diff --git a/.github/actions/cache-prisma-binaries/action.yml b/.github/actions/cache-prisma-binaries/action.yml new file mode 100644 index 00000000000..68615e94c08 --- /dev/null +++ b/.github/actions/cache-prisma-binaries/action.yml @@ -0,0 +1,40 @@ +name: "Cache Prisma binaries" +description: >- + Cache the Prisma CLI and engine binaries that `prisma generate` downloads, so + only the first job on a given prisma-client-py version pays for the download. + + prisma-client-py shells out to `npm install prisma@` whenever its + binary cache directory has no CLI entrypoint, which pulls ~85 MB of query and + schema engines over the network. That normally takes a few seconds, but it is + unbounded: one shard of a proxy-db run took 5m18s on that single step versus + 3.8s on its eleven siblings, which pushed the job past its timeout and got a + fully passing test run cancelled. + + Callers must not set PRISMA_BINARY_CACHE_DIR. The prisma-client-py default + (~/.cache/prisma-python/binaries//) is already + keyed by both versions, so a cache entry can never be served to a run that + expects different binaries. + +runs: + using: composite + steps: + - name: Resolve prisma-client-py version + id: version + shell: bash + run: | + version="$(grep -A1 '^name = "prisma"$' uv.lock | sed -n 's/^version = "\(.*\)"$/\1/p' | head -1)" + if [ -z "${version}" ]; then + echo "could not resolve the prisma package version from uv.lock" >&2 + exit 1 + fi + echo "version=${version}" >> "$GITHUB_OUTPUT" + + - name: Restore Prisma binaries + uses: actions/cache@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.3.0 + with: + # ~/.cache/prisma-python holds the npm install tree prisma-client-py + # drives; ~/.cache/prisma is where @prisma/engines stages its downloads. + path: | + ~/.cache/prisma-python + ~/.cache/prisma + key: ${{ runner.os }}-prisma-binaries-${{ steps.version.outputs.version }} diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md index d34b0ee2e0f..e56f61988ef 100644 --- a/.github/pull_request_template.md +++ b/.github/pull_request_template.md @@ -83,7 +83,11 @@ If you're seeing a delay in your PR being merged, ping the LiteLLM Team on [Slac 🚄 Infrastructure ✅ Test -## Changes +## Caveats (if any) + + ## QA runbook diff --git a/.github/workflows/_test-unit-base.yml b/.github/workflows/_test-unit-base.yml index cee93bde7f2..58208988fca 100644 --- a/.github/workflows/_test-unit-base.yml +++ b/.github/workflows/_test-unit-base.yml @@ -18,10 +18,25 @@ on: type: number default: 2 timeout-minutes: - description: "Job timeout in minutes" + description: >- + Timeout for the test step alone. Setup (checkout, dependency install, + Prisma client generation) gets its own allowance on top, so a slow + runner or a cold binary download can never cancel passing tests. required: false type: number default: 20 + job-timeout-minutes: + description: >- + Backstop for the whole job. Keep it >= `timeout-minutes` plus 35: 30 for + the per-step ceilings on the setup steps below, and 5 for the runner + overhead the job clock charges but no step owns (job init, step + transitions, post-job cleanup). That headroom is what makes the test + budget a floor rather than a hope, since setup cannot overrun into it + without failing its own step first. GitHub expressions have no + arithmetic, so the sum is passed in rather than computed. + required: false + type: number + default: 55 max-failures: description: "Stop after this many failures" required: false @@ -44,30 +59,35 @@ jobs: run: name: Run tests runs-on: ubuntu-latest - timeout-minutes: ${{ inputs.timeout-minutes }} + timeout-minutes: ${{ inputs.job-timeout-minutes }} outputs: decision: ${{ steps.changes.outputs.decision }} steps: - uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 + timeout-minutes: 3 with: persist-credentials: false - name: Detect backend-relevant changes id: changes + timeout-minutes: 2 uses: ./.github/actions/detect-backend-changes - name: Set up Python + timeout-minutes: 3 uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0 with: python-version: "3.12" - name: Set up uv + timeout-minutes: 3 uses: ./.github/actions/setup-uv-with-retries with: version: "0.10.9" - name: Cache uv dependencies + timeout-minutes: 5 uses: actions/cache@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.3.0 with: path: | @@ -79,18 +99,24 @@ jobs: - name: Install dependencies if: steps.changes.outputs.decision != 'skip' + timeout-minutes: 8 run: | .github/scripts/uv_sync_with_retries.sh --frozen --group ci --group proxy-dev --extra google --extra proxy --extra semantic-router --extra saml + - name: Cache Prisma binaries + if: steps.changes.outputs.decision != 'skip' + timeout-minutes: 3 + uses: ./.github/actions/cache-prisma-binaries + - name: Generate Prisma client if: steps.changes.outputs.decision != 'skip' - env: - PRISMA_BINARY_CACHE_DIR: ${{ runner.temp }}/prisma-cache + timeout-minutes: 3 run: | uv run --no-sync prisma generate --schema litellm/proxy/schema.prisma - name: Run tests if: steps.changes.outputs.decision != 'skip' + timeout-minutes: ${{ inputs.timeout-minutes }} env: TEST_PATH: ${{ inputs.test-path }} MAX_FAILURES: ${{ inputs.max-failures }} diff --git a/.github/workflows/check-schema-sync.yml b/.github/workflows/check-schema-sync.yml index 0e5e2804e60..a4e78d2c44c 100644 --- a/.github/workflows/check-schema-sync.yml +++ b/.github/workflows/check-schema-sync.yml @@ -10,6 +10,10 @@ on: permissions: contents: read +concurrency: + group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.sha }} + cancel-in-progress: ${{ github.event_name == 'pull_request' }} + jobs: check-sync: name: Verify schema.prisma copies match root diff --git a/.github/workflows/check-ui-api-types.yml b/.github/workflows/check-ui-api-types.yml index 02543d67a82..dbd663a2efa 100644 --- a/.github/workflows/check-ui-api-types.yml +++ b/.github/workflows/check-ui-api-types.yml @@ -71,10 +71,12 @@ jobs: if: steps.changes.outputs.relevant == 'true' run: .github/scripts/uv_sync_with_retries.sh --frozen --group ci --group proxy-dev --extra google --extra proxy --extra semantic-router + - name: Cache Prisma binaries + if: steps.changes.outputs.relevant == 'true' + uses: ./.github/actions/cache-prisma-binaries + - name: Generate Prisma client if: steps.changes.outputs.relevant == 'true' - env: - PRISMA_BINARY_CACHE_DIR: ${{ runner.temp }}/prisma-cache run: uv run --no-sync prisma generate --schema litellm/proxy/schema.prisma - name: Set up Node.js diff --git a/.github/workflows/conventional-commits.yml b/.github/workflows/conventional-commits.yml index 69ade24d028..eb9eb69f8b6 100644 --- a/.github/workflows/conventional-commits.yml +++ b/.github/workflows/conventional-commits.yml @@ -14,6 +14,10 @@ on: permissions: pull-requests: read +concurrency: + group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.sha }} + cancel-in-progress: ${{ github.event_name == 'pull_request' }} + jobs: lint-pr-title: name: Validate PR title diff --git a/.github/workflows/guard-fork-dependencies.yml b/.github/workflows/guard-fork-dependencies.yml index f4cbdd63cdf..6b366da78d4 100644 --- a/.github/workflows/guard-fork-dependencies.yml +++ b/.github/workflows/guard-fork-dependencies.yml @@ -15,6 +15,10 @@ on: permissions: {} +concurrency: + group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.sha }} + cancel-in-progress: ${{ github.event_name == 'pull_request' }} + jobs: guard: name: Block fork dependency changes diff --git a/.github/workflows/helm_unit_test.yml b/.github/workflows/helm_unit_test.yml index a44d412c781..f95848945a0 100644 --- a/.github/workflows/helm_unit_test.yml +++ b/.github/workflows/helm_unit_test.yml @@ -9,6 +9,10 @@ on: permissions: contents: read +concurrency: + group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.sha }} + cancel-in-progress: ${{ github.event_name == 'pull_request' }} + jobs: unit-test: runs-on: ubuntu-latest diff --git a/.github/workflows/mutation-test.yml b/.github/workflows/mutation-test.yml index da4fe073a6a..68317d5dd12 100644 --- a/.github/workflows/mutation-test.yml +++ b/.github/workflows/mutation-test.yml @@ -57,9 +57,10 @@ jobs: run: | .github/scripts/uv_sync_with_retries.sh --frozen --group ci --group proxy-dev --extra google --extra proxy --extra semantic-router --extra saml + - name: Cache Prisma binaries + uses: ./.github/actions/cache-prisma-binaries + - name: Generate Prisma client - env: - PRISMA_BINARY_CACHE_DIR: ${{ runner.temp }}/prisma-cache run: | uv run --no-sync prisma generate --schema litellm/proxy/schema.prisma diff --git a/.github/workflows/publish-basedpyright-base-counts.yml b/.github/workflows/publish-basedpyright-base-counts.yml index c85d30df0ce..71e196d8361 100644 --- a/.github/workflows/publish-basedpyright-base-counts.yml +++ b/.github/workflows/publish-basedpyright-base-counts.yml @@ -43,12 +43,13 @@ jobs: with: version: "0.10.9" + - name: Cache Prisma binaries + uses: ./.github/actions/cache-prisma-binaries + # The gate provisions its own measurement env (.venv-typecheck: a frozen # uv sync of its canonical dependency groups plus a generated Prisma # client), so no install step here can drift from what local runs measure. - name: Emit basedpyright counts for HEAD - env: - PRISMA_BINARY_CACHE_DIR: ${{ runner.temp }}/prisma-cache run: | python scripts/type_check_gate.py --emit-counts-dir "$RUNNER_TEMP/basedpyright-counts" counts_file=$(ls "$RUNNER_TEMP"/basedpyright-counts/basedpyright-counts-*.json) diff --git a/.github/workflows/test-code-quality.yml b/.github/workflows/test-code-quality.yml index fab05fc2bbb..8f62837d29a 100644 --- a/.github/workflows/test-code-quality.yml +++ b/.github/workflows/test-code-quality.yml @@ -65,6 +65,12 @@ jobs: - name: check_provider_folders_documented run: uv run --no-sync python ./tests/code_coverage_tests/check_provider_folders_documented.py + - name: check_prisma_binary_cache + run: uv run --no-sync python ./tests/code_coverage_tests/check_prisma_binary_cache.py + + - name: check_workflow_startup_safety + run: uv run --no-sync python ./tests/code_coverage_tests/check_workflow_startup_safety.py + - name: router_code_coverage run: uv run --no-sync python ./tests/code_coverage_tests/router_code_coverage.py diff --git a/.github/workflows/test-linting.yml b/.github/workflows/test-linting.yml index 5e333f2a3ca..280ec476cdf 100644 --- a/.github/workflows/test-linting.yml +++ b/.github/workflows/test-linting.yml @@ -11,6 +11,10 @@ on: permissions: contents: read +concurrency: + group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.sha }} + cancel-in-progress: ${{ github.event_name == 'pull_request' }} + jobs: lint: runs-on: ubuntu-latest @@ -67,12 +71,13 @@ jobs: run: | uv sync --frozen --group proxy-dev --group e2e-dev + - name: Cache Prisma binaries + uses: ./.github/actions/cache-prisma-binaries + # basedpyright resolves Prisma's generated client (litellm/proxy/schema.prisma) # only after `prisma generate` writes prisma/client.py et al. Without this the # DB wrappers typed against the generated client would degrade to Unknown. - name: Generate Prisma client - env: - PRISMA_BINARY_CACHE_DIR: ${{ runner.temp }}/prisma-cache run: | uv run --no-sync prisma generate --schema litellm/proxy/schema.prisma @@ -115,7 +120,6 @@ jobs: - name: Check basedpyright budget (delta vs base) env: GH_TOKEN: ${{ github.token }} - PRISMA_BINARY_CACHE_DIR: ${{ runner.temp }}/prisma-cache run: | uv run --no-sync python scripts/type_check_gate.py --base "$GATE_BASE_SHA" diff --git a/.github/workflows/test-litellm-ui-build.yml b/.github/workflows/test-litellm-ui-build.yml index 39f4bc1428a..618b0195b5a 100644 --- a/.github/workflows/test-litellm-ui-build.yml +++ b/.github/workflows/test-litellm-ui-build.yml @@ -10,6 +10,10 @@ on: - litellm_oss_staging - "litellm_**" +concurrency: + group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.sha }} + cancel-in-progress: ${{ github.event_name == 'pull_request' }} + jobs: build-ui: runs-on: ubuntu-latest diff --git a/.github/workflows/test-litellm-ui-lint.yml b/.github/workflows/test-litellm-ui-lint.yml index ecc739a87e2..e03d89ee26a 100644 --- a/.github/workflows/test-litellm-ui-lint.yml +++ b/.github/workflows/test-litellm-ui-lint.yml @@ -10,6 +10,10 @@ on: - litellm_oss_staging - "litellm_**" +concurrency: + group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.sha }} + cancel-in-progress: ${{ github.event_name == 'pull_request' }} + jobs: frontend-lint: runs-on: ubuntu-latest diff --git a/.github/workflows/test-litellm-ui-unit.yml b/.github/workflows/test-litellm-ui-unit.yml index 8f2199017d9..69cbc082d98 100644 --- a/.github/workflows/test-litellm-ui-unit.yml +++ b/.github/workflows/test-litellm-ui-unit.yml @@ -42,6 +42,11 @@ jobs: - name: Install dependencies run: npm ci + - name: Run UI type tests (Vitest) + env: + CI: "true" + run: npm run test:types + - name: Run UI unit tests (Vitest) env: CI: "true" diff --git a/.github/workflows/test-mcp.yml b/.github/workflows/test-mcp.yml index a5a4e722133..05cc13d0af2 100644 --- a/.github/workflows/test-mcp.yml +++ b/.github/workflows/test-mcp.yml @@ -11,6 +11,10 @@ on: permissions: contents: read +concurrency: + group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.sha }} + cancel-in-progress: ${{ github.event_name == 'pull_request' }} + jobs: test: runs-on: ubuntu-latest diff --git a/.github/workflows/test-model-map.yaml b/.github/workflows/test-model-map.yaml index cf4b0eb21a1..c2770e5da4c 100644 --- a/.github/workflows/test-model-map.yaml +++ b/.github/workflows/test-model-map.yaml @@ -11,6 +11,10 @@ on: permissions: contents: read +concurrency: + group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.sha }} + cancel-in-progress: ${{ github.event_name == 'pull_request' }} + jobs: validate-model-prices-json: runs-on: ubuntu-latest diff --git a/.github/workflows/test-terraform-provider.yml b/.github/workflows/test-terraform-provider.yml index 058a2538c15..7ea22825f4f 100644 --- a/.github/workflows/test-terraform-provider.yml +++ b/.github/workflows/test-terraform-provider.yml @@ -92,9 +92,10 @@ jobs: run: | .github/scripts/uv_sync_with_retries.sh --frozen --group ci --group proxy-dev --extra google --extra proxy --extra semantic-router + - name: Cache Prisma binaries + uses: ./.github/actions/cache-prisma-binaries + - name: Generate Prisma client - env: - PRISMA_BINARY_CACHE_DIR: ${{ runner.temp }}/prisma-cache run: | uv run --no-sync prisma generate --schema litellm/proxy/schema.prisma diff --git a/.github/workflows/test-unit-documentation.yml b/.github/workflows/test-unit-documentation.yml index 50589cb5926..c93779c177f 100644 --- a/.github/workflows/test-unit-documentation.yml +++ b/.github/workflows/test-unit-documentation.yml @@ -65,10 +65,12 @@ jobs: run: | .github/scripts/uv_sync_with_retries.sh --frozen --group ci --group proxy-dev --extra google --extra proxy --extra semantic-router + - name: Cache Prisma binaries + if: steps.changes.outputs.decision != 'skip' + uses: ./.github/actions/cache-prisma-binaries + - name: Generate Prisma client if: steps.changes.outputs.decision != 'skip' - env: - PRISMA_BINARY_CACHE_DIR: ${{ runner.temp }}/prisma-cache run: | uv run --no-sync prisma generate --schema litellm/proxy/schema.prisma diff --git a/.github/workflows/test-unit-proxy-db.yml b/.github/workflows/test-unit-proxy-db.yml index 60d2e471862..df212a85885 100644 --- a/.github/workflows/test-unit-proxy-db.yml +++ b/.github/workflows/test-unit-proxy-db.yml @@ -28,6 +28,10 @@ concurrency: # Most of a shard's time is pytest plugin load + xdist worker imports + # pytest-cov instrumentation, not the tests themselves. Keeping per-shard # work low and matching worker count to runner cores is what controls it. +# * `timeout` bounds the pytest step only. Checkout, dependency install, and +# Prisma client generation draw on a separate allowance in the base +# workflow, so slow setup shows up as a slow job rather than as a +# cancelled shard whose tests were passing. # * workers: 4 matches the 4-core ubuntu-latest runner. -n 8 on 4 cores # oversubscribes 2x and workers fight for CPU during their cold-start # imports (measured ~441% CPU for -n 8 locally, i.e. ~55% effective). diff --git a/.github/workflows/test-unit-proxy-endpoints.yml b/.github/workflows/test-unit-proxy-endpoints.yml index 645996f779d..64b92f7d847 100644 --- a/.github/workflows/test-unit-proxy-endpoints.yml +++ b/.github/workflows/test-unit-proxy-endpoints.yml @@ -38,6 +38,8 @@ jobs: tests/test_litellm/proxy/google_endpoints tests/test_litellm/proxy/openai_files_endpoint tests/test_litellm/proxy/batches_endpoints + tests/test_litellm/proxy/fine_tuning_endpoints + tests/test_litellm/proxy/vector_store_files_endpoints tests/test_litellm/proxy/video_endpoints tests/test_litellm/proxy/response_api_endpoints tests/test_litellm/proxy/image_endpoints @@ -74,4 +76,5 @@ jobs: workers: 4 reruns: 2 timeout-minutes: 60 + job-timeout-minutes: 95 artifact-name: proxy-server diff --git a/.github/workflows/test-unit-proxy-legacy.yml b/.github/workflows/test-unit-proxy-legacy.yml index 49aa5f9f51d..e8ca36fb30d 100644 --- a/.github/workflows/test-unit-proxy-legacy.yml +++ b/.github/workflows/test-unit-proxy-legacy.yml @@ -82,10 +82,12 @@ jobs: run: | .github/scripts/uv_sync_with_retries.sh --frozen --group ci --group proxy-dev --extra google --extra proxy --extra semantic-router + - name: Cache Prisma binaries + if: steps.changes.outputs.decision != 'skip' + uses: ./.github/actions/cache-prisma-binaries + - name: Generate Prisma client if: steps.changes.outputs.decision != 'skip' - env: - PRISMA_BINARY_CACHE_DIR: ${{ runner.temp }}/prisma-cache run: | uv run --no-sync prisma generate --schema litellm/proxy/schema.prisma diff --git a/.github/workflows/weekly_load_anomaly.yml b/.github/workflows/weekly_load_anomaly.yml index 4c2103f026d..2dffc889d0e 100644 --- a/.github/workflows/weekly_load_anomaly.yml +++ b/.github/workflows/weekly_load_anomaly.yml @@ -51,9 +51,10 @@ jobs: run: | .github/scripts/uv_sync_with_retries.sh --frozen --group ci --group proxy-dev --extra proxy + - name: Cache Prisma binaries + uses: ./.github/actions/cache-prisma-binaries + - name: Generate Prisma client - env: - PRISMA_BINARY_CACHE_DIR: ${{ runner.temp }}/prisma-cache run: | uv run --no-sync prisma generate --schema litellm/proxy/schema.prisma diff --git a/CLAUDE.md b/CLAUDE.md index 59929143c46..436fa33fa41 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -1,4 +1,12 @@ -Do not write any comments (existing comments can stay) unless explicitly asked to in a user (not system) prompt +Do not write comments unless they are any of: +- absolutely necessary to explain some very complex business logic (in which case, keep it concise and clear) +- used as an input for tools to read and act on. For example: + - entries in `.git-blame-ignore-revs` saying which commit is excluded from git blame + - a lint or type checker suppression like `# mutable-ok` or `# pyright: ignore[reportArgumentType] # ` when introducing a truly unavoidable violation +- a TODO or FIXME + - Not great to have those, but if it's unavoidable, make sure to include a strong, concise reason for why it's there or, better yet, link to a GitHub issue for the follow-up work + +Explanation: The point of this rule is to keep out AI slop comments. AI writes way too many and way too verbose comments. Code comments are, in a way, a violation of DRY code. You must update logic in two locations to change the code, and "hard to change" is literally the definition of tech debt. We should instead aim to write code that is intuitive and clear, even at a glance, to the reader, being both easy to maintain and high performance Don't assume that the existing code is correct or the right way of doing things / good coding patterns. In fact, there are a lot of bad coding practices, overly complex code, code smells, etc. If something doesn't look right, speak up. Feel free to break existing patterns or question weird existing code to make new code high quality, as in: @@ -9,7 +17,7 @@ Don't assume that the existing code is correct or the right way of doing things - easy to maintain/change - modern -In that order of importance +In descending order of importance When adding new features, add meaningful tests. Don't add tests that don't check anything substantial and is there just to make the code coverage pass. Yes, code coverage is important, but I'd rather have no signal whether the code is working than tests that don't fail when code is broken. The goal is to have tests that would fail before the feature was added/if the code was mutated in a way that breaks the feature and succeed only when the feature is fully working. I should run mutation testing and see > 90% kill rate @@ -41,7 +49,7 @@ Python max line length is 120, not 88 When you fix violations gated by `ruff-strict-budget.json`, `type-discipline-budget.json`, or `basedpyright-code-budget.json`, run `make lint-budget-update` and commit the lowered limits so the ceilings ratchet down instead of leaving stale headroom. It measures the working tree, so it must contain exactly the fixes you're committing -`make pre-commit` saves its complete output to a log file in .git (overwriting previous pre-commit logs) and prints that path as its first and last output lines. To inspect a run, read or grep that log instead of re-running the multi-minute checks just to see a different slice +`make check` (f.k.a. `make pre-commit`, which still works identically as an alias) saves its complete output to a log file in .git (overwriting previous logs) and prints that path as its first and last output lines. To inspect a run, read or grep that log instead of re-running the multi-minute checks just to see a different slice If you're trying to create a new function that relies on untyped stuff, instead of adding more Any's and pushing `reportAny` / `reportExplicitAny` closer to their basedpyright ceilings, just validate it in the caller with Pydantic (a model or `TypeAdapter` that returns the typed thing or raises will do) and then pass the now typed variable in diff --git a/Makefile b/Makefile index 493828571b7..94d8c875af5 100644 --- a/Makefile +++ b/Makefile @@ -8,7 +8,7 @@ lint-basedpyright lint-e2e-basedpyright lint-basedpyright-budget-update lint-type-discipline lint-type-discipline-budget-update \ lint-ruff-budget lint-ruff-budget-update lint-budget-update lint-gate \ install-dev install-proxy-dev install-test-deps install-hooks \ - install-helm-unittest check-circular-imports check-import-safety pre-commit \ + install-helm-unittest check-circular-imports check-import-safety check pre-commit \ lint-install lint-fetch-base bootstrap # Default target @@ -22,7 +22,8 @@ help: @echo " make install-test-deps - Install the full local test environment" @echo " make install-helm-unittest - Install helm unittest plugin" @echo " make install-hooks - Install git hooks (Conventional Commits + Branches)" - @echo " make pre-commit - Run CI-equivalent lint on staged files (run before committing)" + @echo " make check - Run CI-equivalent lint on staged files, or on the diff vs the base branch when nothing is staged" + @echo " make pre-commit - Legacy alias for make check" @echo " make format - Apply ruff format code formatting" @echo " make format-check - Check ruff format code formatting (matches CI)" @echo " make lint - Run all linting (Ruff, basedpyright, format check, circular imports, import safety)" @@ -236,13 +237,20 @@ lint-checks: lint-format-check-changed lint-ruff lint-gate lint-type-discipline # Faster linting for local development (only checks changed code) lint-dev: lint-format-changed check-circular-imports check-import-safety -# Run the gating CI checks against your staged files right before committing. Mirrors +# Run the gating CI checks against your changes. Scopes to staged files when anything +# is staged (warning about changed files left unstaged); with nothing staged it falls +# back to the working tree's diff against the merge base with the base branch, so a +# fresh merge commit or an unstaged working tree still gets checked. Mirrors # test-linting.yml (Python), test-litellm-ui-build.yml's frontend-lint (dashboard), and -# check-ui-api-types.yml (API-type drift), skipping any whose files you didn't stage. +# check-ui-api-types.yml (API-type drift), skipping any whose files aren't in scope. # Not auto-installed as a git hook so it never slows an unrelated human commit. -pre-commit: bootstrap +check: bootstrap ./scripts/pre_commit_lint.sh +pre-commit: + @echo "make pre-commit is a legacy alias; use make check" >&2 + @$(MAKE) check + # Testing targets test: install-test-deps $(UV_RUN) pytest tests/ diff --git a/basedpyright-code-budget.json b/basedpyright-code-budget.json index 32b8eb3d4d0..96b689aed74 100644 --- a/basedpyright-code-budget.json +++ b/basedpyright-code-budget.json @@ -1,18 +1,18 @@ { "reportAny": { - "limit": 28842 + "limit": 26391 }, "reportArgumentType": { - "limit": 2634 + "limit": 2614 }, "reportAssignmentType": { - "limit": 329 + "limit": 327 }, "reportAttributeAccessIssue": { "limit": 514 }, "reportCallIssue": { - "limit": 117 + "limit": 114 }, "reportConstantRedefinition": { "limit": 40 @@ -24,7 +24,7 @@ "limit": 19 }, "reportExplicitAny": { - "limit": 9103 + "limit": 8319 }, "reportFunctionMemberAccess": { "limit": 7 @@ -54,10 +54,10 @@ "limit": 0 }, "reportMissingParameterType": { - "limit": 5843 + "limit": 5825 }, "reportMissingTypeArgument": { - "limit": 15816 + "limit": 15695 }, "reportMissingTypeStubs": { "limit": 40 @@ -72,7 +72,7 @@ "limit": 0 }, "reportOptionalMemberAccess": { - "limit": 1078 + "limit": 1077 }, "reportOptionalOperand": { "limit": 0 @@ -90,31 +90,31 @@ "limit": 8 }, "reportReturnType": { - "limit": 218 + "limit": 213 }, "reportTypedDictNotRequiredAccess": { - "limit": 27 + "limit": 26 }, "reportUndefinedVariable": { "limit": 0 }, "reportUnknownArgumentType": { - "limit": 45098 + "limit": 44996 }, "reportUnknownLambdaType": { "limit": 113 }, "reportUnknownMemberType": { - "limit": 39826 + "limit": 39643 }, "reportUnknownParameterType": { - "limit": 20237 + "limit": 20132 }, "reportUnknownVariableType": { - "limit": 31371 + "limit": 31153 }, "reportUnnecessaryCast": { - "limit": 122 + "limit": 118 }, "reportUnnecessaryComparison": { "limit": 701 @@ -123,7 +123,7 @@ "limit": 5 }, "reportUnnecessaryIsInstance": { - "limit": 864 + "limit": 857 }, "reportUntypedBaseClass": { "limit": 0 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 7acdd5dbdaf..dc8f17fb665 100644 --- a/enterprise/litellm_enterprise/proxy/common_utils/check_batch_cost.py +++ b/enterprise/litellm_enterprise/proxy/common_utils/check_batch_cost.py @@ -3,7 +3,7 @@ Polls LiteLLM_ManagedObjectTable to check if the batch job is complete, and if t """ from datetime import datetime, timedelta, timezone -from typing import TYPE_CHECKING, List, Optional, Tuple +from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple from litellm._logging import verbose_proxy_logger from litellm._uuid import uuid @@ -43,11 +43,15 @@ class CheckBatchCost: # the guaranteed-failing primary query on every subsequent cycle. self._has_batch_processed_column: bool = True - async def _get_user_info(self, batch_id, user_id) -> dict: + async def _get_user_info(self, batch_id: str, user_id: Optional[str]) -> Dict[str, Any]: """ 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). + Returns an empty dict when user_id is None: batches created by a team or service + account key carry no user id, and find_unique(where={"user_id": None}) raises. """ + if not user_id: + return {} try: user_row = await self.prisma_client.db.litellm_usertable.find_unique( where={"user_id": user_id} @@ -62,6 +66,66 @@ class CheckBatchCost: verbose_proxy_logger.error(f"CheckBatchCost: could not look up user {user_id} for batch {batch_id}: {e}") return {} + async def _get_key_alias(self, batch_id: str, api_key: str | None) -> str | None: + """Resolve the creating virtual key's alias from its hashed token.""" + if not api_key: + return None + try: + key_row = 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: + verbose_proxy_logger.error(f"CheckBatchCost: could not look up key alias for batch {batch_id}: {e}") + return None + + async def _get_team_alias(self, team_id: str | None) -> str | None: + """Resolve a team's alias from its id.""" + if not team_id: + return None + try: + team_row = 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: + verbose_proxy_logger.error(f"CheckBatchCost: could not look up team alias for team {team_id}: {e}") + return None + + async def _build_creator_attribution_metadata( + self, job: "LiteLLM_ManagedObjectTable", batch_id: str + ) -> Dict[str, Any]: + """ + 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 + is. Rows created before api_key and request_tags were persisted carry only + created_by and team_id, and fall back to those. A named creating key owns + user_api_key_alias; when it has no alias, or the key has since been rotated or + deleted, the field keeps the creating user's alias that _get_user_info filled in, + because a resolvable name is more useful on the spend row than a null. + """ + api_key = getattr(job, "api_key", None) + team_id = getattr(job, "team_id", None) + request_tags = getattr(job, "request_tags", None) + + metadata: Dict[str, Any] = { + "user_api_key_user_id": job.created_by, + "user_api_key": api_key, + "user_api_key_team_id": team_id, + **(await self._get_user_info(batch_id, job.created_by)), + } + + key_alias = await self._get_key_alias(batch_id, api_key) + if key_alias is not None: + metadata["user_api_key_alias"] = key_alias + team_alias = await self._get_team_alias(team_id) + if team_alias is not None: + metadata["user_api_key_team_alias"] = team_alias + if isinstance(request_tags, list) and request_tags: + metadata["tags"] = [tag for tag in request_tags if isinstance(tag, str)] + + return metadata + async def _cleanup_stale_managed_objects(self) -> None: """ Mark managed objects older than MANAGED_OBJECT_STALENESS_CUTOFF_DAYS days @@ -485,9 +549,6 @@ class CheckBatchCost: function_id=str(uuid.uuid4()), ) - creator_user_id = job.created_by - user_info = await self._get_user_info(batch_id, job.created_by) - logging_obj.update_environment_variables( litellm_params={ # set the user-agent header so that S3 callback consumers can easily identify CheckBatchCost callbacks @@ -496,11 +557,7 @@ class CheckBatchCost: "user-agent": CHECK_BATCH_COST_USER_AGENT, } }, - "metadata": { - "user_api_key_user_id": creator_user_id, - "user_api_key_team_id": getattr(job, "team_id", None), - **user_info, - }, + "metadata": await self._build_creator_attribution_metadata(job, batch_id), }, optional_params={}, ) diff --git a/enterprise/litellm_enterprise/proxy/hooks/managed_files.py b/enterprise/litellm_enterprise/proxy/hooks/managed_files.py index 8d589e6207c..37d267fcd6e 100644 --- a/enterprise/litellm_enterprise/proxy/hooks/managed_files.py +++ b/enterprise/litellm_enterprise/proxy/hooks/managed_files.py @@ -3,8 +3,21 @@ import base64 import json +from collections.abc import Mapping, Sequence from types import MappingProxyType -from typing import TYPE_CHECKING, Any, Dict, Final, List, Literal, Optional, Union, cast +from typing import ( + TYPE_CHECKING, + Any, + Dict, + Final, + List, + Literal, + Optional, + Protocol, + TypedDict, + Union, + cast, +) from uuid import NAMESPACE_URL, uuid5 from fastapi import HTTPException @@ -32,9 +45,12 @@ from litellm.proxy._types import ( ) from litellm.proxy.openai_files_endpoints.common_utils import ( _is_base64_encoded_unified_file_id, + apply_unified_file_ids, + ensure_batch_response_managed_file_ids, get_batch_id_from_unified_batch_id, get_content_type_from_file_object, get_model_id_from_unified_batch_id, + map_raw_file_ids_to_unified, normalize_mime_type_for_provider, resolve_managed_output_file_model_name, ) @@ -62,6 +78,9 @@ if TYPE_CHECKING: if TYPE_CHECKING: from opentelemetry.trace import Span as _Span + from prisma.models import ( + LiteLLM_ManagedObjectTable as PrismaManagedObjectRow, + ) from litellm.proxy.utils import InternalUsageCache as _InternalUsageCache from litellm.proxy.utils import PrismaClient as _PrismaClient @@ -75,31 +94,94 @@ else: PrismaClient = Any -def _parse_managed_file_object( - raw_file_object: object, unified_file_id: str -) -> Optional[OpenAIFileObject]: +def _sanitized_parse_error(e: Exception) -> str: + return ( + str(e.errors(include_input=False, include_url=False, include_context=False)) + if isinstance(e, ValidationError) + else type(e).__name__ + ) + + +def _decode_json_blob(blob: object) -> object: + return json.loads(blob) if isinstance(blob, str) else blob + + +def _parse_managed_batch_row(row: "PrismaManagedObjectRow") -> Optional[LiteLLMBatch]: + try: + batch_obj: Final = LiteLLMBatch.model_validate(_decode_json_blob(row.file_object)) + except Exception as e: + verbose_logger.warning(f"Failed to parse batch object {row.unified_object_id}: {_sanitized_parse_error(e)}") + return None + batch_obj.id = row.unified_object_id + return batch_obj + + +def _parse_managed_file_object(raw_file_object: object, unified_file_id: str) -> Optional[OpenAIFileObject]: if raw_file_object is None: return None try: return OpenAIFileObject.model_validate(raw_file_object) - except ValidationError as e: - verbose_logger.warning( - f"Failed to parse managed file object {unified_file_id}: " - f"{e.errors(include_input=False, include_url=False, include_context=False)}" - ) - return None except Exception as e: - verbose_logger.warning( - f"Failed to parse managed file object {unified_file_id}: {type(e).