diff --git a/.circleci/config.yml b/.circleci/config.yml index 6c7bbddb9f..fbbb6deeba 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -21,9 +21,7 @@ commands: - run: name: "Install local version of litellm-enterprise" command: | - cd enterprise - python -m pip install -e . - cd .. + pip install --force-reinstall --no-deps -e enterprise/ setup_litellm_test_deps: steps: - checkout @@ -1458,6 +1456,7 @@ jobs: pip install "respx==0.22.0" pip install "pydantic==2.10.2" pip install "boto3==1.36.0" + pip install "semantic_router==0.1.10" # Run pytest and generate JUnit XML report - run: name: Run tests @@ -1656,7 +1655,7 @@ jobs: - search_coverage.xml - search_coverage # Split litellm_mapped_tests into 3 parallel jobs for 3x faster execution - litellm_mapped_tests_proxy: + litellm_mapped_tests_proxy_part1: docker: - image: cimg/python:3.11 auth: @@ -1667,23 +1666,53 @@ jobs: steps: - setup_litellm_test_deps - run: - name: Run proxy tests + name: Run proxy tests part 1 (high-volume directories) command: | prisma generate - python -m pytest tests/test_litellm/proxy --cov=litellm --cov-report=xml --junitxml=test-results/junit-proxy.xml --durations=10 -n 16 --maxfail=5 --timeout=300 -vv --log-cli-level=WARNING - no_output_timeout: 120m + export PYTHONUNBUFFERED=1 + python -m pytest tests/test_litellm/proxy/guardrails tests/test_litellm/proxy/management_endpoints tests/test_litellm/proxy/_experimental tests/test_litellm/proxy/client tests/test_litellm/proxy/auth --cov=litellm --cov-report=xml --junitxml=test-results/junit-proxy-part1.xml --durations=10 -n 8 --maxfail=5 --timeout=60 -vv --log-cli-level=WARNING -r A + no_output_timeout: 60m - run: name: Rename the coverage files command: | - mv coverage.xml litellm_proxy_tests_coverage.xml - mv .coverage litellm_proxy_tests_coverage + mv coverage.xml litellm_proxy_tests_part1_coverage.xml + mv .coverage litellm_proxy_tests_part1_coverage - store_test_results: path: test-results - persist_to_workspace: root: . paths: - - litellm_proxy_tests_coverage.xml - - litellm_proxy_tests_coverage + - litellm_proxy_tests_part1_coverage.xml + - litellm_proxy_tests_part1_coverage + litellm_mapped_tests_proxy_part2: + docker: + - image: cimg/python:3.11 + auth: + username: ${DOCKERHUB_USERNAME} + password: ${DOCKERHUB_PASSWORD} + working_directory: ~/project + resource_class: xlarge + steps: + - setup_litellm_test_deps + - run: + name: Run proxy tests part 2 (all other tests) + command: | + prisma generate + export PYTHONUNBUFFERED=1 + python -m pytest tests/test_litellm/proxy --ignore=tests/test_litellm/proxy/guardrails --ignore=tests/test_litellm/proxy/management_endpoints --ignore=tests/test_litellm/proxy/_experimental --ignore=tests/test_litellm/proxy/client --ignore=tests/test_litellm/proxy/auth --cov=litellm --cov-report=xml --junitxml=test-results/junit-proxy-part2.xml --durations=10 -n 8 --maxfail=5 --timeout=60 -vv --log-cli-level=WARNING -r A + no_output_timeout: 60m + - run: + name: Rename the coverage files + command: | + mv coverage.xml litellm_proxy_tests_part2_coverage.xml + mv .coverage litellm_proxy_tests_part2_coverage + - store_test_results: + path: test-results + - persist_to_workspace: + root: . + paths: + - litellm_proxy_tests_part2_coverage.xml + - litellm_proxy_tests_part2_coverage litellm_mapped_tests_llms: docker: - image: cimg/python:3.11 @@ -1724,7 +1753,7 @@ jobs: - run: name: Run core tests command: | - python -m pytest tests/test_litellm --ignore=tests/test_litellm/proxy --ignore=tests/test_litellm/llms --ignore=tests/test_litellm/integrations --ignore=tests/test_litellm/litellm_core_utils --cov=litellm --cov-report=xml --junitxml=test-results/junit-core.xml --durations=10 -n 16 --maxfail=5 --timeout=300 -vv --log-cli-level=WARNING + python -m pytest tests/test_litellm --ignore=tests/test_litellm/proxy --ignore=tests/test_litellm/llms --ignore=tests/test_litellm/integrations --ignore=tests/test_litellm/litellm_core_utils --ignore=tests/test_litellm/experimental_mcp_client --cov=litellm --cov-report=xml --junitxml=test-results/junit-core.xml --durations=10 -n 16 --maxfail=5 --timeout=300 -vv --log-cli-level=WARNING no_output_timeout: 120m - run: name: Rename the coverage files @@ -1765,6 +1794,33 @@ jobs: paths: - litellm_core_utils_tests_coverage.xml - litellm_core_utils_tests_coverage + litellm_mapped_tests_mcps: + docker: + - image: cimg/python:3.11 + auth: + username: ${DOCKERHUB_USERNAME} + password: ${DOCKERHUB_PASSWORD} + working_directory: ~/project + resource_class: xlarge + steps: + - setup_litellm_test_deps + - run: + name: Run MCP client tests + command: | + python -m pytest tests/test_litellm/experimental_mcp_client --cov=litellm --cov-report=xml --junitxml=test-results/junit-mcps.xml --durations=10 -n 4 --maxfail=5 --timeout=300 -vv --log-cli-level=WARNING + no_output_timeout: 120m + - run: + name: Rename the coverage files + command: | + mv coverage.xml litellm_mcps_tests_coverage.xml + mv .coverage litellm_mcps_tests_coverage + - store_test_results: + path: test-results + - persist_to_workspace: + root: . + paths: + - litellm_mcps_tests_coverage.xml + - litellm_mcps_tests_coverage litellm_mapped_tests_integrations: docker: - image: cimg/python:3.11 @@ -3597,9 +3653,11 @@ jobs: -p 4000:4000 \ -e DATABASE_URL=postgresql://postgres:postgres@host.docker.internal:5432/circle_test \ -e LITELLM_MASTER_KEY="sk-1234" \ + -e ANTHROPIC_API_KEY=$ANTHROPIC_API_KEY \ -e AWS_ACCESS_KEY_ID=$AWS_ACCESS_KEY_ID \ -e AWS_SECRET_ACCESS_KEY=$AWS_SECRET_ACCESS_KEY \ -e AWS_REGION_NAME="us-east-1" \ + -e LITELLM_LOCAL_ANTHROPIC_BETA_HEADERS="True" \ --add-host host.docker.internal:host-gateway \ --name my-app \ -v $(pwd)/tests/proxy_e2e_anthropic_messages_tests/test_config.yaml:/app/config.yaml \ @@ -3652,7 +3710,7 @@ jobs: python -m venv venv . venv/bin/activate pip install coverage - coverage combine llm_translation_coverage realtime_translation_coverage llm_responses_api_coverage ocr_coverage search_coverage mcp_coverage logging_coverage audio_coverage litellm_router_coverage litellm_router_unit_coverage local_testing_part1_coverage local_testing_part2_coverage litellm_assistants_api_coverage auth_ui_unit_tests_coverage langfuse_coverage caching_coverage litellm_proxy_unit_tests_part1_coverage litellm_proxy_unit_tests_part2_coverage image_gen_coverage pass_through_unit_tests_coverage batches_coverage litellm_security_tests_coverage guardrails_coverage litellm_mapped_tests_coverage + coverage combine llm_translation_coverage realtime_translation_coverage llm_responses_api_coverage ocr_coverage search_coverage mcp_coverage litellm_mcps_tests_coverage logging_coverage audio_coverage litellm_router_coverage litellm_router_unit_coverage local_testing_part1_coverage local_testing_part2_coverage litellm_assistants_api_coverage auth_ui_unit_tests_coverage langfuse_coverage caching_coverage litellm_proxy_unit_tests_part1_coverage litellm_proxy_unit_tests_part2_coverage image_gen_coverage pass_through_unit_tests_coverage batches_coverage litellm_security_tests_coverage guardrails_coverage litellm_mapped_tests_coverage coverage xml - codecov/upload: file: ./coverage.xml @@ -4392,7 +4450,13 @@ workflows: only: - main - /litellm_.*/ - - litellm_mapped_tests_proxy: + - litellm_mapped_tests_proxy_part1: + filters: + branches: + only: + - main + - /litellm_.*/ + - litellm_mapped_tests_proxy_part2: filters: branches: only: @@ -4410,6 +4474,12 @@ workflows: only: - main - /litellm_.*/ + - litellm_mapped_tests_mcps: + filters: + branches: + only: + - main + - /litellm_.*/ - litellm_mapped_tests_integrations: filters: branches: @@ -4469,9 +4539,11 @@ workflows: - llm_responses_api_testing - ocr_testing - search_testing - - litellm_mapped_tests_proxy + - litellm_mapped_tests_proxy_part1 + - litellm_mapped_tests_proxy_part2 - litellm_mapped_tests_llms - litellm_mapped_tests_core + - litellm_mapped_tests_mcps - litellm_mapped_tests_integrations - litellm_mapped_tests_litellm_core_utils - litellm_mapped_enterprise_tests @@ -4548,9 +4620,11 @@ workflows: - llm_responses_api_testing - ocr_testing - search_testing - - litellm_mapped_tests_proxy + - litellm_mapped_tests_proxy_part1 + - litellm_mapped_tests_proxy_part2 - litellm_mapped_tests_llms - litellm_mapped_tests_core + - litellm_mapped_tests_mcps - litellm_mapped_tests_integrations - litellm_mapped_tests_litellm_core_utils - litellm_mapped_enterprise_tests diff --git a/.claude/settings.json b/.claude/settings.json new file mode 100644 index 0000000000..8c1d85f96e --- /dev/null +++ b/.claude/settings.json @@ -0,0 +1,36 @@ +{ + "permissions": { + "allow": [ + "Bash(git show:*)", + "Bash(git worktree add:*)", + "Read(//Users/krrishdholakia/Documents/litellm/**)", + "Read(//Users/krrishdholakia/Documents/litellm-claude-code-guardrails/litellm/types/**)", + "Read(//Users/krrishdholakia/Documents/litellm-claude-code-guardrails/**)", + "Read(//Users/krrishdholakia/Documents/litellm-claude-code-guardrails/litellm/**)", + "Bash(python:*)", + "Bash(python -c \"\nimport sys; sys.path.insert\\(0, ''.''\\)\nfrom litellm.proxy.guardrails.guardrail_hooks.claude_code.guardrail import ClaudeCodeGuardrail, HOSTED_TOOL_PREFIXES\nprint\\(''HOSTED_TOOL_PREFIXES:'', HOSTED_TOOL_PREFIXES\\)\nprint\\(''ClaudeCodeGuardrail imported OK''\\)\n\")", + "Read(//Users/krrishdholakia/Documents/litellm-mcp-jwt-groups/litellm/proxy/**)", + "Read(//Users/krrishdholakia/Documents/litellm-mcp-jwt-groups/**)", + "Bash(poetry run pytest:*)", + "Bash(git add:*)", + "Bash(git commit:*)", + "Bash(poetry run python:*)", + "Bash(poetry run pip:*)", + "Bash(git reset:*)", + "Bash(git cherry-pick:*)", + "Bash(git checkout:*)", + "Read(//Users/krrishdholakia/Documents/litellm/litellm/proxy/guardrails/guardrail_hooks/**)", + "Read(//Users/krrishdholakia/Documents/**)", + "Bash(git -C /Users/krrishdholakia/Documents/litellm-mcp-user-permissions worktree list)", + "Bash(ls:*)" + ], + "additionalDirectories": [ + "/Users/krrishdholakia/Documents/litellm-mcp-group-plan/plan", + "/Users/krrishdholakia/Documents/litellm-claude-code-guardrails/litellm/proxy/guardrails/guardrail_hooks/claude_code", + "/Users/krrishdholakia/Documents/litellm-claude-code-guardrails/litellm/types", + "/Users/krrishdholakia/Documents/litellm-claude-code-guardrails", + "/Users/krrishdholakia/Documents/litellm-mcp-jwt-groups/litellm/proxy", + "/Users/krrishdholakia/Documents/litellm-mcp-jwt-groups/tests/test_litellm/proxy/auth" + ] + } +} diff --git a/.github/actions/helm-oci-chart-releaser/action.yml b/.github/actions/helm-oci-chart-releaser/action.yml index 059277ed88..1823e26283 100644 --- a/.github/actions/helm-oci-chart-releaser/action.yml +++ b/.github/actions/helm-oci-chart-releaser/action.yml @@ -40,38 +40,33 @@ outputs: runs: using: composite steps: + - name: Helm | Setup + uses: azure/setup-helm@v4 + with: + version: v3.20.0 + - name: Helm | Login shell: bash run: echo ${{ inputs.registry_password }} | helm registry login -u ${{ inputs.registry_username }} --password-stdin ${{ inputs.registry }} - env: - HELM_EXPERIMENTAL_OCI: '1' - + - name: Helm | Dependency if: inputs.update_dependencies == 'true' shell: bash run: helm dependency update ${{ inputs.path == null && format('{0}/{1}', 'charts', inputs.name) || inputs.path }} - env: - HELM_EXPERIMENTAL_OCI: '1' - name: Helm | Package shell: bash run: helm package ${{ inputs.path == null && format('{0}/{1}', 'charts', inputs.name) || inputs.path }} --version ${{ inputs.tag }} --app-version ${{ inputs.app_version }} - env: - HELM_EXPERIMENTAL_OCI: '1' - name: Helm | Push shell: bash run: helm push ${{ inputs.name }}-${{ inputs.tag }}.tgz oci://${{ inputs.registry }}/${{ inputs.repository }} - env: - HELM_EXPERIMENTAL_OCI: '1' - name: Helm | Logout shell: bash run: helm registry logout ${{ inputs.registry }} - env: - HELM_EXPERIMENTAL_OCI: '1' - name: Helm | Output id: output shell: bash - run: echo "image=${{ inputs.registry }}/${{ inputs.repository }}/${{ inputs.name }}:${{ inputs.tag }}" >> $GITHUB_OUTPUT \ No newline at end of file + run: echo "image=${{ inputs.registry }}/${{ inputs.repository }}/${{ inputs.name }}:${{ inputs.tag }}" >> $GITHUB_OUTPUT diff --git a/.github/workflows/check_duplicate_issues.yml b/.github/workflows/check_duplicate_issues.yml index 14d6964fcd..b2a298bfcd 100644 --- a/.github/workflows/check_duplicate_issues.yml +++ b/.github/workflows/check_duplicate_issues.yml @@ -2,28 +2,47 @@ name: Check Duplicate Issues on: issues: - types: [opened, edited] + types: [opened] jobs: - check-duplicate: + check-duplicates: + if: github.event.action == 'opened' runs-on: ubuntu-latest permissions: - issues: write contents: read + issues: write steps: - - name: Check for potential duplicates - uses: wow-actions/potential-duplicates@v1 - with: + - name: Install Claude Code + run: npm install -g @anthropic-ai/claude-code + + - name: Check duplicates + env: + ANTHROPIC_API_KEY: ${{ secrets.LITELLM_VIRTUAL_KEY }} + ANTHROPIC_BASE_URL: ${{ secrets.LITELLM_BASE_URL }} GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} - label: potential-duplicate - threshold: 0.6 - reaction: eyes - comment: | - **⚠️ Potential duplicate detected** - - This issue appears similar to existing issue(s): - {{#issues}} - - [#{{number}}]({{html_url}}) - {{title}} ({{accuracy}}% similar) - {{/issues}} - - Please review the linked issue(s) to see if they address your concern. If this is not a duplicate, please provide additional context to help us understand the difference. + PROMPT: | + A new issue has been created in the ${{ github.repository }} repository. + + Issue number: ${{ github.event.issue.number }} + + Lookup this issue with gh issue view ${{ github.event.issue.number }} --repo ${{ github.repository }}. + + Search through existing issues (excluding #${{ github.event.issue.number }}) to find potential duplicates. + + Use gh issue list --repo ${{ github.repository }} with relevant search terms from the new issue's title and description. Try multiple keyword combinations to search broadly. Check both open and recently closed issues. + + Consider: + 1. Similar titles or descriptions + 2. Same error messages or symptoms + 3. Related functionality or components + 4. Similar feature requests + + If you find potential duplicates, post a SINGLE comment on issue #${{ github.event.issue.number }} using gh issue comment ${{ github.event.issue.number }} --repo ${{ github.repository }} with this format: + + _This comment was generated by an LLM and may be inaccurate._ + + This issue might be a duplicate of existing issues. Please check: + - #[issue_number]: [brief description of similarity] + + If you find NO duplicates, do NOT post any comment. Stay silent. + run: claude -p "$PROMPT" --model sonnet --max-turns 10 --allowedTools "Bash(gh issue *)" diff --git a/.github/workflows/check_duplicate_prs.yml b/.github/workflows/check_duplicate_prs.yml new file mode 100644 index 0000000000..5a5f1a89e6 --- /dev/null +++ b/.github/workflows/check_duplicate_prs.yml @@ -0,0 +1,52 @@ +name: Check Duplicate PRs + +on: + pull_request_target: + types: [opened] + +jobs: + check-duplicates: + if: | + github.event.pull_request.user.login != 'ishaan-jaff' && + github.event.pull_request.user.login != 'krrishdholakia' && + github.event.pull_request.user.login != 'actions-user' && + !endsWith(github.event.pull_request.user.login, '[bot]') + runs-on: ubuntu-latest + permissions: + contents: read + pull-requests: write + steps: + - name: Install Claude Code + run: npm install -g @anthropic-ai/claude-code + + - name: Check duplicates + env: + ANTHROPIC_API_KEY: ${{ secrets.LITELLM_VIRTUAL_KEY }} + ANTHROPIC_BASE_URL: ${{ secrets.LITELLM_BASE_URL }} + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + PROMPT: | + A new PR has been opened in the ${{ github.repository }} repository. + + PR number: ${{ github.event.pull_request.number }} + + Lookup this PR with gh pr view ${{ github.event.pull_request.number }} --repo ${{ github.repository }}. + + Search through existing open PRs (excluding #${{ github.event.pull_request.number }}) to find potential duplicates. + + Use gh pr list --repo ${{ github.repository }} with relevant search terms from the new PR's title and description. Try multiple keyword combinations to search broadly. Check both open and recently closed PRs. + + Consider: + 1. Similar titles or descriptions + 2. Same bug fix or feature being implemented + 3. Related functionality or components + 4. Overlapping code changes (same files or areas) + + If you find potential duplicates, post a SINGLE comment on PR #${{ github.event.pull_request.number }} using gh pr comment ${{ github.event.pull_request.number }} --repo ${{ github.repository }} with this format: + + _This comment was generated by an LLM and may be inaccurate._ + + This PR might be a duplicate of existing PRs. Please check: + - #[pr_number]: [brief description of similarity] + + If you find NO duplicates, do NOT post any comment. Stay silent. + run: claude -p "$PROMPT" --model sonnet --max-turns 10 --allowedTools "Bash(gh pr *)" diff --git a/.github/workflows/regenerate-poetry-lock.yml b/.github/workflows/regenerate-poetry-lock.yml new file mode 100644 index 0000000000..c0844f1c70 --- /dev/null +++ b/.github/workflows/regenerate-poetry-lock.yml @@ -0,0 +1,80 @@ +name: Regenerate poetry.lock + +# Runs whenever pyproject.toml is merged into main (the most common cause of +# the "pyproject.toml changed significantly since poetry.lock was last generated" +# CI failure). Can also be triggered manually. +on: + push: + branches: + - main + paths: + - pyproject.toml + workflow_dispatch: + +permissions: + contents: write # needed to push the auto/regenerate-poetry-lock-* branch + pull-requests: write # needed to open the PR and enable auto-merge + +jobs: + regenerate-lock: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: "3.11" + + - name: Install Poetry + run: pip install poetry + + - name: Regenerate poetry.lock + run: poetry lock + + - name: Check whether poetry.lock actually changed + id: diff + run: | + if git diff --quiet poetry.lock; then + echo "changed=false" >> "$GITHUB_OUTPUT" + else + echo "changed=true" >> "$GITHUB_OUTPUT" + fi + + - name: Open PR with the refreshed lock file + if: steps.diff.outputs.changed == 'true' + id: open-pr + run: | + BRANCH="auto/regenerate-poetry-lock-$(date +'%Y%m%d%H%M%S')" + git config user.name "github-actions[bot]" + git config user.email "github-actions[bot]@users.noreply.github.com" + git checkout -b "$BRANCH" + git add poetry.lock + git commit -m "chore: regenerate poetry.lock to match pyproject.toml" + git push -f origin "$BRANCH" + + cat > /tmp/pr-body.md << 'BODY' + Automated regeneration of `poetry.lock` after `pyproject.toml` was updated on `main`. + + Fixes the recurring CI failure: + ``` + pyproject.toml changed significantly since poetry.lock was last generated. + Run `poetry lock` to fix the lock file. + ``` + BODY + + PR_URL=$(gh pr create \ + --title "chore: regenerate poetry.lock to match pyproject.toml" \ + --body-file /tmp/pr-body.md \ + --head "$BRANCH" \ + --base main) + echo "pr_url=$PR_URL" >> "$GITHUB_OUTPUT" + env: + GH_TOKEN: ${{ github.token }} + + - name: Enable auto-merge + if: steps.diff.outputs.changed == 'true' + run: | + gh pr merge "${{ steps.open-pr.outputs.pr_url }}" --auto --squash + env: + GH_TOKEN: ${{ github.token }} diff --git a/.github/workflows/test-litellm-matrix.yml b/.github/workflows/test-litellm-matrix.yml index b442c7dd5f..d0ac28ab41 100644 --- a/.github/workflows/test-litellm-matrix.yml +++ b/.github/workflows/test-litellm-matrix.yml @@ -12,44 +12,107 @@ concurrency: jobs: test: runs-on: ubuntu-latest - timeout-minutes: 15 + timeout-minutes: 20 # Increased from 15 to 20 strategy: fail-fast: false matrix: test-group: # tests/test_litellm split by subdirectory (~560 files total) - - name: "llms" - path: "tests/test_litellm/llms" - workers: 4 + # Vertex AI tests separated for better isolation (prevent auth/env pollution) + - name: "llms-vertex" + path: "tests/test_litellm/llms/vertex_ai" + workers: 1 + reruns: 2 + - name: "llms-other" + path: "tests/test_litellm/llms --ignore=tests/test_litellm/llms/vertex_ai" + workers: 2 + reruns: 2 # tests/test_litellm/proxy split by subdirectory (~180 files total) - name: "proxy-guardrails" path: "tests/test_litellm/proxy/guardrails tests/test_litellm/proxy/management_endpoints tests/test_litellm/proxy/management_helpers" - workers: 4 + workers: 2 + reruns: 2 - name: "proxy-core" path: "tests/test_litellm/proxy/auth tests/test_litellm/proxy/client tests/test_litellm/proxy/db tests/test_litellm/proxy/hooks tests/test_litellm/proxy/policy_engine" - workers: 4 + workers: 2 + reruns: 2 - name: "proxy-misc" path: "tests/test_litellm/proxy/_experimental tests/test_litellm/proxy/agent_endpoints tests/test_litellm/proxy/anthropic_endpoints tests/test_litellm/proxy/common_utils tests/test_litellm/proxy/discovery_endpoints tests/test_litellm/proxy/experimental tests/test_litellm/proxy/google_endpoints tests/test_litellm/proxy/health_endpoints tests/test_litellm/proxy/image_endpoints tests/test_litellm/proxy/middleware tests/test_litellm/proxy/openai_files_endpoint tests/test_litellm/proxy/pass_through_endpoints tests/test_litellm/proxy/prompts tests/test_litellm/proxy/public_endpoints tests/test_litellm/proxy/response_api_endpoints tests/test_litellm/proxy/spend_tracking tests/test_litellm/proxy/ui_crud_endpoints tests/test_litellm/proxy/vector_store_endpoints tests/test_litellm/proxy/test_*.py" - workers: 4 + workers: 2 + reruns: 2 - name: "integrations" path: "tests/test_litellm/integrations" - workers: 4 + workers: 2 + reruns: 3 # Integration tests tend to be flakier - name: "core-utils" path: "tests/test_litellm/litellm_core_utils" workers: 2 - - name: "other" - path: "tests/test_litellm/caching tests/test_litellm/responses tests/test_litellm/secret_managers tests/test_litellm/vector_stores tests/test_litellm/a2a_protocol tests/test_litellm/anthropic_interface tests/test_litellm/completion_extras tests/test_litellm/containers tests/test_litellm/enterprise tests/test_litellm/experimental_mcp_client tests/test_litellm/google_genai tests/test_litellm/images tests/test_litellm/interactions tests/test_litellm/passthrough tests/test_litellm/router_strategy tests/test_litellm/router_utils tests/test_litellm/types" - workers: 4 + reruns: 1 + - name: "other-1" + # responses (5942) + caching (1723) + types (819) ≈ 8.5k lines + path: "tests/test_litellm/responses tests/test_litellm/caching tests/test_litellm/types" + workers: 2 + reruns: 2 + - name: "other-2" + # enterprise (3062) + google_genai (2511) + router_utils (1982) ≈ 7.6k lines + path: "tests/test_litellm/enterprise tests/test_litellm/google_genai tests/test_litellm/router_utils" + workers: 2 + reruns: 2 + - name: "other-3" + # remaining dirs ≈ 8.0k lines + path: "tests/test_litellm/router_strategy tests/test_litellm/secret_managers tests/test_litellm/a2a_protocol tests/test_litellm/anthropic_interface tests/test_litellm/completion_extras tests/test_litellm/containers tests/test_litellm/experimental_mcp_client tests/test_litellm/images tests/test_litellm/interactions tests/test_litellm/passthrough tests/test_litellm/vector_stores" + workers: 2 + reruns: 2 - name: "root" path: "tests/test_litellm/test_*.py" - workers: 4 + workers: 2 + reruns: 2 # tests/proxy_unit_tests split alphabetically (~48 files total) - - name: "proxy-unit-a" - path: "tests/proxy_unit_tests/test_[a-o]*.py" + - name: "proxy-unit-a1" + # test_[a-j]*.py: jwt (1564) + auth_checks (978) + google_gemini (478) + e2e_pod_lock (437) + rest + path: "tests/proxy_unit_tests/test_[a-j]*.py" workers: 2 - - name: "proxy-unit-b" - path: "tests/proxy_unit_tests/test_[p-z]*.py" + reruns: 1 + - name: "proxy-unit-a2" + # test_[k-o]*.py: key_generate_prisma (4346) + key_generate_dynamodb + models_fallback + path: "tests/proxy_unit_tests/test_[k-o]*.py" workers: 2 + reruns: 1 + - name: "proxy-unit-b1" + # lighter config/utility proxy tests (prisma, project, prompt, proxy_[c-r]*) + path: "tests/proxy_unit_tests/test_prisma*.py tests/proxy_unit_tests/test_project*.py tests/proxy_unit_tests/test_prompt*.py tests/proxy_unit_tests/test_proxy_[c-r]*.py" + workers: 2 + reruns: 1 + - name: "proxy-unit-b2" + # proxy_server.py alone (2750 lines) - isolated to avoid blocking smaller tests + path: "tests/proxy_unit_tests/test_proxy_server.py" + workers: 2 + reruns: 1 + - name: "proxy-unit-b3" + # proxy_server_* (618) + proxy_setting_guardrails (71) - smaller server-related tests + path: "tests/proxy_unit_tests/test_proxy_server_*.py tests/proxy_unit_tests/test_proxy_setting_guardrails.py" + workers: 2 + reruns: 1 + - name: "proxy-unit-b4" + # proxy_utils.py alone (2339 lines) - isolated to avoid blocking token counter + path: "tests/proxy_unit_tests/test_proxy_utils.py" + workers: 2 + reruns: 1 + - name: "proxy-unit-b5" + # proxy_token_counter (1279) - runs independently from utils + path: "tests/proxy_unit_tests/test_proxy_token_counter.py" + workers: 2 + reruns: 1 + - name: "proxy-unit-b6" + # test_[r-t]*.py: response_polling (1399) + search_api_logging (202) + server_root (64) + skills_db (261) + realtime_cache (62) + path: "tests/proxy_unit_tests/test_[r-t]*.py" + workers: 2 + reruns: 1 + - name: "proxy-unit-b7" + # test_[u-z]*.py: user_api_key_auth (1136) + zero_cost (590) + update_spend (305) + unit_test_* (206) + ui_path (157) + path: "tests/proxy_unit_tests/test_[u-z]*.py" + workers: 2 + reruns: 1 name: test (${{ matrix.test-group.name }}) @@ -79,12 +142,17 @@ jobs: run: | poetry config virtualenvs.in-project true poetry install --with dev,proxy-dev --extras "proxy semantic-router" - poetry run pip install pytest-retry==1.6.3 pytest-xdist google-genai==1.22.0 \ + # pytest-rerunfailures and pytest-xdist are in pyproject.toml dev dependencies + poetry run pip install google-genai==1.22.0 \ google-cloud-aiplatform>=1.38 fastapi-offline==1.7.3 python-multipart==0.0.22 openapi-core - name: Setup litellm-enterprise run: | - cd enterprise && poetry run pip install -e . && cd .. + poetry run pip install --force-reinstall --no-deps -e enterprise/ + + - name: Generate Prisma client + run: | + poetry run prisma generate --schema litellm/proxy/schema.prisma - name: Run tests - ${{ matrix.test-group.name }} run: | @@ -92,18 +160,7 @@ jobs: --tb=short -vv \ --maxfail=10 \ -n ${{ matrix.test-group.workers }} \ + --reruns ${{ matrix.test-group.reruns }} \ + --reruns-delay 1 \ + --dist=loadscope \ --durations=20 - - # Aggregate job to require all matrix jobs pass - test-complete: - needs: test - runs-on: ubuntu-latest - if: always() - steps: - - name: Check test results - run: | - if [ "${{ needs.test.result }}" != "success" ]; then - echo "Some test groups failed" - exit 1 - fi - echo "All test groups passed!" diff --git a/.github/workflows/test-litellm-ui-build.yml b/.github/workflows/test-litellm-ui-build.yml new file mode 100644 index 0000000000..b0a8b648a4 --- /dev/null +++ b/.github/workflows/test-litellm-ui-build.yml @@ -0,0 +1,32 @@ +name: UI Build Check +permissions: + contents: read + +on: + pull_request: + branches: [main] + +jobs: + build-ui: + runs-on: ubuntu-latest + timeout-minutes: 10 + defaults: + run: + working-directory: ui/litellm-dashboard + + steps: + - name: Checkout repository + uses: actions/checkout@v4 + + - name: Setup Node.js + uses: actions/setup-node@v4 + with: + node-version: "20" + cache: "npm" + cache-dependency-path: ui/litellm-dashboard/package-lock.json + + - name: Install dependencies + run: npm install + + - name: Build + run: npm run build diff --git a/.github/workflows/test-litellm.yml b/.github/workflows/test-litellm.yml index dc9b48c28f..cf6928897b 100644 --- a/.github/workflows/test-litellm.yml +++ b/.github/workflows/test-litellm.yml @@ -42,9 +42,7 @@ jobs: poetry run pip install "openapi-core" - name: Setup litellm-enterprise as local package run: | - cd enterprise - poetry run pip install -e . - cd .. + poetry run pip install --force-reinstall --no-deps -e enterprise/ - name: Run tests run: | poetry run pytest tests/test_litellm --tb=short -vv --maxfail=10 -n 4 --durations=50 diff --git a/.github/workflows/test-mcp.yml b/.github/workflows/test-mcp.yml index e19e67c9c4..2e32aae768 100644 --- a/.github/workflows/test-mcp.yml +++ b/.github/workflows/test-mcp.yml @@ -40,9 +40,7 @@ jobs: - name: Setup litellm-enterprise as local package run: | - cd enterprise - python -m pip install -e . - cd .. + poetry run pip install --force-reinstall --no-deps -e enterprise/ - name: Run MCP tests run: | diff --git a/.github/workflows/test_server_root_path.yml b/.github/workflows/test_server_root_path.yml new file mode 100644 index 0000000000..c359e38bff --- /dev/null +++ b/.github/workflows/test_server_root_path.yml @@ -0,0 +1,96 @@ +name: Test Proxy SERVER_ROOT_PATH Routing +permissions: + contents: read + +on: + pull_request: + branches: [main] + +jobs: + test-server-root-path: + runs-on: ubuntu-latest + timeout-minutes: 15 + + strategy: + matrix: + root_path: ["/api/v1", "/llmproxy"] + + steps: + - name: Checkout repository + uses: actions/checkout@v4 + + - name: Set up Docker Buildx + uses: docker/setup-buildx-action@v3 + + - name: Build Docker image + uses: docker/build-push-action@v5 + with: + context: . + file: ./docker/Dockerfile.non_root + tags: litellm-test:${{ github.sha }} + load: true + cache-from: type=gha + cache-to: type=gha,mode=max + + - name: Start LiteLLM container with SERVER_ROOT_PATH + run: | + docker run -d \ + --name litellm-test \ + -p 4000:4000 \ + -e SERVER_ROOT_PATH="${{ matrix.root_path }}" \ + -e LITELLM_MASTER_KEY="sk-1234" \ + litellm-test:${{ github.sha }} \ + --detailed_debug + + - name: Wait for container to be healthy + run: | + echo "Waiting for LiteLLM to start..." + max_attempts=30 + attempt=0 + + while [ $attempt -lt $max_attempts ]; do + if docker logs litellm-test 2>&1 | grep -q "Uvicorn running"; then + echo "LiteLLM started successfully" + break + fi + attempt=$((attempt + 1)) + echo "Attempt $attempt/$max_attempts - waiting for server to start..." + sleep 2 + done + + if [ $attempt -eq $max_attempts ]; then + echo "Server failed to start within timeout" + docker logs litellm-test + exit 1 + fi + + sleep 5 + + - name: Show container logs + if: always() + run: docker logs litellm-test + + - name: Test UI endpoint with root path + run: | + ROOT_PATH="${{ matrix.root_path }}" + echo "Testing UI at: http://localhost:4000${ROOT_PATH}/ui/" + + for i in 1 2 3; do + content=$(curl -sL --max-time 5 -H "Authorization: Bearer sk-1234" "http://localhost:4000${ROOT_PATH}/ui/") + if echo "$content" | grep -q -E "(html| bool: + """ + Verify the API key from the Authorization header. + + Args: + authorization: Authorization header (Bearer token) + + Returns: + True if valid, raises HTTPException if invalid + """ + if authorization is None: + # Allow requests without authentication for testing + return True + + # Extract token from "Bearer " + if not authorization.startswith("Bearer "): + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="Invalid authorization header format. Expected 'Bearer '", + ) + + token = authorization.replace("Bearer ", "").strip() + + if token not in VALID_API_TOKENS: + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="Invalid API key", + ) + + return True + + +@app.get("/beta/litellm_prompt_management", response_model=PromptResponse) +async def get_prompt( + prompt_id: str = Query(..., description="The ID of the prompt to fetch"), + project_name: Optional[str] = Query( + None, description="Optional project name filter" + ), + slug: Optional[str] = Query(None, description="Optional slug filter"), + version: Optional[str] = Query(None, description="Optional version filter"), + authorization: Optional[str] = Header(None), +) -> PromptResponse: + """ + Get a prompt by ID with optional filtering. + + This endpoint implements the LiteLLM Generic Prompt Management API specification. + + Args: + prompt_id: The ID of the prompt to fetch + project_name: Optional project name for filtering + slug: Optional slug for filtering + version: Optional version for filtering + authorization: Optional Bearer token for authentication + + Returns: + PromptResponse with the prompt template and configuration + + Raises: + HTTPException: 401 if authentication fails, 404 if prompt not found + """ + # Verify authentication + verify_api_key(authorization) + + # Log the request parameters (useful for debugging) + print(f"Fetching prompt: {prompt_id}") + if project_name: + print(f" Project: {project_name}") + if slug: + print(f" Slug: {slug}") + if version: + print(f" Version: {version}") + + # Check if prompt exists + if prompt_id not in PROMPTS_DB: + raise HTTPException( + status_code=status.HTTP_404_NOT_FOUND, + detail=f"Prompt '{prompt_id}' not found. Available prompts: {list(PROMPTS_DB.keys())}", + ) + + # Get the prompt from the database + prompt_data = PROMPTS_DB[prompt_id] + + # Optional: Apply filtering based on project_name, slug, or version + # In a real implementation, you might use these to filter prompts by access control + # or to fetch specific versions from your database + + return PromptResponse(**prompt_data) + + +@app.get("/health") +async def health_check(): + """Health check endpoint""" + return { + "status": "healthy", + "service": "mock-prompt-management-api", + "version": "1.0.0", + } + + +@app.get("/prompts") +async def list_prompts(authorization: Optional[str] = Header(None)): + """ + List all available prompts. + + This is a convenience endpoint (not part of the LiteLLM spec) for + discovering available prompts. + """ + # Verify authentication + verify_api_key(authorization) + + prompts_list = [ + { + "prompt_id": pid, + "model": p.get("prompt_template_model"), + "has_variables": any( + "{" in msg.get("content", "") for msg in p.get("prompt_template", []) + ), + } + for pid, p in PROMPTS_DB.items() + ] + + return {"prompts": prompts_list, "total": len(prompts_list)} + + +@app.get("/prompts/{prompt_id}/variables") +async def get_prompt_variables( + prompt_id: str, authorization: Optional[str] = Header(None) +): + """ + Get all variables in a prompt template. + + This is a convenience endpoint (not part of the LiteLLM spec) for + discovering what variables a prompt expects. + """ + # Verify authentication + verify_api_key(authorization) + + if prompt_id not in PROMPTS_DB: + raise HTTPException( + status_code=status.HTTP_404_NOT_FOUND, + detail=f"Prompt '{prompt_id}' not found", + ) + + prompt_data = PROMPTS_DB[prompt_id] + variables = set() + + # Extract variables from the prompt template + import re + + for message in prompt_data["prompt_template"]: + content = message.get("content", "") + # Find all {variable} patterns + found_vars = re.findall(r"\{(\w+)\}", content) + variables.update(found_vars) + + return { + "prompt_id": prompt_id, + "variables": sorted(list(variables)), + "example_usage": { + "prompt_id": prompt_id, + "prompt_variables": {var: f"<{var}_value>" for var in variables}, + }, + } + + +@app.post("/prompts") +async def create_prompt( + prompt: PromptResponse, authorization: Optional[str] = Header(None) +): + """ + Create a new prompt (convenience endpoint for testing). + + This is NOT part of the LiteLLM spec - it's just for testing purposes. + """ + # Verify authentication + verify_api_key(authorization) + + if prompt.prompt_id in PROMPTS_DB: + raise HTTPException( + status_code=status.HTTP_409_CONFLICT, + detail=f"Prompt '{prompt.prompt_id}' already exists", + ) + + PROMPTS_DB[prompt.prompt_id] = prompt.dict() + + return { + "status": "created", + "prompt_id": prompt.prompt_id, + "message": "Prompt created successfully (in-memory only)", + } + + +# ============================================================================ +# Main +# ============================================================================ + +if __name__ == "__main__": + import uvicorn + + print("=" * 70) + print("Mock Prompt Management API Server") + print("=" * 70) + print(f"\nStarting server on http://localhost:8080") + print(f"\nAvailable prompts: {len(PROMPTS_DB)}") + for prompt_id in PROMPTS_DB.keys(): + print(f" - {prompt_id}") + print(f"\nValid API tokens: {len(VALID_API_TOKENS)}") + print(" - test-token-12345") + print(" - dev-token-67890") + print(" - prod-token-abcdef") + print("\nEndpoints:") + print(" GET /beta/litellm_prompt_management?prompt_id= (LiteLLM spec)") + print(" GET /health (health check)") + print(" GET /prompts (list all prompts)") + print( + " GET /prompts/{id}/variables (get prompt variables)" + ) + print(" POST /prompts (create prompt)") + print("\nExample usage:") + print( + ' curl "http://localhost:8080/beta/litellm_prompt_management?prompt_id=hello-world-prompt"' + ) + print("\nPress CTRL+C to stop the server") + print("=" * 70) + + uvicorn.run(app, host="0.0.0.0", port=8080, log_level="info") diff --git a/deploy/charts/litellm-helm/Chart.yaml b/deploy/charts/litellm-helm/Chart.yaml index 8a08f0b4e2..0f6db331e5 100644 --- a/deploy/charts/litellm-helm/Chart.yaml +++ b/deploy/charts/litellm-helm/Chart.yaml @@ -26,6 +26,10 @@ version: 1.1.0 # It is recommended to use it with quotes. appVersion: v1.80.12 +annotations: + org.opencontainers.image.source: "https://github.com/BerriAI/litellm" + org.opencontainers.image.url: "https://docs.litellm.ai/" + dependencies: - name: "postgresql" version: ">=13.3.0" diff --git a/docs/my-website/blog/claude_code_beta_headers/index.md b/docs/my-website/blog/claude_code_beta_headers/index.md new file mode 100644 index 0000000000..44567f616a --- /dev/null +++ b/docs/my-website/blog/claude_code_beta_headers/index.md @@ -0,0 +1,177 @@ +--- +slug: claude-code-beta-headers-incident +title: "Incident Report: Invalid beta headers with Claude Code" +date: 2026-02-16T10:00:00 +authors: + - name: Sameer Kankute + title: SWE @ LiteLLM (LLM Translation) + url: https://www.linkedin.com/in/sameer-kankute/ + image_url: https://pbs.twimg.com/profile_images/2001352686994907136/ONgNuSk5_400x400.jpg + - name: Ishaan Jaff + title: "CTO, LiteLLM" + url: https://www.linkedin.com/in/reffajnaahsi/ + image_url: https://pbs.twimg.com/profile_images/1613813310264340481/lz54oEiB_400x400.jpg + - name: Krrish Dholakia + title: "CEO, LiteLLM" + url: https://www.linkedin.com/in/krish-d/ + image_url: https://pbs.twimg.com/profile_images/1298587542745358340/DZv3Oj-h_400x400.jpg +tags: [incident-report, anthropic, stability] +hide_table_of_contents: false +--- + +**Date:** February 13, 2026 +**Duration:** ~3 hours +**Severity:** High +**Status:** Resolved + +> **Note:** This fix will be available starting from `v1.81.13-nightly` or higher of LiteLLM. + +## Summary + +Claude Code began sending unsupported Anthropic beta headers to non-Anthropic providers (Bedrock, Azure AI, Vertex AI), causing `invalid beta flag` errors. LiteLLM was forwarding all beta headers without provider-specific validation. Users experienced request failures when routing Claude Code requests through LiteLLM to these providers. + +- **LLM calls to Anthropic:** No impact. +- **LLM calls to Bedrock/Azure/Vertex:** Failed with `invalid beta flag` errors when unsupported headers were present. +- **Cost tracking and routing:** No impact. + +{/* truncate */} + +--- + +## Background + +Anthropic uses beta headers to enable experimental features in Claude. When Claude Code makes API requests, it includes headers like `anthropic-beta: prompt-caching-scope-2026-01-05,advanced-tool-use-2025-11-20`. However, not all providers support all Anthropic beta features. + +Before this incident, LiteLLM forwarded all beta headers to all providers without validation: + +```mermaid +sequenceDiagram + participant CC as Claude Code + participant LP as LiteLLM (old behavior) + participant Provider as Provider (Bedrock/Azure/Vertex) + + CC->>LP: Request with beta headers + Note over CC,LP: anthropic-beta: header1,header2,header3 + + LP->>Provider: Forward ALL headers (no validation) + Note over LP,Provider: anthropic-beta: header1,header2,header3 + + Provider-->>LP: ❌ Error: invalid beta flag + LP-->>CC: Request fails +``` + +Requests succeeded for Anthropic (native support) but failed for other providers when Claude Code sent headers those providers didn't support. + +--- + +## Root cause + +LiteLLM lacked provider-specific beta header validation. When Claude Code introduced new beta features or sent headers that specific providers didn't support, those headers were blindly forwarded, causing provider API errors. + +--- + +## Remediation + +| # | Action | Status | Code | +|---|---|---|---| +| 1 | Create `anthropic_beta_headers_config.json` with provider-specific mappings | ✅ Done | [`anthropic_beta_headers_config.json`](https://github.com/BerriAI/litellm/blob/main/litellm/anthropic_beta_headers_config.json) | +| 2 | Implement strict validation: headers must be explicitly mapped to be forwarded | ✅ Done | [`litellm_logging.py`](https://github.com/BerriAI/litellm/blob/main/litellm/litellm_core_utils/litellm_logging.py) | +| 3 | Add `/reload/anthropic_beta_headers` endpoint for dynamic config updates | ✅ Done | Proxy management endpoints | +| 4 | Add `/schedule/anthropic_beta_headers_reload` for automatic periodic updates | ✅ Done | Proxy management endpoints | +| 5 | Support `LITELLM_ANTHROPIC_BETA_HEADERS_URL` for custom config sources | ✅ Done | Environment configuration | +| 6 | Support `LITELLM_LOCAL_ANTHROPIC_BETA_HEADERS` for air-gapped deployments | ✅ Done | Environment configuration | + +Now LiteLLM validates and transforms headers per-provider: + +```mermaid +sequenceDiagram + participant CC as Claude Code + participant LP as LiteLLM (new behavior) + participant Config as Beta Headers Config + participant Provider as Provider (Bedrock/Azure/Vertex) + + CC->>LP: Request with beta headers + Note over CC,LP: anthropic-beta: header1,header2,header3 + + LP->>Config: Load header mapping for provider + Config-->>LP: Returns mapping (header→value or null) + + Note over LP: Validate & Transform:
1. Check if header exists in mapping
2. Filter out null values
3. Map to provider-specific names + + LP->>Provider: Request with filtered & mapped headers + Note over LP,Provider: anthropic-beta: mapped-header2
(header1, header3 filtered out) + + Provider-->>LP: ✅ Success response + LP-->>CC: Response +``` + +--- + +## Dynamic configuration updates + +A key improvement is zero-downtime configuration updates. When Anthropic releases new beta features, users can update their configuration without restarting: + +```bash +# Manually trigger reload (no restart needed) +curl -X POST "https://your-proxy-url/reload/anthropic_beta_headers" \ + -H "Authorization: Bearer YOUR_ADMIN_TOKEN" + +# Or schedule automatic reloads every 24 hours +curl -X POST "https://your-proxy-url/schedule/anthropic_beta_headers_reload?hours=24" \ + -H "Authorization: Bearer YOUR_ADMIN_TOKEN" +``` + +This prevents future incidents where Claude Code introduces new headers before LiteLLM configuration is updated. + +--- + +## Configuration format + +The `anthropic_beta_headers_config.json` file maps input headers to provider-specific output headers: + +```json +{ + "description": "Mapping of Anthropic beta headers for each provider.", + "anthropic": { + "advanced-tool-use-2025-11-20": "advanced-tool-use-2025-11-20", + "computer-use-2025-01-24": "computer-use-2025-01-24" + }, + "bedrock_converse": { + "advanced-tool-use-2025-11-20": null, + "computer-use-2025-01-24": "computer-use-2025-01-24" + }, + "azure_ai": { + "advanced-tool-use-2025-11-20": "advanced-tool-use-2025-11-20", + "computer-use-2025-01-24": "computer-use-2025-01-24" + } +} +``` + +**Validation rules:** +1. Headers must exist in the mapping for the target provider +2. Headers with `null` values are filtered out (unsupported) +3. Header names can be transformed per-provider (e.g., Bedrock uses different names for some features) + +--- + +## Resolution steps for users + +For users still experiencing issues, update to the latest LiteLLM version if < v1.81.11-nightly: + +```bash +pip install --upgrade litellm +``` + +Or manually reload the configuration without restarting: + +```bash +curl -X POST "https://your-proxy-url/reload/anthropic_beta_headers" \ + -H "Authorization: Bearer YOUR_ADMIN_TOKEN" +``` + +--- + +## Related documentation + +- [Managing Anthropic Beta Headers](../proxy/sync_anthropic_beta_headers.md) - Complete configuration guide +- [`anthropic_beta_headers_config.json`](https://github.com/BerriAI/litellm/blob/main/litellm/anthropic_beta_headers_config.json) - Current configuration file diff --git a/docs/my-website/blog/claude_opus_4_6/index.md b/docs/my-website/blog/claude_opus_4_6/index.md index 3fd7066154..e44420bd57 100644 --- a/docs/my-website/blog/claude_opus_4_6/index.md +++ b/docs/my-website/blog/claude_opus_4_6/index.md @@ -185,7 +185,7 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \ model_list: - model_name: claude-opus-4-6 litellm_params: - model: bedrock/anthropic.claude-opus-4-6-v1:0 + model: bedrock/anthropic.claude-opus-4-6-v1 aws_access_key_id: os.environ/AWS_ACCESS_KEY_ID aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY aws_region_name: us-east-1 @@ -389,6 +389,10 @@ Compaction blocks are also supported in streaming mode. You'll receive: ### Adaptive Thinking +:::note +When using `reasoning_effort` with Claude Opus 4.6, all values (`low`, `medium`, `high`) are mapped to `thinking: {type: "adaptive"}`. To use explicit thinking budgets with `type: "enabled"`, pass the native `thinking` parameter directly (see "Native thinking param" tab below). +::: + @@ -434,6 +438,21 @@ curl --location 'http://0.0.0.0:4000/v1/messages' \ }' ``` + + + +Use the `thinking` parameter directly for adaptive thinking via the SDK: + +```python +import litellm + +response = litellm.completion( + model="anthropic/claude-opus-4-6", + messages=[{"role": "user", "content": "Solve this complex problem: What is the optimal strategy for..."}], + thinking={"type": "adaptive"}, +) +``` + diff --git a/docs/my-website/blog/claude_sonnet_4_6/index.md b/docs/my-website/blog/claude_sonnet_4_6/index.md new file mode 100644 index 0000000000..df54fa0979 --- /dev/null +++ b/docs/my-website/blog/claude_sonnet_4_6/index.md @@ -0,0 +1,283 @@ +--- +slug: claude_sonnet_4_6 +title: "Day 0 Support: Claude Sonnet 4.6" +date: 2026-02-17T10:00:00 +authors: + - name: Ishaan Jaff + title: "CTO, LiteLLM" + url: https://www.linkedin.com/in/reffajnaahsi/ + image_url: https://pbs.twimg.com/profile_images/1613813310264340481/lz54oEiB_400x400.jpg + - name: Krrish Dholakia + title: "CEO, LiteLLM" + url: https://www.linkedin.com/in/krish-d/ + image_url: https://pbs.twimg.com/profile_images/1298587542745358340/DZv3Oj-h_400x400.jpg +description: "Day 0 support for Claude Sonnet 4.6 on LiteLLM AI Gateway - use across Anthropic, Azure, Vertex AI, and Bedrock." +tags: [anthropic, claude, sonnet 4.6] +hide_table_of_contents: false +--- + +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +LiteLLM now supports Claude Sonnet 4.6 on Day 0. Use it across Anthropic, Azure, Vertex AI, and Bedrock through the LiteLLM AI Gateway. + +## Docker Image + +```bash +docker pull ghcr.io/berriai/litellm:v1.81.3-stable.sonnet-4-6 +``` + +## Usage - Anthropic + + + + +**1. Setup config.yaml** + +```yaml +model_list: + - model_name: claude-sonnet-4-6 + litellm_params: + model: anthropic/claude-sonnet-4-6 + api_key: os.environ/ANTHROPIC_API_KEY +``` + +**2. Start the proxy** + +```bash +docker run -d \ + -p 4000:4000 \ + -e ANTHROPIC_API_KEY=$ANTHROPIC_API_KEY \ + -v $(pwd)/config.yaml:/app/config.yaml \ + ghcr.io/berriai/litellm:v1.81.3-stable.sonnet-4-6 \ + --config /app/config.yaml +``` + +**3. Test it!** + +```bash +curl --location 'http://0.0.0.0:4000/chat/completions' \ +--header 'Content-Type: application/json' \ +--header 'Authorization: Bearer $LITELLM_KEY' \ +--data '{ + "model": "claude-sonnet-4-6", + "messages": [ + { + "role": "user", + "content": "what llm are you" + } + ] +}' +``` + + + + + +```python +from litellm import completion + +response = completion( + model="anthropic/claude-sonnet-4-6", + messages=[{"role": "user", "content": "what llm are you"}] +) +print(response.choices[0].message.content) +``` + + + + +## Usage - Azure + + + + +**1. Setup config.yaml** + +```yaml +model_list: + - model_name: claude-sonnet-4-6 + litellm_params: + model: azure_ai/claude-sonnet-4-6 + api_key: os.environ/AZURE_AI_API_KEY + api_base: os.environ/AZURE_AI_API_BASE # https://.services.ai.azure.com +``` + +**2. Start the proxy** + +```bash +docker run -d \ + -p 4000:4000 \ + -e AZURE_AI_API_KEY=$AZURE_AI_API_KEY \ + -e AZURE_AI_API_BASE=$AZURE_AI_API_BASE \ + -v $(pwd)/config.yaml:/app/config.yaml \ + ghcr.io/berriai/litellm:v1.81.3-stable.sonnet-4-6 \ + --config /app/config.yaml +``` + +**3. Test it!** + +```bash +curl --location 'http://0.0.0.0:4000/chat/completions' \ +--header 'Content-Type: application/json' \ +--header 'Authorization: Bearer $LITELLM_KEY' \ +--data '{ + "model": "claude-sonnet-4-6", + "messages": [ + { + "role": "user", + "content": "what llm are you" + } + ] +}' +``` + + + + + +```python +from litellm import completion + +response = completion( + model="azure_ai/claude-sonnet-4-6", + api_key="your-azure-api-key", + api_base="https://.services.ai.azure.com", + messages=[{"role": "user", "content": "what llm are you"}] +) +print(response.choices[0].message.content) +``` + + + + +## Usage - Vertex AI + + + + +**1. Setup config.yaml** + +```yaml +model_list: + - model_name: claude-sonnet-4-6 + litellm_params: + model: vertex_ai/claude-sonnet-4-6 + vertex_project: os.environ/VERTEX_PROJECT + vertex_location: us-east5 +``` + +**2. Start the proxy** + +```bash +docker run -d \ + -p 4000:4000 \ + -e VERTEX_PROJECT=$VERTEX_PROJECT \ + -e GOOGLE_APPLICATION_CREDENTIALS=/app/credentials.json \ + -v $(pwd)/config.yaml:/app/config.yaml \ + -v $(pwd)/credentials.json:/app/credentials.json \ + ghcr.io/berriai/litellm:v1.81.3-stable.sonnet-4-6 \ + --config /app/config.yaml +``` + +**3. Test it!** + +```bash +curl --location 'http://0.0.0.0:4000/chat/completions' \ +--header 'Content-Type: application/json' \ +--header 'Authorization: Bearer $LITELLM_KEY' \ +--data '{ + "model": "claude-sonnet-4-6", + "messages": [ + { + "role": "user", + "content": "what llm are you" + } + ] +}' +``` + + + + + +```python +from litellm import completion + +response = completion( + model="vertex_ai/claude-sonnet-4-6", + vertex_project="your-project-id", + vertex_location="us-east5", + messages=[{"role": "user", "content": "what llm are you"}] +) +print(response.choices[0].message.content) +``` + + + + +## Usage - Bedrock + + + + +**1. Setup config.yaml** + +```yaml +model_list: + - model_name: claude-sonnet-4-6 + litellm_params: + model: bedrock/anthropic.claude-sonnet-4-6-v1 + aws_access_key_id: os.environ/AWS_ACCESS_KEY_ID + aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY + aws_region_name: us-east-1 +``` + +**2. Start the proxy** + +```bash +docker run -d \ + -p 4000:4000 \ + -e AWS_ACCESS_KEY_ID=$AWS_ACCESS_KEY_ID \ + -e AWS_SECRET_ACCESS_KEY=$AWS_SECRET_ACCESS_KEY \ + -v $(pwd)/config.yaml:/app/config.yaml \ + ghcr.io/berriai/litellm:v1.81.3-stable.sonnet-4-6 \ + --config /app/config.yaml +``` + +**3. Test it!** + +```bash +curl --location 'http://0.0.0.0:4000/chat/completions' \ +--header 'Content-Type: application/json' \ +--header 'Authorization: Bearer $LITELLM_KEY' \ +--data '{ + "model": "claude-sonnet-4-6", + "messages": [ + { + "role": "user", + "content": "what llm are you" + } + ] +}' +``` + + + + + +```python +from litellm import completion + +response = completion( + model="bedrock/anthropic.claude-sonnet-4-6-v1", + aws_access_key_id="your-access-key", + aws_secret_access_key="your-secret-key", + aws_region_name="us-east-1", + messages=[{"role": "user", "content": "what llm are you"}] +) +print(response.choices[0].message.content) +``` + + + diff --git a/docs/my-website/blog/gemin_3.1/index.md b/docs/my-website/blog/gemin_3.1/index.md new file mode 100644 index 0000000000..b81595e4bd --- /dev/null +++ b/docs/my-website/blog/gemin_3.1/index.md @@ -0,0 +1,150 @@ +--- +slug: gemini_3_1_pro +title: "DAY 0 Support: Gemini 3.1 Pro on LiteLLM" +date: 2026-02-19T10:00:00 +authors: + - name: Sameer Kankute + title: SWE @ LiteLLM (LLM Translation) + url: https://www.linkedin.com/in/sameer-kankute/ + image_url: https://pbs.twimg.com/profile_images/2001352686994907136/ONgNuSk5_400x400.jpg + - name: Krrish Dholakia + title: "CEO, LiteLLM" + url: https://www.linkedin.com/in/krish-d/ + image_url: https://pbs.twimg.com/profile_images/1298587542745358340/DZv3Oj-h_400x400.jpg + - name: Ishaan Jaff + title: "CTO, LiteLLM" + url: https://www.linkedin.com/in/reffajnaahsi/ + image_url: https://pbs.twimg.com/profile_images/1613813310264340481/lz54oEiB_400x400.jpg +description: "Guide to using Gemini 3.1 Pro on LiteLLM Proxy and SDK with day 0 support." +tags: [gemini, day 0 support, llms] +hide_table_of_contents: false +--- + + +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Gemini 3.1 Pro Day 0 Support + +LiteLLM now supports `gemini-3.1-pro-preview` and all the new API changes along with it. + +## Deploy this version + + + + +``` showLineNumbers title="docker run litellm" +docker run \ +-e STORE_MODEL_IN_DB=True \ +-p 4000:4000 \ +ghcr.io/berriai/litellm:main-v1.81.9-stable.gemini.3.1-pro +``` + + + + + +``` showLineNumbers title="pip install litellm" +pip install litellm==v1.81.9-stable.gemini.3.1-pro +``` + + + + +## What's New + +### 1. New Thinking Levels: `thinkingLevel` with MINIMAL & MEDIUM + +Gemini 3.1 Pro introduces support for **medium** thinking level + +LiteLLM automatically maps the OpenAI `reasoning_effort` parameter to Gemini's `thinkingLevel`, so you can use familiar `reasoning_effort` values (`minimal`, `low`, `medium`, `high`) without changing your code! + +--- +## Supported Endpoints + +LiteLLM provides **full end-to-end support** for Gemini 3.1 Pro on: + +- ✅ `/v1/chat/completions` - OpenAI-compatible chat completions endpoint +- ✅ `/v1/responses` - OpenAI Responses API endpoint (streaming and non-streaming) +- ✅ [`/v1/messages`](../../docs/anthropic_unified) - Anthropic-compatible messages endpoint +- ✅ `/v1/generateContent` – [Google Gemini API](../../docs/generateContent.md) compatible endpoint + +All endpoints support: +- Streaming and non-streaming responses +- Function calling with thought signatures +- Multi-turn conversations +- All Gemini 3-specific features +- Conversion of provider specific thinking related param to thinkingLevel + +## Quick Start + + + + +**Basic Usage with MEDIUM thinking (NEW)** + +```python +from litellm import completion + +# No need to make any changes to your code as we map openai reasoning param to thinkingLevel +response = completion( + model="gemini/gemini-3.1-pro-preview", + messages=[{"role": "user", "content": "Solve this complex math problem: 25 * 4 + 10"}], + reasoning_effort="medium", # NEW: MEDIUM thinking level +) + +print(response.choices[0].message.content) +``` + + + + + +**1. Setup config.yaml** + +```yaml +model_list: + - model_name: gemini-3.1-pro-preview + litellm_params: + model: gemini/gemini-3.1-pro-preview + api_key: os.environ/GEMINI_API_KEY + - model_name: vertex-gemini-3.1-pro-preview + litellm_params: + model: vertex_ai/gemini-3.1-pro-preview +``` + +**2. Start proxy** + +```bash +litellm --config /path/to/config.yaml +``` + +**3. Call with MEDIUM thinking** + +```bash +curl -X POST http://localhost:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer " \ + -d '{ + "model": "gemini-3.1-pro-preview", + "messages": [{"role": "user", "content": "Complex reasoning task"}], + "reasoning_effort": "medium" + }' +``` + + + + +--- + +## `reasoning_effort` Mapping for Gemini 3+ + +| reasoning_effort | thinking_level | +|------------------|----------------| +| `minimal` | `minimal` | +| `low` | `low` | +| `medium` | `medium` | +| `high` | `high` | +| `disable` | `minimal` | +| `none` | `minimal` | + diff --git a/docs/my-website/blog/vllm_embeddings_incident/index.md b/docs/my-website/blog/vllm_embeddings_incident/index.md new file mode 100644 index 0000000000..a1ce815285 --- /dev/null +++ b/docs/my-website/blog/vllm_embeddings_incident/index.md @@ -0,0 +1,117 @@ +--- +slug: vllm-embeddings-incident +title: "Incident Report: vLLM Embeddings Broken by encoding_format Parameter" +date: 2026-02-18T10:00:00 +authors: + - name: Sameer Kankute + title: SWE @ LiteLLM (LLM Translation) + url: https://www.linkedin.com/in/sameer-kankute/ + image_url: https://pbs.twimg.com/profile_images/2001352686994907136/ONgNuSk5_400x400.jpg + - name: Krrish Dholakia + title: "CEO, LiteLLM" + url: https://www.linkedin.com/in/krish-d/ + image_url: https://pbs.twimg.com/profile_images/1298587542745358340/DZv3Oj-h_400x400.jpg + - name: Ishaan Jaff + title: "CTO, LiteLLM" + url: https://www.linkedin.com/in/reffajnaahsi/ + image_url: https://pbs.twimg.com/profile_images/1613813310264340481/lz54oEiB_400x400.jpg +tags: [incident-report, embeddings, vllm] +hide_table_of_contents: false +--- + +**Date:** Feb 16, 2026 +**Duration:** ~3 hours +**Severity:** High (for vLLM embedding users) +**Status:** Resolved + +## Summary + +A commit ([`dbcae4a`](https://github.com/BerriAI/litellm/commit/dbcae4aca5836770d0e9cd43abab0333c3d61ab2)) intended to fix OpenAI SDK behavior broke vLLM embeddings by explicitly passing `encoding_format=None` in API requests. vLLM rejects this with error: `"unknown variant \`\`, expected float or base64"`. + +- **vLLM embedding calls:** Complete failure - all requests rejected +- **Other providers:** No impact - OpenAI and other providers functioned normally +- **Other vLLM functionality:** No impact - only embeddings were affected + +{/* truncate */} + +--- + +## Background + +The `encoding_format` parameter for embeddings specifies whether vectors should be returned as `float` arrays or `base64` encoded strings. Different providers have different expectations: + +- **OpenAI SDK:** If `encoding_format` is omitted, the SDK adds a default value of `"float"` +- **vLLM:** Strictly validates `encoding_format` - only accepts `"float"`, `"base64"`, or complete omission. Rejects `None` or empty string values. + +```mermaid +flowchart TD + A["1. User calls litellm.embedding() + litellm/main.py"] --> B["2. Transform request for provider + litellm/llms/openai_like/embedding/handler.py"] + B --> C["3. Send request to vLLM endpoint"] + C -->|"encoding_format omitted"| D["4a. ✅ vLLM processes request"] + C -->|"encoding_format='float' or 'base64'"| D + C -->|"encoding_format=None or ''"| E["4b. ❌ vLLM rejects with error: + 'unknown variant, expected float or base64'"] + + style D fill:#d4edda,stroke:#28a745 + style E fill:#f8d7da,stroke:#dc3545 + style B fill:#fff3cd,stroke:#ffc107 +``` + +--- + +## Root cause + +A well-intentioned fix for OpenAI SDK behavior inadvertently broke vLLM embeddings: + +**The Breaking Change ([`dbcae4a`](https://github.com/BerriAI/litellm/commit/dbcae4aca5836770d0e9cd43abab0333c3d61ab2)):** + +In `litellm/main.py`, the code was changed to explicitly set `encoding_format=None` instead of omitting it: + +```python +# Added in dbcae4a +if encoding_format is not None: + optional_params["encoding_format"] = encoding_format +else: + # Omitting causes openai sdk to add default value of "float" + optional_params["encoding_format"] = None +``` + +This fix worked correctly for OpenAI - explicitly passing `None` prevented the SDK from adding its default value. However, vLLM's strict parameter validation rejected `None` values, causing all embedding requests to fail. + +--- + +## The Fix + +Fix deployed ([`55348dd`](https://github.com/BerriAI/litellm/commit/55348dd9c51b5b028f676d25ad023b8f052fc071)). The solution filters out `None` and empty string values from `optional_params` before sending requests to OpenAI-like providers (including vLLM). + +**In `litellm/llms/openai_like/embedding/handler.py`:** + +```python +# Before (broken) +data = {"model": model, "input": input, **optional_params} + +# After (fixed) +filtered_optional_params = {k: v for k, v in optional_params.items() if v not in (None, '')} +data = {"model": model, "input": input, **filtered_optional_params} +``` + +This ensures: +- Valid values (`"float"`, `"base64"`) are preserved and sent +- `None` and empty string values are filtered out (parameter omitted entirely) +- OpenAI SDK no longer adds defaults because liteLLM handles the parameter upstream + +--- + +## Remediation + +| # | Action | Status | Code | +|---|---|---|---| +| 1 | Filter `None` and empty string values in OpenAI-like embedding handler | ✅ Done | [`handler.py#L108`](https://github.com/BerriAI/litellm/blob/main/litellm/llms/openai_like/embedding/handler.py#L108) | +| 2 | Unit tests for parameter filtering (None, empty string, valid values) | ✅ Done | [`test_openai_like_embedding.py`](https://github.com/BerriAI/litellm/blob/main/tests/test_litellm/llms/openai_like/embedding/test_openai_like_embedding.py) | +| 3 | Transformation tests for hosted_vllm embedding config | ✅ Done | [`test_hosted_vllm_embedding_transformation.py`](https://github.com/BerriAI/litellm/blob/main/tests/test_litellm/llms/hosted_vllm/embedding/test_hosted_vllm_embedding_transformation.py) | +| 4 | E2E tests with actual vLLM endpoint | ✅ Done | [`test_hosted_vllm_embedding_e2e.py`](https://github.com/BerriAI/litellm/blob/main/tests/test_litellm/llms/hosted_vllm/embedding/test_hosted_vllm_embedding_e2e.py) | +| 5 | Validate JSON payload structure matches vLLM expectations | ✅ Done | Tests verify exact JSON sent to endpoint | + +--- diff --git a/docs/my-website/docs/adding_provider/generic_guardrail_api.md b/docs/my-website/docs/adding_provider/generic_guardrail_api.md index 0931c349e4..eb567a69fc 100644 --- a/docs/my-website/docs/adding_provider/generic_guardrail_api.md +++ b/docs/my-website/docs/adding_provider/generic_guardrail_api.md @@ -237,6 +237,7 @@ litellm_settings: mode: pre_call # or post_call, during_call api_base: https://your-guardrail-api.com api_key: os.environ/YOUR_GUARDRAIL_API_KEY # optional + unreachable_fallback: fail_closed # default: fail_closed. Set to fail_open to proceed if the guardrail endpoint is unreachable (network errors, or HTTP 502/503/504 from an upstream proxy/LB). additional_provider_specific_params: # your custom parameters threshold: 0.8 diff --git a/docs/my-website/docs/adding_provider/generic_prompt_management_api.md b/docs/my-website/docs/adding_provider/generic_prompt_management_api.md new file mode 100644 index 0000000000..d1b119d94c --- /dev/null +++ b/docs/my-website/docs/adding_provider/generic_prompt_management_api.md @@ -0,0 +1,576 @@ +# [BETA] Generic Prompt Management API - Integrate Without a PR + +## The Problem + +As a prompt management provider, integrating with LiteLLM traditionally requires: +- Making a PR to the LiteLLM repository +- Waiting for review and merge +- Maintaining provider-specific code in LiteLLM's codebase +- Updating the integration for changes to your API + +## The Solution + +The **Generic Prompt Management API** lets you integrate with LiteLLM **instantly** by implementing a simple API endpoint. No PR required. + +### Key Benefits + +1. **No PR Needed** - Deploy and integrate immediately +3. **Simple Contract** - One GET endpoint, standard JSON response +4. **Variable Substitution** - Support for prompt variables with `{variable}` syntax +5. **Custom Parameters** - Pass provider-specific query params via config +6. **Full Control** - You own and maintain your prompt management API +7. **Model & Parameters Override** - Optionally override model and parameters from your prompts + +## Get Started in 3 Steps + +### Step 1: Configure LiteLLM + +Add to your `config.yaml`: + +```yaml +prompts: + - prompt_id: "simple_prompt" + litellm_params: + prompt_integration: "generic_prompt_management" + api_base: http://localhost:8080 + api_key: os.environ/YOUR_API_KEY +``` + +### Step 2: Implement Your API Endpoint + +```python +from fastapi import FastAPI +from pydantic import BaseModel + +app = FastAPI() + +@app.get("/beta/litellm_prompt_management") +async def get_prompt(prompt_id: str): + return { + "prompt_id": prompt_id, + "prompt_template": [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "Help me with {task}"} + ], + "prompt_template_model": "gpt-4", + "prompt_template_optional_params": {"temperature": 0.7} + } +``` + +### Step 3: Use in Your App + +```python +from litellm import completion + +response = completion( + model="gpt-4", + prompt_id="simple_prompt", + prompt_variables={"task": "data analysis"}, + messages=[{"role": "user", "content": "I have sales data"}] +) +``` + +That's it! LiteLLM fetches your prompt, applies variables, and makes the request + +## API Contract + +### Endpoint + +Implement `GET /beta/litellm_prompt_management` + +### Request Format + +Your endpoint will receive a GET request with query parameters: + +``` +GET /beta/litellm_prompt_management?prompt_id={prompt_id}&{custom_params} +``` + +**Query Parameters:** +- `prompt_id` (required): The ID of the prompt to fetch +- Custom parameters: Any additional parameters you configured in `provider_specific_query_params` + +**Example:** +``` +GET /beta/litellm_prompt_management?prompt_id=hello-world-prompt-2bac&project_name=litellm&slug=hello-world-prompt-2bac +``` + +### Response Format + +```json +{ + "prompt_id": "hello-world-prompt-2bac", + "prompt_template": [ + { + "role": "system", + "content": "You are a helpful assistant specialized in {domain}." + }, + { + "role": "user", + "content": "Help me with {task}" + } + ], + "prompt_template_model": "gpt-4", + "prompt_template_optional_params": { + "temperature": 0.7, + "max_tokens": 500, + "top_p": 0.9 + } +} +``` + +**Response Fields:** +- `prompt_id` (string, required): The ID of the prompt +- `prompt_template` (array, required): Array of OpenAI-format messages with optional `{variable}` placeholders +- `prompt_template_model` (string, optional): Model to use for this prompt (overrides client model unless `ignore_prompt_manager_model: true`) +- `prompt_template_optional_params` (object, optional): Additional parameters like temperature, max_tokens, etc. (merged with client params unless `ignore_prompt_manager_optional_params: true`) + +## LiteLLM Configuration + +Add to `config.yaml`: + +```yaml +model_list: + - model_name: gpt-3.5-turbo + litellm_params: + model: openai/gpt-3.5-turbo + api_key: os.environ/OPENAI_API_KEY + +prompts: + - prompt_id: "simple_prompt" + litellm_params: + prompt_integration: "generic_prompt_management" + provider_specific_query_params: + project_name: litellm + slug: hello-world-prompt-2bac + api_base: http://localhost:8080 + api_key: os.environ/YOUR_PROMPT_API_KEY # optional + ignore_prompt_manager_model: true # optional, keep client's model + ignore_prompt_manager_optional_params: true # optional, don't merge prompt manager's params (e.g. temperature, max_tokens, etc.) +``` + +### Configuration Parameters + +- `prompt_integration`: Must be `"generic_prompt_management"` +- `provider_specific_query_params`: Custom query parameters sent to your API (optional) +- `api_base`: Base URL of your prompt management API +- `api_key`: Optional API key for authentication (sent as `Bearer` token) +- `ignore_prompt_manager_model`: If `true`, use the model specified by client instead of prompt's model (default: `false`) +- `ignore_prompt_manager_optional_params`: If `true`, don't merge prompt's optional params with client params (default: `false`) + +## Usage + +### Using with LiteLLM SDK + +**Basic usage with prompt ID:** + +```python +from litellm import completion + +response = completion( + model="gpt-4", + prompt_id="simple_prompt", + messages=[{"role": "user", "content": "Additional message"}] +) +``` + +**With prompt variables:** + +```python +response = completion( + model="gpt-4", + prompt_id="simple_prompt", + prompt_variables={ + "domain": "data science", + "task": "analyzing customer churn" + }, + messages=[{"role": "user", "content": "Please provide a detailed analysis"}] +) +``` + +The prompt template will have `{domain}` replaced with "data science" and `{task}` replaced with "analyzing customer churn". + +### Using with LiteLLM Proxy + +**1. Start the proxy with your config:** + +```bash +litellm --config /path/to/config.yaml +``` + +**2. Make requests with prompt_id:** + +```bash +curl http://0.0.0.0:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer sk-1234" \ + -d '{ + "model": "gpt-4", + "prompt_id": "simple_prompt", + "prompt_variables": { + "domain": "healthcare", + "task": "patient risk assessment" + }, + "messages": [ + {"role": "user", "content": "Analyze the following data..."} + ] + }' +``` + +**3. Using with OpenAI SDK:** + +```python +from openai import OpenAI + +client = OpenAI( + base_url="http://0.0.0.0:4000", + api_key="sk-1234" +) + +response = client.chat.completions.create( + model="gpt-4", + messages=[ + {"role": "user", "content": "Analyze the data"} + ], + extra_body={ + "prompt_id": "simple_prompt", + "prompt_variables": { + "domain": "finance", + "task": "fraud detection" + } + } +) +``` + +## Implementation Example + +See [mock_prompt_management_server.py](https://github.com/BerriAI/litellm/blob/main/cookbook/mock_prompt_management_server/mock_prompt_management_server.py) for a complete reference implementation with multiple example prompts, authentication, and convenience endpoints. + +**Minimal FastAPI example:** + +```python +from fastapi import FastAPI, HTTPException, Header +from typing import Optional, Dict, Any, List +from pydantic import BaseModel + +app = FastAPI() + +# In-memory prompt storage (replace with your database) +PROMPTS = { + "hello-world-prompt": { + "prompt_id": "hello-world-prompt", + "prompt_template": [ + { + "role": "system", + "content": "You are a helpful assistant specialized in {domain}." + }, + { + "role": "user", + "content": "Help me with: {task}" + } + ], + "prompt_template_model": "gpt-4", + "prompt_template_optional_params": { + "temperature": 0.7, + "max_tokens": 500 + } + }, + "code-review-prompt": { + "prompt_id": "code-review-prompt", + "prompt_template": [ + { + "role": "system", + "content": "You are an expert code reviewer. Review code for {language}." + }, + { + "role": "user", + "content": "Review the following code:\n\n{code}" + } + ], + "prompt_template_model": "gpt-4-turbo", + "prompt_template_optional_params": { + "temperature": 0.3, + "max_tokens": 1000 + } + } +} + +class PromptResponse(BaseModel): + prompt_id: str + prompt_template: List[Dict[str, str]] + prompt_template_model: Optional[str] = None + prompt_template_optional_params: Optional[Dict[str, Any]] = None + +@app.get("/beta/litellm_prompt_management", response_model=PromptResponse) +async def get_prompt( + prompt_id: str, + authorization: Optional[str] = Header(None), + project_name: Optional[str] = None, + slug: Optional[str] = None, +): + """ + Get a prompt by ID with optional filtering by project_name and slug. + + Args: + prompt_id: The ID of the prompt to fetch + authorization: Optional Bearer token for authentication + project_name: Optional project name filter + slug: Optional slug filter + """ + + # Optional: Validate authorization + if authorization: + token = authorization.replace("Bearer ", "") + # Validate your token here + if not is_valid_token(token): + raise HTTPException(status_code=401, detail="Invalid API key") + + # Optional: Apply additional filtering based on custom params + if project_name or slug: + # You can use these parameters to filter or validate access + # For example, check if the user has access to this project + pass + + # Fetch the prompt from your storage + if prompt_id not in PROMPTS: + raise HTTPException( + status_code=404, + detail=f"Prompt '{prompt_id}' not found" + ) + + prompt_data = PROMPTS[prompt_id] + + return PromptResponse(**prompt_data) + +def is_valid_token(token: str) -> bool: + """Validate API token - implement your logic here""" + # Example: Check against your database or secret store + valid_tokens = ["your-secret-token", "another-valid-token"] + return token in valid_tokens + +# Optional: Health check endpoint +@app.get("/health") +async def health_check(): + return {"status": "healthy"} + +# Optional: List all prompts endpoint +@app.get("/prompts") +async def list_prompts(authorization: Optional[str] = Header(None)): + """List all available prompts""" + if authorization: + token = authorization.replace("Bearer ", "") + if not is_valid_token(token): + raise HTTPException(status_code=401, detail="Invalid API key") + + return { + "prompts": [ + {"prompt_id": pid, "model": p.get("prompt_template_model")} + for pid, p in PROMPTS.items() + ] + } + +if __name__ == "__main__": + import uvicorn + uvicorn.run(app, host="0.0.0.0", port=8080) +``` + +### Running the Example Server + +1. Install dependencies: +```bash +pip install fastapi uvicorn +``` + +2. Save the code above to `prompt_server.py` + +3. Run the server: +```bash +python prompt_server.py +``` + +4. Test the endpoint: +```bash +curl "http://localhost:8080/beta/litellm_prompt_management?prompt_id=hello-world-prompt&project_name=litellm&slug=hello-world-prompt-2bac" +``` + +Expected response: +```json +{ + "prompt_id": "hello-world-prompt", + "prompt_template": [ + { + "role": "system", + "content": "You are a helpful assistant specialized in {domain}." + }, + { + "role": "user", + "content": "Help me with: {task}" + } + ], + "prompt_template_model": "gpt-4", + "prompt_template_optional_params": { + "temperature": 0.7, + "max_tokens": 500 + } +} +``` + +## Advanced Features + +### Variable Substitution + +LiteLLM automatically substitutes variables in your prompt templates using the `{variable}` syntax. Both `{variable}` and `{{variable}}` formats are supported. + +**Example prompt template:** +```json +{ + "prompt_template": [ + { + "role": "system", + "content": "You are an expert in {domain} with {years} years of experience." + } + ] +} +``` + +**Client request:** +```python +completion( + model="gpt-4", + prompt_id="expert_prompt", + prompt_variables={ + "domain": "machine learning", + "years": "10" + } +) +``` + +**Result:** +``` +"You are an expert in machine learning with 10 years of experience." +``` + +### Caching + +LiteLLM automatically caches fetched prompts in memory. The cache key includes: +- `prompt_id` +- `prompt_label` (if provided) +- `prompt_version` (if provided) + +This means your API endpoint is only called once per unique prompt configuration. + +### Model Override Behavior + +**Default behavior (without `ignore_prompt_manager_model`):** +```yaml +prompts: + - prompt_id: "my_prompt" + litellm_params: + prompt_integration: "generic_prompt_management" + api_base: http://localhost:8080 +``` + +If your API returns `"prompt_template_model": "gpt-4"`, LiteLLM will use `gpt-4` regardless of what the client specified. + +**With `ignore_prompt_manager_model: true`:** +```yaml +prompts: + - prompt_id: "my_prompt" + litellm_params: + prompt_integration: "generic_prompt_management" + api_base: http://localhost:8080 + ignore_prompt_manager_model: true +``` + +LiteLLM will use the model specified by the client, ignoring the prompt's model. + +### Parameter Merging Behavior + +**Default behavior (without `ignore_prompt_manager_optional_params`):** + +Client params are merged with prompt params, with prompt params taking precedence: +```python +# Prompt returns: {"temperature": 0.7, "max_tokens": 500} +# Client sends: {"temperature": 0.9, "top_p": 0.95} +# Final params: {"temperature": 0.7, "max_tokens": 500, "top_p": 0.95} +``` + +**With `ignore_prompt_manager_optional_params: true`:** + +Only client params are used: +```python +# Prompt returns: {"temperature": 0.7, "max_tokens": 500} +# Client sends: {"temperature": 0.9, "top_p": 0.95} +# Final params: {"temperature": 0.9, "top_p": 0.95} +``` + +## Security Considerations + +1. **Authentication**: Use the `api_key` parameter to secure your prompt management API +2. **Authorization**: Implement team/user-based access control using the custom query parameters +3. **Rate Limiting**: Add rate limiting to prevent abuse of your API +4. **Input Validation**: Validate all query parameters before processing +5. **HTTPS**: Always use HTTPS in production for encrypted communication +6. **Secrets**: Store API keys in environment variables, not in config files + +## Use Cases + +✅ **Use Generic Prompt Management API when:** +- You want instant integration without waiting for PRs +- You maintain your own prompt management service +- You need full control over prompt versioning and updates +- You want to build custom prompt management features +- You need to integrate with your internal systems + +✅ **Common scenarios:** +- Internal prompt management system for your organization +- Multi-tenant prompt management with team-based access control +- A/B testing different prompt versions +- Prompt experimentation and analytics +- Integration with existing prompt engineering workflows + +## When to Use This + +✅ **Use Generic Prompt Management API when:** +- You want instant integration without waiting for PRs +- You maintain your own prompt management service +- You need full control over updates and features +- You want custom prompt storage and versioning logic + +❌ **Make a PR when:** +- You want deeper integration with LiteLLM internals +- Your integration requires complex LiteLLM-specific logic +- You want to be featured as a built-in provider +- You're building a reusable integration for the community + +## Troubleshooting + +### Prompt not found +- Verify the `prompt_id` matches exactly (case-sensitive) +- Check that your API endpoint is accessible from LiteLLM +- Verify authentication if using `api_key` + +### Variables not substituted +- Ensure variables use `{variable}` or `{{variable}}` syntax +- Check that variable names in `prompt_variables` match template exactly +- Variables are case-sensitive + +### Model not being overridden +- Check if `ignore_prompt_manager_model: true` is set in config +- Verify your API is returning `prompt_template_model` in the response + +### Parameters not being applied +- Check if `ignore_prompt_manager_optional_params: true` is set +- Verify your API is returning `prompt_template_optional_params` +- Ensure parameter names match OpenAI's parameter names + +## Questions? + +This is a **beta API**. We're actively improving it based on feedback. Open an issue or PR if you need additional capabilities. + +## Related Documentation + +- [Prompt Management Overview](../proxy/prompt_management.md) +- [Generic Guardrail API](./generic_guardrail_api.md) +- [LiteLLM Proxy Setup](../proxy/quick_start.md) + diff --git a/docs/my-website/docs/completion/message_sanitization.md b/docs/my-website/docs/completion/message_sanitization.md new file mode 100644 index 0000000000..17482c5933 --- /dev/null +++ b/docs/my-website/docs/completion/message_sanitization.md @@ -0,0 +1,465 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Message Sanitization for Tool Calling for anthropic models + +**Automatically fix common message formatting issues when using tool calling with `modify_params=True`** + +LiteLLM can automatically sanitize messages to handle common issues that occur during tool calling workflows, especially when using OpenAI-compatible clients with providers that have strict message format requirements (like Anthropic Claude). + +## Overview + +When `litellm.modify_params = True` is enabled, LiteLLM automatically sanitizes messages to fix three common issues: + +1. **Orphaned Tool Calls** - Assistant messages with tool_calls but missing tool results +2. **Orphaned Tool Results** - Tool messages that reference non-existent tool_call_ids +3. **Empty Message Content** - Messages with empty or whitespace-only text content + +This ensures your tool calling workflows work seamlessly across different LLM providers without manual message validation. + +## Why Message Sanitization? + +Different LLM providers have varying requirements for message formats, especially during tool calling: + +- **Anthropic Claude** requires every tool_call to have a corresponding tool result +- Some providers reject messages with empty content +- OpenAI-compatible clients may not always maintain perfect message consistency + +Without sanitization, these issues cause API errors that interrupt your workflows. With `modify_params=True`, LiteLLM handles these edge cases automatically. + +## Quick Start + + + + +```python +import litellm + +# Enable automatic message sanitization +litellm.modify_params = True + +# This will work even if messages have formatting issues +response = litellm.completion( + model="anthropic/claude-3-5-sonnet-20241022", + messages=[ + {"role": "user", "content": "What's the weather in Boston?"}, + { + "role": "assistant", + "tool_calls": [ + { + "id": "call_123", + "type": "function", + "function": {"name": "get_weather", "arguments": '{"city": "Boston"}'} + } + ] + # Missing tool result - LiteLLM will add a dummy result automatically + }, + {"role": "user", "content": "Thanks!"} + ], + tools=[{ + "type": "function", + "function": { + "name": "get_weather", + "description": "Get weather for a city", + "parameters": { + "type": "object", + "properties": {"city": {"type": "string"}}, + "required": ["city"] + } + } + }] +) +``` + + + + +```yaml +litellm_settings: + modify_params: true # Enable automatic message sanitization + +model_list: + - model_name: claude-3-5-sonnet + litellm_params: + model: anthropic/claude-3-5-sonnet-20241022 +``` + + + + +## Sanitization Cases + +### Case A: Orphaned Tool Calls (Missing Tool Results) + +**Problem:** An assistant message contains `tool_calls`, but no corresponding tool result messages follow. + +**Solution:** LiteLLM automatically adds dummy tool result messages for any missing tool results. + +**Example:** + +```python +import litellm +litellm.modify_params = True + +# Messages with orphaned tool calls +messages = [ + {"role": "user", "content": "Search for Python tutorials"}, + { + "role": "assistant", + "tool_calls": [ + { + "id": "call_abc123", + "type": "function", + "function": {"name": "web_search", "arguments": '{"query": "Python tutorials"}'} + } + ] + }, + # Missing tool result here! + {"role": "user", "content": "What about JavaScript?"} +] + +# LiteLLM automatically adds: +# { +# "role": "tool", +# "tool_call_id": "call_abc123", +# "content": "[System: Tool execution skipped/interrupted by user. No result provided for tool 'web_search'.]" +# } + +response = litellm.completion( + model="anthropic/claude-3-5-sonnet-20241022", + messages=messages, + tools=[...] +) +``` + +**When this happens:** +- User interrupts tool execution +- Client loses tool results due to network issues +- Conversation flow changes before tool completes +- Multi-turn conversations where tools are optional + +### Case B: Orphaned Tool Results (Invalid tool_call_id) + +**Problem:** A tool message references a `tool_call_id` that doesn't exist in any previous assistant message. + +**Solution:** LiteLLM automatically removes these orphaned tool result messages. + +**Example:** + +```python +import litellm +litellm.modify_params = True + +# Messages with orphaned tool result +messages = [ + {"role": "user", "content": "Hello"}, + {"role": "assistant", "content": "Hi! How can I help?"}, + { + "role": "tool", + "tool_call_id": "call_nonexistent", # This tool_call_id doesn't exist! + "content": "Some result" + } +] + +# LiteLLM automatically removes the orphaned tool message + +response = litellm.completion( + model="anthropic/claude-3-5-sonnet-20241022", + messages=messages +) +``` + +**When this happens:** +- Message history is manually edited +- Tool results are duplicated or mismatched +- Conversation state is restored incorrectly +- Messages are merged from different conversations + +### Case C: Empty Message Content + +**Problem:** User or assistant messages have empty or whitespace-only content. + +**Solution:** LiteLLM replaces empty content with a system placeholder message. + +**Example:** + +```python +import litellm +litellm.modify_params = True + +# Messages with empty content +messages = [ + {"role": "user", "content": ""}, # Empty content + {"role": "assistant", "content": " "}, # Whitespace only +] + +# LiteLLM automatically replaces with: +# {"role": "user", "content": "[System: Empty message content sanitised to satisfy protocol]"} +# {"role": "assistant", "content": "[System: Empty message content sanitised to satisfy protocol]"} + +response = litellm.completion( + model="anthropic/claude-3-5-sonnet-20241022", + messages=messages +) +``` + +**When this happens:** +- UI sends empty messages +- Content is stripped during preprocessing +- Placeholder messages in conversation history +- Edge cases in message construction + +## Configuration + +### Enable Globally + + + + +```python +import litellm + +# Enable for all completion calls +litellm.modify_params = True +``` + + + + +```yaml +litellm_settings: + modify_params: true +``` + + + + +```bash +export LITELLM_MODIFY_PARAMS=True +``` + + + + +### Enable Per-Request + +```python +import litellm + +# Enable only for specific requests +response = litellm.completion( + model="anthropic/claude-3-5-sonnet-20241022", + messages=messages, + modify_params=True # Override global setting +) +``` + +## Supported Providers + +Message sanitization currently works with: + +- ✅ Anthropic (Claude) + +**Note:** While the sanitization logic is provider-agnostic, it is currently only applied in the Anthropic message transformation pipeline. Support for additional providers may be added in future releases. + +## Implementation Details + +### How It Works + +The message sanitization process runs **before** messages are converted to provider-specific formats: + +1. **Input:** OpenAI-format messages with potential issues +2. **Sanitization:** Three helper functions process the messages: + - `_sanitize_empty_text_content()` - Fixes empty content + - `_add_missing_tool_results()` - Adds dummy tool results + - `_is_orphaned_tool_result()` - Identifies orphaned results +3. **Output:** Clean, provider-compatible messages + +### Code Reference + +The sanitization logic is implemented in: +- `litellm/litellm_core_utils/prompt_templates/factory.py` +- Function: `sanitize_messages_for_tool_calling()` + +### Logging + +When sanitization occurs, LiteLLM logs debug messages: + +```python +import litellm +litellm.set_verbose = True # Enable debug logging + +# You'll see logs like: +# "_add_missing_tool_results: Found 1 orphaned tool calls. Adding dummy tool results." +# "_is_orphaned_tool_result: Found orphaned tool result with tool_call_id=call_123" +# "_sanitize_empty_text_content: Replaced empty text content in user message" +``` + +## Best Practices + +### 1. Enable for Production Workflows + +```python +# Recommended for production +litellm.modify_params = True + +# Ensures robust handling of edge cases +response = litellm.completion( + model="anthropic/claude-3-5-sonnet-20241022", + messages=messages, + tools=tools +) +``` + +### 2. Preserve Tool Results When Possible + +While sanitization handles missing tool results, it's better to provide actual results: + +```python +# Good: Provide actual tool results +messages = [ + {"role": "user", "content": "Search for Python"}, + {"role": "assistant", "tool_calls": [...]}, + {"role": "tool", "tool_call_id": "call_123", "content": "Actual search results"} +] + +# Fallback: Sanitization adds dummy result if missing +messages = [ + {"role": "user", "content": "Search for Python"}, + {"role": "assistant", "tool_calls": [...]}, + # Missing tool result - sanitization adds dummy +] +``` + +### 3. Monitor Sanitization Events + +Use logging to track when sanitization occurs: + +```python +import litellm +import logging + +# Enable debug logging +litellm.set_verbose = True +logging.basicConfig(level=logging.DEBUG) + +# Track sanitization events in your application +response = litellm.completion( + model="anthropic/claude-3-5-sonnet-20241022", + messages=messages +) +``` + +### 4. Test Edge Cases + +Ensure your application handles sanitized messages correctly: + +```python +import litellm +litellm.modify_params = True + +# Test orphaned tool calls +test_messages = [ + {"role": "user", "content": "Test"}, + {"role": "assistant", "tool_calls": [{"id": "call_1", "type": "function", "function": {"name": "test", "arguments": "{}"}}]}, + {"role": "user", "content": "Continue"} # No tool result +] + +response = litellm.completion( + model="anthropic/claude-3-5-sonnet-20241022", + messages=test_messages, + tools=[...] +) + +# Verify the response handles the dummy tool result appropriately +``` + +## Related Features + +- **[Drop Params](./drop_params.md)** - Drop unsupported parameters for specific providers +- **[Message Trimming](./message_trimming.md)** - Trim messages to fit token limits +- **[Function Calling](./function_call.md)** - Complete guide to tool/function calling +- **[Reasoning Content](../reasoning_content.md)** - Extended thinking with tool calling + +## Troubleshooting + +### Sanitization Not Working + +**Issue:** Messages still cause errors despite `modify_params=True` + +**Solution:** +1. Verify `modify_params` is enabled: + ```python + import litellm + print(litellm.modify_params) # Should be True + ``` + +2. Check if the issue is provider-specific: + ```python + litellm.set_verbose = True # Enable debug logging + ``` + +3. Ensure you're using a recent version of LiteLLM: + ```bash + pip install --upgrade litellm + ``` + +### Unexpected Dummy Tool Results + +**Issue:** Dummy tool results appear when you expect actual results + +**Cause:** Tool result messages are missing or have incorrect `tool_call_id` + +**Solution:** +1. Verify tool result messages have correct `tool_call_id`: + ```python + # Correct + {"role": "tool", "tool_call_id": "call_123", "content": "result"} + + # Incorrect - will be treated as orphaned + {"role": "tool", "tool_call_id": "wrong_id", "content": "result"} + ``` + +2. Ensure tool results immediately follow assistant messages with tool_calls + +### Performance Impact + +**Issue:** Concerned about performance overhead + +**Details:** Message sanitization has minimal performance impact: +- Runs in O(n) time where n = number of messages +- Only processes messages when `modify_params=True` +- Typically adds < 1ms to request processing time + +## FAQ + +**Q: Does sanitization modify my original messages?** + +A: No, sanitization creates a new list of messages. Your original messages remain unchanged. + +**Q: Can I disable specific sanitization cases?** + +A: Currently, all three cases are handled together when `modify_params=True`. To disable sanitization entirely, set `modify_params=False`. + +**Q: What happens to the dummy tool results?** + +A: Dummy tool results are sent to the LLM provider along with other messages. The model sees them as regular tool results with informative error messages. + +**Q: Does this work with streaming?** + +A: Yes, message sanitization works with both streaming and non-streaming requests. + +**Q: Is this related to `drop_params`?** + +A: No, they're separate features: +- `modify_params` - Modifies/fixes message content and structure +- `drop_params` - Removes unsupported API parameters + +Both can be enabled simultaneously. + +## See Also + +- [Reasoning Content with Tool Calling](../reasoning_content.md) +- [Function Calling Guide](./function_call.md) +- [Bedrock Provider Documentation](../providers/bedrock.md) +- [Anthropic Provider Documentation](../providers/anthropic.md) diff --git a/docs/my-website/docs/completion/usage.md b/docs/my-website/docs/completion/usage.md index c388e5bfee..d610afeae5 100644 --- a/docs/my-website/docs/completion/usage.md +++ b/docs/my-website/docs/completion/usage.md @@ -50,3 +50,51 @@ for chunk in completion: print(chunk.choices[0].delta) ``` + +### Proxy: Always Include Streaming Usage + +When using the LiteLLM Proxy, you can configure it to automatically include usage information in all streaming responses, even if the client doesn't send `stream_options={"include_usage": True}`. + +#### Configuration + +Add the following to your config.yaml: + +```yaml +general_settings: + always_include_stream_usage: true +``` + +Alternatively, configure it through the UI: + +1. Navigate to the LiteLLM Proxy UI +2. Go to `Settings` > `Router Settings` > `General` +3. Find the `always_include_stream_usage` setting +4. Toggle it to `true` +5. Click `Update` to save + +#### How it works + +When `always_include_stream_usage` is enabled: +- All streaming requests will automatically have `stream_options={"include_usage": True}` added +- Clients will receive usage information in the final chunk, even if they didn't explicitly request it +- If a client already provides `stream_options`, `include_usage: True` will be added without overwriting other options +- Non-streaming requests are not affected + +#### Example + +With this setting enabled, a simple streaming request like: + +```bash +curl -X POST http://localhost:4000/v1/chat/completions \ + -H "Authorization: Bearer sk-1234" \ + -H "Content-Type: application/json" \ + -d '{ + "model": "gpt-4o", + "messages": [{"role": "user", "content": "Hello!"}], + "stream": true + }' +``` + +Will automatically receive usage information in the response, without needing to explicitly include `stream_options`. + +``` diff --git a/docs/my-website/docs/completion/web_search.md b/docs/my-website/docs/completion/web_search.md index 9ba66c730f..1f5ba2dee4 100644 --- a/docs/my-website/docs/completion/web_search.md +++ b/docs/my-website/docs/completion/web_search.md @@ -18,7 +18,7 @@ Each provider uses their own search backend: | Provider | Search Engine | Notes | |----------|---------------|-------| -| **OpenAI** (`gpt-4o-search-preview`, `gpt-4o-mini-search-preview`, `gpt-5-search-api`) | OpenAI's internal search | Real-time web data | +| **OpenAI** (`gpt-5-search-api`, `gpt-4o-search-preview`, `gpt-4o-mini-search-preview`) | OpenAI's internal search | Real-time web data | | **xAI** (`grok-3`) | xAI's search + X/Twitter | Real-time social media data | | **Google AI/Vertex** (`gemini-2.0-flash`) | **Google Search** | Uses actual Google search results | | **Anthropic** (`claude-3-5-sonnet`) | Anthropic's web search | Real-time web data | @@ -45,6 +45,19 @@ Use `web_search_options` when you need to: **Anthropic Web Search Models**: Claude models that support web search: `claude-3-5-sonnet-latest`, `claude-3-5-sonnet-20241022`, `claude-3-5-haiku-latest`, `claude-3-5-haiku-20241022`, `claude-3-7-sonnet-20250219` ::: +## OpenAI Web Search: Two Approaches + +OpenAI offers two distinct ways to use web search depending on the endpoint and model: + +| Approach | Endpoint | Models | How to enable | +|----------|----------|--------|---------------| +| **Search Models** | `/chat/completions` | `gpt-5-search-api`, `gpt-4o-search-preview`, `gpt-4o-mini-search-preview` | Pass `web_search_options` parameter | +| **Web Search Tool** | `/responses` | `gpt-5`, `gpt-4.1`, `gpt-4o`, and other regular models | Pass `web_search_preview` tool | + +:::tip Search models search automatically +Search models like `gpt-5-search-api` **automatically search the web** even without the `web_search_options` parameter. Use `web_search_options` to set `search_context_size` (`"low"`, `"medium"`, `"high"`) or specify `user_location` for localized results. +::: + ## `/chat/completions` (litellm.completion) ### Quick Start @@ -56,7 +69,7 @@ Use `web_search_options` when you need to: from litellm import completion response = completion( - model="openai/gpt-4o-search-preview", + model="openai/gpt-5-search-api", messages=[ { "role": "user", @@ -76,31 +89,36 @@ response = completion( ```yaml model_list: - # OpenAI + # OpenAI search models + - model_name: gpt-5-search-api + litellm_params: + model: openai/gpt-5-search-api + api_key: os.environ/OPENAI_API_KEY + - model_name: gpt-4o-search-preview litellm_params: model: openai/gpt-4o-search-preview api_key: os.environ/OPENAI_API_KEY - + # xAI - model_name: grok-3 litellm_params: model: xai/grok-3 api_key: os.environ/XAI_API_KEY - + # Anthropic - model_name: claude-3-5-sonnet-latest litellm_params: model: anthropic/claude-3-5-sonnet-latest api_key: os.environ/ANTHROPIC_API_KEY - + # VertexAI - model_name: gemini-2-flash litellm_params: model: gemini-2.0-flash vertex_project: your-project-id vertex_location: us-central1 - + # Google AI Studio - model_name: gemini-2-flash-studio litellm_params: @@ -108,13 +126,13 @@ model_list: api_key: os.environ/GOOGLE_API_KEY ``` -2. Start the proxy +2. Start the proxy ```bash litellm --config /path/to/config.yaml ``` -3. Test it! +3. Test it! ```python showLineNumbers from openai import OpenAI @@ -126,13 +144,18 @@ client = OpenAI( ) response = client.chat.completions.create( - model="grok-3", # or any other web search enabled model + model="gpt-5-search-api", # or any other web search enabled model messages=[ { "role": "user", "content": "What was a positive news story from today?" } - ] + ], + extra_body={ + "web_search_options": { + "search_context_size": "medium" + } + } ) ``` @@ -149,7 +172,7 @@ from litellm import completion # Customize search context size response = completion( - model="openai/gpt-4o-search-preview", + model="openai/gpt-5-search-api", messages=[ { "role": "user", @@ -257,6 +280,12 @@ response = client.chat.completions.create( ## `/responses` (litellm.responses) +Use the `web_search_preview` tool with models like `gpt-5`, `gpt-4.1`, `gpt-4o`, etc. + +:::info +Search-dedicated models like `gpt-5-search-api` and `gpt-4o-search-preview` do **not** support the `/responses` endpoint. Use them with `/chat/completions` + `web_search_options` instead (see above). +::: + ### Quick Start @@ -266,18 +295,14 @@ response = client.chat.completions.create( from litellm import responses response = responses( - model="openai/gpt-4o", - input=[ - { - "role": "user", - "content": "What was a positive news story from today?" - } - ], + model="openai/gpt-5", + input="What is the capital of France?", tools=[{ "type": "web_search_preview" # enables web search with default medium context size }] ) ``` + @@ -285,19 +310,24 @@ response = responses( ```yaml model_list: - - model_name: gpt-4o + - model_name: gpt-5 litellm_params: - model: openai/gpt-4o + model: openai/gpt-5 + api_key: os.environ/OPENAI_API_KEY + + - model_name: gpt-4.1 + litellm_params: + model: openai/gpt-4.1 api_key: os.environ/OPENAI_API_KEY ``` -2. Start the proxy +2. Start the proxy ```bash litellm --config /path/to/config.yaml ``` -3. Test it! +3. Test it! ```python showLineNumbers from openai import OpenAI @@ -309,11 +339,11 @@ client = OpenAI( ) response = client.responses.create( - model="gpt-4o", + model="gpt-5", tools=[{ "type": "web_search_preview" }], - input="What was a positive news story from today?", + input="What is the capital of France?", ) print(response.output_text) @@ -331,13 +361,8 @@ from litellm import responses # Customize search context size response = responses( - model="openai/gpt-4o", - input=[ - { - "role": "user", - "content": "What was a positive news story from today?" - } - ], + model="openai/gpt-5", + input="What is the capital of France?", tools=[{ "type": "web_search_preview", "search_context_size": "low" # Options: "low", "medium" (default), "high" @@ -358,12 +383,12 @@ client = OpenAI( # Customize search context size response = client.responses.create( - model="gpt-4o", + model="gpt-5", tools=[{ "type": "web_search_preview", "search_context_size": "low" # Options: "low", "medium" (default), "high" }], - input="What was a positive news story from today?", + input="What is the capital of France?", ) print(response.output_text) @@ -417,14 +442,14 @@ model_list: web_search_options: search_context_size: "high" # Options: "low", "medium", "high" - # Different context size for different models - - model_name: gpt-4o-search-preview + # OpenAI search model with custom context size + - model_name: gpt-5-search-api litellm_params: - model: openai/gpt-4o-search-preview + model: openai/gpt-5-search-api api_key: os.environ/OPENAI_API_KEY web_search_options: search_context_size: "low" - + # Gemini with medium context (default) - model_name: gemini-2-flash litellm_params: @@ -449,6 +474,7 @@ Use `litellm.supports_web_search(model="model_name")` -> returns `True` if model ```python showLineNumbers # Check OpenAI models +assert litellm.supports_web_search(model="openai/gpt-5-search-api") == True assert litellm.supports_web_search(model="openai/gpt-4o-search-preview") == True # Check xAI models @@ -472,13 +498,20 @@ assert litellm.supports_web_search(model="gemini/gemini-2.0-flash") == True ```yaml model_list: # OpenAI + - model_name: gpt-5-search-api + litellm_params: + model: openai/gpt-5-search-api + api_key: os.environ/OPENAI_API_KEY + model_info: + supports_web_search: True + - model_name: gpt-4o-search-preview litellm_params: model: openai/gpt-4o-search-preview api_key: os.environ/OPENAI_API_KEY model_info: supports_web_search: True - + # xAI - model_name: grok-3 litellm_params: @@ -533,6 +566,12 @@ Expected Response ```json showLineNumbers { "data": [ + { + "model_group": "gpt-5-search-api", + "providers": ["openai"], + "max_tokens": 128000, + "supports_web_search": true + }, { "model_group": "gpt-4o-search-preview", "providers": ["openai"], diff --git a/docs/my-website/docs/evals_api.md b/docs/my-website/docs/evals_api.md new file mode 100644 index 0000000000..bb66e9fdc0 --- /dev/null +++ b/docs/my-website/docs/evals_api.md @@ -0,0 +1,441 @@ +# /evals + +LiteLLM Proxy supports OpenAI's Evaluations (Evals) API, allowing you to create, manage, and run evaluations to measure model performance against defined testing criteria. + +## What are Evals? + +OpenAI Evals API provides a structured way to: +- **Create Evaluations**: Define testing criteria and data sources for evaluating model outputs +- **Run Evaluations**: Execute evaluations against specific models and datasets +- **Track Results**: Monitor evaluation progress and review detailed results + +## Quick Start + +### Setup LiteLLM Proxy + +First, start your LiteLLM Proxy server: + +```bash +litellm --config config.yaml + +# Proxy will run on http://localhost:4000 +``` + +### Initialize OpenAI Client + +```python +from openai import OpenAI + +# Point to your LiteLLM Proxy +client = OpenAI( + api_key="sk-1234", # Your LiteLLM proxy API key + base_url="http://localhost:4000" # Your proxy URL +) +``` + + +For async operations: + +```python +from openai import AsyncOpenAI + +client = AsyncOpenAI( + api_key="sk-1234", + base_url="http://localhost:4000" +) +``` + +--- + +## Evaluation Management + +### Create an Evaluation + +Create an evaluation with testing criteria and data source configuration. + +#### Example: Sentiment Classification Eval + +```python +from openai import OpenAI + +client = OpenAI( + api_key="sk-1234", + base_url="http://localhost:4000" +) + +# Create evaluation with label model grader +eval_obj = client.evals.create( + name="Sentiment Classification", + data_source_config={ + "type": "stored_completions", + "metadata": {"usecase": "chatbot"} + }, + testing_criteria=[ + { + "type": "label_model", + "model": "gpt-4o-mini", + "input": [ + { + "role": "developer", + "content": "Classify the sentiment of the following statement as one of 'positive', 'neutral', or 'negative'" + }, + { + "role": "user", + "content": "Statement: {{item.input}}" + } + ], + "passing_labels": ["positive"], + "labels": ["positive", "neutral", "negative"], + "name": "Sentiment Grader" + } + ] +) + +# Note: If you want to use model-specific credentials for this evaluation, you can specify the model name in the extra body parameters. + +print(f"Created eval: {eval_obj.id}") +print(f"Eval name: {eval_obj.name}") +``` + +#### Example: Push Notifications Summarizer Monitoring + +This example shows how to monitor prompt changes for regressions in a push notifications summarizer: + +```python +from openai import AsyncOpenAI + +client = AsyncOpenAI( + api_key="sk-1234", + base_url="http://localhost:4000" +) + +# Define data source for stored completions +data_source_config = { + "type": "stored_completions", + "metadata": { + "usecase": "push_notifications_summarizer" + } +} + +# Define grader criteria +GRADER_DEVELOPER_PROMPT = """ +Label the following push notification summary as either correct or incorrect. +The push notification and the summary will be provided below. +A good push notification summary is concise and snappy. +If it is good, then label it as correct, if not, then incorrect. +""" + +GRADER_TEMPLATE_PROMPT = """ +Push notifications: {{item.input}} +Summary: {{sample.output_text}} +""" + +push_notification_grader = { + "name": "Push Notification Summary Grader", + "type": "label_model", + "model": "gpt-4o-mini", + "input": [ + { + "role": "developer", + "content": GRADER_DEVELOPER_PROMPT, + }, + { + "role": "user", + "content": GRADER_TEMPLATE_PROMPT, + }, + ], + "passing_labels": ["correct"], + "labels": ["correct", "incorrect"], +} + +# Create the evaluation +eval_result = await client.evals.create( + name="Push Notification Completion Monitoring", + metadata={"description": "This eval monitors completions"}, + data_source_config=data_source_config, + testing_criteria=[push_notification_grader], +) + +eval_id = eval_result.id +print(f"Created eval: {eval_id}") +``` + +### List Evaluations + +Retrieve a list of all your evaluations with pagination support. + +```python +# List all evaluations +evals_response = client.evals.list( + limit=20, + order="desc" +) + +for eval in evals_response.data: + print(f"Eval ID: {eval.id}, Name: {eval.name}") + +# Check if there are more evals +if evals_response.has_more: + # Fetch next page + next_evals = client.evals.list( + after=evals_response.last_id, + limit=20 + ) +``` + +### Get a Specific Evaluation + +Retrieve details of a specific evaluation by ID. + +```python +eval = client.evals.retrieve( + eval_id="eval_abc123" +) + +print(f"Eval ID: {eval.id}") +print(f"Name: {eval.name}") +print(f"Data Source: {eval.data_source_config}") +print(f"Testing Criteria: {eval.testing_criteria}") +``` + +### Update an Evaluation + +Update evaluation metadata or name. + +```python +updated_eval = client.evals.update( + eval_id="eval_abc123", + name="Updated Evaluation Name", + metadata={ + "version": "2.0", + "updated_by": "user@example.com" + } +) + +print(f"Updated eval: {updated_eval.name}") +``` + +### Delete an Evaluation + +Permanently delete an evaluation. + +```python +delete_response = client.evals.delete( + eval_id="eval_abc123" +) + +print(f"Deleted: {delete_response.deleted}") # True +``` + +--- + +## Evaluation Runs + +### Create a Run + +Execute an evaluation by creating a run. The run processes your data through the model and applies testing criteria. + +#### Using Stored Completions + +First, generate some test data by making chat completions with metadata: + +```python +from openai import AsyncOpenAI +import asyncio + +client = AsyncOpenAI( + api_key="sk-1234", + base_url="http://localhost:4000" +) + +# Generate test data with different prompt versions +push_notification_data = [ + """ +- New message from Sarah: "Can you call me later?" +- Your package has been delivered! +- Flash sale: 20% off electronics for the next 2 hours! +""", + """ +- Weather alert: Thunderstorm expected in your area. +- Reminder: Doctor's appointment at 3 PM. +- John liked your photo on Instagram. +""" +] + +PROMPTS = [ + ( + """ + You are a helpful assistant that summarizes push notifications. + You are given a list of push notifications and you need to collapse them into a single one. + Output only the final summary, nothing else. + """, + "v1" + ), + ( + """ + You are a helpful assistant that summarizes push notifications. + You are given a list of push notifications and you need to collapse them into a single one. + The summary should be longer than it needs to be and include more information than is necessary. + Output only the final summary, nothing else. + """, + "v2" + ) +] + +# Create completions with metadata for tracking +tasks = [] +for notifications in push_notification_data: + for (prompt, version) in PROMPTS: + tasks.append(client.chat.completions.create( + model="gpt-4o-mini", + messages=[ + {"role": "developer", "content": prompt}, + {"role": "user", "content": notifications}, + ], + metadata={ + "prompt_version": version, + "usecase": "push_notifications_summarizer" + } + )) + +await asyncio.gather(*tasks) +``` + +Now create runs to evaluate different prompt versions: + +```python +# Grade prompt_version=v1 +eval_run_result = await client.evals.runs.create( + eval_id=eval_id, + name="v1-run", + data_source={ + "type": "completions", + "source": { + "type": "stored_completions", + "metadata": { + "prompt_version": "v1", + } + } + } +) + +print(f"Run ID: {eval_run_result.id}") +print(f"Status: {eval_run_result.status}") +print(f"Report URL: {eval_run_result.report_url}") + +# Grade prompt_version=v2 +eval_run_result_v2 = await client.evals.runs.create( + eval_id=eval_id, + name="v2-run", + data_source={ + "type": "completions", + "source": { + "type": "stored_completions", + "metadata": { + "prompt_version": "v2", + } + } + } +) + +print(f"Run ID: {eval_run_result_v2.id}") +print(f"Report URL: {eval_run_result_v2.report_url}") +``` + +#### Using Completions with Different Models + +Test how different models perform on the same inputs: + +```python +# Test with GPT-4o using stored completions as input +tasks = [] +for prompt_version in ["v1", "v2"]: + tasks.append(client.evals.runs.create( + eval_id=eval_id, + name=f"gpt-4o-run-{prompt_version}", + data_source={ + "type": "completions", + "input_messages": { + "type": "item_reference", + "item_reference": "item.input", + }, + "model": "gpt-4o", + "source": { + "type": "stored_completions", + "metadata": { + "prompt_version": prompt_version, + } + } + } + )) + +results = await asyncio.gather(*tasks) +for run in results: + print(f"Report URL: {run.report_url}") +``` + +### List Runs + +Get all runs for a specific evaluation. + +```python +# List all runs for an evaluation +runs_response = client.evals.runs.list( + eval_id="eval_abc123", + limit=20, + order="desc" +) + +for run in runs_response.data: + print(f"Run ID: {run.id}") + print(f"Status: {run.status}") + print(f"Name: {run.name}") + if run.result_counts: + print(f"Results: {run.result_counts.passed}/{run.result_counts.total} passed") +``` + +### Get Run Details + +Retrieve detailed information about a specific run, including results. + +```python +run = client.evals.runs.retrieve( + eval_id="eval_abc123", + run_id="run_def456" +) + +print(f"Run ID: {run.id}") +print(f"Status: {run.status}") +print(f"Started: {run.started_at}") +print(f"Completed: {run.completed_at}") + +# Check results +if run.result_counts: + print(f"\nOverall Results:") + print(f"Total: {run.result_counts.total}") + print(f"Passed: {run.result_counts.passed}") + print(f"Failed: {run.result_counts.failed}") + print(f"Error: {run.result_counts.errored}") + +# Per-criteria results +if run.per_testing_criteria_results: + for criteria_result in run.per_testing_criteria_results: + print(f"\nCriteria {criteria_result.testing_criteria_index}:") + print(f" Passed: {criteria_result.result_counts.passed}") + print(f" Average Score: {criteria_result.average_score}") +``` + +### Delete a Run + +Permanently delete a run and its results. + +```python +delete_response = await client.evals.runs.delete( + eval_id="eval_abc123", + run_id="run_def456" +) + +print(f"Deleted: {delete_response.deleted}") # True +print(f"Run ID: {delete_response.run_id}") +``` + diff --git a/docs/my-website/docs/mcp.md b/docs/my-website/docs/mcp.md index 84d10c2593..50973f220f 100644 --- a/docs/my-website/docs/mcp.md +++ b/docs/my-website/docs/mcp.md @@ -808,6 +808,68 @@ If your stdio MCP server needs per-request credentials, you can map HTTP headers In this example, when a client makes a request with the `X-GITHUB_PERSONAL_ACCESS_TOKEN` header, the proxy forwards that value into the stdio process as the `GITHUB_PERSONAL_ACCESS_TOKEN` environment variable. +## Control MCP Access for End Users + +Control which MCP servers end users of your AI application can access (e.g. users of an internal chat UI). Pass the customer ID in the `x-litellm-end-user-id` header to: +- Enforce object permissions (limit which MCP servers they can access) +- Apply customer-specific budgets +- Track spend per customer + +**FastMCP Client Example:** + +```python title="Track customer spend with x-litellm-end-user-id" showLineNumbers +from fastmcp import Client +import asyncio + +# MCP client configuration with customer tracking +config = { + "mcpServers": { + "github": { + "url": "http://localhost:4000/github_mcp/mcp", + "headers": { + "x-litellm-api-key": "Bearer sk-1234", + "x-litellm-end-user-id": "customer_123", # 👈 CUSTOMER ID + "Authorization": "Bearer gho_token" + } + } + } +} + +client = Client(config) + +async def main(): + async with client: + # All MCP calls will be tracked under customer_123 + tools = await client.list_tools() + result = await client.call_tool(tools[0].name, {}) + print(f"Tool result: {result}") + +asyncio.run(main()) +``` + +**Cursor IDE Example:** + +```json title="Cursor config with customer tracking" showLineNumbers +{ + "mcpServers": { + "GitHub": { + "url": "http://localhost:4000/github_mcp/mcp", + "headers": { + "x-litellm-api-key": "Bearer $LITELLM_API_KEY", + "x-litellm-end-user-id": "customer_123" + } + } + } +} +``` + +**What happens:** +- Customer-specific object permissions are enforced (only allowed MCP servers are accessible) +- Customer budgets are applied +- All tool calls are tracked under `customer_123` + +[Learn more about customer management →](./proxy/customers) + ## Using your MCP with client side credentials Use this if you want to pass a client side authentication token to LiteLLM to then pass to your MCP to auth to your MCP. diff --git a/docs/my-website/docs/mcp_oauth.md b/docs/my-website/docs/mcp_oauth.md index 9cd7b1e77b..5c4b70cc5b 100644 --- a/docs/my-website/docs/mcp_oauth.md +++ b/docs/my-website/docs/mcp_oauth.md @@ -242,3 +242,96 @@ curl http://localhost:4000/mcp-rest/tools/call \ | `client_secret` | Yes | OAuth2 client secret. Supports `os.environ/VAR_NAME` | | `token_url` | Yes | Token endpoint URL | | `scopes` | No | List of scopes to request | + +## Debugging OAuth + +When the LiteLLM proxy is hosted remotely and you cannot access server logs, enable **debug headers** to get masked authentication diagnostics in the HTTP response. + +### Enable Debug Mode + +Add the `x-litellm-mcp-debug: true` header to your MCP client request. + +**Claude Code:** + +```bash +claude mcp add --transport http litellm_proxy http://proxy.example.com/atlassian_mcp/mcp \ + --header "x-litellm-api-key: Bearer sk-..." \ + --header "x-litellm-mcp-debug: true" +``` + +**curl:** + +```bash +curl -X POST http://localhost:4000/atlassian_mcp/mcp \ + -H "Content-Type: application/json" \ + -H "x-litellm-api-key: Bearer sk-..." \ + -H "x-litellm-mcp-debug: true" \ + -d '{"jsonrpc":"2.0","id":1,"method":"tools/list","params":{}}' +``` + +### Reading the Debug Response Headers + +The response includes these headers (all sensitive values are masked): + +| Header | Description | +|--------|-------------| +| `x-mcp-debug-inbound-auth` | Which inbound auth headers were present. | +| `x-mcp-debug-oauth2-token` | The OAuth2 token (masked). Shows `SAME_AS_LITELLM_KEY` if the LiteLLM key is leaking. | +| `x-mcp-debug-auth-resolution` | Which auth method was used: `oauth2-passthrough`, `m2m-client-credentials`, `per-request-header`, `static-token`, or `no-auth`. | +| `x-mcp-debug-outbound-url` | The upstream MCP server URL. | +| `x-mcp-debug-server-auth-type` | The `auth_type` configured on the server. | + +**Example — healthy OAuth2 passthrough:** + +``` +x-mcp-debug-inbound-auth: x-litellm-api-key=Bearer****1234; authorization=Bearer****ef01 +x-mcp-debug-oauth2-token: Bearer****ef01 +x-mcp-debug-auth-resolution: oauth2-passthrough +x-mcp-debug-outbound-url: https://mcp.atlassian.com/v1/mcp +x-mcp-debug-server-auth-type: oauth2 +``` + +**Example — LiteLLM key leaking (misconfigured):** + +``` +x-mcp-debug-inbound-auth: authorization=Bearer****1234 +x-mcp-debug-oauth2-token: Bearer****1234 (SAME_AS_LITELLM_KEY - likely misconfigured) +x-mcp-debug-auth-resolution: oauth2-passthrough +x-mcp-debug-outbound-url: https://mcp.atlassian.com/v1/mcp +x-mcp-debug-server-auth-type: oauth2 +``` + +### Common Issues + +#### LiteLLM API key leaking to the MCP server + +**Symptom:** `x-mcp-debug-oauth2-token` shows `SAME_AS_LITELLM_KEY`. + +The `Authorization` header carries the LiteLLM API key instead of an OAuth2 token. The OAuth2 flow never ran because the client already had an `Authorization` header set. + +**Fix:** Move the LiteLLM key to `x-litellm-api-key`: + +```bash +# WRONG — blocks OAuth2 discovery +claude mcp add --transport http my_server http://proxy/mcp/server \ + --header "Authorization: Bearer sk-..." + +# CORRECT — LiteLLM key in dedicated header, Authorization free for OAuth2 +claude mcp add --transport http my_server http://proxy/mcp/server \ + --header "x-litellm-api-key: Bearer sk-..." +``` + +#### No OAuth2 token present + +**Symptom:** `x-mcp-debug-oauth2-token` shows `(none)` and `x-mcp-debug-auth-resolution` shows `no-auth`. + +Check that: +1. The `Authorization` header is NOT set as a static header in the client config. +2. The MCP server in LiteLLM config has `auth_type: oauth2`. +3. The `.well-known/oauth-protected-resource` endpoint returns valid metadata. + +#### M2M token used instead of user token + +**Symptom:** `x-mcp-debug-auth-resolution` shows `m2m-client-credentials`. + +The server has `client_id`/`client_secret`/`token_url` configured so LiteLLM is fetching a machine-to-machine token instead of using the per-user OAuth2 token. To use per-user tokens, remove the client credentials from the server config. diff --git a/docs/my-website/docs/mcp_troubleshoot.md b/docs/my-website/docs/mcp_troubleshoot.md index 27ba0e4d78..57e7bfa674 100644 --- a/docs/my-website/docs/mcp_troubleshoot.md +++ b/docs/my-website/docs/mcp_troubleshoot.md @@ -6,6 +6,39 @@ When LiteLLM acts as an MCP proxy, traffic normally flows `Client → LiteLLM Pr For provisioning steps, transport options, and configuration fields, refer to [mcp.md](./mcp.md). +## Quick Start: Debug with One Command + +The fastest way to debug MCP issues is to enable **debug headers**. Run this curl against your LiteLLM proxy and check the response headers: + +```bash +curl -si -X POST http://localhost:4000/{your_mcp_server}/mcp \ + -H "Content-Type: application/json" \ + -H "x-litellm-api-key: Bearer sk-YOUR_KEY" \ + -H "x-litellm-mcp-debug: true" \ + -d '{"jsonrpc":"2.0","id":1,"method":"tools/list","params":{}}' \ + 2>&1 | grep -i "x-mcp-debug" +``` + +This returns masked diagnostic headers that tell you exactly what's happening with authentication: + +``` +x-mcp-debug-inbound-auth: x-litellm-api-key=Bearer****1234 +x-mcp-debug-oauth2-token: Bearer****ef01 +x-mcp-debug-auth-resolution: oauth2-passthrough +x-mcp-debug-outbound-url: https://mcp.atlassian.com/v1/mcp +x-mcp-debug-server-auth-type: oauth2 +``` + +If you see `SAME_AS_LITELLM_KEY` in `x-mcp-debug-oauth2-token`, your LiteLLM API key is leaking to the MCP server instead of an OAuth2 token. See [Debugging OAuth](./mcp_oauth#debugging-oauth) for the fix and other common issues. + +For Claude Code, add the debug header to your MCP config: + +```bash +claude mcp add --transport http my_server http://localhost:4000/my_mcp/mcp \ + --header "x-litellm-api-key: Bearer sk-..." \ + --header "x-litellm-mcp-debug: true" +``` + ## Locate the Error Source Pin down where the failure occurs before adjusting settings so you do not mix symptoms from separate hops. @@ -13,7 +46,7 @@ Pin down where the failure occurs before adjusting settings so you do not mix sy ### LiteLLM UI / Playground Errors (LiteLLM → MCP) Failures shown on the MCP creation form or within the MCP Tool Testing Playground mean the LiteLLM proxy cannot reach the MCP server. Typical causes are misconfiguration (transport, headers, credentials), MCP/server outages, network/firewall blocks, or inaccessible OAuth metadata. - @@ -22,7 +55,7 @@ Failures shown on the MCP creation form or within the MCP Tool Testing Playgroun **Actions** - Capture LiteLLM proxy logs alongside MCP-server logs (see [Error Log Example](./mcp_troubleshoot#error-log-example-failed-mcp-call)) to inspect the request/response pair and stack traces. -- From the LiteLLM server, run Method 2 ([`curl` smoke test](./mcp_troubleshoot#curl-smoke-test)) against the MCP endpoint to confirm basic connectivity. +- From the LiteLLM server, run a [`curl` smoke test](./mcp_troubleshoot#curl-smoke-test) against the MCP endpoint to confirm basic connectivity. ### Client Traffic Issues (Client → LiteLLM) If only real client requests fail, determine whether LiteLLM ever reaches the MCP hop. @@ -43,7 +76,7 @@ During `/responses` or `/chat/completions`, LiteLLM may trigger MCP tool calls m - Validate MCP connectivity with the [MCP Inspector](./mcp_troubleshoot#mcp-inspector) to ensure the server responds. - Reproduce the same MCP call via the LiteLLM Playground to confirm LiteLLM can complete the MCP hop independently. - @@ -55,6 +88,10 @@ LiteLLM performs metadata discovery per the MCP spec ([section 2.3](https://mode - Use `curl ` (or similar) from the LiteLLM host to ensure the discovery document is reachable and contains the expected authorization/token endpoints. - Record the exact metadata URL, requested scopes, and any static client credentials so support can replay the discovery step if needed. +## Debugging OAuth + +For detailed OAuth2 debugging — including debug header reference, common misconfigurations, and example output — see [Debugging OAuth](./mcp_oauth#debugging-oauth). + ## Verify Connectivity Run lightweight validations before impacting production traffic. @@ -66,7 +103,7 @@ Use the MCP Inspector when you need to test both `Client → LiteLLM` and `Clien 2. Configure and connect: - **Transport Type:** choose the transport the client uses (Streamable HTTP for LiteLLM). - **URL:** the endpoint under test (LiteLLM MCP URL for `Client → LiteLLM`, or the MCP server URL for `Client → MCP`). - - **Custom Headers:** e.g., `Authorization: Bearer `. + - **Custom Headers:** e.g., `x-litellm-api-key: Bearer `. 