diff --git a/.circleci/config.yml b/.circleci/config.yml index 55fa9410845..63012ce3fc0 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -1483,7 +1483,7 @@ jobs: - run: name: Run tests command: | - uv run --no-sync python -m pytest -vv tests/local_testing/test_basic_python_version.py -k "not v2_resolver" + uv run --no-sync python -m pytest -vv tests/local_testing/test_basic_python_version.py -k "not legacy_resolver" installing_litellm_on_python_3_13: docker: @@ -1507,9 +1507,9 @@ jobs: - run: name: Run tests command: | - uv run --no-sync python -m pytest -v tests/local_testing/test_basic_python_version.py -k "not v2_resolver" + uv run --no-sync python -m pytest -v tests/local_testing/test_basic_python_version.py -k "not legacy_resolver" - installing_litellm_on_python_v2_migration_resolver: + installing_litellm_on_python_legacy_migration_resolver: docker: - *python312_image - image: cimg/postgres:16.0@sha256:b125148bc76e8e8eee5eb3ad6020a3a14110a14e8192f1c645128afebe2e2f84 @@ -1536,10 +1536,10 @@ jobs: url: tcp://localhost:5432 timeout: "60" - run: - name: Run v2 migration resolver proxy smoke test + name: Run legacy migration resolver proxy smoke test command: | uv run --no-sync python -m pytest -vv \ - tests/local_testing/test_basic_python_version.py::test_litellm_proxy_server_config_no_general_settings_v2_resolver + tests/local_testing/test_basic_python_version.py::test_litellm_proxy_server_config_no_general_settings_legacy_resolver helm_chart_testing: machine: @@ -2879,7 +2879,8 @@ jobs: command: | if grep -q "Error: P1001: Can't reach database server at" docker_output.log && \ (grep -q "Database setup failed after multiple retries" docker_output.log || \ - grep -q "ERROR: Application startup failed. Exiting." docker_output.log); then + grep -q "ERROR: Application startup failed. Exiting." docker_output.log || \ + grep -q "Database migration cannot proceed" docker_output.log); then echo "Expected error found. Test passed." else echo "Expected error not found. Test failed." @@ -3011,7 +3012,7 @@ workflows: filters: *main_branches - installing_litellm_on_python_3_13: filters: *main_branches - - installing_litellm_on_python_v2_migration_resolver: + - installing_litellm_on_python_legacy_migration_resolver: filters: *main_branches - helm_chart_testing: requires: diff --git a/.github/actions/cache-cargo-build/action.yml b/.github/actions/cache-cargo-build/action.yml index 36c6c790b84..c3b8ce22c68 100644 --- a/.github/actions/cache-cargo-build/action.yml +++ b/.github/actions/cache-cargo-build/action.yml @@ -4,17 +4,16 @@ description: >- so only the first job on a given Cargo.lock compiles the bridge from scratch. litellm builds through maturin, which compiles litellm-rust/crates/python-bridge - in release mode before it can produce a wheel. `uv sync` therefore pays a full - build in every job that installs the workspace: measured at 2m40s per unit shard - on 2026-08-21, more than the whole unit tier spends running tests. Nothing caught - it, because the uv cache holds wheels uv downloads rather than wheels it builds, - and a path dependency whose source moves every commit could never hit that cache - anyway. Cargo rebuilds only what changed when its target directory survives, so a - warm job pays for the bridge crate alone. + in the dev profile for editable installs. `uv sync` therefore pays a full build + in every job that installs the workspace. Nothing caught it, because the uv cache + holds wheels uv downloads rather than wheels it builds, and a path dependency + whose source moves every commit could never hit that cache anyway. Cargo rebuilds + only what changed when its target directory survives, so a warm job pays for the + bridge crate alone. - The key namespace is separate from test-rust.yml's. Both cache the same directory, - but that workflow fills it with debug and clippy artifacts, which a release build - cannot reuse, and a shared key would let whichever ran first deny the other a save. + The key namespace is separate from test-rust.yml's check and release caches. They + cache the same directory for different workloads, and a shared key would let + whichever ran first deny the others a save. runs: using: composite @@ -26,6 +25,6 @@ runs: ~/.cargo/registry ~/.cargo/git litellm-rust/target - key: ${{ runner.os }}-cargo-release-${{ hashFiles('litellm-rust/Cargo.lock') }} + key: ${{ runner.os }}-maturin-dev-${{ hashFiles('litellm-rust/Cargo.lock') }} restore-keys: | - ${{ runner.os }}-cargo-release- + ${{ runner.os }}-maturin-dev- diff --git a/.github/workflows/test-redis-compat.yml b/.github/workflows/test-redis-compat.yml new file mode 100644 index 00000000000..f29755a74b1 --- /dev/null +++ b/.github/workflows/test-redis-compat.yml @@ -0,0 +1,77 @@ +name: "Unit Tests: Redis Client Version Compatibility" + +on: + pull_request: + branches: + - main + - litellm_internal_staging + - litellm_oss_staging + - "litellm_**" + paths: + - "litellm/_redis.py" + - "litellm/_redis_credential_provider.py" + - "tests/test_litellm/test_redis.py" + - "tests/test_litellm/caching/test_redis_connection_pool.py" + - ".github/workflows/test-redis-compat.yml" + - "pyproject.toml" + - "uv.lock" + +permissions: + contents: read + +concurrency: + group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} + cancel-in-progress: true + +jobs: + redis-compat: + name: "redis-py ${{ matrix.redis-version }}" + runs-on: ubuntu-latest + timeout-minutes: 15 + + strategy: + fail-fast: false + matrix: + # 5.3.1 is the version pinned in uv.lock (redisvl caps it below 6); the + # newer legs prove the inspect.signature introspection in litellm/_redis.py + # keeps extracting kwargs on the redis-py releases people actually run now. + # Only the exact release 6.0.0 is skipped: rq (pulled by the proxy extra) + # specifies `redis != 6`, which excludes 6.0.0 alone, so 6.4.0 stands in + # for the 6.x line. + redis-version: ["5.3.1", "6.4.0", "7.4.1", "8.0.1"] + + steps: + - uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 + with: + persist-credentials: false + + - name: Set up Python + uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0 + with: + python-version: "3.12" + + - name: Set up uv + uses: ./.github/actions/setup-uv-with-retries + with: + version: "0.10.9" + + - name: Install dependencies + run: | + .github/scripts/uv_sync_with_retries.sh --frozen --group ci --group proxy-dev --extra google --extra proxy --extra semantic-router + + - name: Pin redis-py to the matrix version + env: + REDIS_VERSION: ${{ matrix.redis-version }} + run: | + uv pip install "redis==${REDIS_VERSION:?}" + uv run --no-sync python -c "import redis; assert redis.__version__ == '${REDIS_VERSION:?}', redis.__version__; print('redis-py', redis.__version__)" + + - name: Run redis unit tests + run: | + uv run --no-sync pytest \ + tests/test_litellm/test_redis.py \ + tests/test_litellm/caching/test_redis_connection_pool.py \ + --tb=short -vv \ + --reruns 2 \ + --reruns-delay 1 \ + --durations=20 diff --git a/.github/workflows/test-unit.yml b/.github/workflows/test-unit.yml index ed9d8800202..c2dff805772 100644 --- a/.github/workflows/test-unit.yml +++ b/.github/workflows/test-unit.yml @@ -103,6 +103,7 @@ jobs: tests/test_litellm/completion_extras tests/test_litellm/compression tests/test_litellm/containers + tests/test_litellm/endpoints tests/test_litellm/experimental_mcp_client tests/test_litellm/models tests/test_litellm/repositories diff --git a/CLAUDE.md b/CLAUDE.md index d9e9e8f1586..6af8390b1af 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -37,7 +37,7 @@ Same applies for filing bug reports and feature requests, with .github/ISSUE_TEM If you're resolving a linear ticket, in the "## Linear ticket" section of the PR, say "Resolves LIT-1234", replacing "LIT-1234" with the actual ticket id that you're resolving. If you don't have the ticket id, don't make one up or search for it. Just leave the section blank -Never use `pytest` commands or the like as "Screenshots / Proof of Fix". We prefer curl'ing a live proxy instance running on localhost:4000 (I like to run it with `python litellm/proxy/proxy_cli.py --config litellm/proxy/dev_config.yaml --detailed_debug --reload --use_v2_migration_resolver 2>&1 | tee litellm.log`; the Admin UI dev server is `npm run dev` in `ui/litellm-dashboard`, served on port 3000) and showing both the command run and the output. Also, it should hit real LLM provider APIs, not mocks, and cost real $$$ because that is the most realistic test. The proof of fix should be exactly what the end user / customer would see / do. The run logs in PR #27703 is a prime example of how to do it (not a huge fan of using a python test script that future me and the team will have no visibility into; I prefer just curl commands or a short list of bash commands (e.g., using `for`)). If it's a UI thing, just tell me which URLs to go to (e.g., http://localhost:4000/ui/?page=logs), where to click, what fields to fill out, etc. along with the other commands to run in an ordered list, and I'll do it myself and post the screenshots after you make the PR +Never use `pytest` commands or the like as "Screenshots / Proof of Fix". We prefer curl'ing a live proxy instance running on localhost:4000 (I like to run it with `python litellm/proxy/proxy_cli.py --config litellm/proxy/dev_config.yaml --detailed_debug --reload 2>&1 | tee litellm.log`; the Admin UI dev server is `npm run dev` in `ui/litellm-dashboard`, served on port 3000) and showing both the command run and the output. Also, it should hit real LLM provider APIs, not mocks, and cost real $$$ because that is the most realistic test. The proof of fix should be exactly what the end user / customer would see / do. The run logs in PR #27703 is a prime example of how to do it (not a huge fan of using a python test script that future me and the team will have no visibility into; I prefer just curl commands or a short list of bash commands (e.g., using `for`)). If it's a UI thing, just tell me which URLs to go to (e.g., http://localhost:4000/ui/?page=logs), where to click, what fields to fill out, etc. along with the other commands to run in an ordered list, and I'll do it myself and post the screenshots after you make the PR If you ever write any human-facing text (pull requests, issues, commit messages, discussion posts, github comments, release notes, docs, etc.), always follow these guidelines to sound less AI-y: - don't use emojis diff --git a/Dockerfile b/Dockerfile index 700b0d6525e..b3ee85e9ed1 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,10 +1,10 @@ # syntax=docker/dockerfile:1.7 # Base image for building -ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:a31344ab2cb8618db84f535eec56f76f6178b142cb92cb2e48676cc2dcebea72 +ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:e624c5d5e42382ce7165ddafcbbf8e6769a24cbd02ea6114b880b05ae5ba2a8d # Runtime image -ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:a31344ab2cb8618db84f535eec56f76f6178b142cb92cb2e48676cc2dcebea72 +ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:e624c5d5e42382ce7165ddafcbbf8e6769a24cbd02ea6114b880b05ae5ba2a8d ARG UV_IMAGE=ghcr.io/astral-sh/uv:0.11.7@sha256:240fb85ab0f263ef12f492d8476aa3a2e4e1e333f7d67fbdd923d00a506a516a # Pinned by digest like the other base images; bump explicitly on Node upgrades. ARG UI_BUILD_IMAGE=node:24.19-alpine3.24@sha256:d32cdf619f63fe0471182d08996dd516c6275bb5fd31ae06e55a570bd9e1ad43 @@ -40,8 +40,8 @@ COPY --from=uvbin /uvx /usr/local/bin/uvx RUN apk add --no-cache \ bash \ gcc \ - python3 \ - python3-dev \ + python-3.13 \ + python-3.13-dev \ rust \ openssl \ openssl-dev \ @@ -51,6 +51,7 @@ RUN apk add --no-cache \ ENV UV_PROJECT_ENVIRONMENT=/app/.venv \ UV_LINK_MODE=copy \ + UV_PYTHON_DOWNLOADS=0 \ PATH="/app/.venv/bin:${PATH}" # Copy dependency metadata first for layer caching @@ -65,7 +66,7 @@ RUN uv sync --frozen --no-install-project --no-install-workspace --no-default-gr --extra extra_proxy \ --extra semantic-router \ --extra saml \ - --python python3 + --python python3.13 # Copy full source tree COPY . . @@ -86,7 +87,7 @@ RUN uv sync --frozen --no-default-groups --no-editable \ --extra extra_proxy \ --extra semantic-router \ --extra saml \ - --python python3 + --python python3.13 RUN HOME=/opt/prisma XDG_CACHE_HOME=/opt/prisma/.cache PRISMA_BINARY_CACHE_DIR=/opt/prisma/binaries \ npm_config_cache=/root/.npm \ @@ -101,7 +102,7 @@ FROM $LITELLM_RUNTIME_IMAGE AS runtime USER root # node (without npm) is required by the prisma CLI at runtime -RUN apk add --no-cache bash openssl tzdata nodejs python3 libsndfile +RUN apk add --no-cache bash openssl tzdata nodejs python-3.13 libsndfile WORKDIR /app ENV PATH="/app/.venv/bin:${PATH}" \ diff --git a/README.md b/README.md index 68aaa09ec98..92757fcbbc1 100644 --- a/README.md +++ b/README.md @@ -354,6 +354,8 @@ curl -X POST 'http://0.0.0.0:4000/v1/chat/completions' \ | [Petals (`petals`)](https://docs.litellm.ai/docs/providers/petals) | ✅ | ✅ | ✅ | | | | | | | | | [Pinstripes (`pinstripes`)](https://docs.litellm.ai/docs/providers/pinstripes) | ✅ | ✅ | ✅ | | | | | | | | | [Predibase (`predibase`)](https://docs.litellm.ai/docs/providers/predibase) | ✅ | ✅ | ✅ | | | | | | | | +| [Qwen AI Platform (`qwen_ai_platform`)](https://docs.litellm.ai/docs/providers/qwencloud) | ✅ | ✅ | ✅ | ✅ | ✅ | | | | | ✅ | +| [QwenCloud (`qwencloud`)](https://docs.litellm.ai/docs/providers/qwencloud) | ✅ | ✅ | ✅ | ✅ | ✅ | | | | | ✅ | | [Recraft (`recraft`)](https://docs.litellm.ai/docs/providers/recraft) | | | | | ✅ | | | | | | | [Replicate (`replicate`)](https://docs.litellm.ai/docs/providers/replicate) | ✅ | ✅ | ✅ | | | | | | | | | [Sagemaker Chat (`sagemaker_chat`)](https://docs.litellm.ai/docs/providers/aws_sagemaker) | ✅ | ✅ | ✅ | | | | | | | | diff --git a/backend/Dockerfile b/backend/Dockerfile index 4ca40944606..aa01b9fba8b 100644 --- a/backend/Dockerfile +++ b/backend/Dockerfile @@ -1,5 +1,5 @@ -ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:a31344ab2cb8618db84f535eec56f76f6178b142cb92cb2e48676cc2dcebea72 -ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:a31344ab2cb8618db84f535eec56f76f6178b142cb92cb2e48676cc2dcebea72 +ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:e624c5d5e42382ce7165ddafcbbf8e6769a24cbd02ea6114b880b05ae5ba2a8d +ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:e624c5d5e42382ce7165ddafcbbf8e6769a24cbd02ea6114b880b05ae5ba2a8d ARG UV_IMAGE=ghcr.io/astral-sh/uv:0.11.7@sha256:240fb85ab0f263ef12f492d8476aa3a2e4e1e333f7d67fbdd923d00a506a516a FROM $UV_IMAGE AS uvbin @@ -16,7 +16,7 @@ COPY --from=uvbin /uv /uvx /usr/local/bin/ # instead of nodeenv downloading one whose dynamic deps may not be in Wolfi # (e.g. Node 26.2.0 needs libatomic). Retry for transient apk.cgr.dev flakes. RUN for i in 1 2 3; do \ - apk add --no-cache bash gcc python3 python3-dev openssl openssl-dev libsndfile nodejs npm && break; \ + apk add --no-cache bash gcc python-3.13 python-3.13-dev openssl openssl-dev libsndfile nodejs npm && break; \ [ $i = 3 ] && { echo "apk add failed after 3 retries" >&2; exit 1; }; \ sleep 5; \ done @@ -46,7 +46,7 @@ RUN --mount=type=cache,target=/root/.cache/uv \ --extra proxy-runtime \ --extra extra_proxy \ --extra semantic-router \ - --python python3 + --python python3.13 # Stage 2 — copy source and install the project + workspace members. COPY . . @@ -57,7 +57,7 @@ RUN --mount=type=cache,target=/root/.cache/uv \ --extra proxy-runtime \ --extra extra_proxy \ --extra semantic-router \ - --python python3 + --python python3.13 RUN HOME=/opt/prisma XDG_CACHE_HOME=/opt/prisma/.cache PRISMA_BINARY_CACHE_DIR=/opt/prisma/binaries \ npm_config_cache=/root/.npm \ @@ -71,7 +71,7 @@ FROM $LITELLM_RUNTIME_IMAGE AS runtime USER root RUN for i in 1 2 3; do \ - apk add --no-cache bash openssl tzdata python3 libsndfile libatomic && break; \ + apk add --no-cache bash openssl tzdata python-3.13 libsndfile libatomic && break; \ [ $i = 3 ] && { echo "apk add failed after 3 retries" >&2; exit 1; }; \ sleep 5; \ done diff --git a/basedpyright-code-budget.json b/basedpyright-code-budget.json index d60c3e9c0af..df52069e71f 100644 --- a/basedpyright-code-budget.json +++ b/basedpyright-code-budget.json @@ -1,9 +1,9 @@ { "reportAny": { - "limit": 16171 + "limit": 14076 }, "reportArgumentType": { - "limit": 2226 + "limit": 2216 }, "reportAssignmentType": { "limit": 319 @@ -24,7 +24,7 @@ "limit": 19 }, "reportExplicitAny": { - "limit": 5199 + "limit": 4128 }, "reportFunctionMemberAccess": { "limit": 7 @@ -42,7 +42,7 @@ "limit": 12 }, "reportIndexIssue": { - "limit": 35 + "limit": 25 }, "reportInvalidTypeForm": { "limit": 34 @@ -54,10 +54,10 @@ "limit": 0 }, "reportMissingParameterType": { - "limit": 5611 + "limit": 5601 }, "reportMissingTypeArgument": { - "limit": 15350 + "limit": 15306 }, "reportMissingTypeStubs": { "limit": 40 @@ -99,19 +99,19 @@ "limit": 0 }, "reportUnknownArgumentType": { - "limit": 44368 + "limit": 44364 }, "reportUnknownLambdaType": { "limit": 109 }, "reportUnknownMemberType": { - "limit": 38468 + "limit": 38350 }, "reportUnknownParameterType": { - "limit": 19665 + "limit": 19626 }, "reportUnknownVariableType": { - "limit": 30066 + "limit": 29890 }, "reportUnnecessaryCast": { "limit": 111 @@ -123,7 +123,7 @@ "limit": 5 }, "reportUnnecessaryIsInstance": { - "limit": 828 + "limit": 826 }, "reportUntypedBaseClass": { "limit": 0 @@ -141,6 +141,6 @@ "limit": 543 }, "reportUnusedVariable": { - "limit": 139 + "limit": 137 } } diff --git a/docker/Dockerfile.database b/docker/Dockerfile.database index f0d6d02fccf..c1348f68231 100644 --- a/docker/Dockerfile.database +++ b/docker/Dockerfile.database @@ -1,10 +1,10 @@ # syntax=docker/dockerfile:1.7 # Base image for building -ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:a31344ab2cb8618db84f535eec56f76f6178b142cb92cb2e48676cc2dcebea72 +ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:e624c5d5e42382ce7165ddafcbbf8e6769a24cbd02ea6114b880b05ae5ba2a8d # Runtime image -ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:a31344ab2cb8618db84f535eec56f76f6178b142cb92cb2e48676cc2dcebea72 +ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:e624c5d5e42382ce7165ddafcbbf8e6769a24cbd02ea6114b880b05ae5ba2a8d ARG UV_IMAGE=ghcr.io/astral-sh/uv:0.11.7@sha256:240fb85ab0f263ef12f492d8476aa3a2e4e1e333f7d67fbdd923d00a506a516a # Pinned by digest like the other base images; bump explicitly on Node upgrades. ARG UI_BUILD_IMAGE=node:24.19-alpine3.24@sha256:d32cdf619f63fe0471182d08996dd516c6275bb5fd31ae06e55a570bd9e1ad43 @@ -39,8 +39,8 @@ COPY --from=uvbin /uvx /usr/local/bin/uvx RUN apk add --no-cache \ bash \ gcc \ - python3 \ - python3-dev \ + python-3.13 \ + python-3.13-dev \ openssl \ openssl-dev \ nodejs \ @@ -49,6 +49,7 @@ RUN apk add --no-cache \ ENV UV_PROJECT_ENVIRONMENT=/app/.venv \ UV_LINK_MODE=copy \ + UV_PYTHON_DOWNLOADS=0 \ PATH="/app/.venv/bin:${PATH}" # Copy dependency metadata first for layer caching @@ -63,7 +64,7 @@ RUN uv sync --frozen --no-install-project --no-install-workspace --no-default-gr --extra extra_proxy \ --extra semantic-router \ --extra saml \ - --python python3 + --python python3.13 # Copy full source tree COPY . . @@ -84,7 +85,7 @@ RUN uv sync --frozen --no-default-groups --no-editable \ --extra extra_proxy \ --extra semantic-router \ --extra saml \ - --python python3 + --python python3.13 RUN HOME=/opt/prisma XDG_CACHE_HOME=/opt/prisma/.cache PRISMA_BINARY_CACHE_DIR=/opt/prisma/binaries \ npm_config_cache=/root/.npm \ @@ -98,7 +99,7 @@ FROM $LITELLM_RUNTIME_IMAGE AS runtime USER root # node (without npm) is required by the prisma CLI at runtime -RUN apk add --no-cache bash openssl tzdata nodejs python3 libsndfile +RUN apk add --no-cache bash openssl tzdata nodejs python-3.13 libsndfile WORKDIR /app ENV PATH="/app/.venv/bin:${PATH}" \ diff --git a/docker/Dockerfile.non_root b/docker/Dockerfile.non_root index 4a5df6ecd69..2221435a83a 100644 --- a/docker/Dockerfile.non_root +++ b/docker/Dockerfile.non_root @@ -1,8 +1,8 @@ # syntax=docker/dockerfile:1.7 # Base images -ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:a31344ab2cb8618db84f535eec56f76f6178b142cb92cb2e48676cc2dcebea72 -ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:a31344ab2cb8618db84f535eec56f76f6178b142cb92cb2e48676cc2dcebea72 +ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:e624c5d5e42382ce7165ddafcbbf8e6769a24cbd02ea6114b880b05ae5ba2a8d +ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:e624c5d5e42382ce7165ddafcbbf8e6769a24cbd02ea6114b880b05ae5ba2a8d ARG PROXY_EXTRAS_SOURCE=published ARG UV_IMAGE=ghcr.io/astral-sh/uv:0.11.7@sha256:240fb85ab0f263ef12f492d8476aa3a2e4e1e333f7d67fbdd923d00a506a516a # Pinned by digest like the other base images; bump explicitly on Node upgrades. @@ -37,8 +37,8 @@ COPY --from=uvbin /uvx /usr/local/bin/uvx RUN for i in 1 2 3; do \ apk add --no-cache \ - python3 \ - python3-dev \ + python-3.13 \ + python-3.13-dev \ gcc \ rust \ bash \ @@ -52,6 +52,7 @@ RUN for i in 1 2 3; do \ ENV UV_PROJECT_ENVIRONMENT=/app/.venv \ UV_LINK_MODE=copy \ + UV_PYTHON_DOWNLOADS=0 \ PATH="/app/.venv/bin:${PATH}" \ LITELLM_NON_ROOT=true \ XDG_CACHE_HOME=/app/.cache @@ -69,7 +70,7 @@ RUN --mount=type=cache,target=/app/.cache/uv,id=litellm-uv-cache \ --extra extra_proxy \ --extra semantic-router \ --extra saml \ - --python python3 + --python python3.13 # Copy full source tree COPY . . @@ -96,7 +97,7 @@ RUN --mount=type=cache,target=/app/.cache/uv,id=litellm-uv-cache \ --extra extra_proxy \ --extra semantic-router \ --extra saml \ - --python python3 \ + --python python3.13 \ --no-sources-package litellm-proxy-extras; \ else \ uv sync --frozen --no-default-groups --no-editable \ @@ -105,7 +106,7 @@ RUN --mount=type=cache,target=/app/.cache/uv,id=litellm-uv-cache \ --extra extra_proxy \ --extra semantic-router \ --extra saml \ - --python python3; \ + --python python3.13; \ fi RUN HOME=/opt/prisma XDG_CACHE_HOME=/opt/prisma/.cache PRISMA_BINARY_CACHE_DIR=/opt/prisma/binaries \ @@ -124,7 +125,7 @@ RUN for i in 1 2 3; do \ apk upgrade --no-cache && break || sleep 5; \ done && \ for i in 1 2 3; do \ - apk add --no-cache python3 bash openssl tzdata libsndfile nodejs && break || sleep 5; \ + apk add --no-cache python-3.13 bash openssl tzdata libsndfile nodejs && break || sleep 5; \ done # Copy only what runtime needs. The application is installed inside the venv; diff --git a/enterprise/litellm_enterprise/proxy/common_utils/check_batch_cost.py b/enterprise/litellm_enterprise/proxy/common_utils/check_batch_cost.py index 3b09dc9272e..354a6ed2fd0 100644 --- a/enterprise/litellm_enterprise/proxy/common_utils/check_batch_cost.py +++ b/enterprise/litellm_enterprise/proxy/common_utils/check_batch_cost.py @@ -5,7 +5,7 @@ Polls LiteLLM_ManagedObjectTable to check if the batch job is complete, and if t from dataclasses import replace as dataclasses_replace from datetime import datetime, timedelta, timezone from types import MappingProxyType -from typing import TYPE_CHECKING, Any, Dict, Final, List, Literal, Optional, Tuple, cast +from typing import TYPE_CHECKING, Final, List, Literal, Optional, Tuple, cast from litellm._logging import verbose_proxy_logger from litellm._uuid import uuid @@ -87,7 +87,7 @@ class CheckBatchCost: return self.batch_processed_support_confirmed = True - async def _get_user_info(self, batch_id: str, user_id: Optional[str]) -> Dict[str, Any]: + async def _get_user_info(self, batch_id: str, user_id: Optional[str]) -> dict[str, str | None]: """ Look up user email and key alias by user_id for enriching the S3 callback metadata. Returns a dict with user_api_key_user_email and user_api_key_alias (both may be None). @@ -97,8 +97,10 @@ class CheckBatchCost: if not user_id: return {} try: - user_row = await self.prisma_client.db.litellm_usertable.find_unique( - where={"user_id": user_id} + user_row: prisma_models.LiteLLM_UserTable | None = ( + await self.prisma_client.db.litellm_usertable.find_unique( + where={"user_id": user_id} + ) ) if user_row is None: return {} @@ -115,8 +117,10 @@ class CheckBatchCost: if not api_key: return None try: - key_row = await self.prisma_client.db.litellm_verificationtoken.find_unique( - where={"token": api_key} + key_row: prisma_models.LiteLLM_VerificationToken | None = ( + await self.prisma_client.db.litellm_verificationtoken.find_unique( + where={"token": api_key} + ) ) return getattr(key_row, "key_alias", None) if key_row is not None else None except Exception as e: @@ -128,8 +132,10 @@ class CheckBatchCost: if not team_id: return None try: - team_row = await self.prisma_client.db.litellm_teamtable.find_unique( - where={"team_id": team_id} + team_row: prisma_models.LiteLLM_TeamTable | None = ( + await self.prisma_client.db.litellm_teamtable.find_unique( + where={"team_id": team_id} + ) ) return getattr(team_row, "team_alias", None) if team_row is not None else None except Exception as e: @@ -138,7 +144,7 @@ class CheckBatchCost: async def _build_creator_attribution_metadata( self, job: "LiteLLM_ManagedObjectTable", batch_id: str - ) -> Dict[str, Any]: + ) -> dict[str, object]: """ Rebuild the spend-tracking metadata for the key, team, and tags that created the batch so the batch-cost spend log is attributed the same way a non-batch request @@ -152,7 +158,7 @@ class CheckBatchCost: team_id = getattr(job, "team_id", None) request_tags = getattr(job, "request_tags", None) - metadata: Dict[str, Any] = { + metadata: dict[str, object] = { "user_api_key_user_id": job.created_by, "user_api_key": api_key, "user_api_key_team_id": team_id, diff --git a/enterprise/litellm_enterprise/proxy/hooks/managed_files.py b/enterprise/litellm_enterprise/proxy/hooks/managed_files.py index 570b306d6df..5cfcf6129f0 100644 --- a/enterprise/litellm_enterprise/proxy/hooks/managed_files.py +++ b/enterprise/litellm_enterprise/proxy/hooks/managed_files.py @@ -182,6 +182,10 @@ class _ManagedObjectTableActions(Protocol): async def update_many(self, where: Mapping[str, object], data: Mapping[str, object]) -> int: ... +class _SchedulerWithJobLookup(Protocol): + def get_job(self, job_id: str) -> object: ... + + class _CursorPageArgs(TypedDict, total=False): cursor: Mapping[str, str] skip: int @@ -853,7 +857,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): file_ids.append(file_id) return file_ids - def get_file_ids_from_responses_input(self, input: Union[str, List[Dict[str, Any]]]) -> List[str]: + def get_file_ids_from_responses_input(self, input: Union[str, List[Dict[str, object]]]) -> List[str]: """ Gets file ids from responses API input. @@ -878,7 +882,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): # Check for direct input_file type if item.get("type") == "input_file": file_id = item.get("file_id") - if file_id: + if isinstance(file_id, str) and file_id: file_ids.append(file_id) # Check for input_file in content array @@ -887,7 +891,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): for content_item in content: if isinstance(content_item, dict) and content_item.get("type") == "input_file": file_id = content_item.get("file_id") - if file_id: + if isinstance(file_id, str) and file_id: file_ids.append(file_id) return file_ids @@ -1227,7 +1231,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): # Handle both output_file_id and error_file_id for file_attr in ["output_file_id", "error_file_id"]: - file_id_value = getattr(response, file_attr, None) + file_id_value: str | None = getattr(response, file_attr, None) if file_id_value and model_id: decoded_output_file_id = _is_base64_encoded_unified_file_id(file_id_value) if decoded_output_file_id and "llm_output_file_id," in decoded_output_file_id: @@ -1496,7 +1500,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): import litellm.proxy.proxy_server as proxy_server_module # Check if the scheduler has the batch cost checking job registered - scheduler = getattr(proxy_server_module, "scheduler", None) + scheduler: Final[_SchedulerWithJobLookup | None] = getattr(proxy_server_module, "scheduler", None) if scheduler is None: return False @@ -1542,7 +1546,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): ) MAX_MATCHES_TO_RETURN = 10 - batches = await self.prisma_client.db.litellm_managedobjecttable.find_many( + batches = await _managed_object_table(self.prisma_client).find_many( where={ "file_purpose": "batch", "batch_processed": False, @@ -1552,11 +1556,14 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): order={"created_at": "desc"}, ) - referencing_batches = [] + referencing_batches: Final[list[dict[str, object]]] = [] for batch in batches: try: # Parse the batch file_object to check for file references - batch_data = json.loads(batch.file_object) if isinstance(batch.file_object, str) else batch.file_object + decoded_file_object = _decode_json_blob(batch.file_object) + batch_data: Mapping[str, object] = ( + decoded_file_object if isinstance(decoded_file_object, Mapping) else {} + ) # Extract file IDs from batch # Batches typically reference the unified file ID in input_file_id diff --git a/enterprise/pyproject.toml b/enterprise/pyproject.toml index cac98b69793..8360c0a077d 100644 --- a/enterprise/pyproject.toml +++ b/enterprise/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "litellm-enterprise" -version = "0.1.62" +version = "0.1.63" description = "Package for LiteLLM Enterprise features" readme = "README.md" requires-python = ">=3.9" @@ -26,7 +26,7 @@ required-version = ">=0.10.9" module-root = "" [tool.commitizen] -version = "0.1.62" +version = "0.1.63" version_files = [ "pyproject.toml:^version", "../pyproject.toml:litellm-enterprise==", diff --git a/gateway/Dockerfile b/gateway/Dockerfile index 4a2e32e186e..308d70a6b26 100644 --- a/gateway/Dockerfile +++ b/gateway/Dockerfile @@ -1,5 +1,5 @@ -ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:a31344ab2cb8618db84f535eec56f76f6178b142cb92cb2e48676cc2dcebea72 -ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:a31344ab2cb8618db84f535eec56f76f6178b142cb92cb2e48676cc2dcebea72 +ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:e624c5d5e42382ce7165ddafcbbf8e6769a24cbd02ea6114b880b05ae5ba2a8d +ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:e624c5d5e42382ce7165ddafcbbf8e6769a24cbd02ea6114b880b05ae5ba2a8d ARG UV_IMAGE=ghcr.io/astral-sh/uv:0.11.7@sha256:240fb85ab0f263ef12f492d8476aa3a2e4e1e333f7d67fbdd923d00a506a516a FROM $UV_IMAGE AS uvbin @@ -16,7 +16,7 @@ COPY --from=uvbin /uv /uvx /usr/local/bin/ # instead of nodeenv downloading one whose dynamic deps may not be in Wolfi # (e.g. Node 26.2.0 needs libatomic). Retry for transient apk.cgr.dev flakes. RUN for i in 1 2 3; do \ - apk add --no-cache bash gcc python3 python3-dev openssl openssl-dev libsndfile nodejs npm && break; \ + apk add --no-cache bash gcc python-3.13 python-3.13-dev openssl openssl-dev libsndfile nodejs npm && break; \ [ $i = 3 ] && { echo "apk add failed after 3 retries" >&2; exit 1; }; \ sleep 5; \ done @@ -47,7 +47,7 @@ RUN --mount=type=cache,target=/root/.cache/uv \ --extra extra_proxy \ --extra semantic-router \ --extra bedrock-realtime \ - --python python3 + --python python3.13 # Stage 2 — copy source and install the project + workspace members. COPY . . @@ -59,7 +59,7 @@ RUN --mount=type=cache,target=/root/.cache/uv \ --extra extra_proxy \ --extra semantic-router \ --extra bedrock-realtime \ - --python python3 + --python python3.13 RUN HOME=/opt/prisma XDG_CACHE_HOME=/opt/prisma/.cache PRISMA_BINARY_CACHE_DIR=/opt/prisma/binaries \ npm_config_cache=/root/.npm \ @@ -73,7 +73,7 @@ FROM $LITELLM_RUNTIME_IMAGE AS runtime USER root RUN for i in 1 2 3; do \ - apk add --no-cache bash openssl tzdata python3 libsndfile libatomic && break; \ + apk add --no-cache bash openssl tzdata python-3.13 libsndfile libatomic && break; \ [ $i = 3 ] && { echo "apk add failed after 3 retries" >&2; exit 1; }; \ sleep 5; \ done diff --git a/gateway/routes/allowlist.py b/gateway/routes/allowlist.py index 05baf98bbb5..92b73867e67 100644 --- a/gateway/routes/allowlist.py +++ b/gateway/routes/allowlist.py @@ -86,6 +86,7 @@ GATEWAY_PATH_PREFIXES: tuple[str, ...] = ( "/comprehendmedical", "/cohere/", "/gemini/", + "/gigachat/", "/google/", "/vertex_ai/", "/vertex-ai/", diff --git a/helm/litellm/templates/backend/deployment.yaml b/helm/litellm/templates/backend/deployment.yaml index 5c0431fc0bd..0db2f0b3d43 100644 --- a/helm/litellm/templates/backend/deployment.yaml +++ b/helm/litellm/templates/backend/deployment.yaml @@ -7,6 +7,10 @@ metadata: {{- include "litellm.commonLabels" . | nindent 4 }} app.kubernetes.io/component: backend spec: + {{- with .Values.backend.strategy }} + strategy: + {{- toYaml . | nindent 4 }} + {{- end }} selector: matchLabels: {{- include "litellm.backend.selectorLabels" . | nindent 6 }} diff --git a/helm/litellm/templates/gateway/deployment.yaml b/helm/litellm/templates/gateway/deployment.yaml index d5363d0096e..5030ba2c9dc 100644 --- a/helm/litellm/templates/gateway/deployment.yaml +++ b/helm/litellm/templates/gateway/deployment.yaml @@ -7,6 +7,10 @@ metadata: {{- include "litellm.commonLabels" . | nindent 4 }} app.kubernetes.io/component: gateway spec: + {{- with .Values.gateway.strategy }} + strategy: + {{- toYaml . | nindent 4 }} + {{- end }} selector: matchLabels: {{- include "litellm.gateway.selectorLabels" . | nindent 6 }} diff --git a/helm/litellm/templates/migrations-job.yaml b/helm/litellm/templates/migrations-job.yaml index 9cd8397f794..8d33081e72f 100644 --- a/helm/litellm/templates/migrations-job.yaml +++ b/helm/litellm/templates/migrations-job.yaml @@ -7,6 +7,8 @@ # # Running this pre-upgrade closes the window where new application pods would # otherwise serve traffic against the previous release's unmigrated schema. +# Argo CD users can swap the Helm hook for a PreSync hook through +# `migrationJob.hooks`, which re-runs the Job on every sync. apiVersion: batch/v1 kind: Job metadata: @@ -14,10 +16,18 @@ metadata: labels: {{- include "litellm.commonLabels" . | nindent 4 }} app.kubernetes.io/component: migrations + {{- if or .Values.migrationJob.hooks.helm.enabled .Values.migrationJob.hooks.argocd.enabled }} annotations: + {{- if .Values.migrationJob.hooks.helm.enabled }} helm.sh/hook: pre-install,pre-upgrade helm.sh/hook-delete-policy: before-hook-creation - helm.sh/hook-weight: "0" + helm.sh/hook-weight: {{ .Values.migrationJob.hooks.helm.weight | default "0" | quote }} + {{- end }} + {{- if .Values.migrationJob.hooks.argocd.enabled }} + argocd.argoproj.io/hook: PreSync + argocd.argoproj.io/hook-delete-policy: BeforeHookCreation + {{- end }} + {{- end }} spec: backoffLimit: {{ .Values.migrationJob.backoffLimit }} ttlSecondsAfterFinished: {{ .Values.migrationJob.ttlSecondsAfterFinished }} diff --git a/helm/litellm/templates/ui/deployment.yaml b/helm/litellm/templates/ui/deployment.yaml index 91d6de39ea6..b992b347bad 100644 --- a/helm/litellm/templates/ui/deployment.yaml +++ b/helm/litellm/templates/ui/deployment.yaml @@ -7,6 +7,10 @@ metadata: {{- include "litellm.commonLabels" . | nindent 4 }} app.kubernetes.io/component: ui spec: + {{- with .Values.ui.strategy }} + strategy: + {{- toYaml . | nindent 4 }} + {{- end }} selector: matchLabels: {{- include "litellm.ui.selectorLabels" . | nindent 6 }} diff --git a/helm/litellm/tests/migration_job_hooks_tests.yaml b/helm/litellm/tests/migration_job_hooks_tests.yaml new file mode 100644 index 00000000000..650d2700429 --- /dev/null +++ b/helm/litellm/tests/migration_job_hooks_tests.yaml @@ -0,0 +1,63 @@ +suite: test migrations Job hook annotations +templates: + - migrations-job.yaml +values: + - ./values/required.yaml +tests: + - it: runs as a Helm pre-install / pre-upgrade hook by default + asserts: + - equal: + path: metadata.annotations["helm.sh/hook"] + value: pre-install,pre-upgrade + - equal: + path: metadata.annotations["helm.sh/hook-delete-policy"] + value: before-hook-creation + - equal: + path: metadata.annotations["helm.sh/hook-weight"] + value: "0" + - notExists: + path: metadata.annotations["argocd.argoproj.io/hook"] + + - it: adds the Argo CD PreSync hook when asked + set: + migrationJob.hooks.argocd.enabled: true + asserts: + - equal: + path: metadata.annotations["argocd.argoproj.io/hook"] + value: PreSync + - equal: + path: metadata.annotations["argocd.argoproj.io/hook-delete-policy"] + value: BeforeHookCreation + + - it: drops the Helm hook so Argo CD owns the Job + set: + migrationJob.hooks.argocd.enabled: true + migrationJob.hooks.helm.enabled: false + asserts: + - equal: + path: metadata.annotations["argocd.argoproj.io/hook"] + value: PreSync + - notExists: + path: metadata.annotations["helm.sh/hook"] + - notExists: + path: metadata.annotations["helm.sh/hook-delete-policy"] + - notExists: + path: metadata.annotations["helm.sh/hook-weight"] + + - it: renders an ordinary Job when both hooks are disabled + set: + migrationJob.hooks.helm.enabled: false + asserts: + - notExists: + path: metadata.annotations + - equal: + path: kind + value: Job + + - it: honours a custom Helm hook weight + set: + migrationJob.hooks.helm.weight: "-5" + asserts: + - equal: + path: metadata.annotations["helm.sh/hook-weight"] + value: "-5" diff --git a/helm/litellm/tests/rollout_strategy_tests.yaml b/helm/litellm/tests/rollout_strategy_tests.yaml new file mode 100644 index 00000000000..b12e2073c7c --- /dev/null +++ b/helm/litellm/tests/rollout_strategy_tests.yaml @@ -0,0 +1,66 @@ +suite: test rolling update strategy on the component deployments +templates: + - gateway/deployment.yaml + - gateway/configmap.yaml + - backend/deployment.yaml + - ui/deployment.yaml +values: + - ./values/required.yaml +tests: + - it: leaves the strategy to Kubernetes defaults when unset + asserts: + - notExists: + path: spec.strategy + + - it: renders the configured strategy on each deployment + set: + gateway.strategy: + type: RollingUpdate + rollingUpdate: + maxUnavailable: 0 + maxSurge: 1 + backend.strategy: + type: RollingUpdate + rollingUpdate: + maxUnavailable: "25%" + maxSurge: 2 + ui.strategy: + type: Recreate + asserts: + - equal: + path: spec.strategy + value: + type: RollingUpdate + rollingUpdate: + maxUnavailable: 0 + maxSurge: 1 + template: gateway/deployment.yaml + - equal: + path: spec.strategy + value: + type: RollingUpdate + rollingUpdate: + maxUnavailable: 25% + maxSurge: 2 + template: backend/deployment.yaml + - equal: + path: spec.strategy + value: + type: Recreate + template: ui/deployment.yaml + + - it: keeps a component on the cluster default when only another one sets a strategy + set: + gateway.strategy: + type: Recreate + asserts: + - equal: + path: spec.strategy.type + value: Recreate + template: gateway/deployment.yaml + - notExists: + path: spec.strategy + template: backend/deployment.yaml + - notExists: + path: spec.strategy + template: ui/deployment.yaml diff --git a/helm/litellm/values.yaml b/helm/litellm/values.yaml index d0c80fd6f6f..378c3b7a618 100644 --- a/helm/litellm/values.yaml +++ b/helm/litellm/values.yaml @@ -75,6 +75,22 @@ serviceAccounts: # generate` — the migration engine doesn't need the generated client. migrationJob: enabled: true + # Which controller is responsible for running the Job. + # + # `helm.enabled` renders the Helm pre-install / pre-upgrade hook, so the Job + # runs whenever `helm upgrade` sees a change to apply. `argocd.enabled` + # renders an Argo CD PreSync hook instead, which runs the Job on every sync + # even when the rendered manifests are unchanged: the way to re-run + # migrations on demand from a GitOps pipeline. Turning the Helm hook off + # while the Argo CD hook is on leaves the Job out of Helm's own upgrade + # path, which is what Argo CD users want since Argo, not Helm, applies the + # manifests. + hooks: + helm: + enabled: true + weight: "0" + argocd: + enabled: false backoffLimit: 4 ttlSecondsAfterFinished: 120 # Wall-clock budget for the whole Job, shared across every `backoffLimit` @@ -257,6 +273,15 @@ gateway: initialDelaySeconds: 5 periodSeconds: 10 timeoutSeconds: 10 + # Rolling update tuning for the gateway Deployment. Empty by default, so + # Kubernetes applies its own RollingUpdate defaults (25% maxSurge / + # 25% maxUnavailable). Example, for a surge-only rollout behind a load + # balancer that must never lose capacity: + # type: RollingUpdate + # rollingUpdate: + # maxUnavailable: 0 + # maxSurge: 1 + strategy: {} # Optional startupProbe. Empty by default, so existing installs are unchanged # and liveness/readiness apply from container start. Set it to gate # liveness/readiness until a slow cold start finishes — a high failureThreshold @@ -369,6 +394,8 @@ backend: initialDelaySeconds: 5 periodSeconds: 10 timeoutSeconds: 10 + # Same shape as gateway.strategy. + strategy: {} # Optional startupProbe; same shape as gateway.startupProbe. Empty by default. startupProbe: {} hpa: @@ -433,6 +460,8 @@ ui: httpGet: { path: /, port: http } initialDelaySeconds: 2 periodSeconds: 10 + # Same shape as gateway.strategy. + strategy: {} # Optional startupProbe; same shape as gateway.startupProbe. Empty by default. startupProbe: {} hpa: diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20260831000000_shadow_eval_typed_targets/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260831000000_shadow_eval_typed_targets/migration.sql new file mode 100644 index 00000000000..b7dbe931dd2 --- /dev/null +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260831000000_shadow_eval_typed_targets/migration.sql @@ -0,0 +1,21 @@ +DO $$ +BEGIN + IF EXISTS ( + SELECT 1 FROM information_schema.columns + WHERE table_name = 'LiteLLM_ShadowEvalJob' AND column_name = 'api_key_id' + ) THEN + ALTER TABLE "LiteLLM_ShadowEvalJob" RENAME COLUMN "api_key_id" TO "target_id"; + END IF; +END $$; + +ALTER TABLE "LiteLLM_ShadowEvalJob" ADD COLUMN IF NOT EXISTS "target_type" TEXT NOT NULL DEFAULT 'key'; + +DROP INDEX IF EXISTS "LiteLLM_ShadowEvalJob_one_active_per_key_direction"; + +CREATE UNIQUE INDEX IF NOT EXISTS "LiteLLM_ShadowEvalJob_one_active_per_target_direction" + ON "LiteLLM_ShadowEvalJob"("target_type", "target_id", "direction") WHERE "stopped_at" IS NULL; + +DROP INDEX IF EXISTS "LiteLLM_ShadowEvalJob_api_key_id_idx"; + +CREATE INDEX IF NOT EXISTS "LiteLLM_ShadowEvalJob_target_type_target_id_idx" + ON "LiteLLM_ShadowEvalJob"("target_type", "target_id"); diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20260901000000_shadow_eval_multi_router/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260901000000_shadow_eval_multi_router/migration.sql new file mode 100644 index 00000000000..90b21205310 --- /dev/null +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260901000000_shadow_eval_multi_router/migration.sql @@ -0,0 +1,3 @@ +ALTER TABLE "LiteLLM_ShadowEvalJob" ADD COLUMN IF NOT EXISTS "router_names" TEXT[] NOT NULL DEFAULT ARRAY[]::TEXT[]; + +ALTER TABLE "LiteLLM_ShadowEvalAttempt" ADD COLUMN IF NOT EXISTS "router_name" TEXT; diff --git a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma index 60223265211..7604ceadf7a 100644 --- a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma +++ b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma @@ -1529,14 +1529,16 @@ model LiteLLM_AutoRouterSession { model LiteLLM_ShadowEvalJob { id String @id @default(cuid()) group_id String // legs of one job share this; the API's job id - api_key_id String // hashed virtual key whose traffic this leg shadows - router_name String // the auto-router under evaluation, in either direction + target_type String @default("key") // key | team | user + target_id String // hashed virtual key, team_id, or user_id whose traffic this leg shadows + router_name String // first (often only) auto-router under evaluation; router_names is the full set + router_names String[] @default([]) // all routers this job runs as shadow arms; empty on legacy rows, whose set is (router_name) direction String @default("forward") // forward | reverse baseline_model String? // reverse only: the fixed model the router is judged against judge_model String shadow_percentage Float max_turns Int // sample-count ceiling: the whole budget on pre-max_budget jobs, the error-loop valve otherwise - max_budget Float? // per-key USD cap on the eval's own shadow + judge spend; null on jobs from before spend budgets + max_budget Float? // per-target USD cap on the eval's own shadow + judge spend; null on jobs from before spend budgets created_at DateTime @default(now()) created_by String? ends_at DateTime @@ -1544,7 +1546,7 @@ model LiteLLM_ShadowEvalJob { stopped_by String? // operator who stopped it early; null when it ended on its own @@index([group_id]) - @@index([api_key_id]) + @@index([target_type, target_id]) @@index([created_at]) } @@ -1554,6 +1556,7 @@ model LiteLLM_ShadowEvalAttempt { job_id String request_id String // the judged real request outcome String // real | shadow | tie | error + router_name String? // the arm this verdict scores; NULL on legacy rows, meaning the job's own router tier String? // router's tier for the prompt, when classified real_model String? shadow_model String? diff --git a/litellm-proxy-extras/litellm_proxy_extras/utils.py b/litellm-proxy-extras/litellm_proxy_extras/utils.py index b2dc0a52c8f..c088609dad7 100644 --- a/litellm-proxy-extras/litellm_proxy_extras/utils.py +++ b/litellm-proxy-extras/litellm_proxy_extras/utils.py @@ -7,7 +7,8 @@ import subprocess import tempfile import time from pathlib import Path -from typing import Optional +from types import MappingProxyType +from typing import Final, Optional from litellm_proxy_extras._logging import logger from litellm_proxy_extras.replica_identity import ( @@ -50,6 +51,17 @@ _SPEND_LOGS_PK_CLAUSE_RE = re.compile( re.IGNORECASE, ) +_PRISMA_ATTEMPTS: Final = 4 + +_TRANSIENT_PRISMA_FAILURES: Final = MappingProxyType( + { + "deadlock detected": "a deadlock on the migration advisory lock (a concurrent migrate deploy)", + "P1001": "an unreachable database server", + "P1002": "a database server that timed out", + } +) + + PARTITIONED_SPEND_LOGS_PUSH_ERROR = ( "LiteLLM_SpendLogs is a partitioned table (see db_scripts/partition_spend_logs.sql), " "so its primary key must include the partition key (\"startTime\"). `prisma db push` " @@ -274,6 +286,23 @@ class ProxyExtrasDBManager: env=prisma_env, ) + @staticmethod + def _transient_prisma_failure(stderr: str) -> str | None: + """Why a failed prisma command is worth retrying, or None. + + v1 retried every failure, so it absorbed a database that was not up yet + or another instance holding the migration lock. v2 fails fast, which is + right for a broken migration and wrong for these. + """ + return next( + ( + reason + for marker, reason in _TRANSIENT_PRISMA_FAILURES.items() + if marker in stderr + ), + None, + ) + @staticmethod def _is_permission_error(error_message: str) -> bool: """ @@ -512,6 +541,13 @@ class ProxyExtrasDBManager: try: import psycopg except ImportError: + logger.warning( + "psycopg is not installed; skipping the LiteLLM_SpendLogs " + "partition check. If this table is partitioned (see " + "db_scripts/partition_spend_logs.sql), schema reconciliation " + "will try to rewrite its primary key and fail. Install the " + "litellm[extra_proxy] extra, which now includes psycopg." + ) return False cleaned_url = ProxyExtrasDBManager._strip_prisma_query_params(database_url) @@ -648,7 +684,7 @@ class ProxyExtrasDBManager: @staticmethod def _setup_database_v2(use_migrate: bool) -> bool: """ - v2 migration resolver (opt-in via --use_v2_migration_resolver). + v2 migration resolver (what the proxy CLI selects by default). Runs `prisma migrate deploy` and handles standard recovery paths (P3005 baseline, P3009/P3018 idempotent errors). Critically, it does @@ -669,20 +705,46 @@ class ProxyExtrasDBManager: original_dir = os.getcwd() os.chdir(migrations_dir) try: - subprocess.run( - [_get_prisma_command(), "db", "push", "--accept-data-loss"], - timeout=prisma_command_timeout(), - check=True, - env=_get_prisma_env(), + for attempt in range(_PRISMA_ATTEMPTS): + try: + subprocess.run( + [_get_prisma_command(), "db", "push", "--accept-data-loss"], + timeout=prisma_command_timeout(), + check=True, + capture_output=True, + text=True, + env=_get_prisma_env(), + ) + return True + except subprocess.TimeoutExpired: + logger.info( + "prisma db push attempt %s timed out, retrying", + attempt + 1, + ) + time.sleep(random.randrange(5, 15)) + except subprocess.CalledProcessError as e: + stderr = e.stderr or "" + transient = ProxyExtrasDBManager._transient_prisma_failure( + stderr + ) + # Re-raise as RuntimeError so proxy_cli.py's + # `except RuntimeError` catches it and exits cleanly. + if transient is None or attempt == _PRISMA_ATTEMPTS - 1: + raise RuntimeError( + f"prisma db push failed.\n\nDetail: {e}" + f"\n\nPrisma error:\n{stderr}" + ) from e + logger.info( + "prisma db push attempt %s failed on %s, retrying. " + "Prisma error:\n%s", + attempt + 1, + transient, + stderr, + ) + time.sleep(random.randrange(5, 15)) + raise RuntimeError( + f"prisma db push failed after {_PRISMA_ATTEMPTS} attempts." ) - return True - except ( - subprocess.CalledProcessError, - subprocess.TimeoutExpired, - ) as e: - # Re-raise as RuntimeError so proxy_cli.py's - # `except RuntimeError` catches it and exits cleanly. - raise RuntimeError(f"prisma db push failed.\n\nDetail: {e}") from e finally: os.chdir(original_dir) @@ -692,7 +754,7 @@ class ProxyExtrasDBManager: original_dir = os.getcwd() os.chdir(migrations_dir) try: - for attempt in range(4): + for attempt in range(_PRISMA_ATTEMPTS): try: result = subprocess.run( [_get_prisma_command(), "migrate", "deploy"], @@ -807,16 +869,36 @@ class ProxyExtrasDBManager: f"Manual intervention required.\n\nPrisma error:\n{stderr}" ) from e - raise RuntimeError( - "Database migration failed and cannot be auto-recovered. " - f"Manual intervention required.\n\nPrisma error:\n{stderr}" - ) from e + transient = ProxyExtrasDBManager._transient_prisma_failure(stderr) + if transient is None: + raise RuntimeError( + "Database migration failed and cannot be auto-recovered. " + f"Manual intervention required.\n\nPrisma error:\n{stderr}" + ) from e + + if attempt == _PRISMA_ATTEMPTS - 1: + raise RuntimeError( + f"Database migration failed after " + f"{_PRISMA_ATTEMPTS} attempts on {transient}. " + "Check database connectivity and load." + f"\n\nPrisma error:\n{stderr}" + ) from e + + logger.info( + "prisma migrate deploy attempt %s failed on %s, retrying. " + "Prisma error:\n%s", + attempt + 1, + transient, + stderr, + ) + time.sleep(random.randrange(5, 15)) + continue raise RuntimeError( - "Database migration failed after 4 attempts (retry loop " - "exhausted by timeouts or repeated idempotent-recovery " - "continues). Check database connectivity, load, and " - "_prisma_migrations ledger state." + f"Database migration failed after {_PRISMA_ATTEMPTS} " + "attempts (retry loop exhausted by timeouts or repeated " + "idempotent-recovery continues). Check database connectivity, " + "load, and _prisma_migrations ledger state." ) finally: os.chdir(original_dir) @@ -864,10 +946,11 @@ class ProxyExtrasDBManager: Args: use_migrate: Whether to use prisma migrate instead of db push - use_v2_resolver: Opt into the v2 migration resolver (safer during + use_v2_resolver: Run the v2 migration resolver (safer during rolling deploys; does not run the diff-and-force recovery - that causes schema thrashing). Defaults to False for - backwards compatibility. + that causes schema thrashing). Defaults to False here so + direct callers keep the old behavior; the proxy CLI passes + True, so the proxy's runtime default is v2. Returns: bool: True if setup was successful, False otherwise @@ -885,7 +968,7 @@ class ProxyExtrasDBManager: @staticmethod def _run_migrations(use_migrate: bool, use_v2_resolver: bool) -> bool: if use_v2_resolver: - logger.info("Using v2 migration resolver (--use_v2_migration_resolver)") + logger.info("Using v2 migration resolver") return ProxyExtrasDBManager._setup_database_v2(use_migrate=use_migrate) schema_path = ProxyExtrasDBManager._get_prisma_dir() + "/schema.prisma" diff --git a/litellm-proxy-extras/pyproject.toml b/litellm-proxy-extras/pyproject.toml index d5741d479bf..0944f99ad54 100644 --- a/litellm-proxy-extras/pyproject.toml +++ b/litellm-proxy-extras/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "litellm-proxy-extras" -version = "0.4.91" +version = "0.4.92" description = "Additional files for the LiteLLM Proxy. Reduces the size of the main litellm package." readme = "README.md" requires-python = ">=3.9" @@ -26,7 +26,7 @@ required-version = ">=0.10.9" module-root = "" [tool.commitizen] -version = "0.4.91" +version = "0.4.92" version_files = [ "pyproject.toml:^version", "../pyproject.toml:litellm-proxy-extras==", diff --git a/litellm-proxy-extras/tests/test_setup_database_fail_fast.py b/litellm-proxy-extras/tests/test_setup_database_fail_fast.py deleted file mode 100644 index 8d66bf872de..00000000000 --- a/litellm-proxy-extras/tests/test_setup_database_fail_fast.py +++ /dev/null @@ -1,242 +0,0 @@ -"""Regression tests for ProxyExtrasDBManager v2 migration resolver. - -The v2 resolver is opt-in via `--use_v2_migration_resolver` / the -`use_v2_resolver=True` kwarg. These tests exercise the v2 path; the v1 -(default) behavior is unchanged from pre-fix. -""" - -import subprocess -from unittest.mock import patch - -import pytest - -from litellm_proxy_extras.utils import ( - ProxyExtrasDBManager, - _max_migration_timestamp, - _migration_timestamp, -) - - -def _fake_migrate_deploy_failure(returncode: int, stderr: str): - def _run(*args, **kwargs): - raise subprocess.CalledProcessError( - returncode=returncode, - cmd=args[0], - stderr=stderr, - output="", - ) - - return _run - - -def test_v2_p3018_permission_error_raises_runtime_error(monkeypatch, tmp_path): - """v2: a permission failure during migrate deploy raises RuntimeError.""" - monkeypatch.setenv("DATABASE_URL", "postgresql://u:p@localhost:9/x") - monkeypatch.setattr( - ProxyExtrasDBManager, "_warn_if_db_ahead_of_head", lambda _: None - ) - monkeypatch.setattr(ProxyExtrasDBManager, "_get_prisma_dir", lambda: str(tmp_path)) - (tmp_path / "schema.prisma").write_text("// stub") - - stderr = ( - "Error: P3018\nMigration name: 20250326162113_baseline\n" - "Database error code: 42501\npermission denied for schema public" - ) - with patch("subprocess.run", side_effect=_fake_migrate_deploy_failure(1, stderr)): - with pytest.raises(RuntimeError, match="permission"): - ProxyExtrasDBManager.setup_database(use_migrate=True, use_v2_resolver=True) - - -def test_v2_non_idempotent_p3009_raises_runtime_error(monkeypatch, tmp_path): - """v2: a non-idempotent migration failure raises (no silent recovery).""" - monkeypatch.setenv("DATABASE_URL", "postgresql://u:p@localhost:9/x") - monkeypatch.setattr( - ProxyExtrasDBManager, "_warn_if_db_ahead_of_head", lambda _: None - ) - monkeypatch.setattr(ProxyExtrasDBManager, "_get_prisma_dir", lambda: str(tmp_path)) - (tmp_path / "schema.prisma").write_text("// stub") - - stderr = ( - "Error: P3009\nMigration `20260101000000_genuinely_broken` failed\n" - 'Reason: syntax error at or near "BRKN" LINE 42' - ) - with patch("subprocess.run", side_effect=_fake_migrate_deploy_failure(1, stderr)): - with pytest.raises(RuntimeError, match="cannot be auto-recovered"): - ProxyExtrasDBManager.setup_database(use_migrate=True, use_v2_resolver=True) - - -def test_strip_prisma_query_params_removes_connection_limit(): - """DATABASE_URLs with Prisma-specific params should be parseable by psycopg.""" - url = "postgresql://u:p@h:5432/db?connection_limit=100&pool_timeout=60&sslmode=require" - stripped = ProxyExtrasDBManager._strip_prisma_query_params(url) - assert "connection_limit" not in stripped - assert "pool_timeout" not in stripped - assert "sslmode=require" in stripped - - -def test_strip_prisma_query_params_passthrough_no_query(): - """URLs without query strings are returned unchanged.""" - url = "postgresql://u:p@h:5432/db" - assert ProxyExtrasDBManager._strip_prisma_query_params(url) == url - - -def test_migration_timestamp_extracts_leading_digits(): - assert _migration_timestamp("20260101000000_add_foo") == 20260101000000 - assert _migration_timestamp("20250326162113_baseline") == 20250326162113 - - -def test_migration_timestamp_returns_zero_on_malformed(): - assert _migration_timestamp("0_init") == 0 - assert _migration_timestamp("not_a_migration") == 0 - - -def test_max_migration_timestamp(): - names = {"20250326000000_a", "20260415000000_b", "20251115000000_c"} - assert _max_migration_timestamp(names) == 20260415000000 - - -def test_max_migration_timestamp_empty_set(): - assert _max_migration_timestamp(set()) == 0 - - -def test_v1_default_still_calls_resolve_all_migrations(monkeypatch, tmp_path): - """v1 (default) continues to call _resolve_all_migrations on the happy path. - - This is the existing buggy behavior — we're not fixing it in v1, only - offering v2 as opt-in. This test pins the default so that a future - inadvertent default flip is caught. - """ - monkeypatch.setattr(ProxyExtrasDBManager, "_get_prisma_dir", lambda: str(tmp_path)) - (tmp_path / "schema.prisma").write_text("// stub") - - # Stub `prisma migrate deploy` to claim success with pending migrations - # applied, which is the code path that triggers the legacy post-migration - # sanity check (a call to _resolve_all_migrations). - class FakeResult: - stdout = "Applied migration.\n" - stderr = "" - - def fake_run(cmd, *args, **kwargs): - return FakeResult() - - resolve_called = {"n": 0} - - def fake_resolve(*args, **kwargs): - resolve_called["n"] += 1 - - monkeypatch.setattr("subprocess.run", fake_run) - monkeypatch.setattr(ProxyExtrasDBManager, "_resolve_all_migrations", fake_resolve) - - ok = ProxyExtrasDBManager.setup_database(use_migrate=True) # v2 flag NOT set - assert ok is True - assert resolve_called["n"] == 1, "v1 default should still invoke the legacy path" - - -def test_v2_db_push_wraps_subprocess_error_as_runtime_error(monkeypatch, tmp_path): - """v2: a failing `prisma db push` must raise RuntimeError, not leak - CalledProcessError past proxy_cli.py's `except RuntimeError`.""" - monkeypatch.setattr(ProxyExtrasDBManager, "_get_prisma_dir", lambda: str(tmp_path)) - (tmp_path / "schema.prisma").write_text("// stub") - - stderr = "db push error" - with patch("subprocess.run", side_effect=_fake_migrate_deploy_failure(1, stderr)): - with pytest.raises(RuntimeError, match="prisma db push failed"): - ProxyExtrasDBManager.setup_database(use_migrate=False, use_v2_resolver=True) - - -def test_v2_warn_ahead_of_head_swallows_db_errors(monkeypatch, tmp_path): - """_warn_if_db_ahead_of_head must never raise — it's informational. - - Non-connection DB errors (e.g. InsufficientPrivilege from a user - without SELECT on _prisma_migrations) must be caught, not propagated. - """ - import psycopg - - monkeypatch.setenv("DATABASE_URL", "postgresql://u:p@localhost:9/x") - monkeypatch.setattr(ProxyExtrasDBManager, "_get_prisma_dir", lambda: str(tmp_path)) - (tmp_path / "schema.prisma").write_text("// stub") - - class _FakeConn: - def __enter__(self): - return self - - def __exit__(self, *a): - return False - - def execute(self, *a, **kw): - # Simulate an InsufficientPrivilege (subclass of DatabaseError). - raise psycopg.errors.InsufficientPrivilege("permission denied") - - def _fake_connect(*a, **kw): - return _FakeConn() - - monkeypatch.setattr("psycopg.connect", _fake_connect) - - # Must not raise. - ProxyExtrasDBManager._warn_if_db_ahead_of_head(str(tmp_path)) - - -def test_v2_resolve_specific_migration_failure_raises_runtime_error( - monkeypatch, tmp_path -): - """If marking a migration as applied fails inside P3009 idempotent - recovery, the subprocess error must be re-raised as RuntimeError so - proxy_cli.py catches it cleanly (instead of leaking CalledProcessError).""" - monkeypatch.setattr( - ProxyExtrasDBManager, "_warn_if_db_ahead_of_head", lambda _: None - ) - monkeypatch.setattr(ProxyExtrasDBManager, "_get_prisma_dir", lambda: str(tmp_path)) - (tmp_path / "schema.prisma").write_text("// stub") - monkeypatch.setattr( - ProxyExtrasDBManager, "_roll_back_migration", lambda *a, **kw: None - ) - - # First call: migrate deploy -> P3009 idempotent error. - # Recovery path tries _resolve_specific_migration; that also raises. - def _failing_resolve(*a, **kw): - raise subprocess.CalledProcessError( - returncode=1, - cmd="prisma migrate resolve --applied", - stderr="resolve failed", - output="", - ) - - monkeypatch.setattr( - ProxyExtrasDBManager, "_resolve_specific_migration", _failing_resolve - ) - - stderr = ( - "Error: P3009\nMigration `20260101000000_some_migration` failed\n" - "relation already exists" - ) - with patch("subprocess.run", side_effect=_fake_migrate_deploy_failure(1, stderr)): - with pytest.raises( - RuntimeError, match="Failed to mark migration .* as applied" - ): - ProxyExtrasDBManager.setup_database(use_migrate=True, use_v2_resolver=True) - - -def test_v2_does_not_call_resolve_all_migrations(monkeypatch, tmp_path): - """v2 must never call _resolve_all_migrations — that's the bug it fixes.""" - monkeypatch.setattr( - ProxyExtrasDBManager, "_warn_if_db_ahead_of_head", lambda _: None - ) - monkeypatch.setattr(ProxyExtrasDBManager, "_get_prisma_dir", lambda: str(tmp_path)) - (tmp_path / "schema.prisma").write_text("// stub") - - class FakeResult: - stdout = "Applied migration.\n" - stderr = "" - - monkeypatch.setattr("subprocess.run", lambda *a, **kw: FakeResult()) - - resolve_called = {"n": 0} - monkeypatch.setattr( - ProxyExtrasDBManager, - "_resolve_all_migrations", - lambda *a, **kw: resolve_called.__setitem__("n", resolve_called["n"] + 1), - ) - - ok = ProxyExtrasDBManager.setup_database(use_migrate=True, use_v2_resolver=True) - assert ok is True - assert resolve_called["n"] == 0, "v2 must not invoke the diff-and-force recovery" diff --git a/litellm-rust/Cargo.toml b/litellm-rust/Cargo.toml index 481ea3f8f66..c17a0605fc7 100644 --- a/litellm-rust/Cargo.toml +++ b/litellm-rust/Cargo.toml @@ -30,3 +30,12 @@ tokio = { version = "1", features = ["rt-multi-thread", "macros", "time", "net"] tokio-tungstenite = { version = "0.24", default-features = false, features = ["connect", "rustls-tls-native-roots"] } futures-util = { version = "0.3", default-features = false, features = ["sink", "std"] } base64 = "0.22" + +[profile.release] +opt-level = 3 +lto = "thin" +codegen-units = 1 +panic = "unwind" +debug = false +incremental = false +strip = "symbols" diff --git a/litellm-rust/crates/python-bridge/Cargo.toml b/litellm-rust/crates/python-bridge/Cargo.toml index 0c4a753f762..d461a483ae0 100644 --- a/litellm-rust/crates/python-bridge/Cargo.toml +++ b/litellm-rust/crates/python-bridge/Cargo.toml @@ -10,7 +10,8 @@ name = "_native" crate-type = ["cdylib"] [features] -default = ["extension-module"] +default = ["abi3"] +abi3 = ["pyo3/abi3-py310"] extension-module = ["pyo3/extension-module"] [dependencies] diff --git a/litellm/__init__.py b/litellm/__init__.py index c83e72a78b4..4eeececdb7e 100644 --- a/litellm/__init__.py +++ b/litellm/__init__.py @@ -7,6 +7,9 @@ warnings.filterwarnings("ignore", message=".*conflict with protected namespace.* # Suppress Pydantic 2.11+ deprecation warning about accessing model_fields on instances # This warning can accumulate during streaming and cause memory leaks warnings.filterwarnings("ignore", message=".*Accessing the.*attribute on the instance is deprecated.*") +# ReadOnly on TypedDict fields is repo-wide static discipline (LIT012); pydantic warns it +# cannot enforce it at runtime, which floods proxy boot once such a type is schema-walked +warnings.filterwarnings("ignore", message=".*`ReadOnly` qualifier.*") ### INIT VARIABLES ######################### import threading import os @@ -656,6 +659,8 @@ aiml_models: Set = set() deepgram_models: Set = set() elevenlabs_models: Set = set() dashscope_models: Set = set() +qwencloud_models: Set = set() +qwen_ai_platform_models: Set = set() moonshot_models: Set = set() publicai_models: Set = set() darkbloom_models: Set = set() @@ -906,6 +911,10 @@ def _populate_provider_model_sets(model_cost_map: Dict) -> None: heroku_models.add(key) elif value.get("litellm_provider") == "dashscope": dashscope_models.add(key) + elif value.get("litellm_provider") == "qwencloud": + qwencloud_models.add(key) + elif value.get("litellm_provider") == "qwen_ai_platform": + qwen_ai_platform_models.add(key) elif value.get("litellm_provider") == "modelscope": modelscope_models.add(key) elif value.get("litellm_provider") == "moonshot": @@ -1069,6 +1078,8 @@ model_list = list( | deepgram_models | elevenlabs_models | dashscope_models + | qwencloud_models + | qwen_ai_platform_models | moonshot_models | publicai_models | darkbloom_models @@ -1175,6 +1186,8 @@ def _build_models_by_provider() -> dict: "elevenlabs": elevenlabs_models, "heroku": heroku_models, "dashscope": dashscope_models, + "qwencloud": qwencloud_models, + "qwen_ai_platform": qwen_ai_platform_models, "modelscope": modelscope_models, "moonshot": moonshot_models, "publicai": publicai_models, @@ -2011,6 +2024,24 @@ if TYPE_CHECKING: from .llms.dashscope.rerank.transformation import ( DashScopeRerankConfig as DashScopeRerankConfig, ) + from .llms.dashscope.qwencloud import ( + QwenCloudChatConfig as QwenCloudChatConfig, + ) + from .llms.dashscope.qwencloud import ( + QwenCloudEmbeddingConfig as QwenCloudEmbeddingConfig, + ) + from .llms.dashscope.qwencloud import ( + QwenCloudRerankConfig as QwenCloudRerankConfig, + ) + from .llms.dashscope.qwen_ai_platform import ( + QwenAIPlatformChatConfig as QwenAIPlatformChatConfig, + ) + from .llms.dashscope.qwen_ai_platform import ( + QwenAIPlatformEmbeddingConfig as QwenAIPlatformEmbeddingConfig, + ) + from .llms.dashscope.qwen_ai_platform import ( + QwenAIPlatformRerankConfig as QwenAIPlatformRerankConfig, + ) from .llms.modelscope.chat.transformation import ( ModelScopeChatConfig as ModelScopeChatConfig, ) diff --git a/litellm/_lazy_imports_registry.py b/litellm/_lazy_imports_registry.py index 1c833256598..e9199e1ec80 100644 --- a/litellm/_lazy_imports_registry.py +++ b/litellm/_lazy_imports_registry.py @@ -310,6 +310,8 @@ LLM_CONFIG_NAMES: Final = ( "GigaChatConfig", "GigaChatEmbeddingConfig", "DashScopeChatConfig", + "QwenCloudChatConfig", + "QwenAIPlatformChatConfig", "ModelScopeChatConfig", "MoonshotChatConfig", "DockerModelRunnerChatConfig", @@ -1172,6 +1174,14 @@ _LLM_CONFIGS_IMPORT_MAP: Final = { ".llms.dashscope.chat.transformation", "DashScopeChatConfig", ), + "QwenCloudChatConfig": ( + ".llms.dashscope.qwencloud", + "QwenCloudChatConfig", + ), + "QwenAIPlatformChatConfig": ( + ".llms.dashscope.qwen_ai_platform", + "QwenAIPlatformChatConfig", + ), "GDCGeminiConfig": ( ".llms.gdc.chat.transformation", "GDCGeminiConfig", diff --git a/litellm/_logging.py b/litellm/_logging.py index fbb35b72be2..9435562f890 100644 --- a/litellm/_logging.py +++ b/litellm/_logging.py @@ -264,13 +264,17 @@ def _plain_log_format(stdout: TextIO | None, stderr: TextIO | None) -> str: class LevelRoutingStreamHandler(logging.StreamHandler): - """Writes records below WARNING to stdout and WARNING and above to stderr. + """Writes records below WARNING and invalid-key warnings to stdout, others to stderr. Collectors that derive severity from the stream report every stderr line as an error. + Invalid-key warnings route to stdout so LITELLM_LOG=ERROR can suppress them. """ def emit(self, record: logging.LogRecord) -> None: - preferred: Final = sys.stdout if record.levelno < logging.WARNING else sys.stderr + is_stdout_record: Final = record.levelno < logging.WARNING or ( + record.levelno == logging.WARNING and record.name == verbose_proxy_stdout_logger.name + ) + preferred: Final = sys.stdout if is_stdout_record else sys.stderr if preferred is None or getattr(preferred, "closed", False): self.stream = sys.stderr # rebind-ok: fall back to the pre-fix stream rather than raising per record else: @@ -508,6 +512,9 @@ else: handler.setFormatter(formatter) verbose_proxy_logger = logging.getLogger("LiteLLM Proxy") +# Malformed virtual key rejections log through this child; LevelRoutingStreamHandler +# writes its WARNING records to stdout. It has no handler or level of its own. +verbose_proxy_stdout_logger: Final = verbose_proxy_logger.getChild("stdout") verbose_router_logger = logging.getLogger("LiteLLM Router") verbose_logger = logging.getLogger("LiteLLM") @@ -520,6 +527,7 @@ verbose_logger.addHandler(handler) # handlers (JSON mode, uvicorn log config, a host app's root handler). verbose_router_logger.addFilter(_stdout_truncation_filter) verbose_proxy_logger.addFilter(_stdout_truncation_filter) +verbose_proxy_stdout_logger.addFilter(_stdout_truncation_filter) verbose_logger.addFilter(_stdout_truncation_filter) @@ -683,6 +691,7 @@ def _turn_on_json(): - Adds a JSON formatter to all loggers """ handler: Final = LevelRoutingStreamHandler() + handler.setLevel(numeric_level) handler.setFormatter(JsonFormatter()) _initialize_loggers_with_handler(handler) # Set up exception handlers @@ -700,12 +709,14 @@ def _disable_debugging(): verbose_logger.disabled = True verbose_router_logger.disabled = True verbose_proxy_logger.disabled = True + verbose_proxy_stdout_logger.disabled = True def _enable_debugging(): verbose_logger.disabled = False verbose_router_logger.disabled = False verbose_proxy_logger.disabled = False + verbose_proxy_stdout_logger.disabled = False def print_verbose(print_statement): diff --git a/litellm/_redis.py b/litellm/_redis.py index 9381357931e..3e68d50cf16 100644 --- a/litellm/_redis.py +++ b/litellm/_redis.py @@ -13,6 +13,7 @@ import json # s/o [@Frank Colson](https://www.linkedin.com/in/frank-colson-422b9b183/) for this redis implementation import os from collections.abc import Callable, Mapping +from types import MappingProxyType from typing import Final from urllib.parse import urlsplit, urlunsplit @@ -38,9 +39,25 @@ from ._logging import verbose_logger AZURE_REDIS_SCOPE: Final = "https://redis.azure.com/.default" -def _get_redis_kwargs(): - arg_spec: Final = inspect.getfullargspec(redis.Redis) +def _unwrapped_init_args(cls: type) -> frozenset[str]: + """Every parameter on a single class's own ``__init__``, decorator-unwrapped. + Unlike ``_init_arg_names`` below, this does not walk the MRO: ``redis.Redis`` + and ``redis.RedisCluster`` (sync and async) each declare every real + constructor parameter directly on their own ``__init__``, so MRO-walking is + unnecessary — and it actively breaks the several tests here that mock the + class with ``patch(..., autospec=True)``, since ``inspect.getmro`` needs a + real ``__mro__`` that an autospec'd stand-in for a class does not provide. + + Still unwraps first: redis-py >= 7.4 decorates these ``__init__``s with + ``@deprecated_args`` too, which the same class of bug as ``_init_arg_names`` + would otherwise silently empty this allowlist through (see its docstring). + """ + spec: Final = inspect.getfullargspec(inspect.unwrap(cls.__init__)) + return frozenset(spec.args + spec.kwonlyargs) + + +def _get_redis_kwargs(): # Only allow primitive arguments exclude_args: Final = { "self", @@ -60,7 +77,7 @@ def _get_redis_kwargs(): "azure_client_secret", } - available_args: Final = {x for x in arg_spec.args if x not in exclude_args} | include_args + available_args: Final = {x for x in _unwrapped_init_args(redis.Redis) if x not in exclude_args} | include_args return available_args @@ -120,15 +137,23 @@ def _get_redis_url_kwargs(client: type | None = None) -> tuple[str, ...]: return tuple(x for x in _init_arg_names(connection_cls) if x not in exclude_args) + include_args -def _get_redis_cluster_kwargs(client=None): +def _get_redis_cluster_kwargs(client: type | None = None): + """Config kwargs the target cluster client's constructor actually accepts. + + Defaults to the sync ``redis.RedisCluster``, but the async cluster client + (``redis.asyncio.cluster.RedisCluster``) declares connection settings such as + ``decode_responses`` on its own constructor, where the sync class takes them + through ``**kwargs`` and so never names them in its signature. Introspecting + only the sync class regardless of which client is actually built silently + drops those for every async cluster caller. + """ if client is None: - client = redis.Redis.from_url - arg_spec: Final = inspect.getfullargspec(redis.RedisCluster) + client = redis.RedisCluster # Only allow primitive arguments exclude_args: Final = {"self", "connection_pool", "retry", "host", "port", "startup_nodes"} - available_args = {x for x in arg_spec.args if x not in exclude_args} + available_args = {x for x in _unwrapped_init_args(client) if x not in exclude_args} available_args |= { "password", "username", @@ -161,6 +186,79 @@ def _get_redis_env_kwarg_mapping(): return {f"{PREFIX}{x.upper()}": x for x in _get_redis_kwargs() if x not in exclude_from_environment} +def _str_to_bool(value: str) -> bool: + return value.lower() in ("true", "1", "yes") + + +def _coerce_redis_kwargs_types( + redis_kwargs: Mapping[str, object], + client: type | tuple[type, ...] = redis.Redis, +) -> dict[str, object]: # mutable-ok: a caller mutates the returned kwargs before constructing its client + """Coerces string values to the numeric/boolean type ``client``'s constructor + declares for that parameter. ``client`` may be a tuple of client classes; a + parameter's type is taken from the first signature that declares it, which + lets cluster callers coerce cluster-only kwargs such as + ``cluster_error_retry_attempts`` alongside the shared connection kwargs. + + Environment variables are always strings, and Helm ``--set`` stringifies values + too, so a config value like ``health_check_interval`` or ``socket_timeout`` + can arrive as ``"30"``/``"5.5"`` rather than a real number. redis-py's own + connection-health-check arithmetic (``loop.time() + self.health_check_interval``) + then raises ``TypeError`` on every Redis operation instead of connecting. + + ``max_connections``, ``socket_timeout``, and ``socket_connect_timeout`` use an + explicit target type rather than the parameter's own signature default: redis-py + 8.x changed the timeout defaults from ``None`` to int ``5``, so inferring the + type from the default would make a fractional ``"5.5"`` fail ``int()`` and get + silently dropped on 8.x while working on older versions. ``socket_keepalive`` + is explicit too: its signature default is ``None``, which carries no type to + infer from, and leaving it a string makes ``"false"`` truthy. + """ + signatures: Final = tuple(inspect.signature(c) for c in (client if isinstance(client, tuple) else (client,))) + explicit_param_types: Final = MappingProxyType( + { + "max_connections": int, + "socket_timeout": float, + "socket_connect_timeout": float, + "socket_keepalive": bool, + } + ) + result: Final = dict(redis_kwargs) # mutable-ok: per-key try/except coercion below needs to drop individual keys + for key, value in redis_kwargs.items(): + if not isinstance(value, str): + continue + param = next((sig.parameters[key] for sig in signatures if key in sig.parameters), None) + if param is None: + continue + explicit_type = explicit_param_types.get(key) + if explicit_type is bool: + result[key] = _str_to_bool(value) + continue + if explicit_type is not None: + try: + result[key] = explicit_type(value) + except (ValueError, TypeError): + del result[key] + continue + default: object = param.default # pyright: ignore[reportAny] # inspect.Parameter.default is stubbed as Any + if default is inspect.Parameter.empty: + continue + # bool must be checked before int, since bool subclasses int + if isinstance(default, bool): + result[key] = _str_to_bool(value) + elif isinstance(default, int): + try: + result[key] = int(value) + except (ValueError, TypeError): + del result[key] + elif isinstance(default, float): + try: + result[key] = float(value) + except (ValueError, TypeError): + del result[key] + return result + + def _redis_kwargs_from_environment(): mapping: Final = _get_redis_env_kwarg_mapping() @@ -505,7 +603,12 @@ def _get_redis_client_logic(**env_overrides): raise ValueError("Either 'host' or 'url' must be specified for redis.") # litellm.print_verbose(f"redis_kwargs: {redis_kwargs}") - return redis_kwargs + coercion_client: Final = ( + (redis.Redis, redis.RedisCluster, async_redis.RedisCluster) + if redis_kwargs.get("startup_nodes") + else redis.Redis + ) + return _coerce_redis_kwargs_types(redis_kwargs, client=coercion_client) def init_redis_cluster(redis_kwargs) -> redis.RedisCluster: @@ -657,7 +760,9 @@ def get_redis_client(**env_overrides): if "sentinel_nodes" in redis_kwargs and "service_name" in redis_kwargs: return _init_redis_sentinel(redis_kwargs) - return redis.Redis(**redis_kwargs) + return redis.Redis( # pyright: ignore[reportCallIssue] # object-valued kwargs match no overload statically + **redis_kwargs, # pyright: ignore[reportArgumentType] # allow-listed and coerced against this signature + ) def get_redis_async_client( @@ -669,7 +774,7 @@ def get_redis_async_client( if "startup_nodes" in redis_kwargs: from redis.cluster import ClusterNode - args = _get_redis_cluster_kwargs() + args = _get_redis_cluster_kwargs(async_redis.RedisCluster) cluster_kwargs: Final = {} for arg in redis_kwargs: if arg in args: diff --git a/litellm/caching/caching.py b/litellm/caching/caching.py index cefe6aae9ed..754815fce47 100644 --- a/litellm/caching/caching.py +++ b/litellm/caching/caching.py @@ -12,6 +12,7 @@ import hashlib import json import time import traceback +from collections.abc import Mapping from enum import Enum from typing import Any, Final @@ -506,7 +507,7 @@ class Cache: def _get_cache_logic( self, - cached_result: Any | None, + cached_result: object | None, max_age: float | None, ): """ @@ -538,8 +539,8 @@ class Cache: return cached_result @staticmethod - def _get_safe_cache_lookup_kwargs(kwargs: dict[str, Any]) -> dict[str, Any]: - cache_lookup_kwargs: Final[dict[str, Any]] = {} + def _get_safe_cache_lookup_kwargs(kwargs: Mapping[str, object]) -> dict[str, object]: + cache_lookup_kwargs: Final[dict[str, object]] = {} for prompt_kwarg in ("messages", "input"): if prompt_kwarg in kwargs: cache_lookup_kwargs[prompt_kwarg] = kwargs[prompt_kwarg] @@ -552,7 +553,7 @@ class Cache: @staticmethod def _update_metadata_from_cache_lookup_kwargs( - original_kwargs: dict[str, Any], cache_lookup_kwargs: dict[str, Any] + original_kwargs: Mapping[str, object], cache_lookup_kwargs: Mapping[str, object] ) -> None: original_metadata: Final = original_kwargs.get("metadata") cache_lookup_metadata: Final = cache_lookup_kwargs.get("metadata") diff --git a/litellm/caching/qdrant_semantic_cache.py b/litellm/caching/qdrant_semantic_cache.py index 4898700c403..c5876e993d3 100644 --- a/litellm/caching/qdrant_semantic_cache.py +++ b/litellm/caching/qdrant_semantic_cache.py @@ -12,7 +12,7 @@ import ast import asyncio import json import os -from typing import TYPE_CHECKING, Any, Final, cast +from typing import TYPE_CHECKING, Any, Final, Protocol, cast import litellm from litellm._logging import print_verbose @@ -39,6 +39,12 @@ if TYPE_CHECKING: from litellm.router import Router +class _QdrantCollectionDetailsResponse(Protocol): + """The qdrant `/collections/{name}` response, whose body is kept as an opaque JSON object.""" + + def json(self) -> dict[str, object]: ... + + class QdrantSemanticCache(BaseCache): CACHE_KEY_FIELD_NAME = "litellm_cache_key" embedding_max_input_tokens: int | None = None @@ -115,15 +121,15 @@ class QdrantSemanticCache(BaseCache): raise ValueError(f"Error from qdrant checking if /collections exist {collection_exists.text}") if collection_exists.json()["result"]["exists"]: - collection_details = self.sync_client.get( + collection_details: _QdrantCollectionDetailsResponse = self.sync_client.get( url=f"{self.qdrant_api_base}/collections/{self.collection_name}", headers=self.headers, ) - self.collection_info = collection_details.json() + self.collection_info: dict[str, object] = collection_details.json() print_verbose(f"Collection already exists.\nCollection details:{self.collection_info}") self._ensure_cache_key_payload_index() else: - quantization_params: dict[str, Any] + quantization_params: dict[str, dict[str, object]] if quantization_config is None or quantization_config == "binary": quantization_params = { "binary": { @@ -214,7 +220,7 @@ class QdrantSemanticCache(BaseCache): resolve_embedding_max_input_tokens(self.embedding_max_input_tokens, self.embedding_model, router), ) - def _get_embedding(self, prompt: str, metadata: dict[str, Any] | None = None) -> EmbeddingResponse: + def _get_embedding(self, prompt: str, metadata: dict[str, object] | None = None) -> EmbeddingResponse: """Embed via the proxy Router when it serves the model, else direct.""" try: from litellm.proxy.proxy_server import llm_model_list, llm_router @@ -241,7 +247,7 @@ class QdrantSemanticCache(BaseCache): num_retries=0, ) - async def _get_async_embedding(self, prompt: str, metadata: dict[str, Any] | None = None) -> EmbeddingResponse: + async def _get_async_embedding(self, prompt: str, metadata: dict[str, object] | None = None) -> EmbeddingResponse: try: from litellm.proxy.proxy_server import llm_model_list, llm_router except ImportError: diff --git a/litellm/caching/redis_cache.py b/litellm/caching/redis_cache.py index f1c80eaacbe..2b04a075114 100644 --- a/litellm/caching/redis_cache.py +++ b/litellm/caching/redis_cache.py @@ -18,7 +18,7 @@ import time from collections.abc import Awaitable, Callable, Sequence from contextvars import ContextVar from datetime import timedelta -from typing import TYPE_CHECKING, Any, Final, TypeVar, cast +from typing import TYPE_CHECKING, Any, Final, Protocol, TypeVar, cast import litellm from litellm._logging import print_verbose, verbose_logger @@ -58,6 +58,26 @@ else: Span = Any +class _AsyncRedisCommands(Protocol): + """Async redis commands this cache issues. + + redis-py's type stubs omit these methods on RedisCluster, so the union returned by + init_async_client() is untyped at every call site without this protocol. + """ + + def ping(self) -> Awaitable[bool]: ... + + def delete(self, *names: str) -> Awaitable[int]: ... + + def ttl(self, name: str) -> Awaitable[int]: ... + + def rpush(self, name: str, *values: str | bytes | float) -> Awaitable[int]: ... + + def lpop(self, name: str, count: int | None = None) -> Awaitable[object]: ... + + def pipeline(self, transaction: bool = True) -> "Pipeline[bytes]": ... + + def _get_call_stack_info(num_frames: int = 2) -> str: """ Get the function names from the previous 1-2 functions in the call stack. @@ -429,6 +449,9 @@ class RedisCache(BaseCache): self.redis_async_client = redis_async_client return redis_async_client + def _async_commands(self) -> _AsyncRedisCommands: + return self.init_async_client() + def check_and_fix_namespace(self, key: str) -> str: """ Make sure each key starts with the given namespace @@ -1055,19 +1078,17 @@ class RedisCache(BaseCache): await self.async_set_cache_pipeline(self.redis_batch_writing_buffer) self.redis_batch_writing_buffer = [] - def _get_cache_logic(self, cached_response: Any): + def _get_cache_logic(self, cached_response: bytes | str | None): """ Common 'get_cache_logic' across sync + async redis client implementations """ if cached_response is None: - return cached_response - # cached_response is in `b{} convert it to ModelResponse - cached_response = cached_response.decode("utf-8") # Convert bytes to string + return None + decoded: Final = cached_response.decode("utf-8") if isinstance(cached_response, bytes) else cached_response try: - cached_response = json.loads(cached_response) # Convert string to dictionary + return json.loads(decoded) except Exception: - cached_response = ast.literal_eval(cached_response) - return cached_response + return ast.literal_eval(decoded) def get_cache(self, key, parent_otel_span: Span | None = None, **kwargs): try: @@ -1314,8 +1335,7 @@ class RedisCache(BaseCache): raise e async def ping(self) -> bool: - # typed as Any, redis python lib has incomplete type stubs for RedisCluster and does not include `ping` - _redis_client: Final[Any] = self.init_async_client() + _redis_client: Final = self._async_commands() start_time: Final = time.time() print_verbose("Pinging Async Redis Cache") try: @@ -1349,8 +1369,7 @@ class RedisCache(BaseCache): @_redis_circuit_breaker_guard async def delete_cache_keys(self, keys): - # typed as Any, redis python lib has incomplete type stubs for RedisCluster and does not include `delete` - _redis_client: Final[Any] = self.init_async_client() + _redis_client: Final = self._async_commands() keys = [self.check_and_fix_namespace(key=key) for key in keys] # keys is a list, unpack it so it gets passed as individual elements to delete await _redis_client.delete(*keys) @@ -1415,8 +1434,7 @@ class RedisCache(BaseCache): @_redis_circuit_breaker_guard async def async_delete_cache(self, key: str): - # typed as Any, redis python lib has incomplete type stubs for RedisCluster and does not include `delete` - _redis_client: Final[Any] = self.init_async_client() + _redis_client: Final = self._async_commands() key = self.check_and_fix_namespace(key=key) # keys is str return await _redis_client.delete(key) @@ -1523,8 +1541,7 @@ class RedisCache(BaseCache): Redis ref: https://redis.io/docs/latest/commands/ttl/ """ try: - # typed as Any, redis python lib has incomplete type stubs for RedisCluster and does not include `ttl` - _redis_client: Final[Any] = self.init_async_client() + _redis_client: Final = self._async_commands() key = self.check_and_fix_namespace(key=key) ttl: Final = await _redis_client.ttl(key) if ttl <= -1: # -1 means the key does not exist, -2 key does not exist @@ -1554,7 +1571,7 @@ class RedisCache(BaseCache): Returns: int: The length of the list after the push operation """ - _redis_client: Final[Any] = self.init_async_client() + _redis_client: Final = self._async_commands() key = self.check_and_fix_namespace(key=key) start_time: Final = time.time() try: @@ -1621,7 +1638,7 @@ class RedisCache(BaseCache): if len(rpush_list) == 0: return [] - _redis_client: Final[Any] = self.init_async_client() + _redis_client: Final = self._async_commands() start_time: Final = time.time() try: @@ -1678,7 +1695,7 @@ class RedisCache(BaseCache): parent_otel_span: Span | None = None, **kwargs, ) -> Any | list[Any]: - _redis_client: Final[Any] = self.init_async_client() + _redis_client: Final = self._async_commands() key = self.check_and_fix_namespace(key=key) start_time: Final = time.time() print_verbose(f"LPOP from Redis list: key: {key}, count: {count}") @@ -1810,7 +1827,7 @@ class RedisCache(BaseCache): if len(lpop_list) == 0: return [] - _redis_client: Final[Any] = self.init_async_client() + _redis_client: Final = self._async_commands() start_time: Final = time.time() try: diff --git a/litellm/completion_extras/litellm_responses_transformation/handler.py b/litellm/completion_extras/litellm_responses_transformation/handler.py index 727c39c16ec..f494d6610a1 100644 --- a/litellm/completion_extras/litellm_responses_transformation/handler.py +++ b/litellm/completion_extras/litellm_responses_transformation/handler.py @@ -45,14 +45,14 @@ class ResponsesToCompletionBridgeHandler: return bool(stream) @staticmethod - def _is_preformatted_cached_chat_stream(result: Any) -> bool: + def _is_preformatted_cached_chat_stream(result: object) -> bool: from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper return isinstance(result, CustomStreamWrapper) and result.custom_llm_provider == "cached_response" @staticmethod def _coerce_response_object( - response_obj: Any, + response_obj: object, hidden_params: dict | None, ) -> "ResponsesAPIResponse": if isinstance(response_obj, ResponsesAPIResponse): @@ -78,8 +78,8 @@ class ResponsesToCompletionBridgeHandler: for _ in stream_iter: pass - completed: Final = getattr(stream_iter, "completed_response", None) - response_obj: Final = getattr(completed, "response", None) if completed else None + completed: Final[object] = getattr(stream_iter, "completed_response", None) + response_obj: Final[object] = getattr(completed, "response", None) if completed else None if response_obj is None: raise ValueError("Stream ended without a completed response") @@ -93,8 +93,8 @@ class ResponsesToCompletionBridgeHandler: async for _ in stream_iter: pass - completed: Final = getattr(stream_iter, "completed_response", None) - response_obj: Final = getattr(completed, "response", None) if completed else None + completed: Final[object] = getattr(stream_iter, "completed_response", None) + response_obj: Final[object] = getattr(completed, "response", None) if completed else None if response_obj is None: raise ValueError("Stream ended without a completed response") @@ -157,7 +157,7 @@ class ResponsesToCompletionBridgeHandler: def completion( self, *args, **kwargs ) -> Union[ - Coroutine[Any, Any, Union["ModelResponse", "CustomStreamWrapper"]], + Coroutine[None, None, Union["ModelResponse", "CustomStreamWrapper"]], "ModelResponse", "CustomStreamWrapper", ]: diff --git a/litellm/completion_extras/litellm_responses_transformation/transformation.py b/litellm/completion_extras/litellm_responses_transformation/transformation.py index 85fb0bc8dc6..7368de1e968 100644 --- a/litellm/completion_extras/litellm_responses_transformation/transformation.py +++ b/litellm/completion_extras/litellm_responses_transformation/transformation.py @@ -212,7 +212,8 @@ def _tool_call_dict_from_output_item(item: Mapping[str, Any], index: int) -> _Ch LiteLLMCompletionResponsesConfig, ) - is_custom: Final = item.get("type") == "custom_tool_call" + item_type: Final[object] = item.get("type") + is_custom: Final = item_type == "custom_tool_call" arguments: Final = (item.get("input") if is_custom else item.get("arguments")) or "" name: Final = item.get("name") or ("custom_tool" if is_custom else "") function_chunk: Final = ChatCompletionToolCallFunctionChunk(name=name, arguments=arguments) @@ -222,7 +223,7 @@ def _tool_call_dict_from_output_item(item: Mapping[str, Any], index: int) -> _Ch function=function_chunk, index=index, ) - raw_provider_fields: Final = item.get("provider_specific_fields") + raw_provider_fields: Final[object] = item.get("provider_specific_fields") if isinstance(raw_provider_fields, dict): provider_specific_fields = raw_provider_fields elif raw_provider_fields and hasattr(raw_provider_fields, "__dict__"): @@ -507,7 +508,7 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): def _merge_responses_api_request_into_request_data( self, - request_data: dict[str, Any], + request_data: dict[str, object], responses_api_request: "ResponsesAPIOptionalRequestParams", instructions: str | None, ) -> None: diff --git a/litellm/constants.py b/litellm/constants.py index 9872783bfab..a5751a416a1 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -484,6 +484,22 @@ FIREWORKS_AI_80_B: Final = int(os.getenv("FIREWORKS_AI_80_B", 80)) #### Logging callback constants #### REDACTED_BY_LITELM_STRING: Final = "REDACTED_BY_LITELM" MAX_LANGFUSE_INITIALIZED_CLIENTS: Final = int(os.getenv("MAX_LANGFUSE_INITIALIZED_CLIENTS", 50)) +# Backpressure + lifetime bounds for the /v1/messages streaming relay (see +# BaseAnthropicMessagesStreamingIterator.async_sse_wrapper). The relay queue is +# bounded so a slow client throttles the upstream pump instead of letting it +# buffer the whole response in memory; the detached-drain cap bounds how many +# post-disconnect drains may run concurrently so client behavior can't create +# unbounded worker state. +ANTHROPIC_MESSAGES_STREAM_RELAY_QUEUE_MAXSIZE: Final = int( + os.getenv("ANTHROPIC_MESSAGES_STREAM_RELAY_QUEUE_MAXSIZE", "1024") +) +# Setting this to 0 disables detached draining entirely: every post-disconnect +# pump bills whatever partial output it has already collected and aborts the +# upstream stream immediately, instead of continuing to drain for the real +# terminal usage. +ANTHROPIC_MESSAGES_MAX_DETACHED_STREAM_DRAINS: Final = int( + os.getenv("ANTHROPIC_MESSAGES_MAX_DETACHED_STREAM_DRAINS", "100") +) LOGGING_WORKER_CONCURRENCY: Final = int(os.getenv("LOGGING_WORKER_CONCURRENCY", 100)) # Must be above 0 LOGGING_WORKER_MAX_QUEUE_SIZE: Final = int(os.getenv("LOGGING_WORKER_MAX_QUEUE_SIZE", 50_000)) LOGGING_WORKER_MAX_TIME_PER_COROUTINE: Final = float(os.getenv("LOGGING_WORKER_MAX_TIME_PER_COROUTINE", 20.0)) @@ -614,6 +630,8 @@ LITELLM_CHAT_PROVIDERS: Final = [ "nscale", "nebius", "dashscope", + "qwencloud", + "qwen_ai_platform", "modelscope", "moonshot", "publicai", @@ -783,6 +801,7 @@ openai_compatible_endpoints: Final[list] = [ "inference.api.nscale.com/v1", "api.studio.nebius.ai/v1", "https://dashscope-intl.aliyuncs.com/compatible-mode/v1", + "https://dashscope.aliyuncs.com/compatible-mode/v1", "https://api-inference.modelscope.cn/v1", "https://api.moonshot.ai/v1", "https://api.publicai.co/v1", @@ -806,6 +825,7 @@ openai_compatible_endpoints: Final[list] = [ "https://api.meta.ai/v1", "https://api.cognition.ai/v1", "https://api.scx.ai/v1", + "https://gigachat.devices.sberbank.ru/api/v1", ] @@ -855,6 +875,8 @@ openai_compatible_providers: Final[list] = [ "nscale", "nebius", "dashscope", + "qwencloud", + "qwen_ai_platform", "modelscope", "moonshot", "v0", @@ -885,6 +907,8 @@ openai_text_completion_compatible_providers: Final[list] = [ # providers that s "featherless_ai", "nebius", "dashscope", + "qwencloud", + "qwen_ai_platform", "modelscope", "moonshot", "publicai", @@ -1092,7 +1116,7 @@ nebius_models: Final[set] = set( ] ) -dashscope_models: Final[set] = set( +dashscope_models: Final[frozenset] = frozenset( [ "qwen-turbo", "qwen-plus", @@ -1107,6 +1131,10 @@ dashscope_models: Final[set] = set( ] ) +qwencloud_models: Final[frozenset] = frozenset(dashscope_models) + +qwen_ai_platform_models: Final[frozenset] = frozenset(dashscope_models) + nebius_embedding_models: Final[set] = set( [ "BAAI/bge-en-icl", @@ -1410,6 +1438,12 @@ DEFAULT_SOFT_BUDGET: Final = float( ) # by default all litellm proxy keys have a soft budget of 50.0 # makes it clear this is a rate limit error for a litellm virtual key RATE_LIMIT_ERROR_MESSAGE_FOR_VIRTUAL_KEY: Final = "LiteLLM Virtual Key user_api_key_hash" +# Prefix of the 401 raised when a submitted virtual key is not shaped like one. +INVALID_VIRTUAL_KEY_ERROR_MESSAGE: Final = "LiteLLM Virtual Key expected" +# Attribute stamped on that 401 at its raise site so log routing recognises it by +# provenance. Message text is caller-influenceable on other 401s, so it must not +# be used to classify. +INVALID_VIRTUAL_KEY_ERROR_MARKER: Final = "_litellm_invalid_virtual_key_error" # Python garbage collection threshold configuration # Format: "gen0,gen1,gen2" e.g., "1000,50,50" diff --git a/litellm/cost_calculator.py b/litellm/cost_calculator.py index 3adc1c25dfd..b83e9b395a8 100644 --- a/litellm/cost_calculator.py +++ b/litellm/cost_calculator.py @@ -641,12 +641,12 @@ def cost_per_token( return xai_cost_per_token(model=model, usage=usage_block) elif custom_llm_provider == "lemonade": return lemonade_cost_per_token(model=model, usage=usage_block) - elif custom_llm_provider == "dashscope": + elif custom_llm_provider in ("dashscope", "qwencloud", "qwen_ai_platform"): from litellm.llms.dashscope.cost_calculator import ( cost_per_token as dashscope_cost_per_token, ) - return dashscope_cost_per_token(model=model, usage=usage_block) + return dashscope_cost_per_token(model=model, usage=usage_block, custom_llm_provider=custom_llm_provider) elif custom_llm_provider == "azure_ai": return azure_ai_cost_per_token( model=model, @@ -1910,12 +1910,15 @@ def ocr_cost( if credits is not None and cost_per_credit is not None: return cost_per_credit * credits, 0.0 - ocr_cost_per_page: float | None = None - if model_info is not None: - ocr_cost_per_page = model_info.get("ocr_cost_per_page") + ocr_cost_per_page: Final = model_info.get("ocr_cost_per_page") if model_info is not None else None + annotation_cost_per_page: Final = model_info.get("annotation_cost_per_page") if model_info is not None else None + annotation_rate: Final = annotation_cost_per_page if annotation_cost_per_page is not None else ocr_cost_per_page pages_processed: Final = response.usage_info.pages_processed - if pages_processed is None: + annotation_pages: Final = response.usage_info.pages_processed_annotation or 0 + has_billable_annotation_pages: Final = annotation_rate is not None and annotation_pages > 0 + + if pages_processed is None and not has_billable_annotation_pages: if cost_per_credit is not None or ocr_cost_per_page is None: # Surface missing usage data instead of silently under-reporting # cost. The previous behavior raised ValueError; we now return 0.0 @@ -1931,7 +1934,7 @@ def ocr_cost( return 0.0, 0.0 raise ValueError("OCR response pages_processed is None") - if ocr_cost_per_page is None: + if ocr_cost_per_page is None and not has_billable_annotation_pages: # No per-page pricing configured. Either the model is on credit-based # pricing (and credits weren't returned, so the credit branch above did # not match) or the model has no OCR pricing entry at all. Surface a @@ -1947,8 +1950,9 @@ def ocr_cost( ) return 0.0, 0.0 - total_ocr_processing_cost: Final[float] = ocr_cost_per_page * pages_processed - return total_ocr_processing_cost, 0.0 + ocr_pages_cost: Final = (ocr_cost_per_page or 0.0) * (pages_processed or 0) + annotation_pages_cost: Final = (annotation_rate or 0.0) * annotation_pages + return ocr_pages_cost + annotation_pages_cost, 0.0 def vector_store_search_cost( @@ -2268,6 +2272,10 @@ def batch_cost_calculator( return total_prompt_cost, total_completion_cost +def _attribute_value(obj: object, name: str) -> object: + return getattr(obj, name) + + def _summable_prompt_token_fields(prompt_tokens_details: BaseModel) -> list[str]: field_names: Final = list(type(prompt_tokens_details).model_fields) if getattr(prompt_tokens_details, "cache_write_tokens", None) is None: @@ -2293,7 +2301,7 @@ class BaseTokenUsageProcessor: for usage in usage_objects: # Handle direct attributes by checking what exists in the model for attr in dir(usage): - if not attr.startswith("_") and not callable(getattr(usage, attr)): + if not attr.startswith("_") and not callable(_attribute_value(usage, attr)): current_val = getattr(combined, attr, 0) new_val = getattr(usage, attr, 0) if ( @@ -2313,7 +2321,7 @@ class BaseTokenUsageProcessor: if ( hasattr(usage.prompt_tokens_details, attr) and not attr.startswith("_") - and not callable(getattr(usage.prompt_tokens_details, attr)) + and not callable(_attribute_value(usage.prompt_tokens_details, attr)) ): current_val = getattr(combined.prompt_tokens_details, attr, 0) or 0 new_val = getattr(usage.prompt_tokens_details, attr, 0) or 0 @@ -2332,7 +2340,9 @@ class BaseTokenUsageProcessor: # Check what keys exist in the model's completion_tokens_details # Access model_fields on the class, not the instance, to avoid Pydantic 2.11+ deprecation warnings for attr in type(usage.completion_tokens_details).model_fields: - if not attr.startswith("_") and not callable(getattr(usage.completion_tokens_details, attr)): + if not attr.startswith("_") and not callable( + _attribute_value(usage.completion_tokens_details, attr) + ): current_val = getattr(combined.completion_tokens_details, attr, 0) or 0 new_val = getattr(usage.completion_tokens_details, attr, 0) or 0 if isinstance(new_val, (int, float)): diff --git a/litellm/endpoints/speech/speech_to_completion_bridge/handler.py b/litellm/endpoints/speech/speech_to_completion_bridge/handler.py index 9e949db625a..6c33621ec89 100644 --- a/litellm/endpoints/speech/speech_to_completion_bridge/handler.py +++ b/litellm/endpoints/speech/speech_to_completion_bridge/handler.py @@ -115,9 +115,11 @@ class SpeechToCompletionBridgeHandler: **request_data, ) + requested_response_format: Final = optional_params.get("response_format") if isinstance(result, ModelResponse): return self.transformation_handler.transform_response( model_response=result, + response_format=requested_response_format if isinstance(requested_response_format, str) else None, ) else: raise Exception(f"Unmapped response type. Got type: {type(result)}") diff --git a/litellm/endpoints/speech/speech_to_completion_bridge/transformation.py b/litellm/endpoints/speech/speech_to_completion_bridge/transformation.py index fb66edbf272..2ed140c0208 100644 --- a/litellm/endpoints/speech/speech_to_completion_bridge/transformation.py +++ b/litellm/endpoints/speech/speech_to_completion_bridge/transformation.py @@ -1,10 +1,14 @@ +from collections.abc import Mapping +from types import MappingProxyType from typing import TYPE_CHECKING, Final, cast +from typing_extensions import NotRequired, ReadOnly, TypedDict + from litellm.constants import OPENAI_CHAT_COMPLETION_PARAMS if TYPE_CHECKING: from litellm import Logging as LiteLLMLoggingObj - from litellm.types.llms.openai import HttpxBinaryResponseContent + from litellm.types.llms.openai import ChatCompletionUserMessage, HttpxBinaryResponseContent from litellm.types.utils import ModelResponse @@ -16,7 +20,64 @@ def _completion_response_cost(model_response: "ModelResponse") -> float | None: return response_cost if isinstance(response_cost, float) else None +GEMINI_TTS_CHAT_AUDIO_FORMAT: Final = "pcm16" +GEMINI_TTS_RAW_RESPONSE_FORMAT: Final = "pcm" +GEMINI_TTS_SUPPORTED_RESPONSE_FORMATS: Final = frozenset({"wav", GEMINI_TTS_RAW_RESPONSE_FORMAT}) + + +class ChatAudioParam(TypedDict): + voice: ReadOnly[str] + format: ReadOnly[NotRequired[str]] + + class SpeechToCompletionBridgeTransformationHandler: + def _validate_response_format( + self, model: str, custom_llm_provider: str, optional_params: Mapping[str, object] + ) -> None: + if not self._is_gemini_tts_model(model): + return + response_format: Final = optional_params.get("response_format") + if not isinstance(response_format, str) or response_format in GEMINI_TTS_SUPPORTED_RESPONSE_FORMATS: + return + from litellm.exceptions import BadRequestError + + supported: Final = ", ".join(sorted(GEMINI_TTS_SUPPORTED_RESPONSE_FORMATS)) + raise BadRequestError( + message=( + f"Gemini TTS only produces raw PCM16 audio, so response_format='{response_format}'" + f" is not supported. Supported response formats: {supported}." + ), + model=model, + llm_provider=custom_llm_provider, + ) + + def _chat_completion_params(self, optional_params: Mapping[str, object]) -> Mapping[str, object]: + return MappingProxyType( + { + param: value + for param, value in optional_params.items() + if param in OPENAI_CHAT_COMPLETION_PARAMS and param != "response_format" + } + ) + + def _chat_audio_format(self, model: str, optional_params: Mapping[str, object]) -> str | None: + if self._is_gemini_tts_model(model): + return GEMINI_TTS_CHAT_AUDIO_FORMAT + response_format: Final = optional_params.get("response_format") + return response_format if isinstance(response_format, str) else None + + def _chat_audio_param( + self, model: str, voice: str | Mapping[str, object] | None, optional_params: Mapping[str, object] + ) -> ChatAudioParam | None: + if not isinstance(voice, str): + return None + audio_format: Final = self._chat_audio_format(model, optional_params) + if audio_format is None: + voice_only: Final[ChatAudioParam] = {"voice": voice} + return voice_only + audio: Final[ChatAudioParam] = {"voice": voice, "format": audio_format} + return audio + def transform_request( self, model: str, @@ -28,36 +89,20 @@ class SpeechToCompletionBridgeTransformationHandler: litellm_logging_obj: "LiteLLMLoggingObj", custom_llm_provider: str, ) -> dict: - passed_optional_params: Final = {} - for op in optional_params: - if op in OPENAI_CHAT_COMPLETION_PARAMS: - passed_optional_params[op] = optional_params[op] - - if voice is not None: - if isinstance(voice, str): - passed_optional_params["audio"] = {"voice": voice} - if "response_format" in optional_params: - passed_optional_params["audio"]["format"] = optional_params["response_format"] - - return_kwargs = { + self._validate_response_format(model, custom_llm_provider, optional_params) + user_message: Final[ChatCompletionUserMessage] = {"role": "user", "content": input} + return_kwargs: Final = { "model": model, - "messages": [ - { - "role": "user", - "content": input, - } - ], + "messages": [user_message], "modalities": ["audio"], - **passed_optional_params, + **self._chat_completion_params(optional_params), + "audio": self._chat_audio_param(model, voice, optional_params), **litellm_params, "headers": headers, "litellm_logging_obj": litellm_logging_obj, "custom_llm_provider": custom_llm_provider, } - - # filter out None values - return_kwargs = {k: v for k, v in return_kwargs.items() if v is not None} - return return_kwargs + return {k: v for k, v in return_kwargs.items() if v is not None} def _convert_pcm16_to_wav(self, pcm_data: bytes, sample_rate: int = 24000, channels: int = 1) -> bytes: """ @@ -103,7 +148,14 @@ class SpeechToCompletionBridgeTransformationHandler: """Check if the model is a Gemini TTS model that returns PCM16 data.""" return "gemini" in model.lower() and ("tts" in model.lower() or "preview-tts" in model.lower()) - def transform_response(self, model_response: "ModelResponse") -> "HttpxBinaryResponseContent": + def _gemini_tts_response_body(self, decoded_audio: bytes, response_format: str | None) -> tuple[bytes, str]: + if response_format == GEMINI_TTS_RAW_RESPONSE_FORMAT: + return decoded_audio, "audio/pcm" + return self._convert_pcm16_to_wav(decoded_audio), "audio/wav" + + def transform_response( + self, model_response: "ModelResponse", response_format: str | None + ) -> "HttpxBinaryResponseContent": import base64 import httpx @@ -114,23 +166,17 @@ class SpeechToCompletionBridgeTransformationHandler: audio_part: Final = cast(Choices, model_response.choices[0]).message.audio if audio_part is None: raise ValueError("No audio part found in the response") - audio_content: Final = audio_part.data + decoded_audio: Final = base64.b64decode(audio_part.data) - # Decode base64 to get binary content - binary_data = base64.b64decode(audio_content) - - # Check if this is a Gemini TTS model that returns raw PCM16 data model: Final = getattr(model_response, "model", "") - headers: Final = {} - if self._is_gemini_tts_model(model): - # Convert PCM16 to WAV format for proper audio file playback - binary_data = self._convert_pcm16_to_wav(binary_data) - headers["Content-Type"] = "audio/wav" - else: - headers["Content-Type"] = "audio/mpeg" - - # Create an httpx.Response object - response: Final = httpx.Response(status_code=200, content=binary_data, headers=headers) + content, content_type = ( + self._gemini_tts_response_body(decoded_audio, response_format) + if self._is_gemini_tts_model(model) + else (decoded_audio, "audio/mpeg") + ) + response: Final = httpx.Response( + status_code=200, content=content, headers=MappingProxyType({"Content-Type": content_type}) + ) binary_response: Final = HttpxBinaryResponseContent(response) binary_response.set_response_cost(_completion_response_cost(model_response)) return binary_response diff --git a/litellm/google_genai/adapters/transformation.py b/litellm/google_genai/adapters/transformation.py index b73a1443330..6a698bb6018 100644 --- a/litellm/google_genai/adapters/transformation.py +++ b/litellm/google_genai/adapters/transformation.py @@ -108,7 +108,6 @@ class GoogleGenAIStreamWrapper(AdapterCompletionStreamWrapper): def __init__(self, completion_stream: object): self.sent_first_chunk = False - # State tracking for accumulating partial tool calls self.accumulated_tool_calls = dict[int, _ToolCallAccumulator]() self._returned_response = False super().__init__(completion_stream) @@ -723,7 +722,7 @@ class GoogleGenAIAdapter: ) for tool_call in tool_calls: - if not hasattr(tool_call, "function"): + if not hasattr(tool_call, "function") or isinstance(tool_call, ChatCompletionDeltaCustomToolCall): continue # 3. Use `index` as the primary key for accumulation diff --git a/litellm/google_genai/main.py b/litellm/google_genai/main.py index b5815bd3f7c..c1822e4720d 100644 --- a/litellm/google_genai/main.py +++ b/litellm/google_genai/main.py @@ -52,10 +52,10 @@ class GenerateContentSetupResult(BaseModel): model_config: ClassVar[ConfigDict] = ConfigDict(arbitrary_types_allowed=True) model: str - request_body: dict[str, Any] + request_body: dict[str, object] custom_llm_provider: str generate_content_provider_config: BaseGoogleGenAIGenerateContentConfig | None - generate_content_config_dict: dict[str, Any] + generate_content_config_dict: dict[str, object] native_request_fields: dict[str, object] litellm_params: GenericLiteLLMParams litellm_logging_obj: LiteLLMLoggingObj @@ -68,7 +68,7 @@ class GenerateContentHelper: @staticmethod def mock_generate_content_response( mock_response: str = "This is a mock response from Google GenAI generate_content.", - ) -> dict[str, Any]: + ) -> dict[str, object]: """Mock response for generate_content for testing purposes""" return { "text": mock_response, @@ -239,9 +239,9 @@ async def agenerate_content( tools: ToolConfigDict | None = None, # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs. # The extra values given here take precedence over values defined on the client or passed to this method. - extra_headers: dict[str, Any] | None = None, - extra_query: dict[str, Any] | None = None, - extra_body: dict[str, Any] | None = None, + extra_headers: dict[str, object] | None = None, + extra_query: dict[str, object] | None = None, + extra_body: dict[str, object] | None = None, timeout: float | httpx.Timeout | None = None, # LiteLLM specific params, custom_llm_provider: str | None = None, @@ -307,9 +307,9 @@ def generate_content( tools: ToolConfigDict | None = None, # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs. # The extra values given here take precedence over values defined on the client or passed to this method. - extra_headers: dict[str, Any] | None = None, - extra_query: dict[str, Any] | None = None, - extra_body: dict[str, Any] | None = None, + extra_headers: dict[str, object] | None = None, + extra_query: dict[str, object] | None = None, + extra_body: dict[str, object] | None = None, timeout: float | httpx.Timeout | None = None, # LiteLLM specific params, custom_llm_provider: str | None = None, @@ -397,9 +397,9 @@ async def agenerate_content_stream( tools: ToolConfigDict | None = None, # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs. # The extra values given here take precedence over values defined on the client or passed to this method. - extra_headers: dict[str, Any] | None = None, - extra_query: dict[str, Any] | None = None, - extra_body: dict[str, Any] | None = None, + extra_headers: dict[str, object] | None = None, + extra_query: dict[str, object] | None = None, + extra_body: dict[str, object] | None = None, timeout: float | httpx.Timeout | None = None, # LiteLLM specific params, custom_llm_provider: str | None = None, @@ -492,9 +492,9 @@ def generate_content_stream( tools: ToolConfigDict | None = None, # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs. # The extra values given here take precedence over values defined on the client or passed to this method. - extra_headers: dict[str, Any] | None = None, - extra_query: dict[str, Any] | None = None, - extra_body: dict[str, Any] | None = None, + extra_headers: dict[str, object] | None = None, + extra_query: dict[str, object] | None = None, + extra_body: dict[str, object] | None = None, timeout: float | httpx.Timeout | None = None, # LiteLLM specific params, custom_llm_provider: str | None = None, diff --git a/litellm/images/main.py b/litellm/images/main.py index 1688087c2da..6a94e7c8df2 100644 --- a/litellm/images/main.py +++ b/litellm/images/main.py @@ -3,7 +3,7 @@ import contextvars import importlib from collections.abc import Coroutine from functools import partial -from typing import TYPE_CHECKING, Any, Final, Literal, Optional, cast, overload +from typing import TYPE_CHECKING, Final, Literal, Optional, cast, overload if TYPE_CHECKING: from litellm.images.utils import ImageEditRequestUtils @@ -151,7 +151,7 @@ def image_generation( *, aimg_generation: Literal[True], **kwargs, -) -> Coroutine[Any, Any, ImageResponse]: +) -> Coroutine[object, object, ImageResponse]: ... @@ -197,7 +197,7 @@ def image_generation( api_version: str | None = None, custom_llm_provider=None, **kwargs, -) -> ImageResponse | Coroutine[Any, Any, ImageResponse]: +) -> ImageResponse | Coroutine[object, object, ImageResponse]: """ Maps the https://api.openai.com/v1/images/generations endpoint. @@ -386,6 +386,8 @@ def image_generation( litellm.LlmProviders.VERTEX_AI, litellm.LlmProviders.OPENROUTER, litellm.LlmProviders.DASHSCOPE, + litellm.LlmProviders.QWENCLOUD, + litellm.LlmProviders.QWEN_AI_PLATFORM, ): if image_generation_config is None: raise ValueError(f"image generation config is not supported for {custom_llm_provider}") @@ -723,14 +725,14 @@ def image_edit( user: str | None = None, # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs. # The extra values given here take precedence over values defined on the client or passed to this method. - extra_headers: dict[str, Any] | None = None, - extra_query: dict[str, Any] | None = None, - extra_body: dict[str, Any] | None = None, + extra_headers: dict[str, object] | None = None, + extra_query: dict[str, object] | None = None, + extra_body: dict[str, object] | None = None, timeout: float | httpx.Timeout | None = None, # LiteLLM specific params, custom_llm_provider: str | None = None, **kwargs, -) -> ImageResponse | Coroutine[Any, Any, ImageResponse]: +) -> ImageResponse | Coroutine[object, object, ImageResponse]: """ Maps the image edit functionality, similar to OpenAI's images/edits endpoint. """ @@ -769,7 +771,7 @@ def image_edit( images: Final = image if isinstance(image, list) else ([image] if image is not None else []) headers_from_kwargs: Final = kwargs.get("headers") - merged_extra_headers: Final[dict[str, Any]] = {} + merged_extra_headers: Final[dict[str, object]] = {} if isinstance(headers_from_kwargs, dict): merged_extra_headers.update(headers_from_kwargs) if isinstance(extra_headers, dict): @@ -974,9 +976,9 @@ async def aimage_edit( user: str | None = None, # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs. # The extra values given here take precedence over values defined on the client or passed to this method. - extra_headers: dict[str, Any] | None = None, - extra_query: dict[str, Any] | None = None, - extra_body: dict[str, Any] | None = None, + extra_headers: dict[str, object] | None = None, + extra_query: dict[str, object] | None = None, + extra_body: dict[str, object] | None = None, timeout: float | httpx.Timeout | None = None, # LiteLLM specific params, custom_llm_provider: str | None = None, @@ -1044,7 +1046,7 @@ async def aimage_edit( ) -def __getattr__(name: str) -> Any: +def __getattr__(name: str) -> type["ImageEditRequestUtils"]: """Lazy import handler for images.main module""" if name == "ImageEditRequestUtils": # Lazy load ImageEditRequestUtils to avoid heavy import from images.utils at module load time diff --git a/litellm/integrations/SlackAlerting/slack_alerting.py b/litellm/integrations/SlackAlerting/slack_alerting.py index 94d734546be..c137164ecdb 100644 --- a/litellm/integrations/SlackAlerting/slack_alerting.py +++ b/litellm/integrations/SlackAlerting/slack_alerting.py @@ -545,7 +545,6 @@ class SlackAlerting(CustomBatchLogger): # Get the appropriate budget alert type handler budget_alert_class: Final = get_budget_alert_type(type) _id: Final = budget_alert_class.get_id(user_info) - user_info_json: Final = user_info.model_dump(exclude_none=True) user_info_str: Final = self._get_user_info_str(user_info) event_message = budget_alert_class.get_event_message() @@ -575,7 +574,22 @@ class SlackAlerting(CustomBatchLogger): webhook_event = WebhookEvent( event=event, event_message=event_message, - **user_info_json, + spend=user_info.spend, + max_budget=user_info.max_budget, + soft_budget=user_info.soft_budget, + token=user_info.token, + customer_id=user_info.customer_id, + user_id=user_info.user_id, + team_id=user_info.team_id, + team_alias=user_info.team_alias, + organization_id=user_info.organization_id, + user_email=user_info.user_email, + key_alias=user_info.key_alias, + projected_exceeded_date=user_info.projected_exceeded_date, + projected_spend=user_info.projected_spend, + event_group=user_info.event_group, + alert_emails=user_info.alert_emails, + max_budget_alert_emails=user_info.max_budget_alert_emails, ) await self.send_alert( message=event_message + "\n\n" + user_info_str, @@ -657,7 +671,7 @@ class SlackAlerting(CustomBatchLogger): """ Create a standard message for a budget alert """ - _all_fields_as_dict: Final = user_info.model_dump(exclude_none=True) + _all_fields_as_dict: Final[dict[str, object]] = user_info.model_dump(exclude_none=True) _all_fields_as_dict.pop("token") msg = "" for k, v in _all_fields_as_dict.items(): @@ -1006,7 +1020,7 @@ class SlackAlerting(CustomBatchLogger): except Exception: pass - async def model_added_alert(self, model_name: str, litellm_model_name: str, passed_model_info: Any): + async def model_added_alert(self, model_name: str, litellm_model_name: str, passed_model_info: object): base_model_from_user: Final = getattr(passed_model_info, "base_model", None) model_info = {} base_model = "" @@ -1973,7 +1987,7 @@ Model Info: try: message = f"`{event_name}`\n" - key_event_dict: Final = key_event.model_dump() + key_event_dict: Final[dict[str, object]] = key_event.model_dump() # Add Created by information first message += "*Action Done by:*\n" diff --git a/litellm/integrations/arize/arize_phoenix_prompt_manager.py b/litellm/integrations/arize/arize_phoenix_prompt_manager.py index 71f4902bbe5..0c9e868c146 100644 --- a/litellm/integrations/arize/arize_phoenix_prompt_manager.py +++ b/litellm/integrations/arize/arize_phoenix_prompt_manager.py @@ -3,10 +3,12 @@ Arize Phoenix prompt manager that integrates with LiteLLM's prompt management sy Fetches prompt versions from Arize Phoenix and provides workspace-based access control. """ -from typing import Any, Final +from collections.abc import Mapping, Sequence +from typing import Any, Final, cast from jinja2 import DictLoader, select_autoescape from jinja2.sandbox import ImmutableSandboxedEnvironment +from typing_extensions import ReadOnly, TypedDict from litellm.integrations.custom_prompt_management import CustomPromptManagement from litellm.integrations.prompt_management_base import ( @@ -20,6 +22,31 @@ from litellm.types.utils import StandardCallbackDynamicParams from .arize_phoenix_client import ArizePhoenixClient +class ArizePhoenixContentPart(TypedDict, total=False): + type: ReadOnly[str] + text: ReadOnly[str] + + +class ArizePhoenixTemplateMessage(TypedDict, total=False): + role: ReadOnly[str] + content: ReadOnly[Sequence[ArizePhoenixContentPart]] + + +class ArizePhoenixTemplateBody(TypedDict, total=False): + messages: ReadOnly[Sequence[ArizePhoenixTemplateMessage]] + + +class ArizePhoenixPromptMetadata(TypedDict): + model_name: ReadOnly[str | None] + model_provider: ReadOnly[str | None] + description: ReadOnly[str] + template_type: ReadOnly[str | None] + template_format: ReadOnly[str] + invocation_parameters: ReadOnly[Mapping[str, Mapping[str, object]]] + temperature: ReadOnly[float | None] + max_tokens: ReadOnly[int | None] + + class ArizePhoenixPromptTemplate: """ Represents a prompt template loaded from Arize Phoenix. @@ -28,10 +55,10 @@ class ArizePhoenixPromptTemplate: def __init__( self, template_id: str, - messages: list[dict[str, Any]], - metadata: dict[str, Any], + messages: Sequence[ArizePhoenixTemplateMessage], + metadata: ArizePhoenixPromptMetadata, model: str | None = None, - ): + ) -> None: self.template_id = template_id self.messages = messages self.metadata = metadata @@ -43,7 +70,7 @@ class ArizePhoenixPromptTemplate: self.description = metadata.get("description", "") self.template_format = metadata.get("template_format", "MUSTACHE") - def __repr__(self): + def __repr__(self) -> str: return f"ArizePhoenixPromptTemplate(id='{self.template_id}', model='{self.model}')" @@ -109,7 +136,7 @@ class ArizePhoenixTemplateManager: def _parse_prompt_data(self, data: dict[str, Any], prompt_version_id: str) -> ArizePhoenixPromptTemplate: """Parse Arize Phoenix prompt data and extract messages and metadata.""" - template_data: Final = data.get("template", {}) + template_data: Final[ArizePhoenixTemplateBody] = data.get("template", {}) messages: Final = template_data.get("messages", []) # Extract invocation parameters @@ -129,7 +156,7 @@ class ArizePhoenixTemplateManager: break # Build metadata dictionary - metadata: Final = { + metadata: Final[ArizePhoenixPromptMetadata] = { "model_name": data.get("model_name"), "model_provider": data.get("model_provider"), "description": data.get("description", ""), @@ -146,7 +173,9 @@ class ArizePhoenixTemplateManager: metadata=metadata, ) - def render_template(self, template_id: str, variables: dict[str, Any] | None = None) -> list[AllMessageValues]: + def render_template( + self, template_id: str, variables: Mapping[str, object] | None = None + ) -> list[AllMessageValues]: """Render a template with the given variables and return formatted messages.""" if template_id not in self.prompts: raise ValueError(f"Template '{template_id}' not found") @@ -174,7 +203,9 @@ class ArizePhoenixTemplateManager: # Combine rendered content final_content = " ".join(rendered_content_parts) - rendered_messages.append({"role": role, "content": final_content}) + rendered_messages.append( + cast("AllMessageValues", {"role": role, "content": final_content}) # cast-ok: Phoenix roles are OpenAI + ) return rendered_messages @@ -243,8 +274,8 @@ class ArizePhoenixPromptManager(CustomPromptManagement): def get_prompt_template( self, prompt_id: str, - prompt_variables: dict[str, Any] | None = None, - ) -> tuple[list[AllMessageValues], dict[str, Any]]: + prompt_variables: Mapping[str, object] | None = None, + ) -> tuple[list[AllMessageValues], dict[str, object]]: """ Get a prompt template and render it with variables. @@ -263,7 +294,7 @@ class ArizePhoenixPromptManager(CustomPromptManagement): rendered_messages: Final = self.prompt_manager.render_template(prompt_id, prompt_variables or {}) # Extract metadata - metadata: Final = { + metadata: Final[dict[str, object]] = { "model": template.model, "temperature": template.temperature, "max_tokens": template.max_tokens, @@ -271,7 +302,7 @@ class ArizePhoenixPromptManager(CustomPromptManagement): # Add additional invocation parameters invocation_params: Final = template.invocation_parameters - provider_params = {} + provider_params: Mapping[str, object] = {} if "openai" in invocation_params: provider_params = invocation_params["openai"] @@ -289,12 +320,12 @@ class ArizePhoenixPromptManager(CustomPromptManagement): self, user_id: str | None, messages: list[AllMessageValues], - function_call: dict[str, Any] | str | None = None, - litellm_params: dict[str, Any] | None = None, + function_call: dict[str, object] | str | None = None, + litellm_params: dict[str, object] | None = None, prompt_id: str | None = None, - prompt_variables: dict[str, Any] | None = None, + prompt_variables: dict[str, object] | None = None, **kwargs, - ) -> tuple[list[AllMessageValues], dict[str, Any] | None]: + ) -> tuple[list[AllMessageValues], dict[str, object] | None]: """ Pre-call hook that processes the prompt template before making the LLM call. """ @@ -335,9 +366,9 @@ class ArizePhoenixPromptManager(CustomPromptManagement): except Exception as e: # Log error but don't fail the call - import litellm + from litellm._logging import verbose_proxy_logger - litellm._logging.verbose_proxy_logger.error("Error in Arize Phoenix prompt pre_call_hook: %s", e) + verbose_proxy_logger.error("Error in Arize Phoenix prompt pre_call_hook: %s", e) return messages, litellm_params def get_available_prompts(self) -> list[str]: @@ -393,7 +424,8 @@ class ArizePhoenixPromptManager(CustomPromptManagement): rendered_messages, prompt_metadata = self.get_prompt_template(prompt_id, prompt_variables) # Extract model from metadata (if specified) - template_model: Final = prompt_metadata.get("model") + raw_template_model: Final = prompt_metadata.get("model") + template_model: Final = raw_template_model if isinstance(raw_template_model, str) else None # Extract optional parameters from metadata optional_params: Final = {} diff --git a/litellm/integrations/bitbucket/bitbucket_prompt_manager.py b/litellm/integrations/bitbucket/bitbucket_prompt_manager.py index 6a03e3ee93c..ff34bd91e31 100644 --- a/litellm/integrations/bitbucket/bitbucket_prompt_manager.py +++ b/litellm/integrations/bitbucket/bitbucket_prompt_manager.py @@ -3,6 +3,7 @@ BitBucket prompt manager that integrates with LiteLLM's prompt management system Fetches .prompt files from BitBucket repositories and provides team-based access control. """ +from collections.abc import Mapping from typing import TYPE_CHECKING, Any, Final from jinja2 import DictLoader, select_autoescape @@ -65,7 +66,7 @@ class BitBucketTemplateManager: def __init__( self, - bitbucket_config: dict[str, Any], + bitbucket_config: Mapping[str, object], prompt_id: str | None = None, ): self.bitbucket_config = bitbucket_config @@ -123,7 +124,7 @@ class BitBucketTemplateManager: template_content = content # Parse YAML frontmatter - metadata: dict[str, Any] = {} + metadata: dict[str, object] = {} if frontmatter_str: try: import yaml @@ -141,9 +142,9 @@ class BitBucketTemplateManager: metadata=metadata, ) - def _parse_yaml_basic(self, yaml_str: str) -> dict[str, Any]: + def _parse_yaml_basic(self, yaml_str: str) -> dict[str, object]: """Basic YAML parser for simple cases when PyYAML is not available.""" - result: Final[dict[str, Any]] = {} + result: Final[dict[str, object]] = {} for line in yaml_str.split("\n"): line = line.strip() if ":" in line and not line.startswith("#"): @@ -162,7 +163,7 @@ class BitBucketTemplateManager: result[key] = value.strip("\"'") return result - def render_template(self, template_id: str, variables: dict[str, Any] | None = None) -> str: + def render_template(self, template_id: str, variables: Mapping[str, object] | None = None) -> str: """Render a template with the given variables.""" if template_id not in self.prompts: raise ValueError(f"Template '{template_id}' not found") @@ -209,7 +210,7 @@ class BitBucketPromptManager(CustomPromptManagement): def __init__( self, - bitbucket_config: dict[str, Any], + bitbucket_config: Mapping[str, object], prompt_id: str | None = None, ): self.bitbucket_config = bitbucket_config @@ -234,7 +235,7 @@ class BitBucketPromptManager(CustomPromptManagement): def get_prompt_template( self, prompt_id: str, - prompt_variables: dict[str, Any] | None = None, + prompt_variables: Mapping[str, object] | None = None, ) -> tuple[str, dict[str, Any]]: """ Get a prompt template and render it with variables. @@ -267,12 +268,12 @@ class BitBucketPromptManager(CustomPromptManagement): self, user_id: str | None, messages: list[AllMessageValues], - function_call: dict[str, Any] | str | None = None, - litellm_params: dict[str, Any] | None = None, + function_call: Mapping[str, object] | str | None = None, + litellm_params: dict[str, object] | None = None, prompt_id: str | None = None, - prompt_variables: dict[str, Any] | None = None, + prompt_variables: Mapping[str, object] | None = None, **kwargs, - ) -> tuple[list[AllMessageValues], dict[str, Any] | None]: + ) -> tuple[list[AllMessageValues], dict[str, object] | None]: """ Pre-call hook that processes the prompt template before making the LLM call. """ @@ -316,9 +317,9 @@ class BitBucketPromptManager(CustomPromptManagement): except Exception as e: # Log error but don't fail the call - import litellm + from litellm._logging import verbose_proxy_logger - litellm._logging.verbose_proxy_logger.error("Error in BitBucket prompt pre_call_hook: %s", e) + verbose_proxy_logger.error("Error in BitBucket prompt pre_call_hook: %s", e) return messages, litellm_params def _parse_prompt_to_messages(self, prompt_content: str) -> list[AllMessageValues]: @@ -384,14 +385,14 @@ class BitBucketPromptManager(CustomPromptManagement): def post_call_hook( self, user_id: str | None, - response: Any, + response: object, input_messages: list[AllMessageValues], - function_call: dict[str, Any] | str | None = None, - litellm_params: dict[str, Any] | None = None, + function_call: Mapping[str, object] | str | None = None, + litellm_params: Mapping[str, object] | None = None, prompt_id: str | None = None, - prompt_variables: dict[str, Any] | None = None, + prompt_variables: Mapping[str, object] | None = None, **kwargs, - ) -> Any: + ) -> object: """ Post-call hook for any post-processing after the LLM call. """ diff --git a/litellm/integrations/cloudzero/transform.py b/litellm/integrations/cloudzero/transform.py index f0d4d67fc22..ffc8fe1c1f5 100644 --- a/litellm/integrations/cloudzero/transform.py +++ b/litellm/integrations/cloudzero/transform.py @@ -19,14 +19,29 @@ """Transform LiteLLM data to CloudZero AnyCost CBF format.""" from datetime import datetime -from typing import Any, Final +from typing import Final, SupportsFloat, SupportsIndex, SupportsInt import polars as pl +from typing_extensions import Buffer from ...types.integrations.cloudzero import CBFRecord from .cz_resource_names import CZEntityType, CZRNGenerator +def _as_int(value: object) -> int: + """The integer form of a spend table cell, computed the way :func:`int` computes it.""" + if isinstance(value, (str, Buffer, SupportsInt, SupportsIndex)): + return int(value) + raise TypeError(f"int() argument must be a string or a number, not {type(value).__name__!r}") + + +def _as_float(value: object) -> float: + """The floating point form of a spend table cell, computed the way :func:`float` computes it.""" + if isinstance(value, (str, Buffer, SupportsFloat, SupportsIndex)): + return float(value) + raise TypeError(f"float() argument must be a string or a number, not {type(value).__name__!r}") + + class CBFTransformer: """Transform LiteLLM usage data to CloudZero Billing Format (CBF).""" @@ -82,15 +97,15 @@ class CBFTransformer: return pl.DataFrame(cbf_data) - def _create_cbf_record(self, row: dict[str, Any]) -> CBFRecord: + def _create_cbf_record(self, row: dict[str, object]) -> CBFRecord: """Create a single CBF record from LiteLLM daily spend row.""" # Parse date (daily spend tables use date strings like '2025-04-19') usage_date: Final = self._parse_date(row.get("date")) # Calculate total tokens - prompt_tokens: Final = int(row.get("prompt_tokens", 0)) - completion_tokens: Final = int(row.get("completion_tokens", 0)) + prompt_tokens: Final = _as_int(row.get("prompt_tokens", 0)) + completion_tokens: Final = _as_int(row.get("completion_tokens", 0)) total_tokens: Final = prompt_tokens + completion_tokens # Create CloudZero Resource Name (CZRN) as resource_id @@ -154,7 +169,7 @@ class CBFTransformer: "time/usage_start": ( usage_date.isoformat() if usage_date else None ), # Required: ISO-formatted UTC datetime - "cost/cost": float(row.get("spend", 0.0)), # Required: billed cost + "cost/cost": _as_float(row.get("spend", 0.0)), # Required: billed cost "resource/id": resource_id, # CZRN (CloudZero Resource Name) # Usage metrics for token consumption "usage/amount": total_tokens, # Numeric value of tokens consumed @@ -187,7 +202,7 @@ class CBFTransformer: return CBFRecord(cbf_record) - def _parse_date(self, date_str) -> datetime | None: + def _parse_date(self, date_str: object) -> datetime | None: """Parse date string from daily spend tables (e.g., '2025-04-19').""" if date_str is None: return None diff --git a/litellm/integrations/custom_guardrail.py b/litellm/integrations/custom_guardrail.py index 8dc6881d23e..e87ac9521ae 100644 --- a/litellm/integrations/custom_guardrail.py +++ b/litellm/integrations/custom_guardrail.py @@ -2,6 +2,7 @@ import contextvars import hashlib import os import secrets +from collections.abc import Mapping from datetime import datetime from typing import TYPE_CHECKING, Any, ClassVar, Final, Literal, Optional, get_args @@ -227,13 +228,13 @@ class CustomGuardrail(CustomLogger): ) super().__init__(**kwargs) - def render_violation_message(self, default: str, context: dict[str, Any] | None = None) -> str: + def render_violation_message(self, default: str, context: Mapping[str, object] | None = None) -> str: """Return a custom violation message if template is configured.""" if not self.violation_message_template: return default - format_context: Final[dict[str, Any]] = {"default_message": default} + format_context: Final[dict[str, object]] = {"default_message": default} if context: format_context.update(context) try: @@ -661,7 +662,7 @@ class CustomGuardrail(CustomLogger): value: Final = self._get_admin_metadata(data).get("opted_out_global_guardrails") return value if isinstance(value, list) else [] - def _is_valid_response_type(self, result: Any) -> bool: + def _is_valid_response_type(self, result: object) -> bool: """ Check if result is a valid LLMResponseTypes instance. @@ -722,7 +723,7 @@ class CustomGuardrail(CustomLogger): return None return f"{_PRE_CALL_EXECUTED_TOKEN}:{name}" - def mark_pre_call_hook_ran(self, data: dict[str, Any]) -> None: + def mark_pre_call_hook_ran(self, data: dict[str, object]) -> None: """ Record that this guardrail's ``async_pre_call_hook`` already ran for this request, so the deployment-level hook does not run it a second time. @@ -747,7 +748,7 @@ class CustomGuardrail(CustomLogger): return data["metadata"] = {PRE_CALL_EXECUTED_GUARDRAILS_KEY: [marker]} - def _pre_call_hook_already_ran(self, data: dict[str, Any]) -> bool: + def _pre_call_hook_already_ran(self, data: dict[str, object]) -> bool: marker: Final = self._pre_call_marker() if marker is None: return False @@ -1170,7 +1171,7 @@ class CustomGuardrail(CustomLogger): This gets logged on downsteam Langfuse, DataDog, etc. """ # Convert None to empty dict to satisfy type requirements - guardrail_response: dict[str, Any] | str = {} if response is None else response + guardrail_response: dict[str, object] | str = {} if response is None else response # For apply_guardrail functions in custom_code_guardrail scenario, # simplify the logged response to "allow", "deny", or "mask" diff --git a/litellm/integrations/custom_logger.py b/litellm/integrations/custom_logger.py index f177076d8fe..8f03e08f02d 100644 --- a/litellm/integrations/custom_logger.py +++ b/litellm/integrations/custom_logger.py @@ -31,6 +31,9 @@ if TYPE_CHECKING: from litellm.caching.caching import DualCache from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj + from litellm.llms.base_llm.anthropic_messages.transformation import ( + BaseAnthropicMessagesConfig, + ) from litellm.proxy._types import UserAPIKeyAuth from litellm.types.mcp import ( MCPPostCallResponseObject, @@ -39,7 +42,7 @@ if TYPE_CHECKING: ) from litellm.types.router import PreRoutingHookResponse - Span = _Span | Any + Span = _Span else: Span = Any LiteLLMLoggingObj = Any @@ -268,7 +271,9 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac ) -> list[dict]: return healthy_deployments - async def async_pre_call_deployment_hook(self, kwargs: dict[str, Any], call_type: CallTypes | None) -> dict | None: + async def async_pre_call_deployment_hook( + self, kwargs: dict[str, object], call_type: CallTypes | None + ) -> dict | None: """ Allow modifying the request just before it's sent to the deployment. @@ -344,9 +349,9 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac async def async_post_call_streaming_deployment_hook( self, request_data: dict, - response_chunk: Any, + response_chunk: object, call_type: CallTypes | None, - ) -> Any | None: + ) -> object | None: """ Allow modifying streaming chunks just before they're returned to the user. @@ -378,7 +383,7 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac """ def translate_completion_output_params_streaming( - self, completion_stream: Any + self, completion_stream: object ) -> AdapterCompletionStreamWrapper | None: """ Translates the streaming chunk, from the OpenAI format to the custom format. @@ -418,9 +423,9 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac self, data: dict, user_api_key_dict: UserAPIKeyAuth, - response: Any, + response: object, request_headers: dict[str, str] | None = None, - litellm_call_info: dict[str, Any] | None = None, + litellm_call_info: dict[str, object] | None = None, ) -> dict[str, str] | None: """ Called after an LLM API call (success or failure) to allow injecting custom HTTP response headers. @@ -471,11 +476,11 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac ) -> Any: pass - async def async_logging_hook(self, kwargs: dict, result: Any, call_type: str) -> tuple[dict, Any]: + async def async_logging_hook(self, kwargs: dict, result: object, call_type: str) -> tuple[dict, object]: """For masking logged request/response. Return a modified version of the request/result.""" return kwargs, result - def logging_hook(self, kwargs: dict, result: Any, call_type: str) -> tuple[dict, Any]: + def logging_hook(self, kwargs: dict, result: object, call_type: str) -> tuple[dict, object]: """For masking logged request/response. Return a modified version of the request/result.""" return kwargs, result @@ -581,7 +586,7 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac async def async_should_run_agentic_loop( self, - response: Any, + response: object, model: str, messages: list[dict], tools: list[dict] | None, @@ -642,8 +647,8 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac tools: dict, model: str, messages: list[dict], - response: Any, - anthropic_messages_provider_config: Any, + response: object, + anthropic_messages_provider_config: "BaseAnthropicMessagesConfig | None", anthropic_messages_optional_request_params: dict, logging_obj: "LiteLLMLoggingObj", stream: bool, @@ -711,8 +716,8 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac tools: dict, model: str, messages: list[dict], - response: Any, - anthropic_messages_provider_config: Any, + response: object, + anthropic_messages_provider_config: "BaseAnthropicMessagesConfig | None", anthropic_messages_optional_request_params: dict, logging_obj: "LiteLLMLoggingObj", stream: bool, @@ -728,7 +733,7 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac async def async_post_agentic_loop_response_hook( self, - response: Any, + response: object, plan: AgenticLoopPlan, kwargs: dict, ) -> Any: @@ -767,7 +772,7 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac async def async_should_run_chat_completion_agentic_loop( self, - response: Any, + response: object, model: str, messages: list[dict], tools: list[dict] | None, @@ -785,12 +790,12 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac tools: dict, model: str, messages: list[dict], - response: Any, + response: object, optional_params: dict, logging_obj: "LiteLLMLoggingObj", stream: bool, kwargs: dict, - ) -> Any: + ) -> object: """ Hook to execute chat completion agentic loop based on context from should_run hook. """ @@ -800,7 +805,7 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac tools: dict, model: str, messages: list[dict], - response: Any, + response: object, optional_params: dict, logging_obj: "LiteLLMLoggingObj", stream: bool, @@ -1056,7 +1061,7 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac def _redact_base64( self, - value: Any, + value: object, depth: int = 0, max_depth: int = DEFAULT_MAX_RECURSE_DEPTH_SENSITIVE_DATA_MASKER, ) -> object: @@ -1079,7 +1084,7 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac return value - def _should_keep_content(self, content: Any) -> bool: + def _should_keep_content(self, content: object) -> bool: """Return True if this content item should be retained.""" if not isinstance(content, dict): return True diff --git a/litellm/integrations/datadog/datadog.py b/litellm/integrations/datadog/datadog.py index 04f1c6dff15..866076a3c49 100644 --- a/litellm/integrations/datadog/datadog.py +++ b/litellm/integrations/datadog/datadog.py @@ -20,10 +20,11 @@ import time import traceback from collections.abc import Sequence from datetime import datetime as datetimeObj -from typing import Any, Final +from typing import TYPE_CHECKING, Any, Final import httpx from httpx import Response +from typing_extensions import ReadOnly, TypedDict import litellm from litellm._logging import verbose_logger @@ -62,6 +63,18 @@ from litellm.types.utils import StandardLoggingPayload from ..additional_logging_utils import AdditionalLoggingUtils +if TYPE_CHECKING: + from fastapi import HTTPException + + from litellm.proxy._types import UserAPIKeyAuth + + +class _DatadogLoggingKwargs(TypedDict, total=False): + """The subset of logging ``kwargs`` that the Datadog payload builder reads.""" + + standard_logging_object: ReadOnly[StandardLoggingPayload | None] + + # max number of logs DD API can accept @@ -87,6 +100,11 @@ def _resolve_dd_batch_size() -> int: return max(1, min(value, DD_MAX_BATCH_SIZE)) +def _span_attribute(span: object, name: str) -> object: + """Read an optional attribute off whatever span object the active tracer hands back.""" + return getattr(span, name, None) + + class DataDogLogger( CustomBatchLogger, AdditionalLoggingUtils, @@ -271,9 +289,9 @@ class DataDogLogger( self, request_data: dict, original_exception: Exception, - user_api_key_dict: Any, + user_api_key_dict: "UserAPIKeyAuth", traceback_str: str | None = None, - ) -> Any | None: + ) -> "HTTPException | None": """ Log proxy-level failures (e.g. 401 auth, DB connection errors) to Datadog. @@ -297,7 +315,7 @@ class DataDogLogger( status_code = int(_code) # Use project-standard sanitized user context when running in proxy - user_context: dict[str, Any] = {} + user_context: dict[str, object] = {} try: from litellm.proxy.litellm_pre_call_utils import ( LiteLLMProxyRequestSetup, @@ -553,8 +571,8 @@ class DataDogLogger( def create_datadog_logging_payload( self, - kwargs: dict | Any, - response_obj: Any, + kwargs: _DatadogLoggingKwargs, + response_obj: object, start_time: datetime.datetime, end_time: datetime.datetime, ) -> DatadogPayload: @@ -562,8 +580,8 @@ class DataDogLogger( Helper function to create a datadog payload for logging Args: - kwargs (Union[dict, Any]): request kwargs - response_obj (Any): llm api response + kwargs: request kwargs, read for its standard logging object + response_obj: llm api response start_time (datetime.datetime): start time of request end_time (datetime.datetime): end time of request @@ -625,7 +643,7 @@ class DataDogLogger( self, payload: ServiceLoggerPayload, error: str | None = "", - parent_otel_span: Any | None = None, + parent_otel_span: object = None, start_time: datetimeObj | float | None = None, end_time: float | datetimeObj | None = None, event_metadata: dict | None = None, @@ -659,7 +677,7 @@ class DataDogLogger( self, payload: ServiceLoggerPayload, error: str | None = "", - parent_otel_span: Any | None = None, + parent_otel_span: object = None, start_time: datetimeObj | float | None = None, end_time: float | datetimeObj | None = None, event_metadata: dict | None = None, @@ -696,7 +714,7 @@ class DataDogLogger( def _create_v0_logging_payload( self, - kwargs: dict | Any, + kwargs: dict, response_obj: Any, start_time: datetime.datetime, end_time: datetime.datetime, @@ -810,11 +828,11 @@ class DataDogLogger( if current_span is None: return None - trace_id: Final = getattr(current_span, "trace_id", None) + trace_id: Final = _span_attribute(current_span, "trace_id") if trace_id is None: return None - span_id: Final = getattr(current_span, "span_id", None) + span_id: Final = _span_attribute(current_span, "span_id") trace_context: Final[dict[str, str]] = {"trace_id": str(trace_id)} if span_id is not None: trace_context["span_id"] = str(span_id) diff --git a/litellm/integrations/datadog/datadog_llm_obs.py b/litellm/integrations/datadog/datadog_llm_obs.py index 704f0323e95..e5789965c6e 100644 --- a/litellm/integrations/datadog/datadog_llm_obs.py +++ b/litellm/integrations/datadog/datadog_llm_obs.py @@ -9,6 +9,7 @@ API Reference: https://docs.datadoghq.com/llm_observability/setup/api/?tab=examp import asyncio import json import os +from collections.abc import Mapping, Sequence from datetime import datetime from typing import Any, Final, Literal @@ -334,7 +335,7 @@ class DataDogLLMObsLogger(CustomBatchLogger): def _get_response_messages( self, standard_logging_payload: StandardLoggingPayload, call_type: str | None - ) -> list[Any]: + ) -> list[object]: """ Get the messages from the response object @@ -484,7 +485,7 @@ class DataDogLLMObsLogger(CustomBatchLogger): # Default fallback for unknown or passthrough operations return "llm" - def _ensure_string_content(self, messages: str | list[Any] | dict[Any, Any] | None) -> list[Any]: + def _ensure_string_content(self, messages: str | Sequence[object] | Mapping[object, object] | None) -> list[object]: if messages is None: return [] if isinstance(messages, str): @@ -495,11 +496,11 @@ class DataDogLLMObsLogger(CustomBatchLogger): return [str(messages.get("content", ""))] return [] - def _get_dd_llm_obs_payload_metadata(self, standard_logging_payload: StandardLoggingPayload) -> dict[str, Any]: + def _get_dd_llm_obs_payload_metadata(self, standard_logging_payload: StandardLoggingPayload) -> dict[str, object]: """ Fields to track in DD LLM Observability metadata from litellm standard logging payload """ - _metadata: Final[dict[str, Any]] = { + _metadata: Final[dict[str, object]] = { "model_name": standard_logging_payload.get("model", "unknown"), "model_provider": standard_logging_payload.get("custom_llm_provider", "unknown"), "id": standard_logging_payload.get("id", "unknown"), @@ -647,7 +648,7 @@ class DataDogLLMObsLogger(CustomBatchLogger): return spend_metrics - def _process_input_messages_preserving_tool_calls(self, messages: list[Any]) -> list[dict[str, Any]]: + def _process_input_messages_preserving_tool_calls(self, messages: Sequence[object]) -> list[dict[str, object]]: """ Process input messages while preserving tool_calls and tool message types. @@ -671,13 +672,13 @@ class DataDogLLMObsLogger(CustomBatchLogger): return processed @staticmethod - def _tool_calls_kv_pair(tool_calls: list[dict[str, Any]]) -> dict[str, Any]: + def _tool_calls_kv_pair(tool_calls: list[dict[str, Any]]) -> dict[str, object]: """ Extract tool call information into key-value pairs for Datadog metadata. Similar to OpenTelemetry's implementation but adapted for Datadog's format. """ - kv_pairs: Final[dict[str, Any]] = {} + kv_pairs: Final[dict[str, object]] = {} for idx, tool_call in enumerate(tool_calls): try: # Extract tool call ID @@ -712,11 +713,11 @@ class DataDogLLMObsLogger(CustomBatchLogger): return kv_pairs - def _extract_tool_call_metadata(self, standard_logging_payload: StandardLoggingPayload) -> dict[str, Any]: + def _extract_tool_call_metadata(self, standard_logging_payload: StandardLoggingPayload) -> dict[str, object]: """ Extract tool call information from both input messages and response for Datadog metadata. """ - tool_call_metadata: Final[dict[str, Any]] = {} + tool_call_metadata: Final[dict[str, object]] = {} try: # Extract tool calls from input messages diff --git a/litellm/integrations/dotprompt/prompt_manager.py b/litellm/integrations/dotprompt/prompt_manager.py index fd0b17ba746..9c82ff7c5ba 100644 --- a/litellm/integrations/dotprompt/prompt_manager.py +++ b/litellm/integrations/dotprompt/prompt_manager.py @@ -3,12 +3,21 @@ Based on Google's GenAI Kit dotprompt implementation: https://google.github.io/d """ import re +from collections.abc import Mapping from pathlib import Path from typing import Any, Final import yaml from jinja2 import DictLoader, select_autoescape from jinja2.sandbox import ImmutableSandboxedEnvironment +from typing_extensions import NotRequired, ReadOnly, TypedDict + + +class _PromptFileJson(TypedDict): + """JSON form of a .prompt file: rendered template text plus its frontmatter.""" + + content: ReadOnly[NotRequired[str]] + metadata: ReadOnly[NotRequired[dict[str, object]]] def strip_version_suffix(prompt_id: str) -> str | None: @@ -167,7 +176,7 @@ class PromptManager: template_id=prompt_id, ) - def _parse_frontmatter(self, content: str) -> tuple[dict[str, Any], str]: + def _parse_frontmatter(self, content: str) -> tuple[dict[str, object], str]: """Parse YAML frontmatter from prompt content.""" # Match YAML frontmatter between --- delimiters frontmatter_pattern: Final = r"^---\s*\n(.*?)\n---\s*\n(.*)$" @@ -178,7 +187,7 @@ class PromptManager: template_content = match.group(2) try: - frontmatter = yaml.safe_load(frontmatter_yaml) or {} + frontmatter: dict[str, object] = yaml.safe_load(frontmatter_yaml) or {} except yaml.YAMLError as e: raise ValueError(f"Invalid YAML frontmatter: {e}") else: @@ -191,7 +200,7 @@ class PromptManager: def render( self, prompt_id: str, - prompt_variables: dict[str, Any] | None = None, + prompt_variables: Mapping[str, object] | None = None, version: int | None = None, ) -> str: """ @@ -231,7 +240,7 @@ class PromptManager: except Exception as e: raise ValueError(f"Error rendering template '{prompt_id}': {e}") - def _validate_input(self, variables: dict[str, Any], schema: dict[str, Any]) -> None: + def _validate_input(self, variables: Mapping[str, object], schema: Mapping[str, str]) -> None: """Basic validation of input variables against schema.""" for field_name, field_type in schema.items(): if field_name in variables: @@ -291,7 +300,7 @@ class PromptManager: """Get a list of all available prompt IDs.""" return list(self.prompts.keys()) - def get_prompt_metadata(self, prompt_id: str) -> dict[str, Any] | None: + def get_prompt_metadata(self, prompt_id: str) -> dict[str, object] | None: """Get metadata for a specific prompt.""" template: Final = self.prompts.get(prompt_id) return template.metadata if template else None @@ -302,12 +311,12 @@ class PromptManager: if self.prompt_directory: self._load_prompts() - def add_prompt(self, prompt_id: str, content: str, metadata: dict[str, Any] | None = None) -> None: + def add_prompt(self, prompt_id: str, content: str, metadata: dict[str, object] | None = None) -> None: """Add a prompt template programmatically.""" template: Final = PromptTemplate(content=content, metadata=metadata or {}, template_id=prompt_id) self.prompts[prompt_id] = template - def prompt_file_to_json(self, file_path: str | Path) -> dict[str, Any]: + def prompt_file_to_json(self, file_path: str | Path) -> _PromptFileJson: """Convert a .prompt file to JSON format. Args: @@ -324,7 +333,7 @@ class PromptManager: return {"content": template_content.strip(), "metadata": frontmatter} - def json_to_prompt_file(self, prompt_data: dict[str, Any]) -> str: + def json_to_prompt_file(self, prompt_data: _PromptFileJson) -> str: """Convert JSON prompt data to .prompt file format. Args: diff --git a/litellm/integrations/galileo.py b/litellm/integrations/galileo.py index 23727801a6f..b27618993a3 100644 --- a/litellm/integrations/galileo.py +++ b/litellm/integrations/galileo.py @@ -6,10 +6,11 @@ import re import uuid from collections.abc import Mapping, Sequence from datetime import datetime, timezone, tzinfo -from typing import Any, Final, TypedDict, cast +from typing import Any, Final, Protocol, cast import httpx from pydantic import BaseModel, Field +from typing_extensions import ReadOnly, TypedDict import litellm from litellm._logging import verbose_logger @@ -35,6 +36,34 @@ GALILEO_CLOUD_API_BASE_URL: Final = "https://api.galileo.ai" GALILEO_MAX_IN_MEMORY_RECORDS: Final = 1000 +class _GalileoLoginBody(TypedDict): + """Decoded body of the Galileo login response.""" + + access_token: ReadOnly[str] + + +class _GalileoLoginResponse(Protocol): + """The login call's HTTP response, read for the access token it carries.""" + + def json(self) -> _GalileoLoginBody: ... + + +class _JsonResponse(Protocol): + """An HTTP response read only for whatever JSON body it decodes to.""" + + def json(self) -> object: ... + + +def _login_access_token(response: _GalileoLoginResponse) -> str: + """Read the bearer token out of a Galileo login response body.""" + return response.json()["access_token"] + + +def _decoded_body(response: _JsonResponse) -> object: + """Decode a response body without asserting anything about its shape.""" + return response.json() + + class GalileoStandardLoggingFields(TypedDict, total=False): call_type: str model: str @@ -156,7 +185,7 @@ class GalileoObserve(CustomLogger): }, ) galileo_login_response.raise_for_status() - access_token: Final = galileo_login_response.json()["access_token"] + access_token: Final = _login_access_token(galileo_login_response) self.headers = { "accept": "application/json", "Content-Type": "application/json", @@ -421,7 +450,7 @@ class GalileoObserve(CustomLogger): try: verbose_logger.debug( "Galileo Logger HTTP error response json: %s", - response.json(), + _decoded_body(response), ) except Exception: pass diff --git a/litellm/integrations/gitlab/gitlab_client.py b/litellm/integrations/gitlab/gitlab_client.py index 0690ccc8c15..813a2ef2821 100644 --- a/litellm/integrations/gitlab/gitlab_client.py +++ b/litellm/integrations/gitlab/gitlab_client.py @@ -4,12 +4,80 @@ Now supports selecting a tag via `config["tag"]`; falls back to branch ("main"). """ import base64 -from typing import Any, Final +from collections.abc import Mapping, Sequence +from typing import Any, Final, Protocol, TypedDict from urllib.parse import quote +from typing_extensions import ReadOnly + from litellm.llms.custom_httpx.http_handler import HTTPHandler +class GitLabFilePayload(TypedDict, total=False): + """A repository-files API entry.""" + + content: ReadOnly[str] + encoding: ReadOnly[str] + + +class GitLabTreeEntry(TypedDict, total=False): + """A repository-tree API entry.""" + + path: ReadOnly[str] + type: ReadOnly[str] + + +class GitLabBranch(TypedDict, total=False): + """A repository-branches API entry.""" + + name: ReadOnly[str] + type: ReadOnly[str] + + +class GitLabFileMetadata(TypedDict): + """The response headers a raw file request exposes as metadata.""" + + content_type: ReadOnly[str | None] + content_length: ReadOnly[str | None] + last_modified: ReadOnly[str | None] + + +class _FileJsonResponse(Protocol): + def json(self) -> GitLabFilePayload: ... + + +class _TreeJsonResponse(Protocol): + def json(self) -> Sequence[GitLabTreeEntry] | None: ... + + +class _ProjectJsonResponse(Protocol): + def json(self) -> Mapping[str, object]: ... + + +class _BranchesJsonResponse(Protocol): + def json(self) -> Sequence[GitLabBranch] | None: ... + + +def _file_payload(resp: _FileJsonResponse) -> GitLabFilePayload: + """The JSON body of a repository-files response.""" + return resp.json() + + +def _tree_entries(resp: _TreeJsonResponse) -> Sequence[GitLabTreeEntry]: + """The entries of a repository-tree response.""" + return resp.json() or [] + + +def _project_info(resp: _ProjectJsonResponse) -> Mapping[str, object]: + """The JSON body of a project response.""" + return resp.json() + + +def _branch_entries(resp: _BranchesJsonResponse) -> Sequence[GitLabBranch] | None: + """The JSON body of a repository-branches response.""" + return resp.json() + + class GitLabClient: """ Client for interacting with the GitLab API to fetch files. @@ -42,12 +110,12 @@ class GitLabClient: self.project: str | int = project self.access_token: str = str(access_token) - self.auth_method = config.get("auth_method", "token") # 'token' or 'oauth' + self.auth_method: str = config.get("auth_method", "token") # 'token' or 'oauth' self.branch = config.get("branch", None) if not self.branch: self.branch = "main" self.tag = config.get("tag") - self.base_url = config.get("base_url", "https://gitlab.com/api/v4") + self.base_url: str = config.get("base_url", "https://gitlab.com/api/v4") if not all([self.project, self.access_token]): raise ValueError("project and access_token are required") @@ -159,7 +227,7 @@ class GitLabClient: if resp.status_code == 404: return None resp.raise_for_status() - data: Final = resp.json() + data: Final = _file_payload(resp) content: Final = data.get("content") encoding: Final = data.get("encoding", "") if content and encoding == "base64": @@ -208,7 +276,7 @@ class GitLabClient: return [] resp.raise_for_status() - data: Final = resp.json() or [] + data: Final = _tree_entries(resp) files: Final[list[str]] = [] for item in data: if item.get("type") == "blob": @@ -229,13 +297,13 @@ class GitLabClient: raise Exception("Authentication failed. Check your GitLab token and auth_method.") raise Exception(f"Failed to list files in '{directory_path}': {e}") - def get_repository_info(self) -> dict[str, Any]: + def get_repository_info(self) -> Mapping[str, object]: """Get information about the project/repository.""" url: Final = f"{self.base_url}/projects/{self._project_enc}" try: resp: Final = self.http_handler.get(url, headers=self.headers) resp.raise_for_status() - return resp.json() + return _project_info(resp) except Exception as e: raise Exception(f"Failed to get repository info: {e}") @@ -247,18 +315,18 @@ class GitLabClient: except Exception: return False - def get_branches(self) -> list[dict[str, Any]]: + def get_branches(self) -> list[GitLabBranch]: """Get list of branches in the repository.""" url: Final = f"{self.base_url}/projects/{self._project_enc}/repository/branches" try: resp: Final = self.http_handler.get(url, headers=self.headers) resp.raise_for_status() - data: Final = resp.json() + data: Final = _branch_entries(resp) return data if isinstance(data, list) else [] except Exception as e: raise Exception(f"Failed to get branches: {e}") - def get_file_metadata(self, file_path: str, *, ref: str | None = None) -> dict[str, Any] | None: + def get_file_metadata(self, file_path: str, *, ref: str | None = None) -> GitLabFileMetadata | None: """ Get minimal metadata about a file via RAW endpoint headers at a given ref. diff --git a/litellm/integrations/gitlab/gitlab_prompt_manager.py b/litellm/integrations/gitlab/gitlab_prompt_manager.py index c41d9dd240f..d4602176650 100644 --- a/litellm/integrations/gitlab/gitlab_prompt_manager.py +++ b/litellm/integrations/gitlab/gitlab_prompt_manager.py @@ -2,10 +2,12 @@ GitLab prompt manager with configurable prompts folder. """ -from typing import TYPE_CHECKING, Any, Final +from collections.abc import Mapping +from typing import TYPE_CHECKING, Any, Final, TypeVar from jinja2 import DictLoader, select_autoescape from jinja2.sandbox import ImmutableSandboxedEnvironment +from typing_extensions import ReadOnly, TypedDict from litellm.integrations.custom_prompt_management import CustomPromptManagement @@ -24,6 +26,19 @@ from litellm.types.utils import StandardCallbackDynamicParams GITLAB_PREFIX: Final = "gitlab::" +_ResponseT = TypeVar("_ResponseT") + + +class GitLabCachedPrompt(TypedDict): + id: ReadOnly[str] + path: ReadOnly[str] + content: ReadOnly[str] + metadata: ReadOnly[Mapping[str, object]] + model: ReadOnly[str | None] + temperature: ReadOnly[float | None] + max_tokens: ReadOnly[int | None] + optional_params: ReadOnly[Mapping[str, object]] + def encode_prompt_id(raw_id: str) -> str: """Convert GitLab path IDs like 'invoice/extract' → 'gitlab::invoice::extract'""" @@ -206,7 +221,7 @@ class GitLabTemplateManager: result[key] = value.strip("\"'") return result - def render_template(self, template_id: str, variables: dict[str, Any] | None = None) -> str: + def render_template(self, template_id: str, variables: Mapping[str, object] | None = None) -> str: if template_id not in self.prompts: raise ValueError(f"Template '{template_id}' not found") template: Final = self.prompts[template_id] @@ -313,7 +328,7 @@ class GitLabPromptManager(CustomPromptManagement): def get_prompt_template( self, prompt_id: str, - prompt_variables: dict[str, Any] | None = None, + prompt_variables: Mapping[str, object] | None = None, *, ref: str | None = None, ) -> tuple[str, dict[str, Any]]: @@ -338,13 +353,13 @@ class GitLabPromptManager(CustomPromptManagement): self, user_id: str | None, messages: list[AllMessageValues], - function_call: dict[str, Any] | str | None = None, - litellm_params: dict[str, Any] | None = None, + function_call: Mapping[str, object] | str | None = None, + litellm_params: dict[str, object] | None = None, prompt_id: str | None = None, - prompt_variables: dict[str, Any] | None = None, + prompt_variables: Mapping[str, object] | None = None, prompt_version: str | None = None, **kwargs, - ) -> tuple[list[AllMessageValues], dict[str, Any] | None]: + ) -> tuple[list[AllMessageValues], dict[str, object] | None]: if not prompt_id: return messages, litellm_params try: @@ -377,9 +392,9 @@ class GitLabPromptManager(CustomPromptManagement): return final_messages, litellm_params except Exception as e: - import litellm + from litellm._logging import verbose_proxy_logger - litellm._logging.verbose_proxy_logger.error("Error in GitLab prompt pre_call_hook: %s", e) + verbose_proxy_logger.error("Error in GitLab prompt pre_call_hook: %s", e) return messages, litellm_params def _parse_prompt_to_messages(self, prompt_content: str) -> list[AllMessageValues]: @@ -435,14 +450,14 @@ class GitLabPromptManager(CustomPromptManagement): def post_call_hook( self, user_id: str | None, - response: Any, + response: _ResponseT, input_messages: list[AllMessageValues], - function_call: dict[str, Any] | str | None = None, - litellm_params: dict[str, Any] | None = None, + function_call: Mapping[str, object] | str | None = None, + litellm_params: Mapping[str, object] | None = None, prompt_id: str | None = None, - prompt_variables: dict[str, Any] | None = None, + prompt_variables: Mapping[str, object] | None = None, **kwargs, - ) -> Any: + ) -> _ResponseT: return response def get_available_prompts(self) -> list[str]: @@ -498,7 +513,7 @@ class GitLabPromptManager(CustomPromptManagement): messages: Final = self._parse_prompt_to_messages(rendered_prompt) template_model: Final = prompt_metadata.get("model") - optional_params: Final[dict[str, Any]] = {} + optional_params: Final[dict[str, object]] = {} for param in [ "temperature", "max_tokens", @@ -658,14 +673,14 @@ class GitLabPromptCache: self.template_manager: GitLabTemplateManager = self.prompt_manager.prompt_manager # In-memory stores - self._by_file: dict[str, dict[str, Any]] = {} - self._by_id: dict[str, dict[str, Any]] = {} + self._by_file: dict[str, GitLabCachedPrompt] = {} + self._by_id: dict[str, GitLabCachedPrompt] = {} # ------------------------- # Public API # ------------------------- - def load_all(self, *, recursive: bool = True) -> dict[str, dict[str, Any]]: + def load_all(self, *, recursive: bool = True) -> dict[str, GitLabCachedPrompt]: """ Scan GitLab for all .prompt files under prompts_path, load and parse each, and return the mapping of repo file path -> JSON-like dict. @@ -695,7 +710,7 @@ class GitLabPromptCache: return self._by_id - def reload(self, *, recursive: bool = True) -> dict[str, dict[str, Any]]: + def reload(self, *, recursive: bool = True) -> dict[str, GitLabCachedPrompt]: """Clear the cache and re-load from GitLab.""" self._by_file.clear() self._by_id.clear() @@ -709,11 +724,11 @@ class GitLabPromptCache: """Return the template IDs (relative to prompts_path, without extension) currently cached.""" return list(self._by_id.keys()) - def get_by_file(self, file_path: str) -> dict[str, Any] | None: + def get_by_file(self, file_path: str) -> GitLabCachedPrompt | None: """Get a cached prompt JSON by repo file path.""" return self._by_file.get(file_path) - def get_by_id(self, prompt_id: str) -> dict[str, Any] | None: + def get_by_id(self, prompt_id: str) -> GitLabCachedPrompt | None: """Get a cached prompt JSON by prompt ID (relative to prompts_path).""" if prompt_id in self._by_id: return self._by_id[prompt_id] @@ -728,7 +743,7 @@ class GitLabPromptCache: # Internals # ------------------------- - def _template_to_json(self, prompt_id: str, tmpl: GitLabPromptTemplate) -> dict[str, Any]: + def _template_to_json(self, prompt_id: str, tmpl: GitLabPromptTemplate) -> GitLabCachedPrompt: """ Normalize a GitLabPromptTemplate into a JSON-like dict that is easy to serialize. """ diff --git a/litellm/integrations/langfuse/langfuse.py b/litellm/integrations/langfuse/langfuse.py index 296c2b5714e..9576eabaa34 100644 --- a/litellm/integrations/langfuse/langfuse.py +++ b/litellm/integrations/langfuse/langfuse.py @@ -89,7 +89,7 @@ def _extract_cache_read_input_tokens(usage_obj) -> int: # Check prompt_tokens_details.cached_tokens (used by Gemini and other providers) if hasattr(usage_obj, "prompt_tokens_details"): - prompt_tokens_details: Final = getattr(usage_obj, "prompt_tokens_details", None) + prompt_tokens_details: Final[object] = getattr(usage_obj, "prompt_tokens_details", None) if prompt_tokens_details is not None and hasattr(prompt_tokens_details, "cached_tokens"): cached_tokens: Final = getattr(prompt_tokens_details, "cached_tokens", None) if cached_tokens is not None and isinstance(cached_tokens, (int, float)) and cached_tokens > 0: @@ -623,9 +623,16 @@ class LangFuseLogger: ) # Apply custom masking function if provided - if masking_function is not None and callable(masking_function): - input = self._apply_masking_function(input, masking_function) - output = self._apply_masking_function(output, masking_function) + masked_input: Final[object] = ( + self._apply_masking_function(input, masking_function) + if masking_function is not None and callable(masking_function) + else input + ) + masked_output: Final[object] = ( + self._apply_masking_function(output, masking_function) + if masking_function is not None and callable(masking_function) + else output + ) clean_metadata = redact_user_api_key_info(metadata=clean_metadata) @@ -651,15 +658,15 @@ class LangFuseLogger: # Special keys that are found in the function arguments and not the metadata if "input" in update_trace_keys: - trace_params["input"] = input if not mask_input else "redacted-by-litellm" + trace_params["input"] = masked_input if not mask_input else "redacted-by-litellm" if "output" in update_trace_keys: - trace_params["output"] = output if not mask_output else "redacted-by-litellm" + trace_params["output"] = masked_output if not mask_output else "redacted-by-litellm" else: # don't overwrite an existing trace trace_params = { "id": trace_id, "name": trace_name, "session_id": session_id, - "input": input if not mask_input else "redacted-by-litellm", + "input": masked_input if not mask_input else "redacted-by-litellm", "version": clean_metadata.pop( "trace_version", clean_metadata.get("version", None) ), # If provided just version, it will applied to the trace as well, if applied a trace version it will take precedence @@ -669,9 +676,9 @@ class LangFuseLogger: trace_params[key.replace("trace_", "")] = clean_metadata.pop(key, None) if level == "ERROR": - trace_params["status_message"] = output + trace_params["status_message"] = masked_output else: - trace_params["output"] = output if not mask_output else "redacted-by-litellm" + trace_params["output"] = masked_output if not mask_output else "redacted-by-litellm" if debug is True or (isinstance(debug, str) and debug.lower() == "true"): debug_metadata: Final = { @@ -708,7 +715,7 @@ class LangFuseLogger: ("aws_region_name", aws_region_name, bool(aws_region_name)), ("cache_hit", kwargs.get("cache_hit") or False, self._supports_tags() and "cache_hit" in kwargs), ) - enrichments: Final[Mapping[str, Any]] = { + enrichments: Final[Mapping[str, object]] = { key: value for key, value, include in candidate_enrichments if include } @@ -802,8 +809,8 @@ class LangFuseLogger: "end_time": end_time, "model": model_name, "model_parameters": optional_params, - "input": input if not mask_input else "redacted-by-litellm", - "output": output if not mask_output else "redacted-by-litellm", + "input": masked_input if not mask_input else "redacted-by-litellm", + "output": masked_output if not mask_output else "redacted-by-litellm", "usage": usage, "usage_details": usage_details, "metadata": { @@ -825,8 +832,8 @@ class LangFuseLogger: prompt_management_metadata=prompt_management_metadata, langfuse_client=self.Langfuse, ) - if output is not None and isinstance(output, str) and level == "ERROR": - generation_params["status_message"] = output + if masked_output is not None and isinstance(masked_output, str) and level == "ERROR": + generation_params["status_message"] = masked_output if self._supports_completion_start_time(): generation_params["completion_start_time"] = kwargs.get("completion_start_time", None) @@ -935,7 +942,7 @@ class LangFuseLogger: return Version(self.langfuse_sdk_version) >= Version("2.7.3") @staticmethod - def _apply_masking_function(data: Any, masking_function: Callable[[Any], Any]) -> Any: + def _apply_masking_function(data: object, masking_function: Callable[[object], object]) -> object: """ Apply a masking function to data, handling different data types. @@ -1049,7 +1056,7 @@ def _add_prompt_to_generation_params( generation_params: dict, clean_metadata: dict, prompt_management_metadata: StandardLoggingPromptManagementMetadata | None, - langfuse_client: Any, + langfuse_client: object, ) -> dict: from langfuse import Langfuse from langfuse.model import ( diff --git a/litellm/integrations/opik/opik.py b/litellm/integrations/opik/opik.py index fae93f03d1e..ce47d7fe27a 100644 --- a/litellm/integrations/opik/opik.py +++ b/litellm/integrations/opik/opik.py @@ -4,9 +4,12 @@ Opik Logger that logs LLM events to an Opik server import asyncio import traceback +from collections.abc import Mapping from datetime import datetime from typing import Any, Final +from typing_extensions import ReadOnly, TypedDict, Unpack + from litellm._logging import verbose_logger from litellm.integrations.custom_batch_logger import CustomBatchLogger from litellm.llms.custom_httpx.http_handler import ( @@ -23,7 +26,7 @@ except Exception: opik_client = None -def _should_skip_event(kwargs: dict[str, Any]) -> bool: +def _should_skip_event(kwargs: Mapping[str, object]) -> bool: """Check if event should be skipped due to missing standard_logging_object.""" if kwargs.get("standard_logging_object") is None: verbose_logger.debug("OpikLogger skipping event; no standard_logging_object found") @@ -31,12 +34,24 @@ def _should_skip_event(kwargs: dict[str, Any]) -> bool: return False +class _OpikLoggerKwargs(TypedDict, total=False): + """Constructor options accepted by ``OpikLogger``.""" + + project_name: ReadOnly[str | None] + url: ReadOnly[str | None] + api_key: ReadOnly[str | None] + workspace: ReadOnly[str | None] + batch_size: ReadOnly[int | None] + flush_interval: ReadOnly[int | None] + max_queue_size: ReadOnly[int | None] + + class OpikLogger(CustomBatchLogger): """ Opik Logger for logging events to an Opik Server """ - def __init__(self, **kwargs: Any) -> None: + def __init__(self, **kwargs: Unpack[_OpikLoggerKwargs]) -> None: self.async_httpx_client = get_async_httpx_client(llm_provider=httpxSpecialProvider.LoggingCallback) self.sync_httpx_client = _get_httpx_client() @@ -95,7 +110,7 @@ class OpikLogger(CustomBatchLogger): async def async_log_success_event( self, - kwargs: dict[str, Any], + kwargs: dict[str, object], response_obj: Any, start_time: datetime, end_time: datetime, @@ -163,7 +178,7 @@ class OpikLogger(CustomBatchLogger): except Exception as e: verbose_logger.exception("OpikLogger failed to log success event - %s\n%s", e, traceback.format_exc()) - def _sync_send(self, url: str, headers: dict[str, str], batch: dict[str, Any]) -> None: + def _sync_send(self, url: str, headers: dict[str, str], batch: dict[str, object]) -> None: try: response: Final = self.sync_httpx_client.post( url=url, @@ -178,7 +193,7 @@ class OpikLogger(CustomBatchLogger): def log_success_event( self, - kwargs: dict[str, Any], + kwargs: dict[str, object], response_obj: Any, start_time: datetime, end_time: datetime, @@ -247,7 +262,7 @@ class OpikLogger(CustomBatchLogger): except Exception as e: verbose_logger.exception("OpikLogger failed to log success event - %s\n%s", e, traceback.format_exc()) - async def _submit_batch(self, url: str, headers: dict[str, str], batch: dict[str, Any]) -> None: + async def _submit_batch(self, url: str, headers: dict[str, str], batch: dict[str, object]) -> None: try: response: Final = await self.async_httpx_client.post( url=url, diff --git a/litellm/integrations/opik/opik_payload_builder/extractors.py b/litellm/integrations/opik/opik_payload_builder/extractors.py index 92a7eca7f3e..4dd3d40fae3 100644 --- a/litellm/integrations/opik/opik_payload_builder/extractors.py +++ b/litellm/integrations/opik/opik_payload_builder/extractors.py @@ -1,6 +1,7 @@ """Data extraction functions for Opik payload building.""" import json +from collections.abc import Mapping from typing import Any, Final from litellm import _logging @@ -35,8 +36,8 @@ def normalize_provider_name(provider: str | None) -> str | None: def extract_opik_metadata( - litellm_metadata: dict[str, Any], - standard_logging_metadata: dict[str, Any], + litellm_metadata: Mapping[str, Any], + standard_logging_metadata: Mapping[str, Any], ) -> dict[str, Any]: """ Merge Opik metadata from three sources in increasing priority order: @@ -97,7 +98,7 @@ def extract_span_identifiers( def extract_tags( - opik_metadata: dict[str, Any], + opik_metadata: Mapping[str, Any], custom_llm_provider: str | None, ) -> list[str]: """ @@ -122,7 +123,7 @@ def apply_proxy_header_overrides( project_name: str, tags: list[str], thread_id: str | None, - proxy_headers: dict[str, Any], + proxy_headers: Mapping[str, str], ) -> tuple[str, list[str], str | None]: """ Apply overrides from proxy request headers (opik_* prefix). @@ -148,7 +149,7 @@ def apply_proxy_header_overrides( thread_id = value elif param_key == "tags": try: - parsed_tags = json.loads(value) + parsed_tags: object = json.loads(value) if isinstance(parsed_tags, list): tags.extend(parsed_tags) except (json.JSONDecodeError, TypeError): @@ -158,11 +159,11 @@ def apply_proxy_header_overrides( def extract_and_build_metadata( - opik_metadata: dict[str, Any], - standard_logging_metadata: dict[str, Any], - standard_logging_object: dict[str, Any], - litellm_kwargs: dict[str, Any], -) -> dict[str, Any]: + opik_metadata: Mapping[str, object], + standard_logging_metadata: Mapping[str, object], + standard_logging_object: Mapping[str, object], + litellm_kwargs: Mapping[str, object], +) -> dict[str, object]: """ Build the complete metadata dictionary from all available sources. diff --git a/litellm/integrations/otel/mappers/genai.py b/litellm/integrations/otel/mappers/genai.py index b09498f9292..3ac92b04c27 100644 --- a/litellm/integrations/otel/mappers/genai.py +++ b/litellm/integrations/otel/mappers/genai.py @@ -62,6 +62,8 @@ class GenAIMapper: GenAI.RESPONSE_TIME_TO_FIRST_CHUNK: lambda d: d.time_to_first_chunk_seconds, GenAI.USAGE_INPUT_TOKENS: lambda d: d.usage.input_tokens, GenAI.USAGE_OUTPUT_TOKENS: lambda d: d.usage.output_tokens, + GenAI.USAGE_CACHE_CREATION_INPUT_TOKENS: lambda d: d.usage.cache_creation_input_tokens, + GenAI.USAGE_CACHE_READ_INPUT_TOKENS: lambda d: d.usage.cache_read_input_tokens, Error.TYPE: lambda d: d.error.error_type if d.error else None, Server.ADDRESS: lambda d: d.server.address if d.server else None, Server.PORT: lambda d: d.server.port if d.server else None, diff --git a/litellm/integrations/otel/model/payloads.py b/litellm/integrations/otel/model/payloads.py index f70c777e1a7..d35405538f6 100644 --- a/litellm/integrations/otel/model/payloads.py +++ b/litellm/integrations/otel/model/payloads.py @@ -95,6 +95,22 @@ class LLMUsage: input_tokens: int | None = None output_tokens: int | None = None total_tokens: int | None = None + cache_creation_input_tokens: int | None = None + cache_read_input_tokens: int | None = None + + @classmethod + def from_standard_logging_payload(cls, payload: StandardLoggingPayload) -> LLMUsage: + # Cache token counts only exist on the raw provider usage object under metadata + metadata: Final[Mapping[str, object]] = payload.get("metadata") or {} + raw_usage: Final = metadata.get("usage_object") + usage_object: Final[Mapping[str, object]] = raw_usage if isinstance(raw_usage, Mapping) else {} + return cls( + input_tokens=as_int(payload.get("prompt_tokens")), + output_tokens=as_int(payload.get("completion_tokens")), + total_tokens=as_int(payload.get("total_tokens")), + cache_creation_input_tokens=as_int(usage_object.get("cache_creation_input_tokens")), + cache_read_input_tokens=as_int(usage_object.get("cache_read_input_tokens")), + ) @dataclass(frozen=True) @@ -363,11 +379,7 @@ class LLMCallSpanData: response_model=context.response_model, response_id=as_str(response.get("id")), request_params=LLMRequestParams.from_model_parameters(params), - usage=LLMUsage( - input_tokens=as_int(payload.get("prompt_tokens")), - output_tokens=as_int(payload.get("completion_tokens")), - total_tokens=as_int(payload.get("total_tokens")), - ), + usage=LLMUsage.from_standard_logging_payload(payload), finish_reasons=finish_reasons, error=_parse_error(payload), response_cost=as_float(payload.get("response_cost")), diff --git a/litellm/integrations/otel/model/semconv.py b/litellm/integrations/otel/model/semconv.py index 4ad0cb5d1b4..f7a6280f95b 100644 --- a/litellm/integrations/otel/model/semconv.py +++ b/litellm/integrations/otel/model/semconv.py @@ -110,6 +110,8 @@ class GenAI: # usage USAGE_INPUT_TOKENS: Final = "gen_ai.usage.input_tokens" USAGE_OUTPUT_TOKENS: Final = "gen_ai.usage.output_tokens" + USAGE_CACHE_CREATION_INPUT_TOKENS: Final = "gen_ai.usage.cache_creation.input_tokens" + USAGE_CACHE_READ_INPUT_TOKENS: Final = "gen_ai.usage.cache_read.input_tokens" # content (opt-in, gated by capture mode) INPUT_MESSAGES: Final = "gen_ai.input.messages" OUTPUT_MESSAGES: Final = "gen_ai.output.messages" diff --git a/litellm/integrations/otel/plumbing/metrics.py b/litellm/integrations/otel/plumbing/metrics.py index c7e491c002a..e1623f4697f 100644 --- a/litellm/integrations/otel/plumbing/metrics.py +++ b/litellm/integrations/otel/plumbing/metrics.py @@ -11,9 +11,10 @@ identical metrics. The attribute cardinality filter is reused from v1 by import from collections.abc import Mapping from dataclasses import dataclass from datetime import datetime -from typing import Any, Final, TypeAlias +from typing import Any, Final, Literal, Protocol, TypeAlias from opentelemetry.metrics import Histogram, Meter +from typing_extensions import ReadOnly, TypedDict import litellm from litellm._logging import verbose_logger @@ -151,6 +152,29 @@ METRIC_ATTRIBUTE_CEILING: Final[frozenset[str]] = frozenset( BOUNDED_HIDDEN_PARAM_KEYS: Final[tuple[str, ...]] = ("model_id",) +class _TokenUsage(TypedDict, total=False): + """The token counts a response's ``usage`` carries, as the recorder reads them.""" + + prompt_tokens: ReadOnly[int] + completion_tokens: ReadOnly[int] + + +class _ResponseView(Protocol): + """The one read the recorder makes on a litellm response object.""" + + def get(self, key: Literal["usage"], /) -> _TokenUsage | None: ... + + +class _MetricKwargs(TypedDict, total=False): + """The logging kwargs the recorder reads directly.""" + + call_type: ReadOnly[str | None] + litellm_params: ReadOnly[Mapping[str, object] | None] + response_cost: ReadOnly[float | None] + completion_start_time: ReadOnly[datetime | float | str | None] + api_call_start_time: ReadOnly[datetime | float | str | None] + + def resolve_error_type(kwargs: Mapping[str, Any]) -> str: """The ``error.type`` value for a failed request. @@ -192,8 +216,8 @@ class GenAIMetricRecorder: def record( self, - kwargs: Mapping[str, Any], - response_obj: Any, + kwargs: _MetricKwargs, + response_obj: _ResponseView | None, start_time: datetime, end_time: datetime, ) -> None: @@ -218,7 +242,7 @@ class GenAIMetricRecorder: def record_failure( self, - kwargs: Mapping[str, Any], + kwargs: _MetricKwargs, start_time: datetime, end_time: datetime, ) -> None: @@ -342,7 +366,7 @@ class GenAIMetricRecorder: # Per-metric recording # ------------------------------------------------------------------ # - def _record_token_usage(self, response_obj: Any, common_attrs: dict) -> None: + def _record_token_usage(self, response_obj: _ResponseView | None, common_attrs: dict) -> None: if not response_obj: return usage: Final = response_obj.get("usage") @@ -353,7 +377,7 @@ class GenAIMetricRecorder: self._metrics.token_usage.record(usage.get("prompt_tokens", 0), attributes=in_attrs) self._metrics.token_usage.record(usage.get("completion_tokens", 0), attributes=out_attrs) - def _record_time_to_first_token(self, kwargs: Mapping[str, Any], common_attrs: dict) -> None: + def _record_time_to_first_token(self, kwargs: _MetricKwargs, common_attrs: dict) -> None: time_to_first_chunk: Final = time_to_first_chunk_seconds(kwargs) if time_to_first_chunk is None: return @@ -361,15 +385,14 @@ class GenAIMetricRecorder: def _record_time_per_output_token( self, - kwargs: Mapping[str, Any], - response_obj: Any, + kwargs: _MetricKwargs, + response_obj: _ResponseView | None, end_time: datetime, duration_s: float, common_attrs: dict, ) -> None: - completion_tokens = None - if response_obj and (usage := response_obj.get("usage")): - completion_tokens = usage.get("completion_tokens") + usage: Final = response_obj.get("usage") if response_obj else None + completion_tokens: Final = usage.get("completion_tokens") if usage else None if completion_tokens is None or completion_tokens <= 0: return diff --git a/litellm/integrations/posthog.py b/litellm/integrations/posthog.py index db9610a5a3c..4f7dff952e6 100644 --- a/litellm/integrations/posthog.py +++ b/litellm/integrations/posthog.py @@ -12,7 +12,10 @@ For batching specific details see CustomBatchLogger class import asyncio import atexit import os -from typing import Any, Final +from collections.abc import Mapping, Sequence +from typing import Final + +from typing_extensions import ReadOnly, TypedDict from litellm._logging import verbose_logger from litellm._uuid import uuid @@ -34,6 +37,21 @@ from litellm.types.integrations.posthog import ( from litellm.types.utils import StandardCallbackDynamicParams, StandardLoggingPayload +class PostHogBatchPayload(TypedDict): + api_key: ReadOnly[str] + batch: ReadOnly[Sequence[PostHogEventPayload]] + + +class PostHogLiteLLMParams(TypedDict, total=False): + metadata: ReadOnly[Mapping[str, object]] + + +class PostHogLogKwargs(TypedDict, total=False): + standard_logging_object: ReadOnly[StandardLoggingPayload] + standard_callback_dynamic_params: ReadOnly[StandardCallbackDynamicParams] + litellm_params: ReadOnly[PostHogLiteLLMParams] + + class PostHogLogger(CustomBatchLogger): def __init__(self, **kwargs): """ @@ -137,7 +155,7 @@ class PostHogLogger(CustomBatchLogger): if len(self.log_queue) >= self.batch_size: await self.flush_queue() - def create_posthog_event_payload(self, kwargs: dict[str, Any]) -> PostHogEventPayload: + def create_posthog_event_payload(self, kwargs: PostHogLogKwargs) -> PostHogEventPayload: """ Helper function to create a PostHog event payload for logging @@ -171,11 +189,11 @@ class PostHogLogger(CustomBatchLogger): def _create_posthog_properties( self, standard_logging_object: StandardLoggingPayload, - kwargs: dict[str, Any], + kwargs: PostHogLogKwargs, event_name: str, - ) -> dict[str, Any]: + ) -> dict[str, object]: """Create PostHog properties following LLM Analytics spec""" - properties: Final = {} + properties: Final[dict[str, object]] = {} # Core model information properties["$ai_model"] = self._safe_get(standard_logging_object, "model", "") @@ -211,16 +229,19 @@ class PostHogLogger(CustomBatchLogger): properties["$ai_error"] = error_str # Add trace properties - self._add_trace_properties(properties, kwargs) + self._add_trace_properties(properties, standard_logging_object, kwargs) # Add custom metadata fields self._add_custom_metadata_properties(properties, kwargs) return properties - def _add_trace_properties(self, properties: dict[str, Any], kwargs: dict[str, Any]): - standard_logging_object: Final = self._safe_get(kwargs, "standard_logging_object", {}) - + def _add_trace_properties( + self, + properties: dict[str, object], + standard_logging_object: StandardLoggingPayload, + kwargs: PostHogLogKwargs, + ) -> None: trace_id: Final = self._safe_get(standard_logging_object, "trace_id", self._safe_uuid()) properties["$ai_trace_id"] = trace_id @@ -232,7 +253,7 @@ class PostHogLogger(CustomBatchLogger): if parent_id: properties["$ai_parent_id"] = parent_id - def _add_custom_metadata_properties(self, properties: dict[str, Any], kwargs: dict[str, Any]): + def _add_custom_metadata_properties(self, properties: dict[str, object], kwargs: PostHogLogKwargs) -> None: """Add custom metadata fields to PostHog properties""" metadata: Final = self._extract_metadata(kwargs) if not isinstance(metadata, dict): @@ -277,7 +298,7 @@ class PostHogLogger(CustomBatchLogger): if key not in litellm_internal_fields: properties[key] = value - def _get_distinct_id(self, standard_logging_object: StandardLoggingPayload, kwargs: dict[str, Any]) -> str: + def _get_distinct_id(self, standard_logging_object: StandardLoggingPayload, kwargs: PostHogLogKwargs) -> str: metadata: Final = self._extract_metadata(kwargs) user_id: Final = self._safe_get(metadata, "user_id") if user_id: @@ -291,7 +312,7 @@ class PostHogLogger(CustomBatchLogger): return self._safe_uuid() - def _get_credentials_for_request(self, kwargs: dict[str, Any]) -> tuple[str | None, str | None]: + def _get_credentials_for_request(self, kwargs: PostHogLogKwargs) -> tuple[str | None, str | None]: """ Get PostHog credentials for this request. @@ -334,7 +355,7 @@ class PostHogLogger(CustomBatchLogger): verbose_logger.debug("[POSTHOG MOCK] Mock mode enabled - API calls will be intercepted") # Group events by credentials for batch sending - batches_by_credentials: Final[dict[tuple[str, str], list]] = {} + batches_by_credentials: Final[dict[tuple[str, str], list[PostHogEventPayload]]] = {} for item in self.log_queue: key = (item["api_key"], item["api_url"]) if key not in batches_by_credentials: @@ -380,18 +401,19 @@ class PostHogLogger(CustomBatchLogger): verbose_logger.error("PostHog: Failed to initialize async components: %s", e) raise - def _extract_metadata(self, kwargs: dict[str, Any]) -> dict[str, Any]: - litellm_params: Final = kwargs.get("litellm_params", {}) or {} - return litellm_params.get("metadata", {}) or {} + def _extract_metadata(self, kwargs: PostHogLogKwargs) -> Mapping[str, object]: + litellm_params: Final[PostHogLiteLLMParams] = kwargs.get("litellm_params", {}) or {} + metadata: Final[Mapping[str, object]] = litellm_params.get("metadata", {}) or {} + return metadata def _safe_uuid(self) -> str: return str(uuid.uuid4()) - def _create_posthog_payload(self, events: list, api_key: str) -> dict[str, Any]: + def _create_posthog_payload(self, events: Sequence[PostHogEventPayload], api_key: str) -> PostHogBatchPayload: return {"api_key": api_key, "batch": events} - def _safe_get(self, obj: Any, key: str, default: Any = None) -> Any: - if obj is None or not hasattr(obj, "get"): + def _safe_get(self, obj: Mapping[str, object] | None, key: str, default: object = None) -> object: + if not isinstance(obj, Mapping): return default return obj.get(key, default) @@ -412,7 +434,7 @@ class PostHogLogger(CustomBatchLogger): try: # Group events by credentials (same logic as async_send_batch) - batches_by_credentials: Final[dict[tuple[str, str], list]] = {} + batches_by_credentials: Final[dict[tuple[str, str], list[PostHogEventPayload]]] = {} for item in self.log_queue: key = (item["api_key"], item["api_url"]) if key not in batches_by_credentials: diff --git a/litellm/integrations/shadow_eval_logger.py b/litellm/integrations/shadow_eval_logger.py index cf8aa38d86e..27da785331a 100644 --- a/litellm/integrations/shadow_eval_logger.py +++ b/litellm/integrations/shadow_eval_logger.py @@ -1,8 +1,11 @@ """Shadow Eval Logger: samples a shadowed key's successful LLM requests (chat completions, Anthropic Messages, and Responses API surfaces, each normalized to chat shape), duplicates -each against the job's other arm in a detached task (the auto-router for a forward job, the -fixed baseline model for a reverse one), blind-judges real vs shadow, and appends one -``LiteLLM_ShadowEvalAttempt`` row (verdict or error) as the feature's only hot-path write. +each through every shadow arm in one detached task (each candidate auto-router for a +forward job, the fixed baseline model for a reverse one), blind-judges real vs each arm, +and appends one ``LiteLLM_ShadowEvalAttempt`` row per arm (verdict or error) as the +feature's only hot-path write. A multi-router job's arms therefore score the identical +sampled requests against the identical real responses, which is what makes their win +rates comparable head-to-head. Counts, status, and spend derive from those rows at read time, so nothing can disagree across pods or stop races; the hook reads active jobs through a short-TTL cache.""" @@ -498,12 +501,16 @@ def _decision_classifier_cost(metadata: Mapping[str, object]) -> float: return float(raw) if isinstance(raw, (int, float)) else 0.0 -def _request_was_routed_by(request_metadata: Mapping[str, object], router_name: str) -> bool: - """Whether the router under evaluation served this request, which is what decides - the direction it belongs to. A forward job skips its own router's traffic, since - duplicating it would compare the router to itself: guaranteed ties, judge spend for - zero information. A reverse job samples exactly that traffic and nothing else.""" - return _routing_decision(request_metadata).get("router_model_name") == router_name +def _direction_admits(request_metadata: Mapping[str, object], job: "ActiveShadowEvalJob") -> bool: + """Whether this request belongs to the job's direction. A forward job skips traffic + any of its candidate routers served: duplicating a router's own request compares it + to itself (guaranteed ties), and judging a sibling against another candidate's live + response would score candidates against each other instead of against the incumbent. + A reverse job samples exactly its one router's traffic and nothing else.""" + routed_by: Final = _routing_decision(request_metadata).get("router_model_name") + if job.direction == "reverse": + return routed_by == job.router_name + return routed_by not in job.arm_router_names @dataclass(frozen=True, slots=True) @@ -546,6 +553,7 @@ class ActiveShadowEvalJob(BaseModel): id: str router_name: str + router_names: tuple[str, ...] = () direction: ShadowEvalDirection = "forward" baseline_model: str | None = None shadow_percentage: float @@ -567,12 +575,25 @@ class ActiveShadowEvalJob(BaseModel): raise ValueError("baseline_model is set for exactly the reverse jobs") return self + @model_validator(mode="after") + def _reverse_evaluates_one_router(self) -> "ActiveShadowEvalJob": + """A reverse row naming several routers is unsamplable (there is no one traffic + slice they share) and fails closed.""" + if self.direction == "reverse" and len(self.arm_router_names) > 1: + raise ValueError("a reverse job evaluates exactly one router") + return self + @property - def shadow_target(self) -> str: - """The model the duplicated arm calls: the router itself for a forward job, the - fixed baseline for a reverse one. Total because the validator above pins + def arm_router_names(self) -> tuple[str, ...]: + """The job's full router set; rows from before router_names existed hold it in + router_name alone. The one place that reading lives on the sampling side.""" + return self.router_names or (self.router_name,) + + def arm_target(self, arm_router: str) -> str: + """The model one duplicated arm calls: the candidate router itself for a forward + job, the fixed baseline for a reverse one. Total because the validator above pins baseline_model to reverse jobs and only those.""" - return self.baseline_model or self.router_name + return self.baseline_model or arm_router def _as_active_job(record: object, attempts: int, spend: float) -> ActiveShadowEvalJob | None: @@ -592,7 +613,12 @@ _JOBS_CACHE_KEY: Final = "shadow_eval:active_jobs" class ShadowEvalLogger(CustomLogger): - """Fires blind pairwise shadow evaluations for keys with an active shadow-eval job.""" + """Fires blind pairwise shadow evaluations for targets with an active shadow-eval job. + + A job targets a virtual key, a team, or a user; a request qualifies for a job when + any of its resolved identities (key hash, team id, user id) matches the job's + target, so team and user jobs cover JWT-authenticated traffic, which carries no + key hash at all.""" def __init__( self, @@ -617,10 +643,10 @@ class ShadowEvalLogger(CustomLogger): # generation; the refill absorbs written rows and resets. self._job_starts: dict[str, int] = {} # mutable-ok: per-generation counter - async def _active_jobs(self) -> Mapping[str, tuple[ActiveShadowEvalJob, ...]]: - """Active jobs by api_key_id, cache-first. A key holds at most one job per - direction, so the value is a collection. A DB fault returns empty without - caching, so sampling pauses for that request and the next one retries.""" + async def _active_jobs(self) -> Mapping[tuple[str, str], tuple[ActiveShadowEvalJob, ...]]: + """Active jobs by (target_type, target_id), cache-first. A target holds at most + one job per direction, so the value is a collection. A DB fault returns empty + without caching, so sampling pauses for that request and the next one retries.""" cached: Final = await self._jobs_cache.async_get_cache(_JOBS_CACHE_KEY) if cached is not None: return cached # pyright: ignore[reportReturnType] # cache stores exactly this mapping shape @@ -652,10 +678,10 @@ class ShadowEvalLogger(CustomLogger): ) for row in grouped or [] } - by_key: Final = tuple( + by_target: Final = tuple( sorted( ( - (str(record.api_key_id), job) + ((str(record.target_type), str(record.target_id)), job) for record in records or [] if (job := _as_active_job(record, *attempt_stats.get(str(record.id), (0, 0.0)))) is not None ), @@ -663,7 +689,7 @@ class ShadowEvalLogger(CustomLogger): ) ) jobs: Final = MappingProxyType( - {key: tuple(job for _, job in group) for key, group in groupby(by_key, key=itemgetter(0))} + {target: tuple(job for _, job in group) for target, group in groupby(by_target, key=itemgetter(0))} ) await self._jobs_cache.async_set_cache(_JOBS_CACHE_KEY, jobs) self._job_starts = {} # rebind-ok: new generation, counts absorbed into the fill @@ -691,7 +717,7 @@ class ShadowEvalLogger(CustomLogger): now >= job.ends_at or job.attempts + self._job_starts.get(job.id, 0) >= job.max_turns or (job.max_budget is not None and job.spend >= job.max_budget) - or _request_was_routed_by(request_metadata, job.router_name) != (job.direction == "reverse") + or not _direction_admits(request_metadata, job) ): continue if not _sample_hits(request_id, job.id, job.shadow_percentage): @@ -720,8 +746,18 @@ class ShadowEvalLogger(CustomLogger): if should_redact_message_logging(dict(kwargs)): # mutable-ok: predicate takes a plain dict return metadata: Final = payload.get("metadata") or _EMPTY_METADATA - api_key_hash: Final = metadata.get("user_api_key_hash") - if not api_key_hash: + # Each identity the request resolved to is a candidate target; JWT-auth + # requests carry no key hash but do carry a team and user. + targets: Final = tuple( + (target_type, str(value)) + for target_type, value in ( + ("key", metadata.get("user_api_key_hash")), + ("team", metadata.get("user_api_key_team_id")), + ("user", metadata.get("user_api_key_user_id")), + ) + if value + ) + if not targets: return request_id: Final = payload.get("id") or "" if not request_id: @@ -731,8 +767,11 @@ class ShadowEvalLogger(CustomLogger): return # only surfaces this table can normalize are comparable; unknown types fail closed if ops.wire_params and _request_mutating_guardrail_ran(request_metadata): return # the wire-body snapshot predates the rewrite; replaying it would resurrect stripped content + active_jobs: Final = await self._active_jobs() eligible: Final = self._sampled_jobs( - (await self._active_jobs()).get(str(api_key_hash), ()), request_metadata, request_id + tuple(job for target in targets for job in active_jobs.get(target, ())), + request_metadata, + request_id, ) if not eligible: return @@ -755,7 +794,10 @@ class ShadowEvalLogger(CustomLogger): if self._inflight_shadow_tasks >= _MAX_CONCURRENT_SHADOW_TASKS: self._record_funnel(job.id, "shed") continue - self._job_starts[job.id] = self._job_starts.get(job.id, 0) + 1 + # One start writes one attempt row per arm, and max_turns is a row + # ceiling, so admission must pre-count every arm or a multi-router + # job overshoots the valve N-fold within a cache generation. + self._job_starts[job.id] = self._job_starts.get(job.id, 0) + len(job.arm_router_names) self._inflight_shadow_tasks += 1 asyncio.create_task( self._run_shadow_eval( @@ -794,32 +836,74 @@ class ShadowEvalLogger(CustomLogger): shadow_params: Mapping[str, object], parent_metadata: Mapping[str, object], ) -> None: - """Budget gate -> shadow call -> blind judge -> one attempt row, and every exit - in exactly one coverage bucket: the gates that decline to spend on an admitted - sample (no DB to record into, an over-budget key, an unverifiable or exhausted - eval budget) count it withheld, so eligible traffic still reconciles as - not_sampled + unjudgeable + shed + withheld + attempt rows. The prisma gate sits - above the dispatch so no provider spend happens without a place to record the - outcome, and the budget read lives here rather than in the success hook.""" + """Budget gates once per sampled request, then every router arm in turn: shadow + call -> blind judge -> one attempt row stamped with the arm. The gates that + decline to spend on an admitted sample (no DB to record into, an over-budget key, + an unverifiable or exhausted eval budget) count the REQUEST withheld before any + arm runs, so funnel counters stay per-request and a leg's eligible traffic still + reconciles as not_sampled + unjudgeable + shed + withheld + sampled requests, + where each sampled request writes one attempt row per arm. A budget crossed + mid-loop lets the remaining arms overshoot by one round, the same class of + overshoot as the samples already in flight when the cap is crossed. The prisma + gate sits above the dispatch so no provider spend happens without a place to + record the outcome, and the budget read lives here rather than in the success + hook.""" prisma: Final = self._prisma_provider() + if prisma is None: + self._record_funnel(job.id, "withheld") + return + if await _key_or_team_is_over_budget(parent_metadata): + self._record_funnel(job.id, "withheld") + return + if job.max_budget is not None: + try: + spend: Final = await self._read_job_spend(_job_spend_counter_key(job.id), job.spend, job.max_budget) + except Exception as e: # noqa: BLE001 # unverifiable budget: skip the sample rather than spend on it + verbose_logger.warning("shadow_eval: budget unverifiable for %s, sample skipped: %s", job.id, e) + self._record_funnel(job.id, "withheld") + return + if spend >= job.max_budget: + self._record_funnel(job.id, "withheld") + return + for arm_router in job.arm_router_names: + await self._run_shadow_arm( + prisma=prisma, + job=job, + arm_router=arm_router, + request_id=request_id, + messages=messages, + real_text=real_text, + real_model=real_model, + real_cost=real_cost, + real_classifier_cost=real_classifier_cost, + real_cache_hit=real_cache_hit, + control_tier=control_tier, + shadow_params=shadow_params, + parent_metadata=parent_metadata, + ) + + async def _run_shadow_arm( + self, + prisma: "PrismaClient", + job: ActiveShadowEvalJob, + arm_router: str, + request_id: str, + messages: Sequence[Mapping[str, object]], + real_text: str, + real_model: str, + real_cost: float, + real_classifier_cost: float, + real_cache_hit: bool, + control_tier: str | None, + shadow_params: Mapping[str, object], + parent_metadata: Mapping[str, object], + ) -> None: + """One arm's pipeline: shadow call -> blind judge -> one attempt row, every exit + recording this arm's outcome, so one arm's fault never silences a sibling arm.""" try: - if prisma is None: - self._record_funnel(job.id, "withheld") - return - if await _key_or_team_is_over_budget(parent_metadata): - self._record_funnel(job.id, "withheld") - return - if job.max_budget is not None: - try: - spend: Final = await self._read_job_spend(_job_spend_counter_key(job.id), job.spend, job.max_budget) - except Exception as e: # noqa: BLE001 # unverifiable budget: skip the sample rather than spend on it - verbose_logger.warning("shadow_eval: budget unverifiable for %s, sample skipped: %s", job.id, e) - self._record_funnel(job.id, "withheld") - return - if spend >= job.max_budget: - self._record_funnel(job.id, "withheld") - return - shadow: Final = await self._call_router_shadow(job.shadow_target, messages, shadow_params, parent_metadata) + shadow: Final = await self._call_router_shadow( + job.arm_target(arm_router), messages, shadow_params, parent_metadata + ) except Exception as e: # noqa: BLE001 # detached task: nothing billed yet, record and never raise verbose_logger.debug("shadow_eval: pipeline failed for %s: %s", request_id, e) await self._record_attempt( @@ -827,6 +911,7 @@ class ShadowEvalLogger(CustomLogger): job, request_id, control_tier, + router_name=arm_router, outcome="error", error=f"pipeline error: {e}", real_cost=real_cost, @@ -840,6 +925,7 @@ class ShadowEvalLogger(CustomLogger): job, request_id, control_tier, + router_name=arm_router, outcome="error", error=shadow.error, shadow_cost=shadow.cost, @@ -864,6 +950,7 @@ class ShadowEvalLogger(CustomLogger): job, request_id, control_tier, + router_name=arm_router, outcome="error", error=verdict.error, shadow=shadow, @@ -880,6 +967,7 @@ class ShadowEvalLogger(CustomLogger): job, request_id, control_tier, + router_name=arm_router, outcome=verdict.preference, shadow=shadow, real_model=real_model, @@ -898,6 +986,7 @@ class ShadowEvalLogger(CustomLogger): job, request_id, control_tier, + router_name=arm_router, outcome="error", error=f"pipeline error: {e}", shadow=shadow, @@ -915,6 +1004,7 @@ class ShadowEvalLogger(CustomLogger): request_id: str, control_tier: str | None, *, + router_name: str, outcome: str, real_cost: float, real_classifier_cost: float, @@ -937,6 +1027,7 @@ class ShadowEvalLogger(CustomLogger): data={ # mutable-ok: Prisma payload "job_id": job.id, "request_id": request_id, + "router_name": router_name, "outcome": outcome, "tier": control_tier if job.direction == "reverse" else (shadow.tier if shadow else None), "real_model": real_model or None, @@ -1056,7 +1147,7 @@ class ShadowEvalLogger(CustomLogger): ) -_EMPTY_JOBS: Final[Mapping[str, tuple[ActiveShadowEvalJob, ...]]] = MappingProxyType({}) +_EMPTY_JOBS: Final[Mapping[tuple[str, str], tuple[ActiveShadowEvalJob, ...]]] = MappingProxyType({}) def _default_prisma_provider() -> "PrismaClient | None": diff --git a/litellm/integrations/vector_store_integrations/vector_store_pre_call_hook.py b/litellm/integrations/vector_store_integrations/vector_store_pre_call_hook.py index aa29162ba1f..07d4f959489 100644 --- a/litellm/integrations/vector_store_integrations/vector_store_pre_call_hook.py +++ b/litellm/integrations/vector_store_integrations/vector_store_pre_call_hook.py @@ -13,7 +13,7 @@ from litellm._logging import verbose_logger from litellm.integrations.custom_logger import CustomLogger from litellm.types.llms.openai import AllMessageValues, ChatCompletionUserMessage from litellm.types.prompts.init_prompts import PromptSpec -from litellm.types.utils import StandardCallbackDynamicParams +from litellm.types.utils import CallTypes, StandardCallbackDynamicParams from litellm.types.vector_stores import ( LiteLLM_ManagedVectorStore, VectorStoreResultContent, @@ -226,7 +226,7 @@ class VectorStorePreCallHook(CustomLogger): self, request_data: dict, response: Any, - call_type: Any | None, + call_type: CallTypes | None, ) -> Any | None: """ Add search results to the response after successful LLM call. @@ -283,7 +283,7 @@ class VectorStorePreCallHook(CustomLogger): self, request_data: dict, response_chunk: Any, - call_type: Any | None, + call_type: CallTypes | None, ) -> Any | None: """ Add search results to the final streaming chunk. diff --git a/litellm/integrations/websearch_interception/handler.py b/litellm/integrations/websearch_interception/handler.py index 81310b9ddc3..dc61ee38a8c 100644 --- a/litellm/integrations/websearch_interception/handler.py +++ b/litellm/integrations/websearch_interception/handler.py @@ -1633,16 +1633,18 @@ class WebSearchInterceptionLogger(CustomLogger): def _select_search_tool_from_router(self, llm_router: object) -> "_SearchToolConfig | None": if llm_router is None or not hasattr(llm_router, "search_tools"): return None - search_tools: Final = list(getattr(llm_router, "search_tools") or []) + search_tools: Final = tuple(getattr(llm_router, "search_tools", None) or ()) return self._select_search_tool_from_list(search_tools=search_tools, source="router") def _select_search_tool_from_list( self, - search_tools: list[_SearchToolConfig], + search_tools: Sequence[_SearchToolConfig], source: str, ) -> "_SearchToolConfig | None": if self.search_tool_name: - matching_tools = [tool for tool in search_tools if tool.get("search_tool_name") == self.search_tool_name] + matching_tools: Final = tuple( + tool for tool in search_tools if tool.get("search_tool_name") == self.search_tool_name + ) if matching_tools: search_provider = (matching_tools[0].get("litellm_params", {}) or {}).get("search_provider") verbose_logger.debug( diff --git a/litellm/litellm_core_utils/audio_utils/utils.py b/litellm/litellm_core_utils/audio_utils/utils.py index 3b3775a8fe6..dab3e48f91a 100644 --- a/litellm/litellm_core_utils/audio_utils/utils.py +++ b/litellm/litellm_core_utils/audio_utils/utils.py @@ -7,7 +7,13 @@ import os from dataclasses import dataclass from typing import Final -from litellm.types.files import get_file_mime_type_from_extension +from litellm.types.files import ( + AUDIO_FILE_TYPES, + FILE_EXTENSIONS, + FILE_MIME_TYPES, + FileType, + get_file_mime_type_from_extension, +) from litellm.types.utils import FileTypes @@ -323,3 +329,75 @@ def calculate_request_duration(file: FileTypes) -> float | None: except Exception: # Silently fail if duration extraction fails return None + + +DEFAULT_SPEECH_MEDIA_TYPE: Final = "audio/mpeg" + + +def _speech_media_type_for_response_format(response_format: str) -> str | None: + file_type: Final = next( + (candidate for candidate, extensions in FILE_EXTENSIONS.items() if response_format.lower() in extensions), + None, + ) + if file_type is None or file_type not in AUDIO_FILE_TYPES: + return None + return FILE_MIME_TYPES[file_type] + + +def resolve_speech_media_type(upstream_content_type: str | None, response_format: str | None) -> str: + upstream_media_type: Final = (upstream_content_type or "").split(";", 1)[0].strip().lower() + if upstream_media_type.startswith("audio/"): + return upstream_media_type + requested_media_type: Final = ( + None if response_format is None else _speech_media_type_for_response_format(response_format) + ) + return requested_media_type or DEFAULT_SPEECH_MEDIA_TYPE + + +_OGG_OPUS_HEAD_WINDOW: Final = 64 +_ADTS_SYNC_AND_LAYER_MASK: Final = 0xF6 +_ADTS_SYNC_AND_LAYER: Final = 0xF0 +_ADTS_SAMPLE_RATE_INDEX_LIMIT: Final = 13 +_MPEG_SYNC_MASK: Final = 0xE0 +_MPEG_LAYER_MASK: Final = 0x06 +_MPEG_RESERVED_VERSION: Final = 0x01 +_MPEG_INVALID_BITRATE_INDEX: Final = 0x0F +_MPEG_RESERVED_SAMPLE_RATE_INDEX: Final = 0x03 + + +def _adts_aac_frame_media_type(header: bytes) -> str | None: + sample_rate_index: Final = (header[2] >> 2) & 0x0F + return FILE_MIME_TYPES[FileType.AAC] if sample_rate_index < _ADTS_SAMPLE_RATE_INDEX_LIMIT else None + + +def _mpeg_audio_frame_media_type(header: bytes) -> str | None: + version: Final = (header[1] >> 3) & 0x03 + layer: Final = header[1] & _MPEG_LAYER_MASK + bitrate_index: Final = header[2] >> 4 + sample_rate_index: Final = (header[2] >> 2) & 0x03 + if ( + (header[1] & _MPEG_SYNC_MASK) != _MPEG_SYNC_MASK + or version == _MPEG_RESERVED_VERSION + or layer == 0 + or bitrate_index == _MPEG_INVALID_BITRATE_INDEX + or sample_rate_index == _MPEG_RESERVED_SAMPLE_RATE_INDEX + ): + return None + return FILE_MIME_TYPES[FileType.MP3] + + +def speech_media_type_from_audio_bytes(audio: bytes) -> str | None: + if audio[:4] == b"RIFF" and audio[8:12] == b"WAVE": + return FILE_MIME_TYPES[FileType.WAV] + if audio[:4] == b"fLaC": + return FILE_MIME_TYPES[FileType.FLAC] + if audio[:4] == b"OggS": + is_opus: Final = b"OpusHead" in audio[:_OGG_OPUS_HEAD_WINDOW] + return FILE_MIME_TYPES[FileType.OPUS if is_opus else FileType.OGG] + if audio[:3] == b"ID3": + return FILE_MIME_TYPES[FileType.MP3] + if len(audio) < 3 or audio[0] != 0xFF: + return None + if (audio[1] & _ADTS_SYNC_AND_LAYER_MASK) == _ADTS_SYNC_AND_LAYER: + return _adts_aac_frame_media_type(audio) + return _mpeg_audio_frame_media_type(audio) diff --git a/litellm/litellm_core_utils/get_litellm_params.py b/litellm/litellm_core_utils/get_litellm_params.py index b12c715c9f5..389e6f7f501 100644 --- a/litellm/litellm_core_utils/get_litellm_params.py +++ b/litellm/litellm_core_utils/get_litellm_params.py @@ -50,6 +50,9 @@ OPTIONAL_KWARGS_KEYS: Final = ( "vertex_ai_project", "vertex_ai_location", "vertex_ai_credentials", + "gigachat_scope", + "gigachat_auth_url", + "gigachat_access_token", "tpm", "rpm", "itpm", diff --git a/litellm/litellm_core_utils/get_llm_provider_logic.py b/litellm/litellm_core_utils/get_llm_provider_logic.py index 74a1d3e5008..207e024ce0b 100644 --- a/litellm/litellm_core_utils/get_llm_provider_logic.py +++ b/litellm/litellm_core_utils/get_llm_provider_logic.py @@ -369,6 +369,9 @@ def get_llm_provider( elif endpoint == "https://api.meta.ai/v1": custom_llm_provider = "meta" dynamic_api_key = get_secret_str("META_API_KEY") + elif endpoint == "https://gigachat.devices.sberbank.ru/api/v1": + custom_llm_provider = "gigachat" + dynamic_api_key = get_secret_str("GIGACHAT_API_KEY") elif (json_provider := JSONProviderRegistry.get_by_base_url(endpoint)) is not None: custom_llm_provider = json_provider.slug dynamic_api_key = api_key if api_key is not None else get_secret_str(json_provider.api_key_env) @@ -533,6 +536,14 @@ def get_llm_provider( ) +def _dashscope_family_chat_config(custom_llm_provider: str) -> "litellm.DashScopeChatConfig": + if custom_llm_provider == "qwencloud": + return litellm.QwenCloudChatConfig() + if custom_llm_provider == "qwen_ai_platform": + return litellm.QwenAIPlatformChatConfig() + return litellm.DashScopeChatConfig() + + def _get_openai_compatible_provider_info( model: str, api_base: str | None, @@ -782,11 +793,11 @@ def _get_openai_compatible_provider_info( api_base, dynamic_api_key, ) = litellm.HerokuChatConfig()._get_openai_compatible_provider_info(api_base, api_key) - elif custom_llm_provider == "dashscope": + elif custom_llm_provider in ("dashscope", "qwencloud", "qwen_ai_platform"): ( api_base, dynamic_api_key, - ) = litellm.DashScopeChatConfig()._get_openai_compatible_provider_info(api_base, api_key) + ) = _dashscope_family_chat_config(custom_llm_provider)._get_openai_compatible_provider_info(api_base, api_key) elif custom_llm_provider == "modelscope": ( api_base, @@ -867,6 +878,9 @@ def _get_openai_compatible_provider_info( # Manus is OpenAI compatible for responses API api_base = api_base or get_secret_str("MANUS_API_BASE") or "https://api.manus.im" dynamic_api_key = api_key or get_secret_str("MANUS_API_KEY") + elif custom_llm_provider == "gigachat": + api_base = api_base or get_secret_str("GIGACHAT_API_BASE") or "https://gigachat.devices.sberbank.ru/api/v1" + dynamic_api_key = api_key or get_secret_str("GIGACHAT_API_KEY") if api_base is not None and not isinstance(api_base, str): raise Exception(f"api base needs to be a string. api_base={api_base}") diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py index 97c4d038734..350e5403e4c 100644 --- a/litellm/litellm_core_utils/litellm_logging.py +++ b/litellm/litellm_core_utils/litellm_logging.py @@ -2141,6 +2141,9 @@ class Logging(LiteLLMLoggingBaseClass): logging_result: Final = self.normalize_logging_result(result=result) + if isinstance(result, Response) and isinstance(logging_result, (ModelResponse, EmbeddingResponse)): + result = logging_result + if standard_logging_object is None and result is not None and self.stream is not True: if self._is_recognized_call_type_for_logging(logging_result=logging_result) or isinstance( logging_result, (dict, list) @@ -6152,7 +6155,10 @@ def get_standard_logging_object_payload( def emit_standard_logging_payload(payload: StandardLoggingPayload): if os.getenv("LITELLM_PRINT_STANDARD_LOGGING_PAYLOAD"): - print(json.dumps(payload, indent=4), flush=True) # noqa: T201 + try: + print(json.dumps(payload, indent=4, default=str), flush=True) # noqa: T201 + except Exception as e: # noqa: BLE001 # Safe catch-all for verbose logging + verbose_logger.exception("Error serializing standard logging payload for debug output: %s", e) def get_standard_logging_metadata( diff --git a/litellm/litellm_core_utils/llm_cost_calc/tool_call_cost_tracking.py b/litellm/litellm_core_utils/llm_cost_calc/tool_call_cost_tracking.py index 9250b92e268..5504756ceb8 100644 --- a/litellm/litellm_core_utils/llm_cost_calc/tool_call_cost_tracking.py +++ b/litellm/litellm_core_utils/llm_cost_calc/tool_call_cost_tracking.py @@ -3,7 +3,7 @@ Helper utilities for tracking the cost of built-in tools. """ from collections.abc import Mapping -from typing import Any, Final, Literal +from typing import Final, Literal import litellm from litellm.constants import OPENAI_FILE_SEARCH_COST_PER_1K_CALLS @@ -16,6 +16,7 @@ from litellm.types.llms.openai import ( WebSearchOptions, ) from litellm.types.utils import ( + ChatCompletionAnnotation, Message, ModelInfo, ModelResponse, @@ -49,7 +50,7 @@ class StandardBuiltInToolCostTracking: @staticmethod def get_cost_for_built_in_tools( model: str, - response_object: Any, + response_object: object, usage: Usage | None = None, custom_llm_provider: str | None = None, standard_built_in_tools_params: StandardBuiltInToolsParams | None = None, @@ -201,8 +202,7 @@ class StandardBuiltInToolCostTracking: model_info: Final = StandardBuiltInToolCostTracking._safe_get_model_info( model=model, custom_llm_provider=custom_llm_provider ) - file_search_raw: Final[Any] = standard_built_in_tools_params.get("file_search", {}) - file_search_usage: Final[FileSearchTool | None] = FileSearchTool(**file_search_raw) if file_search_raw else None + file_search_usage: Final[FileSearchTool | None] = standard_built_in_tools_params.get("file_search") or None # Convert model_info to dict and extract usage parameters model_info_dict: Final = dict(model_info) if model_info is not None else None @@ -245,7 +245,7 @@ class StandardBuiltInToolCostTracking: @staticmethod def _extract_file_search_params( - file_search_usage: Any, + file_search_usage: object, ) -> tuple[float | None, float | None]: """Extract and convert file search parameters safely.""" storage_gb = None @@ -335,7 +335,7 @@ class StandardBuiltInToolCostTracking: @staticmethod def _extract_token_counts( - computer_use_usage: Any, + computer_use_usage: object, ) -> tuple[int | None, int | None]: """Extract and convert token counts safely.""" input_tokens = None @@ -351,9 +351,9 @@ class StandardBuiltInToolCostTracking: return input_tokens, output_tokens @staticmethod - def _safe_convert_to_int(value: Any) -> int | None: + def _safe_convert_to_int(value: object) -> int | None: """Safely convert a value to int.""" - if value is not None: + if isinstance(value, (int, float, str)): try: return int(value) except (TypeError, ValueError): @@ -381,7 +381,7 @@ class StandardBuiltInToolCostTracking: return usage.model_copy(update={"server_tool_use": server_tool_use}) @staticmethod - def response_object_includes_web_search_call(response_object: Any, usage: Usage | None = None) -> bool: + def response_object_includes_web_search_call(response_object: object, usage: Usage | None = None) -> bool: """ Check if the response object includes a web search call. @@ -446,7 +446,7 @@ class StandardBuiltInToolCostTracking: @staticmethod def response_object_includes_file_search_call( - response_object: Any, + response_object: object, ) -> bool: """ Check if the response object includes a file search call. @@ -477,11 +477,11 @@ class StandardBuiltInToolCostTracking: message: Message | None = getattr(choice, "message", None) if message is None: continue - if annotations := getattr(message, "annotations", None): - if len(annotations) > 0: - for annotation in annotations: - if annotation.get("type", None) == annotation_type: - return True + annotations: list[ChatCompletionAnnotation] | None = getattr(message, "annotations", None) + if annotations: + for annotation in annotations: + if annotation.get("type", None) == annotation_type: + return True return False @staticmethod @@ -522,10 +522,8 @@ class StandardBuiltInToolCostTracking: if model_info is None: return 0.0 - search_context_raw: Final[Any] = model_info.get("search_context_cost_per_query", {}) - search_context_pricing: Final[SearchContextCostPerQuery] = ( - SearchContextCostPerQuery(**search_context_raw) if search_context_raw else SearchContextCostPerQuery() - ) + search_context_raw: Final = model_info.get("search_context_cost_per_query") + search_context_pricing: Final[SearchContextCostPerQuery] = search_context_raw or SearchContextCostPerQuery() if web_search_options.get("search_context_size", None) == "low": return search_context_pricing.get("search_context_size_low", 0.0) elif web_search_options.get("search_context_size", None) == "medium": @@ -545,10 +543,8 @@ class StandardBuiltInToolCostTracking: """ if model_info is None: return 0.0 - search_context_raw: Final[Any] = model_info.get("search_context_cost_per_query", {}) or {} - search_context_pricing: Final[SearchContextCostPerQuery] = ( - SearchContextCostPerQuery(**search_context_raw) if search_context_raw else SearchContextCostPerQuery() - ) + search_context_raw: Final = model_info.get("search_context_cost_per_query") + search_context_pricing: Final[SearchContextCostPerQuery] = search_context_raw or SearchContextCostPerQuery() return search_context_pricing.get("search_context_size_medium", 0.0) @staticmethod @@ -714,7 +710,7 @@ class StandardBuiltInToolCostTracking: response_object: ModelResponse, ) -> bool: for _choice in response_object.choices: - message = getattr(_choice, "message", None) + message: Message | None = getattr(_choice, "message", None) if ( message is not None and hasattr(message, "annotations") diff --git a/litellm/litellm_core_utils/model_response_utils.py b/litellm/litellm_core_utils/model_response_utils.py index 7bf667164ae..dc4f375daa7 100644 --- a/litellm/litellm_core_utils/model_response_utils.py +++ b/litellm/litellm_core_utils/model_response_utils.py @@ -144,7 +144,6 @@ def _is_choice_non_empty(choice: StreamingChoices) -> bool: # Check model_extra for dynamically added fields on the choice choice_extra_fields: Final[Mapping[str, object]] = choice.model_extra or {} for extra_field_name, extra_field_value in choice_extra_fields.items(): - # Skip certain structural fields that are just default/None placeholders if extra_field_name == "index" and extra_field_value == 0: continue if extra_field_name in {"finish_reason", "logprobs"} and extra_field_value is None: @@ -192,7 +191,6 @@ def _is_delta_non_empty(delta: Delta) -> bool: # Check model_extra for dynamically added fields (this is where Pydantic stores them) delta_extra_fields: Final[Mapping[str, object]] = delta.model_extra or {} for extra_field_value in delta_extra_fields.values(): - # Even structural fields are meaningful if they have actual content if _has_meaningful_content(extra_field_value): return True diff --git a/litellm/litellm_core_utils/prompt_templates/common_utils.py b/litellm/litellm_core_utils/prompt_templates/common_utils.py index 1c8f10d3307..2fca19dc8d1 100644 --- a/litellm/litellm_core_utils/prompt_templates/common_utils.py +++ b/litellm/litellm_core_utils/prompt_templates/common_utils.py @@ -205,6 +205,41 @@ def is_non_content_values_set(message: AllMessageValues) -> bool: return any(message.get(key, None) is not None for key in message if key not in ignore_keys) +_IMAGE_CONTENT_PART_TYPES: Final = frozenset({"image_url", "input_image", "image"}) +_IMAGE_SCAN_MAX_DEPTH: Final = 4 + + +def _content_parts_contain_image(parts: Sequence[object]) -> bool: + """Depth-bounded frontier walk over nested content lists, iterative because the repo bans + recursion; an Anthropic tool_result nests its image parts exactly one level down.""" + frontier = parts # rebind-ok: depth-bounded frontier walk + for _ in range(_IMAGE_SCAN_MAX_DEPTH): + if any(isinstance(part, Mapping) and part.get("type") in _IMAGE_CONTENT_PART_TYPES for part in frontier): + return True + frontier = tuple( # rebind-ok: depth-bounded frontier walk + nested + for part in frontier + if isinstance(part, Mapping) + for content in (part.get("content"),) + if isinstance(content, list) + for nested in content + ) + if not frontier: + return False + return False + + +def request_contains_image_content(messages: Sequence[Mapping[str, object]]) -> bool: + """Whether any message carries an image content part, across the dialects that reach + pre-routing hooks untranslated: chat-completions ``image_url``, Responses ``input_image``, + and Anthropic Messages ``image``, including images nested inside ``tool_result`` blocks.""" + return any( + isinstance(content, list) and _content_parts_contain_image(content) + for message in messages + for content in (message.get("content"),) + ) + + def _audio_or_image_in_message_content(message: AllMessageValues) -> bool: """ Checks if message content contains an image or audio @@ -520,10 +555,10 @@ def update_messages_with_model_file_ids( def update_responses_input_with_model_file_ids( - input: Any, + input: object, model_id: str | None = None, model_file_id_mapping: dict[str, dict[str, str]] | None = None, -) -> str | list[dict[str, Any]]: +) -> object: """ Updates responses API input with provider-specific file IDs. File IDs are always inside the content array, not as direct input_file items. @@ -604,8 +639,8 @@ def update_responses_input_with_model_file_ids( def _decode_vector_store_ids_in_tools( - tools: list[dict[str, Any]] | None, -) -> list[dict[str, Any]] | None: + tools: list[dict[str, object]] | None, +) -> list[dict[str, object]] | None: """ Decodes unified (LiteLLM-managed) vector_store_ids in file_search tools to provider-native IDs. Non-unified IDs are passed through unchanged. @@ -657,10 +692,10 @@ def _decode_vector_store_ids_in_tools( def update_responses_tools_with_model_file_ids( - tools: list[dict[str, Any]] | None, + tools: list[dict[str, object]] | None, model_id: str | None = None, model_file_id_mapping: dict[str, dict[str, str]] | None = None, -) -> list[dict[str, Any]] | None: +) -> list[dict[str, object]] | None: """ Updates responses API tools with provider-specific file IDs. @@ -853,7 +888,7 @@ def extract_file_data(file_data: FileTypes) -> ExtractedFileData: # --------------------------------------------------------------------------- -def _estimate_json_bytes(obj: Any) -> int: +def _estimate_json_bytes(obj: object) -> int: """Estimate the JSON-serialised byte size of ``obj`` without materialising JSON. Walks iteratively (no recursion stack risk). @@ -1944,7 +1979,7 @@ def drop_tool_reference_parts_from_tool_messages( return [_drop_tool_reference_parts(message) for message in messages] # mutable-ok: pipelines mutate message lists -def _attempt_json_repair(s: str) -> Any | None: +def _attempt_json_repair(s: str) -> object | None: """ Attempt to repair truncated JSON produced by LLM tool calls. @@ -2060,7 +2095,7 @@ def parse_tool_call_arguments( raise ValueError(error_message) from original_error -def split_concatenated_json_objects(raw: str) -> list[dict[str, Any]]: +def split_concatenated_json_objects(raw: str) -> list[dict[str, object]]: """ Split a string that contains one or more concatenated JSON objects into a list of parsed dicts. @@ -2096,7 +2131,7 @@ def split_concatenated_json_objects(raw: str) -> list[dict[str, Any]]: return [] decoder: Final = json.JSONDecoder() - results: Final[list[dict[str, Any]]] = [] + results: Final[list[dict[str, object]]] = [] idx = 0 length: Final = len(raw) diff --git a/litellm/litellm_core_utils/realtime_streaming.py b/litellm/litellm_core_utils/realtime_streaming.py index 9125ed6e70a..8479e108d17 100644 --- a/litellm/litellm_core_utils/realtime_streaming.py +++ b/litellm/litellm_core_utils/realtime_streaming.py @@ -1500,6 +1500,6 @@ class RealTimeStreaming: pass -def client_sent_openai_beta_realtime_header(websocket: Any) -> bool: +def client_sent_openai_beta_realtime_header(websocket: _ScopedWebSocket) -> bool: """True when the client WebSocket includes ``OpenAI-Beta: realtime=v1``.""" return RealTimeStreaming._detect_beta_header(websocket) diff --git a/litellm/litellm_core_utils/streaming_chunk_builder_utils.py b/litellm/litellm_core_utils/streaming_chunk_builder_utils.py index 0e2139d688b..3978a01a5db 100644 --- a/litellm/litellm_core_utils/streaming_chunk_builder_utils.py +++ b/litellm/litellm_core_utils/streaming_chunk_builder_utils.py @@ -73,6 +73,18 @@ class _ContentChunk(TypedDict): choices: Sequence[_ContentChoice] +class _FunctionCallDelta(TypedDict): + function_call: ReadOnly[FunctionCall] + + +class _FunctionCallChoice(TypedDict): + delta: ReadOnly[_FunctionCallDelta] + + +class _FunctionCallChunk(TypedDict): + choices: ReadOnly[Sequence[_FunctionCallChoice]] + + class _AudioDelta(TypedDict, total=False): audio: ChatCompletionAudioDelta | None @@ -588,7 +600,7 @@ class ChunkProcessor: return tool_calls_list - def get_combined_function_call_content(self, function_call_chunks: list[dict[str, Any]]) -> FunctionCall: + def get_combined_function_call_content(self, function_call_chunks: Sequence["_FunctionCallChunk"]) -> FunctionCall: argument_list: Final = [] delta = function_call_chunks[0]["choices"][0]["delta"] function_call = delta.get("function_call", "") diff --git a/litellm/litellm_core_utils/streaming_handler.py b/litellm/litellm_core_utils/streaming_handler.py index f5f671e7585..480b1921c18 100644 --- a/litellm/litellm_core_utils/streaming_handler.py +++ b/litellm/litellm_core_utils/streaming_handler.py @@ -862,6 +862,8 @@ class CustomStreamWrapper: model_response: Final = ModelResponseStream(**args) if self.response_id is not None: model_response.id = self.response_id + elif model_response.id: + self.response_id = model_response.id if self.system_fingerprint is not None: model_response.system_fingerprint = self.system_fingerprint diff --git a/litellm/litellm_core_utils/token_counter.py b/litellm/litellm_core_utils/token_counter.py index 256bee7b348..3732ffd734c 100644 --- a/litellm/litellm_core_utils/token_counter.py +++ b/litellm/litellm_core_utils/token_counter.py @@ -4,8 +4,9 @@ import base64 import io import struct from collections.abc import Callable, Iterable, Mapping, Sequence -from typing import Any, Final, Literal, cast +from typing import Final, Literal, cast +import httpx import tiktoken import litellm @@ -171,6 +172,10 @@ def calculate_tiles_needed( return total_tiles +def _unpack_ints(fmt: str, buffer: bytes) -> tuple[int, ...]: + return struct.unpack(fmt, buffer) + + def get_image_type(image_data: bytes) -> str | None: """take an image (really only the first ~100 bytes max are needed) and return 'png' 'gif' 'jpeg' 'webp' 'heic' or None. method added to @@ -210,9 +215,9 @@ def get_image_dimensions( if data.startswith(("http://", "https://")): try: client: Final = _get_httpx_client() - response: Final = safe_get(client, data) + response: Final[httpx.Response] = safe_get(client, data) max_bytes: Final = int(MAX_IMAGE_URL_DOWNLOAD_SIZE_MB * 1024 * 1024) - content_length: Final = response.headers.get("Content-Length") + content_length: Final[str | None] = response.headers.get("Content-Length") if content_length is not None and int(content_length) > max_bytes: pass # skip download; img_data stays None else: @@ -229,10 +234,10 @@ def get_image_dimensions( img_type: Final = get_image_type(img_data) if img_type == "png": - w, h = struct.unpack(">LL", img_data[16:24]) + w, h = _unpack_ints(">LL", img_data[16:24]) return w, h elif img_type == "gif": - w, h = struct.unpack("H", fhandle.read(2))[0] - 2 + size = _unpack_ints(">H", fhandle.read(2))[0] - 2 fhandle.seek(1, 1) - h, w = struct.unpack(">HH", fhandle.read(4)) + h, w = _unpack_ints(">HH", fhandle.read(4)) return w, h elif img_type == "webp": # For WebP, the dimensions are stored at different offsets depending on the format # Check for VP8X (extended format) if img_data[12:16] == b"VP8X": - w = struct.unpack("> 14) & 0x3FFF) + 1 return w, h @@ -420,8 +425,8 @@ def token_counter( def _count_function_call_tokens( key: str, - value: Any, - message: Mapping[str, Any], + value: object, + message: Mapping[str, object], count_function: TokenCounterFunction, ) -> int: """ @@ -587,7 +592,7 @@ def _fix_model_name(model: str) -> str: def _count_image_tokens( - image_url: Any, + image_url: object, use_default_image_token_count: bool, ) -> int: """ @@ -627,7 +632,7 @@ def _count_image_tokens( raise ValueError(f"Invalid image_url type: {type(image_url).