__name__}" - ) + verbose_logger.warning(f"Failed to parse managed file object {unified_file_id}: {_sanitized_parse_error(e)}") return None +class _ManagedFileRow(Protocol): + unified_file_id: str + file_object: OpenAIFileObject + storage_backend: Optional[str] + storage_url: Optional[str] + created_by: Optional[str] + team_id: Optional[str] + + def model_dump(self) -> Mapping[str, object]: ... + + +class _ManagedFileTableActions(Protocol): + async def find_first(self, where: Mapping[str, object]) -> Optional[_ManagedFileRow]: ... + + async def find_many(self, where: Mapping[str, object]) -> Sequence[_ManagedFileRow]: ... + + async def upsert(self, where: Mapping[str, str], data: Mapping[str, Mapping[str, object]]) -> _ManagedFileRow: ... + + async def delete(self, where: Mapping[str, str]) -> Optional[_ManagedFileRow]: ... + + +class _ManagedObjectTableActions(Protocol): + async def find_first(self, where: Mapping[str, object]) -> "Optional[PrismaManagedObjectRow]": ... + + async def find_many( + self, + where: Mapping[str, object], + take: int, + order: Union[Mapping[str, str], Sequence[Mapping[str, str]]], + cursor: Mapping[str, str] = ..., + skip: int = ..., + ) -> "Sequence[PrismaManagedObjectRow]": ... + + async def upsert( + self, where: Mapping[str, str], data: Mapping[str, Mapping[str, object]] + ) -> "PrismaManagedObjectRow": ... + + async def update_many(self, where: Mapping[str, object], data: Mapping[str, object]) -> int: ... + + +class _CursorPageArgs(TypedDict, total=False): + cursor: Mapping[str, str] + skip: int + + +def _managed_file_table(prisma_client: PrismaClient) -> _ManagedFileTableActions: + return prisma_client.db.litellm_managedfiletable + + +def _managed_object_table(prisma_client: PrismaClient) -> _ManagedObjectTableActions: + return prisma_client.db.litellm_managedobjecttable + + class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): # Class variables or attributes - def __init__( - self, internal_usage_cache: InternalUsageCache, prisma_client: PrismaClient - ): + def __init__(self, internal_usage_cache: InternalUsageCache, prisma_client: PrismaClient): self.internal_usage_cache = internal_usage_cache self.prisma_client = prisma_client @@ -118,9 +200,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): model_mappings: Dict[str, str], user_api_key_dict: UserAPIKeyAuth, ) -> None: - verbose_logger.info( - f"Storing LiteLLM Managed File object with id={file_id} in cache" - ) + verbose_logger.info(f"Storing LiteLLM Managed File object with id={file_id} in cache") if file_object is not None: litellm_managed_file_object = LiteLLM_ManagedFileTable( unified_file_id=file_id, @@ -171,13 +251,11 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): f"storage_url={db_data.get('storage_url')}" ) - result = await self.prisma_client.db.litellm_managedfiletable.upsert( + result = await _managed_file_table(self.prisma_client).upsert( where={"unified_file_id": file_id}, data={"create": db_data, "update": update_data}, ) - verbose_logger.debug( - f"LiteLLM Managed File object with id={file_id} stored in db: {result}" - ) + verbose_logger.debug(f"LiteLLM Managed File object with id={file_id} stored in db: {result}") async def store_unified_object_id( self, @@ -187,10 +265,25 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): model_object_id: str, file_purpose: Literal["batch", "fine-tune", "response"], user_api_key_dict: UserAPIKeyAuth, + request_tags: Sequence[str] | None = None, + persist_attribution: bool = False, + create_if_missing: bool = True, ) -> None: - verbose_logger.info( - f"Storing LiteLLM Managed {file_purpose} object with id={unified_object_id} in cache" - ) + """Persist a managed object row, caching it and upserting it in the DB. + + persist_attribution is set only by the batch create, which is the one caller + that can speak for the creator; it gates the api_key and request_tags columns + that CheckBatchCost bills against, so a later poll or retrieve of the same + batch cannot record itself as the paying key. Like created_by and team_id, + both are written only in the upsert create branch, never on update. + + create_if_missing is cleared by callers that observe a batch they did not + create, such as a poll. They still refresh status and file_object, but a + row absent from the table is left absent rather than created with the + observer as its creator, because created_by and team_id are written from + whoever calls the create branch. + """ + verbose_logger.info(f"Storing LiteLLM Managed {file_purpose} object with id={unified_object_id} in cache") litellm_managed_object = LiteLLM_ManagedObjectTable( unified_object_id=unified_object_id, model_object_id=model_object_id, @@ -203,7 +296,30 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): litellm_parent_otel_span=litellm_parent_otel_span, ) - await self.prisma_client.db.litellm_managedobjecttable.upsert( + from prisma import Json + + api_key = user_api_key_dict.api_key or None + attribution_columns = ( + { + **({"api_key": api_key} if api_key is not None else {}), + **({"request_tags": Json(list(request_tags))} if request_tags else {}), + } + if persist_attribution + else {} + ) + # FIX: Update status and file_object on every operation to keep state in sync + update_columns: Final = { + "file_object": file_object.model_dump_json(), + "status": file_object.status, + "updated_by": user_api_key_dict.user_id, + } + if not create_if_missing: + await _managed_object_table(self.prisma_client).update_many( + where={"unified_object_id": unified_object_id}, + data=update_columns, + ) + return + await _managed_object_table(self.prisma_client).upsert( where={"unified_object_id": unified_object_id}, data={ "create": { @@ -215,12 +331,9 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): "team_id": user_api_key_dict.team_id, "updated_by": user_api_key_dict.user_id, "status": file_object.status, + **attribution_columns, }, - "update": { - "file_object": file_object.model_dump_json(), - "status": file_object.status, - "updated_by": user_api_key_dict.user_id, - }, # FIX: Update status and file_object on every operation to keep state in sync + "update": update_columns, }, ) @@ -240,9 +353,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): return LiteLLM_ManagedFileTable.model_validate(result) ## CHECK DB - db_object = await self.prisma_client.db.litellm_managedfiletable.find_first( - where={"unified_file_id": file_id} - ) + db_object = await _managed_file_table(self.prisma_client).find_first(where={"unified_file_id": file_id}) if db_object: return LiteLLM_ManagedFileTable.model_validate(db_object.model_dump()) @@ -252,9 +363,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): self, file_id: str, litellm_parent_otel_span: Optional[Span] = None ) -> OpenAIFileObject: ## get old value - initial_value = await self.prisma_client.db.litellm_managedfiletable.find_first( - where={"unified_file_id": file_id} - ) + initial_value = await _managed_file_table(self.prisma_client).find_first(where={"unified_file_id": file_id}) if initial_value is None: raise Exception(f"LiteLLM Managed File object with id={file_id} not found") ## delete old value @@ -263,15 +372,11 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): value=None, litellm_parent_otel_span=litellm_parent_otel_span, ) - await self.prisma_client.db.litellm_managedfiletable.delete( - where={"unified_file_id": file_id} - ) + await _managed_file_table(self.prisma_client).delete(where={"unified_file_id": file_id}) return initial_value.file_object - async def can_user_call_unified_file_id( - self, unified_file_id: str, user_api_key_dict: UserAPIKeyAuth - ) -> bool: - managed_file = await self.prisma_client.db.litellm_managedfiletable.find_first( + async def can_user_call_unified_file_id(self, unified_file_id: str, user_api_key_dict: UserAPIKeyAuth) -> bool: + managed_file = await _managed_file_table(self.prisma_client).find_first( where={"unified_file_id": unified_file_id} ) @@ -286,13 +391,9 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): detail=f"File not found: {unified_file_id}", ) - async def can_user_call_unified_object_id( - self, unified_object_id: str, user_api_key_dict: UserAPIKeyAuth - ) -> bool: - managed_object = ( - await self.prisma_client.db.litellm_managedobjecttable.find_first( - where={"unified_object_id": unified_object_id} - ) + async def can_user_call_unified_object_id(self, unified_object_id: str, user_api_key_dict: UserAPIKeyAuth) -> bool: + managed_object = await _managed_object_table(self.prisma_client).find_first( + where={"unified_object_id": unified_object_id} ) if managed_object: @@ -314,34 +415,28 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): provider: Optional[str] = None, target_model_names: Optional[str] = None, llm_router: Optional[Router] = None, - ) -> Dict[str, Any]: + ) -> Dict[str, object]: # Provider filtering is not supported for managed batches # This is because the encoded object ids stored in the managed objects table do not contain the provider information # To support provider filtering, we would need to store the provider information in the encoded object ids if provider: - raise Exception( - "Filtering by 'provider' is not supported when using managed batches." - ) + raise Exception("Filtering by 'provider' is not supported when using managed batches.") # Model name filtering is not supported for managed batches # This is because the encoded object ids stored in the managed objects table do not contain the model name # A hash of the model name + litellm_params for the model name is encoded as the model id. This is not sufficient to reliably map the target model names to the model ids. if target_model_names: - raise Exception( - "Filtering by 'target_model_names' is not supported when using managed batches." - ) + raise Exception("Filtering by 'target_model_names' is not supported when using managed batches.") owner_filter = build_owner_filter(user_api_key_dict) if owner_filter is None: return build_list_page([]) - where_clause: Dict[str, Any] = {"file_purpose": "batch", **owner_filter} + where_clause: Dict[str, object] = {"file_purpose": "batch", **owner_filter} if after: - cursor_row = ( - await self.prisma_client.db.litellm_managedobjecttable.find_first( - where={**where_clause, "unified_object_id": after} - ) + cursor_row = await _managed_object_table(self.prisma_client).find_first( + where={**where_clause, "unified_object_id": after} ) if cursor_row is None: raise HTTPException( @@ -349,12 +444,10 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): detail=f"Invalid 'after' cursor: no batch found with id '{after}'.", ) - page_size = limit or 20 - cursor_args: Dict[str, Any] = ( - {"cursor": {"unified_object_id": after}, "skip": 1} if after else {} - ) + page_size: Final = min(limit or 20, 100) + cursor_args: _CursorPageArgs = {"cursor": {"unified_object_id": after}, "skip": 1} if after else {} - batches = await self.prisma_client.db.litellm_managedobjecttable.find_many( + batches = await _managed_object_table(self.prisma_client).find_many( where=where_clause, take=page_size + 1, order=[{"created_at": "desc"}, {"unified_object_id": "desc"}], @@ -363,25 +456,54 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): has_more = len(batches) > page_size - batch_objects: List[LiteLLMBatch] = [] - for batch in batches[:page_size]: - try: - batch_data = ( - json.loads(batch.file_object) - if isinstance(batch.file_object, str) - else batch.file_object - ) - batch_obj = LiteLLMBatch.model_validate(batch_data) - batch_obj.id = batch.unified_object_id - batch_objects.append(batch_obj) + parsed_rows: Final = tuple( + (row, batch_obj) for row in batches[:page_size] if (batch_obj := _parse_managed_batch_row(row)) is not None + ) + unified_id_by_raw_id: Final = await map_raw_file_ids_to_unified( + raw_file_ids=frozenset( + file_id + for _, batch_obj in parsed_rows + for file_id in (batch_obj.input_file_id, batch_obj.output_file_id, batch_obj.error_file_id) + if file_id and not _is_base64_encoded_unified_file_id(file_id) + ), + prisma_client=self.prisma_client, + ) + resolved_batches: Final = [ + await self._resolve_listed_batch( + row=row, + batch_obj=batch_obj, + unified_id_by_raw_id=unified_id_by_raw_id, + user_api_key_dict=user_api_key_dict, + ) + for row, batch_obj in parsed_rows + ] + return build_list_page( + [batch_obj for batch_obj in resolved_batches if batch_obj is not None], + has_more=has_more, + ) - except Exception as e: - verbose_logger.warning( - f"Failed to parse batch object {batch.unified_object_id}: {e}" - ) - continue - - return build_list_page(batch_objects, has_more=has_more) + async def _resolve_listed_batch( + self, + row: "PrismaManagedObjectRow", + batch_obj: LiteLLMBatch, + unified_id_by_raw_id: Mapping[str, str], + user_api_key_dict: UserAPIKeyAuth, + ) -> Optional[LiteLLMBatch]: + apply_unified_file_ids(batch_obj, unified_id_by_raw_id) + try: + await ensure_batch_response_managed_file_ids( + response=batch_obj, + managed_files_obj=self, + prisma_client=self.prisma_client, + verbose_proxy_logger=verbose_logger, + user_api_key_dict=user_api_key_dict, + db_batch_object=row, + unified_batch_id=_is_base64_encoded_unified_file_id(row.unified_object_id), + ) + except Exception as e: + verbose_logger.warning(f"Failed to resolve managed file ids for batch {row.unified_object_id}: {e}") + return None + return batch_obj async def get_user_created_file_ids( self, user_api_key_dict: UserAPIKeyAuth, model_object_ids: List[str] @@ -398,7 +520,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): if owner_filter is None: return [] - file_ids = await self.prisma_client.db.litellm_managedfiletable.find_many( + file_ids = await _managed_file_table(self.prisma_client).find_many( where={ **owner_filter, "flat_model_file_ids": {"hasSome": model_object_ids}, @@ -407,27 +529,14 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): return [ parsed_file_object.model_copy(update={"id": row.unified_file_id}) for row in file_ids - if ( - parsed_file_object := _parse_managed_file_object( - row.file_object, row.unified_file_id - ) - ) - is not None + if (parsed_file_object := _parse_managed_file_object(row.file_object, row.unified_file_id)) is not None ] - async def check_managed_file_id_access( - self, data: Dict, user_api_key_dict: UserAPIKeyAuth - ) -> bool: + async def check_managed_file_id_access(self, data: Dict, user_api_key_dict: UserAPIKeyAuth) -> bool: retrieve_file_id = cast(Optional[str], data.get("file_id")) - potential_file_id = ( - _is_base64_encoded_unified_file_id(retrieve_file_id) - if retrieve_file_id - else False - ) + potential_file_id = _is_base64_encoded_unified_file_id(retrieve_file_id) if retrieve_file_id else False if potential_file_id and retrieve_file_id: - if await self.can_user_call_unified_file_id( - retrieve_file_id, user_api_key_dict - ): + if await self.can_user_call_unified_file_id(retrieve_file_id, user_api_key_dict): return True else: raise HTTPException( @@ -436,9 +545,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): ) return False - async def check_file_ids_access( - self, file_ids: List[str], user_api_key_dict: UserAPIKeyAuth - ) -> None: + async def check_file_ids_access(self, file_ids: List[str], user_api_key_dict: UserAPIKeyAuth) -> None: """ Check if the user has access to a list of file IDs. Only checks managed (unified) file IDs. @@ -453,9 +560,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): for file_id in file_ids: is_unified_file_id = _is_base64_encoded_unified_file_id(file_id) if is_unified_file_id: - if not await self.can_user_call_unified_file_id( - file_id, user_api_key_dict - ): + if not await self.can_user_call_unified_file_id(file_id, user_api_key_dict): raise HTTPException( status_code=403, detail=f"User {user_api_key_dict.user_id} does not have access to the file {file_id}", @@ -483,10 +588,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): ### HANDLE TRANSFORMATIONS ### # Check both completion and acompletion call types - is_completion_call = ( - call_type == CallTypes.completion.value - or call_type == CallTypes.acompletion.value - ) + is_completion_call = call_type == CallTypes.completion.value or call_type == CallTypes.acompletion.value if is_completion_call: messages = data.get("messages") @@ -499,9 +601,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): # Check if any files are stored in storage backends and need base64 conversion # This is needed for Vertex AI/Gemini which requires base64 content - is_vertex_ai = model and ( - "vertex_ai" in model or "gemini" in model.lower() - ) + is_vertex_ai = model and ("vertex_ai" in model or "gemini" in model.lower()) if is_vertex_ai: await self._convert_storage_files_to_base64( messages=messages, @@ -513,10 +613,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): file_ids, user_api_key_dict.parent_otel_span ) data["model_file_id_mapping"] = model_file_id_mapping - elif ( - call_type == CallTypes.aresponses.value - or call_type == CallTypes.responses.value - ): + elif call_type == CallTypes.aresponses.value or call_type == CallTypes.responses.value: # Handle managed files in responses API input and tools file_ids = [] @@ -543,23 +640,15 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): if tools: unified_vs_ids = self.get_vector_store_ids_from_file_search_tools(tools) if unified_vs_ids: - await self.check_vector_store_ids_access( - unified_vs_ids, user_api_key_dict - ) + await self.check_vector_store_ids_access(unified_vs_ids, user_api_key_dict) elif call_type == CallTypes.afile_content.value: retrieve_file_id = cast(Optional[str], data.get("file_id")) - potential_file_id = ( - _is_base64_encoded_unified_file_id(retrieve_file_id) - if retrieve_file_id - else False - ) + potential_file_id = _is_base64_encoded_unified_file_id(retrieve_file_id) if retrieve_file_id else False if potential_file_id and "llm_output_file_id," in potential_file_id: model_id = self.get_model_id_from_unified_file_id(potential_file_id) if model_id: data["model"] = model_id - data["file_id"] = self.get_output_file_id_from_unified_file_id( - potential_file_id - ) + data["file_id"] = self.get_output_file_id_from_unified_file_id(potential_file_id) elif call_type == CallTypes.acreate_batch.value: input_file_id = cast(Optional[str], data.get("input_file_id")) if input_file_id: @@ -576,10 +665,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): ): accessor_key: Optional[str] = None retrieve_object_id: Optional[str] = None - if ( - call_type == CallTypes.aretrieve_batch.value - or call_type == CallTypes.acancel_batch.value - ): + if call_type == CallTypes.aretrieve_batch.value or call_type == CallTypes.acancel_batch.value: accessor_key = "batch_id" elif ( call_type == CallTypes.acancel_fine_tuning_job.value @@ -591,32 +677,24 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): retrieve_object_id = cast(Optional[str], data.get(accessor_key)) potential_llm_object_id = ( - _is_base64_encoded_unified_file_id(retrieve_object_id) - if retrieve_object_id - else False + _is_base64_encoded_unified_file_id(retrieve_object_id) if retrieve_object_id else False ) if potential_llm_object_id and retrieve_object_id: ## VALIDATE USER HAS ACCESS TO THE OBJECT ## - if not await self.can_user_call_unified_object_id( - retrieve_object_id, user_api_key_dict - ): + if not await self.can_user_call_unified_object_id(retrieve_object_id, user_api_key_dict): raise HTTPException( status_code=403, detail=f"User {user_api_key_dict.user_id} does not have access to the object {retrieve_object_id}", ) ## for managed batch id - get the model id - potential_model_id = get_model_id_from_unified_batch_id( - potential_llm_object_id - ) + potential_model_id = get_model_id_from_unified_batch_id(potential_llm_object_id) if potential_model_id is None: raise Exception( f"LiteLLM Managed {accessor_key} with id={retrieve_object_id} is invalid - does not contain encoded model_id." ) data["model"] = potential_model_id - data[accessor_key] = get_batch_id_from_unified_batch_id( - potential_llm_object_id - ) + data[accessor_key] = get_batch_id_from_unified_batch_id(potential_llm_object_id) elif call_type == CallTypes.acreate_fine_tuning_job.value: input_file_id = cast(Optional[str], data.get("training_file")) if input_file_id: @@ -672,24 +750,18 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): if accessor_key: input_file_id = cast(Optional[str], kwargs.get(accessor_key)) - model_file_id_mapping = cast( - Optional[Dict[str, Dict[str, str]]], kwargs.get("model_file_id_mapping") - ) + model_file_id_mapping = cast(Optional[Dict[str, Dict[str, str]]], kwargs.get("model_file_id_mapping")) # model_info may be at top-level or nested under litellm_metadata # (batch/file operations use litellm_metadata) model_id = cast(Optional[str], kwargs.get("model_info", {}).get("id", None)) if model_id is None: model_id = cast( Optional[str], - kwargs.get("litellm_metadata", {}) - .get("model_info", {}) - .get("id", None), + kwargs.get("litellm_metadata", {}).get("model_info", {}).get("id", None), ) mapped_file_id: Optional[str] = None if input_file_id and model_file_id_mapping and model_id: - mapped_file_id = model_file_id_mapping.get(input_file_id, {}).get( - model_id, None - ) + mapped_file_id = model_file_id_mapping.get(input_file_id, {}).get(model_id, None) if mapped_file_id: kwargs[accessor_key] = mapped_file_id @@ -715,9 +787,7 @@ 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, Any]]]) -> List[str]: """ Gets file ids from responses API input. @@ -749,19 +819,14 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): content = item.get("content") if isinstance(content, list): for content_item in content: - if ( - isinstance(content_item, dict) - and content_item.get("type") == "input_file" - ): + if isinstance(content_item, dict) and content_item.get("type") == "input_file": file_id = content_item.get("file_id") if file_id: file_ids.append(file_id) return file_ids - def get_file_ids_from_responses_tools( - self, tools: List[Dict[str, Any]] - ) -> List[str]: + def get_file_ids_from_responses_tools(self, tools: List[Dict[str, object]]) -> List[str]: """ Gets file ids from responses API tools parameter. @@ -794,9 +859,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): return file_ids - def get_vector_store_ids_from_file_search_tools( - self, tools: List[Dict[str, Any]] - ) -> List[str]: + def get_vector_store_ids_from_file_search_tools(self, tools: List[Dict[str, object]]) -> List[str]: """ Extract unified vector_store_ids from file_search tools. @@ -889,9 +952,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): ), ) - async def get_model_file_id_mapping( - self, file_ids: List[str], litellm_parent_otel_span: Span - ) -> dict: + async def get_model_file_id_mapping(self, file_ids: List[str], litellm_parent_otel_span: Span) -> dict: """ Get model-specific file IDs for a list of proxy file IDs. Returns a dictionary mapping litellm_proxy/ file_id -> model_id -> model_file_id @@ -921,9 +982,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): # Get all cache keys matching the pattern file_id:* for file_id in litellm_managed_file_ids: # Search for any cache key starting with this file_id - unified_file_object = await self.get_unified_file_id( - file_id, litellm_parent_otel_span - ) + unified_file_object = await self.get_unified_file_id(file_id, litellm_parent_otel_span) if unified_file_object: file_id_mapping[file_id] = unified_file_object.model_mappings @@ -941,9 +1000,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): raise Exception("LLM Router not initialized. Ensure models added to proxy.") responses = [] for model in target_model_names_list: - individual_response = await llm_router.acreate_file( - model=model, **_create_file_request - ) + individual_response = await llm_router.acreate_file(model=model, **_create_file_request) responses.append(individual_response) return responses @@ -974,9 +1031,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): model_mappings: Dict[str, str] = {} for file_object in responses: - model_file_id_mapping = file_object._hidden_params.get( - "model_file_id_mapping" - ) + model_file_id_mapping = file_object._hidden_params.get("model_file_id_mapping") if model_file_id_mapping and isinstance(model_file_id_mapping, dict): model_mappings.update(model_file_id_mapping) @@ -991,17 +1046,10 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): # Emit Prometheus metrics for managed file creation prom_logger = self._get_prometheus_logger() if prom_logger: - first_model = ( - target_model_names_list[0] if target_model_names_list else None - ) + first_model = target_model_names_list[0] if target_model_names_list else None first_provider = "" if responses: - first_provider = ( - getattr(responses[0], "_hidden_params", {}).get( - "custom_llm_provider" - ) - or "" - ) + first_provider = getattr(responses[0], "_hidden_params", {}).get("custom_llm_provider") or "" prom_logger.record_managed_file_created( model=first_model or "", api_provider=first_provider, @@ -1044,9 +1092,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): ) # Convert to URL-safe base64 and strip padding - base64_unified_file_id = ( - base64.urlsafe_b64encode(unified_file_id.encode()).decode().rstrip("=") - ) + base64_unified_file_id = base64.urlsafe_b64encode(unified_file_id.encode()).decode().rstrip("=") ## CREATE RESPONSE OBJECT @@ -1063,46 +1109,26 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): return response - def get_unified_generic_response_id( - self, model_id: str, generic_response_id: str - ) -> str: - unified_generic_response_id = ( - SpecialEnums.LITELLM_MANAGED_GENERIC_RESPONSE_COMPLETE_STR.value.format( - model_id, generic_response_id - ) - ) - return ( - base64.urlsafe_b64encode(unified_generic_response_id.encode()) - .decode() - .rstrip("=") + def get_unified_generic_response_id(self, model_id: str, generic_response_id: str) -> str: + unified_generic_response_id = SpecialEnums.LITELLM_MANAGED_GENERIC_RESPONSE_COMPLETE_STR.value.format( + model_id, generic_response_id ) + return base64.urlsafe_b64encode(unified_generic_response_id.encode()).decode().rstrip("=") def get_unified_batch_id(self, batch_id: str, model_id: str) -> str: - unified_batch_id = SpecialEnums.LITELLM_MANAGED_BATCH_COMPLETE_STR.value.format( - model_id, batch_id - ) + unified_batch_id = SpecialEnums.LITELLM_MANAGED_BATCH_COMPLETE_STR.value.format(model_id, batch_id) return base64.urlsafe_b64encode(unified_batch_id.encode()).decode().rstrip("=") - def get_unified_output_file_id( - self, output_file_id: str, model_id: str, model_name: Optional[str] - ) -> str: - deterministic_uuid: Final = uuid5( - uuid5(NAMESPACE_URL, model_id), output_file_id - ) - unified_output_file_id = ( - SpecialEnums.LITELLM_MANAGED_FILE_COMPLETE_STR.value.format( - "application/json", - str(deterministic_uuid), - model_name or "", - output_file_id, - model_id, - ) - ) - return ( - base64.urlsafe_b64encode(unified_output_file_id.encode()) - .decode() - .rstrip("=") + def get_unified_output_file_id(self, output_file_id: str, model_id: str, model_name: Optional[str]) -> str: + deterministic_uuid: Final = uuid5(uuid5(NAMESPACE_URL, model_id), output_file_id) + unified_output_file_id = SpecialEnums.LITELLM_MANAGED_FILE_COMPLETE_STR.value.format( + "application/json", + str(deterministic_uuid), + model_name or "", + output_file_id, + model_id, ) + return base64.urlsafe_b64encode(unified_output_file_id.encode()).decode().rstrip("=") def get_model_id_from_unified_file_id(self, file_id: str) -> str: return file_id.split("llm_output_file_model_id,")[1].split(";")[0] @@ -1110,59 +1136,39 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): def get_output_file_id_from_unified_file_id(self, file_id: str) -> str: marker = "llm_output_file_id," if marker not in file_id: - raise ValueError( - f"Unified id does not contain {marker!r}: {file_id[:80]!r}" - ) + raise ValueError(f"Unified id does not contain {marker!r}: {file_id[:80]!r}") return file_id.split(marker, 1)[1].split(";")[0] async def async_post_call_success_hook( self, data: Dict, user_api_key_dict: UserAPIKeyAuth, response: LLMResponseTypes - ) -> Any: + ) -> LLMResponseTypes: if isinstance(response, LiteLLMBatch): ## Check if unified_file_id is in the response - unified_file_id = response._hidden_params.get( - "unified_file_id" - ) # managed file id - unified_batch_id = response._hidden_params.get( - "unified_batch_id" - ) # managed batch id + unified_file_id = response._hidden_params.get("unified_file_id") # managed file id + unified_batch_id = response._hidden_params.get("unified_batch_id") # managed batch id model_id = cast(Optional[str], response._hidden_params.get("model_id")) model_name = cast(Optional[str], response._hidden_params.get("model_name")) resolved_model_name = resolve_managed_output_file_model_name( - unified_input_file_id=unified_file_id - if isinstance(unified_file_id, str) - else response.input_file_id, + unified_input_file_id=unified_file_id if isinstance(unified_file_id, str) else response.input_file_id, fallback_model_name=model_name, ) original_response_id = response.id if (unified_batch_id or unified_file_id) and model_id: - response.id = self.get_unified_batch_id( - batch_id=response.id, model_id=model_id - ) + response.id = self.get_unified_batch_id(batch_id=response.id, model_id=model_id) # 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) 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 - ): - provider_file_id = ( - self.get_output_file_id_from_unified_file_id( - decoded_output_file_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: + provider_file_id = self.get_output_file_id_from_unified_file_id(decoded_output_file_id) unified_file_id = file_id_value elif decoded_output_file_id: verbose_logger.warning( - f"Skipping {file_attr}={file_id_value!r}: " - "unified id is not a managed file output id" + f"Skipping {file_attr}={file_id_value!r}: unified id is not a managed file output id" ) continue else: @@ -1181,23 +1187,18 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): # Import module and use getattr for better testability with mocks import litellm.proxy.proxy_server as proxy_server_module - _llm_router = getattr( - proxy_server_module, "llm_router", None - ) + _llm_router = getattr(proxy_server_module, "llm_router", None) if _llm_router is not None and model_id: - _creds = ( - _llm_router.get_deployment_credentials_with_provider( - model_id - ) - or {} - ) + _creds = _llm_router.get_deployment_credentials_with_provider(model_id) or {} file_object = await litellm.afile_retrieve( file_id=provider_file_id, **_creds, ) else: file_object = await litellm.afile_retrieve( - custom_llm_provider=model_name.split("/")[0] if model_name and "/" in model_name else "openai", # type: ignore[arg-type] + custom_llm_provider=model_name.split("/")[0] + if model_name and "/" in model_name + else "openai", # type: ignore[arg-type] file_id=provider_file_id, ) verbose_logger.debug( @@ -1251,9 +1252,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): elif isinstance(response, LiteLLMFineTuningJob): ## Check if unified_file_id is in the response - unified_file_id = response._hidden_params.get( - "unified_file_id" - ) # managed file id + unified_file_id = response._hidden_params.get("unified_file_id") # managed file id unified_finetuning_job_id = response._hidden_params.get( "unified_finetuning_job_id" ) # managed finetuning job id @@ -1261,9 +1260,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): model_name = cast(Optional[str], response._hidden_params.get("model_name")) original_response_id = response.id if (unified_file_id or unified_finetuning_job_id) and model_id: - response.id = self.get_unified_generic_response_id( - model_id=model_id, generic_response_id=response.id - ) + response.id = self.get_unified_generic_response_id(model_id=model_id, generic_response_id=response.id) await self.store_unified_object_id( unified_object_id=response.id, file_object=response, @@ -1278,9 +1275,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): """ ## check if file object if hasattr(response, "data") and isinstance(response.data, list): - if all( - isinstance(file_object, FileObject) for file_object in response.data - ): + if all(isinstance(file_object, FileObject) for file_object in response.data): ## Get all file id's ## Check which file id's were created by the user ## Filter the response to only include the files created by the user @@ -1289,21 +1284,34 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): file_object.id for file_object in cast(List[FileObject], response.data) # type: ignore ] - user_created_file_ids = await self.get_user_created_file_ids( - user_api_key_dict, file_ids - ) + user_created_file_ids = await self.get_user_created_file_ids(user_api_key_dict, file_ids) ## Filter the response to only include the files created by the user response.data = user_created_file_ids # type: ignore + self._scope_list_page_cursors(response, user_created_file_ids) return response return response return response + @staticmethod + def _scope_list_page_cursors(response: AsyncCursorPage, data: List[OpenAIFileObject]) -> None: + """Rebuild ``first_id`` / ``last_id`` from the caller-scoped page. + + The upstream cursors point at rows that were just filtered out, so + leaving them in place discloses other callers' file ids. ``has_more`` + is always cleared because ``after`` is never forwarded upstream, so + no further page is reachable through the proxy. + """ + if hasattr(response, "first_id"): + response.first_id = data[0].id if data else None + if hasattr(response, "last_id"): + response.last_id = data[-1].id if data else None + if hasattr(response, "has_more"): + response.has_more = False + async def afile_retrieve( - self, file_id: str, litellm_parent_otel_span: Optional[Span], llm_router=None + self, file_id: str, litellm_parent_otel_span: Optional[Span], llm_router: Optional[Router] = None ) -> OpenAIFileObject: - stored_file_object = await self.get_unified_file_id( - file_id, litellm_parent_otel_span - ) + stored_file_object = await self.get_unified_file_id(file_id, litellm_parent_otel_span) # Case 1 : This is not a managed file if not stored_file_object: @@ -1326,21 +1334,13 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): ) try: - model_id, model_file_id = next( - iter(stored_file_object.model_mappings.items()) - ) - credentials = ( - llm_router.get_deployment_credentials_with_provider(model_id) or {} - ) - response = await litellm.afile_retrieve( - file_id=model_file_id, **credentials - ) + model_id, model_file_id = next(iter(stored_file_object.model_mappings.items())) + credentials = llm_router.get_deployment_credentials_with_provider(model_id) or {} + response = await litellm.afile_retrieve(file_id=model_file_id, **credentials) response.id = file_id # Replace with unified ID return response except Exception as e: - raise Exception( - f"Failed to retrieve file {file_id} from provider: {str(e)}" - ) from e + raise Exception(f"Failed to retrieve file {file_id} from provider: {str(e)}") from e async def afile_list( self, @@ -1377,12 +1377,10 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): return False except Exception as e: - verbose_logger.warning( - f"Error checking batch polling configuration: {e}. Assuming disabled." - ) + verbose_logger.warning(f"Error checking batch polling configuration: {e}. Assuming disabled.") return False - async def _get_batches_referencing_file(self, file_id: str) -> List[Dict[str, Any]]: + async def _get_batches_referencing_file(self, file_id: str) -> List[Dict[str, object]]: """ Find batches that reference this file and still need cost tracking. Find batches that are in non-terminal state and have not yet been processed by CheckBatchCost. @@ -1398,9 +1396,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): # Get model-specific file IDs for this unified file ID if it's a managed file try: - model_file_id_mapping = await self.get_model_file_id_mapping( - [file_id], litellm_parent_otel_span=None - ) + model_file_id_mapping = await self.get_model_file_id_mapping([file_id], litellm_parent_otel_span=None) if model_file_id_mapping and file_id in model_file_id_mapping: # Add all provider file IDs for this unified file @@ -1408,8 +1404,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): file_ids_to_check.extend(provider_file_ids) except Exception as e: verbose_logger.debug( - f"Could not get model file ID mapping for {file_id}: {e}. " - f"Will only check unified file ID." + f"Could not get model file ID mapping for {file_id}: {e}. Will only check unified file ID." ) MAX_MATCHES_TO_RETURN = 10 @@ -1427,11 +1422,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): 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 - ) + batch_data = json.loads(batch.file_object) if isinstance(batch.file_object, str) else batch.file_object # Extract file IDs from batch # Batches typically reference the unified file ID in input_file_id @@ -1440,9 +1431,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): output_file_id = batch_data.get("output_file_id") error_file_id = batch_data.get("error_file_id") - referenced_file_ids = [ - fid for fid in [input_file_id, output_file_id, error_file_id] if fid - ] + referenced_file_ids = [fid for fid in [input_file_id, output_file_id, error_file_id] if fid] # Check if any referenced file ID matches the file we're trying to delete if any(ref_id in file_ids_to_check for ref_id in referenced_file_ids): @@ -1454,9 +1443,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): } ) except Exception as e: - verbose_logger.warning( - f"Error parsing batch object {batch.unified_object_id}: {e}" - ) + verbose_logger.warning(f"Error parsing batch object {batch.unified_object_id}: {e}") continue return referencing_batches @@ -1485,21 +1472,15 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): if referencing_batches: # File is referenced by non-terminal batches and polling is enabled - MAX_BATCHES_IN_ERROR = ( - 5 # Limit batches shown in error message for readability - ) + MAX_BATCHES_IN_ERROR = 5 # Limit batches shown in error message for readability # Show up to MAX_BATCHES_IN_ERROR in the error message batches_to_show = referencing_batches[:MAX_BATCHES_IN_ERROR] - batch_statuses = [ - f"{b['batch_id']}: {b['status']}" for b in batches_to_show - ] + batch_statuses = [f"{b['batch_id']}: {b['status']}" for b in batches_to_show] # Determine the count message count_message = f"{len(referencing_batches)}" - if ( - len(referencing_batches) >= 10 - ): # MAX_MATCHES_TO_RETURN from _get_batches_referencing_file + if len(referencing_batches) >= 10: # MAX_MATCHES_TO_RETURN from _get_batches_referencing_file count_message = "10+" error_message = ( @@ -1540,23 +1521,17 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): await self._check_file_deletion_allowed(file_id) # file_id = convert_b64_uid_to_unified_uid(file_id) - model_file_id_mapping = await self.get_model_file_id_mapping( - [file_id], litellm_parent_otel_span - ) + model_file_id_mapping = await self.get_model_file_id_mapping([file_id], litellm_parent_otel_span) delete_response = None specific_model_file_id_mapping = model_file_id_mapping.get(file_id) if specific_model_file_id_mapping: # Remove conflicting keys from data to avoid duplicate keyword arguments - filtered_data = { - k: v for k, v in data.items() if k not in ("model", "file_id") - } + filtered_data = {k: v for k, v in data.items() if k not in ("model", "file_id")} for model_id, model_file_id in specific_model_file_id_mapping.items(): delete_response = await llm_router.afile_delete(model=model_id, file_id=model_file_id, **filtered_data) # type: ignore - stored_file_object = await self.delete_unified_file_id( - file_id, litellm_parent_otel_span - ) + stored_file_object = await self.delete_unified_file_id(file_id, litellm_parent_otel_span) # Record successful deletion metric only on actual success if stored_file_object or delete_response: @@ -1583,9 +1558,8 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): Get the content of a file from first model that has it """ model_file_id_mapping = data.pop("model_file_id_mapping", None) - model_file_id_mapping = ( - model_file_id_mapping - or await self.get_model_file_id_mapping([file_id], litellm_parent_otel_span) + model_file_id_mapping = model_file_id_mapping or await self.get_model_file_id_mapping( + [file_id], litellm_parent_otel_span ) specific_model_file_id_mapping = model_file_id_mapping.get(file_id) @@ -1598,13 +1572,9 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): # against the deployment's configured bucket, which they only # trust from this immutable server-side snapshot, never from # request params. - credentials = llm_router.get_deployment_credentials_with_provider( - model_id=model_id - ) + credentials = llm_router.get_deployment_credentials_with_provider(model_id=model_id) if credentials is not None: - data["_litellm_internal_model_credentials"] = cast( - Dict, MappingProxyType(dict(credentials)) - ) + data["_litellm_internal_model_credentials"] = cast(Dict, MappingProxyType(dict(credentials))) else: data.pop("_litellm_internal_model_credentials", None) return await llm_router.afile_content(model=model_id, file_id=provider_file_id, **data) # type: ignore @@ -1639,9 +1609,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): # Check database for storage backend info # IMPORTANT: The database stores the base64 encoded unified_file_id (not the decoded version) # So we query with the original file_id (which is base64 encoded) - db_file = await self.prisma_client.db.litellm_managedfiletable.find_first( - where={"unified_file_id": file_id} - ) + db_file = await _managed_file_table(self.prisma_client).find_first(where={"unified_file_id": file_id}) if not db_file or not db_file.storage_backend or not db_file.storage_url: continue @@ -1667,22 +1635,16 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): file_content = await storage_backend.download_file(storage_url) # Determine content type from file object - content_type = self._get_content_type_from_file_object( - db_file.file_object - ) + content_type = self._get_content_type_from_file_object(db_file.file_object) # Convert to base64 base64_data = base64.b64encode(file_content).decode("utf-8") base64_data_uri = f"data:{content_type};base64,{base64_data}" # Update messages to use base64 instead of file_id - self._update_messages_with_base64_data( - messages, file_id, base64_data_uri, content_type - ) + self._update_messages_with_base64_data(messages, file_id, base64_data_uri, content_type) except Exception as e: - verbose_logger.exception( - f"Error converting file {file_id} from storage backend to base64: {str(e)}" - ) + verbose_logger.exception(f"Error converting file {file_id} from storage backend to base64: {str(e)}") # Continue with other files even if one fails continue diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20260713010000_add_ptu_columns_to_daily_team_spend/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260713010000_add_ptu_columns_to_daily_team_spend/migration.sql new file mode 100644 index 00000000000..89a0494431b --- /dev/null +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260713010000_add_ptu_columns_to_daily_team_spend/migration.sql @@ -0,0 +1,2 @@ +-- AlterTable +ALTER TABLE "LiteLLM_DailyTeamSpend" ADD COLUMN IF NOT EXISTS "ptu_flat_cost" DOUBLE PRECISION NOT NULL DEFAULT 0.0; diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20260730000000_add_api_key_and_request_tags_to_managed_object_table/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260730000000_add_api_key_and_request_tags_to_managed_object_table/migration.sql new file mode 100644 index 00000000000..79bc6b24de8 --- /dev/null +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260730000000_add_api_key_and_request_tags_to_managed_object_table/migration.sql @@ -0,0 +1,5 @@ +-- Add api_key and request_tags columns to LiteLLM_ManagedObjectTable +-- Captured at batch-create time so CheckBatchCost can attribute batch-cost spend +-- back to the creating virtual key (and its tags) even when created_by is null. +ALTER TABLE "LiteLLM_ManagedObjectTable" ADD COLUMN IF NOT EXISTS "api_key" TEXT; +ALTER TABLE "LiteLLM_ManagedObjectTable" ADD COLUMN IF NOT EXISTS "request_tags" JSONB DEFAULT '[]'; diff --git a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma index 9c871b65f40..33fd9389b63 100644 --- a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma +++ b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma @@ -30,7 +30,7 @@ model LiteLLM_BudgetTable { end_users LiteLLM_EndUserTable[] // multiple end-users can have the same budget tags LiteLLM_TagTable[] // multiple tags can have the same budget team_membership LiteLLM_TeamMembership[] // budgets of Users within a Team - organization_membership LiteLLM_OrganizationMembership[] // budgets of Users within a Organization + organization_membership LiteLLM_OrganizationMembership[] // budgets of Users within a Organization } // Models on proxy @@ -893,6 +893,7 @@ model LiteLLM_DailyTeamSpend { api_requests BigInt @default(0) successful_requests BigInt @default(0) failed_requests BigInt @default(0) + ptu_flat_cost Float @default(0.0) created_at DateTime @default(now()) updated_at DateTime @updatedAt @@ -985,6 +986,8 @@ model LiteLLM_ManagedObjectTable { // for batches or finetuning jobs which use t created_at DateTime @default(now()) created_by String? team_id String? + api_key String? + request_tags Json? @default("[]") updated_at DateTime @updatedAt updated_by String? diff --git a/litellm/__init__.py b/litellm/__init__.py index e0c7d56361c..bc8a13ec2cd 100644 --- a/litellm/__init__.py +++ b/litellm/__init__.py @@ -197,6 +197,7 @@ standard_logging_payload_excluded_fields: Optional[List[str]] = ( None # Fields to exclude from StandardLoggingPayload before callbacks receive it ) log_raw_request_response: bool = False +request_correlation_in_logs: bool = False redact_messages_in_exceptions: Optional[bool] = False redact_user_api_key_info: Optional[bool] = False # When True (default — preserves historical behavior), the Router appends diff --git a/litellm/_logging.py b/litellm/_logging.py index b9e102e2b3c..6add9d79a5b 100644 --- a/litellm/_logging.py +++ b/litellm/_logging.py @@ -1,4 +1,5 @@ import ast +import contextvars import logging import os import sys @@ -6,12 +7,44 @@ from datetime import datetime from logging import Formatter from typing import Any, Final +import litellm from litellm.litellm_core_utils.safe_json_dumps import safe_dumps from litellm.litellm_core_utils.safe_json_loads import safe_json_loads from litellm.litellm_core_utils.secret_redaction import redact_string set_verbose = False +session_id_var: Final[contextvars.ContextVar[str]] = contextvars.ContextVar("session_id", default="") +trace_id_var: Final[contextvars.ContextVar[str]] = contextvars.ContextVar("trace_id", default="") + +_MAX_CORRELATION_ID_LENGTH: Final = 256 + + +def _sanitize_correlation_id(value: str) -> str: + """Strip control characters, bound length, and redact credential-shaped + content before a caller-controlled trace_id/session_id (e.g. + litellm_session_id, x-litellm-trace-id) is stamped into log lines. + + Without the first two, a caller could embed \\r/\\n or terminal escape + sequences to forge fake log entries, or submit an oversized value repeated + across every log line for the request. Without the redaction, a caller + could smuggle a real credential (e.g. an sk-... key) through this field: + CorrelationContextFilter stamps trace_id/session_id onto the record after + SecretRedactionFilter has already run, so those two fields never otherwise + pass through credential redaction. + """ + stripped: Final = "".join(ch for ch in value if ch.isprintable()) + return _redact_string(stripped[:_MAX_CORRELATION_ID_LENGTH]) + + +def set_session_id(session_id: str) -> "contextvars.Token[str]": + return session_id_var.set(_sanitize_correlation_id(session_id)) + + +def set_trace_id(trace_id: str) -> "contextvars.Token[str]": + return trace_id_var.set(_sanitize_correlation_id(trace_id)) + + if set_verbose is True: logging.warning( "`litellm.set_verbose` is deprecated. Please set `os.environ['LITELLM_LOG'] = 'DEBUG'` for debug logs." @@ -77,6 +110,28 @@ class SecretRedactionFilter(logging.Filter): _secret_filter: Final = SecretRedactionFilter() +class CorrelationContextFilter(logging.Filter): + """Stamps each log record with the current request's trace_id and session_id from contextvars. + + Works in tandem with JsonFormatter: the formatter's record.