3. Open the **Tools** tab and click **List Tools** to verify the MCP alias responds. ### `curl` Smoke Test @@ -79,7 +116,7 @@ curl -X POST https://your-target-domain.example.com/mcp \ -d '{"jsonrpc":"2.0","id":1,"method":"tools/list","params":{}}' ``` -Add `-H "Authorization: Bearer "` when the target is a LiteLLM endpoint that requires authentication. Adjust the headers, or payload to target other MCP methods. Matching failures between `curl` and LiteLLM confirm that the MCP server or network/OAuth layer is the culprit. +Add `-H "x-litellm-api-key: Bearer "` when the target is a LiteLLM endpoint that requires authentication. Adjust the headers or payload to target other MCP methods. Matching failures between `curl` and LiteLLM confirm that the MCP server or network/OAuth layer is the culprit. ## Review Logs diff --git a/docs/my-website/docs/observability/datadog.md b/docs/my-website/docs/observability/datadog.md index 6f785be101..9385b0020c 100644 --- a/docs/my-website/docs/observability/datadog.md +++ b/docs/my-website/docs/observability/datadog.md @@ -253,3 +253,12 @@ LiteLLM supports customizing the following Datadog environment variables \* **Required when using Direct API** (default): `DD_API_KEY` and `DD_SITE` are required \* **Optional when using DataDog Agent**: Set `LITELLM_DD_AGENT_HOST` to use agent mode; `DD_API_KEY` and `DD_SITE` are not required for **Datadog Logs**. (**Note: `DD_API_KEY` IS REQUIRED for Datadog LLM Observability**) +## Automatic Tags + +LiteLLM automatically adds the following tags to your Datadog logs and metrics if the information is available in the request: + +| Tag | Description | Source | +|-----|-------------|--------| +| `team` | The team alias or ID associated with the API Key | `user_api_key_team_alias`, `team_alias`, `user_api_key_team_id`, or `team_id` in metadata | +| `request_tag` | Custom tags passed in the request | `request_tags` in logging payload | + diff --git a/docs/my-website/docs/projects/openai-agents.md b/docs/my-website/docs/projects/openai-agents.md index 95a2191b88..86983e7e51 100644 --- a/docs/my-website/docs/projects/openai-agents.md +++ b/docs/my-website/docs/projects/openai-agents.md @@ -1,22 +1,121 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; # OpenAI Agents SDK -The [OpenAI Agents SDK](https://github.com/openai/openai-agents-python) is a lightweight framework for building multi-agent workflows. -It includes an official LiteLLM extension that lets you use any of the 100+ supported providers (Anthropic, Gemini, Mistral, Bedrock, etc.) +Use OpenAI Agents SDK with any LLM provider through LiteLLM Proxy. + +The [OpenAI Agents SDK](https://github.com/openai/openai-agents-python) is a lightweight framework for building multi-agent workflows. It includes an official LiteLLM extension that lets you use any of the 100+ supported providers. + +## Quick Start + +### 1. Install Dependencies + +```bash +pip install "openai-agents[litellm]" +``` + +### 2. Add Model to Config + +```yaml title="config.yaml" +model_list: + - model_name: gpt-4o + litellm_params: + model: "openai/gpt-4o" + api_key: "os.environ/OPENAI_API_KEY" + + - model_name: claude-sonnet + litellm_params: + model: "anthropic/claude-3-5-sonnet-20241022" + api_key: "os.environ/ANTHROPIC_API_KEY" + + - model_name: gemini-pro + litellm_params: + model: "gemini/gemini-2.0-flash-exp" + api_key: "os.environ/GEMINI_API_KEY" +``` + +### 3. Start LiteLLM Proxy + +```bash +litellm --config config.yaml +``` + +### 4. Use with Proxy + + + ```python from agents import Agent, Runner from agents.extensions.models.litellm_model import LitellmModel +# Point to LiteLLM proxy agent = Agent( name="Assistant", instructions="You are a helpful assistant.", - model=LitellmModel(model="provider/model-name") + model=LitellmModel( + model="claude-sonnet", # Model from config.yaml + api_key="sk-1234", # LiteLLM API key + base_url="http://localhost:4000" + ) ) -result = Runner.run_sync(agent, "your_prompt_here") -print("Result:", result.final_output) +result = await Runner.run(agent, "What is LiteLLM?") +print(result.final_output) ``` -- [GitHub](https://github.com/openai/openai-agents-python) -- [LiteLLM Extension Docs](https://openai.github.io/openai-agents-python/ref/extensions/litellm/) + + + +```python +from agents import Agent, Runner +from agents.extensions.models.litellm_model import LitellmModel + +# Use any provider directly +agent = Agent( + name="Assistant", + instructions="You are a helpful assistant.", + model=LitellmModel( + model="anthropic/claude-3-5-sonnet-20241022", + api_key="your-anthropic-key" + ) +) + +result = await Runner.run(agent, "What is LiteLLM?") +print(result.final_output) +``` + + + + +## Track Usage + +Enable usage tracking to monitor token consumption: + +```python +from agents import Agent, ModelSettings +from agents.extensions.models.litellm_model import LitellmModel + +agent = Agent( + name="Assistant", + model=LitellmModel(model="claude-sonnet", api_key="sk-1234"), + model_settings=ModelSettings(include_usage=True) +) + +result = await Runner.run(agent, "Hello") +print(result.context_wrapper.usage) # Token counts +``` + +## Environment Variables + +| Variable | Value | Description | +|----------|-------|-------------| +| `LITELLM_BASE_URL` | `http://localhost:4000` | LiteLLM proxy URL | +| `LITELLM_API_KEY` | `sk-1234` | Your LiteLLM API key | + +## Related Resources + +- [OpenAI Agents SDK Documentation](https://openai.github.io/openai-agents-python/) +- [LiteLLM Extension Docs](https://openai.github.io/openai-agents-python/models/litellm/) +- [LiteLLM Proxy Quick Start](../proxy/quick_start) diff --git a/docs/my-website/docs/providers/anthropic.md b/docs/my-website/docs/providers/anthropic.md index 446d663c5a..de5a4dc610 100644 --- a/docs/my-website/docs/providers/anthropic.md +++ b/docs/my-website/docs/providers/anthropic.md @@ -1473,6 +1473,20 @@ LiteLLM translates OpenAI's `reasoning_effort` to Anthropic's `thinking` paramet | "medium" | "budget_tokens": 2048 | | "high" | "budget_tokens": 4096 | +:::note +For Claude Opus 4.6, all `reasoning_effort` values (`low`, `medium`, `high`) are mapped to `thinking: {type: "adaptive"}`. To use explicit thinking budgets, pass the native `thinking` parameter directly: + +```python +from litellm import completion + +resp = completion( + model="anthropic/claude-opus-4-6", + messages=[{"role": "user", "content": "What is the capital of France?"}], + thinking={"type": "enabled", "budget_tokens": 1024}, +) +``` +::: + @@ -1614,8 +1628,65 @@ curl http://0.0.0.0:4000/v1/chat/completions \ +#### Adaptive Thinking (Claude Opus 4.6) + + +```python +response = litellm.completion( + model="anthropic/claude-opus-4-6", + messages=[{"role": "user", "content": "What is the optimal strategy for solving this problem?"}], + thinking={"type": "adaptive"}, +) +``` + + + + +```bash +curl http://0.0.0.0:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer $LITELLM_KEY" \ + -d '{ + "model": "anthropic/claude-opus-4-6", + "messages": [{"role": "user", "content": "What is the optimal strategy for solving this problem?"}], + "thinking": {"type": "adaptive"} + }' +``` + + + + +#### Enabled Thinking with Budget + + + + +```python +response = litellm.completion( + model="anthropic/claude-opus-4-6", + messages=[{"role": "user", "content": "What is the capital of France?"}], + thinking={"type": "enabled", "budget_tokens": 5000}, +) +``` + + + + +```bash +curl http://0.0.0.0:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer $LITELLM_KEY" \ + -d '{ + "model": "anthropic/claude-opus-4-6", + "messages": [{"role": "user", "content": "What is the capital of France?"}], + "thinking": {"type": "enabled", "budget_tokens": 5000} + }' +``` + + + ## **Passing Extra Headers to Anthropic API** diff --git a/docs/my-website/docs/providers/dashscope.md b/docs/my-website/docs/providers/dashscope.md index 565776d6c4..3df0fbab1b 100644 --- a/docs/my-website/docs/providers/dashscope.md +++ b/docs/my-website/docs/providers/dashscope.md @@ -1,7 +1,7 @@ -# Dashscope (Qwen API) +# Dashscope API (Qwen models) https://dashscope.console.aliyun.com/ -**We support ALL Qwen models, just set `dashscope/` as a prefix when sending completion requests** +**We support ALL Qwen models (from Alibaba Cloud), just set `dashscope/` as a prefix when sending completion requests** ## API Key ```python @@ -9,6 +9,26 @@ https://dashscope.console.aliyun.com/ os.environ['DASHSCOPE_API_KEY'] ``` +## API Base +You can optionally specify the API base URL depending on your region: + +| Region | API Base | +|--------|----------| +| **International** | `https://dashscope-intl.aliyuncs.com/compatible-mode/v1` | +| **China/Beijing** | `https://dashscope.aliyuncs.com/compatible-mode/v1` | + +```python +# Set via environment variable +os.environ['DASHSCOPE_API_BASE'] = "https://dashscope-intl.aliyuncs.com/compatible-mode/v1" + +# Or pass directly in the completion call +response = completion( + model="dashscope/qwen-turbo", + messages=[{"role": "user", "content": "hello"}], + api_base="https://dashscope-intl.aliyuncs.com/compatible-mode/v1" +) +``` + ## Sample Usage ```python from litellm import completion @@ -43,9 +63,7 @@ for chunk in response: ``` -## Supported Models - ALL Qwen Models Supported! -We support ALL Qwen models, just set `dashscope/` as a prefix when sending completion requests - +## All supported Models [DashScope Model List](https://help.aliyun.com/zh/model-studio/compatibility-of-openai-with-dashscope?spm=a2c4g.11186623.help-menu-2400256.d_2_8_0.1efd516e2tTXBn&scm=20140722.H_2833609._.OR_help-T_cn~zh-V_1#7f9c78ae99pwz) diff --git a/docs/my-website/docs/providers/gemini.md b/docs/my-website/docs/providers/gemini.md index b9ad7820dd..6de2263916 100644 --- a/docs/my-website/docs/providers/gemini.md +++ b/docs/my-website/docs/providers/gemini.md @@ -1196,6 +1196,8 @@ When responding to Computer Use tool calls, include the URL and screenshot: + + ## Thought Signatures Thought signatures are encrypted representations of the model's internal reasoning process for a given turn in a conversation. By passing thought signatures back to the model in subsequent requests, you provide it with the context of its previous thoughts, allowing it to build upon its reasoning and maintain a coherent line of inquiry. diff --git a/docs/my-website/docs/providers/openai.md b/docs/my-website/docs/providers/openai.md index 80645a51ac..23940e1c54 100644 --- a/docs/my-website/docs/providers/openai.md +++ b/docs/my-website/docs/providers/openai.md @@ -230,7 +230,70 @@ os.environ["OPENAI_BASE_URL"] = "https://your_host/v1" # OPTIONAL These also support the `OPENAI_BASE_URL` environment variable, which can be used to specify a custom API endpoint. -## OpenAI Vision Models +### OpenAI Web Search Models + +OpenAI has two ways to use web search, depending on the endpoint: + +| Approach | Endpoint | Models | How to enable | +|----------|----------|--------|---------------| +| **Search Models** | `/chat/completions` | `gpt-5-search-api`, `gpt-4o-search-preview`, `gpt-4o-mini-search-preview` | Pass `web_search_options` parameter | +| **Web Search Tool** | `/responses` | `gpt-5`, `gpt-4.1`, `gpt-4o`, and other regular models | Pass `web_search_preview` tool | + + + + +```python showLineNumbers +from litellm import completion + +response = completion( + model="openai/gpt-5-search-api", + messages=[{"role": "user", "content": "What is the capital of France?"}], + web_search_options={ + "search_context_size": "medium" # Options: "low", "medium", "high" + } +) +``` + + + + +```python showLineNumbers +from litellm import responses + +response = responses( + model="openai/gpt-5", + input="What is the capital of France?", + tools=[{ + "type": "web_search_preview", + "search_context_size": "low" + }] +) +``` + + + + +```yaml +model_list: + # Search model for /chat/completions + - model_name: gpt-5-search-api + litellm_params: + model: openai/gpt-5-search-api + api_key: os.environ/OPENAI_API_KEY + + # Regular model for /responses with web_search_preview tool + - model_name: gpt-5 + litellm_params: + model: openai/gpt-5 + api_key: os.environ/OPENAI_API_KEY +``` + + + + +For full details, see the [Web Search guide](../completion/web_search.md). + +## OpenAI Vision Models | Model Name | Function Call | |-----------------------|-----------------------------------------------------------------| | gpt-4o | `response = completion(model="gpt-4o", messages=messages)` | diff --git a/docs/my-website/docs/providers/openai/responses_api.md b/docs/my-website/docs/providers/openai/responses_api.md index 75eab1afac..7799c93ccf 100644 --- a/docs/my-website/docs/providers/openai/responses_api.md +++ b/docs/my-website/docs/providers/openai/responses_api.md @@ -37,6 +37,24 @@ for event in response: print(event) ``` +#### Web Search +```python showLineNumbers title="OpenAI Responses with Web Search" +import litellm + +response = litellm.responses( + model="openai/gpt-5", + input="What is the capital of France?", + tools=[{ + "type": "web_search_preview", + "search_context_size": "medium" # Options: "low", "medium", "high" + }] +) + +print(response) +``` + +For full details, see the [Web Search guide](../../completion/web_search.md). + #### Image Generation with Streaming ```python showLineNumbers title="OpenAI Streaming Image Generation" import litellm diff --git a/docs/my-website/docs/providers/scaleway.md b/docs/my-website/docs/providers/scaleway.md new file mode 100644 index 0000000000..ea57c24db3 --- /dev/null +++ b/docs/my-website/docs/providers/scaleway.md @@ -0,0 +1,62 @@ + +# Scaleway +LiteLLM supports all [models available on Scaleway Generative APIs ↗](https://www.scaleway.com/en/docs/generative-apis/reference-content/supported-models/). + +## Usage with LiteLLM Python SDK + +```python +import os +from litellm import completion + +os.environ["SCW_SECRET_KEY"] = "your-scaleway-secret-key" + +messages = [{"role": "user", "content": "Write a short poem"}] +response = completion(model="scaleway/qwen3-235b-a22b-instruct-2507", messages=messages) +print(response) +``` + +## Usage with LiteLLM Proxy + +### 1. Set Scaleway models in config.yaml + +```yaml +model_list: + - model_name: scaleway-model + litellm_params: + model: scaleway/qwen3-235b-a22b-instruct-2507 + api_key: "os.environ/SCW_SECRET_KEY" # ensure you have `SCW_SECRET_KEY` in your .env +``` + +### 2. Start proxy + +```bash +litellm --config config.yaml +``` + +### 3. Query proxy + +Assuming the proxy is running on [http://localhost:4000](http://localhost:4000): +```bash +curl http://localhost:4000/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer YOUR_LITELLM_MASTER_KEY" \ + -d '{ + "model": "scaleway-model", + "messages": [ + { + "role": "system", + "content": "You are a helpful assistant." + }, + { + "role": "user", + "content": "Write a short poem" + } + ] + }' +``` +`-H "Authorization: Bearer YOUR_LITELLM_MASTER_KEY" ` is only required if you have set a LiteLLM master key + + +## Supported features + +Scaleway provider supports all features in [Generative APIs reference documentation ↗](https://www.scaleway.com/en/developers/api/generative-apis/), such as streaming, structured outputs and tool calling. diff --git a/docs/my-website/docs/providers/watsonx/rerank.md b/docs/my-website/docs/providers/watsonx/rerank.md new file mode 100644 index 0000000000..0900ce9678 --- /dev/null +++ b/docs/my-website/docs/providers/watsonx/rerank.md @@ -0,0 +1,52 @@ +# watsonx.ai Rerank + +## Overview + +| Property | Details | +|----------|--------------------------------------------------------------------------| +| Description | watsonx.ai rerank integration | +| Provider Route on LiteLLM | `watsonx/` | +| Supported Operations | `/ml/v1/text/rerank` | +| Link to Provider Doc | [IBM WatsonX.ai ↗](https://cloud.ibm.com/apidocs/watsonx-ai#text-rerank) | + +## Quick Start + +### **LiteLLM SDK** + +```python +import os +from litellm import rerank + +os.environ["WATSONX_APIKEY"] = "YOUR_WATSONX_APIKEY" +os.environ["WATSONX_API_BASE"] = "YOUR_WATSONX_API_BASE" +os.environ["WATSONX_PROJECT_ID"] = "YOUR_WATSONX_PROJECT_ID" + +query="Best programming language for beginners?" +documents=[ + "Python is great for beginners due to simple syntax.", + "JavaScript runs in browsers and is versatile.", + "Rust has a steep learning curve but is very safe.", +] + +response = rerank( + model="watsonx/cross-encoder/ms-marco-minilm-l-12-v2", + query=query, + documents=documents, + top_n=2, + return_documents=True, +) + +print(response) +``` + +### **LiteLLM Proxy** + +```yaml +model_list: + - model_name: cross-encoder/ms-marco-minilm-l-12-v2 + litellm_params: + model: watsonx/cross-encoder/ms-marco-minilm-l-12-v2 + api_key: os.environ/WATSONX_APIKEY + api_base: os.environ/WATSONX_API_BASE + project_id: os.environ/WATSONX_PROJECT_ID +``` diff --git a/docs/my-website/docs/proxy/access_groups.md b/docs/my-website/docs/proxy/access_groups.md new file mode 100644 index 0000000000..59904575da --- /dev/null +++ b/docs/my-website/docs/proxy/access_groups.md @@ -0,0 +1,122 @@ +import Image from '@theme/IdealImage'; +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Access Groups + +Access Groups simplify how you define and manage resource access across your organization. Instead of configuring models, MCP servers, and agents separately on each key or team, you create one group that bundles the resources you want to grant, then attach that group to your keys or teams. + +## Overview + +**Access Groups** let you define a reusable set of allowed resources—models, MCP servers, and agents—in a single place. One group can grant access to all three resource types. Simply attach the group to a key or team, and they get access to everything defined in that group. + +- **Unified resource control** – One group controls access to models, MCP servers, and agents together +- **Reusable** – Define once, attach to many keys or teams +- **Easy to maintain** – Update the group (add or remove resources) and all attached keys and teams automatically reflect the change +- **Clear visibility** – See exactly which resources each group grants and which keys/teams use it + + + +### How It Works + +**Key concept:** Define resources in a group → Attach group to key or team → Key/team gets access to all resources in the group + +| Resource Type | What the group controls | +| --------------- | -------------------------------------------------------------------- | +| **Models** | Which LLM models keys/teams can use (e.g., `gpt-4`, `claude-3-opus`) | +| **MCP Servers** | Which MCP servers are available for tool calling | +| **Agents** | Which agents can be invoked | + +## How to Create and Use Access Groups in the UI + +### 1. Navigate to Access Groups + +Go to the Admin UI (e.g. `http://localhost:4000/ui` or your `PROXY_BASE_URL/ui`) and click **Access Groups** in the sidebar. + +![](https://colony-recorder.s3.amazonaws.com/files/2026-02-15/d117fdb2-18c8-49e0-91e6-1f830d2d4b85/ascreenshot_f5822a0ddac64e3383124419d0c66298_text_export.jpeg) + +### 2. Create an Access Group + +Click **Create Access Group** and give your group a name. + +![](https://colony-recorder.s3.amazonaws.com/files/2026-02-15/aefb900d-d106-4436-806c-3608ad19659f/ascreenshot_3f6fed1256604fe3b7038a0778ce3342_text_export.jpeg) + +![](https://colony-recorder.s3.amazonaws.com/files/2026-02-15/0951bb93-61bd-477e-beaf-f58810f8980b/ascreenshot_f0fb5d552fd74ff8a1080e82758fcdc2_text_export.jpeg) + +### 3. Define Resources in the Group + +Use the tabs to select which models, MCP servers, and agents this group grants access to: + +- **Models tab** – Select the LLM models +- **MCP Servers tab** – Select MCP servers (for tool calling) +- **Agents tab** – Select agents + +![](https://colony-recorder.s3.amazonaws.com/files/2026-02-15/37398e8f-cd50-48c9-85e2-c77b2eeb994b/ascreenshot_440ec7906c8f4199b30ef91c903960b9_text_export.jpeg) + +![](https://colony-recorder.s3.amazonaws.com/files/2026-02-15/99d36543-8582-4bb7-a34d-3d5fe0fcf12f/ascreenshot_d9983240955c496892e1f7c38c074045_text_export.jpeg) + +![](https://colony-recorder.s3.amazonaws.com/files/2026-02-15/06fc5919-5c71-4fc3-999b-da7a4800af3f/ascreenshot_db93fdf742b249dc90a4b9d5991d6097_text_export.jpeg) + +### 4. Attach the Access Group to a Key + +When creating or editing a virtual key, expand **Optional Settings** and select your Access Group. The key will inherit access to all models, MCP servers, and agents defined in that group. + +1. Go to **Virtual Keys** and click **+ Create New Key** +2. Expand **Optional Settings** +3. In the Access Group field, select the group you created +4. Save the key + +![](https://colony-recorder.s3.amazonaws.com/files/2026-02-15/cdfa76ab-bf38-4ca4-a97d-2cb50fafe50b/ascreenshot_046daecb57554c28ba553cf6c01f5450_text_export.jpeg) + +![](https://colony-recorder.s3.amazonaws.com/files/2026-02-15/84f08e9c-e9d0-42aa-8317-f385190b6d7d/ascreenshot_2d239716d30f431d9ad494baf7933d6a_text_export.jpeg) + +![](https://colony-recorder.s3.amazonaws.com/files/2026-02-15/41d7b7f9-ac58-4602-b887-c35c9b419dce/ascreenshot_8abd4fef48014dd1b88848411e6d7912_text_export.jpeg) + +![](https://colony-recorder.s3.amazonaws.com/files/2026-02-15/e37b01c0-f2d7-4133-8b2f-ccc51f6769e1/ascreenshot_f495df428ad54cac9ec43b46c3dfc1b1_text_export.jpeg) + +![](https://colony-recorder.s3.amazonaws.com/files/2026-02-15/3fe33cad-6b64-46c3-a66e-6e6e073c3d7a/ascreenshot_f2dcc79ae8af47dd86ade2f85165d3c1_text_export.jpeg) + +### 5. Attach the Access Group to a Team + +You can also attach an Access Group to a team when creating or editing the team. All keys associated with that team will then have access to the resources defined in the group. + +## Use Cases + +### Team-based Access + +Create groups like "Engineering", "Data Science", or "Product" with the models, MCP servers, and agents each team needs. Attach the group to the team—no need to configure each resource on every key. + +### Environment Separation + +- **Production group** – Production models, approved MCP servers, and production agents +- **Development group** – Cost-efficient models, experimental MCP tools, and dev agents + +Attach the appropriate group to keys or teams based on environment. + +### Simplified Onboarding + +New developers get a key with an Access Group instead of manually configuring models, MCP servers, and agents. Add them to the right team or give them a key with the correct group. + +### Centralized Updates + +When you add a new model or MCP server to a group, every key and team attached to that group automatically gains access. Remove a resource from the group and it’s revoked everywhere at once. + +## Access Group vs. Model Access Groups + +LiteLLM has two related concepts: + +| Feature | **Access Groups** (this page) | **Model Access Groups** | +| ---------- | ----------------------------------------------------------------------- | ------------------------------------------------------- | +| Definition | Define in the UI; one group can include models, MCP servers, and agents | Defined in config or via API; groups are model-centric | +| Scope | Models + MCP servers + agents | Models only | +| Attach to | Keys, teams | Keys, teams | +| Use when | You want unified control over models, MCP, and agents from the UI | You need config-based or API-based model access control | + +For config-based model access with `access_groups` in `model_info`, see [Model Access Groups](./model_access_groups.md). + +## Related Documentation + +- [Virtual Keys](./virtual_keys.md) – Creating and managing API keys +- [Role-based Access Controls](./access_control.md) – Organizations, teams, and user roles +- [Model Access Groups](./model_access_groups.md) – Config-based model access groups +- [MCP Control](../mcp_control.md) – MCP server setup and access control diff --git a/docs/my-website/docs/proxy/auto_routing.md b/docs/my-website/docs/proxy/auto_routing.md index 7325dc8227..a04db28d37 100644 --- a/docs/my-website/docs/proxy/auto_routing.md +++ b/docs/my-website/docs/proxy/auto_routing.md @@ -219,3 +219,189 @@ curl -X POST http://localhost:4000/v1/chat/completions \ 3. If a route's similarity score exceeds the threshold, the request is routed to that model 4. If no route matches, the request goes to the default model +--- + +## Complexity Router + +The Complexity Router provides an alternative to semantic routing that uses **rule-based scoring** to classify requests by complexity and route them to appropriate models — with **zero external API calls** and **sub-millisecond latency**. + +### When to Use + +| Feature | Semantic Auto Router | Complexity Router | +|---------|---------------------|-------------------| +| Classification | Embedding-based matching | Rule-based scoring | +| Latency | ~100-500ms (embedding API) | <1ms | +| API Calls | Requires embedding model | None | +| Training | Requires utterance examples | Works out of the box | +| Best For | Intent-based routing | Cost optimization | + +Use **Complexity Router** when you want to: +- Route simple queries to cheaper/faster models (e.g., gpt-4o-mini) +- Route complex queries to more capable models (e.g., claude-sonnet-4) +- Minimize latency overhead from routing decisions +- Avoid additional API costs for embeddings + +### LiteLLM Python SDK + +```python +from litellm import Router + +router = Router( + model_list=[ + # Target models for each tier + { + "model_name": "gpt-4o-mini", + "litellm_params": {"model": "gpt-4o-mini"}, + }, + { + "model_name": "gpt-4o", + "litellm_params": {"model": "gpt-4o"}, + }, + { + "model_name": "claude-sonnet", + "litellm_params": {"model": "claude-sonnet-4-20250514"}, + }, + { + "model_name": "o1-preview", + "litellm_params": {"model": "o1-preview"}, + }, + # Complexity router configuration + { + "model_name": "smart-router", + "litellm_params": { + "model": "auto_router/complexity_router", + "complexity_router_config": { + "tiers": { + "SIMPLE": "gpt-4o-mini", + "MEDIUM": "gpt-4o", + "COMPLEX": "claude-sonnet", + "REASONING": "o1-preview", + }, + }, + "complexity_router_default_model": "gpt-4o", + }, + }, + ], +) +``` + +#### Usage + +```python +# Simple query → routes to gpt-4o-mini +response = await router.acompletion( + model="smart-router", + messages=[{"role": "user", "content": "What is 2+2?"}], +) + +# Complex technical query → routes to claude-sonnet or higher +response = await router.acompletion( + model="smart-router", + messages=[{"role": "user", "content": "Design a distributed microservice architecture with Kubernetes orchestration"}], +) + +# Reasoning request → routes to o1-preview +response = await router.acompletion( + model="smart-router", + messages=[{"role": "user", "content": "Think step by step and reason through this problem carefully..."}], +) +``` + +### LiteLLM Proxy Server + +Add the complexity router to your `config.yaml`: + +```yaml +model_list: + # Target models + - model_name: gpt-4o-mini + litellm_params: + model: gpt-4o-mini + + - model_name: gpt-4o + litellm_params: + model: gpt-4o + + - model_name: claude-sonnet + litellm_params: + model: claude-sonnet-4-20250514 + + - model_name: o1-preview + litellm_params: + model: o1-preview + + # Complexity router + - model_name: smart-router + litellm_params: + model: auto_router/complexity_router + complexity_router_config: + tiers: + SIMPLE: gpt-4o-mini + MEDIUM: gpt-4o + COMPLEX: claude-sonnet + REASONING: o1-preview + complexity_router_default_model: gpt-4o +``` + +### Configuration Options + +#### Tier Boundaries + +Customize the score thresholds for each tier: + +```yaml +complexity_router_config: + tiers: + SIMPLE: gpt-4o-mini + MEDIUM: gpt-4o + COMPLEX: claude-sonnet + REASONING: o1-preview + tier_boundaries: + simple_medium: 0.15 # Below 0.15 → SIMPLE + medium_complex: 0.35 # 0.15-0.35 → MEDIUM + complex_reasoning: 0.60 # 0.35-0.60 → COMPLEX, above → REASONING +``` + +#### Token Thresholds + +Adjust when prompts are considered "short" or "long": + +```yaml +complexity_router_config: + token_thresholds: + simple: 15 # Prompts under 15 tokens are penalized (simple indicator) + complex: 400 # Prompts over 400 tokens get complexity boost +``` + +#### Dimension Weights + +Customize how much each signal contributes to the complexity score: + +```yaml +complexity_router_config: + dimension_weights: + tokenCount: 0.10 # Prompt length + codePresence: 0.30 # Code-related keywords + reasoningMarkers: 0.25 # "step by step", "think through", etc. + technicalTerms: 0.25 # Domain-specific complexity + simpleIndicators: 0.05 # "what is", "define", greetings + multiStepPatterns: 0.03 # "first...then", numbered steps + questionComplexity: 0.02 # Multiple questions +``` + +### How Complexity Routing Works + +The router scores each request across 7 dimensions: + +| Dimension | What It Detects | Effect | +|-----------|-----------------|--------| +| Token Count | Short (<15) or long (>400) prompts | Short = simple, long = complex | +| Code Presence | "function", "class", "api", "database", etc. | Increases complexity | +| Reasoning Markers | "step by step", "think through", "analyze" | Triggers REASONING tier | +| Technical Terms | "architecture", "distributed", "encryption" | Increases complexity | +| Simple Indicators | "what is", "define", "hello" | Decreases complexity | +| Multi-Step Patterns | "first...then", "1. 2. 3." | Increases complexity | +| Question Complexity | Multiple question marks | Increases complexity | + +**Special behavior:** If 2+ reasoning markers are detected in the user message, the request automatically routes to the REASONING tier regardless of the weighted score. + diff --git a/docs/my-website/docs/proxy/config_settings.md b/docs/my-website/docs/proxy/config_settings.md index 38ad9bdd0e..5b255f0188 100644 --- a/docs/my-website/docs/proxy/config_settings.md +++ b/docs/my-website/docs/proxy/config_settings.md @@ -358,7 +358,8 @@ router_settings: | redis_url | str | URL for Redis server. **Known performance issue with Redis URL.** | | cache_responses | boolean | Flag to enable caching LLM Responses, if cache set under `router_settings`. If true, caches responses. Defaults to False. | | router_general_settings | RouterGeneralSettings | [SDK-Only] Router general settings - contains optimizations like 'async_only_mode'. [Docs](../routing.md#router-general-settings) | -| optional_pre_call_checks | List[str] | List of pre-call checks to add to the router. Currently supported: 'router_budget_limiting', 'prompt_caching' | +| optional_pre_call_checks | List[str] | List of pre-call checks to add to the router. Supported: `router_budget_limiting`, `prompt_caching`, `responses_api_deployment_check`, `deployment_affinity`, `forward_client_headers_by_model_group` | +| deployment_affinity_ttl_seconds | int | TTL (seconds) for user-key → deployment affinity mapping when `deployment_affinity` is enabled (configured at Router init / proxy startup). Defaults to `3600` (1 hour). | | ignore_invalid_deployments | boolean | If true, ignores invalid deployments. Default for proxy is True - to prevent invalid models from blocking other models from being loaded. | | search_tools | List[SearchToolTypedDict] | List of search tool configurations for Search API integration. Each tool specifies a search_tool_name and litellm_params with search_provider, api_key, api_base, etc. [Further Docs](../search.md) | | guardrail_list | List[GuardrailTypedDict] | List of guardrail configurations for guardrail load balancing. Enables load balancing across multiple guardrail deployments with the same guardrail_name. [Further Docs](./guardrails/guardrail_load_balancing.md) | @@ -450,6 +451,7 @@ router_settings: | BATCH_STATUS_POLL_INTERVAL_SECONDS | Interval in seconds for polling batch status. Default is 3600 (1 hour) | BATCH_STATUS_POLL_MAX_ATTEMPTS | Maximum number of attempts for polling batch status. Default is 24 (for 24 hours) | BEDROCK_MAX_POLICY_SIZE | Maximum size for Bedrock policy. Default is 75 +| BEDROCK_MIN_THINKING_BUDGET_TOKENS | Minimum thinking budget in tokens for Bedrock reasoning models. Bedrock returns a 400 error if budget_tokens is below this value. Requests with lower values are clamped to this minimum. Default is 1024 | BERRISPEND_ACCOUNT_ID | Account ID for BerriSpend service | BRAINTRUST_API_KEY | API key for Braintrust integration | BRAINTRUST_API_BASE | Base URL for Braintrust API. Default is https://api.braintrustdata.com/v1 @@ -483,6 +485,7 @@ router_settings: | CUSTOM_TIKTOKEN_CACHE_DIR | Custom directory for Tiktoken cache | CONFIDENT_API_KEY | API key for Confident AI (Deepeval) Logging service | COHERE_API_BASE | Base URL for Cohere API. Default is https://api.cohere.com +| COMPETITOR_LLM_TEMPERATURE | Temperature setting for the LLM used in competitor discovery. Default is 0.3 | DATABASE_HOST | Hostname for the database server | DATABASE_NAME | Name of the database | DATABASE_PASSWORD | Password for the database user @@ -492,6 +495,7 @@ router_settings: | DATABASE_USER | Username for database connection | DATABASE_USERNAME | Alias for database user | DATABRICKS_API_BASE | Base URL for Databricks API +| DATABRICKS_API_KEY | API key (Personal Access Token) for Databricks API authentication | DATABRICKS_CLIENT_ID | Client ID for Databricks OAuth M2M authentication (Service Principal application ID) | DATABRICKS_CLIENT_SECRET | Client secret for Databricks OAuth M2M authentication | DATABRICKS_USER_AGENT | Custom user agent string for Databricks API requests. Used for partner telemetry attribution @@ -520,6 +524,7 @@ router_settings: | DEBUG_OTEL | Enable debug mode for OpenTelemetry | DEFAULT_ALLOWED_FAILS | Maximum failures allowed before cooling down a model. Default is 3 | DEFAULT_A2A_AGENT_TIMEOUT | Default timeout in seconds for A2A (Agent-to-Agent) protocol requests. Default is 6000 +| DEFAULT_ACCESS_GROUP_CACHE_TTL | Time-to-live in seconds for cached access group information. Default is 600 (10 minutes) | DEFAULT_ANTHROPIC_CHAT_MAX_TOKENS | Default maximum tokens for Anthropic chat completions. Default is 4096 | DEFAULT_BATCH_SIZE | Default batch size for operations. Default is 512 | DEFAULT_CHUNK_OVERLAP | Default chunk overlap for RAG text splitters. Default is 200 @@ -538,7 +543,7 @@ router_settings: | DEFAULT_IMAGE_WIDTH | Default width for images. Default is 300 | DEFAULT_IN_MEMORY_TTL | Default time-to-live for in-memory cache in seconds. Default is 5 | DEFAULT_MANAGEMENT_OBJECT_IN_MEMORY_CACHE_TTL | Default time-to-live in seconds for management objects (User, Team, Key, Organization) in memory cache. Default is 60 seconds. -| DEFAULT_MAX_LRU_CACHE_SIZE | Default maximum size for LRU cache. Default is 16 +| DEFAULT_MAX_LRU_CACHE_SIZE | Default maximum size for LRU cache. Default is 64 | DEFAULT_MAX_RECURSE_DEPTH | Default maximum recursion depth. Default is 100 | DEFAULT_MAX_RECURSE_DEPTH_SENSITIVE_DATA_MASKER | Default maximum recursion depth for sensitive data masker. Default is 10 | DEFAULT_MAX_RETRIES | Default maximum retry attempts. Default is 2 @@ -548,6 +553,7 @@ router_settings: | DEFAULT_MCP_SEMANTIC_FILTER_EMBEDDING_MODEL | Default embedding model for MCP semantic tool filtering. Default is "text-embedding-3-small" | DEFAULT_MCP_SEMANTIC_FILTER_SIMILARITY_THRESHOLD | Default similarity threshold for MCP semantic tool filtering. Default is 0.3 | DEFAULT_MCP_SEMANTIC_FILTER_TOP_K | Default number of top results to return for MCP semantic tool filtering. Default is 10 +| MCP_NPM_CACHE_DIR | Directory for npm cache used by STDIO MCP servers. In containers the default (~/.npm) may not exist or be read-only. Default is `/tmp/.npm_mcp_cache` | MCP_OAUTH2_TOKEN_CACHE_DEFAULT_TTL | Default TTL in seconds for MCP OAuth2 token cache. Default is 3600 | MCP_OAUTH2_TOKEN_CACHE_MAX_SIZE | Maximum number of entries in MCP OAuth2 token cache. Default is 200 | MCP_OAUTH2_TOKEN_CACHE_MIN_TTL | Minimum TTL in seconds for MCP OAuth2 token cache. Default is 10 @@ -555,7 +561,7 @@ router_settings: | DEFAULT_MOCK_RESPONSE_COMPLETION_TOKEN_COUNT | Default token count for mock response completions. Default is 20 | DEFAULT_MOCK_RESPONSE_PROMPT_TOKEN_COUNT | Default token count for mock response prompts. Default is 10 | DEFAULT_MODEL_CREATED_AT_TIME | Default creation timestamp for models. Default is 1677610602 -| DEFAULT_NUM_WORKERS_LITELLM_PROXY | Default number of workers for LiteLLM proxy. Default is 4. **We strongly recommend setting NUM Workers to Number of vCPUs available** +| DEFAULT_NUM_WORKERS_LITELLM_PROXY | Default number of workers for LiteLLM proxy when `NUM_WORKERS` is not set. Default is 1. **We strongly recommend setting NUM_WORKERS to the number of vCPUs available** (e.g. `NUM_WORKERS=8` or `--num_workers 8`). | DEFAULT_PROMPT_INJECTION_SIMILARITY_THRESHOLD | Default threshold for prompt injection similarity. Default is 0.7 | DEFAULT_POLLING_INTERVAL | Default polling interval for schedulers in seconds. Default is 0.03 | DEFAULT_REASONING_EFFORT_DISABLE_THINKING_BUDGET | Default reasoning effort disable thinking budget. Default is 0 @@ -600,7 +606,6 @@ router_settings: | EMAIL_BUDGET_ALERT_TTL | Time-to-live for budget alert deduplication in seconds. Default is 86400 (24 hours) | ENKRYPTAI_API_BASE | Base URL for EnkryptAI Guardrails API. **Default is https://api.enkryptai.com** | ENKRYPTAI_API_KEY | API key for EnkryptAI Guardrails service -| EXPERIMENTAL_MULTI_INSTANCE_RATE_LIMITING | Flag to enable new multi-instance rate limiting. **Default is False** | FIREWORKS_AI_4_B | Size parameter for Fireworks AI 4B model. Default is 4 | FIREWORKS_AI_16_B | Size parameter for Fireworks AI 16B model. Default is 16 | FIREWORKS_AI_56_B_MOE | Size parameter for Fireworks AI 56B MOE model. Default is 56 @@ -745,9 +750,12 @@ router_settings: | LITERAL_API_KEY | API key for Literal integration | LITERAL_API_URL | API URL for Literal service | LITERAL_BATCH_SIZE | Batch size for Literal operations +| LITELLM_ANTHROPIC_BETA_HEADERS_URL | Custom URL for fetching Anthropic beta headers configuration. Default is the GitHub main branch URL | LITELLM_ANTHROPIC_DISABLE_URL_SUFFIX | Disable automatic URL suffix appending for Anthropic API base URLs. When set to `true`, prevents LiteLLM from automatically adding `/v1/messages` or `/v1/complete` to custom Anthropic API endpoints +| LITELLM_ASSETS_PATH | Path to directory for UI assets and logos. Used when running with read-only filesystem (e.g., Kubernetes). Default is `/var/lib/litellm/assets` in Docker. | LITELLM_CLI_JWT_EXPIRATION_HOURS | Expiration time in hours for CLI-generated JWT tokens. Default is 24 hours | LITELLM_DD_AGENT_HOST | Hostname or IP of DataDog agent for LiteLLM-specific logging. When set, logs are sent to agent instead of direct API +| LITELLM_DEPLOYMENT_ENVIRONMENT | Environment name for the deployment (e.g., "production", "staging"). Used as a fallback when OTEL_ENVIRONMENT_NAME is not set. Sets the `environment` tag in telemetry data | LITELLM_DD_AGENT_PORT | Port of DataDog agent for LiteLLM-specific log intake. Default is 10518 | LITELLM_DD_LLM_OBS_PORT | Port for Datadog LLM Observability agent. Default is 8126 | LITELLM_DONT_SHOW_FEEDBACK_BOX | Flag to hide feedback box in LiteLLM UI @@ -760,11 +768,15 @@ router_settings: | LITELLM_MIGRATION_DIR | Custom migrations directory for prisma migrations, used for baselining db in read-only file systems. | LITELLM_HOSTED_UI | URL of the hosted UI for LiteLLM | LITELLM_UI_API_DOC_BASE_URL | Optional override for the API Reference base URL (used in sample code/docs) when the admin UI runs on a different host than the proxy. Defaults to `PROXY_BASE_URL` when unset. +| LITELLM_UI_PATH | Path to directory for Admin UI files. Used when running with read-only filesystem (e.g., Kubernetes). Default is `/var/lib/litellm/ui` in Docker. | LITELM_ENVIRONMENT | Environment of LiteLLM Instance, used by logging services. Currently only used by DeepEval. | LITELLM_KEY_ROTATION_ENABLED | Enable auto-key rotation for LiteLLM (boolean). Default is false. | LITELLM_KEY_ROTATION_CHECK_INTERVAL_SECONDS | Interval in seconds for how often to run job that auto-rotates keys. Default is 86400 (24 hours). +| LITELLM_KEY_ROTATION_GRACE_PERIOD | Duration to keep old key valid after rotation (e.g. "24h", "2d"). Default is empty (immediate revoke). Used for scheduled rotations and as fallback when not specified in regenerate request. | LITELLM_LICENSE | License key for LiteLLM usage +| LITELLM_LOCAL_ANTHROPIC_BETA_HEADERS | Set to `True` to use the local bundled Anthropic beta headers config only, disabling remote fetching. Default is `False` | LITELLM_LOCAL_MODEL_COST_MAP | Local configuration for model cost mapping in LiteLLM +| LITELLM_LOCAL_POLICY_TEMPLATES | When set to "true", uses local backup policy templates instead of fetching from GitHub. Policy templates are fetched from https://raw.githubusercontent.com/BerriAI/litellm/main/policy_templates.json by default, with automatic fallback to local backup on failure | LITELLM_LOG | Enable detailed logging for LiteLLM | LITELLM_MODEL_COST_MAP_URL | URL for fetching model cost map data. Default is https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json | LITELLM_LOG_FILE | File path to write LiteLLM logs to. When set, logs will be written to both console and the specified file @@ -772,6 +784,10 @@ router_settings: | LITELLM_METER_NAME | Name for OTEL Meter | LITELLM_OTEL_INTEGRATION_ENABLE_EVENTS | Optionally enable semantic logs for OTEL | LITELLM_OTEL_INTEGRATION_ENABLE_METRICS | Optionally enable emantic metrics for OTEL +| LITELLM_ENABLE_PYROSCOPE | If true, enables Pyroscope CPU profiling. Profiles are sent to PYROSCOPE_SERVER_ADDRESS. Off by default. See [Pyroscope profiling](/proxy/pyroscope_profiling). +| PYROSCOPE_APP_NAME | Application name reported to Pyroscope. Required when LITELLM_ENABLE_PYROSCOPE is true. No default. +| PYROSCOPE_SERVER_ADDRESS | Pyroscope server URL to send profiles to. Required when LITELLM_ENABLE_PYROSCOPE is true. No default. +| PYROSCOPE_SAMPLE_RATE | Optional. Sample rate for Pyroscope profiling (integer). No default; when unset, the pyroscope-io library default is used. | LITELLM_MASTER_KEY | Master key for proxy authentication | LITELLM_MODE | Operating mode for LiteLLM (e.g., production, development) | LITELLM_NON_ROOT | Flag to run LiteLLM in non-root mode for enhanced security in Docker containers @@ -791,6 +807,7 @@ router_settings: | LOGGING_WORKER_MAX_QUEUE_SIZE | Maximum size of the logging worker queue. When the queue is full, the worker aggressively clears tasks to make room instead of dropping logs. Default is 50,000 | LOGGING_WORKER_MAX_TIME_PER_COROUTINE | Maximum time in seconds allowed for each coroutine in the logging worker before timing out. Default is 20.0 | LOGGING_WORKER_CLEAR_PERCENTAGE | Percentage of the queue to extract when clearing. Default is 50% +| MAX_COMPETITOR_NAMES | Maximum number of competitor names allowed in policy template enrichment. Default is 100 | MAX_EXCEPTION_MESSAGE_LENGTH | Maximum length for exception messages. Default is 2000 | MAX_ITERATIONS_TO_CLEAR_QUEUE | Maximum number of iterations to attempt when clearing the logging worker queue during shutdown. Default is 200 | MAX_TIME_TO_CLEAR_QUEUE | Maximum time in seconds to spend clearing the logging worker queue during shutdown. Default is 5.0 diff --git a/docs/my-website/docs/proxy/customers.md b/docs/my-website/docs/proxy/customers.md index 1101884c36..50a5f994fa 100644 --- a/docs/my-website/docs/proxy/customers.md +++ b/docs/my-website/docs/proxy/customers.md @@ -2,29 +2,98 @@ import Image from '@theme/IdealImage'; import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; -# Customers / End-User Budgets +# Customers / End-Users -Track spend, set budgets for your customers. +Track spend, set budgets and permissions for your customers. -## Tracking Customer Spend +## Tracking Customer Spend + Permissions ### 1. Make LLM API call w/ Customer ID -Make a /chat/completions call, pass 'user' - First call Works +LiteLLM checks for a customer/end-user ID in the following order (first match wins): -```bash showLineNumbers title="Make request with customer ID" +| Priority | Method | Where | Notes | +|----------|--------|-------|-------| +| 1 | `x-litellm-customer-id` header | Request headers | Standard header, always checked | +| 2 | `x-litellm-end-user-id` header | Request headers | Standard header, always checked | +| 3 | Custom header via `user_header_mappings` | Request headers | Configured in `general_settings` | +| 4 | Custom header via `user_header_name` | Request headers | Deprecated — use `user_header_mappings` | +| 5 | `user` field | Request body | Standard OpenAI field | +| 6 | `litellm_metadata.user` field | Request body | Anthropic-style metadata | +| 7 | `metadata.user_id` field | Request body | Generic metadata pattern | +| 8 | `safety_identifier` field | Request body | Responses API | + +**Option 1: Standard headers** (recommended — no request body modification needed) + +```bash showLineNumbers title="Make request with customer ID in header" curl -X POST 'http://0.0.0.0:4000/chat/completions' \ --header 'Content-Type: application/json' \ - --header 'Authorization: Bearer sk-1234' \ # 👈 YOUR PROXY KEY - --data ' { + --header 'Authorization: Bearer sk-1234' \ + --header 'x-litellm-end-user-id: ishaan3' \ + --data '{ "model": "azure-gpt-3.5", - "user": "ishaan3", # 👈 CUSTOMER ID - "messages": [ - { - "role": "user", - "content": "what time is it" - } - ] + "messages": [{"role": "user", "content": "what time is it"}] + }' +``` + +Both `x-litellm-customer-id` and `x-litellm-end-user-id` are supported and always checked without any configuration. + +**Option 2: `user` field in request body** (OpenAI-compatible) + +```bash showLineNumbers title="Make request with customer ID in body" +curl -X POST 'http://0.0.0.0:4000/chat/completions' \ + --header 'Content-Type: application/json' \ + --header 'Authorization: Bearer sk-1234' \ + --data '{ + "model": "azure-gpt-3.5", + "user": "ishaan3", + "messages": [{"role": "user", "content": "what time is it"}] + }' +``` + +**Option 3: Custom header via `user_header_mappings`** (configurable) + +```yaml showLineNumbers title="config.yaml" +general_settings: + user_header_mappings: + - header_name: "x-my-app-user-id" + litellm_user_role: "customer" +``` + +```bash showLineNumbers title="Make request with custom header" +curl -X POST 'http://0.0.0.0:4000/chat/completions' \ + --header 'Content-Type: application/json' \ + --header 'Authorization: Bearer sk-1234' \ + --header 'x-my-app-user-id: ishaan3' \ + --data '{ + "model": "azure-gpt-3.5", + "messages": [{"role": "user", "content": "what time is it"}] + }' +``` + +**Option 4: `litellm_metadata.user`** (Anthropic-style) + +```bash showLineNumbers title="Make request with litellm_metadata.user" +curl -X POST 'http://0.0.0.0:4000/chat/completions' \ + --header 'Content-Type: application/json' \ + --header 'Authorization: Bearer sk-1234' \ + --data '{ + "model": "claude-3-5-sonnet", + "messages": [{"role": "user", "content": "what time is it"}], + "litellm_metadata": {"user": "ishaan3"} + }' +``` + +**Option 5: `metadata.user_id`** + +```bash showLineNumbers title="Make request with metadata.user_id" +curl -X POST 'http://0.0.0.0:4000/chat/completions' \ + --header 'Content-Type: application/json' \ + --header 'Authorization: Bearer sk-1234' \ + --data '{ + "model": "azure-gpt-3.5", + "messages": [{"role": "user", "content": "what time is it"}], + "metadata": {"user_id": "ishaan3"} }' ``` @@ -123,7 +192,171 @@ Expected Response -## Setting Customer Budgets +## Setting Customer Object Permissions + +Control which resources (MCP servers, vector stores, agents) a customer can access. + +### What are Object Permissions? + +Object permissions allow you to restrict customer access to specific: +- **MCP Servers**: Limit which MCP servers the customer can call +- **MCP Access Groups**: Assign customers to predefined groups of MCP servers +- **MCP Tool Permissions**: Granular control over which tools within an MCP server the customer can use +- **Vector Stores**: Control which vector stores the customer can query +- **Agents**: Restrict which agents the customer can interact with +- **Agent Access Groups**: Assign customers to predefined groups of agents + +### Creating a Customer with Object Permissions + +```bash showLineNumbers title="Create customer with object permissions" +curl -L -X POST 'http://localhost:4000/customer/new' \ +-H 'Authorization: Bearer sk-1234' \ +-H 'Content-Type: application/json' \ +-d '{ + "user_id": "user_1", + "object_permission": { + "mcp_servers": ["server_1", "server_2"], + "mcp_access_groups": ["public_group"], + "mcp_tool_permissions": { + "server_1": ["tool_a", "tool_b"] + }, + "vector_stores": ["vector_store_1"], + "agents": ["agent_1"], + "agent_access_groups": ["basic_agents"] + } + }' +``` + +**Parameters:** +- `mcp_servers` (Optional[List[str]]): List of allowed MCP server IDs +- `mcp_access_groups` (Optional[List[str]]): List of MCP access group names +- `mcp_tool_permissions` (Optional[Dict[str, List[str]]]): Map of server ID to allowed tool names +- `vector_stores` (Optional[List[str]]): List of allowed vector store IDs +- `agents` (Optional[List[str]]): List of allowed agent IDs +- `agent_access_groups` (Optional[List[str]]): List of agent access group names + +**Note:** If `object_permission` is `null` or `{}`, the customer has no object-level restrictions. + +### Updating Customer Object Permissions + +You can update object permissions for existing customers: + +```bash showLineNumbers title="Update customer object permissions" +curl -L -X POST 'http://localhost:4000/customer/update' \ +-H 'Authorization: Bearer sk-1234' \ +-H 'Content-Type: application/json' \ +-d '{ + "user_id": "user_1", + "object_permission": { + "mcp_servers": ["server_3"], + "vector_stores": ["vector_store_2", "vector_store_3"] + } + }' +``` + +### Viewing Customer Object Permissions + +When you query customer info, object permissions are included in the response: + +```bash showLineNumbers title="Get customer info with object permissions" +curl -X GET 'http://0.0.0.0:4000/customer/info?end_user_id=user_1' \ + -H 'Authorization: Bearer sk-1234' +``` + +**Response:** +```json showLineNumbers title="Response with object permissions" +{ + "user_id": "user_1", + "blocked": false, + "alias": "John Doe", + "spend": 0.0, + "object_permission": { + "object_permission_id": "perm_abc123", + "mcp_servers": ["server_1", "server_2"], + "mcp_access_groups": ["public_group"], + "mcp_tool_permissions": { + "server_1": ["tool_a", "tool_b"] + }, + "vector_stores": ["vector_store_1"], + "agents": ["agent_1"], + "agent_access_groups": ["basic_agents"] + }, + "litellm_budget_table": null +} +``` + +### Use Cases + +**1. Tiered Access Control** +Create different permission tiers for your customers: + +```bash showLineNumbers title="Free tier customer" +# Free tier - limited access +curl -L -X POST 'http://localhost:4000/customer/new' \ +-H 'Authorization: Bearer sk-1234' \ +-H 'Content-Type: application/json' \ +-d '{ + "user_id": "free_user", + "budget_id": "free_tier", + "object_permission": { + "mcp_access_groups": ["public_group"], + "agent_access_groups": ["basic_agents"] + } + }' +``` + +```bash showLineNumbers title="Premium tier customer" +# Premium tier - full access +curl -L -X POST 'http://localhost:4000/customer/new' \ +-H 'Authorization: Bearer sk-1234' \ +-H 'Content-Type: application/json' \ +-d '{ + "user_id": "premium_user", + "budget_id": "premium_tier", + "object_permission": { + "mcp_servers": ["server_1", "server_2", "server_3"], + "vector_stores": ["vector_store_1", "vector_store_2"], + "agents": ["agent_1", "agent_2", "agent_3"] + } + }' +``` + +**2. Department-Specific Access** +Restrict customers to resources relevant to their department: + +```bash showLineNumbers title="Sales team customer" +curl -L -X POST 'http://localhost:4000/customer/new' \ +-H 'Authorization: Bearer sk-1234' \ +-H 'Content-Type: application/json' \ +-d '{ + "user_id": "sales_user", + "object_permission": { + "mcp_servers": ["crm_server", "email_server"], + "agents": ["sales_assistant"], + "vector_stores": ["sales_knowledge_base"] + } + }' +``` + +**3. Tool-Level Restrictions** +Grant access to specific tools within an MCP server: + +```bash showLineNumbers title="Limited tool access" +curl -L -X POST 'http://localhost:4000/customer/new' \ +-H 'Authorization: Bearer sk-1234' \ +-H 'Content-Type: application/json' \ +-d '{ + "user_id": "restricted_user", + "object_permission": { + "mcp_servers": ["database_server"], + "mcp_tool_permissions": { + "database_server": ["read_only_query", "get_table_schema"] + } + } + }' +``` + +## Setting Customer Budgets Set customer budgets (e.g. monthly budgets, tpm/rpm limits) on LiteLLM Proxy diff --git a/docs/my-website/docs/proxy/guardrails/policy_templates.md b/docs/my-website/docs/proxy/guardrails/policy_templates.md new file mode 100644 index 0000000000..f0c93ca44c --- /dev/null +++ b/docs/my-website/docs/proxy/guardrails/policy_templates.md @@ -0,0 +1,296 @@ +# Policy Templates + +Policy templates provide pre-configured guardrail policies that you can use as a starting point for your organization. Instead of manually creating policies and guardrails, you can select a template that matches your use case and deploy it with one click. + +## Using Policy Templates + +### In the UI + +1. Navigate to **Policies → Templates** tab in the LiteLLM Admin UI +2. Browse available templates (e.g., "PII Protection", "Cost Control", "HR Compliance") +3. Click **"Use Template"** on any template +4. Review the guardrails that will be created: + - Existing guardrails are marked with a green checkmark + - New guardrails can be selected/deselected +5. Click **"Create X Guardrails & Use Template"** +6. Review and customize the pre-filled policy form +7. Click **"Create Policy"** to save + +### Workflow + +``` +Select Template → Review Guardrails → Create Selected → Edit Policy → Save +``` + +The system automatically: +- ✅ Detects which guardrails already exist +- ✅ Creates only the missing guardrails you select +- ✅ Pre-fills the policy form with template data +- ✅ Lets you customize before saving + +## Available Templates + +Templates are fetched from [GitHub](https://raw.githubusercontent.com/BerriAI/litellm/main/policy_templates.json) with automatic fallback to local backup. + +### Current Templates + +#### 1. Advanced PII Protection (Australia) +- **Complexity:** High +- **Use Case:** Comprehensive PII detection for Australian organizations +- **Guardrails:** + - Australian tax identifiers (TFN, ABN, Medicare) + - Australian passports + - International PII (SSN, passports, national IDs) + - Contact information (email, phone, address) + - Financial data (credit cards, IBAN) + - API credentials (AWS, GitHub, Slack) - **BLOCKS** requests + - Network infrastructure (IP addresses) + - Protected class information (gender, race, religion, disability, etc.) + +#### 2. Baseline PII Protection +- **Complexity:** Low +- **Use Case:** Basic protection for internal tools and testing +- **Guardrails:** + - Australian tax identifiers + - API credentials + - Financial data + +## Creating Your Own Policy Templates + +You can contribute policy templates for the entire LiteLLM community to use. + +### Template Structure + +Templates are defined in JSON format with the following structure: + +```json +{ + "id": "unique-template-id", + "title": "Display Title", + "description": "Detailed description of what this template protects", + "icon": "ShieldCheckIcon", + "iconColor": "text-purple-500", + "iconBg": "bg-purple-50", + "guardrails": [ + "guardrail-name-1", + "guardrail-name-2" + ], + "complexity": "Low|Medium|High", + "guardrailDefinitions": [ + { + "guardrail_name": "example-guardrail", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "patterns": [ + { + "pattern_type": "prebuilt", + "pattern_name": "email", + "action": "MASK" + } + ], + "pattern_redaction_format": "[{pattern_name}_REDACTED]" + }, + "guardrail_info": { + "description": "What this guardrail does" + } + } + ], + "templateData": { + "policy_name": "policy-name", + "description": "Policy description", + "guardrails_add": ["guardrail-name-1", "guardrail-name-2"], + "guardrails_remove": [] + } +} +``` + +### Field Descriptions + +#### Display Fields +- **id**: Unique identifier (lowercase with hyphens) +- **title**: User-facing name shown in UI +- **description**: Detailed explanation of what the template protects +- **icon**: Icon name (must be available in UI icon map) +- **iconColor**: Tailwind CSS text color class +- **iconBg**: Tailwind CSS background color class +- **guardrails**: Array of guardrail names (for display only) +- **complexity**: Badge showing difficulty ("Low", "Medium", or "High") + +#### Guardrail Definitions +- **guardrailDefinitions**: Array of complete guardrail configurations + - Each must be a valid guardrail object that can be sent to `/guardrails` POST endpoint + - If a guardrail already exists, it will be skipped + - Can be empty `[]` if template uses only existing guardrails + +#### Policy Configuration +- **templateData**: Object that pre-fills the policy form + - **policy_name**: Suggested name (user can edit) + - **description**: Policy description + - **guardrails_add**: Array of guardrail names to include + - **guardrails_remove**: Array to remove (usually `[]` for templates) + - **inherit**: (Optional) Parent policy name for inheritance + +### Example Template + +Here's a complete example for a HIPAA compliance template: + +```json +{ + "id": "hipaa-compliance", + "title": "HIPAA Compliance Policy", + "description": "Healthcare compliance policy that masks PHI and enforces HIPAA regulations for healthcare applications.", + "icon": "ShieldCheckIcon", + "iconColor": "text-red-500", + "iconBg": "bg-red-50", + "guardrails": [ + "phi-detector", + "medical-record-blocker", + "patient-id-masker" + ], + "complexity": "High", + "guardrailDefinitions": [ + { + "guardrail_name": "phi-detector", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "patterns": [ + { + "pattern_type": "prebuilt", + "pattern_name": "us_ssn", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "email", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "us_phone", + "action": "MASK" + } + ], + "pattern_redaction_format": "[PHI_REDACTED]" + }, + "guardrail_info": { + "description": "Detects and masks Protected Health Information (PHI)" + } + } + ], + "templateData": { + "policy_name": "hipaa-compliance-policy", + "description": "HIPAA compliance policy for healthcare applications", + "guardrails_add": [ + "phi-detector", + "medical-record-blocker", + "patient-id-masker" + ], + "guardrails_remove": [] + } +} +``` + +## Contributing Templates + +To contribute a policy template for everyone to use: + +### Step 1: Create Your Template JSON + +1. Create a JSON file following the structure above +2. Test it locally by adding it to your local `policy_templates.json` +3. Verify all guardrails work correctly +4. Ensure descriptions are clear and helpful + +### Step 2: Submit a Pull Request + +1. Fork the [LiteLLM repository](https://github.com/BerriAI/litellm) +2. Add your template to `policy_templates.json` at the root +3. Add your template to `litellm/policy_templates_backup.json` (keep both in sync) +4. Create a pull request with: + - Clear description of what the template protects + - Use case examples + - Any relevant compliance frameworks (HIPAA, GDPR, SOC 2, etc.) + +### Guidelines + +**DO:** +- ✅ Use clear, descriptive names +- ✅ Include comprehensive descriptions +- ✅ Test all guardrails thoroughly +- ✅ Document pattern sources (e.g., "Based on NIST guidelines") +- ✅ Group related guardrails logically +- ✅ Consider different complexity levels + +**DON'T:** +- ❌ Include credentials or secrets +- ❌ Use overly broad patterns that may have false positives +- ❌ Duplicate existing templates +- ❌ Use custom code without thorough testing + +## Using Templates Offline + +For air-gapped or offline deployments, set the environment variable: + +```bash +export LITELLM_LOCAL_POLICY_TEMPLATES=true +``` + +This forces the system to use the local backup (`litellm/policy_templates_backup.json`) instead of fetching from GitHub. + +## Template Sources + +- **GitHub (default):** https://raw.githubusercontent.com/BerriAI/litellm/main/policy_templates.json +- **Local backup:** `litellm/policy_templates_backup.json` + +Templates are automatically fetched from GitHub on each request, with fallback to local backup on any failure. + +## Available Pattern Types + +When creating guardrails for templates, you can use these prebuilt patterns: + +### Identity Documents +- `passport_australia`, `passport_us`, `passport_uk`, `passport_germany`, etc. +- `us_ssn`, `us_ssn_no_dash` +- `au_tfn`, `au_abn`, `au_medicare` +- `nl_bsn_contextual` +- `br_cpf`, `br_rg`, `br_cnpj` + +### Financial +- `visa`, `mastercard`, `amex`, `discover`, `credit_card` +- `iban` + +### Contact Information +- `email` +- `us_phone`, `br_phone_landline`, `br_phone_mobile` +- `street_address` +- `br_cep` (Brazilian postal code) + +### Credentials +- `aws_access_key`, `aws_secret_key` +- `github_token` +- `slack_token` +- `generic_api_key` + +### Network +- `ipv4`, `ipv6` + +### Protected Class +- `gender_sexual_orientation` +- `race_ethnicity_national_origin` +- `religion` +- `age_discrimination` +- `disability` +- `marital_family_status` +- `military_status` +- `public_assistance` + +See the [full patterns list](https://github.com/BerriAI/litellm/blob/main/litellm/proxy/guardrails/guardrail_hooks/litellm_content_filter/patterns.json) for all available patterns. + +## Related Docs + +- [Guardrail Policies](./guardrail_policies) +- [Policy Tags](./policy_tags) +- [Content Filter Patterns](../hooks/content_filter) +- [Custom Code Guardrails](../hooks/custom_code) diff --git a/docs/my-website/docs/proxy/logging.md b/docs/my-website/docs/proxy/logging.md index 56fb420e6c..1abb127dfd 100644 --- a/docs/my-website/docs/proxy/logging.md +++ b/docs/my-website/docs/proxy/logging.md @@ -1338,6 +1338,7 @@ litellm_settings: s3_aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY # AWS Secret Access Key for S3 s3_path: my-test-path # [OPTIONAL] set path in bucket you want to write logs to s3_endpoint_url: https://s3.amazonaws.com # [OPTIONAL] S3 endpoint URL, if you want to use Backblaze/cloudflare s3 buckets + s3_use_virtual_hosted_style: false # [OPTIONAL] use virtual-hosted-style URLs (bucket.endpoint/key) instead of path-style (endpoint/bucket/key). Useful for S3-compatible services like MinIO s3_strip_base64_files: false # [OPTIONAL] remove base64 files before storing in s3 ``` diff --git a/docs/my-website/docs/proxy/pass_through.md b/docs/my-website/docs/proxy/pass_through.md index cf8168764b..f47d706414 100644 --- a/docs/my-website/docs/proxy/pass_through.md +++ b/docs/my-website/docs/proxy/pass_through.md @@ -58,6 +58,17 @@ Configure the required authentication and pricing: - The Bria API requires an `api_token` header - Enter your Bria API key as the value for the `api_token` header +**Default Query Parameters (Optional):** +- Add query parameters that will be automatically sent with every request +- Perfect for API versioning, format specifications, or default configurations +- Clients can override these parameters by providing their own values +- Example: `version=v1`, `format=json`, `timeout=30` + + + **Pricing Configuration:** - Set a cost per request (e.g., $12.00 in this example) - This enables cost tracking and billing for your users @@ -112,6 +123,9 @@ general_settings: content-type: application/json accept: application/json forward_headers: true # Forward all incoming headers + default_query_params: # Optional: Default query parameters + version: "v1" # Always send version=v1 + format: "json" # Default format (can be overridden) ``` ### Start and Test @@ -166,6 +180,9 @@ general_settings: auth: boolean # Enable LiteLLM authentication (Enterprise) forward_headers: boolean # Forward all incoming headers include_subpath: boolean # If true, forwards requests to sub-paths (default: false) + methods: list[string] # Optional: HTTP methods (e.g., ["GET", "POST"]). If not specified, all methods are supported. + default_query_params: # Optional: Default query parameters sent with every request + : string # Key-value pairs (e.g., version: "v1", format: "json") headers: # Custom headers to add Authorization: string # Auth header for target API content-type: string # Request content type @@ -177,11 +194,17 @@ general_settings: ### Header Options - **Authorization**: Authentication for the target API -- **content-type**: Request body format specification +- **content-type**: Request body format specification - **accept**: Expected response format - **LANGFUSE_PUBLIC_KEY/SECRET_KEY**: For Langfuse integration - **Custom headers**: Any additional key-value pairs +### Default Query Parameters +- **Parameter precedence**: Client params > URL params > default params +- **Use cases**: API versioning, authentication tokens, format control, feature flags +- **Override capability**: Clients can override any default parameter +- **Examples**: `version: "v1"`, `format: "json"`, `timeout: "30"` + ### Sub-path Routing By default, pass-through endpoints only match the **exact path** specified. To forward requests to sub-paths, set `include_subpath: true`: @@ -201,6 +224,92 @@ general_settings: --- +### Default Query Parameters + +Pass-through endpoints support default query parameters that are automatically added to every request. This is useful for API versioning, format specifications, authentication tokens, or any default configuration. + +#### How It Works + +**Parameter Precedence (highest to lowest priority):** +1. **Client-provided parameters** (in the request URL) +2. **URL parameters** (from the target URL) +3. **Default parameters** (from configuration) + +#### Example Configuration + +```yaml +general_settings: + pass_through_endpoints: + - path: "/api/v1" + target: "https://external-api.com/service?timeout=60" # URL has timeout=60 + default_query_params: + version: "v1" # Always add version=v1 + format: "json" # Default format=json (can be overridden) + auth_level: "basic" # Always add auth_level=basic +``` + +#### Request Examples + +**Client Request:** `GET /api/v1/users` +**Actual Backend Call:** `https://external-api.com/service?version=v1&format=json&auth_level=basic&timeout=60` + +**Client Request:** `GET /api/v1/users?format=xml&custom=value` +**Actual Backend Call:** `https://external-api.com/service?version=v1&auth_level=basic&timeout=60&format=xml&custom=value` +- Client `format=xml` overrides default `format=json` +- Default `version=v1` and `auth_level=basic` are preserved +- URL `timeout=60` is preserved +- Client `custom=value` is added + +#### Use Cases + +- **API Versioning**: Always send `version=v2` to maintain compatibility +- **Authentication**: Add authentication tokens like `api_key=default_key` +- **Format Control**: Default to `format=json` but allow client override +- **Rate Limiting**: Set `rate_limit=standard` as default +- **Feature Flags**: Enable `experimental=false` by default + +--- + +You can configure different target URLs for the same path using different HTTP methods. This is useful when different backends handle different operations: + + + +```yaml +general_settings: + pass_through_endpoints: + # GET requests to /azure/kb go to read API + - path: "/azure/kb" + target: "https://read-api.example.com/knowledge-base" + methods: ["GET"] + headers: + Authorization: "bearer os.environ/READ_API_KEY" + + # POST requests to /azure/kb go to write API + - path: "/azure/kb" + target: "https://write-api.example.com/knowledge-base" + methods: ["POST"] + headers: + Authorization: "bearer os.environ/WRITE_API_KEY" + + # PUT requests to /azure/kb go to update API + - path: "/azure/kb" + target: "https://update-api.example.com/knowledge-base" + methods: ["PUT"] + headers: + Authorization: "bearer os.environ/UPDATE_API_KEY" +``` + +**Key Points:** +- If `methods` is not specified, the endpoint supports all HTTP methods (GET, POST, PUT, DELETE, PATCH) +- Multiple endpoints can share the same path as long as they have different methods +- You can specify multiple methods for a single endpoint: `methods: ["GET", "POST"]` +- This allows you to route to different backends based on the operation type + +--- + ## Advanced: Custom Adapters For complex integrations (like Anthropic/Bedrock clients), you can create custom adapters that translate between different API schemas. diff --git a/docs/my-website/docs/proxy/project_management.md b/docs/my-website/docs/proxy/project_management.md new file mode 100644 index 0000000000..06ed5b4a0d --- /dev/null +++ b/docs/my-website/docs/proxy/project_management.md @@ -0,0 +1,318 @@ +# [Beta] Project Management + +Projects in LiteLLM sit between teams and keys in the organizational hierarchy, enabling fine-grained access control and budget management for specific use cases or applications. + +```mermaid +graph TD + A[Organization] --> B[Team 1] + A --> C[Team 2] + B --> D[Project A] + B --> E[Project B] + C --> F[Project C] + D --> G[API Key 1] + D --> H[API Key 2] + E --> I[API Key 3] + F --> J[API Key 4] + + style A fill:#e1f5ff + style B fill:#fff4e6 + style C fill:#fff4e6 + style D fill:#f3e5f5 + style E fill:#f3e5f5 + style F fill:#f3e5f5 + style G fill:#e8f5e9 + style H fill:#e8f5e9 + style I fill:#e8f5e9 + style J fill:#e8f5e9 +``` + +**Hierarchy**: `Organizations > Teams > Projects > Keys` + +## Quick Start + +This walkthrough shows how to create a project, generate an API key, make requests, and view project-level spend tracking in the UI. + +### Step 1: Create a Project + +```bash showLineNumbers +curl --location 'http://0.0.0.0:4000/project/new' \ +--header 'Authorization: Bearer sk-1234' \ +--header 'Content-Type: application/json' \ +--data '{ + "project_alias": "flight-search-assistant", + "team_id": "ad898803-c8a3-4f4a-976a-a3c372cffa45", + "models": ["gpt-4", "gpt-3.5-turbo"], + "max_budget": 100, + "metadata": { + "use_case_id": "SNOW-12345", + "responsible_ai_id": "RAI-67890" + } +}' | jq +``` + +**Response:** +```json +{ + "project_id": "e402a141-725a-4437-bff5-d47459189716", + "project_alias": "flight-search-assistant", + "team_id": "ad898803-c8a3-4f4a-976a-a3c372cffa45", + "models": ["gpt-4", "gpt-3.5-turbo"], + "max_budget": 100, + ... +} +``` + +### Step 2: Generate API Key for Project + +```bash showLineNumbers +curl 'http://0.0.0.0:4000/key/generate' \ +--header 'Authorization: Bearer sk-1234' \ +--header 'Content-Type: application/json' \ +--data-raw '{ + "models": ["gpt-3.5-turbo", "gpt-4"], + "metadata": {"user": "ishaan@berri.ai"}, + "project_id": "e402a141-725a-4437-bff5-d47459189716" +}' | jq +``` + +**Response:** +```json +{ + "key": "sk-W8VbscpfuyvHm5TkxRYiXA", + "key_name": "sk-...YiXA", + "project_id": "e402a141-725a-4437-bff5-d47459189716", + ... +} +``` + +### Step 3: Use API Key in Chat Completions + +```bash showLineNumbers +curl http://localhost:4000/v1/chat/completions \ +--header 'Content-Type: application/json' \ +--header 'Authorization: Bearer sk-W8VbscpfuyvHm5TkxRYiXA' \ +--data '{ + "model": "gpt-4", + "messages": [{"role": "user", "content": "What is litellm?"}] +}' | jq +``` + +### Step 4: View Project Spend in UI + +Navigate to the **Logs** page in the LiteLLM Admin UI. You'll see the `user_api_key_project_id` tracked in the request metadata: + +![Project Spend Tracking](/img/project_spend.png) + +As shown above, the spend logs metadata includes: +- `"user_api_key_project_id": "e402a141-725a-4437-bff5-d47459189716"` - Links the request to your project +- All costs and token usage are automatically attributed to the project +- You can query and filter logs by project ID for detailed reporting + +## API Endpoints + +### POST /project/new + +Create a new project. + +**Who can call**: Admins or Team Admins + +**Parameters**: +- `project_alias` (string, optional): Human-readable name for the project +- `team_id` (string, required): The team this project belongs to +- `models` (array, optional): List of models the project can access +- `max_budget` (float, optional): Maximum spend budget for the project +- `tpm_limit` (int, optional): Tokens per minute limit +- `rpm_limit` (int, optional): Requests per minute limit +- `budget_duration` (string, optional): Budget reset period (e.g., "30d", "1mo") +- `metadata` (object, optional): Custom metadata for the project +- `blocked` (boolean, optional): Block all API calls for this project + +**Example**: + +```bash +curl --location 'http://0.0.0.0:4000/project/new' \ +--header 'Authorization: Bearer sk-1234' \ +--header 'Content-Type: application/json' \ +--data '{ + "project_alias": "hotel-recommendations", + "team_id": "team-123", + "models": ["claude-3-sonnet"], + "max_budget": 200, + "tpm_limit": 100000, + "metadata": { + "use_case_id": "SNOW-12346", + "cost_center": "travel-products" + } +}' +``` + +**Response**: + +```json +{ + "project_id": "project-def", + "project_alias": "hotel-recommendations", + "team_id": "team-123", + "models": ["claude-3-sonnet"], + "spend": 0.0, + "budget_id": "budget-xyz", + "metadata": { + "use_case_id": "SNOW-12346", + "cost_center": "travel-products" + }, + "created_at": "2025-01-15T10:00:00Z", + "updated_at": "2025-01-15T10:00:00Z" +} +``` + +### POST /project/update + +Update an existing project. + +**Who can call**: Admins or Team Admins + +**Parameters**: +- `project_id` (string, required): The project to update +- `project_alias` (string, optional): Updated project name +- `team_id` (string, optional): Move project to different team +- `models` (array, optional): Updated list of allowed models +- `max_budget` (float, optional): Updated budget +- `tpm_limit` (int, optional): Updated TPM limit +- `rpm_limit` (int, optional): Updated RPM limit +- `metadata` (object, optional): Updated metadata +- `blocked` (boolean, optional): Updated blocked status + +**Example**: + +```bash +curl --location 'http://0.0.0.0:4000/project/update' \ +--header 'Authorization: Bearer sk-1234' \ +--header 'Content-Type: application/json' \ +--data '{ + "project_id": "project-abc", + "max_budget": 200, + "tpm_limit": 200000, + "metadata": { + "status": "production" + } +}' +``` + +### GET /project/info + +Get information about a specific project. + +**Parameters**: +- `project_id` (string, required): Query parameter + +**Example**: + +```bash +curl --location 'http://0.0.0.0:4000/project/info?project_id=project-abc' \ +--header 'Authorization: Bearer sk-1234' +``` + +**Response**: + +```json +{ + "project_id": "project-abc", + "project_alias": "flight-search-assistant", + "team_id": "team-123", + "models": ["gpt-4", "gpt-3.5-turbo"], + "spend": 45.67, + "model_spend": { + "gpt-4": 42.30, + "gpt-3.5-turbo": 3.37 + }, + "litellm_budget_table": { + "budget_id": "budget-xyz", + "max_budget": 100.0, + "tpm_limit": 100000, + "rpm_limit": 100 + }, + "metadata": { + "use_case_id": "SNOW-12345" + } +} +``` + +### GET /project/list + +List all projects the user has access to. + +**Example**: + +```bash +curl --location 'http://0.0.0.0:4000/project/list' \ +--header 'Authorization: Bearer sk-1234' +``` + +**Response**: + +```json +[ + { + "project_id": "project-abc", + "project_alias": "flight-search-assistant", + "team_id": "team-123", + "spend": 45.67 + }, + { + "project_id": "project-def", + "project_alias": "hotel-recommendations", + "team_id": "team-123", + "spend": 23.45 + } +] +``` + +### DELETE /project/delete + +Delete one or more projects. + +**Who can call**: Admins only + +**Parameters**: +- `project_ids` (array, required): List of project IDs to delete + +**Example**: + +```bash +curl --location --request DELETE 'http://0.0.0.0:4000/project/delete' \ +--header 'Authorization: Bearer sk-1234' \ +--header 'Content-Type: application/json' \ +--data '{ + "project_ids": ["project-abc", "project-def"] +}' +``` + +**Note**: Projects with associated API keys cannot be deleted. Delete or reassign the keys first. + +## Model-Specific Quotas + +You can set different quotas for different models within a project: + +```bash +curl --location 'http://0.0.0.0:4000/project/new' \ +--header 'Authorization: Bearer sk-1234' \ +--header 'Content-Type: application/json' \ +--data '{ + "project_alias": "multi-model-project", + "team_id": "team-123", + "models": ["gpt-4", "gpt-3.5-turbo", "claude-3-sonnet"], + "max_budget": 500, + "metadata": { + "model_tpm_limit": { + "gpt-4": 50000, + "gpt-3.5-turbo": 200000, + "claude-3-sonnet": 100000 + }, + "model_rpm_limit": { + "gpt-4": 50, + "gpt-3.5-turbo": 500, + "claude-3-sonnet": 100 + } + } +}' +``` diff --git a/docs/my-website/docs/proxy/prompt_management.md b/docs/my-website/docs/proxy/prompt_management.md index 0c7ff96f53..08307ba99e 100644 --- a/docs/my-website/docs/proxy/prompt_management.md +++ b/docs/my-website/docs/proxy/prompt_management.md @@ -11,6 +11,7 @@ Run experiments or change the specific model (e.g. from gpt-4o to gpt4o-mini fin | Native LiteLLM GitOps (.prompt files) | [Get Started](native_litellm_prompt) | | Langfuse | [Get Started](https://langfuse.com/docs/prompts/get-started) | | Humanloop | [Get Started](../observability/humanloop) | +| Generic Prompt Management API | [Get Started](../adding_provider/generic_prompt_management_api) | ## Onboarding Prompts via config.yaml @@ -34,7 +35,7 @@ prompts: - prompt_id: "my_prompt_id" litellm_params: prompt_id: "my_prompt_id" - prompt_integration: "dotprompt" # or langfuse, bitbucket, gitlab, custom + prompt_integration: "dotprompt" # or langfuse, bitbucket, gitlab, generic_prompt_management, custom # integration-specific parameters below ``` @@ -46,6 +47,7 @@ The `prompt_integration` field determines where and how prompts are loaded: - **`langfuse`**: Fetch prompts from Langfuse prompt management - **`bitbucket`**: Load from BitBucket repository `.prompt` files (team-based access control) - **`gitlab`**: Load from GitLab repository `.prompt` files (team-based access control) +- **`generic_prompt_management`**: Integrate any prompt management system via a simple API endpoint (no PR required) - **`custom`**: Use your own custom prompt management implementation Each integration has its own configuration parameters and access control mechanisms. @@ -207,6 +209,57 @@ System: You are a helpful assistant. User: {{user_message}} ``` + + + + +```yaml +prompts: + - prompt_id: "simple_prompt" + litellm_params: + prompt_integration: "generic_prompt_management" + provider_specific_query_params: + project_name: litellm + slug: hello-world-prompt-2bac + api_base: http://localhost:8080 + api_key: os.environ/GENERIC_PROMPT_API_KEY + ignore_prompt_manager_model: true # optional + ignore_prompt_manager_optional_params: true # optional +``` + +**What you need to implement:** + +A GET endpoint at `/beta/litellm_prompt_management` that returns: + +```json +{ + "prompt_id": "simple_prompt", + "prompt_template": [ + { + "role": "system", + "content": "You are a helpful assistant." + }, + { + "role": "user", + "content": "Help me with {task}" + } + ], + "prompt_template_model": "gpt-4", + "prompt_template_optional_params": { + "temperature": 0.7, + "max_tokens": 500 + } +} +``` + +**Benefits:** +- No PR required - integrate any prompt management system +- Full control over your prompt storage and versioning +- Support for variable substitution with `{variable}` syntax +- Custom query parameters for filtering and access control + +**Learn more:** [Generic Prompt Management API Documentation](../adding_provider/generic_prompt_management_api) + diff --git a/docs/my-website/docs/proxy/pyroscope_profiling.md b/docs/my-website/docs/proxy/pyroscope_profiling.md new file mode 100644 index 0000000000..fa3db3a878 --- /dev/null +++ b/docs/my-website/docs/proxy/pyroscope_profiling.md @@ -0,0 +1,43 @@ +# Grafana Pyroscope CPU profiling + +LiteLLM proxy can send continuous CPU profiles to [Grafana Pyroscope](https://grafana.com/docs/pyroscope/latest/) when enabled via environment variables. This is optional and off by default. + +## Quick start + +1. **Install the optional dependency** (required only when enabling Pyroscope): + + ```bash + pip install pyroscope-io + ``` + + Or install the proxy extra: + + ```bash + pip install "litellm[proxy]" + ``` + +2. **Set environment variables** before starting the proxy: + + | Variable | Required | Description | + |----------|----------|-------------| + | `LITELLM_ENABLE_PYROSCOPE` | Yes (to enable) | Set to `true` to enable Pyroscope profiling. | + | `PYROSCOPE_APP_NAME` | Yes (when enabled) | Application name shown in the Pyroscope UI. | + | `PYROSCOPE_SERVER_ADDRESS` | Yes (when enabled) | Pyroscope server URL (e.g. `http://localhost:4040`). | + | `PYROSCOPE_SAMPLE_RATE` | No | Sample rate (integer). If unset, the pyroscope-io library default is used. | + +3. **Start the proxy**; profiling will begin automatically when the proxy starts. + + ```bash + export LITELLM_ENABLE_PYROSCOPE=true + export PYROSCOPE_APP_NAME=litellm-proxy + export PYROSCOPE_SERVER_ADDRESS=http://localhost:4040 + litellm --config config.yaml + ``` + +4. **View profiles** in the Pyroscope (or Grafana) UI and select your `PYROSCOPE_APP_NAME`. + +## Notes + +- **Optional dependency**: `pyroscope-io` is an optional dependency. If it is not installed and `LITELLM_ENABLE_PYROSCOPE=true`, the proxy will log a warning and continue without profiling. +- **Platform support**: The `pyroscope-io` package uses a native extension and is not available on all platforms (e.g. Windows is excluded by the package). +- **Other settings**: See [Configuration settings](/proxy/config_settings) for all proxy environment variables. diff --git a/docs/my-website/docs/proxy/release_cycle.md b/docs/my-website/docs/proxy/release_cycle.md index 10dd6d8b3c..b3e056b024 100644 --- a/docs/my-website/docs/proxy/release_cycle.md +++ b/docs/my-website/docs/proxy/release_cycle.md @@ -22,4 +22,10 @@ Stable releases come out every week (typically Sunday) - 'patch' bumps: extremely minor addition that doesn't affect any existing functionality or add any user-facing features. (e.g. a 'created_at' column in a database table) - 'minor' bumps: add a new feature or a new database table that is backward compatible. -- 'major' bumps: break backward compatibility. \ No newline at end of file +- 'major' bumps: break backward compatibility. + +### Enterprise Support + + +- Stable releases come out every week. Once a new one is available, we no longer provide support for an older one. +- If there is a MAJOR change (according to semvar conventions - e.g. 1.x.x -> 2.x.x), we can provide support for upto 90 days on the prior stable image. diff --git a/docs/my-website/docs/proxy/request_headers.md b/docs/my-website/docs/proxy/request_headers.md index 090c201f88..d76964611a 100644 --- a/docs/my-website/docs/proxy/request_headers.md +++ b/docs/my-website/docs/proxy/request_headers.md @@ -20,6 +20,10 @@ By default, LiteLLM does not forward client headers to LLM provider APIs. Howeve `x-litellm-spend-logs-metadata`: Optional[str]: JSON string containing custom metadata to include in spend logs. Example: `{"user_id": "12345", "project_id": "proj_abc", "request_type": "chat_completion"}`. [Learn More](../proxy/enterprise#tracking-spend-with-custom-metadata) +`x-litellm-customer-id`: Optional[str]: Standard header for passing a customer/end-user ID. Always checked without any configuration. [Learn More](./customers) + +`x-litellm-end-user-id`: Optional[str]: Standard header for passing a customer/end-user ID. Always checked without any configuration. [Learn More](./customers) + ## Anthropic Headers `anthropic-version` Optional[str]: The version of the Anthropic API to use. diff --git a/docs/my-website/docs/proxy/sync_anthropic_beta_headers.md b/docs/my-website/docs/proxy/sync_anthropic_beta_headers.md new file mode 100644 index 0000000000..e1645082d9 --- /dev/null +++ b/docs/my-website/docs/proxy/sync_anthropic_beta_headers.md @@ -0,0 +1,128 @@ +# Auto Sync Anthropic Beta Headers + +Automatically keep your Anthropic beta headers configuration up to date without restarting your service. **This allows you to support new Anthropic beta features across all providers without restarting your service.** + +## Overview + +When Anthropic releases new beta features (e.g., new tool capabilities, extended context windows), you typically need to restart your LiteLLM service to get the latest beta header mappings for different providers (Anthropic, Bedrock, Vertex AI, Azure AI). + +With auto-sync, LiteLLM automatically pulls the latest configuration from GitHub's [`anthropic_beta_headers_config.json`](https://github.com/BerriAI/litellm/blob/main/litellm/anthropic_beta_headers_config.json) without requiring a restart. This means: + +- **Zero downtime** when new beta features are released +- **Always up-to-date** provider support mappings +- **Automatic updates** - set it once and forget it + +## Quick Start + +**Manual sync:** +```bash +curl -X POST "https://your-proxy-url/reload/anthropic_beta_headers" \ + -H "Authorization: Bearer YOUR_ADMIN_TOKEN" \ + -H "Content-Type: application/json" +``` + +**Automatic sync every 24 hours:** +```bash +curl -X POST "https://your-proxy-url/schedule/anthropic_beta_headers_reload?hours=24" \ + -H "Authorization: Bearer YOUR_ADMIN_TOKEN" \ + -H "Content-Type: application/json" +``` + +## API Endpoints + +| Endpoint | Method | Description | +|----------|--------|-------------| +| `/reload/anthropic_beta_headers` | POST | Manual sync | +| `/schedule/anthropic_beta_headers_reload?hours={hours}` | POST | Schedule periodic sync | +| `/schedule/anthropic_beta_headers_reload` | DELETE | Cancel scheduled sync | +| `/schedule/anthropic_beta_headers_reload/status` | GET | Check sync status | + +**Authentication:** Requires admin role or master key + +## Python Example + +```python +import requests + +def sync_anthropic_beta_headers(proxy_url, admin_token): + response = requests.post( + f"{proxy_url}/reload/anthropic_beta_headers", + headers={"Authorization": f"Bearer {admin_token}"} + ) + return response.json() + +# Usage +result = sync_anthropic_beta_headers("https://your-proxy-url", "your-admin-token") +print(result['message']) +``` + +## Configuration + +**Custom beta headers config URL:** +```bash +export LITELLM_ANTHROPIC_BETA_HEADERS_URL="https://raw.githubusercontent.com/BerriAI/litellm/main/litellm/anthropic_beta_headers_config.json" +``` + +**Use local beta headers config:** +```bash +export LITELLM_LOCAL_ANTHROPIC_BETA_HEADERS=True +``` + +## Scheduling Automatic Reloads + +Schedule automatic reloads to ensure your proxy always has the latest beta header mappings: + +```bash +# Reload every 24 hours +curl -X POST "https://your-proxy-url/schedule/anthropic_beta_headers_reload?hours=24" \ + -H "Authorization: Bearer YOUR_ADMIN_TOKEN" +``` + +**Check reload status:** +```bash +curl -X GET "https://your-proxy-url/schedule/anthropic_beta_headers_reload/status" \ + -H "Authorization: Bearer YOUR_ADMIN_TOKEN" +``` + +**Response:** +```json +{ + "scheduled": true, + "interval_hours": 24, + "last_run": "2026-02-13T10:00:00", + "next_run": "2026-02-14T10:00:00" +} +``` + +**Cancel scheduled reload:** +```bash +curl -X DELETE "https://your-proxy-url/schedule/anthropic_beta_headers_reload" \ + -H "Authorization: Bearer YOUR_ADMIN_TOKEN" +``` + +## Environment Variables + +| Variable | Description | Default | +|----------|-------------|---------| +| `LITELLM_ANTHROPIC_BETA_HEADERS_URL` | URL to fetch beta headers config from | GitHub main branch | +| `LITELLM_LOCAL_ANTHROPIC_BETA_HEADERS` | Set to `True` to use local config only | `False` | + +## How It Works + +1. **Initial Load:** On startup, LiteLLM loads the beta headers configuration from the remote URL (or local file if configured) +2. **Caching:** The configuration is cached in memory to avoid repeated fetches on every request +3. **Scheduled Reload:** If configured, the proxy checks every 10 seconds whether it's time to reload based on your schedule +4. **Manual Reload:** You can trigger an immediate reload via the API endpoint +5. **Multi-Pod Support:** In multi-pod deployments, the reload configuration is stored in the database so all pods stay in sync + +## Benefits + +- **No Restarts Required:** Add support for new Anthropic beta features without downtime +- **Provider Compatibility:** Automatically get updated mappings for Bedrock, Vertex AI, Azure AI, etc. +- **Performance:** Configuration is cached and only reloaded when needed +- **Reliability:** Falls back to local configuration if remote fetch fails + +## Related + +- [Model Cost Map Sync](./sync_models_github.md) - Auto-sync model pricing data +- [Anthropic Beta Headers](../completion/anthropic.md#beta-features) - Using Anthropic beta features diff --git a/docs/my-website/docs/proxy/team_budgets.md b/docs/my-website/docs/proxy/team_budgets.md index 03d1879713..01b07f23a3 100644 --- a/docs/my-website/docs/proxy/team_budgets.md +++ b/docs/my-website/docs/proxy/team_budgets.md @@ -8,7 +8,6 @@ import TabItem from '@theme/TabItem'; # Pre-Requisites - You must set up a Postgres database (e.g. Supabase, Neon, etc.) -- To enable team member rate limits, set the environment variable `EXPERIMENTAL_MULTI_INSTANCE_RATE_LIMITING=true` **before starting the proxy server**. Without this, team member rate limits will not be enforced. ## Default Budget for Auto-Generated JWT Teams diff --git a/docs/my-website/docs/proxy/users.md b/docs/my-website/docs/proxy/users.md index a389f0bd44..8517db51a8 100644 --- a/docs/my-website/docs/proxy/users.md +++ b/docs/my-website/docs/proxy/users.md @@ -68,13 +68,6 @@ You can: **Step-by step tutorial on setting, resetting budgets on Teams here (API or using Admin UI)** -> **Prerequisite:** -> To enable team member rate limits, you must set the environment variable `EXPERIMENTAL_MULTI_INSTANCE_RATE_LIMITING=true` before starting the proxy server. Without this, team member rate limits will not be enforced. - -👉 [https://docs.litellm.ai/docs/proxy/team_budgets](https://docs.litellm.ai/docs/proxy/team_budgets) - -::: - #### **Add budgets to teams** ```shell @@ -822,12 +815,10 @@ Expected Response: } ``` -### [BETA] Multi-instance rate limiting +### Multi-instance rate limiting -Enable multi-instance rate limiting with the env var `EXPERIMENTAL_MULTI_INSTANCE_RATE_LIMITING="True"` **Important Notes:** -- Setting `EXPERIMENTAL_MULTI_INSTANCE_RATE_LIMITING="True"` is required for team member rate limits to function, not just for multi-instance scenarios. - **Rate limits do not apply to proxy admin users.