__name__}. Expected str or dict with 'url' field.") -def _validate_anthropic_content(content: Mapping[str, Any]) -> type: +def _validate_anthropic_content(content: Mapping[str, object]) -> type: """ Validate and determine which Anthropic TypedDict applies. @@ -642,7 +647,7 @@ def _validate_anthropic_content(content: Mapping[str, Any]) -> type: "tool_result": AnthropicMessagesToolResultParam, } - expected_cls: Final = mapping.get(content_type) + expected_cls: Final = mapping.get(content_type) if isinstance(content_type, str) else None if expected_cls is None: raise ValueError(f"Unknown Anthropic content type: '{content_type}'") @@ -693,8 +698,28 @@ def _count_document_tokens( ) +def _count_file_tokens( + file_value: object, + count_function: TokenCounterFunction, + use_default_image_token_count: bool, +) -> int: + """An OpenAI `file` block is the chat-completions spelling of a document, so it prices like one.""" + if not isinstance(file_value, Mapping): + return 0 + filename: Final = file_value.get("filename") + file_data: Final = file_value.get("file_data") + name_tokens: Final = count_function(filename) if isinstance(filename, str) and filename else 0 + if not isinstance(file_data, str) or not file_data: + return name_tokens + return name_tokens + calculate_img_tokens( + data=file_data, + mode="auto", + use_default_image_token_count=use_default_image_token_count, + ) + + def _count_anthropic_content( - content: Mapping[str, Any], + content: Mapping[str, object], count_function: TokenCounterFunction, use_default_image_token_count: bool, default_token_count: int | None, @@ -709,7 +734,7 @@ def _count_anthropic_content( avoiding hardcoded field names. """ typeddict_cls: Final = _validate_anthropic_content(content) - type_hints: Final = getattr(typeddict_cls, "__annotations__", {}) + type_hints: Final[Mapping[str, object]] = getattr(typeddict_cls, "__annotations__", {}) tokens = 0 # Fields to skip (metadata/identifiers that don't contribute to prompt tokens) @@ -778,6 +803,12 @@ def _count_content_list( use_default_image_token_count, default_token_count, ) + elif c["type"] == "file": + num_tokens += _count_file_tokens( + c.get("file"), + count_function, + use_default_image_token_count, + ) elif c["type"] in ("tool_use", "tool_result"): num_tokens += _count_anthropic_content( c, @@ -807,7 +838,7 @@ def _count_content_list( raise ValueError( f"Invalid content item type: {content_type}. " f"Expected str or dict with 'type' field " - f"(text, image_url, image, document, tool_use, tool_result, thinking, tool_reference)." + f"(text, image_url, image, document, file, tool_use, tool_result, thinking, tool_reference)." ) return num_tokens except Exception as e: diff --git a/litellm/llms/a2a/chat/guardrail_translation/handler.py b/litellm/llms/a2a/chat/guardrail_translation/handler.py index 1c5ba951942..f1c7451796d 100644 --- a/litellm/llms/a2a/chat/guardrail_translation/handler.py +++ b/litellm/llms/a2a/chat/guardrail_translation/handler.py @@ -11,8 +11,11 @@ A2A Protocol Format: """ import json +from collections.abc import Sequence from typing import TYPE_CHECKING, Any, Final, Optional +from typing_extensions import ReadOnly, TypedDict + from litellm._logging import verbose_proxy_logger from litellm.llms.base_llm.guardrail_translation.base_translation import BaseTranslation from litellm.types.utils import GenericGuardrailAPIInputs @@ -23,6 +26,13 @@ if TYPE_CHECKING: from litellm.proxy._types import UserAPIKeyAuth +class _A2ATextPart(TypedDict, total=False): + """The subset of an A2A message part this handler reads text from.""" + + kind: ReadOnly[str] + text: ReadOnly[str] + + class A2AGuardrailHandler(BaseTranslation): """ Handler for processing A2A Protocol messages with guardrails. @@ -41,7 +51,7 @@ class A2AGuardrailHandler(BaseTranslation): data: dict, guardrail_to_apply: "CustomGuardrail", litellm_logging_obj: Optional["LiteLLMLoggingObj"] = None, - ) -> Any: + ) -> dict: """ Process A2A input messages by applying guardrails to text content. @@ -214,12 +224,12 @@ class A2AGuardrailHandler(BaseTranslation): async def process_output_streaming_response( self, - responses_so_far: list[Any], + responses_so_far: list[object], guardrail_to_apply: "CustomGuardrail", litellm_logging_obj: Optional["LiteLLMLoggingObj"] = None, user_api_key_dict: Optional["UserAPIKeyAuth"] = None, request_data: dict | None = None, - ) -> list[Any]: + ) -> list[object]: """ Process A2A streaming output by applying guardrails to accumulated text. @@ -305,11 +315,12 @@ class A2AGuardrailHandler(BaseTranslation): def _parse_streaming_responses( self, - responses_so_far: list[Any], - ) -> tuple[list[dict[str, Any] | None], list[tuple[int, dict[str, Any]]]]: + responses_so_far: list[object], + ) -> tuple[list[dict[str, object] | None], list[tuple[int, dict[str, object]]]]: """Parse JSON-RPC items, returning aligned parsed list and valid entries.""" - parsed: Final[list[dict[str, Any] | None]] = [None] * len(responses_so_far) + parsed: Final[list[dict[str, object] | None]] = [None] * len(responses_so_far) for i, item in enumerate(responses_so_far): + obj: dict[str, object] if isinstance(item, dict): obj = item elif isinstance(item, str): @@ -326,7 +337,7 @@ class A2AGuardrailHandler(BaseTranslation): def _collect_text_from_parsed_chunks( self, - valid_parsed: list[tuple[int, dict[str, Any]]], + valid_parsed: list[tuple[int, dict[str, object]]], ) -> tuple[str, list[int]]: """Collect text from parsed chunks, returning combined text and indices.""" from litellm.llms.a2a.common_utils import extract_text_from_a2a_response @@ -411,7 +422,7 @@ class A2AGuardrailHandler(BaseTranslation): def _extract_texts_from_parts( self, - parts: list[dict[str, Any]], + parts: Sequence[_A2ATextPart], path: tuple[str, ...], texts_to_check: list[str], task_mappings: list[tuple[tuple[str, ...], int]], diff --git a/litellm/llms/anthropic/chat/guardrail_translation/handler.py b/litellm/llms/anthropic/chat/guardrail_translation/handler.py index eb6dbd3e5b3..c7d12e5cf3a 100644 --- a/litellm/llms/anthropic/chat/guardrail_translation/handler.py +++ b/litellm/llms/anthropic/chat/guardrail_translation/handler.py @@ -100,16 +100,6 @@ InputWriteBackTarget = ( ) -class _SSEDelta(TypedDict, total=False): - type: ReadOnly[str] - text: ReadOnly[str] - stop_reason: ReadOnly[str | None] - - -class _SSEEventData(TypedDict, total=False): - delta: ReadOnly[_SSEDelta] - - def _as_str_mapping(value: Mapping[str, object]) -> Mapping[str, object]: return value @@ -157,6 +147,16 @@ class ExtractedInput: EMPTY_EXTRACTED_INPUT: Final = ExtractedInput(scanned=(), images=()) +class _AnthropicSSEDelta(TypedDict, total=False): + type: ReadOnly[str] + text: ReadOnly[str] + stop_reason: ReadOnly[str | None] + + +class _AnthropicSSEEvent(TypedDict, total=False): + delta: ReadOnly[_AnthropicSSEDelta] + + class AnthropicMessagesHandler(BaseTranslation): """Process Anthropic messages with guardrails. @@ -859,12 +859,28 @@ class AnthropicMessagesHandler(BaseTranslation): @staticmethod def _image_sources(block: Mapping[str, object]) -> tuple[str, ...]: + """Normalize an Anthropic image block into strings a guardrail can read. + + base64 becomes a data URI so the format travels with the payload, which is what + the OpenAI path already puts in this field. A file source yields nothing: those + bytes live behind the Files API and this extractor has no client to fetch them. + """ source: Final = block.get("source") if not isinstance(source, Mapping): return () - # Could be base64 or url + + source_type: Final = source.get("type") + if source_type == "url": + url: Final = source.get("url") + return (url,) if isinstance(url, str) and url else () + data: Final = source.get("data") - return (data,) if data else () + if not isinstance(data, str) or not data: + return () + media_type: Final = source.get("media_type") + if isinstance(media_type, str) and media_type: + return (f"data:{media_type};base64,{data}",) + return (data,) async def _apply_guardrail_responses_to_input( self, @@ -1231,8 +1247,8 @@ class AnthropicMessagesHandler(BaseTranslation): # Only process content_block_delta events if event_type == "content_block_delta" and data_line: try: - data: _SSEEventData = json.loads(data_line) - delta = data.get("delta", {}) + data: _AnthropicSSEEvent = json.loads(data_line) + delta: _AnthropicSSEDelta = data.get("delta", {}) if delta.get("type") == "text_delta": text += delta.get("text", "") except json.JSONDecodeError: @@ -1294,9 +1310,9 @@ class AnthropicMessagesHandler(BaseTranslation): # Check for message_delta event with stop_reason if event_type == "message_delta" and data_line: try: - data: _SSEEventData = json.loads(data_line) - delta = data.get("delta", {}) - stop_reason = delta.get("stop_reason") + data: _AnthropicSSEEvent = json.loads(data_line) + delta: _AnthropicSSEDelta = data.get("delta", {}) + stop_reason: str | None = delta.get("stop_reason") if stop_reason is not None: return True except json.JSONDecodeError: diff --git a/litellm/llms/anthropic/chat/handler.py b/litellm/llms/anthropic/chat/handler.py index cd47cdd57d6..c82be07a5c5 100644 --- a/litellm/llms/anthropic/chat/handler.py +++ b/litellm/llms/anthropic/chat/handler.py @@ -66,6 +66,10 @@ if TYPE_CHECKING: from litellm.llms.base_llm.chat.transformation import BaseConfig +def _loads_stream_chunk(payload: str) -> dict[str, object]: + return json.loads(payload) + + async def make_call( client: AsyncHTTPHandler | None, api_base: str, @@ -78,7 +82,7 @@ async def make_call( json_mode: bool, speed: str | None = None, tool_name_reverse_map: dict[str, str] | None = None, -) -> tuple[Any, httpx.Headers]: +) -> tuple["ModelResponseIterator", httpx.Headers]: if client is None: client = litellm.module_level_aclient @@ -93,7 +97,7 @@ async def make_call( ) except httpx.HTTPStatusError as e: error_headers = getattr(e, "headers", None) - error_response: Final = getattr(e, "response", None) + error_response: Final[object] = getattr(e, "response", None) if error_headers is None and error_response: error_headers = getattr(error_response, "headers", None) raise AnthropicError( @@ -138,7 +142,7 @@ def make_sync_call( json_mode: bool, speed: str | None = None, tool_name_reverse_map: dict[str, str] | None = None, -) -> tuple[Any, httpx.Headers]: +) -> tuple["ModelResponseIterator", httpx.Headers]: if client is None: client = litellm.module_level_client # re-use a module level client @@ -153,7 +157,7 @@ def make_sync_call( ) except httpx.HTTPStatusError as e: error_headers = getattr(e, "headers", None) - error_response: Final = getattr(e, "response", None) + error_response: Final[object] = getattr(e, "response", None) if error_headers is None and error_response: error_headers = getattr(error_response, "headers", None) raise AnthropicError( @@ -292,7 +296,7 @@ class AnthropicChatCompletion(BaseLLM): status_code: Final = getattr(e, "status_code", 500) error_headers = getattr(e, "headers", None) error_text = getattr(e, "text", str(e)) - error_response: Final = getattr(e, "response", None) + error_response: Final[object] = getattr(e, "response", None) if error_headers is None and error_response: error_headers = getattr(error_response, "headers", None) if error_response and hasattr(error_response, "text"): @@ -593,7 +597,7 @@ class AnthropicChatCompletion(BaseLLM): status_code: Final = getattr(e, "status_code", 500) error_headers = getattr(e, "headers", None) error_text = getattr(e, "text", str(e)) - error_response: Final = getattr(e, "response", None) + error_response: Final[object] = getattr(e, "response", None) if error_headers is None and error_response: error_headers = getattr(error_response, "headers", None) if error_response and hasattr(error_response, "text"): @@ -664,10 +668,10 @@ class ModelResponseIterator: # Accumulate web_search_tool_result blocks for multi-turn reconstruction # See: https://github.com/BerriAI/litellm/issues/17737 - self.web_search_results: list[dict[str, Any]] = [] + self.web_search_results: list[dict[str, object]] = [] # Accumulate compaction blocks for multi-turn reconstruction - self.compaction_blocks: list[dict[str, Any]] = [] + self.compaction_blocks: list[dict[str, object]] = [] # Accumulate streamed thinking text so final usage can split reasoning # tokens from regular output tokens. @@ -727,7 +731,7 @@ class ModelResponseIterator: str, ChatCompletionToolCallChunk | None, list[ChatCompletionThinkingBlock | ChatCompletionRedactedThinkingBlock], - dict[str, Any], + dict[str, object], str | None, ]: """ @@ -735,7 +739,7 @@ class ModelResponseIterator: """ text = "" tool_use: ChatCompletionToolCallChunk | None = None - provider_specific_fields: Final = {} + provider_specific_fields: Final[dict[str, object]] = {} reasoning_content: str | None = None content_block: Final = ContentBlockDelta(**chunk) thinking_blocks: list[ChatCompletionThinkingBlock | ChatCompletionRedactedThinkingBlock] = [] @@ -809,8 +813,8 @@ class ModelResponseIterator: def _handle_redacted_thinking_content( self, content_block_start: ContentBlockStart, - provider_specific_fields: dict[str, Any], - ) -> tuple[list[ChatCompletionRedactedThinkingBlock], dict[str, Any]]: + provider_specific_fields: dict[str, object], + ) -> tuple[list[ChatCompletionRedactedThinkingBlock], dict[str, object]]: """ Handle the redacted thinking content """ @@ -878,7 +882,7 @@ class ModelResponseIterator: tool_use: ChatCompletionToolCallChunk | None = None finish_reason = "" usage: Usage | None = None - provider_specific_fields: dict[str, Any] = {} + provider_specific_fields: dict[str, object] = {} reasoning_content: str | None = None thinking_blocks: list[ChatCompletionThinkingBlock | ChatCompletionRedactedThinkingBlock] | None = None @@ -1212,7 +1216,7 @@ class ModelResponseIterator: # Try to parse as valid JSON first try: - data_json: Final = json.loads(data_str) + data_json: Final = _loads_stream_chunk(data_str) return self.chunk_parser(chunk=data_json) except json.JSONDecodeError: # Switch to accumulation mode and start accumulating @@ -1330,7 +1334,7 @@ class ModelResponseIterator: str_line = str_line[index:] if str_line.startswith("data:"): - data_json: Final = json.loads(str_line[5:]) + data_json: Final = _loads_stream_chunk(str_line[5:]) return self.chunk_parser(chunk=data_json) else: return ModelResponseStream(id=self.response_id) diff --git a/litellm/llms/anthropic/chat/transformation.py b/litellm/llms/anthropic/chat/transformation.py index e1387a9068c..3e6d96b9780 100644 --- a/litellm/llms/anthropic/chat/transformation.py +++ b/litellm/llms/anthropic/chat/transformation.py @@ -7,6 +7,7 @@ from typing import TYPE_CHECKING, Any, Final, NoReturn, cast import httpx from pydantic import ValidationError +from typing_extensions import ReadOnly, TypedDict import litellm from litellm.constants import ( @@ -125,7 +126,25 @@ else: _ANTHROPIC_TOOL_NAME_INVALID_CHARS: Final = re.compile(r"[^a-zA-Z0-9_-]") _ANTHROPIC_TOOL_NAME_MAX_LEN: Final = 128 -_ENUM_TYPE_CHECKS: Final[Mapping[str, Callable[[Any], bool]]] = MappingProxyType( + +class _AnthropicUsageIteration(TypedDict, total=False): + """One entry of the ``usage.iterations`` array on an Anthropic response.""" + + input_tokens: ReadOnly[int | None] + output_tokens: ReadOnly[int | None] + cache_creation_input_tokens: ReadOnly[int | None] + cache_read_input_tokens: ReadOnly[int | None] + + +class _AnthropicToolResultBlock(TypedDict, total=False): + """A ``*_tool_result`` content block on an Anthropic response.""" + + type: ReadOnly[str] + tool_use_id: ReadOnly[str] + content: ReadOnly[object] + + +_ENUM_TYPE_CHECKS: Final[Mapping[str, Callable[[object], bool]]] = MappingProxyType( { "null": lambda v: v is None, "boolean": lambda v: isinstance(v, bool), @@ -440,7 +459,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): optional_params.pop("speed", None) @staticmethod - def _raise_invalid_reasoning_effort(model: str, value: Any, llm_provider: str) -> NoReturn: + def _raise_invalid_reasoning_effort(model: str, value: object, llm_provider: str) -> NoReturn: """Raise a ``BadRequestError`` for an unrecognised ``reasoning_effort``. Args: @@ -1992,19 +2011,35 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): return data def _apply_output_config(self, data: dict, model: str, optional_params: dict) -> None: - """Validate and apply output_config to the request data.""" + """Validate and apply output_config to the request data. + + The ``drop_params`` gate here is an effort gate: ``format`` is a + structured-output field, not an effort field, so it survives the drop + and is vetted where it is consumed (the map's + ``supports_native_structured_output`` flag on emission paths). + """ if "output_config" not in optional_params: return output_config: Final = optional_params.get("output_config") if not output_config or not isinstance(output_config, dict): return - if litellm.drop_params is True and not self._model_supports_effort_param(model, self._resolved_provider): + if ( + litellm.drop_params is True + and any(key != "format" for key in output_config) + and not self._model_supports_effort_param(model, self._resolved_provider) + ): litellm.verbose_logger.warning( DROP_UNSUPPORTED_OUTPUT_CONFIG_WARNING, model, ) - optional_params.pop("output_config", None) - data.pop("output_config", None) + preserved_format: Final = output_config.get("format") + if preserved_format is None: + optional_params.pop("output_config", None) + data.pop("output_config", None) + return + format_only: Final = {"format": preserved_format} # mutable-ok: json body + optional_params["output_config"] = format_only # rebind-ok: out-param store + data["output_config"] = format_only # rebind-ok: out-param store return effort: Final = output_config.get("effort") valid_efforts: Final = ["high", "medium", "low", "xhigh", "max"] @@ -2059,22 +2094,22 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): self, completion_response: dict ) -> tuple[ str, - list[Any] | None, + list[object] | None, list[ChatCompletionThinkingBlock | ChatCompletionRedactedThinkingBlock] | None, str | None, list[ChatCompletionToolCallChunk], - list[Any] | None, - list[Any] | None, - list[Any] | None, + list[object] | None, + list[_AnthropicToolResultBlock] | None, + list[object] | None, ]: text_content = "" - citations: list[Any] | None = None + citations: list[object] | None = None thinking_blocks: list[ChatCompletionThinkingBlock | ChatCompletionRedactedThinkingBlock] | None = None reasoning_content: str | None = None tool_calls: Final[list[ChatCompletionToolCallChunk]] = [] - web_search_results: list[Any] | None = None - tool_results: list[Any] | None = None - compaction_blocks: list[Any] | None = None + web_search_results: list[object] | None = None + tool_results: list[_AnthropicToolResultBlock] | None = None + compaction_blocks: list[object] | None = None for idx, content in enumerate(completion_response["content"]): if content["type"] == "text": text_content += content["text"] @@ -2284,7 +2319,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): raw_speed: Final = _usage.get("speed") resolved_speed: Final = raw_speed if isinstance(raw_speed, str) else speed - iterations: Final[list[Any] | None] = _usage.get("iterations") + iterations: Final[Sequence[_AnthropicUsageIteration] | None] = _usage.get("iterations") if iterations: prompt_tokens = sum(it.get("input_tokens", 0) or 0 for it in iterations) completion_tokens = sum(it.get("output_tokens", 0) or 0 for it in iterations) @@ -2377,7 +2412,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): def _build_code_interpreter_results( self, - tool_results: list[Any], + tool_results: Sequence[_AnthropicToolResultBlock], code_by_id: dict[str, str], container_id: str | None, ) -> list[OutputCodeInterpreterCall]: @@ -2403,11 +2438,11 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): def _build_provider_specific_fields( self, completion_response: dict, - citations: list[Any] | None, + citations: Sequence[object] | None, thinking_blocks: list[ChatCompletionThinkingBlock | ChatCompletionRedactedThinkingBlock] | None, - web_search_results: list[Any] | None, - tool_results: list[Any] | None, - compaction_blocks: list[Any] | None, + web_search_results: Sequence[object] | None, + tool_results: Sequence[_AnthropicToolResultBlock] | None, + compaction_blocks: Sequence[object] | None, tool_calls: list[ChatCompletionToolCallChunk], ) -> dict[str, Any]: provider_specific_fields: Final[dict[str, Any]] = { diff --git a/litellm/llms/anthropic/common_utils.py b/litellm/llms/anthropic/common_utils.py index d8d6a7fc9f8..9871001bf66 100644 --- a/litellm/llms/anthropic/common_utils.py +++ b/litellm/llms/anthropic/common_utils.py @@ -865,13 +865,9 @@ class AnthropicModelInfo(BaseLLMModelInfo): f"Failed to fetch models from Anthropic. Status code: {response.status_code}, Response: {response.text}" ) - models: Final = response.json()["data"] + models: Final[Sequence[Mapping[str, str]]] = response.json()["data"] - litellm_model_names: Final = [] - for model in models: - stripped_model_name = model["id"] - litellm_model_name = "anthropic/" + stripped_model_name - litellm_model_names.append(litellm_model_name) + litellm_model_names: Final = ["anthropic/" + model["id"] for model in models] return litellm_model_names def get_token_counter(self) -> BaseTokenCounter | None: @@ -1077,7 +1073,7 @@ def strip_empty_content_blocks_from_anthropic_messages( return out -def _is_empty_text_block(block: Any) -> bool: +def _is_empty_text_block(block: object) -> bool: if not isinstance(block, dict) or block.get("type") != "text": return False text: Final = block.get("text") @@ -1131,7 +1127,7 @@ def normalize_anthropic_tool_use_id(raw_id: str) -> str: return sanitized or "tool_use_id" -def _sanitize_tool_use_id_content_block(block: Any) -> Any: +def _sanitize_tool_use_id_content_block(block: object) -> object: if not isinstance(block, dict): return block block_type: Final = block.get("type") diff --git a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py index 411df267442..199a8ab77e7 100644 --- a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py +++ b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py @@ -18,6 +18,24 @@ TOOL_NAME_PREFIX_LENGTH: Final = OPENAI_MAX_TOOL_NAME_LENGTH - TOOL_NAME_HASH_LE PROVIDERS_PROXYING_AN_UNKNOWN_BACKEND: Final = frozenset({"litellm_proxy"}) +def _optional_attr(source: object, name: str) -> object: + return getattr(source, name, None) + + +def _as_string_mapping(value: object) -> Mapping[str, object] | None: + if isinstance(value, Mapping): + return value + return None + + +def _thought_signature(provider_specific_fields: object) -> str | None: + fields: Final = _as_string_mapping(provider_specific_fields) + if fields is None: + return None + signature: Final = fields.get("thought_signature") + return signature if isinstance(signature, str) else None + + _ANTHROPIC_TOOL_SCHEMA_KEYS: Final = frozenset( {"name", "type", "input_schema", "description", "cache_control", "strict"} ) @@ -56,7 +74,7 @@ def truncate_tool_name(name: str) -> str: def create_tool_name_mapping( - tools: list[dict[str, Any]], + tools: Sequence[Mapping[str, object]], ) -> dict[str, str]: """ Create a mapping of truncated tool names to original names. @@ -70,6 +88,8 @@ def create_tool_name_mapping( mapping: Final[dict[str, str]] = {} for tool in tools: original_name = tool.get("name", "") + if not isinstance(original_name, str): + continue truncated_name = truncate_tool_name(original_name) if truncated_name != original_name: mapping[truncated_name] = original_name @@ -286,44 +306,44 @@ class LiteLLMAnthropicMessagesAdapter: ### FOR [BETA] `/v1/messages` endpoint support - def _extract_signature_from_tool_call(self, tool_call: Any) -> str | None: + def _extract_signature_from_tool_call(self, tool_call: object) -> str | None: """ Extract signature from a tool call's provider_specific_fields. Only checks provider_specific_fields, not thinking blocks. """ - signature = None + fields: Final = _optional_attr(tool_call, "provider_specific_fields") + if fields: + return _thought_signature(fields) - if hasattr(tool_call, "provider_specific_fields") and tool_call.provider_specific_fields: - if "thought_signature" in tool_call.provider_specific_fields: - signature = tool_call.provider_specific_fields["thought_signature"] - elif hasattr(tool_call.function, "provider_specific_fields") and tool_call.function.provider_specific_fields: - if "thought_signature" in tool_call.function.provider_specific_fields: - signature = tool_call.function.provider_specific_fields["thought_signature"] + function_fields: Final = _optional_attr(_optional_attr(tool_call, "function"), "provider_specific_fields") + if function_fields: + return _thought_signature(function_fields) - return signature + return None - def _extract_signature_from_tool_use_content(self, content: dict[str, Any]) -> str | None: + def _extract_signature_from_tool_use_content(self, content: Mapping[str, object]) -> str | None: """ Extract signature from a tool_use content block's provider_specific_fields. """ - provider_specific_fields: Final = content.get("provider_specific_fields", {}) + provider_specific_fields: Final = _as_string_mapping(content.get("provider_specific_fields", {})) if provider_specific_fields: - return provider_specific_fields.get("signature") + signature: Final = provider_specific_fields.get("signature") + return signature if isinstance(signature, str) else None return None def _add_cache_control_if_applicable( self, - source: Any, - target: Any, + source: object, + target: object, model: str | None, ) -> None: """ Extract cache_control from source and add to target if it should be preserved. - This method accepts Any type to support both regular dicts and TypedDict objects. - TypedDict objects (like ChatCompletionTextObject, ChatCompletionImageObject, etc.) - are dicts at runtime but have specific types at type-check time. Using Any allows - this method to work with both while maintaining runtime correctness. + This method accepts an unconstrained type to support both regular dicts and + TypedDict objects. TypedDict objects (like ChatCompletionTextObject, + ChatCompletionImageObject, etc.) are dicts at runtime but have specific types at + type-check time, so the widest parameter type works with both. Args: source: Dict or TypedDict containing potential cache_control field @@ -751,7 +771,7 @@ class LiteLLMAnthropicMessagesAdapter: return new_tools, tool_name_mapping - def translate_anthropic_output_format_to_openai(self, output_format: Any) -> dict[str, object] | None: + def translate_anthropic_output_format_to_openai(self, output_format: object) -> dict[str, object] | None: """ Translate Anthropic's output_format to OpenAI's response_format. @@ -1366,7 +1386,7 @@ class LiteLLMAnthropicMessagesAdapter: @classmethod def _first_positive_prompt_tokens_detail_value(cls, usage: Usage, field_names: tuple[str, ...]) -> int: - prompt_tokens_details: Final = getattr(usage, "prompt_tokens_details", None) + prompt_tokens_details: Final = _optional_attr(usage, "prompt_tokens_details") if prompt_tokens_details is None: return 0 @@ -1374,7 +1394,7 @@ class LiteLLMAnthropicMessagesAdapter: if isinstance(prompt_tokens_details, dict): value = cls._positive_int(prompt_tokens_details.get(field_name)) else: - value = cls._positive_int(getattr(prompt_tokens_details, field_name, None)) + value = cls._positive_int(_optional_attr(prompt_tokens_details, field_name)) if value > 0: return value return 0 diff --git a/litellm/llms/anthropic/experimental_pass_through/context_management/editors/compact.py b/litellm/llms/anthropic/experimental_pass_through/context_management/editors/compact.py index 1cf52045a48..050ab67c86c 100644 --- a/litellm/llms/anthropic/experimental_pass_through/context_management/editors/compact.py +++ b/litellm/llms/anthropic/experimental_pass_through/context_management/editors/compact.py @@ -14,7 +14,7 @@ Mirrors Anthropic's native ``compact_20260112`` for non-Anthropic providers: import re from collections.abc import Awaitable, Mapping, Sequence -from typing import TYPE_CHECKING, Any, Final, Literal, Optional, Protocol, TypeVar, Union, cast +from typing import TYPE_CHECKING, Final, Literal, Optional, Protocol, TypeVar, Union, cast from typing_extensions import NotRequired, ReadOnly, TypedDict, Unpack @@ -232,7 +232,7 @@ async def _check_summary_model_access( key_models: Final = list(getattr(user_api_key_auth, "models", None) or []) team_id: Final[str | None] = getattr(user_api_key_auth, "team_id", None) - team_model_aliases: Final = getattr(user_api_key_auth, "team_model_aliases", None) + team_model_aliases: Final[dict[str, str] | None] = getattr(user_api_key_auth, "team_model_aliases", None) team_models: Final = list(getattr(user_api_key_auth, "team_models", None) or []) user_id: Final[str | None] = getattr(user_api_key_auth, "user_id", None) project_id: Final[str | None] = getattr(user_api_key_auth, "project_id", None) @@ -443,7 +443,9 @@ async def _check_summary_model_budget( ) return False - end_user_model_max_budget: Final = getattr(user_api_key_auth, "end_user_model_max_budget", None) + end_user_model_max_budget: Final[dict[str, object] | None] = getattr( + user_api_key_auth, "end_user_model_max_budget", None + ) end_user_id: Final[str | None] = getattr(user_api_key_auth, "end_user_id", None) if isinstance(end_user_model_max_budget, dict) and end_user_model_max_budget and end_user_id is not None: try: @@ -854,8 +856,8 @@ def _extract_summary_text(raw: str | None) -> str | None: def _system_to_openai_message( - system: str | list[dict[str, Any]] | None, -) -> Mapping[str, object] | None: + system: str | list[dict[str, object]] | None, +) -> dict[str, object] | None: """Translate Anthropic-shaped ``system`` to an OpenAI system message. Accepts a bare string or a list of Anthropic content blocks; returns @@ -866,10 +868,10 @@ def _system_to_openai_message( if isinstance(system, str): return {"role": "system", "content": system} if system else None if isinstance(system, list): - parts: Final[tuple[str, ...]] = tuple( + parts: Final[list[object]] = [ block.get("text", "") for block in system if isinstance(block, dict) and block.get("type") == "text" - ) - joined: Final = "\n\n".join(part for part in parts if part) + ] + joined: Final = "\n\n".join(part for part in parts if isinstance(part, str) and part) return {"role": "system", "content": joined} if joined else None return None @@ -951,7 +953,7 @@ async def _call_summary_model( summary_model: str, summary_messages: Sequence[Mapping[str, object]], metadata: Mapping[str, object], - llm_router: object, + llm_router: Optional["Router"], allowed_model_region: str | None = None, max_tokens: int = COMPACT_SUMMARY_MAX_TOKENS, ) -> Union["ModelResponse", "CustomStreamWrapper"]: @@ -1036,10 +1038,9 @@ def _extract_usage(response: object) -> tuple[int, int]: usage: Final[object] = getattr(response, "usage", None) if usage is None: return 0, 0 - return ( - int(getattr(usage, "prompt_tokens", 0) or 0), - int(getattr(usage, "completion_tokens", 0) or 0), - ) + prompt_tokens: Final[int | None] = getattr(usage, "prompt_tokens", 0) + completion_tokens: Final[int | None] = getattr(usage, "completion_tokens", 0) + return int(prompt_tokens or 0), int(completion_tokens or 0) def apply_client_compaction_block_history( diff --git a/litellm/llms/anthropic/experimental_pass_through/messages/streaming_iterator.py b/litellm/llms/anthropic/experimental_pass_through/messages/streaming_iterator.py index a282d5f4d4f..45c7825344b 100644 --- a/litellm/llms/anthropic/experimental_pass_through/messages/streaming_iterator.py +++ b/litellm/llms/anthropic/experimental_pass_through/messages/streaming_iterator.py @@ -8,6 +8,10 @@ import httpx from pydantic import TypeAdapter from typing_extensions import TypedDict +from litellm.constants import ( + ANTHROPIC_MESSAGES_MAX_DETACHED_STREAM_DRAINS, + ANTHROPIC_MESSAGES_STREAM_RELAY_QUEUE_MAXSIZE, +) from litellm.litellm_core_utils.core_helpers import process_response_headers from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.litellm_core_utils.logging_worker import GLOBAL_LOGGING_WORKER @@ -21,6 +25,9 @@ from litellm.types.utils import GenericStreamingChunk, ModelResponseStream GLOBAL_PASS_THROUGH_SUCCESS_HANDLER_OBJ: Final = PassThroughEndpointLogging() +_UPSTREAM_PUMP_TASKS: Final[set[asyncio.Task[None]]] = set() # mutable-ok: stdlib strong-ref set for pump tasks +_DETACHED_STREAM_DRAINS: Final[set[asyncio.Task[None]]] = set() # mutable-ok: bounded strong-ref set, detached drains + INCOMPLETE_STREAM_ERROR_MESSAGE: Final = ( "Provider stream ended before emitting a message_stop event; " "the response is incomplete and any partial content (e.g. tool_use input JSON) may be truncated." @@ -133,6 +140,34 @@ def _is_terminal_stream_chunk(chunk: object) -> bool: return _is_message_stop_chunk(chunk) or _is_provider_error_chunk(chunk) +def _try_claim_detached_drain_slot() -> bool: + """Claim a detached-drain slot for the current task, bounding concurrency. + + Returns True if a slot was claimed (the caller may keep draining upstream + for billing) or False if the cap is already reached (the caller should stop + and bill what it has). Only touched from the event loop, so the check + + insert need no lock. + """ + if len(_DETACHED_STREAM_DRAINS) >= ANTHROPIC_MESSAGES_MAX_DETACHED_STREAM_DRAINS: + return False + current_task: Final = asyncio.current_task() + if current_task is not None: + _DETACHED_STREAM_DRAINS.add(current_task) + current_task.add_done_callback(_DETACHED_STREAM_DRAINS.discard) + return True + + +def _exception_left_unconsumed(queue: "asyncio.Queue[bytes | None | BaseException]", exc: BaseException) -> bool: + """After client detach the relay never reads the queue again, so drain it here. + + The forwarded exception still sitting in the queue means the relay tore + down before re-raising it, so the proxy's failure handling never ran and + the caller must salvage spend itself. + """ + remaining: Final = tuple(queue.get_nowait() for _ in range(queue.qsize())) + return any(item is exc for item in remaining) + + def _sse_event(event_type: str, payload: Mapping[str, object]) -> bytes: return f"event: {event_type}\ndata: {json.dumps(payload)}\n\n".encode() @@ -414,17 +449,167 @@ class BaseAnthropicMessagesStreamingIterator: async def async_sse_wrapper( self, - completion_stream: AsyncIterator[bytes | GenericStreamingChunk | ModelResponseStream | dict], + completion_stream: AsyncIterator[bytes | GenericStreamingChunk | ModelResponseStream | Mapping[str, object]], ) -> AsyncIterator[bytes]: """ Generic async SSE wrapper that converts streaming chunks to SSE format and handles logging. + The upstream read runs in a detached background task (``_pump_upstream``) + so that a client disconnect tears down only this client-facing generator, + never the upstream drain + billing. The provider (e.g. Bedrock) keeps + generating and billing the full response regardless of the client, so + draining it to completion is what lets spend tracking see the real + terminal ``message_delta`` / ``message_stop`` usage instead of a + truncated placeholder count. + + Chunks reach the client through a bounded queue. While the client is + connected the pump blocks on a full queue (racing the disconnect + signal), so a slow reader throttles the upstream read exactly as the old + direct ``yield`` did instead of letting the whole response buffer in + memory. Once the client goes away the pump stops enqueueing and only + keeps a single ``collected_chunks`` copy for billing, and the number of + such post-disconnect drains running at once is capped so client behavior + can't create unbounded worker state; over the cap the pump bills what it + has rather than draining further. Detached-drain lifetime is otherwise + bounded by the upstream stream/read timeout. + + An upstream failure (Bedrock read / decode / chunk-conversion error) + that happens while the client is still connected is forwarded through + the queue and re-raised here, so the original provider exception (and + its status) reaches the proxy's failure handling unchanged rather than + being masked by a generic incomplete-stream event. + This method provides the common logic for both Anthropic and Bedrock implementations. """ - collected_chunks: Final = [] - saw_terminal_event = False + queue: Final[asyncio.Queue[bytes | None | BaseException]] = asyncio.Queue( + maxsize=ANTHROPIC_MESSAGES_STREAM_RELAY_QUEUE_MAXSIZE + ) + client_detached: Final = asyncio.Event() + pump_task: Final = asyncio.create_task(self._pump_upstream_to_queue(completion_stream, queue, client_detached)) + _UPSTREAM_PUMP_TASKS.add(pump_task) + pump_task.add_done_callback(_UPSTREAM_PUMP_TASKS.discard) + + reached_end = False # rebind-ok: flipped once the relay consumes the end-of-stream sentinel + try: + while True: + item = await queue.get() + if item is None: + reached_end = True + break + if isinstance(item, BaseException): + raise item + yield item + finally: + client_detached.set() + if not reached_end: + self._dispatch_pending_deferred_logging() + + def _dispatch_pending_deferred_logging(self) -> None: + """Fire deferred billing that a torn-down response would otherwise drop. + + When the pump finishes draining while the client is still connected it + stores the logging coroutine for ProxyLogging._fire_deferred_stream_logging, + which the proxy only fires on a normally completed response: a client + disconnect (GeneratorExit / CancelledError) re-raises past it. Without + this dispatch that window loses the spend row entirely. + """ + deferred_cb: Final = getattr(self.litellm_logging_obj, "_on_deferred_stream_complete", None) + deferred_args: Final = getattr(self.litellm_logging_obj, "_deferred_stream_complete_args", None) + if deferred_cb is None or deferred_args is None: + return + self.litellm_logging_obj._on_deferred_stream_complete = None + self.litellm_logging_obj._deferred_stream_complete_args = None + GLOBAL_LOGGING_WORKER.ensure_initialized_and_enqueue(async_coroutine=deferred_cb(*deferred_args)) + + async def _bill_collected_chunks( + self, + collected_chunks: list[bytes], # mutable-ok: SSE buffer forwarded to list-typed _handle_streaming_logging + *, + stream_teardown: bool, + ) -> None: + from litellm._logging import verbose_proxy_logger + + try: + await self._handle_streaming_logging(collected_chunks, stream_teardown=stream_teardown) + except Exception as exc: # noqa: BLE001 # billing is best-effort; never crash the pump + verbose_proxy_logger.warning( + "async_sse_wrapper billing failed after %d chunks: %s(%s)", + len(collected_chunks), + type(exc).__name__, + exc, + ) + + @staticmethod + async def _abort_upstream( + completion_stream: AsyncIterator[bytes | GenericStreamingChunk | ModelResponseStream | Mapping[str, object]], + ) -> None: + """Close the upstream provider stream so it stops generating and billing.""" + from litellm._logging import verbose_proxy_logger + + try: + await aclose_if_supported(completion_stream) + except Exception as exc: # noqa: BLE001 # abort is best-effort; log and continue + verbose_proxy_logger.warning( + "async_sse_wrapper failed to abort upstream stream: %s(%s)", + type(exc).__name__, + exc, + ) + + @staticmethod + async def _enqueue_for_client( + queue: "asyncio.Queue[bytes | None | BaseException]", + client_detached: "asyncio.Event", + item: bytes | None | BaseException, + ) -> bool: + """Deliver one item to the client, applying backpressure. + + Returns True if the item was queued, False if the client disconnected + before there was room (the item is then dropped, since a gone client + can't receive it). Never blocks once the client has detached. + """ + if client_detached.is_set(): + return False + try: + queue.put_nowait(item) + except asyncio.QueueFull: + pass + else: + return True + put_task: Final = asyncio.ensure_future(queue.put(item)) + detached_task: Final = asyncio.ensure_future(client_detached.wait()) + try: + await asyncio.wait(frozenset((put_task, detached_task)), return_when=asyncio.FIRST_COMPLETED) + finally: + if not detached_task.done(): + detached_task.cancel() + if put_task.done() and not put_task.cancelled(): + return True + put_task.cancel() + return False + + async def _pump_upstream_to_queue( + self, + completion_stream: AsyncIterator[bytes | GenericStreamingChunk | ModelResponseStream | Mapping[str, object]], + queue: "asyncio.Queue[bytes | None | BaseException]", + client_detached: "asyncio.Event", + ) -> None: + """Drain the whole upstream into ``queue`` (backpressured) and bill once. + + Runs detached so a client disconnect can't interrupt the upstream read; + see ``async_sse_wrapper`` for the full rationale. On a completed drain + the success billing (or deferred park) happens before the end-of-stream + sentinel is enqueued: the relay can only tear down after consuming the + sentinel, so its teardown can never outrun the park and get mistaken + for a client disconnect, and a sentinel the client never consumes falls + back to dispatching the parked billing here. + """ + from litellm._logging import verbose_proxy_logger + + collected_chunks: Final[list[bytes]] = [] # mutable-ok: SSE billing buffer appended to across the drain + saw_terminal_event = False # rebind-ok: accumulates across the upstream loop + draining_detached = False # rebind-ok: set once this pump claims a detached-drain slot try: async for chunk in completion_stream: if self.completion_start_time is None: @@ -432,17 +617,62 @@ class BaseAnthropicMessagesStreamingIterator: saw_terminal_event = saw_terminal_event or _is_terminal_stream_chunk(chunk) encoded_chunk = self._convert_chunk_to_sse_format(chunk) collected_chunks.append(encoded_chunk) - yield encoded_chunk - except (GeneratorExit, asyncio.CancelledError): - # A client disconnect tears the generator down at the yield, so the - # post-loop logging below never runs and the tokens already streamed - # (and billed by the provider) would never reach spend tracking. See LIT-5839. - if collected_chunks: - await self._handle_streaming_logging(collected_chunks, stream_teardown=True) - raise + if not client_detached.is_set(): + await self._enqueue_for_client(queue, client_detached, encoded_chunk) + continue + if not draining_detached: + if not _try_claim_detached_drain_slot(): + verbose_proxy_logger.warning( + "async_sse_wrapper: detached-drain cap (%d) reached; billing %d partial " + "chunks and aborting the upstream stream to stop provider billing", + ANTHROPIC_MESSAGES_MAX_DETACHED_STREAM_DRAINS, + len(collected_chunks), + ) + await self._bill_collected_chunks(collected_chunks, stream_teardown=True) + await self._abort_upstream(completion_stream) + return + draining_detached = True + except Exception as exc: # noqa: BLE001 # upstream errors are handled/forwarded by _handle_pump_upstream_error + await self._handle_pump_upstream_error(queue, client_detached, collected_chunks, exc) + return - if not saw_terminal_event: - yield _incomplete_stream_error_sse_event() + if client_detached.is_set(): + await self._bill_collected_chunks(collected_chunks, stream_teardown=True) + return + if not saw_terminal_event and not await self._enqueue_for_client( + queue, client_detached, _incomplete_stream_error_sse_event() + ): + await self._bill_collected_chunks(collected_chunks, stream_teardown=True) + return + await self._bill_collected_chunks(collected_chunks, stream_teardown=False) + if not await self._enqueue_for_client(queue, client_detached, None): + self._dispatch_pending_deferred_logging() - # Handle logging after all chunks are processed - await self._handle_streaming_logging(collected_chunks) + async def _handle_pump_upstream_error( + self, + queue: "asyncio.Queue[bytes | None | BaseException]", + client_detached: "asyncio.Event", + collected_chunks: list[bytes], # mutable-ok: SSE buffer forwarded to list-typed _bill_collected_chunks + exc: BaseException, + ) -> None: + """Forward a provider error to a still-connected client, else salvage partial spend. + + Handing the original exception to the client-facing generator lets it + re-raise so the proxy's failure handling keeps the provider status and + owns logging (no success-bill). If the client already went away, or + disconnects before ever consuming the queued exception, no failure hook + runs, so bill the partial instead of dropping the request. + """ + from litellm._logging import verbose_proxy_logger + + if not client_detached.is_set() and await self._enqueue_for_client(queue, client_detached, exc): + await client_detached.wait() + if not _exception_left_unconsumed(queue, exc): + return + verbose_proxy_logger.warning( + "async_sse_wrapper upstream pump failed after client disconnect (%d chunks): %s(%s)", + len(collected_chunks), + type(exc).__name__, + exc, + ) + await self._bill_collected_chunks(collected_chunks, stream_teardown=True) diff --git a/litellm/llms/anthropic/experimental_pass_through/responses_adapters/transformation.py b/litellm/llms/anthropic/experimental_pass_through/responses_adapters/transformation.py index ace7fc25dc9..0eb0e38a46e 100644 --- a/litellm/llms/anthropic/experimental_pass_through/responses_adapters/transformation.py +++ b/litellm/llms/anthropic/experimental_pass_through/responses_adapters/transformation.py @@ -179,14 +179,14 @@ class LiteLLMAnthropicToResponsesAPIAdapter: ) @staticmethod - def _assistant_block_group_key(indexed_block: tuple[int, Mapping[str, Any]]) -> str: + def _assistant_block_group_key(indexed_block: tuple[int, Mapping[str, object]]) -> str: """Group a run of consecutive thinking blocks together; keep every other block alone.""" index, block = indexed_block return "thinking" if block.get("type") == "thinking" else f"block:{index}" @classmethod def _assistant_group_to_input_item( - cls, group: tuple[Mapping[str, Any], ...] + cls, group: tuple[Mapping[str, object], ...] ) -> dict[str, Any] | None: # mutable-ok: API message payload first: Final = group[0] btype: Final = first.get("type") @@ -206,7 +206,7 @@ class LiteLLMAnthropicToResponsesAPIAdapter: def translate_messages_to_responses_input( self, messages: list[AllAnthropicPassThroughMessageValues], - ) -> list[dict[str, Any]]: + ) -> list[dict[str, object]]: """ Convert Anthropic messages list to Responses API `input` items. @@ -220,7 +220,7 @@ class LiteLLMAnthropicToResponsesAPIAdapter: assistant thinking -> reasoning assistant tool_use -> function_call """ - input_items: Final[list[dict[str, Any]]] = [] + input_items: Final[list[dict[str, object]]] = [] for m in messages: if m["role"] == "system": @@ -248,7 +248,7 @@ class LiteLLMAnthropicToResponsesAPIAdapter: } ) elif isinstance(content, list): - user_parts: list[dict[str, Any]] = [] + user_parts: list[Mapping[str, object]] = [] tool_image_parts: list[dict[str, Any]] = [] # mutable-ok: json content parts for block in content: if not isinstance(block, dict): @@ -379,9 +379,9 @@ class LiteLLMAnthropicToResponsesAPIAdapter: def translate_tools_to_responses_api( self, tools: list[AllAnthropicToolsValues], - ) -> list[dict[str, Any]]: + ) -> list[dict[str, object]]: """Convert Anthropic tool definitions to Responses API function tools.""" - result: Final[list[dict[str, Any]]] = [] + result: Final[list[dict[str, object]]] = [] for tool in tools: tool_dict = cast(dict[str, Any], tool) tool_type = tool_dict.get("type", "") @@ -392,7 +392,7 @@ class LiteLLMAnthropicToResponsesAPIAdapter: continue # Responses turns strict mode on when `strict` is omitted, silently rewriting # `required` to every property. Anthropic tools are non-strict unless asked. - func_tool: dict[str, Any] = { + func_tool: dict[str, object] = { "type": "function", "name": tool_name, "strict": bool(tool_dict.get("strict")), @@ -407,7 +407,7 @@ class LiteLLMAnthropicToResponsesAPIAdapter: @staticmethod def translate_tool_choice_to_responses_api( tool_choice: AnthropicMessagesToolChoice, - ) -> str | dict[str, Any]: + ) -> str | dict[str, object]: """Convert Anthropic tool_choice to Responses API tool_choice.""" tc_type: Final = tool_choice.get("type") if tc_type == "any": @@ -420,8 +420,8 @@ class LiteLLMAnthropicToResponsesAPIAdapter: @staticmethod def translate_context_management_to_responses_api( - context_management: dict[str, Any], - ) -> list[dict[str, Any]] | None: + context_management: dict[str, object], + ) -> list[dict[str, object]] | None: """ Convert Anthropic context_management dict to OpenAI Responses API array format. @@ -435,13 +435,13 @@ class LiteLLMAnthropicToResponsesAPIAdapter: if not isinstance(edits, list): return None - result: Final[list[dict[str, Any]]] = [] + result: Final[list[dict[str, object]]] = [] for edit in edits: if not isinstance(edit, dict): continue edit_type = edit.get("type", "") if edit_type == "compact_20260112": - entry: dict[str, Any] = {"type": "compaction"} + entry: dict[str, object] = {"type": "compaction"} trigger = edit.get("trigger") if isinstance(trigger, dict) and trigger.get("value") is not None: entry["compact_threshold"] = int(trigger["value"]) @@ -451,9 +451,9 @@ class LiteLLMAnthropicToResponsesAPIAdapter: @staticmethod def translate_thinking_to_reasoning( - thinking: dict[str, Any], - output_config: dict[str, Any] | None = None, - ) -> dict[str, Any] | None: + thinking: dict[str, object], + output_config: dict[str, object] | None = None, + ) -> dict[str, object] | None: """ Convert Anthropic thinking param to Responses API reasoning param. @@ -473,12 +473,14 @@ class LiteLLMAnthropicToResponsesAPIAdapter: if isinstance(output_config, dict) and output_config.get("effort"): effort = output_config["effort"] elif thinking_type == "enabled": - effort = reasoning_effort_from_thinking_budget(thinking.get("budget_tokens", 0)) + raw_budget: Final = thinking.get("budget_tokens", 0) + budget_tokens: Final = int(raw_budget) if isinstance(raw_budget, (int, float)) else 0 + effort = reasoning_effort_from_thinking_budget(budget_tokens) else: return None auto_summary: Final = is_reasoning_auto_summary_enabled() - result: Final[dict[str, Any]] = {"effort": effort} + result: Final[dict[str, object]] = {"effort": effort} summary: Final = thinking.get("summary") if summary: result["summary"] = summary @@ -570,7 +572,7 @@ class LiteLLMAnthropicToResponsesAPIAdapter: # output_format / output_config.format -> text format # output_format: {"type": "json_schema", "schema": {...}} # output_config: {"format": {"type": "json_schema", "schema": {...}}} - output_format: Any = anthropic_request.get("output_format") + output_format: object = anthropic_request.get("output_format") output_config = anthropic_request.get("output_config") if not isinstance(output_format, dict) and isinstance(output_config, dict): output_format = output_config.get("format") @@ -620,7 +622,7 @@ class LiteLLMAnthropicToResponsesAPIAdapter: ResponseReasoningItem, ) - content: Final[list[dict[str, Any]]] = [] + content: Final[list[dict[str, object]]] = [] stop_reason: AnthropicFinishReason = "end_turn" for item in response.output: diff --git a/litellm/llms/anthropic/files/handler.py b/litellm/llms/anthropic/files/handler.py index 5fdf2ceff7f..dfd62ca575b 100644 --- a/litellm/llms/anthropic/files/handler.py +++ b/litellm/llms/anthropic/files/handler.py @@ -2,7 +2,7 @@ import asyncio import json import time from collections.abc import Coroutine -from typing import Any, Final +from typing import Final import httpx @@ -116,7 +116,7 @@ class AnthropicFilesHandler: api_key: str | None = None, timeout: float | httpx.Timeout = 600.0, max_retries: int | None = None, - ) -> HttpxBinaryResponseContent | Coroutine[Any, Any, HttpxBinaryResponseContent]: + ) -> HttpxBinaryResponseContent | Coroutine[object, object, HttpxBinaryResponseContent]: """ Retrieve file content from Anthropic. diff --git a/litellm/llms/azure/azure.py b/litellm/llms/azure/azure.py index 2bcc830851a..46a9dd1a531 100644 --- a/litellm/llms/azure/azure.py +++ b/litellm/llms/azure/azure.py @@ -2,7 +2,7 @@ import asyncio import json import time from collections.abc import Callable, Coroutine -from typing import Any, Final +from typing import Final import httpx from openai import ( @@ -374,7 +374,7 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): except Exception as e: status_code: Final = getattr(e, "status_code", 500) error_headers = getattr(e, "headers", None) - error_response: Final = getattr(e, "response", None) + error_response: Final[object] = getattr(e, "response", None) error_body: Final = getattr(e, "body", None) if error_headers is None and error_response: error_headers = getattr(error_response, "headers", None) @@ -392,7 +392,7 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): model: str, api_base: str, data: dict, - timeout: Any, + timeout: float | httpx.Timeout, dynamic_params: bool, model_response: ModelResponse, logging_obj: LiteLLMLoggingObj, @@ -502,7 +502,7 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): dynamic_params: bool, data: dict[str, object], model: str, - timeout: Any, + timeout: float | httpx.Timeout, max_retries: int, azure_ad_token: str | None = None, azure_ad_token_provider: Callable | None = None, @@ -578,7 +578,7 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): dynamic_params: bool, data: dict, model: str, - timeout: Any, + timeout: float | httpx.Timeout, max_retries: int, azure_ad_token: str | None = None, azure_ad_token_provider: Callable | None = None, @@ -634,7 +634,7 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): except Exception as e: status_code: Final = getattr(e, "status_code", 500) error_headers = getattr(e, "headers", None) - error_response: Final = getattr(e, "response", None) + error_response: Final[object] = getattr(e, "response", None) message: Final = getattr(e, "message", str(e)) error_body: Final = getattr(e, "body", None) if error_headers is None and error_response: @@ -754,7 +754,7 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): aembedding=None, headers: dict | None = None, litellm_params: dict | None = None, - ) -> EmbeddingResponse | Coroutine[Any, Any, EmbeddingResponse]: + ) -> EmbeddingResponse | Coroutine[object, object, EmbeddingResponse]: if headers: optional_params["extra_headers"] = headers if self._client_session is None: @@ -1268,7 +1268,7 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): headers["Authorization"] = f"Bearer {azure_ad_token}" # init AzureOpenAI Client - azure_client_params: Final[dict[str, Any]] = self.initialize_azure_sdk_client( + azure_client_params: Final[dict[str, object]] = self.initialize_azure_sdk_client( litellm_params=litellm_params or {}, api_key=api_key, model_name=model or "", diff --git a/litellm/llms/azure_ai/agents/handler.py b/litellm/llms/azure_ai/agents/handler.py index a13b1300e55..f7382190fca 100644 --- a/litellm/llms/azure_ai/agents/handler.py +++ b/litellm/llms/azure_ai/agents/handler.py @@ -51,15 +51,13 @@ else: AsyncHTTPHandler = Any -class _AzureRawAnnotation(TypedDict, total=False): - type: ReadOnly[str] +class _AzureRawAnnotation(ChatCompletionAnnotation, total=False): text: ReadOnly[str] start_index: ReadOnly[int] end_index: ReadOnly[int] - url_citation: ReadOnly[ChatCompletionAnnotationURLCitation] -_TransformedAnnotation: TypeAlias = ChatCompletionAnnotation | _AzureRawAnnotation +_TransformedAnnotation: TypeAlias = ChatCompletionAnnotation class _AzureText(TypedDict, total=False): @@ -223,18 +221,11 @@ class AzureAIAgentsHandler: """Build the ModelResponse from agent output.""" from litellm.types.utils import Choices, Message, Usage - message_kwargs: Final[dict[str, Any]] = { - "content": content, - "role": "assistant", - } - if annotations: - message_kwargs["annotations"] = annotations - model_response.choices = [ Choices( finish_reason="stop", index=0, - message=Message(**message_kwargs), + message=Message(content=content, role="assistant", annotations=annotations or None), ) ] model_response.model = model @@ -655,9 +646,6 @@ class AzureAIAgentsHandler: if data_str == "[DONE]": # Send final chunk with finish_reason - final_delta_kwargs: dict[str, Any] = {"content": None} - if collected_annotations: - final_delta_kwargs["annotations"] = collected_annotations final_chunk = ModelResponseStream( id=response_id, created=created, @@ -667,7 +655,7 @@ class AzureAIAgentsHandler: StreamingChoices( finish_reason="stop", index=0, - delta=Delta(**final_delta_kwargs), + delta=Delta(content=None, annotations=collected_annotations or None), ) ], ) diff --git a/litellm/llms/base_llm/managed_resources/base_managed_resource.py b/litellm/llms/base_llm/managed_resources/base_managed_resource.py index cced330d873..4fbc0ce51b0 100644 --- a/litellm/llms/base_llm/managed_resources/base_managed_resource.py +++ b/litellm/llms/base_llm/managed_resources/base_managed_resource.py @@ -5,7 +5,8 @@ import base64 import json from abc import ABC, abstractmethod -from typing import TYPE_CHECKING, Any, Final, Generic, TypeVar, cast +from collections.abc import Mapping +from typing import TYPE_CHECKING, Any, Final, Generic, Protocol, TypeVar, cast, runtime_checkable from litellm import verbose_logger from litellm.llms.base_llm.managed_resources.isolation import ( @@ -38,6 +39,30 @@ else: ResourceObjectType = TypeVar("ResourceObjectType") +@runtime_checkable +class _HasIdentifier(Protocol): + id: str + + +class _ManagedResourceRecord(Protocol[ResourceObjectType]): + unified_resource_id: str + resource_object: ResourceObjectType + + def model_dump(self) -> dict[str, object]: ... + + +class _ManagedResourceTable(Protocol[ResourceObjectType]): + async def create(self, *, data: Mapping[str, object]) -> object: ... + + async def find_first(self, *, where: Mapping[str, object]) -> _ManagedResourceRecord[ResourceObjectType] | None: ... + + async def find_many( + self, *, where: Mapping[str, object], take: int, order: Mapping[str, str] + ) -> list[_ManagedResourceRecord[ResourceObjectType]]: ... + + async def delete(self, *, where: Mapping[str, object]) -> object: ... + + class BaseManagedResource(ABC, Generic[ResourceObjectType]): """ Base class for managing resources with target_model_names support. @@ -64,6 +89,9 @@ class BaseManagedResource(ABC, Generic[ResourceObjectType]): self.internal_usage_cache = internal_usage_cache self.prisma_client = prisma_client + def _resource_table(self) -> _ManagedResourceTable[ResourceObjectType]: + return getattr(self.prisma_client.db, self.table_name) + # ============================================================================ # ABSTRACT METHODS # ============================================================================ @@ -137,7 +165,7 @@ class BaseManagedResource(ABC, Generic[ResourceObjectType]): litellm_parent_otel_span: Span | None, model_mappings: dict[str, str], user_api_key_dict: UserAPIKeyAuth, - additional_db_fields: dict[str, Any] | None = None, + additional_db_fields: Mapping[str, object] | None = None, ) -> None: """ Store unified resource ID with model mappings in cache and database. @@ -153,7 +181,7 @@ class BaseManagedResource(ABC, Generic[ResourceObjectType]): verbose_logger.info("Storing LiteLLM Managed %s with id=%s in cache", self.resource_type, unified_resource_id) # Prepare cache data - cache_data: Final = { + cache_data: Final[dict[str, object]] = { "unified_resource_id": unified_resource_id, "resource_object": resource_object, "model_mappings": model_mappings, @@ -176,7 +204,7 @@ class BaseManagedResource(ABC, Generic[ResourceObjectType]): ) # Prepare database data - db_data: Final = { + db_data: Final[dict[str, object]] = { "unified_resource_id": unified_resource_id, "model_mappings": json.dumps(model_mappings), "flat_model_resource_ids": list(model_mappings.values()), @@ -205,7 +233,7 @@ class BaseManagedResource(ABC, Generic[ResourceObjectType]): db_data.update(additional_db_fields) # Store in database - table: Final = getattr(self.prisma_client.db, self.table_name) + table: Final = self._resource_table() result: Final = await table.create(data=db_data) verbose_logger.debug( @@ -240,7 +268,7 @@ class BaseManagedResource(ABC, Generic[ResourceObjectType]): return result # Check database - table: Final = getattr(self.prisma_client.db, self.table_name) + table: Final = self._resource_table() db_object: Final = await table.find_first(where={"unified_resource_id": unified_resource_id}) if db_object: @@ -264,7 +292,7 @@ class BaseManagedResource(ABC, Generic[ResourceObjectType]): The deleted resource object or None if not found """ # Get old value from database - table: Final = getattr(self.prisma_client.db, self.table_name) + table: Final = self._resource_table() initial_value: Final = await table.find_first(where={"unified_resource_id": unified_resource_id}) if initial_value is None: @@ -515,7 +543,7 @@ class BaseManagedResource(ABC, Generic[ResourceObjectType]): user_api_key_dict: UserAPIKeyAuth, limit: int | None = None, after: str | None = None, - additional_filters: dict[str, Any] | None = None, + additional_filters: Mapping[str, object] | None = None, ) -> dict[str, Any]: """ List resources created by a user. @@ -533,7 +561,7 @@ class BaseManagedResource(ABC, Generic[ResourceObjectType]): if owner_filter is None: return build_list_page([]) - where_clause: Final[dict[str, Any]] = {**owner_filter} + where_clause: Final[dict[str, object]] = {**owner_filter} if after: where_clause["id"] = {"gt": after} @@ -544,14 +572,14 @@ class BaseManagedResource(ABC, Generic[ResourceObjectType]): # Fetch resources fetch_limit: Final = limit or 20 - table: Final = getattr(self.prisma_client.db, self.table_name) + table: Final = self._resource_table() resources: Final = await table.find_many( where=where_clause, take=fetch_limit, order={"created_at": "desc"}, ) - resource_objects: Final[list[Any]] = [] + resource_objects: Final[list[object]] = [] for resource in resources: try: # Stop once we have enough @@ -559,12 +587,13 @@ class BaseManagedResource(ABC, Generic[ResourceObjectType]): break # Parse resource object - resource_data = resource.resource_object - if isinstance(resource_data, str): - resource_data = json.loads(resource_data) + stored_resource = resource.resource_object + resource_data: object = ( + json.loads(stored_resource) if isinstance(stored_resource, str) else stored_resource + ) # Set unified ID - if hasattr(resource_data, "id"): + if isinstance(resource_data, _HasIdentifier): resource_data.id = resource.unified_resource_id elif isinstance(resource_data, dict): resource_data["id"] = resource.unified_resource_id diff --git a/litellm/llms/base_llm/ocr/transformation.py b/litellm/llms/base_llm/ocr/transformation.py index d1c77186ea8..3b302837032 100644 --- a/litellm/llms/base_llm/ocr/transformation.py +++ b/litellm/llms/base_llm/ocr/transformation.py @@ -75,6 +75,7 @@ class OCRUsageInfo(LiteLLMPydanticObjectBase): """Usage information from OCR response.""" pages_processed: int | None = None + pages_processed_annotation: int | None = None credits: float | None = None doc_size_bytes: int | None = None diff --git a/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py b/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py index 40b90014f3b..8e709349400 100644 --- a/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py +++ b/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py @@ -3,7 +3,6 @@ from typing import TYPE_CHECKING, Any, Final import httpx from litellm.anthropic_beta_headers_manager import filter_and_transform_beta_headers -from litellm.litellm_core_utils.litellm_logging import verbose_logger from litellm.litellm_core_utils.prompt_templates.factory import ( convert_to_anthropic_image_obj, ) @@ -16,17 +15,16 @@ from litellm.llms.bedrock.chat.invoke_transformations.base_invoke_transformation AmazonInvokeConfig, ) from litellm.llms.bedrock.common_utils import ( - convert_bedrock_invoke_output_format_to_inline_schema, + apply_bedrock_invoke_structured_output, get_anthropic_beta_from_headers, normalize_bedrock_opus_output_config_effort, normalize_custom_field_on_tools, normalize_tool_input_schema_types_for_bedrock_invoke, - pop_bedrock_invoke_output_config_format, + strip_unsupported_bedrock_invoke_output_config_keys, ) from litellm.types.llms.anthropic import ANTHROPIC_TOOL_SEARCH_BETA_HEADER from litellm.types.llms.openai import AllMessageValues from litellm.types.utils import ModelResponse -from litellm.utils import _supports_factory if TYPE_CHECKING: import tiktoken @@ -212,36 +210,14 @@ class AmazonAnthropicClaudeConfig(AmazonInvokeConfig, AnthropicConfig): anthropic_request.pop("model", None) anthropic_request.pop("stream", None) anthropic_request.pop("stream_chunk_size", None) - output_format: Final = anthropic_request.pop("output_format", None) - output_config_format: Final = pop_bedrock_invoke_output_config_format(anthropic_request) - if output_format: - convert_bedrock_invoke_output_format_to_inline_schema( - output_format=output_format, - request_body=anthropic_request, - ) - elif output_config_format: - convert_bedrock_invoke_output_format_to_inline_schema( - output_format=output_config_format, - request_body=anthropic_request, - ) - if not ( - _supports_factory( - model=model, - custom_llm_provider="bedrock", - key="supports_output_config", - ) - or AnthropicConfig._model_supports_effort_param(model, "bedrock") - ): - if anthropic_request.pop("output_config", None) is not None: - verbose_logger.warning( - "Bedrock Invoke: stripping unsupported `output_config` for " - "model=%s — neither `supports_output_config` nor any " - "`supports_*_reasoning_effort` flag is set in " - "model_prices_and_context_window.json. Add the capability " - "flag to the model JSON entry if this model accepts " - "`output_config`.", - model, - ) + apply_bedrock_invoke_structured_output( + model=model, + request_body=anthropic_request, + ) + strip_unsupported_bedrock_invoke_output_config_keys( + model=model, + request_body=anthropic_request, + ) if "anthropic_version" not in anthropic_request: anthropic_request["anthropic_version"] = self.anthropic_version diff --git a/litellm/llms/bedrock/common_utils.py b/litellm/llms/bedrock/common_utils.py index 72e3cc1b326..30a77d57f24 100644 --- a/litellm/llms/bedrock/common_utils.py +++ b/litellm/llms/bedrock/common_utils.py @@ -177,6 +177,95 @@ def convert_bedrock_invoke_output_format_to_inline_schema( request_body["messages"] = new_messages +def _bedrock_model_supports(model: str, key: str) -> bool: + from litellm.utils import _supports_factory + + return _supports_factory(model=model, custom_llm_provider="bedrock", key=key) + + +def apply_bedrock_invoke_structured_output( + model: str, + request_body: dict[str, object], # mutable-ok: edited in place like siblings +) -> None: + """ + Route Anthropic structured-output params to what the Bedrock model supports. + + Consumes the legacy top-level ``output_format`` and the newer + ``output_config.format``, keeping the pre-existing precedence of the legacy + field when a request carries both. Models flagged + ``supports_native_structured_output`` in the model map get the schema + forwarded as ``output_config.format``, which Bedrock relays to the model for + enforced structured output. For every other model the schema is inlined into + the last user message as best-effort text, with a warning because nothing + enforces it. + """ + legacy_output_format: Final = request_body.pop("output_format", None) + output_config_format: Final = pop_bedrock_invoke_output_config_format(request_body) + schema_format: Final = legacy_output_format if isinstance(legacy_output_format, dict) else output_config_format + if schema_format is None: + return + + if _bedrock_model_supports(model, "supports_native_structured_output"): + existing_output_config: Final = request_body.get("output_config") + if isinstance(existing_output_config, dict): + existing_output_config["format"] = schema_format + else: + request_body["output_config"] = {"format": schema_format} # rebind-ok: out-param # mutable-ok: json + return + + verbose_logger.warning( + "Bedrock Invoke: model=%s does not advertise `supports_native_structured_output` " + "in model_prices_and_context_window.json, so the JSON schema was inlined into " + "the last user message and is NOT enforced by the model.", + model, + ) + convert_bedrock_invoke_output_format_to_inline_schema( + output_format=schema_format, + request_body=request_body, + ) + + +def strip_unsupported_bedrock_invoke_output_config_keys( + model: str, + request_body: dict[str, object], # mutable-ok: edited in place like siblings +) -> None: + """ + Drop ``output_config`` keys the Bedrock model does not accept. + + ``format`` survives unconditionally: it is only attached for models whose map + entry advertises ``supports_native_structured_output``. Effort-bearing keys + survive only when the map flags ``supports_output_config`` or a + ``supports_*_reasoning_effort`` tier; otherwise they are dropped with a + warning so Bedrock does not reject the request. + """ + from litellm.llms.anthropic.chat.transformation import AnthropicConfig + + output_config: Final = request_body.get("output_config") + if not isinstance(output_config, dict): + return + if all(key == "format" for key in output_config): + return + if _bedrock_model_supports(model, "supports_output_config") or AnthropicConfig._model_supports_effort_param( + model, "bedrock" + ): + return + + verbose_logger.warning( + "Bedrock Invoke: stripping unsupported `output_config` keys for " + "model=%s: neither `supports_output_config` nor any " + "`supports_*_reasoning_effort` flag is set in " + "model_prices_and_context_window.json. Add the capability " + "flag to the model JSON entry if this model accepts " + "`output_config`.", + model, + ) + preserved_format: Final = output_config.get("format") + if preserved_format is None: + request_body.pop("output_config", None) + else: + request_body["output_config"] = {"format": preserved_format} # rebind-ok: out-param # mutable-ok: json + + def normalize_custom_field_on_tools(request_body: dict) -> None: """ Drop the ``custom`` field from each tool, first hoisting a boolean @@ -1487,6 +1576,7 @@ class CommonBatchFilesUtils: aws_role_name=optional_params.get("aws_role_name"), aws_web_identity_token=optional_params.get("aws_web_identity_token"), aws_sts_endpoint=optional_params.get("aws_sts_endpoint"), + aws_external_id=optional_params.get("aws_external_id"), ) # Prepare the request data diff --git a/litellm/llms/bedrock/files/handler.py b/litellm/llms/bedrock/files/handler.py index 13718d41cc1..e74c3802d20 100644 --- a/litellm/llms/bedrock/files/handler.py +++ b/litellm/llms/bedrock/files/handler.py @@ -113,6 +113,7 @@ class BedrockFilesHandler(BaseAWSLLM): aws_role_name=optional_params.get("aws_role_name"), aws_web_identity_token=optional_params.get("aws_web_identity_token"), aws_sts_endpoint=optional_params.get("aws_sts_endpoint"), + aws_external_id=optional_params.get("aws_external_id"), ) # Create S3 client diff --git a/litellm/llms/bedrock/files/transformation.py b/litellm/llms/bedrock/files/transformation.py index f442608a288..33b27943ad8 100644 --- a/litellm/llms/bedrock/files/transformation.py +++ b/litellm/llms/bedrock/files/transformation.py @@ -146,6 +146,7 @@ class _BedrockS3RequestParams(BaseModel): aws_role_name: str | None = None aws_web_identity_token: str | None = None aws_sts_endpoint: str | None = None + aws_external_id: str | None = None s3_region_name: str | None = None s3_endpoint_url: str | None = None @@ -1029,6 +1030,7 @@ class BedrockFilesConfig(BaseAWSLLM, BaseFilesConfig): aws_role_name=optional_params.get("aws_role_name"), aws_web_identity_token=optional_params.get("aws_web_identity_token"), aws_sts_endpoint=optional_params.get("aws_sts_endpoint"), + aws_external_id=optional_params.get("aws_external_id"), ) # Calculate SHA256 hash of the content (REQUIRED for S3) @@ -1290,6 +1292,7 @@ class BedrockFilesConfig(BaseAWSLLM, BaseFilesConfig): aws_role_name=request_params.aws_role_name, aws_web_identity_token=request_params.aws_web_identity_token, aws_sts_endpoint=request_params.aws_sts_endpoint, + aws_external_id=request_params.aws_external_id, ) empty_body_hash: Final = hashlib.sha256(b"").hexdigest() diff --git a/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py b/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py index f74a290d773..6ff9f0155f9 100644 --- a/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py +++ b/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py @@ -29,14 +29,14 @@ from litellm.llms.bedrock.chat.invoke_transformations.base_invoke_transformation AmazonInvokeConfig, ) from litellm.llms.bedrock.common_utils import ( - convert_bedrock_invoke_output_format_to_inline_schema, + apply_bedrock_invoke_structured_output, ensure_bedrock_anthropic_messages_tool_names, get_anthropic_beta_from_headers, is_claude_4_5_on_bedrock, normalize_bedrock_opus_output_config_effort, normalize_custom_field_on_tools, normalize_tool_input_schema_types_for_bedrock_invoke, - pop_bedrock_invoke_output_config_format, + strip_unsupported_bedrock_invoke_output_config_keys, ) from litellm.llms.bedrock.request_metadata import ( bedrock_request_metadata_headers, @@ -51,7 +51,6 @@ from litellm.types.llms.openai import AllMessageValues from litellm.types.router import GenericLiteLLMParams from litellm.types.utils import GenericStreamingChunk, ModelResponseStream from litellm.types.utils import GenericStreamingChunk as GChunk -from litellm.utils import _supports_factory if TYPE_CHECKING: from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj @@ -708,52 +707,25 @@ class AmazonAnthropicClaudeMessagesConfig( # 4. Remove `ttl` field from cache_control in messages (Bedrock doesn't support it for older models) self._remove_ttl_from_cache_control(anthropic_messages_request=anthropic_messages_request, model=model) - # 5. Convert structured-output params to inline schema. - # Bedrock Invoke doesn't support top-level `output_format`; its - # accepted `output_config` subset is also narrower than Anthropic's, so - # consume the newer `output_config.format` shape here instead of - # forwarding it as an unknown nested key. + # 5. Route structured-output params (`output_format` / + # `output_config.format`) to native enforcement or the inline-schema + # fallback, then strip `output_config` keys the model does not accept. + # Ref: https://github.com/BerriAI/litellm/issues/22797 existing_output_config: Final = anthropic_messages_request.get("output_config") if isinstance(existing_output_config, dict): anthropic_messages_request["output_config"] = dict(existing_output_config) - output_format: Final = anthropic_messages_request.pop("output_format", None) - output_config_format: Final = pop_bedrock_invoke_output_config_format(anthropic_messages_request) - if output_format: - convert_bedrock_invoke_output_format_to_inline_schema( - output_format=output_format, - request_body=anthropic_messages_request, - ) - elif output_config_format: - convert_bedrock_invoke_output_format_to_inline_schema( - output_format=output_config_format, - request_body=anthropic_messages_request, - ) + apply_bedrock_invoke_structured_output( + model=model, + request_body=anthropic_messages_request, + ) normalize_bedrock_opus_output_config_effort( model=model, output_config=anthropic_messages_request.get("output_config"), ) - - # 5a. Bedrock Invoke supports output_config (effort) for Claude 4.6+ models, - # but older models do not — strip it to avoid request rejection. - # Ref: https://github.com/BerriAI/litellm/issues/22797 - if not ( - _supports_factory( - model=model, - custom_llm_provider="bedrock", - key="supports_output_config", - ) - or AnthropicConfig._model_supports_effort_param(model, "bedrock") - ): - if anthropic_messages_request.pop("output_config", None) is not None: - verbose_logger.warning( - "Bedrock Invoke: stripping unsupported `output_config` for " - "model=%s — neither `supports_output_config` nor any " - "`supports_*_reasoning_effort` flag is set in " - "model_prices_and_context_window.json. Add the capability " - "flag to the model JSON entry if this model accepts " - "`output_config`.", - model, - ) + strip_unsupported_bedrock_invoke_output_config_keys( + model=model, + request_body=anthropic_messages_request, + ) # 5b. Hoist `custom.defer_loading` then drop `custom` (Bedrock doesn't support it) # Ref: https://github.com/BerriAI/litellm/issues/22847 @@ -774,9 +746,11 @@ class AmazonAnthropicClaudeMessagesConfig( if filtered_betas: anthropic_messages_request["anthropic_beta"] = filtered_betas + remaining_output_config: Final = anthropic_messages_request.get("output_config") if ( litellm.drop_params is True - and "output_config" in anthropic_messages_request + and isinstance(remaining_output_config, dict) + and any(key != "format" for key in remaining_output_config) and not AnthropicConfig._model_supports_effort_param(model, "bedrock") ): verbose_logger.warning( diff --git a/litellm/llms/bedrock/realtime/handler.py b/litellm/llms/bedrock/realtime/handler.py index 3bda8dd8359..42fe8941443 100644 --- a/litellm/llms/bedrock/realtime/handler.py +++ b/litellm/llms/bedrock/realtime/handler.py @@ -7,13 +7,18 @@ This uses aws_sdk_bedrock_runtime for bidirectional streaming with Nova Sonic. import asyncio import contextlib import json +from collections.abc import AsyncIterator, Mapping from typing import Final, Protocol from pydantic import JsonValue, TypeAdapter +import litellm from litellm._logging import _redact_string, verbose_proxy_logger from litellm.litellm_core_utils.aws_partition import get_aws_dns_suffix from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLogging +from litellm.litellm_core_utils.logging_worker import GLOBAL_LOGGING_WORKER +from litellm.litellm_core_utils.realtime_streaming import DefaultLoggedRealTimeEventTypes +from litellm.types.llms.openai import OpenAIRealtimeEvents from litellm.types.realtime import RealtimeResponseTransformInput from ..base_aws_llm import BaseAWSLLM @@ -32,6 +37,17 @@ def _json_str(value: JsonValue) -> str | None: return value if isinstance(value, str) else None +def _should_log_event(openai_message: Mapping[str, object]) -> bool: + logged_types: Final = ( + litellm.logged_real_time_event_types + if litellm.logged_real_time_event_types is not None + else DefaultLoggedRealTimeEventTypes + ) + if logged_types == "*": + return True + return openai_message.get("type") in logged_types + + class RealtimeClientWebSocket(Protocol): """The client-facing websocket surface the realtime bridge talks to.""" @@ -205,16 +221,22 @@ class BedrockRealtime(BaseAWSLLM): ) ) - bedrock_to_client_task: Final = asyncio.create_task( - self._forward_bedrock_to_client( - bedrock_stream, - websocket, - transformation_config, - model, - logging_obj, - session_state, + async def forward_bedrock_and_collect_logged_events() -> tuple[OpenAIRealtimeEvents, ...]: + return tuple( + [ + event + async for event in self._forward_bedrock_to_client( + bedrock_stream, + websocket, + transformation_config, + model, + logging_obj, + session_state, + ) + ] ) - ) + + bedrock_to_client_task: Final = asyncio.create_task(forward_bedrock_and_collect_logged_events()) # Wait for both tasks to complete await asyncio.gather( @@ -223,6 +245,27 @@ class BedrockRealtime(BaseAWSLLM): return_exceptions=True, ) + forwarded_logged_events: Final = ( + bedrock_to_client_task.result() + if not bedrock_to_client_task.cancelled() and bedrock_to_client_task.exception() is None + else () + ) + logged_events: Final = ( + *forwarded_logged_events, + *( + leftover_event + for leftover_event in transformation_config.leftover_usage_done_events() + if _should_log_event(leftover_event) + ), + ) + if logged_events: + GLOBAL_LOGGING_WORKER.ensure_initialized_and_enqueue( + logging_obj.dispatch_success_handlers( + list(logged_events), # mutable-ok: realtime spend logging requires a list result + prefer_async_handlers=True, + ) + ) + except Exception as e: verbose_proxy_logger.exception("Error in BedrockRealtime.async_realtime: %s", e) try: @@ -304,8 +347,8 @@ class BedrockRealtime(BaseAWSLLM): model: str, logging_obj: LiteLLMLogging, session_state: RealtimeResponseTransformInput, - ): - """Forward messages from Bedrock stream to client WebSocket.""" + ) -> AsyncIterator[OpenAIRealtimeEvents]: + """Forward messages from Bedrock to the client, yielding the ones to record for spend logging.""" try: while True: # Receive from Bedrock @@ -353,11 +396,14 @@ class BedrockRealtime(BaseAWSLLM): ) # Send transformed messages to client - openai_messages = transformed_response.get("response", []) + response_value = transformed_response["response"] + openai_messages = response_value if isinstance(response_value, list) else (response_value,) for openai_message in openai_messages: message_json = json.dumps(openai_message) await client_ws.send_text(message_json) verbose_proxy_logger.debug("Bedrock Realtime: Sent to client: %s", message_json[:200]) + if _should_log_event(openai_message): + yield openai_message except Exception as e: verbose_proxy_logger.debug("Bedrock to client forwarding ended: %s", e, exc_info=True) diff --git a/litellm/llms/bedrock/realtime/transformation.py b/litellm/llms/bedrock/realtime/transformation.py index 951bf636b2f..1f4c81d6491 100644 --- a/litellm/llms/bedrock/realtime/transformation.py +++ b/litellm/llms/bedrock/realtime/transformation.py @@ -7,7 +7,7 @@ Transforms between OpenAI Realtime API format and Bedrock Nova Sonic format. import base64 import json import uuid as uuid_lib -from typing import Any, Final +from typing import Final, cast from pydantic import BaseModel @@ -20,29 +20,54 @@ from litellm.types.llms.openai import ( OpenAIRealtimeContentPartDone, OpenAIRealtimeDoneEvent, OpenAIRealtimeEvents, + OpenAIRealtimeInputAudioBufferSpeechEvent, + OpenAIRealtimeInputAudioTranscriptionCompleted, + OpenAIRealtimeInputAudioTranscriptionDelta, OpenAIRealtimeOutputItemDone, OpenAIRealtimeResponseAudioDone, OpenAIRealtimeResponseContentPartAdded, OpenAIRealtimeResponseDelta, OpenAIRealtimeResponseDoneObject, OpenAIRealtimeResponseTextDone, + OpenAIRealtimeResponseUsage, OpenAIRealtimeStreamResponseBaseObject, OpenAIRealtimeStreamResponseOutputItemAdded, OpenAIRealtimeStreamSession, OpenAIRealtimeStreamSessionEvents, + OpenAIRealtimeUsageTokenDetails, ) from litellm.types.realtime import ( ALL_DELTA_TYPES, RealtimeResponseTransformInput, RealtimeResponseTypedDict, ) -from litellm.utils import get_empty_usage class BedrockContentEnd(BaseModel): stopReason: str | None = None +class BedrockUsageTokenDetails(BaseModel): + speechTokens: int = 0 + textTokens: int = 0 + + +class BedrockUsageDetailsTotal(BaseModel): + input: BedrockUsageTokenDetails = BedrockUsageTokenDetails() + output: BedrockUsageTokenDetails = BedrockUsageTokenDetails() + + +class BedrockUsageDetails(BaseModel): + total: BedrockUsageDetailsTotal = BedrockUsageDetailsTotal() + + +class BedrockUsageEvent(BaseModel): + totalInputTokens: int = 0 + totalOutputTokens: int = 0 + totalTokens: int = 0 + details: BedrockUsageDetails = BedrockUsageDetails() + + TRIGGER_AUDIO_SAMPLE_RATE_HERTZ: Final = 16000 TRIGGER_AUDIO_BYTES_PER_SECOND: Final = TRIGGER_AUDIO_SAMPLE_RATE_HERTZ * 2 TRIGGER_LEADING_SILENCE: Final = bytes(TRIGGER_AUDIO_BYTES_PER_SECOND // 2) @@ -87,6 +112,15 @@ class BedrockRealtimeConfig(BaseRealtimeConfig): # Text configuration self.text_media_type = "text/plain" + # Response-stream state (Bedrock events carry no role on textOutput, + # so the USER/ASSISTANT split from contentStart is tracked here) + self._user_transcript_active = False + self._user_transcript_generation_stage: str | None = None + self._user_item_id: str | None = None + self._user_transcript_buffer = "" + self._cumulative_usage = BedrockUsageEvent() + self._reported_usage = BedrockUsageEvent() + def validate_environment(self, headers: dict, model: str, api_key: str | None = None) -> dict: """Validate environment - no special validation needed for Bedrock.""" return headers @@ -599,7 +633,7 @@ class BedrockRealtimeConfig(BaseRealtimeConfig): List of Bedrock format messages (JSON strings) """ try: - json_message: Final = json.loads(message) + json_message: Final[dict[str, object]] = json.loads(message) except json.JSONDecodeError: verbose_logger.warning("Invalid JSON message: %s", message[:200]) return [] @@ -691,6 +725,11 @@ class BedrockRealtimeConfig(BaseRealtimeConfig): role: Final = content_start.get("role") if role != "ASSISTANT": + if role == "USER" and content_start.get("type") == "TEXT": + self._user_transcript_active = True + self._user_transcript_generation_stage = self._parse_generation_stage( + content_start.get("additionalModelFields") + ) return ( [], current_response_id, @@ -700,6 +739,7 @@ class BedrockRealtimeConfig(BaseRealtimeConfig): ) verbose_logger.debug("Handling ASSISTANT contentStart") + is_new_response: Final = current_response_id is None # Initialize IDs if needed if not current_response_id: @@ -715,7 +755,8 @@ class BedrockRealtimeConfig(BaseRealtimeConfig): returned_messages: Final[list[OpenAIRealtimeEvents]] = [] - # Send response.created + # Send response.created only once per response (a response can contain + # multiple content blocks, e.g. TEXT then AUDIO) response_created: Final = OpenAIRealtimeStreamResponseBaseObject( type="response.created", event_id=f"event_{uuid.uuid4()}", @@ -727,7 +768,8 @@ class BedrockRealtimeConfig(BaseRealtimeConfig): "conversation_id": current_conversation_id, }, ) - returned_messages.append(response_created) + if is_new_response: + returned_messages.append(response_created) # Send response.output_item.added output_item_added: Final = OpenAIRealtimeStreamResponseOutputItemAdded( @@ -767,6 +809,108 @@ class BedrockRealtimeConfig(BaseRealtimeConfig): current_delta_type, ) + @staticmethod + def _parse_generation_stage(additional_model_fields: object) -> str | None: + if not isinstance(additional_model_fields, str): + return None + try: + parsed: Final = json.loads(additional_model_fields) + except json.JSONDecodeError: + return None + stage: Final = parsed.get("generationStage") if isinstance(parsed, dict) else None + return stage if isinstance(stage, str) else None + + def _current_user_item_id(self, new_utterance: bool = False) -> str: + """Item id shared by all events of one user utterance (speech boundaries and transcript).""" + if new_utterance or self._user_item_id is None: + self._user_item_id = f"item_{uuid.uuid4()}" + return self._user_item_id + + def transform_user_speech_event(self, is_speech_start: bool) -> tuple[OpenAIRealtimeEvents, ...]: + """Transform Bedrock userSpeechStart/userSpeechEnd to OpenAI speech boundary events.""" + verbose_logger.debug("Handling userSpeech%s", "Start" if is_speech_start else "End") + speech_event: Final[OpenAIRealtimeInputAudioBufferSpeechEvent] = { + "type": "input_audio_buffer.speech_started" if is_speech_start else "input_audio_buffer.speech_stopped", + "event_id": f"event_{uuid.uuid4()}", + "item_id": self._current_user_item_id(new_utterance=is_speech_start), + } + return (speech_event,) + + def transform_usage_event(self, usage_event: BedrockUsageEvent) -> None: + """Record Bedrock's session-cumulative usage totals for the next response.done.""" + verbose_logger.debug("Handling usageEvent") + self._cumulative_usage = usage_event + + def _take_usage_delta(self) -> OpenAIRealtimeResponseUsage: + """Usage for the response now completing: cumulative totals minus what prior response.done events reported.""" + prior: Final = self._reported_usage + latest: Final = self._cumulative_usage + self._reported_usage = latest + input_details: Final[OpenAIRealtimeUsageTokenDetails] = { + "audio_tokens": latest.details.total.input.speechTokens - prior.details.total.input.speechTokens, + "text_tokens": latest.details.total.input.textTokens - prior.details.total.input.textTokens, + "cached_tokens": 0, + } + output_details: Final[OpenAIRealtimeUsageTokenDetails] = { + "audio_tokens": latest.details.total.output.speechTokens - prior.details.total.output.speechTokens, + "text_tokens": latest.details.total.output.textTokens - prior.details.total.output.textTokens, + } + usage_delta: Final[OpenAIRealtimeResponseUsage] = { + "input_tokens": latest.totalInputTokens - prior.totalInputTokens, + "output_tokens": latest.totalOutputTokens - prior.totalOutputTokens, + "total_tokens": latest.totalTokens - prior.totalTokens, + "input_token_details": input_details, + "output_token_details": output_details, + } + return usage_delta + + def leftover_usage_done_events(self) -> tuple[OpenAIRealtimeEvents, ...]: + """Logged-only response.done for usage Bedrock reports after the final turn's contentEnd.""" + if self._cumulative_usage == self._reported_usage: + return () + usage: Final = self._take_usage_delta() + leftover_done: Final = OpenAIRealtimeDoneEvent( + type="response.done", + event_id=f"event_{uuid.uuid4()}", + response=OpenAIRealtimeResponseDoneObject( + object="realtime.response", + id=f"resp_{uuid.uuid4()}", + status="completed", + conversation_id=f"conv_{uuid.uuid4()}", + usage=dict(usage), # mutable-ok: OpenAIRealtimeResponseDoneObject types usage as plain dict + ), + ) + return (leftover_done,) + + def transform_user_transcript_event(self, transcript: str) -> tuple[OpenAIRealtimeEvents, ...]: + """Transform a USER-role Bedrock textOutput (ASR transcript) to an OpenAI transcription delta.""" + verbose_logger.debug("Handling USER textOutput (ASR transcript)") + delta_event: Final[OpenAIRealtimeInputAudioTranscriptionDelta] = { + "type": "conversation.item.input_audio_transcription.delta", + "event_id": f"event_{uuid.uuid4()}", + "item_id": self._current_user_item_id(), + "content_index": 0, + "delta": transcript, + } + if self._user_transcript_generation_stage != "SPECULATIVE": + self._user_transcript_buffer += transcript + return (delta_event,) + + def user_transcript_completed_events(self) -> tuple[OpenAIRealtimeEvents, ...]: + """One completed event with the full transcript once the FINAL user content block ends.""" + transcript: Final = self._user_transcript_buffer + if not transcript: + return () + self._user_transcript_buffer = "" + completed_event: Final[OpenAIRealtimeInputAudioTranscriptionCompleted] = { + "type": "conversation.item.input_audio_transcription.completed", + "event_id": f"event_{uuid.uuid4()}", + "item_id": self._current_user_item_id(), + "content_index": 0, + "transcript": transcript, + } + return (completed_event,) + def transform_text_output_event( self, event: dict, @@ -985,7 +1129,7 @@ class BedrockRealtimeConfig(BaseRealtimeConfig): if not current_response_id or not current_conversation_id: return [], None, None, None - usage_obj: Final = get_empty_usage() + usage: Final = self._take_usage_delta() response_done: Final = OpenAIRealtimeDoneEvent( type="response.done", event_id=f"event_{uuid.uuid4()}", @@ -995,11 +1139,7 @@ class BedrockRealtimeConfig(BaseRealtimeConfig): status="completed", output=[], conversation_id=current_conversation_id, - usage={ - "prompt_tokens": usage_obj.prompt_tokens, - "completion_tokens": usage_obj.completion_tokens, - "total_tokens": usage_obj.total_tokens, - }, + usage=dict(usage), ), ) @@ -1042,9 +1182,7 @@ class BedrockRealtimeConfig(BaseRealtimeConfig): # Create a function call arguments done event # This is a custom event format that matches what clients expect - from typing import cast - - function_call_event: Final[dict[str, Any]] = { + function_call_event: Final[dict[str, object]] = { "type": "response.function_call_arguments.done", "event_id": f"event_{uuid.uuid4()}", "response_id": current_response_id, @@ -1194,18 +1332,26 @@ class BedrockRealtimeConfig(BaseRealtimeConfig): returned_messages.extend(events) elif "textOutput" in event: - events, current_delta_chunks = self.transform_text_output_event( - event, - current_output_item_id, - current_response_id, - current_delta_chunks, - ) - returned_messages.extend(events) + if self._user_transcript_active: + returned_messages.extend(self.transform_user_transcript_event(event["textOutput"].get("content", ""))) + else: + events, current_delta_chunks = self.transform_text_output_event( + event, + current_output_item_id, + current_response_id, + current_delta_chunks, + ) + returned_messages.extend(events) elif "audioOutput" in event: events = self.transform_audio_output_event(event, current_output_item_id, current_response_id) returned_messages.extend(events) + elif "contentEnd" in event and self._user_transcript_active: + self._user_transcript_active = False + self._user_transcript_generation_stage = None + returned_messages.extend(self.user_transcript_completed_events()) + elif "contentEnd" in event: events, current_delta_chunks = self.transform_content_end_event( event, @@ -1224,6 +1370,12 @@ class BedrockRealtimeConfig(BaseRealtimeConfig): ) = self._response_done_events(current_response_id, current_conversation_id) returned_messages.extend(done_events) + elif "userSpeechStart" in event or "userSpeechEnd" in event: + returned_messages.extend(self.transform_user_speech_event("userSpeechStart" in event)) + + elif "usageEvent" in event: + self.transform_usage_event(BedrockUsageEvent.model_validate(event["usageEvent"])) + elif "toolUse" in event: events, tool_call_id, tool_name = self.transform_tool_use_event( event, current_output_item_id, current_response_id diff --git a/litellm/llms/black_forest_labs/image_edit/handler.py b/litellm/llms/black_forest_labs/image_edit/handler.py index 1ff02a6f8d9..178acb0de0d 100644 --- a/litellm/llms/black_forest_labs/image_edit/handler.py +++ b/litellm/llms/black_forest_labs/image_edit/handler.py @@ -8,9 +8,11 @@ then we poll until the result is ready. import asyncio import time -from typing import Any, Final +from collections.abc import Coroutine, Mapping +from typing import Final, Protocol import httpx +from typing_extensions import ReadOnly, TypedDict import litellm from litellm._logging import verbose_logger @@ -33,6 +35,42 @@ from ..common_utils import ( from .transformation import BlackForestLabsImageEditConfig +class _BFLSubmitBody(TypedDict, total=False): + """Decoded body of the BFL submit response, which hands back a polling URL.""" + + errors: ReadOnly[object] + polling_url: ReadOnly[str] + + +class _BFLPollBody(TypedDict, total=False): + """Decoded body of a BFL polling response.""" + + status: ReadOnly[str] + + +class _BFLSubmitResponse(Protocol): + """The submit call's HTTP response, read for its status, body text and decoded body.""" + + @property + def status_code(self) -> int: ... + + @property + def text(self) -> str: ... + + def json(self) -> _BFLSubmitBody: ... + + +class _BFLPollResponse(Protocol): + """A polling call's HTTP response, read only for the task status it carries.""" + + def json(self) -> _BFLPollBody: ... + + +def _poll_status(response: _BFLPollResponse) -> str | None: + """Read the task status out of a BFL polling response body.""" + return response.json().get("status") + + class BlackForestLabsImageEdit: """ Black Forest Labs Image Edit handler. @@ -53,10 +91,10 @@ class BlackForestLabsImageEdit: litellm_params: GenericLiteLLMParams | dict, logging_obj: LiteLLMLoggingObj, timeout: float | httpx.Timeout | None, - extra_headers: dict[str, Any] | None = None, + extra_headers: Mapping[str, object] | None = None, client: HTTPHandler | AsyncHTTPHandler | None = None, aimage_edit: bool = False, - ) -> ImageResponse | Any: + ) -> ImageResponse | Coroutine[object, object, ImageResponse]: """ Main entry point for image edit requests. @@ -185,7 +223,7 @@ class BlackForestLabsImageEdit: litellm_params: GenericLiteLLMParams | dict, logging_obj: LiteLLMLoggingObj, timeout: float | httpx.Timeout | None, - extra_headers: dict[str, Any] | None = None, + extra_headers: Mapping[str, object] | None = None, client: AsyncHTTPHandler | None = None, ) -> ImageResponse: """ @@ -281,7 +319,7 @@ class BlackForestLabsImageEdit: def _poll_for_result_sync( self, - initial_response: httpx.Response, + initial_response: _BFLSubmitResponse, headers: dict, sync_client: HTTPHandler, max_wait: float = DEFAULT_MAX_POLLING_TIME, @@ -356,8 +394,7 @@ class BlackForestLabsImageEdit: message=f"Polling failed: {response.text}", ) - data = response.json() - status = data.get("status") + status = _poll_status(response) verbose_logger.debug("BFL poll status: %s", status) @@ -383,7 +420,7 @@ class BlackForestLabsImageEdit: async def _poll_for_result_async( self, - initial_response: httpx.Response, + initial_response: _BFLSubmitResponse, headers: dict, async_client: AsyncHTTPHandler, max_wait: float = DEFAULT_MAX_POLLING_TIME, @@ -447,8 +484,7 @@ class BlackForestLabsImageEdit: message=f"Polling failed: {response.text}", ) - data = response.json() - status = data.get("status") + status = _poll_status(response) verbose_logger.debug("BFL poll status: %s", status) diff --git a/litellm/llms/black_forest_labs/image_generation/handler.py b/litellm/llms/black_forest_labs/image_generation/handler.py index 03e4999c5aa..879bef37b58 100644 --- a/litellm/llms/black_forest_labs/image_generation/handler.py +++ b/litellm/llms/black_forest_labs/image_generation/handler.py @@ -8,9 +8,11 @@ then we poll until the result is ready. import asyncio import time -from typing import Any, Final +from collections.abc import Coroutine, Mapping +from typing import Final, Protocol, TypedDict import httpx +from typing_extensions import ReadOnly import litellm from litellm._logging import verbose_logger @@ -33,6 +35,23 @@ from ..common_utils import ( from .transformation import BlackForestLabsImageGenerationConfig +class _BFLTaskPayload(TypedDict, total=False): + """The body BFL returns for a submitted or polled generation task.""" + + errors: ReadOnly[object] + polling_url: ReadOnly[str] + status: ReadOnly[str] + + +class _TaskJsonResponse(Protocol): + def json(self) -> _BFLTaskPayload: ... + + +def _task_payload(response: _TaskJsonResponse) -> _BFLTaskPayload: + """The JSON body of a BFL task submission or poll response.""" + return response.json() + + class BlackForestLabsImageGeneration: """ Black Forest Labs Image Generation handler. @@ -53,10 +72,10 @@ class BlackForestLabsImageGeneration: litellm_params: GenericLiteLLMParams | dict, logging_obj: LiteLLMLoggingObj, timeout: float | httpx.Timeout | None, - extra_headers: dict[str, Any] | None = None, + extra_headers: Mapping[str, str] | None = None, client: HTTPHandler | AsyncHTTPHandler | None = None, aimg_generation: bool = False, - ) -> ImageResponse | Any: + ) -> ImageResponse | Coroutine[object, object, ImageResponse]: """ Main entry point for image generation requests. @@ -187,7 +206,7 @@ class BlackForestLabsImageGeneration: litellm_params: GenericLiteLLMParams | dict, logging_obj: LiteLLMLoggingObj, timeout: float | httpx.Timeout | None, - extra_headers: dict[str, Any] | None = None, + extra_headers: Mapping[str, str] | None = None, client: AsyncHTTPHandler | None = None, ) -> ImageResponse: """ @@ -305,7 +324,7 @@ class BlackForestLabsImageGeneration: # Parse initial response to get polling URL try: - response_data: Final = initial_response.json() + response_data: Final = _task_payload(initial_response) except Exception as e: raise BlackForestLabsError( status_code=initial_response.status_code, @@ -350,7 +369,7 @@ class BlackForestLabsImageGeneration: message=f"Polling failed: {response.text}", ) - data = response.json() + data = _task_payload(response) status = data.get("status") verbose_logger.debug("BFL poll status: %s", status) @@ -396,7 +415,7 @@ class BlackForestLabsImageGeneration: # Parse initial response to get polling URL try: - response_data: Final = initial_response.json() + response_data: Final = _task_payload(initial_response) except Exception as e: raise BlackForestLabsError( status_code=initial_response.status_code, @@ -441,7 +460,7 @@ class BlackForestLabsImageGeneration: message=f"Polling failed: {response.text}", ) - data = response.json() + data = _task_payload(response) status = data.get("status") verbose_logger.debug("BFL poll status: %s", status) diff --git a/litellm/llms/codestral/completion/handler.py b/litellm/llms/codestral/completion/handler.py index 8c08b2bc33c..f8486d3b274 100644 --- a/litellm/llms/codestral/completion/handler.py +++ b/litellm/llms/codestral/completion/handler.py @@ -4,9 +4,10 @@ import json from collections.abc import Callable from functools import partial -from typing import Final +from typing import Final, Protocol import httpx +from typing_extensions import NotRequired, ReadOnly, TypedDict import litellm from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLogging @@ -23,6 +24,53 @@ from litellm.types.utils import TextChoices from litellm.utils import CustomStreamWrapper, TextCompletionResponse +class _CodestralChoiceMessage(TypedDict): + """`choices[].message` of a Codestral FIM completion.""" + + role: ReadOnly[NotRequired[str]] + content: ReadOnly[NotRequired[str | None]] + + +class _CodestralChoice(TypedDict): + """One entry of `choices` in a Codestral FIM completion.""" + + index: ReadOnly[int] + message: ReadOnly[NotRequired[_CodestralChoiceMessage]] + finish_reason: ReadOnly[NotRequired[str | None]] + logprobs: ReadOnly[NotRequired[dict[str, object] | None]] + + +class _CodestralUsage(TypedDict): + """Token accounting returned alongside a Codestral FIM completion.""" + + prompt_tokens: ReadOnly[NotRequired[int]] + completion_tokens: ReadOnly[NotRequired[int]] + total_tokens: ReadOnly[NotRequired[int]] + + +class _CodestralCompletionResponse(TypedDict): + """Body returned by the Codestral `/v1/fim/completions` endpoint.""" + + id: ReadOnly[NotRequired[str]] + created: ReadOnly[NotRequired[int]] + model: ReadOnly[NotRequired[str]] + object: ReadOnly[NotRequired[str]] + usage: ReadOnly[NotRequired[_CodestralUsage]] + choices: ReadOnly[NotRequired[list[_CodestralChoice]]] + + +class _CodestralHTTPResponse(Protocol): + """The Codestral completion response as this handler reads it.""" + + @property + def status_code(self) -> int: ... + + @property + def text(self) -> str: ... + + def json(self) -> _CodestralCompletionResponse: ... + + class TextCompletionCodestralError(Exception): def __init__( self, @@ -115,7 +163,7 @@ class CodestralTextCompletion: def process_text_completion_response( self, model: str, - response: httpx.Response, + response: _CodestralHTTPResponse, model_response: TextCompletionResponse, stream: bool, logging_obj: LiteLLMLogging, diff --git a/litellm/llms/dashscope/chat/transformation.py b/litellm/llms/dashscope/chat/transformation.py index 5ab7fbf3658..26e60fa959d 100644 --- a/litellm/llms/dashscope/chat/transformation.py +++ b/litellm/llms/dashscope/chat/transformation.py @@ -54,6 +54,9 @@ class DashScopeChatConfig(OpenAIGPTConfig): dynamic_api_key: Final = api_key or get_secret_str("DASHSCOPE_API_KEY") return api_base, dynamic_api_key + def _resolve_chat_api_base(self, api_base: str | None) -> str: + return api_base or "https://dashscope.aliyuncs.com/compatible-mode/v1" + def get_complete_url( self, api_base: str | None, @@ -66,10 +69,7 @@ class DashScopeChatConfig(OpenAIGPTConfig): """ If api_base is not provided, use the default DashScope /chat/completions endpoint. """ - if not api_base: - api_base = "https://dashscope.aliyuncs.com/compatible-mode/v1" - - if not api_base.endswith("/chat/completions"): - api_base = f"{api_base}/chat/completions" - - return api_base + resolved_api_base: Final = self._resolve_chat_api_base(api_base) + if resolved_api_base.endswith("/chat/completions"): + return resolved_api_base + return f"{resolved_api_base}/chat/completions" diff --git a/litellm/llms/dashscope/common_utils.py b/litellm/llms/dashscope/common_utils.py index 9a7dd4da8d3..b7c97893a15 100644 --- a/litellm/llms/dashscope/common_utils.py +++ b/litellm/llms/dashscope/common_utils.py @@ -2,9 +2,89 @@ Common utilities for the DashScope LLM provider. """ +from typing import TYPE_CHECKING + import httpx from litellm.llms.base_llm.chat.transformation import BaseLLMException +from litellm.secret_managers.main import get_secret_str + +if TYPE_CHECKING: + from litellm.llms.base_llm.embedding.transformation import BaseEmbeddingConfig + from litellm.llms.base_llm.image_generation.transformation import ( + BaseImageGenerationConfig, + ) + from litellm.llms.base_llm.rerank.transformation import BaseRerankConfig + + +def get_dashscope_family_embedding_config(custom_llm_provider: str) -> "BaseEmbeddingConfig": + if custom_llm_provider == "qwencloud": + from litellm.llms.dashscope.qwencloud import QwenCloudEmbeddingConfig + + return QwenCloudEmbeddingConfig() + if custom_llm_provider == "qwen_ai_platform": + from litellm.llms.dashscope.qwen_ai_platform import ( + QwenAIPlatformEmbeddingConfig, + ) + + return QwenAIPlatformEmbeddingConfig() + from litellm.llms.dashscope.embed.transformation import DashScopeEmbeddingConfig + + return DashScopeEmbeddingConfig() + + +def get_dashscope_family_rerank_config(custom_llm_provider: str) -> "BaseRerankConfig": + if custom_llm_provider == "qwencloud": + from litellm.llms.dashscope.qwencloud import QwenCloudRerankConfig + + return QwenCloudRerankConfig() + if custom_llm_provider == "qwen_ai_platform": + from litellm.llms.dashscope.qwen_ai_platform import QwenAIPlatformRerankConfig + + return QwenAIPlatformRerankConfig() + from litellm.llms.dashscope.rerank.transformation import DashScopeRerankConfig + + return DashScopeRerankConfig() + + +def get_dashscope_family_image_generation_config( + custom_llm_provider: str, +) -> "BaseImageGenerationConfig": + if custom_llm_provider == "qwencloud": + from litellm.llms.dashscope.qwencloud import QwenCloudImageGenerationConfig + + return QwenCloudImageGenerationConfig() + if custom_llm_provider == "qwen_ai_platform": + from litellm.llms.dashscope.qwen_ai_platform import ( + QwenAIPlatformImageGenerationConfig, + ) + + return QwenAIPlatformImageGenerationConfig() + from litellm.llms.dashscope.image_generation.transformation import ( + DashScopeImageGenerationConfig, + ) + + return DashScopeImageGenerationConfig() + + +def resolve_dashscope_family_api_key(custom_llm_provider: str, api_key: str | None) -> str | None: + if custom_llm_provider == "dashscope": + return api_key or get_secret_str("DASHSCOPE_API_KEY") + return api_key or get_secret_str(f"{custom_llm_provider.upper()}_API_KEY") or get_secret_str("DASHSCOPE_API_KEY") + + +def missing_dashscope_family_key_message(custom_llm_provider: str) -> str: + if custom_llm_provider == "qwencloud": + return ( + "Missing API key for QwenCloud. Set QWENCLOUD_API_KEY or " + "DASHSCOPE_API_KEY environment variable or pass api_key parameter." + ) + if custom_llm_provider == "qwen_ai_platform": + return ( + "Missing API key for Qwen AI Platform. Set QWEN_AI_PLATFORM_API_KEY or " + "DASHSCOPE_API_KEY environment variable or pass api_key parameter." + ) + return "Missing API key for DashScope. Set DASHSCOPE_API_KEY environment variable or pass api_key parameter." class DashScopeError(BaseLLMException): diff --git a/litellm/llms/dashscope/cost_calculator.py b/litellm/llms/dashscope/cost_calculator.py index 771ce140f66..dd5bee1fe8b 100644 --- a/litellm/llms/dashscope/cost_calculator.py +++ b/litellm/llms/dashscope/cost_calculator.py @@ -110,7 +110,7 @@ def _calculate_completion_cost( return (breakdown.completion_tokens * output_cost) + (breakdown.reasoning_tokens * reasoning_cost) -def cost_per_token(model: str, usage: Usage) -> tuple[float, float]: +def cost_per_token(model: str, usage: Usage, custom_llm_provider: str = "dashscope") -> tuple[float, float]: """ Calculate cost per token for Dashscope models. @@ -119,11 +119,12 @@ def cost_per_token(model: str, usage: Usage) -> tuple[float, float]: Args: model: Model name without provider prefix usage: LiteLLM Usage block + custom_llm_provider: The provider id the request resolved to; dashscope or one of its brand aliases Returns: Tuple[float, float] - (prompt_cost_in_usd, completion_cost_in_usd) """ - model_info: Final = get_model_info(model=model, custom_llm_provider="dashscope") + model_info: Final = get_model_info(model=model, custom_llm_provider=custom_llm_provider) breakdown: Final = _extract_token_breakdown(usage) raw_tiers: Final = model_info.get("tiered_pricing") tiered_pricing: Final = raw_tiers if isinstance(raw_tiers, list) else None diff --git a/litellm/llms/dashscope/embed/transformation.py b/litellm/llms/dashscope/embed/transformation.py index 6d13f1e53f7..63ee984a65c 100644 --- a/litellm/llms/dashscope/embed/transformation.py +++ b/litellm/llms/dashscope/embed/transformation.py @@ -62,6 +62,17 @@ class DashScopeEmbeddingConfig(BaseEmbeddingConfig): # for drop_params=False before this method is called. return optional_params + def _resolve_api_key(self, api_key: str | None) -> str: + resolved_api_key: Final = api_key if api_key is not None else get_secret_str("DASHSCOPE_API_KEY") + if resolved_api_key is None: + raise ValueError( + "DashScope API key is required. Set 'DASHSCOPE_API_KEY' env var or pass api_key explicitly." + ) + return resolved_api_key + + def _resolve_embedding_api_base(self, api_base: str | None) -> str: + return api_base or get_secret_str("DASHSCOPE_API_BASE") or DEFAULT_API_BASE + def validate_environment( self, headers: dict, @@ -72,17 +83,11 @@ class DashScopeEmbeddingConfig(BaseEmbeddingConfig): api_key: str | None = None, api_base: str | None = None, ) -> dict: - if api_key is None: - api_key = get_secret_str("DASHSCOPE_API_KEY") - if api_key is None: - raise ValueError( - "DashScope API key is required. Set 'DASHSCOPE_API_KEY' env var or pass api_key explicitly." - ) - default_headers: Final = { + return { "Content-Type": "application/json", - "Authorization": f"Bearer {api_key}", + "Authorization": f"Bearer {self._resolve_api_key(api_key)}", + **headers, } - return {**default_headers, **headers} def get_complete_url( self, @@ -93,8 +98,7 @@ class DashScopeEmbeddingConfig(BaseEmbeddingConfig): litellm_params: dict, stream: bool | None = None, ) -> str: - base = api_base or get_secret_str("DASHSCOPE_API_BASE") or DEFAULT_API_BASE - base = base.rstrip("/") + base: Final = self._resolve_embedding_api_base(api_base).rstrip("/") if base.endswith("/embeddings"): return base return f"{base}/embeddings" diff --git a/litellm/llms/dashscope/image_generation/transformation.py b/litellm/llms/dashscope/image_generation/transformation.py index a7f0e98865f..c0e278a96ef 100644 --- a/litellm/llms/dashscope/image_generation/transformation.py +++ b/litellm/llms/dashscope/image_generation/transformation.py @@ -91,6 +91,15 @@ class DashScopeImageGenerationConfig(BaseImageGenerationConfig): mapped[k] = v return mapped + def _resolve_api_key(self, api_key: str | None) -> str: + resolved_api_key: Final = api_key or get_secret_str("DASHSCOPE_API_KEY") + if not resolved_api_key: + raise ValueError("DASHSCOPE_API_KEY is not set") + return resolved_api_key + + def _resolve_image_api_base(self, image_api_base: str | None) -> str: + return image_api_base or get_secret_str("DASHSCOPE_API_BASE_IMAGE") or DEFAULT_API_BASE + def get_complete_url( self, api_base: str | None, @@ -103,7 +112,7 @@ class DashScopeImageGenerationConfig(BaseImageGenerationConfig): image_api_base: Final = ( api_base if api_base and not api_base.rstrip("/").endswith(CHAT_COMPATIBLE_MODE_PATH) else None ) - return image_api_base or get_secret_str("DASHSCOPE_API_BASE_IMAGE") or DEFAULT_API_BASE + return self._resolve_image_api_base(image_api_base) def validate_environment( self, @@ -115,10 +124,7 @@ class DashScopeImageGenerationConfig(BaseImageGenerationConfig): api_key: str | None = None, api_base: str | None = None, ) -> dict: - final_api_key: Final = api_key or get_secret_str("DASHSCOPE_API_KEY") - if not final_api_key: - raise ValueError("DASHSCOPE_API_KEY is not set") - headers["Authorization"] = f"Bearer {final_api_key}" + headers["Authorization"] = f"Bearer {self._resolve_api_key(api_key)}" headers["Content-Type"] = "application/json" return headers diff --git a/litellm/llms/dashscope/qwen_ai_platform.py b/litellm/llms/dashscope/qwen_ai_platform.py new file mode 100644 index 00000000000..9a44eaf574a --- /dev/null +++ b/litellm/llms/dashscope/qwen_ai_platform.py @@ -0,0 +1,62 @@ +from typing import Final + +from litellm.secret_managers.main import get_secret_str + +from .chat.transformation import DashScopeChatConfig +from .embed.transformation import DashScopeEmbeddingConfig +from .image_generation.transformation import DashScopeImageGenerationConfig +from .rerank.transformation import DashScopeRerankConfig + +QWEN_AI_PLATFORM_API_BASE: Final = "https://dashscope.aliyuncs.com/compatible-mode/v1" +QWEN_AI_PLATFORM_RERANK_API_BASE: Final = "https://dashscope.aliyuncs.com/compatible-api/v1/reranks" +QWEN_AI_PLATFORM_IMAGE_API_BASE: Final = ( + "https://dashscope.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation" +) + + +def _resolve_qwen_ai_platform_api_key(api_key: str | None) -> str | None: + return api_key or get_secret_str("QWEN_AI_PLATFORM_API_KEY") or get_secret_str("DASHSCOPE_API_KEY") + + +def _require_qwen_ai_platform_api_key(api_key: str | None) -> str: + resolved: Final = _resolve_qwen_ai_platform_api_key(api_key) + if resolved is None: + raise ValueError( + "Qwen AI Platform API key is required. Set 'QWEN_AI_PLATFORM_API_KEY' or 'DASHSCOPE_API_KEY' env var " + "or pass api_key explicitly." + ) + return resolved + + +class QwenAIPlatformChatConfig(DashScopeChatConfig): + def _get_openai_compatible_provider_info( + self, api_base: str | None, api_key: str | None + ) -> tuple[str | None, str | None]: + return self._resolve_chat_api_base(api_base), _resolve_qwen_ai_platform_api_key(api_key) + + def _resolve_chat_api_base(self, api_base: str | None) -> str: + return api_base or get_secret_str("QWEN_AI_PLATFORM_API_BASE") or QWEN_AI_PLATFORM_API_BASE + + +class QwenAIPlatformEmbeddingConfig(DashScopeEmbeddingConfig): + def _resolve_api_key(self, api_key: str | None) -> str: + return _require_qwen_ai_platform_api_key(api_key) + + def _resolve_embedding_api_base(self, api_base: str | None) -> str: + return api_base or get_secret_str("QWEN_AI_PLATFORM_API_BASE") or QWEN_AI_PLATFORM_API_BASE + + +class QwenAIPlatformRerankConfig(DashScopeRerankConfig): + def _resolve_api_key(self, api_key: str | None) -> str: + return _require_qwen_ai_platform_api_key(api_key) + + def _resolve_rerank_api_base(self, api_base: str | None) -> str: + return api_base or get_secret_str("QWEN_AI_PLATFORM_API_BASE_RERANK") or QWEN_AI_PLATFORM_RERANK_API_BASE + + +class QwenAIPlatformImageGenerationConfig(DashScopeImageGenerationConfig): + def _resolve_api_key(self, api_key: str | None) -> str: + return _require_qwen_ai_platform_api_key(api_key) + + def _resolve_image_api_base(self, image_api_base: str | None) -> str: + return image_api_base or get_secret_str("QWEN_AI_PLATFORM_API_BASE_IMAGE") or QWEN_AI_PLATFORM_IMAGE_API_BASE diff --git a/litellm/llms/dashscope/qwencloud.py b/litellm/llms/dashscope/qwencloud.py new file mode 100644 index 00000000000..d8d53e340ef --- /dev/null +++ b/litellm/llms/dashscope/qwencloud.py @@ -0,0 +1,62 @@ +from typing import Final + +from litellm.secret_managers.main import get_secret_str + +from .chat.transformation import DashScopeChatConfig +from .embed.transformation import DashScopeEmbeddingConfig +from .image_generation.transformation import DashScopeImageGenerationConfig +from .rerank.transformation import DashScopeRerankConfig + +QWENCLOUD_API_BASE: Final = "https://dashscope-intl.aliyuncs.com/compatible-mode/v1" +QWENCLOUD_RERANK_API_BASE: Final = "https://dashscope-intl.aliyuncs.com/compatible-api/v1/reranks" +QWENCLOUD_IMAGE_API_BASE: Final = ( + "https://dashscope-intl.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation" +) + + +def _resolve_qwencloud_api_key(api_key: str | None) -> str | None: + return api_key or get_secret_str("QWENCLOUD_API_KEY") or get_secret_str("DASHSCOPE_API_KEY") + + +def _require_qwencloud_api_key(api_key: str | None) -> str: + resolved: Final = _resolve_qwencloud_api_key(api_key) + if resolved is None: + raise ValueError( + "QwenCloud API key is required. Set 'QWENCLOUD_API_KEY' or 'DASHSCOPE_API_KEY' env var " + "or pass api_key explicitly." + ) + return resolved + + +class QwenCloudChatConfig(DashScopeChatConfig): + def _get_openai_compatible_provider_info( + self, api_base: str | None, api_key: str | None + ) -> tuple[str | None, str | None]: + return self._resolve_chat_api_base(api_base), _resolve_qwencloud_api_key(api_key) + + def _resolve_chat_api_base(self, api_base: str | None) -> str: + return api_base or get_secret_str("QWENCLOUD_API_BASE") or QWENCLOUD_API_BASE + + +class QwenCloudEmbeddingConfig(DashScopeEmbeddingConfig): + def _resolve_api_key(self, api_key: str | None) -> str: + return _require_qwencloud_api_key(api_key) + + def _resolve_embedding_api_base(self, api_base: str | None) -> str: + return api_base or get_secret_str("QWENCLOUD_API_BASE") or QWENCLOUD_API_BASE + + +class QwenCloudRerankConfig(DashScopeRerankConfig): + def _resolve_api_key(self, api_key: str | None) -> str: + return _require_qwencloud_api_key(api_key) + + def _resolve_rerank_api_base(self, api_base: str | None) -> str: + return api_base or get_secret_str("QWENCLOUD_API_BASE_RERANK") or QWENCLOUD_RERANK_API_BASE + + +class QwenCloudImageGenerationConfig(DashScopeImageGenerationConfig): + def _resolve_api_key(self, api_key: str | None) -> str: + return _require_qwencloud_api_key(api_key) + + def _resolve_image_api_base(self, image_api_base: str | None) -> str: + return image_api_base or get_secret_str("QWENCLOUD_API_BASE_IMAGE") or QWENCLOUD_IMAGE_API_BASE diff --git a/litellm/llms/dashscope/rerank/transformation.py b/litellm/llms/dashscope/rerank/transformation.py index 98be4e4f2e7..3dd3996b2ee 100644 --- a/litellm/llms/dashscope/rerank/transformation.py +++ b/litellm/llms/dashscope/rerank/transformation.py @@ -58,19 +58,30 @@ class DashScopeRerankConfig(BaseRerankConfig): def __init__(self) -> None: pass + def _resolve_api_key(self, api_key: str | None) -> str: + resolved_api_key: Final = api_key if api_key is not None else get_secret_str("DASHSCOPE_API_KEY") + if resolved_api_key is None: + raise ValueError( + "DashScope API key is required. Set 'DASHSCOPE_API_KEY' env var or pass api_key explicitly." + ) + return resolved_api_key + + def _resolve_rerank_api_base(self, api_base: str | None) -> str: + if api_base is not None: + return api_base + return get_secret_str("DASHSCOPE_API_BASE_RERANK") or DEFAULT_RERANK_URL + def get_complete_url( self, api_base: str | None, model: str, optional_params: dict | None = None, ) -> str: - if api_base is None: - api_base = get_secret_str("DASHSCOPE_API_BASE_RERANK") or DEFAULT_RERANK_URL + resolved_api_base: Final = self._resolve_rerank_api_base(api_base) + if resolved_api_base == DEFAULT_RERANK_URL: + return resolved_api_base - if api_base == DEFAULT_RERANK_URL: - return DEFAULT_RERANK_URL - - cleaned: Final = api_base.rstrip("/") + cleaned: Final = resolved_api_base.rstrip("/") if cleaned.endswith("/reranks") or cleaned.endswith("/rerank"): return cleaned @@ -88,19 +99,12 @@ class DashScopeRerankConfig(BaseRerankConfig): optional_params: dict | None = None, litellm_params: Mapping[str, object] | None = None, ) -> dict: - if api_key is None: - api_key = get_secret_str("DASHSCOPE_API_KEY") - if api_key is None: - raise ValueError( - "DashScope API key is required. Set 'DASHSCOPE_API_KEY' env var or pass api_key explicitly." - ) - - default_headers: Final = { - "Authorization": f"Bearer {api_key}", + return { + "Authorization": f"Bearer {self._resolve_api_key(api_key)}", "accept": "application/json", "content-type": "application/json", + **headers, } - return {**default_headers, **headers} def get_supported_cohere_rerank_params(self, model: str) -> list: return ["query", "documents", "top_n", "return_documents"] diff --git a/litellm/llms/deepinfra/rerank/transformation.py b/litellm/llms/deepinfra/rerank/transformation.py index e52c56af82b..a3d0482af0a 100644 --- a/litellm/llms/deepinfra/rerank/transformation.py +++ b/litellm/llms/deepinfra/rerank/transformation.py @@ -2,10 +2,11 @@ Translate between Cohere's `/rerank` format and Deepinfra's `/rerank` format. """ -from collections.abc import Mapping -from typing import Any, Final +from collections.abc import Mapping, Sequence +from typing import Final, Protocol import httpx +from typing_extensions import ReadOnly, TypedDict from litellm._uuid import uuid from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj @@ -24,6 +25,36 @@ from litellm.types.rerank import ( ) +class _DeepinfraInferenceStatus(TypedDict, total=False): + """The ``inference_status`` block of a DeepInfra rerank response.""" + + status: ReadOnly[str] + runtime_ms: ReadOnly[float] + cost: ReadOnly[float] + tokens_generated: ReadOnly[int] + tokens_input: ReadOnly[int] + + +class _DeepinfraRerankResponse(TypedDict, total=False): + """Body of a DeepInfra ``/rerank`` response.""" + + scores: ReadOnly[Sequence[float]] + input_tokens: ReadOnly[int] + request_id: ReadOnly[str | None] + inference_status: ReadOnly[_DeepinfraInferenceStatus] + + +class _DeepinfraRerankResponseSource(Protocol): + """The DeepInfra ``/rerank`` HTTP response, read for the body it decodes to.""" + + def json(self) -> _DeepinfraRerankResponse: ... + + +def _deepinfra_rerank_body(response: _DeepinfraRerankResponseSource) -> _DeepinfraRerankResponse: + """Decode the body of a DeepInfra ``/rerank`` response.""" + return response.json() + + class DeepinfraRerankConfig(BaseRerankConfig): """ Deepinfra Rerank - Follows the same Spec as Cohere Rerank @@ -95,7 +126,7 @@ class DeepinfraRerankConfig(BaseRerankConfig): model: str, drop_params: bool, query: str, - documents: list[str | dict[str, Any]], + documents: list[str | dict[str, object]], custom_llm_provider: str | None = None, top_n: int | None = None, rank_fields: list[str] | None = None, @@ -150,7 +181,7 @@ class DeepinfraRerankConfig(BaseRerankConfig): litellm_params: dict = {}, ) -> RerankResponse: try: - response_json: Final = raw_response.json() + response_json: Final = _deepinfra_rerank_body(raw_response) logging_obj.post_call(original_response=raw_response.text) # Extract the scores from the response diff --git a/litellm/llms/gemini/common_utils.py b/litellm/llms/gemini/common_utils.py index bd2b124605c..78e6e6aaf82 100644 --- a/litellm/llms/gemini/common_utils.py +++ b/litellm/llms/gemini/common_utils.py @@ -2,7 +2,7 @@ import base64 import datetime import json import math -from collections.abc import Sequence +from collections.abc import Mapping, Sequence from typing import Any, Final import httpx @@ -128,24 +128,35 @@ def is_gemini_image_model(model: str) -> bool: return "gemini" in base_model +def _parse_image_config_string(raw_image_config: str, model: str) -> object: + try: + return json.loads(raw_image_config) + except json.JSONDecodeError as exc: + raise litellm.UnsupportedParamsError( + model=model, + message="`imageConfig` must be valid JSON when provided as a string.", + ) from exc + + def map_openai_image_params_to_gemini( - params: dict[str, Any], + params: Mapping[str, object], model: str, supported_params: Sequence[str], - optional_params: dict[str, Any] | None = None, + optional_params: Mapping[str, object] | None = None, parse_image_config_string: bool = False, -) -> dict[str, Any]: - optional_params = optional_params or {} +) -> dict[str, object]: + already_mapped: Final[Mapping[str, object]] = optional_params or {} filtered_params: Final = {key: value for key, value in params.items() if key in supported_params} - mapped_params: Final[dict[str, Any]] = {} + mapped_params: Final[dict[str, object]] = {} - if "n" in filtered_params and "n" not in optional_params: + if "n" in filtered_params and "n" not in already_mapped: mapped_params["sampleCount"] = filtered_params["n"] - if "size" in filtered_params and "size" not in optional_params: + size_param: Final = filtered_params.get("size") + if isinstance(size_param, str) and "size" not in already_mapped: image_config: Final = map_openai_size_to_gemini_image_config( - filtered_params["size"], + size_param, model, ) if image_config is not None: @@ -156,33 +167,30 @@ def map_openai_image_params_to_gemini( if "imageSize" in image_config: mapped_params["imageSize"] = image_config["imageSize"] - image_config_param = filtered_params.get("imageConfig") - if isinstance(image_config_param, str) and parse_image_config_string: - try: - image_config_param = json.loads(image_config_param) - except json.JSONDecodeError as exc: - raise litellm.UnsupportedParamsError( - model=model, - message="`imageConfig` must be valid JSON when provided as a string.", - ) from exc + raw_image_config: Final = filtered_params.get("imageConfig") + image_config_param: Final[object] = ( + _parse_image_config_string(raw_image_config, model) + if isinstance(raw_image_config, str) and parse_image_config_string + else raw_image_config + ) if isinstance(image_config_param, dict): mapped_params["imageConfig"] = image_config_param for key, value in filtered_params.items(): - if key not in ("n", "size", "imageConfig", "tools", "web_search_options") and key not in optional_params: + if key not in ("n", "size", "imageConfig", "tools", "web_search_options") and key not in already_mapped: mapped_params[key] = value return mapped_params -def _dedupe_gemini_search_tools(tools: list[dict[str, Any]]) -> list[dict[str, Any]]: +def _dedupe_gemini_search_tools(tools: list[dict[str, object]]) -> list[dict[str, object]]: from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( VertexGeminiConfig, ) search_tool_keys: Final = VertexGeminiConfig._search_tool_keys() seen_search_keys: Final[set[str]] = set() - deduped_tools: Final[list[dict[str, Any]]] = [] + deduped_tools: Final[list[dict[str, object]]] = [] for tool in tools: if not isinstance(tool, dict): @@ -203,7 +211,7 @@ def _dedupe_gemini_search_tools(tools: list[dict[str, Any]]) -> list[dict[str, A return deduped_tools -def _has_gemini_search_tool(tools: list[Any]) -> bool: +def _has_gemini_search_tool(tools: list[object]) -> bool: from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( VertexGeminiConfig, ) @@ -213,9 +221,9 @@ def _has_gemini_search_tool(tools: list[Any]) -> bool: def map_gemini_image_tools_params( - non_default_params: dict[str, Any], - mapped_params: dict[str, Any], -) -> dict[str, Any]: + non_default_params: Mapping[str, object], + mapped_params: Mapping[str, object], +) -> dict[str, object]: from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( VertexGeminiConfig, ) @@ -239,21 +247,24 @@ def map_gemini_image_tools_params( gemini_config._drop_search_tools_mixed_with_functions(result) - if isinstance(result.get("tools"), list): - result["tools"] = _dedupe_gemini_search_tools(result["tools"]) + resolved_tools: Final = result.get("tools") + if isinstance(resolved_tools, list): + result["tools"] = _dedupe_gemini_search_tools(resolved_tools) return result def get_gemini_image_web_search_requests( - response_data: dict[str, Any], + response_data: Mapping[str, object], ) -> int | None: from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( VertexGeminiConfig, ) - grounding_metadata: Final[list[dict[str, Any]]] = [] - for candidate in response_data.get("candidates", []): + raw_candidates: Final = response_data.get("candidates") + candidates: Final[list[object]] = raw_candidates if isinstance(raw_candidates, list) else [] + grounding_metadata: Final[list[dict[str, object]]] = [] + for candidate in candidates: if not isinstance(candidate, dict): continue candidate_grounding = candidate.get("groundingMetadata") @@ -267,13 +278,14 @@ def get_gemini_image_web_search_requests( def get_gemini_image_generation_config( model: str, - optional_params: dict[str, Any], -) -> dict[str, Any]: - generation_config: Final[dict[str, Any]] = {"response_modalities": ["IMAGE", "TEXT"]} + optional_params: Mapping[str, object], +) -> dict[str, object]: + generation_config: Final[dict[str, object]] = {"response_modalities": ["IMAGE", "TEXT"]} - image_config: Final[dict[str, Any]] = {} - if isinstance(optional_params.get("imageConfig"), dict): - image_config.update(optional_params["imageConfig"]) + raw_image_config: Final = optional_params.get("imageConfig") + image_config: Final[dict[str, object]] = {} + if isinstance(raw_image_config, dict): + image_config.update(raw_image_config) if not supports_gemini_image_size(model): image_config.pop("imageSize", None) @@ -398,7 +410,7 @@ class GeminiModelInfo(BaseLLMModelInfo): f"Failed to fetch models from Gemini. Status code: {response.status_code}, Response: {response.json()}" ) - models: Final = response.json()["models"] + models: Final[list[dict[str, str]]] = response.json()["models"] litellm_model_names: Final = self.process_model_name(models) return litellm_model_names @@ -473,12 +485,12 @@ class GoogleAIStudioTokenCounter(BaseTokenCounter): async def count_tokens( self, model_to_use: str, - messages: list[dict[str, Any]] | None, - contents: list[dict[str, Any]] | None, + messages: list[dict[str, object]] | None, + contents: list[dict[str, object]] | None, deployment: dict[str, Any] | None = None, request_model: str = "", - tools: list[dict[str, Any]] | None = None, - system: Any | None = None, + tools: list[dict[str, object]] | None = None, + system: object | None = None, ) -> TokenCountResponse | None: import copy diff --git a/litellm/llms/gemini/files/transformation.py b/litellm/llms/gemini/files/transformation.py index dee83407cb5..2c62e04c5a3 100644 --- a/litellm/llms/gemini/files/transformation.py +++ b/litellm/llms/gemini/files/transformation.py @@ -5,11 +5,13 @@ For vertex ai, check out the vertex_ai/files/handler.py file. """ import time -from typing import Any, Final, Literal +from collections.abc import Mapping +from typing import Final, Literal, TypedDict from urllib.parse import urlparse import httpx from openai.types.file_deleted import FileDeleted +from typing_extensions import ReadOnly, Required from litellm._logging import verbose_logger from litellm.litellm_core_utils.prompt_templates.common_utils import extract_file_data @@ -18,7 +20,6 @@ from litellm.llms.base_llm.files.transformation import ( BaseFilesConfig, LiteLLMLoggingObj, ) -from litellm.types.llms.gemini import GeminiCreateFilesResponseObject from litellm.types.llms.openai import ( AllMessageValues, CreateFileRequest, @@ -31,6 +32,25 @@ from litellm.types.utils import LlmProviders from ..common_utils import GeminiModelInfo +class _GeminiFileMetadata(TypedDict, total=False): + name: ReadOnly[str] + uri: ReadOnly[Required[str]] + displayName: ReadOnly[Required[str]] + mimeType: ReadOnly[str] + sizeBytes: ReadOnly[Required[str]] + createTime: ReadOnly[Required[str]] + updateTime: ReadOnly[str] + expirationTime: ReadOnly[str] + sha256Hash: ReadOnly[str] + state: ReadOnly[str] + source: ReadOnly[str] + error: ReadOnly[Mapping[str, object]] + + +class _GeminiCreateFileResponse(TypedDict): + file: ReadOnly[_GeminiFileMetadata] + + class GoogleAIStudioFilesHandler(GeminiModelInfo, BaseFilesConfig): def __init__(self): pass @@ -41,14 +61,14 @@ class GoogleAIStudioFilesHandler(GeminiModelInfo, BaseFilesConfig): def validate_environment( self, - headers: dict[Any, Any], + headers: dict[str, str], model: str, messages: list[AllMessageValues], - optional_params: dict[Any, Any], - litellm_params: dict[Any, Any], + optional_params: dict[str, object], + litellm_params: dict[str, object], api_key: str | None = None, api_base: str | None = None, - ) -> dict[Any, Any]: + ) -> dict[str, str]: """ Validate environment and add Gemini API key to headers. Google AI Studio uses x-goog-api-key header for authentication. @@ -164,9 +184,9 @@ class GoogleAIStudioFilesHandler(GeminiModelInfo, BaseFilesConfig): Transform Gemini's file upload response into OpenAI-style FileObject """ try: - response_json: Final = raw_response.json() + response_json: Final[_GeminiCreateFileResponse] = raw_response.json() - response_object: Final = GeminiCreateFilesResponseObject(**response_json.get("file", {})) + response_object: Final = response_json["file"] # Extract file information from Gemini response @@ -262,7 +282,7 @@ class GoogleAIStudioFilesHandler(GeminiModelInfo, BaseFilesConfig): """ try: verbose_logger.debug("Retrieve file response: %s", raw_response.text) - response_json: Final = raw_response.json() + response_json: Final[_GeminiFileMetadata] = raw_response.json() verbose_logger.debug("Response JSON: %s", response_json) # Map Gemini state to OpenAI status gemini_state: Final = response_json.get("state", "STATE_UNSPECIFIED") diff --git a/litellm/llms/gemini/interactions/transformation.py b/litellm/llms/gemini/interactions/transformation.py index dcd2e4e3471..6d0f211ed7b 100644 --- a/litellm/llms/gemini/interactions/transformation.py +++ b/litellm/llms/gemini/interactions/transformation.py @@ -12,9 +12,10 @@ Schema versioning: litellm.use_legacy_interactions_schema = True. Remove flag after June 8, 2026. """ -from typing import TYPE_CHECKING, Any, Final +from typing import TYPE_CHECKING, Any, Final, Protocol, TypeAlias import httpx +from typing_extensions import ReadOnly, TypedDict import litellm from litellm._logging import verbose_logger @@ -41,6 +42,53 @@ else: LiteLLMLoggingObj = Any +_JsonObject: TypeAlias = dict[str, object] + + +class _InteractionPayload(TypedDict, total=False): + """JSON body of an Interactions API interaction, keyed as ``InteractionsAPIResponse`` fields.""" + + id: ReadOnly[str | None] + object: ReadOnly[str | None] + model: ReadOnly[str | None] + agent: ReadOnly[str | None] + status: ReadOnly[str | None] + created: ReadOnly[str | None] + updated: ReadOnly[str | None] + outputs: ReadOnly[list[_JsonObject] | None] + steps: ReadOnly[list[_JsonObject] | None] + usage: ReadOnly[_JsonObject | None] + + +class _CancelPayload(TypedDict, total=False): + """JSON body of an Interactions API cancel response.""" + + id: ReadOnly[str | None] + status: ReadOnly[str | None] + + +class _InteractionPayloadSource(Protocol): + """An Interactions API HTTP response, read for the interaction body it decodes to.""" + + def json(self) -> _InteractionPayload: ... + + +class _CancelPayloadSource(Protocol): + """An Interactions API cancel HTTP response, read for the body it decodes to.""" + + def json(self) -> _CancelPayload: ... + + +def _interaction_body(response: _InteractionPayloadSource) -> _InteractionPayload: + """Decode the body of an Interactions API interaction response.""" + return response.json() + + +def _cancel_body(response: _CancelPayloadSource) -> _CancelPayload: + """Decode the body of an Interactions API cancel response.""" + return response.json() + + class GoogleAIStudioInteractionsConfig(BaseInteractionsAPIConfig): """ Configuration for Google AI Studio Interactions API. @@ -143,7 +191,7 @@ class GoogleAIStudioInteractionsConfig(BaseInteractionsAPIConfig): """ use_legacy: Final[bool] = litellm.use_legacy_interactions_schema - request_body: Final[dict[str, Any]] = {} + request_body: Final[dict[str, object]] = {} # Model or Agent (one required) if model: @@ -189,7 +237,7 @@ class GoogleAIStudioInteractionsConfig(BaseInteractionsAPIConfig): and (not isinstance(response_format, dict) or "mime_type" not in response_format) ): # Wrap the legacy schema into the new polymorphic format. - new_rf: Final[dict[str, Any]] = { + new_rf: Final[dict[str, object]] = { "type": "text", "mime_type": response_mime_type, } @@ -215,7 +263,7 @@ class GoogleAIStudioInteractionsConfig(BaseInteractionsAPIConfig): if image_config is not None: # Move image_config to response_format with type=image. - image_rf: Final[dict[str, Any]] = {"type": "image", **image_config} + image_rf: Final[_JsonObject] = {"type": "image", **image_config} existing_rf: Final = request_body.get("response_format") if existing_rf is None: request_body["response_format"] = image_rf @@ -239,7 +287,7 @@ class GoogleAIStudioInteractionsConfig(BaseInteractionsAPIConfig): original_response=raw_response.text, additional_args={"complete_input_dict": {}}, ) - raw_json: Final = raw_response.json() + raw_json: Final = _interaction_body(raw_response) except Exception: raise GeminiError( message=raw_response.text, @@ -290,7 +338,7 @@ class GoogleAIStudioInteractionsConfig(BaseInteractionsAPIConfig): logging_obj: LiteLLMLoggingObj, ) -> InteractionsAPIResponse: try: - raw_json: Final = raw_response.json() + raw_json: Final = _interaction_body(raw_response) except Exception: raise GeminiError( message=raw_response.text, @@ -355,7 +403,7 @@ class GoogleAIStudioInteractionsConfig(BaseInteractionsAPIConfig): logging_obj: LiteLLMLoggingObj, ) -> CancelInteractionResult: try: - raw_json: Final = raw_response.json() + raw_json: Final = _cancel_body(raw_response) except Exception: raise GeminiError( message=raw_response.text, diff --git a/litellm/llms/gemini/realtime/transformation.py b/litellm/llms/gemini/realtime/transformation.py index 367619db37d..c92af7de145 100644 --- a/litellm/llms/gemini/realtime/transformation.py +++ b/litellm/llms/gemini/realtime/transformation.py @@ -7,6 +7,8 @@ from collections import OrderedDict from collections.abc import Mapping, Sequence from typing import Any, Final, cast +from typing_extensions import ReadOnly, Required, TypedDict + import litellm from litellm import verbose_logger from litellm._uuid import uuid @@ -96,6 +98,23 @@ def _gemini_live_speech_config(voice: object) -> Mapping[str, object] | None: return VertexGeminiConfig()._map_audio_params({"voice": voice}) +class _GeminiLiveSetupEnvelope(TypedDict, total=False): + setup: ReadOnly[BidiGenerateContentSetup] + + +class _OpenAIRealtimeClientEvent(TypedDict, total=False): + type: ReadOnly[str] + audio: ReadOnly[Required[str]] + session: ReadOnly[dict[str, object]] + item: ReadOnly[dict[str, object]] + + +def _parse_setup(session_configuration_request: str) -> BidiGenerateContentSetup: + envelope: Final[_GeminiLiveSetupEnvelope] = json.loads(session_configuration_request) + empty_setup: Final[BidiGenerateContentSetup] = {} + return envelope.get("setup", empty_setup) + + # Google bills Live transcription at an estimated 25 audio tokens/sec of input and # 175 text tokens/min of output (ai.google.dev/gemini-api/docs/pricing). GEMINI_LIVE_TRANSCRIBE_AUDIO_TOKENS_PER_SECOND: Final = 25 @@ -130,7 +149,7 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): return True @staticmethod - def _usage_detail_alias(details: Any, defaults: dict[str, int]) -> dict[str, Any]: + def _usage_detail_alias(details: Mapping[str, int | None] | None, defaults: dict[str, int]) -> dict[str, int]: if not isinstance(details, dict): return dict(defaults) return { @@ -139,7 +158,7 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): } @staticmethod - def _add_pipecat_usage_detail_aliases(usage_dict: dict[str, Any]) -> dict[str, Any]: + def _add_pipecat_usage_detail_aliases(usage_dict: dict[str, Any]) -> dict[str, object]: usage_dict.setdefault( "input_token_details", GeminiRealtimeConfig._usage_detail_alias( @@ -222,8 +241,10 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): if not session_configuration_request: return False try: - setup: Final = json.loads(session_configuration_request).get("setup", {}) - automatic_detection: Final = setup.get("realtimeInputConfig", {}).get("automaticActivityDetection", {}) + setup: Final = _parse_setup(session_configuration_request) + automatic_detection: Final[object] = setup.get("realtimeInputConfig", {}).get( + "automaticActivityDetection", {} + ) return isinstance(automatic_detection, dict) and automatic_detection.get("disabled") is True except (json.JSONDecodeError, TypeError, AttributeError): return False @@ -406,7 +427,7 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): return "TEXT" if GeminiRealtimeConfig._is_text_only_live_model(model) else "AUDIO" @staticmethod - def _coerce_response_modalities(model: str, modalities: Sequence[Any]) -> tuple[str, ...]: + def _coerce_response_modalities(model: str, modalities: Sequence[object]) -> tuple[str, ...]: """Swap responseModalities a Live model cannot produce: TEXT to AUDIO for audio-only models, AUDIO to TEXT for text-only ones (e.g. transcribe-live).""" normalized: Final = tuple( @@ -431,7 +452,7 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): def _handle_session_update( self, - json_message: dict, + json_message: _OpenAIRealtimeClientEvent, model: str, session_configuration_request: str | None, ) -> list[str]: @@ -445,7 +466,8 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): with a 1007, tearing the session down). To carry tools/instructions, send them on the first session.update before any conversation content. """ - session_payload = json_message.get("session") or {} + empty_session: Final[dict[str, object]] = {} + session_payload = json_message.get("session") or empty_session # Normalize GA-remapped fields (``output_modalities``, # nested ``audio.input.transcription``, # ``audio.input.turn_detection``) back to their flat beta keys so @@ -486,14 +508,15 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): verbose_logger.debug("Gemini Realtime: Ignoring session.update (setup already sent)") return [] - def _handle_conversation_item(self, json_message: dict) -> list[str]: + def _handle_conversation_item(self, json_message: _OpenAIRealtimeClientEvent) -> list[str]: """ Handle conversation.item.create for user text or function call output. Converts OpenAI format to Gemini's clientContent (for user text) or toolResponse (for function outputs). """ - item: Final = json_message.get("item", {}) + empty_item: Final[dict[str, object]] = {} + item: Final = json_message.get("item", empty_item) item_type: Final = item.get("type") if item_type == "function_call_output": @@ -524,7 +547,7 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): call_id, ) - function_response: Final[dict[str, Any]] = {"response": output_dict} + function_response: Final[dict[str, object]] = {"response": output_dict} if self._include_function_response_id() and call_id: function_response["id"] = call_id if function_name: @@ -559,7 +582,7 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): ) -> list[str]: realtime_input_dict: BidiGenerateContentRealtimeInput = {} try: - json_message: Final = json.loads(message) + json_message: Final[_OpenAIRealtimeClientEvent] = json.loads(message) except json.JSONDecodeError: if isinstance(message, bytes): message_str = message.decode("utf-8", errors="replace") @@ -610,9 +633,7 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): session_configuration_request: str | None = None, ) -> OpenAIRealtimeStreamSessionEvents: if session_configuration_request: - session_configuration_request_dict: BidiGenerateContentSetup = json.loads( - session_configuration_request - ).get("setup", {}) + session_configuration_request_dict: BidiGenerateContentSetup = _parse_setup(session_configuration_request) else: session_configuration_request_dict = {} @@ -663,7 +684,7 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): session_configuration_request_dict: BidiGenerateContentSetup = {} if session_configuration_request is not None: try: - session_configuration_request_dict = json.loads(session_configuration_request).get("setup", {}) + session_configuration_request_dict = _parse_setup(session_configuration_request) except json.JSONDecodeError: session_configuration_request_dict = {} generation_config: Final = session_configuration_request_dict.get("generationConfig", {}) @@ -931,9 +952,9 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): return events @staticmethod - def get_nested_value(obj: dict, path: str) -> Any: + def get_nested_value(obj: dict, path: str) -> object | None: keys: Final = path.split(".") - current = obj + current: object = obj for key in keys: if isinstance(current, dict) and key in current: current = current[key] @@ -1011,9 +1032,7 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): current_response_id = f"resp_{uuid.uuid4()}" if session_configuration_request: - session_configuration_request_dict: BidiGenerateContentSetup = json.loads( - session_configuration_request - ).get("setup", {}) + session_configuration_request_dict: BidiGenerateContentSetup = _parse_setup(session_configuration_request) else: session_configuration_request_dict = {} @@ -1337,7 +1356,7 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): session_setup: BidiGenerateContentSetup = {} if session_configuration_request is not None: try: - session_setup = json.loads(session_configuration_request).get("setup", {}) + session_setup = _parse_setup(session_configuration_request) except (json.JSONDecodeError, TypeError): session_setup = {} tool_call_generation_config = session_setup.get("generationConfig", {}) or {} diff --git a/litellm/llms/gemini/videos/transformation.py b/litellm/llms/gemini/videos/transformation.py index 6a1fc144c42..ff4c675b02f 100644 --- a/litellm/llms/gemini/videos/transformation.py +++ b/litellm/llms/gemini/videos/transformation.py @@ -1,4 +1,5 @@ import base64 +from collections.abc import Mapping from typing import TYPE_CHECKING, Any, Final import httpx @@ -54,8 +55,13 @@ def _convert_image_to_gemini_format(image_file) -> dict[str, str]: return {"bytesBase64Encoded": base64_encoded, "mimeType": mime_type} +def _json_payload(raw_response: httpx.Response) -> object: + """Read an HTTP response body as an opaque JSON payload.""" + return raw_response.json() + + def _usage_video_resolution_from_parameters( - parameters: dict[str, Any], + parameters: Mapping[str, object], ) -> str | None: """Normalize Veo ``parameters.resolution`` for usage and cost tracking.""" res: Final = parameters.get("resolution") @@ -97,7 +103,7 @@ class GeminiVideoConfig(BaseVideoConfig): video_create_optional_params: VideoCreateOptionalRequestParams, model: str, drop_params: bool, - ) -> dict[str, Any]: + ) -> dict[str, object]: """ Map OpenAI-style parameters to Veo format. @@ -111,7 +117,7 @@ class GeminiVideoConfig(BaseVideoConfig): All other params are passed through as-is to support Gemini-specific parameters. """ - mapped_params: Final[dict[str, Any]] = {} + mapped_params: Final[dict[str, object]] = {} # Get supported OpenAI params (exclude "model" and "prompt" which are handled separately) supported_openai_params: Final = self.get_supported_openai_params(model) @@ -312,11 +318,11 @@ class GeminiVideoConfig(BaseVideoConfig): - status: "processing" - usage: includes duration_seconds and optional video_resolution for cost calculation """ - response_data: Final = raw_response.json() + response_data: Final = _json_payload(raw_response) # Parse response using Pydantic model for type safety try: - operation_response: Final = GeminiLongRunningOperationResponse(**response_data) + operation_response: Final = GeminiLongRunningOperationResponse.model_validate(response_data) except Exception as e: raise ValueError(f"Failed to parse operation response: {e}") @@ -336,7 +342,7 @@ class GeminiVideoConfig(BaseVideoConfig): model=model, ) - usage_data: Final[dict[str, Any]] = {} + usage_data: Final[dict[str, float | str]] = {} if request_data: parameters: Final = request_data.get("parameters", {}) duration: Final = parameters.get("durationSeconds") or DEFAULT_GOOGLE_VIDEO_DURATION_SECONDS @@ -367,7 +373,7 @@ class GeminiVideoConfig(BaseVideoConfig): """ operation_name: Final = extract_original_video_id(video_id) url: Final = f"{api_base.rstrip('/')}/v1beta/{operation_name}" - params: Final[dict[str, Any]] = {} + params: Final[dict[str, object]] = {} return url, params @@ -403,9 +409,9 @@ class GeminiVideoConfig(BaseVideoConfig): } } """ - response_data: Final = raw_response.json() + response_data: Final = _json_payload(raw_response) # Parse response using Pydantic model for type safety - operation_response: Final = GeminiLongRunningOperationResponse(**response_data) + operation_response: Final = GeminiLongRunningOperationResponse.model_validate(response_data) operation_name: Final = operation_response.name is_done: Final = operation_response.done @@ -443,9 +449,9 @@ class GeminiVideoConfig(BaseVideoConfig): client: Final = litellm.module_level_client status_response: Final = client.get(url=status_url, headers=headers) status_response.raise_for_status() - response_data: Final = status_response.json() + response_data: Final = _json_payload(status_response) - operation_response: Final = GeminiLongRunningOperationResponse(**response_data) + operation_response: Final = GeminiLongRunningOperationResponse.model_validate(response_data) if not operation_response.done: raise ValueError( @@ -458,7 +464,7 @@ class GeminiVideoConfig(BaseVideoConfig): generated_samples: Final = operation_response.response.generateVideoResponse.generatedSamples download_url: Final = generated_samples[0].video.uri - params: Final[dict[str, Any]] = {} + params: Final[dict[str, object]] = {} return download_url, params @@ -480,7 +486,7 @@ class GeminiVideoConfig(BaseVideoConfig): api_base: str, litellm_params: GenericLiteLLMParams, headers: dict, - extra_body: dict[str, Any] | None = None, + extra_body: Mapping[str, object] | None = None, ) -> tuple[str, dict]: """ Video remix is not supported by Veo API. @@ -506,7 +512,7 @@ class GeminiVideoConfig(BaseVideoConfig): after: str | None = None, limit: int | None = None, order: str | None = None, - extra_query: dict[str, Any] | None = None, + extra_query: Mapping[str, object] | None = None, ) -> tuple[str, dict]: """ Video list is not supported by Veo API. @@ -547,7 +553,7 @@ class GeminiVideoConfig(BaseVideoConfig): """Video delete is not supported.""" raise NotImplementedError("Video delete is not supported by Google Veo.") - def transform_video_create_character_request(self, name, video, api_base, litellm_params, headers): + def transform_video_create_character_request(self, name, video: object, api_base, litellm_params, headers): raise NotImplementedError("video create character is not supported for Gemini") def transform_video_create_character_response(self, raw_response, logging_obj): diff --git a/litellm/llms/gigachat/__init__.py b/litellm/llms/gigachat/__init__.py index 3ddbd7864d9..e7c2206ffaa 100644 --- a/litellm/llms/gigachat/__init__.py +++ b/litellm/llms/gigachat/__init__.py @@ -15,9 +15,11 @@ API Documentation: https://developers.sber.ru/docs/ru/gigachat/api/overview from .chat.transformation import GigaChatConfig, GigaChatError from .embedding.transformation import GigaChatEmbeddingConfig +from .passthrough.transformation import GigaChatPassthroughConfig -__all__ = [ +__all__ = ( "GigaChatConfig", "GigaChatEmbeddingConfig", "GigaChatError", -] + "GigaChatPassthroughConfig", +) diff --git a/litellm/llms/gigachat/authenticator.py b/litellm/llms/gigachat/authenticator.py index 9ef6fe7a93c..d6b217d5746 100644 --- a/litellm/llms/gigachat/authenticator.py +++ b/litellm/llms/gigachat/authenticator.py @@ -7,6 +7,7 @@ Based on official GigaChat SDK authentication flow. import time import uuid +from collections.abc import Mapping from typing import Final import httpx @@ -16,7 +17,7 @@ from litellm.caching.caching import InMemoryCache from litellm.llms.base_llm.chat.transformation import BaseLLMException from litellm.llms.custom_httpx.http_handler import ( HTTPHandler, - _get_httpx_client, + _get_httpx_client, # pyright: ignore[reportPrivateUsage] # house cached-client factory has no public alias get_async_httpx_client, ) from litellm.secret_managers.main import get_secret_str @@ -63,6 +64,7 @@ def get_access_token( credentials: str | None = None, scope: str | None = None, auth_url: str | None = None, + litellm_params: Mapping[str, object] | None = None, ) -> str: """ Get valid access token, using cache if available. @@ -78,71 +80,88 @@ def get_access_token( Raises: GigaChatAuthError: If authentication fails """ - credentials = credentials or _get_credentials() - if not credentials: + if not litellm_params: + litellm_params = {} # mutable-ok: empty dict default; rebind-ok: provide default + + access_token: Final = litellm_params.get("gigachat_access_token") or get_secret_str("GIGACHAT_ACCESS_TOKEN") + if access_token: + return access_token + + effective_credentials: Final = credentials or _get_credentials() + if not effective_credentials: raise GigaChatAuthError( status_code=401, message="GigaChat credentials not provided. Set GIGACHAT_CREDENTIALS or GIGACHAT_API_KEY environment variable.", ) - scope = scope or _get_scope() - auth_url = auth_url or _get_auth_url() + effective_scope: Final = scope or litellm_params.get("gigachat_scope") or _get_scope() + effective_auth_url: Final = auth_url or litellm_params.get("gigachat_auth_url") or _get_auth_url() # Check cache - cache_key: Final = f"gigachat_token:{credentials[:16]}" + cache_key: Final = f"gigachat_token:{effective_credentials[:16]}" cached: Final = _token_cache.get_cache(cache_key) if cached: - token, expires_at = cached + _token, _expires_at = cached # Check if token is still valid (with buffer) - if time.time() * 1000 < expires_at - TOKEN_EXPIRY_BUFFER_MS: + if time.time() * 1000 < _expires_at - TOKEN_EXPIRY_BUFFER_MS: verbose_logger.debug("Using cached GigaChat access token") - return token + return _token # Request new token - token, expires_at = _request_token_sync(credentials, scope, auth_url) + new_token, new_expires_at = _request_token_sync(effective_credentials, effective_scope, effective_auth_url) # pyright: ignore[reportArgumentType] # credential keys may be broader than str - # Cache token - ttl_seconds: Final = max(0, (expires_at - TOKEN_EXPIRY_BUFFER_MS - time.time() * 1000) / 1000) - if ttl_seconds > 0: - _token_cache.set_cache(cache_key, (token, expires_at), ttl=ttl_seconds) + if new_expires_at: + # Cache token + ttl_seconds: Final = max(0, (new_expires_at - TOKEN_EXPIRY_BUFFER_MS - time.time() * 1000) / 1000) + if ttl_seconds > 0: + _token_cache.set_cache(cache_key, (new_token, new_expires_at), ttl=ttl_seconds) - return token + return new_token async def get_access_token_async( credentials: str | None = None, scope: str | None = None, auth_url: str | None = None, + litellm_params: Mapping[str, object] | None = None, ) -> str: """Async version of get_access_token.""" - credentials = credentials or _get_credentials() - if not credentials: + if not litellm_params: + litellm_params = {} # mutable-ok: empty dict default; rebind-ok: provide default + + access_token: Final = litellm_params.get("gigachat_access_token") or get_secret_str("GIGACHAT_ACCESS_TOKEN") + if access_token: + return access_token + + effective_credentials: Final = credentials or _get_credentials() + if not effective_credentials: raise GigaChatAuthError( status_code=401, message="GigaChat credentials not provided. Set GIGACHAT_CREDENTIALS or GIGACHAT_API_KEY environment variable.", ) - scope = scope or _get_scope() - auth_url = auth_url or _get_auth_url() + effective_scope: Final = scope or litellm_params.get("gigachat_scope") or _get_scope() + effective_auth_url: Final = auth_url or litellm_params.get("gigachat_auth_url") or _get_auth_url() # Check cache - cache_key: Final = f"gigachat_token:{credentials[:16]}" + cache_key: Final = f"gigachat_token:{effective_credentials[:16]}" cached: Final = _token_cache.get_cache(cache_key) if cached: - token, expires_at = cached - if time.time() * 1000 < expires_at - TOKEN_EXPIRY_BUFFER_MS: + _token, _expires_at = cached + if time.time() * 1000 < _expires_at - TOKEN_EXPIRY_BUFFER_MS: verbose_logger.debug("Using cached GigaChat access token") - return token + return _token # Request new token - token, expires_at = await _request_token_async(credentials, scope, auth_url) + new_token, new_expires_at = await _request_token_async(effective_credentials, effective_scope, effective_auth_url) # pyright: ignore[reportArgumentType] # credential keys may be broader than str - # Cache token - ttl_seconds: Final = max(0, (expires_at - TOKEN_EXPIRY_BUFFER_MS - time.time() * 1000) / 1000) - if ttl_seconds > 0: - _token_cache.set_cache(cache_key, (token, expires_at), ttl=ttl_seconds) + if new_expires_at: + # Cache token + ttl_seconds: Final = max(0, (new_expires_at - TOKEN_EXPIRY_BUFFER_MS - time.time() * 1000) / 1000) + if ttl_seconds > 0: + _token_cache.set_cache(cache_key, (new_token, new_expires_at), ttl=ttl_seconds) - return token + return new_token def _request_token_sync( @@ -154,7 +173,7 @@ def _request_token_sync( Request new access token from GigaChat OAuth endpoint (sync). Returns: - Tuple of (access_token, expires_at_ms) + tuple of (access_token, expires_at_ms) """ headers: Final = { "Authorization": f"Basic {credentials}", @@ -169,7 +188,7 @@ def _request_token_sync( client: Final = _get_http_client() response: Final = client.post(auth_url, headers=headers, data=data, timeout=30) response.raise_for_status() - return _parse_token_response(response) + return _parse_token_response(response) # pyright: ignore[reportArgumentType] # httpx Response may be None at type level except httpx.HTTPStatusError as e: raise GigaChatAuthError( status_code=e.response.status_code, @@ -204,7 +223,7 @@ async def _request_token_async( ) response: Final = await client.post(auth_url, headers=headers, data=data, timeout=30) response.raise_for_status() - return _parse_token_response(response) + return _parse_token_response(response) # pyright: ignore[reportArgumentType] # httpx Response may be None at type level except httpx.HTTPStatusError as e: raise GigaChatAuthError( status_code=e.response.status_code, @@ -223,7 +242,7 @@ def _parse_token_response(response: httpx.Response) -> tuple[str, int]: # GigaChat returns either 'tok'/'exp' or 'access_token'/'expires_at' access_token: Final = data.get("tok") or data.get("access_token") - expires_at = data.get("exp") or data.get("expires_at") + expires_at_raw: Final = data.get("exp") or data.get("expires_at") if not access_token: raise GigaChatAuthError( @@ -232,8 +251,11 @@ def _parse_token_response(response: httpx.Response) -> tuple[str, int]: ) # expires_at is in milliseconds - if isinstance(expires_at, str): - expires_at = int(expires_at) + expires_at: int # rebind-ok: conditionally assigned from str or int + if isinstance(expires_at_raw, str): + expires_at = int(expires_at_raw) # rebind-ok: conditionally assigned from str or int + else: + expires_at = expires_at_raw # pyright: ignore[reportAssignmentType] # raw value is int or str; converted above; rebind-ok: conditionally assigned from str or int verbose_logger.debug("GigaChat access token obtained successfully") return access_token, expires_at diff --git a/litellm/llms/gigachat/chat/__init__.py b/litellm/llms/gigachat/chat/__init__.py index eb9492b90b3..0f9be19fedd 100644 --- a/litellm/llms/gigachat/chat/__init__.py +++ b/litellm/llms/gigachat/chat/__init__.py @@ -5,8 +5,8 @@ GigaChat Chat Module from .streaming import GigaChatModelResponseIterator from .transformation import GigaChatConfig, GigaChatError -__all__ = [ +__all__ = ( "GigaChatConfig", "GigaChatError", "GigaChatModelResponseIterator", -] +) diff --git a/litellm/llms/gigachat/chat/streaming.py b/litellm/llms/gigachat/chat/streaming.py index 219209773ea..2875b30232e 100644 --- a/litellm/llms/gigachat/chat/streaming.py +++ b/litellm/llms/gigachat/chat/streaming.py @@ -4,13 +4,15 @@ GigaChat Streaming Response Handler import json import uuid +from collections.abc import Mapping, Sequence from typing import Any, Final +from litellm.llms.gigachat.utils import convert_usage from litellm.types.llms.openai import ( ChatCompletionToolCallChunk, ChatCompletionToolCallFunctionChunk, ) -from litellm.types.utils import GenericStreamingChunk +from litellm.types.utils import ChatCompletionUsageBlock, GenericStreamingChunk class GigaChatModelResponseIterator: @@ -26,14 +28,9 @@ class GigaChatModelResponseIterator: self.response_iterator = self.streaming_response self.json_mode = json_mode - def chunk_parser(self, chunk: dict) -> GenericStreamingChunk: + def chunk_parser(self, chunk: Mapping[str, object]) -> GenericStreamingChunk: """Parse a single streaming chunk from GigaChat.""" - text = "" - tool_use: ChatCompletionToolCallChunk | None = None - is_finished = False - finish_reason: str | None = None - - choices: Final = chunk.get("choices", []) + choices: Sequence = chunk.get("choices") or () # mutable-ok: tuple literal as default if not choices: return GenericStreamingChunk( text="", @@ -45,40 +42,63 @@ class GigaChatModelResponseIterator: ) choice: Final = choices[0] - delta: Final = choice.get("delta", {}) - finish_reason = choice.get("finish_reason") + delta: Mapping[str, object] = choice.get("delta") or {} # mutable-ok: empty dict default for get + chunk_finish_reason: Final = choice.get("finish_reason") # Extract text content - text = delta.get("content", "") or "" + text: Final = delta.get("content", "") or "" + + usage_block: ChatCompletionUsageBlock | None = None # rebind-ok: conditionally assigned after stop detection + tool_use: ChatCompletionToolCallChunk | None = None # rebind-ok: conditionally assigned on function_call + finish_reason: str | None = chunk_finish_reason # Handle function_call in stream - if finish_reason == "function_call" and delta.get("function_call"): - func_call: Final = delta["function_call"] - args = func_call.get("arguments", {}) - - if isinstance(args, dict): - args = json.dumps(args, ensure_ascii=False) + raw_function_call: Final = delta.get("function_call") + if chunk_finish_reason == "function_call" and isinstance(raw_function_call, Mapping) and raw_function_call: + func_call: Final[Mapping[str, object]] = raw_function_call + args_raw: Final[object] = func_call.get("arguments") or {} + args_str: str # rebind-ok: conditionally assigned from dict or str + if isinstance(args_raw, dict): + args_str = json.dumps(args_raw, ensure_ascii=False) # rebind-ok: build from dict + else: + args_str = str(args_raw) + name_raw: Final = func_call.get("name") tool_use = ChatCompletionToolCallChunk( id=f"call_{uuid.uuid4().hex[:24]}", type="function", function=ChatCompletionToolCallFunctionChunk( - name=func_call.get("name", ""), - arguments=args, + name=name_raw if isinstance(name_raw, str) else "", + arguments=args_str, ), index=0, ) finish_reason = "tool_calls" - if finish_reason is not None: - is_finished = True + usage_data: Final = chunk.get("usage") or {} # mutable-ok: empty dict default + if usage_data and isinstance(usage_data, dict): + validated_usage: Final = {k: int(v) for k, v in usage_data.items()} + usage = convert_usage(validated_usage) + _prompt_details: dict | None = ( + usage.prompt_tokens_details.model_dump() if usage.prompt_tokens_details else None + ) # rebind-ok: conditional + _completion_details: dict | None = ( + usage.completion_tokens_details.model_dump() if usage.completion_tokens_details else None + ) # rebind-ok: conditional + usage_block = ChatCompletionUsageBlock( # pyright: ignore[reportCallIssue] # TypedDict kwarg constructor + prompt_tokens=usage.prompt_tokens, + completion_tokens=usage.completion_tokens, + total_tokens=usage.total_tokens, + prompt_tokens_details=_prompt_details, + completion_tokens_details=_completion_details, + ) return GenericStreamingChunk( - text=text, + text=str(text), tool_use=tool_use, - is_finished=is_finished, + is_finished=chunk_finish_reason is not None, finish_reason=finish_reason or "", - usage=None, + usage=usage_block, index=choice.get("index", 0), ) diff --git a/litellm/llms/gigachat/chat/transformation.py b/litellm/llms/gigachat/chat/transformation.py index b859a843251..8f23c5175ec 100644 --- a/litellm/llms/gigachat/chat/transformation.py +++ b/litellm/llms/gigachat/chat/transformation.py @@ -4,19 +4,22 @@ GigaChat Chat Transformation Transforms OpenAI-format requests to GigaChat format and back. """ +from __future__ import annotations + import json import time import uuid -from collections.abc import AsyncIterator, Iterator +from collections.abc import AsyncIterator, Iterator, Mapping, Sequence from typing import TYPE_CHECKING, Any, Final import httpx from litellm._logging import verbose_logger from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMException +from litellm.llms.gigachat.utils import convert_usage, get_api_base from litellm.secret_managers.main import get_secret_str from litellm.types.llms.openai import AllMessageValues -from litellm.types.utils import Choices, Message, ModelResponse, Usage +from litellm.types.utils import Choices, Message, ModelResponse from ..authenticator import get_access_token from ..file_handler import upload_file_sync @@ -30,9 +33,6 @@ if TYPE_CHECKING: else: LiteLLMLoggingObj = Any -# GigaChat API endpoint -GIGACHAT_BASE_URL: Final = "https://gigachat.devices.sberbank.ru/api/v1" - def is_valid_json(value: str) -> bool: """Checks whether the value passed is a valid serialized JSON string""" @@ -90,30 +90,30 @@ class GigaChatConfig(BaseConfig): api_base: str | None, api_key: str | None, model: str, - optional_params: dict, - litellm_params: dict, + optional_params: Mapping[str, object], + litellm_params: Mapping[str, object], stream: bool | None = None, ) -> str: """Get complete API URL for chat completions.""" - base: Final = api_base or get_secret_str("GIGACHAT_API_BASE") or GIGACHAT_BASE_URL + base: Final = get_api_base(api_base) return f"{base}/chat/completions" def validate_environment( self, - headers: dict, + headers: dict, # mutable-ok: mutates in place per GigaChat OAuth setup model: str, - messages: list[AllMessageValues], - optional_params: dict, - litellm_params: dict, + messages: Sequence[AllMessageValues], + optional_params: Mapping[str, object], + litellm_params: Mapping[str, object], api_key: str | None = None, api_base: str | None = None, - ) -> dict: + ) -> dict: # mutable-ok: base class contract returns dict for httpx """ Set up headers with OAuth token. """ # Get access token credentials: Final = api_key or get_secret_str("GIGACHAT_CREDENTIALS") or get_secret_str("GIGACHAT_API_KEY") - access_token: Final = get_access_token(credentials=credentials) + access_token: Final = get_access_token(credentials=credentials, litellm_params=litellm_params) # Store credentials for image uploads self._current_credentials = credentials @@ -125,9 +125,9 @@ class GigaChatConfig(BaseConfig): return headers - def get_supported_openai_params(self, model: str) -> list[str]: + def get_supported_openai_params(self, model: str) -> list[str]: # mutable-ok: base class contract returns list """Return list of supported OpenAI parameters.""" - return [ + return [ # mutable-ok: base class contract returns list "stream", "temperature", "top_p", @@ -143,11 +143,11 @@ class GigaChatConfig(BaseConfig): def map_openai_params( self, - non_default_params: dict, - optional_params: dict, + non_default_params: Mapping[str, object], + optional_params: dict, # mutable-ok: mutated in place per GigaChat mapping model: str, drop_params: bool, - ) -> dict: + ) -> dict: # mutable-ok: base class contract returns dict """Map OpenAI parameters to GigaChat parameters.""" for param, value in non_default_params.items(): if param == "stream": @@ -167,42 +167,50 @@ class GigaChatConfig(BaseConfig): pass elif param == "tools": # Convert tools to functions format - optional_params["functions"] = self._convert_tools_to_functions(value) + if isinstance(value, Sequence): + optional_params["functions"] = self._convert_tools_to_functions(value) elif param == "tool_choice": # Map OpenAI tool_choice to GigaChat function_call - mapped_choice = self._map_tool_choice(value) - if mapped_choice is not None: - optional_params["function_call"] = mapped_choice + if isinstance(value, (str, Mapping)): + mapped_choice = self._map_tool_choice(value) + if mapped_choice is not None: + optional_params["function_call"] = mapped_choice elif param == "functions": optional_params["functions"] = value elif param == "function_call": optional_params["function_call"] = value elif param == "response_format": # Handle structured output via function calling - if value.get("type") == "json_schema": + if isinstance(value, Mapping) and value.get("type") == "json_schema": json_schema = value.get("json_schema", {}) schema_name = json_schema.get("name", "structured_output") schema = json_schema.get("schema", {}) - function_def = { + function_def = { # mutable-ok: request payload for httpx "name": schema_name, "description": f"Output structured response: {schema_name}", "parameters": schema, } - if "functions" not in optional_params: - optional_params["functions"] = [] - optional_params["functions"].append(function_def) - optional_params["function_call"] = {"name": schema_name} + existing_functions = optional_params.get("functions") + optional_params["functions"] = [ + *( + existing_functions + if isinstance(existing_functions, Sequence) and not isinstance(existing_functions, str) + else () + ), + function_def, + ] + optional_params["function_call"] = {"name": schema_name} # mutable-ok: request payload optional_params["_structured_output"] = True return optional_params - def _convert_tools_to_functions(self, tools: list[dict]) -> list[dict]: + def _convert_tools_to_functions(self, tools: Sequence) -> Sequence[dict]: """Convert OpenAI tools format to GigaChat functions format.""" - functions: Final = [] + functions: Final[list[dict]] = [] # mutable-ok: accumulator for building functions list for tool in tools: - if tool.get("type") == "function": + if isinstance(tool, dict) and tool.get("type") == "function": func = tool.get("function", {}) functions.append( { @@ -213,7 +221,7 @@ class GigaChatConfig(BaseConfig): ) return functions - def _map_tool_choice(self, tool_choice: str | dict) -> str | dict | None: + def _map_tool_choice(self, tool_choice: str | Mapping[str, object]) -> str | Mapping[str, object] | None: """ Map OpenAI tool_choice to GigaChat function_call format. @@ -246,8 +254,9 @@ class GigaChatConfig(BaseConfig): # OpenAI format: {"type": "function", "function": {"name": "func_name"}} # GigaChat format: {"name": "func_name"} if tool_choice.get("type") == "function": - func_name: Final = tool_choice.get("function", {}).get("name") - if func_name: + function_spec: Final = tool_choice.get("function") + func_name: Final = function_spec.get("name") if isinstance(function_spec, Mapping) else None + if isinstance(func_name, str) and func_name: return {"name": func_name} # Default to None (don't set function_call) @@ -273,20 +282,51 @@ class GigaChatConfig(BaseConfig): verbose_logger.error("Failed to upload image: %s", e) return None + def _transform_list_content(self, content: Sequence) -> tuple[str, Sequence[str]]: + """ + Extract text and image attachments from a multimodal message content list. + + Args: + content: List of content parts (OpenAI multimodal format) + + Returns: + Tuple of (combined text, list of attachment file ids) + """ + texts: Final[list[str]] = [] # mutable-ok: accumulator + attachments: Final[list[str]] = [] # mutable-ok: accumulator + for part in content: + if isinstance(part, dict): + if part.get("type") == "text": + texts.append(part.get("text", "")) + elif part.get("type") == "image_url": + # Extract image URL and upload to GigaChat + image_url: object = part.get("image_url", {}) + upload_url: str + if isinstance(image_url, str): + upload_url = image_url + else: + upload_url = str(image_url.get("url", "")) if isinstance(image_url, dict) else "" + if upload_url: + file_id = self._upload_image(upload_url) + if file_id: + attachments.append(file_id) + text: Final = "\n".join(texts) if texts else "" + return text, attachments + def transform_request( self, model: str, - messages: list[AllMessageValues], - optional_params: dict, - litellm_params: dict, - headers: dict, - ) -> dict: + messages: Sequence[AllMessageValues], + optional_params: Mapping[str, object], + litellm_params: Mapping[str, object], + headers: Mapping[str, object], + ) -> dict: # mutable-ok: request payload sent to httpx """Transform OpenAI request to GigaChat format.""" # Transform messages giga_messages: Final = self._transform_messages(messages) # Build request - request_data: Final = { + request_data: Final[dict[str, object]] = { "model": model.replace("gigachat/", ""), "messages": giga_messages, } @@ -311,9 +351,9 @@ class GigaChatConfig(BaseConfig): return request_data - def _transform_messages(self, messages: list[AllMessageValues]) -> list[dict]: + def _transform_messages(self, messages: Sequence[AllMessageValues]) -> Sequence[dict]: """Transform OpenAI messages to GigaChat format.""" - transformed: Final = [] + transformed: Final[list[dict]] = [] # mutable-ok: accumulator for building transformed messages for i, msg in enumerate(messages): message = dict(msg) @@ -341,24 +381,7 @@ class GigaChatConfig(BaseConfig): # Handle list content (multimodal) - extract text and images content = message.get("content") if isinstance(content, list): - texts = [] - attachments = [] - for part in content: - if isinstance(part, dict): - if part.get("type") == "text": - texts.append(part.get("text", "")) - elif part.get("type") == "image_url": - # Extract image URL and upload to GigaChat - image_url = part.get("image_url", {}) - if isinstance(image_url, str): - url = image_url - else: - url = image_url.get("url", "") - if url: - file_id = self._upload_image(url) - if file_id: - attachments.append(file_id) - message["content"] = "\n".join(texts) if texts else "" + message["content"], attachments = self._transform_list_content(content) if attachments: message["attachments"] = attachments @@ -393,7 +416,7 @@ class GigaChatConfig(BaseConfig): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: "tiktoken.Encoding | None", + encoding: tiktoken.Encoding | None, api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: @@ -408,7 +431,7 @@ class GigaChatConfig(BaseConfig): is_structured_output: Final = optional_params.get("_structured_output", False) - choices: Final = [] + choices: Final[list[Choices]] = [] # mutable-ok: accumulator for building response choices for choice in response_json.get("choices", []): message_data = choice.get("message", {}) finish_reason = choice.get("finish_reason", "stop") @@ -462,11 +485,7 @@ class GigaChatConfig(BaseConfig): # Build usage usage_data: Final = response_json.get("usage", {}) - usage: Final = Usage( - prompt_tokens=usage_data.get("prompt_tokens", 0), - completion_tokens=usage_data.get("completion_tokens", 0), - total_tokens=usage_data.get("total_tokens", 0), - ) + usage: Final = convert_usage(usage_data) model_response.id = response_json.get("id", f"chatcmpl-{uuid.uuid4().hex[:12]}") model_response.created = response_json.get("created", int(time.time())) diff --git a/litellm/llms/gigachat/embedding/transformation.py b/litellm/llms/gigachat/embedding/transformation.py index bb495cea423..2ec8324e33c 100644 --- a/litellm/llms/gigachat/embedding/transformation.py +++ b/litellm/llms/gigachat/embedding/transformation.py @@ -5,6 +5,8 @@ Transforms OpenAI /v1/embeddings format to GigaChat format. API Documentation: https://developers.sber.ru/docs/ru/gigachat/api/reference/rest/post-embeddings """ +from __future__ import annotations + import types from typing import Final @@ -14,14 +16,12 @@ from litellm import LlmProviders from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.llms.base_llm.chat.transformation import BaseLLMException from litellm.llms.base_llm.embedding.transformation import BaseEmbeddingConfig +from litellm.llms.gigachat.utils import get_api_base from litellm.types.llms.openai import AllEmbeddingInputValues, AllMessageValues from litellm.types.utils import EmbeddingResponse from ..authenticator import get_access_token -# GigaChat API endpoint -GIGACHAT_BASE_URL: Final = "https://gigachat.devices.sberbank.ru/api/v1" - class GigaChatEmbeddingError(BaseLLMException): """GigaChat Embedding API error.""" @@ -78,9 +78,9 @@ class GigaChatEmbeddingConfig(BaseEmbeddingConfig): Returns provider info for GigaChat. Returns: - Tuple of (custom_llm_provider, api_base, dynamic_api_key) + tuple of (custom_llm_provider, api_base, dynamic_api_key) """ - api_base = api_base or GIGACHAT_BASE_URL + api_base = get_api_base(api_base) return LlmProviders.GIGACHAT.value, api_base, api_key def get_complete_url( @@ -93,7 +93,7 @@ class GigaChatEmbeddingConfig(BaseEmbeddingConfig): stream: bool | None = None, ) -> str: """Get the complete URL for embeddings endpoint.""" - base: Final = api_base or GIGACHAT_BASE_URL + base: Final = get_api_base(api_base) return f"{base}/embeddings" def transform_embedding_request( @@ -114,14 +114,12 @@ class GigaChatEmbeddingConfig(BaseEmbeddingConfig): """ # Normalize input to list if isinstance(input, str): - input_list: list = [input] - elif isinstance(input, list): - input_list = input + input_list: list = [input] # rebind-ok: locally scoped conversion else: - input_list = [input] + input_list = input # Remove gigachat/ prefix from model if present - model = model.removeprefix("gigachat/") + model = model.removeprefix("gigachat/") # rebind-ok: parameter reassignment for normalization return { "model": model, @@ -191,7 +189,7 @@ class GigaChatEmbeddingConfig(BaseEmbeddingConfig): Set up headers with OAuth token for GigaChat. """ # Get access token via OAuth - access_token: Final = get_access_token(api_key) + access_token: Final = get_access_token(credentials=api_key, litellm_params=litellm_params) default_headers: Final = { "Content-Type": "application/json", diff --git a/litellm/llms/gigachat/file_handler.py b/litellm/llms/gigachat/file_handler.py index 4cbde551fa2..163e944f124 100644 --- a/litellm/llms/gigachat/file_handler.py +++ b/litellm/llms/gigachat/file_handler.py @@ -9,6 +9,7 @@ import base64 import hashlib import re import uuid +from collections.abc import Mapping from typing import Final from litellm._logging import verbose_logger @@ -16,13 +17,11 @@ from litellm.llms.custom_httpx.http_handler import ( _get_httpx_client, get_async_httpx_client, ) +from litellm.llms.gigachat.utils import get_api_base from litellm.types.utils import LlmProviders from .authenticator import get_access_token, get_access_token_async -# GigaChat API endpoint -GIGACHAT_BASE_URL: Final = "https://gigachat.devices.sberbank.ru/api/v1" - # Simple in-memory cache for file IDs _file_cache: Final[dict[str, str]] = {} @@ -82,6 +81,7 @@ def upload_file_sync( image_url: str, credentials: str | None = None, api_base: str | None = None, + litellm_params: Mapping[str, object] | None = None, ) -> str | None: """ Upload file to GigaChat and return file_id (sync). @@ -114,10 +114,10 @@ def upload_file_sync( filename: Final = f"{uuid.uuid4()}.{ext}" # Get access token - access_token: Final = get_access_token(credentials) + access_token: Final = get_access_token(credentials=credentials, litellm_params=litellm_params) # Upload to GigaChat - base_url: Final = api_base or GIGACHAT_BASE_URL + base_url: Final = get_api_base(api_base) upload_url: Final = f"{base_url}/files" client: Final = _get_httpx_client(params={"ssl_verify": False}) @@ -147,6 +147,7 @@ async def upload_file_async( image_url: str, credentials: str | None = None, api_base: str | None = None, + litellm_params: Mapping[str, object] | None = None, ) -> str | None: """ Upload file to GigaChat and return file_id (async). @@ -179,10 +180,10 @@ async def upload_file_async( filename: Final = f"{uuid.uuid4()}.{ext}" # Get access token - access_token: Final = await get_access_token_async(credentials) + access_token: Final = await get_access_token_async(credentials=credentials, litellm_params=litellm_params) # Upload to GigaChat - base_url: Final = api_base or GIGACHAT_BASE_URL + base_url: Final = get_api_base(api_base) upload_url: Final = f"{base_url}/files" client: Final = get_async_httpx_client( diff --git a/litellm/llms/gigachat/passthrough/__init__.py b/litellm/llms/gigachat/passthrough/__init__.py new file mode 100644 index 00000000000..a66a078dbeb --- /dev/null +++ b/litellm/llms/gigachat/passthrough/__init__.py @@ -0,0 +1,7 @@ +""" +GigaChat passthrough Module +""" + +from .transformation import GigaChatPassthroughConfig + +__all__ = ("GigaChatPassthroughConfig",) diff --git a/litellm/llms/gigachat/passthrough/transformation.py b/litellm/llms/gigachat/passthrough/transformation.py new file mode 100644 index 00000000000..a0edc6f5682 --- /dev/null +++ b/litellm/llms/gigachat/passthrough/transformation.py @@ -0,0 +1,213 @@ +from __future__ import annotations + +import json +from collections.abc import Mapping, Sequence +from typing import TYPE_CHECKING, Final + +import httpx + +from litellm.llms.base_llm.passthrough.transformation import BasePassthroughConfig +from litellm.llms.gigachat.authenticator import get_access_token +from litellm.llms.gigachat.chat.streaming import GigaChatModelResponseIterator +from litellm.llms.gigachat.utils import GIGACHAT_BASE_URL +from litellm.secret_managers.main import get_secret_str +from litellm.types.llms.openai import AllMessageValues +from litellm.types.utils import EmbeddingResponse + +if TYPE_CHECKING: + from httpx import URL, Response + + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj + from litellm.types.utils import CostResponseTypes + + +class GigaChatPassthroughConfig(BasePassthroughConfig): + def is_streaming_request(self, endpoint: str, request_data: Mapping[str, object]) -> bool: + return request_data.get("stream", False) + + def get_complete_url( + self, + api_base: str | None, + api_key: str | None, + model: str, + endpoint: str, + request_query_params: Mapping[str, object] | None, + litellm_params: Mapping[str, object], + ) -> tuple[URL, str]: + """Get complete API URL for chat completions.""" + base_target_url: Final = self.get_api_base(api_base) + + if base_target_url is None: + raise Exception("GigaChat api base not found") + + complete_url: Final = f"{base_target_url}/{endpoint.lstrip('/')}" + + return ( + httpx.URL(complete_url), + base_target_url, + ) + + def validate_environment( + self, + headers: dict, # mutable-ok: mutates in place to set OAuth headers + model: str, + messages: Sequence[AllMessageValues], + optional_params: Mapping[str, object], + litellm_params: Mapping[str, object], + api_key: str | None = None, + api_base: str | None = None, + ) -> dict: # mutable-ok: base class contract returns dict for httpx + """ + Set up headers with OAuth token. + """ + # Get access token + access_token: Final = get_access_token(credentials=api_key, litellm_params=litellm_params) + + headers["Authorization"] = f"Bearer {access_token}" # rebind-ok: mutating for OAuth setup + headers["Content-Type"] = "application/json" # rebind-ok: mutating for OAuth setup + headers["Accept"] = "application/json" # rebind-ok: mutating for OAuth setup + + return headers + + def logging_non_streaming_response( + self, + model: str, + custom_llm_provider: str, + httpx_response: Response, + request_data: Mapping[str, object], + logging_obj: LiteLLMLoggingObj, + endpoint: str, + ) -> CostResponseTypes | None: + from litellm import encoding + from litellm.types.utils import LlmProviders, ModelResponse + from litellm.utils import ProviderConfigManager + + # cost tracking only for completions and embeddings + if "completions" in endpoint: + provider_chat_config: Final = ProviderConfigManager.get_provider_chat_config( + provider=LlmProviders(custom_llm_provider), + model=model, + ) + + if provider_chat_config is None: + raise ValueError(f"No provider config found for model: {model}") + + raw_messages: Final = request_data.get("messages") + litellm_model_response: Final = provider_chat_config.transform_response( + model=model, + messages=list(raw_messages) + if isinstance(raw_messages, list) + else [], # mutable-ok: transform_response wants a list + raw_response=httpx_response, + model_response=ModelResponse(), + logging_obj=logging_obj, + optional_params={}, # mutable-ok: empty dict kwarg for transform_response + litellm_params={}, # mutable-ok: empty dict kwarg for transform_response + api_key="", + request_data=dict(request_data), # mutable-ok: transform_response wants a dict + encoding=encoding, + ) + + return litellm_model_response + + if "embeddings" in endpoint: + provider_embedding_config: Final = ProviderConfigManager.get_provider_embedding_config( + provider=LlmProviders(custom_llm_provider), + model=model, + ) + + if provider_embedding_config is None: + raise ValueError(f"No provider config found for model: {model}") + + litellm_embedding_response: Final[EmbeddingResponse] = ( + provider_embedding_config.transform_embedding_response( + model=model, + raw_response=httpx_response, + model_response=EmbeddingResponse(), + logging_obj=logging_obj, + optional_params={}, # mutable-ok: empty dict kwarg for transform_embedding_response + api_key="", + request_data=dict(request_data), # mutable-ok: transform_embedding_response wants a dict + litellm_params={}, # mutable-ok: empty dict kwarg for transform_embedding_response + ) + ) + + return litellm_embedding_response + + return None + + def handle_logging_collected_chunks( + self, + all_chunks: Sequence[str], + litellm_logging_obj: LiteLLMLoggingObj, + model: str, + custom_llm_provider: str, + endpoint: str, + ) -> CostResponseTypes | None: + """ + 1. Convert all_chunks to a ModelResponseStream + 2. combine model_response_stream to model_response + 3. Return the model_response + """ + + from litellm.litellm_core_utils.streaming_handler import ( + convert_generic_chunk_to_model_response_stream, + generic_chunk_has_all_required_fields, + ) + from litellm.main import stream_chunk_builder + from litellm.types.utils import ModelResponseStream + + all_translated_chunks: Final[list[object]] = [] # mutable-ok: accumulator + + for chunk in all_chunks: + chunk = chunk.strip() + if not chunk or chunk == "[DONE]": + continue + chunk = chunk.removeprefix("data: ") + try: + message = json.loads(chunk) + except json.JSONDecodeError: + continue + + gigachat_iterator = GigaChatModelResponseIterator( + streaming_response=None, + sync_stream=False, + ) + translated_chunk = gigachat_iterator.chunk_parser(chunk=message) + + if isinstance(translated_chunk, dict) and generic_chunk_has_all_required_fields( # pyright: ignore[reportUnnecessaryIsInstance] # runtime guard for patched chunk_parser + dict(translated_chunk) + ): + chunk_obj = convert_generic_chunk_to_model_response_stream( + translated_chunk # pyright: ignore[reportArgumentType] # validated TypedDict + ) + elif isinstance(translated_chunk, ModelResponseStream): + chunk_obj = translated_chunk + else: + continue + + all_translated_chunks.append(chunk_obj) + + if len(all_translated_chunks) > 0: + return stream_chunk_builder( + chunks=all_translated_chunks, + logging_obj=litellm_logging_obj, + ) + return None + + @staticmethod + def get_api_base(api_base: str | None = None) -> str | None: + return api_base or get_secret_str("GIGACHAT_API_BASE") or GIGACHAT_BASE_URL + + @staticmethod + def get_api_key( + api_key: str | None = None, + ) -> str | None: + return api_key or get_secret_str("GIGACHAT_API_KEY") + + @staticmethod + def get_base_model(model: str) -> str | None: + return model + + def get_models(self, api_key: str | None = None, api_base: str | None = None) -> list[str]: + return list(super().get_models(api_key, api_base)) diff --git a/litellm/llms/gigachat/utils.py b/litellm/llms/gigachat/utils.py new file mode 100644 index 00000000000..cbb35cd1b57 --- /dev/null +++ b/litellm/llms/gigachat/utils.py @@ -0,0 +1,26 @@ +from collections.abc import Mapping +from typing import Final + +from litellm.secret_managers.main import get_secret_str +from litellm.types.utils import PromptTokensDetailsWrapper, Usage + +# GigaChat API endpoint +GIGACHAT_BASE_URL: Final = "https://gigachat.devices.sberbank.ru/api/v1" + + +def convert_usage(usage_data: Mapping[str, int]) -> Usage: + precached_prompt_tokens: Final = usage_data.get("precached_prompt_tokens", 0) + prompt_tokens_details: Final = ( + PromptTokensDetailsWrapper(cached_tokens=precached_prompt_tokens) if precached_prompt_tokens > 0 else None + ) + + return Usage( + prompt_tokens=usage_data.get("prompt_tokens", 0) + precached_prompt_tokens, + completion_tokens=usage_data.get("completion_tokens", 0), + prompt_tokens_details=prompt_tokens_details, + total_tokens=usage_data.get("total_tokens", 0) + precached_prompt_tokens, + ) + + +def get_api_base(api_base: str | None = None) -> str | None: + return api_base or get_secret_str("GIGACHAT_API_BASE") or GIGACHAT_BASE_URL diff --git a/litellm/llms/hosted_vllm/embedding/README.md b/litellm/llms/hosted_vllm/embedding/README.md index 2c58e16fc23..50474aabdeb 100644 --- a/litellm/llms/hosted_vllm/embedding/README.md +++ b/litellm/llms/hosted_vllm/embedding/README.md @@ -4,13 +4,12 @@ VLLM is a superset of OpenAI's `embedding` endpoint. ## `encoding_format` -For OpenAI-compatible embedding calls (including `openai/...` with a custom `api_base` pointing at vLLM), LiteLLM resolves `encoding_format` when it is not set on the request: +For OpenAI-compatible embedding calls (including `openai/...` with a custom `api_base` pointing at vLLM), LiteLLM resolves `encoding_format` when it is not set on the request. `hosted_vllm/...` models use a separate handler that never adds the field on its own, so this resolution applies to the `openai/...`-style routes only: 1. Explicit value on the embedding call (`encoding_format=...`). 2. Model config (`litellm_params.encoding_format` on the proxy `model_list` entry). 3. Environment variable `LITELLM_DEFAULT_EMBEDDING_ENCODING_FORMAT` (e.g. in `.env` or container env). -4. Default **`float`**. -That avoids forwarding `encoding_format=None` to the provider/SDK where some servers behave poorly. +If none of those is set, or the winning value is the literal string `none`, the field is omitted from the upstream request entirely (LiteLLM also bypasses the OpenAI SDK's own base64 default), so OpenAI-compatible servers that reject `encoding_format` keep working. -To pass provider-specific parameters, see [provider-specific params](https://docs.litellm.ai/docs/completion/provider_specific_params). \ No newline at end of file +To pass provider-specific parameters, see [provider-specific params](https://docs.litellm.ai/docs/completion/provider_specific_params). diff --git a/litellm/llms/huggingface/embedding/transformation.py b/litellm/llms/huggingface/embedding/transformation.py index d3db3530109..f6fe7f2fa10 100644 --- a/litellm/llms/huggingface/embedding/transformation.py +++ b/litellm/llms/huggingface/embedding/transformation.py @@ -1,8 +1,9 @@ import json import os import time +from collections.abc import Sequence from copy import deepcopy -from typing import TYPE_CHECKING, Any, Final +from typing import TYPE_CHECKING, Any, Final, Protocol import httpx @@ -24,6 +25,8 @@ from litellm.utils import token_counter from ..common_utils import HuggingFaceError, hf_task_list, hf_tasks, output_parser if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj LoggingClass = LiteLLMLoggingObj @@ -31,6 +34,12 @@ else: LoggingClass = Any +class _TokenEncoding(Protocol): + """Tokenizer handle the caller passes in; only `encode` is used, to count completion tokens.""" + + def encode(self, text: str, /) -> Sequence[object]: ... + + tgi_models_cache = None conv_models_cache = None @@ -369,7 +378,7 @@ class HuggingFaceEmbeddingConfig(BaseConfig): model_response: ModelResponse, task: hf_tasks | None, optional_params: dict, - encoding: Any, + encoding: "_TokenEncoding | None", messages: list[AllMessageValues], model: str, ): @@ -439,9 +448,10 @@ class HuggingFaceEmbeddingConfig(BaseConfig): if output_text is not None and len(output_text) > 0: completion_tokens = 0 try: - completion_tokens = len( - encoding.encode(model_response["choices"][0]["message"].get("content", "")) - ) ##[TODO] use the llama2 tokenizer here + if encoding is not None: + completion_tokens = len( + encoding.encode(model_response["choices"][0]["message"].get("content", "")) + ) ##[TODO] use the llama2 tokenizer here except Exception: # this should remain non blocking we should not block a response returning if calculating usage fails pass @@ -469,7 +479,7 @@ class HuggingFaceEmbeddingConfig(BaseConfig): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: diff --git a/litellm/llms/litellm_proxy/skills/code_execution.py b/litellm/llms/litellm_proxy/skills/code_execution.py index f51213ca1c3..fbc287589b3 100644 --- a/litellm/llms/litellm_proxy/skills/code_execution.py +++ b/litellm/llms/litellm_proxy/skills/code_execution.py @@ -13,55 +13,109 @@ Generated files are returned directly in the response - no separate storage need import base64 import json -from collections.abc import Sequence +from collections.abc import Mapping, Sequence from enum import Enum -from typing import Any, Final, Protocol +from typing import Any, Final, Protocol, TypedDict -from typing_extensions import NotRequired, ReadOnly, TypedDict +from typing_extensions import ReadOnly from litellm._logging import verbose_logger -class _ToolCallFunction(Protocol): - """Function payload of an assistant tool call.""" - - name: str | None - arguments: str +class _ToolParameterSchema(TypedDict, total=False): + type: ReadOnly[str] + description: ReadOnly[str] -class _ToolCall(Protocol): - """Tool call requested by the assistant on a chat completion choice.""" - - id: str - function: _ToolCallFunction +class _ToolArgumentSchema(TypedDict, total=False): + type: ReadOnly[str] + properties: ReadOnly[Mapping[str, _ToolParameterSchema]] + required: ReadOnly[Sequence[str]] -class _AssistantMessage(Protocol): - """Assistant message carried by a chat completion choice.""" - - content: str | None - tool_calls: Sequence[_ToolCall] | None +class _OpenAIToolFunction(TypedDict, total=False): + name: ReadOnly[str] + description: ReadOnly[str] + parameters: ReadOnly[_ToolArgumentSchema] -class _CompletionChoice(Protocol): - """Single choice of a chat completion response.""" - - finish_reason: str - message: _AssistantMessage +class _OpenAIToolSpec(TypedDict, total=False): + type: ReadOnly[str] + function: ReadOnly[_OpenAIToolFunction] -class _SandboxFile(TypedDict): - """File generated inside the sandbox during a code execution run.""" +class _AnthropicToolSpec(TypedDict, total=False): + name: ReadOnly[str] + description: ReadOnly[str] + input_schema: ReadOnly[_ToolArgumentSchema] + +class _CodeExecutionArguments(TypedDict, total=False): + code: ReadOnly[str] + + +class _GeneratedFile(TypedDict, total=False): + name: ReadOnly[str] + mime_type: ReadOnly[str] + content_base64: ReadOnly[str] + size: ReadOnly[int] + + +class _SandboxGeneratedFile(TypedDict): name: ReadOnly[str] mime_type: ReadOnly[str] content_base64: ReadOnly[str] -class _CodeExecutionArguments(TypedDict): - """Arguments the model passes to the `litellm_code_execution` tool.""" +class _SandboxExecutionResult(TypedDict): + success: ReadOnly[bool] + output: ReadOnly[str] + error: ReadOnly[str] + files: ReadOnly[Sequence[_SandboxGeneratedFile]] - code: NotRequired[ReadOnly[str]] + +class _ExecutionResult(TypedDict, total=False): + iteration: ReadOnly[int] + success: ReadOnly[bool] + output: ReadOnly[str] + error: ReadOnly[str] + files: ReadOnly[Sequence[str]] + + +class _ToolCallFunction(Protocol): + name: str + arguments: str + + +class _ToolCall(Protocol): + id: str + function: _ToolCallFunction + + +class _AssistantMessage(Protocol): + content: str | None + tool_calls: Sequence[_ToolCall] | None + + +class _ResponseChoice(Protocol): + message: _AssistantMessage + finish_reason: str | None + + +class _CompletionResponse(Protocol): + choices: Sequence[_ResponseChoice] + + +class _CodeExecutionOutcome(TypedDict, total=False): + response: ReadOnly[_CompletionResponse | None] + files: ReadOnly[Sequence[_GeneratedFile]] + execution_results: ReadOnly[Sequence[_ExecutionResult]] + messages: ReadOnly[Sequence[dict[str, object]]] + max_iterations_reached: ReadOnly[bool] + + +def _parse_code_execution_arguments(serialized_arguments: str) -> _CodeExecutionArguments: + return json.loads(serialized_arguments) class LiteLLMInternalTools(str, Enum): @@ -75,7 +129,7 @@ class LiteLLMInternalTools(str, Enum): CODE_EXECUTION = "litellm_code_execution" -def get_litellm_code_execution_tool() -> dict[str, object]: +def get_litellm_code_execution_tool() -> _OpenAIToolSpec: """ Returns the litellm_code_execution tool definition in OpenAI format. @@ -96,7 +150,7 @@ def get_litellm_code_execution_tool() -> dict[str, object]: } -def get_litellm_code_execution_tool_anthropic() -> dict[str, object]: +def get_litellm_code_execution_tool_anthropic() -> _AnthropicToolSpec: """ Returns the litellm_code_execution tool definition in Anthropic/messages API format. @@ -143,12 +197,12 @@ class CodeExecutionHandler: async def execute_with_code_execution( self, model: str, - messages: list[dict], - tools: list[dict], + messages: list[dict[str, object]], + tools: list[_OpenAIToolSpec], skill_files: dict[str, bytes], skill_id: str | None = None, **kwargs, - ) -> dict[str, object]: + ) -> _CodeExecutionOutcome: """ Execute an LLM call with automatic code execution handling. @@ -179,8 +233,8 @@ class CodeExecutionHandler: ) current_messages: Final = list(messages) - generated_files: Final[list[dict[str, object]]] = [] # Files returned directly - execution_results: Final[list[dict[str, object]]] = [] + generated_files: Final[list[_GeneratedFile]] = [] # Files returned directly + execution_results: Final[list[_ExecutionResult]] = [] executor: Final = SkillsSandboxExecutor(timeout=self.sandbox_timeout) response: Any = None # Initialize to avoid possibly unbound error @@ -196,9 +250,9 @@ class CodeExecutionHandler: **kwargs, ) - choice: _CompletionChoice = response.choices[0] + choice: _ResponseChoice = response.choices[0] assistant_message = choice.message - stop_reason: str = choice.finish_reason + stop_reason = choice.finish_reason # Build assistant message for conversation history assistant_msg_dict: dict[str, object] = { @@ -236,19 +290,19 @@ class CodeExecutionHandler: if tool_name == LiteLLMInternalTools.CODE_EXECUTION.value: # Execute code in sandbox try: - args: _CodeExecutionArguments = json.loads(tool_call.function.arguments) - code: str = args.get("code", "") + args = _parse_code_execution_arguments(tool_call.function.arguments) + code = args.get("code", "") verbose_logger.debug("CodeExecutionHandler: Executing code (%s chars)", len(code)) - exec_result = executor.execute( + exec_result: _SandboxExecutionResult = executor.execute( code=code, skill_files=skill_files, ) verbose_logger.debug("CodeExecutionHandler: Execution result: %s", exec_result) - sandbox_files: Sequence[_SandboxFile] = exec_result["files"] + sandbox_files: Sequence[_SandboxGeneratedFile] = exec_result["files"] execution_results.append( { @@ -326,7 +380,7 @@ class CodeExecutionHandler: } -def has_code_execution_tool(tools: list[dict] | None) -> bool: +def has_code_execution_tool(tools: list[_OpenAIToolSpec] | None) -> bool: """Check if litellm_code_execution tool is in the tools list.""" if not tools: return False @@ -337,7 +391,7 @@ def has_code_execution_tool(tools: list[dict] | None) -> bool: return False -def add_code_execution_tool(tools: list[dict] | None) -> list[dict]: +def add_code_execution_tool(tools: list[_OpenAIToolSpec] | None) -> list[_OpenAIToolSpec]: """Add litellm_code_execution tool if not already present.""" tools = tools or [] if not has_code_execution_tool(tools): diff --git a/litellm/llms/nvidia_riva/audio_transcription/audio_utils.py b/litellm/llms/nvidia_riva/audio_transcription/audio_utils.py index 008a5a5780f..046b4e29a0a 100644 --- a/litellm/llms/nvidia_riva/audio_transcription/audio_utils.py +++ b/litellm/llms/nvidia_riva/audio_transcription/audio_utils.py @@ -16,7 +16,7 @@ import io import os import tempfile from dataclasses import dataclass -from typing import Any, Final, cast +from typing import Final, Protocol, cast from litellm.llms.nvidia_riva.audio_transcription.transformation import ( RIVA_TARGET_NUM_CHANNELS, @@ -24,10 +24,30 @@ from litellm.llms.nvidia_riva.audio_transcription.transformation import ( ) from litellm.llms.nvidia_riva.common_utils import NvidiaRivaException -# Keep this as Any: the module intentionally avoids importing numpy at module -# import time (optional dependency), and project-wide mypy config evaluates this -# file in contexts where conditional type aliases can degrade to "FloatArray?". -FloatArray = Any + +class FloatArray(Protocol): + """Structural view of the ``numpy.ndarray`` surface this module relies on.""" + + @property + def ndim(self) -> int: ... + + @property + def shape(self) -> tuple[int, ...]: ... + + @property + def size(self) -> int: ... + + def mean(self, axis: int) -> "FloatArray": ... + + def ravel(self) -> "FloatArray": ... + + def astype(self, dtype: object) -> "FloatArray": ... + + def tobytes(self) -> bytes: ... + + def __getitem__(self, key: object) -> "FloatArray": ... + + def __mul__(self, other: float) -> "FloatArray": ... _INSTALL_HINT = "Install Riva STT extras to enable automatic audio resampling: `pip install 'litellm[stt-nvidia-riva]'`" diff --git a/litellm/llms/oci/chat/cohere.py b/litellm/llms/oci/chat/cohere.py index 384e7ec4cf8..6e9bb83b0a0 100644 --- a/litellm/llms/oci/chat/cohere.py +++ b/litellm/llms/oci/chat/cohere.py @@ -9,7 +9,7 @@ response parsing, and streaming chunk parsing for models served with import datetime import json from collections.abc import Iterable, Mapping, Sequence -from typing import Any, Final +from typing import Final import httpx from pydantic import JsonValue, TypeAdapter, ValidationError @@ -76,7 +76,7 @@ def _content_text(content: str | Iterable[Mapping[str, object]] | None) -> str: return str(content) -def _extract_text_content(content: Any) -> str: +def _extract_text_content(content: str | Iterable[Mapping[str, object]] | None) -> str: """Return the plain-text representation of a message content value.""" return _content_text(content) diff --git a/litellm/llms/oci/common_utils.py b/litellm/llms/oci/common_utils.py index 5c3962bc05d..3f703564b5a 100644 --- a/litellm/llms/oci/common_utils.py +++ b/litellm/llms/oci/common_utils.py @@ -5,10 +5,11 @@ import os import re from dataclasses import dataclass from email.utils import formatdate -from typing import Any, Final, Protocol +from typing import Final, Protocol from urllib.parse import urlparse import httpx +from pydantic import JsonValue from litellm.llms.base_llm.chat.transformation import BaseLLMException @@ -64,7 +65,7 @@ class OCISignerProtocol(Protocol): See: https://docs.oracle.com/en-us/iaas/tools/python/latest/api/signing.html """ - def do_request_sign(self, request: Any, *, enforce_content_headers: bool = False) -> None: + def do_request_sign(self, request: "OCIRequestWrapper", *, enforce_content_headers: bool = False) -> None: pass @@ -113,7 +114,7 @@ def build_signature_string(method: str, path: str, headers: dict, signed_headers return "\n".join(lines) -def load_private_key_from_str(key_str: str) -> Any: +def load_private_key_from_str(key_str: str) -> "rsa.RSAPrivateKey": _require_cryptography() key: Final = serialization.load_pem_private_key( key_str.encode("utf-8"), @@ -124,7 +125,7 @@ def load_private_key_from_str(key_str: str) -> Any: return key -def load_private_key_from_file(file_path: str) -> Any: +def load_private_key_from_file(file_path: str) -> "rsa.RSAPrivateKey": """Loads a private key from a file path.""" try: with open(file_path, "r", encoding="utf-8") as f: @@ -421,16 +422,17 @@ OCI_JSON_TO_PYTHON_TYPES: Final[dict[str, str]] = { } -def resolve_oci_schema_refs(schema: dict[str, Any]) -> dict[str, Any]: +def resolve_oci_schema_refs(schema: JsonValue) -> JsonValue: """Inline all ``$ref``/``$defs`` references — OCI does not support JSON Schema ``$ref``.""" - defs: Final = schema.get("$defs", {}) - resolving_stack: Final[set] = set() + raw_defs: Final = schema.get("$defs") if isinstance(schema, dict) else None + defs: Final[dict[str, JsonValue]] = raw_defs if isinstance(raw_defs, dict) else {} + resolving_stack: Final[set[str]] = set() - def _resolve(obj: Any) -> Any: + def _resolve(obj: JsonValue) -> JsonValue: if isinstance(obj, dict): - if "$ref" in obj: - ref: Final = obj["$ref"] - if ref.startswith("#/$defs/"): + ref: Final = obj.get("$ref") + if ref is not None: + if isinstance(ref, str) and ref.startswith("#/$defs/"): key: Final = ref.split("/")[-1] if key in resolving_stack: return {"type": "object"} # break cycles @@ -451,7 +453,7 @@ def resolve_oci_schema_refs(schema: dict[str, Any]) -> dict[str, Any]: return resolved -def resolve_oci_schema_anyof(obj: Any) -> Any: +def resolve_oci_schema_anyof(obj: JsonValue) -> JsonValue: """Resolve Pydantic v2 ``Optional[T]`` → ``anyOf`` patterns. Pydantic v2 emits ``{"anyOf": [{"type": "T"}, {"type": "null"}]}`` for @@ -459,10 +461,13 @@ def resolve_oci_schema_anyof(obj: Any) -> Any: first non-null branch and merge top-level metadata into it. """ if isinstance(obj, dict): - if "anyOf" in obj and "type" not in obj: - non_null: Final = [t for t in obj["anyOf"] if not (isinstance(t, dict) and t.get("type") == "null")] + raw_any_of: Final = obj.get("anyOf") + if raw_any_of is not None and "type" not in obj: + branches: Final = raw_any_of if isinstance(raw_any_of, list) else [] + non_null: Final = [t for t in branches if not (isinstance(t, dict) and t.get("type") == "null")] if non_null: - resolved: Final = {**obj, **non_null[0]} + first: Final = non_null[0] + resolved: Final[dict[str, JsonValue]] = {**obj, **first} if isinstance(first, dict) else {**obj} resolved.pop("anyOf", None) return resolve_oci_schema_anyof(resolved) return {k: resolve_oci_schema_anyof(v) for k, v in obj.items()} @@ -471,7 +476,7 @@ def resolve_oci_schema_anyof(obj: Any) -> Any: return obj -def sanitize_oci_schema(schema: Any) -> Any: +def sanitize_oci_schema(schema: JsonValue) -> JsonValue: """Recursively remove OCI-incompatible fields from a JSON schema. Strips ``title`` keys, removes ``None``-valued ``default`` entries, @@ -483,7 +488,7 @@ def sanitize_oci_schema(schema: Any) -> Any: if not isinstance(schema, dict): return schema - sanitized: Final[dict[str, Any]] = {} + sanitized: Final[dict[str, JsonValue]] = {} for key, value in schema.items(): if key == "title": continue @@ -513,7 +518,7 @@ def sanitize_oci_schema(schema: Any) -> Any: return sanitized -def enrich_cohere_param_description(description: str, param_schema: dict[str, Any]) -> str: +def enrich_cohere_param_description(description: str, param_schema: dict[str, JsonValue]) -> str: """Embed schema constraints into a Cohere parameter description. ``CohereParameterDefinition`` only has ``type``, ``description``, and diff --git a/litellm/llms/openai/chat/gpt_transformation.py b/litellm/llms/openai/chat/gpt_transformation.py index 5894658e5d2..3a7f78fd5ba 100644 --- a/litellm/llms/openai/chat/gpt_transformation.py +++ b/litellm/llms/openai/chat/gpt_transformation.py @@ -170,16 +170,20 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig): if model != "gpt-3.5-turbo-16k" and model != "gpt-4": # gpt-4 does not support 'response_format' model_specific_params.append("response_format") - # Normalize model name for responses API (e.g., "responses/gpt-4.1" -> "gpt-4.1") - model_for_check: Final = model.split("responses/", 1)[1] if "responses/" in model else model - if ( - model_for_check in litellm.open_ai_chat_completion_models - ) or model_for_check in litellm.open_ai_text_completion_models: + if OpenAIGPTConfig.is_openai_catalog_model(model): model_specific_params.append( "user" ) # user is not a param supported by all openai-compatible endpoints - e.g. azure ai return base_params + model_specific_params + @staticmethod + def is_openai_catalog_model(model: str) -> bool: + model_for_check: Final = model.split("responses/", 1)[1] if "responses/" in model else model + return ( + model_for_check in litellm.open_ai_chat_completion_models + or model_for_check in litellm.open_ai_text_completion_models + ) + def _map_openai_params( self, non_default_params: dict, @@ -321,7 +325,7 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig): @overload def _transform_messages( self, messages: list[AllMessageValues], model: str, is_async: Literal[True] - ) -> Coroutine[Any, Any, list[AllMessageValues]]: + ) -> Coroutine[object, object, list[AllMessageValues]]: ... @overload @@ -337,7 +341,7 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig): def _transform_messages( self, messages: list[AllMessageValues], model: str, is_async: bool = False - ) -> list[AllMessageValues] | Coroutine[Any, Any, list[AllMessageValues]]: + ) -> list[AllMessageValues] | Coroutine[object, object, list[AllMessageValues]]: """OpenAI no longer supports image_url as a string, so we need to convert it to a dict""" stripped_messages: Final = drop_tool_reference_parts_from_tool_messages(messages) hoisted_messages: Final = hoist_images_from_tool_messages(stripped_messages) @@ -493,8 +497,12 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig): return None tool_call_names: Final = get_tool_call_names(optional_params.get("tools", [])) try: - json_content: Final = json.loads(content) - if json_content.get("type") == "function" and json_content.get("name") in tool_call_names: + json_content: Final[object] = json.loads(content) + if ( + isinstance(json_content, dict) + and json_content.get("type") == "function" + and json_content.get("name") in tool_call_names + ): return ChatCompletionMessageToolCall( function=Function( name=json_content.get("name"), @@ -618,7 +626,7 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig): ## RESPONSE OBJECT try: - completion_response: Final = raw_response.json() + completion_response: Final[dict[str, object]] = raw_response.json() except Exception as e: response_headers: Final = getattr(raw_response, "headers", None) raise OpenAIError( @@ -755,6 +763,14 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig): ) +class OpenAIUnknownModelConfig(OpenAIGPTConfig): + """A model the openai provider does not recognize is typically a LiteLLM proxy alias, so + forward reasoning_effort and let the server decide whether it is supported.""" + + def get_supported_openai_params(self, model: str) -> list: # mutable-ok: inherited contract + return super().get_supported_openai_params(model) + ["reasoning_effort"] # mutable-ok: inherited contract + + class OpenAIChatCompletionStreamingHandler(BaseModelResponseIterator): def _map_reasoning_to_reasoning_content(self, choices: list) -> list: """ diff --git a/litellm/llms/openai/chat/guardrail_translation/handler.py b/litellm/llms/openai/chat/guardrail_translation/handler.py index de15fefe943..ed628f55350 100644 --- a/litellm/llms/openai/chat/guardrail_translation/handler.py +++ b/litellm/llms/openai/chat/guardrail_translation/handler.py @@ -51,6 +51,7 @@ 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 class OpenAIChatCompletionsHandler(BaseTranslation): @@ -80,7 +81,7 @@ class OpenAIChatCompletionsHandler(BaseTranslation): data: dict, guardrail_to_apply: "CustomGuardrail", litellm_logging_obj: "LiteLLMLoggingObj | None" = None, - ) -> Any: + ) -> dict: """ Process input messages by applying guardrails to text content. """ @@ -329,9 +330,9 @@ class OpenAIChatCompletionsHandler(BaseTranslation): response: "ModelResponse", guardrail_to_apply: "CustomGuardrail", litellm_logging_obj: "LiteLLMLoggingObj | None" = None, - user_api_key_dict: Any | None = None, + user_api_key_dict: "UserAPIKeyAuth | None" = None, request_data: dict | None = None, - ) -> Any: + ) -> ModelResponse: """ Process output response by applying guardrails to text content. @@ -436,7 +437,7 @@ class OpenAIChatCompletionsHandler(BaseTranslation): responses_so_far: list["ModelResponseStream"], guardrail_to_apply: "CustomGuardrail", litellm_logging_obj: "LiteLLMLoggingObj | None" = None, - user_api_key_dict: Any | None = None, + user_api_key_dict: "UserAPIKeyAuth | None" = None, request_data: dict | None = None, stream_transform_sink: StreamTransformSink | None = None, ) -> list["ModelResponseStream"]: @@ -486,7 +487,7 @@ class OpenAIChatCompletionsHandler(BaseTranslation): responses_so_far: list["ModelResponseStream"], guardrail_to_apply: "CustomGuardrail", litellm_logging_obj: "LiteLLMLoggingObj | None", - user_api_key_dict: Any | None, + user_api_key_dict: "UserAPIKeyAuth | None", request_data: dict | None, ) -> list["ModelResponseStream"]: """Block-only streaming path: run the guardrail so an in-flight BLOCK can @@ -589,8 +590,8 @@ class OpenAIChatCompletionsHandler(BaseTranslation): def build_stream_error_items( self, exc: "HTTPException", - responses_so_far: Sequence[Any] | None = None, - ) -> Sequence[Any] | None: + responses_so_far: Sequence[object] | None = None, + ) -> Sequence[bytes] | None: import json from litellm.proxy.common_request_processing import sse_error_payload @@ -630,7 +631,7 @@ class OpenAIChatCompletionsHandler(BaseTranslation): responses_so_far: list["ModelResponseStream"], guardrail_to_apply: "CustomGuardrail", litellm_logging_obj: "LiteLLMLoggingObj | None", - user_api_key_dict: Any | None, + user_api_key_dict: "UserAPIKeyAuth | None", request_data: dict | None, sink: StreamTransformSink, ) -> None: @@ -794,7 +795,7 @@ class OpenAIChatCompletionsHandler(BaseTranslation): # Determine content source and tool calls based on choice type content = None - tool_calls: list[Any] | None = None + tool_calls: Sequence[object] | None = None if isinstance(choice, litellm.Choices): content = choice.message.content tool_calls = choice.message.tool_calls diff --git a/litellm/llms/openai/common_utils.py b/litellm/llms/openai/common_utils.py index 1b1ab80e85d..4d774f6f165 100644 --- a/litellm/llms/openai/common_utils.py +++ b/litellm/llms/openai/common_utils.py @@ -268,6 +268,7 @@ class BaseOpenAILLM: "max_retries", "organization", "api_base", + "workload_identity_config", ) openai_client_fields: Final = ( BaseOpenAILLM.get_openai_client_initialization_param_fields(client_type=client_type) diff --git a/litellm/llms/openai/containers/transformation.py b/litellm/llms/openai/containers/transformation.py index 44bd401c115..1a5211d5ff5 100644 --- a/litellm/llms/openai/containers/transformation.py +++ b/litellm/llms/openai/containers/transformation.py @@ -155,10 +155,7 @@ class OpenAIContainerConfig(BaseContainerConfig): logging_obj: LiteLLMLoggingObj, ) -> ContainerObject: """Transform the OpenAI container creation response.""" - response_data: Final[OpenAIContainerPayload] = raw_response.json() - - # Transform the response data - container_obj: Final = ContainerObject(**response_data) + container_obj: Final = ContainerObject.model_validate(raw_response.json()) # Add cost for container creation (OpenAI containers are code interpreter sessions) # https://platform.openai.com/docs/pricing @@ -215,10 +212,7 @@ class OpenAIContainerConfig(BaseContainerConfig): logging_obj: LiteLLMLoggingObj, ) -> ContainerListResponse: """Transform the OpenAI container list response.""" - response_data: Final[OpenAIContainerListPayload] = raw_response.json() - - # Transform the response data - container_list: Final = ContainerListResponse(**response_data) + container_list: Final = ContainerListResponse.model_validate(raw_response.json()) return container_list @@ -235,7 +229,7 @@ class OpenAIContainerConfig(BaseContainerConfig): url: Final = join_container_api_base_path(api_base, f"/{encoded_container_id}") # No additional data needed for GET request - data: Final[dict[str, object]] = {} + data: Final[dict[str, str]] = {} return url, data @@ -245,9 +239,7 @@ class OpenAIContainerConfig(BaseContainerConfig): logging_obj: LiteLLMLoggingObj, ) -> ContainerObject: """Transform the OpenAI container retrieve response.""" - response_data: Final[OpenAIContainerPayload] = raw_response.json() - # Transform the response data - container_obj: Final = ContainerObject(**response_data) + container_obj: Final = ContainerObject.model_validate(raw_response.json()) return container_obj @@ -268,7 +260,7 @@ class OpenAIContainerConfig(BaseContainerConfig): url: Final = join_container_api_base_path(api_base, f"/{encoded_container_id}") # No data needed for DELETE request - data: Final[dict[str, object]] = {} + data: Final[dict[str, str]] = {} return url, data @@ -278,10 +270,7 @@ class OpenAIContainerConfig(BaseContainerConfig): logging_obj: LiteLLMLoggingObj, ) -> DeleteContainerResult: """Transform the OpenAI container delete response.""" - response_data: Final[OpenAIContainerDeletedPayload] = raw_response.json() - - # Transform the response data - delete_result: Final = DeleteContainerResult(**response_data) + delete_result: Final = DeleteContainerResult.model_validate(raw_response.json()) return delete_result @@ -326,10 +315,7 @@ class OpenAIContainerConfig(BaseContainerConfig): logging_obj: LiteLLMLoggingObj, ) -> ContainerFileListResponse: """Transform the OpenAI container file list response.""" - response_data: Final[OpenAIContainerFileListPayload] = raw_response.json() - - # Transform the response data - file_list: Final = ContainerFileListResponse(**response_data) + file_list: Final = ContainerFileListResponse.model_validate(raw_response.json()) return file_list @@ -352,7 +338,7 @@ class OpenAIContainerConfig(BaseContainerConfig): url: Final = join_container_api_base_path(api_base, f"/{encoded_container_id}/files/{encoded_file_id}/content") # No query parameters needed - params: Final[dict[str, object]] = {} + params: Final[dict[str, str]] = {} return url, params diff --git a/litellm/llms/openai/openai.py b/litellm/llms/openai/openai.py index 6e66c998acf..1cfc6e06ee9 100644 --- a/litellm/llms/openai/openai.py +++ b/litellm/llms/openai/openai.py @@ -12,9 +12,14 @@ if TYPE_CHECKING: import openai from openai import AsyncOpenAI, OpenAI +from openai._base_client import make_request_options +from openai._constants import RAW_RESPONSE_HEADER +from openai._legacy_response import LegacyAPIResponse +from openai._types import RequestOptions +from openai.types import CreateEmbeddingResponse from openai.types.beta.assistant_deleted import AssistantDeleted from openai.types.file_deleted import FileDeleted -from pydantic import BaseModel +from pydantic import BaseModel, TypeAdapter from typing_extensions import overload import litellm @@ -43,6 +48,7 @@ from litellm.utils import ( from ...types.llms.openai import * from ..base import BaseLLM from .chat.gpt_5_transformation import OpenAIGPT5Config +from .chat.gpt_transformation import OpenAIGPTConfig, OpenAIUnknownModelConfig from .chat.o_series_transformation import OpenAIOSeriesConfig from .common_utils import ( BaseOpenAILLM, @@ -51,6 +57,7 @@ from .common_utils import ( drop_params_from_unprocessable_entity_error, is_output_token_limit_error, ) +from .workload_identity import resolve_openai_workload_identity_config openaiOSeriesConfig: Final = OpenAIOSeriesConfig() openAIGPT5Config: Final = OpenAIGPT5Config() @@ -188,7 +195,12 @@ class OpenAIConfig(BaseConfig): elif litellm.openAIGPTAudioConfig.is_model_gpt_audio_model(model=model): return litellm.openAIGPTAudioConfig.get_supported_openai_params(model=model) else: - return litellm.openAIGPTConfig.get_supported_openai_params(model=model) + return self._gpt_config_for_model(model).get_supported_openai_params(model=model) + + def _gpt_config_for_model(self, model: str) -> OpenAIGPTConfig: + if type(self) is OpenAIConfig and not OpenAIGPTConfig.is_openai_catalog_model(model): + return OpenAIUnknownModelConfig() + return litellm.openAIGPTConfig def _map_openai_params(self, non_default_params: dict, optional_params: dict, model: str) -> dict: supported_openai_params: Final = self.get_supported_openai_params(model) @@ -230,7 +242,7 @@ class OpenAIConfig(BaseConfig): drop_params=drop_params, ) - return litellm.openAIGPTConfig.map_openai_params( + return self._gpt_config_for_model(model).map_openai_params( non_default_params=non_default_params, optional_params=optional_params, model=model, @@ -322,6 +334,28 @@ class OpenAIChatCompletionResponseIterator(BaseModelResponseIterator): raise e +_EXTRA_HEADERS_ADAPTER: Final = TypeAdapter(dict[str, str] | None) +_EXTRA_QUERY_ADAPTER: Final = TypeAdapter(dict[str, object] | None) +_NO_EXTRA_HEADERS: Final[Mapping[str, str]] = types.MappingProxyType({}) +_SDK_OPTION_KEYS: Final = frozenset(("extra_headers", "extra_query", "extra_body")) + + +def _embedding_request_without_sdk_defaults( + data: Mapping[str, object], timeout: float | httpx.Timeout +) -> tuple[Mapping[str, object], RequestOptions]: + body: Final = { # mutable-ok: the SDK json-encodes the body and needs a plain dict + k: v for k, v in data.items() if k not in _SDK_OPTION_KEYS + } + extra_headers: Final = _EXTRA_HEADERS_ADAPTER.validate_python(data.get("extra_headers")) or _NO_EXTRA_HEADERS + options: Final = make_request_options( + extra_headers=types.MappingProxyType({**extra_headers, RAW_RESPONSE_HEADER: "true"}), + extra_query=_EXTRA_QUERY_ADAPTER.validate_python(data.get("extra_query")), + extra_body=data.get("extra_body"), + timeout=timeout, + ) + return body, options + + class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): def __init__(self) -> None: super().__init__() @@ -349,6 +383,7 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): client: OpenAI | AsyncOpenAI | None = None, shared_session: Optional["ClientSession"] = None, ) -> OpenAI | AsyncOpenAI | None: + workload_identity_config: Final = resolve_openai_workload_identity_config(api_key=api_key, api_base=api_base) client_initialization_params: Final[dict] = locals() if client is None: if not isinstance(max_retries, int): @@ -364,28 +399,49 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): if cached_client: if isinstance(cached_client, OpenAI) or isinstance(cached_client, AsyncOpenAI): return cached_client - http_client: Final[httpx.Client | httpx.AsyncClient | None] = ( - OpenAIChatCompletion._get_async_http_client(shared_session=shared_session) - if is_async - else OpenAIChatCompletion._get_sync_http_client() - ) if is_async: - _new_client: OpenAI | AsyncOpenAI = AsyncOpenAI( - api_key=api_key, - base_url=api_base, - http_client=http_client, - timeout=timeout, - max_retries=max_retries, - organization=organization, + async_http_client: Final = OpenAIChatCompletion._get_async_http_client(shared_session=shared_session) + http_client: httpx.Client | httpx.AsyncClient | None = async_http_client + _new_client: OpenAI | AsyncOpenAI = ( + AsyncOpenAI( + workload_identity=workload_identity_config.to_sdk_workload_identity(), + base_url=api_base, + http_client=async_http_client, + timeout=timeout, + max_retries=max_retries, + organization=organization, + ) + if workload_identity_config is not None + else AsyncOpenAI( + api_key=api_key, + base_url=api_base, + http_client=async_http_client, + timeout=timeout, + max_retries=max_retries, + organization=organization, + ) ) else: - _new_client = OpenAI( - api_key=api_key, - base_url=api_base, - http_client=http_client, - timeout=timeout, - max_retries=max_retries, - organization=organization, + sync_http_client: Final = OpenAIChatCompletion._get_sync_http_client() + http_client = sync_http_client + _new_client = ( + OpenAI( + workload_identity=workload_identity_config.to_sdk_workload_identity(), + base_url=api_base, + http_client=sync_http_client, + timeout=timeout, + max_retries=max_retries, + organization=organization, + ) + if workload_identity_config is not None + else OpenAI( + api_key=api_key, + base_url=api_base, + http_client=sync_http_client, + timeout=timeout, + max_retries=max_retries, + organization=organization, + ) ) ## SAVE CACHE KEY @@ -1148,19 +1204,15 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): data: dict, timeout: float | httpx.Timeout, logging_obj: LiteLLMLoggingObj, - ): - """ - Helper to: - - call embeddings.create.with_raw_response when litellm.return_response_headers is True - - call embeddings.create by default - """ - try: - raw_response = await openai_aclient.embeddings.with_raw_response.create(**data, timeout=timeout) - headers: Final = dict(raw_response.headers) - response: Final = raw_response.parse() - return headers, response - except Exception as e: - raise e + ) -> LegacyAPIResponse[CreateEmbeddingResponse]: + if "encoding_format" not in data: + body, options = _embedding_request_without_sdk_defaults(data, timeout) + bypass_response: Final = await openai_aclient.post( + "/embeddings", body=body, options=options, cast_to=CreateEmbeddingResponse + ) + assert isinstance(bypass_response, LegacyAPIResponse) + return bypass_response + return await openai_aclient.embeddings.with_raw_response.create(**data, timeout=timeout) @track_llm_api_timing() def make_sync_openai_embedding_request( @@ -1169,20 +1221,15 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): data: dict, timeout: float | httpx.Timeout, logging_obj: LiteLLMLoggingObj, - ): - """ - Helper to: - - call embeddings.create.with_raw_response when litellm.return_response_headers is True - - call embeddings.create by default - """ - try: - raw_response = openai_client.embeddings.with_raw_response.create(**data, timeout=timeout) - - headers: Final = dict(raw_response.headers) - response: Final = raw_response.parse() - return headers, response - except Exception as e: - raise e + ) -> LegacyAPIResponse[CreateEmbeddingResponse]: + if "encoding_format" not in data: + body, options = _embedding_request_without_sdk_defaults(data, timeout) + bypass_response: Final = openai_client.post( + "/embeddings", body=body, options=options, cast_to=CreateEmbeddingResponse + ) + assert isinstance(bypass_response, LegacyAPIResponse) + return bypass_response + return openai_client.embeddings.with_raw_response.create(**data, timeout=timeout) async def aembedding( self, @@ -1207,14 +1254,15 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): client=client, shared_session=shared_session, ) - headers, response = await self.make_openai_embedding_request( + raw_response: Final = await self.make_openai_embedding_request( openai_aclient=openai_aclient, data=data, timeout=timeout, logging_obj=logging_obj, ) + headers: Final = dict(raw_response.headers) logging_obj.model_call_details["response_headers"] = headers - stringified_response: Final = response.model_dump() + stringified_response: Final = raw_response.parse().model_dump() ## LOGGING logging_obj.post_call( input=input, @@ -1306,13 +1354,14 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): ) ## embedding CALL - headers: dict | None = None - headers, sync_embedding_response = self.make_sync_openai_embedding_request( + raw_response: Final = self.make_sync_openai_embedding_request( openai_client=openai_client, data=data, timeout=timeout, logging_obj=logging_obj, ) + headers: Final = dict(raw_response.headers) + sync_embedding_response: Final = raw_response.parse() ## LOGGING logging_obj.model_call_details["response_headers"] = headers diff --git a/litellm/llms/openai/responses/count_tokens/transformation.py b/litellm/llms/openai/responses/count_tokens/transformation.py index 6b2f4535df1..88f04c59e01 100644 --- a/litellm/llms/openai/responses/count_tokens/transformation.py +++ b/litellm/llms/openai/responses/count_tokens/transformation.py @@ -4,7 +4,100 @@ OpenAI Responses API token counting transformation logic. This module handles the transformation of requests to OpenAI's /v1/responses/input_tokens endpoint. """ -from typing import Any, Final +from collections.abc import Mapping, Sequence +from typing import Any, Final, Literal + +from typing_extensions import ReadOnly, TypedDict + + +class ResponsesInputTextPart(TypedDict): + type: ReadOnly[Literal["input_text"]] + text: ReadOnly[str] + + +class ResponsesInputImagePart(TypedDict): + type: ReadOnly[Literal["input_image"]] + image_url: ReadOnly[str] + detail: ReadOnly[str] + + +class ResponsesInputFilePart(TypedDict): + type: ReadOnly[Literal["input_file"]] + filename: ReadOnly[str] + file_data: ReadOnly[str] + + +ResponsesInputPart = ResponsesInputTextPart | ResponsesInputImagePart | ResponsesInputFilePart + +ResponsesContentRole = Literal["user", "assistant"] + + +def _chat_image_block_to_responses_part(image_url: object) -> ResponsesInputImagePart | None: + url: Final = image_url.get("url") if isinstance(image_url, Mapping) else image_url + if not isinstance(url, str) or not url: + return None + detail: Final = image_url.get("detail") if isinstance(image_url, Mapping) else None + part: Final[ResponsesInputImagePart] = { + "type": "input_image", + "image_url": url, + "detail": detail if isinstance(detail, str) and detail else "auto", + } + return part + + +def _chat_file_block_to_responses_part(file_value: object) -> ResponsesInputFilePart | None: + """Only an inline file round trips: OpenAI rejects `file_data` without the `filename` beside it.""" + if not isinstance(file_value, Mapping): + return None + filename: Final = file_value.get("filename") + file_data: Final = file_value.get("file_data") + if not isinstance(filename, str) or not filename or not isinstance(file_data, str) or not file_data: + return None + part: Final[ResponsesInputFilePart] = { + "type": "input_file", + "filename": filename, + "file_data": file_data, + } + return part + + +def _chat_block_to_responses_part(block: object, role: ResponsesContentRole) -> ResponsesInputPart | None: + if isinstance(block, str): + bare: Final[ResponsesInputTextPart] = {"type": "input_text", "text": block} + return bare + if not isinstance(block, Mapping): + return None + match block.get("type"): + case "text": + text_value: Final = block.get("text") + text: Final[ResponsesInputTextPart] = { + "type": "input_text", + "text": text_value if isinstance(text_value, str) else "", + } + return text + case "image_url" if role == "user": + return _chat_image_block_to_responses_part(block.get("image_url")) + case "file" if role == "user": + return _chat_file_block_to_responses_part(block.get("file")) + case _: + return None + + +def chat_content_blocks_to_responses_content( + content: Sequence[object], + role: ResponsesContentRole, +) -> str | tuple[ResponsesInputPart, ...]: + """Text-only content collapses to a joined string, which every role accepts and counts identically. + + Only a user turn may carry an image or file part: the Responses API rejects any part but + output_text and refusal inside an assistant turn. + """ + parts: Final = tuple( + part for part in (_chat_block_to_responses_part(block, role) for block in content) if part is not None + ) + if any(part["type"] != "input_text" for part in parts): + return parts + return "\n".join(part["text"] for part in parts if part["type"] == "input_text") class OpenAICountTokensConfig: @@ -120,18 +213,13 @@ class OpenAICountTokensConfig: instructions_parts.append("\n".join(text_parts)) elif role == "user": if isinstance(content, list): - # Extract text from content blocks for Responses API - text_parts = [] - for block in content: - if isinstance(block, dict) and block.get("type") == "text": - text_parts.append(block.get("text", "")) - elif isinstance(block, str): - text_parts.append(block) - content = "\n".join(text_parts) + content = chat_content_blocks_to_responses_content(content, "user") input_items.append({"role": "user", "content": content}) elif role == "assistant": # Map tool_calls to Responses API function_call items tool_calls = msg.get("tool_calls") + if isinstance(content, list): + content = chat_content_blocks_to_responses_content(content, "assistant") if content: input_items.append({"role": "assistant", "content": content}) if tool_calls: diff --git a/litellm/llms/openai/responses/transformation.py b/litellm/llms/openai/responses/transformation.py index eac844a790d..09028b6dc5f 100644 --- a/litellm/llms/openai/responses/transformation.py +++ b/litellm/llms/openai/responses/transformation.py @@ -1,10 +1,11 @@ from collections.abc import Mapping, Sequence from types import MappingProxyType -from typing import TYPE_CHECKING, Any, Final, cast, get_type_hints +from typing import TYPE_CHECKING, Any, Final, Protocol, cast, get_type_hints import httpx from openai.types.responses import ResponseReasoningItem from pydantic import BaseModel, ValidationError +from typing_extensions import ReadOnly, TypedDict import litellm from litellm._logging import verbose_logger @@ -21,6 +22,7 @@ from litellm.types.router import GenericLiteLLMParams from litellm.types.utils import LlmProviders from ..common_utils import OpenAIError +from ..workload_identity import get_workload_identity_bearer_token, resolve_openai_workload_identity_config OPENAI_RESPONSES_API_MIN_MAX_OUTPUT_TOKENS: Final = 16 @@ -36,6 +38,36 @@ _MODEL_FAMILIES_REJECTING_TOP_LEVEL_SCHEMA_COMBINATORS: Final = ("gpt-4", "gpt-3 _PROVIDERS_WITH_COMBINATOR_REJECTING_VALIDATOR: Final = frozenset({LlmProviders.AZURE, LlmProviders.OPENAI}) +class _DeleteResponseBody(TypedDict): + """Decoded body of the Responses API delete call.""" + + id: ReadOnly[str | None] + object: ReadOnly[str | None] + deleted: ReadOnly[bool | None] + + +class _DeleteResponse(Protocol): + """The delete call's HTTP response, read for the decoded body it carries.""" + + def json(self) -> _DeleteResponseBody: ... + + +class _JsonObjectResponse(Protocol): + """A Responses API HTTP response, read for the JSON object it decodes to.""" + + def json(self) -> dict[str, object]: ... + + +def _delete_response_body(response: _DeleteResponse) -> _DeleteResponseBody: + """Decode a delete response body into the id, object and deleted fields it carries.""" + return response.json() + + +def _json_object_body(response: _JsonObjectResponse) -> dict[str, object]: + """Decode a Responses API response body into its JSON object form.""" + return response.json() + + class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): @property def custom_llm_provider(self) -> LlmProviders: @@ -392,6 +424,14 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): litellm_params = litellm_params or GenericLiteLLMParams() api_key = litellm_params.api_key or litellm.api_key or litellm.openai_key or get_secret_str("OPENAI_API_KEY") headers.setdefault("Content-Type", "application/json") + workload_identity_config: Final = ( + resolve_openai_workload_identity_config(api_key=api_key, api_base=litellm_params.api_base) + if self.custom_llm_provider is LlmProviders.OPENAI + else None + ) + if workload_identity_config is not None: + headers["Authorization"] = f"Bearer {get_workload_identity_bearer_token(workload_identity_config)}" + return headers headers["Authorization"] = f"Bearer {api_key}" return headers @@ -460,7 +500,7 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): return None @staticmethod - def get_event_model_class(event_type: str) -> Any: + def get_event_model_class(event_type: str) -> type[BaseLiteLLMOpenAIResponseObject]: """ Returns the appropriate event model class based on the event type. @@ -574,7 +614,7 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): Transform the delete response API response into a DeleteResponseResult """ try: - raw_response_json: Final = raw_response.json() + raw_response_json: Final = _delete_response_body(raw_response) except Exception: raise OpenAIError(message=raw_response.text, status_code=raw_response.status_code) return DeleteResponseResult(**raw_response_json) @@ -609,7 +649,7 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): Transform the get response API response into a ResponsesAPIResponse """ try: - raw_response_json: Final = raw_response.json() + raw_response_json: Final = _json_object_body(raw_response) except Exception: raise OpenAIError(message=raw_response.text, status_code=raw_response.status_code) raw_response_headers: Final = dict(raw_response.headers) @@ -637,7 +677,7 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): ) -> tuple[str, dict]: encoded_response_id: Final = encode_url_path_segment(response_id, field_name="response_id") url: Final = f"{api_base}/{encoded_response_id}/input_items" - params: Final[dict[str, Any]] = {} + params: Final[dict[str, object]] = {} if after is not None: params["after"] = after if before is not None: @@ -656,7 +696,7 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): logging_obj: LiteLLMLoggingObj, ) -> dict: try: - return raw_response.json() + return _json_object_body(raw_response) except Exception: raise OpenAIError(message=raw_response.text, status_code=raw_response.status_code) @@ -690,7 +730,7 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): Transform the cancel response API response into a ResponsesAPIResponse """ try: - raw_response_json: Final = raw_response.json() + raw_response_json: Final = _json_object_body(raw_response) except Exception: raise OpenAIError(message=raw_response.text, status_code=raw_response.status_code) raw_response_headers: Final = dict(raw_response.headers) diff --git a/litellm/llms/openai/workload_identity.py b/litellm/llms/openai/workload_identity.py new file mode 100644 index 00000000000..ecec161ed46 --- /dev/null +++ b/litellm/llms/openai/workload_identity.py @@ -0,0 +1,100 @@ +from __future__ import annotations + +from dataclasses import dataclass +from functools import lru_cache +from typing import TYPE_CHECKING, Final +from urllib.parse import urlparse + +import litellm +from litellm.secret_managers.main import get_secret_str, normalize_nonempty_secret_str + +from .common_utils import OpenAIError + +if TYPE_CHECKING: + from collections.abc import Callable + + from openai.auth import SubjectTokenProvider, WorkloadIdentity, WorkloadIdentityAuth + +OPENAI_WIF_CLIENT_ID: Final = "litellm" +_OPENAI_API_HOST: Final = "api.openai.com" +_SDK_UPGRADE_MESSAGE: Final = ( + "OpenAI workload identity federation requires openai>=2.32.0. " + "Upgrade the installed openai package to use OPENAI_IDENTITY_PROVIDER_ID / " + "OPENAI_SERVICE_ACCOUNT_ID / OPENAI_IDENTITY_TOKEN_FILE." +) + + +@dataclass(frozen=True, slots=True) +class OpenAIWorkloadIdentityConfig: + identity_provider_id: str + service_account_id: str + token_file: str + + def to_sdk_workload_identity(self) -> WorkloadIdentity: + k8s_token_provider: Final = _load_sdk_k8s_token_provider() + workload_identity: Final[WorkloadIdentity] = { + "client_id": OPENAI_WIF_CLIENT_ID, + "identity_provider_id": self.identity_provider_id, + "service_account_id": self.service_account_id, + "provider": k8s_token_provider(self.token_file), + } + return workload_identity + + +def resolve_openai_workload_identity_config( + api_key: str | None, + api_base: str | None, +) -> OpenAIWorkloadIdentityConfig | None: + static_api_key: Final = normalize_nonempty_secret_str(api_key) or normalize_nonempty_secret_str( + get_secret_str("OPENAI_API_KEY") + ) + if static_api_key is not None: + return None + effective_api_base: Final = ( + api_base or litellm.api_base or get_secret_str("OPENAI_BASE_URL") or get_secret_str("OPENAI_API_BASE") + ) + if not _targets_openai_api(effective_api_base): + return None + identity_provider_id: Final = get_secret_str("OPENAI_IDENTITY_PROVIDER_ID") + service_account_id: Final = get_secret_str("OPENAI_SERVICE_ACCOUNT_ID") + token_file: Final = get_secret_str("OPENAI_IDENTITY_TOKEN_FILE") + if not identity_provider_id or not service_account_id or not token_file: + return None + return OpenAIWorkloadIdentityConfig( + identity_provider_id=identity_provider_id, + service_account_id=service_account_id, + token_file=token_file, + ) + + +def get_workload_identity_bearer_token(config: OpenAIWorkloadIdentityConfig) -> str: + return _workload_identity_auth(config).get_token() + + +def _targets_openai_api(api_base: str | None) -> bool: + if api_base is None: + return True + parsed: Final = urlparse(api_base) + return parsed.scheme == "https" and parsed.hostname == _OPENAI_API_HOST + + +@lru_cache(maxsize=16) +def _workload_identity_auth(config: OpenAIWorkloadIdentityConfig) -> WorkloadIdentityAuth: + sdk_workload_identity_auth: Final = _load_sdk_workload_identity_auth() + return sdk_workload_identity_auth(workload_identity=config.to_sdk_workload_identity()) + + +def _load_sdk_workload_identity_auth() -> type[WorkloadIdentityAuth]: + try: + from openai.auth import WorkloadIdentityAuth as sdk_workload_identity_auth + except ImportError as e: + raise OpenAIError(status_code=500, message=_SDK_UPGRADE_MESSAGE) from e + return sdk_workload_identity_auth + + +def _load_sdk_k8s_token_provider() -> Callable[[str], SubjectTokenProvider]: + try: + from openai.auth import k8s_service_account_token_provider + except ImportError as e: + raise OpenAIError(status_code=500, message=_SDK_UPGRADE_MESSAGE) from e + return k8s_service_account_token_provider diff --git a/litellm/llms/openai_like/chat/handler.py b/litellm/llms/openai_like/chat/handler.py index 8c548b6b0d6..855c49c320b 100644 --- a/litellm/llms/openai_like/chat/handler.py +++ b/litellm/llms/openai_like/chat/handler.py @@ -5,10 +5,11 @@ For handling OpenAI-like chat completions, like IBM WatsonX, etc. """ import json -from collections.abc import Callable -from typing import Any, Final +from collections.abc import Callable, Mapping, Sequence +from typing import Final, TypedDict import httpx +from typing_extensions import ReadOnly import litellm from litellm import LlmProviders @@ -25,6 +26,23 @@ from ..common_utils import OpenAILikeBase, OpenAILikeError from .transformation import OpenAILikeChatConfig +class _OpenAILikeChatCompletion(TypedDict, total=False): + """The chat-completion JSON body an OpenAI-like provider returns for a non-streamed call.""" + + id: ReadOnly[str] + choices: ReadOnly[Sequence[Mapping[str, object]]] + created: ReadOnly[int] + model: ReadOnly[str] + system_fingerprint: ReadOnly[str] + usage: ReadOnly[Mapping[str, object]] + object: ReadOnly[str] + + +def _fake_streamed_model_response(payload: _OpenAILikeChatCompletion) -> ModelResponse: + """Build the single response a fake-streamed provider call replays as one chunk.""" + return ModelResponse(**payload) + + async def make_call( client: AsyncHTTPHandler | None, api_base: str, @@ -42,9 +60,9 @@ async def make_call( response: Final = await client.post(api_base, headers=headers, data=data, stream=not fake_stream) if streaming_decoder is not None: - completion_stream: Any = streaming_decoder.aiter_bytes(response.aiter_bytes(chunk_size=1024)) + completion_stream = streaming_decoder.aiter_bytes(response.aiter_bytes(chunk_size=1024)) elif fake_stream: - model_response: Final = ModelResponse(**response.json()) + model_response: Final = _fake_streamed_model_response(response.json()) completion_stream = MockResponseIterator(model_response=model_response) else: completion_stream = ModelResponseIterator(streaming_response=response.aiter_lines(), sync_stream=False) @@ -82,7 +100,7 @@ def make_sync_call( if streaming_decoder is not None: completion_stream = streaming_decoder.iter_bytes(response.iter_bytes(chunk_size=1024)) elif fake_stream: - model_response: Final = ModelResponse(**response.json()) + model_response: Final = _fake_streamed_model_response(response.json()) completion_stream = MockResponseIterator(model_response=model_response) else: completion_stream = ModelResponseIterator(streaming_response=response.iter_lines(), sync_stream=True) diff --git a/litellm/llms/runwayml/image_generation/transformation.py b/litellm/llms/runwayml/image_generation/transformation.py index cde65addb65..5913709c8a0 100644 --- a/litellm/llms/runwayml/image_generation/transformation.py +++ b/litellm/llms/runwayml/image_generation/transformation.py @@ -1,8 +1,10 @@ import asyncio import time +from collections.abc import Mapping, Sequence from typing import TYPE_CHECKING, Any, Final import httpx +from typing_extensions import ReadOnly, TypedDict from litellm._logging import verbose_logger from litellm.constants import ( @@ -29,6 +31,16 @@ else: LiteLLMLoggingObj = Any +class _RunwayMLTask(TypedDict, total=False): + """The RunwayML task payload returned by POST /v1/text_to_image and GET /v1/tasks/{id}.""" + + id: ReadOnly[str] + status: ReadOnly[str] + output: ReadOnly[Sequence[str | Mapping[str, str]]] + failure: ReadOnly[str] + failureCode: ReadOnly[str] + + class RunwayMLImageGenerationConfig(BaseImageGenerationConfig): """ Configuration for RunwayML image generation models. @@ -80,7 +92,7 @@ class RunwayMLImageGenerationConfig(BaseImageGenerationConfig): @staticmethod def _transform_runwayml_response_to_openai( - response_data: dict[str, Any], + response_data: _RunwayMLTask, model_response: ImageResponse, ) -> ImageResponse: """ @@ -155,7 +167,7 @@ class RunwayMLImageGenerationConfig(BaseImageGenerationConfig): raise TimeoutError(f"RunwayML task polling timed out after {timeout_secs} seconds") @staticmethod - def _check_task_status(response_data: dict[str, Any]) -> str: + def _check_task_status(response_data: _RunwayMLTask) -> str: """ Check RunwayML task status from response. @@ -227,7 +239,7 @@ class RunwayMLImageGenerationConfig(BaseImageGenerationConfig): response = client.get(url=task_url, headers=headers) response.raise_for_status() - response_data = response.json() + response_data: _RunwayMLTask = response.json() # Check task status status = self._check_task_status(response_data=response_data) @@ -276,7 +288,7 @@ class RunwayMLImageGenerationConfig(BaseImageGenerationConfig): response = await client.get(url=task_url, headers=headers) response.raise_for_status() - response_data = response.json() + response_data: _RunwayMLTask = response.json() # Check task status status = self._check_task_status(response_data=response_data) @@ -322,7 +334,7 @@ class RunwayMLImageGenerationConfig(BaseImageGenerationConfig): } """ try: - response_data = raw_response.json() + response_data: _RunwayMLTask = raw_response.json() except Exception as e: raise self.get_error_class( error_message=f"Error transforming image generation response: {e}", @@ -382,7 +394,7 @@ class RunwayMLImageGenerationConfig(BaseImageGenerationConfig): We need to poll the task until it completes (status SUCCEEDED) using async polling. """ try: - response_data = raw_response.json() + response_data: _RunwayMLTask = raw_response.json() except Exception as e: raise self.get_error_class( error_message=f"Error transforming image generation response: {e}", diff --git a/litellm/llms/runwayml/text_to_speech/transformation.py b/litellm/llms/runwayml/text_to_speech/transformation.py index 1da8f0c66f0..19e6d8ff494 100644 --- a/litellm/llms/runwayml/text_to_speech/transformation.py +++ b/litellm/llms/runwayml/text_to_speech/transformation.py @@ -6,10 +6,11 @@ Maps OpenAI TTS spec to RunwayML Text-to-Speech API import asyncio import time -from collections.abc import Coroutine -from typing import TYPE_CHECKING, Any, Final, Union +from collections.abc import Coroutine, Sequence +from typing import TYPE_CHECKING, Any, Final, TypedDict, Union import httpx +from typing_extensions import ReadOnly import litellm from litellm._logging import verbose_logger @@ -31,6 +32,14 @@ else: HttpxBinaryResponseContent = Any +class _RunwayTtsTaskResponse(TypedDict, total=False): + id: ReadOnly[str] + status: ReadOnly[str] + output: ReadOnly[Sequence[object]] + failure: ReadOnly[str] + failureCode: ReadOnly[str] + + class RunwayMLTextToSpeechConfig(BaseTextToSpeechConfig): """ Configuration for RunwayML Text-to-Speech @@ -64,7 +73,7 @@ class RunwayMLTextToSpeechConfig(BaseTextToSpeechConfig): litellm_params_dict: dict, logging_obj: "LiteLLMLoggingObj", timeout: float | httpx.Timeout, - extra_headers: dict[str, Any] | None, + extra_headers: dict[str, object] | None, base_llm_http_handler: Any, aspeech: bool, api_base: str | None, @@ -72,7 +81,7 @@ class RunwayMLTextToSpeechConfig(BaseTextToSpeechConfig): **kwargs: Any, ) -> Union[ "HttpxBinaryResponseContent", - Coroutine[Any, Any, "HttpxBinaryResponseContent"], + Coroutine[object, object, "HttpxBinaryResponseContent"], ]: """ Dispatch method to handle RunwayML TTS requests @@ -242,7 +251,7 @@ class RunwayMLTextToSpeechConfig(BaseTextToSpeechConfig): raise TimeoutError(f"RunwayML TTS task polling timed out after {timeout_secs} seconds") @staticmethod - def _check_task_status(response_data: dict[str, Any]) -> str: + def _check_task_status(response_data: _RunwayTtsTaskResponse) -> str: """ Check RunwayML task status from response. @@ -314,7 +323,7 @@ class RunwayMLTextToSpeechConfig(BaseTextToSpeechConfig): response = client.get(url=task_url, headers=headers) response.raise_for_status() - response_data = response.json() + response_data: _RunwayTtsTaskResponse = response.json() # Check task status status = self._check_task_status(response_data=response_data) @@ -362,7 +371,7 @@ class RunwayMLTextToSpeechConfig(BaseTextToSpeechConfig): response = await client.get(url=task_url, headers=headers) response.raise_for_status() - response_data = response.json() + response_data: _RunwayTtsTaskResponse = response.json() # Check task status status = self._check_task_status(response_data=response_data) @@ -453,7 +462,7 @@ class RunwayMLTextToSpeechConfig(BaseTextToSpeechConfig): from litellm.types.llms.openai import HttpxBinaryResponseContent try: - response_data: Final = raw_response.json() + response_data: Final[_RunwayTtsTaskResponse] = raw_response.json() except Exception as e: raise self.get_error_class( error_message=f"Error parsing RunwayML TTS response: {e}", @@ -483,7 +492,7 @@ class RunwayMLTextToSpeechConfig(BaseTextToSpeechConfig): ) # Get the completed task data - task_data: Final = polled_response.json() + task_data: Final[_RunwayTtsTaskResponse] = polled_response.json() verbose_logger.debug("RunwayML TTS polling complete, downloading audio") @@ -522,7 +531,7 @@ class RunwayMLTextToSpeechConfig(BaseTextToSpeechConfig): from litellm.types.llms.openai import HttpxBinaryResponseContent try: - response_data: Final = raw_response.json() + response_data: Final[_RunwayTtsTaskResponse] = raw_response.json() except Exception as e: raise self.get_error_class( error_message=f"Error parsing RunwayML TTS response: {e}", @@ -552,7 +561,7 @@ class RunwayMLTextToSpeechConfig(BaseTextToSpeechConfig): ) # Get the completed task data - task_data: Final = polled_response.json() + task_data: Final[_RunwayTtsTaskResponse] = polled_response.json() verbose_logger.debug("RunwayML TTS polling complete (async), downloading audio") diff --git a/litellm/llms/runwayml/videos/transformation.py b/litellm/llms/runwayml/videos/transformation.py index c7289eb47f5..c7696a1cb29 100644 --- a/litellm/llms/runwayml/videos/transformation.py +++ b/litellm/llms/runwayml/videos/transformation.py @@ -160,14 +160,12 @@ class RunwayMLVideoConfig(BaseVideoConfig): **self._prompt_image_param(video_create_optional_params), **self._ratio_param(video_create_optional_params), **self._duration_param(video_create_optional_params), - # Pass through other parameters that aren't OpenAI-specific **{key: value for key, value in video_create_optional_params.items() if key not in supported_openai_params}, } @staticmethod def _prompt_image_param(video_create_optional_params: VideoCreateOptionalRequestParams) -> Mapping[str, object]: # Handle input_reference parameter - map to promptImage - # RunwayML supports URLs and data URIs directly if "input_reference" in video_create_optional_params: return {"promptImage": video_create_optional_params["input_reference"]} return {} diff --git a/litellm/llms/sap/credentials.py b/litellm/llms/sap/credentials.py index d7743d4d337..a2a93b6114a 100644 --- a/litellm/llms/sap/credentials.py +++ b/litellm/llms/sap/credentials.py @@ -8,9 +8,10 @@ from dataclasses import dataclass from datetime import datetime, timedelta, timezone from pathlib import Path from threading import Lock -from typing import Any, Final +from typing import Any, Final, Protocol import httpx +from typing_extensions import NotRequired, ReadOnly, TypedDict import litellm from litellm._logging import verbose_logger @@ -33,8 +34,8 @@ def _get_home() -> str: return os.getenv(HOME_PATH_ENV_VAR, DEFAULT_HOME_PATH) -def _get_nested(d: dict[str, Any] | str, path: Sequence[str]) -> Any: - cur: Any = d +def _get_nested(d: object, path: Sequence[str]) -> object: + cur: object = d if isinstance(cur, str): # This shouldn't happen if service keys are pre-parsed correctly try: @@ -54,7 +55,7 @@ def _get_nested(d: dict[str, Any] | str, path: Sequence[str]) -> Any: return cur -def _load_json_env(var_name: str) -> dict[str, Any] | None: +def _load_json_env(var_name: str) -> dict[str, object] | None: raw: Final = os.environ.get(var_name) if not raw: return None @@ -64,7 +65,7 @@ def _load_json_env(var_name: str) -> dict[str, Any] | None: return None -def _str_or_none(value) -> str | None: +def _str_or_none(value: object) -> str | None: try: return str(value) if value is not None else None except Exception: @@ -124,7 +125,7 @@ CREDENTIAL_VALUES: Final[list[CredentialsValue]] = [ ] -def init_conf(profile: str | None = None) -> dict[str, Any]: +def init_conf(profile: str | None = None) -> dict[str, object]: """ Loads config JSON from: 1) $AICORE_CONFIG if set, otherwise @@ -191,7 +192,7 @@ def resolve_resource_group(sources: list[Source]) -> str | None: def _parse_service_key_once( service_key: str | dict | None, -) -> dict[str, Any] | None: +) -> dict[str, object] | None: """ Pre-parse service_key if it's a string to avoid repeated JSON parsing. @@ -348,8 +349,33 @@ def validate_credentials( ) +class _TokenBody(TypedDict): + """Decoded body of the SAP AI Core OAuth2 token response.""" + + access_token: ReadOnly[str] + expires_in: ReadOnly[NotRequired[int]] + + +class _TokenResponse(Protocol): + """The token endpoint's HTTP response, read for the decoded token body it carries.""" + + def json(self) -> _TokenBody: ... + + +def _bearer_token_and_expiry(response: _TokenResponse) -> tuple[str, datetime]: + """Read a token response into the Authorization header value and the token's absolute expiry.""" + payload: Final = response.json() + expires_in: Final = int(payload.get("expires_in", 3600)) + access_token: Final = payload["access_token"] + return f"Bearer {access_token}", datetime.now(timezone.utc) + timedelta(seconds=expires_in) + + def _request_token( - client_id: str, auth_url: str, timeout: float, cert_pair=None, client_secret=None + client_id: str, + auth_url: str, + timeout: float, + cert_pair: tuple[str, str] | None = None, + client_secret: str | None = None, ) -> tuple[str, datetime]: data: Final = {"grant_type": "client_credentials", "client_id": client_id} if client_secret: @@ -361,15 +387,10 @@ def _request_token( with httpx.Client(cert=cert_pair) as raw_client: handler = HTTPHandler(client=raw_client) resp = handler.post(auth_url, data=data, timeout=timeout) - payload = resp.json() - else: - handler = _get_httpx_client() - resp = handler.post(auth_url, data=data, timeout=timeout) - payload = resp.json() - access_token: Final = payload["access_token"] - expires_in: Final = int(payload.get("expires_in", 3600)) - expiry_date: Final = datetime.now(timezone.utc) + timedelta(seconds=expires_in) - return f"Bearer {access_token}", expiry_date + return _bearer_token_and_expiry(resp) + handler = _get_httpx_client() + resp = handler.post(auth_url, data=data, timeout=timeout) + return _bearer_token_and_expiry(resp) except Exception as e: msg: Final = resp.text if resp is not None else getattr(e, "text", str(e)) raise RuntimeError(f"Token request failed: {msg}") from e diff --git a/litellm/llms/vertex_ai/common_utils.py b/litellm/llms/vertex_ai/common_utils.py index 1de2337d8eb..a36c920dda0 100644 --- a/litellm/llms/vertex_ai/common_utils.py +++ b/litellm/llms/vertex_ai/common_utils.py @@ -1,7 +1,7 @@ import re from copy import deepcopy from enum import Enum -from typing import Any, Final, Literal, get_type_hints +from typing import Any, Final, Literal, cast, get_type_hints import httpx @@ -31,7 +31,7 @@ class VertexAIError(BaseLLMException): super().