__dict__ loop picks up these + attributes as first-class JSON fields without any formatter-level code. + """ + + def filter(self, record: logging.LogRecord) -> bool: + if not litellm.request_correlation_in_logs: + return True + trace_id: Final = trace_id_var.get() + if trace_id: + record.trace_id = trace_id # rebind-ok: stamping the LogRecord is the Filter interface's contract + session_id: Final = session_id_var.get() + if session_id: + record.session_id = session_id # rebind-ok: stamping the LogRecord is the Filter interface's contract + return True + + +_correlation_filter: Final = CorrelationContextFilter() + + json_logs = bool(os.getenv("JSON_LOGS", False)) # Create a handler for the logger (you may need to adapt this based on your needs) log_level: Final = os.getenv("LITELLM_LOG", "DEBUG") @@ -84,6 +139,7 @@ numeric_level: Final[str] = getattr(logging, log_level.upper()) handler: Final = logging.StreamHandler() handler.setLevel(numeric_level) handler.addFilter(_secret_filter) +handler.addFilter(_correlation_filter) def _try_parse_json_message(message: str) -> dict[str, Any] | None: @@ -146,6 +202,11 @@ def _get_standard_record_attrs() -> frozenset: _STANDARD_RECORD_ATTRS: Final = _get_standard_record_attrs() +# CorrelationContextFilter is the only legitimate source for these two JSON fields; +# see JsonFormatter.format() for why they're excluded from the generic message-content +# and extra-attribute promotion paths. +_RESERVED_CORRELATION_FIELDS: Final = frozenset(("trace_id", "session_id")) + class JsonFormatter(Formatter): def __init__(self): @@ -164,13 +225,18 @@ class JsonFormatter(Formatter): "timestamp": self.formatTime(record), } - # Parse embedded JSON or Python dict repr in message so sub-fields become first-class properties + # Parse embedded JSON or Python dict repr in message so sub-fields become first-class properties. + # trace_id/session_id are excluded here unconditionally (not just "if not already + # set") - CorrelationContextFilter is the only legitimate source for these two + # fields, and a message that merely happens to parse as JSON/dict (e.g. a proxy + # log line dumping raw request headers) must never be able to claim them, even on + # a record the filter hasn't stamped yet (no correlation context active for it). parsed = _try_parse_json_message(message_str) if parsed is None: parsed = _try_parse_embedded_python_dict(message_str) if parsed is not None: for key, value in parsed.items(): - if key not in json_record: + if key not in json_record and key not in _RESERVED_CORRELATION_FIELDS: json_record[key] = value # Include extra attributes passed via logger.debug("msg", extra={...}) @@ -178,6 +244,18 @@ class JsonFormatter(Formatter): if key not in _STANDARD_RECORD_ATTRS and key not in json_record: json_record[key] = value + # trace_id/session_id are reserved: CorrelationContextFilter is the only + # legitimate source for these two fields. Without this, a message string + # that happens to parse as JSON/dict (e.g. a proxy log line dumping raw + # request headers) with a "trace_id"/"session_id" key would have already + # claimed the key at the parsed-message step above, and the extra-attributes + # loop's "key not in json_record" guard would then skip the real value - + # letting a caller-supplied header spoof another request's correlation ids. + for reserved_key in _RESERVED_CORRELATION_FIELDS: + value = getattr(record, reserved_key, None) + if value: + json_record[reserved_key] = value + # Set component/logger only if not already supplied via extra={...} if "component" not in json_record: json_record["component"] = record.name @@ -190,12 +268,34 @@ class JsonFormatter(Formatter): return safe_dumps(json_record) +class CorrelationPlainFormatter(logging.Formatter): + """Appends trace_id/session_id to plain-text log lines stamped by CorrelationContextFilter. + + Mirrors JsonFormatter's handling of these two fields so request_correlation_in_logs + behaves the same whether or not json_logs is enabled. + """ + + def format(self, record: logging.LogRecord) -> str: + formatted: Final = super().format(record) + trace_id: Final = getattr(record, "trace_id", None) + session_id: Final = getattr(record, "session_id", None) + if not trace_id and not session_id: + return formatted + parts: Final = tuple( + p + for p in (f"trace_id={trace_id}" if trace_id else None, f"session_id={session_id}" if session_id else None) + if p + ) + return f"{formatted} [{' '.join(parts)}]" + + # Function to set up exception handlers for JSON logging def _setup_json_exception_handlers(formatter): # Create a handler with JSON formatting for exceptions error_handler: Final = logging.StreamHandler() error_handler.setFormatter(formatter) error_handler.addFilter(_secret_filter) + error_handler.addFilter(_correlation_filter) # Setup excepthook for uncaught exceptions def json_excepthook(exc_type, exc_value, exc_traceback): @@ -243,7 +343,7 @@ if json_logs: handler.setFormatter(JsonFormatter()) _setup_json_exception_handlers(JsonFormatter()) else: - formatter: Final = logging.Formatter( + formatter: Final = CorrelationPlainFormatter( "\033[92m%(asctime)s - %(name)s:%(levelname)s\033[0m: %(filename)s:%(lineno)s - %(message)s", datefmt="%H:%M:%S", ) @@ -346,6 +446,7 @@ def _initialize_loggers_with_handler(handler: logging.Handler): - Prevents bubbling to parent/root (critical to prevent duplicate JSON logs) """ handler.addFilter(_secret_filter) + handler.addFilter(_correlation_filter) for lg in _get_loggers_to_initialize(): lg.handlers.clear() # remove any existing handlers lg.addHandler(handler) # add JSON formatter handler diff --git a/litellm/a2a_protocol/providers/pydantic_ai_agents/transformation.py b/litellm/a2a_protocol/providers/pydantic_ai_agents/transformation.py index 339da998d56..024e8c179c2 100644 --- a/litellm/a2a_protocol/providers/pydantic_ai_agents/transformation.py +++ b/litellm/a2a_protocol/providers/pydantic_ai_agents/transformation.py @@ -6,16 +6,33 @@ This module provides fake streaming by converting non-streaming responses into s """ import asyncio -from collections.abc import AsyncIterator -from typing import Any, Final, cast +from collections.abc import AsyncIterator, Mapping, Sequence +from typing import Any, Final, Protocol, cast, runtime_checkable from uuid import uuid4 +from pydantic import TypeAdapter + from litellm._logging import verbose_logger from litellm.llms.custom_httpx.http_handler import ( AsyncHTTPHandler, get_async_httpx_client, ) +_ANY_KEY_DICT_ADAPTER: Final = TypeAdapter(dict[object, object]) +_STR_KEY_DICT_ADAPTER: Final = TypeAdapter(dict[str, object]) +_LIST_ADAPTER: Final = TypeAdapter(list[object]) +_TEXT_ADAPTER: Final = TypeAdapter(str) + + +@runtime_checkable +class _SupportsModelDump(Protocol): + def model_dump(self, *, mode: str, exclude_none: bool) -> Mapping[str, object]: ... + + +@runtime_checkable +class _SupportsPydanticDict(Protocol): + def dict(self, *, exclude_none: bool) -> Mapping[str, object]: ... + class PydanticAITransformation: """ @@ -28,7 +45,7 @@ class PydanticAITransformation: """ @staticmethod - def _remove_none_values(obj: Any) -> Any: + def _remove_none_values(obj: object) -> object: """ Recursively remove None values from a dict/list structure. @@ -42,14 +59,18 @@ class PydanticAITransformation: Cleaned object with None values removed """ if isinstance(obj, dict): - return {k: PydanticAITransformation._remove_none_values(v) for k, v in obj.items() if v is not None} + typed_dict: Final = _ANY_KEY_DICT_ADAPTER.validate_python(obj) + return {k: PydanticAITransformation._remove_none_values(v) for k, v in typed_dict.items() if v is not None} elif isinstance(obj, list): - return [PydanticAITransformation._remove_none_values(item) for item in obj if item is not None] + typed_list: Final = _LIST_ADAPTER.validate_python(obj) + return [PydanticAITransformation._remove_none_values(item) for item in typed_list if item is not None] else: return obj @staticmethod - def _params_to_dict(params: Any) -> dict[str, Any]: + def _params_to_dict( + params: "_SupportsModelDump | _SupportsPydanticDict | Mapping[str, object]", + ) -> Mapping[str, object]: """ Convert params to a dict, handling Pydantic models. @@ -59,10 +80,10 @@ class PydanticAITransformation: Returns: Dict representation of params """ - if hasattr(params, "model_dump"): + if isinstance(params, _SupportsModelDump): # Pydantic v2 model return params.model_dump(mode="python", exclude_none=True) - elif hasattr(params, "dict"): + elif isinstance(params, _SupportsPydanticDict): # Pydantic v1 model return params.dict(exclude_none=True) elif isinstance(params, dict): @@ -75,12 +96,12 @@ class PydanticAITransformation: async def _poll_for_completion( client: AsyncHTTPHandler, endpoint: str, - task_id: str, + task_id: object, request_id: str, max_attempts: int = 30, poll_interval: float = 0.5, agent_extra_headers: dict[str, str] | None = None, - ) -> dict[str, Any]: + ) -> dict[str, object]: """ Poll for task completion using tasks/get method. @@ -112,10 +133,10 @@ class PydanticAITransformation: }, ) response.raise_for_status() - poll_data = response.json() + poll_data = _STR_KEY_DICT_ADAPTER.validate_python(response.json()) - result = poll_data.get("result", {}) - status = result.get("status", {}) + result = _STR_KEY_DICT_ADAPTER.validate_python(poll_data.get("result", {})) + status = _STR_KEY_DICT_ADAPTER.validate_python(result.get("status", {})) state = status.get("state", "") verbose_logger.debug("Pydantic AI: Poll attempt %s/%s, state=%s", attempt + 1, max_attempts, state) @@ -133,10 +154,10 @@ class PydanticAITransformation: async def _send_and_poll_raw( api_base: str, request_id: str, - params: Any, + params: "_SupportsModelDump | _SupportsPydanticDict | Mapping[str, object]", timeout: float = 60.0, agent_extra_headers: dict[str, str] | None = None, - ) -> dict[str, Any]: + ) -> dict[str, object]: """ Send a request to Pydantic AI agent and return the raw task response. @@ -153,14 +174,16 @@ class PydanticAITransformation: Raw Pydantic AI task response (with history/artifacts) """ # Convert params to dict if it's a Pydantic model - params_dict = PydanticAITransformation._params_to_dict(params) - # Remove None values - FastA2A doesn't accept null for optional fields - params_dict = PydanticAITransformation._remove_none_values(params_dict) + params_dict: Final = _ANY_KEY_DICT_ADAPTER.validate_python( + PydanticAITransformation._remove_none_values(PydanticAITransformation._params_to_dict(params)) + ) # Ensure the message has 'kind': 'message' as required by FastA2A/Pydantic AI if "message" in params_dict: - params_dict["message"]["kind"] = "message" + message_value: Final = _ANY_KEY_DICT_ADAPTER.validate_python(params_dict["message"]) + message_value["kind"] = "message" + params_dict["message"] = message_value # Build A2A JSON-RPC request using message/send method for FastA2A compatibility a2a_request: Final = { @@ -189,11 +212,11 @@ class PydanticAITransformation: }, ) response.raise_for_status() - response_data = response.json() + response_data = _STR_KEY_DICT_ADAPTER.validate_python(response.json()) # Check if task is already completed - result: Final = response_data.get("result", {}) - status: Final = result.get("status", {}) + result: Final = _STR_KEY_DICT_ADAPTER.validate_python(response_data.get("result", {})) + status: Final = _STR_KEY_DICT_ADAPTER.validate_python(result.get("status", {})) state: Final = status.get("state", "") if state != "completed": @@ -217,10 +240,10 @@ class PydanticAITransformation: async def send_non_streaming_request( api_base: str, request_id: str, - params: Any, + params: "_SupportsModelDump | _SupportsPydanticDict | Mapping[str, object]", timeout: float = 60.0, agent_extra_headers: dict[str, str] | None = None, - ) -> dict[str, Any]: + ) -> dict[str, object]: """ Send a non-streaming A2A request to Pydantic AI agent and wait for completion. @@ -253,10 +276,10 @@ class PydanticAITransformation: async def send_and_get_raw_response( api_base: str, request_id: str, - params: Any, + params: "_SupportsModelDump | _SupportsPydanticDict | Mapping[str, object]", timeout: float = 60.0, agent_extra_headers: dict[str, str] | None = None, - ) -> dict[str, Any]: + ) -> dict[str, object]: """ Send a request to Pydantic AI agent and return the raw task response. @@ -282,9 +305,9 @@ class PydanticAITransformation: @staticmethod def _transform_to_a2a_response( - response_data: dict[str, Any], + response_data: Mapping[str, object], request_id: str, - ) -> dict[str, Any]: + ) -> dict[str, object]: """ Transform Pydantic AI task response to standard A2A non-streaming format. @@ -328,7 +351,7 @@ class PydanticAITransformation: } @staticmethod - def _extract_response_text(response_data: dict[str, Any]) -> tuple[str, str, list]: + def _extract_response_text(response_data: Mapping[str, object]) -> tuple[object, object, Sequence[object]]: """ Extract response text from completed task response. @@ -342,52 +365,53 @@ class PydanticAITransformation: Returns: Tuple of (full_text, message_id, parts) """ - result: Final = response_data.get("result", {}) + result: Final = _STR_KEY_DICT_ADAPTER.validate_python(response_data.get("result", {})) # Try to extract from artifacts first (preferred for results) artifacts: Final = result.get("artifacts", []) if artifacts: - for artifact in artifacts: - parts = artifact.get("parts", []) + for artifact in _LIST_ADAPTER.validate_python(artifacts): + parts = _LIST_ADAPTER.validate_python(_STR_KEY_DICT_ADAPTER.validate_python(artifact).get("parts", [])) for part in parts: - if part.get("kind") == "text": - text = part.get("text", "") + if (part_dict := _STR_KEY_DICT_ADAPTER.validate_python(part)).get("kind") == "text": + text = part_dict.get("text", "") if text: return text, str(uuid4()), parts # Fall back to history - get the last agent message - history: Final = result.get("history", []) + history: Final = _LIST_ADAPTER.validate_python(result.get("history", [])) for msg in reversed(history): - if msg.get("role") == "agent": - parts = msg.get("parts", []) - message_id = msg.get("messageId", str(uuid4())) + if (msg_dict := _STR_KEY_DICT_ADAPTER.validate_python(msg)).get("role") == "agent": + parts = _LIST_ADAPTER.validate_python(msg_dict.get("parts", [])) + message_id = msg_dict.get("messageId", str(uuid4())) full_text = "" for part in parts: - if part.get("kind") == "text": - full_text += part.get("text", "") + if (part_dict := _STR_KEY_DICT_ADAPTER.validate_python(part)).get("kind") == "text": + full_text += _TEXT_ADAPTER.validate_python(part_dict.get("text", "")) if full_text: return full_text, message_id, parts # Fall back to message field (original format) message: Final = result.get("message", {}) if message: - parts = message.get("parts", []) - message_id = message.get("messageId", str(uuid4())) + message_dict: Final = _STR_KEY_DICT_ADAPTER.validate_python(message) + parts = _LIST_ADAPTER.validate_python(message_dict.get("parts", [])) + message_id = message_dict.get("messageId", str(uuid4())) full_text = "" for part in parts: - if part.get("kind") == "text": - full_text += part.get("text", "") + if (part_dict := _STR_KEY_DICT_ADAPTER.validate_python(part)).get("kind") == "text": + full_text += _TEXT_ADAPTER.validate_python(part_dict.get("text", "")) return full_text, message_id, parts return "", str(uuid4()), [] @staticmethod async def fake_streaming_from_response( - response_data: dict[str, Any], + response_data: Mapping[str, object], request_id: str, chunk_size: int = 50, delay_ms: int = 10, - ) -> AsyncIterator[dict[str, Any]]: + ) -> AsyncIterator[dict[str, object]]: """ Convert a non-streaming A2A response into fake streaming chunks. @@ -410,12 +434,12 @@ class PydanticAITransformation: full_text, message_id, parts = PydanticAITransformation._extract_response_text(response_data) # Extract input message from raw response for history - result: Final = response_data.get("result", {}) - history: Final = result.get("history", []) - input_message = {} + result: Final = _STR_KEY_DICT_ADAPTER.validate_python(response_data.get("result", {})) + history: Final = _LIST_ADAPTER.validate_python(result.get("history", [])) + input_message = _STR_KEY_DICT_ADAPTER.validate_python({}) for msg in history: - if msg.get("role") == "user": - input_message = msg + if (msg_dict := _STR_KEY_DICT_ADAPTER.validate_python(msg)).get("role") == "user": + input_message = msg_dict break # Generate IDs for streaming events @@ -426,45 +450,49 @@ class PydanticAITransformation: # 1. Emit initial task event (kind: "task", status: "submitted") # Format matches A2ACompletionBridgeTransformation.create_task_event - task_event: Final = { - "jsonrpc": "2.0", - "id": request_id, - "result": { - "contextId": context_id, - "history": [ - { - "contextId": context_id, - "kind": "message", - "messageId": input_message_id, - "parts": input_message.get("parts", [{"kind": "text", "text": ""}]), - "role": "user", - "taskId": task_id, - } - ], - "id": task_id, - "kind": "task", - "status": { - "state": "submitted", + task_event: Final = _STR_KEY_DICT_ADAPTER.validate_python( + { + "jsonrpc": "2.0", + "id": request_id, + "result": { + "contextId": context_id, + "history": [ + { + "contextId": context_id, + "kind": "message", + "messageId": input_message_id, + "parts": input_message.get("parts", [{"kind": "text", "text": ""}]), + "role": "user", + "taskId": task_id, + } + ], + "id": task_id, + "kind": "task", + "status": { + "state": "submitted", + }, }, - }, - } + } + ) yield task_event # 2. Emit status update (kind: "status-update", status: "working") # Format matches A2ACompletionBridgeTransformation.create_status_update_event - working_event: Final = { - "jsonrpc": "2.0", - "id": request_id, - "result": { - "contextId": context_id, - "final": False, - "kind": "status-update", - "status": { - "state": "working", + working_event: Final = _STR_KEY_DICT_ADAPTER.validate_python( + { + "jsonrpc": "2.0", + "id": request_id, + "result": { + "contextId": context_id, + "final": False, + "kind": "status-update", + "status": { + "state": "working", + }, + "taskId": task_id, }, - "taskId": task_id, - }, - } + } + ) yield working_event # Small delay to simulate processing @@ -473,29 +501,32 @@ class PydanticAITransformation: # 3. Emit artifact update chunks (kind: "artifact-update") # Format matches A2ACompletionBridgeTransformation.create_artifact_update_event if full_text: + full_text_str: Final = _TEXT_ADAPTER.validate_python(full_text) # Split text into chunks - for i in range(0, len(full_text), chunk_size): - chunk_text = full_text[i : i + chunk_size] - is_last_chunk = (i + chunk_size) >= len(full_text) + for i in range(0, len(full_text_str), chunk_size): + chunk_text = full_text_str[i : i + chunk_size] + is_last_chunk = (i + chunk_size) >= len(full_text_str) - artifact_event = { - "jsonrpc": "2.0", - "id": request_id, - "result": { - "contextId": context_id, - "kind": "artifact-update", - "taskId": task_id, - "artifact": { - "artifactId": artifact_id, - "parts": [ - { - "kind": "text", - "text": chunk_text, - } - ], + artifact_event = _STR_KEY_DICT_ADAPTER.validate_python( + { + "jsonrpc": "2.0", + "id": request_id, + "result": { + "contextId": context_id, + "kind": "artifact-update", + "taskId": task_id, + "artifact": { + "artifactId": artifact_id, + "parts": [ + { + "kind": "text", + "text": chunk_text, + } + ], + }, }, - }, - } + } + ) yield artifact_event # Add delay between chunks (except for last chunk) @@ -503,19 +534,21 @@ class PydanticAITransformation: await asyncio.sleep(delay_ms / 1000.0) # 4. Emit final status update (kind: "status-update", status: "completed", final: true) - completed_event: Final = { - "jsonrpc": "2.0", - "id": request_id, - "result": { - "contextId": context_id, - "final": True, - "kind": "status-update", - "status": { - "state": "completed", + completed_event: Final = _STR_KEY_DICT_ADAPTER.validate_python( + { + "jsonrpc": "2.0", + "id": request_id, + "result": { + "contextId": context_id, + "final": True, + "kind": "status-update", + "status": { + "state": "completed", + }, + "taskId": task_id, }, - "taskId": task_id, - }, - } + } + ) yield completed_event verbose_logger.info("Pydantic AI: Fake streaming completed for request_id=%s", request_id) diff --git a/litellm/constants.py b/litellm/constants.py index 6f0e9e7afe2..87d6fa1a744 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -280,6 +280,7 @@ TOOL_POLICY_CACHE_TTL_SECONDS: Final = int(os.getenv("TOOL_POLICY_CACHE_TTL_SECO GUARDRAIL_SCANNED_MESSAGES_CACHE_TTL_SECONDS: Final = int( os.getenv("GUARDRAIL_SCANNED_MESSAGES_CACHE_TTL_SECONDS", 24 * 60 * 60) ) +BEDROCK_APPLY_GUARDRAIL_CHUNK_BUDGET_CHARS: Final = 25_000 # Aggregation threshold: default to 80% of the asyncio queue maxsize so the check can always trigger. # Must be < LITELLM_ASYNCIO_QUEUE_MAXSIZE; if set higher the aggregation logic will never fire. MAX_SIZE_IN_MEMORY_QUEUE: Final = int(os.getenv("MAX_SIZE_IN_MEMORY_QUEUE", int(LITELLM_ASYNCIO_QUEUE_MAXSIZE * 0.8))) @@ -1321,6 +1322,7 @@ X_LITELLM_DISABLE_CALLBACKS: Final = "x-litellm-disable-callbacks" LITELLM_METADATA_FIELD: Final = "litellm_metadata" OLD_LITELLM_METADATA_FIELD: Final = "metadata" RETURN_RAW_MODEL_NAME_METADATA_KEY: Final = "_complexity_router_return_raw_model_name" +SESSION_DEPLOYMENT_AFFINITY_TTL_METADATA_KEY: Final = "_session_deployment_affinity_ttl" INTERNAL_CALL_ORIGIN_METADATA_KEY: Final = "internal_call_origin" LITELLM_TRUNCATED_PAYLOAD_FIELD: Final = "litellm_truncated" LITELLM_TRUNCATION_DB_SAFEGUARD_NOTE: Final = ( @@ -1491,6 +1493,8 @@ SPEND_LOG_QUEUE_POLL_INTERVAL: Final = float(os.getenv("SPEND_LOG_QUEUE_POLL_INT SPEND_COUNTER_RESEED_LOCKS_MAX_SIZE: Final = int(os.getenv("SPEND_COUNTER_RESEED_LOCKS_MAX_SIZE", 10000)) DEFAULT_CRON_JOB_LOCK_TTL_SECONDS: Final = int(os.getenv("DEFAULT_CRON_JOB_LOCK_TTL_SECONDS", 60)) # 1 minute PROXY_BUDGET_RESCHEDULER_MIN_TIME: Final = int(os.getenv("PROXY_BUDGET_RESCHEDULER_MIN_TIME", 597)) +RESET_BUDGET_JOB_BATCH_SIZE: Final = max(1, int(os.getenv("RESET_BUDGET_JOB_BATCH_SIZE", "500"))) +RESET_BUDGET_JOB_MAX_CHUNKS_PER_RUN: Final = max(1, int(os.getenv("RESET_BUDGET_JOB_MAX_CHUNKS_PER_RUN", "100"))) PROXY_BATCH_POLLING_INTERVAL: Final = int(os.getenv("PROXY_BATCH_POLLING_INTERVAL", 3600)) MAX_OBJECTS_PER_POLL_CYCLE: Final = max(1, int(os.getenv("MAX_OBJECTS_PER_POLL_CYCLE", 50))) MANAGED_OBJECT_STALENESS_CUTOFF_DAYS: Final = max(1, int(os.getenv("MANAGED_OBJECT_STALENESS_CUTOFF_DAYS", 7))) @@ -1717,3 +1721,18 @@ BROWSER_SECURITY_HEADERS: Final[frozenset[str]] = frozenset( ) UNSAFE_PROXY_RESPONSE_HEADERS: Final[frozenset[str]] = HTTP_FRAMING_HEADERS | BROWSER_SECURITY_HEADERS + +# PTU reservation rollup writes rows to LiteLLM_DailyTeamSpend with this +# sentinel api_key so PTU flat cost stays distinguishable from real per-request +# spend under the table's composite unique constraint. +PTU_SENTINEL_API_KEY: Final[str] = "__ptu_flat_cost__" +PTU_ROLLUP_JOB_ID: Final[str] = "ptu_flat_cost_rollup_job" +PTU_ROLLUP_LOCK_TTL_SECONDS: Final[int] = 900 +# Furthest back the catch-up pass looks for unpriced PTU days when a deployment +# declares no ptu_effective_from, bounding the scan for an open-ended window. +PTU_ROLLUP_MAX_BACKFILL_DAYS: Final[int] = 90 +# Slack allowed when deciding a sentinel row is stale. The row's updated_at and the +# run's cutoff are stamped by different hosts, so clock skew between them must not let +# one run delete a charge another just wrote. A stale row is hours old and a concurrent +# one is seconds old, so a few minutes separates them. +PTU_PRUNE_SKEW_GRACE_SECONDS: Final[int] = 300 diff --git a/litellm/evals/main.py b/litellm/evals/main.py index a25c7a96a8a..2f639d30ca0 100644 --- a/litellm/evals/main.py +++ b/litellm/evals/main.py @@ -7,7 +7,7 @@ import asyncio import contextvars from collections.abc import Coroutine from functools import partial -from typing import Any, Final +from typing import Final import httpx @@ -21,8 +21,10 @@ from litellm.types.llms.openai_evals import ( CancelRunResponse, CreateEvalRequest, CreateRunRequest, + DataSourceConfig, DeleteEvalResponse, Eval, + GraderConfig, ListEvalsParams, ListEvalsResponse, ListRunsParams, @@ -41,13 +43,13 @@ DEFAULT_OPENAI_API_BASE: Final = "https://api.openai.com" @client async def acreate_eval( - data_source_config: dict[str, Any], - testing_criteria: list[dict[str, Any]], + data_source_config: DataSourceConfig, + testing_criteria: list[GraderConfig], name: str | None = None, - metadata: dict[str, Any] | None = None, - extra_headers: dict[str, Any] | None = None, - extra_query: dict[str, Any] | None = None, - extra_body: dict[str, Any] | None = None, + metadata: dict[str, object] | 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, custom_llm_provider: str | None = None, **kwargs, @@ -110,17 +112,17 @@ async def acreate_eval( @client def create_eval( - data_source_config: dict[str, Any], - testing_criteria: list[dict[str, Any]], + data_source_config: DataSourceConfig, + testing_criteria: list[GraderConfig], name: str | None = None, - metadata: dict[str, Any] | None = None, - extra_headers: dict[str, Any] | None = None, - extra_query: dict[str, Any] | None = None, - extra_body: dict[str, Any] | None = None, + metadata: dict[str, object] | 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, custom_llm_provider: str | None = None, **kwargs, -) -> Eval | Coroutine[Any, Any, Eval]: +) -> Eval | Coroutine[object, object, Eval]: """ Create a new evaluation @@ -231,8 +233,8 @@ async def alist_evals( before: str | None = None, order: str | None = None, order_by: str | None = None, - extra_headers: dict[str, Any] | None = None, - extra_query: dict[str, Any] | None = None, + extra_headers: dict[str, object] | None = None, + extra_query: dict[str, object] | None = None, timeout: float | httpx.Timeout | None = None, custom_llm_provider: str | None = None, **kwargs, @@ -300,12 +302,12 @@ def list_evals( before: str | None = None, order: str | None = None, order_by: str | None = None, - extra_headers: dict[str, Any] | None = None, - extra_query: dict[str, Any] | None = None, + extra_headers: dict[str, object] | None = None, + extra_query: dict[str, object] | None = None, timeout: float | httpx.Timeout | None = None, custom_llm_provider: str | None = None, **kwargs, -) -> ListEvalsResponse | Coroutine[Any, Any, ListEvalsResponse]: +) -> ListEvalsResponse | Coroutine[object, object, ListEvalsResponse]: """ List all evaluations @@ -413,8 +415,8 @@ def list_evals( @client async def aget_eval( eval_id: str, - extra_headers: dict[str, Any] | None = None, - extra_query: dict[str, Any] | None = None, + extra_headers: dict[str, object] | None = None, + extra_query: dict[str, object] | None = None, timeout: float | httpx.Timeout | None = None, custom_llm_provider: str | None = None, **kwargs, @@ -470,12 +472,12 @@ async def aget_eval( @client def get_eval( eval_id: str, - extra_headers: dict[str, Any] | None = None, - extra_query: dict[str, Any] | None = None, + extra_headers: dict[str, object] | None = None, + extra_query: dict[str, object] | None = None, timeout: float | httpx.Timeout | None = None, custom_llm_provider: str | None = None, **kwargs, -) -> Eval | Coroutine[Any, Any, Eval]: +) -> Eval | Coroutine[object, object, Eval]: """ Get an evaluation by ID @@ -564,10 +566,10 @@ def get_eval( async def aupdate_eval( eval_id: str, name: str | None = None, - metadata: dict[str, Any] | None = None, - extra_headers: dict[str, Any] | None = None, - extra_query: dict[str, Any] | None = None, - extra_body: dict[str, Any] | None = None, + metadata: dict[str, object] | 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, custom_llm_provider: str | None = None, **kwargs, @@ -630,14 +632,14 @@ async def aupdate_eval( def update_eval( eval_id: str, name: str | None = None, - metadata: dict[str, Any] | None = None, - extra_headers: dict[str, Any] | None = None, - extra_query: dict[str, Any] | None = None, - extra_body: dict[str, Any] | None = None, + metadata: dict[str, object] | 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, custom_llm_provider: str | None = None, **kwargs, -) -> Eval | Coroutine[Any, Any, Eval]: +) -> Eval | Coroutine[object, object, Eval]: """ Update an evaluation @@ -783,8 +785,8 @@ def update_eval( @client async def adelete_eval( eval_id: str, - extra_headers: dict[str, Any] | None = None, - extra_query: dict[str, Any] | None = None, + extra_headers: dict[str, object] | None = None, + extra_query: dict[str, object] | None = None, timeout: float | httpx.Timeout | None = None, custom_llm_provider: str | None = None, **kwargs, @@ -840,12 +842,12 @@ async def adelete_eval( @client def delete_eval( eval_id: str, - extra_headers: dict[str, Any] | None = None, - extra_query: dict[str, Any] | None = None, + extra_headers: dict[str, object] | None = None, + extra_query: dict[str, object] | None = None, timeout: float | httpx.Timeout | None = None, custom_llm_provider: str | None = None, **kwargs, -) -> DeleteEvalResponse | Coroutine[Any, Any, DeleteEvalResponse]: +) -> DeleteEvalResponse | Coroutine[object, object, DeleteEvalResponse]: """ Delete an evaluation @@ -933,8 +935,8 @@ def delete_eval( @client async def acancel_eval( eval_id: str, - extra_headers: dict[str, Any] | None = None, - extra_query: dict[str, Any] | None = None, + extra_headers: dict[str, object] | None = None, + extra_query: dict[str, object] | None = None, timeout: float | httpx.Timeout | None = None, custom_llm_provider: str | None = None, **kwargs, @@ -990,12 +992,12 @@ async def acancel_eval( @client def cancel_eval( eval_id: str, - extra_headers: dict[str, Any] | None = None, - extra_query: dict[str, Any] | None = None, + extra_headers: dict[str, object] | None = None, + extra_query: dict[str, object] | None = None, timeout: float | httpx.Timeout | None = None, custom_llm_provider: str | None = None, **kwargs, -) -> CancelEvalResponse | Coroutine[Any, Any, CancelEvalResponse]: +) -> CancelEvalResponse | Coroutine[object, object, CancelEvalResponse]: """ Cancel a running evaluation @@ -1092,12 +1094,12 @@ def cancel_eval( @client async def acreate_run( eval_id: str, - data_source: dict[str, Any], + data_source: dict[str, object], name: str | None = None, - metadata: dict[str, Any] | None = None, - extra_headers: dict[str, Any] | None = None, - extra_query: dict[str, Any] | None = None, - extra_body: dict[str, Any] | None = None, + metadata: dict[str, object] | 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, custom_llm_provider: str | None = None, **kwargs, @@ -1161,16 +1163,16 @@ async def acreate_run( @client def create_run( eval_id: str, - data_source: dict[str, Any], + data_source: dict[str, object], name: str | None = None, - metadata: dict[str, Any] | None = None, - extra_headers: dict[str, Any] | None = None, - extra_query: dict[str, Any] | None = None, - extra_body: dict[str, Any] | None = None, + metadata: dict[str, object] | 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, custom_llm_provider: str | None = None, **kwargs, -) -> Run | Coroutine[Any, Any, Run]: +) -> Run | Coroutine[object, object, Run]: """ Create a new run for an evaluation @@ -1280,8 +1282,8 @@ async def alist_runs( after: str | None = None, before: str | None = None, order: str | None = None, - extra_headers: dict[str, Any] | None = None, - extra_query: dict[str, Any] | None = None, + extra_headers: dict[str, object] | None = None, + extra_query: dict[str, object] | None = None, timeout: float | httpx.Timeout | None = None, custom_llm_provider: str | None = None, **kwargs, @@ -1349,12 +1351,12 @@ def list_runs( after: str | None = None, before: str | None = None, order: str | None = None, - extra_headers: dict[str, Any] | None = None, - extra_query: dict[str, Any] | None = None, + extra_headers: dict[str, object] | None = None, + extra_query: dict[str, object] | None = None, timeout: float | httpx.Timeout | None = None, custom_llm_provider: str | None = None, **kwargs, -) -> ListRunsResponse | Coroutine[Any, Any, ListRunsResponse]: +) -> ListRunsResponse | Coroutine[object, object, ListRunsResponse]: """ List all runs for an evaluation @@ -1462,8 +1464,8 @@ def list_runs( async def aget_run( eval_id: str, run_id: str, - extra_headers: dict[str, Any] | None = None, - extra_query: dict[str, Any] | None = None, + extra_headers: dict[str, object] | None = None, + extra_query: dict[str, object] | None = None, timeout: float | httpx.Timeout | None = None, custom_llm_provider: str | None = None, **kwargs, @@ -1522,12 +1524,12 @@ async def aget_run( def get_run( eval_id: str, run_id: str, - extra_headers: dict[str, Any] | None = None, - extra_query: dict[str, Any] | None = None, + extra_headers: dict[str, object] | None = None, + extra_query: dict[str, object] | None = None, timeout: float | httpx.Timeout | None = None, custom_llm_provider: str | None = None, **kwargs, -) -> Run | Coroutine[Any, Any, Run]: +) -> Run | Coroutine[object, object, Run]: """ Get a specific run @@ -1618,8 +1620,8 @@ def get_run( async def acancel_run( eval_id: str, run_id: str, - extra_headers: dict[str, Any] | None = None, - extra_query: dict[str, Any] | None = None, + extra_headers: dict[str, object] | None = None, + extra_query: dict[str, object] | None = None, timeout: float | httpx.Timeout | None = None, custom_llm_provider: str | None = None, **kwargs, @@ -1678,12 +1680,12 @@ async def acancel_run( def cancel_run( eval_id: str, run_id: str, - extra_headers: dict[str, Any] | None = None, - extra_query: dict[str, Any] | None = None, + extra_headers: dict[str, object] | None = None, + extra_query: dict[str, object] | None = None, timeout: float | httpx.Timeout | None = None, custom_llm_provider: str | None = None, **kwargs, -) -> CancelRunResponse | Coroutine[Any, Any, CancelRunResponse]: +) -> CancelRunResponse | Coroutine[object, object, CancelRunResponse]: """ Cancel a running run @@ -1783,8 +1785,8 @@ def cancel_run( async def adelete_run( eval_id: str, run_id: str, - extra_headers: dict[str, Any] | None = None, - extra_query: dict[str, Any] | None = None, + extra_headers: dict[str, object] | None = None, + extra_query: dict[str, object] | None = None, timeout: float | httpx.Timeout | None = None, custom_llm_provider: str | None = None, **kwargs, @@ -1843,12 +1845,12 @@ async def adelete_run( def delete_run( eval_id: str, run_id: str, - extra_headers: dict[str, Any] | None = None, - extra_query: dict[str, Any] | None = None, + extra_headers: dict[str, object] | None = None, + extra_query: dict[str, object] | None = None, timeout: float | httpx.Timeout | None = None, custom_llm_provider: str | None = None, **kwargs, -) -> RunDeleteResponse | Coroutine[Any, Any, RunDeleteResponse]: +) -> RunDeleteResponse | Coroutine[object, object, RunDeleteResponse]: """ Delete a run diff --git a/litellm/integrations/arize/_utils.py b/litellm/integrations/arize/_utils.py index 8c494794858..e7e1ab538d5 100644 --- a/litellm/integrations/arize/_utils.py +++ b/litellm/integrations/arize/_utils.py @@ -1,4 +1,5 @@ import json +from collections.abc import Mapping from typing import TYPE_CHECKING, Any, Final from typing_extensions import override @@ -12,7 +13,7 @@ from litellm.litellm_core_utils.redact_messages import ( should_redact_message_logging, ) from litellm.litellm_core_utils.safe_json_dumps import safe_dumps -from litellm.types.utils import StandardLoggingPayload +from litellm.types.utils import CallTypes, StandardLoggingMCPToolCall, StandardLoggingPayload if TYPE_CHECKING: from opentelemetry.trace import Span @@ -22,6 +23,7 @@ from litellm.integrations._types.open_inference import ( ImageAttributes, MessageAttributes, MessageContentAttributes, + OpenInferenceMimeTypeValues, OpenInferenceSpanKindValues, SpanAttributes, ToolCallAttributes, @@ -480,6 +482,7 @@ def set_attributes(span: "Span", kwargs, response_obj, attributes: type[BaseLLMO response_obj_for_attrs, slp, ) + _safe_emit("mcp tool attrs", _maybe_set_mcp_tool_attrs, span, kwargs, slp, response_obj_for_attrs) def _sanitize_optional_params(optional_params: dict | None) -> dict: @@ -538,9 +541,12 @@ def _set_request_attributes( if optional_params.get("user"): safe_set_attribute(span, "llm.user", optional_params.get("user")) - if response_obj and response_obj.get("id"): + if not hasattr(response_obj, "get"): + return + + if response_obj.get("id"): safe_set_attribute(span, "llm.response.id", response_obj.get("id")) - if response_obj and response_obj.get("model"): + if response_obj.get("model"): safe_set_attribute(span, "llm.response.model", response_obj.get("model")) @@ -588,6 +594,8 @@ def _coerce_response_obj_for_attrs(response_obj): - dicts and Pydantic models that already expose `.get` are returned unchanged (preserves all current behavior, including the Responses API flow which relies on Pydantic attribute access). + - Pydantic models without `.get` (e.g. the MCP SDK's `CallToolResult`, + logged for `call_mcp_tool` spans) are dumped to a dict. - `httpx.Response` and other text-only responses (passthrough routes) are JSON-decoded so the standard extraction paths can read fields like `id`, `model`, and `usage`. On failure the original object is returned @@ -595,6 +603,9 @@ def _coerce_response_obj_for_attrs(response_obj): """ if response_obj is None or hasattr(response_obj, "get"): return response_obj + dumped: Final = _to_plain_dict(response_obj) + if isinstance(dumped, dict): + return dumped text: Final = getattr(response_obj, "text", None) if isinstance(text, str) and text: try: @@ -1058,3 +1069,65 @@ def _parse_passthrough_response(raw_response_obj, coerced_response_obj, kwargs): except Exception: return None return None + + +def _maybe_set_mcp_tool_attrs( + span: "Span", + kwargs: Mapping[str, object], + standard_logging_payload: StandardLoggingPayload | None, + coerced_response_obj: object, +) -> None: + """Render `call_mcp_tool` spans as OpenInference TOOL spans. + + MCP tool calls carry neither `messages` nor `choices`, so the generic + extraction paths leave Input/Output blank. The tool name and arguments live + in `metadata.mcp_tool_call_metadata`; the result is an MCP `CallToolResult` + whose `content` is a list of typed parts. + """ + if standard_logging_payload is None: + return + if standard_logging_payload.get("call_type") != CallTypes.call_mcp_tool.value: + return + + metadata: Final = standard_logging_payload.get("metadata") + mcp_meta: Final[StandardLoggingMCPToolCall | None] = metadata.get("mcp_tool_call_metadata") if metadata else None + if mcp_meta is None: + return + + tool_name: Final = mcp_meta.get("name") or mcp_meta.get("namespaced_tool_name") + if tool_name: + safe_set_attribute(span, SpanAttributes.TOOL_NAME, tool_name) + + if should_redact_message_logging(kwargs): # pyright: ignore[reportArgumentType] # reads, never mutates + return + + arguments: Final[object] = mcp_meta.get("arguments") + if arguments is not None: + safe_set_attribute(span, SpanAttributes.INPUT_VALUE, safe_dumps(arguments)) + safe_set_attribute(span, SpanAttributes.INPUT_MIME_TYPE, OpenInferenceMimeTypeValues.JSON.value) + + _set_mcp_tool_output(span, coerced_response_obj) + + +def _has_only_text_parts(content: object) -> bool: + return not isinstance(content, list) or all(_coerce_text([part]) is not None for part in content) + + +def _set_mcp_tool_output(span: "Span", coerced_response_obj: object) -> None: + if not isinstance(coerced_response_obj, Mapping): + return + + content: Final[object] = coerced_response_obj.get("content") + text: Final[str | None] = _coerce_text(content) + if text and _has_only_text_parts(content): + safe_set_attribute(span, SpanAttributes.OUTPUT_VALUE, text) + safe_set_attribute(span, SpanAttributes.OUTPUT_MIME_TYPE, OpenInferenceMimeTypeValues.TEXT.value) + return + + structured: Final[object] = coerced_response_obj.get("structuredContent") + payload: Final[object] = content if content else structured if structured is not None else content + if payload is None: + return + + safe_set_attribute(span, SpanAttributes.OUTPUT_VALUE, safe_dumps(payload)) + safe_set_attribute(span, SpanAttributes.OUTPUT_MIME_TYPE, OpenInferenceMimeTypeValues.JSON.value) diff --git a/litellm/integrations/otel/__init__.py b/litellm/integrations/otel/__init__.py index 94442e96adb..9c1205bb277 100644 --- a/litellm/integrations/otel/__init__.py +++ b/litellm/integrations/otel/__init__.py @@ -57,6 +57,7 @@ from litellm.integrations.otel.model.semconv import ( Metric, Network, NetworkTransport, + RpcSystem, Server, resolve_operation, resolve_provider, @@ -102,6 +103,7 @@ __all__ = [ "ProxyRequestSpanData", "RequestContext", "RequestIdentity", + "RpcSystem", "Server", "ServerInfo", "ServiceSpanData", diff --git a/litellm/integrations/otel/logger.py b/litellm/integrations/otel/logger.py index e273289168a..2c83406afed 100644 --- a/litellm/integrations/otel/logger.py +++ b/litellm/integrations/otel/logger.py @@ -33,6 +33,7 @@ from litellm.integrations.otel.model.payloads import ( is_mcp_list_tools, is_mcp_tool_call, ) +from litellm.integrations.otel.model.semconv import Error from litellm.integrations.otel.model.spans import SpanRole, span_role_for_service from litellm.integrations.otel.model.utils import to_ns from litellm.integrations.otel.plumbing.context import ( @@ -634,18 +635,23 @@ class OpenTelemetryV2(CustomLogger): """Stamp the v2 error.