** - When testing rate limits, use internal user roles (non-admin) to ensure limits are enforced as expected. diff --git a/docs/my-website/docs/proxy/virtual_keys.md b/docs/my-website/docs/proxy/virtual_keys.md index 38ff4ede28..c74aa75ff4 100644 --- a/docs/my-website/docs/proxy/virtual_keys.md +++ b/docs/my-website/docs/proxy/virtual_keys.md @@ -549,11 +549,14 @@ curl 'http://localhost:4000/key/sk-1234/regenerate' \ "models": [ "gpt-4", "gpt-3.5-turbo" - ] + ], + "grace_period": "48h" }' ``` +**Grace period (optional)**: Set `grace_period` (e.g. `"24h"`, `"2d"`, `"1w"`) to keep the old key valid for a transitional period. Both old and new keys work until the grace period elapses, enabling seamless cutover without production downtime. Omitted or empty = immediate revoke. Can also be set via `LITELLM_KEY_ROTATION_GRACE_PERIOD` env var for scheduled rotations. + **Read More** - [Write rotated keys to secrets manager](https://docs.litellm.ai/docs/secret#aws-secret-manager) @@ -640,11 +643,13 @@ Set these environment variables when starting the proxy: |----------|-------------|---------| | `LITELLM_KEY_ROTATION_ENABLED` | Enable the rotation worker | `false` | | `LITELLM_KEY_ROTATION_CHECK_INTERVAL_SECONDS` | How often to scan for keys to rotate (in seconds) | `86400` (24 hours) | +| `LITELLM_KEY_ROTATION_GRACE_PERIOD` | Duration to keep old key valid after rotation (e.g. `24h`, `2d`) | `""` (immediate revoke) | **Example:** ```bash export LITELLM_KEY_ROTATION_ENABLED=true export LITELLM_KEY_ROTATION_CHECK_INTERVAL_SECONDS=3600 # Check every hour +export LITELLM_KEY_ROTATION_GRACE_PERIOD=48h # Keep old key valid for 48h during cutover litellm --config config.yaml ``` diff --git a/docs/my-website/docs/reasoning_content.md b/docs/my-website/docs/reasoning_content.md index 04c6d7ee6c..b5a5809bd4 100644 --- a/docs/my-website/docs/reasoning_content.md +++ b/docs/my-website/docs/reasoning_content.md @@ -642,6 +642,25 @@ model_list: model: openai/responses/gpt-5-mini ``` +**Per-model configuration** (recommended when using Open WebUI or clients that cannot set `extra_body`): + +```yaml +model_list: + - model_name: gpt-5.1 + litellm_params: + model: openai/gpt-5.1 + # String format - uses reasoning_auto_summary for summary when set + reasoning_effort: "high" + model_info: + mode: responses # if using Responses API bridge + + - model_name: gpt-5.1-with-summary + litellm_params: + model: openai/gpt-5.1 + # Dict format - explicit control over effort and summary + reasoning_effort: {"effort": "high", "summary": "detailed"} +``` + diff --git a/docs/my-website/docs/rerank.md b/docs/my-website/docs/rerank.md index 90f685d2bb..9c76883d7f 100644 --- a/docs/my-website/docs/rerank.md +++ b/docs/my-website/docs/rerank.md @@ -8,15 +8,15 @@ LiteLLM Follows the [cohere api request / response for the rerank api](https://c ## Overview -| Feature | Supported | Notes | -|---------|-----------|-------| -| Cost Tracking | ✅ | Works with all supported models | -| Logging | ✅ | Works across all integrations | -| End-user Tracking | ✅ | | -| Fallbacks | ✅ | Works between supported models | -| Loadbalancing | ✅ | Works between supported models | -| Guardrails | ✅ | Applies to input query only (not documents) | -| Supported Providers | Cohere, Together AI, Azure AI, DeepInfra, Nvidia NIM, Infinity, Fireworks AI, Voyage AI | | +| Feature | Supported | Notes | +|---------|-----------------------------------------------------------------------------------------------------|-------| +| Cost Tracking | ✅ | Works with all supported models | +| Logging | ✅ | Works across all integrations | +| End-user Tracking | ✅ | | +| Fallbacks | ✅ | Works between supported models | +| Loadbalancing | ✅ | Works between supported models | +| Guardrails | ✅ | Applies to input query only (not documents) | +| Supported Providers | Cohere, Together AI, Azure AI, DeepInfra, Nvidia NIM, Infinity, Fireworks AI, Voyage AI, watsonx.ai | | ## **LiteLLM Python SDK Usage** ### Quick Start @@ -123,17 +123,18 @@ curl http://0.0.0.0:4000/rerank \ #### ⚡️See all supported models and providers at [models.litellm.ai](https://models.litellm.ai/) -| Provider | Link to Usage | -|-------------|--------------------| -| Cohere (v1 + v2 clients) | [Usage](#quick-start) | -| Together AI| [Usage](../docs/providers/togetherai) | -| Azure AI| [Usage](../docs/providers/azure_ai#rerank-endpoint) | -| Jina AI| [Usage](../docs/providers/jina_ai) | -| AWS Bedrock| [Usage](../docs/providers/bedrock#rerank-api) | -| HuggingFace| [Usage](../docs/providers/huggingface_rerank) | -| Infinity| [Usage](../docs/providers/infinity) | -| vLLM| [Usage](../docs/providers/vllm#rerank-endpoint) | -| DeepInfra| [Usage](../docs/providers/deepinfra#rerank-endpoint) | -| Vertex AI| [Usage](../docs/providers/vertex#rerank-api) | -| Fireworks AI| [Usage](../docs/providers/fireworks_ai#rerank-endpoint) | -| Voyage AI| [Usage](../docs/providers/voyage#rerank) | \ No newline at end of file +| Provider | Link to Usage | +|--------------------------|------------------------------------------------------| +| Cohere (v1 + v2 clients) | [Usage](#quick-start) | +| Together AI | [Usage](../docs/providers/togetherai) | +| Azure AI | [Usage](../docs/providers/azure_ai#rerank-endpoint) | +| Jina AI | [Usage](../docs/providers/jina_ai) | +| AWS Bedrock | [Usage](../docs/providers/bedrock#rerank-api) | +| HuggingFace | [Usage](../docs/providers/huggingface_rerank) | +| Infinity | [Usage](../docs/providers/infinity) | +| vLLM | [Usage](../docs/providers/vllm#rerank-endpoint) | +| DeepInfra | [Usage](../docs/providers/deepinfra#rerank-endpoint) | +| Vertex AI | [Usage](../docs/providers/vertex#rerank-api) | +| Fireworks AI | [Usage](../docs/providers/fireworks_ai#rerank-endpoint) | +| Voyage AI | [Usage](../docs/providers/voyage#rerank) | +| IBM watsonx.ai | [Usage](../docs/providers/watsonx/rerank) | \ No newline at end of file diff --git a/docs/my-website/docs/response_api.md b/docs/my-website/docs/response_api.md index dd2b77712c..b37be2b5bc 100644 --- a/docs/my-website/docs/response_api.md +++ b/docs/my-website/docs/response_api.md @@ -884,7 +884,13 @@ router = litellm.Router( }, }, ], - optional_pre_call_checks=["responses_api_deployment_check"], + # `responses_api_deployment_check` ensures Requests with `previous_response_id` + # are routed to the same deployment. `deployment_affinity` adds sticky sessions + # for requests without `previous_response_id` (useful for implicit caching). + # `session_affinity` adds sticky sessions based on `session_id` metadata. + optional_pre_call_checks=["responses_api_deployment_check", "deployment_affinity", "session_affinity"], + # Optional (default is 3600 seconds / 1 hour) + deployment_affinity_ttl_seconds=3600, ) # Initial request @@ -911,7 +917,18 @@ follow_up = await router.aresponses( #### 1. Setup session continuity on proxy config.yaml -To enable session continuity for Responses API in your LiteLLM proxy, set `optional_pre_call_checks: ["responses_api_deployment_check"]` in your proxy config.yaml. +To enable session continuity for Responses API in your LiteLLM proxy, set `optional_pre_call_checks` in your proxy config.yaml. + +- `responses_api_deployment_check`: high priority routing when `previous_response_id` is provided +- `session_affinity`: sticky sessions based on session id (takes priority over `deployment_affinity`) +- `deployment_affinity`: sticky sessions based on user key (applies even without `previous_response_id`) + +Notes: +- User-key affinity is keyed on `metadata.user_api_key_hash` (the API key hash). The OpenAI `user` request parameter is an end-user identifier and is intentionally not used for deployment affinity. +- Session-ID affinity is keyed on `metadata.session_id`. For proxy requests, this can be passed via the `x-litellm-session-id` HTTP header. For Python SDK requests, you can pass it via `litellm_metadata={"session_id": "value"}` in request args. +- `user_api_key_hash` is already SHA-256, and is used as-is (no double hashing). +- Affinity is scoped by a stable model identifier (the model-map key, e.g. `model_map_information.model_map_key`) so model aliases map to the same stickiness bucket. +- The mapping TTL is controlled by `deployment_affinity_ttl_seconds` (configured on Router init / proxy startup). ```yaml showLineNumbers title="config.yaml with Session Continuity" model_list: @@ -929,7 +946,12 @@ model_list: api_base: https://endpoint2.openai.azure.com router_settings: - optional_pre_call_checks: ["responses_api_deployment_check"] + optional_pre_call_checks: + - responses_api_deployment_check + - session_affinity + - deployment_affinity + # Optional (default is 3600 seconds / 1 hour) + deployment_affinity_ttl_seconds: 3600 ``` #### 2. Use the OpenAI Python SDK to make requests to LiteLLM Proxy @@ -1029,6 +1051,8 @@ For long-running conversations, you can enable **server-side compaction** so tha Supported on the OpenAI Responses API when using the `openai` or `azure` provider. Pass `context_management` with a compaction entry and `compact_threshold` (token count; minimum 1000). When the context crosses the threshold, the server compacts in-stream and continues. Chain turns with `previous_response_id` or by appending output items to your next input array. See [OpenAI Compaction guide](https://developers.openai.com/api/docs/guides/compaction) for details. +> **Note:** You can use openai `context_management` format with Anthropic models via LiteLLM via responses API. LiteLLM will automatically translate this format for Anthropic and handle context management for you. + For explicit control over when compaction runs, use the standalone compact endpoint (`POST /v1/responses/compact`) instead. ### Python SDK @@ -1356,8 +1380,3 @@ Response: - - - - - diff --git a/docs/my-website/docs/search/index.md b/docs/my-website/docs/search/index.md index 551a495261..8a71edead0 100644 --- a/docs/my-website/docs/search/index.md +++ b/docs/my-website/docs/search/index.md @@ -276,6 +276,7 @@ The response follows Perplexity's search format with the following structure: | Firecrawl | `FIRECRAWL_API_KEY` | `firecrawl` | | SearXNG | `SEARXNG_API_BASE` (required) | `searxng` | | Linkup | `LINKUP_API_KEY` | `linkup` | +| DuckDuckGo | `DUCKDUCKGO_API_BASE` | `duckduckgo` | See the individual provider documentation for detailed setup instructions and provider-specific parameters. diff --git a/docs/my-website/docs/troubleshoot.md b/docs/my-website/docs/troubleshoot.md index 179f1c7897..1539e1959f 100644 --- a/docs/my-website/docs/troubleshoot.md +++ b/docs/my-website/docs/troubleshoot.md @@ -1,102 +1,48 @@ -# Troubleshooting & Support - -## Information to Provide When Seeking Help +# Issue Reporting When reporting issues, please include as much of the following as possible. It's okay if you can't provide everything—especially in production scenarios where the trigger might be unknown. Sharing most of this information will help us assist you more effectively. -### 1. LiteLLM Configuration File +## 1. LiteLLM Configuration File Your `config.yaml` file (redact sensitive info like API keys). Include number of workers if not in config. -### 2. Initialization Command +## 2. Initialization Command The command used to start LiteLLM (e.g., `litellm --config config.yaml --num_workers 8 --detailed_debug`). -### 3. LiteLLM Version +## 3. LiteLLM Version -- Current version -- Version when the issue first appeared (if different) +- Current version +- Version when the issue first appeared (if different) - If upgraded, the version changed from → to -### 4. Environment Variables +## 4. Environment Variables Non-sensitive environment variables not in your config (e.g., `NUM_WORKERS`, `LITELLM_LOG`, `LITELLM_MODE`). Do not include passwords or API keys. -### 5. Server Specifications +## 5. Server Specifications CPU cores, RAM, OS, number of instances/replicas, etc. -### 6. Database and Redis Usage +## 6. Database and Redis Usage - **Database:** Using database? (`DATABASE_URL` set), database type and version - **Redis:** Using Redis? Redis version, configuration type (Standalone/Cluster/Sentinel). -### 7. Endpoints +## 7. Endpoints The endpoint(s) you're using that are experiencing issues (e.g., `/chat/completions`, `/embeddings`). -### 8. Request Example +## 8. Request Example A realistic example of the request causing issues, including expected vs. actual response and any error messages. -### 9. Error Logs, Stack Traces, and Metrics +## 9. Error Logs, Stack Traces, and Metrics Full error logs, stack traces, and any images from service metrics (CPU, memory, request rates, etc.) that might help diagnose the issue. --- -## UI Issues - -If you're experiencing issues with the LiteLLM Admin UI, please include the following information in addition to the general details above. - -### 1. Steps to Reproduce - -A clear, step-by-step description of how to trigger the issue (e.g., "Navigate to Settings → Team, click 'Create Team', fill in fields, click submit → error appears"). - -### 2. LiteLLM Version - -The current version of LiteLLM you're running. Check via `litellm --version` or the UI's settings page. - -### 3. Architecture & Deployment Setup - -Distributed environments are a known source of UI issues. Please describe: - -- **Number of LiteLLM instances/replicas** and how they are deployed (e.g., Kubernetes, Docker Compose, ECS) -- **Load balancer** type and configuration (e.g., ALB, Nginx, Cloudflare Tunnel) — include whether sticky sessions are enabled -- **How the UI is accessed** — directly via LiteLLM, through a reverse proxy, or behind an ingress controller -- **Any CDN or caching layers** between the user and the LiteLLM server - -### 4. Network Tab Requests - -Open your browser's Developer Tools (F12 → Network tab), reproduce the issue, and share: - -- The **failing request(s)** — URL, method, status code, and response body -- **Screenshots or HAR export** of the relevant network activity -- Any **CORS or mixed-content errors** shown in the Console tab - -### 5. Environment Variables - -Non-sensitive environment variables related to the UI and proxy setup, such as: - -- `LITELLM_MASTER_KEY` -- `PROXY_BASE_URL` / `LITELLM_PROXY_BASE_URL` -- `UI_BASE_PATH` -- Any SSO-related variables (e.g., `GOOGLE_CLIENT_ID`, `MICROSOFT_TENANT`) - -Do **not** include passwords, secrets, or API keys. - -### 6. Browser & Access Details - -- **Browser** and version (e.g., Chrome 120, Firefox 121) -- **Access URL** used to reach the UI (redact sensitive parts) -- Whether the issue occurs for **all users or specific roles** (Admin, Internal User, etc.) - -### 7. Screenshots or Screen Recordings - -A screenshot or short screen recording of the issue is extremely helpful. Include any visible error messages, toasts, or unexpected behavior. - ---- - ## Support Channels [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version) @@ -109,4 +55,3 @@ Our numbers 📞 +1 (770) 8783-106 / +1 (412) 618-6238 Our emails ✉️ ishaan@berri.ai / krrish@berri.ai [![Chat on WhatsApp](https://img.shields.io/static/v1?label=Chat%20on&message=WhatsApp&color=success&logo=WhatsApp&style=flat-square)](https://wa.link/huol9n) [![Chat on Discord](https://img.shields.io/static/v1?label=Chat%20on&message=Discord&color=blue&logo=Discord&style=flat-square)](https://discord.gg/wuPM9dRgDw) - diff --git a/docs/my-website/docs/troubleshoot/latency_overhead.md b/docs/my-website/docs/troubleshoot/latency_overhead.md new file mode 100644 index 0000000000..cfb2cb43a7 --- /dev/null +++ b/docs/my-website/docs/troubleshoot/latency_overhead.md @@ -0,0 +1,90 @@ +# Latency Overhead Troubleshooting + +Use this guide when you see unexpected latency overhead between LiteLLM proxy and the LLM provider. + +## Quick Checklist + +1. **Collect the `x-litellm-overhead-duration-ms` response header** — this tells you LiteLLM's total overhead on every request. Start here. +2. **Is DEBUG logging enabled?** This is the #1 cause of latency with large payloads. +3. **Are you sending large base64 payloads?** (images, PDFs) — see [Large Payload Overhead](#large-payload-overhead). +4. **Enable detailed timing headers** to pinpoint where time is spent. + +## Diagnostic Headers + +### `x-litellm-overhead-duration-ms` (always on) + +Every response from LiteLLM includes this header. It shows the total latency overhead in milliseconds added by LiteLLM proxy (i.e. total response time minus the LLM API call time). Collect this on every request to understand your baseline overhead. + +```bash +curl -s -D - http://localhost:4000/v1/chat/completions \ + -H "Authorization: Bearer sk-..." \ + -d '{"model": "gpt-4o", "messages": [{"role": "user", "content": "hi"}]}' \ + 2>&1 | grep x-litellm-overhead-duration-ms +``` + +### `x-litellm-callback-duration-ms` (always on) + +Shows time spent building callback/logging payloads (ms). If this is high (>100ms), your payloads may be too large for efficient logging. + +```bash +curl -s -D - http://localhost:4000/v1/chat/completions \ + -H "Authorization: Bearer sk-..." \ + -d '{"model": "gpt-4o", "messages": [{"role": "user", "content": "hi"}]}' \ + 2>&1 | grep x-litellm +``` + +### Detailed Timing Breakdown (opt-in) + +Set `LITELLM_DETAILED_TIMING=true` to get per-phase timing in response headers: + +| Header | What it measures | +|--------|-----------------| +| `x-litellm-timing-pre-processing-ms` | Auth, routing, request processing (before LLM call) | +| `x-litellm-timing-llm-api-ms` | Actual LLM API call duration | +| `x-litellm-timing-post-processing-ms` | Response processing (after LLM returns) | +| `x-litellm-timing-message-copy-ms` | Message copy time in logging layer | + +```bash +# Enable detailed timing +export LITELLM_DETAILED_TIMING=true +``` + +## Large Payload Overhead + +When sending large payloads (>1MB, e.g. base64-encoded images/PDFs), three things can add overhead: + +### 1. DEBUG Logging (most common) + +When `LITELLM_LOG=DEBUG` or `set_verbose=True` is enabled, every request payload is serialized with `json.dumps(indent=4)` synchronously. For a 2MB+ payload, this alone can take **2-5 seconds**. + +**Fix:** Don't use DEBUG logging in production. Use `INFO` level instead: + +```bash +export LITELLM_LOG=INFO +``` + +If you need DEBUG logging but have large payloads, you can increase the size threshold for full payload logging: + +```bash +# Only fully serialize payloads under 100KB for DEBUG logs (default) +export MAX_PAYLOAD_SIZE_FOR_DEBUG_LOG=102400 +``` + +### 2. Base64 in Logging Payloads + +Callback payloads (sent to Langfuse, etc.) include message content. Large base64 strings are automatically truncated to size placeholders in logging payloads. + +You can control the truncation threshold: + +```bash +# Max base64 characters before truncation (default: 64) +export MAX_BASE64_LENGTH_FOR_LOGGING=64 +``` + +## Environment Variables Reference + +| Variable | Default | Description | +|----------|---------|-------------| +| `LITELLM_DETAILED_TIMING` | `false` | Enable per-phase timing headers | +| `MAX_PAYLOAD_SIZE_FOR_DEBUG_LOG` | `102400` | Max payload bytes for full DEBUG serialization | +| `MAX_BASE64_LENGTH_FOR_LOGGING` | `64` | Max base64 chars before truncation in logging | diff --git a/docs/my-website/docs/troubleshoot/prisma_migrations.md b/docs/my-website/docs/troubleshoot/prisma_migrations.md index 9d9cb585b2..79b797d2cd 100644 --- a/docs/my-website/docs/troubleshoot/prisma_migrations.md +++ b/docs/my-website/docs/troubleshoot/prisma_migrations.md @@ -2,6 +2,8 @@ Common Prisma migration issues encountered when upgrading or downgrading LiteLLM proxy versions, and how to fix them. +For a full guide on safely reverting your LiteLLM version, see the **[Safe Rollback Guide](rollback)**. + ## How Prisma Migrations Work in LiteLLM - LiteLLM uses [Prisma](https://www.prisma.io/) to manage its PostgreSQL database schema. @@ -46,6 +48,8 @@ After deleting the entry, restart LiteLLM — it will re-apply the migration on If deleting the migration entry and restarting doesn't resolve the issue, sync the schema directly: +> **Warning:** `prisma db push` can cause **data loss** if the Prisma schema removes columns or tables that exist in your database. Only use this as a last resort and ensure you have a database backup first. + ```bash DATABASE_URL="" prisma db push ``` @@ -76,7 +80,7 @@ DELETE FROM "_prisma_migrations" WHERE migration_name = ''; ``` -3. If that doesn't work, use `prisma db push`: +3. If that doesn't work, use `prisma db push` (see [warning above](#step-2--if-that-doesnt-work-use-prisma-db-push) — back up your database first): ```bash DATABASE_URL="" prisma db push @@ -106,7 +110,7 @@ LIMIT 20; 3. Restart LiteLLM to re-run migrations. -4. If that doesn't work, use `prisma db push`: +4. If that doesn't work, use `prisma db push` (see [warning above](#step-2--if-that-doesnt-work-use-prisma-db-push) — back up your database first): ```bash DATABASE_URL="" prisma db push diff --git a/docs/my-website/docs/troubleshoot/rollback.md b/docs/my-website/docs/troubleshoot/rollback.md new file mode 100644 index 0000000000..a6b8db169a --- /dev/null +++ b/docs/my-website/docs/troubleshoot/rollback.md @@ -0,0 +1,115 @@ +# Safe Rollback Guide + +This guide outlines the process for safely rolling back a LiteLLM Proxy deployment to a previous version. + +We recommend rolling back to the previous [stable release](https://github.com/BerriAI/litellm/releases). Stable releases come out every week and follow the `main-v-stable` tag convention (e.g., `main-v1.77.2-stable`). + +## 1. Determine Rollback Scope + +Before proceeding, identify why you are rolling back: +- **Application Logic Error**: Reverting code changes but keeping the database schema. +- **Database Migration Failure**: Reverting changes that included database schema updates. +- **Performance Regression**: Reverting to a known stable version. + +## 2. Back Up the Database + +> **Always back up before rolling back.** Before making any changes, take a database snapshot or dump. This is your safety net if something goes wrong during the rollback. + +```bash +# PostgreSQL example +pg_dump -h -U -d -F c -f litellm_backup_$(date +%Y%m%d_%H%M%S).dump +``` + +If you are on a managed database (e.g., AWS RDS, GCP Cloud SQL), create a snapshot through your cloud console instead. + +## 3. Pre-Rollback Checks + +Before reverting, review these items: + +- **`LITELLM_SALT_KEY`**: Do **not** change this value during rollback. It is used to encrypt/decrypt your LLM API Key credentials stored in the database. Changing it will make existing credentials unreadable. See [Best Practices for Production](../proxy/prod#8-set-litellm-salt-key). +- **`config.yaml`**: If you added settings specific to the newer version, the older version may not recognize them. Review your config and remove or comment out any settings that were introduced in the version you are rolling back from. +- **`DISABLE_SCHEMA_UPDATE`**: If you use the [Helm PreSync hook for migrations](../proxy/prod#7-use-helm-presync-hook-for-database-migrations-beta) with `DISABLE_SCHEMA_UPDATE=true` on your pods, migrations will **not** auto-run on restart. You will need to handle migration cleanup manually (see Step 5) or re-run the PreSync hook against the older chart version. + +## 4. Revert Application Version + +Revert your deployment to the previous stable Docker image or Helm chart version. + +### Docker +Update your deployment manifest (e.g., K8s Deployment, Docker Compose) to use the previous version: +```yaml +# Example: Reverting to the previous stable release +image: docker.litellm.ai/berriai/litellm:main-v-stable +``` + +See [all available images](https://github.com/orgs/BerriAI/packages). + +### Helm +If you deployed via Helm, use `helm rollback`: +```bash +helm rollback [revision-number] +``` + +## 5. Handle Database Migrations + +If you are rolling back to a version that did not have a specific migration, you may need to resolve the migration state in the database. + +> LiteLLM uses `prisma migrate deploy` for production (enabled via `USE_PRISMA_MIGRATE=True`). If a migration partially failed or you are reverting code that expects an older schema, you need to clean up the migration history in the `_prisma_migrations` table. See [Best Practices for Production](../proxy/prod#9-use-prisma-migrate-deploy). + +### Option A — Delete stale migration entries (recommended) + +Connect to your PostgreSQL database and remove migration entries that belong to the version you are rolling back from. This lets LiteLLM re-apply them cleanly if you upgrade again later. + +```sql +-- View recent migrations +SELECT migration_name, finished_at, rolled_back_at, logs +FROM "_prisma_migrations" +ORDER BY started_at DESC +LIMIT 10; + +-- Delete migration entries from the version you are rolling back from +DELETE FROM "_prisma_migrations" +WHERE migration_name = ''; +``` + +After deleting the entries, restart LiteLLM — it will re-apply the correct migrations for its version on startup. + +> **Note:** If you have `DISABLE_SCHEMA_UPDATE=true` set on your pods, migrations will not auto-run. You need to either temporarily set it to `false`, or re-run the Helm PreSync migration job targeting the older version. + +### Option B — Use `prisma migrate resolve` (if you have CLI access) + +If you have access to the Prisma CLI (e.g., in a local development environment or a debug container with the `litellm-proxy-extras` package installed): + +```bash +DATABASE_URL="" prisma migrate resolve --rolled-back "" +``` + +> **Note:** This requires the Prisma CLI to be available in your environment (installed via `prisma-client-py`). If you don't have CLI access (e.g., no shell into the running container), use **Option A** (direct SQL) instead. + +### Auto-Recovery Logic +LiteLLM's internal `ProxyExtrasDBManager` automatically attempts to handle idempotent migrations. In many cases, simply rolling back the version and restarting the proxy will be enough if the database changes are additive (e.g., new columns or tables). + +## 6. Verification Checklist + +After rolling back, verify the health of the system: + +- [ ] **Health Endpoint**: Confirm the `/health` endpoint returns `200 OK`. +- [ ] **Check Logs**: Ensure no Prisma errors appear — look for `relation "..." does not exist`, `column "..." does not exist`, or `prisma migrate` failures in the logs. +- [ ] **Spend Tracking**: Run a test completion and confirm the spend is recorded in the `LiteLLM_SpendLogs` table. +- [ ] **Billing (Lago)**: If using Lago for billing (e.g., Lago → Stripe), check proxy logs for `Logged Lago Object` to confirm usage events are being sent. +- [ ] **State Consistency**: If using Redis for caching or rate limiting, consider clearing the cache if the newer version changed the cache key structure. +- [ ] **Admin UI**: Verify the Admin UI loads and shows correct data for keys and teams. + +## 7. Troubleshooting + +### "New migrations cannot be applied" +If you see this error after a rollback, it means the database has a migration in a "failed" state. +1. Identify the failed migration name (see the SQL query in Step 5). +2. Delete the failed entry from `_prisma_migrations`. +3. Restart the proxy. + +### "relation X does not exist" +This typically means a migration entry exists in `_prisma_migrations` but the actual table/column was never created or was dropped. +1. Delete the stale migration entry. +2. Restart LiteLLM so it re-runs the migration. + +For more details on Prisma errors, see [Prisma Migrations Troubleshoot](prisma_migrations). diff --git a/docs/my-website/docs/troubleshoot/ui_issues.md b/docs/my-website/docs/troubleshoot/ui_issues.md new file mode 100644 index 0000000000..90912b1dae --- /dev/null +++ b/docs/my-website/docs/troubleshoot/ui_issues.md @@ -0,0 +1,49 @@ +# UI Troubleshooting + +If you're experiencing issues with the LiteLLM Admin UI, please include the following information when reporting. + +## 1. Steps to Reproduce + +A clear, step-by-step description of how to trigger the issue (e.g., "Navigate to Settings → Team, click 'Create Team', fill in fields, click submit → error appears"). + +## 2. LiteLLM Version + +The current version of LiteLLM you're running. Check via `litellm --version` or the UI's settings page. + +## 3. Architecture & Deployment Setup + +Distributed environments are a known source of UI issues. Please describe: + +- **Number of LiteLLM instances/replicas** and how they are deployed (e.g., Kubernetes, Docker Compose, ECS) +- **Load balancer** type and configuration (e.g., ALB, Nginx, Cloudflare Tunnel) — include whether sticky sessions are enabled +- **How the UI is accessed** — directly via LiteLLM, through a reverse proxy, or behind an ingress controller +- **Any CDN or caching layers** between the user and the LiteLLM server + +## 4. Network Tab Requests + +Open your browser's Developer Tools (F12 → Network tab), reproduce the issue, and share: + +- The **failing request(s)** — URL, method, status code, and response body +- **Screenshots or HAR export** of the relevant network activity +- Any **CORS or mixed-content errors** shown in the Console tab + +## 5. Environment Variables + +Non-sensitive environment variables related to the UI and proxy setup, such as: + +- `LITELLM_MASTER_KEY` +- `PROXY_BASE_URL` / `LITELLM_PROXY_BASE_URL` +- `UI_BASE_PATH` +- Any SSO-related variables (e.g., `GOOGLE_CLIENT_ID`, `MICROSOFT_TENANT`) + +Do **not** include passwords, secrets, or API keys. + +## 6. Browser & Access Details + +- **Browser** and version (e.g., Chrome 120, Firefox 121) +- **Access URL** used to reach the UI (redact sensitive parts) +- Whether the issue occurs for **all users or specific roles** (Admin, Internal User, etc.) + +## 7. Screenshots or Screen Recordings + +A screenshot or short screen recording of the issue is extremely helpful. Include any visible error messages, toasts, or unexpected behavior. diff --git a/docs/my-website/docs/tutorials/claude_code_beta_headers.md b/docs/my-website/docs/tutorials/claude_code_beta_headers.md index 4cc6f7ff92..fab90d15e8 100644 --- a/docs/my-website/docs/tutorials/claude_code_beta_headers.md +++ b/docs/my-website/docs/tutorials/claude_code_beta_headers.md @@ -92,9 +92,34 @@ Open `anthropic_beta_headers_config.json` and add the new header to each provide - **Header transformations**: Some providers use different header names (e.g., Bedrock maps `advanced-tool-use-2025-11-20` to `tool-search-tool-2025-10-19`) - **Alphabetical order**: Keep headers sorted alphabetically for maintainability -### Step 3: Restart Your Application +### Step 3: Reload Configuration (No Restart Required!) -After updating the config file, restart your LiteLLM proxy or application: +**Option 1: Dynamic Reload Without Restart** + +Instead of restarting your application, you can dynamically reload the beta headers configuration using environment variables and API endpoints: + +```bash +# Set environment variable to fetch from remote URL (Do this if you want to point it to some other URL) +export LITELLM_ANTHROPIC_BETA_HEADERS_URL="https://raw.githubusercontent.com/BerriAI/litellm/main/litellm/anthropic_beta_headers_config.json" + +# Manually trigger reload via API (no restart needed!) +curl -X POST "https://your-proxy-url/reload/anthropic_beta_headers" \ + -H "Authorization: Bearer YOUR_ADMIN_TOKEN" +``` + +**Option 2: Schedule Automatic Reloads** + +Set up automatic reloading to always stay up-to-date with the latest beta headers: + +```bash +# Reload configuration every 24 hours +curl -X POST "https://your-proxy-url/schedule/anthropic_beta_headers_reload?hours=24" \ + -H "Authorization: Bearer YOUR_ADMIN_TOKEN" +``` + +**Option 3: Traditional Restart** + +If you prefer the traditional approach, restart your LiteLLM proxy or application: ```bash # If using LiteLLM proxy @@ -104,7 +129,11 @@ litellm --config config.yaml # Just restart your Python application ``` -The updated configuration will be loaded automatically. +:::tip Zero-Downtime Updates +With dynamic reloading, you can fix invalid beta header errors **without restarting your service**! This is especially useful in production environments where downtime is costly. + +See [Auto Sync Anthropic Beta Headers](../proxy/sync_anthropic_beta_headers.md) for complete documentation. +::: ## Fixing Invalid Beta Header Errors @@ -215,6 +244,26 @@ Result sent to Bedrock: anthropic-beta: computer-use-2025-01-24 ``` +## Dynamic Configuration Management (No Restart Required!) + +### Environment Variables + +Control how LiteLLM loads the beta headers configuration: + +| Variable | Description | Default | +|----------|-------------|---------| +| `LITELLM_ANTHROPIC_BETA_HEADERS_URL` | URL to fetch config from | GitHub main branch | +| `LITELLM_LOCAL_ANTHROPIC_BETA_HEADERS` | Set to `True` to use local config only | `False` | + +**Example: Use Custom Config URL** +```bash +export LITELLM_ANTHROPIC_BETA_HEADERS_URL="https://your-company.com/custom-beta-headers.json" +``` + +**Example: Use Local Config Only (No Remote Fetching)** +```bash +export LITELLM_LOCAL_ANTHROPIC_BETA_HEADERS=True +``` ## Provider-Specific Notes ### Bedrock diff --git a/docs/my-website/docs/tutorials/claude_mcp.md b/docs/my-website/docs/tutorials/claude_mcp.md index 07c3cead0b..ab27908c8d 100644 --- a/docs/my-website/docs/tutorials/claude_mcp.md +++ b/docs/my-website/docs/tutorials/claude_mcp.md @@ -9,7 +9,7 @@ Note: LiteLLM supports OAuth for MCP servers as well. [Learn more](https://docs. ## Connecting MCP Servers -You can also connect MCP servers to Claude Code via LiteLLM Proxy. +You can connect MCP servers to Claude Code via LiteLLM Proxy. 1. Add the MCP server to your `config.yaml` @@ -23,6 +23,7 @@ In this example, we'll add the Github MCP server to our `config.yaml` mcp_servers: github_mcp: url: "https://api.githubcopilot.com/mcp" + transport: "http" auth_type: oauth2 client_id: os.environ/GITHUB_OAUTH_CLIENT_ID client_secret: os.environ/GITHUB_OAUTH_CLIENT_SECRET @@ -34,31 +35,70 @@ mcp_servers: In this example, we'll add the Atlassian MCP server to our `config.yaml` ```yaml title="config.yaml" showLineNumbers -atlassian_mcp: - server_id: atlassian_mcp_id - url: "https://mcp.atlassian.com/v1/sse" - transport: "sse" - auth_type: oauth2 +mcp_servers: + atlassian_mcp: + url: "https://mcp.atlassian.com/v1/mcp" + transport: "http" + auth_type: oauth2 ``` +:::important +The server name under `mcp_servers:` (e.g. `atlassian_mcp`, `github_mcp`) **must match** the name used in the Claude Code URL path (`/mcp/`). A mismatch will cause a 404 error during OAuth. +::: + 2. Start LiteLLM Proxy +Since Claude Code needs a publicly accessible URL for the OAuth callback, expose your proxy via ngrok or a similar tool. + ```bash litellm --config /path/to/config.yaml # RUNNING on http://0.0.0.0:4000 ``` -3. Use the MCP server in Claude Code - ```bash -claude mcp add --transport http litellm_proxy http://0.0.0.0:4000/github_mcp/mcp --header "Authorization: Bearer sk-LITELLM_VIRTUAL_KEY" +# In a separate terminal — expose proxy for OAuth callbacks +ngrok http 4000 ``` -For MCP servers that require dynamic client registration (such as Atlassian), please set `x-litellm-api-key: Bearer sk-LITELLM_VIRTUAL_KEY` instead of using `Authorization: Bearer LITELLM_VIRTUAL_KEY`. +3. Add the MCP server to Claude Code + + + + +```bash +claude mcp add --transport http litellm-github https://your-ngrok-url.ngrok-free.dev/mcp/github_mcp \ + --header "x-litellm-api-key: Bearer sk-1234" +``` + + + + +```bash +claude mcp add --transport http litellm-atlassian https://your-ngrok-url.ngrok-free.dev/mcp/atlassian_mcp \ + --header "x-litellm-api-key: Bearer sk-1234" +``` + + + + +**Parameter breakdown:** + +| Parameter | Description | +|-----------|-------------| +| `--transport http` | Use HTTP transport for the MCP connection | +| `litellm-atlassian` | The name for this MCP server **on Claude Code** — can be anything you choose | +| `https://your-ngrok-url.ngrok-free.dev/mcp/atlassian_mcp` | The LiteLLM proxy URL. Format: `/mcp/`. The `atlassian_mcp` part **must match** the key under `mcp_servers:` in your LiteLLM proxy config | +| `--header "x-litellm-api-key: Bearer sk-1234"` | Your LiteLLM virtual key for authentication to the proxy | + +You can also add the MCP server directly to your `~/.claude.json` file instead of using `claude mcp add`. [See Claude Code docs](https://docs.anthropic.com/en/docs/claude-code/mcp). + +:::note +For MCP servers that require OAuth (such as Atlassian), use `x-litellm-api-key` instead of `Authorization` for the LiteLLM virtual key. The `Authorization` header is reserved for the OAuth flow. +::: 4. Authenticate via Claude Code @@ -68,24 +108,20 @@ a. Start Claude Code claude ``` -b. Authenticate via Claude Code +b. Open the MCP menu ```bash /mcp ``` -c. Select the MCP server +c. Select the MCP server (e.g. `litellm-atlassian`) -```bash -> litellm_proxy -``` - -d. Start Oauth flow via Claude Code +d. Start the OAuth flow ```bash > 1. Authenticate 2. Reconnect - 3. Disable + 3. Disable ``` e. 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"watchpack": "^2.4.4", + "terser-webpack-plugin": "^5.3.16", + "watchpack": "^2.5.1", "webpack-sources": "^3.3.3" }, "bin": { diff --git a/docs/my-website/package.json b/docs/my-website/package.json index 4af7a168f8..c4fa04c96a 100644 --- a/docs/my-website/package.json +++ b/docs/my-website/package.json @@ -61,10 +61,26 @@ "mermaid": ">=11.10.0", "gray-matter": "4.0.3", "glob": ">=11.1.0", - "tar": ">=7.5.7", + "tar": ">=7.5.8", "@isaacs/brace-expansion": ">=5.0.1", "node-forge": ">=1.3.2", "mdast-util-to-hast": ">=13.2.1", - "lodash-es": ">=4.17.23" + "lodash-es": ">=4.17.23", + "schema-utils@3": { + "ajv": "6.14.0" + }, + "schema-utils@4": { + "ajv": "8.18.0" + }, + "file-loader": { + "ajv": "6.14.0" + }, + "null-loader": { + "ajv": "6.14.0" + }, + "url-loader": { + "ajv": "6.14.0" + }, + "minimatch": "10.2.1" } } \ No newline at end of file diff --git a/docs/my-website/release_notes/v1.81.12.md b/docs/my-website/release_notes/v1.81.12.md new file mode 100644 index 0000000000..a1f1daa2b9 --- /dev/null +++ b/docs/my-website/release_notes/v1.81.12.md @@ -0,0 +1,433 @@ +--- +title: "v1.81.12-stable - Guardrail Policy Templates & Action Builder" +slug: "v1-81-12" +date: 2026-02-14T00:00:00 +authors: + - name: Krrish Dholakia + title: CEO, LiteLLM + url: https://www.linkedin.com/in/krish-d/ + image_url: https://pbs.twimg.com/profile_images/1298587542745358340/DZv3Oj-h_400x400.jpg + - name: Ishaan Jaff + title: CTO, LiteLLM + url: https://www.linkedin.com/in/reffajnaahsi/ + image_url: https://pbs.twimg.com/profile_images/1613813310264340481/lz54oEiB_400x400.jpg +hide_table_of_contents: false +--- + +## Deploy this version + +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; +import Image from '@theme/IdealImage'; + + + + +``` showLineNumbers title="docker run litellm" +docker run \ +-e STORE_MODEL_IN_DB=True \ +-p 4000:4000 \ +ghcr.io/berriai/litellm:main-v1.81.12-stable +``` + + + + +``` showLineNumbers title="pip install litellm" +pip install litellm==1.81.12 +``` + + + + +## Key Highlights + +- **Policy Templates** - [Pre-configured guardrail policy templates for common safety and compliance use-cases (including NSFW, toxic content, and child safety)](../../docs/proxy/guardrails/policy_templates) +- **Guardrail Action Builder** - [Build and customize guardrail policy flows with the new action-builder UI and conditional execution support](../../docs/proxy/guardrails/policy_templates) +- **MCP OAuth2 M2M + Tracing** - [Add machine-to-machine OAuth2 support for MCP servers and OpenTelemetry tracing for MCP calls through AI Gateway](../../docs/mcp) +- **Responses API `shell` Tool & `context_management` support** - [Server-side context management (compaction) and Shell tool support for the OpenAI Responses API](../../docs/response_api) +- **Access Groups** - [Create access groups to manage model, MCP server, and agent access across teams and keys](../../docs/proxy/access_groups) +- **50+ New Bedrock Regional Model Entries** - DeepSeek V3.2, MiniMax M2.1, Kimi K2.5, Qwen3 Coder Next, and NVIDIA Nemotron Nano across multiple regions +- **Add Semgrep & fix OOMs** - [Static analysis rules and out-of-memory fixes](#add-semgrep--fix-ooms) - [PR #20912](https://github.com/BerriAI/litellm/pull/20912) + +--- + +## Add Semgrep & fix OOMs + +This release fixes out-of-memory (OOM) risks from unbounded `asyncio.Queue()` usage. Log queues (e.g. GCS bucket) and DB spend-update queues were previously unbounded and could grow without limit under load. They now use a configurable max size (`LITELLM_ASYNCIO_QUEUE_MAXSIZE`, default 1000); when full, queues flush immediately to make room instead of growing memory. A Semgrep rule (`.semgrep/rules/python/unbounded-memory.yml`) was added to flag similar unbounded-memory patterns in future code. [PR #20912](https://github.com/BerriAI/litellm/pull/20912) + +--- + +## Guardrail Action Builder + +This release adds a visual action builder for guardrail policies with conditional execution support. You can now chain guardrails into multi-step pipelines — if a simple guardrail fails, route to an advanced one instead of immediately blocking. Each step has configurable ON PASS and ON FAIL actions (Next Step, Block, or Allow), and you can test the full pipeline with a sample message before saving. + +![Guardrail Action Builder](../img/release_notes/guard_actions.png) + +### Access Groups + +Access Groups simplify defining resource access across your organization. One group can grant access to models, MCP servers, and agents—simply attach it to a key or team. Create groups in the Admin UI, define which resources each group includes, then assign the group when creating keys or teams. Updates to a group apply automatically to all attached keys and teams. + + + +## New Providers and Endpoints + +### New Providers (2 new providers) + +| Provider | Supported LiteLLM Endpoints | Description | +| -------- | --------------------------- | ----------- | +| [Scaleway](../../docs/providers/scaleway) | `/chat/completions` | Scaleway Generative APIs for chat completions | +| [Sarvam AI](../../docs/providers/sarvam) | `/chat/completions`, `/audio/transcriptions`, `/audio/speech` | Sarvam AI STT and TTS support for Indian languages | + +--- + +## New Models / Updated Models + +#### New Model Support (19 highlighted models) + +| Provider | Model | Context Window | Input ($/1M tokens) | Output ($/1M tokens) | +| -------- | ----- | -------------- | ------------------- | -------------------- | +| AWS Bedrock | `deepseek.v3.2` | 164K | $0.62 | $1.85 | +| AWS Bedrock | `minimax.minimax-m2.1` | 196K | $0.30 | $1.20 | +| AWS Bedrock | `moonshotai.kimi-k2.5` | 262K | $0.60 | $3.00 | +| AWS Bedrock | `moonshotai.kimi-k2-thinking` | 262K | $0.73 | $3.03 | +| AWS Bedrock | `qwen.qwen3-coder-next` | 262K | $0.50 | $1.20 | +| AWS Bedrock | `nvidia.nemotron-nano-3-30b` | 262K | $0.06 | $0.24 | +| Azure AI | `azure_ai/kimi-k2.5` | 262K | $0.60 | $3.00 | +| Vertex AI | `vertex_ai/zai-org/glm-5-maas` | 200K | $1.00 | $3.20 | +| MiniMax | `minimax/MiniMax-M2.5` | 1M | $0.30 | $1.20 | +| MiniMax | `minimax/MiniMax-M2.5-lightning` | 1M | $0.30 | $2.40 | +| Dashscope | `dashscope/qwen3-max` | 258K | Tiered pricing | Tiered pricing | +| Perplexity | `perplexity/preset/pro-search` | - | Per-request | Per-request | +| Perplexity | `perplexity/openai/gpt-4o` | - | Per-request | Per-request | +| Perplexity | `perplexity/openai/gpt-5.2` | - | Per-request | Per-request | +| Vercel AI Gateway | `vercel_ai_gateway/anthropic/claude-opus-4.6` | 200K | $5.00 | $25.00 | +| Vercel AI Gateway | `vercel_ai_gateway/anthropic/claude-sonnet-4` | 200K | $3.00 | $15.00 | +| Vercel AI Gateway | `vercel_ai_gateway/anthropic/claude-haiku-4.5` | 200K | $1.00 | $5.00 | +| Sarvam AI | `sarvam/sarvam-m` | 8K | Free tier | Free tier | +| Anthropic | `fast/claude-opus-4-6` | 1M | $30.00 | $150.00 | + +*Note: AWS Bedrock models are available across multiple regions (us-east-1, us-east-2, us-west-2, eu-central-1, eu-north-1, ap-northeast-1, ap-south-1, ap-southeast-3, sa-east-1). 54 regional model entries were added in total.* + +#### Features + +- **[Anthropic](../../docs/providers/anthropic)** + - Enable non-tool structured outputs on Claude Opus 4.5 and 4.6 using `output_format` param - [PR #20548](https://github.com/BerriAI/litellm/pull/20548) + - Add support for `anthropic_messages` call type in prompt caching - [PR #19233](https://github.com/BerriAI/litellm/pull/19233) + - Managing Anthropic Beta Headers with remote URL fetching - [PR #20935](https://github.com/BerriAI/litellm/pull/20935), [PR #21110](https://github.com/BerriAI/litellm/pull/21110) + - Remove `x-anthropic-billing` block - [PR #20951](https://github.com/BerriAI/litellm/pull/20951) + - Use Authorization Bearer for OAuth tokens instead of x-api-key - [PR #21039](https://github.com/BerriAI/litellm/pull/21039) + - Filter unsupported JSON schema constraints for structured outputs - [PR #20813](https://github.com/BerriAI/litellm/pull/20813) + - New Claude Opus 4.6 features for `/v1/messages` - [PR #20733](https://github.com/BerriAI/litellm/pull/20733) + - Fix `reasoning_effort=None` and `"none"` should return None for Opus 4.6 - [PR #20800](https://github.com/BerriAI/litellm/pull/20800) + +- **[AWS Bedrock](../../docs/providers/bedrock)** + - Extend model support with 4 new beta models - [PR #21035](https://github.com/BerriAI/litellm/pull/21035) + - Add Claude Opus 4.6 to `_supports_tool_search_on_bedrock` - [PR #21017](https://github.com/BerriAI/litellm/pull/21017) + - Correct Bedrock Claude Opus 4.6 model IDs (remove `:0` suffix) - [PR #20564](https://github.com/BerriAI/litellm/pull/20564), [PR #20671](https://github.com/BerriAI/litellm/pull/20671) + - Add `output_config` as supported param - [PR #20748](https://github.com/BerriAI/litellm/pull/20748) + +- **[Vertex AI](../../docs/providers/vertex)** + - Add Vertex GLM-5 model support - [PR #21053](https://github.com/BerriAI/litellm/pull/21053) + - Propagate `extra_headers` anthropic-beta to request body - [PR #20666](https://github.com/BerriAI/litellm/pull/20666) + - Preserve `usageMetadata` in `_hidden_params` - [PR #20559](https://github.com/BerriAI/litellm/pull/20559) + - Map `IMAGE_PROHIBITED_CONTENT` to `content_filter` - [PR #20524](https://github.com/BerriAI/litellm/pull/20524) + - Add RAG ingest for Vertex AI - [PR #21120](https://github.com/BerriAI/litellm/pull/21120) + +- **[OCI / Cohere](../../docs/providers/cohere)** + - OCI Cohere responseFormat/Pydantic support - [PR #20663](https://github.com/BerriAI/litellm/pull/20663) + - Fix OCI Cohere system messages by populating `preambleOverride` - [PR #20958](https://github.com/BerriAI/litellm/pull/20958) + +- **[Perplexity](../../docs/providers/perplexity)** + - Perplexity Research API support with preset search - [PR #20860](https://github.com/BerriAI/litellm/pull/20860) + +- **[MiniMax](../../docs/providers/minimax)** + - Add MiniMax-M2.5 and MiniMax-M2.5-lightning models - [PR #21054](https://github.com/BerriAI/litellm/pull/21054) + +- **[Kimi / Moonshot](../../docs/providers/moonshot)** + - Add Kimi model pricing by region - [PR #20855](https://github.com/BerriAI/litellm/pull/20855) + - Add `moonshotai.kimi-k2.5` - [PR #20863](https://github.com/BerriAI/litellm/pull/20863) + +- **[Dashscope](../../docs/providers/dashscope)** + - Add `dashscope/qwen3-max` model with tiered pricing - [PR #20919](https://github.com/BerriAI/litellm/pull/20919) + +- **[Vercel AI Gateway](../../docs/providers/vercel_ai_gateway)** + - Add new Vercel AI Anthropic models - [PR #20745](https://github.com/BerriAI/litellm/pull/20745) + +- **[Azure AI](../../docs/providers/azure_ai)** + - Add `azure_ai/kimi-k2.5` to Azure model DB - [PR #20896](https://github.com/BerriAI/litellm/pull/20896) + - Support Azure AD token auth for non-Claude azure_ai models - [PR #20981](https://github.com/BerriAI/litellm/pull/20981) + - Fix Azure batches issues - [PR #21092](https://github.com/BerriAI/litellm/pull/21092) + +- **[DeepSeek](../../docs/providers/deepseek)** + - Sync DeepSeek model metadata and add bare-name fallback - [PR #20938](https://github.com/BerriAI/litellm/pull/20938) + +- **[Gemini](../../docs/providers/gemini)** + - Handle image in assistant message for Gemini - [PR #20845](https://github.com/BerriAI/litellm/pull/20845) + - Add missing tpm/rpm for Gemini models - [PR #21175](https://github.com/BerriAI/litellm/pull/21175) + +- **General** + - Add 30 missing models to pricing JSON - [PR #20797](https://github.com/BerriAI/litellm/pull/20797) + - Cleanup 39 deprecated OpenRouter models - [PR #20786](https://github.com/BerriAI/litellm/pull/20786) + - Standardize endpoint `display_name` naming convention - [PR #20791](https://github.com/BerriAI/litellm/pull/20791) + - Fix and stabilize model cost map formatting - [PR #20895](https://github.com/BerriAI/litellm/pull/20895) + - Export `PermissionDeniedError` from `litellm.__init__` - [PR #20960](https://github.com/BerriAI/litellm/pull/20960) + +### Bug Fixes + +- **[Anthropic](../../docs/providers/anthropic)** + - Fix `get_supported_anthropic_messages_params` - [PR #20752](https://github.com/BerriAI/litellm/pull/20752) + - Fix `base_model` name for body and deployment name in URL - [PR #20747](https://github.com/BerriAI/litellm/pull/20747) + +- **[Azure](../../docs/providers/azure/azure)** + - Preserve `content_policy_violation` error details from Azure OpenAI - [PR #20883](https://github.com/BerriAI/litellm/pull/20883) + +- **[Vertex AI](../../docs/providers/vertex)** + - Fix Gemini multi-turn tool calling message formatting (added and reverted) - [PR #20569](https://github.com/BerriAI/litellm/pull/20569), [PR #21051](https://github.com/BerriAI/litellm/pull/21051) + +--- + +## LLM API Endpoints + +#### Features + +- **[Responses API](../../docs/response_api)** + - Add server-side context management (compaction) support - [PR #21058](https://github.com/BerriAI/litellm/pull/21058) + - Add Shell tool support for OpenAI Responses API - [PR #21063](https://github.com/BerriAI/litellm/pull/21063) + - Preserve tool call argument deltas when streaming id is omitted - [PR #20712](https://github.com/BerriAI/litellm/pull/20712) + - Preserve interleaved thinking/redacted_thinking blocks during streaming - [PR #20702](https://github.com/BerriAI/litellm/pull/20702) + +- **[Chat Completions](../../docs/completion/input)** + - Add Web Search support using LiteLLM `/search` (web search interception hook) - [PR #20483](https://github.com/BerriAI/litellm/pull/20483) + - Preserved nullable object fields by carrying schema properties - [PR #19132](https://github.com/BerriAI/litellm/pull/19132) + - Support `prompt_cache_key` for OpenAI and Azure chat completions - [PR #20989](https://github.com/BerriAI/litellm/pull/20989) + +- **[Pass-Through Endpoints](../../docs/pass_through/bedrock)** + - Add support for `langchain_aws` via LiteLLM passthrough - [PR #20843](https://github.com/BerriAI/litellm/pull/20843) + - Add `custom_body` parameter to `endpoint_func` in `create_pass_through_route` - [PR #20849](https://github.com/BerriAI/litellm/pull/20849) + +- **[Vector Stores](../../docs/providers/openai)** + - Add `target_model_names` for vector store endpoints - [PR #21089](https://github.com/BerriAI/litellm/pull/21089) + +- **General** + - Add `output_config` as supported param - [PR #20748](https://github.com/BerriAI/litellm/pull/20748) + - Add managed error file support - [PR #20838](https://github.com/BerriAI/litellm/pull/20838) + +#### Bugs + +- **General** + - Stop leaking Python tracebacks in streaming SSE error responses - [PR #20850](https://github.com/BerriAI/litellm/pull/20850) + - Fix video list pagination cursors not encoded with provider metadata - [PR #20710](https://github.com/BerriAI/litellm/pull/20710) + - Handle `metadata=None` in SDK path retry/error logic - [PR #20873](https://github.com/BerriAI/litellm/pull/20873) + - Fix Spend logs pickle error with Pydantic models and redaction - [PR #20685](https://github.com/BerriAI/litellm/pull/20685) + - Remove duplicate `PerplexityResponsesConfig` from `LLM_CONFIG_NAMES` - [PR #21105](https://github.com/BerriAI/litellm/pull/21105) + +--- + +## Management Endpoints / UI + +#### Features + +- **Access Groups** + - New Access Groups feature for managing model, MCP server, and agent access - [PR #21022](https://github.com/BerriAI/litellm/pull/21022) + - Access Groups table and details page UI - [PR #21165](https://github.com/BerriAI/litellm/pull/21165) + - Refactor `model_ids` to `model_names` for backwards compatibility - [PR #21166](https://github.com/BerriAI/litellm/pull/21166) + +- **Policies** + - Allow connecting Policies to Tags, simulating Policies, viewing key/team counts - [PR #20904](https://github.com/BerriAI/litellm/pull/20904) + - Guardrail pipeline support for conditional sequential execution - [PR #21177](https://github.com/BerriAI/litellm/pull/21177) + - Pipeline flow builder UI for guardrail policies - [PR #21188](https://github.com/BerriAI/litellm/pull/21188) + +- **SSO / Auth** + - New Login With SSO Button - [PR #20908](https://github.com/BerriAI/litellm/pull/20908) + - M2M OAuth2 UI Flow - [PR #20794](https://github.com/BerriAI/litellm/pull/20794) + - Allow Organization and Team Admins to call `/invitation/new` - [PR #20987](https://github.com/BerriAI/litellm/pull/20987) + - Invite User: Email Integration Alert - [PR #20790](https://github.com/BerriAI/litellm/pull/20790) + - Populate identity fields in proxy admin JWT early-return path - [PR #21169](https://github.com/BerriAI/litellm/pull/21169) + +- **Spend Logs** + - Show predefined error codes in filter with user definable fallback - [PR #20773](https://github.com/BerriAI/litellm/pull/20773) + - Paginated searchable model select - [PR #20892](https://github.com/BerriAI/litellm/pull/20892) + - Sorting columns support - [PR #21143](https://github.com/BerriAI/litellm/pull/21143) + - Allow sorting on `/spend/logs/ui` - [PR #20991](https://github.com/BerriAI/litellm/pull/20991) + +- **UI Improvements** + - Navbar: Option to hide Usage Popup - [PR #20910](https://github.com/BerriAI/litellm/pull/20910) + - Model Page: Improve Credentials Messaging - [PR #21076](https://github.com/BerriAI/litellm/pull/21076) + - Fallbacks: Default configurable to 10 models - [PR #21144](https://github.com/BerriAI/litellm/pull/21144) + - Fallback display with arrows and card structure - [PR #20922](https://github.com/BerriAI/litellm/pull/20922) + - Team Info: Migrate to AntD Tabs + Table - [PR #20785](https://github.com/BerriAI/litellm/pull/20785) + - AntD refactoring and 0 cost models fix - [PR #20687](https://github.com/BerriAI/litellm/pull/20687) + - Zscaler AI Guard UI - [PR #21077](https://github.com/BerriAI/litellm/pull/21077) + - Include Config Defined Pass Through Endpoints - [PR #20898](https://github.com/BerriAI/litellm/pull/20898) + - Rename "HTTP" to "Streamable HTTP (Recommended)" in MCP server page - [PR #21000](https://github.com/BerriAI/litellm/pull/21000) + - MCP server discovery UI - [PR #21079](https://github.com/BerriAI/litellm/pull/21079) + +- **Virtual Keys** + - Allow Management keys to access `user/daily/activity` and team - [PR #20124](https://github.com/BerriAI/litellm/pull/20124) + - Skip premium check for empty metadata fields on team/key update - [PR #20598](https://github.com/BerriAI/litellm/pull/20598) + +#### Bugs + +- Logs: Fix Input and Output Copying - [PR #20657](https://github.com/BerriAI/litellm/pull/20657) +- Teams: Fix Available Teams - [PR #20682](https://github.com/BerriAI/litellm/pull/20682) +- Spend Logs: Reset Filters Resets Custom Date Range - [PR #21149](https://github.com/BerriAI/litellm/pull/21149) +- Usage: Request Chart stack variant fix - [PR #20894](https://github.com/BerriAI/litellm/pull/20894) +- Add Auto Router: Description Text Input Focus - [PR #21004](https://github.com/BerriAI/litellm/pull/21004) +- Guardrail Edit: LiteLLM Content Filter Categories - [PR #21002](https://github.com/BerriAI/litellm/pull/21002) +- Add null guard for models in API keys table - [PR #20655](https://github.com/BerriAI/litellm/pull/20655) +- Show error details instead of 'Data Not Available' for failed requests - [PR #20656](https://github.com/BerriAI/litellm/pull/20656) +- Fix Spend Management Tests - [PR #21088](https://github.com/BerriAI/litellm/pull/21088) +- Fix JWT email domain validation error message - [PR #21212](https://github.com/BerriAI/litellm/pull/21212) + +--- + +## AI Integrations + +### Logging + +- **[PostHog](../../docs/observability/posthog_integration)** + - Fix JSON serialization error for non-serializable objects - [PR #20668](https://github.com/BerriAI/litellm/pull/20668) + +- **[Prometheus](../../docs/proxy/logging#prometheus)** + - Sanitize label values to prevent metric scrape failures - [PR #20600](https://github.com/BerriAI/litellm/pull/20600) + +- **[Langfuse](../../docs/proxy/logging#langfuse)** + - Prevent empty proxy request spans from being sent to Langfuse - [PR #19935](https://github.com/BerriAI/litellm/pull/19935) + +- **[OpenTelemetry](../../docs/proxy/logging#otel)** + - Auto-infer `otlp_http` exporter when endpoint is configured - [PR #20438](https://github.com/BerriAI/litellm/pull/20438) + +- **[CloudZero](../../docs/proxy/logging)** + - Update CBF field mappings per LIT-1907 - [PR #20906](https://github.com/BerriAI/litellm/pull/20906) + +- **General** + - Allow `MAX_CALLBACKS` override via env var - [PR #20781](https://github.com/BerriAI/litellm/pull/20781) + - Add `standard_logging_payload_excluded_fields` config option - [PR #20831](https://github.com/BerriAI/litellm/pull/20831) + - Enable `verbose_logger` when `LITELLM_LOG=DEBUG` - [PR #20496](https://github.com/BerriAI/litellm/pull/20496) + - Guard against None `litellm_metadata` in batch logging path - [PR #20832](https://github.com/BerriAI/litellm/pull/20832) + - Propagate model-level tags from config to SpendLogs - [PR #20769](https://github.com/BerriAI/litellm/pull/20769) + +### Guardrails + +- **Policy Templates** + - New Policy Templates: pre-configured guardrail combinations for specific use-cases - [PR #21025](https://github.com/BerriAI/litellm/pull/21025) + - Add NSFW policy template, toxic keywords in multiple languages, child safety content filter, JSON content viewer - [PR #21205](https://github.com/BerriAI/litellm/pull/21205) + - Add toxic/abusive content filter guardrails - [PR #20934](https://github.com/BerriAI/litellm/pull/20934) + +- **Pipeline Execution** + - Add guardrail pipeline support for conditional sequential execution - [PR #21177](https://github.com/BerriAI/litellm/pull/21177) + - Agent Guardrails on streaming output - [PR #21206](https://github.com/BerriAI/litellm/pull/21206) + - Pipeline flow builder UI - [PR #21188](https://github.com/BerriAI/litellm/pull/21188) + +- **[Zscaler AI Guard](../../docs/apply_guardrail)** + - Zscaler AI Guard bug fixes and support during post-call - [PR #20801](https://github.com/BerriAI/litellm/pull/20801) + - Zscaler AI Guard UI - [PR #21077](https://github.com/BerriAI/litellm/pull/21077) + +- **[ZGuard](../../docs/apply_guardrail)** + - Add team policy mapping for ZGuard - [PR #20608](https://github.com/BerriAI/litellm/pull/20608) + +- **General** + - Add logging to all unified guardrails + link to custom code guardrail templates - [PR #20900](https://github.com/BerriAI/litellm/pull/20900) + - Forward request headers + `litellm_version` to generic guardrails - [PR #20729](https://github.com/BerriAI/litellm/pull/20729) + - Empty `guardrails`/`policies` arrays should not trigger enterprise license check - [PR #20567](https://github.com/BerriAI/litellm/pull/20567) + - Fix OpenAI moderation guardrails - [PR #20718](https://github.com/BerriAI/litellm/pull/20718) + - Fix `/v2/guardrails/list` returning sensitive values - [PR #20796](https://github.com/BerriAI/litellm/pull/20796) + - Fix guardrail status error - [PR #20972](https://github.com/BerriAI/litellm/pull/20972) + - Reuse `get_instance_fn` in `initialize_custom_guardrail` - [PR #20917](https://github.com/BerriAI/litellm/pull/20917) + +--- + +## Spend Tracking, Budgets and Rate Limiting + +- **Prevent shared backend model key from being polluted** by per-deployment custom pricing - [PR #20679](https://github.com/BerriAI/litellm/pull/20679) +- **Avoid in-place mutation** in SpendUpdateQueue aggregation - [PR #20876](https://github.com/BerriAI/litellm/pull/20876) + +--- + +## MCP Gateway (12 updates) + +- **MCP M2M OAuth2 Support** - Add support for machine-to-machine OAuth2 for MCP servers - [PR #20788](https://github.com/BerriAI/litellm/pull/20788) +- **MCP Server Discovery UI** - Browse and discover available MCP servers from the UI - [PR #21079](https://github.com/BerriAI/litellm/pull/21079) +- **MCP Tracing** - Add OpenTelemetry tracing for MCP calls running through AI Gateway - [PR #21018](https://github.com/BerriAI/litellm/pull/21018) +- **MCP OAuth2 Debug Headers** - Client-side debug headers for OAuth2 troubleshooting - [PR #21151](https://github.com/BerriAI/litellm/pull/21151) +- **Fix MCP "Session not found" errors** - Resolve session persistence issues - [PR #21040](https://github.com/BerriAI/litellm/pull/21040) +- **Fix MCP OAuth2 root endpoints** returning "MCP server not found" - [PR #20784](https://github.com/BerriAI/litellm/pull/20784) +- **Fix MCP OAuth2 query param merging** when `authorization_url` already contains params - [PR #20968](https://github.com/BerriAI/litellm/pull/20968) +- **Fix MCP SCOPES on Atlassian** issue - [PR #21150](https://github.com/BerriAI/litellm/pull/21150) +- **Fix MCP StreamableHTTP backend** - Use `anyio.fail_after` instead of `asyncio.wait_for` - [PR #20891](https://github.com/BerriAI/litellm/pull/20891) +- **Inject `NPM_CONFIG_CACHE`** into STDIO MCP subprocess env - [PR #21069](https://github.com/BerriAI/litellm/pull/21069) +- **Block spaces and hyphens** in MCP server names and aliases - [PR #21074](https://github.com/BerriAI/litellm/pull/21074) + +--- + +## Performance / Loadbalancing / Reliability improvements (8 improvements) + +- **Remove orphan entries from queue** - Fix memory leak in scheduler queue - [PR #20866](https://github.com/BerriAI/litellm/pull/20866) +- **Remove repeated provider parsing** in budget limiter hot path - [PR #21043](https://github.com/BerriAI/litellm/pull/21043) +- **Use current retry exception** for retry backoff instead of stale exception - [PR #20725](https://github.com/BerriAI/litellm/pull/20725) +- **Add Semgrep & fix OOMs** - Static analysis rules and out-of-memory fixes - [PR #20912](https://github.com/BerriAI/litellm/pull/20912) +- **Add Pyroscope** for continuous profiling and observability - [PR #21167](https://github.com/BerriAI/litellm/pull/21167) +- **Respect `ssl_verify`** with shared aiohttp sessions - [PR #20349](https://github.com/BerriAI/litellm/pull/20349) +- **Fix shared health check serialization** - [PR #21119](https://github.com/BerriAI/litellm/pull/21119) +- **Change model mismatch logs** from WARNING to DEBUG - [PR #20994](https://github.com/BerriAI/litellm/pull/20994) + +--- + +## Database Changes + +### Schema Updates + +| Table | Change Type | Description | PR | Migration | +| ----- | ----------- | ----------- | -- | --------- | +| `LiteLLM_VerificationToken` | New Indexes | Added indexes on `user_id`+`team_id`, `team_id`, and `budget_reset_at`+`expires` | [PR #20736](https://github.com/BerriAI/litellm/pull/20736) | [Migration](https://github.com/BerriAI/litellm/blob/main/litellm-proxy-extras/litellm_proxy_extras/migrations/20260209085821_add_verificationtoken_indexes/migration.sql) | +| `LiteLLM_PolicyAttachmentTable` | New Column | Added `tags` text array for policy-to-tag connections | [PR #21061](https://github.com/BerriAI/litellm/pull/21061) | [Migration](https://github.com/BerriAI/litellm/blob/main/litellm-proxy-extras/litellm_proxy_extras/migrations/20260212103349_adjust_tags_policy_table/migration.sql) | +| `LiteLLM_AccessGroupTable` | New Table | Access groups for managing model, MCP server, and agent access | [PR #21022](https://github.com/BerriAI/litellm/pull/21022) | [Migration](https://github.com/BerriAI/litellm/blob/main/litellm-proxy-extras/litellm_proxy_extras/migrations/20260212143306_add_access_group_table/migration.sql) | +| `LiteLLM_AccessGroupTable` | Column Change | Renamed `access_model_ids` to `access_model_names` | [PR #21166](https://github.com/BerriAI/litellm/pull/21166) | [Migration](https://github.com/BerriAI/litellm/blob/main/litellm-proxy-extras/litellm_proxy_extras/migrations/20260213170952_access_group_change_to_model_name/migration.sql) | +| `LiteLLM_ManagedVectorStoreTable` | New Table | Managed vector store tracking with model mappings | - | [Migration](https://github.com/BerriAI/litellm/blob/main/litellm-proxy-extras/litellm_proxy_extras/migrations/20260213105436_add_managed_vector_store_table/migration.sql) | +| `LiteLLM_TeamTable`, `LiteLLM_VerificationToken` | New Column | Added `access_group_ids` text array | [PR #21022](https://github.com/BerriAI/litellm/pull/21022) | [Migration](https://github.com/BerriAI/litellm/blob/main/litellm-proxy-extras/litellm_proxy_extras/migrations/20260212143306_add_access_group_table/migration.sql) | +| `LiteLLM_GuardrailsTable` | New Column | Added `team_id` text column | - | [Migration](https://github.com/BerriAI/litellm/blob/main/litellm-proxy-extras/litellm_proxy_extras/migrations/20260214094754_schema_sync/migration.sql) | + +--- + +## Documentation Updates (14 updates) + +- LiteLLM Observatory section added to v1.81.9 release notes - [PR #20675](https://github.com/BerriAI/litellm/pull/20675) +- Callback registration optimization added to release notes - [PR #20681](https://github.com/BerriAI/litellm/pull/20681) +- Middleware performance blog post - [PR #20677](https://github.com/BerriAI/litellm/pull/20677) +- UI Team Soft Budget documentation - [PR #20669](https://github.com/BerriAI/litellm/pull/20669) +- UI Contributing and Troubleshooting guide - [PR #20674](https://github.com/BerriAI/litellm/pull/20674) +- Reorganize Admin UI subsection - [PR #20676](https://github.com/BerriAI/litellm/pull/20676) +- SDK proxy authentication (OAuth2/JWT auto-refresh) - [PR #20680](https://github.com/BerriAI/litellm/pull/20680) +- Forward client headers to LLM API documentation fix - [PR #20768](https://github.com/BerriAI/litellm/pull/20768) +- Add docs guide for using policies - [PR #20914](https://github.com/BerriAI/litellm/pull/20914) +- Add native thinking param examples for Claude Opus 4.6 - [PR #20799](https://github.com/BerriAI/litellm/pull/20799) +- Fix Claude Code MCP tutorial - [PR #21145](https://github.com/BerriAI/litellm/pull/21145) +- Add API base URLs for Dashscope (International and China/Beijing) - [PR #21083](https://github.com/BerriAI/litellm/pull/21083) +- Fix `DEFAULT_NUM_WORKERS_LITELLM_PROXY` default (1, not 4) - [PR #21127](https://github.com/BerriAI/litellm/pull/21127) +- Correct ElevenLabs support status in README - [PR #20643](https://github.com/BerriAI/litellm/pull/20643) + +--- + +## New Contributors +* @iver56 made their first contribution in [PR #20643](https://github.com/BerriAI/litellm/pull/20643) +* @eliasaronson made their first contribution in [PR #20666](https://github.com/BerriAI/litellm/pull/20666) +* @NirantK made their first contribution in [PR #19656](https://github.com/BerriAI/litellm/pull/19656) +* @looksgood made their first contribution in [PR #20919](https://github.com/BerriAI/litellm/pull/20919) +* @kelvin-tran made their first contribution in [PR #20548](https://github.com/BerriAI/litellm/pull/20548) +* @bluet made their first contribution in [PR #20873](https://github.com/BerriAI/litellm/pull/20873) +* @itayov made their first contribution in [PR #20729](https://github.com/BerriAI/litellm/pull/20729) +* @CSteigstra made their first contribution in [PR #20960](https://github.com/BerriAI/litellm/pull/20960) +* @rahulrd25 made their first contribution in [PR #20569](https://github.com/BerriAI/litellm/pull/20569) +* @muraliavarma made their first contribution in [PR #20598](https://github.com/BerriAI/litellm/pull/20598) +* @joaokopernico made their first contribution in [PR #21039](https://github.com/BerriAI/litellm/pull/21039) +* @datzscaler made their first contribution in [PR #21077](https://github.com/BerriAI/litellm/pull/21077) +* @atapia27 made their first contribution in [PR #20922](https://github.com/BerriAI/litellm/pull/20922) +* @fpagny made their first contribution in [PR #21121](https://github.com/BerriAI/litellm/pull/21121) +* @aidankovacic-8451 made their first contribution in [PR #21119](https://github.com/BerriAI/litellm/pull/21119) +* @luisgallego-aily made their first contribution in [PR #19935](https://github.com/BerriAI/litellm/pull/19935) + +--- + +## Full Changelog +[v1.81.9.rc.1...v1.81.12.rc.1](https://github.com/BerriAI/litellm/compare/v1.81.9.rc.1...v1.81.12.rc.1) diff --git a/docs/my-website/release_notes/v1.81.14.md b/docs/my-website/release_notes/v1.81.14.md new file mode 100644 index 0000000000..765b730c1a --- /dev/null +++ b/docs/my-website/release_notes/v1.81.14.md @@ -0,0 +1,447 @@ +--- +title: "v1.81.14-stable - Claude Sonnet 4.6, Guardrail Garden & Major Performance Improvements" +slug: "v1-81-14" +date: 2026-02-21T00:00:00 +authors: + - name: Krrish Dholakia + title: CEO, LiteLLM + url: https://www.linkedin.com/in/krish-d/ + image_url: https://pbs.twimg.com/profile_images/1298587542745358340/DZv3Oj-h_400x400.jpg + - name: Ishaan Jaff + title: CTO, LiteLLM + url: https://www.linkedin.com/in/reffajnaahsi/ + image_url: https://pbs.twimg.com/profile_images/1613813310264340481/lz54oEiB_400x400.jpg +hide_table_of_contents: false +--- + +## Deploy this version + +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; +import Image from '@theme/IdealImage'; + + + + +``` showLineNumbers title="docker run litellm" +docker run \ +-e STORE_MODEL_IN_DB=True \ +-p 4000:4000 \ +ghcr.io/berriai/litellm:main-v1.81.14-stable +``` + + + + +``` showLineNumbers title="pip install litellm" +pip install litellm==1.81.14 +``` + + + + +## Key Highlights + +- **Use Claude Sonnet 4.6 on day 0** — [reasoning, computer use, prompt caching, and 200K context, working across Anthropic and Vertex AI from the moment it launched](../../docs/providers/anthropic) +- **Deploy guardrails without writing code** — [Guardrail Garden lets you browse a marketplace of pre-built policies (competitor blockers, GDPR PII, EU AI Act, prompt injection) and deploy in one click](../../docs/proxy/guardrails/policy_templates) +- **Test guardrail policies before shipping** — [upload a CSV dataset to the compliance playground and validate policies against real traffic; get AI-generated policy suggestions with latency overhead estimates](../../docs/proxy/guardrails/policy_templates) +- **Turn any OpenAPI spec into an MCP server** — [paste a spec and get a working MCP server instantly, via API or UI](../../docs/mcp) +- **Call any prompt management system from a single API** — [the new Prompt Management API works with Langfuse, LangSmith, and others without requiring per-integration code](../../docs/proxy/prompt_management) +- **Major performance batch** — 20+ targeted optimizations across router algorithms, logging overhead, cost calculator, and connection management — meaningfully lower latency and CPU overhead on every request + +--- + +This release includes the largest single batch of performance work since v1.74. The most impactful change moves async/sync callback sorting from per-request to registration time (~30% speedup for callback-heavy deployments). On top of that: Pydantic round-trips eliminated from the logging hot path, OpenAI client init params pre-computed once at startup, quadratic deployment scan removed from usage-based routing, and several O(n²) → O(1) fixes in the router's team filter and model list lookups. Combined, these changes add up for high-throughput deployments that were hitting CPU ceilings. + +--- + +## New Providers and Endpoints + +### New Providers (1 new provider) + +| Provider | Supported LiteLLM Endpoints | Description | +| -------- | --------------------------- | ----------- | +| [IBM watsonx.ai](../../docs/providers/watsonx) | `/rerank` | Rerank support for IBM watsonx.ai models | + +### New LLM API Endpoints (1 new endpoint) + +| Endpoint | Method | Description | Documentation | +| -------- | ------ | ----------- | ------------- | +| `/v1/evals` | POST/GET | OpenAI-compatible Evals API for model evaluation | [Docs](../../docs/evals_api) | + +--- + +## New Models / Updated Models + +#### New Model Support (13 new models) + +| Provider | Model | Context Window | Input ($/1M tokens) | Output ($/1M tokens) | Features | +| -------- | ----- | -------------- | ------------------- | -------------------- | -------- | +| Anthropic | `claude-sonnet-4-6` | 200K | $3.00 | $15.00 | Reasoning, computer use, prompt caching, vision, PDF | +| Vertex AI | `vertex_ai/claude-opus-4-6@default` | 1M | $5.00 | $25.00 | Reasoning, computer use, prompt caching | +| Google Gemini | `gemini/gemini-3.1-pro-preview` | 1M | $2.00 | $12.00 | Audio, video, images, PDF | +| Google Gemini | `gemini/gemini-3.1-pro-preview-customtools` | 1M | $2.00 | $12.00 | Custom tools | +| GitHub Copilot | `github_copilot/gpt-5.3-codex` | 128K | - | - | Responses API, function calling, vision | +| GitHub Copilot | `github_copilot/claude-opus-4.6-fast` | 128K | - | - | Chat completions, function calling, vision | +| Mistral | `mistral/devstral-small-latest` | 256K | $0.10 | $0.30 | Function calling, response schema | +| Mistral | `mistral/devstral-latest` | 256K | $0.40 | $2.00 | Function calling, response schema | +| Mistral | `mistral/devstral-medium-latest` | 256K | $0.40 | $2.00 | Function calling, response schema | +| OpenRouter | `openrouter/minimax/minimax-m2.5` | 196K | $0.30 | $1.10 | Function calling, reasoning, prompt caching | +| Fireworks AI | `fireworks_ai/accounts/fireworks/models/glm-4p7` | - | - | - | Chat completions | +| Fireworks AI | `fireworks_ai/accounts/fireworks/models/minimax-m2p1` | - | - | - | Chat completions | +| Fireworks AI | `fireworks_ai/accounts/fireworks/models/kimi-k2p5` | - | - | - | Chat completions | + +#### Features + +- **[Anthropic](../../docs/providers/anthropic)** + - Day 0 support for Claude Sonnet 4.6 with reasoning, computer use, and 200K context - [PR #21401](https://github.com/BerriAI/litellm/pull/21401) + - Add Claude Sonnet 4.6 pricing - [PR #21395](https://github.com/BerriAI/litellm/pull/21395) + - Add day 0 feature support for Claude Sonnet 4.6 (streaming, function calling, vision) - [PR #21448](https://github.com/BerriAI/litellm/pull/21448) + - Add `reasoning` effort and extended thinking support for Sonnet 4.6 - [PR #21598](https://github.com/BerriAI/litellm/pull/21598) + - Fix empty system messages in `translate_system_message` - [PR #21630](https://github.com/BerriAI/litellm/pull/21630) + - Sanitize Anthropic messages for multi-turn compatibility - [PR #21464](https://github.com/BerriAI/litellm/pull/21464) + - Map `websearch` tool from `/v1/messages` to `/chat/completions` - [PR #21465](https://github.com/BerriAI/litellm/pull/21465) + - Forward `reasoning` field as `reasoning_content` in delta streaming - [PR #21468](https://github.com/BerriAI/litellm/pull/21468) + - Add server-side compaction translation from OpenAI to Anthropic format - [PR #21555](https://github.com/BerriAI/litellm/pull/21555) + +- **[AWS Bedrock](../../docs/providers/bedrock)** + - Native structured outputs API support (`outputConfig.textFormat`) - [PR #21222](https://github.com/BerriAI/litellm/pull/21222) + - Support `nova/` and `nova-2/` spec prefixes for custom imported models - [PR #21359](https://github.com/BerriAI/litellm/pull/21359) + - Broaden Nova 2 model detection to support all `nova-2-*` variants - [PR #21358](https://github.com/BerriAI/litellm/pull/21358) + - Add Accept header for AgentCore MCP server requests - [PR #21551](https://github.com/BerriAI/litellm/pull/21551) + - Clamp `thinking.budget_tokens` to minimum 1024 - [PR #21306](https://github.com/BerriAI/litellm/pull/21306) + - Fix `parallel_tool_calls` mapping for Bedrock Converse - [PR #21659](https://github.com/BerriAI/litellm/pull/21659) + +- **[Google Gemini / Vertex AI](../../docs/providers/gemini)** + - Day 0 support for `gemini-3.1-pro-preview` - [PR #21568](https://github.com/BerriAI/litellm/pull/21568) + - Fix `_map_reasoning_effort_to_thinking_level` for all Gemini 3 family models - [PR #21654](https://github.com/BerriAI/litellm/pull/21654) + - Add reasoning support via config for Gemini models - [PR #21663](https://github.com/BerriAI/litellm/pull/21663) + +- **[Databricks](../../docs/providers/databricks)** + - Add Databricks to supported providers for response schema - [PR #21368](https://github.com/BerriAI/litellm/pull/21368) + - Native Responses API support for Databricks GPT models - [PR #21460](https://github.com/BerriAI/litellm/pull/21460) + +- **[GitHub Copilot](../../docs/providers/github_copilot)** + - Add `github_copilot/gpt-5.3-codex` and `github_copilot/claude-opus-4.6-fast` models - [PR #21316](https://github.com/BerriAI/litellm/pull/21316) + - Fix unsupported params for ChatGPT Codex - [PR #21209](https://github.com/BerriAI/litellm/pull/21209) + - Allow GitHub model aliases to reuse upstream model metadata - [PR #21497](https://github.com/BerriAI/litellm/pull/21497) + +- **[Mistral](../../docs/providers/mistral)** + - Add `devstral-2512` model aliases (`devstral-small-latest`, `devstral-latest`, `devstral-medium-latest`) - [PR #21372](https://github.com/BerriAI/litellm/pull/21372) + +- **[IBM watsonx.ai](../../docs/providers/watsonx)** + - Add native rerank support - [PR #21303](https://github.com/BerriAI/litellm/pull/21303) + +- **[xAI](../../docs/providers/xai)** + - Fix usage object in xAI responses - [PR #21559](https://github.com/BerriAI/litellm/pull/21559) + +- **[Dashscope](../../docs/providers/dashscope)** + - Remove list-to-str transformation that caused incorrect request formatting - [PR #21547](https://github.com/BerriAI/litellm/pull/21547) + +- **[hosted_vllm](../../docs/providers/vllm)** + - Convert thinking blocks to content blocks for multi-turn conversations - [PR #21557](https://github.com/BerriAI/litellm/pull/21557) + +- **[OCI / Oracle](../../docs/providers/oci_cohere)** + - Fix Grok output pricing - [PR #21329](https://github.com/BerriAI/litellm/pull/21329) + +- **[AU Anthropic](../../docs/providers/anthropic)** + - Fix `au.anthropic.claude-opus-4-6-v1` model ID - [PR #20731](https://github.com/BerriAI/litellm/pull/20731) + +- **General** + - Add routing based on reasoning support — skip deployments that don't support reasoning when `thinking` params are present - [PR #21302](https://github.com/BerriAI/litellm/pull/21302) + - Add `stop` as supported param for OpenAI and Azure - [PR #21539](https://github.com/BerriAI/litellm/pull/21539) + - Add `store` and other missing params to `OPENAI_CHAT_COMPLETION_PARAMS` - [PR #21195](https://github.com/BerriAI/litellm/pull/21195), [PR #21360](https://github.com/BerriAI/litellm/pull/21360) + - Preserve `provider_specific_fields` from proxy responses - [PR #21220](https://github.com/BerriAI/litellm/pull/21220) + - Add default usage data configuration - [PR #21550](https://github.com/BerriAI/litellm/pull/21550) + +### Bug Fixes + +- **[AWS Bedrock](../../docs/providers/bedrock)** + - Fix service_tier cost propagation - [PR #21172](https://github.com/BerriAI/litellm/pull/21172) + - Fix per-image pricing for multimodal embeddings - [PR #21646](https://github.com/BerriAI/litellm/pull/21646) + - Use `batch_` prefix for Vertex AI batch IDs in `encode_file_id_with_model` - [PR #21624](https://github.com/BerriAI/litellm/pull/21624) + +- **[Bedrock Converse](../../docs/providers/bedrock)** + - Fix Anthropic usage object to match v1/messages spec - [PR #21295](https://github.com/BerriAI/litellm/pull/21295) + +- **[Fireworks AI](../../docs/providers/fireworks_ai)** + - Add missing model pricing for `glm-4p7`, `minimax-m2p1`, `kimi-k2p5` - [PR #21642](https://github.com/BerriAI/litellm/pull/21642) + +- **[Responses API](../../docs/response_api)** + - Fix `use None` instead of `Reasoning()` for reasoning parameter - [PR #21103](https://github.com/BerriAI/litellm/pull/21103) + - Preserve metadata for custom callbacks on codex/responses path - [PR #21243](https://github.com/BerriAI/litellm/pull/21243) + +--- + +## LLM API Endpoints + +#### Features + +- **[Responses API](../../docs/response_api)** + - Return `finish_reason='tool_calls'` when response contains function_call items - [PR #19745](https://github.com/BerriAI/litellm/pull/19745) + - Eliminate per-chunk thread spawning in async streaming path for significantly better throughput - [PR #21709](https://github.com/BerriAI/litellm/pull/21709) + +- **[Evals API](../../docs/evals_api)** + - Add support for OpenAI Evals API - [PR #21375](https://github.com/BerriAI/litellm/pull/21375) + +- **[Batch API](../../docs/batches)** + - Add file deletion criteria with batch references - [PR #21456](https://github.com/BerriAI/litellm/pull/21456) + - Misc bug fixes for managed batches - [PR #21157](https://github.com/BerriAI/litellm/pull/21157) + +- **[Pass-Through Endpoints](../../docs/pass_through/bedrock)** + - Add method-based routing for passthrough endpoints - [PR #21543](https://github.com/BerriAI/litellm/pull/21543) + - Preserve and forward OAuth Authorization headers through proxy layer - [PR #19912](https://github.com/BerriAI/litellm/pull/19912) + +- **[Websearch / Tool Calling](../../docs/completion/input)** + - Add DuckDuckGo as a search tool - [PR #21467](https://github.com/BerriAI/litellm/pull/21467) + - Fix `pre_call_deployment_hook` not triggering via proxy router for websearch - [PR #21433](https://github.com/BerriAI/litellm/pull/21433) + +- **General** + - Exclude tool params for models without function calling support - [PR #21244](https://github.com/BerriAI/litellm/pull/21244) + - Add `store` param to OpenAI chat completion params - [PR #21195](https://github.com/BerriAI/litellm/pull/21195) + - Add reasoning support via config for per-model reasoning configuration - [PR #21663](https://github.com/BerriAI/litellm/pull/21663) + +#### Bugs + +- **General** + - Fix `api_base` resolution error for models with multiple potential endpoints - [PR #21658](https://github.com/BerriAI/litellm/pull/21658) + - Fix session grouping broken for dict rows from `query_raw` - [PR #21435](https://github.com/BerriAI/litellm/pull/21435) + +--- + +## Management Endpoints / UI + +#### Features + +- **Access Groups** + - Add Access Group Selector to Create and Edit flow for Keys/Teams - [PR #21234](https://github.com/BerriAI/litellm/pull/21234) + +- **Virtual Keys** + - Fix virtual key grace period from env/UI - [PR #20321](https://github.com/BerriAI/litellm/pull/20321) + - Fix key expiry default duration - [PR #21362](https://github.com/BerriAI/litellm/pull/21362) + - Key Last Active Tracking — see when a key was last used - [PR #21545](https://github.com/BerriAI/litellm/pull/21545) + - Fix `/v1/models` returning wildcard instead of expanded models for BYOK team keys - [PR #21408](https://github.com/BerriAI/litellm/pull/21408) + - Return `failed_tokens` in delete_verification_tokens response - [PR #21609](https://github.com/BerriAI/litellm/pull/21609) + +- **Models + Endpoints** + - Add Model Settings Modal to Models & Endpoints page - [PR #21516](https://github.com/BerriAI/litellm/pull/21516) + - Allow `store_model_in_db` to be set via database (not just config) - [PR #21511](https://github.com/BerriAI/litellm/pull/21511) + - Fix `input_cost_per_token` masked/hidden in Model Info UI - [PR #21723](https://github.com/BerriAI/litellm/pull/21723) + - Fix credentials for UI-created models in batch file uploads - [PR #21502](https://github.com/BerriAI/litellm/pull/21502) + - Resolve credentials for UI-created models - [PR #21502](https://github.com/BerriAI/litellm/pull/21502) + +- **Teams** + - Allow team members to view entire team usage - [PR #21537](https://github.com/BerriAI/litellm/pull/21537) + - Fix service account visibility for team members - [PR #21627](https://github.com/BerriAI/litellm/pull/21627) + - Organization Info page: show member email, AntD tabs, reusable MemberTable - [PR #21745](https://github.com/BerriAI/litellm/pull/21745) + +- **Usage / Spend Logs** + - Allow filtering Usage by User - [PR #21351](https://github.com/BerriAI/litellm/pull/21351) + - Inject Credential Name as Tag for Usage Page filtering - [PR #21715](https://github.com/BerriAI/litellm/pull/21715) + - Prefix credential tags and update Tag usage banner - [PR #21739](https://github.com/BerriAI/litellm/pull/21739) + - Show retry count for requests in Logs view - [PR #21704](https://github.com/BerriAI/litellm/pull/21704) + - Fix Aggregated Daily Activity Endpoint performance - [PR #21613](https://github.com/BerriAI/litellm/pull/21613) + +- **SSO / Auth** + - Fix SSO PKCE support in multi-pod Kubernetes deployments - [PR #20314](https://github.com/BerriAI/litellm/pull/20314) + - Preserve SSO role regardless of `role_mappings` config - [PR #21503](https://github.com/BerriAI/litellm/pull/21503) + +- **Proxy CLI / Master Key** + - Fix master key rotation Prisma validation errors - [PR #21330](https://github.com/BerriAI/litellm/pull/21330) + - Handle missing `DATABASE_URL` in `append_query_params` - [PR #21239](https://github.com/BerriAI/litellm/pull/21239) + +- **Project Management** + - Add Project Management APIs for organizing resources - [PR #21078](https://github.com/BerriAI/litellm/pull/21078) + +- **UI Improvements** + - Content Filters: help edit/view categories and 1-click add with pagination - [PR #21223](https://github.com/BerriAI/litellm/pull/21223) + - Playground: test fallbacks with UI - [PR #21007](https://github.com/BerriAI/litellm/pull/21007) + - Add `forward_client_headers_to_llm_api` toggle to general settings - [PR #21776](https://github.com/BerriAI/litellm/pull/21776) + - Fix `is_premium()` debug log spam on every request - [PR #20841](https://github.com/BerriAI/litellm/pull/20841) + +#### Bugs + +- Spend Logs: Fix cost calculation - [PR #21152](https://github.com/BerriAI/litellm/pull/21152) +- Logs: Fix table not updating and pagination issues - [PR #21708](https://github.com/BerriAI/litellm/pull/21708) +- Fix `/get_image` ignoring `UI_LOGO_PATH` when `cached_logo.jpg` exists - [PR #21637](https://github.com/BerriAI/litellm/pull/21637) +- Fix duplicate URL in `tagsSpendLogsCall` query string - [PR #20909](https://github.com/BerriAI/litellm/pull/20909) +- Preserve `key_alias` and `team_id` metadata in `/user/daily/activity/aggregated` after key deletion or regeneration - [PR #20684](https://github.com/BerriAI/litellm/pull/20684) +- Uncomment `response_model` in `user_info` endpoint - [PR #17430](https://github.com/BerriAI/litellm/pull/17430) +- Allow `internal_user_viewer` to access RAG endpoints; restrict ingest to existing vector stores - [PR #21508](https://github.com/BerriAI/litellm/pull/21508) +- Suppress warning for `litellm-dashboard` team in agent permission handler - [PR #21721](https://github.com/BerriAI/litellm/pull/21721) + +--- + +## AI Integrations + +### Logging + +- **[DataDog](../../docs/proxy/logging#datadog)** + - Add `team` tag to logs, metrics, and cost management - [PR #21449](https://github.com/BerriAI/litellm/pull/21449) + +- **[Prometheus](../../docs/proxy/logging#prometheus)** + - Fix double-counting of `litellm_proxy_total_requests_metric` - [PR #21159](https://github.com/BerriAI/litellm/pull/21159) + - Guard against None metadata in Prometheus metrics - [PR #21489](https://github.com/BerriAI/litellm/pull/21489) + - Add ASGI middleware for improved Prometheus metrics collection - [PR #20434](https://github.com/BerriAI/litellm/pull/20434) + +- **[Langfuse](../../docs/proxy/logging#langfuse)** + - Improve Langfuse test isolation (multiple stability fixes) - [PR #21214](https://github.com/BerriAI/litellm/pull/21214) + +- **General** + - Fix cost to 0 for cached responses in logging - [PR #21816](https://github.com/BerriAI/litellm/pull/21816) + - Improve streaming proxy throughput by fixing middleware and logging bottlenecks - [PR #21501](https://github.com/BerriAI/litellm/pull/21501) + - Reduce proxy overhead for large base64 payloads - [PR #21594](https://github.com/BerriAI/litellm/pull/21594) + - Close streaming connections to prevent connection pool exhaustion - [PR #21213](https://github.com/BerriAI/litellm/pull/21213) + +### Guardrails + +- **Guardrail Garden** + - Launch Guardrail Garden — a marketplace for pre-built guardrails deployable in one click - [PR #21732](https://github.com/BerriAI/litellm/pull/21732) + - Redesign guardrail creation form with vertical stepper UI - [PR #21727](https://github.com/BerriAI/litellm/pull/21727) + - Add guardrail jump link in log detail view - [PR #21437](https://github.com/BerriAI/litellm/pull/21437) + - Guardrail tracing UI: show policy, detection method, and match details - [PR #21349](https://github.com/BerriAI/litellm/pull/21349) + +- **AI Policy Templates** + - Seven new ready-to-deploy policy templates ship in this release: + - GDPR Art. 32 EU PII Protection - [PR #21340](https://github.com/BerriAI/litellm/pull/21340) + - EU AI Act Article 5 (5 sub-guardrails, with French language support) - [PR #21342](https://github.com/BerriAI/litellm/pull/21342), [PR #21453](https://github.com/BerriAI/litellm/pull/21453), [PR #21427](https://github.com/BerriAI/litellm/pull/21427) + - Prompt injection detection - [PR #21520](https://github.com/BerriAI/litellm/pull/21520) + - Aviation and UAE topic filters with tag-based routing - [PR #21518](https://github.com/BerriAI/litellm/pull/21518) + - Airline off-topic restriction - [PR #21607](https://github.com/BerriAI/litellm/pull/21607) + - SQL injection - [PR #21806](https://github.com/BerriAI/litellm/pull/21806) + - AI-powered policy template suggestions with latency overhead estimates - [PR #21589](https://github.com/BerriAI/litellm/pull/21589), [PR #21608](https://github.com/BerriAI/litellm/pull/21608), [PR #21620](https://github.com/BerriAI/litellm/pull/21620) + +- **Compliance Checker** + - Add compliance checker endpoints + UI panel - [PR #21432](https://github.com/BerriAI/litellm/pull/21432) + - CSV dataset upload to compliance playground for batch testing - [PR #21526](https://github.com/BerriAI/litellm/pull/21526) + +- **Built-in Guardrails** + - Competitor name blocker: blocks by name, handles streaming, supports name variations, and splits pre/post call - [PR #21719](https://github.com/BerriAI/litellm/pull/21719), [PR #21533](https://github.com/BerriAI/litellm/pull/21533) + - Topic blocker with both keyword and embedding-based implementations - [PR #21713](https://github.com/BerriAI/litellm/pull/21713) + - Insults content filter - [PR #21729](https://github.com/BerriAI/litellm/pull/21729) + - MCP Security guardrail to block unregistered MCP servers - [PR #21429](https://github.com/BerriAI/litellm/pull/21429) + +- **[Generic Guardrails](../../docs/proxy/guardrails)** + - Add configurable fallback to handle generic guardrail endpoint connection failures - [PR #21245](https://github.com/BerriAI/litellm/pull/21245) + +- **[Presidio](../../docs/proxy/guardrails)** + - Fix Presidio controls configuration - [PR #21798](https://github.com/BerriAI/litellm/pull/21798) + +- **[LakeraAI](../../docs/proxy/guardrails)** + - Avoid `KeyError` on missing `LAKERA_API_KEY` during initialization - [PR #21422](https://github.com/BerriAI/litellm/pull/21422) + +### Prompt Management + +- **Prompt Management API** + - New API to interact with prompt management integrations without requiring a PR - [PR #17800](https://github.com/BerriAI/litellm/pull/17800), [PR #17946](https://github.com/BerriAI/litellm/pull/17946) + - Fix prompt registry configuration issues - [PR #21402](https://github.com/BerriAI/litellm/pull/21402) + +--- + +## Spend Tracking, Budgets and Rate Limiting + +- **Fix Bedrock service_tier cost propagation** — costs from service-tier responses now correctly flow through to spend tracking - [PR #21172](https://github.com/BerriAI/litellm/pull/21172) +- **Fix cost for cached responses** — cached responses now correctly log $0 cost instead of re-billing - [PR #21816](https://github.com/BerriAI/litellm/pull/21816) +- **Aggregate daily activity endpoint performance** — faster queries for `/user/daily/activity/aggregated` - [PR #21613](https://github.com/BerriAI/litellm/pull/21613) +- **Preserve key_alias and team_id metadata** in `/user/daily/activity/aggregated` after key deletion or regeneration - [PR #20684](https://github.com/BerriAI/litellm/pull/20684) +- **Inject Credential Name as Tag** for granular usage page filtering by credential - [PR #21715](https://github.com/BerriAI/litellm/pull/21715) + +--- + +## MCP Gateway + +- **OpenAPI-to-MCP** — Convert any OpenAPI spec to an MCP server via API or UI - [PR #21575](https://github.com/BerriAI/litellm/pull/21575), [PR #21662](https://github.com/BerriAI/litellm/pull/21662) +- **MCP User Permissions** — Fine-grained permissions for end users on MCP servers - [PR #21462](https://github.com/BerriAI/litellm/pull/21462) +- **MCP Security Guardrail** — Block calls to unregistered MCP servers - [PR #21429](https://github.com/BerriAI/litellm/pull/21429) +- **Fix StreamableHTTPSessionManager** — Revert to stateless mode to prevent session state issues - [PR #21323](https://github.com/BerriAI/litellm/pull/21323) +- **Fix Bedrock AgentCore Accept header** — Add required Accept header for AgentCore MCP server requests - [PR #21551](https://github.com/BerriAI/litellm/pull/21551) + +--- + +## Performance / Loadbalancing / Reliability improvements + +**Logging & callback overhead** + +- Move async/sync callback separation from per-request to callback registration time — ~30% speedup for callback-heavy deployments - [PR #20354](https://github.com/BerriAI/litellm/pull/20354) +- Skip Pydantic Usage round-trip in logging payload — reduces serialization overhead per request - [PR #21003](https://github.com/BerriAI/litellm/pull/21003) +- Skip duplicate `get_standard_logging_object_payload` calls for non-streaming requests - [PR #20440](https://github.com/BerriAI/litellm/pull/20440) +- Reuse `LiteLLM_Params` object across the request lifecycle - [PR #20593](https://github.com/BerriAI/litellm/pull/20593) +- Optimize `add_litellm_data_to_request` hot path - [PR #20526](https://github.com/BerriAI/litellm/pull/20526) +- Optimize `model_dump_with_preserved_fields` - [PR #20882](https://github.com/BerriAI/litellm/pull/20882) +- Pre-compute OpenAI client init params at module load instead of per-request - [PR #20789](https://github.com/BerriAI/litellm/pull/20789) +- Reduce proxy overhead for large base64 payloads - [PR #21594](https://github.com/BerriAI/litellm/pull/21594) +- Improve streaming proxy throughput by fixing middleware and logging bottlenecks - [PR #21501](https://github.com/BerriAI/litellm/pull/21501) +- Eliminate per-chunk thread spawning in Responses API async streaming - [PR #21709](https://github.com/BerriAI/litellm/pull/21709) + +**Cost calculation** + +- Optimize `completion_cost()` with early-exit and caching - [PR #20448](https://github.com/BerriAI/litellm/pull/20448) +- Cost calculator: reduce repeated lookups and dict copies - [PR #20541](https://github.com/BerriAI/litellm/pull/20541) + +**Router & load balancing** + +- Remove quadratic deployment scan in usage-based routing v2 - [PR #21211](https://github.com/BerriAI/litellm/pull/21211) +- Avoid O(n²) membership scans in team deployment filter - [PR #21210](https://github.com/BerriAI/litellm/pull/21210) +- Avoid O(n) alias scan for non-alias `get_model_list` lookups - [PR #21136](https://github.com/BerriAI/litellm/pull/21136) +- Increase default LRU cache size to reduce multi-model cache thrash - [PR #21139](https://github.com/BerriAI/litellm/pull/21139) +- Cache `get_model_access_groups()` no-args result on Router - [PR #20374](https://github.com/BerriAI/litellm/pull/20374) +- Deployment affinity routing callback — route to the same deployment for a session - [PR #19143](https://github.com/BerriAI/litellm/pull/19143) +- Complexity-based auto routing — new router strategy that routes based on request complexity - [PR #21789](https://github.com/BerriAI/litellm/pull/21789) +- Session-ID-based routing — use `session_id` for consistent routing within a session - [PR #21763](https://github.com/BerriAI/litellm/pull/21763) + +**Connection management & reliability** + +- Fix Redis connection pool reliability — prevent connection exhaustion under load - [PR #21717](https://github.com/BerriAI/litellm/pull/21717) +- Fix Prisma connection self-heal for auth and runtime reconnection (reverted, will be re-introduced with fixes) - [PR #21706](https://github.com/BerriAI/litellm/pull/21706) +- Make `PodLockManager.release_lock` atomic compare-and-delete - [PR #21226](https://github.com/BerriAI/litellm/pull/21226) + +--- + +## Database Changes + +### Schema Updates + +| Table | Change Type | Description | PR | +| ----- | ----------- | ----------- | -- | +| `LiteLLM_DeletedVerificationToken` | New Column | Added `project_id` column | [PR #21587](https://github.com/BerriAI/litellm/pull/21587) | +| `LiteLLM_ProjectTable` | New Table | Project management for organizing resources | [PR #21078](https://github.com/BerriAI/litellm/pull/21078) | +| `LiteLLM_VerificationToken` | New Column | Added `last_active` timestamp for key activity tracking | [PR #21545](https://github.com/BerriAI/litellm/pull/21545) | +| `LiteLLM_ManagedVectorStoreTable` | Migration | Make vector store migration idempotent | [PR #21325](https://github.com/BerriAI/litellm/pull/21325) | + +--- + +## Documentation Updates + +- Add OpenAI Agents SDK with LiteLLM guide - [PR #21311](https://github.com/BerriAI/litellm/pull/21311) +- Access Groups documentation - [PR #21236](https://github.com/BerriAI/litellm/pull/21236) +- Anthropic beta headers documentation - [PR #21320](https://github.com/BerriAI/litellm/pull/21320) +- Latency overhead troubleshooting guide - [PR #21600](https://github.com/BerriAI/litellm/pull/21600), [PR #21603](https://github.com/BerriAI/litellm/pull/21603) +- Add rollback safety check guide - [PR #21743](https://github.com/BerriAI/litellm/pull/21743) +- Incident report: vLLM Embeddings broken by encoding_format parameter - [PR #21474](https://github.com/BerriAI/litellm/pull/21474) +- Incident report: Claude Code beta headers - [PR #21485](https://github.com/BerriAI/litellm/pull/21485) +- Mark v1.81.12 as stable - [PR #21809](https://github.com/BerriAI/litellm/pull/21809) + +--- + +## New Contributors + +* @mjkam made their first contribution in [PR #21306](https://github.com/BerriAI/litellm/pull/21306) +* @saneroen made their first contribution in [PR #21243](https://github.com/BerriAI/litellm/pull/21243) +* @vincentkoc made their first contribution in [PR #21239](https://github.com/BerriAI/litellm/pull/21239) +* @felixti made their first contribution in [PR #19745](https://github.com/BerriAI/litellm/pull/19745) +* @anttttti made their first contribution in [PR #20731](https://github.com/BerriAI/litellm/pull/20731) +* @ndgigliotti made their first contribution in [PR #21222](https://github.com/BerriAI/litellm/pull/21222) +* @iamadamreed made their first contribution in [PR #19912](https://github.com/BerriAI/litellm/pull/19912) +* @sahukanishka made their first contribution in [PR #21220](https://github.com/BerriAI/litellm/pull/21220) +* @namabile made their first contribution in [PR #21195](https://github.com/BerriAI/litellm/pull/21195) +* @stronk7 made their first contribution in [PR #21372](https://github.com/BerriAI/litellm/pull/21372) +* @ZeroAurora made their first contribution in [PR #21547](https://github.com/BerriAI/litellm/pull/21547) +* @SolitudePy made their first contribution in [PR #21497](https://github.com/BerriAI/litellm/pull/21497) +* @SherifWaly made their first contribution in [PR #21557](https://github.com/BerriAI/litellm/pull/21557) +* @dkindlund made their first contribution in [PR #21633](https://github.com/BerriAI/litellm/pull/21633) +* @cagojeiger made their first contribution in [PR #21664](https://github.com/BerriAI/litellm/pull/21664) + +--- + +## Full Changelog +[v1.81.12.rc.1...v1.81.14.rc.1](https://github.com/BerriAI/litellm/compare/v1.81.12.rc.1...v1.81.14.rc.1) diff --git a/docs/my-website/release_notes/v1.81.6.md b/docs/my-website/release_notes/v1.81.6.md index d349afa65f..1e948aa37b 100644 --- a/docs/my-website/release_notes/v1.81.6.md +++ b/docs/my-website/release_notes/v1.81.6.md @@ -14,6 +14,14 @@ authors: hide_table_of_contents: false --- +:::danger Known Issue - CPU Usage + +This release had known issues with CPU usage. This has been fixed in [v1.81.9-stable](./v1-81-9). + +**We recommend using v1.81.9-stable instead.** + +::: + ## Deploy this version import Tabs from '@theme/Tabs'; diff --git a/docs/my-website/release_notes/v1.81.9.md b/docs/my-website/release_notes/v1.81.9.md index c34d3056ca..c7659442c4 100644 --- a/docs/my-website/release_notes/v1.81.9.md +++ b/docs/my-website/release_notes/v1.81.9.md @@ -1,5 +1,5 @@ --- -title: "[Preview] v1.81.9 - Control which MCP Servers are exposed on the Internet" +title: "v1.81.9 - Control which MCP Servers are exposed on the Internet" slug: "v1-81-9" date: 2026-02-07T00:00:00 authors: @@ -14,6 +14,16 @@ authors: hide_table_of_contents: false --- +:::info Stable Release Branch + +For each stable release, we now maintain a dedicated branch with the format `litellm_stable_release_branch_x_xx_xx` for the version. + +This allows easier patching for day 0 model launches. + +**Branch for v1.81.9:** [litellm_stable_release_branch_1_81_9](https://github.com/BerriAI/litellm/tree/litellm_stable_release_branch_1_81_9) + +::: + ## Deploy this version import Tabs from '@theme/Tabs'; @@ -27,7 +37,7 @@ import Image from '@theme/IdealImage'; docker run \ -e STORE_MODEL_IN_DB=True \ -p 4000:4000 \ -ghcr.io/berriai/litellm:main-v1.81.9.rc.1 +ghcr.io/berriai/litellm:main-v1.81.9-stable ``` diff --git a/docs/my-website/sidebars.js b/docs/my-website/sidebars.js index 579b569910..be5fcd3313 100644 --- a/docs/my-website/sidebars.js +++ b/docs/my-website/sidebars.js @@ -97,6 +97,7 @@ const sidebars = { label: "Policies", items: [ "proxy/guardrails/guardrail_policies", + "proxy/guardrails/policy_templates", "proxy/guardrails/policy_tags", ], }, @@ -106,7 +107,8 @@ const sidebars = { items: [ "proxy/alerting", "proxy/pagerduty", - "proxy/prometheus" + "proxy/prometheus", + "proxy/pyroscope_profiling" ] }, { @@ -118,6 +120,13 @@ const sidebars = { type: "category", label: "[Beta] Prompt Management", items: [ + { + type: "category", + label: "Contributing to Prompt Management", + items: [ + "adding_provider/generic_prompt_management_api", + ] + }, "proxy/litellm_prompt_management", "proxy/custom_prompt_management", "proxy/native_litellm_prompt", @@ -174,6 +183,7 @@ const sidebars = { "tutorials/copilotkit_sdk", "tutorials/google_adk", "tutorials/livekit_xai_realtime", + "projects/openai-agents" ] }, @@ -400,6 +410,7 @@ const sidebars = { items: [ "proxy/users", "proxy/team_budgets", + "proxy/project_management", "proxy/ui_team_soft_budget_alerts", "proxy/tag_budgets", "proxy/customers", @@ -465,6 +476,7 @@ const sidebars = { "proxy/model_access_guide", "proxy/model_access", "proxy/model_access_groups", + "proxy/access_groups", "proxy/team_model_add" ] }, @@ -569,6 +581,7 @@ const sidebars = { "proxy/managed_finetuning", ] }, + "evals_api", "generateContent", "apply_guardrail", "bedrock_invoke", @@ -769,13 +782,13 @@ const sidebars = { "providers/bedrock_batches", "providers/bedrock_realtime_with_audio", "providers/aws_polly", - "providers/bedrock_vector_store", - ] - }, - "providers/litellm_proxy", - "providers/abliteration", - "providers/ai21", - "providers/aiml", + "providers/bedrock_vector_store", + ] + }, + "providers/litellm_proxy", + "providers/abliteration", + "providers/ai21", + "providers/aiml", "providers/aleph_alpha", "providers/amazon_nova", "providers/anyscale", @@ -873,6 +886,7 @@ const sidebars = { }, "providers/sambanova", "providers/sap", + "providers/scaleway", "providers/stability", "providers/synthetic", "providers/snowflake", @@ -931,6 +945,7 @@ const sidebars = { "providers/anthropic_tool_search", "guides/code_interpreter", "completion/message_trimming", + "completion/message_sanitization", "completion/model_alias", "completion/mock_requests", "completion/predict_outputs", @@ -1002,6 +1017,7 @@ const sidebars = { "tutorials/presidio_pii_masking", "tutorials/elasticsearch_logging", "tutorials/gemini_realtime_with_audio", + "tutorials/claude_code_beta_headers", { type: "category", label: "LiteLLM Python SDK Tutorials", @@ -1096,22 +1112,37 @@ const sidebars = { "proxy_server", ], }, - "troubleshoot", { type: "category", - label: "Issue Reporting", + label: "Troubleshooting", items: [ - "troubleshoot/prisma_migrations", - "troubleshoot/cpu_issues", - "troubleshoot/memory_issues", - "troubleshoot/spend_queue_warnings", - "troubleshoot/max_callbacks", + "troubleshoot/ui_issues", + "mcp_troubleshoot", + { + type: "category", + label: "Performance / Latency", + items: [ + "troubleshoot/latency_overhead", + "troubleshoot/cpu_issues", + "troubleshoot/memory_issues", + "troubleshoot/spend_queue_warnings", + "troubleshoot/max_callbacks", + "troubleshoot/prisma_migrations", + ], + }, + "troubleshoot/rollback", + "troubleshoot", ], }, { type: "category", label: "Blog", items: [ + { + type: "link", + label: "Day 0 Support: Claude Sonnet 4.6", + href: "/blog/claude_sonnet_4_6", + }, { type: "link", label: "Incident: Broken Model Cost Map", diff --git a/docs/my-website/src/pages/troubleshoot.md b/docs/my-website/src/pages/troubleshoot.md deleted file mode 100644 index 05dbf56caa..0000000000 --- a/docs/my-website/src/pages/troubleshoot.md +++ /dev/null @@ -1,11 +0,0 @@ -# Troubleshooting - -## Stable Version - -If you're running into problems with installation / Usage -Use the stable version of litellm - -``` -pip install litellm==0.1.345 -``` - diff --git a/docs/my-website/static/img/project_spend.png b/docs/my-website/static/img/project_spend.png new file mode 100644 index 0000000000..955d1786ba Binary files /dev/null and b/docs/my-website/static/img/project_spend.png differ diff --git a/enterprise/dist/litellm_enterprise-0.1.32-py3-none-any.whl b/enterprise/dist/litellm_enterprise-0.1.32-py3-none-any.whl new file mode 100644 index 0000000000..0c87c72c98 Binary files /dev/null and b/enterprise/dist/litellm_enterprise-0.1.32-py3-none-any.whl differ diff --git a/enterprise/dist/litellm_enterprise-0.1.32.tar.gz b/enterprise/dist/litellm_enterprise-0.1.32.tar.gz new file mode 100644 index 0000000000..4f0ac1a9b2 Binary files /dev/null and b/enterprise/dist/litellm_enterprise-0.1.32.tar.gz differ diff --git a/enterprise/enterprise_hooks/__init__.py b/enterprise/enterprise_hooks/__init__.py index 9eb1c8960a..e93c8c9150 100644 --- a/enterprise/enterprise_hooks/__init__.py +++ b/enterprise/enterprise_hooks/__init__.py @@ -1,11 +1,15 @@ from typing import Dict, Literal, Type, Union from litellm_enterprise.proxy.hooks.managed_files import _PROXY_LiteLLMManagedFiles +from litellm_enterprise.proxy.hooks.managed_vector_stores import ( + _PROXY_LiteLLMManagedVectorStores, +) from litellm.integrations.custom_logger import CustomLogger ENTERPRISE_PROXY_HOOKS: Dict[str, Type[CustomLogger]] = { "managed_files": _PROXY_LiteLLMManagedFiles, + "managed_vector_stores": _PROXY_LiteLLMManagedVectorStores, } @@ -13,6 +17,7 @@ def get_enterprise_proxy_hook( hook_name: Union[ Literal[ "managed_files", + "managed_vector_stores", "max_parallel_requests", ], str, diff --git a/enterprise/litellm_enterprise/enterprise_callbacks/pagerduty/pagerduty.py b/enterprise/litellm_enterprise/enterprise_callbacks/pagerduty/pagerduty.py index e481cdc995..b6c9104b23 100644 --- a/enterprise/litellm_enterprise/enterprise_callbacks/pagerduty/pagerduty.py +++ b/enterprise/litellm_enterprise/enterprise_callbacks/pagerduty/pagerduty.py @@ -1,309 +1,311 @@ -""" -PagerDuty Alerting Integration - -Handles two types of alerts: -- High LLM API Failure Rate. Configure X fails in Y seconds to trigger an alert. -- High Number of Hanging LLM Requests. Configure X hangs in Y seconds to trigger an alert. - -Note: This is a Free feature on the regular litellm docker image. - -However, this is under the enterprise license -""" - -import asyncio -import os -from datetime import datetime, timedelta, timezone -from typing import List, Literal, Optional, Union - -from litellm._logging import verbose_logger -from litellm.caching import DualCache -from litellm.integrations.SlackAlerting.slack_alerting import SlackAlerting -from litellm.llms.custom_httpx.http_handler import ( - AsyncHTTPHandler, - get_async_httpx_client, - httpxSpecialProvider, -) -from litellm.proxy._types import UserAPIKeyAuth -from litellm.types.integrations.pagerduty import ( - AlertingConfig, - PagerDutyInternalEvent, - PagerDutyPayload, - PagerDutyRequestBody, -) -from litellm.types.utils import ( - CallTypesLiteral, - StandardLoggingPayload, - StandardLoggingPayloadErrorInformation, -) - -PAGERDUTY_DEFAULT_FAILURE_THRESHOLD = 60 -PAGERDUTY_DEFAULT_FAILURE_THRESHOLD_WINDOW_SECONDS = 60 -PAGERDUTY_DEFAULT_HANGING_THRESHOLD_SECONDS = 60 -PAGERDUTY_DEFAULT_HANGING_THRESHOLD_WINDOW_SECONDS = 600 - - -class PagerDutyAlerting(SlackAlerting): - """ - Tracks failed requests and hanging requests separately. - If threshold is crossed for either type, triggers a PagerDuty alert. - """ - - def __init__( - self, alerting_args: Optional[Union[AlertingConfig, dict]] = None, **kwargs - ): - super().