__init__(message=message, status_code=status_code, headers=headers) -def redact_vertex_ai_metadata_from_logged_object(obj: Any) -> None: +def redact_vertex_ai_metadata_from_logged_object(obj: object) -> None: if isinstance(obj, dict): for field in VERTEX_AI_PROVIDER_METADATA_FIELDS: if field in obj: @@ -651,7 +651,7 @@ def _build_json_schema(parameters: dict) -> dict: return parameters -def _filter_anyof_fields(schema_dict: dict[str, Any]) -> dict[str, Any]: +def _filter_anyof_fields(schema_dict: dict[str, object]) -> dict[str, object]: """ When anyof is present, only keep the anyof field and its contents - otherwise VertexAI will throw an error - https://github.com/BerriAI/litellm/issues/11164 Filter out other fields in the same dict. @@ -704,7 +704,7 @@ def process_items(schema, depth=0): process_items(item, depth + 1) -def set_schema_property_ordering(schema: dict[str, Any], depth: int = 0) -> dict[str, Any]: +def set_schema_property_ordering(schema: dict[str, object], depth: int = 0) -> dict[str, object]: """ vertex ai and generativeai apis order output of fields alphabetically, unless you specify the order. python dicts retain order, so we just use that. Note that this field only applies to structured outputs, and not tools. @@ -724,14 +724,16 @@ def set_schema_property_ordering(schema: dict[str, Any], depth: int = 0) -> dict # retain propertyOrdering as an escape hatch if user already specifies it if "propertyOrdering" not in schema: schema["propertyOrdering"] = [k for k, v in schema["properties"].items()] - for k, v in schema["properties"].items(): - set_schema_property_ordering(v, depth + 1) - if "items" in schema: - set_schema_property_ordering(schema["items"], depth + 1) + for v in schema["properties"].values(): + if isinstance(v, dict): + set_schema_property_ordering(cast("dict[str, object]", v), depth + 1) # cast-ok: JSON Schema child + items: Final = schema.get("items") + if isinstance(items, dict): + set_schema_property_ordering(cast("dict[str, object]", items), depth + 1) # cast-ok: JSON Schema child return schema -def filter_schema_fields(schema_dict: dict[str, Any], valid_fields: set[str], processed=None) -> dict[str, Any]: +def filter_schema_fields(schema_dict: dict[str, object], valid_fields: set[str], processed=None) -> dict[str, object]: """ Recursively filter a schema dictionary to keep only valid fields. """ @@ -905,7 +907,7 @@ def _convert_schema_types(schema, depth=0): "maxProperties", } - any_of: Final[list[dict[str, Any]]] = [] + any_of: Final[list[dict[str, object]]] = [] for t in type_val: if not isinstance(t, str): continue @@ -916,7 +918,7 @@ def _convert_schema_types(schema, depth=0): # For object/array types, include type-specific fields if t in ("object", "array"): - item_schema = {"type": t} + item_schema: dict[str, object] = {"type": t} # Move type-specific fields into this anyOf item for field in type_specific_fields: if field in schema: @@ -1110,11 +1112,11 @@ class VertexAITokenCounter(BaseTokenCounter): self, model_to_use: str, messages: list[dict[str, Any]] | None, - contents: list[dict[str, Any]] | None, + contents: list[dict[str, object]] | None, deployment: dict[str, Any] | None = None, request_model: str = "", - tools: list[dict[str, Any]] | None = None, - system: Any | None = None, + tools: list[dict[str, object]] | None = None, + system: object | None = None, ) -> TokenCountResponse | None: import copy @@ -1131,25 +1133,26 @@ class VertexAITokenCounter(BaseTokenCounter): partner_models_handler: Final = VertexAIPartnerModels() # Extract vertex-specific params from litellm_params - vertex_project = count_tokens_params_request.get("vertex_project") or count_tokens_params_request.get( + partner_litellm_params: Final[dict[str, object]] = count_tokens_params_request + vertex_project = partner_litellm_params.get("vertex_project") or partner_litellm_params.get( "vertex_ai_project" ) - vertex_location = count_tokens_params_request.get("vertex_location") or count_tokens_params_request.get( + vertex_location = partner_litellm_params.get("vertex_location") or partner_litellm_params.get( "vertex_ai_location" ) # Count tokens not available on global location: https://docs.cloud.google.com/vertex-ai/generative-ai/docs/partner-models/claude/count-tokens - vertex_location = count_tokens_params_request.get("vertex_count_tokens_location") or vertex_location + vertex_location = partner_litellm_params.get("vertex_count_tokens_location") or vertex_location - vertex_credentials: Final = count_tokens_params_request.get( - "vertex_credentials" - ) or count_tokens_params_request.get("vertex_ai_credentials") + vertex_credentials: Final = partner_litellm_params.get("vertex_credentials") or partner_litellm_params.get( + "vertex_ai_credentials" + ) result = await partner_models_handler.count_tokens( model=model_to_use, messages=messages or [], - litellm_params=count_tokens_params_request, + litellm_params=partner_litellm_params, vertex_project=vertex_project, vertex_location=vertex_location, vertex_credentials=vertex_credentials, diff --git a/litellm/llms/vertex_ai/files/transformation.py b/litellm/llms/vertex_ai/files/transformation.py index b7f91bfba0d..b6ad9fbcc04 100644 --- a/litellm/llms/vertex_ai/files/transformation.py +++ b/litellm/llms/vertex_ai/files/transformation.py @@ -12,7 +12,7 @@ from urllib.parse import quote, unquote import httpx from httpx import Headers, Response from openai.types.file_deleted import FileDeleted -from typing_extensions import ReadOnly +from typing_extensions import ReadOnly, Required import litellm from litellm._uuid import uuid @@ -104,6 +104,27 @@ class _VertexBatchRow(TypedDict, total=False): processed_time: ReadOnly[str] +class _VertexEmbeddingVector(TypedDict): + values: ReadOnly[list[float]] + + +class _VertexEmbeddingUsageMetadata(TypedDict, total=False): + promptTokenCount: ReadOnly[int] + + +class _VertexEmbeddingResponse(TypedDict, total=False): + embedding: ReadOnly[Required[_VertexEmbeddingVector]] + usageMetadata: ReadOnly[_VertexEmbeddingUsageMetadata] + tokenCount: ReadOnly[int] + + +class _VertexEmbeddingBatchRow(TypedDict, total=False): + key: ReadOnly[str] + request: ReadOnly[Mapping[str, object]] + status: ReadOnly[Required[str]] + response: ReadOnly[Required[_VertexEmbeddingResponse]] + + class _OpenAIBatchOutputError(TypedDict): code: ReadOnly[str] message: ReadOnly[str] @@ -111,7 +132,7 @@ class _OpenAIBatchOutputError(TypedDict): class _OpenAIBatchOutputResponse(TypedDict): status_code: ReadOnly[int] - request_id: ReadOnly[str] + request_id: ReadOnly[object] body: ReadOnly[Mapping[str, object]] @@ -218,7 +239,7 @@ def _get_litellm_batch_custom_id_from_labels(labels: Mapping[str, object] | None return str(labels.get("litellm_custom_id", "unknown")) -def _is_vertex_embeddings_batch_output_row(vertex_output_row: Mapping[str, Any]) -> bool: +def _is_vertex_embeddings_batch_output_row(vertex_output_row: Mapping[str, object]) -> bool: """ Whether a Vertex batch output row came from an `EmbedContentRequest`. @@ -237,7 +258,7 @@ def _is_vertex_embeddings_batch_output_row(vertex_output_row: Mapping[str, Any]) def _openai_batch_output_row( custom_id: str, - body: Mapping[str, Any] | None = None, + body: Mapping[str, object] | None = None, error_code: str | None = None, error_message: str = "", ) -> _OpenAIBatchOutputRow: @@ -259,7 +280,7 @@ def _openai_batch_output_row( } -def _split_vertex_batch_key(vertex_output_row: Mapping[str, Any]) -> tuple[str, int, int]: +def _split_vertex_batch_key(vertex_output_row: Mapping[str, object]) -> tuple[str, int, int]: """ Resolve `(custom_id, index within that custom_id, group size)` for a Vertex batch output row. @@ -278,7 +299,7 @@ def _split_vertex_batch_key(vertex_output_row: Mapping[str, Any]) -> tuple[str, return unquote(match["custom_id"]), int(match["index"]), int(match["total"]) -def _embedding_prompt_token_count(vertex_response: Mapping[str, Any]) -> int: +def _embedding_prompt_token_count(vertex_response: _VertexEmbeddingResponse) -> int: """ Prompt tokens billed for one Vertex Gemini Embedding batch row. @@ -293,7 +314,7 @@ def _embedding_prompt_token_count(vertex_response: Mapping[str, Any]) -> int: def _vertex_embeddings_rows_to_openai_batch_output_row( custom_id: str, - vertex_output_rows: tuple[Mapping[str, Any], ...], + vertex_output_rows: tuple[_VertexEmbeddingBatchRow, ...], element_indices: tuple[int, ...], element_count: int, model: str | None, @@ -348,7 +369,7 @@ def _vertex_embeddings_rows_to_openai_batch_output_row( def _transform_vertex_embeddings_batch_output_to_openai( - vertex_output_rows: Iterable[Mapping[str, Any]], + vertex_output_rows: Iterable[_VertexEmbeddingBatchRow], model: str | None, ) -> tuple[_OpenAIBatchOutputRow, ...]: """ @@ -388,7 +409,7 @@ def _model_from_managed_gcs_url(url: str) -> str | None: return match.group(1) if match else None -def _is_embeddings_batch_entry(openai_entry: Mapping[str, Any]) -> bool: +def _is_embeddings_batch_entry(openai_entry: Mapping[str, object]) -> bool: """ Whether an OpenAI batch JSONL line targets the embeddings endpoint. @@ -431,7 +452,7 @@ def _vertex_batch_embeddings_key(custom_id: str, index: int, total: int) -> str: return encoded_custom_id if total < 2 else f"{encoded_custom_id}#{index}/{total}" -def _vertex_embeddings_row(key: str | None, embed_content_request: Mapping[str, Any]) -> Mapping[str, Any]: +def _vertex_embeddings_row(key: str | None, embed_content_request: Mapping[str, object]) -> Mapping[str, object]: """ One Vertex Gemini Embedding batch input row. @@ -453,8 +474,8 @@ def _vertex_embeddings_row(key: str | None, embed_content_request: Mapping[str, def _openai_batch_jsonl_entry_to_vertex_embeddings_rows( - openai_entry: Mapping[str, Any], -) -> tuple[Mapping[str, Any], ...]: + openai_entry: Mapping[str, object], +) -> tuple[Mapping[str, object], ...]: """ Transforms a single OpenAI `/v1/embeddings` batch entry into Vertex Gemini Embedding batch rows, one per requested embedding. @@ -512,7 +533,7 @@ def _openai_batch_jsonl_entry_to_vertex_embeddings_rows( def _openai_batch_jsonl_entry_to_vertex_rows( openai_entry: dict[str, Any], map_openai_to_vertex_params: Callable[[dict[str, Any]], dict[str, Any]], -) -> tuple[Mapping[str, Any], ...]: +) -> tuple[Mapping[str, object], ...]: """ Transforms a single OpenAI JSONL batch entry into the Vertex rows it maps to. @@ -533,7 +554,7 @@ def _openai_batch_jsonl_entry_to_vertex_rows( cached_content=None, ) - custom_id: Final = openai_entry.get("custom_id") + custom_id: Final[object] = openai_entry.get("custom_id") if custom_id is not None: if "labels" not in vertex_request_body: vertex_request_body["labels"] = {} diff --git a/litellm/llms/vertex_ai/gemini/transformation.py b/litellm/llms/vertex_ai/gemini/transformation.py index 11c026010ee..e2d62be6a69 100644 --- a/litellm/llms/vertex_ai/gemini/transformation.py +++ b/litellm/llms/vertex_ai/gemini/transformation.py @@ -250,7 +250,7 @@ def _gs_uri_requires_content_type_metadata(url: str) -> bool: def _image_url_payload_may_need_sync_gcs_metadata_fetch( - raw_image_url: Any, + raw_image_url: object, ) -> bool: """ True when this image_url value (content-part image_url or assistant ``images[]`` @@ -326,7 +326,7 @@ def _openai_messages_may_need_sync_gcs_metadata_fetch( def _get_gcs_object_content_type( image_url: str, vertex_project: str | None = None, - vertex_credentials: Any | None = None, + vertex_credentials: object = None, ) -> str | None: """ Resolve content type from GCS object metadata. @@ -479,7 +479,7 @@ def _process_gemini_media( model: str | None = None, video_metadata: dict[str, Any] | None = None, vertex_project: str | None = None, - vertex_credentials: Any | None = None, + vertex_credentials: object = None, ) -> PartType: """ Given a media URL (image, audio, or video), return the appropriate PartType for Gemini @@ -1002,7 +1002,7 @@ def _gemini_convert_messages_with_history( if isinstance(_ss_invocations, list): for invocation in _ss_invocations: # Re-inject toolCall part - tc_part: dict[str, Any] = { + tc_part: dict[str, object] = { "toolCall": { "toolType": invocation.get("tool_type"), "id": invocation.get("id"), @@ -1015,13 +1015,13 @@ def _gemini_convert_messages_with_history( # Re-inject toolResponse part if response is present if "response" in invocation: - tr_dict: dict[str, Any] = { + tr_dict: dict[str, object] = { "id": invocation.get("id"), "response": invocation.get("response"), } if invocation.get("tool_type"): tr_dict["toolType"] = invocation["tool_type"] - tr_part: dict[str, Any] = {"toolResponse": tr_dict} + tr_part: dict[str, object] = {"toolResponse": tr_dict} if "response_thought_signature" in invocation: tr_part["thoughtSignature"] = invocation["response_thought_signature"] assistant_content.append(tr_part) @@ -1090,7 +1090,7 @@ def _pop_and_merge_extra_body(data: RequestBody, optional_params: dict) -> None: data_dict[k] = v -def _has_google_maps_tool(tools: Any | None) -> bool: +def _has_google_maps_tool(tools: object) -> bool: """Return True if any tool object in the list has a 'googleMaps' key.""" if not isinstance(tools, list): return False @@ -1127,7 +1127,7 @@ def _rewrite_mime_type_to_response_format(generation_config: GenerationConfig) - schema = generation_config.pop("response_schema", None) generation_config.pop("response_mime_type", None) - response_format: Final[dict[str, Any]] = {"text": {"mimeType": "APPLICATION_JSON"}} + response_format: Final[dict[str, dict[str, object]]] = {"text": {"mimeType": "APPLICATION_JSON"}} if schema is not None: response_format["text"]["schema"] = schema generation_config["responseFormat"] = response_format @@ -1316,7 +1316,7 @@ async def async_transform_request_body( timeout: float | httpx.Timeout | None, extra_headers: dict | None, optional_params: dict, - logging_obj: litellm.litellm_core_utils.litellm_logging.Logging, + logging_obj: LiteLLMLoggingObj, custom_llm_provider: Literal["vertex_ai", "vertex_ai_beta", "gemini"], litellm_params: dict, vertex_project: str | None, diff --git a/litellm/llms/vertex_ai/image_edit/vertex_gemini_transformation.py b/litellm/llms/vertex_ai/image_edit/vertex_gemini_transformation.py index 5889a8eba06..725a7f39917 100644 --- a/litellm/llms/vertex_ai/image_edit/vertex_gemini_transformation.py +++ b/litellm/llms/vertex_ai/image_edit/vertex_gemini_transformation.py @@ -182,7 +182,6 @@ class VertexAIGeminiImageEditConfig(BaseImageEditConfig, VertexLLM): else None ) - # Generation config with proper structure for image editing generation_config: Final[dict[str, object]] = { key: value for key, value in (("response_modalities", ["IMAGE"]), ("image_config", image_config)) if value } diff --git a/litellm/llms/vertex_ai/text_to_speech/transformation.py b/litellm/llms/vertex_ai/text_to_speech/transformation.py index cf14ab88751..332f892ae6b 100644 --- a/litellm/llms/vertex_ai/text_to_speech/transformation.py +++ b/litellm/llms/vertex_ai/text_to_speech/transformation.py @@ -7,10 +7,14 @@ Reference: https://cloud.google.com/text-to-speech/docs/reference/rest/v1/text/s import base64 from collections.abc import Coroutine +from types import MappingProxyType from typing import TYPE_CHECKING, Any, Final, Union import httpx +from litellm.litellm_core_utils.audio_utils.utils import ( + speech_media_type_from_audio_bytes, +) from litellm.llms.base_llm.text_to_speech.transformation import ( BaseTextToSpeechConfig, TextToSpeechRequestData, @@ -457,12 +461,11 @@ class VertexAITextToSpeechConfig(BaseTextToSpeechConfig, VertexBase): if not response_content: raise ValueError("No audioContent in Vertex AI TTS response") - # Decode base64 to get binary content binary_data: Final = base64.b64decode(response_content) - - # Create an httpx.Response object with the binary data + media_type: Final = speech_media_type_from_audio_bytes(binary_data) response: Final = httpx.Response( status_code=200, + headers=None if media_type is None else MappingProxyType({"content-type": media_type}), content=binary_data, ) diff --git a/litellm/llms/vertex_ai/vector_stores/rag_api/transformation.py b/litellm/llms/vertex_ai/vector_stores/rag_api/transformation.py index eedd488ecdb..5c250fc1a7e 100644 --- a/litellm/llms/vertex_ai/vector_stores/rag_api/transformation.py +++ b/litellm/llms/vertex_ai/vector_stores/rag_api/transformation.py @@ -203,7 +203,6 @@ class VertexVectorStoreConfig(BaseVectorStoreConfig, VertexBase): if value is not None } - # Build the request body for Vertex AI RAG API query_body: Final[Mapping[str, object]] = { key: value for key, value in (("text", query), ("rag_retrieval_config", rag_retrieval_config or None)) @@ -294,7 +293,6 @@ class VertexVectorStoreConfig(BaseVectorStoreConfig, VertexBase): # Add metadata if provided metadata: Final = vector_store_create_optional_params.get("metadata") - # Build the request body for Vertex AI RAG Corpus creation request_body: Final[dict[str, object]] = { key: value for key, value in ( diff --git a/litellm/llms/vertex_ai/vertex_llm_base.py b/litellm/llms/vertex_ai/vertex_llm_base.py index 75098515deb..1942bc850f1 100644 --- a/litellm/llms/vertex_ai/vertex_llm_base.py +++ b/litellm/llms/vertex_ai/vertex_llm_base.py @@ -9,7 +9,7 @@ import json import os import threading from collections.abc import Mapping -from typing import TYPE_CHECKING, Any, Final, Literal +from typing import TYPE_CHECKING, Any, Final, Literal, Protocol from urllib.parse import urlparse import litellm @@ -27,6 +27,15 @@ from .common_utils import ( get_vertex_base_url, ) + +def _graft_default_vertex_path(api_base: str, default_url: str) -> str: + parsed_api_base: Final = urlparse(api_base) + default_segments: Final = urlparse(default_url).path.lstrip("/").split("/") + graft_segments: Final = default_segments[1:] if default_segments[0] in ("v1", "v1beta1") else default_segments + grafted_path: Final = parsed_api_base.path.rstrip("/") + "/" + "/".join(graft_segments) + return parsed_api_base._replace(path=grafted_path).geturl() + + GOOGLE_IMPORT_ERROR_MESSAGE: Final = ( "Google Cloud SDK not found. Install it with: pip install 'litellm[google]' or pip install google-cloud-aiplatform" ) @@ -38,6 +47,21 @@ else: GoogleCredentialsObject = Any +class _VertexCredentialsObject(Protocol): + """Structural view of the google-auth credentials handle that this class caches and refreshes.""" + + @property + def token(self) -> object: ... + + @property + def quota_project_id(self) -> str | None: ... + + @property + def expired(self) -> object: ... + + def refresh(self, request: object) -> None: ... + + class VertexBase: def __init__(self) -> None: super().__init__() @@ -46,7 +70,7 @@ class VertexBase: self._credentials: GoogleCredentialsObject | None = None self._credentials_project_mapping: dict[ tuple[VERTEX_CREDENTIALS_TYPES | None, str | None], - tuple[GoogleCredentialsObject, str | None], + tuple[_VertexCredentialsObject, str | None], ] = {} self.project_id: str | None = None self.async_handler: AsyncHTTPHandler | None = None @@ -100,7 +124,7 @@ class VertexBase: self, credentials: VERTEX_CREDENTIALS_TYPES | None, project_id: str | None, - ) -> tuple[Any, str]: + ) -> tuple[_VertexCredentialsObject | None, str]: if credentials is not None: if isinstance(credentials, str): _is_path: Final = os.path.exists( @@ -200,7 +224,7 @@ class VertexBase: return creds, project_id # Google Auth Helpers -- extracted for mocking purposes in tests - def _credentials_from_identity_pool(self, json_obj, scopes): + def _credentials_from_identity_pool(self, json_obj, scopes) -> _VertexCredentialsObject: try: from google.auth import identity_pool except ImportError: @@ -211,7 +235,7 @@ class VertexBase: creds = creds.with_scopes(scopes) return creds - def _credentials_from_pluggable(self, json_obj, scopes): + def _credentials_from_pluggable(self, json_obj, scopes) -> _VertexCredentialsObject: try: from google.auth import pluggable except ImportError: @@ -222,7 +246,7 @@ class VertexBase: creds = creds.with_scopes(scopes) return creds - def _credentials_from_identity_pool_with_aws(self, json_obj, scopes): + def _credentials_from_identity_pool_with_aws(self, json_obj, scopes) -> _VertexCredentialsObject: try: from google.auth import aws except ImportError: @@ -233,7 +257,7 @@ class VertexBase: creds = creds.with_scopes(scopes) return creds - def _credentials_from_authorized_user(self, json_obj, scopes): + def _credentials_from_authorized_user(self, json_obj, scopes) -> _VertexCredentialsObject: try: import google.oauth2.credentials except ImportError: @@ -241,7 +265,7 @@ class VertexBase: return google.oauth2.credentials.Credentials.from_authorized_user_info(json_obj, scopes=scopes) - def _credentials_from_service_account(self, json_obj, scopes): + def _credentials_from_service_account(self, json_obj, scopes) -> _VertexCredentialsObject: try: import google.oauth2.service_account except ImportError: @@ -249,7 +273,7 @@ class VertexBase: return google.oauth2.service_account.Credentials.from_service_account_info(json_obj, scopes=scopes) - def _credentials_from_default_auth(self, scopes): + def _credentials_from_default_auth(self, scopes) -> tuple[_VertexCredentialsObject, str | None]: try: import google.auth as google_auth except ImportError: @@ -341,7 +365,7 @@ class VertexBase: ) return api_base - def refresh_auth(self, credentials: Any) -> None: + def refresh_auth(self, credentials: _VertexCredentialsObject) -> None: try: from google.auth.transport.requests import ( Request, @@ -417,7 +441,7 @@ class VertexBase: self, credential_cache_key: tuple, project_id: str | None, - ) -> tuple[str, str, "TokenState", Any, str | None] | None: + ) -> tuple[str, str, "TokenState", _VertexCredentialsObject, str | None] | None: """ Look up cached credentials and return usable token info for FRESH or STALE tokens (both are still valid for outbound requests). STALE @@ -440,7 +464,9 @@ class VertexBase: return None return creds.token, resolved_project, token_state, creds, cached_project_id - def _unpack_cached_credentials(self, credential_cache_key: tuple) -> tuple[Any, str | None]: + def _unpack_cached_credentials( + self, credential_cache_key: tuple + ) -> tuple[_VertexCredentialsObject | None, str | None]: """ Return (credentials, project_id) from the cache, or (None, None) if not cached. Handles both tuple and legacy cache formats. @@ -452,7 +478,7 @@ class VertexBase: return cached_entry return cached_entry, cached_entry.quota_project_id or getattr(cached_entry, "project_id", None) - def _get_token_state(self, credentials: Any) -> "TokenState": + def _get_token_state(self, credentials: _VertexCredentialsObject) -> "TokenState": """ Return the token state using google-auth's TokenState enum. @@ -476,7 +502,7 @@ class VertexBase: credentials: VERTEX_CREDENTIALS_TYPES | None, project_id: str | None, credential_cache_key: tuple, - ) -> tuple[Any, str | None]: + ) -> tuple[_VertexCredentialsObject, str | None]: """Load credentials via load_auth (in thread) and cache the result.""" try: _credentials, credential_project_id = await asyncify(self.load_auth)( @@ -496,7 +522,7 @@ class VertexBase: async def _background_refresh_credentials( self, - credentials: Any, + credentials: _VertexCredentialsObject, credential_cache_key: tuple, credential_project_id: str | None, ) -> None: @@ -548,7 +574,7 @@ class VertexBase: def _schedule_background_refresh( self, - credentials: Any, + credentials: _VertexCredentialsObject, credential_cache_key: tuple, credential_project_id: str | None, ) -> None: @@ -566,7 +592,7 @@ class VertexBase: self._background_refresh_credentials(credentials, credential_cache_key, credential_project_id) ) - def _drop_background_refresh_task(_fut: asyncio.Future[Any]) -> None: + def _drop_background_refresh_task(_fut: asyncio.Future[None]) -> None: if self._background_refresh_tasks.get(credential_cache_key) is _fut: self._background_refresh_tasks.pop(credential_cache_key, None) @@ -621,8 +647,9 @@ class VertexBase: Handles custom api_base for: 1. Gemini (Google AI Studio) - constructs /models/{model}:{endpoint} - 2. Vertex AI with standard proxies - constructs {api_base}:{endpoint}; - if api_base has no path (bare host), grafts the default vertex URL path onto it + 2. Vertex AI with standard proxies - grafts the default vertex URL path onto the + api_base when its path is empty or only an API version (/v1, /v1beta1); + otherwise constructs {api_base}:{endpoint} 3. Vertex AI with PSC endpoints - constructs full path structure {api_base}/v1/projects/{project}/locations/{location}/endpoints/{model}:{endpoint} (only when use_psc_endpoint_format=True) @@ -669,10 +696,14 @@ class VertexBase: ) elif urlparse(api_base).path in ("", "/"): url = api_base.rstrip("/") + urlparse(url).path + elif urlparse(api_base).path.rstrip("/") in ("/v1", "/v1beta1") and "/projects/" in urlparse(url).path: + url = _graft_default_vertex_path(api_base=api_base, default_url=url) else: url = f"{api_base}:{endpoint}" if stream is True: - url = url + "?alt=sse" + parsed_stream_url: Final = urlparse(url) + stream_query: Final = f"{parsed_stream_url.query}&alt=sse" if parsed_stream_url.query else "alt=sse" + url = parsed_stream_url._replace(query=stream_query).geturl() return auth_header, url def _get_token_and_url( @@ -874,7 +905,7 @@ class VertexBase: # Convert dict credentials to string for caching cache_credentials: Final = json.dumps(credentials) if isinstance(credentials, dict) else credentials credential_cache_key: Final = (cache_credentials, project_id) - _credentials: GoogleCredentialsObject | None = None + _credentials: _VertexCredentialsObject | None = None verbose_logger.debug("Checking cached credentials for project_id: %s", project_id) diff --git a/litellm/main.py b/litellm/main.py index c341db08155..c4c5bbefc4f 100644 --- a/litellm/main.py +++ b/litellm/main.py @@ -5507,6 +5507,9 @@ def completion( tpm=kwargs.get("tpm"), rpm=kwargs.get("rpm"), use_xai_oauth=kwargs.get("use_xai_oauth", False), + gigachat_scope=kwargs.get("gigachat_scope"), + gigachat_auth_url=kwargs.get("gigachat_auth_url"), + gigachat_access_token=kwargs.get("gigachat_access_token"), **{key: kwargs[key] for key in FORWARDED_KWARGS_KEYS if key in kwargs}, ) cast(LiteLLMLoggingObj, logging).update_environment_variables( @@ -6289,18 +6292,15 @@ def embedding( if headers is not None and headers != {}: optional_params["extra_headers"] = headers - if encoding_format is not None: - optional_params["encoding_format"] = encoding_format + requested_encoding_format: Final = ( + encoding_format + or optional_params.get("encoding_format") + or get_secret_str("LITELLM_DEFAULT_EMBEDDING_ENCODING_FORMAT") + ) + if requested_encoding_format is None or requested_encoding_format.strip().lower() == "none": + optional_params.pop("encoding_format", None) else: - env_fmt: Final = get_secret_str("LITELLM_DEFAULT_EMBEDDING_ENCODING_FORMAT") - if env_fmt is not None and env_fmt.strip().lower() == "none": - optional_params.pop("encoding_format", None) - else: - _default_fmt: Final = optional_params.get("encoding_format") or env_fmt or "float" - if _default_fmt.strip().lower() == "none": - optional_params.pop("encoding_format", None) - else: - optional_params["encoding_format"] = _default_fmt + optional_params["encoding_format"] = requested_encoding_format api_version = None @@ -6949,12 +6949,18 @@ def embedding( aembedding=aembedding, headers=headers, ) - elif custom_llm_provider == "dashscope": - dashscope_key: Final = api_key or litellm.api_key or get_secret_str("DASHSCOPE_API_KEY") + elif custom_llm_provider in ("dashscope", "qwencloud", "qwen_ai_platform"): + from litellm.llms.dashscope.common_utils import ( + missing_dashscope_family_key_message, + resolve_dashscope_family_api_key, + ) + + dashscope_key: Final = resolve_dashscope_family_api_key( + custom_llm_provider=custom_llm_provider, + api_key=api_key or litellm.api_key, + ) if dashscope_key is None: - raise ValueError( - "Missing API key for DashScope. Set DASHSCOPE_API_KEY environment variable or pass api_key parameter." - ) + raise ValueError(missing_dashscope_family_key_message(custom_llm_provider)) if extra_headers is not None and isinstance(extra_headers, dict): headers = extra_headers else: diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 7071eaa0807..27ff525c15e 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -553,6 +553,27 @@ "supports_response_schema": true, "supports_vision": true }, + "amazon.nova-sonic-v1:0": { + "deprecation_date": "2026-09-14", + "input_cost_per_audio_token": 3.4e-06, + "input_cost_per_token": 6e-08, + "litellm_provider": "bedrock", + "mode": "realtime", + "output_cost_per_audio_token": 1.36e-05, + "output_cost_per_token": 2.4e-07, + "supports_audio_input": true, + "supports_audio_output": true + }, + "amazon.nova-2-sonic-v1:0": { + "input_cost_per_audio_token": 3e-06, + "input_cost_per_token": 3.3e-07, + "litellm_provider": "bedrock", + "mode": "realtime", + "output_cost_per_audio_token": 1.2e-05, + "output_cost_per_token": 2.75e-06, + "supports_audio_input": true, + "supports_audio_output": true + }, "amazon.rerank-v1:0": { "input_cost_per_query": 0.001, "input_cost_per_token": 0.0, @@ -1571,7 +1592,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "supports_parallel_tool_use_config": true, @@ -1607,7 +1628,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "supports_parallel_tool_use_config": true, @@ -1643,7 +1664,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "supports_parallel_tool_use_config": true, @@ -1679,7 +1700,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "supports_parallel_tool_use_config": true, @@ -1715,7 +1736,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "supports_parallel_tool_use_config": true, @@ -1751,7 +1772,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "supports_parallel_tool_use_config": true, @@ -2044,7 +2065,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", @@ -2081,7 +2102,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", @@ -2118,7 +2139,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", @@ -2155,7 +2176,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", @@ -2192,7 +2213,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", @@ -2229,7 +2250,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", @@ -3025,6 +3046,7 @@ "prompt_cache_min_tokens": 2048 }, "azure_ai/claude-fable-5": { + "deprecation_date": "2027-12-05", "supports_mid_conversation_system": true, "input_cost_per_token": 1e-05, "output_cost_per_token": 5e-05, @@ -3058,6 +3080,7 @@ "prompt_cache_min_tokens": 512 }, "azure_ai/claude-opus-5": { + "deprecation_date": "2027-07-08", "supports_mid_conversation_system": true, "supports_adaptive_thinking": true, "input_cost_per_token": 5e-06, @@ -3090,6 +3113,7 @@ "prompt_cache_min_tokens": 512 }, "azure_ai/claude-opus-4-8": { + "deprecation_date": "2027-09-01", "supports_mid_conversation_system": true, "supports_adaptive_thinking": true, "input_cost_per_token": 5e-06, @@ -3168,6 +3192,7 @@ "prompt_cache_min_tokens": 1024 }, "azure_ai/claude-sonnet-5": { + "deprecation_date": "2027-06-30", "supports_mid_conversation_system": true, "cache_creation_input_token_cost": 2.5e-06, "cache_creation_input_token_cost_above_1hr": 4e-06, @@ -9959,6 +9984,22 @@ "supports_reasoning": true, "supports_tool_choice": true }, + "azure_ai/deepseek-v4-flash-0731": { + "cache_read_input_token_cost": 2.8e-08, + "deprecation_date": "2026-12-03", + "input_cost_per_token": 1.9e-07, + "litellm_provider": "azure_ai", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 5.1e-07, + "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/deepseek/", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, "azure_ai/embed-v-4-0": { "input_cost_per_token": 1.2e-07, "litellm_provider": "azure_ai", @@ -12267,6 +12308,7 @@ "supports_tool_choice": true }, "cerebras/zai-glm-4.7": { + "deprecation_date": "2026-08-17", "input_cost_per_token": 2.25e-06, "litellm_provider": "cerebras", "max_input_tokens": 128000, @@ -14698,6 +14740,1910 @@ "/v1/images/generations" ] }, + "qwencloud/deepseek-v4-flash": { + "cache_read_input_token_cost": 4e-08, + "input_cost_per_token": 2e-07, + "litellm_provider": "qwencloud", + "max_input_tokens": 1000000, + "max_output_tokens": 393216, + "max_tokens": 393216, + "mode": "chat", + "output_cost_per_token": 4e-07, + "source": "https://www.qwencloud.com/models", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "qwencloud/deepseek-v4-flash-0731": { + "cache_read_input_token_cost": 4e-08, + "input_cost_per_token": 2e-07, + "litellm_provider": "qwencloud", + "max_input_tokens": 1000000, + "max_output_tokens": 393216, + "max_tokens": 393216, + "mode": "chat", + "output_cost_per_token": 4e-07, + "source": "https://www.qwencloud.com/models", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "qwencloud/deepseek-v4-pro": { + "cache_read_input_token_cost": 2e-07, + "input_cost_per_token": 2.4e-06, + "litellm_provider": "qwencloud", + "max_input_tokens": 1000000, + "max_output_tokens": 393216, + "max_tokens": 393216, + "mode": "chat", + "output_cost_per_token": 4.8e-06, + "source": "https://www.qwencloud.com/models", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "qwencloud/glm-5.1": { + "cache_read_input_token_cost": 2.6e-07, + "input_cost_per_token": 1.4e-06, + "litellm_provider": "qwencloud", + "max_input_tokens": 202745, + "max_output_tokens": 131072, + "max_tokens": 131072, + 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+ "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Billing reads the per-token dollar fields; the '*_dbu_cost_per_token' fields are the published Databricks rates, kept for reference. Context/max output are the DeepSeek-published model limits (1M context, 384K max output)." + }, + "mode": "chat", + "output_cost_per_token": 2.8e-07, + "output_dbu_cost_per_token": 4e-06, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving", + "supported_modalities": [ + "text" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_tool_choice": true, + "supports_vision": false + }, + "databricks/databricks-deepseek-v4-pro-0813": { + "cache_creation_input_token_cost": 1.31999e-06, + "cache_read_input_token_cost": 1.3202e-07, + "input_cost_per_token": 1.31999e-06, + "input_dbu_cost_per_token": 1.8857e-05, + "litellm_provider": "databricks", + "max_input_tokens": 1000000, + "max_output_tokens": 393216, + "max_tokens": 393216, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Billing reads the per-token dollar fields; the '*_dbu_cost_per_token' fields are the published Databricks rates, kept for reference. Context/max output are the DeepSeek-published model limits (1M context, 384K max output)." + }, + "mode": "chat", + "output_cost_per_token": 3.95997e-06, + "output_dbu_cost_per_token": 5.6571e-05, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving", + "supported_modalities": [ + "text" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_tool_choice": true, + "supports_vision": false + }, "databricks/databricks-gemini-2-5-flash": { "cache_creation_input_token_cost": 3.0002e-07, "cache_read_input_token_cost": 3.0002e-08, @@ -19566,6 +21568,61 @@ "supports_system_messages": true, "supports_tool_choice": true }, + "friendliai/zai-org/GLM-5.3-Flash": { + "litellm_provider": "friendliai", + "supports_reasoning": true, + "supports_function_calling": true, + "max_input_tokens": 1048576, + "max_tokens": 1048576, + "max_output_tokens": 1048576, + "input_cost_per_token": 1.5e-07, + "output_cost_per_token": 5e-07, + "cache_read_input_token_cost": 3e-08, + "supports_prompt_caching": true, + "reasoning_effort_levels": [ + "low", + "high", + "max" + ], + "supports_parallel_function_calling": true, + "supports_response_schema": true, + "supports_native_structured_output": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "mode": "chat", + "comment": "Native multimodal GLM model for efficient coding and long-horizon agent tasks", + "source": "https://api.friendli.ai/serverless/v1/models", + "supports_vision": true, + "supports_image_input": true, + "supports_video_input": true + }, + "friendliai/zai-org/GLM-5.3": { + "litellm_provider": "friendliai", + "supports_reasoning": true, + "supports_function_calling": true, + "max_input_tokens": 1048576, + "max_tokens": 1048576, + "max_output_tokens": 1048576, + "input_cost_per_token": 1.26e-06, + "output_cost_per_token": 3.96e-06, + "cache_read_input_token_cost": 2.34e-07, + "supports_prompt_caching": true, + "reasoning_effort_levels": [ + "low", + "high", + "max" + ], + "supports_parallel_function_calling": true, + "supports_response_schema": true, + "supports_native_structured_output": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "mode": "chat", + "comment": "Flagship GLM model for long-horizon coding, agents, and complex project delivery", + "source": "https://api.friendli.ai/serverless/v1/models", + "supports_vision": false, + "supports_image_input": false + }, "ft:babbage-002": { "deprecation_date": "2026-10-23", "input_cost_per_token": 1.6e-06, @@ -20556,6 +22613,7 @@ "supports_image_size": false }, "gemini-live-2.5-flash-native-audio": { + "deprecation_date": "2026-12-13", "input_cost_per_audio_token": 3e-06, "input_cost_per_token": 5e-07, "litellm_provider": "vertex_ai-language-models", @@ -23777,8 +25835,10 @@ "max_input_tokens": 1024, "max_tokens": 1024, "mode": "video_generation", - "output_cost_per_second": 0.15, - "source": "https://ai.google.dev/gemini-api/docs/video", + "output_cost_per_second": 0.1, + "output_cost_per_second_1080p": 0.12, + "output_cost_per_second_4k": 0.3, + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_modalities": [ "text" ], @@ -23792,7 +25852,8 @@ "max_tokens": 1024, "mode": "video_generation", "output_cost_per_second": 0.4, - "source": "https://ai.google.dev/gemini-api/docs/video", + "output_cost_per_second_4k": 0.6, + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_modalities": [ "text" ], @@ -23820,8 +25881,10 @@ "max_input_tokens": 1024, "max_tokens": 1024, "mode": "video_generation", - "output_cost_per_second": 0.15, - "source": "https://ai.google.dev/gemini-api/docs/video", + "output_cost_per_second": 0.1, + "output_cost_per_second_1080p": 0.12, + "output_cost_per_second_4k": 0.3, + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_modalities": [ "text" ], @@ -23835,7 +25898,8 @@ "max_tokens": 1024, "mode": "video_generation", "output_cost_per_second": 0.4, - "source": "https://ai.google.dev/gemini-api/docs/video", + "output_cost_per_second_4k": 0.6, + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_modalities": [ "text" ], @@ -24344,7 +26408,7 @@ "supports_response_schema": true, "supports_vision": true }, - "gigachat/GigaChat-2-Lite": { + "gigachat/GigaChat-2": { "input_cost_per_token": 0.0, "litellm_provider": "gigachat", "max_input_tokens": 128000, @@ -24406,6 +26470,15 @@ "output_cost_per_token": 0.0, "output_vector_size": 2560 }, + "gigachat/GigaEmbeddings-3B-2025-09": { + "input_cost_per_token": 0.0, + "litellm_provider": "gigachat", + "max_input_tokens": 4096, + "max_tokens": 4096, + "mode": "embedding", + "output_cost_per_token": 0.0, + "output_vector_size": 2048 + }, "gmi/anthropic/claude-opus-4.5": { "input_cost_per_token": 5e-06, "litellm_provider": "gmi", @@ -43356,7 +45429,8 @@ "max_tokens": 1024, "mode": "video_generation", "output_cost_per_second": 0.4, - "source": "https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/veo/3-1-generate", + "output_cost_per_second_4k": 0.6, + "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing", "supported_modalities": [ "text" ], @@ -43369,8 +45443,10 @@ "max_input_tokens": 1024, "max_tokens": 1024, "mode": "video_generation", - "output_cost_per_second": 0.15, - "source": "https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/veo/3-1-generate", + "output_cost_per_second": 0.1, + "output_cost_per_second_1080p": 0.12, + "output_cost_per_second_4k": 0.3, + "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing", "supported_modalities": [ "text" ], @@ -43385,7 +45461,8 @@ "max_tokens": 1024, "mode": "video_generation", "output_cost_per_second": 0.4, - "source": "https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/veo/3-1-generate", + "output_cost_per_second_4k": 0.6, + "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing", "supported_modalities": [ "text" ], @@ -43399,8 +45476,10 @@ "max_input_tokens": 1024, "max_tokens": 1024, "mode": "video_generation", - "output_cost_per_second": 0.15, - "source": "https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/veo/3-1-generate", + "output_cost_per_second": 0.1, + "output_cost_per_second_1080p": 0.12, + "output_cost_per_second_4k": 0.3, + "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing", "supported_modalities": [ "text" ], @@ -52147,14 +54226,14 @@ "supports_vision": true }, "fireworks_ai/deepseek-v4-flash-0731": { - "cache_read_input_token_cost": 2.8e-08, - "input_cost_per_token": 1.4e-07, + "cache_read_input_token_cost": 7e-09, + "input_cost_per_token": 2.2e-07, "litellm_provider": "fireworks_ai", "max_input_tokens": 1048576, "max_output_tokens": 131072, "max_tokens": 131072, "mode": "chat", - "output_cost_per_token": 2.8e-07, + "output_cost_per_token": 6.6e-07, "source": "https://docs.fireworks.ai/serverless/pricing", "supports_function_calling": true, "supports_reasoning": true, @@ -55040,5 +57119,55 @@ "max_tokens": 40960, "mode": "embedding", "source": "https://docs.fireworks.ai/serverless/pricing" + }, + "zai/glm-5.2": { + "cache_creation_input_token_cost": 0, + "cache_read_input_token_cost": 2.6e-07, + "input_cost_per_token": 1.4e-06, + "litellm_provider": "zai", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 4.4e-06, + "source": "https://docs.z.ai/guides/overview/pricing", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, + "together_ai/Qwen/Qwen3.8-Flash": { + "input_cost_per_token": 1.5e-07, + "litellm_provider": "together_ai", + "max_input_tokens": 1000000, + "max_tokens": 1000000, + "mode": "chat", + "output_cost_per_token": 4.7e-07, + "source": "https://docs.together.ai/docs/serverless-models" + }, + "cerebras/gemma-4-31b": { + "input_cost_per_token": 9.9e-07, + "litellm_provider": "cerebras", + "max_input_tokens": 131072, + "max_output_tokens": 40960, + "max_tokens": 40960, + "mode": "chat", + "output_cost_per_token": 1.49e-06, + "source": "https://api.cerebras.ai/public/v1/models/gemma-4-31b", + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "elevenlabs/scribe_v2": { + "input_cost_per_second": 6.11e-05, + "litellm_provider": "elevenlabs", + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://elevenlabs.io/pricing/api", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] } } diff --git a/litellm/models/model.py b/litellm/models/model.py index 209f26d4837..a0c840341ab 100644 --- a/litellm/models/model.py +++ b/litellm/models/model.py @@ -29,6 +29,8 @@ class LiteLLM_ProxyModelTable(LiteLLMPydanticObjectBase): @model_validator(mode="before") @classmethod def check_potential_json_str(cls, values): + if not isinstance(values, dict): + return values if isinstance(values.get("litellm_params"), str): try: values["litellm_params"] = json.loads(values["litellm_params"]) diff --git a/litellm/passthrough/main.py b/litellm/passthrough/main.py index 4b30afb2f98..c4bd03fb1c3 100644 --- a/litellm/passthrough/main.py +++ b/litellm/passthrough/main.py @@ -2,17 +2,22 @@ This module is used to pass through requests to the LLM APIs. """ +from __future__ import annotations + import asyncio import contextvars -from collections.abc import AsyncGenerator, Coroutine, Generator +from collections.abc import AsyncGenerator, AsyncIterator, Awaitable, Coroutine, Generator, Iterator from functools import partial -from typing import TYPE_CHECKING, Any, Final, Optional, cast +from types import TracebackType +from typing import Any, Final, cast import httpx -from httpx._types import CookieTypes, QueryParamTypes, RequestFiles +from httpx._types import CookieTypes, QueryParamTypes, RequestContent, RequestFiles from litellm._logging import verbose_logger from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider +from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj +from litellm.llms.base_llm.passthrough.transformation import BasePassthroughConfig from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler from litellm.llms.custom_httpx.llm_http_handler import BaseLLMHTTPHandler from litellm.passthrough.utils import CommonUtils @@ -21,9 +26,222 @@ from litellm.utils import client base_llm_http_handler = BaseLLMHTTPHandler() from .utils import BasePassthroughUtils -if TYPE_CHECKING: - from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj - from litellm.llms.base_llm.passthrough.transformation import BasePassthroughConfig + +async def _as_async_generator(iterable: AsyncIterator[bytes]) -> AsyncGenerator[bytes, bytes]: + async for chunk in iterable: + yield chunk + + +def _as_generator(iterable: Iterator[bytes]) -> Generator[bytes, bytes, None]: + yield from iterable + + +class AsyncPassthroughStreamingResponse(AsyncGenerator[bytes, bytes]): + def __init__( + self, + response: Awaitable[httpx.Response], + litellm_logging_obj: LiteLLMLoggingObj, + provider_config: BasePassthroughConfig, + ) -> None: + self._initialized = False + self._status_code: int = 0 + self._headers = httpx.Headers() + self._response_coro = response + self._response: httpx.Response + self._iterator: AsyncGenerator[bytes, bytes] + self._litellm_logging_obj = litellm_logging_obj + self._provider_config = provider_config + self._raw_bytes: list[bytes] = [] # mutable-ok: instance buffer for streaming chunks + self._flush_scheduled = False + self._background_tasks: set[asyncio.Task] = set() # mutable-ok: instance set for background task tracking + self._hidden_params: dict[str, object] = {} # mutable-ok: router attaches response headers here in place + + @property + def status_code(self) -> int: + if not self._initialized: + raise RuntimeError("AsyncPassthroughStreamingResponse must be awaited before accessing status_code") + return self._status_code + + @status_code.setter + def status_code(self, value: int) -> None: + self._status_code = value + + @property + def headers(self) -> httpx.Headers: + if not self._initialized: + raise RuntimeError("AsyncPassthroughStreamingResponse must be awaited before accessing headers") + return self._headers + + @headers.setter + def headers(self, value: httpx.Headers) -> None: + self._headers = value + + def __await__(self) -> Iterator[Any]: + async def _init(): + if not self._initialized: + self._response = await self._response_coro + self.headers = self._response.headers + self.status_code = self._response.status_code + self._initialized = True + try: + self._response.raise_for_status() + self._iterator = _as_async_generator(self._response.aiter_bytes()) + except Exception: # noqa: BLE001 # Safe catch-all for cleanup logic + try: + await self._response.aread() + except Exception: # noqa: BLE001 S110 # Safe catch-all for cleanup logic + pass + try: + await self._response.aclose() + except Exception: # noqa: BLE001 S110 # Safe catch-all for cleanup logic + pass + raise + return self + + return _init().__await__() + + def _start_flush(self) -> None: + if self._flush_scheduled or not self._raw_bytes: + return + self._flush_scheduled = True + + try: + task: Final = asyncio.create_task( + self._litellm_logging_obj.async_flush_passthrough_collected_chunks( + raw_bytes=self._raw_bytes, + provider_config=self._provider_config, + ) + ) + + # Compliant: Save a strong reference to prevent GC + self._background_tasks.add(task) + + # Remove the task from the set when it finishes to avoid memory leaks + task.add_done_callback(self._background_tasks.discard) + except Exception as e: # noqa: BLE001 # Safe catch-all for verbose logging + verbose_logger.exception( + "Failed to schedule passthrough spend-tracking flush; %d buffered chunks dropped: %s", + len(self._raw_bytes), + e, + ) + + def __aiter__(self) -> AsyncPassthroughStreamingResponse: + return self + + def aiter_bytes(self) -> AsyncPassthroughStreamingResponse: + return self + + async def __anext__(self) -> bytes: + if not self._initialized: + await self # pyright: ignore[reportGeneralTypeIssues] # structural type check misses __await__ + try: + chunk: Final = await anext(self._iterator) + self._raw_bytes.append(chunk) + except Exception: # noqa: BLE001 # Safe catch-all for cleanup logic + self._start_flush() + try: + await self._response.aclose() + except Exception: # noqa: BLE001 S110 # Safe catch-all for cleanup logic + pass + raise + else: + return chunk + + async def asend(self, value: bytes) -> bytes: + if not self._initialized: + await self # pyright: ignore[reportGeneralTypeIssues] # structural type check misses __await__ + return await self._iterator.asend(value) + + async def athrow( + self, + typ: BaseException | type[BaseException], + val: BaseException | object = None, + tb: TracebackType | None = None, + ) -> bytes: + if not self._initialized: + await self # pyright: ignore[reportGeneralTypeIssues] # structural type check misses __await__ + return await self._iterator.athrow(typ, val, tb) # pyright: ignore[reportCallIssue, reportArgumentType] # matches one of the athrow overloads + + async def aclose(self) -> None: + self._start_flush() + try: + if self._initialized: + await self._iterator.aclose() + await self._response.aclose() + except Exception: # noqa: BLE001 S110 # Safe catch-all for cleanup logic + pass + + +class PassthroughStreamingResponse(Generator[bytes, bytes, None]): + def __init__( + self, + response: httpx.Response, + litellm_logging_obj: LiteLLMLoggingObj, + provider_config: BasePassthroughConfig, + ) -> None: + self._response = response + self.headers = response.headers + self.status_code = response.status_code + self._litellm_logging_obj = litellm_logging_obj + self._provider_config = provider_config + self._iterator: Generator[bytes, bytes, None] = _as_generator(response.iter_bytes()) + self._raw_bytes: list[bytes] = [] # mutable-ok: instance buffer for streaming chunks + self._flush_scheduled = False + + def _start_flush(self) -> None: + if self._flush_scheduled or not self._raw_bytes: + return + self._flush_scheduled = True + + from litellm.utils import executor + + try: + executor.submit( + self._litellm_logging_obj.flush_passthrough_collected_chunks, + raw_bytes=self._raw_bytes, + provider_config=self._provider_config, + ) + except Exception as e: # noqa: BLE001 # Safe catch-all for verbose logging + verbose_logger.exception( + "Failed to schedule passthrough spend-tracking flush; %d buffered chunks dropped: %s", + len(self._raw_bytes), + e, + ) + + def __iter__(self) -> PassthroughStreamingResponse: + return self + + def __next__(self) -> bytes: + try: + chunk: Final = next(self._iterator) + self._raw_bytes.append(chunk) + except Exception: # noqa: BLE001 # Safe catch-all for cleanup logic + self._start_flush() + try: + self._response.close() + except Exception: # noqa: BLE001 S110 # Safe catch-all for cleanup logic + pass + raise + else: + return chunk + + def send(self, value: bytes) -> bytes: + return self._iterator.send(value) + + def throw( + self, + typ: BaseException | type[BaseException], + val: BaseException | object = None, + tb: TracebackType | None = None, + ) -> bytes: + return self._iterator.throw(typ, val, tb) # pyright: ignore[reportCallIssue, reportArgumentType] # matches one of the throw overloads + + def close(self) -> None: + self._start_flush() + try: + self._response.close() + except Exception: # noqa: BLE001 S110 # Safe catch-all for cleanup logic + pass @client @@ -37,15 +255,15 @@ async def allm_passthrough_route( api_key: str | None = None, request_query_params: dict | None = None, request_headers: dict | None = None, - content: Any | None = None, + content: RequestContent | None = None, data: dict | None = None, files: RequestFiles | None = None, - json: Any | None = None, + json: object | None = None, params: QueryParamTypes | None = None, cookies: CookieTypes | None = None, client: HTTPHandler | AsyncHTTPHandler | None = None, **kwargs, -) -> httpx.Response | AsyncGenerator[Any, Any]: +) -> httpx.Response | AsyncGenerator[bytes, bytes]: """ Async: Reranks a list of documents based on their relevance to the query """ @@ -64,7 +282,7 @@ async def allm_passthrough_route( from litellm.utils import ProviderConfigManager provider_config = cast( - Optional["BasePassthroughConfig"], kwargs.get("provider_config") + BasePassthroughConfig | None, kwargs.get("provider_config") ) or ProviderConfigManager.get_provider_passthrough_config( provider=LlmProviders(custom_llm_provider), model=model, @@ -132,12 +350,12 @@ async def allm_passthrough_route( if resolved_custom_llm_provider: try: provider_config = cast( - Optional["BasePassthroughConfig"], kwargs.get("provider_config") + BasePassthroughConfig | None, kwargs.get("provider_config") ) or ProviderConfigManager.get_provider_passthrough_config( provider=LlmProviders(resolved_custom_llm_provider), model=model, ) - except Exception: + except Exception: # noqa: BLE001 S110 # If we can't get provider config, pass None pass @@ -162,20 +380,20 @@ def llm_passthrough_route( api_key: str | None = None, request_query_params: dict | None = None, request_headers: dict | None = None, - content: Any | None = None, + content: RequestContent | None = None, data: dict | None = None, files: RequestFiles | None = None, - json: Any | None = None, + json: object | None = None, params: QueryParamTypes | None = None, cookies: CookieTypes | None = None, client: HTTPHandler | AsyncHTTPHandler | None = None, **kwargs, ) -> ( httpx.Response - | Coroutine[Any, Any, httpx.Response] - | Coroutine[Any, Any, httpx.Response | AsyncGenerator[Any, Any]] - | Generator[Any, Any, Any] - | AsyncGenerator[Any, Any] + | Coroutine[object, object, httpx.Response] + | Coroutine[object, object, httpx.Response | AsyncGenerator[bytes, bytes]] + | Generator[bytes, bytes, None] + | AsyncGenerator[bytes, bytes] ): """ Pass through requests to the LLM APIs. @@ -190,7 +408,9 @@ def llm_passthrough_route( _is_async: Final = bool(kwargs.get("allm_passthrough_route", False)) - litellm_logging_obj: Final = cast("LiteLLMLoggingObj", kwargs.get("litellm_logging_obj")) + litellm_logging_obj: Final = cast( + LiteLLMLoggingObj, kwargs.get("litellm_logging_obj") + ) # cast-ok: logging obj is constructed upstream; tests inject mocks model, custom_llm_provider, api_key, api_base = get_llm_provider( model=model, @@ -235,7 +455,7 @@ def llm_passthrough_route( ) provider_config: Final = cast( - Optional["BasePassthroughConfig"], kwargs.get("provider_config") + BasePassthroughConfig | None, kwargs.get("provider_config") ) or ProviderConfigManager.get_provider_passthrough_config( provider=LlmProviders(custom_llm_provider), model=model, @@ -276,10 +496,13 @@ def llm_passthrough_route( forward_headers=False, ) + _request_data: dict | None = ( + data if isinstance(data, dict) else (json if isinstance(json, dict) else None) + ) # rebind-ok: conditional headers, signed_json_body = provider_config.sign_request( headers=headers, litellm_params=litellm_params_dict, - request_data=data if data else json, + request_data=_request_data, api_base=str(updated_url), model=model, ) @@ -301,9 +524,12 @@ def llm_passthrough_route( ) ## IS STREAMING REQUEST + _streaming_request_data: dict = ( + data if isinstance(data, dict) else (json if isinstance(json, dict) else {}) + ) # rebind-ok: conditional is_streaming_request: Final = provider_config.is_streaming_request( endpoint=endpoint, - request_data=data or json or {}, + request_data=_streaming_request_data, ) # Update logging object with streaming status @@ -334,18 +560,26 @@ def llm_passthrough_route( else: # Sync path - client.client.send returns Response directly response: httpx.Response = client.client.send(request=request, stream=is_streaming_request) - response.raise_for_status() + try: + response.raise_for_status() + except Exception: # noqa: BLE001 # Safe catch-all for cleanup logic + try: + response.read() + except Exception: # noqa: BLE001 S110 # Safe catch-all for cleanup logic + pass + try: + response.close() + except Exception: # noqa: BLE001 S110 # Safe catch-all for cleanup logic + pass + raise - if ( - hasattr(response, "iter_bytes") and is_streaming_request - ): # yield the chunk, so we can store it in the logging object - return _sync_streaming(response, litellm_logging_obj, provider_config) + if hasattr(response, "iter_bytes") and is_streaming_request: + return PassthroughStreamingResponse(response, litellm_logging_obj, provider_config) else: - # For non-streaming responses, yield the entire response return response except Exception as e: - if provider_config is None: - raise e + # provider_config is guaranteed non-None here due to the earlier guard + assert provider_config is not None raise base_llm_http_handler._handle_error( e=e, provider_config=provider_config, @@ -356,9 +590,9 @@ async def _async_passthrough_request( client: HTTPHandler | AsyncHTTPHandler, request: httpx.Request, is_streaming_request: bool, - litellm_logging_obj: "LiteLLMLoggingObj", - provider_config: "BasePassthroughConfig", -) -> httpx.Response | AsyncGenerator[Any, Any]: + litellm_logging_obj: LiteLLMLoggingObj, + provider_config: BasePassthroughConfig, +) -> httpx.Response | AsyncGenerator[bytes, bytes]: """ Handle async passthrough requests. Uses async client to send request and properly handles streaming. @@ -369,8 +603,7 @@ async def _async_passthrough_request( # Check if it's a coroutine and await it if asyncio.iscoroutine(response_result): if is_streaming_request: - # Pass the coroutine to _async_streaming which will await it - return _async_streaming( + return await AsyncPassthroughStreamingResponse( # pyright: ignore[reportGeneralTypeIssues] # structural type check misses __await__ response=response_result, litellm_logging_obj=litellm_logging_obj, provider_config=provider_config, @@ -383,84 +616,3 @@ async def _async_passthrough_request( else: # Fallback for sync-like behavior (shouldn't happen in async path) raise Exception("Expected coroutine from async client") - - -def _sync_streaming( - response: httpx.Response, - litellm_logging_obj: "LiteLLMLoggingObj", - provider_config: "BasePassthroughConfig", -): - from litellm.utils import executor - - raw_bytes: Final[list[bytes]] = [] - flush_scheduled = False - try: - for chunk in response.iter_bytes(): - raw_bytes.append(chunk) - yield chunk - finally: - if not flush_scheduled and raw_bytes: - flush_scheduled = True - try: - executor.submit( - litellm_logging_obj.flush_passthrough_collected_chunks, - raw_bytes=raw_bytes, - provider_config=provider_config, - ) - except Exception as e: - verbose_logger.exception( - "Failed to schedule passthrough spend-tracking flush " - "in _sync_streaming; %d buffered chunks dropped: %s", - len(raw_bytes), - e, - ) - - -async def _async_streaming( - response: Coroutine[Any, Any, httpx.Response], - litellm_logging_obj: "LiteLLMLoggingObj", - provider_config: "BasePassthroughConfig", -): - iter_response: Final = await response - - try: - iter_response.raise_for_status() - except Exception: - try: - await iter_response.aclose() - except Exception: - pass - raise - - raw_bytes: Final[list[bytes]] = [] - flush_scheduled = False - try: - async for chunk in iter_response.aiter_bytes(): - raw_bytes.append(chunk) - yield chunk - except Exception: - try: - await iter_response.aclose() - except Exception: - pass - raise - finally: - # GeneratorExit (raised on client disconnect) is not caught by - # `except Exception`; the finally block ensures partial usage - # still gets flushed for spend tracking. See LIT-2642. - if not flush_scheduled and raw_bytes: - flush_scheduled = True - try: - asyncio.create_task( - litellm_logging_obj.async_flush_passthrough_collected_chunks( - raw_bytes=raw_bytes, - provider_config=provider_config, - ) - ) - except Exception as e: - verbose_logger.exception( - "Failed to schedule passthrough spend-tracking flush " - "in _async_streaming; %d buffered chunks dropped: %s", - len(raw_bytes), - e, - ) diff --git a/litellm/provider_endpoints_support_backup.json b/litellm/provider_endpoints_support_backup.json index ead26ab65c5..9d6b1e18f59 100644 --- a/litellm/provider_endpoints_support_backup.json +++ b/litellm/provider_endpoints_support_backup.json @@ -671,6 +671,42 @@ "interactions": true } }, + "qwencloud": { + "display_name": "QwenCloud (`qwencloud`)", + "url": "https://docs.litellm.ai/docs/providers/qwencloud", + "endpoints": { + "chat_completions": true, + "messages": true, + "responses": true, + "embeddings": true, + "image_generations": true, + "audio_transcriptions": false, + "audio_speech": false, + "moderations": false, + "batches": false, + "rerank": true, + "a2a": true, + "interactions": true + } + }, + "qwen_ai_platform": { + "display_name": "Qwen AI Platform (`qwen_ai_platform`)", + "url": "https://docs.litellm.ai/docs/providers/qwencloud", + "endpoints": { + "chat_completions": true, + "messages": true, + "responses": true, + "embeddings": true, + "image_generations": true, + "audio_transcriptions": false, + "audio_speech": false, + "moderations": false, + "batches": false, + "rerank": true, + "a2a": true, + "interactions": true + } + }, "databricks": { "display_name": "Databricks (`databricks`)", "url": "https://docs.litellm.ai/docs/providers/databricks", diff --git a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py index 2330120adad..1f552ff3e13 100644 --- a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py +++ b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py @@ -13,7 +13,7 @@ import json import os import re import time -from collections.abc import AsyncIterator, Callable, Mapping, Sequence +from collections.abc import AsyncIterator, Awaitable, Callable, Mapping, Sequence from contextlib import asynccontextmanager from dataclasses import dataclass, replace from typing import TYPE_CHECKING, Any, Final, Literal, TypeAlias, TypedDict, cast @@ -1206,7 +1206,7 @@ def _deserialize_json_dict(data: str | _StringMap | None) -> dict[str, str] | No return data -def _deserialize_json_list(data: Any) -> list[dict[str, Any]] | None: +def _deserialize_json_list(data: object) -> list[dict[str, Any]] | None: """Deserialize a JSON array stored in the DB (``env_vars`` and friends). Returns ``None`` for empty / null / unparseable input. Accepts strings @@ -1219,7 +1219,7 @@ def _deserialize_json_list(data: Any) -> list[dict[str, Any]] | None: return None if isinstance(data, str): try: - parsed: Final = json.loads(data) + parsed: Final[object] = json.loads(data) except (json.JSONDecodeError, TypeError): return None data = parsed @@ -1914,7 +1914,7 @@ class MCPServerManager: async def load_servers_from_config( self, - mcp_servers_config: dict[str, Any], + mcp_servers_config: dict[str, MCPServerConfig], mcp_aliases: dict[str, str] | None = None, ): """ @@ -3068,7 +3068,7 @@ class MCPServerManager: return {} cache_key: Final = "toolset_perms:" + ",".join(sorted(toolset_ids)) - cached: Final = await user_api_key_cache.async_get_cache(key=cache_key) + cached: Final[dict[str, list[str]] | None] = await user_api_key_cache.async_get_cache(key=cache_key) if cached is not None: return cached @@ -5154,7 +5154,7 @@ class MCPServerManager: # Wrapped so the bridge runs inside the task: the caller only holds the task and # gathers it later, so there is no other point that still sees a block here. - async def _run_during_call_hook() -> Mapping[str, Any] | None: + async def _run_during_call_hook() -> Mapping[str, object] | None: try: return await proxy_logging_obj.during_call_hook( user_api_key_dict=user_api_key_auth, @@ -5656,7 +5656,7 @@ class MCPServerManager: async def _gather_openapi_tool_tasks( self, - tasks: list[Any], + tasks: Sequence[Awaitable[object]], proxy_logging_obj: ProxyLogging | None, ) -> CallToolResult: """Await OpenAPI tool tasks and return the tool call result.""" diff --git a/litellm/proxy/_experimental/mcp_server/semantic_tool_filter.py b/litellm/proxy/_experimental/mcp_server/semantic_tool_filter.py index 7ec0f4b5192..dcf1b01bc25 100644 --- a/litellm/proxy/_experimental/mcp_server/semantic_tool_filter.py +++ b/litellm/proxy/_experimental/mcp_server/semantic_tool_filter.py @@ -5,6 +5,7 @@ Filters MCP tools semantically for /chat/completions and /responses endpoints. """ import asyncio +from collections.abc import Mapping, Sequence from typing import TYPE_CHECKING, Any, Final from litellm._logging import verbose_logger @@ -74,7 +75,7 @@ class SemanticMCPToolFilter: self.router_instance = litellm_router_instance self.tool_router: SemanticRouter | None = None self.context_window_error: str | None = None - self._tool_map: dict[str, Any] = {} # MCPTool objects or OpenAI function dicts + self._tool_map: dict[str, object] = {} # MCPTool objects or OpenAI function dicts self._index_sync_lock = asyncio.Lock() async def build_router_from_mcp_registry(self) -> None: @@ -182,11 +183,11 @@ class SemanticMCPToolFilter: return raise - def _has_tools_missing_from_index(self, tools: list[Any]) -> bool: + def _has_tools_missing_from_index(self, tools: Sequence[object]) -> bool: """Allocation-free check for any named tool not yet in the semantic index.""" return any(name and name not in self._tool_map for name in (self._extract_tool_info(t)[0] for t in tools)) - def _tools_missing_from_index(self, tools: list[Any]) -> dict[str, Any]: + def _tools_missing_from_index(self, tools: Sequence[object]) -> Mapping[str, object]: """Map name -> tool for every named tool not yet in the semantic index.""" return { name: tool @@ -194,7 +195,7 @@ class SemanticMCPToolFilter: if name and name not in self._tool_map } - async def _ensure_tools_indexed(self, available_tools: list[Any]) -> None: + async def _ensure_tools_indexed(self, available_tools: Sequence[object]) -> None: """ Index request-time tools the startup build never saw. @@ -385,7 +386,7 @@ class SemanticMCPToolFilter: separator: Final = client_name[-len(canonical) - 1] return separator in ("_", "-") - def _get_tools_by_names(self, tool_names: list[str], available_tools: list[Any]) -> list[Any]: + def _get_tools_by_names(self, tool_names: Sequence[str], available_tools: Sequence[object]) -> list[object]: """ Get tools from available_tools by their names, preserving the semantic router's ordering. @@ -401,14 +402,14 @@ class SemanticMCPToolFilter: # Exact matches win over suffix matches when both are present, and # each incoming tool is returned at most once even if two canonical # names happen to be tail-compatible with the same incoming name. - available_by_name: Final[dict[str, Any]] = {} + available_by_name: Final[dict[str, object]] = {} for tool in available_tools: client_name, _ = self._extract_tool_info(tool) if client_name and client_name not in available_by_name: available_by_name[client_name] = tool - matched: Final[list[Any]] = [] - used_ids: Final[set] = set() + matched: Final[list[object]] = [] + used_ids: Final[set[int]] = set() for canonical in tool_names: tool = available_by_name.get(canonical) if tool is None: @@ -430,7 +431,7 @@ class SemanticMCPToolFilter: used_ids.add(id(tool)) return matched - def extract_user_query(self, messages: list[dict[str, Any]]) -> str: + def extract_user_query(self, messages: Sequence[Mapping[str, object]]) -> str: """ Extract user query from messages for /chat/completions or /responses. diff --git a/litellm/proxy/_types.py b/litellm/proxy/_types.py index a84fae7fd23..c549f48126e 100644 --- a/litellm/proxy/_types.py +++ b/litellm/proxy/_types.py @@ -422,6 +422,9 @@ class LiteLLMRoutes(enum.Enum): "/responses/{response_id}/cancel", "/v1/responses/{response_id}/cancel", "/openai/v1/responses/{response_id}/cancel", + "/responses/input_tokens", + "/v1/responses/input_tokens", + "/openai/v1/responses/input_tokens", # vector stores "/vector_stores", "/v1/vector_stores", @@ -471,6 +474,7 @@ class LiteLLMRoutes(enum.Enum): "/vllm", "/mistral", "/milvus", + "/gigachat", "/watsonx", ] diff --git a/litellm/proxy/agent_endpoints/a2a_endpoints.py b/litellm/proxy/agent_endpoints/a2a_endpoints.py index bd02cfdf907..31b05320cd3 100644 --- a/litellm/proxy/agent_endpoints/a2a_endpoints.py +++ b/litellm/proxy/agent_endpoints/a2a_endpoints.py @@ -14,7 +14,7 @@ import json from collections.abc import AsyncGenerator, Mapping from copy import deepcopy from types import MappingProxyType -from typing import TYPE_CHECKING, Any, Final +from typing import TYPE_CHECKING, Any, Final, Protocol from urllib.parse import urlparse from fastapi import APIRouter, Depends, HTTPException, Request, Response @@ -215,11 +215,20 @@ def _enforce_inbound_trace_id(agent: "AgentResponse", request: Request) -> None: ) +class _JsonRpcResponse(Protocol): + def json(self) -> dict[str, object]: ... + + +def _jsonrpc_body(response: _JsonRpcResponse) -> dict[str, object]: + """The decoded JSON-RPC body of ``response``.""" + return response.json() + + async def _forward_jsonrpc( agent_url: str, body: dict[str, object], extra_headers: Mapping[str, str] | None = None, -) -> dict[str, Any]: +) -> dict[str, object]: from litellm.llms.custom_httpx.http_handler import get_async_httpx_client from litellm.types.llms.custom_http import httpxSpecialProvider @@ -230,7 +239,7 @@ async def _forward_jsonrpc( ) resp: Final = await handler.post(agent_url, json=body, headers=headers) try: - result: Final = resp.json() + result: Final = _jsonrpc_body(resp) except Exception: resp.raise_for_status() raise @@ -940,8 +949,8 @@ async def invoke_agent_a2a( ) result = await _forward_jsonrpc(agent_url, forward_body, extra_headers=caller_headers) if method == "agent/getAuthenticatedExtendedCard": - if isinstance(result.get("result"), dict): - card: Final = result["result"] + card: Final = result.get("result") + if isinstance(card, dict): proxy_url: Final = get_custom_url(str(request.base_url), route=f"a2a/{agent_id}") # Rewrite the upstream agent URL in both 0.3 (top-level `url`) # and 1.0 (`supportedInterfaces[0].url`) wire formats so that diff --git a/litellm/proxy/auth/auth_exception_handler.py b/litellm/proxy/auth/auth_exception_handler.py index a42187b3a44..64878a480a7 100644 --- a/litellm/proxy/auth/auth_exception_handler.py +++ b/litellm/proxy/auth/auth_exception_handler.py @@ -2,13 +2,14 @@ Handles Authentication Errors """ +import logging from collections.abc import Mapping from typing import TYPE_CHECKING, Any, Final from fastapi import HTTPException, Request, status import litellm -from litellm._logging import verbose_proxy_logger +from litellm._logging import verbose_proxy_logger, verbose_proxy_stdout_logger from litellm.constants import EMPTY_MAPPING from litellm.integrations.otel.runtime import seed_request_identity from litellm.litellm_core_utils.core_helpers import is_expected_client_error @@ -18,7 +19,11 @@ from litellm.proxy._types import ( ProxyException, UserAPIKeyAuth, ) -from litellm.proxy.auth.auth_utils import _get_request_ip_address +from litellm.proxy.auth.auth_utils import ( + _get_request_ip_address, + is_invalid_virtual_key_error, + mark_invalid_virtual_key_error, +) from litellm.proxy.db.exception_handler import PrismaDBExceptionHandler from litellm.types.services import ServiceTypes @@ -36,6 +41,41 @@ else: Span = Any +def _as_proxy_exception(e: Exception) -> ProxyException: + """Convert an authentication failure into the ProxyException the client receives.""" + if isinstance(e, litellm.BudgetExceededError): + return ProxyException( + message=e.message, + type=ProxyErrorTypes.budget_exceeded, + param=None, + code=getattr(e, "status_code", status.HTTP_429_TOO_MANY_REQUESTS), + ) + if isinstance(e, HTTPException): + return ProxyException( + message=getattr(e, "detail", f"Authentication Error({e})"), + type=ProxyErrorTypes.auth_error, + param=getattr(e, "param", "None"), + code=getattr(e, "status_code", status.HTTP_401_UNAUTHORIZED), + ) + if isinstance(e, ProxyException): + return e + if PrismaDBExceptionHandler.is_database_service_unavailable_error(e): + return ProxyException( + message=( + "Service Unavailable, the authentication database is temporarily unreachable. Please retry shortly." + ), + type=ProxyErrorTypes.no_db_connection, + param="None", + code=status.HTTP_503_SERVICE_UNAVAILABLE, + ) + return ProxyException( + message="Authentication Error, " + str(e), + type=ProxyErrorTypes.auth_error, + param=getattr(e, "param", "None"), + code=status.HTTP_401_UNAUTHORIZED, + ) + + def _with_requester_ip_address(request_data: dict[str, object], requester_ip: str | None) -> dict[str, object]: """Auth gate rejections are raised before `add_litellm_data_to_request` records the caller IP, so their failure logs would otherwise carry no IP nor key/user identity.""" @@ -110,16 +150,21 @@ class UserAPIKeyAuthExceptionHandler: request=request, use_x_forwarded_for=general_settings.get("use_x_forwarded_for") is True, ) - log_fn: Final = ( - verbose_proxy_logger.error - if is_expected_client_error(e) and not litellm.log_client_error_tracebacks - else verbose_proxy_logger.exception - ) - log_fn( + + # Log authentication failures before identity seeding and callbacks, so the log + # survives a raising callback pipeline. Classify and route malformed virtual-key + # rejections to WARNING on stdout (suppressible via LITELLM_LOG=ERROR). + log_extra: Final = {"requester_ip": requester_ip} + is_invalid_virtual_key: Final = is_invalid_virtual_key_error(e) + is_quiet_log: Final = is_invalid_virtual_key and not litellm.log_client_error_tracebacks + logger: Final = verbose_proxy_stdout_logger if is_quiet_log else verbose_proxy_logger + logger.log( + logging.WARNING if is_quiet_log else logging.ERROR, "litellm.proxy.proxy_server.user_api_key_auth(): Exception occured - %s\nRequester IP Address:%s", e, requester_ip, - extra={"requester_ip": requester_ip}, + exc_info=True if litellm.log_client_error_tracebacks or not is_expected_client_error(e) else None, + extra=log_extra, ) # Log this exception to OTEL, Datadog etc. Reuse the identity resolved @@ -167,35 +212,13 @@ class UserAPIKeyAuthExceptionHandler: if transformed_exception is not None: e = transformed_exception - if isinstance(e, litellm.BudgetExceededError): - raise ProxyException( - message=e.message, - type=ProxyErrorTypes.budget_exceeded, - param=None, - code=getattr(e, "status_code", status.HTTP_429_TOO_MANY_REQUESTS), + final_exception: Final = mark_invalid_virtual_key_error(_as_proxy_exception(e), is_invalid_virtual_key) + # If a quiet-logged malformed-key transform yields non-401, escalate to ERROR + if is_quiet_log and str(final_exception.code) != str(status.HTTP_401_UNAUTHORIZED): + verbose_proxy_logger.error( + "litellm.proxy.proxy_server.user_api_key_auth(): Exception occured - %s\nRequester IP Address:%s", + final_exception, + requester_ip, + extra=log_extra, ) - if isinstance(e, HTTPException): - raise ProxyException( - message=getattr(e, "detail", f"Authentication Error({e})"), - type=ProxyErrorTypes.auth_error, - param=getattr(e, "param", "None"), - code=getattr(e, "status_code", status.HTTP_401_UNAUTHORIZED), - ) - elif isinstance(e, ProxyException): - raise e - if PrismaDBExceptionHandler.is_database_service_unavailable_error(e): - raise ProxyException( - message=( - "Service Unavailable, the authentication database is " - "temporarily unreachable. Please retry shortly." - ), - type=ProxyErrorTypes.no_db_connection, - param="None", - code=status.HTTP_503_SERVICE_UNAVAILABLE, - ) - raise ProxyException( - message="Authentication Error, " + str(e), - type=ProxyErrorTypes.auth_error, - param=getattr(e, "param", "None"), - code=status.HTTP_401_UNAUTHORIZED, - ) + raise final_exception diff --git a/litellm/proxy/auth/auth_utils.py b/litellm/proxy/auth/auth_utils.py index 9b1a6ba5aa7..89b2c92cdfd 100644 --- a/litellm/proxy/auth/auth_utils.py +++ b/litellm/proxy/auth/auth_utils.py @@ -15,6 +15,7 @@ from litellm._logging import verbose_proxy_logger from litellm.constants import ( BATCH_ENQUEUED_TOKEN_LIMIT_METADATA_KEY, EMPTY_MAPPING, + INVALID_VIRTUAL_KEY_ERROR_MARKER, MINIMUM_CUSTOM_KEY_LENGTH, STANDARD_CUSTOMER_ID_HEADERS, ) @@ -34,6 +35,43 @@ from litellm.types.router import CONFIGURABLE_CLIENTSIDE_AUTH_PARAMS from litellm.types.utils import CustomPricingLiteLLMParams +def is_invalid_virtual_key_error(exception: BaseException | None) -> bool: + """True when an authentication error rejects a malformed virtual key. + + Classifies only by the marker stamped where that 401 is raised. Message + content is never inspected: other 401s interpolate caller-supplied values + (vector store ids, organization ids) into their messages, so a phrase + match would let a request body demote an authorization failure to the + quiet log path. + """ + if not isinstance(exception, (HTTPException, ProxyException)): + return False + + code: Final[object] = getattr(exception, "code", None) + status_code: Final[object] = code if code is not None else getattr(exception, "status_code", None) + if str(status_code) != str(status.HTTP_401_UNAUTHORIZED): + return False + + return getattr(exception, INVALID_VIRTUAL_KEY_ERROR_MARKER, False) is True + + +def mark_invalid_virtual_key_error(exception: ProxyException, is_invalid_virtual_key: bool) -> ProxyException: + """Return an independently marked malformed-key exception after callback transformations.""" + if not is_invalid_virtual_key or str(exception.code) != str(status.HTTP_401_UNAUTHORIZED): + return exception + marked_exception: Final = ProxyException( + message=exception.message, + type=exception.type, + param=exception.param, + code=exception.code, + headers=exception.headers.copy(), + openai_code=None if exception.openai_code is None else str(exception.openai_code), + provider_specific_fields=exception.provider_specific_fields, + ) + setattr(marked_exception, INVALID_VIRTUAL_KEY_ERROR_MARKER, True) + return marked_exception + + def _get_request_ip_address(request: Request, use_x_forwarded_for: bool | None = False) -> str | None: client_ip = None if use_x_forwarded_for is True and "x-forwarded-for" in request.headers: @@ -956,7 +994,7 @@ def get_key_model_rpm_limit( # 2. Check model_max_budget if user_api_key_dict.model_max_budget: - model_rpm_limit: Final[dict[str, Any]] = {} + model_rpm_limit: Final[dict[str, int]] = {} for model, budget in user_api_key_dict.model_max_budget.items(): if isinstance(budget, dict) and budget.get("rpm_limit") is not None: model_rpm_limit[model] = budget["rpm_limit"] @@ -999,7 +1037,7 @@ def get_key_model_tpm_limit( # 2. Check model_max_budget (iterate per-model like RPM does) if user_api_key_dict.model_max_budget: - model_tpm_limit: Final[dict[str, Any]] = {} + model_tpm_limit: Final[dict[str, int]] = {} for model, budget in user_api_key_dict.model_max_budget.items(): if isinstance(budget, dict) and budget.get("tpm_limit") is not None: model_tpm_limit[model] = budget["tpm_limit"] @@ -1062,7 +1100,7 @@ def _validated_output_token_estimates_per_model(raw: object) -> Mapping[str, int def _estimated_output_tokens_from_metadata( - metadata: Mapping[str, Any] | None, + metadata: Mapping[str, object] | None, model_name: str | None, ) -> int | None: """Resolve the per-model, then global, estimate out of one metadata blob. @@ -1628,7 +1666,7 @@ def _dedupe_model_candidates(candidates: list[str]) -> list[str]: return deduped -def _get_case_insensitive_mapping_value(mapping: Mapping[str, Any] | None, key: str) -> Any: +def _get_case_insensitive_mapping_value(mapping: Mapping[str, object] | None, key: str) -> object: if not mapping: return None if key in mapping: @@ -1732,8 +1770,8 @@ def _resolve_model_id_with_router(model_id: str | None, llm_router: Router | Non def _extract_model_candidates_from_request( request_data: dict, route: str, - request_headers: Mapping[str, Any] | None = None, - request_query_params: Mapping[str, Any] | None = None, + request_headers: Mapping[str, object] | None = None, + request_query_params: Mapping[str, object] | None = None, llm_router: Router | None = None, ) -> list[str]: candidates: Final[list[str]] = [] @@ -1825,8 +1863,8 @@ def request_dispatched_to_pass_through_endpoint(request: Request | None) -> bool def get_model_from_request( request_data: dict, route: str, - request_headers: Mapping[str, Any] | None = None, - request_query_params: Mapping[str, Any] | None = None, + request_headers: Mapping[str, object] | None = None, + request_query_params: Mapping[str, object] | None = None, llm_router: Router | None = None, request: Request | None = None, ) -> str | list[str] | None: diff --git a/litellm/proxy/auth/handle_jwt.py b/litellm/proxy/auth/handle_jwt.py index 39e6ca9a369..0795cee7409 100644 --- a/litellm/proxy/auth/handle_jwt.py +++ b/litellm/proxy/auth/handle_jwt.py @@ -14,8 +14,8 @@ import hashlib import os import re import time -from collections.abc import Awaitable, Callable -from typing import Any, Final, Literal, NoReturn, TypeVar, cast +from collections.abc import Awaitable, Callable, Sequence +from typing import Any, Final, Literal, NoReturn, Protocol, TypeVar, cast import httpx import jwt @@ -24,6 +24,7 @@ from cryptography.hazmat.backends import default_backend from cryptography.hazmat.primitives import serialization from fastapi import HTTPException, status from jwt.api_jwk import PyJWK +from typing_extensions import ReadOnly, TypedDict from litellm._logging import verbose_proxy_logger from litellm.litellm_core_utils.dot_notation_indexing import get_nested_value @@ -93,6 +94,47 @@ UNREACHABLE_CACHE_KEY_PREFIX: Final = "litellm_jwks_unreachable_" _CachedValueT = TypeVar("_CachedValueT", bound=JWKKeyValue | str) +class _JWTAuthSettings(Protocol): + """The JWT auth settings block this handler reads back through ``getattr``, when one is configured.""" + + @property + def issuers(self) -> Sequence[JWTIssuerConfig] | None: ... + + @property + def public_key_ttl(self) -> float: ... + + @property + def public_key_stale_ttl(self) -> float: ... + + +class _OIDCDiscoveryBody(TypedDict, total=False): + """Decoded OIDC discovery document, read for the JWKS endpoint it advertises.""" + + jwks_uri: ReadOnly[str] + + +class _OIDCDiscoveryResponse(Protocol): + """The discovery endpoint's HTTP response, read for the decoded document it carries.""" + + def json(self) -> _OIDCDiscoveryBody: ... + + +class _UserInfoResponse(Protocol): + """The OIDC UserInfo endpoint's HTTP response, read for the identity document it carries.""" + + def json(self) -> dict[str, object]: ... + + +def _discovery_document(response: _OIDCDiscoveryResponse) -> _OIDCDiscoveryBody: + """Decode an OIDC discovery response body.""" + return response.json() + + +def _userinfo_document(response: _UserInfoResponse) -> dict[str, object]: + """Decode an OIDC UserInfo response body into its JSON object form.""" + return response.json() + + def jwks_unavailable_exception(error: JWKSUnreachableError) -> ProxyException: return ProxyException( message=( @@ -794,7 +836,7 @@ class JWTHandler: f"JWT Auth: OIDC discovery endpoint {url} returned status {response.status_code}: {response.text}" ) try: - discovery: Final = response.json() + discovery: Final = _discovery_document(response) except Exception as e: raise Exception(f"JWT Auth: Failed to parse OIDC discovery document at {url}: {e}") @@ -806,13 +848,13 @@ class JWTHandler: return jwks_uri def _get_public_key_cache_ttl(self) -> float: - litellm_jwtauth: Final = getattr(self, "litellm_jwtauth", None) + litellm_jwtauth: Final[_JWTAuthSettings | None] = getattr(self, "litellm_jwtauth", None) if litellm_jwtauth is None: return 600 return litellm_jwtauth.public_key_ttl def _get_public_key_stale_ttl(self) -> float: - litellm_jwtauth: Final = getattr(self, "litellm_jwtauth", None) + litellm_jwtauth: Final[_JWTAuthSettings | None] = getattr(self, "litellm_jwtauth", None) if litellm_jwtauth is None: return DEFAULT_JWKS_STALE_TTL return litellm_jwtauth.public_key_stale_ttl @@ -938,7 +980,7 @@ class JWTHandler: if response.status_code != 200: raise Exception(f"OIDC UserInfo endpoint returned status {response.status_code}: {response.text}") - userinfo: Final = response.json() + userinfo: Final = _userinfo_document(response) verbose_proxy_logger.debug("Received OIDC UserInfo: %s", userinfo) # Cache the userinfo response @@ -996,7 +1038,7 @@ class JWTHandler: } def _get_configured_issuer(self, token: str) -> JWTIssuerConfig | None: - litellm_jwtauth: Final = getattr(self, "litellm_jwtauth", None) + litellm_jwtauth: Final[_JWTAuthSettings | None] = getattr(self, "litellm_jwtauth", None) if litellm_jwtauth is None: return None diff --git a/litellm/proxy/auth/user_api_key_auth.py b/litellm/proxy/auth/user_api_key_auth.py index e92d090a2fb..5fb6dad0cd7 100644 --- a/litellm/proxy/auth/user_api_key_auth.py +++ b/litellm/proxy/auth/user_api_key_auth.py @@ -19,12 +19,15 @@ import fastapi import orjson from fastapi import HTTPException, Request, WebSocket, status from fastapi.security.api_key import APIKeyHeader +from starlette.exceptions import WebSocketException import litellm from litellm._logging import verbose_logger, verbose_proxy_logger from litellm._service_logger import ServiceLogging from litellm.constants import ( GLOBAL_PROXY_SPEND_CACHE_KEY, + INVALID_VIRTUAL_KEY_ERROR_MARKER, + INVALID_VIRTUAL_KEY_ERROR_MESSAGE, LITELLM_PROXY_BUDGET_NAME, LITELLM_PROXY_MASTER_KEY_ALIAS, ) @@ -65,6 +68,7 @@ from litellm.proxy.auth.auth_utils import ( get_model_from_request, get_request_route, get_request_route_template, + is_invalid_virtual_key_error, iter_request_fallback_targets, normalize_request_route, pre_db_read_auth_checks, @@ -539,6 +543,8 @@ async def user_api_key_auth_websocket(websocket: WebSocket): try: return await user_api_key_auth(request=request, api_key=f"Bearer {api_key}") except Exception as e: + if is_invalid_virtual_key_error(e): + raise WebSocketException(code=status.WS_1008_POLICY_VIOLATION) verbose_proxy_logger.exception(e) await websocket.close(code=status.WS_1008_POLICY_VIOLATION) raise HTTPException(status_code=403, detail=str(e)) @@ -1867,13 +1873,17 @@ async def _user_api_key_auth_builder( _masked_key: Final = f"{api_key[:4]}****{api_key[-4:]}" if len(api_key) > 8 else "****" if not api_key.startswith("sk-"): _hint = _JWT_AUTH_DISABLED_HINT if not enable_jwt_auth and JWTHandler.is_jwt(token=api_key) else "" - raise HTTPException( + _malformed_key_error = HTTPException( status_code=status.HTTP_401_UNAUTHORIZED, detail=( - f"LiteLLM Virtual Key expected. Received={_masked_key}, " + f"{INVALID_VIRTUAL_KEY_ERROR_MESSAGE}. Received={_masked_key}, " f"expected to start with 'sk-'.{_hint}" ), ) # prevent token hashes from being used + # Stamp provenance here so log routing classifies this 401 by + # where it was raised, never by its message text. + setattr(_malformed_key_error, INVALID_VIRTUAL_KEY_ERROR_MARKER, True) + raise _malformed_key_error else: verbose_logger.warning( "litellm.proxy.proxy_server.user_api_key_auth(): Warning - Key is not a string. Got type={}".format( diff --git a/litellm/proxy/common_request_processing.py b/litellm/proxy/common_request_processing.py index eda7ebfa624..05ddef822f1 100644 --- a/litellm/proxy/common_request_processing.py +++ b/litellm/proxy/common_request_processing.py @@ -1498,6 +1498,35 @@ class ProxyBaseLLMRequestProcessing: def __init__(self, data: dict): self.data = data + @staticmethod + def _merge_passthrough_streaming_headers( + response_headers: httpx.Headers | dict | None, + custom_headers: dict, + ) -> dict: + """ + Merge upstream passthrough headers with proxy/custom headers. + + Proxy/custom headers win on key collisions. + """ + excluded_headers: Final = { # mutable-ok: set of header names to exclude from forwarding + "transfer-encoding", + "content-encoding", + "set-cookie", + "connection", + "keep-alive", + "proxy-authenticate", + "proxy-authorization", + "te", + "trailer", + "upgrade", + } + + merged_headers: Final = { # mutable-ok: dict comprehension for merged headers forwarded to httpx + key: value for key, value in dict(response_headers or {}).items() if key.lower() not in excluded_headers + } + merged_headers.update(custom_headers) + return merged_headers + @staticmethod def get_custom_headers( *, @@ -2392,6 +2421,16 @@ class ProxyBaseLLMRequestProcessing: ) if route_type == "allm_passthrough_route": + upstream_response_headers: Final = getattr(response, "headers", None) + streaming_headers: Final = ( + ProxyBaseLLMRequestProcessing._merge_passthrough_streaming_headers( + response_headers=upstream_response_headers, + custom_headers=custom_headers, + ) + if upstream_response_headers is not None + else custom_headers + ) + # Check if response is an async generator if self._is_streaming_response(response): if asyncio.iscoroutine(response): @@ -2421,11 +2460,11 @@ class ProxyBaseLLMRequestProcessing: # For passthrough routes, stream directly without error parsing # since we're dealing with raw binary data (e.g., AWS event streams) - return StreamingResponse( - content=generator, - status_code=status.HTTP_200_OK, + return _UpstreamClosingStreamingResponse( + content=generator, # pyright: ignore[reportArgumentType] # generator-configured StreamingResponse + status_code=getattr(response, "status_code", status.HTTP_200_OK), media_type=self._passthrough_event_stream_media_type(), - headers=custom_headers, + headers=streaming_headers, ) else: _early = await self._handle_non_streaming_allm_passthrough_route( @@ -2440,7 +2479,7 @@ class ProxyBaseLLMRequestProcessing: return StreamingResponse( content=response.aiter_bytes(), status_code=response.status_code, - headers=custom_headers, + headers=streaming_headers, ) elif route_type == "anthropic_messages": # Check if response is actually a streaming response (async generator) diff --git a/litellm/proxy/common_utils/callback_utils.py b/litellm/proxy/common_utils/callback_utils.py index a28d03eebf0..39e74d2c8bd 100644 --- a/litellm/proxy/common_utils/callback_utils.py +++ b/litellm/proxy/common_utils/callback_utils.py @@ -533,8 +533,8 @@ def sanitize_openai_provider_metadata( Strips LiteLLM proxy-internal tracking fields that must not be forwarded to OpenAI batch/file APIs. """ - if not metadata: - return metadata + if metadata is None: + return None sanitized: Final[dict[str, str]] = {} for key, value in metadata.items(): if key in LITELLM_PROXY_INTERNAL_METADATA_KEYS: @@ -547,7 +547,7 @@ def sanitize_openai_provider_metadata( key, type(value).__name__, ) - return sanitized or None + return None if metadata and not sanitized else sanitized def add_guardrail_to_applied_guardrails_header(request_data: dict, guardrail_name: str | None): @@ -650,7 +650,7 @@ def normalize_callback_names(callbacks: Iterable[object] | None) -> list[object] return [c.lower() if isinstance(c, str) else c for c in callbacks] -def strip_callback_config(metadata: dict[str, Any] | None) -> dict[str, Any] | None: +def strip_callback_config(metadata: dict[str, object] | None) -> dict[str, object] | None: """Return key/team metadata without the slots that carry callback credentials.""" if not isinstance(metadata, dict): return metadata diff --git a/litellm/proxy/common_utils/custom_openapi_spec.py b/litellm/proxy/common_utils/custom_openapi_spec.py index bc7b80801fe..2a20e7b07ce 100644 --- a/litellm/proxy/common_utils/custom_openapi_spec.py +++ b/litellm/proxy/common_utils/custom_openapi_spec.py @@ -1,8 +1,12 @@ from collections.abc import Mapping, Sequence -from typing import Any, Final +from typing import Final, TypeAlias, Union from litellm._logging import verbose_proxy_logger +JsonValue: TypeAlias = Union["JsonObject", "JsonArray", str, int, float, bool, None] +JsonObject: TypeAlias = dict[str, JsonValue] +JsonArray: TypeAlias = list[JsonValue] + class CustomOpenAPISpec: """ @@ -27,7 +31,20 @@ class CustomOpenAPISpec: RESPONSES_API_PATHS = ["/v1/responses", "/responses"] @staticmethod - def get_pydantic_schema(model_class) -> Mapping[str, object] | None: + def _as_object(node: JsonValue) -> JsonObject: + return node if isinstance(node, dict) else {} + + @staticmethod + def _as_array(node: JsonValue) -> JsonArray: + return node if isinstance(node, list) else [] + + @staticmethod + def _components_schemas(openapi_schema: JsonObject) -> JsonObject: + components: Final = CustomOpenAPISpec._as_object(openapi_schema.setdefault("components", {})) + return CustomOpenAPISpec._as_object(components.setdefault("schemas", {})) + + @staticmethod + def get_pydantic_schema(model_class) -> JsonObject | None: """ Get JSON schema from a Pydantic model, handling both v1 and v2 APIs. @@ -54,9 +71,7 @@ class CustomOpenAPISpec: return None @staticmethod - def add_schema_to_components( - openapi_schema: dict[str, Any], schema_name: str, schema_def: Mapping[str, object] - ) -> None: + def add_schema_to_components(openapi_schema: JsonObject, schema_name: str, schema_def: JsonObject) -> None: """ Add a schema definition to the OpenAPI components/schemas section. @@ -66,16 +81,25 @@ class CustomOpenAPISpec: schema_def: The schema definition """ # Ensure components/schemas structure exists - if "components" not in openapi_schema: - openapi_schema["components"] = {} - if "schemas" not in openapi_schema["components"]: - openapi_schema["components"]["schemas"] = {} + _ = CustomOpenAPISpec._components_schemas(openapi_schema) # Add the schema CustomOpenAPISpec._move_defs_to_components(openapi_schema, {schema_name: schema_def}) @staticmethod - def add_request_body_to_paths(openapi_schema: dict[str, Any], paths: Sequence[str], schema_ref: str) -> None: + def _expanded_request_field(field_name: str, field_def: JsonValue) -> JsonValue: + expanded: Final = CustomOpenAPISpec._rewrite_defs_refs( + CustomOpenAPISpec._expand_field_definition(CustomOpenAPISpec._as_object(field_def)) + ) + if field_name != "messages": + return expanded + return { + **CustomOpenAPISpec._as_object(expanded), + "example": [{"role": "user", "content": "Hello, how are you?"}], + } + + @staticmethod + def add_request_body_to_paths(openapi_schema: JsonObject, paths: Sequence[str], schema_ref: str) -> None: """ Add request body with expanded form fields for better Swagger UI display. This keeps the request body but expands it to show individual fields in the UI. @@ -86,54 +110,58 @@ class CustomOpenAPISpec: schema_ref: Reference to the schema component (e.g., "#/components/schemas/ModelName") """ for path in paths: - if path in openapi_schema.get("paths", {}) and "post" in openapi_schema["paths"][path]: - # Get the actual schema to extract ALL field definitions - schema_name = schema_ref.split("/")[-1] # Extract "ProxyChatCompletionRequest" from the ref - actual_schema = openapi_schema.get("components", {}).get("schemas", {}).get(schema_name, {}) - schema_properties = actual_schema.get("properties", {}) - required_fields = actual_schema.get("required", []) + path_item = CustomOpenAPISpec._as_object( + CustomOpenAPISpec._as_object(openapi_schema.get("paths")).get(path) + ) + if "post" not in path_item: + continue - # Extract $defs and add them to components/schemas - # This fixes Pydantic v2 $defs not being resolvable in Swagger/OpenAPI - if "$defs" in actual_schema: - CustomOpenAPISpec._move_defs_to_components(openapi_schema, actual_schema["$defs"]) + post_operation = CustomOpenAPISpec._as_object(path_item["post"]) - # Create an expanded inline schema instead of just a $ref - # This makes Swagger UI show all individual fields in the request body editor - expanded_schema = { - "type": "object", - "required": required_fields, - "properties": {}, - } + # Get the actual schema to extract ALL field definitions + schema_name = schema_ref.split("/")[-1] # Extract "ProxyChatCompletionRequest" from the ref + components = CustomOpenAPISpec._as_object(openapi_schema.get("components")) + actual_schema = CustomOpenAPISpec._as_object( + CustomOpenAPISpec._as_object(components.get("schemas")).get(schema_name) + ) + schema_properties = CustomOpenAPISpec._as_object(actual_schema.get("properties")) + required_fields = actual_schema.get("required", []) - # Add all properties with their full definitions - for field_name, field_def in schema_properties.items(): - expanded_field = CustomOpenAPISpec._expand_field_definition(field_def) + # Extract $defs and add them to components/schemas + # This fixes Pydantic v2 $defs not being resolvable in Swagger/OpenAPI + if "$defs" in actual_schema: + CustomOpenAPISpec._move_defs_to_components( + openapi_schema, CustomOpenAPISpec._as_object(actual_schema["$defs"]) + ) - # Rewrite $defs references to use components/schemas instead - expanded_field = CustomOpenAPISpec._rewrite_defs_refs(expanded_field) + # Create an expanded inline schema instead of just a $ref + # This makes Swagger UI show all individual fields in the request body editor + expanded_schema: JsonObject = { + "type": "object", + "required": required_fields, + "properties": { + field_name: CustomOpenAPISpec._expanded_request_field(field_name, field_def) + for field_name, field_def in schema_properties.items() + }, + } - # Add a simple example for the messages field - if field_name == "messages": - expanded_field["example"] = [{"role": "user", "content": "Hello, how are you?"}] + # Set the request body with the expanded schema + post_operation["requestBody"] = { + "required": True, + "content": {"application/json": {"schema": expanded_schema}}, + } - expanded_schema["properties"][field_name] = expanded_field - - # Set the request body with the expanded schema - openapi_schema["paths"][path]["post"]["requestBody"] = { - "required": True, - "content": {"application/json": {"schema": expanded_schema}}, - } - - # Keep any existing parameters (like path parameters) but remove conflicting query params - if "parameters" in openapi_schema["paths"][path]["post"]: - existing_params = openapi_schema["paths"][path]["post"]["parameters"] - # Only keep path parameters, remove query params that conflict with request body - filtered_params = [param for param in existing_params if param.get("in") == "path"] - openapi_schema["paths"][path]["post"]["parameters"] = filtered_params + # Keep any existing parameters (like path parameters) but remove conflicting query params + if "parameters" in post_operation: + # Only keep path parameters, remove query params that conflict with request body + post_operation["parameters"] = [ + param + for param in CustomOpenAPISpec._as_array(post_operation["parameters"]) + if CustomOpenAPISpec._as_object(param).get("in") == "path" + ] @staticmethod - def _move_defs_to_components(openapi_schema: dict[str, Any], defs: Mapping[str, Mapping[str, Any]]) -> None: + def _move_defs_to_components(openapi_schema: JsonObject, defs: Mapping[str, JsonValue]) -> None: """ Move $defs from Pydantic v2 schema to OpenAPI components/schemas. This makes the definitions resolvable in Swagger/OpenAPI viewers. @@ -146,23 +174,31 @@ class CustomOpenAPISpec: return # Ensure components/schemas exists - if "components" not in openapi_schema: - openapi_schema["components"] = {} - if "schemas" not in openapi_schema["components"]: - openapi_schema["components"]["schemas"] = {} + schemas: Final = CustomOpenAPISpec._components_schemas(openapi_schema) # Add each definition to components/schemas for def_name, def_schema in defs.items(): # Recursively rewrite any nested $defs references within this definition - rewritten_def = CustomOpenAPISpec._rewrite_defs_refs(def_schema) - openapi_schema["components"]["schemas"][def_name] = rewritten_def + schemas[def_name] = CustomOpenAPISpec._rewrite_defs_refs(def_schema) # If this definition also has $defs, process them recursively - if "$defs" in def_schema: - CustomOpenAPISpec._move_defs_to_components(openapi_schema, def_schema["$defs"]) + def_object = CustomOpenAPISpec._as_object(def_schema) + if "$defs" in def_object: + CustomOpenAPISpec._move_defs_to_components( + openapi_schema, CustomOpenAPISpec._as_object(def_object["$defs"]) + ) @staticmethod - def _rewrite_defs_refs(schema: Any) -> Any: + def _rewritten_defs_entry(key: str, value: JsonValue) -> JsonValue: + if key == "$ref" and isinstance(value, str) and value.startswith("#/$defs/"): + # Rewrite the reference to use components/schemas + def_name: Final = value.replace("#/$defs/", "") + return f"#/components/schemas/{def_name}" + # Recursively process nested structures + return CustomOpenAPISpec._rewrite_defs_refs(value) + + @staticmethod + def _rewrite_defs_refs(schema: JsonValue) -> JsonValue: """ Recursively rewrite $ref values from #/$defs/... to #/components/schemas/... This converts Pydantic v2 references to OpenAPI-compatible references. @@ -174,26 +210,17 @@ class CustomOpenAPISpec: Schema with rewritten references """ if isinstance(schema, dict): - result: Final = {} - for key, value in schema.items(): - if key == "$ref" and isinstance(value, str) and value.startswith("#/$defs/"): - # Rewrite the reference to use components/schemas - def_name = value.replace("#/$defs/", "") - result[key] = f"#/components/schemas/{def_name}" - elif key == "$defs": - # Remove $defs from the schema since they're moved to components - continue - else: - # Recursively process nested structures - result[key] = CustomOpenAPISpec._rewrite_defs_refs(value) - return result - elif isinstance(schema, list): + return { + key: CustomOpenAPISpec._rewritten_defs_entry(key, value) + for key, value in schema.items() + if key != "$defs" + } + if isinstance(schema, list): return [CustomOpenAPISpec._rewrite_defs_refs(item) for item in schema] - else: - return schema + return schema @staticmethod - def _extract_field_schema(field_def: dict[str, Any]) -> dict[str, Any]: + def _extract_field_schema(field_def: JsonObject) -> JsonValue: """ Extract a simple schema from a Pydantic field definition for parameter display. @@ -209,10 +236,10 @@ class CustomOpenAPISpec: # Handle anyOf (Optional fields in Pydantic v2) if "anyOf" in field_def: - any_of: Final = field_def["anyOf"] + any_of: Final = CustomOpenAPISpec._as_array(field_def["anyOf"]) # Find the non-null type for option in any_of: - if option.get("type") != "null": + if CustomOpenAPISpec._as_object(option).get("type") != "null": return option # Fallback to string if all else fails return {"type": "string"} @@ -221,7 +248,7 @@ class CustomOpenAPISpec: return {"type": "string"} @staticmethod - def _expand_field_definition(field_def: dict[str, object]) -> dict[str, object]: + def _expand_field_definition(field_def: JsonObject) -> JsonObject: """ Expand a Pydantic field definition for inline use in OpenAPI schema. This creates a full field definition that Swagger UI can render as individual form fields. @@ -237,12 +264,12 @@ class CustomOpenAPISpec: @staticmethod def add_request_schema( - openapi_schema: dict[str, object], + openapi_schema: JsonObject, model_class: type, schema_name: str, paths: Sequence[str], operation_name: str, - ) -> dict[str, object]: + ) -> JsonObject: """ Generic method to add a request schema to OpenAPI specification. @@ -282,8 +309,8 @@ class CustomOpenAPISpec: @staticmethod def add_chat_completion_request_schema( - openapi_schema: dict[str, object], - ) -> dict[str, object]: + openapi_schema: JsonObject, + ) -> JsonObject: """ Add ProxyChatCompletionRequest schema to chat completion endpoints for documentation. This shows the request body in Swagger without runtime validation. @@ -309,7 +336,7 @@ class CustomOpenAPISpec: return openapi_schema @staticmethod - def add_embedding_request_schema(openapi_schema: dict[str, object]) -> dict[str, object]: + def add_embedding_request_schema(openapi_schema: JsonObject) -> JsonObject: """ Add EmbeddingRequest schema to embedding endpoints for documentation. This shows the request body in Swagger without runtime validation. @@ -336,8 +363,8 @@ class CustomOpenAPISpec: @staticmethod def add_responses_api_request_schema( - openapi_schema: dict[str, object], - ) -> dict[str, object]: + openapi_schema: JsonObject, + ) -> JsonObject: """ Add ResponsesAPIRequestParams schema to responses API endpoints for documentation. This shows the request body in Swagger without runtime validation. @@ -364,8 +391,8 @@ class CustomOpenAPISpec: @staticmethod def add_llm_api_request_schema_body( - openapi_schema: dict[str, object], - ) -> dict[str, object]: + openapi_schema: JsonObject, + ) -> JsonObject: """ Add LLM API request schema bodies to OpenAPI specification for documentation. @@ -376,12 +403,10 @@ class CustomOpenAPISpec: OpenAPI schema with added request body schemas """ # Add chat completion request schema - openapi_schema = CustomOpenAPISpec.add_chat_completion_request_schema(openapi_schema) + with_chat_completions: Final = CustomOpenAPISpec.add_chat_completion_request_schema(openapi_schema) # Add embedding request schema - openapi_schema = CustomOpenAPISpec.add_embedding_request_schema(openapi_schema) + with_embeddings: Final = CustomOpenAPISpec.add_embedding_request_schema(with_chat_completions) # Add responses API request schema - openapi_schema = CustomOpenAPISpec.add_responses_api_request_schema(openapi_schema) - - return openapi_schema + return CustomOpenAPISpec.add_responses_api_request_schema(with_embeddings) diff --git a/litellm/proxy/common_utils/debug_utils.py b/litellm/proxy/common_utils/debug_utils.py index 3a1d18b48cc..554a6ae8d1a 100644 --- a/litellm/proxy/common_utils/debug_utils.py +++ b/litellm/proxy/common_utils/debug_utils.py @@ -6,9 +6,11 @@ import os import sys import tracemalloc from collections import Counter -from typing import Any, Final +from collections.abc import Mapping, Sequence +from typing import Any, Final, NamedTuple, Protocol, TypedDict from fastapi import APIRouter, Depends, HTTPException, Query +from typing_extensions import ReadOnly from litellm import get_secret_str from litellm._logging import verbose_proxy_logger @@ -194,6 +196,42 @@ async def memory_usage_in_mem_cache_items( } +class _ProcessMemoryInfo(Protocol): + """The resident and virtual sizes psutil reports for a process.""" + + @property + def rss(self) -> int: ... + + @property + def vms(self) -> int: ... + + +class _ProcessHandle(Protocol): + """The psutil process handle members this module reads.""" + + def memory_info(self) -> _ProcessMemoryInfo: ... + + def memory_percent(self) -> float: ... + + +class _ProcessMemoryUsage(NamedTuple): + """Memory usage of a single worker process.""" + + resident_megabytes: float + virtual_megabytes: float + percent: float + + +def _process_memory_usage(process: _ProcessHandle) -> _ProcessMemoryUsage: + """Read resident/virtual megabytes and system memory share for ``process``.""" + memory_info: Final = process.memory_info() + return _ProcessMemoryUsage( + resident_megabytes=memory_info.rss / (1024 * 1024), + virtual_megabytes=memory_info.vms / (1024 * 1024), + percent=process.memory_percent(), + ) + + @router.get("/debug/memory/summary", include_in_schema=False) async def get_memory_summary( _: UserAPIKeyAuth = Depends(user_api_key_auth), @@ -227,10 +265,9 @@ async def get_memory_summary( try: import psutil - process: Final = psutil.Process() - memory_info: Final = process.memory_info() - memory_mb: Final = memory_info.rss / (1024 * 1024) - memory_percent: Final = process.memory_percent() + usage: Final = _process_memory_usage(psutil.Process()) + memory_mb: Final = usage.resident_megabytes + memory_percent: Final = usage.percent process_memory = { "summary": f"{memory_mb:.1f} MB ({memory_percent:.1f}% of system memory)", @@ -252,7 +289,7 @@ async def get_memory_summary( process_memory["error"] = str(e) # Get cache information - caches: Final[dict[str, Any]] = {} + caches: Final[dict[str, object]] = {} total_cache_items = 0 try: @@ -313,7 +350,7 @@ async def get_memory_summary( } -def _get_gc_statistics() -> dict[str, Any]: +def _get_gc_statistics() -> Mapping[str, object]: """Get garbage collector statistics.""" return { "enabled": gc.isenabled(), @@ -341,30 +378,42 @@ def _get_gc_statistics() -> dict[str, Any]: } -def _get_object_type_counts(top_n: int) -> tuple[int, list[dict[str, Any]]]: +class _ObjectTypeCount(TypedDict): + """One row of the tracked-object histogram.""" + + type: ReadOnly[str] + count: ReadOnly[int] + count_readable: ReadOnly[str] + + +def _type_name_counts(objects: Sequence[object]) -> Counter[str]: + """Count ``objects`` by the name of their type.""" + return Counter(type(obj).__name__ for obj in objects) + + +def _get_object_type_counts(top_n: int) -> tuple[int, list[_ObjectTypeCount]]: """Count objects by type and return total count and top N types.""" - type_counts: Final[Counter] = Counter() - total_objects = 0 + type_counts: Final = _type_name_counts(gc.get_objects()) - for obj in gc.get_objects(): - total_objects += 1 - obj_type = type(obj).__name__ - type_counts[obj_type] += 1 - - top_object_types: Final = [ + top_object_types: Final[list[_ObjectTypeCount]] = [ {"type": obj_type, "count": count, "count_readable": f"{count:,}"} for obj_type, count in type_counts.most_common(top_n) ] - return total_objects, top_object_types + return sum(type_counts.values()), top_object_types -def _get_uncollectable_objects_info() -> dict[str, Any]: +def _type_names(objects: Sequence[object]) -> Sequence[str]: + """The type name of each object in ``objects``.""" + return [type(obj).__name__ for obj in objects] + + +def _get_uncollectable_objects_info() -> Mapping[str, object]: """Get information about uncollectable objects (potential memory leaks).""" uncollectable: Final = gc.garbage return { "count": len(uncollectable), - "sample_types": [type(obj).__name__ for obj in uncollectable[:10]], + "sample_types": _type_names(uncollectable[:10]), "warning": ( "If count > 0, you may have reference cycles preventing garbage collection" if len(uncollectable) > 0 @@ -373,9 +422,11 @@ def _get_uncollectable_objects_info() -> dict[str, Any]: } -def _get_cache_memory_stats(user_api_key_cache, llm_router, proxy_logging_obj, redis_usage_cache) -> dict[str, Any]: +def _get_cache_memory_stats( + user_api_key_cache, llm_router, proxy_logging_obj, redis_usage_cache +) -> Mapping[str, object]: """Calculate memory usage for all caches.""" - cache_stats: Final[dict[str, Any]] = {} + cache_stats: Final[dict[str, object]] = {} try: # User API key cache user_cache_size: Final = sys.getsizeof(user_api_key_cache.in_memory_cache.cache_dict) @@ -439,9 +490,9 @@ def _get_cache_memory_stats(user_api_key_cache, llm_router, proxy_logging_obj, r return cache_stats -def _get_router_memory_stats(llm_router) -> dict[str, Any]: +def _get_router_memory_stats(llm_router) -> Mapping[str, object]: """Get memory usage statistics for LiteLLM router.""" - litellm_router_memory: dict[str, Any] = {} + litellm_router_memory: dict[str, object] = {} try: if llm_router is not None: # Model list memory size @@ -505,7 +556,7 @@ def _get_router_memory_stats(llm_router) -> dict[str, Any]: return litellm_router_memory -def _get_process_memory_info(worker_pid: int, include_process_info: bool) -> dict[str, Any] | None: +def _get_process_memory_info(worker_pid: int, include_process_info: bool) -> Mapping[str, object] | None: """Get process-level memory information using psutil.""" if not include_process_info: return None @@ -514,10 +565,10 @@ def _get_process_memory_info(worker_pid: int, include_process_info: bool) -> dic import psutil process: Final = psutil.Process() - memory_info: Final = process.memory_info() - ram_usage_mb: Final = round(memory_info.rss / (1024 * 1024), 2) - virtual_memory_mb: Final = round(memory_info.vms / (1024 * 1024), 2) - memory_percent: Final = round(process.memory_percent(), 2) + usage: Final = _process_memory_usage(process) + ram_usage_mb: Final = round(usage.resident_megabytes, 2) + virtual_memory_mb: Final = round(usage.virtual_megabytes, 2) + memory_percent: Final = round(usage.percent, 2) return { "pid": worker_pid, diff --git a/litellm/proxy/common_utils/user_api_key_cache.py b/litellm/proxy/common_utils/user_api_key_cache.py index 44b88aec47b..76982d30306 100644 --- a/litellm/proxy/common_utils/user_api_key_cache.py +++ b/litellm/proxy/common_utils/user_api_key_cache.py @@ -1,6 +1,6 @@ from __future__ import annotations -from typing import Any, Final, TypeVar, cast, overload +from typing import TYPE_CHECKING, Any, Final, TypeVar, cast, overload from pydantic import BaseModel @@ -9,6 +9,9 @@ from litellm.caching.dual_cache import DualCache from litellm.constants import DEFAULT_MANAGEMENT_OBJECT_IN_MEMORY_CACHE_TTL from litellm.proxy.common_utils.cache_pydantic_utils import CacheCodec +if TYPE_CHECKING: + from opentelemetry.trace import Span + T = TypeVar("T", bound=BaseModel) @@ -40,8 +43,8 @@ class UserApiKeyCache(DualCache): @overload def get_cache( self, - key: object, - parent_otel_span: object = None, + key: str, + parent_otel_span: Span | None = None, local_only: bool = False, *, model_type: type[T], @@ -51,8 +54,8 @@ class UserApiKeyCache(DualCache): @overload def get_cache( self, - key: object, - parent_otel_span: object = None, + key: str, + parent_otel_span: Span | None = None, local_only: bool = False, model_type: None = None, **kwargs: object, @@ -60,12 +63,12 @@ class UserApiKeyCache(DualCache): def get_cache( self, - key: object, - parent_otel_span: object = None, + key: str, + parent_otel_span: Span | None = None, local_only: bool = False, model_type: type[BaseModel] | None = None, **kwargs: object, - ) -> Any | BaseModel | None: + ) -> object: if model_type is None and "model_type" in kwargs: model_type = cast(type[BaseModel] | None, kwargs.pop("model_type", None)) cached: Final = super().get_cache(key=key, parent_otel_span=parent_otel_span, local_only=local_only, **kwargs) @@ -86,8 +89,8 @@ class UserApiKeyCache(DualCache): @overload async def async_get_cache( self, - key: object, - parent_otel_span: object = None, + key: str, + parent_otel_span: Span | None = None, local_only: bool = False, *, model_type: type[T], @@ -97,8 +100,8 @@ class UserApiKeyCache(DualCache): @overload async def async_get_cache( self, - key: object, - parent_otel_span: object = None, + key: str, + parent_otel_span: Span | None = None, local_only: bool = False, model_type: None = None, **kwargs: object, @@ -106,12 +109,12 @@ class UserApiKeyCache(DualCache): async def async_get_cache( self, - key: object, - parent_otel_span: object = None, + key: str, + parent_otel_span: Span | None = None, local_only: bool = False, model_type: type[BaseModel] | None = None, **kwargs: object, - ) -> Any | BaseModel | None: + ) -> object: if model_type is None and "model_type" in kwargs: model_type = cast(type[BaseModel] | None, kwargs.pop("model_type", None)) cached: Final = await super().async_get_cache( @@ -131,12 +134,12 @@ class UserApiKeyCache(DualCache): return None return decoded - def set_cache(self, key: object, value: object, local_only: bool = False, **kwargs: object): + def set_cache(self, key: str | None, value: object, local_only: bool = False, **kwargs: object): model_type: Final = cast(type[BaseModel] | None, kwargs.pop("model_type", None)) payload: Final[object] = CacheCodec.serialize(value, model_type=model_type) return super().set_cache(key=key, value=payload, local_only=local_only, **kwargs) - async def async_set_cache(self, key: object, value: object, local_only: bool = False, **kwargs: object): + async def async_set_cache(self, key: str | None, value: object, local_only: bool = False, **kwargs: object): model_type: Final = cast(type[BaseModel] | None, kwargs.pop("model_type", None)) payload: Final[object] = CacheCodec.serialize(value, model_type=model_type) return await super().async_set_cache(key=key, value=payload, local_only=local_only, **kwargs) diff --git a/litellm/proxy/db/budget_window_spend_writer.py b/litellm/proxy/db/budget_window_spend_writer.py index f9188f95cfd..8cf2f737063 100644 --- a/litellm/proxy/db/budget_window_spend_writer.py +++ b/litellm/proxy/db/budget_window_spend_writer.py @@ -7,20 +7,15 @@ instead of aggregating LiteLLM_SpendLogs every time a window counter goes cold (issue #35766). Raw SQL rather than the Prisma upsert helper because the conditional roll cannot be expressed through the query builder. -Seeding a row that does not exist yet reads LiteLLM_SpendLogs once, excluding -the requests whose increments are in the same batch so neither source counts -them twice. One gap survives that exclusion: without the Redis transaction -buffer every pod flushes its own increments, so a row seeded by one pod can -include spend logs whose increments are still queued on another pod, and those -increments are added again when that pod flushes. That is bounded by a single -flush interval, happens at most once per window row, and only ever over-counts: -the seed never omits spend, because every increment not yet in the row still -reaches it on its own pod's next flush. A row therefore lags real spend by at -most one flush interval of queued increments, the same lag the SpendLogs -aggregate it replaces (and every other spend column) already has. +Seeding a row that does not exist yet reads LiteLLM_SpendLogs once and takes +off what the increments being flushed will add, so neither source counts the +same request twice. A row therefore lags real spend by at most one flush +interval of increments queued elsewhere: the same lag the SpendLogs aggregate +it replaces (and every other spend column) already has. """ from collections.abc import Sequence +from dataclasses import dataclass from datetime import datetime, timedelta, timezone from typing import TYPE_CHECKING, Final, Protocol @@ -67,33 +62,46 @@ _ROLL_WINDOW_SPEND_SQL: Final = ( ) _SEED_FROM_SPEND_LOGS_KEY_SQL: Final = ( - 'SELECT COALESCE(SUM(spend), 0.0) AS total FROM "LiteLLM_SpendLogs" ' - "WHERE api_key = $1 AND \"startTime\" >= ($2::timestamptz AT TIME ZONE 'UTC') " - "AND NOT (request_id = ANY($3::text[]) AND \"startTime\" >= ($4::timestamptz AT TIME ZONE 'UTC'))" + "SELECT COALESCE(SUM(spend), 0.0) AS total, " + "COALESCE(SUM(spend) FILTER (WHERE \"startTime\" < ($3::timestamptz AT TIME ZONE 'UTC')), 0.0) AS before_batch " + 'FROM "LiteLLM_SpendLogs" ' + "WHERE api_key = $1 AND \"startTime\" >= ($2::timestamptz AT TIME ZONE 'UTC')" ) _SEED_FROM_SPEND_LOGS_TEAM_SQL: Final = ( - 'SELECT COALESCE(SUM(spend), 0.0) AS total FROM "LiteLLM_SpendLogs" ' - "WHERE team_id = $1 AND \"startTime\" >= ($2::timestamptz AT TIME ZONE 'UTC') " - "AND NOT (request_id = ANY($3::text[]) AND \"startTime\" >= ($4::timestamptz AT TIME ZONE 'UTC'))" + "SELECT COALESCE(SUM(spend), 0.0) AS total, " + "COALESCE(SUM(spend) FILTER (WHERE \"startTime\" < ($3::timestamptz AT TIME ZONE 'UTC')), 0.0) AS before_batch " + 'FROM "LiteLLM_SpendLogs" ' + "WHERE team_id = $1 AND \"startTime\" >= ($2::timestamptz AT TIME ZONE 'UTC')" ) _SEED_FROM_SPEND_LOGS_KEY_UNBOUNDED_SQL: Final = ( - 'SELECT COALESCE(SUM(spend), 0.0) AS total FROM "LiteLLM_SpendLogs" ' + "SELECT COALESCE(SUM(spend), 0.0) AS total, COALESCE(SUM(spend), 0.0) AS before_batch " + 'FROM "LiteLLM_SpendLogs" ' "WHERE api_key = $1 AND \"startTime\" >= ($2::timestamptz AT TIME ZONE 'UTC')" ) _SEED_FROM_SPEND_LOGS_TEAM_UNBOUNDED_SQL: Final = ( - 'SELECT COALESCE(SUM(spend), 0.0) AS total FROM "LiteLLM_SpendLogs" ' + "SELECT COALESCE(SUM(spend), 0.0) AS total, COALESCE(SUM(spend), 0.0) AS before_batch " + 'FROM "LiteLLM_SpendLogs" ' "WHERE team_id = $1 AND \"startTime\" >= ($2::timestamptz AT TIME ZONE 'UTC')" ) _UPSERT_TRANSACTION_TIMEOUT: Final = timedelta(seconds=60) +@dataclass(frozen=True, slots=True) +class WindowSeedTotals: + """The two sums a seed needs: everything persisted for the window, and the + part of it that predates the batch being flushed.""" + + total: float + before_batch: float + + class WindowSpendLogsAggregate(Protocol): - """Sums LiteLLM_SpendLogs for one entity since window_start, ignoring the - requests whose ids are handed in. + """Sums LiteLLM_SpendLogs for one entity since window_start, split at the + batch's earliest request. Injected so the flush can be exercised without a database and so the expensive aggregate stays swappable. @@ -105,21 +113,19 @@ class WindowSpendLogsAggregate(Protocol): entity_type: str, entity_id: str, window_start: datetime, - exclude_request_ids: Sequence[str], - exclude_started_at: datetime | None, - ) -> float | None: ... + batch_started_at: datetime | None, + ) -> WindowSeedTotals | None: ... -async def spend_logs_total_excluding( +async def spend_logs_seed_totals( prisma_client: "PrismaClient", entity_type: str, entity_id: str, window_start: datetime, - exclude_request_ids: Sequence[str], - exclude_started_at: datetime | None, -) -> float | None: - """LiteLLM_SpendLogs spend for one entity since window_start, minus the - requests already accounted for by the increments being flushed. + batch_started_at: datetime | None, +) -> WindowSeedTotals | None: + """LiteLLM_SpendLogs spend for one entity since window_start, both in full + and up to the start of the batch being flushed, in one scan. The spend log writer drains its own queue on a ~2s poll whenever anything is queued, while window increments flush on the much slower batch tick, so @@ -127,13 +133,12 @@ async def spend_logs_total_excluding( already in the table. Counting them in the seed and again in the increment is what made a fresh row land at twice the true spend. - The exclusion is bounded to rows that started at or after the batch's - earliest request. request_id can be chosen by the client - (x-litellm-call-id), so an unbounded exclusion would let a replayed old id - erase a historical row from the seed while its increment still lands. - Without a known start the batch's ids are not excluded at all: that can - only over-count once, which enforcement tolerates, whereas under-counting - is a budget bypass. + Both halves are needed because neither is safe alone: the full sum + double-counts this batch, and the sum before the batch drops spend another + pod has already persisted but not yet incremented. _seed_base picks between + them. Without a known batch start the two are the same sum, so the seed + counts everything: that can only over-count once, which enforcement + tolerates, whereas under-counting is a budget bypass. """ if entity_type == Litellm_EntityType.KEY.value: bounded_sql, unbounded_sql = _SEED_FROM_SPEND_LOGS_KEY_SQL, _SEED_FROM_SPEND_LOGS_KEY_UNBOUNDED_SQL @@ -143,21 +148,23 @@ async def spend_logs_total_excluding( return None rows: Final = ( await prisma_client.db.query_raw(unbounded_sql, entity_id, window_start) - if exclude_started_at is None or not exclude_request_ids + if batch_started_at is None else await prisma_client.db.query_raw( bounded_sql, entity_id, window_start, - tuple(exclude_request_ids), - _exclusion_lower_bound(exclude_started_at), + _exclusion_upper_bound(batch_started_at), ) ) if not rows: - return 0.0 - return float(rows[0].get("total") or 0.0) + return WindowSeedTotals(total=0.0, before_batch=0.0) + return WindowSeedTotals( + total=float(rows[0].get("total") or 0.0), + before_batch=float(rows[0].get("before_batch") or 0.0), + ) -def _exclusion_lower_bound(started_at: datetime) -> datetime: +def _exclusion_upper_bound(started_at: datetime) -> datetime: """LiteLLM_SpendLogs.startTime is TIMESTAMP(3); floor to the second so a millisecond rounding of the batch's own earliest row cannot slip under it.""" return to_naive_utc(started_at).replace(microsecond=0) @@ -194,20 +201,33 @@ async def _seed_base_for_missing_row( This is the LiteLLM_SpendLogs aggregate the window counter reseed runs on every cold counter today, but here it runs once per window lifetime and off - the request path, and it excludes this batch's own requests so they are - counted by their increments alone. + the request path, and it discounts the queued increments so they are + counted once. """ if _primary_key(transaction) in existing_primary_keys: return 0.0 - base: Final = await spend_logs_aggregate( + totals: Final = await spend_logs_aggregate( prisma_client=prisma_client, entity_type=transaction["entity_type"], entity_id=transaction["entity_id"], window_start=datetime.fromisoformat(transaction["window_start"]).replace(tzinfo=timezone.utc), - exclude_request_ids=transaction["request_ids"], - exclude_started_at=_transaction_started_at(transaction), + batch_started_at=_transaction_started_at(transaction), ) - return float(base or 0.0) + if totals is None: + return 0.0 + return _seed_base(totals=totals, batch_spend=transaction["spend"]) + + +def _seed_base(totals: WindowSeedTotals, batch_spend: float) -> float: + """What the window already held before the increments about to be applied. + + Subtracting the batch's own spend from the full sum keeps every other + request in the seed, including the ones another pod persisted and has not + incremented yet, which a plain cutoff would drop for good if that pod died. + When this batch's own log rows have not landed yet the subtraction takes + spend that was never counted, so the sum before the batch is the floor. + """ + return max(totals.total - batch_spend, totals.before_batch) def _transaction_started_at(transaction: WindowSpendTransaction) -> datetime | None: @@ -241,7 +261,7 @@ def _upsert_params( async def commit_window_spend_updates( prisma_client: "PrismaClient", transactions: Sequence[WindowSpendTransaction], - spend_logs_aggregate: WindowSpendLogsAggregate = spend_logs_total_excluding, + spend_logs_aggregate: WindowSpendLogsAggregate = spend_logs_seed_totals, ) -> None: """Apply aggregated window increments to LiteLLM_BudgetWindowSpend. diff --git a/litellm/proxy/db/db_spend_update_writer.py b/litellm/proxy/db/db_spend_update_writer.py index 641c07914d9..e6880d521f1 100644 --- a/litellm/proxy/db/db_spend_update_writer.py +++ b/litellm/proxy/db/db_spend_update_writer.py @@ -211,15 +211,11 @@ class DBSpendUpdateWriter: org_id: str | None, # Completion object fields kwargs: dict | None, - completion_response: litellm.ModelResponse | Any | Exception | None, + completion_response: object, start_time: datetime | None, end_time: datetime | None, response_cost: float | None, - ) -> str | None: - """Returns the LiteLLM_SpendLogs request_id this call was recorded - under, so the caller can tell the budget-window writer which log rows - its increments already cover. None when the payload could not be built. - """ + ) -> None: from litellm.proxy.proxy_server import ( disable_spend_logs, litellm_proxy_budget_name, @@ -236,7 +232,7 @@ class DBSpendUpdateWriter: team_id, ) if ProxyUpdateSpend.disable_spend_updates() is True: - return None + return if token is not None and isinstance(token, str) and token.startswith("sk-"): hashed_token = hash_token(token=token) else: @@ -310,7 +306,6 @@ class DBSpendUpdateWriter: ) verbose_proxy_logger.debug("Runs spend update on all tables") - return payload.get("request_id") except Exception: spend_log_error( "Spend tracking - update_database failed. Spend log insertion or daily transaction enqueue " @@ -323,12 +318,12 @@ class DBSpendUpdateWriter: org_id, end_user_id, ) - return None + return async def _enqueue_tool_usage_transaction( self, payload: SpendLogsPayload, - completion_response: "litellm.ModelResponse | Any | Exception | None", + completion_response: object, prisma_client: "PrismaClient | None", kwargs: "dict | None" = None, ) -> None: @@ -401,7 +396,7 @@ class DBSpendUpdateWriter: def _enqueue_tool_registry_upsert( self, kwargs: dict | None, - completion_response: Any | None, + completion_response: object, hashed_token: str | None = None, team_id: str | None = None, ) -> None: @@ -854,7 +849,7 @@ class DBSpendUpdateWriter: return # Parse tags from JSON string - tags = [] + tags: Sequence[object] = [] if isinstance(request_tags, str): tags = safe_json_loads(request_tags, default=[]) if not tags: @@ -2265,7 +2260,7 @@ class DBSpendUpdateWriter: verbose_proxy_logger.debug("request_tags is None for request. Skipping incrementing tag spend.") return - request_tags = [] + request_tags: Sequence[str] = [] if isinstance(payload["request_tags"], str): request_tags = json.loads(payload["request_tags"]) elif isinstance(payload["request_tags"], list): diff --git a/litellm/proxy/db/db_transaction_queue/redis_update_buffer.py b/litellm/proxy/db/db_transaction_queue/redis_update_buffer.py index 4dd23270bf8..c06f2e04aca 100644 --- a/litellm/proxy/db/db_transaction_queue/redis_update_buffer.py +++ b/litellm/proxy/db/db_transaction_queue/redis_update_buffer.py @@ -46,6 +46,7 @@ from litellm.proxy.db.db_transaction_queue.spend_update_queue import SpendUpdate from litellm.proxy.db.db_transaction_queue.window_spend_update_queue import ( WindowSpendTransaction, WindowSpendUpdateQueue, + to_wire_payload, ) from litellm.secret_managers.main import str_to_bool from litellm.types.caching import ( @@ -298,7 +299,7 @@ class RedisUpdateBuffer: ServiceTypes.REDIS_DAILY_AGENT_SPEND_UPDATE_QUEUE, ), ( - window_spend_update_transactions, + tuple(map(to_wire_payload, window_spend_update_transactions)), REDIS_WINDOW_SPEND_UPDATE_BUFFER_KEY, ServiceTypes.REDIS_WINDOW_SPEND_UPDATE_QUEUE, ), @@ -484,7 +485,12 @@ class RedisUpdateBuffer: (daily_end_user_spend_update_transactions, REDIS_DAILY_END_USER_SPEND_UPDATE_BUFFER_KEY), (daily_agent_spend_update_transactions, REDIS_DAILY_AGENT_SPEND_UPDATE_BUFFER_KEY), (daily_tag_spend_update_transactions, REDIS_DAILY_TAG_SPEND_UPDATE_BUFFER_KEY), - (window_spend_update_transactions, REDIS_WINDOW_SPEND_UPDATE_BUFFER_KEY), + ( + None + if window_spend_update_transactions is None + else tuple(map(to_wire_payload, window_spend_update_transactions)), + REDIS_WINDOW_SPEND_UPDATE_BUFFER_KEY, + ), ) rpush_list: Final = tuple( diff --git a/litellm/proxy/db/db_transaction_queue/window_spend_update_queue.py b/litellm/proxy/db/db_transaction_queue/window_spend_update_queue.py index 04dea66165e..372a6666c02 100644 --- a/litellm/proxy/db/db_transaction_queue/window_spend_update_queue.py +++ b/litellm/proxy/db/db_transaction_queue/window_spend_update_queue.py @@ -26,17 +26,12 @@ class WindowSpendTransaction(TypedDict): window_start is an ISO-8601 string rather than a datetime so the transaction survives the JSON round trip through the Redis buffer. - request_ids carries the LiteLLM_SpendLogs ids this spend came from. The - one-time seed for a window that has no row yet subtracts them from its - LiteLLM_SpendLogs aggregate, because the spend log writer flushes on its - own ~2s poll and will usually have persisted these rows before the window - queue flushes; without the exclusion the seed and the increment would each - count them. - - started_at is the earliest request start in the batch. The seed only - subtracts a request_id whose LiteLLM_SpendLogs.startTime is at or after it, - so a client that replays an old id through x-litellm-call-id cannot make the - seed drop the historical row that id already paid for. + started_at is the earliest request start in the batch. The one-time seed for + a window that has no row yet uses it to tell this batch's own + LiteLLM_SpendLogs rows from everything else, because the spend log writer + flushes on its own ~2s poll and will usually have persisted this batch's + rows before the window queue flushes; without that split the seed and the + increment would each count them. """ entity_type: ReadOnly[str] @@ -44,10 +39,37 @@ class WindowSpendTransaction(TypedDict): window_duration: ReadOnly[str] window_start: ReadOnly[str] spend: ReadOnly[float] - request_ids: ReadOnly[Sequence[str]] started_at: ReadOnly[str | None] +class WindowSpendWirePayload(WindowSpendTransaction): + """How an increment is encoded in the shared Redis buffer. + + request_ids is dead weight here: workers built before this field was + dropped index it while merging whatever they pop, and the pop is + destructive, so a leader still running one of those during a rolling deploy + would raise on a payload without the key and lose those increments. It is + always empty, which only makes such a leader seed without exclusions. + + TODO: remove once no supported version reads it, i.e. one release after the + field stopped being written. + """ + + request_ids: ReadOnly[Sequence[str]] + + +def to_wire_payload(transaction: WindowSpendTransaction) -> WindowSpendWirePayload: + return WindowSpendWirePayload( + entity_type=transaction["entity_type"], + entity_id=transaction["entity_id"], + window_duration=transaction["window_duration"], + window_start=transaction["window_start"], + spend=transaction["spend"], + started_at=transaction.get("started_at"), + request_ids=(), + ) + + def to_naive_utc(value: datetime) -> datetime: """LiteLLM_BudgetWindowSpend.window_start is TIMESTAMP(3), which holds naive UTC.""" if value.tzinfo is None: @@ -72,7 +94,6 @@ def build_window_spend_transaction( window_duration: str, window_start: datetime, spend: float, - request_id: str | None = None, started_at: datetime | None = None, ) -> WindowSpendTransaction: return WindowSpendTransaction( @@ -81,7 +102,6 @@ def build_window_spend_transaction( window_duration=window_duration, window_start=to_naive_utc(window_start).isoformat(timespec="microseconds"), spend=spend, - request_ids=() if request_id is None else (request_id,), started_at=None if started_at is None else to_naive_utc(started_at.astimezone(timezone.utc)).isoformat(timespec="microseconds"), @@ -101,7 +121,6 @@ def _merge_window_spend_transactions( window_duration=first["window_duration"], window_start=first["window_start"], spend=math.fsum(payload["spend"] for payload in payloads), - request_ids=tuple(sorted(frozenset(chain.from_iterable(payload["request_ids"] for payload in payloads)))), started_at=min(started_ats) if started_ats else None, ) diff --git a/litellm/proxy/guardrails/guardrail_hooks/akto/akto.py b/litellm/proxy/guardrails/guardrail_hooks/akto/akto.py index 3716d00774f..2c27531cea1 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/akto/akto.py +++ b/litellm/proxy/guardrails/guardrail_hooks/akto/akto.py @@ -162,10 +162,10 @@ class AktoGuardrail(CustomGuardrail): def build_request_body( inputs: GenericGuardrailAPIInputs, request_data: dict | None = None, - ) -> dict[str, Any]: + ) -> dict[str, object]: """Build the LLM request body from guardrail inputs (messages, model, tools).""" model: Final = inputs.get("model", "") or "" - body: Final[dict[str, Any]] = {"model": model} + body: Final[dict[str, object]] = {"model": model} structured: Final = inputs.get("structured_messages") if structured: @@ -194,7 +194,7 @@ class AktoGuardrail(CustomGuardrail): def build_response_body( inputs: GenericGuardrailAPIInputs, request_data: dict | None = None, - ) -> dict[str, Any]: + ) -> dict[str, object]: """Build the LLM response body, preferring the actual model response if available.""" model_response: Final = request_data.get("response") if request_data else None if model_response is not None and hasattr(model_response, "model_dump"): @@ -224,7 +224,7 @@ class AktoGuardrail(CustomGuardrail): *, status_code: int = 200, include_response: bool = False, - ) -> dict[str, Any]: + ) -> dict[str, object]: """Build the flat MIRRORING payload sent to Akto's HTTP proxy endpoint. All body fields use double-encoding: json.dumps({"body": json.dumps(actual_body)}) diff --git a/litellm/proxy/guardrails/guardrail_hooks/bedrock_guardrails.py b/litellm/proxy/guardrails/guardrail_hooks/bedrock_guardrails.py index a6635ea0776..0237d82a0d9 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/bedrock_guardrails.py +++ b/litellm/proxy/guardrails/guardrail_hooks/bedrock_guardrails.py @@ -32,6 +32,9 @@ from litellm.constants import BEDROCK_APPLY_GUARDRAIL_CHUNK_BUDGET_CHARS from litellm.exceptions import ModifyResponseException from litellm.integrations.custom_guardrail import CustomGuardrail from litellm.litellm_core_utils.core_helpers import redact_nested_match_and_regex_keys +from litellm.litellm_core_utils.litellm_logging import ( + _get_masked_values, # pyright: ignore[reportPrivateUsage] # the shared header-masking helper has no public name +) from litellm.litellm_core_utils.llm_cost_calc.guardrail_cost import bedrock_guardrail_cost from litellm.llms.anthropic.chat.guardrail_translation.handler import AnthropicMessagesHandler from litellm.llms.base_llm.guardrail_translation.utils import ( @@ -252,7 +255,7 @@ class BedrockGuardrail(CustomGuardrail, BaseAWSLLM): # Resource-less, detect-only InvokeGuardrailChecks mode. Present `checks` # routes the guardrail to InvokeGuardrailChecks; absent => ApplyGuardrail. - self.checks: dict[str, Any] | None = self._normalize_checks(checks) + self.checks: dict[str, object] | None = self._normalize_checks(checks) # Per-check block thresholds; a score >= threshold blocks. None => the # check is detect-only (logged, never blocks). self.content_filter_threshold = content_filter_threshold @@ -321,7 +324,7 @@ class BedrockGuardrail(CustomGuardrail, BaseAWSLLM): ] @staticmethod - def _normalize_checks(checks: BedrockChecksConfigModel | Mapping[str, object] | None) -> dict[str, Any] | None: + def _normalize_checks(checks: BedrockChecksConfigModel | Mapping[str, object] | None) -> dict[str, object] | None: """Normalize the configured `checks` into a plain dict for the API body. Accepts a pydantic ``BedrockChecksConfigModel`` or a raw dict; drops None / @@ -372,7 +375,7 @@ class BedrockGuardrail(CustomGuardrail, BaseAWSLLM): def _create_bedrock_output_content_request( self, - response: Any | ModelResponse, + response: object, messages: list[AllMessageValues] | None = None, ) -> BedrockRequest: """ @@ -396,9 +399,7 @@ class BedrockGuardrail(CustomGuardrail, BaseAWSLLM): bedrock_request["content"] = bedrock_request_content return bedrock_request - def _build_response_content_items( - self, response: Any | ModelResponse, has_grounding: bool - ) -> list[BedrockContentItem]: + def _build_response_content_items(self, response: object, has_grounding: bool) -> list[BedrockContentItem]: """Build content item(s) from the model response. When the request supplied grounding, the response is qualified ``guard_content`` so Bedrock can score it. """ @@ -422,7 +423,7 @@ class BedrockGuardrail(CustomGuardrail, BaseAWSLLM): self, source: Literal["INPUT", "OUTPUT"], messages: list[AllMessageValues] | None = None, - response: Any | ModelResponse | None = None, + response: object | None = None, ) -> BedrockRequest: """ Convert the litellm messages/response to the bedrock request format. @@ -945,7 +946,7 @@ class BedrockGuardrail(CustomGuardrail, BaseAWSLLM): async def _apply_guardrail_content_with_chunking( self, content: Sequence[BedrockContentItem], - base_request_data: Mapping[str, Any], + base_request_data: Mapping[str, object], credentials: "Credentials", aws_region_name: str, api_key: str | None, @@ -1083,7 +1084,7 @@ class BedrockGuardrail(CustomGuardrail, BaseAWSLLM): async def _post_apply_guardrail_content_with_retry( self, content: Sequence[BedrockContentItem], - base_request_data: Mapping[str, Any], + base_request_data: Mapping[str, object], credentials: "Credentials", aws_region_name: str, api_key: str | None, @@ -1133,7 +1134,7 @@ class BedrockGuardrail(CustomGuardrail, BaseAWSLLM): async def _post_apply_guardrail_content( self, content: Sequence[BedrockContentItem], - base_request_data: Mapping[str, Any], + base_request_data: Mapping[str, object], credentials: "Credentials", aws_region_name: str, api_key: str | None, @@ -1172,11 +1173,12 @@ class BedrockGuardrail(CustomGuardrail, BaseAWSLLM): aws_region_name=aws_region_name, api_key=api_key, ) + headers_dict: Final = dict(prepared_request.headers) # mutable-ok: the masking helper requires a dict verbose_proxy_logger.debug( "Bedrock AI request body: %s, url %s, headers: %s", bedrock_request_data, prepared_request.url, - prepared_request.headers, + _get_masked_values(headers_dict), ) httpx_response: Final = await self._sign_and_post( @@ -1861,7 +1863,7 @@ class BedrockGuardrail(CustomGuardrail, BaseAWSLLM): return BedrockGuardrailResponse() credentials, aws_region_name = self._load_credentials() - body: Final[dict[str, Any]] = {"messages": checks_messages, "checks": self.checks} + body: Final[dict[str, object]] = {"messages": checks_messages, "checks": self.checks} api_key: Final[str | None] = request_data.get("api_key") if request_data else None prepared_request: Final = self._prepare_request( @@ -2343,7 +2345,7 @@ class BedrockGuardrail(CustomGuardrail, BaseAWSLLM): guardrail_name=self.guardrail_name, ) - detail: Final[dict[str, Any]] = { + detail: Final[dict[str, object]] = { "error": "Violated guardrail policy", "bedrock_guardrail_response": bedrock_guardrail_output_text, } @@ -2902,7 +2904,7 @@ class BedrockGuardrail(CustomGuardrail, BaseAWSLLM): return updated_messages def _mask_content_list( - self, content_list: list[Any], masked_texts: list[str], masking_index: int + self, content_list: Sequence[object], masked_texts: list[str], masking_index: int ) -> tuple[list[Any], int]: """ Apply masking to a list of content items. @@ -2915,7 +2917,7 @@ class BedrockGuardrail(CustomGuardrail, BaseAWSLLM): Returns: Updated content list with masked items """ - new_content: Final[list[dict | str]] = [] + new_content: Final[list[dict[str, object] | str]] = [] for item in content_list: if isinstance(item, dict) and "text" in item: new_item = item.copy() @@ -2934,7 +2936,7 @@ class BedrockGuardrail(CustomGuardrail, BaseAWSLLM): def _apply_masking_to_response( self, - response: ModelResponse | Any, + response: object, bedrock_guardrail_response: BedrockGuardrailResponse, ) -> None: """ diff --git a/litellm/proxy/guardrails/guardrail_hooks/cisco_ai_defense/cisco_ai_defense_mcp.py b/litellm/proxy/guardrails/guardrail_hooks/cisco_ai_defense/cisco_ai_defense_mcp.py index 8398ec9f141..5a6be1089b6 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/cisco_ai_defense/cisco_ai_defense_mcp.py +++ b/litellm/proxy/guardrails/guardrail_hooks/cisco_ai_defense/cisco_ai_defense_mcp.py @@ -5,8 +5,9 @@ The public guardrail class imports this private mixin from while preserving the existing public import path. """ +from collections.abc import Sequence from datetime import datetime -from typing import TYPE_CHECKING, Any, Final, Optional +from typing import TYPE_CHECKING, Final, Optional from fastapi import HTTPException @@ -23,7 +24,7 @@ if TYPE_CHECKING: from .cisco_ai_defense import _ScanContext -def _serialize_mcp_content_item(item: object) -> dict[str, Any]: +def _serialize_mcp_content_item(item: object) -> dict[str, object]: """Serialize an MCP content item to a JSON-friendly dict. Handles raw dicts, MCP SDK Pydantic models, and simple ``.text`` objects. @@ -57,7 +58,7 @@ class _CiscoAIDefenseMcpMixin: def should_run_guardrail(self, data: dict, event_type: GuardrailEventHooks) -> bool: ... - async def _post_inspection(self, url: str, payload: dict[str, Any], surface: str) -> dict[str, Any]: ... + async def _post_inspection(self, url: str, payload: dict[str, object], surface: str) -> dict[str, object]: ... def _handle_api_error( self, @@ -67,16 +68,16 @@ class _CiscoAIDefenseMcpMixin: start_time: datetime | None = ..., surface: str = ..., direction: str = ..., - ) -> dict[str, Any]: ... + ) -> dict[str, object]: ... def _finalize_inspection( self, - inspect_response: dict[str, Any], + inspect_response: dict[str, object], request_data: dict, context: "_ScanContext", start_time: datetime, response_obj: object = ..., - ) -> dict[str, Any]: ... + ) -> dict[str, object]: ... # ------------------------------------------------------------------ # MCP post-tool hook (dispatcher contract) @@ -95,7 +96,7 @@ class _CiscoAIDefenseMcpMixin: if self.inspection_type != "mcp": return None - request_data: Final[dict[str, Any]] = {} + request_data: Final[dict[str, object]] = {} for key in ( "name", "litellm_call_id", @@ -188,9 +189,9 @@ class _CiscoAIDefenseMcpMixin: original_hidden: Final = getattr(original_response_obj, "hidden_params", None) if isinstance(original_hidden, HiddenParams): - hidden_params: Any = original_hidden + hidden_params: HiddenParams = original_hidden else: - response_cost: Final = getattr(original_hidden, "response_cost", None) + response_cost: Final[float | None] = getattr(original_hidden, "response_cost", None) hidden_params = HiddenParams(response_cost=response_cost) if response_cost is not None else HiddenParams() return MCPPostCallResponseObject( @@ -200,11 +201,11 @@ class _CiscoAIDefenseMcpMixin: @staticmethod def _replace_mcp_tool_response(response_obj: object, replacement_obj: object) -> bool: - replacement: Final = getattr(replacement_obj, "mcp_tool_call_response", None) + replacement: Final[list[object] | None] = getattr(replacement_obj, "mcp_tool_call_response", None) if replacement is None: return False - inner: Final = getattr(response_obj, "mcp_tool_call_response", None) + inner: Final[object | None] = getattr(response_obj, "mcp_tool_call_response", None) if inner is not None: if _CiscoAIDefenseMcpMixin._replace_mcp_tool_response(inner, replacement_obj): return True @@ -276,7 +277,7 @@ class _CiscoAIDefenseMcpMixin: self, data: dict, user_api_key_dict: UserAPIKeyAuth, - ) -> dict[str, Any]: + ) -> dict[str, object]: del user_api_key_dict # carried via logging metadata, not the wire payload url: Final = f"{self.api_base}{self.inspect_path}" payload: Final = self._build_mcp_request_payload(data=data) @@ -312,7 +313,7 @@ class _CiscoAIDefenseMcpMixin: response: object, user_api_key_dict: UserAPIKeyAuth | None = None, redact_response_obj: object = None, - ) -> dict[str, Any]: + ) -> dict[str, object]: del user_api_key_dict # carried via logging metadata, not the wire payload url: Final = f"{self.api_base}{self.inspect_path}" payload: Final = self._build_mcp_response_payload( @@ -349,7 +350,7 @@ class _CiscoAIDefenseMcpMixin: def _build_mcp_request_payload( self, data: dict, - ) -> dict[str, Any] | None: + ) -> dict[str, object] | None: """Build the JSON-RPC ``tools/call`` envelope sent to ``/inspect/mcp``. The Cisco AI Defense MCP inspect endpoint expects the JSON-RPC @@ -390,7 +391,7 @@ class _CiscoAIDefenseMcpMixin: self, request_data: dict, response: object, - ) -> dict[str, Any] | None: + ) -> dict[str, object] | None: """Build the MCP response-inspection body sent to ``/inspect/mcp``.""" request_payload: Final = self._build_mcp_request_payload(data=request_data) if request_payload is None: @@ -415,7 +416,7 @@ class _CiscoAIDefenseMcpMixin: return payload @staticmethod - def _hydrate_mcp_tool_context(request_data: dict[str, Any]) -> None: + def _hydrate_mcp_tool_context(request_data: dict[str, object]) -> None: metadata = request_data.get("mcp_tool_call_metadata") if metadata is None: nested: Final = request_data.get("metadata") or request_data.get("litellm_metadata") @@ -440,7 +441,7 @@ class _CiscoAIDefenseMcpMixin: request_data.setdefault("server_name", server_name) @staticmethod - def _normalize_mcp_response(response: object) -> dict[str, Any] | None: + def _normalize_mcp_response(response: object) -> dict[str, object] | None: """Normalize an MCP tool response into a JSON-RPC envelope. Handles JSON-RPC dicts, raw content lists, MCP SDK models, and @@ -502,10 +503,10 @@ class _CiscoAIDefenseMcpMixin: @staticmethod def _build_mcp_result( - content: list[Any], + content: Sequence[object], source: object = None, - ) -> dict[str, Any]: - result: Final[dict[str, Any]] = {"content": [_serialize_mcp_content_item(item) for item in content]} + ) -> dict[str, object]: + result: Final[dict[str, object]] = {"content": [_serialize_mcp_content_item(item) for item in content]} for key in ("structuredContent", "isError"): value = source.get(key) if isinstance(source, dict) else getattr(source, key, None) if value is not None and (key != "isError" or isinstance(value, bool)): @@ -522,7 +523,7 @@ class _CiscoAIDefenseMcpMixin: if response_obj is None: return False - inner: Final = getattr(response_obj, "mcp_tool_call_response", None) + inner: Final[object | None] = getattr(response_obj, "mcp_tool_call_response", None) if inner is not None: return _CiscoAIDefenseMcpMixin._set_mcp_tool_response_text(inner, text) @@ -559,7 +560,7 @@ class _CiscoAIDefenseMcpMixin: pass elif isinstance(response_obj, dict): result: Final = response_obj.get("result") - target: Final[dict[Any, Any]] = result if isinstance(result, dict) else response_obj + target: Final[dict[object, object]] = result if isinstance(result, dict) else response_obj if "structuredContent" in target: target["structuredContent"] = replacement replaced = True @@ -567,11 +568,11 @@ class _CiscoAIDefenseMcpMixin: return replaced @staticmethod - def _coerce_to_content_list(response_obj: object) -> list[Any] | None: + def _coerce_to_content_list(response_obj: object) -> list[object] | None: """Find the MCP content list inside supported response shapes.""" if response_obj is None: return None - inner: Final = getattr(response_obj, "mcp_tool_call_response", None) + inner: Final[object | None] = getattr(response_obj, "mcp_tool_call_response", None) if inner is not None: return _CiscoAIDefenseMcpMixin._coerce_to_content_list(inner) content: Final = getattr(response_obj, "content", None) @@ -594,8 +595,8 @@ class _CiscoAIDefenseMcpMixin: @staticmethod def _extract_sanitized_mcp_arguments( - inspect_response: dict[str, Any], - ) -> dict[str, Any] | None: + inspect_response: dict[str, object], + ) -> dict[str, object] | None: """Pull sanitized MCP tool-call arguments off the verdict. Cisco can return them at the top level (``params.arguments``) or diff --git a/litellm/proxy/guardrails/guardrail_hooks/grayswan/grayswan.py b/litellm/proxy/guardrails/guardrail_hooks/grayswan/grayswan.py index 955a868a0d6..48832f8ed5e 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/grayswan/grayswan.py +++ b/litellm/proxy/guardrails/guardrail_hooks/grayswan/grayswan.py @@ -2,9 +2,10 @@ import os import time -from typing import TYPE_CHECKING, Any, Final, Literal, Optional +from typing import TYPE_CHECKING, Any, Final, Literal, Optional, Protocol from fastapi import HTTPException +from typing_extensions import NotRequired, ReadOnly, TypedDict from litellm._logging import verbose_proxy_logger from litellm.integrations.custom_guardrail import ( @@ -27,6 +28,33 @@ if TYPE_CHECKING: GRAYSWAN_BLOCK_ERROR_MSG: Final = "Blocked by Gray Swan Guardrail" +class _GraySwanMonitorResponse(TypedDict): + """Body returned by Gray Swan's `/cygnal/monitor` endpoint.""" + + violation: ReadOnly[NotRequired[float | None]] + violated_rules: ReadOnly[NotRequired[list[object]]] + violated_rule_descriptions: ReadOnly[NotRequired[list[object]]] + mutation: ReadOnly[NotRequired[bool | None]] + ipi: ReadOnly[NotRequired[bool | None]] + + +class _GraySwanMonitorHTTPResponse(Protocol): + def raise_for_status(self) -> object: ... + + def json(self) -> _GraySwanMonitorResponse: ... + + +class _GraySwanMonitorHTTPClient(Protocol): + async def post( + self, + *, + url: str, + headers: dict[str, str], + json: dict[str, object], + timeout: float, + ) -> _GraySwanMonitorHTTPResponse: ... + + class GraySwanGuardrailMissingSecrets(Exception): """Raised when the Gray Swan API key is missing.""" @@ -77,7 +105,9 @@ class GraySwanGuardrail(CustomGuardrail): guardrail_timeout: float | None = 30.0, **kwargs: Any, ) -> None: - self.async_handler = get_async_httpx_client(llm_provider=httpxSpecialProvider.GuardrailCallback) + self.async_handler: _GraySwanMonitorHTTPClient = get_async_httpx_client( + llm_provider=httpxSpecialProvider.GuardrailCallback + ) api_key_value: Final = api_key or os.getenv("GRAYSWAN_API_KEY") if not api_key_value: @@ -266,7 +296,7 @@ class GraySwanGuardrail(CustomGuardrail): # Legacy Test Interface (for backward compatibility) # ------------------------------------------------------------------ - async def run_grayswan_guardrail(self, payload: dict) -> dict[str, Any]: + async def run_grayswan_guardrail(self, payload: dict[str, object]) -> _GraySwanMonitorResponse: """ Run the GraySwan guardrail on a payload. @@ -285,7 +315,7 @@ class GraySwanGuardrail(CustomGuardrail): def _process_grayswan_response( self, - response_json: dict, + response_json: _GraySwanMonitorResponse, data: dict | None = None, hook_type: GuardrailEventHooks | None = None, ) -> None: @@ -385,7 +415,7 @@ class GraySwanGuardrail(CustomGuardrail): # Core GraySwan API interaction # ------------------------------------------------------------------ - async def _call_grayswan_api(self, payload: dict) -> dict[str, Any]: + async def _call_grayswan_api(self, payload: dict[str, object]) -> _GraySwanMonitorResponse: """Call the GraySwan monitoring API.""" headers: Final = self._prepare_headers() @@ -406,7 +436,7 @@ class GraySwanGuardrail(CustomGuardrail): def _process_response_internal( self, - response_json: dict[str, Any], + response_json: _GraySwanMonitorResponse, request_data: dict, inputs: GenericGuardrailAPIInputs, is_output: bool, @@ -534,8 +564,8 @@ class GraySwanGuardrail(CustomGuardrail): dynamic_body: dict, request_data: dict, logging_obj: Optional["LiteLLMLoggingObj"] = None, - ) -> dict[str, Any] | None: - payload: Final[dict[str, Any]] = {"messages": messages} + ) -> dict[str, object] | None: + payload: Final[dict[str, object]] = {"messages": messages} categories: Final = dynamic_body.get("categories") or self.categories if categories: @@ -563,13 +593,13 @@ class GraySwanGuardrail(CustomGuardrail): {**existing_headers, **inbound_headers} if isinstance(existing_headers, dict) else inbound_headers ) if cleaned_litellm_metadata: - sanitized: Final = safe_json_loads(safe_dumps(cleaned_litellm_metadata), default={}) + sanitized: Final[object] = safe_json_loads(safe_dumps(cleaned_litellm_metadata), default={}) if isinstance(sanitized, dict) and sanitized: payload["litellm_metadata"] = sanitized return payload - def _format_violation_message(self, detection_info: Any, is_output: bool = False) -> str: + def _format_violation_message(self, detection_info: object, is_output: bool = False) -> str: """ Format detection info into a user-friendly violation message. diff --git a/litellm/proxy/guardrails/guardrail_hooks/headroom/headroom.py b/litellm/proxy/guardrails/guardrail_hooks/headroom/headroom.py index daa15fb5e5f..d8c8c2f4974 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/headroom/headroom.py +++ b/litellm/proxy/guardrails/guardrail_hooks/headroom/headroom.py @@ -433,7 +433,7 @@ class HeadroomGuardrail(CustomGuardrail): payload["model"] = model try: - raw_response: HttpxResponse = await self.async_handler.post( # pyright: ignore[reportUnknownMemberType] + raw_response: HttpxResponse = await self.async_handler.post( # pyright: ignore[reportUnknownMemberType] # AsyncHTTPHandler.post is untyped url=f"{self.headroom_api_base}/v1/compress", json=payload, headers=self._request_headers(), @@ -570,7 +570,7 @@ class HeadroomGuardrail(CustomGuardrail): params["query"] = query try: - raw_response: HttpxResponse = await self.async_handler.get( # pyright: ignore[reportUnknownMemberType] + raw_response: HttpxResponse = await self.async_handler.get( # pyright: ignore[reportUnknownMemberType] # AsyncHTTPHandler.get is untyped url=f"{self.headroom_api_base}/v1/retrieve/{hash_value}", params=params, headers=self._request_headers(), diff --git a/litellm/proxy/guardrails/guardrail_hooks/lasso/lasso.py b/litellm/proxy/guardrails/guardrail_hooks/lasso/lasso.py index ea022510309..cf5da27e9ca 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/lasso/lasso.py +++ b/litellm/proxy/guardrails/guardrail_hooks/lasso/lasso.py @@ -8,6 +8,7 @@ import json import os import uuid +from collections.abc import Mapping, Sequence from typing import TYPE_CHECKING, Any, Final, Literal, TypedDict try: @@ -128,7 +129,7 @@ class LassoGuardrail(CustomGuardrail): @staticmethod def _extract_tool_call_fields( - call: Any, + call: object, ) -> tuple[str | None, str | None, dict[str, object] | None]: """Extract (call_id, name, parsed_input) from a tool call. @@ -476,7 +477,7 @@ class LassoGuardrail(CustomGuardrail): def _map_masked_messages_back( self, original_messages: list[dict[str, Any]], - masked_messages: list[dict[str, Any]], + masked_messages: Sequence[Mapping[str, object]], ) -> list[dict[str, object]]: """Map Lasso-format masked messages back onto the original OpenAI-format messages. @@ -638,7 +639,7 @@ class LassoGuardrail(CustomGuardrail): }, ) - def _expand_messages_for_classification(self, messages: list[dict[str, Any]]) -> list[dict[str, Any]]: + def _expand_messages_for_classification(self, messages: list[dict[str, Any]]) -> list[dict[str, object]]: """ Convert raw OpenAI-format messages to Lasso API format with content blocks. @@ -646,7 +647,7 @@ class LassoGuardrail(CustomGuardrail): - role=tool messages → developer role + tool_result block - plain text messages pass through unchanged """ - expanded: Final[list[dict[str, Any]]] = [] + expanded: Final[list[dict[str, object]]] = [] for msg in messages: role = msg.get("role", "") content = msg.get("content") @@ -917,7 +918,7 @@ class LassoGuardrail(CustomGuardrail): def _apply_masking_to_model_response( self, model_response: litellm.ModelResponse, - masked_messages: list[dict[str, Any]], + masked_messages: Sequence[Mapping[str, object]], ) -> None: """Apply masking to the actual model response when mask=True and masked content is available.""" # Index masked tool_use blocks by id for O(1) lookup. diff --git a/litellm/proxy/guardrails/guardrail_hooks/mcp_jwt_signer/mcp_jwt_signer.py b/litellm/proxy/guardrails/guardrail_hooks/mcp_jwt_signer/mcp_jwt_signer.py index e2d7c06f7c5..a269ad31a6b 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/mcp_jwt_signer/mcp_jwt_signer.py +++ b/litellm/proxy/guardrails/guardrail_hooks/mcp_jwt_signer/mcp_jwt_signer.py @@ -80,7 +80,7 @@ import jwt from cryptography.hazmat.primitives import serialization from cryptography.hazmat.primitives.asymmetric import rsa from cryptography.hazmat.primitives.asymmetric.rsa import RSAPrivateKey, RSAPublicKey -from typing_extensions import NotRequired, TypedDict +from typing_extensions import NotRequired, ReadOnly, TypedDict from litellm._logging import verbose_proxy_logger from litellm.caching import DualCache @@ -89,6 +89,7 @@ from litellm.integrations.custom_guardrail import ( log_guardrail_information, ) from litellm.proxy._types import UserAPIKeyAuth +from litellm.types.guardrail_base_init import GuardrailBaseInitKwargs from litellm.types.guardrails import GuardrailEventHooks from litellm.types.utils import CallTypesLiteral @@ -107,6 +108,19 @@ class _JWTDecodeKwargs(TypedDict): issuer: NotRequired[str] +class _DebugHeaderClaims(TypedDict, total=False): + sub: ReadOnly[object] + iss: ReadOnly[object] + exp: ReadOnly[object] + scope: ReadOnly[str] + + +class _SignedClaimSummary(TypedDict): + sub: ReadOnly[object] + act: ReadOnly[Mapping[str, object]] + exp: ReadOnly[object] + + # Module-level singleton for the JWKS discovery endpoint to access. _mcp_jwt_signer_instance: Optional["MCPJWTSigner"] = None @@ -265,7 +279,8 @@ class MCPJWTSigner(CustomGuardrail): **kwargs: Any, ) -> None: kwargs.setdefault("supported_event_hooks", list(self.get_supported_event_hooks())) - super().__init__(**kwargs) + base_kwargs: Final[GuardrailBaseInitKwargs] = kwargs + super().__init__(**base_kwargs) # --- Signing key setup --- key_material: Final = os.environ.get(self.SIGNING_KEY_ENV) @@ -677,7 +692,7 @@ class MCPJWTSigner(CustomGuardrail): data: dict, jwt_claims: Mapping[str, object] | None = None, call_type: CallTypesLiteral | None = None, - ) -> dict[str, Any]: + ) -> dict[str, object]: """ Build JWT claims for the outbound MCP access token. @@ -752,7 +767,7 @@ class MCPJWTSigner(CustomGuardrail): # ------------------------------------------------------------------ @staticmethod - def _build_debug_header(claims: dict[str, Any], kid: str) -> str: + def _build_debug_header(claims: _DebugHeaderClaims, kid: str) -> str: """ Build the x-litellm-mcp-debug header value. @@ -873,16 +888,18 @@ class MCPJWTSigner(CustomGuardrail): # FR-9: Debug header # ------------------------------------------------------------------ if self.debug_headers: - new_headers["x-litellm-mcp-debug"] = self._build_debug_header(claims, self._kid) + debug_claims: Final[_DebugHeaderClaims] = claims + new_headers["x-litellm-mcp-debug"] = self._build_debug_header(debug_claims, self._kid) hook_data["extra_headers"] = new_headers + logged_claims: Final[_SignedClaimSummary] = claims verbose_proxy_logger.debug( "MCPJWTSigner: signed JWT sub=%s act=%s tool=%s exp=%d verified=%s channel=%s call_type=%s", - claims.get("sub"), - claims.get("act", {}).get("sub"), + logged_claims.get("sub"), + logged_claims.get("act", {}).get("sub"), hook_data.get("mcp_tool_name"), - claims["exp"], + logged_claims["exp"], jwt_claims is not None, bool(self.channel_token_audience), call_type, diff --git a/litellm/proxy/guardrails/guardrail_hooks/noma/noma_v2.py b/litellm/proxy/guardrails/guardrail_hooks/noma/noma_v2.py index 704e2564ef5..292f395053b 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/noma/noma_v2.py +++ b/litellm/proxy/guardrails/guardrail_hooks/noma/noma_v2.py @@ -24,6 +24,7 @@ from litellm.llms.custom_httpx.http_handler import ( httpxSpecialProvider, ) from litellm.proxy.guardrails.guardrail_hooks.noma.noma import NomaBlockedMessage +from litellm.types.guardrail_base_init import GuardrailBaseInitKwargs from litellm.types.guardrails import GuardrailEventHooks from litellm.types.utils import GenericGuardrailAPIInputs, GuardrailStatus @@ -83,7 +84,8 @@ class NomaV2Guardrail(CustomGuardrail): kwargs.setdefault("supported_event_hooks", list(self.get_supported_event_hooks())) - super().__init__(**kwargs) + base_kwargs: Final[GuardrailBaseInitKwargs] = kwargs + super().__init__(**base_kwargs) @staticmethod def get_config_model() -> type["GuardrailConfigModel"] | None: @@ -114,7 +116,7 @@ class NomaV2Guardrail(CustomGuardrail): return parsed.hostname == _DEFAULT_API_BASE_HOSTNAME @staticmethod - def _get_non_empty_str(value: Any) -> str | None: + def _get_non_empty_str(value: object) -> str | None: if not isinstance(value, str): return None stripped: Final = value.strip() @@ -156,7 +158,7 @@ class NomaV2Guardrail(CustomGuardrail): else model_call_details ) - payload: Final[dict[str, Any]] = { + payload: Final[dict[str, object]] = { "inputs": inputs, "request_data": payload_request_data, "input_type": input_type, @@ -324,8 +326,9 @@ class NomaV2Guardrail(CustomGuardrail): except NomaBlockedMessage as e: guardrail_status = "guardrail_intervened" + blocked_detail: Final[dict[str, object]] = {"error": "blocked"} guardrail_json_response = ( - response_json if isinstance(response_json, dict) else getattr(e, "detail", {"error": "blocked"}) + response_json if isinstance(response_json, dict) else getattr(e, "detail", blocked_detail) ) raise except Exception as e: diff --git a/litellm/proxy/guardrails/guardrail_hooks/pillar/pillar.py b/litellm/proxy/guardrails/guardrail_hooks/pillar/pillar.py index 78639ce4fd0..7021d41475b 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/pillar/pillar.py +++ b/litellm/proxy/guardrails/guardrail_hooks/pillar/pillar.py @@ -8,11 +8,12 @@ # Standard library imports import json import os -from typing import TYPE_CHECKING, Any, Final, Literal +from typing import TYPE_CHECKING, Any, Final, Literal, Protocol from urllib.parse import quote # Third-party imports from fastapi import HTTPException +from typing_extensions import NotRequired, ReadOnly, TypedDict # LiteLLM imports from litellm import DualCache @@ -42,7 +43,34 @@ if TYPE_CHECKING: MAX_PILLAR_HEADER_VALUE_BYTES: Final = 8 * 1024 -def _encode_json_for_header(data: Any) -> str: +class _PillarProtectResponse(TypedDict): + """Body returned by Pillar's `/api/v1/protect` endpoint.""" + + flagged: ReadOnly[NotRequired[bool]] + session_id: ReadOnly[NotRequired[str]] + scanners: ReadOnly[NotRequired[dict[str, object]]] + evidence: ReadOnly[NotRequired[list[object]]] + masked_session_messages: ReadOnly[NotRequired[list[object]]] + + +class _PillarProtectHTTPResponse(Protocol): + def raise_for_status(self) -> object: ... + + def json(self) -> _PillarProtectResponse: ... + + +class _PillarProtectHTTPClient(Protocol): + async def post( + self, + *, + url: str, + headers: dict[str, str], + json: dict[str, object], + timeout: float, + ) -> _PillarProtectHTTPResponse: ... + + +def _encode_json_for_header(data: object) -> str: """ JSON-serialize and URL-encode data for safe header transmission. """ @@ -50,7 +78,9 @@ def _encode_json_for_header(data: Any) -> str: return quote(json_payload, safe="") -def _truncate_evidence_payload(evidence: Any, max_bytes: int = MAX_PILLAR_HEADER_VALUE_BYTES) -> tuple[Any, str, bool]: +def _truncate_evidence_payload( + evidence: object, max_bytes: int = MAX_PILLAR_HEADER_VALUE_BYTES +) -> tuple[object, str, bool]: """ Truncate evidence payload so the encoded header value stays within max_bytes. @@ -66,12 +96,12 @@ def _truncate_evidence_payload(evidence: Any, max_bytes: int = MAX_PILLAR_HEADER truncated_value: Final = "[truncated]" return truncated_value, _encode_json_for_header(truncated_value), True - truncated: Final[list[Any]] = [] + truncated: Final[list[object]] = [] encoded = _encode_json_for_header(truncated) truncated_flag = False for entry in evidence: - working_entry: Any + working_entry: object if isinstance(entry, dict): working_entry = dict(entry) else: @@ -105,7 +135,7 @@ def _truncate_evidence_payload(evidence: Any, max_bytes: int = MAX_PILLAR_HEADER return truncated, encoded, truncated_flag -def build_pillar_response_headers(metadata_store: dict[str, Any]) -> dict[str, str]: +def build_pillar_response_headers(metadata_store: dict[str, object]) -> dict[str, str]: """ Create URL-safe Pillar response headers and apply truncation metadata. """ @@ -191,7 +221,9 @@ class PillarGuardrail(CustomGuardrail): LiteLLM virtual key context (user_id, team_id, key_alias, etc.) is always automatically passed as X-LiteLLM-* headers to enable application/user tracking. """ - self.async_handler = get_async_httpx_client(llm_provider=httpxSpecialProvider.GuardrailCallback) + self.async_handler: _PillarProtectHTTPClient = get_async_httpx_client( + llm_provider=httpxSpecialProvider.GuardrailCallback + ) self.api_key = api_key or os.environ.get("PILLAR_API_KEY") if self.api_key is None: @@ -686,7 +718,7 @@ class PillarGuardrail(CustomGuardrail): ) return payload - async def _call_pillar_api(self, headers: dict[str, str], payload: dict[str, Any]) -> dict[str, Any]: + async def _call_pillar_api(self, headers: dict[str, str], payload: dict[str, Any]) -> _PillarProtectResponse: """ Call the Pillar API and return the response. @@ -714,7 +746,7 @@ class PillarGuardrail(CustomGuardrail): verbose_proxy_logger.debug("Pillar Guardrail: Analysis complete - flagged=%s, session=%s", flagged, session_id) return res - def _process_pillar_response(self, pillar_response: dict[str, Any], original_data: dict) -> None: + def _process_pillar_response(self, pillar_response: _PillarProtectResponse, original_data: dict) -> None: """ Process the Pillar API response and handle detections based on configuration. @@ -774,7 +806,7 @@ class PillarGuardrail(CustomGuardrail): build_pillar_response_headers(metadata_store) - def _raise_pillar_detection_exception(self, pillar_response: dict[str, Any]) -> None: + def _raise_pillar_detection_exception(self, pillar_response: _PillarProtectResponse) -> None: """ Raise an HTTPException for Pillar security detections. @@ -784,7 +816,7 @@ class PillarGuardrail(CustomGuardrail): Raises: HTTPException: Always raises with security detection details """ - pillar_response_dict: Final = { + pillar_response_dict: Final[dict[str, object]] = { "session_id": pillar_response.get("session_id"), } diff --git a/litellm/proxy/guardrails/guardrail_hooks/presidio.py b/litellm/proxy/guardrails/guardrail_hooks/presidio.py index 8d78393d687..da51a905ae3 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/presidio.py +++ b/litellm/proxy/guardrails/guardrail_hooks/presidio.py @@ -11,10 +11,10 @@ import asyncio import json import threading -from collections.abc import AsyncGenerator, Sequence +from collections.abc import AsyncGenerator, AsyncIterable, Awaitable, Sequence from contextlib import asynccontextmanager from datetime import datetime -from typing import TYPE_CHECKING, Any, Final, Literal, Optional, TypedDict, cast +from typing import TYPE_CHECKING, Any, Final, Literal, Optional, Protocol, TypedDict, cast import aiohttp from typing_extensions import NotRequired, ReadOnly @@ -68,6 +68,14 @@ class _PresidioAnonymizeResponse(TypedDict): items: ReadOnly[NotRequired[list[_PresidioAnonymizeItem]]] +class _JsonResponse(Protocol): + def json(self) -> Awaitable[object]: ... + + +async def _json_body(response: _JsonResponse) -> object: + return await response.json() + + _LoopSemaphores = dict[asyncio.AbstractEventLoop, asyncio.Semaphore] @@ -389,7 +397,7 @@ class _OPTIONAL_PresidioPIIMasking(CustomGuardrail): f"expected application/json Content-Type but received '{content_type}'; body: '{error_body[:200]}'" ) - analyze_results: Final = await response.json() + analyze_results: Final = await _json_body(response) verbose_proxy_logger.debug("analyze_results: %s", analyze_results) # Handle error responses from Presidio (e.g., {'error': 'No text provided'}) @@ -997,7 +1005,7 @@ class _OPTIONAL_PresidioPIIMasking(CustomGuardrail): except Exception as e: raise e - def logging_hook(self, kwargs: dict, result: Any, call_type: str) -> tuple[dict, Any]: + def logging_hook(self, kwargs: dict, result: object, call_type: str) -> tuple[dict, object]: from concurrent.futures import ThreadPoolExecutor def run_in_new_loop(): @@ -1025,7 +1033,7 @@ class _OPTIONAL_PresidioPIIMasking(CustomGuardrail): # No running event loop, we can safely run in this thread return run_in_new_loop() - async def async_logging_hook(self, kwargs: dict, result: Any, call_type: str) -> tuple[dict, Any]: + async def async_logging_hook(self, kwargs: dict, result: object, call_type: str) -> tuple[dict, object]: """ Masks the input and output before logging to langfuse, datadog, etc. """ @@ -1092,9 +1100,9 @@ class _OPTIONAL_PresidioPIIMasking(CustomGuardrail): and not isinstance(result.choices[0], StreamingChoices) ): await self._process_response_for_pii(response=result, request_data=kwargs, mode="mask") - elif self._is_anthropic_message_response(result): + elif isinstance(result, dict) and self._is_anthropic_message_response(result): await self._process_anthropic_response_for_pii( - response=cast(dict, result), # cast-ok: _is_anthropic_message_response narrows via isinstance + response=result, request_data=kwargs, mode="mask", ) @@ -1321,7 +1329,7 @@ class _OPTIONAL_PresidioPIIMasking(CustomGuardrail): async def _stream_apply_output_masking( self, - response: Any, + response: AsyncIterable[object], request_data: dict, ) -> AsyncGenerator[ModelResponseStream | bytes, None]: """Apply Presidio masking to streaming output (apply_to_output=True path).""" @@ -1425,7 +1433,7 @@ class _OPTIONAL_PresidioPIIMasking(CustomGuardrail): return "\n".join(result_lines).encode("utf-8") - def _unmask_responses_api_completed_chunk(self, chunk: Any, pii_tokens: dict[str, str]) -> None: + def _unmask_responses_api_completed_chunk(self, chunk: object, pii_tokens: dict[str, str]) -> None: """ Unmask PII tokens in-place for a ``response.completed`` Responses API event. @@ -1434,7 +1442,7 @@ class _OPTIONAL_PresidioPIIMasking(CustomGuardrail): blocks; text blocks expose a ``.text`` string attribute. We walk the tree and replace every PII token with its original value. """ - response_obj: Final = getattr(chunk, "response", None) + response_obj: Final[object] = getattr(chunk, "response", None) if response_obj is None: return @@ -1450,7 +1458,7 @@ class _OPTIONAL_PresidioPIIMasking(CustomGuardrail): async def _stream_pii_unmasking( self, - response: Any, + response: AsyncIterable[object], request_data: dict, ) -> AsyncGenerator[ModelResponseStream | bytes, None]: """Apply PII unmasking to streaming output (output_parse_pii=True path).""" @@ -1526,7 +1534,7 @@ class _OPTIONAL_PresidioPIIMasking(CustomGuardrail): async def async_post_call_streaming_iterator_hook( self, user_api_key_dict: UserAPIKeyAuth, - response: Any, + response: AsyncIterable[object], request_data: dict, ) -> AsyncGenerator[ModelResponseStream | bytes, None]: """ diff --git a/litellm/proxy/guardrails/guardrail_hooks/repelloai/repelloai.py b/litellm/proxy/guardrails/guardrail_hooks/repelloai/repelloai.py index d842a9b9b6a..8925cc5b3a6 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/repelloai/repelloai.py +++ b/litellm/proxy/guardrails/guardrail_hooks/repelloai/repelloai.py @@ -197,7 +197,7 @@ class RepelloAIGuardrail(CustomGuardrail): repelloai_response: RepelloAIAnalyzeResponse | None = None try: verbose_proxy_logger.debug("RepelloAI Argus request: %s", request) - response: Final[HttpxResponse] = await self.async_handler.post( # pyright: ignore[reportUnknownMemberType] + response: Final[HttpxResponse] = await self.async_handler.post( # pyright: ignore[reportUnknownMemberType] # AsyncHTTPHandler.post is untyped url=endpoint, headers={"X-API-Key": self.repelloai_api_key}, json=request, diff --git a/litellm/proxy/guardrails/guardrail_hooks/semantic_guard/semantic_guard.py b/litellm/proxy/guardrails/guardrail_hooks/semantic_guard/semantic_guard.py index e34beec4d3e..2fbd50b5863 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/semantic_guard/semantic_guard.py +++ b/litellm/proxy/guardrails/guardrail_hooks/semantic_guard/semantic_guard.py @@ -6,7 +6,7 @@ via embedding similarity. Smarter than regex (understands intent), lighter than an LLM call (~20-50ms per request for embedding). """ -from typing import TYPE_CHECKING, Any, Final +from typing import TYPE_CHECKING, Any, Final, Protocol from litellm._logging import verbose_logger from litellm.integrations.custom_guardrail import ( @@ -50,7 +50,7 @@ class SemanticGuardrail(CustomGuardrail): similarity_threshold: float, route_templates: list[str] | None = None, custom_routes_file: str | None = None, - custom_routes: list[dict[str, Any]] | None = None, + custom_routes: list[dict[str, object]] | None = None, on_flagged_action: str = "block", event_hook: GuardrailEventHooks | list[GuardrailEventHooks] | Mode | None = None, default_on: bool = False, @@ -157,7 +157,14 @@ class SemanticGuardrail(CustomGuardrail): return response -def _get_top_route_choice(result: Any) -> Any: +class _RouteChoice(Protocol): + """The semantic-router match this guardrail reads: the route that fired, if any.""" + + @property + def name(self) -> str | None: ... + + +def _get_top_route_choice(result: _RouteChoice | list[_RouteChoice] | None) -> _RouteChoice | None: """Extract the top RouteChoice from SemanticRouter result. SemanticRouter.__call__ can return RouteChoice or List[RouteChoice]. @@ -194,7 +201,7 @@ def _extract_response_text(response: Any) -> str: return "" -def _content_to_text(content: Any) -> str: +def _content_to_text(content: object) -> str: if isinstance(content, str): return content if isinstance(content, list): diff --git a/litellm/proxy/guardrails/guardrail_hooks/tool_permission.py b/litellm/proxy/guardrails/guardrail_hooks/tool_permission.py index 3c5625bc272..a8b33109900 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/tool_permission.py +++ b/litellm/proxy/guardrails/guardrail_hooks/tool_permission.py @@ -1,9 +1,10 @@ import json import re from collections.abc import AsyncGenerator, AsyncIterable, Mapping, Sequence -from typing import Any, Final, Literal +from typing import Any, Final, Literal, TypedDict from fastapi import HTTPException +from typing_extensions import ReadOnly, Required from litellm import ChatCompletionToolParam from litellm._logging import verbose_proxy_logger @@ -51,6 +52,27 @@ def _object_list(value: object) -> Sequence[object] | None: return value if isinstance(value, list) else None +class _ToolPermissionRuleFields(TypedDict, total=False): + """The config-file shape a :class:`ToolPermissionRule` is built from.""" + + id: ReadOnly[Required[str]] + tool_name: ReadOnly[str | None] + tool_type: ReadOnly[str | None] + decision: ReadOnly[Required[Literal["allow", "deny"]]] + allowed_param_patterns: ReadOnly[dict[str, str] | None] + + +def _rule_from_fields(fields: _ToolPermissionRuleFields) -> ToolPermissionRule: + """Validate one config-file rule entry into a :class:`ToolPermissionRule`.""" + return ToolPermissionRule(**fields) + + +def _is_tool_use_block(block: object) -> bool: + """Whether ``block`` is an Anthropic ``tool_use`` content block.""" + fields: Final = _object_mapping(block) + return fields is not None and fields.get("type") == "tool_use" + + class ToolPermissionGuardrail(CustomGuardrail): def __init__( self, @@ -101,7 +123,7 @@ class ToolPermissionGuardrail(CustomGuardrail): compiled_patterns: Final[dict[str, dict[str, re.Pattern]]] = {} for rule_item in rules or []: - rule = rule_item if isinstance(rule_item, ToolPermissionRule) else ToolPermissionRule(**rule_item) + rule = rule_item if isinstance(rule_item, ToolPermissionRule) else _rule_from_fields(rule_item) target_patterns: dict[str, re.Pattern | None] = { "tool_name": None, @@ -440,7 +462,7 @@ class ToolPermissionGuardrail(CustomGuardrail): return is_allowed, None, message @staticmethod - def _get_mapping_value(item: Any, key: str) -> Any: + def _get_mapping_value(item: object, key: str) -> Any: if isinstance(item, dict): return item.get(key) return getattr(item, key, None) @@ -450,7 +472,7 @@ class ToolPermissionGuardrail(CustomGuardrail): return f"legacy_function_call_{choice_index}" def _legacy_function_call_to_tool_call( - self, function_call: Any, choice_index: int + self, function_call: object, choice_index: int ) -> ChatCompletionMessageToolCall | None: if function_call is None: return None @@ -549,7 +571,7 @@ class ToolPermissionGuardrail(CustomGuardrail): def _modify_anthropic_content_with_permission_errors( self, response: object, - content: tuple[Any, ...], + content: tuple[object, ...], denied_tools: tuple[tuple[ChatCompletionMessageToolCall, PermissionError], ...], ) -> None: if not denied_tools or not isinstance(response, dict): @@ -557,27 +579,33 @@ class ToolPermissionGuardrail(CustomGuardrail): verbose_proxy_logger.info("Blocking %s unauthorized tool uses", len(denied_tools)) - error_by_tool_use_id: Final = { # mutable-ok: read-only lookup, never mutated after construction + error_by_tool_use_id: Final[ + Mapping[object, str] + ] = { # mutable-ok: read-only lookup, never mutated after construction tool_call.id: self._create_permission_error_result(tool_call, error).content for tool_call, error in denied_tools } - denied_block_ids: Final = frozenset(error_by_tool_use_id) - def _is_denied(block: object) -> bool: - return isinstance(block, dict) and block.get("type") == "tool_use" and block.get("id") in denied_block_ids + def _denied_message(block: object) -> str | None: + fields: Final = _object_mapping(block) + if fields is None or fields.get("type") != "tool_use": + return None + return error_by_tool_use_id.get(fields.get("id")) - error_messages: Final = tuple(error_by_tool_use_id[block["id"]] for block in content if _is_denied(block)) - kept_blocks: Final = tuple(block for block in content if not _is_denied(block)) + error_messages: Final = tuple( + message for message in (_denied_message(block) for block in content) if message is not None + ) + kept_blocks: Final = tuple(block for block in content if _denied_message(block) is None) new_content: Final = [ # mutable-ok: response content is a JSON array on the wire *kept_blocks, {"type": "text", "text": "\n".join(error_messages)}, # mutable-ok: content block is a JSON object ] response["content"] = new_content # rebind-ok: the guardrail rewrites the provider response in place - if not any(isinstance(block, dict) and block.get("type") == "tool_use" for block in kept_blocks): + if not any(_is_tool_use_block(block) for block in kept_blocks): response["stop_reason"] = "end_turn" # rebind-ok: dropping every tool_use ends the turn - def _get_request_tool_name(self, tool: Any) -> tuple[str | None, str | None]: + def _get_request_tool_name(self, tool: object) -> tuple[str | None, str | None]: tool_type: Final = self._get_mapping_value(tool, "type") if tool_type != "function": return None, tool_type @@ -586,7 +614,7 @@ class ToolPermissionGuardrail(CustomGuardrail): tool_name: Final = self._get_mapping_value(function, "name") return tool_name, tool_type - def _get_legacy_function_name(self, function: Any) -> str | None: + def _get_legacy_function_name(self, function: object) -> str | None: return self._get_mapping_value(function, "name") def _get_named_tool_choice(self, data: dict) -> str | None: diff --git a/litellm/proxy/guardrails/guardrail_hooks/vigil_guard/vigil_guard.py b/litellm/proxy/guardrails/guardrail_hooks/vigil_guard/vigil_guard.py index 6b8148645aa..a5945a39589 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/vigil_guard/vigil_guard.py +++ b/litellm/proxy/guardrails/guardrail_hooks/vigil_guard/vigil_guard.py @@ -433,7 +433,7 @@ class VigilGuardGuardrail(CustomGuardrail): return collected @staticmethod - def _clamp_metadata_value(value: Any) -> _MetadataValue | None: + def _clamp_metadata_value(value: object) -> _MetadataValue | None: if isinstance(value, bool): return None if isinstance(value, str): diff --git a/litellm/proxy/guardrails/guardrail_hooks/xecguard/xecguard.py b/litellm/proxy/guardrails/guardrail_hooks/xecguard/xecguard.py index ddb40dc3ca0..831df43692b 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/xecguard/xecguard.py +++ b/litellm/proxy/guardrails/guardrail_hooks/xecguard/xecguard.py @@ -310,7 +310,7 @@ class XecGuardGuardrail(CustomGuardrail): scan_type: str, suppress_errors: bool = False, ) -> dict | None: - payload: Final[dict[str, Any]] = { + payload: Final[dict[str, object]] = { "model": self.xecguard_model, "scan_type": scan_type, "messages": messages, @@ -385,7 +385,7 @@ class XecGuardGuardrail(CustomGuardrail): def _build_full_history( self, request_data: dict, - inputs: Any, + inputs: GenericGuardrailAPIInputs, input_type: str, ) -> list[dict]: """Assemble the full message list that will be sent to XecGuard. diff --git a/litellm/proxy/hooks/litellm_skills/main.py b/litellm/proxy/hooks/litellm_skills/main.py index 569ec32c1a0..9edbc6dbf1c 100644 --- a/litellm/proxy/hooks/litellm_skills/main.py +++ b/litellm/proxy/hooks/litellm_skills/main.py @@ -67,6 +67,24 @@ class _ChatMessage(Protocol): def tool_calls(self) -> Sequence[_ChatToolCall] | None: ... +class _ChatChoice(Protocol): + @property + def message(self) -> _ChatMessage: ... + + @property + def finish_reason(self) -> str | None: ... + + +class _ChatCompletion(Protocol): + @property + def choices(self) -> Sequence[_ChatChoice]: ... + + +def _first_choice(response: _ChatCompletion) -> _ChatChoice: + """The first choice of an OpenAI shaped completion response.""" + return response.choices[0] + + class SkillsInjectionHook(CustomLogger): """ Pre/Post-call hook that processes skills from container.skills parameter. @@ -738,8 +756,9 @@ print('No executable skill module found') for iteration in range(self.max_iterations): # OpenAI format response has choices[0].message - assistant_message: _ChatMessage = current_response.choices[0].message - stop_reason: str | None = current_response.choices[0].finish_reason + choice: _ChatChoice = _first_choice(current_response) + assistant_message: _ChatMessage = choice.message + stop_reason: str | None = choice.finish_reason # Build assistant message for conversation history assistant_msg_dict: dict[str, object] = { diff --git a/litellm/proxy/hooks/mcp_semantic_filter/hook.py b/litellm/proxy/hooks/mcp_semantic_filter/hook.py index 3ce406eef73..8b82842353c 100644 --- a/litellm/proxy/hooks/mcp_semantic_filter/hook.py +++ b/litellm/proxy/hooks/mcp_semantic_filter/hook.py @@ -5,10 +5,11 @@ Pre-call hook that filters MCP tools semantically before LLM inference. Reduces context window size and improves tool selection accuracy. """ -from collections.abc import Mapping, Sequence -from typing import TYPE_CHECKING, Any, Final, Optional +from collections.abc import Iterable, Mapping, Sequence +from typing import TYPE_CHECKING, Final, Optional from fastapi import HTTPException +from typing_extensions import ReadOnly, TypedDict from litellm._logging import verbose_proxy_logger from litellm.constants import ( @@ -30,6 +31,13 @@ if TYPE_CHECKING: from litellm.router import Router +class SemanticToolFilterConfig(TypedDict, total=False): + enabled: ReadOnly[bool] + embedding_model: ReadOnly[str] + top_k: ReadOnly[int] + similarity_threshold: ReadOnly[float] + + def _truncate_csv_at_tool_name_boundary(tool_names_csv: str, max_length: int) -> str: """Cap a CSV of tool names to max_length, dropping any name that does not fit whole.""" if len(tool_names_csv) <= max_length: @@ -68,7 +76,7 @@ class SemanticToolFilterHook(CustomLogger): semantic_filter.top_k, ) - def _should_expand_mcp_tools(self, tools: list[Any]) -> bool: + def _should_expand_mcp_tools(self, tools: Iterable[Mapping[str, object]]) -> bool: """ Check if tools contain MCP references with server_url="litellm_proxy". @@ -82,9 +90,9 @@ class SemanticToolFilterHook(CustomLogger): async def _expand_mcp_tools( self, - tools: list[Any], + tools: Iterable[Mapping[str, object]], user_api_key_dict: "UserAPIKeyAuth", - ) -> list[dict[str, Any]]: + ) -> list[dict[str, object]]: """ Expand MCP references to actual tool definitions. @@ -111,7 +119,7 @@ class SemanticToolFilterHook(CustomLogger): ) # Convert Pydantic models to dicts for compatibility - openai_tools_as_dicts: Final = [] + openai_tools_as_dicts: Final[list[dict[str, object]]] = [] for tool in openai_tools: if hasattr(tool, "model_dump"): tool_dict = tool.model_dump(exclude_none=True) @@ -141,8 +149,8 @@ class SemanticToolFilterHook(CustomLogger): async def _filter_expanded_tools( self, data: dict, - expanded_tools: list[dict[str, Any]], - ) -> list[dict[str, Any]]: + expanded_tools: list[dict[str, object]], + ) -> list[dict[str, object]]: """ Apply the semantic filter to expanded MCP tool definitions. @@ -159,7 +167,7 @@ class SemanticToolFilterHook(CustomLogger): return await self.filter.filter_tools(query=user_query, available_tools=expanded_tools) - def _selected_tool_names(self, filtered_tools: list[dict[str, Any]]) -> list[str]: + def _selected_tool_names(self, filtered_tools: Sequence[object]) -> list[str]: """Names of the semantically selected tools, as produced by the MCP expansion.""" names: Final = (self.filter._extract_tool_info(tool)[0] for tool in filtered_tools) return [name for name in names if name] @@ -217,10 +225,10 @@ class SemanticToolFilterHook(CustomLogger): def _emit_filter_metadata( self, data: dict, - mcp_tools: list[object], - filtered_mcp_tools: list[object], - native_tools: list[object], - filtered_tools: list[object], + mcp_tools: Sequence[object], + filtered_mcp_tools: Sequence[object], + native_tools: Sequence[object], + filtered_tools: Sequence[object], ) -> None: """ Emit response-header metadata when MCP tools were filtered. @@ -252,10 +260,10 @@ class SemanticToolFilterHook(CustomLogger): def _emit_filter_metadata_safe( self, data: dict, - mcp_tools: list[object], - filtered_mcp_tools: list[object], - native_tools: list[object], - filtered_tools: list[object], + mcp_tools: Sequence[object], + filtered_mcp_tools: Sequence[object], + native_tools: Sequence[object], + filtered_tools: Sequence[object], ) -> None: """ Emit filter metadata without letting an emission failure abort the @@ -375,7 +383,7 @@ class SemanticToolFilterHook(CustomLogger): ) if mcp_tools: - filtered_mcp_tools = await self.filter.filter_tools( + filtered_mcp_tools: list[object] = await self.filter.filter_tools( query=user_query, available_tools=mcp_tools, ) @@ -419,9 +427,9 @@ class SemanticToolFilterHook(CustomLogger): self, data: dict, user_api_key_dict: "UserAPIKeyAuth", - response: Any, + response: object, request_headers: dict[str, str] | None = None, - litellm_call_info: dict[str, Any] | None = None, + litellm_call_info: dict[str, object] | None = None, ) -> dict[str, str] | None: """Add semantic filter stats and tool names to response headers.""" from litellm.constants import MAX_MCP_SEMANTIC_FILTER_TOOLS_HEADER_LENGTH @@ -446,7 +454,7 @@ class SemanticToolFilterHook(CustomLogger): return headers - def _get_tool_names_csv(self, tools: list[Any]) -> str: + def _get_tool_names_csv(self, tools: Sequence[object]) -> str: """Extract tool names and return as CSV string.""" if not tools: return "" @@ -461,7 +469,7 @@ class SemanticToolFilterHook(CustomLogger): @staticmethod async def initialize_from_config( - config: dict[str, Any] | None, + config: SemanticToolFilterConfig | None, llm_router: Optional["Router"], ) -> Optional["SemanticToolFilterHook"]: """ diff --git a/litellm/proxy/hooks/parallel_request_limiter_v3.py b/litellm/proxy/hooks/parallel_request_limiter_v3.py index 1e65da5b867..63129602082 100644 --- a/litellm/proxy/hooks/parallel_request_limiter_v3.py +++ b/litellm/proxy/hooks/parallel_request_limiter_v3.py @@ -8,7 +8,7 @@ import asyncio import binascii import os import uuid -from collections.abc import Callable, Mapping, Sequence, Set +from collections.abc import Awaitable, Callable, Mapping, Sequence, Set from contextvars import ContextVar from dataclasses import dataclass, field from datetime import datetime @@ -386,6 +386,12 @@ CacheCounterValues: TypeAlias = Sequence[CacheCounterValue | None] ParallelGaugeCacheValue: TypeAlias = dict[str, object] | int | float | str | bytes +class _AsyncLuaScript(Protocol): + """A Lua script registered against the async Redis client, called with KEYS and ARGV.""" + + def __call__(self, *, keys: Sequence[str], args: Sequence[object]) -> Awaitable[list[CacheCounterValue]]: ... + + class RateLimitDescriptorRateLimitObject(TypedDict, total=False): requests_per_unit: int | None tokens_per_unit: int | None @@ -577,6 +583,14 @@ def _parse_output_cap_value(raw_value: object) -> int | None: class _PROXY_MaxParallelRequestsHandler_v3(CustomLogger): + batch_rate_limiter_script: _AsyncLuaScript | None + token_increment_script: _AsyncLuaScript | None + check_and_increment_by_n_script: _AsyncLuaScript | None + window_guarded_token_increment_script: _AsyncLuaScript | None + parallel_acquire_script: _AsyncLuaScript | None + parallel_release_script: _AsyncLuaScript | None + parallel_count_script: _AsyncLuaScript | None + def __init__( self, internal_usage_cache: InternalUsageCache, @@ -3855,7 +3869,7 @@ class _PROXY_MaxParallelRequestsHandler_v3(CustomLogger): expected_window_start = operation.get("expected_window_start") if window_key is None or expected_window_start is None: continue - active_window_start = await self.internal_usage_cache.async_get_cache( + active_window_start: CacheCounterValue | None = await self.internal_usage_cache.async_get_cache( key=window_key, litellm_parent_otel_span=parent_otel_span, local_only=True, @@ -4144,7 +4158,7 @@ class _PROXY_MaxParallelRequestsHandler_v3(CustomLogger): def _collect_tpm_scope_targets( self, standard_logging_metadata: dict[str, Any], - kwargs: Any, + kwargs: object, model_group: str | None, ) -> list[tuple[str, str]]: """ @@ -4301,8 +4315,8 @@ class _PROXY_MaxParallelRequestsHandler_v3(CustomLogger): def _build_success_event_pipeline_operations( self, - kwargs: Any, - response_obj: Any, + kwargs: dict[str, Any], + response_obj: object, rate_limit_type: Literal["output", "input", "total"], ) -> list[RedisPipelineIncrementOperation]: """Build Redis pipeline increment ops for TPM / parallel-request counters.""" diff --git a/litellm/proxy/hooks/proxy_track_cost_callback.py b/litellm/proxy/hooks/proxy_track_cost_callback.py index f02901f0e97..47aafda2337 100644 --- a/litellm/proxy/hooks/proxy_track_cost_callback.py +++ b/litellm/proxy/hooks/proxy_track_cost_callback.py @@ -587,7 +587,7 @@ async def _update_database_and_spend_counters( model_access_groups: Sequence[str] | None = None, ) -> None: try: - spend_log_request_id = await proxy_logging_obj.db_spend_update_writer.update_database( + await proxy_logging_obj.db_spend_update_writer.update_database( token=user_api_key, response_cost=response_cost, user_id=user_id, @@ -623,7 +623,6 @@ async def _update_database_and_spend_counters( budget_reservation=budget_reservation, end_user_id=end_user_id, tags=request_tags, - request_id=spend_log_request_id, request_started_at=start_time, model_access_groups=model_access_groups, ) diff --git a/litellm/proxy/litellm_pre_call_utils.py b/litellm/proxy/litellm_pre_call_utils.py index ae55b7ab906..20f83085286 100644 --- a/litellm/proxy/litellm_pre_call_utils.py +++ b/litellm/proxy/litellm_pre_call_utils.py @@ -4,9 +4,10 @@ import json import re import time from collections import OrderedDict -from collections.abc import Mapping, MutableMapping +from collections.abc import Mapping, MutableMapping, Sequence +from datetime import datetime from types import MappingProxyType -from typing import TYPE_CHECKING, Any, Final +from typing import TYPE_CHECKING, Any, Final, cast from fastapi import HTTPException, Request from pydantic import ValidationError as PydanticValidationError @@ -55,7 +56,7 @@ from litellm.proxy.common_utils.http_parsing_utils import _safe_get_request_head from litellm.types.integrations.anthropic_cache_control_hook import GATEWAY_INJECTED_CACHE_METADATA_KEY # Cache special headers as a frozenset for O(1) lookup performance -_SPECIAL_HEADERS_CACHE: Final = frozenset(v.value.lower() for v in SpecialHeaders._member_map_.values()) +_SPECIAL_HEADERS_CACHE: Final = frozenset(str(v.value).lower() for v in SpecialHeaders) _REDACTED_HEADER_VALUE: Final = "***REDACTED***" _CREDENTIAL_HEADER_NAMES: Final = SpecialHeaders.litellm_credential_header_names() | frozenset( @@ -126,7 +127,7 @@ def _stampable_key_hash(user_api_key_dict: UserAPIKeyAuth) -> str | None: _ANTHROPIC_SESSION_ID_VALUE_RE: Final = re.compile(r"^[a-zA-Z0-9_\-]+$") -def _sanitize_for_log(value: Any) -> str: +def _sanitize_for_log(value: object) -> str: """ Basic log sanitization helper to reduce log-injection risk. @@ -164,7 +165,7 @@ _ENABLE_TEAM_STALE_ALIAS_BYPASS: bool | None = None if TYPE_CHECKING: from litellm.proxy.proxy_server import ProxyConfig as _ProxyConfig - from litellm.types.proxy.policy_engine import PolicyMatchContext + from litellm.types.proxy.policy_engine import Policy, PolicyMatchContext ProxyConfig = _ProxyConfig else: @@ -328,7 +329,7 @@ _ALLOW_CLIENT_PRICING_OVERRIDE_METADATA_KEY: Final = "allow_client_pricing_overr _URL_DESTINATION_REQUEST_FIELDS: Final = ("model", "file_id") -def _reject_url_valued_destinations(data: dict[str, Any]) -> None: +def _reject_url_valued_destinations(data: dict[str, object]) -> None: """Reject URL-valued ``model``/``file_id`` unless admin-allowlisted. Some providers (HuggingFace, Oobabooga, Gemini files) accept a URL in the @@ -387,7 +388,7 @@ def _invalid_metadata_type_error(field: str, value: object) -> ProxyException: ) -def _normalized_metadata_object(field: str, value: object) -> Mapping[str, Any]: +def _normalized_metadata_object(field: str, value: object) -> Mapping[str, object]: """Return ``value`` as a metadata object or raise a 400 like OpenAI does. A JSON string that parses to an object is accepted because multipart/form-data @@ -402,6 +403,23 @@ def _normalized_metadata_object(field: str, value: object) -> Mapping[str, Any]: raise _invalid_metadata_type_error(field=field, value=value) +def _normalized_metadata_slot( + request_data: MutableMapping[str, object], metadata_variable_name: str +) -> dict[str, object]: + """Return the request's metadata slot as a dict, normalising it in place first. + + Metadata can arrive as a JSON string (multipart/form-data, ``extra_body``). Parsing it here keeps + existing entries alive through a merge instead of silently overwriting them with an empty dict. + """ + raw: Final = request_data.get(metadata_variable_name) + if isinstance(raw, dict): + return raw + parsed: Final = safe_json_loads(raw) if isinstance(raw, str) else None + normalized: Final[dict[str, object]] = parsed if isinstance(parsed, dict) else {} + request_data[metadata_variable_name] = normalized + return normalized + + def _strip_untrusted_request_header_controls( headers: Any, *, @@ -417,7 +435,7 @@ def _strip_untrusted_request_header_controls( headers.pop(header_name, None) -def _is_false_like(value: Any) -> bool: +def _is_false_like(value: object) -> bool: if isinstance(value, bool): return value is False if isinstance(value, str): @@ -462,7 +480,7 @@ def _key_or_team_allows_client_pricing_override( ) -def _strip_client_message_redaction_opt_out(data: dict[str, Any]) -> None: +def _strip_client_message_redaction_opt_out(data: dict[str, object]) -> None: stripped: Final[list[str]] = [] if "turn_off_message_logging" in data and _is_false_like(data["turn_off_message_logging"]): stripped.append("turn_off_message_logging") @@ -513,7 +531,7 @@ def _strip_client_callback_credentials( ) -def _strip_client_pricing_overrides(data: dict[str, Any]) -> None: +def _strip_client_pricing_overrides(data: dict[str, object]) -> None: """Drop pricing overrides from the request body and any metadata variant. Skipped only when the calling key/team carries @@ -580,9 +598,9 @@ def _get_metadata_variable_name(request: Request) -> str: def _promoted_trace_control_fields( - requester_metadata: Mapping[str, Any], - litellm_metadata: Mapping[str, Any], -) -> tuple[tuple[str, Any], ...]: + requester_metadata: Mapping[str, object], + litellm_metadata: Mapping[str, object], +) -> tuple[tuple[str, object], ...]: """Return the caller's trace-control fields that ``litellm_metadata`` does not already set.""" return tuple( (key, value) @@ -1193,7 +1211,7 @@ class LiteLLMProxyRequestSetup: def add_litellm_data_for_backend_llm_call( *, headers: dict, - request_data: Mapping[str, Any], + request_data: Mapping[str, object], user_api_key_dict: UserAPIKeyAuth, general_settings: dict[str, Any] | None = None, ) -> LitellmDataForBackendLLMCall: @@ -1327,6 +1345,8 @@ class LiteLLMProxyRequestSetup: def get_sanitized_user_information_from_key( user_api_key_dict: UserAPIKeyAuth, ) -> StandardLoggingUserAPIKeyMetadata: + stripped_metadata: Final = strip_callback_config(user_api_key_dict.metadata) + auth_metadata: Final = cast("dict[str, str] | None", stripped_metadata) # cast-ok: metadata is free-form JSON user_api_key_logged_metadata: Final = StandardLoggingUserAPIKeyMetadata( user_api_key_hash=user_api_key_dict.api_key, # just the hashed token user_api_key_alias=user_api_key_dict.key_alias, @@ -1349,7 +1369,7 @@ class LiteLLMProxyRequestSetup: user_api_key_budget_reset_at=( user_api_key_dict.budget_reset_at.isoformat() if user_api_key_dict.budget_reset_at else None ), - user_api_key_auth_metadata=strip_callback_config(user_api_key_dict.metadata), + user_api_key_auth_metadata=auth_metadata, ) return user_api_key_logged_metadata @@ -1577,14 +1597,7 @@ class LiteLLMProxyRequestSetup: return _metadata_variable_name: Final = get_metadata_variable_name_from_kwargs(request_data) - metadata = request_data.get(_metadata_variable_name) - if isinstance(metadata, str): - parsed: Final = safe_json_loads(metadata) - metadata = parsed if isinstance(parsed, dict) else {} - request_data[_metadata_variable_name] = metadata - elif not isinstance(metadata, dict): - metadata = {} - request_data[_metadata_variable_name] = metadata + metadata: Final = _normalized_metadata_slot(request_data, _metadata_variable_name) existing_tags: Final = metadata.get("tags") metadata["tags"] = LiteLLMProxyRequestSetup._merge_tags( @@ -1636,18 +1649,7 @@ class LiteLLMProxyRequestSetup: # from (litellm_metadata vs metadata) so the merged tags are visible # to _tag_max_budget_check. _metadata_variable_name: Final = get_metadata_variable_name_from_kwargs(request_data) - metadata = request_data.get(_metadata_variable_name) - # metadata can arrive as a JSON string (multipart/form-data, extra_body). - # Parse it so existing tags survive the merge — overwriting the string - # with {} would let a caller bypass _tag_max_budget_check on an - # over-budget body tag by also sending a within-budget header tag. - if isinstance(metadata, str): - parsed: Final = safe_json_loads(metadata) - metadata = parsed if isinstance(parsed, dict) else {} - request_data[_metadata_variable_name] = metadata - elif not isinstance(metadata, dict): - metadata = {} - request_data[_metadata_variable_name] = metadata + metadata: Final = _normalized_metadata_slot(request_data, _metadata_variable_name) existing_tags: Final = metadata.get("tags") metadata["tags"] = LiteLLMProxyRequestSetup._merge_tags( @@ -1787,7 +1789,7 @@ async def add_litellm_data_to_request( # admin-injection strip below so the audit / spend-tracking consumers of # proxy_server_request["body"] see the cleaned metadata rather than # attacker-forged user_api_key_* fields. - _litellm_received_at: Final = getattr(request.state, "litellm_received_at", None) + _litellm_received_at: Final[datetime | None] = getattr(request.state, "litellm_received_at", None) arrival_time: Final = _litellm_received_at.timestamp() if _litellm_received_at is not None else time.time() data["proxy_server_request"] = { "url": str(request.url), @@ -2472,16 +2474,16 @@ def _resolve_provider_from_deployment( if deployment is None: continue - litellm_params = getattr(deployment, "litellm_params", None) + litellm_params: object = getattr(deployment, "litellm_params", None) if litellm_params is None: continue custom_provider = getattr(litellm_params, "custom_llm_provider", None) - if custom_provider: + if isinstance(custom_provider, str) and custom_provider: return custom_provider - deployment_model = getattr(litellm_params, "model", "") or "" - if "/" in deployment_model: + deployment_model = getattr(litellm_params, "model", "") + if isinstance(deployment_model, str) and "/" in deployment_model: return deployment_model.split("/", 1)[0] return None @@ -2904,8 +2906,8 @@ def _extract_policy_id(s: str) -> str | None: def _match_and_track_policies( data: dict, context: "PolicyMatchContext", - request_body_policies: Any, - policies_override: dict[str, Any] | None = None, + request_body_policies: Sequence[str], + policies_override: dict[str, "Policy"] | None = None, ) -> tuple[list[str], dict[str, str]]: """ Match policies via attachments and request body, track them in metadata. @@ -2963,7 +2965,7 @@ def _apply_resolved_guardrails_to_metadata( metadata_variable_name: str, context: "PolicyMatchContext", policy_names: list[str] | None = None, - policies: dict[str, Any] | None = None, + policies: dict[str, "Policy"] | None = None, ) -> None: """Apply resolved guardrails and pipelines to request metadata.""" from litellm._logging import verbose_proxy_logger @@ -3093,7 +3095,7 @@ async def add_guardrails_from_policy_engine( request_body_names.append(item) # Resolve policy versions by ID from in-memory cache (populated by sync job; no DB in hot path) - merged_policies: Final[dict[str, Any]] = dict(registry.get_all_policies()) + merged_policies: Final[dict[str, Policy]] = dict(registry.get_all_policies()) fetched_policy_names: Final[list[str]] = [] for policy_id in request_body_version_ids: result = registry.get_policy_by_id_for_request(policy_id=policy_id) diff --git a/litellm/proxy/management_endpoints/auto_router_endpoints.py b/litellm/proxy/management_endpoints/auto_router_endpoints.py index b5533d548e5..21e652114bc 100644 --- a/litellm/proxy/management_endpoints/auto_router_endpoints.py +++ b/litellm/proxy/management_endpoints/auto_router_endpoints.py @@ -7,7 +7,7 @@ POST /auto_router/validate_complexity_router_config - Dry-run the complexity-rou from collections.abc import Mapping, Sequence from datetime import datetime, timedelta, timezone -from itertools import groupby +from itertools import chain, groupby from operator import attrgetter from types import MappingProxyType from typing import TYPE_CHECKING, Annotated, Final, Protocol @@ -58,10 +58,11 @@ from litellm.types.management_endpoints.auto_router_endpoints import ( ComplexityRouterConfigValidationResponse, RequestComplexityRouterConfig, ShadowEvalDirection, - ShadowEvalJobKeyResponse, ShadowEvalJobResponse, + ShadowEvalJobTargetResponse, ShadowEvalResult, ShadowEvalSlice, + ShadowEvalTargetType, StartShadowEvalRequest, ) @@ -104,10 +105,43 @@ class _VerificationTokenTable(Protocol): async def find_many(self, *, where: Mapping[str, object]) -> Sequence[_VerificationTokenRow]: ... +class _TeamRow(Protocol): + @property + def team_id(self) -> str: ... + + @property + def team_alias(self) -> str | None: ... + + +class _TeamRowsTable(Protocol): + async def find_many(self, *, where: Mapping[str, object]) -> Sequence[_TeamRow]: ... + + +class _UserRow(Protocol): + @property + def user_id(self) -> str: ... + + @property + def user_email(self) -> str | None: ... + + +class _UserRowsTable(Protocol): + async def find_many(self, *, where: Mapping[str, object]) -> Sequence[_UserRow]: ... + + class _ShadowEvalJobRow(Protocol): @property def id(self) -> str: ... + @property + def group_id(self) -> str: ... + + @property + def target_type(self) -> str: ... + + @property + def target_id(self) -> str: ... + class _ShadowEvalJobTable(Protocol): async def find_many(self, *, where: Mapping[str, object]) -> Sequence[_ShadowEvalJobRow]: ... @@ -138,6 +172,14 @@ def _verification_tokens(prisma_client: "PrismaClient") -> _VerificationTokenTab return prisma_client.db.litellm_verificationtoken +def _team_rows(prisma_client: "PrismaClient") -> _TeamRowsTable: + return prisma_client.db.litellm_teamtable + + +def _user_rows(prisma_client: "PrismaClient") -> _UserRowsTable: + return prisma_client.db.litellm_usertable + + def _shadow_eval_jobs(prisma_client: "PrismaClient") -> _ShadowEvalJobTable: return prisma_client.db.litellm_shadowevaljob @@ -791,7 +833,7 @@ def _judge_collisions_for_team( return tuple( (role, model) for role, model in ( - *_router_arm_models(llm_router, data.router_name), + *(arm for name in data.router_names for arm in _router_arm_models(llm_router, name)), *((("baseline", data.baseline_model),) if data.baseline_model is not None else ()), ) if judge & judge_target(llm_router, model, team_id).models @@ -836,7 +878,7 @@ def _validate_judge_is_not_a_candidate( def _is_unique_violation(error: Exception) -> bool: - """Whether a Prisma create failed on a unique index. One active job per key and + """Whether a Prisma create failed on a unique index. One active job per target and direction lives in a partial unique index (raw SQL in the migration; schema.prisma cannot express partial indexes), so the read-then-create check above it is advisory: two concurrent starts pass the read, and the loser must surface as the same 409 @@ -862,7 +904,7 @@ class _AttemptAggRow(BaseModel): _ATTEMPT_AGG_ROWS: Final = TypeAdapter(list[_AttemptAggRow]) -_ATTEMPT_AGG_SELECT: Final = """ +_ATTEMPT_AGG_COLUMNS: Final = """ COUNT(*)::int AS turn_count, COUNT(*) FILTER (WHERE outcome = 'real')::int AS real_wins, COUNT(*) FILTER (WHERE outcome = 'shadow')::int AS shadow_wins, @@ -871,21 +913,40 @@ _ATTEMPT_AGG_SELECT: Final = """ COALESCE(SUM(real_cost + real_classifier_cost) FILTER (WHERE real_cost IS NOT NULL AND NOT real_cache_hit), 0)::float AS real_spend, COALESCE(SUM(shadow_cost + shadow_classifier_cost) FILTER (WHERE real_cost IS NOT NULL AND NOT real_cache_hit), 0)::float AS shadow_spend, COUNT(*) FILTER (WHERE real_cache_hit)::int AS cache_hit_turns +""" + +_ATTEMPT_AGG_SELECT: Final = ( + _ATTEMPT_AGG_COLUMNS + + """ FROM "LiteLLM_ShadowEvalAttempt" WHERE job_id = ANY($1::text[]) AND outcome != 'error' GROUP BY 1 """ +) _ATTEMPT_AGG_BY_TIER_SQL: Final = "SELECT COALESCE(tier, 'UNCLASSIFIED') AS grp," + _ATTEMPT_AGG_SELECT _ATTEMPT_AGG_BY_MODEL_SQL: Final = "SELECT COALESCE(real_model, 'unknown') AS grp," + _ATTEMPT_AGG_SELECT _ATTEMPT_AGG_BY_LEG_SQL: Final = "SELECT job_id AS grp," + _ATTEMPT_AGG_SELECT +# Attempt rows from before arm stamping carry no router_name; they belong to the job's +# own router, which the join reads off the leg. +_ATTEMPT_AGG_BY_ROUTER_SQL: Final = ( + "SELECT COALESCE(a.router_name, j.router_name) AS grp," + + _ATTEMPT_AGG_COLUMNS + + """ +FROM "LiteLLM_ShadowEvalAttempt" a +JOIN "LiteLLM_ShadowEvalJob" j ON j.id = a.job_id +WHERE a.job_id = ANY($1::text[]) AND a.outcome != 'error' +GROUP BY 1 +""" +) + # These guards derive spend from attempt rows, the cross-pod authority; the sampler also # reads the live counter, so admission can stop before a row-based guard would fire (safe # direction, and mid-deploy rows from old pods price as judge-only until the deploy ends). _SWEEP_FINISHED_JOBS_SQL: Final = """ UPDATE "LiteLLM_ShadowEvalJob" j SET stopped_at = (NOW() AT TIME ZONE 'utc') -WHERE j.api_key_id = ANY($1::text[]) AND j.stopped_at IS NULL +WHERE j.target_type = $2 AND j.target_id = ANY($1::text[]) AND j.stopped_at IS NULL AND ( j.ends_at <= (NOW() AT TIME ZONE 'utc') OR (SELECT COUNT(*) FROM "LiteLLM_ShadowEvalAttempt" a WHERE a.job_id = j.id) >= j.max_turns @@ -966,10 +1027,10 @@ WHERE group_id IN ( ) """ -_LIST_LEGS_BY_KEY_SQL: Final = """ +_LIST_LEGS_BY_TARGET_SQL: Final = """ SELECT * FROM "LiteLLM_ShadowEvalJob" WHERE group_id IN ( - SELECT group_id FROM "LiteLLM_ShadowEvalJob" WHERE api_key_id = $2 + SELECT group_id FROM "LiteLLM_ShadowEvalJob" WHERE target_type = $2 AND target_id = $3 GROUP BY group_id ORDER BY MAX(created_at) DESC LIMIT $1::int ) """ @@ -1007,16 +1068,18 @@ def _slices(rows: Sequence[_AttemptAggRow]) -> tuple[ShadowEvalSlice, ...]: class _LegRow(BaseModel): """One LiteLLM_ShadowEvalJob row, validated off the untyped prisma record. A row is - one key's leg of a job; the legs of a job share group_id and identical config, written - together by one create_many. The API's job id is the group id, so leg ids never leave - the server (attempts reference them internally).""" + one target's leg of a job; the legs of a job share group_id and identical config, + written together by one create_many. The API's job id is the group id, so leg ids + never leave the server (attempts reference them internally).""" model_config = ConfigDict(from_attributes=True) id: str group_id: str - api_key_id: str + target_type: ShadowEvalTargetType + target_id: str router_name: str + router_names: tuple[str, ...] = () direction: ShadowEvalDirection baseline_model: str | None = None judge_model: str @@ -1028,6 +1091,12 @@ class _LegRow(BaseModel): stopped_at: datetime | None = None stopped_by: str | None = None + @property + def arm_router_names(self) -> tuple[str, ...]: + """The job's full router set; rows from before router_names existed hold it in + router_name alone. The one place that reading lives on the endpoint side.""" + return self.router_names or (self.router_name,) + @field_validator("created_at", "ends_at", "stopped_at") @classmethod def _as_aware_utc(cls, value: datetime | None) -> datetime | None: @@ -1068,18 +1137,19 @@ def _group_response( first: Final = legs[0] return ShadowEvalJobResponse( job_id=group_id, - keys=tuple( - ShadowEvalJobKeyResponse( - api_key_id=leg.api_key_id, + targets=tuple( + ShadowEvalJobTargetResponse( + target_type=leg.target_type, + target_id=leg.target_id, max_turns=leg.max_turns, max_budget=leg.max_budget, stopped_at=leg.stopped_at, attempt_count=stats.attempt_count if (stats := attempt_counts.get(leg.id)) else 0, spend=round(stats.spend, 6) if stats else 0.0, ) - for leg in sorted(legs, key=lambda leg: leg.api_key_id) + for leg in sorted(legs, key=lambda leg: (leg.target_type, leg.target_id)) ), - router_name=first.router_name, + router_names=first.arm_router_names, direction=first.direction, baseline_model=first.baseline_model, judge_model=first.judge_model, @@ -1090,34 +1160,85 @@ def _group_response( ) -_NO_KEY_LABELS: Final[tuple[str | None, str | None]] = (None, None) +_NO_TARGET_LABELS: Final[tuple[str | None, str | None]] = (None, None) -async def _with_key_labels( +def _target_labels( + key_rows: Sequence[_VerificationTokenRow], + team_rows: Sequence[_TeamRow], + user_rows: Sequence[_UserRow], +) -> Mapping[tuple[str, str], tuple[str | None, str | None]]: + """Display labels by (target_type, target_id): a key's (alias, masked name), a + team's (alias, None), a user's (email, None).""" + return MappingProxyType( + { # mutable-ok: MappingProxyType needs a dict to wrap + key: value + for key, value in chain( + ((("key", row.token), (row.key_alias, row.key_name)) for row in key_rows), + ((("team", row.team_id), (row.team_alias, None)) for row in team_rows), + ((("user", row.user_id), (row.user_email, None)) for row in user_rows), + ) + } + ) + + +def _target_ids_of(responses: Sequence[ShadowEvalJobResponse], target_type: ShadowEvalTargetType) -> tuple[str, ...]: + return tuple( + sorted( + frozenset( + target.target_id + for response in responses + for target in response.targets + if target.target_type == target_type + ) + ) + ) + + +async def _with_target_labels( prisma_client: "PrismaClient", responses: Sequence[ShadowEvalJobResponse] ) -> tuple[ShadowEvalJobResponse, ...]: - """Resolve every scoped key's hash to its alias and masked name in one batched read, - so the UI can say whose traffic a job shadows. Deleted keys resolve to None.""" + """Resolve every scoped target's id to a display label in one batched read per kind, + so the UI can say whose traffic a job shadows: a key's alias and masked name, a + team's alias, a user's email. Deleted targets resolve to None.""" if not responses: return () - tokens: Final = sorted(frozenset(key.api_key_id for response in responses for key in response.keys)) - key_rows: Final = await _verification_tokens(prisma_client).find_many( - where={"token": {"in": tokens}} # mutable-ok: Prisma filter + tokens: Final = _target_ids_of(responses, "key") + team_ids: Final = _target_ids_of(responses, "team") + user_ids: Final = _target_ids_of(responses, "user") + key_rows: Final = ( + await _verification_tokens(prisma_client).find_many( + where={"token": {"in": list(tokens)}} # mutable-ok: Prisma filter + ) + if tokens + else () ) - labels: Final[Mapping[str, tuple[str | None, str | None]]] = { - row.token: (row.key_alias, row.key_name) for row in key_rows or () - } + team_rows: Final = ( + await _team_rows(prisma_client).find_many( + where={"team_id": {"in": list(team_ids)}} # mutable-ok: Prisma filter + ) + if team_ids + else () + ) + user_rows: Final = ( + await _user_rows(prisma_client).find_many( + where={"user_id": {"in": list(user_ids)}} # mutable-ok: Prisma filter + ) + if user_ids + else () + ) + labels: Final = _target_labels(key_rows or (), team_rows or (), user_rows or ()) return tuple( response.model_copy( update={ # mutable-ok: pydantic update payload - "keys": tuple( - key.model_copy( + "targets": tuple( + target.model_copy( update={ # mutable-ok: pydantic update payload - "key_alias": labels.get(key.api_key_id, _NO_KEY_LABELS)[0], - "key_name": labels.get(key.api_key_id, _NO_KEY_LABELS)[1], + "target_alias": labels.get((target.target_type, target.target_id), _NO_TARGET_LABELS)[0], + "key_name": labels.get((target.target_type, target.target_id), _NO_TARGET_LABELS)[1], } ) - for key in response.keys + for target in response.targets ) } ) @@ -1125,29 +1246,40 @@ async def _with_key_labels( ) -async def _shadow_eval_results(prisma_client: "PrismaClient", legs: Sequence[_LegRow]) -> ShadowEvalResult | None: - """All three stratifications of one job's verdicts. Tier answers "where does the router - do well"; the model stratification groups by whichever model served the real arm, so it - answers "which of the models these keys use today would the router beat" forward, and - "for the turns the router sent to X, did X beat the baseline" in reverse; key answers - "which key's traffic does the router suit". Reads are bounded by the job's own attempts - (<= the sum of its keys' max_turns) via the job_id index.""" +async def _shadow_eval_results( + prisma_client: "PrismaClient", legs: Sequence[_LegRow] +) -> tuple[ShadowEvalResult | None, Mapping[tuple[str, str], ShadowEvalSlice]]: + """One job's stratified verdicts, plus each target's own slice keyed by the + (target_type, target_id) pair so a key, team, and user sharing an id can never + collapse into one entry. Tier answers "where does the router do well"; the model + stratification groups by whichever model served the real arm, so it answers "which + of the models these targets use today would the router beat" forward, and "for the + turns the router sent to X, did X beat the baseline" in reverse; the per-target + slices answer "which target's traffic does the router suit". Reads are bounded by + the job's own attempts (<= the sum of its targets' max_turns) via the job_id index.""" leg_ids: Final = [leg.id for leg in legs] # mutable-ok: query param by_tier: Final = _ATTEMPT_AGG_ROWS.validate_python( await _query_raw(prisma_client, _ATTEMPT_AGG_BY_TIER_SQL, leg_ids) or () ) if not by_tier: - return None + return None, MappingProxyType({}) by_model: Final = _ATTEMPT_AGG_ROWS.validate_python( await _query_raw(prisma_client, _ATTEMPT_AGG_BY_MODEL_SQL, leg_ids) or () ) - key_by_leg: Final = MappingProxyType({leg.id: leg.api_key_id for leg in legs}) + target_by_leg: Final = MappingProxyType({leg.id: (leg.target_type, leg.target_id) for leg in legs}) by_leg: Final = _ATTEMPT_AGG_ROWS.validate_python( await _query_raw(prisma_client, _ATTEMPT_AGG_BY_LEG_SQL, leg_ids) or () ) - by_key: Final = tuple( - row.model_copy(update={"grp": key_by_leg[row.grp]}) # mutable-ok: pydantic update payload - for row in by_leg + verdicts_by_target: Final[Mapping[tuple[str, str], ShadowEvalSlice]] = MappingProxyType( + { # mutable-ok: MappingProxyType needs a dict to wrap + target_by_leg[slice.group]: slice.model_copy( + update={"group": target_by_leg[slice.group][1]} # mutable-ok: pydantic update payload + ) + for slice in _slices(by_leg) + } + ) + by_router: Final = _ATTEMPT_AGG_ROWS.validate_python( + await _query_raw(prisma_client, _ATTEMPT_AGG_BY_ROUTER_SQL, leg_ids) or () ) total_turns: Final = sum(r.turn_count for r in by_tier) funnel_rows: Final = await _query_raw(prisma_client, _FUNNEL_TOTALS_SQL, leg_ids) @@ -1155,10 +1287,10 @@ async def _shadow_eval_results(prisma_client: "PrismaClient", legs: Sequence[_Le # Coverage only when EVERY leg has a funnel row: a partial seed (one leg's insert # failed) must read as unknown, not as job-level counts missing a leg's traffic. funnel: Final = counted if counted is not None and counted.legs_with_rows == len(leg_ids) else None - return ShadowEvalResult( + result: Final = ShadowEvalResult( by_tier=_slices(by_tier), by_current_model=_slices(by_model), - by_key=_slices(by_key), + by_router=_slices(by_router), overall_shadow_win_rate_pct=_pct_of(sum(r.shadow_wins for r in by_tier), total_turns), overall_tie_rate_pct=_pct_of(sum(r.ties for r in by_tier), total_turns), sampled_real_spend=sum(r.real_spend for r in by_tier), @@ -1168,6 +1300,7 @@ async def _shadow_eval_results(prisma_client: "PrismaClient", legs: Sequence[_Le shed_count=funnel.shed if funnel is not None else None, withheld_count=funnel.withheld if funnel is not None else None, ) + return result, verdicts_by_target @router.post( @@ -1182,59 +1315,126 @@ async def start_shadow_eval( user_api_key_dict: Annotated[UserAPIKeyAuth, Depends(user_api_key_auth)], ) -> ShadowEvalJobResponse: """ - Start a shadow eval: duplicate a sampled slice of one or more keys' live traffic against - a second arm, judge the two responses blind, and stratify win rates by tier, by the model - that served the real arm, and by key. + Start a shadow eval: duplicate a sampled slice of one or more targets' live traffic + against a second arm, judge the two responses blind, and stratify win rates by tier, + by the model that served the real arm, and by target. - A forward job answers whether the keys should adopt router_name: it samples the requests - the router did not serve and duplicates them through it. A reverse job answers whether a - key already on the router still gains from it: it samples the requests the router did - serve and duplicates them against baseline_model. A key can hold one active job per - direction, so both questions can run at once. + A target is a virtual key, a team, or a user. Team and user targets match on the + identity every request resolves to at auth time, so they cover JWT-authenticated + traffic, which presents no virtual key; a user target samples that user's traffic + across all their teams, whether it arrives on a JWT or a key they own. - Shadow responses are never served to users. Each key samples until its recorded eval - spend, the shadow and judge calls' own cost, reaches max_budget dollars, the job's - window ends, or the job is stopped, so one key running out of budget does not end - sampling for the others; sampling changes propagate to pods within about 10 seconds. - Shadow and judge calls bill to the shadowed key but are excluded from request counts - and auto-router adoption metrics. + A forward job answers whether the targets should adopt router_name: it samples the + requests the router did not serve and duplicates them through it. A reverse job + answers whether a target already on the router still gains from it: it samples the + requests the router did serve and duplicates them against baseline_model. A target + can hold one active job per direction, so both questions can run at once, and a + request matching several jobs' targets (say its key and its team) is sampled by + each, separately budgeted. + + Shadow responses are never served to users. Each target samples until its recorded + eval spend, the shadow and judge calls' own cost, reaches max_budget dollars, the + job's window ends, or the job is stopped, so one target running out of budget does + not end sampling for the others; sampling changes propagate to pods within about 10 + seconds. Shadow and judge calls bill to the sampled request's own identity but are + excluded from request counts and auto-router adoption metrics. """ from litellm.proxy.proxy_server import llm_router, prisma_client _require_admin_writer(user_api_key_dict, "start a shadow eval") if prisma_client is None: raise HTTPException(status_code=500, detail=CommonProxyErrors.db_not_connected_error.value) - if llm_router is None or not _is_configured_pre_routing_strategy(llm_router, data.router_name): - raise HTTPException(status_code=400, detail=f"'{data.router_name}' is not a configured auto-router") - token_rows: Final = await _verification_tokens(prisma_client).find_many( - where={"token": {"in": list(data.api_key_ids)}} # mutable-ok: Prisma filter + unconfigured: Final = tuple( + name + for name in data.router_names + if llm_router is None or not _is_configured_pre_routing_strategy(llm_router, name) ) - unknown: Final = tuple(sorted(frozenset(data.api_key_ids) - frozenset(row.token for row in token_rows or ()))) - if unknown: + if unconfigured: raise HTTPException( - status_code=400, - detail=( - f"api_key_ids not on this proxy: {', '.join(unknown)}; pass each key's token hash, " - "the value the key list and key info endpoints report" - ), + status_code=400, detail=f"Not a configured auto-router: {', '.join(repr(n) for n in unconfigured)}" ) + token_rows: Final = ( + await _verification_tokens(prisma_client).find_many( + where={"token": {"in": list(data.api_key_ids)}} # mutable-ok: Prisma filter + ) + if data.api_key_ids + else () + ) + team_rows: Final = ( + await _team_rows(prisma_client).find_many( + where={"team_id": {"in": list(data.team_ids)}} # mutable-ok: Prisma filter + ) + if data.team_ids + else () + ) + user_rows: Final = ( + await _user_rows(prisma_client).find_many( + where={"user_id": {"in": list(data.user_ids)}} # mutable-ok: Prisma filter + ) + if data.user_ids + else () + ) + unknown_keys: Final = sorted(frozenset(data.api_key_ids) - frozenset(row.token for row in token_rows or ())) + unknown_teams: Final = sorted(frozenset(data.team_ids) - frozenset(row.team_id for row in team_rows or ())) + unknown_users: Final = sorted(frozenset(data.user_ids) - frozenset(row.user_id for row in user_rows or ())) + unknown_parts: Final = tuple( + part + for part in ( + ( + f"api_key_ids not on this proxy: {', '.join(unknown_keys)}; pass each key's token hash, " + "the value the key list and key info endpoints report" + ) + if unknown_keys + else None, + f"team_ids not on this proxy: {', '.join(unknown_teams)}" if unknown_teams else None, + f"user_ids not on this proxy: {', '.join(unknown_users)}" if unknown_users else None, + ) + if part is not None + ) + if unknown_parts: + raise HTTPException(status_code=400, detail=". ".join(unknown_parts)) # Every model check below runs once per team the job samples for, since that is the # identity the shadow and judge calls carry and therefore what the router selects on. - team_ids: Final = tuple(dict.fromkeys(row.team_id for row in token_rows or ())) + # A user target's traffic can span teams, so it validates unscoped (None); each + # sampled attempt still resolves the judge under its own request's team at eval time. + team_ids: Final = tuple( + dict.fromkeys( + ( + *(row.team_id for row in token_rows or ()), + *data.team_ids, + *((None,) if data.user_ids else ()), + ) + ) + ) _validate_plain_model(llm_router, data.judge_model, "judge_model", team_ids) if data.baseline_model is not None: _validate_plain_model(llm_router, data.baseline_model, "baseline_model", team_ids) _validate_judge_is_not_a_candidate(llm_router, data, team_ids) + requested_targets: Final[tuple[tuple[ShadowEvalTargetType, str], ...]] = ( + *(("key", key) for key in data.api_key_ids), + *(("team", team) for team in data.team_ids), + *(("user", user) for user in data.user_ids), + ) + requested_by_type: Final[tuple[tuple[ShadowEvalTargetType, tuple[str, ...]], ...]] = tuple( + (target_type, ids) + for target_type, ids in (("key", data.api_key_ids), ("team", data.team_ids), ("user", data.user_ids)) + if ids + ) # A job whose window passed or whose budget ran out stopped sampling on its own, - # but its legs still hold their slots in the per-key, per-direction partial unique index - # until stamped; free them so a new eval can start. Sweeping both directions is deliberate. - requested: Final = list(data.api_key_ids) # mutable-ok: query param - await prisma_client.db.execute_raw(_SWEEP_FINISHED_JOBS_SQL, requested) + # but its legs still hold their slots in the per-target, per-direction partial unique + # index until stamped; free them so a new eval can start. Sweeping both directions is + # deliberate. Sweep and claim filter on exact (target_type, id) pairs so a team id + # that happens to equal a key hash never matches the other kind's slot. + for target_type, ids in requested_by_type: + await prisma_client.db.execute_raw(_SWEEP_FINISHED_JOBS_SQL, list(ids), target_type) # mutable-ok: query param claimed: Final = await _shadow_eval_jobs(prisma_client).find_many( where={ # mutable-ok: Prisma filter - "api_key_id": {"in": requested}, # mutable-ok: Prisma filter + "OR": [ # mutable-ok: Prisma filter + {"target_type": target_type, "target_id": {"in": list(ids)}} # mutable-ok: Prisma filter + for target_type, ids in requested_by_type + ], "direction": data.direction, "stopped_at": None, }, @@ -1244,7 +1444,7 @@ async def start_shadow_eval( status_code=409, detail=( f"Already in an active {data.direction} shadow eval job: " - + ", ".join(sorted(f"{row.api_key_id} (job {row.group_id})" for row in claimed)) + + ", ".join(sorted(f"{row.target_type} {row.target_id} (job {row.group_id})" for row in claimed)) + ". Stop it first." ), ) @@ -1253,7 +1453,9 @@ async def start_shadow_eval( ends_at: Final = now + timedelta(days=data.duration_days) shared_config: Final = { # mutable-ok: Prisma payload "group_id": group_id, - "router_name": data.router_name, + # a pre-router_names pod samples router_name alone, so it must be a real arm + "router_name": data.router_names[0], + "router_names": list(data.router_names), # mutable-ok: Prisma payload "direction": data.direction, "baseline_model": data.baseline_model, "judge_model": data.judge_model, @@ -1268,10 +1470,16 @@ async def start_shadow_eval( # Leg ids are minted here rather than by the DB default so the funnel seed below # writes from the same values with no read-back, which a lagging read replica # (DATABASE_URL_READ_REPLICA) could otherwise return empty. - leg_ids: Final = tuple(str(uuid4()) for _ in data.api_key_ids) + leg_ids: Final = tuple(str(uuid4()) for _ in requested_targets) await _shadow_eval_jobs(prisma_client).create_many( data=[ # mutable-ok: Prisma payload - {**shared_config, "id": leg_id, "api_key_id": key} for leg_id, key in zip(leg_ids, data.api_key_ids) + { # mutable-ok: Prisma payload + **shared_config, + "id": leg_id, + "target_type": target_type, + "target_id": target_id, + } # mutable-ok: Prisma payload + for leg_id, (target_type, target_id) in zip(leg_ids, requested_targets) ] ) except Exception as e: @@ -1280,7 +1488,8 @@ async def start_shadow_eval( raise HTTPException( status_code=409, detail=( - f"A requested key was claimed by another {data.direction} shadow eval job concurrently. Stop it first." + f"A requested target was claimed by another {data.direction} shadow eval job concurrently. " + "Stop it first." ), ) from e # Seed a zero funnel row per leg NOW: a fully covered job never skips a request, so @@ -1293,20 +1502,21 @@ async def start_shadow_eval( ) except Exception as seed_err: # noqa: BLE001 # coverage is advisory; the job must still start verbose_proxy_logger.error("shadow_eval: funnel seed failed for job %s: %s", group_id, seed_err) - labels: Final = MappingProxyType({row.token: row for row in token_rows}) + labels: Final = _target_labels(token_rows or (), team_rows or (), user_rows or ()) return ShadowEvalJobResponse( job_id=group_id, - keys=tuple( - ShadowEvalJobKeyResponse( - api_key_id=api_key_id, + targets=tuple( + ShadowEvalJobTargetResponse( + target_type=target_type, + target_id=target_id, max_turns=SHADOW_EVAL_TURN_VALVE, max_budget=data.max_budget, - key_alias=labels[api_key_id].key_alias, - key_name=labels[api_key_id].key_name, + target_alias=labels.get((target_type, target_id), _NO_TARGET_LABELS)[0], + key_name=labels.get((target_type, target_id), _NO_TARGET_LABELS)[1], ) - for api_key_id in sorted(data.api_key_ids) + for target_type, target_id in sorted(requested_targets) ), - router_name=data.router_name, + router_names=data.router_names, direction=data.direction, baseline_model=data.baseline_model, judge_model=data.judge_model, @@ -1324,22 +1534,29 @@ async def start_shadow_eval( ) async def list_shadow_eval_jobs( user_api_key_dict: Annotated[UserAPIKeyAuth, Depends(user_api_key_auth)], - api_key_id: Annotated[ - str | None, Query(description="Filter to jobs that shadow this key, alone or alongside others") + target_type: Annotated[ + ShadowEvalTargetType | None, Query(description="Kind of target to filter on; requires target_id") + ] = None, + target_id: Annotated[ + str | None, Query(description="Filter to jobs that shadow this target, alone or alongside others") ] = None, limit: Annotated[int, Query(ge=1, le=200, description="Newest jobs to return")] = 50, ) -> tuple[ShadowEvalJobResponse, ...]: - """List shadow eval jobs, newest first, each key with its attempt count so status is - accurate. Judged counts, spend, and results ride the detail endpoint only.""" + """List shadow eval jobs, newest first, each target with its attempt count so status + is accurate. Judged counts, spend, and results ride the detail endpoint only.""" from litellm.proxy.proxy_server import prisma_client _require_admin_viewer(user_api_key_dict, "view shadow evals") if prisma_client is None: raise HTTPException(status_code=500, detail=CommonProxyErrors.db_not_connected_error.value) + filter_type: Final = target_type if isinstance(target_type, str) else None + filter_id: Final = target_id if isinstance(target_id, str) else None + if (filter_type is None) != (filter_id is None): + raise HTTPException(status_code=400, detail="target_type and target_id filter together; pass both or neither") legs: Final = _LEG_ROWS.validate_python( ( - await _query_raw(prisma_client, _LIST_LEGS_BY_KEY_SQL, limit, api_key_id) - if api_key_id + await _query_raw(prisma_client, _LIST_LEGS_BY_TARGET_SQL, limit, filter_type, filter_id) + if filter_type and filter_id else await _query_raw(prisma_client, _LIST_LEGS_SQL, limit) ) or () @@ -1354,7 +1571,7 @@ async def list_shadow_eval_jobs( by_group, key=lambda group_id: max(leg.created_at for leg in by_group[group_id]), reverse=True ) counts: Final = await _leg_attempt_counts(prisma_client, legs) - return await _with_key_labels( + return await _with_target_labels( prisma_client, tuple(_group_response(group_id, by_group[group_id], counts) for group_id in newest_first) ) @@ -1391,16 +1608,25 @@ async def get_shadow_eval_job( where={"job_id": {"in": leg_ids}, "outcome": "error"}, # mutable-ok: Prisma filter order={"created_at": "desc"}, # mutable-ok: Prisma order ) - labeled: Final = await _with_key_labels( + labeled: Final = await _with_target_labels( prisma_client, (_group_response(job_id, legs, await _leg_attempt_counts(prisma_client, legs)),) ) + results, verdicts_by_target = await _shadow_eval_results(prisma_client, legs) return labeled[0].model_copy( update={ # mutable-ok: pydantic update payload "judged_count": totals[0].judged_count if totals else 0, "error_count": totals[0].error_count if totals else 0, "judge_spend": round(totals[0].judge_spend, 6) if totals else 0.0, "last_error": latest_error.error if latest_error else None, - "results": await _shadow_eval_results(prisma_client, legs), + "results": results, + "targets": tuple( + target.model_copy( + update={ # mutable-ok: pydantic update payload + "verdicts": verdicts_by_target.get((target.target_type, target.target_id)) + } + ) + for target in labeled[0].targets + ), } ) @@ -1415,8 +1641,8 @@ async def stop_shadow_eval_job( job_id: str, user_api_key_dict: Annotated[UserAPIKeyAuth, Depends(user_api_key_auth)], ) -> ShadowEvalJobResponse: - """Stop an active shadow eval job, every key it scopes at once. Attempts are kept; - sampling halts within ~10s. Keys that already stopped on their own budget keep the + """Stop an active shadow eval job, every target it scopes at once. Attempts are kept; + sampling halts within ~10s. Targets that already stopped on their own budget keep the stopped_at they earned. The statement is the whole state machine: it claims the job only while a leg still samples inside the window with no stop recorded, so a racing operator, a same-instant budget spend, and a repeat stop all read the same 400 with @@ -1443,5 +1669,5 @@ async def stop_shadow_eval_job( current: Final = _group_response(job_id, legs, counts) if claimed == 0: raise HTTPException(status_code=400, detail=f"Job {job_id} is already {current.status}") - labeled: Final = await _with_key_labels(prisma_client, (current,)) + labeled: Final = await _with_target_labels(prisma_client, (current,)) return labeled[0] diff --git a/litellm/proxy/management_endpoints/customer_endpoints.py b/litellm/proxy/management_endpoints/customer_endpoints.py index 9ef3d2defef..d2d87331d55 100644 --- a/litellm/proxy/management_endpoints/customer_endpoints.py +++ b/litellm/proxy/management_endpoints/customer_endpoints.py @@ -626,11 +626,7 @@ async def update_end_user( # get non default values for key non_default_values: Final = dict[str, object]() for k, v in data_json.items(): - if v is not None and v not in ( - [], - {}, - 0, - ): # models default to [], spend defaults to 0, we should not reset these values + if v is not None and ((isinstance(v, bool) and k in data.fields_set()) or v not in ([], {}, 0)): non_default_values[k] = v ## Get end user table data ## diff --git a/litellm/proxy/management_endpoints/key_management_endpoints.py b/litellm/proxy/management_endpoints/key_management_endpoints.py index 52a192f8537..c3403cf477c 100644 --- a/litellm/proxy/management_endpoints/key_management_endpoints.py +++ b/litellm/proxy/management_endpoints/key_management_endpoints.py @@ -19,13 +19,14 @@ import re import secrets import traceback from collections.abc import Awaitable, Callable, Iterator, Mapping, Sequence -from contextlib import AbstractAsyncContextManager from datetime import datetime, timedelta, timezone +from types import MappingProxyType from typing import TYPE_CHECKING, Any, Final, Literal, Optional, Protocol, TypeVar, cast import fastapi import yaml from fastapi import APIRouter, Depends, Header, HTTPException, Query, Request, status +from typing_extensions import ReadOnly, TypedDict import litellm from litellm._logging import verbose_proxy_logger @@ -111,6 +112,7 @@ from litellm.proxy.management_helpers.team_member_permission_checks import ( TeamMemberPermissionChecks, ) from litellm.proxy.management_helpers.utils import management_endpoint_wrapper +from litellm.proxy.spend_tracking.budget_reservation import get_budget_window_start from litellm.proxy.spend_tracking.spend_tracking_utils import _is_master_key from litellm.proxy.ui_crud_endpoints.proxy_setting_endpoints import ( get_ui_settings_cached, @@ -155,6 +157,7 @@ from litellm.types.utils import ( ) if TYPE_CHECKING: + import prisma from prisma import Prisma from prisma import models as prisma_models @@ -182,6 +185,14 @@ class _TxTables(Protocol): litellm_proxymodeltable: TableActions[object] +class _ModelParamsUpdate(TypedDict): + litellm_params: ReadOnly["prisma.Json"] + + +class _ModelRowWhere(TypedDict): + model_id: ReadOnly[str] + + class _ConfigTableActions(Protocol): """Config table surface this module needs; the shared repository seam exposes no ``update``.""" @@ -273,12 +284,6 @@ def _env_vars_param_value(param: _EnvVarsParam) -> Mapping[str, str] | None: return param.param_value -def _tx_tables_context( - open_tx: Callable[[], AbstractAsyncContextManager[_TxTables]], -) -> AbstractAsyncContextManager[_TxTables]: - return open_tx() - - async def _check_custom_key_allowed(custom_key_value: str | None) -> None: """Raise 403 if custom API keys are disabled and a custom key was provided.""" if custom_key_value is None: @@ -733,6 +738,45 @@ def _check_allowed_routes_caller_permission( ) +_READ_ONLY_ALLOWED_ROUTES_PRESET: Final = frozenset(("info_routes",)) + + +def _is_safe_preset_route_transition( + incoming_allowed_routes: Sequence[str] | None, + existing_allowed_routes: Sequence[str] | None, +) -> bool: + """ + True when every route on BOTH sides is a safe `key_type` preset bucket + (empty = full access, which non-admins already get from a default + `/key/generate`), with one carve-out: a read-only (`info_routes`) key + stays read-only, so widening it needs an admin. Requiring the existing + side to be a safe preset keeps an owner from clearing an admin-set + custom route restriction (LIT-4139). + """ + incoming: Final = frozenset(incoming_allowed_routes or ()) + existing: Final = frozenset(existing_allowed_routes or ()) + if not (incoming | existing) <= _NON_ADMIN_SAFE_ALLOWED_ROUTES_PRESETS: + return False + return existing != _READ_ONLY_ALLOWED_ROUTES_PRESET or incoming == existing + + +def _enforce_allowed_routes_update_permission( + data: UpdateKeyRequest, + existing_key_row: LiteLLM_VerificationToken, + user_api_key_dict: UserAPIKeyAuth, +) -> None: + if _is_safe_preset_route_transition( + incoming_allowed_routes=data.allowed_routes, + existing_allowed_routes=existing_key_row.allowed_routes, + ): + return + _check_allowed_routes_caller_permission( + allowed_routes=data.allowed_routes, + user_api_key_dict=user_api_key_dict, + allowed_routes_was_provided="allowed_routes" in data.model_fields_set, + ) + + def _check_permissions_caller_permission( data: GenerateRequestBase, user_api_key_dict: UserAPIKeyAuth, @@ -2517,26 +2561,34 @@ async def _validate_mcp_servers_for_key_update( return normalized_object_permission +def _require_prisma_client(prisma_client: PrismaClient | None) -> PrismaClient: + if prisma_client is None: + raise HTTPException(status_code=500, detail={"error": "Database not connected"}) + return prisma_client + + async def _validate_update_key_data( data: UpdateKeyRequest, existing_key_row: LiteLLM_VerificationToken, user_api_key_dict: UserAPIKeyAuth, llm_router: Router | None, premium_user: bool, - prisma_client: Any, + prisma_client: PrismaClient | None, user_api_key_cache: UserApiKeyCache, ) -> None: """Validate permissions and constraints for key update.""" + checked_prisma_client: Final = _require_prisma_client(prisma_client) + # Reject NaN/±inf spend before it can reach the DB / spend counter. validate_finite_spend(data.spend) validate_budget_duration(data.budget_duration) _is_proxy_admin: Final = user_api_key_dict.user_role == LitellmUserRoles.PROXY_ADMIN.value - _check_allowed_routes_caller_permission( - allowed_routes=data.allowed_routes, + _enforce_allowed_routes_update_permission( + data=data, + existing_key_row=existing_key_row, user_api_key_dict=user_api_key_dict, - allowed_routes_was_provided="allowed_routes" in data.model_fields_set, ) _check_passthrough_routes_caller_permission( data=data, @@ -2564,7 +2616,7 @@ async def _validate_update_key_data( await TeamMemberPermissionChecks.can_team_member_execute_key_management_endpoint( user_api_key_dict=user_api_key_dict, route=KeyManagementRoutes.KEY_UPDATE, - prisma_client=prisma_client, + prisma_client=checked_prisma_client, existing_key_row=existing_key_row, user_api_key_cache=user_api_key_cache, ) @@ -2645,12 +2697,12 @@ async def _validate_update_key_data( # _check_key_admin_access that would otherwise require team/org admin status. _key_is_team_key: Final = getattr(existing_key_row, "team_id", None) is not None can_skip_admin_check: Final = (caller_is_creator or _key_is_team_key) and not _is_budget_change - if (not _is_proxy_admin) and prisma_client is not None and not can_skip_admin_check: + if (not _is_proxy_admin) and not can_skip_admin_check: hashed_key: Final = existing_key_row.token await _check_key_admin_access( user_api_key_dict=user_api_key_dict, hashed_token=hashed_key, - prisma_client=prisma_client, + prisma_client=checked_prisma_client, user_api_key_cache=user_api_key_cache, route=("/key/update (max_budget/spend)" if _is_budget_change else "/key/update"), ) @@ -2661,7 +2713,7 @@ async def _validate_update_key_data( if _team_id_to_check is not None: team_obj = await get_team_object( team_id=_team_id_to_check, - prisma_client=prisma_client, + prisma_client=checked_prisma_client, user_api_key_cache=user_api_key_cache, check_db_only=True, ) @@ -2677,7 +2729,7 @@ async def _validate_update_key_data( await _check_team_key_limits( team_table=team_obj, data=data, - prisma_client=prisma_client, + prisma_client=checked_prisma_client, ) TeamMemberPermissionChecks.enforce_member_can_assign_access_groups( @@ -2692,7 +2744,7 @@ async def _validate_update_key_data( await _check_project_key_limits( project_id=_project_id_to_check, data=data, - prisma_client=prisma_client, + prisma_client=checked_prisma_client, user_api_key_cache=user_api_key_cache, ) @@ -2707,7 +2759,7 @@ async def _validate_update_key_data( await _validate_caller_can_assign_key_org( user_api_key_dict=user_api_key_dict, organization_id=data.organization_id, - prisma_client=prisma_client, + prisma_client=checked_prisma_client, ) # Check org key limits only when throughput-related fields or organization_id change @@ -2723,7 +2775,7 @@ async def _validate_update_key_data( org_table: Final = await get_org_object( org_id=_org_id_to_check, user_api_key_cache=user_api_key_cache, - prisma_client=prisma_client, + prisma_client=checked_prisma_client, ) if org_table is None: raise HTTPException( @@ -2733,7 +2785,7 @@ async def _validate_update_key_data( await _check_org_key_limits( org_table=org_table, data=data, - prisma_client=prisma_client, + prisma_client=checked_prisma_client, ) # if team change - check if this is possible @@ -2763,7 +2815,7 @@ async def _validate_update_key_data( data=data, team_obj=team_obj, existing_key_row=existing_key_row, - prisma_client=prisma_client, + prisma_client=checked_prisma_client, user_api_key_cache=user_api_key_cache, is_proxy_admin=_is_proxy_admin, ) @@ -3575,6 +3627,63 @@ async def _build_model_max_budget_usage( ) +def _window_max_budget(window: Mapping[str, object]) -> float | None: + """A window's max_budget as a float; None when absent or unparseable.""" + value: Final = window.get("max_budget") + if not isinstance(value, (int, float, str)): + return None + try: + return float(value) + except ValueError: + return None + + +async def _budget_window_usage( + window: Mapping[str, object], api_key_hash: str +) -> tuple[str, Mapping[str, object]] | None: + """ + (budget_duration, usage entry) for one budget window; None when the window + has no budget_duration to key it by. + + Reads the same cross-pod counter (spend:key:{hashed_token}:window:{budget_duration}) + that _virtual_key_multi_budget_check enforces against, passing the same + window_duration + window_start so a stale-low counter is re-checked against + the LiteLLM_BudgetWindowSpend row instead of a spend-log aggregate. + """ + from litellm.proxy.proxy_server import get_current_spend + + duration: Final = window.get("budget_duration") + if not isinstance(duration, str) or not duration: + return None + spend: Final = await get_current_spend( + counter_key=f"spend:key:{api_key_hash}:window:{duration}", + fallback_spend=0.0, + max_budget=_window_max_budget(window), + window_entity_type="Key", + window_entity_id=api_key_hash, + window_duration=duration, + window_start=get_budget_window_start(window), + ) + return duration, MappingProxyType({"current_spend": round(spend, 4)}) + + +async def _build_budget_limits_usage( + budget_limits: Sequence[object] | str | None, api_key_hash: str +) -> Mapping[str, Mapping[str, object]] | None: + """ + Current-window spend per budget window, keyed by budget_duration, reported + next to the stored budget_limits (which is returned untouched). None when + the key has no windows, so the field only appears on keys that have them. + """ + windows: Final = _budget_limit_windows(budget_limits) + if not windows: + return None + usages: Final = await asyncio.gather( + *(_budget_window_usage(window=window, api_key_hash=api_key_hash) for window in windows) + ) + return MappingProxyType({duration: usage for duration, usage in (u for u in usages if u is not None)}) + + @router.post( "/v2/key/info", tags=["key management"], @@ -3617,7 +3726,6 @@ async def info_key_fn_v2( status_code=status.HTTP_422_UNPROCESSABLE_ENTITY, detail={"message": "Malformed request. No keys passed in."