* attributes on the FastAPI-owned SERVER span for a failure that dies before any LLM-call span exists (malformed body, auth / validation rejection). Called from the proxy's global exception handler via - ``_close_dangling_otel_server_span``. The instrumentor still owns the span's - status and lifecycle, so this only decorates it — never sets status, never - ends it — and emits no exception event, matching v1's SERVER-span behavior - and avoiding a duplicate of the event ``async_post_call_failure_hook`` or - the ``auth`` phase span already records.""" + ``_close_dangling_otel_server_span``, which swallows the exception into a + ``JSONResponse`` so the instrumentor never sees it and leaves the span + ``UNSET``; the status is set here instead (v1 did the same from the handler) + so a failed request reads as failed and not merely as a span carrying an + error message. The instrumentor still owns the span's lifecycle, so this + never ends it. The exception event is recorded only when nothing stamped + this span already — ``async_post_call_failure_hook`` and the ``auth`` phase + span record their own, and a second event would duplicate it — while the + attributes are always restamped so ``error.code`` stays pinned to the real + response status.""" if span is None or not is_recordable_span(span): return + already_stamped: Final = Error.TYPE in (getattr(span, "attributes", None) or ()) stamp_error( span, _span_error_from_exception(exception, status_code=status_code), - record_event=False, - set_status=False, + record_event=not already_stamped, ) async def async_post_call_failure_hook( diff --git a/litellm/integrations/otel/mappers/genai.py b/litellm/integrations/otel/mappers/genai.py index af56734bec1..032441535e0 100644 --- a/litellm/integrations/otel/mappers/genai.py +++ b/litellm/integrations/otel/mappers/genai.py @@ -31,7 +31,9 @@ from litellm.integrations.otel.model.semconv import ( MCP, Error, GenAI, + JsonRpc, LiteLLM, + RpcSystem, Server, ) from litellm.integrations.otel.model.spans import db_system @@ -94,11 +96,14 @@ class GenAIMapper: _MCP_ATTRS: dict[str, Callable[[MCPToolCallSpanData], AttrValue | None]] = { GenAI.OPERATION_NAME: lambda d: d.operation.value, + JsonRpc.SYSTEM: lambda d: RpcSystem.JSONRPC.value if d.server_address and d.server_port else None, MCP.METHOD_NAME: lambda d: d.method, MCP.SESSION_ID: lambda d: d.session_id, GenAI.TOOL_NAME: lambda d: d.tool_name or None, GenAI.TOOL_CALL_ARGUMENTS: lambda d: d.arguments_json, GenAI.TOOL_CALL_RESULT: lambda d: d.result_json, + Server.ADDRESS: lambda d: d.server_address, + Server.PORT: lambda d: d.server_port, LiteLLM.MCP_SERVER_NAME: lambda d: d.server_name, LiteLLM.CALL_ID: lambda d: d.identity.call_id or None, f"{LiteLLM.COST_PREFIX}total": lambda d: d.response_cost, diff --git a/litellm/integrations/otel/model/payloads.py b/litellm/integrations/otel/model/payloads.py index 02499010624..aba9cc80240 100644 --- a/litellm/integrations/otel/model/payloads.py +++ b/litellm/integrations/otel/model/payloads.py @@ -364,6 +364,34 @@ class LLMCallSpanData: # --- the MCP tool-call model ------------------------------------------------- # +def _upstream_address_port(resource: str | None) -> tuple[str | None, int | None]: + """Split a redacted MCP server origin into ``server.address`` / ``server.port``. + + ``mcp_server_resource`` is a scheme + host + port origin with userinfo, path, + query and fragment already stripped. The port falls back to the scheme default + when the origin omits it, because a consumer that keys a downstream dependency + off the address renders a missing port as ``0``. + + The origin is rebuilt without its IPv6 brackets upstream, so reading the port can + raise on an address the host check still admits: a zone-scoped ``fe80::1%25eth0`` + leaves a truthy hostname of ``fe80`` behind. Both halves are read inside the guard + so an unparseable origin yields no address rather than propagating out of span + construction, matching how the redactor guards the same split. + """ + if not resource: + return None, None + try: + parsed: Final = urlsplit(resource) + hostname: Final = parsed.hostname + port: Final = parsed.port + except ValueError: + return None, None + if not hostname: + return None, None + default_port: Final = 443 if parsed.scheme == "https" else 80 if parsed.scheme == "http" else None + return hostname, port or default_port + + @dataclass(frozen=True) class MCPToolCallSpanData: """One MCP ``tools/call`` execution, parsed from a closed request's payload. @@ -378,6 +406,8 @@ class MCPToolCallSpanData: method: str tool_name: str server_name: str | None + server_address: str | None + server_port: int | None session_id: str | None arguments_json: str | None result_json: str | None @@ -390,11 +420,14 @@ class MCPToolCallSpanData: cls, payload: StandardLoggingPayload, capture_content: bool = False ) -> MCPToolCallSpanData: meta: Final = _mcp_tool_call_metadata(cast(Mapping[str, object], payload)) + address, port = _upstream_address_port(as_str(meta.get("mcp_server_resource")) or None) return cls( operation=resolve_operation(as_str(payload.get("call_type"))), method=MCPMethod.TOOLS_CALL.value, tool_name=as_str(meta.get("name")) or "", server_name=as_str(meta.get("mcp_server_name")), + server_address=address, + server_port=port, session_id=as_str(meta.get("mcp_session_id")), arguments_json=( _json_or_none(meta.get("arguments")) if capture_content and meta.get("arguments") is not None else None diff --git a/litellm/integrations/otel/model/semconv.py b/litellm/integrations/otel/model/semconv.py index 24f0b947b08..3d585c36b67 100644 --- a/litellm/integrations/otel/model/semconv.py +++ b/litellm/integrations/otel/model/semconv.py @@ -130,11 +130,25 @@ class JsonRpc: """JSON-RPC keys carried on MCP spans. The error/status code lives in the ``rpc.*`` namespace per semconv, not ``jsonrpc.*``.""" + SYSTEM: Final = "rpc.system" REQUEST_ID: Final = "jsonrpc.request.id" PROTOCOL_VERSION: Final = "jsonrpc.protocol.version" RESPONSE_STATUS_CODE: Final = "rpc.response.status_code" +class RpcSystem(str, Enum): + """Well-known values for ``rpc.system``. MCP frames every message as JSON-RPC 2.0. + + Naming the system also classifies the span: a CLIENT span carrying none of the + ``rpc.*``/``http.*``/``db.*``/``messaging.*`` families records no span type or + subtype in backends that derive those from the attribute family. It is emitted + only alongside ``server.address``/``server.port``, since a backend that reads it + as a downstream dependency names that dependency from the server address. + """ + + JSONRPC = "jsonrpc" + + class NetworkTransport(str, Enum): """Well-known values for ``network.transport``.""" diff --git a/litellm/integrations/websearch_interception/handler.py b/litellm/integrations/websearch_interception/handler.py index 9748db2dcd2..f7f27459768 100644 --- a/litellm/integrations/websearch_interception/handler.py +++ b/litellm/integrations/websearch_interception/handler.py @@ -10,7 +10,7 @@ import asyncio import math import uuid from collections.abc import AsyncIterator, Mapping, Sequence -from typing import TYPE_CHECKING, Any, Final, cast +from typing import TYPE_CHECKING, Any, Final, TypedDict, cast import litellm from litellm._logging import verbose_logger @@ -42,7 +42,12 @@ from litellm.types.integrations.websearch_interception import ( WebSearchInterceptionConfig, ) from litellm.types.llms.openai import AllMessageValues -from litellm.types.utils import CallTypes, LlmProviders +from litellm.types.utils import ( + AgenticLoopParams, + CallTypes, + LlmProviders, + StandardLoggingUserAPIKeyMetadata, +) from litellm.utils import ProviderConfigManager if TYPE_CHECKING: @@ -68,6 +73,18 @@ WEBSEARCH_EMIT_NATIVE_BLOCKS_KEY: Final = "_websearch_interception_emit_native_b WEBSEARCH_NATIVE_BLOCKS_METADATA_KEY: Final = "websearch_native_blocks" +class _PlanMetadataView(TypedDict): + websearch_native_blocks: Sequence[Mapping[str, object]] | None + + +class _AgenticLoopParamsView(TypedDict): + agentic_loop_params: AgenticLoopParams + + +class _WebSearchSettingsView(TypedDict): + websearch_interception_params: WebSearchInterceptionConfig + + class WebSearchInterceptionLogger(CustomLogger): """ CustomLogger that intercepts WebSearch tool calls for models that don't @@ -265,7 +282,7 @@ class WebSearchInterceptionLogger(CustomLogger): return None # Check if request has tools with native web_search - tools: Final = kwargs.get("tools") + tools: Final[Sequence[dict[str, object]] | None] = kwargs.get("tools") if not tools: return None @@ -314,7 +331,9 @@ class WebSearchInterceptionLogger(CustomLogger): return kwargs - def _convert_responses_tools(self, kwargs: Mapping[str, object], tools: list[dict[str, object]]) -> dict | None: + def _convert_responses_tools( + self, kwargs: Mapping[str, object], tools: Sequence[dict[str, object]] + ) -> dict[str, object] | None: """Convert Responses API web search tools to the LiteLLM standard function tool.""" if not any(is_web_search_tool_responses(tool) for tool in tools): return None @@ -379,7 +398,7 @@ class WebSearchInterceptionLogger(CustomLogger): ) @staticmethod - def _tool_name(tool: dict[str, Any]) -> str | None: + def _tool_name(tool: Mapping[str, object]) -> object: """Effective tool name, handling OpenAI ``function`` wrapper shape.""" fn: Final = tool.get("function") if tool.get("type") == "function" and isinstance(fn, dict): @@ -387,7 +406,7 @@ class WebSearchInterceptionLogger(CustomLogger): return tool.get("name") @classmethod - def _sync_forced_tool_choice(cls, tool_choice: Any, converted_tools: list[dict[str, object]]) -> object: + def _sync_forced_tool_choice(cls, tool_choice: object, converted_tools: Sequence[Mapping[str, object]]) -> object: """Repoint a forced ``tool_choice`` at ``litellm_web_search`` when it names a web-search tool that was just converted away. @@ -455,7 +474,7 @@ class WebSearchInterceptionLogger(CustomLogger): kwargs[WEBSEARCH_EMIT_NATIVE_BLOCKS_KEY] = True # Convert native web search tools to LiteLLM standard - converted_tools: Final = [] + converted_tools: Final[list[dict[str, object]]] = [] for tool in tools: if is_web_search_tool(tool): standard_tool = get_litellm_web_search_tool() @@ -826,7 +845,10 @@ class WebSearchInterceptionLogger(CustomLogger): Anthropic-native clients (Claude Desktop, the Anthropic SDK) can render citations / sources alongside the model's textual reply. """ - native_blocks: Final = plan.metadata.get(WEBSEARCH_NATIVE_BLOCKS_METADATA_KEY) + metadata_view: Final[_PlanMetadataView] = { + "websearch_native_blocks": plan.metadata.get(WEBSEARCH_NATIVE_BLOCKS_METADATA_KEY) + } + native_blocks: Final = metadata_view["websearch_native_blocks"] if not native_blocks: return response return self._inject_native_blocks(response, native_blocks) @@ -1271,8 +1293,10 @@ class WebSearchInterceptionLogger(CustomLogger): kwargs_for_followup: Final = self._prepare_followup_kwargs(kwargs) if logging_obj is not None: - agentic_params: Final = logging_obj.model_call_details.get("agentic_loop_params", {}) - full_model_name = agentic_params.get("model", model) + agentic_view: Final[_AgenticLoopParamsView] = { + "agentic_loop_params": logging_obj.model_call_details.get("agentic_loop_params", {}) + } + full_model_name = agentic_view["agentic_loop_params"].get("model", model) verbose_logger.debug( "WebSearchInterception: Built anthropic request patch [call_id=%s model=%s messages=%d searches=%d]", _call_id, @@ -1316,6 +1340,7 @@ class WebSearchInterceptionLogger(CustomLogger): search_tool: Final = self._select_search_tool_from_router(llm_router=llm_router) search_provider: str | None = None search_litellm_params: dict[str, Any] = {} + search_tool_name: Final = self._selected_search_tool_name(search_tool=search_tool) if search_tool is not None: await self._authorize_search_tool(search_tool=search_tool, kwargs=kwargs) search_litellm_params = dict(search_tool.get("litellm_params", {}) or {}) @@ -1332,12 +1357,30 @@ class WebSearchInterceptionLogger(CustomLogger): verbose_logger.debug( "WebSearchInterception: Executing search for '%s' using provider '%s'", query, search_provider ) + user_api_key_auth: Final = self._get_user_api_key_auth_from_kwargs(kwargs) + search_metadata: Final = ( + None + if user_api_key_auth is None + else self._build_search_request_metadata( + user_api_key_auth=user_api_key_auth, + search_tool_name=search_tool_name, + ) + ) search_kwargs: Final = { key: value for key, value in search_litellm_params.items() if key != "search_provider" and value is not None } - result: Final = await litellm.asearch(query=query, search_provider=search_provider, **search_kwargs) + result: Final = ( + await litellm.asearch(query=query, search_provider=search_provider, **search_kwargs) + if search_metadata is None + else await litellm.asearch( + query=query, + search_provider=search_provider, + litellm_metadata=search_metadata, + **search_kwargs, + ) + ) # Format using transformation function search_result_text: Final = WebSearchTransformation.format_search_response(result) @@ -1394,6 +1437,35 @@ class WebSearchInterceptionLogger(CustomLogger): team_object=team_object, ) + @staticmethod + def _build_search_request_metadata( + user_api_key_auth: "UserAPIKeyAuth", + search_tool_name: str | None, + ) -> Mapping[str, object]: + """ + Spend-tracking metadata for the intercepted search, so its provider cost is logged + and billed against the key/user/team that made the originating LLM request instead + of being dropped by the proxy's spend hook for lack of an owner. + """ + from litellm.proxy.litellm_pre_call_utils import LiteLLMProxyRequestSetup + + user_api_key_metadata: Final[StandardLoggingUserAPIKeyMetadata] = ( + LiteLLMProxyRequestSetup.get_sanitized_user_information_from_key(user_api_key_dict=user_api_key_auth) + ) + return { # mutable-ok: litellm's metadata channel is a plain dict its logging path reads and enriches + **user_api_key_metadata, + "model_group": search_tool_name, + "user_api_key": user_api_key_auth.api_key, + "user_api_key_auth": user_api_key_auth, + } + + @staticmethod + def _selected_search_tool_name(search_tool: Mapping[str, object] | None) -> str | None: + if search_tool is None: + return None + search_tool_name: Final = search_tool.get("search_tool_name") + return search_tool_name if isinstance(search_tool_name, str) and search_tool_name else None + @staticmethod def _get_user_api_key_auth_from_kwargs(kwargs: Mapping[str, object] | None) -> "UserAPIKeyAuth | None": if not kwargs: @@ -1621,7 +1693,7 @@ class WebSearchInterceptionLogger(CustomLogger): @staticmethod def initialize_from_proxy_config( litellm_settings: dict[str, Any], - callback_specific_params: dict[str, Any], + callback_specific_params: Mapping[str, object], ) -> "WebSearchInterceptionLogger": """ Static method to initialize WebSearchInterceptionLogger from proxy config. @@ -1645,7 +1717,10 @@ class WebSearchInterceptionLogger(CustomLogger): # Get websearch_interception_params from litellm_settings or callback_specific_params websearch_params: WebSearchInterceptionConfig = {} if "websearch_interception_params" in litellm_settings: - websearch_params = litellm_settings["websearch_interception_params"] + settings_view: Final[_WebSearchSettingsView] = { + "websearch_interception_params": litellm_settings["websearch_interception_params"] + } + websearch_params = settings_view["websearch_interception_params"] elif "websearch_interception" in callback_specific_params and isinstance( callback_specific_params["websearch_interception"], dict ): diff --git a/litellm/litellm_core_utils/cli_token_utils.py b/litellm/litellm_core_utils/cli_token_utils.py index fe986dd5fce..a44ce431f4e 100644 --- a/litellm/litellm_core_utils/cli_token_utils.py +++ b/litellm/litellm_core_utils/cli_token_utils.py @@ -8,6 +8,7 @@ This module has no dependencies on proxy code and can be safely imported at the import json import os import time +from collections.abc import Mapping from pathlib import Path from typing import Final @@ -71,7 +72,7 @@ def get_litellm_gateway_api_key( return token_data["key"] -def is_cli_token_fresh(token_data: dict, buffer_hours: float = 0.1) -> bool: +def is_cli_token_fresh(token_data: Mapping[str, object], buffer_hours: float = 0.1) -> bool: """Check whether a cached CLI token (as stored in token.json) is still within its expiration window. Used by `lite auth print-token` to fail fast, without a network round trip, once the cached token is past diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py index 99721c3ffa2..a3ff048e92a 100644 --- a/litellm/litellm_core_utils/litellm_logging.py +++ b/litellm/litellm_core_utils/litellm_logging.py @@ -10,7 +10,7 @@ import subprocess import sys import time import traceback -from collections.abc import Callable +from collections.abc import Callable, Mapping from datetime import datetime as dt_object from functools import lru_cache from typing import TYPE_CHECKING, Any, Final, Literal, Optional, Union, cast @@ -25,7 +25,15 @@ from litellm import ( log_raw_request_response, turn_off_message_logging, ) -from litellm._logging import _is_debugging_on, _redact_string, verbose_logger +from litellm._logging import ( + _is_debugging_on, + _redact_string, + session_id_var, + set_session_id, + set_trace_id, + trace_id_var, + verbose_logger, +) from litellm._uuid import uuid from litellm.batches.batch_utils import _handle_completed_batch from litellm.caching.caching import DualCache, InMemoryCache @@ -313,6 +321,7 @@ class Logging(LiteLLMLoggingBaseClass): applied_guardrails: list[str] | None = None, kwargs: dict | None = None, log_raw_request_response: bool = False, + supports_correlation_logging: bool = True, ): _input: Final[str | None] = messages # save original value of messages if messages is not None: @@ -338,6 +347,36 @@ class Logging(LiteLLMLoggingBaseClass): self.call_type = call_type self.litellm_call_id = litellm_call_id self.litellm_trace_id: str = litellm_trace_id if litellm_trace_id else str(uuid.uuid4()) + + # Capture the pre-call *value* (not a contextvars.Token) so restoration works + # even if this attempt's own logging ends up dispatched onto a different + # asyncio Task/context (e.g. via asyncio.create_task or the logging worker) - + # a Token can only be reset in the exact Context where it was created. + self._pre_call_trace_id: str = trace_id_var.get() + self._pre_call_session_id: str = session_id_var.get() + _sid: Final = kwargs.get("litellm_session_id") if kwargs else None + self.litellm_session_id: str = str(_sid) if _sid else "" + # supports_correlation_logging is False for calls originating from the + # sync client entry point (wrapper() in utils.py): a plain OS thread + # has no per-call context isolation the way an asyncio Task does, and + # a thread pool's worker threads are recycled across unrelated + # requests, so stamping trace_id/session_id there risks one request's + # ids leaking into a different, later request on the same thread. Sync + # support is deferred to a follow-up PR with its own safe-restore + # mechanism; async calls (the proxy's only call path) are unaffected. + if supports_correlation_logging: + set_trace_id(self.litellm_trace_id) + set_session_id(self.litellm_session_id) + # set_trace_id()/set_session_id() sanitize (strip control chars, bound + # length) before storing, so the contextvar's actual value can differ + # from self.litellm_trace_id/litellm_session_id. Capture what was + # really stored - _restore_correlation_context_if_unclaimed() must + # compare against this, not the raw ids, or a caller-supplied id + # containing control characters/oversized input would never match + # and cleanup would be skipped forever. + self._own_trace_id: str = trace_id_var.get() + self._own_session_id: str = session_id_var.get() + self.function_id = function_id self.streaming_chunks: list[Any] = [] # for generating complete stream response self.sync_streaming_chunks: list[Any] = [] # for generating complete stream response @@ -1992,7 +2031,67 @@ class Logging(LiteLLMLoggingBaseClass): if complete_streaming_response is not None: await self.async_success_handler(result=complete_streaming_response) - def success_handler(self, result=None, start_time=None, end_time=None, cache_hit=None, **kwargs): + def _restore_correlation_context(self) -> None: + """Restore trace_id/session_id contextvars to their pre-call value. + + Without this, a nested LiteLLM call sharing the same asyncio Task as an + outer request (e.g. a guardrail's own LLM-as-judge call, an MCP sampling + call) would leave the outer request's subsequent log lines stamped with + the nested call's trace_id/session_id instead of its own. + + Uses a plain set() of the captured pre-call value rather than + contextvars.Token-based reset(), since this can end up called from a + different asyncio Task/context than __init__ ran in (e.g. the request + task's own wrapper() finally block, plus async_success_handler + dispatched separately via asyncio.create_task/the logging worker) - + reset() only works in the exact Context a Token was created in and + raises otherwise. Deliberately NOT idempotent/guarded: each distinct + Task that calls this needs its own restore to actually take effect in + that Task's view of the contextvars, so calling it multiple times + (once per Task involved in this attempt) is required, not just safe. + """ + set_trace_id(self._pre_call_trace_id) + set_session_id(self._pre_call_session_id) + + def _restore_correlation_context_if_unclaimed(self) -> None: + """Guarded variant for __del__-triggered cleanup only. + + __del__ can fire arbitrarily late (delayed by cyclic GC, possibly + after the consuming Task/thread has already moved on to a different, + still-active call). Unconditionally restoring in that case would + stomp the active call's trace_id/session_id with this abandoned + stream's stale pre-call snapshot. Only restore if the contextvars + still hold the ids *this* call set - i.e. nothing has claimed them + since - so an unrelated active call is never overwritten. + """ + if trace_id_var.get() == self._own_trace_id and session_id_var.get() == self._own_session_id: + self._restore_correlation_context() + + def success_handler( + self, + result: Any = None, # heterogeneous response object; varies by call type (ANN401 ignored, see ruff-strict.toml) + start_time: datetime.datetime | None = None, + end_time: datetime.datetime | None = None, + cache_hit: bool | None = None, + **kwargs: Any, # kwargs-ok: forwarded to _success_handler_body + ) -> None: + """Restores trace_id/session_id contextvars once this attempt's own success + logging (including any nested calls its callbacks trigger) is fully done.""" + try: + return self._success_handler_body( + result=result, start_time=start_time, end_time=end_time, cache_hit=cache_hit, **kwargs + ) + finally: + self._restore_correlation_context() + + def _success_handler_body( + self, + result: Any = None, # heterogeneous response object; varies by call type (ANN401 ignored, see ruff-strict.toml) + start_time: datetime.datetime | None = None, + end_time: datetime.datetime | None = None, + cache_hit: bool | None = None, + **kwargs: Any, # kwargs-ok: forwarded from success_handler + ) -> None: verbose_logger.debug("Logging Details LiteLLM-Success Call: Cache_hit=%s", cache_hit) if not self.should_run_logging(event_type="sync_success"): # prevent double logging return @@ -2399,7 +2498,31 @@ class Logging(LiteLLMLoggingBaseClass): e, ) - async def async_success_handler(self, result=None, start_time=None, end_time=None, cache_hit=None, **kwargs): + async def async_success_handler( + self, + result: Any = None, # heterogeneous response object; varies by call type (ANN401 ignored, see ruff-strict.toml) + start_time: datetime.datetime | None = None, + end_time: datetime.datetime | None = None, + cache_hit: bool | None = None, + **kwargs: Any, # kwargs-ok: forwarded to _async_success_handler_body + ) -> None: + """Restores trace_id/session_id contextvars once this attempt's own success + logging (including any nested calls its callbacks trigger) is fully done.""" + try: + return await self._async_success_handler_body( + result=result, start_time=start_time, end_time=end_time, cache_hit=cache_hit, **kwargs + ) + finally: + self._restore_correlation_context() + + async def _async_success_handler_body( + self, + result: Any = None, # heterogeneous response object; varies by call type (ANN401 ignored, see ruff-strict.toml) + start_time: datetime.datetime | None = None, + end_time: datetime.datetime | None = None, + cache_hit: bool | None = None, + **kwargs: Any, # kwargs-ok: forwarded from async_success_handler + ) -> None: """ Implementing async callbacks, to handle asyncio event loop issues when custom integrations need to use async functions. """ @@ -2791,7 +2914,32 @@ class Logging(LiteLLMLoggingBaseClass): kwargs=self.model_call_details, ) - def failure_handler(self, exception, traceback_exception, start_time=None, end_time=None): + def failure_handler( + self, + exception: Exception, + traceback_exception: str, + start_time: datetime.datetime | None = None, + end_time: datetime.datetime | None = None, + ) -> None: + """Restores trace_id/session_id contextvars once this attempt's own failure + logging (including any nested calls its callbacks trigger) is fully done.""" + try: + return self._failure_handler_body( + exception=exception, + traceback_exception=traceback_exception, + start_time=start_time, + end_time=end_time, + ) + finally: + self._restore_correlation_context() + + def _failure_handler_body( + self, + exception: Exception, + traceback_exception: str, + start_time: datetime.datetime | None = None, + end_time: datetime.datetime | None = None, + ) -> None: verbose_logger.debug("Logging Details LiteLLM-Failure Call: %s", litellm.failure_callback) if not self.should_run_logging(event_type="sync_failure"): # prevent double logging return @@ -2960,7 +3108,32 @@ class Logging(LiteLLMLoggingBaseClass): "LiteLLM.LoggingError: [Non-Blocking] Exception occurred while failure logging %s", e ) - async def async_failure_handler(self, exception, traceback_exception, start_time=None, end_time=None): + async def async_failure_handler( + self, + exception: Exception, + traceback_exception: str, + start_time: datetime.datetime | None = None, + end_time: datetime.datetime | None = None, + ) -> None: + """Restores trace_id/session_id contextvars once this attempt's own failure + logging (including any nested calls its callbacks trigger) is fully done.""" + try: + return await self._async_failure_handler_body( + exception=exception, + traceback_exception=traceback_exception, + start_time=start_time, + end_time=end_time, + ) + finally: + self._restore_correlation_context() + + async def _async_failure_handler_body( + self, + exception: Exception, + traceback_exception: str, + start_time: datetime.datetime | None = None, + end_time: datetime.datetime | None = None, + ) -> None: """ Implementing async callbacks, to handle asyncio event loop issues when custom integrations need to use async functions. """ @@ -5061,33 +5234,61 @@ class StandardLoggingPayloadSetup: return end_time_float - start_time_float @staticmethod - def _get_standard_logging_payload_trace_id( + def get_standard_logging_payload_trace_id( logging_obj: Logging, - litellm_params: dict, + litellm_params: Mapping[str, Any], ) -> str: """ Returns the `litellm_trace_id` for this request This helps link sessions when multiple requests are made in a single session + + Gated behind `litellm.request_correlation_in_logs`: + - Off (default): legacy behavior, preserved for backward compatibility - + `litellm_session_id` takes priority over `litellm_trace_id` since historically + this field doubled as the session-grouping field. + - On: `litellm_trace_id` takes priority - trace_id and session_id are independent, + see `get_standard_logging_payload_session_id` for session tracking. """ dynamic_litellm_session_id: Final = litellm_params.get("litellm_session_id") dynamic_litellm_trace_id: Final = litellm_params.get("litellm_trace_id") + metadata: Final = litellm_params.get("metadata") + metadata_session_id: Final = metadata.get("session_id") if metadata else None + metadata_trace_id: Final = metadata.get("trace_id") if metadata else None - # Note: we recommend using `litellm_session_id` for session tracking - # `litellm_trace_id` is an internal litellm param + ordered_candidates: Final[tuple[Any, Any, Any, Any]] = ( + (dynamic_litellm_trace_id, dynamic_litellm_session_id, metadata_trace_id, metadata_session_id) + if litellm.request_correlation_in_logs + else (dynamic_litellm_session_id, dynamic_litellm_trace_id, metadata_session_id, metadata_trace_id) + ) + for candidate in ordered_candidates: + if candidate: + return str(candidate) + return logging_obj.litellm_trace_id + + @staticmethod + def get_standard_logging_payload_session_id( + logging_obj: Logging, + litellm_params: Mapping[str, Any], + ) -> str: + """ + Returns the end-user/conversation `litellm_session_id` for this request, independent of trace_id. + + Only populated when `litellm.request_correlation_in_logs` is enabled - off by default + to avoid changing existing StandardLoggingPayload shape for callers who haven't opted in. + Unlike `get_standard_logging_payload_trace_id`, this never falls back to a generated + per-call trace id: it's empty when the caller never supplied a session id. + """ + if not litellm.request_correlation_in_logs: + return "" + dynamic_litellm_session_id: Final = litellm_params.get("litellm_session_id") if dynamic_litellm_session_id: return str(dynamic_litellm_session_id) - elif dynamic_litellm_trace_id: - return str(dynamic_litellm_trace_id) - # Fallback: use metadata.session_id or metadata.trace_id for call chaining - metadata: Final = litellm_params.get("metadata") or {} - metadata_session_id: Final = metadata.get("session_id") - metadata_trace_id: Final = metadata.get("trace_id") + metadata: Final = litellm_params.get("metadata") + metadata_session_id: Final = metadata.get("session_id") if metadata else None if metadata_session_id: return str(metadata_session_id) - if metadata_trace_id: - return str(metadata_trace_id) - return logging_obj.litellm_trace_id + return logging_obj.litellm_session_id @staticmethod def _get_user_agent_tags(proxy_server_request: dict) -> list[str] | None: @@ -5392,7 +5593,11 @@ def get_standard_logging_object_payload( payload: Final[StandardLoggingPayload] = StandardLoggingPayload( id=str(id), litellm_call_id=kwargs.get("litellm_call_id") or litellm_params.get("litellm_call_id"), - trace_id=StandardLoggingPayloadSetup._get_standard_logging_payload_trace_id( + trace_id=StandardLoggingPayloadSetup.get_standard_logging_payload_trace_id( + logging_obj=logging_obj, + litellm_params=litellm_params, + ), + session_id=StandardLoggingPayloadSetup.get_standard_logging_payload_session_id( logging_obj=logging_obj, litellm_params=litellm_params, ), diff --git a/litellm/litellm_core_utils/llm_cost_calc/utils.py b/litellm/litellm_core_utils/llm_cost_calc/utils.py index 6574b261774..b94851794f0 100644 --- a/litellm/litellm_core_utils/llm_cost_calc/utils.py +++ b/litellm/litellm_core_utils/llm_cost_calc/utils.py @@ -49,6 +49,12 @@ _SERVICE_TIER_TO_COST_KEY_SUFFIX: Final[Mapping[str, str]] = MappingProxyType( } ) +_INCLUSIVE_THRESHOLD_PROVIDERS: Final = frozenset({"xai"}) + + +def _uses_inclusive_token_thresholds(custom_llm_provider: str | None) -> bool: + return custom_llm_provider in _INCLUSIVE_THRESHOLD_PROVIDERS + def _get_token_detail_value(details: object, key: str) -> int | None: if isinstance(details, dict): @@ -202,7 +208,11 @@ def _parse_above_token_threshold(key: str) -> float: def _get_token_base_cost( - model_info: ModelInfo, usage: Usage, service_tier: str | None = None + model_info: ModelInfo, + usage: Usage, + service_tier: str | None = None, + *, + threshold_is_inclusive: bool = False, ) -> tuple[float, float, float, float, float]: """ Return prompt cost, completion cost, and cache costs for a given model and usage. @@ -210,6 +220,9 @@ def _get_token_base_cost( If input_tokens > threshold and `input_cost_per_token_above_[x]k_tokens` or `input_cost_per_token_above_[x]_tokens` is set, then we use the corresponding threshold cost for all token types. + `threshold_is_inclusive` switches that comparison to >=, for providers such as xAI + that bill the higher tier once the prompt reaches the threshold. + Returns: Tuple[float, float, float, float] - (prompt_cost, completion_cost, cache_creation_cost, cache_read_cost) """ @@ -262,7 +275,7 @@ def _get_token_base_cost( # Handle both formats: _above_128k_tokens and _above_128_tokens threshold_str = key.split("_above_")[1].split("_tokens")[0] threshold = _parse_above_token_threshold(key) - if usage.prompt_tokens > threshold: + if usage.prompt_tokens > threshold or (threshold_is_inclusive and usage.prompt_tokens == threshold): # Prefer a service_tier-specific above-threshold key when available, # e.g. input_cost_per_token_priority_above_200k_tokens for Gemini # ON_DEMAND_PRIORITY. Falls back to the standard key automatically @@ -777,7 +790,12 @@ def generic_cost_per_token( cache_creation_cost, cache_creation_cost_above_1hr, cache_read_cost, - ) = _get_token_base_cost(model_info=model_info, usage=usage, service_tier=service_tier) + ) = _get_token_base_cost( + model_info=model_info, + usage=usage, + service_tier=service_tier, + threshold_is_inclusive=_uses_inclusive_token_thresholds(custom_llm_provider), + ) prompt_cost = _calculate_input_cost( prompt_tokens_details=prompt_tokens_details, @@ -909,7 +927,12 @@ def get_token_type_cost_breakdown( cache_creation_cost_rate, cache_creation_cost_above_1hr_rate, cache_read_cost_rate, - ) = _get_token_base_cost(model_info=model_info, usage=usage, service_tier=service_tier) + ) = _get_token_base_cost( + model_info=model_info, + usage=usage, + service_tier=service_tier, + threshold_is_inclusive=_uses_inclusive_token_thresholds(custom_llm_provider), + ) reasoning_tokens = ( _parse_completion_tokens_details(usage)["reasoning_tokens"] @@ -996,9 +1019,13 @@ def calculate_image_response_cost_from_usage( input_tokens_details: Final = getattr(usage, "input_tokens_details", None) prompt_tokens_details: PromptTokensDetailsWrapper | None = None if input_tokens_details is not None: + # input_tokens_details may be a dict (e.g. OpenAI image edit responses) + # or an object; read it tolerantly like the output side below, so image + # input tokens are priced at input_cost_per_image_token instead of + # silently falling back to the text rate. prompt_tokens_details = PromptTokensDetailsWrapper( - text_tokens=getattr(input_tokens_details, "text_tokens", None), - image_tokens=getattr(input_tokens_details, "image_tokens", None), + text_tokens=_get_token_detail_value(input_tokens_details, "text_tokens"), + image_tokens=_get_token_detail_value(input_tokens_details, "image_tokens"), cached_tokens=0, ) diff --git a/litellm/litellm_core_utils/realtime_streaming.py b/litellm/litellm_core_utils/realtime_streaming.py index 858d10df53b..d68bdc4a250 100644 --- a/litellm/litellm_core_utils/realtime_streaming.py +++ b/litellm/litellm_core_utils/realtime_streaming.py @@ -1,5 +1,6 @@ import asyncio import json +from collections.abc import Mapping, Sequence from typing import TYPE_CHECKING, Any, Final, Protocol, cast import litellm @@ -20,11 +21,24 @@ from .litellm_logging import Logging as LiteLLMLogging if TYPE_CHECKING: from websockets.asyncio.client import ClientConnection + from litellm.types.guardrails import GuardrailEventHooks + CLIENT_CONNECTION_CLASS = ClientConnection else: CLIENT_CONNECTION_CLASS = Any +class _ClientWebSocketExceptions(Protocol): + ConnectionClosed: type[Exception] + + +class _ClientWebSocket(Protocol): + exceptions: _ClientWebSocketExceptions + + async def send_text(self, data: str) -> None: ... + async def receive_text(self) -> str: ... + + class RealtimeEventNormalizer(Protocol): def should_drop(self, event: object) -> bool: ... def normalize(self, event: dict) -> dict: ... @@ -48,13 +62,13 @@ class RealTimeStreaming: logging_obj: LiteLLMLogging, provider_config: BaseRealtimeConfig | None = None, model: str = "", - user_api_key_dict: Any | None = None, + user_api_key_dict: object | None = None, request_data: dict | None = None, backend_uses_beta_protocol: bool | None = None, force_transcription_model: str | None = None, event_normalizer: RealtimeEventNormalizer | None = None, ): - self.websocket = websocket + self.websocket: _ClientWebSocket = websocket self.backend_ws = backend_ws self.logging_obj = logging_obj self.messages: list[OpenAIRealtimeEvents] = [] @@ -127,7 +141,7 @@ class RealTimeStreaming: ] ) _CLIENT_AUDIO_BUFFER_COMMIT_TYPES = frozenset(["input_audio_buffer.commit", "input_audio_buffer.end"]) - _AUDIO_FORMAT_MAP: dict[str, dict[str, Any]] = { + _AUDIO_FORMAT_MAP: dict[str, dict[str, str | int]] = { "pcm16": {"type": "audio/pcm", "rate": 24000}, "g711_ulaw": {"type": "audio/G711-ulaw", "rate": 8000}, "g711_alaw": {"type": "audio/G711-alaw", "rate": 8000}, @@ -281,6 +295,7 @@ class RealTimeStreaming: if event_obj.get("type") != "response.done": return response: Final = cast(dict[str, Any], event_obj.get("response", {})) + item: Mapping[str, object] for item in response.get("output", []): if item.get("type") == "function_call": self.tool_calls.append( @@ -384,7 +399,7 @@ class RealTimeStreaming: return message try: - message_obj: Final = json.loads(message) + message_obj: Final[Mapping[str, object]] = json.loads(message) except (json.JSONDecodeError, TypeError): return message @@ -487,7 +502,7 @@ class RealTimeStreaming: if self._backend_setup_complete and not self._flushing_pending_messages_until_setup: return False try: - msg_obj: Final = json.loads(message) + msg_obj: Final[Mapping[str, object]] = json.loads(message) except (json.JSONDecodeError, TypeError): return False return msg_obj.get("type") in RealTimeStreaming._CLIENT_AUDIO_BUFFER_TYPES @@ -555,7 +570,7 @@ class RealTimeStreaming: def _event_to_client_json(self, event: dict) -> str: return json.dumps(self._normalize_event_for_ga_client(event)) - async def _send_event_to_client(self, event: Any, event_str: str) -> bool: + async def _send_event_to_client(self, event: object, event_str: str) -> bool: if self._should_drop_event_from_client(event): return False if isinstance(event, dict): @@ -595,12 +610,12 @@ class RealTimeStreaming: def _make_disable_auto_response_message(self) -> str: """Return a session.update that disables VAD auto-response.""" - turn_detection: Final[dict[str, Any]] = { + turn_detection: Final[dict[str, str | bool]] = { "type": "server_vad", "create_response": False, } if self._backend_uses_beta_protocol: - session: dict[str, Any] = {"turn_detection": turn_detection} + session: dict[str, object] = {"turn_detection": turn_detection} else: session = { "type": "realtime", @@ -654,7 +669,7 @@ class RealTimeStreaming: def _has_realtime_guardrails_for_event_hooks( self, - event_hooks: list[Any], + event_hooks: Sequence["GuardrailEventHooks"], ) -> bool: """Return True if any callback would run for one of ``event_hooks``.""" from litellm.integrations.custom_guardrail import CustomGuardrail @@ -699,7 +714,7 @@ class RealTimeStreaming: transcript: str, item_id: str | None = None, pre_block_backend_message: str | None = None, - event_hooks: list[Any] | None = None, + event_hooks: Sequence["GuardrailEventHooks"] | None = None, ) -> bool: """ Run registered guardrails on realtime text (transcript, user message, tool output). @@ -753,7 +768,7 @@ class RealTimeStreaming: raise # Extract the human-readable error from the detail dict (HTTPException) # or fall back to str(e) for plain ValueError. - detail = getattr(e, "detail", None) + detail: object | None = getattr(e, "detail", None) if isinstance(detail, dict): safe_msg = detail.get("error") or str(e) elif detail is not None: @@ -826,7 +841,7 @@ class RealTimeStreaming: return True return False - async def _handle_provider_config_message(self, raw_response) -> None: + async def _handle_provider_config_message(self, raw_response: str) -> None: """Process a backend message when a provider_config is set (transformed path).""" returned_object: Final = self.provider_config.transform_realtime_response( raw_response, @@ -910,7 +925,7 @@ class RealTimeStreaming: await self._send_event_to_client(event, event_str) @staticmethod - def _parse_backend_event(raw_response: str) -> dict | None: + def _parse_backend_event(raw_response: str) -> dict[str, object] | None: """Parse a backend frame once. Returns None for non-JSON or non-object frames.""" try: event: Final = json.loads(raw_response) @@ -1020,7 +1035,7 @@ class RealTimeStreaming: objects and any test doubles that expose a .scope dict. """ try: - headers: Final = websocket.scope.get("headers", []) + headers: Final[Sequence[tuple[bytes | str, bytes | str]]] = websocket.scope.get("headers", []) for name, value in headers: if isinstance(name, bytes): name = name.decode("latin-1") @@ -1071,9 +1086,9 @@ class RealTimeStreaming: session["output_modalities"] = ["text"] # 3-7. Lift flat audio fields into the nested audio object - audio: Final[dict[str, Any]] = {} - inp: Final[dict[str, Any]] = {} - out: Final[dict[str, Any]] = {} + audio: Final[dict[str, object]] = {} + inp: Final[dict[str, object]] = {} + out: Final[dict[str, object]] = {} # voice → audio.output.voice if "voice" in session: @@ -1190,7 +1205,7 @@ class RealTimeStreaming: # model; check them with the same guardrail used for # user text so an attacker cannot smuggle blocked # content into a function_call_output. - output = item.get("output", "") + output: object = item.get("output", "") output_text = output if isinstance(output, str) else json.dumps(output) if output_text: # Build the sanitized function_call_output up @@ -1241,7 +1256,7 @@ class RealTimeStreaming: # interaction turn. continue elif item.get("role") == "user": - content_list = item.get("content", []) + content_list: Sequence[object] = item.get("content", []) texts = [ c.get("text", "") for c in content_list @@ -1280,7 +1295,7 @@ class RealTimeStreaming: and not self._guardrail_turn_detection_update_sent and self._has_audio_transcription_guardrails() ): - session = msg_obj.setdefault("session", {}) + session: object = msg_obj.setdefault("session", {}) if isinstance(session, dict): existing_td = session.get("turn_detection") if not isinstance(existing_td, dict): diff --git a/litellm/litellm_core_utils/streaming_chunk_builder_utils.py b/litellm/litellm_core_utils/streaming_chunk_builder_utils.py index 3f967e29002..886ba6a3a18 100644 --- a/litellm/litellm_core_utils/streaming_chunk_builder_utils.py +++ b/litellm/litellm_core_utils/streaming_chunk_builder_utils.py @@ -3,7 +3,7 @@ import time from collections.abc import Iterator, Mapping, Sequence from itertools import groupby from types import MappingProxyType -from typing import TYPE_CHECKING, Any, Final, Union, cast +from typing import TYPE_CHECKING, Any, Final, TypedDict, Union, cast from litellm._logging import verbose_logger from litellm.types.llms.openai import ( @@ -30,6 +30,7 @@ from litellm.types.utils import ( from litellm.utils import print_verbose, token_counter if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging from litellm.types.litellm_core_utils.streaming_chunk_builder_utils import ( UsagePerChunk, ) @@ -39,6 +40,60 @@ if TYPE_CHECKING: ) +class _ThinkingBlockFragment(TypedDict, total=False): + type: str | None + data: str | None + thinking: str | None + signature: str | None + + +class _ThinkingDelta(TypedDict, total=False): + thinking_blocks: Sequence[_ThinkingBlockFragment] + + +class _ThinkingChoice(TypedDict, total=False): + delta: _ThinkingDelta + + +class _ThinkingChunk(TypedDict): + choices: Sequence[_ThinkingChoice] + + +class _ContentChoice(TypedDict, total=False): + delta: Mapping[str, str | None] + + +class _ContentChunk(TypedDict): + choices: Sequence[_ContentChoice] + + +class _AudioDelta(TypedDict, total=False): + audio: ChatCompletionAudioDelta | None + + +class _AudioChoice(TypedDict, total=False): + delta: _AudioDelta + + +class _AudioChunk(TypedDict): + choices: Sequence[_AudioChoice] + + +class _UsageBearingChunk(TypedDict, total=False): + usage: Usage | None + _hidden_params: Mapping[str, str] + + +class _UsageSummary(TypedDict): + prompt_tokens: int | None + completion_tokens: int | None + cache_creation_input_tokens: int | None + cache_read_input_tokens: int | None + completion_tokens_details: CompletionTokensDetails | None + prompt_tokens_details: PromptTokensDetailsWrapper | None + cost: float | None + + def capture_cache_creation_token_details( prompt_tokens_details: PromptTokensDetailsWrapper | None, current: CacheCreationTokenDetails | None, @@ -78,7 +133,7 @@ class ChunkProcessor: return [] first_chunk: Final = chunks[0] - first_hidden_params: dict[str, Any] = {} + first_hidden_params: dict[str, object] = {} if isinstance(first_chunk, dict): candidate = first_chunk.get("_hidden_params", {}) if isinstance(candidate, dict): @@ -115,8 +170,8 @@ class ChunkProcessor: @staticmethod def apply_provider_assembled_streaming_metadata( response: ModelResponse, - chunks: list[Any], - logging_obj: Any | None = None, + chunks: list[object], + logging_obj: "Logging | None" = None, ) -> None: if not chunks: return @@ -456,7 +511,7 @@ class ChunkProcessor: ) def get_combined_content( - self, chunks: list[dict[str, Any]], delta_key: str = "content" + self, chunks: Sequence["_ContentChunk"], delta_key: str = "content" ) -> ChatCompletionAssistantContentValue: content_list: Final[list[str]] = [] for chunk in chunks: @@ -475,7 +530,7 @@ class ChunkProcessor: return combined_content def get_combined_thinking_content( - self, chunks: list[dict[str, Any]] + self, chunks: Sequence["_ThinkingChunk"] ) -> list[Union["ChatCompletionThinkingBlock", "ChatCompletionRedactedThinkingBlock"]] | None: from litellm.types.llms.openai import ( ChatCompletionRedactedThinkingBlock, @@ -532,10 +587,10 @@ class ChunkProcessor: return thinking_blocks return None - def get_combined_reasoning_content(self, chunks: list[dict[str, Any]]) -> ChatCompletionAssistantContentValue: + def get_combined_reasoning_content(self, chunks: Sequence["_ContentChunk"]) -> ChatCompletionAssistantContentValue: return self.get_combined_content(chunks, delta_key="reasoning_content") - def get_combined_audio_content(self, chunks: list[dict[str, Any]]) -> ChatCompletionAudioResponse: + def get_combined_audio_content(self, chunks: Sequence["_AudioChunk"]) -> ChatCompletionAudioResponse: base64_data_list: Final[list[str]] = [] transcript_list: Final[list[str]] = [] expires_at: int | None = None @@ -544,7 +599,7 @@ class ChunkProcessor: for chunk in chunks: choices = chunk["choices"] for choice in choices: - delta = choice.get("delta") or {} + delta: _AudioDelta = choice.get("delta") or {} audio: ChatCompletionAudioDelta | None = delta.get("audio") if audio is not None: for k, v in audio.items(): @@ -565,7 +620,7 @@ class ChunkProcessor: id=id, ) - def _usage_chunk_calculation_helper(self, usage_chunk: Usage) -> dict: + def _usage_chunk_calculation_helper(self, usage_chunk: Usage) -> "_UsageSummary": prompt_tokens = 0 completion_tokens = 0 ## anthropic prompt caching information ## @@ -623,8 +678,8 @@ class ChunkProcessor: return reasoning_tokens @staticmethod - def _extract_usage_chunk(chunk: dict[str, Any] | ModelResponse | ModelResponseStream) -> Usage | None: - usage_chunk: Usage | dict[str, Any] | None = None + def _extract_usage_chunk(chunk: "_UsageBearingChunk | ModelResponse | ModelResponseStream") -> Usage | None: + usage_chunk: Usage | None = None if hasattr(chunk, "usage") and chunk.usage is not None: usage_chunk = chunk.usage elif "usage" in chunk: @@ -640,7 +695,7 @@ class ChunkProcessor: def _calculate_usage_per_chunk( self, - chunks: list[dict[str, Any] | ModelResponse], + chunks: Sequence["_UsageBearingChunk | ModelResponse"], ) -> "UsagePerChunk": from litellm.types.litellm_core_utils.streaming_chunk_builder_utils import ( UsagePerChunk, @@ -721,13 +776,7 @@ class ChunkProcessor: "web_search_requests", ) - prompt_tokens_details = ( - cast( - PromptTokensDetailsWrapper | None, - usage_chunk_dict["prompt_tokens_details"], - ) - or prompt_tokens_details - ) + prompt_tokens_details = usage_chunk_dict["prompt_tokens_details"] or prompt_tokens_details cache_creation_token_details = capture_cache_creation_token_details( prompt_tokens_details, cache_creation_token_details @@ -758,7 +807,7 @@ class ChunkProcessor: @staticmethod def _reset_anthropic_cursor_completion_tokens( - chunks: list[dict[str, Any] | ModelResponse], + chunks: Sequence["_UsageBearingChunk | ModelResponse"], completion_tokens: int, completion_usage_updates: int, ) -> int: @@ -797,7 +846,7 @@ class ChunkProcessor: def calculate_usage( self, - chunks: list[dict[str, Any] | ModelResponse], + chunks: Sequence["_UsageBearingChunk | ModelResponse"], model: str, completion_output: str, messages: list | None = None, @@ -851,8 +900,8 @@ class ChunkProcessor: setattr(returned_usage, "cache_read_input_tokens", cache_read_input_tokens) # for anthropic if completion_tokens_details is not None: if isinstance(completion_tokens_details, CompletionTokensDetails): - returned_usage.completion_tokens_details = CompletionTokensDetailsWrapper( - **completion_tokens_details.model_dump() + returned_usage.completion_tokens_details = CompletionTokensDetailsWrapper.model_validate( + completion_tokens_details.model_dump() ) else: returned_usage.completion_tokens_details = completion_tokens_details diff --git a/litellm/litellm_core_utils/streaming_handler.py b/litellm/litellm_core_utils/streaming_handler.py index 68465d06b15..2dc71abee3e 100644 --- a/litellm/litellm_core_utils/streaming_handler.py +++ b/litellm/litellm_core_utils/streaming_handler.py @@ -213,7 +213,75 @@ class CustomStreamWrapper: def __aiter__(self) -> AsyncIterator["ModelResponseStream"]: return self + def _restore_consumer_correlation_context(self, *, guarded: bool = False) -> None: + """Restore trace_id/session_id in the *consuming* thread/task/context. + + wrapper_async() deliberately skips restoring correlation context when + it returns a stream, so log lines emitted while the caller iterates it + still carry this call's ids (see request_correlation_in_logs). + wrapper() (the sync path) never stamps anything in the first place - + see Logging.__init__'s supports_correlation_logging - so this method + is an inert no-op for sync-created streams, harmless to call anyway + since the class is shared between __next__ and __anext__. + But the terminal success/failure handlers this stream dispatches to + finish the job run on a *different* Task/thread (asyncio.create_task, + threading.Thread, or the shared executor) - restoring there fixes up + that detached context, not the one actually running the caller's + `for`/`async for` loop. Call this at every point control genuinely + returns to that consuming context: natural exhaustion (StopIteration/ + StopAsyncIteration), a raised failure, or explicit aclose(). Never let + this raise - it must not break the caller's actual stream handling. + + guarded=True (only __del__ uses this) skips the restore unless the + contextvars still hold the ids this stream's own call set, so a + delayed finalizer never overwrites a different, still-active call + that has since taken over the same Task/thread's context. + """ + try: + logging_obj: Final = getattr(self, "logging_obj", None) + if logging_obj is None: + return + method_name: Final = ( + "_restore_correlation_context_if_unclaimed" if guarded else "_restore_correlation_context" + ) + restore: Final = getattr(logging_obj, method_name, None) + if restore is not None: + restore() + except Exception as restore_error: # noqa: BLE001 # best-effort cleanup; must not raise into the caller + verbose_logger.debug("could not restore correlation context: %s", restore_error) + + def __del__(self) -> None: + """Best-effort correlation-context cleanup for an abandoned async stream. + + Only meaningfully applies to streams created by wrapper_async(): it + leaves contextvars "open" across the caller's iteration, so if the + caller never fully consumes the stream - stops early, drops the + reference, cancels it - none of the exit points + _restore_consumer_correlation_context() is called from ever run. For a + sync stream (wrapper()), this is a no-op in practice: wrapper() never + stamps trace_id/session_id for sync calls in the first place (see + Logging.__init__'s supports_correlation_logging), so there is nothing + for this to clean up. + + This is a best-effort fallback, not a guarantee: __del__ timing is + unpredictable (delayed by cyclic GC, not guaranteed at interpreter + shutdown, and may run on a different thread), so this can only reduce + how long the leak persists, not eliminate it. That's an acceptable + trade specifically because its blast radius is bounded to the one + asyncio Task this stream's own call ran in - each async call has its + own copy of the contextvars, and Tasks (unlike a thread pool's worker + threads) are never recycled across requests, so a delayed or missed + cleanup here can never misattribute a *different* request's logs. + guarded=True additionally ensures it never clobbers a different, + still-active call's context within that same Task if this fires late. + """ + self._restore_consumer_correlation_context(guarded=True) + async def aclose(self): + # Restore the consumer's outer context only after the underlying + # provider stream's own close (and its diagnostic logging below, if + # closing fails) completes - not before - so those log lines still + # carry this closing stream's own trace_id/session_id. if self.completion_stream is not None: stream_to_close: Final = self.completion_stream self.completion_stream = None @@ -233,6 +301,7 @@ class CustomStreamWrapper: "CustomStreamWrapper.aclose: error closing completion_stream: %s", e, ) + self._restore_consumer_correlation_context() def check_send_stream_usage(self, stream_options: dict | None): return stream_options is not None and stream_options.get("include_usage", False) is True @@ -1839,6 +1908,7 @@ class CustomStreamWrapper: if self.sent_stream_usage is False and self.send_stream_usage is True: self.sent_stream_usage = True return response + self._restore_consumer_correlation_context() raise # Re-raise StopIteration else: self.sent_last_chunk = True @@ -1852,6 +1922,19 @@ class CustomStreamWrapper: processed_chunk, cache_hit, ) # log response + # Deliberately do NOT restore context here even though + # completion_stream is already exhausted: this chunk is still + # real data belonging to this call, and the caller's own + # (application-level) log statements processing it run + # immediately after this return, in this same synchronous + # frame - restoring first would make those lines carry the + # wrong ids, which is exactly what leaving context open during + # iteration is meant to prevent (see + # _restore_consumer_correlation_context's docstring). A caller + # that keeps iterating gets cleaned up on its next __next__() + # call (immediate StopIteration, handled above); one that + # stops right here relies on aclose() or the best-effort + # __del__ guard instead. return processed_chunk except Exception as e: traceback_exception: Final = traceback.format_exc() @@ -1879,8 +1962,12 @@ class CustomStreamWrapper: cache_hit = False if self.custom_llm_provider is not None and self.custom_llm_provider == "cached_response": cache_hit = True - self._check_max_streaming_duration() try: + # Inside the try (not before it) so a raised litellm.Timeout flows + # through the same except Exception -> _handle_stream_fallback_error + # path as every other failure, restoring the consumer's correlation + # context - a check before the try would bypass that entirely. + self._check_max_streaming_duration() if self.completion_stream is None: await self.fetch_stream() @@ -2083,10 +2170,17 @@ class CustomStreamWrapper: ) ) + self._restore_consumer_correlation_context() raise StopAsyncIteration # Re-raise StopIteration else: self.sent_last_chunk = True processed_chunk: Final = self.finish_reason_handler() + # see sync __next__'s sibling branch: deliberately do NOT restore + # here - this chunk is still this call's own data, and restoring + # before returning it would corrupt the caller's own log + # statements processing it. A caller that keeps iterating gets + # cleaned up on the next __anext__() call; one that stops here + # relies on aclose() or the best-effort __del__ guard. return processed_chunk def _log_stream_failure_and_raise(self, e: Exception) -> NoReturn: @@ -2138,7 +2232,12 @@ class CustomStreamWrapper: """ from litellm.exceptions import MidStreamFallbackError - # Map to OpenAI exception format + # Map to OpenAI exception format. Some providers' mappers (e.g. + # _map_anthropic_exception, _map_aleph_alpha_exception) synchronously + # log a debug diagnostic (the raw status code) as part of mapping - + # restore the consumer's outer context only after this completes, so + # that diagnostic log line still carries the failing stream's own + # trace_id/session_id instead of the consumer's (or an empty one). if isinstance(e, OpenAIError): mapped_exception: Exception = e else: @@ -2152,6 +2251,7 @@ class CustomStreamWrapper: ) except Exception as mapping_error: mapped_exception = mapping_error + self._restore_consumer_correlation_context() def _normalize_status_code(exc: Exception) -> int | None: """Best-effort status_code extraction.""" diff --git a/litellm/llms/anthropic/experimental_pass_through/adapters/handler.py b/litellm/llms/anthropic/experimental_pass_through/adapters/handler.py index a9751489473..48d8a03d549 100644 --- a/litellm/llms/anthropic/experimental_pass_through/adapters/handler.py +++ b/litellm/llms/anthropic/experimental_pass_through/adapters/handler.py @@ -1,11 +1,13 @@ -from collections.abc import AsyncIterator, Coroutine, Iterator +from collections.abc import AsyncIterator, Coroutine, Iterator, Mapping from typing import ( TYPE_CHECKING, - Any, Final, + TypeAlias, cast, ) +from typing_extensions import TypedDict + import litellm from litellm._logging import verbose_logger from litellm.litellm_core_utils.asyncify import run_async_function @@ -33,8 +35,17 @@ if TYPE_CHECKING: # Anthropic-only keys already mapped by the translator; strip on extra_kwargs re-merge. ANTHROPIC_ONLY_REQUEST_KEYS: Final[frozenset[str]] = frozenset({"output_config"}) +_AnthropicMessages: TypeAlias = "list[dict[str, object]]" +_AnthropicSystem: TypeAlias = "str | list[dict[str, object]] | None" +_ContextManagementSpec: TypeAlias = "dict[str, object] | list[dict[str, object]] | None" -def _messages_have_compaction_block(messages: list[dict]) -> bool: + +class _CompletionKwargs(TypedDict, total=False, extra_items=object): + model: str + custom_llm_provider: str + + +def _messages_have_compaction_block(messages: _AnthropicMessages) -> bool: """Return True when any message carries a ``compaction`` content block.""" for msg in messages: content = msg.get("content") @@ -54,8 +65,10 @@ def _proxy_router_fallback() -> "Router | None": return _proxy_router -def _extract_proxy_litellm_metadata(kwargs: dict[str, Any]) -> dict[str, Any] | None: - """Return ``kwargs["litellm_metadata"]`` when it's a dict; ``None`` otherwise. +def _extract_proxy_litellm_metadata( + kwargs: Mapping[str, object], +) -> "tuple[dict[str, object], UserAPIKeyAuth | None] | tuple[None, None]": + """Return ``(kwargs["litellm_metadata"], its user_api_key_auth)`` when it's a dict; ``(None, None)`` otherwise. The proxy attaches its auth/spend-attribution fields (``user_api_key``, ``user_api_key_team_id``, ``litellm_call_id``, the full ``UserAPIKeyAuth`` @@ -68,18 +81,19 @@ def _extract_proxy_litellm_metadata(kwargs: dict[str, Any]) -> dict[str, Any] | """ litellm_metadata: Final = kwargs.get("litellm_metadata") if not isinstance(litellm_metadata, dict): - return None - return litellm_metadata + return None, None + user_api_key_auth: Final[UserAPIKeyAuth | None] = litellm_metadata.get("user_api_key_auth") + return litellm_metadata, user_api_key_auth async def _prepare_context_managed_request( *, model: str, - messages: list[dict], - tools: list[dict] | None, - system: Any | None, - context_management_spec: Any, - litellm_metadata: dict | None, + messages: _AnthropicMessages, + tools: list[dict[str, object]] | None, + system: _AnthropicSystem, + context_management_spec: _ContextManagementSpec, + litellm_metadata: dict[str, object] | None, additional_drop_params: list[str] | None, llm_router: "Router | None", user_api_key_auth: "UserAPIKeyAuth | None" = None, @@ -102,11 +116,11 @@ async def _prepare_context_managed_request( if polyfill_will_run: history_result: PolyfillResult | None = None - working_messages: list[dict] = messages - working_system: Any | None = system + working_messages: _AnthropicMessages = messages + working_system: _AnthropicSystem = system else: history_result = apply_client_compaction_block_history( - messages=cast(list[dict[str, Any]], messages), + messages=messages, system=system, ) working_messages = history_result.messages if history_result is not None else messages @@ -136,7 +150,7 @@ async def _prepare_context_managed_request( # to non-Anthropic backends that would reject them. if polyfill_will_run and history_result is None: history_result = apply_client_compaction_block_history( - messages=cast(list[dict[str, Any]], messages), + messages=messages, system=system, ) return history_result @@ -144,7 +158,7 @@ async def _prepare_context_managed_request( def _polyfill_will_run( *, - context_management_spec: Any, + context_management_spec: _ContextManagementSpec, additional_drop_params: list[str] | None, ) -> bool: """Return True when ``compact_20260112`` will run via the polyfill dispatcher. @@ -171,7 +185,7 @@ def _polyfill_will_run( def _spec_has_non_compact_edits( *, - context_management_spec: Any, + context_management_spec: _ContextManagementSpec, additional_drop_params: list[str] | None, ) -> bool: """Return True when the spec includes edits other than ``compact_20260112``. @@ -209,9 +223,9 @@ def _context_management_explicitly_dropped(additional_drop_params: list[str] | N def _normalize_spec_edits( *, - context_management_spec: Any, + context_management_spec: _ContextManagementSpec, additional_drop_params: list[str] | None, -) -> list[dict[str, Any]] | None: +) -> list[dict[str, object]] | None: """Return the normalized ``edits`` list, or ``None`` if the polyfill won't run. Delegates spec-shape normalization to the dispatcher's ``_normalize_spec`` @@ -236,11 +250,11 @@ def _normalize_spec_edits( async def _run_polyfill_if_enabled( *, model: str, - messages: list[dict], - tools: list[dict] | None, - system: Any | None, - context_management_spec: Any, - litellm_metadata: dict | None, + messages: _AnthropicMessages, + tools: list[dict[str, object]] | None, + system: _AnthropicSystem, + context_management_spec: _ContextManagementSpec, + litellm_metadata: dict[str, object] | None, additional_drop_params: list[str] | None, llm_router: "Router | None", user_api_key_auth: "UserAPIKeyAuth | None" = None, @@ -304,9 +318,9 @@ ANTHROPIC_ADAPTER: Final = AnthropicAdapter() class LiteLLMMessagesToCompletionTransformationHandler: @staticmethod def _route_openai_thinking_to_responses_api_if_needed( - completion_kwargs: dict[str, Any], + completion_kwargs: _CompletionKwargs, *, - thinking: dict[str, Any] | None, + thinking: Mapping[str, object] | None, ) -> None: """ When users call `litellm.anthropic.messages.*` with a non-Anthropic model and @@ -369,7 +383,7 @@ class LiteLLMMessagesToCompletionTransformationHandler: @staticmethod def _normalize_reasoning_effort( - completion_kwargs: dict[str, Any], + completion_kwargs: _CompletionKwargs, ) -> None: """ Normalize reasoning_effort values based on target model capabilities. @@ -385,7 +399,7 @@ class LiteLLMMessagesToCompletionTransformationHandler: if reasoning_effort is None: return - model: Final = cast(str, completion_kwargs.get("model", "")) + model: Final = completion_kwargs.get("model", "") custom_llm_provider: Final = completion_kwargs.get("custom_llm_provider") if isinstance(reasoning_effort, str): @@ -407,21 +421,21 @@ class LiteLLMMessagesToCompletionTransformationHandler: def _prepare_completion_kwargs( *, max_tokens: int, - messages: list[dict], + messages: _AnthropicMessages, model: str, - metadata: dict | None = None, + metadata: dict[str, object] | None = None, stop_sequences: list[str] | None = None, stream: bool | None = False, - system: str | list[dict[str, Any]] | None = None, + system: _AnthropicSystem = None, temperature: float | None = None, - thinking: dict | None = None, - tool_choice: dict | None = None, - tools: list[dict] | None = None, + thinking: dict[str, object] | None = None, + tool_choice: dict[str, object] | None = None, + tools: list[dict[str, object]] | None = None, top_k: int | None = None, top_p: float | None = None, - output_format: dict | None = None, - extra_kwargs: dict[str, Any] | None = None, - ) -> tuple[dict[str, Any], dict[str, str]]: + output_format: dict[str, object] | None = None, + extra_kwargs: Mapping[str, object] | None = None, + ) -> tuple[_CompletionKwargs, dict[str, str]]: """Prepare kwargs for litellm.completion/acompletion. Returns: @@ -433,7 +447,7 @@ class LiteLLMMessagesToCompletionTransformationHandler: Logging as LiteLLMLoggingObject, ) - request_data: Final = { + request_data: Final[dict[str, object]] = { "model": model, "messages": messages, "max_tokens": max_tokens, @@ -478,7 +492,7 @@ class LiteLLMMessagesToCompletionTransformationHandler: if openai_request is None: raise ValueError("Failed to translate request to OpenAI format") - completion_kwargs: Final[dict[str, Any]] = dict(openai_request) + completion_kwargs: Final[_CompletionKwargs] = {**openai_request} if stream: completion_kwargs["stream"] = stream @@ -528,19 +542,19 @@ class LiteLLMMessagesToCompletionTransformationHandler: @staticmethod async def async_anthropic_messages_handler( max_tokens: int, - messages: list[dict], + messages: _AnthropicMessages, model: str, - metadata: dict | None = None, + metadata: dict[str, object] | None = None, stop_sequences: list[str] | None = None, stream: bool | None = False, system: str | None = None, temperature: float | None = None, - thinking: dict | None = None, - tool_choice: dict | None = None, - tools: list[dict] | None = None, + thinking: dict[str, object] | None = None, + tool_choice: dict[str, object] | None = None, + tools: list[dict[str, object]] | None = None, top_k: int | None = None, top_p: float | None = None, - output_format: dict | None = None, + output_format: dict[str, object] | None = None, **kwargs, ) -> AnthropicMessagesResponse | AsyncIterator[bytes] | Iterator[bytes]: """Handle non-Anthropic models asynchronously using the adapter""" @@ -551,10 +565,7 @@ class LiteLLMMessagesToCompletionTransformationHandler: requested_router if requested_router is not None else _proxy_router_fallback() ) - proxy_litellm_metadata: Final = _extract_proxy_litellm_metadata(kwargs) - user_api_key_auth: Final[UserAPIKeyAuth | None] = ( - proxy_litellm_metadata.get("user_api_key_auth") if proxy_litellm_metadata is not None else None - ) + proxy_litellm_metadata, user_api_key_auth = _extract_proxy_litellm_metadata(kwargs) polyfill_result: Final = await _prepare_context_managed_request( model=model, @@ -618,19 +629,19 @@ class LiteLLMMessagesToCompletionTransformationHandler: @staticmethod def anthropic_messages_handler( max_tokens: int, - messages: list[dict], + messages: _AnthropicMessages, model: str, - metadata: dict | None = None, + metadata: dict[str, object] | None = None, stop_sequences: list[str] | None = None, stream: bool | None = False, system: str | None = None, temperature: float | None = None, - thinking: dict | None = None, - tool_choice: dict | None = None, - tools: list[dict] | None = None, + thinking: dict[str, object] | None = None, + tool_choice: dict[str, object] | None = None, + tools: list[dict[str, object]] | None = None, top_k: int | None = None, top_p: float | None = None, - output_format: dict | None = None, + output_format: dict[str, object] | None = None, _is_async: bool = False, **kwargs, ) -> ( @@ -688,10 +699,7 @@ class LiteLLMMessagesToCompletionTransformationHandler: if context_management is None and not _messages_have_compaction_block(messages): polyfill_result: PolyfillResult | None = None else: - proxy_litellm_metadata: Final = _extract_proxy_litellm_metadata(kwargs) - user_api_key_auth: Final[UserAPIKeyAuth | None] = ( - proxy_litellm_metadata.get("user_api_key_auth") if proxy_litellm_metadata is not None else None - ) + proxy_litellm_metadata, user_api_key_auth = _extract_proxy_litellm_metadata(kwargs) polyfill_result = run_async_function( _prepare_context_managed_request, model=model, diff --git a/litellm/llms/anthropic/experimental_pass_through/messages/mcp_handler.py b/litellm/llms/anthropic/experimental_pass_through/messages/mcp_handler.py index 6ba129f5a7d..c1f10c245f8 100644 --- a/litellm/llms/anthropic/experimental_pass_through/messages/mcp_handler.py +++ b/litellm/llms/anthropic/experimental_pass_through/messages/mcp_handler.py @@ -7,8 +7,8 @@ tool through a ``tool_use`` content block, and results are fed back as ``tool_result`` blocks in a user message. """ -from collections.abc import AsyncIterator, Mapping, Sequence -from typing import Any, Final +from collections.abc import AsyncIterator, Awaitable, Callable, Iterator, Mapping, Sequence +from typing import Any, Final, NamedTuple from litellm._logging import verbose_logger from litellm.responses.mcp.request_context import MCPRequestContext @@ -24,14 +24,18 @@ from litellm.types.llms.anthropic_messages.anthropic_response import ( MAX_MCP_TOOL_USE_ITERATIONS: Final = 10 -def _get_response_content(response: AnthropicMessagesResponse) -> Sequence[Mapping[str, Any]]: +class _AnthropicMessagesCall(NamedTuple): + fn: Callable[..., Awaitable[AnthropicMessagesResponse | Iterator[bytes] | AsyncIterator[object]]] + + +def _get_response_content(response: AnthropicMessagesResponse) -> Sequence[Mapping[str, object]]: content: Final = response.get("content") if not isinstance(content, list): return () return tuple(block for block in content if isinstance(block, dict)) -def _extract_tool_use_blocks(response: AnthropicMessagesResponse) -> Sequence[Mapping[str, Any]]: +def _extract_tool_use_blocks(response: AnthropicMessagesResponse) -> Sequence[Mapping[str, object]]: """Return the ``tool_use`` content blocks the model emitted.""" return tuple(block for block in _get_response_content(response) if block.get("type") == "tool_use") @@ -41,7 +45,7 @@ def _get_stop_reason(response: AnthropicMessagesResponse) -> str | None: return stop_reason if isinstance(stop_reason, str) else None -def _build_tool_result_message(tool_results: Sequence[Mapping[str, Any]]) -> AnthropicMessagesUserMessageParam: +def _build_tool_result_message(tool_results: Sequence[Mapping[str, object]]) -> AnthropicMessagesUserMessageParam: """Turn executed tool results into the user message Anthropic expects.""" return AnthropicMessagesUserMessageParam( role="user", @@ -58,11 +62,11 @@ def _build_tool_result_message(tool_results: Sequence[Mapping[str, Any]]) -> Ant async def anthropic_messages_with_mcp( max_tokens: int, - messages: Sequence[Mapping[str, Any]], + messages: Sequence[Mapping[str, object]], model: str, - tools: Sequence[Mapping[str, Any]] | None = None, + tools: Sequence[Mapping[str, object]] | None = None, **kwargs: Any, # kwargs-ok: forwarded verbatim to litellm.anthropic_messages, which owns the param contract -) -> AnthropicMessagesResponse | AsyncIterator[Any]: +) -> AnthropicMessagesResponse | Iterator[bytes] | AsyncIterator[object]: """ Expand litellm_proxy MCP references for `/v1/messages` and run the tool loop. @@ -81,7 +85,7 @@ async def anthropic_messages_with_mcp( mcp_references, other_tools = LiteLLM_Proxy_MCP_Handler._parse_mcp_tools(tools) if not mcp_references: - return await litellm.anthropic_messages( + return await _AnthropicMessagesCall(fn=litellm.anthropic_messages).fn( max_tokens=max_tokens, messages=list(messages), model=model, @@ -114,7 +118,7 @@ async def anthropic_messages_with_mcp( ) stream: Final = bool(kwargs.pop("stream", False)) - base_call_args: Final[Mapping[str, Any]] = { + base_call_args: Final[Mapping[str, object]] = { "max_tokens": max_tokens, "model": model, "tools": all_tools or None, @@ -123,10 +127,12 @@ async def anthropic_messages_with_mcp( } if not should_auto_execute: - return await litellm.anthropic_messages(messages=list(messages), stream=stream, **base_call_args) + return await _AnthropicMessagesCall(fn=litellm.anthropic_messages).fn( + messages=list(messages), stream=stream, **base_call_args + ) - working_messages: Sequence[Mapping[str, Any]] = tuple(messages) - response: AnthropicMessagesResponse = await litellm.anthropic_messages( + working_messages: Sequence[Mapping[str, object]] = tuple(messages) + response: AnthropicMessagesResponse = await _AnthropicMessagesCall(fn=litellm.anthropic_messages).fn( messages=list(working_messages), stream=False, **base_call_args ) @@ -161,7 +167,9 @@ async def anthropic_messages_with_mcp( {"role": "assistant", "content": list(_get_response_content(response))}, _build_tool_result_message(tool_results), ) - response = await litellm.anthropic_messages(messages=list(working_messages), stream=False, **base_call_args) + response = await _AnthropicMessagesCall(fn=litellm.anthropic_messages).fn( + messages=list(working_messages), stream=False, **base_call_args + ) else: verbose_logger.warning( "MCP tool loop hit its %s iteration cap for model %s; returning the last response", diff --git a/litellm/llms/anthropic/experimental_pass_through/responses_adapters/handler.py b/litellm/llms/anthropic/experimental_pass_through/responses_adapters/handler.py index 5e05ebc3c63..9210719dd59 100644 --- a/litellm/llms/anthropic/experimental_pass_through/responses_adapters/handler.py +++ b/litellm/llms/anthropic/experimental_pass_through/responses_adapters/handler.py @@ -8,7 +8,12 @@ from collections.abc import AsyncIterator, Coroutine from typing import Any, Final import litellm -from litellm.types.llms.anthropic import AnthropicMessagesRequest +from litellm.types.llms.anthropic import ( + AllAnthropicToolsValues, + AnthropicMessagesRequest, + AnthropicOutputConfig, + AnthropicOutputSchema, +) from litellm.types.llms.anthropic_messages.anthropic_response import ( AnthropicMessagesResponse, ) @@ -27,24 +32,24 @@ def _build_responses_kwargs( model: str, context_management: dict | None = None, metadata: dict | None = None, - output_config: dict | None = None, + output_config: AnthropicOutputConfig | None = None, stop_sequences: list[str] | None = None, stream: bool | None = False, system: str | None = None, temperature: float | None = None, thinking: dict | None = None, tool_choice: dict | None = None, - tools: list[dict] | None = None, + tools: list[AllAnthropicToolsValues | dict] | None = None, top_k: int | None = None, top_p: float | None = None, - output_format: dict | None = None, + output_format: AnthropicOutputSchema | None = None, extra_kwargs: dict[str, Any] | None = None, ) -> dict[str, Any]: """ Build the kwargs dict to pass directly to litellm.responses() / litellm.aresponses(). """ # Build a typed AnthropicMessagesRequest for the adapter - request_data: Final[dict[str, Any]] = { + request_data: Final[AnthropicMessagesRequest] = { "model": model, "messages": messages, "max_tokens": max_tokens, @@ -128,19 +133,19 @@ class LiteLLMMessagesToResponsesAPIHandler: model: str, context_management: dict | None = None, metadata: dict | None = None, - output_config: dict | None = None, + output_config: AnthropicOutputConfig | None = None, stop_sequences: list[str] | None = None, stream: bool | None = False, system: str | None = None, temperature: float | None = None, thinking: dict | None = None, tool_choice: dict | None = None, - tools: list[dict] | None = None, + tools: list[AllAnthropicToolsValues | dict] | None = None, top_k: int | None = None, top_p: float | None = None, - output_format: dict | None = None, + output_format: AnthropicOutputSchema | None = None, **kwargs, - ) -> AnthropicMessagesResponse | AsyncIterator: + ) -> AnthropicMessagesResponse | AsyncIterator[bytes]: responses_kwargs: Final = _build_responses_kwargs( max_tokens=max_tokens, messages=messages, @@ -179,23 +184,23 @@ class LiteLLMMessagesToResponsesAPIHandler: model: str, context_management: dict | None = None, metadata: dict | None = None, - output_config: dict | None = None, + output_config: AnthropicOutputConfig | None = None, stop_sequences: list[str] | None = None, stream: bool | None = False, system: str | None = None, temperature: float | None = None, thinking: dict | None = None, tool_choice: dict | None = None, - tools: list[dict] | None = None, + tools: list[AllAnthropicToolsValues | dict] | None = None, top_k: int | None = None, top_p: float | None = None, - output_format: dict | None = None, + output_format: AnthropicOutputSchema | None = None, _is_async: bool = False, **kwargs, ) -> ( AnthropicMessagesResponse - | AsyncIterator[Any] - | Coroutine[Any, Any, AnthropicMessagesResponse | AsyncIterator[Any]] + | AsyncIterator[bytes] + | Coroutine[None, None, AnthropicMessagesResponse | AsyncIterator[bytes]] ): if _is_async: return LiteLLMMessagesToResponsesAPIHandler.async_anthropic_messages_handler( diff --git a/litellm/llms/azure/assistants.py b/litellm/llms/azure/assistants.py index 671e4633af4..f7b419405ac 100644 --- a/litellm/llms/azure/assistants.py +++ b/litellm/llms/azure/assistants.py @@ -1,8 +1,9 @@ from collections.abc import Coroutine, Iterable -from typing import Any, Final, Literal +from typing import Any, Final, Literal, TypedDict import httpx from openai import AsyncAzureOpenAI, AzureOpenAI +from openai.types.shared_params.metadata import Metadata from typing_extensions import overload from ...types.llms.openai import ( @@ -22,6 +23,16 @@ from ...types.llms.openai import ( from .common_utils import BaseAzureLLM +class _RunThreadStreamData(TypedDict): + thread_id: str + assistant_id: str + additional_instructions: str | None + instructions: str | None + metadata: Metadata | None + model: str | None + tools: Iterable[AssistantToolParam] | None + + class AzureAssistantsAPI(BaseAzureLLM): def __init__(self) -> None: super().