__init__() - _api_key = os.getenv("PAGERDUTY_API_KEY") - if not _api_key: - raise ValueError("PAGERDUTY_API_KEY is not set") - - self.api_key: str = _api_key - alerting_args = alerting_args or {} - self.pagerduty_alerting_args: AlertingConfig = AlertingConfig( - failure_threshold=alerting_args.get( - "failure_threshold", PAGERDUTY_DEFAULT_FAILURE_THRESHOLD - ), - failure_threshold_window_seconds=alerting_args.get( - "failure_threshold_window_seconds", - PAGERDUTY_DEFAULT_FAILURE_THRESHOLD_WINDOW_SECONDS, - ), - hanging_threshold_seconds=alerting_args.get( - "hanging_threshold_seconds", PAGERDUTY_DEFAULT_HANGING_THRESHOLD_SECONDS - ), - hanging_threshold_window_seconds=alerting_args.get( - "hanging_threshold_window_seconds", - PAGERDUTY_DEFAULT_HANGING_THRESHOLD_WINDOW_SECONDS, - ), - ) - - # Separate storage for failures vs. hangs - self._failure_events: List[PagerDutyInternalEvent] = [] - self._hanging_events: List[PagerDutyInternalEvent] = [] - - # ------------------ MAIN LOGIC ------------------ # - - async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time): - """ - Record a failure event. Only send an alert to PagerDuty if the - configured *failure* threshold is exceeded in the specified window. - """ - now = datetime.now(timezone.utc) - standard_logging_payload: Optional[StandardLoggingPayload] = kwargs.get( - "standard_logging_object" - ) - if not standard_logging_payload: - raise ValueError( - "standard_logging_object is required for PagerDutyAlerting" - ) - - # Extract error details - error_info: Optional[StandardLoggingPayloadErrorInformation] = ( - standard_logging_payload.get("error_information") or {} - ) - _meta = standard_logging_payload.get("metadata") or {} - - self._failure_events.append( - PagerDutyInternalEvent( - failure_event_type="failed_response", - timestamp=now, - error_class=error_info.get("error_class"), - error_code=error_info.get("error_code"), - error_llm_provider=error_info.get("llm_provider"), - user_api_key_hash=_meta.get("user_api_key_hash"), - user_api_key_alias=_meta.get("user_api_key_alias"), - user_api_key_spend=_meta.get("user_api_key_spend"), - user_api_key_max_budget=_meta.get("user_api_key_max_budget"), - user_api_key_budget_reset_at=_meta.get("user_api_key_budget_reset_at"), - user_api_key_org_id=_meta.get("user_api_key_org_id"), - user_api_key_team_id=_meta.get("user_api_key_team_id"), - user_api_key_user_id=_meta.get("user_api_key_user_id"), - user_api_key_team_alias=_meta.get("user_api_key_team_alias"), - user_api_key_end_user_id=_meta.get("user_api_key_end_user_id"), - user_api_key_user_email=_meta.get("user_api_key_user_email"), - user_api_key_request_route=_meta.get("user_api_key_request_route"), - user_api_key_auth_metadata=_meta.get("user_api_key_auth_metadata"), - ) - ) - - # Prune + Possibly alert - window_seconds = self.pagerduty_alerting_args.get( - "failure_threshold_window_seconds", 60 - ) - threshold = self.pagerduty_alerting_args.get("failure_threshold", 1) - - # If threshold is crossed, send PD alert for failures - await self._send_alert_if_thresholds_crossed( - events=self._failure_events, - window_seconds=window_seconds, - threshold=threshold, - alert_prefix="High LLM API Failure Rate", - ) - - async def async_pre_call_hook( - self, - user_api_key_dict: UserAPIKeyAuth, - cache: DualCache, - data: dict, - call_type: CallTypesLiteral, - ) -> Optional[Union[Exception, str, dict]]: - """ - Example of detecting hanging requests by waiting a given threshold. - If the request didn't finish by then, we treat it as 'hanging'. - """ - verbose_logger.info("Inside Proxy Logging Pre-call hook!") - asyncio.create_task( - self.hanging_response_handler( - request_data=data, user_api_key_dict=user_api_key_dict - ) - ) - return None - - async def hanging_response_handler( - self, request_data: Optional[dict], user_api_key_dict: UserAPIKeyAuth - ): - """ - Checks if request completed by the time 'hanging_threshold_seconds' elapses. - If not, we classify it as a hanging request. - """ - verbose_logger.debug( - f"Inside Hanging Response Handler!..sleeping for {self.pagerduty_alerting_args.get('hanging_threshold_seconds', PAGERDUTY_DEFAULT_HANGING_THRESHOLD_SECONDS)} seconds" - ) - await asyncio.sleep( - self.pagerduty_alerting_args.get( - "hanging_threshold_seconds", PAGERDUTY_DEFAULT_HANGING_THRESHOLD_SECONDS - ) - ) - - if await self._request_is_completed(request_data=request_data): - return # It's not hanging if completed - - # Otherwise, record it as hanging - self._hanging_events.append( - PagerDutyInternalEvent( - failure_event_type="hanging_response", - timestamp=datetime.now(timezone.utc), - error_class="HangingRequest", - error_code="HangingRequest", - error_llm_provider="HangingRequest", - user_api_key_hash=user_api_key_dict.api_key, - user_api_key_alias=user_api_key_dict.key_alias, - user_api_key_spend=user_api_key_dict.spend, - user_api_key_max_budget=user_api_key_dict.max_budget, - user_api_key_budget_reset_at=( - user_api_key_dict.budget_reset_at.isoformat() - if user_api_key_dict.budget_reset_at - else None - ), - user_api_key_org_id=user_api_key_dict.org_id, - user_api_key_team_id=user_api_key_dict.team_id, - user_api_key_user_id=user_api_key_dict.user_id, - user_api_key_team_alias=user_api_key_dict.team_alias, - user_api_key_end_user_id=user_api_key_dict.end_user_id, - user_api_key_user_email=user_api_key_dict.user_email, - user_api_key_request_route=user_api_key_dict.request_route, - user_api_key_auth_metadata=user_api_key_dict.metadata, - ) - ) - - # Prune + Possibly alert - window_seconds = self.pagerduty_alerting_args.get( - "hanging_threshold_window_seconds", - PAGERDUTY_DEFAULT_HANGING_THRESHOLD_WINDOW_SECONDS, - ) - threshold: int = self.pagerduty_alerting_args.get( - "hanging_threshold_fails", PAGERDUTY_DEFAULT_HANGING_THRESHOLD_SECONDS - ) - - # If threshold is crossed, send PD alert for hangs - await self._send_alert_if_thresholds_crossed( - events=self._hanging_events, - window_seconds=window_seconds, - threshold=threshold, - alert_prefix="High Number of Hanging LLM Requests", - ) - - # ------------------ HELPERS ------------------ # - - async def _send_alert_if_thresholds_crossed( - self, - events: List[PagerDutyInternalEvent], - window_seconds: int, - threshold: int, - alert_prefix: str, - ): - """ - 1. Prune old events - 2. If threshold is reached, build alert, send to PagerDuty - 3. Clear those events - """ - cutoff = datetime.now(timezone.utc) - timedelta(seconds=window_seconds) - pruned = [e for e in events if e.get("timestamp", datetime.min) > cutoff] - - # Update the reference list - events.clear() - events.extend(pruned) - - # Check threshold - verbose_logger.debug( - f"Have {len(events)} events in the last {window_seconds} seconds. Threshold is {threshold}" - ) - if len(events) >= threshold: - # Build short summary of last N events - error_summaries = self._build_error_summaries(events, max_errors=5) - alert_message = ( - f"{alert_prefix}: {len(events)} in the last {window_seconds} seconds." - ) - custom_details = {"recent_errors": error_summaries} - - await self.send_alert_to_pagerduty( - alert_message=alert_message, - custom_details=custom_details, - ) - - # Clear them after sending an alert, so we don't spam - events.clear() - - def _build_error_summaries( - self, events: List[PagerDutyInternalEvent], max_errors: int = 5 - ) -> List[PagerDutyInternalEvent]: - """ - Build short text summaries for the last `max_errors`. - Example: "ValueError (code: 500, provider: openai)" - """ - recent = events[-max_errors:] - summaries = [] - for fe in recent: - # If any of these is None, show "N/A" to avoid messing up the summary string - fe.pop("timestamp") - summaries.append(fe) - return summaries - - async def send_alert_to_pagerduty(self, alert_message: str, custom_details: dict): - """ - Send [critical] Alert to PagerDuty - - https://developer.pagerduty.com/api-reference/YXBpOjI3NDgyNjU-pager-duty-v2-events-api - """ - try: - verbose_logger.debug(f"Sending alert to PagerDuty: {alert_message}") - async_client: AsyncHTTPHandler = get_async_httpx_client( - llm_provider=httpxSpecialProvider.LoggingCallback - ) - payload: PagerDutyRequestBody = PagerDutyRequestBody( - payload=PagerDutyPayload( - summary=alert_message, - severity="critical", - source="LiteLLM Alert", - component="LiteLLM", - custom_details=custom_details, - ), - routing_key=self.api_key, - event_action="trigger", - ) - - return await async_client.post( - url="https://events.pagerduty.com/v2/enqueue", - json=dict(payload), - headers={"Content-Type": "application/json"}, - ) - except Exception as e: - verbose_logger.exception(f"Error sending alert to PagerDuty: {e}") +""" +PagerDuty Alerting Integration + +Handles two types of alerts: +- High LLM API Failure Rate. Configure X fails in Y seconds to trigger an alert. +- High Number of Hanging LLM Requests. Configure X hangs in Y seconds to trigger an alert. + +Note: This is a Free feature on the regular litellm docker image. + +However, this is under the enterprise license +""" + +import asyncio +import os +from datetime import datetime, timedelta, timezone +from typing import List, Optional, Union + +from litellm._logging import verbose_logger +from litellm.caching import DualCache +from litellm.integrations.SlackAlerting.slack_alerting import SlackAlerting +from litellm.llms.custom_httpx.http_handler import ( + AsyncHTTPHandler, + get_async_httpx_client, + httpxSpecialProvider, +) +from litellm.proxy._types import UserAPIKeyAuth +from litellm.types.integrations.pagerduty import ( + AlertingConfig, + PagerDutyInternalEvent, + PagerDutyPayload, + PagerDutyRequestBody, +) +from litellm.types.utils import ( + CallTypesLiteral, + StandardLoggingPayload, + StandardLoggingPayloadErrorInformation, +) + +PAGERDUTY_DEFAULT_FAILURE_THRESHOLD = 60 +PAGERDUTY_DEFAULT_FAILURE_THRESHOLD_WINDOW_SECONDS = 60 +PAGERDUTY_DEFAULT_HANGING_THRESHOLD_SECONDS = 60 +PAGERDUTY_DEFAULT_HANGING_THRESHOLD_WINDOW_SECONDS = 600 + + +class PagerDutyAlerting(SlackAlerting): + """ + Tracks failed requests and hanging requests separately. + If threshold is crossed for either type, triggers a PagerDuty alert. + """ + + def __init__( + self, alerting_args: Optional[Union[AlertingConfig, dict]] = None, **kwargs + ): + super().__init__() + _api_key = os.getenv("PAGERDUTY_API_KEY") + if not _api_key: + raise ValueError("PAGERDUTY_API_KEY is not set") + + self.api_key: str = _api_key + alerting_args = alerting_args or {} + self.pagerduty_alerting_args: AlertingConfig = AlertingConfig( + failure_threshold=alerting_args.get( + "failure_threshold", PAGERDUTY_DEFAULT_FAILURE_THRESHOLD + ), + failure_threshold_window_seconds=alerting_args.get( + "failure_threshold_window_seconds", + PAGERDUTY_DEFAULT_FAILURE_THRESHOLD_WINDOW_SECONDS, + ), + hanging_threshold_seconds=alerting_args.get( + "hanging_threshold_seconds", PAGERDUTY_DEFAULT_HANGING_THRESHOLD_SECONDS + ), + hanging_threshold_window_seconds=alerting_args.get( + "hanging_threshold_window_seconds", + PAGERDUTY_DEFAULT_HANGING_THRESHOLD_WINDOW_SECONDS, + ), + ) + + # Separate storage for failures vs. hangs + self._failure_events: List[PagerDutyInternalEvent] = [] + self._hanging_events: List[PagerDutyInternalEvent] = [] + + # ------------------ MAIN LOGIC ------------------ # + + async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time): + """ + Record a failure event. Only send an alert to PagerDuty if the + configured *failure* threshold is exceeded in the specified window. + """ + now = datetime.now(timezone.utc) + standard_logging_payload: Optional[StandardLoggingPayload] = kwargs.get( + "standard_logging_object" + ) + if not standard_logging_payload: + raise ValueError( + "standard_logging_object is required for PagerDutyAlerting" + ) + + # Extract error details + error_info: Optional[StandardLoggingPayloadErrorInformation] = ( + standard_logging_payload.get("error_information") or {} + ) + _meta = standard_logging_payload.get("metadata") or {} + + self._failure_events.append( + PagerDutyInternalEvent( + failure_event_type="failed_response", + timestamp=now, + error_class=error_info.get("error_class"), + error_code=error_info.get("error_code"), + error_llm_provider=error_info.get("llm_provider"), + user_api_key_hash=_meta.get("user_api_key_hash"), + user_api_key_alias=_meta.get("user_api_key_alias"), + user_api_key_spend=_meta.get("user_api_key_spend"), + user_api_key_max_budget=_meta.get("user_api_key_max_budget"), + user_api_key_budget_reset_at=_meta.get("user_api_key_budget_reset_at"), + user_api_key_org_id=_meta.get("user_api_key_org_id"), + user_api_key_team_id=_meta.get("user_api_key_team_id"), + user_api_key_project_id=_meta.get("user_api_key_project_id"), + user_api_key_user_id=_meta.get("user_api_key_user_id"), + user_api_key_team_alias=_meta.get("user_api_key_team_alias"), + user_api_key_end_user_id=_meta.get("user_api_key_end_user_id"), + user_api_key_user_email=_meta.get("user_api_key_user_email"), + user_api_key_request_route=_meta.get("user_api_key_request_route"), + user_api_key_auth_metadata=_meta.get("user_api_key_auth_metadata"), + ) + ) + + # Prune + Possibly alert + window_seconds = self.pagerduty_alerting_args.get( + "failure_threshold_window_seconds", 60 + ) + threshold = self.pagerduty_alerting_args.get("failure_threshold", 1) + + # If threshold is crossed, send PD alert for failures + await self._send_alert_if_thresholds_crossed( + events=self._failure_events, + window_seconds=window_seconds, + threshold=threshold, + alert_prefix="High LLM API Failure Rate", + ) + + async def async_pre_call_hook( + self, + user_api_key_dict: UserAPIKeyAuth, + cache: DualCache, + data: dict, + call_type: CallTypesLiteral, + ) -> Optional[Union[Exception, str, dict]]: + """ + Example of detecting hanging requests by waiting a given threshold. + If the request didn't finish by then, we treat it as 'hanging'. + """ + verbose_logger.info("Inside Proxy Logging Pre-call hook!") + asyncio.create_task( + self.hanging_response_handler( + request_data=data, user_api_key_dict=user_api_key_dict + ) + ) + return None + + async def hanging_response_handler( + self, request_data: Optional[dict], user_api_key_dict: UserAPIKeyAuth + ): + """ + Checks if request completed by the time 'hanging_threshold_seconds' elapses. + If not, we classify it as a hanging request. + """ + verbose_logger.debug( + f"Inside Hanging Response Handler!..sleeping for {self.pagerduty_alerting_args.get('hanging_threshold_seconds', PAGERDUTY_DEFAULT_HANGING_THRESHOLD_SECONDS)} seconds" + ) + await asyncio.sleep( + self.pagerduty_alerting_args.get( + "hanging_threshold_seconds", PAGERDUTY_DEFAULT_HANGING_THRESHOLD_SECONDS + ) + ) + + if await self._request_is_completed(request_data=request_data): + return # It's not hanging if completed + + # Otherwise, record it as hanging + self._hanging_events.append( + PagerDutyInternalEvent( + failure_event_type="hanging_response", + timestamp=datetime.now(timezone.utc), + error_class="HangingRequest", + error_code="HangingRequest", + error_llm_provider="HangingRequest", + user_api_key_hash=user_api_key_dict.api_key, + user_api_key_alias=user_api_key_dict.key_alias, + user_api_key_spend=user_api_key_dict.spend, + user_api_key_max_budget=user_api_key_dict.max_budget, + user_api_key_budget_reset_at=( + user_api_key_dict.budget_reset_at.isoformat() + if user_api_key_dict.budget_reset_at + else None + ), + user_api_key_org_id=user_api_key_dict.org_id, + user_api_key_team_id=user_api_key_dict.team_id, + user_api_key_project_id=user_api_key_dict.project_id, + user_api_key_user_id=user_api_key_dict.user_id, + user_api_key_team_alias=user_api_key_dict.team_alias, + user_api_key_end_user_id=user_api_key_dict.end_user_id, + user_api_key_user_email=user_api_key_dict.user_email, + user_api_key_request_route=user_api_key_dict.request_route, + user_api_key_auth_metadata=user_api_key_dict.metadata, + ) + ) + + # Prune + Possibly alert + window_seconds = self.pagerduty_alerting_args.get( + "hanging_threshold_window_seconds", + PAGERDUTY_DEFAULT_HANGING_THRESHOLD_WINDOW_SECONDS, + ) + threshold: int = self.pagerduty_alerting_args.get( + "hanging_threshold_fails", PAGERDUTY_DEFAULT_HANGING_THRESHOLD_SECONDS + ) + + # If threshold is crossed, send PD alert for hangs + await self._send_alert_if_thresholds_crossed( + events=self._hanging_events, + window_seconds=window_seconds, + threshold=threshold, + alert_prefix="High Number of Hanging LLM Requests", + ) + + # ------------------ HELPERS ------------------ # + + async def _send_alert_if_thresholds_crossed( + self, + events: List[PagerDutyInternalEvent], + window_seconds: int, + threshold: int, + alert_prefix: str, + ): + """ + 1. Prune old events + 2. If threshold is reached, build alert, send to PagerDuty + 3. Clear those events + """ + cutoff = datetime.now(timezone.utc) - timedelta(seconds=window_seconds) + pruned = [e for e in events if e.get("timestamp", datetime.min) > cutoff] + + # Update the reference list + events.clear() + events.extend(pruned) + + # Check threshold + verbose_logger.debug( + f"Have {len(events)} events in the last {window_seconds} seconds. Threshold is {threshold}" + ) + if len(events) >= threshold: + # Build short summary of last N events + error_summaries = self._build_error_summaries(events, max_errors=5) + alert_message = ( + f"{alert_prefix}: {len(events)} in the last {window_seconds} seconds." + ) + custom_details = {"recent_errors": error_summaries} + + await self.send_alert_to_pagerduty( + alert_message=alert_message, + custom_details=custom_details, + ) + + # Clear them after sending an alert, so we don't spam + events.clear() + + def _build_error_summaries( + self, events: List[PagerDutyInternalEvent], max_errors: int = 5 + ) -> List[PagerDutyInternalEvent]: + """ + Build short text summaries for the last `max_errors`. + Example: "ValueError (code: 500, provider: openai)" + """ + recent = events[-max_errors:] + summaries = [] + for fe in recent: + # If any of these is None, show "N/A" to avoid messing up the summary string + fe.pop("timestamp") + summaries.append(fe) + return summaries + + async def send_alert_to_pagerduty(self, alert_message: str, custom_details: dict): + """ + Send [critical] Alert to PagerDuty + + https://developer.pagerduty.com/api-reference/YXBpOjI3NDgyNjU-pager-duty-v2-events-api + """ + try: + verbose_logger.debug(f"Sending alert to PagerDuty: {alert_message}") + async_client: AsyncHTTPHandler = get_async_httpx_client( + llm_provider=httpxSpecialProvider.LoggingCallback + ) + payload: PagerDutyRequestBody = PagerDutyRequestBody( + payload=PagerDutyPayload( + summary=alert_message, + severity="critical", + source="LiteLLM Alert", + component="LiteLLM", + custom_details=custom_details, + ), + routing_key=self.api_key, + event_action="trigger", + ) + + return await async_client.post( + url="https://events.pagerduty.com/v2/enqueue", + json=dict(payload), + headers={"Content-Type": "application/json"}, + ) + except Exception as e: + verbose_logger.exception(f"Error sending alert to PagerDuty: {e}") diff --git a/enterprise/litellm_enterprise/proxy/auth/route_checks.py b/enterprise/litellm_enterprise/proxy/auth/route_checks.py index 6f7cf9143f..fc57292a8d 100644 --- a/enterprise/litellm_enterprise/proxy/auth/route_checks.py +++ b/enterprise/litellm_enterprise/proxy/auth/route_checks.py @@ -41,6 +41,10 @@ class EnterpriseRouteChecks: return get_secret_bool("DISABLE_ADMIN_ENDPOINTS") is True + # Routes that should remain accessible even when LLM API endpoints are disabled. + # These are read-only model listing routes needed by the Admin UI. + LLM_API_EXEMPT_ROUTES = ["/models", "/v1/models"] + @staticmethod def should_call_route(route: str): """ @@ -58,6 +62,7 @@ class EnterpriseRouteChecks: ) elif ( RouteChecks.is_llm_api_route(route=route) + and route not in EnterpriseRouteChecks.LLM_API_EXEMPT_ROUTES and EnterpriseRouteChecks.is_llm_api_route_disabled() ): raise HTTPException( 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 bb25e4f062..bf8bc46f72 100644 --- a/enterprise/litellm_enterprise/proxy/common_utils/check_batch_cost.py +++ b/enterprise/litellm_enterprise/proxy/common_utils/check_batch_cost.py @@ -4,7 +4,7 @@ Polls LiteLLM_ManagedObjectTable to check if the batch job is complete, and if t from litellm._uuid import uuid from datetime import datetime -from typing import TYPE_CHECKING, Optional, cast +from typing import TYPE_CHECKING, Optional from litellm._logging import verbose_proxy_logger @@ -35,14 +35,11 @@ class CheckBatchCost: - if not, return False - if so, return True """ - from litellm_enterprise.proxy.hooks.managed_files import ( - _PROXY_LiteLLMManagedFiles, - ) - from litellm.batches.batch_utils import ( _get_file_content_as_dictionary, calculate_batch_cost_and_usage, ) + from litellm.files.main import afile_content from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLogging from litellm.proxy.openai_files_endpoints.common_utils import ( @@ -102,31 +99,41 @@ class CheckBatchCost: continue ## RETRIEVE THE BATCH JOB OUTPUT FILE - managed_files_obj = cast( - Optional[_PROXY_LiteLLMManagedFiles], - self.proxy_logging_obj.get_proxy_hook("managed_files"), - ) if ( response.status == "completed" and response.output_file_id is not None - and managed_files_obj is not None ): verbose_proxy_logger.info( f"Batch ID: {batch_id} is complete, tracking cost and usage" ) - # track cost - model_file_id_mapping = { - response.output_file_id: {model_id: response.output_file_id} - } - _file_content = await managed_files_obj.afile_content( - file_id=response.output_file_id, - litellm_parent_otel_span=None, - llm_router=self.llm_router, - model_file_id_mapping=model_file_id_mapping, + + # This background job runs as default_user_id, so going through the HTTP endpoint + # would trigger check_managed_file_id_access and get 403. Instead, extract the raw + # provider file ID and call afile_content directly with deployment credentials. + raw_output_file_id = response.output_file_id + decoded = _is_base64_encoded_unified_file_id(raw_output_file_id) + if decoded: + try: + raw_output_file_id = decoded.split("llm_output_file_id,")[1].split(";")[0] + except (IndexError, AttributeError): + pass + + credentials = self.llm_router.get_deployment_credentials_with_provider(model_id) or {} + _file_content = await afile_content( + file_id=raw_output_file_id, + **credentials, ) + # Access content - handle both direct attribute and method call + if hasattr(_file_content, 'content'): + content_bytes = _file_content.content + elif hasattr(_file_content, 'read'): + content_bytes = await _file_content.read() + else: + content_bytes = _file_content + file_content_as_dict = _get_file_content_as_dictionary( - _file_content.content + content_bytes ) deployment_info = self.llm_router.get_deployment(model_id=model_id) @@ -143,11 +150,15 @@ class CheckBatchCost: custom_llm_provider=custom_llm_provider, ) + # Pass deployment model_info so custom batch pricing + # (input_cost_per_token_batches etc.) is used for cost calc + deployment_model_info = deployment_info.model_info.model_dump() if deployment_info.model_info else {} batch_cost, batch_usage, batch_models = ( await calculate_batch_cost_and_usage( file_content_dictionary=file_content_as_dict, custom_llm_provider=llm_provider, # type: ignore model_name=model_name, + model_info=deployment_model_info, ) ) logging_obj = LiteLLMLogging( diff --git a/enterprise/litellm_enterprise/proxy/hooks/managed_files.py b/enterprise/litellm_enterprise/proxy/hooks/managed_files.py index a41b3f3bf6..bda20e2f74 100644 --- a/enterprise/litellm_enterprise/proxy/hooks/managed_files.py +++ b/enterprise/litellm_enterprise/proxy/hooks/managed_files.py @@ -230,12 +230,14 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): if managed_file: return managed_file.created_by == user_id - return False + raise HTTPException( + status_code=404, + 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: - ## check if the user has access to the unified object id ## check if the user has access to the unified object id user_id = user_api_key_dict.user_id managed_object = ( @@ -246,7 +248,10 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): if managed_object: return managed_object.created_by == user_id - return True # don't raise error if managed object is not found + raise HTTPException( + status_code=404, + detail=f"Object not found: {unified_object_id}", + ) async def list_user_batches( self, @@ -911,15 +916,24 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): ) setattr(response, file_attr, unified_file_id) - # Fetch the actual file object from the provider + # Use llm_router credentials when available. Without credentials, + # Azure and other auth-required providers return 500/401. file_object = None try: - # Use litellm to retrieve the file object from the provider - from litellm import afile_retrieve - file_object = await afile_retrieve( - custom_llm_provider=model_name.split("/")[0] if model_name and "/" in model_name else "openai", - file_id=original_file_id - ) + # 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) + if _llm_router is not None and model_id: + _creds = _llm_router.get_deployment_credentials_with_provider(model_id) or {} + file_object = await litellm.afile_retrieve( + file_id=original_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", + file_id=original_file_id, + ) verbose_logger.debug( f"Successfully retrieved file object for {file_attr}={original_file_id}" ) @@ -1004,8 +1018,12 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): raise Exception(f"LiteLLM Managed File object with id={file_id} not found") # Case 2: Managed file and the file object exists in the database + # The stored file_object has the raw provider ID. Replace with the unified ID + # so callers see a consistent ID (matching Case 3 which does response.id = file_id). if stored_file_object and stored_file_object.file_object: - return stored_file_object.file_object + # Use model_copy to ensure the ID update persists (Pydantic v2 compatibility) + response = stored_file_object.file_object.model_copy(update={"id": file_id}) + return response # Case 3: Managed file exists in the database but not the file object (for. e.g the batch task might not have run) # So we fetch the file object from the provider. We deliberately do not store the result to avoid interfering with batch cost tracking code. @@ -1033,6 +1051,168 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): """Handled in files_endpoints.py""" return [] + def _is_batch_polling_enabled(self) -> bool: + """ + Check if batch cost tracking is actually enabled and running. + Returns: + bool: True if batch cost tracking is active, False otherwise + """ + try: + # Import here to avoid circular dependencies + import litellm.proxy.proxy_server as proxy_server_module + + # Check if the scheduler has the batch cost checking job registered + scheduler = getattr(proxy_server_module, 'scheduler', None) + if scheduler is None: + return False + + # Check if the check_batch_cost_job exists in the scheduler + try: + job = scheduler.get_job('check_batch_cost_job') + if job is not None: + return True + except Exception: + # Job not found or scheduler doesn't support get_job + pass + + return False + except Exception as e: + 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]]: + """ + Find batches in non-terminal states that reference this file. + + Non-terminal states: validating, in_progress, finalizing + Terminal states: completed, complete, failed, expired, cancelled + + Args: + file_id: The unified file ID to check + + Returns: + List of batch objects referencing this file in non-terminal state + (max 10 for error message display) + """ + # Prepare list of file IDs to check (both unified and provider IDs) + file_ids_to_check = [file_id] + + # 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 + ) + + if model_file_id_mapping and file_id in model_file_id_mapping: + # Add all provider file IDs for this unified file + provider_file_ids = list(model_file_id_mapping[file_id].values()) + 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." + ) + MAX_MATCHES_TO_RETURN = 10 + + batches = await self.prisma_client.db.litellm_managedobjecttable.find_many( + where={ + "file_purpose": "batch", + "status": {"in": ["validating", "in_progress", "finalizing"]}, + }, + take=MAX_MATCHES_TO_RETURN, + order={"created_at": "desc"}, + ) + + referencing_batches = [] + 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 + + # Extract file IDs from batch + # Batches typically reference the unified file ID in input_file_id + # Output and error files are generated by the provider + input_file_id = batch_data.get("input_file_id") + 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] + + # 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): + referencing_batches.append({ + "batch_id": batch.unified_object_id, + "status": batch.status, + "created_at": batch.created_at, + }) + except Exception as e: + verbose_logger.warning( + f"Error parsing batch object {batch.unified_object_id}: {e}" + ) + continue + + return referencing_batches + + async def _check_file_deletion_allowed(self, file_id: str) -> None: + """ + Check if file deletion should be blocked due to batch references. + + Blocks deletion if: + 1. File is referenced by any batch in non-terminal state, AND + 2. Batch polling is configured (user wants cost tracking) + + Args: + file_id: The unified file ID to check + + Raises: + HTTPException: If file deletion should be blocked + """ + # Check if batch polling is enabled + if not self._is_batch_polling_enabled(): + # Batch polling not configured, allow deletion + return + + # Check if file is referenced by any non-terminal batches + referencing_batches = await self._get_batches_referencing_file(file_id) + + 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 + + # 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] + + # Determine the count message + count_message = f"{len(referencing_batches)}" + if len(referencing_batches) >= 10: # MAX_MATCHES_TO_RETURN from _get_batches_referencing_file + count_message = "10+" + + error_message = ( + f"Cannot delete file {file_id}. " + f"The file is referenced by {count_message} batch(es) in non-terminal state" + ) + + # Add specific batch details if not too many + if len(referencing_batches) <= MAX_BATCHES_IN_ERROR: + error_message += f": {', '.join(batch_statuses)}. " + else: + error_message += f" (showing {MAX_BATCHES_IN_ERROR} most recent): {', '.join(batch_statuses)}. " + + error_message += ( + f"To delete this file before complete cost tracking, please delete or cancel the referencing batch(es) first. " + f"Alternatively, wait for all batches to complete processing." + ) + + raise HTTPException( + status_code=400, + detail=error_message, + ) + async def afile_delete( self, file_id: str, @@ -1041,6 +1221,9 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): **data: Dict, ) -> OpenAIFileObject: + # Check if file deletion should be blocked due to batch references + 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 diff --git a/enterprise/litellm_enterprise/proxy/hooks/managed_vector_stores.py b/enterprise/litellm_enterprise/proxy/hooks/managed_vector_stores.py new file mode 100644 index 0000000000..254d816039 --- /dev/null +++ b/enterprise/litellm_enterprise/proxy/hooks/managed_vector_stores.py @@ -0,0 +1,464 @@ +# What is this? +## This hook is used to manage vector stores with target_model_names support +## It allows creating vector stores across multiple models and managing them with unified IDs + +from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union, cast + +from fastapi import HTTPException + +import litellm +from litellm import Router, verbose_logger +from litellm._uuid import uuid +from litellm.integrations.custom_logger import CustomLogger +from litellm.llms.base_llm.managed_resources import BaseManagedResource +from litellm.llms.base_llm.managed_resources.utils import ( + generate_unified_id_string, + is_base64_encoded_unified_id, +) +from litellm.proxy._types import UserAPIKeyAuth +from litellm.types.vector_stores import ( + VectorStoreCreateOptionalRequestParams, + VectorStoreCreateResponse, +) + +if TYPE_CHECKING: + from opentelemetry.trace import Span as _Span + + from litellm.proxy.utils import InternalUsageCache as _InternalUsageCache + from litellm.proxy.utils import PrismaClient as _PrismaClient + + Span = Union[_Span, Any] + InternalUsageCache = _InternalUsageCache + PrismaClient = _PrismaClient +else: + Span = Any + InternalUsageCache = Any + PrismaClient = Any + + +class _PROXY_LiteLLMManagedVectorStores( + CustomLogger, BaseManagedResource[VectorStoreCreateResponse] +): + """ + Managed vector stores with target_model_names support. + + This class provides functionality to: + - Create vector stores across multiple models + - Retrieve vector stores by unified ID + - Delete vector stores from all models + - List vector stores created by a user + """ + + def __init__( + self, internal_usage_cache: InternalUsageCache, prisma_client: PrismaClient + ): + CustomLogger.__init__(self) + BaseManagedResource.__init__(self, internal_usage_cache, prisma_client) + + # ============================================================================ + # ABSTRACT METHOD IMPLEMENTATIONS + # ============================================================================ + + @property + def resource_type(self) -> str: + """Return the resource type identifier.""" + return "vector_store" + + @property + def table_name(self) -> str: + """Return the database table name for vector stores.""" + # Prisma converts model name LiteLLM_ManagedVectorStoreTable to litellm_managedvectorstoretable + return "litellm_managedvectorstoretable" + + def get_unified_resource_id_format( + self, + resource_object: VectorStoreCreateResponse, + target_model_names_list: List[str], + ) -> str: + """ + Generate the format string for the unified vector store ID. + + Format: + litellm_proxy:vector_store;unified_id,;target_model_names,;resource_id,;model_id, + """ + # VectorStoreCreateResponse is a TypedDict, so resource_object is a dictionary + # Extract provider resource ID from the response + provider_resource_id = resource_object.get("id", "") + + # Model ID is stored in hidden params if the response object supports it + # For TypedDict responses, we need to check if _hidden_params was added + hidden_params: Dict[str, Any] = {} + if hasattr(resource_object, "_hidden_params"): + hidden_params = getattr(resource_object, "_hidden_params", {}) or {} + model_id = hidden_params.get("model_id", "") + + return generate_unified_id_string( + resource_type=self.resource_type, + unified_uuid=str(uuid.uuid4()), + target_model_names=target_model_names_list, + provider_resource_id=provider_resource_id, + model_id=model_id, + ) + + async def create_resource_for_model( + self, + llm_router: Router, + model: str, + request_data: Dict[str, Any], + litellm_parent_otel_span: Span, + ) -> VectorStoreCreateResponse: + """ + Create a vector store for a specific model. + + Args: + llm_router: LiteLLM router instance + model: Model name to create vector store for + request_data: Request data for vector store creation + litellm_parent_otel_span: OpenTelemetry span for tracing + + Returns: + VectorStoreCreateResponse from the provider + """ + # Use the router to create the vector store + response = await llm_router.avector_store_create( + model=model, **request_data + ) + return response + + # ============================================================================ + # VECTOR STORE CRUD OPERATIONS + # ============================================================================ + + async def acreate_vector_store( + self, + create_request: VectorStoreCreateOptionalRequestParams, + llm_router: Router, + target_model_names_list: List[str], + litellm_parent_otel_span: Span, + user_api_key_dict: UserAPIKeyAuth, + ) -> VectorStoreCreateResponse: + """ + Create a vector store across multiple models. + + Args: + create_request: Vector store creation request parameters + llm_router: LiteLLM router instance + target_model_names_list: List of target model names + litellm_parent_otel_span: OpenTelemetry span for tracing + user_api_key_dict: User API key authentication details + + Returns: + VectorStoreCreateResponse with unified ID + """ + verbose_logger.info( + f"Creating managed vector store for models: {target_model_names_list}" + ) + + # Create vector store for each model + # Convert TypedDict to Dict[str, Any] for base class compatibility + request_data_dict: Dict[str, Any] = dict(create_request) + responses = await self.create_resource_for_each_model( + llm_router=llm_router, + request_data=request_data_dict, + target_model_names_list=target_model_names_list, + litellm_parent_otel_span=litellm_parent_otel_span, + ) + + # Generate unified ID + unified_id = self.generate_unified_resource_id( + resource_objects=responses, + target_model_names_list=target_model_names_list, + ) + + # Extract model mappings from responses + model_mappings: Dict[str, str] = {} + for response in responses: + hidden_params = getattr(response, "_hidden_params", {}) or {} + model_id = hidden_params.get("model_id") + if model_id: + # VectorStoreCreateResponse is a TypedDict, use dict access + model_mappings[model_id] = response["id"] + + verbose_logger.debug( + f"Created vector stores with model mappings: {model_mappings}" + ) + + # Store in database + await self.store_unified_resource_id( + unified_resource_id=unified_id, + resource_object=responses[0], # Store first response as template + litellm_parent_otel_span=litellm_parent_otel_span, + model_mappings=model_mappings, + user_api_key_dict=user_api_key_dict, + ) + + # Return response with unified ID + # VectorStoreCreateResponse is a TypedDict, so we need to create a new dict with the unified ID + response = responses[0].copy() + response["id"] = unified_id + + verbose_logger.info( + f"Successfully created managed vector store with unified ID: {unified_id}" + ) + + return response + + async def alist_vector_stores( + self, + user_api_key_dict: UserAPIKeyAuth, + limit: Optional[int] = None, + after: Optional[str] = None, + order: Optional[str] = None, + ) -> Dict[str, Any]: + """ + List vector stores created by a user. + + Args: + user_api_key_dict: User API key authentication details + limit: Maximum number of vector stores to return + after: Cursor for pagination + order: Sort order ('asc' or 'desc') + + Returns: + Dictionary with list of vector stores and pagination info + """ + # Use the base class method + return await self.list_user_resources( + user_api_key_dict=user_api_key_dict, + limit=limit, + after=after, + ) + + # ============================================================================ + # ACCESS CONTROL + # ============================================================================ + + async def check_vector_store_access( + self, vector_store_id: str, user_api_key_dict: UserAPIKeyAuth + ) -> bool: + """ + Check if user has access to a vector store. + + Args: + vector_store_id: The unified vector store ID + user_api_key_dict: User API key authentication details + + Returns: + True if user has access, False otherwise + """ + is_unified_id = is_base64_encoded_unified_id(vector_store_id) + + if is_unified_id: + # Check access for managed vector store + return await self.can_user_access_unified_resource_id( + vector_store_id, + user_api_key_dict, + ) + + # Not a managed vector store, allow access + return True + + async def check_managed_vector_store_access( + self, data: Dict, user_api_key_dict: UserAPIKeyAuth + ) -> bool: + """ + Check if user has access to a managed vector store in request data. + + Args: + data: Request data containing vector_store_id + user_api_key_dict: User API key authentication details + + Returns: + True if this is a managed vector store and user has access + + Raises: + HTTPException: If user doesn't have access + """ + vector_store_id = cast(Optional[str], data.get("vector_store_id")) + is_unified_id = ( + is_base64_encoded_unified_id(vector_store_id) + if vector_store_id + else False + ) + + if is_unified_id and vector_store_id: + if await self.can_user_access_unified_resource_id( + vector_store_id, user_api_key_dict + ): + return True + else: + raise HTTPException( + status_code=403, + detail=f"User {user_api_key_dict.user_id} does not have access to vector store {vector_store_id}", + ) + + return False + + # ============================================================================ + # PRE-CALL HOOK (For Router Integration) + # ============================================================================ + + async def async_pre_call_hook( + self, + user_api_key_dict: UserAPIKeyAuth, + cache: Any, + data: Dict, + call_type: str, + ) -> Union[Exception, str, Dict, None]: + """ + Pre-call hook to handle vector store operations. + + This hook intercepts vector store requests and: + - Validates access for managed vector stores + - Transforms unified IDs to provider-specific IDs + - Adds model routing information + + Args: + user_api_key_dict: User API key authentication details + cache: Cache instance + data: Request data + call_type: Type of call being made + + Returns: + Modified request data or None + """ + from litellm.llms.base_llm.managed_resources.utils import ( + is_base64_encoded_unified_id, + parse_unified_id, + ) + + # Handle vector store search operations + if call_type == "avector_store_search": + vector_store_id = data.get("vector_store_id") + + if vector_store_id: + # Check if it's a managed vector store ID + decoded_id = is_base64_encoded_unified_id(vector_store_id) + + if decoded_id: + verbose_logger.debug( + f"Processing managed vector store search: {vector_store_id}" + ) + + # Check access + has_access = await self.can_user_access_unified_resource_id( + vector_store_id, user_api_key_dict + ) + + if not has_access: + raise HTTPException( + status_code=403, + detail=f"User {user_api_key_dict.user_id} does not have access to vector store {vector_store_id}", + ) + + # Parse the unified ID to extract components + parsed_id = parse_unified_id(vector_store_id) + + if parsed_id: + # Extract the model ID and provider resource ID + model_id = parsed_id.get("model_id") + provider_resource_id = parsed_id.get("provider_resource_id") + target_model_names = parsed_id.get("target_model_names", []) + + verbose_logger.debug( + f"Decoded vector store - model_id: {model_id}, provider_resource_id: {provider_resource_id}, target_model_names: {target_model_names}" + ) + + # Determine which model to use for routing + # Priority: model_id (deployment ID) > first target_model_name + routing_model = None + if model_id: + routing_model = model_id + elif target_model_names and len(target_model_names) > 0: + routing_model = target_model_names[0] + + # Set the model for routing + if routing_model: + data["model"] = routing_model + verbose_logger.info( + f"Routing vector store search to model: {routing_model}" + ) + + # Replace the unified ID with the provider-specific ID + if provider_resource_id: + data["vector_store_id"] = provider_resource_id + verbose_logger.debug( + f"Replaced unified ID with provider resource ID: {provider_resource_id}" + ) + + # Handle vector store retrieve/delete operations + elif call_type in ("avector_store_retrieve", "avector_store_delete"): + await self.check_managed_vector_store_access(data, user_api_key_dict) + + # If it's a managed vector store, we'll handle it in the endpoint + # No need to transform here as the endpoint will route to the hook + + return data + + # ============================================================================ + # POST-CALL HOOK (For Response Transformation) + # ============================================================================ + + async def async_post_call_success_hook( + self, + data: Dict, + user_api_key_dict: UserAPIKeyAuth, + response: Any, + ) -> Any: + """ + Post-call hook to transform responses. + + This hook can be used to transform responses if needed. + For now, it just passes through the response. + + Args: + data: Request data + user_api_key_dict: User API key authentication details + response: Response from the provider + + Returns: + Potentially modified response + """ + # Currently no transformation needed + return response + + # ============================================================================ + # DEPLOYMENT FILTERING + # ============================================================================ + + async def async_filter_deployments( # type: ignore[override] + self, + model: str, + healthy_deployments: List, + messages: Optional[List] = None, + request_kwargs: Optional[Dict] = None, + parent_otel_span: Optional[Span] = None, + ) -> List[Dict]: + """ + Filter deployments based on vector store availability. + + This is used by the router to select only deployments that have + the vector store available. + + Note: This method signature is a compromise between CustomLogger and BaseManagedResource + parent classes which have incompatible signatures. The type: ignore[override] is necessary + due to this multiple inheritance conflict. + + Args: + model: Model name + healthy_deployments: List of healthy deployments + messages: Messages (unused for vector stores, required by CustomLogger interface) + request_kwargs: Request kwargs containing vector_store_id and mappings + parent_otel_span: OpenTelemetry span for tracing + + Returns: + Filtered list of deployments + """ + return await BaseManagedResource.async_filter_deployments( + self, + model=model, + healthy_deployments=healthy_deployments, + request_kwargs=request_kwargs, + parent_otel_span=parent_otel_span, + resource_id_key="vector_store_id", + ) diff --git a/enterprise/pyproject.toml b/enterprise/pyproject.toml index eca5cdb97d..55720934f0 100644 --- a/enterprise/pyproject.toml +++ b/enterprise/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "litellm-enterprise" -version = "0.1.31" +version = "0.1.32" description = "Package for LiteLLM Enterprise features" authors = ["BerriAI"] readme = "README.md" @@ -22,7 +22,7 @@ requires = ["poetry-core"] build-backend = "poetry.core.masonry.api" [tool.commitizen] -version = "0.1.31" +version = "0.1.32" version_files = [ "pyproject.toml:version", "../requirements.txt:litellm-enterprise==", diff --git a/litellm-proxy-extras/build_and_publish.md b/litellm-proxy-extras/build_and_publish.md new file mode 100644 index 0000000000..6bf16b9946 --- /dev/null +++ b/litellm-proxy-extras/build_and_publish.md @@ -0,0 +1,127 @@ +# Build & Publish `litellm-proxy-extras` + +This runbook covers building and publishing a new version of the `litellm-proxy-extras` PyPI package. For use by litellm engineers only. + +## Prerequisites + +- All `schema.prisma` files are in sync (see [migration_runbook.md](./migration_runbook.md) Step 0) +- Migration has been generated and committed +- You are in the `litellm-proxy-extras/` directory + +## Step 1: Bump the Version + +### Option A: Automatic Version Bump (Recommended) + +Use commitizen to automatically bump the version across all files: + +```bash +cd litellm-proxy-extras +cz bump --increment patch +``` + +This will automatically: +- Bump the version in `pyproject.toml` (both `[tool.poetry].version` and `[tool.commitizen].version`) +- Update the version in `../requirements.txt` +- Update the version in `../pyproject.toml` (root) +- Create a git commit with the version bump + +Then skip to Step 3 (Install Build Dependencies). + +### Option B: Manual Version Bump + +Update the version in `pyproject.toml`: + +```bash +cd litellm-proxy-extras + +# Check current version +grep 'version' pyproject.toml +``` + +Edit `pyproject.toml` and bump the version (both `[tool.poetry].version` and `[tool.commitizen].version`). + +#### Step 2: Update Version in Root Package Files (Manual Only) + +After bumping the version in `litellm-proxy-extras/pyproject.toml`, you **must** also update the version reference in the root-level files: + +| File | Line to update | +|------|---------------| +| `requirements.txt` | `litellm-proxy-extras==X.Y.Z` | +| `pyproject.toml` (root) | `litellm-proxy-extras = {version = "X.Y.Z", optional = true}` | + +```bash +# From the repo root — replace OLD with NEW version +sed -i '' 's/litellm-proxy-extras==OLD/litellm-proxy-extras==NEW/' requirements.txt +sed -i '' 's/litellm-proxy-extras = {version = "OLD"/litellm-proxy-extras = {version = "NEW"/' pyproject.toml +``` + +> **Do NOT skip this step.** The main `litellm` package pins the extras version — if you don't update these, users will install the old version. + +## Step 3: Install Build Dependencies + +```bash +pip install build twine +``` + +## Step 4: Clean Old Artifacts + +```bash +rm -rf dist/ build/ *.egg-info +``` + +## Step 5: Build the Package + +```bash +python3 -m build +``` + +This creates `.tar.gz` and `.whl` files in the `dist/` directory. + +Verify the build output: + +```bash +ls -la dist/ +``` + +## Step 6: Upload to PyPI + +```bash +twine upload dist/* +``` + +You will be prompted for your PyPI API token: + +``` +Enter your API token: pypi-... +``` + +> Use `__token__` as the username and your PyPI API token as the password. + +## Quick Reference (Copy-Paste) + +```bash +cd litellm-proxy-extras +rm -rf dist/ build/ *.egg-info +python3 -m build +twine upload dist/* +``` + +--- + +## Do you want to build and publish a new `litellm-proxy-extras` package? (y/n) + +If **yes**, run the following commands in order: + +```bash +cd litellm-proxy-extras +pip install build twine +rm -rf dist/ build/ *.egg-info +python3 -m build +twine upload dist/* +``` + +When `twine upload` runs, enter your PyPI credentials: +- **Username:** `__token__` +- **Password:** *(paste your PyPI API key)* + +If **no**, you're done — no package publish needed. diff --git a/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.36-py3-none-any.whl b/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.36-py3-none-any.whl new file mode 100644 index 0000000000..c98d9cfcfa Binary files /dev/null and b/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.36-py3-none-any.whl differ diff --git a/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.36.tar.gz b/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.36.tar.gz new file mode 100644 index 0000000000..c8c3340462 Binary files /dev/null and b/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.36.tar.gz differ diff --git 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a/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.45.tar.gz b/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.45.tar.gz new file mode 100644 index 0000000000..5680b26dbf Binary files /dev/null and b/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.45.tar.gz differ diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20251113000000_add_project_table/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20251113000000_add_project_table/migration.sql new file mode 100644 index 0000000000..f1d3129bb3 --- /dev/null +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20251113000000_add_project_table/migration.sql @@ -0,0 +1,35 @@ +-- CreateTable +CREATE TABLE "LiteLLM_ProjectTable" ( + "project_id" TEXT NOT NULL, + "project_alias" TEXT, + "team_id" TEXT, + "budget_id" TEXT, + "metadata" JSONB NOT NULL DEFAULT '{}', + "models" TEXT[], + "spend" DOUBLE PRECISION NOT NULL DEFAULT 0.0, + "model_spend" JSONB NOT NULL DEFAULT '{}', + "blocked" BOOLEAN NOT NULL DEFAULT false, + "object_permission_id" TEXT, + "created_at" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP, + "created_by" TEXT NOT NULL, + "updated_at" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP, + "updated_by" TEXT NOT NULL, + + CONSTRAINT "LiteLLM_ProjectTable_pkey" PRIMARY KEY ("project_id") +); + +-- AddForeignKey +ALTER TABLE "LiteLLM_ProjectTable" ADD CONSTRAINT "LiteLLM_ProjectTable_team_id_fkey" FOREIGN KEY ("team_id") REFERENCES "LiteLLM_TeamTable"("team_id") ON DELETE SET NULL ON UPDATE CASCADE; + +-- AddForeignKey +ALTER TABLE "LiteLLM_ProjectTable" ADD CONSTRAINT "LiteLLM_ProjectTable_budget_id_fkey" FOREIGN KEY ("budget_id") REFERENCES "LiteLLM_BudgetTable"("budget_id") ON DELETE SET NULL ON UPDATE CASCADE; + +-- AddForeignKey +ALTER TABLE "LiteLLM_ProjectTable" ADD CONSTRAINT "LiteLLM_ProjectTable_object_permission_id_fkey" FOREIGN KEY ("object_permission_id") REFERENCES "LiteLLM_ObjectPermissionTable"("object_permission_id") ON DELETE SET NULL ON UPDATE CASCADE; + +-- AlterTable: Add project_id to LiteLLM_VerificationToken +ALTER TABLE "LiteLLM_VerificationToken" ADD COLUMN "project_id" TEXT; + +-- AddForeignKey +ALTER TABLE "LiteLLM_VerificationToken" ADD CONSTRAINT "LiteLLM_VerificationToken_project_id_fkey" FOREIGN KEY ("project_id") REFERENCES "LiteLLM_ProjectTable"("project_id") ON DELETE SET NULL ON UPDATE CASCADE; + diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20251113000001_add_project_fields/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20251113000001_add_project_fields/migration.sql new file mode 100644 index 0000000000..48328b4d6a --- /dev/null +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20251113000001_add_project_fields/migration.sql @@ -0,0 +1,5 @@ +-- AlterTable: Add new fields to LiteLLM_ProjectTable +ALTER TABLE "LiteLLM_ProjectTable" ADD COLUMN "description" TEXT; +ALTER TABLE "LiteLLM_ProjectTable" ADD COLUMN "model_rpm_limit" JSONB NOT NULL DEFAULT '{}'; +ALTER TABLE "LiteLLM_ProjectTable" ADD COLUMN "model_tpm_limit" JSONB NOT NULL DEFAULT '{}'; + diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20260131150814_add_team_user_to_vector_stores/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260131150814_add_team_user_to_vector_stores/migration.sql index 2032f76a5d..1f5dc311bd 100644 --- a/litellm-proxy-extras/litellm_proxy_extras/migrations/20260131150814_add_team_user_to_vector_stores/migration.sql +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260131150814_add_team_user_to_vector_stores/migration.sql @@ -1,10 +1,13 @@ -- AlterTable -ALTER TABLE "LiteLLM_ManagedVectorStoresTable" ADD COLUMN "team_id" TEXT, -ADD COLUMN "user_id" TEXT; +ALTER TABLE "LiteLLM_ManagedVectorStoresTable" + ADD COLUMN IF NOT EXISTS "team_id" TEXT, + ADD COLUMN IF NOT EXISTS "user_id" TEXT; -- CreateIndex -CREATE INDEX "LiteLLM_ManagedVectorStoresTable_team_id_idx" ON "LiteLLM_ManagedVectorStoresTable"("team_id"); +CREATE INDEX IF NOT EXISTS "LiteLLM_ManagedVectorStoresTable_team_id_idx" + ON "LiteLLM_ManagedVectorStoresTable"("team_id"); -- CreateIndex -CREATE INDEX "LiteLLM_ManagedVectorStoresTable_user_id_idx" ON "LiteLLM_ManagedVectorStoresTable"("user_id"); +CREATE INDEX IF NOT EXISTS "LiteLLM_ManagedVectorStoresTable_user_id_idx" + ON "LiteLLM_ManagedVectorStoresTable"("user_id"); diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20260203120000_add_deprecated_verification_token_table/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260203120000_add_deprecated_verification_token_table/migration.sql new file mode 100644 index 0000000000..51d8844419 --- /dev/null +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260203120000_add_deprecated_verification_token_table/migration.sql @@ -0,0 +1,19 @@ +-- CreateTable +CREATE TABLE "LiteLLM_DeprecatedVerificationToken" ( + "id" TEXT NOT NULL, + "token" TEXT NOT NULL, + "active_token_id" TEXT NOT NULL, + "revoke_at" TIMESTAMP(3) NOT NULL, + "created_at" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP, + + CONSTRAINT "LiteLLM_DeprecatedVerificationToken_pkey" PRIMARY KEY ("id") +); + +-- CreateIndex +CREATE UNIQUE INDEX "LiteLLM_DeprecatedVerificationToken_token_key" ON "LiteLLM_DeprecatedVerificationToken"("token"); + +-- CreateIndex +CREATE INDEX "LiteLLM_DeprecatedVerificationToken_token_revoke_at_idx" ON "LiteLLM_DeprecatedVerificationToken"("token", "revoke_at"); + +-- CreateIndex +CREATE INDEX "LiteLLM_DeprecatedVerificationToken_revoke_at_idx" ON "LiteLLM_DeprecatedVerificationToken"("revoke_at"); diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20260212143306_add_access_group_table/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260212143306_add_access_group_table/migration.sql new file mode 100644 index 0000000000..67e75e84c4 --- /dev/null +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260212143306_add_access_group_table/migration.sql @@ -0,0 +1,33 @@ +-- AlterTable +ALTER TABLE "LiteLLM_DeletedTeamTable" ADD COLUMN "access_group_ids" TEXT[] DEFAULT ARRAY[]::TEXT[]; + +-- AlterTable +ALTER TABLE "LiteLLM_DeletedVerificationToken" ADD COLUMN "access_group_ids" TEXT[] DEFAULT ARRAY[]::TEXT[]; + +-- AlterTable +ALTER TABLE "LiteLLM_TeamTable" ADD COLUMN "access_group_ids" TEXT[] DEFAULT ARRAY[]::TEXT[]; + +-- AlterTable +ALTER TABLE "LiteLLM_VerificationToken" ADD COLUMN "access_group_ids" TEXT[] DEFAULT ARRAY[]::TEXT[]; + +-- CreateTable +CREATE TABLE "LiteLLM_AccessGroupTable" ( + "access_group_id" TEXT NOT NULL, + "access_group_name" TEXT NOT NULL, + "description" TEXT, + "access_model_ids" TEXT[] DEFAULT ARRAY[]::TEXT[], + "access_mcp_server_ids" TEXT[] DEFAULT ARRAY[]::TEXT[], + "access_agent_ids" TEXT[] DEFAULT ARRAY[]::TEXT[], + "assigned_team_ids" TEXT[] DEFAULT ARRAY[]::TEXT[], + "assigned_key_ids" TEXT[] DEFAULT ARRAY[]::TEXT[], + "created_at" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP, + "created_by" TEXT, + "updated_at" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP, + "updated_by" TEXT, + + CONSTRAINT "LiteLLM_AccessGroupTable_pkey" PRIMARY KEY ("access_group_id") +); + +-- CreateIndex +CREATE UNIQUE INDEX "LiteLLM_AccessGroupTable_access_group_name_key" ON "LiteLLM_AccessGroupTable"("access_group_name"); + diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20260213105436_add_managed_vector_store_table/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260213105436_add_managed_vector_store_table/migration.sql new file mode 100644 index 0000000000..0835875220 --- /dev/null +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260213105436_add_managed_vector_store_table/migration.sql @@ -0,0 +1,22 @@ +-- CreateTable +CREATE TABLE "LiteLLM_ManagedVectorStoreTable" ( + "id" TEXT NOT NULL, + "unified_resource_id" TEXT NOT NULL, + "resource_object" JSONB, + "model_mappings" JSONB NOT NULL, + "flat_model_resource_ids" TEXT[] DEFAULT ARRAY[]::TEXT[], + "storage_backend" TEXT, + "storage_url" TEXT, + "created_at" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP, + "created_by" TEXT, + "updated_at" TIMESTAMP(3) NOT NULL, + "updated_by" TEXT, + + CONSTRAINT "LiteLLM_ManagedVectorStoreTable_pkey" PRIMARY KEY ("id") +); + +-- CreateIndex +CREATE UNIQUE INDEX "LiteLLM_ManagedVectorStoreTable_unified_resource_id_key" ON "LiteLLM_ManagedVectorStoreTable"("unified_resource_id"); + +-- CreateIndex +CREATE INDEX "LiteLLM_ManagedVectorStoreTable_unified_resource_id_idx" ON "LiteLLM_ManagedVectorStoreTable"("unified_resource_id"); diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20260213170952_access_group_change_to_model_name/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260213170952_access_group_change_to_model_name/migration.sql new file mode 100644 index 0000000000..c940d3aca8 --- /dev/null +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260213170952_access_group_change_to_model_name/migration.sql @@ -0,0 +1,3 @@ +-- AlterTable +ALTER TABLE "LiteLLM_AccessGroupTable" DROP COLUMN "access_model_ids", +ADD COLUMN "access_model_names" TEXT[] DEFAULT ARRAY[]::TEXT[]; diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20260214094754_schema_sync/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260214094754_schema_sync/migration.sql new file mode 100644 index 0000000000..b5d5b97858 --- /dev/null +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260214094754_schema_sync/migration.sql @@ -0,0 +1,3 @@ +-- AlterTable +ALTER TABLE "LiteLLM_GuardrailsTable" ADD COLUMN "team_id" TEXT; + diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20260214124140_baseline_diff/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260214124140_baseline_diff/migration.sql new file mode 100644 index 0000000000..2f725d8380 --- /dev/null +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260214124140_baseline_diff/migration.sql @@ -0,0 +1,2 @@ +-- This is an empty migration. + diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20260214163027_add_pipeline_to_policy_table/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260214163027_add_pipeline_to_policy_table/migration.sql new file mode 100644 index 0000000000..e57b9ef29c --- /dev/null +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260214163027_add_pipeline_to_policy_table/migration.sql @@ -0,0 +1,3 @@ +-- AlterTable +ALTER TABLE "LiteLLM_PolicyTable" ADD COLUMN "pipeline" JSONB; + diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20260214185341_object_permissions_for_end_users/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260214185341_object_permissions_for_end_users/migration.sql new file mode 100644 index 0000000000..5c5dc6fd6f --- /dev/null +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260214185341_object_permissions_for_end_users/migration.sql @@ -0,0 +1,6 @@ +-- AlterTable +ALTER TABLE "LiteLLM_EndUserTable" ADD COLUMN "object_permission_id" TEXT; + +-- AddForeignKey +ALTER TABLE "LiteLLM_EndUserTable" ADD CONSTRAINT "LiteLLM_EndUserTable_object_permission_id_fkey" FOREIGN KEY ("object_permission_id") REFERENCES "LiteLLM_ObjectPermissionTable"("object_permission_id") ON DELETE SET NULL ON UPDATE CASCADE; + diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20260218231534_add_last_active_to_key_table/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260218231534_add_last_active_to_key_table/migration.sql new file mode 100644 index 0000000000..ded1856059 --- /dev/null +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260218231534_add_last_active_to_key_table/migration.sql @@ -0,0 +1,6 @@ +-- AlterTable +ALTER TABLE "LiteLLM_DeletedVerificationToken" ADD COLUMN "last_active" TIMESTAMP(3); + +-- AlterTable +ALTER TABLE "LiteLLM_VerificationToken" ADD COLUMN "last_active" TIMESTAMP(3); + diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20260219105005_add_project_id_to_deleted_keys/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260219105005_add_project_id_to_deleted_keys/migration.sql new file mode 100644 index 0000000000..59bdc86adb --- /dev/null +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260219105005_add_project_id_to_deleted_keys/migration.sql @@ -0,0 +1,3 @@ +-- AlterTable +ALTER TABLE "LiteLLM_DeletedVerificationToken" ADD COLUMN "project_id" TEXT; + diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20260220124742_add_spec_path_to_mcp_servers/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260220124742_add_spec_path_to_mcp_servers/migration.sql new file mode 100644 index 0000000000..4f4e72a879 --- /dev/null +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260220124742_add_spec_path_to_mcp_servers/migration.sql @@ -0,0 +1,2 @@ +-- AlterTable +ALTER TABLE "LiteLLM_MCPServerTable" ADD COLUMN "spec_path" TEXT; diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20260220153844_add_composite_index_aggregate_tables/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260220153844_add_composite_index_aggregate_tables/migration.sql new file mode 100644 index 0000000000..a10f123b02 --- /dev/null +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260220153844_add_composite_index_aggregate_tables/migration.sql @@ -0,0 +1,36 @@ +-- DropIndex +DROP INDEX "LiteLLM_DailyAgentSpend_agent_id_idx"; + +-- DropIndex +DROP INDEX "LiteLLM_DailyEndUserSpend_end_user_id_idx"; + +-- DropIndex +DROP INDEX "LiteLLM_DailyOrganizationSpend_organization_id_idx"; + +-- DropIndex +DROP INDEX "LiteLLM_DailyTagSpend_tag_idx"; + +-- DropIndex +DROP INDEX "LiteLLM_DailyTeamSpend_team_id_idx"; + +-- DropIndex +DROP INDEX "LiteLLM_DailyUserSpend_user_id_idx"; + +-- CreateIndex +CREATE INDEX "LiteLLM_DailyAgentSpend_agent_id_date_idx" ON "LiteLLM_DailyAgentSpend"("agent_id", "date"); + +-- CreateIndex +CREATE INDEX "LiteLLM_DailyEndUserSpend_end_user_id_date_idx" ON "LiteLLM_DailyEndUserSpend"("end_user_id", "date"); + +-- CreateIndex +CREATE INDEX "LiteLLM_DailyOrganizationSpend_organization_id_date_idx" ON "LiteLLM_DailyOrganizationSpend"("organization_id", "date"); + +-- CreateIndex +CREATE INDEX "LiteLLM_DailyTagSpend_tag_date_idx" ON "LiteLLM_DailyTagSpend"("tag", "date"); + +-- CreateIndex +CREATE INDEX "LiteLLM_DailyTeamSpend_team_id_date_idx" ON "LiteLLM_DailyTeamSpend"("team_id", "date"); + +-- CreateIndex +CREATE INDEX "LiteLLM_DailyUserSpend_user_id_date_idx" ON "LiteLLM_DailyUserSpend"("user_id", "date"); + diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20260221000000_ensure_project_id_verification_token/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260221000000_ensure_project_id_verification_token/migration.sql new file mode 100644 index 0000000000..697928c85d --- /dev/null +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260221000000_ensure_project_id_verification_token/migration.sql @@ -0,0 +1,5 @@ +-- Ensure project_id column exists in LiteLLM_VerificationToken. +-- The original migration (20251113000000_add_project_table) adds this column, +-- but if it failed partway through (e.g. LiteLLM_ProjectTable already existed) +-- and was resolved as idempotent, the ALTER TABLE step may have been skipped. +ALTER TABLE "LiteLLM_VerificationToken" ADD COLUMN IF NOT EXISTS "project_id" TEXT; diff --git a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma index 558dfcc951..777e9c6b97 100644 --- a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma +++ b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma @@ -24,6 +24,7 @@ model LiteLLM_BudgetTable { updated_at DateTime @default(now()) @updatedAt @map("updated_at") updated_by String organization LiteLLM_OrganizationTable[] // multiple orgs can have the same budget + projects LiteLLM_ProjectTable[] // multiple projects can have the same budget keys LiteLLM_VerificationToken[] // multiple keys can have the same budget end_users LiteLLM_EndUserTable[] // multiple end-users can have the same budget tags LiteLLM_TagTable[] // multiple tags can have the same budget @@ -128,12 +129,41 @@ model LiteLLM_TeamTable { model_max_budget Json @default("{}") router_settings Json? @default("{}") team_member_permissions String[] @default([]) + access_group_ids String[] @default([]) policies String[] @default([]) model_id Int? @unique // id for LiteLLM_ModelTable -> stores team-level model aliases allow_team_guardrail_config Boolean @default(false) // if true, team admin can configure guardrails for this team litellm_organization_table LiteLLM_OrganizationTable? @relation(fields: [organization_id], references: [organization_id]) litellm_model_table LiteLLM_ModelTable? @relation(fields: [model_id], references: [id]) object_permission LiteLLM_ObjectPermissionTable? @relation(fields: [object_permission_id], references: [object_permission_id]) + projects LiteLLM_ProjectTable[] +} + +// Projects sit between teams and keys for use-case management +model LiteLLM_ProjectTable { + project_id String @id @default(uuid()) + project_alias String? + description String? + team_id String? + budget_id String? + metadata Json @default("{}") + models String[] + spend Float @default(0.0) + model_spend Json @default("{}") + model_rpm_limit Json @default("{}") + model_tpm_limit Json @default("{}") + blocked Boolean @default(false) + object_permission_id String? + created_at DateTime @default(now()) @map("created_at") + created_by String + updated_at DateTime @default(now()) @updatedAt @map("updated_at") + updated_by String + + // Relations + litellm_team_table LiteLLM_TeamTable? @relation(fields: [team_id], references: [team_id]) + litellm_budget_table LiteLLM_BudgetTable? @relation(fields: [budget_id], references: [budget_id]) + keys LiteLLM_VerificationToken[] + object_permission LiteLLM_ObjectPermissionTable? @relation(fields: [object_permission_id], references: [object_permission_id]) } // Audit table for deleted teams - preserves spend and team information for historical tracking @@ -161,6 +191,54 @@ model LiteLLM_DeletedTeamTable { model_max_budget Json @default("{}") router_settings Json? @default("{}") team_member_permissions String[] @default([]) + access_group_ids String[] @default([]) + policies String[] @default([]) + model_id Int? // id for LiteLLM_ModelTable -> stores team-level model aliases + allow_team_guardrail_config Boolean @default(false) + + // Original timestamps from team creation/updates + created_at DateTime? @map("created_at") + updated_at DateTime? @map("updated_at") + + // Deletion metadata + deleted_at DateTime @default(now()) @map("deleted_at") + deleted_by String? @map("deleted_by") // User who deleted the team + deleted_by_api_key String? @map("deleted_by_api_key") // API key hash that performed the deletion + litellm_changed_by String? @map("litellm_changed_by") // From litellm-changed-by header if provided + + @@index([team_id]) + @@index([deleted_at]) + @@index([organization_id]) + @@index([team_alias]) + @@index([created_at]) +} + +// Audit table for deleted teams - preserves spend and team information for historical tracking +model LiteLLM_DeletedTeamTable { + id String @id @default(uuid()) + team_id String // Original team_id + team_alias String? + organization_id String? + object_permission_id String? + admins String[] + members String[] + members_with_roles Json @default("{}") + metadata Json @default("{}") + max_budget Float? + soft_budget Float? + spend Float @default(0.0) + models String[] + max_parallel_requests Int? + tpm_limit BigInt? + rpm_limit BigInt? + budget_duration String? + budget_reset_at DateTime? + blocked Boolean @default(false) + model_spend Json @default("{}") + model_max_budget Json @default("{}") + router_settings Json? @default("{}") + team_member_permissions String[] @default([]) + access_group_ids String[] @default([]) policies String[] @default([]) model_id Int? // id for LiteLLM_ModelTable -> stores team-level model aliases allow_team_guardrail_config Boolean @default(false) @@ -228,9 +306,11 @@ model LiteLLM_ObjectPermissionTable { agents String[] @default([]) agent_access_groups String[] @default([]) teams LiteLLM_TeamTable[] + projects LiteLLM_ProjectTable[] verification_tokens LiteLLM_VerificationToken[] organizations LiteLLM_OrganizationTable[] users LiteLLM_UserTable[] + end_users LiteLLM_EndUserTable[] } // Holds the MCP server configuration @@ -281,6 +361,7 @@ model LiteLLM_VerificationToken { router_settings Json? @default("{}") user_id String? team_id String? + project_id String? permissions Json @default("{}") max_parallel_requests Int? metadata Json @default("{}") @@ -293,6 +374,7 @@ model LiteLLM_VerificationToken { allowed_cache_controls String[] @default([]) allowed_routes String[] @default([]) policies String[] @default([]) + access_group_ids String[] @default([]) model_spend Json @default("{}") model_max_budget Json @default("{}") budget_id String? @@ -302,6 +384,7 @@ model LiteLLM_VerificationToken { created_by String? updated_at DateTime? @default(now()) @updatedAt @map("updated_at") updated_by String? + last_active DateTime? // When this key was last used rotation_count Int? @default(0) // Number of times key has been rotated auto_rotate Boolean? @default(false) // Whether this key should be auto-rotated rotation_interval String? // How often to rotate (e.g., "30d", "90d") @@ -309,6 +392,7 @@ model LiteLLM_VerificationToken { key_rotation_at DateTime? // When this key should next be rotated litellm_budget_table LiteLLM_BudgetTable? @relation(fields: [budget_id], references: [budget_id]) litellm_organization_table LiteLLM_OrganizationTable? @relation(fields: [organization_id], references: [organization_id]) + litellm_project_table LiteLLM_ProjectTable? @relation(fields: [project_id], references: [project_id]) object_permission LiteLLM_ObjectPermissionTable? @relation(fields: [object_permission_id], references: [object_permission_id]) // SELECT COUNT(*) FROM (SELECT "public"."LiteLLM_VerificationToken"."token" FROM "public"."LiteLLM_VerificationToken" WHERE ("public"."LiteLLM_VerificationToken"."user_id" = $1 AND ("public"."LiteLLM_VerificationToken"."team_id" IS NULL OR "public"."LiteLLM_VerificationToken"."team_id" <> $2)) OFFSET $3 ) AS "sub" @@ -322,6 +406,19 @@ model LiteLLM_VerificationToken { @@index([budget_reset_at, expires]) } +// Deprecated keys during grace period - allows old key to work until revoke_at +model LiteLLM_DeprecatedVerificationToken { + id String @id @default(uuid()) + token String // Hashed old key + active_token_id String // Current token hash in LiteLLM_VerificationToken + revoke_at DateTime // When the old key stops working + created_at DateTime @default(now()) @map("created_at") + + @@unique([token]) + @@index([token, revoke_at]) + @@index([revoke_at]) +} + // Audit table for deleted keys - preserves spend and key information for historical tracking model LiteLLM_DeletedVerificationToken { id String @id @default(uuid()) @@ -336,6 +433,7 @@ model LiteLLM_DeletedVerificationToken { config Json @default("{}") user_id String? team_id String? + project_id String? permissions Json @default("{}") max_parallel_requests Int? metadata Json @default("{}") @@ -348,6 +446,7 @@ model LiteLLM_DeletedVerificationToken { allowed_cache_controls String[] @default([]) allowed_routes String[] @default([]) policies String[] @default([]) + access_group_ids String[] @default([]) model_spend Json @default("{}") model_max_budget Json @default("{}") router_settings Json? @default("{}") @@ -358,6 +457,7 @@ model LiteLLM_DeletedVerificationToken { created_by String? // Original creator updated_at DateTime? // Last update timestamp before deletion updated_by String? // Last user who updated before deletion + last_active DateTime? // When this key was last used before deletion rotation_count Int? @default(0) auto_rotate Boolean? @default(false) rotation_interval String? @@ -386,7 +486,9 @@ model LiteLLM_EndUserTable { allowed_model_region String? // require all user requests to use models in this specific region default_model String? // use along with 'allowed_model_region'. if no available model in region, default to this model. budget_id String? + object_permission_id String? litellm_budget_table LiteLLM_BudgetTable? @relation(fields: [budget_id], references: [budget_id]) + object_permission LiteLLM_ObjectPermissionTable? @relation(fields: [object_permission_id], references: [object_permission_id]) blocked Boolean @default(false) } @@ -428,7 +530,7 @@ model LiteLLM_SpendLogs { custom_llm_provider String? @default("") // litellm used custom_llm_provider api_base String? @default("") user String? @default("") - metadata Json? @default("{}") + metadata Json? @default("{}") // project_id stored here cache_hit String? @default("") cache_key String? @default("") request_tags Json? @default("[]") @@ -558,7 +660,7 @@ model LiteLLM_DailyUserSpend { @@unique([user_id, date, api_key, model, custom_llm_provider, mcp_namespaced_tool_name, endpoint]) @@index([date]) - @@index([user_id]) + @@index([user_id, date]) @@index([api_key]) @@index([model]) @@index([mcp_namespaced_tool_name]) @@ -589,7 +691,7 @@ model LiteLLM_DailyOrganizationSpend { @@unique([organization_id, date, api_key, model, custom_llm_provider, mcp_namespaced_tool_name, endpoint]) @@index([date]) - @@index([organization_id]) + @@index([organization_id, date]) @@index([api_key]) @@index([model]) @@index([mcp_namespaced_tool_name]) @@ -619,7 +721,7 @@ model LiteLLM_DailyEndUserSpend { updated_at DateTime @updatedAt @@unique([end_user_id, date, api_key, model, custom_llm_provider, mcp_namespaced_tool_name, endpoint]) @@index([date]) - @@index([end_user_id]) + @@index([end_user_id, date]) @@index([api_key]) @@index([model]) @@index([mcp_namespaced_tool_name]) @@ -649,7 +751,7 @@ model LiteLLM_DailyAgentSpend { updated_at DateTime @updatedAt @@unique([agent_id, date, api_key, model, custom_llm_provider, mcp_namespaced_tool_name, endpoint]) @@index([date]) - @@index([agent_id]) + @@index([agent_id, date]) @@index([api_key]) @@index([model]) @@index([mcp_namespaced_tool_name]) @@ -680,7 +782,7 @@ model LiteLLM_DailyTeamSpend { @@unique([team_id, date, api_key, model, custom_llm_provider, mcp_namespaced_tool_name, endpoint]) @@index([date]) - @@index([team_id]) + @@index([team_id, date]) @@index([api_key]) @@index([model]) @@index([mcp_namespaced_tool_name]) @@ -712,7 +814,7 @@ model LiteLLM_DailyTagSpend { @@unique([tag, date, api_key, model, custom_llm_provider, mcp_namespaced_tool_name, endpoint]) @@index([date]) - @@index([tag]) + @@index([tag, date]) @@index([api_key]) @@index([model]) @@index([mcp_namespaced_tool_name]) @@ -766,6 +868,22 @@ model LiteLLM_ManagedObjectTable { // for batches or finetuning jobs which use t @@index([model_object_id]) } +model LiteLLM_ManagedVectorStoreTable { + id String @id @default(uuid()) + unified_resource_id String @unique // The base64 encoded unified vector store ID + resource_object Json? // Stores the VectorStoreCreateResponse + model_mappings Json // Maps model_id -> provider_vector_store_id + flat_model_resource_ids String[] @default([]) // Flat list of provider vector store IDs for faster querying + storage_backend String? // Storage backend name (if applicable) + storage_url String? // Storage URL (if applicable) + created_at DateTime @default(now()) + created_by String? + updated_at DateTime @updatedAt + updated_by String? + + @@index([unified_resource_id]) +} + model LiteLLM_ManagedVectorStoresTable { vector_store_id String @id custom_llm_provider String @@ -900,6 +1018,7 @@ model LiteLLM_PolicyTable { guardrails_add String[] @default([]) guardrails_remove String[] @default([]) condition Json? @default("{}") // Policy conditions (e.g., model matching) + pipeline Json? // Optional guardrail pipeline (mode + steps[]) created_at DateTime @default(now()) created_by String? updated_at DateTime @default(now()) @updatedAt @@ -920,3 +1039,23 @@ model LiteLLM_PolicyAttachmentTable { updated_at DateTime @default(now()) @updatedAt updated_by String? } + +//Unified Access Groups table for storing unified access groups +model LiteLLM_AccessGroupTable { + access_group_id String @id @default(uuid()) + access_group_name String @unique + description String? + + // Resource memberships - explicit arrays per type + access_model_names String[] @default([]) + access_mcp_server_ids String[] @default([]) + access_agent_ids String[] @default([]) + + assigned_team_ids String[] @default([]) + assigned_key_ids String[] @default([]) + + created_at DateTime @default(now()) + created_by String? + updated_at DateTime @default(now()) @updatedAt + updated_by String? +} \ No newline at end of file diff --git a/litellm-proxy-extras/migration_runbook.md b/litellm-proxy-extras/migration_runbook.md index 93948f24b1..3310b1626a 100644 --- a/litellm-proxy-extras/migration_runbook.md +++ b/litellm-proxy-extras/migration_runbook.md @@ -2,7 +2,35 @@ This is a runbook for creating and running database migrations for the LiteLLM proxy. For use for litellm engineers only. -## Quick Start +## Step 0: Sync All `schema.prisma` Files + +Before doing anything else, make sure all `schema.prisma` files in the repo are in sync. There are multiple copies that must match: + +| File | Purpose | +|------|---------| +| `schema.prisma` (repo root) | Source of truth | +| `litellm/proxy/schema.prisma` | Used by the proxy server | +| `litellm-proxy-extras/litellm_proxy_extras/schema.prisma` | Used for migration generation | + +**Sync process:** + +```bash +# 1. Diff all schema files against the root source of truth +diff schema.prisma litellm/proxy/schema.prisma +diff schema.prisma litellm-proxy-extras/litellm_proxy_extras/schema.prisma + +# 2. If there are differences, copy the root schema to all locations +cp schema.prisma litellm/proxy/schema.prisma +cp schema.prisma litellm-proxy-extras/litellm_proxy_extras/schema.prisma + +# 3. Verify all files are now identical +diff schema.prisma litellm/proxy/schema.prisma && echo "proxy schema in sync" || echo "MISMATCH" +diff schema.prisma litellm-proxy-extras/litellm_proxy_extras/schema.prisma && echo "extras schema in sync" || echo "MISMATCH" +``` + +> **Do NOT proceed to migration generation until all schema files are identical.** + +## Step 1: Quick Start — Generate Migration ```bash # Install deps (one time) @@ -43,8 +71,13 @@ rm -rf litellm-proxy-extras/litellm_proxy_extras/migrations/[empty_dir] ## Rules -- Update `schema.prisma` first +- Sync all `schema.prisma` files first (Step 0) +- Update `schema.prisma` at the repo root first, then sync copies - Review generated SQL before committing - Use descriptive migration names - Never edit existing migration files - Commit schema + migration together + +--- + +**Done with migration?** See [build_and_publish.md](./build_and_publish.md) to publish a new `litellm-proxy-extras` package. diff --git a/litellm-proxy-extras/pyproject.toml b/litellm-proxy-extras/pyproject.toml index ceeb8c8bb4..3f5581408a 100644 --- a/litellm-proxy-extras/pyproject.toml +++ b/litellm-proxy-extras/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "litellm-proxy-extras" -version = "0.4.35" +version = "0.4.46" description = "Additional files for the LiteLLM Proxy. Reduces the size of the main litellm package." authors = ["BerriAI"] readme = "README.md" @@ -22,7 +22,7 @@ requires = ["poetry-core"] build-backend = "poetry.core.masonry.api" [tool.commitizen] -version = "0.4.35" +version = "0.4.46" version_files = [ "pyproject.toml:version", "../requirements.txt:litellm-proxy-extras==", diff --git a/litellm/__init__.py b/litellm/__init__.py index 0fdbac63fe..a994db85b1 100644 --- a/litellm/__init__.py +++ b/litellm/__init__.py @@ -338,6 +338,10 @@ model_cost_map_url: str = os.getenv( "LITELLM_MODEL_COST_MAP_URL", "https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json", ) +anthropic_beta_headers_url: str = os.getenv( + "LITELLM_ANTHROPIC_BETA_HEADERS_URL", + "https://raw.githubusercontent.com/BerriAI/litellm/main/litellm/anthropic_beta_headers_config.json", +) suppress_debug_info = False dynamodb_table_name: Optional[str] = None s3_callback_params: Optional[Dict] = None @@ -1148,6 +1152,28 @@ from .skills.main import ( delete_skill, adelete_skill, ) +from .evals.main import ( + create_eval, + acreate_eval, + list_evals, + alist_evals, + get_eval, + aget_eval, + delete_eval, + adelete_eval, + cancel_eval, + acancel_eval, + create_run, + acreate_run, + list_runs, + alist_runs, + get_run, + aget_run, + delete_run, + adelete_run, + cancel_run, + acancel_run, +) from .integrations import * from .llms.custom_httpx.async_client_cleanup import close_litellm_async_clients from .exceptions import ( @@ -1329,6 +1355,7 @@ if TYPE_CHECKING: from .llms.vertex_ai.rerank.transformation import VertexAIRerankConfig as VertexAIRerankConfig from .llms.fireworks_ai.rerank.transformation import FireworksAIRerankConfig as FireworksAIRerankConfig from .llms.voyage.rerank.transformation import VoyageRerankConfig as VoyageRerankConfig + from .llms.watsonx.rerank.transformation import IBMWatsonXRerankConfig as IBMWatsonXRerankConfig from .llms.clarifai.chat.transformation import ClarifaiConfig as ClarifaiConfig from .llms.ai21.chat.transformation import AI21ChatConfig as AI21ChatConfig from .llms.meta_llama.chat.transformation import LlamaAPIConfig as LlamaAPIConfig @@ -1396,6 +1423,7 @@ if TYPE_CHECKING: from .llms.volcengine.responses.transformation import VolcEngineResponsesAPIConfig as VolcEngineResponsesAPIConfig from .llms.manus.responses.transformation import ManusResponsesAPIConfig as ManusResponsesAPIConfig from .llms.perplexity.responses.transformation import PerplexityResponsesConfig as PerplexityResponsesConfig + from .llms.databricks.responses.transformation import DatabricksResponsesAPIConfig as DatabricksResponsesAPIConfig from .llms.gemini.interactions.transformation import GoogleAIStudioInteractionsConfig as GoogleAIStudioInteractionsConfig from .llms.openai.chat.o_series_transformation import OpenAIOSeriesConfig as OpenAIOSeriesConfig, OpenAIOSeriesConfig as OpenAIO1Config from .llms.anthropic.skills.transformation import AnthropicSkillsConfig as AnthropicSkillsConfig @@ -1728,6 +1756,37 @@ def __getattr__(name: str) -> Any: _globals["_service_logger"] = litellm._service_logger return _globals["_service_logger"] + # Lazy load evals module functions + if name in ["acreate_eval", "alist_evals", "aget_eval", "aupdate_eval", "adelete_eval", "acancel_eval", + "create_eval", "list_evals", "get_eval", "update_eval", "delete_eval", "cancel_eval", + "acreate_run", "alist_runs", "aget_run", "acancel_run", "adelete_run", + "create_run", "list_runs", "get_run", "cancel_run", "delete_run"]: + from litellm.evals.main import ( + acreate_eval, + alist_evals, + aget_eval, + aupdate_eval, + adelete_eval, + acancel_eval, + create_eval, + list_evals, + get_eval, + update_eval, + delete_eval, + cancel_eval, + acreate_run, + alist_runs, + aget_run, + acancel_run, + adelete_run, + create_run, + list_runs, + get_run, + cancel_run, + delete_run, + ) + return locals()[name] + raise AttributeError(f"module {__name__!r} has no attribute {name!r}") diff --git a/litellm/_lazy_imports_registry.py b/litellm/_lazy_imports_registry.py index ebe9af9d85..943acc6320 100644 --- a/litellm/_lazy_imports_registry.py +++ b/litellm/_lazy_imports_registry.py @@ -155,6 +155,7 @@ LLM_CONFIG_NAMES = ( "VertexAIRerankConfig", "FireworksAIRerankConfig", "VoyageRerankConfig", + "IBMWatsonXRerankConfig", "ClarifaiConfig", "AI21ChatConfig", "LlamaAPIConfig", @@ -227,6 +228,7 @@ LLM_CONFIG_NAMES = ( "LiteLLMProxyResponsesAPIConfig", "VolcEngineResponsesAPIConfig", "PerplexityResponsesConfig", + "DatabricksResponsesAPIConfig", "GoogleAIStudioInteractionsConfig", "OpenAIOSeriesConfig", "AnthropicSkillsConfig", @@ -275,7 +277,6 @@ LLM_CONFIG_NAMES = ( "LmStudioEmbeddingConfig", "NscaleConfig", "PerplexityChatConfig", - "PerplexityResponsesConfig", "AzureOpenAIO1Config", "IBMWatsonXAIConfig", "IBMWatsonXChatConfig", @@ -672,6 +673,7 @@ _LLM_CONFIGS_IMPORT_MAP = { "FireworksAIRerankConfig", ), "VoyageRerankConfig": (".llms.voyage.rerank.transformation", "VoyageRerankConfig"), + "IBMWatsonXRerankConfig": (".llms.watsonx.rerank.transformation", "IBMWatsonXRerankConfig"), "ClarifaiConfig": (".llms.clarifai.chat.transformation", "ClarifaiConfig"), "AI21ChatConfig": (".llms.ai21.chat.transformation", "AI21ChatConfig"), "LlamaAPIConfig": (".llms.meta_llama.chat.transformation", "LlamaAPIConfig"), @@ -907,6 +909,10 @@ _LLM_CONFIGS_IMPORT_MAP = { ".llms.perplexity.responses.transformation", "PerplexityResponsesConfig", ), + "DatabricksResponsesAPIConfig": ( + ".llms.databricks.responses.transformation", + "DatabricksResponsesAPIConfig", + ), "GoogleAIStudioInteractionsConfig": ( ".llms.gemini.interactions.transformation", "GoogleAIStudioInteractionsConfig", diff --git a/litellm/_redis.py b/litellm/_redis.py index a86ebd9ea9..c61582abd1 100644 --- a/litellm/_redis.py +++ b/litellm/_redis.py @@ -381,6 +381,8 @@ def get_redis_async_client( ) -> Union[async_redis.Redis, async_redis.RedisCluster]: redis_kwargs = _get_redis_client_logic(**env_overrides) if "url" in redis_kwargs and redis_kwargs["url"] is not None: + if connection_pool is not None: + return async_redis.Redis(connection_pool=connection_pool) args = _get_redis_url_kwargs(client=async_redis.Redis.from_url) url_kwargs = {} for arg in redis_kwargs: @@ -461,9 +463,16 @@ def get_redis_connection_pool(**env_overrides): redis_kwargs = _get_redis_client_logic(**env_overrides) verbose_logger.debug("get_redis_connection_pool: redis_kwargs", redis_kwargs) if "url" in redis_kwargs and redis_kwargs["url"] is not None: - return async_redis.BlockingConnectionPool.from_url( - timeout=REDIS_CONNECTION_POOL_TIMEOUT, url=redis_kwargs["url"] - ) + pool_kwargs = {"timeout": REDIS_CONNECTION_POOL_TIMEOUT, "url": redis_kwargs["url"]} + if "max_connections" in redis_kwargs: + try: + pool_kwargs["max_connections"] = int(redis_kwargs["max_connections"]) + except (TypeError, ValueError): + verbose_logger.warning( + "REDIS: invalid max_connections value %r, ignoring", + redis_kwargs["max_connections"], + ) + return async_redis.BlockingConnectionPool.from_url(**pool_kwargs) connection_class = async_redis.Connection if "ssl" in redis_kwargs: connection_class = async_redis.SSLConnection diff --git a/litellm/_service_logger.py b/litellm/_service_logger.py index b67d0d8606..8f9a3c5083 100644 --- a/litellm/_service_logger.py +++ b/litellm/_service_logger.py @@ -312,10 +312,12 @@ class ServiceLogging(CustomLogger): _duration, type(_duration) ) ) # invalid _duration value + # Batch polling callbacks (check_batch_cost) don't include call_type in kwargs. + # Use .get() to avoid KeyError. await self.async_service_success_hook( service=ServiceTypes.LITELLM, duration=_duration, - call_type=kwargs["call_type"], + call_type=kwargs.get("call_type", "unknown") ) except Exception as e: raise e diff --git a/litellm/anthropic_beta_headers_config.json b/litellm/anthropic_beta_headers_config.json index 5edb8067a0..9b79a38214 100644 --- a/litellm/anthropic_beta_headers_config.json +++ b/litellm/anthropic_beta_headers_config.json @@ -2,8 +2,8 @@ "description": "Mapping of Anthropic beta headers for each provider. Keys are input header names, values are provider-specific header names (or null if unsupported). Only headers present in mapping keys with non-null values can be forwarded.", "anthropic": { "advanced-tool-use-2025-11-20": "advanced-tool-use-2025-11-20", - "bash_20241022": "bash_20241022", - "bash_20250124": "bash_20250124", + "bash_20241022": null, + "bash_20250124": null, "code-execution-2025-08-25": "code-execution-2025-08-25", "compact-2026-01-12": "compact-2026-01-12", "computer-use-2025-01-24": "computer-use-2025-01-24", @@ -13,26 +13,27 @@ "effort-2025-11-24": "effort-2025-11-24", "fast-mode-2026-02-01": "fast-mode-2026-02-01", "files-api-2025-04-14": "files-api-2025-04-14", - "structured-output-2024-03-01": "structured-output-2024-03-01", + "structured-output-2024-03-01": null, "fine-grained-tool-streaming-2025-05-14": "fine-grained-tool-streaming-2025-05-14", "interleaved-thinking-2025-05-14": "interleaved-thinking-2025-05-14", "mcp-client-2025-11-20": "mcp-client-2025-11-20", "mcp-client-2025-04-04": "mcp-client-2025-04-04", - "mcp-servers-2025-12-04": "mcp-servers-2025-12-04", + "mcp-servers-2025-12-04": null, + "oauth-2025-04-20": "oauth-2025-04-20", "output-128k-2025-02-19": "output-128k-2025-02-19", "prompt-caching-scope-2026-01-05": "prompt-caching-scope-2026-01-05", "skills-2025-10-02": "skills-2025-10-02", "structured-outputs-2025-11-13": "structured-outputs-2025-11-13", - "text_editor_20241022": "text_editor_20241022", - "text_editor_20250124": "text_editor_20250124", + "text_editor_20241022": null, + "text_editor_20250124": null, "token-efficient-tools-2025-02-19": "token-efficient-tools-2025-02-19", "web-fetch-2025-09-10": "web-fetch-2025-09-10", "web-search-2025-03-05": "web-search-2025-03-05" }, "azure_ai": { "advanced-tool-use-2025-11-20": "advanced-tool-use-2025-11-20", - "bash_20241022": "bash_20241022", - "bash_20250124": "bash_20250124", + "bash_20241022": null, + "bash_20250124": null, "code-execution-2025-08-25": "code-execution-2025-08-25", "compact-2026-01-12": null, "computer-use-2025-01-24": "computer-use-2025-01-24", @@ -46,7 +47,7 @@ "interleaved-thinking-2025-05-14": "interleaved-thinking-2025-05-14", "mcp-client-2025-11-20": "mcp-client-2025-11-20", "mcp-client-2025-04-04": "mcp-client-2025-04-04", - "mcp-servers-2025-12-04": "mcp-servers-2025-12-04", + "mcp-servers-2025-12-04": null, "output-128k-2025-02-19": null, "structured-output-2024-03-01": null, "prompt-caching-scope-2026-01-05": "prompt-caching-scope-2026-01-05", @@ -59,14 +60,14 @@ "web-search-2025-03-05": "web-search-2025-03-05" }, "bedrock_converse": { - "advanced-tool-use-2025-11-20": "tool-search-tool-2025-10-19", + "advanced-tool-use-2025-11-20": null, "bash_20241022": null, "bash_20250124": null, "code-execution-2025-08-25": null, "compact-2026-01-12": null, "computer-use-2025-01-24": "computer-use-2025-01-24", "computer-use-2025-11-24": "computer-use-2025-11-24", - "context-1m-2025-08-07": null, + "context-1m-2025-08-07": "context-1m-2025-08-07", "context-management-2025-06-27": "context-management-2025-06-27", "effort-2025-11-24": null, "fast-mode-2026-02-01": null, @@ -84,7 +85,7 @@ "text_editor_20241022": null, "text_editor_20250124": null, "token-efficient-tools-2025-02-19": null, - "tool-search-tool-2025-10-19": "tool-search-tool-2025-10-19", + "tool-search-tool-2025-10-19": null, "web-fetch-2025-09-10": null, "web-search-2025-03-05": null }, @@ -147,5 +148,35 @@ "tool-search-tool-2025-10-19": "tool-search-tool-2025-10-19", "web-fetch-2025-09-10": null, "web-search-2025-03-05": "web-search-2025-03-05" + }, + "databricks": { + "advanced-tool-use-2025-11-20": "advanced-tool-use-2025-11-20", + "bash_20241022": null, + "bash_20250124": null, + "code-execution-2025-08-25": "code-execution-2025-08-25", + "compact-2026-01-12": "compact-2026-01-12", + "computer-use-2025-01-24": "computer-use-2025-01-24", + "computer-use-2025-11-24": "computer-use-2025-11-24", + "context-1m-2025-08-07": "context-1m-2025-08-07", + "context-management-2025-06-27": "context-management-2025-06-27", + "effort-2025-11-24": "effort-2025-11-24", + "fast-mode-2026-02-01": "fast-mode-2026-02-01", + "files-api-2025-04-14": "files-api-2025-04-14", + "structured-output-2024-03-01": null, + "fine-grained-tool-streaming-2025-05-14": "fine-grained-tool-streaming-2025-05-14", + "interleaved-thinking-2025-05-14": "interleaved-thinking-2025-05-14", + "mcp-client-2025-11-20": "mcp-client-2025-11-20", + "mcp-client-2025-04-04": "mcp-client-2025-04-04", + "mcp-servers-2025-12-04": null, + "oauth-2025-04-20": "oauth-2025-04-20", + "output-128k-2025-02-19": "output-128k-2025-02-19", + "prompt-caching-scope-2026-01-05": "prompt-caching-scope-2026-01-05", + "skills-2025-10-02": "skills-2025-10-02", + "structured-outputs-2025-11-13": "structured-outputs-2025-11-13", + "text_editor_20241022": null, + "text_editor_20250124": null, + "token-efficient-tools-2025-02-19": "token-efficient-tools-2025-02-19", + "web-fetch-2025-09-10": "web-fetch-2025-09-10", + "web-search-2025-03-05": "web-search-2025-03-05" } } \ No newline at end of file diff --git a/litellm/anthropic_beta_headers_manager.py b/litellm/anthropic_beta_headers_manager.py index 9730ae0269..24df6296b9 100644 --- a/litellm/anthropic_beta_headers_manager.py +++ b/litellm/anthropic_beta_headers_manager.py @@ -5,28 +5,167 @@ This module provides utilities to: 1. Load beta header configuration from JSON (mapping of supported headers per provider) 2. Filter and map beta headers based on provider support 3. Handle provider-specific header name mappings (e.g., advanced-tool-use -> tool-search-tool) +4. Support remote fetching and caching similar to model cost map Design: - JSON config contains mapping of beta headers for each provider - Keys are input header names, values are provider-specific header names (or null if unsupported) - Only headers present in mapping keys with non-null values can be forwarded - This enforces stricter validation than the previous unsupported list approach + +Configuration can be loaded from: +- Remote URL (default): Fetches from GitHub repository +- Local file: Set LITELLM_LOCAL_ANTHROPIC_BETA_HEADERS=True to use bundled config only + +Environment Variables: +- LITELLM_LOCAL_ANTHROPIC_BETA_HEADERS: Set to "True" to disable remote fetching +- LITELLM_ANTHROPIC_BETA_HEADERS_URL: Custom URL for remote config (optional) """ import json import os +from importlib.resources import files from typing import Dict, List, Optional, Set +import httpx + from litellm.litellm_core_utils.litellm_logging import verbose_logger # Cache for the loaded configuration _BETA_HEADERS_CONFIG: Optional[Dict] = None +class GetAnthropicBetaHeadersConfig: + """ + Handles fetching, validating, and loading the Anthropic beta headers configuration. + + Similar to GetModelCostMap, this class manages the lifecycle of the beta headers + configuration with support for remote fetching and local fallback. + """ + + @staticmethod + def load_local_beta_headers_config() -> Dict: + """Load the local backup beta headers config bundled with the package.""" + try: + content = json.loads( + files("litellm") + .joinpath("anthropic_beta_headers_config.json") + .read_text(encoding="utf-8") + ) + return content + except Exception as e: + verbose_logger.error(f"Failed to load local beta headers config: {e}") + # Return empty config as fallback + return { + "anthropic": {}, + "azure_ai": {}, + "bedrock": {}, + "bedrock_converse": {}, + "vertex_ai": {}, + "provider_aliases": {} + } + + @staticmethod + def _check_is_valid_dict(fetched_config: dict) -> bool: + """Check if fetched config is a non-empty dict with expected structure.""" + if not isinstance(fetched_config, dict): + verbose_logger.warning( + "LiteLLM: Fetched beta headers config is not a dict (type=%s). " + "Falling back to local backup.", + type(fetched_config).__name__, + ) + return False + + if len(fetched_config) == 0: + verbose_logger.warning( + "LiteLLM: Fetched beta headers config is empty. " + "Falling back to local backup.", + ) + return False + + # Check for at least one provider key + provider_keys = ["anthropic", "azure_ai", "bedrock", "bedrock_converse", "vertex_ai"] + has_provider = any(key in fetched_config for key in provider_keys) + + if not has_provider: + verbose_logger.warning( + "LiteLLM: Fetched beta headers config missing provider keys. " + "Falling back to local backup.", + ) + return False + + return True + + @classmethod + def validate_beta_headers_config(cls, fetched_config: dict) -> bool: + """ + Validate the integrity of a fetched beta headers config. + + Returns True if all checks pass, False otherwise. + """ + return cls._check_is_valid_dict(fetched_config) + + @staticmethod + def fetch_remote_beta_headers_config(url: str, timeout: int = 5) -> dict: + """ + Fetch the beta headers config from a remote URL. + + Returns the parsed JSON dict. Raises on network/parse errors + (caller is expected to handle). + """ + response = httpx.get(url, timeout=timeout) + response.raise_for_status() + return response.json() + + +def get_beta_headers_config(url: str) -> dict: + """ + Public entry point — returns the beta headers config dict. + + 1. If ``LITELLM_LOCAL_ANTHROPIC_BETA_HEADERS`` is set, uses the local backup only. + 2. Otherwise fetches from ``url``, validates integrity, and falls back + to the local backup on any failure. + + Args: + url: URL to fetch the remote beta headers configuration from + + Returns: + Dict containing the beta headers configuration + """ + # Check if local-only mode is enabled + if os.getenv("LITELLM_LOCAL_ANTHROPIC_BETA_HEADERS", "").lower() == "true": + # verbose_logger.debug("Using local Anthropic beta headers config (LITELLM_LOCAL_ANTHROPIC_BETA_HEADERS=True)") + return GetAnthropicBetaHeadersConfig.load_local_beta_headers_config() + + try: + content = GetAnthropicBetaHeadersConfig.fetch_remote_beta_headers_config(url) + except Exception as e: + verbose_logger.warning( + "LiteLLM: Failed to fetch remote beta headers config from %s: %s. " + "Falling back to local backup.", + url, + str(e), + ) + return GetAnthropicBetaHeadersConfig.load_local_beta_headers_config() + + # Validate the fetched config + if not GetAnthropicBetaHeadersConfig.validate_beta_headers_config(fetched_config=content): + verbose_logger.warning( + "LiteLLM: Fetched beta headers config failed integrity check. " + "Using local backup instead. url=%s", + url, + ) + return GetAnthropicBetaHeadersConfig.load_local_beta_headers_config() + + return content + + def _load_beta_headers_config() -> Dict: """ - Load the beta headers configuration from JSON file. - Uses caching to avoid repeated file reads. + Load the beta headers configuration. + Uses caching to avoid repeated fetches/file reads. + + This function is called by all public API functions and manages the global cache. Returns: Dict containing the beta headers configuration @@ -36,26 +175,27 @@ def _load_beta_headers_config() -> Dict: if _BETA_HEADERS_CONFIG is not None: return _BETA_HEADERS_CONFIG - config_path = os.path.join( - os.path.dirname(__file__), - "anthropic_beta_headers_config.json" - ) + # Get the URL from environment or use default + from litellm import anthropic_beta_headers_url - try: - with open(config_path, "r") as f: - _BETA_HEADERS_CONFIG = json.load(f) - verbose_logger.debug(f"Loaded beta headers config from {config_path}") - return _BETA_HEADERS_CONFIG - except Exception as e: - verbose_logger.error(f"Failed to load beta headers config: {e}") - # Return empty config as fallback (empty mappings) - return { - "anthropic": {}, - "azure_ai": {}, - "bedrock": {}, - "bedrock_converse": {}, - "vertex_ai": {} - } + _BETA_HEADERS_CONFIG = get_beta_headers_config(url=anthropic_beta_headers_url) + verbose_logger.debug("Loaded and cached beta headers config") + + return _BETA_HEADERS_CONFIG + + +def reload_beta_headers_config() -> Dict: + """ + Force reload the beta headers configuration from source (remote or local). + Clears the cache and fetches fresh configuration. + + Returns: + Dict containing the newly loaded beta headers configuration + """ + global _BETA_HEADERS_CONFIG + _BETA_HEADERS_CONFIG = None + verbose_logger.info("Reloading beta headers config (cache cleared)") + return _load_beta_headers_config() def get_provider_name(provider: str) -> str: diff --git a/litellm/batches/batch_utils.py b/litellm/batches/batch_utils.py index f80eae20f3..29bd99c2a6 100644 --- a/litellm/batches/batch_utils.py +++ b/litellm/batches/batch_utils.py @@ -8,7 +8,7 @@ import litellm from litellm._logging import verbose_logger from litellm._uuid import uuid from litellm.types.llms.openai import Batch -from litellm.types.utils import CallTypes, ModelResponse, Usage +from litellm.types.utils import CallTypes, ModelInfo, ModelResponse, Usage from litellm.utils import token_counter @@ -16,14 +16,22 @@ async def calculate_batch_cost_and_usage( file_content_dictionary: List[dict], custom_llm_provider: Literal["openai", "azure", "vertex_ai", "hosted_vllm", "anthropic"], model_name: Optional[str] = None, + model_info: Optional[ModelInfo] = None, ) -> Tuple[float, Usage, List[str]]: """ - Calculate the cost and usage of a batch + Calculate the cost and usage of a batch. + + Args: + model_info: Optional deployment-level model info with custom batch + pricing. Threaded through to batch_cost_calculator so that + deployment-specific pricing (e.g. input_cost_per_token_batches) + is used instead of the global cost map. """ batch_cost = _batch_cost_calculator( custom_llm_provider=custom_llm_provider, file_content_dictionary=file_content_dictionary, model_name=model_name, + model_info=model_info, ) batch_usage = _get_batch_job_total_usage_from_file_content( file_content_dictionary=file_content_dictionary, @@ -39,11 +47,19 @@ async def _handle_completed_batch( batch: Batch, custom_llm_provider: Literal["openai", "azure", "vertex_ai", "hosted_vllm", "anthropic"], model_name: Optional[str] = None, + litellm_params: Optional[dict] = None, ) -> Tuple[float, Usage, List[str]]: - """Helper function to process a completed batch and handle logging""" + """Helper function to process a completed batch and handle logging + + Args: + batch: The batch object + custom_llm_provider: The LLM provider + model_name: Optional model name + litellm_params: Optional litellm parameters containing credentials (api_key, api_base, etc.) + """ # Get batch results file_content_dictionary = await _get_batch_output_file_content_as_dictionary( - batch, custom_llm_provider + batch, custom_llm_provider, litellm_params=litellm_params ) # Calculate costs and usage @@ -86,6 +102,7 @@ def _batch_cost_calculator( file_content_dictionary: List[dict], custom_llm_provider: Literal["openai", "azure", "vertex_ai", "hosted_vllm", "anthropic"] = "openai", model_name: Optional[str] = None, + model_info: Optional[ModelInfo] = None, ) -> float: """ Calculate the cost of a batch based on the output file id @@ -100,6 +117,7 @@ def _batch_cost_calculator( total_cost = _get_batch_job_cost_from_file_content( file_content_dictionary=file_content_dictionary, custom_llm_provider=custom_llm_provider, + model_info=model_info, ) verbose_logger.debug("total_cost=%s", total_cost) return total_cost @@ -187,9 +205,16 @@ def calculate_vertex_ai_batch_cost_and_usage( async def _get_batch_output_file_content_as_dictionary( batch: Batch, custom_llm_provider: Literal["openai", "azure", "vertex_ai", "hosted_vllm", "anthropic"] = "openai", + litellm_params: Optional[dict] = None, ) -> List[dict]: """ Get the batch output file content as a list of dictionaries + + Args: + batch: The batch object + custom_llm_provider: The LLM provider + litellm_params: Optional litellm parameters containing credentials (api_key, api_base, etc.) + Required for Azure and other providers that need authentication """ from litellm.files.main import afile_content from litellm.proxy.openai_files_endpoints.common_utils import ( @@ -211,13 +236,50 @@ async def _get_batch_output_file_content_as_dictionary( except (IndexError, AttributeError) as e: verbose_logger.error(f"Failed to extract LLM output file ID from unified file ID: {batch.output_file_id}, error: {e}") - _file_content = await afile_content( - file_id=file_id, - custom_llm_provider=custom_llm_provider, - ) + # Build kwargs for afile_content with credentials from litellm_params + file_content_kwargs = { + "file_id": file_id, + "custom_llm_provider": custom_llm_provider, + } + + # Extract and add credentials for file access + credentials = _extract_file_access_credentials(litellm_params) + file_content_kwargs.update(credentials) + + _file_content = await afile_content(**file_content_kwargs) return _get_file_content_as_dictionary(_file_content.content) +def _extract_file_access_credentials(litellm_params: Optional[dict]) -> dict: + """ + Extract credentials from litellm_params for file access operations. + + This method extracts relevant authentication and configuration parameters + needed for accessing files across different providers (Azure, Vertex AI, etc.). + + Args: + litellm_params: Dictionary containing litellm parameters with credentials + + Returns: + Dictionary containing only the credentials needed for file access + """ + credentials = {} + + if litellm_params: + # List of credential keys that should be passed to file operations + credential_keys = [ + "api_key", "api_base", "api_version", "organization", + "azure_ad_token", "azure_ad_token_provider", + "vertex_project", "vertex_location", "vertex_credentials", + "timeout", "max_retries" + ] + for key in credential_keys: + if key in litellm_params: + credentials[key] = litellm_params[key] + + return credentials + + def _get_file_content_as_dictionary(file_content: bytes) -> List[dict]: """ Get the file content as a list of dictionaries from JSON Lines format @@ -238,10 +300,13 @@ def _get_file_content_as_dictionary(file_content: bytes) -> List[dict]: def _get_batch_job_cost_from_file_content( file_content_dictionary: List[dict], custom_llm_provider: Literal["openai", "azure", "vertex_ai", "hosted_vllm", "anthropic"] = "openai", + model_info: Optional[ModelInfo] = None, ) -> float: """ Get the cost of a batch job from the file content """ + from litellm.cost_calculator import batch_cost_calculator + try: total_cost: float = 0.0 # parse the file content as json @@ -251,11 +316,22 @@ def _get_batch_job_cost_from_file_content( for _item in file_content_dictionary: if _batch_response_was_successful(_item): _response_body = _get_response_from_batch_job_output_file(_item) - total_cost += litellm.completion_cost( - completion_response=_response_body, - custom_llm_provider=custom_llm_provider, - call_type=CallTypes.aretrieve_batch.value, - ) + if model_info is not None: + usage = _get_batch_job_usage_from_response_body(_response_body) + model = _response_body.get("model", "") + prompt_cost, completion_cost = batch_cost_calculator( + usage=usage, + model=model, + custom_llm_provider=custom_llm_provider, + model_info=model_info, + ) + total_cost += prompt_cost + completion_cost + else: + total_cost += litellm.completion_cost( + completion_response=_response_body, + custom_llm_provider=custom_llm_provider, + call_type=CallTypes.aretrieve_batch.value, + ) verbose_logger.debug("total_cost=%s", total_cost) return total_cost except Exception as e: diff --git a/litellm/caching/dual_cache.py b/litellm/caching/dual_cache.py index 3edc3f4282..6df570c72b 100644 --- a/litellm/caching/dual_cache.py +++ b/litellm/caching/dual_cache.py @@ -12,7 +12,8 @@ import asyncio import time import traceback from concurrent.futures import ThreadPoolExecutor -from typing import TYPE_CHECKING, Any, List, Optional, Union +from threading import Lock +from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union if TYPE_CHECKING: from litellm.types.caching import RedisPipelineIncrementOperation @@ -71,6 +72,7 @@ class DualCache(BaseCache): self.last_redis_batch_access_time = LimitedSizeOrderedDict( max_size=default_max_redis_batch_cache_size ) + self._last_redis_batch_access_time_lock = Lock() self.redis_batch_cache_expiry = ( default_redis_batch_cache_expiry or litellm.default_redis_batch_cache_expiry @@ -236,22 +238,46 @@ class DualCache(BaseCache): except Exception: verbose_logger.error(traceback.format_exc()) - def get_redis_batch_keys( + def _reserve_redis_batch_keys( self, current_time: float, keys: List[str], result: List[Any], - ) -> List[str]: - sublist_keys = [] - for key, value in zip(keys, result): - if value is None: + ) -> Tuple[List[str], Dict[str, Optional[float]]]: + """ + Atomically choose keys to fetch from Redis and reserve their access time. + This prevents check-then-act races under concurrent async callers. + """ + sublist_keys: List[str] = [] + previous_access_times: Dict[str, Optional[float]] = {} + + with self._last_redis_batch_access_time_lock: + for key, value in zip(keys, result): + if value is not None: + continue + if ( key not in self.last_redis_batch_access_time or current_time - self.last_redis_batch_access_time[key] >= self.redis_batch_cache_expiry ): sublist_keys.append(key) - return sublist_keys + previous_access_times[key] = self.last_redis_batch_access_time.get( + key + ) + self.last_redis_batch_access_time[key] = current_time + + return sublist_keys, previous_access_times + + def _rollback_redis_batch_key_reservations( + self, previous_access_times: Dict[str, Optional[float]] + ) -> None: + with self._last_redis_batch_access_time_lock: + for key, previous_time in previous_access_times.items(): + if previous_time is None: + self.last_redis_batch_access_time.pop(key, None) + else: + self.last_redis_batch_access_time[key] = previous_time async def async_batch_get_cache( self, @@ -276,19 +302,23 @@ class DualCache(BaseCache): - check the redis cache """ current_time = time.time() - sublist_keys = self.get_redis_batch_keys(current_time, keys, result) + sublist_keys, previous_access_times = self._reserve_redis_batch_keys( + current_time, keys, result + ) - # Only hit Redis if the last access time was more than 5 seconds ago + # Only hit Redis if enough time has passed since last access. if len(sublist_keys) > 0: - # If not found in in-memory cache, try fetching from Redis - redis_result = await self.redis_cache.async_batch_get_cache( - sublist_keys, parent_otel_span=parent_otel_span - ) - - # Update the last access time for ALL queried keys - # This includes keys with None values to throttle repeated Redis queries - for key in sublist_keys: - self.last_redis_batch_access_time[key] = current_time + try: + # If not found in in-memory cache, try fetching from Redis + redis_result = await self.redis_cache.async_batch_get_cache( + sublist_keys, parent_otel_span=parent_otel_span + ) + except Exception: + # Do not throttle subsequent callers if the Redis read fails. + self._rollback_redis_batch_key_reservations( + previous_access_times + ) + raise # Short-circuit if redis_result is None or contains only None values if redis_result is None or all(v is None for v in redis_result.values()): diff --git a/litellm/caching/llm_caching_handler.py b/litellm/caching/llm_caching_handler.py index 16eb824f4c..5dc16a224c 100644 --- a/litellm/caching/llm_caching_handler.py +++ b/litellm/caching/llm_caching_handler.py @@ -8,6 +8,25 @@ from .in_memory_cache import InMemoryCache class LLMClientCache(InMemoryCache): + def _remove_key(self, key: str) -> None: + """Close async clients before evicting them to prevent connection pool leaks.""" + value = self.cache_dict.get(key) + super()._remove_key(key) + if value is not None: + close_fn = getattr(value, "aclose", None) or getattr( + value, "close", None + ) + if close_fn and asyncio.iscoroutinefunction(close_fn): + try: + asyncio.get_running_loop().create_task(close_fn()) + except RuntimeError: + pass + elif close_fn and callable(close_fn): + try: + close_fn() + except Exception: + pass + def update_cache_key_with_event_loop(self, key): """ Add the event loop to the cache key, to prevent event loop closed errors. diff --git a/litellm/caching/redis_cache.py b/litellm/caching/redis_cache.py index 03d09ecc04..dcc2df5f91 100644 --- a/litellm/caching/redis_cache.py +++ b/litellm/caching/redis_cache.py @@ -1105,6 +1105,10 @@ class RedisCache(BaseCache): async def disconnect(self): await self.async_redis_conn_pool.disconnect(inuse_connections=True) + try: + self.redis_client.close() + except Exception as e: + verbose_logger.debug("Error closing sync Redis client: %s", e) async def test_connection(self) -> dict: """ diff --git a/litellm/completion_extras/litellm_responses_transformation/transformation.py b/litellm/completion_extras/litellm_responses_transformation/transformation.py index b2a5e2fa59..35fc93bbeb 100644 --- a/litellm/completion_extras/litellm_responses_transformation/transformation.py +++ b/litellm/completion_extras/litellm_responses_transformation/transformation.py @@ -62,9 +62,7 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): def __init__(self): pass - def _handle_raw_dict_response_item( - self, item: Dict[str, Any], index: int - ) -> Tuple[Optional[Any], int]: + def _handle_raw_dict_response_item(self, item: Dict[str, Any], index: int) -> Tuple[Optional[Any], int]: """ Handle raw dict response items from Responses API (e.g., GPT-5 Codex format). @@ -107,13 +105,9 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): if item_type == "function_call": # Extract provider_specific_fields if present and pass through as-is provider_specific_fields = item.get("provider_specific_fields") - if provider_specific_fields and not isinstance( - provider_specific_fields, dict - ): + if provider_specific_fields and not isinstance(provider_specific_fields, dict): provider_specific_fields = ( - dict(provider_specific_fields) - if hasattr(provider_specific_fields, "__dict__") - else {} + dict(provider_specific_fields) if hasattr(provider_specific_fields, "__dict__") else {} ) tool_call_dict = { @@ -129,9 +123,7 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): if provider_specific_fields: tool_call_dict["provider_specific_fields"] = provider_specific_fields # Also add to function's provider_specific_fields for consistency - tool_call_dict["function"][ - "provider_specific_fields" - ] = provider_specific_fields + tool_call_dict["function"]["provider_specific_fields"] = provider_specific_fields msg = Message( content=None, @@ -169,7 +161,8 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): "type": "message", "role": role, "content": self._convert_content_to_responses_format( - content, role # type: ignore + content, + role, # type: ignore ), } ) @@ -186,7 +179,8 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): elif isinstance(content, list): # Transform list content to Responses API format tool_output = self._convert_content_to_responses_format( - content, "user" # Use "user" role to get input_* types + content, + "user", # Use "user" role to get input_* types ) else: # Fallback: convert unexpected types to input_text @@ -219,14 +213,90 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): { "type": "message", "role": role, - "content": self._convert_content_to_responses_format( - content, cast(str, role) - ), + "content": self._convert_content_to_responses_format(content, cast(str, role)), } ) return input_items, instructions + def _map_optional_params_to_responses_api_request( + self, + optional_params: dict, + responses_api_request: "ResponsesAPIOptionalRequestParams", + ) -> None: + """Map optional_params into responses_api_request (mutates in place).""" + for key, value in optional_params.items(): + if value is None: + continue + if key in ("max_tokens", "max_completion_tokens"): + responses_api_request["max_output_tokens"] = value + elif key == "tools" and value is not None: + responses_api_request["tools"] = ( + self._convert_tools_to_responses_format( + cast(List[Dict[str, Any]], value) + ) + ) + elif key == "response_format": + text_format = self._transform_response_format_to_text_format(value) + if text_format: + responses_api_request["text"] = text_format # type: ignore + elif key in ResponsesAPIOptionalRequestParams.