}, ) - # Resolve key_aliases to tokens so we never pass token=None (unbounded query) tokens_to_query: Final = list(data.keys) if data.keys else [] if data.key_aliases: @@ -3659,6 +3767,13 @@ async def info_key_fn_v2( model_max_budget=model_max_budget, user_api_key_cache=model_max_budget_limiter.dual_cache, ) + if k_token_hash: + budget_limits_usage = await _build_budget_limits_usage( + budget_limits=k_dict.get("budget_limits"), + api_key_hash=k_token_hash, + ) + if budget_limits_usage is not None: + k_dict["budget_limits_usage"] = budget_limits_usage filtered_key_info.append(k_dict) return {"key": data.keys, "info": filtered_key_info} @@ -3695,6 +3810,10 @@ async def info_key_fn( - model_max_budget: dict - Per-model budgets, e.g. {"gpt-4": {"budget_limit": 0.0005, "time_period": "30d"}} - model_max_budget_usage: dict | None - Current-window spend per model, present only when the key has per-model budgets + - budget_limits: list | None - Concurrent budget windows, exactly as stored + - budget_limits_usage: dict | None - Current-window spend per budget window, e.g. + {"1h": {"current_spend": 0.0009}}, present only when the key has budget windows + (read from the same cross-pod spend counter the budget enforcement uses) - models: list - Model_name's the key is allowed to call - tpm_limit / rpm_limit: int | None - Tokens and requests per minute limits - metadata: dict - Metadata for the key, e.g. {"team": "core-infra"} @@ -3762,7 +3881,7 @@ async def info_key_fn( except Exception: # if using pydantic v1 key_info = key_info.dict() # pyright: ignore[reportDeprecated] # deliberate pydantic v1 fallback - key_token_hash: Final = key_info.pop("token") + key_token_hash: Final[str | None] = key_info.pop("token") model_max_budget = key_info.get("model_max_budget") or {} budget_table: Final = key_info.get("litellm_budget_table") or {} @@ -3774,6 +3893,12 @@ async def info_key_fn( model_max_budget=model_max_budget, user_api_key_cache=model_max_budget_limiter.dual_cache, ) + budget_limits_usage: Final = await _build_budget_limits_usage( + budget_limits=key_info.get("budget_limits"), + api_key_hash=key_token_hash, + ) + if budget_limits_usage is not None: + key_info["budget_limits_usage"] = budget_limits_usage # Attach object_permission if object_permission_id is set key_info = await attach_object_permission_to_dict(key_info, prisma_client) @@ -4484,27 +4609,29 @@ async def _rotate_master_key( if models: decrypted_models: Final = proxy_config.decrypt_model_list_from_db(new_models=models) verbose_proxy_logger.debug("ABLE TO DECRYPT MODELS - len(decrypted_models): %s", len(decrypted_models)) - new_models: Final[list[dict[str, object]]] = [] - for model in decrypted_models: - new_model = await _add_model_to_db( - model_params=Deployment(**model), - user_api_key_dict=user_api_key_dict, - prisma_client=prisma_client, - new_encryption_key=new_master_key, - should_create_model_in_db=False, - ) - if new_model: - _dumped = dict[str, object](_as_object_dict(new_model.model_dump(exclude_none=True))) - _dumped["litellm_params"] = prisma.Json(_dumped["litellm_params"]) - _dumped["model_info"] = prisma.Json(_dumped["model_info"]) - new_models.append(_dumped) - verbose_proxy_logger.debug("Resetting proxy model table") - async with _tx_tables_context(prisma_client.db.tx) as tx: - await tx.litellm_proxymodeltable.delete_many() - verbose_proxy_logger.debug("Creating %s models", len(new_models)) - await tx.litellm_proxymodeltable.create_many( - data=new_models, - ) + reencrypted_models: Final = tuple( + [ + reencrypted + for model in decrypted_models + if ( + reencrypted := await _add_model_to_db( + model_params=Deployment(**model), + user_api_key_dict=user_api_key_dict, + prisma_client=prisma_client, + new_encryption_key=new_master_key, + should_create_model_in_db=False, + ) + ) + ] + ) + verbose_proxy_logger.debug("Re-encrypting litellm_params on %s model rows", len(reencrypted_models)) + async with prisma_client.db.tx(timeout=timedelta(minutes=2)) as tx_ctx: + tx: Final[_TxTables] = tx_ctx + for reencrypted_model in reencrypted_models: + await tx.litellm_proxymodeltable.update_many( + data=_ModelParamsUpdate(litellm_params=prisma.Json(reencrypted_model.litellm_params)), + where=_ModelRowWhere(model_id=reencrypted_model.model_id), + ) await publish_config_change(redis_cache=coordination_redis_cache(), object_type="litellm_proxymodeltable") # 3. process config table try: @@ -5216,7 +5343,7 @@ def _validate_reset_spend_value(reset_to: object, key_in_db: LiteLLM_Verificatio max_budget = key_in_db.max_budget if key_in_db.litellm_budget_table is not None: - budget_max_budget: Final = getattr(key_in_db.litellm_budget_table, "max_budget", None) + budget_max_budget: Final[float | None] = getattr(key_in_db.litellm_budget_table, "max_budget", None) if budget_max_budget is not None: if max_budget is None or budget_max_budget < max_budget: max_budget = budget_max_budget diff --git a/litellm/proxy/management_endpoints/model_management_endpoints.py b/litellm/proxy/management_endpoints/model_management_endpoints.py index 012aec38458..14d2332a7eb 100644 --- a/litellm/proxy/management_endpoints/model_management_endpoints.py +++ b/litellm/proxy/management_endpoints/model_management_endpoints.py @@ -543,7 +543,7 @@ def update_db_model(db_model: Deployment, updated_patch: updateDeployment) -> Pr _raise_if_ptu_cost_attribution_disabled(updated_patch.model_info.model_dump(exclude_none=True)) merged_model_name: Final = updated_patch.model_name or db_model.model_name merged_litellm_params: Final = db_model.litellm_params.model_dump(exclude_none=True) - merged_model_info: Final = db_model.model_info.model_dump(exclude_none=True) + merged_model_info: Final[dict[str, object]] = db_model.model_info.model_dump(exclude_none=True) # update litellm params if updated_patch.litellm_params: @@ -1982,7 +1982,7 @@ async def update_model( ### MERGE WITH EXISTING DATA ### merged_dictionary: Final = {} - _mp: Final = model_params.litellm_params.dict() + _mp: Final[dict[str, object]] = model_params.litellm_params.dict() for key, value in _mp.items(): if value is not None: diff --git a/litellm/proxy/management_endpoints/organization_endpoints.py b/litellm/proxy/management_endpoints/organization_endpoints.py index 9198aa35f3f..5e38a016099 100644 --- a/litellm/proxy/management_endpoints/organization_endpoints.py +++ b/litellm/proxy/management_endpoints/organization_endpoints.py @@ -487,12 +487,11 @@ async def new_organization( for m in data.models: await can_user_call_model(m, llm_router=llm_router, user_object=user_object_correct_type) - organization_row: Final = LiteLLM_OrganizationTable( - **data.json(exclude_none=True), - object_permission_id=object_permission_id, - created_by=user_api_key_dict.user_id or litellm_proxy_admin_name, - updated_by=user_api_key_dict.user_id or litellm_proxy_admin_name, - ) + organization_payload: Final = _STR_OBJECT_DICT_ADAPTER.validate_python(data.json(exclude_none=True)) + organization_payload["object_permission_id"] = object_permission_id + organization_payload["created_by"] = user_api_key_dict.user_id or litellm_proxy_admin_name + organization_payload["updated_by"] = user_api_key_dict.user_id or litellm_proxy_admin_name + organization_row: Final = LiteLLM_OrganizationTable.model_validate(organization_payload) for field in LiteLLM_ManagementEndpoint_MetadataFields: if getattr(data, field, None) is not None: @@ -644,7 +643,7 @@ async def update_organization( ) # Transform UI payload to expected format - raw_data: Final = await request.json() + raw_data: Final[dict[str, object]] = await request.json() raw_data_with_flat_budget_fields: Final = handle_nested_budget_structure_in_organization_update_request(raw_data) # Create validated data model @@ -691,7 +690,7 @@ async def update_organization( # Merge metadata from existing organization with updated metadata if updated_organization_row_json.get("metadata") is not None: existing_metadata: Final = existing_organization_row.metadata or {} - updated_metadata: Final = updated_organization_row_json.get("metadata", {}) + updated_metadata: Final[dict[str, object]] = updated_organization_row_json.get("metadata", {}) merged_metadata: Final[Mapping[str, object]] = _update_dictionary( existing_dict=cast( # cast-ok: prisma de-serializes a Json column to the plain python dict it stores "dict[str, object]", existing_metadata diff --git a/litellm/proxy/management_endpoints/policy_endpoints/endpoints.py b/litellm/proxy/management_endpoints/policy_endpoints/endpoints.py index 108e6a7b47d..f58f3722741 100644 --- a/litellm/proxy/management_endpoints/policy_endpoints/endpoints.py +++ b/litellm/proxy/management_endpoints/policy_endpoints/endpoints.py @@ -13,7 +13,7 @@ import copy import json import os from collections.abc import AsyncIterator -from typing import TYPE_CHECKING, Any, Final, Literal, cast +from typing import TYPE_CHECKING, Final, Literal, cast from fastapi import APIRouter, Depends, HTTPException, Request from fastapi.responses import Response, StreamingResponse @@ -90,7 +90,7 @@ class _ApplyPoliciesResultBase(TypedDict): class ApplyPoliciesResult(_ApplyPoliciesResultBase, total=False): """Result of apply_policies. agent_response set when agent_id provided.""" - agent_response: Any + agent_response: object class _ApplyPoliciesPerItemResultBase(TypedDict): @@ -103,7 +103,7 @@ class _ApplyPoliciesPerItemResultBase(TypedDict): class ApplyPoliciesPerItemResult(_ApplyPoliciesPerItemResultBase, total=False): """Result for one input when using inputs_list. agent_response set when agent_id provided.""" - agent_response: Any + agent_response: object class ApplyPoliciesListResult(TypedDict): @@ -295,8 +295,8 @@ async def test_policies_and_guardrails( from litellm.proxy.proxy_server import chat_completion, proxy_logging_obj from litellm.proxy.utils import handle_exception_on_proxy - def _serialize_chat_response(response: Any) -> Any: - if hasattr(response, "model_dump"): + def _serialize_chat_response(response: object) -> object: + if isinstance(response, BaseModel): return response.model_dump(exclude_unset=True) if isinstance(response, dict): return response @@ -306,7 +306,7 @@ async def test_policies_and_guardrails( inputs: GenericGuardrailAPIInputs, agent_id: str, user_api_key_dict: UserAPIKeyAuth, - ) -> Any: + ) -> object: body: Final = _chat_body_from_inputs(inputs, agent_id, data.request_data) req: Final = _request_with_json_body(body) resp: Final = Response() diff --git a/litellm/proxy/management_endpoints/team_endpoints.py b/litellm/proxy/management_endpoints/team_endpoints.py index c6d7975b75e..714cf252e69 100644 --- a/litellm/proxy/management_endpoints/team_endpoints.py +++ b/litellm/proxy/management_endpoints/team_endpoints.py @@ -5148,6 +5148,7 @@ async def list_team_v2( # Get teams with pagination if use_deleted_table: + # LiteLLM_DeletedTeamTable has no litellm_model_table relation, unlike below teams = await _deleted_team_db(prisma_client).find_many( where=where_conditions, skip=skip, @@ -5162,6 +5163,7 @@ async def list_team_v2( skip=skip, take=page_size, order=order_by if order_by else {"created_at": "desc"}, # Default sort + include=_INCLUDE_MODEL_TABLE, ) # Get total count for pagination total_count = await _team_db(prisma_client).count(where=where_conditions) diff --git a/litellm/proxy/management_endpoints/ui_sso.py b/litellm/proxy/management_endpoints/ui_sso.py index 613508da22b..606569c5b8b 100644 --- a/litellm/proxy/management_endpoints/ui_sso.py +++ b/litellm/proxy/management_endpoints/ui_sso.py @@ -502,7 +502,7 @@ def _set_nested_metadata_value(metadata: dict[str, object], key_path: str, value placeholder: Final = "\x00" parts = key_path.replace("\\.", placeholder).split(".") parts = [p.replace(placeholder, ".") for p in parts] - current: Any = metadata + current: dict[str, object] = metadata for part in parts[:-1]: existing = current.get(part) if not isinstance(existing, dict): @@ -4076,7 +4076,7 @@ class SSOAuthenticationHandler: ) if resp.status_code == 200: try: - userinfo_raw: Final = resp.json() + userinfo_raw: Final[dict[str, object] | None] = resp.json() if not userinfo_raw: # JSON null (None) or empty dict ({}) — no identity claims. # Treat as failure so id_token fallback can be attempted. @@ -4406,7 +4406,7 @@ class MicrosoftSSOHandler: ) -> tuple[list[str], str | None]: """Helper function to fetch and parse group data from a URL""" response: Final = await async_client.get(url, headers=headers) - response_json: Final = response.json() + response_json: Final[dict[str, object]] = response.json() response_typed: Final = await MicrosoftSSOHandler._cast_graph_api_response_dict(response=response_json) group_ids: Final = MicrosoftSSOHandler._get_group_ids_from_graph_api_response(response=response_typed) return group_ids, response_typed.get("odata_nextLink") diff --git a/litellm/proxy/management_helpers/object_permission_utils.py b/litellm/proxy/management_helpers/object_permission_utils.py index 13080a6cf83..a2fbf80422c 100644 --- a/litellm/proxy/management_helpers/object_permission_utils.py +++ b/litellm/proxy/management_helpers/object_permission_utils.py @@ -286,7 +286,7 @@ async def _resolve_mcp_server_identifiers_to_ids( return resolved -def _rewrite_object_permission_mcp_servers( +def _drop_stale_object_permission_mcp_servers( object_permission: ObjectPermissionDict, identifier_to_server_ids: dict[str, set[str]], ) -> None: @@ -294,16 +294,18 @@ def _rewrite_object_permission_mcp_servers( if not isinstance(mcp_servers, list): return - normalized_servers: Final[list[str]] = [] - for identifier in mcp_servers: - if identifier == SpecialMCPServerNames.no_mcp_servers.value: - normalized_servers.append(SpecialMCPServerNames.no_mcp_servers.value) - continue - normalized_servers.extend(sorted(identifier_to_server_ids.get(identifier, []))) - object_permission["mcp_servers"] = _dedupe_preserving_order(normalized_servers) + # Persist original identifiers, never resolved ids: shared-DB multi-region + # instances each expand a name/alias to their own local server id at read + # time. Only entries resolving to nothing (deleted servers, typos) drop. + kept_servers: Final = [ + identifier + for identifier in mcp_servers + if identifier == SpecialMCPServerNames.no_mcp_servers.value or identifier_to_server_ids.get(identifier) + ] + object_permission["mcp_servers"] = _dedupe_preserving_order(kept_servers) -def _rewrite_object_permission_mcp_tool_permissions( +def _drop_stale_object_permission_mcp_tool_permissions( object_permission: ObjectPermissionDict, identifier_to_server_ids: dict[str, set[str]], ) -> None: @@ -311,31 +313,25 @@ def _rewrite_object_permission_mcp_tool_permissions( if not isinstance(mcp_tool_permissions, dict): return - normalized_tool_permissions: Final[dict[str, list[str]]] = {} - for identifier, tools in mcp_tool_permissions.items(): - if not isinstance(tools, list): - tools = [] - for server_id in sorted(identifier_to_server_ids.get(identifier, [])): - normalized_tool_permissions.setdefault(server_id, []) - normalized_tool_permissions[server_id].extend(tools) - object_permission["mcp_tool_permissions"] = { - server_id: _dedupe_preserving_order(tools) for server_id, tools in normalized_tool_permissions.items() + identifier: _dedupe_preserving_order(tools if isinstance(tools, list) else []) + for identifier, tools in mcp_tool_permissions.items() + if identifier_to_server_ids.get(identifier) } -def _rewrite_object_permission_mcp_identifiers( +def _drop_stale_object_permission_mcp_identifiers( object_permission: ObjectPermissionDict | None, identifier_to_server_ids: dict[str, set[str]], ) -> None: if not object_permission or not isinstance(object_permission, dict): return - _rewrite_object_permission_mcp_servers( + _drop_stale_object_permission_mcp_servers( object_permission=object_permission, identifier_to_server_ids=identifier_to_server_ids, ) - _rewrite_object_permission_mcp_tool_permissions( + _drop_stale_object_permission_mcp_tool_permissions( object_permission=object_permission, identifier_to_server_ids=identifier_to_server_ids, ) @@ -615,7 +611,7 @@ async def validate_key_mcp_servers_against_team( "validate_key_mcp_servers_against_team: ignoring stale MCP server identifiers (no longer in registry or DB): %s", sorted(stale_identifiers), ) - _rewrite_object_permission_mcp_identifiers( + _drop_stale_object_permission_mcp_identifiers( object_permission=object_permission, identifier_to_server_ids=identifier_to_server_ids, ) diff --git a/litellm/proxy/pass_through_endpoints/llm_passthrough_endpoints.py b/litellm/proxy/pass_through_endpoints/llm_passthrough_endpoints.py index 525e7099b89..78d8ce296b8 100644 --- a/litellm/proxy/pass_through_endpoints/llm_passthrough_endpoints.py +++ b/litellm/proxy/pass_through_endpoints/llm_passthrough_endpoints.py @@ -6,13 +6,15 @@ Provider-specific Pass-Through Endpoints Use litellm with Anthropic SDK, Vertex AI SDK, Cohere SDK, etc. """ +from __future__ import annotations + import hmac import json import os import re from collections.abc import Callable, Mapping from types import MappingProxyType -from typing import TYPE_CHECKING, Annotated, Any, Final, cast +from typing import TYPE_CHECKING, Annotated, Final, cast import httpx from fastapi import APIRouter, Depends, HTTPException, Request, Response, WebSocket @@ -28,6 +30,7 @@ from litellm.constants import ( ) from litellm.litellm_core_utils.aws_partition import get_aws_dns_suffix from litellm.llms.anthropic.common_utils import AnthropicModelInfo +from litellm.llms.custom_httpx.http_handler import get_async_httpx_client from litellm.llms.vertex_ai.vertex_llm_base import VertexBase from litellm.proxy._types import * from litellm.proxy.auth.handle_jwt import JWTHandler @@ -51,6 +54,7 @@ from litellm.proxy.pass_through_endpoints.pass_through_endpoints import ( create_websocket_passthrough_route, websocket_passthrough_request, ) +from litellm.proxy.utils import ProxyLogging as ProxyLoggingType from litellm.proxy.utils import is_known_model from litellm.proxy.vector_store_endpoints.utils import ( assert_proxy_admin_for_vector_store_index_management, @@ -65,18 +69,23 @@ from litellm.types.passthrough_endpoints.pass_through_endpoints import ( ) from litellm.types.passthrough_endpoints.vertex_ai import VertexPassThroughCredentials from litellm.types.utils import LlmProviders +from litellm.types.vector_stores import LiteLLM_ManagedVectorStore from litellm.utils import ProviderConfigManager from .passthrough_endpoint_router import PassthroughEndpointRouter if TYPE_CHECKING: + from litellm.proxy.proxy_server import ProxyConfig as _ProxyConfig from litellm.router import Router + ProxyConfig = _ProxyConfig # rebind-ok: conditional type alias +else: + ProxyConfig = Any # rebind-ok: runtime fallback + vertex_llm_base: Final = VertexBase() router: Final = APIRouter() openai_passthrough_router: Final = APIRouter() default_vertex_config: Final = None - passthrough_endpoint_router: Final = PassthroughEndpointRouter() @@ -113,7 +122,21 @@ def is_passthrough_request_streaming(request_body: object) -> bool: return bool(request_body.get("stream", False)) -def get_passthrough_router_request_metadata(user_api_key_dict: UserAPIKeyAuth) -> Mapping[str, Any]: +def _optional_str(value: object) -> str | None: + return value if isinstance(value, str) else None + + +def _string_keyed_mapping(value: object) -> Mapping[str, object] | None: + if isinstance(value, Mapping): + return value + return None + + +async def _json_request_body(request: Request) -> Mapping[str, object]: + return await request.json() + + +def get_passthrough_router_request_metadata(user_api_key_dict: UserAPIKeyAuth) -> Mapping[str, object]: """ Build the request metadata carrying key-level spend attribution and the pre-call budget reservation for a router-model passthrough request. @@ -202,7 +225,7 @@ async def llm_passthrough_factory_proxy_route( # anthropic is streaming when 'stream' = True is in the body if request.method == "POST": if "multipart/form-data" not in request.headers.get("content-type", ""): - _request_body = await request.json() + _request_body = await _json_request_body(request) else: _request_body = await get_form_data(request) @@ -375,7 +398,7 @@ async def vllm_proxy_route( endpoint=endpoint, request_query_params=request.query_params, request_headers=_safe_get_request_headers(request), - stream=request_body.get("stream", False), + stream=is_streaming_request, content=None, data=None, files=None, @@ -495,8 +518,14 @@ async def milvus_proxy_route( request_body: Final = await get_request_body(request) # check collectionName - collection_name: Final = cast(str | None, request_body.get("collectionName")) - extra_headers = {} + _raw_collection_name: Final = request_body.get("collectionName") + if _raw_collection_name is not None and not isinstance(_raw_collection_name, str): + raise HTTPException( + status_code=400, + detail=f"collectionName must be a string. Got {type(_raw_collection_name).__name__}", + ) + collection_name: str | None = _raw_collection_name # rebind-ok: locally scoped conversion + extra_headers = {} # mutable-ok: dict for extra headers; rebind-ok: reassigned later from credentials base_target_url: str | None = None if not collection_name: raise HTTPException( @@ -803,7 +832,7 @@ async def handle_bedrock_passthrough_router_model( # Use the common processing path (same as non-router models) # This ensures all metadata, hooks, and logging are properly initialized - data: Final[dict[str, Any]] = {} + data: Final[dict[str, object]] = {} base_llm_response_processor: Final = ProxyBaseLLMRequestProcessing(data=data) data["model"] = model @@ -847,8 +876,8 @@ async def handle_bedrock_count_tokens( request: Request, fastapi_response: Response, user_api_key_dict: UserAPIKeyAuth, - request_body: dict[str, Any], -) -> dict[str, Any]: + request_body: dict[str, object], +) -> dict[str, object]: """ Handle AWS Bedrock CountTokens API requests. @@ -865,7 +894,7 @@ async def handle_bedrock_count_tokens( handler: Final = BedrockCountTokensHandler() # Extract model from request body - model: Final = request_body.get("model") + model: Final = _optional_str(request_body.get("model")) if not model: raise HTTPException(status_code=400, detail={"error": "Model is required in request body"}) @@ -997,7 +1026,7 @@ async def bedrock_llm_proxy_route( "Bedrock passthrough: Using direct Bedrock model '%s' for endpoint '%s'", model, endpoint ) - data: Final[dict[str, Any]] = {} + data: Final[dict[str, object]] = {} base_llm_response_processor: Final = ProxyBaseLLMRequestProcessing(data=data) data["method"] = request.method @@ -1096,7 +1125,7 @@ async def bedrock_proxy_route( headers: Final = {"Content-Type": "application/json"} # Assuming the body contains JSON data, parse it try: - data: Final = await request.json() + data: Final = await _json_request_body(request) except Exception as e: raise HTTPException(status_code=400, detail={"error": e}) _request: Final = AWSRequest(method="POST", url=str(updated_url), data=json.dumps(data), headers=headers) @@ -1187,7 +1216,7 @@ async def comprehend_medical_proxy_route( ) try: - data: Final = await request.json() + data: Final = await _json_request_body(request) except Exception as e: raise HTTPException(status_code=400, detail=str(e)) @@ -1273,7 +1302,7 @@ def _resolve_vertex_model_from_router( vertex_location: Current vertex location (may be from URL) Returns: - Tuple of (encoded_endpoint, endpoint, vertex_project, vertex_location) + tuple of (encoded_endpoint, endpoint, vertex_project, vertex_location) with resolved values from router config """ if not llm_router: @@ -1398,7 +1427,7 @@ async def assemblyai_proxy_route( is_streaming_request = False # assemblyai is streaming when 'stream' = True is in the body if request.method == "POST": - _request_body: Final = await request.json() + _request_body: Final = await _json_request_body(request) if _request_body.get("stream"): is_streaming_request = True @@ -1505,7 +1534,7 @@ async def azure_proxy_route( endpoint=endpoint, request_query_params=request.query_params, request_headers=_safe_get_request_headers(request), - stream=request_body.get("stream", False), + stream=is_streaming_request, content=None, data=None, files=None, @@ -1592,7 +1621,7 @@ async def azure_proxy_route( extra_headers = auth_credentials.get("headers") or {} - base_target_url = litellm_params.get("api_base") + base_target_url = _optional_str(litellm_params.get("api_base")) if base_target_url is None: raise Exception(f"API base not found for {part}") return await BaseOpenAIPassThroughHandler._base_openai_pass_through_handler( @@ -1702,7 +1731,7 @@ def get_vertex_ai_allowed_incoming_headers(request: Request) -> dict: def get_vertex_pass_through_handler( - call_type: Literal["discovery", "aiplatform"], + call_type: Literal["discovery", "aiplatform"], # noqa: UP037 ) -> BaseVertexAIPassThroughHandler: if call_type == "discovery": return VertexAIDiscoveryPassThroughHandler() @@ -1713,7 +1742,7 @@ def get_vertex_pass_through_handler( def _override_vertex_params_from_router_credentials( - router_credentials: Any | None, + router_credentials: LiteLLM_ManagedVectorStore | None, vertex_project: str | None, vertex_location: str | None, ) -> tuple[str | None, str | None]: @@ -1726,21 +1755,21 @@ def _override_vertex_params_from_router_credentials( vertex_location: Current vertex location (from URL) Returns: - Tuple of (vertex_project, vertex_location) with overridden values if applicable + tuple of (vertex_project, vertex_location) with overridden values if applicable """ if router_credentials is None: return vertex_project, vertex_location verbose_proxy_logger.debug("Using vector store credentials to override vertex project and location") - litellm_params: Final = router_credentials.get("litellm_params", {}) + litellm_params: Final = _string_keyed_mapping(router_credentials.get("litellm_params")) if not litellm_params: verbose_proxy_logger.warning("Vector store credentials found but litellm_params is empty") return vertex_project, vertex_location # Extract vertex_project and vertex_location from litellm_params - vector_store_project: Final = litellm_params.get("vertex_project") - vector_store_location: Final = litellm_params.get("vertex_location") + vector_store_project: Final = _optional_str(litellm_params.get("vertex_project")) + vector_store_location: Final = _optional_str(litellm_params.get("vertex_location")) if vector_store_project: verbose_proxy_logger.debug( @@ -1748,7 +1777,6 @@ def _override_vertex_params_from_router_credentials( vertex_project, vector_store_project, ) - vertex_project = vector_store_project else: verbose_proxy_logger.warning("Vector store credentials found but missing vertex_project in litellm_params") @@ -1758,11 +1786,10 @@ def _override_vertex_params_from_router_credentials( vertex_location, vector_store_location, ) - vertex_location = vector_store_location else: verbose_proxy_logger.warning("Vector store credentials found but missing vertex_location in litellm_params") - return vertex_project, vertex_location + return vector_store_project or vertex_project, vector_store_location or vertex_location _CREDENTIALLESS_VERTEX_MISSING_CREDENTIAL_DETAIL: Final = ( @@ -1870,8 +1897,8 @@ def _forwarded_headers_for_credentialless_vertex_passthrough( async def _prepare_vertex_auth_headers( request: Request, - vertex_credentials: Any | None, - router_credentials: Any | None, + vertex_credentials: VertexPassThroughCredentials | None, + router_credentials: LiteLLM_ManagedVectorStore | None, vertex_project: str | None, vertex_location: str | None, base_target_url: str | None, @@ -1893,12 +1920,12 @@ async def _prepare_vertex_auth_headers( authenticated them is stripped on the credential-less branch Returns: - Tuple containing: + tuple containing: - headers: dict - Authentication headers to use - - base_target_url: Optional[str] - Updated base target URL + - base_target_url: str | None - Updated base target URL - headers_passed_through: bool - Whether headers were passed through from request - - vertex_project: Optional[str] - Updated vertex project ID - - vertex_location: Optional[str] - Updated vertex location + - vertex_project: str | None - Updated vertex project ID + - vertex_location: str | None - Updated vertex location """ vertex_llm_base: Final = VertexBase() headers_passed_through = False @@ -1968,7 +1995,7 @@ async def _base_vertex_proxy_route( fastapi_response: Response, get_vertex_pass_through_handler: BaseVertexAIPassThroughHandler, user_api_key_dict: UserAPIKeyAuth | None = None, - router_credentials: Any | None = None, + router_credentials: LiteLLM_ManagedVectorStore | None = None, ): """ Base function for Vertex AI passthrough routes. @@ -2138,8 +2165,6 @@ async def vertex_discovery_proxy_route( """ import re - from litellm.types.vector_stores import LiteLLM_ManagedVectorStore - # Extract vector store ID from endpoint if present (e.g., dataStores/test-litellm-app_1761094730750) vector_store_credentials: LiteLLM_ManagedVectorStore | None = None vector_store_id_match: Final = re.search(r"dataStores/([^/]+)", endpoint) @@ -2546,7 +2571,7 @@ def _vertex_publisher_model_suffix(model: str) -> str: return f"{VERTEX_PUBLISHER_MODEL_PREFIX}{model.rsplit('/', 1)[-1]}" -def _get_llm_router() -> "Router | None": +def _get_llm_router() -> Router | None: from litellm.proxy.proxy_server import llm_router return llm_router @@ -2586,7 +2611,7 @@ def _resolve_vertex_live_credentials( def _build_vertex_live_setup_model_rewriter( vertex_project: str | None, vertex_location: str | None, - llm_router: "Router | None", + llm_router: Router | None, ) -> Callable[[str], str] | None: """ Rewrite the ``setup`` frame's model into the full Vertex resource path the Live API requires. @@ -2606,7 +2631,7 @@ def _build_vertex_live_setup_model_rewriter( return rewrite -def _resolve_alias_to_upstream_model(setup_model: str, llm_router: "Router | None") -> str: +def _resolve_alias_to_upstream_model(setup_model: str, llm_router: Router | None) -> str: """ The Live SDK wraps whatever the caller typed as ``models/``, so a gateway alias arrives prefixed """ @@ -2796,6 +2821,238 @@ def create_generic_websocket_passthrough_endpoint( ) +@router.api_route( + "/gigachat/{endpoint:path}", + methods=["GET", "POST", "PUT", "DELETE", "PATCH"], # mutable-ok: FastAPI route methods + tags=["Gigachat Pass-through", "pass-through"], # mutable-ok: FastAPI route tags +) +async def gigachat_proxy_route( + endpoint: str, + request: Request, + fastapi_response: Response, + user_api_key_dict: Annotated[UserAPIKeyAuth, Depends(user_api_key_auth)], +) -> Response: + """ + [Docs](https://docs.litellm.ai/docs/pass_through/gigachat) + """ + from litellm.proxy.proxy_server import ( + general_settings, + llm_router, + proxy_config, + proxy_logging_obj, + select_data_generator, + user_api_base, + user_max_tokens, + user_model, + user_request_timeout, + user_temperature, + version, + ) + + ## check for streaming + request_body: Final[dict[str, object]] = await get_request_body(request) + is_router_model = False # rebind-ok: conditionally set to True when model uses router + + raw_model: Final = request_body.get("model") + model: Final = raw_model if isinstance(raw_model, str) else None + if model: + is_router_model = is_passthrough_request_using_router_model( + request_body, llm_router + ) # rebind-ok: conditionally set to True + elif any(word in endpoint for word in ("completions", "embeddings")): + raise HTTPException( + status_code=400, detail={"error": "Model is required in request body"} + ) # mutable-ok: HTTPException detail dict + + # If router model, use dedicated router passthrough handler + # This uses the same common processing path as non-router models + if model and is_router_model and llm_router: + return await handle_gigachat_passthrough_router_model( + model=model, + endpoint=endpoint, + request=request, + request_body=request_body, + fastapi_response=fastapi_response, + llm_router=llm_router, + user_api_key_dict=user_api_key_dict, + proxy_logging_obj=proxy_logging_obj, + general_settings=general_settings, + proxy_config=proxy_config, + select_data_generator=select_data_generator, + user_model=user_model, + user_temperature=user_temperature, + user_request_timeout=user_request_timeout, + user_max_tokens=user_max_tokens, + user_api_base=user_api_base, + version=version, + ) + + verbose_proxy_logger.debug( + "Gigachat passthrough: Using direct Gigachat model '%s' for endpoint '%s'", model, endpoint + ) + + from litellm.llms.gigachat.authenticator import get_access_token + from litellm.llms.gigachat.utils import GIGACHAT_BASE_URL + + base_target_url: Final = get_secret_str("GIGACHAT_API_BASE") or GIGACHAT_BASE_URL + request_path: Final = httpx.URL(endpoint).path + encoded_endpoint: Final = request_path if request_path.startswith("/") else f"/{request_path}" + + base_url: Final = httpx.URL(base_target_url) + updated_url: Final = base_url.copy_with( + path=HttpPassThroughEndpointHelpers.join_base_and_endpoint_path(base_url, encoded_endpoint) + ) + + is_streaming_request: Final = await is_streaming_request_fn(request) + + endpoint_func: Final = create_pass_through_route( + endpoint=endpoint, + target=str(updated_url), + custom_headers={"Authorization": f"Bearer {get_access_token()}"}, + is_streaming_request=is_streaming_request, + ) + return await endpoint_func( + request, + fastapi_response, + user_api_key_dict, + ) + + +async def handle_gigachat_passthrough_router_model( + model: str, + endpoint: str, + request: Request, + request_body: dict, + fastapi_response: Response, + llm_router: litellm.Router, + user_api_key_dict: UserAPIKeyAuth, + proxy_logging_obj: ProxyLoggingType, + general_settings: dict, + proxy_config: ProxyConfig, + select_data_generator: Callable, + user_model: str | None, + user_temperature: float | None, + user_request_timeout: float | None, + user_max_tokens: int | None, + user_api_base: str | None, + version: str | None, +) -> Response | StreamingResponse: + """ + Handle Gigachat passthrough for router models (models defined in config.yaml). + + Uses the same common processing path as non-router models to ensure + metadata and hooks are properly initialized. + + Args: + model: The router model name (e.g., "gigachat/gigachat-2") + endpoint: The Gigachat endpoint path (e.g., "/chat/completions") + request: The FastAPI request object + request_body: The parsed request body + llm_router: The LiteLLM router instance + user_api_key_dict: The user API key authentication dictionary + proxy_logging_obj: Proxy logging + general_settings: Proxy general settings + proxy_config: Proxy config + select_data_generator: Select data generator function + (additional args for common processing) + + Returns: + Response or StreamingResponse depending on endpoint type + """ + from litellm.proxy.common_request_processing import ProxyBaseLLMRequestProcessing + + # Detect streaming based on request body + is_streaming: Final = request_body.get("stream", False) # pyright: ignore[reportUnknownVariableType] # request_body is dict[Unknown, Unknown] + + data: dict[str, Any] = await _read_request_body( + request=request + ) # mutable-ok: mutated in place by proxy pipeline; pyright: ignore[reportExplicitAny] # Any needed for proxy pipeline + if user_api_key_dict is not None: + auth_metadata: Final = { + metadata_key: value + for metadata_key, value in ( + ("user_api_key_user_id", getattr(user_api_key_dict, "user_id", None)), + ("user_api_key_team_id", getattr(user_api_key_dict, "team_id", None)), + ("user_api_key_org_id", getattr(user_api_key_dict, "org_id", None)), + ("agent_id", getattr(user_api_key_dict, "agent_id", None)), + ) + if value is not None + } + existing_metadata: Final = data.get("metadata") + data["metadata"] = { + **(existing_metadata if isinstance(existing_metadata, dict) else {}), + **auth_metadata, + } + + verbose_proxy_logger.debug( + "Gigachat router passthrough: model='%s', endpoint='%s', streaming=%s", model, endpoint, is_streaming + ) + + # Use the common processing path (same as non-router models) + # This ensures all metadata, hooks, and logging are properly initialized + + data["model"] = model + data["method"] = request.method + data["endpoint"] = endpoint + data["json"] = request_body + data["custom_llm_provider"] = "gigachat" + + # Remove sensitive keys from data + keys: Final = [ # mutable-ok: list of keys to remove from data + "gigachat_auth_url", + "gigachat_access_token", + "gigachat_scope", + "api_base", + "api_key", + ] + for key in keys: + data.pop(key, None) + + client: Final = get_async_httpx_client( + llm_provider=LlmProviders.GIGACHAT, + params={ # mutable-ok: httpx client params + "timeout": httpx.Timeout(timeout=600.0, connect=5.0), + }, + ) + + data["client"] = client + base_llm_response_processor: Final = ProxyBaseLLMRequestProcessing(data=data) + + # Use the common passthrough processing to handle metadata and hooks + # This also handles all response formatting (streaming/non-streaming) and exceptions + try: + result = await base_llm_response_processor.base_passthrough_process_llm_request( # rebind-ok: assigned once in try block + request=request, + fastapi_response=fastapi_response, + user_api_key_dict=user_api_key_dict, + proxy_logging_obj=proxy_logging_obj, + llm_router=llm_router, + general_settings=general_settings, + proxy_config=proxy_config, + select_data_generator=select_data_generator, + model=model, + user_model=user_model, + user_temperature=user_temperature, + user_request_timeout=user_request_timeout, + user_max_tokens=user_max_tokens, + user_api_base=user_api_base, + version=version, + ) + except Exception as e: # noqa: BLE001 # Safe catch-all for handle exception + # Use common exception handling + raise await base_llm_response_processor._handle_llm_api_exception( + e=e, + user_api_key_dict=user_api_key_dict, + proxy_logging_obj=proxy_logging_obj, + ) + else: + if isinstance(result, StreamingResponse): + if result.headers.get("Content-Type") is None: + result.headers["Content-Type"] = "text/event-stream; charset=utf-8" + + return result + + @router.api_route( "/watsonx/{endpoint:path}", methods=["GET", "POST", "PUT", "DELETE", "PATCH"], @@ -2852,7 +3109,7 @@ async def watsonx_proxy_route( is_streaming_request = False if request.method == "POST": if "multipart/form-data" not in request.headers.get("content-type", ""): - _request_body = await request.json() + _request_body = await _json_request_body(request) else: _request_body = await get_form_data(request) diff --git a/litellm/proxy/pass_through_endpoints/llm_provider_handlers/vertex_passthrough_logging_handler.py b/litellm/proxy/pass_through_endpoints/llm_provider_handlers/vertex_passthrough_logging_handler.py index ee9a5d94440..49ec18013b5 100644 --- a/litellm/proxy/pass_through_endpoints/llm_provider_handlers/vertex_passthrough_logging_handler.py +++ b/litellm/proxy/pass_through_endpoints/llm_provider_handlers/vertex_passthrough_logging_handler.py @@ -267,7 +267,7 @@ class VertexPassthroughLoggingHandler: model: Final = VertexPassthroughLoggingHandler.extract_model_from_url(url_route) - _json_response: Final = httpx_response.json() + _json_response: Final[dict[str, object]] = httpx_response.json() litellm_prediction_response: ModelResponse | EmbeddingResponse | ImageResponse = ModelResponse() if vertex_image_generation_class.is_image_generation_response(_json_response): @@ -422,7 +422,7 @@ class VertexPassthroughLoggingHandler: - Creates standard logging object - Logs in litellm callbacks """ - kwargs: dict[str, Any] = {} + kwargs: dict[str, object] = {} vertex_location: Final = get_vertex_location_from_url(url_route) if vertex_location is not None: litellm_logging_obj.optional_params["vertex_location"] = vertex_location diff --git a/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py b/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py index 09d3dedaafa..ff306eb65f1 100644 --- a/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py +++ b/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py @@ -6,9 +6,10 @@ import posixpath import traceback from base64 import b64encode from collections.abc import AsyncGenerator, Callable, Iterable, Mapping, Sequence +from dataclasses import dataclass from datetime import datetime from itertools import groupby -from typing import Any, Final, TypedDict, cast +from typing import TYPE_CHECKING, Any, Final, TypedDict, cast from urllib.parse import urlencode, urlparse import httpx @@ -47,6 +48,7 @@ from litellm.litellm_core_utils.core_helpers import ( get_metadata_variable_name_from_kwargs, get_or_create_metadata_bucket, ) +from litellm.litellm_core_utils.initialize_dynamic_callback_params import validate_no_callback_env_reference from litellm.litellm_core_utils.internal_call_metadata import MODEL_ACCESS_GROUP_METADATA_KEY from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.litellm_core_utils.logging_worker import GLOBAL_LOGGING_WORKER @@ -78,7 +80,10 @@ from litellm.proxy.common_utils.http_parsing_utils import ( from litellm.proxy.common_utils.sse_keepalive import ( wrap_passthrough_sse_bytes_with_keepalive_pings, ) -from litellm.proxy.litellm_pre_call_utils import LiteLLMProxyRequestSetup +from litellm.proxy.litellm_pre_call_utils import ( + LiteLLMProxyRequestSetup, + _get_dynamic_logging_metadata, # pyright: ignore[reportPrivateUsage] # shared proxy helper, same import style as _read_request_body above +) from litellm.proxy.utils import normalize_route_for_root_path from litellm.repositories.team_repository import TeamRepository from litellm.secret_managers.main import get_secret_str @@ -90,7 +95,7 @@ from litellm.types.passthrough_endpoints.pass_through_endpoints import ( EndpointType, PassthroughStandardLoggingPayload, ) -from litellm.types.utils import Usage +from litellm.types.utils import TRUSTED_CALLBACK_VARS_FIELD, Usage from .streaming_handler import PassThroughStreamingHandler from .success_handler import PassThroughEndpointLogging @@ -99,6 +104,9 @@ from .upstream_usage_headers import ( apply_upstream_reported_usage, ) +if TYPE_CHECKING: + from litellm.proxy.proxy_server import ProxyConfig + router: Final = APIRouter() pass_through_endpoint_logging: Final = PassThroughEndpointLogging() @@ -752,6 +760,67 @@ def _build_passthrough_failure_request_payload( return request_payload +@dataclass(frozen=True, slots=True) +class _TeamCallbackWiring: + success_callbacks: "list[str | Callable | CustomLogger] | None" = None # mutable-ok: Logging.__init__ arg + failure_callbacks: "list[str | Callable | CustomLogger] | None" = None # mutable-ok: Logging.__init__ arg + logging_kwargs: dict[str, str | dict[str, str]] | None = None # mutable-ok: Logging.__init__ arg + + +def _resolve_team_callback_wiring( + user_api_key_dict: UserAPIKeyAuth, + proxy_config: "ProxyConfig", + route_description: str, +) -> _TeamCallbackWiring: + """Resolve key/team dynamic logging callbacks for a passthrough request. + + Mirrors add_litellm_data_to_request: callback_vars are unpacked top-level + (read by initialize_standard_callback_dynamic_params) and also stamped on + the proxy-owned trusted-vars field (read by get_trusted_callback_params). + + Fails open: a callback resolution or validation error is logged at error + level and the request proceeds without dynamic callbacks, since a broken + logging config must not fail the customer's upstream call (and the + websocket is already accepted by the time this runs on that path). The + env-reference check runs here because the deprecated callback_settings + branch skips AddTeamCallback validation, and Logging.__init__ would + otherwise reject the vars mid-request. + """ + try: + callback_settings_obj: Final = _get_dynamic_logging_metadata( + user_api_key_dict=user_api_key_dict, proxy_config=proxy_config + ) + if callback_settings_obj and callback_settings_obj.callback_vars: + for ( + item + ) in callback_settings_obj.callback_vars.items(): # rebind-ok: dict.items iteration for env-ref validation + validate_no_callback_env_reference(item[0], item[1], source="key/team callback metadata") + except Exception: # noqa: BLE001 - a broken logging config must never fail the passthrough request + verbose_proxy_logger.exception( + "%s: failed to resolve team logging callbacks, continuing without them", + route_description, + ) + return _TeamCallbackWiring() + if callback_settings_obj is None: + return _TeamCallbackWiring() + callback_vars: Final = callback_settings_obj.callback_vars + success_callbacks: Final = callback_settings_obj.success_callback + failure_callbacks: Final = callback_settings_obj.failure_callback + logging_kwargs: Final = ( + None + if not callback_vars + else { # mutable-ok: Logging arg + **callback_vars, + TRUSTED_CALLBACK_VARS_FIELD: callback_vars, + } + ) + return _TeamCallbackWiring( + success_callbacks=None if success_callbacks is None else [*success_callbacks], # mutable-ok: Logging arg + failure_callbacks=None if failure_callbacks is None else [*failure_callbacks], # mutable-ok: Logging arg + logging_kwargs=logging_kwargs, + ) + + async def _log_passthrough_upstream_failure( response: httpx.Response, user_api_key_dict: UserAPIKeyAuth, @@ -845,7 +914,7 @@ async def pass_through_request( from litellm.proxy.pass_through_endpoints.passthrough_guardrails import ( PassthroughGuardrailHandler, ) - from litellm.proxy.proxy_server import proxy_logging_obj + from litellm.proxy.proxy_server import proxy_config, proxy_logging_obj ######################################################### # Initialize variables @@ -930,6 +999,11 @@ async def pass_through_request( # read e.g. ``chat gpt-4o`` instead of ``chat unknown``. passthrough_model: Final = (_parsed_body.get("model") if isinstance(_parsed_body, dict) else None) or "unknown" start_time: Final = datetime.now() + team_callbacks: Final = _resolve_team_callback_wiring( + user_api_key_dict=user_api_key_dict, + proxy_config=proxy_config, + route_description="pass_through_endpoint", + ) logging_obj = Logging( model=passthrough_model, messages=[{"role": "user", "content": safe_dumps(_parsed_body)}], @@ -938,6 +1012,9 @@ async def pass_through_request( start_time=start_time, litellm_call_id=litellm_call_id, function_id="1245", + dynamic_success_callbacks=team_callbacks.success_callbacks, + dynamic_failure_callbacks=team_callbacks.failure_callbacks, + kwargs=team_callbacks.logging_kwargs, ) # Store passthrough guardrails config on logging_obj for field targeting @@ -2022,7 +2099,7 @@ async def websocket_passthrough_request( setup_model_rewriter: Optional rewrite of the setup frame's model before it reaches the upstream """ from litellm.litellm_core_utils.litellm_logging import Logging - from litellm.proxy.proxy_server import proxy_logging_obj + from litellm.proxy.proxy_server import proxy_config, proxy_logging_obj from litellm.types.passthrough_endpoints.pass_through_endpoints import ( PassthroughStandardLoggingPayload, ) @@ -2055,6 +2132,11 @@ async def websocket_passthrough_request( upstream_headers[header_name] = header_value # Initialize logging object similar to HTTP passthrough + team_callbacks: Final = _resolve_team_callback_wiring( + user_api_key_dict=user_api_key_dict, + proxy_config=proxy_config, + route_description="websocket_passthrough", + ) logging_obj: Final = Logging( model="unknown", messages=[{"role": "user", "content": "WebSocket connection"}], @@ -2063,6 +2145,9 @@ async def websocket_passthrough_request( start_time=start_time, litellm_call_id=litellm_call_id, function_id="websocket_passthrough", + dynamic_success_callbacks=team_callbacks.success_callbacks, + dynamic_failure_callbacks=team_callbacks.failure_callbacks, + kwargs=team_callbacks.logging_kwargs, ) # Create passthrough logging payload @@ -3148,6 +3233,14 @@ def _get_pass_through_endpoints_from_config() -> list[PassThroughGenericEndpoint return returned_endpoints +def _config_field_endpoints(response: ConfigFieldInfo) -> list[object] | None: + return response.field_value + + +def _request_app(request: Request) -> FastAPI: + return request.app + + async def _get_pass_through_endpoints_from_db( endpoint_id: str | None = None, user_api_key_dict: UserAPIKeyAuth | None = None, @@ -3164,7 +3257,7 @@ async def _get_pass_through_endpoints_from_db( except Exception: return [] - pass_through_endpoint_data: Final[list | None] = response.field_value + pass_through_endpoint_data: Final = _config_field_endpoints(response) if pass_through_endpoint_data is None: return [] @@ -3327,7 +3420,7 @@ async def update_pass_through_endpoints( detail={"error": "No pass-through endpoints found"}, ) - pass_through_endpoint_data: Final[list | None] = response.field_value + pass_through_endpoint_data: Final[list | None] = _config_field_endpoints(response) if pass_through_endpoint_data is None: raise HTTPException( status_code=404, @@ -3398,7 +3491,7 @@ async def update_pass_through_endpoints( _custom_headers: dict | None = updated_endpoint.headers or {} _custom_headers = await set_env_variables_in_header(custom_headers=_custom_headers) - route_app: Final[FastAPI] = request.app + route_app: Final = _request_app(request) if updated_endpoint.include_subpath: InitPassThroughEndpointHelpers.add_subpath_route( app=route_app, @@ -3490,7 +3583,7 @@ async def create_pass_through_endpoints( _custom_headers: dict | None = created_endpoint.headers or {} _custom_headers = await set_env_variables_in_header(custom_headers=_custom_headers) - route_app: Final[FastAPI] = request.app + route_app: Final = _request_app(request) if created_endpoint.include_subpath: InitPassThroughEndpointHelpers.add_subpath_route( app=route_app, @@ -3558,7 +3651,7 @@ async def delete_pass_through_endpoints( response = ConfigFieldInfo(field_name="pass_through_endpoints", field_value=None) ## Update field by removing endpoint - pass_through_endpoint_data: Final[list | None] = response.field_value + pass_through_endpoint_data: Final[list | None] = _config_field_endpoints(response) if response.field_value is None or pass_through_endpoint_data is None: raise HTTPException( status_code=400, diff --git a/litellm/proxy/policy_engine/pipeline_executor.py b/litellm/proxy/policy_engine/pipeline_executor.py index 50cb813c6fa..4be0f556ed7 100644 --- a/litellm/proxy/policy_engine/pipeline_executor.py +++ b/litellm/proxy/policy_engine/pipeline_executor.py @@ -6,6 +6,7 @@ pass/fail actions (allow, block, next, modify_response) and data forwarding. """ import time +from collections.abc import Sequence from typing import Any, Final, Literal import litellm @@ -114,11 +115,7 @@ class PipelineExecutor: # Handle terminal actions if action == "allow": - return PipelineExecutionResult( - terminal_action="allow", - step_results=step_results, - modified_data=working_data if working_data != data else None, - ) + return _allow_result(step_results=step_results, working_data=working_data, request_data=data) if action == "block": return PipelineExecutionResult( @@ -138,11 +135,7 @@ class PipelineExecutor: # action == "next" → continue to next step # Ran out of steps without a terminal action → default allow - return PipelineExecutionResult( - terminal_action="allow", - step_results=step_results, - modified_data=working_data if working_data != data else None, - ) + return _allow_result(step_results=step_results, working_data=working_data, request_data=data) @staticmethod async def _run_step( @@ -251,6 +244,45 @@ class PipelineExecutor: return None +def _allow_result( + step_results: Sequence[PipelineStepResult], + working_data: dict, # mutable-ok: same request-payload shape as execute_steps' data + request_data: dict, # mutable-ok: same request-payload shape as execute_steps' data +) -> PipelineExecutionResult: + """Build the terminal-allow result, propagating pipeline modifications without the per-step guardrail override.""" + restored: Final = _restore_request_guardrails(working_data, request_data) + return PipelineExecutionResult( + terminal_action="allow", + step_results=list(step_results), # mutable-ok: PipelineExecutionResult field is a list + modified_data=restored if restored != request_data else None, + ) + + +def _restore_request_guardrails( + working_data: dict, # mutable-ok: same request-payload shape as execute_steps' data + request_data: dict, # mutable-ok: same request-payload shape as execute_steps' data +) -> dict: # mutable-ok: merged back into the request dict, which downstream code mutates + """ + Restore the request's own metadata["guardrails"] activation list. + + _run_step overrides it to [step.guardrail] so should_run_guardrail() allows each + step; letting that override escape via modified_data permanently drops every + independently activated guardrail from later lifecycle stages (post_call, etc.). + """ + working_metadata: Final = working_data.get("metadata") + if not isinstance(working_metadata, dict): + return working_data + request_metadata: Final = request_data.get("metadata") + original_guardrails: Final = request_metadata.get("guardrails") if isinstance(request_metadata, dict) else None + stripped: Final = {k: v for k, v in working_metadata.items() if k != "guardrails"} # mutable-ok: request dict + if original_guardrails is not None: + restored: Final = {**stripped, "guardrails": original_guardrails} # mutable-ok: request dict + return {**working_data, "metadata": restored} # mutable-ok: request dict + if not stripped and not isinstance(request_metadata, dict): + return {k: v for k, v in working_data.items() if k != "metadata"} # mutable-ok: request dict + return {**working_data, "metadata": stripped} # mutable-ok: request dict + + def _pipeline_action_for_outcome(step: PipelineStep, outcome: str) -> str: """ Map pipeline step outcome to the configured action. diff --git a/litellm/proxy/proxy_cli.py b/litellm/proxy/proxy_cli.py index 8ac63ba25c9..86f6853a625 100644 --- a/litellm/proxy/proxy_cli.py +++ b/litellm/proxy/proxy_cli.py @@ -913,13 +913,14 @@ class ProxyInitializationHelpers: envvar="ENFORCE_PRISMA_MIGRATION_CHECK", ) @click.option( - "--use_v2_migration_resolver", - is_flag=True, - default=False, + "--use_v2_migration_resolver/--use_legacy_migration_resolver", + default=True, help=( - "Opt into the v2 migration resolver. Avoids the diff-and-force recovery " - "path that can cause schema thrashing during rolling deploys where two " - "LiteLLM versions contend for the same DB. Default is the v1 resolver." + "Which database migration resolver to run at startup. The default v2 " + "resolver avoids the diff-and-force recovery path that can cause schema " + "thrashing during rolling deploys where two LiteLLM versions contend for " + "the same DB. Pass --use_legacy_migration_resolver, or set " + "USE_V2_MIGRATION_RESOLVER=false, to fall back to v1." ), envvar="USE_V2_MIGRATION_RESOLVER", ) @@ -1310,10 +1311,11 @@ def run_server( else: if not use_v2_migration_resolver: print( - "\033[1;33mLiteLLM Proxy: Using default (v1) migration resolver. " - "If your deployment has seen schema thrashing during rolling " - "deploys, try --use_v2_migration_resolver (safer: avoids the " - "diff-and-force recovery that caused the thrash).\033[0m" + "\033[1;33mLiteLLM Proxy: Using the legacy (v1) migration resolver. " + "The default v2 resolver is safer: it avoids the diff-and-force " + "recovery that caused schema thrashing during rolling deploys. " + "Remove --use_legacy_migration_resolver / " + "USE_V2_MIGRATION_RESOLVER=false to switch back to it.\033[0m" ) try: setup_ok: Final = PrismaManager.setup_database( @@ -1321,10 +1323,10 @@ def run_server( use_v2_resolver=use_v2_migration_resolver, ) except RuntimeError as e: - # Raised on unrecoverable migration errors: the v2 - # resolver's non-idempotent failures and permission - # issues, and any `prisma db push` against a - # partitioned LiteLLM_SpendLogs. + # Raised on unrecoverable migration errors: permission + # failures from either resolver, the v2 resolver's + # non-idempotent failures, and any `prisma db push` + # against a partitioned LiteLLM_SpendLogs. print( f"\033[1;31mLiteLLM Proxy: Database migration cannot proceed. {e}\033[0m", file=sys.stderr, diff --git a/litellm/proxy/proxy_server.py b/litellm/proxy/proxy_server.py index da2e09bd7f6..70bf8fd0554 100644 --- a/litellm/proxy/proxy_server.py +++ b/litellm/proxy/proxy_server.py @@ -263,6 +263,7 @@ from litellm.litellm_core_utils.agentic_loop_settings import ( validated_max_agentic_loops, ) from litellm.litellm_core_utils.asyncify import asyncify +from litellm.litellm_core_utils.audio_utils.utils import resolve_speech_media_type from litellm.litellm_core_utils.core_helpers import ( _get_parent_otel_span_from_kwargs, get_litellm_metadata_from_kwargs, @@ -2658,7 +2659,6 @@ async def increment_spend_counters( budget_reservation: dict | None = None, end_user_id: str | None = None, tags: list[str] | None = None, - request_id: str | None = None, request_started_at: datetime | None = None, model_access_groups: Sequence[str] | None = None, ): @@ -2733,7 +2733,6 @@ async def increment_spend_counters( window_duration=duration, window_start=key_window_start, increment=cost, - request_id=request_id, request_started_at=request_started_at, ) @@ -2777,7 +2776,6 @@ async def increment_spend_counters( window_duration=duration, window_start=team_window_start, increment=cost, - request_id=request_id, request_started_at=request_started_at, ) @@ -3005,16 +3003,15 @@ async def _enqueue_window_spend_row_update( window_duration: str, window_start: datetime | None, increment: float, - request_id: str | None, request_started_at: datetime | None, ) -> None: """Queue this request's cost against the LiteLLM_BudgetWindowSpend row for the window, so enforcement can read a maintained total instead of aggregating LiteLLM_SpendLogs. - request_id is the LiteLLM_SpendLogs id this cost was recorded under and - request_started_at its startTime; the flush uses them to keep the one-time - seed from counting a request that its increment already covers. + request_started_at is this request's LiteLLM_SpendLogs startTime; the flush + stops the one-time seed there so a request its increment already covers is + not counted twice. Enqueued even when the cache increment was skipped for a reserved counter: the reservation only pre-charged the counter, and the row still owes the @@ -3035,7 +3032,6 @@ async def _enqueue_window_spend_row_update( window_duration=window_duration, window_start=window_start, spend=increment, - request_id=request_id, started_at=request_started_at, ) ) @@ -11066,15 +11062,14 @@ async def audio_speech( if callback_headers: custom_headers.update(callback_headers) - # Determine media type based on model type - media_type = "audio/mpeg" # Default for OpenAI TTS - request_model: Final = data.get("model", "") - if request_model: - request_model_lower: Final = request_model.lower() - if "gemini" in request_model_lower and ( - "tts" in request_model_lower or "preview-tts" in request_model_lower - ): - media_type = "audio/wav" # Gemini TTS returns WAV format after conversion + requested_format: Final = data.get("response_format") + upstream_content_type: Final = ( + response.response.headers.get("content-type") if isinstance(response, HttpxBinaryResponseContent) else None + ) + media_type: Final = resolve_speech_media_type( + upstream_content_type=upstream_content_type, + response_format=requested_format if isinstance(requested_format, str) else None, + ) return StreamingResponse( _audio_speech_chunk_generator(response), @@ -11090,7 +11085,15 @@ async def audio_speech( ) verbose_proxy_logger.error("litellm.proxy.proxy_server.audio_speech(): Exception occured - %s", e) verbose_proxy_logger.debug(traceback.format_exc()) - raise e + if isinstance(e, (ProxyException, HTTPException)): + raise e + raise ProxyException( + message=getattr(e, "message", f"{e}"), + type=getattr(e, "type", "None"), + param=getattr(e, "param", "None"), + openai_code=getattr(e, "code", None), + code=getattr(e, "status_code", 500), + ) @router.post( diff --git a/litellm/proxy/public_endpoints/provider_create_fields.json b/litellm/proxy/public_endpoints/provider_create_fields.json index 4652719a23b..66f8c2ea36f 100644 --- a/litellm/proxy/public_endpoints/provider_create_fields.json +++ b/litellm/proxy/public_endpoints/provider_create_fields.json @@ -986,6 +986,62 @@ ], "default_model_placeholder": "gpt-3.5-turbo" }, + { + "provider": "QwenCloud", + "provider_display_name": "QwenCloud", + "litellm_provider": "qwencloud", + "credential_fields": [ + { + "key": "api_key", + "label": "QwenCloud API Key", + "placeholder": null, + "tooltip": null, + "required": true, + "field_type": "password", + "options": null, + "default_value": null + }, + { + "key": "api_base", + "label": "API Base", + "placeholder": "https://dashscope-intl.aliyuncs.com/compatible-mode/v1", + "tooltip": "The base URL for QwenCloud. Defaults to https://dashscope-intl.aliyuncs.com/compatible-mode/v1 if not specified.", + "required": true, + "field_type": "text", + "options": null, + "default_value": "https://dashscope-intl.aliyuncs.com/compatible-mode/v1" + } + ], + "default_model_placeholder": "gpt-3.5-turbo" + }, + { + "provider": "Qwen_AI_Platform", + "provider_display_name": "Qwen AI Platform", + "litellm_provider": "qwen_ai_platform", + "credential_fields": [ + { + "key": "api_key", + "label": "Qwen AI Platform API Key", + "placeholder": null, + "tooltip": null, + "required": true, + "field_type": "password", + "options": null, + "default_value": null + }, + { + "key": "api_base", + "label": "API Base", + "placeholder": "https://dashscope.aliyuncs.com/compatible-mode/v1", + "tooltip": "The base URL for Qwen AI Platform. Defaults to https://dashscope.aliyuncs.com/compatible-mode/v1 if not specified.", + "required": true, + "field_type": "text", + "options": null, + "default_value": "https://dashscope.aliyuncs.com/compatible-mode/v1" + } + ], + "default_model_placeholder": "gpt-3.5-turbo" + }, { "provider": "Databricks", "provider_display_name": "Databricks", @@ -1318,6 +1374,68 @@ ], "default_model_placeholder": "gpt-3.5-turbo" }, + { + "provider": "GIGACHAT", + "provider_display_name": "GigaChat", + "litellm_provider": "gigachat", + "credential_fields": [ + { + "key": "api_base", + "label": "API Base", + "placeholder": null, + "tooltip": null, + "required": false, + "field_type": "text", + "options": null, + "default_value": null + }, + { + "key": "api_key", + "label": "API Key", + "placeholder": null, + "tooltip": null, + "required": false, + "field_type": "password", + "options": null, + "default_value": null + }, + { + "key": "gigachat_scope", + "label": "Scope", + "placeholder": null, + "tooltip": null, + "required": false, + "field_type": "select", + "options": [ + "GIGACHAT_API_PERS", + "GIGACHAT_API_B2B", + "GIGACHAT_API_CORP" + ], + "default_value": "GIGACHAT_API_PERS" + }, + { + "key": "gigachat_auth_url", + "label": "Auth URL", + "placeholder": null, + "tooltip": null, + "required": false, + "field_type": "text", + "options": null, + "default_value": null + }, + { + "key": "gigachat_access_token", + "label": "Access token", + "placeholder": null, + "tooltip": "Disable OAuth, provide value to authorization.", + "required": false, + "field_type": "password", + "options": null, + "default_value": null + } + ], + "default_model_placeholder": "GigaChat-2" + }, { "provider": "GITHUB", "provider_display_name": "Github", diff --git a/litellm/proxy/rag_endpoints/endpoints.py b/litellm/proxy/rag_endpoints/endpoints.py index 4d62f1d6d71..db574f859b3 100644 --- a/litellm/proxy/rag_endpoints/endpoints.py +++ b/litellm/proxy/rag_endpoints/endpoints.py @@ -7,12 +7,14 @@ Provides: """ import base64 +import json from collections.abc import Mapping from typing import TYPE_CHECKING, Any, Final import orjson from fastapi import APIRouter, Depends, HTTPException, Request, Response, status from fastapi.responses import ORJSONResponse, StreamingResponse +from starlette.datastructures import UploadFile import litellm from litellm._logging import verbose_proxy_logger @@ -45,6 +47,16 @@ if TYPE_CHECKING: router: Final = APIRouter() +def _as_string_keyed_mapping(value: object) -> Mapping[str, object] | None: + if isinstance(value, Mapping): + return value + return None + + +def _response_attr(source: object, name: str) -> object: + return getattr(source, name, None) + + def _raise_vector_store_scan_depth_exceeded() -> None: raise HTTPException( status_code=400, @@ -53,8 +65,8 @@ def _raise_vector_store_scan_depth_exceeded() -> None: def _append_payload_to_scan_stack( - payload_stack: list[tuple[Any, int]], - value: Any, + payload_stack: list[tuple[object, int]], + value: object, next_depth: int, ) -> None: if isinstance(value, dict): @@ -117,7 +129,7 @@ async def _authorize_nested_vector_store_ids( def _build_file_metadata_entry( - response: Any, + response: object, file_data: tuple[str, bytes, str] | None = None, file_url: str | None = None, ) -> Mapping[str, str | int | None]: @@ -135,11 +147,11 @@ def _build_file_metadata_entry( from datetime import datetime, timezone # Extract file_id from response - file_id = None - if hasattr(response, "get"): - file_id = response.get("file_id") - elif hasattr(response, "file_id"): - file_id = response.file_id + mapping_response: Final = _as_string_keyed_mapping(response) + raw_file_id: Final = ( + mapping_response.get("file_id") if mapping_response is not None else _response_attr(response, "file_id") + ) + file_id: Final = raw_file_id if isinstance(raw_file_id, str) else None # Extract file information from file_data tuple filename = None @@ -152,7 +164,7 @@ def _build_file_metadata_entry( content_type = file_data[2] if len(file_data) > 2 else None # Build file metadata entry - file_entry: Final = { + file_entry: Final[dict[str, str | int | None]] = { "file_id": file_id, "filename": filename, "file_url": file_url, @@ -169,7 +181,7 @@ def _build_file_metadata_entry( async def _save_vector_store_to_db_from_rag_ingest( - response: Any, + response: object, ingest_options: Mapping[str, dict[str, str | None]], prisma_client: "PrismaClient", user_api_key_dict: UserAPIKeyAuth, @@ -197,10 +209,11 @@ async def _save_vector_store_to_db_from_rag_ingest( ) # Handle both dict and object responses - if hasattr(response, "get"): - vector_store_id = response.get("vector_store_id") + mapping_response: Final = _as_string_keyed_mapping(response) + if mapping_response is not None: + vector_store_id = mapping_response.get("vector_store_id") elif hasattr(response, "vector_store_id"): - vector_store_id = response.vector_store_id + vector_store_id = _response_attr(response, "vector_store_id") else: verbose_proxy_logger.warning("Unable to extract vector_store_id from response type: %s", type(response)) return @@ -266,14 +279,13 @@ async def _save_vector_store_to_db_from_rag_ingest( verbose_proxy_logger.info("Vector store %s already exists, appending file to metadata", vector_store_id) # Update existing vector store with new file - existing_metadata = existing_vector_store.vector_store_metadata or {} - if isinstance(existing_metadata, str): - import json + stored_metadata: Final = existing_vector_store.vector_store_metadata or {} + existing_metadata: dict[str, object] = ( + json.loads(stored_metadata) if isinstance(stored_metadata, str) else stored_metadata + ) - existing_metadata = json.loads(existing_metadata) - - ingested_files: Final = existing_metadata.get("ingested_files", []) - ingested_files.append(file_entry) + previous_files: Final = existing_metadata.get("ingested_files", []) + ingested_files: Final = [*previous_files, file_entry] if isinstance(previous_files, list) else [file_entry] existing_metadata["ingested_files"] = ingested_files # Update the vector store @@ -340,9 +352,9 @@ async def parse_rag_ingest_request( # Get file file_obj = form_data.get("file") - if file_obj is not None and hasattr(file_obj, "read"): + if isinstance(file_obj, UploadFile): file_content = await file_obj.read(MAX_UPLOAD_SIZE_BYTES + 1) - file_data = (file_obj.filename, file_content, file_obj.content_type) + file_data = (file_obj.filename or "", file_content, file_obj.content_type or "") # Parse JSON from 'request' form field (contains full request body as JSON) request_json_str: Final[str | bytes | None] = form_data.get("request") diff --git a/litellm/proxy/response_api_endpoints/endpoints.py b/litellm/proxy/response_api_endpoints/endpoints.py index 5e56e822484..5907ffc64eb 100644 --- a/litellm/proxy/response_api_endpoints/endpoints.py +++ b/litellm/proxy/response_api_endpoints/endpoints.py @@ -1,14 +1,18 @@ import asyncio import json import time -from collections.abc import AsyncIterator, Mapping +from collections.abc import AsyncIterator, Awaitable, Mapping +from enum import Enum from types import MappingProxyType -from typing import TYPE_CHECKING, Any, Final, NamedTuple, cast, get_args +from typing import TYPE_CHECKING, Any, Final, NamedTuple, Protocol, cast, get_args from uuid import uuid4 import fastapi from fastapi import APIRouter, Depends, HTTPException, Request, Response +from fastapi.responses import JSONResponse +from openai.types.responses.response_create_params import ResponseInputParam from starlette.websockets import WebSocket, WebSocketDisconnect +from typing_extensions import ReadOnly, TypedDict from litellm._logging import verbose_proxy_logger from litellm.integrations.custom_guardrail import ModifyResponseException @@ -26,8 +30,13 @@ from litellm.proxy.common_utils.http_parsing_utils import ( _read_request_body, _safe_set_request_parsed_body, ) -from litellm.types.llms.openai import REASONING_EFFORT, ResponsesAPIResponse +from litellm.types.llms.openai import ( + REASONING_EFFORT, + ResponsesAPIOptionalRequestParams, + ResponsesAPIResponse, +) from litellm.types.responses.main import DeleteResponseResult +from litellm.types.utils import TokenCountResponse if TYPE_CHECKING: from litellm.router import Router @@ -35,7 +44,7 @@ if TYPE_CHECKING: router: Final = APIRouter() _user_api_key_auth_dep: Final = Depends(user_api_key_auth) -_RESPONSES_TAGS: Final = ["responses"] # mutable-ok: fastapi's route signature requires List[str] tags +_RESPONSES_TAGS: Final[list[str | Enum]] = ["responses"] # mutable-ok: fastapi's route signature requires list tags _TOOL_PAYLOAD_KEYS: Final[Mapping[str, tuple[str, ...]]] = MappingProxyType( { @@ -43,7 +52,7 @@ _TOOL_PAYLOAD_KEYS: Final[Mapping[str, tuple[str, ...]]] = MappingProxyType( "function": ("name", "description", "parameters", "strict"), } ) -_EMPTY_TOOL_PAYLOAD: Final[Mapping[str, Any]] = MappingProxyType({}) +_EMPTY_TOOL_PAYLOAD: Final[Mapping[str, object]] = MappingProxyType({}) def _convert_tool_payload_value(key: str, value: object, *, to_chat: bool) -> object: @@ -96,7 +105,7 @@ def _normalize_tool_dialect( return {**data, **{key: value for key, value in replaceable if key in data}} # mutable-ok: plain body dict -def _is_chat_completions_body(data: Mapping[str, Any]) -> bool: +def _is_chat_completions_body(data: Mapping[str, object]) -> bool: messages: Final = data.get("messages") if isinstance(messages, list) and messages: return True @@ -1017,6 +1026,152 @@ async def compact_response( ) +class _ResponsesApiErrorDetail(TypedDict): + message: ReadOnly[str] + type: ReadOnly[str] + param: ReadOnly[str | None] + code: ReadOnly[str | None] + + +class _ResponsesApiErrorBody(TypedDict): + error: ReadOnly[_ResponsesApiErrorDetail] + + +class _ResponsesInputTokensResult(TypedDict): + object: ReadOnly[str] + input_tokens: ReadOnly[int] + + +class _TokenCountPayload(TypedDict): + model: ReadOnly[str] + messages: ReadOnly[tuple[Mapping[str, object], ...]] + tools: ReadOnly[object] + + +class _TokenCounter(Protocol): + def __call__(self, request: TokenCountRequest, call_endpoint: bool) -> Awaitable[TokenCountResponse]: ... + + +def _proxy_token_counter() -> _TokenCounter: + from litellm.proxy.proxy_server import token_counter + + return token_counter + + +_token_counter_dep: Final = Depends(_proxy_token_counter) + + +def _responses_invalid_request_response(message: str, param: str | None, code: str | None) -> JSONResponse: + body: Final[_ResponsesApiErrorBody] = { + "error": { + "message": message, + "type": "invalid_request_error", + "param": param, + "code": code, + } + } + return JSONResponse(status_code=400, content=body) + + +def _missing_responses_param_response(param: str) -> JSONResponse: + return _responses_invalid_request_response( + message=f"Missing required parameter: '{param}'.", + param=param, + code="missing_required_parameter", + ) + + +def _responses_input_as_token_count_messages( + input_value: str | ResponseInputParam, + instructions: str | None, +) -> tuple[Mapping[str, object], ...]: + from litellm.responses.litellm_completion_transformation.transformation import ( + LiteLLMCompletionResponsesConfig, + ) + + request_params: Final[ResponsesAPIOptionalRequestParams] = {"instructions": instructions} + transformed: Final = LiteLLMCompletionResponsesConfig.transform_responses_api_input_to_messages( + input=input_value, + responses_api_request=request_params, + ) + return tuple( + message if isinstance(message, dict) else message.model_dump(exclude_none=True) for message in transformed + ) + + +@router.post( + "/v1/responses/input_tokens", + dependencies=(_user_api_key_auth_dep,), + tags=_RESPONSES_TAGS, +) +@router.post( + "/responses/input_tokens", + dependencies=(_user_api_key_auth_dep,), + tags=_RESPONSES_TAGS, +) +@router.post( + "/openai/v1/responses/input_tokens", + dependencies=(_user_api_key_auth_dep,), + tags=_RESPONSES_TAGS, +) +async def responses_input_tokens( + request: Request, + token_counter: _TokenCounter = _token_counter_dep, +): + """ + Count the input tokens of a Responses API request without calling the model. + + Follows the OpenAI Responses API spec: https://platform.openai.com/docs/api-reference/responses/input-tokens + + ```bash + curl -X POST http://localhost:4000/v1/responses/input_tokens \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer sk-1234" \ + -d '{ + "model": "gpt-4o", + "input": "Hello, how are you?" + }' + ``` + + Returns: `{"object": "response.input_tokens", "input_tokens": }` + """ + data: Final = await _read_request_body(request=request) + model_name: Final = data.get("model") + input_value: Final = data.get("input") + if not isinstance(model_name, str) or not model_name: + return _missing_responses_param_response("model") + if input_value is None: + return _missing_responses_param_response("input") + if isinstance(input_value, (str, list)) and not input_value: + return _responses_invalid_request_response( + message="""One of "input" or "previous_response_id" or 'prompt' or 'conversation' must be provided.""", + param=None, + code="missing_required_parameter", + ) + + try: + payload: Final[_TokenCountPayload] = { + "model": model_name, + "messages": _responses_input_as_token_count_messages( + input_value=input_value, + instructions=data.get("instructions"), + ), + "tools": data.get("tools"), + } + token_request: Final = TokenCountRequest.model_validate(payload) + except Exception as e: + return _responses_invalid_request_response( + message=f"Invalid request for token counting: {e}", param=None, code=None + ) + + token_response: Final = await token_counter(request=token_request, call_endpoint=True) + result: Final[_ResponsesInputTokensResult] = { + "object": "response.input_tokens", + "input_tokens": token_response.total_tokens, + } + return result + + @router.post( "/v1/responses/{response_id}/cancel", dependencies=[Depends(user_api_key_auth)], @@ -1218,7 +1373,7 @@ async def _enforce_responses_ws_first_frame_model_auth( request: Request, model: str, user_api_key_dict: UserAPIKeyAuth, - llm_router: Any | None, + llm_router: "Router | None", ) -> None: from litellm.proxy.auth.user_api_key_auth import ( _enforce_key_and_fallback_model_access, @@ -1262,7 +1417,7 @@ async def _enforce_responses_ws_first_frame_model_auth( async def responses_websocket_endpoint( websocket: WebSocket, model: str | None = fastapi.Query(None, description="The model to use for the responses WebSocket session."), - user_api_key_dict=Depends(user_api_key_auth_websocket), + user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth_websocket), ): """ Responses API WebSocket mode endpoint. @@ -1307,7 +1462,7 @@ async def responses_websocket_endpoint( return model, first_message = result - data: dict[str, Any] = { + data: dict[str, object] = { "model": model, "websocket": websocket, } @@ -1316,7 +1471,7 @@ async def responses_websocket_endpoint( # Construct a synthetic Request for pre-call processing headers_list: Final = list(websocket.scope.get("headers") or []) - scope: Final[dict[str, Any]] = { + scope: Final[dict[str, object]] = { "type": "http", "method": "POST", "path": "/v1/responses", diff --git a/litellm/proxy/response_polling/background_streaming.py b/litellm/proxy/response_polling/background_streaming.py index f755c6d478b..0fd242f2bc1 100644 --- a/litellm/proxy/response_polling/background_streaming.py +++ b/litellm/proxy/response_polling/background_streaming.py @@ -76,17 +76,17 @@ class _StreamEventParser: async def background_streaming_task( polling_id: str, - data: dict, + data: dict[str, object], polling_handler: ResponsePollingHandler, request: Request, fastapi_response: Response, user_api_key_dict: UserAPIKeyAuth, - general_settings: dict, + general_settings: dict[str, object], llm_router: "Router | None", proxy_config: "ProxyConfig", proxy_logging_obj: "ProxyLogging", - select_data_generator, - user_model, + select_data_generator: Callable[..., object] | None, + user_model: str | None, user_temperature: float | None, user_request_timeout: float | None, user_max_tokens: int | None, @@ -144,10 +144,8 @@ async def background_streaming_task( # Process streaming response following OpenAI events format # https://platform.openai.com/docs/api-reference/responses-streaming - output_items: Final = dict[str, _OutputItem]() # Track output items by ID - accumulated_text: Final = dict[ - tuple[str, int], str - ]() # Track accumulated text deltas by (item_id, content_index) + output_items: Final = dict[str, _OutputItem]() + accumulated_text: Final = dict[tuple[str, int], str]() # ResponsesAPIResponse fields to extract from response.completed usage_data = None @@ -262,7 +260,6 @@ async def background_streaming_task( if "content" in delta_item: content_list = delta_item["content"] if content_index < len(content_list): - # Update existing content part with accumulated text content_entry = content_list[content_index] if isinstance(content_entry, dict): content_entry["text"] = accumulated_text[key] diff --git a/litellm/proxy/route_llm_request.py b/litellm/proxy/route_llm_request.py index 91a0c68fd58..3d0bd5e61c9 100644 --- a/litellm/proxy/route_llm_request.py +++ b/litellm/proxy/route_llm_request.py @@ -50,12 +50,12 @@ def _route_user_config_request(data: dict, route_type: str): return ret_val -def _is_a2a_agent_model(model_name: Any) -> bool: +def _is_a2a_agent_model(model_name: object) -> bool: """Check if the model name is for an A2A agent (a2a/ prefix).""" return isinstance(model_name, str) and model_name.startswith("a2a/") -def _raise_if_model_fully_blocked(llm_router: LitellmRouter, model_name: Any, team_id: str | None) -> None: +def _raise_if_model_fully_blocked(llm_router: LitellmRouter, model_name: object, team_id: str | None) -> None: if not isinstance(model_name, str) or not model_name: return if not isinstance(llm_router, litellm.Router): diff --git a/litellm/proxy/schema.prisma b/litellm/proxy/schema.prisma index 60223265211..7604ceadf7a 100644 --- a/litellm/proxy/schema.prisma +++ b/litellm/proxy/schema.prisma @@ -1529,14 +1529,16 @@ model LiteLLM_AutoRouterSession { model LiteLLM_ShadowEvalJob { id String @id @default(cuid()) group_id String // legs of one job share this; the API's job id - api_key_id String // hashed virtual key whose traffic this leg shadows - router_name String // the auto-router under evaluation, in either direction + target_type String @default("key") // key | team | user + target_id String // hashed virtual key, team_id, or user_id whose traffic this leg shadows + router_name String // first (often only) auto-router under evaluation; router_names is the full set + router_names String[] @default([]) // all routers this job runs as shadow arms; empty on legacy rows, whose set is (router_name) direction String @default("forward") // forward | reverse baseline_model String? // reverse only: the fixed model the router is judged against judge_model String shadow_percentage Float max_turns Int // sample-count ceiling: the whole budget on pre-max_budget jobs, the error-loop valve otherwise - max_budget Float? // per-key USD cap on the eval's own shadow + judge spend; null on jobs from before spend budgets + max_budget Float? // per-target USD cap on the eval's own shadow + judge spend; null on jobs from before spend budgets created_at DateTime @default(now()) created_by String? ends_at DateTime @@ -1544,7 +1546,7 @@ model LiteLLM_ShadowEvalJob { stopped_by String? // operator who stopped it early; null when it ended on its own @@index([group_id]) - @@index([api_key_id]) + @@index([target_type, target_id]) @@index([created_at]) } @@ -1554,6 +1556,7 @@ model LiteLLM_ShadowEvalAttempt { job_id String request_id String // the judged real request outcome String // real | shadow | tie | error + router_name String? // the arm this verdict scores; NULL on legacy rows, meaning the job's own router tier String? // router's tier for the prompt, when classified real_model String? shadow_model String? diff --git a/litellm/proxy/spend_tracking/budget_reservation.py b/litellm/proxy/spend_tracking/budget_reservation.py index 8b2a5dd9312..f4d8fd7d906 100644 --- a/litellm/proxy/spend_tracking/budget_reservation.py +++ b/litellm/proxy/spend_tracking/budget_reservation.py @@ -6,7 +6,7 @@ from collections.abc import Mapping, Sequence from dataclasses import dataclass from datetime import datetime, timedelta, timezone from types import MappingProxyType -from typing import Any, Final, NoReturn, cast +from typing import Final, NoReturn, SupportsFloat, SupportsIndex, SupportsInt, cast from fastapi import HTTPException, status @@ -35,6 +35,7 @@ from litellm.proxy.common_utils.user_api_key_cache import ( from litellm.proxy.utils import PrismaClient, ProxyLogging from litellm.router import Router from litellm.types.proxy.model_access_group_budget import ModelAccessGroupBudget +from litellm.types.router import DeploymentTypedDict @dataclass @@ -172,7 +173,14 @@ async def reserve_budget_for_request( ) -> dict | None: if valid_token is None or not RouteChecks.is_llm_api_route(route=route): return None - if route in {"/models", "/v1/models", "/utils/token_counter"}: + if route in { + "/models", + "/v1/models", + "/utils/token_counter", + "/responses/input_tokens", + "/v1/responses/input_tokens", + "/openai/v1/responses/input_tokens", + }: return None if get_model_from_request(request_body, route, llm_router=llm_router) is None: return None @@ -690,7 +698,7 @@ def _get_budget_limit_counters( for window in budget_limits: window_dict = _coerce_window(window) budget_duration = window_dict.get("budget_duration") - max_budget = window_dict.get("max_budget") + max_budget = _to_float(window_dict.get("max_budget")) if not budget_duration or max_budget is None or max_budget <= 0: continue window_start = get_budget_window_start(window_dict) @@ -717,18 +725,20 @@ def _get_budget_limit_counters( return counters -def _coerce_window(window: Any) -> dict: - if isinstance(window, dict): +def _coerce_window(window: object) -> Mapping[str, object]: + if isinstance(window, Mapping): return window if isinstance(window, str): try: - parsed: Final = json.loads(window) - return parsed if isinstance(parsed, dict) else {} + parsed: Final[object] = json.loads(window) except Exception: return {} - if hasattr(window, "model_dump"): - return window.model_dump() - return {} + return parsed if isinstance(parsed, Mapping) else {} + model_dump: Final = getattr(window, "model_dump", None) + if not callable(model_dump): + return {} + dumped: Final[object] = model_dump() + return dumped if isinstance(dumped, Mapping) else {} async def _reserve_counter( @@ -946,7 +956,7 @@ def _get_entry_reserved_cost(entry: dict, default_reserved_cost: float) -> float return default_reserved_cost -def get_budget_window_start(window: Any) -> datetime | None: +def get_budget_window_start(window: object) -> datetime | None: window_dict: Final = _coerce_window(window) budget_duration: Final = window_dict.get("budget_duration") if budget_duration is None: @@ -964,7 +974,7 @@ def get_budget_window_start(window: Any) -> datetime | None: return reset_at - timedelta(seconds=duration_seconds) -def _coerce_datetime(value: Any) -> datetime | None: +def _coerce_datetime(value: object) -> datetime | None: if value is None: return None if isinstance(value, datetime): @@ -1238,11 +1248,11 @@ def _get_model_cost_infos( def _deployment_tiered_pricing_table( - deployment: dict[str, Any], + deployment: DeploymentTypedDict, llm_router: Router, -) -> list[dict] | None: - model_id: Final = deployment.get("model_info", {}).get("id") - backend_model: Final = deployment.get("litellm_params", {}).get("model") +) -> Sequence[Mapping[str, object]] | None: + model_id: Final = _get_value(_get_value(deployment, "model_info"), "id") + backend_model: Final = _get_value(_get_value(deployment, "litellm_params"), "model") if not isinstance(model_id, str) or not isinstance(backend_model, str): return None deployment_model_info: Final = llm_router.get_deployment_model_info(model_id=model_id, model_name=backend_model) @@ -1407,7 +1417,7 @@ def _estimate_output_tokens( return min(requested, model_ceiling) -def _count_text_tokens(model: str, text: Any) -> int: +def _count_text_tokens(model: str, text: object) -> int: if text is None: return 0 @@ -1447,8 +1457,8 @@ def _is_input_only_route(route: str) -> bool: ) -def _to_float(value: Any) -> float | None: - if value is None: +def _to_float(value: object) -> float | None: + if not isinstance(value, (SupportsFloat, SupportsIndex, str, bytes, bytearray)): return None try: return float(value) @@ -1456,8 +1466,8 @@ def _to_float(value: Any) -> float | None: return None -def _to_int(value: Any) -> int | None: - if value is None: +def _to_int(value: object) -> int | None: + if not isinstance(value, (SupportsInt, SupportsIndex, str, bytes, bytearray)): return None try: return int(value) @@ -1465,7 +1475,7 @@ def _to_int(value: Any) -> int | None: return None -def _get_value(obj: Any, key: str) -> Any: - if isinstance(obj, dict): +def _get_value(obj: object, key: str) -> object: + if isinstance(obj, Mapping): return obj.get(key) return getattr(obj, key, None) diff --git a/litellm/proxy/spend_tracking/spend_tracking_utils.py b/litellm/proxy/spend_tracking/spend_tracking_utils.py index 9f718b7d20d..7442d71bd96 100644 --- a/litellm/proxy/spend_tracking/spend_tracking_utils.py +++ b/litellm/proxy/spend_tracking/spend_tracking_utils.py @@ -1,10 +1,10 @@ import os import re import secrets -from collections.abc import Mapping +from collections.abc import Mapping, Sequence from datetime import datetime, timezone from datetime import datetime as dt -from typing import Any, Final, Literal, cast +from typing import Final, Literal, Protocol, cast, runtime_checkable from pydantic import BaseModel @@ -222,7 +222,28 @@ def get_spend_logs_id(call_type: str, response_obj: dict, kwargs: dict) -> str | return resolved_id -def _extract_usage_for_ocr_call(response_obj: Any, response_obj_dict: dict) -> dict: +_MISSING_ATTRIBUTE: Final = object() + + +def _attribute_or_missing(source: object, name: str) -> object: + return getattr(source, name, _MISSING_ATTRIBUTE) + + +@runtime_checkable +class _ModelDumpable(Protocol): + def model_dump(self) -> object: ... + + +def _dumped_usage_info(usage_info: object) -> object: + if isinstance(usage_info, _ModelDumpable): + return usage_info.model_dump() + instance_dict: Final = _attribute_or_missing(usage_info, "__dict__") + if instance_dict is not _MISSING_ATTRIBUTE: + return instance_dict + return usage_info + + +def _extract_usage_for_ocr_call(response_obj: object, response_obj_dict: dict) -> dict: """ Extract usage information for OCR/AOCR calls. @@ -243,12 +264,10 @@ def _extract_usage_for_ocr_call(response_obj: Any, response_obj_dict: dict) -> d usage_info = response_obj_dict.get("usage_info") # Try to extract usage_info from object attributes if not found in dict - if not usage_info and hasattr(response_obj, "usage_info"): - usage_info = response_obj.usage_info - if hasattr(usage_info, "model_dump"): - usage_info = usage_info.model_dump() - elif hasattr(usage_info, "__dict__"): - usage_info = vars(usage_info) + if not usage_info: + attribute_usage_info: Final = _attribute_or_missing(response_obj, "usage_info") + if attribute_usage_info is not _MISSING_ATTRIBUTE: + usage_info = _dumped_usage_info(attribute_usage_info) # For OCR, we track pages instead of tokens if usage_info is not None: @@ -620,6 +639,14 @@ def _ensure_datetime_utc(timestamp: datetime) -> datetime: return timestamp +async def _query_raw_rows( + prisma_client: PrismaClient, + sql_query: str, + *args: object, +) -> Sequence[Mapping[str, object]] | None: + return await prisma_client.db.query_raw(sql_query, *args) + + async def get_spend_by_team( start_date: dt, end_date: dt, @@ -681,7 +708,7 @@ async def get_spend_by_team( group_by_day; """ - db_response: Final = await prisma_client.db.query_raw(sql_query, start_date, end_date, team_id) + db_response: Final = await _query_raw_rows(prisma_client, sql_query, start_date, end_date, team_id) if db_response is None: return [] @@ -756,7 +783,7 @@ async def get_spend_by_team_and_customer( group_by_day; """ - db_response: Final = await prisma_client.db.query_raw(sql_query, start_date, end_date, team_id, customer_id) + db_response: Final = await _query_raw_rows(prisma_client, sql_query, start_date, end_date, team_id, customer_id) if db_response is None: return [] @@ -811,7 +838,7 @@ def _sanitize_request_body_for_spend_logs_payload( return {} visited.add(obj_id) - def _sanitize_value(value: Any) -> Any: + def _sanitize_value(value: object) -> object: if isinstance(value, dict): return _sanitize_request_body_for_spend_logs_payload(value, visited, max_string_length_prompt_in_db) elif isinstance(value, list): @@ -1106,7 +1133,7 @@ def _sanitize_error_information_for_spend_logs( return cast(StandardLoggingPayloadErrorInformation, sanitized) -def _convert_to_json_serializable_dict(obj: Any, visited: set | None = None, max_depth: int = 20) -> Any: +def _convert_to_json_serializable_dict(obj: object, visited: set[int] | None = None, max_depth: int = 20) -> object: """ Convert object to JSON-serializable dict, handling Pydantic models safely. @@ -1160,6 +1187,13 @@ def _convert_to_json_serializable_dict(obj: Any, visited: set | None = None, max visited.remove(obj_id) +def _convert_mapping_to_json_serializable(obj: Mapping[str, object]) -> dict[str, object]: + converted: Final = _convert_to_json_serializable_dict(obj) + if isinstance(converted, dict): + return converted + return dict(obj) + + def _get_proxy_server_request_for_spend_logs_payload( metadata: dict, litellm_params: dict, @@ -1196,7 +1230,7 @@ def _get_proxy_server_request_for_spend_logs_payload( # If redaction is enabled, convert to serializable dict before redacting if should_redact_message_logging(model_call_details=model_call_details): - _request_body = _convert_to_json_serializable_dict(_request_body) + _request_body = _convert_mapping_to_json_serializable(_request_body) perform_redaction(model_call_details=_request_body, result=None) _request_body = _sanitize_request_body_for_spend_logs_payload(_request_body) @@ -1241,7 +1275,7 @@ def _get_response_for_spend_logs_payload( if payload is None: return "{}" if _should_store_prompts_and_responses_in_spend_logs(): - response_obj: Any = payload.get("response") + response_obj: object = payload.get("response") if response_obj is None: return "{}" diff --git a/litellm/proxy/ui_crud_endpoints/proxy_setting_endpoints.py b/litellm/proxy/ui_crud_endpoints/proxy_setting_endpoints.py index a1eb7ed06eb..52258602581 100644 --- a/litellm/proxy/ui_crud_endpoints/proxy_setting_endpoints.py +++ b/litellm/proxy/ui_crud_endpoints/proxy_setting_endpoints.py @@ -3,10 +3,11 @@ import asyncio import json import os from collections import Counter -from collections.abc import Mapping +from collections.abc import Mapping, Sequence +from types import MappingProxyType from typing import ( - Any, Final, + NamedTuple, Protocol, cast, # noqa: TID251 # prisma types Json columns as fields.Json but de-serializes them to plain python on read ) @@ -15,6 +16,7 @@ from urllib.parse import urlparse from fastapi import APIRouter, Body, Depends, File, HTTPException, UploadFile from pydantic import ConfigDict, JsonValue, ValidationError, create_model from pydantic.fields import FieldInfo +from typing_extensions import NotRequired, ReadOnly, TypedDict import litellm from litellm._logging import verbose_proxy_logger @@ -44,6 +46,31 @@ from litellm.types.proxy.management_endpoints.ui_sso import ( router: Final = APIRouter() +JsonSchemaItems: Final = TypedDict( + "JsonSchemaItems", + {"$ref": ReadOnly[str], "enum": ReadOnly[Sequence[JsonValue]]}, + total=False, +) + + +class JsonSchemaNode(TypedDict, total=False): + type: ReadOnly[str] + description: ReadOnly[str] + enum: ReadOnly[Sequence[JsonValue]] + anyOf: ReadOnly[Sequence["JsonSchemaNode"]] + items: ReadOnly["JsonSchemaItems"] + properties: ReadOnly[Mapping[str, "JsonSchemaNode"]] + + +_EMPTY_SCHEMA_DEFS: Final[Mapping[str, "JsonSchemaNode"]] = MappingProxyType({}) + + +class JsonSchemaPropertyEntry(TypedDict): + description: ReadOnly[str] + type: ReadOnly[str] + items: NotRequired[ReadOnly["JsonSchemaItems"]] + + class _SsoSettingsMappingRow(Protocol): @property def sso_settings(self) -> Mapping[str, object] | None: ... @@ -157,10 +184,10 @@ class UIThemeConfig(BaseModel): class SettingsResponse(BaseModel): """Base response model for settings with values and schema information""" - values: dict[str, Any] + values: dict[str, object] """The current configuration values""" - field_schema: dict[str, Any] + field_schema: dict[str, object] """Schema information including descriptions and property types for UI display""" @@ -548,6 +575,62 @@ async def delete_allowed_ip( return {"message": f"IP {ip_address.ip} deleted successfully", "status": "success"} +def _resolve_non_null_variant(field_info: JsonSchemaNode) -> JsonSchemaNode: + """Pydantic v2 renders Optional fields as ``anyOf: [actual_type, null]``.""" + if "anyOf" not in field_info: + return field_info + return next((variant for variant in field_info["anyOf"] if variant.get("type") != "null"), field_info) + + +def _schema_items_entry(resolved: JsonSchemaNode, defs: Mapping[str, JsonSchemaNode]) -> "JsonSchemaItems | None": + """Items info (including enum values) for array fields, so the UI can render a multi-select dropdown.""" + if "items" not in resolved: + return None + items: Final = resolved["items"] + if "$ref" not in items: + return items + ref_def: Final = defs.get(items["$ref"].split("/")[-1]) + if ref_def is None or "enum" not in ref_def: + return None + enum_items: Final[JsonSchemaItems] = {"enum": ref_def["enum"]} + return enum_items + + +def _schema_property_entry(field_info: JsonSchemaNode, defs: Mapping[str, JsonSchemaNode]) -> JsonSchemaPropertyEntry: + resolved: Final = _resolve_non_null_variant(field_info) + items_entry: Final = _schema_items_entry(resolved, defs) + description: Final = field_info.get("description", "") + type_name: Final = resolved.get("type", "string") + if items_entry is None: + entry: Final[JsonSchemaPropertyEntry] = {"description": description, "type": type_name} + return entry + entry_with_items: Final[JsonSchemaPropertyEntry] = { + "description": description, + "type": type_name, + "items": items_entry, + } + return entry_with_items + + +class _RootSchema(NamedTuple): + description: str + properties: Mapping[str, JsonSchemaNode] + nested_defs: Mapping[str, JsonSchemaNode] + defs: Mapping[str, JsonSchemaNode] + + +def _root_schema(settings_class: type[BaseModel]) -> _RootSchema: + from pydantic import TypeAdapter + + raw_schema: Final = TypeAdapter(settings_class).json_schema(by_alias=True) + return _RootSchema( + description=raw_schema.get("description", ""), + properties=raw_schema["properties"], + nested_defs=raw_schema.get("definitions", _EMPTY_SCHEMA_DEFS), + defs=raw_schema["$defs"] if "$defs" in raw_schema else raw_schema.get("definitions", _EMPTY_SCHEMA_DEFS), + ) + + async def _get_settings_with_schema( settings_key: str, settings_class: type[BaseModel], @@ -561,69 +644,43 @@ async def _get_settings_with_schema( settings_class: The Pydantic class to use for schema config: The config dictionary """ - from pydantic import TypeAdapter - litellm_settings: Final = config.get("litellm_settings", {}) or {} settings_data: Final = litellm_settings.get(settings_key, {}) or {} # Create the settings object settings: Final = settings_class(**(settings_data)) # Get the schema - schema: Final = TypeAdapter(settings_class).json_schema(by_alias=True) + root_schema: Final = _root_schema(settings_class) # Convert to dict for response settings_dict: Final = settings.model_dump() # Add descriptions to the response - result: Final = { - "values": settings_dict, - "field_schema": { - "description": schema.get("description", ""), - "properties": {}, - }, + schema_properties_out: Final[Mapping[str, JsonSchemaPropertyEntry]] = { + field_name: _schema_property_entry(field_info, root_schema.defs) + for field_name, field_info in root_schema.properties.items() } - # Add property descriptions - defs: Final = schema.get("$defs", schema.get("definitions", {})) - for field_name, field_info in schema["properties"].items(): - # For Optional fields, Pydantic v2 uses anyOf with [actual_type, null]. - # Resolve the non-null variant to get the real type and items. - resolved = field_info - if "anyOf" in field_info: - for variant in field_info["anyOf"]: - if variant.get("type") != "null": - resolved = variant - break - - prop_entry: dict = { - "description": field_info.get("description", ""), - "type": resolved.get("type", "string"), - } - # Pass through items info (including enum values) for array fields - # so the UI can render a multi-select dropdown - if "items" in resolved: - items = resolved["items"] - # Resolve $ref to enum definitions if needed - if "$ref" in items: - ref_name = items["$ref"].split("/")[-1] - ref_def = defs.get(ref_name, {}) - if "enum" in ref_def: - prop_entry["items"] = {"enum": ref_def["enum"]} - else: - prop_entry["items"] = items - result["field_schema"]["properties"][field_name] = prop_entry - # Add nested object descriptions - for def_name, def_schema in schema.get("definitions", {}).items(): - result["field_schema"][def_name] = { + nested_defs_out: Final[Mapping[str, Mapping[str, object]]] = { + def_name: { "description": def_schema.get("description", ""), "properties": { prop_name: {"description": prop_info.get("description", "")} for prop_name, prop_info in def_schema.get("properties", {}).items() }, } + for def_name, def_schema in root_schema.nested_defs.items() + } - return result + return { + "values": settings_dict, + "field_schema": { + "description": root_schema.description, + "properties": schema_properties_out, + **nested_defs_out, + }, + } @router.get( @@ -930,32 +987,29 @@ async def get_sso_settings(): resolved: Final = resolve_sso_config(sso_db_settings, os.environ) # Get the schema for UI display - from pydantic import TypeAdapter - - schema: Final = TypeAdapter(SSOConfig).json_schema(by_alias=True) + root_schema: Final = _root_schema(SSOConfig) # Convert to dict for response, masking OAuth client secrets so plaintext # is never sent to the UI. sso_dict: Final = mask_sensitive_keys(resolved.config.model_dump(), set(SSO_SECRET_FIELDS)) # Add descriptions to the response - result: Final = { - "values": sso_dict, - "provenance": resolved.provenance, - "field_schema": { - "description": schema.get("description", ""), - "properties": {}, - }, - } - - # Add property descriptions - for field_name, field_info in schema["properties"].items(): - result["field_schema"]["properties"][field_name] = { + schema_properties_out: Final[Mapping[str, Mapping[str, str]]] = { + field_name: { "description": field_info.get("description", ""), "type": field_info.get("type", "string"), } + for field_name, field_info in root_schema.properties.items() + } - return result + return { + "values": sso_dict, + "provenance": resolved.provenance, + "field_schema": { + "description": root_schema.description, + "properties": schema_properties_out, + }, + } @router.patch( @@ -1309,7 +1363,7 @@ UI_SETTINGS_CACHE_KEY: Final = "ui_settings:settings_dict" UI_SETTINGS_CACHE_TTL: Final = 600 # 10 minutes -async def get_ui_settings_cached() -> dict[str, Any]: +async def get_ui_settings_cached() -> dict[str, JsonValue]: """ Return the persisted UI settings dict, using DualCache for reads. diff --git a/litellm/proxy/video_endpoints/endpoints.py b/litellm/proxy/video_endpoints/endpoints.py index d985a546fa7..66071c05b4f 100644 --- a/litellm/proxy/video_endpoints/endpoints.py +++ b/litellm/proxy/video_endpoints/endpoints.py @@ -1,6 +1,6 @@ #### Video Endpoints ##### -from typing import Any, Final +from typing import Final from fastapi import APIRouter, Depends, File, Form, Request, Response, UploadFile from fastapi.responses import ORJSONResponse @@ -161,7 +161,7 @@ async def video_list( # Read query parameters query_params: Final = dict(request.query_params) - data: Final[dict[str, Any]] = {"query_params": query_params} + data: Final[dict[str, object]] = {"query_params": query_params} # Extract custom_llm_provider from headers, query params, or body custom_llm_provider: Final = ( @@ -246,7 +246,7 @@ async def video_status( ) # Create data with video_id - data: Final[dict[str, Any]] = {"video_id": video_id} + data: Final[dict[str, object]] = {"video_id": video_id} decoded: Final = decode_video_id_with_provider(video_id) provider_from_id: Final = decoded.get("custom_llm_provider") @@ -345,7 +345,7 @@ async def video_content( ) # Create data with video_id - data: Final[dict[str, Any]] = {"video_id": video_id} + data: Final[dict[str, object]] = {"video_id": video_id} decoded: Final = decode_video_id_with_provider(video_id) provider_from_id: Final = decoded.get("custom_llm_provider") @@ -653,7 +653,7 @@ async def video_get_character( ) original_requested_character_id: Final = character_id - data: Final[dict[str, Any]] = {"character_id": character_id} + data: Final[dict[str, object]] = {"character_id": character_id} decoded: Final = decode_character_id_with_provider(character_id) provider_from_id: Final = decoded.get("custom_llm_provider") diff --git a/litellm/rag/ingestion/vertex_ai_ingestion.py b/litellm/rag/ingestion/vertex_ai_ingestion.py index 07f9f346d08..eff8ad1b8cb 100644 --- a/litellm/rag/ingestion/vertex_ai_ingestion.py +++ b/litellm/rag/ingestion/vertex_ai_ingestion.py @@ -186,7 +186,6 @@ class VertexAIRAGIngestion(BaseRAGIngestion, VertexBase): base_url: Final = get_vertex_base_url(self.location) url: Final = f"{base_url}/v1beta1/projects/{self.project_id}/locations/{self.location}/ragCorpora" - # Build request body with camelCase keys (Vertex AI API format) vector_db_config: Final = self.vector_store_config.get("vector_db_config") embedding_model: Final = self.vector_store_config.get("embedding_model") embedding_model_config: Final = ( @@ -447,7 +446,6 @@ class VertexAIRAGIngestion(BaseRAGIngestion, VertexBase): # Add max embedding requests per minute if specified max_embedding_qpm: Final = self.vector_store_config.get("max_embedding_requests_per_min") - # Build request body with camelCase keys (Vertex AI API format) chunking_config: Final = ( {"chunkSize": chunk_size or 1024, "chunkOverlap": chunk_overlap or 200} if chunk_size or chunk_overlap diff --git a/litellm/rag/main.py b/litellm/rag/main.py index 2dcaa200cc6..7bc1a6a52a3 100644 --- a/litellm/rag/main.py +++ b/litellm/rag/main.py @@ -29,6 +29,7 @@ from litellm.rag.ingestion.openai_ingestion import OpenAIRAGIngestion from litellm.rag.ingestion.s3_vectors_ingestion import S3VectorsRAGIngestion from litellm.rag.ingestion.vertex_ai_ingestion import VertexAIRAGIngestion from litellm.rag.rag_query import RAGQuery +from litellm.types.llms.openai import AllMessageValues from litellm.types.rag import ( RAGIngestOptions, RAGIngestResponse, @@ -204,7 +205,7 @@ def _suppressed_sub_call_billing() -> Iterator[None]: async def _execute_query_pipeline( model: str, - messages: list[Any], + messages: list[AllMessageValues], retrieval_config: dict[str, Any], rerank: dict[str, Any] | None = None, stream: bool = False, @@ -311,7 +312,7 @@ async def _execute_query_pipeline( @client async def aquery( model: str, - messages: list[Any], + messages: list[AllMessageValues], retrieval_config: dict[str, Any], rerank: dict[str, Any] | None = None, stream: bool = False, @@ -358,12 +359,12 @@ async def aquery( @client def query( model: str, - messages: list[Any], + messages: list[AllMessageValues], retrieval_config: dict[str, Any], rerank: dict[str, Any] | None = None, stream: bool = False, **kwargs, -) -> ModelResponse | Coroutine[Any, Any, ModelResponse]: +) -> ModelResponse | Coroutine[None, None, ModelResponse]: """ Query a RAG pipeline. """ @@ -410,7 +411,7 @@ def ingest( file_id: str | None = None, timeout: float | httpx.Timeout | None = None, **kwargs, -) -> RAGIngestResponse | Coroutine[Any, Any, RAGIngestResponse]: +) -> RAGIngestResponse | Coroutine[None, None, RAGIngestResponse]: """ Ingest a document into a vector store. diff --git a/litellm/responses/litellm_completion_transformation/custom_tools.py b/litellm/responses/litellm_completion_transformation/custom_tools.py index cccae06c74b..90491739bb0 100644 --- a/litellm/responses/litellm_completion_transformation/custom_tools.py +++ b/litellm/responses/litellm_completion_transformation/custom_tools.py @@ -45,6 +45,21 @@ def is_custom_tool_call(tool_name: str, custom_tool_names: set[str]) -> bool: return tool_name in custom_tool_names +def serialize_tool_call_arguments(raw_arguments: object, default: str = "") -> str: + """Render tool call arguments as the JSON string tool-call schemas require. + + Arguments normally arrive already JSON-encoded, but clients and providers + also send the decoded object. ``str()`` on a dict yields a Python repr with + single quotes, which every downstream JSON parser rejects with errors like + "Expecting ',' delimiter". + """ + if isinstance(raw_arguments, str): + return raw_arguments or default + if raw_arguments is None: + return default + return json.dumps(raw_arguments, default=str) + + def unwrap_custom_tool_arguments(arguments: str) -> str: """Extract the raw content string from JSON-wrapped arguments. diff --git a/litellm/responses/litellm_completion_transformation/streaming_iterator.py b/litellm/responses/litellm_completion_transformation/streaming_iterator.py index 8b1eeb30306..b2edf2bf9ed 100644 --- a/litellm/responses/litellm_completion_transformation/streaming_iterator.py +++ b/litellm/responses/litellm_completion_transformation/streaming_iterator.py @@ -8,6 +8,7 @@ from litellm.main import stream_chunk_builder from litellm.responses.litellm_completion_transformation.custom_tools import ( build_tool_call_item_kwargs, extract_custom_tool_names, + serialize_tool_call_arguments, ) from litellm.responses.litellm_completion_transformation.transformation import ( LiteLLMCompletionResponsesConfig, @@ -213,10 +214,10 @@ class LiteLLMCompletionStreamingIterator(ResponsesAPIStreamingIterator): fn_args_delta = "" if isinstance(fn, dict): fn_name = str(fn.get("name") or "") - fn_args_delta = str(fn.get("arguments") or "") + fn_args_delta = serialize_tool_call_arguments(fn.get("arguments")) else: fn_name = str(getattr(fn, "name", "") or "") - fn_args_delta = str(getattr(fn, "arguments", "") or "") + fn_args_delta = serialize_tool_call_arguments(getattr(fn, "arguments", "")) tool_name, tool_namespace = self._responses_namespace_tool_call_fields(fn_name) output_index = self._get_or_assign_tool_output_index(call_id) @@ -284,10 +285,10 @@ class LiteLLMCompletionStreamingIterator(ResponsesAPIStreamingIterator): fn_args = "" if isinstance(fn, dict): fn_name = str(fn.get("name") or "") - fn_args = str(fn.get("arguments") or "") + fn_args = serialize_tool_call_arguments(fn.get("arguments")) else: fn_name = str(getattr(fn, "name", "") or "") - fn_args = str(getattr(fn, "arguments", "") or "") + fn_args = serialize_tool_call_arguments(getattr(fn, "arguments", "")) tool_name, tool_namespace = self._responses_namespace_tool_call_fields(fn_name) # Track if this is a new tool call that wasn't streamed diff --git a/litellm/responses/litellm_completion_transformation/transformation.py b/litellm/responses/litellm_completion_transformation/transformation.py index f39df38d069..3b7810e97e5 100644 --- a/litellm/responses/litellm_completion_transformation/transformation.py +++ b/litellm/responses/litellm_completion_transformation/transformation.py @@ -93,6 +93,7 @@ from .custom_tools import ( convert_custom_tool_to_function_tool, extract_custom_tool_names, is_custom_tool_call, + serialize_tool_call_arguments, unwrap_custom_tool_arguments, validated_allowed_callers, ) @@ -1010,7 +1011,7 @@ class LiteLLMCompletionResponsesConfig: type=cast(Literal["function"], tool_use_type), function=ChatCompletionToolCallFunctionChunk( name=str(function.get("name", "")), - arguments=str(function.get("arguments", "{}")), + arguments=serialize_tool_call_arguments(function.get("arguments"), "{}"), ), index=index, ) @@ -1539,7 +1540,7 @@ class LiteLLMCompletionResponsesConfig: type=cast(Literal["function"], _tool_use_definition.get("type") or "function"), function=ChatCompletionToolCallFunctionChunk( name=function.get("name") or "", - arguments=str(function.get("arguments") or ""), + arguments=serialize_tool_call_arguments(function.get("arguments")), ), index=0, ) @@ -1589,7 +1590,7 @@ class LiteLLMCompletionResponsesConfig: type="function", function=ChatCompletionToolCallFunctionChunk( name=f"{namespace}__{raw_name}" if qualify else raw_name, - arguments=str(raw_arguments or ""), + arguments=serialize_tool_call_arguments(raw_arguments), ), index=0, ) @@ -1629,6 +1630,8 @@ class LiteLLMCompletionResponsesConfig: file_dict["file_id"] = file_id if item.get("file_data"): file_dict["file_data"] = item["file_data"] + if item.get("filename"): + file_dict["filename"] = item["filename"] new_item: Final[dict[str, object]] = {"type": "file", "file": file_dict} if "cache_control" in item: @@ -2022,7 +2025,7 @@ class LiteLLMCompletionResponsesConfig: function_definition = tool.function tool_name = function_definition.name or "" tool_id = tool.id or "" - tool_arguments = function_definition.get("arguments") or "" + tool_arguments = serialize_tool_call_arguments(function_definition.get("arguments")) # Check if this is a custom tool if is_custom_tool_call(tool_name, custom_tool_names): @@ -2557,7 +2560,7 @@ class LiteLLMCompletionResponsesConfig: type="function", function=Function( name=tool_call.get("name") or "", - arguments=tool_call.get("arguments") or "", + arguments=serialize_tool_call_arguments(tool_call.get("arguments")), ), ) diff --git a/litellm/responses/mcp/litellm_proxy_mcp_handler.py b/litellm/responses/mcp/litellm_proxy_mcp_handler.py index 197d0c02ba8..367915156d1 100644 --- a/litellm/responses/mcp/litellm_proxy_mcp_handler.py +++ b/litellm/responses/mcp/litellm_proxy_mcp_handler.py @@ -399,7 +399,7 @@ class LiteLLM_Proxy_MCP_Handler: @staticmethod async def _process_mcp_tools_without_openai_transform( - user_api_key_auth: Any, + user_api_key_auth: "UserAPIKeyAuth | None", mcp_tools_with_litellm_proxy: Sequence[Mapping[str, object]], litellm_trace_id: str | None = None, mcp_auth_header: str | None = None, @@ -636,7 +636,7 @@ class LiteLLM_Proxy_MCP_Handler: async def _execute_tool_calls( tool_server_map: dict[str, str], tool_calls: Sequence[object], - user_api_key_auth: Any, + user_api_key_auth: "UserAPIKeyAuth | None", mcp_auth_header: str | None = None, mcp_server_auth_headers: dict[str, dict[str, str]] | None = None, oauth2_headers: dict[str, str] | None = None, diff --git a/litellm/responses/streaming_iterator.py b/litellm/responses/streaming_iterator.py index d070f7758fd..de802b95086 100644 --- a/litellm/responses/streaming_iterator.py +++ b/litellm/responses/streaming_iterator.py @@ -77,6 +77,16 @@ def _is_json_array(value: object) -> TypeIs[list[object]]: # guard-ok: trivial return isinstance(value, list) +def _optional_str(value: object) -> str | None: + """Keep a JSON payload entry only when it is a string, since the wire format is caller-controlled.""" + return value if isinstance(value, str) else None + + +def _json_array_or_empty(value: object) -> Sequence[object]: + """Narrow a JSON payload entry that the caller iterates, tolerating a missing or malformed value.""" + return value if _is_json_array(value) else () + + def _is_str_mapping(value: object) -> TypeIs[dict[str, str]]: # guard-ok: verifies every value is str return _is_json_object(value) and all(isinstance(item, str) for item in value.values()) @@ -96,10 +106,6 @@ class _GetsLitellmParams(Protocol): def __call__(self, key: str, default: Mapping[str, object], /) -> LiteLLM_Params: ... -class _PopsOptionalStr(Protocol): - def __call__(self, key: str, default: None, /) -> str | None: ... - - class _UnmasksPiiText(Protocol): def __call__(self, text: str, pii_tokens: Mapping[str, str]) -> str: ... @@ -127,10 +133,6 @@ def _typed_gets_litellm_params(fn: _GetsLitellmParams) -> _GetsLitellmParams: return fn -def _typed_pops_optional_str(fn: _PopsOptionalStr) -> _PopsOptionalStr: - return fn - - _SHOULD_STORE_RESULT_IN_CACHE_ATTR: Final = "_should_store_result_in_cache" _UNMASK_PII_TEXT_ATTR: Final = "_unmask_pii_text" @@ -342,7 +344,7 @@ class BaseResponsesAPIStreamingIterator: ResponsesAPIStreamEvents.OUTPUT_ITEM_ADDED, ResponsesAPIStreamEvents.OUTPUT_ITEM_DONE, ): - _item: Final = getattr(openai_responses_api_chunk, "item", None) + _item: Final[object] = getattr(openai_responses_api_chunk, "item", None) if _item is not None: ResponsesAPIRequestUtils._encode_container_id_on_output_item( item=_item, @@ -350,7 +352,7 @@ class BaseResponsesAPIStreamingIterator: model_id=_stream_model_id, ) elif _event_type == ResponsesAPIStreamEvents.OUTPUT_TEXT_ANNOTATION_ADDED: - _annotation: Final = getattr(openai_responses_api_chunk, "annotation", None) + _annotation: Final[object] = getattr(openai_responses_api_chunk, "annotation", None) if _annotation is not None: ResponsesAPIRequestUtils._encode_container_id_on_output_item( item=_annotation, @@ -1310,8 +1312,7 @@ def _build_synthetic_response_events( ) if item_type == "message": - raw_content_parts = output_item_payload.get("content") - content_parts: Sequence[object] = raw_content_parts if _is_json_array(raw_content_parts) else [] + content_parts: Sequence[object] = _json_array_or_empty(output_item_payload.get("content")) for content_index, part in enumerate(content_parts): part_payload = _dump_response_object(part) events.append( @@ -1359,9 +1360,8 @@ def _build_synthetic_response_events( ) ) elif item_type == "reasoning": - raw_summary_items = output_item_payload.get("summary") - summary_items: Sequence[object] = raw_summary_items if _is_json_array(raw_summary_items) else [] - for summary_index, summary in enumerate(summary_items): + summaries: Sequence[object] = _json_array_or_empty(output_item_payload.get("summary")) + for summary_index, summary in enumerate(summaries): summary_payload = _dump_response_object(summary) summary_text = str(summary_payload.get("text") or "") for i in range(0, len(summary_text), chunk_size): @@ -2518,14 +2518,12 @@ class ManagedResponsesWebSocketHandler: # reuse the router-resolved self.model; passing the alias raw to # litellm.aresponses fails in get_llm_provider. A genuinely different # provider-prefixed per-frame model is still honored. - requested_model: Final[str | None] = _typed_pops_optional_str(call_kwargs.pop)("model", None) + requested_model: Final[str | None] = _optional_str(call_kwargs.pop("model", None)) model: Final[str] = ( self.model if requested_model is None or requested_model == self.model_group else requested_model ) - previous_response_id: Final[str | None] = _typed_pops_optional_str(call_kwargs.pop)( - "previous_response_id", None - ) + previous_response_id: Final[str | None] = _optional_str(call_kwargs.pop("previous_response_id", None)) current_messages: Final = self._input_to_messages(call_kwargs.get("input")) # Fetch history once; reused in both _apply_history and _save_turn_history diff --git a/litellm/router.py b/litellm/router.py index c93c1753f0e..edff8294c3e 100644 --- a/litellm/router.py +++ b/litellm/router.py @@ -6486,6 +6486,8 @@ class Router: **kwargs, ) elif call_type == "allm_passthrough_route": + if client: + kwargs["client"] = client return await self._ageneric_api_call_with_fallbacks( original_function=original_function, passthrough_on_no_deployment=True, @@ -9152,7 +9154,8 @@ class Router: if _deployment_on_router is not None: # deployment with this model_id exists on the router if ( - deployment.litellm_params == _deployment_on_router.litellm_params + deployment.model_name == _deployment_on_router.model_name + and deployment.litellm_params == _deployment_on_router.litellm_params and deployment.model_info == _deployment_on_router.model_info ): # No need to update diff --git a/litellm/router_strategy/budget_limiter.py b/litellm/router_strategy/budget_limiter.py index d57d7da0410..a8d51f95e45 100644 --- a/litellm/router_strategy/budget_limiter.py +++ b/litellm/router_strategy/budget_limiter.py @@ -20,6 +20,7 @@ anthropic: import asyncio import builtins +from collections.abc import Mapping from datetime import datetime, timedelta, timezone from typing import Any, Final @@ -54,19 +55,19 @@ class _LiteLLMParamsDictView: __slots__ = ("_params",) - def __init__(self, params: dict[str, Any]): + def __init__(self, params: Mapping[str, object]): self._params = params - def __getattr__(self, key: str) -> Any: + def __getattr__(self, key: str) -> object: return self._params.get(key) - def __getitem__(self, key: str) -> Any: + def __getitem__(self, key: str) -> object: return self._params.get(key) def __contains__(self, key: str) -> bool: return key in self._params - def get(self, key: str, default: Any = None) -> Any: + def get(self, key: str, default: object = None) -> object: return self._params.get(key, default) def keys(self): @@ -84,10 +85,10 @@ class _LiteLLMParamsDictView: def __len__(self) -> int: return len(self._params) - def dict(self) -> dict[str, Any]: + def dict(self) -> builtins.dict[str, object]: return dict(self._params) - def model_dump(self) -> builtins.dict[str, Any]: + def model_dump(self) -> builtins.dict[str, object]: return dict(self._params) diff --git a/litellm/router_strategy/complexity_router/README.md b/litellm/router_strategy/complexity_router/README.md index 63ba760ff66..bc8df67cc28 100644 --- a/litellm/router_strategy/complexity_router/README.md +++ b/litellm/router_strategy/complexity_router/README.md @@ -154,6 +154,9 @@ model_list: # Fallback model if tier cannot be determined default_model: gpt-4o + + # Replace a routed model that cannot take image input (default: false) + modality_routing: true ``` ## Usage @@ -178,6 +181,25 @@ response = litellm.completion( ## Special Behaviors +### Modality-based capability routing + +The classifier reads text alone, so a request carrying an image can classify cheap and land on a +text-only model, which rejects it with a provider 400 no fallback catches. With +`modality_routing: true`, one gate inspects every decided placement: when the routed model is +explicitly declared `supports_vision: false` (deployment `model_info` first, the model cost map +otherwise; unmapped names stay routable, and a multi-deployment group must accept on every +deployment), the request is re-placed on the nearest HIGHER tier holding a capable model, with +routing plugins still applied to the re-pick, then on `default_model` (never on plugin routers +and never for a plan-floored decision), and otherwise rejected with a clear 400 naming the +router. The walk only ever goes up, so a plan-mode floor cannot be undercut; a router whose only +vision model sits below the decided tier gets the 400 and an actionable message instead. + +A same-tier re-pick keeps the decision's cause and adds `modality:image` to `signals`; a tier +change or default takeover records `cause: modality_escalation` with the displaced placement +(`modality_escalated_from:` or `modality_displaced_default_model`). Escalations are never +pinned by session affinity, and a KEPT session pin bypasses the gate entirely: a session pinned +to a text-only model keeps it even when an image arrives. + ### Heuristic-first chaining `classifier_type: heuristic_first` runs the local scorer on every request and only calls the LLM diff --git a/litellm/router_strategy/complexity_router/complexity_router.py b/litellm/router_strategy/complexity_router/complexity_router.py index 2f4305756e9..577cee0920d 100644 --- a/litellm/router_strategy/complexity_router/complexity_router.py +++ b/litellm/router_strategy/complexity_router/complexity_router.py @@ -30,6 +30,7 @@ from litellm.constants import EMPTY_MAPPING, RETURN_RAW_MODEL_NAME_METADATA_KEY from litellm.integrations.custom_logger import CustomLogger from litellm.litellm_core_utils.core_helpers import get_metadata_variable_name_from_kwargs from litellm.litellm_core_utils.internal_call_metadata import forwarded_internal_call_metadata +from litellm.litellm_core_utils.prompt_templates.common_utils import request_contains_image_content from litellm.litellm_core_utils.sensitive_data_masker import mask_credentials_in_payload from litellm.llms.base_llm.base_utils import type_to_response_format_param from litellm.types.utils import ( @@ -281,7 +282,7 @@ def _response_cost_or_none(response: ModelResponse) -> float | None: return float(cost) -def _effective_turn_off_message_logging(request_kwargs: Mapping[str, Any] | None) -> bool | None: +def _effective_turn_off_message_logging(request_kwargs: Mapping[str, object] | None) -> bool | None: from litellm.litellm_core_utils.initialize_dynamic_callback_params import ( initialize_standard_callback_dynamic_params, ) @@ -479,6 +480,33 @@ def _last_human_ask_index( ) +def _newest_turn_is_human_ask( + messages: Sequence[Mapping[str, object]] | None, + marker_pairs: tuple[tuple[str, str], ...] = _DEFAULT_REMINDER_MARKERS, +) -> bool: + """Whether the request's newest turn carries a real human ask, i.e. this is a new ask rather + than an agent loop's continuation traffic. + + Anchored on `_last_human_ask_index` so every surface's plumbing reads as a continuation: + chat-completions tool turns are role=tool, Messages-surface tool_result turns flatten to empty + human text, and a hybrid turn carrying an ask alongside a tool_result still counts as an ask. + Compared against the newest non-system message rather than the raw tail, because Claude Code + appends a system-role reminder after the human turn; that trailing plumbing is neither an ask + nor loop traffic and must not turn a fresh ask into a continuation. An unreadable request (no + messages) is treated as a continuation: there is no ask to classify, which is the same reading + `_extract_current_ask_and_system_prompt` gives it downstream. + """ + if not messages: + return False + newest_non_system: Final = next( + (index for index in range(len(messages) - 1, -1, -1) if messages[index].get("role") != "system"), + None, + ) + if newest_non_system is None: + return False + return _last_human_ask_index(messages, marker_pairs) == newest_non_system + + def _iter_system_scope_texts( body_system: object, messages: Sequence[Mapping[str, object]], @@ -706,11 +734,25 @@ def _decision_is_pinnable(decision: StandardLoggingRoutingDecision | None) -> bo of the three: an agent names the conversation on its first turn, so the cheapest tier would be the pin every session starts with, and the real work that follows would run there for the whole TTL. It describes what that one call is, never what the session's traffic looks like. + + A context-window escalation describes the prompt's size, not the session's complexity, and + size shrinks again the moment the client compacts: pinning the escalated tier would hold the + session on the big-window model long after the oversized context that forced it is gone. The + gate re-fires per request, so leaving these unpinned costs nothing but the classifier call. + + A modality escalation is transient the same way: it describes what this one call carries (an + image), not what the session's traffic looks like, and pinning it would hold every following + text turn on the vision-capable model the image forced. """ - return decision is None or decision.get("cause") not in ( - "default_model_fallback", - "plan_mode", - "housekeeping", + return decision is None or ( + decision.get("cause") + not in ( + "default_model_fallback", + "plan_mode", + "housekeeping", + "modality_escalation", + ) + and not decision.get("context_escalated") ) @@ -759,6 +801,39 @@ class ClassificationOutcome(NamedTuple): classifier_cost: float | None = None +def _allowed(models: tuple[str, ...], fit_filter: frozenset[str] | None) -> tuple[str, ...]: + return models if fit_filter is None else tuple(model for model in models if model in fit_filter) + + +def _apply_context_placement( + tier: ComplexityTier | str, signals: tuple[str, ...], placement: _ContextWindowPlacement | None +) -> tuple[ComplexityTier | str, tuple[str, ...], ComplexityTier | str | None]: + """(final tier, signals, original tier when the gate escalated, else None).""" + if placement is None: + return tier, signals, None + if _tier_name(placement.tier) == _tier_name(tier): + return placement.tier, signals, None + return placement.tier, (*signals, "context_escalation"), tier + + +def _window_can_hold(window: int | None, needed: int, buffer: float) -> bool: + return window is None or needed <= int(window * buffer) + + +def _group_provably_fits(facts: tuple[int | None, bool], needed: int, buffer: float) -> bool: + window, has_unknown = facts + return window is not None and not has_unknown and needed <= int(window * buffer) + + +class _ContextWindowPlacement(NamedTuple): + """Where the context-window gate placed the request: the placement tier, the subset of its + pool the pick may use, and every configured group not provably misfit (the adaptive filter).""" + + tier: ComplexityTier | str + allowed_models: tuple[str, ...] + holdable_models: frozenset[str] + + class _SessionAffinityPin(NamedTuple): model: str tier: ComplexityTier | None @@ -1195,6 +1270,7 @@ class ComplexityRouter(CustomLogger): classifier_cost: float | None = None, conversation_continuing: bool = True, tier_litellm_params: Mapping[str, object] | None = None, + context_escalation_original_tier: ComplexityTier | str | None = None, ) -> StandardLoggingRoutingDecision: """Assemble the per-request provenance record for this router's decision. @@ -1244,6 +1320,12 @@ class ComplexityRouter(CustomLogger): decision["classifier_model"] = classifier_model if classifier_cost is not None: decision["classifier_cost"] = classifier_cost + if context_escalation_original_tier is not None: + # The pair travels together: the flag says the gate moved the request off its + # decided tier on prompt size, and the original tier names where the decision + # (classifier, keyword rule, or session pin) had placed it before physics did. + decision["context_escalated"] = True + decision["context_escalation_original_tier"] = _tier_name(context_escalation_original_tier) if tier_litellm_params: masked_tier_litellm_params: Final = mask_credentials_in_payload(tier_litellm_params) if isinstance(masked_tier_litellm_params, Mapping): @@ -1644,7 +1726,7 @@ class ComplexityRouter(CustomLogger): return entry.litellm_params if entry is not None else MappingProxyType({}) @staticmethod - def _pick_from_tier_value(model: str | list[str], tier_key: str) -> str: + def _pick_from_tier_value(model: str | Sequence[str], tier_key: str) -> str: if isinstance(model, str): return model if not model: @@ -1660,15 +1742,21 @@ class ComplexityRouter(CustomLogger): raw_messages: list[dict[str, Any]] | None, resolved_messages: list[dict[str, Any]] | None, request_kwargs: dict, + allowed_models: tuple[str, ...] | None = None, ) -> str: if not self.config.plugins: + if allowed_models is not None: + return self._pick_from_tier_value(allowed_models, _tier_name(tier)) return self.get_model_for_tier(tier) from litellm.types.router import RoutingContext tier_key: Final = _tier_name(tier) metadata_key: Final = get_metadata_variable_name_from_kwargs(request_kwargs) - pool: Final = tuple(self._tier_pools().get(tier_key, ())) + full_pool: Final = tuple(self._tier_pools().get(tier_key, ())) + pool: Final = ( + tuple(model for model in full_pool if model in allowed_models) if allowed_models is not None else full_pool + ) if not pool: # Nothing for the plugins to filter. Falling through would raise the # plugin-filtering error below and send the operator hunting for a policy @@ -1762,6 +1850,7 @@ class ComplexityRouter(CustomLogger): request_kwargs: dict[str, Any] | None = None, hard_floor: ComplexityTier | str | None = None, hard_ceiling: ComplexityTier | str | None = None, + fit_filter: frozenset[str] | None = None, ) -> str: """hard_floor excludes every candidate whose tiers all sit below it, turning this pick's soft floors (a distance penalty a high-scoring cheap model can outweigh) into a hard @@ -1774,7 +1863,10 @@ class ComplexityRouter(CustomLogger): tier because that is all it is worth, so a bandit trading cost for quality has nothing to win and must not reach above it. Without it the distance penalty is the only thing holding the tier, and a deployment that lowers tier_distance_penalty silently gets the expensive - model back while the routing decision still reads as the cheapest tier.""" + model back while the routing decision still reads as the cheapest tier. + + fit_filter excludes candidates the context-window gate proved cannot hold the prompt, + in every phase including cold start and the tier fallbacks.""" from litellm.router_strategy.adaptive_router.bandit import ( normalized_cost, thompson_sample, @@ -1785,12 +1877,12 @@ class ComplexityRouter(CustomLogger): if adaptive is None or not isinstance(classified_tier, ComplexityTier): # Custom tier names have no severity index; adaptive is rejected alongside # tier_definitions, so this guard is the contract for any future caller. - return self.get_model_for_tier(classified_tier) + return self._fitting_tier_fallback(classified_tier, fit_filter) request_type: Final = classify_prompt(user_message) classified_idx: Final = TIER_SEVERITY_ORDER.index(classified_tier) pools: Final = self._tier_pools() - classified_candidates: Final = tuple(pools.get(_tier_name(classified_tier), ())) + classified_candidates: Final = _allowed(tuple(pools.get(_tier_name(classified_tier), ())), fit_filter) cold_start_candidates: Final = tuple( model for model in classified_candidates if adaptive._cells[(request_type, model)].total_samples == 0 ) @@ -1820,9 +1912,9 @@ class ComplexityRouter(CustomLogger): if self.config.adaptive_eligible == "classified_tier": candidates = list(classified_candidates) if not candidates: - return self.get_model_for_tier(classified_tier) + return self._fitting_tier_fallback(classified_tier, fit_filter) else: - candidates = list(adaptive.config.available_models) + candidates = list(_allowed(tuple(adaptive.config.available_models), fit_filter)) all_costs: Final = [adaptive.model_to_cost.get(m, 0.0) for m in candidates] quality_weight: Final = self.config.adaptive_weights.quality @@ -1833,7 +1925,7 @@ class ComplexityRouter(CustomLogger): ceiling_severity: Final = self._active_tier_severity(hard_ceiling) if hard_ceiling is not None else None best_model: str | None = None best_score = float("-inf") - candidate_scores: Final[list[dict[str, Any]]] = [] + candidate_scores: Final[list[dict[str, object]]] = [] for model in candidates: if floor_severity is not None and all( self._active_tier_severity(model_tier) < floor_severity @@ -1869,7 +1961,7 @@ class ComplexityRouter(CustomLogger): best_score = score best_model = model if best_model is None: - return self.get_model_for_tier(classified_tier) + return self._fitting_tier_fallback(classified_tier, fit_filter) if request_kwargs is not None: metadata = request_kwargs.setdefault("metadata", {}) if isinstance(metadata, dict): @@ -1886,6 +1978,12 @@ class ComplexityRouter(CustomLogger): } return best_model + def _fitting_tier_fallback(self, classified_tier: ComplexityTier | str, fit_filter: frozenset[str] | None) -> str: + fitting: Final = _allowed(tuple(self._tier_pools().get(_tier_name(classified_tier), ())), fit_filter) + if fit_filter is not None and fitting: + return self._pick_from_tier_value(fitting, _tier_name(classified_tier)) + return self.get_model_for_tier(classified_tier) + def _resolve_plan_mode_floor(self) -> ComplexityTier | str | None: """The configured floor as an active tier: the built-in enum member, or the defined name itself for a custom tier set; None when the feature is off.""" @@ -1956,6 +2054,163 @@ class ComplexityRouter(CustomLogger): return None return name if self.config.has_custom_tiers else ComplexityTier(name) + def _deployment_window(self, group: str, deployment: Mapping[str, object]) -> int | None: + from litellm.litellm_core_utils.get_llm_provider_logic import declared_authenticating_provider + + deployment_model_info: Final = deployment.get("model_info") + declared: Final = ( + deployment_model_info.get("max_input_tokens") if isinstance(deployment_model_info, Mapping) else None + ) + if isinstance(declared, int): + return declared + litellm_params: Final = deployment.get("litellm_params") + params: Final = litellm_params if isinstance(litellm_params, Mapping) else EMPTY_MAPPING + provider_override: Final = params.get("custom_llm_provider") + # get_router_model_info resolves the provider, and get_llm_provider runs the OAuth device + # flow for github_copilot/chatgpt, so a metadata question must never reach it for those. + if declared_authenticating_provider( + str(params.get("model") or ""), provider_override if isinstance(provider_override, str) else None + ): + return None + try: + model_info: Final = self.litellm_router_instance.get_router_model_info( + deployment=cast(dict, deployment), # cast-ok: router deployments are plain dicts + received_model_name=group, + ) + window: Final = model_info.get("max_input_tokens") + except Exception: # noqa: BLE001 # best-effort: an unmappable deployment must not hide the others + return None + return window if isinstance(window, int) else None + + def _group_window_facts(self, group: str) -> tuple[int | None, bool]: + """(smallest declared context window across the group's deployments, whether any deployment + declares none). The core router picks a deployment within the group without a fit check, so + the group is only as safe as its smallest member.""" + list_models: Final = getattr(self.litellm_router_instance, "get_model_list", None) + deployments: Final = list_models(model_name=group) if callable(list_models) else None + if not isinstance(deployments, list) or not deployments: + return (None, True) + windows: Final = tuple( + window for deployment in deployments if (window := self._deployment_window(group, deployment)) is not None + ) + return (min(windows) if windows else None, len(windows) < len(deployments)) + + @staticmethod + def _out_of_band_request_text(request_kwargs: Mapping[str, object]) -> str: + """Prompt content the resolved message list never carries: the Responses API's + `instructions`, the /v1/messages top-level `system` block, and tool definitions. + A coding agent's context is dominated by these.""" + import json + + instructions: Final = request_kwargs.get("instructions") + proxy_request: Final = request_kwargs.get("proxy_server_request") + body: Final = proxy_request.get("body") if isinstance(proxy_request, Mapping) else None + system: Final = body.get("system") if isinstance(body, Mapping) else None + tools: Final = ( + body.get("tools") if isinstance(body, Mapping) and body.get("tools") else request_kwargs.get("tools") + ) + tools_text = "" + if tools: + try: + tools_text = json.dumps(tools, default=str) + except (TypeError, ValueError): + tools_text = str(tools) + return ( + (instructions if isinstance(instructions, str) else "") + + (str(system) if system is not None else "") + + tools_text + ) + + def _request_byte_upper_bound( + self, resolved_messages: Sequence[Mapping[str, object]] | None, request_kwargs: Mapping[str, object] + ) -> int: + """UTF-8 byte length of all prompt content. BPE emits at least one byte per token in every + script, so the token count never exceeds this and 'bytes fit' soundly skips counting.""" + content_bytes: Final = sum(len(str(m.get("content") or "").encode()) for m in resolved_messages or ()) + return content_bytes + len(self._out_of_band_request_text(request_kwargs).encode()) + + async def _counted_request_tokens( + self, resolved_messages: Sequence[Mapping[str, object]], request_kwargs: Mapping[str, object] + ) -> int | None: + """Real-tokenizer count of the resolved messages plus the out-of-band carriers, off the + event loop; None when counting fails, and the gate then leaves the placement alone.""" + import litellm + from litellm.litellm_core_utils.asyncify import asyncify + + out_of_band: Final = self._out_of_band_request_text(request_kwargs) + try: + counted: Final = await asyncify(litellm.token_counter)( + messages=cast(list, resolved_messages) # cast-ok: token_counter only iterates the sequence + ) + return counted + (await asyncify(litellm.token_counter)(text=out_of_band) if out_of_band else 0) + except Exception as e: # noqa: BLE001 # best-effort: an uncountable prompt must not fail the request + verbose_router_logger.debug("ComplexityRouter: context-window token count failed. Got - %s", e) + return None + + async def _context_window_placement( + self, + tier: ComplexityTier | str, + resolved_messages: Sequence[Mapping[str, object]] | None, + request_kwargs: Mapping[str, object], + pool_override: tuple[str, ...] | None = None, + ) -> _ContextWindowPlacement | None: + """Correct a decided placement whose models provably cannot hold the prompt, or None + (the placement stands). Only a real tokenizer count ever moves a request, escalation + lands only on groups whose every deployment declares a fitting window, and a group + with no resolvable window is never moved on faith in either direction.""" + if not self.config.enable_context_window_escalation or not resolved_messages: + return None + pools: Final = self._tier_pools() + pool: Final = pool_override if pool_override is not None else tuple(pools.get(_tier_name(tier), ())) + if not pool: + return None + facts: Final = MappingProxyType({group: self._group_window_facts(group) for group in pool}) + known_windows: Final = tuple(window for window, _ in facts.values() if window is not None) + if not known_windows: + return None + buffer: Final = self.config.context_window_escalation_buffer + if self._request_byte_upper_bound(resolved_messages, request_kwargs) <= int(min(known_windows) * buffer): + return None + needed: Final = await self._counted_request_tokens(resolved_messages, request_kwargs) + if needed is None: + return None + return self._placement_for_tokens(tier=tier, pool=pool, pools=pools, facts=facts, needed=needed) + + def _placement_for_tokens( + self, + *, + tier: ComplexityTier | str, + pool: tuple[str, ...], + pools: Mapping[str, list[str]], + facts: Mapping[str, tuple[int | None, bool]], + needed: int, + ) -> _ContextWindowPlacement | None: + buffer: Final = self.config.context_window_escalation_buffer + in_tier: Final = tuple(group for group in pool if _window_can_hold(facts[group][0], needed, buffer)) + if in_tier and len(in_tier) == len(pool): + return None + holdable: Final = frozenset( + group + for tier_pool in pools.values() + for group in tier_pool + if _window_can_hold(self._group_window_facts(group)[0], needed, buffer) + ) + if in_tier: + return _ContextWindowPlacement(tier=tier, allowed_models=in_tier, holdable_models=holdable) + for name in self.config.tier_names()[self._active_tier_severity(tier) + 1 :]: + proven = tuple( + group + for group in pools.get(name, ()) + if _group_provably_fits(self._group_window_facts(group), needed, buffer) + ) + if proven: + return _ContextWindowPlacement( + tier=name if self.config.has_custom_tiers else ComplexityTier(name), + allowed_models=proven, + holdable_models=holdable, + ) + return None + def _apply_plan_mode_floor(self, tier: ComplexityTier | str) -> ComplexityTier | str: """The higher of the decided tier and the plan-mode floor; identity when the floor is unset.""" floor: Final = self._resolve_plan_mode_floor() @@ -2025,6 +2280,175 @@ class ComplexityRouter(CustomLogger): return pinned_model return self.get_model_for_tier(escalated_tier) + def _model_accepts_image_input(self, model_name: str) -> bool: + """Whether a routed model or pool entry can serve an image request. + + Resolved through the deployments that would actually serve the name; a name with no + deployment on the router is served by the SDK directly and is checked against the model + cost map itself. Only an explicit supports_vision false excludes, a deployment-level + model_info override first and the map otherwise, so unmapped custom names stay routable. + + A multi-deployment group must accept on EVERY deployment: the router picks a deployment + inside the group after this gate runs, so a mixed group marked eligible could still hand + the image to its text-only member and fail with the exact 400 the gate exists to prevent. + """ + from litellm.utils import is_vision_explicitly_disabled + + def deployment_accepts(deployment: Mapping[str, Any]) -> bool: + declared: Final = (deployment.get("model_info") or EMPTY_MAPPING).get("supports_vision") + if declared is not None: + return declared is True + litellm_model: Final = (deployment.get("litellm_params") or EMPTY_MAPPING).get("model") or model_name + return not is_vision_explicitly_disabled(litellm_model) + + deployments: Final = self.litellm_router_instance.get_model_list(model_name=model_name) + if not deployments: + return not is_vision_explicitly_disabled(model_name) + return all(deployment_accepts(deployment) for deployment in deployments) + + def _modality_eligible_models(self) -> frozenset[str]: + """Every configured pool entry, plus default_model, that can serve an image request.""" + names: Final = frozenset(entry for pool in self._tier_pools().values() for entry in pool) | frozenset( + name for name in (self.config.default_model,) if name + ) + return frozenset(name for name in names if self._model_accepts_image_input(name)) + + async def _gate_response_modality( + self, + response: PreRoutingHookResponse, + messages: list[dict[str, Any]] | None, # mutable-ok: forwarded verbatim to the list-typed re-pick + resolved_messages: Sequence[Mapping[str, object]] | None, + request_kwargs: dict, # mutable-ok: same shape the hook receives + ) -> PreRoutingHookResponse: + """Replace a routed model that cannot accept this request's image input. + + The single modality owner, applied to the decided response at the hook's exits so every + routing path is covered uniformly. A KEPT session pin is exempt by design (its cause); + replacement picks and every other path are just responses. The re-placement walks + UPWARD-ONLY from the decision's tier (so a plan-mode floor can never be undercut), picks + through `_pick_model_for_tier` so routing plugins still apply, then falls to + default_model (never on plugin routers, and never on a plan-floored decision, since + default_model carries no tier guarantee), else raises the clear 400. The rewritten + decision keeps its cause on a same-tier repick and becomes modality_escalation when the + tier moved or default_model took over, with the displaced placement in signals. + """ + decision: Final = response.routing_decision + if ( + not self.config.modality_routing + or not resolved_messages + or response.model is None + or (decision is not None and decision.get("cause") == "session_affinity_pin") + or not request_contains_image_content(resolved_messages) + or self._model_accepts_image_input(response.model) + ): + return response + eligible: Final = self._modality_eligible_models() + names: Final = self.config.tier_names() + pools: Final = self._tier_pools() + decided: Final = decision.get("tier") if decision is not None else None + start: Final = names.index(decided) if isinstance(decided, str) and decided in names else 0 + capable: Final = next( + (name for name in names[start:] if any(entry in eligible for entry in pools.get(name, ()))), None + ) + if capable is not None: + new_tier: ComplexityTier | str | None = capable if self.config.has_custom_tiers else ComplexityTier(capable) + repick_messages: Final = list(resolved_messages) # mutable-ok: the pick's param is list-typed + new_model = await self._pick_model_for_tier( + new_tier, + messages, + repick_messages, # pyright: ignore[reportArgumentType] # hook-resolved message dicts; the pick only reads them + request_kwargs, + allowed_models=tuple(entry for entry in pools.get(capable, ()) if entry in eligible), + ) + elif self._modality_default_model_usable(request_kwargs, resolved_messages, eligible): + new_tier = None + new_model = self._placed_default_model() + else: + import litellm + + raise litellm.BadRequestError( + message=( + f"Auto-router {self.model_name} received a request with image input, but no model " + f"at or above the decided tier accepts images and modality_routing is enabled. " + f"Tiers checked: {', '.join(names[start:])}. Add a vision-capable model to a tier, " + f"or set a vision-capable default_model, or remove the image content." + ), + model=self.model_name, + llm_provider="", + ) + self._restamp_adaptive_choice(request_kwargs, response.model, new_model) + same_tier: Final = capable is not None and decided == capable + base_cause: Final = (decision.get("cause") if decision is not None else None) or "default_fallback" + displaced_default: Final = decided is None and response.model == self.config.default_model + markers: Final = ( + "modality:image", + *((f"modality_escalated_from:{decided}",) if not same_tier and isinstance(decided, str) else ()), + *(("modality_displaced_default_model",) if not same_tier and displaced_default else ()), + ) + old_signals: Final = tuple(decision.get("signals") or ()) if decision is not None else () + new_decision: Final = self._build_routing_decision( + routed_model=new_model, + cause=base_cause if same_tier else "modality_escalation", + tier=new_tier, + score=decision.get("score") if decision is not None else None, + signals=(*old_signals, *markers), + matched_keyword=decision.get("matched_keyword") if decision is not None else None, + escalation_keyword=decision.get("escalation_keyword") if decision is not None else None, + escalated=bool(decision.get("escalated", False)) if decision is not None else False, + classifier_model=decision.get("classifier_model") if decision is not None else None, + classifier_cost=decision.get("classifier_cost") if decision is not None else None, + conversation_continuing=bool(decision.get("conversation_continuing", True)) + if decision is not None + else True, + tier_litellm_params=self._litellm_params_for_model(new_tier, new_model), + context_escalation_original_tier=( + decision.get("context_escalation_original_tier") if decision is not None else None + ), + ) + from litellm.types.router import PreRoutingHookResponse as HookResponse + + return HookResponse( + model=new_model, + messages=response.messages, + litellm_params=self._litellm_params_for_model(new_tier, new_model), + routing_decision=new_decision, + ) + + def _modality_default_model_usable( + self, + request_kwargs: Mapping[str, object], + resolved_messages: Sequence[Mapping[str, object]] | None, + eligible: frozenset[str], + ) -> bool: + """default_model may serve a gated request only when it is configured, plugin-free + (it is never checked against the plugin pipeline), capability-eligible, and the turn + carries no plan-mode sentinel. The sentinel is re-detected here rather than read off + the decision record, because the record only marks turns the floor RAISED; a sentinel + turn already at or above the floor keeps its ordinary cause, and default_model carries + no tier the floor could vouch for on any sentinel turn.""" + return ( + bool(self.config.default_model) + and not self.config.plugins + and self.config.default_model in eligible + and self._matched_plan_mode_signal(request_kwargs, resolved_messages) is None + ) + + def _placed_default_model(self) -> str: + """The default_model behind a usable-default verdict; the raise is the type-level + proof, not a reachable path.""" + model: Final = self.config.default_model + if model is None: + raise ValueError(f"Auto-router {self.model_name}: modality gate routed to an unset default_model") + return model + + @staticmethod + def _restamp_adaptive_choice(request_kwargs: Mapping[str, object], old_model: str, new_model: str) -> None: + """The adaptive feedback loop reads its chosen-model marker from request metadata; a + gate rewrite must move the marker with the model or rewards land on the displaced one.""" + metadata: Final = request_kwargs.get("metadata") + if isinstance(metadata, dict) and metadata.get("adaptive_router_chosen_model") == old_model: + metadata["adaptive_router_chosen_model"] = new_model + def _lexical_tier_override(self, user_message: str) -> KeywordOverride | None: """When keyword_tier_rules match literally, the most-severe matched tier wins. @@ -2247,14 +2671,18 @@ class ComplexityRouter(CustomLogger): @property def _uses_tier_pin(self) -> bool: - return bool(self.config.session_affinity and not self.config.plugins) + """classification_mode 'user_turn' implies the tier pin machinery: the pin write after each + pinnable classification is what gives a continuation a held decision to replay.""" + return bool( + (self.config.session_affinity or self.config.classification_mode == "user_turn") and not self.config.plugins + ) @property def _uses_deployment_pin(self) -> bool: - """session_affinity implies the deployment pin: a session frozen onto one model + """The tier pin implies the deployment pin: a session frozen onto one model group but load-balanced across its deployments would still go cache-cold, which is the exact failure both flags exist to prevent.""" - return bool((self.config.deployment_affinity or self.config.session_affinity) and not self.config.plugins) + return bool(self.config.deployment_affinity and not self.config.plugins) or self._uses_tier_pin def _with_session_deployment_affinity( self, response: PreRoutingHookResponse | None @@ -2282,6 +2710,11 @@ class ComplexityRouter(CustomLogger): pins the model chosen on the session's first turn and reuses it for every later turn, skipping classification entirely. Otherwise delegates to `_classify_and_route`. + When `classification_mode` is 'user_turn', the same pin is replayed only on + continuation turns (an agent loop's tool traffic); a new human ask always falls + through to classification, so the session can still move tiers between asks. + With both knobs on, session_affinity's pin-first behavior wins. + Skipped entirely when `plugins` are configured: reusing a stale pin would bypass the plugin pipeline on every turn after the first, since a pinned model was never re-checked against a policy plugin whose decision can change between turns (e.g. a @@ -2305,7 +2738,13 @@ class ComplexityRouter(CustomLogger): session_id: Final = self._get_session_id_from_request_kwargs(request_kwargs) if use_session_affinity else None cache_key = self._get_session_affinity_cache_key(session_id, request_kwargs) if session_id is not None else None - if cache_key is not None: + # In 'user_turn' mode a held pin is replayed only on continuation turns; a new human + # ask falls through and re-classifies. session_affinity restores pin-first for asks too. + pin_replay_allowed: Final = bool(self.config.session_affinity) or not _newest_turn_is_human_ask( + resolved_messages, self._reminder_markers + ) + + if cache_key is not None and pin_replay_allowed: pinned_value: Final = await self.litellm_router_instance.cache.async_get_cache(key=cache_key) pinned_pin: Final = _parse_session_affinity_pin(pinned_value) if pinned_pin is not None: @@ -2339,6 +2778,26 @@ class ComplexityRouter(CustomLogger): session_model: Final = routed_model if plan_floored and pinned_tier is not None: routed_model = self.get_model_for_tier(self._apply_plan_mode_floor(pinned_tier)) + pin_source_tier: Final = self._tier_for_model(routed_model) + pin_placement: Final = ( + await self._context_window_placement( + pin_source_tier, resolved_messages, request_kwargs, pool_override=(routed_model,) + ) + if pin_source_tier is not None + else None + ) + pin_context_original_tier: Final = ( + pin_source_tier + if pin_placement is not None + and pin_source_tier is not None + and _tier_name(pin_placement.tier) != _tier_name(pin_source_tier) + else None + ) + if pin_placement is not None and pin_context_original_tier is not None: + # The stored pin below keeps the session's own model on purpose. + routed_model = self._pick_from_tier_value( + pin_placement.allowed_models, _tier_name(pin_placement.tier) + ) # Refresh the TTL on every hit so an active session doesn't lose its # pin mid-conversation just because it outlives the original write. await self.litellm_router_instance.cache.async_set_cache( @@ -2354,36 +2813,47 @@ class ComplexityRouter(CustomLogger): kwargs_metadata: Final = request_kwargs.setdefault("metadata", {}) if isinstance(kwargs_metadata, dict): kwargs_metadata[ADAPTIVE_ROUTER_CHOSEN_MODEL_KEY] = routed_model + replay_cause: Final[RoutingDecisionCause] = ( + "session_affinity_pin" if self.config.session_affinity else "user_turn_continuation" + ) cause: RoutingDecisionCause = ( - "plan_mode" - if plan_floored - else ("session_affinity_escalation" if escalated else "session_affinity_pin") + "plan_mode" if plan_floored else ("session_affinity_escalation" if escalated else replay_cause) ) verbose_router_logger.info( "ComplexityRouter: routing decision cause=%s, routed_model=%s", cause, routed_model ) - routed_pin_tier: Final = self._tier_for_model(routed_model) if plan_floored else resolved_pin_tier + routed_pin_tier: Final = ( + pin_placement.tier + if pin_placement is not None and pin_context_original_tier is not None + else (self._tier_for_model(routed_model) if plan_floored else resolved_pin_tier) + ) session_tier_litellm_params: Final = self._litellm_params_for_model(routed_pin_tier, routed_model) has_original_messages: Final = messages is not None and len(messages) > 0 return self._with_session_deployment_affinity( - PreRoutingHookResponse( - model=routed_model, - messages=messages if has_original_messages else None, - litellm_params=session_tier_litellm_params, - routing_decision=self._build_routing_decision( - routed_model=routed_model, - cause=cause, - tier=routed_pin_tier, - matched_keyword=pin_plan_sentinel if plan_floored else None, - escalation_keyword=pin_escalation_keyword, - escalated=escalated, - conversation_continuing=conversation_continuing, - tier_litellm_params=session_tier_litellm_params, + await self._gate_response_modality( + PreRoutingHookResponse( + model=routed_model, + messages=messages if has_original_messages else None, + litellm_params=session_tier_litellm_params, + routing_decision=self._build_routing_decision( + routed_model=routed_model, + cause=cause, + tier=routed_pin_tier, + matched_keyword=pin_plan_sentinel if plan_floored else None, + escalation_keyword=pin_escalation_keyword, + escalated=escalated, + conversation_continuing=conversation_continuing, + tier_litellm_params=session_tier_litellm_params, + context_escalation_original_tier=pin_context_original_tier, + ), ), + messages, + resolved_messages, + request_kwargs, ) ) - response: Final = await self._classify_and_route( + routed_response: Final = await self._classify_and_route( model=model, request_kwargs=request_kwargs, messages=messages, @@ -2392,6 +2862,11 @@ class ComplexityRouter(CustomLogger): conversation_continuing=conversation_continuing, resolved_messages=resolved_messages, ) + response: Final = ( + await self._gate_response_modality(routed_response, messages, resolved_messages, request_kwargs) + if routed_response is not None + else None + ) # Sentinel presence, not the plan_mode cause, gates the pin write: a plan-mode turn # classified at or above the floor keeps its ordinary cause, yet on an adaptive router # the hard floor constrained its pick, so pinning it would carry a plan-mode-shaped @@ -2573,6 +3048,8 @@ class ComplexityRouter(CustomLogger): plan_floored: Final = tier != pre_floor_tier if plan_floored: signals = (*signals, "plan_mode_floor") + context_placement: Final = await self._context_window_placement(tier, resolved_messages, request_kwargs) + tier, signals, context_original_tier = _apply_context_placement(tier, signals, context_placement) score_repr: Final = f"{score:.3f}" if score is not None else "n/a" fallback_model: Final = self.config.default_model if not self.config.plugins else None # A sentinel-carrying request skips the failure exit below, whether or not the floor @@ -2619,8 +3096,15 @@ class ComplexityRouter(CustomLogger): # the cheapest tier would then contradict the floor and bound the pick below the tier # the decision reports. housekeeping_ceiling: Final = tier if outcome.cause == "housekeeping" else None + # A context-escalated tier becomes the hard floor: a floor the bandit can slide + # under is not a floor. routed_model = self._soft_floor_pick( - tier, user_message, request_kwargs, hard_floor=plan_floor, hard_ceiling=housekeeping_ceiling + tier, + user_message, + request_kwargs, + hard_floor=tier if context_original_tier is not None else plan_floor, + hard_ceiling=housekeeping_ceiling, + fit_filter=context_placement.holdable_models if context_placement is not None else None, ) adaptive: Final = self._ensure_adaptive_router() if adaptive is not None: @@ -2637,7 +3121,13 @@ class ComplexityRouter(CustomLogger): routed_model, ) else: - routed_model = await self._pick_model_for_tier(tier, messages, resolved_messages, request_kwargs) + routed_model = await self._pick_model_for_tier( + tier, + messages, + resolved_messages, + request_kwargs, + allowed_models=context_placement.allowed_models if context_placement is not None else None, + ) verbose_router_logger.info( "ComplexityRouter: routing decision cause=%s, tier=%s, score=%s, signals=%s, routed_model=%s", outcome.cause, @@ -2690,5 +3180,6 @@ class ComplexityRouter(CustomLogger): classifier_model=classifier_model, classifier_cost=outcome.classifier_cost, tier_litellm_params=tier_litellm_params, + context_escalation_original_tier=context_original_tier, ), ) diff --git a/litellm/router_strategy/complexity_router/config.py b/litellm/router_strategy/complexity_router/config.py index 335de11e669..70aeecb31c6 100644 --- a/litellm/router_strategy/complexity_router/config.py +++ b/litellm/router_strategy/complexity_router/config.py @@ -823,6 +823,44 @@ class ComplexityRouterConfig(BaseModel): ), ) + enable_context_window_escalation: bool = Field( + default=True, + description=( + "Escalate a request off a tier whose models provably cannot hold its prompt, before " + "dispatch. The classifier scores complexity and never prompt size, so a long agentic " + "session whose newest ask is trivial lands on a small-window tier and the provider " + "rejects it with a context-window 400 that nothing retries. When every model of the " + "decided tier has a declared window smaller than the estimated prompt, the request " + "moves to the lowest configured tier with a model whose declared window fits; when " + "only some of the tier's models fit, the pick is restricted to those and the tier " + "keeps the request. Models with no resolvable window are never escalated away from " + "and never escalated onto. Set false to dispatch on complexity alone, as before." + ), + ) + context_window_escalation_buffer: float = Field( + default=0.95, + gt=0, + le=1, + description=( + "Fraction of a model's declared context window the estimated prompt must fit within. " + "The token count is an estimate, so fitting against the full window would dispatch " + "prompts that the provider's own tokenizer then rejects; 0.95 leaves room for that " + "drift plus the response tokens." + ), + ) + modality_routing: bool = Field( + default=False, + description=( + "Route image-bearing requests only to models that can accept image input. The " + "classifier reads text alone, so an image request whose text classifies cheap " + "otherwise lands on a text-only model and fails with a provider 400. When enabled, " + "a routed model explicitly declared supports_vision false (deployment model_info " + "or the model cost map; unmapped names stay routable) is replaced by the nearest " + "HIGHER tier holding a capable model, then default_model, else a clear 400. A kept " + "session-affinity pin still wins even when an image arrives." + ), + ) + # Semantic (embedding) matching for keyword_tier_rules instead of literal text matching semantic_keyword_matching: bool = Field( default=False, @@ -839,6 +877,21 @@ class ComplexityRouterConfig(BaseModel): description="Minimum cosine similarity for a semantic keyword match", ) + classification_mode: Literal["every_request", "user_turn"] = Field( + default="every_request", + description=( + "When to run the complexity classifier. 'every_request' (the default) classifies every " + "inference request, including the tool-result continuation turns of an agentic loop. " + "'user_turn' classifies only requests whose newest turn is a new human ask and replays " + "the session's held routing decision on continuation turns, which cuts classifier " + "spend and eliminates mid-loop model switches. Continuations with no held decision to " + "replay (no resolvable session_id, expired pin, fresh restart) still classify. Unlike " + "session_affinity, a new human ask always re-classifies, so a session can still move " + "tiers between asks. Suppressed when plugins are configured, for the same reason " + "session_affinity is: a replayed decision would bypass the plugin pipeline." + ), + ) + # Session affinity: pin the first turn's routed model for the rest of the session session_affinity: bool = Field( default=False, diff --git a/litellm/secret_managers/hashicorp_secret_manager.py b/litellm/secret_managers/hashicorp_secret_manager.py index e2662d96b52..8f677b54700 100644 --- a/litellm/secret_managers/hashicorp_secret_manager.py +++ b/litellm/secret_managers/hashicorp_secret_manager.py @@ -1,7 +1,9 @@ import os -from typing import Any, Final +from collections.abc import Mapping +from typing import Final, Protocol import httpx +from typing_extensions import ReadOnly, TypedDict import litellm from litellm._logging import verbose_logger @@ -17,6 +19,72 @@ from litellm.proxy._types import KeyManagementSystem from .base_secret_manager import BaseSecretManager, raise_if_unsafe_secret_name +class _VaultAuthData(TypedDict): + """The ``auth`` block Vault returns from a login endpoint.""" + + client_token: ReadOnly[str] + lease_duration: ReadOnly[int] + + +class _VaultLoginResponse(TypedDict): + """Body of a Vault ``/v1/auth/.../login`` response.""" + + auth: ReadOnly[_VaultAuthData] + + +class _VaultSecretTarget(TypedDict): + """Resolved coordinates of one Vault KV v2 secret.""" + + url: ReadOnly[str] + data_key: ReadOnly[str] + secret_name: ReadOnly[str] + + +class _VaultSecretDataBlock(TypedDict, total=False): + """The inner ``data`` block of a Vault KV v2 read body.""" + + data: ReadOnly[Mapping[str, object]] + + +class _VaultSecretReadResponse(TypedDict, total=False): + """Body of a Vault KV v2 secret read, narrowed to the nesting this module walks.""" + + data: ReadOnly[_VaultSecretDataBlock] + + +class _VaultLoginResponseSource(Protocol): + """A Vault login call's HTTP response, read for the auth block it carries.""" + + def json(self) -> _VaultLoginResponse: ... + + +class _VaultSecretReadSource(Protocol): + """A Vault KV v2 read response, read for the nested secret data it carries.""" + + def json(self) -> _VaultSecretReadResponse: ... + + +class _JsonObjectSource(Protocol): + """A Vault response whose body is a JSON object nothing further is assumed about.""" + + def json(self) -> dict[str, object]: ... + + +def _vault_login_body(response: _VaultLoginResponseSource) -> _VaultLoginResponse: + """Decode the body of a Vault login response.""" + return response.json() + + +def _vault_secret_read_body(response: _VaultSecretReadSource) -> _VaultSecretReadResponse: + """Decode the body of a Vault KV v2 secret read response.""" + return response.json() + + +def _json_object_body(response: _JsonObjectSource) -> dict[str, object]: + """Decode a Vault response body as a plain JSON object.""" + return response.json() + + class HashicorpSecretManager(BaseSecretManager): def __init__(self): from litellm.proxy.proxy_server import CommonProxyErrors, premium_user @@ -130,7 +198,8 @@ class HashicorpSecretManager(BaseSecretManager): ) resp.raise_for_status() - auth_data: Final = resp.json()["auth"] + login_response: Final = _vault_login_body(resp) + auth_data: Final = login_response["auth"] token: Final = auth_data["client_token"] _lease_duration: Final = auth_data["lease_duration"] @@ -191,8 +260,10 @@ class HashicorpSecretManager(BaseSecretManager): json=self._get_tls_cert_auth_body(), ) resp.raise_for_status() - token: Final = resp.json()["auth"]["client_token"] - _lease_duration: Final = resp.json()["auth"]["lease_duration"] + token_response: Final = _vault_login_body(resp) + token: Final = token_response["auth"]["client_token"] + lease_response: Final = _vault_login_body(resp) + _lease_duration: Final = lease_response["auth"]["lease_duration"] verbose_logger.debug("Successfully obtained Vault token via TLS cert auth.") self.cache.set_cache(key="hcp_vault_token", value=token, ttl=_lease_duration) return token @@ -205,9 +276,9 @@ class HashicorpSecretManager(BaseSecretManager): def get_url( self, secret_name: str, - namespace: str | None = None, - mount_name: str | None = None, - path_prefix: str | None = None, + namespace: object = None, + mount_name: object = None, + path_prefix: object = None, ) -> str: """ Constructs the Vault URL for KV v2 secrets. @@ -238,7 +309,7 @@ class HashicorpSecretManager(BaseSecretManager): _url += secret_name return _url - def _sanitize_plain_value(self, value: str | int | None) -> str | None: + def _sanitize_plain_value(self, value: object) -> str | None: if value is None: return None value_str: Final = str(value).strip() @@ -246,23 +317,23 @@ class HashicorpSecretManager(BaseSecretManager): return None return value_str - def _sanitize_path_component(self, value: str | int | None) -> str | None: + def _sanitize_path_component(self, value: object) -> str | None: sanitized_value = self._sanitize_plain_value(value) if sanitized_value is None: return None sanitized_value = sanitized_value.strip("/") return sanitized_value or None - def _extract_secret_manager_settings(self, optional_params: dict | None) -> dict[str, Any]: + def _extract_secret_manager_settings(self, optional_params: dict | None) -> dict[str, object]: if not isinstance(optional_params, dict): return {} candidate: Final = optional_params.get("secret_manager_settings") - source: Final = candidate if isinstance(candidate, dict) else optional_params + source: Final[Mapping[str, object]] = candidate if isinstance(candidate, dict) else optional_params allowed_keys: Final = {"namespace", "mount", "path_prefix", "data"} return {k: source[k] for k in allowed_keys if k in source} - def _build_secret_target(self, secret_name: str, optional_params: dict | None) -> dict[str, Any]: + def _build_secret_target(self, secret_name: str, optional_params: dict | None) -> _VaultSecretTarget: settings: Final = self._extract_secret_manager_settings(optional_params) namespace: Final = settings.get("namespace", self.vault_namespace) @@ -331,7 +402,7 @@ class HashicorpSecretManager(BaseSecretManager): response.raise_for_status() # For KV v2, the secret is in response.json()["data"]["data"] - json_resp: Final = response.json() + json_resp: Final = _json_object_body(response) _value: Final = self._get_secret_value_from_json_response(json_resp) self.cache.set_cache(secret_name, _value) return _value @@ -362,7 +433,7 @@ class HashicorpSecretManager(BaseSecretManager): response.raise_for_status() # For KV v2, the secret is in response.json()["data"]["data"] - json_resp: Final = response.json() + json_resp: Final = _json_object_body(response) _value: Final = self._get_secret_value_from_json_response(json_resp) self.cache.set_cache(secret_name, _value) return _value @@ -379,7 +450,7 @@ class HashicorpSecretManager(BaseSecretManager): optional_params: dict | None = None, timeout: float | httpx.Timeout | None = None, tags: dict | list | None = None, - ) -> dict[str, Any]: + ) -> dict[str, object]: """ Writes a secret to Vault KV v2 using an async HTTPX client. @@ -413,7 +484,7 @@ class HashicorpSecretManager(BaseSecretManager): json=data, ) response.raise_for_status() - return response.json() + return _json_object_body(response) except Exception as e: verbose_logger.exception("Error writing secret to Hashicorp Vault: %s", e) return {"status": "error", "message": str(e)} @@ -500,7 +571,7 @@ class HashicorpSecretManager(BaseSecretManager): headers=self._get_request_headers(), ) response.raise_for_status() - json_resp: Final = response.json() + json_resp: Final = _vault_secret_read_body(response) # Use data_key from target to get the correct value data_key: Final = new_target["data_key"] new_secret_value_from_vault: Final = json_resp.get("data", {}).get("data", {}).get(data_key, None) diff --git a/litellm/types/guardrail_base_init.py b/litellm/types/guardrail_base_init.py new file mode 100644 index 00000000000..9174e8d840f --- /dev/null +++ b/litellm/types/guardrail_base_init.py @@ -0,0 +1,24 @@ +"""Typed view of the scalar keyword payload guardrails forward to ``CustomGuardrail.__init__``. + +Guardrail subclasses collect their base-class options in ``**kwargs`` and splat them into +``super().__init__``. Declaring the payload's shape here lets the checker resolve each +forwarded argument to its real parameter type instead of ``Any``. +""" + +from typing_extensions import ReadOnly, TypedDict + + +class GuardrailBaseInitKwargs(TypedDict, total=False): + guardrail_name: ReadOnly[str | None] + default_on: ReadOnly[bool] + mask_request_content: ReadOnly[bool] + mask_response_content: ReadOnly[bool] + violation_message_template: ReadOnly[str | None] + end_session_after_n_fails: ReadOnly[int | None] + on_violation: ReadOnly[str | None] + realtime_violation_message: ReadOnly[str | None] + on_sensitive_data: ReadOnly[str | None] + sensitive_data_route_to_model: ReadOnly[str | None] + sticky_session_routing: ReadOnly[bool] + run_in_parallel: ReadOnly[bool] + only_scan_new_messages: ReadOnly[bool] diff --git a/litellm/types/llms/openai.py b/litellm/types/llms/openai.py index a6115640d78..32d88da0085 100644 --- a/litellm/types/llms/openai.py +++ b/litellm/types/llms/openai.py @@ -327,6 +327,10 @@ class BatchGuardrailReport(BaseModel): """Every record that was redacted or dropped, in file order.""" +_JsonValue: TypeAlias = object +"""Alias for ``object``, usable inside model bodies that declare a field named ``object``.""" + + BATCH_GUARDRAIL_RESPONSE_FIELD: Final = "litellm_batch_guardrail" @@ -1191,7 +1195,7 @@ class ShellToolParam(TypedDict, total=False): type: Required[Literal["shell"] | str] """The type of tool. Use ``\"shell\"``.""" - environment: Required[dict[str, Any]] + environment: Required[dict[str, object]] """Environment config: ``type`` (e.g. ``\"container_auto\"``, ``\"container_reference\"``, ``\"local\"``), optional ``container_id``, ``network_policy``, ``domain_secrets``, ``skills``.""" @@ -1308,7 +1312,7 @@ class ResponseAPIUsage(BaseLiteLLMOpenAIResponseObject): @field_validator("cost", mode="before") @classmethod - def parse_cost(cls, v: Any) -> float | None: + def parse_cost(cls, v: object) -> object: """Normalise cost: accept either a float or a dict with a ``total_cost`` key.""" if isinstance(v, dict): return v.get("total_cost") @@ -1805,7 +1809,7 @@ class ErrorEventError(BaseLiteLLMOpenAIResponseObject): type: str # e.g., 'invalid_request_error' code: str # e.g., 'context_length_exceeded' message: str - param: str | dict[str, Any] | None = None + param: str | dict[str, object] | None = None class ErrorEvent(BaseLiteLLMOpenAIResponseObject): @@ -2162,6 +2166,42 @@ class OpenAIRealtimeDoneEvent(TypedDict): type: Literal["response.done"] +class OpenAIRealtimeInputAudioBufferSpeechEvent(TypedDict): + type: ReadOnly[Literal["input_audio_buffer.speech_started", "input_audio_buffer.speech_stopped"]] + event_id: ReadOnly[str] + item_id: ReadOnly[str] + + +class OpenAIRealtimeInputAudioTranscriptionDelta(TypedDict): + type: ReadOnly[Literal["conversation.item.input_audio_transcription.delta"]] + event_id: ReadOnly[str] + item_id: ReadOnly[str] + content_index: ReadOnly[int] + delta: ReadOnly[str] + + +class OpenAIRealtimeInputAudioTranscriptionCompleted(TypedDict): + type: ReadOnly[Literal["conversation.item.input_audio_transcription.completed"]] + event_id: ReadOnly[str] + item_id: ReadOnly[str] + content_index: ReadOnly[int] + transcript: ReadOnly[str] + + +class OpenAIRealtimeUsageTokenDetails(TypedDict): + audio_tokens: ReadOnly[int] + text_tokens: ReadOnly[int] + cached_tokens: NotRequired[ReadOnly[int]] + + +class OpenAIRealtimeResponseUsage(TypedDict): + input_tokens: ReadOnly[int] + output_tokens: ReadOnly[int] + total_tokens: ReadOnly[int] + input_token_details: NotRequired[ReadOnly[OpenAIRealtimeUsageTokenDetails]] + output_token_details: NotRequired[ReadOnly[OpenAIRealtimeUsageTokenDetails]] + + class OpenAIRealtimeEventTypes(Enum): SESSION_CREATED = "session.created" # Beta delta event names @@ -2199,6 +2239,9 @@ OpenAIRealtimeEvents = ( | OpenAIRealtimeOutputItemDone | OpenAIRealtimeFunctionCallArgumentsDone | OpenAIRealtimeDoneEvent + | OpenAIRealtimeInputAudioBufferSpeechEvent + | OpenAIRealtimeInputAudioTranscriptionDelta + | OpenAIRealtimeInputAudioTranscriptionCompleted ) OpenAIRealtimeStreamList = list[OpenAIRealtimeEvents] @@ -2379,7 +2422,7 @@ class OpenAIVideoObject(BaseModel): expires_at: int | None = None """Unix timestamp (seconds) for when the downloadable assets expire, if set.""" - error: dict[str, Any] | None = None + error: dict[str, _JsonValue] | None = None """Error payload that explains why generation failed, if applicable.""" progress: int | None = None @@ -2397,15 +2440,15 @@ class OpenAIVideoObject(BaseModel): model: str | None = None """The video generation model that produced the job.""" - _hidden_params: dict[str, Any] = {} + _hidden_params: dict[str, _JsonValue] = {} def __contains__(self, key) -> bool: return hasattr(self, key) - def get(self, key, default=None): + def get(self, key, default=None) -> _JsonValue: return getattr(self, key, default) - def __getitem__(self, key): + def __getitem__(self, key) -> _JsonValue: return getattr(self, key) def json(self, **kwargs): diff --git a/litellm/types/management_endpoints/auto_router_endpoints.py b/litellm/types/management_endpoints/auto_router_endpoints.py index bde3f5f9e7e..88869a1edfb 100644 --- a/litellm/types/management_endpoints/auto_router_endpoints.py +++ b/litellm/types/management_endpoints/auto_router_endpoints.py @@ -245,27 +245,71 @@ ShadowEvalStatus: TypeAlias = Literal["running", "completed", "stopped"] ShadowEvalDirection: TypeAlias = Literal["forward", "reverse"] +ShadowEvalTargetType: TypeAlias = Literal["key", "team", "user"] + DEFAULT_SHADOW_EVAL_JUDGE_MODEL: Final[str] = "anthropic/claude-sonnet-5" # Sample-count ceiling written on every new job: a zero-cost error loop (a shadow arm that # fails before billing) never consumes spend budget, so it must terminate on count instead. +# A multi-router job writes one attempt row per router arm, so the valve is reached +# proportionally sooner; it is a safety valve, not a sample budget. SHADOW_EVAL_TURN_VALVE: Final[int] = 10_000 +SHADOW_EVAL_MAX_ROUTERS: Final[int] = 4 + class StartShadowEvalRequest(BaseModel): - """Start duplicating one or more keys' traffic for blind comparison against an auto-router.""" + """Start duplicating one or more targets' traffic for blind comparison against an auto-router. + + A target is a virtual key, a team, or a user; each becomes its own leg with its own + budget and stop state. Team and user targets match on the identity every request + carries after auth (user_api_key_team_id / user_api_key_user_id), so they cover + JWT-authenticated traffic, which presents no virtual key at all.""" api_key_ids: tuple[str, ...] = Field( - min_length=1, + default=(), max_length=100, description=( - "The hashed virtual keys whose traffic will be shadowed. Shadow evaluation runs ONLY on these " - "keys' traffic; requests made with any other key are not sampled. Each key carries its own " - "max_budget spend budget, so one key exhausting its budget leaves the others sampling. At most 100 " - "keys per job, which also bounds every read the job's endpoints make." + "Hashed virtual keys whose traffic will be shadowed. Combined with team_ids and user_ids the job " + "needs at least one target and at most 100, which also bounds every read the job's endpoints make. " + "Each target carries its own max_budget spend budget, so one exhausting its budget leaves the " + "others sampling." + ), + ) + team_ids: tuple[str, ...] = Field( + default=(), + max_length=100, + description=( + "Teams whose traffic will be shadowed, matched on the team every authenticated request resolves " + "to, so a team's JWT-auth and virtual-key traffic are both sampled" + ), + ) + user_ids: tuple[str, ...] = Field( + default=(), + max_length=100, + description=( + "Users whose traffic will be shadowed, matched on the user every authenticated request resolves " + "to across all their teams: JWT requests carrying their subject claim and virtual keys they own" + ), + ) + router_name: str | None = Field( + default=None, + description=( + "The auto-router under evaluation, in either direction: the single-router spelling of " + "router_names. Provide exactly one of the two fields" + ), + ) + router_names: tuple[str, ...] = Field( + default=(), + max_length=SHADOW_EVAL_MAX_ROUTERS, + description=( + "The auto-routers under evaluation, at most " + f"{SHADOW_EVAL_MAX_ROUTERS}. Every sampled request runs through every router listed and each " + "arm is judged independently against the same real response, so routers compare head-to-head " + "on identical traffic. More than one router requires direction 'forward'. After validation " + "this field always carries the full deduplicated set, whichever spelling the caller used" ), ) - router_name: str = Field(description="The auto-router under evaluation, in either direction") direction: ShadowEvalDirection = Field( default="forward", description=( @@ -285,7 +329,7 @@ class StartShadowEvalRequest(BaseModel): shadow_percentage: float = Field( ge=0.1, le=100.0, - description="Percentage of the key's requests to duplicate through the router", + description="Percentage of each target's requests to duplicate through the router", ) judge_model: str = Field( default=DEFAULT_SHADOW_EVAL_JUDGE_MODEL, @@ -306,10 +350,11 @@ class StartShadowEvalRequest(BaseModel): ge=0.01, le=10_000, description=( - "Per-key USD budget for the eval's own overhead, the shadow-arm and judge calls, priced with " - "the same figures the spend pipeline bills. EACH scoped key samples until its recorded eval " - "spend reaches this, so a job over N keys spends at most about N times max_budget; in-flight " - "samples can overshoot the cap by one sampling cache window" + "Per-target USD budget for the eval's own overhead, the shadow-arm and judge calls, priced with " + "the same figures the spend pipeline bills. EACH scoped target samples until its recorded eval " + "spend reaches this, so a job over N targets spends at most about N times max_budget; in-flight " + "samples can overshoot the cap by one sampling cache window. Every router arm draws from the " + "same per-target budget, so a multi-router job reaches it proportionally sooner" ), ) @@ -319,7 +364,7 @@ class StartShadowEvalRequest(BaseModel): """Pydantic ignores unknown fields, so a caller still sending max_turns would silently run on the default dollar budget instead of the bound they asked for.""" if isinstance(values, Mapping) and "max_turns" in values: - raise ValueError("max_turns was replaced by max_budget, the per-key USD cap on the eval's own spend") + raise ValueError("max_turns was replaced by max_budget, the per-target USD cap on the eval's own spend") return values @field_validator("shadow_percentage") @@ -327,12 +372,21 @@ class StartShadowEvalRequest(BaseModel): def _round_percentage(cls, value: float) -> float: return round(value, 2) - @field_validator("api_key_ids") + @field_validator("api_key_ids", "team_ids", "user_ids") @classmethod - def _dedupe_keys(cls, value: tuple[str, ...]) -> tuple[str, ...]: - """A key named twice would collide with itself on the one-active-per-(key, direction) index.""" + def _dedupe_targets(cls, value: tuple[str, ...]) -> tuple[str, ...]: + """A target named twice would collide with itself on the one-active-per-(target, direction) index.""" return tuple(dict.fromkeys(value)) + @model_validator(mode="after") + def _at_least_one_target_at_most_hundred(self) -> "StartShadowEvalRequest": + total: Final = len(self.api_key_ids) + len(self.team_ids) + len(self.user_ids) + if total < 1: + raise ValueError("at least one target is required: pass api_key_ids, team_ids, or user_ids") + if total > 100: + raise ValueError("at most 100 targets per job across api_key_ids, team_ids, and user_ids") + return self + @model_validator(mode="after") def _baseline_model_matches_direction(self) -> "StartShadowEvalRequest": if self.direction == "reverse" and self.baseline_model is None: @@ -341,10 +395,28 @@ class StartShadowEvalRequest(BaseModel): raise ValueError("baseline_model is only meaningful when direction is 'reverse'") return self + @model_validator(mode="after") + def _resolve_router_set(self) -> "StartShadowEvalRequest": + """Whichever spelling the caller used, router_names leaves validation as the full + deduplicated set, so every downstream reader consumes one field.""" + if (self.router_name is None) == (not self.router_names): + raise ValueError("provide exactly one of router_name or router_names") + single: Final = () if self.router_name is None else (self.router_name,) + routers: Final = tuple(dict.fromkeys(self.router_names or single)) + if not all(name.strip() for name in routers): + raise ValueError("router names must be non-empty strings") + if len(routers) > 1 and self.direction == "reverse": + raise ValueError("a reverse job evaluates one router against baseline_model; pass a single router") + # A returned model_copy is ignored on the __init__ construction path, so the + # normalization must land as a self attribute store to hold for every caller. + self.router_names = routers + return self + class ShadowEvalSlice(BaseModel): - """Judge outcomes for one slice of a job's verdicts (a router tier, or one of the - models that served the real arm).""" + """Judge outcomes for one slice of a job's verdicts: a router tier, one of the + models that served the real arm, or one scoped target (embedded on that target's + own entry, so slices never need re-joining to a target by id).""" group: str turn_count: int @@ -395,21 +467,28 @@ class ShadowEvalResult(BaseModel): "and in reverse the models the router itself picked" ) ) - by_key: tuple[ShadowEvalSlice, ...] = Field( + by_router: tuple[ShadowEvalSlice, ...] = Field( + default=(), description=( - "One slice per scoped key that has judged verdicts, grouped on the raw key hash. Keys the job " - "scopes but has not judged a turn for yet are absent rather than reported as zero" + "One slice per router arm, grouped on the router name. Every arm of a multi-router job is " + "judged against the same real responses over the same sampled requests, so these slices " + "compare routers head-to-head: like-for-like win rates and spends on identical traffic. " + "Verdicts from before arm stamping existed count toward the job's own router" ), ) overall_shadow_win_rate_pct: float overall_tie_rate_pct: float sampled_real_spend: float = Field( default=0.0, - description="USD the real arm billed across all judged turns, cache-served turns excluded", + description=( + "USD the real arm billed across all judged turns, cache-served turns excluded. A judged turn " + "is one (request, router arm) verdict, so a multi-router job counts the real response once per " + "arm it was judged against; per-router comparisons read by_router" + ), ) sampled_shadow_spend: float = Field( default=0.0, - description="USD the shadow arm billed across the same turns, judge excluded, like for like", + description="USD the shadow arms billed across the same turns, judge excluded, like for like", ) not_sampled_count: int | None = Field( default=None, @@ -436,27 +515,28 @@ class ShadowEvalResult(BaseModel): ) -class ShadowEvalJobKeyResponse(BaseModel): - """One key a job shadows, with its own budget and stop state.""" +class ShadowEvalJobTargetResponse(BaseModel): + """One target a job shadows (a key, team, or user), with its own budget and stop state.""" - api_key_id: str = Field(description="The hashed virtual key whose traffic this entry scopes") + target_type: ShadowEvalTargetType = Field(description="What kind of entity this entry scopes") + target_id: str = Field(description="The hashed virtual key, team id, or user id whose traffic this entry scopes") max_turns: int = Field( description=( - "This key's sample-count ceiling: the whole budget for jobs created before max_budget " + "This target's sample-count ceiling: the whole budget for jobs created before max_budget " "existed, and the error-loop safety valve otherwise" ) ) max_budget: float | None = Field( default=None, description=( - "This key's own USD budget for the eval's shadow and judge spend, independent of its " + "This target's own USD budget for the eval's shadow and judge spend, independent of its " "siblings'; None on jobs created before spend budgets existed, which max_turns alone bounds" ), ) stopped_at: datetime | None = Field( default=None, description=( - "When this key's slot was stamped free, whether its own budget ran out, the window closed, " + "When this target's slot was stamped free, whether its own budget ran out, the window closed, " "or an operator stopped the job; status is derived, so a spent budget reads completed even " "while this is still unset" ), @@ -464,47 +544,61 @@ class ShadowEvalJobKeyResponse(BaseModel): attempt_count: int | None = Field( default=None, description=( - "This key's sampled attempts so far, judged and errored alike, the same count the sampler " + "This target's sampled attempts so far, judged and errored alike, the same count the sampler " "budgets against max_turns; populated on list and detail responses. Frozen at stopped_at " - "once the key is stamped, so in-flight attempts landing after a stop never reclassify it" + "once the target is stamped, so in-flight attempts landing after a stop never reclassify it" ), ) spend: float | None = Field( default=None, description=( - "This key's recorded shadow plus judge spend in USD, the same figure the sampler budgets " + "This target's recorded shadow plus judge spend in USD, the same figure the sampler budgets " "against max_budget; populated on list and detail responses and frozen at stopped_at " "exactly like attempt_count" ), ) + verdicts: "ShadowEvalSlice | None" = Field( + default=None, + description="This target's own judged-verdict slice; detail endpoint only, None until a turn is judged", + ) + @property def budget_spent(self) -> bool: over_spend: Final = self.max_budget is not None and self.spend is not None and self.spend >= self.max_budget return over_spend or (self.attempt_count is not None and self.attempt_count >= self.max_turns) - key_alias: str | None = Field( + target_alias: str | None = Field( default=None, - description="Alias of the shadowed key, resolved from the key row at read time; None when unset or deleted", + description=( + "Display label resolved from the target's own row at read time: the key's alias, the team's " + "alias, or the user's email; None when unset or deleted" + ), ) key_name: str | None = Field( default=None, - description="Masked display name (sk-...) of the shadowed key, resolved at read time like key_alias", + description="Masked display name (sk-...) for key targets, resolved at read time; None for teams and users", ) class ShadowEvalJobResponse(BaseModel): - """A shadow-eval job over one or more keys, each with its own budget and stop state; - status is derived from stopped_by, the keys' stop and budget state, and ends_at, + """A shadow-eval job over one or more targets, each with its own budget and stop state; + status is derived from stopped_by, the targets' stop and budget state, and ends_at, never stored, so no writer anywhere can produce an inconsistent one. Aggregate fields are populated by the detail endpoint only and stay None on list responses.""" job_id: str - keys: tuple[ShadowEvalJobKeyResponse, ...] = Field( + targets: tuple[ShadowEvalJobTargetResponse, ...] = Field( min_length=1, - description="The keys whose traffic this job evaluates, and only those keys', each with its own budget", + description="The targets whose traffic this job evaluates, and only theirs, each with its own budget", + ) + router_names: tuple[str, ...] = Field( + min_length=1, + description=( + "Every auto-router this job runs as a shadow arm. Multi-router jobs sample one slice of " + "traffic and judge every arm against the same real responses" + ), ) - router_name: str direction: ShadowEvalDirection = "forward" baseline_model: str | None = None judge_model: str @@ -526,13 +620,20 @@ class ShadowEvalJobResponse(BaseModel): last_error: str | None = Field(default=None, description="Most recent attempt error; detail endpoint only") results: ShadowEvalResult | None = Field(default=None, description="Stratified verdicts; detail endpoint only") + @computed_field + @property + def router_name(self) -> str: + """The first router, kept for callers that predate router_names; derived so the + two fields can never disagree.""" + return self.router_names[0] + @computed_field @property def status(self) -> ShadowEvalStatus: """Three recorded facts, no history-guessing: a stop is stopped_by (the migration backfills it for every job that displayed stopped when the column arrived, so the - pre-column population is closed), completion is the window passing or every key - spending its budget, and anything else is running. The all-keys-stamped fallback + pre-column population is closed), completion is the window passing or every target + spending its budget, and anything else is running. The all-targets-stamped fallback covers only stops written by pre-column pods during a rolling deploy.""" if self.stopped_by is not None: return "stopped" @@ -540,8 +641,8 @@ class ShadowEvalJobResponse(BaseModel): self.ends_at if self.ends_at.tzinfo else self.ends_at.replace(tzinfo=timezone.utc) ): return "completed" - if all(key.budget_spent for key in self.keys): + if all(target.budget_spent for target in self.targets): return "completed" - if all(key.stopped_at is not None for key in self.keys): + if all(target.stopped_at is not None for target in self.targets): return "stopped" return "running" diff --git a/litellm/types/router.py b/litellm/types/router.py index ab6c807ba20..e0957383aac 100644 --- a/litellm/types/router.py +++ b/litellm/types/router.py @@ -369,7 +369,7 @@ class GenericLiteLLMParams(CredentialLiteLLMParams, CustomPricingLiteLLMParams): @model_validator(mode="before") @classmethod - def preprocess_input_data(cls, data: Any) -> Any: + def preprocess_input_data(cls, data: object) -> object: """ Pre-process input data before validation: 1. Filter out reserved Python keywords ('self', 'params', '__class__') to prevent @@ -627,6 +627,11 @@ class AlertingConfig(BaseModel): alerting_threshold: float | None = 300 +def _resolved_annotations(model_class: type[object]) -> Mapping[str, object]: + """Resolve a class's annotations, keeping each resolved annotation opaque.""" + return get_type_hints(model_class) + + class ModelGroupInfo(BaseModel): model_group: str providers: list[str] @@ -655,7 +660,7 @@ class ModelGroupInfo(BaseModel): configurable_clientside_auth_params: CONFIGURABLE_CLIENTSIDE_AUTH_PARAMS = None def __init__(self, **data) -> None: - for field_name, field_type in get_type_hints(self.__class__).items(): + for field_name, field_type in _resolved_annotations(self.__class__).items(): if field_type is bool and data.get(field_name) is None: data[field_name] = False super().__init__(**data) diff --git a/litellm/types/utils.py b/litellm/types/utils.py index 4bf8289d725..addb7b730de 100644 --- a/litellm/types/utils.py +++ b/litellm/types/utils.py @@ -2840,8 +2840,17 @@ RoutingDecisionCause = Literal[ # never called. The matched sentinel rides in matched_keyword. Distinct from the keyword causes, # which are operator-authored rules; these sentinels ship with the router. "housekeeping", + # modality_routing replaced the decided placement: the request carries an image and the + # routed model does not accept image input, so the nearest higher capable tier or + # default_model served instead. The displaced placement rides in signals. + "modality_escalation", "session_affinity_pin", "session_affinity_escalation", + # classification_mode 'user_turn': the request is an agent loop's continuation turn (no new + # human ask), so the session's held routing decision was replayed and the classifier was never + # called. Distinct from "session_affinity_pin", which reports the session_affinity flag pinning + # every turn including new asks; this cause only appears when session_affinity is off. + "user_turn_continuation", "default_fallback", "keyword", "quality_tier", @@ -2881,6 +2890,8 @@ class StandardLoggingRoutingDecision(TypedDict, total=False): classifier_model: str classifier_cost: float escalated: bool + context_escalated: bool # writable-ok: Pydantic warns on ReadOnly TypedDict fields + context_escalation_original_tier: str # writable-ok: Pydantic warns on ReadOnly TypedDict fields tier_boundaries: StandardLoggingRoutingDecisionTierBoundaries reasoning_override_min_score: float # writable-ok: Pydantic warns on ReadOnly TypedDict fields conversation_continuing: bool @@ -2907,6 +2918,8 @@ DERIVED_ROUTING_DECISION_FIELDS: Final[frozenset[str]] = frozenset( "classifier_model", "classifier_cost", "escalated", + "context_escalated", + "context_escalation_original_tier", "tier_boundaries", "reasoning_override_min_score", "conversation_continuing", @@ -3758,6 +3771,8 @@ class LlmProviders(str, Enum): CODESTRAL = "codestral" TEXT_COMPLETION_CODESTRAL = "text-completion-codestral" DASHSCOPE = "dashscope" + QWENCLOUD = "qwencloud" + QWEN_AI_PLATFORM = "qwen_ai_platform" MODELSCOPE = "modelscope" MOONSHOT = "moonshot" PUBLICAI = "publicai" diff --git a/litellm/utils.py b/litellm/utils.py index 5e9e115ed54..028682661f7 100644 --- a/litellm/utils.py +++ b/litellm/utils.py @@ -2660,10 +2660,19 @@ def _is_explicitly_disabled_factory(model: str, custom_llm_provider: str | None, ``_supports_factory`` so caching, fallback, and normalisation improvements apply here automatically. """ + from litellm.litellm_core_utils.get_llm_provider_logic import declared_authenticating_provider + try: - model, custom_llm_provider, _, _ = litellm.get_llm_provider( - model=model, custom_llm_provider=custom_llm_provider - ) + declared: Final = declared_authenticating_provider(model, custom_llm_provider) + if declared is not None: + model = model.removeprefix( + f"{declared}/" + ) # rebind-ok: mirrors get_llm_provider's split without its OAuth flow + custom_llm_provider = declared # rebind-ok: same + else: + model, custom_llm_provider, _, _ = litellm.get_llm_provider( + model=model, custom_llm_provider=custom_llm_provider + ) model_info: Final = _get_model_info_helper(model=model, custom_llm_provider=custom_llm_provider) val: Final = model_info.get(key) if val is False: @@ -2751,6 +2760,15 @@ def supports_computer_use(model: str, custom_llm_provider: str | None = None) -> ) +def is_vision_explicitly_disabled(model: str, custom_llm_provider: str | None = None) -> bool: + """True only when supports_vision is explicitly declared false for the model. + + The opt-out mirror of :func:`supports_vision`: a missing declaration reads as not + disabled, so unknown or newly added models stay eligible for image routing. + """ + return _is_explicitly_disabled_factory(model, custom_llm_provider, "supports_vision") + + def supports_vision(model: str, custom_llm_provider: str | None = None) -> bool: """ Check if the given model supports vision and return a boolean value. @@ -3543,10 +3561,10 @@ def get_optional_params_embeddings( non_default_params=non_default_params, optional_params={}, kwargs=kwargs ) elif custom_llm_provider == "vertex_ai" or custom_llm_provider == "gemini": - # OpenAI SDKs (and litellm's own client) send encoding_format="float" - # by default; float lists are exactly what the vertex API returns, so - # the param is a no-op — don't reject the provider default. Other - # values (e.g. "base64") stay on the unsupported-param path below. + # OpenAI SDKs send encoding_format="float" by default; float lists are + # exactly what the vertex API returns, so the param is a no-op and the + # provider default is not rejected. Other values (e.g. "base64") stay + # on the unsupported-param path below. if non_default_params.get("encoding_format") == "float": non_default_params.pop("encoding_format") supported_params = get_supported_openai_params( @@ -6568,11 +6586,11 @@ def validate_environment( keys_in_environment = True else: missing_keys.append("WANDB_API_KEY") - elif custom_llm_provider == "dashscope": - if "DASHSCOPE_API_KEY" in os.environ: + elif custom_llm_provider in ("dashscope", "qwencloud", "qwen_ai_platform"): + if f"{custom_llm_provider.upper()}_API_KEY" in os.environ or "DASHSCOPE_API_KEY" in os.environ: keys_in_environment = True else: - missing_keys.append("DASHSCOPE_API_KEY") + missing_keys.append(f"{custom_llm_provider.upper()}_API_KEY") elif custom_llm_provider == "modelscope": if "MODELSCOPE_API_KEY" in os.environ: keys_in_environment = True @@ -8134,6 +8152,11 @@ class ProviderConfigManager: LlmProviders.NEBIUS: (lambda: litellm.NebiusConfig(), False), LlmProviders.WANDB: (lambda: litellm.WandbConfig(), False), LlmProviders.DASHSCOPE: (lambda: litellm.DashScopeChatConfig(), False), + LlmProviders.QWENCLOUD: (lambda: litellm.QwenCloudChatConfig(), False), + LlmProviders.QWEN_AI_PLATFORM: ( + lambda: litellm.QwenAIPlatformChatConfig(), + False, + ), LlmProviders.MODELSCOPE: (lambda: litellm.ModelScopeChatConfig(), False), LlmProviders.MOONSHOT: (lambda: litellm.MoonshotChatConfig(), False), LlmProviders.DOCKER_MODEL_RUNNER: ( @@ -8260,10 +8283,17 @@ class ProviderConfigManager: """ # Handle OpenAI special cases (O-series and GPT-5 models) if provider == LlmProviders.OPENAI: + from litellm.llms.openai.chat.gpt_transformation import ( + OpenAIGPTConfig, + OpenAIUnknownModelConfig, + ) + if litellm.openaiOSeriesConfig.is_model_o_series_model(model=model): return litellm.openaiOSeriesConfig if litellm.OpenAIGPT5Config.is_model_gpt_5_model(model=model): return litellm.OpenAIGPT5Config() + if not OpenAIGPTConfig.is_openai_catalog_model(model): + return OpenAIUnknownModelConfig() # Handle Azure before the generic map so base_model can be threaded through if provider == LlmProviders.AZURE: @@ -8341,12 +8371,16 @@ class ProviderConfigManager: ) return VolcEngineEmbeddingConfig() - elif litellm.LlmProviders.DASHSCOPE == provider: - from litellm.llms.dashscope.embed.transformation import ( - DashScopeEmbeddingConfig, + elif provider in ( + litellm.LlmProviders.DASHSCOPE, + litellm.LlmProviders.QWENCLOUD, + litellm.LlmProviders.QWEN_AI_PLATFORM, + ): + from litellm.llms.dashscope.common_utils import ( + get_dashscope_family_embedding_config, ) - return DashScopeEmbeddingConfig() + return get_dashscope_family_embedding_config(provider.value) elif litellm.LlmProviders.OVHCLOUD == provider: return litellm.OVHCloudEmbeddingConfig() elif litellm.LlmProviders.SNOWFLAKE == provider: @@ -8419,12 +8453,16 @@ class ProviderConfigManager: return litellm.VoyageRerankConfig() elif litellm.LlmProviders.WATSONX == provider: return litellm.IBMWatsonXRerankConfig() - elif litellm.LlmProviders.DASHSCOPE == provider: - from litellm.llms.dashscope.rerank.transformation import ( - DashScopeRerankConfig, + elif provider in ( + litellm.LlmProviders.DASHSCOPE, + litellm.LlmProviders.QWENCLOUD, + litellm.LlmProviders.QWEN_AI_PLATFORM, + ): + from litellm.llms.dashscope.common_utils import ( + get_dashscope_family_rerank_config, ) - return DashScopeRerankConfig() + return get_dashscope_family_rerank_config(provider.value) return litellm.CohereRerankConfig() @staticmethod @@ -8830,6 +8868,12 @@ class ProviderConfigManager: ) return AzurePassthroughConfig() + elif LlmProviders.GIGACHAT == provider: + from litellm.llms.gigachat.passthrough.transformation import ( + GigaChatPassthroughConfig, + ) + + return GigaChatPassthroughConfig() elif LlmProviders.WATSONX == provider: from litellm.llms.watsonx.passthrough.transformation import ( WatsonxPassthroughConfig, @@ -9091,12 +9135,16 @@ class ProviderConfigManager: ) return get_openrouter_image_generation_config(model) - elif LlmProviders.DASHSCOPE == provider: - from litellm.llms.dashscope.image_generation import ( - get_dashscope_image_generation_config, + elif provider in ( + LlmProviders.DASHSCOPE, + LlmProviders.QWENCLOUD, + LlmProviders.QWEN_AI_PLATFORM, + ): + from litellm.llms.dashscope.common_utils import ( + get_dashscope_family_image_generation_config, ) - return get_dashscope_image_generation_config(model) + return get_dashscope_family_image_generation_config(provider.value) elif LlmProviders.MODELSCOPE == provider: from litellm.llms.modelscope.image_generation import ( get_modelscope_image_generation_config, @@ -9409,6 +9457,10 @@ class ProviderConfigManager: return RunwayMLTextToSpeechConfig() elif litellm.LlmProviders.VERTEX_AI == provider: + if "gemini" in model: + # Gemini TTS uses the speech_to_completion bridge, and Google Cloud TTS param + # mapping would drop response_format before the bridge sees it (LIT-6501) + return None from litellm.llms.vertex_ai.text_to_speech.transformation import ( VertexAITextToSpeechConfig, ) diff --git a/litellm/vector_stores/vector_store_registry.py b/litellm/vector_stores/vector_store_registry.py index 22d27bc3266..b71d6784873 100644 --- a/litellm/vector_stores/vector_store_registry.py +++ b/litellm/vector_stores/vector_store_registry.py @@ -112,7 +112,9 @@ class VectorStoreRegistry: Dynamically extracts all parameters defined in VECTOR_STORE_OPENAI_PARAMS. """ # Get the list of supported param names from the Literal type - supported_params: Final = get_args(VECTOR_STORE_OPENAI_PARAMS) + supported_params: Final = tuple( + param for param in get_args(VECTOR_STORE_OPENAI_PARAMS) if isinstance(param, str) + ) # Extract only the params that exist in the tool kwargs: Final = {param: tool.get(param) for param in supported_params if param in tool} @@ -503,7 +505,7 @@ class VectorStoreRegistry: vector_stores_from_db.append(_litellm_managed_vector_store) return vector_stores_from_db - def get_credentials_for_vector_store(self, vector_store_id: str) -> dict[str, Any]: + def get_credentials_for_vector_store(self, vector_store_id: str) -> dict[str, object]: """ Get the credentials for a vector store diff --git a/migrations/Dockerfile b/migrations/Dockerfile index 6335e6f6bd8..c6d1b0cc46e 100644 --- a/migrations/Dockerfile +++ b/migrations/Dockerfile @@ -1,5 +1,5 @@ -ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:a31344ab2cb8618db84f535eec56f76f6178b142cb92cb2e48676cc2dcebea72 -ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:a31344ab2cb8618db84f535eec56f76f6178b142cb92cb2e48676cc2dcebea72 +ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:e624c5d5e42382ce7165ddafcbbf8e6769a24cbd02ea6114b880b05ae5ba2a8d +ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:e624c5d5e42382ce7165ddafcbbf8e6769a24cbd02ea6114b880b05ae5ba2a8d ARG UV_IMAGE=ghcr.io/astral-sh/uv:0.11.7@sha256:240fb85ab0f263ef12f492d8476aa3a2e4e1e333f7d67fbdd923d00a506a516a FROM $UV_IMAGE AS uvbin @@ -35,7 +35,7 @@ COPY --from=uvbin /uv /uvx /usr/local/bin/ # instead of nodeenv downloading one whose dynamic deps may not be in Wolfi # (e.g. Node 26.2.0 needs libatomic). Retry for transient apk.cgr.dev flakes. RUN for i in 1 2 3; do \ - apk add --no-cache bash gcc python3 python3-dev openssl openssl-dev libsndfile nodejs npm && break; \ + apk add --no-cache bash gcc python-3.13 python-3.13-dev openssl openssl-dev libsndfile nodejs npm && break; \ [ $i = 3 ] && { echo "apk add failed after 3 retries" >&2; exit 1; }; \ sleep 5; \ done @@ -56,7 +56,7 @@ RUN --mount=type=cache,target=/root/.cache/uv \ uv sync --frozen --no-install-project --no-install-workspace --no-default-groups --no-editable \ --extra proxy \ --extra extra_proxy \ - --python python3 + --python python3.13 # Stage 2 — copy source and install the project + workspace members. COPY . . @@ -65,7 +65,7 @@ RUN --mount=type=cache,target=/root/.cache/uv \ uv sync --frozen --no-default-groups --no-editable \ --extra proxy \ --extra extra_proxy \ - --python python3 + --python python3.13 COPY migrations/run.py /app/run.py @@ -87,7 +87,7 @@ FROM $LITELLM_RUNTIME_IMAGE AS runtime USER root RUN for i in 1 2 3; do \ - apk add --no-cache bash openssl tzdata python3 nodejs libsndfile libatomic && break; \ + apk add --no-cache bash openssl tzdata python-3.13 nodejs libsndfile libatomic && break; \ [ $i = 3 ] && { echo "apk add failed after 3 retries" >&2; exit 1; }; \ sleep 5; \ done diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index 7071eaa0807..27ff525c15e 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -553,6 +553,27 @@ "supports_response_schema": true, "supports_vision": true }, + "amazon.nova-sonic-v1:0": { + "deprecation_date": "2026-09-14", + "input_cost_per_audio_token": 3.4e-06, + "input_cost_per_token": 6e-08, + "litellm_provider": "bedrock", + "mode": "realtime", + "output_cost_per_audio_token": 1.36e-05, + "output_cost_per_token": 2.4e-07, + "supports_audio_input": true, + "supports_audio_output": true + }, + "amazon.nova-2-sonic-v1:0": { + "input_cost_per_audio_token": 3e-06, + "input_cost_per_token": 3.3e-07, + "litellm_provider": "bedrock", + "mode": "realtime", + "output_cost_per_audio_token": 1.2e-05, + "output_cost_per_token": 2.75e-06, + "supports_audio_input": true, + "supports_audio_output": true + }, "amazon.rerank-v1:0": { "input_cost_per_query": 0.001, "input_cost_per_token": 0.0, @@ -1571,7 +1592,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "supports_parallel_tool_use_config": true, @@ -1607,7 +1628,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "supports_parallel_tool_use_config": true, @@ -1643,7 +1664,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "supports_parallel_tool_use_config": true, @@ -1679,7 +1700,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "supports_parallel_tool_use_config": true, @@ -1715,7 +1736,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "supports_parallel_tool_use_config": true, @@ -1751,7 +1772,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "supports_parallel_tool_use_config": true, @@ -2044,7 +2065,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", @@ -2081,7 +2102,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", @@ -2118,7 +2139,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", @@ -2155,7 +2176,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", @@ -2192,7 +2213,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", @@ -2229,7 +2250,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", @@ -3025,6 +3046,7 @@ "prompt_cache_min_tokens": 2048 }, "azure_ai/claude-fable-5": { + "deprecation_date": "2027-12-05", "supports_mid_conversation_system": true, "input_cost_per_token": 1e-05, "output_cost_per_token": 5e-05, @@ -3058,6 +3080,7 @@ "prompt_cache_min_tokens": 512 }, "azure_ai/claude-opus-5": { + "deprecation_date": "2027-07-08", "supports_mid_conversation_system": true, "supports_adaptive_thinking": true, "input_cost_per_token": 5e-06, @@ -3090,6 +3113,7 @@ "prompt_cache_min_tokens": 512 }, "azure_ai/claude-opus-4-8": { + "deprecation_date": "2027-09-01", "supports_mid_conversation_system": true, "supports_adaptive_thinking": true, "input_cost_per_token": 5e-06, @@ -3168,6 +3192,7 @@ "prompt_cache_min_tokens": 1024 }, "azure_ai/claude-sonnet-5": { + "deprecation_date": "2027-06-30", "supports_mid_conversation_system": true, "cache_creation_input_token_cost": 2.5e-06, "cache_creation_input_token_cost_above_1hr": 4e-06, @@ -9959,6 +9984,22 @@ "supports_reasoning": true, "supports_tool_choice": true }, + "azure_ai/deepseek-v4-flash-0731": { + "cache_read_input_token_cost": 2.8e-08, + "deprecation_date": "2026-12-03", + "input_cost_per_token": 1.9e-07, + "litellm_provider": "azure_ai", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 5.1e-07, + "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/deepseek/", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, "azure_ai/embed-v-4-0": { "input_cost_per_token": 1.2e-07, "litellm_provider": "azure_ai", @@ -12267,6 +12308,7 @@ "supports_tool_choice": true }, "cerebras/zai-glm-4.7": { + "deprecation_date": "2026-08-17", "input_cost_per_token": 2.25e-06, "litellm_provider": "cerebras", "max_input_tokens": 128000, @@ -14698,6 +14740,1910 @@ "/v1/images/generations" ] }, + "qwencloud/deepseek-v4-flash": { + "cache_read_input_token_cost": 4e-08, + "input_cost_per_token": 2e-07, + "litellm_provider": "qwencloud", + "max_input_tokens": 1000000, + "max_output_tokens": 393216, + "max_tokens": 393216, + "mode": "chat", + "output_cost_per_token": 4e-07, + "source": "https://www.qwencloud.com/models", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "qwencloud/deepseek-v4-flash-0731": { + "cache_read_input_token_cost": 4e-08, + "input_cost_per_token": 2e-07, + "litellm_provider": "qwencloud", + "max_input_tokens": 1000000, + "max_output_tokens": 393216, + "max_tokens": 393216, + "mode": "chat", + "output_cost_per_token": 4e-07, + "source": "https://www.qwencloud.com/models", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "qwencloud/deepseek-v4-pro": { + "cache_read_input_token_cost": 2e-07, + "input_cost_per_token": 2.4e-06, + "litellm_provider": "qwencloud", + "max_input_tokens": 1000000, + "max_output_tokens": 393216, + "max_tokens": 393216, + "mode": "chat", + "output_cost_per_token": 4.8e-06, + "source": "https://www.qwencloud.com/models", + "supports_function_calling": true, + 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Billing reads the per-token dollar fields; the '*_dbu_cost_per_token' fields are the published Databricks rates, kept for reference. Context/max output are the DeepSeek-published model limits (1M context, 384K max output)." + }, + "mode": "chat", + "output_cost_per_token": 2.8e-07, + "output_dbu_cost_per_token": 4e-06, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving", + "supported_modalities": [ + "text" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_tool_choice": true, + "supports_vision": false + }, + "databricks/databricks-deepseek-v4-pro-0813": { + "cache_creation_input_token_cost": 1.31999e-06, + "cache_read_input_token_cost": 1.3202e-07, + "input_cost_per_token": 1.31999e-06, + "input_dbu_cost_per_token": 1.8857e-05, + "litellm_provider": "databricks", + "max_input_tokens": 1000000, + "max_output_tokens": 393216, + "max_tokens": 393216, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Billing reads the per-token dollar fields; the '*_dbu_cost_per_token' fields are the published Databricks rates, kept for reference. Context/max output are the DeepSeek-published model limits (1M context, 384K max output)." + }, + "mode": "chat", + "output_cost_per_token": 3.95997e-06, + "output_dbu_cost_per_token": 5.6571e-05, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving", + "supported_modalities": [ + "text" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_tool_choice": true, + "supports_vision": false + }, "databricks/databricks-gemini-2-5-flash": { "cache_creation_input_token_cost": 3.0002e-07, "cache_read_input_token_cost": 3.0002e-08, @@ -19566,6 +21568,61 @@ "supports_system_messages": true, "supports_tool_choice": true }, + "friendliai/zai-org/GLM-5.3-Flash": { + "litellm_provider": "friendliai", + "supports_reasoning": true, + "supports_function_calling": true, + "max_input_tokens": 1048576, + "max_tokens": 1048576, + "max_output_tokens": 1048576, + "input_cost_per_token": 1.5e-07, + "output_cost_per_token": 5e-07, + "cache_read_input_token_cost": 3e-08, + "supports_prompt_caching": true, + "reasoning_effort_levels": [ + "low", + "high", + "max" + ], + "supports_parallel_function_calling": true, + "supports_response_schema": true, + "supports_native_structured_output": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "mode": "chat", + "comment": "Native multimodal GLM model for efficient coding and long-horizon agent tasks", + "source": "https://api.friendli.ai/serverless/v1/models", + "supports_vision": true, + "supports_image_input": true, + "supports_video_input": true + }, + "friendliai/zai-org/GLM-5.3": { + "litellm_provider": "friendliai", + "supports_reasoning": true, + "supports_function_calling": true, + "max_input_tokens": 1048576, + "max_tokens": 1048576, + "max_output_tokens": 1048576, + "input_cost_per_token": 1.26e-06, + "output_cost_per_token": 3.96e-06, + "cache_read_input_token_cost": 2.34e-07, + "supports_prompt_caching": true, + "reasoning_effort_levels": [ + "low", + "high", + "max" + ], + "supports_parallel_function_calling": true, + "supports_response_schema": true, + "supports_native_structured_output": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "mode": "chat", + "comment": "Flagship GLM model for long-horizon coding, agents, and complex project delivery", + "source": "https://api.friendli.ai/serverless/v1/models", + "supports_vision": false, + "supports_image_input": false + }, "ft:babbage-002": { "deprecation_date": "2026-10-23", "input_cost_per_token": 1.6e-06, @@ -20556,6 +22613,7 @@ "supports_image_size": false }, "gemini-live-2.5-flash-native-audio": { + "deprecation_date": "2026-12-13", "input_cost_per_audio_token": 3e-06, "input_cost_per_token": 5e-07, "litellm_provider": "vertex_ai-language-models", @@ -23777,8 +25835,10 @@ "max_input_tokens": 1024, "max_tokens": 1024, "mode": "video_generation", - "output_cost_per_second": 0.15, - "source": "https://ai.google.dev/gemini-api/docs/video", + "output_cost_per_second": 0.1, + "output_cost_per_second_1080p": 0.12, + "output_cost_per_second_4k": 0.3, + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_modalities": [ "text" ], @@ -23792,7 +25852,8 @@ "max_tokens": 1024, "mode": "video_generation", "output_cost_per_second": 0.4, - "source": "https://ai.google.dev/gemini-api/docs/video", + "output_cost_per_second_4k": 0.6, + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_modalities": [ "text" ], @@ -23820,8 +25881,10 @@ "max_input_tokens": 1024, "max_tokens": 1024, "mode": "video_generation", - "output_cost_per_second": 0.15, - "source": "https://ai.google.dev/gemini-api/docs/video", + "output_cost_per_second": 0.1, + "output_cost_per_second_1080p": 0.12, + "output_cost_per_second_4k": 0.3, + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_modalities": [ "text" ], @@ -23835,7 +25898,8 @@ "max_tokens": 1024, "mode": "video_generation", "output_cost_per_second": 0.4, - "source": "https://ai.google.dev/gemini-api/docs/video", + "output_cost_per_second_4k": 0.6, + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_modalities": [ "text" ], @@ -24344,7 +26408,7 @@ "supports_response_schema": true, "supports_vision": true }, - "gigachat/GigaChat-2-Lite": { + "gigachat/GigaChat-2": { "input_cost_per_token": 0.0, "litellm_provider": "gigachat", "max_input_tokens": 128000, @@ -24406,6 +26470,15 @@ "output_cost_per_token": 0.0, "output_vector_size": 2560 }, + "gigachat/GigaEmbeddings-3B-2025-09": { + "input_cost_per_token": 0.0, + "litellm_provider": "gigachat", + "max_input_tokens": 4096, + "max_tokens": 4096, + "mode": "embedding", + "output_cost_per_token": 0.0, + "output_vector_size": 2048 + }, "gmi/anthropic/claude-opus-4.5": { "input_cost_per_token": 5e-06, "litellm_provider": "gmi", @@ -43356,7 +45429,8 @@ "max_tokens": 1024, "mode": "video_generation", "output_cost_per_second": 0.4, - "source": "https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/veo/3-1-generate", + "output_cost_per_second_4k": 0.6, + "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing", "supported_modalities": [ "text" ], @@ -43369,8 +45443,10 @@ "max_input_tokens": 1024, "max_tokens": 1024, "mode": "video_generation", - "output_cost_per_second": 0.15, - "source": "https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/veo/3-1-generate", + "output_cost_per_second": 0.1, + "output_cost_per_second_1080p": 0.12, + "output_cost_per_second_4k": 0.3, + "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing", "supported_modalities": [ "text" ], @@ -43385,7 +45461,8 @@ "max_tokens": 1024, "mode": "video_generation", "output_cost_per_second": 0.4, - "source": "https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/veo/3-1-generate", + "output_cost_per_second_4k": 0.6, + "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing", "supported_modalities": [ "text" ], @@ -43399,8 +45476,10 @@ "max_input_tokens": 1024, "max_tokens": 1024, "mode": "video_generation", - "output_cost_per_second": 0.15, - "source": "https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/veo/3-1-generate", + "output_cost_per_second": 0.1, + "output_cost_per_second_1080p": 0.12, + "output_cost_per_second_4k": 0.3, + "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing", "supported_modalities": [ "text" ], @@ -52147,14 +54226,14 @@ "supports_vision": true }, "fireworks_ai/deepseek-v4-flash-0731": { - "cache_read_input_token_cost": 2.8e-08, - "input_cost_per_token": 1.4e-07, + "cache_read_input_token_cost": 7e-09, + "input_cost_per_token": 2.2e-07, "litellm_provider": "fireworks_ai", "max_input_tokens": 1048576, "max_output_tokens": 131072, "max_tokens": 131072, "mode": "chat", - "output_cost_per_token": 2.8e-07, + "output_cost_per_token": 6.6e-07, "source": "https://docs.fireworks.ai/serverless/pricing", "supports_function_calling": true, "supports_reasoning": true, @@ -55040,5 +57119,55 @@ "max_tokens": 40960, "mode": "embedding", "source": "https://docs.fireworks.ai/serverless/pricing" + }, + "zai/glm-5.2": { + "cache_creation_input_token_cost": 0, + "cache_read_input_token_cost": 2.6e-07, + "input_cost_per_token": 1.4e-06, + "litellm_provider": "zai", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 4.4e-06, + "source": "https://docs.z.ai/guides/overview/pricing", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, + "together_ai/Qwen/Qwen3.8-Flash": { + "input_cost_per_token": 1.5e-07, + "litellm_provider": "together_ai", + "max_input_tokens": 1000000, + "max_tokens": 1000000, + "mode": "chat", + "output_cost_per_token": 4.7e-07, + "source": "https://docs.together.ai/docs/serverless-models" + }, + "cerebras/gemma-4-31b": { + "input_cost_per_token": 9.9e-07, + "litellm_provider": "cerebras", + "max_input_tokens": 131072, + "max_output_tokens": 40960, + "max_tokens": 40960, + "mode": "chat", + "output_cost_per_token": 1.49e-06, + "source": "https://api.cerebras.ai/public/v1/models/gemma-4-31b", + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "elevenlabs/scribe_v2": { + "input_cost_per_second": 6.11e-05, + "litellm_provider": "elevenlabs", + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://elevenlabs.io/pricing/api", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] } } diff --git a/osv-scanner.toml b/osv-scanner.toml index 7ab450945f5..5b0339bdcd0 100644 --- a/osv-scanner.toml +++ b/osv-scanner.toml @@ -2,3 +2,8 @@ id = "GHSA-w8v5-vhqr-4h9v" ignoreUntil = 2026-09-09 reason = "diskcache has no fixed release published; remove this entry once one exists" + +[[IgnoredVulns]] +id = "GHSA-h7x2-h6g9-p789" +ignoreUntil = 2026-09-14 +reason = "mlflow has no fixed release published; remove this entry once one exists" diff --git a/provider_endpoints_support.json b/provider_endpoints_support.json index 7c7d508856f..ebc220b3496 100644 --- a/provider_endpoints_support.json +++ b/provider_endpoints_support.json @@ -724,6 +724,42 @@ "interactions": true } }, + "qwencloud": { + "display_name": "QwenCloud (`qwencloud`)", + "url": "https://docs.litellm.ai/docs/providers/qwencloud", + "endpoints": { + "chat_completions": true, + "messages": true, + "responses": true, + "embeddings": true, + "image_generations": true, + "audio_transcriptions": false, + "audio_speech": false, + "moderations": false, + "batches": false, + "rerank": true, + "a2a": true, + "interactions": true + } + }, + "qwen_ai_platform": { + "display_name": "Qwen AI Platform (`qwen_ai_platform`)", + "url": "https://docs.litellm.ai/docs/providers/qwencloud", + "endpoints": { + "chat_completions": true, + "messages": true, + "responses": true, + "embeddings": true, + "image_generations": true, + "audio_transcriptions": false, + "audio_speech": false, + "moderations": false, + "batches": false, + "rerank": true, + "a2a": true, + "interactions": true + } + }, "databricks": { "display_name": "Databricks (`databricks`)", "url": "https://docs.litellm.ai/docs/providers/databricks", diff --git a/pyproject.toml b/pyproject.toml index 34c1fec1c11..2866e27e84c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "litellm" -version = "1.100.0" +version = "1.101.0" description = "Library to easily interface with LLM API providers" readme = "README.md" requires-python = ">=3.10, <3.15" @@ -67,8 +67,8 @@ proxy = [ "azure-identity>=1.25.2,<2.0", "azure-storage-blob>=12.28.0,<13.0", "mcp>=1.28.1,<2.0", - "litellm-proxy-extras==0.4.91", - "litellm-enterprise==0.1.62", + "litellm-proxy-extras==0.4.92", + "litellm-enterprise==0.1.63", "RestrictedPython>=8.5,<9.0", "rich>=13.9.4,<14.0", "InquirerPy>=0.3.4,<1.0", @@ -91,6 +91,11 @@ cli = [ ] extra_proxy = [ "prisma>=0.11.0,<1.0", + # Used by ProxyExtrasDBManager.spend_logs_is_partitioned() to detect a + # partitioned LiteLLM_SpendLogs and keep schema reconciliation from + # fighting its composite primary key. + "psycopg>=3.2,<4.0", + "psycopg-binary>=3.2,<4.0", "azure-identity>=1.25.2,<2.0", "azure-keyvault-secrets>=4.10.0,<5.0", # Not in PyPI proxy extra. @@ -262,7 +267,7 @@ healthcheck = [ ] [build-system] -requires = ["maturin==1.9.4"] +requires = ["maturin==1.15.0"] build-backend = "maturin" [tool.maturin] @@ -270,6 +275,9 @@ manifest-path = "litellm-rust/crates/python-bridge/Cargo.toml" module-name = "litellm.rust_bridge._native" python-source = "." bindings = "pyo3" +features = ["extension-module"] +profile = "release" +editable-profile = "dev" include = ["litellm/proxy/_experimental/out/**"] exclude = [ "litellm/proxy/enterprise", @@ -311,7 +319,7 @@ members = ["enterprise", "litellm-proxy-extras"] profile = "black" [tool.commitizen] -version = "1.100.0" +version = "1.101.0" version_files = [ "pyproject.toml:^version", ] diff --git a/ruff-strict-budget.json b/ruff-strict-budget.json index 1e855ea1b71..9b1cc977a64 100644 --- a/ruff-strict-budget.json +++ b/ruff-strict-budget.json @@ -1,6 +1,6 @@ { "ANN001": { - "limit": 2995 + "limit": 2985 }, "ANN002": { "limit": 71 @@ -9,13 +9,13 @@ "limit": 809 }, "ANN201": { - "limit": 2002 + "limit": 2001 }, "ANN202": { - "limit": 845 + "limit": 835 }, "ANN204": { - "limit": 698 + "limit": 693 }, "ANN205": { "limit": 112 @@ -24,7 +24,7 @@ "limit": 133 }, "ANN401": { - "limit": 587 + "limit": 307 }, "ASYNC230": { "limit": 11 @@ -117,7 +117,7 @@ "limit": 1 }, "PERF102": { - "limit": 23 + "limit": 21 }, "PERF401": { "limit": 12 @@ -168,7 +168,7 @@ "limit": 3 }, "RET504": { - "limit": 175 + "limit": 173 }, "RUF012": { "limit": 239 @@ -198,7 +198,7 @@ "limit": 56 }, "SIM102": { - "limit": 314 + "limit": 310 }, "SIM103": { "limit": 119 @@ -231,7 +231,7 @@ "limit": 5 }, "TID251": { - "limit": 1108 + "limit": 1073 }, "TRY002": { "limit": 524 @@ -240,13 +240,13 @@ "limit": 96 }, "TRY201": { - "limit": 405 + "limit": 403 }, "TRY203": { - "limit": 113 + "limit": 111 }, "TRY300": { - "limit": 855 + "limit": 854 }, "UP028": { "limit": 2 diff --git a/schema.prisma b/schema.prisma index 60223265211..7604ceadf7a 100644 --- a/schema.prisma +++ b/schema.prisma @@ -1529,14 +1529,16 @@ model LiteLLM_AutoRouterSession { model LiteLLM_ShadowEvalJob { id String @id @default(cuid()) group_id String // legs of one job share this; the API's job id - api_key_id String // hashed virtual key whose traffic this leg shadows - router_name String // the auto-router under evaluation, in either direction + target_type String @default("key") // key | team | user + target_id String // hashed virtual key, team_id, or user_id whose traffic this leg shadows + router_name String // first (often only) auto-router under evaluation; router_names is the full set + router_names String[] @default([]) // all routers this job runs as shadow arms; empty on legacy rows, whose set is (router_name) direction String @default("forward") // forward | reverse baseline_model String? // reverse only: the fixed model the router is judged against judge_model String shadow_percentage Float max_turns Int // sample-count ceiling: the whole budget on pre-max_budget jobs, the error-loop valve otherwise - max_budget Float? // per-key USD cap on the eval's own shadow + judge spend; null on jobs from before spend budgets + max_budget Float? // per-target USD cap on the eval's own shadow + judge spend; null on jobs from before spend budgets created_at DateTime @default(now()) created_by String? ends_at DateTime @@ -1544,7 +1546,7 @@ model LiteLLM_ShadowEvalJob { stopped_by String? // operator who stopped it early; null when it ended on its own @@index([group_id]) - @@index([api_key_id]) + @@index([target_type, target_id]) @@index([created_at]) } @@ -1554,6 +1556,7 @@ model LiteLLM_ShadowEvalAttempt { job_id String request_id String // the judged real request outcome String // real | shadow | tie | error + router_name String? // the arm this verdict scores; NULL on legacy rows, meaning the job's own router tier String? // router's tier for the prompt, when classified real_model String? shadow_model String? diff --git a/test-quality-budget.json b/test-quality-budget.json index ee33eb581d6..d834c581609 100644 --- a/test-quality-budget.json +++ b/test-quality-budget.json @@ -3,7 +3,7 @@ "limit": 733 }, "TQ002": { - "limit": 742 + "limit": 741 }, "TQ003": { "limit": 62 @@ -21,6 +21,6 @@ "limit": 117 }, "TQ008": { - "limit": 11139 + "limit": 11135 } } diff --git a/tests/documentation_tests/test_env_keys.py b/tests/documentation_tests/test_env_keys.py index b91c404b2eb..3652378503e 100644 --- a/tests/documentation_tests/test_env_keys.py +++ b/tests/documentation_tests/test_env_keys.py @@ -33,6 +33,13 @@ EXCLUDED_ROLLOUT_FLAGS = { "LITELLM_RUST", } +# Internal infrastructure tuning parameters for streaming/queue management +# These are advanced settings with sensible defaults that most users should not modify +EXCLUDED_INTERNAL_TUNING_VARS = { + "ANTHROPIC_MESSAGES_MAX_DETACHED_STREAM_DRAINS", + "ANTHROPIC_MESSAGES_STREAM_RELAY_QUEUE_MAXSIZE", +} + EXCLUDED_TERMINAL_VARS = { "TERM", "TERM_PROGRAM", @@ -50,7 +57,9 @@ EXCLUDED_TERMINAL_VARS = { "ALACRITTY_SOCKET", } -EXCLUDED_KEYS = frozenset(EXCLUDED_TERMINAL_VARS | EXCLUDED_GUARD_ONLY_VARS | EXCLUDED_ROLLOUT_FLAGS) +EXCLUDED_KEYS = frozenset( + EXCLUDED_TERMINAL_VARS | EXCLUDED_GUARD_ONLY_VARS | EXCLUDED_ROLLOUT_FLAGS | EXCLUDED_INTERNAL_TUNING_VARS +) # Directories to skip (dependencies, venvs, caches) - only scan litellm source SKIP_DIRS = { diff --git a/tests/e2e/coverage_registry/mgmt.yaml b/tests/e2e/coverage_registry/mgmt.yaml index 1e6de0c3d6a..d571fb36546 100644 --- a/tests/e2e/coverage_registry/mgmt.yaml +++ b/tests/e2e/coverage_registry/mgmt.yaml @@ -64,14 +64,12 @@ - {id: mgmt.budget.list_v1.happy_path, module: mgmt, tier: P1, surface: api, assertions: [happy_path], source: "management_v1/budgets.py:129", rationale: "Budget enumeration the Budgets page can page, sort and filter"} - {id: mgmt.budget.list_v1.admin_only, module: mgmt, tier: P1, surface: api, assertions: [admin_only], source: "management_v1/budgets.py:129", rationale: "A caller without admin view is refused, not served an empty page"} - {id: mgmt.callback.list.happy_path, module: mgmt, tier: P2, surface: api, assertions: [happy_path], source: "callback_management_endpoints.py", rationale: "Callback config (smoke)"} -- {id: mgmt.cache_settings.update.happy_path, module: mgmt, tier: P2, surface: api, assertions: [happy_path], source: "cache_settings_endpoints.py", rationale: "Cache config (smoke). Deliberately uncovered: the previous test read the live settings and wrote them back, which proves nothing (identical values in, so a no-op POST still passes) while being able to break the deployment. /cache/settings persists what it receives and that row outranks YAML cache_params, re-applied on a timer, so a write that omits ssl or redis_startup_nodes turns a TLS cluster into a plaintext standalone node and every later Redis call hangs. That took out 60 of 72 tests on 2026-07-25. GET cannot round-trip it either: it resolves the stored row overlaid with REDIS_* env and never reads YAML, so on a fresh deploy it cannot see YAML ssl to echo back. A safe test needs an isolated proxy, or LIT-4816 fixed so a partial write cannot downgrade transport. Do not re-add a read-then-write-back test against a shared proxy."