__init__() @@ -212,9 +223,9 @@ class AzureAssistantsAPI(BaseAzureLLM): response_obj: OpenAIMessage | None = None if getattr(thread_message, "status", None) is None: thread_message.status = "completed" - response_obj = OpenAIMessage(**thread_message.dict()) + response_obj = OpenAIMessage.model_validate(thread_message.dict()) else: - response_obj = OpenAIMessage(**thread_message.dict()) + response_obj = OpenAIMessage.model_validate(thread_message.dict()) return response_obj # fmt: off @@ -301,9 +312,9 @@ class AzureAssistantsAPI(BaseAzureLLM): response_obj: OpenAIMessage | None = None if getattr(thread_message, "status", None) is None: thread_message.status = "completed" - response_obj = OpenAIMessage(**thread_message.dict()) + response_obj = OpenAIMessage.model_validate(thread_message.dict()) else: - response_obj = OpenAIMessage(**thread_message.dict()) + response_obj = OpenAIMessage.model_validate(thread_message.dict()) return response_obj async def async_get_messages( @@ -443,7 +454,7 @@ class AzureAssistantsAPI(BaseAzureLLM): message_thread: Final = await openai_client.beta.threads.create(**data) - return Thread(**message_thread.dict()) + return Thread.model_validate(message_thread.dict()) # fmt: off @@ -539,7 +550,7 @@ class AzureAssistantsAPI(BaseAzureLLM): message_thread: Final = azure_openai_client.beta.threads.create(**data) - return Thread(**message_thread.dict()) + return Thread.model_validate(message_thread.dict()) async def async_get_thread( self, @@ -566,7 +577,7 @@ class AzureAssistantsAPI(BaseAzureLLM): response: Final = await openai_client.beta.threads.retrieve(thread_id=thread_id) - return Thread(**response.dict()) + return Thread.model_validate(response.dict()) # fmt: off @@ -642,7 +653,7 @@ class AzureAssistantsAPI(BaseAzureLLM): response: Final = openai_client.beta.threads.retrieve(thread_id=thread_id) - return Thread(**response.dict()) + return Thread.model_validate(response.dict()) # def delete_thread(self): # pass @@ -730,7 +741,8 @@ class AzureAssistantsAPI(BaseAzureLLM): event_handler: AssistantEventHandler | None, litellm_params: dict | None = None, ) -> AssistantStreamManager[AssistantEventHandler]: - data: Final[dict[str, Any]] = { + stream_fn: Final = client.beta.threads.runs.stream + base_data: Final[_RunThreadStreamData] = { "thread_id": thread_id, "assistant_id": assistant_id, "additional_instructions": additional_instructions, @@ -740,8 +752,8 @@ class AzureAssistantsAPI(BaseAzureLLM): "tools": tools, } if event_handler is not None: - data["event_handler"] = event_handler - return client.beta.threads.runs.stream(**data) + return stream_fn(**base_data, event_handler=event_handler) + return stream_fn(**base_data) # fmt: off 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 85fda3a6522..fad7e7558c2 100644 --- a/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py +++ b/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py @@ -67,6 +67,8 @@ class AmazonAnthropicClaudeMessagesConfig( DEFAULT_BEDROCK_ANTHROPIC_API_VERSION = "bedrock-2023-05-31" + WEBSEARCH_INTERCEPTION_DOCS_URL = "https://docs.litellm.ai/docs/integrations/websearch_interception" + @property def custom_llm_provider(self) -> str | None: return "bedrock" @@ -572,6 +574,45 @@ class AmazonAnthropicClaudeMessagesConfig( return filtered_betas + @staticmethod + def _reject_unsupported_web_search_tools(anthropic_messages_request: dict[str, object], model: str) -> None: + """ + Bedrock's Anthropic endpoints cannot execute Anthropic's server-side + ``web_search_*`` tool; forwarding it returns an opaque + "The provided request is not valid" 400 from Bedrock. Fail fast with an + error that names the problem and the fix instead. + + When web search interception is enabled + (``litellm_settings.callbacks: ["websearch_interception"]``), the tool + is converted to a regular function tool before this transform runs, so + this guard never fires. + """ + from litellm.integrations.websearch_interception.tools import ( + is_anthropic_native_web_search_tool, + ) + + tools: Final = anthropic_messages_request.get("tools") + if not isinstance(tools, list): + return + web_search_tool: Final = next( + (t for t in tools if isinstance(t, dict) and is_anthropic_native_web_search_tool(t)), + None, + ) + if web_search_tool is None: + return + raise litellm.BadRequestError( + message=( + f"Bedrock does not support Anthropic's server-side web search tool " + f"(tool type '{web_search_tool.get('type')}', model '{model}'). " + "To use web search with this model, enable LiteLLM's web search interception " + "so the proxy executes the search instead: " + f"{AmazonAnthropicClaudeMessagesConfig.WEBSEARCH_INTERCEPTION_DOCS_URL}. " + "Alternatively, remove the web_search tool from the request." + ), + model=model, + llm_provider="bedrock", + ) + def _strip_unsupported_bedrock_invoke_fields( self, anthropic_messages_request: dict, @@ -630,6 +671,8 @@ class AmazonAnthropicClaudeMessagesConfig( ############## BEDROCK Invoke SPECIFIC TRANSFORMATION ### ######################################################### + self._reject_unsupported_web_search_tools(anthropic_messages_request=anthropic_messages_request, model=model) + # 1. anthropic_version is required for all claude models if "anthropic_version" not in anthropic_messages_request: anthropic_messages_request["anthropic_version"] = self.DEFAULT_BEDROCK_ANTHROPIC_API_VERSION diff --git a/litellm/llms/vertex_ai/batches/handler.py b/litellm/llms/vertex_ai/batches/handler.py index 29ebd5c2a75..6481b67fad7 100644 --- a/litellm/llms/vertex_ai/batches/handler.py +++ b/litellm/llms/vertex_ai/batches/handler.py @@ -14,7 +14,7 @@ from litellm.llms.custom_httpx.http_handler import ( _get_httpx_client, get_async_httpx_client, ) -from litellm.llms.vertex_ai.common_utils import get_vertex_base_url +from litellm.llms.vertex_ai.common_utils import VertexAIError, get_vertex_base_url from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import VertexLLM from litellm.types.llms.openai import CreateBatchRequest from litellm.types.llms.vertex_ai import ( @@ -98,9 +98,6 @@ class VertexAIBatchPrediction(VertexLLM): data=json.dumps(vertex_batch_request), ) - if response.status_code != 200: - raise Exception(f"Error: {response.status_code} {response.text}") - _json_response: Final = response.json() vertex_batch_response = VertexAIBatchTransformation.transform_vertex_ai_batch_response_to_openai_batch_response( response=_json_response @@ -130,8 +127,6 @@ class VertexAIBatchPrediction(VertexLLM): error_body[:1000], ) raise - if response.status_code != 200: - raise Exception(f"Error: {response.status_code} {response.text}") _json_response: Final = response.json() vertex_batch_response = VertexAIBatchTransformation.transform_vertex_ai_batch_response_to_openai_batch_response( @@ -243,7 +238,9 @@ class VertexAIBatchPrediction(VertexLLM): ) if response.status_code != 200: - raise Exception(f"Error: {response.status_code} {response.text}") + raise VertexAIError( + status_code=response.status_code, message=f"Error: {response.status_code} {response.text}" + ) _json_response: Final = response.json() vertex_batch_response = VertexAIBatchTransformation.transform_vertex_ai_batch_response_to_openai_batch_response( @@ -293,7 +290,9 @@ class VertexAIBatchPrediction(VertexLLM): headers=headers, ) if response.status_code != 200: - raise Exception(f"Error: {response.status_code} {response.text}") + raise VertexAIError( + status_code=response.status_code, message=f"Error: {response.status_code} {response.text}" + ) _json_response: Final = response.json() vertex_batch_response = VertexAIBatchTransformation.transform_vertex_ai_batch_response_to_openai_batch_response( @@ -366,7 +365,9 @@ class VertexAIBatchPrediction(VertexLLM): ) if response.status_code != 200: - raise Exception(f"Error: {response.status_code} {response.text}") + raise VertexAIError( + status_code=response.status_code, message=f"Error: {response.status_code} {response.text}" + ) _json_response: Final = response.json() vertex_batch_response: Final = ( @@ -391,7 +392,9 @@ class VertexAIBatchPrediction(VertexLLM): params=params, ) if response.status_code != 200: - raise Exception(f"Error: {response.status_code} {response.text}") + raise VertexAIError( + status_code=response.status_code, message=f"Error: {response.status_code} {response.text}" + ) _json_response: Final = response.json() vertex_batch_response: Final = ( @@ -461,7 +464,7 @@ class VertexAIBatchPrediction(VertexLLM): sync_handler: Final = _get_httpx_client() try: - response: Final = sync_handler.post( + sync_handler.post( url=api_base, headers=headers, data=json.dumps({}), @@ -475,9 +478,6 @@ class VertexAIBatchPrediction(VertexLLM): ) raise - if response.status_code != 200: - raise Exception(f"Error: {response.status_code} {response.text}") - # HTTPHandler.get() does not accept a timeout parameter retrieve_response: Final = sync_handler.get( url=retrieve_api_base, @@ -489,7 +489,10 @@ class VertexAIBatchPrediction(VertexLLM): retrieve_response.status_code, retrieve_response.text[:1000], ) - raise Exception(f"Error: {retrieve_response.status_code} {retrieve_response.text}") + raise VertexAIError( + status_code=retrieve_response.status_code, + message=f"Error: {retrieve_response.status_code} {retrieve_response.text}", + ) _json_response: Final = retrieve_response.json() vertex_batch_response = VertexAIBatchTransformation.transform_vertex_ai_batch_response_to_openai_batch_response( @@ -508,7 +511,7 @@ class VertexAIBatchPrediction(VertexLLM): llm_provider=litellm.LlmProviders.VERTEX_AI, ) try: - response: Final = await client.post( + await client.post( url=api_base, headers=headers, data=json.dumps({}), @@ -521,8 +524,6 @@ class VertexAIBatchPrediction(VertexLLM): e.response.text[:1000], ) raise - if response.status_code != 200: - raise Exception(f"Error: {response.status_code} {response.text}") # AsyncHTTPHandler.get() does not accept a timeout parameter retrieve_response: Final = await client.get( @@ -535,7 +536,10 @@ class VertexAIBatchPrediction(VertexLLM): retrieve_response.status_code, retrieve_response.text[:1000], ) - raise Exception(f"Error: {retrieve_response.status_code} {retrieve_response.text}") + raise VertexAIError( + status_code=retrieve_response.status_code, + message=f"Error: {retrieve_response.status_code} {retrieve_response.text}", + ) _json_response: Final = retrieve_response.json() vertex_batch_response = VertexAIBatchTransformation.transform_vertex_ai_batch_response_to_openai_batch_response( diff --git a/litellm/llms/vertex_ai/batches/transformation.py b/litellm/llms/vertex_ai/batches/transformation.py index 66951715488..f284b47292b 100644 --- a/litellm/llms/vertex_ai/batches/transformation.py +++ b/litellm/llms/vertex_ai/batches/transformation.py @@ -1,7 +1,9 @@ from typing import Any, Final +from urllib.parse import unquote from litellm._uuid import uuid from litellm.llms.vertex_ai.common_utils import ( + VertexAIError, _convert_vertex_datetime_to_openai_datetime, ) from litellm.types.llms.openai import BatchJobStatus, CreateBatchRequest @@ -199,16 +201,40 @@ class VertexAIBatchTransformation: gcs_file_uri format: gs://litellm-testing-bucket/litellm-vertex-files/publishers/google/models/gemini-1.5-flash-001/e9412502-2c91-42a6-8e61-f5c294cc0fc8 returns: "publishers/google/models/gemini-1.5-flash-001" + + Raises a 400 `VertexAIError` when the uri carries no parseable model path. """ - from urllib.parse import unquote - - decoded_uri: Final = unquote(gcs_file_uri) - - model_path: Final = decoded_uri.split("publishers/")[1] - parts: Final = model_path.split("/") - model: Final = f"publishers/{'/'.join(parts[:3])}" + model: Final = cls._parse_model_from_gcs_file(gcs_file_uri) + if model is None: + raise VertexAIError( + status_code=400, + message=( + "Vertex AI batch creation requires the model to be part of `input_file_id`, but " + f"'{gcs_file_uri}' contains no 'publishers//models/' path segment. " + "Either upload the input file through LiteLLM (POST /v1/files with " + "custom_llm_provider=vertex_ai), which encodes the model into the returned file id, or " + "pass a uri of the form " + "gs:////publishers//models//" + ), + ) return model + @classmethod + def _parse_model_from_gcs_file(cls, gcs_file_uri: str) -> str | None: + """ + Returns the `publishers//models/` path from a gcs uri, or None if the uri + does not contain one. + """ + _, separator, model_path = unquote(gcs_file_uri).partition("publishers/") + if not separator: + return None + + parts: Final = model_path.split("/") + if len(parts) < 3 or parts[1] != "models" or not parts[2]: + return None + + return f"publishers/{'/'.join(parts[:3])}" + @classmethod def is_unmanaged_gcs_batch_input_file_id(cls, input_file_id: str | None) -> bool: """ @@ -216,7 +242,11 @@ class VertexAIBatchTransformation: LiteLLM-managed unified file id) with a `publishers/` model path that `_get_model_from_gcs_file` can parse. """ - return input_file_id is not None and input_file_id.startswith("gs://") and "publishers/" in input_file_id + return ( + input_file_id is not None + and input_file_id.startswith("gs://") + and cls._parse_model_from_gcs_file(input_file_id) is not None + ) @classmethod def get_bare_model_name_from_gcs_file(cls, gcs_file_uri: str) -> str: diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index d954e33da9c..951c114b0a9 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -2808,6 +2808,7 @@ }, "azure/codex-mini": { "cache_read_input_token_cost": 3.75e-07, + "deprecation_date": "2026-11-15", "input_cost_per_token": 1.5e-06, "litellm_provider": "azure", "max_input_tokens": 200000, @@ -3627,7 +3628,7 @@ "comment": "Flat cost of $0.14 per M input tokens for Azure AI Foundry Model Router infrastructure. Use pattern: azure_ai/model_router/ where deployment-name is your Azure deployment (e.g., azure-model-router)" }, "azure/eu/gpt-4o-2024-08-06": { - "deprecation_date": "2026-02-27", + "deprecation_date": "2027-04-14", "cache_read_input_token_cost": 1.375e-06, "input_cost_per_token": 2.75e-06, "litellm_provider": "azure", @@ -3644,7 +3645,7 @@ "supports_vision": true }, "azure/eu/gpt-4o-2024-11-20": { - "deprecation_date": "2026-03-01", + "deprecation_date": "2027-04-14", "cache_creation_input_token_cost": 1.38e-06, "input_cost_per_token": 2.75e-06, "litellm_provider": "azure", @@ -3661,6 +3662,7 @@ }, "azure/eu/gpt-4o-mini-2024-07-18": { "cache_read_input_token_cost": 8.3e-08, + "deprecation_date": "2027-04-14", "input_cost_per_token": 1.65e-07, "litellm_provider": "azure", "max_input_tokens": 128000, @@ -3742,6 +3744,7 @@ }, "azure/eu/gpt-5-2025-08-07": { "cache_read_input_token_cost": 1.375e-07, + "deprecation_date": "2027-02-09", "input_cost_per_token": 1.375e-06, "litellm_provider": "azure", "max_input_tokens": 272000, @@ -3774,6 +3777,7 @@ }, "azure/eu/gpt-5-mini-2025-08-07": { "cache_read_input_token_cost": 2.75e-08, + "deprecation_date": "2027-02-09", "input_cost_per_token": 2.75e-07, "litellm_provider": "azure", "max_input_tokens": 272000, @@ -3840,6 +3844,7 @@ }, "azure/eu/gpt-5.1-chat": { "cache_read_input_token_cost": 1.4e-07, + "deprecation_date": "2026-06-29", "input_cost_per_token": 1.38e-06, "litellm_provider": "azure", "max_input_tokens": 128000, @@ -3934,6 +3939,7 @@ }, "azure/eu/gpt-5-nano-2025-08-07": { "cache_read_input_token_cost": 5.5e-09, + "deprecation_date": "2027-02-09", "input_cost_per_token": 5.5e-08, "litellm_provider": "azure", "max_input_tokens": 272000, @@ -3966,6 +3972,7 @@ }, "azure/eu/o1-2024-12-17": { "cache_read_input_token_cost": 8.25e-06, + "deprecation_date": "2026-10-21", "input_cost_per_token": 1.65e-05, "litellm_provider": "azure", "max_input_tokens": 200000, @@ -4011,6 +4018,7 @@ }, "azure/eu/o3-mini-2025-01-31": { "cache_read_input_token_cost": 6.05e-07, + "deprecation_date": "2026-10-01", "input_cost_per_token": 1.21e-06, "input_cost_per_token_batches": 6.05e-07, "litellm_provider": "azure", @@ -4027,7 +4035,7 @@ }, "azure/global-standard/gpt-4o-2024-08-06": { "cache_read_input_token_cost": 1.25e-06, - "deprecation_date": "2026-02-27", + "deprecation_date": "2027-04-14", "input_cost_per_token": 2.5e-06, "litellm_provider": "azure", "max_input_tokens": 128000, @@ -4044,7 +4052,7 @@ }, "azure/global-standard/gpt-4o-2024-11-20": { "cache_read_input_token_cost": 1.25e-06, - "deprecation_date": "2026-03-01", + "deprecation_date": "2027-04-14", "input_cost_per_token": 2.5e-06, "litellm_provider": "azure", "max_input_tokens": 128000, @@ -4073,7 +4081,7 @@ "supports_vision": true }, "azure/global/gpt-4o-2024-08-06": { - "deprecation_date": "2026-02-27", + "deprecation_date": "2027-04-14", "cache_read_input_token_cost": 1.25e-06, "input_cost_per_token": 2.5e-06, "litellm_provider": "azure", @@ -4090,7 +4098,7 @@ "supports_vision": true }, "azure/global/gpt-4o-2024-11-20": { - "deprecation_date": "2026-03-01", + "deprecation_date": "2027-04-14", "cache_read_input_token_cost": 1.25e-06, "input_cost_per_token": 2.5e-06, "litellm_provider": "azure", @@ -4142,6 +4150,7 @@ }, "azure/global/gpt-5.1-chat": { "cache_read_input_token_cost": 1.25e-07, + "deprecation_date": "2026-06-29", "input_cost_per_token": 1.25e-06, "litellm_provider": "azure", "max_input_tokens": 128000, @@ -4476,7 +4485,7 @@ "supports_web_search": false }, "azure/gpt-4.1-2025-04-14": { - "deprecation_date": "2026-11-04", + "deprecation_date": "2027-04-14", "cache_read_input_token_cost": 5e-07, "input_cost_per_token": 2e-06, "input_cost_per_token_batches": 1e-06, @@ -4543,7 +4552,7 @@ "supports_web_search": false }, "azure/gpt-4.1-mini-2025-04-14": { - "deprecation_date": "2026-11-04", + "deprecation_date": "2027-04-14", "cache_read_input_token_cost": 1e-07, "input_cost_per_token": 4e-07, "input_cost_per_token_batches": 2e-07, @@ -4609,7 +4618,7 @@ "supports_vision": true }, "azure/gpt-4.1-nano-2025-04-14": { - "deprecation_date": "2026-11-04", + "deprecation_date": "2026-10-14", "cache_read_input_token_cost": 2.5e-08, "input_cost_per_token": 1e-07, "input_cost_per_token_batches": 5e-08, @@ -4677,6 +4686,7 @@ "supports_vision": true }, "azure/gpt-4o-2024-05-13": { + "deprecation_date": "2026-10-01", "input_cost_per_token": 5e-06, "litellm_provider": "azure", "max_input_tokens": 128000, @@ -4691,7 +4701,7 @@ "supports_vision": true }, "azure/gpt-4o-2024-08-06": { - "deprecation_date": "2026-02-27", + "deprecation_date": "2027-04-14", "cache_read_input_token_cost": 1.25e-06, "input_cost_per_token": 2.5e-06, "litellm_provider": "azure", @@ -4708,7 +4718,7 @@ "supports_vision": true }, "azure/gpt-4o-2024-11-20": { - "deprecation_date": "2026-03-01", + "deprecation_date": "2027-04-14", "cache_read_input_token_cost": 1.25e-06, "input_cost_per_token": 2.75e-06, "litellm_provider": "azure", @@ -4725,6 +4735,7 @@ "supports_vision": true }, "azure/gpt-audio-2025-08-28": { + "deprecation_date": "2027-03-02", "input_cost_per_audio_token": 4e-05, "input_cost_per_token": 2.5e-06, "litellm_provider": "azure", @@ -4756,6 +4767,7 @@ "supports_vision": false }, "azure/gpt-audio-1.5-2026-02-23": { + "deprecation_date": "2027-08-24", "input_cost_per_audio_token": 4e-05, "input_cost_per_token": 2.5e-06, "litellm_provider": "azure", @@ -4787,6 +4799,7 @@ "supports_vision": false }, "azure/gpt-audio-mini-2025-10-06": { + "deprecation_date": "2027-04-06", "input_cost_per_audio_token": 1e-05, "input_cost_per_token": 6e-07, "litellm_provider": "azure", @@ -4866,6 +4879,7 @@ }, "azure/gpt-4o-mini-2024-07-18": { "cache_read_input_token_cost": 7.5e-08, + "deprecation_date": "2027-04-14", "input_cost_per_token": 1.65e-07, "litellm_provider": "azure", "max_input_tokens": 128000, @@ -4933,6 +4947,7 @@ "azure/gpt-realtime-2025-08-28": { "cache_creation_input_audio_token_cost": 4e-06, "cache_read_input_token_cost": 4e-06, + "deprecation_date": "2027-03-02", "input_cost_per_audio_token": 3.2e-05, "input_cost_per_image": 5e-06, "input_cost_per_token": 4e-06, @@ -4965,6 +4980,7 @@ "azure/gpt-realtime-1.5-2026-02-23": { "cache_creation_input_audio_token_cost": 4e-06, "cache_read_input_token_cost": 4e-06, + "deprecation_date": "2027-08-24", "input_cost_per_audio_token": 3.2e-05, "input_cost_per_image": 5e-06, "input_cost_per_token": 4e-06, @@ -5102,6 +5118,7 @@ "supports_tool_choice": true }, "azure/gpt-4o-transcribe": { + "deprecation_date": "2026-10-15", "input_cost_per_audio_token": 2.5e-06, "input_cost_per_token": 2.5e-06, "litellm_provider": "azure", @@ -5114,6 +5131,7 @@ ] }, "azure/gpt-4o-transcribe-diarize": { + "deprecation_date": "2027-04-15", "input_cost_per_audio_token": 2.5e-06, "input_cost_per_token": 2.5e-06, "litellm_provider": "azure", @@ -5145,6 +5163,7 @@ "azure/gpt-5.1-2025-11-13": { "cache_read_input_token_cost": 1.25e-07, "cache_read_input_token_cost_priority": 2.5e-07, + "deprecation_date": "2027-05-15", "input_cost_per_token": 1.25e-06, "input_cost_per_token_priority": 2.5e-06, "litellm_provider": "azure", @@ -5182,6 +5201,7 @@ "azure/gpt-5.1-chat-2025-11-13": { "cache_read_input_token_cost": 1.25e-07, "cache_read_input_token_cost_priority": 2.5e-07, + "deprecation_date": "2026-06-29", "input_cost_per_token": 1.25e-06, "input_cost_per_token_priority": 2.5e-06, "litellm_provider": "azure", @@ -5218,6 +5238,7 @@ "azure/gpt-5.1-codex-2025-11-13": { "cache_read_input_token_cost": 1.25e-07, "cache_read_input_token_cost_priority": 2.5e-07, + "deprecation_date": "2027-05-15", "input_cost_per_token": 1.25e-06, "input_cost_per_token_priority": 2.5e-06, "litellm_provider": "azure", @@ -5251,6 +5272,7 @@ "azure/gpt-5.1-codex-mini-2025-11-13": { "cache_read_input_token_cost": 2.5e-08, "cache_read_input_token_cost_priority": 4.5e-08, + "deprecation_date": "2027-05-15", "input_cost_per_token": 2.5e-07, "input_cost_per_token_priority": 4.5e-07, "litellm_provider": "azure", @@ -5315,6 +5337,7 @@ }, "azure/gpt-5-2025-08-07": { "cache_read_input_token_cost": 1.25e-07, + "deprecation_date": "2027-02-09", "input_cost_per_token": 1.25e-06, "litellm_provider": "azure", "max_input_tokens": 272000, @@ -5347,6 +5370,7 @@ }, "azure/gpt-5-chat": { "cache_read_input_token_cost": 1.25e-07, + "deprecation_date": "2026-06-29", "input_cost_per_token": 1.25e-06, "litellm_provider": "azure", "max_input_tokens": 128000, @@ -5380,6 +5404,7 @@ }, "azure/gpt-5-chat-latest": { "cache_read_input_token_cost": 1.25e-07, + "deprecation_date": "2026-06-29", "input_cost_per_token": 1.25e-06, "litellm_provider": "azure", "max_input_tokens": 128000, @@ -5412,6 +5437,7 @@ }, "azure/gpt-5-codex": { "cache_read_input_token_cost": 1.25e-07, + "deprecation_date": "2027-03-17", "input_cost_per_token": 1.25e-06, "litellm_provider": "azure", "max_input_tokens": 272000, @@ -5474,6 +5500,7 @@ }, "azure/gpt-5-mini-2025-08-07": { "cache_read_input_token_cost": 2.5e-08, + "deprecation_date": "2027-02-09", "input_cost_per_token": 2.5e-07, "litellm_provider": "azure", "max_input_tokens": 272000, @@ -5538,6 +5565,7 @@ }, "azure/gpt-5-nano-2025-08-07": { "cache_read_input_token_cost": 5e-09, + "deprecation_date": "2027-02-09", "input_cost_per_token": 5e-08, "litellm_provider": "azure", "max_input_tokens": 272000, @@ -5569,6 +5597,7 @@ "supports_vision": true }, "azure/gpt-5-pro": { + "deprecation_date": "2027-04-07", "input_cost_per_token": 1.5e-05, "litellm_provider": "azure", "max_input_tokens": 272000, @@ -5633,6 +5662,7 @@ }, "azure/gpt-5.1-chat": { "cache_read_input_token_cost": 1.25e-07, + "deprecation_date": "2026-06-29", "input_cost_per_token": 1.25e-06, "litellm_provider": "azure", "max_input_tokens": 128000, @@ -5697,6 +5727,7 @@ }, "azure/gpt-5.1-codex-max": { "cache_read_input_token_cost": 1.25e-07, + "deprecation_date": "2027-05-18", "input_cost_per_token": 1.25e-06, "litellm_provider": "azure", "max_input_tokens": 272000, @@ -5791,6 +5822,7 @@ "azure/gpt-5.2-2025-12-11": { "cache_read_input_token_cost": 1.75e-07, "cache_read_input_token_cost_priority": 3.5e-07, + "deprecation_date": "2027-06-08", "input_cost_per_token": 1.75e-06, "input_cost_per_token_priority": 3.5e-06, "litellm_provider": "azure", @@ -5827,6 +5859,7 @@ "azure/gpt-5.2-chat": { "cache_read_input_token_cost": 1.75e-07, "cache_read_input_token_cost_priority": 3.5e-07, + "deprecation_date": "2026-06-29", "input_cost_per_token": 1.75e-06, "input_cost_per_token_priority": 3.5e-06, "litellm_provider": "azure", @@ -5861,6 +5894,7 @@ "azure/gpt-5.2-chat-2025-12-11": { "cache_read_input_token_cost": 1.75e-07, "cache_read_input_token_cost_priority": 3.5e-07, + "deprecation_date": "2026-05-13", "input_cost_per_token": 1.75e-06, "input_cost_per_token_priority": 3.5e-06, "litellm_provider": "azure", @@ -5894,6 +5928,7 @@ }, "azure/gpt-5.2-codex": { "cache_read_input_token_cost": 1.75e-07, + "deprecation_date": "2027-07-13", "input_cost_per_token": 1.75e-06, "litellm_provider": "azure", "max_input_tokens": 272000, @@ -5925,6 +5960,7 @@ "azure/gpt-5.3-chat": { "cache_read_input_token_cost": 1.75e-07, "cache_read_input_token_cost_priority": 3.5e-07, + "deprecation_date": "2026-06-29", "input_cost_per_token": 1.75e-06, "input_cost_per_token_priority": 3.5e-06, "litellm_provider": "azure", @@ -5958,6 +5994,7 @@ }, "azure/gpt-5.3-codex": { "cache_read_input_token_cost": 1.75e-07, + "deprecation_date": "2027-08-24", "input_cost_per_token": 1.75e-06, "litellm_provider": "azure", "max_input_tokens": 272000, @@ -6164,6 +6201,7 @@ "cache_read_input_token_cost_above_272k_tokens": 5e-07, "cache_read_input_token_cost_priority": 5e-07, "cache_read_input_token_cost_above_272k_tokens_priority": 1e-06, + "deprecation_date": "2027-09-02", "input_cost_per_token": 2.5e-06, "input_cost_per_token_above_272k_tokens": 5e-06, "input_cost_per_token_priority": 5e-06, @@ -6203,6 +6241,7 @@ "azure/us/gpt-5.4-2026-03-05": { "cache_read_input_token_cost": 2.8e-07, "cache_read_input_token_cost_priority": 5.5e-07, + "deprecation_date": "2027-09-02", "input_cost_per_token": 2.75e-06, "input_cost_per_token_priority": 5.5e-06, "output_cost_per_token": 1.65e-05, @@ -6238,6 +6277,7 @@ "azure/eu/gpt-5.4-2026-03-05": { "cache_read_input_token_cost": 2.8e-07, "cache_read_input_token_cost_priority": 5.5e-07, + "deprecation_date": "2027-09-02", "input_cost_per_token": 2.75e-06, "input_cost_per_token_priority": 5.5e-06, "output_cost_per_token": 1.65e-05, @@ -6308,6 +6348,7 @@ "azure/gpt-5.4-pro-2026-03-05": { "cache_read_input_token_cost": 3e-06, "cache_read_input_token_cost_above_272k_tokens": 6e-06, + "deprecation_date": "2027-09-07", "input_cost_per_token": 3e-05, "input_cost_per_token_above_272k_tokens": 6e-05, "litellm_provider": "azure", @@ -6390,6 +6431,7 @@ "cache_read_input_token_cost_above_272k_tokens": 1e-06, "cache_read_input_token_cost_priority": 1e-06, "cache_read_input_token_cost_above_272k_tokens_priority": 2e-06, + "deprecation_date": "2028-01-11", "input_cost_per_token": 5e-06, "input_cost_per_token_above_272k_tokens": 1e-05, "input_cost_per_token_priority": 1e-05, @@ -6435,6 +6477,7 @@ "cache_read_input_token_cost_above_272k_tokens": 4e-07, "cache_read_input_token_cost_priority": 4e-07, "cache_read_input_token_cost_above_272k_tokens_priority": 8e-07, + "deprecation_date": "2028-01-11", "input_cost_per_token": 2e-06, "input_cost_per_token_above_272k_tokens": 4e-06, "input_cost_per_token_priority": 4e-06, @@ -6480,6 +6523,7 @@ "cache_read_input_token_cost_above_272k_tokens": 4e-08, "cache_read_input_token_cost_priority": 4e-08, "cache_read_input_token_cost_above_272k_tokens_priority": 8e-08, + "deprecation_date": "2028-01-11", "input_cost_per_token": 2e-07, "input_cost_per_token_above_272k_tokens": 4e-07, "input_cost_per_token_priority": 4e-07, @@ -6566,6 +6610,7 @@ "cache_read_input_token_cost": 5.5e-07, "cache_read_input_token_cost_above_272k_tokens": 1.1e-06, "cache_read_input_token_cost_priority": 1.375e-06, + "deprecation_date": "2028-01-11", "input_cost_per_token": 5.5e-06, "input_cost_per_token_above_272k_tokens": 1.1e-05, "input_cost_per_token_priority": 1.375e-05, @@ -6608,6 +6653,7 @@ "cache_read_input_token_cost": 2.2e-07, "cache_read_input_token_cost_above_272k_tokens": 4.4e-07, "cache_read_input_token_cost_priority": 5.5e-07, + "deprecation_date": "2028-01-11", "input_cost_per_token": 2.2e-06, "input_cost_per_token_above_272k_tokens": 4.4e-06, "input_cost_per_token_priority": 5.5e-06, @@ -6650,6 +6696,7 @@ "cache_read_input_token_cost": 2.2e-08, "cache_read_input_token_cost_above_272k_tokens": 4.4e-08, "cache_read_input_token_cost_priority": 5.5e-08, + "deprecation_date": "2028-01-11", "input_cost_per_token": 2.2e-07, "input_cost_per_token_above_272k_tokens": 4.4e-07, "input_cost_per_token_priority": 5.5e-07, @@ -6734,6 +6781,7 @@ "cache_read_input_token_cost": 5.5e-07, "cache_read_input_token_cost_above_272k_tokens": 1.1e-06, "cache_read_input_token_cost_priority": 1.375e-06, + "deprecation_date": "2028-01-11", "input_cost_per_token": 5.5e-06, "input_cost_per_token_above_272k_tokens": 1.1e-05, "input_cost_per_token_priority": 1.375e-05, @@ -6776,6 +6824,7 @@ "cache_read_input_token_cost": 2.2e-07, "cache_read_input_token_cost_above_272k_tokens": 4.4e-07, "cache_read_input_token_cost_priority": 5.5e-07, + "deprecation_date": "2028-01-11", "input_cost_per_token": 2.2e-06, "input_cost_per_token_above_272k_tokens": 4.4e-06, "input_cost_per_token_priority": 5.5e-06, @@ -6818,6 +6867,7 @@ "cache_read_input_token_cost": 2.2e-08, "cache_read_input_token_cost_above_272k_tokens": 4.4e-08, "cache_read_input_token_cost_priority": 5.5e-08, + "deprecation_date": "2028-01-11", "input_cost_per_token": 2.2e-07, "input_cost_per_token_above_272k_tokens": 4.4e-07, "input_cost_per_token_priority": 5.5e-07, @@ -7216,6 +7266,7 @@ }, "azure/gpt-5.4-mini-2026-03-17": { "cache_read_input_token_cost": 7.5e-08, + "deprecation_date": "2027-09-21", "input_cost_per_token": 7.5e-07, "litellm_provider": "azure", "max_input_tokens": 272000, @@ -7286,6 +7337,7 @@ }, "azure/gpt-5.4-nano-2026-03-17": { "cache_read_input_token_cost": 2e-08, + "deprecation_date": "2027-09-21", "input_cost_per_token": 2e-07, "litellm_provider": "azure", "max_input_tokens": 272000, @@ -7321,6 +7373,7 @@ }, "azure/gpt-image-1": { "cache_read_input_token_cost": 1.25e-06, + "deprecation_date": "2026-10-23", "input_cost_per_image_token": 1e-05, "input_cost_per_token": 5e-06, "litellm_provider": "azure", @@ -7432,6 +7485,7 @@ }, "azure/gpt-image-1-mini": { "cache_read_input_token_cost": 2e-07, + "deprecation_date": "2027-04-07", "input_cost_per_image_token": 2.5e-06, "input_cost_per_token": 2e-06, "litellm_provider": "azure", @@ -7456,6 +7510,7 @@ }, "azure/gpt-image-1.5-2025-12-16": { "cache_read_input_token_cost": 1.25e-06, + "deprecation_date": "2027-06-16", "input_cost_per_token": 5e-06, "input_cost_per_image_token": 8e-06, "litellm_provider": "azure", @@ -7483,6 +7538,7 @@ }, "azure/gpt-image-2-2026-04-21": { "cache_read_input_token_cost": 1.25e-06, + "deprecation_date": "2027-10-21", "input_cost_per_token": 5e-06, "input_cost_per_image_token": 8e-06, "litellm_provider": "azure", @@ -7613,6 +7669,7 @@ }, "azure/o1-2024-12-17": { "cache_read_input_token_cost": 7.5e-06, + "deprecation_date": "2026-10-21", "input_cost_per_token": 1.5e-05, "litellm_provider": "azure", "max_input_tokens": 200000, @@ -7718,7 +7775,7 @@ "supports_vision": true }, "azure/o3-2025-04-16": { - "deprecation_date": "2026-04-16", + "deprecation_date": "2026-10-21", "cache_read_input_token_cost": 5e-07, "input_cost_per_token": 2e-06, "litellm_provider": "azure", @@ -7749,6 +7806,7 @@ }, "azure/o3-deep-research": { "cache_read_input_token_cost": 2.5e-06, + "deprecation_date": "2026-12-26", "input_cost_per_token": 1e-05, "litellm_provider": "azure", "max_input_tokens": 200000, @@ -7796,6 +7854,7 @@ }, "azure/o3-mini-2025-01-31": { "cache_read_input_token_cost": 5.5e-07, + "deprecation_date": "2026-10-01", "input_cost_per_token": 1.1e-06, "litellm_provider": "azure", "max_input_tokens": 200000, @@ -7839,6 +7898,7 @@ "supports_vision": true }, "azure/o3-pro-2025-06-10": { + "deprecation_date": "2026-12-17", "input_cost_per_token": 2e-05, "input_cost_per_token_batches": 1e-05, "litellm_provider": "azure", @@ -7899,6 +7959,7 @@ }, "azure/o4-mini-2025-04-16": { "cache_read_input_token_cost": 2.75e-07, + "deprecation_date": "2026-10-16", "input_cost_per_token": 1.1e-06, "litellm_provider": "azure", "max_input_tokens": 200000, @@ -7939,6 +8000,7 @@ "output_cost_per_token": 0.0 }, "azure/text-embedding-3-large": { + "deprecation_date": "2028-02-09", "input_cost_per_token": 1.3e-07, "litellm_provider": "azure", "max_input_tokens": 8191, @@ -7947,7 +8009,7 @@ "output_cost_per_token": 0.0 }, "azure/text-embedding-3-small": { - "deprecation_date": "2026-04-30", + "deprecation_date": "2028-02-09", "input_cost_per_token": 2e-08, "litellm_provider": "azure", "max_input_tokens": 8191, @@ -7956,6 +8018,7 @@ "output_cost_per_token": 0.0 }, "azure/text-embedding-ada-002": { + "deprecation_date": "2028-02-09", "input_cost_per_token": 1e-07, "litellm_provider": "azure", "max_input_tokens": 8191, @@ -7987,17 +8050,19 @@ ] }, "azure/tts-1": { + "deprecation_date": "2026-12-15", "input_cost_per_character": 1.5e-05, "litellm_provider": "azure", "mode": "audio_speech" }, "azure/tts-1-hd": { + "deprecation_date": "2026-12-15", "input_cost_per_character": 3e-05, "litellm_provider": "azure", "mode": "audio_speech" }, "azure/us/gpt-4.1-2025-04-14": { - "deprecation_date": "2026-11-04", + "deprecation_date": "2027-04-14", "cache_read_input_token_cost": 5.5e-07, "input_cost_per_token": 2.2e-06, "input_cost_per_token_batches": 1.1e-06, @@ -8031,7 +8096,7 @@ "supports_web_search": false }, "azure/us/gpt-4.1-mini-2025-04-14": { - "deprecation_date": "2026-11-04", + "deprecation_date": "2027-04-14", "cache_read_input_token_cost": 1.1e-07, "input_cost_per_token": 4.4e-07, "input_cost_per_token_batches": 2.2e-07, @@ -8065,7 +8130,7 @@ "supports_web_search": false }, "azure/us/gpt-4.1-nano-2025-04-14": { - "deprecation_date": "2026-11-04", + "deprecation_date": "2026-10-14", "cache_read_input_token_cost": 2.5e-08, "input_cost_per_token": 1.1e-07, "input_cost_per_token_batches": 