__annotations__.keys(): + responses_api_request[key] = value # type: ignore + elif key == "previous_response_id": + responses_api_request["previous_response_id"] = value + elif key == "reasoning_effort": + responses_api_request["reasoning"] = self._map_reasoning_effort(value) + elif key == "web_search_options": + self._add_web_search_tool(responses_api_request, value) + + def _build_sanitized_litellm_params( + self, litellm_params: dict + ) -> Dict[str, Any]: + """Build sanitized litellm_params with merged metadata.""" + responses_optional_param_keys = set( + ResponsesAPIOptionalRequestParams.__annotations__.keys() + ) + sanitized: Dict[str, Any] = { + key: value + for key, value in litellm_params.items() + if key not in responses_optional_param_keys + } + legacy_metadata = litellm_params.get("metadata") + existing_litellm_metadata = litellm_params.get("litellm_metadata") + merged_litellm_metadata: Dict[str, Any] = {} + if isinstance(legacy_metadata, dict): + merged_litellm_metadata.update(legacy_metadata) + if isinstance(existing_litellm_metadata, dict): + merged_litellm_metadata.update(existing_litellm_metadata) + if merged_litellm_metadata: + sanitized["litellm_metadata"] = merged_litellm_metadata + else: + sanitized.pop("litellm_metadata", None) + return sanitized + + def _merge_responses_api_request_into_request_data( + self, + request_data: Dict[str, Any], + responses_api_request: "ResponsesAPIOptionalRequestParams", + instructions: Optional[str], + ) -> None: + """Add non-None values from responses_api_request into request_data.""" + for key, value in responses_api_request.items(): + if value is None: + continue + if key == "instructions" and instructions: + request_data["instructions"] = instructions + elif key == "stream_options" and isinstance(value, dict): + request_data["stream_options"] = value.get("include_obfuscation") + elif key == "user" and isinstance(value, str): + # OpenAI API requires user param to be max 64 chars - truncate if longer + if len(value) <= 64: + request_data["user"] = value + else: + request_data["user"] = value[:64] + else: + request_data[key] = value + def transform_request( self, model: str, @@ -251,34 +321,10 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): if instructions: responses_api_request["instructions"] = instructions - # Map optional parameters - for key, value in optional_params.items(): - if value is None: - continue - if key in ("max_tokens", "max_completion_tokens"): - responses_api_request["max_output_tokens"] = value - elif key == "tools" and value is not None: - # Convert chat completion tools to responses API tools format - responses_api_request["tools"] = ( - self._convert_tools_to_responses_format( - cast(List[Dict[str, Any]], value) - ) - ) - elif key == "response_format": - # Convert response_format to text.format - text_format = self._transform_response_format_to_text_format(value) - if text_format: - responses_api_request["text"] = text_format # type: ignore - elif key in ResponsesAPIOptionalRequestParams.__annotations__.keys(): - responses_api_request[key] = value # type: ignore - elif key == "previous_response_id": - responses_api_request["previous_response_id"] = value - elif key == "reasoning_effort": - responses_api_request["reasoning"] = self._map_reasoning_effort(value) - elif key == "web_search_options": - self._add_web_search_tool(responses_api_request, value) + self._map_optional_params_to_responses_api_request( + optional_params, responses_api_request + ) - # Get stream parameter from litellm_params if not in optional_params stream = optional_params.get("stream") or litellm_params.get("stream", False) verbose_logger.debug(f"Chat provider: Stream parameter: {stream}") @@ -290,9 +336,7 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): previous_response_id = optional_params.get("previous_response_id") if previous_response_id: # Use the existing session handler for responses API - verbose_logger.debug( - f"Chat provider: Warning ignoring previous response ID: {previous_response_id}" - ) + verbose_logger.debug(f"Chat provider: Warning ignoring previous response ID: {previous_response_id}") # Convert back to responses API format for the actual request @@ -302,26 +346,9 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): setattr(litellm_logging_obj, "call_type", CallTypes.responses.value) - responses_optional_param_keys = set( - ResponsesAPIOptionalRequestParams.__annotations__.keys() + sanitized_litellm_params = self._build_sanitized_litellm_params( + litellm_params ) - sanitized_litellm_params: Dict[str, Any] = { - key: value - for key, value in litellm_params.items() - if key not in responses_optional_param_keys - } - - legacy_metadata = litellm_params.get("metadata") - existing_litellm_metadata = litellm_params.get("litellm_metadata") - merged_litellm_metadata: Dict[str, Any] = {} - if isinstance(legacy_metadata, dict): - merged_litellm_metadata.update(legacy_metadata) - if isinstance(existing_litellm_metadata, dict): - merged_litellm_metadata.update(existing_litellm_metadata) - if merged_litellm_metadata: - sanitized_litellm_params["litellm_metadata"] = merged_litellm_metadata - else: - sanitized_litellm_params.pop("litellm_metadata", None) request_data = { "model": api_model, @@ -331,22 +358,11 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): "client": client, } - verbose_logger.debug( - f"Chat provider: Final request model={api_model}, input_items={len(input_items)}" - ) + verbose_logger.debug(f"Chat provider: Final request model={api_model}, input_items={len(input_items)}") - # Add non-None values from responses_api_request - for key, value in responses_api_request.items(): - if value is not None: - if key == "instructions" and instructions: - request_data["instructions"] = instructions - elif key == "stream_options" and isinstance(value, dict): - request_data["stream_options"] = value.get("include_obfuscation") - elif key == "user": # string can't be longer than 64 characters - if isinstance(value, str) and len(value) <= 64: - request_data["user"] = value - else: - request_data[key] = value + self._merge_responses_api_request_into_request_data( + request_data, responses_api_request, instructions + ) if headers: request_data["extra_headers"] = headers @@ -422,9 +438,11 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): LiteLLMCompletionResponsesConfig, ) - tool_call_dict = LiteLLMCompletionResponsesConfig.convert_response_function_tool_call_to_chat_completion_tool_call( - tool_call_item=item, - index=tool_call_index, + tool_call_dict = ( + LiteLLMCompletionResponsesConfig.convert_response_function_tool_call_to_chat_completion_tool_call( + tool_call_item=item, + index=tool_call_index, + ) ) accumulated_tool_calls.append(tool_call_dict) tool_call_index += 1 @@ -444,9 +462,7 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): tool_calls=accumulated_tool_calls, reasoning_content=reasoning_content, ) - choices.append( - Choices(message=msg, finish_reason="tool_calls", index=index) - ) + choices.append(Choices(message=msg, finish_reason="tool_calls", index=index)) reasoning_content = None return choices @@ -482,17 +498,10 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): ) if len(choices) == 0: - if ( - raw_response.incomplete_details is not None - and raw_response.incomplete_details.reason is not None - ): - raise ValueError( - f"{model} unable to complete request: {raw_response.incomplete_details.reason}" - ) + if raw_response.incomplete_details is not None and raw_response.incomplete_details.reason is not None: + raise ValueError(f"{model} unable to complete request: {raw_response.incomplete_details.reason}") else: - raise ValueError( - f"Unknown items in responses API response: {raw_response.output}" - ) + raise ValueError(f"Unknown items in responses API response: {raw_response.output}") setattr(model_response, "choices", choices) @@ -501,11 +510,9 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): setattr( model_response, "usage", - ResponseAPILoggingUtils._transform_response_api_usage_to_chat_usage( - raw_response.usage - ), + ResponseAPILoggingUtils._transform_response_api_usage_to_chat_usage(raw_response.usage), ) - + # Preserve hidden params from the ResponsesAPIResponse, especially the headers # which contain important provider information like x-request-id raw_response_hidden_params = getattr(raw_response, "_hidden_params", {}) @@ -522,24 +529,18 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): model_response._hidden_params[key] = merged_headers else: model_response._hidden_params[key] = value - + return model_response def get_model_response_iterator( self, - streaming_response: Union[ - Iterator[str], AsyncIterator[str], "ModelResponse", "BaseModel" - ], + streaming_response: Union[Iterator[str], AsyncIterator[str], "ModelResponse", "BaseModel"], sync_stream: bool, json_mode: Optional[bool] = False, ) -> BaseModelResponseIterator: - return OpenAiResponsesToChatCompletionStreamIterator( - streaming_response, sync_stream, json_mode - ) + return OpenAiResponsesToChatCompletionStreamIterator(streaming_response, sync_stream, json_mode) - def _convert_content_str_to_input_text( - self, content: str, role: str - ) -> Dict[str, Any]: + def _convert_content_str_to_input_text(self, content: str, role: str) -> Dict[str, Any]: if role == "user" or role == "system" or role == "tool": return {"type": "input_text", "text": content} else: @@ -566,9 +567,7 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): if actual_image_url is None: raise ValueError(f"Invalid image URL: {content_image_url}") - image_param = ResponseInputImageParam( - image_url=actual_image_url, detail="auto", type="input_image" - ) + image_param = ResponseInputImageParam(image_url=actual_image_url, detail="auto", type="input_image") if detail: image_param["detail"] = detail @@ -577,31 +576,29 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): def _convert_content_to_responses_format( self, - content: Union[ - str, - Iterable[ - Union["OpenAIMessageContentListBlock", "ChatCompletionThinkingBlock"] - ], + content: Optional[ + Union[ + str, + Iterable[Union["OpenAIMessageContentListBlock", "ChatCompletionThinkingBlock"]], + ] ], role: str, ) -> List[Dict[str, Any]]: """Convert chat completion content to responses API format""" from litellm.types.llms.openai import ChatCompletionImageObject - verbose_logger.debug( - f"Chat provider: Converting content to responses format - input type: {type(content)}" - ) + verbose_logger.debug(f"Chat provider: Converting content to responses format - input type: {type(content)}") - if isinstance(content, str): + if content is None: + return [self._convert_content_str_to_input_text("", role)] + elif isinstance(content, str): result = [self._convert_content_str_to_input_text(content, role)] verbose_logger.debug(f"Chat provider: String content -> {result}") return result elif isinstance(content, list): result = [] for i, item in enumerate(content): - verbose_logger.debug( - f"Chat provider: Processing content item {i}: {type(item)} = {item}" - ) + verbose_logger.debug(f"Chat provider: Processing content item {i}: {type(item)} = {item}") if isinstance(item, str): converted = self._convert_content_str_to_input_text(item, role) result.append(converted) @@ -610,9 +607,7 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): # Handle multimodal content original_type = item.get("type") if original_type == "text": - converted = self._convert_content_str_to_input_text( - item.get("text", ""), role - ) + converted = self._convert_content_str_to_input_text(item.get("text", ""), role) result.append(converted) verbose_logger.debug(f"Chat provider: text -> {converted}") elif original_type == "image_url": @@ -624,18 +619,14 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): ), ) result.append(converted) - verbose_logger.debug( - f"Chat provider: image_url -> {converted}" - ) + verbose_logger.debug(f"Chat provider: image_url -> {converted}") else: # Try to map other types to responses API format item_type = original_type or "input_text" if item_type == "image": converted = {"type": "input_image", **item} result.append(converted) - verbose_logger.debug( - f"Chat provider: image -> {converted}" - ) + verbose_logger.debug(f"Chat provider: image -> {converted}") elif item_type in [ "input_text", "input_image", @@ -647,18 +638,12 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): ]: # Already in responses API format result.append(item) - verbose_logger.debug( - f"Chat provider: passthrough -> {item}" - ) + verbose_logger.debug(f"Chat provider: passthrough -> {item}") else: # Default to input_text for unknown types - converted = self._convert_content_str_to_input_text( - str(item.get("text", item)), role - ) + converted = self._convert_content_str_to_input_text(str(item.get("text", item)), role) result.append(converted) - verbose_logger.debug( - f"Chat provider: unknown({original_type}) -> {converted}" - ) + verbose_logger.debug(f"Chat provider: unknown({original_type}) -> {converted}") verbose_logger.debug(f"Chat provider: Final converted content: {result}") return result else: @@ -666,17 +651,13 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): verbose_logger.debug(f"Chat provider: Other content type -> {result}") return result - def _convert_tools_to_responses_format( - self, tools: List[Dict[str, Any]] - ) -> List["ALL_RESPONSES_API_TOOL_PARAMS"]: + def _convert_tools_to_responses_format(self, tools: List[Dict[str, Any]]) -> List["ALL_RESPONSES_API_TOOL_PARAMS"]: """Convert chat completion tools to responses API tools format""" responses_tools: List["ALL_RESPONSES_API_TOOL_PARAMS"] = [] for tool in tools: # convert function tool from chat completion to responses API format if tool.get("type") == "function": - function_tool = cast( - ChatCompletionToolParamFunctionChunk, tool.get("function") - ) + function_tool = cast(ChatCompletionToolParamFunctionChunk, tool.get("function")) responses_tools.append( FunctionToolParam( name=function_tool["name"], @@ -702,9 +683,7 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): if not extra_body: return optional_params - supported_responses_api_params = set( - ResponsesAPIOptionalRequestParams.__annotations__.keys() - ) + supported_responses_api_params = set(ResponsesAPIOptionalRequestParams.__annotations__.keys()) # Also include params we handle specially supported_responses_api_params.update( { @@ -722,9 +701,7 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): return optional_params - def _map_reasoning_effort( - self, reasoning_effort: Union[str, Dict[str, Any]] - ) -> Optional[Reasoning]: + def _map_reasoning_effort(self, reasoning_effort: Union[str, Dict[str, Any]]) -> Optional[Reasoning]: # If dict is passed, convert it directly to Reasoning object if isinstance(reasoning_effort, dict): return Reasoning(**reasoning_effort) # type: ignore[typeddict-item] @@ -732,8 +709,7 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): # Check if auto-summary is enabled via flag or environment variable # Priority: litellm.reasoning_auto_summary flag > LITELLM_REASONING_AUTO_SUMMARY env var auto_summary_enabled = ( - litellm.reasoning_auto_summary - or os.getenv("LITELLM_REASONING_AUTO_SUMMARY", "false").lower() == "true" + litellm.reasoning_auto_summary or os.getenv("LITELLM_REASONING_AUTO_SUMMARY", "false").lower() == "true" ) # If string is passed, map with optional summary based on flag/env var @@ -744,11 +720,15 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): elif reasoning_effort == "xhigh": return Reasoning(effort="xhigh", summary="detailed") if auto_summary_enabled else Reasoning(effort="xhigh") # type: ignore[typeddict-item] elif reasoning_effort == "medium": - return Reasoning(effort="medium", summary="detailed") if auto_summary_enabled else Reasoning(effort="medium") + return ( + Reasoning(effort="medium", summary="detailed") if auto_summary_enabled else Reasoning(effort="medium") + ) elif reasoning_effort == "low": return Reasoning(effort="low", summary="detailed") if auto_summary_enabled else Reasoning(effort="low") elif reasoning_effort == "minimal": - return Reasoning(effort="minimal", summary="detailed") if auto_summary_enabled else Reasoning(effort="minimal") + return ( + Reasoning(effort="minimal", summary="detailed") if auto_summary_enabled else Reasoning(effort="minimal") + ) return None def _add_web_search_tool( @@ -827,7 +807,7 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): return {"format": {"type": "text"}} return None - + @staticmethod def _convert_annotations_to_chat_format( annotations: Optional[List[Any]], @@ -880,9 +860,7 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): class OpenAiResponsesToChatCompletionStreamIterator(BaseModelResponseIterator): - def __init__( - self, streaming_response, sync_stream: bool, json_mode: Optional[bool] = False - ): + def __init__(self, streaming_response, sync_stream: bool, json_mode: Optional[bool] = False): super().__init__(streaming_response, sync_stream, json_mode) def _handle_string_chunk( @@ -895,9 +873,7 @@ class OpenAiResponsesToChatCompletionStreamIterator(BaseModelResponseIterator): if not str_line or str_line.startswith("event:"): # ignore. - return GenericStreamingChunk( - text="", tool_use=None, is_finished=False, finish_reason="", usage=None - ) + return GenericStreamingChunk(text="", tool_use=None, is_finished=False, finish_reason="", usage=None) index = str_line.find("data:") if index != -1: str_line = str_line[index + 5 :] @@ -960,13 +936,9 @@ class OpenAiResponsesToChatCompletionStreamIterator(BaseModelResponseIterator): if output_item.get("type") == "function_call": # Extract provider_specific_fields if present provider_specific_fields = output_item.get("provider_specific_fields") - if provider_specific_fields and not isinstance( - provider_specific_fields, dict - ): + if provider_specific_fields and not isinstance(provider_specific_fields, dict): provider_specific_fields = ( - dict(provider_specific_fields) - if hasattr(provider_specific_fields, "__dict__") - else {} + dict(provider_specific_fields) if hasattr(provider_specific_fields, "__dict__") else {} ) function_chunk = ChatCompletionToolCallFunctionChunk( @@ -975,9 +947,7 @@ class OpenAiResponsesToChatCompletionStreamIterator(BaseModelResponseIterator): ) if provider_specific_fields: - function_chunk["provider_specific_fields"] = ( - provider_specific_fields - ) + function_chunk["provider_specific_fields"] = provider_specific_fields tool_call_chunk = ChatCompletionToolCallChunk( id=output_item.get("call_id"), @@ -1012,9 +982,7 @@ class OpenAiResponsesToChatCompletionStreamIterator(BaseModelResponseIterator): id=None, index=0, type="function", - function=ChatCompletionToolCallFunctionChunk( - name=None, arguments=content_part - ), + function=ChatCompletionToolCallFunctionChunk(name=None, arguments=content_part), ) ] ), @@ -1023,22 +991,16 @@ class OpenAiResponsesToChatCompletionStreamIterator(BaseModelResponseIterator): ] ) else: - raise ValueError( - f"Chat provider: Invalid function argument delta {parsed_chunk}" - ) + raise ValueError(f"Chat provider: Invalid function argument delta {parsed_chunk}") elif event_type == "response.output_item.done": # New output item added output_item = parsed_chunk.get("item", {}) if output_item.get("type") == "function_call": # Extract provider_specific_fields if present provider_specific_fields = output_item.get("provider_specific_fields") - if provider_specific_fields and not isinstance( - provider_specific_fields, dict - ): + if provider_specific_fields and not isinstance(provider_specific_fields, dict): provider_specific_fields = ( - dict(provider_specific_fields) - if hasattr(provider_specific_fields, "__dict__") - else {} + dict(provider_specific_fields) if hasattr(provider_specific_fields, "__dict__") else {} ) function_chunk = ChatCompletionToolCallFunctionChunk( @@ -1048,9 +1010,7 @@ class OpenAiResponsesToChatCompletionStreamIterator(BaseModelResponseIterator): # Add provider_specific_fields to function if present if provider_specific_fields: - function_chunk["provider_specific_fields"] = ( - provider_specific_fields - ) + function_chunk["provider_specific_fields"] = provider_specific_fields tool_call_chunk = ChatCompletionToolCallChunk( id=output_item.get("call_id"), @@ -1114,21 +1074,31 @@ class OpenAiResponsesToChatCompletionStreamIterator(BaseModelResponseIterator): elif event_type == "response.completed": # Response is fully complete - now we can signal is_finished=True # This ensures we don't prematurely end the stream before tool_calls arrive + + # Check if response contains function_call items in output + # to determine correct finish_reason + response_data = parsed_chunk.get("response", {}) + output_items = response_data.get("output", []) if response_data else [] + + has_function_calls = any( + item.get("type") == "function_call" for item in output_items if isinstance(item, dict) + ) + + finish_reason = "tool_calls" if has_function_calls else "stop" + return ModelResponseStream( choices=[ StreamingChoices( index=0, delta=Delta(content=""), - finish_reason="stop", + finish_reason=finish_reason, ) ] ) else: pass # For any unhandled event types, create a minimal valid chunk or skip - verbose_logger.debug( - f"Chat provider: Unhandled event type '{event_type}', creating empty chunk" - ) + verbose_logger.debug(f"Chat provider: Unhandled event type '{event_type}', creating empty chunk") # Return a minimal valid chunk for unknown events return ModelResponseStream( @@ -1151,9 +1121,5 @@ class OpenAiResponsesToChatCompletionStreamIterator(BaseModelResponseIterator): Returns: ModelResponseStream: OpenAI-formatted streaming chunk """ - verbose_logger.debug( - f"Chat provider: transform_streaming_response called with chunk: {chunk}" - ) - return OpenAiResponsesToChatCompletionStreamIterator.translate_responses_chunk_to_openai_stream( - chunk - ) + verbose_logger.debug(f"Chat provider: transform_streaming_response called with chunk: {chunk}") + return OpenAiResponsesToChatCompletionStreamIterator.translate_responses_chunk_to_openai_stream(chunk) diff --git a/litellm/constants.py b/litellm/constants.py index 88c57d3ce4..89992b459c 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -49,6 +49,19 @@ DEFAULT_REPLICATE_POLLING_DELAY_SECONDS = int( ) DEFAULT_IMAGE_TOKEN_COUNT = int(os.getenv("DEFAULT_IMAGE_TOKEN_COUNT", 250)) +# Maximum number of base64 characters to keep in logging payloads. +# Data URIs exceeding this are replaced with a size placeholder. +# Set to 0 to disable truncation. +MAX_BASE64_LENGTH_FOR_LOGGING = int( + os.getenv("MAX_BASE64_LENGTH_FOR_LOGGING", 64) +) + +# When true, adds detailed per-phase timing breakdown headers to responses. +# Headers: x-litellm-timing-{pre-processing,llm-api,post-processing,message-copy}-ms +LITELLM_DETAILED_TIMING = ( + os.getenv("LITELLM_DETAILED_TIMING", "false").lower() == "true" +) + # Model cost map validation constants MODEL_COST_MAP_MIN_MODEL_COUNT = int( os.getenv("MODEL_COST_MAP_MIN_MODEL_COUNT", 50) @@ -91,6 +104,14 @@ MAX_MCP_SEMANTIC_FILTER_TOOLS_HEADER_LENGTH = int( os.getenv("MAX_MCP_SEMANTIC_FILTER_TOOLS_HEADER_LENGTH", 150) ) +# Semantic Guard Defaults +DEFAULT_SEMANTIC_GUARD_EMBEDDING_MODEL = str( + os.getenv("DEFAULT_SEMANTIC_GUARD_EMBEDDING_MODEL", "text-embedding-3-small") +) +DEFAULT_SEMANTIC_GUARD_SIMILARITY_THRESHOLD = float( + os.getenv("DEFAULT_SEMANTIC_GUARD_SIMILARITY_THRESHOLD", 0.75) +) + # MCP OAuth2 Client Credentials Defaults MCP_OAUTH2_TOKEN_EXPIRY_BUFFER_SECONDS = int( os.getenv("MCP_OAUTH2_TOKEN_EXPIRY_BUFFER_SECONDS", "60") @@ -101,9 +122,12 @@ MCP_OAUTH2_TOKEN_CACHE_MAX_SIZE = int( MCP_OAUTH2_TOKEN_CACHE_DEFAULT_TTL = int( os.getenv("MCP_OAUTH2_TOKEN_CACHE_DEFAULT_TTL", "3600") ) -MCP_OAUTH2_TOKEN_CACHE_MIN_TTL = int( - os.getenv("MCP_OAUTH2_TOKEN_CACHE_MIN_TTL", "10") -) + +# Default npm cache directory for STDIO MCP servers. +# npm/npx needs a writable cache dir; in containers the default (~/.npm) +# may not exist or be read-only. /tmp is always writable. +MCP_NPM_CACHE_DIR = os.getenv("MCP_NPM_CACHE_DIR", "/tmp/.npm_mcp_cache") +MCP_OAUTH2_TOKEN_CACHE_MIN_TTL = int(os.getenv("MCP_OAUTH2_TOKEN_CACHE_MIN_TTL", "10")) LITELLM_UI_ALLOW_HEADERS = [ "x-litellm-semantic-filter", @@ -126,7 +150,7 @@ DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_FLASH_LITE = int( # Maximum number of callbacks that can be registered # This prevents callbacks from exponentially growing and consuming CPU resources # Override with LITELLM_MAX_CALLBACKS env var for large deployments (e.g., many teams with guardrails) -MAX_CALLBACKS = get_env_int("LITELLM_MAX_CALLBACKS", 30) +MAX_CALLBACKS = get_env_int("LITELLM_MAX_CALLBACKS", 100) # Generic fallback for unknown models DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET = int( @@ -162,15 +186,19 @@ _DEFAULT_TTL_FOR_HTTPX_CLIENTS = 3600 # 1 hour, re-use the same httpx client fo # Aiohttp connection pooling - prevents memory leaks from unbounded connection growth # Set to 0 for unlimited (not recommended for production) AIOHTTP_CONNECTOR_LIMIT = int(os.getenv("AIOHTTP_CONNECTOR_LIMIT", 300)) -AIOHTTP_CONNECTOR_LIMIT_PER_HOST = int(os.getenv("AIOHTTP_CONNECTOR_LIMIT_PER_HOST", 50)) +AIOHTTP_CONNECTOR_LIMIT_PER_HOST = int( + os.getenv("AIOHTTP_CONNECTOR_LIMIT_PER_HOST", 50) +) AIOHTTP_KEEPALIVE_TIMEOUT = int(os.getenv("AIOHTTP_KEEPALIVE_TIMEOUT", 120)) AIOHTTP_TTL_DNS_CACHE = int(os.getenv("AIOHTTP_TTL_DNS_CACHE", 300)) # enable_cleanup_closed is only needed for Python versions with the SSL leak bug # Fixed in Python 3.12.7+ and 3.13.1+ (see https://github.com/python/cpython/pull/118960) # Reference: https://github.com/aio-libs/aiohttp/blob/master/aiohttp/connector.py#L74-L78 -AIOHTTP_NEEDS_CLEANUP_CLOSED = ( - (3, 13, 0) <= sys.version_info < (3, 13, 1) or sys.version_info < (3, 12, 7) -) +AIOHTTP_NEEDS_CLEANUP_CLOSED = (3, 13, 0) <= sys.version_info < ( + 3, + 13, + 1, +) or sys.version_info < (3, 12, 7) # WebSocket constants # Default to None (unlimited) to match OpenAI's official agents SDK behavior @@ -208,15 +236,15 @@ REDIS_UPDATE_BUFFER_KEY = "litellm_spend_update_buffer" REDIS_DAILY_SPEND_UPDATE_BUFFER_KEY = "litellm_daily_spend_update_buffer" REDIS_DAILY_TEAM_SPEND_UPDATE_BUFFER_KEY = "litellm_daily_team_spend_update_buffer" REDIS_DAILY_ORG_SPEND_UPDATE_BUFFER_KEY = "litellm_daily_org_spend_update_buffer" -REDIS_DAILY_END_USER_SPEND_UPDATE_BUFFER_KEY = "litellm_daily_end_user_spend_update_buffer" +REDIS_DAILY_END_USER_SPEND_UPDATE_BUFFER_KEY = ( + "litellm_daily_end_user_spend_update_buffer" +) REDIS_DAILY_AGENT_SPEND_UPDATE_BUFFER_KEY = "litellm_daily_agent_spend_update_buffer" REDIS_DAILY_TAG_SPEND_UPDATE_BUFFER_KEY = "litellm_daily_tag_spend_update_buffer" MAX_REDIS_BUFFER_DEQUEUE_COUNT = int(os.getenv("MAX_REDIS_BUFFER_DEQUEUE_COUNT", 100)) MAX_SIZE_IN_MEMORY_QUEUE = int(os.getenv("MAX_SIZE_IN_MEMORY_QUEUE", 2000)) # Bounds asyncio.Queue() instances (log queues, spend update queues, etc.) to prevent unbounded memory growth -LITELLM_ASYNCIO_QUEUE_MAXSIZE = int( - os.getenv("LITELLM_ASYNCIO_QUEUE_MAXSIZE", 1000) -) +LITELLM_ASYNCIO_QUEUE_MAXSIZE = int(os.getenv("LITELLM_ASYNCIO_QUEUE_MAXSIZE", 1000)) MAX_IN_MEMORY_QUEUE_FLUSH_COUNT = int( os.getenv("MAX_IN_MEMORY_QUEUE_FLUSH_COUNT", 1000) ) @@ -280,7 +308,9 @@ MIN_NON_ZERO_TEMPERATURE = float(os.getenv("MIN_NON_ZERO_TEMPERATURE", 0.0001)) REPEATED_STREAMING_CHUNK_LIMIT = int( os.getenv("REPEATED_STREAMING_CHUNK_LIMIT", 100) ) # catch if model starts looping the same chunk while streaming. Uses high default to prevent false positives. -DEFAULT_MAX_LRU_CACHE_SIZE = int(os.getenv("DEFAULT_MAX_LRU_CACHE_SIZE", 16)) +# Shared maxsize for functools.lru_cache usage across hot paths. +# Defaulted to 64 to avoid cache thrash in multi-model production workloads. +DEFAULT_MAX_LRU_CACHE_SIZE = int(os.getenv("DEFAULT_MAX_LRU_CACHE_SIZE", 64)) _REALTIME_BODY_CACHE_SIZE = 1000 # Keep realtime helper caches bounded; workloads rarely exceed 1k models/intents INITIAL_RETRY_DELAY = float(os.getenv("INITIAL_RETRY_DELAY", 0.5)) MAX_RETRY_DELAY = float(os.getenv("MAX_RETRY_DELAY", 8.0)) @@ -312,6 +342,9 @@ NON_LLM_CONNECTION_TIMEOUT = int( MAX_EXCEPTION_MESSAGE_LENGTH = int(os.getenv("MAX_EXCEPTION_MESSAGE_LENGTH", 2000)) MAX_STRING_LENGTH_PROMPT_IN_DB = int(os.getenv("MAX_STRING_LENGTH_PROMPT_IN_DB", 2048)) BEDROCK_MAX_POLICY_SIZE = int(os.getenv("BEDROCK_MAX_POLICY_SIZE", 75)) +BEDROCK_MIN_THINKING_BUDGET_TOKENS = int( + os.getenv("BEDROCK_MIN_THINKING_BUDGET_TOKENS", 1024) +) REPLICATE_POLLING_DELAY_SECONDS = float( os.getenv("REPLICATE_POLLING_DELAY_SECONDS", 0.5) ) @@ -338,7 +371,9 @@ MAX_SIZE_PER_ITEM_IN_MEMORY_CACHE_IN_KB = int( DEFAULT_MAX_TOKENS_FOR_TRITON = int(os.getenv("DEFAULT_MAX_TOKENS_FOR_TRITON", 2000)) #### Networking settings #### request_timeout: float = float(os.getenv("REQUEST_TIMEOUT", 6000)) # time in seconds -DEFAULT_A2A_AGENT_TIMEOUT: float = float(os.getenv("DEFAULT_A2A_AGENT_TIMEOUT", 6000)) # 10 minutes +DEFAULT_A2A_AGENT_TIMEOUT: float = float( + os.getenv("DEFAULT_A2A_AGENT_TIMEOUT", 6000) +) # 10 minutes # Patterns that indicate a localhost/internal URL in A2A agent cards that should be # replaced with the original base_url. This is a common misconfiguration where # developers deploy agents with development URLs in their agent cards. @@ -390,8 +425,12 @@ DD_TRACER_STREAMING_CHUNK_YIELD_RESOURCE = os.getenv( "DD_TRACER_STREAMING_CHUNK_YIELD_RESOURCE", "streaming.chunk.yield" ) -EMAIL_BUDGET_ALERT_TTL = int(os.getenv("EMAIL_BUDGET_ALERT_TTL", 24 * 60 * 60)) # 24 hours in seconds -EMAIL_BUDGET_ALERT_MAX_SPEND_ALERT_PERCENTAGE = float(os.getenv("EMAIL_BUDGET_ALERT_MAX_SPEND_ALERT_PERCENTAGE", 0.8)) # 80% of max budget +EMAIL_BUDGET_ALERT_TTL = int( + os.getenv("EMAIL_BUDGET_ALERT_TTL", 24 * 60 * 60) +) # 24 hours in seconds +EMAIL_BUDGET_ALERT_MAX_SPEND_ALERT_PERCENTAGE = float( + os.getenv("EMAIL_BUDGET_ALERT_MAX_SPEND_ALERT_PERCENTAGE", 0.8) +) # 80% of max budget ############### LLM Provider Constants ############### ### ANTHROPIC CONSTANTS ### ANTHROPIC_TOKEN_COUNTING_BETA_VERSION = os.getenv( @@ -560,6 +599,11 @@ OPENAI_CHAT_COMPLETION_PARAMS = [ "thinking", "web_search_options", "service_tier", + "prompt_cache_key", + "prompt_cache_retention", + "safety_identifier", + "verbosity", + "store", ] OPENAI_TRANSCRIPTION_PARAMS = [ @@ -621,6 +665,7 @@ DEFAULT_CHAT_COMPLETION_PARAM_VALUES = { "prompt_cache_retention": None, "store": None, "metadata": None, + "context_management": None, } openai_compatible_endpoints: List = [ @@ -1011,16 +1056,19 @@ BEDROCK_EMBEDDING_PROVIDERS_LITERAL = Literal[ BEDROCK_CONVERSE_MODELS = [ "qwen.qwen3-coder-480b-a35b-v1:0", + "qwen.qwen3-coder-next", "qwen.qwen3-235b-a22b-2507-v1:0", "qwen.qwen3-coder-30b-a3b-v1:0", "qwen.qwen3-32b-v1:0", "deepseek.v3-v1:0", + "deepseek.v3.2", "openai.gpt-oss-20b-1:0", "openai.gpt-oss-120b-1:0", "anthropic.claude-haiku-4-5-20251001-v1:0", "anthropic.claude-sonnet-4-5-20250929-v1:0", "anthropic.claude-opus-4-6-v1:0", "anthropic.claude-opus-4-6-v1", + "anthropic.claude-sonnet-4-6", "anthropic.claude-opus-4-1-20250805-v1:0", "anthropic.claude-opus-4-20250514-v1:0", "anthropic.claude-sonnet-4-20250514-v1:0", @@ -1057,6 +1105,8 @@ BEDROCK_CONVERSE_MODELS = [ "amazon.nova-pro-v1:0", "writer.palmyra-x4-v1:0", "writer.palmyra-x5-v1:0", + "minimax.minimax-m2.1", + "moonshotai.kimi-k2.5", ] @@ -1141,7 +1191,17 @@ known_tokenizer_config = { } -OPENAI_FINISH_REASONS = ["stop", "length", "function_call", "content_filter", "null", "finish_reason_unspecified", "malformed_function_call", "guardrail_intervened", "eos"] +OPENAI_FINISH_REASONS = [ + "stop", + "length", + "function_call", + "content_filter", + "null", + "finish_reason_unspecified", + "malformed_function_call", + "guardrail_intervened", + "eos", +] HUMANLOOP_PROMPT_CACHE_TTL_SECONDS = int( os.getenv("HUMANLOOP_PROMPT_CACHE_TTL_SECONDS", 60) ) # 1 minute @@ -1231,6 +1291,9 @@ LITELLM_KEY_ROTATION_ENABLED = os.getenv("LITELLM_KEY_ROTATION_ENABLED", "false" LITELLM_KEY_ROTATION_CHECK_INTERVAL_SECONDS = int( os.getenv("LITELLM_KEY_ROTATION_CHECK_INTERVAL_SECONDS", 86400) ) # 24 hours default +LITELLM_KEY_ROTATION_GRACE_PERIOD: str = os.getenv( + "LITELLM_KEY_ROTATION_GRACE_PERIOD", "" +) # Duration to keep old key valid after rotation (e.g. "24h", "2d"); empty = immediate revoke (default) UI_SESSION_TOKEN_TEAM_ID = "litellm-dashboard" LITELLM_PROXY_ADMIN_NAME = "default_user_id" @@ -1241,8 +1304,8 @@ CLI_SSO_SESSION_CACHE_KEY_PREFIX = "cli_sso_session" CLI_JWT_TOKEN_NAME = "cli-jwt-token" # Support both CLI_JWT_EXPIRATION_HOURS and LITELLM_CLI_JWT_EXPIRATION_HOURS for backwards compatibility CLI_JWT_EXPIRATION_HOURS = int( - os.getenv("CLI_JWT_EXPIRATION_HOURS") - or os.getenv("LITELLM_CLI_JWT_EXPIRATION_HOURS") + os.getenv("CLI_JWT_EXPIRATION_HOURS") + or os.getenv("LITELLM_CLI_JWT_EXPIRATION_HOURS") or 24 ) @@ -1333,6 +1396,9 @@ SPECIAL_LITELLM_AUTH_TOKEN = ["ui-token"] DEFAULT_MANAGEMENT_OBJECT_IN_MEMORY_CACHE_TTL = int( os.getenv("DEFAULT_MANAGEMENT_OBJECT_IN_MEMORY_CACHE_TTL", 60) ) +DEFAULT_ACCESS_GROUP_CACHE_TTL = int( + os.getenv("DEFAULT_ACCESS_GROUP_CACHE_TTL", 600) +) # Sentry Scrubbing Configuration SENTRY_DENYLIST = [ @@ -1423,12 +1489,21 @@ MICROSOFT_USER_EMAIL_ATTRIBUTE = str( MICROSOFT_USER_DISPLAY_NAME_ATTRIBUTE = str( os.getenv("MICROSOFT_USER_DISPLAY_NAME_ATTRIBUTE", "displayName") ) -MICROSOFT_USER_ID_ATTRIBUTE = str( - os.getenv("MICROSOFT_USER_ID_ATTRIBUTE", "id") -) +MICROSOFT_USER_ID_ATTRIBUTE = str(os.getenv("MICROSOFT_USER_ID_ATTRIBUTE", "id")) MICROSOFT_USER_FIRST_NAME_ATTRIBUTE = str( os.getenv("MICROSOFT_USER_FIRST_NAME_ATTRIBUTE", "givenName") ) MICROSOFT_USER_LAST_NAME_ATTRIBUTE = str( os.getenv("MICROSOFT_USER_LAST_NAME_ATTRIBUTE", "surname") ) + +# Maximum payload size (in bytes) to fully serialize for DEBUG logging. +# Payloads larger than this are truncated to avoid multi-second json.dumps blocking the response. +MAX_PAYLOAD_SIZE_FOR_DEBUG_LOG = int( + os.getenv("MAX_PAYLOAD_SIZE_FOR_DEBUG_LOG", 102400) +) # 100 KB + +# Policy template enrichment +MAX_COMPETITOR_NAMES = int(os.getenv("MAX_COMPETITOR_NAMES", 100)) +COMPETITOR_LLM_TEMPERATURE = float(os.getenv("COMPETITOR_LLM_TEMPERATURE", 0.3)) +DEFAULT_COMPETITOR_DISCOVERY_MODEL = "gpt-4o-mini" diff --git a/litellm/cost_calculator.py b/litellm/cost_calculator.py index 4ea22dbd90..74c1afb0cc 100644 --- a/litellm/cost_calculator.py +++ b/litellm/cost_calculator.py @@ -74,6 +74,7 @@ from litellm.llms.vertex_ai.cost_calculator import ( from litellm.llms.vertex_ai.cost_calculator import cost_router as google_cost_router from litellm.llms.xai.cost_calculator import cost_per_token as xai_cost_per_token from litellm.responses.utils import ResponseAPILoggingUtils +from litellm.types.agents import LiteLLMSendMessageResponse from litellm.types.llms.openai import ( HttpxBinaryResponseContent, ImageGenerationRequestQuality, @@ -118,6 +119,42 @@ if TYPE_CHECKING: else: LitellmLoggingObject = Any +# Pre-resolved CallTypes enum values for fast membership checks +_A2A_CALL_TYPES = frozenset({ + CallTypes.asend_message.value, + CallTypes.send_message.value, +}) + +_VIDEO_CALL_TYPES = frozenset({ + CallTypes.create_video.value, + CallTypes.acreate_video.value, + CallTypes.video_remix.value, + CallTypes.avideo_remix.value, +}) + +_SPEECH_CALL_TYPES = frozenset({ + CallTypes.speech.value, + CallTypes.aspeech.value, +}) + +_TRANSCRIPTION_CALL_TYPES = frozenset({ + CallTypes.atranscription.value, + CallTypes.transcription.value, +}) + +_RERANK_CALL_TYPES = frozenset({ + CallTypes.rerank.value, + CallTypes.arerank.value, +}) + +_SEARCH_CALL_TYPES = frozenset({ + CallTypes.search.value, + CallTypes.asearch.value, +}) + +_AREALTIME_CALL_TYPE = CallTypes.arealtime.value +_MCP_CALL_TYPE = CallTypes.call_mcp_tool.value + def _cost_per_token_custom_pricing_helper( prompt_tokens: float = 0, @@ -150,32 +187,33 @@ def _get_additional_costs( ) -> Optional[dict]: """ Calculate additional costs beyond standard token costs. - + This function delegates to provider-specific config classes to calculate any additional costs like routing fees, infrastructure costs, etc. - + Args: model: The model name custom_llm_provider: The provider name (optional) prompt_tokens: Number of prompt tokens completion_tokens: Number of completion tokens - + Returns: Optional dictionary with cost names and amounts, or None if no additional costs """ if not custom_llm_provider: return None - + try: config_class = None if custom_llm_provider == "azure_ai": from litellm.llms.azure_ai.common_utils import AzureFoundryModelInfo + config_class = AzureFoundryModelInfo.get_azure_ai_config_for_model(model) # Add more providers here as needed # elif custom_llm_provider == "other_provider": # config_class = get_other_provider_config(model) - - if config_class and hasattr(config_class, 'calculate_additional_costs'): + + if config_class and hasattr(config_class, "calculate_additional_costs"): return config_class.calculate_additional_costs( model=model, prompt_tokens=prompt_tokens, @@ -183,7 +221,7 @@ def _get_additional_costs( ) except Exception as e: verbose_logger.debug(f"Error calculating additional costs: {e}") - + return None @@ -446,7 +484,9 @@ def cost_per_token( # noqa: PLR0915 elif custom_llm_provider == "anthropic": return anthropic_cost_per_token(model=model, usage=usage_block) elif custom_llm_provider == "bedrock": - return bedrock_cost_per_token(model=model, usage=usage_block) + return bedrock_cost_per_token( + model=model, usage=usage_block, service_tier=service_tier + ) elif custom_llm_provider == "openai": return openai_cost_per_token( model=model, usage=usage_block, service_tier=service_tier @@ -748,6 +788,8 @@ def _infer_call_type( return "image_generation" elif isinstance(completion_response, TextCompletionResponse): return "text_completion" + elif isinstance(completion_response, LiteLLMSendMessageResponse): + return "send_message" return call_type @@ -1037,9 +1079,9 @@ def completion_cost( # noqa: PLR0915 or isinstance(completion_response, dict) ): # tts returns a custom class if isinstance(completion_response, dict): - usage_obj: Optional[ - Union[dict, Usage] - ] = completion_response.get("usage", {}) + usage_obj: Optional[Union[dict, Usage]] = ( + completion_response.get("usage", {}) + ) else: usage_obj = getattr(completion_response, "usage", {}) if isinstance(usage_obj, BaseModel) and not _is_known_usage_objects( @@ -1115,10 +1157,7 @@ def completion_cost( # noqa: PLR0915 completion_tokens = token_counter(model=model, text=completion) # Handle A2A calls before model check - A2A doesn't require a model - if call_type in ( - CallTypes.asend_message.value, - CallTypes.send_message.value, - ): + if call_type in _A2A_CALL_TYPES: from litellm.a2a_protocol.cost_calculator import A2ACostCalculator return A2ACostCalculator.calculate_a2a_cost( @@ -1154,12 +1193,7 @@ def completion_cost( # noqa: PLR0915 optional_params=optional_params, call_type=call_type, ) - elif ( - call_type == CallTypes.create_video.value - or call_type == CallTypes.acreate_video.value - or call_type == CallTypes.video_remix.value - or call_type == CallTypes.avideo_remix.value - ): + elif call_type in _VIDEO_CALL_TYPES: ### VIDEO GENERATION COST CALCULATION ### usage_obj = getattr(completion_response, "usage", None) if completion_response is not None and usage_obj: @@ -1188,22 +1222,13 @@ def completion_cost( # noqa: PLR0915 duration_seconds=0.0, # Default to 0 if no duration available custom_llm_provider=custom_llm_provider, ) - elif ( - call_type == CallTypes.speech.value - or call_type == CallTypes.aspeech.value - ): + elif call_type in _SPEECH_CALL_TYPES: prompt_characters = litellm.utils._count_characters(text=prompt) - elif ( - call_type == CallTypes.atranscription.value - or call_type == CallTypes.transcription.value - ): + elif call_type in _TRANSCRIPTION_CALL_TYPES: audio_transcription_file_duration = getattr( completion_response, "duration", 0.0 ) - elif ( - call_type == CallTypes.rerank.value - or call_type == CallTypes.arerank.value - ): + elif call_type in _RERANK_CALL_TYPES: if completion_response is not None and isinstance( completion_response, RerankResponse ): @@ -1222,10 +1247,7 @@ def completion_cost( # noqa: PLR0915 billed_units.get("search_units") or 1 ) # cohere charges per request by default. completion_tokens = search_units - elif ( - call_type == CallTypes.search.value - or call_type == CallTypes.asearch.value - ): + elif call_type in _SEARCH_CALL_TYPES: from litellm.search import search_provider_cost_per_query # Extract number_of_queries from optional_params or default to 1 @@ -1294,7 +1316,7 @@ def completion_cost( # noqa: PLR0915 ) return _final_cost - elif call_type == CallTypes.arealtime.value and isinstance( + elif call_type == _AREALTIME_CALL_TYPE and isinstance( completion_response, LiteLLMRealtimeStreamLoggingObject ): if ( @@ -1313,7 +1335,7 @@ def completion_cost( # noqa: PLR0915 custom_llm_provider=custom_llm_provider, litellm_model_name=model, ) - elif call_type == CallTypes.call_mcp_tool.value: + elif call_type == _MCP_CALL_TYPE: from litellm.proxy._experimental.mcp_server.cost_calculator import ( MCPCostCalculator, ) @@ -1387,21 +1409,26 @@ def completion_cost( # noqa: PLR0915 cache_creation_input_tokens=cache_creation_input_tokens, cache_read_input_tokens=cache_read_input_tokens, usage_object=cost_per_token_usage_object, - call_type=cast(CallTypesLiteral, call_type), + call_type=call_type, audio_transcription_file_duration=audio_transcription_file_duration, rerank_billed_units=rerank_billed_units, service_tier=service_tier, response=completion_response, ) - + # Get additional costs from provider (e.g., routing fees, infrastructure costs) - additional_costs = _get_additional_costs( - model=model, - custom_llm_provider=custom_llm_provider, - prompt_tokens=prompt_tokens, - completion_tokens=completion_tokens, - ) - + # Only azure_ai implements additional costs + if custom_llm_provider == "azure_ai": + additional_costs = _get_additional_costs( + model=model, + custom_llm_provider=custom_llm_provider, + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + ) + else: + additional_costs = None + + _final_cost = ( prompt_tokens_cost_usd_dollar + completion_tokens_cost_usd_dollar ) @@ -1892,9 +1919,16 @@ def batch_cost_calculator( usage: Usage, model: str, custom_llm_provider: Optional[str] = None, + model_info: Optional[ModelInfo] = None, ) -> Tuple[float, float]: """ - Calculate the cost of a batch job + Calculate the cost of a batch job. + + Args: + model_info: Optional deployment-level model info containing custom + batch pricing (e.g. input_cost_per_token_batches). When provided, + skips the global litellm.get_model_info() lookup so that + deployment-specific pricing is used. """ _, custom_llm_provider, _, _ = litellm.get_llm_provider( @@ -1907,12 +1941,13 @@ def batch_cost_calculator( custom_llm_provider, ) - try: - model_info: Optional[ModelInfo] = litellm.get_model_info( - model=model, custom_llm_provider=custom_llm_provider - ) - except Exception: - model_info = None + if model_info is None: + try: + model_info = litellm.get_model_info( + model=model, custom_llm_provider=custom_llm_provider + ) + except Exception: + model_info = None if not model_info: return 0.0, 0.0 @@ -2134,4 +2169,3 @@ def handle_realtime_stream_cost_calculation( return total_cost - diff --git a/litellm/evals/__init__.py b/litellm/evals/__init__.py new file mode 100644 index 0000000000..89dfb62b2b --- /dev/null +++ b/litellm/evals/__init__.py @@ -0,0 +1,33 @@ +""" +Evals API operations +""" + +from .main import ( + acancel_eval, + acreate_eval, + adelete_eval, + aget_eval, + alist_evals, + aupdate_eval, + cancel_eval, + create_eval, + delete_eval, + get_eval, + list_evals, + update_eval, +) + +__all__ = [ + "acreate_eval", + "alist_evals", + "aget_eval", + "aupdate_eval", + "adelete_eval", + "acancel_eval", + "create_eval", + "list_evals", + "get_eval", + "update_eval", + "delete_eval", + "cancel_eval", +] diff --git a/litellm/evals/main.py b/litellm/evals/main.py new file mode 100644 index 0000000000..a39c283915 --- /dev/null +++ b/litellm/evals/main.py @@ -0,0 +1,1944 @@ +""" +Main entry point for Evals API operations +Provides create, list, get, update, delete, and cancel operations for evals +""" + +import asyncio +import contextvars +from functools import partial +from typing import Any, Coroutine, Dict, List, Optional, Union + +import httpx + +import litellm +from litellm.constants import request_timeout +from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj +from litellm.llms.base_llm.evals.transformation import BaseEvalsAPIConfig +from litellm.llms.custom_httpx.llm_http_handler import BaseLLMHTTPHandler +from litellm.types.llms.openai_evals import ( + CancelEvalResponse, + CancelRunResponse, + CreateEvalRequest, + CreateRunRequest, + DeleteEvalResponse, + Eval, + ListEvalsParams, + ListEvalsResponse, + ListRunsParams, + ListRunsResponse, + Run, + RunDeleteResponse, + UpdateEvalRequest, +) +from litellm.types.router import GenericLiteLLMParams +from litellm.utils import ProviderConfigManager, client + +# Initialize HTTP handler +base_llm_http_handler = BaseLLMHTTPHandler() +DEFAULT_OPENAI_API_BASE = "https://api.openai.com" + + +@client +async def acreate_eval( + data_source_config: Dict[str, Any], + testing_criteria: List[Dict[str, Any]], + name: Optional[str] = None, + metadata: Optional[Dict[str, Any]] = None, + extra_headers: Optional[Dict[str, Any]] = None, + extra_query: Optional[Dict[str, Any]] = None, + extra_body: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + custom_llm_provider: Optional[str] = None, + **kwargs, +) -> Eval: + """ + Async: Create a new evaluation + + Args: + data_source_config: Configuration for the data source + testing_criteria: List of graders for all eval runs + name: Optional name for the evaluation + metadata: Optional additional metadata (max 16 key-value pairs) + extra_headers: Additional headers for the request + extra_query: Additional query parameters + extra_body: Additional body parameters + timeout: Request timeout + custom_llm_provider: Provider name (e.g., 'openai') + **kwargs: Additional parameters + + Returns: + Eval object + """ + local_vars = locals() + try: + loop = asyncio.get_event_loop() + kwargs["acreate_eval"] = True + + func = partial( + create_eval, + data_source_config=data_source_config, + testing_criteria=testing_criteria, + name=name, + metadata=metadata, + extra_headers=extra_headers, + extra_query=extra_query, + extra_body=extra_body, + timeout=timeout, + custom_llm_provider=custom_llm_provider, + **kwargs, + ) + + ctx = contextvars.copy_context() + func_with_context = partial(ctx.run, func) + init_response = await loop.run_in_executor(None, func_with_context) + + if asyncio.iscoroutine(init_response): + response = await init_response + else: + response = init_response + return response + except Exception as e: + raise litellm.exception_type( + model=None, + custom_llm_provider=custom_llm_provider, + original_exception=e, + completion_kwargs=local_vars, + extra_kwargs=kwargs, + ) + + +@client +def create_eval( + data_source_config: Dict[str, Any], + testing_criteria: List[Dict[str, Any]], + name: Optional[str] = None, + metadata: Optional[Dict[str, Any]] = None, + extra_headers: Optional[Dict[str, Any]] = None, + extra_query: Optional[Dict[str, Any]] = None, + extra_body: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + custom_llm_provider: Optional[str] = None, + **kwargs, +) -> Union[Eval, Coroutine[Any, Any, Eval]]: + """ + Create a new evaluation + + Args: + data_source_config: Configuration for the data source + testing_criteria: List of graders for all eval runs + name: Optional name for the evaluation + metadata: Optional additional metadata (max 16 key-value pairs) + extra_headers: Additional headers for the request + extra_query: Additional query parameters + extra_body: Additional body parameters + timeout: Request timeout + custom_llm_provider: Provider name (e.g., 'openai') + **kwargs: Additional parameters + + Returns: + Eval object + """ + local_vars = locals() + try: + litellm_logging_obj: LiteLLMLoggingObj = kwargs.get("litellm_logging_obj") # type: ignore + litellm_call_id: Optional[str] = kwargs.get("litellm_call_id", None) + _is_async = kwargs.pop("acreate_eval", False) is True + + # Get LiteLLM parameters + litellm_params = GenericLiteLLMParams(**kwargs) + + # Determine provider + if custom_llm_provider is None: + custom_llm_provider = "openai" + + # Get provider config + evals_api_provider_config: Optional[BaseEvalsAPIConfig] = ( + ProviderConfigManager.get_provider_evals_api_config( # type: ignore + provider=litellm.LlmProviders(custom_llm_provider), + ) + ) + + if evals_api_provider_config is None: + raise ValueError( + f"CREATE eval is not supported for {custom_llm_provider}" + ) + + # Build create request + create_request: CreateEvalRequest = { + "data_source_config": data_source_config, # type: ignore + "testing_criteria": testing_criteria, # type: ignore + } + if name is not None: + create_request["name"] = name + + # Merge extra_body if provided + if extra_body: + create_request.update(extra_body) # type: ignore + + # Validate environment and get headers + headers = extra_headers or {} + headers = evals_api_provider_config.validate_environment( + headers=headers, litellm_params=litellm_params + ) + + # Transform request + request_body = evals_api_provider_config.transform_create_eval_request( + create_request=create_request, + litellm_params=litellm_params, + headers=headers, + ) + + # Get API base and URL + api_base = litellm_params.api_base or DEFAULT_OPENAI_API_BASE + url = evals_api_provider_config.get_complete_url( + api_base=api_base, endpoint="evals" + ) + + # Pre-call logging + litellm_logging_obj.update_environment_variables( + model=None, + optional_params=request_body, + litellm_params={ + "litellm_call_id": litellm_call_id, + }, + custom_llm_provider=custom_llm_provider, + ) + + # Make HTTP request + response = base_llm_http_handler.create_eval_handler( # type: ignore + url=url, + request_body=request_body, + evals_api_provider_config=evals_api_provider_config, + custom_llm_provider=custom_llm_provider, + litellm_params=litellm_params, + logging_obj=litellm_logging_obj, + extra_headers=headers, + timeout=timeout or request_timeout, + _is_async=_is_async, + client=kwargs.get("client"), + shared_session=kwargs.get("shared_session"), + ) + + return response + except Exception as e: + raise litellm.exception_type( + model=None, + custom_llm_provider=custom_llm_provider, + original_exception=e, + completion_kwargs=local_vars, + extra_kwargs=kwargs, + ) + + +@client +async def alist_evals( + limit: Optional[int] = None, + after: Optional[str] = None, + before: Optional[str] = None, + order: Optional[str] = None, + order_by: Optional[str] = None, + extra_headers: Optional[Dict[str, Any]] = None, + extra_query: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + custom_llm_provider: Optional[str] = None, + **kwargs, +) -> ListEvalsResponse: + """ + Async: List all evaluations + + Args: + limit: Number of results to return per page (max 100, default 20) + after: Cursor for pagination - returns evals after this ID + before: Cursor for pagination - returns evals before this ID + order: Sort order ('asc' or 'desc', default 'desc') + order_by: Field to sort by ('created_at' or 'updated_at', default 'created_at') + extra_headers: Additional headers for the request + extra_query: Additional query parameters + timeout: Request timeout + custom_llm_provider: Provider name (e.g., 'openai') + **kwargs: Additional parameters + + Returns: + ListEvalsResponse object + """ + local_vars = locals() + try: + loop = asyncio.get_event_loop() + kwargs["alist_evals"] = True + + func = partial( + list_evals, + limit=limit, + after=after, + before=before, + order=order, + order_by=order_by, + extra_headers=extra_headers, + extra_query=extra_query, + timeout=timeout, + custom_llm_provider=custom_llm_provider, + **kwargs, + ) + + ctx = contextvars.copy_context() + func_with_context = partial(ctx.run, func) + init_response = await loop.run_in_executor(None, func_with_context) + + if asyncio.iscoroutine(init_response): + response = await init_response + else: + response = init_response + return response + except Exception as e: + raise litellm.exception_type( + model=None, + custom_llm_provider=custom_llm_provider, + original_exception=e, + completion_kwargs=local_vars, + extra_kwargs=kwargs, + ) + + +@client +def list_evals( + limit: Optional[int] = None, + after: Optional[str] = None, + before: Optional[str] = None, + order: Optional[str] = None, + order_by: Optional[str] = None, + extra_headers: Optional[Dict[str, Any]] = None, + extra_query: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + custom_llm_provider: Optional[str] = None, + **kwargs, +) -> Union[ListEvalsResponse, Coroutine[Any, Any, ListEvalsResponse]]: + """ + List all evaluations + + Args: + limit: Number of results to return per page (max 100, default 20) + after: Cursor for pagination - returns evals after this ID + before: Cursor for pagination - returns evals before this ID + order: Sort order ('asc' or 'desc', default 'desc') + order_by: Field to sort by ('created_at' or 'updated_at', default 'created_at') + extra_headers: Additional headers for the request + extra_query: Additional query parameters + timeout: Request timeout + custom_llm_provider: Provider name (e.g., 'openai') + **kwargs: Additional parameters + + Returns: + ListEvalsResponse object + """ + local_vars = locals() + try: + litellm_logging_obj: LiteLLMLoggingObj = kwargs.get("litellm_logging_obj") # type: ignore + litellm_call_id: Optional[str] = kwargs.get("litellm_call_id", None) + _is_async = kwargs.pop("alist_evals", False) is True + + # Get LiteLLM parameters + litellm_params = GenericLiteLLMParams(**kwargs) + + # Determine provider + if custom_llm_provider is None: + custom_llm_provider = "openai" + + # Get provider config + evals_api_provider_config: Optional[BaseEvalsAPIConfig] = ( + ProviderConfigManager.get_provider_evals_api_config( # type: ignore + provider=litellm.LlmProviders(custom_llm_provider), + ) + ) + + if evals_api_provider_config is None: + raise ValueError(f"LIST evals is not supported for {custom_llm_provider}") + + # Build list parameters + list_params: ListEvalsParams = {} + if limit is not None: + list_params["limit"] = limit + if after is not None: + list_params["after"] = after + if before is not None: + list_params["before"] = before + if order is not None: + list_params["order"] = order # type: ignore + if order_by is not None: + list_params["order_by"] = order_by # type: ignore + + # Merge extra_query if provided + if extra_query: + list_params.update(extra_query) # type: ignore + + # Validate environment and get headers + headers = extra_headers or {} + headers = evals_api_provider_config.validate_environment( + headers=headers, litellm_params=litellm_params + ) + + # Transform request + url, query_params = evals_api_provider_config.transform_list_evals_request( + list_params=list_params, + litellm_params=litellm_params, + headers=headers, + ) + + # Pre-call logging + litellm_logging_obj.update_environment_variables( + model=None, + optional_params=query_params, + litellm_params={ + "litellm_call_id": litellm_call_id, + }, + custom_llm_provider=custom_llm_provider, + ) + + # Make HTTP request + response = base_llm_http_handler.list_evals_handler( # type: ignore + url=url, + query_params=query_params, + evals_api_provider_config=evals_api_provider_config, + custom_llm_provider=custom_llm_provider, + litellm_params=litellm_params, + logging_obj=litellm_logging_obj, + extra_headers=headers, + timeout=timeout or request_timeout, + _is_async=_is_async, + client=kwargs.get("client"), + shared_session=kwargs.get("shared_session"), + ) + + return response + except Exception as e: + raise litellm.exception_type( + model=None, + custom_llm_provider=custom_llm_provider, + original_exception=e, + completion_kwargs=local_vars, + extra_kwargs=kwargs, + ) + + +@client +async def aget_eval( + eval_id: str, + extra_headers: Optional[Dict[str, Any]] = None, + extra_query: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + custom_llm_provider: Optional[str] = None, + **kwargs, +) -> Eval: + """ + Async: Get an evaluation by ID + + Args: + eval_id: The ID of the evaluation to fetch + extra_headers: Additional headers for the request + extra_query: Additional query parameters + timeout: Request timeout + custom_llm_provider: Provider name (e.g., 'openai') + **kwargs: Additional parameters + + Returns: + Eval object + """ + local_vars = locals() + try: + loop = asyncio.get_event_loop() + kwargs["aget_eval"] = True + + func = partial( + get_eval, + eval_id=eval_id, + extra_headers=extra_headers, + extra_query=extra_query, + timeout=timeout, + custom_llm_provider=custom_llm_provider, + **kwargs, + ) + + ctx = contextvars.copy_context() + func_with_context = partial(ctx.run, func) + init_response = await loop.run_in_executor(None, func_with_context) + + if asyncio.iscoroutine(init_response): + response = await init_response + else: + response = init_response + return response + except Exception as e: + raise litellm.exception_type( + model=None, + custom_llm_provider=custom_llm_provider, + original_exception=e, + completion_kwargs=local_vars, + extra_kwargs=kwargs, + ) + + +@client +def get_eval( + eval_id: str, + extra_headers: Optional[Dict[str, Any]] = None, + extra_query: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + custom_llm_provider: Optional[str] = None, + **kwargs, +) -> Union[Eval, Coroutine[Any, Any, Eval]]: + """ + Get an evaluation by ID + + Args: + eval_id: The ID of the evaluation to fetch + extra_headers: Additional headers for the request + extra_query: Additional query parameters + timeout: Request timeout + custom_llm_provider: Provider name (e.g., 'openai') + **kwargs: Additional parameters + + Returns: + Eval object + """ + local_vars = locals() + try: + litellm_logging_obj: LiteLLMLoggingObj = kwargs.get("litellm_logging_obj") # type: ignore + litellm_call_id: Optional[str] = kwargs.get("litellm_call_id", None) + _is_async = kwargs.pop("aget_eval", False) is True + + # Get LiteLLM parameters + litellm_params = GenericLiteLLMParams(**kwargs) + + # Determine provider + if custom_llm_provider is None: + custom_llm_provider = "openai" + + # Get provider config + evals_api_provider_config: Optional[BaseEvalsAPIConfig] = ( + ProviderConfigManager.get_provider_evals_api_config( # type: ignore + provider=litellm.LlmProviders(custom_llm_provider), + ) + ) + + if evals_api_provider_config is None: + raise ValueError(f"GET eval is not supported for {custom_llm_provider}") + + # Validate environment and get headers + headers = extra_headers or {} + headers = evals_api_provider_config.validate_environment( + headers=headers, litellm_params=litellm_params + ) + + # Transform request + api_base = litellm_params.api_base or DEFAULT_OPENAI_API_BASE + url, headers = evals_api_provider_config.transform_get_eval_request( + eval_id=eval_id, + api_base=api_base, + litellm_params=litellm_params, + headers=headers, + ) + + # Pre-call logging + litellm_logging_obj.update_environment_variables( + model=None, + optional_params={"eval_id": eval_id}, + litellm_params={ + "litellm_call_id": litellm_call_id, + }, + custom_llm_provider=custom_llm_provider, + ) + + # Make HTTP request + response = base_llm_http_handler.get_eval_handler( # type: ignore + url=url, + evals_api_provider_config=evals_api_provider_config, + custom_llm_provider=custom_llm_provider, + litellm_params=litellm_params, + logging_obj=litellm_logging_obj, + extra_headers=headers, + timeout=timeout or request_timeout, + _is_async=_is_async, + client=kwargs.get("client"), + shared_session=kwargs.get("shared_session"), + ) + + return response + except Exception as e: + raise litellm.exception_type( + model=None, + custom_llm_provider=custom_llm_provider, + original_exception=e, + completion_kwargs=local_vars, + extra_kwargs=kwargs, + ) + + +@client +async def aupdate_eval( + eval_id: str, + name: Optional[str] = None, + metadata: Optional[Dict[str, Any]] = None, + extra_headers: Optional[Dict[str, Any]] = None, + extra_query: Optional[Dict[str, Any]] = None, + extra_body: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + custom_llm_provider: Optional[str] = None, + **kwargs, +) -> Eval: + """ + Async: Update an evaluation + + Args: + eval_id: The ID of the evaluation to update + name: Updated name + metadata: Updated metadata + extra_headers: Additional headers for the request + extra_query: Additional query parameters + extra_body: Additional body parameters + timeout: Request timeout + custom_llm_provider: Provider name (e.g., 'openai') + **kwargs: Additional parameters + + Returns: + Eval object + """ + local_vars = locals() + try: + loop = asyncio.get_event_loop() + kwargs["aupdate_eval"] = True + + func = partial( + update_eval, + eval_id=eval_id, + name=name, + metadata=metadata, + extra_headers=extra_headers, + extra_query=extra_query, + extra_body=extra_body, + timeout=timeout, + custom_llm_provider=custom_llm_provider, + **kwargs, + ) + + ctx = contextvars.copy_context() + func_with_context = partial(ctx.run, func) + init_response = await loop.run_in_executor(None, func_with_context) + + if asyncio.iscoroutine(init_response): + response = await init_response + else: + response = init_response + return response + except Exception as e: + raise litellm.exception_type( + model=None, + custom_llm_provider=custom_llm_provider, + original_exception=e, + completion_kwargs=local_vars, + extra_kwargs=kwargs, + ) + + +@client +def update_eval( + eval_id: str, + name: Optional[str] = None, + metadata: Optional[Dict[str, Any]] = None, + extra_headers: Optional[Dict[str, Any]] = None, + extra_query: Optional[Dict[str, Any]] = None, + extra_body: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + custom_llm_provider: Optional[str] = None, + **kwargs, +) -> Union[Eval, Coroutine[Any, Any, Eval]]: + """ + Update an evaluation + + Args: + eval_id: The ID of the evaluation to update + name: Updated name + metadata: Updated metadata + extra_headers: Additional headers for the request + extra_query: Additional query parameters + extra_body: Additional body parameters + timeout: Request timeout + custom_llm_provider: Provider name (e.g., 'openai') + **kwargs: Additional parameters + + Returns: + Eval object + """ + local_vars = locals() + try: + litellm_logging_obj: LiteLLMLoggingObj = kwargs.get("litellm_logging_obj") # type: ignore + litellm_call_id: Optional[str] = kwargs.get("litellm_call_id", None) + _is_async = kwargs.pop("aupdate_eval", False) is True + + # Get LiteLLM parameters + litellm_params = GenericLiteLLMParams(**kwargs) + + # Determine provider + if custom_llm_provider is None: + custom_llm_provider = "openai" + + # Get provider config + evals_api_provider_config: Optional[BaseEvalsAPIConfig] = ( + ProviderConfigManager.get_provider_evals_api_config( # type: ignore + provider=litellm.LlmProviders(custom_llm_provider), + ) + ) + + if evals_api_provider_config is None: + raise ValueError( + f"UPDATE eval is not supported for {custom_llm_provider}" + ) + + # Build update request + update_request: UpdateEvalRequest = {} + if name is not None: + update_request["name"] = name + + # Filter metadata to exclude internal LiteLLM fields + if metadata is not None: + # List of internal LiteLLM metadata keys that should NOT be sent to OpenAI + internal_keys = { + "headers", "requester_metadata", "user_api_key_hash", "user_api_key_alias", + "user_api_key_spend", "user_api_key_max_budget", "user_api_key_team_id", + "user_api_key_user_id", "user_api_key_org_id", "user_api_key_team_alias", + "user_api_key_end_user_id", "user_api_key_user_email", "user_api_key_request_route", + "user_api_key_budget_reset_at", "user_api_key_auth_metadata", "user_api_key", + "user_api_end_user_max_budget", "user_api_key_auth", "litellm_api_version", + "global_max_parallel_requests", "user_api_key_team_max_budget", + "user_api_key_team_spend", "user_api_key_model_max_budget", + "user_api_key_user_spend", "user_api_key_user_max_budget", + "user_api_key_metadata", "endpoint", "litellm_parent_otel_span", + "requester_ip_address", "user_agent", + } + # Only include user-provided metadata keys + filtered_metadata = {k: v for k, v in metadata.items() if k not in internal_keys} + if filtered_metadata: # Only add if there's user metadata + update_request["metadata"] = filtered_metadata + + # Merge extra_body if provided + if extra_body: + update_request.update(extra_body) # type: ignore + + # Validate environment and get headers + headers = extra_headers or {} + headers = evals_api_provider_config.validate_environment( + headers=headers, litellm_params=litellm_params + ) + + # Transform request + api_base = litellm_params.api_base or DEFAULT_OPENAI_API_BASE + url, headers, request_body = evals_api_provider_config.transform_update_eval_request( + eval_id=eval_id, + update_request=update_request, + api_base=api_base, + litellm_params=litellm_params, + headers=headers, + ) + + # Pre-call logging + litellm_logging_obj.update_environment_variables( + model=None, + optional_params=request_body, + litellm_params={ + "litellm_call_id": litellm_call_id, + }, + custom_llm_provider=custom_llm_provider, + ) + + # Make HTTP request + response = base_llm_http_handler.update_eval_handler( # type: ignore + url=url, + request_body=request_body, + evals_api_provider_config=evals_api_provider_config, + custom_llm_provider=custom_llm_provider, + litellm_params=litellm_params, + logging_obj=litellm_logging_obj, + extra_headers=headers, + timeout=timeout or request_timeout, + _is_async=_is_async, + client=kwargs.get("client"), + shared_session=kwargs.get("shared_session"), + ) + + return response + except Exception as e: + raise litellm.exception_type( + model=None, + custom_llm_provider=custom_llm_provider, + original_exception=e, + completion_kwargs=local_vars, + extra_kwargs=kwargs, + ) + + +@client +async def adelete_eval( + eval_id: str, + extra_headers: Optional[Dict[str, Any]] = None, + extra_query: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + custom_llm_provider: Optional[str] = None, + **kwargs, +) -> DeleteEvalResponse: + """ + Async: Delete an evaluation + + Args: + eval_id: The ID of the evaluation to delete + extra_headers: Additional headers for the request + extra_query: Additional query parameters + timeout: Request timeout + custom_llm_provider: Provider name (e.g., 'openai') + **kwargs: Additional parameters + + Returns: + DeleteEvalResponse object + """ + local_vars = locals() + try: + loop = asyncio.get_event_loop() + kwargs["adelete_eval"] = True + + func = partial( + delete_eval, + eval_id=eval_id, + extra_headers=extra_headers, + extra_query=extra_query, + timeout=timeout, + custom_llm_provider=custom_llm_provider, + **kwargs, + ) + + ctx = contextvars.copy_context() + func_with_context = partial(ctx.run, func) + init_response = await loop.run_in_executor(None, func_with_context) + + if asyncio.iscoroutine(init_response): + response = await init_response + else: + response = init_response + return response + except Exception as e: + raise litellm.exception_type( + model=None, + custom_llm_provider=custom_llm_provider, + original_exception=e, + completion_kwargs=local_vars, + extra_kwargs=kwargs, + ) + + +@client +def delete_eval( + eval_id: str, + extra_headers: Optional[Dict[str, Any]] = None, + extra_query: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + custom_llm_provider: Optional[str] = None, + **kwargs, +) -> Union[DeleteEvalResponse, Coroutine[Any, Any, DeleteEvalResponse]]: + """ + Delete an evaluation + + Args: + eval_id: The ID of the evaluation to delete + extra_headers: Additional headers for the request + extra_query: Additional query parameters + timeout: Request timeout + custom_llm_provider: Provider name (e.g., 'openai') + **kwargs: Additional parameters + + Returns: + DeleteEvalResponse object + """ + local_vars = locals() + try: + litellm_logging_obj: LiteLLMLoggingObj = kwargs.get("litellm_logging_obj") # type: ignore + litellm_call_id: Optional[str] = kwargs.get("litellm_call_id", None) + _is_async = kwargs.pop("adelete_eval", False) is True + + # Get LiteLLM parameters + litellm_params = GenericLiteLLMParams(**kwargs) + + # Determine provider + if custom_llm_provider is None: + custom_llm_provider = "openai" + + # Get provider config + evals_api_provider_config: Optional[BaseEvalsAPIConfig] = ( + ProviderConfigManager.get_provider_evals_api_config( # type: ignore + provider=litellm.LlmProviders(custom_llm_provider), + ) + ) + + if evals_api_provider_config is None: + raise ValueError(f"DELETE eval is not supported for {custom_llm_provider}") + + # Validate environment and get headers + headers = extra_headers or {} + headers = evals_api_provider_config.validate_environment( + headers=headers, litellm_params=litellm_params + ) + + # Transform request + api_base = litellm_params.api_base or DEFAULT_OPENAI_API_BASE + url, headers = evals_api_provider_config.transform_delete_eval_request( + eval_id=eval_id, + api_base=api_base, + litellm_params=litellm_params, + headers=headers, + ) + + # Pre-call logging + litellm_logging_obj.update_environment_variables( + model=None, + optional_params={"eval_id": eval_id}, + litellm_params={ + "litellm_call_id": litellm_call_id, + }, + custom_llm_provider=custom_llm_provider, + ) + + # Make HTTP request + response = base_llm_http_handler.delete_eval_handler( # type: ignore + url=url, + evals_api_provider_config=evals_api_provider_config, + custom_llm_provider=custom_llm_provider, + litellm_params=litellm_params, + logging_obj=litellm_logging_obj, + extra_headers=headers, + timeout=timeout or request_timeout, + _is_async=_is_async, + client=kwargs.get("client"), + shared_session=kwargs.get("shared_session"), + ) + + return response + except Exception as e: + raise litellm.exception_type( + model=None, + custom_llm_provider=custom_llm_provider, + original_exception=e, + completion_kwargs=local_vars, + extra_kwargs=kwargs, + ) + + +@client +async def acancel_eval( + eval_id: str, + extra_headers: Optional[Dict[str, Any]] = None, + extra_query: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + custom_llm_provider: Optional[str] = None, + **kwargs, +) -> CancelEvalResponse: + """ + Async: Cancel a running evaluation + + Args: + eval_id: The ID of the evaluation to cancel + extra_headers: Additional headers for the request + extra_query: Additional query parameters + timeout: Request timeout + custom_llm_provider: Provider name (e.g., 'openai') + **kwargs: Additional parameters + + Returns: + CancelEvalResponse object + """ + local_vars = locals() + try: + loop = asyncio.get_event_loop() + kwargs["acancel_eval"] = True + + func = partial( + cancel_eval, + eval_id=eval_id, + extra_headers=extra_headers, + extra_query=extra_query, + timeout=timeout, + custom_llm_provider=custom_llm_provider, + **kwargs, + ) + + ctx = contextvars.copy_context() + func_with_context = partial(ctx.run, func) + init_response = await loop.run_in_executor(None, func_with_context) + + if asyncio.iscoroutine(init_response): + response = await init_response + else: + response = init_response + return response + except Exception as e: + raise litellm.exception_type( + model=None, + custom_llm_provider=custom_llm_provider, + original_exception=e, + completion_kwargs=local_vars, + extra_kwargs=kwargs, + ) + + +@client +def cancel_eval( + eval_id: str, + extra_headers: Optional[Dict[str, Any]] = None, + extra_query: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + custom_llm_provider: Optional[str] = None, + **kwargs, +) -> Union[CancelEvalResponse, Coroutine[Any, Any, CancelEvalResponse]]: + """ + Cancel a running evaluation + + Args: + eval_id: The ID of the evaluation to cancel + extra_headers: Additional headers for the request + extra_query: Additional query parameters + timeout: Request timeout + custom_llm_provider: Provider name (e.g., 'openai') + **kwargs: Additional parameters + + Returns: + CancelEvalResponse object + """ + local_vars = locals() + try: + litellm_logging_obj: LiteLLMLoggingObj = kwargs.get("litellm_logging_obj") # type: ignore + litellm_call_id: Optional[str] = kwargs.get("litellm_call_id", None) + _is_async = kwargs.pop("acancel_eval", False) is True + + # Get LiteLLM parameters + litellm_params = GenericLiteLLMParams(**kwargs) + + # Determine provider + if custom_llm_provider is None: + custom_llm_provider = "openai" + + # Get provider config + evals_api_provider_config: Optional[BaseEvalsAPIConfig] = ( + ProviderConfigManager.get_provider_evals_api_config( # type: ignore + provider=litellm.LlmProviders(custom_llm_provider), + ) + ) + + if evals_api_provider_config is None: + raise ValueError(f"CANCEL eval is not supported for {custom_llm_provider}") + + # Validate environment and get headers + headers = extra_headers or {} + headers = evals_api_provider_config.validate_environment( + headers=headers, litellm_params=litellm_params + ) + + # Transform request + api_base = litellm_params.api_base or DEFAULT_OPENAI_API_BASE + url, headers, request_body = evals_api_provider_config.transform_cancel_eval_request( + eval_id=eval_id, + api_base=api_base, + litellm_params=litellm_params, + headers=headers, + ) + + # Pre-call logging + litellm_logging_obj.update_environment_variables( + model=None, + optional_params={"eval_id": eval_id}, + litellm_params={ + "litellm_call_id": litellm_call_id, + }, + custom_llm_provider=custom_llm_provider, + ) + + # Make HTTP request + response = base_llm_http_handler.cancel_eval_handler( # type: ignore + url=url, + evals_api_provider_config=evals_api_provider_config, + custom_llm_provider=custom_llm_provider, + litellm_params=litellm_params, + logging_obj=litellm_logging_obj, + extra_headers=headers, + timeout=timeout or request_timeout, + _is_async=_is_async, + client=kwargs.get("client"), + shared_session=kwargs.get("shared_session"), + ) + + return response + except Exception as e: + raise litellm.exception_type( + model=None, + custom_llm_provider=custom_llm_provider, + original_exception=e, + completion_kwargs=local_vars, + extra_kwargs=kwargs, + ) + + +# =================================== +# Run API Functions +# =================================== + + +@client +async def acreate_run( + eval_id: str, + data_source: Dict[str, Any], + name: Optional[str] = None, + metadata: Optional[Dict[str, Any]] = None, + extra_headers: Optional[Dict[str, Any]] = None, + extra_query: Optional[Dict[str, Any]] = None, + extra_body: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + custom_llm_provider: Optional[str] = None, + **kwargs, +) -> Run: + """ + Async: Create a new run for an evaluation + + Args: + eval_id: The ID of the evaluation to run + data_source: Data source configuration for the run (can be jsonl, completions, or responses type) + name: Optional name for the run + metadata: Optional additional metadata + extra_headers: Additional headers for the request + extra_query: Additional query parameters + extra_body: Additional body parameters + timeout: Request timeout + custom_llm_provider: Provider name (e.g., 'openai') + **kwargs: Additional parameters + + Returns: + Run object + """ + local_vars = locals() + try: + loop = asyncio.get_event_loop() + kwargs["acreate_run"] = True + + func = partial( + create_run, + eval_id=eval_id, + data_source=data_source, + name=name, + metadata=metadata, + extra_headers=extra_headers, + extra_query=extra_query, + extra_body=extra_body, + timeout=timeout, + custom_llm_provider=custom_llm_provider, + **kwargs, + ) + + ctx = contextvars.copy_context() + func_with_context = partial(ctx.run, func) + init_response = await loop.run_in_executor(None, func_with_context) + + if asyncio.iscoroutine(init_response): + response = await init_response + else: + response = init_response + return response + except Exception as e: + raise litellm.exception_type( + model=None, + custom_llm_provider=custom_llm_provider, + original_exception=e, + completion_kwargs=local_vars, + extra_kwargs=kwargs, + ) + + +@client +def create_run( + eval_id: str, + data_source: Dict[str, Any], + name: Optional[str] = None, + metadata: Optional[Dict[str, Any]] = None, + extra_headers: Optional[Dict[str, Any]] = None, + extra_query: Optional[Dict[str, Any]] = None, + extra_body: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + custom_llm_provider: Optional[str] = None, + **kwargs, +) -> Union[Run, Coroutine[Any, Any, Run]]: + """ + Create a new run for an evaluation + + Args: + eval_id: The ID of the evaluation to run + data_source: Data source configuration for the run (can be jsonl, completions, or responses type) + name: Optional name for the run + metadata: Optional additional metadata + extra_headers: Additional headers for the request + extra_query: Additional query parameters + extra_body: Additional body parameters + timeout: Request timeout (default 600s for long-running operations) + custom_llm_provider: Provider name (e.g., 'openai') + **kwargs: Additional parameters + + Returns: + Run object + """ + local_vars = locals() + try: + litellm_logging_obj: LiteLLMLoggingObj = kwargs.get("litellm_logging_obj") # type: ignore + litellm_call_id: Optional[str] = kwargs.get("litellm_call_id", None) + _is_async = kwargs.pop("acreate_run", False) is True + + # Get LiteLLM parameters + litellm_params = GenericLiteLLMParams(**kwargs) + + # Determine provider + if custom_llm_provider is None: + custom_llm_provider = "openai" + + # Get provider config + evals_api_provider_config: Optional[BaseEvalsAPIConfig] = ( + ProviderConfigManager.get_provider_evals_api_config( # type: ignore + provider=litellm.LlmProviders(custom_llm_provider), + ) + ) + + if evals_api_provider_config is None: + raise ValueError( + f"CREATE run is not supported for {custom_llm_provider}" + ) + + # Build create request + create_request: CreateRunRequest = { + "data_source": data_source, # type: ignore + } + if name is not None: + create_request["name"] = name + # if metadata is not None: + # create_request["metadata"] = metadata + + # Merge extra_body if provided + if extra_body: + create_request.update(extra_body) # type: ignore + + # Validate environment and get headers + headers = extra_headers or {} + headers = evals_api_provider_config.validate_environment( + headers=headers, litellm_params=litellm_params + ) + + # Transform request + api_base = litellm_params.api_base or DEFAULT_OPENAI_API_BASE + url, request_body = evals_api_provider_config.transform_create_run_request( + eval_id=eval_id, + create_request=create_request, + litellm_params=litellm_params, + headers=headers, + ) + + # Pre-call logging + litellm_logging_obj.update_environment_variables( + model=None, + optional_params=request_body, + litellm_params={ + "litellm_call_id": litellm_call_id, + }, + custom_llm_provider=custom_llm_provider, + ) + + # Make HTTP request (default 600s timeout for long-running operations) + response = base_llm_http_handler.create_run_handler( # type: ignore + url=url, + request_body=request_body, + evals_api_provider_config=evals_api_provider_config, + custom_llm_provider=custom_llm_provider, + litellm_params=litellm_params, + logging_obj=litellm_logging_obj, + extra_headers=headers, + timeout=timeout or httpx.Timeout(timeout=600.0, connect=5.0), + _is_async=_is_async, + client=kwargs.get("client"), + shared_session=kwargs.get("shared_session"), + ) + + return response + except Exception as e: + raise litellm.exception_type( + model=None, + custom_llm_provider=custom_llm_provider, + original_exception=e, + completion_kwargs=local_vars, + extra_kwargs=kwargs, + ) + + +@client +async def alist_runs( + eval_id: str, + limit: Optional[int] = None, + after: Optional[str] = None, + before: Optional[str] = None, + order: Optional[str] = None, + extra_headers: Optional[Dict[str, Any]] = None, + extra_query: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + custom_llm_provider: Optional[str] = None, + **kwargs, +) -> ListRunsResponse: + """ + Async: List all runs for an evaluation + + Args: + eval_id: The ID of the evaluation + limit: Number of results to return per page (max 100, default 20) + after: Cursor for pagination - returns runs after this ID + before: Cursor for pagination - returns runs before this ID + order: Sort order ('asc' or 'desc', default 'desc') + extra_headers: Additional headers for the request + extra_query: Additional query parameters + timeout: Request timeout + custom_llm_provider: Provider name (e.g., 'openai') + **kwargs: Additional parameters + + Returns: + ListRunsResponse object + """ + local_vars = locals() + try: + loop = asyncio.get_event_loop() + kwargs["alist_runs"] = True + + func = partial( + list_runs, + eval_id=eval_id, + limit=limit, + after=after, + before=before, + order=order, + extra_headers=extra_headers, + extra_query=extra_query, + timeout=timeout, + custom_llm_provider=custom_llm_provider, + **kwargs, + ) + + ctx = contextvars.copy_context() + func_with_context = partial(ctx.run, func) + init_response = await loop.run_in_executor(None, func_with_context) + + if asyncio.iscoroutine(init_response): + response = await init_response + else: + response = init_response + return response + except Exception as e: + raise litellm.exception_type( + model=None, + custom_llm_provider=custom_llm_provider, + original_exception=e, + completion_kwargs=local_vars, + extra_kwargs=kwargs, + ) + + +@client +def list_runs( + eval_id: str, + limit: Optional[int] = None, + after: Optional[str] = None, + before: Optional[str] = None, + order: Optional[str] = None, + extra_headers: Optional[Dict[str, Any]] = None, + extra_query: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + custom_llm_provider: Optional[str] = None, + **kwargs, +) -> Union[ListRunsResponse, Coroutine[Any, Any, ListRunsResponse]]: + """ + List all runs for an evaluation + + Args: + eval_id: The ID of the evaluation + limit: Number of results to return per page (max 100, default 20) + after: Cursor for pagination - returns runs after this ID + before: Cursor for pagination - returns runs before this ID + order: Sort order ('asc' or 'desc', default 'desc') + extra_headers: Additional headers for the request + extra_query: Additional query parameters + timeout: Request timeout + custom_llm_provider: Provider name (e.g., 'openai') + **kwargs: Additional parameters + + Returns: + ListRunsResponse object + """ + local_vars = locals() + try: + litellm_logging_obj: LiteLLMLoggingObj = kwargs.get("litellm_logging_obj") # type: ignore + litellm_call_id: Optional[str] = kwargs.get("litellm_call_id", None) + _is_async = kwargs.pop("alist_runs", False) is True + + # Get LiteLLM parameters + litellm_params = GenericLiteLLMParams(**kwargs) + + # Determine provider + if custom_llm_provider is None: + custom_llm_provider = "openai" + + # Get provider config + evals_api_provider_config: Optional[BaseEvalsAPIConfig] = ( + ProviderConfigManager.get_provider_evals_api_config( # type: ignore + provider=litellm.LlmProviders(custom_llm_provider), + ) + ) + + if evals_api_provider_config is None: + raise ValueError(f"LIST runs is not supported for {custom_llm_provider}") + + # Build list parameters + list_params: ListRunsParams = {} + if limit is not None: + list_params["limit"] = limit + if after is not None: + list_params["after"] = after + if before is not None: + list_params["before"] = before + if order is not None: + list_params["order"] = order # type: ignore + + # Merge extra_query if provided + if extra_query: + list_params.update(extra_query) # type: ignore + + # Validate environment and get headers + headers = extra_headers or {} + headers = evals_api_provider_config.validate_environment( + headers=headers, litellm_params=litellm_params + ) + + # Transform request + url, query_params = evals_api_provider_config.transform_list_runs_request( + eval_id=eval_id, + list_params=list_params, + litellm_params=litellm_params, + headers=headers, + ) + + # Pre-call logging + litellm_logging_obj.update_environment_variables( + model=None, + optional_params={"eval_id": eval_id, **query_params}, + litellm_params={ + "litellm_call_id": litellm_call_id, + }, + custom_llm_provider=custom_llm_provider, + ) + + # Make HTTP request + response = base_llm_http_handler.list_runs_handler( # type: ignore + url=url, + query_params=query_params, + evals_api_provider_config=evals_api_provider_config, + custom_llm_provider=custom_llm_provider, + litellm_params=litellm_params, + logging_obj=litellm_logging_obj, + extra_headers=headers, + timeout=timeout or request_timeout, + _is_async=_is_async, + client=kwargs.get("client"), + shared_session=kwargs.get("shared_session"), + ) + + return response + except Exception as e: + raise litellm.exception_type( + model=None, + custom_llm_provider=custom_llm_provider, + original_exception=e, + completion_kwargs=local_vars, + extra_kwargs=kwargs, + ) + + +@client +async def aget_run( + eval_id: str, + run_id: str, + extra_headers: Optional[Dict[str, Any]] = None, + extra_query: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + custom_llm_provider: Optional[str] = None, + **kwargs, +) -> Run: + """ + Async: Get a specific run + + Args: + eval_id: The ID of the evaluation + run_id: The ID of the run to retrieve + extra_headers: Additional headers for the request + extra_query: Additional query parameters + timeout: Request timeout + custom_llm_provider: Provider name (e.g., 'openai') + **kwargs: Additional parameters + + Returns: + Run object + """ + local_vars = locals() + try: + loop = asyncio.get_event_loop() + kwargs["aget_run"] = True + + func = partial( + get_run, + eval_id=eval_id, + run_id=run_id, + extra_headers=extra_headers, + extra_query=extra_query, + timeout=timeout, + custom_llm_provider=custom_llm_provider, + **kwargs, + ) + + ctx = contextvars.copy_context() + func_with_context = partial(ctx.run, func) + init_response = await loop.run_in_executor(None, func_with_context) + + if asyncio.iscoroutine(init_response): + response = await init_response + else: + response = init_response + return response + except Exception as e: + raise litellm.exception_type( + model=None, + custom_llm_provider=custom_llm_provider, + original_exception=e, + completion_kwargs=local_vars, + extra_kwargs=kwargs, + ) + + +@client +def get_run( + eval_id: str, + run_id: str, + extra_headers: Optional[Dict[str, Any]] = None, + extra_query: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + custom_llm_provider: Optional[str] = None, + **kwargs, +) -> Union[Run, Coroutine[Any, Any, Run]]: + """ + Get a specific run + + Args: + eval_id: The ID of the evaluation + run_id: The ID of the run to retrieve + extra_headers: Additional headers for the request + extra_query: Additional query parameters + timeout: Request timeout + custom_llm_provider: Provider name (e.g., 'openai') + **kwargs: Additional parameters + + Returns: + Run object + """ + local_vars = locals() + try: + litellm_logging_obj: LiteLLMLoggingObj = kwargs.get("litellm_logging_obj") # type: ignore + litellm_call_id: Optional[str] = kwargs.get("litellm_call_id", None) + _is_async = kwargs.pop("aget_run", False) is True + + # Get LiteLLM parameters + litellm_params = GenericLiteLLMParams(**kwargs) + + # Determine provider + if custom_llm_provider is None: + custom_llm_provider = "openai" + + # Get provider config + evals_api_provider_config: Optional[BaseEvalsAPIConfig] = ( + ProviderConfigManager.get_provider_evals_api_config( # type: ignore + provider=litellm.LlmProviders(custom_llm_provider), + ) + ) + + if evals_api_provider_config is None: + raise ValueError(f"GET run is not supported for {custom_llm_provider}") + + # Validate environment and get headers + headers = extra_headers or {} + headers = evals_api_provider_config.validate_environment( + headers=headers, litellm_params=litellm_params + ) + + # Transform request + api_base = litellm_params.api_base or DEFAULT_OPENAI_API_BASE + url, headers = evals_api_provider_config.transform_get_run_request( + eval_id=eval_id, + run_id=run_id, + api_base=api_base, + litellm_params=litellm_params, + headers=headers, + ) + + # Pre-call logging + litellm_logging_obj.update_environment_variables( + model=None, + optional_params={"eval_id": eval_id, "run_id": run_id}, + litellm_params={ + "litellm_call_id": litellm_call_id, + }, + custom_llm_provider=custom_llm_provider, + ) + + # Make HTTP request + response = base_llm_http_handler.get_run_handler( # type: ignore + url=url, + evals_api_provider_config=evals_api_provider_config, + custom_llm_provider=custom_llm_provider, + litellm_params=litellm_params, + logging_obj=litellm_logging_obj, + extra_headers=headers, + timeout=timeout or request_timeout, + _is_async=_is_async, + client=kwargs.get("client"), + shared_session=kwargs.get("shared_session"), + ) + + return response + except Exception as e: + raise litellm.exception_type( + model=None, + custom_llm_provider=custom_llm_provider, + original_exception=e, + completion_kwargs=local_vars, + extra_kwargs=kwargs, + ) + + +@client +async def acancel_run( + eval_id: str, + run_id: str, + extra_headers: Optional[Dict[str, Any]] = None, + extra_query: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + custom_llm_provider: Optional[str] = None, + **kwargs, +) -> CancelRunResponse: + """ + Async: Cancel a running run + + Args: + eval_id: The ID of the evaluation + run_id: The ID of the run to cancel + extra_headers: Additional headers for the request + extra_query: Additional query parameters + timeout: Request timeout + custom_llm_provider: Provider name (e.g., 'openai') + **kwargs: Additional parameters + + Returns: + CancelRunResponse object + """ + local_vars = locals() + try: + loop = asyncio.get_event_loop() + kwargs["acancel_run"] = True + + func = partial( + cancel_run, + eval_id=eval_id, + run_id=run_id, + extra_headers=extra_headers, + extra_query=extra_query, + timeout=timeout, + custom_llm_provider=custom_llm_provider, + **kwargs, + ) + + ctx = contextvars.copy_context() + func_with_context = partial(ctx.run, func) + init_response = await loop.run_in_executor(None, func_with_context) + + if asyncio.iscoroutine(init_response): + response = await init_response + else: + response = init_response + return response + except Exception as e: + raise litellm.exception_type( + model=None, + custom_llm_provider=custom_llm_provider, + original_exception=e, + completion_kwargs=local_vars, + extra_kwargs=kwargs, + ) + + +@client +def cancel_run( + eval_id: str, + run_id: str, + extra_headers: Optional[Dict[str, Any]] = None, + extra_query: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + custom_llm_provider: Optional[str] = None, + **kwargs, +) -> Union[CancelRunResponse, Coroutine[Any, Any, CancelRunResponse]]: + """ + Cancel a running run + + Args: + eval_id: The ID of the evaluation + run_id: The ID of the run to cancel + extra_headers: Additional headers for the request + extra_query: Additional query parameters + timeout: Request timeout + custom_llm_provider: Provider name (e.g., 'openai') + **kwargs: Additional parameters + + Returns: + CancelRunResponse object + """ + local_vars = locals() + try: + litellm_logging_obj: LiteLLMLoggingObj = kwargs.get("litellm_logging_obj") # type: ignore + litellm_call_id: Optional[str] = kwargs.get("litellm_call_id", None) + _is_async = kwargs.pop("acancel_run", False) is True + + # Get LiteLLM parameters + litellm_params = GenericLiteLLMParams(**kwargs) + + # Determine provider + if custom_llm_provider is None: + custom_llm_provider = "openai" + + # Get provider config + evals_api_provider_config: Optional[BaseEvalsAPIConfig] = ( + ProviderConfigManager.get_provider_evals_api_config( # type: ignore + provider=litellm.LlmProviders(custom_llm_provider), + ) + ) + + if evals_api_provider_config is None: + raise ValueError(f"CANCEL run is not supported for {custom_llm_provider}") + + # Validate environment and get headers + headers = extra_headers or {} + headers = evals_api_provider_config.validate_environment( + headers=headers, litellm_params=litellm_params + ) + + # Transform request + api_base = litellm_params.api_base or DEFAULT_OPENAI_API_BASE + url, headers, request_body = evals_api_provider_config.transform_cancel_run_request( + eval_id=eval_id, + run_id=run_id, + api_base=api_base, + litellm_params=litellm_params, + headers=headers, + ) + + # Pre-call logging + litellm_logging_obj.update_environment_variables( + model=None, + optional_params={"eval_id": eval_id, "run_id": run_id}, + litellm_params={ + "litellm_call_id": litellm_call_id, + }, + custom_llm_provider=custom_llm_provider, + ) + + # Make HTTP request + response = base_llm_http_handler.cancel_run_handler( # type: ignore + url=url, + evals_api_provider_config=evals_api_provider_config, + custom_llm_provider=custom_llm_provider, + litellm_params=litellm_params, + logging_obj=litellm_logging_obj, + extra_headers=headers, + timeout=timeout or request_timeout, + _is_async=_is_async, + client=kwargs.get("client"), + shared_session=kwargs.get("shared_session"), + ) + + return response + except Exception as e: + raise litellm.exception_type( + model=None, + custom_llm_provider=custom_llm_provider, + original_exception=e, + completion_kwargs=local_vars, + extra_kwargs=kwargs, + ) + + +# =================================== +# Delete Run API Functions +# =================================== + + +@client +async def adelete_run( + eval_id: str, + run_id: str, + extra_headers: Optional[Dict[str, Any]] = None, + extra_query: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + custom_llm_provider: Optional[str] = None, + **kwargs, +) -> RunDeleteResponse: + """ + Async: Delete a run + + Args: + eval_id: The ID of the evaluation + run_id: The ID of the run to delete + extra_headers: Additional headers for the request + extra_query: Additional query parameters + timeout: Request timeout + custom_llm_provider: Provider name (e.g., 'openai') + **kwargs: Additional parameters + + Returns: + RunDeleteResponse object + """ + local_vars = locals() + try: + loop = asyncio.get_event_loop() + kwargs["adelete_run"] = True + + func = partial( + delete_run, + eval_id=eval_id, + run_id=run_id, + extra_headers=extra_headers, + extra_query=extra_query, + timeout=timeout, + custom_llm_provider=custom_llm_provider, + **kwargs, + ) + + ctx = contextvars.copy_context() + func_with_context = partial(ctx.run, func) + init_response = await loop.run_in_executor(None, func_with_context) + + if asyncio.iscoroutine(init_response): + response = await init_response + else: + response = init_response + return response + except Exception as e: + raise litellm.exception_type( + model=None, + custom_llm_provider=custom_llm_provider, + original_exception=e, + completion_kwargs=local_vars, + extra_kwargs=kwargs, + ) + + +@client +def delete_run( + eval_id: str, + run_id: str, + extra_headers: Optional[Dict[str, Any]] = None, + extra_query: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + custom_llm_provider: Optional[str] = None, + **kwargs, +) -> Union[RunDeleteResponse, Coroutine[Any, Any, RunDeleteResponse]]: + """ + Delete a run + + Args: + eval_id: The ID of the evaluation + run_id: The ID of the run to delete + extra_headers: Additional headers for the request + extra_query: Additional query parameters + timeout: Request timeout + custom_llm_provider: Provider name (e.g., 'openai') + **kwargs: Additional parameters + + Returns: + RunDeleteResponse object + """ + local_vars = locals() + try: + litellm_logging_obj: LiteLLMLoggingObj = kwargs.get("litellm_logging_obj") # type: ignore + litellm_call_id: Optional[str] = kwargs.get("litellm_call_id", None) + _is_async = kwargs.pop("adelete_run", False) is True + + # Get LiteLLM parameters + litellm_params = GenericLiteLLMParams(**kwargs) + + # Determine provider + if custom_llm_provider is None: + custom_llm_provider = "openai" + + # Get provider config + evals_api_provider_config: Optional[BaseEvalsAPIConfig] = ( + ProviderConfigManager.get_provider_evals_api_config( # type: ignore + provider=litellm.LlmProviders(custom_llm_provider), + ) + ) + + if evals_api_provider_config is None: + raise ValueError(f"DELETE run is not supported for {custom_llm_provider}") + + # Validate environment and get headers + headers = extra_headers or {} + headers = evals_api_provider_config.validate_environment( + headers=headers, litellm_params=litellm_params + ) + + # Transform request + api_base = litellm_params.api_base or DEFAULT_OPENAI_API_BASE + url, headers, request_body = evals_api_provider_config.transform_delete_run_request( + eval_id=eval_id, + run_id=run_id, + api_base=api_base, + litellm_params=litellm_params, + headers=headers, + ) + + # Pre-call logging + litellm_logging_obj.update_environment_variables( + model=None, + optional_params={"eval_id": eval_id, "run_id": run_id}, + litellm_params={ + "litellm_call_id": litellm_call_id, + }, + custom_llm_provider=custom_llm_provider, + ) + + # Make HTTP request + response = base_llm_http_handler.delete_run_handler( # type: ignore + url=url, + evals_api_provider_config=evals_api_provider_config, + custom_llm_provider=custom_llm_provider, + litellm_params=litellm_params, + logging_obj=litellm_logging_obj, + extra_headers=headers, + timeout=timeout or request_timeout, + _is_async=_is_async, + client=kwargs.get("client"), + shared_session=kwargs.get("shared_session"), + ) + + return response + except Exception as e: + raise litellm.exception_type( + model=None, + custom_llm_provider=custom_llm_provider, + original_exception=e, + completion_kwargs=local_vars, + extra_kwargs=kwargs, + ) diff --git a/litellm/integrations/SlackAlerting/budget_alert_types.py b/litellm/integrations/SlackAlerting/budget_alert_types.py index 205c5c89e3..ea80b25854 100644 --- a/litellm/integrations/SlackAlerting/budget_alert_types.py +++ b/litellm/integrations/SlackAlerting/budget_alert_types.py @@ -74,6 +74,14 @@ class ProjectedLimitExceededAlert(BaseBudgetAlertType): return user_info.token or "default_id" +class ProjectBudgetAlert(BaseBudgetAlertType): + def get_event_message(self) -> str: + return "Project Budget: " + + def get_id(self, user_info: CallInfo) -> str: + return user_info.token or "default_id" + + def get_budget_alert_type( type: Literal[ "token_budget", @@ -84,6 +92,7 @@ def get_budget_alert_type( "organization_budget", "proxy_budget", "projected_limit_exceeded", + "project_budget", ], ) -> BaseBudgetAlertType: """Factory function to get the appropriate budget alert type class""" @@ -97,6 +106,7 @@ def get_budget_alert_type( "organization_budget": OrganizationBudgetAlert(), "token_budget": TokenBudgetAlert(), "projected_limit_exceeded": ProjectedLimitExceededAlert(), + "project_budget": ProjectBudgetAlert(), } if type in alert_types: diff --git a/litellm/integrations/SlackAlerting/slack_alerting.py b/litellm/integrations/SlackAlerting/slack_alerting.py index 8fb3e132de..a525856db8 100644 --- a/litellm/integrations/SlackAlerting/slack_alerting.py +++ b/litellm/integrations/SlackAlerting/slack_alerting.py @@ -538,6 +538,7 @@ class SlackAlerting(CustomBatchLogger): "organization_budget", "proxy_budget", "projected_limit_exceeded", + "project_budget", ], user_info: CallInfo, ): @@ -1378,9 +1379,13 @@ Model Info: """ if self.alerting is None: return - + # Start periodic flush if not already started - if not self.periodic_started and self.alerting is not None and len(self.alerting) > 0: + if ( + not self.periodic_started + and self.alerting is not None + and len(self.alerting) > 0 + ): asyncio.create_task(self.periodic_flush()) self.periodic_started = True diff --git a/litellm/integrations/cloudzero/transform.py b/litellm/integrations/cloudzero/transform.py index c36833a6db..b40a71da1c 100644 --- a/litellm/integrations/cloudzero/transform.py +++ b/litellm/integrations/cloudzero/transform.py @@ -141,7 +141,7 @@ class CBFTransformer: # Required CBF fields 'time/usage_start': usage_date.isoformat() if usage_date else None, # Required: ISO-formatted UTC datetime 'cost/cost': float(row.get('spend', 0.0)), # Required: billed cost - 'resource/id': model, # Send model name + 'resource/id': resource_id, # CZRN (CloudZero Resource Name) # Usage metrics for token consumption 'usage/amount': total_tokens, # Numeric value of tokens consumed diff --git a/litellm/integrations/custom_guardrail.py b/litellm/integrations/custom_guardrail.py index 407bc581f7..4a1e3e41e9 100644 --- a/litellm/integrations/custom_guardrail.py +++ b/litellm/integrations/custom_guardrail.py @@ -26,10 +26,16 @@ from litellm.types.utils import ( CallTypes, GenericGuardrailAPIInputs, GuardrailStatus, + GuardrailTracingDetail, LLMResponseTypes, StandardLoggingGuardrailInformation, ) +try: + from fastapi.exceptions import HTTPException +except ImportError: + HTTPException = None # type: ignore + if TYPE_CHECKING: from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj dc = DualCache() @@ -515,9 +521,15 @@ class CustomGuardrail(CustomLogger): masked_entity_count: Optional[Dict[str, int]] = None, guardrail_provider: Optional[str] = None, event_type: Optional[GuardrailEventHooks] = None, + tracing_detail: Optional[GuardrailTracingDetail] = None, ) -> None: """ Builds `StandardLoggingGuardrailInformation` and adds it to the request metadata so it can be used for logging to DataDog, Langfuse, etc. + + Args: + tracing_detail: Optional typed dict with provider-specific tracing fields + (guardrail_id, policy_template, detection_method, confidence_score, + classification, match_details, patterns_checked, alert_recipients). """ if isinstance(guardrail_json_response, Exception): guardrail_json_response = str(guardrail_json_response) @@ -554,6 +566,7 @@ class CustomGuardrail(CustomLogger): end_time=end_time, duration=duration, masked_entity_count=masked_entity_count, + **(tracing_detail or {}), ) def _append_guardrail_info(container: dict) -> None: @@ -624,7 +637,9 @@ class CustomGuardrail(CustomLogger): This gets logged on downsteam Langfuse, DataDog, etc. """ # Convert None to empty dict to satisfy type requirements - guardrail_response = {} if response is None else response + guardrail_response: Union[Dict[str, Any], str] = ( + {} if response is None else response + ) # For apply_guardrail functions in custom_code_guardrail scenario, # simplify the logged response to "allow", "deny", or "mask" @@ -648,6 +663,27 @@ class CustomGuardrail(CustomLogger): ) return response + @staticmethod + def _is_guardrail_intervention(e: Exception) -> bool: + """ + Returns True if the exception represents an intentional guardrail block + (this was logged previously as an API failure - guardrail_failed_to_respond). + + Guardrails signal intentional blocks by raising: + - HTTPException with status 400 (content policy violation) + - ModifyResponseException (passthrough mode violation) + """ + + if isinstance(e, ModifyResponseException): + return True + if ( + HTTPException is not None + and isinstance(e, HTTPException) + and e.status_code == 400 + ): + return True + return False + def _process_error( self, e: Exception, @@ -662,6 +698,11 @@ class CustomGuardrail(CustomLogger): This gets logged on downsteam Langfuse, DataDog, etc. """ + guardrail_status: GuardrailStatus = ( + "guardrail_intervened" + if self._is_guardrail_intervention(e) + else "guardrail_failed_to_respond" + ) # For custom_code_guardrail scenario, log as "deny" instead of full exception # Check if this is from custom_code_guardrail by checking the class name guardrail_response: Union[Exception, str] = e @@ -671,7 +712,7 @@ class CustomGuardrail(CustomLogger): self.add_standard_logging_guardrail_information_to_request_data( guardrail_json_response=guardrail_response, request_data=request_data, - guardrail_status="guardrail_failed_to_respond", + guardrail_status=guardrail_status, duration=duration, start_time=start_time, end_time=end_time, @@ -781,8 +822,8 @@ def log_guardrail_information(func): - during_call - post_call """ - import asyncio import functools + import inspect def _infer_event_type_from_function_name( func_name: str, @@ -863,7 +904,7 @@ def log_guardrail_information(func): @functools.wraps(func) def wrapper(*args, **kwargs): - if asyncio.iscoroutinefunction(func): + if inspect.iscoroutinefunction(func): return async_wrapper(*args, **kwargs) return sync_wrapper(*args, **kwargs) diff --git a/litellm/integrations/datadog/datadog_cost_management.py b/litellm/integrations/datadog/datadog_cost_management.py index 2eb94b59dd..a961d4f924 100644 --- a/litellm/integrations/datadog/datadog_cost_management.py +++ b/litellm/integrations/datadog/datadog_cost_management.py @@ -93,7 +93,9 @@ class DatadogCostManagementLogger(CustomBatchLogger): Aggregates costs by Provider, Model, and Date. Returns a list of DatadogFOCUSCostEntry. """ - aggregator: Dict[Tuple[str, str, str, Tuple[Tuple[str, str], ...]], DatadogFOCUSCostEntry] = {} + aggregator: Dict[ + Tuple[str, str, str, Tuple[Tuple[str, str], ...]], DatadogFOCUSCostEntry + ] = {} for log in logs: try: @@ -167,10 +169,20 @@ class DatadogCostManagementLogger(CustomBatchLogger): metadata = log.get("metadata", {}) if metadata: # Add user info - if "user_api_key_alias" in metadata: + # Add user info + if metadata.get("user_api_key_alias"): tags["user"] = str(metadata["user_api_key_alias"]) - if "user_api_key_team_alias" in metadata: - tags["team"] = str(metadata["user_api_key_team_alias"]) + + # Add Team Tag + team_tag = ( + metadata.get("user_api_key_team_alias") + or metadata.get("team_alias") # type: ignore + or metadata.get("user_api_key_team_id") + or metadata.get("team_id") # type: ignore + ) + + if team_tag: + tags["team"] = str(team_tag) # model_group is not in StandardLoggingMetadata TypedDict, so we need to access it via dict.get() model_group = metadata.get("model_group") # type: ignore[misc] if model_group: diff --git a/litellm/integrations/datadog/datadog_handler.py b/litellm/integrations/datadog/datadog_handler.py index e2f30f2f61..0406f1e5d2 100644 --- a/litellm/integrations/datadog/datadog_handler.py +++ b/litellm/integrations/datadog/datadog_handler.py @@ -55,4 +55,15 @@ def get_datadog_tags( request_tags = standard_logging_object.get("request_tags", []) or [] tags.extend(f"request_tag:{tag}" for tag in request_tags) + # Add Team Tag + metadata = standard_logging_object.get("metadata", {}) or {} + team_tag = ( + metadata.get("user_api_key_team_alias") + or metadata.get("team_alias") + or metadata.get("user_api_key_team_id") + or metadata.get("team_id") + ) + if team_tag: + tags.append(f"team:{team_tag}") + return ",".join(tags) diff --git a/litellm/integrations/langfuse/langfuse_otel.py b/litellm/integrations/langfuse/langfuse_otel.py index 8955d3619f..b96ec72b04 100644 --- a/litellm/integrations/langfuse/langfuse_otel.py +++ b/litellm/integrations/langfuse/langfuse_otel.py @@ -1,6 +1,7 @@ import base64 import json # <--- NEW import os +from datetime import datetime from typing import TYPE_CHECKING, Any, Optional, Union from litellm._logging import verbose_logger @@ -392,6 +393,22 @@ class LangfuseOtelLogger(OpenTelemetry): return dynamic_headers + def create_litellm_proxy_request_started_span( + self, + start_time: datetime, + headers: dict, + ) -> Optional[Span]: + """ + Override to prevent creating empty proxy request spans. + + Langfuse should only receive spans for actual LLM calls, not for + internal proxy operations (auth, postgres, proxy_pre_call, etc.). + + By returning None, we prevent the parent span from being created, + which in turn prevents empty traces from being sent to Langfuse. + """ + return None + async def async_service_success_hook(self, *args, **kwargs): """ Langfuse should not receive service success logs. diff --git a/litellm/integrations/opentelemetry.py b/litellm/integrations/opentelemetry.py index b847180174..35362a71cc 100644 --- a/litellm/integrations/opentelemetry.py +++ b/litellm/integrations/opentelemetry.py @@ -1051,23 +1051,15 @@ class OpenTelemetry(CustomLogger): # See: https://github.com/open-telemetry/opentelemetry-python/pull/4676 # TODO: Refactor to use the proper OTEL Logs API instead of directly creating SDK LogRecords - from opentelemetry._logs import ( - SeverityNumber, - get_logger, - ) - - # MyPy evaluates both branches of try/except imports and can fail when - # newer OTEL stubs remove/relocate symbols. Gate the typing import so - # only the canonical location is type-checked. - if TYPE_CHECKING: - from opentelemetry.sdk._logs._internal import LogRecord as SdkLogRecord - else: - try: - from opentelemetry.sdk._logs import ( - LogRecord as SdkLogRecord, # type: ignore[attr-defined] - ) - except ImportError: - from opentelemetry.sdk._logs._internal import LogRecord as SdkLogRecord + from opentelemetry._logs import SeverityNumber, get_logger + try: + from opentelemetry.sdk._logs import ( # type: ignore[attr-defined] # OTEL < 1.39.0 + LogRecord as SdkLogRecord, + ) + except ImportError: + from opentelemetry.sdk._logs._internal import ( + LogRecord as SdkLogRecord, # type: ignore[attr-defined] # OTEL >= 1.39.0 + ) otel_logger = get_logger(LITELLM_LOGGER_NAME) diff --git a/litellm/integrations/prometheus.py b/litellm/integrations/prometheus.py index 1675201f1f..4c7afd5a57 100644 --- a/litellm/integrations/prometheus.py +++ b/litellm/integrations/prometheus.py @@ -22,6 +22,10 @@ from typing import ( import litellm from litellm._logging import print_verbose, verbose_logger from litellm.integrations.custom_logger import CustomLogger +from litellm.litellm_core_utils.core_helpers import ( + get_litellm_metadata_from_kwargs, + get_metadata_variable_name_from_kwargs, +) from litellm.proxy._types import ( LiteLLM_DeletedVerificationToken, LiteLLM_TeamTable, @@ -1055,16 +1059,16 @@ class PrometheusLogger(CustomLogger): enum_values=enum_values, ) - if ( - standard_logging_payload["stream"] is True - ): # log successful streaming requests from logging event hook. - _labels = prometheus_label_factory( - supported_enum_labels=self.get_labels_for_metric( - metric_name="litellm_proxy_total_requests_metric" - ), - enum_values=enum_values, - ) - self.litellm_proxy_total_requests_metric.labels(**_labels).inc() + # increment litellm_proxy_total_requests_metric for all successful requests + # (both streaming and non-streaming) in this single location to prevent + # double-counting that occurs when async_post_call_success_hook also increments + _labels = prometheus_label_factory( + supported_enum_labels=self.get_labels_for_metric( + metric_name="litellm_proxy_total_requests_metric" + ), + enum_values=enum_values, + ) + self.litellm_proxy_total_requests_metric.labels(**_labels).inc() def _increment_token_metrics( self, @@ -1086,13 +1090,6 @@ class PrometheusLogger(CustomLogger): ): _tags = standard_logging_payload["request_tags"] - _labels = prometheus_label_factory( - supported_enum_labels=self.get_labels_for_metric( - metric_name="litellm_proxy_total_requests_metric" - ), - enum_values=enum_values, - ) - _labels = prometheus_label_factory( supported_enum_labels=self.get_labels_for_metric( metric_name="litellm_total_tokens_metric" @@ -1655,49 +1652,12 @@ class PrometheusLogger(CustomLogger): ): """ Proxy level tracking - triggered when the proxy responds with a success response to the client + + Note: litellm_proxy_total_requests_metric is NOT incremented here to avoid + double-counting. It is incremented in async_log_success_event which fires + for all successful requests (both streaming and non-streaming). """ - try: - from litellm.litellm_core_utils.litellm_logging import ( - StandardLoggingPayloadSetup, - ) - - if self._should_skip_metrics_for_invalid_key( - user_api_key_dict=user_api_key_dict - ): - return - - _metadata = data.get("metadata", {}) or {} - enum_values = UserAPIKeyLabelValues( - end_user=user_api_key_dict.end_user_id, - hashed_api_key=user_api_key_dict.api_key, - api_key_alias=user_api_key_dict.key_alias, - requested_model=data.get("model", ""), - team=user_api_key_dict.team_id, - team_alias=user_api_key_dict.team_alias, - user=user_api_key_dict.user_id, - user_email=user_api_key_dict.user_email, - status_code="200", - route=user_api_key_dict.request_route, - tags=StandardLoggingPayloadSetup._get_request_tags( - litellm_params=data, - proxy_server_request=data.get("proxy_server_request", {}), - ), - client_ip=_metadata.get("requester_ip_address"), - user_agent=_metadata.get("user_agent"), - ) - _labels = prometheus_label_factory( - supported_enum_labels=self.get_labels_for_metric( - metric_name="litellm_proxy_total_requests_metric" - ), - enum_values=enum_values, - ) - self.litellm_proxy_total_requests_metric.labels(**_labels).inc() - - except Exception as e: - verbose_logger.exception( - "prometheus Layer Error(): Exception occured - {}".format(str(e)) - ) - pass + pass def _safe_get(self, obj: Any, key: str, default: Any = None) -> Any: """Get value from dict or Pydantic model.""" @@ -2004,7 +1964,7 @@ class PrometheusLogger(CustomLogger): api_base = standard_logging_payload["api_base"] _litellm_params = request_kwargs.get("litellm_params", {}) or {} - _metadata = _litellm_params.get("metadata", {}) + _metadata = get_litellm_metadata_from_kwargs(request_kwargs) litellm_model_name = request_kwargs.get("model", None) llm_provider = _litellm_params.get("custom_llm_provider", None) _model_info = _metadata.get("model_info") or {} @@ -2220,7 +2180,8 @@ class PrometheusLogger(CustomLogger): original_model_group, kwargs, ) - _metadata = kwargs.get("metadata", {}) + _metadata_key = get_metadata_variable_name_from_kwargs(kwargs) + _metadata = kwargs.get(_metadata_key) or {} standard_metadata: StandardLoggingMetadata = ( StandardLoggingPayloadSetup.get_standard_logging_metadata( metadata=_metadata @@ -2265,7 +2226,8 @@ class PrometheusLogger(CustomLogger): kwargs, ) _new_model = kwargs.get("model") - _metadata = kwargs.get("metadata", {}) + _metadata_key = get_metadata_variable_name_from_kwargs(kwargs) + _metadata = kwargs.get(_metadata_key) or {} _tags = cast(List[str], kwargs.get("tags") or []) standard_metadata: StandardLoggingMetadata = ( StandardLoggingPayloadSetup.get_standard_logging_metadata( diff --git a/litellm/integrations/s3_v2.py b/litellm/integrations/s3_v2.py index 534b85e475..eddc80dbc1 100644 --- a/litellm/integrations/s3_v2.py +++ b/litellm/integrations/s3_v2.py @@ -51,6 +51,7 @@ class S3Logger(CustomBatchLogger, BaseAWSLLM): s3_use_team_prefix: bool = False, s3_strip_base64_files: bool = False, s3_use_key_prefix: bool = False, + s3_use_virtual_hosted_style: bool = False, **kwargs, ): try: @@ -78,7 +79,8 @@ class S3Logger(CustomBatchLogger, BaseAWSLLM): s3_path=s3_path, s3_use_team_prefix=s3_use_team_prefix, s3_strip_base64_files=s3_strip_base64_files, - s3_use_key_prefix=s3_use_key_prefix + s3_use_key_prefix=s3_use_key_prefix, + s3_use_virtual_hosted_style=s3_use_virtual_hosted_style ) verbose_logger.debug(f"s3 logger using endpoint url {s3_endpoint_url}") @@ -135,6 +137,7 @@ class S3Logger(CustomBatchLogger, BaseAWSLLM): s3_use_team_prefix: bool = False, s3_strip_base64_files: bool = False, s3_use_key_prefix: bool = False, + s3_use_virtual_hosted_style: bool = False, ): """ Initialize the s3 params for this logging callback @@ -217,6 +220,11 @@ class S3Logger(CustomBatchLogger, BaseAWSLLM): or s3_strip_base64_files ) + self.s3_use_virtual_hosted_style = ( + bool(litellm.s3_callback_params.get("s3_use_virtual_hosted_style", False)) + or s3_use_virtual_hosted_style + ) + return async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): @@ -247,8 +255,14 @@ class S3Logger(CustomBatchLogger, BaseAWSLLM): standard_logging_payload=kwargs.get("standard_logging_object", None), ) + # afile_delete and other non-model call types never produce a standard_logging_object, + # so s3_batch_logging_element is None. Skip gracefully instead of raising ValueError. if s3_batch_logging_element is None: - raise ValueError("s3_batch_logging_element is None") + verbose_logger.debug( + "s3 Logging - skipping event, no standard_logging_object for call_type=%s", + kwargs.get("call_type", "unknown"), + ) + return verbose_logger.debug( "\ns3 Logger - Logging payload = %s", s3_batch_logging_element @@ -302,13 +316,20 @@ class S3Logger(CustomBatchLogger, BaseAWSLLM): url = f"https://{self.s3_bucket_name}.s3.