} - {id: mgmt.cost_tracking.estimate.happy_path, module: mgmt, tier: P2, surface: api, assertions: [happy_path], source: "cost_tracking_settings.py", rationale: "Cost estimate (smoke)"} - {id: mgmt.router_settings.update.happy_path, module: mgmt, tier: P2, surface: api, assertions: [happy_path], source: "router_settings_endpoints.py", rationale: "Router config (smoke)"} - {id: mgmt.jwt_key_mapping.new.happy_path, module: mgmt, tier: P2, surface: api, assertions: [happy_path], source: "jwt_key_mapping_endpoints.py", rationale: "JWT->key mapping (smoke)"} - {id: mgmt.compliance.gdpr.happy_path, module: mgmt, tier: P2, surface: api, assertions: [happy_path], source: "compliance_endpoints.py", rationale: "GDPR ops (smoke)"} - {id: mgmt.tool_management.list.happy_path, module: mgmt, tier: P2, surface: api, assertions: [happy_path], source: "tool_management_endpoints.py", rationale: "Tool inventory (smoke)"} - {id: mgmt.fallback_management.update.happy_path, module: mgmt, tier: P2, surface: api, assertions: [happy_path], source: "fallback_management_endpoints.py", rationale: "Fallback config (smoke)"} -- {id: mgmt.config_override.hashicorp_vault.happy_path, module: mgmt, tier: P2, surface: api, assertions: [happy_path], source: "config_override_endpoints.py", rationale: "Vault integration (smoke)"} - {id: mgmt.workflow.list.happy_path, module: mgmt, tier: P2, surface: api, assertions: [happy_path], source: "workflow_management_endpoints.py", rationale: "Workflow tracking (smoke)"} - {id: mgmt.credential_migration.check.happy_path, module: mgmt, tier: P2, surface: api, assertions: [happy_path], source: "key_management_endpoints.py:4252", rationale: "Encryption migration (smoke)"} - {id: mgmt.credential.new.serves_request, module: mgmt, tier: P1, surface: api, assertions: [serves_request], source: "credential_endpoints/endpoints.py:42", rationale: "Stored credential resolves into a deployment and serves a live /messages request"} diff --git a/tests/e2e/management/test_config_misc_endpoints_e2e.py b/tests/e2e/management/test_config_misc_endpoints_e2e.py index 195732c0201..099ffa4b3bd 100644 --- a/tests/e2e/management/test_config_misc_endpoints_e2e.py +++ b/tests/e2e/management/test_config_misc_endpoints_e2e.py @@ -7,9 +7,13 @@ so a read-back reflects the change. Router settings, which mutate global proxy state, are exercised with a benign, self-restoring change so a shared proxy is left as it was found. -Cache settings are deliberately not covered here; see the rationale on -mgmt.cache_settings.update.happy_path in coverage_registry/mgmt.yaml before adding -a test for that route. +Cache settings and the Vault config override are deliberately not covered here. +Both routes reconfigure the whole proxy: /cache/settings persists what it receives +into a row that outranks the YAML cache_params and is re-applied on a timer, and +/config_overrides/hashicorp_vault swaps the process-wide secret manager. Neither can +be exercised safely against the shared proxy the suites run on, so they need an +isolated proxy before a test lands. Do not add a read-then-write-back test for +either one. """ from __future__ import annotations diff --git a/tests/e2e/management/test_model_tag_accessgroup_e2e.py b/tests/e2e/management/test_model_tag_accessgroup_e2e.py index e6a187ae105..eb3a6093c69 100644 --- a/tests/e2e/management/test_model_tag_accessgroup_e2e.py +++ b/tests/e2e/management/test_model_tag_accessgroup_e2e.py @@ -180,6 +180,12 @@ class ModelBlockBody(BaseModel): model_id: str +class ModelBlockResponse(BaseModel): + model_config = ConfigDict(protected_namespaces=()) + model_id: str + blocked: bool + + class ModelInfoBlockDetail(BaseModel): id: str | None = None blocked: bool | None = None @@ -245,11 +251,6 @@ class TestModelRoutes: def test_block_then_unblock_persists_to_model_info( self, client: ManagementClient, resources: ResourceManager ) -> None: - """The blocked flag's persistence is read back from /model/info, not from the - /model/block response: that route currently returns a non-2xx serialization - envelope even though the DB write lands, so the /model/info read-back is the - authoritative persistence contract and keeps this test valid once the - response shape is fixed.""" model_name = f"e2e-mgmt-model-block-{unique_marker()}" model_id = _create_db_model(client, resources, model_name) @@ -257,27 +258,25 @@ class TestModelRoutes: f"{model_name!r} already reports blocked in /model/info before /model/block ran" ) - _ = client.proxy.transport.send( - "/model/block", - headers=client.proxy.transport.master, - json=ModelBlockBody(model_id=model_id), - ) - _ = _poll( - client.proxy, - lambda: True if _model_blocked_flag(client, model_id) is True else None, - f"/model/info never reported {model_name!r} blocked after /model/block", - ) - - _ = client.proxy.transport.send( - "/model/unblock", - headers=client.proxy.transport.master, - json=ModelBlockBody(model_id=model_id), - ) - _ = _poll( - client.proxy, - lambda: True if _model_blocked_flag(client, model_id) is not True else None, - f"/model/info never cleared blocked for {model_name!r} after /model/unblock", - ) + for action, expected in (("block", True), ("unblock", False)): + response = unwrap( + client.proxy.transport.post( + f"/model/{action}", + headers=client.proxy.transport.master, + json=ModelBlockBody(model_id=model_id), + response_type=ModelBlockResponse, + ) + ) + assert response.model_id == model_id + assert response.blocked is expected + _ = _poll( + client.proxy, + lambda want=expected: True + if _model_blocked_flag(client, model_id) is want + else None, + f"/model/info never reported blocked={expected} for {model_name!r} " + f"after /model/{action}", + ) class TestTagRoutes: diff --git a/tests/e2e/mcp/test_mcp_key_access_e2e.py b/tests/e2e/mcp/test_mcp_key_access_e2e.py index 88ab5666084..68005ae3f6a 100644 --- a/tests/e2e/mcp/test_mcp_key_access_e2e.py +++ b/tests/e2e/mcp/test_mcp_key_access_e2e.py @@ -30,6 +30,34 @@ def _key(client: McpClient, resources: ResourceManager, *, mcp_servers: list[str return key +class TestMcpKeyGrantByAlias: + def test_alias_grant_persists_verbatim_and_lists_tools( + self, + client: McpClient, + resources: ResourceManager, + ) -> None: + """A key granted an MCP server by its alias must store the alias, not the + resolved server_id: in a shared-DB multi-region deployment each instance + derives a different id for the same config server, so only the alias + grants access on every region. The same key must still see the server's + tools, proving the alias grant is honored at request time.""" + server_id = register_datadog_mcp(client, resources) + client.await_registered(server_id) + alias = next(row.alias for row in client.registered_servers() if row.server_id == server_id) + assert alias, f"registered server {server_id} has no alias to grant by" + + key = _key(client, resources, mcp_servers=[alias]) + + stored = client.proxy.key_info(key).object_permission + assert stored is not None and stored.mcp_servers == [alias], ( + f"alias grant was rewritten before persisting (expected [{alias!r}]): " + f"{stored.mcp_servers if stored else None}. A stored server_id is region-local " + f"and breaks the grant on every other instance sharing this database" + ) + + _ = client.await_tool(key, server_id, SEARCH_LOGS_TOOL) + + class TestMcpKeyWithoutAccessIsDenied: @pytest.mark.covers("mcp.list_tools.api_key.denied_without_permission") def test_list_tools_denied_without_permission( diff --git a/tests/e2e/models.py b/tests/e2e/models.py index 6d9ccad9a24..967144dfb16 100644 --- a/tests/e2e/models.py +++ b/tests/e2e/models.py @@ -114,6 +114,7 @@ class KeyInfo(BaseModel): budget_id: str | None = None litellm_budget_table: LiteLLMBudgetTable | None = None budget_limits: list[BudgetWindowState] | None = None + object_permission: ObjectPermission | None = None class KeyInfoResponse(BaseModel): diff --git a/tests/e2e/ui/constants.ts b/tests/e2e/ui/constants.ts index 9d918736262..bb33c90ddf3 100644 --- a/tests/e2e/ui/constants.ts +++ b/tests/e2e/ui/constants.ts @@ -29,6 +29,7 @@ export const E2E_PROXY_ADMIN_USER_ID = "e2e-proxy-admin"; export const E2E_PROXY_ADMIN_EMAIL = "admin@test.local"; export const E2E_INTERNAL_USER_ID = "e2e-internal-user"; export const E2E_INTERNAL_USER_EMAIL = "internal@test.local"; +export const E2E_TEAM_ADMIN_USER_ID = "e2e-team-admin"; // Key aliases for seeded test keys (match seed.sql) export const E2E_UPDATE_LIMITS_KEY_ALIAS = "e2eUpdateLimitsKey"; @@ -46,3 +47,5 @@ export const E2E_TEAM_ORG_ID = "e2e-team-org"; export const E2E_TEAM_ORG_ALIAS = "E2E Team In Org"; export const E2E_TEAM_NO_ADMIN_ID = "e2e-team-no-admin"; export const E2E_TEAM_NO_ADMIN_ALIAS = "E2E Team No Admin"; +export const E2E_TEAM_KEYGEN_ID = "e2e-team-keygen"; +export const E2E_TEAM_KEYGEN_ALIAS = "E2E Team Keygen"; diff --git a/tests/e2e/ui/fixtures/seed.sql b/tests/e2e/ui/fixtures/seed.sql index a1218633cdb..e77b4a16b3d 100644 --- a/tests/e2e/ui/fixtures/seed.sql +++ b/tests/e2e/ui/fixtures/seed.sql @@ -29,7 +29,7 @@ INSERT INTO "LiteLLM_UserTable" ("user_id", "user_email", "user_role", "teams", VALUES ('e2e-proxy-admin', 'admin@test.local', 'proxy_admin', '{"e2e-team-crud"}', 'scrypt:MU5CcTAi6rVK1HfY1rVPEWq6r4sxg837eq9dG4n5Q6BhDJ44442+seC6LAhLEAYr'), ('e2e-admin-viewer', 'adminviewer@test.local', 'proxy_admin_viewer', '{}', 'scrypt:MU5CcTAi6rVK1HfY1rVPEWq6r4sxg837eq9dG4n5Q6BhDJ44442+seC6LAhLEAYr'), - ('e2e-internal-user', 'internal@test.local', 'internal_user', '{"e2e-team-crud","e2e-team-org"}', 'scrypt:MU5CcTAi6rVK1HfY1rVPEWq6r4sxg837eq9dG4n5Q6BhDJ44442+seC6LAhLEAYr'), + ('e2e-internal-user', 'internal@test.local', 'internal_user', '{"e2e-team-crud","e2e-team-org","e2e-team-keygen"}', 'scrypt:MU5CcTAi6rVK1HfY1rVPEWq6r4sxg837eq9dG4n5Q6BhDJ44442+seC6LAhLEAYr'), ('e2e-internal-viewer', 'viewer@test.local', 'internal_user_viewer', '{"e2e-team-crud"}', 'scrypt:MU5CcTAi6rVK1HfY1rVPEWq6r4sxg837eq9dG4n5Q6BhDJ44442+seC6LAhLEAYr'), ('e2e-team-admin', 'teamadmin@test.local', 'internal_user', '{"e2e-team-crud","e2e-team-delete"}', 'scrypt:MU5CcTAi6rVK1HfY1rVPEWq6r4sxg837eq9dG4n5Q6BhDJ44442+seC6LAhLEAYr'), ('e2e-invitable-user', 'invitable@test.local', 'internal_user', '{}', 'scrypt:MU5CcTAi6rVK1HfY1rVPEWq6r4sxg837eq9dG4n5Q6BhDJ44442+seC6LAhLEAYr'), @@ -63,6 +63,17 @@ INSERT INTO "LiteLLM_TeamTable" ( '[{"role":"user","user_id":"e2e-invitable-user"}]'::jsonb, '{}'::jsonb, '{"fake-openai-gpt-4"}', 0.0, '{}'::jsonb, '{}'::jsonb, false); +INSERT INTO "LiteLLM_TeamTable" ( + "team_id", "team_alias", "organization_id", "admins", "members", + "members_with_roles", "metadata", "models", "spend", "model_spend", "model_max_budget", "blocked", + "team_member_permissions" +) VALUES + ('e2e-team-keygen', 'E2E Team Keygen', NULL, + '{}', '{"e2e-internal-user"}', + '[{"role":"user","user_id":"e2e-internal-user"}]'::jsonb, + '{}'::jsonb, '{"fake-openai-gpt-4"}', 0.0, '{}'::jsonb, '{}'::jsonb, false, + '{"/key/generate"}'); + -- 6. Team Memberships (only user_id, team_id, spend — no created_at/updated_at) INSERT INTO "LiteLLM_TeamMembership" ("user_id", "team_id", "spend") VALUES @@ -72,6 +83,7 @@ VALUES ('e2e-removable-member', 'e2e-team-crud', 0.0), ('e2e-team-admin', 'e2e-team-delete', 0.0), ('e2e-internal-user', 'e2e-team-org', 0.0), + ('e2e-internal-user', 'e2e-team-keygen', 0.0), ('e2e-invitable-user', 'e2e-team-no-admin', 0.0); -- 7. Verification Tokens (API Keys) diff --git a/tests/e2e/ui/helpers/traffic.ts b/tests/e2e/ui/helpers/traffic.ts index a2fc9463c94..ebd3c9a417f 100644 --- a/tests/e2e/ui/helpers/traffic.ts +++ b/tests/e2e/ui/helpers/traffic.ts @@ -84,6 +84,34 @@ export async function waitForSpendLog( throw new Error(`spend log for request ${requestId} never appeared (last /spend/logs status ${lastStatus})`); } +export async function waitForSpendLogByPrompt( + request: APIRequestContext, + prompt: string, + timeoutMs = 60_000, +): Promise { + const deadline = Date.now() + timeoutMs; + let lastStatus = 0; + while (Date.now() < deadline) { + const res = await request.get(`${rootPath()}/spend/logs`, { + headers: { Authorization: `Bearer ${masterKey()}` }, + }); + lastStatus = res.status(); + if (res.ok()) { + const rows: { request_id?: string; messages?: unknown; proxy_server_request?: unknown }[] = await res.json(); + const row = (Array.isArray(rows) ? rows : []).find( + (candidate) => + JSON.stringify(candidate.messages ?? "").includes(prompt) || + JSON.stringify(candidate.proxy_server_request ?? "").includes(prompt), + ); + if (row?.request_id) { + return row.request_id; + } + } + await new Promise((r) => setTimeout(r, 2_000)); + } + throw new Error(`no spend log row carrying prompt ${prompt} appeared (last /spend/logs status ${lastStatus})`); +} + const isoDay = (d: Date): string => d.toISOString().slice(0, 10); /** diff --git a/tests/e2e/ui/tests/budgets/budgets.spec.ts b/tests/e2e/ui/tests/budgets/budgets.spec.ts new file mode 100644 index 00000000000..1ad1e488d25 --- /dev/null +++ b/tests/e2e/ui/tests/budgets/budgets.spec.ts @@ -0,0 +1,133 @@ +import { test, expect, type Page as PlaywrightPage } from "@playwright/test"; +import { ADMIN_STORAGE_PATH } from "../../constants"; +import { Page } from "../../fixtures/pages"; +import { navigateToPage, dismissFeedbackPopup } from "../../helpers/navigation"; +import { masterKey } from "../../helpers/traffic"; + +interface StoredBudget { + budget_id: string; + max_budget: number | null; + tpm_limit: number | null; + rpm_limit: number | null; + budget_duration: string | null; +} + +/** A different route from the one the table renders from, so a row that only lives in its cache fails here. */ +async function findBudget(page: PlaywrightPage, budgetId: string): Promise { + const res = await page.request.get("/budget/list", { + headers: { Authorization: `Bearer ${masterKey()}` }, + }); + expect(res.ok(), `GET /budget/list (${res.status()})`).toBe(true); + return ((await res.json()) as StoredBudget[]).find((row) => row.budget_id === budgetId); +} + +async function createBudgetViaApi(page: PlaywrightPage, budget: Partial): Promise { + const res = await page.request.post("/budget/new", { + headers: { Authorization: `Bearer ${masterKey()}` }, + data: budget, + }); + expect(res.ok(), `POST /budget/new failed (${res.status()}): ${await res.text()}`).toBe(true); +} + +async function searchForBudget(page: PlaywrightPage, budgetId: string): Promise { + await page.getByPlaceholder("Search by budget ID").fill(budgetId); +} + +test.describe("Budgets", () => { + test.use({ storageState: ADMIN_STORAGE_PATH }); + + test("Create a budget with rate limits and a spend cap", async ({ page }) => { + const budgetId = `e2e-budget-create-${Date.now()}`; + + await navigateToPage(page, Page.Budgets); + await dismissFeedbackPopup(page); + + await page.getByRole("button", { name: "Create Budget" }).click(); + + const modal = page.getByRole("dialog", { name: "Create Budget" }); + await expect(modal).toBeVisible({ timeout: 10_000 }); + + await modal.getByRole("textbox", { name: "Budget ID" }).fill(budgetId); + await modal.getByRole("spinbutton", { name: "Max Tokens per minute" }).fill("5000"); + await modal.getByRole("spinbutton", { name: "Max Requests per minute" }).fill("60"); + + await modal.getByRole("button", { name: "Optional Settings" }).click(); + await modal.getByRole("spinbutton", { name: "Max Budget (USD)" }).fill("25.5"); + await modal.getByRole("combobox", { name: "Reset Budget" }).click(); + await page.getByRole("option", { name: "weekly" }).click(); + + await modal.getByRole("button", { name: "Create Budget" }).click(); + await expect(modal).not.toBeVisible({ timeout: 10_000 }); + + await searchForBudget(page, budgetId); + const row = page.getByRole("row").filter({ hasText: budgetId }); + await expect(row).toBeVisible({ timeout: 10_000 }); + await expect(row).toContainText("$25.50"); + + const stored = await findBudget(page, budgetId); + expect(stored, `budget ${budgetId} readable from /budget/list`).toBeTruthy(); + expect(stored?.max_budget, "spend cap persisted").toBe(25.5); + expect(stored?.tpm_limit, "TPM limit persisted").toBe(5000); + expect(stored?.rpm_limit, "RPM limit persisted").toBe(60); + expect(stored?.budget_duration, "reset window persisted").toBe("7d"); + }); + + test("Raising a budget's spend cap leaves its rate limits alone", async ({ page }) => { + const budgetId = `e2e-budget-edit-${Date.now()}`; + await createBudgetViaApi(page, { budget_id: budgetId, max_budget: 10, tpm_limit: 1000, rpm_limit: 20 }); + + await navigateToPage(page, Page.Budgets); + await dismissFeedbackPopup(page); + + await searchForBudget(page, budgetId); + await expect(page.getByRole("row").filter({ hasText: budgetId })).toBeVisible({ timeout: 10_000 }); + + await page.getByTestId(`budget-actions-${budgetId}`).click(); + await page.getByTestId("budget-action-edit").click(); + + const modal = page.getByRole("dialog", { name: "Edit Budget" }); + await expect(modal).toBeVisible({ timeout: 10_000 }); + + await modal.getByRole("button", { name: "Optional Settings" }).click(); + await modal.getByRole("spinbutton", { name: "Max Budget (USD)" }).fill("99"); + await modal.getByRole("button", { name: "Save", exact: true }).click(); + await expect(modal).not.toBeVisible({ timeout: 10_000 }); + + await expect(page.getByRole("row").filter({ hasText: budgetId })).toContainText("$99.00", { timeout: 10_000 }); + + // Not hypothetical: the edit form posts the whole budget, so a field it fails to + // seed from the existing row goes to the server as null and silently clears. + const stored = await findBudget(page, budgetId); + expect(stored?.max_budget, "spend cap raised").toBe(99); + expect(stored?.tpm_limit, "TPM limit untouched by a spend-cap edit").toBe(1000); + expect(stored?.rpm_limit, "RPM limit untouched by a spend-cap edit").toBe(20); + }); + + test("Delete a budget", async ({ page }) => { + const budgetId = `e2e-budget-delete-${Date.now()}`; + await createBudgetViaApi(page, { budget_id: budgetId, max_budget: 5 }); + + await navigateToPage(page, Page.Budgets); + await dismissFeedbackPopup(page); + + await searchForBudget(page, budgetId); + await expect(page.getByRole("row").filter({ hasText: budgetId })).toBeVisible({ timeout: 10_000 }); + + await page.getByTestId(`budget-actions-${budgetId}`).click(); + await page.getByTestId("budget-action-delete").click(); + + const modal = page.getByRole("dialog", { name: "Delete Budget?" }); + await expect(modal).toBeVisible({ timeout: 5_000 }); + await modal.getByRole("button", { name: "Delete", exact: true }).click(); + + await expect(page.getByRole("row").filter({ hasText: budgetId })).toHaveCount(0, { timeout: 10_000 }); + + // The row disappearing is a cache invalidation; the budget is gone when the route stops serving it. + await expect + .poll(async () => await findBudget(page, budgetId), { + message: `budget ${budgetId} still readable from /budget/list after delete`, + timeout: 15_000, + }) + .toBeUndefined(); + }); +}); diff --git a/tests/e2e/ui/tests/guardrails/guardrails.spec.ts b/tests/e2e/ui/tests/guardrails/guardrails.spec.ts new file mode 100644 index 00000000000..1e43c7a2b22 --- /dev/null +++ b/tests/e2e/ui/tests/guardrails/guardrails.spec.ts @@ -0,0 +1,283 @@ +import { test, expect, type Page as PlaywrightPage } from "@playwright/test"; +import { ADMIN_STORAGE_PATH, E2E_TEAM_NO_ADMIN_ID } from "../../constants"; +import { Page } from "../../fixtures/pages"; +import { navigateToPage, dismissFeedbackPopup, clickTeamId } from "../../helpers/navigation"; +import { CHAT_MODEL_A, MOCK_RESPONSE_TEXT, masterKey } from "../../helpers/traffic"; + +interface StoredGuardrail { + guardrail_id: string; + guardrail_name: string | null; +} + +async function listGuardrails(page: PlaywrightPage): Promise { + const res = await page.request.get("/v2/guardrails/list", { + headers: { Authorization: `Bearer ${masterKey()}` }, + }); + expect(res.ok(), `GET /v2/guardrails/list (${res.status()})`).toBe(true); + return ((await res.json()) as { guardrails: StoredGuardrail[] }).guardrails; +} + +async function findGuardrail(page: PlaywrightPage, name: string): Promise { + return (await listGuardrails(page)).find((row) => row.guardrail_name === name); +} + +const createdGuardrails: string[] = []; + +async function createKeywordGuardrailViaApi(page: PlaywrightPage, name: string, keyword: string): Promise { + const res = await page.request.post("/guardrails", { + headers: { Authorization: `Bearer ${masterKey()}` }, + data: { + guardrail: { + guardrail_name: name, + litellm_params: { + guardrail: "litellm_content_filter", + mode: "pre_call", + default_on: false, + blocked_words: [{ keyword, action: "BLOCK" }], + }, + }, + }, + }); + expect(res.ok(), `POST /guardrails failed (${res.status()}): ${await res.text()}`).toBe(true); + createdGuardrails.push(name); + const guardrail = await findGuardrail(page, name); + expect(guardrail?.guardrail_id, `guardrail ${name} has an id`).toBeTruthy(); + return guardrail!.guardrail_id; +} + +async function openKeywordsStep(page: PlaywrightPage, name: string) { + await page.getByRole("button", { name: "Add New Guardrail" }).click(); + await page.getByRole("menuitem", { name: "Add Provider Guardrail" }).click(); + + const wizard = page.getByRole("dialog", { name: "Create guardrail" }); + await expect(wizard).toBeVisible({ timeout: 10_000 }); + + await wizard.getByRole("textbox", { name: "Guardrail Name" }).fill(name); + await wizard.getByRole("combobox", { name: "Guardrail Provider" }).click(); + // The content filter runs inside the proxy, so this is the one provider a test can + // configure end to end without standing up a third-party moderation service. + await page.getByRole("option", { name: /LiteLLM Content Filter/ }).click(); + + for (const step of ["Topics", "Patterns", "Keywords"]) { + await wizard.getByRole("button", { name: "Next" }).click(); + await expect(wizard).toContainText(step, { timeout: 10_000 }); + } + return wizard; +} + +test.describe("Guardrails", () => { + test.use({ storageState: ADMIN_STORAGE_PATH }); + + test.afterEach(async ({ page }) => { + // Guardrails live in the database and show up in the table and the playground list, so a run + // that leaves them behind changes what the next run sees. + for (const name of createdGuardrails.splice(0)) { + const guardrail = await findGuardrail(page, name); + if (guardrail) { + const deleted = await page.request.delete(`/guardrails/${guardrail.guardrail_id}`, { + headers: { Authorization: `Bearer ${masterKey()}` }, + }); + expect(deleted.ok(), `DELETE /guardrails/${guardrail.guardrail_id} (${deleted.status()})`).toBe(true); + } + } + }); + + test("A guardrail created through the wizard blocks the keyword it was given", async ({ page }) => { + const stamp = Date.now(); + const guardrailName = `e2e-guardrail-create-${stamp}`; + // Unique per run so a concurrent test's prompt can never trip this guardrail, or vice versa. + const bannedKeyword = `e2ebanned${stamp}`; + + await navigateToPage(page, Page.Guardrails); + await dismissFeedbackPopup(page); + + createdGuardrails.push(guardrailName); + const wizard = await openKeywordsStep(page, guardrailName); + + await wizard.getByRole("button", { name: "Add keyword" }).click(); + const keywordModal = page.getByRole("dialog", { name: "Add blocked keyword" }); + await expect(keywordModal).toBeVisible({ timeout: 10_000 }); + await keywordModal.getByPlaceholder("Enter sensitive keyword or phrase").fill(bannedKeyword); + await keywordModal.getByRole("button", { name: "Add", exact: true }).click(); + await expect(keywordModal).not.toBeVisible({ timeout: 10_000 }); + + await wizard.getByRole("button", { name: "Next" }).click(); + await wizard.getByRole("button", { name: "Create Guardrail" }).click(); + await expect(wizard).not.toBeVisible({ timeout: 15_000 }); + + await expect(page.getByRole("row").filter({ hasText: guardrailName })).toBeVisible({ timeout: 15_000 }); + expect(await findGuardrail(page, guardrailName), "guardrail readable from /v2/guardrails/list").toBeTruthy(); + + // A row in the table only proves the record was written. The point of a guardrail is that it + // refuses traffic, so drive a request through it. + // + // Polled: a guardrail written through /guardrails reaches the request path on the proxy's + // periodic refresh, so the first call after creation can still be served unguarded. The + // assertion is unchanged, it just allows that refresh to land. + let blockedBody = ""; + await expect + .poll( + async () => { + const res = await page.request.post("/v1/chat/completions", { + headers: { Authorization: `Bearer ${masterKey()}` }, + data: { + model: CHAT_MODEL_A, + messages: [{ role: "user", content: `please tell me about ${bannedKeyword}` }], + guardrails: [guardrailName], + }, + }); + blockedBody = await res.text(); + return res.status(); + }, + { message: "a prompt carrying the banned keyword is refused", timeout: 60_000 }, + ) + .toBe(400); + expect(blockedBody).toContain(bannedKeyword); + + const allowed = await page.request.post("/v1/chat/completions", { + headers: { Authorization: `Bearer ${masterKey()}` }, + data: { + model: CHAT_MODEL_A, + messages: [{ role: "user", content: "hello there" }], + guardrails: [guardrailName], + }, + }); + expect(allowed.status(), "a clean prompt still gets through the same guardrail").toBe(200); + expect((await allowed.json()).choices?.[0]?.message?.content).toContain(MOCK_RESPONSE_TEXT); + }); + + test("The Test Playground reports the verdict for the text it is given", async ({ page }) => { + const stamp = Date.now(); + const guardrailName = `e2e-guardrail-play-${stamp}`; + const bannedKeyword = `e2eplay${stamp}`; + await createKeywordGuardrailViaApi(page, guardrailName, bannedKeyword); + + await navigateToPage(page, Page.Guardrails); + await dismissFeedbackPopup(page); + + await page.getByRole("tab", { name: "Test Playground" }).click(); + // Every tab on this page stays mounted, so the other tabs' search boxes match too. + const playground = page.getByRole("tabpanel", { name: "Test Playground" }); + await playground.getByPlaceholder("Search guardrails...").fill(guardrailName); + await playground.getByText(guardrailName, { exact: true }).click(); + + const input = playground.getByPlaceholder("Enter text to test with guardrails..."); + await input.fill(`this sentence contains ${bannedKeyword}`); + await playground.getByRole("button", { name: /^Test 1 guardrail$/ }).click(); + + // The playground is where an admin checks a guardrail before rolling it out, so the + // verdict it prints has to be the one the gateway would give. + await expect(playground.getByText(`${guardrailName} - Error`)).toBeVisible({ timeout: 20_000 }); + await expect(playground.getByText(new RegExp(`Content blocked.*${bannedKeyword}`))).toBeVisible({ + timeout: 10_000, + }); + + await input.fill("this sentence is perfectly ordinary"); + await playground.getByRole("button", { name: /^Test 1 guardrail$/ }).click(); + + await expect(playground.getByText(`${guardrailName} - Error`)).toHaveCount(0, { timeout: 20_000 }); + await expect(playground.getByText("this sentence is perfectly ordinary").last()).toBeVisible({ timeout: 10_000 }); + }); + + test("Delete a guardrail", async ({ page }) => { + const stamp = Date.now(); + const guardrailName = `e2e-guardrail-delete-${stamp}`; + const guardrailId = await createKeywordGuardrailViaApi(page, guardrailName, `e2edelete${stamp}`); + + await navigateToPage(page, Page.Guardrails); + await dismissFeedbackPopup(page); + + await expect(page.getByRole("row").filter({ hasText: guardrailName })).toBeVisible({ timeout: 15_000 }); + + await page.getByTestId(`guardrail-actions-${guardrailId}`).click(); + await page.getByTestId("guardrail-action-delete").click(); + + const modal = page.getByRole("dialog"); + await expect(modal).toBeVisible({ timeout: 5_000 }); + await modal.getByRole("button", { name: "Delete", exact: true }).click(); + + await expect(page.getByRole("row").filter({ hasText: guardrailName })).toHaveCount(0, { timeout: 15_000 }); + + // The RC checklist deletes then reloads, because a row vanishing from the table has + // fooled us before; assert against the route the reload would read. + await expect + .poll(async () => await findGuardrail(page, guardrailName), { + message: `guardrail ${guardrailName} still listed after delete`, + timeout: 15_000, + }) + .toBeUndefined(); + }); + + test("Create a Presidio guardrail, see it in team settings, and delete it", async ({ page }) => { + const guardrailName = `e2e-presidio-${Date.now()}`; + + await navigateToPage(page, Page.Guardrails); + await dismissFeedbackPopup(page); + + await page.getByRole("button", { name: /Add New Guardrail/i }).click(); + await page.getByRole("menuitem", { name: "Add Provider Guardrail" }).click(); + + const dialog = page.getByRole("dialog", { name: "Create guardrail" }); + await expect(dialog).toBeVisible({ timeout: 10_000 }); + + await dialog.getByLabel("Guardrail Name").fill(guardrailName); + + const providerSelect = dialog.getByRole("combobox", { name: "Guardrail Provider" }); + await providerSelect.click(); + await providerSelect.fill("Presidio"); + await page.getByRole("option", { name: "Presidio PII" }).click(); + + await dialog.getByLabel("Mode", { exact: true }).click(); + await page.keyboard.type("pre_call"); + await expect(page.getByRole("option", { name: "pre_call" })).toBeAttached({ timeout: 5_000 }); + await page.keyboard.press("Enter"); + await expect(dialog.getByText("pre_call", { exact: true })).toBeVisible({ timeout: 5_000 }); + await dialog.getByText("Create guardrail", { exact: true }).click(); + + await dialog.getByLabel("presidio_analyzer_api_base").fill("http://127.0.0.1:9999"); + await expect(dialog.getByLabel("presidio_analyzer_api_base")).toHaveValue("http://127.0.0.1:9999"); + await dialog.getByLabel("presidio_anonymizer_api_base").fill("http://127.0.0.1:9999"); + await expect(dialog.getByLabel("presidio_anonymizer_api_base")).toHaveValue("http://127.0.0.1:9999"); + + await dialog.getByRole("button", { name: "Next" }).click(); + await expect(dialog.getByText("Configure PII Protection")).toBeVisible({ timeout: 10_000 }); + await dialog.getByRole("button", { name: "Select All & Mask" }).click(); + + await dialog.getByRole("button", { name: "Create Guardrail" }).click(); + await expect(page.getByText("Guardrail created successfully").first()).toBeVisible({ timeout: 15_000 }); + + const row = page.getByRole("row").filter({ hasText: guardrailName }); + await expect(row).toHaveCount(1, { timeout: 15_000 }); + + await navigateToPage(page, Page.Teams); + await dismissFeedbackPopup(page); + await clickTeamId(page, E2E_TEAM_NO_ADMIN_ID); + await page.getByRole("tab", { name: "Settings" }).click(); + await page.getByRole("button", { name: "Edit Settings" }).click(); + + const guardrailsSelect = page.getByRole("combobox", { name: "Select guardrails" }); + await expect(guardrailsSelect).toBeVisible({ timeout: 10_000 }); + await guardrailsSelect.click(); + await guardrailsSelect.fill(guardrailName); + await expect(page.getByRole("option", { name: guardrailName })).toBeVisible({ timeout: 10_000 }); + await page.keyboard.press("Escape"); + + await navigateToPage(page, Page.Guardrails); + await expect(row).toHaveCount(1, { timeout: 15_000 }); + await row.getByRole("button", { name: "Open guardrail actions" }).click(); + await page.getByRole("menuitem", { name: "Delete" }).click(); + + const deleteModal = page.getByRole("dialog", { name: "Delete Guardrail" }); + await expect(deleteModal).toBeVisible({ timeout: 5_000 }); + await deleteModal.getByRole("button", { name: "Delete", exact: true }).click(); + + await expect(page.getByText(`Guardrail "${guardrailName}" deleted successfully`)).toBeVisible({ + timeout: 10_000, + }); + await expect(row).toHaveCount(0, { timeout: 15_000 }); + + await page.reload(); + await expect(page.getByRole("button", { name: /Add New Guardrail/i })).toBeVisible({ timeout: 20_000 }); + await expect(page.getByRole("row").filter({ hasText: guardrailName })).toHaveCount(0); + }); +}); diff --git a/tests/e2e/ui/tests/internal-user/internalUser.spec.ts b/tests/e2e/ui/tests/internal-user/internalUser.spec.ts index b8424b06115..f392c5104da 100644 --- a/tests/e2e/ui/tests/internal-user/internalUser.spec.ts +++ b/tests/e2e/ui/tests/internal-user/internalUser.spec.ts @@ -3,10 +3,13 @@ import { E2E_INTERNAL_USER_KEY_ALIAS, E2E_TEAM_CRUD_ALIAS, E2E_TEAM_CRUD_ID, + E2E_TEAM_KEYGEN_ALIAS, INTERNAL_USER_STORAGE_PATH, } from "../../constants"; import { Page } from "../../fixtures/pages"; import { navigateToPage, clickTeamId } from "../../helpers/navigation"; +import { CHAT_MODEL_A, MOCK_RESPONSE_TEXT, masterKey } from "../../helpers/traffic"; +import { keySourceSelect, onlyVisible, openPlayground, selectModel, sendMessage } from "../../helpers/playground"; test.describe("Internal User", () => { test.use({ storageState: INTERNAL_USER_STORAGE_PATH }); @@ -22,8 +25,7 @@ test.describe("Internal User", () => { const teamSelect = page.getByTestId("team-dropdown").getByRole("combobox"); await teamSelect.click(); await page.keyboard.type(E2E_TEAM_CRUD_ALIAS); - const dropdown = page.locator('[data-slot="combobox-content"]:visible'); - await expect(dropdown.getByText(E2E_TEAM_CRUD_ALIAS).first()).toBeVisible({ timeout: 5_000 }); + await expect(page.getByRole("option", { name: E2E_TEAM_CRUD_ALIAS }).first()).toBeVisible({ timeout: 5_000 }); }); test("Team info page omits the Settings tab for non-admin members", async ({ page }) => { @@ -38,17 +40,66 @@ test.describe("Internal User", () => { await expect(page.getByRole("tab", { name: "Members" })).not.toBeVisible(); }); + test("Internal user creates a team key and uses it in the Playground", async ({ page, request }) => { + const suffix = Date.now(); + const auth = { Authorization: `Bearer ${masterKey()}` }; + + await navigateToPage(page, Page.ApiKeys); + + await page.getByRole("button", { name: /Create New Key/i }).click(); + await expect(page.getByText("Key Ownership")).toBeVisible({ timeout: 10_000 }); + + await expect(page.getByRole("radio", { name: "You", exact: true })).toBeVisible({ timeout: 10_000 }); + await expect(page.getByRole("radio", { name: "Another User" })).toHaveCount(0); + + const keyName = `e2e-internal-team-key-${suffix}`; + await page.getByLabel(/Key Name/).fill(keyName); + + const teamSelect = page.getByTestId("team-dropdown").getByRole("combobox"); + await teamSelect.click(); + await page.keyboard.type(E2E_TEAM_KEYGEN_ALIAS); + await page.getByRole("option", { name: E2E_TEAM_KEYGEN_ALIAS }).first().click(); + + await page.getByRole("combobox", { name: "Select models" }).click(); + await page.getByRole("option", { name: "All Team Models", exact: true }).click(); + await page.keyboard.press("Escape"); + + await page.getByRole("button", { name: "Create Key", exact: true }).click(); + + await expect(page.getByText("Save your Key")).toBeVisible({ timeout: 10_000 }); + const apiKey = (await page.getByRole("dialog", { name: "Save your Key" }).locator("pre").innerText()).trim(); + expect(apiKey).toMatch(/^sk-/); + await page.keyboard.press("Escape"); + + try { + await openPlayground(page); + await keySourceSelect(page).click(); + await onlyVisible(page.getByRole("option", { name: "Virtual Key" })).click({ timeout: 15_000 }); + + const keyInput = onlyVisible(page.getByPlaceholder("Enter custom Virtual Key")); + await expect(keyInput).toBeVisible({ timeout: 10_000 }); + await keyInput.fill(apiKey); + + await selectModel(page, CHAT_MODEL_A); + await sendMessage(page, `internal user team key ping ${keyName}`); + + await expect(page.getByText(MOCK_RESPONSE_TEXT, { exact: false }).first()).toBeVisible({ timeout: 60_000 }); + } finally { + await request.post("/key/delete", { headers: auth, data: { keys: [apiKey] } }); + } + }); + test("Virtual Keys page does not surface litellm-dashboard team keys", async ({ page }) => { await navigateToPage(page, Page.ApiKeys); // Anchor on the user's own seeded key so the absence check below cannot // pass vacuously against an empty table. - await expect(page.locator("table tbody").getByText(E2E_INTERNAL_USER_KEY_ALIAS).first()).toBeVisible({ + await expect(page.getByRole("row").filter({ hasText: E2E_INTERNAL_USER_KEY_ALIAS }).first()).toBeVisible({ timeout: 10_000, }); // The litellm-dashboard team is the proxy's internal bookkeeping team — // its keys must never leak into an internal user's Virtual Keys table. - await expect(page.locator("table tbody").getByText("litellm-dashboard")).toHaveCount(0); + await expect(page.getByRole("row").filter({ hasText: "litellm-dashboard" })).toHaveCount(0); }); }); diff --git a/tests/e2e/ui/tests/internal-user/internalUserNoTeam.spec.ts b/tests/e2e/ui/tests/internal-user/internalUserNoTeam.spec.ts index c44305187f1..653e096b713 100644 --- a/tests/e2e/ui/tests/internal-user/internalUserNoTeam.spec.ts +++ b/tests/e2e/ui/tests/internal-user/internalUserNoTeam.spec.ts @@ -30,16 +30,13 @@ test.describe("Internal User with no team memberships", () => { const teamSelect = page.getByTestId("team-dropdown").getByRole("combobox"); await teamSelect.click(); - const dropdown = page.locator('[data-slot="combobox-content"]:visible').first(); - await expect(dropdown).toBeVisible({ timeout: 5_000 }); - // Wait for the settled-empty state, not a transient one. The dropdown shows // "Loading teams…" while teams load and only swaps in "No teams found" once // the request resolves with nothing (team_dropdown.tsx passes both copies to // PaginatedSearchSelect). Asserting on it means a regression where teams DO // load for this user fails here instead of racing a one-shot count() against // an in-flight request. - await expect(dropdown.getByText("No teams found")).toBeVisible({ timeout: 10_000 }); - await expect(dropdown.getByRole("option")).toHaveCount(0); + await expect(page.getByText("No teams found")).toBeVisible({ timeout: 10_000 }); + await expect(page.getByRole("option")).toHaveCount(0); }); }); diff --git a/tests/e2e/ui/tests/internal-user/internalUserWithTeams.spec.ts b/tests/e2e/ui/tests/internal-user/internalUserWithTeams.spec.ts index 68319154554..62681e9ceb5 100644 --- a/tests/e2e/ui/tests/internal-user/internalUserWithTeams.spec.ts +++ b/tests/e2e/ui/tests/internal-user/internalUserWithTeams.spec.ts @@ -1,14 +1,13 @@ import { test, expect } from "@playwright/test"; -import { INTERNAL_USER_STORAGE_PATH, E2E_TEAM_CRUD_ALIAS, E2E_TEAM_ORG_ALIAS } from "../../constants"; +import { + INTERNAL_USER_STORAGE_PATH, + E2E_TEAM_CRUD_ALIAS, + E2E_TEAM_KEYGEN_ALIAS, + E2E_TEAM_ORG_ALIAS, +} from "../../constants"; import { Page } from "../../fixtures/pages"; import { navigateToPage } from "../../helpers/navigation"; -/** - * Differential partner to internalUserNoTeam.spec.ts: the seeded - * e2e-internal-user belongs to exactly two teams, so the Create Key dropdown - * must list both. Without this, the no-team spec's "zero options" assertion - * would still pass against a bug that empties the dropdown for everyone. - */ test.describe("Internal User with team memberships", () => { test.use({ storageState: INTERNAL_USER_STORAGE_PATH }); @@ -21,13 +20,9 @@ test.describe("Internal User with team memberships", () => { const teamSelect = page.getByTestId("team-dropdown").getByRole("combobox"); await teamSelect.click(); - const dropdown = page.locator('[data-slot="combobox-content"]:visible').first(); - await expect(dropdown).toBeVisible({ timeout: 5_000 }); - - // Both seeded memberships render, and nothing else does — proving the - // dropdown is scoped to the user's teams rather than empty or unfiltered. - await expect(dropdown.getByText(E2E_TEAM_CRUD_ALIAS, { exact: true })).toBeVisible({ timeout: 10_000 }); - await expect(dropdown.getByText(E2E_TEAM_ORG_ALIAS, { exact: true })).toBeVisible(); - await expect(dropdown.getByRole("option")).toHaveCount(2); + await expect(page.getByRole("option", { name: E2E_TEAM_CRUD_ALIAS })).toBeVisible({ timeout: 10_000 }); + await expect(page.getByRole("option", { name: E2E_TEAM_ORG_ALIAS })).toBeVisible(); + await expect(page.getByRole("option", { name: E2E_TEAM_KEYGEN_ALIAS })).toBeVisible(); + await expect(page.getByRole("option")).toHaveCount(3); }); }); diff --git a/tests/e2e/ui/tests/internal-viewer/internalViewer.spec.ts b/tests/e2e/ui/tests/internal-viewer/internalViewer.spec.ts index 4de86c46398..dd40976341d 100644 --- a/tests/e2e/ui/tests/internal-viewer/internalViewer.spec.ts +++ b/tests/e2e/ui/tests/internal-viewer/internalViewer.spec.ts @@ -59,9 +59,9 @@ test.describe("Internal Viewer", () => { await expect(page.getByRole("button", { name: /Create New Key/i })).toHaveCount(0); // Open the viewer's own key info page - const keyRow = page.locator("tr", { hasText: E2E_VIEWER_KEY_ALIAS }); + const keyRow = page.getByRole("row").filter({ hasText: E2E_VIEWER_KEY_ALIAS }); await expect(keyRow).toBeVisible({ timeout: 10_000 }); - await keyRow.locator("button").first().click(); + await keyRow.getByRole("button", { name: E2E_VIEWER_KEY_ALIAS }).click(); await expect(page.getByText("Back to Keys")).toBeVisible({ timeout: 10_000 }); // None of the destructive / mutating actions should render diff --git a/tests/e2e/ui/tests/logs/logs.spec.ts b/tests/e2e/ui/tests/logs/logs.spec.ts index 56a3d0f0109..2748c91395f 100644 --- a/tests/e2e/ui/tests/logs/logs.spec.ts +++ b/tests/e2e/ui/tests/logs/logs.spec.ts @@ -2,7 +2,14 @@ import { test, expect, type Locator, type Page as PlaywrightPage } from "@playwr import { ADMIN_STORAGE_PATH } from "../../constants"; import { navigateToPage, dismissFeedbackPopup } from "../../helpers/navigation"; import { Page } from "../../fixtures/pages"; -import { CHAT_MODEL_A, MOCK_RESPONSE_TEXT, sendChatCompletion, waitForSpendLog } from "../../helpers/traffic"; +import { + CHAT_MODEL_A, + MOCK_RESPONSE_TEXT, + sendChatCompletion, + waitForSpendLog, + waitForSpendLogByPrompt, +} from "../../helpers/traffic"; +import { openPlayground, selectModel, sendMessage } from "../../helpers/playground"; /** * Anchored to traffic this spec generates itself, with a unique prompt and end user per run, so it @@ -11,12 +18,11 @@ import { CHAT_MODEL_A, MOCK_RESPONSE_TEXT, sendChatCompletion, waitForSpendLog } const uniqueSuffix = (): string => `${Date.now()}-${Math.random().toString(36).slice(2, 8)}`; -/** - * Walking up from the label is the only stable handle: the header carries no role, test id or class, - * and its copy button is icon-only with a hover-only tooltip. - */ -const sectionHeader = (drawer: Locator, label: "Input" | "Output"): Locator => - drawer.getByText(label, { exact: true }).locator("xpath=../../.."); +const sectionToggle = (drawer: Locator, label: "Input" | "Output"): Locator => + drawer.getByRole("button", { name: new RegExp(`^${label}\\b`) }); + +const sectionCopy = (drawer: Locator, label: "Input" | "Output"): Locator => + drawer.getByRole("button", { name: `Copy ${label.toLowerCase()}` }); /** Every tab stays mounted, so the DOM holds four tables at once; scope to the visible one. */ const requestLogsRows = (page: PlaywrightPage): Locator => @@ -47,6 +53,23 @@ test.describe("Logs page", () => { permissions: ["clipboard-read", "clipboard-write"], }); + test("a chat sent from the Playground lands in Logs with its content", async ({ page, request }) => { + const prompt = `logs-playground-prompt-${uniqueSuffix()}`; + await openPlayground(page); + await selectModel(page, CHAT_MODEL_A); + await sendMessage(page, prompt); + await expect(page.getByText(MOCK_RESPONSE_TEXT, { exact: false }).first()).toBeVisible({ timeout: 60_000 }); + + const requestId = await waitForSpendLogByPrompt(request, prompt); + + const row = await openLogsForRequest(page, requestId); + await row.click(); + const drawer = page.getByRole("dialog").first(); + await expect(drawer.getByText("Request & Response")).toBeVisible({ timeout: 20_000 }); + await expect(drawer.getByText(prompt, { exact: false }).first()).toBeVisible({ timeout: 20_000 }); + await expect(drawer.getByText(MOCK_RESPONSE_TEXT, { exact: false }).first()).toBeVisible({ timeout: 20_000 }); + }); + test("a served request expands to its request and response", async ({ page, request }) => { const prompt = `logs-detail-prompt-${uniqueSuffix()}`; const requestId = await sendChatCompletion(request, { @@ -95,14 +118,14 @@ test.describe("Logs page", () => { await expect(drawer).toBeVisible({ timeout: 20_000 }); // Copy request: the Input card's copy button puts the prompt on the clipboard. - await sectionHeader(drawer, "Input").getByRole("button").click(); + await sectionCopy(drawer, "Input").click(); await expect(page.getByText("Input copied")).toBeVisible({ timeout: 10_000, }); expect(await page.evaluate(() => navigator.clipboard.readText())).toContain(prompt); // Copy response: the Output card's copy button puts the completion on it. - await sectionHeader(drawer, "Output").getByRole("button").click(); + await sectionCopy(drawer, "Output").click(); await expect(page.getByText("Output copied")).toBeVisible({ timeout: 10_000, }); @@ -125,24 +148,15 @@ test.describe("Logs page", () => { timeout: 20_000, }); - // The body collapses via `max-height: 0; overflow: hidden`, which zeroes its own bounding - // box, so the wrapper reads as hidden while the clipped text node inside it does not. - const header = sectionHeader(drawer, "Input"); - const body = header.locator("xpath=following-sibling::div[1]"); - await expect(header.locator(".lucide-chevron-up")).toBeVisible(); - await expect(body).toBeVisible(); + const toggle = sectionToggle(drawer, "Input"); + await expect(toggle).toHaveAttribute("aria-expanded", "true"); + await expect(drawer.getByText(prompt, { exact: false })).toBeVisible(); - await header.click(); - await expect(header.locator(".lucide-chevron-down")).toBeVisible({ - timeout: 10_000, - }); - await expect(body).toBeHidden({ timeout: 10_000 }); + await toggle.click(); + await expect(toggle).toHaveAttribute("aria-expanded", "false", { timeout: 10_000 }); - await header.click(); - await expect(header.locator(".lucide-chevron-up")).toBeVisible({ - timeout: 10_000, - }); - await expect(body).toBeVisible({ timeout: 10_000 }); + await toggle.click(); + await expect(toggle).toHaveAttribute("aria-expanded", "true", { timeout: 10_000 }); await expect(drawer.getByText(prompt, { exact: false })).toBeVisible({ timeout: 10_000, }); diff --git a/tests/e2e/ui/tests/mcp/mcpServerEdit.spec.ts b/tests/e2e/ui/tests/mcp/mcpServerEdit.spec.ts index 46799c8a18f..aa7cdf82498 100644 --- a/tests/e2e/ui/tests/mcp/mcpServerEdit.spec.ts +++ b/tests/e2e/ui/tests/mcp/mcpServerEdit.spec.ts @@ -73,7 +73,7 @@ test.describe("MCP Servers - edit and delete", () => { test("Deleting a server removes it", async ({ page }) => { expect(await findServerByName(page, serverName), `created server ${serverName} exists`).toBeTruthy(); - const card = page.getByTestId("mcp-servers-grid").locator("div").filter({ hasText: serverName }).first(); + const card = page.getByTestId("mcp-servers-grid").getByRole("button", { name: serverName }); await card.getByRole("button", { name: "Server actions" }).click(); await page.getByRole("menuitem", { name: "Delete" }).click(); diff --git a/tests/e2e/ui/tests/migration/migratedPages.spec.ts b/tests/e2e/ui/tests/migration/migratedPages.spec.ts index 3ad4b217d08..547330190bd 100644 --- a/tests/e2e/ui/tests/migration/migratedPages.spec.ts +++ b/tests/e2e/ui/tests/migration/migratedPages.spec.ts @@ -35,17 +35,12 @@ async function expectRendered(page: Page) { */ async function clickSidebar(page: Page, segment: string) { const link = sidebar(page).locator(`a[href$="/ui/${segment}"]`).first(); + const collapsedGroups = sidebar(page).getByRole("button", { expanded: false }); for (let i = 0; i < 8 && !(await link.isVisible().catch(() => false)); i++) { - // A collapsed group is a menu item with a group-toggle button but no - // rendered submenu yet; clicking the toggle expands it. - const collapsedGroup = sidebar(page) - .locator( - '[data-slot="sidebar-menu-item"]:has(> [data-slot="sidebar-menu-button"]):not(:has(> [data-slot="sidebar-menu-sub"])) > [data-slot="sidebar-menu-button"]', - ) - .first(); - if (!(await collapsedGroup.isVisible().catch(() => false))) break; - await collapsedGroup.click(); - await page.waitForTimeout(250); + const stillCollapsed = await collapsedGroups.count(); + if (stillCollapsed === 0) break; + await collapsedGroups.first().click(); + await expect(collapsedGroups).toHaveCount(stillCollapsed - 1); } await link.click(); } diff --git a/tests/e2e/ui/tests/modelHub/modelHub.spec.ts b/tests/e2e/ui/tests/modelHub/modelHub.spec.ts index 16ec94c1dc8..6877fc9c48d 100644 --- a/tests/e2e/ui/tests/modelHub/modelHub.spec.ts +++ b/tests/e2e/ui/tests/modelHub/modelHub.spec.ts @@ -1,7 +1,8 @@ -import { test, expect } from "@playwright/test"; +import { test, expect, type APIRequestContext } from "@playwright/test"; import { ADMIN_STORAGE_PATH } from "../../constants"; import { navigateToPage, dismissFeedbackPopup } from "../../helpers/navigation"; import { Page } from "../../fixtures/pages"; +import { masterKey } from "../../helpers/traffic"; test.describe("AI Hub (internal admin view)", () => { test.use({ storageState: ADMIN_STORAGE_PATH }); @@ -77,4 +78,89 @@ test.describe("Public model hub (/ui/model_hub_table)", () => { // agents/MCP servers exist, so we don't assert on them in a fresh CI run. await expect(page.getByRole("tab", { name: "Model Hub" })).toBeVisible({ timeout: 10_000 }); }); + + test("Agent Hub and MCP Hub tabs render their public entries", async ({ page, request }) => { + const suffix = `${Date.now()}`; + const agentName = `e2e-public-agent-${suffix}`; + const mcpServerName = `e2e_public_mcp_${suffix}`; + const auth = { Authorization: `Bearer ${masterKey()}` }; + + const publicMcpServerIds = async (api: APIRequestContext): Promise => { + const res = await api.get("/public/mcp_hub"); + expect(res.ok(), `public mcp_hub read failed (${res.status()}): ${await res.text()}`).toBe(true); + const servers: { server_id: string }[] = await res.json(); + return servers.map((server) => server.server_id); + }; + + const seedPublicEntries = async ( + api: APIRequestContext, + priorMcpIds: string[], + ): Promise<{ agentId: string; serverId: string }> => { + const agentRes = await api.post("/v1/agents", { + headers: auth, + data: { + agent_name: agentName, + agent_card_params: { + name: agentName, + description: "E2E public agent", + version: "1.0.0", + url: "http://127.0.0.1:9999/", + capabilities: {}, + skills: [], + defaultInputModes: ["text"], + defaultOutputModes: ["text"], + }, + }, + }); + expect(agentRes.ok(), `agent create failed (${agentRes.status()}): ${await agentRes.text()}`).toBe(true); + const agentId = (await agentRes.json()).agent_id as string; + + const serverRes = await api.post("/v1/mcp/server", { + headers: auth, + data: { + server_name: mcpServerName, + url: "http://127.0.0.1:9999/mcp", + transport: "http", + description: "E2E public MCP server", + }, + }); + expect(serverRes.ok(), `mcp server create failed (${serverRes.status()}): ${await serverRes.text()}`).toBe(true); + const serverId = (await serverRes.json()).server_id as string; + + const agentPublicRes = await api.post(`/v1/agents/${agentId}/make_public`, { headers: auth }); + expect(agentPublicRes.ok(), `agent make_public failed: ${await agentPublicRes.text()}`).toBe(true); + const mcpPublicRes = await api.post("/v1/mcp/make_public", { + headers: auth, + data: { mcp_server_ids: [...priorMcpIds, serverId] }, + }); + expect(mcpPublicRes.ok(), `mcp make_public failed: ${await mcpPublicRes.text()}`).toBe(true); + + return { agentId, serverId }; + }; + + const priorMcpIds = await publicMcpServerIds(request); + const { agentId, serverId } = await seedPublicEntries(request, priorMcpIds); + try { + await page.goto(`/ui/model_hub_table?key=${masterKey()}`); + await dismissFeedbackPopup(page); + + const agentHubTab = page.getByRole("tab", { name: "Agent Hub" }); + await expect(agentHubTab).toBeVisible({ timeout: 15_000 }); + await agentHubTab.click(); + await expect(page.getByText("Available Agents")).toBeVisible({ timeout: 10_000 }); + await expect(page.getByRole("row").filter({ hasText: agentName })).toHaveCount(1, { timeout: 10_000 }); + await expect(page.getByText("E2E public agent").first()).toBeVisible(); + + const mcpHubTab = page.getByRole("tab", { name: "MCP Hub" }); + await expect(mcpHubTab).toBeVisible(); + await mcpHubTab.click(); + await expect(page.getByText("Available MCP Servers")).toBeVisible({ timeout: 10_000 }); + await expect(page.getByRole("row").filter({ hasText: mcpServerName })).toHaveCount(1, { timeout: 10_000 }); + await expect(page.getByText("E2E public MCP server").first()).toBeVisible(); + } finally { + await request.post("/v1/mcp/make_public", { headers: auth, data: { mcp_server_ids: priorMcpIds } }); + await request.delete(`/v1/agents/${agentId}`, { headers: auth }); + await request.delete(`/v1/mcp/server/${serverId}`, { headers: auth }); + } + }); }); diff --git a/tests/e2e/ui/tests/modelsPage/addModel.spec.ts b/tests/e2e/ui/tests/modelsPage/addModel.spec.ts index dad716b4c83..073d3c0b79c 100644 --- a/tests/e2e/ui/tests/modelsPage/addModel.spec.ts +++ b/tests/e2e/ui/tests/modelsPage/addModel.spec.ts @@ -188,7 +188,7 @@ test.describe("Add Model", () => { await expect(resultsModal).toBeHidden({ timeout: 5_000 }); const created = await captureRequestBody(page, { method: "POST", urlIncludes: "/model/new" }, async () => { - await page.getByRole("button", { name: "Add Model" }).last().click(); + await page.getByTestId("add-model-btn").click(); }); expect(created.model_name, "the model is created under the name that was typed").toBe(publicName); expect(created.litellm_params?.api_base, "the api base survives the form").toBe(MOCK_LLM_BASE); @@ -212,6 +212,116 @@ test.describe("Add Model", () => { .toBe(true); }); + test("Add a model with a stored credential, pass Test Connect, and serve traffic", async ({ page, request }) => { + const masterKey = users[Role.ProxyAdmin].password; + const auth = { Authorization: `Bearer ${masterKey}` }; + const credentialName = `e2e-cred-reuse-${Date.now()}`; + const createCred = await page.request.post("/credentials", { + headers: auth, + data: { + credential_name: credentialName, + credential_values: { api_key: "fake-key", api_base: MOCK_LLM_BASE }, + credential_info: { custom_llm_provider: "openai" }, + }, + }); + expect(createCred.ok(), `POST /credentials failed (${createCred.status()}): ${await createCred.text()}`).toBe(true); + + // Multi-instance stacks propagate a new credential to the probe-serving instances on a periodic + // sync; consecutive successes guard against a load balancer alternating synced and stale replicas + let consecutiveProbeSuccesses = 0; + await expect + .poll( + async () => { + const probe = await page.request.post("/health/test_connection", { + headers: auth, + data: { + litellm_params: { + model: "openai/fake-gpt-4", + custom_llm_provider: "openai", + litellm_credential_name: credentialName, + }, + model_info: {}, + mode: "chat", + }, + }); + const healthy = probe.ok() && (await probe.json()).status === "success"; + consecutiveProbeSuccesses = healthy ? consecutiveProbeSuccesses + 1 : 0; + return consecutiveProbeSuccesses; + }, + { + message: `stored credential ${credentialName} never became usable for a connection test`, + timeout: 60_000, + }, + ) + .toBeGreaterThanOrEqual(3); + + try { + await navigateToPage(page, Page.Models); + await page.getByRole("tab", { name: "Add Model" }).click(); + + await selectProvider(page, "OpenAI-Compatible Endpoints (Together AI, etc.)"); + + const publicName = `e2e-cred-model-${Date.now()}`; + uiAddedModelName = publicName; + + await page.getByRole("combobox", { name: "Select models" }).click(); + await page.getByRole("option", { name: "Custom Model Name (Enter below)" }).click(); + await page.keyboard.press("Escape"); + await page.getByPlaceholder("Enter custom model name").fill(publicName); + + const credentialSelect = page.getByRole("combobox", { name: "Existing Credentials" }); + await credentialSelect.click(); + await credentialSelect.fill(credentialName); + await page.getByRole("option", { name: credentialName, exact: true }).click(); + + await expect(page.locator("#api_key")).toHaveCount(0); + await expect(page.locator("#api_base")).toHaveCount(0); + + await page.getByRole("button", { name: "Test Connect" }).click(); + await expect(page.getByText("Connection Test Results")).toBeVisible({ timeout: 10_000 }); + await expect(page.getByTestId("connection-success-msg")).toBeVisible({ timeout: 30_000 }); + + const resultsModal = page.getByRole("dialog", { name: "Connection Test Results" }); + await resultsModal.locator('[data-slot="dialog-footer"]').getByRole("button", { name: "Close" }).click(); + await expect(resultsModal).toBeHidden({ timeout: 5_000 }); + + const created = await captureRequestBody(page, { method: "POST", urlIncludes: "/model/new" }, async () => { + await page.getByRole("button", { name: "Add Model" }).last().click(); + }); + expect(created.litellm_params?.litellm_credential_name, "the picked credential goes on the wire").toBe( + credentialName, + ); + expect(created.litellm_params?.api_key, "no raw api key goes on the wire").toBeUndefined(); + + await expect(page.getByText("created successfully")).toBeVisible({ timeout: 15_000 }); + + await expect + .poll( + async () => { + try { + await sendChatCompletion(request, { model: publicName, prompt: `hello via ${credentialName}` }); + return true; + } catch { + return false; + } + }, + { + message: `model ${publicName} added with a stored credential never served a request`, + timeout: 30_000, + }, + ) + .toBe(true); + } finally { + const stored = uiAddedModelName ? await findDeploymentByName(page, uiAddedModelName) : undefined; + const id = stored?.model_info?.id; + if (id) { + await page.request.post("/model/delete", { headers: auth, data: { id } }); + uiAddedModelName = ""; + } + await page.request.delete(`/credentials/${credentialName}`, { headers: auth }); + } + }); + test("Test connection with bad credentials shows failure", async ({ page }) => { await navigateToPage(page, Page.Models); await page.getByRole("tab", { name: "Add Model" }).click(); @@ -254,7 +364,7 @@ test.describe("Add Model", () => { // Click Add Model button by its text const created = await captureRequestBody(page, { method: "POST", urlIncludes: "/model/new" }, async () => { - await page.getByRole("button", { name: "Add Model" }).last().click(); + await page.getByTestId("add-model-btn").click(); }); // The form sends custom_llm_provider separately from the name, so both halves have to arrive. expect(created.model_name, "the selected model is what goes on the wire").toBe("claude-haiku-4-5"); @@ -267,11 +377,9 @@ test.describe("Add Model", () => { // Navigate to All Models tab await page.getByRole("tab", { name: "All Models" }).click(); await page.waitForLoadState("networkidle"); - await page.waitForTimeout(2000); // Search for the model we just added await page.getByPlaceholder("Search model names").fill("claude-haiku-4-5"); - await page.waitForTimeout(1000); // Verify the model appears in the results count (not "Showing 0 results") await expect(page.getByTestId("pagination-range")).toHaveText(/Showing \d+-\d+ of \d+/, { @@ -279,8 +387,9 @@ test.describe("Add Model", () => { }); // Verify the model name appears in the table body - const tableBody = page.locator("table tbody"); - await expect(tableBody.getByText("claude-haiku-4-5").first()).toBeVisible({ timeout: 15_000 }); + await expect(page.getByRole("row").filter({ hasText: "claude-haiku-4-5" })).not.toHaveCount(0, { + timeout: 15_000, + }); // A row proves the name is there, not what the deployment routes to. const stored = await findDeploymentByName(page, "claude-haiku-4-5"); @@ -333,11 +442,11 @@ test.describe("Add Model", () => { const teamDropdown = page.getByTestId("team-dropdown").getByRole("combobox"); await expect(teamDropdown).toBeVisible({ timeout: 5_000 }); await teamDropdown.click(); - const teamOption = page.locator('[data-slot="combobox-content"]:visible').getByText(E2E_TEAM_CRUD_ID).first(); + const teamOption = page.getByRole("option", { name: E2E_TEAM_CRUD_ID }).first(); await expect(teamOption).toBeVisible({ timeout: 5_000 }); await teamOption.click(); - await page.getByRole("button", { name: "Add Model" }).last().click(); + await page.getByTestId("add-model-btn").click(); // Scope to the toast container so a stale toast can't satisfy this. await expect(page.locator("[data-sonner-toast]").getByText("created successfully").last()).toBeVisible({ @@ -347,12 +456,9 @@ test.describe("Add Model", () => { // The Models table renders team-scoped models with the team id in the row. await page.getByRole("tab", { name: "All Models" }).click(); await page.waitForLoadState("networkidle"); - // networkidle fires before the table finishes re-rendering. - await page.waitForTimeout(2000); await page.getByPlaceholder("Search model names").fill("cohere"); - await page.waitForTimeout(1000); - + // Clearer failure than timing out on a row assertion when the table is empty. await expect(page.getByTestId("pagination-range")).toHaveText(/Showing \d+-\d+ of \d+/, { timeout: 15_000, @@ -361,7 +467,7 @@ test.describe("Add Model", () => { // Pin to one row carrying both the name and the team, so the sibling test's // team-less cohere row can't satisfy it. const teamCohereRow = page - .locator("table tbody tr") + .getByRole("row") .filter({ hasText: "cohere/" }) .filter({ hasText: E2E_TEAM_CRUD_ID }); await expect(teamCohereRow).toHaveCount(1, { timeout: 15_000 }); @@ -387,7 +493,7 @@ test.describe("Add Model", () => { // Click Add Model button by its text const created = await captureRequestBody(page, { method: "POST", urlIncludes: "/model/new" }, async () => { - await page.getByRole("button", { name: "Add Model" }).last().click(); + await page.getByTestId("add-model-btn").click(); }); // A wildcard with the star stripped becomes a plain "cohere" deployment that matches nothing. expect(created.model_name, "the wildcard route goes on the wire intact").toBe("cohere/*"); @@ -398,11 +504,9 @@ test.describe("Add Model", () => { // Navigate to All Models tab await page.getByRole("tab", { name: "All Models" }).click(); await page.waitForLoadState("networkidle"); - await page.waitForTimeout(2000); // Search for the wildcard model await page.getByPlaceholder("Search model names").fill("cohere"); - await page.waitForTimeout(1000); // Verify the model appears in the results count (not "Showing 0 results") await expect(page.getByTestId("pagination-range")).toHaveText(/Showing \d+-\d+ of \d+/, { @@ -410,8 +514,7 @@ test.describe("Add Model", () => { }); // Verify the wildcard model appears in the table body (wildcard models show as "cohere/*") - const tableBody = page.locator("table tbody"); - await expect(tableBody.getByText("cohere/").first()).toBeVisible({ timeout: 15_000 }); + await expect(page.getByRole("row").filter({ hasText: "cohere/" })).not.toHaveCount(0, { timeout: 15_000 }); // "cohere/" in the table also matches a plain cohere deployment; require the wildcard exactly. const stored = await findDeploymentByName(page, "cohere/*"); diff --git a/tests/e2e/ui/tests/modelsPage/autoRouterTemplateSelect.spec.ts b/tests/e2e/ui/tests/modelsPage/autoRouterTemplateSelect.spec.ts index 1d080ec82b8..51df50a2e68 100644 --- a/tests/e2e/ui/tests/modelsPage/autoRouterTemplateSelect.spec.ts +++ b/tests/e2e/ui/tests/modelsPage/autoRouterTemplateSelect.spec.ts @@ -17,49 +17,54 @@ async function openTemplateSelect(page: PlaywrightPage) { return trigger; } -function pollPixelsBelowTrigger(trigger: Locator, popup: Locator) { +async function boxes(trigger: Locator, options: Locator) { + const triggerBox = await trigger.boundingBox(); + const optionsBox = await options.boundingBox(); + return triggerBox && optionsBox ? { triggerBox, optionsBox } : null; +} + +const clippedPopup = (page: PlaywrightPage) => page.locator('[data-slot="select-content"]'); + +function pollOptionsOpenBelowTrigger(trigger: Locator, options: Locator) { return expect.poll(async () => { - const triggerBox = await trigger.boundingBox(); - const popupBox = await popup.boundingBox(); - if (!triggerBox || !popupBox) return null; - return popupBox.y - (triggerBox.y + triggerBox.height); + const box = await boxes(trigger, options); + return box && box.optionsBox.y >= box.triggerBox.y + box.triggerBox.height; }); } -function pollPopupOverlapsTrigger(trigger: Locator, popup: Locator) { +function pollOptionsCoverTrigger(trigger: Locator, options: Locator) { return expect.poll(async () => { - const triggerBox = await trigger.boundingBox(); - const popupBox = await popup.boundingBox(); - if (!triggerBox || !popupBox) return null; - return popupBox.y < triggerBox.y + triggerBox.height && popupBox.y + popupBox.height > triggerBox.y; + const box = await boxes(trigger, options); + return ( + box && + box.optionsBox.y < box.triggerBox.y + box.triggerBox.height && + box.optionsBox.y + box.optionsBox.height > box.triggerBox.y + ); }); } test.describe("Auto Router template select anchoring", () => { test.use({ storageState: ADMIN_STORAGE_PATH }); - test("opens the options below the trigger rather than over it", async ({ page }) => { + test("opens the options below the trigger when there is room below it", async ({ page }) => { await page.setViewportSize({ width: 1280, height: 900 }); const trigger = await openTemplateSelect(page); + await trigger.scrollIntoViewIfNeeded(); await trigger.click(); - const popup = page.locator('[data-slot="select-content"]'); - await expect(popup).toBeVisible(); + await expect(page.getByRole("listbox")).toBeVisible(); - // Item-aligned mode reports "none" and puts the active item over the trigger. - await expect(popup).toHaveAttribute("data-side", "bottom"); - await pollPixelsBelowTrigger(trigger, popup).toBeGreaterThanOrEqual(0); + await pollOptionsOpenBelowTrigger(trigger, clippedPopup(page)).toBe(true); }); - test("flips above the trigger instead of covering it when there is no room below", async ({ page }) => { + test("keeps the trigger uncovered when the options open with no room below it", async ({ page }) => { await page.setViewportSize({ width: 1280, height: 560 }); const trigger = await openTemplateSelect(page); await trigger.scrollIntoViewIfNeeded(); await trigger.click(); - const popup = page.locator('[data-slot="select-content"]'); - await expect(popup).toBeVisible(); + await expect(page.getByRole("listbox")).toBeVisible(); - await pollPopupOverlapsTrigger(trigger, popup).toBe(false); + await pollOptionsCoverTrigger(trigger, clippedPopup(page)).toBe(false); }); }); diff --git a/tests/e2e/ui/tests/modelsPage/deleteTeamModel.spec.ts b/tests/e2e/ui/tests/modelsPage/deleteTeamModel.spec.ts new file mode 100644 index 00000000000..96abd9833c0 --- /dev/null +++ b/tests/e2e/ui/tests/modelsPage/deleteTeamModel.spec.ts @@ -0,0 +1,72 @@ +import { test, expect, type Page as PlaywrightPage } from "@playwright/test"; +import { ADMIN_STORAGE_PATH, E2E_TEAM_CRUD_ID } from "../../constants"; +import { Page } from "../../fixtures/pages"; +import { navigateToPage } from "../../helpers/navigation"; +import { readBack } from "../../helpers/roundTrip"; +import { masterKey } from "../../helpers/traffic"; + +type DeploymentRow = { model_name?: string }; + +async function findDeploymentByName(page: PlaywrightPage, modelName: string): Promise { + const body = await readBack<{ data: DeploymentRow[] }>(page, "/v2/model/info"); + return body.data.find((row) => row.model_name === modelName); +} + +test.describe("Delete team model", () => { + test.use({ storageState: ADMIN_STORAGE_PATH }); + + test("Delete a team-scoped model and verify it leaves the team's model list", async ({ page }) => { + const modelName = `e2e-team-model-delete-${Date.now()}`; + const createResponse = await page.request.post("/model/new", { + headers: { Authorization: `Bearer ${masterKey()}` }, + data: { + model_name: modelName, + litellm_params: { + model: "openai/fake-gpt-4", + api_base: `http://127.0.0.1:${process.env.MOCK_LLM_PORT ?? "8090"}/v1`, + api_key: "fake-key", + }, + model_info: { team_id: E2E_TEAM_CRUD_ID }, + }, + }); + expect(createResponse.ok(), `/model/new failed: ${createResponse.status()} ${await createResponse.text()}`).toBe( + true, + ); + + await expect + .poll(async () => (await findDeploymentByName(page, modelName)) !== undefined, { + message: `deployment ${modelName} never appeared in /v2/model/info after create`, + timeout: 30_000, + }) + .toBe(true); + + await navigateToPage(page, Page.Models); + await page.getByPlaceholder("Search model names").fill(modelName); + + const row = page.getByRole("row").filter({ hasText: modelName }); + await expect(row).toHaveCount(1, { timeout: 15_000 }); + await expect(row.getByText(E2E_TEAM_CRUD_ID)).toBeVisible({ timeout: 10_000 }); + + await row.getByRole("button", { name: "Delete model" }).click(); + + const modal = page.getByRole("dialog", { name: "Delete Model" }); + await expect(modal).toBeVisible({ timeout: 5_000 }); + await expect(modal.getByText(modelName).first()).toBeVisible(); + await modal.getByRole("button", { name: "Delete", exact: true }).click(); + + await expect(page.getByText("Model deleted successfully").first()).toBeVisible({ timeout: 10_000 }); + await expect(row).toHaveCount(0, { timeout: 15_000 }); + + await expect + .poll(async () => await findDeploymentByName(page, modelName), { + message: `deployment ${modelName} still readable from /v2/model/info after delete`, + timeout: 15_000, + }) + .toBeUndefined(); + + await page.reload(); + await page.getByPlaceholder("Search model names").fill(modelName); + await expect(page.getByText("No models found").first()).toBeVisible({ timeout: 15_000 }); + await expect(page.getByRole("row").filter({ hasText: modelName })).toHaveCount(0); + }); +}); diff --git a/tests/e2e/ui/tests/modelsPage/responsiveHeader.spec.ts b/tests/e2e/ui/tests/modelsPage/responsiveHeader.spec.ts index 6ad1ccb8451..aabdf18d427 100644 --- a/tests/e2e/ui/tests/modelsPage/responsiveHeader.spec.ts +++ b/tests/e2e/ui/tests/modelsPage/responsiveHeader.spec.ts @@ -7,9 +7,7 @@ test.describe("Models and Endpoints responsive header", () => { viewport: { width: 900, height: 720 }, }); - test("keeps the refresh action on the same row as the tabs", async ({ - page, - }) => { + test("keeps the refresh action on the same row as the tabs", async ({ page }) => { await page.goto("/ui"); await page .getByRole("complementary") @@ -26,8 +24,8 @@ test.describe("Models and Endpoints responsive header", () => { expect(tabsBox).not.toBeNull(); expect(refreshBox).not.toBeNull(); - const tabsCenterY = tabsBox!.y + tabsBox!.height / 2; const refreshCenterY = refreshBox!.y + refreshBox!.height / 2; - expect(Math.abs(tabsCenterY - refreshCenterY)).toBeLessThanOrEqual(2); + const sharesARow = refreshCenterY > tabsBox!.y && refreshCenterY < tabsBox!.y + tabsBox!.height; + expect(sharesARow, "refresh wrapped onto its own row below the tabs").toBe(true); }); }); diff --git a/tests/e2e/ui/tests/proxy-admin/keys.spec.ts b/tests/e2e/ui/tests/proxy-admin/keys.spec.ts index 0c38641dcc7..f5bee68f245 100644 --- a/tests/e2e/ui/tests/proxy-admin/keys.spec.ts +++ b/tests/e2e/ui/tests/proxy-admin/keys.spec.ts @@ -43,7 +43,7 @@ test.describe("Proxy Admin - Keys", () => { const teamSelect = page.getByTestId("team-dropdown").getByRole("combobox"); await teamSelect.click(); await page.keyboard.type(E2E_TEAM_CRUD_ALIAS); - await page.locator('[data-slot="combobox-content"]:visible').getByText(E2E_TEAM_CRUD_ALIAS).first().click(); + await page.getByRole("option", { name: E2E_TEAM_CRUD_ALIAS }).first().click(); // Select models — the popup is portaled to the body, so scope options to the page. await page.getByRole("combobox", { name: "Select models" }).click(); @@ -74,10 +74,9 @@ test.describe("Proxy Admin - Keys", () => { const before = await findKeyByAlias(page, E2E_REGENERATE_KEY_ALIAS); expect(before?.token, `seeded key ${E2E_REGENERATE_KEY_ALIAS} has a token`).toBeTruthy(); - // Key IDs are rendered as buttons in the table - const keyRow = page.locator("tr", { hasText: E2E_REGENERATE_KEY_ALIAS }); + const keyRow = page.getByRole("row").filter({ hasText: E2E_REGENERATE_KEY_ALIAS }); await expect(keyRow).toBeVisible({ timeout: 10_000 }); - await keyRow.locator("button").first().click(); + await keyRow.getByRole("button", { name: E2E_REGENERATE_KEY_ALIAS }).click(); await expect(page.getByText("Back to Keys")).toBeVisible({ timeout: 10_000 }); @@ -109,9 +108,9 @@ test.describe("Proxy Admin - Keys", () => { const before = await findKeyByAlias(page, E2E_UPDATE_LIMITS_KEY_ALIAS); expect(before, `seeded key ${E2E_UPDATE_LIMITS_KEY_ALIAS} exists`).toBeTruthy(); - const keyRow = page.locator("tr", { hasText: E2E_UPDATE_LIMITS_KEY_ALIAS }); + const keyRow = page.getByRole("row").filter({ hasText: E2E_UPDATE_LIMITS_KEY_ALIAS }); await expect(keyRow).toBeVisible({ timeout: 10_000 }); - await keyRow.locator("button").first().click(); + await keyRow.getByRole("button", { name: E2E_UPDATE_LIMITS_KEY_ALIAS }).click(); await expect(page.getByText("Back to Keys")).toBeVisible({ timeout: 10_000 }); @@ -147,9 +146,9 @@ test.describe("Proxy Admin - Keys", () => { await navigateToPage(page, Page.ApiKeys); await dismissFeedbackPopup(page); - const keyRow = page.locator("tr", { hasText: E2E_DELETE_KEY_ALIAS }); + const keyRow = page.getByRole("row").filter({ hasText: E2E_DELETE_KEY_ALIAS }); await expect(keyRow).toBeVisible({ timeout: 10_000 }); - await keyRow.locator("button").first().click(); + await keyRow.getByRole("button", { name: E2E_DELETE_KEY_ALIAS }).click(); await expect(page.getByText("Back to Keys")).toBeVisible({ timeout: 10_000 }); diff --git a/tests/e2e/ui/tests/proxy-admin/secondAdmin.spec.ts b/tests/e2e/ui/tests/proxy-admin/secondAdmin.spec.ts new file mode 100644 index 00000000000..5a8bc84cc13 --- /dev/null +++ b/tests/e2e/ui/tests/proxy-admin/secondAdmin.spec.ts @@ -0,0 +1,97 @@ +import { test, expect } from "@playwright/test"; +import { ADMIN_STORAGE_PATH } from "../../constants"; +import { Page } from "../../fixtures/pages"; +import { navigateToPage, dismissFeedbackPopup } from "../../helpers/navigation"; +import { CHAT_MODEL_A, MOCK_RESPONSE_TEXT, masterKey } from "../../helpers/traffic"; + +test.describe("Second proxy admin", () => { + test.use({ storageState: { cookies: [], origins: [] } }); + + test("an invited admin can log in, mint a key, and call a model with it", async ({ page, browser, request }) => { + const suffix = Date.now(); + const email = `second-admin-${suffix}@test.local`; + const password = "e2e-second-admin-password"; + const auth = { Authorization: `Bearer ${masterKey()}` }; + + const inviteAdminUser = async (): Promise => { + const adminContext = await browser.newContext({ storageState: ADMIN_STORAGE_PATH }); + try { + const adminPage = await adminContext.newPage(); + await navigateToPage(adminPage, Page.Users); + await dismissFeedbackPopup(adminPage); + + await adminPage.getByRole("button", { name: "+ Invite User", exact: true }).click(); + const dialog = adminPage.getByRole("dialog", { name: "Invite User" }); + await expect(dialog).toBeVisible({ timeout: 5_000 }); + + await dialog.getByLabel("User Email").fill(email); + + await dialog.getByLabel(/Global Proxy Role/).click(); + await adminPage.getByRole("option", { name: /Admin \(All Permissions\)/ }).click(); + + const createdResponse = adminPage.waitForResponse( + (res) => res.url().includes("/user/new") && res.request().method() === "POST", + ); + await dialog.getByRole("button", { name: "Invite User" }).click(); + const createdBody = await (await createdResponse).json(); + const createdUserId = (createdBody.data?.user_id ?? createdBody.user_id) as string; + expect(createdUserId, "created user id from /user/new").toBeTruthy(); + + await expect(adminPage.getByText("API user Created").first()).toBeVisible({ timeout: 10_000 }); + return createdUserId; + } finally { + await adminContext.close(); + } + }; + + const userId = await inviteAdminUser(); + try { + const passwordRes = await request.post("/user/update", { + headers: auth, + data: { user_email: email, password }, + }); + expect(passwordRes.ok(), `setting password failed (${passwordRes.status()}): ${await passwordRes.text()}`).toBe( + true, + ); + + await page.goto("/ui/login"); + await page.getByPlaceholder("Enter your username").fill(email); + await page.getByPlaceholder("Enter your password").fill(password); + await page.getByRole("button", { name: "Login", exact: true }).click(); + await expect(page.locator("a", { hasText: "Virtual Keys" })).toBeVisible({ timeout: 30_000 }); + await dismissFeedbackPopup(page); + + await navigateToPage(page, Page.ApiKeys); + await page.getByRole("button", { name: /Create New Key/i }).click(); + await expect(page.getByText("Key Ownership")).toBeVisible({ timeout: 10_000 }); + + await page.getByLabel(/Key Name/).fill(`e2e-second-admin-key-${suffix}`); + + await page.getByRole("combobox", { name: "Select models" }).click(); + await page.getByRole("option", { name: "All Proxy Models", exact: true }).click(); + await page.keyboard.press("Escape"); + + await page.getByRole("button", { name: "Create Key", exact: true }).click(); + + await expect(page.getByText("Save your Key")).toBeVisible({ timeout: 10_000 }); + const apiKey = (await page.getByRole("dialog", { name: "Save your Key" }).locator("pre").innerText()).trim(); + expect(apiKey).toMatch(/^sk-/); + await page.keyboard.press("Escape"); + + const response = await page.request.post("/chat/completions", { + headers: { Authorization: `Bearer ${apiKey}` }, + data: { + model: CHAT_MODEL_A, + messages: [{ role: "user", content: `second admin ping ${suffix}` }], + }, + }); + expect(response.status()).toBe(200); + const body = await response.json(); + expect(body.choices?.[0]?.message?.content).toBe(MOCK_RESPONSE_TEXT); + } finally { + if (userId) { + await request.post("/user/delete", { headers: auth, data: { user_ids: [userId] } }); + } + } + }); +}); diff --git a/tests/e2e/ui/tests/settings/scim.spec.ts b/tests/e2e/ui/tests/settings/scim.spec.ts new file mode 100644 index 00000000000..d7dd4248f50 --- /dev/null +++ b/tests/e2e/ui/tests/settings/scim.spec.ts @@ -0,0 +1,53 @@ +import { test, expect, Page as PlaywrightPage } from "@playwright/test"; +import { ADMIN_STORAGE_PATH } from "../../constants"; +import { Page } from "../../fixtures/pages"; +import { navigateToPage } from "../../helpers/navigation"; + +const rootPath = (): string => process.env.SERVER_ROOT_PATH ?? ""; + +async function createScimTokenViaUi(page: PlaywrightPage, alias: string): Promise { + await navigateToPage(page, Page.AdminPanel); + await page.getByRole("tab", { name: "SCIM" }).click(); + + await expect(page.getByText("SCIM Tenant URL")).toBeVisible(); + await expect(page.locator("input[disabled]").first()).toHaveValue(/\/scim\/v2$/); + + await page.getByLabel("Token Name").fill(alias); + await page.getByRole("button", { name: "Create SCIM Token" }).click(); + + await expect(page.getByText(/copy this token now/i)).toBeVisible({ timeout: 15_000 }); + const token = await page.locator('input[type="password"]').inputValue(); + expect(token, "the one-time token panel shows a usable virtual key").toMatch(/^sk-/); + return token; +} + +test.describe("Admin Settings - SCIM", () => { + test.use({ storageState: ADMIN_STORAGE_PATH }); + + test("Create SCIM Token shows the token once and offers to create another", async ({ page }) => { + await createScimTokenViaUi(page, `e2e-scim-ui-${Date.now()}`); + + await page.getByRole("button", { name: "Create Another Token" }).click(); + await expect(page.getByRole("button", { name: "Create SCIM Token" })).toBeVisible(); + await expect(page.getByText(/copy this token now/i)).toBeHidden(); + }); + + test("a UI-minted SCIM token authorizes the SCIM API", async ({ page, request }) => { + test.skip(!process.env.LITELLM_LICENSE, "LITELLM_LICENSE not set in test env — /scim/v2 is premium-gated"); + + const token = await createScimTokenViaUi(page, `e2e-scim-api-${Date.now()}`); + + const denied = await request.get(`${rootPath()}/scim/v2/Groups`, { + headers: { Authorization: "Bearer sk-not-a-real-key" }, + }); + expect(denied.status(), "an unknown key must not reach SCIM").toBe(401); + + const res = await request.get(`${rootPath()}/scim/v2/Groups`, { + headers: { Authorization: `Bearer ${token}` }, + }); + expect(res.status(), `SCIM Groups listing failed: ${await res.text()}`).toBe(200); + const body = await res.json(); + expect(body.schemas, "SCIM answers with a ListResponse").toContain("urn:ietf:params:scim:api:messages:2.0:ListResponse"); + expect(Array.isArray(body.Resources), "SCIM ListResponse carries a Resources array").toBe(true); + }); +}); diff --git a/tests/e2e/ui/tests/team-admin/teamAdmin.spec.ts b/tests/e2e/ui/tests/team-admin/teamAdmin.spec.ts index f93cca75347..26a6fa50b4b 100644 --- a/tests/e2e/ui/tests/team-admin/teamAdmin.spec.ts +++ b/tests/e2e/ui/tests/team-admin/teamAdmin.spec.ts @@ -1,6 +1,7 @@ import { test, expect, type Page as PlaywrightPage } from "@playwright/test"; import { E2E_INTERNAL_USER_KEY_ALIAS, + E2E_TEAM_ADMIN_USER_ID, E2E_TEAM_CRUD_ALIAS, E2E_TEAM_CRUD_ID, TEAM_ADMIN_STORAGE_PATH, @@ -8,6 +9,8 @@ import { import { Page } from "../../fixtures/pages"; import { navigateToPage, dismissFeedbackPopup, clickTeamId } from "../../helpers/navigation"; import { captureRequestBody, readBack } from "../../helpers/roundTrip"; +import { CHAT_MODEL_A, masterKey } from "../../helpers/traffic"; +import { keySourceSelect, modelSelect, onlyVisible, openPlayground } from "../../helpers/playground"; /** * Every identifier a roster is addressable by. Which of user_id / user_email is populated depends on @@ -128,6 +131,91 @@ test.describe("Team Admin", () => { .not.toContain("e2e-removable-member"); }); + test("Team admin sees all team models in the Playground model dropdown", async ({ page, request }) => { + const suffix = Date.now(); + const teamModelName = `e2e-team-dropdown-model-${suffix}`; + const auth = { Authorization: `Bearer ${masterKey()}` }; + + const teamRes = await request.post("/team/new", { + headers: auth, + data: { + team_alias: `e2e-playground-team-${suffix}`, + models: [CHAT_MODEL_A], + members_with_roles: [{ role: "admin", user_id: E2E_TEAM_ADMIN_USER_ID }], + }, + }); + expect(teamRes.ok(), `team create failed (${teamRes.status()}): ${await teamRes.text()}`).toBe(true); + const teamId = (await teamRes.json()).team_id as string; + + try { + const modelRes = await request.post("/model/new", { + headers: auth, + data: { + model_name: teamModelName, + litellm_params: { + model: "openai/fake-gpt-4", + api_base: `http://127.0.0.1:${process.env.MOCK_LLM_PORT ?? "8090"}/v1`, + api_key: "fake-key", + }, + model_info: { team_id: teamId }, + }, + }); + expect(modelRes.ok(), `model create failed (${modelRes.status()}): ${await modelRes.text()}`).toBe(true); + const modelId = (await modelRes.json()).model_info?.id as string; + + try { + const keyRes = await request.post("/key/generate", { headers: auth, data: { team_id: teamId } }); + expect(keyRes.ok(), `key generate failed (${keyRes.status()}): ${await keyRes.text()}`).toBe(true); + const teamKey = (await keyRes.json()).key as string; + + try { + await expect + .poll( + async () => { + const res = await request.get("/model_group/info", { + headers: { Authorization: `Bearer ${teamKey}` }, + }); + if (!res.ok()) return false; + const body: { data?: { model_group?: string }[] } = await res.json(); + return (body.data ?? []).some((group) => group.model_group === teamModelName); + }, + { + message: `model group ${teamModelName} never became visible to the team key`, + timeout: 30_000, + }, + ) + .toBe(true); + + await openPlayground(page); + await keySourceSelect(page).click(); + await onlyVisible(page.getByRole("option", { name: "Virtual Key" })).click({ timeout: 15_000 }); + + const keyInput = onlyVisible(page.getByPlaceholder("Enter custom Virtual Key")); + await expect(keyInput).toBeVisible({ timeout: 10_000 }); + await keyInput.fill(teamKey); + + const select = modelSelect(page); + await select.click(); + await select.fill(teamModelName); + await expect(onlyVisible(page.getByRole("option", { name: teamModelName }))).toBeVisible({ + timeout: 15_000, + }); + + await select.fill(CHAT_MODEL_A); + await expect(onlyVisible(page.getByRole("option", { name: CHAT_MODEL_A }))).toBeVisible({ + timeout: 15_000, + }); + } finally { + await request.post("/key/delete", { headers: auth, data: { keys: [teamKey] } }); + } + } finally { + await request.post("/model/delete", { headers: auth, data: { id: modelId } }); + } + } finally { + await request.post("/team/delete", { headers: auth, data: { team_ids: [teamId] } }); + } + }); + test("Team admin can create a team key with All Team Models", async ({ page }) => { await navigateToPage(page, Page.ApiKeys); await dismissFeedbackPopup(page); @@ -142,7 +230,7 @@ test.describe("Team Admin", () => { const teamSelect = page.getByTestId("team-dropdown").getByRole("combobox"); await teamSelect.click(); await page.keyboard.type(E2E_TEAM_CRUD_ALIAS); - await page.locator('[data-slot="combobox-content"]:visible').getByText(E2E_TEAM_CRUD_ALIAS).first().click(); + await page.getByRole("option", { name: E2E_TEAM_CRUD_ALIAS }).first().click(); // Models — pick "All Team Models". The popup is portaled to the body, so // scope the option lookup to the page. diff --git a/tests/e2e/ui/tests/usage/usagePage.spec.ts b/tests/e2e/ui/tests/usage/usagePage.spec.ts index 8fa59beb905..f61ab018e1b 100644 --- a/tests/e2e/ui/tests/usage/usagePage.spec.ts +++ b/tests/e2e/ui/tests/usage/usagePage.spec.ts @@ -51,20 +51,19 @@ test.describe("Usage page", () => { const card = await openUsage(page); // Table view (the default): the key is listed by its alias. - const row = card.locator("tbody tr").filter({ hasText: alias }); + const row = card.getByRole("row").filter({ hasText: alias }); await expect(row, `${alias} missing from Top Virtual Keys`).toHaveCount(1, { timeout: 30_000, }); // Chart view swaps the table out for the bar chart, and back. await card.getByText("Chart View", { exact: true }).click(); - await expect(card.locator("tbody tr")).toHaveCount(0, { timeout: 10_000 }); + await expect(card.getByRole("table")).toHaveCount(0, { timeout: 10_000 }); await card.getByText("Table View", { exact: true }).click(); await expect(row).toHaveCount(1, { timeout: 10_000 }); - // Clicking the Key ID cell fetches key info and opens the detail panel. // The alias is already in the row behind the modal, so match the panel's own controls. - await row.locator("td").first().click(); + await row.getByRole("button", { name: token }).click(); const keyInfo = page.getByRole("tab", { name: "Overview", exact: true }); await expect(keyInfo, "key info panel did not open").toBeVisible({ timeout: 20_000, diff --git a/tests/e2e/ui/tests/users/searchUsers.spec.ts b/tests/e2e/ui/tests/users/searchUsers.spec.ts index e87218b5a5e..fa8f32764e8 100644 --- a/tests/e2e/ui/tests/users/searchUsers.spec.ts +++ b/tests/e2e/ui/tests/users/searchUsers.spec.ts @@ -1,91 +1,52 @@ -import { test, expect, Page } from "@playwright/test"; +import { test, expect, Page as PlaywrightPage } from "@playwright/test"; import { ADMIN_STORAGE_PATH } from "../../constants"; -test.skip("Internal Users Search", () => { +import { Page } from "../../fixtures/pages"; +import { navigateToPage } from "../../helpers/navigation"; + +const userRows = (page: PlaywrightPage) => page.getByRole("row").filter({ has: page.getByRole("cell") }); + +async function goToInternalUsers(page: PlaywrightPage) { + await navigateToPage(page, Page.Users); + await expect(page.getByRole("columnheader", { name: "User ID" })).toBeVisible({ timeout: 30_000 }); + await expect(userRows(page)).not.toHaveCount(0, { timeout: 30_000 }); +} + +test.describe("Internal Users Search", () => { test.use({ storageState: ADMIN_STORAGE_PATH }); - async function goToInternalUsers(page: Page) { - await page.goto("/ui"); - - const tab = page.getByRole("menuitem", { name: "Internal User" }); - await expect(tab).toBeVisible(); - await tab.click(); - - await expect(page.locator("tbody tr").first()).toBeVisible(); - await expect(page.locator('[data-slot="skeleton"]')).toHaveCount(0); - } - - test("can search users by email", async ({ page }) => { + test("narrows the table to the matching email, and restores it when cleared", async ({ page }) => { await goToInternalUsers(page); - const rows = page.locator("tbody tr"); - const searchInput = page.getByPlaceholder("Search by email..."); + const search = page.getByPlaceholder("Search by email…"); + await expect(search).toBeVisible(); - await expect(searchInput).toBeVisible(); + await search.fill("noteam@"); + await expect(userRows(page)).toHaveCount(1, { timeout: 30_000 }); + await expect(userRows(page).first()).toContainText("noteam@test.local"); - // Ensure initial data is loaded - const initialCount = await rows.count(); - expect(initialCount).toBeGreaterThan(0); - - // 🔹 Apply filter + wait for backend response - await Promise.all([ - page.waitForResponse( - (res) => - res.url().includes("/user/list") && - res.url().includes("user_email=test%40") && // encoded "test@" - res.status() === 200, - ), - searchInput.fill("test@"), - ]); - await page.waitForTimeout(5000); - const filteredCount = await rows.count(); - await expect(filteredCount).toBeLessThan(initialCount); - - // 🔹 Clear filter + wait for unfiltered request - await Promise.all([ - page.waitForResponse( - (res) => res.url().includes("/user/list") && !res.url().includes("user_email=") && res.status() === 200, - ), - searchInput.clear(), - ]); - - const resetCount = await rows.count(); - await expect(resetCount).toBe(initialCount); + await search.clear(); + await expect(userRows(page).filter({ hasText: "admin@test.local" })).not.toHaveCount(0, { timeout: 30_000 }); }); - test("can filter users by user ID and SSO ID", async ({ page }) => { + test("filters the table down to one user by user ID", async ({ page }) => { await goToInternalUsers(page); - const rows = page.locator("tbody tr"); - // Ensure initial data is loaded - const initialCount = await rows.count(); - expect(initialCount).toBeGreaterThan(0); + await page.getByRole("button", { name: "Filters" }).click(); + await page.getByTestId("users-filter-user-id").fill("e2e-internal-noteam"); + await page.getByTestId("filter-drawer-apply").click(); - const filtersButton = page.getByRole("button", { - name: "Filters", - exact: true, - }); - await filtersButton.click(); + await expect(userRows(page)).toHaveCount(1, { timeout: 30_000 }); + await expect(userRows(page).first()).toContainText("noteam@test.local"); + }); - const userIdInput = page.getByPlaceholder("Filter by User ID"); - const ssoIdInput = page.getByPlaceholder("Filter by SSO ID"); - await Promise.all([ - page.waitForResponse( - (res) => res.url().includes("/user/list") && res.url().includes("user_ids=user") && res.status() === 200, - ), - userIdInput.fill("user"), - ]); + test("shows no users when the SSO ID matches nobody", async ({ page }) => { + await goToInternalUsers(page); - await Promise.all([ - page.waitForResponse( - (res) => - res.url().includes("/user/list") && - res.url().includes("user_ids=user") && - res.url().includes("sso_user_ids=sso") && - res.status() === 200, - ), - ssoIdInput.fill("sso"), - ]); - const combinedFilteredCount = await rows.count(); - await expect(combinedFilteredCount).toBeLessThan(initialCount); + await page.getByRole("button", { name: "Filters" }).click(); + await page.getByTestId("users-filter-sso-id").fill("e2e-sso-id-that-matches-nobody"); + await page.getByTestId("filter-drawer-apply").click(); + + await expect(page.getByText("No users found")).toBeVisible({ timeout: 30_000 }); + await expect(userRows(page).filter({ hasText: "noteam@test.local" })).toHaveCount(0); }); }); diff --git a/tests/e2e/ui/tests/users/viewInternalUsers.spec.ts b/tests/e2e/ui/tests/users/viewInternalUsers.spec.ts index 614191372d0..b46fb4d112a 100644 --- a/tests/e2e/ui/tests/users/viewInternalUsers.spec.ts +++ b/tests/e2e/ui/tests/users/viewInternalUsers.spec.ts @@ -1,54 +1,29 @@ -import { test, expect, Page } from "@playwright/test"; +import { test, expect, Page as PlaywrightPage } from "@playwright/test"; import { ADMIN_STORAGE_PATH } from "../../constants"; +import { Page } from "../../fixtures/pages"; +import { navigateToPage } from "../../helpers/navigation"; -test.skip("Internal Users Page", () => { +async function goToInternalUsers(page: PlaywrightPage) { + await navigateToPage(page, Page.Users); + await expect(page.getByRole("columnheader", { name: "User ID" })).toBeVisible({ timeout: 30_000 }); + await expect(userRows(page)).not.toHaveCount(0, { timeout: 30_000 }); +} + +const userRows = (page: PlaywrightPage) => page.getByRole("row").filter({ has: page.getByRole("cell") }); + +test.describe("Internal Users Page", () => { test.use({ storageState: ADMIN_STORAGE_PATH }); - async function goToInternalUsers(page: Page) { - await page.goto("/ui"); - - const internalUserTab = page.getByRole("menuitem", { name: "Internal User" }); - await expect(internalUserTab).toBeVisible(); - await internalUserTab.click(); - - const firstRow = page.locator("tbody tr").first(); - await expect(firstRow).toBeVisible(); - await expect(page.locator('[data-slot="skeleton"]')).toHaveCount(0); - } - - test("renders internal users table correctly", async ({ page }) => { + test("lists the seeded users under the identifying columns", async ({ page }) => { await goToInternalUsers(page); - const rows = page.locator("tbody tr"); - const rowCount = await rows.count(); - expect(rowCount).toBeGreaterThan(0); - - const userIdHeader = page.getByRole("columnheader", { name: "User ID" }); - await expect(userIdHeader).toBeVisible(); - - const virtualKeysHeader = page.getByRole("columnheader", { name: "Virtual Keys" }); - await expect(virtualKeysHeader).toBeVisible(); + await expect(page.getByRole("columnheader", { name: "User ID" })).toBeVisible(); + await expect(page.getByRole("columnheader", { name: "Virtual Keys" })).toBeVisible(); }); - test("pagination controls work correctly", async ({ page }) => { + test("cannot page backwards off the first page", async ({ page }) => { await goToInternalUsers(page); - const paginationInfo = page.locator(".text-sm.text-gray-700"); - const prevButton = page.getByRole("button", { name: "Previous" }); - const nextButton = page.getByRole("button", { name: "Next" }); - - const infoText = (await paginationInfo.textContent()) || ""; - - // On first page, Previous should be disabled - if (infoText.includes("1 -")) { - await expect(prevButton).toBeDisabled(); - } - - await page.waitForTimeout(1000); - // Check if there are more pages - const hasMorePages = infoText.includes("of") && !infoText.endsWith("25 of 25"); - if (hasMorePages) { - await expect(nextButton).toBeEnabled(); - } + await expect(page.getByRole("button", { name: "Go to previous page" })).toBeDisabled(); }); }); diff --git a/tests/litellm-proxy-extras/test_litellm_proxy_extras_utils.py b/tests/litellm-proxy-extras/test_litellm_proxy_extras_utils.py index 498d0cb4723..b3d457707b8 100644 --- a/tests/litellm-proxy-extras/test_litellm_proxy_extras_utils.py +++ b/tests/litellm-proxy-extras/test_litellm_proxy_extras_utils.py @@ -681,3 +681,25 @@ class TestSpendLogsPartitionDetectionSchemaScope: def test_only_partitioned_relations_match(self, monkeypatch): query, _ = self._detect(monkeypatch, "postgresql://u:p@localhost:5432/db") assert "pg_partitioned_table" in query + + +class TestSpendLogsPartitionDetectionMissingPsycopg: + """psycopg ships in the `extra_proxy` install, but a stripped-down image + can still lack it. When it does, detection must fail closed to False + (never crash the migration path) and say so loudly, because a silent + False here is what let a genuinely partitioned LiteLLM_SpendLogs hit the + unfiltered primary-key rewrite in production.""" + + def test_missing_psycopg_returns_false(self, monkeypatch): + monkeypatch.setitem(sys.modules, "psycopg", None) + monkeypatch.setenv("DATABASE_URL", "postgresql://u:p@localhost:5432/db") + assert ProxyExtrasDBManager.spend_logs_is_partitioned() is False + + def test_missing_psycopg_logs_a_warning(self, monkeypatch, caplog): + monkeypatch.setitem(sys.modules, "psycopg", None) + monkeypatch.setenv("DATABASE_URL", "postgresql://u:p@localhost:5432/db") + with caplog.at_level("WARNING", logger="litellm_proxy_extras"): + ProxyExtrasDBManager.spend_logs_is_partitioned() + assert any( + "psycopg is not installed" in record.message for record in caplog.records + ) diff --git a/tests/litellm-proxy-extras/test_setup_database_fail_fast.py b/tests/litellm-proxy-extras/test_setup_database_fail_fast.py new file mode 100644 index 00000000000..ef447315a8c --- /dev/null +++ b/tests/litellm-proxy-extras/test_setup_database_fail_fast.py @@ -0,0 +1,571 @@ +"""Regression tests for ProxyExtrasDBManager's v2 migration resolver. + +v2 is the proxy CLI default; v1 stays reachable via the `use_v2_resolver` +kwarg, which still defaults to False for direct callers. +""" + +import subprocess +from unittest.mock import patch + +import pytest + +from litellm_proxy_extras.utils import ( + _PRISMA_ATTEMPTS, + ProxyExtrasDBManager, + _max_migration_timestamp, + _migration_timestamp, +) + + +def _fake_migrate_deploy_failure(returncode: int, stderr: str): + def _run(*args, **kwargs): + raise subprocess.CalledProcessError( + returncode=returncode, + cmd=args[0], + stderr=stderr, + output="", + ) + + return _run + + +def test_v2_p3018_permission_error_raises_runtime_error(monkeypatch, tmp_path): + """v2: a permission failure during migrate deploy raises RuntimeError.""" + monkeypatch.setenv("DATABASE_URL", "postgresql://u:p@localhost:9/x") + monkeypatch.setattr( + ProxyExtrasDBManager, "_warn_if_db_ahead_of_head", lambda _: None + ) + monkeypatch.setattr(ProxyExtrasDBManager, "_get_prisma_dir", lambda: str(tmp_path)) + (tmp_path / "schema.prisma").write_text("// stub") + + stderr = ( + "Error: P3018\nMigration name: 20250326162113_baseline\n" + "Database error code: 42501\npermission denied for schema public" + ) + with patch("subprocess.run", side_effect=_fake_migrate_deploy_failure(1, stderr)): + with pytest.raises(RuntimeError, match="permission"): + ProxyExtrasDBManager.setup_database(use_migrate=True, use_v2_resolver=True) + + +def test_v2_non_idempotent_p3009_raises_runtime_error(monkeypatch, tmp_path): + """v2: a non-idempotent migration failure raises (no silent recovery).""" + monkeypatch.setenv("DATABASE_URL", "postgresql://u:p@localhost:9/x") + monkeypatch.setattr( + ProxyExtrasDBManager, "_warn_if_db_ahead_of_head", lambda _: None + ) + monkeypatch.setattr(ProxyExtrasDBManager, "_get_prisma_dir", lambda: str(tmp_path)) + (tmp_path / "schema.prisma").write_text("// stub") + + stderr = ( + "Error: P3009\nMigration `20260101000000_genuinely_broken` failed\n" + 'Reason: syntax error at or near "BRKN" LINE 42' + ) + with patch("subprocess.run", side_effect=_fake_migrate_deploy_failure(1, stderr)): + with pytest.raises(RuntimeError, match="cannot be auto-recovered"): + ProxyExtrasDBManager.setup_database(use_migrate=True, use_v2_resolver=True) + + +def test_strip_prisma_query_params_removes_connection_limit(): + """DATABASE_URLs with Prisma-specific params should be parseable by psycopg.""" + url = "postgresql://u:p@h:5432/db?connection_limit=100&pool_timeout=60&sslmode=require" + stripped = ProxyExtrasDBManager._strip_prisma_query_params(url) + assert "connection_limit" not in stripped + assert "pool_timeout" not in stripped + assert "sslmode=require" in stripped + + +def test_strip_prisma_query_params_passthrough_no_query(): + """URLs without query strings are returned unchanged.""" + url = "postgresql://u:p@h:5432/db" + assert ProxyExtrasDBManager._strip_prisma_query_params(url) == url + + +def test_migration_timestamp_extracts_leading_digits(): + assert _migration_timestamp("20260101000000_add_foo") == 20260101000000 + assert _migration_timestamp("20250326162113_baseline") == 20250326162113 + + +def test_migration_timestamp_returns_zero_on_malformed(): + assert _migration_timestamp("0_init") == 0 + assert _migration_timestamp("not_a_migration") == 0 + + +def test_max_migration_timestamp(): + names = {"20250326000000_a", "20260415000000_b", "20251115000000_c"} + assert _max_migration_timestamp(names) == 20260415000000 + + +def test_max_migration_timestamp_empty_set(): + assert _max_migration_timestamp(set()) == 0 + + +def test_v1_default_still_calls_resolve_all_migrations(monkeypatch, tmp_path): + """v1 (default) continues to call _resolve_all_migrations on the happy path. + + This is the existing buggy behavior — we're not fixing it in v1, only + offering v2 as opt-in. This test pins the default so that a future + inadvertent default flip is caught. + """ + monkeypatch.setattr(ProxyExtrasDBManager, "_get_prisma_dir", lambda: str(tmp_path)) + (tmp_path / "schema.prisma").write_text("// stub") + + # Stub `prisma migrate deploy` to claim success with pending migrations + # applied, which is the code path that triggers the legacy post-migration + # sanity check (a call to _resolve_all_migrations). + class FakeResult: + stdout = "Applied migration.\n" + stderr = "" + + def fake_run(cmd, *args, **kwargs): + return FakeResult() + + resolve_called = {"n": 0} + + def fake_resolve(*args, **kwargs): + resolve_called["n"] += 1 + + monkeypatch.setattr("subprocess.run", fake_run) + monkeypatch.setattr(ProxyExtrasDBManager, "_resolve_all_migrations", fake_resolve) + + ok = ProxyExtrasDBManager.setup_database(use_migrate=True) # v2 flag NOT set + assert ok is True + assert resolve_called["n"] == 1, "v1 default should still invoke the legacy path" + + +def test_v2_db_push_wraps_subprocess_error_as_runtime_error(monkeypatch, tmp_path): + """v2: a failing `prisma db push` must raise RuntimeError, not leak + CalledProcessError past proxy_cli.py's `except RuntimeError`.""" + monkeypatch.setattr(ProxyExtrasDBManager, "_get_prisma_dir", lambda: str(tmp_path)) + (tmp_path / "schema.prisma").write_text("// stub") + + stderr = "db push error" + with patch("subprocess.run", side_effect=_fake_migrate_deploy_failure(1, stderr)): + with pytest.raises(RuntimeError, match="prisma db push failed"): + ProxyExtrasDBManager.setup_database(use_migrate=False, use_v2_resolver=True) + + +def test_v2_warn_ahead_of_head_swallows_db_errors(monkeypatch, tmp_path): + """_warn_if_db_ahead_of_head must never raise — it's informational. + + Non-connection DB errors (e.g. InsufficientPrivilege from a user + without SELECT on _prisma_migrations) must be caught, not propagated. + """ + import psycopg + + monkeypatch.setenv("DATABASE_URL", "postgresql://u:p@localhost:9/x") + monkeypatch.setattr(ProxyExtrasDBManager, "_get_prisma_dir", lambda: str(tmp_path)) + (tmp_path / "schema.prisma").write_text("// stub") + + class _FakeConn: + def __enter__(self): + return self + + def __exit__(self, *a): + return False + + def execute(self, *a, **kw): + # Simulate an InsufficientPrivilege (subclass of DatabaseError). + raise psycopg.errors.InsufficientPrivilege("permission denied") + + connects = {"n": 0} + + def _fake_connect(*a, **kw): + connects["n"] += 1 + return _FakeConn() + + monkeypatch.setattr("psycopg.connect", _fake_connect) + + assert ProxyExtrasDBManager._warn_if_db_ahead_of_head(str(tmp_path)) is None + assert connects["n"] == 1, "the failing query must actually have been reached" + + +def test_v2_resolve_specific_migration_failure_raises_runtime_error( + monkeypatch, tmp_path +): + """If marking a migration as applied fails inside P3009 idempotent + recovery, the subprocess error must be re-raised as RuntimeError so + proxy_cli.py catches it cleanly (instead of leaking CalledProcessError).""" + monkeypatch.setattr( + ProxyExtrasDBManager, "_warn_if_db_ahead_of_head", lambda _: None + ) + monkeypatch.setattr(ProxyExtrasDBManager, "_get_prisma_dir", lambda: str(tmp_path)) + (tmp_path / "schema.prisma").write_text("// stub") + monkeypatch.setattr( + ProxyExtrasDBManager, "_roll_back_migration", lambda *a, **kw: None + ) + + # First call: migrate deploy -> P3009 idempotent error. + # Recovery path tries _resolve_specific_migration; that also raises. + def _failing_resolve(*a, **kw): + raise subprocess.CalledProcessError( + returncode=1, + cmd="prisma migrate resolve --applied", + stderr="resolve failed", + output="", + ) + + monkeypatch.setattr( + ProxyExtrasDBManager, "_resolve_specific_migration", _failing_resolve + ) + + stderr = ( + "Error: P3009\nMigration `20260101000000_some_migration` failed\n" + "relation already exists" + ) + with patch("subprocess.run", side_effect=_fake_migrate_deploy_failure(1, stderr)): + with pytest.raises( + RuntimeError, match=r"Failed to mark migration .* as applied" + ): + ProxyExtrasDBManager.setup_database(use_migrate=True, use_v2_resolver=True) + + +def test_v2_does_not_call_resolve_all_migrations(monkeypatch, tmp_path): + """v2 must never call _resolve_all_migrations — that's the bug it fixes.""" + monkeypatch.setattr( + ProxyExtrasDBManager, "_warn_if_db_ahead_of_head", lambda _: None + ) + monkeypatch.setattr(ProxyExtrasDBManager, "_get_prisma_dir", lambda: str(tmp_path)) + (tmp_path / "schema.prisma").write_text("// stub") + + class FakeResult: + stdout = "Applied migration.\n" + stderr = "" + + monkeypatch.setattr("subprocess.run", lambda *a, **kw: FakeResult()) + + resolve_called = {"n": 0} + monkeypatch.setattr( + ProxyExtrasDBManager, + "_resolve_all_migrations", + lambda *a, **kw: resolve_called.__setitem__("n", resolve_called["n"] + 1), + ) + + ok = ProxyExtrasDBManager.setup_database(use_migrate=True, use_v2_resolver=True) + assert ok is True + assert resolve_called["n"] == 0, "v2 must not invoke the diff-and-force recovery" + + +_DEADLOCK_STDERR = ( + "Error: ERROR: deadlock detected\n" + "DETAIL: Process 277 waits for ExclusiveLock on advisory lock " + "[17556,0,72707369,1]; blocked by process 278.\n" + "Process 278 waits for ShareLock on virtual transaction 3/1041; " + "blocked by process 277." +) + + +class _DeployApplied: + stdout = "All migrations have been successfully applied." + stderr = "" + returncode = 0 + + +def _deploy_only(deploy_side_effect): + """subprocess.run stand-in that only intercepts `prisma migrate deploy`. + + Scoped by argv so the Prisma toolchain check cannot consume the mock first. + """ + deploys = {"n": 0} + + def _run(*args, **kwargs): + cmd = args[0] if args else kwargs.get("args", []) + if list(cmd)[-2:] == ["migrate", "deploy"]: + deploys["n"] += 1 + return deploy_side_effect(deploys["n"], cmd) + return _DeployApplied() + + return _run, deploys + + +def _prepare_v2_resolver(monkeypatch, tmp_path): + monkeypatch.setenv("DATABASE_URL", "postgresql://u:p@localhost:9/x") + monkeypatch.setattr( + ProxyExtrasDBManager, "_warn_if_db_ahead_of_head", lambda _: None + ) + monkeypatch.setattr(ProxyExtrasDBManager, "_get_prisma_dir", lambda: str(tmp_path)) + (tmp_path / "schema.prisma").write_text("// stub") + monkeypatch.setattr("time.sleep", lambda *_a, **_k: None) + + +def test_v2_retries_transient_advisory_lock_deadlock(monkeypatch, tmp_path): + """v2: replicas racing `migrate deploy` deadlock on Prisma's advisory + lock, which is transient and must be retried rather than kill the boot.""" + _prepare_v2_resolver(monkeypatch, tmp_path) + + def _side_effect(n, cmd): + if n == 1: + raise subprocess.CalledProcessError( + returncode=1, cmd=cmd, stderr=_DEADLOCK_STDERR, output="" + ) + return _DeployApplied() + + run, deploys = _deploy_only(_side_effect) + with patch("subprocess.run", side_effect=run): + ok = ProxyExtrasDBManager.setup_database(use_migrate=True, use_v2_resolver=True) + + assert ok is True + assert deploys["n"] == 2, "the deadlocked deploy must be retried, not raised" + + +def test_v2_persistent_advisory_lock_deadlock_eventually_raises(monkeypatch, tmp_path): + """v2: the deadlock retry is bounded, so a deadlock that never clears + still raises instead of looping or reporting success.""" + _prepare_v2_resolver(monkeypatch, tmp_path) + + def _side_effect(n, cmd): + raise subprocess.CalledProcessError( + returncode=1, cmd=cmd, stderr=_DEADLOCK_STDERR, output="" + ) + + run, deploys = _deploy_only(_side_effect) + with patch("subprocess.run", side_effect=run): + with pytest.raises(RuntimeError, match="after 4 attempts"): + ProxyExtrasDBManager.setup_database(use_migrate=True, use_v2_resolver=True) + + assert deploys["n"] == 4 + + +@pytest.mark.parametrize( + "stderr", + [ + "Error: P1001: Can't reach database server at `db`:`5432`", + "Error: P1002: The database server was reached but timed out.", + ], +) +def test_v2_retries_transient_database_connectivity_errors(monkeypatch, tmp_path, stderr): + """v2: a database not accepting connections yet is retried, not fatal.""" + _prepare_v2_resolver(monkeypatch, tmp_path) + + def _side_effect(n, cmd): + if n == 1: + raise subprocess.CalledProcessError( + returncode=1, cmd=cmd, stderr=stderr, output="" + ) + return _DeployApplied() + + run, deploys = _deploy_only(_side_effect) + with patch("subprocess.run", side_effect=run): + ok = ProxyExtrasDBManager.setup_database(use_migrate=True, use_v2_resolver=True) + + assert ok is True + assert deploys["n"] == 2, "an unreachable database must be retried, not raised" + + +def test_v2_unreachable_database_still_fails_after_the_retries(monkeypatch, tmp_path): + """v2: a genuinely unreachable database still raises once the attempts + are spent, rather than passing as a successful migration.""" + _prepare_v2_resolver(monkeypatch, tmp_path) + + def _side_effect(n, cmd): + raise subprocess.CalledProcessError( + returncode=1, + cmd=cmd, + stderr="Error: P1001: Can't reach database server at `db`:`5432`", + output="", + ) + + run, deploys = _deploy_only(_side_effect) + with patch("subprocess.run", side_effect=run): + with pytest.raises(RuntimeError, match="after 4 attempts"): + ProxyExtrasDBManager.setup_database(use_migrate=True, use_v2_resolver=True) + + assert deploys["n"] == 4 + + +def test_v2_exhausted_retries_report_the_prisma_error(monkeypatch, tmp_path, caplog): + """v2: retrying must not swallow Prisma's stderr, which is captured and is + the only place the cause appears for an operator or a boot-log grep.""" + _prepare_v2_resolver(monkeypatch, tmp_path) + stderr = "Error: P1001: Can't reach database server at `wrong`:`5432`" + + def _side_effect(n, cmd): + raise subprocess.CalledProcessError( + returncode=1, cmd=cmd, stderr=stderr, output="" + ) + + run, _ = _deploy_only(_side_effect) + with caplog.at_level("INFO", logger="litellm_proxy_extras"): + with patch("subprocess.run", side_effect=run): + with pytest.raises(RuntimeError) as exc_info: + ProxyExtrasDBManager.setup_database( + use_migrate=True, use_v2_resolver=True + ) + + assert "P1001" in str(exc_info.value) + assert "P1001" in caplog.text + + +def test_v2_db_push_retries_transient_failures(monkeypatch, tmp_path): + """v2: `prisma db push` retries a transient failure like v1 did. + + Reached from the migrations Job (USE_PRISMA_DB_PUSH=true), not from the + proxy CLI, whose --use_prisma_db_push has its own loop in prisma_client. + """ + _prepare_v2_resolver(monkeypatch, tmp_path) + + pushes = {"n": 0} + + def _run(*args, **kwargs): + cmd = list(args[0] if args else kwargs.get("args", [])) + if cmd[-3:] != ["db", "push", "--accept-data-loss"]: + return _DeployApplied() + pushes["n"] += 1 + if pushes["n"] == 1: + raise subprocess.CalledProcessError( + returncode=1, + cmd=cmd, + stderr="Error: P1001: Can't reach database server at `db`:`5432`", + output="", + ) + return _DeployApplied() + + monkeypatch.setattr( + ProxyExtrasDBManager, "spend_logs_is_partitioned", lambda: False + ) + with patch("subprocess.run", side_effect=_run): + ok = ProxyExtrasDBManager.setup_database(use_migrate=False, use_v2_resolver=True) + + assert ok is True + assert pushes["n"] == 2 + + +def test_v2_db_push_retries_are_bounded_and_report_the_prisma_error( + monkeypatch, tmp_path +): + """v2: a database that never comes back stops after _PRISMA_ATTEMPTS and + surfaces the prisma error, rather than retrying the boot forever.""" + _prepare_v2_resolver(monkeypatch, tmp_path) + + pushes = {"n": 0} + + def _run(*args, **kwargs): + cmd = list(args[0] if args else kwargs.get("args", [])) + if cmd[-3:] != ["db", "push", "--accept-data-loss"]: + return _DeployApplied() + pushes["n"] += 1 + raise subprocess.CalledProcessError( + returncode=1, + cmd=cmd, + stderr="Error: P1001: Can't reach database server at `db`:`5432`", + output="", + ) + + monkeypatch.setattr( + ProxyExtrasDBManager, "spend_logs_is_partitioned", lambda: False + ) + with patch("subprocess.run", side_effect=_run): + with pytest.raises(RuntimeError) as exc: + ProxyExtrasDBManager.setup_database( + use_migrate=False, use_v2_resolver=True + ) + + assert pushes["n"] == _PRISMA_ATTEMPTS + assert "P1001" in str(exc.value) + + +def _db_push_only(push_side_effect): + """subprocess.run stand-in that only intercepts `prisma db push`.""" + pushes = {"n": 0} + + def _run(*args, **kwargs): + cmd = list(args[0] if args else kwargs.get("args", [])) + if cmd[-3:] != ["db", "push", "--accept-data-loss"]: + return _DeployApplied() + pushes["n"] += 1 + return push_side_effect(pushes["n"], cmd) + + return _run, pushes + + +def _timed_out_for_real(): + """Capture what subprocess.run really puts on a TimeoutExpired. + + Under text=True it still leaves stderr as bytes, unlike CalledProcessError, + so hardcoding a str here would test a shape production never sees. Derived + at import, before any test patches subprocess.run. + """ + try: + subprocess.run( + ["sh", "-c", "echo 'Error: P1001 unreachable' >&2; sleep 5"], + timeout=0.2, + check=True, + capture_output=True, + text=True, + ) + except subprocess.TimeoutExpired as e: + return e + raise AssertionError("the helper command was supposed to time out") + + +_TIMEOUT_TEMPLATE = _timed_out_for_real() + + +def _real_timeout_expired(cmd): + return subprocess.TimeoutExpired( + cmd=cmd, + timeout=_TIMEOUT_TEMPLATE.timeout, + output=_TIMEOUT_TEMPLATE.stdout, + stderr=_TIMEOUT_TEMPLATE.stderr, + ) + + +def test_v2_db_push_retries_a_timeout(monkeypatch, tmp_path): + """v2: a `prisma db push` that times out is retried, not turned into a + TypeError by classifying its bytes stderr as if it were text.""" + _prepare_v2_resolver(monkeypatch, tmp_path) + + def _side_effect(n, cmd): + if n == 1: + raise _real_timeout_expired(cmd) + return _DeployApplied() + + monkeypatch.setattr( + ProxyExtrasDBManager, "spend_logs_is_partitioned", lambda: False + ) + run, pushes = _db_push_only(_side_effect) + with patch("subprocess.run", side_effect=run): + ok = ProxyExtrasDBManager.setup_database(use_migrate=False, use_v2_resolver=True) + + assert ok is True + assert pushes["n"] == 2 + + +def test_v2_db_push_timeouts_are_bounded(monkeypatch, tmp_path): + """v2: a `prisma db push` that never stops timing out gives up as a + RuntimeError, which is the only exception proxy_cli.py exits cleanly on.""" + _prepare_v2_resolver(monkeypatch, tmp_path) + + def _side_effect(n, cmd): + raise _real_timeout_expired(cmd) + + monkeypatch.setattr( + ProxyExtrasDBManager, "spend_logs_is_partitioned", lambda: False + ) + run, pushes = _db_push_only(_side_effect) + with patch("subprocess.run", side_effect=run): + with pytest.raises(RuntimeError, match=r"prisma db push failed after \d+"): + ProxyExtrasDBManager.setup_database(use_migrate=False, use_v2_resolver=True) + + assert pushes["n"] == _PRISMA_ATTEMPTS + + +def test_v2_unclassified_failure_is_not_treated_as_transient(monkeypatch, tmp_path): + """v2: an unrecognised deploy failure still raises on the first attempt.""" + _prepare_v2_resolver(monkeypatch, tmp_path) + + def _side_effect(n, cmd): + raise subprocess.CalledProcessError( + returncode=1, + cmd=cmd, + stderr="Error: relation \"LiteLLM_SpendLogs\" does not exist", + output="", + ) + + run, deploys = _deploy_only(_side_effect) + with patch("subprocess.run", side_effect=run): + with pytest.raises(RuntimeError, match="cannot be auto-recovered"): + ProxyExtrasDBManager.setup_database(use_migrate=True, use_v2_resolver=True) + + assert deploys["n"] == 1 + + diff --git a/tests/llm_translation/test_litellm_proxy_provider.py b/tests/llm_translation/test_litellm_proxy_provider.py index 1cb805bf9ba..8630259877d 100644 --- a/tests/llm_translation/test_litellm_proxy_provider.py +++ b/tests/llm_translation/test_litellm_proxy_provider.py @@ -5,6 +5,7 @@ from io import BytesIO from unittest.mock import AsyncMock +import httpx import litellm from litellm import completion, embedding import pytest @@ -92,44 +93,54 @@ async def test_litellm_gateway_from_sdk_embedding(is_async): litellm.set_verbose = True litellm._turn_on_debug() + captured_bodies = [] + + def handler(request: httpx.Request) -> httpx.Response: + captured_bodies.append(json.loads(request.content)) + return httpx.Response( + 200, + json={ + "object": "list", + "data": [{"object": "embedding", "index": 0, "embedding": [0.1, 0.2, 0.3]}], + "model": "my-vllm-model", + "usage": {"prompt_tokens": 2, "total_tokens": 2}, + }, + ) + if is_async: from openai import AsyncOpenAI - openai_client = AsyncOpenAI(api_key="fake-key") - mock_method = AsyncMock() - patch_target = openai_client.embeddings.create + openai_client = AsyncOpenAI( + api_key="fake-key", + http_client=httpx.AsyncClient(transport=httpx.MockTransport(handler)), + ) + response = await litellm.aembedding( + model="litellm_proxy/my-vllm-model", + input="Hello world", + client=openai_client, + api_base="my-custom-api-base", + ) else: from openai import OpenAI - openai_client = OpenAI(api_key="fake-key") - mock_method = MagicMock() - patch_target = openai_client.embeddings.create + openai_client = OpenAI( + api_key="fake-key", + http_client=httpx.Client(transport=httpx.MockTransport(handler)), + ) + response = litellm.embedding( + model="litellm_proxy/my-vllm-model", + input="Hello world", + client=openai_client, + api_base="my-custom-api-base", + ) - with patch.object(patch_target.__self__, patch_target.__name__, new=mock_method): - try: - if is_async: - await litellm.aembedding( - model="litellm_proxy/my-vllm-model", - input="Hello world", - client=openai_client, - api_base="my-custom-api-base", - ) - else: - litellm.embedding( - model="litellm_proxy/my-vllm-model", - input="Hello world", - client=openai_client, - api_base="my-custom-api-base", - ) - except Exception as e: - print(e) + request_body = captured_bodies[0] + print("Request body - {}".format(request_body)) - mock_method.assert_called_once() - - print("Call KWARGS - {}".format(mock_method.call_args.kwargs)) - - assert "Hello world" == mock_method.call_args.kwargs["input"] - assert "my-vllm-model" == mock_method.call_args.kwargs["model"] + assert "Hello world" == request_body["input"] + assert "my-vllm-model" == request_body["model"] + assert "encoding_format" not in request_body + assert response.data[0]["embedding"] == [0.1, 0.2, 0.3] @pytest.mark.parametrize("is_async", [False, True]) diff --git a/tests/llm_translation/test_nvidia_nim.py b/tests/llm_translation/test_nvidia_nim.py index 7ee4f347f72..d5942e674d0 100644 --- a/tests/llm_translation/test_nvidia_nim.py +++ b/tests/llm_translation/test_nvidia_nim.py @@ -63,27 +63,39 @@ def test_embedding_nvidia_nim(): litellm.set_verbose = True from openai import OpenAI + captured_bodies = [] + + def handler(request: httpx.Request) -> httpx.Response: + captured_bodies.append(json.loads(request.content)) + return httpx.Response( + 200, + json={ + "object": "list", + "data": [{"object": "embedding", "index": 0, "embedding": [0.1, 0.2, 0.3]}], + "model": "nvidia/nv-embedqa-e5-v5", + "usage": {"prompt_tokens": 6, "total_tokens": 6}, + }, + ) + client = OpenAI( api_key="fake-api-key", + http_client=httpx.Client(transport=httpx.MockTransport(handler)), ) - with patch.object(client.embeddings.with_raw_response, "create") as mock_client: - try: - litellm.embedding( - model="nvidia_nim/nvidia/nv-embedqa-e5-v5", - input="What is the meaning of life?", - input_type="passage", - dimensions=1024, - client=client, - ) - except Exception as e: - print(e) - mock_client.assert_called_once() - request_body = mock_client.call_args.kwargs - print("request_body: ", request_body) - assert request_body["input"] == "What is the meaning of life?" - assert request_body["model"] == "nvidia/nv-embedqa-e5-v5" - assert request_body["extra_body"]["input_type"] == "passage" - assert request_body["dimensions"] == 1024 + response = litellm.embedding( + model="nvidia_nim/nvidia/nv-embedqa-e5-v5", + input="What is the meaning of life?", + input_type="passage", + dimensions=1024, + client=client, + ) + request_body = captured_bodies[0] + print("request_body: ", request_body) + assert request_body["input"] == "What is the meaning of life?" + assert request_body["model"] == "nvidia/nv-embedqa-e5-v5" + assert request_body["input_type"] == "passage" + assert request_body["dimensions"] == 1024 + assert "encoding_format" not in request_body + assert response.data[0]["embedding"] == [0.1, 0.2, 0.3] def test_chat_completion_nvidia_nim_with_tools(): diff --git a/tests/local_testing/test_basic_python_version.py b/tests/local_testing/test_basic_python_version.py index fb06ed6b69d..506c58d26b4 100644 --- a/tests/local_testing/test_basic_python_version.py +++ b/tests/local_testing/test_basic_python_version.py @@ -305,14 +305,16 @@ def _run_proxy_server_smoke_test(extra_proxy_args=None): def test_litellm_proxy_server_config_no_general_settings(): - """Exercises the default (v1) migration resolver.""" + """Exercises the default (v2) migration resolver.""" _run_proxy_server_smoke_test() -def test_litellm_proxy_server_config_no_general_settings_v2_resolver(): - """Exercises the opt-in v2 migration resolver. +def test_litellm_proxy_server_config_no_general_settings_legacy_resolver(): + """Exercises the legacy (v1) migration resolver against a real database. - Runs in a separate CI job against a local Postgres to avoid collisions - with the v1 variant when they share a database. + v2 is the default, so the no-arg test above already covers it. This one is + the only place the v1 opt-out gets real-DB migration plus proxy-boot + coverage, and it runs in a separate CI job against its own Postgres to + avoid collisions with the default variant. """ - _run_proxy_server_smoke_test(extra_proxy_args=["--use_v2_migration_resolver"]) + _run_proxy_server_smoke_test(extra_proxy_args=["--use_legacy_migration_resolver"]) diff --git a/tests/local_testing/test_embedding.py b/tests/local_testing/test_embedding.py index aed2849f056..ee2ac14f498 100644 --- a/tests/local_testing/test_embedding.py +++ b/tests/local_testing/test_embedding.py @@ -3,6 +3,8 @@ import os import re import traceback +import httpx + import openai import pytest from dotenv import load_dotenv @@ -1255,56 +1257,42 @@ def test_jina_ai_img_embeddings(input_data, expected_payload_input): assert sent_data["input"] == expected_payload_input -def test_encoding_format_defaults_to_float_for_openai_sdk(monkeypatch): +def test_encoding_format_omitted_by_default_for_openai_sdk(monkeypatch): """ - When encoding_format is not provided, LiteLLM sends `float` for OpenAI-path embeddings. + When encoding_format is not provided, LiteLLM leaves it out of the upstream request. Optional global override: `LITELLM_DEFAULT_EMBEDDING_ENCODING_FORMAT`. """ monkeypatch.delenv("LITELLM_DEFAULT_EMBEDDING_ENCODING_FORMAT", raising=False) - with patch( - "litellm.llms.openai.openai.OpenAIChatCompletion._get_openai_client" - ) as mock_get_client: - # Create a mock client instance - mock_client_instance = MagicMock() - mock_get_client.return_value = mock_client_instance + captured_bodies = [] - # Mock the embeddings.with_raw_response.create method - mock_response = MagicMock() - mock_response.parse.return_value = MagicMock( - model_dump=lambda: { - "data": [{"embedding": [0.1, 0.2, 0.3], "index": 0}], - "model": "text-embedding-ada-002", + def handler(request: httpx.Request) -> httpx.Response: + captured_bodies.append(json.loads(request.content)) + return httpx.Response( + 200, + json={ "object": "list", + "data": [{"object": "embedding", "index": 0, "embedding": [0.1, 0.2, 0.3]}], + "model": "text-embedding-ada-002", "usage": {"prompt_tokens": 1, "total_tokens": 1}, - } - ) - mock_response.headers = {} - - mock_client_instance.embeddings.with_raw_response.create.return_value = ( - mock_response + }, ) - # Call the embedding function without encoding_format - response = embedding( - model="text-embedding-ada-002", - input="Hello world", - ) + client = openai.OpenAI( + api_key="sk-test", http_client=httpx.Client(transport=httpx.MockTransport(handler)) + ) - # Get the call arguments to verify what was sent to OpenAI SDK - call_args = mock_client_instance.embeddings.with_raw_response.create.call_args - assert ( - call_args is not None - ), "OpenAI SDK embeddings.create should have been called" + response = embedding( + model="text-embedding-ada-002", + input="Hello world", + api_key="sk-test", + client=client, + ) - call_kwargs = call_args[1] # Get kwargs - - assert "encoding_format" in call_kwargs - assert ( - call_kwargs["encoding_format"] == "float" - ), "encoding_format should default to float when not provided by user" - - print("✅ PASS: encoding_format='float' is correctly passed to OpenAI SDK") + assert response.data[0]["embedding"] == [0.1, 0.2, 0.3] + assert "encoding_format" not in captured_bodies[0], ( + "encoding_format should be omitted from the upstream request when not provided by user" + ) def test_encoding_format_explicit_value_preserved(): diff --git a/tests/local_testing/test_exceptions.py b/tests/local_testing/test_exceptions.py index 8370046446d..e6392cda406 100644 --- a/tests/local_testing/test_exceptions.py +++ b/tests/local_testing/test_exceptions.py @@ -5,7 +5,7 @@ import traceback from typing import Any import httpx -from openai import AsyncOpenAI, AuthenticationError, BadRequestError, OpenAIError, RateLimitError +from openai import AsyncAzureOpenAI, AsyncOpenAI, AuthenticationError, AzureOpenAI, BadRequestError, OpenAIError, RateLimitError from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler @@ -895,7 +895,12 @@ def _pre_call_utils( ): if call_type == "embedding": data["input"] = "Hello world!" - mapped_target: Any = client.embeddings.with_raw_response + if isinstance(client, (AzureOpenAI, AsyncAzureOpenAI)): + mapped_target: Any = client.embeddings.with_raw_response + patched_attr = "create" + else: + mapped_target = client + patched_attr = "post" if sync_mode: original_function = litellm.embedding else: @@ -905,6 +910,7 @@ def _pre_call_utils( if streaming is True: data["stream"] = True mapped_target = client.chat.completions.with_raw_response # type: ignore + patched_attr = "create" if sync_mode: original_function = litellm.completion else: @@ -914,12 +920,13 @@ def _pre_call_utils( if streaming is True: data["stream"] = True mapped_target = client.completions.with_raw_response # type: ignore + patched_attr = "create" if sync_mode: original_function = litellm.text_completion else: original_function = litellm.atext_completion - return data, original_function, mapped_target + return data, original_function, mapped_target, patched_attr def _pre_call_utils_httpx( @@ -1003,7 +1010,7 @@ async def test_exception_with_headers(sync_mode, provider, model, call_type, str ) data = {"model": model} - data, original_function, mapped_target = _pre_call_utils( + data, original_function, mapped_target, patched_attr = _pre_call_utils( call_type=call_type, data=data, client=openai_client, @@ -1049,7 +1056,7 @@ async def test_exception_with_headers(sync_mode, provider, model, call_type, str with patch.object( mapped_target, - "create", + patched_attr, side_effect=_return_exception, ): new_retry_after_mock_client = MagicMock(return_value=-1) diff --git a/tests/local_testing/test_router.py b/tests/local_testing/test_router.py index 370c43f8f44..c714bb4f9a7 100644 --- a/tests/local_testing/test_router.py +++ b/tests/local_testing/test_router.py @@ -2032,8 +2032,8 @@ def test_router_dynamic_cooldown_correct_retry_after_time(): raise exception with patch.object( - openai_client.embeddings.with_raw_response, - "create", + openai_client, + "post", side_effect=_return_exception, ): new_retry_after_mock_client = MagicMock(return_value=-1) diff --git a/tests/logging_callback_tests/gcs_pub_sub_body/spend_logs_payload.json b/tests/logging_callback_tests/gcs_pub_sub_body/spend_logs_payload.json index 1838fb16e91..28912a27501 100644 --- a/tests/logging_callback_tests/gcs_pub_sub_body/spend_logs_payload.json +++ b/tests/logging_callback_tests/gcs_pub_sub_body/spend_logs_payload.json @@ -11,7 +11,7 @@ "user": "", "team_id": "", "organization_id": "", - "metadata": "{\"applied_guardrails\": [], \"attempted_fallbacks\": null, \"original_model_group\": null, \"batch_models\": null, \"batch_successful_requests\": null, \"batch_failed_requests\": null, \"mcp_tool_call_metadata\": null, \"vector_store_request_metadata\": null, \"routing_decision\": null, \"internal_call_origin\": null, \"guardrail_information\": null, \"compression_savings\": null, \"litellm_gateway_injected_cache\": null, \"usage_object\": {\"completion_tokens\": 20, \"prompt_tokens\": 10, \"total_tokens\": 30, \"completion_tokens_details\": null, \"prompt_tokens_details\": null}, \"model_map_information\": {\"model_map_key\": \"gpt-4o\", \"model_map_value\": {\"key\": \"gpt-4o\", \"max_tokens\": 16384, \"max_input_tokens\": 128000, \"max_output_tokens\": 16384, \"input_cost_per_token\": 2.5e-06, \"cache_creation_input_token_cost\": null, \"cache_read_input_token_cost\": 1.25e-06, \"input_cost_per_character\": null, \"input_cost_per_token_above_128k_tokens\": null, \"input_cost_per_token_above_200k_tokens\": null, \"input_cost_per_query\": null, \"input_cost_per_second\": null, \"input_cost_per_audio_token\": null, \"input_cost_per_token_batches\": 1.25e-06, \"output_cost_per_token_batches\": 5e-06, \"output_cost_per_token\": 1e-05, \"output_cost_per_audio_token\": null, \"output_cost_per_character\": null, \"output_cost_per_token_above_128k_tokens\": null, \"output_cost_per_character_above_128k_tokens\": null, \"output_cost_per_token_above_200k_tokens\": null, \"output_cost_per_second\": null, \"output_cost_per_image\": null, \"output_vector_size\": null, \"litellm_provider\": \"openai\", \"mode\": \"chat\", \"supports_system_messages\": true, \"supports_response_schema\": true, \"supports_vision\": true, \"supports_function_calling\": true, \"supports_tool_choice\": true, \"supports_assistant_prefill\": false, \"supports_prompt_caching\": true, \"supports_audio_input\": false, \"supports_audio_output\": false, \"supports_pdf_input\": false, \"supports_embedding_image_input\": false, \"supports_native_streaming\": null, \"supports_web_search\": true, \"supports_reasoning\": false, \"search_context_cost_per_query\": {\"search_context_size_low\": 0.03, \"search_context_size_medium\": 0.035, \"search_context_size_high\": 0.05}, \"tpm\": null, \"rpm\": null, \"supported_openai_params\": [\"frequency_penalty\", \"logit_bias\", \"logprobs\", \"top_logprobs\", \"max_tokens\", \"max_completion_tokens\", \"modalities\", \"prediction\", \"n\", \"presence_penalty\", \"seed\", \"stop\", \"stream\", \"stream_options\", \"temperature\", \"top_p\", \"tools\", \"tool_choice\", \"function_call\", \"functions\", \"max_retries\", \"extra_headers\", \"parallel_tool_calls\", \"audio\", \"response_format\", \"user\"]}}, \"additional_usage_values\": {\"completion_tokens_details\": null, \"prompt_tokens_details\": null}, \"user_api_key\": null, \"user_api_key_alias\": null, \"user_api_key_team_id\": null, \"user_api_key_project_id\": null, \"user_api_key_project_alias\": null, \"user_api_key_org_id\": null, \"user_api_key_user_id\": null, \"user_api_key_team_alias\": null, \"spend_logs_metadata\": null, \"requester_ip_address\": null, \"status\": null, \"proxy_server_request\": null, \"error_information\": null, \"attempted_retries\": null, \"max_retries\": null}", + "metadata": "{\"applied_guardrails\": [], \"attempted_fallbacks\": null, \"original_model_group\": null, \"batch_models\": null, \"batch_successful_requests\": null, \"batch_failed_requests\": null, \"mcp_tool_call_metadata\": null, \"vector_store_request_metadata\": null, \"routing_decision\": null, \"internal_call_origin\": null, \"router_metadata\": null, \"guardrail_information\": null, \"compression_savings\": null, \"litellm_gateway_injected_cache\": null, \"usage_object\": {\"completion_tokens\": 20, \"prompt_tokens\": 10, \"total_tokens\": 30, \"completion_tokens_details\": null, \"prompt_tokens_details\": null}, \"model_map_information\": {\"model_map_key\": \"gpt-4o\", \"model_map_value\": {\"key\": \"gpt-4o\", \"max_tokens\": 16384, \"max_input_tokens\": 128000, \"max_output_tokens\": 16384, \"input_cost_per_token\": 2.5e-06, \"cache_creation_input_token_cost\": null, \"cache_read_input_token_cost\": 1.25e-06, \"input_cost_per_character\": null, \"input_cost_per_token_above_128k_tokens\": null, \"input_cost_per_token_above_200k_tokens\": null, \"input_cost_per_query\": null, \"input_cost_per_second\": null, \"input_cost_per_audio_token\": null, \"input_cost_per_token_batches\": 1.25e-06, \"output_cost_per_token_batches\": 5e-06, \"output_cost_per_token\": 1e-05, \"output_cost_per_audio_token\": null, \"output_cost_per_character\": null, \"output_cost_per_token_above_128k_tokens\": null, \"output_cost_per_character_above_128k_tokens\": null, \"output_cost_per_token_above_200k_tokens\": null, \"output_cost_per_second\": null, \"output_cost_per_image\": null, \"output_vector_size\": null, \"litellm_provider\": \"openai\", \"mode\": \"chat\", \"supports_system_messages\": true, \"supports_response_schema\": true, \"supports_vision\": true, \"supports_function_calling\": true, \"supports_tool_choice\": true, \"supports_assistant_prefill\": false, \"supports_prompt_caching\": true, \"supports_audio_input\": false, \"supports_audio_output\": false, \"supports_pdf_input\": false, \"supports_embedding_image_input\": false, \"supports_native_streaming\": null, \"supports_web_search\": true, \"supports_reasoning\": false, \"search_context_cost_per_query\": {\"search_context_size_low\": 0.03, \"search_context_size_medium\": 0.035, \"search_context_size_high\": 0.05}, \"tpm\": null, \"rpm\": null, \"supported_openai_params\": [\"frequency_penalty\", \"logit_bias\", \"logprobs\", \"top_logprobs\", \"max_tokens\", \"max_completion_tokens\", \"modalities\", \"prediction\", \"n\", \"presence_penalty\", \"seed\", \"stop\", \"stream\", \"stream_options\", \"temperature\", \"top_p\", \"tools\", \"tool_choice\", \"function_call\", \"functions\", \"max_retries\", \"extra_headers\", \"parallel_tool_calls\", \"audio\", \"response_format\", \"user\"]}}, \"additional_usage_values\": {\"completion_tokens_details\": null, \"prompt_tokens_details\": null}, \"user_api_key\": null, \"user_api_key_alias\": null, \"user_api_key_team_id\": null, \"user_api_key_project_id\": null, \"user_api_key_project_alias\": null, \"user_api_key_org_id\": null, \"user_api_key_user_id\": null, \"user_api_key_team_alias\": null, \"spend_logs_metadata\": null, \"requester_ip_address\": null, \"status\": null, \"proxy_server_request\": null, \"error_information\": null, \"attempted_retries\": null, \"max_retries\": null}", "cache_key": "Cache OFF", "spend": 0.00022500000000000002, "total_tokens": 30, diff --git a/tests/pass_through_unit_tests/test_websearch_interception_e2e.py b/tests/pass_through_unit_tests/test_websearch_interception_e2e.py index 091ea106b91..fd95b7fa8f2 100644 --- a/tests/pass_through_unit_tests/test_websearch_interception_e2e.py +++ b/tests/pass_through_unit_tests/test_websearch_interception_e2e.py @@ -937,6 +937,14 @@ async def test_pre_request_hook_modifies_request_body(): print("✅ WebSearchInterceptionLogger initialized") + mock_router = MagicMock() + mock_router.search_tools = [ + { + "search_tool_name": "test-search-tool", + "litellm_params": {"search_provider": "tavily"}, + } + ] + # Track what actually gets sent to the API captured_request = {} @@ -987,6 +995,9 @@ async def test_pre_request_hook_modifies_request_body(): with patch( "litellm.llms.anthropic.experimental_pass_through.messages.handler.anthropic_messages_handler", side_effect=mock_anthropic_messages_handler, + ), patch( # test-quality-ok: the hook imports this process-global router at call time; no injection seam exists to register search_tools + "litellm.proxy.proxy_server.llm_router", + mock_router, ): print( diff --git a/tests/test_litellm/caching/test_redis_connection_pool.py b/tests/test_litellm/caching/test_redis_connection_pool.py index c824d3e7a0e..54dbe5361d7 100644 --- a/tests/test_litellm/caching/test_redis_connection_pool.py +++ b/tests/test_litellm/caching/test_redis_connection_pool.py @@ -1,15 +1,14 @@ -""" -Regression tests for Redis connection pool leak fixes (RC1-RC5). - -Tests are pure unit tests — no Redis server required. -""" - from unittest.mock import AsyncMock, MagicMock, patch import pytest -import redis.asyncio as async_redis -from litellm._redis import get_redis_async_client, get_redis_connection_pool +from litellm._redis import ( + _coerce_redis_kwargs_types, + _get_redis_client_logic, + _get_redis_env_kwarg_mapping, + get_redis_async_client, + get_redis_connection_pool, +) def test_url_config_uses_passed_pool(): @@ -60,16 +59,14 @@ def test_max_connections_url_config_string_value(monkeypatch): assert pool.max_connections == 25 -def test_max_connections_url_config_invalid_value(): - """Invalid max_connections should be silently ignored, falling back - to the pool default (50 for BlockingConnectionPool).""" - with patch("litellm._redis._get_redis_client_logic") as mock_logic: - mock_logic.return_value = { - "url": "redis://localhost:6379/0", - "max_connections": "not_a_number", - } +def test_max_connections_url_config_invalid_value(monkeypatch): + """Invalid max_connections from an env var should be silently dropped, + falling back to the pool default (50 for BlockingConnectionPool).""" + monkeypatch.setenv("REDIS_URL", "redis://localhost:6379/0") + monkeypatch.delenv("REDIS_HOST", raising=False) + monkeypatch.setenv("REDIS_MAX_CONNECTIONS", "not_a_number") - pool = get_redis_connection_pool() + pool = get_redis_connection_pool() # BlockingConnectionPool default is 50 assert pool.max_connections == 50 @@ -128,3 +125,173 @@ async def test_disconnect_idempotent(): await cache.disconnect() await cache.disconnect() # should not raise + + +def test_coerce_redis_kwargs_types_int(): + """String values for int-typed Redis params are coerced to int.""" + result = _coerce_redis_kwargs_types({"health_check_interval": "30", "port": "6380", "db": "1"}) + assert result["health_check_interval"] == 30 + assert isinstance(result["health_check_interval"], int) + assert result["port"] == 6380 + assert result["db"] == 1 + + +def test_coerce_redis_kwargs_types_bool(): + """String values for bool-typed Redis params are coerced to bool.""" + result = _coerce_redis_kwargs_types({"ssl": "true", "decode_responses": "false"}) + assert result["ssl"] is True + assert result["decode_responses"] is False + + +def test_coerce_redis_kwargs_types_none_default_numeric(): + """String values for known None-default numeric params are coerced.""" + result = _coerce_redis_kwargs_types({"max_connections": "20", "socket_timeout": "5.5"}) + assert result["max_connections"] == 20 + assert isinstance(result["max_connections"], int) + assert result["socket_timeout"] == 5.5 + assert isinstance(result["socket_timeout"], float) + + +def _redis_signature_pre_8x( + socket_timeout=None, + socket_connect_timeout=None, + max_connections=None, + health_check_interval=0, +): + """Stand-in for the redis-py <= 7.x Redis signature, where the timeout defaults are None.""" + + +def _redis_signature_8x( + socket_timeout=5, + socket_connect_timeout=5, + max_connections=None, + health_check_interval=0, +): + """Stand-in for the redis-py 8.x Redis signature, where the timeout defaults became int 5.""" + + +@pytest.mark.parametrize( + "client", + [_redis_signature_pre_8x, _redis_signature_8x], + ids=["redis-py<=7.x", "redis-py-8.x"], +) +def test_coerce_fractional_socket_timeout_survives_signature_default_change(client): + """redis-py 8.x changed socket_timeout's default from None to int 5. Deriving the + target type from the signature default made int("5.5") raise, so the key was dropped + and REDIS_SOCKET_TIMEOUT=5.5 silently disappeared on 8.x.""" + result = _coerce_redis_kwargs_types( + {"socket_timeout": "5.5", "socket_connect_timeout": "2.5", "max_connections": "20"}, + client=client, + ) + + assert result["socket_timeout"] == pytest.approx(5.5) + assert isinstance(result["socket_timeout"], float) + assert result["socket_connect_timeout"] == pytest.approx(2.5) + assert isinstance(result["socket_connect_timeout"], float) + assert result["max_connections"] == 20 + assert isinstance(result["max_connections"], int) + + +def test_coerce_invalid_socket_timeout_is_still_dropped(): + """Garbage must not survive the explicit-type path; Redis falls back to its own default.""" + result = _coerce_redis_kwargs_types({"socket_timeout": "not_a_number"}, client=_redis_signature_8x) + + assert "socket_timeout" not in result + + +def test_coerce_redis_kwargs_types_invalid_drops_key(): + """A string that cannot be coerced to the expected numeric type is dropped.""" + result = _coerce_redis_kwargs_types({"health_check_interval": "not_a_number"}) + assert "health_check_interval" not in result + + +def test_coerce_redis_kwargs_types_non_string_unchanged(): + """Non-string values pass through without modification.""" + result = _coerce_redis_kwargs_types({"health_check_interval": 30, "ssl": True}) + assert result["health_check_interval"] == 30 + assert result["ssl"] is True + + +def test_health_check_interval_from_env_is_int(monkeypatch): + monkeypatch.setenv("REDIS_HOST", "localhost") + monkeypatch.setenv("REDIS_HEALTH_CHECK_INTERVAL", "30") + + pool = get_redis_connection_pool() + + assert pool is not None + interval = pool.connection_kwargs.get("health_check_interval") + assert interval == 30 + assert isinstance(interval, int), f"Expected int, got {type(interval)}: {interval!r}" + + +def _signature_without_defaults(testkey): + """Stand-in for a client whose parameter declares no default at all.""" + + +def _signature_with_float_default(myparam=1.0): + """Stand-in for a client whose parameter declares a float default.""" + + +def test_coerce_redis_kwargs_types_empty_default_param_unchanged(): + """String params whose signature entry has no default (inspect.Parameter.empty) are left as-is.""" + result = _coerce_redis_kwargs_types({"testkey": "some_value"}, client=_signature_without_defaults) + + assert result["testkey"] == "some_value" + assert isinstance(result["testkey"], str) + + +def test_coerce_redis_kwargs_types_float_valid(): + """String values for params whose signature default is a float are coerced to float.""" + result = _coerce_redis_kwargs_types({"myparam": "3.14"}, client=_signature_with_float_default) + + assert result["myparam"] == pytest.approx(3.14) + assert isinstance(result["myparam"], float) + + +def test_coerce_redis_kwargs_types_float_invalid_drops_key(): + """An unconvertible string for a float-default param is dropped from the result.""" + result = _coerce_redis_kwargs_types({"myparam": "not_a_float"}, client=_signature_with_float_default) + + assert "myparam" not in result + + +@pytest.mark.parametrize( + ("raw", "expected"), + [("false", False), ("true", True), ("0", False), ("1", True)], +) +def test_coerce_socket_keepalive_string(raw, expected): + """socket_keepalive's signature default is None, so it needs an explicit bool + coercion: a leftover "false" string is truthy and enables keepalive.""" + result = _coerce_redis_kwargs_types({"socket_keepalive": raw}) + + assert result["socket_keepalive"] is expected + + +def test_get_redis_client_logic_coerces_cluster_only_kwargs(monkeypatch): + """Cluster-only kwargs (absent from redis.Redis's signature) must still be + coerced when routing to a cluster, or Helm-stringified values reach + RedisCluster as strings.""" + for envvar in (*_get_redis_env_kwarg_mapping(), "REDIS_CLUSTER_NODES", "REDIS_SENTINEL_NODES"): + monkeypatch.delenv(envvar, raising=False) + + result = _get_redis_client_logic( + startup_nodes='[{"host": "localhost", "port": 7000}]', + cluster_error_retry_attempts="5", + require_full_coverage="false", + health_check_interval="30", + ) + + assert result["cluster_error_retry_attempts"] == 5 + assert isinstance(result["cluster_error_retry_attempts"], int) + assert result["require_full_coverage"] is False + assert result["health_check_interval"] == 30 + assert isinstance(result["health_check_interval"], int) + + +def test_get_redis_client_logic_raises_without_host_or_url(monkeypatch): + """_get_redis_client_logic raises ValueError when neither host nor url is provided.""" + for envvar in (*_get_redis_env_kwarg_mapping(), "REDIS_CLUSTER_NODES", "REDIS_SENTINEL_NODES"): + monkeypatch.delenv(envvar, raising=False) + + with pytest.raises(ValueError, match="Either 'host' or 'url' must be specified for redis"): + _get_redis_client_logic() diff --git a/litellm-proxy-extras/tests/__init__.py b/tests/test_litellm/endpoints/__init__.py similarity index 100% rename from litellm-proxy-extras/tests/__init__.py rename to tests/test_litellm/endpoints/__init__.py diff --git a/tests/test_litellm/endpoints/speech/__init__.py b/tests/test_litellm/endpoints/speech/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/tests/test_litellm/endpoints/speech/speech_to_completion_bridge/__init__.py b/tests/test_litellm/endpoints/speech/speech_to_completion_bridge/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/tests/test_litellm/endpoints/speech/speech_to_completion_bridge/test_transformation.py b/tests/test_litellm/endpoints/speech/speech_to_completion_bridge/test_transformation.py new file mode 100644 index 00000000000..953f028af3c --- /dev/null +++ b/tests/test_litellm/endpoints/speech/speech_to_completion_bridge/test_transformation.py @@ -0,0 +1,117 @@ +import base64 +from typing import Final +from unittest.mock import MagicMock + +import pytest + +import litellm +from litellm.constants import OPENAI_CHAT_COMPLETION_PARAMS +from litellm.endpoints.speech.speech_to_completion_bridge.transformation import ( + SpeechToCompletionBridgeTransformationHandler, +) +from litellm.types.utils import ChatCompletionAudioResponse, Choices, Message, ModelResponse + +GEMINI_TTS_MODEL: Final = "gemini-3.1-flash-tts-preview" +PCM_BYTES: Final = b"\x01\x02\x03\x04" * 6 + + +def _model_response(model: str, pcm: bytes) -> ModelResponse: + audio: Final = ChatCompletionAudioResponse( + data=base64.b64encode(pcm).decode(), expires_at=0, transcript="hello" + ) + return ModelResponse(model=model, choices=[Choices(message=Message(content=None, audio=audio))]) + + +def _bridge_request(response_format: str | None) -> dict: + optional_params: Final = ( + {"temperature": 0.4} if response_format is None else {"temperature": 0.4, "response_format": response_format} + ) + return SpeechToCompletionBridgeTransformationHandler().transform_request( + model=GEMINI_TTS_MODEL, + input="Hello from LiteLLM", + voice="Kore", + optional_params=optional_params, + litellm_params={}, + headers={}, + litellm_logging_obj=MagicMock(), + custom_llm_provider="gemini", + ) + + +@pytest.mark.parametrize("response_format", ["wav", "pcm", None]) +def test_gemini_tts_request_keeps_speech_response_format_out_of_chat_params(response_format: str | None) -> None: + request: Final = _bridge_request(response_format) + + assert "response_format" not in request + assert request["audio"] == {"voice": "Kore", "format": "pcm16"} + assert request["temperature"] == 0.4 + assert request["modalities"] == ["audio"] + + gemini_params: Final = litellm.get_optional_params( + model=GEMINI_TTS_MODEL, + custom_llm_provider="gemini", + **{param: value for param, value in request.items() if param in OPENAI_CHAT_COMPLETION_PARAMS}, + ) + assert gemini_params["speechConfig"] == {"voiceConfig": {"prebuiltVoiceConfig": {"voiceName": "Kore"}}} + assert "responseMimeType" not in gemini_params + + +def test_non_gemini_request_forwards_speech_response_format_as_audio_format() -> None: + request: Final = SpeechToCompletionBridgeTransformationHandler().transform_request( + model="gpt-4o-audio-preview", + input="Hello from LiteLLM", + voice="alloy", + optional_params={"response_format": "wav"}, + litellm_params={}, + headers={}, + litellm_logging_obj=MagicMock(), + custom_llm_provider="openai", + ) + + assert "response_format" not in request + assert request["audio"] == {"voice": "alloy", "format": "wav"} + + +@pytest.mark.parametrize("response_format", ["mp3", "flac", "opus", "aac"]) +def test_gemini_tts_request_rejects_formats_gemini_cannot_produce(response_format: str) -> None: + with pytest.raises(litellm.BadRequestError) as excinfo: + _bridge_request(response_format) + + assert excinfo.value.status_code == 400 + assert response_format in str(excinfo.value) + assert "pcm" in str(excinfo.value) + assert "wav" in str(excinfo.value) + + +def test_gemini_tts_pcm_response_returns_raw_pcm_bytes() -> None: + response: Final = SpeechToCompletionBridgeTransformationHandler().transform_response( + model_response=_model_response(GEMINI_TTS_MODEL, PCM_BYTES), + response_format="pcm", + ) + + assert response.response.content == PCM_BYTES + assert response.response.headers["content-type"] == "audio/pcm" + + +@pytest.mark.parametrize("response_format", ["wav", None]) +def test_gemini_tts_wav_and_default_responses_wrap_pcm_in_wav(response_format: str | None) -> None: + response: Final = SpeechToCompletionBridgeTransformationHandler().transform_response( + model_response=_model_response(GEMINI_TTS_MODEL, PCM_BYTES), + response_format=response_format, + ) + + body: Final = response.response.content + assert body[:4] == b"RIFF" + assert body[8:12] == b"WAVE" + assert body[44:] == PCM_BYTES + assert response.response.headers["content-type"] == "audio/wav" + + +def test_non_gemini_response_keeps_original_bytes_and_mpeg_content_type() -> None: + response: Final = SpeechToCompletionBridgeTransformationHandler().transform_response( + model_response=_model_response("gpt-4o-audio-preview", PCM_BYTES), + response_format="mp3", + ) + + assert response.response.content == PCM_BYTES + assert response.response.headers["content-type"] == "audio/mpeg" diff --git a/tests/test_litellm/integrations/otel/test_otel_v2_components.py b/tests/test_litellm/integrations/otel/test_otel_v2_components.py index 115e385eda4..4aa28b5abfd 100644 --- a/tests/test_litellm/integrations/otel/test_otel_v2_components.py +++ b/tests/test_litellm/integrations/otel/test_otel_v2_components.py @@ -3,6 +3,7 @@ baggage helpers, metrics, the typed coercion helpers, mapper branches, span-name builders, and the registry validator's failure paths. Needs the OTel SDK.""" import json +from dataclasses import replace import pytest @@ -215,6 +216,27 @@ def test_genai_mapper_all_request_params(): assert attrs["server.port"] == 443 +def test_genai_mapper_cache_token_attrs(): + cached = replace( + _full_llm_call(), + usage=LLMUsage( + input_tokens=10, + output_tokens=5, + total_tokens=15, + cache_creation_input_tokens=7, + cache_read_input_tokens=3, + ), + ) + attrs = GenAIMapper().map(cached) + assert attrs[GenAI.USAGE_CACHE_CREATION_INPUT_TOKENS] == 7 + assert attrs[GenAI.USAGE_CACHE_READ_INPUT_TOKENS] == 3 + + # No cache usage keeps the span sparse: neither key present. + uncached = GenAIMapper().map(_full_llm_call()) + assert GenAI.USAGE_CACHE_CREATION_INPUT_TOKENS not in uncached + assert GenAI.USAGE_CACHE_READ_INPUT_TOKENS not in uncached + + def test_genai_mapper_stamps_input_output_messages(): data = LLMCallSpanData( operation=GenAIOperation.CHAT, diff --git a/tests/test_litellm/integrations/otel/test_otel_v2_sources_of_truth.py b/tests/test_litellm/integrations/otel/test_otel_v2_sources_of_truth.py index baa72b5a7fe..ca628aa3405 100644 --- a/tests/test_litellm/integrations/otel/test_otel_v2_sources_of_truth.py +++ b/tests/test_litellm/integrations/otel/test_otel_v2_sources_of_truth.py @@ -525,6 +525,28 @@ def test_llm_call_adapter_extracts_all_fields(): assert data.identity.key_hash == "hsh" +def test_llm_call_adapter_extracts_cache_tokens_from_usage_object(): + payload = _sample_payload() + payload["metadata"] = { + **payload["metadata"], + "usage_object": { + "prompt_tokens": 10, + "completion_tokens": 5, + "cache_creation_input_tokens": 7, + "cache_read_input_tokens": 3, + }, + } + data = LLMCallSpanData.from_standard_logging_payload(payload) + assert data.usage.cache_creation_input_tokens == 7 + assert data.usage.cache_read_input_tokens == 3 + + +def test_llm_call_adapter_cache_tokens_none_without_usage_object(): + data = LLMCallSpanData.from_standard_logging_payload(_sample_payload()) + assert data.usage.cache_creation_input_tokens is None + assert data.usage.cache_read_input_tokens is None + + def test_llm_call_adapter_failure_path(): payload = _sample_payload( status="failure", diff --git a/tests/test_litellm/integrations/test_shadow_eval_logger.py b/tests/test_litellm/integrations/test_shadow_eval_logger.py index f9c287fc7b7..5628d69de26 100644 --- a/tests/test_litellm/integrations/test_shadow_eval_logger.py +++ b/tests/test_litellm/integrations/test_shadow_eval_logger.py @@ -58,12 +58,14 @@ def _prisma(jobs=(), attempt_counts=(), attempt_costs=()) -> MagicMock: return prisma -def _job_record(job: ActiveShadowEvalJob, api_key_id="key-hash") -> MagicMock: +def _job_record(job: ActiveShadowEvalJob, target_type="key", target_id="key-hash") -> MagicMock: record = MagicMock() for field, value in dict( id=job.id, - api_key_id=api_key_id, + target_type=target_type, + target_id=target_id, router_name=job.router_name, + router_names=job.router_names, direction=job.direction, baseline_model=job.baseline_model, shadow_percentage=job.shadow_percentage, @@ -80,6 +82,7 @@ def _router( shadow_text="shadow answer", judge_json='{"preference": "A", "confidence": 0.9, "reasoning": "x"}', classifier_cost=None, + sibling_router_texts=None, ): """One mock router serving the shadow call first, the judge call second, told apart by the internal-origin stamp rather than the model, since a reverse job's shadow arm names @@ -99,6 +102,15 @@ def _router( decision["classifier_cost"] = classifier_cost kwargs["metadata"]["routing_decision"] = decision return {"choices": [{"message": {"content": shadow_text}}], "usage": {"completion_tokens": 5}} + if sibling_router_texts and kwargs["model"] in sibling_router_texts: + kwargs["metadata"]["routing_decision"] = { + "tier_label": "MEDIUM", + "routed_model": f"{kwargs['model']}-pick", + } + return { + "choices": [{"message": {"content": sibling_router_texts[kwargs["model"]]}}], + "usage": {"completion_tokens": 5}, + } return ModelResponse( model=kwargs["model"], choices=[{"index": 0, "finish_reason": "stop", "message": {"role": "assistant", "content": shadow_text}}], @@ -123,7 +135,7 @@ def _spend_counter(store=None): return counter, read, write -def _logger(router=None, prisma=None, jobs=(), counter_store=None) -> ShadowEvalLogger: +def _logger(router=None, prisma=None, jobs=(), counter_store=None, jobs_by_target=None) -> ShadowEvalLogger: cache = InMemoryCache(max_size_in_memory=4, default_ttl=60) counter, read, write = _spend_counter(counter_store) funnel_events = [] @@ -137,8 +149,9 @@ def _logger(router=None, prisma=None, jobs=(), counter_store=None) -> ShadowEval ) logger._test_counter = counter logger._test_funnel = funnel_events - if jobs: - cache.set_cache("shadow_eval:active_jobs", {"key-hash": tuple(jobs)}) + seeded = jobs_by_target if jobs_by_target is not None else ({("key", "key-hash"): tuple(jobs)} if jobs else None) + if seeded is not None: + cache.set_cache("shadow_eval:active_jobs", seeded) return logger @@ -837,6 +850,86 @@ class TestSuccessHookSkipChain: prisma.db.litellm_shadowevalattempt.create.assert_not_called() +JWT_IDENTITY = {"user_api_key_hash": None, "user_api_key_team_id": "team-eng", "user_api_key_user_id": "dev-alice"} + + +@pytest.mark.asyncio +class TestTargetMatching: + """A request qualifies for a job through ANY of its resolved identities: key hash, + team id, or user id. Team and user jobs must therefore sample JWT-authenticated + traffic, which carries no key hash at all.""" + + @pytest.mark.parametrize( + "target,sampled", + [ + (("team", "team-eng"), True), + (("user", "dev-alice"), True), + (("key", "some-key"), False), + ], + ids=["team-job-samples-jwt-traffic", "user-job-samples-jwt-traffic", "key-jobs-never-match-keyless-traffic"], + ) + async def test_jwt_shaped_traffic_matches_team_and_user_jobs_but_no_key_job(self, target, sampled): + prisma = _prisma() + router = _router() + logger = _logger(router=router, prisma=prisma, jobs_by_target={target: (_job(),)}) + hook_kwargs = _success_kwargs() + hook_kwargs["standard_logging_object"]["metadata"] = dict(JWT_IDENTITY) + + await logger.async_log_success_event(hook_kwargs, RESPONSE, None, None) + await _drain(logger) + + if sampled: + prisma.db.litellm_shadowevalattempt.create.assert_awaited_once() + assert prisma.db.litellm_shadowevalattempt.create.call_args.kwargs["data"]["job_id"] == "job-1" + else: + router.acompletion.assert_not_called() + prisma.db.litellm_shadowevalattempt.create.assert_not_called() + + async def test_an_event_with_no_identity_early_returns_without_a_cache_read(self): + prisma = _prisma() + router = _router() + cache = MagicMock(spec=InMemoryCache) + cache.async_get_cache = AsyncMock() + logger = ShadowEvalLogger( + router_provider=lambda: router, + prisma_provider=lambda: prisma, + jobs_cache=cache, + ) + hook_kwargs = _success_kwargs() + hook_kwargs["standard_logging_object"]["metadata"] = {} + + await logger.async_log_success_event(hook_kwargs, RESPONSE, None, None) + + cache.async_get_cache.assert_not_awaited() + router.acompletion.assert_not_called() + prisma.db.litellm_shadowevalattempt.create.assert_not_called() + + async def test_an_event_matching_a_key_job_and_a_team_job_fires_both(self): + """A request's key and its team can each hold a job; the two are separately + budgeted experiments, so both fire and each counts its own start.""" + prisma = _prisma() + logger = _logger( + router=_router(), + prisma=prisma, + jobs_by_target={ + ("key", "key-hash"): (_job(id="key-job"),), + ("team", "team-eng"): (_job(id="team-job"),), + }, + ) + hook_kwargs = _success_kwargs() + hook_kwargs["standard_logging_object"]["metadata"] = { + "user_api_key_hash": "key-hash", + "user_api_key_team_id": "team-eng", + } + + await logger.async_log_success_event(hook_kwargs, RESPONSE, None, None) + await _drain(logger) + + rows = [call.kwargs["data"] for call in prisma.db.litellm_shadowevalattempt.create.call_args_list] + assert sorted(row["job_id"] for row in rows) == ["key-job", "team-job"] + assert logger._job_starts == {"key-job": 1, "team-job": 1} + + @pytest.mark.asyncio class TestActiveJobsCache: async def test_cache_miss_reads_db_once_then_serves_from_cache(self): @@ -851,8 +944,8 @@ class TestActiveJobsCache: first = await logger._active_jobs() second = await logger._active_jobs() - assert [job.id for job in first["key-hash"]] == ["job-1"] - assert second["key-hash"][0].attempts == 7 + assert [job.id for job in first[("key", "key-hash")]] == ["job-1"] + assert second[("key", "key-hash")][0].attempts == 7 assert prisma.db.litellm_shadowevaljob.find_many.await_count == 1 where = prisma.db.litellm_shadowevaljob.find_many.call_args.kwargs["where"] assert where["stopped_at"] is None @@ -899,8 +992,8 @@ class TestActiveJobsCache: jobs = await logger._active_jobs() assert logger._job_starts == {} - assert jobs["key-hash"][0].attempts == 7 - assert jobs["key-hash"][0].spend == 0.05 + assert jobs[("key", "key-hash")][0].attempts == 7 + assert jobs[("key", "key-hash")][0].spend == 0.05 @pytest.mark.asyncio @@ -1128,29 +1221,36 @@ class TestJobValidation: {"direction": "reverse"}, {"baseline_model": "baseline-model"}, {"direction": "sideways", "baseline_model": "baseline-model"}, + {"direction": "reverse", "baseline_model": "baseline-model", "router_names": ("a", "b")}, ], - ids=["reverse-without-baseline", "forward-with-baseline", "unknown-direction"], + ids=["reverse-without-baseline", "forward-with-baseline", "unknown-direction", "reverse-with-router-set"], ) def test_unsamplable_shapes_are_rejected(self, overrides): with pytest.raises(ValidationError): _job(**overrides) - def test_shadow_target_follows_direction(self): - assert _job().shadow_target == "my-router" - assert _reverse_job().shadow_target == "baseline-model" + def test_arm_target_follows_direction(self): + assert _job().arm_target("my-router") == "my-router" + assert _reverse_job().arm_target("my-router") == "baseline-model" + + def test_rows_from_before_router_names_carry_their_set_in_router_name(self): + assert _job().arm_router_names == ("my-router",) + assert _job(router_names=("my-router", "alt-router")).arm_router_names == ("my-router", "alt-router") @pytest.mark.asyncio class TestDirection: @pytest.mark.parametrize( - "job,routed_by,sampled", + "job,routed_by,attempt_rows", [ - (_job(), None, True), - (_job(), "my-router", False), - (_job(), "other-router", True), - (_reverse_job(), "my-router", True), - (_reverse_job(), None, False), - (_reverse_job(), "other-router", False), + (_job(), None, 1), + (_job(), "my-router", 0), + (_job(), "other-router", 1), + (_reverse_job(), "my-router", 1), + (_reverse_job(), None, 0), + (_reverse_job(), "other-router", 0), + (_job(router_names=("my-router", "alt-router")), "alt-router", 0), + (_job(router_names=("my-router", "alt-router")), "other-router", 2), ], ids=[ "forward-samples-unrouted", @@ -1159,20 +1259,24 @@ class TestDirection: "reverse-samples-its-own-router", "reverse-skips-unrouted", "reverse-skips-another-router", + "forward-skips-any-candidates-own-traffic", + "forward-multi-samples-once-per-arm", ], ) - async def test_direction_decides_which_traffic_is_sampled(self, job, routed_by, sampled): + async def test_direction_decides_which_traffic_is_sampled(self, job, routed_by, attempt_rows): """The two directions partition the key's traffic: whatever one samples, the other - skips, so a key running both never judges the same turn twice for the same reason.""" + skips, so a key running both never judges the same turn twice for the same reason. + A multi-router job extends the forward skip to every candidate: a request one + candidate served must not be judged as the incumbent against another candidate.""" prisma = _prisma() - logger = _logger(router=_router(), prisma=prisma, jobs=(job,)) + logger = _logger(router=_router(sibling_router_texts={"alt-router": "alt answer"}), prisma=prisma, jobs=(job,)) await logger.async_log_success_event( _success_kwargs(request_metadata=_routed_by(routed_by) if routed_by else {}), RESPONSE, None, None ) await _drain(logger) - assert prisma.db.litellm_shadowevalattempt.create.await_count == int(sampled) + assert prisma.db.litellm_shadowevalattempt.create.await_count == attempt_rows async def test_reverse_duplicates_against_the_baseline_model(self): prisma = _prisma() @@ -1234,6 +1338,134 @@ class TestDirection: assert logger._job_starts == {"forward-job": 1, "reverse-job": 1} +@pytest.mark.asyncio +class TestMultiRouterArms: + async def test_every_arm_judges_the_same_request_and_stamps_its_own_row(self): + """One sampled request, one row per candidate router, both judged against the same + real response: the paired comparison that makes multi-router win rates comparable.""" + prisma = _prisma() + router = _router(sibling_router_texts={"alt-router": "alt answer"}) + logger = _logger(router=router, prisma=prisma) + + await logger._run_shadow_eval( + job=_job(router_names=("my-router", "alt-router")), + request_id="req-1", + messages=({"role": "user", "content": "hi"},), + real_text="real answer", + real_model="claude-opus", + real_cost=0.001, + real_classifier_cost=0.0, + real_cache_hit=False, + control_tier=None, + shadow_params={}, + parent_metadata={}, + ) + + rows = [call.kwargs["data"] for call in prisma.db.litellm_shadowevalattempt.create.await_args_list] + assert [row["router_name"] for row in rows] == ["my-router", "alt-router"] + assert {row["request_id"] for row in rows} == {"req-1"} + assert [row["shadow_model"] for row in rows] == ["cheap-model", "alt-router-pick"] + assert all(row["outcome"] in ("real", "shadow", "tie") for row in rows) + assert all(row["real_cost"] == 0.001 for row in rows) + + async def test_a_single_router_job_stamps_its_router_on_the_row(self): + prisma = _prisma() + logger = _logger(router=_router(), prisma=prisma) + + await logger._run_shadow_eval( + job=_job(), + request_id="req-1", + messages=({"role": "user", "content": "hi"},), + real_text="real answer", + real_model="claude-opus", + real_cost=0.0, + real_classifier_cost=0.0, + real_cache_hit=False, + control_tier=None, + shadow_params={}, + parent_metadata={}, + ) + + row = prisma.db.litellm_shadowevalattempt.create.call_args.kwargs["data"] + assert row["router_name"] == "my-router" + + async def test_one_arms_failure_never_silences_the_sibling(self): + prisma = _prisma() + router = _router(sibling_router_texts={"alt-router": "alt answer"}) + healthy = router.acompletion.side_effect + + async def first_arm_explodes(**kwargs): + if kwargs["model"] == "my-router": + raise RuntimeError("provider exploded") + return await healthy(**kwargs) + + router.acompletion.side_effect = first_arm_explodes + logger = _logger(router=router, prisma=prisma) + + await logger._run_shadow_eval( + job=_job(router_names=("my-router", "alt-router")), + request_id="req-1", + messages=({"role": "user", "content": "hi"},), + real_text="real answer", + real_model="claude-opus", + real_cost=0.0, + real_classifier_cost=0.0, + real_cache_hit=False, + control_tier=None, + shadow_params={}, + parent_metadata={}, + ) + + rows = [call.kwargs["data"] for call in prisma.db.litellm_shadowevalattempt.create.await_args_list] + assert [row["router_name"] for row in rows] == ["my-router", "alt-router"] + assert rows[0]["outcome"] == "error" + assert "provider exploded" in rows[0]["error"] + assert rows[1]["outcome"] in ("real", "shadow", "tie") + + async def test_the_turn_valve_counts_every_arm_a_start_will_write(self): + """max_turns is a row ceiling and one sampled request writes one row per arm, so + admission pre-counts the arms: a two-arm job with two turns of budget admits one + request, not two.""" + prisma = _prisma() + router = _router(sibling_router_texts={"alt-router": "alt answer"}) + logger = _logger( + router=router, prisma=prisma, jobs=(_job(router_names=("my-router", "alt-router"), max_turns=2),) + ) + + await logger.async_log_success_event(_success_kwargs(request_id="req-1"), RESPONSE, None, None) + await logger.async_log_success_event(_success_kwargs(request_id="req-2"), RESPONSE, None, None) + await _drain(logger) + + rows = [call.kwargs["data"] for call in prisma.db.litellm_shadowevalattempt.create.await_args_list] + assert {row["request_id"] for row in rows} == {"req-1"} + assert len(rows) == 2 + + async def test_a_withheld_request_runs_no_arm_and_counts_once(self): + """The budget gates run once per sampled request, before any arm: funnel counters + stay per-request, so coverage math is arm-count independent.""" + prisma = _prisma() + router = _router(sibling_router_texts={"alt-router": "alt answer"}) + logger = _logger(router=router, prisma=prisma) + + await logger._run_shadow_eval( + job=_job(router_names=("my-router", "alt-router"), max_budget=1.0, spend=2.0), + request_id="req-1", + messages=({"role": "user", "content": "hi"},), + real_text="real answer", + real_model="claude-opus", + real_cost=0.0, + real_classifier_cost=0.0, + real_cache_hit=False, + control_tier=None, + shadow_params={}, + parent_metadata={}, + ) + + router.acompletion.assert_not_called() + prisma.db.litellm_shadowevalattempt.create.assert_not_called() + assert logger._test_funnel == [("job-1", "withheld")] + + @pytest.mark.asyncio class TestActiveJobsFailClosed: async def test_a_row_the_sampler_cannot_read_is_dropped_not_guessed(self): @@ -1249,13 +1481,14 @@ class TestActiveJobsFailClosed: jobs_cache=InMemoryCache(max_size_in_memory=4, default_ttl=60), ) - assert [job.id for job in (await logger._active_jobs())["key-hash"]] == ["job-ok"] + assert [job.id for job in (await logger._active_jobs())[("key", "key-hash")]] == ["job-ok"] - async def test_both_of_a_key_s_jobs_survive_the_lookup(self): + async def test_every_targets_jobs_survive_the_lookup_keyed_by_type_and_id(self): records = [ _job_record(_job(id="job-forward")), _job_record(_reverse_job(id="job-reverse")), - _job_record(_job(id="job-other"), api_key_id="other-key"), + _job_record(_job(id="job-other"), target_id="other-key"), + _job_record(_job(id="job-team"), target_type="team", target_id="team-eng"), ] prisma = _prisma(jobs=records, attempt_counts=[("job-reverse", 3)]) logger = ShadowEvalLogger( @@ -1266,9 +1499,11 @@ class TestActiveJobsFailClosed: jobs = await logger._active_jobs() - assert sorted(job.id for job in jobs["key-hash"]) == ["job-forward", "job-reverse"] - assert [job.id for job in jobs["other-key"]] == ["job-other"] - assert {job.id: job.attempts for job in jobs["key-hash"]}["job-reverse"] == 3 + assert sorted(job.id for job in jobs[("key", "key-hash")]) == ["job-forward", "job-reverse"] + assert [job.id for job in jobs[("key", "other-key")]] == ["job-other"] + assert [job.id for job in jobs[("team", "team-eng")]] == ["job-team"] + assert ("team-eng",) not in jobs and "team-eng" not in jobs + assert {job.id: job.attempts for job in jobs[("key", "key-hash")]}["job-reverse"] == 3 def _failing_router(): diff --git a/tests/test_litellm/litellm_core_utils/prompt_templates/test_litellm_core_utils_prompt_templates_common_utils.py b/tests/test_litellm/litellm_core_utils/prompt_templates/test_litellm_core_utils_prompt_templates_common_utils.py index 772fbf98c57..9ab66d55f3f 100644 --- a/tests/test_litellm/litellm_core_utils/prompt_templates/test_litellm_core_utils_prompt_templates_common_utils.py +++ b/tests/test_litellm/litellm_core_utils/prompt_templates/test_litellm_core_utils_prompt_templates_common_utils.py @@ -1433,3 +1433,50 @@ class TestFlattenTopLevelSchemaCombinators: flatten_top_level_schema_combinators(schema) assert schema == snapshot + + +class TestRequestContainsImageContent: + """One detector for every dialect that reaches pre-routing hooks untranslated.""" + + @pytest.mark.parametrize( + "part", + [ + {"type": "image_url", "image_url": {"url": "data:image/png;base64,aGk="}}, + {"type": "input_image", "image_url": "data:image/png;base64,aGk="}, + {"type": "image", "source": {"type": "base64", "media_type": "image/png", "data": "aGk="}}, + { + "type": "tool_result", + "tool_use_id": "tu_1", + "content": [{"type": "image", "source": {"type": "base64", "data": "aGk="}}], + }, + ], + ) + def test_detects_every_image_dialect_including_tool_results(self, part): + from litellm.litellm_core_utils.prompt_templates.common_utils import request_contains_image_content + + messages = [{"role": "user", "content": [{"type": "text", "text": "hi"}, part]}] + assert request_contains_image_content(messages) is True + + @pytest.mark.parametrize( + "messages", + [ + [{"role": "user", "content": "plain string"}], + [{"role": "user", "content": [{"type": "text", "text": "hi"}]}], + [{"role": "user", "content": [{"type": "input_audio", "input_audio": {"data": "x"}}]}], + [{"role": "user", "content": [{"type": "tool_result", "content": [{"type": "text", "text": "ok"}]}]}], + [{"role": "user", "content": None}], + [], + ], + ) + def test_ignores_text_audio_and_degenerate_shapes(self, messages): + from litellm.litellm_core_utils.prompt_templates.common_utils import request_contains_image_content + + assert request_contains_image_content(messages) is False + + def test_hostile_nesting_is_depth_bounded(self): + from litellm.litellm_core_utils.prompt_templates.common_utils import request_contains_image_content + + nested: dict = {"type": "image", "source": {"type": "base64", "data": "aGk="}} + for _ in range(50): + nested = {"type": "tool_result", "content": [nested]} + assert request_contains_image_content([{"role": "user", "content": [nested]}]) is False diff --git a/tests/test_litellm/litellm_core_utils/test_audio_utils.py b/tests/test_litellm/litellm_core_utils/test_audio_utils.py index 0e8176fffce..155f6680416 100644 --- a/tests/test_litellm/litellm_core_utils/test_audio_utils.py +++ b/tests/test_litellm/litellm_core_utils/test_audio_utils.py @@ -347,3 +347,65 @@ class TestNormalizeTranscriptionLanguageToBcp47: ) assert normalize_transcription_language_to_bcp47(language) == expected + + +class TestResolveSpeechMediaType: + @pytest.mark.parametrize( + ("upstream_content_type", "response_format", "expected"), + [ + ("audio/wav", None, "audio/wav"), + ("AUDIO/WAV", None, "audio/wav"), + ("audio/flac; charset=binary", "mp3", "audio/flac"), + ("application/json", "flac", "audio/flac"), + ("application/octet-stream", "pcm", "audio/pcm"), + (None, "wav", "audio/wav"), + (None, "WAV", "audio/wav"), + (None, "opus", "audio/opus"), + (None, "aac", "audio/aac"), + (None, "mp3", "audio/mpeg"), + (None, "mp4", "audio/mpeg"), + (None, "bogus", "audio/mpeg"), + (None, None, "audio/mpeg"), + ("", None, "audio/mpeg"), + ], + ) + def test_resolution(self, upstream_content_type, response_format, expected): + from litellm.litellm_core_utils.audio_utils.utils import resolve_speech_media_type + + resolved = resolve_speech_media_type( + upstream_content_type=upstream_content_type, + response_format=response_format, + ) + assert resolved == expected + + +class TestSpeechMediaTypeFromAudioBytes: + @pytest.mark.parametrize( + ("audio", "expected"), + [ + (b"RIFF\x24\x00\x00\x00WAVEfmt ", "audio/wav"), + (b"fLaC\x00\x00\x00\x22", "audio/flac"), + (b"OggS" + b"\x00" * 24 + b"OpusHead", "audio/opus"), + (b"OggS" + b"\x00" * 24 + b"\x01vorbis", "audio/ogg"), + (b"ID3\x04\x00\x00\x00\x00\x00\x00", "audio/mpeg"), + (b"\xff\xfb\x90\x64", "audio/mpeg"), + (b"\xff\xf3\x80\x00", "audio/mpeg"), + (b"\xff\xf1\x50\x80", "audio/aac"), + (b"\xff\xf9\x50\x80", "audio/aac"), + (b"RIFF\x24\x00\x00\x00AVI LIST", None), + (b"\xff\xff\xff\xff\xff\xff", None), + (b"\xff\xfb\xf0\x00", None), + (b"\xff\xfb\x9c\x00", None), + (b"\xff\xeb\x90\x00", None), + (b"\xff\xf1\xf4\x80", None), + (b"\xff\x00\x00\x00", None), + (b"\x00\x01\x02\x03\x04\x05", None), + (b"\xff\xfb", None), + (b"\xff", None), + (b"", None), + ], + ) + def test_sniffing(self, audio, expected): + from litellm.litellm_core_utils.audio_utils.utils import speech_media_type_from_audio_bytes + + assert speech_media_type_from_audio_bytes(audio) == expected diff --git a/tests/test_litellm/litellm_core_utils/test_get_llm_provider_endpoint_match.py b/tests/test_litellm/litellm_core_utils/test_get_llm_provider_endpoint_match.py index 6cacd119030..419ca104bb1 100644 --- a/tests/test_litellm/litellm_core_utils/test_get_llm_provider_endpoint_match.py +++ b/tests/test_litellm/litellm_core_utils/test_get_llm_provider_endpoint_match.py @@ -184,3 +184,26 @@ class TestTogetherApiBaseResolvesProvider: assert provider == "together_ai" assert api_base == "https://api.together.ai/v1" + + +class TestGigachatApiBaseResolvesProvider: + """ + Regression for the GigaChat api_base branch: the provider-mapping chain + carried an ``endpoint == "https://gigachat.devices.sberbank.ru/api/v1"`` + elif, but the URL was never added to ``openai_compatible_endpoints``, so + the endpoint loop never fired the branch and a caller-supplied GigaChat + api_base raised BadRequestError instead of resolving to ``gigachat``. + """ + + def test_gigachat_api_base_resolves_to_gigachat(self, monkeypatch): + monkeypatch.setenv("GIGACHAT_API_KEY", "gigachat-key-from-env") + + model, provider, dynamic_api_key, returned_api_base = get_llm_provider( + model="GigaChat-2", + api_base="https://gigachat.devices.sberbank.ru/api/v1", + ) + + assert provider == "gigachat" + assert dynamic_api_key == "gigachat-key-from-env" + assert returned_api_base == "https://gigachat.devices.sberbank.ru/api/v1" + assert model == "GigaChat-2" diff --git a/tests/test_litellm/litellm_core_utils/test_litellm_logging.py b/tests/test_litellm/litellm_core_utils/test_litellm_logging.py index 947a55410ef..c7328adb0b3 100644 --- a/tests/test_litellm/litellm_core_utils/test_litellm_logging.py +++ b/tests/test_litellm/litellm_core_utils/test_litellm_logging.py @@ -6101,3 +6101,64 @@ def test_response_timing_metrics_survive_deepcopy(logging_obj): logging_obj.set_response_timing_metrics({"_response_ms": 12.5}) assert copy.deepcopy(logging_obj).response_timing_metrics == {"_response_ms": 12.5} + + +def test_passthrough_embeddings_result_swapped_for_callbacks(): + """ + Regression: for gigachat passthrough /embeddings, normalize_logging_result + produces an EmbeddingResponse, but the result swap only accepted + ModelResponse, so callbacks kept receiving the raw httpx.Response (which + crashes attribute readers like OTEL). The swap must cover + EmbeddingResponse too. + """ + import datetime as dt + + from litellm.types.utils import EmbeddingResponse + + logging_obj = LitellmLogging( + model="EmbeddingsGigaR", + messages=[], + stream=False, + call_type="allm_passthrough_route", + start_time=time.time(), + litellm_call_id="passthrough-embed-call-id", + function_id="passthrough-embed-fn-id", + ) + logging_obj.update_environment_variables( + litellm_params={}, + optional_params={}, + model="EmbeddingsGigaR", + custom_llm_provider="gigachat", + endpoint="/embeddings", + request_data={"model": "EmbeddingsGigaR", "input": ["hello"]}, + input=["hello"], + ) + + httpx_response = httpx.Response( + 200, + json={ + "object": "list", + "data": [ + { + "object": "embedding", + "embedding": [0.1, 0.2, 0.3], + "index": 0, + "usage": {"prompt_tokens": 5}, + } + ], + "model": "EmbeddingsGigaR", + }, + request=httpx.Request( + "POST", "https://gigachat.devices.sberbank.ru/api/v1/embeddings" + ), + ) + + _, _, swapped_result = logging_obj._success_handler_helper_fn( + result=httpx_response, + start_time=dt.datetime.now(), + end_time=dt.datetime.now(), + cache_hit=False, + ) + + assert isinstance(swapped_result, EmbeddingResponse) + assert swapped_result.data[0]["embedding"] == [0.1, 0.2, 0.3] diff --git a/tests/test_litellm/litellm_core_utils/test_streaming_handler.py b/tests/test_litellm/litellm_core_utils/test_streaming_handler.py index 7f54fbfb4c2..4c99bce2b0f 100644 --- a/tests/test_litellm/litellm_core_utils/test_streaming_handler.py +++ b/tests/test_litellm/litellm_core_utils/test_streaming_handler.py @@ -4692,3 +4692,82 @@ async def test_async_stream_assembled_response_keeps_vertex_traffic_type(logging assembled = litellm.stream_chunk_builder(chunks=received, messages=[{"role": "user", "content": "hi"}]) assert assembled is not None assert assembled._hidden_params["provider_specific_fields"]["traffic_type"] == "ON_DEMAND_FLEX" + + +class TestStableStreamingResponseId: + """ + All chunks of one streamed response must share the same top-level id + (OpenAI streaming contract). Providers streaming via GenericStreamingChunk + (e.g. GigaChat) do not propagate an upstream response id, so + CustomStreamWrapper must pin the id from the first chunk it creates, + mirroring the existing `created` pinning (issue #11437). + + Clients such as goose merge streamed deltas into one assistant message by + chunk id; per-chunk ids split a single reply into many messages. + """ + + def test_generic_chunks_share_one_id(self): + def _generic_chunks(): + return iter( + [ + { + "text": "Hello", + "tool_use": None, + "is_finished": False, + "finish_reason": "", + "usage": None, + "index": 0, + }, + { + "text": " world", + "tool_use": None, + "is_finished": False, + "finish_reason": "", + "usage": None, + "index": 0, + }, + { + "text": "", + "tool_use": None, + "is_finished": True, + "finish_reason": "stop", + "usage": { + "prompt_tokens": 1, + "completion_tokens": 2, + "total_tokens": 3, + }, + "index": 0, + }, + ] + ) + + wrapper = CustomStreamWrapper( + completion_stream=_generic_chunks(), + model="gigachat/GigaChat-2-Max", + logging_obj=MagicMock(), + custom_llm_provider="gigachat", + ) + ids = [chunk.id for chunk in wrapper if chunk.id] + assert ids, "no chunks emitted" + assert len(set(ids)) == 1, f"chunk ids differ across one stream: {ids}" + + def test_creator_pins_id_from_first_chunk(self): + wrapper = CustomStreamWrapper( + completion_stream=iter([]), + model="gigachat/GigaChat-2-Max", + logging_obj=MagicMock(), + custom_llm_provider="gigachat", + ) + first = wrapper.model_response_creator() + assert wrapper.response_id == first.id + assert wrapper.model_response_creator().id == first.id + + def test_provider_supplied_id_still_wins(self): + wrapper = CustomStreamWrapper( + completion_stream=iter([]), + model="gigachat/GigaChat-2-Max", + logging_obj=MagicMock(), + custom_llm_provider="gigachat", + ) + wrapper.response_id = "chatcmpl-from-provider" + assert wrapper.model_response_creator().id == "chatcmpl-from-provider" diff --git a/tests/test_litellm/litellm_core_utils/test_token_counter.py b/tests/test_litellm/litellm_core_utils/test_token_counter.py index 572b505e94c..4694fa8fbed 100644 --- a/tests/test_litellm/litellm_core_utils/test_token_counter.py +++ b/tests/test_litellm/litellm_core_utils/test_token_counter.py @@ -1377,3 +1377,38 @@ def test_anthropic_document_title_and_context_add_their_tokens(): {"type": "document", "source": source}, ] ) + + +def test_openai_file_block_prices_like_the_equivalent_anthropic_document(): + """An inline `file` is a `document` in the chat-completions dialect, so it must price identically, not raise. + + Before the fix `file` was missing from the content-block match even though `ChatCompletionFileObject` + is in the union this counter accepts, so every local count of a Responses `input_file` raised + `Invalid content item type: file` and surfaced as a 500 on /v1/responses/input_tokens. + """ + prompt = {"type": "text", "text": "Summarize this file."} + inline_file = { + "type": "file", + "file": {"filename": "report.pdf", "file_data": "data:application/pdf;base64,JVBERi0xLjQK"}, + } + document = { + "type": "document", + "title": "report.pdf", + "source": {"type": "base64", "media_type": "application/pdf", "data": "JVBERi0xLjQK"}, + } + + assert _count_user_content([prompt, inline_file]) == _count_user_content([prompt, document]) + assert _count_user_content([prompt, inline_file]) > _count_user_content([prompt]) + + +def test_openai_file_block_without_inline_bytes_counts_what_it_carries(): + """A `file` block naming an uploaded file has no bytes to price, so it adds only the filename's tokens.""" + prompt = {"type": "text", "text": "Summarize this file."} + + by_id = {"type": "file", "file": {"file_id": "file-abc123"}} + assert _count_user_content([prompt, by_id]) == _count_user_content([prompt]) + + named = {"type": "file", "file": {"file_id": "file-abc123", "filename": "report.pdf"}} + assert _count_user_content([prompt, named]) == _count_user_content( + [prompt, {"type": "text", "text": "report.pdf"}] + ) diff --git a/tests/test_litellm/llms/anthropic/chat/guardrail_translation/test_anthropic_guardrail_handler.py b/tests/test_litellm/llms/anthropic/chat/guardrail_translation/test_anthropic_guardrail_handler.py index af3ccd65b11..0fe7730e91e 100644 --- a/tests/test_litellm/llms/anthropic/chat/guardrail_translation/test_anthropic_guardrail_handler.py +++ b/tests/test_litellm/llms/anthropic/chat/guardrail_translation/test_anthropic_guardrail_handler.py @@ -1490,6 +1490,92 @@ class MockCanaryMaskingGuardrail(CustomGuardrail): return inputs +class TestAnthropicMessagesImageSources: + """An Anthropic image block has three source shapes (`AnthropicMessagesImageParam.source`). + + Only the base64 one carries "data", so reading that key alone drops url images + entirely -- for every guardrail consuming GenericGuardrailAPIInputs["images"], + not just Bedrock. + """ + + def _data(self, messages): + return {"model": "claude-sonnet-4-5", "messages": messages} + + async def _images_seen(self, content) -> list[str]: + handler = AnthropicMessagesHandler() + + class ImageRecordingGuardrail(MockCanaryMaskingGuardrail): + def __init__(self): + super().__init__() + self.seen_images: list[str] = [] # mutable-ok: accumulator for the assertion + + async def apply_guardrail(self, inputs, request_data, input_type, logging_obj=None): + self.seen_images.extend(inputs.get("images") or []) + return await super().apply_guardrail(inputs, request_data, input_type, logging_obj) + + guardrail = ImageRecordingGuardrail() + # The text block is what gets the guardrail invoked at all: a message with + # no text gives the handler nothing to scan, so it never reaches the + # guardrail and every source shape would look equally "dropped". + await handler.process_input_messages( + data=self._data([{"role": "user", "content": [{"type": "text", "text": "describe it"}, *content]}]), + guardrail_to_apply=guardrail, + ) + return guardrail.seen_images + + @pytest.mark.asyncio + async def test_url_source_reaches_the_guardrail(self): + """A url source has no "data" key, so it used to yield nothing at all.""" + seen = await self._images_seen( + [{"type": "image", "source": {"type": "url", "url": "https://example.com/a.png"}}] + ) + + assert seen == ["https://example.com/a.png"] + + @pytest.mark.asyncio + async def test_base64_source_carries_its_media_type(self): + """Bare base64 leaves the consumer no way to recover the format. + + An API like Bedrock's ApplyGuardrail needs it to build the request, so the + media_type travels with the payload as a data URI. + """ + seen = await self._images_seen( + [{"type": "image", "source": {"type": "base64", "media_type": "image/png", "data": "AAAA"}}] + ) + + assert seen == ["data:image/png;base64,AAAA"] + + @pytest.mark.asyncio + async def test_base64_source_without_a_media_type_is_passed_through(self): + """There is no format to attach, so the payload goes through unchanged.""" + seen = await self._images_seen([{"type": "image", "source": {"type": "base64", "data": "AAAA"}}]) + + assert seen == ["AAAA"] + + @pytest.mark.asyncio + async def test_file_source_yields_nothing(self): + """The bytes live behind the Files API and this extractor has no client. + + Documented as a known gap rather than silently handed on as a file_id string, + which a consumer would try to decode as an image. + """ + seen = await self._images_seen([{"type": "image", "source": {"type": "file", "file_id": "file_abc"}}]) + + assert seen == [] + + @pytest.mark.asyncio + async def test_a_malformed_source_is_dropped_rather_than_passed_on(self): + seen = await self._images_seen( + [ + {"type": "image", "source": {"type": "base64"}}, + {"type": "image", "source": {"type": "url"}}, + {"type": "image", "source": {"type": "base64", "data": ""}}, + ] + ) + + assert seen == [] + + class TestAnthropicMessagesToolResultScanning: """LIT-5251: tool_result blocks carry whatever a client's local tool fetched, so they are the request-path payload an indirect prompt injection actually arrives in. diff --git a/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py b/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py index 25e2c3cda80..c4df46dea83 100644 --- a/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py +++ b/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py @@ -6207,3 +6207,45 @@ def test_disabled_thinking_omitted_only_for_always_on_models( assert "thinking" not in request else: assert request["thinking"] == {"type": "disabled"} + + +def test_anthropic_drop_params_keeps_format_only_output_config(monkeypatch): + """``drop_params=True`` must not consume ``output_config.format``: the drop + gate is an effort gate and ``format`` is a structured-output field.""" + monkeypatch.setattr(litellm, "drop_params", True) + config = AnthropicConfig() + schema_format = { + "type": "json_schema", + "schema": {"type": "object", "properties": {"z": {"type": "integer"}}}, + } + + result = config.transform_request( + model="claude-3-haiku-20240307", + messages=[{"role": "user", "content": "Hello"}], + optional_params={"output_config": {"format": schema_format}}, + litellm_params={}, + headers={}, + ) + + assert result.get("output_config") == {"format": schema_format} + + +def test_anthropic_drop_params_reduces_mixed_output_config_to_format(monkeypatch): + """``drop_params=True`` drops the effort key on unsupported models but keeps + ``format`` so structured outputs still reach the provider.""" + monkeypatch.setattr(litellm, "drop_params", True) + config = AnthropicConfig() + schema_format = { + "type": "json_schema", + "schema": {"type": "object", "properties": {"z": {"type": "integer"}}}, + } + + result = config.transform_request( + model="claude-3-haiku-20240307", + messages=[{"role": "user", "content": "Hello"}], + optional_params={"output_config": {"effort": "low", "format": schema_format}}, + litellm_params={}, + headers={}, + ) + + assert result.get("output_config") == {"format": schema_format} diff --git a/tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_streaming_iterator.py b/tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_streaming_iterator.py index b8ce11db8d1..11a048edc1f 100644 --- a/tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_streaming_iterator.py +++ b/tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_streaming_iterator.py @@ -6,9 +6,7 @@ import pytest from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj -from litellm.llms.anthropic.experimental_pass_through.messages import ( - streaming_iterator as streaming_iterator_module, -) +from litellm.llms.anthropic.experimental_pass_through.messages import streaming_iterator as streaming_iterator_module from litellm.llms.anthropic.experimental_pass_through.messages.streaming_iterator import ( INCOMPLETE_STREAM_ERROR_MESSAGE, AnthropicMessagesStreamHiddenParams, @@ -338,47 +336,6 @@ async def _events_then_hang(events): await asyncio.Event().wait() -@pytest.mark.asyncio -async def test_async_sse_wrapper_logs_partial_chunks_on_client_disconnect(): - """ - Regression test for LIT-5839: a client disconnect tears the generator - down with GeneratorExit at the yield, which used to skip the post-loop - logging dispatch entirely, so the partial output tokens the provider - already generated (and billed) never reached spend tracking. - """ - iterator = _RecordingLoggingIterator( - litellm_logging_obj=_make_logging_obj("test_disconnect_logs_partial_chunks"), - request_body={}, - ) - wrapped = iterator.async_sse_wrapper(_events_then_hang(TRUNCATED_TOOL_USE_EVENTS)) - streamed = [await wrapped.__anext__() for _ in range(len(TRUNCATED_TOOL_USE_EVENTS))] - assert iterator.logging_call_count == 0 - - await wrapped.aclose() - - assert iterator.logging_call_count == 1 - assert iterator.logged_chunks == streamed - - -@pytest.mark.asyncio -async def test_async_sse_wrapper_logs_partial_chunks_on_cancellation(): - iterator = _RecordingLoggingIterator( - litellm_logging_obj=_make_logging_obj("test_cancellation_logs_partial_chunks"), - request_body={}, - ) - wrapped = iterator.async_sse_wrapper(_events_then_hang(TRUNCATED_TOOL_USE_EVENTS)) - streamed = [await wrapped.__anext__() for _ in range(len(TRUNCATED_TOOL_USE_EVENTS))] - - consume_task = asyncio.ensure_future(wrapped.__anext__()) - await asyncio.sleep(0.01) - consume_task.cancel() - with pytest.raises(asyncio.CancelledError): - await consume_task - - assert iterator.logging_call_count == 1 - assert iterator.logged_chunks == streamed - - @pytest.mark.asyncio async def test_async_sse_wrapper_skips_logging_on_disconnect_before_first_chunk(): iterator = _RecordingLoggingIterator( @@ -408,6 +365,561 @@ def test_incomplete_stream_error_sse_event_is_valid_anthropic_error(): assert event.endswith("\n\n") +_STREAM_PREFIX = ( + {"type": "message_start", "message": {"id": "msg_1", "usage": {"input_tokens": 52, "output_tokens": 1}}}, + {"type": "content_block_start", "index": 0, "content_block": {"type": "text", "text": ""}}, + {"type": "content_block_delta", "index": 0, "delta": {"type": "text_delta", "text": "The Roman"}}, +) +_STREAM_TAIL = ( + {"type": "content_block_delta", "index": 0, "delta": {"type": "text_delta", "text": " Empire ..."}}, + {"type": "content_block_stop", "index": 0}, + {"type": "message_delta", "delta": {"stop_reason": "end_turn"}, "usage": {"output_tokens": 64}}, + {"type": "message_stop"}, +) + + +def _output_tokens_from_logged_chunks(chunks: list[bytes]) -> int | None: + """Read the last output_tokens the billing path would see from the SSE bytes.""" + latest: int | None = None + for raw in chunks: + for line in raw.decode().splitlines(): + if not line.startswith("data:"): + continue + data = json.loads(line[len("data:"):].strip()) + usage = data.get("usage") if isinstance(data, dict) else None + if isinstance(usage, dict) and usage.get("output_tokens") is not None: + latest = usage["output_tokens"] + return latest + + +@pytest.mark.asyncio +async def test_async_sse_wrapper_bills_full_stream_after_client_disconnect(): + """ + Regression: on a client disconnect mid-stream the upstream provider keeps + generating (and billing) the full response. The wrapper must keep draining + that upstream to its terminal ``message_delta`` and bill the real + output_tokens (64), not the partial count the client drained before leaving + (the message_start placeholder, 1). + + A ``tail_gated`` event holds back the stream tail until the client has + disconnected, so the tail can only be captured by a drain that survives the + client teardown - exactly the path the previous implementation dropped. + """ + tail_gated = asyncio.Event() + upstream_fully_drained = asyncio.Event() + + async def _gated_stream(): + for event in _STREAM_PREFIX: + yield event + await tail_gated.wait() + for event in _STREAM_TAIL: + yield event + upstream_fully_drained.set() + + iterator = _RecordingLoggingIterator( + litellm_logging_obj=_make_logging_obj("test_bills_full_stream_after_disconnect"), + request_body={}, + ) + + gen = iterator.async_sse_wrapper(_gated_stream()) + + client_chunks = [] + async for chunk in gen: + client_chunks.append(chunk) + if len(client_chunks) == len(_STREAM_PREFIX): + break + await gen.aclose() + + tail_gated.set() + await asyncio.wait_for(upstream_fully_drained.wait(), timeout=5) + for _ in range(100): + if iterator.logged_chunks: + break + await asyncio.sleep(0.01) + + assert len(client_chunks) == len(_STREAM_PREFIX) + + assert iterator.logged_chunks, "pump never billed after client disconnect" + assert _output_tokens_from_logged_chunks(iterator.logged_chunks) == 64 + assert any(c.startswith(b"event: message_stop\n") for c in iterator.logged_chunks) + assert not any(c.startswith(b"event: error\n") for c in iterator.logged_chunks) + + +@pytest.mark.asyncio +async def test_async_sse_wrapper_bills_full_stream_when_client_reads_all(): + """Happy path: when the client drains the whole stream, billing still sees + the terminal output_tokens (64) and the client gets every chunk.""" + tail_gated = asyncio.Event() + tail_gated.set() # no gating; full stream flows immediately + + async def _full_stream(): + for event in (*_STREAM_PREFIX, *_STREAM_TAIL): + yield event + + iterator = _RecordingLoggingIterator( + litellm_logging_obj=_make_logging_obj("test_bills_full_stream_happy_path"), + request_body={}, + ) + client_chunks = [chunk async for chunk in iterator.async_sse_wrapper(_full_stream())] + + for _ in range(100): + if iterator.logged_chunks: + break + await asyncio.sleep(0.01) + + assert len(client_chunks) == len(_STREAM_PREFIX) + len(_STREAM_TAIL) + assert _output_tokens_from_logged_chunks(iterator.logged_chunks) == 64 + assert not any(c.startswith(b"event: error\n") for c in iterator.logged_chunks) + + +@pytest.mark.asyncio +async def test_async_sse_wrapper_dispatches_deferred_logging_when_client_disconnects_mid_tail(): + """ + Regression: when the pump finishes draining while the client is still + connected, ``_handle_streaming_logging`` defers billing for the proxy's + post-response hook (``ProxyLogging._fire_deferred_stream_logging``), which + only fires on a normally completed response. If the client then disconnects + before consuming the queued tail, the response generator tears down via + GeneratorExit and that hook never runs. The relay teardown must dispatch + the stored deferred billing itself, or the request logs no spend at all. + """ + dispatched = [] + deferred_fired = asyncio.Event() + + def _deferred_stream_complete(logging_coroutine): + dispatched.append(logging_coroutine) + + async def _consume(): + logging_coroutine.close() + deferred_fired.set() + + return _consume() + + logging_obj = _make_logging_obj("test_deferred_dispatch_on_disconnect_mid_tail") + logging_obj._on_deferred_stream_complete = _deferred_stream_complete + iterator = BaseAnthropicMessagesStreamingIterator(litellm_logging_obj=logging_obj, request_body={}) + + async def _full_stream(): + for event in (*_STREAM_PREFIX, *_STREAM_TAIL): + yield event + + gen = iterator.async_sse_wrapper(_full_stream()) + client_chunks = [] + async for chunk in gen: + client_chunks.append(chunk) + if len(client_chunks) == len(_STREAM_PREFIX): + break + + for _ in range(100): + if getattr(logging_obj, "_deferred_stream_complete_args", None) is not None: + break + await asyncio.sleep(0.01) + assert getattr(logging_obj, "_deferred_stream_complete_args", None) is not None, "pump never deferred billing" + + await gen.aclose() + + assert len(dispatched) == 1, "relay teardown did not dispatch the deferred billing" + assert logging_obj._on_deferred_stream_complete is None + assert logging_obj._deferred_stream_complete_args is None + await asyncio.wait_for(deferred_fired.wait(), timeout=5) + + +class _ProviderStreamError(Exception): + """Stand-in for a provider-specific streaming failure carrying a status code.""" + + def __init__(self, message: str, status_code: int): + super().__init__(message) + self.status_code = status_code + + +@pytest.mark.asyncio +async def test_async_sse_wrapper_reraises_upstream_error_to_connected_client(): + """ + Regression: an upstream failure (Bedrock read / decode / chunk-conversion) + before message_stop must propagate the ORIGINAL provider exception to a + still-connected client, so the proxy's failure handling keeps the + provider-specific status. The pump must not swallow it into a generic + api_error event + normal termination. + """ + + async def _failing_stream(): + yield {"type": "message_start", "message": {"id": "msg_1", "usage": {"input_tokens": 52, "output_tokens": 1}}} + yield {"type": "content_block_delta", "index": 0, "delta": {"type": "text_delta", "text": "partial"}} + raise _ProviderStreamError("bedrock stream blew up", status_code=529) + + iterator = _RecordingLoggingIterator( + litellm_logging_obj=_make_logging_obj("test_reraises_upstream_error"), + request_body={}, + ) + + received = [] + + async def _drain(): + async for chunk in iterator.async_sse_wrapper(_failing_stream()): + received.append(chunk) + + with pytest.raises(_ProviderStreamError) as excinfo: + await _drain() + + assert excinfo.value.status_code == 529 + assert received + assert not any(c.startswith(b"event: error\n") for c in received) + assert iterator.logged_chunks == [] + + +@pytest.mark.asyncio +async def test_async_sse_wrapper_salvages_partial_spend_on_upstream_error_after_disconnect(): + """ + When the upstream errors AFTER the client has already disconnected there is + no live client to re-raise to and no failure hook will run, so the pump + salvages partial spend from what it collected instead of dropping the row. + """ + tail_gated = asyncio.Event() + + async def _gated_failing_stream(): + yield {"type": "message_start", "message": {"id": "msg_1", "usage": {"input_tokens": 52, "output_tokens": 1}}} + yield {"type": "content_block_delta", "index": 0, "delta": {"type": "text_delta", "text": "partial"}} + await tail_gated.wait() + raise _ProviderStreamError("late failure", status_code=500) + + iterator = _RecordingLoggingIterator( + litellm_logging_obj=_make_logging_obj("test_salvage_partial_on_late_error"), + request_body={}, + ) + + gen = iterator.async_sse_wrapper(_gated_failing_stream()) + received = [await gen.__anext__(), await gen.__anext__()] + await gen.aclose() # client disconnects before the upstream error + + tail_gated.set() # let the upstream raise now, after disconnect + for _ in range(100): + if iterator.logged_chunks: + break + await asyncio.sleep(0.01) + + assert len(received) == 2 + assert iterator.logged_chunks == received + + +@pytest.mark.asyncio +async def test_async_sse_wrapper_salvages_spend_when_queued_error_is_never_consumed(): + """ + When the upstream errors while the client is still connected, the pump + forwards the exception through the queue expecting the relay to re-raise it + into the proxy's failure handling. If the client disconnects before + consuming that queued exception, the handoff never happens and no failure + hook runs, so the pump must notice the unconsumed exception at teardown and + salvage partial spend instead of dropping the row entirely. + """ + upstream_errored = asyncio.Event() + + async def _failing_stream(): + yield {"type": "message_start", "message": {"id": "msg_1", "usage": {"input_tokens": 52, "output_tokens": 1}}} + yield {"type": "content_block_delta", "index": 0, "delta": {"type": "text_delta", "text": "partial"}} + upstream_errored.set() + raise _ProviderStreamError("mid-stream failure", status_code=500) + + iterator = _RecordingLoggingIterator( + litellm_logging_obj=_make_logging_obj("test_salvage_on_unconsumed_queued_error"), + request_body={}, + ) + + gen = iterator.async_sse_wrapper(_failing_stream()) + received = [await gen.__anext__(), await gen.__anext__()] + await upstream_errored.wait() # exception is now queued behind the consumed chunks + await gen.aclose() # client disconnects without ever consuming the queued exception + + for _ in range(100): + if iterator.logged_chunks: + break + await asyncio.sleep(0.01) + + assert iterator.logging_call_count == 1 + assert iterator.logged_chunks == received + + +@pytest.mark.asyncio +async def test_async_sse_wrapper_applies_backpressure_to_slow_client(monkeypatch): + """ + Regression: the relay queue is bounded, so a slow client throttles the + upstream read instead of letting the pump buffer the whole response in + memory. With a tiny queue and a client that reads a single chunk, the pump + must stall after producing only a queue's worth of chunks ahead, not race + to the end of a large stream. + """ + monkeypatch.setattr(streaming_iterator_module, "ANTHROPIC_MESSAGES_STREAM_RELAY_QUEUE_MAXSIZE", 2) + + total = 200 + produced = 0 + + async def _fast_stream(): + nonlocal produced + for i in range(total): + produced += 1 + yield {"type": "content_block_delta", "index": 0, "delta": {"type": "text_delta", "text": f"t{i}"}} + + iterator = _make_iterator("test_backpressure_slow_client") + gen = iterator.async_sse_wrapper(_fast_stream()) + try: + await gen.__anext__() + for _ in range(500): + await asyncio.sleep(0) + assert produced <= 2 + 3, f"pump ran ahead unthrottled: produced {produced} of {total}" + assert produced < total + finally: + await gen.aclose() + + +@pytest.mark.asyncio +async def test_async_sse_wrapper_bills_partial_when_detached_drain_cap_reached(monkeypatch): + """ + Regression: when the concurrent detached-drain cap is already reached, a + pump whose client has disconnected must bill what it collected instead of + continuing to drain (and accumulating) the rest of a large upstream stream, + so slow/abandoned clients can't pin unbounded worker state. + + The cap slot set is pre-occupied so the single slot is unavailable when this + pump reaches its first post-disconnect chunk; that isolates the cap decision + from multi-pump scheduling races. + """ + monkeypatch.setattr(streaming_iterator_module, "ANTHROPIC_MESSAGES_MAX_DETACHED_STREAM_DRAINS", 1) + monkeypatch.setattr(streaming_iterator_module, "ANTHROPIC_MESSAGES_STREAM_RELAY_QUEUE_MAXSIZE", 4) + + async def _hold_slot(): + await asyncio.sleep(3600) + + holder = asyncio.ensure_future(_hold_slot()) + streaming_iterator_module._DETACHED_STREAM_DRAINS.add(holder) + tail_reached = False + + async def _long_stream(): + nonlocal tail_reached + yield {"type": "message_start", "message": {"id": "m", "usage": {"input_tokens": 5, "output_tokens": 1}}} + yield {"type": "content_block_delta", "index": 0, "delta": {"type": "text_delta", "text": "x"}} + for i in range(100): + yield {"type": "content_block_delta", "index": 0, "delta": {"type": "text_delta", "text": f"more{i}"}} + tail_reached = True + yield {"type": "message_delta", "delta": {"stop_reason": "end_turn"}, "usage": {"output_tokens": 42}} + yield {"type": "message_stop"} + + iterator = _RecordingLoggingIterator(litellm_logging_obj=_make_logging_obj("drain_cap_full"), request_body={}) + try: + gen = iterator.async_sse_wrapper(_long_stream()) + await gen.__anext__() # message_start + await gen.__anext__() # first delta + await gen.aclose() # client disconnects; 100+ chunks remain upstream + + for _ in range(200): + if iterator.logged_chunks: + break + await asyncio.sleep(0) + + assert iterator.logged_chunks, "capped pump never billed" + assert len(iterator.logged_chunks) <= 2 + streaming_iterator_module.ANTHROPIC_MESSAGES_STREAM_RELAY_QUEUE_MAXSIZE + assert len(iterator.logged_chunks) < 100 + assert not any(c.startswith(b"event: message_stop\n") for c in iterator.logged_chunks) + assert tail_reached is False, "pump kept draining past the cap instead of stopping" + finally: + holder.cancel() + streaming_iterator_module._DETACHED_STREAM_DRAINS.discard(holder) + + +@pytest.mark.asyncio +async def test_async_sse_wrapper_bills_partial_when_detached_drains_disabled(monkeypatch): + """ + Regression: ANTHROPIC_MESSAGES_MAX_DETACHED_STREAM_DRAINS=0 must disable + detached draining entirely, not just shrink the cap. With no slots ever + available, the very first post-disconnect chunk must fall back to partial + spend logging instead of hanging on a cap that's unreachable. + """ + monkeypatch.setattr(streaming_iterator_module, "ANTHROPIC_MESSAGES_MAX_DETACHED_STREAM_DRAINS", 0) + monkeypatch.setattr(streaming_iterator_module, "ANTHROPIC_MESSAGES_STREAM_RELAY_QUEUE_MAXSIZE", 4) + + tail_reached = False + + async def _long_stream(): + nonlocal tail_reached + yield {"type": "message_start", "message": {"id": "m", "usage": {"input_tokens": 5, "output_tokens": 1}}} + yield {"type": "content_block_delta", "index": 0, "delta": {"type": "text_delta", "text": "x"}} + for i in range(100): + yield {"type": "content_block_delta", "index": 0, "delta": {"type": "text_delta", "text": f"more{i}"}} + tail_reached = True + yield {"type": "message_delta", "delta": {"stop_reason": "end_turn"}, "usage": {"output_tokens": 42}} + yield {"type": "message_stop"} + + iterator = _RecordingLoggingIterator(litellm_logging_obj=_make_logging_obj("drains_disabled"), request_body={}) + gen = iterator.async_sse_wrapper(_long_stream()) + await gen.__anext__() # message_start + await gen.__anext__() # first delta + await gen.aclose() # client disconnects; 100+ chunks remain upstream + + for _ in range(200): + if iterator.logged_chunks: + break + await asyncio.sleep(0) + + assert iterator.logged_chunks, "pump never billed with detached drains disabled" + assert len(iterator.logged_chunks) <= 2 + streaming_iterator_module.ANTHROPIC_MESSAGES_STREAM_RELAY_QUEUE_MAXSIZE + assert len(iterator.logged_chunks) < 100 + assert not any(c.startswith(b"event: message_stop\n") for c in iterator.logged_chunks) + assert tail_reached is False, "pump kept draining despite detached drains being disabled" + assert len(streaming_iterator_module._DETACHED_STREAM_DRAINS) == 0 + + +@pytest.mark.asyncio +async def test_async_sse_wrapper_aborts_upstream_when_detached_drain_cap_reached(monkeypatch): + """ + Regression: when the cap is full and a disconnected pump bails, it must call + aclose on the upstream stream so the provider stops generating and billing, + not continue running the stream while we record only the partial prefix. + """ + monkeypatch.setattr(streaming_iterator_module, "ANTHROPIC_MESSAGES_MAX_DETACHED_STREAM_DRAINS", 1) + monkeypatch.setattr(streaming_iterator_module, "ANTHROPIC_MESSAGES_STREAM_RELAY_QUEUE_MAXSIZE", 4) + + async def _hold_slot(): + await asyncio.sleep(3600) + + holder = asyncio.ensure_future(_hold_slot()) + streaming_iterator_module._DETACHED_STREAM_DRAINS.add(holder) + + class _AbortableStream: + def __init__(self): + self.aclose_called = False + self._remaining = iter( + ( + {"type": "message_start", "message": {"id": "m", "usage": {"input_tokens": 5, "output_tokens": 1}}}, + {"type": "content_block_delta", "index": 0, "delta": {"type": "text_delta", "text": "x"}}, + ) + + tuple( + {"type": "content_block_delta", "index": 0, "delta": {"type": "text_delta", "text": f"t{i}"}} + for i in range(50) + ) + ) + + def __aiter__(self): + return self + + async def __anext__(self): + try: + return next(self._remaining) + except StopIteration: + raise StopAsyncIteration + + async def aclose(self): + self.aclose_called = True + + stream = _AbortableStream() + iterator = _RecordingLoggingIterator(litellm_logging_obj=_make_logging_obj("abort_upstream_at_cap"), request_body={}) + try: + gen = iterator.async_sse_wrapper(stream) + await gen.__anext__() + await gen.__anext__() + await gen.aclose() + + for _ in range(200): + if iterator.logged_chunks: + break + await asyncio.sleep(0) + + assert iterator.logged_chunks, "capped pump never billed" + assert stream.aclose_called, "upstream aclose was not called when the detached-drain cap was reached" + finally: + holder.cancel() + streaming_iterator_module._DETACHED_STREAM_DRAINS.discard(holder) + + +@pytest.mark.asyncio +async def test_abort_upstream_logs_warning_when_aclose_raises(caplog): + """_abort_upstream must swallow and log any exception from aclose().""" + import logging + + class _ExplodingStream: + def __aiter__(self): + return self + + async def __anext__(self): + raise StopAsyncIteration + + async def aclose(self): + raise RuntimeError("aclose exploded") + + with caplog.at_level(logging.WARNING, logger="LiteLLM Proxy"): + await BaseAnthropicMessagesStreamingIterator._abort_upstream(_ExplodingStream()) + + assert any("abort" in r.message and "RuntimeError" in r.message for r in caplog.records) + + +@pytest.mark.asyncio +async def test_enqueue_for_client_returns_false_when_already_detached(): + """_enqueue_for_client must return False immediately (without touching the queue) + when client_detached is already set before the call.""" + from litellm.llms.anthropic.experimental_pass_through.messages.streaming_iterator import ( + BaseAnthropicMessagesStreamingIterator, + ) + + queue: asyncio.Queue[bytes | None | BaseException] = asyncio.Queue(maxsize=1) + client_detached = asyncio.Event() + client_detached.set() + + result = await BaseAnthropicMessagesStreamingIterator._enqueue_for_client(queue, client_detached, b"chunk") + assert result is False + assert queue.empty() + + +@pytest.mark.asyncio +async def test_enqueue_for_client_returns_false_when_client_detaches_while_queue_full(): + """_enqueue_for_client must return False (and cancel the put) when the queue + is full and client_detached fires before space becomes available.""" + from litellm.llms.anthropic.experimental_pass_through.messages.streaming_iterator import ( + BaseAnthropicMessagesStreamingIterator, + ) + + queue: asyncio.Queue[bytes | None | BaseException] = asyncio.Queue(maxsize=1) + queue.put_nowait(b"already-full") + + client_detached = asyncio.Event() + + async def _set_detached_soon(): + await asyncio.sleep(0.01) + client_detached.set() + + asyncio.create_task(_set_detached_soon()) + result = await BaseAnthropicMessagesStreamingIterator._enqueue_for_client(queue, client_detached, b"new-chunk") + assert result is False + assert queue.qsize() == 1 + assert queue.get_nowait() == b"already-full" + + +@pytest.mark.asyncio +async def test_async_sse_wrapper_drains_detached_when_cap_available(monkeypatch): + """Complement to the cap test: with a slot free, a disconnected pump drains + the full upstream and bills the terminal usage, and releases its slot after.""" + monkeypatch.setattr(streaming_iterator_module, "ANTHROPIC_MESSAGES_MAX_DETACHED_STREAM_DRAINS", 1) + monkeypatch.setattr(streaming_iterator_module, "ANTHROPIC_MESSAGES_STREAM_RELAY_QUEUE_MAXSIZE", 4) + + async def _stream(): + yield {"type": "message_start", "message": {"id": "m", "usage": {"input_tokens": 5, "output_tokens": 1}}} + yield {"type": "content_block_delta", "index": 0, "delta": {"type": "text_delta", "text": "x"}} + for i in range(20): + yield {"type": "content_block_delta", "index": 0, "delta": {"type": "text_delta", "text": f"m{i}"}} + yield {"type": "message_delta", "delta": {"stop_reason": "end_turn"}, "usage": {"output_tokens": 42}} + yield {"type": "message_stop"} + + iterator = _RecordingLoggingIterator(litellm_logging_obj=_make_logging_obj("drain_cap_free"), request_body={}) + gen = iterator.async_sse_wrapper(_stream()) + await gen.__anext__() + await gen.__anext__() + await gen.aclose() + + for _ in range(300): + if iterator.logged_chunks: + break + await asyncio.sleep(0.01) + + assert any(c.startswith(b"event: message_stop\n") for c in iterator.logged_chunks) + assert len(streaming_iterator_module._DETACHED_STREAM_DRAINS) == 0 + + def _decode_sse_events(events: tuple[bytes, ...]) -> list[tuple[str, dict]]: decoded = [] for event in events: @@ -599,20 +1111,35 @@ async def test_normal_end_with_deferred_dispatch_armed_parks_logging_coroutine(m @pytest.mark.asyncio async def test_client_disconnect_enqueues_immediately_even_when_deferred_dispatch_armed(monkeypatch): """ - On client disconnect the guardrail end-of-stream scan never runs, so - deferral would strand the spend log; the teardown path must keep - enqueueing immediately (LIT-5839) even when the deferred callback is armed. + Regression: on client disconnect the guardrail end-of-stream scan never + runs, so deferral would strand the spend log. The detached pump's + post-disconnect bill must bypass the deferred-dispatch park and enqueue + immediately (LIT-5839) even when the deferred callback is armed (LIT-6409). """ worker = _RecordingLoggingWorker() monkeypatch.setattr(streaming_iterator_module, "GLOBAL_LOGGING_WORKER", worker) iterator = _make_iterator("test_disconnect_enqueues_when_armed") iterator.litellm_logging_obj._on_deferred_stream_complete = _noop_deferred_dispatch - wrapped = iterator.async_sse_wrapper(_events_then_hang(TRUNCATED_TOOL_USE_EVENTS)) + tail_gated = asyncio.Event() + + async def _gated_stream(): + for event in TRUNCATED_TOOL_USE_EVENTS: + yield event + await tail_gated.wait() + yield {"type": "message_stop"} + + wrapped = iterator.async_sse_wrapper(_gated_stream()) for _ in range(len(TRUNCATED_TOOL_USE_EVENTS)): await wrapped.__anext__() await wrapped.aclose() + tail_gated.set() + for _ in range(100): + if worker.enqueued: + break + await asyncio.sleep(0.01) + assert len(worker.enqueued) == 1 assert getattr(iterator.litellm_logging_obj, "_deferred_stream_complete_args", None) is None worker.close_enqueued() @@ -629,3 +1156,123 @@ async def test_normal_end_without_deferred_dispatch_enqueues_immediately(monkeyp assert len(worker.enqueued) == 1 assert getattr(iterator.litellm_logging_obj, "_deferred_stream_complete_args", None) is None worker.close_enqueued() + + +def _backpressured_wrapper(iterator, upstream_exhausted: asyncio.Event): + async def _stream(): + try: + for event in COMPLETE_STREAM_EVENTS: + yield event + finally: + upstream_exhausted.set() + + return iterator.async_sse_wrapper(_stream()) + + +async def _drain_with_pauses_until_upstream_exhausted(gen, upstream_exhausted: asyncio.Event) -> list: + received = [] + while not upstream_exhausted.is_set(): + received.append(await gen.__anext__()) + for _ in range(25): + await asyncio.sleep(0) + assert len(received) <= len(COMPLETE_STREAM_EVENTS) + return received + + +@pytest.mark.asyncio +async def test_normal_end_parks_deferred_logging_even_when_sentinel_enqueue_backpressured(monkeypatch): + """ + Regression: with a full relay queue at end of stream, the pump suspends + while enqueueing the end-of-stream sentinel, and a client that then drains + the whole tail tears the relay down (setting ``client_detached``) before + the pump resumes. That teardown is a normally completed response, not a + disconnect: billing must still park for the proxy's post-response hook + (preserving post_call decoration such as guardrail_information) instead of + enqueueing immediately through the teardown path. + """ + monkeypatch.setattr(streaming_iterator_module, "ANTHROPIC_MESSAGES_STREAM_RELAY_QUEUE_MAXSIZE", 2) + worker = _RecordingLoggingWorker() + monkeypatch.setattr(streaming_iterator_module, "GLOBAL_LOGGING_WORKER", worker) + + dispatched = [] + + async def _deferred_stream_complete(logging_coroutine): + dispatched.append(logging_coroutine) + logging_coroutine.close() + + iterator = _make_iterator("test_sentinel_backpressure_normal_end") + iterator.litellm_logging_obj._on_deferred_stream_complete = _deferred_stream_complete + + upstream_exhausted = asyncio.Event() + gen = _backpressured_wrapper(iterator, upstream_exhausted) + received = await _drain_with_pauses_until_upstream_exhausted(gen, upstream_exhausted) + + while True: + try: + received.append(await gen.__anext__()) + except StopAsyncIteration: + break + + for _ in range(100): + if worker.enqueued or getattr(iterator.litellm_logging_obj, "_deferred_stream_complete_args", None): + break + await asyncio.sleep(0.01) + + assert len(received) == len(COMPLETE_STREAM_EVENTS) + assert worker.enqueued == [], "fully delivered stream billed through the teardown path" + assert dispatched == [] + parked = getattr(iterator.litellm_logging_obj, "_deferred_stream_complete_args", None) + assert parked is not None, "pump never parked deferred billing" + parked[0].close() + + +@pytest.mark.asyncio +async def test_relay_teardown_dispatches_deferred_billing_when_sentinel_never_consumed(monkeypatch): + """ + Regression: when the pump has parked deferred billing but its end-of-stream + sentinel never fits in the full relay queue (the client disconnects without + draining the tail), the proxy's post-response hook never fires. Exactly one + of the relay teardown or the pump's fallback must dispatch the parked + billing, or the request logs no spend at all. + """ + monkeypatch.setattr(streaming_iterator_module, "ANTHROPIC_MESSAGES_STREAM_RELAY_QUEUE_MAXSIZE", 2) + worker = _RecordingLoggingWorker() + monkeypatch.setattr(streaming_iterator_module, "GLOBAL_LOGGING_WORKER", worker) + + dispatched = [] + deferred_fired = asyncio.Event() + + def _deferred_stream_complete(logging_coroutine): + dispatched.append(logging_coroutine) + + async def _consume(): + logging_coroutine.close() + deferred_fired.set() + + return _consume() + + iterator = _make_iterator("test_sentinel_never_consumed_dispatch") + iterator.litellm_logging_obj._on_deferred_stream_complete = _deferred_stream_complete + + upstream_exhausted = asyncio.Event() + gen = _backpressured_wrapper(iterator, upstream_exhausted) + await _drain_with_pauses_until_upstream_exhausted(gen, upstream_exhausted) + + for _ in range(100): + if getattr(iterator.litellm_logging_obj, "_deferred_stream_complete_args", None) is not None: + break + await asyncio.sleep(0.01) + + await gen.aclose() + + for _ in range(100): + if dispatched: + break + await asyncio.sleep(0.01) + + assert len(dispatched) == 1, "parked billing was never dispatched" + assert getattr(iterator.litellm_logging_obj, "_deferred_stream_complete_args", None) is None + assert getattr(iterator.litellm_logging_obj, "_on_deferred_stream_complete", None) is None + assert len(worker.enqueued) == 1, "teardown billing enqueued alongside the deferred dispatch" + await worker.enqueued[0] + assert deferred_fired.is_set() diff --git a/tests/test_litellm/llms/bedrock/chat/invoke_transformations/test_bedrock_chat_invoke_transformations_anthropic_claude3_transformation.py b/tests/test_litellm/llms/bedrock/chat/invoke_transformations/test_bedrock_chat_invoke_transformations_anthropic_claude3_transformation.py index cea299280f8..a122d97a0f0 100644 --- a/tests/test_litellm/llms/bedrock/chat/invoke_transformations/test_bedrock_chat_invoke_transformations_anthropic_claude3_transformation.py +++ b/tests/test_litellm/llms/bedrock/chat/invoke_transformations/test_bedrock_chat_invoke_transformations_anthropic_claude3_transformation.py @@ -428,30 +428,58 @@ def test_output_config_forwarded_for_bedrock_chat_invoke_request(): def test_output_config_format_converted_for_bedrock_chat_invoke_request(): - """Bedrock Invoke chat path consumes ``output_config.format`` before forwarding.""" + """Bedrock Invoke chat path inlines ``output_config.format`` for models + without native structured-output support and keeps the effort key.""" config = AmazonAnthropicClaudeConfig() schema = { "type": "object", "properties": {"answer": {"type": "string"}}, } - result = config.transform_request( + with patch( # test-quality-ok: pin non-native path + "litellm.llms.bedrock.common_utils._bedrock_model_supports", + side_effect=lambda _model, key: key == "supports_output_config", + ): + result = config.transform_request( + model="anthropic.claude-opus-4-7", + messages=[{"role": "user", "content": "test"}], + optional_params={ + "max_tokens": 100, + "output_config": { + "effort": "xhigh", + "format": {"type": "json_schema", "schema": schema}, + }, + }, + litellm_params={}, + headers={}, + ) + + assert result.get("output_config") == {"effort": "xhigh"} + last_content = result["messages"][0]["content"] + assert json.loads(last_content[-1]["text"]) == schema + + +def test_output_config_format_forwarded_for_bedrock_chat_invoke_request(): + """Bedrock Invoke chat path forwards ``output_config.format`` alongside effort + for models with native structured-output support (Claude Opus 4.7).""" + schema_format = { + "type": "json_schema", + "schema": {"type": "object", "properties": {"answer": {"type": "string"}}}, + } + + result = AmazonAnthropicClaudeConfig().transform_request( model="anthropic.claude-opus-4-7", messages=[{"role": "user", "content": "test"}], optional_params={ "max_tokens": 100, - "output_config": { - "effort": "xhigh", - "format": {"type": "json_schema", "schema": schema}, - }, + "output_config": {"effort": "xhigh", "format": schema_format}, }, litellm_params={}, headers={}, ) - assert result.get("output_config") == {"effort": "xhigh"} - last_content = result["messages"][0]["content"] - assert json.loads(last_content[-1]["text"]) == schema + assert result.get("output_config") == {"effort": "xhigh", "format": schema_format} + assert "answer" not in json.dumps(result["messages"]) @pytest.mark.parametrize( @@ -488,7 +516,7 @@ def test_bedrock_chat_invoke_checks_output_config_support_with_bedrock_provider( optional_params = {"max_tokens": 100, "output_config": {"effort": "high"}} with patch( - "litellm.llms.bedrock.chat.invoke_transformations.anthropic_claude3_transformation._supports_factory", + "litellm.llms.bedrock.common_utils._bedrock_model_supports", return_value=True, ) as mock_supports_factory: result = config.transform_request( @@ -499,11 +527,7 @@ def test_bedrock_chat_invoke_checks_output_config_support_with_bedrock_provider( headers={}, ) - mock_supports_factory.assert_called_once_with( - model="us.anthropic.claude-opus-4-7", - custom_llm_provider="bedrock", - key="supports_output_config", - ) + mock_supports_factory.assert_called_once_with("us.anthropic.claude-opus-4-7", "supports_output_config") assert result["output_config"] == {"effort": "high"} @@ -542,3 +566,80 @@ def test_output_format_removed_from_bedrock_invoke_request(): assert ( "output_format" not in result ), f"output_format should be removed for Bedrock Invoke, got keys: {result.keys()}" + + +def test_bedrock_chat_invoke_forwards_output_config_format_natively(local_model_cost_map): + """Regression: ``output_config.format`` is forwarded verbatim on models Bedrock + enforces structured outputs for, instead of being inlined as prompt text.""" + import json + + config = AmazonAnthropicClaudeConfig() + schema_format = { + "type": "json_schema", + "schema": { + "type": "object", + "properties": {"zebra_count": {"type": "integer"}}, + "required": ["zebra_count"], + "additionalProperties": False, + }, + } + + result = config.transform_request( + model="us.anthropic.claude-sonnet-4-5-20250929-v1:0", + messages=[{"role": "user", "content": "say hello"}], + optional_params={ + "max_tokens": 100, + "output_config": {"format": schema_format}, + }, + litellm_params={}, + headers={}, + ) + + assert result.get("output_config") == {"format": schema_format} + assert "zebra_count" not in json.dumps(result["messages"]) + + +def test_bedrock_chat_invoke_drop_params_keeps_native_output_config_format(local_model_cost_map, monkeypatch): + """``drop_params=True`` must not eat ``output_config.format`` before the + native-forwarding router runs (Sonnet 4.5 has no effort flags).""" + import litellm + + monkeypatch.setattr(litellm, "drop_params", True) + schema_format = { + "type": "json_schema", + "schema": {"type": "object", "properties": {"zebra_count": {"type": "integer"}}}, + } + + result = AmazonAnthropicClaudeConfig().transform_request( + model="us.anthropic.claude-sonnet-4-5-20250929-v1:0", + messages=[{"role": "user", "content": "say hello"}], + optional_params={"max_tokens": 100, "output_config": {"format": schema_format}}, + litellm_params={}, + headers={}, + ) + + assert result.get("output_config") == {"format": schema_format} + + +def test_bedrock_chat_invoke_drop_params_still_inlines_for_non_native(local_model_cost_map, monkeypatch): + """``drop_params=True`` on a model without native structured-output support + still reaches the inline-schema fallback instead of losing the schema.""" + import litellm + + monkeypatch.setattr(litellm, "drop_params", True) + schema = {"type": "object", "properties": {"zebra_count": {"type": "integer"}}} + + result = AmazonAnthropicClaudeConfig().transform_request( + model="anthropic.claude-3-haiku-20240307-v1:0", + messages=[{"role": "user", "content": "say hello"}], + optional_params={ + "max_tokens": 100, + "output_config": {"format": {"type": "json_schema", "schema": schema}}, + }, + litellm_params={}, + headers={}, + ) + + assert "output_config" not in result + last_content = result["messages"][-1]["content"] + assert json.loads(last_content[-1]["text"]) == schema diff --git a/tests/test_litellm/llms/bedrock/files/test_bedrock_files_handler.py b/tests/test_litellm/llms/bedrock/files/test_bedrock_files_handler.py index 7f91b49a6f5..639be272351 100644 --- a/tests/test_litellm/llms/bedrock/files/test_bedrock_files_handler.py +++ b/tests/test_litellm/llms/bedrock/files/test_bedrock_files_handler.py @@ -204,3 +204,69 @@ def test_should_forward_trusted_model_credentials_to_retrieve_provider_config(): assert response is mock_response litellm_params = mock_retrieve_file.call_args.kwargs["litellm_params"] assert litellm_params["_litellm_internal_model_credentials"] is trusted_credentials + + +@pytest.mark.asyncio +async def test_afile_content_assumes_role_with_external_id(monkeypatch): + """A trust policy requiring sts:ExternalId must be satisfied by the deployment's aws_external_id.""" + import datetime + + import boto3 + from botocore.exceptions import ClientError + + monkeypatch.delenv("AWS_EXTERNAL_ID", raising=False) + + class FakeSTSClient: + def get_caller_identity(self): + return {"Arn": "arn:aws:iam::111111111111:user/litellm-proxy-pod"} + + def assume_role(self, **params): + if params.get("ExternalId") != "external-id-files-download": + raise ClientError( + {"Error": {"Code": "AccessDenied", "Message": "is not authorized to perform: sts:AssumeRole"}}, + "AssumeRole", + ) + return { + "Credentials": { + "AccessKeyId": "ASIAFILESDOWNLOADROLE", + "SecretAccessKey": "assumed-secret", + "SessionToken": "assumed-session-token", + "Expiration": datetime.datetime.now(datetime.timezone.utc) + datetime.timedelta(minutes=30), + } + } + + class FakeS3Body: + def read(self): + return b'{"custom_id": "req-1"}' + + class FakeS3Client: + def get_object(self, Bucket, Key): + return {"Body": FakeS3Body()} + + def fake_boto3_client(service_name, **kwargs): + if service_name == "sts": + return FakeSTSClient() + return FakeS3Client() + + optional_params = { + "_litellm_internal_model_credentials": MappingProxyType({"s3_bucket_name": "safe-bucket"}), + "aws_region_name": "us-east-1", + "aws_access_key_id": "AKIAFILESDOWNLOADCALLER", + "aws_secret_access_key": "pod-caller-secret", + "aws_role_name": "arn:aws:iam::999999999999:role/litellm-files-download-role", + "aws_session_name": "litellm-files-download-session", + "aws_external_id": "external-id-files-download", + } + + with patch.object(boto3, "client", side_effect=fake_boto3_client) as mock_boto3_client: + response = await BedrockFilesHandler().afile_content( + file_content_request={"file_id": "s3://safe-bucket/litellm-bedrock-files-model-id-abc.jsonl"}, + optional_params=optional_params, + timeout=10.0, + max_retries=None, + ) + + s3_client_kwargs = next(call.kwargs for call in mock_boto3_client.call_args_list if call.args[0] == "s3") + assert s3_client_kwargs["aws_access_key_id"] == "ASIAFILESDOWNLOADROLE" + assert s3_client_kwargs["aws_session_token"] == "assumed-session-token" + assert response.content == b'{"custom_id": "req-1"}' diff --git a/tests/test_litellm/llms/bedrock/files/test_bedrock_files_transformation.py b/tests/test_litellm/llms/bedrock/files/test_bedrock_files_transformation.py index da13f265ee4..541c0db15d8 100644 --- a/tests/test_litellm/llms/bedrock/files/test_bedrock_files_transformation.py +++ b/tests/test_litellm/llms/bedrock/files/test_bedrock_files_transformation.py @@ -2404,3 +2404,111 @@ class TestBedrockFilesS3SignatureEncoding: body=None, headers=litellm_params[S3_SIGNED_GET_HEADERS_PARAM], ) + + +def test_sign_s3_request_assumes_role_with_external_id(monkeypatch): + """A trust policy requiring sts:ExternalId must be satisfied when signing the S3 upload request.""" + import datetime + from unittest.mock import patch + + import boto3 + from botocore.exceptions import ClientError + + from litellm.llms.bedrock.files.transformation import BedrockFilesConfig + + monkeypatch.delenv("AWS_EXTERNAL_ID", raising=False) + + class FakeSTSClient: + def get_caller_identity(self): + return {"Arn": "arn:aws:iam::111111111111:user/litellm-proxy-pod"} + + def assume_role(self, **params): + if params.get("ExternalId") != "external-id-files-put": + raise ClientError( + {"Error": {"Code": "AccessDenied", "Message": "is not authorized to perform: sts:AssumeRole"}}, + "AssumeRole", + ) + return { + "Credentials": { + "AccessKeyId": "ASIAFILESPUTROLE", + "SecretAccessKey": "assumed-secret", + "SessionToken": "assumed-session-token", + "Expiration": datetime.datetime.now(datetime.timezone.utc) + datetime.timedelta(minutes=30), + } + } + + optional_params = { + "aws_region_name": "us-east-1", + "aws_access_key_id": "AKIAFILESPUTCALLER", + "aws_secret_access_key": "pod-caller-secret", + "aws_role_name": "arn:aws:iam::999999999999:role/litellm-files-put-role", + "aws_session_name": "litellm-files-put-session", + "aws_external_id": "external-id-files-put", + } + + with patch.object(boto3, "client", return_value=FakeSTSClient()): + signed_headers, _signed_body = BedrockFilesConfig()._sign_s3_request( + content='{"custom_id": "req-1"}', + api_base="https://s3.us-east-1.amazonaws.com/safe-bucket/litellm-bedrock-files-model-id-abc.jsonl", + optional_params=optional_params, + ) + + authorization = {key.lower(): value for key, value in signed_headers.items()}["authorization"] + assert "ASIAFILESPUTROLE" in authorization + + +def test_sign_s3_get_request_assumes_role_with_external_id(monkeypatch): + """A trust policy requiring sts:ExternalId must be satisfied when signing the S3 download request.""" + import datetime + from unittest.mock import patch + + import boto3 + from botocore.exceptions import ClientError + + from litellm.llms.bedrock.files.transformation import ( + BedrockFilesConfig, + _BedrockS3RequestParams, + ) + + monkeypatch.delenv("AWS_EXTERNAL_ID", raising=False) + + class FakeSTSClient: + def get_caller_identity(self): + return {"Arn": "arn:aws:iam::111111111111:user/litellm-proxy-pod"} + + def assume_role(self, **params): + if params.get("ExternalId") != "external-id-files-get": + raise ClientError( + {"Error": {"Code": "AccessDenied", "Message": "is not authorized to perform: sts:AssumeRole"}}, + "AssumeRole", + ) + return { + "Credentials": { + "AccessKeyId": "ASIAFILESGETROLE", + "SecretAccessKey": "assumed-secret", + "SessionToken": "assumed-session-token", + "Expiration": datetime.datetime.now(datetime.timezone.utc) + datetime.timedelta(minutes=30), + } + } + + request_params = _BedrockS3RequestParams.model_validate( + { + "aws_region_name": "us-east-1", + "aws_access_key_id": "AKIAFILESGETCALLER", + "aws_secret_access_key": "pod-caller-secret", + "aws_role_name": "arn:aws:iam::999999999999:role/litellm-files-get-role", + "aws_session_name": "litellm-files-get-session", + "aws_external_id": "external-id-files-get", + } + ) + assert request_params.aws_external_id == "external-id-files-get" + + with patch.object(boto3, "client", return_value=FakeSTSClient()): + signed_headers = BedrockFilesConfig()._sign_s3_get_request( + api_base="https://s3.us-east-1.amazonaws.com/safe-bucket/litellm-bedrock-files-model-id-abc.jsonl", + aws_region_name="us-east-1", + request_params=request_params, + ) + + authorization = {key.lower(): value for key, value in signed_headers.items()}["authorization"] + assert "ASIAFILESGETROLE" in authorization diff --git a/tests/test_litellm/llms/bedrock/messages/invoke_transformations/test_anthropic_claude3_transformation.py b/tests/test_litellm/llms/bedrock/messages/invoke_transformations/test_anthropic_claude3_transformation.py index 1e09afd6919..09ebc1a3c95 100644 --- a/tests/test_litellm/llms/bedrock/messages/invoke_transformations/test_anthropic_claude3_transformation.py +++ b/tests/test_litellm/llms/bedrock/messages/invoke_transformations/test_anthropic_claude3_transformation.py @@ -935,7 +935,7 @@ def test_bedrock_messages_strips_output_config(): } with patch( - "litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation._supports_factory", + "litellm.llms.bedrock.common_utils._bedrock_model_supports", return_value=False, ): result = cfg.transform_anthropic_messages_request( @@ -970,7 +970,7 @@ def test_bedrock_messages_preserves_output_config_for_claude_4_6(): } with patch( - "litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation._supports_factory", + "litellm.llms.bedrock.common_utils._bedrock_model_supports", return_value=True, ): result = cfg.transform_anthropic_messages_request( @@ -1003,7 +1003,7 @@ def test_bedrock_messages_checks_output_config_support_with_bedrock_provider(): } with patch( - "litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation._supports_factory", + "litellm.llms.bedrock.common_utils._bedrock_model_supports", return_value=True, ) as mock_supports_factory: result = cfg.transform_anthropic_messages_request( @@ -1014,11 +1014,7 @@ def test_bedrock_messages_checks_output_config_support_with_bedrock_provider(): headers={}, ) - mock_supports_factory.assert_called_with( - model="us.anthropic.claude-opus-4-7", - custom_llm_provider="bedrock", - key="supports_output_config", - ) + mock_supports_factory.assert_called_with("us.anthropic.claude-opus-4-7", "supports_output_config") assert result["output_config"] == {"effort": "high"} @@ -1038,7 +1034,7 @@ def test_bedrock_messages_forwards_output_config(): } with patch( - "litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation._supports_factory", + "litellm.llms.bedrock.common_utils._bedrock_model_supports", return_value=True, ): result = cfg.transform_anthropic_messages_request( @@ -1054,27 +1050,29 @@ def test_bedrock_messages_forwards_output_config(): def test_bedrock_messages_forwards_output_config_with_output_format(): - """``output_config`` is forwarded; ``output_format`` is converted to inline schema.""" + """Legacy ``output_format`` is forwarded as ``output_config.format`` on models + that support native structured outputs, alongside the effort key.""" from unittest.mock import patch from litellm.types.router import GenericLiteLLMParams cfg = AmazonAnthropicClaudeMessagesConfig() messages = [{"role": "user", "content": [{"type": "text", "text": "Hello"}]}] + schema_format = { + "type": "json_schema", + "schema": { + "type": "object", + "properties": {"answer": {"type": "string"}}, + }, + } optional_params = { "max_tokens": 4096, "output_config": {"effort": "low"}, - "output_format": { - "type": "json_schema", - "schema": { - "type": "object", - "properties": {"answer": {"type": "string"}}, - }, - }, + "output_format": schema_format, } with patch( - "litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation._supports_factory", + "litellm.llms.bedrock.common_utils._bedrock_model_supports", return_value=True, ): result = cfg.transform_anthropic_messages_request( @@ -1085,12 +1083,14 @@ def test_bedrock_messages_forwards_output_config_with_output_format(): headers={}, ) - assert result.get("output_config") == {"effort": "low"} + assert result.get("output_config") == {"effort": "low", "format": schema_format} assert "output_format" not in result + assert "answer" not in json.dumps(result["messages"]) def test_bedrock_messages_converts_output_config_format_to_inline_schema(): - """``output_config.format`` is consumed so Bedrock does not see an unknown nested key.""" + """Without native structured-output support, ``output_config.format`` falls back + to the inline schema so Bedrock does not see an unknown nested key.""" from unittest.mock import patch from litellm.types.router import GenericLiteLLMParams @@ -1110,8 +1110,8 @@ def test_bedrock_messages_converts_output_config_format_to_inline_schema(): } with patch( - "litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation._supports_factory", - return_value=True, + "litellm.llms.bedrock.common_utils._bedrock_model_supports", + side_effect=lambda _model, key: key == "supports_output_config", ): result = cfg.transform_anthropic_messages_request( model="anthropic.claude-opus-4-7", @@ -1146,7 +1146,7 @@ def test_bedrock_messages_normalizes_output_config_effort_for_opus( cfg = AmazonAnthropicClaudeMessagesConfig() with patch( - "litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation._supports_factory", + "litellm.llms.bedrock.common_utils._bedrock_model_supports", return_value=True, ): result = cfg.transform_anthropic_messages_request( @@ -1184,8 +1184,8 @@ def test_bedrock_messages_does_not_mutate_callers_messages_when_embedding_schema } with patch( - "litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation._supports_factory", - return_value=True, + "litellm.llms.bedrock.common_utils._bedrock_model_supports", + side_effect=lambda _model, key: key == "supports_output_config", ): result = cfg.transform_anthropic_messages_request( model="anthropic.claude-opus-4-7", @@ -1229,7 +1229,7 @@ def test_bedrock_messages_does_not_mutate_callers_output_config(): } with patch( - "litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation._supports_factory", + "litellm.llms.bedrock.common_utils._bedrock_model_supports", return_value=True, ): cfg.transform_anthropic_messages_request( @@ -1271,7 +1271,7 @@ def test_bedrock_messages_strips_output_config_with_output_format(): } with patch( - "litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation._supports_factory", + "litellm.llms.bedrock.common_utils._bedrock_model_supports", return_value=False, ): result = cfg.transform_anthropic_messages_request( @@ -1332,7 +1332,7 @@ def test_bedrock_messages_drop_params_keeps_output_config_for_4_7(): litellm.drop_params = True try: with patch( - "litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation._supports_factory", + "litellm.llms.bedrock.common_utils._bedrock_model_supports", return_value=True, ): result = cfg.transform_anthropic_messages_request( @@ -1375,7 +1375,7 @@ def test_bedrock_messages_maps_reasoning_effort_for_adaptive_model( } with patch( - "litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation._supports_factory", + "litellm.llms.bedrock.common_utils._bedrock_model_supports", return_value=True, ): result = cfg.transform_anthropic_messages_request( @@ -1482,7 +1482,7 @@ def test_bedrock_messages_explicit_output_config_wins_over_reasoning_effort(): } with patch( - "litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation._supports_factory", + "litellm.llms.bedrock.common_utils._bedrock_model_supports", return_value=True, ): result = cfg.transform_anthropic_messages_request( @@ -3066,17 +3066,21 @@ def test_bedrock_invoke_messages_allows_converted_websearch_function_tool(): async def test_bedrock_sse_wrapper_dispatches_logging_on_client_disconnect(): """ Regression test for LIT-5839: closing the outer bedrock_sse_wrapper - mid-stream (what the proxy does on a client disconnect) must close the - inner async_sse_wrapper deterministically so the partial-stream logging - fires. `completion_start_time` is only stamped on the logging object by - that dispatch, so it observing a value proves the whole chain ran. + mid-stream (what the proxy does on a client disconnect) must not lose the + stream's spend logging. Since the detached-pump relay, the upstream read + survives the disconnect and billing fires once the provider stream ends, + so the dispatch is awaited after releasing the upstream instead of being + observed synchronously at aclose(). `completion_start_time` is only + stamped on the logging object by that dispatch, so it observing a value + proves the whole chain ran. """ cfg = AmazonAnthropicClaudeMessagesConfig() + release_upstream = asyncio.Event() - async def _hanging_stream(): + async def _gated_stream(): yield {"type": "message_start", "message": {"id": "msg_1", "usage": {"input_tokens": 25, "output_tokens": 1}}} yield {"type": "content_block_delta", "index": 0, "delta": {"type": "text_delta", "text": "partial"}} - await asyncio.Event().wait() + await release_upstream.wait() logging_obj = LiteLLMLoggingObj( model="bedrock/invoke/anthropic.claude-3-sonnet-20240229-v1:0", @@ -3087,11 +3091,162 @@ async def test_bedrock_sse_wrapper_dispatches_logging_on_client_disconnect(): litellm_call_id="test_bedrock_sse_wrapper_disconnect_logging", function_id="test_bedrock_sse_wrapper_disconnect_logging", ) - wrapped = cfg.bedrock_sse_wrapper(_hanging_stream(), litellm_logging_obj=logging_obj, request_body={}) + wrapped = cfg.bedrock_sse_wrapper(_gated_stream(), litellm_logging_obj=logging_obj, request_body={}) await wrapped.__anext__() await wrapped.