6e-08, @@ -8098,7 +8163,7 @@ "supports_vision": true }, "azure/us/gpt-4o-2024-08-06": { - "deprecation_date": "2026-02-27", + "deprecation_date": "2027-04-14", "cache_read_input_token_cost": 1.375e-06, "input_cost_per_token": 2.75e-06, "litellm_provider": "azure", @@ -8115,7 +8180,7 @@ "supports_vision": true }, "azure/us/gpt-4o-2024-11-20": { - "deprecation_date": "2026-03-01", + "deprecation_date": "2027-04-14", "cache_creation_input_token_cost": 1.38e-06, "input_cost_per_token": 2.75e-06, "litellm_provider": "azure", @@ -8132,6 +8197,7 @@ }, "azure/us/gpt-4o-mini-2024-07-18": { "cache_read_input_token_cost": 8.3e-08, + "deprecation_date": "2027-04-14", "input_cost_per_token": 1.65e-07, "litellm_provider": "azure", "max_input_tokens": 128000, @@ -8213,6 +8279,7 @@ }, "azure/us/gpt-5-2025-08-07": { "cache_read_input_token_cost": 1.375e-07, + "deprecation_date": "2027-02-09", "input_cost_per_token": 1.375e-06, "litellm_provider": "azure", "max_input_tokens": 272000, @@ -8245,6 +8312,7 @@ }, "azure/us/gpt-5-mini-2025-08-07": { "cache_read_input_token_cost": 2.75e-08, + "deprecation_date": "2027-02-09", "input_cost_per_token": 2.75e-07, "litellm_provider": "azure", "max_input_tokens": 272000, @@ -8277,6 +8345,7 @@ }, "azure/us/gpt-5-nano-2025-08-07": { "cache_read_input_token_cost": 5.5e-09, + "deprecation_date": "2027-02-09", "input_cost_per_token": 5.5e-08, "litellm_provider": "azure", "max_input_tokens": 272000, @@ -8343,6 +8412,7 @@ }, "azure/us/gpt-5.1-chat": { "cache_read_input_token_cost": 1.4e-07, + "deprecation_date": "2026-06-29", "input_cost_per_token": 1.38e-06, "litellm_provider": "azure", "max_input_tokens": 128000, @@ -8437,6 +8507,7 @@ }, "azure/us/o1-2024-12-17": { "cache_read_input_token_cost": 8.25e-06, + "deprecation_date": "2026-10-21", "input_cost_per_token": 1.65e-05, "litellm_provider": "azure", "max_input_tokens": 200000, @@ -8481,7 +8552,7 @@ "supports_vision": false }, "azure/us/o3-2025-04-16": { - "deprecation_date": "2026-04-16", + "deprecation_date": "2026-10-21", "cache_read_input_token_cost": 5.5e-07, "input_cost_per_token": 2.2e-06, "litellm_provider": "azure", @@ -8512,6 +8583,7 @@ }, "azure/us/o3-mini-2025-01-31": { "cache_read_input_token_cost": 6.05e-07, + "deprecation_date": "2026-10-01", "input_cost_per_token": 1.21e-06, "input_cost_per_token_batches": 6.05e-07, "litellm_provider": "azure", @@ -8528,6 +8600,7 @@ }, "azure/us/o4-mini-2025-04-16": { "cache_read_input_token_cost": 3.1e-07, + "deprecation_date": "2026-10-16", "input_cost_per_token": 1.21e-06, "litellm_provider": "azure", "max_input_tokens": 200000, @@ -8544,6 +8617,7 @@ "supports_vision": true }, "azure/whisper-1": { + "deprecation_date": "2026-12-15", "input_cost_per_second": 0.0001, "litellm_provider": "azure", "mode": "audio_transcription", @@ -11396,6 +11470,7 @@ "output_cost_per_token": 5e-07 }, "chatgpt-4o-latest": { + "deprecation_date": "2026-02-17", "input_cost_per_token": 5e-06, "litellm_provider": "openai", "max_input_tokens": 128000, @@ -11499,6 +11574,7 @@ "cache_creation_input_token_cost": 3e-07, "cache_creation_input_token_cost_above_1hr": 6e-06, "cache_read_input_token_cost": 3e-08, + "deprecation_date": "2026-04-20", "input_cost_per_token": 2.5e-07, "litellm_provider": "anthropic", "max_input_tokens": 200000, @@ -11517,7 +11593,7 @@ "cache_creation_input_token_cost": 1.875e-05, "cache_creation_input_token_cost_above_1hr": 6e-06, "cache_read_input_token_cost": 1.5e-06, - "deprecation_date": "2026-05-01", + "deprecation_date": "2026-01-05", "input_cost_per_token": 1.5e-05, "litellm_provider": "anthropic", "max_input_tokens": 200000, @@ -11535,6 +11611,7 @@ "claude-4-opus-20250514": { "cache_creation_input_token_cost": 1.875e-05, "cache_read_input_token_cost": 1.5e-06, + "deprecation_date": "2026-06-15", "input_cost_per_token": 1.5e-05, "litellm_provider": "anthropic", "max_input_tokens": 200000, @@ -11563,6 +11640,7 @@ "cache_creation_input_token_cost_above_200k_tokens": 7.5e-06, "cache_read_input_token_cost": 3e-07, "cache_read_input_token_cost_above_200k_tokens": 6e-07, + "deprecation_date": "2026-06-15", "input_cost_per_token": 3e-06, "input_cost_per_token_above_200k_tokens": 6e-06, "litellm_provider": "anthropic", @@ -11776,7 +11854,8 @@ "supports_native_structured_output": true, "supports_tool_choice": true, "supports_vision": true, - "prompt_cache_min_tokens": 1024 + "prompt_cache_min_tokens": 1024, + "deprecation_date": "2026-08-05" }, "claude-opus-4-1-20250805": { "cache_creation_input_token_cost": 1.875e-05, @@ -11812,7 +11891,7 @@ "cache_creation_input_token_cost_above_1hr": 3e-05, "cache_read_input_token_cost": 1.5e-06, "input_cost_per_token": 1.5e-05, - "deprecation_date": "2026-05-14", + "deprecation_date": "2026-06-15", "litellm_provider": "anthropic", "max_input_tokens": 200000, "max_output_tokens": 32000, @@ -12153,7 +12232,7 @@ "prompt_cache_min_tokens": 1024 }, "claude-sonnet-4-20250514": { - "deprecation_date": "2026-05-14", + "deprecation_date": "2026-06-15", "cache_creation_input_token_cost": 3.75e-06, "cache_creation_input_token_cost_above_1hr": 6e-06, "cache_read_input_token_cost": 3e-07, @@ -12508,6 +12587,7 @@ }, "codex-mini-latest": { "cache_read_input_token_cost": 3.75e-07, + "deprecation_date": "2026-02-12", "input_cost_per_token": 1.5e-06, "litellm_provider": "openai", "max_input_tokens": 200000, @@ -12746,6 +12826,7 @@ "supports_vision": true }, "dall-e-2": { + "deprecation_date": "2026-05-12", "input_cost_per_image": 0.02, "litellm_provider": "openai", "mode": "image_generation", @@ -12756,6 +12837,7 @@ ] }, "dall-e-3": { + "deprecation_date": "2026-05-12", "input_cost_per_image": 0.04, "litellm_provider": "openai", "mode": "image_generation", @@ -15471,6 +15553,7 @@ "output_cost_per_token": 1.85e-06, "supports_function_calling": true, "supports_reasoning": true, + "supports_native_structured_output": true, "supports_tool_choice": true, "source": "https://aws.amazon.com/bedrock/pricing/" }, @@ -17259,6 +17342,7 @@ "supports_tool_choice": true }, "ft:babbage-002": { + "deprecation_date": "2026-10-23", "input_cost_per_token": 1.6e-06, "input_cost_per_token_batches": 2e-07, "litellm_provider": "text-completion-openai", @@ -17270,6 +17354,7 @@ "output_cost_per_token_batches": 2e-07 }, "ft:davinci-002": { + "deprecation_date": "2026-10-23", "input_cost_per_token": 1.2e-05, "input_cost_per_token_batches": 1e-06, "litellm_provider": "text-completion-openai", @@ -17281,6 +17366,7 @@ "output_cost_per_token_batches": 1e-06 }, "ft:gpt-3.5-turbo": { + "deprecation_date": "2026-10-23", "input_cost_per_token": 3e-06, "input_cost_per_token_batches": 1.5e-06, "litellm_provider": "openai", @@ -17294,6 +17380,7 @@ "supports_tool_choice": true }, "ft:gpt-3.5-turbo-0125": { + "deprecation_date": "2026-10-23", "input_cost_per_token": 3e-06, "litellm_provider": "openai", "max_input_tokens": 16385, @@ -17327,6 +17414,7 @@ "supports_tool_choice": true }, "ft:gpt-4-0613": { + "deprecation_date": "2026-10-23", "input_cost_per_token": 3e-05, "litellm_provider": "openai", "max_input_tokens": 8192, @@ -17433,6 +17521,7 @@ }, "ft:gpt-4.1-nano-2025-04-14": { "cache_read_input_token_cost": 5e-08, + "deprecation_date": "2026-10-23", "input_cost_per_token": 2e-07, "input_cost_per_token_batches": 1e-07, "litellm_provider": "openai", @@ -17451,6 +17540,7 @@ }, "ft:o4-mini-2025-04-16": { "cache_read_input_token_cost": 1e-06, + "deprecation_date": "2026-10-23", "input_cost_per_token": 4e-06, "input_cost_per_token_batches": 2e-06, "litellm_provider": "openai", @@ -17702,6 +17792,46 @@ "tpm": 8000000, "supports_image_size": false }, + "gemini-3-pro-image": { + "input_cost_per_image": 0.0011, + "input_cost_per_token": 2e-06, + "input_cost_per_token_batches": 1e-06, + "litellm_provider": "vertex_ai-language-models", + "max_input_tokens": 65536, + "max_output_tokens": 32768, + "max_tokens": 32768, + "mode": "image_generation", + "output_cost_per_image": 0.134, + "output_cost_per_image_token": 0.00012, + "output_cost_per_token": 1.2e-05, + "output_cost_per_token_batches": 6e-06, + "source": "https://ai.google.dev/gemini-api/docs/pricing#gemini-3-pro-image", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/completions", + "/v1/batch" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text", + "image" + ], + "supports_function_calling": false, + "supports_prompt_caching": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_vision": true, + "supports_web_search": true, + "search_context_cost_per_query": { + "search_context_size_low": 0.014, + "search_context_size_medium": 0.014, + "search_context_size_high": 0.014 + }, + "web_search_billing_unit": "per_query" + }, "gemini-3-pro-image-preview": { "input_cost_per_image": 0.0011, "input_cost_per_token": 2e-06, @@ -17742,6 +17872,44 @@ }, "web_search_billing_unit": "per_query" }, + "gemini-3.1-flash-image": { + "input_cost_per_image": 0.00056, + "input_cost_per_token": 5e-07, + "litellm_provider": "vertex_ai-language-models", + "max_input_tokens": 65536, + "max_output_tokens": 32768, + "max_tokens": 32768, + "mode": "image_generation", + "output_cost_per_image": 0.0672, + "output_cost_per_image_token": 6e-05, + "output_cost_per_token": 3e-06, + "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing#gemini-models", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/completions", + "/v1/batch" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text", + "image" + ], + "supports_function_calling": false, + "supports_prompt_caching": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_vision": true, + "supports_web_search": true, + "search_context_cost_per_query": { + "search_context_size_low": 0.014, + "search_context_size_medium": 0.014, + "search_context_size_high": 0.014 + }, + "web_search_billing_unit": "per_query" + }, "gemini-3.1-flash-image-preview": { "input_cost_per_image": 0.00056, "input_cost_per_token": 5e-07, @@ -18847,6 +19015,7 @@ }, "gemini/gemini-robotics-er-1.5-preview": { "cache_read_input_token_cost": 0, + "deprecation_date": "2026-04-30", "input_cost_per_token": 3e-07, "input_cost_per_audio_token": 1e-06, "litellm_provider": "gemini", @@ -19089,6 +19258,7 @@ "uses_embed_content": true }, "gemini/gemini-embedding-001": { + "deprecation_date": "2028-05-14", "input_cost_per_token": 1.5e-07, "litellm_provider": "gemini", "max_input_tokens": 2048, @@ -19101,6 +19271,7 @@ "tpm": 10000000 }, "gemini/gemini-embedding-2-preview": { + "deprecation_date": "2026-08-10", "input_cost_per_audio_per_second": 0.00016, "input_cost_per_image": 0.00012, "input_cost_per_token": 2e-07, @@ -19310,6 +19481,7 @@ }, "gemini/gemini-2.5-flash-image": { "cache_read_input_token_cost": 3e-08, + "deprecation_date": "2026-10-02", "input_cost_per_audio_token": 1e-06, "input_cost_per_token": 3e-07, "litellm_provider": "gemini", @@ -19389,7 +19561,6 @@ ], "supports_function_calling": false, "supports_prompt_caching": true, - "supports_reasoning": false, "supports_response_schema": true, "supports_system_messages": true, "supports_vision": true, @@ -19399,9 +19570,11 @@ "search_context_size_medium": 0.014, "search_context_size_high": 0.014 }, - "web_search_billing_unit": "per_query" + "web_search_billing_unit": "per_query", + "supports_reasoning": false }, "gemini/gemini-3-pro-image-preview": { + "deprecation_date": "2026-06-25", "input_cost_per_image": 0.0011, "input_cost_per_token": 2e-06, "input_cost_per_token_batches": 1e-06, @@ -19487,6 +19660,7 @@ "web_search_billing_unit": "per_query" }, "gemini/gemini-3.1-flash-image-preview": { + "deprecation_date": "2026-06-25", "input_cost_per_token": 2.5e-07, "input_cost_per_token_batches": 1.25e-07, "litellm_provider": "gemini", @@ -19618,6 +19792,7 @@ }, "gemini/gemini-2.5-flash-lite-preview-09-2025": { "cache_read_input_token_cost": 1e-08, + "deprecation_date": "2026-03-31", "input_cost_per_audio_token": 3e-07, "input_cost_per_token": 1e-07, "litellm_provider": "gemini", @@ -19999,6 +20174,7 @@ }, "gemini/gemini-3.1-flash-lite-preview": { "cache_read_input_token_cost": 2.5e-08, + "deprecation_date": "2026-05-25", "input_cost_per_audio_token": 5e-07, "input_cost_per_token": 2.5e-07, "litellm_provider": "gemini", @@ -20051,6 +20227,7 @@ "cache_read_input_token_cost": 2.5e-08, "cache_read_input_token_cost_flex": 1.25e-08, "cache_read_input_token_cost_priority": 4.5e-08, + "deprecation_date": "2027-05-07", "input_cost_per_audio_token": 5e-07, "input_cost_per_token": 2.5e-07, "input_cost_per_token_batches": 1.25e-07, @@ -20818,18 +20995,21 @@ "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing" }, "gemini/imagen-4.0-fast-generate-001": { + "deprecation_date": "2026-08-17", "litellm_provider": "gemini", "mode": "image_generation", "output_cost_per_image": 0.02, "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing" }, "gemini/imagen-4.0-generate-001": { + "deprecation_date": "2026-08-17", "litellm_provider": "gemini", "mode": "image_generation", "output_cost_per_image": 0.04, "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing" }, "gemini/imagen-4.0-ultra-generate-001": { + "deprecation_date": "2026-08-17", "litellm_provider": "gemini", "mode": "image_generation", "output_cost_per_image": 0.06, @@ -20913,6 +21093,7 @@ "supports_web_search": false }, "gemini/veo-2.0-generate-001": { + "deprecation_date": "2026-06-30", "litellm_provider": "gemini", "max_input_tokens": 1024, "max_tokens": 1024, @@ -21004,8 +21185,7 @@ "max_tokens": 16000, "mode": "chat", "supported_endpoints": [ - "/v1/chat/completions", - "/v1/messages" + "/v1/chat/completions" ], "supports_function_calling": true, "supports_parallel_function_calling": true, @@ -21018,8 +21198,7 @@ "max_tokens": 16000, "mode": "chat", "supported_endpoints": [ - "/v1/chat/completions", - "/v1/messages" + "/v1/chat/completions" ], "supports_function_calling": true, "supports_parallel_function_calling": true, @@ -21071,8 +21250,7 @@ "max_tokens": 16000, "mode": "chat", "supported_endpoints": [ - "/v1/chat/completions", - "/v1/messages" + "/v1/chat/completions" ], "supports_function_calling": true, "supports_parallel_function_calling": true, @@ -21950,6 +22128,7 @@ "supports_vision": true }, "gpt-3.5-turbo": { + "deprecation_date": "2026-10-23", "input_cost_per_token": 5e-07, "litellm_provider": "openai", "max_input_tokens": 16385, @@ -21963,6 +22142,7 @@ "supports_tool_choice": true }, "gpt-3.5-turbo-0125": { + "deprecation_date": "2026-10-23", "input_cost_per_token": 5e-07, "litellm_provider": "openai", "max_input_tokens": 16385, @@ -22022,6 +22202,7 @@ "output_cost_per_token": 2e-06 }, "gpt-4": { + "deprecation_date": "2026-10-23", "input_cost_per_token": 3e-05, "litellm_provider": "openai", "max_input_tokens": 8192, @@ -22062,7 +22243,7 @@ "supports_tool_choice": true }, "gpt-4-0613": { - "deprecation_date": "2025-06-06", + "deprecation_date": "2026-10-23", "input_cost_per_token": 3e-05, "litellm_provider": "openai", "max_input_tokens": 8192, @@ -22076,7 +22257,7 @@ "supports_tool_choice": true }, "gpt-4-1106-preview": { - "deprecation_date": "2026-03-26", + "deprecation_date": "2026-10-23", "input_cost_per_token": 1e-05, "litellm_provider": "openai", "max_input_tokens": 128000, @@ -22091,6 +22272,7 @@ "supports_tool_choice": true }, "gpt-4-turbo": { + "deprecation_date": "2026-10-23", "input_cost_per_token": 1e-05, "litellm_provider": "openai", "max_input_tokens": 128000, @@ -22107,6 +22289,7 @@ "supports_vision": true }, "gpt-4-turbo-2024-04-09": { + "deprecation_date": "2026-10-23", "input_cost_per_token": 1e-05, "litellm_provider": "openai", "max_input_tokens": 128000, @@ -22288,6 +22471,7 @@ "gpt-4.1-nano": { "cache_read_input_token_cost": 2.5e-08, "cache_read_input_token_cost_priority": 5e-08, + "deprecation_date": "2026-10-23", "input_cost_per_token": 1e-07, "input_cost_per_token_batches": 5e-08, "input_cost_per_token_priority": 2e-07, @@ -22324,6 +22508,7 @@ "gpt-4.1-nano-2025-04-14": { "cache_read_input_token_cost": 2.5e-08, "cache_read_input_token_cost_priority": 5e-08, + "deprecation_date": "2026-10-23", "input_cost_per_token": 1e-07, "input_cost_per_token_priority": 2e-07, "input_cost_per_token_batches": 5e-08, @@ -22381,6 +22566,7 @@ "supports_vision": true }, "gpt-4o-2024-05-13": { + "deprecation_date": "2026-10-23", "input_cost_per_token": 5e-06, "input_cost_per_token_batches": 2.5e-06, "input_cost_per_token_priority": 8.75e-06, @@ -22447,6 +22633,7 @@ "supports_vision": true }, "gpt-4o-audio-preview": { + "deprecation_date": "2027-01-20", "input_cost_per_audio_token": 4e-05, "input_cost_per_token": 2.5e-06, "litellm_provider": "openai", @@ -22464,6 +22651,7 @@ "supports_tool_choice": true }, "gpt-4o-audio-preview-2024-12-17": { + "deprecation_date": "2027-01-20", "input_cost_per_audio_token": 4e-05, "input_cost_per_token": 2.5e-06, "litellm_provider": "openai", @@ -22481,6 +22669,7 @@ "supports_tool_choice": true }, "gpt-4o-audio-preview-2025-06-03": { + "deprecation_date": "2027-01-20", "input_cost_per_audio_token": 4e-05, "input_cost_per_token": 2.5e-06, "litellm_provider": "openai", @@ -22498,6 +22687,7 @@ "supports_tool_choice": true }, "gpt-audio": { + "deprecation_date": "2027-01-20", "input_cost_per_audio_token": 3.2e-05, "input_cost_per_token": 2.5e-06, "litellm_provider": "openai", @@ -22567,6 +22757,7 @@ "supports_vision": false }, "gpt-audio-2025-08-28": { + "deprecation_date": "2027-01-20", "input_cost_per_audio_token": 3.2e-05, "input_cost_per_token": 2.5e-06, "litellm_provider": "openai", @@ -22603,6 +22794,7 @@ "supports_vision": false }, "gpt-audio-mini": { + "deprecation_date": "2027-01-20", "input_cost_per_audio_token": 1e-05, "input_cost_per_token": 6e-07, "litellm_provider": "openai", @@ -22639,6 +22831,7 @@ "supports_vision": false }, "gpt-audio-mini-2025-10-06": { + "deprecation_date": "2026-07-23", "input_cost_per_audio_token": 1e-05, "input_cost_per_token": 6e-07, "litellm_provider": "openai", @@ -22762,6 +22955,7 @@ "supports_vision": true }, "gpt-4o-mini-audio-preview": { + "deprecation_date": "2027-01-20", "input_cost_per_audio_token": 1e-05, "input_cost_per_token": 1.5e-07, "litellm_provider": "openai", @@ -22779,6 +22973,7 @@ "supports_tool_choice": true }, "gpt-4o-mini-audio-preview-2024-12-17": { + "deprecation_date": "2027-01-20", "input_cost_per_audio_token": 1e-05, "input_cost_per_token": 1.5e-07, "litellm_provider": "openai", @@ -22798,6 +22993,7 @@ "gpt-4o-mini-realtime-preview": { "cache_creation_input_audio_token_cost": 3e-07, "cache_read_input_token_cost": 3e-07, + "deprecation_date": "2027-01-20", "input_cost_per_audio_token": 1e-05, "input_cost_per_token": 6e-07, "litellm_provider": "openai", @@ -22817,6 +23013,7 @@ "gpt-4o-mini-realtime-preview-2024-12-17": { "cache_creation_input_audio_token_cost": 3e-07, "cache_read_input_token_cost": 3e-07, + "deprecation_date": "2027-01-20", "input_cost_per_audio_token": 1e-05, "input_cost_per_token": 6e-07, "litellm_provider": "openai", @@ -22861,6 +23058,7 @@ }, "gpt-4o-mini-search-preview-2025-03-11": { "cache_read_input_token_cost": 7.5e-08, + "deprecation_date": "2026-07-23", "input_cost_per_token": 1.5e-07, "input_cost_per_token_batches": 7.5e-08, "litellm_provider": "openai", @@ -22911,6 +23109,7 @@ }, "gpt-4o-realtime-preview": { "cache_read_input_token_cost": 2.5e-06, + "deprecation_date": "2027-01-20", "input_cost_per_audio_token": 4e-05, "input_cost_per_token": 5e-06, "litellm_provider": "openai", @@ -22929,6 +23128,7 @@ }, "gpt-4o-realtime-preview-2024-12-17": { "cache_read_input_token_cost": 2.5e-06, + "deprecation_date": "2027-01-20", "input_cost_per_audio_token": 4e-05, "input_cost_per_token": 5e-06, "litellm_provider": "openai", @@ -22947,6 +23147,7 @@ }, "gpt-4o-realtime-preview-2025-06-03": { "cache_read_input_token_cost": 2.5e-06, + "deprecation_date": "2027-01-20", "input_cost_per_audio_token": 4e-05, "input_cost_per_token": 5e-06, "litellm_provider": "openai", @@ -22991,6 +23192,7 @@ }, "gpt-4o-search-preview-2025-03-11": { "cache_read_input_token_cost": 1.25e-06, + "deprecation_date": "2026-07-23", "input_cost_per_token": 2.5e-06, "input_cost_per_token_batches": 1.25e-06, "litellm_provider": "openai", @@ -23023,6 +23225,7 @@ }, "gpt-image-1.5": { "cache_read_input_token_cost": 1.25e-06, + "deprecation_date": "2026-12-01", "input_cost_per_token": 5e-06, "litellm_provider": "openai", "mode": "image_generation", @@ -23037,6 +23240,7 @@ }, "gpt-image-1.5-2025-12-16": { "cache_read_input_token_cost": 1.25e-06, + "deprecation_date": "2026-12-01", "input_cost_per_token": 5e-06, "litellm_provider": "openai", "mode": "image_generation", @@ -23532,6 +23736,7 @@ "gpt-5.1-chat-latest": { "cache_read_input_token_cost": 1.25e-07, "cache_read_input_token_cost_priority": 2.5e-07, + "deprecation_date": "2026-07-23", "input_cost_per_token": 1.25e-06, "input_cost_per_token_priority": 2.5e-06, "litellm_provider": "openai", @@ -23651,6 +23856,7 @@ "gpt-5.2-chat-latest": { "cache_read_input_token_cost": 1.75e-07, "cache_read_input_token_cost_priority": 3.5e-07, + "deprecation_date": "2026-08-10", "input_cost_per_token": 1.75e-06, "input_cost_per_token_priority": 3.5e-06, "litellm_provider": "openai", @@ -23689,6 +23895,7 @@ "gpt-5.3-chat-latest": { "cache_read_input_token_cost": 1.75e-07, "cache_read_input_token_cost_priority": 3.5e-07, + "deprecation_date": "2026-08-10", "input_cost_per_token": 1.75e-06, "input_cost_per_token_priority": 3.5e-06, "litellm_provider": "openai", @@ -24571,7 +24778,7 @@ "input_cost_per_token": 1.5e-05, "input_cost_per_token_batches": 7.5e-06, "litellm_provider": "openai", - "max_input_tokens": 128000, + "max_input_tokens": 400000, "max_output_tokens": 272000, "max_tokens": 272000, "mode": "responses", @@ -24604,10 +24811,11 @@ "supports_minimal_reasoning_effort": true }, "gpt-5-pro-2025-10-06": { + "deprecation_date": "2026-12-11", "input_cost_per_token": 1.5e-05, "input_cost_per_token_batches": 7.5e-06, "litellm_provider": "openai", - "max_input_tokens": 128000, + "max_input_tokens": 400000, "max_output_tokens": 272000, "max_tokens": 272000, "mode": "responses", @@ -24643,6 +24851,7 @@ "cache_read_input_token_cost": 1.25e-07, "cache_read_input_token_cost_flex": 6.25e-08, "cache_read_input_token_cost_priority": 2.5e-07, + "deprecation_date": "2026-12-11", "input_cost_per_token": 1.25e-06, "input_cost_per_token_flex": 6.25e-07, "input_cost_per_token_priority": 2.5e-06, @@ -24718,6 +24927,7 @@ }, "gpt-5-chat-latest": { "cache_read_input_token_cost": 1.25e-07, + "deprecation_date": "2026-07-23", "input_cost_per_token": 1.25e-06, "litellm_provider": "openai", "max_input_tokens": 128000, @@ -24753,6 +24963,7 @@ }, "gpt-5-codex": { "cache_read_input_token_cost": 1.25e-07, + "deprecation_date": "2026-07-23", "input_cost_per_token": 1.25e-06, "litellm_provider": "openai", "max_input_tokens": 272000, @@ -24788,6 +24999,7 @@ "gpt-5.1-codex": { "cache_read_input_token_cost": 1.25e-07, "cache_read_input_token_cost_priority": 2.5e-07, + "deprecation_date": "2026-07-23", "input_cost_per_token": 1.25e-06, "input_cost_per_token_priority": 2.5e-06, "litellm_provider": "openai", @@ -24824,6 +25036,7 @@ }, "gpt-5.1-codex-max": { "cache_read_input_token_cost": 1.25e-07, + "deprecation_date": "2026-07-23", "input_cost_per_token": 1.25e-06, "litellm_provider": "openai", "max_input_tokens": 272000, @@ -24859,6 +25072,7 @@ "gpt-5.1-codex-mini": { "cache_read_input_token_cost": 2.5e-08, "cache_read_input_token_cost_priority": 4.5e-08, + "deprecation_date": "2026-07-23", "input_cost_per_token": 2.5e-07, "input_cost_per_token_priority": 4.5e-07, "litellm_provider": "openai", @@ -24896,6 +25110,7 @@ "gpt-5.2-codex": { "cache_read_input_token_cost": 1.75e-07, "cache_read_input_token_cost_priority": 3.5e-07, + "deprecation_date": "2026-07-23", "input_cost_per_token": 1.75e-06, "input_cost_per_token_priority": 3.5e-06, "litellm_provider": "openai", @@ -25013,6 +25228,7 @@ "cache_read_input_token_cost": 2.5e-08, "cache_read_input_token_cost_flex": 1.25e-08, "cache_read_input_token_cost_priority": 4.5e-08, + "deprecation_date": "2026-12-11", "input_cost_per_token": 2.5e-07, "input_cost_per_token_flex": 1.25e-07, "input_cost_per_token_priority": 4.5e-07, @@ -25094,6 +25310,7 @@ "gpt-5-nano-2025-08-07": { "cache_read_input_token_cost": 5e-09, "cache_read_input_token_cost_flex": 2.5e-09, + "deprecation_date": "2026-12-11", "input_cost_per_token": 5e-08, "input_cost_per_token_priority": 2.5e-06, "input_cost_per_token_flex": 2.5e-08, @@ -25133,6 +25350,7 @@ }, "gpt-image-1": { "cache_read_input_token_cost": 1.25e-06, + "deprecation_date": "2026-10-23", "input_cost_per_image_token": 1e-05, "input_cost_per_token": 5e-06, "litellm_provider": "openai", @@ -25145,6 +25363,7 @@ }, "gpt-image-1-mini": { "cache_read_input_token_cost": 2e-07, + "deprecation_date": "2026-12-01", "input_cost_per_image_token": 2.5e-06, "input_cost_per_token": 2e-06, "litellm_provider": "openai", @@ -25158,6 +25377,7 @@ "gpt-realtime": { "cache_creation_input_audio_token_cost": 4e-07, "cache_read_input_token_cost": 4e-07, + "deprecation_date": "2027-01-20", "input_cost_per_audio_token": 3.2e-05, "input_cost_per_image": 5e-06, "input_cost_per_token": 4e-06, @@ -25324,6 +25544,7 @@ "gpt-realtime-mini": { "cache_creation_input_audio_token_cost": 3e-07, "cache_read_input_audio_token_cost": 3e-07, + "deprecation_date": "2027-01-20", "input_cost_per_audio_token": 1e-05, "input_cost_per_token": 6e-07, "litellm_provider": "openai", @@ -25355,6 +25576,7 @@ "gpt-realtime-2025-08-28": { "cache_creation_input_audio_token_cost": 4e-07, "cache_read_input_token_cost": 4e-07, + "deprecation_date": "2027-01-20", "input_cost_per_audio_token": 3.2e-05, "input_cost_per_image": 5e-06, "input_cost_per_token": 4e-06, @@ -29183,6 +29405,7 @@ }, "o1": { "cache_read_input_token_cost": 7.5e-06, + "deprecation_date": "2026-10-23", "input_cost_per_token": 1.5e-05, "litellm_provider": "openai", "max_input_tokens": 200000, @@ -29202,6 +29425,7 @@ }, "o1-2024-12-17": { "cache_read_input_token_cost": 7.5e-06, + "deprecation_date": "2026-10-23", "input_cost_per_token": 1.5e-05, "litellm_provider": "openai", "max_input_tokens": 200000, @@ -29220,6 +29444,7 @@ "supports_vision": true }, "o1-pro": { + "deprecation_date": "2026-10-23", "input_cost_per_token": 0.00015, "input_cost_per_token_batches": 7.5e-05, "litellm_provider": "openai", @@ -29252,6 +29477,7 @@ "supports_vision": true }, "o1-pro-2025-03-19": { + "deprecation_date": "2026-10-23", "input_cost_per_token": 0.00015, "input_cost_per_token_batches": 7.5e-05, "litellm_provider": "openai", @@ -29325,6 +29551,7 @@ "cache_read_input_token_cost": 5e-07, "cache_read_input_token_cost_flex": 2.5e-07, "cache_read_input_token_cost_priority": 8.75e-07, + "deprecation_date": "2026-12-11", "input_cost_per_token": 2e-06, "input_cost_per_token_flex": 1e-06, "input_cost_per_token_priority": 3.5e-06, @@ -29361,6 +29588,7 @@ }, "o3-deep-research": { "cache_read_input_token_cost": 2.5e-06, + "deprecation_date": "2026-07-23", "input_cost_per_token": 1e-05, "input_cost_per_token_batches": 5e-06, "litellm_provider": "openai", @@ -29395,6 +29623,7 @@ }, "o3-deep-research-2025-06-26": { "cache_read_input_token_cost": 2.5e-06, + "deprecation_date": "2026-07-23", "input_cost_per_token": 1e-05, "input_cost_per_token_batches": 5e-06, "litellm_provider": "openai", @@ -29429,6 +29658,7 @@ }, "o3-mini": { "cache_read_input_token_cost": 5.5e-07, + "deprecation_date": "2026-10-23", "input_cost_per_token": 1.1e-06, "litellm_provider": "openai", "max_input_tokens": 200000, @@ -29446,6 +29676,7 @@ }, "o3-mini-2025-01-31": { "cache_read_input_token_cost": 5.5e-07, + "deprecation_date": "2026-10-23", "input_cost_per_token": 1.1e-06, "litellm_provider": "openai", "max_input_tokens": 200000, @@ -29493,6 +29724,7 @@ "supports_web_search": true }, "o3-pro-2025-06-10": { + "deprecation_date": "2026-12-11", "input_cost_per_token": 2e-05, "input_cost_per_token_batches": 1e-05, "litellm_provider": "openai", @@ -29527,6 +29759,7 @@ "cache_read_input_token_cost": 2.75e-07, "cache_read_input_token_cost_flex": 1.375e-07, "cache_read_input_token_cost_priority": 5e-07, + "deprecation_date": "2026-10-23", "input_cost_per_token": 1.1e-06, "input_cost_per_token_flex": 5.5e-07, "input_cost_per_token_priority": 2e-06, @@ -29552,6 +29785,7 @@ "cache_read_input_token_cost": 2.75e-07, "cache_read_input_token_cost_flex": 1.375e-07, "cache_read_input_token_cost_priority": 5e-07, + "deprecation_date": "2026-10-23", "input_cost_per_token": 1.1e-06, "input_cost_per_token_flex": 5.5e-07, "input_cost_per_token_priority": 2e-06, @@ -29575,6 +29809,7 @@ }, "o4-mini-deep-research": { "cache_read_input_token_cost": 5e-07, + "deprecation_date": "2026-07-23", "input_cost_per_token": 2e-06, "input_cost_per_token_batches": 1e-06, "litellm_provider": "openai", @@ -29609,6 +29844,7 @@ }, "o4-mini-deep-research-2025-06-26": { "cache_read_input_token_cost": 5e-07, + "deprecation_date": "2026-07-23", "input_cost_per_token": 2e-06, "input_cost_per_token_batches": 1e-06, "litellm_provider": "openai", @@ -32000,6 +32236,22 @@ "supports_reasoning": true, "supports_tool_choice": true }, + "openrouter/z-ai/glm-5.1": { + "input_cost_per_token": 1.05e-06, + "output_cost_per_token": 3.5e-06, + "cache_read_input_token_cost": 5.25e-07, + "cache_creation_input_token_cost": 0.0, + "litellm_provider": "openrouter", + "max_input_tokens": 202752, + "max_output_tokens": 65535, + "max_tokens": 65535, + "mode": "chat", + "source": "https://openrouter.ai/z-ai/glm-5.1", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, "openrouter/minimax/minimax-m2.1": { "input_cost_per_token": 2.7e-07, "output_cost_per_token": 1.2e-06, @@ -39994,69 +40246,86 @@ }, "xai/grok-4.20-multi-agent-beta-0309": { "cache_read_input_token_cost": 2e-07, - "input_cost_per_token": 2e-06, + "input_cost_per_token": 1.25e-06, "litellm_provider": "xai", - "max_input_tokens": 2000000, - "max_output_tokens": 2000000, - "max_tokens": 2000000, + "max_input_tokens": 1000000, + "max_output_tokens": 1000000, + "max_tokens": 1000000, "mode": "chat", - "output_cost_per_token": 6e-06, + "output_cost_per_token": 2.5e-06, "source": "https://docs.x.ai/docs/models", "supports_function_calling": true, "supports_prompt_caching": true, "supports_reasoning": true, "supports_tool_choice": true, "supports_vision": true, - "supports_web_search": true + "supports_web_search": true, + "input_cost_per_token_above_200k_tokens": 2.5e-06, + "output_cost_per_token_above_200k_tokens": 5e-06, + "cache_read_input_token_cost_above_200k_tokens": 4e-07, + "supports_response_schema": true }, "xai/grok-4.20-beta-0309-reasoning": { "cache_read_input_token_cost": 2e-07, - "input_cost_per_token": 2e-06, + "input_cost_per_token": 1.25e-06, "litellm_provider": "xai", - "max_input_tokens": 2000000, - "max_output_tokens": 2000000, - "max_tokens": 2000000, + "max_input_tokens": 1000000, + "max_output_tokens": 1000000, + "max_tokens": 1000000, "mode": "chat", - "output_cost_per_token": 6e-06, + "output_cost_per_token": 2.5e-06, "source": "https://docs.x.ai/docs/models", "supports_function_calling": true, "supports_prompt_caching": true, "supports_reasoning": true, "supports_tool_choice": true, "supports_vision": true, - "supports_web_search": true + "supports_web_search": true, + "input_cost_per_token_above_200k_tokens": 2.5e-06, + "output_cost_per_token_above_200k_tokens": 5e-06, + "cache_read_input_token_cost_above_200k_tokens": 4e-07, + "supports_response_schema": true }, "xai/grok-4.20-0309-reasoning": { "cache_read_input_token_cost": 2e-07, - "input_cost_per_token": 2e-06, + "input_cost_per_token": 1.25e-06, "litellm_provider": "xai", - "max_input_tokens": 2000000, - "max_output_tokens": 2000000, - "max_tokens": 2000000, + "max_input_tokens": 1000000, + "max_output_tokens": 1000000, + "max_tokens": 1000000, "mode": "chat", - "output_cost_per_token": 6e-06, + "output_cost_per_token": 2.5e-06, "source": "https://docs.x.ai/docs/models", "supports_function_calling": true, "supports_reasoning": true, "supports_tool_choice": true, "supports_vision": true, - "supports_web_search": true + "supports_web_search": true, + "input_cost_per_token_above_200k_tokens": 2.5e-06, + "output_cost_per_token_above_200k_tokens": 5e-06, + "cache_read_input_token_cost_above_200k_tokens": 4e-07, + "supports_prompt_caching": true, + "supports_response_schema": true }, "xai/grok-4.20-beta-0309-non-reasoning": { "cache_read_input_token_cost": 2e-07, - "input_cost_per_token": 2e-06, + "input_cost_per_token": 1.25e-06, "litellm_provider": "xai", - "max_input_tokens": 2000000, - "max_output_tokens": 2000000, - "max_tokens": 2000000, + "max_input_tokens": 1000000, + "max_output_tokens": 1000000, + "max_tokens": 1000000, "mode": "chat", - "output_cost_per_token": 6e-06, + "output_cost_per_token": 2.5e-06, "source": "https://docs.x.ai/docs/models", "supports_function_calling": true, "supports_prompt_caching": true, "supports_tool_choice": true, "supports_vision": true, - "supports_web_search": true + "supports_web_search": true, + "input_cost_per_token_above_200k_tokens": 2.5e-06, + "output_cost_per_token_above_200k_tokens": 5e-06, + "cache_read_input_token_cost_above_200k_tokens": 4e-07, + "supports_response_schema": true }, "xai/grok-4.3": { "cache_read_input_token_cost": 2e-07, @@ -40101,8 +40370,8 @@ "supports_web_search": true }, "xai/grok-4.5": { - "cache_read_input_token_cost": 5e-07, - "cache_read_input_token_cost_above_200k_tokens": 1e-06, + "cache_read_input_token_cost": 3e-07, + "cache_read_input_token_cost_above_200k_tokens": 6e-07, "input_cost_per_token": 2e-06, "input_cost_per_token_above_200k_tokens": 4e-06, "litellm_provider": "xai", @@ -40122,8 +40391,8 @@ "supports_web_search": true }, "xai/grok-4.5-latest": { - "cache_read_input_token_cost": 5e-07, - "cache_read_input_token_cost_above_200k_tokens": 1e-06, + "cache_read_input_token_cost": 3e-07, + "cache_read_input_token_cost_above_200k_tokens": 6e-07, "input_cost_per_token": 2e-06, "input_cost_per_token_above_200k_tokens": 4e-06, "litellm_provider": "xai", @@ -40156,51 +40425,64 @@ "supports_web_search": true }, "xai/grok-code-fast": { - "cache_read_input_token_cost": 2e-08, - "input_cost_per_token": 2e-07, + "cache_read_input_token_cost": 2e-07, + "input_cost_per_token": 1e-06, "litellm_provider": "xai", "max_input_tokens": 256000, "max_output_tokens": 256000, "max_tokens": 256000, "mode": "chat", - "output_cost_per_token": 1.5e-06, + "output_cost_per_token": 2e-06, "source": "https://docs.x.ai/docs/models", "supports_function_calling": true, "supports_prompt_caching": true, "supports_reasoning": true, - "supports_tool_choice": true + "supports_tool_choice": true, + "input_cost_per_token_above_200k_tokens": 2e-06, + "output_cost_per_token_above_200k_tokens": 4e-06, + "cache_read_input_token_cost_above_200k_tokens": 4e-07, + "supports_response_schema": true, + "supports_vision": true }, "xai/grok-code-fast-1": { - "cache_read_input_token_cost": 2e-08, - "input_cost_per_token": 2e-07, + "cache_read_input_token_cost": 2e-07, + "input_cost_per_token": 1e-06, "litellm_provider": "xai", "max_input_tokens": 256000, "max_output_tokens": 256000, "max_tokens": 256000, "mode": "chat", - "output_cost_per_token": 1.5e-06, + "output_cost_per_token": 2e-06, "source": "https://docs.x.ai/docs/models", "supports_function_calling": true, "supports_prompt_caching": true, "supports_reasoning": true, "supports_tool_choice": true, - "deprecation_date": "2026-05-15" + "input_cost_per_token_above_200k_tokens": 2e-06, + "output_cost_per_token_above_200k_tokens": 4e-06, + "cache_read_input_token_cost_above_200k_tokens": 4e-07, + "supports_response_schema": true, + "supports_vision": true }, "xai/grok-code-fast-1-0825": { - "cache_read_input_token_cost": 2e-08, - "input_cost_per_token": 2e-07, + "cache_read_input_token_cost": 2e-07, + "input_cost_per_token": 1e-06, "litellm_provider": "xai", "max_input_tokens": 256000, "max_output_tokens": 256000, "max_tokens": 256000, "mode": "chat", - "output_cost_per_token": 1.5e-06, + "output_cost_per_token": 2e-06, "source": "https://docs.x.ai/docs/models", "supports_function_calling": true, "supports_prompt_caching": true, "supports_reasoning": true, "supports_tool_choice": true, - "deprecation_date": "2026-05-15" + "input_cost_per_token_above_200k_tokens": 2e-06, + "output_cost_per_token_above_200k_tokens": 4e-06, + "cache_read_input_token_cost_above_200k_tokens": 4e-07, + "supports_response_schema": true, + "supports_vision": true }, "xai/grok-vision-beta": { "input_cost_per_image": 5e-06, @@ -40240,6 +40522,7 @@ "mode": "chat", "supports_function_calling": true, "supports_reasoning": true, + "supports_native_structured_output": true, "supports_system_messages": true, "supports_tool_choice": true, "source": "https://aws.amazon.com/bedrock/pricing/" @@ -40273,6 +40556,21 @@ "supports_tool_choice": true, "source": "https://docs.z.ai/guides/overview/pricing" }, + "zai/glm-5.1": { + "cache_creation_input_token_cost": 0, + "cache_read_input_token_cost": 2.6e-07, + "input_cost_per_token": 1.4e-06, + "output_cost_per_token": 4.4e-06, + "litellm_provider": "zai", + "max_input_tokens": 200000, + "max_output_tokens": 128000, + "mode": "chat", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_tool_choice": true, + "source": "https://docs.z.ai/guides/overview/pricing" + }, "zai/glm-5-code": { "cache_creation_input_token_cost": 0, "cache_read_input_token_cost": 3e-07, @@ -40303,6 +40601,21 @@ "supports_tool_choice": true, "source": "https://docs.z.ai/guides/overview/pricing" }, + "zai/glm-4.7-flash": { + "cache_creation_input_token_cost": 0, + "cache_read_input_token_cost": 0, + "input_cost_per_token": 0, + "output_cost_per_token": 0, + "litellm_provider": "zai", + "max_input_tokens": 200000, + "max_output_tokens": 128000, + "mode": "chat", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_tool_choice": true, + "source": "https://docs.z.ai/guides/overview/pricing" + }, "zai/glm-4.6": { "cache_creation_input_token_cost": 0, "cache_read_input_token_cost": 1.1e-07, @@ -40407,6 +40720,7 @@ "mode": "chat" }, "openai/sora-2": { + "deprecation_date": "2026-09-24", "litellm_provider": "openai", "mode": "video_generation", "output_cost_per_video_per_second": 0.1, @@ -40420,6 +40734,7 @@ ] }, "openai/sora-2-pro": { + "deprecation_date": "2026-09-24", "litellm_provider": "openai", "mode": "video_generation", "output_cost_per_video_per_second": 0.3, @@ -44338,6 +44653,7 @@ ] }, "gpt-4o-mini-tts-2025-03-20": { + "deprecation_date": "2026-07-23", "input_cost_per_token": 2.5e-06, "litellm_provider": "openai", "mode": "audio_speech", @@ -44374,6 +44690,7 @@ ] }, "gpt-4o-mini-transcribe-2025-03-20": { + "deprecation_date": "2027-01-20", "input_cost_per_audio_token": 1.25e-06, "input_cost_per_token": 1.25e-06, "litellm_provider": "openai", @@ -44444,6 +44761,7 @@ "cache_creation_input_audio_token_cost": 3e-07, "cache_read_input_audio_token_cost": 3e-07, "cache_read_input_token_cost": 6e-08, + "deprecation_date": "2026-07-23", "input_cost_per_audio_token": 1e-05, "input_cost_per_image": 8e-07, "input_cost_per_token": 6e-07, @@ -44524,6 +44842,7 @@ "supports_audio_input": true }, "sora-2": { + "deprecation_date": "2026-09-24", "litellm_provider": "openai", "mode": "video_generation", "output_cost_per_video_per_second": 0.1, @@ -44537,6 +44856,7 @@ ] }, "sora-2-pro": { + "deprecation_date": "2026-09-24", "litellm_provider": "openai", "mode": "video_generation", "output_cost_per_video_per_second": 0.3, @@ -44564,6 +44884,7 @@ }, "chatgpt-image-latest": { "cache_read_input_token_cost": 1.25e-06, + "deprecation_date": "2026-12-01", "input_cost_per_image_token": 1e-05, "input_cost_per_token": 5e-06, "litellm_provider": "openai", @@ -45714,6 +46035,7 @@ "supports_response_schema": true }, "snowflake/claude-sonnet-4-6": { + "supports_adaptive_thinking": true, "max_tokens": 16384, "max_input_tokens": 200000, "max_output_tokens": 16384, @@ -46135,40 +46457,6 @@ "supports_tool_choice": true, "supports_vision": false }, - "darkbloom/gemma-4-26b": { - "input_cost_per_token": 3e-08, - "litellm_provider": "darkbloom", - "max_input_tokens": 131072, - "max_output_tokens": 32768, - "max_tokens": 32768, - "mode": "chat", - "output_cost_per_token": 1.65e-07, - "source": "https://www.darkbloom.dev/", - "supported_endpoints": [ - "/v1/chat/completions" - ], - "supports_function_calling": true, - "supports_native_streaming": true, - "supports_system_messages": true, - "supports_tool_choice": true - }, - "darkbloom/gpt-oss-20b": { - "input_cost_per_token": 1.45e-08, - "litellm_provider": "darkbloom", - "max_input_tokens": 131072, - "max_output_tokens": 32768, - "max_tokens": 32768, - "mode": "chat", - "output_cost_per_token": 7e-08, - "source": "https://www.darkbloom.dev/", - "supported_endpoints": [ - "/v1/chat/completions" - ], - "supports_function_calling": true, - "supports_native_streaming": true, - "supports_system_messages": true, - "supports_tool_choice": true - }, "deepseek/deepseek-v4-pro": { "cache_creation_input_token_cost": 0.0, "cache_read_input_token_cost": 3.625e-09, @@ -46325,6 +46613,336 @@ "supports_reasoning": false, "source": "https://pinstripes.io/pricing" }, + "darkbloom/gemma-4-26b": { + "input_cost_per_token": 3e-08, + "litellm_provider": "darkbloom", + "max_input_tokens": 131072, + "max_output_tokens": 32768, + "max_tokens": 32768, + "mode": "chat", + "output_cost_per_token": 1.65e-07, + "source": "https://www.darkbloom.dev/", + "supported_endpoints": [ + "/v1/chat/completions" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_system_messages": true, + "supports_tool_choice": true + }, + "darkbloom/gpt-oss-20b": { + "input_cost_per_token": 1.45e-08, + "litellm_provider": "darkbloom", + "max_input_tokens": 131072, + "max_output_tokens": 32768, + "max_tokens": 32768, + "mode": "chat", + "output_cost_per_token": 7e-08, + "source": "https://www.darkbloom.dev/", + "supported_endpoints": [ + "/v1/chat/completions" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_system_messages": true, + "supports_tool_choice": true + }, + "xai/grok-4.20-0309-non-reasoning": { + "cache_read_input_token_cost": 2e-07, + "input_cost_per_token": 1.25e-06, + "litellm_provider": "xai", + "max_input_tokens": 1000000, + "max_output_tokens": 1000000, + "max_tokens": 1000000, + "mode": "chat", + "output_cost_per_token": 2.5e-06, + "source": "https://docs.x.ai/docs/models", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_tool_choice": true, + "supports_vision": true, + "supports_web_search": true, + "input_cost_per_token_above_200k_tokens": 2.5e-06, + "output_cost_per_token_above_200k_tokens": 5e-06, + "cache_read_input_token_cost_above_200k_tokens": 4e-07, + "supports_response_schema": true + }, + "xai/grok-4.20-multi-agent-0309": { + "cache_read_input_token_cost": 2e-07, + "input_cost_per_token": 1.25e-06, + "litellm_provider": "xai", + "max_input_tokens": 1000000, + "max_output_tokens": 1000000, + "max_tokens": 1000000, + "mode": "chat", + "output_cost_per_token": 2.5e-06, + "source": "https://docs.x.ai/docs/models", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_tool_choice": true, + "supports_vision": true, + "supports_web_search": true, + "input_cost_per_token_above_200k_tokens": 2.5e-06, + "output_cost_per_token_above_200k_tokens": 5e-06, + "cache_read_input_token_cost_above_200k_tokens": 4e-07, + "supports_response_schema": true + }, + "xai/grok-build-0.1": { + "cache_read_input_token_cost": 2e-07, + "input_cost_per_token": 1e-06, + "litellm_provider": "xai", + "max_input_tokens": 256000, + "max_output_tokens": 256000, + "max_tokens": 256000, + "mode": "chat", + "output_cost_per_token": 2e-06, + "source": "https://docs.x.ai/docs/models", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_tool_choice": true, + "input_cost_per_token_above_200k_tokens": 2e-06, + "output_cost_per_token_above_200k_tokens": 4e-06, + "cache_read_input_token_cost_above_200k_tokens": 4e-07, + "supports_response_schema": true, + "supports_vision": true + }, + "gpt-transcribe": { + "input_cost_per_second": 7.5e-05, + "litellm_provider": "openai", + "mode": "audio_transcription", + "source": "https://platform.openai.com/docs/models/gpt-transcribe", + "supported_endpoints": [ + "/v1/audio/transcriptions", + "/v1/realtime/transcription_sessions" + ], + "supported_modalities": [ + "text", + "audio" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_input": true + }, + "gpt-live-transcribe": { + "input_cost_per_second": 0.0002833333333333333, + "litellm_provider": "openai", + "mode": "audio_transcription", + "source": "https://platform.openai.com/docs/models/gpt-live-transcribe", + "supported_endpoints": [ + "/v1/realtime", + "/v1/realtime/transcription_sessions" + ], + "supported_modalities": [ + "text", + "audio" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_input": true + }, + "gpt-realtime-translate": { + "input_cost_per_second": 0.0005666666666666667, + "litellm_provider": "openai", + "max_input_tokens": 16000, + "max_output_tokens": 2000, + "max_tokens": 2000, + "mode": "realtime", + "source": "https://platform.openai.com/docs/models/gpt-realtime-translate", + "supported_modalities": [ + "audio" + ], + "supported_output_modalities": [ + "text", + "audio" + ], + "supports_audio_input": true, + "supports_audio_output": true + }, + "claude-mythos-5": { + "cache_creation_input_token_cost": 1.25e-05, + "cache_creation_input_token_cost_above_1hr": 2e-05, + "cache_read_input_token_cost": 1e-06, + "input_cost_per_token": 1e-05, + "litellm_provider": "anthropic", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 5e-05, + "prompt_cache_min_tokens": 512, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "source": "https://docs.claude.com/en/docs/about-claude/models/overview", + "supports_adaptive_thinking": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_max_reasoning_effort": true, + "supports_output_config": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true + }, + "claude-mythos-preview": { + "cache_creation_input_token_cost": 1.25e-05, + "cache_creation_input_token_cost_above_1hr": 2e-05, + "cache_read_input_token_cost": 1e-06, + "input_cost_per_token": 1e-05, + "litellm_provider": "anthropic", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 5e-05, + "prompt_cache_min_tokens": 512, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "source": "https://docs.claude.com/en/docs/about-claude/models/overview", + "supports_adaptive_thinking": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_max_reasoning_effort": true, + "supports_output_config": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true + }, + "gemini/gemini-robotics-er-2-streaming-preview": { + "input_cost_per_audio_token": 2e-06, + "input_cost_per_token": 2e-06, + "litellm_provider": "gemini", + "mode": "chat", + "output_cost_per_token": 1e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.014, + "search_context_size_low": 0.014, + "search_context_size_medium": 0.014 + }, + "source": "https://ai.google.dev/gemini-api/docs/pricing", + "supported_endpoints": [ + "/v1/realtime" + ], + "supported_modalities": [ + "text", + "image", + "audio", + "video" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_input": true, + "supports_function_calling": true, + "supports_video_input": true, + "supports_vision": true, + "supports_web_search": true, + "web_search_billing_unit": "per_query" + }, + "mistral/mistral-small-2603": { + "input_cost_per_token": 1.5e-07, + "litellm_provider": "mistral", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 6e-07, + "source": "https://docs.mistral.ai/models/model-cards/mistral-small-4-0-26-03", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "mistral/labs-leanstral-1-5": { + "input_cost_per_token": 0.0, + "litellm_provider": "mistral", + "max_input_tokens": 262144, + "max_output_tokens": 131072, + "max_tokens": 131072, + "mode": "chat", + "output_cost_per_token": 0.0, + "source": "https://docs.mistral.ai/models/model-cards/leanstral-1-5", + "supports_function_calling": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "mistral/mistral-moderation-2603": { + "input_cost_per_token": 0.0, + "litellm_provider": "mistral", + "max_input_tokens": 131072, + "mode": "moderation", + "output_cost_per_token": 0.0, + "source": "https://docs.mistral.ai/models/model-cards/mistral-moderation-26-03" + }, + "mistral/voxtral-mini-2602": { + "input_cost_per_second": 5e-05, + "litellm_provider": "mistral", + "mode": "audio_transcription", + "source": "https://docs.mistral.ai/models/model-cards/voxtral-mini-transcribe-26-02", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ], + "supported_modalities": [ + "audio" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_input": true + }, + "mistral/voxtral-mini-transcribe-realtime-2602": { + "input_cost_per_second": 0.0001, + "litellm_provider": "mistral", + "mode": "audio_transcription", + "source": "https://docs.mistral.ai/models/model-cards/voxtral-mini-transcribe-realtime-26-02", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ], + "supported_modalities": [ + "audio" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_input": true + }, + "mistral/voxtral-mini-tts-2603": { + "litellm_provider": "mistral", + "mode": "audio_speech", + "output_cost_per_character": 1.6e-05, + "source": "https://docs.mistral.ai/models/model-cards/voxtral-tts-26-03", + "supported_endpoints": [ + "/v1/audio/speech" + ], + "supported_modalities": [ + "text" + ], + "supported_output_modalities": [ + "audio" + ], + "supports_audio_output": true + }, "fallback_generalizations": { "rules": [ { diff --git a/litellm/proxy/_experimental/mcp_server/rest_endpoints.py b/litellm/proxy/_experimental/mcp_server/rest_endpoints.py index 76618e0f742..e285feb77ee 100644 --- a/litellm/proxy/_experimental/mcp_server/rest_endpoints.py +++ b/litellm/proxy/_experimental/mcp_server/rest_endpoints.py @@ -109,7 +109,7 @@ if MCP_AVAILABLE: ############ MCP Server REST API Routes ################# async def _safe_fire_mcp_tool_call_logging( logging_obj: Any | None, - result: Any, + result: "CallToolResult", start_time: datetime, end_time: datetime, user_api_key_auth: UserAPIKeyAuth | None = None, diff --git a/litellm/proxy/_experimental/mcp_server/sampling_handler.py b/litellm/proxy/_experimental/mcp_server/sampling_handler.py index 0896c344f05..a9a3367cd93 100644 --- a/litellm/proxy/_experimental/mcp_server/sampling_handler.py +++ b/litellm/proxy/_experimental/mcp_server/sampling_handler.py @@ -15,6 +15,8 @@ from collections.abc import Mapping, Sequence from typing import Any, Final, NamedTuple, Optional, Protocol, Union, runtime_checkable if typing.TYPE_CHECKING: + from collections.abc import Awaitable, Callable + from fastapi import Request from mcp.client.session import ClientSession from mcp.shared.context import RequestContext @@ -28,8 +30,9 @@ if typing.TYPE_CHECKING: ToolUseContent, ) + from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper from litellm.proxy._types import UserAPIKeyAuth - from litellm.proxy.utils import ProxyLogging + from litellm.types.utils import ModelResponse from fastapi import HTTPException from pydantic import TypeAdapter @@ -1016,7 +1019,7 @@ async def _run_budget_checks( general_settings=general_settings or {}, route="/chat/completions", llm_router=_llm_router, - proxy_logging_obj=typing.cast("ProxyLogging", _proxy_logging_obj), + proxy_logging_obj=_proxy_logging_obj, valid_token=user_api_key_auth, request=dummy_request, ) @@ -1176,15 +1179,19 @@ async def _build_completion_kwargs( ) +class _AcompletionCall(NamedTuple): + fn: "Callable[..., Awaitable[ModelResponse | CustomStreamWrapper]]" + + async def _run_guardrails_and_call_llm( - completion_kwargs: dict[str, Any], + completion_kwargs: dict[str, object], user_api_key_auth: "UserAPIKeyAuth", ) -> Any: try: from litellm.proxy.proxy_server import proxy_logging_obj as _plo if _plo is not None: - completion_kwargs = await typing.cast("ProxyLogging", _plo).pre_call_hook( + completion_kwargs = await _plo.pre_call_hook( user_api_key_dict=user_api_key_auth, data=completion_kwargs, call_type="acompletion", @@ -1204,10 +1211,10 @@ async def _run_guardrails_and_call_llm( from litellm.proxy.proxy_server import llm_router if llm_router is not None: - return await llm_router.acompletion(**completion_kwargs) - return await litellm.acompletion(**completion_kwargs) + return await _AcompletionCall(fn=llm_router.acompletion).fn(**completion_kwargs) + return await _AcompletionCall(fn=litellm.acompletion).fn(**completion_kwargs) except ImportError: - return await litellm.acompletion(**completion_kwargs) + return await _AcompletionCall(fn=litellm.acompletion).fn(**completion_kwargs) async def handle_sampling_create_message( diff --git a/litellm/proxy/_experimental/mcp_server/server.py b/litellm/proxy/_experimental/mcp_server/server.py index 1c6ad84ddb4..49a1f1314f0 100644 --- a/litellm/proxy/_experimental/mcp_server/server.py +++ b/litellm/proxy/_experimental/mcp_server/server.py @@ -13,9 +13,9 @@ import time import traceback import types import uuid -from collections.abc import AsyncIterator, Callable, Mapping +from collections.abc import AsyncIterator, Callable, Mapping, Sequence from datetime import datetime -from typing import TYPE_CHECKING, Any, Final, cast +from typing import TYPE_CHECKING, Any, Final, Protocol import httpx from fastapi import FastAPI, HTTPException @@ -145,7 +145,7 @@ try: ) # Robust auth lookup keyed by session_object. - _session_obj_auth_storage: "weakref.WeakKeyDictionary[Any, MCPAuthenticatedUser]" = weakref.WeakKeyDictionary() + _session_obj_auth_storage: "weakref.WeakKeyDictionary[object, MCPAuthenticatedUser]" = weakref.WeakKeyDictionary() except ImportError as e: verbose_logger.debug("MCP module not found: %s", e) MCP_AVAILABLE = False @@ -493,14 +493,14 @@ if MCP_AVAILABLE: def _gateway_create_initialization_options( self, notification_options: NotificationOptions | None = None, - experimental_capabilities: dict[str, dict[str, Any]] | None = None, + experimental_capabilities: dict[str, dict[str, object]] | None = None, ) -> InitializationOptions: opts: Final = Server.create_initialization_options( self, notification_options=notification_options, experimental_capabilities=experimental_capabilities or {}, ) - updates: Final[dict[str, Any]] = {} + updates: Final[dict[str, str]] = {} merged: Final = _mcp_gateway_initialize_instructions.get() if merged is not None: updates["instructions"] = merged @@ -549,6 +549,17 @@ if MCP_AVAILABLE: _stateful_session_locks: Final[dict[str, asyncio.Lock]] = {} _stateful_session_active_request_counts: Final[dict[str, int]] = {} + class _TerminableTransport(Protocol): + async def terminate(self) -> None: ... + + class _TransportRegistry(Protocol): + def __contains__(self, session_id: object, /) -> bool: ... + + def pop(self, session_id: str, default: None, /) -> "_TerminableTransport | None": ... + + def _stateful_server_instances() -> _TransportRegistry: + return getattr(session_manager_stateful, "_server_instances", {}) + def _remove_stateful_session_tracking(session_id: str) -> None: _stateful_session_auth_contexts.pop(session_id, None) _stateful_session_auth_context_last_seen.pop(session_id, None) @@ -578,8 +589,8 @@ if MCP_AVAILABLE: ) -> None: """Terminate expired stateful sessions and drop their auth contexts.""" now = time.monotonic() if now is None else now - server_instances: Final = getattr(session_manager_stateful, "_server_instances", {}) - expired_session_ids: Final = [] + server_instances: Final = _stateful_server_instances() + expired_session_ids: Final[list[str]] = [] for session_id, last_seen in _stateful_session_auth_context_last_seen.items(): if _stateful_session_active_request_counts.get(session_id, 0) > 0: continue @@ -619,7 +630,7 @@ if MCP_AVAILABLE: session may proceed, or ``False`` when the caller is already at the cap with every session in flight (the new ``initialize`` should be rejected). """ - server_instances: Final = getattr(session_manager_stateful, "_server_instances", {}) + server_instances: Final = _stateful_server_instances() def _owned_live_session_ids() -> list[str]: return [ @@ -778,7 +789,7 @@ if MCP_AVAILABLE: get_virtual_tool_definitions, ) - return [Tool(**d) for d in get_virtual_tool_definitions()] + return [Tool.model_validate(d) for d in get_virtual_tool_definitions()] # Get mcp_servers from context variable verbose_logger.debug("MCP list_tools - Calling _list_mcp_tools") @@ -847,7 +858,7 @@ if MCP_AVAILABLE: async def _build_virtual_call_logging_obj( name: str, - arguments: dict[str, Any], + arguments: dict[str, object], user_api_key_auth: UserAPIKeyAuth, ) -> LiteLLMLoggingObj | None: """Run the pre-call pipeline (guardrails + logging setup) for a virtual @@ -885,7 +896,7 @@ if MCP_AVAILABLE: async def _dispatch_virtual_mcp_tool( name: str, - arguments: dict[str, Any] | None, + arguments: dict[str, object] | None, user_api_key_auth: UserAPIKeyAuth | None, client_ip: str | None, mcp_servers: list[str] | None = None, @@ -957,7 +968,7 @@ if MCP_AVAILABLE: ) @server.call_tool() - async def mcp_server_tool_call(name: str, arguments: dict[str, Any] | None) -> CallToolResult: + async def mcp_server_tool_call(name: str, arguments: dict[str, object] | None) -> CallToolResult: """ Call a specific tool with the provided arguments Args: @@ -1621,7 +1632,7 @@ if MCP_AVAILABLE: async def _get_user_oauth_extra_headers_from_db( server: MCPServer, user_api_key_auth: UserAPIKeyAuth | None, - prefetched_creds: dict[str, dict[str, Any]] | None = None, + prefetched_creds: 'Mapping[str, "OAuthCredentialPayload"] | None' = None, ) -> dict[str, str] | None: """Stored OAuth2 token for (user, server) as an ``Authorization: Bearer`` header, or None. @@ -1646,7 +1657,7 @@ if MCP_AVAILABLE: Returns a dict keyed by server_id to avoid N+1 queries in asyncio.gather loops. """ - user_id: Final = getattr(user_api_key_auth, "user_id", None) if user_api_key_auth else None + user_id: Final[str | None] = getattr(user_api_key_auth, "user_id", None) if user_api_key_auth else None if not user_id: return {} try: @@ -1871,7 +1882,7 @@ if MCP_AVAILABLE: list_tools_start_time: Final = datetime.now() litellm_logging_obj: LiteLLMLoggingObj | None = None - list_tools_request_data: dict[str, Any] = {} + list_tools_request_data: dict[str, object] = {} if log_list_tools_to_spendlogs: # This is intentionally minimal: only async_success_handler / post_call_failure_hook @@ -1879,7 +1890,7 @@ if MCP_AVAILABLE: list_tools_call_id: Final = str(uuid.uuid4()) # Derive trace_id from raw_headers when not explicitly passed (same as A2A / MCP call_tool) effective_litellm_trace_id: Final = litellm_trace_id or get_chain_id_from_headers(raw_headers) - spend_logs_metadata: Final[dict[str, Any]] = { + spend_logs_metadata: Final[dict[str, object]] = { "mcp_operation": "list_tools", } if isinstance(list_tools_log_source, str): @@ -2615,7 +2626,7 @@ if MCP_AVAILABLE: async def execute_mcp_tool( name: str, - arguments: dict[str, Any], + arguments: dict[str, object], allowed_mcp_servers: list[MCPServer], start_time: datetime, user_api_key_auth: UserAPIKeyAuth | None = None, @@ -2882,7 +2893,7 @@ if MCP_AVAILABLE: _request_auth_header.reset(_auth_token) _request_extra_headers.reset(_extra_token) _request_resolved_auth_headers.reset(_resolved_token) - response = CallToolResult(content=cast(Any, local_content), isError=False) + response = CallToolResult(content=local_content, isError=False) # Try managed MCP server tool (the name is bare; the prefix boundary was # already resolved above against this server's registered prefixes) @@ -2956,7 +2967,7 @@ if MCP_AVAILABLE: arguments = hook_result["arguments"] # pyright: ignore[reportAny] # hook returns untyped args local_content = await _handle_local_mcp_tool(original_tool_name, arguments) - response = CallToolResult(content=cast(Any, local_content), isError=False) + response = CallToolResult(content=local_content, isError=False) return await _run_post_mcp_call_guardrails( result=response, @@ -3003,7 +3014,7 @@ if MCP_AVAILABLE: async def _fire_mcp_tool_call_logging( logging_obj: LiteLLMLoggingObj, - result: Any, + result: CallToolResult, start_time: datetime, end_time: datetime, user_api_key_auth: UserAPIKeyAuth | None = None, @@ -3070,7 +3081,7 @@ if MCP_AVAILABLE: @client async def call_mcp_tool( name: str, - arguments: dict[str, Any] | None = None, + arguments: dict[str, object] | None = None, user_api_key_auth: UserAPIKeyAuth | None = None, mcp_auth_header: str | None = None, mcp_servers: list[str] | None = None, @@ -3161,7 +3172,7 @@ if MCP_AVAILABLE: async def mcp_get_prompt( name: str, - arguments: dict[str, Any] | None = None, + arguments: dict[str, object] | None = None, user_api_key_auth: UserAPIKeyAuth | None = None, mcp_auth_header: str | None = None, mcp_servers: list[str] | None = None, @@ -3262,7 +3273,7 @@ if MCP_AVAILABLE: def _get_standard_logging_mcp_tool_call( name: str, - arguments: dict[str, Any], + arguments: dict[str, object], server_name: str | None, session_id: str | None = None, ) -> StandardLoggingMCPToolCall: @@ -3291,13 +3302,13 @@ if MCP_AVAILABLE: async def _handle_managed_mcp_tool( server_name: str, name: str, - arguments: dict[str, Any], + arguments: dict[str, object], user_api_key_auth: UserAPIKeyAuth | None = 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, raw_headers: dict[str, str] | None = None, - litellm_logging_obj: Any | None = None, + litellm_logging_obj: LiteLLMLoggingObj | None = None, host_progress_callback: Callable | None = None, ) -> CallToolResult: """Handle tool execution for managed server tools""" @@ -3320,7 +3331,7 @@ if MCP_AVAILABLE: return call_tool_result async def _handle_local_mcp_tool( - name: str, arguments: dict[str, Any] + name: str, arguments: dict[str, object] ) -> list[TextContent | ImageContent | EmbeddedResource]: """ Handle tool execution for local registry tools @@ -3426,7 +3437,8 @@ if MCP_AVAILABLE: Extract mcp-session-id from ASGI scope headers. Returns None if not present. """ - for header_name, header_value in scope.get("headers", []): + scope_headers: Final[Sequence[tuple[bytes | str, bytes | str]]] = scope.get("headers", []) + for header_name, header_value in scope_headers: name = header_name if isinstance(header_name, bytes) else header_name.encode() if name.lower() == b"mcp-session-id": return header_value.decode() if isinstance(header_value, bytes) else str(header_value) @@ -3528,7 +3540,7 @@ if MCP_AVAILABLE: if message.get("type") != "http.request": break - body = message.get("body", b"") or b"" + body: bytes = message.get("body", b"") or b"" if body: # Only retain up to the remaining peek budget for sniffing. # The full ``message`` is already in memory (delivered by @@ -3571,9 +3583,9 @@ if MCP_AVAILABLE: Fixes https://github.com/BerriAI/litellm/issues/20992 """ _mcp_session_header: Final = b"mcp-session-id" - _headers: Final = scope.get("headers", []) + _headers: Final[Sequence[tuple[bytes | str, bytes | str]]] = scope.get("headers", []) - def _normalize_header_name(header_name: Any) -> bytes | None: + def _normalize_header_name(header_name: object) -> bytes | None: if isinstance(header_name, bytes): return header_name.lower() if isinstance(header_name, str): @@ -3902,7 +3914,8 @@ if MCP_AVAILABLE: def _get_authorization_header_from_scope(scope: Scope) -> str | None: """First ``Authorization`` header value in the ASGI scope, or None.""" - for key, value in scope.get("headers", []): + scope_headers: Final[Sequence[tuple[bytes, bytes]]] = scope.get("headers", []) + for key, value in scope_headers: if key.lower() == b"authorization": return value.decode("latin-1") return None @@ -3921,7 +3934,8 @@ if MCP_AVAILABLE: ``MCPRequestHandler.process_mcp_request``), and forwarding it upstream would leak the proxy key to a third-party MCP server. """ - has_litellm_key_header: Final = any(key.lower() == b"x-litellm-api-key" for key, _ in scope.get("headers", [])) + scope_headers: Final[Sequence[tuple[bytes, bytes]]] = scope.get("headers", []) + has_litellm_key_header: Final = any(key.lower() == b"x-litellm-api-key" for key, _ in scope_headers) if not has_litellm_key_header: return None return _get_authorization_header_from_scope(scope) @@ -4115,7 +4129,7 @@ if MCP_AVAILABLE: async def handle_streamable_http_mcp(scope: Scope, receive: Receive, send: Send) -> None: """Handle MCP requests through StreamableHTTP.""" try: - path: Final = scope.get("path", "") + path: Final[str] = scope.get("path", "") ( user_api_key_auth, mcp_auth_header, @@ -4135,7 +4149,8 @@ if MCP_AVAILABLE: ) # Strip any client-supplied x-mcp-toolset-id to prevent forgery. - scope["headers"] = [(k, v) for k, v in scope.get("headers", []) if k.lower() != b"x-mcp-toolset-id"] + scope_headers: Final[Sequence[tuple[bytes, bytes]]] = scope.get("headers", []) + scope["headers"] = [(k, v) for k, v in scope_headers if k.lower() != b"x-mcp-toolset-id"] # Apply toolset scope if set server-side via ContextVar (set by # /toolset/{name}/mcp and /{name}/mcp route handlers in proxy_server.py). @@ -4436,7 +4451,7 @@ if MCP_AVAILABLE: async def handle_sse_mcp(scope: Scope, receive: Receive, send: Send) -> None: """Handle MCP requests through SSE.""" try: - path: Final = scope.get("path", "") + path: Final[str] = scope.get("path", "") ( user_api_key_auth, mcp_auth_header, @@ -4456,7 +4471,8 @@ if MCP_AVAILABLE: ) # Strip any client-supplied x-mcp-toolset-id to prevent forgery. - scope["headers"] = [(k, v) for k, v in scope.get("headers", []) if k.lower() != b"x-mcp-toolset-id"] + scope_headers: Final[Sequence[tuple[bytes, bytes]]] = scope.get("headers", []) + scope["headers"] = [(k, v) for k, v in scope_headers if k.lower() != b"x-mcp-toolset-id"] # Apply toolset scope if set server-side via ContextVar so the # downstream probe list matches the fully-authorized server set @@ -4680,7 +4696,8 @@ if MCP_AVAILABLE: ) -> Send: async def wrapped_send(message: Message) -> None: if message.get("type") == "http.response.start": - for key, value in message.get("headers", []): + response_headers: Final[Sequence[tuple[bytes | str, bytes | str]]] = message.get("headers", []) + for key, value in response_headers: header_name = key if isinstance(key, bytes) else str(key).encode() if header_name.lower() == b"mcp-session-id": session_id = value.decode() if isinstance(value, bytes) else str(value) diff --git a/litellm/proxy/_experimental/out/404.html b/litellm/proxy/_experimental/out/404.html index d27d44fd770..b4775167856 100644 --- a/litellm/proxy/_experimental/out/404.html +++ b/litellm/proxy/_experimental/out/404.html @@ -1 +1 @@ -404: This page could not be found.LiteLLM Dashboard

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t,{apiKeySource:r,accessToken:o,apiKey:a,inputMessage:n,chatHistory:s,selectedTags:l,selectedVectorStores:c,selectedGuardrails:d,selectedPolicies:u,selectedMCPServers:p,mcpServers:m,mcpServerToolRestrictions:h,selectedVoice:f,endpointType:g,selectedModel:b,selectedSdk:v,proxySettings:x}=e,y="session"===r?o:a,_=window.location.origin,k=x?.LITELLM_UI_API_DOC_BASE_URL;k&&k.trim()?_=k:x?.PROXY_BASE_URL&&(_=x.PROXY_BASE_URL);let w=n||"Your prompt here",j=w.replace(/\\/g,"\\\\").replace(/"/g,'\\"').replace(/\n/g,"\\n"),S=s.filter(e=>!e.isImage).map(({role:e,content:t})=>({role:e,content:t})),C={};l.length>0&&(C.tags=l),c.length>0&&(C.vector_stores=c),d.length>0&&(C.guardrails=d),u.length>0&&(C.policies=u);let z=b||"your-model-name",R="azure"===v?`import openai - -client = openai.AzureOpenAI( - api_key="${y||"YOUR_LITELLM_API_KEY"}", - azure_endpoint="${_}", - api_version="2024-02-01" -)`:`import openai - -client = openai.OpenAI( - api_key="${y||"YOUR_LITELLM_API_KEY"}", - base_url="${_}" 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or data:application/pdf;base64,{base64_file} -# } -# } -# ] -# } -# ]${r} -# ) -# print(response_with_file) -`;break}case i.RESPONSES:{let e=Object.keys(C).length>0,r="";if(e){let e=JSON.stringify({metadata:C},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();r=`, - extra_body=${e}`}let o=S.length>0?S:[{role:"user",content:w}];t=` -import base64 - -# Helper function to encode images to base64 -def encode_image(image_path): - with open(image_path, "rb") as image_file: - return base64.b64encode(image_file.read()).decode('utf-8') - -# Example with text only -response = client.responses.create( - model="${z}", - input=${JSON.stringify(o,null,4)}${r} -) - -print(response.output_text) - -# Example with image or PDF (uncomment and provide file path to use) -# base64_file = encode_image("path/to/your/file.jpg") # or .pdf -# response_with_file = client.responses.create( -# model="${z}", -# input=[ -# { -# "role": "user", -# "content": [ -# {"type": "input_text", "text": "${j}"}, -# { -# "type": "input_image", -# "image_url": f"data:image/jpeg;base64,{base64_file}", # or data:application/pdf;base64,{base64_file} -# }, -# ], -# } -# ]${r} -# ) -# print(response_with_file.output_text) -`;break}case i.IMAGE:t="azure"===v?` -# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI. -# This snippet uses 'client.images.generate' and will create a new image based on your prompt. -# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context. -import os -import requests -import json -import time -from PIL import Image - -result = client.images.generate( - model="${z}", - prompt="${n}", - n=1 -) - -json_response = json.loads(result.model_dump_json()) - -# Set the directory for the stored image -image_dir = os.path.join(os.curdir, 'images') - -# If the directory doesn't exist, create it -if not os.path.isdir(image_dir): - os.mkdir(image_dir) - -# Initialize the image path -image_filename = f"generated_image_{int(time.time())}.png" -image_path = os.path.join(image_dir, image_filename) - -try: - # Retrieve the generated image - if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"): - image_url = json_response["data"][0]["url"] - generated_image = requests.get(image_url).content - with open(image_path, "wb") as image_file: - image_file.write(generated_image) - - print(f"Image saved to {image_path}") - # Display the image - image = Image.open(image_path) - image.show() - else: - print("Could not find image URL in response.") - print("Full response:", json_response) -except Exception as e: - print(f"An error occurred: {e}") - print("Full response:", json_response) -`:` -import base64 -import os -import time -import json -from PIL import Image -import requests - -# Helper function to encode images to base64 -def encode_image(image_path): - with open(image_path, "rb") as image_file: - return base64.b64encode(image_file.read()).decode('utf-8') - -# Helper function to create a file (simplified for this example) -def create_file(image_path): - # In a real implementation, this would upload the file to OpenAI - # For this example, we'll just return a placeholder ID - return f"file_{os.path.basename(image_path).replace('.', '_')}" - -# The prompt entered by the user -prompt = "${j}" - -# Encode images to base64 -base64_image1 = encode_image("body-lotion.png") -base64_image2 = encode_image("soap.png") - -# Create file IDs -file_id1 = create_file("body-lotion.png") -file_id2 = create_file("incense-kit.png") - -response = client.responses.create( - model="${z}", - input=[ - { - "role": "user", - "content": [ - {"type": "input_text", "text": prompt}, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image1}", - }, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image2}", - }, - { - "type": "input_image", - "file_id": file_id1, 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this._client.delete(rt`/v1/messages/batches/${e}?beta=true`,{...s,headers:s7([{"anthropic-beta":[...r??[],"message-batches-2024-09-24"].toString()},s?.headers])})}cancel(e,t={},s){let{betas:r}=t??{};return this._client.post(rt`/v1/messages/batches/${e}/cancel?beta=true`,{...s,headers:s7([{"anthropic-beta":[...r??[],"message-batches-2024-09-24"].toString()},s?.headers])})}async results(e,t={},s){let r=await this.retrieve(e);if(!r.results_url)throw new se(`No batch \`results_url\`; Has it finished processing? ${r.processing_status} - ${r.id}`);let{betas:a}=t??{};return this._client.get(r.results_url,{...s,headers:s7([{"anthropic-beta":[...a??[],"message-batches-2024-09-24"].toString(),Accept:"application/binary"},s?.headers]),stream:!0,__binaryResponse:!0})._thenUnwrap((e,t)=>rg.fromResponse(t.response,t.controller))}}let 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r>=0&&s.splice(r,1),this}once(e,t){return(t6(this,Z,"f")[e]||(t6(this,Z,"f")[e]=[])).push({listener:t,once:!0}),this}emitted(e){return new Promise((t,s)=>{t3(this,er,!0,"f"),"error"!==e&&this.once("error",s),this.once(e,t)})}async done(){t3(this,er,!0,"f"),await t6(this,X,"f")}get currentMessage(){return t6(this,H,"f")}async finalMessage(){return await this.done(),t6(this,F,"m",el).call(this)}async finalText(){return await this.done(),t6(this,F,"m",eo).call(this)}_emit(e,...t){if(t6(this,ee,"f"))return;"end"===e&&(t3(this,ee,!0,"f"),t6(this,Y,"f").call(this));let s=t6(this,Z,"f")[e];if(s&&(t6(this,Z,"f")[e]=s.filter(e=>!e.once),s.forEach(({listener:e})=>e(...t))),"abort"===e){let e=t[0];t6(this,er,"f")||s?.length||Promise.reject(e),t6(this,K,"f").call(this,e),t6(this,Q,"f").call(this,e),this._emit("end");return}if("error"===e){let e=t[0];t6(this,er,"f")||s?.length||Promise.reject(e),t6(this,K,"f").call(this,e),t6(this,Q,"f").call(this,e),this._emit("end")}}_emitFinal(){this.receivedMessages.at(-1)&&this._emit("finalMessage",t6(this,F,"m",el).call(this))}async _fromReadableStream(e,t){let s,r=t?.signal;r&&(r.aborted&&this.controller.abort(),s=this.controller.abort.bind(this.controller),r.addEventListener("abort",s));try{t6(this,F,"m",ed).call(this),this._connected(null);let t=sU.fromReadableStream(e,this.controller);for await(let e of t)t6(this,F,"m",eu).call(this,e);if(t.controller.signal?.aborted)throw new ss;t6(this,F,"m",em).call(this)}finally{r&&s&&r.removeEventListener("abort",s)}}[(H=new WeakMap,J=new WeakMap,V=new WeakMap,G=new WeakMap,K=new WeakMap,X=new WeakMap,Y=new WeakMap,Q=new WeakMap,Z=new WeakMap,ee=new WeakMap,et=new WeakMap,es=new WeakMap,er=new WeakMap,ea=new WeakMap,en=new WeakMap,ei=new WeakMap,ec=new WeakMap,F=new WeakSet,el=function(){if(0===this.receivedMessages.length)throw new se("stream ended without producing a Message with role=assistant");return this.receivedMessages.at(-1)},eo=function(){if(0===this.receivedMessages.length)throw new se("stream ended without producing a Message with role=assistant");let e=this.receivedMessages.at(-1).content.filter(e=>"text"===e.type).map(e=>e.text);if(0===e.length)throw new se("stream ended without producing a content block with type=text");return e.join(" ")},ed=function(){this.ended||t3(this,H,void 0,"f")},eu=function(e){if(this.ended)return;let t=t6(this,F,"m",eh).call(this,e);switch(this._emit("streamEvent",e,t),e.type){case"content_block_delta":{let s=t.content.at(-1);switch(e.delta.type){case"text_delta":"text"===s.type&&this._emit("text",e.delta.text,s.text||"");break;case"citations_delta":"text"===s.type&&this._emit("citation",e.delta.citation,s.citations??[]);break;case"input_json_delta":rG(s)&&s.input&&this._emit("inputJson",e.delta.partial_json,s.input);break;case"thinking_delta":"thinking"===s.type&&this._emit("thinking",e.delta.thinking,s.thinking);break;case"signature_delta":"thinking"===s.type&&this._emit("signature",s.signature);break;default:rX(e.delta)}break}case"message_stop":this._addMessageParam(t),this._addMessage(rH(t,t6(this,J,"f"),{logger:t6(this,ei,"f")}),!0);break;case"content_block_stop":this._emit("contentBlock",t.content.at(-1));break;case"message_start":t3(this,H,t,"f")}},em=function(){if(this.ended)throw new se("stream has ended, this shouldn't happen");let e=t6(this,H,"f");if(!e)throw new se("request ended without sending any chunks");return t3(this,H,void 0,"f"),rH(e,t6(this,J,"f"),{logger:t6(this,ei,"f")})},eh=function(e){let t=t6(this,H,"f");if("message_start"===e.type){if(t)throw new se(`Unexpected event order, got ${e.type} before receiving "message_stop"`);return e.message}if(!t)throw new se(`Unexpected event order, got ${e.type} before "message_start"`);switch(e.type){case"message_stop":case"content_block_stop":return t;case"message_delta":return t.stop_reason=e.delta.stop_reason,t.stop_sequence=e.delta.stop_sequence,t.usage.output_tokens=e.usage.output_tokens,null!=e.usage.input_tokens&&(t.usage.input_tokens=e.usage.input_tokens),null!=e.usage.cache_creation_input_tokens&&(t.usage.cache_creation_input_tokens=e.usage.cache_creation_input_tokens),null!=e.usage.cache_read_input_tokens&&(t.usage.cache_read_input_tokens=e.usage.cache_read_input_tokens),null!=e.usage.server_tool_use&&(t.usage.server_tool_use=e.usage.server_tool_use),t;case"content_block_start":return t.content.push({...e.content_block}),t;case"content_block_delta":{let s=t.content.at(e.index);switch(e.delta.type){case"text_delta":s?.type==="text"&&(t.content[e.index]={...s,text:(s.text||"")+e.delta.text});break;case"citations_delta":s?.type==="text"&&(t.content[e.index]={...s,citations:[...s.citations??[],e.delta.citation]});break;case"input_json_delta":if(s&&rG(s)){let r=s[rV]||"";r+=e.delta.partial_json;let a={...s};Object.defineProperty(a,rV,{value:r,enumerable:!1,writable:!0}),r&&(a.input=rj(r)),t.content[e.index]=a}break;case"thinking_delta":s?.type==="thinking"&&(t.content[e.index]={...s,thinking:s.thinking+e.delta.thinking});break;case"signature_delta":s?.type==="thinking"&&(t.content[e.index]={...s,signature:e.delta.signature});break;default:rX(e.delta)}return t}}},Symbol.asyncIterator)](){let e=[],t=[],s=!1;return this.on("streamEvent",s=>{let r=t.shift();r?r.resolve(s):e.push(s)}),this.on("end",()=>{for(let e of(s=!0,t))e.resolve(void 0);t.length=0}),this.on("abort",e=>{for(let r of(s=!0,t))r.reject(e);t.length=0}),this.on("error",e=>{for(let r of(s=!0,t))r.reject(e);t.length=0}),{next:async()=>e.length?{value:e.shift(),done:!1}:s?{value:void 0,done:!0}:new Promise((e,s)=>t.push({resolve:e,reject:s})).then(e=>e?{value:e,done:!1}:{value:void 0,done:!0}),return:async()=>(this.abort(),{value:void 0,done:!0})}}toReadableStream(){return new sU(this[Symbol.asyncIterator].bind(this),this.controller).toReadableStream()}}function rX(e){}class rY extends s6{create(e,t){return this._client.post("/v1/messages/batches",{body:e,...t})}retrieve(e,t){return this._client.get(rt`/v1/messages/batches/${e}`,t)}list(e={},t){return this._client.getAPIList("/v1/messages/batches",sV,{query:e,...t})}delete(e,t){return this._client.delete(rt`/v1/messages/batches/${e}`,t)}cancel(e,t){return this._client.post(rt`/v1/messages/batches/${e}/cancel`,t)}async results(e,t){let s=await this.retrieve(e);if(!s.results_url)throw new se(`No batch \`results_url\`; Has it finished processing? 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