{self.s3_region_name}.amazonaws.com/{batch_logging_element.s3_object_key}" if self.s3_endpoint_url and self.s3_bucket_name: - url = ( - self.s3_endpoint_url - + "/" - + self.s3_bucket_name - + "/" - + batch_logging_element.s3_object_key - ) + if self.s3_use_virtual_hosted_style: + # Virtual-hosted-style: bucket.endpoint/key + endpoint_host = self.s3_endpoint_url.replace("https://", "").replace("http://", "") + protocol = "https://" if self.s3_endpoint_url.startswith("https://") else "http://" + url = f"{protocol}{self.s3_bucket_name}.{endpoint_host}/{batch_logging_element.s3_object_key}" + else: + # Path-style: endpoint/bucket/key + url = ( + self.s3_endpoint_url + + "/" + + self.s3_bucket_name + + "/" + + batch_logging_element.s3_object_key + ) # Convert JSON to string json_string = safe_dumps(batch_logging_element.payload) @@ -456,13 +477,20 @@ class S3Logger(CustomBatchLogger, BaseAWSLLM): url = f"https://{self.s3_bucket_name}.s3.{self.s3_region_name}.amazonaws.com/{batch_logging_element.s3_object_key}" if self.s3_endpoint_url and self.s3_bucket_name: - url = ( - self.s3_endpoint_url - + "/" - + self.s3_bucket_name - + "/" - + batch_logging_element.s3_object_key - ) + if self.s3_use_virtual_hosted_style: + # Virtual-hosted-style: bucket.endpoint/key + endpoint_host = self.s3_endpoint_url.replace("https://", "").replace("http://", "") + protocol = "https://" if self.s3_endpoint_url.startswith("https://") else "http://" + url = f"{protocol}{self.s3_bucket_name}.{endpoint_host}/{batch_logging_element.s3_object_key}" + else: + # Path-style: endpoint/bucket/key + url = ( + self.s3_endpoint_url + + "/" + + self.s3_bucket_name + + "/" + + batch_logging_element.s3_object_key + ) # Convert JSON to string json_string = safe_dumps(batch_logging_element.payload) @@ -550,13 +578,20 @@ class S3Logger(CustomBatchLogger, BaseAWSLLM): url = f"https://{self.s3_bucket_name}.s3.{self.s3_region_name}.amazonaws.com/{s3_object_key}" if self.s3_endpoint_url and self.s3_bucket_name: - url = ( - self.s3_endpoint_url - + "/" - + self.s3_bucket_name - + "/" - + s3_object_key - ) + if self.s3_use_virtual_hosted_style: + # Virtual-hosted-style: bucket.endpoint/key + endpoint_host = self.s3_endpoint_url.replace("https://", "").replace("http://", "") + protocol = "https://" if self.s3_endpoint_url.startswith("https://") else "http://" + url = f"{protocol}{self.s3_bucket_name}.{endpoint_host}/{s3_object_key}" + else: + # Path-style: endpoint/bucket/key + url = ( + self.s3_endpoint_url + + "/" + + self.s3_bucket_name + + "/" + + s3_object_key + ) # Prepare the request for GET operation # For GET requests, we need x-amz-content-sha256 with hash of empty string @@ -618,4 +653,4 @@ class S3Logger(CustomBatchLogger, BaseAWSLLM): verbose_logger.exception( f"Error retrieving object {object_key} from cold storage: {str(e)}" ) - return None + return None \ No newline at end of file diff --git a/litellm/integrations/websearch_interception/handler.py b/litellm/integrations/websearch_interception/handler.py index 1277cac51d..d7858d71eb 100644 --- a/litellm/integrations/websearch_interception/handler.py +++ b/litellm/integrations/websearch_interception/handler.py @@ -16,6 +16,7 @@ from litellm.constants import LITELLM_WEB_SEARCH_TOOL_NAME from litellm.integrations.custom_logger import CustomLogger from litellm.integrations.websearch_interception.tools import ( get_litellm_web_search_tool, + get_litellm_web_search_tool_openai, is_web_search_tool, is_web_search_tool_chat_completion, ) @@ -77,7 +78,13 @@ class WebSearchInterceptionLogger(CustomLogger): that we can intercept and execute ourselves. """ # Check if this is for an enabled provider - custom_llm_provider = kwargs.get("litellm_params", {}).get("custom_llm_provider", "") + # Try top-level kwargs first, then nested litellm_params, then derive from model name + custom_llm_provider = kwargs.get("custom_llm_provider", "") or kwargs.get("litellm_params", {}).get("custom_llm_provider", "") + if not custom_llm_provider: + try: + _, custom_llm_provider, _, _ = litellm.get_llm_provider(model=kwargs.get("model", "")) + except Exception: + custom_llm_provider = "" if custom_llm_provider not in self.enabled_providers: return None @@ -101,7 +108,7 @@ class WebSearchInterceptionLogger(CustomLogger): for tool in tools: if is_web_search_tool(tool): # Convert to LiteLLM standard web search tool - converted_tool = get_litellm_web_search_tool() + converted_tool = get_litellm_web_search_tool_openai() converted_tools.append(converted_tool) verbose_logger.debug( f"WebSearchInterception: Converted {tool.get('name', 'unknown')} " @@ -111,8 +118,9 @@ class WebSearchInterceptionLogger(CustomLogger): # Keep other tools as-is converted_tools.append(tool) - # Return modified kwargs with converted tools - return {"tools": converted_tools} + # Update tools in-place and return full kwargs + kwargs["tools"] = converted_tools + return kwargs @classmethod def from_config_yaml( diff --git a/litellm/integrations/websearch_interception/tools.py b/litellm/integrations/websearch_interception/tools.py index c39d150fb1..7ef2b35004 100644 --- a/litellm/integrations/websearch_interception/tools.py +++ b/litellm/integrations/websearch_interception/tools.py @@ -49,6 +49,39 @@ def get_litellm_web_search_tool() -> Dict[str, Any]: } +def get_litellm_web_search_tool_openai() -> Dict[str, Any]: + """ + Get the standard LiteLLM web search tool definition in OpenAI format. + + Used by async_pre_call_deployment_hook which runs in the chat completions + path where tools must be in OpenAI format (type: "function" with + function.parameters). + + Returns: + Dict containing the OpenAI-style tool definition. + """ + return { + "type": "function", + "function": { + "name": LITELLM_WEB_SEARCH_TOOL_NAME, + "description": ( + "Search the web for information. Use this when you need current " + "information or answers to questions that require up-to-date data." + ), + "parameters": { + "type": "object", + "properties": { + "query": { + "type": "string", + "description": "The search query to execute" + } + }, + "required": ["query"] + } + } + } + + def is_web_search_tool_chat_completion(tool: Dict[str, Any]) -> bool: """ Check if a tool is a web search tool for Chat Completions API (strict check). diff --git a/litellm/litellm_core_utils/api_route_to_call_types.py b/litellm/litellm_core_utils/api_route_to_call_types.py index 4146ff6d6a..2ae9986ce9 100644 --- a/litellm/litellm_core_utils/api_route_to_call_types.py +++ b/litellm/litellm_core_utils/api_route_to_call_types.py @@ -3,6 +3,9 @@ Dictionary mapping API routes to their corresponding CallTypes in LiteLLM. This dictionary maps each API endpoint to the CallTypes that can be used for that route. Each route can have both async (prefixed with 'a') and sync call types. + +Route patterns may contain placeholders like {agent_id}, {model}, {batch_id}; these +match a single path segment when resolving call types for a concrete path. """ from typing import List, Optional @@ -10,17 +13,43 @@ from typing import List, Optional from litellm.types.utils import API_ROUTE_TO_CALL_TYPES, CallTypes +def _route_matches_pattern(route: str, pattern: str) -> bool: + """ + Return True if the concrete route matches the pattern. + Pattern segments like {param} match any single path segment. + """ + route_parts = route.strip("/").split("/") + pattern_parts = pattern.strip("/").split("/") + if len(route_parts) != len(pattern_parts): + return False + for r, p in zip(route_parts, pattern_parts): + if p.startswith("{") and p.endswith("}"): + continue + if r != p: + return False + return True + + def get_call_types_for_route(route: str) -> Optional[List[CallTypes]]: """ Get the list of CallTypes for a given API route. + Supports both exact keys and dynamic patterns (e.g. /a2a/my-agent/message/send + matches /a2a/{agent_id}/message/send). + Args: - route: API route path (e.g., "/chat/completions") + route: API route path (e.g., "/chat/completions" or "/a2a/my-pydantic-agent/message/send") Returns: List of CallTypes for that route, or None if route not found """ - return API_ROUTE_TO_CALL_TYPES.get(route, None) + exact = API_ROUTE_TO_CALL_TYPES.get(route, None) + if exact is not None: + return exact + for pattern, call_types in API_ROUTE_TO_CALL_TYPES.items(): + if _route_matches_pattern(route, pattern): + return call_types + return None def get_routes_for_call_type(call_type: CallTypes) -> list: diff --git a/litellm/litellm_core_utils/exception_mapping_utils.py b/litellm/litellm_core_utils/exception_mapping_utils.py index 03fbdd463d..dde44cced3 100644 --- a/litellm/litellm_core_utils/exception_mapping_utils.py +++ b/litellm/litellm_core_utils/exception_mapping_utils.py @@ -70,6 +70,11 @@ class ExceptionCheckers: Check if an error string indicates a context window exceeded error. """ _error_str_lowercase = error_str.lower() + # Exclude param validation errors (e.g. OpenAI "user" param max 64 chars) + if "string_above_max_length" in _error_str_lowercase: + return False + if "invalid 'user'" in _error_str_lowercase and "string too long" in _error_str_lowercase: + return False known_exception_substrings = [ "exceed context limit", "this model's maximum context length is", diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py index 82a7af64f9..258df1bb90 100644 --- a/litellm/litellm_core_utils/litellm_logging.py +++ b/litellm/litellm_core_utils/litellm_logging.py @@ -1,5431 +1,5512 @@ -# What is this? -## Common Utility file for Logging handler -# Logging function -> log the exact model details + what's being sent | Non-Blocking -import copy -import datetime -import json -import os -import re -import subprocess -import sys -import time -import traceback -from datetime import datetime as dt_object -from functools import lru_cache -from typing import ( - TYPE_CHECKING, - Any, - Callable, - Dict, - List, - Literal, - Optional, - Tuple, - Type, - Union, - cast, -) - -from httpx import Response -from pydantic import BaseModel - -import litellm -from litellm import ( - _custom_logger_compatible_callbacks_literal, - json_logs, - log_raw_request_response, - turn_off_message_logging, -) -from litellm._logging import _is_debugging_on, verbose_logger -from litellm._uuid import uuid -from litellm.batches.batch_utils import _handle_completed_batch -from litellm.caching.caching import DualCache, InMemoryCache -from litellm.caching.caching_handler import LLMCachingHandler -from litellm.constants import ( - DEFAULT_MOCK_RESPONSE_COMPLETION_TOKEN_COUNT, - DEFAULT_MOCK_RESPONSE_PROMPT_TOKEN_COUNT, - SENTRY_DENYLIST, - SENTRY_PII_DENYLIST, -) -from litellm.cost_calculator import ( - RealtimeAPITokenUsageProcessor, - _select_model_name_for_cost_calc, -) -from litellm.integrations.agentops import AgentOps -from litellm.integrations.anthropic_cache_control_hook import AnthropicCacheControlHook -from litellm.integrations.arize.arize import ArizeLogger -from litellm.integrations.custom_guardrail import CustomGuardrail -from litellm.integrations.custom_logger import CustomLogger -from litellm.integrations.deepeval.deepeval import DeepEvalLogger -from litellm.integrations.mlflow import MlflowLogger -from litellm.integrations.sqs import SQSLogger -from litellm.litellm_core_utils.core_helpers import reconstruct_model_name -from litellm.litellm_core_utils.get_litellm_params import get_litellm_params -from litellm.litellm_core_utils.llm_cost_calc.tool_call_cost_tracking import ( - StandardBuiltInToolCostTracking, -) -from litellm.litellm_core_utils.model_param_helper import ModelParamHelper -from litellm.litellm_core_utils.redact_messages import ( - redact_message_input_output_from_custom_logger, - redact_message_input_output_from_logging, -) -from litellm.llms.base_llm.ocr.transformation import OCRResponse -from litellm.llms.base_llm.search.transformation import SearchResponse -from litellm.responses.utils import ResponseAPILoggingUtils -from litellm.types.agents import LiteLLMSendMessageResponse -from litellm.types.containers.main import ContainerObject -from litellm.types.llms.openai import ( - AllMessageValues, - Batch, - FineTuningJob, - HttpxBinaryResponseContent, - OpenAIFileObject, - OpenAIModerationResponse, - ResponseAPIUsage, - ResponseCompletedEvent, - ResponsesAPIResponse, -) -from litellm.types.mcp import MCPPostCallResponseObject -from litellm.types.prompts.init_prompts import PromptSpec -from litellm.types.rerank import RerankResponse -from litellm.types.utils import ( - CachingDetails, - CallTypes, - CostBreakdown, - CostResponseTypes, - CustomPricingLiteLLMParams, - DynamicPromptManagementParamLiteral, - EmbeddingResponse, - GuardrailStatus, - ImageResponse, - LiteLLMBatch, - LiteLLMLoggingBaseClass, - LiteLLMRealtimeStreamLoggingObject, - ModelResponse, - ModelResponseStream, - RawRequestTypedDict, - StandardBuiltInToolsParams, - StandardCallbackDynamicParams, - StandardLoggingAdditionalHeaders, - StandardLoggingHiddenParams, - StandardLoggingMCPToolCall, - StandardLoggingMetadata, - StandardLoggingModelCostFailureDebugInformation, - StandardLoggingModelInformation, - StandardLoggingPayload, - StandardLoggingPayloadErrorInformation, - StandardLoggingPayloadStatus, - StandardLoggingPayloadStatusFields, - StandardLoggingPromptManagementMetadata, - StandardLoggingVectorStoreRequest, - TextCompletionResponse, - TranscriptionResponse, - Usage, -) -from litellm.types.videos.main import VideoObject -from litellm.utils import _get_base_model_from_metadata, executor, print_verbose - -from ..integrations.argilla import ArgillaLogger -from ..integrations.arize.arize_phoenix import ArizePhoenixLogger -from ..integrations.athina import AthinaLogger -from ..integrations.azure_sentinel.azure_sentinel import AzureSentinelLogger -from ..integrations.azure_storage.azure_storage import AzureBlobStorageLogger -from ..integrations.custom_prompt_management import CustomPromptManagement -from ..integrations.datadog.datadog import DataDogLogger -from ..integrations.datadog.datadog_llm_obs import DataDogLLMObsLogger -from ..integrations.dotprompt import DotpromptManager -from ..integrations.dynamodb import DyanmoDBLogger -from ..integrations.galileo import GalileoObserve -from ..integrations.gcs_bucket.gcs_bucket import GCSBucketLogger -from ..integrations.gcs_pubsub.pub_sub import GcsPubSubLogger -from ..integrations.greenscale import GreenscaleLogger -from ..integrations.helicone import HeliconeLogger -from ..integrations.humanloop import HumanloopLogger -from ..integrations.lago import LagoLogger -from ..integrations.langfuse.langfuse import LangFuseLogger -from ..integrations.langfuse.langfuse_handler import LangFuseHandler -from ..integrations.langfuse.langfuse_prompt_management import LangfusePromptManagement -from ..integrations.langsmith import LangsmithLogger -from ..integrations.literal_ai import LiteralAILogger -from ..integrations.logfire_logger import LogfireLevel, LogfireLogger -from ..integrations.lunary import LunaryLogger -from ..integrations.openmeter import OpenMeterLogger -from ..integrations.opik.opik import OpikLogger -from ..integrations.posthog import PostHogLogger -from ..integrations.prompt_layer import PromptLayerLogger -from ..integrations.s3 import S3Logger -from ..integrations.s3_v2 import S3Logger as S3V2Logger -from ..integrations.supabase import Supabase -from ..integrations.traceloop import TraceloopLogger -from .exception_mapping_utils import _get_response_headers -from .initialize_dynamic_callback_params import ( - initialize_standard_callback_dynamic_params as _initialize_standard_callback_dynamic_params, -) -from .specialty_caches.dynamic_logging_cache import DynamicLoggingCache - -if TYPE_CHECKING: - from litellm.llms.base_llm.passthrough.transformation import BasePassthroughConfig -try: - from litellm_enterprise.enterprise_callbacks.callback_controls import ( - EnterpriseCallbackControls, - ) - from litellm_enterprise.enterprise_callbacks.pagerduty.pagerduty import ( - PagerDutyAlerting, - ) - from litellm_enterprise.enterprise_callbacks.send_emails.resend_email import ( - ResendEmailLogger, - ) - from litellm_enterprise.enterprise_callbacks.send_emails.sendgrid_email import ( - SendGridEmailLogger, - ) - from litellm_enterprise.enterprise_callbacks.send_emails.smtp_email import ( - SMTPEmailLogger, - ) - from litellm_enterprise.litellm_core_utils.litellm_logging import ( - StandardLoggingPayloadSetup as EnterpriseStandardLoggingPayloadSetup, - ) - - from litellm.integrations.generic_api.generic_api_callback import GenericAPILogger - - EnterpriseStandardLoggingPayloadSetupVAR: Optional[ - Type[EnterpriseStandardLoggingPayloadSetup] - ] = EnterpriseStandardLoggingPayloadSetup -except Exception as e: - verbose_logger.debug( - f"[Non-Blocking] Unable to import GenericAPILogger - LiteLLM Enterprise Feature - {str(e)}" - ) - GenericAPILogger = CustomLogger # type: ignore - ResendEmailLogger = CustomLogger # type: ignore - SendGridEmailLogger = CustomLogger # type: ignore - SMTPEmailLogger = CustomLogger # type: ignore - PagerDutyAlerting = CustomLogger # type: ignore - EnterpriseCallbackControls = None # type: ignore - EnterpriseStandardLoggingPayloadSetupVAR = None -_in_memory_loggers: List[Any] = [] - -_STANDARD_LOGGING_METADATA_KEYS: frozenset = frozenset( - StandardLoggingMetadata.__annotations__.keys() -) - -### GLOBAL VARIABLES ### - -# Cache custom pricing keys as frozenset for O(1) lookups instead of looping through 49 keys -_CUSTOM_PRICING_KEYS: frozenset = frozenset( - CustomPricingLiteLLMParams.model_fields.keys() -) - -sentry_sdk_instance = None -capture_exception = None -add_breadcrumb = None -slack_app = None -alerts_channel = None -heliconeLogger = None -athinaLogger = None -promptLayerLogger = None -logfireLogger = None -weightsBiasesLogger = None -customLogger = None -langFuseLogger = None -openMeterLogger = None -lagoLogger = None -dataDogLogger = None -prometheusLogger = None -dynamoLogger = None -s3Logger = None -greenscaleLogger = None -lunaryLogger = None -supabaseClient = None -deepevalLogger = None -callback_list: Optional[List[str]] = [] -user_logger_fn = None -additional_details: Optional[Dict[str, str]] = {} -local_cache: Optional[Dict[str, str]] = {} -last_fetched_at = None -last_fetched_at_keys = None - - -#### -class ServiceTraceIDCache: - def __init__(self) -> None: - self.cache = InMemoryCache() - - def get_cache(self, litellm_call_id: str, service_name: str) -> Optional[str]: - key_name = "{}:{}".format(service_name, litellm_call_id) - response = self.cache.get_cache(key=key_name) - return response - - def set_cache(self, litellm_call_id: str, service_name: str, trace_id: str) -> None: - key_name = "{}:{}".format(service_name, litellm_call_id) - self.cache.set_cache(key=key_name, value=trace_id) - return None - - -in_memory_trace_id_cache = ServiceTraceIDCache() -in_memory_dynamic_logger_cache = DynamicLoggingCache() - -# Cached lazy import for PrometheusLogger -# Module-level cache to avoid repeated imports while preserving memory benefits -_PrometheusLogger = None - - -def _get_cached_prometheus_logger(): - """ - Get cached PrometheusLogger class. - Lazy imports on first call to avoid loading prometheus.py and utils.py at import time (60MB saved). - Subsequent calls use cached class for better performance. - """ - global _PrometheusLogger - if _PrometheusLogger is None: - from litellm.integrations.prometheus import PrometheusLogger - - _PrometheusLogger = PrometheusLogger - return _PrometheusLogger - - -class Logging(LiteLLMLoggingBaseClass): - global supabaseClient, promptLayerLogger, weightsBiasesLogger, logfireLogger, capture_exception, add_breadcrumb, lunaryLogger, logfireLogger, prometheusLogger, slack_app - custom_pricing: bool = False - stream_options = None - litellm_request_debug: bool = False - - def __init__( - self, - model: str, - messages, - stream, - call_type, - start_time, - litellm_call_id: str, - function_id: str, - litellm_trace_id: Optional[str] = None, - dynamic_input_callbacks: Optional[ - List[Union[str, Callable, CustomLogger]] - ] = None, - dynamic_success_callbacks: Optional[ - List[Union[str, Callable, CustomLogger]] - ] = None, - dynamic_async_success_callbacks: Optional[ - List[Union[str, Callable, CustomLogger]] - ] = None, - dynamic_failure_callbacks: Optional[ - List[Union[str, Callable, CustomLogger]] - ] = None, - dynamic_async_failure_callbacks: Optional[ - List[Union[str, Callable, CustomLogger]] - ] = None, - applied_guardrails: Optional[List[str]] = None, - kwargs: Optional[Dict] = None, - log_raw_request_response: bool = False, - ): - _input: Optional[str] = messages # save original value of messages - if messages is not None: - if isinstance(messages, str): - messages = [ - {"role": "user", "content": messages} - ] # convert text completion input to the chat completion format - elif ( - isinstance(messages, list) - and len(messages) > 0 - and isinstance(messages[0], str) - ): - new_messages = [] - for m in messages: - new_messages.append({"role": "user", "content": m}) - messages = new_messages - - self.model = model - self.messages = copy.deepcopy(messages) if messages is not None else None - self.stream = stream - self.start_time = start_time # log the call start time - 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()) - ) - 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 - self.log_raw_request_response = log_raw_request_response - - # Initialize dynamic callbacks - self.dynamic_input_callbacks: Optional[ - List[Union[str, Callable, CustomLogger]] - ] = dynamic_input_callbacks - self.dynamic_success_callbacks: Optional[ - List[Union[str, Callable, CustomLogger]] - ] = dynamic_success_callbacks - self.dynamic_async_success_callbacks: Optional[ - List[Union[str, Callable, CustomLogger]] - ] = dynamic_async_success_callbacks - self.dynamic_failure_callbacks: Optional[ - List[Union[str, Callable, CustomLogger]] - ] = dynamic_failure_callbacks - self.dynamic_async_failure_callbacks: Optional[ - List[Union[str, Callable, CustomLogger]] - ] = dynamic_async_failure_callbacks - - # Process dynamic callbacks - self.process_dynamic_callbacks() - - ## DYNAMIC LANGFUSE / GCS / logging callback KEYS ## - self.standard_callback_dynamic_params: StandardCallbackDynamicParams = ( - self.initialize_standard_callback_dynamic_params(kwargs) - ) - self.standard_built_in_tools_params: StandardBuiltInToolsParams = ( - self.initialize_standard_built_in_tools_params(kwargs) - ) - ## TIME TO FIRST TOKEN LOGGING ## - self.completion_start_time: Optional[datetime.datetime] = None - self._llm_caching_handler: Optional[LLMCachingHandler] = None - - # INITIAL LITELLM_PARAMS - litellm_params = {} - if kwargs is not None: - litellm_params = get_litellm_params(**kwargs) - litellm_params = scrub_sensitive_keys_in_metadata(litellm_params) - - self.litellm_params = litellm_params - - # Initialize cost breakdown field - self.cost_breakdown: Optional[CostBreakdown] = None - - # Init Caching related details - self.caching_details: Optional[CachingDetails] = None - - # Passthrough endpoint guardrails config for field targeting - self.passthrough_guardrails_config: Optional[Dict[str, Any]] = None - - self.model_call_details: Dict[str, Any] = { - "litellm_trace_id": litellm_trace_id, - "litellm_call_id": litellm_call_id, - "input": _input, - "litellm_params": litellm_params, - "applied_guardrails": applied_guardrails, - "model": model, - } - - def process_dynamic_callbacks(self): - """ - Initializes CustomLogger compatible callbacks in self.dynamic_* callbacks - - If a callback is in litellm._known_custom_logger_compatible_callbacks, it needs to be intialized and added to the respective dynamic_* callback list. - """ - # Process input callbacks - self.dynamic_input_callbacks = self._process_dynamic_callback_list( - self.dynamic_input_callbacks, dynamic_callbacks_type="input" - ) - - # Process failure callbacks - self.dynamic_failure_callbacks = self._process_dynamic_callback_list( - self.dynamic_failure_callbacks, dynamic_callbacks_type="failure" - ) - - # Process async failure callbacks - self.dynamic_async_failure_callbacks = self._process_dynamic_callback_list( - self.dynamic_async_failure_callbacks, dynamic_callbacks_type="async_failure" - ) - - # Process success callbacks - self.dynamic_success_callbacks = self._process_dynamic_callback_list( - self.dynamic_success_callbacks, dynamic_callbacks_type="success" - ) - - # Process async success callbacks - self.dynamic_async_success_callbacks = self._process_dynamic_callback_list( - self.dynamic_async_success_callbacks, dynamic_callbacks_type="async_success" - ) - - def _process_dynamic_callback_list( - self, - callback_list: Optional[List[Union[str, Callable, CustomLogger]]], - dynamic_callbacks_type: Literal[ - "input", "success", "failure", "async_success", "async_failure" - ], - ) -> Optional[List[Union[str, Callable, CustomLogger]]]: - """ - Helper function to initialize CustomLogger compatible callbacks in self.dynamic_* callbacks - - - If a callback is in litellm._known_custom_logger_compatible_callbacks, - replace the string with the initialized callback class. - - If dynamic callback is a "success" callback that is a known_custom_logger_compatible_callbacks then add it to dynamic_async_success_callbacks - - If dynamic callback is a "failure" callback that is a known_custom_logger_compatible_callbacks then add it to dynamic_failure_callbacks - """ - if callback_list is None: - return None - - processed_list: List[Union[str, Callable, CustomLogger]] = [] - for callback in callback_list: - if ( - isinstance(callback, str) - and callback in litellm._known_custom_logger_compatible_callbacks - ): - callback_class = _init_custom_logger_compatible_class( - callback, internal_usage_cache=None, llm_router=None # type: ignore - ) - if callback_class is not None: - processed_list.append(callback_class) - - # If processing dynamic_success_callbacks, add to dynamic_async_success_callbacks - if dynamic_callbacks_type == "success": - if self.dynamic_async_success_callbacks is None: - self.dynamic_async_success_callbacks = [] - self.dynamic_async_success_callbacks.append(callback_class) - elif dynamic_callbacks_type == "failure": - if self.dynamic_async_failure_callbacks is None: - self.dynamic_async_failure_callbacks = [] - self.dynamic_async_failure_callbacks.append(callback_class) - else: - processed_list.append(callback) - return processed_list - - def initialize_standard_callback_dynamic_params( - self, kwargs: Optional[Dict] = None - ) -> StandardCallbackDynamicParams: - """ - Initialize the standard callback dynamic params from the kwargs - - checks if langfuse_secret_key, gcs_bucket_name in kwargs and sets the corresponding attributes in StandardCallbackDynamicParams - """ - - return _initialize_standard_callback_dynamic_params(kwargs) - - def initialize_standard_built_in_tools_params( - self, kwargs: Optional[Dict] = None - ) -> StandardBuiltInToolsParams: - """ - Initialize the standard built-in tools params from the kwargs - - checks if web_search_options in kwargs or tools and sets the corresponding attribute in StandardBuiltInToolsParams - """ - return StandardBuiltInToolsParams( - web_search_options=StandardBuiltInToolCostTracking._get_web_search_options( - kwargs or {} - ), - file_search=StandardBuiltInToolCostTracking._get_file_search_tool_call( - kwargs or {} - ), - ) - - def update_environment_variables( - self, - litellm_params: Dict, - optional_params: Dict, - model: Optional[str] = None, - user: Optional[str] = None, - **additional_params, - ): - self.optional_params = optional_params - if model is not None: - self.model = model - self.user = user - self.litellm_params = { - **self.litellm_params, - **scrub_sensitive_keys_in_metadata(litellm_params), - } - self.litellm_request_debug = litellm_params.get("litellm_request_debug", False) - self.logger_fn = litellm_params.get("logger_fn", None) - if _is_debugging_on() or self.litellm_request_debug: - verbose_logger.debug(f"self.optional_params: {self.optional_params}") - - self.model_call_details.update( - { - "model": self.model, - "messages": self.messages, - "optional_params": self.optional_params, - "litellm_params": self.litellm_params, - "start_time": self.start_time, - "stream": self.stream, - "user": user, - "call_type": str(self.call_type), - "litellm_call_id": self.litellm_call_id, - "completion_start_time": self.completion_start_time, - "standard_callback_dynamic_params": self.standard_callback_dynamic_params, - **self.optional_params, - **additional_params, - } - ) - - ## check if stream options is set ## - used by CustomStreamWrapper for easy instrumentation - if "stream_options" in additional_params: - self.stream_options = additional_params["stream_options"] - ## check if custom pricing set ## - if any( - litellm_params.get(key) is not None - for key in _CUSTOM_PRICING_KEYS & litellm_params.keys() - ): - self.custom_pricing = True - - if "custom_llm_provider" in self.model_call_details: - self.custom_llm_provider = self.model_call_details["custom_llm_provider"] - - def update_messages(self, messages: List[AllMessageValues]): - """ - Update the logged value of the messages in the model_call_details - - Allows pre-call hooks to update the messages before the call is made - """ - self.messages = messages - self.model_call_details["messages"] = messages - - def should_run_prompt_management_hooks( - self, - non_default_params: Dict, - prompt_id: Optional[str] = None, - tools: Optional[List[Dict]] = None, - ) -> bool: - """ - Return True if prompt management hooks should be run - """ - if prompt_id: - return True - - if self._should_run_prompt_management_hooks_without_prompt_id( - non_default_params=non_default_params, - tools=tools, - ): - return True - - return False - - def _should_run_prompt_management_hooks_without_prompt_id( - self, - non_default_params: Dict, - tools: Optional[List[Dict]] = None, - ) -> bool: - """ - Certain prompt management hooks don't need a `prompt_id` to be passed in, they are triggered by dynamic params - - eg. AnthropicCacheControlHook and BedrockKnowledgeBaseHook both don't require a `prompt_id` to be passed in, they are triggered by dynamic params - """ - for param in non_default_params: - if param in DynamicPromptManagementParamLiteral.list_all_params(): - return True - - ############################################################################# - # Check if Vector Store / Knowledge Base hooks should be applied to the prompt - ############################################################################# - if litellm.vector_store_registry is not None: - if litellm.vector_store_registry.get_vector_store_to_run( - non_default_params=non_default_params, tools=tools - ): - return True - return False - - def get_chat_completion_prompt( - self, - model: str, - messages: List[AllMessageValues], - non_default_params: Dict, - prompt_variables: Optional[dict], - prompt_id: Optional[str] = None, - prompt_spec: Optional[PromptSpec] = None, - prompt_management_logger: Optional[CustomLogger] = None, - prompt_label: Optional[str] = None, - prompt_version: Optional[int] = None, - ) -> Tuple[str, List[AllMessageValues], dict]: - custom_logger = ( - prompt_management_logger - or self.get_custom_logger_for_prompt_management( - model=model, - non_default_params=non_default_params, - prompt_id=prompt_id, - prompt_spec=prompt_spec, - dynamic_callback_params=self.standard_callback_dynamic_params, - ) - ) - - if custom_logger: - ( - model, - messages, - non_default_params, - ) = custom_logger.get_chat_completion_prompt( - model=model, - messages=messages, - non_default_params=non_default_params or {}, - prompt_id=prompt_id, - prompt_spec=prompt_spec, - prompt_variables=prompt_variables, - dynamic_callback_params=self.standard_callback_dynamic_params, - prompt_label=prompt_label, - prompt_version=prompt_version, - ) - self.messages = messages - return model, messages, non_default_params - - async def async_get_chat_completion_prompt( - self, - model: str, - messages: List[AllMessageValues], - non_default_params: Dict, - prompt_variables: Optional[dict], - prompt_id: Optional[str] = None, - prompt_spec: Optional[PromptSpec] = None, - prompt_management_logger: Optional[CustomLogger] = None, - tools: Optional[List[Dict]] = None, - prompt_label: Optional[str] = None, - prompt_version: Optional[int] = None, - ) -> Tuple[str, List[AllMessageValues], dict]: - custom_logger = ( - prompt_management_logger - or self.get_custom_logger_for_prompt_management( - model=model, - tools=tools, - non_default_params=non_default_params, - prompt_id=prompt_id, - prompt_spec=prompt_spec, - dynamic_callback_params=self.standard_callback_dynamic_params, - ) - ) - - if custom_logger: - ( - model, - messages, - non_default_params, - ) = await custom_logger.async_get_chat_completion_prompt( - model=model, - messages=messages, - non_default_params=non_default_params or {}, - prompt_id=prompt_id, - prompt_spec=prompt_spec, - prompt_variables=prompt_variables, - dynamic_callback_params=self.standard_callback_dynamic_params, - litellm_logging_obj=self, - tools=tools, - prompt_label=prompt_label, - prompt_version=prompt_version, - ) - self.messages = messages - return model, messages, non_default_params - - def _auto_detect_prompt_management_logger( - self, - prompt_id: str, - prompt_spec: Optional[PromptSpec], - dynamic_callback_params: StandardCallbackDynamicParams, - ) -> Optional[CustomLogger]: - """ - Auto-detect which prompt management system owns the given prompt_id. - - This allows a user to just pass prompt_id in the completion call and it will be auto-detected which system owns this prompt. - - Args: - prompt_id: The prompt ID to check - dynamic_callback_params: Dynamic callback parameters for should_run_prompt_management checks - - Returns: - A CustomLogger instance if a matching prompt management system is found, None otherwise - """ - prompt_management_loggers = ( - litellm.logging_callback_manager.get_custom_loggers_for_type( - callback_type=CustomPromptManagement - ) - ) - - for logger in prompt_management_loggers: - if isinstance(logger, CustomPromptManagement): - try: - if logger.should_run_prompt_management( - prompt_id=prompt_id, - prompt_spec=prompt_spec, - dynamic_callback_params=dynamic_callback_params, - ): - self.model_call_details[ - "prompt_integration" - ] = logger.__class__.__name__ - return logger - except Exception: - # If check fails, continue to next logger - continue - - return None - - def get_custom_logger_for_prompt_management( - self, - model: str, - non_default_params: Dict, - tools: Optional[List[Dict]] = None, - prompt_id: Optional[str] = None, - prompt_spec: Optional[PromptSpec] = None, - dynamic_callback_params: Optional[StandardCallbackDynamicParams] = None, - ) -> Optional[CustomLogger]: - """ - Get a custom logger for prompt management based on model name or available callbacks. - - Args: - model: The model name to check for prompt management integration - non_default_params: Non-default parameters passed to the completion call - tools: Optional tools passed to the completion call - prompt_id: Optional prompt ID to auto-detect which system owns this prompt - dynamic_callback_params: Dynamic callback parameters for should_run_prompt_management checks - - Returns: - A CustomLogger instance if one is found, None otherwise - """ - # First check if model starts with a known custom logger compatible callback - # This takes precedence for backward compatibility - for callback_name in litellm._known_custom_logger_compatible_callbacks: - if model.startswith(callback_name): - custom_logger = _init_custom_logger_compatible_class( - logging_integration=callback_name, - internal_usage_cache=None, - llm_router=None, - ) - if custom_logger is not None: - self.model_call_details["prompt_integration"] = model.split("/")[0] - return custom_logger - - # If prompt_id is provided, try to auto-detect which system has this prompt - if prompt_id and dynamic_callback_params is not None: - auto_detected_logger = self._auto_detect_prompt_management_logger( - prompt_id=prompt_id, - prompt_spec=prompt_spec, - dynamic_callback_params=dynamic_callback_params, - ) - if auto_detected_logger is not None: - return auto_detected_logger - - # Then check for any registered CustomPromptManagement loggers (fallback) - prompt_management_loggers = ( - litellm.logging_callback_manager.get_custom_loggers_for_type( - callback_type=CustomPromptManagement - ) - ) - - if prompt_management_loggers: - logger = prompt_management_loggers[0] - self.model_call_details["prompt_integration"] = logger.__class__.__name__ - return logger - - if anthropic_cache_control_logger := AnthropicCacheControlHook.get_custom_logger_for_anthropic_cache_control_hook( - non_default_params - ): - self.model_call_details[ - "prompt_integration" - ] = anthropic_cache_control_logger.__class__.__name__ - return anthropic_cache_control_logger - - ######################################################### - # Vector Store / Knowledge Base hooks - ######################################################### - if litellm.vector_store_registry is not None: - vector_store_custom_logger = _init_custom_logger_compatible_class( - logging_integration="vector_store_pre_call_hook", - internal_usage_cache=None, - llm_router=None, - ) - self.model_call_details[ - "prompt_integration" - ] = vector_store_custom_logger.__class__.__name__ - # Add to global callbacks so post-call hooks are invoked - if ( - vector_store_custom_logger - and vector_store_custom_logger not in litellm.callbacks - ): - litellm.logging_callback_manager.add_litellm_callback( - vector_store_custom_logger - ) - return vector_store_custom_logger - - return None - - def get_custom_logger_for_anthropic_cache_control_hook( - self, non_default_params: Dict - ) -> Optional[CustomLogger]: - if non_default_params.get("cache_control_injection_points", None): - custom_logger = _init_custom_logger_compatible_class( - logging_integration="anthropic_cache_control_hook", - internal_usage_cache=None, - llm_router=None, - ) - return custom_logger - return None - - def _get_raw_request_body(self, data: Optional[Union[dict, str]]) -> dict: - if data is None: - return {"error": "Received empty dictionary for raw request body"} - if isinstance(data, str): - try: - return json.loads(data) - except Exception: - return { - "error": "Unable to parse raw request body. Got - {}".format(data) - } - return data - - def _get_masked_api_base(self, api_base: str) -> str: - if "key=" in api_base: - # Find the position of "key=" in the string - key_index = api_base.find("key=") + 4 - # Mask the last 5 characters after "key=" - masked_api_base = api_base[:key_index] + "*" * 5 + api_base[-4:] - else: - masked_api_base = api_base - return str(masked_api_base) - - def _pre_call(self, input, api_key, model=None, additional_args={}): - """ - Common helper function across the sync + async pre-call function - """ - - self.model_call_details["input"] = input - self.model_call_details["api_key"] = api_key - self.model_call_details["additional_args"] = additional_args - self.model_call_details["log_event_type"] = "pre_api_call" - if ( - model - ): # if model name was changes pre-call, overwrite the initial model call name with the new one - self.model_call_details["model"] = model - self.model_call_details["litellm_params"][ - "api_base" - ] = self._get_masked_api_base(additional_args.get("api_base", "")) - - def pre_call(self, input, api_key, model=None, additional_args={}): # noqa: PLR0915 - # Log the exact input to the LLM API - litellm.error_logs["PRE_CALL"] = locals() - try: - self._pre_call( - input=input, - api_key=api_key, - model=model, - additional_args=additional_args, - ) - - # User Logging -> if you pass in a custom logging function - self._print_llm_call_debugging_log( - api_base=additional_args.get("api_base", ""), - headers=additional_args.get("headers", {}), - additional_args=additional_args, - ) - # log raw request to provider (like LangFuse) -- if opted in. - if ( - self.log_raw_request_response is True - or log_raw_request_response is True - ): - _litellm_params = self.model_call_details.get("litellm_params", {}) - _metadata = _litellm_params.get("metadata", {}) or {} - try: - # [Non-blocking Extra Debug Information in metadata] - if turn_off_message_logging is True: - _metadata[ - "raw_request" - ] = "redacted by litellm. \ - 'litellm.turn_off_message_logging=True'" - else: - curl_command = self._get_request_curl_command( - api_base=additional_args.get("api_base", ""), - headers=additional_args.get("headers", {}), - additional_args=additional_args, - data=additional_args.get("complete_input_dict", {}), - ) - - _metadata["raw_request"] = str(curl_command) - # split up, so it's easier to parse in the UI - self.model_call_details[ - "raw_request_typed_dict" - ] = RawRequestTypedDict( - raw_request_api_base=str( - additional_args.get("api_base") or "" - ), - raw_request_body=self._get_raw_request_body( - additional_args.get("complete_input_dict", {}) - ), - # NOTE: setting ignore_sensitive_headers to True will cause - # the Authorization header to be leaked when calls to the health - # endpoint are made and fail. - raw_request_headers=self._get_masked_headers( - additional_args.get("headers", {}) or {}, - ), - error=None, - ) - except Exception as e: - self.model_call_details[ - "raw_request_typed_dict" - ] = RawRequestTypedDict( - error=str(e), - ) - _metadata[ - "raw_request" - ] = "Unable to Log \ - raw request: {}".format( - str(e) - ) - if getattr(self, "logger_fn", None) and callable(self.logger_fn): - try: - self.logger_fn( - self.model_call_details - ) # Expectation: any logger function passed in by the user should accept a dict object - except Exception as e: - verbose_logger.exception( - "LiteLLM.LoggingError: [Non-Blocking] Exception occurred while logging {}".format( - str(e) - ) - ) - - self.model_call_details["api_call_start_time"] = datetime.datetime.now() - # Input Integration Logging -> If you want to log the fact that an attempt to call the model was made - callbacks = litellm.input_callback + (self.dynamic_input_callbacks or []) - for callback in callbacks: - try: - if callback == "supabase" and supabaseClient is not None: - verbose_logger.debug("reaches supabase for logging!") - model = self.model_call_details["model"] - messages = self.model_call_details["input"] - verbose_logger.debug(f"supabaseClient: {supabaseClient}") - supabaseClient.input_log_event( - model=model, - messages=messages, - end_user=self.model_call_details.get("user", "default"), - litellm_call_id=self.litellm_params["litellm_call_id"], - print_verbose=print_verbose, - ) - elif callback == "sentry" and add_breadcrumb: - try: - details_to_log = copy.deepcopy(self.model_call_details) - except Exception: - details_to_log = self.model_call_details - if litellm.turn_off_message_logging: - # make a copy of the _model_Call_details and log it - details_to_log.pop("messages", None) - details_to_log.pop("input", None) - details_to_log.pop("prompt", None) - - add_breadcrumb( - category="litellm.llm_call", - message=f"Model Call Details pre-call: {details_to_log}", - level="info", - ) - - elif isinstance(callback, CustomLogger): # custom logger class - callback.log_pre_api_call( - model=self.model, - messages=self.messages, - kwargs=self.model_call_details, - ) - elif ( - callable(callback) and customLogger is not None - ): # custom logger functions - customLogger.log_input_event( - model=self.model, - messages=self.messages, - kwargs=self.model_call_details, - print_verbose=print_verbose, - callback_func=callback, - ) - except Exception as e: - verbose_logger.exception( - "litellm.Logging.pre_call(): Exception occured - {}".format( - str(e) - ) - ) - verbose_logger.debug( - f"LiteLLM.Logging: is sentry capture exception initialized {capture_exception}" - ) - if capture_exception: # log this error to sentry for debugging - capture_exception(e) - except Exception as e: - verbose_logger.exception( - "LiteLLM.LoggingError: [Non-Blocking] Exception occurred while logging {}".format( - str(e) - ) - ) - verbose_logger.error( - f"LiteLLM.Logging: is sentry capture exception initialized {capture_exception}" - ) - if capture_exception: # log this error to sentry for debugging - capture_exception(e) - - def _print_llm_call_debugging_log( - self, - api_base: str, - headers: dict, - additional_args: dict, - ): - """ - Internal debugging helper function - - Prints the RAW curl command sent from LiteLLM - """ - if _is_debugging_on() or self.litellm_request_debug: - if json_logs: - masked_headers = self._get_masked_headers(headers) - if self.litellm_request_debug: - verbose_logger.warning( # .warning ensures this shows up in all environments - "POST Request Sent from LiteLLM", - extra={"api_base": {api_base}, **masked_headers}, - ) - else: - verbose_logger.debug( - "POST Request Sent from LiteLLM", - extra={"api_base": {api_base}, **masked_headers}, - ) - else: - headers = additional_args.get("headers", {}) - if headers is None: - headers = {} - data = additional_args.get("complete_input_dict", {}) - api_base = str(additional_args.get("api_base", "")) - curl_command = self._get_request_curl_command( - api_base=api_base, - headers=headers, - additional_args=additional_args, - data=data, - ) - if self.litellm_request_debug: - verbose_logger.warning( - f"\033[92m{curl_command}\033[0m\n" - ) # .warning ensures this shows up in all environments - else: - verbose_logger.debug(f"\033[92m{curl_command}\033[0m\n") - - def _get_request_body(self, data: dict) -> str: - return str(data) - - def _get_request_curl_command( - self, api_base: str, headers: Optional[dict], additional_args: dict, data: dict - ) -> str: - masked_api_base = self._get_masked_api_base(api_base) - if headers is None: - headers = {} - curl_command = "\n\nPOST Request Sent from LiteLLM:\n" - curl_command += "curl -X POST \\\n" - curl_command += f"{masked_api_base} \\\n" - masked_headers = self._get_masked_headers(headers) - formatted_headers = " ".join( - [f"-H '{k}: {v}'" for k, v in masked_headers.items()] - ) - curl_command += ( - f"{formatted_headers} \\\n" if formatted_headers.strip() != "" else "" - ) - curl_command += f"-d '{self._get_request_body(data)}'\n" - if additional_args.get("request_str", None) is not None: - # print the sagemaker / bedrock client request - curl_command = "\nRequest Sent from LiteLLM:\n" - request_str = additional_args.get("request_str", "") - curl_command += request_str - elif api_base == "": - curl_command = str(self.model_call_details) - return curl_command - - def _get_masked_headers( - self, headers: dict, ignore_sensitive_headers: bool = False - ) -> dict: - """ - Internal debugging helper function - - Masks the headers of the request sent from LiteLLM - """ - return _get_masked_values( - headers, ignore_sensitive_values=ignore_sensitive_headers - ) - - def post_call( - self, original_response, input=None, api_key=None, additional_args={} - ): - # Log the exact result from the LLM API, for streaming - log the type of response received - litellm.error_logs["POST_CALL"] = locals() - if isinstance(original_response, dict): - original_response = json.dumps(original_response) - try: - self.model_call_details["input"] = input - self.model_call_details["api_key"] = api_key - self.model_call_details["original_response"] = original_response - self.model_call_details["additional_args"] = additional_args - self.model_call_details["log_event_type"] = "post_api_call" - - if self.litellm_request_debug: - attr = "warning" - else: - attr = "debug" - - if json_logs: - callattr = getattr(verbose_logger, attr) - callattr( - "RAW RESPONSE:\n{}\n\n".format( - self.model_call_details.get( - "original_response", self.model_call_details - ) - ), - ) - else: - callattr = getattr(verbose_logger, attr) - callattr( - "RAW RESPONSE:\n{}\n\n".format( - self.model_call_details.get( - "original_response", self.model_call_details - ) - ) - ) - if getattr(self, "logger_fn", None) and callable(self.logger_fn): - try: - self.logger_fn( - self.model_call_details - ) # Expectation: any logger function passed in by the user should accept a dict object - except Exception as e: - verbose_logger.exception( - "LiteLLM.LoggingError: [Non-Blocking] Exception occurred while logging {}".format( - str(e) - ) - ) - original_response = redact_message_input_output_from_logging( - model_call_details=( - self.model_call_details - if hasattr(self, "model_call_details") - else {} - ), - result=original_response, - ) - # Input Integration Logging -> If you want to log the fact that an attempt to call the model was made - - callbacks = litellm.input_callback + (self.dynamic_input_callbacks or []) - for callback in callbacks: - try: - if callback == "sentry" and add_breadcrumb: - verbose_logger.debug("reaches sentry breadcrumbing") - try: - details_to_log = copy.deepcopy(self.model_call_details) - except Exception: - details_to_log = self.model_call_details - if litellm.turn_off_message_logging: - # make a copy of the _model_Call_details and log it - details_to_log.pop("messages", None) - details_to_log.pop("input", None) - details_to_log.pop("prompt", None) - - add_breadcrumb( - category="litellm.llm_call", - message=f"Model Call Details post-call: {details_to_log}", - level="info", - ) - elif isinstance(callback, CustomLogger): # custom logger class - callback.log_post_api_call( - kwargs=self.model_call_details, - response_obj=None, - start_time=self.start_time, - end_time=None, - ) - except Exception as e: - verbose_logger.exception( - "LiteLLM.LoggingError: [Non-Blocking] Exception occurred while post-call logging with integrations {}".format( - str(e) - ) - ) - verbose_logger.debug( - f"LiteLLM.Logging: is sentry capture exception initialized {capture_exception}" - ) - if capture_exception: # log this error to sentry for debugging - capture_exception(e) - except Exception as e: - verbose_logger.exception( - "LiteLLM.LoggingError: [Non-Blocking] Exception occurred while logging {}".format( - str(e) - ) - ) - - async def async_post_mcp_tool_call_hook( - self, - kwargs: dict, - response_obj: Any, - start_time: datetime.datetime, - end_time: datetime.datetime, - ): - """ - Post MCP Tool Call Hook - - Use this to modify the MCP tool call response before it is returned to the user. - """ - from litellm.types.llms.base import HiddenParams - from litellm.types.mcp import MCPPostCallResponseObject - - callbacks = self.get_combined_callback_list( - dynamic_success_callbacks=self.dynamic_success_callbacks, - global_callbacks=litellm.success_callback, - ) - post_mcp_tool_call_response_obj: MCPPostCallResponseObject = ( - MCPPostCallResponseObject( - mcp_tool_call_response=response_obj, hidden_params=HiddenParams() - ) - ) - for callback in callbacks: - try: - if isinstance(callback, CustomLogger): - response: Optional[ - MCPPostCallResponseObject - ] = await callback.async_post_mcp_tool_call_hook( - kwargs=kwargs, - response_obj=post_mcp_tool_call_response_obj, - start_time=start_time, - end_time=end_time, - ) - ###################################################################### - # if any of the callbacks modify the response, use the modified response - # current implementation returns the first modified response - ###################################################################### - if response is not None: - response_obj = self._parse_post_mcp_call_hook_response( - response=response - ) - except Exception as e: - verbose_logger.exception( - "LiteLLM.LoggingError: [Non-Blocking] Exception occurred while logging {}".format( - str(e) - ) - ) - return response_obj - - def _parse_post_mcp_call_hook_response( - self, response: Optional[MCPPostCallResponseObject] - ) -> Any: - """ - Parse the response from the post_mcp_tool_call_hook - - 1. Unpack the mcp_tool_call_response - 2. save the updated response_cost to the model_call_details - """ - if response is None: - return None - self.model_call_details["response_cost"] = response.hidden_params.response_cost - return response.mcp_tool_call_response - - def get_response_ms(self) -> float: - return ( - self.model_call_details.get("end_time", datetime.datetime.now()) - - self.model_call_details.get("start_time", datetime.datetime.now()) - ).total_seconds() * 1000 - - def set_cost_breakdown( - self, - input_cost: float, - output_cost: float, - total_cost: float, - cost_for_built_in_tools_cost_usd_dollar: float, - additional_costs: Optional[dict] = None, - original_cost: Optional[float] = None, - discount_percent: Optional[float] = None, - discount_amount: Optional[float] = None, - margin_percent: Optional[float] = None, - margin_fixed_amount: Optional[float] = None, - margin_total_amount: Optional[float] = None, - ) -> None: - """ - Helper method to store cost breakdown in the logging object. - - Args: - input_cost: Cost of input/prompt tokens - output_cost: Cost of output/completion tokens - cost_for_built_in_tools_cost_usd_dollar: Cost of built-in tools - total_cost: Total cost of request - additional_costs: Free-form additional costs dict (e.g., {"azure_model_router_flat_cost": 0.00014}) - original_cost: Cost before discount - discount_percent: Discount percentage (0.05 = 5%) - discount_amount: Discount amount in USD - margin_percent: Margin percentage applied (0.10 = 10%) - margin_fixed_amount: Fixed margin amount in USD - margin_total_amount: Total margin added in USD - """ - - self.cost_breakdown = CostBreakdown( - input_cost=input_cost, - output_cost=output_cost, - total_cost=total_cost, - tool_usage_cost=cost_for_built_in_tools_cost_usd_dollar, - ) - - # Store additional costs if provided (free-form dict for extensibility) - if additional_costs and isinstance(additional_costs, dict) and len(additional_costs) > 0: - self.cost_breakdown["additional_costs"] = additional_costs - - # Store discount information if provided - if original_cost is not None: - self.cost_breakdown["original_cost"] = original_cost - if discount_percent is not None: - self.cost_breakdown["discount_percent"] = discount_percent - if discount_amount is not None: - self.cost_breakdown["discount_amount"] = discount_amount - - # Store margin information if provided - if margin_percent is not None: - self.cost_breakdown["margin_percent"] = margin_percent - if margin_fixed_amount is not None: - self.cost_breakdown["margin_fixed_amount"] = margin_fixed_amount - if margin_total_amount is not None: - self.cost_breakdown["margin_total_amount"] = margin_total_amount - - def _response_cost_calculator( - self, - result: Union[ - ModelResponse, - ModelResponseStream, - EmbeddingResponse, - ImageResponse, - TranscriptionResponse, - TextCompletionResponse, - HttpxBinaryResponseContent, - RerankResponse, - Batch, - FineTuningJob, - ResponsesAPIResponse, - ResponseCompletedEvent, - OpenAIFileObject, - LiteLLMRealtimeStreamLoggingObject, - OpenAIModerationResponse, - "SearchResponse", - ], - cache_hit: Optional[bool] = None, - litellm_model_name: Optional[str] = None, - router_model_id: Optional[str] = None, - ) -> Optional[float]: - """ - Calculate response cost using result + logging object variables. - - used for consistent cost calculation across response headers + logging integrations. - """ - - if isinstance(result, BaseModel) and hasattr(result, "_hidden_params"): - hidden_params = getattr(result, "_hidden_params", {}) - if ( - "response_cost" in hidden_params - and hidden_params["response_cost"] is not None - ): # use cost if already calculated - return hidden_params["response_cost"] - elif ( - router_model_id is None and "model_id" in hidden_params - ): # use model_id if not already set - router_model_id = hidden_params["model_id"] - - ## RESPONSE COST ## - custom_pricing = use_custom_pricing_for_model( - litellm_params=( - self.litellm_params if hasattr(self, "litellm_params") else None - ) - ) - - prompt = "" # use for tts cost calc - _input = self.model_call_details.get("input", None) - if _input is not None and isinstance(_input, str): - prompt = _input - - if cache_hit is None: - cache_hit = self.model_call_details.get("cache_hit", False) - - try: - response_cost_calculator_kwargs = { - "response_object": result, - "model": litellm_model_name or self.model, - "cache_hit": cache_hit, - "custom_llm_provider": self.model_call_details.get( - "custom_llm_provider", None - ), - "base_model": _get_base_model_from_metadata( - model_call_details=self.model_call_details - ), - "call_type": self.call_type, - "optional_params": self.optional_params, - "custom_pricing": custom_pricing, - "prompt": prompt, - "standard_built_in_tools_params": self.standard_built_in_tools_params, - "router_model_id": router_model_id, - "litellm_logging_obj": self, - "service_tier": ( - self.optional_params.get("service_tier") - if self.optional_params - else None - ), - } - except Exception as e: # error creating kwargs for cost calculation - debug_info = StandardLoggingModelCostFailureDebugInformation( - error_str=str(e), - traceback_str=_get_traceback_str_for_error(str(e)), - ) - verbose_logger.debug( - f"response_cost_failure_debug_information: {debug_info}" - ) - self.model_call_details[ - "response_cost_failure_debug_information" - ] = debug_info - return None - - try: - response_cost = litellm.response_cost_calculator( - **response_cost_calculator_kwargs - ) - - verbose_logger.debug(f"response_cost: {response_cost}") - return response_cost - except Exception as e: # error calculating cost - debug_info = StandardLoggingModelCostFailureDebugInformation( - error_str=str(e), - traceback_str=_get_traceback_str_for_error(str(e)), - model=response_cost_calculator_kwargs["model"], - cache_hit=response_cost_calculator_kwargs["cache_hit"], - custom_llm_provider=response_cost_calculator_kwargs[ - "custom_llm_provider" - ], - base_model=response_cost_calculator_kwargs["base_model"], - call_type=response_cost_calculator_kwargs["call_type"], - custom_pricing=response_cost_calculator_kwargs["custom_pricing"], - ) - verbose_logger.debug( - f"response_cost_failure_debug_information: {debug_info}" - ) - self.model_call_details[ - "response_cost_failure_debug_information" - ] = debug_info - - return None - - async def _response_cost_calculator_async( - self, - result: Union[ - ModelResponse, - ModelResponseStream, - EmbeddingResponse, - ImageResponse, - TranscriptionResponse, - TextCompletionResponse, - HttpxBinaryResponseContent, - RerankResponse, - Batch, - FineTuningJob, - ], - cache_hit: Optional[bool] = None, - ) -> Optional[float]: - return self._response_cost_calculator(result=result, cache_hit=cache_hit) - - def should_run_logging( - self, - event_type: Literal[ - "async_success", "sync_success", "async_failure", "sync_failure" - ], - stream: bool = False, - ) -> bool: - try: - if self.model_call_details.get(f"has_logged_{event_type}", False) is True: - return False - - return True - except Exception: - return True - - def has_run_logging( - self, - event_type: Literal[ - "async_success", "sync_success", "async_failure", "sync_failure" - ], - ) -> None: - if self.stream is not None and self.stream is True: - """ - Ignore check on stream, as there can be multiple chunks - """ - return - self.model_call_details[f"has_logged_{event_type}"] = True - return - - def should_run_callback( - self, callback: litellm.CALLBACK_TYPES, litellm_params: dict, event_hook: str - ) -> bool: - if litellm.global_disable_no_log_param: - return True - - if litellm_params.get("no-log", False) is True: - # proxy cost tracking cal backs should run - - if not ( - isinstance(callback, CustomLogger) - and "_PROXY_" in callback.__class__.__name__ - ): - verbose_logger.debug( - f"no-log request, skipping logging for {event_hook} event" - ) - return False - - # Check for dynamically disabled callbacks via headers - if ( - EnterpriseCallbackControls is not None - and EnterpriseCallbackControls.is_callback_disabled_dynamically( - callback=callback, - litellm_params=litellm_params, - standard_callback_dynamic_params=self.standard_callback_dynamic_params, - ) - ): - verbose_logger.debug( - f"Callback {callback} disabled via x-litellm-disable-callbacks header for {event_hook} event" - ) - return False - - return True - - def _update_completion_start_time(self, completion_start_time: datetime.datetime): - self.completion_start_time = completion_start_time - self.model_call_details["completion_start_time"] = self.completion_start_time - - def normalize_logging_result(self, result: Any) -> Any: - """ - Some endpoints return a different type of result than what is expected by the logging system. - This function is used to normalize the result to the expected type. - """ - logging_result = result - if self.call_type == CallTypes.arealtime.value and isinstance(result, list): - combined_usage_object = RealtimeAPITokenUsageProcessor.collect_and_combine_usage_from_realtime_stream_results( - results=result - ) - logging_result = ( - RealtimeAPITokenUsageProcessor.create_logging_realtime_object( - usage=combined_usage_object, - results=result, - ) - ) - - elif ( - self.call_type == CallTypes.llm_passthrough_route.value - or self.call_type == CallTypes.allm_passthrough_route.value - ) and isinstance(result, Response): - from litellm.utils import ProviderConfigManager - - provider_config = ProviderConfigManager.get_provider_passthrough_config( - provider=self.model_call_details.get("custom_llm_provider", ""), - model=self.model, - ) - if provider_config is not None: - logging_result = provider_config.logging_non_streaming_response( - model=self.model, - custom_llm_provider=self.model_call_details.get( - "custom_llm_provider", "" - ), - httpx_response=result, - request_data=self.model_call_details.get("request_data", {}), - logging_obj=self, - endpoint=self.model_call_details.get("endpoint", ""), - ) - return logging_result - - def _process_hidden_params_and_response_cost( - self, - logging_result, - start_time, - end_time, - ): - hidden_params = getattr(logging_result, "_hidden_params", {}) - if hidden_params: - if self.model_call_details.get("litellm_params") is not None: - self.model_call_details["litellm_params"].setdefault("metadata", {}) - if self.model_call_details["litellm_params"]["metadata"] is None: - self.model_call_details["litellm_params"]["metadata"] = {} - self.model_call_details["litellm_params"]["metadata"]["hidden_params"] = getattr(logging_result, "_hidden_params", {}) # type: ignore - - if "response_cost" in hidden_params: - self.model_call_details["response_cost"] = hidden_params["response_cost"] - else: - self.model_call_details["response_cost"] = self._response_cost_calculator( - result=logging_result - ) - - self.model_call_details[ - "standard_logging_object" - ] = get_standard_logging_object_payload( - kwargs=self.model_call_details, - init_response_obj=logging_result, - start_time=start_time, - end_time=end_time, - logging_obj=self, - status="success", - standard_built_in_tools_params=self.standard_built_in_tools_params, - ) - - def _transform_usage_objects(self, result): - if isinstance(result, ResponsesAPIResponse): - result = result.model_copy() - transformed_usage = ( - ResponseAPILoggingUtils._transform_response_api_usage_to_chat_usage( - result.usage - ) - ) - setattr(result, "usage", transformed_usage) - if ( - standard_logging_payload := self.model_call_details.get( - "standard_logging_object" - ) - ) is not None: - response_dict = ( - result.model_dump() - if hasattr(result, "model_dump") - else dict(result) - ) - # Ensure usage is properly included with transformed chat format - if transformed_usage is not None: - response_dict["usage"] = ( - transformed_usage.model_dump() - if hasattr(transformed_usage, "model_dump") - else dict(transformed_usage) - ) - standard_logging_payload["response"] = response_dict - elif isinstance(result, TranscriptionResponse): - from litellm.litellm_core_utils.llm_cost_calc.usage_object_transformation import ( - TranscriptionUsageObjectTransformation, - ) - - result = result.model_copy() - transformed_usage = TranscriptionUsageObjectTransformation.transform_transcription_usage_object(result.usage) # type: ignore - setattr(result, "usage", transformed_usage) - return result - - def _success_handler_helper_fn( - self, - result=None, - start_time=None, - end_time=None, - cache_hit=None, - standard_logging_object: Optional[StandardLoggingPayload] = None, - ): - try: - if start_time is None: - start_time = self.start_time - if end_time is None: - end_time = datetime.datetime.now() - if self.completion_start_time is None: - self.completion_start_time = end_time - self.model_call_details[ - "completion_start_time" - ] = self.completion_start_time - - self.model_call_details["log_event_type"] = "successful_api_call" - self.model_call_details["end_time"] = end_time - self.model_call_details["cache_hit"] = cache_hit - - if self.call_type == CallTypes.anthropic_messages.value: - result = self._handle_anthropic_messages_response_logging(result=result) - elif ( - self.call_type == CallTypes.generate_content.value - or self.call_type == CallTypes.agenerate_content.value - ): - result = self._handle_non_streaming_google_genai_generate_content_response_logging( - result=result - ) - elif ( - self.call_type == CallTypes.asend_message.value - or self.call_type == CallTypes.send_message.value - ): - result = self._handle_a2a_response_logging(result=result) - - logging_result = self.normalize_logging_result(result=result) - - if ( - standard_logging_object is None - and result is not None - and self.stream is not True - ): - if self._is_recognized_call_type_for_logging( - logging_result=logging_result - ): - self._process_hidden_params_and_response_cost( - logging_result=logging_result, - start_time=start_time, - end_time=end_time, - ) - elif isinstance(result, dict) or isinstance(result, list): - self.model_call_details[ - "standard_logging_object" - ] = get_standard_logging_object_payload( - kwargs=self.model_call_details, - init_response_obj=result, - start_time=start_time, - end_time=end_time, - logging_obj=self, - status="success", - standard_built_in_tools_params=self.standard_built_in_tools_params, - ) - elif standard_logging_object is not None: - self.model_call_details[ - "standard_logging_object" - ] = standard_logging_object - else: - self.model_call_details["response_cost"] = None - - result = self._transform_usage_objects(result=result) - - if ( - litellm.max_budget - and self.stream is False - and result is not None - and isinstance(result, dict) - and "content" in result - ): - time_diff = (end_time - start_time).total_seconds() - float_diff = float(time_diff) - litellm._current_cost += litellm.completion_cost( - model=self.model, - prompt="", - completion=getattr(result, "content", ""), - total_time=float_diff, - standard_built_in_tools_params=self.standard_built_in_tools_params, - ) - - return start_time, end_time, result - except Exception as e: - raise Exception(f"[Non-Blocking] LiteLLM.Success_Call Error: {str(e)}") - - def _is_recognized_call_type_for_logging( - self, - logging_result: Any, - ): - """ - Returns True if the call type is recognized for logging (eg. ModelResponse, ModelResponseStream, etc.) - """ - if ( - isinstance(logging_result, ModelResponse) - or isinstance(logging_result, ModelResponseStream) - or isinstance(logging_result, EmbeddingResponse) - or isinstance(logging_result, ImageResponse) - or isinstance(logging_result, TranscriptionResponse) - or isinstance(logging_result, TextCompletionResponse) - or isinstance(logging_result, HttpxBinaryResponseContent) # tts - or isinstance(logging_result, RerankResponse) - or isinstance(logging_result, FineTuningJob) - or isinstance(logging_result, LiteLLMBatch) - or isinstance(logging_result, ResponsesAPIResponse) - or isinstance(logging_result, OpenAIFileObject) - or isinstance(logging_result, LiteLLMRealtimeStreamLoggingObject) - or isinstance(logging_result, OpenAIModerationResponse) - or isinstance(logging_result, OCRResponse) # OCR - or isinstance(logging_result, SearchResponse) # Search API - or isinstance(logging_result, dict) - and logging_result.get("object") == "vector_store.search_results.page" - or isinstance(logging_result, dict) - and logging_result.get("object") == "search" # Search API (dict format) - or isinstance(logging_result, VideoObject) - or isinstance(logging_result, ContainerObject) - or isinstance(logging_result, LiteLLMSendMessageResponse) # A2A - or (self.call_type == CallTypes.call_mcp_tool.value) - ): - return True - return False - - def _flush_passthrough_collected_chunks_helper( - self, - raw_bytes: List[bytes], - provider_config: "BasePassthroughConfig", - ) -> Optional["CostResponseTypes"]: - all_chunks = provider_config._convert_raw_bytes_to_str_lines(raw_bytes) - complete_streaming_response = provider_config.handle_logging_collected_chunks( - all_chunks=all_chunks, - litellm_logging_obj=self, - model=self.model, - custom_llm_provider=self.model_call_details.get("custom_llm_provider", ""), - endpoint=self.model_call_details.get("endpoint", ""), - ) - return complete_streaming_response - - def flush_passthrough_collected_chunks( - self, - raw_bytes: List[bytes], - provider_config: "BasePassthroughConfig", - ): - """ - Flush collected chunks from the logging object - This is used to log the collected chunks once streaming is done on passthrough endpoints - - 1. Decode the raw bytes to string lines - 2. Get the complete streaming response from the provider config - 3. Log the complete streaming response (trigger success handler) - This is used for passthrough endpoints - """ - complete_streaming_response = self._flush_passthrough_collected_chunks_helper( - raw_bytes=raw_bytes, - provider_config=provider_config, - ) - - if complete_streaming_response is not None: - self.success_handler(result=complete_streaming_response) - return - - async def async_flush_passthrough_collected_chunks( - self, - raw_bytes: List[bytes], - provider_config: "BasePassthroughConfig", - ): - complete_streaming_response = self._flush_passthrough_collected_chunks_helper( - raw_bytes=raw_bytes, - provider_config=provider_config, - ) - - if complete_streaming_response is not None: - await self.async_success_handler(result=complete_streaming_response) - return - - def success_handler( # noqa: PLR0915 - self, result=None, start_time=None, end_time=None, cache_hit=None, **kwargs - ): - verbose_logger.debug( - f"Logging Details LiteLLM-Success Call: Cache_hit={cache_hit}" - ) - if not self.should_run_logging( - event_type="sync_success" - ): # prevent double logging - return - start_time, end_time, result = self._success_handler_helper_fn( - start_time=start_time, - end_time=end_time, - result=result, - cache_hit=cache_hit, - standard_logging_object=kwargs.get("standard_logging_object", None), - ) - litellm_params = self.model_call_details.get("litellm_params", {}) - is_sync_request = ( - litellm_params.get(CallTypes.acompletion.value, False) is not True - and litellm_params.get(CallTypes.aresponses.value, False) is not True - and litellm_params.get(CallTypes.aembedding.value, False) is not True - and litellm_params.get(CallTypes.aimage_generation.value, False) is not True - and litellm_params.get(CallTypes.atranscription.value, False) is not True - ) - try: - ## BUILD COMPLETE STREAMED RESPONSE - complete_streaming_response: Optional[ - Union[ModelResponse, TextCompletionResponse, ResponsesAPIResponse] - ] = None - if "complete_streaming_response" in self.model_call_details: - return # break out of this. - complete_streaming_response = self._get_assembled_streaming_response( - result=result, - start_time=start_time, - end_time=end_time, - is_async=False, - streaming_chunks=self.sync_streaming_chunks, - ) - if complete_streaming_response is not None: - verbose_logger.debug( - "Logging Details LiteLLM-Success Call streaming complete" - ) - self.model_call_details[ - "complete_streaming_response" - ] = complete_streaming_response - self.model_call_details[ - "response_cost" - ] = self._response_cost_calculator(result=complete_streaming_response) - ## STANDARDIZED LOGGING PAYLOAD - self.model_call_details[ - "standard_logging_object" - ] = get_standard_logging_object_payload( - kwargs=self.model_call_details, - init_response_obj=complete_streaming_response, - start_time=start_time, - end_time=end_time, - logging_obj=self, - status="success", - standard_built_in_tools_params=self.standard_built_in_tools_params, - ) - if ( - standard_logging_payload := self.model_call_details.get( - "standard_logging_object" - ) - ) is not None: - # Only emit for sync requests (async_success_handler handles async) - if is_sync_request: - emit_standard_logging_payload(standard_logging_payload) - callbacks = self.get_combined_callback_list( - dynamic_success_callbacks=self.dynamic_success_callbacks, - global_callbacks=litellm.success_callback, - ) - - ## REDACT MESSAGES ## - result = redact_message_input_output_from_logging( - model_call_details=( - self.model_call_details - if hasattr(self, "model_call_details") - else {} - ), - result=result, - ) - ## LOGGING HOOK ## - for callback in callbacks: - if isinstance(callback, CustomLogger): - self.model_call_details, result = callback.logging_hook( - kwargs=self.model_call_details, - result=result, - call_type=self.call_type, - ) - - self.has_run_logging(event_type="sync_success") - for callback in callbacks: - try: - should_run = self.should_run_callback( - callback=callback, - litellm_params=litellm_params, - event_hook="success_handler", - ) - if not should_run: - continue - if callback == "promptlayer" and promptLayerLogger is not None: - print_verbose("reaches promptlayer for logging!") - promptLayerLogger.log_event( - kwargs=self.model_call_details, - response_obj=result, - start_time=start_time, - end_time=end_time, - print_verbose=print_verbose, - ) - if callback == "supabase" and supabaseClient is not None: - print_verbose("reaches supabase for logging!") - kwargs = self.model_call_details - - # this only logs streaming once, complete_streaming_response exists i.e when stream ends - if self.stream: - if "complete_streaming_response" not in kwargs: - continue - else: - print_verbose("reaches supabase for streaming logging!") - result = kwargs["complete_streaming_response"] - - model = kwargs["model"] - messages = kwargs["messages"] - optional_params = kwargs.get("optional_params", {}) - litellm_params = kwargs.get("litellm_params", {}) - supabaseClient.log_event( - model=model, - messages=messages, - end_user=optional_params.get("user", "default"), - response_obj=result, - start_time=start_time, - end_time=end_time, - litellm_call_id=( - current_call_id - if ( - current_call_id := litellm_params.get( - "litellm_call_id" - ) - ) - is not None - else str(uuid.uuid4()) - ), - print_verbose=print_verbose, - ) - if callback == "wandb" and weightsBiasesLogger is not None: - print_verbose("reaches wandb for logging!") - weightsBiasesLogger.log_event( - kwargs=self.model_call_details, - response_obj=result, - start_time=start_time, - end_time=end_time, - print_verbose=print_verbose, - ) - if callback == "logfire" and logfireLogger is not None: - verbose_logger.debug("reaches logfire for success logging!") - kwargs = {} - for k, v in self.model_call_details.items(): - if ( - k != "original_response" - ): # copy.deepcopy raises errors as this could be a coroutine - kwargs[k] = v - - # this only logs streaming once, complete_streaming_response exists i.e when stream ends - if self.stream: - if "complete_streaming_response" not in kwargs: - continue - else: - print_verbose("reaches logfire for streaming logging!") - result = kwargs["complete_streaming_response"] - - logfireLogger.log_event( - kwargs=self.model_call_details, - response_obj=result, - start_time=start_time, - end_time=end_time, - print_verbose=print_verbose, - level=LogfireLevel.INFO.value, # type: ignore - ) - - if callback == "lunary" and lunaryLogger is not None: - print_verbose("reaches lunary for logging!") - model = self.model - kwargs = self.model_call_details - - input = kwargs.get("messages", kwargs.get("input", None)) - - type = ( - "embed" - if self.call_type == CallTypes.embedding.value - else "llm" - ) - - # this only logs streaming once, complete_streaming_response exists i.e when stream ends - if self.stream: - if "complete_streaming_response" not in kwargs: - continue - else: - result = kwargs["complete_streaming_response"] - - lunaryLogger.log_event( - type=type, - kwargs=kwargs, - event="end", - model=model, - input=input, - user_id=kwargs.get("user", None), - # user_props=self.model_call_details.get("user_props", None), - extra=kwargs.get("optional_params", {}), - response_obj=result, - start_time=start_time, - end_time=end_time, - run_id=self.litellm_call_id, - print_verbose=print_verbose, - ) - if callback == "helicone" and heliconeLogger is not None: - print_verbose("reaches helicone for logging!") - model = self.model - messages = self.model_call_details["input"] - kwargs = self.model_call_details - - # this only logs streaming once, complete_streaming_response exists i.e when stream ends - if self.stream: - if "complete_streaming_response" not in kwargs: - continue - else: - print_verbose("reaches helicone for streaming logging!") - result = kwargs["complete_streaming_response"] - - heliconeLogger.log_success( - model=model, - messages=messages, - response_obj=result, - start_time=start_time, - end_time=end_time, - print_verbose=print_verbose, - kwargs=kwargs, - ) - if callback == "langfuse": - global langFuseLogger - print_verbose("reaches langfuse for success logging!") - kwargs = {} - for k, v in self.model_call_details.items(): - if ( - k != "original_response" - ): # copy.deepcopy raises errors as this could be a coroutine - kwargs[k] = v - # this only logs streaming once, complete_streaming_response exists i.e when stream ends - if self.stream: - verbose_logger.debug( - f"is complete_streaming_response in kwargs: {kwargs.get('complete_streaming_response', None)}" - ) - if complete_streaming_response is None: - continue - else: - print_verbose("reaches langfuse for streaming logging!") - result = kwargs["complete_streaming_response"] - - langfuse_logger_to_use = LangFuseHandler.get_langfuse_logger_for_request( - globalLangfuseLogger=langFuseLogger, - standard_callback_dynamic_params=self.standard_callback_dynamic_params, - in_memory_dynamic_logger_cache=in_memory_dynamic_logger_cache, - ) - if langfuse_logger_to_use is not None: - _response = langfuse_logger_to_use.log_event_on_langfuse( - kwargs=kwargs, - response_obj=result, - start_time=start_time, - end_time=end_time, - user_id=kwargs.get("user", None), - ) - if _response is not None and isinstance(_response, dict): - _trace_id = _response.get("trace_id", None) - if _trace_id is not None: - in_memory_trace_id_cache.set_cache( - litellm_call_id=self.litellm_call_id, - service_name="langfuse", - trace_id=_trace_id, - ) - if callback == "greenscale" and greenscaleLogger is not None: - kwargs = {} - for k, v in self.model_call_details.items(): - if ( - k != "original_response" - ): # copy.deepcopy raises errors as this could be a coroutine - kwargs[k] = v - # this only logs streaming once, complete_streaming_response exists i.e when stream ends - if self.stream: - verbose_logger.debug( - f"is complete_streaming_response in kwargs: {kwargs.get('complete_streaming_response', None)}" - ) - if complete_streaming_response is None: - continue - else: - print_verbose( - "reaches greenscale for streaming logging!" - ) - result = kwargs["complete_streaming_response"] - - greenscaleLogger.log_event( - kwargs=kwargs, - response_obj=result, - start_time=start_time, - end_time=end_time, - print_verbose=print_verbose, - ) - if callback == "athina" and athinaLogger is not None: - deep_copy = {} - for k, v in self.model_call_details.items(): - deep_copy[k] = v - athinaLogger.log_event( - kwargs=deep_copy, - response_obj=result, - start_time=start_time, - end_time=end_time, - print_verbose=print_verbose, - ) - if callback == "traceloop": - deep_copy = {} - for k, v in self.model_call_details.items(): - if k != "original_response": - deep_copy[k] = v - traceloopLogger.log_event( - kwargs=deep_copy, - response_obj=result, - start_time=start_time, - end_time=end_time, - user_id=kwargs.get("user", None), - print_verbose=print_verbose, - ) - if callback == "s3": - global s3Logger - if s3Logger is None: - s3Logger = S3Logger() - if self.stream: - if "complete_streaming_response" in self.model_call_details: - print_verbose( - "S3Logger Logger: Got Stream Event - Completed Stream Response" - ) - s3Logger.log_event( - kwargs=self.model_call_details, - response_obj=self.model_call_details[ - "complete_streaming_response" - ], - start_time=start_time, - end_time=end_time, - print_verbose=print_verbose, - ) - else: - print_verbose( - "S3Logger Logger: Got Stream Event - No complete stream response as yet" - ) - else: - s3Logger.log_event( - kwargs=self.model_call_details, - response_obj=result, - start_time=start_time, - end_time=end_time, - print_verbose=print_verbose, - ) - - if callback == "openmeter" and is_sync_request: - global openMeterLogger - if openMeterLogger is None: - print_verbose("Instantiates openmeter client") - openMeterLogger = OpenMeterLogger() - if self.stream and complete_streaming_response is None: - openMeterLogger.log_stream_event( - kwargs=self.model_call_details, - response_obj=result, - start_time=start_time, - end_time=end_time, - ) - else: - if self.stream and complete_streaming_response: - self.model_call_details[ - "complete_response" - ] = self.model_call_details.get( - "complete_streaming_response", {} - ) - result = self.model_call_details["complete_response"] - openMeterLogger.log_success_event( - kwargs=self.model_call_details, - response_obj=result, - start_time=start_time, - end_time=end_time, - ) - if ( - isinstance(callback, CustomLogger) - and is_sync_request - and self.call_type - != CallTypes.pass_through.value # pass-through endpoints call async_log_success_event - ): # custom logger class - if self.stream and complete_streaming_response is None: - callback.log_stream_event( - kwargs=self.model_call_details, - response_obj=result, - start_time=start_time, - end_time=end_time, - ) - else: - if self.stream and complete_streaming_response: - self.model_call_details[ - "complete_response" - ] = self.model_call_details.get( - "complete_streaming_response", {} - ) - result = self.model_call_details["complete_response"] - - callback.log_success_event( - kwargs=self.model_call_details, - response_obj=result, - start_time=start_time, - end_time=end_time, - ) - if ( - callable(callback) is True - and is_sync_request - and customLogger is not None - ): # custom logger functions - print_verbose( - "success callbacks: Running Custom Callback Function - {}".format( - callback - ) - ) - - customLogger.log_event( - kwargs=self.model_call_details, - response_obj=result, - start_time=start_time, - end_time=end_time, - print_verbose=print_verbose, - callback_func=callback, - ) - - except Exception as e: - print_verbose( - f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while success logging with integrations {traceback.format_exc()}" - ) - print_verbose( - f"LiteLLM.Logging: is sentry capture exception initialized {capture_exception}" - ) - if capture_exception: # log this error to sentry for debugging - capture_exception(e) - # Track callback logging failures in Prometheus - try: - self._handle_callback_failure(callback=callback) - except Exception: - pass - except Exception as e: - verbose_logger.exception( - "LiteLLM.LoggingError: [Non-Blocking] Exception occurred while success logging {}".format( - str(e) - ), - ) - - async def async_success_handler( # noqa: PLR0915 - self, result=None, start_time=None, end_time=None, cache_hit=None, **kwargs - ): - """ - Implementing async callbacks, to handle asyncio event loop issues when custom integrations need to use async functions. - """ - print_verbose( - "Logging Details LiteLLM-Async Success Call, cache_hit={}".format(cache_hit) - ) - if not self.should_run_logging( - event_type="async_success" - ): # prevent double logging - return - - ## CALCULATE COST FOR BATCH JOBS - if self.call_type == CallTypes.aretrieve_batch.value and isinstance( - result, LiteLLMBatch - ): - litellm_params = self.litellm_params or {} - litellm_metadata = litellm_params.get("litellm_metadata", {}) - if ( - litellm_metadata.get("batch_ignore_default_logging", False) is True - ): # polling job will query these frequently, don't spam db logs - return - - from litellm.proxy.openai_files_endpoints.common_utils import ( - _is_base64_encoded_unified_file_id, - ) - - # check if file id is a unified file id - is_base64_unified_file_id = _is_base64_encoded_unified_file_id(result.id) - - batch_cost = kwargs.get("batch_cost", None) - batch_usage = kwargs.get("batch_usage", None) - batch_models = kwargs.get("batch_models", None) - has_explicit_batch_data = all( - x is not None for x in (batch_cost, batch_usage, batch_models) - ) - - should_compute_batch_data = ( - not is_base64_unified_file_id - or not has_explicit_batch_data - and result.status == "completed" - ) - if has_explicit_batch_data: - result._hidden_params["response_cost"] = batch_cost - result._hidden_params["batch_models"] = batch_models - result.usage = batch_usage - - elif should_compute_batch_data: - ( - response_cost, - batch_usage, - batch_models, - ) = await _handle_completed_batch( - batch=result, - custom_llm_provider=self.custom_llm_provider, - ) - - result._hidden_params["response_cost"] = response_cost - result._hidden_params["batch_models"] = batch_models - result.usage = batch_usage - - start_time, end_time, result = self._success_handler_helper_fn( - start_time=start_time, - end_time=end_time, - result=result, - cache_hit=cache_hit, - standard_logging_object=kwargs.get("standard_logging_object", None), - ) - - ## BUILD COMPLETE STREAMED RESPONSE - if "async_complete_streaming_response" in self.model_call_details: - return # break out of this. - complete_streaming_response: Optional[ - Union[ModelResponse, TextCompletionResponse, ResponsesAPIResponse] - ] = self._get_assembled_streaming_response( - result=result, - start_time=start_time, - end_time=end_time, - is_async=True, - streaming_chunks=self.streaming_chunks, - ) - - if complete_streaming_response is not None: - print_verbose("Async success callbacks: Got a complete streaming response") - - self.model_call_details[ - "async_complete_streaming_response" - ] = complete_streaming_response - - try: - if self.model_call_details.get("cache_hit", False) is True: - self.model_call_details["response_cost"] = 0.0 - else: - # check if base_model set on azure - _get_base_model_from_metadata( - model_call_details=self.model_call_details - ) - # base_model defaults to None if not set on model_info - self.model_call_details[ - "response_cost" - ] = self._response_cost_calculator( - result=complete_streaming_response - ) - - verbose_logger.debug( - f"Model={self.model}; cost={self.model_call_details['response_cost']}" - ) - except litellm.NotFoundError: - verbose_logger.warning( - f"Model={self.model} not found in completion cost map. Setting 'response_cost' to None" - ) - self.model_call_details["response_cost"] = None - - ## STANDARDIZED LOGGING PAYLOAD - self.model_call_details[ - "standard_logging_object" - ] = get_standard_logging_object_payload( - kwargs=self.model_call_details, - init_response_obj=complete_streaming_response, - start_time=start_time, - end_time=end_time, - logging_obj=self, - status="success", - standard_built_in_tools_params=self.standard_built_in_tools_params, - ) - - # print standard logging payload - if ( - standard_logging_payload := self.model_call_details.get( - "standard_logging_object" - ) - ) is not None: - emit_standard_logging_payload(standard_logging_payload) - elif self.call_type == "pass_through_endpoint": - print_verbose( - "Async success callbacks: Got a pass-through endpoint response" - ) - - self.model_call_details["async_complete_streaming_response"] = result - - # cost calculation not possible for pass-through - self.model_call_details["response_cost"] = None - - ## STANDARDIZED LOGGING PAYLOAD - self.model_call_details[ - "standard_logging_object" - ] = get_standard_logging_object_payload( - kwargs=self.model_call_details, - init_response_obj=result, - start_time=start_time, - end_time=end_time, - logging_obj=self, - status="success", - standard_built_in_tools_params=self.standard_built_in_tools_params, - ) - - # print standard logging payload - if ( - standard_logging_payload := self.model_call_details.get( - "standard_logging_object" - ) - ) is not None: - emit_standard_logging_payload(standard_logging_payload) - callbacks = self.get_combined_callback_list( - dynamic_success_callbacks=self.dynamic_async_success_callbacks, - global_callbacks=litellm._async_success_callback, - ) - - result = redact_message_input_output_from_logging( - model_call_details=( - self.model_call_details if hasattr(self, "model_call_details") else {} - ), - result=result, - ) - - ## LOGGING HOOK ## - - for callback in callbacks: - if isinstance(callback, CustomGuardrail): - from litellm.types.guardrails import GuardrailEventHooks - - if ( - callback.should_run_guardrail( - data=self.model_call_details, - event_type=GuardrailEventHooks.logging_only, - ) - is not True - ): - continue - - self.model_call_details, result = await callback.async_logging_hook( - kwargs=self.model_call_details, - result=result, - call_type=self.call_type, - ) - elif isinstance(callback, CustomLogger): - result = redact_message_input_output_from_custom_logger( - result=result, litellm_logging_obj=self, custom_logger=callback - ) - self.model_call_details, result = await callback.async_logging_hook( - kwargs=self.model_call_details, - result=result, - call_type=self.call_type, - ) - - self.has_run_logging(event_type="async_success") - - for callback in callbacks: - # check if callback can run for this request - litellm_params = self.model_call_details.get("litellm_params", {}) - should_run = self.should_run_callback( - callback=callback, - litellm_params=litellm_params, - event_hook="async_success_handler", - ) - if not should_run: - continue - try: - if callback == "openmeter" and openMeterLogger is not None: - if self.stream is True: - if ( - "async_complete_streaming_response" - in self.model_call_details - ): - await openMeterLogger.async_log_success_event( - kwargs=self.model_call_details, - response_obj=self.model_call_details[ - "async_complete_streaming_response" - ], - start_time=start_time, - end_time=end_time, - ) - else: - await openMeterLogger.async_log_stream_event( # [TODO]: move this to being an async log stream event function - kwargs=self.model_call_details, - response_obj=result, - start_time=start_time, - end_time=end_time, - ) - else: - await openMeterLogger.async_log_success_event( - kwargs=self.model_call_details, - response_obj=result, - start_time=start_time, - end_time=end_time, - ) - - if isinstance(callback, CustomLogger): # custom logger class - model_call_details: Dict = self.model_call_details - ################################## - # call redaction hook for custom logger - model_call_details = callback.redact_standard_logging_payload_from_model_call_details( - model_call_details=model_call_details - ) - ################################## - if self.stream is True: - if "async_complete_streaming_response" in model_call_details: - await callback.async_log_success_event( - kwargs=model_call_details, - response_obj=model_call_details[ - "async_complete_streaming_response" - ], - start_time=start_time, - end_time=end_time, - ) - else: - await callback.async_log_stream_event( # [TODO]: move this to being an async log stream event function - kwargs=model_call_details, - response_obj=result, - start_time=start_time, - end_time=end_time, - ) - else: - await callback.async_log_success_event( - kwargs=model_call_details, - response_obj=result, - start_time=start_time, - end_time=end_time, - ) - if callable(callback): # custom logger functions - global customLogger - if customLogger is None: - customLogger = CustomLogger() - if self.stream: - if ( - "async_complete_streaming_response" - in self.model_call_details - ): - await customLogger.async_log_event( - kwargs=self.model_call_details, - response_obj=self.model_call_details[ - "async_complete_streaming_response" - ], - start_time=start_time, - end_time=end_time, - print_verbose=print_verbose, - callback_func=callback, - ) - else: - await customLogger.async_log_event( - kwargs=self.model_call_details, - response_obj=result, - start_time=start_time, - end_time=end_time, - print_verbose=print_verbose, - callback_func=callback, - ) - if callback == "dynamodb": - global dynamoLogger - if dynamoLogger is None: - dynamoLogger = DyanmoDBLogger() - if self.stream: - if ( - "async_complete_streaming_response" - in self.model_call_details - ): - print_verbose( - "DynamoDB Logger: Got Stream Event - Completed Stream Response" - ) - await dynamoLogger._async_log_event( - kwargs=self.model_call_details, - response_obj=self.model_call_details[ - "async_complete_streaming_response" - ], - start_time=start_time, - end_time=end_time, - print_verbose=print_verbose, - ) - else: - print_verbose( - "DynamoDB Logger: Got Stream Event - No complete stream response as yet" - ) - else: - await dynamoLogger._async_log_event( - kwargs=self.model_call_details, - response_obj=result, - start_time=start_time, - end_time=end_time, - print_verbose=print_verbose, - ) - except Exception: - verbose_logger.error( - f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while success logging {traceback.format_exc()}" - ) - self._handle_callback_failure(callback=callback) - pass - - def _handle_callback_failure(self, callback: Any): - """ - Handle callback logging failures by incrementing Prometheus metrics. - - Works for both sync and async contexts since Prometheus counter increment is synchronous. - - Args: - callback: The callback that failed - """ - try: - callback_name = self._get_callback_name(callback) - - all_callbacks = litellm.logging_callback_manager._get_all_callbacks() - - for callback_obj in all_callbacks: - if hasattr(callback_obj, "increment_callback_logging_failure"): - callback_obj.increment_callback_logging_failure(callback_name=callback_name) # type: ignore - break # Only increment once - - except Exception as e: - verbose_logger.debug(f"Error in _handle_callback_failure: {str(e)}") - - def _failure_handler_helper_fn( - self, exception, traceback_exception, start_time=None, end_time=None - ): - if start_time is None: - start_time = self.start_time - if end_time is None: - end_time = datetime.datetime.now() - - # on some exceptions, model_call_details is not always initialized, this ensures that we still log those exceptions - if not hasattr(self, "model_call_details"): - self.model_call_details = {} - - self.model_call_details["log_event_type"] = "failed_api_call" - self.model_call_details["exception"] = exception - self.model_call_details["traceback_exception"] = traceback_exception - self.model_call_details["end_time"] = end_time - self.model_call_details.setdefault("original_response", None) - self.model_call_details["response_cost"] = 0 - - if hasattr(exception, "headers") and isinstance(exception.headers, dict): - self.model_call_details.setdefault("litellm_params", {}) - metadata = ( - self.model_call_details["litellm_params"].get("metadata", {}) or {} - ) - metadata.update(exception.headers) - - ## STANDARDIZED LOGGING PAYLOAD - - self.model_call_details[ - "standard_logging_object" - ] = get_standard_logging_object_payload( - kwargs=self.model_call_details, - init_response_obj={}, - start_time=start_time, - end_time=end_time, - logging_obj=self, - status="failure", - error_str=str(exception), - original_exception=exception, - standard_built_in_tools_params=self.standard_built_in_tools_params, - ) - return start_time, end_time - - async def special_failure_handlers(self, exception: Exception): - """ - Custom events, emitted for specific failures. - - Currently just for router model group rate limit error - """ - from litellm.types.router import RouterErrors - - litellm_params: dict = self.model_call_details.get("litellm_params") or {} - metadata = litellm_params.get("metadata") or {} - - ## BASE CASE ## check if rate limit error for model group size 1 - is_base_case = False - if metadata.get("model_group_size") is not None: - model_group_size = metadata.get("model_group_size") - if isinstance(model_group_size, int) and model_group_size == 1: - is_base_case = True - ## check if special error ## - if ( - RouterErrors.no_deployments_available.value not in str(exception) - and is_base_case is False - ): - return - - ## get original model group ## - - model_group = metadata.get("model_group") or None - for callback in litellm._async_failure_callback: - if isinstance(callback, CustomLogger): # custom logger class - await callback.log_model_group_rate_limit_error( - exception=exception, - original_model_group=model_group, - kwargs=self.model_call_details, - ) # type: ignore - - def failure_handler( # noqa: PLR0915 - self, exception, traceback_exception, start_time=None, end_time=None - ): - verbose_logger.debug( - f"Logging Details LiteLLM-Failure Call: {litellm.failure_callback}" - ) - if not self.should_run_logging( - event_type="sync_failure" - ): # prevent double logging - return - litellm_params = self.model_call_details.get("litellm_params", {}) - is_sync_request = ( - litellm_params.get(CallTypes.acompletion.value, False) is not True - and litellm_params.get(CallTypes.aresponses.value, False) is not True - and litellm_params.get(CallTypes.aembedding.value, False) is not True - and litellm_params.get(CallTypes.aimage_generation.value, False) is not True - and litellm_params.get(CallTypes.atranscription.value, False) is not True - ) - - try: - start_time, end_time = self._failure_handler_helper_fn( - exception=exception, - traceback_exception=traceback_exception, - start_time=start_time, - end_time=end_time, - ) - callbacks = self.get_combined_callback_list( - dynamic_success_callbacks=self.dynamic_failure_callbacks, - global_callbacks=litellm.failure_callback, - ) - - result = None # result sent to all loggers, init this to None incase it's not created - - result = redact_message_input_output_from_logging( - model_call_details=( - self.model_call_details - if hasattr(self, "model_call_details") - else {} - ), - result=result, - ) - self.has_run_logging(event_type="sync_failure") - for callback in callbacks: - try: - should_run = self.should_run_callback( - callback=callback, - litellm_params=litellm_params, - event_hook="failure_handler", - ) - if not should_run: - continue - if callback == "lunary" and lunaryLogger is not None: - print_verbose("reaches lunary for logging error!") - - model = self.model - - input = self.model_call_details["input"] - - _type = ( - "embed" - if self.call_type == CallTypes.embedding.value - else "llm" - ) - - lunaryLogger.log_event( - kwargs=self.model_call_details, - type=_type, - event="error", - user_id=self.model_call_details.get("user", "default"), - model=model, - input=input, - error=traceback_exception, - run_id=self.litellm_call_id, - start_time=start_time, - end_time=end_time, - print_verbose=print_verbose, - ) - if callback == "sentry": - print_verbose("sending exception to sentry") - if capture_exception: - capture_exception(exception) - else: - print_verbose( - f"capture exception not initialized: {capture_exception}" - ) - elif callback == "supabase" and supabaseClient is not None: - print_verbose("reaches supabase for logging!") - print_verbose(f"supabaseClient: {supabaseClient}") - supabaseClient.log_event( - model=self.model if hasattr(self, "model") else "", - messages=self.messages, - end_user=self.model_call_details.get("user", "default"), - response_obj=result, - start_time=start_time, - end_time=end_time, - litellm_call_id=self.model_call_details["litellm_call_id"], - print_verbose=print_verbose, - ) - if ( - callable(callback) and customLogger is not None - ): # custom logger functions - customLogger.log_event( - kwargs=self.model_call_details, - response_obj=result, - start_time=start_time, - end_time=end_time, - print_verbose=print_verbose, - callback_func=callback, - ) - if ( - isinstance(callback, CustomLogger) and is_sync_request - ): # custom logger class - callback.log_failure_event( - start_time=start_time, - end_time=end_time, - response_obj=result, - kwargs=self.model_call_details, - ) - if callback == "langfuse": - global langFuseLogger - verbose_logger.debug("reaches langfuse for logging failure") - kwargs = {} - for k, v in self.model_call_details.items(): - if ( - k != "original_response" - ): # copy.deepcopy raises errors as this could be a coroutine - kwargs[k] = v - # this only logs streaming once, complete_streaming_response exists i.e when stream ends - langfuse_logger_to_use = LangFuseHandler.get_langfuse_logger_for_request( - globalLangfuseLogger=langFuseLogger, - standard_callback_dynamic_params=self.standard_callback_dynamic_params, - in_memory_dynamic_logger_cache=in_memory_dynamic_logger_cache, - ) - _response = langfuse_logger_to_use.log_event_on_langfuse( - start_time=start_time, - end_time=end_time, - response_obj=None, - user_id=kwargs.get("user", None), - status_message=str(exception), - level="ERROR", - kwargs=self.model_call_details, - ) - if _response is not None and isinstance(_response, dict): - _trace_id = _response.get("trace_id", None) - if _trace_id is not None: - in_memory_trace_id_cache.set_cache( - litellm_call_id=self.litellm_call_id, - service_name="langfuse", - trace_id=_trace_id, - ) - if callback == "traceloop": - traceloopLogger.log_event( - start_time=start_time, - end_time=end_time, - response_obj=None, - user_id=self.model_call_details.get("user", None), - print_verbose=print_verbose, - status_message=str(exception), - level="ERROR", - kwargs=self.model_call_details, - ) - if callback == "logfire" and logfireLogger is not None: - verbose_logger.debug("reaches logfire for failure logging!") - kwargs = {} - for k, v in self.model_call_details.items(): - if ( - k != "original_response" - ): # copy.deepcopy raises errors as this could be a coroutine - kwargs[k] = v - kwargs["exception"] = exception - - logfireLogger.log_event( - kwargs=kwargs, - response_obj=result, - start_time=start_time, - end_time=end_time, - level=LogfireLevel.ERROR.value, # type: ignore - print_verbose=print_verbose, - ) - - except Exception as e: - print_verbose( - f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while failure logging with integrations {str(e)}" - ) - print_verbose( - f"LiteLLM.Logging: is sentry capture exception initialized {capture_exception}" - ) - if capture_exception: # log this error to sentry for debugging - capture_exception(e) - except Exception as e: - verbose_logger.exception( - "LiteLLM.LoggingError: [Non-Blocking] Exception occurred while failure logging {}".format( - str(e) - ) - ) - - async def async_failure_handler( - self, exception, traceback_exception, start_time=None, end_time=None - ): - """ - Implementing async callbacks, to handle asyncio event loop issues when custom integrations need to use async functions. - """ - await self.special_failure_handlers(exception=exception) - if not self.should_run_logging( - event_type="async_failure" - ): # prevent double logging - return - start_time, end_time = self._failure_handler_helper_fn( - exception=exception, - traceback_exception=traceback_exception, - start_time=start_time, - end_time=end_time, - ) - - callbacks = self.get_combined_callback_list( - dynamic_success_callbacks=self.dynamic_async_failure_callbacks, - global_callbacks=litellm._async_failure_callback, - ) - - result = None # result sent to all loggers, init this to None incase it's not created - - self.has_run_logging(event_type="async_failure") - for callback in callbacks: - try: - litellm_params = self.model_call_details.get("litellm_params", {}) - should_run = self.should_run_callback( - callback=callback, - litellm_params=litellm_params, - event_hook="async_failure_handler", - ) - if not should_run: - continue - if isinstance(callback, CustomLogger): # custom logger class - await callback.async_log_failure_event( - kwargs=self.model_call_details, - response_obj=result, - start_time=start_time, - end_time=end_time, - ) # type: ignore - if ( - callable(callback) and customLogger is not None - ): # custom logger functions - await customLogger.async_log_event( - kwargs=self.model_call_details, - response_obj=result, - start_time=start_time, - end_time=end_time, - print_verbose=print_verbose, - callback_func=callback, - ) - except Exception as e: - verbose_logger.exception( - "LiteLLM.LoggingError: [Non-Blocking] Exception occurred while failure \ - logging {}\nCallback={}".format( - str(e), callback - ) - ) - # Track callback logging failures in Prometheus - self._handle_callback_failure(callback=callback) - - def _get_trace_id(self, service_name: Literal["langfuse"]) -> Optional[str]: - """ - For the given service (e.g. langfuse), return the trace_id actually logged. - - Used for constructing the url in slack alerting. - - Returns: - - str: The logged trace id - - None: If trace id not yet emitted. - """ - trace_id: Optional[str] = None - if service_name == "langfuse": - trace_id = in_memory_trace_id_cache.get_cache( - litellm_call_id=self.litellm_call_id, service_name=service_name - ) - - return trace_id - - def _get_callback_object(self, service_name: Literal["langfuse"]) -> Optional[Any]: - """ - Return dynamic callback object. - - Meant to solve issue when doing key-based/team-based logging - """ - global langFuseLogger - - if service_name == "langfuse": - if langFuseLogger is None or ( - ( - self.standard_callback_dynamic_params.get("langfuse_public_key") - is not None - and self.standard_callback_dynamic_params.get("langfuse_public_key") - != langFuseLogger.public_key - ) - or ( - self.standard_callback_dynamic_params.get("langfuse_public_key") - is not None - and self.standard_callback_dynamic_params.get("langfuse_public_key") - != langFuseLogger.public_key - ) - or ( - self.standard_callback_dynamic_params.get("langfuse_host") - is not None - and self.standard_callback_dynamic_params.get("langfuse_host") - != langFuseLogger.langfuse_host - ) - ): - return LangFuseLogger( - langfuse_public_key=self.standard_callback_dynamic_params.get( - "langfuse_public_key" - ), - langfuse_secret=self.standard_callback_dynamic_params.get( - "langfuse_secret" - ), - langfuse_host=self.standard_callback_dynamic_params.get( - "langfuse_host" - ), - ) - return langFuseLogger - - return None - - def handle_sync_success_callbacks_for_async_calls( - self, - result: Any, - start_time: datetime.datetime, - end_time: datetime.datetime, - cache_hit: Optional[Any] = None, - ) -> None: - """ - Handles calling success callbacks for Async calls. - - Why: Some callbacks - `langfuse`, `s3` are sync callbacks. We need to call them in the executor. - """ - if self._should_run_sync_callbacks_for_async_calls() is False: - return - - executor.submit( - self.success_handler, - result, - start_time, - end_time, - cache_hit, - ) - - def _should_run_sync_callbacks_for_async_calls(self) -> bool: - """ - Returns: - - bool: True if sync callbacks should be run for async calls. eg. `langfuse`, `s3` - """ - _combined_sync_callbacks = self.get_combined_callback_list( - dynamic_success_callbacks=self.dynamic_success_callbacks, - global_callbacks=litellm.success_callback, - ) - _filtered_success_callbacks = self._remove_internal_custom_logger_callbacks( - _combined_sync_callbacks - ) - _filtered_success_callbacks = self._remove_internal_litellm_callbacks( - _filtered_success_callbacks - ) - return len(_filtered_success_callbacks) > 0 - - def get_combined_callback_list( - self, dynamic_success_callbacks: Optional[List], global_callbacks: List - ) -> List: - if dynamic_success_callbacks is None: - return global_callbacks - return list(set(dynamic_success_callbacks + global_callbacks)) - - def _remove_internal_litellm_callbacks(self, callbacks: List) -> List: - """ - Creates a filtered list of callbacks, excluding internal LiteLLM callbacks. - - Args: - callbacks: List of callback functions/strings to filter - - Returns: - List of filtered callbacks with internal ones removed - """ - filtered = [ - cb for cb in callbacks if not self._is_internal_litellm_proxy_callback(cb) - ] - - verbose_logger.debug(f"Filtered callbacks: {filtered}") - return filtered - - def _get_callback_name(self, cb) -> str: - """ - Helper to get the name of a callback function - - Args: - cb: The callback object/function/string to get the name of - - Returns: - The name of the callback - """ - if isinstance(cb, str): - return cb - if hasattr(cb, "__name__"): - return cb.