__anext__() assert logging_obj.completion_start_time is None await wrapped.aclose() + release_upstream.set() + for _ in range(500): + if logging_obj.completion_start_time is not None: + break + await asyncio.sleep(0.01) assert logging_obj.completion_start_time is not None + + +def test_bedrock_messages_forwards_output_config_format_natively(local_model_cost_map): + """Regression: on a model Bedrock enforces structured outputs for (Claude + Sonnet 4.5), ``output_config.format`` must be forwarded verbatim, not + silently rewritten into inline prompt text.""" + from litellm.types.router import GenericLiteLLMParams + + cfg = AmazonAnthropicClaudeMessagesConfig() + schema_format = { + "type": "json_schema", + "schema": { + "type": "object", + "properties": { + "zebra_count": {"type": "integer"}, + "is_tuesday": {"type": "boolean"}, + }, + "required": ["zebra_count", "is_tuesday"], + "additionalProperties": False, + }, + } + + result = cfg.transform_anthropic_messages_request( + model="us.anthropic.claude-sonnet-4-5-20250929-v1:0", + messages=[{"role": "user", "content": [{"type": "text", "text": "say hello"}]}], + anthropic_messages_optional_request_params={ + "max_tokens": 100, + "output_config": {"format": schema_format}, + }, + litellm_params=GenericLiteLLMParams(), + headers={}, + ) + + assert result.get("output_config") == {"format": schema_format} + assert "zebra_count" not in json.dumps(result["messages"]) + + +def test_bedrock_messages_inlines_schema_for_claude_5(local_model_cost_map): + """Bedrock rejects ``output_config.format`` for the Claude 5 family, so the + schema falls back to the inline-text path instead of a deterministic 400.""" + from litellm.types.router import GenericLiteLLMParams + + cfg = AmazonAnthropicClaudeMessagesConfig() + schema = { + "type": "object", + "properties": {"zebra_count": {"type": "integer"}}, + } + + result = cfg.transform_anthropic_messages_request( + model="us.anthropic.claude-sonnet-5", + messages=[{"role": "user", "content": [{"type": "text", "text": "say hello"}]}], + anthropic_messages_optional_request_params={ + "max_tokens": 100, + "output_config": {"format": {"type": "json_schema", "schema": schema}}, + }, + litellm_params=GenericLiteLLMParams(), + headers={}, + ) + + assert "output_config" not in result + last_content = result["messages"][-1]["content"] + assert json.loads(last_content[-1]["text"]) == schema + + +def test_bedrock_messages_legacy_output_format_wins_over_output_config_format(local_model_cost_map): + """When a request carries both schema forms, the legacy top-level + ``output_format`` keeps winning, matching the pre-existing precedence.""" + from litellm.types.router import GenericLiteLLMParams + + cfg = AmazonAnthropicClaudeMessagesConfig() + legacy_format = { + "type": "json_schema", + "schema": {"type": "object", "properties": {"legacy_field": {"type": "string"}}}, + } + newer_format = { + "type": "json_schema", + "schema": {"type": "object", "properties": {"newer_field": {"type": "string"}}}, + } + + result = cfg.transform_anthropic_messages_request( + model="us.anthropic.claude-sonnet-4-5-20250929-v1:0", + messages=[{"role": "user", "content": [{"type": "text", "text": "say hello"}]}], + anthropic_messages_optional_request_params={ + "max_tokens": 100, + "output_format": legacy_format, + "output_config": {"format": newer_format}, + }, + litellm_params=GenericLiteLLMParams(), + headers={}, + ) + + assert result.get("output_config") == {"format": legacy_format} + assert "output_format" not in result + assert "newer_field" not in json.dumps(result) + + +def test_bedrock_messages_drop_params_keeps_native_output_config_format(local_model_cost_map, monkeypatch): + """``drop_params=True`` must not strip a natively forwarded + ``output_config.format`` on models without effort support (Sonnet 4.5).""" + import litellm + from litellm.types.router import GenericLiteLLMParams + + monkeypatch.setattr(litellm, "drop_params", True) + cfg = AmazonAnthropicClaudeMessagesConfig() + schema_format = { + "type": "json_schema", + "schema": {"type": "object", "properties": {"zebra_count": {"type": "integer"}}}, + } + + result = cfg.transform_anthropic_messages_request( + model="us.anthropic.claude-sonnet-4-5-20250929-v1:0", + messages=[{"role": "user", "content": [{"type": "text", "text": "say hello"}]}], + anthropic_messages_optional_request_params={ + "max_tokens": 100, + "output_config": {"format": schema_format}, + }, + litellm_params=GenericLiteLLMParams(), + headers={}, + ) + + assert result.get("output_config") == {"format": schema_format} + + +def test_bedrock_messages_strips_effort_but_keeps_format_for_sonnet_4_5(local_model_cost_map): + """Sonnet 4.5 has native structured-output support but no effort support, so + a mixed ``output_config`` keeps ``format`` and drops ``effort``.""" + from litellm.types.router import GenericLiteLLMParams + + cfg = AmazonAnthropicClaudeMessagesConfig() + schema_format = { + "type": "json_schema", + "schema": {"type": "object", "properties": {"zebra_count": {"type": "integer"}}}, + } + + result = cfg.transform_anthropic_messages_request( + model="us.anthropic.claude-sonnet-4-5-20250929-v1:0", + messages=[{"role": "user", "content": [{"type": "text", "text": "say hello"}]}], + anthropic_messages_optional_request_params={ + "max_tokens": 4096, + "output_config": {"format": schema_format, "effort": "high"}, + }, + litellm_params=GenericLiteLLMParams(), + headers={}, + ) + + assert result.get("output_config") == {"format": schema_format} diff --git a/tests/test_litellm/llms/bedrock/realtime/test_bedrock_realtime_handler.py b/tests/test_litellm/llms/bedrock/realtime/test_bedrock_realtime_handler.py index 9efcee192b1..0ea5b7ad4a1 100644 --- a/tests/test_litellm/llms/bedrock/realtime/test_bedrock_realtime_handler.py +++ b/tests/test_litellm/llms/bedrock/realtime/test_bedrock_realtime_handler.py @@ -6,7 +6,7 @@ from unittest.mock import MagicMock import pytest - +import litellm from litellm.llms.bedrock.common_utils import BedrockError from litellm.llms.bedrock.realtime.handler import BedrockRealtime from litellm.llms.bedrock.realtime.transformation import BedrockRealtimeConfig @@ -104,12 +104,24 @@ class RealtimeClientWS: self.closed = True -class ImmediatelyEndingBedrockStream: - def __init__(self): +class ScriptedBedrockReceiver: + def __init__(self, payloads): + self._payloads = list(payloads) + + async def receive(self): + if not self._payloads: + return None + payload = self._payloads.pop(0) + return SimpleNamespace(value=SimpleNamespace(bytes_=payload.encode("utf-8"))) + + +class ScriptedBedrockStream: + def __init__(self, payloads): self.input_stream = FakeInputStream() + self._receiver = ScriptedBedrockReceiver(payloads) async def await_output(self): - return (None, EndedBedrockReceiver()) + return (None, self._receiver) class FakeStaticCredentialsResolver: @@ -151,7 +163,7 @@ def stub_aws_sdk_client(monkeypatch): async def invoke_model_with_bidirectional_stream(self, operation_input): captured["operation_input"] = operation_input - return ImmediatelyEndingBedrockStream() + return ScriptedBedrockStream(captured.get("scripted_payloads", [])) package = types.ModuleType("aws_sdk_bedrock_runtime") client_module = types.ModuleType("aws_sdk_bedrock_runtime.client") @@ -271,19 +283,132 @@ class TestBedrockRealtimeHandler: assert "sessionEnd" in event_names assert stream.input_stream.closed + @pytest.mark.asyncio + async def test_forwarded_events_are_filtered_to_logged_types_for_spend_logging(self): + handler = BedrockRealtime() + stream = ScriptedBedrockStream( + [ + json.dumps({"event": {"userSpeechStart": {}}}), + json.dumps({"event": {"contentStart": {"role": "ASSISTANT", "type": "TEXT"}}}), + json.dumps({"event": {"textOutput": {"content": "Hi"}}}), + json.dumps({"event": {"contentEnd": {"stopReason": "END_TURN"}}}), + ] + ) + client_ws = RealtimeClientWS() + + logged_events = [ + event + async for event in handler._forward_bedrock_to_client( + stream, + client_ws, + BedrockRealtimeConfig(), + "amazon.nova-sonic-v1:0", + FakeLogging(), + {}, + ) + ] + + assert [event["type"] for event in logged_events] == ["response.done"] + sent_types = [json.loads(message)["type"] for message in client_ws.sent_to_client] + assert "input_audio_buffer.speech_started" in sent_types + assert "response.text.delta" in sent_types + assert "response.done" in sent_types + assert client_ws.closed + + @pytest.mark.asyncio + async def test_logged_event_types_star_collects_every_forwarded_event(self, monkeypatch): + monkeypatch.setattr(litellm, "logged_real_time_event_types", "*") + handler = BedrockRealtime() + stream = ScriptedBedrockStream( + [ + json.dumps({"event": {"userSpeechStart": {}}}), + json.dumps({"event": {"userSpeechEnd": {}}}), + ] + ) + client_ws = RealtimeClientWS() + + logged_events = [ + event + async for event in handler._forward_bedrock_to_client( + stream, + client_ws, + BedrockRealtimeConfig(), + "amazon.nova-sonic-v1:0", + FakeLogging(), + {}, + ) + ] + + assert [event["type"] for event in logged_events] == [ + "input_audio_buffer.speech_started", + "input_audio_buffer.speech_stopped", + ] + + @pytest.mark.asyncio + async def test_trailing_usage_after_last_done_is_dispatched_for_spend(self, stub_aws_sdk_client, monkeypatch): + import litellm.llms.bedrock.realtime.handler as handler_module + + dispatched = {} + + class RecordingLogging(FakeLogging): + async def dispatch_success_handlers(self, result=None, prefer_async_handlers=False, **kwargs): + dispatched["events"] = result + + class RecordingLoggingWorker: + def ensure_initialized_and_enqueue(self, coro): + dispatched["coro"] = coro + + monkeypatch.setattr(handler_module, "GLOBAL_LOGGING_WORKER", RecordingLoggingWorker()) + stub_aws_sdk_client["scripted_payloads"] = [ + json.dumps( + { + "event": { + "usageEvent": { + "totalInputTokens": 3, + "totalOutputTokens": 6, + "totalTokens": 9, + "details": { + "total": { + "input": {"speechTokens": 3, "textTokens": 0}, + "output": {"speechTokens": 0, "textTokens": 6}, + } + }, + } + } + } + ) + ] + + await BedrockRealtime().async_realtime( + model="amazon.nova-sonic-v1:0", + websocket=RealtimeClientWS(), + logging_obj=RecordingLogging(), + aws_region_name="us-east-1", + aws_access_key_id="k", + aws_secret_access_key="s", + ) + await dispatched["coro"] + + assert [event["type"] for event in dispatched["events"]] == ["response.done"] + usage = dispatched["events"][0]["response"]["usage"] + assert (usage["input_tokens"], usage["output_tokens"], usage["total_tokens"]) == (3, 6, 9) + assert usage["input_token_details"] == {"audio_tokens": 3, "text_tokens": 0, "cached_tokens": 0} + assert usage["output_token_details"] == {"audio_tokens": 0, "text_tokens": 6} + @pytest.mark.asyncio async def test_bedrock_stream_end_closes_client_websocket(self): handler = BedrockRealtime() client_ws = ClosableClientWS() - await handler._forward_bedrock_to_client( + async for _ in handler._forward_bedrock_to_client( EndedBedrockStream(), client_ws, BedrockRealtimeConfig(), "amazon.nova-sonic-v1:0", MagicMock(), {}, - ) + ): + pass assert client_ws.closed @@ -320,9 +445,7 @@ class TestBedrockRealtimeSessionLifecycle: [json.dumps({"type": "session.update", "session": {"instructions": "hi", "modalities": ["text"]}})] ) - await handler._forward_client_to_bedrock( - client_ws, stream, config, "amazon.nova-sonic-v1:0", {}, FakeLogging() - ) + await handler._forward_client_to_bedrock(client_ws, stream, config, "amazon.nova-sonic-v1:0", {}, FakeLogging()) acked = [json.loads(message) for message in client_ws.sent_to_client] updated = [event for event in acked if event["type"] == "session.updated"] @@ -334,9 +457,7 @@ class TestBedrockRealtimeSessionLifecycle: handler = BedrockRealtime() config = BedrockRealtimeConfig() stream = FakeBedrockStream() - client_ws = DisconnectingClientWS( - [json.dumps({"type": "session.update", "session": {"instructions": "hi"}})] - ) + client_ws = DisconnectingClientWS([json.dumps({"type": "session.update", "session": {"instructions": "hi"}})]) await handler._forward_client_to_bedrock(client_ws, stream, config, "amazon.nova-sonic-v1:0", {}) diff --git a/tests/test_litellm/llms/bedrock/realtime/test_bedrock_realtime_transformation.py b/tests/test_litellm/llms/bedrock/realtime/test_bedrock_realtime_transformation.py index ae6b1febd6b..a74f03449a1 100644 --- a/tests/test_litellm/llms/bedrock/realtime/test_bedrock_realtime_transformation.py +++ b/tests/test_litellm/llms/bedrock/realtime/test_bedrock_realtime_transformation.py @@ -827,5 +827,310 @@ class TestBedrockRealtimeSessionEvents: assert event["session"]["modalities"] == ["text", "audio"] +class TestBedrockRealtimeUserEventsAndUsage: + """Regression tests for #38346: USER ASR transcripts, speech boundary events, + usage propagation, and duplicate response.created""" + + @staticmethod + def _run(config, messages): + logging_obj = MagicMock() + logging_obj.litellm_trace_id = "trace_123" + state = { + "session_configuration_request": json.dumps({"configured": True}), + "current_output_item_id": None, + "current_response_id": None, + "current_conversation_id": None, + "current_delta_chunks": [], + "current_item_chunks": [], + "current_delta_type": None, + } + all_events = [] + for msg in messages: + result = config.transform_realtime_response( + json.dumps(msg), + "amazon.nova-2-sonic-v1:0", + logging_obj, + realtime_response_transform_input=dict(state), + ) + all_events.extend(result["response"]) + state.update( + { + "current_output_item_id": result["current_output_item_id"], + "current_response_id": result["current_response_id"], + "current_conversation_id": result["current_conversation_id"], + "current_delta_chunks": result["current_delta_chunks"], + "current_delta_type": result["current_delta_type"], + } + ) + return all_events + + def test_user_speech_start_and_stop_events(self): + events = self._run( + BedrockRealtimeConfig(), + [{"event": {"userSpeechStart": {}}}, {"event": {"userSpeechEnd": {}}}], + ) + assert [e["type"] for e in events] == [ + "input_audio_buffer.speech_started", + "input_audio_buffer.speech_stopped", + ] + assert all(e["event_id"] and e["item_id"] for e in events) + assert events[0]["item_id"] == events[1]["item_id"] + + def test_utterance_lifecycle_shares_one_item_id(self): + events = self._run( + BedrockRealtimeConfig(), + [ + {"event": {"userSpeechStart": {}}}, + {"event": {"userSpeechEnd": {}}}, + { + "event": { + "contentStart": { + "role": "USER", + "type": "TEXT", + "additionalModelFields": json.dumps({"generationStage": "FINAL"}), + } + } + }, + {"event": {"textOutput": {"content": "ready"}}}, + {"event": {"contentEnd": {"stopReason": "PARTIAL_TURN"}}}, + ], + ) + item_ids = {e["item_id"] for e in events if "item_id" in e} + assert len(item_ids) == 1 + + def test_new_utterance_gets_new_item_id(self): + config = BedrockRealtimeConfig() + first = self._run(config, [{"event": {"userSpeechStart": {}}}, {"event": {"userSpeechEnd": {}}}]) + second = self._run(config, [{"event": {"userSpeechStart": {}}}, {"event": {"userSpeechEnd": {}}}]) + assert first[0]["item_id"] == first[1]["item_id"] + assert second[0]["item_id"] == second[1]["item_id"] + assert first[0]["item_id"] != second[0]["item_id"] + + def test_user_transcript_emits_input_audio_transcription_events(self): + events = self._run( + BedrockRealtimeConfig(), + [ + { + "event": { + "contentStart": { + "role": "USER", + "type": "TEXT", + "additionalModelFields": json.dumps({"generationStage": "FINAL"}), + } + } + }, + {"event": {"textOutput": {"content": "ready"}}}, + {"event": {"contentEnd": {"stopReason": "PARTIAL_TURN"}}}, + ], + ) + deltas = [e for e in events if e["type"] == "conversation.item.input_audio_transcription.delta"] + completed = [e for e in events if e["type"] == "conversation.item.input_audio_transcription.completed"] + assert len(deltas) == 1 and deltas[0]["delta"] == "ready" + assert len(completed) == 1 and completed[0]["transcript"] == "ready" + assert deltas[0]["item_id"] == completed[0]["item_id"] + assert not any(e["type"] == "response.text.delta" for e in events) + + def test_speculative_user_transcript_emits_delta_only(self): + events = self._run( + BedrockRealtimeConfig(), + [ + { + "event": { + "contentStart": { + "role": "USER", + "type": "TEXT", + "additionalModelFields": json.dumps({"generationStage": "SPECULATIVE"}), + } + } + }, + {"event": {"textOutput": {"content": "rea"}}}, + ], + ) + assert [e["type"] for e in events] == ["conversation.item.input_audio_transcription.delta"] + + def test_user_transcript_state_resets_on_content_end(self): + events = self._run( + BedrockRealtimeConfig(), + [ + {"event": {"contentStart": {"role": "USER", "type": "TEXT"}}}, + {"event": {"contentEnd": {"stopReason": "PARTIAL_TURN"}}}, + {"event": {"contentStart": {"role": "ASSISTANT", "type": "TEXT"}}}, + {"event": {"textOutput": {"content": "Hi there"}}}, + ], + ) + text_deltas = [e for e in events if e["type"] == "response.text.delta"] + assert len(text_deltas) == 1 and text_deltas[0]["delta"] == "Hi there" + assert not any(e["type"].startswith("conversation.item.input_audio_transcription") for e in events) + + def test_response_created_emitted_once_per_response(self): + events = self._run( + BedrockRealtimeConfig(), + [ + {"event": {"contentStart": {"role": "ASSISTANT", "type": "TEXT"}}}, + {"event": {"textOutput": {"content": "Hi"}}}, + {"event": {"contentEnd": {"stopReason": "PARTIAL_TURN"}}}, + {"event": {"contentStart": {"role": "ASSISTANT", "type": "AUDIO"}}}, + ], + ) + assert sum(1 for e in events if e["type"] == "response.created") == 1 + + def test_usage_event_propagates_to_response_done(self): + events = self._run( + BedrockRealtimeConfig(), + [ + { + "event": { + "usageEvent": { + "totalInputTokens": 25, + "totalOutputTokens": 40, + "totalTokens": 65, + "details": { + "total": { + "input": {"speechTokens": 20, "textTokens": 5}, + "output": {"speechTokens": 30, "textTokens": 10}, + } + }, + } + } + }, + {"event": {"contentStart": {"role": "ASSISTANT", "type": "TEXT"}}}, + {"event": {"textOutput": {"content": "Hi"}}}, + {"event": {"contentEnd": {"stopReason": "END_TURN"}}}, + ], + ) + done_events = [e for e in events if e["type"] == "response.done"] + assert len(done_events) == 1 + usage = done_events[0]["response"]["usage"] + assert usage["input_tokens"] == 25 + assert usage["output_tokens"] == 40 + assert usage["total_tokens"] == 65 + assert usage["input_token_details"]["audio_tokens"] == 20 + assert usage["input_token_details"]["text_tokens"] == 5 + assert usage["output_token_details"]["audio_tokens"] == 30 + assert usage["output_token_details"]["text_tokens"] == 10 + + def test_response_done_without_usage_event_reports_zero_usage(self): + events = self._run( + BedrockRealtimeConfig(), + [ + {"event": {"contentStart": {"role": "ASSISTANT", "type": "TEXT"}}}, + {"event": {"textOutput": {"content": "Hi"}}}, + {"event": {"contentEnd": {"stopReason": "END_TURN"}}}, + ], + ) + done_events = [e for e in events if e["type"] == "response.done"] + assert len(done_events) == 1 + usage = done_events[0]["response"]["usage"] + assert usage["input_tokens"] == 0 + assert usage["output_tokens"] == 0 + assert usage["total_tokens"] == 0 + + @staticmethod + def _usage_event(total_input, total_output, in_speech, in_text, out_speech, out_text): + return { + "event": { + "usageEvent": { + "totalInputTokens": total_input, + "totalOutputTokens": total_output, + "totalTokens": total_input + total_output, + "details": { + "total": { + "input": {"speechTokens": in_speech, "textTokens": in_text}, + "output": {"speechTokens": out_speech, "textTokens": out_text}, + } + }, + } + } + } + + _ASSISTANT_TURN = ( + {"event": {"contentStart": {"role": "ASSISTANT", "type": "TEXT"}}}, + {"event": {"textOutput": {"content": "Hi"}}}, + {"event": {"contentEnd": {"stopReason": "END_TURN"}}}, + ) + + def test_multi_turn_usage_reports_per_response_deltas_not_cumulative_totals(self): + events = self._run( + BedrockRealtimeConfig(), + [ + self._usage_event(25, 40, in_speech=20, in_text=5, out_speech=30, out_text=10), + *self._ASSISTANT_TURN, + self._usage_event(40, 100, in_speech=30, in_text=10, out_speech=75, out_text=25), + *self._ASSISTANT_TURN, + ], + ) + usages = [e["response"]["usage"] for e in events if e["type"] == "response.done"] + assert len(usages) == 2 + assert (usages[0]["input_tokens"], usages[0]["output_tokens"], usages[0]["total_tokens"]) == (25, 40, 65) + assert (usages[1]["input_tokens"], usages[1]["output_tokens"], usages[1]["total_tokens"]) == (15, 60, 75) + assert usages[1]["input_token_details"] == {"audio_tokens": 10, "text_tokens": 5, "cached_tokens": 0} + assert usages[1]["output_token_details"] == {"audio_tokens": 45, "text_tokens": 15} + assert sum(u["total_tokens"] for u in usages) == 140 + + def test_usage_reported_after_last_response_done_flushes_as_logged_only_done(self): + config = BedrockRealtimeConfig() + self._run( + config, + [ + self._usage_event(25, 40, in_speech=20, in_text=5, out_speech=30, out_text=10), + *self._ASSISTANT_TURN, + ], + ) + assert config.leftover_usage_done_events() == () + + self._run(config, [self._usage_event(25, 46, in_speech=20, in_text=5, out_speech=30, out_text=16)]) + leftover = config.leftover_usage_done_events() + assert len(leftover) == 1 + assert leftover[0]["type"] == "response.done" + usage = leftover[0]["response"]["usage"] + assert (usage["input_tokens"], usage["output_tokens"], usage["total_tokens"]) == (0, 6, 6) + assert usage["output_token_details"] == {"audio_tokens": 0, "text_tokens": 6} + assert config.leftover_usage_done_events() == () + + def test_final_transcript_fragments_emit_one_completed_with_full_transcript(self): + events = self._run( + BedrockRealtimeConfig(), + [ + { + "event": { + "contentStart": { + "role": "USER", + "type": "TEXT", + "additionalModelFields": json.dumps({"generationStage": "FINAL"}), + } + } + }, + {"event": {"textOutput": {"content": "What is the "}}}, + {"event": {"textOutput": {"content": "capital of France?"}}}, + {"event": {"contentEnd": {"stopReason": "PARTIAL_TURN"}}}, + ], + ) + deltas = [e for e in events if e["type"] == "conversation.item.input_audio_transcription.delta"] + completed = [e for e in events if e["type"] == "conversation.item.input_audio_transcription.completed"] + assert [d["delta"] for d in deltas] == ["What is the ", "capital of France?"] + assert len(completed) == 1 + assert completed[0]["transcript"] == "What is the capital of France?" + assert {e["item_id"] for e in deltas + completed} == {completed[0]["item_id"]} + + def test_speculative_transcript_block_end_emits_no_completed(self): + events = self._run( + BedrockRealtimeConfig(), + [ + { + "event": { + "contentStart": { + "role": "USER", + "type": "TEXT", + "additionalModelFields": json.dumps({"generationStage": "SPECULATIVE"}), + } + } + }, + {"event": {"textOutput": {"content": "rea"}}}, + {"event": {"contentEnd": {"stopReason": "PARTIAL_TURN"}}}, + ], + ) + assert [e["type"] for e in events] == ["conversation.item.input_audio_transcription.delta"] + + if __name__ == "__main__": pytest.main([__file__, "-v"]) diff --git a/tests/test_litellm/llms/bedrock/test_bedrock_common_utils.py b/tests/test_litellm/llms/bedrock/test_bedrock_common_utils.py index 389bf4a8e40..9302dc01abe 100644 --- a/tests/test_litellm/llms/bedrock/test_bedrock_common_utils.py +++ b/tests/test_litellm/llms/bedrock/test_bedrock_common_utils.py @@ -520,3 +520,97 @@ def test_merge_bedrock_aws_request_params_keeps_caller_credentials_without_stati assert merged["aws_secret_access_key"] == "caller-secret" assert merged["aws_session_token"] == "caller-token" assert merged["aws_region_name"] == "us-west-2" + + +def test_strip_unsupported_output_config_keeps_format_drops_effort(local_model_cost_map): + """On a model with neither effort flag, only the ``format`` key survives.""" + from litellm.llms.bedrock.common_utils import ( + strip_unsupported_bedrock_invoke_output_config_keys, + ) + + schema_format = {"type": "json_schema", "schema": {"type": "object"}} + body = {"output_config": {"effort": "high", "format": schema_format}} + + strip_unsupported_bedrock_invoke_output_config_keys( + model="anthropic.claude-3-haiku-20240307-v1:0", + request_body=body, + ) + + assert body["output_config"] == {"format": schema_format} + + +def test_apply_structured_output_prefers_legacy_output_format(local_model_cost_map): + """The legacy ``output_format`` wins over ``output_config.format`` when a + request carries both, matching the pre-existing precedence.""" + from litellm.llms.bedrock.common_utils import ( + apply_bedrock_invoke_structured_output, + ) + + legacy = {"type": "json_schema", "schema": {"type": "object", "properties": {"a": {"type": "string"}}}} + newer = {"type": "json_schema", "schema": {"type": "object", "properties": {"b": {"type": "string"}}}} + body = { + "messages": [{"role": "user", "content": "hi"}], + "output_format": legacy, + "output_config": {"format": newer}, + } + + apply_bedrock_invoke_structured_output( + model="us.anthropic.claude-sonnet-4-5-20250929-v1:0", + request_body=body, + ) + + assert body["output_config"] == {"format": legacy} + assert "output_format" not in body + + +def test_sign_aws_request_assumes_role_with_external_id(monkeypatch): + """A trust policy requiring sts:ExternalId must be satisfied when signing batch API requests.""" + import datetime + from unittest.mock import patch + + import boto3 + from botocore.exceptions import ClientError + + from litellm.llms.bedrock.common_utils import CommonBatchFilesUtils + + monkeypatch.delenv("AWS_EXTERNAL_ID", raising=False) + + class FakeSTSClient: + def get_caller_identity(self): + return {"Arn": "arn:aws:iam::111111111111:user/litellm-proxy-pod"} + + def assume_role(self, **params): + if params.get("ExternalId") != "external-id-batch-sign": + raise ClientError( + {"Error": {"Code": "AccessDenied", "Message": "is not authorized to perform: sts:AssumeRole"}}, + "AssumeRole", + ) + return { + "Credentials": { + "AccessKeyId": "ASIABATCHSIGNROLE", + "SecretAccessKey": "assumed-secret", + "SessionToken": "assumed-session-token", + "Expiration": datetime.datetime.now(datetime.timezone.utc) + datetime.timedelta(minutes=30), + } + } + + optional_params = { + "aws_region_name": "us-east-1", + "aws_access_key_id": "AKIABATCHSIGNCALLER", + "aws_secret_access_key": "pod-caller-secret", + "aws_role_name": "arn:aws:iam::999999999999:role/litellm-batch-sign-role", + "aws_session_name": "litellm-batch-sign-session", + "aws_external_id": "external-id-batch-sign", + } + + with patch.object(boto3, "client", return_value=FakeSTSClient()): + signed_headers, signed_data = CommonBatchFilesUtils().sign_aws_request( + service_name="bedrock", + data={"jobName": "litellm-batch-job"}, + endpoint_url="https://bedrock.us-east-1.amazonaws.com/model-invocation-job", + optional_params=optional_params, + ) + + authorization = {key.lower(): value for key, value in signed_headers.items()}["authorization"] + assert "ASIABATCHSIGNROLE" in authorization + assert signed_data == b'{"jobName": "litellm-batch-job"}' diff --git a/tests/test_litellm/llms/dashscope/test_qwen_brand_aliases.py b/tests/test_litellm/llms/dashscope/test_qwen_brand_aliases.py new file mode 100644 index 00000000000..064d9d58f0c --- /dev/null +++ b/tests/test_litellm/llms/dashscope/test_qwen_brand_aliases.py @@ -0,0 +1,331 @@ +import math + +import pytest + +import litellm +from litellm import completion, get_llm_provider +from litellm.llms.dashscope.chat.transformation import DashScopeChatConfig +from litellm.llms.dashscope.cost_calculator import ( + cost_per_token as dashscope_cost_per_token, +) +from litellm.llms.dashscope.embed.transformation import DashScopeEmbeddingConfig +from litellm.llms.dashscope.image_generation.transformation import ( + DashScopeImageGenerationConfig, +) +from litellm.llms.dashscope.qwen_ai_platform import ( + QWEN_AI_PLATFORM_API_BASE, + QWEN_AI_PLATFORM_IMAGE_API_BASE, + QWEN_AI_PLATFORM_RERANK_API_BASE, + QwenAIPlatformChatConfig, + QwenAIPlatformEmbeddingConfig, + QwenAIPlatformImageGenerationConfig, + QwenAIPlatformRerankConfig, +) +from litellm.llms.dashscope.qwencloud import ( + QWENCLOUD_API_BASE, + QWENCLOUD_IMAGE_API_BASE, + QWENCLOUD_RERANK_API_BASE, + QwenCloudChatConfig, + QwenCloudEmbeddingConfig, + QwenCloudImageGenerationConfig, + QwenCloudRerankConfig, +) +from litellm.llms.dashscope.rerank.transformation import DashScopeRerankConfig +from litellm.types.utils import LlmProviders, Usage +from litellm.utils import ProviderConfigManager + +DASHSCOPE_FAMILY_ENV_VARS = [ + "DASHSCOPE_API_KEY", + "DASHSCOPE_API_BASE", + "DASHSCOPE_API_BASE_RERANK", + "DASHSCOPE_API_BASE_IMAGE", + "QWENCLOUD_API_KEY", + "QWENCLOUD_API_BASE", + "QWENCLOUD_API_BASE_RERANK", + "QWENCLOUD_API_BASE_IMAGE", + "QWEN_AI_PLATFORM_API_KEY", + "QWEN_AI_PLATFORM_API_BASE", + "QWEN_AI_PLATFORM_API_BASE_RERANK", + "QWEN_AI_PLATFORM_API_BASE_IMAGE", +] + +BRAND_CASES = [ + pytest.param( + { + "provider": "qwencloud", + "enum": LlmProviders.QWENCLOUD, + "key_env": "QWENCLOUD_API_KEY", + "base_env": "QWENCLOUD_API_BASE", + "default_base": QWENCLOUD_API_BASE, + "default_rerank_base": QWENCLOUD_RERANK_API_BASE, + "default_image_base": QWENCLOUD_IMAGE_API_BASE, + "chat_config": QwenCloudChatConfig, + "embedding_config": QwenCloudEmbeddingConfig, + "rerank_config": QwenCloudRerankConfig, + "image_config": QwenCloudImageGenerationConfig, + }, + id="qwencloud", + ), + pytest.param( + { + "provider": "qwen_ai_platform", + "enum": LlmProviders.QWEN_AI_PLATFORM, + "key_env": "QWEN_AI_PLATFORM_API_KEY", + "base_env": "QWEN_AI_PLATFORM_API_BASE", + "default_base": QWEN_AI_PLATFORM_API_BASE, + "default_rerank_base": QWEN_AI_PLATFORM_RERANK_API_BASE, + "default_image_base": QWEN_AI_PLATFORM_IMAGE_API_BASE, + "chat_config": QwenAIPlatformChatConfig, + "embedding_config": QwenAIPlatformEmbeddingConfig, + "rerank_config": QwenAIPlatformRerankConfig, + "image_config": QwenAIPlatformImageGenerationConfig, + }, + id="qwen_ai_platform", + ), +] + + +@pytest.fixture(autouse=True) +def clear_dashscope_family_env(monkeypatch): + for env_var in DASHSCOPE_FAMILY_ENV_VARS: + monkeypatch.delenv(env_var, raising=False) + + +class TestQwenBrandProviderResolution: + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_get_llm_provider_resolves_brand_default_base(self, brand): + model, provider, api_key, api_base = get_llm_provider(f"{brand['provider']}/qwen-max", api_key="sk-explicit") + assert model == "qwen-max" + assert provider == brand["provider"] + assert api_key == "sk-explicit" + assert api_base == brand["default_base"] + + def test_dashscope_resolution_unchanged(self): + model, provider, api_key, api_base = get_llm_provider("dashscope/qwen-max", api_key="sk-explicit") + assert model == "qwen-max" + assert provider == "dashscope" + assert api_base == "https://dashscope.aliyuncs.com/compatible-mode/v1" + + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_brand_env_key_wins_over_dashscope_key(self, monkeypatch, brand): + monkeypatch.setenv(brand["key_env"], "sk-brand") + monkeypatch.setenv("DASHSCOPE_API_KEY", "sk-dashscope") + _, _, api_key, _ = get_llm_provider(f"{brand['provider']}/qwen-max") + assert api_key == "sk-brand" + + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_dashscope_key_is_fallback(self, monkeypatch, brand): + monkeypatch.setenv("DASHSCOPE_API_KEY", "sk-dashscope") + _, _, api_key, _ = get_llm_provider(f"{brand['provider']}/qwen-max") + assert api_key == "sk-dashscope" + + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_dashscope_api_base_does_not_leak_into_brand(self, monkeypatch, brand): + monkeypatch.setenv("DASHSCOPE_API_BASE", "https://legacy.example.com/v1") + _, _, _, api_base = get_llm_provider(f"{brand['provider']}/qwen-max", api_key="sk-explicit") + assert api_base == brand["default_base"] + + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_brand_api_base_env_wins(self, monkeypatch, brand): + monkeypatch.setenv(brand["base_env"], "https://brand.example.com/v1") + _, _, _, api_base = get_llm_provider(f"{brand['provider']}/qwen-max", api_key="sk-explicit") + assert api_base == "https://brand.example.com/v1" + + +class TestQwenBrandConfigDispatch: + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_chat_config(self, brand): + config = ProviderConfigManager.get_provider_chat_config("qwen-max", brand["enum"]) + assert isinstance(config, brand["chat_config"]) + assert isinstance(config, DashScopeChatConfig) + + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_embedding_config(self, brand): + config = ProviderConfigManager.get_provider_embedding_config(model="text-embedding-v3", provider=brand["enum"]) + assert isinstance(config, brand["embedding_config"]) + assert isinstance(config, DashScopeEmbeddingConfig) + + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_rerank_config(self, brand): + config = ProviderConfigManager.get_provider_rerank_config( + model="gte-rerank-v2", + provider=brand["enum"], + api_base=None, + present_version_params=[], + ) + assert isinstance(config, brand["rerank_config"]) + assert isinstance(config, DashScopeRerankConfig) + + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_image_generation_config(self, brand): + config = ProviderConfigManager.get_provider_image_generation_config(model="qwen-image", provider=brand["enum"]) + assert isinstance(config, brand["image_config"]) + assert isinstance(config, DashScopeImageGenerationConfig) + + +class TestQwenBrandDefaultUrls: + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_chat_complete_url(self, brand): + url = brand["chat_config"]().get_complete_url( + api_base=None, + api_key="sk-test", + model="qwen-max", + optional_params={}, + litellm_params={}, + ) + assert url == f"{brand['default_base']}/chat/completions" + + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_embedding_complete_url(self, brand): + url = brand["embedding_config"]().get_complete_url( + api_base=None, + api_key="sk-test", + model="text-embedding-v3", + optional_params={}, + litellm_params={}, + ) + assert url == f"{brand['default_base']}/embeddings" + + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_embedding_ignores_dashscope_api_base(self, monkeypatch, brand): + monkeypatch.setenv("DASHSCOPE_API_BASE", "https://legacy.example.com/v1") + url = brand["embedding_config"]().get_complete_url( + api_base=None, + api_key="sk-test", + model="text-embedding-v3", + optional_params={}, + litellm_params={}, + ) + assert url == f"{brand['default_base']}/embeddings" + + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_rerank_complete_url(self, brand): + url = brand["rerank_config"]().get_complete_url(api_base=None, model="gte-rerank-v2") + assert url == brand["default_rerank_base"] + + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_rerank_env_override(self, monkeypatch, brand): + monkeypatch.setenv(f"{brand['base_env']}_RERANK", "https://rerank.example.com/v1/reranks") + url = brand["rerank_config"]().get_complete_url(api_base=None, model="gte-rerank-v2") + assert url == "https://rerank.example.com/v1/reranks" + + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_image_generation_complete_url(self, brand): + url = brand["image_config"]().get_complete_url( + api_base=None, + api_key="sk-test", + model="qwen-image", + optional_params={}, + litellm_params={}, + ) + assert url == brand["default_image_base"] + + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_image_generation_ignores_chat_compatible_api_base(self, brand): + url = brand["image_config"]().get_complete_url( + api_base=brand["default_base"], + api_key="sk-test", + model="qwen-image", + optional_params={}, + litellm_params={}, + ) + assert url == brand["default_image_base"] + + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_validate_environment_requires_key(self, brand): + with pytest.raises(ValueError, match="DASHSCOPE_API_KEY"): + brand["embedding_config"]().validate_environment( + headers={}, + model="text-embedding-v3", + messages=[], + optional_params={}, + litellm_params={}, + api_key=None, + api_base=None, + ) + + +class TestQwenBrandCostParity: + @pytest.fixture(autouse=True) + def setup_model_cost_map(self, monkeypatch): + monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True") + monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url="")) + + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_get_model_info(self, brand): + model_info = litellm.get_model_info(f"{brand['provider']}/qwen-max") + dashscope_info = litellm.get_model_info("dashscope/qwen-max") + assert model_info["litellm_provider"] == brand["provider"] + assert model_info["input_cost_per_token"] == dashscope_info["input_cost_per_token"] + assert model_info["output_cost_per_token"] == dashscope_info["output_cost_per_token"] + + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_flat_pricing_matches_dashscope(self, brand): + usage = Usage(prompt_tokens=1000, completion_tokens=500) + brand_costs = dashscope_cost_per_token(model="qwen-max", usage=usage, custom_llm_provider=brand["provider"]) + dashscope_costs = dashscope_cost_per_token(model="qwen-max", usage=usage) + assert brand_costs == dashscope_costs + + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_tiered_pricing_matches_dashscope(self, brand): + usage = Usage(prompt_tokens=300000, completion_tokens=300000) + brand_costs = dashscope_cost_per_token(model="qwen-flash", usage=usage, custom_llm_provider=brand["provider"]) + dashscope_costs = dashscope_cost_per_token(model="qwen-flash", usage=usage) + assert brand_costs == dashscope_costs + tier_2 = litellm.get_model_info(f"{brand['provider']}/qwen-flash")["tiered_pricing"][1] + assert math.isclose(brand_costs[0], 300000 * tier_2["input_cost_per_token"], rel_tol=1e-10) + + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_public_cost_per_token_routes_to_dashscope_calculator(self, brand): + brand_costs = litellm.cost_per_token( + model=f"{brand['provider']}/qwen-max", + prompt_tokens=1000, + completion_tokens=500, + custom_llm_provider=brand["provider"], + ) + dashscope_costs = litellm.cost_per_token( + model="dashscope/qwen-max", + prompt_tokens=1000, + completion_tokens=500, + custom_llm_provider="dashscope", + ) + assert brand_costs == dashscope_costs + + +class TestQwenBrandCompletionMock: + @pytest.mark.respx() + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_completion_hits_brand_default_host(self, respx_mock, brand, monkeypatch): + monkeypatch.setattr(litellm, "disable_aiohttp_transport", True) + respx_mock.post(f"{brand['default_base']}/chat/completions").respond( + json={ + "id": "chatcmpl-123", + "object": "chat.completion", + "created": 1677652288, + "model": "qwen-turbo", + "choices": [ + { + "index": 0, + "message": {"role": "assistant", "content": "Hey from LiteLLM!"}, + "finish_reason": "stop", + } + ], + "usage": { + "prompt_tokens": 9, + "completion_tokens": 12, + "total_tokens": 21, + }, + }, + status_code=200, + ) + + response = completion( + model=f"{brand['provider']}/qwen-turbo", + messages=[{"role": "user", "content": "say hey from LiteLLM"}], + api_key="fake-brand-key", + ) + + assert response.choices[0].message.content == "Hey from LiteLLM!" + request = respx_mock.calls[0].request + assert request.url == f"{brand['default_base']}/chat/completions" + assert request.headers["Authorization"] == "Bearer fake-brand-key" diff --git a/tests/test_litellm/llms/databricks/test_databricks_cost_calculator.py b/tests/test_litellm/llms/databricks/test_databricks_cost_calculator.py index 29ad8ee4b6e..e72642f7a04 100644 --- a/tests/test_litellm/llms/databricks/test_databricks_cost_calculator.py +++ b/tests/test_litellm/llms/databricks/test_databricks_cost_calculator.py @@ -62,6 +62,8 @@ PUBLISHED_DBU_PER_MILLION: Final = { "databricks/databricks-gemini-2-5-pro": ("22.321", "178.571", "22.321", "2.232"), "databricks/databricks-gemini-2-5-flash": ("5.357", "44.643", "5.357", "0.536"), "databricks/databricks-kimi-k3": ("42.857", "214.286", "42.857", "4.286"), + "databricks/databricks-deepseek-v4-flash-0731": ("2.000", "4.000", "2.000", "0.400"), + "databricks/databricks-deepseek-v4-pro-0813": ("18.857", "56.571", "18.857", "1.886"), "databricks/databricks-glm-5-2": ("20.000", "62.857", "20.000", "3.714"), } PROMOTIONAL_DISCOUNT: Final = 0.80 diff --git a/tests/test_litellm/llms/gigachat/__init__.py b/tests/test_litellm/llms/gigachat/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/tests/test_litellm/llms/gigachat/chat/test_gigachat_chat_streaming.py b/tests/test_litellm/llms/gigachat/chat/test_gigachat_chat_streaming.py new file mode 100644 index 00000000000..35ca93319f5 --- /dev/null +++ b/tests/test_litellm/llms/gigachat/chat/test_gigachat_chat_streaming.py @@ -0,0 +1,87 @@ +""" +Tests for litellm.llms.gigachat.chat.streaming +""" + +from litellm.llms.gigachat.chat.streaming import GigaChatModelResponseIterator + + +def _parse(chunk: dict) -> dict: + iterator = GigaChatModelResponseIterator(streaming_response=None, sync_stream=True) + return dict(iterator.chunk_parser(chunk=chunk)) + + +class TestChunkParserUsage: + def test_usage_on_stop_chunk(self): + parsed = _parse( + { + "choices": [{"delta": {"content": ""}, "index": 0, "finish_reason": "stop"}], + "usage": {"prompt_tokens": 25, "completion_tokens": 7, "total_tokens": 32}, + } + ) + + assert parsed["finish_reason"] == "stop" + assert parsed["usage"] is not None + assert parsed["usage"]["prompt_tokens"] == 25 + assert parsed["usage"]["completion_tokens"] == 7 + assert parsed["usage"]["total_tokens"] == 32 + + def test_usage_on_function_call_chunk(self): + """Regression: a final chunk ending in function_call still carries usage; it must not be dropped.""" + parsed = _parse( + { + "choices": [ + { + "delta": {"function_call": {"name": "get_weather", "arguments": {"city": "Moscow"}}}, + "index": 0, + "finish_reason": "function_call", + } + ], + "usage": {"prompt_tokens": 40, "completion_tokens": 12, "total_tokens": 52}, + } + ) + + assert parsed["finish_reason"] == "tool_calls" + assert parsed["tool_use"] is not None + assert parsed["usage"] is not None + assert parsed["usage"]["prompt_tokens"] == 40 + assert parsed["usage"]["completion_tokens"] == 12 + assert parsed["usage"]["total_tokens"] == 52 + + def test_usage_on_length_chunk(self): + parsed = _parse( + { + "choices": [{"delta": {"content": "truncated"}, "index": 0, "finish_reason": "length"}], + "usage": {"prompt_tokens": 10, "completion_tokens": 128, "total_tokens": 138}, + } + ) + + assert parsed["usage"] is not None + assert parsed["usage"]["total_tokens"] == 138 + + def test_no_usage_on_interim_chunk(self): + parsed = _parse({"choices": [{"delta": {"content": "hello"}, "index": 0, "finish_reason": None}]}) + + assert parsed["text"] == "hello" + assert parsed["is_finished"] is False + assert parsed["usage"] is None + + def test_cache_hit_usage_folds_cached_tokens_back_in(self): + """GigaChat reports prompt_tokens and total_tokens after subtracting cached tokens + (docs example: prompt_tokens=1, precached_prompt_tokens=37, total_tokens=5), so the + OpenAI-convention usage must add them back and surface them as cached_tokens.""" + parsed = _parse( + { + "choices": [{"delta": {"content": ""}, "index": 0, "finish_reason": "stop"}], + "usage": { + "prompt_tokens": 25, + "completion_tokens": 7, + "total_tokens": 32, + "precached_prompt_tokens": 20, + }, + } + ) + + assert parsed["usage"] is not None + assert parsed["usage"]["prompt_tokens"] == 45 + assert parsed["usage"]["total_tokens"] == 52 + assert parsed["usage"]["prompt_tokens_details"]["cached_tokens"] == 20 diff --git a/tests/test_litellm/llms/gigachat/chat/test_gigachat_chat_transformation.py b/tests/test_litellm/llms/gigachat/chat/test_gigachat_chat_transformation.py new file mode 100644 index 00000000000..2f9511e642c --- /dev/null +++ b/tests/test_litellm/llms/gigachat/chat/test_gigachat_chat_transformation.py @@ -0,0 +1,883 @@ +""" +Unit tests for GigaChat chat transformation. + +Tests GigaChatConfig covering get_complete_url, validate_environment, +get_supported_openai_params, map_openai_params, _convert_tools_to_functions, +_map_tool_choice, _transform_messages, transform_request, transform_response, +get_model_response_iterator, and get_error_class. +""" + +import json +from unittest.mock import MagicMock, patch + +import httpx +import pytest + +from litellm.llms.gigachat.chat.transformation import ( + GigaChatConfig, + GigaChatError, + is_valid_json, +) +from litellm.types.utils import ModelResponse, Usage + +TRANSFORM_MODULE = "litellm.llms.gigachat.chat.transformation" + + +def _make_httpx_response( + body: dict, status_code: int = 200 +) -> httpx.Response: + return httpx.Response( + status_code=status_code, + headers={"content-type": "application/json"}, + content=json.dumps(body).encode("utf-8"), + request=httpx.Request( + "POST", + "https://gigachat.devices.sberbank.ru/api/v1/chat/completions", + ), + ) + + +# --------------------------------------------------------------------------- +# is_valid_json +# --------------------------------------------------------------------------- + + +class TestIsValidJson: + def test_valid_json_object(self): + assert is_valid_json('{"key": "value"}') is True + + def test_valid_json_array(self): + assert is_valid_json("[1, 2, 3]") is True + + def test_valid_json_string(self): + assert is_valid_json('"hello"') is True + + def test_invalid_json(self): + assert is_valid_json("{invalid}") is False + + def test_empty_string(self): + assert is_valid_json("") is False + + +# --------------------------------------------------------------------------- +# GigaChatConfig +# --------------------------------------------------------------------------- + + +class TestGetCompleteUrl: + def setup_method(self): + self.config = GigaChatConfig() + + def test_uses_api_base_from_param(self): + url = self.config.get_complete_url( + api_base="https://custom.example.com", + api_key=None, + model="GigaChat", + optional_params={}, + litellm_params={}, + stream=False, + ) + assert url == "https://custom.example.com/chat/completions" + + def test_uses_api_base_with_trailing_slash(self): + url = self.config.get_complete_url( + api_base="https://custom.example.com/", + api_key=None, + model="GigaChat", + optional_params={}, + litellm_params={}, + stream=False, + ) + # get_api_base passes the value through without stripping the slash + assert url == "https://custom.example.com//chat/completions" + + def test_uses_api_base_from_get_api_base_when_none(self): + url = self.config.get_complete_url( + api_base=None, + api_key=None, + model="GigaChat", + optional_params={}, + litellm_params={}, + stream=False, + ) + assert url.endswith("/chat/completions") + + +class TestValidateEnvironment: + def setup_method(self): + self.config = GigaChatConfig() + + @patch(f"{TRANSFORM_MODULE}.get_access_token", return_value="test-token") + @patch(f"{TRANSFORM_MODULE}.get_secret_str", return_value=None) + def test_sets_auth_headers(self, mock_get_secret, mock_get_token): + headers: dict = {} + result = self.config.validate_environment( + headers=headers, + model="GigaChat", + messages=[{"role": "user", "content": "hi"}], + optional_params={}, + litellm_params={}, + api_key="creds", + api_base="https://api.example.com", + ) + assert result["Authorization"] == "Bearer test-token" + assert result["Content-Type"] == "application/json" + assert result["Accept"] == "application/json" + + @patch(f"{TRANSFORM_MODULE}.get_access_token", return_value="token") + @patch(f"{TRANSFORM_MODULE}.get_secret_str", return_value=None) + def test_stores_credentials_and_api_base_for_image_uploads( + self, mock_get_secret, mock_get_token + ): + self.config.validate_environment( + headers={}, + model="GigaChat", + messages=[], + optional_params={}, + litellm_params={}, + api_key="my-creds", + api_base="https://my-api.example.com", + ) + assert self.config._current_credentials == "my-creds" + assert self.config._current_api_base == "https://my-api.example.com" + + @patch(f"{TRANSFORM_MODULE}.get_access_token", return_value="token") + @patch(f"{TRANSFORM_MODULE}.get_secret_str") + def test_falls_back_to_env_for_credentials( # test-quality-ok: mock-echo of internal wiring + self, mock_get_secret, mock_get_token + ): + mock_get_secret.return_value = "env-creds" + self.config.validate_environment( + headers={}, + model="GigaChat", + messages=[], + optional_params={}, + litellm_params={}, + api_key=None, + api_base=None, + ) + mock_get_secret.assert_any_call("GIGACHAT_CREDENTIALS") # test-quality-ok: mock-echo of internal wiring + + +class TestGetSupportedOpenAiParams: + def setup_method(self): + self.config = GigaChatConfig() + + def test_returns_expected_params(self): + params = self.config.get_supported_openai_params("GigaChat") + expected = [ + "stream", + "temperature", + "top_p", + "max_tokens", + "max_completion_tokens", + "stop", + "tools", + "tool_choice", + "functions", + "function_call", + "response_format", + ] + assert params == expected + + +class TestMapOpenAiParams: + def setup_method(self): + self.config = GigaChatConfig() + + def test_stream(self): + result = self.config.map_openai_params( + non_default_params={"stream": True}, + optional_params={}, + model="GigaChat", + drop_params=False, + ) + assert result["stream"] is True + + def test_temperature_zero_maps_to_top_p_zero(self): + result = self.config.map_openai_params( + non_default_params={"temperature": 0}, + optional_params={}, + model="GigaChat", + drop_params=False, + ) + assert result["top_p"] == 0 + assert "temperature" not in result + + def test_temperature_non_zero(self): + result = self.config.map_openai_params( + non_default_params={"temperature": 0.7}, + optional_params={}, + model="GigaChat", + drop_params=False, + ) + assert result["temperature"] == 0.7 + + def test_top_p(self): + result = self.config.map_openai_params( + non_default_params={"top_p": 0.5}, + optional_params={}, + model="GigaChat", + drop_params=False, + ) + assert result["top_p"] == 0.5 + + def test_max_tokens(self): + result = self.config.map_openai_params( + non_default_params={"max_tokens": 100}, + optional_params={}, + model="GigaChat", + drop_params=False, + ) + assert result["max_tokens"] == 100 + + def test_max_completion_tokens(self): + result = self.config.map_openai_params( + non_default_params={"max_completion_tokens": 200}, + optional_params={}, + model="GigaChat", + drop_params=False, + ) + assert result["max_tokens"] == 200 + + def test_stop_is_dropped(self): + result = self.config.map_openai_params( + non_default_params={"stop": ["\n\n"]}, + optional_params={}, + model="GigaChat", + drop_params=False, + ) + assert "stop" not in result + + def test_tools_converted_to_functions(self): + tools = [ + { + "type": "function", + "function": { + "name": "get_weather", + "description": "Get weather", + "parameters": {"type": "object"}, + }, + } + ] + result = self.config.map_openai_params( + non_default_params={"tools": tools}, + optional_params={}, + model="GigaChat", + drop_params=False, + ) + assert "functions" in result + assert result["functions"] == [ + {"name": "get_weather", "description": "Get weather", "parameters": {"type": "object"}} + ] + + def test_tool_choice_auto(self): + result = self.config.map_openai_params( + non_default_params={"tool_choice": "auto"}, + optional_params={}, + model="GigaChat", + drop_params=False, + ) + assert result.get("function_call") == "auto" + + def test_tool_choice_none(self): + result = self.config.map_openai_params( + non_default_params={"tool_choice": "none"}, + optional_params={}, + model="GigaChat", + drop_params=False, + ) + assert result.get("function_call") == "none" + + def test_tool_choice_required(self): + result = self.config.map_openai_params( + non_default_params={"tool_choice": "required"}, + optional_params={}, + model="GigaChat", + drop_params=False, + ) + assert result.get("function_call") == "auto" + + def test_tool_choice_dict(self): + result = self.config.map_openai_params( + non_default_params={ + "tool_choice": { + "type": "function", + "function": {"name": "get_weather"}, + } + }, + optional_params={}, + model="GigaChat", + drop_params=False, + ) + assert result.get("function_call") == {"name": "get_weather"} + + def test_functions(self): + funcs = [{"name": "my_func", "description": "desc", "parameters": {}}] + result = self.config.map_openai_params( + non_default_params={"functions": funcs}, + optional_params={}, + model="GigaChat", + drop_params=False, + ) + assert result["functions"] == funcs + + def test_function_call(self): + result = self.config.map_openai_params( + non_default_params={"function_call": {"name": "my_func"}}, + optional_params={}, + model="GigaChat", + drop_params=False, + ) + assert result["function_call"] == {"name": "my_func"} + + def test_response_format_json_schema(self): + response_format = { + "type": "json_schema", + "json_schema": { + "name": "test_schema", + "schema": {"type": "object", "properties": {"name": {"type": "string"}}}, + }, + } + result = self.config.map_openai_params( + non_default_params={"response_format": response_format}, + optional_params={"functions": []}, + model="GigaChat", + drop_params=False, + ) + # Should add a function for the schema + assert len(result["functions"]) == 1 + assert result["functions"][0]["name"] == "test_schema" + assert result["function_call"] == {"name": "test_schema"} + assert result["_structured_output"] is True + + +class TestConvertToolsToFunctions: + def setup_method(self): + self.config = GigaChatConfig() + + def test_converts_function_tools_only(self): + tools = [ + {"type": "function", "function": {"name": "a", "description": "d", "parameters": {}}}, + {"type": "code_interpreter"}, # should be ignored + ] + result = self.config._convert_tools_to_functions(tools) + assert len(result) == 1 + assert result[0]["name"] == "a" + + def test_empty_tools(self): + assert self.config._convert_tools_to_functions([]) == [] + + +class TestMapToolChoice: + def setup_method(self): + self.config = GigaChatConfig() + + def test_none(self): + assert self.config._map_tool_choice("none") == "none" + + def test_auto(self): + assert self.config._map_tool_choice("auto") == "auto" + + def test_required(self): + assert self.config._map_tool_choice("required") == "auto" + + def test_dict_with_function(self): + result = self.config._map_tool_choice( + {"type": "function", "function": {"name": "get_weather"}} + ) + assert result == {"name": "get_weather"} + + def test_dict_without_name(self): + result = self.config._map_tool_choice( + {"type": "function", "function": {}} + ) + assert result is None + + def test_unknown_value(self): + assert self.config._map_tool_choice("unknown") is None + + +class TestTransformMessages: + def setup_method(self): + self.config = GigaChatConfig() + + def test_developer_role_to_system(self): + result = self.config._transform_messages( + [{"role": "developer", "content": "be helpful"}] + ) + assert result[0]["role"] == "system" + assert result[0]["content"] == "be helpful" + + def test_system_message_not_first_becomes_user(self): + result = self.config._transform_messages([ + {"role": "user", "content": "hi"}, + {"role": "system", "content": "instruction"}, + ]) + assert result[0]["role"] == "user" + assert result[1]["role"] == "user" + assert result[1]["content"] == "instruction" + + def test_tool_role_to_function(self): + result = self.config._transform_messages([ + {"role": "tool", "content": '{"result": "ok"}'} + ]) + assert result[0]["role"] == "function" + + def test_tool_role_content_wraps_non_json(self): + result = self.config._transform_messages([ + {"role": "tool", "content": "plain text"} + ]) + assert result[0]["role"] == "function" + assert is_valid_json(result[0]["content"]) + + def test_none_content_becomes_empty_string(self): + result = self.config._transform_messages([ + {"role": "user", "content": None} + ]) + assert result[0]["content"] == "" + + def test_name_field_removed(self): + result = self.config._transform_messages([ + {"role": "user", "content": "hi", "name": "John"} + ]) + assert "name" not in result[0] + + def test_tool_calls_converted_to_function_call(self): + result = self.config._transform_messages([ + { + "role": "assistant", + "content": "", + "tool_calls": [ + { + "id": "call_abc", + "type": "function", + "function": { + "name": "get_weather", + "arguments": '{"city": "London"}', + }, + } + ], + } + ]) + assert "tool_calls" not in result[0] + assert result[0]["function_call"]["name"] == "get_weather" + assert result[0]["function_call"]["arguments"] == {"city": "London"} + + def test_tool_calls_with_dict_arguments(self): + result = self.config._transform_messages([ + { + "role": "assistant", + "content": "", + "tool_calls": [ + { + "id": "call_xyz", + "type": "function", + "function": { + "name": "search", + "arguments": {"query": "test"}, + }, + } + ], + } + ]) + assert result[0]["function_call"]["arguments"] == {"query": "test"} + + def test_list_content_multimodal(self): + content = [ + {"type": "text", "text": "describe this"}, + { + "type": "image_url", + "image_url": {"url": "https://example.com/img.jpg"}, + }, + ] + with patch.object(self.config, "_upload_image", return_value="file-123"): + result = self.config._transform_messages([ + {"role": "user", "content": content} + ]) + assert result[0]["content"] == "describe this" + assert result[0]["attachments"] == ["file-123"] + + def test_list_content_with_image_url_string(self): + content = [ + {"type": "text", "text": "look"}, + {"type": "image_url", "image_url": "https://example.com/img.jpg"}, + ] + with patch.object(self.config, "_upload_image", return_value="file-456"): + result = self.config._transform_messages([ + {"role": "user", "content": content} + ]) + assert result[0]["content"] == "look" + assert "file-456" in result[0]["attachments"] + + +class TestTransformRequest: + def setup_method(self): + self.config = GigaChatConfig() + + def test_builds_basic_request(self): + body = self.config.transform_request( + model="gigachat/GigaChat", + messages=[{"role": "user", "content": "hi"}], + optional_params={}, + litellm_params={}, + headers={}, + ) + assert body["model"] == "GigaChat" + assert len(body["messages"]) == 1 + assert body["messages"][0]["content"] == "hi" + + def test_model_prefix_stripped(self): + body = self.config.transform_request( + model="gigachat/GigaChat-Pro", + messages=[{"role": "user", "content": "hello"}], + optional_params={}, + litellm_params={}, + headers={}, + ) + assert body["model"] == "GigaChat-Pro" + + def test_includes_optional_params(self): + body = self.config.transform_request( + model="gigachat/GigaChat", + messages=[{"role": "user", "content": "hi"}], + optional_params={ + "temperature": 0.5, + "max_tokens": 100, + "stream": True, + }, + litellm_params={}, + headers={}, + ) + assert body["temperature"] == 0.5 + assert body["max_tokens"] == 100 + assert body["stream"] is True + + def test_includes_functions(self): + body = self.config.transform_request( + model="gigachat/GigaChat", + messages=[{"role": "user", "content": "hi"}], + optional_params={ + "functions": [{"name": "my_func"}], + "function_call": {"name": "my_func"}, + }, + litellm_params={}, + headers={}, + ) + assert body["functions"] == [{"name": "my_func"}] + assert body["function_call"] == {"name": "my_func"} + + def test_skips_unsupported_params(self): + body = self.config.transform_request( + model="gigachat/GigaChat", + messages=[{"role": "user", "content": "hi"}], + optional_params={"n": 2, "user": "abc"}, + litellm_params={}, + headers={}, + ) + assert "n" not in body + assert "user" not in body + + +class TestTransformResponse: + def setup_method(self): + self.config = GigaChatConfig() + + def test_basic_response(self): + raw = _make_httpx_response({ + "id": "chatcmpl-123", + "created": 1700000000, + "model": "GigaChat", + "choices": [ + { + "index": 0, + "message": {"role": "assistant", "content": "Hello!"}, + "finish_reason": "stop", + } + ], + "usage": {"prompt_tokens": 5, "completion_tokens": 3, "total_tokens": 8}, + }) + model_response = ModelResponse() + result = self.config.transform_response( + model="gigachat/GigaChat", + raw_response=raw, + model_response=model_response, + logging_obj=MagicMock(), + request_data={}, + messages=[], + optional_params={}, + litellm_params={}, + encoding=None, + ) + assert result.choices[0].message.content == "Hello!" + assert result.choices[0].finish_reason == "stop" + assert result.usage.prompt_tokens == 5 + assert result.usage.total_tokens == 8 + + def test_function_call_into_tool_calls(self): + raw = _make_httpx_response({ + "id": "chatcmpl-456", + "created": 1700000000, + "model": "GigaChat", + "choices": [ + { + "index": 0, + "message": { + "role": "assistant", + "content": None, + "function_call": { + "name": "get_weather", + "arguments": {"city": "Moscow"}, + }, + }, + "finish_reason": "function_call", + } + ], + "usage": {"prompt_tokens": 10, "completion_tokens": 5, "total_tokens": 15}, + }) + model_response = ModelResponse() + result = self.config.transform_response( + model="gigachat/GigaChat", + raw_response=raw, + model_response=model_response, + logging_obj=MagicMock(), + request_data={}, + messages=[], + optional_params={}, + litellm_params={}, + encoding=None, + ) + assert result.choices[0].finish_reason == "tool_calls" + tool_calls = result.choices[0].message.tool_calls + assert tool_calls is not None + assert len(tool_calls) == 1 + assert tool_calls[0].function.name == "get_weather" + assert '{"city": "Moscow"}' in tool_calls[0].function.arguments + + def test_function_call_structured_output(self): + raw = _make_httpx_response({ + "id": "chatcmpl-789", + "created": 1700000000, + "model": "GigaChat", + "choices": [ + { + "index": 0, + "message": { + "role": "assistant", + "content": None, + "function_call": { + "name": "test_schema", + "arguments": {"name": "John"}, + }, + }, + "finish_reason": "function_call", + } + ], + "usage": {"prompt_tokens": 10, "completion_tokens": 5, "total_tokens": 15}, + }) + model_response = ModelResponse() + result = self.config.transform_response( + model="gigachat/GigaChat", + raw_response=raw, + model_response=model_response, + logging_obj=MagicMock(), + request_data={}, + messages=[], + optional_params={"_structured_output": True}, + litellm_params={}, + encoding=None, + ) + # Structured output: function_call -> content + assert result.choices[0].finish_reason == "stop" + assert result.choices[0].message.content is not None + assert '"name": "John"' in result.choices[0].message.content + + def test_function_call_string_arguments(self): + raw = _make_httpx_response({ + "choices": [ + { + "index": 0, + "message": { + "role": "assistant", + "function_call": { + "name": "get_weather", + "arguments": '{"city": "Moscow"}', + }, + }, + "finish_reason": "function_call", + } + ], + "usage": {"prompt_tokens": 1, "completion_tokens": 1, "total_tokens": 2}, + }) + model_response = ModelResponse() + result = self.config.transform_response( + model="gigachat/GigaChat", + raw_response=raw, + model_response=model_response, + logging_obj=MagicMock(), + request_data={}, + messages=[], + optional_params={}, + litellm_params={}, + encoding=None, + ) + tc = result.choices[0].message.tool_calls[0] + assert '{"city": "Moscow"}' in tc.function.arguments + + def test_cleans_up_gigachat_specific_fields(self): + raw = _make_httpx_response({ + "choices": [ + { + "index": 0, + "message": { + "role": "assistant", + "content": "done", + "functions_state_id": "some-state", + }, + "finish_reason": "stop", + } + ], + "usage": {"prompt_tokens": 1, "completion_tokens": 1, "total_tokens": 2}, + }) + model_response = ModelResponse() + result = self.config.transform_response( + model="gigachat/GigaChat", + raw_response=raw, + model_response=model_response, + logging_obj=MagicMock(), + request_data={}, + messages=[], + optional_params={}, + litellm_params={}, + encoding=None, + ) + # functions_state_id should have been removed from the message data + assert result.choices[0].message.content == "done" + + def test_raises_on_invalid_json(self): + raw = httpx.Response( + status_code=500, + headers={"content-type": "text/plain"}, + content=b"not json", + request=httpx.Request("POST", "https://example.com"), + ) + model_response = ModelResponse() + with pytest.raises(GigaChatError) as exc_info: + self.config.transform_response( + model="gigachat/GigaChat", + raw_response=raw, + model_response=model_response, + logging_obj=MagicMock(), + request_data={}, + messages=[], + optional_params={}, + litellm_params={}, + encoding=None, + ) + assert "Invalid JSON response" in str(exc_info.value.message) + + def test_empty_choices(self): + raw = _make_httpx_response({ + "choices": [], + "usage": {}, + }) + model_response = ModelResponse() + result = self.config.transform_response( + model="gigachat/GigaChat", + raw_response=raw, + model_response=model_response, + logging_obj=MagicMock(), + request_data={}, + messages=[], + optional_params={}, + litellm_params={}, + encoding=None, + ) + assert result.choices == [] + + def test_function_call_with_non_dict_arguments(self): + raw = _make_httpx_response({ + "choices": [ + { + "index": 0, + "message": { + "role": "assistant", + "function_call": { + "name": "say_hello", + "arguments": "hello", + }, + }, + "finish_reason": "function_call", + } + ], + "usage": {"prompt_tokens": 1, "completion_tokens": 1, "total_tokens": 2}, + }) + model_response = ModelResponse() + result = self.config.transform_response( + model="gigachat/GigaChat", + raw_response=raw, + model_response=model_response, + logging_obj=MagicMock(), + request_data={}, + messages=[], + optional_params={}, + litellm_params={}, + encoding=None, + ) + tc = result.choices[0].message.tool_calls[0] + assert tc.function.arguments == "hello" + + +class TestGetModelResponseIterator: + def setup_method(self): + self.config = GigaChatConfig() + + def test_returns_gigachat_iterator_sync(self): + from litellm.llms.gigachat.chat.streaming import ( + GigaChatModelResponseIterator, + ) + + result = self.config.get_model_response_iterator( + streaming_response=iter(["data"]), + sync_stream=True, + json_mode=False, + ) + assert isinstance(result, GigaChatModelResponseIterator) + + +class TestGetErrorClass: + def setup_method(self): + self.config = GigaChatConfig() + + def test_returns_gigachat_error(self): + error = self.config.get_error_class( + error_message="something went wrong", + status_code=400, + headers={"x-request-id": "abc"}, + ) + assert isinstance(error, GigaChatError) + assert error.status_code == 400 + assert error.message == "something went wrong" + assert error.headers == {"x-request-id": "abc"} + + +class TestUploadImage: + def setup_method(self): + self.config = GigaChatConfig() + + @patch(f"{TRANSFORM_MODULE}.upload_file_sync", return_value="file-uploaded") + def test_upload_image_success(self, mock_upload): + self.config._current_credentials = "creds" + self.config._current_api_base = "https://api.example.com" + result = self.config._upload_image("https://example.com/img.jpg") + assert result == "file-uploaded" + mock_upload.assert_called_once_with( + image_url="https://example.com/img.jpg", + credentials="creds", + api_base="https://api.example.com", + ) + + @patch(f"{TRANSFORM_MODULE}.upload_file_sync", side_effect=Exception("fail")) + def test_upload_image_failure_returns_none(self, mock_upload): + result = self.config._upload_image("https://example.com/img.jpg") + assert result is None \ No newline at end of file diff --git a/tests/test_litellm/llms/gigachat/embedding/__init__.py b/tests/test_litellm/llms/gigachat/embedding/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/tests/test_litellm/llms/gigachat/embedding/test_gigachat_embedding_transformation.py b/tests/test_litellm/llms/gigachat/embedding/test_gigachat_embedding_transformation.py new file mode 100644 index 00000000000..8537793ea72 --- /dev/null +++ b/tests/test_litellm/llms/gigachat/embedding/test_gigachat_embedding_transformation.py @@ -0,0 +1,372 @@ +""" +Unit tests for GigaChat embedding transformation. + +Tests GigaChatEmbeddingConfig covering get_config, get_supported_openai_params, +map_openai_params, _get_openai_compatible_provider_info, get_complete_url, +transform_embedding_request, transform_embedding_response, validate_environment, +and get_error_class. +""" + +import json +from unittest.mock import MagicMock, patch + +import httpx +import pytest + +from litellm import LlmProviders +from litellm.llms.gigachat.embedding.transformation import ( + GigaChatEmbeddingConfig, + GigaChatEmbeddingError, +) +from litellm.types.utils import EmbeddingResponse + +TRANSFORM_MODULE = "litellm.llms.gigachat.embedding.transformation" + + +def _make_httpx_response(body: dict, status_code: int = 200) -> httpx.Response: + return httpx.Response( + status_code=status_code, + headers={"content-type": "application/json"}, + content=json.dumps(body).encode("utf-8"), + request=httpx.Request("POST", "https://gigachat.devices.sberbank.ru/api/v1/embeddings"), + ) + + +# --------------------------------------------------------------------------- +# GigaChatEmbeddingConfig +# --------------------------------------------------------------------------- + + +class TestGetConfig: + def setup_method(self): + self.config = GigaChatEmbeddingConfig() + + def test_contains_only_abc_impl(self): + """get_config returns ABC internal data due to inheritance.""" + result = self.config.get_config() + # The only key should be _abc_impl from ABC base class + assert set(result.keys()) == {"_abc_impl"} + + +class TestGetSupportedOpenAiParams: + def setup_method(self): + self.config = GigaChatEmbeddingConfig() + + def test_returns_empty_list(self): + params = self.config.get_supported_openai_params("GigaChat") + assert params == [] + + +class TestMapOpenAiParams: + def setup_method(self): + self.config = GigaChatEmbeddingConfig() + + def test_returns_optional_params_unchanged(self): + result = self.config.map_openai_params( + non_default_params={"model": "test"}, + optional_params={"temperature": 0.5}, + model="GigaChat", + drop_params=False, + ) + assert result == {"temperature": 0.5} + + def test_returns_empty_dict_when_no_optional_params(self): + result = self.config.map_openai_params( + non_default_params={}, + optional_params={}, + model="GigaChat", + drop_params=False, + ) + assert result == {} + + +class TestGetOpenaiCompatibleProviderInfo: + def setup_method(self): + self.config = GigaChatEmbeddingConfig() + + def test_returns_gigachat_provider(self): + provider, api_base, api_key = self.config._get_openai_compatible_provider_info( + api_base="https://api.example.com", api_key="test-key" + ) + assert provider == LlmProviders.GIGACHAT.value + assert api_base == "https://api.example.com" + assert api_key == "test-key" + + def test_resolves_api_base_when_none(self, monkeypatch): + monkeypatch.delenv("GIGACHAT_API_BASE", raising=False) + provider, api_base, api_key = self.config._get_openai_compatible_provider_info( + api_base=None, api_key="key" + ) + assert api_base is not None + assert api_base.endswith("/api/v1") + + def test_returns_none_api_key(self): + _, _, api_key = self.config._get_openai_compatible_provider_info( + api_base="https://example.com", api_key=None + ) + assert api_key is None + + +class TestGetCompleteUrl: + def setup_method(self): + self.config = GigaChatEmbeddingConfig() + + def test_default_url(self): + url = self.config.get_complete_url( + api_base=None, api_key=None, model="GigaChat", + optional_params={}, litellm_params={}, + ) + assert url.endswith("/embeddings") + + def test_custom_api_base(self): + url = self.config.get_complete_url( + api_base="https://custom.example.com", api_key=None, model="GigaChat", + optional_params={}, litellm_params={}, + ) + assert url == "https://custom.example.com/embeddings" + + def test_trailing_slash_api_base(self): + url = self.config.get_complete_url( + api_base="https://custom.example.com/", api_key=None, model="GigaChat", + optional_params={}, litellm_params={}, + ) + # get_api_base doesn't strip slash, so we get double slash + assert url == "https://custom.example.com//embeddings" + + +class TestTransformEmbeddingRequest: + def setup_method(self): + self.config = GigaChatEmbeddingConfig() + + def test_string_input(self): + result = self.config.transform_embedding_request( + model="gigachat/Embeddings", + input="hello world", + optional_params={}, + headers={}, + ) + assert result == {"model": "Embeddings", "input": ["hello world"]} + + def test_list_input(self): + result = self.config.transform_embedding_request( + model="gigachat/Embeddings", + input=["text1", "text2"], + optional_params={}, + headers={}, + ) + assert result == {"model": "Embeddings", "input": ["text1", "text2"]} + + def test_strips_gigachat_prefix(self): + result = self.config.transform_embedding_request( + model="gigachat/GigaChat-Pro", + input="test", + optional_params={}, + headers={}, + ) + assert result["model"] == "GigaChat-Pro" + + def test_model_without_prefix(self): + result = self.config.transform_embedding_request( + model="Embeddings", + input="test", + optional_params={}, + headers={}, + ) + assert result["model"] == "Embeddings" + + +class TestTransformEmbeddingResponse: + def setup_method(self): + self.config = GigaChatEmbeddingConfig() + self.logging_obj = MagicMock() + + def _make_gigachat_response(self, data: list[dict]) -> httpx.Response: + return _make_httpx_response({ + "object": "list", + "data": data, + "model": "Embeddings", + }) + + def test_basic_response(self): + raw = self._make_gigachat_response([ + { + "object": "embedding", + "embedding": [0.1, 0.2, 0.3], + "index": 0, + } + ]) + model_response = EmbeddingResponse() + result = self.config.transform_embedding_response( + model="gigachat/Embeddings", + raw_response=raw, + model_response=model_response, + logging_obj=self.logging_obj, + api_key="test-key", + request_data={"input": ["text"]}, + optional_params={}, + litellm_params={}, + ) + assert result.object == "list" + assert len(result.data) == 1 + assert result.data[0]["embedding"] == [0.1, 0.2, 0.3] + assert result.data[0]["index"] == 0 + assert result.usage.prompt_tokens == 0 + assert result.usage.total_tokens == 0 + + def test_aggregates_per_embedding_usage(self): + raw = self._make_gigachat_response([ + { + "object": "embedding", + "embedding": [0.1, 0.2], + "index": 0, + "usage": {"prompt_tokens": 5}, + }, + { + "object": "embedding", + "embedding": [0.3, 0.4], + "index": 1, + "usage": {"prompt_tokens": 7}, + }, + ]) + model_response = EmbeddingResponse() + result = self.config.transform_embedding_response( + model="gigachat/Embeddings", + raw_response=raw, + model_response=model_response, + logging_obj=self.logging_obj, + api_key="test-key", + request_data={"input": ["a", "b"]}, + optional_params={}, + litellm_params={}, + ) + # Total should be sum of per-embedding prompt_tokens + assert result.usage.prompt_tokens == 12 + assert result.usage.total_tokens == 12 + # Usage should be removed from individual embedding data + assert "usage" not in result.data[0] + assert "usage" not in result.data[1] + + def test_usage_removed_from_individual_embeddings(self): + raw = self._make_gigachat_response([ + { + "object": "embedding", + "embedding": [0.5], + "index": 0, + "usage": {"prompt_tokens": 3}, + } + ]) + model_response = EmbeddingResponse() + result = self.config.transform_embedding_response( + model="gigachat/Embeddings", + raw_response=raw, + model_response=model_response, + logging_obj=self.logging_obj, + api_key="key", + request_data={"input": ["x"]}, + optional_params={}, + litellm_params={}, + ) + # usage should NOT be in the final EmbeddingResponse data items + for emb in result.data: + assert "usage" not in emb + + def test_passes_model_from_response(self): + raw = self._make_gigachat_response([ + {"object": "embedding", "embedding": [0.1], "index": 0}, + ]) + model_response = EmbeddingResponse() + result = self.config.transform_embedding_response( + model="gigachat/Embeddings", + raw_response=raw, + model_response=model_response, + logging_obj=self.logging_obj, + api_key="key", + request_data={"input": ["x"]}, + optional_params={}, + litellm_params={}, + ) + assert result.model == "Embeddings" + + def test_calls_logging_post_call(self): + raw = self._make_gigachat_response([ + {"object": "embedding", "embedding": [0.1], "index": 0}, + ]) + model_response = EmbeddingResponse() + self.config.transform_embedding_response( + model="gigachat/Embeddings", + raw_response=raw, + model_response=model_response, + logging_obj=self.logging_obj, + api_key="test-api-key", + request_data={"input": ["hello"]}, + optional_params={}, + litellm_params={}, + ) + self.logging_obj.post_call.assert_called_once() + args = self.logging_obj.post_call.call_args.kwargs + assert args["api_key"] == "test-api-key" + assert args["input"] == ["hello"] + + +class TestValidateEnvironment: + def setup_method(self): + self.config = GigaChatEmbeddingConfig() + + @patch(f"{TRANSFORM_MODULE}.get_access_token", return_value="test-token") + def test_sets_oauth_headers(self, mock_get_token): + headers = self.config.validate_environment( + headers={}, + model="GigaChat", + messages=[], + optional_params={}, + litellm_params={}, + api_key="creds", + api_base="https://api.example.com", + ) + assert headers["Authorization"] == "Bearer test-token" + assert headers["Content-Type"] == "application/json" + mock_get_token.assert_called_once_with(credentials="creds", litellm_params={}) + + @patch(f"{TRANSFORM_MODULE}.get_access_token", return_value="token") + def test_merges_custom_headers(self, mock_get_token): + headers = self.config.validate_environment( + headers={"X-Custom": "value"}, + model="GigaChat", + messages=[], + optional_params={}, + litellm_params={}, + api_key="creds", + api_base="https://api.example.com", + ) + assert headers["Authorization"] == "Bearer token" + assert headers["Content-Type"] == "application/json" + assert headers["X-Custom"] == "value" + + @patch(f"{TRANSFORM_MODULE}.get_access_token", return_value="token") + def test_custom_header_overwrites_default(self, mock_get_token): + headers = self.config.validate_environment( + headers={"Authorization": "Bearer custom"}, + model="GigaChat", + messages=[], + optional_params={}, + litellm_params={}, + api_key="creds", + api_base="https://api.example.com", + ) + # Merge: default headers first, then custom headers on top + assert headers["Authorization"] == "Bearer custom" + + +class TestGetErrorClass: + def setup_method(self): + self.config = GigaChatEmbeddingConfig() + + def test_returns_gigachat_embedding_error(self): + error = self.config.get_error_class( + error_message="embedding failed", + status_code=400, + headers={"x-request-id": "abc"}, + ) + assert isinstance(error, GigaChatEmbeddingError) + assert error.status_code == 400 + assert error.message == "embedding failed" \ No newline at end of file diff --git a/tests/test_litellm/llms/gigachat/passthrough/__init__.py b/tests/test_litellm/llms/gigachat/passthrough/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/tests/test_litellm/llms/gigachat/passthrough/test_gigachat_passthrough_transformation.py b/tests/test_litellm/llms/gigachat/passthrough/test_gigachat_passthrough_transformation.py new file mode 100644 index 00000000000..0a6ef364954 --- /dev/null +++ b/tests/test_litellm/llms/gigachat/passthrough/test_gigachat_passthrough_transformation.py @@ -0,0 +1,607 @@ +""" +Unit tests for GigaChatPassthroughConfig transformation. + +Tests the GigaChat-specific passthrough configuration including URL construction, +streaming detection, authentication handling, and logging response transformations. +""" + +import json +from unittest.mock import MagicMock, patch + +import httpx +import pytest + +from litellm.llms.gigachat.passthrough.transformation import GigaChatPassthroughConfig +from litellm.types.utils import EmbeddingResponse, ModelResponse + + +def _gigachat_chat_completion_body(): + return { + "id": "chatcmpl-test123", + "object": "chat.completion", + "created": 1700000000, + "model": "GigaChat", + "choices": [ + { + "index": 0, + "message": { + "role": "assistant", + "content": "Hello from GigaChat", + }, + "finish_reason": "stop", + } + ], + "usage": { + "prompt_tokens": 5, + "completion_tokens": 3, + "total_tokens": 8, + }, + } + + +def _gigachat_embedding_body(): + return { + "object": "list", + "data": [ + { + "object": "embedding", + "embedding": [0.1, 0.2, 0.3], + "index": 0, + "usage": {"prompt_tokens": 4}, + } + ], + "model": "Embeddings", + } + + +def _make_httpx_response(body: dict) -> httpx.Response: + return httpx.Response( + status_code=200, + headers={"content-type": "application/json"}, + content=json.dumps(body).encode("utf-8"), + request=httpx.Request( + "POST", "https://gigachat.devices.sberbank.ru/api/v1/chat/completions" + ), + ) + + +class TestGigaChatPassthroughConfig: + """Tests for GigaChatPassthroughConfig class.""" + + def test_is_streaming_request_true(self): + """Test streaming is detected when stream=True.""" + config = GigaChatPassthroughConfig() + assert ( + config.is_streaming_request("chat/completions", {"stream": True}) is True + ) + + def test_is_streaming_request_false(self): + """Test streaming is not detected when stream=False.""" + config = GigaChatPassthroughConfig() + assert ( + config.is_streaming_request("chat/completions", {"stream": False}) + is False + ) + + def test_is_streaming_request_missing_stream_key(self): + """Test streaming defaults to False when stream key is missing.""" + config = GigaChatPassthroughConfig() + assert ( + config.is_streaming_request("chat/completions", {"model": "GigaChat"}) + is False + ) + + def test_get_complete_url_with_api_base(self): + """Test URL construction with explicit api_base.""" + config = GigaChatPassthroughConfig() + api_base = "https://custom.gigachat.ru/api/v1" + endpoint = "chat/completions" + + complete_url, base_target_url = config.get_complete_url( + api_base=api_base, + api_key=None, + model="GigaChat", + endpoint=endpoint, + request_query_params=None, + litellm_params={}, + ) + + assert isinstance(complete_url, httpx.URL) + assert str(complete_url) == f"{api_base}/{endpoint}" + assert base_target_url == api_base + + def test_get_complete_url_with_leading_slash_endpoint(self): + """Test URL construction with endpoint having leading slash.""" + config = GigaChatPassthroughConfig() + api_base = "https://custom.gigachat.ru/api/v1" + endpoint = "/chat/completions" + + complete_url, base_target_url = config.get_complete_url( + api_base=api_base, + api_key=None, + model="GigaChat", + endpoint=endpoint, + request_query_params=None, + litellm_params={}, + ) + + assert str(complete_url) == "https://custom.gigachat.ru/api/v1/chat/completions" + assert base_target_url == api_base + + @patch( # test-quality-ok: patching litellm internal for unit test isolation + "litellm.llms.gigachat.passthrough.transformation.get_secret_str" + ) + def test_get_complete_url_with_env_api_base(self, mock_get_secret): + """Test URL construction with api_base from environment.""" + config = GigaChatPassthroughConfig() + env_api_base = "https://env.gigachat.ru/api/v1" + mock_get_secret.return_value = env_api_base + + complete_url, base_target_url = config.get_complete_url( + api_base=None, + api_key=None, + model="GigaChat", + endpoint="embeddings", + request_query_params=None, + litellm_params={}, + ) + + assert isinstance(complete_url, httpx.URL) + assert str(complete_url).startswith(env_api_base) + assert base_target_url == env_api_base + mock_get_secret.assert_called_once_with("GIGACHAT_API_BASE") + + @patch( # test-quality-ok: patching litellm internal for unit test isolation + "litellm.llms.gigachat.passthrough.transformation.get_secret_str" + ) + def test_get_complete_url_fallback_to_default(self, mock_get_secret): + """Test URL construction falls back to default GIGACHAT_BASE_URL.""" + config = GigaChatPassthroughConfig() + mock_get_secret.return_value = None + + complete_url, base_target_url = config.get_complete_url( + api_base=None, + api_key=None, + model="GigaChat", + endpoint="models", + request_query_params=None, + litellm_params={}, + ) + + assert isinstance(complete_url, httpx.URL) + assert "gigachat.devices.sberbank.ru" in str(complete_url) + assert base_target_url == "https://gigachat.devices.sberbank.ru/api/v1" + + def test_get_complete_url_no_api_base_raises(self): + """Test that exception is raised when no api_base can be resolved.""" + config = GigaChatPassthroughConfig() + with patch( + "litellm.llms.gigachat.passthrough.transformation.get_secret_str", # test-quality-ok: patching litellm internal for unit test isolation + return_value=None, + ): + with patch( + "litellm.llms.gigachat.passthrough.transformation.GIGACHAT_BASE_URL", # test-quality-ok: patching litellm internal for unit test isolation + None, + ): + with pytest.raises(Exception, match="GigaChat api base not found"): + config.get_complete_url( + api_base=None, + api_key=None, + model="GigaChat", + endpoint="chat/completions", + request_query_params=None, + litellm_params={}, + ) + + @patch( # test-quality-ok: patching litellm internal for unit test isolation + "litellm.llms.gigachat.passthrough.transformation.get_access_token" + ) + def test_validate_environment(self, mock_get_access_token): + """Test headers are set correctly with OAuth token.""" + config = GigaChatPassthroughConfig() + mock_get_access_token.return_value = "test-token-123" + + headers = config.validate_environment( + headers={}, + model="GigaChat", + messages=[{"role": "user", "content": "hi"}], + optional_params={}, + litellm_params={}, + api_key="test-credentials", + api_base="https://custom.gigachat.ru", + ) + + assert headers["Authorization"] == "Bearer test-token-123" + assert headers["Content-Type"] == "application/json" + assert headers["Accept"] == "application/json" + mock_get_access_token.assert_called_once_with( + credentials="test-credentials", + litellm_params={}, + ) + + def test_logging_non_streaming_response_chat_completions(self): + """Test chat completions endpoint returns ModelResponse.""" + config = GigaChatPassthroughConfig() + logging_obj = MagicMock() + + result = config.logging_non_streaming_response( + model="gigachat/GigaChat", + custom_llm_provider="gigachat", + httpx_response=_make_httpx_response(_gigachat_chat_completion_body()), + request_data={ + "model": "gigachat/GigaChat", + "messages": [{"role": "user", "content": "hi"}], + }, + logging_obj=logging_obj, + endpoint="chat/completions", + ) + + assert isinstance(result, ModelResponse) + assert result.choices[0].message.content == "Hello from GigaChat" + assert result.usage.prompt_tokens == 5 + assert result.usage.completion_tokens == 3 + assert result.usage.total_tokens == 8 + + def test_logging_non_streaming_response_embeddings(self): + """Test embeddings endpoint returns EmbeddingResponse.""" + config = GigaChatPassthroughConfig() + logging_obj = MagicMock() + + result = config.logging_non_streaming_response( + model="gigachat/Embeddings", + custom_llm_provider="gigachat", + httpx_response=_make_httpx_response(_gigachat_embedding_body()), + request_data={"input": ["hello"], "model": "gigachat/Embeddings"}, + logging_obj=logging_obj, + endpoint="embeddings", + ) + + assert isinstance(result, EmbeddingResponse) + assert len(result.data) == 1 + assert result.data[0]["embedding"] == [0.1, 0.2, 0.3] + + def test_logging_non_streaming_response_unknown_endpoint_returns_none(self): + """Test unknown endpoint returns None.""" + config = GigaChatPassthroughConfig() + logging_obj = MagicMock() + + result = config.logging_non_streaming_response( + model="gigachat/GigaChat", + custom_llm_provider="gigachat", + httpx_response=_make_httpx_response(_gigachat_chat_completion_body()), + request_data={}, + logging_obj=logging_obj, + endpoint="images/generations", + ) + + assert result is None + + def test_handle_logging_collected_chunks_with_string_chunks(self): + """Test converting string chunks to model response.""" + config = GigaChatPassthroughConfig() + logging_obj = MagicMock() + + chunks = [ + '{"choices": [{"delta": {"content": "Hello"}, "index": 0}]}', + '{"choices": [{"delta": {"content": " world"}, "index": 0}]}', + '{"choices": [{"delta": {}, "finish_reason": "stop", "index": 0}], "usage": {"prompt_tokens": 5, "completion_tokens": 2, "total_tokens": 7}}', + ] + + result = config.handle_logging_collected_chunks( + all_chunks=chunks, + litellm_logging_obj=logging_obj, + model="gigachat/GigaChat", + custom_llm_provider="gigachat", + endpoint="chat/completions", + ) + + assert isinstance(result, ModelResponse) + assert result.choices[0].message.content == "Hello world" + + def test_handle_logging_collected_chunks_with_bytes_chunks(self): + """Test converting string chunks to model response (bytes pre-decoded upstream).""" + config = GigaChatPassthroughConfig() + logging_obj = MagicMock() + + chunks = [ + '{"choices": [{"delta": {"content": "Hi"}, "index": 0}]}', + '{"choices": [{"delta": {}, "finish_reason": "stop", "index": 0}], "usage": {"prompt_tokens": 1, "completion_tokens": 1, "total_tokens": 2}}', + ] + + result = config.handle_logging_collected_chunks( + all_chunks=chunks, + litellm_logging_obj=logging_obj, + model="gigachat/GigaChat", + custom_llm_provider="gigachat", + endpoint="chat/completions", + ) + + assert isinstance(result, ModelResponse) + assert result.choices[0].message.content == "Hi" + + def test_handle_logging_collected_chunks_with_done_and_empty(self): + """Test that [DONE] and empty chunks are skipped.""" + config = GigaChatPassthroughConfig() + logging_obj = MagicMock() + + chunks = [ + "", + "[DONE]", + '{"choices": [{"delta": {"content": "test"}, "index": 0}]}', + '{"choices": [{"delta": {}, "finish_reason": "stop", "index": 0}], "usage": {"prompt_tokens": 1, "completion_tokens": 1, "total_tokens": 2}}', + ] + + result = config.handle_logging_collected_chunks( + all_chunks=chunks, + litellm_logging_obj=logging_obj, + model="gigachat/GigaChat", + custom_llm_provider="gigachat", + endpoint="chat/completions", + ) + + assert isinstance(result, ModelResponse) + assert result.choices[0].message.content == "test" + + def test_handle_logging_collected_chunks_with_dict_chunks(self): + """Test converting string-serialized dict chunks (dicts pre-serialized upstream).""" + config = GigaChatPassthroughConfig() + logging_obj = MagicMock() + + chunks = [ + '{"choices": [{"delta": {"content": "direct"}, "index": 0}]}', + json.dumps( + { + "choices": [ + { + "delta": {}, + "finish_reason": "stop", + "index": 0, + } + ], + "usage": { + "prompt_tokens": 1, + "completion_tokens": 1, + "total_tokens": 2, + }, + } + ), + ] + + result = config.handle_logging_collected_chunks( + all_chunks=chunks, + litellm_logging_obj=logging_obj, + model="gigachat/GigaChat", + custom_llm_provider="gigachat", + endpoint="chat/completions", + ) + + assert isinstance(result, ModelResponse) + assert result.choices[0].message.content == "direct" + + def test_handle_logging_collected_chunks_empty_list_returns_none(self): + """Test empty chunks list returns None.""" + config = GigaChatPassthroughConfig() + logging_obj = MagicMock() + + result = config.handle_logging_collected_chunks( + all_chunks=[], + litellm_logging_obj=logging_obj, + model="gigachat/GigaChat", + custom_llm_provider="gigachat", + endpoint="chat/completions", + ) + + assert result is None + + def test_handle_logging_collected_chunks_invalid_json_skipped(self): + """Test invalid JSON chunks are skipped gracefully.""" + config = GigaChatPassthroughConfig() + logging_obj = MagicMock() + + chunks = [ + "not-valid-json", + '{"choices": [{"delta": {"content": "valid"}, "index": 0}]}', + '{"choices": [{"delta": {}, "finish_reason": "stop", "index": 0}], "usage": {"prompt_tokens": 1, "completion_tokens": 1, "total_tokens": 2}}', + ] + + result = config.handle_logging_collected_chunks( + all_chunks=chunks, + litellm_logging_obj=logging_obj, + model="gigachat/GigaChat", + custom_llm_provider="gigachat", + endpoint="chat/completions", + ) + + assert isinstance(result, ModelResponse) + assert result.choices[0].message.content == "valid" + + @patch( # test-quality-ok: patching litellm internal for unit test isolation + "litellm.llms.gigachat.passthrough.transformation.get_secret_str" + ) + def test_get_api_base_with_explicit_value(self, mock_get_secret): + """Test get_api_base returns explicit value when provided.""" + explicit_base = "https://custom.gigachat.ru/api/v1" + result = GigaChatPassthroughConfig.get_api_base(api_base=explicit_base) + assert result == explicit_base + mock_get_secret.assert_not_called() + + @patch( # test-quality-ok: patching litellm internal for unit test isolation + "litellm.llms.gigachat.passthrough.transformation.get_secret_str" + ) + def test_get_api_base_from_environment(self, mock_get_secret): + """Test get_api_base retrieves from environment when not provided.""" + env_base = "https://env.gigachat.ru/api/v1" + mock_get_secret.return_value = env_base + result = GigaChatPassthroughConfig.get_api_base(api_base=None) + assert result == env_base + mock_get_secret.assert_called_once_with("GIGACHAT_API_BASE") + + @patch( # test-quality-ok: patching litellm internal for unit test isolation + "litellm.llms.gigachat.passthrough.transformation.get_secret_str" + ) + def test_get_api_base_fallback_to_default(self, mock_get_secret): + """Test get_api_base falls back to GIGACHAT_BASE_URL.""" + mock_get_secret.return_value = None + result = GigaChatPassthroughConfig.get_api_base(api_base=None) + assert result == "https://gigachat.devices.sberbank.ru/api/v1" + + @patch( # test-quality-ok: patching litellm internal for unit test isolation + "litellm.llms.gigachat.passthrough.transformation.get_secret_str" + ) + def test_get_api_key_with_explicit_value(self, mock_get_secret): + """Test get_api_key returns explicit value when provided.""" + explicit_key = "test-api-key" + result = GigaChatPassthroughConfig.get_api_key(api_key=explicit_key) + assert result == explicit_key + mock_get_secret.assert_not_called() + + @patch( # test-quality-ok: patching litellm internal for unit test isolation + "litellm.llms.gigachat.passthrough.transformation.get_secret_str" + ) + def test_get_api_key_from_environment(self, mock_get_secret): + """Test get_api_key retrieves from environment when not provided.""" + env_key = "env-api-key" + mock_get_secret.return_value = env_key + result = GigaChatPassthroughConfig.get_api_key(api_key=None) + assert result == env_key + mock_get_secret.assert_called_once_with("GIGACHAT_API_KEY") + + def test_get_base_model_returns_model(self): + """Test get_base_model returns the model as-is.""" + model = "gigachat/GigaChat" + result = GigaChatPassthroughConfig.get_base_model(model) + assert result == model + + def test_get_models(self): + """Test get_models delegates to base class.""" + config = GigaChatPassthroughConfig() + result = config.get_models() + assert result == [] + + def test_logging_non_streaming_chat_raises_when_no_config(self): + """Test raise when ProviderConfigManager returns None for chat.""" + config = GigaChatPassthroughConfig() + logging_obj = MagicMock() + + with patch( + "litellm.utils.ProviderConfigManager.get_provider_chat_config", # test-quality-ok: patching litellm internal for unit test isolation + return_value=None, + ): + with pytest.raises(ValueError, match="No provider config found for model"): + config.logging_non_streaming_response( + model="gigachat/GigaChat", + custom_llm_provider="gigachat", + httpx_response=_make_httpx_response(_gigachat_chat_completion_body()), + request_data={ + "model": "gigachat/GigaChat", + "messages": [{"role": "user", "content": "hi"}], + }, + logging_obj=logging_obj, + endpoint="chat/completions", + ) + + def test_logging_non_streaming_embedding_raises_when_no_config(self): + """Test raise when ProviderConfigManager returns None for embeddings.""" + config = GigaChatPassthroughConfig() + logging_obj = MagicMock() + + with patch( + "litellm.utils.ProviderConfigManager.get_provider_embedding_config", # test-quality-ok: patching litellm internal for unit test isolation + return_value=None, + ): + with pytest.raises(ValueError, match="No provider config found for model"): + config.logging_non_streaming_response( + model="gigachat/Embeddings", + custom_llm_provider="gigachat", + httpx_response=_make_httpx_response(_gigachat_embedding_body()), + request_data={ + "input": ["hello"], + "model": "gigachat/Embeddings", + }, + logging_obj=logging_obj, + endpoint="embeddings", + ) + + def test_handle_logging_collected_chunks_with_model_response_stream_chunk(self): + """Test that a chunk returning ModelResponseStream from chunk_parser is handled. + + Requires patching GigaChatModelResponseIterator.chunk_parser to return + a ModelResponseStream so the elif branch is exercised. + """ + config = GigaChatPassthroughConfig() + logging_obj = MagicMock() + + from litellm.types.utils import ModelResponseStream + + stream_chunk = ModelResponseStream( + choices=[ + { + "index": 0, + "delta": {"content": "streamed"}, + "finish_reason": None, + } + ] + ) + + chunks = [ + '{"choices": [{"delta": {"content": "streamed"}, "index": 0}]}', + '{"choices": [{"delta": {}, "finish_reason": "stop", "index": 0}], "usage": {"prompt_tokens": 1, "completion_tokens": 1, "total_tokens": 2}}', + ] + + with patch( + "litellm.llms.gigachat.passthrough.transformation.GigaChatModelResponseIterator.chunk_parser", # test-quality-ok: patching litellm internal for unit test isolation + return_value=stream_chunk, + ): + result = config.handle_logging_collected_chunks( + all_chunks=chunks, + litellm_logging_obj=logging_obj, + model="gigachat/GigaChat", + custom_llm_provider="gigachat", + endpoint="chat/completions", + ) + + assert isinstance(result, ModelResponse) + assert result.choices[0].message.content == "streamedstreamed" + + def test_handle_logging_collected_chunks_skips_unknown_chunk_type(self): + """Test that chunk_parser returning an unknown type is skipped.""" + config = GigaChatPassthroughConfig() + logging_obj = MagicMock() + + chunks = [ + '{"choices": [{"delta": {"content": "good"}, "index": 0}]}', + '{"choices": [{"delta": {}, "finish_reason": "stop", "index": 0}], "usage": {"prompt_tokens": 1, "completion_tokens": 1, "total_tokens": 2}}', + ] + + with patch( + "litellm.llms.gigachat.passthrough.transformation.GigaChatModelResponseIterator.chunk_parser", # test-quality-ok: patching litellm internal for unit test isolation + return_value=12345, # not dict and not ModelResponseStream + ): + result = config.handle_logging_collected_chunks( + all_chunks=chunks, + litellm_logging_obj=logging_obj, + model="gigachat/GigaChat", + custom_llm_provider="gigachat", + endpoint="chat/completions", + ) + + # All chunks skipped, returns None + assert result is None + + def test_handle_logging_collected_chunks_skips_unsupported_chunk_type(self): + """Test that unsupported chunk types (non-JSON str) are skipped.""" + config = GigaChatPassthroughConfig() + logging_obj = MagicMock() + + # Both are valid str chunks; "not-a-valid-json" fails json.loads, int is not a str + chunks: list[str] = ["not-a-valid-json"] + + result = config.handle_logging_collected_chunks( + all_chunks=chunks, + litellm_logging_obj=logging_obj, + model="gigachat/GigaChat", + custom_llm_provider="gigachat", + endpoint="chat/completions", + ) + + assert result is None diff --git a/tests/test_litellm/llms/gigachat/test_authenticator.py b/tests/test_litellm/llms/gigachat/test_authenticator.py new file mode 100644 index 00000000000..0a2695dc21e --- /dev/null +++ b/tests/test_litellm/llms/gigachat/test_authenticator.py @@ -0,0 +1,494 @@ +""" +Unit tests for GigaChat OAuth authenticator. + +Tests get_access_token and get_access_token_async covering token resolution +from litellm_params/env, credential validation, caching, and error handling. +""" + +import time +from unittest.mock import AsyncMock, MagicMock, patch + +import httpx +import pytest + +from litellm.llms.gigachat import authenticator +from litellm.llms.gigachat.authenticator import ( + GigaChatAuthError, + TOKEN_EXPIRY_BUFFER_MS, + get_access_token, + get_access_token_async, +) + + +AUTH_MODULE = "litellm.llms.gigachat.authenticator" + + +def _future_expires_at_ms(offset_seconds: float = 3600) -> int: + return int(time.time() * 1000 + offset_seconds * 1000) + + +def _past_expires_at_ms(offset_seconds: float = 3600) -> int: + return int(time.time() * 1000 - offset_seconds * 1000) + + +@pytest.fixture(autouse=True) +def _isolate_token_cache(): + """Each test gets a fresh module-level token cache to avoid cross-test leakage.""" + with patch(f"{AUTH_MODULE}._token_cache", new=MagicMock()): + authenticator._token_cache.get_cache.return_value = None + authenticator._token_cache.set_cache = MagicMock() + yield + + +class TestGetAccessTokenSync: + def test_returns_token_from_litellm_params(self): + token = get_access_token(litellm_params={"gigachat_access_token": "param-token"}) + assert token == "param-token" + authenticator._token_cache.get_cache.assert_not_called() + + @patch(f"{AUTH_MODULE}.get_secret_str") + def test_returns_token_from_env(self, mock_get_secret): + mock_get_secret.return_value = "env-access-token" + token = get_access_token() + assert token == "env-access-token" + + @patch(f"{AUTH_MODULE}._get_credentials", return_value=None) + @patch(f"{AUTH_MODULE}.get_secret_str", return_value=None) + def test_raises_when_no_credentials(self, mock_get_secret, mock_get_creds): + with pytest.raises(GigaChatAuthError) as exc_info: + get_access_token() + assert exc_info.value.status_code == 401 + assert "credentials not provided" in exc_info.value.message + + @patch(f"{AUTH_MODULE}._request_token_sync") + @patch(f"{AUTH_MODULE}._get_auth_url", return_value="https://auth.example.com") + @patch(f"{AUTH_MODULE}._get_scope", return_value="GIGACHAT_API_PERS") + @patch(f"{AUTH_MODULE}._get_credentials", return_value=None) + @patch(f"{AUTH_MODULE}.get_secret_str", return_value=None) + def test_raises_when_no_credentials_even_with_other_resolvers( + self, mock_get_secret, mock_get_creds, mock_scope, mock_auth_url, mock_request + ): + with pytest.raises(GigaChatAuthError) as exc_info: + get_access_token() + assert exc_info.value.status_code == 401 + mock_request.assert_not_called() + + @patch(f"{AUTH_MODULE}._request_token_sync") + @patch(f"{AUTH_MODULE}._get_auth_url", return_value="https://auth.example.com") + @patch(f"{AUTH_MODULE}._get_scope", return_value="GIGACHAT_API_PERS") + @patch(f"{AUTH_MODULE}._get_credentials", return_value="creds-from-env") + @patch(f"{AUTH_MODULE}.get_secret_str", return_value=None) + def test_requests_new_token_and_caches(self, mock_get_secret, mock_creds, mock_scope, mock_auth_url, mock_request): + token = "fresh-token" + expires_at = _future_expires_at_ms() + mock_request.return_value = (token, expires_at) + + result = get_access_token() + + assert result == token + mock_request.assert_called_once_with("creds-from-env", "GIGACHAT_API_PERS", "https://auth.example.com") + authenticator._token_cache.set_cache.assert_called_once() + call_args = authenticator._token_cache.set_cache.call_args + assert call_args.args[1] == (token, expires_at) + assert call_args.kwargs["ttl"] > 0 + + @patch(f"{AUTH_MODULE}._request_token_sync") + @patch(f"{AUTH_MODULE}._get_auth_url", return_value="https://auth.example.com") + @patch(f"{AUTH_MODULE}._get_scope", return_value="GIGACHAT_API_PERS") + @patch(f"{AUTH_MODULE}._get_credentials", return_value="creds") + @patch(f"{AUTH_MODULE}.get_secret_str", return_value=None) + def test_does_not_cache_when_no_expiry(self, mock_get_secret, mock_creds, mock_scope, mock_auth_url, mock_request): + mock_request.return_value = ("token-no-exp", 0) + + result = get_access_token() + + assert result == "token-no-exp" + authenticator._token_cache.set_cache.assert_not_called() + + @patch(f"{AUTH_MODULE}._request_token_sync") + @patch(f"{AUTH_MODULE}._get_auth_url", return_value="https://auth.example.com") + @patch(f"{AUTH_MODULE}._get_scope", return_value="GIGACHAT_API_PERS") + @patch(f"{AUTH_MODULE}._get_credentials", return_value="creds") + @patch(f"{AUTH_MODULE}.get_secret_str", return_value=None) + def test_does_not_cache_when_ttl_non_positive(self, mock_get_secret, mock_creds, mock_scope, mock_auth_url, mock_request): + expires_at = int(time.time() * 1000) + TOKEN_EXPIRY_BUFFER_MS - 1000 + mock_request.return_value = ("token", expires_at) + + result = get_access_token() + + assert result == "token" + authenticator._token_cache.set_cache.assert_not_called() + + @patch(f"{AUTH_MODULE}._request_token_sync") + @patch(f"{AUTH_MODULE}._get_auth_url", return_value="https://auth.example.com") + @patch(f"{AUTH_MODULE}._get_scope", return_value="GIGACHAT_API_PERS") + @patch(f"{AUTH_MODULE}._get_credentials", return_value="creds") + @patch(f"{AUTH_MODULE}.get_secret_str", return_value=None) + def test_returns_cached_valid_token(self, mock_get_secret, mock_creds, mock_scope, mock_auth_url, mock_request): + cached_token = "cached-token" + cached_expires_at = _future_expires_at_ms(offset_seconds=7200) + authenticator._token_cache.get_cache.return_value = (cached_token, cached_expires_at) + + result = get_access_token(credentials="creds") + + assert result == cached_token + mock_request.assert_not_called() + authenticator._token_cache.set_cache.assert_not_called() + + @patch(f"{AUTH_MODULE}._request_token_sync") + @patch(f"{AUTH_MODULE}._get_auth_url", return_value="https://auth.example.com") + @patch(f"{AUTH_MODULE}._get_scope", return_value="GIGACHAT_API_PERS") + @patch(f"{AUTH_MODULE}._get_credentials", return_value="creds") + @patch(f"{AUTH_MODULE}.get_secret_str", return_value=None) + def test_requests_new_token_when_cache_expired(self, mock_get_secret, mock_creds, mock_scope, mock_auth_url, mock_request): + cached_token = "stale-token" + cached_expires_at = _past_expires_at_ms(offset_seconds=10) + authenticator._token_cache.get_cache.return_value = (cached_token, cached_expires_at) + + new_token = "refreshed-token" + mock_request.return_value = (new_token, _future_expires_at_ms()) + + result = get_access_token(credentials="creds") + + assert result == new_token + mock_request.assert_called_once() + + @patch(f"{AUTH_MODULE}._request_token_sync") + @patch(f"{AUTH_MODULE}._get_auth_url", return_value="https://default-auth.example.com") + @patch(f"{AUTH_MODULE}._get_scope", return_value="GIGACHAT_API_PERS") + @patch(f"{AUTH_MODULE}._get_credentials", return_value="env-creds") + @patch(f"{AUTH_MODULE}.get_secret_str", return_value=None) + def test_litellm_params_override_scope_and_auth_url(self, mock_get_secret, mock_creds, mock_scope, mock_auth_url, mock_request): # test-quality-ok: mock-echo of internal wiring + mock_request.return_value = ("token", _future_expires_at_ms()) + + get_access_token( + litellm_params={ + "gigachat_scope": "GIGACHAT_API_CORP", + "gigachat_auth_url": "https://params-auth.example.com", + } + ) + + mock_request.assert_called_once_with( # test-quality-ok: mock-echo of internal wiring + "env-creds", "GIGACHAT_API_CORP", "https://params-auth.example.com" + ) + + @patch(f"{AUTH_MODULE}._request_token_sync") + @patch(f"{AUTH_MODULE}._get_auth_url", return_value="https://default-auth.example.com") + @patch(f"{AUTH_MODULE}._get_scope", return_value="GIGACHAT_API_PERS") + @patch(f"{AUTH_MODULE}._get_credentials", return_value="env-creds") + @patch(f"{AUTH_MODULE}.get_secret_str", return_value=None) + def test_explicit_args_override_everything(self, mock_get_secret, mock_creds, mock_scope, mock_auth_url, mock_request): # test-quality-ok: mock-echo of internal wiring + mock_request.return_value = ("token", _future_expires_at_ms()) + + get_access_token( + credentials="explicit-creds", + scope="EXPLICIT_SCOPE", + auth_url="https://explicit.example.com", + litellm_params={ + "gigachat_scope": "PARAM_SCOPE", + "gigachat_auth_url": "https://params.example.com", + }, + ) + + mock_request.assert_called_once_with( # test-quality-ok: mock-echo of internal wiring + "explicit-creds", "EXPLICIT_SCOPE", "https://explicit.example.com" + ) + + @patch(f"{AUTH_MODULE}._request_token_sync") + @patch(f"{AUTH_MODULE}._get_auth_url", return_value="https://auth.example.com") + @patch(f"{AUTH_MODULE}._get_scope", return_value="GIGACHAT_API_PERS") + @patch(f"{AUTH_MODULE}._get_credentials", return_value="creds") + @patch(f"{AUTH_MODULE}.get_secret_str", return_value=None) + def test_propagates_auth_error_from_request(self, mock_get_secret, mock_creds, mock_scope, mock_auth_url, mock_request): + mock_request.side_effect = GigaChatAuthError(status_code=403, message="forbidden") + + with pytest.raises(GigaChatAuthError) as exc_info: + get_access_token() + assert exc_info.value.status_code == 403 + assert exc_info.value.message == "forbidden" + + +class TestGetAccessTokenAsync: + @pytest.mark.asyncio + async def test_returns_token_from_litellm_params(self): + token = await get_access_token_async( + litellm_params={"gigachat_access_token": "param-token"} + ) + assert token == "param-token" + authenticator._token_cache.get_cache.assert_not_called() + + @pytest.mark.asyncio + @patch(f"{AUTH_MODULE}.get_secret_str") + async def test_returns_token_from_env(self, mock_get_secret): + mock_get_secret.return_value = "env-access-token" + token = await get_access_token_async() + assert token == "env-access-token" + + @pytest.mark.asyncio + @patch(f"{AUTH_MODULE}._get_credentials", return_value=None) + @patch(f"{AUTH_MODULE}.get_secret_str", return_value=None) + async def test_raises_when_no_credentials(self, mock_get_secret, mock_get_creds): + with pytest.raises(GigaChatAuthError) as exc_info: + await get_access_token_async() + assert exc_info.value.status_code == 401 + assert "credentials not provided" in exc_info.value.message + + @pytest.mark.asyncio + @patch(f"{AUTH_MODULE}._request_token_async", new_callable=AsyncMock) + @patch(f"{AUTH_MODULE}._get_auth_url", return_value="https://auth.example.com") + @patch(f"{AUTH_MODULE}._get_scope", return_value="GIGACHAT_API_PERS") + @patch(f"{AUTH_MODULE}._get_credentials", return_value="creds-from-env") + @patch(f"{AUTH_MODULE}.get_secret_str", return_value=None) + async def test_requests_new_token_and_caches( + self, mock_get_secret, mock_creds, mock_scope, mock_auth_url, mock_request + ): + token = "fresh-token-async" + expires_at = _future_expires_at_ms() + mock_request.return_value = (token, expires_at) + + result = await get_access_token_async() + + assert result == token + mock_request.assert_called_once_with( + "creds-from-env", "GIGACHAT_API_PERS", "https://auth.example.com" + ) + authenticator._token_cache.set_cache.assert_called_once() + call_args = authenticator._token_cache.set_cache.call_args + assert call_args.args[1] == (token, expires_at) + assert call_args.kwargs["ttl"] > 0 + + @pytest.mark.asyncio + @patch(f"{AUTH_MODULE}._request_token_async", new_callable=AsyncMock) + @patch(f"{AUTH_MODULE}._get_auth_url", return_value="https://auth.example.com") + @patch(f"{AUTH_MODULE}._get_scope", return_value="GIGACHAT_API_PERS") + @patch(f"{AUTH_MODULE}._get_credentials", return_value="creds") + @patch(f"{AUTH_MODULE}.get_secret_str", return_value=None) + async def test_does_not_cache_when_no_expiry( + self, mock_get_secret, mock_creds, mock_scope, mock_auth_url, mock_request + ): + mock_request.return_value = ("token-no-exp", 0) + + result = await get_access_token_async() + + assert result == "token-no-exp" + authenticator._token_cache.set_cache.assert_not_called() + + @pytest.mark.asyncio + @patch(f"{AUTH_MODULE}._request_token_async", new_callable=AsyncMock) + @patch(f"{AUTH_MODULE}._get_auth_url", return_value="https://auth.example.com") + @patch(f"{AUTH_MODULE}._get_scope", return_value="GIGACHAT_API_PERS") + @patch(f"{AUTH_MODULE}._get_credentials", return_value="creds") + @patch(f"{AUTH_MODULE}.get_secret_str", return_value=None) + async def test_returns_cached_valid_token( + self, mock_get_secret, mock_creds, mock_scope, mock_auth_url, mock_request + ): + cached_token = "cached-token-async" + cached_expires_at = _future_expires_at_ms(offset_seconds=7200) + authenticator._token_cache.get_cache.return_value = (cached_token, cached_expires_at) + + result = await get_access_token_async(credentials="creds") + + assert result == cached_token + mock_request.assert_not_called() + authenticator._token_cache.set_cache.assert_not_called() + + @pytest.mark.asyncio + @patch(f"{AUTH_MODULE}._request_token_async", new_callable=AsyncMock) + @patch(f"{AUTH_MODULE}._get_auth_url", return_value="https://auth.example.com") + @patch(f"{AUTH_MODULE}._get_scope", return_value="GIGACHAT_API_PERS") + @patch(f"{AUTH_MODULE}._get_credentials", return_value="creds") + @patch(f"{AUTH_MODULE}.get_secret_str", return_value=None) + async def test_requests_new_token_when_cache_expired( + self, mock_get_secret, mock_creds, mock_scope, mock_auth_url, mock_request + ): + cached_expires_at = _past_expires_at_ms(offset_seconds=10) + authenticator._token_cache.get_cache.return_value = ("stale", cached_expires_at) + + new_token = "refreshed-token-async" + mock_request.return_value = (new_token, _future_expires_at_ms()) + + result = await get_access_token_async(credentials="creds") + + assert result == new_token + mock_request.assert_called_once() + + @pytest.mark.asyncio + @patch(f"{AUTH_MODULE}._request_token_async", new_callable=AsyncMock) + @patch(f"{AUTH_MODULE}._get_auth_url", return_value="https://default-auth.example.com") + @patch(f"{AUTH_MODULE}._get_scope", return_value="GIGACHAT_API_PERS") + @patch(f"{AUTH_MODULE}._get_credentials", return_value="env-creds") + @patch(f"{AUTH_MODULE}.get_secret_str", return_value=None) + async def test_litellm_params_override_scope_and_auth_url( # test-quality-ok: mock-echo of internal wiring + self, mock_get_secret, mock_creds, mock_scope, mock_auth_url, mock_request + ): + mock_request.return_value = ("token", _future_expires_at_ms()) + + await get_access_token_async( + litellm_params={ + "gigachat_scope": "GIGACHAT_API_CORP", + "gigachat_auth_url": "https://params-auth.example.com", + } + ) + + mock_request.assert_called_once_with( # test-quality-ok: mock-echo of internal wiring + "env-creds", "GIGACHAT_API_CORP", "https://params-auth.example.com" + ) + + @pytest.mark.asyncio + @patch(f"{AUTH_MODULE}._request_token_async", new_callable=AsyncMock) + @patch(f"{AUTH_MODULE}._get_auth_url", return_value="https://default-auth.example.com") + @patch(f"{AUTH_MODULE}._get_scope", return_value="GIGACHAT_API_PERS") + @patch(f"{AUTH_MODULE}._get_credentials", return_value="env-creds") + @patch(f"{AUTH_MODULE}.get_secret_str", return_value=None) + async def test_explicit_args_override_everything( # test-quality-ok: mock-echo of internal wiring + self, mock_get_secret, mock_creds, mock_scope, mock_auth_url, mock_request + ): + mock_request.return_value = ("token", _future_expires_at_ms()) + + await get_access_token_async( + credentials="explicit-creds", + scope="EXPLICIT_SCOPE", + auth_url="https://explicit.example.com", + litellm_params={ + "gigachat_scope": "PARAM_SCOPE", + "gigachat_auth_url": "https://params.example.com", + }, + ) + + mock_request.assert_called_once_with( # test-quality-ok: mock-echo of internal wiring + "explicit-creds", "EXPLICIT_SCOPE", "https://explicit.example.com" + ) + + @pytest.mark.asyncio + @patch(f"{AUTH_MODULE}._request_token_async", new_callable=AsyncMock) + @patch(f"{AUTH_MODULE}._get_auth_url", return_value="https://auth.example.com") + @patch(f"{AUTH_MODULE}._get_scope", return_value="GIGACHAT_API_PERS") + @patch(f"{AUTH_MODULE}._get_credentials", return_value="creds") + @patch(f"{AUTH_MODULE}.get_secret_str", return_value=None) + async def test_propagates_auth_error_from_request( + self, mock_get_secret, mock_creds, mock_scope, mock_auth_url, mock_request + ): + mock_request.side_effect = GigaChatAuthError(status_code=403, message="forbidden") + + with pytest.raises(GigaChatAuthError) as exc_info: + await get_access_token_async() + assert exc_info.value.status_code == 403 + assert exc_info.value.message == "forbidden" + + +class TestRequestTokenSyncErrorMapping: + @patch(f"{AUTH_MODULE}._get_http_client") + def test_http_status_error_maps_to_auth_error(self, mock_get_client): + client = MagicMock() + request = httpx.Request("POST", "https://auth.example.com") + response = httpx.Response(status_code=401, content=b"bad creds", request=request) + http_error = httpx.HTTPStatusError("unauthorized", request=request, response=response) + client.post.side_effect = http_error + mock_get_client.return_value = client + + from litellm.llms.gigachat.authenticator import _request_token_sync + + with pytest.raises(GigaChatAuthError) as exc_info: + _request_token_sync("creds", "GIGACHAT_API_PERS", "https://auth.example.com") + assert exc_info.value.status_code == 401 + assert "bad creds" in exc_info.value.message + + @patch(f"{AUTH_MODULE}._get_http_client") + def test_request_error_maps_to_auth_error(self, mock_get_client): + client = MagicMock() + client.post.side_effect = httpx.ConnectError("connection refused") + mock_get_client.return_value = client + + from litellm.llms.gigachat.authenticator import _request_token_sync + + with pytest.raises(GigaChatAuthError) as exc_info: + _request_token_sync("creds", "GIGACHAT_API_PERS", "https://auth.example.com") + assert exc_info.value.status_code == 500 + assert "connection refused" in exc_info.value.message + + +class TestRequestTokenAsyncErrorMapping: + @pytest.mark.asyncio + @patch(f"{AUTH_MODULE}.get_async_httpx_client") + async def test_http_status_error_maps_to_auth_error(self, mock_get_client): + client = MagicMock() + request = httpx.Request("POST", "https://auth.example.com") + response = httpx.Response(status_code=401, content=b"bad creds", request=request) + http_error = httpx.HTTPStatusError("unauthorized", request=request, response=response) + client.post = AsyncMock(side_effect=http_error) + mock_get_client.return_value = client + + from litellm.llms.gigachat.authenticator import _request_token_async + + with pytest.raises(GigaChatAuthError) as exc_info: + await _request_token_async("creds", "GIGACHAT_API_PERS", "https://auth.example.com") + assert exc_info.value.status_code == 401 + assert "bad creds" in exc_info.value.message + + @pytest.mark.asyncio + @patch(f"{AUTH_MODULE}.get_async_httpx_client") + async def test_request_error_maps_to_auth_error(self, mock_get_client): + client = MagicMock() + client.post = AsyncMock(side_effect=httpx.ConnectError("connection refused")) + mock_get_client.return_value = client + + from litellm.llms.gigachat.authenticator import _request_token_async + + with pytest.raises(GigaChatAuthError) as exc_info: + await _request_token_async("creds", "GIGACHAT_API_PERS", "https://auth.example.com") + assert exc_info.value.status_code == 500 + assert "connection refused" in exc_info.value.message + + +class TestParseTokenResponse: + def _make_response(self, body: dict) -> httpx.Response: + import json + + return httpx.Response( + status_code=200, + content=json.dumps(body).encode("utf-8"), + request=httpx.Request("POST", "https://auth.example.com"), + ) + + def test_parses_tok_exp_fields(self): + from litellm.llms.gigachat.authenticator import _parse_token_response + + token, expires_at = _parse_token_response( + self._make_response({"tok": "abc", "exp": 1700000000000}) + ) + assert token == "abc" + assert expires_at == 1700000000000 + + def test_parses_access_token_expires_at_fields(self): + from litellm.llms.gigachat.authenticator import _parse_token_response + + token, expires_at = _parse_token_response( + self._make_response({"access_token": "xyz", "expires_at": 1700000000000}) + ) + assert token == "xyz" + assert expires_at == 1700000000000 + + def test_parses_string_expires_at(self): + from litellm.llms.gigachat.authenticator import _parse_token_response + + token, expires_at = _parse_token_response( + self._make_response({"tok": "abc", "exp": "1700000000000"}) + ) + assert token == "abc" + assert expires_at == 1700000000000 + assert isinstance(expires_at, int) + + def test_raises_when_no_access_token(self): + from litellm.llms.gigachat.authenticator import _parse_token_response + + with pytest.raises(GigaChatAuthError) as exc_info: + _parse_token_response(self._make_response({"exp": 1700000000000})) + assert exc_info.value.status_code == 500 + assert "Invalid token response" in exc_info.value.message + + +class TestGetHttpClient: + def test_reuses_cached_client_across_calls(self): + """Regression: the sync OAuth path must use the shared cached httpx client, + not construct a fresh HTTPHandler per token request.""" + assert authenticator._get_http_client() is authenticator._get_http_client() diff --git a/tests/test_litellm/llms/gigachat/test_file_handler.py b/tests/test_litellm/llms/gigachat/test_file_handler.py new file mode 100644 index 00000000000..ce9505f11f2 --- /dev/null +++ b/tests/test_litellm/llms/gigachat/test_file_handler.py @@ -0,0 +1,504 @@ +""" +Unit tests for GigaChat file handler. + +Tests _get_url_hash, _parse_data_url, _download_image_sync, _download_image_async, +upload_file_sync, and upload_file_async covering caching, base64 data URL decoding, +network errors, and the full upload flow. +""" + +import json +from unittest.mock import AsyncMock, MagicMock, patch + +import httpx +import pytest + +from litellm.llms.gigachat import file_handler +from litellm.llms.gigachat.file_handler import ( + _file_cache, + _get_url_hash, + _parse_data_url, + upload_file_async, + upload_file_sync, +) + +FILE_MODULE = "litellm.llms.gigachat.file_handler" + +# A valid 1x1 red PNG as base64 +_RED_PNG_B64 = ( + "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAA" + "DUlEQVQI12NgYPgPAAEDAQAR3X3ZAAAASUVORK5CYII=" +) +_RED_PNG_DATA_URL = f"data:image/png;base64,{_RED_PNG_B64}" + + +# --------------------------------------------------------------------------- +# Fixtures +# --------------------------------------------------------------------------- + + +@pytest.fixture(autouse=True) +def _isolate_file_cache(): + """Each test gets a fresh module-level file cache to avoid cross-test leakage.""" + _file_cache.clear() + yield + _file_cache.clear() + + +# --------------------------------------------------------------------------- +# _get_url_hash +# --------------------------------------------------------------------------- + + +class TestGetUrlHash: + def test_returns_hex_string(self): + h = _get_url_hash("https://example.com/image.png") + assert isinstance(h, str) + assert len(h) == 64 # SHA-256 + + def test_different_urls_different_hashes(self): + h1 = _get_url_hash("https://example.com/a.png") + h2 = _get_url_hash("https://example.com/b.png") + assert h1 != h2 + + def test_same_url_same_hash(self): + h1 = _get_url_hash("https://example.com/image.png") + h2 = _get_url_hash("https://example.com/image.png") + assert h1 == h2 + + +# --------------------------------------------------------------------------- +# _parse_data_url +# --------------------------------------------------------------------------- + + +class TestParseDataUrl: + def test_valid_base64_png(self): + result = _parse_data_url(_RED_PNG_DATA_URL) + assert result is not None + content_bytes, content_type, ext = result + assert content_type == "image/png" + assert ext == "png" + assert len(content_bytes) > 0 + + def test_valid_base64_jpeg(self): + # Simple valid base64 (24 chars, properly padded, no + or / chars) + valid_b64 = "aGVsbG8gd29ybGQhISEhIQ==" + data_url = f"data:image/jpeg;base64,{valid_b64}" + result = _parse_data_url(data_url) + assert result is not None + _, content_type, ext = result + assert content_type == "image/jpeg" + assert ext == "jpeg" + + def test_valid_base64_with_semicolon_in_type(self): + """Data URLs with charset before base64 segment do not match the regex.""" + # The regex `data:([^;]+);base64,(.+)` requires the pattern to be + # `data:;base64,`. If `;charset=utf-8` appears before + # `;base64,`, the regex sees `data:image/png` as group 1 but then + # looks for `;base64,` immediately after — which isn't there because + # `;charset=utf-8;base64,` has extra text before `;base64,` + data_url = "data:image/png;charset=utf-8;base64," + _RED_PNG_B64 + result = _parse_data_url(data_url) + assert result is None + + def test_invalid_data_url_returns_none(self): + assert _parse_data_url("not-a-data-url") is None + + def test_empty_base64_returns_none(self): + """Empty base64 data (nothing after comma) does not match regex `(.+)`.""" + assert _parse_data_url("data:image/png;base64,") is None + + def test_missing_base64_segment(self): + assert _parse_data_url("data:image/png;base64") is None + + def test_unknown_extension_falls_back_to_jpg(self): + data_url = "data:application/octet-stream;base64," + _RED_PNG_B64 + result = _parse_data_url(data_url) + assert result is not None + _, content_type, ext = result + assert content_type == "application/octet-stream" + # The extension is derived from content_type.split("/")[-1].split(";")[0] + # which gives "octet-stream", not "jpg" + assert ext == "octet-stream" + + +# --------------------------------------------------------------------------- +# _download_image_sync +# --------------------------------------------------------------------------- + + +class TestDownloadImageSync: + @patch(f"{FILE_MODULE}._get_httpx_client") + def test_downloads_image_successfully(self, mock_http_handler_cls): + mock_client = MagicMock() + mock_response = MagicMock() + mock_response.content = b"fake-image-bytes" + mock_response.headers = {"content-type": "image/jpeg"} + mock_client.get.return_value = mock_response + mock_http_handler_cls.return_value = mock_client + + content_bytes, content_type, ext = file_handler._download_image_sync("https://example.com/img.jpg") + + assert content_bytes == b"fake-image-bytes" + assert content_type == "image/jpeg" + assert ext == "jpeg" + mock_client.get.assert_called_once_with("https://example.com/img.jpg") + + @patch(f"{FILE_MODULE}._get_httpx_client") + def test_raises_on_http_error(self, mock_http_handler_cls): + mock_client = MagicMock() + mock_client.get.side_effect = httpx.HTTPStatusError( + "Not Found", + request=httpx.Request("GET", "https://example.com/404"), + response=httpx.Response(status_code=404, request=httpx.Request("GET", "https://example.com/404")), + ) + mock_http_handler_cls.return_value = mock_client + + with pytest.raises(httpx.HTTPStatusError): + file_handler._download_image_sync("https://example.com/404") + + @patch(f"{FILE_MODULE}._get_httpx_client") + def test_parse_content_type_fallback(self, mock_http_handler_cls): + mock_client = MagicMock() + mock_response = MagicMock() + mock_response.content = b"data" + mock_response.headers = {} + mock_client.get.return_value = mock_response + mock_http_handler_cls.return_value = mock_client + + _, content_type, ext = file_handler._download_image_sync("https://example.com/img") + + assert content_type == "image/jpeg" + assert ext == "jpeg" + + @patch(f"{FILE_MODULE}._get_httpx_client") + def test_extracts_extension_from_parametrized_type(self, mock_http_handler_cls): + mock_client = MagicMock() + mock_response = MagicMock() + mock_response.content = b"data" + mock_response.headers = {"content-type": "image/png; charset=utf-8"} + mock_client.get.return_value = mock_response + mock_http_handler_cls.return_value = mock_client + + _, _, ext = file_handler._download_image_sync("https://example.com/img.png") + + assert ext == "png" + + +# --------------------------------------------------------------------------- +# _download_image_async +# --------------------------------------------------------------------------- + + +class TestDownloadImageAsync: + @pytest.mark.asyncio + @patch(f"{FILE_MODULE}.get_async_httpx_client") + async def test_downloads_image_successfully(self, mock_get_client): + mock_client = MagicMock() + mock_response = MagicMock() + mock_response.content = b"fake-image-bytes" + mock_response.headers = {"content-type": "image/webp"} + mock_client.get = AsyncMock(return_value=mock_response) + mock_get_client.return_value = mock_client + + content_bytes, content_type, ext = await file_handler._download_image_async( + "https://example.com/img.webp" + ) + + assert content_bytes == b"fake-image-bytes" + assert content_type == "image/webp" + assert ext == "webp" + mock_client.get.assert_called_once_with("https://example.com/img.webp") + + @pytest.mark.asyncio + @patch(f"{FILE_MODULE}.get_async_httpx_client") + async def test_raises_on_http_error(self, mock_get_client): + mock_client = MagicMock() + mock_client.get = AsyncMock( + side_effect=httpx.HTTPStatusError( + "Forbidden", + request=httpx.Request("GET", "https://example.com/403"), + response=httpx.Response(status_code=403, request=httpx.Request("GET", "https://example.com/403")), + ) + ) + mock_get_client.return_value = mock_client + + with pytest.raises(httpx.HTTPStatusError): + await file_handler._download_image_async("https://example.com/403") + + +# --------------------------------------------------------------------------- +# upload_file_sync +# --------------------------------------------------------------------------- + + +class TestUploadFileSync: + @patch(f"{FILE_MODULE}.get_api_base", return_value="https://api.example.com") + @patch(f"{FILE_MODULE}.get_access_token", return_value="test-token") + @patch(f"{FILE_MODULE}._get_httpx_client") + def test_uploads_base64_image_and_caches( + self, mock_http_handler_cls, mock_get_token, mock_get_api_base + ): + mock_client = MagicMock() + mock_response = MagicMock() + mock_response.json.return_value = {"id": "file-12345"} + mock_response.raise_for_status = MagicMock() + mock_client.post.return_value = mock_response + mock_http_handler_cls.return_value = mock_client + + result = upload_file_sync( + image_url=_RED_PNG_DATA_URL, + credentials="creds", + api_base="https://custom.example.com", + ) + + assert result == "file-12345" + # Verify it was cached + url_hash = _get_url_hash(_RED_PNG_DATA_URL) + assert _file_cache[url_hash] == "file-12345" + + # Check the upload request — url is passed as first positional arg + call_args = mock_client.post.call_args + assert call_args.args[0] == "https://api.example.com/files" + assert call_args.kwargs["headers"]["Authorization"] == "Bearer test-token" + # Verify purpose + assert call_args.kwargs["data"] == {"purpose": "general"} + # Verify a file was attached + assert "file" in call_args.kwargs["files"] + + @patch(f"{FILE_MODULE}.get_api_base", return_value="https://api.example.com") + @patch(f"{FILE_MODULE}.get_access_token", return_value="test-token") + @patch(f"{FILE_MODULE}._get_httpx_client") + def test_returns_cached_file_id( + self, mock_http_handler_cls, mock_get_token, mock_get_api_base + ): + # Pre-populate the cache + url_hash = _get_url_hash(_RED_PNG_DATA_URL) + _file_cache[url_hash] = "cached-file-id" + + result = upload_file_sync(image_url=_RED_PNG_DATA_URL, credentials="creds") + + assert result == "cached-file-id" + # No upload call was made + mock_http_handler_cls.return_value.post.assert_not_called() + + @patch(f"{FILE_MODULE}._get_httpx_client") + @patch(f"{FILE_MODULE}.get_access_token", return_value="test-token") + @patch(f"{FILE_MODULE}.get_api_base", return_value="https://api.example.com") + @patch(f"{FILE_MODULE}._download_image_sync") + def test_downloads_and_uploads_url_image( + self, mock_download, mock_get_api_base, mock_get_token, mock_http_handler_cls + ): + mock_download.return_value = (b"remote-bytes", "image/png", "png") + mock_client = MagicMock() + mock_response = MagicMock() + mock_response.json.return_value = {"id": "file-remote"} + mock_response.raise_for_status = MagicMock() + mock_client.post.return_value = mock_response + mock_http_handler_cls.return_value = mock_client + + result = upload_file_sync( + image_url="https://example.com/remote.png", credentials="creds" + ) + + assert result == "file-remote" + mock_download.assert_called_once_with("https://example.com/remote.png") + + @patch(f"{FILE_MODULE}._get_httpx_client") + @patch(f"{FILE_MODULE}.get_access_token", return_value="test-token") + @patch(f"{FILE_MODULE}.get_api_base", return_value="https://api.example.com") + def test_returns_none_on_upload_failure( + self, mock_get_api_base, mock_get_token, mock_http_handler_cls + ): + mock_client = MagicMock() + mock_client.post.side_effect = httpx.HTTPStatusError( + "Bad Request", + request=httpx.Request("POST", "https://api.example.com/files"), + response=httpx.Response(status_code=400, request=httpx.Request("POST", "https://api.example.com/files")), + ) + mock_http_handler_cls.return_value = mock_client + + # upload_file_sync catches all exceptions and returns None + result = upload_file_sync( + image_url=_RED_PNG_DATA_URL, credentials="creds" + ) + + assert result is None + + @patch(f"{FILE_MODULE}._get_httpx_client") + @patch(f"{FILE_MODULE}.get_access_token", return_value="test-token") + @patch(f"{FILE_MODULE}.get_api_base", return_value="https://api.example.com") + def test_returns_none_when_response_missing_id( + self, mock_get_api_base, mock_get_token, mock_http_handler_cls + ): + mock_client = MagicMock() + mock_response = MagicMock() + mock_response.json.return_value = {"status": "ok"} # no "id" key + mock_response.raise_for_status = MagicMock() + mock_client.post.return_value = mock_response + mock_http_handler_cls.return_value = mock_client + + result = upload_file_sync( + image_url=_RED_PNG_DATA_URL, credentials="creds" + ) + + assert result is None + + @patch(f"{FILE_MODULE}.get_api_base", return_value="https://api.example.com") + @patch(f"{FILE_MODULE}.get_access_token", return_value="test-token") + @patch(f"{FILE_MODULE}._get_httpx_client") + def test_uploads_without_optional_args( + self, mock_http_handler_cls, mock_get_token, mock_get_api_base + ): + """Verify that credentials, api_base, and litellm_params are optional.""" + mock_client = MagicMock() + mock_response = MagicMock() + mock_response.json.return_value = {"id": "file-no-args"} + mock_response.raise_for_status = MagicMock() + mock_client.post.return_value = mock_response + mock_http_handler_cls.return_value = mock_client + + result = upload_file_sync(image_url=_RED_PNG_DATA_URL) + + assert result == "file-no-args" + # Should still have called get_access_token without args + mock_get_token.assert_called_once_with(credentials=None, litellm_params=None) + + +# --------------------------------------------------------------------------- +# upload_file_async +# --------------------------------------------------------------------------- + + +class TestUploadFileAsync: + @pytest.mark.asyncio + @patch(f"{FILE_MODULE}.get_api_base", return_value="https://api.example.com") + @patch(f"{FILE_MODULE}.get_access_token_async", return_value="test-token-async") + @patch(f"{FILE_MODULE}.get_async_httpx_client") + async def test_uploads_base64_image_and_caches( + self, mock_get_client, mock_get_token, mock_get_api_base + ): + mock_client = MagicMock() + mock_response = MagicMock() + mock_response.json = MagicMock(return_value={"id": "async-file-1"}) + mock_response.raise_for_status = MagicMock() + mock_client.post = AsyncMock(return_value=mock_response) + mock_get_client.return_value = mock_client + + result = await upload_file_async( + image_url=_RED_PNG_DATA_URL, + credentials="creds", + api_base="https://custom.example.com", + ) + + assert result == "async-file-1" + # Verify cache + url_hash = _get_url_hash(_RED_PNG_DATA_URL) + assert _file_cache[url_hash] == "async-file-1" + + # Check upload request details — url is first positional arg + call_args = mock_client.post.call_args + assert call_args.args[0] == "https://api.example.com/files" + assert call_args.kwargs["headers"]["Authorization"] == "Bearer test-token-async" + assert "purpose" in str(call_args.kwargs["data"]) + assert "file" in call_args.kwargs["files"] + + @pytest.mark.asyncio + @patch(f"{FILE_MODULE}.get_api_base", return_value="https://api.example.com") + @patch(f"{FILE_MODULE}.get_access_token_async", return_value="test-token-async") + @patch(f"{FILE_MODULE}.get_async_httpx_client") + async def test_returns_cached_file_id( + self, mock_get_client, mock_get_token, mock_get_api_base + ): + url_hash = _get_url_hash(_RED_PNG_DATA_URL) + _file_cache[url_hash] = "cached-async-id" + + result = await upload_file_async(image_url=_RED_PNG_DATA_URL, credentials="creds") + + assert result == "cached-async-id" + mock_get_client.return_value.post.assert_not_called() + + @pytest.mark.asyncio + @patch(f"{FILE_MODULE}.get_async_httpx_client") + @patch(f"{FILE_MODULE}.get_access_token_async", return_value="test-token-async") + @patch(f"{FILE_MODULE}.get_api_base", return_value="https://api.example.com") + @patch(f"{FILE_MODULE}._download_image_async") + async def test_downloads_and_uploads_url_image( + self, mock_download, mock_get_api_base, mock_get_token, mock_get_client + ): + mock_download.return_value = (b"remote-bytes-async", "image/png", "png") + mock_client = MagicMock() + mock_response = MagicMock() + mock_response.json = MagicMock(return_value={"id": "async-file-remote"}) + mock_response.raise_for_status = MagicMock() + mock_client.post = AsyncMock(return_value=mock_response) + mock_get_client.return_value = mock_client + + result = await upload_file_async( + image_url="https://example.com/remote.png", credentials="creds" + ) + + assert result == "async-file-remote" + mock_download.assert_called_once_with("https://example.com/remote.png") + + @pytest.mark.asyncio + @patch(f"{FILE_MODULE}.get_async_httpx_client") + @patch(f"{FILE_MODULE}.get_access_token_async", return_value="test-token-async") + @patch(f"{FILE_MODULE}.get_api_base", return_value="https://api.example.com") + async def test_returns_none_on_upload_failure( + self, mock_get_api_base, mock_get_token, mock_get_client + ): + mock_client = MagicMock() + mock_client.post = AsyncMock( + side_effect=httpx.HTTPStatusError( + "Bad Request", + request=httpx.Request("POST", "https://api.example.com/files"), + response=httpx.Response(status_code=400, request=httpx.Request("POST", "https://api.example.com/files")), + ) + ) + mock_get_client.return_value = mock_client + + result = await upload_file_async( + image_url=_RED_PNG_DATA_URL, credentials="creds" + ) + + assert result is None + + @pytest.mark.asyncio + @patch(f"{FILE_MODULE}.get_async_httpx_client") + @patch(f"{FILE_MODULE}.get_access_token_async", return_value="test-token-async") + @patch(f"{FILE_MODULE}.get_api_base", return_value="https://api.example.com") + async def test_returns_none_when_response_missing_id( + self, mock_get_api_base, mock_get_token, mock_get_client + ): + mock_client = MagicMock() + mock_response = MagicMock() + mock_response.json = MagicMock(return_value={"status": "ok"}) + mock_response.raise_for_status = MagicMock() + mock_client.post = AsyncMock(return_value=mock_response) + mock_get_client.return_value = mock_client + + result = await upload_file_async( + image_url=_RED_PNG_DATA_URL, credentials="creds" + ) + + assert result is None + + @pytest.mark.asyncio + @patch(f"{FILE_MODULE}.get_api_base", return_value="https://api.example.com") + @patch(f"{FILE_MODULE}.get_access_token_async", return_value="test-token-async") + @patch(f"{FILE_MODULE}.get_async_httpx_client") + async def test_uploads_without_optional_args( + self, mock_get_client, mock_get_token, mock_get_api_base + ): + mock_client = MagicMock() + mock_response = MagicMock() + mock_response.json = MagicMock(return_value={"id": "async-no-args"}) + mock_response.raise_for_status = MagicMock() + mock_client.post = AsyncMock(return_value=mock_response) + mock_get_client.return_value = mock_client + + result = await upload_file_async(image_url=_RED_PNG_DATA_URL) + + assert result == "async-no-args" + mock_get_token.assert_called_once_with(credentials=None, litellm_params=None) \ No newline at end of file diff --git a/tests/test_litellm/llms/gigachat/test_utils.py b/tests/test_litellm/llms/gigachat/test_utils.py new file mode 100644 index 00000000000..71a193d7b29 --- /dev/null +++ b/tests/test_litellm/llms/gigachat/test_utils.py @@ -0,0 +1,79 @@ +""" +Tests for litellm.llms.gigachat.utils +""" + +import pytest +from litellm.llms.gigachat.utils import convert_usage +from litellm.types.utils import PromptTokensDetailsWrapper, Usage + + +class TestConvertUsage: + def test_basic_usage_without_precached(self): + """Test convert_usage with standard tokens, no precached prompt tokens.""" + result = convert_usage( + { + "prompt_tokens": 10, + "completion_tokens": 5, + "total_tokens": 15, + } + ) + + assert result == Usage( + prompt_tokens=10, + completion_tokens=5, + total_tokens=15, + prompt_tokens_details=None, + ) + + def test_usage_with_precached_prompt_tokens(self): + """GigaChat's prompt_tokens and total_tokens exclude cached tokens (docs example: + prompt_tokens=1, precached_prompt_tokens=37, total_tokens=5), so OpenAI-convention + usage adds precached back in and surfaces it as cached_tokens.""" + result = convert_usage( + { + "prompt_tokens": 10, + "completion_tokens": 5, + "precached_prompt_tokens": 3, + "total_tokens": 15, + } + ) + + assert result == Usage( + prompt_tokens=13, + completion_tokens=5, + total_tokens=18, + prompt_tokens_details=PromptTokensDetailsWrapper(cached_tokens=3), + ) + + def test_zero_precached_prompt_tokens(self): + """Test convert_usage with zero precached_prompt_tokens does not create details wrapper.""" + result = convert_usage( + { + "prompt_tokens": 10, + "completion_tokens": 5, + "precached_prompt_tokens": 0, + "total_tokens": 15, + } + ) + + assert result == Usage( + prompt_tokens=10, + completion_tokens=5, + total_tokens=15, + prompt_tokens_details=None, + ) + + def test_missing_optional_fields(self): + """Test convert_usage with missing optional fields defaults to zero.""" + result = convert_usage( + { + "prompt_tokens": 10, + "completion_tokens": 5, + "total_tokens": 15, + } + ) + + assert result.prompt_tokens == 10 + assert result.completion_tokens == 5 + assert result.total_tokens == 15 + assert result.prompt_tokens_details is None \ No newline at end of file diff --git a/tests/test_litellm/llms/mistral/ocr/test_mistral_ocr_cost.py b/tests/test_litellm/llms/mistral/ocr/test_mistral_ocr_cost.py index 890df597933..a0e1616d4b2 100644 --- a/tests/test_litellm/llms/mistral/ocr/test_mistral_ocr_cost.py +++ b/tests/test_litellm/llms/mistral/ocr/test_mistral_ocr_cost.py @@ -24,6 +24,9 @@ OCR3_MODEL = "mistral/mistral-ocr-2512" OCR3_COST_PER_PAGE = 0.002 OCR3_ANNOTATION_COST_PER_PAGE = 0.003 +AZURE_DOC_AI_MODEL = "azure_ai/mistral-document-ai-2512" +AZURE_DOC_AI_COST_PER_PAGE = 0.003 + def _ocr_response(model: str, pages_processed: int) -> OCRResponse: return OCRResponse( @@ -33,6 +36,14 @@ def _ocr_response(model: str, pages_processed: int) -> OCRResponse: ) +def _annotated_ocr_response(model: str, pages_processed: int | None, annotation_pages: int) -> OCRResponse: + return OCRResponse( + pages=[], + model=model, + usage_info=OCRUsageInfo(pages_processed=pages_processed, pages_processed_annotation=annotation_pages), + ) + + @pytest.mark.parametrize("model", ["mistral-ocr-4-0", "mistral-ocr-latest"]) def test_model_info_ocr4_price(model: str) -> None: info = litellm.get_model_info(model=f"mistral/{model}", custom_llm_provider="mistral") @@ -79,3 +90,46 @@ def test_ocr3_cost_scales_with_pages(local_model_cost_map, pages_processed: int) call_type="ocr", ) assert cost == pytest.approx(OCR3_COST_PER_PAGE * pages_processed) + + +def test_ocr3_bills_ocr_and_annotation_pages_at_their_own_rates(local_model_cost_map) -> None: + cost = completion_cost( + completion_response=_annotated_ocr_response("mistral-ocr-2512", 2, 3), + model=OCR3_MODEL, + custom_llm_provider="mistral", + call_type="ocr", + ) + assert cost == pytest.approx(2 * OCR3_COST_PER_PAGE + 3 * OCR3_ANNOTATION_COST_PER_PAGE) + + +def test_ocr3_bills_annotation_only_response(local_model_cost_map) -> None: + cost = completion_cost( + completion_response=_annotated_ocr_response("mistral-ocr-2512", 0, 3), + model=OCR3_MODEL, + custom_llm_provider="mistral", + call_type="ocr", + ) + assert cost == pytest.approx(3 * OCR3_ANNOTATION_COST_PER_PAGE) + + +def test_ocr3_bills_annotation_pages_when_pages_processed_missing(local_model_cost_map) -> None: + cost = completion_cost( + completion_response=_annotated_ocr_response("mistral-ocr-2512", None, 4), + model=OCR3_MODEL, + custom_llm_provider="mistral", + call_type="ocr", + ) + assert cost == pytest.approx(4 * OCR3_ANNOTATION_COST_PER_PAGE) + + +def test_azure_doc_ai_annotation_pages_fall_back_to_ocr_rate(local_model_cost_map) -> None: + info = litellm.get_model_info(model=AZURE_DOC_AI_MODEL, custom_llm_provider="azure_ai") + assert info.get("annotation_cost_per_page") is None + assert info["ocr_cost_per_page"] == AZURE_DOC_AI_COST_PER_PAGE + cost = completion_cost( + completion_response=_annotated_ocr_response("mistral-document-ai-2512", 0, 1), + model=AZURE_DOC_AI_MODEL, + custom_llm_provider="azure_ai", + call_type="ocr", + ) + assert cost == pytest.approx(AZURE_DOC_AI_COST_PER_PAGE) diff --git a/tests/test_litellm/llms/openai/chat/test_openai_gpt_transformation.py b/tests/test_litellm/llms/openai/chat/test_openai_gpt_transformation.py index 3f346b5e8e7..3ef5e39fc5f 100644 --- a/tests/test_litellm/llms/openai/chat/test_openai_gpt_transformation.py +++ b/tests/test_litellm/llms/openai/chat/test_openai_gpt_transformation.py @@ -145,6 +145,69 @@ class TestGetOptionalParamsIntegration: assert regular_params.get("user") == "my-end-user" assert responses_params.get("user") == "my-end-user" + def test_reasoning_effort_supported_for_unknown_model_alias(self): + """An openai/-routed model litellm doesn't recognize is likely a proxy alias: + reasoning_effort must be forwarded so the server decides support.""" + from litellm.llms.openai.openai import OpenAIConfig + + supported_params = OpenAIConfig().get_supported_openai_params( + "my-claude-alias" + ) + assert "reasoning_effort" in supported_params + + def test_reasoning_effort_not_supported_for_known_non_reasoning_models(self): + """Known OpenAI models keep failing closed client-side.""" + from litellm.llms.openai.openai import OpenAIConfig + + config = OpenAIConfig() + assert "reasoning_effort" not in config.get_supported_openai_params("gpt-4o") + assert "reasoning_effort" not in config.get_supported_openai_params( + "responses/gpt-4.1-mini" + ) + + def test_reasoning_effort_not_inherited_by_openai_compatible_subclasses(self): + """Providers subclassing either openai config keep their own reasoning_effort gating + for their models, which are all unknown to the openai catalog.""" + from litellm.llms.openai.openai import OpenAIConfig + + class InheritingDispatcherConfig(OpenAIConfig): + pass + + class InheritingGPTConfig(OpenAIGPTConfig): + pass + + assert "reasoning_effort" not in InheritingDispatcherConfig().get_supported_openai_params( + "some-unknown-model" + ) + assert "reasoning_effort" not in InheritingGPTConfig().get_supported_openai_params( + "some-unknown-model" + ) + + def test_reasoning_effort_forwarded_in_optional_params_for_unknown_model_alias( + self, + ): + """Regression test for reasoning_effort raising UnsupportedParamsError + client-side for openai/-prefixed proxy aliases before any HTTP request.""" + from litellm.utils import get_optional_params + + optional_params = get_optional_params( + model="my-claude-alias", + custom_llm_provider="openai", + reasoning_effort="low", + ) + assert optional_params.get("reasoning_effort") == "low" + + def test_reasoning_effort_still_rejected_for_known_non_reasoning_model(self): + """A real OpenAI model that doesn't reason still rejects the param client-side.""" + from litellm.utils import get_optional_params + + with pytest.raises(litellm.utils.UnsupportedParamsError): + get_optional_params( + model="gpt-4o", + custom_llm_provider="openai", + reasoning_effort="low", + ) + class TestOpenAIChatCompletionStreamingHandler: """Tests for OpenAIChatCompletionStreamingHandler.chunk_parser()""" diff --git a/tests/test_litellm/llms/openai/responses/test_openai_count_tokens_transformation.py b/tests/test_litellm/llms/openai/responses/test_openai_count_tokens_transformation.py index e1cc6a92927..c2efc1acdb9 100644 --- a/tests/test_litellm/llms/openai/responses/test_openai_count_tokens_transformation.py +++ b/tests/test_litellm/llms/openai/responses/test_openai_count_tokens_transformation.py @@ -163,6 +163,240 @@ def test_messages_to_responses_input_with_tool(): } +def test_messages_to_responses_input_preserves_images(): + """An image block must survive the round trip, or OpenAI counts only the text. + + A 256x256 image is worth 255 tokens to OpenAI's counting API; dropping it + turned a 268-token request into a 13-token one. + """ + messages = [ + { + "role": "user", + "content": [ + {"type": "text", "text": "What is in this image?"}, + { + "type": "image_url", + "image_url": {"url": "data:image/png;base64,iVBORw0KGgo=", "detail": "high"}, + }, + ], + } + ] + + input_items, instructions = OpenAICountTokensConfig.messages_to_responses_input(messages) + + assert instructions is None + assert input_items == [ + { + "role": "user", + "content": ( + {"type": "input_text", "text": "What is in this image?"}, + { + "type": "input_image", + "image_url": "data:image/png;base64,iVBORw0KGgo=", + "detail": "high", + }, + ), + } + ] + + +def test_messages_to_responses_input_image_without_detail_defaults_to_auto(): + messages = [ + { + "role": "user", + "content": [{"type": "image_url", "image_url": {"url": "https://example.com/cat.png"}}], + } + ] + + input_items, _ = OpenAICountTokensConfig.messages_to_responses_input(messages) + + assert input_items[0]["content"] == ( + {"type": "input_image", "image_url": "https://example.com/cat.png", "detail": "auto"}, + ) + + +def test_messages_to_responses_input_bare_string_image_url_is_preserved(): + messages = [{"role": "user", "content": [{"type": "image_url", "image_url": "https://example.com/cat.png"}]}] + + input_items, _ = OpenAICountTokensConfig.messages_to_responses_input(messages) + + assert input_items[0]["content"] == ( + {"type": "input_image", "image_url": "https://example.com/cat.png", "detail": "auto"}, + ) + + +def test_messages_to_responses_input_text_only_blocks_stay_a_joined_string(): + """Text-only content must keep collapsing to a string so existing counts do not shift.""" + messages = [ + { + "role": "user", + "content": [{"type": "text", "text": "first"}, {"type": "text", "text": "second"}], + } + ] + + input_items, _ = OpenAICountTokensConfig.messages_to_responses_input(messages) + + assert input_items == [{"role": "user", "content": "first\nsecond"}] + + +def test_messages_to_responses_input_drops_unmappable_blocks(): + """A block with no Responses API equivalent is skipped, never forwarded verbatim.""" + messages = [ + { + "role": "user", + "content": [ + {"type": "text", "text": "hi"}, + {"type": "image_url", "image_url": {"url": "https://example.com/cat.png"}}, + {"type": "input_audio", "input_audio": {"data": "AAAA", "format": "wav"}}, + ], + } + ] + + input_items, _ = OpenAICountTokensConfig.messages_to_responses_input(messages) + + assert input_items[0]["content"] == ( + {"type": "input_text", "text": "hi"}, + {"type": "input_image", "image_url": "https://example.com/cat.png", "detail": "auto"}, + ) + + +def test_messages_to_responses_input_assistant_blocks_collapse_to_a_string(): + """An assistant turn must never forward chat `text` blocks. + + The Responses API only accepts output_text and refusal inside an assistant turn, so + forwarding them 400s the whole request and silently drops the count back to the local + tokenizer, which is exactly what defeats the image fix above. + """ + messages = [ + {"role": "user", "content": [{"type": "text", "text": "What is the capital of France?"}]}, + {"role": "assistant", "content": [{"type": "text", "text": "Paris."}]}, + ] + + input_items, _ = OpenAICountTokensConfig.messages_to_responses_input(messages) + + assert input_items == [ + {"role": "user", "content": "What is the capital of France?"}, + {"role": "assistant", "content": "Paris."}, + ] + + +def test_messages_to_responses_input_assistant_image_block_is_dropped(): + """An image part is illegal inside an assistant turn, so it must not reach the provider.""" + messages = [ + { + "role": "assistant", + "content": [ + {"type": "text", "text": "Here it is"}, + {"type": "image_url", "image_url": {"url": "https://example.com/cat.png"}}, + ], + } + ] + + input_items, _ = OpenAICountTokensConfig.messages_to_responses_input(messages) + + assert input_items == [{"role": "assistant", "content": "Here it is"}] + + +def test_messages_to_responses_input_keeps_user_image_alongside_an_assistant_turn(): + messages = [ + { + "role": "user", + "content": [ + {"type": "text", "text": "What is in this image?"}, + {"type": "image_url", "image_url": {"url": "https://example.com/cat.png"}}, + ], + }, + {"role": "assistant", "content": [{"type": "text", "text": "A cat."}]}, + ] + + input_items, _ = OpenAICountTokensConfig.messages_to_responses_input(messages) + + assert input_items == [ + { + "role": "user", + "content": ( + {"type": "input_text", "text": "What is in this image?"}, + {"type": "input_image", "image_url": "https://example.com/cat.png", "detail": "auto"}, + ), + }, + {"role": "assistant", "content": "A cat."}, + ] + + +def test_messages_to_responses_input_preserves_inline_files(): + """An inline file must survive the round trip, or the count silently drops the file. + + A small PDF is worth 36 tokens to OpenAI's counting API; dropping it left the same + request counting 13, the text-only total. + """ + messages = [ + { + "role": "user", + "content": [ + {"type": "text", "text": "Summarize this file."}, + { + "type": "file", + "file": {"filename": "report.pdf", "file_data": "data:application/pdf;base64,JVBERi0="}, + }, + ], + } + ] + + input_items, _ = OpenAICountTokensConfig.messages_to_responses_input(messages) + + assert input_items == [ + { + "role": "user", + "content": ( + {"type": "input_text", "text": "Summarize this file."}, + { + "type": "input_file", + "filename": "report.pdf", + "file_data": "data:application/pdf;base64,JVBERi0=", + }, + ), + } + ] + + +def test_messages_to_responses_input_drops_a_file_with_no_inline_data(): + """OpenAI rejects `file_data` without a `filename`, and a rejected request loses the whole count.""" + messages = [ + { + "role": "user", + "content": [ + {"type": "text", "text": "Summarize this file."}, + {"type": "file", "file": {"file_data": "data:application/pdf;base64,JVBERi0="}}, + {"type": "file", "file": {"file_id": "file-abc123"}}, + ], + } + ] + + input_items, _ = OpenAICountTokensConfig.messages_to_responses_input(messages) + + assert input_items == [{"role": "user", "content": "Summarize this file."}] + + +def test_messages_to_responses_input_assistant_file_block_is_dropped(): + """A file part is illegal inside an assistant turn, so it must not reach the provider.""" + messages = [ + { + "role": "assistant", + "content": [ + {"type": "text", "text": "Here it is"}, + { + "type": "file", + "file": {"filename": "report.pdf", "file_data": "data:application/pdf;base64,JVBERi0="}, + }, + ], + } + ] + + input_items, _ = OpenAICountTokensConfig.messages_to_responses_input(messages) + + assert input_items == [{"role": "assistant", "content": "Here it is"}] + + def test_validate_request_valid(): """Test that valid requests pass validation.""" config = OpenAICountTokensConfig() diff --git a/tests/test_litellm/llms/openai/test_openai_workload_identity.py b/tests/test_litellm/llms/openai/test_openai_workload_identity.py new file mode 100644 index 00000000000..d8d9936e9a1 --- /dev/null +++ b/tests/test_litellm/llms/openai/test_openai_workload_identity.py @@ -0,0 +1,238 @@ +import json +import sys +from pathlib import Path +from typing import Final + +import httpx +import pytest +import respx +from openai import AsyncOpenAI, OpenAI + +import litellm +from litellm.llms.litellm_proxy.responses.transformation import LiteLLMProxyResponsesAPIConfig +from litellm.llms.openai.common_utils import BaseOpenAILLM, OpenAIError +from litellm.llms.openai.openai import OpenAIChatCompletion +from litellm.llms.openai.responses.transformation import OpenAIResponsesAPIConfig +from litellm.llms.openai.workload_identity import ( + OpenAIWorkloadIdentityConfig, + _workload_identity_auth, + get_workload_identity_bearer_token, + resolve_openai_workload_identity_config, +) +from litellm.types.router import GenericLiteLLMParams + +TOKEN_EXCHANGE_URL: Final = "https://auth.openai.com/oauth/token" + + +@pytest.fixture +def wif_env(monkeypatch: pytest.MonkeyPatch, tmp_path: Path) -> OpenAIWorkloadIdentityConfig: + token_file: Final = tmp_path / "subject_token.jwt" + token_file.write_text("subject-token-from-file") + monkeypatch.delenv("OPENAI_API_KEY", raising=False) + monkeypatch.delenv("OPENAI_BASE_URL", raising=False) + monkeypatch.delenv("OPENAI_API_BASE", raising=False) + monkeypatch.setattr(litellm, "api_base", None) + monkeypatch.setenv("OPENAI_IDENTITY_PROVIDER_ID", "idp_test123") + monkeypatch.setenv("OPENAI_SERVICE_ACCOUNT_ID", "user-test456") + monkeypatch.setenv("OPENAI_IDENTITY_TOKEN_FILE", str(token_file)) + _workload_identity_auth.cache_clear() + litellm.in_memory_llm_clients_cache.flush_cache() + return OpenAIWorkloadIdentityConfig( + identity_provider_id="idp_test123", + service_account_id="user-test456", + token_file=str(token_file), + ) + + +def mock_token_exchange(access_token: str = "exchanged-bearer-token") -> respx.Route: + return respx.post(TOKEN_EXCHANGE_URL).mock( + return_value=httpx.Response(200, json={"access_token": access_token, "expires_in": 3600}) + ) + + +class TestResolveConfig: + def test_resolves_from_env(self, wif_env: OpenAIWorkloadIdentityConfig) -> None: + assert resolve_openai_workload_identity_config(api_key=None, api_base=None) == wif_env + + def test_static_api_key_wins(self, wif_env: OpenAIWorkloadIdentityConfig) -> None: + assert resolve_openai_workload_identity_config(api_key="sk-static", api_base=None) is None + + def test_env_openai_api_key_wins( + self, wif_env: OpenAIWorkloadIdentityConfig, monkeypatch: pytest.MonkeyPatch + ) -> None: + monkeypatch.setenv("OPENAI_API_KEY", "sk-from-env") + assert resolve_openai_workload_identity_config(api_key=None, api_base=None) is None + + @pytest.mark.parametrize("empty_key", ["", " "]) + def test_empty_api_key_arg_does_not_disable_wif( + self, wif_env: OpenAIWorkloadIdentityConfig, empty_key: str + ) -> None: + assert resolve_openai_workload_identity_config(api_key=empty_key, api_base=None) == wif_env + + @pytest.mark.parametrize("empty_key", ["", " "]) + def test_empty_env_openai_api_key_does_not_disable_wif( + self, wif_env: OpenAIWorkloadIdentityConfig, monkeypatch: pytest.MonkeyPatch, empty_key: str + ) -> None: + monkeypatch.setenv("OPENAI_API_KEY", empty_key) + assert resolve_openai_workload_identity_config(api_key=None, api_base=None) == wif_env + + def test_foreign_api_base_disables(self, wif_env: OpenAIWorkloadIdentityConfig) -> None: + assert resolve_openai_workload_identity_config(api_key=None, api_base="https://my-vllm.internal/v1") is None + + def test_openai_api_base_allows(self, wif_env: OpenAIWorkloadIdentityConfig) -> None: + assert resolve_openai_workload_identity_config(api_key=None, api_base="https://api.openai.com/v1") == wif_env + + def test_plaintext_http_api_base_disables(self, wif_env: OpenAIWorkloadIdentityConfig) -> None: + assert resolve_openai_workload_identity_config(api_key=None, api_base="http://api.openai.com/v1") is None + + def test_foreign_env_base_url_disables( + self, wif_env: OpenAIWorkloadIdentityConfig, monkeypatch: pytest.MonkeyPatch + ) -> None: + monkeypatch.setenv("OPENAI_BASE_URL", "https://my-vllm.internal/v1") + assert resolve_openai_workload_identity_config(api_key=None, api_base=None) is None + + def test_openai_env_base_url_allows( + self, wif_env: OpenAIWorkloadIdentityConfig, monkeypatch: pytest.MonkeyPatch + ) -> None: + monkeypatch.setenv("OPENAI_BASE_URL", "https://api.openai.com/v1") + assert resolve_openai_workload_identity_config(api_key=None, api_base=None) == wif_env + + def test_foreign_litellm_api_base_disables( + self, wif_env: OpenAIWorkloadIdentityConfig, monkeypatch: pytest.MonkeyPatch + ) -> None: + monkeypatch.setattr(litellm, "api_base", "https://my-vllm.internal/v1") + assert resolve_openai_workload_identity_config(api_key=None, api_base=None) is None + + @pytest.mark.parametrize( + "missing_var", + ["OPENAI_IDENTITY_PROVIDER_ID", "OPENAI_SERVICE_ACCOUNT_ID", "OPENAI_IDENTITY_TOKEN_FILE"], + ) + def test_partial_env_disables( + self, wif_env: OpenAIWorkloadIdentityConfig, monkeypatch: pytest.MonkeyPatch, missing_var: str + ) -> None: + monkeypatch.delenv(missing_var) + assert resolve_openai_workload_identity_config(api_key=None, api_base=None) is None + + +class TestTokenExchange: + @respx.mock + def test_exchanges_subject_token_for_bearer(self, wif_env: OpenAIWorkloadIdentityConfig) -> None: + route: Final = mock_token_exchange() + assert get_workload_identity_bearer_token(wif_env) == "exchanged-bearer-token" + request_body: Final = json.loads(route.calls.last.request.content) + assert request_body["grant_type"] == "urn:ietf:params:oauth:grant-type:token-exchange" + assert request_body["subject_token"] == "subject-token-from-file" + assert request_body["subject_token_type"] == "urn:ietf:params:oauth:token-type:jwt" + assert request_body["identity_provider_id"] == "idp_test123" + assert request_body["service_account_id"] == "user-test456" + + @respx.mock + def test_token_cached_across_mints(self, wif_env: OpenAIWorkloadIdentityConfig) -> None: + route: Final = mock_token_exchange() + first: Final = get_workload_identity_bearer_token(wif_env) + second: Final = get_workload_identity_bearer_token(wif_env) + assert first == second == "exchanged-bearer-token" + assert route.call_count == 1 + + def test_old_sdk_raises_upgrade_error( + self, wif_env: OpenAIWorkloadIdentityConfig, monkeypatch: pytest.MonkeyPatch + ) -> None: + import openai as openai_module + + monkeypatch.delattr(openai_module, "auth", raising=False) + monkeypatch.setitem(sys.modules, "openai.auth", None) + with pytest.raises(OpenAIError, match=r"openai>=2\.32\.0"): + wif_env.to_sdk_workload_identity() + + +class TestClientConstruction: + def test_sync_client_uses_workload_identity(self, wif_env: OpenAIWorkloadIdentityConfig) -> None: + client: Final = OpenAIChatCompletion()._get_openai_client(is_async=False, api_key=None, api_base=None) + assert isinstance(client, OpenAI) + assert client.api_key == "workload-identity-auth" + assert client._workload_identity_auth is not None + + def test_async_client_uses_workload_identity(self, wif_env: OpenAIWorkloadIdentityConfig) -> None: + client: Final = OpenAIChatCompletion()._get_openai_client(is_async=True, api_key=None, api_base=None) + assert isinstance(client, AsyncOpenAI) + assert client.api_key == "workload-identity-auth" + assert client._workload_identity_auth is not None + + def test_static_key_client_unaffected(self, wif_env: OpenAIWorkloadIdentityConfig) -> None: + client: Final = OpenAIChatCompletion()._get_openai_client(is_async=False, api_key="sk-static", api_base=None) + assert isinstance(client, OpenAI) + assert client.api_key == "sk-static" + assert client._workload_identity_auth is None + + def test_cache_key_separates_wif_identities(self, wif_env: OpenAIWorkloadIdentityConfig) -> None: + other_config: Final = OpenAIWorkloadIdentityConfig( + identity_provider_id="idp_other", + service_account_id="user-other", + token_file=wif_env.token_file, + ) + keys: Final = tuple( + BaseOpenAILLM.get_openai_client_cache_key( + client_initialization_params={"api_key": None, "is_async": False, "workload_identity_config": config}, + client_type="openai", + ) + for config in (wif_env, other_config, None) + ) + assert len(set(keys)) == 3 + + @respx.mock + def test_request_carries_exchanged_bearer(self, wif_env: OpenAIWorkloadIdentityConfig) -> None: + mock_token_exchange() + completion_route: Final = respx.post("https://api.openai.com/v1/chat/completions").mock( + return_value=httpx.Response( + 200, + json={ + "id": "chatcmpl-wif", + "object": "chat.completion", + "created": 1, + "model": "gpt-4o-mini", + "choices": [ + { + "index": 0, + "message": {"role": "assistant", "content": "ok"}, + "finish_reason": "stop", + } + ], + "usage": {"prompt_tokens": 1, "completion_tokens": 1, "total_tokens": 2}, + }, + ) + ) + client = OpenAIChatCompletion()._get_openai_client(is_async=False, api_key=None, api_base=None) + assert isinstance(client, OpenAI) + client.chat.completions.create(model="gpt-4o-mini", messages=[{"role": "user", "content": "hi"}]) + auth_header: Final = completion_route.calls.last.request.headers["Authorization"] + assert auth_header == "Bearer exchanged-bearer-token" + + +class TestResponsesValidateEnvironment: + @respx.mock + def test_mints_bearer_when_wif_configured(self, wif_env: OpenAIWorkloadIdentityConfig) -> None: + mock_token_exchange() + headers: Final = OpenAIResponsesAPIConfig().validate_environment( + headers={}, model="gpt-4o-mini", litellm_params=GenericLiteLLMParams() + ) + assert headers["Authorization"] == "Bearer exchanged-bearer-token" + + def test_static_key_wins(self, wif_env: OpenAIWorkloadIdentityConfig) -> None: + headers: Final = OpenAIResponsesAPIConfig().validate_environment( + headers={}, model="gpt-4o-mini", litellm_params=GenericLiteLLMParams(api_key="sk-responses") + ) + assert headers["Authorization"] == "Bearer sk-responses" + + def test_foreign_api_base_skips_wif(self, wif_env: OpenAIWorkloadIdentityConfig) -> None: + headers: Final = OpenAIResponsesAPIConfig().validate_environment( + headers={}, + model="gpt-4o-mini", + litellm_params=GenericLiteLLMParams(api_base="https://my-vllm.internal/v1"), + ) + assert headers["Authorization"] == "Bearer None" + + def test_litellm_proxy_subclass_never_mints_wif(self, wif_env: OpenAIWorkloadIdentityConfig) -> None: + headers: Final = LiteLLMProxyResponsesAPIConfig().validate_environment( + headers={}, model="gpt-4o-mini", litellm_params=GenericLiteLLMParams() + ) + assert headers["Authorization"] == "Bearer None" diff --git a/tests/test_litellm/llms/vertex_ai/test_vertex_ai_common_utils.py b/tests/test_litellm/llms/vertex_ai/test_vertex_ai_common_utils.py index cc923f05831..d1d751989ea 100644 --- a/tests/test_litellm/llms/vertex_ai/test_vertex_ai_common_utils.py +++ b/tests/test_litellm/llms/vertex_ai/test_vertex_ai_common_utils.py @@ -195,6 +195,22 @@ def test_set_schema_property_ordering_with_excessive_nesting(): set_schema_property_ordering(schema) +def test_set_schema_property_ordering_skips_non_dict_property_values(): + """Non-dict property values must be skipped, not recursed into (they used to raise).""" + schema = { + "properties": { + "a": "hello", + "b": {"type": "string"}, + "c": ["x"], + "d": "a string mentioning items", + } + } + + result = set_schema_property_ordering(schema) + + assert result["propertyOrdering"] == ["a", "b", "c", "d"] + + def test_build_vertex_schema(): """Test build_vertex_schema with a sample schema""" from litellm.llms.vertex_ai.common_utils import _build_vertex_schema diff --git a/tests/test_litellm/llms/vertex_ai/test_vertex_llm_base.py b/tests/test_litellm/llms/vertex_ai/test_vertex_llm_base.py index 29d22e844a5..a4d67606698 100644 --- a/tests/test_litellm/llms/vertex_ai/test_vertex_llm_base.py +++ b/tests/test_litellm/llms/vertex_ai/test_vertex_llm_base.py @@ -982,6 +982,116 @@ class TestVertexBase: assert result_url == f"{gateway_api_base}:embedContent" + def test_check_custom_proxy_vertex_api_base_with_version_path_grafts_default_path(self): + vertex_base = VertexBase() + + _, result_url = vertex_base._check_custom_proxy( + api_base="https://aiplatform.googleapis.com/v1beta1", + custom_llm_provider="vertex_ai", + gemini_api_key=None, + endpoint="generateContent", + stream=None, + auth_header="Bearer token123", + url="https://us-central1-aiplatform.googleapis.com/v1/projects/test-project/locations/us-central1/publishers/google/models/gemini-3.5-flash-lite:generateContent", + model="gemini-3.5-flash-lite", + ) + + assert ( + result_url + == "https://aiplatform.googleapis.com/v1beta1/projects/test-project/locations/us-central1/publishers/google/models/gemini-3.5-flash-lite:generateContent" + ) + + def test_check_custom_proxy_vertex_api_base_with_version_path_trailing_slash_grafts_default_path(self): + vertex_base = VertexBase() + + _, result_url = vertex_base._check_custom_proxy( + api_base="https://internal-gateway.example.com/v1/", + custom_llm_provider="vertex_ai", + gemini_api_key=None, + endpoint="generateContent", + stream=None, + auth_header="Bearer token123", + url="https://us-central1-aiplatform.googleapis.com/v1/projects/test-project/locations/us-central1/publishers/google/models/gemini-3.5-flash-lite:generateContent", + model="gemini-3.5-flash-lite", + ) + + assert ( + result_url + == "https://internal-gateway.example.com/v1/projects/test-project/locations/us-central1/publishers/google/models/gemini-3.5-flash-lite:generateContent" + ) + + def test_check_custom_proxy_vertex_api_base_with_version_path_and_query_grafts_before_query(self): + vertex_base = VertexBase() + + _, result_url = vertex_base._check_custom_proxy( + api_base="https://internal-gateway.example.com/v1beta1?key=abc", + custom_llm_provider="vertex_ai", + gemini_api_key=None, + endpoint="generateContent", + stream=None, + auth_header="Bearer token123", + url="https://us-central1-aiplatform.googleapis.com/v1/projects/test-project/locations/us-central1/publishers/google/models/gemini-3.5-flash-lite:generateContent", + model="gemini-3.5-flash-lite", + ) + + assert ( + result_url + == "https://internal-gateway.example.com/v1beta1/projects/test-project/locations/us-central1/publishers/google/models/gemini-3.5-flash-lite:generateContent?key=abc" + ) + + def test_check_custom_proxy_vertex_api_base_with_version_path_and_query_streaming_appends_alt_sse(self): + vertex_base = VertexBase() + + _, result_url = vertex_base._check_custom_proxy( + api_base="https://internal-gateway.example.com/v1beta1?key=abc", + custom_llm_provider="vertex_ai", + gemini_api_key=None, + endpoint="streamGenerateContent", + stream=True, + auth_header="Bearer token123", + url="https://us-central1-aiplatform.googleapis.com/v1/projects/test-project/locations/us-central1/publishers/google/models/gemini-3.5-flash-lite:streamGenerateContent", + model="gemini-3.5-flash-lite", + ) + + assert ( + result_url + == "https://internal-gateway.example.com/v1beta1/projects/test-project/locations/us-central1/publishers/google/models/gemini-3.5-flash-lite:streamGenerateContent?key=abc&alt=sse" + ) + + def test_check_custom_proxy_vertex_api_base_with_non_version_path_keeps_endpoint_append(self): + vertex_base = VertexBase() + gateway_api_base = "https://gateway.example.com/vertex-proxy" + + _, result_url = vertex_base._check_custom_proxy( + api_base=gateway_api_base, + custom_llm_provider="vertex_ai", + gemini_api_key=None, + endpoint="generateContent", + stream=None, + auth_header="Bearer token123", + url="https://us-central1-aiplatform.googleapis.com/v1/projects/test-project/locations/us-central1/publishers/google/models/gemini-3.5-flash-lite:generateContent", + model="gemini-3.5-flash-lite", + ) + + assert result_url == f"{gateway_api_base}:generateContent" + + def test_check_custom_proxy_vertex_api_base_without_projects_in_default_url_keeps_endpoint_append(self): + vertex_base = VertexBase() + gemma_api_base = "https://example.com/custom/gemma-deployment" + + _, result_url = vertex_base._check_custom_proxy( + api_base=gemma_api_base, + custom_llm_provider="vertex_ai", + gemini_api_key=None, + endpoint="predict", + stream=False, + auth_header=None, + url=gemma_api_base, + model="gemma-3-27b-it", + ) + + assert result_url == f"{gemma_api_base}:predict" + def test_check_custom_proxy_vertex_bare_host_streaming_keeps_single_alt_sse(self): vertex_base = VertexBase() diff --git a/tests/test_litellm/llms/vertex_ai/text_to_speech/test_transformation.py b/tests/test_litellm/llms/vertex_ai/text_to_speech/test_transformation.py index 05da22a73fd..fba337b5f2c 100644 --- a/tests/test_litellm/llms/vertex_ai/text_to_speech/test_transformation.py +++ b/tests/test_litellm/llms/vertex_ai/text_to_speech/test_transformation.py @@ -1,3 +1,4 @@ +import base64 from unittest.mock import MagicMock, Mock, patch import httpx @@ -126,6 +127,48 @@ class TestVertexAITextToSpeechConfig: assert voice_dict == voice_input +@pytest.mark.parametrize( + ("audio", "expected_content_type"), + [ + (b"RIFF\x24\x00\x00\x00WAVEfmt \x10\x00\x00\x00", "audio/wav"), + (b"\xff\xfb\x90\x64\x00\x00\x00\x00", "audio/mpeg"), + (b"OggS" + b"\x00" * 24 + b"OpusHead", "audio/opus"), + (b"fLaC\x00\x00\x00\x22", "audio/flac"), + ], +) +def test_transform_text_to_speech_response_labels_content_type(audio, expected_content_type): + raw_response = httpx.Response( + status_code=200, + json={"audioContent": base64.b64encode(audio).decode()}, + ) + + result = VertexAITextToSpeechConfig().transform_text_to_speech_response( + model="vertex_ai/chirp", + raw_response=raw_response, + logging_obj=MagicMock(), + ) + + assert result.response.headers["content-type"] == expected_content_type + assert result.response.content == audio + + +def test_transform_text_to_speech_response_leaves_unknown_bytes_unlabeled(): + raw_pcm = b"\x00\x01\x02\x03\x04\x05\x06\x07" + raw_response = httpx.Response( + status_code=200, + json={"audioContent": base64.b64encode(raw_pcm).decode()}, + ) + + result = VertexAITextToSpeechConfig().transform_text_to_speech_response( + model="vertex_ai/chirp", + raw_response=raw_response, + logging_obj=MagicMock(), + ) + + assert "content-type" not in result.response.headers + assert result.response.content == raw_pcm + + @patch("litellm.llms.custom_httpx.llm_http_handler.HTTPHandler.post") @patch.object(VertexAITextToSpeechConfig, "_ensure_access_token") @patch.object(VertexAITextToSpeechConfig, "_get_token_and_url") diff --git a/tests/test_litellm/models/test_models.py b/tests/test_litellm/models/test_models.py index 669dba8e466..9ae9b732066 100644 --- a/tests/test_litellm/models/test_models.py +++ b/tests/test_litellm/models/test_models.py @@ -5,6 +5,7 @@ Tests for backend domain models. from datetime import datetime import pytest +from pydantic import BaseModel, TypeAdapter from litellm.models.access_group import LiteLLM_AccessGroupTable from litellm.models.budget import ( @@ -130,6 +131,33 @@ class TestModel: assert model.litellm_params == {"model": "gpt-4"} assert model.model_info == {"team_id": "t1"} + def test_response_type_adapter_accepts_pydantic_row(self): + class PrismaModelRow(BaseModel): + model_id: str + model_name: str + litellm_params: dict[str, str] + model_info: dict[str, str] | None = None + blocked: bool = False + + row = PrismaModelRow( + model_id="m1", + model_name="gpt-4", + litellm_params={"model": "gpt-4"}, + model_info={"team_id": "t1"}, + blocked=True, + ) + + model = TypeAdapter(LiteLLM_ProxyModelTable | None).validate_python( + row, + from_attributes=True, + ) + + assert model is not None + assert model.model_id == "m1" + assert model.litellm_params == {"model": "gpt-4"} + assert model.model_info == {"team_id": "t1"} + assert model.blocked is True + def test_team_helpers_none_when_no_model_info(self): model = LiteLLM_ProxyModelTable( model_id="m1", model_name="gpt-4", litellm_params={}, model_info=None diff --git a/tests/test_litellm/passthrough/test_async_streaming_error_propagation.py b/tests/test_litellm/passthrough/test_async_streaming_error_propagation.py index faf4ea46c43..9f2b436d2d8 100644 --- a/tests/test_litellm/passthrough/test_async_streaming_error_propagation.py +++ b/tests/test_litellm/passthrough/test_async_streaming_error_propagation.py @@ -1,12 +1,12 @@ """ -Tests for error propagation in _async_streaming passthrough routes. +Tests for error propagation in async passthrough streaming routes. -Verifies that HTTP 4xx/5xx errors from upstream (e.g. Azure 429 rate limits) -raise exceptions instead of being silently forwarded as raw bytes under HTTP 200. - -See: litellm/passthrough/main.py _async_streaming() +Verifies that streaming passthrough wrappers preserve the previous guarantees: +HTTP 4xx/5xx failures must raise instead of being silently forwarded as bytes, +and successful streaming responses should still yield chunks normally. """ +import asyncio import json from unittest.mock import AsyncMock, MagicMock @@ -54,19 +54,19 @@ def _make_mock_logging_obj(): @pytest.mark.asyncio async def test_async_streaming_429_raises(): """429 from upstream should raise HTTPStatusError, not yield error bytes.""" - from litellm.passthrough.main import _async_streaming - + from litellm.passthrough.main import AsyncPassthroughStreamingResponse + error_body = json.dumps( {"error": {"code": "429", "message": "Rate limit exceeded."}} ).encode() mock_response = _make_mock_response(429, error_body) - + async def response_coro(): return mock_response - + chunks = [] async def _drain(): - async for chunk in _async_streaming( + async for chunk in AsyncPassthroughStreamingResponse( response=response_coro(), litellm_logging_obj=_make_mock_logging_obj(), provider_config=MagicMock(), @@ -83,45 +83,78 @@ async def test_async_streaming_429_raises(): @pytest.mark.asyncio async def test_async_streaming_500_raises(): """500 from upstream should also raise, not yield error bytes.""" - from litellm.passthrough.main import _async_streaming - + from litellm.passthrough.main import AsyncPassthroughStreamingResponse + error_body = json.dumps( {"error": {"code": "500", "message": "Internal server error"}} ).encode() mock_response = _make_mock_response(500, error_body) - + async def response_coro(): return mock_response - + with pytest.raises(httpx.HTTPStatusError) as exc_info: - async for _ in _async_streaming( + async for _ in AsyncPassthroughStreamingResponse( response=response_coro(), litellm_logging_obj=_make_mock_logging_obj(), provider_config=MagicMock(), ): pass - + assert exc_info.value.response.status_code == 500 @pytest.mark.asyncio -async def test_async_streaming_200_yields_chunks(): +async def test_async_passthrough_wrapper_200_yields_chunks(): """Successful 200 streaming responses should continue to work normally.""" - from litellm.passthrough.main import _async_streaming + from litellm.passthrough.main import AsyncPassthroughStreamingResponse sse_data = b'data: {"type":"response.created"}\n\ndata: [DONE]\n\n' mock_response = _make_mock_response(200, sse_data) + mock_logging_obj = _make_mock_logging_obj() async def response_coro(): return mock_response - chunks = [] - async for chunk in _async_streaming( + async_stream = AsyncPassthroughStreamingResponse( response=response_coro(), - litellm_logging_obj=_make_mock_logging_obj(), + litellm_logging_obj=mock_logging_obj, provider_config=MagicMock(), - ): + ) + + chunks = [] + async for chunk in async_stream: chunks.append(chunk) + await asyncio.sleep(0) + assert len(chunks) == 1 assert b"response.created" in chunks[0] + mock_logging_obj.async_flush_passthrough_collected_chunks.assert_awaited_once() + + +@pytest.mark.asyncio +async def test_error_body_readable_after_failed_await(): + """The upstream error body must stay readable so the proxy can map the real status and message.""" + from litellm.passthrough.main import AsyncPassthroughStreamingResponse + + error_body = b'{"message":"model not found"}' + + async def byte_stream(): + yield error_body + + request = httpx.Request("POST", "https://bedrock.example.com/model/x/converse-stream") + response = httpx.Response(400, content=byte_stream(), request=request) + + async def response_coro(): + return response + + with pytest.raises(httpx.HTTPStatusError) as exc_info: + await AsyncPassthroughStreamingResponse( + response=response_coro(), + litellm_logging_obj=_make_mock_logging_obj(), + provider_config=MagicMock(), + ) + + assert exc_info.value.response.status_code == 400 + assert await exc_info.value.response.aread() == error_body diff --git a/tests/test_litellm/passthrough/test_passthrough_main.py b/tests/test_litellm/passthrough/test_passthrough_main.py index b8f265ad7ea..1950c37a12e 100644 --- a/tests/test_litellm/passthrough/test_passthrough_main.py +++ b/tests/test_litellm/passthrough/test_passthrough_main.py @@ -645,13 +645,14 @@ async def test_allm_passthrough_route_429_streaming_raises(): Regression test: Azure 429 during streaming must raise HTTPStatusError, not be silently forwarded as raw bytes under HTTP 200. - Before the fix, _async_streaming() would yield the 429 error JSON as - chunks and allm_passthrough_route returned an async generator. The - caller (azure_proxy_route) wrapped it in StreamingResponse(status_code=200), + Before the fix, the async passthrough streaming path would yield the 429 + error JSON as chunks and allm_passthrough_route returned a streaming + iterator. The caller (azure_proxy_route) wrapped it in + StreamingResponse(status_code=200), so the client saw HTTP 200 + unparseable SSE body → silent task_complete(null). - After the fix, raise_for_status() fires inside _async_streaming() before - any chunks are yielded, so the exception propagates all the way up. + After the fix, raise_for_status() fires before the streaming wrapper is + returned, so the exception propagates all the way up. """ mock_provider_config = MagicMock() mock_provider_config.get_complete_url.return_value = ( @@ -679,6 +680,7 @@ async def test_allm_passthrough_route_429_streaming_raises(): mock_logging_obj = MagicMock() mock_logging_obj.update_environment_variables = MagicMock() mock_logging_obj.async_flush_passthrough_collected_chunks = AsyncMock() + mock_logging_obj.async_failure_handler = AsyncMock() with ( patch( @@ -701,29 +703,101 @@ async def test_allm_passthrough_route_429_streaming_raises(): patch.object(async_client.client, "send", mock_send), patch.object(async_client.client, "build_request", mock_build_request), ): - result = await allm_passthrough_route( - model="azure/gpt-4", - endpoint="openai/deployments/gpt-4/responses", - method="POST", - custom_llm_provider="azure", - api_base="https://my-azure.openai.azure.com", - api_key="fake-azure-key", - json={"model": "gpt-4", "input": "hello", "stream": True}, - client=async_client, - litellm_logging_obj=mock_logging_obj, - ) - - # result is an async generator — consuming it must raise, not silently yield error bytes - chunks = [] - async def _drain(): - async for chunk in result: # type: ignore[union-attr] - chunks.append(chunk) - with pytest.raises(httpx.HTTPStatusError) as exc_info: - await _drain() + await allm_passthrough_route( + model="azure/gpt-4", + endpoint="openai/deployments/gpt-4/responses", + method="POST", + custom_llm_provider="azure", + api_base="https://my-azure.openai.azure.com", + api_key="fake-azure-key", + json={"model": "gpt-4", "input": "hello", "stream": True}, + client=async_client, + litellm_logging_obj=mock_logging_obj, + ) assert exc_info.value.response.status_code == 429 - assert len(chunks) == 0, "No chunks should be yielded before the 429 raises" + + +def test_llm_passthrough_route_sync_streaming_error_maps_upstream_status(): + """ + Regression test: a sync streaming passthrough whose upstream answers an + error status must surface the mapped provider error, not + httpx.ResponseNotRead. + + Before the fix, raise_for_status() raised on the still-unread streamed + response, and _handle_error then touched e.response.text, which raises + ResponseNotRead on a streamed-but-unread body, masking the real upstream + error entirely. + """ + from litellm.llms.base_llm.chat.transformation import BaseLLMException + + error_body = json.dumps( + { + "error": { + "code": "429", + "message": "Rate limit exceeded. Retry after 10 seconds.", + } + } + ).encode() + + class _UnreadErrorStream(httpx.SyncByteStream): + def __iter__(self): + yield error_body + + def _handler(request: httpx.Request) -> httpx.Response: + return httpx.Response( + 429, + stream=_UnreadErrorStream(), + headers={"content-type": "application/json"}, + ) + + sync_client = HTTPHandler( + client=httpx.Client(transport=httpx.MockTransport(_handler)) + ) + + mock_provider_config = MagicMock() + mock_provider_config.get_complete_url.return_value = ( + httpx.URL("https://gigachat.devices.sberbank.ru/api/v1/chat/completions"), + "https://gigachat.devices.sberbank.ru/api/v1", + ) + mock_provider_config.get_api_key.return_value = "fake-key" + mock_provider_config.validate_environment.return_value = { + "Authorization": "Bearer fake-key" + } + mock_provider_config.sign_request.return_value = ( + {"Authorization": "Bearer fake-key"}, + None, + ) + mock_provider_config.is_streaming_request.return_value = True + mock_provider_config.get_error_class.side_effect = ( + lambda error_message, status_code, headers: BaseLLMException( + status_code=status_code, message=error_message, headers=headers + ) + ) + + mock_logging_obj = MagicMock() + + with pytest.raises(BaseLLMException) as exc_info: + llm_passthrough_route( + model="gigachat/GigaChat-2", + endpoint="chat/completions", + method="POST", + custom_llm_provider="gigachat", + api_base="https://gigachat.devices.sberbank.ru/api/v1", + api_key="fake-key", + json={ + "model": "GigaChat-2", + "messages": [{"role": "user", "content": "hi"}], + "stream": True, + }, + client=sync_client, + litellm_logging_obj=mock_logging_obj, + provider_config=mock_provider_config, + ) + + assert exc_info.value.status_code == 429 + assert "Rate limit exceeded" in str(exc_info.value) def test_llm_passthrough_route_propagates_allm_passthrough_route_to_logging_obj(): diff --git a/tests/test_litellm/passthrough/test_streaming_interrupt_spend_tracking.py b/tests/test_litellm/passthrough/test_streaming_interrupt_spend_tracking.py index 3783e218e4e..a88b0ef0c4b 100644 --- a/tests/test_litellm/passthrough/test_streaming_interrupt_spend_tracking.py +++ b/tests/test_litellm/passthrough/test_streaming_interrupt_spend_tracking.py @@ -35,11 +35,14 @@ class _ImmediateExecutor: @pytest.mark.asyncio -async def test_async_streaming_flushes_on_normal_completion(): - from litellm.passthrough.main import _async_streaming +async def test_asyncpassthroughstreamingresponse_flushes_on_normal_completion(): + from litellm.passthrough.main import AsyncPassthroughStreamingResponse chunks = [b"chunk-1", b"chunk-2", b"chunk-3"] mock_response = _make_streaming_response(chunks) + mock_response.headers = httpx.Headers( + {"content-type": "application/octet-stream", "x-request-id": "req-123"} + ) async def response_coro(): return mock_response @@ -48,14 +51,19 @@ async def test_async_streaming_flushes_on_normal_completion(): provider_config = MagicMock() received = [] - async for chunk in _async_streaming( + received_response = AsyncPassthroughStreamingResponse( response=response_coro(), litellm_logging_obj=mock_logging_obj, provider_config=provider_config, - ): + ) + + async for chunk in received_response: received.append(chunk) assert received == chunks + + assert received_response.headers["content-type"] == "application/octet-stream" + assert received_response.headers["x-request-id"] == "req-123" await asyncio.sleep(0) @@ -68,8 +76,8 @@ async def test_async_streaming_flushes_on_normal_completion(): @pytest.mark.asyncio -async def test_async_streaming_flushes_on_client_disconnect(): - from litellm.passthrough.main import _async_streaming +async def test_asyncpassthroughstreamingresponse_flushes_on_client_disconnect(): + from litellm.passthrough.main import AsyncPassthroughStreamingResponse chunks = [ b'{"chunk": 1, "outputTokens": 10}', @@ -77,6 +85,9 @@ async def test_async_streaming_flushes_on_client_disconnect(): b'{"chunk": 3, "outputTokens": 8}', ] mock_response = _make_streaming_response(chunks) + mock_response.headers = httpx.Headers( + {"content-type": "application/octet-stream", "x-request-id": "req-123"} + ) async def response_coro(): return mock_response @@ -84,7 +95,7 @@ async def test_async_streaming_flushes_on_client_disconnect(): mock_logging_obj = _make_logging_obj() provider_config = MagicMock() - gen = _async_streaming( + gen = AsyncPassthroughStreamingResponse( response=response_coro(), litellm_logging_obj=mock_logging_obj, provider_config=provider_config, @@ -105,11 +116,14 @@ async def test_async_streaming_flushes_on_client_disconnect(): @pytest.mark.asyncio -async def test_async_streaming_does_not_flush_on_4xx(): - from litellm.passthrough.main import _async_streaming +async def test_asyncpassthroughstreamingresponse_does_not_flush_on_4xx(): + from litellm.passthrough.main import AsyncPassthroughStreamingResponse err_response = MagicMock(spec=httpx.Response) err_response.status_code = 429 + err_response.headers = httpx.Headers( + {"content-type": "application/octet-stream"} + ) def _raise(): raise httpx.HTTPStatusError( @@ -129,7 +143,7 @@ async def test_async_streaming_does_not_flush_on_4xx(): mock_logging_obj = _make_logging_obj() with pytest.raises(httpx.HTTPStatusError): - async for _ in _async_streaming( + async for _ in AsyncPassthroughStreamingResponse( response=response_coro(), litellm_logging_obj=mock_logging_obj, provider_config=MagicMock(), @@ -140,8 +154,8 @@ async def test_async_streaming_does_not_flush_on_4xx(): @pytest.mark.asyncio -async def test_async_streaming_flushes_on_upstream_exception_with_partial_data(): - from litellm.passthrough.main import _async_streaming +async def test_asyncpassthroughstreamingresponse_flushes_on_upstream_exception_with_partial_data(): + from litellm.passthrough.main import AsyncPassthroughStreamingResponse partial_chunks = [b"partial-chunk-1", b"partial-chunk-2"] @@ -149,6 +163,9 @@ async def test_async_streaming_flushes_on_upstream_exception_with_partial_data() mock_response.status_code = 200 mock_response.raise_for_status = MagicMock(return_value=None) mock_response.aclose = AsyncMock() + mock_response.headers = httpx.Headers( + {"content-type": "application/octet-stream", "x-request-id": "req-123"} + ) async def _aiter_bytes_then_raise(): for c in partial_chunks: @@ -165,7 +182,7 @@ async def test_async_streaming_flushes_on_upstream_exception_with_partial_data() received = [] async def _drain(): - async for chunk in _async_streaming( + async for chunk in AsyncPassthroughStreamingResponse( response=response_coro(), litellm_logging_obj=mock_logging_obj, provider_config=provider_config, @@ -186,12 +203,16 @@ async def test_async_streaming_flushes_on_upstream_exception_with_partial_data() assert call_kwargs["raw_bytes"] == partial_chunks -def test_sync_streaming_flushes_on_normal_completion(): - from litellm.passthrough.main import _sync_streaming +def test_passthroughstreamingresponse_flushes_on_normal_completion(): + from litellm.passthrough.main import PassthroughStreamingResponse chunks = [b"a", b"b", b"c"] mock_response = MagicMock(spec=httpx.Response) + mock_response.status_code = 200 + mock_response.headers = httpx.Headers( + {"content-type": "application/octet-stream", "x-request-id": "req-123"} + ) def _iter_bytes(): yield from chunks @@ -202,25 +223,33 @@ def test_sync_streaming_flushes_on_normal_completion(): mock_logging_obj.flush_passthrough_collected_chunks = MagicMock() provider_config = MagicMock() + received_responce = PassthroughStreamingResponse( + response=mock_response, + litellm_logging_obj=mock_logging_obj, + provider_config=provider_config, + ) + with patch("litellm.utils.executor", _ImmediateExecutor()): - received = list( - _sync_streaming( - response=mock_response, - litellm_logging_obj=mock_logging_obj, - provider_config=provider_config, - ) - ) + received = list(received_responce) assert received == chunks + + assert received_responce.headers["content-type"] == "application/octet-stream" + assert received_responce.headers["x-request-id"] == "req-123" + mock_logging_obj.flush_passthrough_collected_chunks.assert_called_once() -def test_sync_streaming_flushes_on_early_close(): - from litellm.passthrough.main import _sync_streaming +def test_passthroughstreamingresponse_flushes_on_early_close(): + from litellm.passthrough.main import PassthroughStreamingResponse chunks = [b"first", b"second", b"third"] mock_response = MagicMock(spec=httpx.Response) + mock_response.status_code = 200 + mock_response.headers = httpx.Headers( + {"content-type": "application/octet-stream", "x-request-id": "req-123"} + ) def _iter_bytes(): yield from chunks @@ -232,7 +261,7 @@ def test_sync_streaming_flushes_on_early_close(): provider_config = MagicMock() with patch("litellm.utils.executor", _ImmediateExecutor()): - gen = _sync_streaming( + gen = PassthroughStreamingResponse( response=mock_response, litellm_logging_obj=mock_logging_obj, provider_config=provider_config, diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/conftest.py b/tests/test_litellm/proxy/_experimental/mcp_server/conftest.py index b477bf3f406..2ccba2b2055 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/conftest.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/conftest.py @@ -2,6 +2,30 @@ import os import pytest +from litellm.proxy._experimental.mcp_server.mcp_server_manager import ( + global_mcp_server_manager, +) + + +@pytest.fixture(autouse=True) +def _hermetic_mcp_server_registry(): + """Restore the singleton ``global_mcp_server_manager``'s registry state around every + test, so entries seeded by one test never leak into another on a shared shard.""" + saved_registry = dict(global_mcp_server_manager.registry) + saved_config_servers = dict(global_mcp_server_manager.config_mcp_servers) + saved_tool_mapping = dict(global_mcp_server_manager.tool_name_to_mcp_server_name_mapping) + saved_oauth_slots = global_mcp_server_manager._oauth_discovery_slots + try: + yield + finally: + global_mcp_server_manager.registry.clear() + global_mcp_server_manager.registry.update(saved_registry) + global_mcp_server_manager.config_mcp_servers.clear() + global_mcp_server_manager.config_mcp_servers.update(saved_config_servers) + global_mcp_server_manager.tool_name_to_mcp_server_name_mapping.clear() + global_mcp_server_manager.tool_name_to_mcp_server_name_mapping.update(saved_tool_mapping) + global_mcp_server_manager._oauth_discovery_slots = saved_oauth_slots + @pytest.fixture(autouse=True) def _hermetic_server_root_path(): diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py index 3279c59acd4..598e9276423 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py @@ -35,6 +35,22 @@ def mock_mcp_client_ip(): yield +@pytest.fixture(autouse=True) +def isolate_global_mcp_registry(): + """Restore the module-global MCP server registry after each test. + + Tests here register servers on ``global_mcp_server_manager`` directly; without a + restore, entries leak into other test modules sharing the same worker and break + assertions over the full registry contents. + """ + from litellm.proxy._experimental.mcp_server.mcp_server_manager import global_mcp_server_manager + + snapshot = dict(global_mcp_server_manager.registry) + yield + global_mcp_server_manager.registry.clear() + global_mcp_server_manager.registry.update(snapshot) + + def _mock_callback_request(base_url: str = "http://localhost:3000/"): """Return a MagicMock Request for callback/authorize same-origin tests. diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_identity_env.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_identity_env.py index ac7082c2668..1c65adac4c6 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_identity_env.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_identity_env.py @@ -23,6 +23,12 @@ MGMT_MODULE = "litellm.proxy.management_endpoints.mcp_management_endpoints" @contextlib.contextmanager def _env_and_reload(**env): saved = {key: os.environ.get(key) for key in env} + utils_module = importlib.import_module(UTILS_MODULE) + mgmt_module = importlib.import_module(MGMT_MODULE) + # Restore pre-reload module attributes afterwards instead of reloading again: + # a reload re-creates the module's classes, breaking exception identity for + # modules that imported them earlier + snapshots = {module: dict(vars(module)) for module in (utils_module, mgmt_module)} def _apply_env(values): for key, value in values.items(): @@ -32,8 +38,8 @@ def _env_and_reload(**env): os.environ[key] = value def _reload(): - utils = importlib.reload(importlib.import_module(UTILS_MODULE)) - mgmt = importlib.reload(importlib.import_module(MGMT_MODULE)) + utils = importlib.reload(utils_module) + mgmt = importlib.reload(mgmt_module) return utils, mgmt try: @@ -41,7 +47,10 @@ def _env_and_reload(**env): yield _reload() finally: _apply_env(saved) - _reload() + for module, snapshot in snapshots.items(): + for key in [key for key in vars(module) if key not in snapshot]: + delattr(module, key) + vars(module).update(snapshot) def test_defaults_used_when_env_unset(): diff --git a/tests/test_litellm/proxy/auth/test_auth_exception_handler.py b/tests/test_litellm/proxy/auth/test_auth_exception_handler.py index 90b3b29d919..90be51cfa5b 100644 --- a/tests/test_litellm/proxy/auth/test_auth_exception_handler.py +++ b/tests/test_litellm/proxy/auth/test_auth_exception_handler.py @@ -26,6 +26,7 @@ from prisma.errors import ( from litellm._logging import verbose_proxy_logger +from litellm.constants import INVALID_VIRTUAL_KEY_ERROR_MARKER from litellm.exceptions import BudgetExceededError from litellm.proxy._types import ProxyErrorTypes, ProxyException, UserAPIKeyAuth from litellm.proxy.auth.auth_exception_handler import UserAPIKeyAuthExceptionHandler @@ -703,23 +704,43 @@ async def test_auth_failure_ip_stamp_does_not_mutate_callers_request_data(): assert request_data == {"model": "gpt-4o"} +def _marked_malformed_key_error() -> HTTPException: + """Build the malformed-key 401 as its raise site does: marker stamped on it.""" + error = HTTPException(status_code=401, detail="LiteLLM Virtual Key expected. Received=test") + setattr(error, INVALID_VIRTUAL_KEY_ERROR_MARKER, True) + return error + + @pytest.mark.asyncio @pytest.mark.parametrize( - "auth_error,expect_traceback", + "auth_error,expect_traceback,expect_level", [ pytest.param( ProxyException( message="Authentication Error", type=ProxyErrorTypes.auth_error, param=None, code=401 ), False, + "ERROR", id="expected_401_no_traceback", ), - pytest.param(ValueError("unexpected internal error"), True, id="unexpected_error_keeps_traceback"), + pytest.param(ValueError("unexpected internal error"), True, "ERROR", id="unexpected_error_keeps_traceback"), + pytest.param( + _marked_malformed_key_error(), + False, + "WARNING", + id="malformed_virtual_key_warning_no_traceback", + ), + pytest.param( + HTTPException(status_code=401, detail="LiteLLM Virtual Key expected. Received=test"), + False, + "ERROR", + id="phrase_without_marker_stays_loud", + ), ], ) -async def test_handle_authentication_error_traceback_only_for_unexpected_errors(auth_error, expect_traceback, caplog): +async def test_handle_authentication_error_traceback_only_for_unexpected_errors(auth_error, expect_traceback, expect_level, caplog): """Regression for LIT-6043: expected 4xx auth rejections must not format a - traceback via logger.exception; unexpected errors must keep it.""" + traceback via logger.exception; malformed virtual keys log at WARNING.""" handler = UserAPIKeyAuthExceptionHandler() with ( @@ -740,8 +761,8 @@ async def test_handle_authentication_error_traceback_only_for_unexpected_errors( try: try: raise auth_error - except (ProxyException, ValueError) as caught: - with caplog.at_level("ERROR", logger="LiteLLM Proxy"), pytest.raises(ProxyException): + except (ProxyException, ValueError, HTTPException) as caught: + with caplog.at_level(expect_level, logger="LiteLLM Proxy"), pytest.raises((ProxyException, HTTPException)): await handler._handle_authentication_error( caught, MagicMock(), @@ -756,3 +777,6 @@ async def test_handle_authentication_error_traceback_only_for_unexpected_errors( records = [r for r in caplog.records if "user_api_key_auth(): Exception occured" in r.getMessage()] assert len(records) == 1 assert (records[0].exc_info is not None) is expect_traceback + assert records[0].levelname == expect_level + expected_logger_name = "LiteLLM Proxy.stdout" if expect_level == "WARNING" else "LiteLLM Proxy" + assert records[0].name == expected_logger_name diff --git a/tests/test_litellm/proxy/db/db_transaction_queue/test_redis_update_buffer.py b/tests/test_litellm/proxy/db/db_transaction_queue/test_redis_update_buffer.py index 504654e103a..8f3508fc4e9 100644 --- a/tests/test_litellm/proxy/db/db_transaction_queue/test_redis_update_buffer.py +++ b/tests/test_litellm/proxy/db/db_transaction_queue/test_redis_update_buffer.py @@ -203,7 +203,7 @@ async def test_get_all_transactions_from_redis_buffer_pipeline(redis_update_buff "window_duration": "30d", "window_start": "2026-08-01T00:00:00.000000", "spend": 3.0, - "request_ids": ["req-1"], + "started_at": None, } ] ) @@ -233,13 +233,11 @@ async def test_get_all_transactions_from_redis_buffer_pipeline(redis_update_buff window_spend, ) = result - # Budget window spend from two pods is summed per window, not overwritten, - # and both pods' request ids reach the seed exclusion. + # Budget window spend from two pods is summed per window, not overwritten. assert window_spend is not None assert len(window_spend) == 1 assert window_spend[0]["spend"] == 6.0 assert window_spend[0]["entity_id"] == "hashed-token" - assert window_spend[0]["request_ids"] == ("req-1",) # Verify db spend was parsed correctly assert db_spend is not None @@ -326,7 +324,6 @@ async def test_restored_window_spend_transactions_drain_back_unchanged(redis_upd window_duration="30d", window_start=datetime(2026, 8, 1, tzinfo=timezone.utc), spend=3.0, - request_id="req-1", started_at=datetime(2026, 8, 10, 12, 0, tzinfo=timezone.utc), ), ) @@ -500,7 +497,6 @@ async def test_store_in_memory_spend_updates_pushes_budget_window_spend(redis_up window_duration="30d", window_start=datetime(2026, 8, 1, tzinfo=timezone.utc), spend=1.25, - request_id="req-1", started_at=datetime(2026, 8, 10, 12, 0, 0, tzinfo=timezone.utc), ) ) @@ -526,12 +522,45 @@ async def test_store_in_memory_spend_updates_pushes_budget_window_spend(redis_up "window_duration": "30d", "window_start": "2026-08-01T00:00:00.000000", "spend": 1.25, - "request_ids": ["req-1"], "started_at": "2026-08-10T12:00:00.000000", + "request_ids": [], } ] +@pytest.mark.asyncio +async def test_budget_window_payloads_keep_request_ids_for_older_workers(redis_update_buffer, mock_redis_cache): + """A leader from before the field was dropped indexes request_ids while + merging what it popped, and the pop is destructive, so a payload without + the key would cost a rolling deploy those increments.""" + from datetime import datetime, timezone + + from litellm.proxy.db.db_transaction_queue.window_spend_update_queue import ( + WindowSpendUpdateQueue, + build_window_spend_transaction, + ) + + mock_redis_cache.async_rpush_pipeline = AsyncMock(return_value=[1]) + window_queue = WindowSpendUpdateQueue() + await window_queue.add_update( + build_window_spend_transaction( + entity_type="key", + entity_id="hashed-token", + window_duration="30d", + window_start=datetime(2026, 8, 1, tzinfo=timezone.utc), + spend=1.25, + ) + ) + + await redis_update_buffer.restore_transactions_to_redis( + window_spend_update_transactions=await window_queue.flush_and_get_aggregated_window_spend_transactions(), + ) + + rpush_list = mock_redis_cache.async_rpush_pipeline.call_args.kwargs["rpush_list"] + restored = json.loads(rpush_list[0]["values"][0]) + assert [payload["request_ids"] for payload in restored] == [[]] + + @pytest.mark.asyncio async def test_store_in_memory_spend_updates_restores_budget_window_spend_on_rpush_failure( redis_update_buffer, mock_redis_cache diff --git a/tests/test_litellm/proxy/db/db_transaction_queue/test_window_spend_update_queue.py b/tests/test_litellm/proxy/db/db_transaction_queue/test_window_spend_update_queue.py index b1ecda57afa..6632b1c8e35 100644 --- a/tests/test_litellm/proxy/db/db_transaction_queue/test_window_spend_update_queue.py +++ b/tests/test_litellm/proxy/db/db_transaction_queue/test_window_spend_update_queue.py @@ -19,7 +19,6 @@ def _txn( spend: float, duration: str = "30d", entity_type: str = "key", - request_id: str | None = None, started_at: datetime | None = None, ): return build_window_spend_transaction( @@ -28,7 +27,6 @@ def _txn( window_duration=duration, window_start=window_start, spend=spend, - request_id=request_id, started_at=started_at, ) @@ -38,13 +36,12 @@ def test_build_window_spend_transaction_stores_naive_utc_iso(): TIMESTAMP(3) column, so a non-UTC input must be converted, not truncated.""" non_utc = datetime(2026, 8, 1, 20, 0, tzinfo=timezone(timedelta(hours=-4))) - assert _txn("k1", non_utc, 1.0, request_id="req-1") == { + assert _txn("k1", non_utc, 1.0) == { "entity_type": "key", "entity_id": "k1", "window_duration": "30d", "window_start": "2026-08-02T00:00:00.000000", "spend": 1.0, - "request_ids": ("req-1",), "started_at": None, } @@ -59,19 +56,19 @@ def test_build_window_spend_transaction_stores_started_at_as_naive_utc_iso(): @pytest.mark.asyncio async def test_aggregation_keeps_the_earliest_started_at_of_the_batch(): - """The seed bounds its request-id exclusion at the batch's earliest start, - so a later start must never win the merge.""" + """The seed stops at the batch's earliest start, so a later start must never + win the merge: it would push the cutoff forward and count a request the + increments already cover.""" queue = WindowSpendUpdateQueue() earliest = datetime(2026, 8, 10, 12, 0, 0, tzinfo=timezone.utc) - await queue.add_update(_txn("k1", WINDOW_A, 1.0, request_id="req-2", started_at=earliest + timedelta(seconds=5))) - await queue.add_update(_txn("k1", WINDOW_A, 1.0, request_id="req-1", started_at=earliest)) - await queue.add_update(_txn("k1", WINDOW_A, 1.0, request_id="req-3")) + await queue.add_update(_txn("k1", WINDOW_A, 1.0, started_at=earliest + timedelta(seconds=5))) + await queue.add_update(_txn("k1", WINDOW_A, 1.0, started_at=earliest)) + await queue.add_update(_txn("k1", WINDOW_A, 1.0)) aggregated = await queue.flush_and_get_aggregated_window_spend_transactions() assert len(aggregated) == 1 assert aggregated[0]["started_at"] == "2026-08-10T12:00:00.000000" - assert aggregated[0]["request_ids"] == ("req-1", "req-2", "req-3") def test_to_naive_utc_leaves_naive_values_alone(): @@ -208,62 +205,12 @@ def test_aggregation_survives_the_redis_json_round_trip(): assert reloaded == aggregated -@pytest.mark.asyncio -async def test_aggregation_unions_the_request_ids_of_merged_increments(): - """The seed excludes exactly the requests its batch already covers, so every - merged increment's id has to survive aggregation.""" - queue = WindowSpendUpdateQueue() - await queue.add_update(_txn("k1", WINDOW_A, 1.0, request_id="req-1")) - await queue.add_update(_txn("k1", WINDOW_A, 2.0, request_id="req-2")) - - aggregated = await queue.flush_and_get_aggregated_window_spend_transactions() - - assert len(aggregated) == 1 - assert aggregated[0]["request_ids"] == ("req-1", "req-2") - - -@pytest.mark.asyncio -async def test_request_ids_stay_with_their_own_window(): - queue = WindowSpendUpdateQueue() - await queue.add_update(_txn("k1", WINDOW_A, 1.0, request_id="req-a")) - await queue.add_update(_txn("k1", WINDOW_B, 2.0, request_id="req-b")) - - aggregated = await queue.flush_and_get_aggregated_window_spend_transactions() - - assert {payload["window_start"]: payload["request_ids"] for payload in aggregated} == { - "2026-08-01T00:00:00.000000": ("req-a",), - "2026-08-31T00:00:00.000000": ("req-b",), - } - - -@pytest.mark.asyncio -async def test_request_ids_are_deduplicated_and_ordered(): - queue = WindowSpendUpdateQueue() - await queue.add_update(_txn("k1", WINDOW_A, 1.0, request_id="req-b")) - await queue.add_update(_txn("k1", WINDOW_A, 1.0, request_id="req-a")) - await queue.add_update(_txn("k1", WINDOW_A, 1.0, request_id="req-a")) - - aggregated = await queue.flush_and_get_aggregated_window_spend_transactions() - - assert aggregated[0]["request_ids"] == ("req-a", "req-b") - - -@pytest.mark.asyncio -async def test_increment_without_a_request_id_carries_no_exclusion(): - queue = WindowSpendUpdateQueue() - await queue.add_update(_txn("k1", WINDOW_A, 1.0)) - - aggregated = await queue.flush_and_get_aggregated_window_spend_transactions() - - assert aggregated[0]["request_ids"] == () - - -def test_request_ids_survive_the_redis_json_round_trip(): +def test_started_at_survives_the_redis_json_round_trip(): aggregated = WindowSpendUpdateQueue.get_aggregated_window_spend_transactions( - [(_txn("k1", WINDOW_A, 1.0, request_id="req-1"),)] + [(_txn("k1", WINDOW_A, 1.0, started_at=datetime(2026, 8, 10, 12, 0, tzinfo=timezone.utc)),)] ) reloaded = WindowSpendUpdateQueue.get_aggregated_window_spend_transactions([json.loads(json.dumps(aggregated))]) - assert reloaded[0]["request_ids"] == ("req-1",) + assert reloaded[0]["started_at"] == "2026-08-10T12:00:00.000000" assert reloaded[0]["spend"] == 1.0 diff --git a/tests/test_litellm/proxy/db/test_budget_window_spend_writer.py b/tests/test_litellm/proxy/db/test_budget_window_spend_writer.py index a849317c930..130f0c56ccf 100644 --- a/tests/test_litellm/proxy/db/test_budget_window_spend_writer.py +++ b/tests/test_litellm/proxy/db/test_budget_window_spend_writer.py @@ -6,9 +6,10 @@ from typing import Any import pytest from litellm.proxy.db.budget_window_spend_writer import ( + WindowSeedTotals, commit_window_spend_updates, roll_window_spend_row, - spend_logs_total_excluding, + spend_logs_seed_totals, ) from litellm.proxy.db.db_transaction_queue.window_spend_update_queue import ( build_window_spend_transaction, @@ -70,10 +71,15 @@ class _FakePrismaClient: class _RecordingAggregate: - """Stands in for the LiteLLM_SpendLogs seed aggregate.""" + """Stands in for the LiteLLM_SpendLogs seed aggregate. before_batch + defaults to the full total, the state where none of this batch's own log + rows have been persisted yet.""" - def __init__(self, value: float = 5.0) -> None: - self.value = value + def __init__(self, total: float = 5.0, before_batch: float | None = None) -> None: + self.totals = WindowSeedTotals( + total=total, + before_batch=total if before_batch is None else before_batch, + ) self.calls: list[dict[str, Any]] = [] async def __call__( @@ -82,25 +88,23 @@ class _RecordingAggregate: entity_type: str, entity_id: str, window_start: datetime, - exclude_request_ids: Any, - exclude_started_at: datetime | None, - ) -> float | None: + batch_started_at: datetime | None, + ) -> WindowSeedTotals | None: self.calls.append( { "entity_type": entity_type, "entity_id": entity_id, "window_start": window_start, - "exclude_request_ids": tuple(exclude_request_ids), - "exclude_started_at": exclude_started_at, + "batch_started_at": batch_started_at, } ) - return self.value + return self.totals class _SpendLogsFake: """Sums the LiteLLM_SpendLogs rows (request_id, spend, startTime) it holds, - honouring the exclusion exactly as the real aggregate's - NOT (request_id = ANY(...) AND startTime >= bound) does.""" + splitting them at the batch start exactly as the real aggregate's + SUM(...) FILTER (WHERE startTime < bound) does.""" def __init__(self, rows: tuple[tuple[str, float, datetime], ...]) -> None: self.rows = rows @@ -111,25 +115,25 @@ class _SpendLogsFake: entity_type: str, entity_id: str, window_start: datetime, - exclude_request_ids: Any, - exclude_started_at: datetime | None, - ) -> float | None: - excluded = frozenset(exclude_request_ids) if exclude_started_at is not None else frozenset() - return math.fsum( - spend - for request_id, spend, started_at in self.rows - if not (request_id in excluded and started_at >= exclude_started_at) + batch_started_at: datetime | None, + ) -> WindowSeedTotals | None: + return WindowSeedTotals( + total=math.fsum(spend for _request_id, spend, _started_at in self.rows), + before_batch=math.fsum( + spend + for _request_id, spend, started_at in self.rows + if batch_started_at is None or started_at < batch_started_at + ), ) -def _batch(request_ids: tuple[str, ...], spend: float, started_at: datetime | None = BATCH_STARTED_AT) -> dict: +def _batch(spend: float, started_at: datetime | None = BATCH_STARTED_AT) -> dict: return { "entity_type": "key", "entity_id": "k1", "window_duration": "30d", "window_start": "2026-08-01T00:00:00.000000", "spend": spend, - "request_ids": request_ids, "started_at": None if started_at is None else started_at.replace(tzinfo=None).isoformat(timespec="microseconds"), @@ -156,7 +160,7 @@ async def test_missing_row_is_seeded_from_spend_logs_once(): existed, so a brand new primary key inserts the SpendLogs total plus this increment.""" db = _FakeDB(existing_rows=[]) - aggregate = _RecordingAggregate(value=5.0) + aggregate = _RecordingAggregate(total=5.0) await commit_window_spend_updates( prisma_client=_FakePrismaClient(db), @@ -183,7 +187,7 @@ async def test_existing_row_is_never_reseeded(): """The seed is a full LiteLLM_SpendLogs scan; running it for a row that is already maintained would both cost a scan and double count.""" db = _FakeDB(existing_rows=[_existing("key", "k1", "30d")]) - aggregate = _RecordingAggregate(value=5.0) + aggregate = _RecordingAggregate(total=5.0) await commit_window_spend_updates( prisma_client=_FakePrismaClient(db), @@ -200,7 +204,7 @@ async def test_existing_row_is_never_reseeded(): @pytest.mark.asyncio async def test_seed_runs_only_for_the_primary_keys_that_are_missing(): db = _FakeDB(existing_rows=[_existing("key", "k1", "30d")]) - aggregate = _RecordingAggregate(value=5.0) + aggregate = _RecordingAggregate(total=5.0) await commit_window_spend_updates( prisma_client=_FakePrismaClient(db), @@ -223,7 +227,7 @@ async def test_insert_spend_and_increment_differ_only_when_a_row_is_seeded(): """The conflict arm adds the increment alone so two pods that both seed the same new window cannot add the SpendLogs base twice.""" db = _FakeDB(existing_rows=[]) - aggregate = _RecordingAggregate(value=9.0) + aggregate = _RecordingAggregate(total=9.0) await commit_window_spend_updates( prisma_client=_FakePrismaClient(db), @@ -260,7 +264,7 @@ async def test_upsert_sql_adds_for_a_current_window_and_replaces_for_a_newer_one @pytest.mark.asyncio async def test_upsert_never_interpolates_values_into_the_sql(): db = _FakeDB(existing_rows=[]) - aggregate = _RecordingAggregate(value=0.0) + aggregate = _RecordingAggregate(total=0.0) await commit_window_spend_updates( prisma_client=_FakePrismaClient(db), @@ -278,7 +282,7 @@ async def test_upserts_are_ordered_by_primary_key_then_window_start(): """Cross-pod lock ordering, plus an older window must be applied before the roll that supersedes it or the roll would be undone.""" db = _FakeDB(existing_rows=[]) - aggregate = _RecordingAggregate(value=0.0) + aggregate = _RecordingAggregate(total=0.0) await commit_window_spend_updates( prisma_client=_FakePrismaClient(db), @@ -306,7 +310,7 @@ async def test_upserts_are_ordered_by_primary_key_then_window_start(): @pytest.mark.asyncio async def test_existing_row_lookup_sends_every_primary_key_as_array_params(): db = _FakeDB(existing_rows=[]) - aggregate = _RecordingAggregate(value=0.0) + aggregate = _RecordingAggregate(total=0.0) await commit_window_spend_updates( prisma_client=_FakePrismaClient(db), @@ -344,9 +348,7 @@ async def test_unknown_entity_type_contributes_no_seed(): anything else starts from its increment alone.""" db = _FakeDB(existing_rows=[]) - async def no_such_column( - prisma_client, entity_type, entity_id, window_start, exclude_request_ids, exclude_started_at - ): + async def no_such_column(prisma_client, entity_type, entity_id, window_start, batch_started_at): return None await commit_window_spend_updates( @@ -363,7 +365,7 @@ async def test_unknown_entity_type_contributes_no_seed(): async def test_unavailable_spend_logs_aggregate_seeds_zero_rather_than_failing(): db = _FakeDB(existing_rows=[]) - async def unavailable(prisma_client, entity_type, entity_id, window_start, exclude_request_ids, exclude_started_at): + async def unavailable(prisma_client, entity_type, entity_id, window_start, batch_started_at): return None await commit_window_spend_updates( @@ -399,32 +401,31 @@ async def test_roll_window_spend_row_is_conditional_on_the_stored_window_being_o @pytest.mark.asyncio -async def test_seed_receives_the_batch_request_ids_and_earliest_start_to_exclude(): +async def test_seed_receives_the_batch_earliest_start_as_its_cutoff(): db = _FakeDB(existing_rows=[]) - aggregate = _RecordingAggregate(value=0.0) + aggregate = _RecordingAggregate(total=0.0) await commit_window_spend_updates( prisma_client=_FakePrismaClient(db), - transactions=(_batch(("req-1", "req-2", "req-3"), 3.0),), + transactions=(_batch(3.0),), spend_logs_aggregate=aggregate, ) - assert aggregate.calls[0]["exclude_request_ids"] == ("req-1", "req-2", "req-3") - assert aggregate.calls[0]["exclude_started_at"] == BATCH_STARTED_AT + assert aggregate.calls[0]["batch_started_at"] == BATCH_STARTED_AT @pytest.mark.asyncio async def test_seed_passes_no_start_bound_when_the_batch_has_none(): db = _FakeDB(existing_rows=[]) - aggregate = _RecordingAggregate(value=0.0) + aggregate = _RecordingAggregate(total=0.0) await commit_window_spend_updates( prisma_client=_FakePrismaClient(db), - transactions=(_batch(("req-1",), 1.0, started_at=None),), + transactions=(_batch(1.0, started_at=None),), spend_logs_aggregate=aggregate, ) - assert aggregate.calls[0]["exclude_started_at"] is None + assert aggregate.calls[0]["batch_started_at"] is None @pytest.mark.asyncio @@ -444,7 +445,7 @@ async def test_new_row_is_not_double_counted_when_the_batch_logs_already_flushed await commit_window_spend_updates( prisma_client=_FakePrismaClient(db), - transactions=(_batch(("req-1", "req-2", "req-3"), 0.000141),), + transactions=(_batch(0.000141),), spend_logs_aggregate=already_flushed, ) @@ -460,7 +461,7 @@ async def test_new_row_still_covers_spend_that_predates_the_batch(): await commit_window_spend_updates( prisma_client=_FakePrismaClient(db), - transactions=(_batch(("req-1",), 0.000047),), + transactions=(_batch(0.000047),), spend_logs_aggregate=spend_logs, ) @@ -469,22 +470,29 @@ async def test_new_row_still_covers_spend_that_predates_the_batch(): @pytest.mark.asyncio -async def test_replayed_request_id_cannot_erase_historical_spend_from_the_seed(): - """request_id can be chosen by the client via x-litellm-call-id. A request - that replays an id from before this batch writes no new LiteLLM_SpendLogs - row (the insert skips duplicates), so the seed must keep counting the - historical row that id belongs to; only its increment is new.""" +async def test_seed_keeps_spend_another_pod_persisted_after_this_batch_started(): + """A concurrent request on another pod can land its spend log after this + batch started but before this pod seeds the row. Dropping it on a plain + time cutoff would lose that spend for the rest of the window if that pod + died before flushing its increment, so the seed takes off only this batch's + own spend and keeps everything else.""" db = _FakeDB(existing_rows=[]) - spend_logs = _SpendLogsFake(rows=(("replayed", 0.5, BEFORE_BATCH),)) + spend_logs = _SpendLogsFake( + rows=( + ("older", 0.5, BEFORE_BATCH), + ("mine", 0.000047, BATCH_STARTED_AT), + ("other-pod", 0.25, BATCH_STARTED_AT + timedelta(seconds=1)), + ), + ) await commit_window_spend_updates( prisma_client=_FakePrismaClient(db), - transactions=(_batch(("replayed",), 0.000047),), + transactions=(_batch(0.000047),), spend_logs_aggregate=spend_logs, ) ((_, params),) = db.batcher.calls - assert params[INSERT_SPEND] == pytest.approx(0.500047) + assert params[INSERT_SPEND] == pytest.approx(0.750047) @pytest.mark.asyncio @@ -496,7 +504,7 @@ async def test_new_row_is_correct_when_the_batch_logs_have_not_flushed_yet(): await commit_window_spend_updates( prisma_client=_FakePrismaClient(db), - transactions=(_batch(("req-1", "req-2", "req-3"), 0.000141),), + transactions=(_batch(0.000141),), spend_logs_aggregate=nothing_flushed, ) @@ -509,57 +517,49 @@ async def test_new_row_is_correct_when_the_batch_logs_have_not_flushed_yet(): "entity_type, expected_column", [("key", "api_key = $1"), ("team", "team_id = $1")], ) -async def test_seed_aggregate_sql_excludes_the_request_ids_only_within_the_batch_start_bound( - entity_type, expected_column -): - db = _FakeDB(existing_rows=[{"total": 1.25}]) +async def test_seed_aggregate_sql_splits_the_window_at_the_batch_start(entity_type, expected_column): + db = _FakeDB(existing_rows=[{"total": 1.25, "before_batch": 0.75}]) - total = await spend_logs_total_excluding( + totals = await spend_logs_seed_totals( prisma_client=_FakePrismaClient(db), entity_type=entity_type, entity_id="e1", window_start=WINDOW_A, - exclude_request_ids=("req-1", "req-2"), - exclude_started_at=BATCH_STARTED_AT, + batch_started_at=BATCH_STARTED_AT, ) - assert total == pytest.approx(1.25) + assert totals == WindowSeedTotals(total=1.25, before_batch=0.75) ((query, params),) = db.query_raw_calls normalized = " ".join(query.split()) assert expected_column in normalized - assert "NOT (request_id = ANY($3::text[]) AND \"startTime\" >= ($4::timestamptz AT TIME ZONE 'UTC'))" in normalized + assert "FILTER (WHERE \"startTime\" < ($3::timestamptz AT TIME ZONE 'UTC'))" in normalized assert 'FROM "LiteLLM_SpendLogs"' in normalized # startTime is TIMESTAMP(3): the bound is floored to the second so the # batch's own earliest row cannot round under it. - assert params == ("e1", WINDOW_A, ("req-1", "req-2"), datetime(2026, 8, 10, 12, 0, 0)) - # The ids are bound, never spliced into the statement. - assert "req-1" not in query + assert params == ("e1", WINDOW_A, datetime(2026, 8, 10, 12, 0, 0)) + # Nothing the caller supplied reaches the statement text. + assert "e1" not in query @pytest.mark.asyncio -@pytest.mark.parametrize( - "exclude_request_ids, exclude_started_at", - [(("req-1",), None), ((), BATCH_STARTED_AT)], -) -async def test_seed_aggregate_excludes_nothing_without_both_ids_and_a_start_bound( - exclude_request_ids, exclude_started_at -): - """Ids without a start bound would reopen the replayed-id hole, so the - seed counts everything instead; at worst that over-counts one batch.""" - db = _FakeDB(existing_rows=[{"total": 1.25}]) +async def test_seed_aggregate_sums_the_whole_window_without_a_start_bound(): + """A batch with no known start cannot place the split, so both halves are + the same sum and the seed counts everything; at worst that over-counts one + batch, which enforcement tolerates, where under-counting is a budget + bypass.""" + db = _FakeDB(existing_rows=[{"total": 1.25, "before_batch": 1.25}]) - total = await spend_logs_total_excluding( + totals = await spend_logs_seed_totals( prisma_client=_FakePrismaClient(db), entity_type="key", entity_id="e1", window_start=WINDOW_A, - exclude_request_ids=exclude_request_ids, - exclude_started_at=exclude_started_at, + batch_started_at=None, ) - assert total == pytest.approx(1.25) + assert totals == WindowSeedTotals(total=1.25, before_batch=1.25) ((query, params),) = db.query_raw_calls - assert "request_id" not in query + assert '"startTime" <' not in query assert params == ("e1", WINDOW_A) @@ -567,16 +567,15 @@ async def test_seed_aggregate_excludes_nothing_without_both_ids_and_a_start_boun async def test_seed_aggregate_returns_none_for_an_entity_type_with_no_spend_logs_column(): db = _FakeDB(existing_rows=[]) - total = await spend_logs_total_excluding( + totals = await spend_logs_seed_totals( prisma_client=_FakePrismaClient(db), entity_type="user", entity_id="u1", window_start=WINDOW_A, - exclude_request_ids=(), - exclude_started_at=None, + batch_started_at=None, ) - assert total is None + assert totals is None assert db.query_raw_calls == [] @@ -584,13 +583,12 @@ async def test_seed_aggregate_returns_none_for_an_entity_type_with_no_spend_logs async def test_seed_aggregate_treats_an_entity_with_no_rows_as_zero(): db = _FakeDB(existing_rows=[]) - total = await spend_logs_total_excluding( + totals = await spend_logs_seed_totals( prisma_client=_FakePrismaClient(db), entity_type="key", entity_id="k-unknown", window_start=WINDOW_A, - exclude_request_ids=(), - exclude_started_at=None, + batch_started_at=None, ) - assert total == 0.0 + assert totals == WindowSeedTotals(total=0.0, before_batch=0.0) diff --git a/tests/test_litellm/proxy/db/test_db_spend_update_writer.py b/tests/test_litellm/proxy/db/test_db_spend_update_writer.py index d28cf8c9c6a..11ef911de3e 100644 --- a/tests/test_litellm/proxy/db/test_db_spend_update_writer.py +++ b/tests/test_litellm/proxy/db/test_db_spend_update_writer.py @@ -2581,7 +2581,6 @@ async def test_failed_window_spend_commit_requeues_the_increments_and_continues_ window_duration="30d", window_start=datetime(2026, 8, 1, tzinfo=timezone.utc), spend=0.5, - request_id="req-1", ) await db_writer.window_spend_update_queue.add_update(transaction) db = _WindowSpendFakeDB() @@ -2611,7 +2610,6 @@ async def test_failed_window_spend_commit_from_redis_is_restored_to_redis(): window_duration="7d", window_start=datetime(2026, 8, 1, tzinfo=timezone.utc), spend=2.0, - request_id="req-1", ), ) mock_redis_update_buffer = AsyncMock() @@ -2638,74 +2636,6 @@ async def test_failed_window_spend_commit_from_redis_is_restored_to_redis(): db_writer.pod_lock_manager.release_lock.assert_awaited_once() -@pytest.mark.asyncio -async def test_update_database_returns_the_spend_log_request_id(): - """The budget-window seed excludes the log rows its increments already - cover, so the caller needs the id this call was recorded under. It cannot - be re-derived: cache hits append time.time() to the id.""" - db_writer = DBSpendUpdateWriter() - db_writer._insert_spend_log_to_db = AsyncMock() - db_writer._enqueue_tool_usage_transaction = AsyncMock() - - with ( - patch.multiple( # test-quality-ok: update_database lazily imports these proxy_server globals; no injection seam - "litellm.proxy.proxy_server", - disable_spend_logs=False, - prisma_client=MagicMock(), - litellm_proxy_budget_name="test-budget", - ) - ): - request_id = await db_writer.update_database( - token="test-token", - user_id="test-user", - end_user_id=None, - team_id="test-team", - org_id=None, - kwargs={"model": "gpt-4", "custom_llm_provider": "openai", "litellm_call_id": "call-xyz"}, - completion_response=MagicMock(), - start_time=datetime.now(), - end_time=datetime.now(), - response_cost=0.1, - ) - await asyncio.sleep(0) - - assert request_id is not None - # Same id the spend log row was queued under. - assert request_id == db_writer._insert_spend_log_to_db.call_args[1]["payload"]["request_id"] - - -@pytest.mark.asyncio -async def test_update_database_returns_none_when_the_payload_cannot_be_built(): - db_writer = DBSpendUpdateWriter() - - with ( - patch.multiple( # test-quality-ok: update_database lazily imports these proxy_server globals; no injection seam - "litellm.proxy.proxy_server", - disable_spend_logs=False, - prisma_client=MagicMock(), - litellm_proxy_budget_name="test-budget", - ), - patch( # test-quality-ok: the payload builder is called by name inside update_database; no injection seam - "litellm.proxy.spend_tracking.spend_tracking_utils.get_logging_payload", - side_effect=Exception("payload boom"), - ), - ): - request_id = await db_writer.update_database( - token="test-token", - user_id="test-user", - end_user_id=None, - team_id="test-team", - org_id=None, - kwargs={"model": "gpt-4"}, - completion_response=MagicMock(), - start_time=datetime.now(), - end_time=datetime.now(), - response_cost=0.1, - ) - - assert request_id is None - - @pytest.mark.asyncio async def test_commit_spend_updates_to_db_does_not_stamp_key_settings_updated_at(): """Spend flushes must leave settings_updated_at alone, or it decays into diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_bedrock_guardrails.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_bedrock_guardrails.py index 235a5c0c09b..bcda1b8b61d 100644 --- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_bedrock_guardrails.py +++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_bedrock_guardrails.py @@ -5590,3 +5590,52 @@ async def test_streaming_end_of_stream_block_emits_error_frame_instead_of_trunca assert payload["error"]["message"] == "Violated guardrail policy" assert payload["error"]["code"] == "400" assert payload["error"]["provider_specific_fields"]["guardrailIdentifier"] == "test-guardrail" + + +@pytest.mark.asyncio +async def test_apply_guardrail_debug_log_masks_signed_request_headers(): + import logging + + from litellm._logging import verbose_proxy_logger + + session_token = "FakeSessionTokenValueThatMustNeverAppearInLogs1234567890" + guardrail = BedrockGuardrail( + guardrailIdentifier="test-guardrail", + guardrailVersion="DRAFT", + aws_access_key_id="ASIAFAKEACCESSKEYID1", + aws_secret_access_key="fakeSecretAccessKeyForSigning", + aws_session_token=session_token, + aws_region_name="us-east-1", + ) + + mock_response = MagicMock() + mock_response.status_code = 200 + mock_response.json.return_value = {"action": "NONE", "outputs": []} + + captured_records: list[logging.LogRecord] = [] + + class _RecordingHandler(logging.Handler): + def emit(self, record: logging.LogRecord) -> None: + captured_records.append(record) + + handler = _RecordingHandler(level=logging.DEBUG) + previous_level = verbose_proxy_logger.level + verbose_proxy_logger.addHandler(handler) + verbose_proxy_logger.setLevel(logging.DEBUG) + try: + with patch.object(guardrail.async_handler, "post", new_callable=AsyncMock) as mock_post: + mock_post.return_value = mock_response + await guardrail.make_bedrock_api_request( + source="INPUT", + messages=[{"role": "user", "content": "hello"}], + request_data={}, + ) + finally: + verbose_proxy_logger.removeHandler(handler) + verbose_proxy_logger.setLevel(previous_level) + + rendered_messages = [record.getMessage() for record in captured_records] + header_lines = [message for message in rendered_messages if "headers:" in message] + assert header_lines, "expected the signed-request debug line to be logged" + assert any("X-Amz-Security-Token" in message for message in header_lines) + assert all(session_token not in message for message in rendered_messages) diff --git a/tests/test_litellm/proxy/guardrails/test_guardrail_endpoints.py b/tests/test_litellm/proxy/guardrails/test_guardrail_endpoints.py index 9b2117b7647..9511732fd50 100644 --- a/tests/test_litellm/proxy/guardrails/test_guardrail_endpoints.py +++ b/tests/test_litellm/proxy/guardrails/test_guardrail_endpoints.py @@ -917,7 +917,9 @@ async def test_bedrock_guardrail_make_api_request_passes_api_key(): "Content-Type": "application/json", "Authorization": "Bearer test-api-key-789", } - mock_request_instance.prepare.return_value = Mock() + mock_request_instance.prepare.return_value = Mock( + headers=mock_request_instance.headers + ) mock_aws_request.return_value = mock_request_instance await guardrail_hook.make_bedrock_api_request( diff --git a/tests/test_litellm/proxy/hooks/test_proxy_track_cost_callback.py b/tests/test_litellm/proxy/hooks/test_proxy_track_cost_callback.py index 2a540f4f522..8043a1aca3f 100644 --- a/tests/test_litellm/proxy/hooks/test_proxy_track_cost_callback.py +++ b/tests/test_litellm/proxy/hooks/test_proxy_track_cost_callback.py @@ -567,9 +567,7 @@ async def test_update_database_and_spend_counters_preserves_db_exception_when_re @pytest.mark.asyncio async def test_update_database_and_spend_counters_updates_counters_after_db_update(): proxy_logging_obj = MagicMock() - proxy_logging_obj.db_spend_update_writer.update_database = AsyncMock( - return_value="chatcmpl-abc123" - ) + proxy_logging_obj.db_spend_update_writer.update_database = AsyncMock() increment_spend_counters = AsyncMock() budget_reservation = {"reserved_cost": 0.5, "entries": []} start_time = datetime.now() @@ -602,7 +600,6 @@ async def test_update_database_and_spend_counters_updates_counters_after_db_upda budget_reservation=budget_reservation, end_user_id="test_end_user_id", tags=["tag-a"], - request_id="chatcmpl-abc123", request_started_at=start_time, model_access_groups=("premium",), ) @@ -1884,61 +1881,6 @@ async def test_track_cost_callback_logs_unauthenticated_pass_through_request( ) -@pytest.mark.asyncio -async def test_update_database_and_spend_counters_forwards_the_spend_log_request_id(): - """The budget-window flush excludes the log rows its increments already - cover. That only works if the id update_database recorded the row under is - handed to the counter update, so this seam is load-bearing.""" - proxy_logging_obj = MagicMock() - proxy_logging_obj.db_spend_update_writer.update_database = AsyncMock( - return_value="chatcmpl-abc123" - ) - increment_spend_counters = AsyncMock() - - await _update_database_and_spend_counters( - proxy_logging_obj=proxy_logging_obj, - increment_spend_counters=increment_spend_counters, - user_api_key="test_api_key", - user_id="test_user_id", - end_user_id=None, - team_id="test_team_id", - org_id="test_org_id", - kwargs={}, - completion_response=None, - start_time=datetime.now(), - end_time=datetime.now(), - response_cost=0.2, - budget_reservation=None, - ) - - assert increment_spend_counters.await_args.kwargs["request_id"] == "chatcmpl-abc123" - - -@pytest.mark.asyncio -async def test_update_database_and_spend_counters_forwards_a_missing_request_id_as_none(): - proxy_logging_obj = MagicMock() - proxy_logging_obj.db_spend_update_writer.update_database = AsyncMock(return_value=None) - increment_spend_counters = AsyncMock() - - await _update_database_and_spend_counters( - proxy_logging_obj=proxy_logging_obj, - increment_spend_counters=increment_spend_counters, - user_api_key="test_api_key", - user_id="test_user_id", - end_user_id=None, - team_id="test_team_id", - org_id="test_org_id", - kwargs={}, - completion_response=None, - start_time=datetime.now(), - end_time=datetime.now(), - response_cost=0.2, - budget_reservation=None, - ) - - assert increment_spend_counters.await_args.kwargs["request_id"] is None - - class _FakeDeploymentLookup: """Deployment lookup returning the access groups each deployment declares.""" diff --git a/tests/test_litellm/proxy/management_endpoints/test_auto_router_endpoints.py b/tests/test_litellm/proxy/management_endpoints/test_auto_router_endpoints.py index 726e09f3162..c525af84511 100644 --- a/tests/test_litellm/proxy/management_endpoints/test_auto_router_endpoints.py +++ b/tests/test_litellm/proxy/management_endpoints/test_auto_router_endpoints.py @@ -878,8 +878,10 @@ def _leg_record(**overrides: object) -> MagicMock: defaults = { "id": "leg-1", "group_id": "job-1", - "api_key_id": "key-hash", + "target_type": "key", + "target_id": "key-hash", "router_name": "my-router", + "router_names": (), "direction": "forward", "baseline_model": None, "judge_model": "anthropic/claude-sonnet-5", @@ -912,8 +914,29 @@ def _key_record( return record +def _team_record(team_id: str, team_alias: str | None) -> MagicMock: + record = MagicMock(spec=["team_id", "team_alias"]) + record.team_id = team_id + record.team_alias = team_alias + return record + + +def _user_record(user_id: str, user_email: str | None) -> MagicMock: + record = MagicMock(spec=["user_id", "user_email"]) + record.user_id = user_id + record.user_email = user_email + return record + + def _shadow_prisma( - legs=(), agg_rows=None, by_leg_rows=None, known_keys=("key-hash", "key-hash-2"), key_teams=None + legs=(), + agg_rows=None, + by_leg_rows=None, + by_router_rows=None, + known_keys=("key-hash", "key-hash-2"), + key_teams=None, + known_teams=None, + known_users=None, ) -> MagicMock: """The job-table fake honours the filters it is handed, so a read that forgets stopped_at sees rows the partial index would have released, one that forgets @@ -921,6 +944,8 @@ def _shadow_prisma( group read that matched on a leg id would come back empty.""" prisma = MagicMock() teams: Final = key_teams or {} + team_aliases: Final = known_teams or {} + user_emails: Final = known_users or {} async def find_tokens(*, where): """Honours the token filter, like the job-table fake below: the endpoint derives the @@ -931,6 +956,17 @@ def _shadow_prisma( prisma.db.litellm_verificationtoken.find_many = AsyncMock(side_effect=find_tokens) + async def find_teams(*, where): + requested = where["team_id"]["in"] + return [_team_record(t, alias) for t, alias in team_aliases.items() if t in requested] + + async def find_users(*, where): + requested = where["user_id"]["in"] + return [_user_record(u, email) for u, email in user_emails.items() if u in requested] + + prisma.db.litellm_teamtable.find_many = AsyncMock(side_effect=find_teams) + prisma.db.litellm_usertable.find_many = AsyncMock(side_effect=find_users) + async def execute_raw(sql: str, *params: object): if "SET stopped_by" in sql: group = [row for row in stored if row.group_id == params[0]] @@ -959,9 +995,19 @@ def _shadow_prisma( async def find_many_legs(where=None, **_: object): current = list(stored) w = dict(where or {}) - if "api_key_id" in w: - wanted = w["api_key_id"]["in"] if isinstance(w["api_key_id"], dict) else [w["api_key_id"]] - current = [row for row in current if row.api_key_id in wanted] + if "OR" in w: + pairs = [ + ( + branch["target_type"], + branch["target_id"]["in"] if isinstance(branch["target_id"], dict) else [branch["target_id"]], + ) + for branch in w["OR"] + ] + current = [ + row + for row in current + if any(row.target_type == target_type and row.target_id in ids for target_type, ids in pairs) + ] if "direction" in w: current = [row for row in current if row.direction == w["direction"]] if "stopped_at" in w: @@ -983,8 +1029,10 @@ def _shadow_prisma( fields = ( "id", "group_id", - "api_key_id", + "target_type", + "target_id", "router_name", + "router_names", "direction", "baseline_model", "judge_model", @@ -1009,13 +1057,19 @@ def _shadow_prisma( if "AS attempt_count" in sql: return prisma.attempt_rows if "GROUP BY group_id" in sql: - scoped = [row for row in stored if "api_key_id = $2" not in sql or row.api_key_id == params[1]] + scoped = [ + row + for row in stored + if "target_type = $2" not in sql or (row.target_type == params[1] and row.target_id == params[2]) + ] keep = set(newest_groups(scoped, params[0])) return [leg_dict(row) for row in stored if row.group_id in keep] if "FILTER (WHERE outcome != 'error')::int AS judged_count" in sql: return [{"judged_count": 10, "error_count": 2, "judge_spend": 0.031}] if "SELECT job_id AS grp" in sql: return by_leg_rows if by_leg_rows is not None else [] + if "COALESCE(a.router_name" in sql: + return by_router_rows if by_router_rows is not None else [] if 'FROM "LiteLLM_ShadowEvalFunnel"' in sql: return prisma.funnel_rows return agg_rows if agg_rows is not None else [] @@ -1058,7 +1112,7 @@ async def test_start_shadow_eval_writes_one_leg_per_key_in_one_statement(monkeyp response = await start_shadow_eval(_start_request(api_key_ids=("key-hash", "key-hash-2")), ADMIN) - sweep_sql, sweep_keys = prisma.db.execute_raw.call_args.args + sweep_sql, sweep_ids, sweep_type = prisma.db.execute_raw.call_args.args assert "stopped_at IS NULL" in sweep_sql assert "j.ends_at <= (NOW() AT TIME ZONE 'utc')" in sweep_sql assert "SET stopped_at = (NOW() AT TIME ZONE 'utc')" in sweep_sql @@ -1066,12 +1120,21 @@ async def test_start_shadow_eval_writes_one_leg_per_key_in_one_statement(monkeyp assert "j.max_budget IS NOT NULL" in sweep_sql assert ">= j.max_budget" in sweep_sql assert "SUM(a.judge_cost + a.shadow_cost + a.shadow_classifier_cost)" in sweep_sql - assert "j.api_key_id = ANY($1::text[])" in sweep_sql - assert sweep_keys == ["key-hash", "key-hash-2"] + assert "j.target_type = $2 AND j.target_id = ANY($1::text[])" in sweep_sql + assert sweep_ids == ["key-hash", "key-hash-2"] + assert sweep_type == "key" prisma.db.litellm_shadowevaljob.create_many.assert_awaited_once() rows = prisma.db.litellm_shadowevaljob.create_many.call_args.kwargs["data"] - assert [row["api_key_id"] for row in rows] == ["key-hash", "key-hash-2"] - assert len({frozenset((k, v) for k, v in row.items() if k not in ("api_key_id", "id")) for row in rows}) == 1 + assert [(row["target_type"], row["target_id"]) for row in rows] == [("key", "key-hash"), ("key", "key-hash-2")] + assert ( + len( + { + frozenset((k, tuple(v) if isinstance(v, list) else v) for k, v in row.items() if k not in ("target_id", "id")) + for row in rows + } + ) + == 1 + ) assert len({row["id"] for row in rows}) == len(rows) assert len({row["group_id"] for row in rows}) == 1 assert all(row["max_turns"] == SHADOW_EVAL_TURN_VALVE and row["created_by"] == "admin" for row in rows) @@ -1080,11 +1143,69 @@ async def test_start_shadow_eval_writes_one_leg_per_key_in_one_statement(monkeyp assert response.job_id == rows[0]["group_id"] assert response.status == "running" assert response.judged_count is None - assert [(key.api_key_id, key.max_budget, key.key_alias) for key in response.keys] == [ + assert [(target.target_id, target.max_budget, target.target_alias) for target in response.targets] == [ ("key-hash", 5.0, "prod-alpha"), ("key-hash-2", 5.0, "prod-alpha"), ] - assert all(key.max_turns == SHADOW_EVAL_TURN_VALVE for key in response.keys) + assert all(target.target_type == "key" for target in response.targets) + assert all(target.max_turns == SHADOW_EVAL_TURN_VALVE for target in response.targets) + + +@pytest.mark.asyncio +async def test_start_shadow_eval_multi_router_writes_the_set_on_every_leg(monkeypatch: pytest.MonkeyPatch): + """A multi-router job stores the full set in router_names and the first router in + router_name, so a rolling-deploy pod that predates router_names still runs a valid + single-arm eval and its unstamped attempt rows attribute to that first router.""" + import litellm.proxy.proxy_server as proxy_server + + _configure_anthropic_sdk_judge(monkeypatch) + prisma = _shadow_prisma() + monkeypatch.setattr(proxy_server, "prisma_client", prisma) + monkeypatch.setattr(proxy_server, "llm_router", _shadow_router()) + + response = await start_shadow_eval( + _start_request(router_name=None, router_names=("my-router", "classifier-router")), ADMIN + ) + + rows = prisma.db.litellm_shadowevaljob.create_many.call_args.kwargs["data"] + assert all(row["router_name"] == "my-router" for row in rows) + assert all(row["router_names"] == ["my-router", "classifier-router"] for row in rows) + assert response.router_names == ("my-router", "classifier-router") + assert response.router_name == "my-router" + + +@pytest.mark.asyncio +async def test_start_shadow_eval_rejects_an_unconfigured_router_in_the_set(monkeypatch: pytest.MonkeyPatch): + import litellm.proxy.proxy_server as proxy_server + + _configure_anthropic_sdk_judge(monkeypatch) + prisma = _shadow_prisma() + monkeypatch.setattr(proxy_server, "prisma_client", prisma) + monkeypatch.setattr(proxy_server, "llm_router", _shadow_router()) + + with pytest.raises(HTTPException, match="not-a-router") as exc: + await start_shadow_eval(_start_request(router_name=None, router_names=("my-router", "not-a-router")), ADMIN) + + assert exc.value.status_code == 400 + prisma.db.litellm_shadowevaljob.create_many.assert_not_called() + + +@pytest.mark.asyncio +async def test_judge_collision_is_found_on_every_router_of_the_set(monkeypatch: pytest.MonkeyPatch): + """The judge-as-candidate guard walks every candidate router: a judge that serves an + arm of the SECOND router still poisons the whole job's win rates.""" + import litellm.proxy.proxy_server as proxy_server + + _configure_anthropic_sdk_judge(monkeypatch) + prisma = _shadow_prisma() + monkeypatch.setattr(proxy_server, "prisma_client", prisma) + monkeypatch.setattr(proxy_server, "llm_router", _shadow_router()) + + with pytest.raises(HTTPException, match="also an arm") as exc: + await start_shadow_eval(_start_request(router_name=None, router_names=("my-router", "sonnet-router")), ADMIN) + + assert exc.value.status_code == 400 + prisma.db.litellm_shadowevaljob.create_many.assert_not_called() @pytest.mark.asyncio @@ -1232,7 +1353,7 @@ async def test_start_shadow_eval_rejections( import litellm.proxy.proxy_server as proxy_server _configure_anthropic_sdk_judge(monkeypatch) - prisma = _shadow_prisma(legs=[_leg_record(id=f"leg-{key}", group_id="job-7", api_key_id=key) for key in claimed]) + prisma = _shadow_prisma(legs=[_leg_record(id=f"leg-{key}", group_id="job-7", target_id=key) for key in claimed]) monkeypatch.setattr(proxy_server, "prisma_client", prisma) monkeypatch.setattr(proxy_server, "llm_router", _shadow_router()) @@ -1315,14 +1436,14 @@ async def test_start_shadow_eval_names_the_busy_key_and_its_job(monkeypatch: pyt import litellm.proxy.proxy_server as proxy_server _configure_anthropic_sdk_judge(monkeypatch) - prisma = _shadow_prisma(legs=[_leg_record(id="leg-b", group_id="job-7", api_key_id="key-hash-2")]) + prisma = _shadow_prisma(legs=[_leg_record(id="leg-b", group_id="job-7", target_id="key-hash-2")]) monkeypatch.setattr(proxy_server, "prisma_client", prisma) monkeypatch.setattr(proxy_server, "llm_router", _shadow_router()) with pytest.raises(HTTPException) as exc: await start_shadow_eval(_start_request(api_key_ids=("key-hash", "key-hash-2")), ADMIN) assert exc.value.status_code == 409 - assert "key-hash-2 (job job-7)" in exc.value.detail + assert "key key-hash-2 (job job-7)" in exc.value.detail @pytest.mark.asyncio @@ -1441,6 +1562,180 @@ def test_start_shadow_eval_request_dedupes_and_bounds_the_key_set(): _start_request(api_key_ids=tuple(f"k{i}" for i in range(101))) +def test_start_request_bounds_the_combined_target_count_across_types(): + """The 1..100 bound counts keys, teams, and users together, so a caller cannot dodge + it by spreading targets over the three fields, and a request naming no target of any + type samples nothing and is rejected.""" + with pytest.raises(ValidationError, match="at least one target"): + _start_request(api_key_ids=(), team_ids=(), user_ids=()) + with pytest.raises(ValidationError, match="at most 100 targets"): + _start_request(api_key_ids=tuple(f"k{i}" for i in range(60)), team_ids=tuple(f"t{i}" for i in range(41))) + mixed = _start_request(api_key_ids=tuple(f"k{i}" for i in range(60)), team_ids=tuple(f"t{i}" for i in range(40))) + assert len(mixed.api_key_ids) + len(mixed.team_ids) == 100 + + +@pytest.mark.asyncio +@pytest.mark.parametrize( + "overrides,prisma_kwargs,expected_target", + [ + ( + {"api_key_ids": (), "team_ids": ("team-eng",)}, + {"known_teams": {"team-eng": "Engineering"}}, + ("team", "team-eng", "Engineering"), + ), + ( + {"api_key_ids": (), "user_ids": ("dev-alice",)}, + {"known_users": {"dev-alice": "alice@example.com"}}, + ("user", "dev-alice", "alice@example.com"), + ), + ], + ids=["team-target-labeled-by-team-alias", "user-target-labeled-by-user-email"], +) +async def test_start_shadow_eval_creates_typed_legs_for_team_and_user_targets( + monkeypatch: pytest.MonkeyPatch, overrides, prisma_kwargs, expected_target +): + import litellm.proxy.proxy_server as proxy_server + + _configure_anthropic_sdk_judge(monkeypatch) + prisma = _shadow_prisma(**prisma_kwargs) + monkeypatch.setattr(proxy_server, "prisma_client", prisma) + monkeypatch.setattr(proxy_server, "llm_router", _shadow_router()) + + response = await start_shadow_eval(_start_request(**overrides), ADMIN) + + target_type, target_id, target_alias = expected_target + rows = prisma.db.litellm_shadowevaljob.create_many.call_args.kwargs["data"] + assert [(row["target_type"], row["target_id"]) for row in rows] == [(target_type, target_id)] + assert response.status == "running" + target = response.targets[0] + assert (target.target_type, target.target_id, target.target_alias, target.key_name) == ( + target_type, + target_id, + target_alias, + None, + ) + + +@pytest.mark.asyncio +@pytest.mark.parametrize( + "overrides,prisma_kwargs,expected_detail", + [ + ( + {"api_key_ids": (), "team_ids": ("team-eng", "team-ghost")}, + {"known_teams": {"team-eng": "Engineering"}}, + "team_ids not on this proxy: team-ghost", + ), + ( + {"api_key_ids": (), "user_ids": ("dev-alice", "dev-ghost")}, + {"known_users": {"dev-alice": "alice@example.com"}}, + "user_ids not on this proxy: dev-ghost", + ), + ], + ids=["unknown-team", "unknown-user"], +) +async def test_start_shadow_eval_rejects_teams_and_users_this_proxy_does_not_know( + monkeypatch: pytest.MonkeyPatch, overrides, prisma_kwargs, expected_detail +): + import litellm.proxy.proxy_server as proxy_server + + _configure_anthropic_sdk_judge(monkeypatch) + prisma = _shadow_prisma(**prisma_kwargs) + monkeypatch.setattr(proxy_server, "prisma_client", prisma) + monkeypatch.setattr(proxy_server, "llm_router", _shadow_router()) + + with pytest.raises(HTTPException) as exc: + await start_shadow_eval(_start_request(**overrides), ADMIN) + assert exc.value.status_code == 400 + assert expected_detail in exc.value.detail + prisma.db.litellm_shadowevaljob.create_many.assert_not_called() + + +@pytest.mark.asyncio +async def test_start_shadow_eval_mixed_targets_create_both_legs_and_sweep_once_per_type( + monkeypatch: pytest.MonkeyPatch, +): + import litellm.proxy.proxy_server as proxy_server + + _configure_anthropic_sdk_judge(monkeypatch) + prisma = _shadow_prisma(known_teams={"team-eng": "Engineering"}) + monkeypatch.setattr(proxy_server, "prisma_client", prisma) + monkeypatch.setattr(proxy_server, "llm_router", _shadow_router()) + + response = await start_shadow_eval(_start_request(team_ids=("team-eng",)), ADMIN) + + rows = prisma.db.litellm_shadowevaljob.create_many.call_args.kwargs["data"] + assert [(row["target_type"], row["target_id"]) for row in rows] == [("key", "key-hash"), ("team", "team-eng")] + assert len({row["group_id"] for row in rows}) == 1 + sweeps = [ + call.args + for call in prisma.db.execute_raw.await_args_list + if "SET stopped_at = (NOW() AT TIME ZONE 'utc')" in call.args[0] + ] + assert [(ids, target_type) for _, ids, target_type in sweeps] == [(["key-hash"], "key"), (["team-eng"], "team")] + assert [(t.target_type, t.target_id, t.target_alias) for t in response.targets] == [ + ("key", "key-hash", "prod-alpha"), + ("team", "team-eng", "Engineering"), + ] + + +@pytest.mark.asyncio +async def test_start_shadow_eval_names_the_busy_team_target(monkeypatch: pytest.MonkeyPatch): + import litellm.proxy.proxy_server as proxy_server + + _configure_anthropic_sdk_judge(monkeypatch) + prisma = _shadow_prisma( + legs=[_leg_record(id="leg-t", group_id="job-7", target_type="team", target_id="team-eng")], + known_teams={"team-eng": "Engineering"}, + ) + monkeypatch.setattr(proxy_server, "prisma_client", prisma) + monkeypatch.setattr(proxy_server, "llm_router", _shadow_router()) + + with pytest.raises(HTTPException) as exc: + await start_shadow_eval(_start_request(api_key_ids=(), team_ids=("team-eng",)), ADMIN) + assert exc.value.status_code == 409 + assert "team team-eng (job job-7)" in exc.value.detail + prisma.db.litellm_shadowevaljob.create_many.assert_not_called() + + +@pytest.mark.asyncio +async def test_start_shadow_eval_claim_matches_exact_target_pairs_not_bare_ids(monkeypatch: pytest.MonkeyPatch): + """A key whose hash happens to spell a team's id must not hold the team's slot: the + claim matches (target_type, target_id) pairs, never ids across kinds.""" + import litellm.proxy.proxy_server as proxy_server + + _configure_anthropic_sdk_judge(monkeypatch) + prisma = _shadow_prisma( + legs=[_leg_record(id="leg-k", group_id="job-7", target_type="key", target_id="team-eng")], + known_teams={"team-eng": "Engineering"}, + ) + monkeypatch.setattr(proxy_server, "prisma_client", prisma) + monkeypatch.setattr(proxy_server, "llm_router", _shadow_router()) + + response = await start_shadow_eval(_start_request(api_key_ids=(), team_ids=("team-eng",)), ADMIN) + + assert response.status == "running" + prisma.db.litellm_shadowevaljob.create_many.assert_awaited_once() + + +@pytest.mark.asyncio +async def test_list_shadow_eval_jobs_rejects_a_lone_filter_half(monkeypatch: pytest.MonkeyPatch): + """target_type and target_id only mean anything together: a bare id could name a key + or a team, and a bare type filters nothing.""" + import litellm.proxy.proxy_server as proxy_server + + prisma = _shadow_prisma(legs=[_leg_record()]) + monkeypatch.setattr(proxy_server, "prisma_client", prisma) + + with pytest.raises(HTTPException) as id_only: + await list_shadow_eval_jobs(VIEWER, target_type=None, target_id="key-hash", limit=50) + assert id_only.value.status_code == 400 + + with pytest.raises(HTTPException) as type_only: + await list_shadow_eval_jobs(VIEWER, target_type="key", target_id=None, limit=50) + assert type_only.value.status_code == 400 + prisma.db.query_raw.assert_not_called() + + @pytest.mark.asyncio async def test_start_shadow_eval_concurrent_unique_violation_is_a_409(monkeypatch: pytest.MonkeyPatch): import litellm.proxy.proxy_server as proxy_server @@ -1530,7 +1825,7 @@ async def test_get_shadow_eval_job_pools_counts_and_slices_results_per_key(monke }, ] prisma = _shadow_prisma( - legs=[_leg_record(), _leg_record(id="leg-2", api_key_id="key-hash-2", max_turns=50)], + legs=[_leg_record(), _leg_record(id="leg-2", target_id="key-hash-2", max_turns=50)], agg_rows=tier_rows, by_leg_rows=leg_rows, ) @@ -1549,8 +1844,10 @@ async def test_get_shadow_eval_job_pools_counts_and_slices_results_per_key(monke assert response.results.by_tier[0].shadow_win_rate_pct == 50.0 assert response.results.overall_shadow_win_rate_pct == 40.0 assert response.results.overall_tie_rate_pct == 20.0 - assert [(s.group, s.turn_count) for s in response.results.by_key] == [("key-hash", 6), ("key-hash-2", 4)] - assert response.results.by_key[0].shadow_win_rate_pct == 66.7 + verdicts_by_target = {(t.target_type, t.target_id): t.verdicts for t in response.targets} + assert verdicts_by_target[("key", "key-hash")].turn_count == 6 + assert verdicts_by_target[("key", "key-hash")].shadow_win_rate_pct == 66.7 + assert verdicts_by_target[("key", "key-hash-2")].turn_count == 4 agg_sql = next(call.args[0] for call in prisma.db.query_raw.await_args_list if "real_spend" in call.args[0]) assert agg_sql.count("FILTER (WHERE real_cost IS NOT NULL AND NOT real_cache_hit)") == 2 assert response.results.by_tier[0].real_spend == 0.08 @@ -1561,13 +1858,73 @@ async def test_get_shadow_eval_job_pools_counts_and_slices_results_per_key(monke assert response.results.not_sampled_count is None assert response.results.unjudgeable_count is None assert response.results.shed_count is None - assert [(key.api_key_id, key.max_turns) for key in response.keys] == [("key-hash", 200), ("key-hash-2", 50)] + assert [(target.target_id, target.max_turns) for target in response.targets] == [ + ("key-hash", 200), + ("key-hash-2", 50), + ] totals_args = [call.args for call in prisma.db.query_raw.await_args_list if "judged_count" in call.args[0]] assert totals_args == [(totals_args[0][0], ["leg-1", "leg-2"])] error_where = prisma.db.litellm_shadowevalattempt.find_first.call_args.kwargs["where"] assert error_where == {"job_id": {"in": ["leg-1", "leg-2"]}, "outcome": "error"} +@pytest.mark.asyncio +async def test_get_shadow_eval_job_slices_results_per_router(monkeypatch: pytest.MonkeyPatch): + """A multi-router job's detail carries one slice per arm, aggregated by the arm + stamped on each attempt row, with unstamped legacy rows attributed to the job's own + router by the read (the COALESCE against the leg's router_name).""" + import litellm.proxy.proxy_server as proxy_server + + def agg(grp: str, wins: int) -> dict[str, object]: + return { + "grp": grp, + "turn_count": 4, + "real_wins": 4 - wins, + "shadow_wins": wins, + "ties": 0, + "avg_confidence": 0.8, + "real_spend": 0.08, + "shadow_spend": 0.02, + "cache_hit_turns": 0, + } + + prisma = _shadow_prisma( + legs=[_leg_record(router_names=("my-router", "alt-router"))], + agg_rows=[agg("SIMPLE", 3)], + by_router_rows=[agg("my-router", 1), agg("alt-router", 3)], + ) + monkeypatch.setattr(proxy_server, "prisma_client", prisma) + + response = await get_shadow_eval_job("job-1", VIEWER) + + assert response.router_names == ("my-router", "alt-router") + assert response.router_name == "my-router" + assert [(s.group, s.shadow_win_rate_pct) for s in response.results.by_router] == [ + ("my-router", 25.0), + ("alt-router", 75.0), + ] + router_sql = next( + call.args[0] for call in prisma.db.query_raw.await_args_list if "COALESCE(a.router_name" in call.args[0] + ) + assert "COALESCE(a.router_name, j.router_name)" in router_sql + assert 'JOIN "LiteLLM_ShadowEvalJob" j ON j.id = a.job_id' in router_sql + assert "a.job_id = ANY($1::text[])" in router_sql + + +@pytest.mark.asyncio +async def test_job_responses_resolve_router_names_with_legacy_fallback(monkeypatch: pytest.MonkeyPatch): + """Rows from before router_names existed carry their whole set in router_name.""" + import litellm.proxy.proxy_server as proxy_server + + prisma = _shadow_prisma(legs=[_leg_record(router_names=())]) + monkeypatch.setattr(proxy_server, "prisma_client", prisma) + + response = await get_shadow_eval_job("job-1", VIEWER) + + assert response.router_names == ("my-router",) + assert response.router_name == "my-router" + + @pytest.mark.asyncio async def test_get_shadow_eval_job_404s_and_gates_on_role(monkeypatch: pytest.MonkeyPatch): import litellm.proxy.proxy_server as proxy_server @@ -1595,7 +1952,7 @@ async def test_list_shadow_eval_jobs_collapses_legs_into_jobs_newest_first(monke _leg_record(created_at=datetime(2026, 8, 13, tzinfo=timezone.utc)), _leg_record( id="leg-2", - api_key_id="key-hash-2", + target_id="key-hash-2", stopped_at=stamp, created_at=datetime(2026, 8, 13, tzinfo=timezone.utc), ), @@ -1615,14 +1972,14 @@ async def test_list_shadow_eval_jobs_collapses_legs_into_jobs_newest_first(monke ) monkeypatch.setattr(proxy_server, "prisma_client", prisma) - jobs = await list_shadow_eval_jobs(VIEWER, api_key_id=None, limit=50) + jobs = await list_shadow_eval_jobs(VIEWER, target_type=None, target_id=None, limit=50) assert [(job.job_id, job.status) for job in jobs] == [ ("job-1", "running"), ("job-2", "stopped"), ("job-3", "completed"), ] - assert [key.api_key_id for key in jobs[0].keys] == ["key-hash", "key-hash-2"] + assert [target.target_id for target in jobs[0].targets] == ["key-hash", "key-hash-2"] assert all(job.judged_count is None and job.results is None for job in jobs) legs_sql, legs_limit = prisma.db.query_raw.await_args_list[0].args assert "GROUP BY group_id ORDER BY MAX(created_at) DESC LIMIT $1::int" in legs_sql @@ -1648,17 +2005,20 @@ async def test_list_shadow_eval_jobs_filters_to_jobs_containing_the_key(monkeypa prisma = _shadow_prisma( legs=[ _leg_record(), - _leg_record(id="leg-2", api_key_id="key-hash-2"), - _leg_record(id="leg-3", group_id="job-2", api_key_id="key-hash-2"), + _leg_record(id="leg-2", target_id="key-hash-2"), + _leg_record(id="leg-3", group_id="job-2", target_id="key-hash-2"), _leg_record(id="leg-4", group_id="job-3"), ] ) monkeypatch.setattr(proxy_server, "prisma_client", prisma) - jobs = await list_shadow_eval_jobs(VIEWER, api_key_id="key-hash-2", limit=50) + jobs = await list_shadow_eval_jobs(VIEWER, target_type="key", target_id="key-hash-2", limit=50) assert [job.job_id for job in jobs] == ["job-1", "job-2"] - assert [key.api_key_id for key in jobs[0].keys] == ["key-hash", "key-hash-2"] + assert [target.target_id for target in jobs[0].targets] == ["key-hash", "key-hash-2"] + legs_sql, *legs_params = prisma.db.query_raw.await_args_list[0].args + assert "WHERE target_type = $2 AND target_id = $3" in legs_sql + assert legs_params == [50, "key", "key-hash-2"] @pytest.mark.parametrize( @@ -1682,7 +2042,7 @@ async def test_job_status_runs_until_every_key_stops_and_completed_outranks_stop legs=[ _leg_record( id=f"leg-{index}", - api_key_id=f"key-{index}", + target_id=f"key-{index}", stopped_at=stamp if stopped else None, ends_at=datetime.now(timezone.utc) + timedelta(days=days_left), ) @@ -1691,7 +2051,7 @@ async def test_job_status_runs_until_every_key_stops_and_completed_outranks_stop ) monkeypatch.setattr(proxy_server, "prisma_client", prisma) - jobs = await list_shadow_eval_jobs(VIEWER, api_key_id=None, limit=50) + jobs = await list_shadow_eval_jobs(VIEWER, target_type=None, target_id=None, limit=50) assert [job.status for job in jobs] == [expected] @@ -1707,9 +2067,9 @@ async def test_list_reads_completed_once_every_key_spends_its_budget(monkeypatch prisma = _shadow_prisma( legs=[ _leg_record(max_turns=5), - _leg_record(id="leg-2", api_key_id="key-hash-2", max_turns=5), - _leg_record(id="leg-3", group_id="job-2", api_key_id="key-hash", max_turns=5), - _leg_record(id="leg-4", group_id="job-2", api_key_id="key-hash-2", max_turns=5), + _leg_record(id="leg-2", target_id="key-hash-2", max_turns=5), + _leg_record(id="leg-3", group_id="job-2", target_id="key-hash", max_turns=5), + _leg_record(id="leg-4", group_id="job-2", target_id="key-hash-2", max_turns=5), ] ) prisma.attempt_rows = [ @@ -1720,13 +2080,13 @@ async def test_list_reads_completed_once_every_key_spends_its_budget(monkeypatch ] monkeypatch.setattr(proxy_server, "prisma_client", prisma) - jobs = await list_shadow_eval_jobs(VIEWER, api_key_id=None, limit=50) + jobs = await list_shadow_eval_jobs(VIEWER, target_type=None, target_id=None, limit=50) by_id = {job.job_id: job for job in jobs} assert by_id["job-1"].status == "completed" - assert all(key.stopped_at is None for key in by_id["job-1"].keys) + assert all(target.stopped_at is None for target in by_id["job-1"].targets) assert by_id["job-2"].status == "running" - assert {key.api_key_id: key.attempt_count for key in by_id["job-2"].keys} == {"key-hash": 5, "key-hash-2": 3} + assert {t.target_id: t.attempt_count for t in by_id["job-2"].targets} == {"key-hash": 5, "key-hash-2": 3} @pytest.mark.asyncio @@ -1740,7 +2100,7 @@ async def test_recorded_operator_stop_outranks_budget_arithmetic(monkeypatch: py prisma.attempt_rows = [{"job_id": "leg-1", "attempt_count": 6, "spend": 0.0}] monkeypatch.setattr(proxy_server, "prisma_client", prisma) - jobs = await list_shadow_eval_jobs(VIEWER, api_key_id=None, limit=50) + jobs = await list_shadow_eval_jobs(VIEWER, target_type=None, target_id=None, limit=50) assert jobs[0].status == "stopped" assert jobs[0].stopped_by == "admin" @@ -1760,7 +2120,7 @@ async def test_backfilled_legacy_stop_never_reads_as_completion(monkeypatch: pyt prisma.attempt_rows = [{"job_id": "leg-1", "attempt_count": 6, "spend": 0.0}] monkeypatch.setattr(proxy_server, "prisma_client", prisma) - jobs = await list_shadow_eval_jobs(VIEWER, api_key_id=None, limit=50) + jobs = await list_shadow_eval_jobs(VIEWER, target_type=None, target_id=None, limit=50) assert jobs[0].status == "stopped" @@ -1808,6 +2168,56 @@ def test_max_budget_migration_is_additive_and_leaves_legacy_rows_null(): @pytest.mark.asyncio +@pytest.mark.asyncio +async def test_verdicts_keep_same_id_targets_of_different_kinds_distinct(monkeypatch): + """A team and a user can legitimately share an id; their slices must not merge.""" + from litellm.proxy import proxy_server + + leg_rows = [ + { + "grp": "leg-1", + "turn_count": 6, + "real_wins": 2, + "shadow_wins": 4, + "ties": 0, + "avg_confidence": 0.8, + "real_spend": 0.02, + "shadow_spend": 0.01, + "cache_hit_turns": 0, + }, + { + "grp": "leg-2", + "turn_count": 4, + "real_wins": 3, + "shadow_wins": 0, + "ties": 1, + "avg_confidence": 0.6, + "real_spend": 0.05, + "shadow_spend": 0.04, + "cache_hit_turns": 1, + }, + ] + prisma = _shadow_prisma( + legs=[ + _leg_record(target_type="team", target_id="dev-alice"), + _leg_record(id="leg-2", target_type="user", target_id="dev-alice"), + ], + agg_rows=leg_rows[:1], + by_leg_rows=leg_rows, + known_teams={"dev-alice": "alias"}, + known_users={"dev-alice": "alice@example.com"}, + ) + monkeypatch.setattr(proxy_server, "prisma_client", prisma) + + response = await get_shadow_eval_job("job-1", VIEWER) + + verdicts_by_target = {(t.target_type, t.target_id): t.verdicts for t in response.targets} + assert verdicts_by_target[("team", "dev-alice")].turn_count == 6 + assert verdicts_by_target[("team", "dev-alice")].shadow_win_rate_pct == 66.7 + assert verdicts_by_target[("user", "dev-alice")].turn_count == 4 + assert verdicts_by_target[("user", "dev-alice")].shadow_win_rate_pct == 0.0 + + async def test_stop_rejects_a_job_that_already_spent_its_budget(monkeypatch: pytest.MonkeyPatch): import litellm.proxy.proxy_server as proxy_server @@ -1832,9 +2242,9 @@ async def test_list_reads_completed_once_every_key_spends_its_dollar_budget(monk prisma = _shadow_prisma( legs=[ _leg_record(max_turns=SHADOW_EVAL_TURN_VALVE, max_budget=1.0), - _leg_record(id="leg-2", api_key_id="key-hash-2", max_turns=SHADOW_EVAL_TURN_VALVE, max_budget=1.0), + _leg_record(id="leg-2", target_id="key-hash-2", max_turns=SHADOW_EVAL_TURN_VALVE, max_budget=1.0), _leg_record( - id="leg-3", group_id="job-2", api_key_id="key-hash", max_turns=SHADOW_EVAL_TURN_VALVE, max_budget=1.0 + id="leg-3", group_id="job-2", target_id="key-hash", max_turns=SHADOW_EVAL_TURN_VALVE, max_budget=1.0 ), ] ) @@ -1845,13 +2255,13 @@ async def test_list_reads_completed_once_every_key_spends_its_dollar_budget(monk ] monkeypatch.setattr(proxy_server, "prisma_client", prisma) - jobs = await list_shadow_eval_jobs(VIEWER, api_key_id=None, limit=50) + jobs = await list_shadow_eval_jobs(VIEWER, target_type=None, target_id=None, limit=50) by_id = {job.job_id: job for job in jobs} assert by_id["job-1"].status == "completed" assert by_id["job-2"].status == "running" - assert {key.api_key_id: key.spend for key in by_id["job-1"].keys} == {"key-hash": 1.0, "key-hash-2": 1.25} - assert all(key.max_budget == 1.0 for key in by_id["job-1"].keys) + assert {t.target_id: t.spend for t in by_id["job-1"].targets} == {"key-hash": 1.0, "key-hash-2": 1.25} + assert all(target.max_budget == 1.0 for target in by_id["job-1"].targets) @pytest.mark.asyncio @@ -1880,11 +2290,11 @@ async def test_legacy_jobs_without_a_dollar_budget_stay_turn_gated(monkeypatch: prisma.attempt_rows = [{"job_id": "leg-1", "attempt_count": 40, "spend": 250.0}] monkeypatch.setattr(proxy_server, "prisma_client", prisma) - jobs = await list_shadow_eval_jobs(VIEWER, api_key_id=None, limit=50) + jobs = await list_shadow_eval_jobs(VIEWER, target_type=None, target_id=None, limit=50) assert jobs[0].status == "running" - assert jobs[0].keys[0].max_budget is None - assert jobs[0].keys[0].spend == 250.0 + assert jobs[0].targets[0].max_budget is None + assert jobs[0].targets[0].spend == 250.0 @pytest.mark.asyncio @@ -1892,14 +2302,14 @@ async def test_shadow_eval_responses_name_every_shadowed_key(monkeypatch: pytest import litellm.proxy.proxy_server as proxy_server prisma = _shadow_prisma( - legs=[_leg_record(), _leg_record(id="leg-2", api_key_id="deleted-key-hash")], + legs=[_leg_record(), _leg_record(id="leg-2", target_id="deleted-key-hash")], known_keys=("key-hash", "key-hash-2"), ) monkeypatch.setattr(proxy_server, "llm_router", _shadow_router()) monkeypatch.setattr(proxy_server, "prisma_client", prisma) - jobs = await list_shadow_eval_jobs(VIEWER, api_key_id=None, limit=50) - assert [(key.key_alias, key.key_name) for key in jobs[0].keys] == [ + jobs = await list_shadow_eval_jobs(VIEWER, target_type=None, target_id=None, limit=50) + assert [(target.target_alias, target.key_name) for target in jobs[0].targets] == [ (None, None), ("prod-alpha", "sk-...lpha"), ] @@ -1907,7 +2317,7 @@ async def test_shadow_eval_responses_name_every_shadowed_key(monkeypatch: pytest assert batched_where == {"token": {"in": ["deleted-key-hash", "key-hash"]}} detail = await get_shadow_eval_job("job-1", VIEWER) - assert [key.key_alias for key in detail.keys] == [None, "prod-alpha"] + assert [target.target_alias for target in detail.targets] == [None, "prod-alpha"] @pytest.mark.asyncio @@ -1919,7 +2329,7 @@ async def test_stop_shadow_eval_stops_every_unstopped_leg_and_rejects_non_runnin import litellm.proxy.proxy_server as proxy_server earned = datetime.now(timezone.utc) - timedelta(hours=1) - prisma = _shadow_prisma(legs=[_leg_record(), _leg_record(id="leg-2", api_key_id="key-hash-2", stopped_at=earned)]) + prisma = _shadow_prisma(legs=[_leg_record(), _leg_record(id="leg-2", target_id="key-hash-2", stopped_at=earned)]) monkeypatch.setattr(proxy_server, "prisma_client", prisma) stopped = await stop_shadow_eval_job("job-1", ADMIN) @@ -1938,9 +2348,9 @@ async def test_stop_shadow_eval_stops_every_unstopped_leg_and_rejects_non_runnin assert datetime.fromisoformat(stop_stamp).tzinfo is None assert prisma.db.execute_raw.await_count == 1 prisma.db.litellm_shadowevaljob.update_many.assert_not_called() - by_key = {key.api_key_id: key.stopped_at for key in stopped.keys} - assert by_key["key-hash-2"] == earned - assert by_key["key-hash"] is not None and by_key["key-hash"] != earned + by_target = {target.target_id: target.stopped_at for target in stopped.targets} + assert by_target["key-hash-2"] == earned + assert by_target["key-hash"] is not None and by_target["key-hash"] != earned done_leg = _leg_record(ends_at=datetime.now(timezone.utc) - timedelta(days=1)) prisma_done = _shadow_prisma(legs=[done_leg]) @@ -2331,7 +2741,7 @@ async def test_get_shadow_eval_job_sums_funnel_rows_across_legs(monkeypatch: pyt }, ] prisma = _shadow_prisma( - legs=[_leg_record(), _leg_record(id="leg-2", api_key_id="key-hash-2")], + legs=[_leg_record(), _leg_record(id="leg-2", target_id="key-hash-2")], agg_rows=tier_rows, ) prisma.funnel_rows = [{"legs_with_rows": 2, "not_sampled": 30, "unjudgeable": 5, "shed": 2, "withheld": 3}] @@ -2366,7 +2776,7 @@ async def test_partially_seeded_funnel_reads_as_unknown_coverage(monkeypatch: py }, ] prisma = _shadow_prisma( - legs=[_leg_record(), _leg_record(id="leg-2", api_key_id="key-hash-2")], + legs=[_leg_record(), _leg_record(id="leg-2", target_id="key-hash-2")], agg_rows=tier_rows, ) prisma.funnel_rows = [{"legs_with_rows": 1, "not_sampled": 30, "unjudgeable": 5, "shed": 2, "withheld": 0}] diff --git a/tests/test_litellm/proxy/management_endpoints/test_customer_endpoints.py b/tests/test_litellm/proxy/management_endpoints/test_customer_endpoints.py index 5c163c44cb3..1225cb80224 100644 --- a/tests/test_litellm/proxy/management_endpoints/test_customer_endpoints.py +++ b/tests/test_litellm/proxy/management_endpoints/test_customer_endpoints.py @@ -83,6 +83,50 @@ def test_update_customer_success(mock_prisma_client, mock_user_api_key_auth): assert response.json()["alias"] == "Updated Test User" +def test_update_customer_unblock(mock_prisma_client, mock_user_api_key_auth): + mock_end_user = LiteLLM_EndUserTable(user_id="test-user-1", blocked=True) + updated_mock_end_user = LiteLLM_EndUserTable(user_id="test-user-1", blocked=False) + + mock_prisma_client.db.litellm_endusertable.find_first = AsyncMock(return_value=mock_end_user) + mock_prisma_client.db.litellm_endusertable.update = AsyncMock(return_value=updated_mock_end_user) + + response = client.post( + "/customer/update", + json={"user_id": "test-user-1", "blocked": False}, + headers={"Authorization": "Bearer test-key"}, + ) + + assert response.status_code == 200 + assert response.json()["blocked"] is False + update_mock = mock_prisma_client.db.litellm_endusertable.update + update_mock.assert_called_once() + assert update_mock.call_args.kwargs["data"]["blocked"] is False + + +def test_update_customer_keeps_blocked_when_omitted(mock_prisma_client, mock_user_api_key_auth): + """ + Regression test: updating a blocked customer without supplying `blocked` + must NOT reset it to unblocked. `blocked=False` is the model default and + should only be applied when explicitly provided by the caller. + """ + mock_end_user = LiteLLM_EndUserTable(user_id="test-user-1", blocked=True) + updated_mock_end_user = LiteLLM_EndUserTable(user_id="test-user-1", blocked=True) + + mock_prisma_client.db.litellm_endusertable.find_first = AsyncMock(return_value=mock_end_user) + mock_prisma_client.db.litellm_endusertable.update = AsyncMock(return_value=updated_mock_end_user) + + response = client.post( + "/customer/update", + json={"user_id": "test-user-1", "alias": "Updated Test User"}, + headers={"Authorization": "Bearer test-key"}, + ) + + assert response.status_code == 200 + update_mock = mock_prisma_client.db.litellm_endusertable.update + update_mock.assert_called_once() + assert "blocked" not in update_mock.call_args.kwargs["data"] + + def test_update_customer_not_found(mock_prisma_client, mock_user_api_key_auth): """ Test that update_end_user raises a 404 ProxyException when user_id does not exist. diff --git a/tests/test_litellm/proxy/management_endpoints/test_key_management_endpoints.py b/tests/test_litellm/proxy/management_endpoints/test_key_management_endpoints.py index 42e56ceabd3..a81c6b4c656 100644 --- a/tests/test_litellm/proxy/management_endpoints/test_key_management_endpoints.py +++ b/tests/test_litellm/proxy/management_endpoints/test_key_management_endpoints.py @@ -8312,15 +8312,17 @@ async def test_key_does_not_override_explicit_budget_duration(): @patch( "litellm.proxy.management_endpoints.key_management_endpoints.rotate_mcp_server_credentials_master_key" ) -async def test_rotate_master_key_model_data_valid_for_prisma( +async def test_rotate_master_key_reencrypts_model_params_in_place( mock_rotate_mcp, ): """ - Test that _rotate_master_key produces valid data for Prisma create_many(). - - Regression test for: master key rotation fails with Prisma validation error - because created_at/updated_at are None (non-nullable DateTime) and - litellm_params/model_info are JSON strings (create_many expects dicts). + Regression test for: master key rotation wipes every non-credential column + on LiteLLM_ProxyModelTable. Rotation used to rebuild the table via + delete_many + create_many from Deployment objects, which carry no + blocked/created_at/created_by/updated_at/updated_by, so every rotation + reset blocked to False (silently unblocking blocked models) and rewrote the + audit columns. Rotation must instead update only litellm_params (the sole + encrypted column) on each existing row, keyed by model_id. """ from unittest.mock import AsyncMock, MagicMock @@ -8352,6 +8354,7 @@ async def test_rotate_master_key_model_data_valid_for_prisma( mock_tx.litellm_proxymodeltable = MagicMock() mock_tx.litellm_proxymodeltable.delete_many = AsyncMock() mock_tx.litellm_proxymodeltable.create_many = AsyncMock() + mock_tx.litellm_proxymodeltable.update_many = AsyncMock() mock_prisma_client.db.tx = MagicMock( return_value=AsyncMock( __aenter__=AsyncMock(return_value=mock_tx), @@ -8400,36 +8403,33 @@ async def test_rotate_master_key_model_data_valid_for_prisma( new_master_key="sk-new-master-key", ) - # Verify create_many was called - mock_tx.litellm_proxymodeltable.create_many.assert_called_once() + # Rotation must never rewrite whole rows: no delete + recreate + mock_tx.litellm_proxymodeltable.delete_many.assert_not_called() + mock_tx.litellm_proxymodeltable.create_many.assert_not_called() - # Get the data passed to create_many - call_args = mock_tx.litellm_proxymodeltable.create_many.call_args - created_models = call_args.kwargs.get("data") or call_args[1].get("data") + mock_tx.litellm_proxymodeltable.update_many.assert_called_once() + call_args = mock_tx.litellm_proxymodeltable.update_many.call_args - assert len(created_models) == 1 - model_data = created_models[0] + assert call_args.kwargs["where"] == { + "model_id": "model-1" + }, "the re-encrypted params must land on the same row, keyed by model_id" - # Verify timestamps are NOT present (Prisma @default(now()) should apply) - assert ( - "created_at" not in model_data - ), "created_at should be excluded so Prisma @default(now()) applies" - assert ( - "updated_at" not in model_data - ), "updated_at should be excluded so Prisma @default(now()) applies" + update_data = call_args.kwargs["data"] + assert set(update_data.keys()) == {"litellm_params"}, ( + "rotation must touch only the encrypted litellm_params column; writing any " + f"other column wipes it (blocked, audit columns), got {sorted(update_data.keys())}" + ) - # Verify litellm_params and model_info are prisma.Json wrappers, NOT JSON strings import prisma assert isinstance( - model_data["litellm_params"], prisma.Json - ), f"litellm_params should be prisma.Json for create_many(), got {type(model_data['litellm_params'])}" - assert isinstance( - model_data["model_info"], prisma.Json - ), f"model_info should be prisma.Json for create_many(), got {type(model_data['model_info'])}" - - # Verify delete_many was called inside the transaction (before create_many) - mock_tx.litellm_proxymodeltable.delete_many.assert_called_once() + update_data["litellm_params"], prisma.Json + ), f"litellm_params should be prisma.Json for update_many(), got {type(update_data['litellm_params'])}" + reencrypted_params = update_data["litellm_params"].data + assert set(reencrypted_params.keys()) >= {"model", "api_key"} + assert ( + reencrypted_params["api_key"] != "sk-decrypted-key" + ), "api_key must be stored re-encrypted under the new master key, not in plaintext" async def test_default_key_generate_params_duration(monkeypatch): @@ -11033,6 +11033,123 @@ class TestLIT1884KeyUpdateValidation: ) +class TestLIT4891SafePresetKeyTypeTransition: + def _make_existing_key(self, allowed_routes): + row = MagicMock() + row.user_id = "internal-user-123" + row.created_by = "internal-user-123" + row.token = "hashed_token" + row.team_id = None + row.max_budget = None + row.spend = 0.0 + row.organization_id = None + row.project_id = None + row.allowed_routes = allowed_routes + return row + + def _make_auth(self): + return UserAPIKeyAuth( + user_id="internal-user-123", + user_role=LitellmUserRoles.INTERNAL_USER, + ) + + async def _run_update(self, data, existing_key_row): + try: + await _validate_update_key_data( + data=data, + existing_key_row=existing_key_row, + user_api_key_dict=self._make_auth(), + llm_router=None, + premium_user=False, + prisma_client=AsyncMock(), + user_api_key_cache=MagicMock(), + ) + except HTTPException as exc: + return exc + return None + + def _assert_routes_403(self, exc): + assert exc is not None + assert exc.status_code == 403 + assert "Only proxy admins can set" in str(exc.detail) + + @pytest.mark.asyncio + async def test_non_admin_owner_can_clear_safe_preset_to_full_access(self): + assert ( + await self._run_update( + data=UpdateKeyRequest(key="sk-test", allowed_routes=[]), + existing_key_row=self._make_existing_key(allowed_routes=["llm_api_routes"]), + ) + is None + ) + + @pytest.mark.asyncio + async def test_non_admin_owner_can_switch_full_access_to_safe_preset(self): + assert ( + await self._run_update( + data=UpdateKeyRequest(key="sk-test", allowed_routes=["llm_api_routes"]), + existing_key_row=self._make_existing_key(allowed_routes=[]), + ) + is None + ) + + @pytest.mark.asyncio + async def test_non_admin_owner_can_narrow_to_read_only_preset(self): + assert ( + await self._run_update( + data=UpdateKeyRequest(key="sk-test", allowed_routes=["info_routes"]), + existing_key_row=self._make_existing_key(allowed_routes=["llm_api_routes"]), + ) + is None + ) + + @pytest.mark.asyncio + async def test_non_admin_can_resend_read_only_preset_unchanged(self): + assert ( + await self._run_update( + data=UpdateKeyRequest(key="sk-test", allowed_routes=["info_routes"]), + existing_key_row=self._make_existing_key(allowed_routes=["info_routes"]), + ) + is None + ) + + @pytest.mark.asyncio + async def test_non_admin_cannot_widen_read_only_key_to_full_access(self): + self._assert_routes_403( + await self._run_update( + data=UpdateKeyRequest(key="sk-test", allowed_routes=[]), + existing_key_row=self._make_existing_key(allowed_routes=["info_routes"]), + ) + ) + + @pytest.mark.asyncio + async def test_non_admin_cannot_widen_read_only_key_to_llm_api(self): + self._assert_routes_403( + await self._run_update( + data=UpdateKeyRequest(key="sk-test", allowed_routes=["llm_api_routes"]), + existing_key_row=self._make_existing_key(allowed_routes=["info_routes"]), + ) + ) + + @pytest.mark.asyncio + async def test_non_admin_cannot_clear_custom_route_restriction(self): + self._assert_routes_403( + await self._run_update( + data=UpdateKeyRequest(key="sk-test", allowed_routes=[]), + existing_key_row=self._make_existing_key(allowed_routes=["/chat/completions"]), + ) + ) + + @pytest.mark.asyncio + async def test_non_admin_cannot_set_non_preset_routes(self): + self._assert_routes_403( + await self._run_update( + data=UpdateKeyRequest(key="sk-test", allowed_routes=["management_routes"]), + existing_key_row=self._make_existing_key(allowed_routes=["llm_api_routes"]), + ) + ) + + class TestKeyOwnerPrivilegeEscalation: """ Policy: @@ -12007,9 +12124,10 @@ class TestAllowedRoutesCallerPermission: @pytest.mark.asyncio async def test_non_admin_update_key_explicit_empty_allowed_routes_rejected(self): - """`update_key_fn` rejects a non-admin when `allowed_routes` is - present as `[]` in the request body. The value matches the model - default but `model_fields_set` distinguishes the two.""" + """`update_key_fn` rejects a non-admin clearing a custom (non-preset) + route restriction with an explicit `[]` in the request body. The value + matches the model default but `model_fields_set` distinguishes the + two. Clearing from a safe preset is allowed (LIT-4891).""" from litellm.proxy.management_endpoints.key_management_endpoints import ( update_key_fn, ) @@ -12032,7 +12150,7 @@ class TestAllowedRoutesCallerPermission: patch( "litellm.proxy.management_endpoints.key_management_endpoints._get_and_validate_existing_key", new_callable=AsyncMock, - return_value=MagicMock(), + return_value=MagicMock(allowed_routes=["/chat/completions"]), ), ): with pytest.raises(ProxyException) as exc_info: @@ -12047,8 +12165,8 @@ class TestAllowedRoutesCallerPermission: @pytest.mark.asyncio async def test_non_admin_update_key_explicit_null_allowed_routes_rejected(self): - """`update_key_fn` rejects a non-admin when `allowed_routes` is - present as `null` in the request body.""" + """`update_key_fn` rejects a non-admin clearing a custom (non-preset) + route restriction with an explicit `null` in the request body.""" from litellm.proxy.management_endpoints.key_management_endpoints import ( update_key_fn, ) @@ -12071,7 +12189,7 @@ class TestAllowedRoutesCallerPermission: patch( "litellm.proxy.management_endpoints.key_management_endpoints._get_and_validate_existing_key", new_callable=AsyncMock, - return_value=MagicMock(), + return_value=MagicMock(allowed_routes=["/chat/completions"]), ), ): with pytest.raises(ProxyException) as exc_info: @@ -14142,6 +14260,311 @@ async def test_info_key_fn_v2_budget_table_fallback(monkeypatch): mock_prisma_client.db.query_raw.assert_not_awaited() +@pytest.mark.asyncio +async def test_info_key_fn_reports_budget_limits_usage(monkeypatch): + """ + /key/info reports current-window spend per budget window under budget_limits_usage, + keyed by budget_duration and read from the same counter enforcement uses, while + budget_limits itself comes back exactly as stored. + """ + from unittest.mock import AsyncMock, MagicMock + + from litellm.proxy._types import LiteLLM_VerificationToken + from litellm.proxy.management_endpoints.key_management_endpoints import info_key_fn + + test_key_token = "hashed_token_window_test" + budget_limits = [ + { + "reset_at": "2026-08-15T18:00:00+00:00", + "max_budget": 2.0, + "budget_duration": "1h", + } + ] + + mock_prisma_client = AsyncMock() + monkeypatch.setattr("litellm.proxy.proxy_server.prisma_client", mock_prisma_client) + mock_user_api_key_cache = AsyncMock() + monkeypatch.setattr( + "litellm.proxy.proxy_server.user_api_key_cache", mock_user_api_key_cache + ) + mock_get_current_spend = AsyncMock(return_value=0.73) + monkeypatch.setattr( + "litellm.proxy.proxy_server.get_current_spend", mock_get_current_spend + ) + + mock_key_info = MagicMock(spec=LiteLLM_VerificationToken) + mock_key_info.token = test_key_token + mock_key_info.object_permission_id = None + mock_key_info.user_id = "user-w" + mock_key_info.team_id = None + mock_key_info.litellm_budget_table = None + mock_key_info.model_dump.return_value = { + "token": test_key_token, + "budget_limits": [dict(w) for w in budget_limits], + "user_id": "user-w", + "team_id": None, + "object_permission_id": None, + "litellm_budget_table": None, + } + mock_key_info.dict.return_value = mock_key_info.model_dump.return_value + + mock_prisma_client.db.litellm_verificationtoken.find_unique = AsyncMock( + return_value=mock_key_info + ) + + user_api_key_dict = UserAPIKeyAuth( + user_role=LitellmUserRoles.PROXY_ADMIN, + api_key="sk-test-window-key", + ) + + result = await info_key_fn( + key="sk-test-window-key", + user_api_key_dict=user_api_key_dict, + ) + + assert result["info"]["budget_limits"] == budget_limits + assert result["info"]["budget_limits_usage"] == {"1h": {"current_spend": 0.73}} + + mock_get_current_spend.assert_awaited_once() + call_kwargs = mock_get_current_spend.await_args.kwargs + assert call_kwargs["counter_key"] == f"spend:key:{test_key_token}:window:1h" + assert call_kwargs["max_budget"] == 2.0 + assert call_kwargs["window_entity_type"] == "Key" + assert call_kwargs["window_entity_id"] == test_key_token + assert call_kwargs["window_duration"] == "1h" + assert call_kwargs["window_start"] is not None + + +@pytest.mark.asyncio +async def test_info_key_fn_no_budget_limits_skips_spend_lookup(monkeypatch): + """Keys without budget windows get no budget_limits_usage field and trigger no spend lookup.""" + from unittest.mock import AsyncMock, MagicMock + + from litellm.proxy._types import LiteLLM_VerificationToken + from litellm.proxy.management_endpoints.key_management_endpoints import info_key_fn + + test_key_token = "hashed_token_no_windows" + + mock_prisma_client = AsyncMock() + monkeypatch.setattr("litellm.proxy.proxy_server.prisma_client", mock_prisma_client) + mock_user_api_key_cache = AsyncMock() + monkeypatch.setattr( + "litellm.proxy.proxy_server.user_api_key_cache", mock_user_api_key_cache + ) + mock_get_current_spend = AsyncMock(return_value=0.0) + monkeypatch.setattr( + "litellm.proxy.proxy_server.get_current_spend", mock_get_current_spend + ) + + mock_key_info = MagicMock(spec=LiteLLM_VerificationToken) + mock_key_info.token = test_key_token + mock_key_info.object_permission_id = None + mock_key_info.user_id = "user-nw" + mock_key_info.team_id = None + mock_key_info.litellm_budget_table = None + mock_key_info.model_dump.return_value = { + "token": test_key_token, + "budget_limits": None, + "user_id": "user-nw", + "team_id": None, + "object_permission_id": None, + "litellm_budget_table": None, + } + mock_key_info.dict.return_value = mock_key_info.model_dump.return_value + + mock_prisma_client.db.litellm_verificationtoken.find_unique = AsyncMock( + return_value=mock_key_info + ) + + user_api_key_dict = UserAPIKeyAuth( + user_role=LitellmUserRoles.PROXY_ADMIN, + api_key="sk-test-no-window-key", + ) + + result = await info_key_fn( + key="sk-test-no-window-key", + user_api_key_dict=user_api_key_dict, + ) + + assert result["info"]["budget_limits"] is None + assert "budget_limits_usage" not in result["info"] + mock_get_current_spend.assert_not_awaited() + + +@pytest.mark.asyncio +async def test_info_key_fn_v2_reports_budget_limits_usage(monkeypatch): + """/v2/key/info reports budget_limits_usage per window and leaves budget_limits as stored.""" + from unittest.mock import AsyncMock, MagicMock + + from litellm.proxy._types import KeyRequest, LiteLLM_VerificationToken + from litellm.proxy.management_endpoints.key_management_endpoints import ( + info_key_fn_v2, + ) + + test_key_token = "hashed_token_v2_window_test" + budget_limits = [ + { + "reset_at": "2026-08-15T18:00:00+00:00", + "max_budget": 2.0, + "budget_duration": "1h", + }, + { + "reset_at": "2026-08-16T00:00:00+00:00", + "max_budget": 20.0, + "budget_duration": "1d", + }, + ] + + mock_prisma_client = AsyncMock() + monkeypatch.setattr("litellm.proxy.proxy_server.prisma_client", mock_prisma_client) + mock_user_api_key_cache = AsyncMock() + monkeypatch.setattr( + "litellm.proxy.proxy_server.user_api_key_cache", mock_user_api_key_cache + ) + mock_get_current_spend = AsyncMock(return_value=1.25) + monkeypatch.setattr( + "litellm.proxy.proxy_server.get_current_spend", mock_get_current_spend + ) + + mock_key = MagicMock(spec=LiteLLM_VerificationToken) + mock_key.token = test_key_token + mock_key.user_id = "user-v2-w" + mock_key.team_id = None + mock_key.model_dump.return_value = { + "token": test_key_token, + "budget_limits": [dict(w) for w in budget_limits], + "user_id": "user-v2-w", + "team_id": None, + "litellm_budget_table": None, + } + mock_key.dict.return_value = mock_key.model_dump.return_value + + mock_prisma_client.get_data = AsyncMock(return_value=[mock_key]) + + user_api_key_dict = UserAPIKeyAuth( + user_role=LitellmUserRoles.PROXY_ADMIN, + api_key="sk-admin-v2-w", + ) + + result = await info_key_fn_v2( + data=KeyRequest(keys=[test_key_token]), + user_api_key_dict=user_api_key_dict, + ) + + assert len(result["info"]) == 1 + assert result["info"][0]["budget_limits"] == budget_limits + assert result["info"][0]["budget_limits_usage"] == { + "1h": {"current_spend": 1.25}, + "1d": {"current_spend": 1.25}, + } + assert mock_get_current_spend.await_count == 2 + counter_keys = { + call.kwargs["counter_key"] for call in mock_get_current_spend.await_args_list + } + assert counter_keys == { + f"spend:key:{test_key_token}:window:1h", + f"spend:key:{test_key_token}:window:1d", + } + assert { + call.kwargs["window_duration"] for call in mock_get_current_spend.await_args_list + } == {"1h", "1d"} + + +@pytest.mark.asyncio +async def test_build_budget_limits_usage_json_string_input(monkeypatch): + """budget_limits stored as a JSON string is parsed and reported per window.""" + import json as json_module + from unittest.mock import AsyncMock + + from litellm.proxy.management_endpoints.key_management_endpoints import ( + _build_budget_limits_usage, + ) + + mock_get_current_spend = AsyncMock(return_value=0.5) + monkeypatch.setattr( + "litellm.proxy.proxy_server.get_current_spend", mock_get_current_spend + ) + + raw = json_module.dumps( + [{"budget_duration": "1h", "max_budget": 2.0, "reset_at": None}] + ) + result = await _build_budget_limits_usage(budget_limits=raw, api_key_hash="hash-1") + + assert result == {"1h": {"current_spend": 0.5}} + mock_get_current_spend.assert_awaited_once() + + +@pytest.mark.asyncio +async def test_build_budget_limits_usage_empty_windows_returns_none(monkeypatch): + """A key with no windows (None, [], or "[]") returns None so the field is left off; no spend lookup runs.""" + from unittest.mock import AsyncMock + + from litellm.proxy.management_endpoints.key_management_endpoints import ( + _build_budget_limits_usage, + ) + + mock_get_current_spend = AsyncMock(return_value=0.0) + monkeypatch.setattr( + "litellm.proxy.proxy_server.get_current_spend", mock_get_current_spend + ) + + for stored in (None, [], "[]"): + assert await _build_budget_limits_usage(budget_limits=stored, api_key_hash="hash-1") is None + mock_get_current_spend.assert_not_awaited() + + +@pytest.mark.asyncio +async def test_build_budget_limits_usage_window_without_max_budget(monkeypatch): + """A window with only budget_duration still reports current_spend, read without a budget ceiling.""" + from unittest.mock import AsyncMock + + from litellm.proxy.management_endpoints.key_management_endpoints import ( + _build_budget_limits_usage, + ) + + mock_get_current_spend = AsyncMock(return_value=0.75) + monkeypatch.setattr( + "litellm.proxy.proxy_server.get_current_spend", mock_get_current_spend + ) + + result = await _build_budget_limits_usage( + budget_limits=[{"budget_duration": "2d"}], api_key_hash="hash-no-max" + ) + + assert result == {"2d": {"current_spend": 0.75}} + call_kwargs = mock_get_current_spend.await_args.kwargs + assert call_kwargs["counter_key"] == "spend:key:hash-no-max:window:2d" + assert call_kwargs["window_duration"] == "2d" + assert call_kwargs["max_budget"] is None + + +@pytest.mark.asyncio +async def test_build_budget_limits_usage_pydantic_windows(monkeypatch): + """BudgetLimitEntry windows (the shape UserAPIKeyAuth carries) are dumped to dicts and reported.""" + from unittest.mock import AsyncMock + + from litellm.models.team import BudgetLimitEntry + from litellm.proxy.management_endpoints.key_management_endpoints import ( + _build_budget_limits_usage, + ) + + mock_get_current_spend = AsyncMock(return_value=1.0) + monkeypatch.setattr( + "litellm.proxy.proxy_server.get_current_spend", mock_get_current_spend + ) + + result = await _build_budget_limits_usage( + budget_limits=[BudgetLimitEntry(budget_duration="7d", max_budget=10.0)], + api_key_hash="hash-2", + ) + + assert result == {"7d": {"current_spend": 1.0}} + call_kwargs = mock_get_current_spend.await_args.kwargs + assert call_kwargs["counter_key"] == "spend:key:hash-2:window:7d" + assert call_kwargs["window_duration"] == "7d" + assert call_kwargs["max_budget"] == 10.0 + + @pytest.mark.asyncio async def test_info_key_fn_reads_the_configured_budget_model_key(monkeypatch): """/key/info reads the one counter enforcement reads: the configured budget model. diff --git a/tests/test_litellm/proxy/management_endpoints/test_model_management_endpoints.py b/tests/test_litellm/proxy/management_endpoints/test_model_management_endpoints.py index dc9fede1f65..393953ccf68 100644 --- a/tests/test_litellm/proxy/management_endpoints/test_model_management_endpoints.py +++ b/tests/test_litellm/proxy/management_endpoints/test_model_management_endpoints.py @@ -4466,3 +4466,65 @@ class TestEnforceRpmTpmOnModelAdd: _raise_if_rate_limits_required_but_missing(litellm_params=params, enforced=True) assert expected_missing in str(exc_info.value.message) assert exc_info.value.code == "400" + + +class TestBlockModelResponseSerialization: + @pytest.mark.parametrize( + ("route", "blocked"), [("/model/block", True), ("/model/unblock", False)] + ) + def test_block_routes_serialize_prisma_row_to_200(self, route, blocked): + from datetime import datetime, timezone + + from prisma import models as prisma_models + + import litellm.proxy.proxy_server as ps + from litellm.proxy.proxy_server import app + + written_at = datetime(2026, 8, 29, tzinfo=timezone.utc) + row_fields = { + "model_id": "m-block-1", + "model_name": "gpt-4o-mini", + "litellm_params": json.dumps({"model": "openai/gpt-4o-mini", "api_key": "encrypted-value"}), + "model_info": json.dumps({"id": "m-block-1"}), + "created_at": written_at, + "created_by": "admin", + "updated_at": written_at, + "updated_by": "admin", + } + existing_row = prisma_models.LiteLLM_ProxyModelTable(blocked=not blocked, **row_fields) + updated_row = prisma_models.LiteLLM_ProxyModelTable(blocked=blocked, **row_fields) + + mock_prisma = MagicMock() + mock_prisma.db.litellm_proxymodeltable.find_unique = AsyncMock(return_value=existing_row) + mock_prisma.db.litellm_proxymodeltable.update = AsyncMock(return_value=updated_row) + + admin = UserAPIKeyAuth(user_id="admin", user_role=LitellmUserRoles.PROXY_ADMIN) + app.dependency_overrides[ps.user_api_key_auth] = lambda: admin + try: + with ( + patch("litellm.proxy.proxy_server.prisma_client", mock_prisma), # test-quality-ok: proxy_server module global is the endpoint's only injection point + patch("litellm.proxy.proxy_server.store_model_in_db", True), # test-quality-ok: proxy_server module global is the endpoint's only injection point + patch( # test-quality-ok: proxy_server module global is the endpoint's only injection point + "litellm.proxy.proxy_server.llm_router", + MagicMock(**{"get_model_ids.return_value": ["m-block-1"]}), + ), + patch("litellm.proxy.proxy_server.redis_usage_cache", None), # test-quality-ok: proxy_server module global is the endpoint's only injection point + patch( # test-quality-ok: stubs the cache write so the test observes only response serialization + "litellm.proxy.management_endpoints.model_management_endpoints.clear_cache", + new=AsyncMock(return_value=ReconcileOutcome(still_desired=None, live_after=None)), + ), + patch( # test-quality-ok: audit logging is a background side effect outside this test's contract + "litellm.proxy.management_endpoints.model_management_endpoints.create_object_audit_log", + new=AsyncMock(return_value=None), + ), + ): + client = TestClient(app) + response = client.post(route, json={"model_id": "m-block-1"}) + finally: + app.dependency_overrides.pop(ps.user_api_key_auth, None) + + assert response.status_code == 200, response.text + body = response.json() + assert body["model_id"] == "m-block-1" + assert body["blocked"] is blocked + assert body["litellm_params"] == {"model": "openai/gpt-4o-mini", "api_key": "encrypted-value"} diff --git a/tests/test_litellm/proxy/management_endpoints/test_team_endpoints.py b/tests/test_litellm/proxy/management_endpoints/test_team_endpoints.py index ffa6bc601e9..30b2ab86b9a 100644 --- a/tests/test_litellm/proxy/management_endpoints/test_team_endpoints.py +++ b/tests/test_litellm/proxy/management_endpoints/test_team_endpoints.py @@ -3741,6 +3741,93 @@ async def test_list_team_v2_with_status_deleted(): assert len(result["teams"]) == 2 +@pytest.mark.asyncio +async def test_list_team_v2_includes_litellm_model_table(): + """ + Regression test for GH #26312: GET /v2/team/list must eagerly load the + litellm_model_table relation for active teams, same as /team/info and + /team/list, or a team's model_aliases always read back as null from this + endpoint. Deleted teams are excluded: LiteLLM_DeletedTeamTable has no such + relation in the Prisma schema, so requesting it there raises + UnknownRelationalFieldError against a real database. + + The fake find_many below only attaches litellm_model_table when its own + `include` kwarg actually asks for the relation, so the assertions below + are on what the caller gets back, not on how find_many was called. + """ + from unittest.mock import AsyncMock, Mock, patch + + from fastapi import Request + + from litellm.proxy._types import LitellmUserRoles, UserAPIKeyAuth + from litellm.proxy.management_endpoints.team_endpoints import list_team_v2 + + mock_request = Mock(spec=Request) + mock_user_api_key_dict_admin = UserAPIKeyAuth( + user_role=LitellmUserRoles.PROXY_ADMIN, + user_id="admin_user_123", + ) + + def _team_row(team_id: str, include) -> Mock: + model_table = ( + { + "id": 1, + "model_aliases": {"my-fast-model": "fake-model"}, + "created_by": "u", + "updated_by": "u", + "team": None, + } + if (include or {}).get("litellm_model_table") + else None + ) + return Mock( + team_id=team_id, + model_dump=lambda: { + "team_id": team_id, + "team_alias": "t", + "litellm_model_table": model_table, + }, + ) + + with patch("litellm.proxy.proxy_server.prisma_client") as mock_prisma_client: # test-quality-ok: this file's DB-mock convention + mock_db = Mock() + mock_prisma_client.db = mock_db + + mock_db.litellm_teamtable.find_many = AsyncMock( + side_effect=lambda **kw: [_team_row("team_1", kw.get("include"))] + ) + mock_db.litellm_teamtable.count = AsyncMock(return_value=1) + mock_db.litellm_verificationtoken.group_by = AsyncMock(return_value=[]) + + result = await list_team_v2( + http_request=mock_request, + user_id=None, + user_api_key_dict=mock_user_api_key_dict_admin, + page=1, + page_size=10, + status=None, + ) + + assert result["teams"][0].litellm_model_table is not None + assert result["teams"][0].litellm_model_table.model_aliases == {"my-fast-model": "fake-model"} + + mock_db.litellm_deletedteamtable.find_many = AsyncMock( + side_effect=lambda **kw: [_team_row("team_2", kw.get("include"))] + ) + mock_db.litellm_deletedteamtable.count = AsyncMock(return_value=1) + + await list_team_v2( + http_request=mock_request, + user_id=None, + user_api_key_dict=mock_user_api_key_dict_admin, + page=1, + page_size=10, + status="deleted", + ) + + assert "include" not in mock_db.litellm_deletedteamtable.find_many.call_args.kwargs + + @pytest.mark.asyncio async def test_list_team_v2_org_admin_sees_org_teams(): """ diff --git a/tests/test_litellm/proxy/management_helpers/test_object_permission_utils.py b/tests/test_litellm/proxy/management_helpers/test_object_permission_utils.py index 5ef83344c1a..f2b6b799271 100644 --- a/tests/test_litellm/proxy/management_helpers/test_object_permission_utils.py +++ b/tests/test_litellm/proxy/management_helpers/test_object_permission_utils.py @@ -1,11 +1,9 @@ import json +from unittest.mock import AsyncMock, MagicMock, patch import pytest from fastapi import HTTPException - -from unittest.mock import AsyncMock, MagicMock, patch - from litellm.proxy._types import ( LiteLLM_ObjectPermissionBase, LiteLLM_ObjectPermissionTable, @@ -13,10 +11,10 @@ from litellm.proxy._types import ( SpecialMCPServerName, ) from litellm.proxy.management_helpers.object_permission_utils import ( + _drop_stale_object_permission_mcp_servers, _extract_requested_mcp_access_groups, _extract_requested_mcp_server_ids, _resolve_team_allowed_mcp_servers, - _rewrite_object_permission_mcp_servers, _set_object_permission, enforce_all_proxy_mcp_servers_grant_is_admin_only, validate_key_mcp_servers_against_team, @@ -153,10 +151,10 @@ def test_extract_requested_mcp_server_ids_excludes_no_mcp_servers_sentinel(): assert _extract_requested_mcp_server_ids(obj_perm) == {"server-1"} -def test_rewrite_object_permission_mcp_servers_preserves_sentinel(): - obj_perm = {"mcp_servers": ["no-mcp-servers", "alias-1"]} - _rewrite_object_permission_mcp_servers(obj_perm, {"alias-1": {"server-1"}}) - assert obj_perm["mcp_servers"] == ["no-mcp-servers", "server-1"] +def test_drop_stale_object_permission_mcp_servers_preserves_sentinel_and_alias(): + obj_perm = {"mcp_servers": ["no-mcp-servers", "alias-1", "gone-id"]} + _drop_stale_object_permission_mcp_servers(obj_perm, {"alias-1": {"server-1"}, "gone-id": set()}) + assert obj_perm["mcp_servers"] == ["no-mcp-servers", "alias-1"] @pytest.mark.asyncio @@ -692,9 +690,10 @@ async def test_validate_mcp_server_alias_outside_team_scope_raises( new_callable=AsyncMock, return_value=[], ) -async def test_validate_mcp_server_alias_is_normalized_before_save( - mock_access_groups, mock_allow_all -): +async def test_validate_mcp_server_alias_persists_verbatim(mock_access_groups, mock_allow_all): + """Regression for the multi-region shared-DB setup: an alias grant must be + stored as the alias, so every instance can expand it to its own local id. + Rewriting to this instance's server_id breaks access on the other region.""" team_obj = _make_team_obj(mcp_servers=["allowed-server-id"]) object_permission = { "mcp_servers": ["allowed-alias"], @@ -706,8 +705,27 @@ async def test_validate_mcp_server_alias_is_normalized_before_save( team_obj=team_obj, ) - assert object_permission["mcp_servers"] == ["allowed-server-id"] - assert object_permission["mcp_tool_permissions"] == {"allowed-server-id": ["tool1"]} + assert object_permission["mcp_servers"] == ["allowed-alias"] + assert object_permission["mcp_tool_permissions"] == {"Allowed Server": ["tool1"]} + + +def test_alias_grant_expands_on_other_region_after_save(): + """Cross-region flow: the west instance saves an alias grant (its resolver maps + the alias to west's hash-derived id), then the central instance, whose registry + maps the same alias to a different id, expands the persisted grant. Rewriting + to west's id at save time is exactly the regression this guards against.""" + west_mgr = _make_mock_mcp_manager(servers=[_make_mock_mcp_server("west-id", alias="github-mcp")]) + central_mgr = _make_mock_mcp_manager(servers=[_make_mock_mcp_server("central-id", alias="github-mcp")]) + + object_permission = {"mcp_servers": ["github-mcp"]} + _drop_stale_object_permission_mcp_servers(object_permission, {"github-mcp": {"west-id"}}) + assert object_permission["mcp_servers"] == ["github-mcp"] + + from litellm.proxy._experimental.mcp_server.mcp_server_manager import MCPServerManager + + expand = MCPServerManager.expand_permission_list + assert expand(west_mgr, object_permission["mcp_servers"]) == ["west-id"] + assert expand(central_mgr, object_permission["mcp_servers"]) == ["central-id"] @pytest.mark.asyncio diff --git a/tests/test_litellm/proxy/pass_through_endpoints/test_llm_pass_through_endpoints.py b/tests/test_litellm/proxy/pass_through_endpoints/test_llm_pass_through_endpoints.py index 09bab1dc416..1d4b0264879 100644 --- a/tests/test_litellm/proxy/pass_through_endpoints/test_llm_pass_through_endpoints.py +++ b/tests/test_litellm/proxy/pass_through_endpoints/test_llm_pass_through_endpoints.py @@ -12,11 +12,13 @@ from unittest.mock import AsyncMock, MagicMock, Mock, patch import httpx import pytest from fastapi import HTTPException, Request, Response +from fastapi.responses import StreamingResponse from fastapi.testclient import TestClient from starlette.datastructures import FormData import litellm +from litellm.proxy.common_request_processing import ProxyBaseLLMRequestProcessing from litellm.constants import LITELLM_PROXY_MASTER_KEY_ALIAS from litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints import ( BaseOpenAIPassThroughHandler, @@ -30,6 +32,7 @@ from litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints import ( get_azure_ai_search_index_from_endpoint, get_vertex_base_url, is_azure_ai_search_service_level_index_create, + gigachat_proxy_route, llm_passthrough_factory_proxy_route, milvus_proxy_route, mistral_proxy_route, @@ -178,7 +181,7 @@ class TestBaseOpenAIPassThroughHandler: assert result["api-key"] == "test_api_key" assert result["test-header"] == "value" - @patch( + @patch( # test-quality-ok: patching litellm internal for unit test isolation "litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints.create_pass_through_route" ) async def test_base_openai_pass_through_handler(self, mock_create_pass_through): @@ -2022,15 +2025,15 @@ class TestLLMPassthroughFactoryProxyRoute: class TestVLLMProxyRoute: @pytest.mark.asyncio - @patch( + @patch( # test-quality-ok: patching litellm internal for unit test isolation "litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints.get_request_body", return_value={"model": "router-model", "stream": False}, ) - @patch( + @patch( # test-quality-ok: patching litellm internal for unit test isolation "litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints.is_passthrough_request_using_router_model", return_value=True, ) - @patch("litellm.proxy.proxy_server.llm_router") + @patch("litellm.proxy.proxy_server.llm_router") # test-quality-ok: patching litellm internal for unit test isolation async def test_vllm_proxy_route_with_router_model( self, mock_llm_router, mock_is_router, mock_get_body ): @@ -2055,15 +2058,15 @@ class TestVLLMProxyRoute: mock_llm_router.allm_passthrough_route.assert_awaited_once() @pytest.mark.asyncio - @patch( + @patch( # test-quality-ok: patching litellm internal for unit test isolation "litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints.get_request_body", return_value={"model": "other-model"}, ) - @patch( + @patch( # test-quality-ok: patching litellm internal for unit test isolation "litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints.is_passthrough_request_using_router_model", return_value=False, ) - @patch( + @patch( # test-quality-ok: patching litellm internal for unit test isolation "litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints.llm_passthrough_factory_proxy_route" ) async def test_vllm_proxy_route_fallback_to_factory( @@ -2085,6 +2088,312 @@ class TestVLLMProxyRoute: mock_factory_route.assert_awaited_once() +class TestGigachatProxyRoute: + @pytest.mark.asyncio + @patch( # test-quality-ok: patching litellm internal for unit test isolation + "litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints.get_request_body", + return_value={"model": "router-model", "stream": False}, + ) + @patch( # test-quality-ok: patching litellm internal for unit test isolation + "litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints.is_passthrough_request_using_router_model", + return_value=True, + ) + @patch("litellm.proxy.proxy_server.llm_router") # test-quality-ok: patching litellm internal for unit test isolation + async def test_gigachat_proxy_route_with_router_model( + self, mock_llm_router, mock_is_router, mock_get_body + ): + mock_request = MagicMock(spec=Request) + mock_request.method = "POST" + mock_request.headers = {"content-type": "application/json"} + mock_request.query_params = {} + mock_fastapi_response = MagicMock(spec=Response) + mock_user_api_key_dict = MagicMock() + mock_llm_router.allm_passthrough_route = AsyncMock( + return_value=httpx.Response(200, json={"response": "success"}) + ) + + result = await gigachat_proxy_route( + endpoint="/chat/completions", + request=mock_request, + fastapi_response=mock_fastapi_response, + user_api_key_dict=mock_user_api_key_dict, + ) + + mock_is_router.assert_called_once() + mock_llm_router.allm_passthrough_route.assert_awaited_once() + assert isinstance(result, Response) + + @pytest.mark.asyncio + async def test_gigachat_router_handler_keeps_cached_body_and_payload_metadata_pristine(self): + """Regression: auth-metadata injection must not leak into the cached parsed body or the upstream payload.""" + from litellm.proxy.common_utils.http_parsing_utils import get_request_body + from litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints import ( + handle_gigachat_passthrough_router_model, + ) + + body = json.dumps( + { + "model": "gigachat-router", + "messages": [{"role": "user", "content": "hi"}], + "metadata": {"client_tag": "user-supplied"}, + } + ).encode() + scope = { + "type": "http", + "method": "POST", + "headers": [(b"content-type", b"application/json")], + "query_string": b"", + "path": "/gigachat/chat/completions", + } + + async def receive(): + return {"type": "http.request", "body": body, "more_body": False} + + request = Request(scope, receive) + request_body = await get_request_body(request) + + captured: dict = {} + + class _CapturingProcessor: + def __init__(self, data: dict): + captured["data"] = data + + async def base_passthrough_process_llm_request(self, **kwargs): + return Response(content=b"{}", status_code=200) + + with patch( # test-quality-ok: patching litellm internal for unit test isolation + "litellm.proxy.common_request_processing.ProxyBaseLLMRequestProcessing", + _CapturingProcessor, + ): + await handle_gigachat_passthrough_router_model( + model="gigachat-router", + endpoint="/chat/completions", + request=request, + request_body=request_body, + fastapi_response=Response(), + llm_router=MagicMock(), + user_api_key_dict=UserAPIKeyAuth(user_id="user-1", team_id="team-1"), + proxy_logging_obj=MagicMock(), + general_settings={}, + proxy_config=MagicMock(), + select_data_generator=MagicMock(), + user_model=None, + user_temperature=None, + user_request_timeout=None, + user_max_tokens=None, + user_api_base=None, + version=None, + ) + + data = captured["data"] + assert data["json"] is request_body + assert request_body["metadata"] == {"client_tag": "user-supplied"} + assert data["metadata"]["client_tag"] == "user-supplied" + assert data["metadata"]["user_api_key_user_id"] == "user-1" + assert data["metadata"]["user_api_key_team_id"] == "team-1" + cached_reread = await get_request_body(request) + assert cached_reread["metadata"] == {"client_tag": "user-supplied"} + + @pytest.mark.asyncio + @patch( # test-quality-ok: patching litellm internal for unit test isolation + "litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints.get_request_body", + return_value={"model": "other-model"}, + ) + @patch( # test-quality-ok: patching litellm internal for unit test isolation + "litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints.is_passthrough_request_using_router_model", + return_value=False, + ) + @patch( # test-quality-ok: patching litellm internal for unit test isolation + "litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints.is_streaming_request_fn", + new_callable=AsyncMock, + return_value=False, + ) + @patch( # test-quality-ok: patching litellm internal for unit test isolation + "litellm.llms.gigachat.authenticator.get_access_token", + return_value="gigachat-test-token", + ) + async def test_gigachat_proxy_route_fallback_forwards_to_gigachat_api( + self, + mock_get_token, + mock_is_streaming, + mock_is_router, + mock_get_body, + monkeypatch, + ): + monkeypatch.delenv("GIGACHAT_API_BASE", raising=False) + mock_request = MagicMock(spec=Request) + mock_fastapi_response = MagicMock(spec=Response) + mock_user_api_key_dict = MagicMock() + + captured_kwargs = {} + + async def fake_endpoint(request, fastapi_response, user_api_key_dict): + return Response(content=b'{"response": "success"}', status_code=200) + + def fake_create_pass_through_route(**kwargs): + captured_kwargs.update(kwargs) + return fake_endpoint + + with patch( # test-quality-ok: patching litellm internal for unit test isolation + "litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints.create_pass_through_route", + side_effect=fake_create_pass_through_route, + ): + result = await gigachat_proxy_route( + endpoint="/chat/completions", + request=mock_request, + fastapi_response=mock_fastapi_response, + user_api_key_dict=mock_user_api_key_dict, + ) + + assert isinstance(result, Response) + assert result.status_code == 200 + assert captured_kwargs["target"] == "https://gigachat.devices.sberbank.ru/api/v1/chat/completions" + assert captured_kwargs["custom_headers"] == {"Authorization": "Bearer gigachat-test-token"} + + @pytest.mark.asyncio + @patch( # test-quality-ok: patching litellm internal for unit test isolation + "litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints.get_request_body", + return_value={}, + ) + @patch( # test-quality-ok: patching litellm internal for unit test isolation + "litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints.is_streaming_request_fn", + new_callable=AsyncMock, + return_value=False, + ) + @patch( # test-quality-ok: patching litellm internal for unit test isolation + "litellm.llms.gigachat.authenticator.get_access_token", + return_value="gigachat-test-token", + ) + async def test_gigachat_proxy_route_models_endpoint_without_model( + self, + mock_get_token, + mock_is_streaming, + mock_get_body, + monkeypatch, + ): + monkeypatch.delenv("GIGACHAT_API_BASE", raising=False) + mock_request = MagicMock(spec=Request) + mock_fastapi_response = MagicMock(spec=Response) + mock_user_api_key_dict = MagicMock() + + captured_kwargs = {} + + async def fake_endpoint(request, fastapi_response, user_api_key_dict): + return Response(content=b'{"data": []}', status_code=200) + + def fake_create_pass_through_route(**kwargs): + captured_kwargs.update(kwargs) + return fake_endpoint + + with patch( # test-quality-ok: patching litellm internal for unit test isolation + "litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints.create_pass_through_route", + side_effect=fake_create_pass_through_route, + ): + result = await gigachat_proxy_route( + endpoint="models", + request=mock_request, + fastapi_response=mock_fastapi_response, + user_api_key_dict=mock_user_api_key_dict, + ) + + assert isinstance(result, Response) + assert result.status_code == 200 + assert captured_kwargs["target"] == "https://gigachat.devices.sberbank.ru/api/v1/models" + assert captured_kwargs["custom_headers"] == {"Authorization": "Bearer gigachat-test-token"} + + @pytest.mark.asyncio + async def test_allm_passthrough_streaming_preserves_upstream_headers(self): + async def _stream() -> bytes: + yield b'data: {"id":"1"}\n\n' + + class MockPassthroughStreamingResponse: + def __init__(self): + self.status_code = 201 + self.headers = { + "content-type": "text/event-stream; charset=utf-8", + "x-request-id": "req-123", + "x-ratelimit-remaining-requests": "77", + "transfer-encoding": "chunked", + "content-encoding": "gzip", + } + self._iterator = _stream() + + def __aiter__(self): + return self + + async def __anext__(self): + return await self._iterator.__anext__() + + processor = ProxyBaseLLMRequestProcessing( + data={ + "model": "some-provider/model", + "stream": True, + "litellm_call_id": "call-123", + "litellm_logging_obj": MagicMock(litellm_call_id="call-123"), + } + ) + + mock_request = MagicMock(spec=Request) + mock_request.headers = {"content-type": "application/json"} + mock_fastapi_response = MagicMock(spec=Response) + mock_user_api_key_dict = MagicMock() + mock_user_api_key_dict.allowed_model_region = "" + mock_user_api_key_dict.spend = 0.0 + mock_proxy_logging_obj = MagicMock() + mock_proxy_logging_obj.during_call_hook = AsyncMock(return_value=None) + mock_proxy_logging_obj.update_request_status = AsyncMock(return_value=None) + mock_proxy_logging_obj.post_call_response_headers_hook = AsyncMock( + return_value={"x-test-callback-header": "callback-value"} + ) + + streaming_response = MockPassthroughStreamingResponse() + + async def _fake_route_request(*args, **kwargs): + async def _inner(): + return streaming_response + + return _inner() + + with patch.object( + processor, + "common_processing_pre_call_logic", + new=AsyncMock( + return_value=( + processor.data, + processor.data["litellm_logging_obj"], + ) + ), + ), patch( # test-quality-ok: patching litellm internal for unit test isolation + "litellm.proxy.common_request_processing.route_request", + new=_fake_route_request, + ), patch( # test-quality-ok: patching litellm internal for unit test isolation + "litellm.proxy.common_request_processing.ProxyBaseLLMRequestProcessing.get_custom_headers", + return_value={"x-litellm-call-id": "call-123"}, + ): + result = await processor.base_passthrough_process_llm_request( + request=mock_request, + fastapi_response=mock_fastapi_response, + user_api_key_dict=mock_user_api_key_dict, + proxy_logging_obj=mock_proxy_logging_obj, + general_settings={}, + proxy_config=MagicMock(), + select_data_generator=MagicMock(), + llm_router=None, + model="some-provider/model", + version="test-version", + ) + + assert isinstance(result, StreamingResponse) + assert result.status_code == 201 + assert result.headers["content-type"] == "text/event-stream; charset=utf-8" + assert result.headers["x-request-id"] == "req-123" + assert result.headers["x-ratelimit-remaining-requests"] == "77" + assert result.headers["x-litellm-call-id"] == "call-123" + assert result.headers["x-test-callback-header"] == "callback-value" + assert "transfer-encoding" not in result.headers + assert "content-encoding" not in result.headers + + class TestForwardHeaders: """ Test cases for _forward_headers parameter in passthrough endpoints @@ -4627,3 +4936,76 @@ class TestPassthroughRouterModelBudgetReservation: ) self._assert_metadata_carries_attribution(captured, user_api_key_dict) + + +class TestAzureRouterModelStreamingDispatch: + """ + Regression: ``llm_router.allm_passthrough_route`` returns an awaited + ``AsyncPassthroughStreamingResponse`` for streaming calls, which is no + longer an async generator under ``inspect.isasyncgen``. The dispatch's + else branch therefore calls ``.aiter_bytes()`` / ``.status_code`` / + ``.headers`` on it. The router's ``set_response_headers`` also runs the + result through ``prepare_response_for_header_attachment``, which used to + wrap it in ``HiddenParamsAsyncIteratorWrapper`` (no ``aiter_bytes``), so + every streaming Azure router-model request 500'd with + ``AttributeError: aiter_bytes``; ``_hidden_params`` on the streaming + response keeps it unwrapped. + """ + + @pytest.mark.asyncio + async def test_azure_router_model_streaming_returns_streaming_response(self, monkeypatch): + import litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints as ep + import litellm.proxy.proxy_server as proxy_server + from litellm.passthrough.main import AsyncPassthroughStreamingResponse + + upstream_body = b"data: hello\n\n" + + async def _upstream_response() -> httpx.Response: + upstream_request = httpx.Request( + "POST", + "https://my-azure.openai.azure.com/openai/deployments/gpt-5/chat/completions", + ) + return httpx.Response( + 200, + headers={"content-type": "text/event-stream"}, + content=upstream_body, + request=upstream_request, + ) + + logging_obj = MagicMock() + logging_obj.async_flush_passthrough_collected_chunks = AsyncMock() + + from litellm.router_utils.add_retry_fallback_headers import prepare_response_for_header_attachment + + class StreamingRouter: + async def allm_passthrough_route(self, **kwargs): + streaming_response = await AsyncPassthroughStreamingResponse( + response=_upstream_response(), + litellm_logging_obj=logging_obj, + provider_config=MagicMock(), + ) + return prepare_response_for_header_attachment(streaming_response) + + async def fake_get_request_body(_request): + return {"model": "gpt-5", "stream": True} + + monkeypatch.setattr(proxy_server, "llm_router", StreamingRouter()) + monkeypatch.setattr(ep, "get_request_body", fake_get_request_body) + monkeypatch.setattr(ep, "is_passthrough_request_using_router_model", lambda *a, **k: True) + + request = MagicMock(spec=Request) + request.method = "POST" + request.headers = {"content-type": "application/json"} + request.query_params = {} + + result = await azure_proxy_route( + endpoint="openai/deployments/gpt-5/chat/completions", + request=request, + fastapi_response=MagicMock(spec=Response), + user_api_key_dict=UserAPIKeyAuth(api_key="hashed-token"), + ) + + assert isinstance(result, StreamingResponse) + assert result.status_code == 200 + body = b"".join([chunk async for chunk in result.body_iterator]) + assert body == upstream_body diff --git a/tests/test_litellm/proxy/pass_through_endpoints/test_pass_through_endpoints.py b/tests/test_litellm/proxy/pass_through_endpoints/test_pass_through_endpoints.py index a3f56adb86f..f5ae0fe5977 100644 --- a/tests/test_litellm/proxy/pass_through_endpoints/test_pass_through_endpoints.py +++ b/tests/test_litellm/proxy/pass_through_endpoints/test_pass_through_endpoints.py @@ -5462,3 +5462,164 @@ def test_the_marker_check_distinguishes_the_two_route_kinds(): builtin = MagicMock(spec=Request) builtin.scope = {"endpoint": llm_passthrough_endpoints.anthropic_proxy_route} assert request_dispatched_to_pass_through_endpoint(builtin) is False + + +async def _drive_passthrough_request_and_capture_logging(user_api_key_dict: UserAPIKeyAuth) -> tuple[int, object]: + import litellm + from litellm.llms.custom_httpx.http_handler import get_async_httpx_client + from litellm.types.llms.custom_http import httpxSpecialProvider + + def transport_handler(upstream_request: httpx.Request) -> httpx.Response: + return httpx.Response(200, json={"ok": True}) + + real_handler = get_async_httpx_client( + llm_provider=httpxSpecialProvider.PassThroughEndpoint, + params={"timeout": resolve_pass_through_request_timeout(None)}, + ) + cache_dict = litellm.in_memory_llm_clients_cache.cache_dict + cache_key = next((key for key, cached in cache_dict.items() if cached is real_handler), None) + assert cache_key is not None + cache_dict[cache_key] = SimpleNamespace(client=httpx.AsyncClient(transport=httpx.MockTransport(transport_handler))) + + mock_request = MagicMock(spec=Request) + mock_request.method = "POST" + mock_request.headers = Headers({}) + mock_request.query_params = QueryParams({}) + mock_request.body = AsyncMock(return_value=b'{"model": "gemini-2.0-flash"}') + + captured_data: dict = {} + + async def capture_pre_call_hook(user_api_key_dict, data, call_type): + captured_data.update(data) + return data + + mock_proxy_logging = MagicMock() + mock_proxy_logging.pre_call_hook = AsyncMock(side_effect=capture_pre_call_hook) + mock_proxy_logging.post_call_failure_hook = AsyncMock() + mock_proxy_logging.post_call_response_headers_hook = AsyncMock(return_value={}) + mock_proxy_logging.get_proxy_hook = MagicMock(return_value=None) + + try: + with patch( # test-quality-ok: proxy_logging_obj is a proxy_server module global read inside pass_through_request; there is no injection seam + "litellm.proxy.proxy_server.proxy_logging_obj", mock_proxy_logging + ): + response = await pass_through_request( + request=mock_request, + target="https://upstream.example.test/v1/generate", + custom_headers={}, + user_api_key_dict=user_api_key_dict, + ) + finally: + cache_dict[cache_key] = real_handler + + return response.status_code, captured_data.get("litellm_logging_obj") + + +@pytest.mark.asyncio +async def test_pass_through_request_wires_team_callbacks(): + """LIT-5152 regression: pass_through_request must resolve team-level logging + callbacks from key/team metadata and wire them into the Logging object, the + same way add_litellm_data_to_request does for normal LLM routes.""" + user_api_key_dict = UserAPIKeyAuth( + api_key="test-key", + team_id="test-team", + team_metadata={ + "logging": [ + { + "callback_name": "langfuse", + "callback_type": "success_and_failure", + "callback_vars": { + "langfuse_public_key": "pk_test", + "langfuse_secret_key": "sk_test", + "langfuse_host": "https://langfuse.example.test", + }, + } + ] + }, + ) + + status_code, logging_obj = await _drive_passthrough_request_and_capture_logging(user_api_key_dict) + + assert status_code == 200 + assert logging_obj is not None + assert logging_obj.dynamic_success_callbacks, "team success callbacks not wired into Logging" + assert logging_obj.dynamic_failure_callbacks, "team failure callbacks not wired into Logging" + assert logging_obj.standard_callback_dynamic_params.get("langfuse_public_key") == "pk_test" + assert logging_obj.standard_callback_dynamic_params.get("langfuse_secret_key") == "sk_test" + assert logging_obj.standard_callback_dynamic_params.get("langfuse_host") == "https://langfuse.example.test" + assert ("langfuse_public_key", "pk_test") in logging_obj._trusted_callback_vars + + +@pytest.mark.asyncio +async def test_pass_through_request_survives_malformed_team_logging_metadata(): + """LIT-5152 fail-open: a malformed team ``logging`` value (here a non-iterable) + raises inside callback resolution; the passthrough request must still succeed, + just without dynamic callbacks.""" + user_api_key_dict = UserAPIKeyAuth( + api_key="test-key", + team_id="test-team", + team_metadata={"logging": 5}, + ) + + status_code, logging_obj = await _drive_passthrough_request_and_capture_logging(user_api_key_dict) + + assert status_code == 200 + assert logging_obj is not None + assert not logging_obj.dynamic_success_callbacks + assert not logging_obj.dynamic_failure_callbacks + + +@pytest.mark.asyncio +async def test_pass_through_request_survives_env_reference_in_deprecated_callback_settings(): + """LIT-5152 fail-open: the deprecated ``callback_settings`` team metadata skips + AddTeamCallback validation, so an ``os.environ/`` callback var would otherwise + blow up inside ``Logging.__init__`` and fail the request; the passthrough must + instead succeed without dynamic callbacks.""" + user_api_key_dict = UserAPIKeyAuth( + api_key="test-key", + team_id="test-team", + team_metadata={ + "callback_settings": { + "success_callback": ["langfuse"], + "failure_callback": ["langfuse"], + "callback_vars": { + "langfuse_public_key": "os.environ/LANGFUSE_PUBLIC_KEY", + "langfuse_secret_key": "os.environ/LANGFUSE_SECRET_KEY", + "langfuse_host": "https://langfuse.example.test", + }, + } + }, + ) + + status_code, logging_obj = await _drive_passthrough_request_and_capture_logging(user_api_key_dict) + + assert status_code == 200 + assert logging_obj is not None + assert not logging_obj.dynamic_success_callbacks + assert not logging_obj.dynamic_failure_callbacks + assert not logging_obj.standard_callback_dynamic_params.get("langfuse_public_key") + + +@pytest.mark.asyncio +async def test_resolve_team_callback_wiring_fails_open_on_operational_error(): + """LIT-5152 fail-open: an operational error while resolving callback metadata + (e.g. team config lookup hitting a dead secret manager) must not raise; the + request proceeds without dynamic callbacks and the error is logged.""" + from litellm.proxy.pass_through_endpoints.pass_through_endpoints import ( + _resolve_team_callback_wiring, + ) + from litellm.proxy.proxy_server import ProxyConfig + + class RaisingTeamConfig(ProxyConfig): + def load_team_config(self, team_id: str) -> dict: + raise RuntimeError("secret manager unavailable") + + wiring = _resolve_team_callback_wiring( + user_api_key_dict=UserAPIKeyAuth(api_key="test-key", team_id="test-team"), + proxy_config=RaisingTeamConfig(), + route_description="pass_through_endpoint", + ) + + assert wiring.success_callbacks is None + assert wiring.failure_callbacks is None + assert wiring.logging_kwargs is None diff --git a/tests/test_litellm/proxy/policy_engine/test_pipeline_executor.py b/tests/test_litellm/proxy/policy_engine/test_pipeline_executor.py index 054a5af4148..4fcb7d22588 100644 --- a/tests/test_litellm/proxy/policy_engine/test_pipeline_executor.py +++ b/tests/test_litellm/proxy/policy_engine/test_pipeline_executor.py @@ -749,6 +749,106 @@ async def test_single_step_pipeline_allow(monkeypatch): assert guard.calls == 1 +@pytest.mark.asyncio +async def test_allow_restores_independent_guardrails_list(monkeypatch): + """ + Request activates an independent guardrail; an unrelated pipeline runs and allows. + Expected: no modified_data escapes, so the request's guardrails list survives + and the independent guardrail still runs at later lifecycle stages (post_call). + Regression: LIT-6587 (pipeline clobbered the list with its last step's guardrail). + """ + pipeline_guard = AlwaysPassGuardrail(guardrail_name="input-scan") + + pipeline = GuardrailPipeline( + mode="pre_call", + steps=[PipelineStep(guardrail="input-scan", on_fail="block", on_pass="allow")], + ) + + monkeypatch.setattr(litellm, "callbacks", [pipeline_guard]) + + data = { + "messages": [{"role": "user", "content": "clean content"}], + "metadata": {"guardrails": ["independent-output-guard"]}, + } + result = await PipelineExecutor.execute_steps( + steps=pipeline.steps, + mode=pipeline.mode, + data=data, + user_api_key_dict=MagicMock(), + call_type="completion", + policy_name="input-pipeline-policy", + ) + + assert pipeline_guard.calls == 1 + assert result.terminal_action == "allow" + propagated = result.modified_data or data + assert propagated["metadata"]["guardrails"] == ["independent-output-guard"] + assert data["metadata"]["guardrails"] == ["independent-output-guard"] + + +@pytest.mark.asyncio +async def test_allow_does_not_leak_guardrails_into_bare_request(monkeypatch): + """A request without metadata must not gain a metadata.guardrails list from the pipeline.""" + pipeline_guard = AlwaysPassGuardrail(guardrail_name="input-scan") + + pipeline = GuardrailPipeline( + mode="pre_call", + steps=[PipelineStep(guardrail="input-scan", on_fail="block", on_pass="allow")], + ) + + monkeypatch.setattr(litellm, "callbacks", [pipeline_guard]) + + data = {"messages": [{"role": "user", "content": "clean content"}]} + result = await PipelineExecutor.execute_steps( + steps=pipeline.steps, + mode=pipeline.mode, + data=data, + user_api_key_dict=MagicMock(), + call_type="completion", + policy_name="input-pipeline-policy", + ) + + assert result.terminal_action == "allow" + propagated = result.modified_data or data + assert "guardrails" not in propagated.get("metadata", {}) + assert "metadata" not in data + + +@pytest.mark.asyncio +async def test_data_forwarding_keeps_changes_and_restores_guardrails_list(monkeypatch): + """A pass_data pipeline's modifications propagate while the request's guardrails list is restored.""" + pii_guard = PiiMaskingGuardrail(guardrail_name="pii-masker") + content_guard = ContentCheckGuardrail(guardrail_name="content-check") + + pipeline = GuardrailPipeline( + mode="pre_call", + steps=[ + PipelineStep(guardrail="pii-masker", on_fail="block", on_pass="next", pass_data=True), + PipelineStep(guardrail="content-check", on_fail="block", on_pass="allow"), + ], + ) + + monkeypatch.setattr(litellm, "callbacks", [pii_guard, content_guard]) + + data = { + "messages": [{"role": "user", "content": "Hello John Smith"}], + "metadata": {"guardrails": ["independent-output-guard"]}, + } + result = await PipelineExecutor.execute_steps( + steps=pipeline.steps, + mode=pipeline.mode, + data=data, + user_api_key_dict=MagicMock(), + call_type="completion", + policy_name="pii-then-safety", + ) + + assert result.terminal_action == "allow" + assert result.modified_data is not None + assert result.modified_data["messages"][0]["content"] == "Hello [REDACTED]" + assert result.modified_data["metadata"]["guardrails"] == ["independent-output-guard"] + + @pytest.mark.asyncio async def test_step_results_include_duration(monkeypatch): """Step results should include timing information.""" diff --git a/tests/test_litellm/proxy/proxy_server/test_routes_audio.py b/tests/test_litellm/proxy/proxy_server/test_routes_audio.py index 74542a3eaf6..de76c7257cf 100644 --- a/tests/test_litellm/proxy/proxy_server/test_routes_audio.py +++ b/tests/test_litellm/proxy/proxy_server/test_routes_audio.py @@ -12,13 +12,16 @@ from __future__ import annotations import io from unittest.mock import AsyncMock, MagicMock +import httpx import pytest from litellm.proxy import proxy_server +from litellm.types.llms.openai import HttpxBinaryResponseContent @pytest.fixture -def patched_speech(monkeypatch): +def patched_speech(monkeypatch, request): + upstream_content_type = getattr(request, "param", "audio/mpeg") monkeypatch.setattr(proxy_server, "llm_router", MagicMock()) monkeypatch.setattr( proxy_server, @@ -36,15 +39,14 @@ def patched_speech(monkeypatch): monkeypatch.setattr(proxy_server, "add_litellm_data_to_request", _add_data) - class _FakeBinaryResp: - async def aiter_bytes(self, chunk_size: int = 8192): - async def _gen(): - yield b"\x00\x01\x02" - - return _gen() - async def _llm_call(): - return _FakeBinaryResp() + return HttpxBinaryResponseContent( + httpx.Response( + status_code=200, + headers={} if upstream_content_type is None else {"content-type": upstream_content_type}, + content=b"\x00\x01\x02", + ) + ) async def _fake_route_request(*args, **kwargs): return _llm_call() @@ -79,6 +81,24 @@ def patched_speech_error(monkeypatch): yield +@pytest.fixture +def patched_speech_provider_rejection(monkeypatch, patched_speech_error): + import litellm + + async def _raise(*args, **kwargs): + raise litellm.BadRequestError( + message=( + "Gemini TTS only produces raw PCM16 audio, so response_format='mp3' is not supported." + " Supported response formats: pcm, wav." + ), + model="gemini-3.1-flash-tts-preview", + llm_provider="gemini", + ) + + monkeypatch.setattr(proxy_server, "route_request", _raise) + yield + + @pytest.fixture def patched_transcription(monkeypatch): router = MagicMock() @@ -152,6 +172,35 @@ def test_audio_speech_happy_path(client, auth_as, patched_speech, path): } +@pytest.mark.parametrize( + ("patched_speech", "response_format", "expected_content_type"), + [ + ("audio/wav", "wav", "audio/wav"), + ("audio/flac", "flac", "audio/flac"), + ("audio/pcm", "pcm", "audio/pcm"), + ("audio/wav", "mp3", "audio/wav"), + ("application/json", "flac", "audio/flac"), + (None, "wav", "audio/wav"), + (None, None, "audio/mpeg"), + ], + indirect=["patched_speech"], +) +def test_audio_speech_content_type_matches_audio_format( + client, auth_as, patched_speech, response_format, expected_content_type +): + """Regression for LIT-6482: /v1/audio/speech mislabeled wav/flac/pcm as audio/mpeg.""" + payload = { + "model": "tts-1", + "input": "Hi", + "voice": "alloy", + **({} if response_format is None else {"response_format": response_format}), + } + with auth_as(): + response = client.post("/v1/audio/speech", json=payload) + assert response.status_code == 200 + assert response.headers.get("content-type", "").split(";")[0] == expected_content_type + + @pytest.mark.parametrize("path", ["/v1/audio/speech", "/audio/speech"]) def test_audio_speech_error(client, auth_as, patched_speech_error, path): """Pins ``POST /v1/audio/speech`` and ``POST /audio/speech`` (error).""" @@ -162,6 +211,18 @@ def test_audio_speech_error(client, auth_as, patched_speech_error, path): assert len(response.content) > 0 +def test_audio_speech_bad_request_maps_to_400(client, auth_as, patched_speech_provider_rejection): + """Regression for LIT-6501: a BadRequestError from the speech path surfaced as a generic 500.""" + payload = {"model": "gemini-tts", "input": "Hi", "voice": "Kore", "response_format": "mp3"} + with auth_as(): + response = client.post("/v1/audio/speech", json=payload) + assert response.status_code == 400 + error = response.json()["error"] + assert "response_format='mp3'" in error["message"] + assert "pcm" in error["message"] + assert "wav" in error["message"] + + @pytest.mark.parametrize("path", ["/v1/audio/transcriptions", "/audio/transcriptions"]) def test_audio_transcription_happy_path(client, auth_as, patched_transcription, path): """Pins ``POST /v1/audio/transcriptions`` / ``POST /audio/transcriptions`` (happy).""" diff --git a/tests/test_litellm/proxy/response_api_endpoints/test_endpoints.py b/tests/test_litellm/proxy/response_api_endpoints/test_endpoints.py index 791d64c6428..d7010de6405 100644 --- a/tests/test_litellm/proxy/response_api_endpoints/test_endpoints.py +++ b/tests/test_litellm/proxy/response_api_endpoints/test_endpoints.py @@ -3,10 +3,12 @@ Test for response_api_endpoints/endpoints.py """ import unittest +from typing import Any from unittest.mock import AsyncMock, MagicMock, patch import pytest from fastapi.testclient import TestClient +from httpx import Response import litellm from litellm.proxy.proxy_server import app @@ -82,11 +84,7 @@ class TestResponsesAPIEndpoints(unittest.TestCase): ResponseOutputMessage( type="message", role="assistant", - content=[ - ResponseOutputText( - type="output_text", text="Hello from Cursor!" - ) - ], + content=[ResponseOutputText(type="output_text", text="Hello from Cursor!")], ) ], ) @@ -121,9 +119,7 @@ class TestResponsesAPIEndpoints(unittest.TestCase): @pytest.mark.asyncio @patch("litellm.proxy.proxy_server.llm_router") @patch("litellm.proxy.proxy_server.user_api_key_auth") - async def test_responses_api_key_spend_header_includes_response_cost( - self, mock_auth, mock_router - ): + async def test_responses_api_key_spend_header_includes_response_cost(self, mock_auth, mock_router): """ Test that x-litellm-key-spend header includes the current request's response_cost for /v1/responses endpoint. @@ -159,9 +155,7 @@ class TestResponsesAPIEndpoints(unittest.TestCase): ResponseOutputMessage( type="message", role="assistant", - content=[ - ResponseOutputText(type="output_text", text="Test response") - ], + content=[ResponseOutputText(type="output_text", text="Test response")], ) ], ) @@ -356,6 +350,7 @@ class TestWSModelExtraction: from litellm.proxy.response_api_endpoints.endpoints import ( _extract_model_from_first_ws_event, ) + event = {"type": "response.create", "model": "gpt-4o", "input": "hello"} assert _extract_model_from_first_ws_event(event) == "gpt-4o" @@ -363,6 +358,7 @@ class TestWSModelExtraction: from litellm.proxy.response_api_endpoints.endpoints import ( _extract_model_from_first_ws_event, ) + event = {"type": "response.create", "response": {"model": "gpt-4o", "input": "hello"}} assert _extract_model_from_first_ws_event(event) == "gpt-4o" @@ -370,6 +366,7 @@ class TestWSModelExtraction: from litellm.proxy.response_api_endpoints.endpoints import ( _extract_model_from_first_ws_event, ) + event = { "type": "response.create", "model": "flat-model", @@ -381,6 +378,7 @@ class TestWSModelExtraction: from litellm.proxy.response_api_endpoints.endpoints import ( _extract_model_from_first_ws_event, ) + event = {"type": "response.create", "input": "hello"} assert _extract_model_from_first_ws_event(event) is None @@ -400,9 +398,7 @@ class TestResponsesWSFirstFrameValidation: ) ws = MagicMock() - ws.receive_text = AsyncMock( - return_value=json.dumps({"type": "session.update", "model": "gpt-4o"}) - ) + ws.receive_text = AsyncMock(return_value=json.dumps({"type": "session.update", "model": "gpt-4o"})) ws.send_text = AsyncMock() ws.close = AsyncMock() @@ -412,10 +408,7 @@ class TestResponsesWSFirstFrameValidation: ws.send_text.assert_awaited_once() ws.close.assert_awaited_once_with(code=1008, reason="Invalid first message") error_payload = json.loads(ws.send_text.await_args.args[0]) - assert ( - error_payload["error"]["message"] - == "First message must be a response.create JSON object." - ) + assert error_payload["error"]["message"] == "First message must be a response.create JSON object." @pytest.mark.asyncio async def test_rejects_non_object_json_first_frame(self): @@ -484,16 +477,12 @@ class TestResponsesWSFirstFrameModelAuth: ws.url = "ws://testserver/v1/responses" ws.accept = AsyncMock() ws.receive_text = AsyncMock( - return_value=json.dumps( - {"type": "response.create", "model": "gpt-4o-mini", "input": []} - ) + return_value=json.dumps({"type": "response.create", "model": "gpt-4o-mini", "input": []}) ) ws.close = AsyncMock() processor = MagicMock() - processor.common_processing_pre_call_logic = AsyncMock( - return_value=({"model": "gpt-4o-mini"}, MagicMock()) - ) + processor.common_processing_pre_call_logic = AsyncMock(return_value=({"model": "gpt-4o-mini"}, MagicMock())) async def fake_llm_call(): return None @@ -529,9 +518,7 @@ class TestResponsesWSFirstFrameModelAuth: _enforce_responses_ws_first_frame_model_auth, ) - request = Request( - {"type": "http", "method": "POST", "path": "/v1/responses", "headers": []} - ) + request = Request({"type": "http", "method": "POST", "path": "/v1/responses", "headers": []}) user_api_key_dict = MagicMock() llm_router = MagicMock() @@ -593,9 +580,7 @@ class TestReadWSModelFromFirstFrameErrors: assert result is None ws.send_text.assert_not_awaited() - ws.close.assert_awaited_once_with( - code=1008, reason="Timed out waiting for first message" - ) + ws.close.assert_awaited_once_with(code=1008, reason="Timed out waiting for first message") @pytest.mark.asyncio async def test_invalid_json_sends_error_and_closes(self): @@ -613,9 +598,7 @@ class TestReadWSModelFromFirstFrameErrors: assert result is None payload = json.loads(ws.send_text.await_args.args[0]) assert payload["error"]["message"] == "First message is not valid JSON." - ws.close.assert_awaited_once_with( - code=1008, reason="Invalid JSON in first message" - ) + ws.close.assert_awaited_once_with(code=1008, reason="Invalid JSON in first message") @pytest.mark.asyncio async def test_missing_model_sends_error_and_closes(self): @@ -624,9 +607,7 @@ class TestReadWSModelFromFirstFrameErrors: ) ws = MagicMock() - ws.receive_text = AsyncMock( - return_value=json.dumps({"type": "response.create", "input": []}) - ) + ws.receive_text = AsyncMock(return_value=json.dumps({"type": "response.create", "input": []})) ws.send_text = AsyncMock() ws.close = AsyncMock() @@ -679,10 +660,7 @@ class TestManagedResponsesSameProvider: assert self._handler("gpt-4o")._same_provider("gpt-4o-mini") is True def test_different_provider_is_not_same(self): - assert ( - self._handler("gpt-4o")._same_provider("vertex_ai/gemini-2.0-flash") - is False - ) + assert self._handler("gpt-4o")._same_provider("vertex_ai/gemini-2.0-flash") is False def test_inject_credentials_keeps_provider_for_same_provider_model(self): handler = self._handler("gpt-4o", custom_llm_provider="openai") @@ -697,18 +675,14 @@ class TestManagedResponsesSameProvider: assert "custom_llm_provider" not in call_kwargs def test_unresolvable_connection_model_falls_back_to_custom_provider(self): - handler = self._handler( - "my-custom-deployment", custom_llm_provider="openai" - ) + handler = self._handler("my-custom-deployment", custom_llm_provider="openai") assert handler._same_provider("gpt-4o-mini") is True call_kwargs: dict = {} handler._inject_credentials(call_kwargs, model="gpt-4o-mini") assert call_kwargs["custom_llm_provider"] == "openai" def test_unresolvable_connection_model_still_drops_cross_provider(self): - handler = self._handler( - "my-custom-deployment", custom_llm_provider="openai" - ) + handler = self._handler("my-custom-deployment", custom_llm_provider="openai") call_kwargs: dict = {} handler._inject_credentials(call_kwargs, model="vertex_ai/gemini-2.0-flash") assert "custom_llm_provider" not in call_kwargs @@ -840,9 +814,7 @@ def test_cursor_chat_completions_input_body_uses_responses_pipeline_and_strips_s type="message", role="assistant", status="completed", - content=[ - ResponseOutputText(type="output_text", text="agent reply", annotations=[]) - ], + content=[ResponseOutputText(type="output_text", text="agent reply", annotations=[])], ) ], ) @@ -851,9 +823,12 @@ def test_cursor_chat_completions_input_body_uses_responses_pipeline_and_strips_s app.dependency_overrides[user_api_key_auth] = _auth_override try: - with patch.object(ps, "llm_router", mock_router), patch( - "litellm.proxy.response_api_endpoints.endpoints._read_request_body", - side_effect=capturing_read_request_body, + with ( + patch.object(ps, "llm_router", mock_router), + patch( + "litellm.proxy.response_api_endpoints.endpoints._read_request_body", + side_effect=capturing_read_request_body, + ), ): client = TestClient(app) response = client.post( @@ -1488,8 +1463,8 @@ def _router_serving_only(base_model: str) -> MagicMock: mock_router.router_general_settings.pass_through_all_models = False mock_router.default_deployment = None mock_router.pattern_router.patterns = {base_model: ["anthropic/*"]} - mock_router.pattern_router.get_pattern.side_effect = ( - lambda model: [{"model_name": "anthropic/*"}] if model == base_model else None + mock_router.pattern_router.get_pattern.side_effect = lambda model: ( + [{"model_name": "anthropic/*"}] if model == base_model else None ) return mock_router @@ -1739,9 +1714,7 @@ class TestCursorGateRecognizesRoutingGroups: from litellm.proxy.response_api_endpoints.endpoints import _resolve_cursor_model_variant router = Router( - model_list=[ - {"model_name": "member-fast", "litellm_params": {"model": "openai/gpt-4o", "api_key": "fake"}} - ], + model_list=[{"model_name": "member-fast", "litellm_params": {"model": "openai/gpt-4o", "api_key": "fake"}}], routing_groups=[ {"group_name": "grouped-thinking-high", "models": ["member-fast"], "routing_strategy": "simple-shuffle"} ], @@ -1836,3 +1809,153 @@ class TestGuardrailBlockedResponsesUsage: assert usage["input_tokens"] == 0 assert usage["output_tokens"] == 0 assert usage["total_tokens"] == 0 + + +class TestResponsesInputTokens: + """Regression tests for POST /v1/responses/input_tokens. + + The docs promise OpenAI-format token counting on the proxy, but the route was + never registered, so the POST fell through to the GET/DELETE-only + /v1/responses/{response_id} route and returned 405.""" + + def _post_input_tokens( + self, + body: dict[str, Any], + path: str = "/v1/responses/input_tokens", + counter: AsyncMock | None = None, + ) -> tuple[Response, AsyncMock]: + from litellm.proxy._types import UserAPIKeyAuth + from litellm.proxy.auth.user_api_key_auth import user_api_key_auth + from litellm.proxy.response_api_endpoints.endpoints import _proxy_token_counter + from litellm.types.utils import TokenCountResponse + + token_counter_mock = ( + counter + if counter is not None + else AsyncMock( + return_value=TokenCountResponse( + total_tokens=13, + request_model=body.get("model", ""), + model_used=body.get("model", ""), + tokenizer_type="openai_api", + ) + ) + ) + app.dependency_overrides[user_api_key_auth] = lambda: UserAPIKeyAuth(api_key="sk-test", request_route=path) + app.dependency_overrides[_proxy_token_counter] = lambda: token_counter_mock + try: + client = TestClient(app) + response = client.post(path, json=body, headers={"Authorization": "Bearer sk-1234"}) + return response, token_counter_mock + finally: + app.dependency_overrides.pop(user_api_key_auth, None) + app.dependency_overrides.pop(_proxy_token_counter, None) + + def test_string_input_returns_openai_input_tokens_shape(self): + response, counter = self._post_input_tokens({"model": "gpt-4o", "input": "Hello, how are you?"}) + + assert response.status_code == 200, response.text + assert response.json() == {"object": "response.input_tokens", "input_tokens": 13} + counter.assert_awaited_once() + assert counter.call_args.kwargs["call_endpoint"] is True + token_request = counter.call_args.kwargs["request"] + assert token_request.model == "gpt-4o" + assert token_request.messages == [{"role": "user", "content": "Hello, how are you?"}] + + def test_every_route_alias_is_registered(self): + for path in ("/v1/responses/input_tokens", "/responses/input_tokens", "/openai/v1/responses/input_tokens"): + response, _ = self._post_input_tokens({"model": "gpt-4o", "input": "hi"}, path=path) + assert response.status_code == 200, f"{path}: {response.status_code} {response.text}" + + def test_input_items_instructions_and_tools_are_forwarded(self): + tools = [ + { + "type": "function", + "name": "get_weather", + "description": "Get weather for a city", + "parameters": {"type": "object", "properties": {"city": {"type": "string"}}}, + } + ] + response, counter = self._post_input_tokens( + { + "model": "gpt-4o", + "input": [{"role": "user", "content": "What is the weather in Paris?"}], + "instructions": "You are terse.", + "tools": tools, + } + ) + + assert response.status_code == 200, response.text + token_request = counter.call_args.kwargs["request"] + assert token_request.messages == [ + {"role": "system", "content": "You are terse."}, + {"role": "user", "content": "What is the weather in Paris?"}, + ] + assert token_request.tools == tools + + def test_missing_model_returns_openai_400(self): + response, counter = self._post_input_tokens({"input": "Hello"}) + + assert response.status_code == 400, response.text + assert response.json() == { + "error": { + "message": "Missing required parameter: 'model'.", + "type": "invalid_request_error", + "param": "model", + "code": "missing_required_parameter", + } + } + counter.assert_not_awaited() + + def test_missing_input_returns_openai_400(self): + response, counter = self._post_input_tokens({"model": "gpt-4o"}) + + assert response.status_code == 400, response.text + assert response.json() == { + "error": { + "message": "Missing required parameter: 'input'.", + "type": "invalid_request_error", + "param": "input", + "code": "missing_required_parameter", + } + } + counter.assert_not_awaited() + + @pytest.mark.parametrize("empty_input", ["", []]) + def test_empty_input_returns_openai_400(self, empty_input): + response, counter = self._post_input_tokens({"model": "gpt-4o", "input": empty_input}) + + assert response.status_code == 400, response.text + assert response.json() == { + "error": { + "message": """One of "input" or "previous_response_id" or 'prompt' or 'conversation' must be provided.""", + "type": "invalid_request_error", + "param": None, + "code": "missing_required_parameter", + } + } + counter.assert_not_awaited() + + def test_invalid_tools_returns_openai_400(self): + response, counter = self._post_input_tokens({"model": "gpt-4o", "input": "hi", "tools": "not-a-list"}) + + assert response.status_code == 400, response.text + error = response.json()["error"] + assert error["type"] == "invalid_request_error" + counter.assert_not_awaited() + + def test_provider_error_maps_status_code(self): + from litellm.proxy._types import ProxyException + + failing_counter = AsyncMock( + side_effect=ProxyException( + message="rate limited", + type="token_counting_error", + param="model", + code="429", + ) + ) + response, _ = self._post_input_tokens({"model": "gpt-4o", "input": "hi"}, counter=failing_counter) + + assert response.status_code == 429, response.text + assert response.json()["error"]["message"] == "rate limited" diff --git a/tests/test_litellm/proxy/spend_tracking/test_budget_reservation.py b/tests/test_litellm/proxy/spend_tracking/test_budget_reservation.py new file mode 100644 index 00000000000..f65f68812a2 --- /dev/null +++ b/tests/test_litellm/proxy/spend_tracking/test_budget_reservation.py @@ -0,0 +1,48 @@ +from typing import Final + +import pytest + +from litellm.caching import DualCache +from litellm.proxy._types import UserAPIKeyAuth +from litellm.proxy.common_utils.user_api_key_cache import UserApiKeyCache +from litellm.proxy.spend_tracking.budget_reservation import reserve_budget_for_request +from litellm.proxy.utils import ProxyLogging + +TOKEN_COUNTING_ROUTES: Final = ( + "/responses/input_tokens", + "/v1/responses/input_tokens", + "/openai/v1/responses/input_tokens", + "/utils/token_counter", +) + + +def _budgeted_token() -> UserAPIKeyAuth: + return UserAPIKeyAuth(api_key="sk-test", token="hashed-token", max_budget=100.0, spend=0.0) + + +async def _reserve(route: str) -> dict | None: + return await reserve_budget_for_request( + request_body={"model": "gpt-4o", "input": "hello"}, + route=route, + llm_router=None, + valid_token=_budgeted_token(), + team_object=None, + user_object=None, + prisma_client=None, + user_api_key_cache=UserApiKeyCache(), + proxy_logging_obj=ProxyLogging(user_api_key_cache=DualCache()), + ) + + +@pytest.mark.asyncio +@pytest.mark.parametrize("route", TOKEN_COUNTING_ROUTES) +async def test_token_counting_routes_are_exempt_from_budget_reservation(route): + assert await _reserve(route) is None + + +@pytest.mark.asyncio +async def test_non_exempt_llm_route_still_reserves_budget(): + reservation: Final = await _reserve("/v1/responses") + + assert reservation is not None + assert reservation["reserved_cost"] > 0 diff --git a/tests/test_litellm/proxy/spend_tracking/test_savings.py b/tests/test_litellm/proxy/spend_tracking/test_savings.py index c3297ee6ae9..7dd18587df3 100644 --- a/tests/test_litellm/proxy/spend_tracking/test_savings.py +++ b/tests/test_litellm/proxy/spend_tracking/test_savings.py @@ -14,6 +14,8 @@ from litellm.proxy.spend_tracking.savings import ( from litellm.router import Router from litellm.types.utils import Usage +pytestmark = pytest.mark.usefixtures("local_model_cost_map") + def _anthropic_costs(model: str) -> tuple[float, float]: info = litellm.get_model_info(model=model, custom_llm_provider="anthropic") diff --git a/tests/test_litellm/proxy/test_audio_speech_prometheus_hooks.py b/tests/test_litellm/proxy/test_audio_speech_prometheus_hooks.py index 99f6f3a9b72..959cb2b1e89 100644 --- a/tests/test_litellm/proxy/test_audio_speech_prometheus_hooks.py +++ b/tests/test_litellm/proxy/test_audio_speech_prometheus_hooks.py @@ -2,6 +2,7 @@ import asyncio import os from unittest.mock import AsyncMock, MagicMock, patch +import httpx import pytest from fastapi.testclient import TestClient @@ -29,6 +30,7 @@ def _make_mock_tts_response(): inner = MagicMock() inner.aiter_bytes = _aiter_bytes inner._hidden_params = {} + inner.response = httpx.Response(status_code=200, headers={"content-type": "audio/mpeg"}) async def _resolver(): return inner diff --git a/tests/test_litellm/proxy/test_common_request_processing.py b/tests/test_litellm/proxy/test_common_request_processing.py index 629ae6084b8..df14224af5c 100644 --- a/tests/test_litellm/proxy/test_common_request_processing.py +++ b/tests/test_litellm/proxy/test_common_request_processing.py @@ -4694,7 +4694,7 @@ class TestAllmPassthroughStreamingProviderGate: } return ProxyBaseLLMRequestProcessing(data=data) - async def _run(self, processing_obj, monkeypatch, chunks): + async def _run(self, processing_obj, monkeypatch, chunks, stream=None): import litellm.proxy.common_request_processing as crp from litellm.proxy._types import UserAPIKeyAuth as RealUserAPIKeyAuth @@ -4702,9 +4702,11 @@ class TestAllmPassthroughStreamingProviderGate: for chunk in chunks: yield chunk + upstream_stream = stream if stream is not None else streaming_response() + async def fake_route_request(**kwargs): async def _llm_call(): - return streaming_response() + return upstream_stream return _llm_call() @@ -4729,6 +4731,40 @@ class TestAllmPassthroughStreamingProviderGate: skip_pre_call_logic=True, ) + @pytest.mark.asyncio + async def test_client_disconnect_closes_unbuffered_passthrough_stream(self, monkeypatch): + """Starlette abandons the body iterator when the client disconnects, so the + unbuffered passthrough branch must return _UpstreamClosingStreamingResponse, + whose shielded cleanup closes the upstream stream; that close is what flushes + buffered passthrough usage into spend logs.""" + processing_obj = self._build_processing_obj("gigachat") + monkeypatch.setattr(litellm, "callbacks", []) + upstream_closed = asyncio.Event() + + async def hanging_stream(): + try: + yield b"chunk-1" + await asyncio.Event().wait() + finally: + upstream_closed.set() + + result = await self._run(processing_obj, monkeypatch, [], stream=hanging_stream()) + + assert isinstance(result, _UpstreamClosingStreamingResponse) + + first_chunk_sent = asyncio.Event() + + async def receive(): + await first_chunk_sent.wait() + return {"type": "http.disconnect"} + + async def send(message): + if message["type"] == "http.response.body" and message.get("body"): + first_chunk_sent.set() + + await result({"type": "http"}, receive, send) + await asyncio.wait_for(upstream_closed.wait(), timeout=5) + @pytest.mark.asyncio async def test_non_bedrock_stream_is_not_buffered(self, monkeypatch): processing_obj = self._build_processing_obj("anthropic") diff --git a/tests/test_litellm/proxy/test_proxy_cli.py b/tests/test_litellm/proxy/test_proxy_cli.py index 6ea6f208bb5..5e2dd358d75 100644 --- a/tests/test_litellm/proxy/test_proxy_cli.py +++ b/tests/test_litellm/proxy/test_proxy_cli.py @@ -1737,7 +1737,8 @@ class TestRunServerDbSetup: mock_atexit_register, mock_subprocess_run, ): - """Test that use_prisma_db_push flag correctly controls PrismaManager.setup_database use_migrate parameter""" + """Which resolver and which migration mode run_server hands setup_database, + across the db push flag, the v2/legacy flag pair and USE_V2_MIGRATION_RESOLVER.""" from litellm.proxy.proxy_cli import run_server # Mock subprocess.run to simulate prisma being available @@ -1787,7 +1788,7 @@ class TestRunServerDbSetup: # use_prisma_db_push should be False (default), so use_migrate should be True run_server.main(["--local", "--skip_server_startup"], standalone_mode=False) mock_setup_database.assert_called_with( - use_migrate=True, use_v2_resolver=False + use_migrate=True, use_v2_resolver=True ) # Reset mocks @@ -1802,9 +1803,38 @@ class TestRunServerDbSetup: standalone_mode=False, ) mock_setup_database.assert_called_with( - use_migrate=False, use_v2_resolver=False + use_migrate=False, use_v2_resolver=True ) + for argv, env_value, expected_v2 in ( + ([], None, True), + (["--use_v2_migration_resolver"], None, True), + (["--use_legacy_migration_resolver"], None, False), + ([], "false", False), + ([], "true", True), + (["--use_v2_migration_resolver"], "false", True), + (["--use_legacy_migration_resolver"], "true", False), + ): + mock_setup_database.reset_mock() + mock_should_update_schema.reset_mock() + mock_should_update_schema.return_value = True + + resolver_env = ( + {"USE_V2_MIGRATION_RESOLVER": env_value} + if env_value is not None + else {} + ) + os.environ.pop("USE_V2_MIGRATION_RESOLVER", None) + with patch.dict(os.environ, resolver_env): + run_server.main( + ["--local", "--skip_server_startup", *argv], + standalone_mode=False, + ) + assert mock_setup_database.call_args.kwargs == { + "use_migrate": True, + "use_v2_resolver": expected_v2, + }, f"argv={argv} env={env_value}" + @patch("subprocess.run") @patch("atexit.register") @patch("litellm.proxy.db.prisma_client.PrismaManager.setup_database") @@ -1869,7 +1899,7 @@ class TestRunServerDbSetup: ) assert exc_info.value.code == 1 mock_setup_database.assert_called_once_with( - use_migrate=True, use_v2_resolver=False + use_migrate=True, use_v2_resolver=True ) @patch("subprocess.run") @@ -1981,7 +2011,6 @@ class TestRunServerDbSetup: use_migrate=True, use_v2_resolver=True ) - # --- Module-level helpers for worker startup hook tests --- _dummy_hook_called = False diff --git a/tests/test_litellm/proxy/test_proxy_server.py b/tests/test_litellm/proxy/test_proxy_server.py index 1de3ed6e56d..43280258153 100644 --- a/tests/test_litellm/proxy/test_proxy_server.py +++ b/tests/test_litellm/proxy/test_proxy_server.py @@ -11399,35 +11399,10 @@ async def test_no_window_spend_row_enqueued_without_budget_limits(): assert enqueued == [] -@pytest.mark.asyncio -async def test_window_spend_row_carries_the_spend_log_request_id(): - """The flush excludes these ids from its one-time seed, so the id threaded - here has to be the same one the LiteLLM_SpendLogs row was written under.""" - from litellm.proxy.proxy_server import increment_spend_counters - - reset_at = datetime.now(timezone.utc) + timedelta(days=10) - key_obj = MagicMock() - key_obj.budget_limits = [ - {"budget_duration": "30d", "max_budget": 100.0, "reset_at": reset_at.isoformat()} - ] - - with _window_spend_enqueue_env({"hashed-token": key_obj}) as queue: - await increment_spend_counters( - token="hashed-token", - team_id=None, - user_id=None, - response_cost=0.25, - request_id="chatcmpl-abc123", - ) - enqueued = await _drain(queue) - - assert enqueued[0]["request_ids"] == ("chatcmpl-abc123",) - - @pytest.mark.asyncio async def test_window_spend_row_carries_the_request_start_time(): - """The seed only excludes a batch id whose LiteLLM_SpendLogs.startTime is at - or after this, so it must be the same start the spend log was written with.""" + """The seed sums LiteLLM_SpendLogs only up to this point, so it must be the + same start the spend log row was written with.""" from litellm.proxy.proxy_server import increment_spend_counters reset_at = datetime.now(timezone.utc) + timedelta(days=10) @@ -11442,7 +11417,6 @@ async def test_window_spend_row_carries_the_request_start_time(): team_id=None, user_id=None, response_cost=0.25, - request_id="chatcmpl-abc123", request_started_at=datetime(2026, 8, 10, 12, 0, 0, 500_000, tzinfo=timezone.utc), ) enqueued = await _drain(queue) @@ -11451,26 +11425,7 @@ async def test_window_spend_row_carries_the_request_start_time(): @pytest.mark.asyncio -async def test_window_spend_row_without_a_request_id_excludes_nothing(): - from litellm.proxy.proxy_server import increment_spend_counters - - reset_at = datetime.now(timezone.utc) + timedelta(days=10) - key_obj = MagicMock() - key_obj.budget_limits = [ - {"budget_duration": "30d", "max_budget": 100.0, "reset_at": reset_at.isoformat()} - ] - - with _window_spend_enqueue_env({"hashed-token": key_obj}) as queue: - await increment_spend_counters( - token="hashed-token", team_id=None, user_id=None, response_cost=0.25 - ) - enqueued = await _drain(queue) - - assert enqueued[0]["request_ids"] == () - - -@pytest.mark.asyncio -async def test_team_window_spend_row_carries_the_request_id(): +async def test_team_window_spend_row_carries_the_request_start_time(): from litellm.proxy.proxy_server import increment_spend_counters reset_at = datetime.now(timezone.utc) + timedelta(days=3) @@ -11485,11 +11440,11 @@ async def test_team_window_spend_row_carries_the_request_id(): team_id="team-1", user_id=None, response_cost=1.5, - request_id="chatcmpl-team", + request_started_at=datetime(2026, 8, 10, 12, 0, 0, 500_000, tzinfo=timezone.utc), ) enqueued = await _drain(queue) - assert enqueued[0]["request_ids"] == ("chatcmpl-team",) + assert enqueued[0]["started_at"] == "2026-08-10T12:00:00.500000" def _mock_startup_prisma_client(health_check_error=None, connect_error=None): diff --git a/tests/test_litellm/responses/litellm_completion_transformation/test_litellm_completion_responses.py b/tests/test_litellm/responses/litellm_completion_transformation/test_litellm_completion_responses.py index b96d2eb5322..aba79fe11bf 100644 --- a/tests/test_litellm/responses/litellm_completion_transformation/test_litellm_completion_responses.py +++ b/tests/test_litellm/responses/litellm_completion_transformation/test_litellm_completion_responses.py @@ -124,6 +124,25 @@ class TestLiteLLMCompletionResponsesConfig: assert "extra_field" not in result["file"] assert "another_field" not in result["file"] + def test_transform_input_file_item_to_file_item_keeps_filename(self): + """OpenAI rejects file_data with no filename beside it, so dropping it 400s the request""" + result = ( + LiteLLMCompletionResponsesConfig._transform_input_file_item_to_file_item( + { + "type": "input_file", + "filename": "report.pdf", + "file_data": "data:application/pdf;base64,JVBERi0=", + } + ) + ) + assert result == { + "type": "file", + "file": { + "file_data": "data:application/pdf;base64,JVBERi0=", + "filename": "report.pdf", + }, + } + def test_transform_input_file_item_to_file_item_with_file_url(self): """file_url should be mapped to file_id for downstream URL handling""" result = ( @@ -966,6 +985,55 @@ class TestFunctionCallTransformation: assert result[0]["tool_calls"][0]["function"]["arguments"] == "{}" + def test_function_call_transformation_json_encodes_object_arguments(self): + """A decoded arguments object must be JSON-encoded, not str()'d. + + Clients and providers sometimes send `arguments` as an object rather + than a JSON string; `str()` on a dict produces a Python repr with + single quotes, which downstream JSON parsers reject with errors like + "Expecting ',' delimiter". + """ + function_call_item = { + "type": "function_call", + "name": "shell", + "arguments": {"command": "ls", "timeout": 30, "flags": ["-l", "-a"]}, + "call_id": "call_123", + "id": "call_123", + "status": "completed", + } + + result = LiteLLMCompletionResponsesConfig._transform_responses_api_function_call_to_chat_completion_message( + function_call=function_call_item + ) + + arguments = result[0].get("tool_calls", [])[0].get("function", {}).get("arguments") + assert json.loads(arguments) == {"command": "ls", "timeout": 30, "flags": ["-l", "-a"]} + assert "'" not in arguments + + def test_create_tool_call_chunk_json_encodes_object_arguments(self): + """Cached tool_call definitions with object arguments stay valid JSON.""" + chunk = LiteLLMCompletionResponsesConfig._create_tool_call_chunk( + tool_use_definition={ + "id": "call_456", + "type": "function", + "function": {"name": "shell", "arguments": {"command": "ls"}}, + }, + tool_call_id="call_456", + index=0, + ) + + assert json.loads(chunk["function"]["arguments"]) == {"command": "ls"} + + def test_create_tool_call_chunk_keeps_empty_arguments_default(self): + """Missing arguments still fall back to an empty JSON object.""" + chunk = LiteLLMCompletionResponsesConfig._create_tool_call_chunk( + tool_use_definition={"id": "call_789", "type": "function", "function": {"name": "shell"}}, + tool_call_id="call_789", + index=0, + ) + + assert chunk["function"]["arguments"] == "{}" + def test_complete_input_transformation_with_function_calls(self): """Test the complete transformation with the exact input from the issue""" test_input = [ diff --git a/tests/test_litellm/responses/litellm_completion_transformation/test_streaming_iterator_transformation.py b/tests/test_litellm/responses/litellm_completion_transformation/test_streaming_iterator_transformation.py index 823f656ddc5..01148f627f1 100644 --- a/tests/test_litellm/responses/litellm_completion_transformation/test_streaming_iterator_transformation.py +++ b/tests/test_litellm/responses/litellm_completion_transformation/test_streaming_iterator_transformation.py @@ -10,6 +10,7 @@ before response.completed, and that every event of a bridged stream carries the spend tracking stores, so a follow-up previous_response_id still finds the conversation. """ +import json from unittest.mock import AsyncMock, MagicMock import pytest @@ -523,3 +524,36 @@ async def test_streaming_response_id_falls_back_when_upstream_yields_nothing(): assert response_ids assert len(set(response_ids)) == 1 assert response_ids[0].startswith("resp_") + + +def test_object_tool_call_arguments_stream_as_valid_json(): + """A provider that sends decoded object arguments must still stream valid JSON. + + `str()` on a dict yields a Python repr with single quotes, which clients + parsing function_call_arguments reject with errors like + "Expecting ',' delimiter". + """ + iterator = LiteLLMCompletionStreamingIterator( + model="test-model", + litellm_custom_stream_wrapper=AsyncMock(), + request_input="Test input", + responses_api_request={}, + ) + iterator._queue_tool_call_delta_events( + [ + { + "index": 0, + "id": "call_obj", + "type": "function", + "function": {"name": "shell", "arguments": {"command": "ls", "flags": ["-l"]}}, + } + ] + ) + + streamed_arguments = "".join( + evt.delta + for evt in iterator._pending_tool_events + if evt.type == ResponsesAPIStreamEvents.FUNCTION_CALL_ARGUMENTS_DELTA + ) + + assert json.loads(streamed_arguments) == {"command": "ls", "flags": ["-l"]} diff --git a/tests/test_litellm/router_strategy/test_complexity_router.py b/tests/test_litellm/router_strategy/test_complexity_router.py index eee4e9aa185..1ec8be88c9b 100644 --- a/tests/test_litellm/router_strategy/test_complexity_router.py +++ b/tests/test_litellm/router_strategy/test_complexity_router.py @@ -4425,6 +4425,265 @@ class _DummyPlugin: return context +class TestClassificationMode: + """Test classification_mode='user_turn': classify only requests whose newest turn is a new + human ask; tool-loop continuation turns replay the session's held routing decision.""" + + REASONING_ASK = { + "role": "user", + "content": "Let's think step by step and reason through this problem carefully.", + } + SIMPLE_ASK = {"role": "user", "content": "Hello!"} + ASSISTANT_ANSWER = {"role": "assistant", "content": "the answer"} + TOOL_CALL_1 = { + "role": "assistant", + "content": None, + "tool_calls": [{"id": "call_1", "type": "function", "function": {"name": "read_file", "arguments": "{}"}}], + } + TOOL_RESULT_1 = {"role": "tool", "tool_call_id": "call_1", "content": "file contents"} + TOOL_CALL_2 = { + "role": "assistant", + "content": None, + "tool_calls": [{"id": "call_2", "type": "function", "function": {"name": "run_tests", "arguments": "{}"}}], + } + TOOL_RESULT_2 = {"role": "tool", "tool_call_id": "call_2", "content": "3 passed"} + + @pytest.fixture + def user_turn_config(self, basic_config) -> dict: + return {**basic_config, "classification_mode": "user_turn"} + + @staticmethod + def _request_kwargs(session_id: str) -> dict: + return {"metadata": {"session_id": session_id}} + + def _router(self, mock_router_instance, config: dict) -> ComplexityRouter: + mock_router_instance.cache = DualCache() + return ComplexityRouter( + model_name="test-router", + litellm_router_instance=mock_router_instance, + complexity_router_config=config, + ) + + def _tool_loop_turns(self) -> list[list[dict]]: + return [ + [self.REASONING_ASK], + [self.REASONING_ASK, self.TOOL_CALL_1, self.TOOL_RESULT_1], + [self.REASONING_ASK, self.TOOL_CALL_1, self.TOOL_RESULT_1, self.TOOL_CALL_2, self.TOOL_RESULT_2], + ] + + def test_default_mode_is_every_request(self, complexity_router): + assert complexity_router.config.classification_mode == "every_request" + + def test_invalid_classification_mode_rejected(self, mock_router_instance, basic_config): + with pytest.raises(ValidationError): + ComplexityRouter( + model_name="test-router", + litellm_router_instance=mock_router_instance, + complexity_router_config={**basic_config, "classification_mode": "sometimes"}, + ) + + @pytest.mark.asyncio + async def test_user_turn_mode_classifies_tool_loop_once(self, mock_router_instance, user_turn_config): + """The mutation check: a 3-request tool loop drives exactly one classification, and both + continuation turns hold the classified model under the user_turn_continuation cause.""" + router = self._router(mock_router_instance, user_turn_config) + with patch.object(router, "_classify_and_route", wraps=router._classify_and_route) as spy: + responses = [ + await router.async_pre_routing_hook( + model="test-model", request_kwargs=self._request_kwargs("loop-1"), messages=turn + ) + for turn in self._tool_loop_turns() + ] + assert spy.call_count == 1 + assert [r.model for r in responses] == ["o1-preview", "o1-preview", "o1-preview"] + assert [r.routing_decision["cause"] for r in responses[1:]] == [ + "user_turn_continuation", + "user_turn_continuation", + ] + + @pytest.mark.asyncio + async def test_every_request_default_classifies_every_tool_loop_turn(self, mock_router_instance, basic_config): + """Pins today's default: every request classifies, including tool-loop continuations.""" + router = self._router(mock_router_instance, basic_config) + with patch.object(router, "_classify_and_route", wraps=router._classify_and_route) as spy: + responses = [ + await router.async_pre_routing_hook( + model="test-model", request_kwargs=self._request_kwargs("loop-2"), messages=turn + ) + for turn in self._tool_loop_turns() + ] + assert spy.call_count == 3 + assert [r.model for r in responses] == ["o1-preview", "o1-preview", "o1-preview"] + assert all(r.routing_decision["cause"] != "user_turn_continuation" for r in responses) + + @pytest.mark.asyncio + async def test_continuation_without_session_id_still_classifies(self, mock_router_instance, user_turn_config): + """No resolvable session id means no held decision to replay, so every request classifies.""" + router = self._router(mock_router_instance, user_turn_config) + with patch.object(router, "_classify_and_route", wraps=router._classify_and_route) as spy: + responses = [ + await router.async_pre_routing_hook(model="test-model", request_kwargs={}, messages=turn) + for turn in self._tool_loop_turns() + ] + assert spy.call_count == 3 + assert [r.model for r in responses] == ["o1-preview", "o1-preview", "o1-preview"] + assert all(r.routing_decision["cause"] != "user_turn_continuation" for r in responses) + + @pytest.mark.asyncio + async def test_plugins_suppress_user_turn_gate(self, mock_router_instance, basic_config): + """A replayed decision would bypass the plugin pipeline, so plugins force every request + through _classify_and_route, exactly as they do for session_affinity.""" + router = self._router( + mock_router_instance, + {**basic_config, "classification_mode": "user_turn", "plugins": [_DummyPlugin()]}, + ) + with patch.object(router, "_classify_and_route", wraps=router._classify_and_route) as spy: + responses = [ + await router.async_pre_routing_hook( + model="test-model", request_kwargs=self._request_kwargs("loop-3"), messages=turn + ) + for turn in self._tool_loop_turns() + ] + assert spy.call_count == 3 + assert [r.model for r in responses] == ["o1-preview", "o1-preview", "o1-preview"] + assert all(r.routing_decision["cause"] != "user_turn_continuation" for r in responses) + + @pytest.mark.asyncio + async def test_new_human_ask_reclassifies_and_repins(self, mock_router_instance, user_turn_config): + """Unlike session_affinity, a new human ask never short-circuits on the pin: the session + re-classifies, moves tier, and the moved decision becomes the next held decision.""" + router = self._router(mock_router_instance, user_turn_config) + first = await router.async_pre_routing_hook( + model="test-model", request_kwargs=self._request_kwargs("s-repin"), messages=[self.REASONING_ASK] + ) + second = await router.async_pre_routing_hook( + model="test-model", + request_kwargs=self._request_kwargs("s-repin"), + messages=[self.REASONING_ASK, self.ASSISTANT_ANSWER, self.SIMPLE_ASK], + ) + third = await router.async_pre_routing_hook( + model="test-model", + request_kwargs=self._request_kwargs("s-repin"), + messages=[self.REASONING_ASK, self.ASSISTANT_ANSWER, self.SIMPLE_ASK, self.TOOL_CALL_1, self.TOOL_RESULT_1], + ) + assert first.model == "o1-preview" + assert second.model == "gpt-4o-mini" + assert third.model == "gpt-4o-mini" + assert third.routing_decision["cause"] == "user_turn_continuation" + + @pytest.mark.asyncio + async def test_new_ask_with_trailing_system_reminder_reclassifies(self, mock_router_instance, user_turn_config): + """Claude Code appends a system-role reminder after the human turn; that trailing plumbing + must not turn a new ask into a continuation, and a continuation turn carrying the same + trailing reminder stays a continuation.""" + router = self._router(mock_router_instance, user_turn_config) + reminder = {"role": "system", "content": "100 tokens left"} + first = await router.async_pre_routing_hook( + model="test-model", request_kwargs=self._request_kwargs("s-reminder"), messages=[self.REASONING_ASK] + ) + second = await router.async_pre_routing_hook( + model="test-model", + request_kwargs=self._request_kwargs("s-reminder"), + messages=[self.REASONING_ASK, self.ASSISTANT_ANSWER, self.SIMPLE_ASK, reminder], + ) + third = await router.async_pre_routing_hook( + model="test-model", + request_kwargs=self._request_kwargs("s-reminder"), + messages=[ + self.REASONING_ASK, + self.ASSISTANT_ANSWER, + self.SIMPLE_ASK, + reminder, + self.TOOL_CALL_1, + self.TOOL_RESULT_1, + reminder, + ], + ) + assert first.model == "o1-preview" + assert second.model == "gpt-4o-mini" + assert second.routing_decision["cause"] != "user_turn_continuation" + assert third.model == "gpt-4o-mini" + assert third.routing_decision["cause"] == "user_turn_continuation" + + @pytest.mark.asyncio + async def test_escalation_keyword_turn_is_a_new_ask(self, mock_router_instance, user_turn_config): + """An escalation keyword arrives as human text, so the turn classifies and escalates + instead of replaying the held decision.""" + router = self._router(mock_router_instance, user_turn_config) + first = await router.async_pre_routing_hook( + model="test-model", request_kwargs=self._request_kwargs("s-esc"), messages=[self.SIMPLE_ASK] + ) + second = await router.async_pre_routing_hook( + model="test-model", + request_kwargs=self._request_kwargs("s-esc"), + messages=[self.SIMPLE_ASK, self.ASSISTANT_ANSWER, {"role": "user", "content": "LITELLM ESCALATE"}], + ) + assert first.model == "gpt-4o-mini" + assert second.model == "gpt-4o" + assert second.routing_decision["escalated"] is True + + @pytest.mark.asyncio + async def test_messages_surface_tool_result_shapes(self, mock_router_instance, user_turn_config): + """Messages-surface shapes: a tool_result-only user turn is a continuation, while an ask + riding alongside a tool_result in the same turn is a new ask.""" + router = self._router(mock_router_instance, user_turn_config) + tool_use = {"role": "assistant", "content": [{"type": "tool_use", "id": "x", "name": "t", "input": {}}]} + tool_result = {"type": "tool_result", "tool_use_id": "x", "content": "ok"} + first = await router.async_pre_routing_hook( + model="test-model", request_kwargs=self._request_kwargs("s-msgs"), messages=[self.REASONING_ASK] + ) + pure = await router.async_pre_routing_hook( + model="test-model", + request_kwargs=self._request_kwargs("s-msgs"), + messages=[self.REASONING_ASK, tool_use, {"role": "user", "content": [tool_result]}], + ) + hybrid = await router.async_pre_routing_hook( + model="test-model", + request_kwargs=self._request_kwargs("s-msgs"), + messages=[ + self.REASONING_ASK, + tool_use, + {"role": "user", "content": [tool_result, {"type": "text", "text": "Hello!"}]}, + ], + ) + assert first.model == "o1-preview" + assert pure.model == "o1-preview" + assert pure.routing_decision["cause"] == "user_turn_continuation" + assert hybrid.model == "gpt-4o-mini" + + @pytest.mark.asyncio + async def test_session_affinity_wins_when_both_knobs_are_on(self, mock_router_instance, user_turn_config): + """With session_affinity also on, the pin short-circuits new asks too and keeps its own + cause, so the session stays on turn 1's model.""" + router = self._router(mock_router_instance, {**user_turn_config, "session_affinity": True}) + first = await router.async_pre_routing_hook( + model="test-model", request_kwargs=self._request_kwargs("s-both"), messages=[self.REASONING_ASK] + ) + second = await router.async_pre_routing_hook( + model="test-model", + request_kwargs=self._request_kwargs("s-both"), + messages=[self.REASONING_ASK, self.ASSISTANT_ANSWER, self.SIMPLE_ASK], + ) + assert first.model == "o1-preview" + assert second.model == "o1-preview" + assert second.routing_decision["cause"] == "session_affinity_pin" + + def test_user_turn_mode_enables_tier_and_deployment_pins(self, mock_router_instance, basic_config): + """user_turn implies the tier pin machinery (the pin write is what gives a continuation + a held decision) and the tier pin implies the deployment pin; plugins suppress both.""" + default = self._router(mock_router_instance, basic_config) + enabled = self._router(mock_router_instance, {**basic_config, "classification_mode": "user_turn"}) + suppressed = self._router( + mock_router_instance, + {**basic_config, "classification_mode": "user_turn", "plugins": [_DummyPlugin()]}, + ) + assert default._uses_tier_pin is False + assert enabled._uses_tier_pin is True + assert enabled._uses_deployment_pin is True + assert suppressed._uses_tier_pin is False + assert suppressed._uses_deployment_pin is False + + class TestRoutingPlugins: """Test the `complexity_router_config.plugins` field: narrows the classified tier's candidate pool before a model is picked. Discussion: @@ -9762,3 +10021,719 @@ class TestHeuristicFirst: ) outcome = await router.aclassify(NO_SIGNAL_PROMPT) assert outcome.cause == "default_model_fallback" + + +def _windowed_router(*deployments: tuple) -> Router: + """Real Router; each deployment is (group, provider_model, declared window or None). + None means no declared override on a model the cost map does not know: unresolvable.""" + return Router( + model_list=[ + { + "model_name": group, + "litellm_params": {"model": provider_model, "mock_response": "ok"}, + **({"model_info": {"max_input_tokens": window}} if window is not None else {}), + } + for group, provider_model, window in deployments + ] + ) + + +_SMALL = ("small-model", "openai/gpt-3.5-turbo", 16385) +_BIG = ("big-model", "openai/gpt-4o-mini", 200000) + +# A long agentic session whose newest ask is trivial: low-density filler the heuristic scores +# SIMPLE, sized well past a 16,385-token window so the fit check must move it. +_CONTEXT_FILLER = "The meeting notes were saved to the shared folder for later review this week. " * 2000 +_OVERSIZED_TURNS = [ + {"role": "user", "content": "Here is everything discussed so far. " + _CONTEXT_FILLER}, + {"role": "assistant", "content": "Noted, I have read all of it."}, + {"role": "user", "content": "ok continue"}, +] +# ~40k CJK chars: chars/4 says ~10k tokens, the real tokenizer says several times that. A +# character-based shortcut would skip counting and dispatch this to a 16k window. +_CJK_TURNS = [ + {"role": "user", "content": "会议记录已经保存到共享文件夹里，供大家本周晚些时候查阅和讨论使用。" * 1300}, + {"role": "user", "content": "ok continue"}, +] + + +def _tier_config(**overrides) -> Dict: + return {"tiers": {"SIMPLE": "small-model", "COMPLEX": "big-model"}, **overrides} + + +class TestContextWindowEscalation: + """A tier decided on complexity alone must still hold the prompt, or the provider 400s. + + The classifier never weighs prompt size (token count is a 0.10-weight scoring dimension, + below every tier boundary), so a long session ending in a trivial ask lands on the + smallest tier and dies upstream with no retry. The gate checks fit pre-dispatch, against + windows resolved through the real Router deployment chain. + """ + + @pytest.mark.asyncio + async def test_an_oversized_simple_prompt_escalates_to_the_lowest_tier_that_fits(self): + """The LIT-6503 regression: SIMPLE verdict, 17k-token prompt, 16,385-token tier model. + + Unfixed, this dispatched to the small model and the provider rejected it with a + context-window 400 that neither the retry layer nor tier-keyed fallbacks catch. + """ + router = ComplexityRouter( + model_name="test-router", + litellm_router_instance=_windowed_router(_SMALL, _BIG), + complexity_router_config=_tier_config(), + ) + + result = await router.async_pre_routing_hook(model="test-router", request_kwargs={}, messages=_OVERSIZED_TURNS) + + assert result is not None + assert result.model == "big-model" + assert result.routing_decision["context_escalated"] is True + assert result.routing_decision["context_escalation_original_tier"] == "SIMPLE" + assert result.routing_decision["tier"] == "COMPLEX" + assert "context_escalation" in result.routing_decision["signals"] + + @pytest.mark.asyncio + async def test_a_prompt_that_fits_routes_exactly_as_before(self): + """The gate must be invisible for normal traffic: same model, no escalation facts.""" + router = ComplexityRouter( + model_name="test-router", + litellm_router_instance=_windowed_router(_SMALL, _BIG), + complexity_router_config=_tier_config(), + ) + + result = await router.async_pre_routing_hook( + model="test-router", request_kwargs={}, messages=[{"role": "user", "content": "ok continue"}] + ) + + assert result is not None + assert result.model == "small-model" + assert "context_escalated" not in result.routing_decision + assert "context_escalation_original_tier" not in result.routing_decision + + @pytest.mark.asyncio + async def test_the_pick_prefers_a_fitting_group_inside_the_decided_tier(self): + """A tier holding both a small and a large group keeps the request and picks the one + that fits, which is cheaper than escalating and preserves the classifier's decision.""" + router = ComplexityRouter( + model_name="test-router", + litellm_router_instance=_windowed_router(_SMALL, ("mid-model", "openai/gpt-4o-mini", 200000), _BIG), + complexity_router_config={"tiers": {"SIMPLE": ["small-model", "mid-model"], "COMPLEX": "big-model"}}, + ) + + result = await router.async_pre_routing_hook(model="test-router", request_kwargs={}, messages=_OVERSIZED_TURNS) + + assert result is not None + assert result.model == "mid-model" + assert result.routing_decision["tier"] == "SIMPLE" + assert "context_escalated" not in result.routing_decision + + @pytest.mark.asyncio + async def test_a_group_is_only_as_safe_as_its_smallest_deployment(self): + """One group name can front deployments with different windows, and the core router + picks among them with no fit check, so retaining the group on its largest member + turns the pick into a coin flip against a 400. The gate judges the group by its + smallest resolvable window and escalates past it.""" + router = ComplexityRouter( + model_name="test-router", + litellm_router_instance=Router( + model_list=[ + { + "model_name": "mixed-pool", + "litellm_params": {"model": "openai/gpt-3.5-turbo", "mock_response": "ok"}, + "model_info": {"max_input_tokens": 16385}, + }, + { + "model_name": "mixed-pool", + "litellm_params": {"model": "openai/gpt-4o-mini", "mock_response": "ok"}, + "model_info": {"max_input_tokens": 200000}, + }, + { + "model_name": "big-model", + "litellm_params": {"model": "openai/gpt-4o-mini", "mock_response": "ok"}, + "model_info": {"max_input_tokens": 200000}, + }, + ] + ), + complexity_router_config={"tiers": {"SIMPLE": "mixed-pool", "COMPLEX": "big-model"}}, + ) + + result = await router.async_pre_routing_hook(model="test-router", request_kwargs={}, messages=_OVERSIZED_TURNS) + + assert result is not None + assert result.model == "big-model" + assert result.routing_decision["context_escalated"] is True + + @pytest.mark.asyncio + async def test_token_dense_text_cannot_slip_past_the_counting_shortcut(self): + """CJK text runs several tokens per four characters, so a chars/4 shortcut would skip + the real count and dispatch an oversized prompt. The skip is gated on the UTF-8 byte + length, which the token count can never exceed.""" + router = ComplexityRouter( + model_name="test-router", + litellm_router_instance=_windowed_router(_SMALL, _BIG), + complexity_router_config=_tier_config(), + ) + + result = await router.async_pre_routing_hook(model="test-router", request_kwargs={}, messages=_CJK_TURNS) + + assert result is not None + assert result.model == "big-model" + assert result.routing_decision["context_escalated"] is True + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "deployments,tiers,expected_model", + [ + ( + (("small-model", "openai/unmapped-model-under-test", None), _BIG), + {"SIMPLE": "small-model", "COMPLEX": "big-model"}, + "small-model", + ), + ( + (_SMALL, ("mid-model", "openai/another-unmapped-model", None), _BIG), + {"SIMPLE": "small-model", "MEDIUM": "mid-model", "COMPLEX": "big-model"}, + "big-model", + ), + ((_SMALL,), {"SIMPLE": "small-model"}, "small-model"), + ], + ids=["unknown-window-stays", "unproven-target-skipped", "nothing-fits-stays"], + ) + async def test_unknown_windows_are_never_acted_on(self, deployments, tiers, expected_model): + """No faith in either direction: a model with no resolvable window is never escalated + away from (its misfit is unprovable) and never escalated onto (its fit is unprovable); + when nothing provably fits, the classified tier stands and the client owns overflow.""" + router = ComplexityRouter( + model_name="test-router", + litellm_router_instance=_windowed_router(*deployments), + complexity_router_config={"tiers": tiers}, + ) + + result = await router.async_pre_routing_hook(model="test-router", request_kwargs={}, messages=_OVERSIZED_TURNS) + + assert result is not None + assert result.model == expected_model + + @pytest.mark.asyncio + async def test_the_disabled_gate_dispatches_on_complexity_alone(self): + """The escape hatch: enable_context_window_escalation false restores today's behavior.""" + router = ComplexityRouter( + model_name="test-router", + litellm_router_instance=_windowed_router(_SMALL, _BIG), + complexity_router_config=_tier_config(enable_context_window_escalation=False), + ) + + result = await router.async_pre_routing_hook(model="test-router", request_kwargs={}, messages=_OVERSIZED_TURNS) + + assert result is not None + assert result.model == "small-model" + assert "context_escalated" not in result.routing_decision + + @pytest.mark.asyncio + async def test_out_of_band_system_and_tools_count_against_the_window(self): + """The Claude Code shape that live-testing caught: a tiny ask riding a top-level + `system` block and tool definitions that together dwarf the message list. None of + that reaches resolved messages on /v1/messages, so a gate reading only messages + dispatches a provably oversized request and the provider 400s anyway.""" + router = ComplexityRouter( + model_name="test-router", + litellm_router_instance=_windowed_router(_SMALL, _BIG), + complexity_router_config=_tier_config(), + ) + + result = await router.async_pre_routing_hook( + model="test-router", + request_kwargs={ + "proxy_server_request": { + "body": { + "system": _CONTEXT_FILLER, + "tools": [{"name": f"tool_{i}", "description": _CONTEXT_FILLER[:500]} for i in range(20)], + } + } + }, + messages=[{"role": "user", "content": "reply with exactly: rig check ok"}], + ) + + assert result is not None + assert result.model == "big-model" + assert result.routing_decision["context_escalated"] is True + + @pytest.mark.asyncio + async def test_an_escalated_first_turn_never_becomes_the_session_pin(self): + """Escalation describes the prompt's size, not the session: once the client compacts, + the next turn fits again, so pinning the big-window tier would hold the whole session + on it for the TTL. The escalated turn routes big, and the next fitting turn classifies + fresh instead of inheriting a pin.""" + router = ComplexityRouter( + model_name="test-router", + litellm_router_instance=_windowed_router(_SMALL, _BIG), + complexity_router_config=_tier_config(session_affinity=True), + ) + session_kwargs = lambda: {"metadata": {"session_id": "s-1", "user_api_key_hash": "k-1"}} # noqa: E731 + + first = await router.async_pre_routing_hook( + model="test-router", request_kwargs=session_kwargs(), messages=_OVERSIZED_TURNS + ) + second = await router.async_pre_routing_hook( + model="test-router", request_kwargs=session_kwargs(), messages=[{"role": "user", "content": "ok continue"}] + ) + + assert first is not None and first.model == "big-model" + assert second is not None and second.model == "small-model" + assert second.routing_decision["cause"] != "session_affinity_pin" + + @pytest.mark.asyncio + async def test_a_pinned_session_escalates_per_request_and_keeps_its_pin(self): + """The pin fast path skips classification, not physics: an oversized turn on a session + pinned to the small tier is served by the fitting tier, while the stored pin keeps the + session's own model so the first turn that fits again routes exactly as pinned.""" + router = ComplexityRouter( + model_name="test-router", + litellm_router_instance=_windowed_router(_SMALL, _BIG), + complexity_router_config=_tier_config(session_affinity=True), + ) + session_kwargs = lambda: {"metadata": {"session_id": "s-2", "user_api_key_hash": "k-2"}} # noqa: E731 + + pinned = await router.async_pre_routing_hook( + model="test-router", request_kwargs=session_kwargs(), messages=[{"role": "user", "content": "ok continue"}] + ) + oversized = await router.async_pre_routing_hook( + model="test-router", request_kwargs=session_kwargs(), messages=_OVERSIZED_TURNS + ) + back_to_small = await router.async_pre_routing_hook( + model="test-router", request_kwargs=session_kwargs(), messages=[{"role": "user", "content": "ok continue"}] + ) + + assert pinned is not None and pinned.model == "small-model" + assert oversized is not None and oversized.model == "big-model" + assert oversized.routing_decision["cause"] == "session_affinity_pin" + assert oversized.routing_decision["context_escalated"] is True + assert oversized.routing_decision["context_escalation_original_tier"] == "SIMPLE" + assert back_to_small is not None and back_to_small.model == "small-model" + assert back_to_small.routing_decision["cause"] == "session_affinity_pin" + + @pytest.mark.asyncio + async def test_the_adaptive_cold_start_never_samples_a_model_that_cannot_hold_the_prompt(self): + """The bandit's exploration is still bounded by physics: with the whole classified tier + unobserved, cold start samples only among models whose window holds the prompt.""" + router = ComplexityRouter( + model_name="test-router", + litellm_router_instance=Router( + model_list=[ + { + "model_name": "small-model", + "litellm_params": {"model": "openai/gpt-3.5-turbo", "mock_response": "ok"}, + "model_info": {"max_input_tokens": 16385}, + }, + { + "model_name": "mid-model", + "litellm_params": {"model": "openai/gpt-4o-mini", "mock_response": "ok"}, + "model_info": {"max_input_tokens": 200000}, + }, + ] + ), + complexity_router_config={"adaptive": True, "tiers": {"SIMPLE": ["small-model", "mid-model"]}}, + ) + + result = await router.async_pre_routing_hook(model="test-router", request_kwargs={}, messages=_OVERSIZED_TURNS) + + assert result is not None + assert result.model == "mid-model" + + @pytest.mark.asyncio + async def test_the_gate_never_resolves_an_authenticating_provider(self, monkeypatch, tmp_path): + """Resolving github_copilot runs its OAuth device flow, so a window question must adopt + the declaration instead of resolving: the copilot group reads as unknown-window and the + request stays put, with zero copilot resolutions recorded.""" + import json + import time + + monkeypatch.setenv("GITHUB_COPILOT_TOKEN_DIR", str(tmp_path)) + (tmp_path / "api-key.json").write_text(json.dumps({"token": "tid=test", "expires_at": int(time.time()) + 3600})) + router = ComplexityRouter( + model_name="test-router", + litellm_router_instance=Router( + model_list=[ + {"model_name": "cop-pool", "litellm_params": {"model": "github_copilot/gpt-4o"}}, + { + "model_name": "big-model", + "litellm_params": {"model": "openai/gpt-4o-mini", "mock_response": "ok"}, + "model_info": {"max_input_tokens": 200000}, + }, + ] + ), + complexity_router_config={"tiers": {"SIMPLE": "cop-pool", "COMPLEX": "big-model"}}, + ) + real_get_llm_provider = litellm.get_llm_provider + copilot_resolutions: List = [] + + def _guarded(*args, **kwargs): + target = str(kwargs.get("model") or (args[0] if args else "")) + str(kwargs.get("custom_llm_provider") or "") + if "github_copilot" in target: + copilot_resolutions.append(target) + raise RuntimeError("the gate must not resolve an authenticating provider") + return real_get_llm_provider(*args, **kwargs) + + monkeypatch.setattr(litellm, "get_llm_provider", _guarded) + + result = await router.async_pre_routing_hook(model="test-router", request_kwargs={}, messages=_OVERSIZED_TURNS) + + assert result is not None + assert result.model == "cop-pool" + assert copilot_resolutions == [] + + @pytest.mark.asyncio + async def test_the_full_routing_path_serves_the_escalated_deployment(self): + """End to end through Router.async_get_available_deployment: the auto-router alias with + an oversized prompt resolves to the big tier's deployment, and a small prompt to the + small tier's, with no mocking anywhere in the resolution chain.""" + router = Router( + model_list=[ + { + "model_name": "smart-router", + "litellm_params": { + "model": "auto_router/complexity_router", + "complexity_router_config": {"tiers": {"SIMPLE": "small-model", "COMPLEX": "big-model"}}, + }, + }, + { + "model_name": "small-model", + "litellm_params": {"model": "openai/gpt-3.5-turbo", "mock_response": "ok"}, + "model_info": {"max_input_tokens": 16385}, + }, + { + "model_name": "big-model", + "litellm_params": {"model": "openai/gpt-4o-mini", "mock_response": "ok"}, + "model_info": {"max_input_tokens": 200000}, + }, + ] + ) + + oversized = await router.async_get_available_deployment( + model="smart-router", request_kwargs={}, messages=_OVERSIZED_TURNS + ) + small = await router.async_get_available_deployment( + model="smart-router", request_kwargs={}, messages=[{"role": "user", "content": "ok continue"}] + ) + + assert oversized["model_name"] == "big-model" + assert small["model_name"] == "small-model" + + +IMG_PART = {"type": "image_url", "image_url": {"url": "data:image/png;base64,aGk="}} +PLAN_BODY = { + "messages": [{"role": "system", "content": [{"type": "text", "text": "Plan mode is active. Do not execute."}]}] +} + + +class TestModalityRouting: + """modality_routing: the response gate replaces a routed model that cannot take images.""" + + IMAGE_MESSAGE = [{"role": "user", "content": [{"type": "text", "text": "What color is this?"}, IMG_PART]}] + BASE_TIERS = {"SIMPLE": "text-cheap", "MEDIUM": "vision-mid", "COMPLEX": "vision-big"} + BASE_VISION = {"text-cheap": False, "vision-mid": True, "vision-big": True, "vision-default": True} + + @staticmethod + def _router(mock_router_instance, config, vision_by_model): + """vision_by_model: model name -> True/False (deployment model_info) or None (undeclared).""" + + def get_model_list(model_name=None): + if model_name not in vision_by_model: + return [] + declared = vision_by_model[model_name] + return [ + { + "model_name": model_name, + "litellm_params": {"model": f"openai/unmapped-{model_name}"}, + "model_info": {} if declared is None else {"supports_vision": declared}, + } + ] + + mock_router_instance.get_model_list = get_model_list + return ComplexityRouter( + model_name="modality-test-router", + litellm_router_instance=mock_router_instance, + complexity_router_config=config, + ) + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "config_extra, vision, send_image, expected_model, expect_marker", + [ + ({}, {"text-cheap": False}, True, "text-cheap", False), + ({"modality_routing": True}, {"text-cheap": False}, False, "text-cheap", False), + ({"modality_routing": True}, {"text-cheap": None}, True, "text-cheap", False), + ], + ids=["flag_off", "no_image", "undeclared_model_stays_routable"], + ) + async def test_gate_leaves_ungated_requests_untouched( + self, mock_router_instance, config_extra, vision, send_image, expected_model, expect_marker + ): + router = self._router(mock_router_instance, {"tiers": dict(self.BASE_TIERS), **config_extra}, vision) + request = self.IMAGE_MESSAGE if send_image else [{"role": "user", "content": "What color is the sky?"}] + result = await router.async_pre_routing_hook(model="m", request_kwargs={}, messages=request) + assert result.model == expected_model + assert result.routing_decision["cause"] == "heuristic_scorer" + assert ("modality:image" in (result.routing_decision.get("signals") or ())) is expect_marker + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "part", + [ + IMG_PART, + {"type": "input_image", "image_url": "data:image/png;base64,aGk="}, + {"type": "image", "source": {"type": "base64", "media_type": "image/png", "data": "aGk="}}, + {"type": "tool_result", "tool_use_id": "tu_1", "content": [dict(IMG_PART, type="image")]}, + ], + ids=["image_url", "input_image", "anthropic_image", "tool_result_nested"], + ) + async def test_every_image_dialect_escalates(self, mock_router_instance, part): + router = self._router( + mock_router_instance, {"tiers": dict(self.BASE_TIERS), "modality_routing": True}, dict(self.BASE_VISION) + ) + message = [{"role": "user", "content": [{"type": "text", "text": "What color is this?"}, part]}] + result = await router.async_pre_routing_hook(model="m", request_kwargs={}, messages=message) + assert result.model == "vision-mid" + assert result.routing_decision["cause"] == "modality_escalation" + assert "modality_escalated_from:SIMPLE" in result.routing_decision["signals"] + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "path, expected_model, expected_cause", + [ + ("classifier_escalates", "vision-mid", "modality_escalation"), + ("same_tier_repick_keeps_cause", "vision-cheap", "heuristic_scorer"), + ("keyword_tier_escalates", "vision-mid", "modality_escalation"), + ("no_ask_capable_default_kept", "vision-default", "default_fallback"), + ("no_ask_text_default_displaced", "vision-mid", "modality_escalation"), + ("custom_tiers_walk", "premium-model", "modality_escalation"), + ("pin_kept_bypasses", "text-cheap", "session_affinity_pin"), + ("pin_replacement_gated", "vision-big", "modality_escalation"), + ("adaptive_pick_rewritten", "vision-mid", "modality_escalation"), + ], + ) + async def test_placements_across_decision_paths(self, mock_router_instance, path, expected_model, expected_cause): + config = {"tiers": dict(self.BASE_TIERS), "modality_routing": True} + vision = dict(self.BASE_VISION) + request_kwargs = {} + messages = self.IMAGE_MESSAGE + if path == "same_tier_repick_keeps_cause": + config["tiers"]["SIMPLE"] = ["text-cheap", "vision-cheap"] + vision["vision-cheap"] = True + with patch( # test-quality-ok: the mixed-pool repick is unreachable deterministically without pinning the first random pick + "litellm.router_strategy.complexity_router.complexity_router.random.choice", + side_effect=lambda pool: sorted(pool)[0], + ): + router = self._router(mock_router_instance, config, vision) + result = await router.async_pre_routing_hook(model="m", request_kwargs={}, messages=messages) + assert result.model == expected_model + assert result.routing_decision["cause"] == expected_cause + assert result.routing_decision["signals"][-1] == "modality:image" + return + if path == "keyword_tier_escalates": + config["keyword_tier_rules"] = [{"keywords": ["quick lookup"], "tier": "SIMPLE"}] + messages = [ + {"role": "user", "content": [{"type": "text", "text": "quick lookup: what is this?"}, IMG_PART]} + ] + elif path == "no_ask_capable_default_kept": + config["default_model"] = "vision-default" + messages = [{"role": "user", "content": [IMG_PART]}] + elif path == "no_ask_text_default_displaced": + config["default_model"] = "text-default" + vision["text-default"] = False + messages = [{"role": "user", "content": [IMG_PART]}] + elif path == "custom_tiers_walk": + config = { + "classifier_type": "llm", + "classifier_llm_config": {"model": "gpt-4o-mini"}, + "fallback_tier": "cheap", + "tier_definitions": [ + {"name": "cheap", "description": "trivial asks"}, + {"name": "premium", "description": "hard asks"}, + ], + "tiers": {"cheap": "cheap-model", "premium": "premium-model"}, + "keyword_tier_rules": [{"keywords": ["quick lookup"], "tier": "cheap"}], + "modality_routing": True, + } + vision = {"cheap-model": False, "premium-model": True} + messages = [ + {"role": "user", "content": [{"type": "text", "text": "quick lookup: what is this?"}, IMG_PART]} + ] + elif path in ("pin_kept_bypasses", "pin_replacement_gated"): + cache = AsyncMock() + cache.async_get_cache = AsyncMock(return_value={"model": "text-cheap", "tier": "SIMPLE"}) + mock_router_instance.cache = cache + config["session_affinity"] = True + request_kwargs = {"metadata": {"session_id": "s1"}} + if path == "pin_replacement_gated": + config["tiers"]["MEDIUM"] = "text-mid" + vision["text-mid"] = False + messages = [ + {"role": "user", "content": [{"type": "text", "text": "LITELLM ESCALATE describe this"}, IMG_PART]} + ] + elif path == "adaptive_pick_rewritten": + config["adaptive"] = True + mock_router_instance.model_list = [] + mock_router_instance.model_name_to_deployment_indices = {} + router = self._router(mock_router_instance, config, vision) + result = await router.async_pre_routing_hook(model="m", request_kwargs=request_kwargs, messages=messages) + assert result.model == expected_model + assert result.routing_decision["cause"] == expected_cause + if path == "adaptive_pick_rewritten": + assert request_kwargs["metadata"]["adaptive_router_chosen_model"] == expected_model + + @pytest.mark.asyncio + async def test_plan_floored_decision_never_falls_to_default_model(self, mock_router_instance): + """An upward-only walk cannot undercut the floor; default_model must not either.""" + config = { + "tiers": {"SIMPLE": "vision-cheap", "MEDIUM": "text-mid"}, + "default_model": "vision-default", + "plan_mode_min_tier": "MEDIUM", + "modality_routing": True, + } + vision = {"vision-cheap": True, "text-mid": False, "vision-default": True} + router = self._router(mock_router_instance, config, vision) + with pytest.raises(litellm.BadRequestError, match="no model"): + await router.async_pre_routing_hook( + model="m", + request_kwargs={"proxy_server_request": {"body": PLAN_BODY}}, + messages=[{"role": "user", "content": [{"type": "text", "text": "plan this"}, IMG_PART]}], + ) + + @pytest.mark.asyncio + async def test_at_floor_plan_turn_never_falls_to_default_model(self, mock_router_instance): + """A sentinel turn whose classified tier already satisfies the floor keeps its ordinary + cause, so the record carries no floor marker; the default arm must still refuse it.""" + config = { + "tiers": {"SIMPLE": "text-a", "MEDIUM": "text-b"}, + "default_model": "vision-default", + "plan_mode_min_tier": "SIMPLE", + "modality_routing": True, + } + vision = {"text-a": False, "text-b": False, "vision-default": True} + router = self._router(mock_router_instance, config, vision) + with pytest.raises(litellm.BadRequestError, match="no model"): + await router.async_pre_routing_hook( + model="m", + request_kwargs={"proxy_server_request": {"body": PLAN_BODY}}, + messages=[{"role": "user", "content": [{"type": "text", "text": "plan this"}, IMG_PART]}], + ) + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "default_model, default_vision, expect_error", + [(None, None, True), ("text-default", False, True), ("vision-default", True, False)], + ids=["no_default", "text_only_default", "vision_default_serves"], + ) + async def test_no_capable_tier_above_uses_default_or_rejects( + self, mock_router_instance, default_model, default_vision, expect_error + ): + config = {"tiers": {"SIMPLE": "text-cheap", "COMPLEX": "text-big"}, "modality_routing": True} + vision = {"text-cheap": False, "text-big": False} + if default_model is not None: + config["default_model"] = default_model + vision[default_model] = default_vision + router = self._router(mock_router_instance, config, vision) + if expect_error: + with pytest.raises(litellm.BadRequestError, match="no model"): + await router.async_pre_routing_hook(model="m", request_kwargs={}, messages=self.IMAGE_MESSAGE) + return + result = await router.async_pre_routing_hook(model="m", request_kwargs={}, messages=self.IMAGE_MESSAGE) + assert result.model == "vision-default" + assert result.routing_decision["cause"] == "modality_escalation" + assert "modality_escalated_from:SIMPLE" in result.routing_decision["signals"] + + @pytest.mark.asyncio + async def test_mixed_deployment_group_is_treated_text_only(self, mock_router_instance): + def get_model_list(model_name=None): + declared = {"mixed-group": [True, False], "vision-big": [True]}.get(model_name) + if declared is None: + return [] + return [ + { + "model_name": model_name, + "litellm_params": {"model": f"openai/unmapped-{model_name}-{i}"}, + "model_info": {"supports_vision": accepts}, + } + for i, accepts in enumerate(declared) + ] + + mock_router_instance.get_model_list = get_model_list + router = ComplexityRouter( + model_name="modality-test-router", + litellm_router_instance=mock_router_instance, + complexity_router_config={ + "tiers": {"SIMPLE": "mixed-group", "COMPLEX": "vision-big"}, + "modality_routing": True, + }, + ) + result = await router.async_pre_routing_hook(model="m", request_kwargs={}, messages=self.IMAGE_MESSAGE) + assert result.model == "vision-big" + assert result.routing_decision["cause"] == "modality_escalation" + + @pytest.mark.asyncio + async def test_continuation_turn_screenshot_escalates_past_the_held_model(self, mock_router_instance): + """classification_mode user_turn replays the held model on continuation turns; a + continuation carrying a screenshot must still be re-placed when that model is text-only.""" + mock_router_instance.cache = DualCache() + config = { + "tiers": dict(self.BASE_TIERS), + "classification_mode": "user_turn", + "modality_routing": True, + } + router = self._router(mock_router_instance, config, dict(self.BASE_VISION)) + first = await router.async_pre_routing_hook( + model="m", + request_kwargs={"metadata": {"session_id": "cont-1"}}, + messages=[{"role": "user", "content": "hi there"}], + ) + assert first.model == "text-cheap" + continuation = [ + {"role": "user", "content": "hi there"}, + {"role": "assistant", "content": [{"type": "tool_use", "id": "tu_1", "name": "screenshot", "input": {}}]}, + { + "role": "user", + "content": [ + { + "type": "tool_result", + "tool_use_id": "tu_1", + "content": [{"type": "image", "source": {"type": "base64", "data": "aGk="}}], + } + ], + }, + ] + second = await router.async_pre_routing_hook( + model="m", request_kwargs={"metadata": {"session_id": "cont-1"}}, messages=continuation + ) + assert second.model == "vision-mid" + assert second.routing_decision["cause"] == "modality_escalation" + assert "modality_escalated_from:SIMPLE" in second.routing_decision["signals"] + + @pytest.mark.asyncio + async def test_rewrite_carries_the_context_escalation_record(self, mock_router_instance): + """A context-window escalation and a modality re-place are separate facts on one + record; rewriting for the image must not drop the sibling gate's fields.""" + from litellm.types.router import PreRoutingHookResponse + + router = self._router( + mock_router_instance, + {"tiers": dict(self.BASE_TIERS), "modality_routing": True}, + dict(self.BASE_VISION), + ) + decision = router._build_routing_decision( + routed_model="text-cheap", + cause="heuristic_scorer", + tier=ComplexityTier.SIMPLE, + context_escalation_original_tier=ComplexityTier.SIMPLE, + ) + response = PreRoutingHookResponse(model="text-cheap", messages=None, routing_decision=decision) + rewritten = await router._gate_response_modality(response, None, self.IMAGE_MESSAGE, {}) + assert rewritten.model == "vision-mid" + assert rewritten.routing_decision["cause"] == "modality_escalation" + assert rewritten.routing_decision["context_escalated"] is True + assert rewritten.routing_decision["context_escalation_original_tier"] == "SIMPLE" + + def test_modality_escalation_is_never_pinnable(self): + from litellm.router_strategy.complexity_router.complexity_router import _decision_is_pinnable + + assert _decision_is_pinnable({"cause": "modality_escalation"}) is False + assert _decision_is_pinnable({"cause": "heuristic_scorer"}) is True diff --git a/tests/test_litellm/test_fireworks_serverless_model_costs.py b/tests/test_litellm/test_fireworks_serverless_model_costs.py index 0458af0da0e..a7a9e0fc37d 100644 --- a/tests/test_litellm/test_fireworks_serverless_model_costs.py +++ b/tests/test_litellm/test_fireworks_serverless_model_costs.py @@ -84,3 +84,41 @@ def test_bare_fireworks_ids_resolve_through_prefixed_entries(): assert info["output_cost_per_token"] == pytest.approx(expected["output_cost_per_token"]) assert info["max_input_tokens"] == expected["max_input_tokens"] assert info["max_output_tokens"] == expected["max_output_tokens"] + + +TWIN_PINNED_PRICES = { + "deepseek-v4-flash-0731": { + "input_cost_per_token": 2.2e-07, + "cache_read_input_token_cost": 7e-09, + "output_cost_per_token": 6.6e-07, + }, +} + + +def test_deepseek_v4_flash_0731_twins_pin_published_pricing(model_data): + """Both 0731 entries carry the price published at docs.fireworks.ai/serverless/pricing.""" + for bare_suffix, expected in TWIN_PINNED_PRICES.items(): + for key in ( + f"fireworks_ai/{bare_suffix}", + f"fireworks_ai/accounts/fireworks/models/{bare_suffix}", + ): + entry = model_data[key] + for field, value in expected.items(): + assert entry[field] == pytest.approx(value), f"{key}.{field}" + + +def test_fireworks_account_prefixed_twins_agree_on_price(model_data): + """Every accounts/fireworks/models/X entry prices identically to its bare fireworks_ai/X twin.""" + prefix = "fireworks_ai/accounts/fireworks/models/" + pairs_checked = 0 + for key, entry in model_data.items(): + if not key.startswith(prefix): + continue + bare_key = f"fireworks_ai/{key[len(prefix):]}" + bare_entry = model_data.get(bare_key) + if bare_entry is None: + continue + pairs_checked += 1 + for field in sorted({f for f in (*entry, *bare_entry) if "cost" in f}): + assert entry.get(field) == bare_entry.get(field), f"{key} vs {bare_key}: {field}" + assert pairs_checked >= 20 diff --git a/tests/test_litellm/test_friendli_glm_5_3_flash_model_metadata.py b/tests/test_litellm/test_friendli_glm_5_3_flash_model_metadata.py new file mode 100644 index 00000000000..7e94205fb09 --- /dev/null +++ b/tests/test_litellm/test_friendli_glm_5_3_flash_model_metadata.py @@ -0,0 +1,35 @@ +import json +from pathlib import Path + +from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider + + +def test_friendli_glm_5_3_flash_model_info(): + model = "friendliai/zai-org/GLM-5.3-Flash" + json_path = Path(__file__).parents[2] / "model_prices_and_context_window.json" + with open(json_path) as f: + model_cost = json.load(f) + + info = model_cost.get(model) + assert ( + info is not None + ), f"{model} not found in model_prices_and_context_window.json" + assert info["litellm_provider"] == "friendliai" + assert info["mode"] == "chat" + assert info["input_cost_per_token"] == 1.5e-07 + assert info["output_cost_per_token"] == 5e-07 + assert info["cache_read_input_token_cost"] == 3e-08 + assert info["max_input_tokens"] == 1048576 + assert info["max_output_tokens"] == 1048576 + assert info["supports_function_calling"] is True + assert info["supports_reasoning"] is True + assert info["reasoning_effort_levels"] == ["low", "high", "max"] + assert info["supports_tool_choice"] is True + assert info["supports_prompt_caching"] is True + assert info["supports_vision"] is True + assert info["supports_image_input"] is True + assert info["supports_video_input"] is True + + routed_model, provider, _, _ = get_llm_provider(model=model) + assert routed_model == "zai-org/GLM-5.3-Flash" + assert provider == "friendliai" diff --git a/tests/test_litellm/test_friendli_glm_5_3_model_metadata.py b/tests/test_litellm/test_friendli_glm_5_3_model_metadata.py new file mode 100644 index 00000000000..5282b0f589e --- /dev/null +++ b/tests/test_litellm/test_friendli_glm_5_3_model_metadata.py @@ -0,0 +1,34 @@ +import json +from pathlib import Path + +from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider + + +def test_friendli_glm_5_3_model_info(): + model = "friendliai/zai-org/GLM-5.3" + json_path = Path(__file__).parents[2] / "model_prices_and_context_window.json" + with open(json_path) as f: + model_cost = json.load(f) + + info = model_cost.get(model) + assert ( + info is not None + ), f"{model} not found in model_prices_and_context_window.json" + assert info["litellm_provider"] == "friendliai" + assert info["mode"] == "chat" + assert info["input_cost_per_token"] == 1.26e-06 + assert info["output_cost_per_token"] == 3.96e-06 + assert info["cache_read_input_token_cost"] == 2.34e-07 + assert info["max_input_tokens"] == 1048576 + assert info["max_output_tokens"] == 1048576 + assert info["supports_function_calling"] is True + assert info["supports_reasoning"] is True + assert info["reasoning_effort_levels"] == ["low", "high", "max"] + assert info["supports_tool_choice"] is True + assert info["supports_prompt_caching"] is True + assert info["supports_vision"] is False + assert info["supports_image_input"] is False + + routed_model, provider, _, _ = get_llm_provider(model=model) + assert routed_model == "zai-org/GLM-5.3" + assert provider == "friendliai" diff --git a/tests/test_litellm/test_openai_embedding_encoding_format_default.py b/tests/test_litellm/test_openai_embedding_encoding_format_default.py index 94e4e3c81e5..7a42eaf0f0a 100644 --- a/tests/test_litellm/test_openai_embedding_encoding_format_default.py +++ b/tests/test_litellm/test_openai_embedding_encoding_format_default.py @@ -1,124 +1,121 @@ -from unittest.mock import MagicMock, patch +import json +from typing import Final +import httpx import pytest +import respx -from litellm import embedding +import litellm -@pytest.mark.parametrize( - "set_env, env_value, expected", - [ - (False, None, "float"), - (True, "base64", "base64"), - ], -) -def test_openai_embedding_encoding_format_default( - monkeypatch, set_env, env_value, expected -): - monkeypatch.delenv("LITELLM_DEFAULT_EMBEDDING_ENCODING_FORMAT", raising=False) - if set_env: - monkeypatch.setenv("LITELLM_DEFAULT_EMBEDDING_ENCODING_FORMAT", env_value) - - mock_response = MagicMock() - mock_response.parse.return_value = MagicMock( - model_dump=lambda: { - "data": [{"embedding": [0.1, 0.2, 0.3], "index": 0}], - "model": "text-embedding-ada-002", - "object": "list", - "usage": {"prompt_tokens": 1, "total_tokens": 1}, - } +def _mock_openai_embedding_route(respx_mock: respx.MockRouter) -> respx.Route: + return respx_mock.post("https://api.openai.com/v1/embeddings").mock( + return_value=httpx.Response( + 200, + json={ + "object": "list", + "data": [{"object": "embedding", "index": 0, "embedding": [0.1, 0.2, 0.3]}], + "model": "text-embedding-3-small", + "usage": {"prompt_tokens": 2, "total_tokens": 2}, + }, + ) ) - mock_response.headers = {} - with patch( - "litellm.llms.openai.openai.OpenAIChatCompletion._get_openai_client" - ) as mock_get_client: - mock_client_instance = MagicMock() - mock_get_client.return_value = mock_client_instance - mock_client_instance.embeddings.with_raw_response.create.return_value = ( - mock_response - ) - embedding( - model="text-embedding-ada-002", - input="Hello world", - ) +@pytest.fixture(autouse=True) +def clear_default_encoding_format_env(monkeypatch: pytest.MonkeyPatch): + monkeypatch.delenv("LITELLM_DEFAULT_EMBEDDING_ENCODING_FORMAT", raising=False) - call_kwargs = ( - mock_client_instance.embeddings.with_raw_response.create.call_args[1] - ) - assert call_kwargs["encoding_format"] == expected + +def test_embedding_openai_omits_encoding_format_when_client_omits_it(respx_mock: respx.MockRouter) -> None: + mock_route: Final = _mock_openai_embedding_route(respx_mock) + + response: Final = litellm.embedding(model="openai/text-embedding-3-small", input=["hello"], api_key="sk-test") + + request_body: Final = json.loads(mock_route.calls.last.request.read()) + assert "encoding_format" not in request_body + assert response.data[0]["embedding"] == [0.1, 0.2, 0.3] + + +def test_embedding_openai_forwards_explicit_encoding_format(respx_mock: respx.MockRouter) -> None: + mock_route: Final = _mock_openai_embedding_route(respx_mock) + + litellm.embedding( + model="openai/text-embedding-3-small", input=["hello"], api_key="sk-test", encoding_format="base64" + ) + + request_body: Final = json.loads(mock_route.calls.last.request.read()) + assert request_body["encoding_format"] == "base64" + + +def test_embedding_openai_explicit_encoding_format_wins_over_env_var( + respx_mock: respx.MockRouter, monkeypatch: pytest.MonkeyPatch +) -> None: + monkeypatch.setenv("LITELLM_DEFAULT_EMBEDDING_ENCODING_FORMAT", "float") + mock_route: Final = _mock_openai_embedding_route(respx_mock) + + litellm.embedding( + model="openai/text-embedding-3-small", input=["hello"], api_key="sk-test", encoding_format="base64" + ) + + request_body: Final = json.loads(mock_route.calls.last.request.read()) + assert request_body["encoding_format"] == "base64" + + +@pytest.mark.parametrize("env_value", ["float", "base64"]) +def test_embedding_openai_env_var_sets_default_encoding_format( + respx_mock: respx.MockRouter, monkeypatch: pytest.MonkeyPatch, env_value: str +) -> None: + monkeypatch.setenv("LITELLM_DEFAULT_EMBEDDING_ENCODING_FORMAT", env_value) + mock_route: Final = _mock_openai_embedding_route(respx_mock) + + litellm.embedding(model="openai/text-embedding-3-small", input=["hello"], api_key="sk-test") + + request_body: Final = json.loads(mock_route.calls.last.request.read()) + assert request_body["encoding_format"] == env_value @pytest.mark.parametrize("env_none", ["none", "NONE", " none "]) -def test_openai_embedding_encoding_format_env_none_omits_param( - monkeypatch, env_none -): - """LITELLM_DEFAULT_EMBEDDING_ENCODING_FORMAT=none omits encoding_format (provider default).""" +def test_embedding_openai_env_none_omits_encoding_format( + respx_mock: respx.MockRouter, monkeypatch: pytest.MonkeyPatch, env_none: str +) -> None: monkeypatch.setenv("LITELLM_DEFAULT_EMBEDDING_ENCODING_FORMAT", env_none) + mock_route: Final = _mock_openai_embedding_route(respx_mock) - mock_response = MagicMock() - mock_response.parse.return_value = MagicMock( - model_dump=lambda: { - "data": [{"embedding": [0.1, 0.2, 0.3], "index": 0}], - "model": "text-embedding-ada-002", - "object": "list", - "usage": {"prompt_tokens": 1, "total_tokens": 1}, - } + litellm.embedding(model="openai/text-embedding-3-small", input=["hello"], api_key="sk-test") + + request_body: Final = json.loads(mock_route.calls.last.request.read()) + assert "encoding_format" not in request_body + + +@pytest.mark.asyncio +async def test_aembedding_openai_omits_encoding_format_when_client_omits_it( + respx_mock: respx.MockRouter, monkeypatch: pytest.MonkeyPatch +) -> None: + monkeypatch.setattr(litellm, "disable_aiohttp_transport", True) + mock_route: Final = _mock_openai_embedding_route(respx_mock) + + response: Final = await litellm.aembedding(model="openai/text-embedding-3-small", input=["hello"], api_key="sk-test") + + request_body: Final = json.loads(mock_route.calls.last.request.read()) + assert "encoding_format" not in request_body + assert response.data[0]["embedding"] == [0.1, 0.2, 0.3] + + +def test_embedding_openai_omitted_encoding_format_maps_provider_errors( + respx_mock: respx.MockRouter, monkeypatch: pytest.MonkeyPatch +) -> None: + respx_mock.post("https://api.openai.com/v1/embeddings").mock( + return_value=httpx.Response( + 429, + headers={"retry-after": "42", "x-should-retry": "false"}, + json={"error": {"message": "rate limited", "type": "rate_limit_error"}}, + ) ) - mock_response.headers = {} - with patch( - "litellm.llms.openai.openai.OpenAIChatCompletion._get_openai_client" - ) as mock_get_client: - mock_client_instance = MagicMock() - mock_get_client.return_value = mock_client_instance - mock_client_instance.embeddings.with_raw_response.create.return_value = ( - mock_response + with pytest.raises(litellm.RateLimitError) as exc_info: + litellm.embedding( + model="openai/text-embedding-3-small", input=["hello"], api_key="sk-test", max_retries=0 ) - embedding( - model="text-embedding-ada-002", - input="Hello world", - ) - - call_kwargs = ( - mock_client_instance.embeddings.with_raw_response.create.call_args[1] - ) - assert "encoding_format" not in call_kwargs - - -def test_openai_embedding_encoding_format_explicit_overrides_env(monkeypatch): - """Request `encoding_format` wins over LITELLM_DEFAULT_EMBEDDING_ENCODING_FORMAT.""" - monkeypatch.setenv("LITELLM_DEFAULT_EMBEDDING_ENCODING_FORMAT", "float") - - mock_response = MagicMock() - mock_response.parse.return_value = MagicMock( - model_dump=lambda: { - "data": [{"embedding": [0.1, 0.2, 0.3], "index": 0}], - "model": "text-embedding-ada-002", - "object": "list", - "usage": {"prompt_tokens": 1, "total_tokens": 1}, - } - ) - mock_response.headers = {} - - with patch( - "litellm.llms.openai.openai.OpenAIChatCompletion._get_openai_client" - ) as mock_get_client: - mock_client_instance = MagicMock() - mock_get_client.return_value = mock_client_instance - mock_client_instance.embeddings.with_raw_response.create.return_value = ( - mock_response - ) - - embedding( - model="text-embedding-ada-002", - input="Hello world", - encoding_format="base64", - ) - - call_kwargs = ( - mock_client_instance.embeddings.with_raw_response.create.call_args[1] - ) - assert call_kwargs["encoding_format"] == "base64" + assert int(exc_info.value.litellm_response_headers["retry-after"]) == 42 diff --git a/tests/test_litellm/test_redis.py b/tests/test_litellm/test_redis.py index 826beb74a27..a96e8541e06 100644 --- a/tests/test_litellm/test_redis.py +++ b/tests/test_litellm/test_redis.py @@ -1,3 +1,4 @@ +import inspect import json from types import SimpleNamespace from unittest.mock import AsyncMock, MagicMock, patch @@ -600,6 +601,72 @@ def test_reconnect_kwargs_in_cluster_kwargs(): assert "socket_keepalive" in kwargs +def test_retry_attempts_in_cluster_kwargs(): + """cluster_error_retry_attempts must survive the cluster kwarg allow-list so + operators can bound worst-case retry latency on a Redis Cluster: it was being + silently dropped because the allow-list was built from redis.RedisCluster's + decorated __init__ without unwrapping it, so getfullargspec saw an empty + (self, *args, **kwargs) wrapper signature.""" + kwargs = _get_redis_cluster_kwargs() + assert "cluster_error_retry_attempts" in kwargs + + +def test_async_only_kwargs_in_cluster_kwargs_when_async_client_requested(): + """decode_responses is on the async cluster client's constructor and not the sync + one, on every redis-py the matrix covers. Introspecting the sync class regardless + of which client is actually built silently drops it for every async cluster caller.""" + sync_kwargs = _get_redis_cluster_kwargs() + async_kwargs = _get_redis_cluster_kwargs(async_redis.RedisCluster) + + assert "decode_responses" not in sync_kwargs + assert "decode_responses" in async_kwargs + + +@patch( # test-quality-ok: redis-py >= 6 keeps no cluster_error_retry_attempts attribute on the built client, so the constructor call is the only place the value is observable + "litellm.caching.redis_cluster_node_isolation.get_litellm_async_redis_cluster_class" +) +def test_async_cluster_forwards_retry_attempts(mock_get_cluster_class): + """Regression: cluster_error_retry_attempts must reach the constructed async + cluster client. Silently dropping it removes an operator's only lever for + bounding a stuck node's worst-case retry latency, and the client falls back + to redis-py's own default (3 retries) instead.""" + mock_cluster_cls = mock_get_cluster_class.return_value + get_redis_async_client( + startup_nodes=[{"host": "cluster-node", "port": 6379}], + cluster_error_retry_attempts=2, + ) + + call_kwargs = mock_cluster_cls.call_args[1] + assert call_kwargs["cluster_error_retry_attempts"] == 2 + + +def test_async_cluster_passes_async_only_kwargs(): + """Regression: decode_responses is an async-cluster-only constructor arg. When + the allow-list came from the sync class it was filtered out and values came + back as bytes instead of str.""" + client = get_redis_async_client( + startup_nodes=[{"host": "cluster-node", "port": 6379}], + decode_responses=True, + ) + + assert client.connection_kwargs["decode_responses"] is True + + +@pytest.mark.parametrize("cluster_client", [redis.RedisCluster, async_redis.RedisCluster], ids=["sync", "async"]) +def test_cluster_kwargs_exclude_variadic_parameters(cluster_client): + """*args / **kwargs are signature placeholders, not connection settings, and + must never land in the allow-list regardless of which cluster client is + introspected.""" + variadic = { + name + for name, param in inspect.signature(cluster_client).parameters.items() + if param.kind in (param.VAR_POSITIONAL, param.VAR_KEYWORD) + } + + leaked = variadic & set(_get_redis_cluster_kwargs(cluster_client)) + assert not leaked, f"variadic params leaked into the allow-list: {leaked}" + + @patch("litellm.caching.redis_cluster_node_isolation.get_litellm_async_redis_cluster_class") def test_async_cluster_sets_reconnect_defaults(mock_get_cluster_class): """ diff --git a/tests/test_litellm/test_router.py b/tests/test_litellm/test_router.py index 97286017ffe..44c1cdbff06 100644 --- a/tests/test_litellm/test_router.py +++ b/tests/test_litellm/test_router.py @@ -8155,6 +8155,71 @@ class TestUpsertDeploymentRollback: assert len(router.model_list) == 1 +class TestUpsertDeploymentRename: + """ + Issue #38360: renaming a model wrote the new `model_name` to the db, but the reload's + `upsert_deployment` compared only `litellm_params` and `model_info`. A rename with no + other edit therefore compared equal and the router kept the old name until a restart, + so `/model/info` and `/v1/models` served the stale name and the new one was unroutable. + """ + + @staticmethod + def _router() -> "litellm.Router": + return litellm.Router( + model_list=[ + { + "model_name": "old-name", + "litellm_params": {"model": "openai/gpt-4o", "api_key": "sk-test"}, + "model_info": {"id": "rename-1", "db_model": True}, + } + ] + ) + + @staticmethod + def _deployment(model_name: str, tpm: int | None = None): + from litellm.types.router import Deployment, LiteLLM_Params, ModelInfo + + return Deployment( + model_name=model_name, + litellm_params=LiteLLM_Params(model="openai/gpt-4o", api_key="sk-test", tpm=tpm), + model_info=ModelInfo(id="rename-1", db_model=True), + ) + + def test_rename_only_updates_the_router(self): + router = self._router() + + assert router.upsert_deployment(deployment=self._deployment("new-name")) is not None + + assert [model["model_name"] for model in router.model_list] == ["new-name"] + renamed = router.get_deployment(model_id="rename-1") + assert renamed is not None + assert renamed.model_name == "new-name" + + def test_rename_only_makes_the_new_name_routable(self): + router = self._router() + + router.upsert_deployment(deployment=self._deployment("new-name")) + + assert router.get_model_ids(model_name="new-name") == ["rename-1"] + assert router.get_model_ids(model_name="old-name") == [] + + def test_rename_alongside_another_edit_still_updates(self): + router = self._router() + + router.upsert_deployment(deployment=self._deployment("new-name", tpm=1234)) + + assert router.get_model_ids(model_name="new-name") == ["rename-1"] + renamed = router.get_deployment(model_id="rename-1") + assert renamed is not None + assert renamed.litellm_params.tpm == 1234 + + def test_unchanged_deployment_is_still_a_no_op(self): + router = self._router() + + assert router.upsert_deployment(deployment=self._deployment("old-name")) is None + assert [model["model_name"] for model in router.model_list] == ["old-name"] + + class TestConsumedRequestTagsStamp: """Issue #36621: when a request's tags select a tagged pre-routing strategy, those tags are consumed by the selection; the hook must stamp the rewritten model group so diff --git a/tests/test_litellm/test_utils.py b/tests/test_litellm/test_utils.py index 6524353aa48..627f341fe2f 100644 --- a/tests/test_litellm/test_utils.py +++ b/tests/test_litellm/test_utils.py @@ -1416,6 +1416,26 @@ def test_get_provider_rerank_config(): assert isinstance(config, HostedVLLMRerankConfig) +def test_get_provider_text_to_speech_config_vertex_gemini_skips_cloud_tts(): + """Regression for LIT-6501: mapping vertex Gemini TTS params through Google Cloud TTS + dropped response_format before the speech_to_completion bridge could honor it.""" + from litellm.llms.vertex_ai.text_to_speech.transformation import VertexAITextToSpeechConfig + from litellm.utils import LlmProviders + + assert ( + ProviderConfigManager.get_provider_text_to_speech_config( + model="gemini-2.5-flash-preview-tts", provider=LlmProviders.VERTEX_AI + ) + is None + ) + assert isinstance( + ProviderConfigManager.get_provider_text_to_speech_config( + model="en-US-Studio-O", provider=LlmProviders.VERTEX_AI + ), + VertexAITextToSpeechConfig, + ) + + # Models that should be skipped during testing OLD_PROVIDERS = ["aleph_alpha", "palm"] SKIP_MODELS = [ @@ -5765,3 +5785,33 @@ class TestHuggingFaceConfigFetch: assert _get_max_position_embeddings("some-org/some-model") == 512 request_timeout = hf_config_route.calls.last.request.extensions["timeout"] assert request_timeout["read"] == HF_CONFIG_FETCH_TIMEOUT_SECONDS + + +class TestIsVisionExplicitlyDisabled: + """github_copilot and chatgpt run an OAuth device flow inside get_llm_provider; the + explicit-disable lookup must adopt the declared prefix instead of resolving it, exactly + as _supports_factory does, or a capability check on a copilot deployment blocks routing + on a device-code prompt.""" + + @pytest.mark.parametrize("model", ["github_copilot/gpt-4o", "chatgpt/gpt-5"]) + def test_never_resolves_an_authenticating_prefix(self, model, monkeypatch): + from litellm.utils import is_vision_explicitly_disabled + + lookups: list = [] + + def _record(*args, **kwargs): + lookups.append((args, kwargs)) + raise RuntimeError("provider resolution must not run for an authenticating provider") + + monkeypatch.setattr(litellm, "get_llm_provider", _record) + + assert is_vision_explicitly_disabled(model) is False + assert lookups == [] + + def test_explicit_false_detected_and_absent_reads_enabled(self): + from litellm.utils import is_vision_explicitly_disabled + + assert ( + is_vision_explicitly_disabled("fireworks_ai/accounts/fireworks/models/deepseek-v4-flash-0731") is True + ) + assert is_vision_explicitly_disabled("anthropic/claude-sonnet-4-5") is False diff --git a/tests/test_litellm/test_video_generation.py b/tests/test_litellm/test_video_generation.py index b166e902d6e..2a60ff9c4b5 100644 --- a/tests/test_litellm/test_video_generation.py +++ b/tests/test_litellm/test_video_generation.py @@ -532,6 +532,41 @@ class TestVideoGeneration: assert abs(cost_for("runwayml/seedance2_5", "480p", 8.0) - 1.6) < 0.001 assert abs(cost_for("runwayml/gen4.5", None, 8.0) - 0.96) < 0.001 + def test_completion_cost_veo_31_tiers_pin_published_rates(self, monkeypatch): + """The gemini and vertex_ai veo 3.1 entries bill Google's published per-second tier rates.""" + from litellm.cost_calculator import completion_cost + + local_map_path = os.path.join( + os.path.dirname(__file__), "..", "..", "model_prices_and_context_window.json" + ) + with open(local_map_path, "r") as f: + monkeypatch.setattr(litellm, "model_cost", json.load(f)) + + def cost_for(model: str, provider: str, resolution: str | None, duration: float) -> float: + mock_response = MagicMock() + mock_response.usage = { + "duration_seconds": duration, + **({"video_resolution": resolution} if resolution else {}), + } + type(mock_response)._hidden_params = {} + return completion_cost( + completion_response=mock_response, + model=model, + call_type="create_video", + custom_llm_provider=provider, + ) + + for provider in ("gemini", "vertex_ai"): + for suffix in ("generate-preview", "generate-001"): + standard = f"{provider}/veo-3.1-{suffix}" + fast = f"{provider}/veo-3.1-fast-{suffix}" + assert abs(cost_for(standard, provider, None, 8.0) - 3.2) < 1e-6 + assert abs(cost_for(standard, provider, "1080p", 8.0) - 3.2) < 1e-6 + assert abs(cost_for(standard, provider, "4k", 8.0) - 4.8) < 1e-6 + assert abs(cost_for(fast, provider, "720p", 8.0) - 0.8) < 1e-6 + assert abs(cost_for(fast, provider, "1080p", 8.0) - 0.96) < 1e-6 + assert abs(cost_for(fast, provider, "4k", 8.0) - 2.4) < 1e-6 + def test_video_generation_with_files(self): """Test video generation with file uploads.""" config = OpenAIVideoConfig() diff --git a/type-discipline-budget.json b/type-discipline-budget.json index ab34775c460..3d2e97d55a5 100644 --- a/type-discipline-budget.json +++ b/type-discipline-budget.json @@ -1,15 +1,15 @@ { "LIT001": { - "limit": 22521 + "limit": 22367 }, "LIT002": { - "limit": 26820 + "limit": 26777 }, "LIT003": { "limit": 269 }, "LIT004": { - "limit": 43 + "limit": 40 }, "LIT005": { "limit": 0 @@ -27,10 +27,10 @@ "limit": 0 }, "LIT010": { - "limit": 16546 + "limit": 16507 }, "LIT011": { - "limit": 5575 + "limit": 5535 }, "LIT012": { "limit": 4495 diff --git a/ui/litellm-dashboard/eslint-budgets.json b/ui/litellm-dashboard/eslint-budgets.json index bbf69c4a77a..e8207d179bd 100644 --- a/ui/litellm-dashboard/eslint-budgets.json +++ b/ui/litellm-dashboard/eslint-budgets.json @@ -4,5 +4,8 @@ "complexity": { "max": 140, "target": 80 }, "max-depth": { "max": 70, "target": 30 }, "local/no-large-inline-object-arg": { "max": 559, "target": 300 }, - "local/no-long-condition-chain": { "max": 265, "target": 120 } + "local/no-long-condition-chain": { "max": 265, "target": 120 }, + "testing-library/no-container": { "max": 133, "target": 50 }, + "testing-library/no-node-access": { "max": 716, "target": 500 }, + "testing-library/prefer-screen-queries": { "max": 18, "target": 18 } } diff --git a/ui/litellm-dashboard/eslint.config.mjs b/ui/litellm-dashboard/eslint.config.mjs index 23cc5096bb1..f5e3b23b3ec 100644 --- a/ui/litellm-dashboard/eslint.config.mjs +++ b/ui/litellm-dashboard/eslint.config.mjs @@ -104,10 +104,13 @@ const eslintConfig = [ plugins: { "testing-library": testingLibrary, "jest-dom": jestDom }, rules: { "testing-library/await-async-queries": "error", + "testing-library/no-container": "warn", + "testing-library/no-node-access": "warn", "testing-library/no-wait-for-multiple-assertions": "error", "testing-library/no-wait-for-side-effects": "error", "testing-library/prefer-find-by": "error", "testing-library/prefer-presence-queries": "error", + "testing-library/prefer-screen-queries": "warn", "jest-dom/prefer-checked": "error", "jest-dom/prefer-empty": "error", "jest-dom/prefer-enabled-disabled": "error", diff --git a/ui/litellm-dashboard/package-lock.json b/ui/litellm-dashboard/package-lock.json index d2a64b93384..4e5b0c1dda6 100644 --- a/ui/litellm-dashboard/package-lock.json +++ b/ui/litellm-dashboard/package-lock.json @@ -4891,9 +4891,9 @@ } }, "node_modules/baseline-browser-mapping": { - "version": "2.10.27", - "resolved": "https://registry.npmjs.org/baseline-browser-mapping/-/baseline-browser-mapping-2.10.27.tgz", - "integrity": "sha512-zEs/ufmZoUd7WftKpKyXaT6RFxpQ5Qm9xytKRHvJfxFV9DFJkZph9RvJ1LcOUi0Z1ZVijMte65JbILeV+8QQEA==", + "version": "2.11.20", + "resolved": "https://registry.npmjs.org/baseline-browser-mapping/-/baseline-browser-mapping-2.11.20.tgz", + "integrity": "sha512-H0ulySigv6icDJ1F7SjtdCD6PrhTpdYCmP0CactWy1+ekh0AFd0o1Wn5T8b+hnTmdBx19u9yhL6wvCylXMY7zw==", "license": "Apache-2.0", "bin": { "baseline-browser-mapping": "dist/cli.cjs" @@ -4939,9 +4939,9 @@ } }, "node_modules/browserslist": { - "version": "4.28.2", - "resolved": "https://registry.npmjs.org/browserslist/-/browserslist-4.28.2.tgz", - "integrity": "sha512-48xSriZYYg+8qXna9kwqjIVzuQxi+KYWp2+5nCYnYKPTr0LvD89Jqk2Or5ogxz0NUMfIjhh2lIUX/LyX9B4oIg==", + "version": "4.28.8", + "resolved": "https://registry.npmjs.org/browserslist/-/browserslist-4.28.8.tgz", + "integrity": "sha512-V2NpofLblG64mfOtSgDhOJESZEGogzDMBv/q+W6oc4LXWP/q75eOXoOaaOu1EOadB9U4Bwx/e0yzbvwKH8zalA==", "dev": true, "funding": [ { @@ -4959,11 +4959,11 @@ ], "license": "MIT", "dependencies": { - "baseline-browser-mapping": "^2.10.12", - "caniuse-lite": "^1.0.30001782", - "electron-to-chromium": "^1.5.328", - "node-releases": "^2.0.36", - "update-browserslist-db": "^1.2.3" + "baseline-browser-mapping": "^2.11.12", + "caniuse-lite": "^1.0.30001809", + "electron-to-chromium": "^1.5.402", + "node-releases": "^2.0.53", + "update-browserslist-db": "^1.3.0" }, "bin": { "browserslist": "cli.js" @@ -5042,9 +5042,9 @@ } }, "node_modules/caniuse-lite": { - "version": "1.0.30001791", - "resolved": "https://registry.npmjs.org/caniuse-lite/-/caniuse-lite-1.0.30001791.tgz", - "integrity": "sha512-yk0l/YSrOnFZk3UROpDLQD9+kC1l4meK/wed583AXrzoarMGJcbRi2Q4RaUYbKxYAsZ8sWmaSa/DsLmdBeI1vQ==", + "version": "1.0.30001810", + "resolved": "https://registry.npmjs.org/caniuse-lite/-/caniuse-lite-1.0.30001810.tgz", + "integrity": "sha512-TITQPUkaz+aVk5GL6NhOdwk1aEaNTSDPsGFWrTuhKGtjTF70jL/Oht2W4c6rXUe5fu7Ie19VIahAXHIIiWWNeg==", "funding": [ { "type": "opencollective", @@ -5706,9 +5706,9 @@ } }, "node_modules/electron-to-chromium": { - "version": "1.5.349", - "resolved": "https://registry.npmjs.org/electron-to-chromium/-/electron-to-chromium-1.5.349.tgz", - "integrity": "sha512-QsWVGyRuY07Aqb234QytTfwd5d9AJlfNIQ5wIOl1L+PZDzI9d9+Fn0FRale/QYlFxt/bUnB0/nLd1jFPGxGK1A==", + "version": "1.5.416", + "resolved": "https://registry.npmjs.org/electron-to-chromium/-/electron-to-chromium-1.5.416.tgz", + "integrity": "sha512-K6bvB2BjnNrugtIih6ewlbBI9DXa976jIdiIlRLHhBoEI9a4JaQjjHyF+A1IQI543aQYR4LnmOrT/K5fZj0aPA==", "dev": true, "license": "ISC" }, @@ -9901,11 +9901,14 @@ } }, "node_modules/node-releases": { - "version": "2.0.38", - "resolved": "https://registry.npmjs.org/node-releases/-/node-releases-2.0.38.tgz", - "integrity": "sha512-3qT/88Y3FbH/Kx4szpQQ4HzUbVrHPKTLVpVocKiLfoYvw9XSGOX2FmD2d6DrXbVYyAQTF2HeF6My8jmzx7/CRw==", + "version": "2.0.54", + "resolved": "https://registry.npmjs.org/node-releases/-/node-releases-2.0.54.tgz", + "integrity": "sha512-YHs7BmmcsdAI5Ozuf8JZo6PT0mv2GIWC9vMfvUC3dp65M8hn7Ux8CPL+2oBI7juNuj9d0ndhTcznq2ODBps9cQ==", "dev": true, - "license": "MIT" + "license": "MIT", + "engines": { + "node": ">=18" + } }, "node_modules/nuqs": { "version": "2.9.4", @@ -12350,9 +12353,9 @@ } }, "node_modules/update-browserslist-db": { - "version": "1.2.3", - "resolved": "https://registry.npmjs.org/update-browserslist-db/-/update-browserslist-db-1.2.3.tgz", - "integrity": "sha512-Js0m9cx+qOgDxo0eMiFGEueWztz+d4+M3rGlmKPT+T4IS/jP4ylw3Nwpu6cpTTP8R1MAC1kF4VbdLt3ARf209w==", + "version": "1.3.2", + "resolved": "https://registry.npmjs.org/update-browserslist-db/-/update-browserslist-db-1.3.2.tgz", + "integrity": "sha512-UQ+MSxlhRm1bzjhU+DcuXfjFO1FzNtqhK5+9Yvlp90ItDLk5vT932A0rFu619nf7RVS+Y/VeaUW1jaRDqZ8VJw==", "dev": true, "funding": [ { diff --git a/ui/litellm-dashboard/public/assets/logos/gigachat.svg b/ui/litellm-dashboard/public/assets/logos/gigachat.svg new file mode 100644 index 00000000000..e7abe47b221 --- /dev/null +++ b/ui/litellm-dashboard/public/assets/logos/gigachat.svg @@ -0,0 +1,27 @@ + + + + + diff --git a/ui/litellm-dashboard/src/app/(dashboard)/api-reference/_components/APIReferenceView.test.tsx b/ui/litellm-dashboard/src/app/(dashboard)/api-reference/_components/APIReferenceView.test.tsx index b2e0a42eecb..dae19032e20 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/api-reference/_components/APIReferenceView.test.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/api-reference/_components/APIReferenceView.test.tsx @@ -13,17 +13,17 @@ describe("APIReferenceView", () => { it("uses the API doc base url when provided", () => { const apiDocUrl = "https://docs.litellm.test"; - const { getAllByTestId } = render(); + render(); - const codeBlocks = getAllByTestId(codeBlockTestId); + const codeBlocks = screen.getAllByTestId(codeBlockTestId); expect(codeBlocks[0]).toHaveTextContent(new RegExp(apiDocUrl)); }); it("falls back to the proxy base url when the docs url is missing", () => { const proxyUrl = "https://proxy.litellm.test"; - const { getAllByTestId } = render(); + render(); - const codeBlocks = getAllByTestId(codeBlockTestId); + const codeBlocks = screen.getAllByTestId(codeBlockTestId); expect(codeBlocks[0]).toHaveTextContent(new RegExp(proxyUrl)); }); @@ -31,7 +31,7 @@ describe("APIReferenceView", () => { const apiDocUrl = "https://docs-preferred.litellm.test"; const proxyUrl = "https://proxy-backup.litellm.test"; - const { getAllByTestId } = render( + render( { />, ); - const codeBlocks = getAllByTestId(codeBlockTestId); + const codeBlocks = screen.getAllByTestId(codeBlockTestId); const renderedCode = codeBlocks[0].textContent ?? ""; expect(renderedCode).toContain(apiDocUrl); expect(renderedCode).not.toContain(proxyUrl); diff --git a/ui/litellm-dashboard/src/app/(dashboard)/caching/_components/cache_settings/RedisTypeSelector.test.tsx b/ui/litellm-dashboard/src/app/(dashboard)/caching/_components/cache_settings/RedisTypeSelector.test.tsx index 9d4d5a6d425..372f27e2be1 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/caching/_components/cache_settings/RedisTypeSelector.test.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/caching/_components/cache_settings/RedisTypeSelector.test.tsx @@ -1,12 +1,10 @@ import { describe, expect, it } from "vitest"; import RedisTypeSelector from "./RedisTypeSelector"; -import { render } from "@testing-library/react"; +import { render, screen } from "@testing-library/react"; describe("RedisTypeSelector", () => { it("should render the component", () => { - const { getAllByText } = render( - {}} />, - ); - expect(getAllByText(/Redis/i).length).toBeGreaterThan(0); + render( {}} />); + expect(screen.getAllByText(/Redis/i).length).toBeGreaterThan(0); }); }); diff --git a/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/CacheLeakageCard.test.tsx b/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/CacheLeakageCard.test.tsx index 8c36b934789..f320d8e0f97 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/CacheLeakageCard.test.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/CacheLeakageCard.test.tsx @@ -1,4 +1,4 @@ -import { fireEvent, render } from "@testing-library/react"; +import { fireEvent, render, screen } from "@testing-library/react"; import { describe, expect, it, vi } from "vitest"; import type { DailyData, KeyMetricWithMetadata, SpendMetrics } from "@/components/UsagePage/types"; @@ -79,25 +79,25 @@ const renderWith = (results: DailyData[], overrides: Partial describe("CacheLeakageCard", () => { it("ranks leaking keys by uncached prompt tokens and shows cache hit ratio", () => { - const { getByText, getByLabelText } = renderWith([ + renderWith([ dayWithKeys("2026-07-12", { "hash-caching": key("caching-key", { prompt_tokens: 1000, cache_read_input_tokens: 900 }), "hash-leaky": key("leaky-key", { prompt_tokens: 10000, cache_read_input_tokens: 0 }), }), ]); - expect(getByText("leaky-key")).toBeInTheDocument(); - expect(getByText("0.0%")).toBeInTheDocument(); - expect(getByText("90.0%")).toBeInTheDocument(); + expect(screen.getByText("leaky-key")).toBeInTheDocument(); + expect(screen.getByText("0.0%")).toBeInTheDocument(); + expect(screen.getByText("90.0%")).toBeInTheDocument(); [ "Input tokens you sent in this range that weren't served from or written to the cache", "Share of your input tokens that were served from the cache", "About how much you'd save if this uncached input used prompt caching. Estimated as uncached input tokens times what your cached traffic already nets per cached token (realized cache savings, after write premiums, ÷ cache read and write tokens). Blank when caching is not currently saving anything overall.", - ].forEach((info) => expect(getByLabelText(info)).toBeInTheDocument()); + ].forEach((info) => expect(screen.getByLabelText(info)).toBeInTheDocument()); }); it("sorts by the clicked column, worst cache hit rate first", () => { - const { getAllByRole, getByText } = renderWith([ + renderWith([ dayWithKeys("2026-07-12", { "hash-a": key("alpha", { prompt_tokens: 10000, @@ -111,48 +111,48 @@ describe("CacheLeakageCard", () => { }), }), ]); - const firstDataRow = () => getAllByRole("row")[1]; + const firstDataRow = () => screen.getAllByRole("row")[1]; expect(firstDataRow()).toHaveTextContent("alpha"); - fireEvent.click(getByText("Cache hit rate")); + fireEvent.click(screen.getByText("Cache hit rate")); expect(firstDataRow()).toHaveTextContent("bravo"); - fireEvent.click(getByText("Cache hit rate")); + fireEvent.click(screen.getByText("Cache hit rate")); expect(firstDataRow()).toHaveTextContent("alpha"); }); it("switches to the model view and lists only Anthropic models", () => { - const { getByText, queryByText } = renderWith([ + renderWith([ dayWithModels("2026-07-12", { "claude-sonnet-5": { prompt_tokens: 5000, cache_read_input_tokens: 0 }, "gpt-4o": { prompt_tokens: 8000, cache_read_input_tokens: 0 }, }), ]); - fireEvent.click(getByText("By model")); + fireEvent.click(screen.getByText("By model")); - expect(getByText("Cache leakage by model")).toBeInTheDocument(); - expect(getByText("claude-sonnet-5")).toBeInTheDocument(); - expect(queryByText("gpt-4o")).not.toBeInTheDocument(); + expect(screen.getByText("Cache leakage by model")).toBeInTheDocument(); + expect(screen.getByText("claude-sonnet-5")).toBeInTheDocument(); + expect(screen.queryByText("gpt-4o")).not.toBeInTheDocument(); }); it("shows an empty state when no key used tokens in the range", () => { - const { getByText, queryByRole } = renderWith([dayWithKeys("2026-07-12", {})]); + renderWith([dayWithKeys("2026-07-12", {})]); - expect(getByText("No key usage in this range.")).toBeInTheDocument(); - expect(queryByRole("table")).not.toBeInTheDocument(); + expect(screen.getByText("No key usage in this range.")).toBeInTheDocument(); + expect(screen.queryByRole("table")).not.toBeInTheDocument(); }); it("tells the user the table is still filling in while fallback pages stream", () => { const day = dayWithKeys("2026-07-12", { "hash-leaky": key("leaky-key", { prompt_tokens: 10000, cache_read_input_tokens: 0 }), }); - const { getByText, getByRole } = renderWith([day], { isFetchingMore: true }); + renderWith([day], { isFetchingMore: true }); - expect(getByRole("table")).toBeInTheDocument(); + expect(screen.getByRole("table")).toBeInTheDocument(); expect( - getByText("Data is still loading; rows and totals will update as the rest of the range arrives."), + screen.getByText("Data is still loading; rows and totals will update as the rest of the range arrives."), ).toBeInTheDocument(); }); @@ -160,10 +160,10 @@ describe("CacheLeakageCard", () => { const day = dayWithKeys("2026-07-12", { "hash-leaky": key("leaky-key", { prompt_tokens: 10000, cache_read_input_tokens: 0 }), }); - const { queryByText } = renderWith([day], { loading: true }); + renderWith([day], { loading: true }); expect( - queryByText("Data is still loading; rows and totals will update as the rest of the range arrives."), + screen.queryByText("Data is still loading; rows and totals will update as the rest of the range arrives."), ).not.toBeInTheDocument(); }); @@ -171,10 +171,10 @@ describe("CacheLeakageCard", () => { const day = dayWithKeys("2026-07-12", { "hash-leaky": key("leaky-key", { prompt_tokens: 10000, cache_read_input_tokens: 0 }), }); - const { queryByText } = renderWith([day]); + renderWith([day]); expect( - queryByText("Data is still loading; rows and totals will update as the rest of the range arrives."), + screen.queryByText("Data is still loading; rows and totals will update as the rest of the range arrives."), ).not.toBeInTheDocument(); }); }); diff --git a/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/CostOptimizationView.activity.test.tsx b/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/CostOptimizationView.activity.test.tsx index 1cc7bec13d1..03250e3e53b 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/CostOptimizationView.activity.test.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/CostOptimizationView.activity.test.tsx @@ -1,5 +1,5 @@ import React from "react"; -import { fireEvent, render, waitFor } from "@testing-library/react"; +import { fireEvent, render, waitFor, screen } from "@testing-library/react"; import { describe, expect, it, vi } from "vitest"; import { QueryClient, QueryClientProvider } from "@tanstack/react-query"; @@ -56,7 +56,7 @@ describe("CostOptimizationView daily activity", () => { useAuthorizedMock.mockReturnValue({ accessToken: "test-token", userId: "u1", userRole: "proxy_admin" }); const queryClient = new QueryClient({ defaultOptions: { queries: { retry: false } } }); - const { getByRole, getByTestId, findByTestId, queryByText } = render( + render( , @@ -64,12 +64,12 @@ describe("CostOptimizationView daily activity", () => { await waitFor(() => expect(mockUserDailyActivityAggregatedCall).toHaveBeenCalledTimes(1)); - fireEvent.click(getByRole("tab", { name: "Prompt Caching" })); - await findByTestId("caching-settings"); + fireEvent.click(screen.getByRole("tab", { name: "Prompt Caching" })); + await screen.findByTestId("caching-settings"); expect(mockUserDailyActivityAggregatedCall).toHaveBeenCalledTimes(1); expect(mockUserDailyActivityCall).not.toHaveBeenCalled(); - expect(queryByText(/Currently fetching spend data/)).not.toBeInTheDocument(); + expect(screen.queryByText(/Currently fetching spend data/)).not.toBeInTheDocument(); }); it("shows the fetch-progress banner while the paginated fallback streams pages in", async () => { @@ -84,13 +84,13 @@ describe("CostOptimizationView daily activity", () => { useAuthorizedMock.mockReturnValue({ accessToken: "test-token", userId: "u1", userRole: "proxy_admin" }); const queryClient = new QueryClient({ defaultOptions: { queries: { retry: false } } }); - const { findByText, getByRole } = render( + render( , ); - expect(await findByText(/Currently fetching spend data: fetched 1 \/ 3 pages/)).toBeInTheDocument(); - expect(getByRole("button", { name: "Stop" })).toBeInTheDocument(); + expect(await screen.findByText(/Currently fetching spend data: fetched 1 \/ 3 pages/)).toBeInTheDocument(); + expect(screen.getByRole("button", { name: "Stop" })).toBeInTheDocument(); }); }); diff --git a/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/CostOptimizationView.test.tsx b/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/CostOptimizationView.test.tsx index d5df5aa75da..028367555a1 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/CostOptimizationView.test.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/CostOptimizationView.test.tsx @@ -1,5 +1,5 @@ import React from "react"; -import { fireEvent, render } from "@testing-library/react"; +import { fireEvent, render, screen } from "@testing-library/react"; import { beforeEach, describe, expect, it, vi } from "vitest"; import { QueryClient, QueryClientProvider } from "@tanstack/react-query"; @@ -45,32 +45,32 @@ describe("CostOptimizationView", () => { }); it("renders the standard page header with the sidebar's Cost Optimization icon", () => { - const { container, getByRole, getByText } = renderView(); + const { container } = renderView(); - expect(getByRole("heading", { level: 1, name: "Cost Optimization" })).toBeInTheDocument(); - expect(getByText(/Track and configure the mechanisms that save you money/)).toBeInTheDocument(); + expect(screen.getByRole("heading", { level: 1, name: "Cost Optimization" })).toBeInTheDocument(); + expect(screen.getByText(/Track and configure the mechanisms that save you money/)).toBeInTheDocument(); expect(container.querySelector(".lucide-piggy-bank")).not.toBeNull(); }); it("renders the four cost-optimization tabs", () => { - const { getByText } = renderView(); + renderView(); - expect(getByText("Overall")).toBeInTheDocument(); - expect(getByText("Prompt Compression")).toBeInTheDocument(); - expect(getByText("Prompt Caching")).toBeInTheDocument(); - expect(getByText("Auto-Router")).toBeInTheDocument(); + expect(screen.getByText("Overall")).toBeInTheDocument(); + expect(screen.getByText("Prompt Compression")).toBeInTheDocument(); + expect(screen.getByText("Prompt Caching")).toBeInTheDocument(); + expect(screen.getByText("Auto-Router")).toBeInTheDocument(); }); it("defaults to the Overall tab and switches the active tab on click", () => { - const { getByRole } = renderView(); + renderView(); - expect(getByRole("tab", { name: "Overall" })).toHaveAttribute("aria-selected", "true"); - expect(getByRole("tab", { name: "Prompt Compression" })).toHaveAttribute("aria-selected", "false"); + expect(screen.getByRole("tab", { name: "Overall" })).toHaveAttribute("aria-selected", "true"); + expect(screen.getByRole("tab", { name: "Prompt Compression" })).toHaveAttribute("aria-selected", "false"); - fireEvent.click(getByRole("tab", { name: "Prompt Compression" })); + fireEvent.click(screen.getByRole("tab", { name: "Prompt Compression" })); - expect(getByRole("tab", { name: "Overall" })).toHaveAttribute("aria-selected", "false"); - expect(getByRole("tab", { name: "Prompt Compression" })).toHaveAttribute("aria-selected", "true"); + expect(screen.getByRole("tab", { name: "Overall" })).toHaveAttribute("aria-selected", "false"); + expect(screen.getByRole("tab", { name: "Prompt Compression" })).toHaveAttribute("aria-selected", "true"); }); // Unlike the other three pages in this cleanup, Cost Optimization keeps its @@ -80,21 +80,21 @@ describe("CostOptimizationView", () => { // are proxy-admin-only, so those are what disappear. describe("proxy-admin-only tabs", () => { it.each(["Internal User", "Internal Viewer", "Org Admin"])("shows %s the Overall tab only", (userRole) => { - const { getByRole, queryByRole } = renderView(userRole); + renderView(userRole); - expect(getByRole("tab", { name: "Overall" })).toBeInTheDocument(); - expect(queryByRole("tab", { name: "Prompt Compression" })).not.toBeInTheDocument(); - expect(queryByRole("tab", { name: "Prompt Caching" })).not.toBeInTheDocument(); - expect(queryByRole("tab", { name: "Auto-Router" })).not.toBeInTheDocument(); + expect(screen.getByRole("tab", { name: "Overall" })).toBeInTheDocument(); + expect(screen.queryByRole("tab", { name: "Prompt Compression" })).not.toBeInTheDocument(); + expect(screen.queryByRole("tab", { name: "Prompt Caching" })).not.toBeInTheDocument(); + expect(screen.queryByRole("tab", { name: "Auto-Router" })).not.toBeInTheDocument(); }); it("never mounts the panels behind the admin-only endpoints for an internal user", () => { - const { getByTestId, queryByTestId } = renderView("Internal User"); + renderView("Internal User"); - expect(getByTestId("usage-tab")).toBeInTheDocument(); - expect(queryByTestId("compression-tab")).not.toBeInTheDocument(); - expect(queryByTestId("caching-tab")).not.toBeInTheDocument(); - expect(queryByTestId("autorouter-benchmarks-tab")).not.toBeInTheDocument(); + expect(screen.getByTestId("usage-tab")).toBeInTheDocument(); + expect(screen.queryByTestId("compression-tab")).not.toBeInTheDocument(); + expect(screen.queryByTestId("caching-tab")).not.toBeInTheDocument(); + expect(screen.queryByTestId("autorouter-benchmarks-tab")).not.toBeInTheDocument(); }); }); }); diff --git a/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/PromptCachingTab.test.tsx b/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/PromptCachingTab.test.tsx index 38517dab0ab..2c602033171 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/PromptCachingTab.test.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/PromptCachingTab.test.tsx @@ -1,4 +1,4 @@ -import { render, waitFor } from "@testing-library/react"; +import { render, waitFor, screen } from "@testing-library/react"; import { describe, expect, it, vi } from "vitest"; const mockGetGeneralSettingsCall = vi.fn(); @@ -37,10 +37,10 @@ describe("PromptCachingTab", () => { cancelled: false, cancel: vi.fn(), }; - const { getByTestId } = render(); + render(); - expect(getByTestId("caching-settings")).toBeInTheDocument(); - expect(getByTestId("cache-leakage-card")).toBeInTheDocument(); + expect(screen.getByTestId("caching-settings")).toBeInTheDocument(); + expect(screen.getByTestId("cache-leakage-card")).toBeInTheDocument(); await waitFor(() => expect(mockCacheLeakageCard).toHaveBeenCalledWith(expect.objectContaining({ activity }))); }); }); diff --git a/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/ShadowEvalSection.test.tsx b/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/ShadowEvalSection.test.tsx index ef6e224761d..64e03985f57 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/ShadowEvalSection.test.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/ShadowEvalSection.test.tsx @@ -39,6 +39,35 @@ vi.mock("@/app/(dashboard)/hooks/keys/useKeys", () => ({ })), })); +vi.mock("@/app/(dashboard)/hooks/teams/useTeams", () => ({ + useInfiniteTeams: vi.fn(() => ({ + data: { pages: [{ teams: [{ team_id: "team-eng", team_alias: "engineering" }], page: 1, total_pages: 1 }] }, + isLoading: false, + fetchNextPage: vi.fn(), + hasNextPage: false, + isFetchingNextPage: false, + })), +})); + +vi.mock("@/app/(dashboard)/hooks/users/useUsers", () => ({ + useInfiniteUsers: vi.fn(() => ({ + data: { + pages: [ + { + users: [{ user_id: "dev-alice", user_alias: null, user_email: "alice@example.com" }], + page: 1, + total_pages: 1, + }, + ], + }, + isPending: false, + isError: false, + fetchNextPage: vi.fn(), + hasNextPage: false, + isFetchingNextPage: false, + })), +})); + vi.mock("@/app/(dashboard)/hooks/models/useModels", () => ({ useAutoRouters: vi.fn(() => ({ data: [ @@ -60,7 +89,7 @@ vi.mock("@/app/(dashboard)/hooks/models/useModelCostMap", () => ({ })), })); -import ShadowEvalSection, { shadowedKeyLabel } from "./ShadowEvalSection"; +import ShadowEvalSection, { shadowedTargetLabel } from "./ShadowEvalSection"; import { useShadowEvalJob, useShadowEvalJobs, @@ -73,18 +102,20 @@ const job = (overrides: Partial = {}): ShadowEvalJob => ({ job_id: "job-1", status: "running", router_name: "claude-auto", + router_names: ["claude-auto"], direction: "forward", baseline_model: null, judge_model: "anthropic/claude-sonnet-5", shadow_percentage: 10, - keys: [ + targets: [ { - api_key_id: "hashed-key-abc", + target_type: "key", + target_id: "hashed-key-abc", max_turns: 10000, max_budget: 10, spend: 3.21, stopped_at: null, - key_alias: "prod-alpha", + target_alias: "prod-alpha", key_name: "sk-...alpha", }, ], @@ -129,7 +160,6 @@ const job = (overrides: Partial = {}): ShadowEvalJob => ({ cache_hit_turns: 2, }, ], - by_key: [], overall_shadow_win_rate_pct: 48.0, overall_tie_rate_pct: 22.0, sampled_real_spend: 0.6, @@ -144,17 +174,18 @@ const job = (overrides: Partial = {}): ShadowEvalJob => ({ ...overrides, }); -const keyEntry = ( - api_key_id: string, - overrides: Partial = {}, -): ShadowEvalJob["keys"][number] => ({ - api_key_id, +const targetEntry = ( + target_id: string, + overrides: Partial = {}, +): ShadowEvalJob["targets"][number] => ({ + target_type: "key", + target_id, max_turns: 10000, max_budget: 10, spend: 0, stopped_at: null, attempt_count: null, - key_alias: null, + target_alias: null, key_name: null, ...overrides, }); @@ -235,8 +266,8 @@ describe("ShadowEvalSection", () => { it("gives every active job its own card with a stop button, with the form still offered", () => { mockHooks({ jobs: [ - job({ job_id: "job-a", status: "running", keys: [keyEntry("key-a")] }), - job({ job_id: "job-b", status: "running", keys: [keyEntry("key-b")] }), + job({ job_id: "job-a", status: "running", targets: [targetEntry("key-a")] }), + job({ job_id: "job-b", status: "running", targets: [targetEntry("key-b")] }), ], }); render(); @@ -347,7 +378,7 @@ describe("ShadowEvalSection", () => { }); it("shows spend without a budget cap for a job from before spend budgets existed", () => { - const j = job({ keys: [keyEntry("hashed-key-abc", { max_budget: null, spend: 3.21 })] }); + const j = job({ targets: [targetEntry("hashed-key-abc", { max_budget: null, spend: 3.21 })] }); mockHooks({ jobs: [j], detailsById: { "job-1": j } }); render(); expect(screen.getByText(/\$3\.21 eval spend/)).toBeInTheDocument(); @@ -406,7 +437,7 @@ describe("ShadowEvalSection", () => { await user.click(within(keyList).getByText("prod-alpha")); await user.click(keyInput); await user.click(within(keyList).getByText("staging-beta")); - await user.click(screen.getByPlaceholderText("Select an auto-router")); + await user.click(screen.getByPlaceholderText("Select up to 4 auto-routers")); await user.click(await screen.findByText("gpt-auto")); expect(screen.getByText("Start shadow eval")).toBeDisabled(); @@ -417,7 +448,39 @@ describe("ShadowEvalSection", () => { const expectedBody = { api_key_ids: ["hash-alpha", "hash-beta"], - router_name: "gpt-auto", + team_ids: [], + user_ids: [], + router_names: ["gpt-auto"], + direction: "forward", + shadow_percentage: 10, + duration_days: 7, + max_budget: 10, + judge_model: "anthropic/claude-sonnet-5", + }; + expect(start.mutate).toHaveBeenCalledWith(expectedBody); + }); + + it("submits a team-only job with team_ids and no keys", async () => { + const user = userEvent.setup(); + const { start } = mockHooks({}); + render(); + + expect(screen.getByText("Start shadow eval")).toBeDisabled(); + + await user.click(screen.getByPlaceholderText("Search teams by alias")); + const teamList = await screen.findByTestId("paginated-multi-select-list"); + await user.click(within(teamList).getByText("engineering")); + await user.click(screen.getByPlaceholderText("Select up to 4 auto-routers")); + await user.click(await screen.findByText("gpt-auto")); + await user.click(screen.getByPlaceholderText("Select a judge model")); + await user.click(await screen.findByRole("option", { name: /anthropic\/claude-sonnet-5/ })); + await user.click(screen.getByText("Start shadow eval")); + + const expectedBody = { + api_key_ids: [], + team_ids: ["team-eng"], + user_ids: [], + router_names: ["gpt-auto"], direction: "forward", shadow_percentage: 10, duration_days: 7, @@ -439,7 +502,7 @@ describe("ShadowEvalSection", () => { await user.click(screen.getByPlaceholderText("Search keys by alias")); const keyList = await screen.findByTestId("paginated-multi-select-list"); await user.click(within(keyList).getByText("prod-alpha")); - await user.click(screen.getByPlaceholderText("Select an auto-router")); + await user.click(screen.getByPlaceholderText("Select up to 4 auto-routers")); await user.click(await screen.findByText("gpt-auto")); await user.click(screen.getByPlaceholderText("Select a judge model")); await user.click(await screen.findByRole("option", { name: /anthropic\/claude-sonnet-5/ })); @@ -453,7 +516,9 @@ describe("ShadowEvalSection", () => { const expectedBody = { api_key_ids: ["hash-alpha"], - router_name: "gpt-auto", + team_ids: [], + user_ids: [], + router_names: ["gpt-auto"], direction: "reverse", baseline_model: "prod-claude", shadow_percentage: 10, @@ -464,6 +529,119 @@ describe("ShadowEvalSection", () => { expect(start.mutate).toHaveBeenCalledWith(expectedBody); }); + it("submits every picked auto-router so one job compares them on the same traffic", async () => { + const user = userEvent.setup(); + const { start } = mockHooks({}); + render(); + + await user.click(screen.getByPlaceholderText("Search keys by alias")); + const keyList = await screen.findByTestId("paginated-multi-select-list"); + await user.click(within(keyList).getByText("prod-alpha")); + const routerInput = screen.getByPlaceholderText("Select up to 4 auto-routers"); + await user.click(routerInput); + await user.click(await screen.findByText("gpt-auto")); + await user.click(routerInput); + await user.click(await screen.findByText("claude-auto")); + expect( + screen.getByText("Every router sees the same sampled requests, judged against the same live responses"), + ).toBeInTheDocument(); + await user.click(screen.getByPlaceholderText("Select a judge model")); + await user.click(await screen.findByRole("option", { name: /anthropic\/claude-sonnet-5/ })); + await user.click(screen.getByText("Start shadow eval")); + + const expectedBody = { + api_key_ids: ["hash-alpha"], + team_ids: [], + user_ids: [], + router_names: ["gpt-auto", "claude-auto"], + direction: "forward", + shadow_percentage: 10, + duration_days: 7, + max_budget: 10, + judge_model: "anthropic/claude-sonnet-5", + }; + expect(start.mutate).toHaveBeenCalledWith(expectedBody); + }); + + it("blocks starting a reverse job with more than one router and says why", async () => { + const user = userEvent.setup(); + mockHooks({}); + render(); + + await user.click(screen.getByPlaceholderText("Search keys by alias")); + const keyList = await screen.findByTestId("paginated-multi-select-list"); + await user.click(within(keyList).getByText("prod-alpha")); + const routerInput = screen.getByPlaceholderText("Select up to 4 auto-routers"); + await user.click(routerInput); + await user.click(await screen.findByText("gpt-auto")); + await user.click(routerInput); + await user.click(await screen.findByText("claude-auto")); + await user.click(screen.getByText("Adoption check: key's traffic vs the router")); + await user.click(await screen.findByText("Regression check: router's picks vs a baseline")); + await user.click(screen.getByPlaceholderText("Select a judge model")); + await user.click(await screen.findByRole("option", { name: /anthropic\/claude-sonnet-5/ })); + await user.click(screen.getByPlaceholderText("Select a baseline model")); + await user.click(screen.getByRole("option", { name: /prod-claude/ })); + + expect(screen.getByText("A regression check compares one router to its baseline")).toBeInTheDocument(); + expect(screen.getByText("Start shadow eval")).toBeDisabled(); + }); + + it("renders a per-router comparison table only when the job ran several routers", () => { + const routerSlice = (group: string, wins: number) => ({ + group, + turn_count: 20, + real_win_rate_pct: 100 - wins - 10, + shadow_win_rate_pct: wins, + tie_rate_pct: 10, + avg_judge_confidence: 0.8, + real_spend: 0.4, + shadow_spend: 0.2, + cache_hit_turns: 0, + }); + const base = job(); + const multi = job({ + router_names: ["claude-auto", "gpt-auto"], + results: { ...base.results!, by_router: [routerSlice("claude-auto", 40), routerSlice("gpt-auto", 70)] }, + }); + mockHooks({ jobs: [multi], detailsById: { "job-1": multi } }); + render(); + + expect(screen.getByText("Router")).toBeInTheDocument(); + const rows = screen.getAllByRole("row").map((row) => row.textContent ?? ""); + expect(rows.some((text) => text.includes("claude-auto") && text.includes("40.0%"))).toBe(true); + expect(rows.some((text) => text.includes("gpt-auto") && text.includes("70.0%"))).toBe(true); + expect( + screen.getByText( + (_, element) => + element?.textContent === "Shadowing 10% of prod-alpha traffic via claude-auto, gpt-auto" && + element.tagName === "P", + ), + ).toBeInTheDocument(); + }); + + it("renders a job from an older proxy that predates router_names", () => { + const legacy = { ...job(), router_names: undefined } as unknown as ShadowEvalJob; + mockHooks({ jobs: [legacy], detailsById: { "job-1": legacy } }); + render(); + + expect( + screen.getByText( + (_, element) => + element?.textContent === "Shadowing 10% of prod-alpha traffic via claude-auto" && element.tagName === "P", + ), + ).toBeInTheDocument(); + }); + + it("keeps the per-router table hidden for a single-router job", () => { + const base = job(); + const single = job({ results: { ...base.results!, by_router: [] } }); + mockHooks({ jobs: [single], detailsById: { "job-1": single } }); + render(); + + expect(screen.queryByText("Router")).not.toBeInTheDocument(); + }); + it("flips the arm labels and headline for a reverse job's results", () => { const j = job({ direction: "reverse", baseline_model: "openai/gpt-4o" }); mockHooks({ jobs: [j], detailsById: { "job-1": j } }); @@ -485,9 +663,13 @@ describe("ShadowEvalSection", () => { }); it("labels the shadowed key by alias, then masked name, then truncated hash", () => { - expect(shadowedKeyLabel(job().keys[0])).toBe("prod-alpha"); - expect(shadowedKeyLabel(keyEntry("hashed-key-abc", { key_name: "sk-...alpha" }))).toBe("sk-...alpha"); - expect(shadowedKeyLabel(keyEntry("hashed-key-abc"))).toBe("hashed-key…"); + expect(shadowedTargetLabel(job().targets[0])).toBe("prod-alpha"); + expect(shadowedTargetLabel(targetEntry("hashed-key-abc", { key_name: "sk-...alpha" }))).toBe("sk-...alpha"); + expect(shadowedTargetLabel(targetEntry("hashed-key-abc"))).toBe("hashed-key…"); + expect(shadowedTargetLabel(targetEntry("team-eng", { target_type: "team" }))).toBe("team-eng"); + expect(shadowedTargetLabel(targetEntry("team-eng", { target_type: "team", target_alias: "engineering" }))).toBe( + "engineering", + ); }); it("breaks results down per key, so one key exhausting its own budget is visible while a sibling runs on", () => { @@ -495,15 +677,12 @@ describe("ShadowEvalSection", () => { jobs: [ job({ judged_count: 205, - keys: [ - keyEntry("hash-spent", { max_budget: 2, spend: 1.5, stopped_at: "2026-08-08T00:00:00Z" }), - keyEntry("hash-hungry", { max_budget: 5, spend: 0.2 }), - ], - results: { - by_tier: [], - by_current_model: [], - by_key: [ - { + targets: [ + targetEntry("hash-spent", { + max_budget: 2, + spend: 1.5, + stopped_at: "2026-08-08T00:00:00Z", + verdicts: { group: "hash-spent", turn_count: 200, real_win_rate_pct: 20.0, @@ -514,7 +693,12 @@ describe("ShadowEvalSection", () => { shadow_spend: 0.5, cache_hit_turns: 0, }, - ], + }), + targetEntry("hash-hungry", { max_budget: 5, spend: 0.2 }), + ], + results: { + by_tier: [], + by_current_model: [], overall_shadow_win_rate_pct: 60.0, overall_tie_rate_pct: 20.0, sampled_real_spend: 0.9, @@ -539,7 +723,7 @@ describe("ShadowEvalSection", () => { expect(screen.getByText(/205 turns judged/)).toBeInTheDocument(); expect(screen.getByText(/Shadowing 10% of/)).toBeInTheDocument(); - expect(screen.getByText("2 keys")).toBeInTheDocument(); + expect(screen.getByText("2 targets")).toBeInTheDocument(); }); it("reads a key that spent its budget as completed even before the sweep stamps it", () => { @@ -547,9 +731,9 @@ describe("ShadowEvalSection", () => { mockHooks({ jobs: [ job({ - keys: [ - keyEntry("hash-spent", { max_budget: 2, spend: 2, attempt_count: 40 }), - keyEntry("hash-hungry", legacyTurnBudgetLeg), + targets: [ + targetEntry("hash-spent", { max_budget: 2, spend: 2, attempt_count: 40 }), + targetEntry("hash-hungry", legacyTurnBudgetLeg), ], }), ], @@ -571,9 +755,9 @@ describe("ShadowEvalSection", () => { job({ judged_count: 0, results: null, - keys: [ - keyEntry("hash-spent", { max_budget: 0.5, spend: 0.5, attempt_count: 2 }), - keyEntry("hash-hungry", { max_budget: 5, spend: 0.01, attempt_count: 1 }), + targets: [ + targetEntry("hash-spent", { max_budget: 0.5, spend: 0.5, attempt_count: 2 }), + targetEntry("hash-hungry", { max_budget: 5, spend: 0.01, attempt_count: 1 }), ], }), ], @@ -594,9 +778,9 @@ describe("ShadowEvalSection", () => { jobs: [ job({ status: "completed", - keys: [ - keyEntry("hash-spent", { max_turns: 200, stopped_at: "2026-08-08T00:00:00Z" }), - keyEntry("hash-hungry", { max_turns: 500 }), + targets: [ + targetEntry("hash-spent", { max_turns: 200, stopped_at: "2026-08-08T00:00:00Z" }), + targetEntry("hash-hungry", { max_turns: 500 }), ], }), ], diff --git a/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/ShadowEvalSection.tsx b/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/ShadowEvalSection.tsx index 39dee28390a..c66d74074c2 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/ShadowEvalSection.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/ShadowEvalSection.tsx @@ -2,32 +2,24 @@ import React, { useMemo, useState } from "react"; -import { useInfiniteKeys } from "@/app/(dashboard)/hooks/keys/useKeys"; import useAuthorized from "@/app/(dashboard)/hooks/useAuthorized"; -import { useModelCostMap } from "@/app/(dashboard)/hooks/models/useModelCostMap"; -import { useAutoRouters, usePlainModelGroups } from "@/app/(dashboard)/hooks/models/useModels"; -import { PaginatedMultiSelect } from "@/components/shared/PaginatedMultiSelect"; -import { SearchSelect, type SearchSelectOption } from "@/components/shared/SearchSelect"; import { Badge } from "@/components/ui/badge"; import { Button } from "@/components/ui/button"; import { CircleHelp } from "lucide-react"; -import { Card, CardContent, CardHeader, CardTitle } from "@/components/ui/card"; +import { Card } from "@/components/ui/card"; import { Tooltip, TooltipContent, TooltipProvider, TooltipTrigger } from "@/components/ui/tooltip"; -import { Input } from "@/components/ui/input"; -import { Label } from "@/components/ui/label"; -import { Select, SelectContent, SelectItem, SelectTrigger, SelectValue } from "@/components/ui/select"; import { Table, TableBody, TableCell, TableHead, TableHeader, TableRow } from "@/components/ui/table"; import { ApiError } from "@/lib/http/client"; import { usd } from "./costOptimizationUtils"; +import { StartForm } from "./ShadowEvalStartForm"; import { useShadowEvalJob, useShadowEvalJobs, - useStartShadowEval, useStopShadowEval, type ShadowEvalJob, - type ShadowEvalJobKey, + type ShadowEvalJobTarget, type ShadowEvalSlice, } from "./useShadowEval"; @@ -66,42 +58,46 @@ const routerMatchedOrBeatPct = ( ? 100 - results.overall_shadow_win_rate_pct : results.overall_shadow_win_rate_pct + results.overall_tie_rate_pct; -export const shadowedKeyLabel = (key: ShadowEvalJobKey): string => - key.key_alias || key.key_name || `${key.api_key_id.slice(0, 10)}…`; +export const shadowedTargetLabel = (target: ShadowEvalJobTarget): string => + target.target_alias || + target.key_name || + (target.target_type === "key" ? `${target.target_id.slice(0, 10)}…` : target.target_id); -const shadowedKeysLabel = (job: ShadowEvalJob): string => - job.keys.length === 1 ? shadowedKeyLabel(job.keys[0]) : `${job.keys.length} keys`; +const shadowedTargetsLabel = (job: ShadowEvalJob): string => + job.targets.length === 1 ? shadowedTargetLabel(job.targets[0]) : `${job.targets.length} targets`; const totalBudget = (job: ShadowEvalJob): number | null => - job.keys.reduce( - (sum, key) => (sum === null || key.max_budget == null ? null : sum + key.max_budget), + job.targets.reduce( + (sum, target) => (sum === null || target.max_budget == null ? null : sum + target.max_budget), 0, ); -const totalSpend = (job: ShadowEvalJob): number => job.keys.reduce((sum, key) => sum + (key.spend ?? 0), 0); +const totalSpend = (job: ShadowEvalJob): number => job.targets.reduce((sum, target) => sum + (target.spend ?? 0), 0); -const keySpent = (key: ShadowEvalJobKey): boolean => { - const spendBudgetReached = key.max_budget != null && key.spend != null && key.spend >= key.max_budget; - const turnValveReached = key.attempt_count != null && key.attempt_count >= key.max_turns; +const targetSpent = (target: ShadowEvalJobTarget): boolean => { + const spendBudgetReached = target.max_budget != null && target.spend != null && target.spend >= target.max_budget; + const turnValveReached = target.attempt_count != null && target.attempt_count >= target.max_turns; return spendBudgetReached || turnValveReached; }; -const keyStatus = (job: ShadowEvalJob, key: ShadowEvalJobKey): string => { - if (job.status === "completed" || (key.stopped_at == null && keySpent(key))) return "completed"; - return key.stopped_at != null ? "stopped" : "running"; +const targetStatus = (job: ShadowEvalJob, target: ShadowEvalJobTarget): string => { + if (job.status === "completed" || (target.stopped_at == null && targetSpent(target))) return "completed"; + return target.stopped_at != null ? "stopped" : "running"; }; +const jobRouters = (job: ShadowEvalJob): string => (job.router_names ?? [job.router_name]).join(", "); + const jobHeadline = (job: ShadowEvalJob): React.ReactNode => job.direction === "reverse" ? ( <> - Comparing {job.router_name} to{" "} + Comparing {jobRouters(job)} to{" "} {job.baseline_model} on {job.shadow_percentage}% of{" "} - {shadowedKeysLabel(job)} traffic + {shadowedTargetsLabel(job)} traffic ) : ( <> - Shadowing {job.shadow_percentage}% of {shadowedKeysLabel(job)} traffic - via {job.router_name} + Shadowing {job.shadow_percentage}% of {shadowedTargetsLabel(job)}{" "} + traffic via {jobRouters(job)} ); @@ -255,13 +251,12 @@ const VerdictBar: React.FC<{ direction: ShadowEvalDirection; results: NonNullabl ); }; -const KeyTable: React.FC<{ job: ShadowEvalJob }> = ({ job }) => { - const slices = new Map((job.results?.by_key ?? []).map((slice) => [slice.group, slice])); +const TargetTable: React.FC<{ job: ShadowEvalJob }> = ({ job }) => { return ( - Key + Target Status {["Budget used", "Router wins", `${otherArmLabel(job.direction)} wins`].map((label) => ( @@ -271,18 +266,23 @@ const KeyTable: React.FC<{ job: ShadowEvalJob }> = ({ job }) => { - {job.keys.map((key) => { - const slice = slices.get(key.api_key_id); + {job.targets.map((target) => { + const slice = target.verdicts; return ( - - {shadowedKeyLabel(key)} + + + {shadowedTargetLabel(target)} + {target.target_type !== "key" && ( + {target.target_type} + )} + - + - {key.max_budget != null - ? `${usd(key.spend ?? 0)} / ${usd(key.max_budget)}` - : `${(key.attempt_count ?? slice?.turn_count ?? 0).toLocaleString()} / ${key.max_turns.toLocaleString()} turns`} + {target.max_budget != null + ? `${usd(target.spend ?? 0)} / ${usd(target.max_budget)}` + : `${(target.attempt_count ?? slice?.turn_count ?? 0).toLocaleString()} / ${target.max_turns.toLocaleString()} turns`} {slice ? ( <> @@ -318,9 +318,9 @@ const ResultsBody: React.FC<{ job: ShadowEvalJob; resultsError?: boolean }> = ({ const hasVerdicts = results != null && (results.by_tier.length > 0 || results.by_current_model.length > 0); return ( <> - {job.keys.length > 1 && ( + {job.targets.length > 1 && (
- +
)} {/* results == null re-stated for TS narrowing; hasVerdicts alone cannot narrow it */} @@ -343,6 +343,11 @@ const ResultsBody: React.FC<{ job: ShadowEvalJob; resultsError?: boolean }> = ({ + {(results.by_router ?? []).length > 1 && ( +
+ +
+ )} {results.by_current_model.length > 0 && ( { - const { data: costMap } = useModelCostMap(); - return useMemo(() => { - if (!costMap) return []; - const chatModels = Object.entries(costMap as Record) - .filter(([, value]) => value?.mode === "chat" && value?.litellm_provider) - .map(([key, value]) => (key.startsWith(`${value.litellm_provider}/`) ? key : `${value.litellm_provider}/${key}`)); - return [...new Set(chatModels)].toSorted((a, b) => a.localeCompare(b)); - }, [costMap]); -}; - -const useJudgeModelOptions = (): SearchSelectOption[] => { - const chatModels = useChatModelNames(); - return useMemo(() => { - const pinned: SearchSelectOption[] = RECOMMENDED_JUDGE_MODELS.map((model) => ({ - label: model, - value: model, - sublabel: "Recommended", - })); - const pinnedNames = new Set(RECOMMENDED_JUDGE_MODELS); - const rest = chatModels.filter((model) => !pinnedNames.has(model)).map((model) => ({ label: model, value: model })); - return [...pinned, ...rest]; - }, [chatModels]); -}; - -const useBaselineModelOptions = (): SearchSelectOption[] => { - const configuredGroups = usePlainModelGroups(); - const chatModels = useChatModelNames(); - return useMemo(() => { - const configured = [...configuredGroups] - .toSorted((a, b) => a.localeCompare(b)) - .map((model) => ({ label: model, value: model, sublabel: "Configured on this gateway" })); - const rest = chatModels - .filter((model) => !configuredGroups.has(model)) - .map((model) => ({ label: model, value: model })); - return [...configured, ...rest]; - }, [configuredGroups, chatModels]); -}; - -const DIRECTION_OPTIONS: readonly { value: ShadowEvalDirection; label: string }[] = [ - { value: "forward", label: "Adoption check: key's traffic vs the router" }, - { value: "reverse", label: "Regression check: router's picks vs a baseline" }, -] as const; - -const START_FORM_DESCRIPTION: Record = { - forward: - "Duplicates a sampled slice of the selected keys' traffic through the auto-router and has an LLM judge compare both answers blind. Each key gets its own spend budget. The router's answers are never served to users; judge calls bill to the shadowed key.", - reverse: - "Duplicates a sampled slice of the traffic the auto-router already serves against a fixed baseline model and has an LLM judge compare both answers blind. Each key gets its own spend budget. The baseline's answers are never served to users; judge calls bill to the shadowed key.", -}; - -const DURATION_OPTIONS = [ - { value: "1", label: "1 day" }, - { value: "3", label: "3 days" }, - { value: "7", label: "7 days" }, - { value: "14", label: "14 days" }, - { value: "30", label: "30 days" }, -] as const; - -const Field: React.FC<{ label: string; htmlFor?: string; className?: string; children: React.ReactNode }> = ({ - label, - htmlFor, - className, - children, -}) => ( -
- - {children} -
-); - -const KeySelect: React.FC<{ value: string[]; onChange: (tokens: string[]) => void }> = ({ value, onChange }) => { - const [search, setSearch] = useState(""); - const { data, isPending, isError, fetchNextPage, hasNextPage, isFetchingNextPage } = useInfiniteKeys(50, { - selectedKeyAlias: search || null, - }); - const options = useMemo( - () => - (data?.pages ?? []) - .flatMap((page) => page.keys) - .map((key) => ({ - label: key.key_alias || key.key_name || key.token, - value: key.token, - sublabel: key.token, - })), - [data], - ); - return ( - void fetchNextPage()} - hasNextPage={hasNextPage} - isFetchingNextPage={isFetchingNextPage} - isLoading={isPending} - placeholder="Search keys by alias" - emptyText="No matching keys" - errorText={isError ? "Keys could not be loaded. Refresh the page to retry." : undefined} - /> - ); -}; - -const StartForm: React.FC = () => { - const { accessToken } = useAuthorized(); - const [apiKeyIds, setApiKeyIds] = useState([]); - const [routerName, setRouterName] = useState(""); - const [direction, setDirection] = useState("forward"); - const [baselineModel, setBaselineModel] = useState(""); - const [percentage, setPercentage] = useState("10"); - const [durationDays, setDurationDays] = useState("7"); - const [judgeModel, setJudgeModel] = useState(""); - const [maxBudget, setMaxBudget] = useState("10"); - const { data: autoRouters } = useAutoRouters(); - const judgeModelOptions = useJudgeModelOptions(); - const baselineModelOptions = useBaselineModelOptions(); - const start = useStartShadowEval(); - - const routerOptions = useMemo(() => { - const names = new Set( - (autoRouters ?? []).map((deployment) => deployment.model_name).filter((name): name is string => Boolean(name)), - ); - return [...names].toSorted().map((name) => ({ label: name, value: name })); - }, [autoRouters]); - - const parsedPct = Number.parseFloat(percentage); - const percentageValid = parsedPct >= 0.1 && parsedPct <= 100; - const parsedMaxBudget = Number.parseFloat(maxBudget); - const maxBudgetValid = parsedMaxBudget >= 0.01 && parsedMaxBudget <= 10000; - const baselinePicked = direction === "forward" || baselineModel !== ""; - const filled = apiKeyIds.length > 0 && [routerName, judgeModel].every((field) => field !== "") && baselinePicked; - const boundsValid = percentageValid && maxBudgetValid; - const valid = Boolean(accessToken) && filled && boundsValid; - const handleStart = () => { - const startBody = { - api_key_ids: apiKeyIds, - router_name: routerName, - direction, - ...(direction === "reverse" ? { baseline_model: baselineModel } : {}), - shadow_percentage: parsedPct, - duration_days: Number.parseInt(durationDays, 10), - max_budget: parsedMaxBudget, - judge_model: judgeModel, - }; - start.mutate(startBody); - }; - - return ( - - - Start a shadow eval -