__name__ - if hasattr(cb, "__func__"): - return cb.__func__.__name__ - if hasattr(cb, "__class__"): - return cb.__class__.__name__ - return str(cb) - - def _is_internal_litellm_proxy_callback(self, cb) -> bool: - """Helper to check if a callback is internal""" - INTERNAL_PREFIXES = [ - "_PROXY", - "_service_logger.ServiceLogging", - "sync_deployment_callback_on_success", - ] - if isinstance(cb, str): - return False - - if not callable(cb): - return True - - cb_name = self._get_callback_name(cb) - return any(prefix in cb_name for prefix in INTERNAL_PREFIXES) - - def _remove_internal_custom_logger_callbacks(self, callbacks: List) -> List: - """ - Removes internal custom logger callbacks from the list. - """ - _new_callbacks = [] - for _c in callbacks: - if isinstance(_c, CustomLogger): - continue - elif ( - isinstance(_c, str) - and _c in litellm._known_custom_logger_compatible_callbacks - ): - continue - _new_callbacks.append(_c) - return _new_callbacks - - def _get_assembled_streaming_response( - self, - result: Union[ - ModelResponse, - TextCompletionResponse, - ModelResponseStream, - ResponseCompletedEvent, - Any, - ], - start_time: datetime.datetime, - end_time: datetime.datetime, - is_async: bool, - streaming_chunks: List[Any], - ) -> Optional[Union[ModelResponse, TextCompletionResponse, ResponsesAPIResponse]]: - if isinstance(result, ModelResponse): - return result - elif isinstance(result, TextCompletionResponse): - return result - elif isinstance(result, ResponseCompletedEvent): - ## return unified Usage object - if isinstance(result.response.usage, ResponseAPIUsage): - transformed_usage = ( - ResponseAPILoggingUtils._transform_response_api_usage_to_chat_usage( - result.response.usage - ) - ) - # Set as dict instead of Usage object so model_dump() serializes it correctly - setattr( - result.response, - "usage", - ( - transformed_usage.model_dump() - if hasattr(transformed_usage, "model_dump") - else dict(transformed_usage) - ), - ) - return result.response - else: - return None - return None - - def _handle_anthropic_messages_response_logging(self, result: Any) -> ModelResponse: - """ - Handles logging for Anthropic messages responses. - - Args: - result: The response object from the model call - - Returns: - The the response object from the model call - - - For Non-streaming responses, we need to transform the response to a ModelResponse object. - - For streaming responses, anthropic_messages handler calls success_handler with a assembled ModelResponse. - """ - import httpx - - if self.stream and isinstance(result, ModelResponse): - return result - elif isinstance(result, ModelResponse): - return result - - httpx_response = self.model_call_details.get("httpx_response", None) - if httpx_response and isinstance(httpx_response, httpx.Response): - result = litellm.AnthropicConfig().transform_response( - raw_response=httpx_response, - model_response=litellm.ModelResponse(), - model=self.model, - messages=[], - logging_obj=self, - optional_params={}, - api_key="", - request_data={}, - encoding=litellm.encoding, - json_mode=False, - litellm_params={}, - ) - else: - from litellm.types.llms.anthropic import AnthropicResponse - - pydantic_result = AnthropicResponse.model_validate(result) - import httpx - - result = litellm.AnthropicConfig().transform_parsed_response( - completion_response=pydantic_result.model_dump(), - raw_response=httpx.Response( - status_code=200, - headers={}, - ), - model_response=litellm.ModelResponse(), - json_mode=None, - ) - return result - - def _handle_non_streaming_google_genai_generate_content_response_logging( - self, result: Any - ) -> ModelResponse: - """ - Handles logging for Google GenAI generate content responses. - """ - import httpx - - httpx_response = self.model_call_details.get("httpx_response", None) - if httpx_response is None: - raise ValueError("Google GenAI Generate Content: httpx_response is None") - dict_result = httpx_response.json() - result = litellm.VertexGeminiConfig()._transform_google_generate_content_to_openai_model_response( - completion_response=dict_result, - model_response=litellm.ModelResponse(), - model=self.model, - logging_obj=self, - raw_response=httpx.Response( - status_code=200, - headers={}, - ), - ) - return result - - def _handle_a2a_response_logging(self, result: Any) -> Any: - """ - Handles logging for A2A (Agent-to-Agent) responses. - - Adds usage from model_call_details to the result if available. - Uses Pydantic's model_copy to avoid modifying the original response. - - Args: - result: The LiteLLMSendMessageResponse from the A2A call - - Returns: - The response object with usage added if available - """ - # Get usage from model_call_details (set by asend_message) - usage = self.model_call_details.get("usage") - if usage is None: - return result - - # Deep copy result and add usage - result_copy = result.model_copy(deep=True) - result_copy.usage = ( - usage.model_dump() if hasattr(usage, "model_dump") else dict(usage) - ) - return result_copy - - -def _get_masked_values( - sensitive_object: dict, - ignore_sensitive_values: bool = False, - mask_all_values: bool = False, - unmasked_length: int = 4, - number_of_asterisks: Optional[int] = 4, -) -> dict: - """ - Internal debugging helper function - - Masks the headers of the request sent from LiteLLM - - Args: - masked_length: Optional length for the masked portion (number of *). If set, will use exactly this many * - regardless of original string length. The total length will be unmasked_length + masked_length. - """ - sensitive_keywords = [ - "authorization", - "token", - "key", - "secret", - "vertex_credentials", - ] - return { - k: ( - # If ignore_sensitive_values is True, or if this key doesn't contain sensitive keywords, return original value - v - if ignore_sensitive_values - or not any( - sensitive_keyword in k.lower() - for sensitive_keyword in sensitive_keywords - ) - else ( - # Apply masking to sensitive keys - ( - v[: unmasked_length // 2] - + "*" * number_of_asterisks - + v[-unmasked_length // 2 :] - ) - if ( - isinstance(v, str) - and len(v) > unmasked_length - and number_of_asterisks is not None - ) - else ( - ( - v[: unmasked_length // 2] - + "*" * (len(v) - unmasked_length) - + v[-unmasked_length // 2 :] - ) - if (isinstance(v, str) and len(v) > unmasked_length) - else ("*****" if isinstance(v, str) else v) - ) - ) - ) - for k, v in sensitive_object.items() - } - - -def set_callbacks(callback_list, function_id=None): # noqa: PLR0915 - """ - Globally sets the callback client - """ - global sentry_sdk_instance, capture_exception, add_breadcrumb, slack_app, alerts_channel, traceloopLogger, athinaLogger, heliconeLogger, supabaseClient, lunaryLogger, promptLayerLogger, langFuseLogger, customLogger, weightsBiasesLogger, logfireLogger, dynamoLogger, s3Logger, dataDogLogger, prometheusLogger, greenscaleLogger, openMeterLogger, deepevalLogger - - try: - for callback in callback_list: - if callback == "sentry": - try: - import sentry_sdk - except ImportError: - print_verbose("Package 'sentry_sdk' is missing. Installing it...") - subprocess.check_call( - [sys.executable, "-m", "pip", "install", "sentry_sdk"] - ) - import sentry_sdk - from sentry_sdk.scrubber import EventScrubber - - sentry_sdk_instance = sentry_sdk - sentry_trace_rate = ( - os.environ.get("SENTRY_API_TRACE_RATE") - if "SENTRY_API_TRACE_RATE" in os.environ - else "1.0" - ) - sentry_sample_rate = ( - os.environ.get("SENTRY_API_SAMPLE_RATE") - if "SENTRY_API_SAMPLE_RATE" in os.environ - else "1.0" - ) - sentry_sdk_instance.init( - dsn=os.environ.get("SENTRY_DSN"), - traces_sample_rate=float(sentry_trace_rate), # type: ignore - sample_rate=float( - sentry_sample_rate if sentry_sample_rate else 1.0 - ), - send_default_pii=False, # Prevent sending Personal Identifiable Information - event_scrubber=EventScrubber( - denylist=SENTRY_DENYLIST, pii_denylist=SENTRY_PII_DENYLIST - ), - environment=os.environ.get("SENTRY_ENVIRONMENT", "production"), - ) - capture_exception = sentry_sdk_instance.capture_exception - add_breadcrumb = sentry_sdk_instance.add_breadcrumb - elif callback == "slack": - try: - from slack_bolt import App - except ImportError: - print_verbose("Package 'slack_bolt' is missing. Installing it...") - subprocess.check_call( - [sys.executable, "-m", "pip", "install", "slack_bolt"] - ) - from slack_bolt import App - slack_app = App( - token=os.environ.get("SLACK_API_TOKEN"), - signing_secret=os.environ.get("SLACK_API_SECRET"), - ) - alerts_channel = os.environ["SLACK_API_CHANNEL"] - print_verbose(f"Initialized Slack App: {slack_app}") - elif callback == "traceloop": - traceloopLogger = TraceloopLogger() - elif callback == "athina": - athinaLogger = AthinaLogger() - print_verbose("Initialized Athina Logger") - elif callback == "helicone": - heliconeLogger = HeliconeLogger() - elif callback == "lunary": - lunaryLogger = LunaryLogger() - elif callback == "promptlayer": - promptLayerLogger = PromptLayerLogger() - elif callback == "langfuse": - langFuseLogger = LangFuseLogger( - langfuse_public_key=None, langfuse_secret=None, langfuse_host=None - ) - elif callback == "openmeter": - openMeterLogger = OpenMeterLogger() - elif callback == "datadog": - dataDogLogger = DataDogLogger() - elif callback == "dynamodb": - dynamoLogger = DyanmoDBLogger() - elif callback == "s3": - s3Logger = S3Logger() - elif callback == "wandb": - from litellm.integrations.weights_biases import WeightsBiasesLogger - - weightsBiasesLogger = WeightsBiasesLogger() - elif callback == "logfire": - logfireLogger = LogfireLogger() - elif callback == "supabase": - print_verbose("instantiating supabase") - supabaseClient = Supabase() - elif callback == "greenscale": - greenscaleLogger = GreenscaleLogger() - print_verbose("Initialized Greenscale Logger") - elif callable(callback): - customLogger = CustomLogger() - except Exception as e: - raise e - return None - - -def _init_custom_logger_compatible_class( # noqa: PLR0915 - logging_integration: _custom_logger_compatible_callbacks_literal, - internal_usage_cache: Optional[DualCache], - llm_router: Optional[ - Any - ], # expect litellm.Router, but typing errors due to circular import - custom_logger_init_args: Optional[dict] = {}, -) -> Optional[CustomLogger]: - """ - Initialize a custom logger compatible class - """ - try: - custom_logger_init_args = custom_logger_init_args or {} - if logging_integration == "agentops": # Add AgentOps initialization - for callback in _in_memory_loggers: - if isinstance(callback, AgentOps): - return callback # type: ignore - - agentops_logger = AgentOps() - _in_memory_loggers.append(agentops_logger) - return agentops_logger # type: ignore - elif logging_integration == "lago": - for callback in _in_memory_loggers: - if isinstance(callback, LagoLogger): - return callback # type: ignore - - lago_logger = LagoLogger() - _in_memory_loggers.append(lago_logger) - return lago_logger # type: ignore - elif logging_integration == "openmeter": - for callback in _in_memory_loggers: - if isinstance(callback, OpenMeterLogger): - return callback # type: ignore - - _openmeter_logger = OpenMeterLogger() - _in_memory_loggers.append(_openmeter_logger) - return _openmeter_logger # type: ignore - elif logging_integration == "posthog": - for callback in _in_memory_loggers: - if isinstance(callback, PostHogLogger): - return callback # type: ignore - - _posthog_logger = PostHogLogger() - _in_memory_loggers.append(_posthog_logger) - return _posthog_logger # type: ignore - elif logging_integration == "braintrust": - from litellm.integrations.braintrust_logging import BraintrustLogger - - for callback in _in_memory_loggers: - if isinstance(callback, BraintrustLogger): - return callback # type: ignore - - braintrust_logger = BraintrustLogger() - _in_memory_loggers.append(braintrust_logger) - return braintrust_logger # type: ignore - elif logging_integration == "langsmith": - for callback in _in_memory_loggers: - if isinstance(callback, LangsmithLogger): - return callback # type: ignore - - _langsmith_logger = LangsmithLogger() - _in_memory_loggers.append(_langsmith_logger) - return _langsmith_logger # type: ignore - elif logging_integration == "argilla": - for callback in _in_memory_loggers: - if isinstance(callback, ArgillaLogger): - return callback # type: ignore - - _argilla_logger = ArgillaLogger() - _in_memory_loggers.append(_argilla_logger) - return _argilla_logger # type: ignore - elif logging_integration == "literalai": - for callback in _in_memory_loggers: - if isinstance(callback, LiteralAILogger): - return callback # type: ignore - - _literalai_logger = LiteralAILogger() - _in_memory_loggers.append(_literalai_logger) - return _literalai_logger # type: ignore - elif logging_integration == "prometheus": - PrometheusLogger = _get_cached_prometheus_logger() - - for callback in _in_memory_loggers: - if isinstance(callback, PrometheusLogger): - return callback # type: ignore - - _prometheus_logger = PrometheusLogger() - _in_memory_loggers.append(_prometheus_logger) - return _prometheus_logger # type: ignore - elif logging_integration == "datadog": - for callback in _in_memory_loggers: - if isinstance(callback, DataDogLogger): - return callback # type: ignore - - _datadog_logger = DataDogLogger() - _in_memory_loggers.append(_datadog_logger) - return _datadog_logger # type: ignore - elif logging_integration == "datadog_llm_observability": - _datadog_llm_obs_logger = DataDogLLMObsLogger() - _in_memory_loggers.append(_datadog_llm_obs_logger) - return _datadog_llm_obs_logger # type: ignore - elif logging_integration == "azure_sentinel": - for callback in _in_memory_loggers: - if isinstance(callback, AzureSentinelLogger): - return callback # type: ignore - - _azure_sentinel_logger = AzureSentinelLogger() - _in_memory_loggers.append(_azure_sentinel_logger) - return _azure_sentinel_logger # type: ignore - elif logging_integration == "gcs_bucket": - for callback in _in_memory_loggers: - if isinstance(callback, GCSBucketLogger): - return callback # type: ignore - - _gcs_bucket_logger = GCSBucketLogger() - _in_memory_loggers.append(_gcs_bucket_logger) - return _gcs_bucket_logger # type: ignore - elif logging_integration == "s3_v2": - for callback in _in_memory_loggers: - if isinstance(callback, S3V2Logger): - return callback # type: ignore - - _s3_v2_logger = S3V2Logger() - _in_memory_loggers.append(_s3_v2_logger) - return _s3_v2_logger # type: ignore - elif logging_integration == "aws_sqs": - for callback in _in_memory_loggers: - if isinstance(callback, SQSLogger): - return callback # type: ignore - - _aws_sqs_logger = SQSLogger() - _in_memory_loggers.append(_aws_sqs_logger) - return _aws_sqs_logger # type: ignore - elif logging_integration == "azure_storage": - for callback in _in_memory_loggers: - if isinstance(callback, AzureBlobStorageLogger): - return callback # type: ignore - - _azure_storage_logger = AzureBlobStorageLogger() - _in_memory_loggers.append(_azure_storage_logger) - return _azure_storage_logger # type: ignore - elif logging_integration == "opik": - for callback in _in_memory_loggers: - if isinstance(callback, OpikLogger): - return callback # type: ignore - - _opik_logger = OpikLogger() - _in_memory_loggers.append(_opik_logger) - return _opik_logger # type: ignore - elif logging_integration == "arize": - from litellm.integrations.opentelemetry import ( - OpenTelemetry, - OpenTelemetryConfig, - ) - - arize_config = ArizeLogger.get_arize_config() - if arize_config.endpoint is None: - raise ValueError( - "No valid endpoint found for Arize, please set 'ARIZE_ENDPOINT' to your GRPC endpoint or 'ARIZE_HTTP_ENDPOINT' to your HTTP endpoint" - ) - otel_config = OpenTelemetryConfig( - exporter=arize_config.protocol, - endpoint=arize_config.endpoint, - service_name=arize_config.project_name, - ) - - os.environ[ - "OTEL_EXPORTER_OTLP_TRACES_HEADERS" - ] = f"space_id={arize_config.space_key or arize_config.space_id},api_key={arize_config.api_key}" - for callback in _in_memory_loggers: - if ( - isinstance(callback, ArizeLogger) - and callback.callback_name == "arize" - ): - return callback # type: ignore - _arize_otel_logger = ArizeLogger(config=otel_config, callback_name="arize") - _in_memory_loggers.append(_arize_otel_logger) - return _arize_otel_logger # type: ignore - elif logging_integration == "arize_phoenix": - from litellm.integrations.opentelemetry import ( - OpenTelemetry, - OpenTelemetryConfig, - ) - - arize_phoenix_config = ArizePhoenixLogger.get_arize_phoenix_config() - otel_config = OpenTelemetryConfig( - exporter=arize_phoenix_config.protocol, - endpoint=arize_phoenix_config.endpoint, - headers=arize_phoenix_config.otlp_auth_headers, - ) - if arize_phoenix_config.project_name: - existing_attrs = os.environ.get("OTEL_RESOURCE_ATTRIBUTES", "") - # Add openinference.project.name attribute - if existing_attrs: - os.environ[ - "OTEL_RESOURCE_ATTRIBUTES" - ] = f"{existing_attrs},openinference.project.name={arize_phoenix_config.project_name}" - else: - os.environ[ - "OTEL_RESOURCE_ATTRIBUTES" - ] = f"openinference.project.name={arize_phoenix_config.project_name}" - - # Set Phoenix project name from environment variable - phoenix_project_name = os.environ.get("PHOENIX_PROJECT_NAME", None) - if phoenix_project_name: - existing_attrs = os.environ.get("OTEL_RESOURCE_ATTRIBUTES", "") - # Add openinference.project.name attribute - if existing_attrs: - os.environ[ - "OTEL_RESOURCE_ATTRIBUTES" - ] = f"{existing_attrs},openinference.project.name={phoenix_project_name}" - else: - os.environ[ - "OTEL_RESOURCE_ATTRIBUTES" - ] = f"openinference.project.name={phoenix_project_name}" - - # auth can be disabled on local deployments of arize phoenix - if arize_phoenix_config.otlp_auth_headers is not None: - os.environ[ - "OTEL_EXPORTER_OTLP_TRACES_HEADERS" - ] = arize_phoenix_config.otlp_auth_headers - - for callback in _in_memory_loggers: - if ( - isinstance(callback, ArizePhoenixLogger) - and callback.callback_name == "arize_phoenix" - ): - return callback # type: ignore - _arize_phoenix_otel_logger = ArizePhoenixLogger( - config=otel_config, callback_name="arize_phoenix" - ) - _in_memory_loggers.append(_arize_phoenix_otel_logger) - return _arize_phoenix_otel_logger # type: ignore - elif logging_integration == "levo": - from litellm.integrations.levo.levo import LevoLogger - from litellm.integrations.opentelemetry import ( - OpenTelemetry, - OpenTelemetryConfig, - ) - - levo_config = LevoLogger.get_levo_config() - otel_config = OpenTelemetryConfig( - exporter=levo_config.protocol, - endpoint=levo_config.endpoint, - headers=levo_config.otlp_auth_headers, - ) - - # Check if LevoLogger instance already exists - for callback in _in_memory_loggers: - if ( - isinstance(callback, LevoLogger) - and callback.callback_name == "levo" - ): - return callback # type: ignore - - _levo_otel_logger = LevoLogger(config=otel_config, callback_name="levo") - _in_memory_loggers.append(_levo_otel_logger) - return _levo_otel_logger # type: ignore - elif logging_integration == "otel": - from litellm.integrations.opentelemetry import OpenTelemetry - - for callback in _in_memory_loggers: - if type(callback) is OpenTelemetry: - return callback # type: ignore - otel_logger = OpenTelemetry( - **_get_custom_logger_settings_from_proxy_server( - callback_name=logging_integration - ) - ) - _in_memory_loggers.append(otel_logger) - return otel_logger # type: ignore - - elif logging_integration == "galileo": - for callback in _in_memory_loggers: - if isinstance(callback, GalileoObserve): - return callback # type: ignore - - galileo_logger = GalileoObserve() - _in_memory_loggers.append(galileo_logger) - return galileo_logger # type: ignore - elif logging_integration == "cloudzero": - from litellm.integrations.cloudzero.cloudzero import CloudZeroLogger - - for callback in _in_memory_loggers: - if isinstance(callback, CloudZeroLogger): - return callback # type: ignore - cloudzero_logger = CloudZeroLogger() - _in_memory_loggers.append(cloudzero_logger) - return cloudzero_logger # type: ignore - elif logging_integration == "focus": - from litellm.integrations.focus.focus_logger import FocusLogger - - for callback in _in_memory_loggers: - if isinstance(callback, FocusLogger): - return callback # type: ignore - focus_logger = FocusLogger() - _in_memory_loggers.append(focus_logger) - return focus_logger # type: ignore - elif logging_integration == "deepeval": - for callback in _in_memory_loggers: - if isinstance(callback, DeepEvalLogger): - return callback # type: ignore - deepeval_logger = DeepEvalLogger() - _in_memory_loggers.append(deepeval_logger) - return deepeval_logger # type: ignore - - elif logging_integration == "logfire": - if "LOGFIRE_TOKEN" not in os.environ: - raise ValueError("LOGFIRE_TOKEN not found in environment variables") - from litellm.integrations.opentelemetry import ( - OpenTelemetry, - OpenTelemetryConfig, - ) - - logfire_base_url = os.getenv( - "LOGFIRE_BASE_URL", "https://logfire-api.pydantic.dev" - ) - otel_config = OpenTelemetryConfig( - exporter="otlp_http", - endpoint=f"{logfire_base_url.rstrip('/')}/v1/traces", - headers=f"Authorization={os.getenv('LOGFIRE_TOKEN')}", - ) - for callback in _in_memory_loggers: - if isinstance(callback, OpenTelemetry): - return callback # type: ignore - _otel_logger = OpenTelemetry(config=otel_config) - _in_memory_loggers.append(_otel_logger) - return _otel_logger # type: ignore - elif logging_integration == "dynamic_rate_limiter": - from litellm.proxy.hooks.dynamic_rate_limiter import ( - _PROXY_DynamicRateLimitHandler, - ) - - for callback in _in_memory_loggers: - if isinstance(callback, _PROXY_DynamicRateLimitHandler): - return callback # type: ignore - - if internal_usage_cache is None: - raise Exception( - "Internal Error: Cache cannot be empty - internal_usage_cache={}".format( - internal_usage_cache - ) - ) - - dynamic_rate_limiter_obj = _PROXY_DynamicRateLimitHandler( - internal_usage_cache=internal_usage_cache - ) - - if llm_router is not None and isinstance(llm_router, litellm.Router): - dynamic_rate_limiter_obj.update_variables(llm_router=llm_router) - _in_memory_loggers.append(dynamic_rate_limiter_obj) - return dynamic_rate_limiter_obj # type: ignore - elif logging_integration == "dynamic_rate_limiter_v3": - from litellm.proxy.hooks.dynamic_rate_limiter_v3 import ( - _PROXY_DynamicRateLimitHandlerV3, - ) - - for callback in _in_memory_loggers: - if isinstance(callback, _PROXY_DynamicRateLimitHandlerV3): - return callback # type: ignore - - if internal_usage_cache is None: - raise Exception( - "Internal Error: Cache cannot be empty - internal_usage_cache={}".format( - internal_usage_cache - ) - ) - - dynamic_rate_limiter_obj_v3 = _PROXY_DynamicRateLimitHandlerV3( - internal_usage_cache=internal_usage_cache - ) - - if llm_router is not None and isinstance(llm_router, litellm.Router): - dynamic_rate_limiter_obj_v3.update_variables(llm_router=llm_router) - _in_memory_loggers.append(dynamic_rate_limiter_obj_v3) - return dynamic_rate_limiter_obj_v3 # type: ignore - elif logging_integration == "langtrace": - if "LANGTRACE_API_KEY" not in os.environ: - raise ValueError("LANGTRACE_API_KEY not found in environment variables") - - from litellm.integrations.opentelemetry import ( - OpenTelemetry, - OpenTelemetryConfig, - ) - - otel_config = OpenTelemetryConfig( - exporter="otlp_http", - endpoint="https://langtrace.ai/api/trace", - ) - os.environ[ - "OTEL_EXPORTER_OTLP_TRACES_HEADERS" - ] = f"api_key={os.getenv('LANGTRACE_API_KEY')}" - for callback in _in_memory_loggers: - if ( - isinstance(callback, OpenTelemetry) - and callback.callback_name == "langtrace" - ): - return callback # type: ignore - _otel_logger = OpenTelemetry(config=otel_config, callback_name="langtrace") - _in_memory_loggers.append(_otel_logger) - return _otel_logger # type: ignore - - elif logging_integration == "mlflow": - for callback in _in_memory_loggers: - if isinstance(callback, MlflowLogger): - return callback # type: ignore - - _mlflow_logger = MlflowLogger() - _in_memory_loggers.append(_mlflow_logger) - return _mlflow_logger # type: ignore - elif logging_integration == "langfuse": - for callback in _in_memory_loggers: - if isinstance(callback, LangfusePromptManagement): - return callback - - langfuse_logger = LangfusePromptManagement() - _in_memory_loggers.append(langfuse_logger) - return langfuse_logger # type: ignore - elif logging_integration == "langfuse_otel": - from litellm.integrations.langfuse.langfuse_otel import LangfuseOtelLogger - - for callback in _in_memory_loggers: - if ( - isinstance(callback, LangfuseOtelLogger) - and callback.callback_name == "langfuse_otel" - ): - return callback # type: ignore - # Allow LangfuseOtelLogger to initialize its own config safely - # This prevents startup crashes if LANGFUSE keys are not in env (e.g. for dynamic usage) - _otel_logger = LangfuseOtelLogger( - config=None, callback_name="langfuse_otel" - ) - _in_memory_loggers.append(_otel_logger) - return _otel_logger # type: ignore - elif logging_integration == "weave_otel": - from litellm.integrations.opentelemetry import OpenTelemetryConfig - from litellm.integrations.weave.weave_otel import ( - WeaveOtelLogger, - get_weave_otel_config, - ) - - weave_otel_config = get_weave_otel_config() - - otel_config = OpenTelemetryConfig( - exporter=weave_otel_config.protocol, - endpoint=weave_otel_config.endpoint, - headers=weave_otel_config.otlp_auth_headers, - ) - - for callback in _in_memory_loggers: - if ( - isinstance(callback, WeaveOtelLogger) - and callback.callback_name == "weave_otel" - ): - return callback # type: ignore - _otel_logger = WeaveOtelLogger( - config=otel_config, callback_name="weave_otel" - ) - _in_memory_loggers.append(_otel_logger) - return _otel_logger # type: ignore - elif logging_integration == "pagerduty": - for callback in _in_memory_loggers: - if isinstance(callback, PagerDutyAlerting): - return callback - pagerduty_logger = PagerDutyAlerting(**custom_logger_init_args) - _in_memory_loggers.append(pagerduty_logger) - return pagerduty_logger # type: ignore - elif logging_integration == "anthropic_cache_control_hook": - for callback in _in_memory_loggers: - if isinstance(callback, AnthropicCacheControlHook): - return callback - anthropic_cache_control_hook = AnthropicCacheControlHook() - _in_memory_loggers.append(anthropic_cache_control_hook) - return anthropic_cache_control_hook # type: ignore - elif logging_integration == "vector_store_pre_call_hook": - from litellm.integrations.vector_store_integrations.vector_store_pre_call_hook import ( - VectorStorePreCallHook, - ) - - for callback in _in_memory_loggers: - if isinstance(callback, VectorStorePreCallHook): - return callback - vector_store_pre_call_hook = VectorStorePreCallHook() - _in_memory_loggers.append(vector_store_pre_call_hook) - return vector_store_pre_call_hook # type: ignore - elif logging_integration == "gcs_pubsub": - for callback in _in_memory_loggers: - if isinstance(callback, GcsPubSubLogger): - return callback - _gcs_pubsub_logger = GcsPubSubLogger() - _in_memory_loggers.append(_gcs_pubsub_logger) - return _gcs_pubsub_logger # type: ignore - elif logging_integration == "generic_api": - for callback in _in_memory_loggers: - if isinstance(callback, GenericAPILogger): - return callback - generic_api_logger = GenericAPILogger() - _in_memory_loggers.append(generic_api_logger) - return generic_api_logger # type: ignore - elif logging_integration == "resend_email": - for callback in _in_memory_loggers: - if isinstance(callback, ResendEmailLogger): - return callback - resend_email_logger = ResendEmailLogger() - _in_memory_loggers.append(resend_email_logger) - return resend_email_logger # type: ignore - elif logging_integration == "sendgrid_email": - for callback in _in_memory_loggers: - if isinstance(callback, SendGridEmailLogger): - return callback - sendgrid_email_logger = SendGridEmailLogger() - _in_memory_loggers.append(sendgrid_email_logger) - return sendgrid_email_logger # type: ignore - elif logging_integration == "smtp_email": - for callback in _in_memory_loggers: - if isinstance(callback, SMTPEmailLogger): - return callback - smtp_email_logger = SMTPEmailLogger() - _in_memory_loggers.append(smtp_email_logger) - return smtp_email_logger # type: ignore - elif logging_integration == "humanloop": - for callback in _in_memory_loggers: - if isinstance(callback, HumanloopLogger): - return callback - - humanloop_logger = HumanloopLogger() - _in_memory_loggers.append(humanloop_logger) - return humanloop_logger # type: ignore - elif logging_integration == "dotprompt": - for callback in _in_memory_loggers: - if isinstance(callback, DotpromptManager): - return callback - - dotprompt_logger = DotpromptManager() - _in_memory_loggers.append(dotprompt_logger) - return dotprompt_logger # type: ignore - elif logging_integration == "bitbucket": - from litellm.integrations.bitbucket.bitbucket_prompt_manager import ( - BitBucketPromptManager, - ) - - for callback in _in_memory_loggers: - if isinstance(callback, BitBucketPromptManager): - return callback - - # Get global BitBucket config - bitbucket_config = getattr(litellm, "global_bitbucket_config", None) - if bitbucket_config is None: - raise ValueError( - "BitBucket configuration not found. Please set litellm.global_bitbucket_config first." - ) - - bitbucket_logger = BitBucketPromptManager(bitbucket_config=bitbucket_config) - _in_memory_loggers.append(bitbucket_logger) - return bitbucket_logger # type: ignore - elif logging_integration == "gitlab": - from litellm.integrations.gitlab.gitlab_prompt_manager import ( - GitLabPromptManager, - ) - - for callback in _in_memory_loggers: - if isinstance(callback, GitLabPromptManager): - return callback - - # Get global BitBucket config - gitlab_config = getattr(litellm, "global_gitlab_config", None) - if gitlab_config is None: - raise ValueError( - "Gitlab configuration not found. Please set litellm.global_gitlab_config first." - ) - - gitlab_logger = GitLabPromptManager(gitlab_config=gitlab_config) - _in_memory_loggers.append(gitlab_logger) - return gitlab_logger # type: ignore - return None - except Exception as e: - verbose_logger.exception( - f"[Non-Blocking Error] Error initializing custom logger: {e}" - ) - return None - return None - - -def get_custom_logger_compatible_class( # noqa: PLR0915 - logging_integration: _custom_logger_compatible_callbacks_literal, -) -> Optional[CustomLogger]: - try: - if logging_integration == "lago": - for callback in _in_memory_loggers: - if isinstance(callback, LagoLogger): - return callback - elif logging_integration == "openmeter": - for callback in _in_memory_loggers: - if isinstance(callback, OpenMeterLogger): - return callback - elif logging_integration == "braintrust": - from litellm.integrations.braintrust_logging import BraintrustLogger - - for callback in _in_memory_loggers: - if isinstance(callback, BraintrustLogger): - return callback - elif logging_integration == "galileo": - for callback in _in_memory_loggers: - if isinstance(callback, GalileoObserve): - return callback - elif logging_integration == "cloudzero": - from litellm.integrations.cloudzero.cloudzero import CloudZeroLogger - - for callback in _in_memory_loggers: - if isinstance(callback, CloudZeroLogger): - return callback - elif logging_integration == "focus": - from litellm.integrations.focus.focus_logger import FocusLogger - - for callback in _in_memory_loggers: - if isinstance(callback, FocusLogger): - return callback - elif logging_integration == "deepeval": - for callback in _in_memory_loggers: - if isinstance(callback, DeepEvalLogger): - return callback - elif logging_integration == "langsmith": - for callback in _in_memory_loggers: - if isinstance(callback, LangsmithLogger): - return callback - elif logging_integration == "argilla": - for callback in _in_memory_loggers: - if isinstance(callback, ArgillaLogger): - return callback - elif logging_integration == "literalai": - for callback in _in_memory_loggers: - if isinstance(callback, LiteralAILogger): - return callback - elif logging_integration == "prometheus": - PrometheusLogger = _get_cached_prometheus_logger() - for callback in _in_memory_loggers: - if isinstance(callback, PrometheusLogger): - return callback - elif logging_integration == "datadog": - for callback in _in_memory_loggers: - if isinstance(callback, DataDogLogger): - return callback - elif logging_integration == "datadog_llm_observability": - for callback in _in_memory_loggers: - if isinstance(callback, DataDogLLMObsLogger): - return callback - elif logging_integration == "azure_sentinel": - for callback in _in_memory_loggers: - if isinstance(callback, AzureSentinelLogger): - return callback - elif logging_integration == "gcs_bucket": - for callback in _in_memory_loggers: - if isinstance(callback, GCSBucketLogger): - return callback - elif logging_integration == "s3_v2": - for callback in _in_memory_loggers: - if isinstance(callback, S3V2Logger): - return callback - elif logging_integration == "aws_sqs": - for callback in _in_memory_loggers: - if isinstance(callback, SQSLogger): - return callback - _aws_sqs_logger = SQSLogger() - _in_memory_loggers.append(_aws_sqs_logger) - return _aws_sqs_logger # type: ignore - elif logging_integration == "azure_storage": - for callback in _in_memory_loggers: - if isinstance(callback, AzureBlobStorageLogger): - return callback - elif logging_integration == "opik": - for callback in _in_memory_loggers: - if isinstance(callback, OpikLogger): - return callback - elif logging_integration == "langfuse": - for callback in _in_memory_loggers: - if isinstance(callback, LangfusePromptManagement): - return callback - elif logging_integration == "otel": - from litellm.integrations.opentelemetry import OpenTelemetry - - for callback in _in_memory_loggers: - if isinstance(callback, OpenTelemetry): - return callback - elif logging_integration == "arize": - if "ARIZE_API_KEY" not in os.environ: - raise ValueError("ARIZE_API_KEY not found in environment variables") - for callback in _in_memory_loggers: - if ( - isinstance(callback, ArizeLogger) - and callback.callback_name == "arize" - ): - return callback - elif logging_integration == "logfire": - if "LOGFIRE_TOKEN" not in os.environ: - raise ValueError("LOGFIRE_TOKEN not found in environment variables") - from litellm.integrations.opentelemetry import OpenTelemetry - - for callback in _in_memory_loggers: - if isinstance(callback, OpenTelemetry): - return callback # type: ignore - - elif logging_integration == "dynamic_rate_limiter": - from litellm.proxy.hooks.dynamic_rate_limiter import ( - _PROXY_DynamicRateLimitHandler, - ) - - for callback in _in_memory_loggers: - if isinstance(callback, _PROXY_DynamicRateLimitHandler): - return callback # type: ignore - elif logging_integration == "dynamic_rate_limiter_v3": - from litellm.proxy.hooks.dynamic_rate_limiter_v3 import ( - _PROXY_DynamicRateLimitHandlerV3, - ) - - for callback in _in_memory_loggers: - if isinstance(callback, _PROXY_DynamicRateLimitHandlerV3): - return callback # type: ignore - - elif logging_integration == "langtrace": - from litellm.integrations.opentelemetry import OpenTelemetry - - if "LANGTRACE_API_KEY" not in os.environ: - raise ValueError("LANGTRACE_API_KEY not found in environment variables") - - for callback in _in_memory_loggers: - if ( - isinstance(callback, OpenTelemetry) - and callback.callback_name == "langtrace" - ): - return callback - - elif logging_integration == "mlflow": - for callback in _in_memory_loggers: - if isinstance(callback, MlflowLogger): - return callback - elif logging_integration == "pagerduty": - for callback in _in_memory_loggers: - if isinstance(callback, PagerDutyAlerting): - return callback - elif logging_integration == "anthropic_cache_control_hook": - for callback in _in_memory_loggers: - if isinstance(callback, AnthropicCacheControlHook): - return callback - elif logging_integration == "vector_store_pre_call_hook": - from litellm.integrations.vector_store_integrations.vector_store_pre_call_hook import ( - VectorStorePreCallHook, - ) - - for callback in _in_memory_loggers: - if isinstance(callback, VectorStorePreCallHook): - return callback - elif logging_integration == "gcs_pubsub": - for callback in _in_memory_loggers: - if isinstance(callback, GcsPubSubLogger): - return callback - elif logging_integration == "generic_api": - for callback in _in_memory_loggers: - if isinstance(callback, GenericAPILogger): - return callback - elif logging_integration == "resend_email": - for callback in _in_memory_loggers: - if isinstance(callback, ResendEmailLogger): - return callback - elif logging_integration == "sendgrid_email": - for callback in _in_memory_loggers: - if isinstance(callback, SendGridEmailLogger): - return callback - elif logging_integration == "smtp_email": - for callback in _in_memory_loggers: - if isinstance(callback, SMTPEmailLogger): - return callback - return None - - except Exception as e: - verbose_logger.exception( - f"[Non-Blocking Error] Error getting custom logger: {e}" - ) - return None - - -def _get_custom_logger_settings_from_proxy_server(callback_name: str) -> Dict: - """ - Get the settings for a custom logger from the proxy server config.yaml - - Proxy server config.yaml defines callback_settings as: - - callback_settings: - otel: - message_logging: False - """ - if litellm.callback_settings: - return dict(litellm.callback_settings.get(callback_name, {})) - return {} - - -def use_custom_pricing_for_model(litellm_params: Optional[dict]) -> bool: - """ - Check if the model uses custom pricing - - Returns True if any of `SPECIAL_MODEL_INFO_PARAMS` are present in `litellm_params` or `model_info` - """ - if litellm_params is None: - return False - - # Check litellm_params using set intersection (only check keys that exist in both) - matching_keys = _CUSTOM_PRICING_KEYS & litellm_params.keys() - for key in matching_keys: - if litellm_params.get(key) is not None: - return True - - # Check model_info - metadata: dict = litellm_params.get("metadata", {}) or {} - model_info: dict = metadata.get("model_info", {}) or {} - - if model_info: - matching_keys = _CUSTOM_PRICING_KEYS & model_info.keys() - for key in matching_keys: - if model_info.get(key) is not None: - return True - - return False - - -def is_valid_sha256_hash(value: str) -> bool: - # Check if the value is a valid SHA-256 hash (64 hexadecimal characters) - return bool(re.fullmatch(r"[a-fA-F0-9]{64}", value)) - - -class StandardLoggingPayloadSetup: - @staticmethod - def cleanup_timestamps( - start_time: Union[dt_object, float], - end_time: Union[dt_object, float], - completion_start_time: Union[dt_object, float], - ) -> Tuple[float, float, float]: - """ - Convert datetime objects to floats - - Args: - start_time: Union[dt_object, float] - end_time: Union[dt_object, float] - completion_start_time: Union[dt_object, float] - - Returns: - Tuple[float, float, float]: A tuple containing the start time, end time, and completion start time as floats. - """ - - if isinstance(start_time, datetime.datetime): - start_time_float = start_time.timestamp() - elif isinstance(start_time, float): - start_time_float = start_time - else: - raise ValueError( - f"start_time is required, got={start_time} of type {type(start_time)}" - ) - - if isinstance(end_time, datetime.datetime): - end_time_float = end_time.timestamp() - elif isinstance(end_time, float): - end_time_float = end_time - else: - raise ValueError( - f"end_time is required, got={end_time} of type {type(end_time)}" - ) - - if isinstance(completion_start_time, datetime.datetime): - completion_start_time_float = completion_start_time.timestamp() - elif isinstance(completion_start_time, float): - completion_start_time_float = completion_start_time - else: - completion_start_time_float = end_time_float - - return start_time_float, end_time_float, completion_start_time_float - - @staticmethod - def append_system_prompt_messages( - kwargs: Optional[Dict] = None, messages: Optional[Any] = None - ): - """ - Append system prompt messages to the messages - """ - if kwargs is not None: - if kwargs.get("system") is not None and isinstance( - kwargs.get("system"), str - ): - if messages is None: - return [{"role": "system", "content": kwargs.get("system")}] - elif isinstance(messages, list): - if len(messages) == 0: - return [{"role": "system", "content": kwargs.get("system")}] - # check for duplicates - if messages[0].get("role") == "system" and messages[0].get( - "content" - ) == kwargs.get("system"): - return messages - messages = [ - {"role": "system", "content": kwargs.get("system")} - ] + messages - elif isinstance(messages, str): - messages = [ - {"role": "system", "content": kwargs.get("system")}, - {"role": "user", "content": messages}, - ] - return messages - - return messages - - @staticmethod - def merge_litellm_metadata(litellm_params: dict) -> dict: - """ - Merge both litellm_metadata and metadata from litellm_params. - - litellm_metadata contains model-related fields, metadata contains user API key fields. - We need both for complete standard logging payload. - - Args: - litellm_params: Dictionary containing metadata and litellm_metadata - - Returns: - dict: Merged metadata with user API key fields taking precedence - """ - merged_metadata: dict = {} - - # Start with metadata (user API key fields) - but skip non-serializable objects - if litellm_params.get("metadata") and isinstance( - litellm_params.get("metadata"), dict - ): - for key, value in litellm_params["metadata"].items(): - # Skip non-serializable objects like UserAPIKeyAuth - if key == "user_api_key_auth": - continue - merged_metadata[key] = value - - # Then merge litellm_metadata (model-related fields) - this will NOT overwrite existing keys - if litellm_params.get("litellm_metadata") and isinstance( - litellm_params.get("litellm_metadata"), dict - ): - for key, value in litellm_params["litellm_metadata"].items(): - if ( - key not in merged_metadata - ): # Don't overwrite existing keys from metadata - merged_metadata[key] = value - - return merged_metadata - - @staticmethod - def get_standard_logging_metadata( - metadata: Optional[Dict[str, Any]], - litellm_params: Optional[dict] = None, - prompt_integration: Optional[str] = None, - applied_guardrails: Optional[List[str]] = None, - mcp_tool_call_metadata: Optional[StandardLoggingMCPToolCall] = None, - vector_store_request_metadata: Optional[ - List[StandardLoggingVectorStoreRequest] - ] = None, - usage_object: Optional[dict] = None, - proxy_server_request: Optional[dict] = None, - start_time: Optional[dt_object] = None, - response_id: Optional[str] = None, - ) -> StandardLoggingMetadata: - """ - Clean and filter the metadata dictionary to include only the specified keys in StandardLoggingMetadata. - - Args: - metadata (Optional[Dict[str, Any]]): The original metadata dictionary. - - Returns: - StandardLoggingMetadata: A StandardLoggingMetadata object containing the cleaned metadata. - - Note: - - If the input metadata is None or not a dictionary, an empty StandardLoggingMetadata object is returned. - - If 'user_api_key' is present in metadata and is a valid SHA256 hash, it's stored as 'user_api_key_hash'. - """ - - prompt_management_metadata: Optional[ - StandardLoggingPromptManagementMetadata - ] = None - if litellm_params is not None: - prompt_id = cast(Optional[str], litellm_params.get("prompt_id", None)) - prompt_variables = cast( - Optional[dict], litellm_params.get("prompt_variables", None) - ) - - if prompt_id is not None and prompt_integration is not None: - prompt_management_metadata = StandardLoggingPromptManagementMetadata( - prompt_id=prompt_id, - prompt_variables=prompt_variables, - prompt_integration=prompt_integration, - ) - - # Initialize with default values - clean_metadata = StandardLoggingMetadata( - user_api_key_hash=None, - user_api_key_alias=None, - user_api_key_spend=None, - user_api_key_max_budget=None, - user_api_key_budget_reset_at=None, - user_api_key_team_id=None, - user_api_key_org_id=None, - user_api_key_user_id=None, - user_api_key_team_alias=None, - user_api_key_user_email=None, - user_api_key_end_user_id=None, - user_api_key_request_route=None, - spend_logs_metadata=None, - requester_ip_address=None, - user_agent=None, - requester_metadata=None, - prompt_management_metadata=prompt_management_metadata, - applied_guardrails=applied_guardrails, - mcp_tool_call_metadata=mcp_tool_call_metadata, - vector_store_request_metadata=vector_store_request_metadata, - usage_object=usage_object, - requester_custom_headers=None, - cold_storage_object_key=None, - user_api_key_auth_metadata=None, - ) - if isinstance(metadata, dict): - for key in metadata.keys() & _STANDARD_LOGGING_METADATA_KEYS: - clean_metadata[key] = metadata[key] # type: ignore - - user_api_key = metadata.get("user_api_key") - if user_api_key and isinstance(user_api_key, str) and is_valid_sha256_hash(user_api_key): - clean_metadata["user_api_key_hash"] = user_api_key - _potential_requester_metadata = metadata.get( - "metadata", None - ) # check if user passed metadata in the sdk request - e.g. metadata for langsmith logging - https://docs.litellm.ai/docs/observability/langsmith_integration#set-langsmith-fields - if ( - clean_metadata["requester_metadata"] is None - and _potential_requester_metadata is not None - and isinstance(_potential_requester_metadata, dict) - ): - clean_metadata["requester_metadata"] = _potential_requester_metadata - - if ( - EnterpriseStandardLoggingPayloadSetupVAR - and proxy_server_request is not None - ): - clean_metadata = EnterpriseStandardLoggingPayloadSetupVAR.apply_enterprise_specific_metadata( - standard_logging_metadata=clean_metadata, - proxy_server_request=proxy_server_request, - ) - - # Generate cold storage object key if cold storage is configured - if start_time is not None and response_id is not None: - cold_storage_object_key = ( - StandardLoggingPayloadSetup._generate_cold_storage_object_key( - start_time=start_time, - response_id=response_id, - team_alias=clean_metadata.get("user_api_key_team_alias"), - ) - ) - if cold_storage_object_key: - clean_metadata["cold_storage_object_key"] = cold_storage_object_key - - return clean_metadata - - @staticmethod - def get_usage_from_response_obj( - response_obj: Optional[dict], combined_usage_object: Optional[Usage] = None - ) -> Usage: - ## BASE CASE ## - if combined_usage_object is not None: - return combined_usage_object - if response_obj is None: - return Usage( - prompt_tokens=0, - completion_tokens=0, - total_tokens=0, - ) - - usage = response_obj.get("usage", None) or {} - if usage is None or ( - not isinstance(usage, dict) and not isinstance(usage, Usage) - ): - return Usage( - prompt_tokens=0, - completion_tokens=0, - total_tokens=0, - ) - elif isinstance(usage, Usage): - return usage - elif isinstance(usage, ResponseAPIUsage): - return ResponseAPILoggingUtils._transform_response_api_usage_to_chat_usage( - usage - ) - elif isinstance(usage, dict): - if ResponseAPILoggingUtils._is_response_api_usage(usage): - return ( - ResponseAPILoggingUtils._transform_response_api_usage_to_chat_usage( - usage - ) - ) - return Usage(**usage) - - raise ValueError(f"usage is required, got={usage} of type {type(usage)}") - - @staticmethod - def get_model_cost_information( - base_model: Optional[str], - custom_pricing: Optional[bool], - custom_llm_provider: Optional[str], - init_response_obj: Union[Any, BaseModel, dict], - ) -> StandardLoggingModelInformation: - model_cost_name = _select_model_name_for_cost_calc( - model=None, - completion_response=init_response_obj, # type: ignore - base_model=base_model, - custom_pricing=custom_pricing, - ) - if model_cost_name is None: - model_cost_information = StandardLoggingModelInformation( - model_map_key="", model_map_value=None - ) - else: - try: - _model_cost_information = litellm.get_model_info( - model=model_cost_name, custom_llm_provider=custom_llm_provider - ) - model_cost_information = StandardLoggingModelInformation( - model_map_key=model_cost_name, - model_map_value=_model_cost_information, - ) - except Exception: - verbose_logger.debug( # keep in debug otherwise it will trigger on every call - "Model={} is not mapped in model cost map. Defaulting to None model_cost_information for standard_logging_payload".format( - model_cost_name - ) - ) - model_cost_information = StandardLoggingModelInformation( - model_map_key=model_cost_name, model_map_value=None - ) - return model_cost_information - - @staticmethod - def get_final_response_obj( - response_obj: dict, init_response_obj: Union[Any, BaseModel, dict], kwargs: dict - ) -> Optional[Union[dict, str, list]]: - """ - Get final response object after redacting the message input/output from logging - """ - if response_obj: - final_response_obj: Optional[Union[dict, str, list]] = response_obj - elif isinstance(init_response_obj, list) or isinstance(init_response_obj, str): - final_response_obj = init_response_obj - else: - final_response_obj = {} - - modified_final_response_obj = redact_message_input_output_from_logging( - model_call_details=kwargs, - result=final_response_obj, - ) - - if modified_final_response_obj is not None and isinstance( - modified_final_response_obj, BaseModel - ): - final_response_obj = modified_final_response_obj.model_dump() - else: - final_response_obj = modified_final_response_obj - - return final_response_obj - - @staticmethod - def get_additional_headers( - additiona_headers: Optional[dict], - ) -> Optional[StandardLoggingAdditionalHeaders]: - if additiona_headers is None: - return None - - additional_logging_headers: StandardLoggingAdditionalHeaders = {} - - for key in StandardLoggingAdditionalHeaders.__annotations__.keys(): - _key = key.lower() - _key = _key.replace("_", "-") - if _key in additiona_headers: - try: - additional_logging_headers[key] = int(additiona_headers[_key]) # type: ignore - except (ValueError, TypeError): - verbose_logger.debug( - f"Could not convert {additiona_headers[_key]} to int for key {key}." - ) - return additional_logging_headers - - @staticmethod - def get_hidden_params( - hidden_params: Optional[dict], - ) -> StandardLoggingHiddenParams: - clean_hidden_params = StandardLoggingHiddenParams( - model_id=None, - cache_key=None, - api_base=None, - response_cost=None, - additional_headers=None, - litellm_overhead_time_ms=None, - batch_models=None, - litellm_model_name=None, - usage_object=None, - ) - if hidden_params is not None: - for key in StandardLoggingHiddenParams.__annotations__.keys(): - if key in hidden_params: - if key == "additional_headers": - clean_hidden_params[ - "additional_headers" - ] = StandardLoggingPayloadSetup.get_additional_headers( - hidden_params[key] - ) - else: - clean_hidden_params[key] = hidden_params[key] # type: ignore - return clean_hidden_params - - @staticmethod - def strip_trailing_slash(api_base: Optional[str]) -> Optional[str]: - if api_base: - if api_base.endswith("//"): - return api_base.rstrip("/") - if api_base[-1] == "/": - return api_base[:-1] - return api_base - - @staticmethod - def _generate_cold_storage_object_key( - start_time: dt_object, - response_id: str, - team_alias: Optional[str] = None, - ) -> Optional[str]: - """ - Generate cold storage object key in the same format as S3Logger. - - Args: - start_time: The start time of the request - response_id: The response ID - team_alias: Optional team alias for team-based prefixing - - Returns: - Optional[str]: The generated object key or None if cold storage not configured - """ - # Generate object key in same format as S3Logger - from litellm.integrations.s3 import get_s3_object_key - - # Only generate object key if cold storage is configured - cold_storage_custom_logger = litellm.cold_storage_custom_logger - if cold_storage_custom_logger is None: - return None - - try: - # Generate file name in same format as litellm.utils.get_logging_id - s3_file_name = f"time-{start_time.strftime('%H-%M-%S-%f')}_{response_id}" - - # Get the actual s3_path from the configured cold storage logger instance - s3_path = "" # default value - - # Try to get the actual logger instance from the logger name - try: - custom_logger = litellm.logging_callback_manager.get_active_custom_logger_for_callback_name( - cold_storage_custom_logger - ) - if ( - custom_logger - and hasattr(custom_logger, "s3_path") - and getattr(custom_logger, "s3_path") - ): - s3_path = getattr(custom_logger, "s3_path") - except Exception: - # If any error occurs in getting the logger instance, use default empty s3_path - pass - - s3_object_key = get_s3_object_key( - s3_path=s3_path, # Use actual s3_path from logger configuration - prefix="", # Don't split by team alias for cold storage - start_time=start_time, - s3_file_name=s3_file_name, - ) - - return s3_object_key - except Exception: - # If any error occurs in generating the key, return None - return None - - @staticmethod - def get_error_information( - original_exception: Optional[Exception], - traceback_str: Optional[str] = None, - ) -> StandardLoggingPayloadErrorInformation: - from litellm.constants import MAXIMUM_TRACEBACK_LINES_TO_LOG - - # Check for 'code' first (used by ProxyException), then fall back to 'status_code' (used by LiteLLM exceptions) - # Ensure error_code is always a string for Prisma Python JSON field compatibility - error_code_attr = getattr(original_exception, "code", None) - if error_code_attr is not None and str(error_code_attr) not in ("", "None"): - error_status: str = str(error_code_attr) - else: - status_code_attr = getattr(original_exception, "status_code", None) - error_status = str(status_code_attr) if status_code_attr is not None else "" - error_class: str = ( - str(original_exception.__class__.__name__) if original_exception else "" - ) - _llm_provider_in_exception = getattr(original_exception, "llm_provider", "") - - # Get traceback information (first 100 lines) - traceback_info = traceback_str or "" - if original_exception: - tb = getattr(original_exception, "__traceback__", None) - if tb: - tb_lines = traceback.format_tb(tb) - traceback_info += "".join( - tb_lines[:MAXIMUM_TRACEBACK_LINES_TO_LOG] - ) # Limit to first 100 lines - - # Get additional error details - error_message = str(original_exception) - - return StandardLoggingPayloadErrorInformation( - error_code=error_status, - error_class=error_class, - llm_provider=_llm_provider_in_exception, - traceback=traceback_info, - error_message=error_message if original_exception else "", - ) - - @staticmethod - def get_response_time( - start_time_float: float, - end_time_float: float, - completion_start_time_float: float, - stream: bool, - ) -> float: - """ - Get the response time for the LLM response - - Args: - start_time_float: float - start time of the LLM call - end_time_float: float - end time of the LLM call - completion_start_time_float: float - time to first token of the LLM response (for streaming responses) - stream: bool - True when a stream response is returned - - Returns: - float: The response time for the LLM response - """ - if stream is True: - return completion_start_time_float - start_time_float - else: - return end_time_float - start_time_float - - @staticmethod - def _get_standard_logging_payload_trace_id( - logging_obj: Logging, - litellm_params: dict, - ) -> str: - """ - Returns the `litellm_trace_id` for this request - - This helps link sessions when multiple requests are made in a single session - """ - dynamic_litellm_session_id = litellm_params.get("litellm_session_id") - dynamic_litellm_trace_id = litellm_params.get("litellm_trace_id") - - # Note: we recommend using `litellm_session_id` for session tracking - # `litellm_trace_id` is an internal litellm param - if dynamic_litellm_session_id: - return str(dynamic_litellm_session_id) - elif dynamic_litellm_trace_id: - return str(dynamic_litellm_trace_id) - else: - return logging_obj.litellm_trace_id - - @staticmethod - def _get_user_agent_tags(proxy_server_request: dict) -> Optional[List[str]]: - """ - Return the user agent tags from the proxy server request for spend tracking - """ - if litellm.disable_add_user_agent_to_request_tags is True: - return None - user_agent_tags: Optional[List[str]] = None - headers = proxy_server_request.get("headers", {}) - if headers is not None and isinstance(headers, dict): - if "user-agent" in headers: - user_agent = headers["user-agent"] - if user_agent is not None: - if user_agent_tags is None: - user_agent_tags = [] - user_agent_part: Optional[str] = None - if "/" in user_agent: - user_agent_part = user_agent.split("/")[0] - if user_agent_part is not None: - user_agent_tags.append("User-Agent: " + user_agent_part) - if user_agent is not None: - user_agent_tags.append("User-Agent: " + user_agent) - return user_agent_tags - - @staticmethod - def _get_extra_header_tags(proxy_server_request: dict) -> Optional[List[str]]: - """ - Extract additional header tags for spend tracking based on config. - """ - extra_headers: List[str] = ( - getattr(litellm, "extra_spend_tag_headers", None) or [] - ) - if not extra_headers: - return None - - headers = proxy_server_request.get("headers", {}) - if not isinstance(headers, dict): - return None - - header_tags = [] - for header_name in extra_headers: - header_value = headers.get(header_name) - if header_value: - header_tags.append(f"{header_name}: {header_value}") - - return header_tags if header_tags else None - - @staticmethod - def _get_request_tags( - litellm_params: dict, proxy_server_request: dict - ) -> List[str]: - # check for 'tags' in both 'metadata' and 'litellm_metadata' - metadata = litellm_params.get("metadata") or {} - litellm_metadata = litellm_params.get("litellm_metadata") or {} - if metadata.get("tags", []): - request_tags = metadata.get("tags", []).copy() - elif litellm_metadata.get("tags", []): - request_tags = litellm_metadata.get("tags", []).copy() - else: - request_tags = [] - user_agent_tags = StandardLoggingPayloadSetup._get_user_agent_tags( - proxy_server_request - ) - additional_header_tags = StandardLoggingPayloadSetup._get_extra_header_tags( - proxy_server_request - ) - if user_agent_tags is not None: - request_tags.extend(user_agent_tags) - if additional_header_tags is not None: - request_tags.extend(additional_header_tags) - return request_tags - - -def _get_status_fields( - status: StandardLoggingPayloadStatus, - guardrail_information: Optional[List[dict]], - error_str: Optional[str], -) -> "StandardLoggingPayloadStatusFields": - """ - Determine status fields based on request status and guardrail information. - - Args: - status: Overall request status ("success" or "failure") - guardrail_information: Guardrail information from metadata - error_str: Error string if any - - Returns: - StandardLoggingPayloadStatusFields with llm_api_status and guardrail_status - """ - # Mapping for legacy guardrail status values to new GuardrailStatus values - GUARDRAIL_STATUS_MAP: Dict[str, GuardrailStatus] = { - "success": "success", - "blocked": "guardrail_intervened", # legacy - "guardrail_intervened": "guardrail_intervened", # direct - "failure": "guardrail_failed_to_respond", # legacy - "guardrail_failed_to_respond": "guardrail_failed_to_respond", # direct - "not_run": "not_run", - } - - # Set LLM API status - llm_api_status: StandardLoggingPayloadStatus = status - - ######################################################### - # Map - guardrail_information.guardrail_status to guardrail_status - ######################################################### - guardrail_status: GuardrailStatus = "not_run" - if guardrail_information and isinstance(guardrail_information, list): - for information in guardrail_information: - if isinstance(information, dict): - raw_status = information.get("guardrail_status", "not_run") - if raw_status != "not_run": - guardrail_status = GUARDRAIL_STATUS_MAP.get(raw_status, "not_run") - break - - return StandardLoggingPayloadStatusFields( - llm_api_status=llm_api_status, guardrail_status=guardrail_status - ) - - -def _extract_response_obj_and_hidden_params( - init_response_obj: Union[Any, BaseModel, dict], - original_exception: Optional[Exception], -) -> Tuple[dict, Optional[dict]]: - """Extract response_obj and hidden_params from init_response_obj.""" - hidden_params: Optional[dict] = None - if init_response_obj is None: - response_obj = {} - elif isinstance(init_response_obj, BaseModel): - response_obj = init_response_obj.model_dump() - hidden_params = getattr(init_response_obj, "_hidden_params", None) - elif isinstance(init_response_obj, dict): - response_obj = init_response_obj - else: - response_obj = {} - - if original_exception is not None and hidden_params is None: - response_headers = _get_response_headers(original_exception) - if response_headers is not None: - hidden_params = dict( - StandardLoggingHiddenParams( - additional_headers=StandardLoggingPayloadSetup.get_additional_headers( - dict(response_headers) - ), - model_id=None, - cache_key=None, - api_base=None, - response_cost=None, - litellm_overhead_time_ms=None, - batch_models=None, - litellm_model_name=None, - usage_object=None, - ) - ) - - return response_obj, hidden_params - - -def get_standard_logging_object_payload( - kwargs: Optional[dict], - init_response_obj: Union[Any, BaseModel, dict], - start_time: dt_object, - end_time: dt_object, - logging_obj: Logging, - status: StandardLoggingPayloadStatus, - error_str: Optional[str] = None, - original_exception: Optional[Exception] = None, - standard_built_in_tools_params: Optional[StandardBuiltInToolsParams] = None, -) -> Optional[StandardLoggingPayload]: - try: - kwargs = kwargs or {} - - response_obj, hidden_params = _extract_response_obj_and_hidden_params( - init_response_obj, original_exception - ) - - # standardize this function to be used across, s3, dynamoDB, langfuse logging - litellm_params = kwargs.get("litellm_params", {}) or {} - proxy_server_request = litellm_params.get("proxy_server_request") or {} - - # Merge both litellm_metadata and metadata to get complete metadata - metadata: dict = StandardLoggingPayloadSetup.merge_litellm_metadata( - litellm_params - ) - - completion_start_time = kwargs.get("completion_start_time", end_time) - call_type = kwargs.get("call_type") - cache_hit = kwargs.get("cache_hit", False) - usage = StandardLoggingPayloadSetup.get_usage_from_response_obj( - response_obj=response_obj, - combined_usage_object=cast( - Optional[Usage], kwargs.get("combined_usage_object") - ), - ) - - id = response_obj.get("id", kwargs.get("litellm_call_id")) - - _model_id = metadata.get("model_info", {}).get("id", "") - _model_group = metadata.get("model_group", "") - - request_tags = StandardLoggingPayloadSetup._get_request_tags( - litellm_params=litellm_params, proxy_server_request=proxy_server_request - ) - - # cleanup timestamps - ( - start_time_float, - end_time_float, - completion_start_time_float, - ) = StandardLoggingPayloadSetup.cleanup_timestamps( - start_time=start_time, - end_time=end_time, - completion_start_time=completion_start_time, - ) - response_time = StandardLoggingPayloadSetup.get_response_time( - start_time_float=start_time_float, - end_time_float=end_time_float, - completion_start_time_float=completion_start_time_float, - stream=kwargs.get("stream", False), - ) - # clean up litellm hidden params - clean_hidden_params = StandardLoggingPayloadSetup.get_hidden_params( - hidden_params - ) - - # clean up litellm metadata - clean_metadata = StandardLoggingPayloadSetup.get_standard_logging_metadata( - metadata=metadata, - litellm_params=litellm_params, - prompt_integration=kwargs.get("prompt_integration", None), - applied_guardrails=kwargs.get("applied_guardrails", None), - mcp_tool_call_metadata=kwargs.get("mcp_tool_call_metadata", None), - vector_store_request_metadata=kwargs.get( - "vector_store_request_metadata", None - ), - usage_object=usage.model_dump(), - proxy_server_request=proxy_server_request, - start_time=start_time, - response_id=id, - ) - _request_body = proxy_server_request.get("body", {}) - end_user_id = clean_metadata["user_api_key_end_user_id"] or _request_body.get( - "user", None - ) # maintain backwards compatibility with old request body check - - saved_cache_cost: float = 0.0 - if cache_hit is True: - id = f"{id}_cache_hit{time.time()}" # do not duplicate the request id - saved_cache_cost = ( - logging_obj._response_cost_calculator( - result=init_response_obj, cache_hit=False # type: ignore - ) - or 0.0 - ) - - ## Get model cost information ## - base_model = _get_base_model_from_metadata(model_call_details=kwargs) - custom_pricing = use_custom_pricing_for_model(litellm_params=litellm_params) - - model_cost_information = StandardLoggingPayloadSetup.get_model_cost_information( - base_model=base_model, - custom_pricing=custom_pricing, - custom_llm_provider=kwargs.get("custom_llm_provider"), - init_response_obj=init_response_obj, - ) - response_cost: float = kwargs.get("response_cost", 0) or 0.0 - - error_information = StandardLoggingPayloadSetup.get_error_information( - original_exception=original_exception, - ) - - ## get final response object ## - final_response_obj = StandardLoggingPayloadSetup.get_final_response_obj( - response_obj=response_obj, - init_response_obj=init_response_obj, - kwargs=kwargs, - ) - - stream: Optional[bool] = None - if ( - kwargs.get("complete_streaming_response") is not None - or kwargs.get("async_complete_streaming_response") is not None - ) and kwargs.get("stream") is True: - stream = True - - # Reconstruct full model name with provider prefix for logging - # This ensures Bedrock models like "us.anthropic.claude-3-5-sonnet-20240620-v1:0" - # are logged as "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0" - custom_llm_provider = cast(Optional[str], kwargs.get("custom_llm_provider")) - model_name = reconstruct_model_name( - kwargs.get("model", "") or "", custom_llm_provider, metadata - ) - - payload: StandardLoggingPayload = StandardLoggingPayload( - id=str(id), - trace_id=StandardLoggingPayloadSetup._get_standard_logging_payload_trace_id( - logging_obj=logging_obj, - litellm_params=litellm_params, - ), - call_type=call_type or "", - cache_hit=cache_hit, - stream=stream, - status=status, - status_fields=_get_status_fields( - status=status, - guardrail_information=metadata.get( - "standard_logging_guardrail_information", None - ), - error_str=error_str, - ), - custom_llm_provider=custom_llm_provider, - saved_cache_cost=saved_cache_cost, - startTime=start_time_float, - endTime=end_time_float, - completionStartTime=completion_start_time_float, - response_time=response_time, - model=model_name, - metadata=clean_metadata, - cache_key=clean_hidden_params["cache_key"], - response_cost=response_cost, - cost_breakdown=logging_obj.cost_breakdown, - total_tokens=usage.total_tokens, - prompt_tokens=usage.prompt_tokens, - completion_tokens=usage.completion_tokens, - request_tags=request_tags, - end_user=end_user_id or "", - api_base=StandardLoggingPayloadSetup.strip_trailing_slash( - litellm_params.get("api_base", "") - ) - or "", - model_group=_model_group, - model_id=_model_id, - requester_ip_address=clean_metadata.get("requester_ip_address", None), - user_agent=clean_metadata.get("user_agent", None), - messages=StandardLoggingPayloadSetup.append_system_prompt_messages( - kwargs=kwargs, messages=kwargs.get("messages") - ), - response=final_response_obj, - model_parameters=ModelParamHelper.get_standard_logging_model_parameters( - kwargs.get("optional_params", None) or {} - ), - hidden_params=clean_hidden_params, - model_map_information=model_cost_information, - error_str=error_str, - error_information=error_information, - response_cost_failure_debug_info=kwargs.get( - "response_cost_failure_debug_information" - ), - guardrail_information=metadata.get( - "standard_logging_guardrail_information", None - ), - standard_built_in_tools_params=standard_built_in_tools_params, - ) - - # emit_standard_logging_payload(payload) - Moved to success_handler to prevent double emitting - - return payload - except Exception as e: - verbose_logger.exception( - "Error creating standard logging object - {}".format(str(e)) - ) - return None - - -def emit_standard_logging_payload(payload: StandardLoggingPayload): - if os.getenv("LITELLM_PRINT_STANDARD_LOGGING_PAYLOAD"): - print(json.dumps(payload, indent=4)) # noqa - - -def get_standard_logging_metadata( - metadata: Optional[Dict[str, Any]], -) -> StandardLoggingMetadata: - """ - Clean and filter the metadata dictionary to include only the specified keys in StandardLoggingMetadata. - - Args: - metadata (Optional[Dict[str, Any]]): The original metadata dictionary. - - Returns: - StandardLoggingMetadata: A StandardLoggingMetadata object containing the cleaned metadata. - - Note: - - If the input metadata is None or not a dictionary, an empty StandardLoggingMetadata object is returned. - - If 'user_api_key' is present in metadata and is a valid SHA256 hash, it's stored as 'user_api_key_hash'. - """ - # Initialize with default values - clean_metadata = StandardLoggingMetadata( - user_api_key_hash=None, - user_api_key_alias=None, - user_api_key_spend=None, - user_api_key_max_budget=None, - user_api_key_budget_reset_at=None, - user_api_key_team_id=None, - user_api_key_org_id=None, - user_api_key_user_id=None, - user_api_key_user_email=None, - user_api_key_team_alias=None, - spend_logs_metadata=None, - requester_ip_address=None, - user_agent=None, - requester_metadata=None, - user_api_key_end_user_id=None, - prompt_management_metadata=None, - applied_guardrails=None, - mcp_tool_call_metadata=None, - vector_store_request_metadata=None, - usage_object=None, - requester_custom_headers=None, - user_api_key_request_route=None, - cold_storage_object_key=None, - user_api_key_auth_metadata=None, - ) - if isinstance(metadata, dict): - # Update the clean_metadata with values from input metadata that match StandardLoggingMetadata fields - for key in StandardLoggingMetadata.__annotations__.keys(): - if key in metadata: - clean_metadata[key] = metadata[key] # type: ignore - - if metadata.get("user_api_key") is not None: - if is_valid_sha256_hash(str(metadata.get("user_api_key"))): - clean_metadata["user_api_key_hash"] = metadata.get( - "user_api_key" - ) # this is the hash - return clean_metadata - - -def scrub_sensitive_keys_in_metadata(litellm_params: Optional[dict]): - if litellm_params is None: - litellm_params = {} - - metadata = litellm_params.get("metadata", {}) or {} - - ## Extract provider-specific callable values (like langfuse_masking_function) - ## Store them separately so only the intended logger can access them - ## This prevents callables from leaking to other logging integrations - if "langfuse_masking_function" in metadata: - masking_fn = metadata.pop("langfuse_masking_function", None) - if callable(masking_fn): - litellm_params["_langfuse_masking_function"] = masking_fn - litellm_params["metadata"] = metadata - - ## check user_api_key_metadata for sensitive logging keys - cleaned_user_api_key_metadata = {} - if "user_api_key_metadata" in metadata and isinstance( - metadata["user_api_key_metadata"], dict - ): - for k, v in metadata["user_api_key_metadata"].items(): - if k == "logging": # prevent logging user logging keys - cleaned_user_api_key_metadata[ - k - ] = "scrubbed_by_litellm_for_sensitive_keys" - else: - cleaned_user_api_key_metadata[k] = v - - metadata["user_api_key_metadata"] = cleaned_user_api_key_metadata - litellm_params["metadata"] = metadata - - return litellm_params - - -# integration helper function -def modify_integration(integration_name, integration_params): - global supabaseClient - if integration_name == "supabase": - if "table_name" in integration_params: - Supabase.supabase_table_name = integration_params["table_name"] - - -@lru_cache(maxsize=16) -def _get_traceback_str_for_error(error_str: str) -> str: - """ - function wrapped with lru_cache to limit the number of times `traceback.format_exc()` is called - """ - return traceback.format_exc() - - -from decimal import Decimal - -# used for unit testing -from typing import Any, Dict, List, Optional, Union - - -def create_dummy_standard_logging_payload() -> StandardLoggingPayload: - # First create the nested objects with proper typing - model_info = StandardLoggingModelInformation( - model_map_key="gpt-3.5-turbo", model_map_value=None - ) - - metadata = StandardLoggingMetadata( # type: ignore - user_api_key_hash=str("test_hash"), - user_api_key_alias=str("test_alias"), - user_api_key_team_id=str("test_team"), - user_api_key_user_id=str("test_user"), - user_api_key_team_alias=str("test_team_alias"), - user_api_key_org_id=None, - spend_logs_metadata=None, - requester_ip_address=str("127.0.0.1"), - requester_metadata=None, - user_api_key_end_user_id=str("test_end_user"), - ) - - hidden_params = StandardLoggingHiddenParams( - model_id=None, - cache_key=None, - api_base=None, - response_cost=None, - additional_headers=None, - litellm_overhead_time_ms=None, - batch_models=None, - litellm_model_name=None, - usage_object=None, - ) - - # Convert numeric values to appropriate types - response_cost = Decimal("0.1") - start_time = Decimal("1234567890.0") - end_time = Decimal("1234567891.0") - completion_start_time = Decimal("1234567890.5") - saved_cache_cost = Decimal("0.0") - - # Create messages and response with proper typing - messages: List[Dict[str, str]] = [{"role": "user", "content": "Hello, world!"}] - response: Dict[str, List[Dict[str, Dict[str, str]]]] = { - "choices": [{"message": {"content": "Hi there!"}}] - } - - # Main payload initialization - return StandardLoggingPayload( # type: ignore - id=str("test_id"), - call_type=str("completion"), - stream=bool(False), - response_cost=response_cost, - response_cost_failure_debug_info=None, - status=str("success"), - total_tokens=int( - DEFAULT_MOCK_RESPONSE_PROMPT_TOKEN_COUNT - + DEFAULT_MOCK_RESPONSE_COMPLETION_TOKEN_COUNT - ), - prompt_tokens=int(DEFAULT_MOCK_RESPONSE_PROMPT_TOKEN_COUNT), - completion_tokens=int(DEFAULT_MOCK_RESPONSE_COMPLETION_TOKEN_COUNT), - startTime=start_time, - endTime=end_time, - completionStartTime=completion_start_time, - model_map_information=model_info, - model=str("gpt-3.5-turbo"), - model_id=str("model-123"), - model_group=str("openai-gpt"), - custom_llm_provider=str("openai"), - api_base=str("https://api.openai.com"), - metadata=metadata, - cache_hit=bool(False), - cache_key=None, - saved_cache_cost=saved_cache_cost, - request_tags=[], - end_user=None, - requester_ip_address=str("127.0.0.1"), - messages=messages, - response=response, - error_str=None, - model_parameters={"stream": True}, - hidden_params=hidden_params, - ) +# What is this? +## Common Utility file for Logging handler +# Logging function -> log the exact model details + what's being sent | Non-Blocking +import copy +import datetime +import json +import os +import re +import subprocess +import sys +import time +import traceback +from datetime import datetime as dt_object +from functools import lru_cache +from typing import ( + TYPE_CHECKING, + Any, + Callable, + Dict, + List, + Literal, + Optional, + Tuple, + Type, + Union, + cast, +) + +from httpx import Response +from pydantic import BaseModel + +import litellm +from litellm import ( + _custom_logger_compatible_callbacks_literal, + json_logs, + log_raw_request_response, + turn_off_message_logging, +) +from litellm._logging import _is_debugging_on, verbose_logger +from litellm._uuid import uuid +from litellm.batches.batch_utils import _handle_completed_batch +from litellm.caching.caching import DualCache, InMemoryCache +from litellm.caching.caching_handler import LLMCachingHandler +from litellm.constants import ( + DEFAULT_MOCK_RESPONSE_COMPLETION_TOKEN_COUNT, + DEFAULT_MOCK_RESPONSE_PROMPT_TOKEN_COUNT, + SENTRY_DENYLIST, + SENTRY_PII_DENYLIST, +) +from litellm.cost_calculator import ( + RealtimeAPITokenUsageProcessor, + _select_model_name_for_cost_calc, +) +from litellm.integrations.agentops import AgentOps +from litellm.integrations.anthropic_cache_control_hook import AnthropicCacheControlHook +from litellm.integrations.arize.arize import ArizeLogger +from litellm.integrations.custom_guardrail import CustomGuardrail +from litellm.integrations.custom_logger import CustomLogger +from litellm.integrations.deepeval.deepeval import DeepEvalLogger +from litellm.integrations.mlflow import MlflowLogger +from litellm.integrations.sqs import SQSLogger +from litellm.litellm_core_utils.core_helpers import reconstruct_model_name +from litellm.litellm_core_utils.get_litellm_params import get_litellm_params +from litellm.litellm_core_utils.llm_cost_calc.tool_call_cost_tracking import ( + StandardBuiltInToolCostTracking, +) +from litellm.litellm_core_utils.logging_utils import truncate_base64_in_messages +from litellm.litellm_core_utils.model_param_helper import ModelParamHelper +from litellm.litellm_core_utils.redact_messages import ( + redact_message_input_output_from_custom_logger, + redact_message_input_output_from_logging, +) +from litellm.llms.base_llm.ocr.transformation import OCRResponse +from litellm.llms.base_llm.search.transformation import SearchResponse +from litellm.responses.utils import ResponseAPILoggingUtils +from litellm.types.agents import LiteLLMSendMessageResponse +from litellm.types.containers.main import ContainerObject +from litellm.types.llms.openai import ( + AllMessageValues, + Batch, + FineTuningJob, + HttpxBinaryResponseContent, + OpenAIFileObject, + OpenAIModerationResponse, + ResponseAPIUsage, + ResponseCompletedEvent, + ResponsesAPIResponse, +) +from litellm.types.mcp import MCPPostCallResponseObject +from litellm.types.prompts.init_prompts import PromptSpec +from litellm.types.rerank import RerankResponse +from litellm.types.utils import ( + CachingDetails, + CallTypes, + CostBreakdown, + CostResponseTypes, + CustomPricingLiteLLMParams, + DynamicPromptManagementParamLiteral, + EmbeddingResponse, + GuardrailStatus, + ImageResponse, + LiteLLMBatch, + LiteLLMLoggingBaseClass, + LiteLLMRealtimeStreamLoggingObject, + ModelResponse, + ModelResponseStream, + RawRequestTypedDict, + StandardBuiltInToolsParams, + StandardCallbackDynamicParams, + StandardLoggingAdditionalHeaders, + StandardLoggingHiddenParams, + StandardLoggingMCPToolCall, + StandardLoggingMetadata, + StandardLoggingModelCostFailureDebugInformation, + StandardLoggingModelInformation, + StandardLoggingPayload, + StandardLoggingPayloadErrorInformation, + StandardLoggingPayloadStatus, + StandardLoggingPayloadStatusFields, + StandardLoggingPromptManagementMetadata, + StandardLoggingVectorStoreRequest, + TextCompletionResponse, + TranscriptionResponse, + Usage, +) +from litellm.types.videos.main import VideoObject +from litellm.utils import _get_base_model_from_metadata, executor, print_verbose + +from ..integrations.argilla import ArgillaLogger +from ..integrations.arize.arize_phoenix import ArizePhoenixLogger +from ..integrations.athina import AthinaLogger +from ..integrations.azure_sentinel.azure_sentinel import AzureSentinelLogger +from ..integrations.azure_storage.azure_storage import AzureBlobStorageLogger +from ..integrations.custom_prompt_management import CustomPromptManagement +from ..integrations.datadog.datadog import DataDogLogger +from ..integrations.datadog.datadog_llm_obs import DataDogLLMObsLogger +from ..integrations.dotprompt import DotpromptManager +from ..integrations.dynamodb import DyanmoDBLogger +from ..integrations.galileo import GalileoObserve +from ..integrations.gcs_bucket.gcs_bucket import GCSBucketLogger +from ..integrations.gcs_pubsub.pub_sub import GcsPubSubLogger +from ..integrations.greenscale import GreenscaleLogger +from ..integrations.helicone import HeliconeLogger +from ..integrations.humanloop import HumanloopLogger +from ..integrations.lago import LagoLogger +from ..integrations.langfuse.langfuse import LangFuseLogger +from ..integrations.langfuse.langfuse_handler import LangFuseHandler +from ..integrations.langfuse.langfuse_prompt_management import LangfusePromptManagement +from ..integrations.langsmith import LangsmithLogger +from ..integrations.literal_ai import LiteralAILogger +from ..integrations.logfire_logger import LogfireLevel, LogfireLogger +from ..integrations.lunary import LunaryLogger +from ..integrations.openmeter import OpenMeterLogger +from ..integrations.opik.opik import OpikLogger +from ..integrations.posthog import PostHogLogger +from ..integrations.prompt_layer import PromptLayerLogger +from ..integrations.s3 import S3Logger +from ..integrations.s3_v2 import S3Logger as S3V2Logger +from ..integrations.supabase import Supabase +from ..integrations.traceloop import TraceloopLogger +from .exception_mapping_utils import _get_response_headers +from .initialize_dynamic_callback_params import ( + initialize_standard_callback_dynamic_params as _initialize_standard_callback_dynamic_params, +) +from .specialty_caches.dynamic_logging_cache import DynamicLoggingCache + +if TYPE_CHECKING: + from litellm.llms.base_llm.passthrough.transformation import BasePassthroughConfig +try: + from litellm_enterprise.enterprise_callbacks.callback_controls import ( + EnterpriseCallbackControls, + ) + from litellm_enterprise.enterprise_callbacks.pagerduty.pagerduty import ( + PagerDutyAlerting, + ) + from litellm_enterprise.enterprise_callbacks.send_emails.resend_email import ( + ResendEmailLogger, + ) + from litellm_enterprise.enterprise_callbacks.send_emails.sendgrid_email import ( + SendGridEmailLogger, + ) + from litellm_enterprise.enterprise_callbacks.send_emails.smtp_email import ( + SMTPEmailLogger, + ) + from litellm_enterprise.litellm_core_utils.litellm_logging import ( + StandardLoggingPayloadSetup as EnterpriseStandardLoggingPayloadSetup, + ) + + from litellm.integrations.generic_api.generic_api_callback import GenericAPILogger + + EnterpriseStandardLoggingPayloadSetupVAR: Optional[ + Type[EnterpriseStandardLoggingPayloadSetup] + ] = EnterpriseStandardLoggingPayloadSetup +except Exception as e: + verbose_logger.debug( + f"[Non-Blocking] Unable to import GenericAPILogger - LiteLLM Enterprise Feature - {str(e)}" + ) + GenericAPILogger = CustomLogger # type: ignore + ResendEmailLogger = CustomLogger # type: ignore + SendGridEmailLogger = CustomLogger # type: ignore + SMTPEmailLogger = CustomLogger # type: ignore + PagerDutyAlerting = CustomLogger # type: ignore + EnterpriseCallbackControls = None # type: ignore + EnterpriseStandardLoggingPayloadSetupVAR = None +_in_memory_loggers: List[Any] = [] + +_STANDARD_LOGGING_METADATA_KEYS: frozenset = frozenset( + StandardLoggingMetadata.__annotations__.keys() +) + +### GLOBAL VARIABLES ### + +# Cache custom pricing keys as frozenset for O(1) lookups instead of looping through 49 keys +_CUSTOM_PRICING_KEYS: frozenset = frozenset( + CustomPricingLiteLLMParams.model_fields.keys() +) + +sentry_sdk_instance = None +capture_exception = None +add_breadcrumb = None +slack_app = None +alerts_channel = None +heliconeLogger = None +athinaLogger = None +promptLayerLogger = None +logfireLogger = None +weightsBiasesLogger = None +customLogger = None +langFuseLogger = None +openMeterLogger = None +lagoLogger = None +dataDogLogger = None +prometheusLogger = None +dynamoLogger = None +s3Logger = None +greenscaleLogger = None +lunaryLogger = None +supabaseClient = None +deepevalLogger = None +callback_list: Optional[List[str]] = [] +user_logger_fn = None +additional_details: Optional[Dict[str, str]] = {} +local_cache: Optional[Dict[str, str]] = {} +last_fetched_at = None +last_fetched_at_keys = None + + +#### +class ServiceTraceIDCache: + def __init__(self) -> None: + self.cache = InMemoryCache() + + def get_cache(self, litellm_call_id: str, service_name: str) -> Optional[str]: + key_name = "{}:{}".format(service_name, litellm_call_id) + response = self.cache.get_cache(key=key_name) + return response + + def set_cache(self, litellm_call_id: str, service_name: str, trace_id: str) -> None: + key_name = "{}:{}".format(service_name, litellm_call_id) + self.cache.set_cache(key=key_name, value=trace_id) + return None + + +in_memory_trace_id_cache = ServiceTraceIDCache() +in_memory_dynamic_logger_cache = DynamicLoggingCache() + +# Cached lazy import for PrometheusLogger +# Module-level cache to avoid repeated imports while preserving memory benefits +_PrometheusLogger = None + + +def _get_cached_prometheus_logger(): + """ + Get cached PrometheusLogger class. + Lazy imports on first call to avoid loading prometheus.py and utils.py at import time (60MB saved). + Subsequent calls use cached class for better performance. + """ + global _PrometheusLogger + if _PrometheusLogger is None: + from litellm.integrations.prometheus import PrometheusLogger + + _PrometheusLogger = PrometheusLogger + return _PrometheusLogger + + +class Logging(LiteLLMLoggingBaseClass): + global supabaseClient, promptLayerLogger, weightsBiasesLogger, logfireLogger, capture_exception, add_breadcrumb, lunaryLogger, logfireLogger, prometheusLogger, slack_app + custom_pricing: bool = False + stream_options = None + litellm_request_debug: bool = False + + def __init__( + self, + model: str, + messages, + stream, + call_type, + start_time, + litellm_call_id: str, + function_id: str, + litellm_trace_id: Optional[str] = None, + dynamic_input_callbacks: Optional[ + List[Union[str, Callable, CustomLogger]] + ] = None, + dynamic_success_callbacks: Optional[ + List[Union[str, Callable, CustomLogger]] + ] = None, + dynamic_async_success_callbacks: Optional[ + List[Union[str, Callable, CustomLogger]] + ] = None, + dynamic_failure_callbacks: Optional[ + List[Union[str, Callable, CustomLogger]] + ] = None, + dynamic_async_failure_callbacks: Optional[ + List[Union[str, Callable, CustomLogger]] + ] = None, + applied_guardrails: Optional[List[str]] = None, + kwargs: Optional[Dict] = None, + log_raw_request_response: bool = False, + ): + _input: Optional[str] = messages # save original value of messages + if messages is not None: + if isinstance(messages, str): + messages = [ + {"role": "user", "content": messages} + ] # convert text completion input to the chat completion format + elif ( + isinstance(messages, list) + and len(messages) > 0 + and isinstance(messages[0], str) + ): + new_messages = [] + for m in messages: + new_messages.append({"role": "user", "content": m}) + messages = new_messages + + self.model = model + # Shallow copy of the outer list only (inner message dicts are shared). + # Safe because the logging layer does not mutate individual message dicts. + _copy_start = time.time() + self.messages = copy.copy(messages) if messages is not None else None + self.message_copy_duration_ms: float = (time.time() - _copy_start) * 1000 + self.callback_duration_ms: float = 0.0 + self.stream = stream + self.start_time = start_time # log the call start time + 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()) + ) + 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 + self.log_raw_request_response = log_raw_request_response + + # Initialize dynamic callbacks + self.dynamic_input_callbacks: Optional[ + List[Union[str, Callable, CustomLogger]] + ] = dynamic_input_callbacks + self.dynamic_success_callbacks: Optional[ + List[Union[str, Callable, CustomLogger]] + ] = dynamic_success_callbacks + self.dynamic_async_success_callbacks: Optional[ + List[Union[str, Callable, CustomLogger]] + ] = dynamic_async_success_callbacks + self.dynamic_failure_callbacks: Optional[ + List[Union[str, Callable, CustomLogger]] + ] = dynamic_failure_callbacks + self.dynamic_async_failure_callbacks: Optional[ + List[Union[str, Callable, CustomLogger]] + ] = dynamic_async_failure_callbacks + + # Process dynamic callbacks + self.process_dynamic_callbacks() + + ## DYNAMIC LANGFUSE / GCS / logging callback KEYS ## + self.standard_callback_dynamic_params: StandardCallbackDynamicParams = ( + self.initialize_standard_callback_dynamic_params(kwargs) + ) + self.standard_built_in_tools_params: StandardBuiltInToolsParams = ( + self.initialize_standard_built_in_tools_params(kwargs) + ) + ## TIME TO FIRST TOKEN LOGGING ## + self.completion_start_time: Optional[datetime.datetime] = None + self._llm_caching_handler: Optional[LLMCachingHandler] = None + + # INITIAL LITELLM_PARAMS + litellm_params = {} + if kwargs is not None: + litellm_params = get_litellm_params(**kwargs) + litellm_params = scrub_sensitive_keys_in_metadata(litellm_params) + + self.litellm_params = litellm_params + + # Initialize cost breakdown field + self.cost_breakdown: Optional[CostBreakdown] = None + + # Init Caching related details + self.caching_details: Optional[CachingDetails] = None + + # Passthrough endpoint guardrails config for field targeting + self.passthrough_guardrails_config: Optional[Dict[str, Any]] = None + + self.model_call_details: Dict[str, Any] = { + "litellm_trace_id": litellm_trace_id, + "litellm_call_id": litellm_call_id, + "input": _input, + "litellm_params": litellm_params, + "applied_guardrails": applied_guardrails, + "model": model, + } + + def process_dynamic_callbacks(self): + """ + Initializes CustomLogger compatible callbacks in self.dynamic_* callbacks + + If a callback is in litellm._known_custom_logger_compatible_callbacks, it needs to be intialized and added to the respective dynamic_* callback list. + """ + # Process input callbacks + self.dynamic_input_callbacks = self._process_dynamic_callback_list( + self.dynamic_input_callbacks, dynamic_callbacks_type="input" + ) + + # Process failure callbacks + self.dynamic_failure_callbacks = self._process_dynamic_callback_list( + self.dynamic_failure_callbacks, dynamic_callbacks_type="failure" + ) + + # Process async failure callbacks + self.dynamic_async_failure_callbacks = self._process_dynamic_callback_list( + self.dynamic_async_failure_callbacks, dynamic_callbacks_type="async_failure" + ) + + # Process success callbacks + self.dynamic_success_callbacks = self._process_dynamic_callback_list( + self.dynamic_success_callbacks, dynamic_callbacks_type="success" + ) + + # Process async success callbacks + self.dynamic_async_success_callbacks = self._process_dynamic_callback_list( + self.dynamic_async_success_callbacks, dynamic_callbacks_type="async_success" + ) + + def _process_dynamic_callback_list( + self, + callback_list: Optional[List[Union[str, Callable, CustomLogger]]], + dynamic_callbacks_type: Literal[ + "input", "success", "failure", "async_success", "async_failure" + ], + ) -> Optional[List[Union[str, Callable, CustomLogger]]]: + """ + Helper function to initialize CustomLogger compatible callbacks in self.dynamic_* callbacks + + - If a callback is in litellm._known_custom_logger_compatible_callbacks, + replace the string with the initialized callback class. + - If dynamic callback is a "success" callback that is a known_custom_logger_compatible_callbacks then add it to dynamic_async_success_callbacks + - If dynamic callback is a "failure" callback that is a known_custom_logger_compatible_callbacks then add it to dynamic_failure_callbacks + """ + if callback_list is None: + return None + + processed_list: List[Union[str, Callable, CustomLogger]] = [] + for callback in callback_list: + if ( + isinstance(callback, str) + and callback in litellm._known_custom_logger_compatible_callbacks + ): + callback_class = _init_custom_logger_compatible_class( + callback, internal_usage_cache=None, llm_router=None # type: ignore + ) + if callback_class is not None: + processed_list.append(callback_class) + + # If processing dynamic_success_callbacks, add to dynamic_async_success_callbacks + if dynamic_callbacks_type == "success": + if self.dynamic_async_success_callbacks is None: + self.dynamic_async_success_callbacks = [] + self.dynamic_async_success_callbacks.append(callback_class) + elif dynamic_callbacks_type == "failure": + if self.dynamic_async_failure_callbacks is None: + self.dynamic_async_failure_callbacks = [] + self.dynamic_async_failure_callbacks.append(callback_class) + else: + processed_list.append(callback) + return processed_list + + def initialize_standard_callback_dynamic_params( + self, kwargs: Optional[Dict] = None + ) -> StandardCallbackDynamicParams: + """ + Initialize the standard callback dynamic params from the kwargs + + checks if langfuse_secret_key, gcs_bucket_name in kwargs and sets the corresponding attributes in StandardCallbackDynamicParams + """ + + return _initialize_standard_callback_dynamic_params(kwargs) + + def initialize_standard_built_in_tools_params( + self, kwargs: Optional[Dict] = None + ) -> StandardBuiltInToolsParams: + """ + Initialize the standard built-in tools params from the kwargs + + checks if web_search_options in kwargs or tools and sets the corresponding attribute in StandardBuiltInToolsParams + """ + return StandardBuiltInToolsParams( + web_search_options=StandardBuiltInToolCostTracking._get_web_search_options( + kwargs or {} + ), + file_search=StandardBuiltInToolCostTracking._get_file_search_tool_call( + kwargs or {} + ), + ) + + def update_environment_variables( + self, + litellm_params: Dict, + optional_params: Dict, + model: Optional[str] = None, + user: Optional[str] = None, + **additional_params, + ): + self.optional_params = optional_params + if model is not None: + self.model = model + self.user = user + self.litellm_params = { + **self.litellm_params, + **scrub_sensitive_keys_in_metadata(litellm_params), + } + self.litellm_request_debug = litellm_params.get("litellm_request_debug", False) + self.logger_fn = litellm_params.get("logger_fn", None) + if _is_debugging_on() or self.litellm_request_debug: + verbose_logger.debug(f"self.optional_params: {self.optional_params}") + + self.model_call_details.update( + { + "model": self.model, + "messages": self.messages, + "optional_params": self.optional_params, + "litellm_params": self.litellm_params, + "start_time": self.start_time, + "stream": self.stream, + "user": user, + "call_type": str(self.call_type), + "litellm_call_id": self.litellm_call_id, + "completion_start_time": self.completion_start_time, + "standard_callback_dynamic_params": self.standard_callback_dynamic_params, + **self.optional_params, + **additional_params, + } + ) + + ## check if stream options is set ## - used by CustomStreamWrapper for easy instrumentation + if "stream_options" in additional_params: + self.stream_options = additional_params["stream_options"] + ## check if custom pricing set ## + if any( + litellm_params.get(key) is not None + for key in _CUSTOM_PRICING_KEYS & litellm_params.keys() + ): + self.custom_pricing = True + + if "custom_llm_provider" in self.model_call_details: + self.custom_llm_provider = self.model_call_details["custom_llm_provider"] + + def update_messages(self, messages: List[AllMessageValues]): + """ + Update the logged value of the messages in the model_call_details + + Allows pre-call hooks to update the messages before the call is made + """ + self.messages = messages + self.model_call_details["messages"] = messages + + def should_run_prompt_management_hooks( + self, + non_default_params: Dict, + prompt_id: Optional[str] = None, + tools: Optional[List[Dict]] = None, + ) -> bool: + """ + Return True if prompt management hooks should be run + """ + if prompt_id: + return True + + if self._should_run_prompt_management_hooks_without_prompt_id( + non_default_params=non_default_params, + tools=tools, + ): + return True + + return False + + def _should_run_prompt_management_hooks_without_prompt_id( + self, + non_default_params: Dict, + tools: Optional[List[Dict]] = None, + ) -> bool: + """ + Certain prompt management hooks don't need a `prompt_id` to be passed in, they are triggered by dynamic params + + eg. AnthropicCacheControlHook and BedrockKnowledgeBaseHook both don't require a `prompt_id` to be passed in, they are triggered by dynamic params + """ + for param in non_default_params: + if param in DynamicPromptManagementParamLiteral.list_all_params(): + return True + + ############################################################################# + # Check if Vector Store / Knowledge Base hooks should be applied to the prompt + ############################################################################# + if litellm.vector_store_registry is not None: + if litellm.vector_store_registry.get_vector_store_to_run( + non_default_params=non_default_params, tools=tools + ): + return True + return False + + def get_chat_completion_prompt( + self, + model: str, + messages: List[AllMessageValues], + non_default_params: Dict, + prompt_variables: Optional[dict], + prompt_id: Optional[str] = None, + prompt_spec: Optional[PromptSpec] = None, + prompt_management_logger: Optional[CustomLogger] = None, + prompt_label: Optional[str] = None, + prompt_version: Optional[int] = None, + ) -> Tuple[str, List[AllMessageValues], dict]: + custom_logger = ( + prompt_management_logger + or self.get_custom_logger_for_prompt_management( + model=model, + non_default_params=non_default_params, + prompt_id=prompt_id, + prompt_spec=prompt_spec, + dynamic_callback_params=self.standard_callback_dynamic_params, + ) + ) + + if custom_logger: + ( + model, + messages, + non_default_params, + ) = custom_logger.get_chat_completion_prompt( + model=model, + messages=messages, + non_default_params=non_default_params or {}, + prompt_id=prompt_id, + prompt_spec=prompt_spec, + prompt_variables=prompt_variables, + dynamic_callback_params=self.standard_callback_dynamic_params, + prompt_label=prompt_label, + prompt_version=prompt_version, + ) + self.messages = messages + return model, messages, non_default_params + + async def async_get_chat_completion_prompt( + self, + model: str, + messages: List[AllMessageValues], + non_default_params: Dict, + prompt_variables: Optional[dict], + prompt_id: Optional[str] = None, + prompt_spec: Optional[PromptSpec] = None, + prompt_management_logger: Optional[CustomLogger] = None, + tools: Optional[List[Dict]] = None, + prompt_label: Optional[str] = None, + prompt_version: Optional[int] = None, + ) -> Tuple[str, List[AllMessageValues], dict]: + custom_logger = ( + prompt_management_logger + or self.get_custom_logger_for_prompt_management( + model=model, + tools=tools, + non_default_params=non_default_params, + prompt_id=prompt_id, + prompt_spec=prompt_spec, + dynamic_callback_params=self.standard_callback_dynamic_params, + ) + ) + + if custom_logger: + ( + model, + messages, + non_default_params, + ) = await custom_logger.async_get_chat_completion_prompt( + model=model, + messages=messages, + non_default_params=non_default_params or {}, + prompt_id=prompt_id, + prompt_spec=prompt_spec, + prompt_variables=prompt_variables, + dynamic_callback_params=self.standard_callback_dynamic_params, + litellm_logging_obj=self, + tools=tools, + prompt_label=prompt_label, + prompt_version=prompt_version, + ) + self.messages = messages + return model, messages, non_default_params + + def _auto_detect_prompt_management_logger( + self, + prompt_id: str, + prompt_spec: Optional[PromptSpec], + dynamic_callback_params: StandardCallbackDynamicParams, + ) -> Optional[CustomLogger]: + """ + Auto-detect which prompt management system owns the given prompt_id. + + This allows a user to just pass prompt_id in the completion call and it will be auto-detected which system owns this prompt. + + Args: + prompt_id: The prompt ID to check + dynamic_callback_params: Dynamic callback parameters for should_run_prompt_management checks + + Returns: + A CustomLogger instance if a matching prompt management system is found, None otherwise + """ + prompt_management_loggers = ( + litellm.logging_callback_manager.get_custom_loggers_for_type( + callback_type=CustomPromptManagement + ) + ) + + for logger in prompt_management_loggers: + if isinstance(logger, CustomPromptManagement): + try: + if logger.should_run_prompt_management( + prompt_id=prompt_id, + prompt_spec=prompt_spec, + dynamic_callback_params=dynamic_callback_params, + ): + self.model_call_details[ + "prompt_integration" + ] = logger.