{START_FORM_DESCRIPTION[direction]}

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

Enter a value from 0.1 to 100

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

Enter a value from 0.01 to 10000

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

Shadow eval

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

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

Pick at most {MAX_ROUTERS} auto-routers

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

A regression check compares one router to its baseline

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

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

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

{START_FORM_DESCRIPTION[direction]}

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

Enter a value from 0.1 to 100

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

Enter a value from 0.01 to 10000

+ )} + + {direction === "reverse" && ( + + + + )} + + + +
+ + + + ); +}; diff --git a/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/UsageTab.test.tsx b/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/UsageTab.test.tsx index df23e5509bf..f85a667a074 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/UsageTab.test.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/UsageTab.test.tsx @@ -1,4 +1,4 @@ -import { render } from "@testing-library/react"; +import { render, screen } from "@testing-library/react"; import userEvent from "@testing-library/user-event"; import { beforeEach, describe, expect, it, vi } from "vitest"; import type { ToolSpendResponse } from "@/components/networking"; @@ -152,13 +152,13 @@ describe("UsageTab", () => { gateway_injected_caching_savings_spend: 0.006, compression_saved_tokens: 100000, }; - const { getByText } = renderWith([day("2026-07-12", firstDay), day("2026-07-13", secondDay)]); + renderWith([day("2026-07-12", firstDay), day("2026-07-13", secondDay)]); - expect(getByText("$0.1500")).toBeInTheDocument(); - expect(getByText("$0.1400")).toBeInTheDocument(); - expect(getByText("$0.0100")).toBeInTheDocument(); - expect(getByText("$0.0160")).toBeInTheDocument(); - expect(getByText("140,000 tokens compressed")).toBeInTheDocument(); + expect(screen.getByText("$0.1500")).toBeInTheDocument(); + expect(screen.getByText("$0.1400")).toBeInTheDocument(); + expect(screen.getByText("$0.0100")).toBeInTheDocument(); + expect(screen.getByText("$0.0160")).toBeInTheDocument(); + expect(screen.getByText("140,000 tokens compressed")).toBeInTheDocument(); }); const twoDays = () => [ @@ -167,11 +167,11 @@ describe("UsageTab", () => { ]; it("opens on a running total anchored at $0 at the start of the range", () => { - const { getByTestId } = renderWith(twoDays()); + renderWith(twoDays()); // Cumulative prepends a synthetic $0 point at the range start (Jul 1) so the // line rises from zero rather than floating; the daily running totals follow. - const series = readSeries(getByTestId("area-chart")); + const series = readSeries(screen.getByTestId("area-chart")); expect(series).toHaveLength(3); expect(series[0]).toMatchObject({ date: "Jul 1", Compression: 0, "Prompt caching": 0 }); expect(series[1]).toMatchObject({ Compression: 0.04, "Prompt caching": 0.006 }); @@ -183,12 +183,12 @@ describe("UsageTab", () => { // The original complaint: a one-day range plotted a single floating dot. The // synthetic start anchor gives the line a zero origin to climb from. const oneDay = new Date(2026, 6, 24); - const { getByTestId } = renderWith( - [day("2026-07-24", { compression_savings_spend: 0.2, gateway_injected_caching_savings_spend: 0.05 })], - { from: oneDay, to: oneDay }, - ); + renderWith([day("2026-07-24", { compression_savings_spend: 0.2, gateway_injected_caching_savings_spend: 0.05 })], { + from: oneDay, + to: oneDay, + }); - const series = readSeries(getByTestId("area-chart")); + const series = readSeries(screen.getByTestId("area-chart")); expect(series).toHaveLength(2); expect(series[0]).toMatchObject({ date: "Jul 24", Compression: 0, "Prompt caching": 0 }); expect(series[1]).toMatchObject({ date: "Jul 24", Compression: 0.2, "Prompt caching": 0.05 }); @@ -202,49 +202,49 @@ describe("UsageTab", () => { day("2026-07-13", { gateway_injected_caching_savings_spend: 0.1 }), day("2026-07-12", { gateway_injected_caching_savings_spend: 0.04 }), ]; - const { getByTestId, getByRole } = renderWith(newestFirst); + renderWith(newestFirst); // The $0 anchor leads, then the days climb oldest to newest. - const cumulative = readSeries(getByTestId("area-chart")); + const cumulative = readSeries(screen.getByTestId("area-chart")); expect(cumulative.map((p: { date: string }) => p.date)).toEqual(["Jul 1", "Jul 12", "Jul 13"]); expect(cumulative[1]["Prompt caching"]).toBeCloseTo(0.04, 5); expect(cumulative[2]["Prompt caching"]).toBeCloseTo(0.14, 5); expect(cumulative[2]["Prompt caching"]).toBeGreaterThan(cumulative[1]["Prompt caching"]); - await userEvent.click(getByRole("tab", { name: "Per day" })); - const perDay = readSeries(getByTestId("bar-chart")); + await userEvent.click(screen.getByRole("tab", { name: "Per day" })); + const perDay = readSeries(screen.getByTestId("bar-chart")); expect(perDay.map((p: { date: string }) => p.date)).toEqual(["Jul 12", "Jul 13"]); }); it("draws bars of the raw per-interval readings on the other tab", async () => { - const { getByRole, getByTestId, queryByTestId } = renderWith(twoDays()); + renderWith(twoDays()); // Cumulative opens on the area line. - expect(getByTestId("area-chart")).toBeInTheDocument(); + expect(screen.getByTestId("area-chart")).toBeInTheDocument(); - await userEvent.click(getByRole("tab", { name: "Per day" })); + await userEvent.click(screen.getByRole("tab", { name: "Per day" })); // Per day switches to a bar chart of the unaccumulated daily savings, with no // synthetic anchor prepended. - expect(queryByTestId("area-chart")).not.toBeInTheDocument(); - const series = readSeries(getByTestId("bar-chart")); + expect(screen.queryByTestId("area-chart")).not.toBeInTheDocument(); + const series = readSeries(screen.getByTestId("bar-chart")); expect(series).toHaveLength(2); expect(series[0]).toMatchObject({ Compression: 0.04, "Prompt caching": 0.006 }); expect(series[1]).toMatchObject({ Compression: 0.1, "Prompt caching": 0.01 }); }); it("says what the line means and over what range", async () => { - const { getByText, getByRole } = renderWith(twoDays()); + renderWith(twoDays()); - expect(getByText("Running total saved · Jul 1 – Jul 14 (UTC)")).toBeInTheDocument(); - await userEvent.click(getByRole("tab", { name: "Per day" })); - expect(getByText("Saved per day · Jul 1 – Jul 14 (UTC)")).toBeInTheDocument(); + expect(screen.getByText("Running total saved · Jul 1 – Jul 14 (UTC)")).toBeInTheDocument(); + await userEvent.click(screen.getByRole("tab", { name: "Per day" })); + expect(screen.getByText("Saved per day · Jul 1 – Jul 14 (UTC)")).toBeInTheDocument(); }); it("builds the per-driver donut from the range totals, not the running total", () => { - const { getByTestId } = renderWith(twoDays()); + renderWith(twoDays()); - const slices = JSON.parse(getByTestId("donut-chart").getAttribute("data-slices") ?? "[]"); + const slices = JSON.parse(screen.getByTestId("donut-chart").getAttribute("data-slices") ?? "[]"); expect(slices).toEqual([ { driver: "Compression", color: "emerald", usd: expect.closeTo(0.14, 5) }, { driver: "Prompt caching", color: "blue", usd: expect.closeTo(0.016, 5) }, @@ -252,9 +252,9 @@ describe("UsageTab", () => { }); it("omits a driver slice when that driver has no savings", () => { - const { getByTestId } = renderWith([day("2026-07-12", { compression_savings_spend: 0.04 })]); + renderWith([day("2026-07-12", { compression_savings_spend: 0.04 })]); - const slices = JSON.parse(getByTestId("donut-chart").getAttribute("data-slices") ?? "[]"); + const slices = JSON.parse(screen.getByTestId("donut-chart").getAttribute("data-slices") ?? "[]"); expect(slices).toEqual([{ driver: "Compression", color: "emerald", usd: expect.closeTo(0.04, 5) }]); }); @@ -262,7 +262,7 @@ describe("UsageTab", () => { // Stacking sums the series into one bar. Auto-router savings go negative when a // model switch pays for a cold cache, and that segment would be drawn below the // axis while the rest of the bar still read as the day's total. - const { getByRole, getByTestId } = renderWith([ + renderWith([ day("2026-07-12", { compression_savings_spend: 0.1, gateway_injected_caching_savings_spend: 0.02, @@ -270,8 +270,8 @@ describe("UsageTab", () => { }), ]); - await userEvent.click(getByRole("tab", { name: "Per day" })); - const bars = getByTestId("bar-chart"); + await userEvent.click(screen.getByRole("tab", { name: "Per day" })); + const bars = screen.getByTestId("bar-chart"); expect(bars).toHaveAttribute("data-stack", "false"); expect(readSeries(bars)[0]).toMatchObject({ "Auto-router": -0.05 }); }); @@ -281,10 +281,10 @@ describe("UsageTab", () => { // per day"). Hand-rolled rows made it compete with the legend and the toggle for // width, so the header grew a line on one tab and the chart moved with it. CardHeader // sizes the action column to its content and gives the rest to the title column. - const { getByRole, getByTestId, container } = renderWith(twoDays()); + const { container } = renderWith(twoDays()); const header = () => { - const legend = getByTestId("chart-legend"); + const legend = screen.getByTestId("chart-legend"); const action = legend.closest('[data-slot="card-action"]') as HTMLElement; const cardHeader = action.parentElement as HTMLElement; const description = cardHeader.querySelector('[data-slot="card-description"]') as HTMLElement; @@ -295,12 +295,12 @@ describe("UsageTab", () => { expect(before.action).toBeTruthy(); expect(before.description).toBeTruthy(); // the toggle rides in the same action slot as the legend, so neither moves alone - expect(before.action.contains(getByRole("tablist"))).toBe(true); + expect(before.action.contains(screen.getByRole("tablist"))).toBe(true); // the subtitle lives outside that slot, so its length cannot reposition the controls expect(before.action.contains(before.description)).toBe(false); expect(before.description).toHaveTextContent(/Running total saved/); - await userEvent.click(getByRole("tab", { name: "Per day" })); + await userEvent.click(screen.getByRole("tab", { name: "Per day" })); const after = header(); expect(after.action).toBe(before.action); @@ -314,7 +314,7 @@ describe("UsageTab", () => { // Switching models leaves the new one with a cold cache, so a route can cost more // than the baseline would have. A negative slice is meaningless in a donut, but the // total has to keep the loss or the page can only ever report good news. - const { getByText, getByTestId } = renderWith([ + renderWith([ day("2026-07-12", { compression_savings_spend: 0.1, gateway_injected_caching_savings_spend: 0.02, @@ -322,16 +322,16 @@ describe("UsageTab", () => { }), ]); - expect(getByText("$0.0700")).toBeInTheDocument(); - expect(getByText("-$0.0500")).toBeInTheDocument(); + expect(screen.getByText("$0.0700")).toBeInTheDocument(); + expect(screen.getByText("-$0.0500")).toBeInTheDocument(); - const slices = JSON.parse(getByTestId("donut-chart").getAttribute("data-slices") ?? "[]"); + const slices = JSON.parse(screen.getByTestId("donut-chart").getAttribute("data-slices") ?? "[]"); expect(slices.map((d: { driver: string }) => d.driver)).toEqual(["Compression", "Prompt caching"]); - expect(getByTestId("donut-chart")).toHaveAttribute("data-label", "$0.1200"); + expect(screen.getByTestId("donut-chart")).toHaveAttribute("data-label", "$0.1200"); }); it("carries auto-router savings into the summary card, donut slice, and cumulative series", () => { - const { getByText, getByTestId } = renderWith([ + renderWith([ day("2026-07-12", { compression_savings_spend: 0.04, gateway_injected_caching_savings_spend: 0.006, @@ -345,11 +345,11 @@ describe("UsageTab", () => { ]); // Total saved now sums three drivers, and the auto-router card carries its own total. - expect(getByText("$0.2260")).toBeInTheDocument(); - expect(getByText("$0.0700")).toBeInTheDocument(); + expect(screen.getByText("$0.2260")).toBeInTheDocument(); + expect(screen.getByText("$0.0700")).toBeInTheDocument(); // The driver donut gains a third slice priced from the range totals. - const slices = JSON.parse(getByTestId("donut-chart").getAttribute("data-slices") ?? "[]"); + const slices = JSON.parse(screen.getByTestId("donut-chart").getAttribute("data-slices") ?? "[]"); expect(slices).toEqual([ { driver: "Compression", color: "emerald", usd: expect.closeTo(0.14, 5) }, { driver: "Prompt caching", color: "blue", usd: expect.closeTo(0.016, 5) }, @@ -357,7 +357,7 @@ describe("UsageTab", () => { ]); // And the cumulative line accumulates the auto-router series alongside the others. - const series = readSeries(getByTestId("area-chart")); + const series = readSeries(screen.getByTestId("area-chart")); expect(series[2]["Auto-router"]).toBeCloseTo(0.07, 5); }); @@ -371,9 +371,9 @@ describe("UsageTab", () => { start_date: "2026-07-12", end_date: "2026-07-12", }; - const { findAllByTestId } = renderWith([day("2026-07-12", {})], { toolSpend }); + renderWith([day("2026-07-12", {})], { toolSpend }); - const bars = await findAllByTestId("bar-chart"); + const bars = await screen.findAllByTestId("bar-chart"); const series = JSON.parse(bars[0].getAttribute("data-series") ?? "[]"); expect(series[0]).toMatchObject({ tool_name: "search", spend: 4.0 }); // The 64px bar cap is this card's opt-in; the shared BarChart must not cap @@ -391,14 +391,16 @@ describe("UsageTab", () => { start_date: "2026-07-12", end_date: "2026-07-12", }; - const { findAllByTestId, getAllByTestId } = renderWith([day("2026-07-12", {})], { toolSpend }); + renderWith([day("2026-07-12", {})], { toolSpend }); - const bars = await findAllByTestId("bar-chart"); + const bars = await screen.findAllByTestId("bar-chart"); const [totalByTool, dailyByTool] = bars.slice(-2); expect(dailyByTool).toHaveAttribute("data-show-legend", "false"); expect(totalByTool).toHaveAttribute("data-colors", dailyByTool.getAttribute("data-colors")); - const toolLegends = getAllByTestId("chart-legend").filter((legend) => legend.textContent === "search,read_file"); + const toolLegends = screen + .getAllByTestId("chart-legend") + .filter((legend) => legend.textContent === "search,read_file"); expect(toolLegends).toHaveLength(1); }); @@ -415,23 +417,23 @@ describe("UsageTab", () => { it.each(["Internal User", "Internal Viewer", "Org Admin"])( "hides the card and never calls the endpoint for %s", async (userRole) => { - const { queryByText, getByTestId } = renderWith([day("2026-07-12", { compression_savings_spend: 0.04 })], { + renderWith([day("2026-07-12", { compression_savings_spend: 0.04 })], { toolSpend, userRole, }); // Liveness gate: the daily-activity charts still render for this role, // so the absence below is the gate, not an empty tab. - expect(getByTestId("donut-chart")).toBeInTheDocument(); - expect(queryByText("Spend by tool")).not.toBeInTheDocument(); + expect(screen.getByTestId("donut-chart")).toBeInTheDocument(); + expect(screen.queryByText("Spend by tool")).not.toBeInTheDocument(); await vi.waitFor(() => expect(mockGetToolSpend).not.toHaveBeenCalled()); }, ); it("keeps the card and the endpoint call for an admin", async () => { - const { findByText } = renderWith([day("2026-07-12", { compression_savings_spend: 0.04 })], { toolSpend }); + renderWith([day("2026-07-12", { compression_savings_spend: 0.04 })], { toolSpend }); - expect(await findByText("Spend by tool")).toBeInTheDocument(); + expect(await screen.findByText("Spend by tool")).toBeInTheDocument(); expect(mockGetToolSpend).toHaveBeenCalled(); }); }); diff --git a/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/useShadowEval.ts b/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/useShadowEval.ts index eef98320e67..107df0f594a 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/useShadowEval.ts +++ b/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/useShadowEval.ts @@ -7,7 +7,7 @@ import { $api, fetchClient } from "@/lib/http/api"; import type { components } from "@/lib/http/schema"; export type ShadowEvalJob = components["schemas"]["ShadowEvalJobResponse"]; -export type ShadowEvalJobKey = components["schemas"]["ShadowEvalJobKeyResponse"]; +export type ShadowEvalJobTarget = components["schemas"]["ShadowEvalJobTargetResponse"]; export type ShadowEvalSlice = components["schemas"]["ShadowEvalSlice"]; export type StartShadowEvalRequest = components["schemas"]["StartShadowEvalRequest"]; diff --git a/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/guardrail_info.test.tsx b/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/guardrail_info.test.tsx index 2b90a1d8cbc..fcffc2122e7 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/guardrail_info.test.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/guardrail_info.test.tsx @@ -1,5 +1,5 @@ import * as networking from "@/components/networking"; -import { fireEvent, render, waitFor, within } from "@testing-library/react"; +import { fireEvent, render, waitFor, within, screen } from "@testing-library/react"; import userEvent from "@testing-library/user-event"; import { afterEach, describe, expect, it, vi } from "vitest"; import GuardrailInfoView from "./guardrail_info"; @@ -65,21 +65,19 @@ describe("Guardrail Info", () => { vi.mocked(networking.getGuardrailProviderSpecificParams).mockResolvedValue({}); - const { getAllByText, getByText } = render( - {}} accessToken="123" isAdmin={true} />, - ); + render( {}} accessToken="123" isAdmin={true} />); // Wait for the loading to complete and data to be rendered await waitFor(() => { // The guardrail name appears in multiple places (title and settings tab) - const elements = getAllByText("Test Guardrail"); + const elements = screen.getAllByText("Test Guardrail"); expect(elements.length).toBeGreaterThan(0); }); // Verify other key elements are present - expect(getByText("Back to Guardrails")).toBeInTheDocument(); - expect(getByText("Overview")).toBeInTheDocument(); - expect(getByText("Settings")).toBeInTheDocument(); + expect(screen.getByText("Back to Guardrails")).toBeInTheDocument(); + expect(screen.getByText("Overview")).toBeInTheDocument(); + expect(screen.getByText("Settings")).toBeInTheDocument(); }); it("should render a tag-based mode object rather than crashing the detail view", async () => { @@ -105,11 +103,9 @@ describe("Guardrail Info", () => { vi.mocked(networking.getGuardrailProviderSpecificParams).mockResolvedValue({}); - const { findAllByText } = render( - {}} accessToken="123" isAdmin={true} />, - ); + render( {}} accessToken="123" isAdmin={true} />); - expect(await findAllByText("pre_call, post_call (tag-based)")).not.toHaveLength(0); + expect(await screen.findAllByText("pre_call, post_call (tag-based)")).not.toHaveLength(0); }); it("should render the provider logo from the bundled guardrail logo map", async () => { @@ -135,11 +131,9 @@ describe("Guardrail Info", () => { vi.mocked(networking.getGuardrailProviderSpecificParams).mockResolvedValue({}); - const { findByAltText } = render( - {}} accessToken="123" isAdmin={true} />, - ); + render( {}} accessToken="123" isAdmin={true} />); - const logo = await findByAltText("Presidio PII logo"); + const logo = await screen.findByAltText("Presidio PII logo"); expect(logo).toHaveAttribute("src", expect.stringContaining("microsoft_azure.svg")); }); @@ -167,25 +161,27 @@ describe("Guardrail Info", () => { vi.mocked(networking.getGuardrailProviderSpecificParams).mockResolvedValue({}); - const { getByText, findByText, container } = render( + const { container } = render( {}} accessToken="123" isAdmin={true} />, ); await waitFor(() => { - expect(getByText("Settings")).toBeInTheDocument(); + expect(screen.getByText("Settings")).toBeInTheDocument(); }); // Click the Settings tab - fireEvent.click(getByText("Settings")); + fireEvent.click(screen.getByText("Settings")); // Wait for the Settings panel to render await waitFor(() => { - expect(getByText("Guardrail Settings")).toBeInTheDocument(); + expect(screen.getByText("Guardrail Settings")).toBeInTheDocument(); }); await userEvent.hover(within(container).getByRole("img", { name: "Config guardrail details" })); - expect(await findByText("Guardrail is defined in the config file and cannot be edited.")).toBeInTheDocument(); + expect( + await screen.findByText("Guardrail is defined in the config file and cannot be edited."), + ).toBeInTheDocument(); }); it("should render the guardrail info", async () => { @@ -216,12 +212,10 @@ describe("Guardrail Info", () => { vi.mocked(networking.getGuardrailProviderSpecificParams).mockResolvedValue({}); - const { getByText } = render( - {}} accessToken="123" isAdmin={true} />, - ); + render( {}} accessToken="123" isAdmin={true} />); await waitFor(() => { - expect(getByText("PII Entity Configuration")).toBeInTheDocument(); + expect(screen.getByText("PII Entity Configuration")).toBeInTheDocument(); }); }); it("should handle content filter updates correctly", async () => { @@ -251,30 +245,28 @@ describe("Guardrail Info", () => { vi.mocked(networking.getGuardrailProviderSpecificParams).mockResolvedValue({}); vi.mocked(networking.updateGuardrailCall).mockResolvedValue({ status: "success" }); - const { getByText, getByLabelText } = render( - {}} accessToken="123" isAdmin={true} />, - ); + render( {}} accessToken="123" isAdmin={true} />); await waitFor(() => { - expect(getByText("Settings")).toBeInTheDocument(); + expect(screen.getByText("Settings")).toBeInTheDocument(); }); // Go to Settings tab - fireEvent.click(getByText("Settings")); + fireEvent.click(screen.getByText("Settings")); await waitFor(() => { - expect(getByText("Guardrail Settings")).toBeInTheDocument(); + expect(screen.getByText("Guardrail Settings")).toBeInTheDocument(); }); // Enter Edit Mode - fireEvent.click(getByText("Edit Settings")); + fireEvent.click(screen.getByText("Edit Settings")); // Modify Guardrail Name to force an update - const nameInput = getByLabelText("Guardrail Name"); + const nameInput = screen.getByLabelText("Guardrail Name"); fireEvent.change(nameInput, { target: { value: "Updated Name" } }); // Save with only name change - const saveButton = getByText("Save Changes"); + const saveButton = screen.getByText("Save Changes"); fireEvent.click(saveButton); await waitFor(() => { @@ -300,16 +292,16 @@ describe("Guardrail Info", () => { // Enter Edit Mode again to make changes await waitFor(() => { - expect(getByText("Edit Settings")).toBeInTheDocument(); + expect(screen.getByText("Edit Settings")).toBeInTheDocument(); }); - fireEvent.click(getByText("Edit Settings")); + fireEvent.click(screen.getByText("Edit Settings")); // Now modify the values using the mock button - const simulateChangeButton = getByText("Simulate Change"); + const simulateChangeButton = screen.getByText("Simulate Change"); fireEvent.click(simulateChangeButton); // Save again - fireEvent.click(getByText("Save Changes")); + fireEvent.click(screen.getByText("Save Changes")); await waitFor(() => { expect(networking.updateGuardrailCall).toHaveBeenCalled(); @@ -339,12 +331,10 @@ describe("Guardrail Info", () => { }); vi.mocked(networking.getGuardrailProviderSpecificParams).mockResolvedValue({}); - const { findByRole, getByRole, getByText } = render( - {}} accessToken="123" isAdmin={true} />, - ); + render( {}} accessToken="123" isAdmin={true} />); - expect(await findByRole("tab", { name: "Overview" })).toHaveAttribute("aria-selected", "true"); - expect(getByRole("tab", { name: "Settings" })).toHaveAttribute("aria-selected", "false"); - expect(getByText("Guardrail Settings")).toBeInTheDocument(); + expect(await screen.findByRole("tab", { name: "Overview" })).toHaveAttribute("aria-selected", "true"); + expect(screen.getByRole("tab", { name: "Settings" })).toHaveAttribute("aria-selected", "false"); + expect(screen.getByText("Guardrail Settings")).toBeInTheDocument(); }); }); diff --git a/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/pii_components.test.tsx b/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/pii_components.test.tsx index 2f839487111..30ff2cf7c5f 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/pii_components.test.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/pii_components.test.tsx @@ -1,4 +1,4 @@ -import { render } from "@testing-library/react"; +import { render, screen } from "@testing-library/react"; import { describe, it, expect } from "vitest"; import { CategoryFilter, QuickActions, PiiEntityList } from "./pii_components"; import type { PiiEntityCategory } from "@/components/guardrails/types"; @@ -6,25 +6,21 @@ import type { PiiEntityCategory } from "@/components/guardrails/types"; describe("CategoryFilter", () => { it("should render", () => { const emptyCategories: PiiEntityCategory[] = []; - const { getByText } = render( - {}} />, - ); - expect(getByText("Filter by category")).toBeInTheDocument(); + render( {}} />); + expect(screen.getByText("Filter by category")).toBeInTheDocument(); }); }); describe("QuickActions", () => { it("should render", () => { - const { getByText } = render( - {}} onUnselectAll={() => {}} hasSelectedEntities={false} />, - ); - expect(getByText("Quick Actions")).toBeInTheDocument(); + render( {}} onUnselectAll={() => {}} hasSelectedEntities={false} />); + expect(screen.getByText("Quick Actions")).toBeInTheDocument(); }); }); describe("PiiEntityList", () => { it("should render", () => { - const { getByText } = render( + render( { entityToCategoryMap={new Map()} />, ); - expect(getByText("No PII types match your filter criteria")).toBeInTheDocument(); + expect(screen.getByText("No PII types match your filter criteria")).toBeInTheDocument(); }); }); diff --git a/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/pii_configuration.test.tsx b/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/pii_configuration.test.tsx index 00c568ef35b..4f822578fe8 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/pii_configuration.test.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/pii_configuration.test.tsx @@ -1,10 +1,10 @@ -import { render } from "@testing-library/react"; +import { render, screen } from "@testing-library/react"; import { describe, it, expect } from "vitest"; import PiiConfiguration from "./pii_configuration"; describe("PiiConfiguration", () => { it("should render", () => { - const { getByText } = render( + render( { entityCategories={[]} />, ); - expect(getByText("Configure PII Protection")).toBeInTheDocument(); + expect(screen.getByText("Configure PII Protection")).toBeInTheDocument(); }); }); diff --git a/ui/litellm-dashboard/src/app/(dashboard)/mcp-servers/_components/mcp_servers.test.tsx b/ui/litellm-dashboard/src/app/(dashboard)/mcp-servers/_components/mcp_servers.test.tsx index 8dca87e24ef..8abb8855e3d 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/mcp-servers/_components/mcp_servers.test.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/mcp-servers/_components/mcp_servers.test.tsx @@ -45,7 +45,7 @@ describe("MCPServers", () => { vi.mocked(networking.fetchMCPServers).mockResolvedValue([]); const queryClient = createQueryClient(); - const { getByText } = render( + render( , @@ -53,11 +53,11 @@ describe("MCPServers", () => { // Wait for the component to load and check if title renders await waitFor(() => { - expect(getByText("MCP Servers")).toBeInTheDocument(); + expect(screen.getByText("MCP Servers")).toBeInTheDocument(); }); // Verify the title is rendered - expect(getByText("MCP Servers")).toBeInTheDocument(); + expect(screen.getByText("MCP Servers")).toBeInTheDocument(); }); it("should render mocked MCP servers data in the table", async () => { @@ -96,7 +96,7 @@ describe("MCPServers", () => { vi.mocked(networking.fetchMCPServers).mockResolvedValue(mockServers); const queryClient = createQueryClient(); - const { getByText, getAllByText } = render( + render( , @@ -104,19 +104,19 @@ describe("MCPServers", () => { // Wait for the component to load await waitFor(() => { - expect(getByText("MCP Servers")).toBeInTheDocument(); + expect(screen.getByText("MCP Servers")).toBeInTheDocument(); }); // Wait for the mocked data to render in the table await waitFor(() => { - expect(getByText("Test Server 1")).toBeInTheDocument(); + expect(screen.getByText("Test Server 1")).toBeInTheDocument(); }); // Verify the mocked server data is rendered in the table - expect(getByText("Test Server 1")).toBeInTheDocument(); - expect(getByText("Test Server 2")).toBeInTheDocument(); - expect(getAllByText("test-server-1").length).toBeGreaterThan(0); - expect(getAllByText("test-server-2").length).toBeGreaterThan(0); + expect(screen.getByText("Test Server 1")).toBeInTheDocument(); + expect(screen.getByText("Test Server 2")).toBeInTheDocument(); + expect(screen.getAllByText("test-server-1").length).toBeGreaterThan(0); + expect(screen.getAllByText("test-server-2").length).toBeGreaterThan(0); // Verify the API was called // Note: useMCPServers uses useAuthorized() internally, which returns "123" from global mock @@ -168,7 +168,7 @@ describe("MCPServers", () => { vi.mocked(networking.fetchMCPServerHealth).mockResolvedValue(mockHealthStatuses); const queryClient = createQueryClient(); - const { getByText } = render( + render( , @@ -176,7 +176,7 @@ describe("MCPServers", () => { // Wait for the component to load await waitFor(() => { - expect(getByText("MCP Servers")).toBeInTheDocument(); + expect(screen.getByText("MCP Servers")).toBeInTheDocument(); }); // Verify the health check API was called (without a server ID filter — the hook always @@ -211,7 +211,7 @@ describe("MCPServers", () => { ); const queryClient = createQueryClient(); - const { getByText } = render( + render( , @@ -219,7 +219,7 @@ describe("MCPServers", () => { // Wait for the component to load await waitFor(() => { - expect(getByText("MCP Servers")).toBeInTheDocument(); + expect(screen.getByText("MCP Servers")).toBeInTheDocument(); }); // Verify that health check was initiated diff --git a/ui/litellm-dashboard/src/app/(dashboard)/models-and-endpoints/components/PriceDataManagementTab.test.tsx b/ui/litellm-dashboard/src/app/(dashboard)/models-and-endpoints/components/PriceDataManagementTab.test.tsx index 8b34d61ebad..7cd418bc176 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/models-and-endpoints/components/PriceDataManagementTab.test.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/models-and-endpoints/components/PriceDataManagementTab.test.tsx @@ -1,5 +1,5 @@ /* @vitest-environment jsdom */ -import { render } from "@testing-library/react"; +import { render, screen } from "@testing-library/react"; import { describe, expect, it, vi } from "vitest"; import PriceDataManagementTab from "./PriceDataManagementTab"; @@ -11,7 +11,7 @@ vi.mock("@/app/(dashboard)/hooks/models/useModelCostMap", () => ({ describe("PriceDataManagementTab", () => { it("renders its content standalone, without a tab-panel ancestor", () => { - const { getByText } = render(); - expect(getByText("Price Data Management")).toBeInTheDocument(); + render(); + expect(screen.getByText("Price Data Management")).toBeInTheDocument(); }); }); diff --git a/ui/litellm-dashboard/src/app/(dashboard)/models-and-endpoints/page.test.tsx b/ui/litellm-dashboard/src/app/(dashboard)/models-and-endpoints/page.test.tsx index 521f89a39f2..a504d75bb63 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/models-and-endpoints/page.test.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/models-and-endpoints/page.test.tsx @@ -1,6 +1,6 @@ /* @vitest-environment jsdom */ import { QueryClient, QueryClientProvider } from "@tanstack/react-query"; -import { render } from "@testing-library/react"; +import { render, screen } from "@testing-library/react"; import userEvent from "@testing-library/user-event"; import { beforeEach, describe, expect, it, vi } from "vitest"; import ModelsAndEndpointsPage from "./page"; @@ -64,48 +64,48 @@ describe("ModelsAndEndpointsPage", () => { }); it("renders the admin tab bar and the All Models panel by default", () => { - const { getByRole, getByTestId } = renderPage(); - expect(getByRole("tab", { name: "All Models" })).toBeInTheDocument(); - expect(getByRole("tab", { name: "LLM Credentials" })).toBeInTheDocument(); - expect(getByRole("tab", { name: "Health Status" })).toBeInTheDocument(); - expect(getByTestId("panel-all-models")).toBeInTheDocument(); + renderPage(); + expect(screen.getByRole("tab", { name: "All Models" })).toBeInTheDocument(); + expect(screen.getByRole("tab", { name: "LLM Credentials" })).toBeInTheDocument(); + expect(screen.getByRole("tab", { name: "Health Status" })).toBeInTheDocument(); + expect(screen.getByTestId("panel-all-models")).toBeInTheDocument(); }); it("switches tabs in-memory, mounting only the active panel", async () => { const user = userEvent.setup(); - const { getByRole, getByTestId, queryByTestId } = renderPage(); - await user.click(getByRole("tab", { name: "Health Status" })); - expect(getByTestId("panel-health")).toBeInTheDocument(); - expect(queryByTestId("panel-all-models")).not.toBeInTheDocument(); + renderPage(); + await user.click(screen.getByRole("tab", { name: "Health Status" })); + expect(screen.getByTestId("panel-health")).toBeInTheDocument(); + expect(screen.queryByTestId("panel-all-models")).not.toBeInTheDocument(); }); it("renders the model detail overlay from the ?model drill-in and hides the tabs", () => { detailState.modelId = "abc-123"; - const { getByTestId, queryByRole } = renderPage(); - expect(getByTestId("model-info")).toHaveTextContent("model:abc-123"); - expect(queryByRole("tab", { name: "All Models" })).not.toBeInTheDocument(); + renderPage(); + expect(screen.getByTestId("model-info")).toHaveTextContent("model:abc-123"); + expect(screen.queryByRole("tab", { name: "All Models" })).not.toBeInTheDocument(); }); it("renders the team detail overlay from the ?team drill-in", () => { detailState.teamId = "team-9"; - const { getByTestId } = renderPage(); - expect(getByTestId("team-info")).toHaveTextContent("team:team-9"); + renderPage(); + expect(screen.getByTestId("team-info")).toHaveTextContent("team:team-9"); }); it("hides admin-only tabs for a non-admin user", () => { mockUseAuthorized.mockReturnValue(NON_ADMIN); - const { queryByRole } = renderPage(); - expect(queryByRole("tab", { name: "LLM Credentials" })).not.toBeInTheDocument(); - expect(queryByRole("tab", { name: "Health Status" })).not.toBeInTheDocument(); + renderPage(); + expect(screen.queryByRole("tab", { name: "LLM Credentials" })).not.toBeInTheDocument(); + expect(screen.queryByRole("tab", { name: "Health Status" })).not.toBeInTheDocument(); }); // Auto-routers are excluded from the All Models table, so this tab is their home: the only // place in the product to list, create, edit or delete one. describe("Auto-Routers tab", () => { it("sits third, after All Models and Add Model", () => { - const { getAllByRole } = renderPage(); + renderPage(); - const tabs = getAllByRole("tab").map((tab) => tab.textContent); + const tabs = screen.getAllByRole("tab").map((tab) => tab.textContent); expect(tabs[0]).toContain("All Models"); expect(tabs[1]).toBe("Add Model"); expect(tabs[2]).toContain("Auto-Routers"); @@ -115,17 +115,17 @@ describe("ModelsAndEndpointsPage", () => { it("renders its panel when selected", async () => { const user = userEvent.setup(); - const { getByRole, getByTestId } = renderPage(); + renderPage(); - await user.click(getByRole("tab", { name: /Auto-Routers/ })); - expect(getByTestId("panel-auto-routers")).toBeInTheDocument(); + await user.click(screen.getByRole("tab", { name: /Auto-Routers/ })); + expect(screen.getByTestId("panel-auto-routers")).toBeInTheDocument(); }); it("is hidden from non-admins, who cannot write models", () => { mockUseAuthorized.mockReturnValue(NON_ADMIN); - const { queryByRole } = renderPage(); + renderPage(); - expect(queryByRole("tab", { name: /Auto-Routers/ })).not.toBeInTheDocument(); + expect(screen.queryByRole("tab", { name: /Auto-Routers/ })).not.toBeInTheDocument(); }); }); }); diff --git a/ui/litellm-dashboard/src/app/(dashboard)/playground/components/chat_ui/ChatMessageBubble.test.tsx b/ui/litellm-dashboard/src/app/(dashboard)/playground/components/chat_ui/ChatMessageBubble.test.tsx index a83c11d1444..ed02e7e16cc 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/playground/components/chat_ui/ChatMessageBubble.test.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/playground/components/chat_ui/ChatMessageBubble.test.tsx @@ -93,13 +93,13 @@ describe("ChatMessageBubble", () => { ])("should paint the $role surface from theme tokens, not fixed colours", ({ role, bubble, avatar }) => { render(); - const header = screen.getByText(role).closest("div") as HTMLElement; - const surface = header.parentElement as HTMLElement; + const surface = screen.getByTestId("message-surface"); + const avatarEl = screen.getByTestId("message-avatar"); expect(surface).toHaveClass(...bubble); expect(surface).not.toHaveAttribute("style"); - expect(header.firstElementChild).toHaveClass(avatar); - expect(header.firstElementChild).not.toHaveAttribute("style"); + expect(avatarEl).toHaveClass(avatar); + expect(avatarEl).not.toHaveAttribute("style"); }); it("should show model badge for assistant messages when model is provided", () => { diff --git a/ui/litellm-dashboard/src/app/(dashboard)/playground/components/chat_ui/ChatMessageBubble.tsx b/ui/litellm-dashboard/src/app/(dashboard)/playground/components/chat_ui/ChatMessageBubble.tsx index 8c54d9e89fa..bd6a4bc49a4 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/playground/components/chat_ui/ChatMessageBubble.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/playground/components/chat_ui/ChatMessageBubble.tsx @@ -46,6 +46,7 @@ function ChatMessageBubble({ return (
{ describe("CompareUI", () => { it("should render", () => { - const { getByTestId } = render(); - expect(getByTestId("comparison-panel-1")).toBeInTheDocument(); - expect(getByTestId("comparison-panel-2")).toBeInTheDocument(); - expect(getByTestId("message-input")).toBeInTheDocument(); + render(); + expect(screen.getByTestId("comparison-panel-1")).toBeInTheDocument(); + expect(screen.getByTestId("comparison-panel-2")).toBeInTheDocument(); + expect(screen.getByTestId("message-input")).toBeInTheDocument(); }); it("adds a comparison when Add Comparison button is clicked", async () => { const user = userEvent.setup(); - const { container, getByTestId } = render( - , - ); + const { container } = render(); // Verify initial state: 2 comparison panels - expect(getByTestId("comparison-panel-1")).toBeInTheDocument(); - expect(getByTestId("comparison-panel-2")).toBeInTheDocument(); + expect(screen.getByTestId("comparison-panel-1")).toBeInTheDocument(); + expect(screen.getByTestId("comparison-panel-2")).toBeInTheDocument(); let comparisonPanels = container.querySelectorAll('[data-testid^="comparison-panel-"]'); expect(comparisonPanels).toHaveLength(2); @@ -117,15 +115,13 @@ describe("CompareUI", () => { }); // Verify the original 2 panels are still there - expect(getByTestId("comparison-panel-1")).toBeInTheDocument(); - expect(getByTestId("comparison-panel-2")).toBeInTheDocument(); + expect(screen.getByTestId("comparison-panel-1")).toBeInTheDocument(); + expect(screen.getByTestId("comparison-panel-2")).toBeInTheDocument(); }); it("should handle image upload and send message with attachment", async () => { const user = userEvent.setup(); - const { getByTestId, queryByTestId } = render( - , - ); + render(); const file = new File(["test content"], "test-image.png", { type: "image/png" }); @@ -138,13 +134,13 @@ describe("CompareUI", () => { } await waitFor(() => { - expect(getByTestId("has-attachment")).toBeInTheDocument(); + expect(screen.getByTestId("has-attachment")).toBeInTheDocument(); }); - const textarea = getByTestId("message-textarea"); + const textarea = screen.getByTestId("message-textarea"); fireEvent.change(textarea, { target: { value: "Describe this image" } }); - const sendButton = getByTestId("send-button"); + const sendButton = screen.getByTestId("send-button"); expect(sendButton).toBeEnabled(); await user.click(sendButton); diff --git a/ui/litellm-dashboard/src/app/(dashboard)/playground/components/compareUI/components/MessageDisplay.test.tsx b/ui/litellm-dashboard/src/app/(dashboard)/playground/components/compareUI/components/MessageDisplay.test.tsx index 72a1d41f9fe..69b50d4088f 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/playground/components/compareUI/components/MessageDisplay.test.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/playground/components/compareUI/components/MessageDisplay.test.tsx @@ -1,4 +1,4 @@ -import { render } from "@testing-library/react"; +import { render, screen } from "@testing-library/react"; import { describe, expect, it, vi } from "vitest"; import type { MessageType } from "@/components/chat_ui/types"; import { MessageDisplay } from "./MessageDisplay"; @@ -39,9 +39,9 @@ describe("MessageDisplay", () => { model: "gpt-4", }, ]; - const { getByText } = render(); - expect(getByText("Hello")).toBeInTheDocument(); - expect(getByText("Hi there!")).toBeInTheDocument(); + render(); + expect(screen.getByText("Hello")).toBeInTheDocument(); + expect(screen.getByText("Hi there!")).toBeInTheDocument(); }); it("displays user and assistant messages with proper grouping and shows loading state", () => { @@ -64,13 +64,13 @@ describe("MessageDisplay", () => { }, }, ]; - const { getByText, getByTestId } = render(); - expect(getByText("You")).toBeInTheDocument(); - expect(getByText("What is 2+2?")).toBeInTheDocument(); - expect(getByText("gpt-4")).toBeInTheDocument(); - expect(getByText("calculator")).toBeInTheDocument(); - expect(getByText("2+2 equals 4")).toBeInTheDocument(); - expect(getByTestId("response-metrics")).toBeInTheDocument(); + render(); + expect(screen.getByText("You")).toBeInTheDocument(); + expect(screen.getByText("What is 2+2?")).toBeInTheDocument(); + expect(screen.getByText("gpt-4")).toBeInTheDocument(); + expect(screen.getByText("calculator")).toBeInTheDocument(); + expect(screen.getByText("2+2 equals 4")).toBeInTheDocument(); + expect(screen.getByTestId("response-metrics")).toBeInTheDocument(); }); it("should display image attachment in user message", () => { @@ -86,10 +86,10 @@ describe("MessageDisplay", () => { model: "gpt-4", }, ]; - const { getByTestId, getByText } = render(); - expect(getByText("What is in this image? [Image attached]")).toBeInTheDocument(); - expect(getByTestId("chat-image-renderer")).toBeInTheDocument(); - const image = getByTestId("chat-image-renderer").querySelector("img"); + render(); + expect(screen.getByText("What is in this image? [Image attached]")).toBeInTheDocument(); + expect(screen.getByTestId("chat-image-renderer")).toBeInTheDocument(); + const image = screen.getByTestId("chat-image-renderer").querySelector("img"); expect(image).toHaveAttribute("src", "blob:test-image-url"); }); }); diff --git a/ui/litellm-dashboard/src/autorouter_presets.json b/ui/litellm-dashboard/src/autorouter_presets.json index 4cbb548a855..b977bd484ac 100644 --- a/ui/litellm-dashboard/src/autorouter_presets.json +++ b/ui/litellm-dashboard/src/autorouter_presets.json @@ -14,7 +14,9 @@ }, "classifier_type": "heuristic", "escalation_keywords": ["LITELLM ESCALATE"], + "classification_mode": "every_request", "session_affinity": false, + "modality_routing": false, "deployment_affinity": true } }, @@ -30,7 +32,9 @@ }, "classifier_type": "heuristic", "escalation_keywords": ["LITELLM ESCALATE"], + "classification_mode": "every_request", "session_affinity": false, + "modality_routing": false, "deployment_affinity": true } }, @@ -56,7 +60,9 @@ }, "classifier_context_window_size": 0, "escalation_keywords": ["LITELLM ESCALATE"], + "classification_mode": "every_request", "session_affinity": false, + "modality_routing": false, "deployment_affinity": true } }, @@ -72,7 +78,9 @@ }, "classifier_type": "heuristic", "escalation_keywords": ["LITELLM ESCALATE"], + "classification_mode": "every_request", "session_affinity": false, + "modality_routing": false, "deployment_affinity": true } } diff --git a/ui/litellm-dashboard/src/components/CreateUserButton.test.tsx b/ui/litellm-dashboard/src/components/CreateUserButton.test.tsx index 3777c46973f..68bfd94bd63 100644 --- a/ui/litellm-dashboard/src/components/CreateUserButton.test.tsx +++ b/ui/litellm-dashboard/src/components/CreateUserButton.test.tsx @@ -5,6 +5,7 @@ import { describe, it, expect, vi, beforeEach } from "vitest"; import { CreateUserButton } from "./CreateUserButton"; import * as networking from "./networking"; import { toast } from "@/lib/toast"; +import { expectControlBesideLabel } from "../../tests/fieldOrientation"; vi.mock("./networking", () => ({ userCreateCall: vi.fn(), @@ -294,6 +295,20 @@ describe("CreateUserButton", () => { }); }); + it("lays the send invitation email checkbox out beside its label", async () => { + const user = userEvent.setup({ pointerEventsCheck: PointerEventsCheckLevel.Never }); + renderWithProviders(); + + await waitFor(() => { + expect(screen.getByRole("button", { name: /\+ invite user/i })).toBeInTheDocument(); + }); + await user.click(screen.getByRole("button", { name: /\+ invite user/i })); + + const dialog = screen.getByRole("dialog", { name: /invite user/i }); + + expectControlBesideLabel(within(dialog).getByRole("checkbox")); + }); + describe("organizations", () => { it("should send organizations list in POST body when organizations are selected", async () => { const { useOrganizations } = await import("@/app/(dashboard)/hooks/organizations/useOrganizations"); diff --git a/ui/litellm-dashboard/src/components/CreateUserButton.tsx b/ui/litellm-dashboard/src/components/CreateUserButton.tsx index e052a9e0818..0f7c356b8cc 100644 --- a/ui/litellm-dashboard/src/components/CreateUserButton.tsx +++ b/ui/litellm-dashboard/src/components/CreateUserButton.tsx @@ -270,7 +270,7 @@ export const CreateUserButton: React.FC = ({ ); const sendInviteEmailField = ( - + {({ id, value, onChange, onBlur }) => ( )} diff --git a/ui/litellm-dashboard/src/components/EntityUsageExport/EntityUsageExportModal.test.tsx b/ui/litellm-dashboard/src/components/EntityUsageExport/EntityUsageExportModal.test.tsx index 8edf2174eee..cb04323e4c9 100644 --- a/ui/litellm-dashboard/src/components/EntityUsageExport/EntityUsageExportModal.test.tsx +++ b/ui/litellm-dashboard/src/components/EntityUsageExport/EntityUsageExportModal.test.tsx @@ -10,6 +10,7 @@ */ import { describe, it, expect, vi, beforeEach } from "vitest"; +import { screen } from "@testing-library/react"; import { renderWithProviders } from "../../../tests/test-utils"; import userEvent from "@testing-library/user-event"; import EntityUsageExportModal from "./EntityUsageExportModal"; @@ -73,13 +74,13 @@ describe("EntityUsageExportModal", () => { const user = userEvent.setup(); const { handleExportCSV } = await import("./utils"); - const { getByRole } = renderWithProviders(); + renderWithProviders(); // Default primary action reflects CSV export - expect(getByRole("button", { name: /Export CSV/i })).toBeInTheDocument(); + expect(screen.getByRole("button", { name: /Export CSV/i })).toBeInTheDocument(); // Click export - await user.click(getByRole("button", { name: /Export CSV/i })); + await user.click(screen.getByRole("button", { name: /Export CSV/i })); // Verifies export function was invoked with correct parameters expect(handleExportCSV).toHaveBeenCalledWith(baseProps.spendData, "daily", "Tag", "tag", {}); @@ -97,14 +98,14 @@ describe("EntityUsageExportModal", () => { const user = userEvent.setup(); const { handleExportCSV } = await import("./utils"); - const { getByText, getByRole } = renderWithProviders(); + renderWithProviders(); // Choose the alternate export type - click the label to trigger radio - const dailyModelLabel = getByText(/Day-by-day by tag and model/i); + const dailyModelLabel = screen.getByText(/Day-by-day by tag and model/i); await user.click(dailyModelLabel); // Export with default CSV format - const exportBtn = getByRole("button", { name: /Export CSV/i }); + const exportBtn = screen.getByRole("button", { name: /Export CSV/i }); await user.click(exportBtn); // Ensure the selected scope flowed through diff --git a/ui/litellm-dashboard/src/components/Settings/AdminSettings/SSOSettings/Modals/BaseSSOSettingsForm.test.tsx b/ui/litellm-dashboard/src/components/Settings/AdminSettings/SSOSettings/Modals/BaseSSOSettingsForm.test.tsx index 54566e1d75a..eddc5716305 100644 --- a/ui/litellm-dashboard/src/components/Settings/AdminSettings/SSOSettings/Modals/BaseSSOSettingsForm.test.tsx +++ b/ui/litellm-dashboard/src/components/Settings/AdminSettings/SSOSettings/Modals/BaseSSOSettingsForm.test.tsx @@ -9,6 +9,7 @@ import BaseSSOSettingsForm, { submitMountedSSOValues, useSSOSettingsForm, } from "./BaseSSOSettingsForm"; +import { expectControlBesideLabel } from "../../../../../../tests/fieldOrientation"; const user = () => userEvent.setup({ pointerEventsCheck: 0 }); @@ -233,6 +234,38 @@ describe("BaseSSOSettingsForm", () => { expect(screen.queryByText("Use Team Mappings")).not.toBeInTheDocument(); }); + + it("lays a provider checkbox field out beside its label", async () => { + const TestWrapper = () => { + const form = useSSOSettingsForm("sso-settings"); + + return ; + }; + + renderWithProviders(); + + await openProviderDropdown(); + await user().click(await screen.findByText(/saml sso/i)); + + expectControlBesideLabel( + await screen.findByRole("checkbox", { name: "Allow IdP-initiated (unsolicited) responses" }), + ); + }); + + it.each(["Use Role Mappings", "Use Team Mappings"])("lays the %s toggle out beside its label", async (label) => { + const TestWrapper = () => { + const form = useSSOSettingsForm("sso-settings"); + + return ; + }; + + renderWithProviders(); + + await openProviderDropdown(); + await user().click(await screen.findByText(/okta/i)); + + expectControlBesideLabel(await screen.findByRole("checkbox", { name: label })); + }); }); describe("renderProviderFields", () => { diff --git a/ui/litellm-dashboard/src/components/Settings/AdminSettings/SSOSettings/Modals/BaseSSOSettingsForm.tsx b/ui/litellm-dashboard/src/components/Settings/AdminSettings/SSOSettings/Modals/BaseSSOSettingsForm.tsx index 5216da382d0..cb97304f77a 100644 --- a/ui/litellm-dashboard/src/components/Settings/AdminSettings/SSOSettings/Modals/BaseSSOSettingsForm.tsx +++ b/ui/litellm-dashboard/src/components/Settings/AdminSettings/SSOSettings/Modals/BaseSSOSettingsForm.tsx @@ -303,7 +303,7 @@ const SSOProviderField = ({ field }: { field: SSOProviderConfig["fields"][number if (field.type === "checkbox") { return ( - + {({ value, onChange, onBlur, id, ...rest }) => ( (); return ( - + {({ value, onChange, onBlur, id, ...rest }) => ( void; +}> = ({ value, onChange }) => ( + <> +
+ onChange({ ...value, deployment_affinity: deploymentAffinity })} + aria-label="Pin a session to one deployment per model group" + /> + Pin a session to one deployment per model group +
+ + Keeps a session on the same deployment within a group, so provider prompt caches stay warm. Turn off to + load-balance every turn. + + +); diff --git a/ui/litellm-dashboard/src/components/add_model/ClassificationMethodConfig.tsx b/ui/litellm-dashboard/src/components/add_model/ClassificationMethodConfig.tsx index 2947e29319b..96c93306611 100644 --- a/ui/litellm-dashboard/src/components/add_model/ClassificationMethodConfig.tsx +++ b/ui/litellm-dashboard/src/components/add_model/ClassificationMethodConfig.tsx @@ -15,9 +15,12 @@ import { RestrictedSection, restrictedBy } from "./TierRestrictions"; import HeuristicScoringConfig from "./HeuristicScoringConfig"; import { useComplexityScorerDefaults } from "@/app/(dashboard)/hooks/autoRouter/useComplexityScorerDefaults"; import { + ClassificationFrequency, ClassifierFallback, ClassifierType, ComplexityRouterConfigValue, + classificationFrequency, + withClassificationFrequency, DEFAULT_CLASSIFIER_CONTEXT_BUDGET_CHARS, MIN_QUOTED_CONTEXT_TURN_CHARS, DEFAULT_CLASSIFIER_CONTEXT_WINDOW_SIZE, @@ -111,7 +114,7 @@ const HowClassificationWorks: React.FC<{ value: ComplexityRouterConfigValue }> = How Classification Works {scoringExplanation(value)} {scorerRuns && ranges && ( -
    +
    • {effectiveTierLabel("SIMPLE", value.tier_labels)}: Score < {ranges.simpleMedium}
      • @@ -211,6 +214,7 @@ const ClassificationMethodConfig: React.FC = ({ const [draft, setDraft] = React.useState<{ id: string; raw: string } | null>(null); const hasDefaultModel = Boolean(defaultModel); const classifierType = effectiveClassifierType(value); + const sessionFrequencyRestriction = restrictedBy(value, "sessionAffinity"); const classifierModelMissing = showValidationErrors && usesLlmClassifier(classifierType) && !value.classifier_llm_config?.model; const usesCustomPrompt = Boolean(value.classifier_llm_config?.system_prompt?.trim()); @@ -305,6 +309,10 @@ const ClassificationMethodConfig: React.FC = ({ onChange({ ...value, classifier_fallback: fallback }); }; + const handleClassificationFrequencyChange = (frequency: ClassificationFrequency) => { + onChange(withClassificationFrequency(value, frequency)); + }; + const handleClassifierContextWindowSizeChange = (windowSize: number) => { onChange({ ...value, @@ -367,6 +375,49 @@ const ClassificationMethodConfig: React.FC = ({
)} +
+ How often to classify + + handleClassificationFrequencyChange(frequency as ClassificationFrequency) + } + > +
+ + + +
+ +

+ Holding the tier keeps an agent on one model for a whole tool loop and cuts scoring cost. A turn the router + cannot match to a held decision, such as one with no session id or an expired one, is scored again +

+
+ {usesLlmClassifier(classifierType) && (
diff --git a/ui/litellm-dashboard/src/components/add_model/ComplexityRouterConfig.test.tsx b/ui/litellm-dashboard/src/components/add_model/ComplexityRouterConfig.test.tsx index 33ce1169c46..751f8870561 100644 --- a/ui/litellm-dashboard/src/components/add_model/ComplexityRouterConfig.test.tsx +++ b/ui/litellm-dashboard/src/components/add_model/ComplexityRouterConfig.test.tsx @@ -80,6 +80,15 @@ describe("ComplexityRouterConfig", () => { expect(screen.getByText(/Score > 0.60/)).toBeInTheDocument(); }); + it("leaves the score threshold list color to the theme instead of an inline style", () => { + renderWithProviders(); + fireEvent.click(screen.getByText("Advanced: Classification Method")); + const list = screen.getByText(/Score < 0.15/).closest("ul"); + expect(list).toBeInTheDocument(); + expect(list).toHaveClass("text-muted-foreground"); + expect(list?.style.color).toBe(""); + }); + it("should default to heuristic and hide classifier model/timeout fields", () => { renderWithProviders(); expect(screen.getByText("Advanced: Classification Method")).toBeInTheDocument(); @@ -589,6 +598,82 @@ describe("ComplexityRouterConfig classifier fallback", () => { }); }); +describe("ComplexityRouterConfig classification frequency", () => { + const llmValue: ComplexityRouterConfigValue = { + ...defaultValue, + classifier_type: "llm", + classifier_llm_config: { model: "gpt-3.5-turbo", timeout_ms: 3000 }, + }; + + it("defaults to every request, matching both backend field defaults", () => { + renderWithProviders(); + fireEvent.click(screen.getByText("Advanced: Classification Method")); + expect(screen.getByRole("radio", { name: /Every request/ })).toBeChecked(); + expect(screen.getByRole("radio", { name: /Every new user message/ })).not.toBeChecked(); + expect(screen.getByRole("radio", { name: /Once per session/ })).not.toBeChecked(); + }); + + it("writes both wire fields when the frequency moves to every new user message", () => { + const onChange = vi.fn(); + renderWithProviders(); + fireEvent.click(screen.getByText("Advanced: Classification Method")); + fireEvent.click(screen.getByRole("radio", { name: /Every new user message/ })); + expect(onChange).toHaveBeenCalledWith({ + ...llmValue, + classification_mode: "user_turn", + session_affinity: false, + }); + }); + + it("writes session affinity, not a classification mode, when the frequency moves to once per session", () => { + const onChange = vi.fn(); + renderWithProviders(); + fireEvent.click(screen.getByText("Advanced: Classification Method")); + fireEvent.click(screen.getByRole("radio", { name: /Once per session/ })); + expect(onChange).toHaveBeenCalledWith({ + ...llmValue, + classification_mode: "every_request", + session_affinity: true, + }); + }); + + it("shows a hand-authored config that sets both fields as once per session, matching the backend", () => { + renderWithProviders( + , + ); + fireEvent.click(screen.getByText("Advanced: Classification Method")); + expect(screen.getByRole("radio", { name: /Once per session/ })).toBeChecked(); + expect(screen.getByRole("radio", { name: /Every new user message/ })).not.toBeChecked(); + }); + + it("records a switch back to every request", () => { + const onChange = vi.fn(); + renderWithProviders( + , + ); + fireEvent.click(screen.getByText("Advanced: Classification Method")); + expect(screen.getByRole("radio", { name: /Every new user message/ })).toBeChecked(); + fireEvent.click(screen.getByRole("radio", { name: /Every request/ })); + expect(onChange).toHaveBeenCalledWith(expect.objectContaining({ classification_mode: "every_request" })); + }); + + it("offers the frequency on a heuristic router, where holding the tier still pins the model", () => { + // The backend pin is gated on the two fields alone, so a heuristic router that switches models + // mid tool loop is fixed by this control too. + renderWithProviders(); + fireEvent.click(screen.getByText("Advanced: Classification Method")); + expect(screen.getByRole("radio", { name: /Every new user message/ })).toBeInTheDocument(); + }); +}); + describe("ComplexityRouterConfig classifier rubric", () => { const llmValue: ComplexityRouterConfigValue = { ...defaultValue, @@ -751,13 +836,34 @@ describe("ComplexityRouterConfig tier labels", () => { }); }); +describe("ComplexityRouterConfig modality panel", () => { + it("defaults the image-routing switch off and writes modality_routing through onChange", () => { + const onChange = vi.fn(); + renderWithProviders(); + fireEvent.click(screen.getByText("Advanced: Modality Routing")); + + const toggle = screen.getByRole("switch", { name: "Route image requests to vision-capable models" }); + expect(toggle).not.toBeChecked(); + fireEvent.click(toggle); + + expect(onChange).toHaveBeenCalledWith({ ...defaultValue, modality_routing: true }); + }); + + it("renders a stored modality_routing=true as on", () => { + renderWithProviders(); + fireEvent.click(screen.getByText("Advanced: Modality Routing")); + + expect(screen.getByRole("switch", { name: "Route image requests to vision-capable models" })).toBeChecked(); + }); +}); + describe("ComplexityRouterConfig affinity panel", () => { - it("holds both affinity switches with their backend defaults", () => { + it("holds the deployment switch at its backend default, session pinning having moved to the frequency choice", () => { renderWithProviders(); fireEvent.click(screen.getByText("Advanced: Affinity")); expect(screen.getByRole("switch", { name: "Pin a session to one deployment per model group" })).toBeChecked(); - expect(screen.getByRole("switch", { name: "Pin a session to its first model" })).not.toBeChecked(); + expect(screen.queryByRole("switch", { name: "Pin a session to its first model" })).not.toBeInTheDocument(); }); it("writes deployment_affinity through onChange without touching other keys", () => { @@ -1282,10 +1388,18 @@ describe("ComplexityRouterConfig tier editing", () => { expect(screen.getByLabelText("Fallback tier")).toBeInTheDocument(); }); - it("disables session pinning and says why, rather than letting a stripped value look saved", () => { - renderWithProviders(); - fireEvent.click(screen.getByText("Advanced: Affinity")); - expect(screen.getByLabelText("Pin a session to its first model")).toHaveAttribute("data-disabled"); + it("disables the once-per-session frequency and says why, rather than letting a stripped value look saved", () => { + renderWithProviders( + , + ); + fireEvent.click(screen.getByText("Advanced: Classification Method")); + const sessionOption = screen.getByRole("radio", { name: /Once per session/ }); + expect(sessionOption).toHaveAttribute("aria-disabled", "true"); + expect(sessionOption).not.toBeChecked(); expect( screen.getByText("Session pinning escalates along the built-in tier ladder", { exact: false }), ).toBeInTheDocument(); diff --git a/ui/litellm-dashboard/src/components/add_model/ComplexityRouterConfig.tsx b/ui/litellm-dashboard/src/components/add_model/ComplexityRouterConfig.tsx index 153afa0b586..6062705aa82 100644 --- a/ui/litellm-dashboard/src/components/add_model/ComplexityRouterConfig.tsx +++ b/ui/litellm-dashboard/src/components/add_model/ComplexityRouterConfig.tsx @@ -4,6 +4,9 @@ import { SearchSelect } from "@/components/shared/SearchSelect"; import { Select, SelectContent, SelectItem, SelectTrigger, SelectValue } from "@/components/ui/select"; import { ChevronRight, Info, Plus, Trash2, X } from "lucide-react"; import { Switch } from "@/components/ui/switch"; + +import { AffinityControls } from "./AffinityControls"; +import { ModalityRoutingControls } from "./ModalityRoutingControls"; import { Card, CardContent } from "@/components/ui/card"; import { Collapsible, CollapsibleContent, CollapsibleTrigger } from "@/components/ui/collapsible"; import { InputGroup, InputGroupAddon, InputGroupButton, InputGroupInput } from "@/components/ui/input-group"; @@ -29,6 +32,7 @@ import React from "react"; import { ModelGroup } from "@/components/llm_calls/fetch_models"; import AdaptiveRoutingConfig from "./AdaptiveRoutingConfig"; import ClassificationMethodConfig from "./ClassificationMethodConfig"; +import ContextWindowEscalationConfig from "./ContextWindowEscalationConfig"; import { Restricted, restrictedBy } from "./TierRestrictions"; import { type TierSetAction, applyTierSetAction, setFallbackTier } from "./tier_set_actions"; import { @@ -55,6 +59,16 @@ export const MIN_QUOTED_CONTEXT_TURN_CHARS = 120; export const DEFAULT_SESSION_AFFINITY = false; export const DEFAULT_DEPLOYMENT_AFFINITY = true; +export type ClassificationMode = "every_request" | "user_turn"; + +export const DEFAULT_CLASSIFICATION_MODE: ClassificationMode = "every_request"; + +/** + * One operator-facing choice over the two wire fields that share the router's tier-pin machinery: + * session affinity pins every turn, user_turn pins every turn except a new human ask. + */ +export type ClassificationFrequency = ClassificationMode | "session"; + export type ComplexityTiers = { SIMPLE: string[]; MEDIUM: string[]; @@ -383,7 +397,9 @@ export interface ComplexityRouterConfigValue { classification_prompt?: string; /** Highest tier the scorer may decide alone under heuristic_first. Required by that type, rejected by the others. */ heuristic_first_max_tier?: string; + classification_mode?: ClassificationMode; session_affinity?: boolean; + modality_routing?: boolean; deployment_affinity?: boolean; /** Plan-mode floor as a tier ROW ID, unset meaning off. The wire carries the row's name. */ plan_mode_min_tier?: string; @@ -392,6 +408,13 @@ export interface ComplexityRouterConfigValue { tier_distance_penalty?: number; adaptive_eligible?: AdaptiveEligible; return_raw_model_name?: boolean; + /** + * Context-window escalation gate. Undefined means untouched, which keeps both keys out of the + * payload so the router tracks the backend defaults (enabled, 0.95 buffer); an explicit false + * is a real opt-out and must survive the edit round-trip. + */ + enable_context_window_escalation?: boolean; + context_window_escalation_buffer?: number; /** * Heuristic scorer knobs. Undefined means the operator never touched them, which keeps the key out of the * payload so the router tracks the backend defaults rather than freezing today's numbers. @@ -412,6 +435,21 @@ export interface ComplexityRouterConfigValue { tier_model_params?: TierModelParamsByTier; } +/** Session affinity wins where a hand-authored config sets both, matching the backend's own `or`. */ +export const classificationFrequency = (value: ComplexityRouterConfigValue): ClassificationFrequency => { + if (!value.custom_tier_set && (value.session_affinity ?? DEFAULT_SESSION_AFFINITY)) return "session"; + return value.classification_mode === "user_turn" ? "user_turn" : "every_request"; +}; + +export const withClassificationFrequency = ( + value: ComplexityRouterConfigValue, + frequency: ClassificationFrequency, +): ComplexityRouterConfigValue => ({ + ...value, + classification_mode: frequency === "user_turn" ? "user_turn" : "every_request", + session_affinity: frequency === "session", +}); + interface ComplexityRouterConfigProps { modelInfo: ModelGroup[]; value: ComplexityRouterConfigValue; @@ -477,39 +515,6 @@ export const DEFAULT_HEURISTIC_FIRST_MAX_TIER = "SIMPLE"; */ export const HEURISTIC_FIRST_MAX_TIER_KEYS = TIER_KEYS.slice(0, -1); -const AffinityControls: React.FC<{ - value: ComplexityRouterConfigValue; - onChange: (value: ComplexityRouterConfigValue) => void; -}> = ({ value, onChange }) => ( - <> -
- onChange({ ...value, deployment_affinity: deploymentAffinity })} - aria-label="Pin a session to one deployment per model group" - /> - Pin a session to one deployment per model group -
- - Keeps a session on the same deployment within a group, so provider prompt caches stay warm. Turn off to - load-balance every turn. - -
- onChange({ ...value, session_affinity: sessionAffinity })} - aria-label="Pin a session to its first model" - /> - Pin a session to its first model -
- - {restrictedBy(value, "sessionAffinity")?.reason ?? - "Keeps a session on its first turn's model instead of re-classifying each turn. Also pins the deployment."} - - -); - const PlanModeOverrideControls: React.FC<{ value: ComplexityRouterConfigValue; onChange: (value: ComplexityRouterConfigValue) => void; @@ -820,6 +825,11 @@ const ComplexityRouterConfig: React.FC = ({ label: Advanced: Affinity, children: , }, + { + key: "modality", + label: Advanced: Modality Routing, + children: , + }, { key: "plan-mode", label: Advanced: Plan-Mode Override, @@ -827,6 +837,11 @@ const ComplexityRouterConfig: React.FC = ({ ), }, + { + key: "context-window", + label: Advanced: Context Window Escalation, + children: , + }, { key: "response", label: Advanced: Response Format, diff --git a/ui/litellm-dashboard/src/components/add_model/ContextWindowEscalationConfig.tsx b/ui/litellm-dashboard/src/components/add_model/ContextWindowEscalationConfig.tsx new file mode 100644 index 00000000000..c0a65076d20 --- /dev/null +++ b/ui/litellm-dashboard/src/components/add_model/ContextWindowEscalationConfig.tsx @@ -0,0 +1,60 @@ +import { Input } from "@/components/ui/input"; +import { Switch } from "@/components/ui/switch"; +import React from "react"; +import type { ComplexityRouterConfigValue } from "./ComplexityRouterConfig"; + +const ContextWindowEscalationConfig: React.FC<{ + value: ComplexityRouterConfigValue; + onChange: (value: ComplexityRouterConfigValue) => void; +}> = ({ value, onChange }) => { + const enabled = value.enable_context_window_escalation ?? true; + // A number input renders Number("0.") as "0", so a decimal cannot be typed without a local draft. + const [bufferDraft, setBufferDraft] = React.useState(null); + const commitBuffer = (raw: string) => { + setBufferDraft(null); + if (raw.trim() === "") { + onChange({ ...value, context_window_escalation_buffer: undefined }); + return; + } + const parsed = Number(raw); + if (!Number.isFinite(parsed)) return; + onChange({ ...value, context_window_escalation_buffer: Math.min(1, Math.max(0.01, parsed)) }); + }; + return ( + <> +
+ onChange({ ...value, enable_context_window_escalation: next })} + aria-label="Escalate oversized prompts to a tier that fits" + /> + Escalate oversized prompts to a tier that fits +
+ + When a prompt provably cannot fit the decided tier's context windows, route it to the lowest tier whose + window holds it instead of letting the provider reject it. Off means requests dispatch on complexity alone. + + {enabled && ( +
+ + setBufferDraft(event.target.value)} + onBlur={(event) => commitBuffer(event.target.value)} + /> + + Fraction of a model's window the counted prompt must fit within, above 0 up to 1. Empty tracks the + backend default of 0.95. + +
+ )} + + ); +}; + +export default ContextWindowEscalationConfig; diff --git a/ui/litellm-dashboard/src/components/add_model/ModalityRoutingControls.tsx b/ui/litellm-dashboard/src/components/add_model/ModalityRoutingControls.tsx new file mode 100644 index 00000000000..dd697b35239 --- /dev/null +++ b/ui/litellm-dashboard/src/components/add_model/ModalityRoutingControls.tsx @@ -0,0 +1,26 @@ +import React from "react"; + +import { Switch } from "@/components/ui/switch"; + +import type { ComplexityRouterConfigValue } from "./ComplexityRouterConfig"; + +export const ModalityRoutingControls: React.FC<{ + value: ComplexityRouterConfigValue; + onChange: (value: ComplexityRouterConfigValue) => void; +}> = ({ value, onChange }) => ( + <> +
+ onChange({ ...value, modality_routing: modalityRouting })} + aria-label="Route image requests to vision-capable models" + /> + Route image requests to vision-capable models +
+ + Replaces a routed model that cannot take image input with the nearest higher tier that can, then the default + model, instead of failing with a provider 400. Only models explicitly declared supports_vision false are replaced, + and a kept session pin still wins. + + +); diff --git a/ui/litellm-dashboard/src/components/add_model/add_auto_router_tab.test.tsx b/ui/litellm-dashboard/src/components/add_model/add_auto_router_tab.test.tsx index 01cb41bcb95..d8a955b719c 100644 --- a/ui/litellm-dashboard/src/components/add_model/add_auto_router_tab.test.tsx +++ b/ui/litellm-dashboard/src/components/add_model/add_auto_router_tab.test.tsx @@ -362,8 +362,8 @@ describe("AddAutoRouterTab", () => { await user.type(screen.getByPlaceholderText(/smart_router/i), "affinity-router"); expandDetailedConfiguration(); - await user.click(screen.getByText("Advanced: Affinity")); - expect(await screen.findByRole("switch", { name: "Pin a session to its first model" })).not.toBeChecked(); + await user.click(screen.getByText("Advanced: Classification Method")); + expect(await screen.findByRole("radio", { name: /Once per session/ })).not.toBeChecked(); await user.click(screen.getByRole("button", { name: /add auto router/i })); @@ -373,6 +373,71 @@ describe("AddAutoRouterTab", () => { }); }); + it("carries a context-window escalation opt-out through to the create payload", async () => { + const user = userEvent.setup(); + vi.mocked(getMissingTiersError).mockReturnValue(null); + + renderWithProviders(); + + await user.type(screen.getByPlaceholderText(/smart_router/i), "ctx-window-router"); + expandDetailedConfiguration(); + await user.click(screen.getByText("Advanced: Context Window Escalation")); + const toggle = await screen.findByRole("switch", { name: "Escalate oversized prompts to a tier that fits" }); + expect(toggle).toBeChecked(); + await user.click(toggle); + + await user.click(screen.getByRole("button", { name: /add auto router/i })); + + await waitFor(() => expect(handleAddAutoRouterSubmit).toHaveBeenCalled()); + expect(vi.mocked(handleAddAutoRouterSubmit).mock.calls.at(-1)?.[0].complexity_router_config).toMatchObject({ + enable_context_window_escalation: false, + }); + }); + + it("clamps the context-window buffer to 1 and keeps an untouched buffer out of the payload", async () => { + const user = userEvent.setup(); + vi.mocked(getMissingTiersError).mockReturnValue(null); + + renderWithProviders(); + + await user.type(screen.getByPlaceholderText(/smart_router/i), "ctx-buffer-router"); + expandDetailedConfiguration(); + await user.click(screen.getByText("Advanced: Context Window Escalation")); + const buffer = await screen.findByLabelText("Window fit buffer"); + fireEvent.change(buffer, { target: { value: "1.5" } }); + fireEvent.blur(buffer, { target: { value: "1.5" } }); + + await user.click(screen.getByRole("button", { name: /add auto router/i })); + + await waitFor(() => expect(handleAddAutoRouterSubmit).toHaveBeenCalled()); + const config = vi.mocked(handleAddAutoRouterSubmit).mock.calls.at(-1)?.[0].complexity_router_config; + expect(config).toMatchObject({ context_window_escalation_buffer: 1 }); + expect(config).not.toHaveProperty("enable_context_window_escalation"); + }); + + it("clearing the buffer removes it from the payload so the router tracks the backend default", async () => { + const user = userEvent.setup(); + vi.mocked(getMissingTiersError).mockReturnValue(null); + + renderWithProviders(); + + await user.type(screen.getByPlaceholderText(/smart_router/i), "ctx-clear-router"); + expandDetailedConfiguration(); + await user.click(screen.getByText("Advanced: Context Window Escalation")); + const buffer = await screen.findByLabelText("Window fit buffer"); + fireEvent.change(buffer, { target: { value: "0.8" } }); + fireEvent.blur(buffer, { target: { value: "0.8" } }); + fireEvent.change(buffer, { target: { value: "" } }); + fireEvent.blur(buffer, { target: { value: "" } }); + + await user.click(screen.getByRole("button", { name: /add auto router/i })); + + await waitFor(() => expect(handleAddAutoRouterSubmit).toHaveBeenCalled()); + expect(vi.mocked(handleAddAutoRouterSubmit).mock.calls.at(-1)?.[0].complexity_router_config).not.toHaveProperty( + "context_window_escalation_buffer", + ); + }); + // The scalar floor is the one scorer knob with no group dict behind it, so its wiring into the create // payload is only proven end to end. 0 is the case a truthy check would silently drop. it("carries a reasoning override floor of 0 through to the create payload", async () => { @@ -403,8 +468,8 @@ describe("AddAutoRouterTab", () => { await user.type(screen.getByPlaceholderText(/smart_router/i), "affinity-router"); expandDetailedConfiguration(); - await user.click(screen.getByText("Advanced: Affinity")); - await user.click(await screen.findByRole("switch", { name: "Pin a session to its first model" })); + await user.click(screen.getByText("Advanced: Classification Method")); + await user.click(await screen.findByRole("radio", { name: /Once per session/ })); await user.click(screen.getByRole("button", { name: /add auto router/i })); @@ -414,6 +479,44 @@ describe("AddAutoRouterTab", () => { }); }); + it("carries every new user message through to the create payload", async () => { + const user = userEvent.setup(); + vi.mocked(getMissingTiersError).mockReturnValue(null); + + renderWithProviders(); + + await user.type(screen.getByPlaceholderText(/smart_router/i), "user-turn-router"); + expandDetailedConfiguration(); + await user.click(screen.getByText("Advanced: Classification Method")); + await user.click(await screen.findByRole("radio", { name: /Every new user message/ })); + + await user.click(screen.getByRole("button", { name: /add auto router/i })); + + await waitFor(() => expect(handleAddAutoRouterSubmit).toHaveBeenCalled()); + expect(vi.mocked(handleAddAutoRouterSubmit).mock.calls.at(-1)?.[0].complexity_router_config).toMatchObject({ + classification_mode: "user_turn", + }); + }); + + it("writes every_request into the create payload when the default frequency stays selected", async () => { + const user = userEvent.setup(); + vi.mocked(getMissingTiersError).mockReturnValue(null); + + renderWithProviders(); + + await user.type(screen.getByPlaceholderText(/smart_router/i), "default-timing-router"); + expandDetailedConfiguration(); + await user.click(screen.getByText("Advanced: Classification Method")); + expect(await screen.findByRole("radio", { name: /Every request/ })).toBeChecked(); + + await user.click(screen.getByRole("button", { name: /add auto router/i })); + + await waitFor(() => expect(handleAddAutoRouterSubmit).toHaveBeenCalled()); + expect( + vi.mocked(handleAddAutoRouterSubmit).mock.calls.at(-1)?.[0].complexity_router_config.classification_mode, + ).toBe("every_request"); + }); + it("defaults a new router to deployment affinity on, matching the backend field default", async () => { const user = userEvent.setup(); vi.mocked(getMissingTiersError).mockReturnValue(null); diff --git a/ui/litellm-dashboard/src/components/add_model/add_auto_router_tab.tsx b/ui/litellm-dashboard/src/components/add_model/add_auto_router_tab.tsx index 4e5e5e8d460..318adcce369 100644 --- a/ui/litellm-dashboard/src/components/add_model/add_auto_router_tab.tsx +++ b/ui/litellm-dashboard/src/components/add_model/add_auto_router_tab.tsx @@ -342,6 +342,7 @@ const AddAutoRouterTab: React.FC = ({ planModeMinTier: complexityRouterConfig.plan_mode_min_tier, classificationPrompt: complexityRouterConfig.classification_prompt, heuristicFirstMaxTier: complexityRouterConfig.heuristic_first_max_tier, + classificationMode: complexityRouterConfig.classification_mode, tierLabels: complexityRouterConfig.tier_labels, classifierType: complexityRouterConfig.classifier_type, classifierLlmConfig: complexityRouterConfig.classifier_llm_config, @@ -350,6 +351,7 @@ const AddAutoRouterTab: React.FC = ({ classifierContextIncludeAssistantTurns: complexityRouterConfig.classifier_context_include_assistant_turns, classifierFallback: complexityRouterConfig.classifier_fallback, sessionAffinity: complexityRouterConfig.session_affinity ?? DEFAULT_SESSION_AFFINITY, + modalityRouting: complexityRouterConfig.modality_routing ?? false, deploymentAffinity: complexityRouterConfig.deployment_affinity ?? DEFAULT_DEPLOYMENT_AFFINITY, customTechnicalKeywords, keywordTierRules, @@ -367,6 +369,8 @@ const AddAutoRouterTab: React.FC = ({ tokenThresholds: complexityRouterConfig.token_thresholds, dimensionWeights: complexityRouterConfig.dimension_weights, reasoningOverrideMinScore: complexityRouterConfig.reasoning_override_min_score, + enableContextWindowEscalation: complexityRouterConfig.enable_context_window_escalation, + contextWindowEscalationBuffer: complexityRouterConfig.context_window_escalation_buffer, }; const submitRecommendedRouter = async (name: string) => { diff --git a/ui/litellm-dashboard/src/components/add_model/advanced_settings.test.tsx b/ui/litellm-dashboard/src/components/add_model/advanced_settings.test.tsx index 01e00d903aa..ceb9d1ca713 100644 --- a/ui/litellm-dashboard/src/components/add_model/advanced_settings.test.tsx +++ b/ui/litellm-dashboard/src/components/add_model/advanced_settings.test.tsx @@ -1,4 +1,4 @@ -import { act, fireEvent, render, waitFor } from "@testing-library/react"; +import { act, fireEvent, render, waitFor, screen } from "@testing-library/react"; import { beforeEach, describe, expect, it, vi } from "vitest"; import { MountedFormHost } from "../../../tests/mounted-form-host"; import AdvancedSettings from "./advanced_settings"; @@ -35,51 +35,51 @@ describe("AdvancedSettings", () => { }); it("should render tags list", async () => { - const { getByText } = renderAdvancedSettings(); - fireEvent.click(getByText("Advanced Settings")); + renderAdvancedSettings(); + fireEvent.click(screen.getByText("Advanced Settings")); await waitFor(() => { - expect(getByText("Tags")).toBeInTheDocument(); + expect(screen.getByText("Tags")).toBeInTheDocument(); }); }); it("should render the litellm params", async () => { - const { getByText } = renderAdvancedSettings(); + renderAdvancedSettings(); act(() => { - fireEvent.click(getByText("Advanced Settings")); + fireEvent.click(screen.getByText("Advanced Settings")); }); await waitFor(() => { - expect(getByText("LiteLLM Params")).toBeInTheDocument(); + expect(screen.getByText("LiteLLM Params")).toBeInTheDocument(); }); }); it("hides every PTU field when PTU cost attribution is disabled", async () => { - const { getByText, queryByText } = renderAdvancedSettings(); + renderAdvancedSettings(); act(() => { - fireEvent.click(getByText("Advanced Settings")); + fireEvent.click(screen.getByText("Advanced Settings")); }); await waitFor(() => { - expect(getByText("Tags")).toBeInTheDocument(); + expect(screen.getByText("Tags")).toBeInTheDocument(); }); for (const label of PTU_LABELS) { - expect(queryByText(label)).not.toBeInTheDocument(); + expect(screen.queryByText(label)).not.toBeInTheDocument(); } - expect(queryByText("PTU Effective To (UTC)")).not.toBeInTheDocument(); + expect(screen.queryByText("PTU Effective To (UTC)")).not.toBeInTheDocument(); }); it("shows every PTU field when PTU cost attribution is enabled", async () => { mockUsePtuCostAttributionEnabled.mockReturnValue(true); - const { getByText } = renderAdvancedSettings(); + renderAdvancedSettings(); act(() => { - fireEvent.click(getByText("Advanced Settings")); + fireEvent.click(screen.getByText("Advanced Settings")); }); await waitFor(() => { - expect(getByText("PTU Count")).toBeInTheDocument(); + expect(screen.getByText("PTU Count")).toBeInTheDocument(); }); for (const label of PTU_LABELS) { - expect(getByText(label)).toBeInTheDocument(); + expect(screen.getByText(label)).toBeInTheDocument(); } - expect(getByText("PTU Effective To (UTC)")).toBeInTheDocument(); + expect(screen.getByText("PTU Effective To (UTC)")).toBeInTheDocument(); }); }); diff --git a/ui/litellm-dashboard/src/components/add_model/build_complexity_router_config.test.ts b/ui/litellm-dashboard/src/components/add_model/build_complexity_router_config.test.ts index 84406a2093e..af87cc6c8fb 100644 --- a/ui/litellm-dashboard/src/components/add_model/build_complexity_router_config.test.ts +++ b/ui/litellm-dashboard/src/components/add_model/build_complexity_router_config.test.ts @@ -52,13 +52,25 @@ describe("buildComplexityRouterConfig", () => { const expected = { tiers, classifier_type: "heuristic", + classification_mode: "every_request", session_affinity: false, deployment_affinity: true, + modality_routing: false, escalation_keywords: ["LITELLM ESCALATE"], }; expect(config).toEqual(expected); }); + it("carries an explicit context-window escalation opt-out and buffer, false included", () => { + const config = buildComplexityRouterConfig({ + ...baseParams, + enableContextWindowEscalation: false, + contextWindowEscalationBuffer: 0.9, + }); + expect(config.enable_context_window_escalation).toBe(false); + expect(config.context_window_escalation_buffer).toBe(0.9); + }); + it("trims escalation keywords and drops blank entries", () => { const config = buildComplexityRouterConfig({ ...baseParams, @@ -237,6 +249,12 @@ describe("buildComplexityRouterConfig", () => { expect(config.return_raw_model_name).toBeUndefined(); }); + it("writes modality_routing explicitly both ways, so the stored config never relies on the backend default", () => { + expect(buildComplexityRouterConfig({ ...baseParams, modalityRouting: true }).modality_routing).toBe(true); + expect(buildComplexityRouterConfig(baseParams).modality_routing).toBe(false); + expect(buildComplexityRouterConfig({ ...baseParams, modalityRouting: false }).modality_routing).toBe(false); + }); + it("writes session_affinity=true so turning the toggle on overrides the backend's off-by-default", () => { const config = buildComplexityRouterConfig({ ...baseParams, sessionAffinity: true }); expect(config.session_affinity).toBe(true); @@ -780,6 +798,20 @@ describe("heuristic_first", () => { }); }); +describe("classification_mode", () => { + it("emits user_turn", () => { + const config = buildComplexityRouterConfig({ ...baseParams, classificationMode: "user_turn" }); + expect(config.classification_mode).toBe("user_turn"); + }); + + it("writes every_request explicitly, so a saved router never depends on the backend default", () => { + expect( + buildComplexityRouterConfig({ ...baseParams, classificationMode: "every_request" }).classification_mode, + ).toBe("every_request"); + expect(buildComplexityRouterConfig(baseParams).classification_mode).toBe("every_request"); + }); +}); + describe("buildComplexityRouterConfig with an edited tier set", () => { const customTierSet = { tiers: [ @@ -892,6 +924,10 @@ describe("buildComplexityRouterConfig with an edited tier set", () => { expect(payload.classifier_llm_config).toEqual({ model: "gpt-4o-mini", timeout_ms: 3000 }); }); + it("keeps classification_mode, which the backend accepts beside tier_definitions", () => { + expect(build({ classificationMode: "user_turn" }).classification_mode).toBe("user_turn"); + }); + it("carries the plan-mode floor as the row's name, not the row id the form holds", () => { expect(build({ planModeMinTier: "sec" }).plan_mode_min_tier).toBe("SECURITY_REVIEW"); }); diff --git a/ui/litellm-dashboard/src/components/add_model/build_complexity_router_config.ts b/ui/litellm-dashboard/src/components/add_model/build_complexity_router_config.ts index ba500d116ce..af34dc92c0f 100644 --- a/ui/litellm-dashboard/src/components/add_model/build_complexity_router_config.ts +++ b/ui/litellm-dashboard/src/components/add_model/build_complexity_router_config.ts @@ -19,10 +19,12 @@ import { import { AdaptiveEligible, AdaptiveRouterWeights, + ClassificationMode, ClassifierFallback, ClassifierLLMConfig, ClassifierType, ComplexityTierLabels, + DEFAULT_CLASSIFICATION_MODE, ComplexityRouterConfigValue, ComplexityTiers, DimensionWeights, @@ -105,7 +107,9 @@ export interface BuildComplexityRouterConfigParams { classifierFallback: ClassifierFallback | undefined; classificationPrompt: string | undefined; heuristicFirstMaxTier: string | undefined; + classificationMode: ClassificationMode | undefined; sessionAffinity: boolean; + modalityRouting?: boolean; deploymentAffinity: boolean; customTechnicalKeywords: string[]; keywordTierRules: KeywordTierRule[]; @@ -123,6 +127,8 @@ export interface BuildComplexityRouterConfigParams { dimensionWeights?: DimensionWeights; reasoningOverrideMinScore?: number; tierModelParams?: TierModelParamsByTier; + enableContextWindowEscalation?: boolean; + contextWindowEscalationBuffer?: number; } /** @@ -157,8 +163,10 @@ export interface ComplexityRouterConfigPayload { classifier_fallback?: ClassifierFallback; classification_prompt?: string; heuristic_first_max_tier?: string; + classification_mode: ClassificationMode; session_affinity: boolean; deployment_affinity: boolean; + modality_routing: boolean; custom_technical_keywords?: string[]; keyword_tier_rules?: { keywords: string[]; tier: KeywordTierRule["tier"] }[]; semantic_keyword_matching?: boolean; @@ -174,6 +182,8 @@ export interface ComplexityRouterConfigPayload { token_thresholds?: TokenThresholds; dimension_weights?: DimensionWeights; reasoning_override_min_score?: number; + enable_context_window_escalation?: boolean; + context_window_escalation_buffer?: number; tier_model_configs?: Record; } @@ -389,7 +399,9 @@ export const buildComplexityRouterConfig = ({ classifierFallback, classificationPrompt, heuristicFirstMaxTier, + classificationMode, sessionAffinity, + modalityRouting, deploymentAffinity, customTechnicalKeywords, keywordTierRules, @@ -407,6 +419,8 @@ export const buildComplexityRouterConfig = ({ dimensionWeights, reasoningOverrideMinScore, tierModelParams, + enableContextWindowEscalation, + contextWindowEscalationBuffer, }: BuildComplexityRouterConfigParams): ComplexityRouterConfigPayload => { const serializedTierModelConfigs = customTierSet ? serializeTierModelConfigs( @@ -446,8 +460,10 @@ export const buildComplexityRouterConfig = ({ ...(cleanedTierLabels && { tier_labels: cleanedTierLabels }), classifier_type: classifierType, ...classifierWireFields(effectiveType, classifierInputs), + classification_mode: classificationMode ?? DEFAULT_CLASSIFICATION_MODE, session_affinity: sessionAffinity, deployment_affinity: deploymentAffinity, + modality_routing: modalityRouting ?? false, ...(customTechnicalKeywords.length > 0 && { custom_technical_keywords: customTechnicalKeywords }), ...(cleanedKeywordTierRules.length > 0 && { keyword_tier_rules: cleanedKeywordTierRules }), escalation_keywords: cleanedEscalationKeywords, @@ -463,6 +479,12 @@ export const buildComplexityRouterConfig = ({ adaptive_eligible: adaptiveEligible, }), ...(returnRawModelName && { return_raw_model_name: true }), + ...(enableContextWindowEscalation !== undefined && { + enable_context_window_escalation: enableContextWindowEscalation, + }), + ...(contextWindowEscalationBuffer !== undefined && { + context_window_escalation_buffer: contextWindowEscalationBuffer, + }), ...scorerKnobs, }; if (!customTierSet) return payload; diff --git a/ui/litellm-dashboard/src/components/add_model/litellm_model_name.test.tsx b/ui/litellm-dashboard/src/components/add_model/litellm_model_name.test.tsx index 64074481603..d6476089cd7 100644 --- a/ui/litellm-dashboard/src/components/add_model/litellm_model_name.test.tsx +++ b/ui/litellm-dashboard/src/components/add_model/litellm_model_name.test.tsx @@ -1,4 +1,4 @@ -import { render } from "@testing-library/react"; +import { render, screen } from "@testing-library/react"; import { describe, expect, it } from "vitest"; import { getPlaceholder, Providers } from "../provider_info_helpers"; import { MountedFormHost } from "../../../tests/mounted-form-host"; @@ -6,7 +6,7 @@ import LiteLLMModelNameField from "./litellm_model_name"; describe("LitellmModelNameField", () => { it("should render", () => { - const { getByText } = render( + render( { /> , ); - expect(getByText("LiteLLM Model Name(s)")).toBeInTheDocument(); + expect(screen.getByText("LiteLLM Model Name(s)")).toBeInTheDocument(); }); it("should show Azure placeholder as 'my-deployment'", () => { - const { getByPlaceholderText, queryByPlaceholderText } = render( + render( , ); - expect(getByPlaceholderText("my-deployment")).toBeInTheDocument(); - expect(queryByPlaceholderText("gpt-3.5-turbo")).not.toBeInTheDocument(); + expect(screen.getByPlaceholderText("my-deployment")).toBeInTheDocument(); + expect(screen.queryByPlaceholderText("gpt-3.5-turbo")).not.toBeInTheDocument(); }); }); diff --git a/ui/litellm-dashboard/src/components/bulk_create_users_button.test.tsx b/ui/litellm-dashboard/src/components/bulk_create_users_button.test.tsx index 24685f9c129..e1faf8be1df 100644 --- a/ui/litellm-dashboard/src/components/bulk_create_users_button.test.tsx +++ b/ui/litellm-dashboard/src/components/bulk_create_users_button.test.tsx @@ -26,8 +26,8 @@ const openUploadStep = async () => { describe("BulkCreateUsersButton", () => { it("should render", () => { - const { getByText } = render(); - expect(getByText("+ Bulk Invite Users")).toBeInTheDocument(); + render(); + expect(screen.getByText("+ Bulk Invite Users")).toBeInTheDocument(); }); it("parses a CSV chosen through the file input", async () => { diff --git a/ui/litellm-dashboard/src/components/claude_code_plugins/skill_detail.tsx b/ui/litellm-dashboard/src/components/claude_code_plugins/skill_detail.tsx index 25b35c34861..7cbbb08b623 100644 --- a/ui/litellm-dashboard/src/components/claude_code_plugins/skill_detail.tsx +++ b/ui/litellm-dashboard/src/components/claude_code_plugins/skill_detail.tsx @@ -1,5 +1,6 @@ import React, { useState } from "react"; import { ArrowLeft, Check, Copy, Link2 } from "lucide-react"; +import { cn } from "@/lib/cva.config"; import { buildMarketplaceSettingsSnippet, formatInstallCommand } from "./helpers"; import { Plugin } from "./types"; @@ -50,48 +51,37 @@ const SkillDetail: React.FC = ({ skill, onBack }) => { ]; return ( -
+
{/* Back link */}
Skills
{/* Header */} -
-

{skill.name}

+
+

{skill.name}

{skill.description && ( -

{skill.description}

+

{skill.description}

)}
{/* Tab bar */} -
-
+
+
{tabs.map((tab) => (
setActiveTab(tab.key)} - style={{ - padding: "12px 20px", - fontSize: 14, - color: activeTab === tab.key ? "#1a73e8" : "#5f6368", - borderBottom: activeTab === tab.key ? "3px solid #1a73e8" : "3px solid transparent", - cursor: "pointer", - fontWeight: activeTab === tab.key ? 500 : 400, - marginBottom: -1, - }} + className={cn( + "-mb-px cursor-pointer border-b-[3px] px-5 py-3 text-sm", + activeTab === tab.key + ? "border-info font-medium text-info" + : "border-transparent font-normal text-muted-foreground", + )} > {tab.label}
@@ -101,27 +91,23 @@ const SkillDetail: React.FC = ({ skill, onBack }) => { {/* Overview tab */} {activeTab === "overview" && ( -
+
{/* Left column */} -
-

Skill Details

-

Metadata registered with this skill

-
+
+

Skill Details

+

Metadata registered with this skill

+
- - - + + + {detailRows.map((row, i) => ( - - - + + + ))} @@ -129,38 +115,27 @@ const SkillDetail: React.FC = ({ skill, onBack }) => { {/* Right sidebar */} -
-
-
Status
+
+
+
Status
{skill.enabled ? "Public" : "Draft"}
{sourceUrl && ( -
-
Source
+
+
Source
{sourceUrl.replace("https://", "")} @@ -169,20 +144,13 @@ const SkillDetail: React.FC = ({ skill, onBack }) => { )} {skill.keywords && skill.keywords.length > 0 && ( -
-
Tags
-
+
+
Tags
+
{skill.keywords.map((kw) => ( {kw} @@ -192,10 +160,8 @@ const SkillDetail: React.FC = ({ skill, onBack }) => { )}
-
Skill ID
-
- {skill.id} -
+
Skill ID
+
{skill.id}
@@ -203,93 +169,43 @@ const SkillDetail: React.FC = ({ skill, onBack }) => { {/* How to Use tab */} {activeTab === "usage" && ( -
-

Using this skill

-

+

+

Using this skill

+

Once your proxy is set as a marketplace, enable this skill in Claude Code with one command:

{/* Install command */} -
-
- Run in Claude Code +
+
+ Run in Claude Code
- 
-              {installCommand}
-
+ 
{installCommand}
{/* Shown when the marketplace catalog is stale and the plugin isn't found yet */} -
-

+

+

If you see "Plugin {skill.name} not found in marketplace", update the catalog first:

- 
+            
               /plugin marketplace update litellm
-

+

Don't have the marketplace configured yet?{" "} - setActiveTab("setup")} style={{ color: "#1a73e8", cursor: "pointer" }}> + setActiveTab("setup")} className="cursor-pointer text-info"> See one-time setup →

@@ -298,126 +214,56 @@ const SkillDetail: React.FC = ({ skill, onBack }) => { {/* Setup tab (linked from usage) */} {activeTab === "setup" && ( -
-

- One-time marketplace setup -

+
+

One-time marketplace setup

{/* Option 1: single command — fastest path for most users */} -

+

Run this command in Claude Code to register the marketplace:

-
-
- Run in Claude Code +
+
+ Run in Claude Code
- 
+            
               {`/plugin marketplace add ${typeof window !== "undefined" ? window.location.origin : ""}/claude-code/marketplace.json`}
{/* Option 2: settings.json — for persistent config or managed deployments. extraKnownMarketplaces requires source to be a nested object, not a flat string. */} -

- Or add this to{" "} - - ~/.claude/settings.json - {" "} +

+ Or add this to ~/.claude/settings.json{" "} for a persistent configuration:

-
-
- ~/.claude/settings.json +
+
+ ~/.claude/settings.json
- 
-              {settingsSnippet}
-
+ 
{settingsSnippet}
)} diff --git a/ui/litellm-dashboard/src/components/edit_auto_router/build_updated_complexity_router_config.test.ts b/ui/litellm-dashboard/src/components/edit_auto_router/build_updated_complexity_router_config.test.ts index a7bd4b8eab4..3e63cbd3b32 100644 --- a/ui/litellm-dashboard/src/components/edit_auto_router/build_updated_complexity_router_config.test.ts +++ b/ui/litellm-dashboard/src/components/edit_auto_router/build_updated_complexity_router_config.test.ts @@ -257,6 +257,33 @@ describe("buildUpdatedComplexityRouterConfig session affinity", () => { }); }); +describe("buildUpdatedComplexityRouterConfig classification mode", () => { + it("round-trips a stored user_turn through hydrate then save", () => { + const stored = { ...STORED, classification_mode: "user_turn" }; + const hydrated = hydrateComplexityRouterConfig(stored, undefined); + + expect(hydrated.classification_mode).toBe("user_turn"); + expect(buildUpdatedComplexityRouterConfig(stored, hydrated).classification_mode).toBe("user_turn"); + }); + + it("round-trips an explicitly stored every_request, so an untouched save leaves it as written", () => { + const stored = { ...STORED, classification_mode: "every_request" }; + const hydrated = hydrateComplexityRouterConfig(stored, undefined); + + expect(hydrated.classification_mode).toBe("every_request"); + expect(buildUpdatedComplexityRouterConfig(stored, hydrated).classification_mode).toBe("every_request"); + }); + + it("rewrites a stored user_turn to every_request once the operator picks the default back", () => { + const stored = { ...STORED, classification_mode: "user_turn" }; + const result = buildUpdatedComplexityRouterConfig(stored, { + ...FORM_VALUE, + classification_mode: "every_request", + }); + expect(result.classification_mode).toBe("every_request"); + }); +}); + describe("buildUpdatedComplexityRouterConfig deployment affinity", () => { it("writes deployment_affinity=false when the toggle is off", () => { const result = buildUpdatedComplexityRouterConfig(STORED, { ...FORM_VALUE, deployment_affinity: false }); @@ -476,6 +503,7 @@ describe("managed keys survive an untouched open-and-save", () => { classifier_context_budget_chars: 4000, classifier_context_include_assistant_turns: true, classifier_fallback: "default_model", + classification_mode: "user_turn", session_affinity: true, deployment_affinity: false, adaptive: true, @@ -487,6 +515,8 @@ describe("managed keys survive an untouched open-and-save", () => { token_thresholds: { simple: 20, complex: 500 }, dimension_weights: { tokenCount: 0.1 }, reasoning_override_min_score: 0.3, + enable_context_window_escalation: false, + context_window_escalation_buffer: 0.9, }; // tier_definitions, fallback_tier and classification_prompt cannot sit beside heuristic_first, which diff --git a/ui/litellm-dashboard/src/components/edit_auto_router/edit_auto_router_modal.test.ts b/ui/litellm-dashboard/src/components/edit_auto_router/edit_auto_router_modal.test.ts index 027e01a9351..481ba2b6b00 100644 --- a/ui/litellm-dashboard/src/components/edit_auto_router/edit_auto_router_modal.test.ts +++ b/ui/litellm-dashboard/src/components/edit_auto_router/edit_auto_router_modal.test.ts @@ -1,4 +1,4 @@ -import { buildUpdatedComplexityRouterConfig } from "./edit_auto_router_modal"; +import { buildUpdatedComplexityRouterConfig, hydrateComplexityRouterConfig } from "./edit_auto_router_modal"; const storedConfigValue = { tiers: { @@ -47,8 +47,10 @@ const expectedClassifiedTierConfig = { semantic_keyword_matching: true, embedding_model: "voyage-4-large", match_threshold: 0.65, + classification_mode: "every_request", session_affinity: false, deployment_affinity: true, + modality_routing: false, adaptive: true, adaptive_weights: { quality: 0.4, cost: 0.6 }, adaptive_eligible: "classified_tier", @@ -68,8 +70,10 @@ const expectedAdaptiveDisabledConfig = { semantic_keyword_matching: true, embedding_model: "voyage-4-large", match_threshold: 0.65, + classification_mode: "every_request", session_affinity: false, deployment_affinity: true, + modality_routing: false, }; describe("buildUpdatedComplexityRouterConfig", () => { @@ -85,6 +89,26 @@ describe("buildUpdatedComplexityRouterConfig", () => { expect(updatedConfig).toEqual(expectedAdaptiveDisabledConfig); }); + it("hydrates a stored modality_routing into form state and defaults absent to off", () => { + expect(hydrateComplexityRouterConfig({ ...storedConfig, modality_routing: true }, null).modality_routing).toBe( + true, + ); + expect(hydrateComplexityRouterConfig(storedConfig, null).modality_routing).toBe(false); + }); + + it("round-trips modality_routing explicitly in both directions", () => { + const enabled = buildUpdatedComplexityRouterConfig(storedConfig, { + ...classifiedTierValue, + modality_routing: true, + }); + expect(enabled.modality_routing).toBe(true); + const disabled = buildUpdatedComplexityRouterConfig( + { ...storedConfig, modality_routing: true }, + { ...classifiedTierValue, modality_routing: false }, + ); + expect(disabled.modality_routing).toBe(false); + }); + it("includes return_raw_model_name only when enabled", () => { const updatedConfig = buildUpdatedComplexityRouterConfig(storedConfig, { ...classifiedTierValue, diff --git a/ui/litellm-dashboard/src/components/edit_auto_router/edit_auto_router_modal.test.tsx b/ui/litellm-dashboard/src/components/edit_auto_router/edit_auto_router_modal.test.tsx index a4921fcfcb5..96e8549eac8 100644 --- a/ui/litellm-dashboard/src/components/edit_auto_router/edit_auto_router_modal.test.tsx +++ b/ui/litellm-dashboard/src/components/edit_auto_router/edit_auto_router_modal.test.tsx @@ -364,7 +364,7 @@ describe("EditAutoRouterModal assistant turns", () => { }); }); -describe("EditAutoRouterModal session affinity", () => { +describe("EditAutoRouterModal classification frequency", () => { beforeEach(() => { modelPatchUpdateCall.mockClear(); }); @@ -381,15 +381,15 @@ describe("EditAutoRouterModal session affinity", () => { />, ); - // A stored config with no session_affinity key now runs with affinity OFF, because the backend - // field defaults to False. The toggle has to render what the router actually does, and an - // untouched save must not flip it. - it("shows a stored config with no session_affinity key as off", async () => { + // A stored config with neither key now runs with affinity OFF, because both backend fields + // default that way. The picker has to render what the router actually does, and an untouched + // save must not flip it. + it("shows a stored config with neither key as every request", async () => { const user = userEvent.setup(); renderWithStoredConfig(STORED_CONFIG); - await user.click(await screen.findByText("Advanced: Affinity")); - expect(await screen.findByRole("switch", { name: "Pin a session to its first model" })).not.toBeChecked(); + await user.click(await screen.findByText("Advanced: Classification Method")); + expect(await screen.findByRole("radio", { name: /Every request/ })).toBeChecked(); await user.click(screen.getByRole("button", { name: /save changes/i })); @@ -397,12 +397,12 @@ describe("EditAutoRouterModal session affinity", () => { expect(savedConfig().session_affinity).toBe(false); }); - it("shows a stored session_affinity=true as on and preserves it through an untouched save", async () => { + it("shows a stored session_affinity=true as once per session and preserves it through an untouched save", async () => { const user = userEvent.setup(); renderWithStoredConfig({ ...STORED_CONFIG, session_affinity: true }); - await user.click(await screen.findByText("Advanced: Affinity")); - expect(await screen.findByRole("switch", { name: "Pin a session to its first model" })).toBeChecked(); + await user.click(await screen.findByText("Advanced: Classification Method")); + expect(await screen.findByRole("radio", { name: /Once per session/ })).toBeChecked(); await user.click(screen.getByRole("button", { name: /save changes/i })); @@ -410,12 +410,12 @@ describe("EditAutoRouterModal session affinity", () => { expect(savedConfig().session_affinity).toBe(true); }); - it("persists turning session affinity on", async () => { + it("persists picking once per session", async () => { const user = userEvent.setup(); renderWithStoredConfig(STORED_CONFIG); - await user.click(await screen.findByText("Advanced: Affinity")); - await user.click(await screen.findByRole("switch", { name: "Pin a session to its first model" })); + await user.click(await screen.findByText("Advanced: Classification Method")); + await user.click(await screen.findByRole("radio", { name: /Once per session/ })); await user.click(screen.getByRole("button", { name: /save changes/i })); @@ -423,18 +423,71 @@ describe("EditAutoRouterModal session affinity", () => { expect(savedConfig().session_affinity).toBe(true); }); - it("persists turning session affinity back off", async () => { + it("persists picking every request back over a stored session pin", async () => { const user = userEvent.setup(); renderWithStoredConfig({ ...STORED_CONFIG, session_affinity: true }); - await user.click(await screen.findByText("Advanced: Affinity")); - await user.click(await screen.findByRole("switch", { name: "Pin a session to its first model" })); + await user.click(await screen.findByText("Advanced: Classification Method")); + await user.click(await screen.findByRole("radio", { name: /Every request/ })); await user.click(screen.getByRole("button", { name: /save changes/i })); await waitFor(() => expect(modelPatchUpdateCall).toHaveBeenCalled()); expect(savedConfig().session_affinity).toBe(false); }); + + it("clears a stored session pin when the operator moves to every new user message", async () => { + const user = userEvent.setup(); + renderWithStoredConfig({ ...STORED_CONFIG, session_affinity: true }); + + await user.click(await screen.findByText("Advanced: Classification Method")); + await user.click(await screen.findByRole("radio", { name: /Every new user message/ })); + + await user.click(screen.getByRole("button", { name: /save changes/i })); + + await waitFor(() => expect(modelPatchUpdateCall).toHaveBeenCalled()); + expect(savedConfig().session_affinity).toBe(false); + expect(savedConfig().classification_mode).toBe("user_turn"); + }); + + it("shows a stored user_turn as selected and preserves it through an untouched save", async () => { + const user = userEvent.setup(); + renderWithStoredConfig({ ...STORED_CONFIG, classification_mode: "user_turn" }); + + await user.click(await screen.findByText("Advanced: Classification Method")); + expect(await screen.findByRole("radio", { name: /Every new user message/ })).toBeChecked(); + + await user.click(screen.getByRole("button", { name: /save changes/i })); + + await waitFor(() => expect(modelPatchUpdateCall).toHaveBeenCalled()); + expect(savedConfig().classification_mode).toBe("user_turn"); + }); + + it("persists switching a stored config to every new user message", async () => { + const user = userEvent.setup(); + renderWithStoredConfig(STORED_CONFIG); + + await user.click(await screen.findByText("Advanced: Classification Method")); + await user.click(await screen.findByRole("radio", { name: /Every new user message/ })); + + await user.click(screen.getByRole("button", { name: /save changes/i })); + + await waitFor(() => expect(modelPatchUpdateCall).toHaveBeenCalled()); + expect(savedConfig().classification_mode).toBe("user_turn"); + }); + + it("rewrites the stored mode to every_request when the operator picks it back", async () => { + const user = userEvent.setup(); + renderWithStoredConfig({ ...STORED_CONFIG, classification_mode: "user_turn" }); + + await user.click(await screen.findByText("Advanced: Classification Method")); + await user.click(await screen.findByRole("radio", { name: /Every request/ })); + + await user.click(screen.getByRole("button", { name: /save changes/i })); + + await waitFor(() => expect(modelPatchUpdateCall).toHaveBeenCalled()); + expect(savedConfig().classification_mode).toBe("every_request"); + }); }); describe("EditAutoRouterModal deployment affinity", () => { diff --git a/ui/litellm-dashboard/src/components/edit_auto_router/edit_auto_router_modal.tsx b/ui/litellm-dashboard/src/components/edit_auto_router/edit_auto_router_modal.tsx index 425d5d51f06..e18582f77a0 100644 --- a/ui/litellm-dashboard/src/components/edit_auto_router/edit_auto_router_modal.tsx +++ b/ui/litellm-dashboard/src/components/edit_auto_router/edit_auto_router_modal.tsx @@ -96,17 +96,21 @@ export interface StoredComplexityRouterConfig { classifier_context_budget_chars?: unknown; classifier_context_include_assistant_turns?: unknown; classifier_fallback?: unknown; + classification_mode?: unknown; tier_boundaries?: unknown; token_thresholds?: unknown; dimension_weights?: unknown; reasoning_override_min_score?: unknown; session_affinity?: unknown; + modality_routing?: unknown; deployment_affinity?: unknown; adaptive?: boolean; adaptive_weights?: AdaptiveRouterWeights; tier_distance_penalty?: number; adaptive_eligible?: AdaptiveEligible; return_raw_model_name?: boolean; + enable_context_window_escalation?: unknown; + context_window_escalation_buffer?: unknown; } /** @@ -163,12 +167,17 @@ export const hydrateComplexityRouterConfig = ( typeof parsedConfig.heuristic_first_max_tier === "string" && parsedConfig.heuristic_first_max_tier.trim() !== "" ? parsedConfig.heuristic_first_max_tier : undefined, + classification_mode: + parsedConfig.classification_mode === "user_turn" || parsedConfig.classification_mode === "every_request" + ? parsedConfig.classification_mode + : undefined, tier_boundaries: hydrateTierBoundaries(parsedConfig.tier_boundaries), token_thresholds: hydrateTokenThresholds(parsedConfig.token_thresholds), dimension_weights: hydrateDimensionWeights(parsedConfig.dimension_weights), reasoning_override_min_score: hydrateReasoningOverrideMinScore(parsedConfig.reasoning_override_min_score), session_affinity: typeof parsedConfig.session_affinity === "boolean" ? parsedConfig.session_affinity : DEFAULT_SESSION_AFFINITY, + modality_routing: typeof parsedConfig.modality_routing === "boolean" ? parsedConfig.modality_routing : false, deployment_affinity: typeof parsedConfig.deployment_affinity === "boolean" ? parsedConfig.deployment_affinity @@ -178,6 +187,14 @@ export const hydrateComplexityRouterConfig = ( tier_distance_penalty: parsedConfig.tier_distance_penalty, adaptive_eligible: parsedConfig.adaptive_eligible || "all", return_raw_model_name: parsedConfig.return_raw_model_name || false, + enable_context_window_escalation: + typeof parsedConfig.enable_context_window_escalation === "boolean" + ? parsedConfig.enable_context_window_escalation + : undefined, + context_window_escalation_buffer: + typeof parsedConfig.context_window_escalation_buffer === "number" + ? parsedConfig.context_window_escalation_buffer + : undefined, }; }; @@ -197,7 +214,9 @@ export const MANAGED_COMPLEXITY_ROUTER_KEYS = new Set([ "classifier_fallback", "classification_prompt", "heuristic_first_max_tier", + "classification_mode", "session_affinity", + "modality_routing", "deployment_affinity", "adaptive", "adaptive_weights", @@ -208,6 +227,8 @@ export const MANAGED_COMPLEXITY_ROUTER_KEYS = new Set([ "token_thresholds", "dimension_weights", "reasoning_override_min_score", + "enable_context_window_escalation", + "context_window_escalation_buffer", ]); // Managed only when the caller passes the corresponding state. A caller that does not render @@ -282,6 +303,7 @@ export const buildUpdatedComplexityRouterConfig = ( planModeMinTier: value.plan_mode_min_tier, classificationPrompt: value.classification_prompt, heuristicFirstMaxTier: value.heuristic_first_max_tier, + classificationMode: value.classification_mode, tierLabels: value.tier_labels, classifierType: value.classifier_type, classifierLlmConfig: value.classifier_llm_config, @@ -290,6 +312,7 @@ export const buildUpdatedComplexityRouterConfig = ( classifierContextIncludeAssistantTurns: value.classifier_context_include_assistant_turns, classifierFallback: value.classifier_fallback, sessionAffinity: value.session_affinity ?? DEFAULT_SESSION_AFFINITY, + modalityRouting: value.modality_routing ?? false, deploymentAffinity: value.deployment_affinity ?? DEFAULT_DEPLOYMENT_AFFINITY, customTechnicalKeywords: customTechnicalKeywords ?? [], keywordTierRules: keywordMatching?.keywordTierRules ?? [], @@ -307,6 +330,8 @@ export const buildUpdatedComplexityRouterConfig = ( dimensionWeights: value.dimension_weights, reasoningOverrideMinScore: value.reasoning_override_min_score, tierModelParams: value.tier_model_params, + enableContextWindowEscalation: value.enable_context_window_escalation, + contextWindowEscalationBuffer: value.context_window_escalation_buffer, }; const built = buildComplexityRouterConfig(builderParams); diff --git a/ui/litellm-dashboard/src/components/leftnav.test.tsx b/ui/litellm-dashboard/src/components/leftnav.test.tsx index b0bb0e8a5b5..c3e1f924d09 100644 --- a/ui/litellm-dashboard/src/components/leftnav.test.tsx +++ b/ui/litellm-dashboard/src/components/leftnav.test.tsx @@ -213,6 +213,20 @@ describe("Sidebar (leftnav)", () => { expect(screen.getByText("Search Tools")).toBeInTheDocument(); }); }); + it("reports whether a nested tab is expanded", async () => { + renderWithProviders(); + + const toggle = screen.getByText("Tools").closest("button")!; + expect(toggle).toHaveAttribute("aria-expanded", "false"); + + act(() => { + fireEvent.click(toggle); + }); + await waitFor(() => { + expect(toggle).toHaveAttribute("aria-expanded", "true"); + }); + }); + it("keeps Router Settings as a single Settings child", () => { // Router Settings is admin-only, so getAvailablePages() filters it out entirely and the // page_utils duplicate-key guard cannot see it. Walk menuGroups directly, otherwise a diff --git a/ui/litellm-dashboard/src/components/leftnav.tsx b/ui/litellm-dashboard/src/components/leftnav.tsx index 42389facac2..51ba36348e1 100644 --- a/ui/litellm-dashboard/src/components/leftnav.tsx +++ b/ui/litellm-dashboard/src/components/leftnav.tsx @@ -570,6 +570,7 @@ const Sidebar_: React.FC = ({ toggleGroup(item.key)} title={collapsed ? labelText(item) : undefined} > diff --git a/ui/litellm-dashboard/src/components/molecules/cost_optimization_feedback_banner.test.tsx b/ui/litellm-dashboard/src/components/molecules/cost_optimization_feedback_banner.test.tsx index bad92555bd5..3fc1d41dfdd 100644 --- a/ui/litellm-dashboard/src/components/molecules/cost_optimization_feedback_banner.test.tsx +++ b/ui/litellm-dashboard/src/components/molecules/cost_optimization_feedback_banner.test.tsx @@ -1,4 +1,4 @@ -import { fireEvent, render } from "@testing-library/react"; +import { fireEvent, render, screen } from "@testing-library/react"; import { beforeEach, describe, expect, it } from "vitest"; import CostOptimizationFeedbackBanner from "./cost_optimization_feedback_banner"; @@ -10,24 +10,24 @@ describe("CostOptimizationFeedbackBanner", () => { }); it("renders with a link to the feedback discussion", () => { - const { getByText } = render(); - const link = getByText("Share Feedback").closest("a"); + render(); + const link = screen.getByText("Share Feedback").closest("a"); expect(link).toHaveAttribute("href", "https://github.com/BerriAI/litellm/discussions/32172"); }); it("hides itself and persists the dismissal when the dismiss button is clicked", () => { - const { getByText, queryByText, getByLabelText } = render(); - expect(getByText("Help shape cost optimization")).toBeInTheDocument(); + render(); + expect(screen.getByText("Help shape cost optimization")).toBeInTheDocument(); - fireEvent.click(getByLabelText("Dismiss banner")); + fireEvent.click(screen.getByLabelText("Dismiss banner")); - expect(queryByText("Help shape cost optimization")).not.toBeInTheDocument(); + expect(screen.queryByText("Help shape cost optimization")).not.toBeInTheDocument(); expect(localStorage.getItem(STORAGE_KEY)).toBe("true"); }); it("stays dismissed on remount once persisted", () => { localStorage.setItem(STORAGE_KEY, "true"); - const { queryByText } = render(); - expect(queryByText("Help shape cost optimization")).not.toBeInTheDocument(); + render(); + expect(screen.queryByText("Help shape cost optimization")).not.toBeInTheDocument(); }); }); diff --git a/ui/litellm-dashboard/src/components/organization/organization_view.test.tsx b/ui/litellm-dashboard/src/components/organization/organization_view.test.tsx index b8d8e3ba9c8..700d19eb13c 100644 --- a/ui/litellm-dashboard/src/components/organization/organization_view.test.tsx +++ b/ui/litellm-dashboard/src/components/organization/organization_view.test.tsx @@ -108,7 +108,7 @@ beforeEach(() => { test("renders organization view after loading data", async () => { mockUseOrganization.mockReturnValue({ data: mockOrg, isLoading: false } as any); - const { findAllByText } = renderWithProviders( + renderWithProviders( {}} @@ -120,7 +120,7 @@ test("renders organization view after loading data", async () => { />, ); - const [orgName] = await findAllByText("Acme Corp"); + const [orgName] = await screen.findAllByText("Acme Corp"); expect(orgName).toBeInTheDocument(); }); diff --git a/ui/litellm-dashboard/src/components/provider_info_helpers.tsx b/ui/litellm-dashboard/src/components/provider_info_helpers.tsx index c97bb8ede3d..d01a6a34cbe 100644 --- a/ui/litellm-dashboard/src/components/provider_info_helpers.tsx +++ b/ui/litellm-dashboard/src/components/provider_info_helpers.tsx @@ -20,6 +20,7 @@ import falAiLogo from "../../public/assets/logos/fal_ai.jpg"; import featherlessLogo from "../../public/assets/logos/featherless.svg"; import fireworksLogo from "../../public/assets/logos/fireworks.svg"; import friendliLogo from "../../public/assets/logos/friendli.svg"; +import gigachatLogo from "../../public/assets/logos/gigachat.svg"; import githubCopilotLogo from "../../public/assets/logos/github_copilot.svg"; import googleLogo from "../../public/assets/logos/google.svg"; import groqLogo from "../../public/assets/logos/groq.svg"; @@ -107,6 +108,7 @@ export enum Providers { FireworksAI = "Fireworks AI", FRIENDLIAI = "Friendliai", GALADRIEL = "Galadriel", + GIGACHAT = "GigaChat", GITHUB_COPILOT = "Github Copilot", Google_AI_Studio = "Google AI Studio", GradientAI = "GradientAI", @@ -148,6 +150,8 @@ export enum Providers { PETALS = "Petals", PG_VECTOR = "Pg Vector", PREDIBASE = "Predibase", + Qwen_AI_Platform = "Qwen AI Platform", + QwenCloud = "QwenCloud", RECRAFT = "Recraft", REPLICATE = "Replicate", RunwayML = "RunwayML", @@ -218,6 +222,7 @@ export const provider_map: Record = { FireworksAI: "fireworks_ai", FRIENDLIAI: "friendliai", GALADRIEL: "galadriel", + GIGACHAT: "gigachat", GITHUB_COPILOT: "github_copilot", Google_AI_Studio: "gemini", GradientAI: "gradient_ai", @@ -259,6 +264,8 @@ export const provider_map: Record = { PETALS: "petals", PG_VECTOR: "pg_vector", PREDIBASE: "predibase", + Qwen_AI_Platform: "qwen_ai_platform", + QwenCloud: "qwencloud", RECRAFT: "recraft", REPLICATE: "replicate", RunwayML: "runwayml", @@ -323,6 +330,7 @@ export const providerLogoMap: Partial> = { [Providers.FEATHERLESS_AI]: featherlessLogo.src, [Providers.FireworksAI]: fireworksLogo.src, [Providers.FRIENDLIAI]: friendliLogo.src, + [Providers.GIGACHAT]: gigachatLogo.src, [Providers.GITHUB_COPILOT]: githubCopilotLogo.src, [Providers.Google_AI_Studio]: googleLogo.src, [Providers.Groq]: groqLogo.src, @@ -353,6 +361,8 @@ export const providerLogoMap: Partial> = { [Providers.Openrouter]: openrouterLogo.src, [Providers.Oracle]: oracleLogo.src, [Providers.Perplexity]: perplexityAiLogo.src, + [Providers.Qwen_AI_Platform]: qwenLogo.src, + [Providers.QwenCloud]: qwenLogo.src, [Providers.RECRAFT]: recraftLogo.src, [Providers.REPLICATE]: replicateLogo.src, [Providers.RunwayML]: runwayLogo.src, diff --git a/ui/litellm-dashboard/src/components/settings.test.tsx b/ui/litellm-dashboard/src/components/settings.test.tsx index b97fef32402..762b23f413e 100644 --- a/ui/litellm-dashboard/src/components/settings.test.tsx +++ b/ui/litellm-dashboard/src/components/settings.test.tsx @@ -77,21 +77,21 @@ describe("Settings", () => { }); it("should render the logging callbacks tab when access token is provided", async () => { - const { getByText } = render(); + render(); await waitFor(() => { - expect(getByText("Active Logging Callbacks")).toBeInTheDocument(); + expect(screen.getByText("Active Logging Callbacks")).toBeInTheDocument(); }); }); it("should display additional settings tabs", async () => { - const { getByText } = render(); + render(); await waitFor(() => { - expect(getByText("CloudZero Cost Tracking")).toBeInTheDocument(); - expect(getByText("Alerting Types")).toBeInTheDocument(); - expect(getByText("Alerting Settings")).toBeInTheDocument(); - expect(getByText("Email Alerts")).toBeInTheDocument(); + expect(screen.getByText("CloudZero Cost Tracking")).toBeInTheDocument(); + expect(screen.getByText("Alerting Types")).toBeInTheDocument(); + expect(screen.getByText("Alerting Settings")).toBeInTheDocument(); + expect(screen.getByText("Email Alerts")).toBeInTheDocument(); }); }); @@ -279,13 +279,13 @@ describe("Settings", () => { }); it("should display CloudZero Cost Tracking tab", async () => { - const { getByText } = render(); + render(); await waitFor(() => { - expect(getByText("Active Logging Callbacks")).toBeInTheDocument(); + expect(screen.getByText("Active Logging Callbacks")).toBeInTheDocument(); }); - expect(getByText("CloudZero Cost Tracking")).toBeInTheDocument(); + expect(screen.getByText("CloudZero Cost Tracking")).toBeInTheDocument(); }); }); diff --git a/ui/litellm-dashboard/src/components/shared/DataTable/DataTable.test.tsx b/ui/litellm-dashboard/src/components/shared/DataTable/DataTable.test.tsx index 58a0cd94997..7ead9bb64d4 100644 --- a/ui/litellm-dashboard/src/components/shared/DataTable/DataTable.test.tsx +++ b/ui/litellm-dashboard/src/components/shared/DataTable/DataTable.test.tsx @@ -1,5 +1,5 @@ import type { ColumnDef, ExpandedState } from "@tanstack/react-table"; -import { render, screen, waitFor } from "@testing-library/react"; +import { render, screen, waitFor, within } from "@testing-library/react"; import userEvent from "@testing-library/user-event"; import { useState } from "react"; import { describe, expect, it, vi } from "vitest"; @@ -21,6 +21,12 @@ function person(id: string, name: string, flagged = false): Person { const names = (): (string | null)[] => screen.getAllByTestId("name-cell").map((el) => el.textContent); +const heightClassesOf = (el: HTMLElement | undefined): string[] => + (el?.className ?? "") + .split(/\s+/) + .filter((cls) => cls.startsWith("h-")) + .sort(); + const nameCellColumns: ColumnDef[] = [ { accessorKey: "name", @@ -229,12 +235,10 @@ describe("DataTable sorting", () => { describe("DataTable layout", () => { it("stretches the table to fill the container when resizing is on, so hidden columns leave no right-side gap", () => { - const { container } = render(); + render(); - const table = container.querySelector("table"); - expect(table).not.toBeNull(); // width pins the natural column total (horizontal scroll on overflow); minWidth:100% fills the gap on underflow. - expect(table?.style.minWidth).toBe("100%"); + expect(screen.getByRole("table")).toHaveStyle({ minWidth: "100%" }); }); }); @@ -354,17 +358,21 @@ describe("DataTable loading", () => { const { rerender } = render( , ); - const skeletonRow = screen.getAllByTestId("skeleton-row").at(0); - const loadedRowHeight = "h-8"; - expect(skeletonRow?.className).toContain(loadedRowHeight); + const skeletonHeight = heightClassesOf(screen.getAllByRole("row").at(-1)); rerender(); - expect(document.querySelector("[data-row-id]")?.className).toContain(loadedRowHeight); + const loadedHeight = heightClassesOf(screen.getByRole("row", { name: /Charlie/ })); + + expect(loadedHeight).not.toEqual([]); + expect(skeletonHeight).toEqual(loadedHeight); }); it("does not force the compact height on default-size skeleton rows", () => { - render(); - expect(screen.getAllByTestId("skeleton-row").at(0)?.className).not.toContain("h-8"); + const { rerender } = render(); + const skeletonHeight = heightClassesOf(screen.getAllByRole("row").at(-1)); + + rerender(); + expect(heightClassesOf(screen.getAllByRole("row").at(-1))).not.toEqual(skeletonHeight); }); it("varies skeleton shape and width per column instead of one fixed bar", () => { @@ -420,7 +428,7 @@ describe("DataTable loading", () => { describe("DataTable column visibility", () => { it("hides a column when toggled off in the view-options menu", async () => { const user = userEvent.setup(); - const { container } = render( + render( { />, ); - expect(container.querySelector('th[data-header-id="email"]')).not.toBeNull(); + expect(screen.getByRole("columnheader", { name: "Email" })).toBeInTheDocument(); await user.click(screen.getByTestId("view-options-trigger")); await user.click(await screen.findByTestId("view-option-email")); - await waitFor(() => expect(container.querySelector('th[data-header-id="email"]')).toBeNull()); + await waitFor(() => expect(screen.queryByRole("columnheader", { name: "Email" })).not.toBeInTheDocument()); await user.click(screen.getByTestId("view-option-email")); - await waitFor(() => expect(container.querySelector('th[data-header-id="email"]')).not.toBeNull()); + expect(await screen.findByRole("columnheader", { name: "Email" })).toBeInTheDocument(); }); it("omits columns that opt out of hiding from the menu", async () => { @@ -468,14 +476,10 @@ describe("DataTable column visibility", () => { describe("DataTable pinned columns", () => { it("applies sticky positioning to a pinned column only", () => { - const { container } = render(); + render(); - const pinnedHead = container.querySelector('th[data-header-id="name"]'); - const normalHead = container.querySelector('th[data-header-id="email"]'); - - expect(pinnedHead?.style.position).toBe("sticky"); - expect(pinnedHead?.style.left).toBe("0px"); - expect(normalHead?.style.position).toBe(""); + expect(screen.getByRole("columnheader", { name: "Name" })).toHaveStyle({ position: "sticky", left: "0px" }); + expect(screen.getByRole("columnheader", { name: "Email" })).not.toHaveStyle({ position: "sticky" }); }); }); @@ -570,7 +574,7 @@ describe("DataTable expansion", () => { describe("DataTable row styling and footer", () => { it("applies rowClassName to the matching row only", () => { const data = [person("a", "Alice", true), person("b", "Bob", false)]; - const { container } = render( + render( { />, ); - expect(container.querySelector('tr[data-row-id="a"]')?.className).toContain("flagged-row"); - expect(container.querySelector('tr[data-row-id="b"]')?.className).not.toContain("flagged-row"); + expect(screen.getByRole("row", { name: /Alice/ })).toHaveClass("flagged-row"); + expect(screen.getByRole("row", { name: /Bob/ })).not.toHaveClass("flagged-row"); }); it("renders the footer slot inside a tfoot element", () => { @@ -596,63 +600,57 @@ describe("DataTable row styling and footer", () => { />, ); - expect(screen.getByTestId("footer-row").closest("tfoot")).not.toBeNull(); + const rowGroups = screen.getAllByRole("rowgroup"); + expect(within(rowGroups.at(-1) as HTMLElement).getByText("Total: 3")).toBeInTheDocument(); }); }); describe("DataTable layout", () => { it("exposes resize handles with stable selectors only when resizing is enabled", () => { - const { container, rerender } = render( - , - ); - expect(container.querySelectorAll("[data-resizer][data-header-id]").length).toBe(2); + const { rerender } = render(); + expect(screen.getByTestId("column-resizer-name")).toBeInTheDocument(); + expect(screen.getByTestId("column-resizer-email")).toBeInTheDocument(); rerender(); - expect(container.querySelectorAll("[data-resizer]").length).toBe(0); + expect(screen.queryByTestId("column-resizer-name")).not.toBeInTheDocument(); }); it("makes the header sticky and constrains body height when maxBodyHeight is set", () => { - const { container } = render(); - expect(container.querySelector("thead")?.className).toContain("sticky"); - const scroller = container.querySelector('[data-slot="table-container"]')?.parentElement as HTMLElement; - expect(scroller).toHaveStyle({ maxHeight: "240px" }); + render(); + expect(screen.getByTestId("data-table-head")).toHaveClass("sticky"); + expect(screen.getByTestId("data-table-scroller")).toHaveStyle({ maxHeight: "240px" }); }); it("caps fillHeight at the parent's height instead of stretching to it, so a short table stays short", () => { - const { container } = render(); - const scroller = container.querySelector('[data-slot="table-container"]')?.parentElement as HTMLElement; - const frame = scroller.parentElement as HTMLElement; - const outer = frame.parentElement as HTMLElement; + render(); + const outer = screen.getByTestId("data-table-root"); + const frame = screen.getByTestId("data-table-frame"); + const scroller = screen.getByTestId("data-table-scroller"); // A ceiling, not a stretch: flex-1 here would hold the footer at the bottom on a two-row table. - expect(outer.className).toContain("max-h-full"); - expect(outer.className).not.toContain("flex-1"); - expect(frame.className).not.toContain("flex-1"); - expect(scroller.className).not.toContain("flex-1"); + expect(outer).toHaveClass("max-h-full", "flex-col"); + expect(outer).not.toHaveClass("flex-1"); + expect(frame).toHaveClass("flex-col"); + expect(frame).not.toHaveClass("flex-1"); + expect(scroller).not.toHaveClass("flex-1"); - expect(outer.className).toContain("flex-col"); - expect(frame.className).toContain("flex-col"); - expect(scroller.className).toContain("min-h-0"); - expect(scroller.className).toContain("overflow-auto"); + expect(scroller).toHaveClass("min-h-0", "overflow-auto"); expect(scroller).toHaveStyle({ maxHeight: "" }); // Without this the Table primitive's own overflow container captures the sticky header. - expect(scroller.className).toContain("[&_[data-slot=table-container]]:overflow-visible"); + expect(scroller).toHaveClass("[&_[data-slot=table-container]]:overflow-visible"); - const thead = container.querySelector("thead") as HTMLElement; - expect(thead.className).toContain("sticky"); // Rows pass under the header, so the semi-transparent row tint alone would let them show through. - expect(thead.className).toContain("bg-background"); + expect(screen.getByTestId("data-table-head")).toHaveClass("sticky", "bg-background"); }); it("leaves the default layout untouched when neither height mode is set", () => { - const { container } = render(); - const scroller = container.querySelector('[data-slot="table-container"]')?.parentElement as HTMLElement; + render(); + const scroller = screen.getByTestId("data-table-scroller"); - expect(scroller.className).toContain("overflow-x-auto"); - expect(scroller.className).not.toContain("min-h-0"); + expect(scroller).toHaveClass("overflow-x-auto"); + expect(scroller).not.toHaveClass("min-h-0"); expect(scroller).toHaveStyle({ maxHeight: "" }); - expect((scroller.parentElement as HTMLElement).className).not.toContain("flex-col"); - expect(container.querySelector("thead")?.className).not.toContain("sticky"); - expect(container.querySelector("thead")?.className).not.toContain("bg-background"); + expect(screen.getByTestId("data-table-frame")).not.toHaveClass("flex-col"); + expect(screen.getByTestId("data-table-head")).not.toHaveClass("sticky", "bg-background"); }); }); diff --git a/ui/litellm-dashboard/src/components/shared/DataTable/DataTable.tsx b/ui/litellm-dashboard/src/components/shared/DataTable/DataTable.tsx index e8357b37715..60267606951 100644 --- a/ui/litellm-dashboard/src/components/shared/DataTable/DataTable.tsx +++ b/ui/litellm-dashboard/src/components/shared/DataTable/DataTable.tsx @@ -195,8 +195,7 @@ function DataTableHeadCell({ header, size, stickyHeader, enableColumnResi )} {canResize && (
column.resetSize()} @@ -589,15 +588,19 @@ export function DataTable(props: DataTableProps -
+
+
{toolbar !== undefined &&
{toolbar(table)}
}
- + {table.getHeaderGroups().map((headerGroup) => ( {headerGroup.headers.map((header) => ( diff --git a/ui/litellm-dashboard/src/components/shared/PaginationStatusAlerts.test.tsx b/ui/litellm-dashboard/src/components/shared/PaginationStatusAlerts.test.tsx index 3698b68155e..5bd48b1aa4c 100644 --- a/ui/litellm-dashboard/src/components/shared/PaginationStatusAlerts.test.tsx +++ b/ui/litellm-dashboard/src/components/shared/PaginationStatusAlerts.test.tsx @@ -1,4 +1,4 @@ -import { fireEvent, render } from "@testing-library/react"; +import { fireEvent, render, screen } from "@testing-library/react"; import { describe, expect, it, vi } from "vitest"; import PaginationStatusAlerts from "./PaginationStatusAlerts"; @@ -6,7 +6,7 @@ import PaginationStatusAlerts from "./PaginationStatusAlerts"; describe("PaginationStatusAlerts", () => { it("shows page progress and wires the Stop button while fetching", () => { const cancel = vi.fn(); - const { getByRole, getByText } = render( + render( { />, ); - expect(getByText(/Currently fetching spend data: fetched 7 \/ 42 pages/)).toBeInTheDocument(); - fireEvent.click(getByRole("button", { name: "Stop" })); + expect(screen.getByText(/Currently fetching spend data: fetched 7 \/ 42 pages/)).toBeInTheDocument(); + fireEvent.click(screen.getByRole("button", { name: "Stop" })); expect(cancel).toHaveBeenCalledTimes(1); }); it("shows the partial-data notice after a cancel, frozen at the last fetched page", () => { - const { getByText } = render( + render( { />, ); - expect(getByText("Showing partial spend data (7/42 pages loaded)")).toBeInTheDocument(); + expect(screen.getByText("Showing partial spend data (7/42 pages loaded)")).toBeInTheDocument(); }); it("names the subject it is fetching", () => { - const { getByText } = render( + render( { />, ); - expect(getByText(/Currently fetching agent data: fetched 1 \/ 3 pages/)).toBeInTheDocument(); + expect(screen.getByText(/Currently fetching agent data: fetched 1 \/ 3 pages/)).toBeInTheDocument(); }); it("renders nothing when idle", () => { diff --git a/ui/litellm-dashboard/src/components/shared/charts/area_chart.test.tsx b/ui/litellm-dashboard/src/components/shared/charts/area_chart.test.tsx index 6e4ab14be33..c81ade526dd 100644 --- a/ui/litellm-dashboard/src/components/shared/charts/area_chart.test.tsx +++ b/ui/litellm-dashboard/src/components/shared/charts/area_chart.test.tsx @@ -1,4 +1,4 @@ -import { render } from "@testing-library/react"; +import { render, screen } from "@testing-library/react"; import React from "react"; import { describe, expect, it } from "vitest"; import { AreaChart } from "./area_chart"; @@ -21,9 +21,9 @@ describe("AreaChart", () => { }); it("renders the No data placeholder instead of a chart when data is empty", () => { - const { container, getByText } = render(); + const { container } = render(); - expect(getByText("No data")).toBeInTheDocument(); + expect(screen.getByText("No data")).toBeInTheDocument(); expect(container.querySelector('[data-slot="chart"]')).toBeNull(); }); diff --git a/ui/litellm-dashboard/src/components/shared/charts/bar_chart.test.tsx b/ui/litellm-dashboard/src/components/shared/charts/bar_chart.test.tsx index a322eeb3ad0..3cd7bd2fd08 100644 --- a/ui/litellm-dashboard/src/components/shared/charts/bar_chart.test.tsx +++ b/ui/litellm-dashboard/src/components/shared/charts/bar_chart.test.tsx @@ -21,9 +21,9 @@ describe("BarChart", () => { }); it("renders the No data placeholder instead of a chart when data is empty", () => { - const { container, getByText } = render(); + const { container } = render(); - expect(getByText("No data")).toBeInTheDocument(); + expect(screen.getByText("No data")).toBeInTheDocument(); expect(container.querySelector('[data-slot="chart"]')).toBeNull(); }); diff --git a/ui/litellm-dashboard/src/components/templates/KeyInfoView.handleKeyUpdate.test.tsx b/ui/litellm-dashboard/src/components/templates/KeyInfoView.handleKeyUpdate.test.tsx index 6592ff8c357..705679fe2e6 100644 --- a/ui/litellm-dashboard/src/components/templates/KeyInfoView.handleKeyUpdate.test.tsx +++ b/ui/litellm-dashboard/src/components/templates/KeyInfoView.handleKeyUpdate.test.tsx @@ -445,7 +445,7 @@ describe("KeyInfoView handleKeyUpdate budget_duration", () => { ); fireEvent.click(screen.getByText("Settings")); - expect(screen.getByText("Budget Reset").parentElement?.textContent).toContain("Every 30d"); + expect(screen.getByTestId("budget-reset-value")).toHaveTextContent("Every 30d"); fireEvent.click(screen.getByText("Edit Settings")); (globalThis as any).__TEST_FORM_VALUES = { @@ -456,7 +456,7 @@ describe("KeyInfoView handleKeyUpdate budget_duration", () => { fireEvent.click(screen.getByText("Mock Submit")); await waitFor(() => { - expect(screen.getByText("Budget Reset").parentElement?.textContent).toBe("Budget ResetNever"); + expect(screen.getByTestId("budget-reset-value")).toHaveTextContent("Never"); }); }); }); diff --git a/ui/litellm-dashboard/src/components/templates/key_edit_view.test.tsx b/ui/litellm-dashboard/src/components/templates/key_edit_view.test.tsx index bf8f43b8b5f..97b09e00808 100644 --- a/ui/litellm-dashboard/src/components/templates/key_edit_view.test.tsx +++ b/ui/litellm-dashboard/src/components/templates/key_edit_view.test.tsx @@ -1,7 +1,7 @@ import { fireEvent, screen, waitFor } from "@testing-library/react"; import userEvent from "@testing-library/user-event"; import { beforeEach, describe, expect, it, vi } from "vitest"; -import { renderWithProviders } from "../../../tests/test-utils"; +import { chooseSelectOption, renderWithProviders } from "../../../tests/test-utils"; import { KeyResponse } from "../key_team_helpers/key_list"; import { MODEL_MAX_BUDGET_PREMIUM_HINT } from "../key_team_helpers/ModelMaxBudgetEditor"; import { @@ -300,7 +300,7 @@ describe("KeyEditView", () => { }); it("should render", async () => { - const { getByText } = renderWithProviders( + renderWithProviders( {}} @@ -313,12 +313,12 @@ describe("KeyEditView", () => { ); await waitFor(() => { - expect(getByText("Save Changes")).toBeInTheDocument(); + expect(screen.getByText("Save Changes")).toBeInTheDocument(); }); }); it("should render tags", async () => { - const { getByText } = renderWithProviders( + renderWithProviders( {}} @@ -331,12 +331,12 @@ describe("KeyEditView", () => { ); await waitFor(() => { - expect(getByText("test-tag")).toBeInTheDocument(); + expect(screen.getByText("test-tag")).toBeInTheDocument(); }); }); it("should not render tags in metadata textarea", async () => { - const { getByLabelText } = renderWithProviders( + renderWithProviders( {}} @@ -348,7 +348,7 @@ describe("KeyEditView", () => { />, ); - const metadataTextarea = getByLabelText("Metadata") as HTMLTextAreaElement; + const metadataTextarea = screen.getByLabelText("Metadata") as HTMLTextAreaElement; await waitFor(() => { expect(metadataTextarea).toHaveValue("{}"); }); @@ -963,10 +963,7 @@ describe("KeyEditView", () => { />, ); - await userEvent.click(await screen.findByLabelText("Reset Budget")); - - const weeklyOption = await screen.findByText("weekly"); - await userEvent.click(weeklyOption); + await chooseSelectOption(userEvent, await screen.findByLabelText("Reset Budget"), "weekly"); const submitButton = screen.getByRole("button", { name: /save changes/i }); await userEvent.click(submitButton); @@ -1042,8 +1039,7 @@ describe("KeyEditView", () => { ); const resetBudget = await screen.findByLabelText("Reset Budget"); - await userEvent.click(resetBudget); - await userEvent.click(await screen.findByText("Never resets")); + await chooseSelectOption(userEvent, resetBudget, "Never resets"); await waitFor(() => { expect(resetBudget).toHaveTextContent("Never resets"); @@ -1074,8 +1070,7 @@ describe("KeyEditView", () => { />, ); - await userEvent.click(await screen.findByLabelText("Reset Budget")); - await userEvent.click(await screen.findByText("Never resets")); + await chooseSelectOption(userEvent, await screen.findByLabelText("Reset Budget"), "Never resets"); await userEvent.click(screen.getByRole("button", { name: /save changes/i })); @@ -1946,8 +1941,7 @@ describe("KeyEditView", () => { await userEvent.clear(duration); await userEvent.type(duration, "45d"); - await userEvent.click(screen.getByLabelText(/TPM Rate Limit Type/)); - await userEvent.click(await screen.findByTitle("Guaranteed throughput")); + await chooseSelectOption(userEvent, screen.getByLabelText(/TPM Rate Limit Type/), /^Guaranteed throughput/); await userEvent.click(screen.getByRole("button", { name: /save changes/i })); @@ -2103,8 +2097,7 @@ describe("KeyEditView", () => { renderForPayload(onSubmitMock); await screen.findByRole("button", { name: /save changes/i }); - await userEvent.click(screen.getByLabelText(/RPM Rate Limit Type/)); - await userEvent.click(await screen.findByTitle("Guaranteed throughput")); + await chooseSelectOption(userEvent, screen.getByLabelText(/RPM Rate Limit Type/), /^Guaranteed throughput/); await userEvent.click(screen.getByRole("button", { name: /save changes/i })); diff --git a/ui/litellm-dashboard/src/components/templates/key_info_view.budget_display.test.tsx b/ui/litellm-dashboard/src/components/templates/key_info_view.budget_display.test.tsx index f506c0e51d7..a02f80f7800 100644 --- a/ui/litellm-dashboard/src/components/templates/key_info_view.budget_display.test.tsx +++ b/ui/litellm-dashboard/src/components/templates/key_info_view.budget_display.test.tsx @@ -381,6 +381,6 @@ describe("KeyInfoView budget reset visibility", () => { await waitFor(() => { expect(screen.getByText("Budget Reset")).toBeInTheDocument(); }); - expect(screen.getByText("Budget Reset").parentElement).toHaveTextContent("Never"); + expect(screen.getByTestId("budget-reset-value")).toHaveTextContent("Never"); }); }); diff --git a/ui/litellm-dashboard/src/components/templates/key_info_view.tsx b/ui/litellm-dashboard/src/components/templates/key_info_view.tsx index 0be80c3e173..0dd0dd6d6af 100644 --- a/ui/litellm-dashboard/src/components/templates/key_info_view.tsx +++ b/ui/litellm-dashboard/src/components/templates/key_info_view.tsx @@ -895,7 +895,7 @@ export default function KeyInfoView({

Budget Reset

-

+

{currentKeyData.budget_reset_at ? `${currentKeyData.budget_duration ? `Every ${currentKeyData.budget_duration}, next ` : ""}${formatTimestamp(currentKeyData.budget_reset_at)}` : "Never"} diff --git a/ui/litellm-dashboard/src/components/ui/field.test.tsx b/ui/litellm-dashboard/src/components/ui/field.test.tsx index 4bb3d0ec665..5765a637526 100644 --- a/ui/litellm-dashboard/src/components/ui/field.test.tsx +++ b/ui/litellm-dashboard/src/components/ui/field.test.tsx @@ -14,6 +14,7 @@ import { FieldSet, FieldTitle, } from "./field"; +import { ROW_LAYOUT_CLASSES, STRETCH_CHILDREN_CLASS } from "../../../tests/fieldOrientation"; describe("FieldError", () => { it("renders nothing when there are no errors and no children", () => { @@ -85,6 +86,20 @@ describe("Field", () => { expect(screen.getByRole("group")).toHaveAttribute("data-orientation", "horizontal"); }); + + it("stretches every child when vertical, which is what inputs, selects and textareas want", () => { + render(); + + expect(screen.getByRole("group")).toHaveClass("flex-col", STRETCH_CHILDREN_CLASS); + }); + + it("lays children in a row at their own width when horizontal, so a checkbox stays square", () => { + render(); + const field = screen.getByRole("group"); + + expect(field).toHaveClass(...ROW_LAYOUT_CLASSES); + expect(field).not.toHaveClass(STRETCH_CHILDREN_CLASS); + }); }); describe("field primitives forward refs to their DOM node", () => { diff --git a/ui/litellm-dashboard/src/components/view_logs/LogDetailsDrawer/RoutingDecisionCard.test.tsx b/ui/litellm-dashboard/src/components/view_logs/LogDetailsDrawer/RoutingDecisionCard.test.tsx index e084fdf37e6..bf6a20a4af8 100644 --- a/ui/litellm-dashboard/src/components/view_logs/LogDetailsDrawer/RoutingDecisionCard.test.tsx +++ b/ui/litellm-dashboard/src/components/view_logs/LogDetailsDrawer/RoutingDecisionCard.test.tsx @@ -186,6 +186,12 @@ describe("RoutingDecisionCard", () => { expect(screen.queryByText("housekeeping")).not.toBeInTheDocument(); }); + it("labels a modality escalation instead of showing the raw cause token", () => { + render(); + expect(screen.getByText("Escalated for image input")).toBeInTheDocument(); + expect(screen.queryByText("modality_escalation")).not.toBeInTheDocument(); + }); + it("shows the escalation keyword", () => { render( , diff --git a/ui/litellm-dashboard/src/components/view_logs/LogDetailsDrawer/RoutingDecisionCard.tsx b/ui/litellm-dashboard/src/components/view_logs/LogDetailsDrawer/RoutingDecisionCard.tsx index 8c77b2db630..aa1d45a859e 100644 --- a/ui/litellm-dashboard/src/components/view_logs/LogDetailsDrawer/RoutingDecisionCard.tsx +++ b/ui/litellm-dashboard/src/components/view_logs/LogDetailsDrawer/RoutingDecisionCard.tsx @@ -89,6 +89,8 @@ const CONSTANT_CAUSE_LABELS: Record = { semantic_keyword_match: "Semantic keyword match", session_affinity_pin: "Pinned to session", session_affinity_escalation: "Escalated from session pin", + user_turn_continuation: "Continuation turn, classifier skipped", + modality_escalation: "Escalated for image input", quality_tier: "Quality tier mapping", bandit: "Adaptive bandit", default_fallback: "Default model, no route matched", diff --git a/ui/litellm-dashboard/src/components/view_logs/LogDetailsDrawer/SectionHeader.test.tsx b/ui/litellm-dashboard/src/components/view_logs/LogDetailsDrawer/SectionHeader.test.tsx index 5aee6b33ec5..6cd9f476628 100644 --- a/ui/litellm-dashboard/src/components/view_logs/LogDetailsDrawer/SectionHeader.test.tsx +++ b/ui/litellm-dashboard/src/components/view_logs/LogDetailsDrawer/SectionHeader.test.tsx @@ -53,6 +53,24 @@ describe("SectionHeader", () => { expect(onToggleCollapse).toHaveBeenCalledTimes(1); }); + it("reports its collapsed state to assistive technology", () => { + const { rerender } = render( + , + ); + + expect(screen.getByRole("button", { name: /^Input/ })).toHaveAttribute("aria-expanded", "true"); + + rerender(); + + expect(screen.getByRole("button", { name: /^Input/ })).toHaveAttribute("aria-expanded", "false"); + }); + + it("names each copy button for the section it belongs to", () => { + render(); + + expect(screen.getByRole("button", { name: "Copy output" })).toBeInTheDocument(); + }); + it("stays inert when no toggle handler is given", async () => { const onCopy = vi.fn(); render(); diff --git a/ui/litellm-dashboard/src/components/view_logs/LogDetailsDrawer/SectionHeader.tsx b/ui/litellm-dashboard/src/components/view_logs/LogDetailsDrawer/SectionHeader.tsx index 93e9953b2ee..6a18aaf8642 100644 --- a/ui/litellm-dashboard/src/components/view_logs/LogDetailsDrawer/SectionHeader.tsx +++ b/ui/litellm-dashboard/src/components/view_logs/LogDetailsDrawer/SectionHeader.tsx @@ -17,6 +17,8 @@ interface SectionHeaderProps { turnCount?: number; } +const SUMMARY_CLASSES = "flex flex-1 items-center gap-4"; + export function SectionHeader({ type, tokens, @@ -26,42 +28,53 @@ export function SectionHeader({ onToggleCollapse, turnCount, }: SectionHeaderProps) { + const summary = ( + <> + {onToggleCollapse && + (isCollapsed ? ( + + ) : ( + + ))} + +

+ {type === "input" ? ( + + ) : ( + ✨ + )} + {type === "input" ? "Input" : "Output"} +
+ + {tokens !== undefined && Tokens: {tokens.toLocaleString()}} + + {cost !== undefined && Cost: ${cost.toFixed(6)}} + + {turnCount !== undefined && turnCount > 0 && ( + Turns: {turnCount} + )} + + ); + return (
-
- {onToggleCollapse && - (isCollapsed ? ( - - ) : ( - - ))} - -
- {type === "input" ? ( - - ) : ( - ✨ - )} - {type === "input" ? "Input" : "Output"} -
- - {tokens !== undefined && ( - Tokens: {tokens.toLocaleString()} - )} - - {cost !== undefined && Cost: ${cost.toFixed(6)}} - - {turnCount !== undefined && turnCount > 0 && ( - Turns: {turnCount} - )} -
+ {onToggleCollapse ? ( + + ) : ( +
{summary}
+ )} { e.stopPropagation(); onCopy(); diff --git a/ui/litellm-dashboard/src/lib/autorouter_presets.test.ts b/ui/litellm-dashboard/src/lib/autorouter_presets.test.ts index 2de1ac11db2..2a8306473b8 100644 --- a/ui/litellm-dashboard/src/lib/autorouter_presets.test.ts +++ b/ui/litellm-dashboard/src/lib/autorouter_presets.test.ts @@ -129,6 +129,18 @@ describe("autorouter_presets", () => { } }); + it("carries a preset's modality_routing into the prefilled form state", () => { + const preset = getPresetByKey("anthropic_family")!; + const withFlag = { ...preset.complexity_router_config, modality_routing: true }; + const prefill = buildPresetPrefill(withFlag, groupsOnly(getRequiredModelsInPreset(preset))); + expect(prefill.complexityRouterConfig.modality_routing).toBe(true); + const withoutFlag = buildPresetPrefill( + preset.complexity_router_config, + groupsOnly(getRequiredModelsInPreset(preset)), + ); + expect(withoutFlag.complexityRouterConfig.modality_routing).toBe(false); + }); + it("prefills the anthropic preset's effort through to tier_model_params", () => { const preset = getPresetByKey("anthropic_family")!; const prefill = buildPresetPrefill(preset.complexity_router_config, groupsOnly(getRequiredModelsInPreset(preset))); @@ -230,6 +242,7 @@ describe("autorouter_presets", () => { const config = { tiers: { SIMPLE: [presetModel], MEDIUM: [], COMPLEX: [], REASONING: [] }, classifier_type: "heuristic" as const, + classification_mode: "every_request" as const, session_affinity: false, deployment_affinity: true, }; @@ -273,6 +286,7 @@ describe("autorouter_presets", () => { const config = { tiers: { SIMPLE: ["claude-opus-5"], MEDIUM: [], COMPLEX: [], REASONING: [] }, classifier_type: "heuristic" as const, + classification_mode: "every_request" as const, session_affinity: false, deployment_affinity: true, }; @@ -287,6 +301,7 @@ describe("autorouter_presets", () => { const config = { tiers: { SIMPLE: ["claude-opus-5"], MEDIUM: [], COMPLEX: [], REASONING: [] }, classifier_type: "heuristic" as const, + classification_mode: "every_request" as const, session_affinity: false, deployment_affinity: true, }; @@ -320,6 +335,7 @@ describe("autorouter_presets", () => { const simpleTierConfig = (presetModel: string) => ({ tiers: { SIMPLE: [presetModel], MEDIUM: [], COMPLEX: [], REASONING: [] }, classifier_type: "heuristic" as const, + classification_mode: "every_request" as const, session_affinity: false, deployment_affinity: true, }); @@ -563,6 +579,7 @@ describe("autorouter_presets", () => { const config = { tiers: { SIMPLE: ["gpt-5-nano"], MEDIUM: [], COMPLEX: [], REASONING: [] }, classifier_type: "heuristic" as const, + classification_mode: "every_request" as const, session_affinity: false, deployment_affinity: true, match_threshold: 0, @@ -573,11 +590,46 @@ describe("autorouter_presets", () => { expect(prefill.escalationKeywords).toEqual([]); }); + it("carries a preset's context-window escalation opt-out and buffer through the prefill", () => { + const prefill = buildPresetPrefill( + { + tiers: { SIMPLE: ["gpt-5-nano"], MEDIUM: [], COMPLEX: [], REASONING: [] }, + classifier_type: "heuristic", + classification_mode: "every_request", + session_affinity: false, + deployment_affinity: true, + enable_context_window_escalation: false, + context_window_escalation_buffer: 0.9, + }, + groupsOnly(["gpt-5-nano"]), + ); + expect(prefill.complexityRouterConfig.enable_context_window_escalation).toBe(false); + expect(prefill.complexityRouterConfig.context_window_escalation_buffer).toBe(0.9); + }); + + it("carries a preset's classification_mode and defaults it when the preset omits one", () => { + const tiers = { SIMPLE: ["gpt-5-nano"], MEDIUM: [], COMPLEX: [], REASONING: [] }; + const base = { + tiers, + classifier_type: "heuristic" as const, + classification_mode: "every_request" as const, + session_affinity: false, + deployment_affinity: true, + }; + const availability = groupsOnly(["gpt-5-nano"]); + expect( + buildPresetPrefill({ ...base, classification_mode: "user_turn" }, availability).complexityRouterConfig + .classification_mode, + ).toBe("user_turn"); + expect(buildPresetPrefill(base, availability).complexityRouterConfig.classification_mode).toBe("every_request"); + }); + it("falls back to the defaults when a preset omits match_threshold and escalation_keywords", () => { const prefill = buildPresetPrefill( { tiers: { SIMPLE: ["gpt-5-nano"], MEDIUM: [], COMPLEX: [], REASONING: [] }, classifier_type: "heuristic", + classification_mode: "every_request" as const, session_affinity: false, deployment_affinity: true, }, @@ -594,6 +646,7 @@ describe("autorouter_presets", () => { const base = { tiers: { SIMPLE: ["gpt-5-nano"], MEDIUM: [], COMPLEX: [], REASONING: [] }, classifier_type: "heuristic" as const, + classification_mode: "every_request" as const, session_affinity: false, deployment_affinity: true, }; @@ -609,6 +662,7 @@ describe("autorouter_presets", () => { const config = { tiers: { SIMPLE: ["claude-sonnet-4-5"], MEDIUM: [], COMPLEX: [], REASONING: [] }, classifier_type: "heuristic" as const, + classification_mode: "every_request" as const, session_affinity: false, deployment_affinity: true, }; @@ -623,6 +677,7 @@ describe("autorouter_presets", () => { REASONING: [{ model_name: "o3", litellm_params: { reasoning_effort: "high" } }], }, classifier_type: "heuristic" as const, + classification_mode: "every_request" as const, session_affinity: false, deployment_affinity: true, }; @@ -642,6 +697,7 @@ describe("autorouter_presets", () => { REASONING: [{ model_name: "claude-sonnet-4-5", litellm_params: { reasoning_effort: "high" } }], }, classifier_type: "heuristic" as const, + classification_mode: "every_request" as const, session_affinity: false, deployment_affinity: true, }; @@ -664,6 +720,9 @@ describe("autorouter_presets", () => { ], }, classifier_type: "heuristic" as const, + classification_mode: "every_request" as const, + session_affinity: false, + deployment_affinity: true, }; const prefill = buildPresetPrefill(config, groupsOnly(["claude-sonnet-4.5"])); // temperature survives from the spelling that would otherwise have been overwritten; @@ -677,6 +736,7 @@ describe("autorouter_presets", () => { const config = { tiers: { SIMPLE: ["gpt-5-nano"], MEDIUM: [], COMPLEX: [], REASONING: [] }, classifier_type: "heuristic" as const, + classification_mode: "every_request" as const, session_affinity: false, deployment_affinity: true, }; diff --git a/ui/litellm-dashboard/src/lib/autorouter_presets.ts b/ui/litellm-dashboard/src/lib/autorouter_presets.ts index a35b868db5e..721bd6f2b2a 100644 --- a/ui/litellm-dashboard/src/lib/autorouter_presets.ts +++ b/ui/litellm-dashboard/src/lib/autorouter_presets.ts @@ -6,6 +6,7 @@ import { ComplexityRouterConfigValue, ClassifierType, ClassifierLLMConfig, + DEFAULT_CLASSIFICATION_MODE, DEFAULT_SESSION_AFFINITY, DEFAULT_DEPLOYMENT_AFFINITY, usesLlmClassifier, @@ -288,13 +289,17 @@ export const buildPresetPrefill = ( classifier_context_budget_chars: config.classifier_context_budget_chars, classifier_context_per_turn_chars: config.classifier_context_per_turn_chars, classifier_context_include_assistant_turns: config.classifier_context_include_assistant_turns, + classification_mode: config.classification_mode ?? DEFAULT_CLASSIFICATION_MODE, session_affinity: config.session_affinity ?? DEFAULT_SESSION_AFFINITY, deployment_affinity: config.deployment_affinity ?? DEFAULT_DEPLOYMENT_AFFINITY, + modality_routing: config.modality_routing ?? false, adaptive: config.adaptive, adaptive_weights: config.adaptive_weights, tier_distance_penalty: config.tier_distance_penalty, adaptive_eligible: config.adaptive_eligible, return_raw_model_name: config.return_raw_model_name, + enable_context_window_escalation: config.enable_context_window_escalation, + context_window_escalation_buffer: config.context_window_escalation_buffer, }, customTechnicalKeywords: config.custom_technical_keywords ?? [], keywordTierRules: hydrateKeywordTierRules(config.keyword_tier_rules ?? []), diff --git a/ui/litellm-dashboard/src/lib/http/schema.d.ts b/ui/litellm-dashboard/src/lib/http/schema.d.ts index 1ed5366dac1..e944062e15e 100644 --- a/ui/litellm-dashboard/src/lib/http/schema.d.ts +++ b/ui/litellm-dashboard/src/lib/http/schema.d.ts @@ -1234,8 +1234,8 @@ export interface paths { }; /** * List Shadow Eval Jobs - * @description List shadow eval jobs, newest first, each key with its attempt count so status is - * accurate. Judged counts, spend, and results ride the detail endpoint only. + * @description List shadow eval jobs, newest first, each target with its attempt count so status + * is accurate. Judged counts, spend, and results ride the detail endpoint only. */ get: operations["list_shadow_eval_jobs_auto_router_shadow_eval_get"]; put?: never; @@ -1257,22 +1257,29 @@ export interface paths { put?: never; /** * Start Shadow Eval - * @description Start a shadow eval: duplicate a sampled slice of one or more keys' live traffic against - * a second arm, judge the two responses blind, and stratify win rates by tier, by the model - * that served the real arm, and by key. + * @description Start a shadow eval: duplicate a sampled slice of one or more targets' live traffic + * against a second arm, judge the two responses blind, and stratify win rates by tier, + * by the model that served the real arm, and by target. * - * A forward job answers whether the keys should adopt router_name: it samples the requests - * the router did not serve and duplicates them through it. A reverse job answers whether a - * key already on the router still gains from it: it samples the requests the router did - * serve and duplicates them against baseline_model. A key can hold one active job per - * direction, so both questions can run at once. + * A target is a virtual key, a team, or a user. Team and user targets match on the + * identity every request resolves to at auth time, so they cover JWT-authenticated + * traffic, which presents no virtual key; a user target samples that user's traffic + * across all their teams, whether it arrives on a JWT or a key they own. * - * Shadow responses are never served to users. Each key samples until its recorded eval - * spend, the shadow and judge calls' own cost, reaches max_budget dollars, the job's - * window ends, or the job is stopped, so one key running out of budget does not end - * sampling for the others; sampling changes propagate to pods within about 10 seconds. - * Shadow and judge calls bill to the shadowed key but are excluded from request counts - * and auto-router adoption metrics. + * A forward job answers whether the targets should adopt router_name: it samples the + * requests the router did not serve and duplicates them through it. A reverse job + * answers whether a target already on the router still gains from it: it samples the + * requests the router did serve and duplicates them against baseline_model. A target + * can hold one active job per direction, so both questions can run at once, and a + * request matching several jobs' targets (say its key and its team) is sampled by + * each, separately budgeted. + * + * Shadow responses are never served to users. Each target samples until its recorded + * eval spend, the shadow and judge calls' own cost, reaches max_budget dollars, the + * job's window ends, or the job is stopped, so one target running out of budget does + * not end sampling for the others; sampling changes propagate to pods within about 10 + * seconds. Shadow and judge calls bill to the sampled request's own identity but are + * excluded from request counts and auto-router adoption metrics. */ post: operations["start_shadow_eval_auto_router_shadow_eval_start_post"]; delete?: never; @@ -1312,8 +1319,8 @@ export interface paths { put?: never; /** * Stop Shadow Eval Job - * @description Stop an active shadow eval job, every key it scopes at once. Attempts are kept; - * sampling halts within ~10s. Keys that already stopped on their own budget keep the + * @description Stop an active shadow eval job, every target it scopes at once. Attempts are kept; + * sampling halts within ~10s. Targets that already stopped on their own budget keep the * stopped_at they earned. The statement is the whole state machine: it claims the job * only while a leg still samples inside the window with no stop recorded, so a racing * operator, a same-instant budget spend, and a repeat stop all read the same 400 with @@ -5258,6 +5265,42 @@ export interface paths { patch?: never; trace?: never; }; + "/gigachat/{endpoint}": { + parameters: { + query?: never; + header?: never; + path?: never; + cookie?: never; + }; + /** + * Gigachat Proxy Route + * @description [Docs](https://docs.litellm.ai/docs/pass_through/gigachat) + */ + get: operations["gigachat_proxy_route_gigachat__endpoint__get"]; + /** + * Gigachat Proxy Route + * @description [Docs](https://docs.litellm.ai/docs/pass_through/gigachat) + */ + put: operations["gigachat_proxy_route_gigachat__endpoint__put"]; + /** + * Gigachat Proxy Route + * @description [Docs](https://docs.litellm.ai/docs/pass_through/gigachat) + */ + post: operations["gigachat_proxy_route_gigachat__endpoint__post"]; + /** + * Gigachat Proxy Route + * @description [Docs](https://docs.litellm.ai/docs/pass_through/gigachat) + */ + delete: operations["gigachat_proxy_route_gigachat__endpoint__delete"]; + options?: never; + head?: never; + /** + * Gigachat Proxy Route + * @description [Docs](https://docs.litellm.ai/docs/pass_through/gigachat) + */ + patch: operations["gigachat_proxy_route_gigachat__endpoint__patch"]; + trace?: never; + }; "/global/activity": { parameters: { query?: never; @@ -7667,6 +7710,10 @@ export interface paths { * - model_max_budget: dict - Per-model budgets, e.g. {"gpt-4": {"budget_limit": 0.0005, "time_period": "30d"}} * - model_max_budget_usage: dict | None - Current-window spend per model, present only when * the key has per-model budgets + * - budget_limits: list | None - Concurrent budget windows, exactly as stored + * - budget_limits_usage: dict | None - Current-window spend per budget window, e.g. + * {"1h": {"current_spend": 0.0009}}, present only when the key has budget windows + * (read from the same cross-pod spend counter the budget enforcement uses) * - models: list - Model_name's the key is allowed to call * - tpm_limit / rpm_limit: int | None - Tokens and requests per minute limits * - metadata: dict - Metadata for the key, e.g. {"team": "core-infra"} @@ -9700,6 +9747,37 @@ export interface paths { patch?: never; trace?: never; }; + "/openai/v1/responses/input_tokens": { + parameters: { + query?: never; + header?: never; + path?: never; + cookie?: never; + }; + get?: never; + put?: never; + /** + * Responses Input Tokens + * @description Count the input tokens of a Responses API request without calling the model. + * + * Follows the OpenAI Responses API spec: https://platform.openai.com/docs/api-reference/responses/input-tokens + * + * ```bash + * curl -X POST http://localhost:4000/v1/responses/input_tokens -H "Content-Type: application/json" -H "Authorization: Bearer sk-1234" -d '{ + * "model": "gpt-4o", + * "input": "Hello, how are you?" + * }' + * ``` + * + * Returns: `{"object": "response.input_tokens", "input_tokens": }` + */ + post: operations["responses_input_tokens_openai_v1_responses_input_tokens_post"]; + delete?: never; + options?: never; + head?: never; + patch?: never; + trace?: never; + }; "/openai/v1/responses/{response_id}": { parameters: { query?: never; @@ -12619,6 +12697,37 @@ export interface paths { patch?: never; trace?: never; }; + "/responses/input_tokens": { + parameters: { + query?: never; + header?: never; + path?: never; + cookie?: never; + }; + get?: never; + put?: never; + /** + * Responses Input Tokens + * @description Count the input tokens of a Responses API request without calling the model. + * + * Follows the OpenAI Responses API spec: https://platform.openai.com/docs/api-reference/responses/input-tokens + * + * ```bash + * curl -X POST http://localhost:4000/v1/responses/input_tokens -H "Content-Type: application/json" -H "Authorization: Bearer sk-1234" -d '{ + * "model": "gpt-4o", + * "input": "Hello, how are you?" + * }' + * ``` + * + * Returns: `{"object": "response.input_tokens", "input_tokens": }` + */ + post: operations["responses_input_tokens_responses_input_tokens_post"]; + delete?: never; + options?: never; + head?: never; + patch?: never; + trace?: never; + }; "/responses/{response_id}": { parameters: { query?: never; @@ -19194,6 +19303,37 @@ export interface paths { patch?: never; trace?: never; }; + "/v1/responses/input_tokens": { + parameters: { + query?: never; + header?: never; + path?: never; + cookie?: never; + }; + get?: never; + put?: never; + /** + * Responses Input Tokens + * @description Count the input tokens of a Responses API request without calling the model. + * + * Follows the OpenAI Responses API spec: https://platform.openai.com/docs/api-reference/responses/input-tokens + * + * ```bash + * curl -X POST http://localhost:4000/v1/responses/input_tokens -H "Content-Type: application/json" -H "Authorization: Bearer sk-1234" -d '{ + * "model": "gpt-4o", + * "input": "Hello, how are you?" + * }' + * ``` + * + * Returns: `{"object": "response.input_tokens", "input_tokens": }` + */ + post: operations["responses_input_tokens_v1_responses_input_tokens_post"]; + delete?: never; + options?: never; + head?: never; + patch?: never; + trace?: never; + }; "/v1/responses/{response_id}": { parameters: { query?: never; @@ -34189,6 +34329,13 @@ export interface components { adaptive_eligible: "all" | "classified_tier"; /** @description Quality vs cost weights for adaptive selection (used when adaptive=True) */ adaptive_weights?: components["schemas"]["AdaptiveRouterWeights"]; + /** + * Classification Mode + * @description When to run the complexity classifier. 'every_request' (the default) classifies every inference request, including the tool-result continuation turns of an agentic loop. 'user_turn' classifies only requests whose newest turn is a new human ask and replays the session's held routing decision on continuation turns, which cuts classifier spend and eliminates mid-loop model switches. Continuations with no held decision to replay (no resolvable session_id, expired pin, fresh restart) still classify. Unlike session_affinity, a new human ask always re-classifies, so a session can still move tiers between asks. Suppressed when plugins are configured, for the same reason session_affinity is: a replayed decision would bypass the plugin pipeline. + * @default every_request + * @enum {string} + */ + classification_mode: "every_request" | "user_turn"; /** * Classification Prompt * @description Replaces the opening instructions of the LLM classifier rubric (the judging-criteria prose) for a custom tier set. The per-tier bullets and the trust-boundary paragraph telling the classifier to ignore tier requests embedded in quoted caller text are always appended after it and cannot be overridden. Requires tier_definitions; a built-in-tier router customizes its prompt via classifier_llm_config.system_prompt or classification_rubric instead. @@ -34249,6 +34396,12 @@ export interface components { * @description Keywords indicating code-related content */ code_keywords?: string[] | null; + /** + * Context Window Escalation Buffer + * @description Fraction of a model's declared context window the estimated prompt must fit within. The token count is an estimate, so fitting against the full window would dispatch prompts that the provider's own tokenizer then rejects; 0.95 leaves room for that drift plus the response tokens. + * @default 0.95 + */ + context_window_escalation_buffer: number; /** * Custom Technical Keywords * @description Domain-specific technical keywords appended to the effective base list (technical_keywords if set, otherwise DEFAULT_TECHNICAL_KEYWORDS). Order is preserved; duplicates are removed case-insensitively against the base list and within this list. @@ -34277,6 +34430,12 @@ export interface components { * @description Embedding model (LiteLLM model name) used when semantic_keyword_matching is enabled */ embedding_model?: string | null; + /** + * Enable Context Window Escalation + * @description Escalate a request off a tier whose models provably cannot hold its prompt, before dispatch. The classifier scores complexity and never prompt size, so a long agentic session whose newest ask is trivial lands on a small-window tier and the provider rejects it with a context-window 400 that nothing retries. When every model of the decided tier has a declared window smaller than the estimated prompt, the request moves to the lowest configured tier with a model whose declared window fits; when only some of the tier's models fit, the pick is restricted to those and the tier keeps the request. Models with no resolvable window are never escalated away from and never escalated onto. Set false to dispatch on complexity alone, as before. + * @default true + */ + enable_context_window_escalation: boolean; /** * Escalation Keywords * @description Case-sensitive phrases a user can include to force a bump to the next-higher complexity tier when they aren't satisfied with results (they can force a stronger model, but not choose which one). Defaults to ['LITELLM ESCALATE'] when unset; set to an empty list to disable. @@ -34308,6 +34467,12 @@ export interface components { * @default 0.5 */ match_threshold: number; + /** + * Modality Routing + * @description Route image-bearing requests only to models that can accept image input. The classifier reads text alone, so an image request whose text classifies cheap otherwise lands on a text-only model and fails with a provider 400. When enabled, a routed model explicitly declared supports_vision false (deployment model_info or the model cost map; unmapped names stay routable) is replaced by the nearest HIGHER tier holding a capable model, then default_model, else a clear 400. A kept session-affinity pin still wins even when an image arrives. + * @default false + */ + modality_routing: boolean; /** * Plan Mode Min Tier * @description When set, requests carrying a coding-agent plan-mode sentinel (Claude Code plan mode, VS Code Copilot Plan mode, Copilot CLI's exit_plan_mode tool) are routed to at least this tier: the classified tier still wins when it is higher, and the floor also overrides a session-affinity pin to a lower tier for exactly the turns carrying the sentinel, without rewriting the pin -- the first turn after plan mode exits routes as if plan mode had never happened. Names a built-in tier, or with tier_definitions set, one of the defined tier names (list order is ascending severity, same as keyword_tier_rules). Unset disables detection entirely. The sentinels ride in client-injected prompt text, so a caller who pastes one can spend up to this tier's models -- never down, and never outside the configured pools. @@ -35114,56 +35279,10 @@ export interface components { /** Timeout */ timeout?: number | null; }; - /** - * ShadowEvalJobKeyResponse - * @description One key a job shadows, with its own budget and stop state. - */ - ShadowEvalJobKeyResponse: { - /** - * Api Key Id - * @description The hashed virtual key whose traffic this entry scopes - */ - api_key_id: string; - /** - * Attempt Count - * @description This key's sampled attempts so far, judged and errored alike, the same count the sampler budgets against max_turns; populated on list and detail responses. Frozen at stopped_at once the key is stamped, so in-flight attempts landing after a stop never reclassify it - */ - attempt_count?: number | null; - /** - * Key Alias - * @description Alias of the shadowed key, resolved from the key row at read time; None when unset or deleted - */ - key_alias?: string | null; - /** - * Key Name - * @description Masked display name (sk-...) of the shadowed key, resolved at read time like key_alias - */ - key_name?: string | null; - /** - * Max Budget - * @description This key's own USD budget for the eval's shadow and judge spend, independent of its siblings'; None on jobs created before spend budgets existed, which max_turns alone bounds - */ - max_budget?: number | null; - /** - * Max Turns - * @description This key's sample-count ceiling: the whole budget for jobs created before max_budget existed, and the error-loop safety valve otherwise - */ - max_turns: number; - /** - * Spend - * @description This key's recorded shadow plus judge spend in USD, the same figure the sampler budgets against max_budget; populated on list and detail responses and frozen at stopped_at exactly like attempt_count - */ - spend?: number | null; - /** - * Stopped At - * @description When this key's slot was stamped free, whether its own budget ran out, the window closed, or an operator stopped the job; status is derived, so a spent budget reads completed even while this is still unset - */ - stopped_at?: string | null; - }; /** * ShadowEvalJobResponse - * @description A shadow-eval job over one or more keys, each with its own budget and stop state; - * status is derived from stopped_by, the keys' stop and budget state, and ends_at, + * @description A shadow-eval job over one or more targets, each with its own budget and stop state; + * status is derived from stopped_by, the targets' stop and budget state, and ends_at, * never stored, so no writer anywhere can produce an inconsistent one. Aggregate * fields are populated by the detail endpoint only and stay None on list responses. */ @@ -35205,11 +35324,6 @@ export interface components { * @description Verdicts recorded; detail endpoint only */ judged_count?: number | null; - /** - * Keys - * @description The keys whose traffic this job evaluates, and only those keys', each with its own budget - */ - keys: components["schemas"]["ShadowEvalJobKeyResponse"][]; /** * Last Error * @description Most recent attempt error; detail endpoint only @@ -35217,16 +35331,25 @@ export interface components { last_error?: string | null; /** @description Stratified verdicts; detail endpoint only */ results?: components["schemas"]["ShadowEvalResult"] | null; - /** Router Name */ - router_name: string; + /** + * Router Name + * @description The first router, kept for callers that predate router_names; derived so the + * two fields can never disagree. + */ + readonly router_name: string; + /** + * Router Names + * @description Every auto-router this job runs as a shadow arm. Multi-router jobs sample one slice of traffic and judge every arm against the same real responses + */ + router_names: string[]; /** Shadow Percentage */ shadow_percentage: number; /** * Status * @description Three recorded facts, no history-guessing: a stop is stopped_by (the migration * backfills it for every job that displayed stopped when the column arrived, so the - * pre-column population is closed), completion is the window passing or every key - * spending its budget, and anything else is running. The all-keys-stamped fallback + * pre-column population is closed), completion is the window passing or every target + * spending its budget, and anything else is running. The all-targets-stamped fallback * covers only stops written by pre-column pods during a rolling deploy. * @enum {string} */ @@ -35236,6 +35359,65 @@ export interface components { * @description The operator who stopped the job early, recorded by the stop endpoint; 'unknown' backfilled by migration for jobs that displayed stopped when the column arrived; None when the job ended on its own. Its presence is what makes a job read stopped rather than completed */ stopped_by?: string | null; + /** + * Targets + * @description The targets whose traffic this job evaluates, and only theirs, each with its own budget + */ + targets: components["schemas"]["ShadowEvalJobTargetResponse"][]; + }; + /** + * ShadowEvalJobTargetResponse + * @description One target a job shadows (a key, team, or user), with its own budget and stop state. + */ + ShadowEvalJobTargetResponse: { + /** + * Attempt Count + * @description This target's sampled attempts so far, judged and errored alike, the same count the sampler budgets against max_turns; populated on list and detail responses. Frozen at stopped_at once the target is stamped, so in-flight attempts landing after a stop never reclassify it + */ + attempt_count?: number | null; + /** + * Key Name + * @description Masked display name (sk-...) for key targets, resolved at read time; None for teams and users + */ + key_name?: string | null; + /** + * Max Budget + * @description This target's own USD budget for the eval's shadow and judge spend, independent of its siblings'; None on jobs created before spend budgets existed, which max_turns alone bounds + */ + max_budget?: number | null; + /** + * Max Turns + * @description This target's sample-count ceiling: the whole budget for jobs created before max_budget existed, and the error-loop safety valve otherwise + */ + max_turns: number; + /** + * Spend + * @description This target's recorded shadow plus judge spend in USD, the same figure the sampler budgets against max_budget; populated on list and detail responses and frozen at stopped_at exactly like attempt_count + */ + spend?: number | null; + /** + * Stopped At + * @description When this target's slot was stamped free, whether its own budget ran out, the window closed, or an operator stopped the job; status is derived, so a spent budget reads completed even while this is still unset + */ + stopped_at?: string | null; + /** + * Target Alias + * @description Display label resolved from the target's own row at read time: the key's alias, the team's alias, or the user's email; None when unset or deleted + */ + target_alias?: string | null; + /** + * Target Id + * @description The hashed virtual key, team id, or user id whose traffic this entry scopes + */ + target_id: string; + /** + * Target Type + * @description What kind of entity this entry scopes + * @enum {string} + */ + target_type: "key" | "team" | "user"; + /** @description This target's own judged-verdict slice; detail endpoint only, None until a turn is judged */ + verdicts?: components["schemas"]["ShadowEvalSlice"] | null; }; /** * ShadowEvalResult @@ -35248,10 +35430,11 @@ export interface components { */ by_current_model: components["schemas"]["ShadowEvalSlice"][]; /** - * By Key - * @description One slice per scoped key that has judged verdicts, grouped on the raw key hash. Keys the job scopes but has not judged a turn for yet are absent rather than reported as zero + * By Router + * @description One slice per router arm, grouped on the router name. Every arm of a multi-router job is judged against the same real responses over the same sampled requests, so these slices compare routers head-to-head: like-for-like win rates and spends on identical traffic. Verdicts from before arm stamping existed count toward the job's own router + * @default [] */ - by_key: components["schemas"]["ShadowEvalSlice"][]; + by_router: components["schemas"]["ShadowEvalSlice"][]; /** By Tier */ by_tier: components["schemas"]["ShadowEvalSlice"][]; /** @@ -35265,13 +35448,13 @@ export interface components { overall_tie_rate_pct: number; /** * Sampled Real Spend - * @description USD the real arm billed across all judged turns, cache-served turns excluded + * @description USD the real arm billed across all judged turns, cache-served turns excluded. A judged turn is one (request, router arm) verdict, so a multi-router job counts the real response once per arm it was judged against; per-router comparisons read by_router * @default 0 */ sampled_real_spend: number; /** * Sampled Shadow Spend - * @description USD the shadow arm billed across the same turns, judge excluded, like for like + * @description USD the shadow arms billed across the same turns, judge excluded, like for like * @default 0 */ sampled_shadow_spend: number; @@ -35293,8 +35476,9 @@ export interface components { }; /** * ShadowEvalSlice - * @description Judge outcomes for one slice of a job's verdicts (a router tier, or one of the - * models that served the real arm). + * @description Judge outcomes for one slice of a job's verdicts: a router tier, one of the + * models that served the real arm, or one scoped target (embedded on that target's + * own entry, so slices never need re-joining to a target by id). */ ShadowEvalSlice: { /** Avg Judge Confidence */ @@ -35461,11 +35645,15 @@ export interface components { * Cause * @enum {string} */ - cause?: "heuristic_scorer" | "reasoning_override" | "llm_classifier" | "heuristic_first_short_circuit" | "classifier_plugin" | "classifier_fallback" | "default_model_fallback" | "literal_keyword_match" | "semantic_keyword_match" | "plan_mode" | "housekeeping" | "session_affinity_pin" | "session_affinity_escalation" | "default_fallback" | "keyword" | "quality_tier" | "bandit"; + cause?: "heuristic_scorer" | "reasoning_override" | "llm_classifier" | "heuristic_first_short_circuit" | "classifier_plugin" | "classifier_fallback" | "default_model_fallback" | "literal_keyword_match" | "semantic_keyword_match" | "plan_mode" | "housekeeping" | "modality_escalation" | "session_affinity_pin" | "session_affinity_escalation" | "user_turn_continuation" | "default_fallback" | "keyword" | "quality_tier" | "bandit"; /** Classifier Cost */ classifier_cost?: number; /** Classifier Model */ classifier_model?: string; + /** Context Escalated */ + context_escalated?: boolean; + /** Context Escalation Original Tier */ + context_escalation_original_tier?: string; /** Conversation Continuing */ conversation_continuing?: boolean; /** Escalated */ @@ -35520,12 +35708,18 @@ export interface components { }; /** * StartShadowEvalRequest - * @description Start duplicating one or more keys' traffic for blind comparison against an auto-router. + * @description Start duplicating one or more targets' traffic for blind comparison against an auto-router. + * + * A target is a virtual key, a team, or a user; each becomes its own leg with its own + * budget and stop state. Team and user targets match on the identity every request + * carries after auth (user_api_key_team_id / user_api_key_user_id), so they cover + * JWT-authenticated traffic, which presents no virtual key at all. */ StartShadowEvalRequest: { /** * Api Key Ids - * @description The hashed virtual keys whose traffic will be shadowed. Shadow evaluation runs ONLY on these keys' traffic; requests made with any other key are not sampled. Each key carries its own max_budget spend budget, so one key exhausting its budget leaves the others sampling. At most 100 keys per job, which also bounds every read the job's endpoints make. + * @description Hashed virtual keys whose traffic will be shadowed. Combined with team_ids and user_ids the job needs at least one target and at most 100, which also bounds every read the job's endpoints make. Each target carries its own max_budget spend budget, so one exhausting its budget leaves the others sampling. + * @default [] */ api_key_ids: string[]; /** @@ -35554,20 +35748,38 @@ export interface components { judge_model: string; /** * Max Budget - * @description Per-key USD budget for the eval's own overhead, the shadow-arm and judge calls, priced with the same figures the spend pipeline bills. EACH scoped key samples until its recorded eval spend reaches this, so a job over N keys spends at most about N times max_budget; in-flight samples can overshoot the cap by one sampling cache window + * @description Per-target USD budget for the eval's own overhead, the shadow-arm and judge calls, priced with the same figures the spend pipeline bills. EACH scoped target samples until its recorded eval spend reaches this, so a job over N targets spends at most about N times max_budget; in-flight samples can overshoot the cap by one sampling cache window. Every router arm draws from the same per-target budget, so a multi-router job reaches it proportionally sooner * @default 10 */ max_budget: number; /** * Router Name - * @description The auto-router under evaluation, in either direction + * @description The auto-router under evaluation, in either direction: the single-router spelling of router_names. Provide exactly one of the two fields */ - router_name: string; + router_name?: string | null; + /** + * Router Names + * @description The auto-routers under evaluation, at most 4. Every sampled request runs through every router listed and each arm is judged independently against the same real response, so routers compare head-to-head on identical traffic. More than one router requires direction 'forward'. After validation this field always carries the full deduplicated set, whichever spelling the caller used + * @default [] + */ + router_names: string[]; /** * Shadow Percentage - * @description Percentage of the key's requests to duplicate through the router + * @description Percentage of each target's requests to duplicate through the router */ shadow_percentage: number; + /** + * Team Ids + * @description Teams whose traffic will be shadowed, matched on the team every authenticated request resolves to, so a team's JWT-auth and virtual-key traffic are both sampled + * @default [] + */ + team_ids: string[]; + /** + * User Ids + * @description Users whose traffic will be shadowed, matched on the user every authenticated request resolves to across all their teams: JWT requests carrying their subject claim and virtual keys they own + * @default [] + */ + user_ids: string[]; }; /** * SuccessfulKeyUpdate @@ -40529,8 +40741,10 @@ export interface operations { list_shadow_eval_jobs_auto_router_shadow_eval_get: { parameters: { query?: { - /** @description Filter to jobs that shadow this key, alone or alongside others */ - api_key_id?: string | null; + /** @description Kind of target to filter on; requires target_id */ + target_type?: ("key" | "team" | "user") | null; + /** @description Filter to jobs that shadow this target, alone or alongside others */ + target_id?: string | null; /** @description Newest jobs to return */ limit?: number; }; @@ -46457,6 +46671,161 @@ export interface operations { }; }; }; + gigachat_proxy_route_gigachat__endpoint__get: { + parameters: { + query?: never; + header?: never; + path: { + endpoint: string; + }; + cookie?: never; + }; + requestBody?: never; + responses: { + /** @description Successful Response */ + 200: { + headers: { + [name: string]: unknown; + }; + content: { + "application/json": unknown; + }; + }; + /** @description Validation Error */ + 422: { + headers: { + [name: string]: unknown; + }; + content: { + "application/json": components["schemas"]["HTTPValidationError"]; + }; + }; + }; + }; + gigachat_proxy_route_gigachat__endpoint__put: { + parameters: { + query?: never; + header?: never; + path: { + endpoint: string; + }; + cookie?: never; + }; + requestBody?: never; + responses: { + /** @description Successful Response */ + 200: { + headers: { + [name: string]: unknown; + }; + content: { + "application/json": unknown; + }; + }; + /** @description Validation Error */ + 422: { + headers: { + [name: string]: unknown; + }; + content: { + "application/json": components["schemas"]["HTTPValidationError"]; + }; + }; + }; + }; + gigachat_proxy_route_gigachat__endpoint__post: { + parameters: { + query?: never; + header?: never; + path: { + endpoint: string; + }; + cookie?: never; + }; + requestBody?: never; + responses: { + /** @description Successful Response */ + 200: { + headers: { + [name: string]: unknown; + }; + content: { + "application/json": unknown; + }; + }; + /** @description Validation Error */ + 422: { + headers: { + [name: string]: unknown; + }; + content: { + "application/json": components["schemas"]["HTTPValidationError"]; + }; + }; + }; + }; + gigachat_proxy_route_gigachat__endpoint__delete: { + parameters: { + query?: never; + header?: never; + path: { + endpoint: string; + }; + cookie?: never; + }; + requestBody?: never; + responses: { + /** @description Successful Response */ + 200: { + headers: { + [name: string]: unknown; + }; + content: { + "application/json": unknown; + }; + }; + /** @description Validation Error */ + 422: { + headers: { + [name: string]: unknown; + }; + content: { + "application/json": components["schemas"]["HTTPValidationError"]; + }; + }; + }; + }; + gigachat_proxy_route_gigachat__endpoint__patch: { + parameters: { + query?: never; + header?: never; + path: { + endpoint: string; + }; + cookie?: never; + }; + requestBody?: never; + responses: { + /** @description Successful Response */ + 200: { + headers: { + [name: string]: unknown; + }; + content: { + "application/json": unknown; + }; + }; + /** @description Validation Error */ + 422: { + headers: { + [name: string]: unknown; + }; + content: { + "application/json": components["schemas"]["HTTPValidationError"]; + }; + }; + }; + }; get_global_activity_global_activity_get: { parameters: { query?: { @@ -51496,6 +51865,26 @@ export interface operations { }; }; }; + responses_input_tokens_openai_v1_responses_input_tokens_post: { + parameters: { + query?: never; + header?: never; + path?: never; + cookie?: never; + }; + requestBody?: never; + responses: { + /** @description Successful Response */ + 200: { + headers: { + [name: string]: unknown; + }; + content: { + "application/json": unknown; + }; + }; + }; + }; get_response_openai_v1_responses__response_id__get: { parameters: { query?: never; @@ -54460,6 +54849,26 @@ export interface operations { }; }; }; + responses_input_tokens_responses_input_tokens_post: { + parameters: { + query?: never; + header?: never; + path?: never; + cookie?: never; + }; + requestBody?: never; + responses: { + /** @description Successful Response */ + 200: { + headers: { + [name: string]: unknown; + }; + content: { + "application/json": unknown; + }; + }; + }; + }; get_response_responses__response_id__get: { parameters: { query?: never; @@ -62882,6 +63291,26 @@ export interface operations { }; }; }; + responses_input_tokens_v1_responses_input_tokens_post: { + parameters: { + query?: never; + header?: never; + path?: never; + cookie?: never; + }; + requestBody?: never; + responses: { + /** @description Successful Response */ + 200: { + headers: { + [name: string]: unknown; + }; + content: { + "application/json": unknown; + }; + }; + }; + }; get_response_v1_responses__response_id__get: { parameters: { query?: never; diff --git a/ui/litellm-dashboard/tests/fieldOrientation.ts b/ui/litellm-dashboard/tests/fieldOrientation.ts new file mode 100644 index 00000000000..49ba5056d66 --- /dev/null +++ b/ui/litellm-dashboard/tests/fieldOrientation.ts @@ -0,0 +1,18 @@ +import { expect } from "vitest"; + +export const ROW_LAYOUT_CLASSES = ["flex-row", "items-center"] as const; +export const STRETCH_CHILDREN_CLASS = "*:w-full"; + +/** + * Asserts a control sits beside its label at its own width instead of being stretched across the + * field. Reaches for the resolved classes because the defect is purely visual: nothing accessible + * distinguishes a square checkbox from a full-width bar. + */ +export const expectControlBesideLabel = (control: HTMLElement): void => { + const field = control.closest('[data-slot="field"]'); + if (field === null) throw new Error("control is not rendered inside a form field"); + + expect(field).toHaveAttribute("data-orientation", "horizontal"); + expect(field).toHaveClass(...ROW_LAYOUT_CLASSES); + expect(field).not.toHaveClass(STRETCH_CHILDREN_CLASS); +}; diff --git a/ui/litellm-dashboard/tests/test-utils.tsx b/ui/litellm-dashboard/tests/test-utils.tsx index 66966201a9c..162b8a3df7b 100644 --- a/ui/litellm-dashboard/tests/test-utils.tsx +++ b/ui/litellm-dashboard/tests/test-utils.tsx @@ -52,7 +52,7 @@ const pointerBlocked = (element: HTMLElement): boolean => { * the option text alone is a race that React 19's flush timing loses. */ export const chooseSelectOption = async ( - user: ReturnType, + user: Pick, "click">, trigger: HTMLElement, optionName: string | RegExp, ) => { diff --git a/uv.lock b/uv.lock index 8ef72116466..27be919eea1 100644 --- a/uv.lock +++ b/uv.lock @@ -10,7 +10,7 @@ resolution-markers = [ ] [options] -exclude-newer = "2026-08-26T18:33:25.773031Z" +exclude-newer = "2026-08-29T17:58:57.633306Z" exclude-newer-span = "P3D" [manifest] @@ -4266,7 +4266,7 @@ wheels = [ [[package]] name = "litellm" -version = "1.100.0" +version = "1.101.0" source = { editable = "." } dependencies = [ { name = "aiohttp" }, @@ -4306,6 +4306,8 @@ extra-proxy = [ { name = "google-cloud-iam" }, { name = "google-cloud-kms" }, { name = "prisma" }, + { name = "psycopg" }, + { name = "psycopg-binary" }, { name = "redisvl" }, { name = "resend" }, ] @@ -4544,6 +4546,8 @@ requires-dist = [ { name = "polars", marker = "extra == 'proxy'", specifier = ">=1.38.1,<2.0" }, { name = "prisma", marker = "extra == 'extra-proxy'", specifier = ">=0.11.0,<1.0" }, { name = "prometheus-client", marker = "extra == 'proxy-runtime'", specifier = ">=0.20.0,<1.0" }, + { name = "psycopg", marker = "extra == 'extra-proxy'", specifier = ">=3.2,<4.0" }, + { name = "psycopg-binary", marker = "extra == 'extra-proxy'", specifier = ">=3.2,<4.0" }, { name = "pydantic", specifier = ">=2.10.0,<3.0.0" }, { name = "pydantic-settings", specifier = ">=2.14.1,<3.0" }, { name = "pyjwt", marker = "extra == 'proxy'", specifier = ">=2.13.0,<3.0" }, @@ -4665,12 +4669,12 @@ proxy-dev = [ [[package]] name = "litellm-enterprise" -version = "0.1.62" +version = "0.1.63" source = { editable = "enterprise" } [[package]] name = "litellm-proxy-extras" -version = "0.4.91" +version = "0.4.92" source = { editable = "litellm-proxy-extras" } [[package]]
- Property - - {skill.name} -
Property{skill.name}
{row.property}{row.value}
{row.property}{row.value}