__class__.__name__ + return logger + except Exception: + # If check fails, continue to next logger + continue + + return None + + def get_custom_logger_for_prompt_management( + self, + model: str, + non_default_params: Dict, + tools: Optional[List[Dict]] = None, + prompt_id: Optional[str] = None, + prompt_spec: Optional[PromptSpec] = None, + dynamic_callback_params: Optional[StandardCallbackDynamicParams] = None, + ) -> Optional[CustomLogger]: + """ + Get a custom logger for prompt management based on model name or available callbacks. + + Args: + model: The model name to check for prompt management integration + non_default_params: Non-default parameters passed to the completion call + tools: Optional tools passed to the completion call + prompt_id: Optional prompt ID to auto-detect which system owns this prompt + dynamic_callback_params: Dynamic callback parameters for should_run_prompt_management checks + + Returns: + A CustomLogger instance if one is found, None otherwise + """ + # First check if model starts with a known custom logger compatible callback + # This takes precedence for backward compatibility + for callback_name in litellm._known_custom_logger_compatible_callbacks: + if model.startswith(callback_name): + custom_logger = _init_custom_logger_compatible_class( + logging_integration=callback_name, + internal_usage_cache=None, + llm_router=None, + ) + if custom_logger is not None: + self.model_call_details["prompt_integration"] = model.split("/")[0] + return custom_logger + + # If prompt_id is provided, try to auto-detect which system has this prompt + if prompt_id and dynamic_callback_params is not None: + auto_detected_logger = self._auto_detect_prompt_management_logger( + prompt_id=prompt_id, + prompt_spec=prompt_spec, + dynamic_callback_params=dynamic_callback_params, + ) + if auto_detected_logger is not None: + return auto_detected_logger + + # Then check for any registered CustomPromptManagement loggers (fallback) + prompt_management_loggers = ( + litellm.logging_callback_manager.get_custom_loggers_for_type( + callback_type=CustomPromptManagement + ) + ) + + if prompt_management_loggers: + logger = prompt_management_loggers[0] + self.model_call_details["prompt_integration"] = logger.__class__.__name__ + return logger + + if anthropic_cache_control_logger := AnthropicCacheControlHook.get_custom_logger_for_anthropic_cache_control_hook( + non_default_params + ): + self.model_call_details[ + "prompt_integration" + ] = anthropic_cache_control_logger.__class__.__name__ + return anthropic_cache_control_logger + + ######################################################### + # Vector Store / Knowledge Base hooks + ######################################################### + if litellm.vector_store_registry is not None: + vector_store_custom_logger = _init_custom_logger_compatible_class( + logging_integration="vector_store_pre_call_hook", + internal_usage_cache=None, + llm_router=None, + ) + self.model_call_details[ + "prompt_integration" + ] = vector_store_custom_logger.__class__.__name__ + # Add to global callbacks so post-call hooks are invoked + if ( + vector_store_custom_logger + and vector_store_custom_logger not in litellm.callbacks + ): + litellm.logging_callback_manager.add_litellm_callback( + vector_store_custom_logger + ) + return vector_store_custom_logger + + return None + + def get_custom_logger_for_anthropic_cache_control_hook( + self, non_default_params: Dict + ) -> Optional[CustomLogger]: + if non_default_params.get("cache_control_injection_points", None): + custom_logger = _init_custom_logger_compatible_class( + logging_integration="anthropic_cache_control_hook", + internal_usage_cache=None, + llm_router=None, + ) + return custom_logger + return None + + def _get_raw_request_body(self, data: Optional[Union[dict, str]]) -> dict: + if data is None: + return {"error": "Received empty dictionary for raw request body"} + if isinstance(data, str): + try: + return json.loads(data) + except Exception: + return { + "error": "Unable to parse raw request body. Got - {}".format(data) + } + return data + + def _get_masked_api_base(self, api_base: str) -> str: + if "key=" in api_base: + # Find the position of "key=" in the string + key_index = api_base.find("key=") + 4 + # Mask the last 5 characters after "key=" + masked_api_base = api_base[:key_index] + "*" * 5 + api_base[-4:] + else: + masked_api_base = api_base + return str(masked_api_base) + + def _pre_call(self, input, api_key, model=None, additional_args={}): + """ + Common helper function across the sync + async pre-call function + """ + + self.model_call_details["input"] = input + self.model_call_details["api_key"] = api_key + self.model_call_details["additional_args"] = additional_args + self.model_call_details["log_event_type"] = "pre_api_call" + if ( + model + ): # if model name was changes pre-call, overwrite the initial model call name with the new one + self.model_call_details["model"] = model + self.model_call_details["litellm_params"][ + "api_base" + ] = self._get_masked_api_base(additional_args.get("api_base", "")) + + def pre_call(self, input, api_key, model=None, additional_args={}): # noqa: PLR0915 + # Log the exact input to the LLM API + litellm.error_logs["PRE_CALL"] = locals() + try: + self._pre_call( + input=input, + api_key=api_key, + model=model, + additional_args=additional_args, + ) + + # User Logging -> if you pass in a custom logging function + self._print_llm_call_debugging_log( + api_base=additional_args.get("api_base", ""), + headers=additional_args.get("headers", {}), + additional_args=additional_args, + ) + # log raw request to provider (like LangFuse) -- if opted in. + if ( + self.log_raw_request_response is True + or log_raw_request_response is True + ): + _litellm_params = self.model_call_details.get("litellm_params", {}) + _metadata = _litellm_params.get("metadata", {}) or {} + try: + # [Non-blocking Extra Debug Information in metadata] + if turn_off_message_logging is True: + _metadata[ + "raw_request" + ] = "redacted by litellm. \ + 'litellm.turn_off_message_logging=True'" + else: + curl_command = self._get_request_curl_command( + api_base=additional_args.get("api_base", ""), + headers=additional_args.get("headers", {}), + additional_args=additional_args, + data=additional_args.get("complete_input_dict", {}), + ) + + _metadata["raw_request"] = str(curl_command) + # split up, so it's easier to parse in the UI + self.model_call_details[ + "raw_request_typed_dict" + ] = RawRequestTypedDict( + raw_request_api_base=str( + additional_args.get("api_base") or "" + ), + raw_request_body=self._get_raw_request_body( + additional_args.get("complete_input_dict", {}) + ), + # NOTE: setting ignore_sensitive_headers to True will cause + # the Authorization header to be leaked when calls to the health + # endpoint are made and fail. + raw_request_headers=self._get_masked_headers( + additional_args.get("headers", {}) or {}, + ), + error=None, + ) + except Exception as e: + self.model_call_details[ + "raw_request_typed_dict" + ] = RawRequestTypedDict( + error=str(e), + ) + _metadata[ + "raw_request" + ] = "Unable to Log \ + raw request: {}".format( + str(e) + ) + if getattr(self, "logger_fn", None) and callable(self.logger_fn): + try: + self.logger_fn( + self.model_call_details + ) # Expectation: any logger function passed in by the user should accept a dict object + except Exception as e: + verbose_logger.exception( + "LiteLLM.LoggingError: [Non-Blocking] Exception occurred while logging {}".format( + str(e) + ) + ) + + self.model_call_details["api_call_start_time"] = datetime.datetime.now() + # Input Integration Logging -> If you want to log the fact that an attempt to call the model was made + callbacks = litellm.input_callback + (self.dynamic_input_callbacks or []) + for callback in callbacks: + try: + if callback == "supabase" and supabaseClient is not None: + verbose_logger.debug("reaches supabase for logging!") + model = self.model_call_details["model"] + messages = self.model_call_details["input"] + verbose_logger.debug(f"supabaseClient: {supabaseClient}") + supabaseClient.input_log_event( + model=model, + messages=messages, + end_user=self.model_call_details.get("user", "default"), + litellm_call_id=self.litellm_params["litellm_call_id"], + print_verbose=print_verbose, + ) + elif callback == "sentry" and add_breadcrumb: + try: + details_to_log = copy.deepcopy(self.model_call_details) + except Exception: + details_to_log = self.model_call_details + if litellm.turn_off_message_logging: + # make a copy of the _model_Call_details and log it + details_to_log.pop("messages", None) + details_to_log.pop("input", None) + details_to_log.pop("prompt", None) + + add_breadcrumb( + category="litellm.llm_call", + message=f"Model Call Details pre-call: {details_to_log}", + level="info", + ) + + elif isinstance(callback, CustomLogger): # custom logger class + callback.log_pre_api_call( + model=self.model, + messages=self.messages, + kwargs=self.model_call_details, + ) + elif ( + callable(callback) and customLogger is not None + ): # custom logger functions + customLogger.log_input_event( + model=self.model, + messages=self.messages, + kwargs=self.model_call_details, + print_verbose=print_verbose, + callback_func=callback, + ) + except Exception as e: + verbose_logger.exception( + "litellm.Logging.pre_call(): Exception occured - {}".format( + str(e) + ) + ) + verbose_logger.debug( + f"LiteLLM.Logging: is sentry capture exception initialized {capture_exception}" + ) + if capture_exception: # log this error to sentry for debugging + capture_exception(e) + except Exception as e: + verbose_logger.exception( + "LiteLLM.LoggingError: [Non-Blocking] Exception occurred while logging {}".format( + str(e) + ) + ) + verbose_logger.error( + f"LiteLLM.Logging: is sentry capture exception initialized {capture_exception}" + ) + if capture_exception: # log this error to sentry for debugging + capture_exception(e) + + def _print_llm_call_debugging_log( + self, + api_base: str, + headers: dict, + additional_args: dict, + ): + """ + Internal debugging helper function + + Prints the RAW curl command sent from LiteLLM + """ + if _is_debugging_on() or self.litellm_request_debug: + if json_logs: + masked_headers = self._get_masked_headers(headers) + if self.litellm_request_debug: + verbose_logger.warning( # .warning ensures this shows up in all environments + "POST Request Sent from LiteLLM", + extra={"api_base": {api_base}, **masked_headers}, + ) + else: + verbose_logger.debug( + "POST Request Sent from LiteLLM", + extra={"api_base": {api_base}, **masked_headers}, + ) + else: + headers = additional_args.get("headers", {}) + if headers is None: + headers = {} + data = additional_args.get("complete_input_dict", {}) + api_base = str(additional_args.get("api_base", "")) + curl_command = self._get_request_curl_command( + api_base=api_base, + headers=headers, + additional_args=additional_args, + data=data, + ) + if self.litellm_request_debug: + verbose_logger.warning( + f"\033[92m{curl_command}\033[0m\n" + ) # .warning ensures this shows up in all environments + else: + verbose_logger.debug(f"\033[92m{curl_command}\033[0m\n") + + def _get_request_body(self, data: dict) -> str: + return str(data) + + def _get_request_curl_command( + self, api_base: str, headers: Optional[dict], additional_args: dict, data: dict + ) -> str: + masked_api_base = self._get_masked_api_base(api_base) + if headers is None: + headers = {} + curl_command = "\n\nPOST Request Sent from LiteLLM:\n" + curl_command += "curl -X POST \\\n" + curl_command += f"{masked_api_base} \\\n" + masked_headers = self._get_masked_headers(headers) + formatted_headers = " ".join( + [f"-H '{k}: {v}'" for k, v in masked_headers.items()] + ) + curl_command += ( + f"{formatted_headers} \\\n" if formatted_headers.strip() != "" else "" + ) + curl_command += f"-d '{self._get_request_body(data)}'\n" + if additional_args.get("request_str", None) is not None: + # print the sagemaker / bedrock client request + curl_command = "\nRequest Sent from LiteLLM:\n" + request_str = additional_args.get("request_str", "") + curl_command += request_str + elif api_base == "": + curl_command = str(self.model_call_details) + return curl_command + + def _get_masked_headers( + self, headers: dict, ignore_sensitive_headers: bool = False + ) -> dict: + """ + Internal debugging helper function + + Masks the headers of the request sent from LiteLLM + """ + return _get_masked_values( + headers, ignore_sensitive_values=ignore_sensitive_headers + ) + + def post_call( + self, original_response, input=None, api_key=None, additional_args={} + ): + # Log the exact result from the LLM API, for streaming - log the type of response received + litellm.error_logs["POST_CALL"] = locals() + if isinstance(original_response, dict): + original_response = json.dumps(original_response) + try: + self.model_call_details["input"] = input + self.model_call_details["api_key"] = api_key + self.model_call_details["original_response"] = original_response + self.model_call_details["additional_args"] = additional_args + self.model_call_details["log_event_type"] = "post_api_call" + + if self.litellm_request_debug: + attr = "warning" + else: + attr = "debug" + + if json_logs: + callattr = getattr(verbose_logger, attr) + callattr( + "RAW RESPONSE:\n{}\n\n".format( + self.model_call_details.get( + "original_response", self.model_call_details + ) + ), + ) + else: + callattr = getattr(verbose_logger, attr) + callattr( + "RAW RESPONSE:\n{}\n\n".format( + self.model_call_details.get( + "original_response", self.model_call_details + ) + ) + ) + if getattr(self, "logger_fn", None) and callable(self.logger_fn): + try: + self.logger_fn( + self.model_call_details + ) # Expectation: any logger function passed in by the user should accept a dict object + except Exception as e: + verbose_logger.exception( + "LiteLLM.LoggingError: [Non-Blocking] Exception occurred while logging {}".format( + str(e) + ) + ) + original_response = redact_message_input_output_from_logging( + model_call_details=( + self.model_call_details + if hasattr(self, "model_call_details") + else {} + ), + result=original_response, + ) + # Input Integration Logging -> If you want to log the fact that an attempt to call the model was made + + callbacks = litellm.input_callback + (self.dynamic_input_callbacks or []) + for callback in callbacks: + try: + if callback == "sentry" and add_breadcrumb: + verbose_logger.debug("reaches sentry breadcrumbing") + try: + details_to_log = copy.deepcopy(self.model_call_details) + except Exception: + details_to_log = self.model_call_details + if litellm.turn_off_message_logging: + # make a copy of the _model_Call_details and log it + details_to_log.pop("messages", None) + details_to_log.pop("input", None) + details_to_log.pop("prompt", None) + + add_breadcrumb( + category="litellm.llm_call", + message=f"Model Call Details post-call: {details_to_log}", + level="info", + ) + elif isinstance(callback, CustomLogger): # custom logger class + callback.log_post_api_call( + kwargs=self.model_call_details, + response_obj=None, + start_time=self.start_time, + end_time=None, + ) + except Exception as e: + verbose_logger.exception( + "LiteLLM.LoggingError: [Non-Blocking] Exception occurred while post-call logging with integrations {}".format( + str(e) + ) + ) + verbose_logger.debug( + f"LiteLLM.Logging: is sentry capture exception initialized {capture_exception}" + ) + if capture_exception: # log this error to sentry for debugging + capture_exception(e) + except Exception as e: + verbose_logger.exception( + "LiteLLM.LoggingError: [Non-Blocking] Exception occurred while logging {}".format( + str(e) + ) + ) + + async def async_post_mcp_tool_call_hook( + self, + kwargs: dict, + response_obj: Any, + start_time: datetime.datetime, + end_time: datetime.datetime, + ): + """ + Post MCP Tool Call Hook + + Use this to modify the MCP tool call response before it is returned to the user. + """ + from litellm.types.llms.base import HiddenParams + from litellm.types.mcp import MCPPostCallResponseObject + + callbacks = self.get_combined_callback_list( + dynamic_success_callbacks=self.dynamic_success_callbacks, + global_callbacks=litellm.success_callback, + ) + post_mcp_tool_call_response_obj: MCPPostCallResponseObject = ( + MCPPostCallResponseObject( + mcp_tool_call_response=response_obj, hidden_params=HiddenParams() + ) + ) + for callback in callbacks: + try: + if isinstance(callback, CustomLogger): + response: Optional[ + MCPPostCallResponseObject + ] = await callback.async_post_mcp_tool_call_hook( + kwargs=kwargs, + response_obj=post_mcp_tool_call_response_obj, + start_time=start_time, + end_time=end_time, + ) + ###################################################################### + # if any of the callbacks modify the response, use the modified response + # current implementation returns the first modified response + ###################################################################### + if response is not None: + response_obj = self._parse_post_mcp_call_hook_response( + response=response + ) + except Exception as e: + verbose_logger.exception( + "LiteLLM.LoggingError: [Non-Blocking] Exception occurred while logging {}".format( + str(e) + ) + ) + return response_obj + + def _parse_post_mcp_call_hook_response( + self, response: Optional[MCPPostCallResponseObject] + ) -> Any: + """ + Parse the response from the post_mcp_tool_call_hook + + 1. Unpack the mcp_tool_call_response + 2. save the updated response_cost to the model_call_details + """ + if response is None: + return None + self.model_call_details["response_cost"] = response.hidden_params.response_cost + return response.mcp_tool_call_response + + def get_response_ms(self) -> float: + return ( + self.model_call_details.get("end_time", datetime.datetime.now()) + - self.model_call_details.get("start_time", datetime.datetime.now()) + ).total_seconds() * 1000 + + def set_cost_breakdown( + self, + input_cost: float, + output_cost: float, + total_cost: float, + cost_for_built_in_tools_cost_usd_dollar: float, + additional_costs: Optional[dict] = None, + original_cost: Optional[float] = None, + discount_percent: Optional[float] = None, + discount_amount: Optional[float] = None, + margin_percent: Optional[float] = None, + margin_fixed_amount: Optional[float] = None, + margin_total_amount: Optional[float] = None, + ) -> None: + """ + Helper method to store cost breakdown in the logging object. + + Args: + input_cost: Cost of input/prompt tokens + output_cost: Cost of output/completion tokens + cost_for_built_in_tools_cost_usd_dollar: Cost of built-in tools + total_cost: Total cost of request + additional_costs: Free-form additional costs dict (e.g., {"azure_model_router_flat_cost": 0.00014}) + original_cost: Cost before discount + discount_percent: Discount percentage (0.05 = 5%) + discount_amount: Discount amount in USD + margin_percent: Margin percentage applied (0.10 = 10%) + margin_fixed_amount: Fixed margin amount in USD + margin_total_amount: Total margin added in USD + """ + + self.cost_breakdown = CostBreakdown( + input_cost=input_cost, + output_cost=output_cost, + total_cost=total_cost, + tool_usage_cost=cost_for_built_in_tools_cost_usd_dollar, + ) + + # Store additional costs if provided (free-form dict for extensibility) + if ( + additional_costs + and isinstance(additional_costs, dict) + and len(additional_costs) > 0 + ): + self.cost_breakdown["additional_costs"] = additional_costs + + # Store discount information if provided + if original_cost is not None: + self.cost_breakdown["original_cost"] = original_cost + if discount_percent is not None: + self.cost_breakdown["discount_percent"] = discount_percent + if discount_amount is not None: + self.cost_breakdown["discount_amount"] = discount_amount + + # Store margin information if provided + if margin_percent is not None: + self.cost_breakdown["margin_percent"] = margin_percent + if margin_fixed_amount is not None: + self.cost_breakdown["margin_fixed_amount"] = margin_fixed_amount + if margin_total_amount is not None: + self.cost_breakdown["margin_total_amount"] = margin_total_amount + + def _response_cost_calculator( + self, + result: Union[ + ModelResponse, + ModelResponseStream, + EmbeddingResponse, + ImageResponse, + TranscriptionResponse, + TextCompletionResponse, + HttpxBinaryResponseContent, + RerankResponse, + Batch, + FineTuningJob, + ResponsesAPIResponse, + ResponseCompletedEvent, + OpenAIFileObject, + LiteLLMRealtimeStreamLoggingObject, + OpenAIModerationResponse, + "SearchResponse", + ], + cache_hit: Optional[bool] = None, + litellm_model_name: Optional[str] = None, + router_model_id: Optional[str] = None, + ) -> Optional[float]: + """ + Calculate response cost using result + logging object variables. + + used for consistent cost calculation across response headers + logging integrations. + """ + + if cache_hit is None: + cache_hit = self.model_call_details.get("cache_hit", False) + + if cache_hit is True: + return 0.0 + + if isinstance(result, BaseModel) and hasattr(result, "_hidden_params"): + hidden_params = getattr(result, "_hidden_params", {}) + if ( + "response_cost" in hidden_params + and hidden_params["response_cost"] is not None + ): # use cost if already calculated + return hidden_params["response_cost"] + elif ( + router_model_id is None and "model_id" in hidden_params + ): # use model_id if not already set + router_model_id = hidden_params["model_id"] + + ## RESPONSE COST ## + custom_pricing = use_custom_pricing_for_model( + litellm_params=( + self.litellm_params if hasattr(self, "litellm_params") else None + ) + ) + + prompt = "" # use for tts cost calc + _input = self.model_call_details.get("input", None) + if _input is not None and isinstance(_input, str): + prompt = _input + + if cache_hit is None: + cache_hit = self.model_call_details.get("cache_hit", False) + + try: + response_cost_calculator_kwargs = { + "response_object": result, + "model": litellm_model_name or self.model, + "cache_hit": cache_hit, + "custom_llm_provider": self.model_call_details.get( + "custom_llm_provider", None + ), + "base_model": _get_base_model_from_metadata( + model_call_details=self.model_call_details + ), + "call_type": self.call_type, + "optional_params": self.optional_params, + "custom_pricing": custom_pricing, + "prompt": prompt, + "standard_built_in_tools_params": self.standard_built_in_tools_params, + "router_model_id": router_model_id, + "litellm_logging_obj": self, + "service_tier": ( + self.optional_params.get("service_tier") + if self.optional_params + else None + ), + } + except Exception as e: # error creating kwargs for cost calculation + debug_info = StandardLoggingModelCostFailureDebugInformation( + error_str=str(e), + traceback_str=_get_traceback_str_for_error(str(e)), + ) + verbose_logger.debug( + f"response_cost_failure_debug_information: {debug_info}" + ) + self.model_call_details[ + "response_cost_failure_debug_information" + ] = debug_info + return None + + try: + response_cost = litellm.response_cost_calculator( + **response_cost_calculator_kwargs + ) + + verbose_logger.debug(f"response_cost: {response_cost}") + return response_cost + except Exception as e: # error calculating cost + debug_info = StandardLoggingModelCostFailureDebugInformation( + error_str=str(e), + traceback_str=_get_traceback_str_for_error(str(e)), + model=response_cost_calculator_kwargs["model"], + cache_hit=response_cost_calculator_kwargs["cache_hit"], + custom_llm_provider=response_cost_calculator_kwargs[ + "custom_llm_provider" + ], + base_model=response_cost_calculator_kwargs["base_model"], + call_type=response_cost_calculator_kwargs["call_type"], + custom_pricing=response_cost_calculator_kwargs["custom_pricing"], + ) + verbose_logger.debug( + f"response_cost_failure_debug_information: {debug_info}" + ) + self.model_call_details[ + "response_cost_failure_debug_information" + ] = debug_info + + return None + + async def _response_cost_calculator_async( + self, + result: Union[ + ModelResponse, + ModelResponseStream, + EmbeddingResponse, + ImageResponse, + TranscriptionResponse, + TextCompletionResponse, + HttpxBinaryResponseContent, + RerankResponse, + Batch, + FineTuningJob, + ], + cache_hit: Optional[bool] = None, + ) -> Optional[float]: + return self._response_cost_calculator(result=result, cache_hit=cache_hit) + + def should_run_logging( + self, + event_type: Literal[ + "async_success", "sync_success", "async_failure", "sync_failure" + ], + stream: bool = False, + ) -> bool: + try: + if self.model_call_details.get(f"has_logged_{event_type}", False) is True: + return False + + return True + except Exception: + return True + + def has_run_logging( + self, + event_type: Literal[ + "async_success", "sync_success", "async_failure", "sync_failure" + ], + ) -> None: + if self.stream is not None and self.stream is True: + """ + Ignore check on stream, as there can be multiple chunks + """ + return + self.model_call_details[f"has_logged_{event_type}"] = True + return + + def should_run_callback( + self, callback: litellm.CALLBACK_TYPES, litellm_params: dict, event_hook: str + ) -> bool: + if litellm.global_disable_no_log_param: + return True + + if litellm_params.get("no-log", False) is True: + # proxy cost tracking cal backs should run + + if not ( + isinstance(callback, CustomLogger) + and "_PROXY_" in callback.__class__.__name__ + ): + verbose_logger.debug( + f"no-log request, skipping logging for {event_hook} event" + ) + return False + + # Check for dynamically disabled callbacks via headers + if ( + EnterpriseCallbackControls is not None + and EnterpriseCallbackControls.is_callback_disabled_dynamically( + callback=callback, + litellm_params=litellm_params, + standard_callback_dynamic_params=self.standard_callback_dynamic_params, + ) + ): + verbose_logger.debug( + f"Callback {callback} disabled via x-litellm-disable-callbacks header for {event_hook} event" + ) + return False + + return True + + def _update_completion_start_time(self, completion_start_time: datetime.datetime): + self.completion_start_time = completion_start_time + self.model_call_details["completion_start_time"] = self.completion_start_time + + def normalize_logging_result(self, result: Any) -> Any: + """ + Some endpoints return a different type of result than what is expected by the logging system. + This function is used to normalize the result to the expected type. + """ + logging_result = result + if self.call_type == CallTypes.arealtime.value and isinstance(result, list): + combined_usage_object = RealtimeAPITokenUsageProcessor.collect_and_combine_usage_from_realtime_stream_results( + results=result + ) + logging_result = ( + RealtimeAPITokenUsageProcessor.create_logging_realtime_object( + usage=combined_usage_object, + results=result, + ) + ) + + elif ( + self.call_type == CallTypes.llm_passthrough_route.value + or self.call_type == CallTypes.allm_passthrough_route.value + ) and isinstance(result, Response): + from litellm.utils import ProviderConfigManager + + provider_config = ProviderConfigManager.get_provider_passthrough_config( + provider=self.model_call_details.get("custom_llm_provider", ""), + model=self.model, + ) + if provider_config is not None: + logging_result = provider_config.logging_non_streaming_response( + model=self.model, + custom_llm_provider=self.model_call_details.get( + "custom_llm_provider", "" + ), + httpx_response=result, + request_data=self.model_call_details.get("request_data", {}), + logging_obj=self, + endpoint=self.model_call_details.get("endpoint", ""), + ) + return logging_result + + def _process_hidden_params_and_response_cost( + self, + logging_result, + start_time, + end_time, + ): + hidden_params = getattr(logging_result, "_hidden_params", {}) + if hidden_params: + if self.model_call_details.get("litellm_params") is not None: + self.model_call_details["litellm_params"].setdefault("metadata", {}) + if self.model_call_details["litellm_params"]["metadata"] is None: + self.model_call_details["litellm_params"]["metadata"] = {} + self.model_call_details["litellm_params"]["metadata"]["hidden_params"] = getattr(logging_result, "_hidden_params", {}) # type: ignore + + if "response_cost" in hidden_params: + self.model_call_details["response_cost"] = hidden_params["response_cost"] + else: + self.model_call_details["response_cost"] = self._response_cost_calculator( + result=logging_result + ) + + self.model_call_details[ + "standard_logging_object" + ] = self._build_standard_logging_payload( + logging_result, start_time, end_time + ) + + if ( + standard_logging_payload := self.model_call_details.get( + "standard_logging_object" + ) + ) is not None: + emit_standard_logging_payload(standard_logging_payload) + + def _build_standard_logging_payload( + self, init_response_obj: Any, start_time: Any, end_time: Any + ) -> Any: + """Build StandardLoggingPayload and accumulate its construction time.""" + _start = time.time() + payload = get_standard_logging_object_payload( + kwargs=self.model_call_details, + init_response_obj=init_response_obj, + start_time=start_time, + end_time=end_time, + logging_obj=self, + status="success", + standard_built_in_tools_params=self.standard_built_in_tools_params, + ) + self.callback_duration_ms += (time.time() - _start) * 1000 + return payload + + def _transform_usage_objects(self, result): + if isinstance(result, ResponsesAPIResponse): + result = result.model_copy() + transformed_usage = ( + ResponseAPILoggingUtils._transform_response_api_usage_to_chat_usage( + result.usage + ) + ) + setattr(result, "usage", transformed_usage) + if ( + standard_logging_payload := self.model_call_details.get( + "standard_logging_object" + ) + ) is not None: + response_dict = ( + result.model_dump() + if hasattr(result, "model_dump") + else dict(result) + ) + # Ensure usage is properly included with transformed chat format + if transformed_usage is not None: + response_dict["usage"] = ( + transformed_usage.model_dump() + if hasattr(transformed_usage, "model_dump") + else dict(transformed_usage) + ) + standard_logging_payload["response"] = response_dict + elif isinstance(result, TranscriptionResponse): + from litellm.litellm_core_utils.llm_cost_calc.usage_object_transformation import ( + TranscriptionUsageObjectTransformation, + ) + + result = result.model_copy() + transformed_usage = TranscriptionUsageObjectTransformation.transform_transcription_usage_object(result.usage) # type: ignore + setattr(result, "usage", transformed_usage) + return result + + def _success_handler_helper_fn( + self, + result=None, + start_time=None, + end_time=None, + cache_hit=None, + standard_logging_object: Optional[StandardLoggingPayload] = None, + ): + try: + if start_time is None: + start_time = self.start_time + if end_time is None: + end_time = datetime.datetime.now() + if self.completion_start_time is None: + self.completion_start_time = end_time + self.model_call_details[ + "completion_start_time" + ] = self.completion_start_time + + self.model_call_details["log_event_type"] = "successful_api_call" + self.model_call_details["end_time"] = end_time + self.model_call_details["cache_hit"] = cache_hit + + if self.call_type == CallTypes.anthropic_messages.value: + result = self._handle_anthropic_messages_response_logging(result=result) + elif ( + self.call_type == CallTypes.generate_content.value + or self.call_type == CallTypes.agenerate_content.value + ): + result = self._handle_non_streaming_google_genai_generate_content_response_logging( + result=result + ) + elif ( + self.call_type == CallTypes.asend_message.value + or self.call_type == CallTypes.send_message.value + ): + result = self._handle_a2a_response_logging(result=result) + + logging_result = self.normalize_logging_result(result=result) + + if ( + standard_logging_object is None + and result is not None + and self.stream is not True + ): + if self._is_recognized_call_type_for_logging( + logging_result=logging_result + ): + self._process_hidden_params_and_response_cost( + logging_result=logging_result, + start_time=start_time, + end_time=end_time, + ) + elif isinstance(result, dict) or isinstance(result, list): + self.model_call_details[ + "standard_logging_object" + ] = self._build_standard_logging_payload( + result, start_time, end_time + ) + if ( + standard_logging_payload := self.model_call_details.get( + "standard_logging_object" + ) + ) is not None: + emit_standard_logging_payload(standard_logging_payload) + elif standard_logging_object is not None: + self.model_call_details[ + "standard_logging_object" + ] = standard_logging_object + else: + self.model_call_details["response_cost"] = None + + result = self._transform_usage_objects(result=result) + + if ( + litellm.max_budget + and self.stream is False + and result is not None + and isinstance(result, dict) + and "content" in result + ): + time_diff = (end_time - start_time).total_seconds() + float_diff = float(time_diff) + litellm._current_cost += litellm.completion_cost( + model=self.model, + prompt="", + completion=getattr(result, "content", ""), + total_time=float_diff, + standard_built_in_tools_params=self.standard_built_in_tools_params, + ) + + return start_time, end_time, result + except Exception as e: + raise Exception(f"[Non-Blocking] LiteLLM.Success_Call Error: {str(e)}") + + def _is_recognized_call_type_for_logging( + self, + logging_result: Any, + ): + """ + Returns True if the call type is recognized for logging (eg. ModelResponse, ModelResponseStream, etc.) + """ + if ( + isinstance(logging_result, ModelResponse) + or isinstance(logging_result, ModelResponseStream) + or isinstance(logging_result, EmbeddingResponse) + or isinstance(logging_result, ImageResponse) + or isinstance(logging_result, TranscriptionResponse) + or isinstance(logging_result, TextCompletionResponse) + or isinstance(logging_result, HttpxBinaryResponseContent) # tts + or isinstance(logging_result, RerankResponse) + or isinstance(logging_result, FineTuningJob) + or isinstance(logging_result, LiteLLMBatch) + or isinstance(logging_result, ResponsesAPIResponse) + or isinstance(logging_result, OpenAIFileObject) + or isinstance(logging_result, LiteLLMRealtimeStreamLoggingObject) + or isinstance(logging_result, OpenAIModerationResponse) + or isinstance(logging_result, OCRResponse) # OCR + or isinstance(logging_result, SearchResponse) # Search API + or isinstance(logging_result, dict) + and logging_result.get("object") == "vector_store.search_results.page" + or isinstance(logging_result, dict) + and logging_result.get("object") == "search" # Search API (dict format) + or isinstance(logging_result, VideoObject) + or isinstance(logging_result, ContainerObject) + or isinstance(logging_result, LiteLLMSendMessageResponse) # A2A + or (self.call_type == CallTypes.call_mcp_tool.value) + ): + return True + return False + + def _flush_passthrough_collected_chunks_helper( + self, + raw_bytes: List[bytes], + provider_config: "BasePassthroughConfig", + ) -> Optional["CostResponseTypes"]: + all_chunks = provider_config._convert_raw_bytes_to_str_lines(raw_bytes) + complete_streaming_response = provider_config.handle_logging_collected_chunks( + all_chunks=all_chunks, + litellm_logging_obj=self, + model=self.model, + custom_llm_provider=self.model_call_details.get("custom_llm_provider", ""), + endpoint=self.model_call_details.get("endpoint", ""), + ) + return complete_streaming_response + + def flush_passthrough_collected_chunks( + self, + raw_bytes: List[bytes], + provider_config: "BasePassthroughConfig", + ): + """ + Flush collected chunks from the logging object + This is used to log the collected chunks once streaming is done on passthrough endpoints + + 1. Decode the raw bytes to string lines + 2. Get the complete streaming response from the provider config + 3. Log the complete streaming response (trigger success handler) + This is used for passthrough endpoints + """ + complete_streaming_response = self._flush_passthrough_collected_chunks_helper( + raw_bytes=raw_bytes, + provider_config=provider_config, + ) + + if complete_streaming_response is not None: + self.success_handler(result=complete_streaming_response) + return + + async def async_flush_passthrough_collected_chunks( + self, + raw_bytes: List[bytes], + provider_config: "BasePassthroughConfig", + ): + complete_streaming_response = self._flush_passthrough_collected_chunks_helper( + raw_bytes=raw_bytes, + provider_config=provider_config, + ) + + if complete_streaming_response is not None: + await self.async_success_handler(result=complete_streaming_response) + return + + def success_handler( # noqa: PLR0915 + self, result=None, start_time=None, end_time=None, cache_hit=None, **kwargs + ): + verbose_logger.debug( + f"Logging Details LiteLLM-Success Call: Cache_hit={cache_hit}" + ) + if not self.should_run_logging( + event_type="sync_success" + ): # prevent double logging + return + start_time, end_time, result = self._success_handler_helper_fn( + start_time=start_time, + end_time=end_time, + result=result, + cache_hit=cache_hit, + standard_logging_object=kwargs.get("standard_logging_object", None), + ) + litellm_params = self.model_call_details.get("litellm_params", {}) + is_sync_request = ( + litellm_params.get(CallTypes.acompletion.value, False) is not True + and litellm_params.get(CallTypes.aresponses.value, False) is not True + and litellm_params.get(CallTypes.aembedding.value, False) is not True + and litellm_params.get(CallTypes.aimage_generation.value, False) is not True + and litellm_params.get(CallTypes.atranscription.value, False) is not True + ) + try: + ## BUILD COMPLETE STREAMED RESPONSE + complete_streaming_response: Optional[ + Union[ModelResponse, TextCompletionResponse, ResponsesAPIResponse] + ] = None + if "complete_streaming_response" in self.model_call_details: + return # break out of this. + complete_streaming_response = self._get_assembled_streaming_response( + result=result, + start_time=start_time, + end_time=end_time, + is_async=False, + streaming_chunks=self.sync_streaming_chunks, + ) + if complete_streaming_response is not None: + verbose_logger.debug( + "Logging Details LiteLLM-Success Call streaming complete" + ) + self.model_call_details[ + "complete_streaming_response" + ] = complete_streaming_response + self.model_call_details[ + "response_cost" + ] = self._response_cost_calculator(result=complete_streaming_response) + ## STANDARDIZED LOGGING PAYLOAD + self.model_call_details[ + "standard_logging_object" + ] = self._build_standard_logging_payload( + complete_streaming_response, start_time, end_time + ) + if ( + standard_logging_payload := self.model_call_details.get( + "standard_logging_object" + ) + ) is not None: + # Only emit for sync requests (async_success_handler handles async) + if is_sync_request: + emit_standard_logging_payload(standard_logging_payload) + callbacks = self.get_combined_callback_list( + dynamic_success_callbacks=self.dynamic_success_callbacks, + global_callbacks=litellm.success_callback, + ) + + ## REDACT MESSAGES ## + result = redact_message_input_output_from_logging( + model_call_details=( + self.model_call_details + if hasattr(self, "model_call_details") + else {} + ), + result=result, + ) + ## LOGGING HOOK ## + for callback in callbacks: + if isinstance(callback, CustomGuardrail): + from litellm.types.guardrails import GuardrailEventHooks + + if ( + callback.should_run_guardrail( + data=self.model_call_details, + event_type=GuardrailEventHooks.logging_only, + ) + is not True + ): + continue + + self.model_call_details, result = callback.logging_hook( + kwargs=self.model_call_details, + result=result, + call_type=self.call_type, + ) + elif isinstance(callback, CustomLogger): + self.model_call_details, result = callback.logging_hook( + kwargs=self.model_call_details, + result=result, + call_type=self.call_type, + ) + + self.has_run_logging(event_type="sync_success") + for callback in callbacks: + try: + should_run = self.should_run_callback( + callback=callback, + litellm_params=litellm_params, + event_hook="success_handler", + ) + if not should_run: + continue + if callback == "promptlayer" and promptLayerLogger is not None: + print_verbose("reaches promptlayer for logging!") + promptLayerLogger.log_event( + kwargs=self.model_call_details, + response_obj=result, + start_time=start_time, + end_time=end_time, + print_verbose=print_verbose, + ) + if callback == "supabase" and supabaseClient is not None: + print_verbose("reaches supabase for logging!") + kwargs = self.model_call_details + + # this only logs streaming once, complete_streaming_response exists i.e when stream ends + if self.stream: + if "complete_streaming_response" not in kwargs: + continue + else: + print_verbose("reaches supabase for streaming logging!") + result = kwargs["complete_streaming_response"] + + model = kwargs["model"] + messages = kwargs["messages"] + optional_params = kwargs.get("optional_params", {}) + litellm_params = kwargs.get("litellm_params", {}) + supabaseClient.log_event( + model=model, + messages=messages, + end_user=optional_params.get("user", "default"), + response_obj=result, + start_time=start_time, + end_time=end_time, + litellm_call_id=( + current_call_id + if ( + current_call_id := litellm_params.get( + "litellm_call_id" + ) + ) + is not None + else str(uuid.uuid4()) + ), + print_verbose=print_verbose, + ) + if callback == "wandb" and weightsBiasesLogger is not None: + print_verbose("reaches wandb for logging!") + weightsBiasesLogger.log_event( + kwargs=self.model_call_details, + response_obj=result, + start_time=start_time, + end_time=end_time, + print_verbose=print_verbose, + ) + if callback == "logfire" and logfireLogger is not None: + verbose_logger.debug("reaches logfire for success logging!") + kwargs = {} + for k, v in self.model_call_details.items(): + if ( + k != "original_response" + ): # copy.deepcopy raises errors as this could be a coroutine + kwargs[k] = v + + # this only logs streaming once, complete_streaming_response exists i.e when stream ends + if self.stream: + if "complete_streaming_response" not in kwargs: + continue + else: + print_verbose("reaches logfire for streaming logging!") + result = kwargs["complete_streaming_response"] + + logfireLogger.log_event( + kwargs=self.model_call_details, + response_obj=result, + start_time=start_time, + end_time=end_time, + print_verbose=print_verbose, + level=LogfireLevel.INFO.value, # type: ignore + ) + + if callback == "lunary" and lunaryLogger is not None: + print_verbose("reaches lunary for logging!") + model = self.model + kwargs = self.model_call_details + + input = kwargs.get("messages", kwargs.get("input", None)) + + type = ( + "embed" + if self.call_type == CallTypes.embedding.value + else "llm" + ) + + # this only logs streaming once, complete_streaming_response exists i.e when stream ends + if self.stream: + if "complete_streaming_response" not in kwargs: + continue + else: + result = kwargs["complete_streaming_response"] + + lunaryLogger.log_event( + type=type, + kwargs=kwargs, + event="end", + model=model, + input=input, + user_id=kwargs.get("user", None), + # user_props=self.model_call_details.get("user_props", None), + extra=kwargs.get("optional_params", {}), + response_obj=result, + start_time=start_time, + end_time=end_time, + run_id=self.litellm_call_id, + print_verbose=print_verbose, + ) + if callback == "helicone" and heliconeLogger is not None: + print_verbose("reaches helicone for logging!") + model = self.model + messages = self.model_call_details["input"] + kwargs = self.model_call_details + + # this only logs streaming once, complete_streaming_response exists i.e when stream ends + if self.stream: + if "complete_streaming_response" not in kwargs: + continue + else: + print_verbose("reaches helicone for streaming logging!") + result = kwargs["complete_streaming_response"] + + heliconeLogger.log_success( + model=model, + messages=messages, + response_obj=result, + start_time=start_time, + end_time=end_time, + print_verbose=print_verbose, + kwargs=kwargs, + ) + if callback == "langfuse": + global langFuseLogger + print_verbose("reaches langfuse for success logging!") + kwargs = {} + for k, v in self.model_call_details.items(): + if ( + k != "original_response" + ): # copy.deepcopy raises errors as this could be a coroutine + kwargs[k] = v + # this only logs streaming once, complete_streaming_response exists i.e when stream ends + if self.stream: + verbose_logger.debug( + f"is complete_streaming_response in kwargs: {kwargs.get('complete_streaming_response', None)}" + ) + if complete_streaming_response is None: + continue + else: + print_verbose("reaches langfuse for streaming logging!") + result = kwargs["complete_streaming_response"] + + langfuse_logger_to_use = LangFuseHandler.get_langfuse_logger_for_request( + globalLangfuseLogger=langFuseLogger, + standard_callback_dynamic_params=self.standard_callback_dynamic_params, + in_memory_dynamic_logger_cache=in_memory_dynamic_logger_cache, + ) + if langfuse_logger_to_use is not None: + _response = langfuse_logger_to_use.log_event_on_langfuse( + kwargs=kwargs, + response_obj=result, + start_time=start_time, + end_time=end_time, + user_id=kwargs.get("user", None), + ) + if _response is not None and isinstance(_response, dict): + _trace_id = _response.get("trace_id", None) + if _trace_id is not None: + in_memory_trace_id_cache.set_cache( + litellm_call_id=self.litellm_call_id, + service_name="langfuse", + trace_id=_trace_id, + ) + if callback == "greenscale" and greenscaleLogger is not None: + kwargs = {} + for k, v in self.model_call_details.items(): + if ( + k != "original_response" + ): # copy.deepcopy raises errors as this could be a coroutine + kwargs[k] = v + # this only logs streaming once, complete_streaming_response exists i.e when stream ends + if self.stream: + verbose_logger.debug( + f"is complete_streaming_response in kwargs: {kwargs.get('complete_streaming_response', None)}" + ) + if complete_streaming_response is None: + continue + else: + print_verbose( + "reaches greenscale for streaming logging!" + ) + result = kwargs["complete_streaming_response"] + + greenscaleLogger.log_event( + kwargs=kwargs, + response_obj=result, + start_time=start_time, + end_time=end_time, + print_verbose=print_verbose, + ) + if callback == "athina" and athinaLogger is not None: + deep_copy = {} + for k, v in self.model_call_details.items(): + deep_copy[k] = v + athinaLogger.log_event( + kwargs=deep_copy, + response_obj=result, + start_time=start_time, + end_time=end_time, + print_verbose=print_verbose, + ) + if callback == "traceloop": + deep_copy = {} + for k, v in self.model_call_details.items(): + if k != "original_response": + deep_copy[k] = v + traceloopLogger.log_event( + kwargs=deep_copy, + response_obj=result, + start_time=start_time, + end_time=end_time, + user_id=kwargs.get("user", None), + print_verbose=print_verbose, + ) + if callback == "s3": + global s3Logger + if s3Logger is None: + s3Logger = S3Logger() + if self.stream: + if "complete_streaming_response" in self.model_call_details: + print_verbose( + "S3Logger Logger: Got Stream Event - Completed Stream Response" + ) + s3Logger.log_event( + kwargs=self.model_call_details, + response_obj=self.model_call_details[ + "complete_streaming_response" + ], + start_time=start_time, + end_time=end_time, + print_verbose=print_verbose, + ) + else: + print_verbose( + "S3Logger Logger: Got Stream Event - No complete stream response as yet" + ) + else: + s3Logger.log_event( + kwargs=self.model_call_details, + response_obj=result, + start_time=start_time, + end_time=end_time, + print_verbose=print_verbose, + ) + + if callback == "openmeter" and is_sync_request: + global openMeterLogger + if openMeterLogger is None: + print_verbose("Instantiates openmeter client") + openMeterLogger = OpenMeterLogger() + if self.stream and complete_streaming_response is None: + openMeterLogger.log_stream_event( + kwargs=self.model_call_details, + response_obj=result, + start_time=start_time, + end_time=end_time, + ) + else: + if self.stream and complete_streaming_response: + self.model_call_details[ + "complete_response" + ] = self.model_call_details.get( + "complete_streaming_response", {} + ) + result = self.model_call_details["complete_response"] + openMeterLogger.log_success_event( + kwargs=self.model_call_details, + response_obj=result, + start_time=start_time, + end_time=end_time, + ) + if ( + isinstance(callback, CustomLogger) + and is_sync_request + and self.call_type + != CallTypes.pass_through.value # pass-through endpoints call async_log_success_event + ): # custom logger class + if self.stream and complete_streaming_response is None: + callback.log_stream_event( + kwargs=self.model_call_details, + response_obj=result, + start_time=start_time, + end_time=end_time, + ) + else: + if self.stream and complete_streaming_response: + self.model_call_details[ + "complete_response" + ] = self.model_call_details.get( + "complete_streaming_response", {} + ) + result = self.model_call_details["complete_response"] + + callback.log_success_event( + kwargs=self.model_call_details, + response_obj=result, + start_time=start_time, + end_time=end_time, + ) + if ( + callable(callback) is True + and is_sync_request + and customLogger is not None + ): # custom logger functions + print_verbose( + "success callbacks: Running Custom Callback Function - {}".format( + callback + ) + ) + + customLogger.log_event( + kwargs=self.model_call_details, + response_obj=result, + start_time=start_time, + end_time=end_time, + print_verbose=print_verbose, + callback_func=callback, + ) + + except Exception as e: + print_verbose( + f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while success logging with integrations {traceback.format_exc()}" + ) + print_verbose( + f"LiteLLM.Logging: is sentry capture exception initialized {capture_exception}" + ) + if capture_exception: # log this error to sentry for debugging + capture_exception(e) + # Track callback logging failures in Prometheus + try: + self._handle_callback_failure(callback=callback) + except Exception: + pass + except Exception as e: + verbose_logger.exception( + "LiteLLM.LoggingError: [Non-Blocking] Exception occurred while success logging {}".format( + str(e) + ), + ) + + async def async_success_handler( # noqa: PLR0915 + self, result=None, start_time=None, end_time=None, cache_hit=None, **kwargs + ): + """ + Implementing async callbacks, to handle asyncio event loop issues when custom integrations need to use async functions. + """ + print_verbose( + "Logging Details LiteLLM-Async Success Call, cache_hit={}".format(cache_hit) + ) + if not self.should_run_logging( + event_type="async_success" + ): # prevent double logging + return + + ## CALCULATE COST FOR BATCH JOBS + if self.call_type == CallTypes.aretrieve_batch.value and isinstance( + result, LiteLLMBatch + ): + litellm_params = self.litellm_params or {} + litellm_metadata = litellm_params.get("litellm_metadata") or {} + if ( + litellm_metadata.get("batch_ignore_default_logging", False) is True + ): # polling job will query these frequently, don't spam db logs + return + + from litellm.proxy.openai_files_endpoints.common_utils import ( + _is_base64_encoded_unified_file_id, + ) + + # check if file id is a unified file id + is_base64_unified_file_id = _is_base64_encoded_unified_file_id(result.id) + + batch_cost = kwargs.get("batch_cost", None) + batch_usage = kwargs.get("batch_usage", None) + batch_models = kwargs.get("batch_models", None) + has_explicit_batch_data = all( + x is not None for x in (batch_cost, batch_usage, batch_models) + ) + + should_compute_batch_data = ( + not is_base64_unified_file_id + or not has_explicit_batch_data + and result.status == "completed" + ) + if has_explicit_batch_data: + result._hidden_params["response_cost"] = batch_cost + result._hidden_params["batch_models"] = batch_models + result.usage = batch_usage + + elif should_compute_batch_data: + ( + response_cost, + batch_usage, + batch_models, + ) = await _handle_completed_batch( + batch=result, + custom_llm_provider=self.custom_llm_provider, + litellm_params=self.litellm_params, + ) + + result._hidden_params["response_cost"] = response_cost + result._hidden_params["batch_models"] = batch_models + result.usage = batch_usage + + start_time, end_time, result = self._success_handler_helper_fn( + start_time=start_time, + end_time=end_time, + result=result, + cache_hit=cache_hit, + standard_logging_object=kwargs.get("standard_logging_object", None), + ) + + ## BUILD COMPLETE STREAMED RESPONSE + if "async_complete_streaming_response" in self.model_call_details: + return # break out of this. + complete_streaming_response: Optional[ + Union[ModelResponse, TextCompletionResponse, ResponsesAPIResponse] + ] = self._get_assembled_streaming_response( + result=result, + start_time=start_time, + end_time=end_time, + is_async=True, + streaming_chunks=self.streaming_chunks, + ) + + if complete_streaming_response is not None: + print_verbose("Async success callbacks: Got a complete streaming response") + + self.model_call_details[ + "async_complete_streaming_response" + ] = complete_streaming_response + + try: + if self.model_call_details.get("cache_hit", False) is True: + self.model_call_details["response_cost"] = 0.0 + else: + # check if base_model set on azure + _get_base_model_from_metadata( + model_call_details=self.model_call_details + ) + # base_model defaults to None if not set on model_info + self.model_call_details[ + "response_cost" + ] = self._response_cost_calculator( + result=complete_streaming_response + ) + + verbose_logger.debug( + f"Model={self.model}; cost={self.model_call_details['response_cost']}" + ) + except litellm.NotFoundError: + verbose_logger.warning( + f"Model={self.model} not found in completion cost map. Setting 'response_cost' to None" + ) + self.model_call_details["response_cost"] = None + + ## STANDARDIZED LOGGING PAYLOAD + self.model_call_details[ + "standard_logging_object" + ] = self._build_standard_logging_payload( + complete_streaming_response, start_time, end_time + ) + + # print standard logging payload + if ( + standard_logging_payload := self.model_call_details.get( + "standard_logging_object" + ) + ) is not None: + emit_standard_logging_payload(standard_logging_payload) + elif self.call_type == "pass_through_endpoint": + print_verbose( + "Async success callbacks: Got a pass-through endpoint response" + ) + + self.model_call_details["async_complete_streaming_response"] = result + + # cost calculation not possible for pass-through + self.model_call_details["response_cost"] = None + + ## STANDARDIZED LOGGING PAYLOAD + self.model_call_details[ + "standard_logging_object" + ] = self._build_standard_logging_payload( + result, start_time, end_time + ) + + # print standard logging payload + if ( + standard_logging_payload := self.model_call_details.get( + "standard_logging_object" + ) + ) is not None: + emit_standard_logging_payload(standard_logging_payload) + callbacks = self.get_combined_callback_list( + dynamic_success_callbacks=self.dynamic_async_success_callbacks, + global_callbacks=litellm._async_success_callback, + ) + + result = redact_message_input_output_from_logging( + model_call_details=( + self.model_call_details if hasattr(self, "model_call_details") else {} + ), + result=result, + ) + + ## LOGGING HOOK ## + + for callback in callbacks: + if isinstance(callback, CustomGuardrail): + from litellm.types.guardrails import GuardrailEventHooks + + if ( + callback.should_run_guardrail( + data=self.model_call_details, + event_type=GuardrailEventHooks.logging_only, + ) + is not True + ): + continue + + self.model_call_details, result = await callback.async_logging_hook( + kwargs=self.model_call_details, + result=result, + call_type=self.call_type, + ) + elif isinstance(callback, CustomLogger): + result = redact_message_input_output_from_custom_logger( + result=result, litellm_logging_obj=self, custom_logger=callback + ) + self.model_call_details, result = await callback.async_logging_hook( + kwargs=self.model_call_details, + result=result, + call_type=self.call_type, + ) + + self.has_run_logging(event_type="async_success") + + for callback in callbacks: + # check if callback can run for this request + litellm_params = self.model_call_details.get("litellm_params", {}) + should_run = self.should_run_callback( + callback=callback, + litellm_params=litellm_params, + event_hook="async_success_handler", + ) + if not should_run: + continue + try: + if callback == "openmeter" and openMeterLogger is not None: + if self.stream is True: + if ( + "async_complete_streaming_response" + in self.model_call_details + ): + await openMeterLogger.async_log_success_event( + kwargs=self.model_call_details, + response_obj=self.model_call_details[ + "async_complete_streaming_response" + ], + start_time=start_time, + end_time=end_time, + ) + else: + await openMeterLogger.async_log_stream_event( # [TODO]: move this to being an async log stream event function + kwargs=self.model_call_details, + response_obj=result, + start_time=start_time, + end_time=end_time, + ) + else: + await openMeterLogger.async_log_success_event( + kwargs=self.model_call_details, + response_obj=result, + start_time=start_time, + end_time=end_time, + ) + + if isinstance(callback, CustomLogger): # custom logger class + model_call_details: Dict = self.model_call_details + ################################## + # call redaction hook for custom logger + model_call_details = callback.redact_standard_logging_payload_from_model_call_details( + model_call_details=model_call_details + ) + ################################## + if self.stream is True: + if "async_complete_streaming_response" in model_call_details: + await callback.async_log_success_event( + kwargs=model_call_details, + response_obj=model_call_details[ + "async_complete_streaming_response" + ], + start_time=start_time, + end_time=end_time, + ) + else: + await callback.async_log_stream_event( # [TODO]: move this to being an async log stream event function + kwargs=model_call_details, + response_obj=result, + start_time=start_time, + end_time=end_time, + ) + else: + await callback.async_log_success_event( + kwargs=model_call_details, + response_obj=result, + start_time=start_time, + end_time=end_time, + ) + if callable(callback): # custom logger functions + global customLogger + if customLogger is None: + customLogger = CustomLogger() + if self.stream: + if ( + "async_complete_streaming_response" + in self.model_call_details + ): + await customLogger.async_log_event( + kwargs=self.model_call_details, + response_obj=self.model_call_details[ + "async_complete_streaming_response" + ], + start_time=start_time, + end_time=end_time, + print_verbose=print_verbose, + callback_func=callback, + ) + else: + await customLogger.async_log_event( + kwargs=self.model_call_details, + response_obj=result, + start_time=start_time, + end_time=end_time, + print_verbose=print_verbose, + callback_func=callback, + ) + if callback == "dynamodb": + global dynamoLogger + if dynamoLogger is None: + dynamoLogger = DyanmoDBLogger() + if self.stream: + if ( + "async_complete_streaming_response" + in self.model_call_details + ): + print_verbose( + "DynamoDB Logger: Got Stream Event - Completed Stream Response" + ) + await dynamoLogger._async_log_event( + kwargs=self.model_call_details, + response_obj=self.model_call_details[ + "async_complete_streaming_response" + ], + start_time=start_time, + end_time=end_time, + print_verbose=print_verbose, + ) + else: + print_verbose( + "DynamoDB Logger: Got Stream Event - No complete stream response as yet" + ) + else: + await dynamoLogger._async_log_event( + kwargs=self.model_call_details, + response_obj=result, + start_time=start_time, + end_time=end_time, + print_verbose=print_verbose, + ) + except Exception: + verbose_logger.error( + f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while success logging {traceback.format_exc()}" + ) + self._handle_callback_failure(callback=callback) + pass + + def _handle_callback_failure(self, callback: Any): + """ + Handle callback logging failures by incrementing Prometheus metrics. + + Works for both sync and async contexts since Prometheus counter increment is synchronous. + + Args: + callback: The callback that failed + """ + try: + callback_name = self._get_callback_name(callback) + + all_callbacks = litellm.logging_callback_manager._get_all_callbacks() + + for callback_obj in all_callbacks: + if hasattr(callback_obj, "increment_callback_logging_failure"): + callback_obj.increment_callback_logging_failure(callback_name=callback_name) # type: ignore + break # Only increment once + + except Exception as e: + verbose_logger.debug(f"Error in _handle_callback_failure: {str(e)}") + + def _failure_handler_helper_fn( + self, exception, traceback_exception, start_time=None, end_time=None + ): + if start_time is None: + start_time = self.start_time + if end_time is None: + end_time = datetime.datetime.now() + + # on some exceptions, model_call_details is not always initialized, this ensures that we still log those exceptions + if not hasattr(self, "model_call_details"): + self.model_call_details = {} + + self.model_call_details["log_event_type"] = "failed_api_call" + self.model_call_details["exception"] = exception + self.model_call_details["traceback_exception"] = traceback_exception + self.model_call_details["end_time"] = end_time + self.model_call_details.setdefault("original_response", None) + self.model_call_details["response_cost"] = 0 + + if hasattr(exception, "headers") and isinstance(exception.headers, dict): + self.model_call_details.setdefault("litellm_params", {}) + metadata = ( + self.model_call_details["litellm_params"].get("metadata", {}) or {} + ) + metadata.update(exception.headers) + + ## STANDARDIZED LOGGING PAYLOAD + + self.model_call_details[ + "standard_logging_object" + ] = get_standard_logging_object_payload( + kwargs=self.model_call_details, + init_response_obj={}, + start_time=start_time, + end_time=end_time, + logging_obj=self, + status="failure", + error_str=str(exception), + original_exception=exception, + standard_built_in_tools_params=self.standard_built_in_tools_params, + ) + return start_time, end_time + + async def special_failure_handlers(self, exception: Exception): + """ + Custom events, emitted for specific failures. + + Currently just for router model group rate limit error + """ + from litellm.types.router import RouterErrors + + litellm_params: dict = self.model_call_details.get("litellm_params") or {} + metadata = litellm_params.get("metadata") or {} + + ## BASE CASE ## check if rate limit error for model group size 1 + is_base_case = False + if metadata.get("model_group_size") is not None: + model_group_size = metadata.get("model_group_size") + if isinstance(model_group_size, int) and model_group_size == 1: + is_base_case = True + ## check if special error ## + if ( + RouterErrors.no_deployments_available.value not in str(exception) + and is_base_case is False + ): + return + + ## get original model group ## + + model_group = metadata.get("model_group") or None + for callback in litellm._async_failure_callback: + if isinstance(callback, CustomLogger): # custom logger class + await callback.log_model_group_rate_limit_error( + exception=exception, + original_model_group=model_group, + kwargs=self.model_call_details, + ) # type: ignore + + def failure_handler( # noqa: PLR0915 + self, exception, traceback_exception, start_time=None, end_time=None + ): + verbose_logger.debug( + f"Logging Details LiteLLM-Failure Call: {litellm.failure_callback}" + ) + if not self.should_run_logging( + event_type="sync_failure" + ): # prevent double logging + return + litellm_params = self.model_call_details.get("litellm_params", {}) + is_sync_request = ( + litellm_params.get(CallTypes.acompletion.value, False) is not True + and litellm_params.get(CallTypes.aresponses.value, False) is not True + and litellm_params.get(CallTypes.aembedding.value, False) is not True + and litellm_params.get(CallTypes.aimage_generation.value, False) is not True + and litellm_params.get(CallTypes.atranscription.value, False) is not True + ) + + try: + start_time, end_time = self._failure_handler_helper_fn( + exception=exception, + traceback_exception=traceback_exception, + start_time=start_time, + end_time=end_time, + ) + callbacks = self.get_combined_callback_list( + dynamic_success_callbacks=self.dynamic_failure_callbacks, + global_callbacks=litellm.failure_callback, + ) + + result = None # result sent to all loggers, init this to None incase it's not created + + result = redact_message_input_output_from_logging( + model_call_details=( + self.model_call_details + if hasattr(self, "model_call_details") + else {} + ), + result=result, + ) + self.has_run_logging(event_type="sync_failure") + for callback in callbacks: + try: + should_run = self.should_run_callback( + callback=callback, + litellm_params=litellm_params, + event_hook="failure_handler", + ) + if not should_run: + continue + if callback == "lunary" and lunaryLogger is not None: + print_verbose("reaches lunary for logging error!") + + model = self.model + + input = self.model_call_details["input"] + + _type = ( + "embed" + if self.call_type == CallTypes.embedding.value + else "llm" + ) + + lunaryLogger.log_event( + kwargs=self.model_call_details, + type=_type, + event="error", + user_id=self.model_call_details.get("user", "default"), + model=model, + input=input, + error=traceback_exception, + run_id=self.litellm_call_id, + start_time=start_time, + end_time=end_time, + print_verbose=print_verbose, + ) + if callback == "sentry": + print_verbose("sending exception to sentry") + if capture_exception: + capture_exception(exception) + else: + print_verbose( + f"capture exception not initialized: {capture_exception}" + ) + elif callback == "supabase" and supabaseClient is not None: + print_verbose("reaches supabase for logging!") + print_verbose(f"supabaseClient: {supabaseClient}") + supabaseClient.log_event( + model=self.model if hasattr(self, "model") else "", + messages=self.messages, + end_user=self.model_call_details.get("user", "default"), + response_obj=result, + start_time=start_time, + end_time=end_time, + litellm_call_id=self.model_call_details["litellm_call_id"], + print_verbose=print_verbose, + ) + if ( + callable(callback) and customLogger is not None + ): # custom logger functions + customLogger.log_event( + kwargs=self.model_call_details, + response_obj=result, + start_time=start_time, + end_time=end_time, + print_verbose=print_verbose, + callback_func=callback, + ) + if ( + isinstance(callback, CustomLogger) and is_sync_request + ): # custom logger class + callback.log_failure_event( + start_time=start_time, + end_time=end_time, + response_obj=result, + kwargs=self.model_call_details, + ) + if callback == "langfuse": + global langFuseLogger + verbose_logger.debug("reaches langfuse for logging failure") + kwargs = {} + for k, v in self.model_call_details.items(): + if ( + k != "original_response" + ): # copy.deepcopy raises errors as this could be a coroutine + kwargs[k] = v + # this only logs streaming once, complete_streaming_response exists i.e when stream ends + langfuse_logger_to_use = LangFuseHandler.get_langfuse_logger_for_request( + globalLangfuseLogger=langFuseLogger, + standard_callback_dynamic_params=self.standard_callback_dynamic_params, + in_memory_dynamic_logger_cache=in_memory_dynamic_logger_cache, + ) + _response = langfuse_logger_to_use.log_event_on_langfuse( + start_time=start_time, + end_time=end_time, + response_obj=None, + user_id=kwargs.get("user", None), + status_message=str(exception), + level="ERROR", + kwargs=self.model_call_details, + ) + if _response is not None and isinstance(_response, dict): + _trace_id = _response.get("trace_id", None) + if _trace_id is not None: + in_memory_trace_id_cache.set_cache( + litellm_call_id=self.litellm_call_id, + service_name="langfuse", + trace_id=_trace_id, + ) + if callback == "traceloop": + traceloopLogger.log_event( + start_time=start_time, + end_time=end_time, + response_obj=None, + user_id=self.model_call_details.get("user", None), + print_verbose=print_verbose, + status_message=str(exception), + level="ERROR", + kwargs=self.model_call_details, + ) + if callback == "logfire" and logfireLogger is not None: + verbose_logger.debug("reaches logfire for failure logging!") + kwargs = {} + for k, v in self.model_call_details.items(): + if ( + k != "original_response" + ): # copy.deepcopy raises errors as this could be a coroutine + kwargs[k] = v + kwargs["exception"] = exception + + logfireLogger.log_event( + kwargs=kwargs, + response_obj=result, + start_time=start_time, + end_time=end_time, + level=LogfireLevel.ERROR.value, # type: ignore + print_verbose=print_verbose, + ) + + except Exception as e: + print_verbose( + f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while failure logging with integrations {str(e)}" + ) + print_verbose( + f"LiteLLM.Logging: is sentry capture exception initialized {capture_exception}" + ) + if capture_exception: # log this error to sentry for debugging + capture_exception(e) + except Exception as e: + verbose_logger.exception( + "LiteLLM.LoggingError: [Non-Blocking] Exception occurred while failure logging {}".format( + str(e) + ) + ) + + async def async_failure_handler( + self, exception, traceback_exception, start_time=None, end_time=None + ): + """ + Implementing async callbacks, to handle asyncio event loop issues when custom integrations need to use async functions. + """ + await self.special_failure_handlers(exception=exception) + if not self.should_run_logging( + event_type="async_failure" + ): # prevent double logging + return + start_time, end_time = self._failure_handler_helper_fn( + exception=exception, + traceback_exception=traceback_exception, + start_time=start_time, + end_time=end_time, + ) + + callbacks = self.get_combined_callback_list( + dynamic_success_callbacks=self.dynamic_async_failure_callbacks, + global_callbacks=litellm._async_failure_callback, + ) + + result = None # result sent to all loggers, init this to None incase it's not created + + self.has_run_logging(event_type="async_failure") + for callback in callbacks: + try: + litellm_params = self.model_call_details.get("litellm_params", {}) + should_run = self.should_run_callback( + callback=callback, + litellm_params=litellm_params, + event_hook="async_failure_handler", + ) + if not should_run: + continue + if isinstance(callback, CustomLogger): # custom logger class + await callback.async_log_failure_event( + kwargs=self.model_call_details, + response_obj=result, + start_time=start_time, + end_time=end_time, + ) # type: ignore + if ( + callable(callback) and customLogger is not None + ): # custom logger functions + await customLogger.async_log_event( + kwargs=self.model_call_details, + response_obj=result, + start_time=start_time, + end_time=end_time, + print_verbose=print_verbose, + callback_func=callback, + ) + except Exception as e: + verbose_logger.exception( + "LiteLLM.LoggingError: [Non-Blocking] Exception occurred while failure \ + logging {}\nCallback={}".format( + str(e), callback + ) + ) + # Track callback logging failures in Prometheus + self._handle_callback_failure(callback=callback) + + def _get_trace_id(self, service_name: Literal["langfuse"]) -> Optional[str]: + """ + For the given service (e.g. langfuse), return the trace_id actually logged. + + Used for constructing the url in slack alerting. + + Returns: + - str: The logged trace id + - None: If trace id not yet emitted. + """ + trace_id: Optional[str] = None + if service_name == "langfuse": + trace_id = in_memory_trace_id_cache.get_cache( + litellm_call_id=self.litellm_call_id, service_name=service_name + ) + + return trace_id + + def _get_callback_object(self, service_name: Literal["langfuse"]) -> Optional[Any]: + """ + Return dynamic callback object. + + Meant to solve issue when doing key-based/team-based logging + """ + global langFuseLogger + + if service_name == "langfuse": + if langFuseLogger is None or ( + ( + self.standard_callback_dynamic_params.get("langfuse_public_key") + is not None + and self.standard_callback_dynamic_params.get("langfuse_public_key") + != langFuseLogger.public_key + ) + or ( + self.standard_callback_dynamic_params.get("langfuse_public_key") + is not None + and self.standard_callback_dynamic_params.get("langfuse_public_key") + != langFuseLogger.public_key + ) + or ( + self.standard_callback_dynamic_params.get("langfuse_host") + is not None + and self.standard_callback_dynamic_params.get("langfuse_host") + != langFuseLogger.langfuse_host + ) + ): + return LangFuseLogger( + langfuse_public_key=self.standard_callback_dynamic_params.get( + "langfuse_public_key" + ), + langfuse_secret=self.standard_callback_dynamic_params.get( + "langfuse_secret" + ), + langfuse_host=self.standard_callback_dynamic_params.get( + "langfuse_host" + ), + ) + return langFuseLogger + + return None + + def handle_sync_success_callbacks_for_async_calls( + self, + result: Any, + start_time: datetime.datetime, + end_time: datetime.datetime, + cache_hit: Optional[Any] = None, + ) -> None: + """ + Handles calling success callbacks for Async calls. + + Why: Some callbacks - `langfuse`, `s3` are sync callbacks. We need to call them in the executor. + """ + if self._should_run_sync_callbacks_for_async_calls() is False: + return + + executor.submit( + self.success_handler, + result, + start_time, + end_time, + cache_hit, + ) + + def _should_run_sync_callbacks_for_async_calls(self) -> bool: + """ + Returns: + - bool: True if sync callbacks should be run for async calls. eg. `langfuse`, `s3` + """ + _combined_sync_callbacks = self.get_combined_callback_list( + dynamic_success_callbacks=self.dynamic_success_callbacks, + global_callbacks=litellm.success_callback, + ) + _filtered_success_callbacks = self._remove_internal_custom_logger_callbacks( + _combined_sync_callbacks + ) + _filtered_success_callbacks = self._remove_internal_litellm_callbacks( + _filtered_success_callbacks + ) + return len(_filtered_success_callbacks) > 0 + + def get_combined_callback_list( + self, dynamic_success_callbacks: Optional[List], global_callbacks: List + ) -> List: + if dynamic_success_callbacks is None: + return list(global_callbacks) + return list(set(dynamic_success_callbacks + global_callbacks)) + + def _remove_internal_litellm_callbacks(self, callbacks: List) -> List: + """ + Creates a filtered list of callbacks, excluding internal LiteLLM callbacks. + + Args: + callbacks: List of callback functions/strings to filter + + Returns: + List of filtered callbacks with internal ones removed + """ + filtered = [ + cb for cb in callbacks if not self._is_internal_litellm_proxy_callback(cb) + ] + + verbose_logger.debug(f"Filtered callbacks: {filtered}") + return filtered + + def _get_callback_name(self, cb) -> str: + """ + Helper to get the name of a callback function + + Args: + cb: The callback object/function/string to get the name of + + Returns: + The name of the callback + """ + if isinstance(cb, str): + return cb + if hasattr(cb, "__name__"): + return cb.__name__ + if hasattr(cb, "__func__"): + return cb.__func__.__name__ + if hasattr(cb, "__class__"): + return cb.__class__.__name__ + return str(cb) + + def _is_internal_litellm_proxy_callback(self, cb) -> bool: + """Helper to check if a callback is internal""" + INTERNAL_PREFIXES = [ + "_PROXY", + "_service_logger.ServiceLogging", + "sync_deployment_callback_on_success", + ] + if isinstance(cb, str): + return False + + if not callable(cb): + return True + + cb_name = self._get_callback_name(cb) + return any(prefix in cb_name for prefix in INTERNAL_PREFIXES) + + def _remove_internal_custom_logger_callbacks(self, callbacks: List) -> List: + """ + Removes internal custom logger callbacks from the list. + """ + _new_callbacks = [] + for _c in callbacks: + if isinstance(_c, CustomLogger): + continue + elif ( + isinstance(_c, str) + and _c in litellm._known_custom_logger_compatible_callbacks + ): + continue + _new_callbacks.append(_c) + return _new_callbacks + + def _get_assembled_streaming_response( + self, + result: Union[ + ModelResponse, + TextCompletionResponse, + ModelResponseStream, + ResponseCompletedEvent, + Any, + ], + start_time: datetime.datetime, + end_time: datetime.datetime, + is_async: bool, + streaming_chunks: List[Any], + ) -> Optional[Union[ModelResponse, TextCompletionResponse, ResponsesAPIResponse]]: + if self.stream is not True: + return None + if isinstance(result, ModelResponse): + return result + elif isinstance(result, TextCompletionResponse): + return result + elif isinstance(result, ResponseCompletedEvent): + ## return unified Usage object + if isinstance(result.response.usage, ResponseAPIUsage): + transformed_usage = ( + ResponseAPILoggingUtils._transform_response_api_usage_to_chat_usage( + result.response.usage + ) + ) + # Set as dict instead of Usage object so model_dump() serializes it correctly + setattr( + result.response, + "usage", + ( + transformed_usage.model_dump() + if hasattr(transformed_usage, "model_dump") + else dict(transformed_usage) + ), + ) + return result.response + else: + return None + return None + + def _handle_anthropic_messages_response_logging(self, result: Any) -> ModelResponse: + """ + Handles logging for Anthropic messages responses. + + Args: + result: The response object from the model call + + Returns: + The the response object from the model call + + - For Non-streaming responses, we need to transform the response to a ModelResponse object. + - For streaming responses, anthropic_messages handler calls success_handler with a assembled ModelResponse. + """ + import httpx + + if self.stream and isinstance(result, ModelResponse): + return result + elif isinstance(result, ModelResponse): + return result + + httpx_response = self.model_call_details.get("httpx_response", None) + if httpx_response and isinstance(httpx_response, httpx.Response): + result = litellm.AnthropicConfig().transform_response( + raw_response=httpx_response, + model_response=litellm.ModelResponse(), + model=self.model, + messages=[], + logging_obj=self, + optional_params={}, + api_key="", + request_data={}, + encoding=litellm.encoding, + json_mode=False, + litellm_params={}, + ) + else: + from litellm.types.llms.anthropic import AnthropicResponse + + pydantic_result = AnthropicResponse.model_validate(result) + import httpx + + result = litellm.AnthropicConfig().transform_parsed_response( + completion_response=pydantic_result.model_dump(), + raw_response=httpx.Response( + status_code=200, + headers={}, + ), + model_response=litellm.ModelResponse(), + json_mode=None, + ) + return result + + def _handle_non_streaming_google_genai_generate_content_response_logging( + self, result: Any + ) -> ModelResponse: + """ + Handles logging for Google GenAI generate content responses. + """ + import httpx + + httpx_response = self.model_call_details.get("httpx_response", None) + if httpx_response is None: + raise ValueError("Google GenAI Generate Content: httpx_response is None") + dict_result = httpx_response.json() + result = litellm.VertexGeminiConfig()._transform_google_generate_content_to_openai_model_response( + completion_response=dict_result, + model_response=litellm.ModelResponse(), + model=self.model, + logging_obj=self, + raw_response=httpx.Response( + status_code=200, + headers={}, + ), + ) + return result + + def _handle_a2a_response_logging(self, result: Any) -> Any: + """ + Handles logging for A2A (Agent-to-Agent) responses. + + Adds usage from model_call_details to the result if available. + Uses Pydantic's model_copy to avoid modifying the original response. + + Args: + result: The LiteLLMSendMessageResponse from the A2A call + + Returns: + The response object with usage added if available + """ + # Get usage from model_call_details (set by asend_message) + usage = self.model_call_details.get("usage") + if usage is None: + return result + + # Deep copy result and add usage + result_copy = result.model_copy(deep=True) + result_copy.usage = ( + usage.model_dump() if hasattr(usage, "model_dump") else dict(usage) + ) + return result_copy + + +def _get_masked_values( + sensitive_object: dict, + ignore_sensitive_values: bool = False, + mask_all_values: bool = False, + unmasked_length: int = 4, + number_of_asterisks: Optional[int] = 4, +) -> dict: + """ + Internal debugging helper function + + Masks the headers of the request sent from LiteLLM + + Args: + masked_length: Optional length for the masked portion (number of *). If set, will use exactly this many * + regardless of original string length. The total length will be unmasked_length + masked_length. + """ + sensitive_keywords = [ + "authorization", + "token", + "key", + "secret", + "vertex_credentials", + ] + return { + k: ( + # If ignore_sensitive_values is True, or if this key doesn't contain sensitive keywords, return original value + v + if ignore_sensitive_values + or not any( + sensitive_keyword in k.lower() + for sensitive_keyword in sensitive_keywords + ) + else ( + # Apply masking to sensitive keys + ( + v[: unmasked_length // 2] + + "*" * number_of_asterisks + + v[-unmasked_length // 2 :] + ) + if ( + isinstance(v, str) + and len(v) > unmasked_length + and number_of_asterisks is not None + ) + else ( + ( + v[: unmasked_length // 2] + + "*" * (len(v) - unmasked_length) + + v[-unmasked_length // 2 :] + ) + if (isinstance(v, str) and len(v) > unmasked_length) + else ("*****" if isinstance(v, str) else v) + ) + ) + ) + for k, v in sensitive_object.items() + } + + +def set_callbacks(callback_list, function_id=None): # noqa: PLR0915 + """ + Globally sets the callback client + """ + global sentry_sdk_instance, capture_exception, add_breadcrumb, slack_app, alerts_channel, traceloopLogger, athinaLogger, heliconeLogger, supabaseClient, lunaryLogger, promptLayerLogger, langFuseLogger, customLogger, weightsBiasesLogger, logfireLogger, dynamoLogger, s3Logger, dataDogLogger, prometheusLogger, greenscaleLogger, openMeterLogger, deepevalLogger + + try: + for callback in callback_list: + if callback == "sentry": + try: + import sentry_sdk + except ImportError: + print_verbose("Package 'sentry_sdk' is missing. Installing it...") + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "sentry_sdk"] + ) + import sentry_sdk + from sentry_sdk.scrubber import EventScrubber + + sentry_sdk_instance = sentry_sdk + sentry_trace_rate = ( + os.environ.get("SENTRY_API_TRACE_RATE") + if "SENTRY_API_TRACE_RATE" in os.environ + else "1.0" + ) + sentry_sample_rate = ( + os.environ.get("SENTRY_API_SAMPLE_RATE") + if "SENTRY_API_SAMPLE_RATE" in os.environ + else "1.0" + ) + sentry_sdk_instance.init( + dsn=os.environ.get("SENTRY_DSN"), + traces_sample_rate=float(sentry_trace_rate), # type: ignore + sample_rate=float( + sentry_sample_rate if sentry_sample_rate else 1.0 + ), + send_default_pii=False, # Prevent sending Personal Identifiable Information + event_scrubber=EventScrubber( + denylist=SENTRY_DENYLIST, pii_denylist=SENTRY_PII_DENYLIST + ), + environment=os.environ.get("SENTRY_ENVIRONMENT", "production"), + ) + capture_exception = sentry_sdk_instance.capture_exception + add_breadcrumb = sentry_sdk_instance.add_breadcrumb + elif callback == "slack": + try: + from slack_bolt import App + except ImportError: + print_verbose("Package 'slack_bolt' is missing. Installing it...") + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "slack_bolt"] + ) + from slack_bolt import App + slack_app = App( + token=os.environ.get("SLACK_API_TOKEN"), + signing_secret=os.environ.get("SLACK_API_SECRET"), + ) + alerts_channel = os.environ["SLACK_API_CHANNEL"] + print_verbose(f"Initialized Slack App: {slack_app}") + elif callback == "traceloop": + traceloopLogger = TraceloopLogger() + elif callback == "athina": + athinaLogger = AthinaLogger() + print_verbose("Initialized Athina Logger") + elif callback == "helicone": + heliconeLogger = HeliconeLogger() + elif callback == "lunary": + lunaryLogger = LunaryLogger() + elif callback == "promptlayer": + promptLayerLogger = PromptLayerLogger() + elif callback == "langfuse": + langFuseLogger = LangFuseLogger( + langfuse_public_key=None, langfuse_secret=None, langfuse_host=None + ) + elif callback == "openmeter": + openMeterLogger = OpenMeterLogger() + elif callback == "datadog": + dataDogLogger = DataDogLogger() + elif callback == "dynamodb": + dynamoLogger = DyanmoDBLogger() + elif callback == "s3": + s3Logger = S3Logger() + elif callback == "wandb": + from litellm.integrations.weights_biases import WeightsBiasesLogger + + weightsBiasesLogger = WeightsBiasesLogger() + elif callback == "logfire": + logfireLogger = LogfireLogger() + elif callback == "supabase": + print_verbose("instantiating supabase") + supabaseClient = Supabase() + elif callback == "greenscale": + greenscaleLogger = GreenscaleLogger() + print_verbose("Initialized Greenscale Logger") + elif callable(callback): + customLogger = CustomLogger() + except Exception as e: + raise e + return None + + +def _init_custom_logger_compatible_class( # noqa: PLR0915 + logging_integration: _custom_logger_compatible_callbacks_literal, + internal_usage_cache: Optional[DualCache], + llm_router: Optional[ + Any + ], # expect litellm.Router, but typing errors due to circular import + custom_logger_init_args: Optional[dict] = {}, +) -> Optional[CustomLogger]: + """ + Initialize a custom logger compatible class + """ + try: + custom_logger_init_args = custom_logger_init_args or {} + if logging_integration == "agentops": # Add AgentOps initialization + for callback in _in_memory_loggers: + if isinstance(callback, AgentOps): + return callback # type: ignore + + agentops_logger = AgentOps() + _in_memory_loggers.append(agentops_logger) + return agentops_logger # type: ignore + elif logging_integration == "lago": + for callback in _in_memory_loggers: + if isinstance(callback, LagoLogger): + return callback # type: ignore + + lago_logger = LagoLogger() + _in_memory_loggers.append(lago_logger) + return lago_logger # type: ignore + elif logging_integration == "openmeter": + for callback in _in_memory_loggers: + if isinstance(callback, OpenMeterLogger): + return callback # type: ignore + + _openmeter_logger = OpenMeterLogger() + _in_memory_loggers.append(_openmeter_logger) + return _openmeter_logger # type: ignore + elif logging_integration == "posthog": + for callback in _in_memory_loggers: + if isinstance(callback, PostHogLogger): + return callback # type: ignore + + _posthog_logger = PostHogLogger() + _in_memory_loggers.append(_posthog_logger) + return _posthog_logger # type: ignore + elif logging_integration == "braintrust": + from litellm.integrations.braintrust_logging import BraintrustLogger + + for callback in _in_memory_loggers: + if isinstance(callback, BraintrustLogger): + return callback # type: ignore + + braintrust_logger = BraintrustLogger() + _in_memory_loggers.append(braintrust_logger) + return braintrust_logger # type: ignore + elif logging_integration == "langsmith": + for callback in _in_memory_loggers: + if isinstance(callback, LangsmithLogger): + return callback # type: ignore + + _langsmith_logger = LangsmithLogger() + _in_memory_loggers.append(_langsmith_logger) + return _langsmith_logger # type: ignore + elif logging_integration == "argilla": + for callback in _in_memory_loggers: + if isinstance(callback, ArgillaLogger): + return callback # type: ignore + + _argilla_logger = ArgillaLogger() + _in_memory_loggers.append(_argilla_logger) + return _argilla_logger # type: ignore + elif logging_integration == "literalai": + for callback in _in_memory_loggers: + if isinstance(callback, LiteralAILogger): + return callback # type: ignore + + _literalai_logger = LiteralAILogger() + _in_memory_loggers.append(_literalai_logger) + return _literalai_logger # type: ignore + elif logging_integration == "prometheus": + PrometheusLogger = _get_cached_prometheus_logger() + + for callback in _in_memory_loggers: + if isinstance(callback, PrometheusLogger): + return callback # type: ignore + + _prometheus_logger = PrometheusLogger() + _in_memory_loggers.append(_prometheus_logger) + return _prometheus_logger # type: ignore + elif logging_integration == "datadog": + for callback in _in_memory_loggers: + if isinstance(callback, DataDogLogger): + return callback # type: ignore + + _datadog_logger = DataDogLogger() + _in_memory_loggers.append(_datadog_logger) + return _datadog_logger # type: ignore + elif logging_integration == "datadog_llm_observability": + _datadog_llm_obs_logger = DataDogLLMObsLogger() + _in_memory_loggers.append(_datadog_llm_obs_logger) + return _datadog_llm_obs_logger # type: ignore + elif logging_integration == "azure_sentinel": + for callback in _in_memory_loggers: + if isinstance(callback, AzureSentinelLogger): + return callback # type: ignore + + _azure_sentinel_logger = AzureSentinelLogger() + _in_memory_loggers.append(_azure_sentinel_logger) + return _azure_sentinel_logger # type: ignore + elif logging_integration == "gcs_bucket": + for callback in _in_memory_loggers: + if isinstance(callback, GCSBucketLogger): + return callback # type: ignore + + _gcs_bucket_logger = GCSBucketLogger() + _in_memory_loggers.append(_gcs_bucket_logger) + return _gcs_bucket_logger # type: ignore + elif logging_integration == "s3_v2": + for callback in _in_memory_loggers: + if isinstance(callback, S3V2Logger): + return callback # type: ignore + + _s3_v2_logger = S3V2Logger() + _in_memory_loggers.append(_s3_v2_logger) + return _s3_v2_logger # type: ignore + elif logging_integration == "aws_sqs": + for callback in _in_memory_loggers: + if isinstance(callback, SQSLogger): + return callback # type: ignore + + _aws_sqs_logger = SQSLogger() + _in_memory_loggers.append(_aws_sqs_logger) + return _aws_sqs_logger # type: ignore + elif logging_integration == "azure_storage": + for callback in _in_memory_loggers: + if isinstance(callback, AzureBlobStorageLogger): + return callback # type: ignore + + _azure_storage_logger = AzureBlobStorageLogger() + _in_memory_loggers.append(_azure_storage_logger) + return _azure_storage_logger # type: ignore + elif logging_integration == "opik": + for callback in _in_memory_loggers: + if isinstance(callback, OpikLogger): + return callback # type: ignore + + _opik_logger = OpikLogger() + _in_memory_loggers.append(_opik_logger) + return _opik_logger # type: ignore + elif logging_integration == "arize": + from litellm.integrations.opentelemetry import ( + OpenTelemetry, + OpenTelemetryConfig, + ) + + arize_config = ArizeLogger.get_arize_config() + if arize_config.endpoint is None: + raise ValueError( + "No valid endpoint found for Arize, please set 'ARIZE_ENDPOINT' to your GRPC endpoint or 'ARIZE_HTTP_ENDPOINT' to your HTTP endpoint" + ) + otel_config = OpenTelemetryConfig( + exporter=arize_config.protocol, + endpoint=arize_config.endpoint, + service_name=arize_config.project_name, + ) + + os.environ[ + "OTEL_EXPORTER_OTLP_TRACES_HEADERS" + ] = f"space_id={arize_config.space_key or arize_config.space_id},api_key={arize_config.api_key}" + for callback in _in_memory_loggers: + if ( + isinstance(callback, ArizeLogger) + and callback.callback_name == "arize" + ): + return callback # type: ignore + _arize_otel_logger = ArizeLogger(config=otel_config, callback_name="arize") + _in_memory_loggers.append(_arize_otel_logger) + return _arize_otel_logger # type: ignore + elif logging_integration == "arize_phoenix": + from litellm.integrations.opentelemetry import ( + OpenTelemetry, + OpenTelemetryConfig, + ) + + arize_phoenix_config = ArizePhoenixLogger.get_arize_phoenix_config() + otel_config = OpenTelemetryConfig( + exporter=arize_phoenix_config.protocol, + endpoint=arize_phoenix_config.endpoint, + headers=arize_phoenix_config.otlp_auth_headers, + ) + if arize_phoenix_config.project_name: + existing_attrs = os.environ.get("OTEL_RESOURCE_ATTRIBUTES", "") + # Add openinference.project.name attribute + if existing_attrs: + os.environ[ + "OTEL_RESOURCE_ATTRIBUTES" + ] = f"{existing_attrs},openinference.project.name={arize_phoenix_config.project_name}" + else: + os.environ[ + "OTEL_RESOURCE_ATTRIBUTES" + ] = f"openinference.project.name={arize_phoenix_config.project_name}" + + # Set Phoenix project name from environment variable + phoenix_project_name = os.environ.get("PHOENIX_PROJECT_NAME", None) + if phoenix_project_name: + existing_attrs = os.environ.get("OTEL_RESOURCE_ATTRIBUTES", "") + # Add openinference.project.name attribute + if existing_attrs: + os.environ[ + "OTEL_RESOURCE_ATTRIBUTES" + ] = f"{existing_attrs},openinference.project.name={phoenix_project_name}" + else: + os.environ[ + "OTEL_RESOURCE_ATTRIBUTES" + ] = f"openinference.project.name={phoenix_project_name}" + + # auth can be disabled on local deployments of arize phoenix + if arize_phoenix_config.otlp_auth_headers is not None: + os.environ[ + "OTEL_EXPORTER_OTLP_TRACES_HEADERS" + ] = arize_phoenix_config.otlp_auth_headers + + for callback in _in_memory_loggers: + if ( + isinstance(callback, ArizePhoenixLogger) + and callback.callback_name == "arize_phoenix" + ): + return callback # type: ignore + _arize_phoenix_otel_logger = ArizePhoenixLogger( + config=otel_config, callback_name="arize_phoenix" + ) + _in_memory_loggers.append(_arize_phoenix_otel_logger) + return _arize_phoenix_otel_logger # type: ignore + elif logging_integration == "levo": + from litellm.integrations.levo.levo import LevoLogger + from litellm.integrations.opentelemetry import ( + OpenTelemetry, + OpenTelemetryConfig, + ) + + levo_config = LevoLogger.get_levo_config() + otel_config = OpenTelemetryConfig( + exporter=levo_config.protocol, + endpoint=levo_config.endpoint, + headers=levo_config.otlp_auth_headers, + ) + + # Check if LevoLogger instance already exists + for callback in _in_memory_loggers: + if ( + isinstance(callback, LevoLogger) + and callback.callback_name == "levo" + ): + return callback # type: ignore + + _levo_otel_logger = LevoLogger(config=otel_config, callback_name="levo") + _in_memory_loggers.append(_levo_otel_logger) + return _levo_otel_logger # type: ignore + elif logging_integration == "otel": + from litellm.integrations.opentelemetry import OpenTelemetry + + for callback in _in_memory_loggers: + if type(callback) is OpenTelemetry: + return callback # type: ignore + otel_logger = OpenTelemetry( + **_get_custom_logger_settings_from_proxy_server( + callback_name=logging_integration + ) + ) + _in_memory_loggers.append(otel_logger) + return otel_logger # type: ignore + + elif logging_integration == "galileo": + for callback in _in_memory_loggers: + if isinstance(callback, GalileoObserve): + return callback # type: ignore + + galileo_logger = GalileoObserve() + _in_memory_loggers.append(galileo_logger) + return galileo_logger # type: ignore + elif logging_integration == "cloudzero": + from litellm.integrations.cloudzero.cloudzero import CloudZeroLogger + + for callback in _in_memory_loggers: + if isinstance(callback, CloudZeroLogger): + return callback # type: ignore + cloudzero_logger = CloudZeroLogger() + _in_memory_loggers.append(cloudzero_logger) + return cloudzero_logger # type: ignore + elif logging_integration == "focus": + from litellm.integrations.focus.focus_logger import FocusLogger + + for callback in _in_memory_loggers: + if isinstance(callback, FocusLogger): + return callback # type: ignore + focus_logger = FocusLogger() + _in_memory_loggers.append(focus_logger) + return focus_logger # type: ignore + elif logging_integration == "deepeval": + for callback in _in_memory_loggers: + if isinstance(callback, DeepEvalLogger): + return callback # type: ignore + deepeval_logger = DeepEvalLogger() + _in_memory_loggers.append(deepeval_logger) + return deepeval_logger # type: ignore + + elif logging_integration == "logfire": + if "LOGFIRE_TOKEN" not in os.environ: + raise ValueError("LOGFIRE_TOKEN not found in environment variables") + from litellm.integrations.opentelemetry import ( + OpenTelemetry, + OpenTelemetryConfig, + ) + + logfire_base_url = os.getenv( + "LOGFIRE_BASE_URL", "https://logfire-api.pydantic.dev" + ) + otel_config = OpenTelemetryConfig( + exporter="otlp_http", + endpoint=f"{logfire_base_url.rstrip('/')}/v1/traces", + headers=f"Authorization={os.getenv('LOGFIRE_TOKEN')}", + ) + for callback in _in_memory_loggers: + if isinstance(callback, OpenTelemetry): + return callback # type: ignore + _otel_logger = OpenTelemetry(config=otel_config) + _in_memory_loggers.append(_otel_logger) + return _otel_logger # type: ignore + elif logging_integration == "dynamic_rate_limiter": + from litellm.proxy.hooks.dynamic_rate_limiter import ( + _PROXY_DynamicRateLimitHandler, + ) + + for callback in _in_memory_loggers: + if isinstance(callback, _PROXY_DynamicRateLimitHandler): + return callback # type: ignore + + if internal_usage_cache is None: + raise Exception( + "Internal Error: Cache cannot be empty - internal_usage_cache={}".format( + internal_usage_cache + ) + ) + + dynamic_rate_limiter_obj = _PROXY_DynamicRateLimitHandler( + internal_usage_cache=internal_usage_cache + ) + + if llm_router is not None and isinstance(llm_router, litellm.Router): + dynamic_rate_limiter_obj.update_variables(llm_router=llm_router) + _in_memory_loggers.append(dynamic_rate_limiter_obj) + return dynamic_rate_limiter_obj # type: ignore + elif logging_integration == "dynamic_rate_limiter_v3": + from litellm.proxy.hooks.dynamic_rate_limiter_v3 import ( + _PROXY_DynamicRateLimitHandlerV3, + ) + + for callback in _in_memory_loggers: + if isinstance(callback, _PROXY_DynamicRateLimitHandlerV3): + return callback # type: ignore + + if internal_usage_cache is None: + raise Exception( + "Internal Error: Cache cannot be empty - internal_usage_cache={}".format( + internal_usage_cache + ) + ) + + dynamic_rate_limiter_obj_v3 = _PROXY_DynamicRateLimitHandlerV3( + internal_usage_cache=internal_usage_cache + ) + + if llm_router is not None and isinstance(llm_router, litellm.Router): + dynamic_rate_limiter_obj_v3.update_variables(llm_router=llm_router) + _in_memory_loggers.append(dynamic_rate_limiter_obj_v3) + return dynamic_rate_limiter_obj_v3 # type: ignore + elif logging_integration == "langtrace": + if "LANGTRACE_API_KEY" not in os.environ: + raise ValueError("LANGTRACE_API_KEY not found in environment variables") + + from litellm.integrations.opentelemetry import ( + OpenTelemetry, + OpenTelemetryConfig, + ) + + otel_config = OpenTelemetryConfig( + exporter="otlp_http", + endpoint="https://langtrace.ai/api/trace", + ) + os.environ[ + "OTEL_EXPORTER_OTLP_TRACES_HEADERS" + ] = f"api_key={os.getenv('LANGTRACE_API_KEY')}" + for callback in _in_memory_loggers: + if ( + isinstance(callback, OpenTelemetry) + and callback.callback_name == "langtrace" + ): + return callback # type: ignore + _otel_logger = OpenTelemetry(config=otel_config, callback_name="langtrace") + _in_memory_loggers.append(_otel_logger) + return _otel_logger # type: ignore + + elif logging_integration == "mlflow": + for callback in _in_memory_loggers: + if isinstance(callback, MlflowLogger): + return callback # type: ignore + + _mlflow_logger = MlflowLogger() + _in_memory_loggers.append(_mlflow_logger) + return _mlflow_logger # type: ignore + elif logging_integration == "langfuse": + for callback in _in_memory_loggers: + if isinstance(callback, LangfusePromptManagement): + return callback + + langfuse_logger = LangfusePromptManagement() + _in_memory_loggers.append(langfuse_logger) + return langfuse_logger # type: ignore + elif logging_integration == "langfuse_otel": + from litellm.integrations.langfuse.langfuse_otel import LangfuseOtelLogger + + for callback in _in_memory_loggers: + if ( + isinstance(callback, LangfuseOtelLogger) + and callback.callback_name == "langfuse_otel" + ): + return callback # type: ignore + # Allow LangfuseOtelLogger to initialize its own config safely + # This prevents startup crashes if LANGFUSE keys are not in env (e.g. for dynamic usage) + _otel_logger = LangfuseOtelLogger( + config=None, callback_name="langfuse_otel" + ) + _in_memory_loggers.append(_otel_logger) + return _otel_logger # type: ignore + elif logging_integration == "weave_otel": + from litellm.integrations.opentelemetry import OpenTelemetryConfig + from litellm.integrations.weave.weave_otel import ( + WeaveOtelLogger, + get_weave_otel_config, + ) + + weave_otel_config = get_weave_otel_config() + + otel_config = OpenTelemetryConfig( + exporter=weave_otel_config.protocol, + endpoint=weave_otel_config.endpoint, + headers=weave_otel_config.otlp_auth_headers, + ) + + for callback in _in_memory_loggers: + if ( + isinstance(callback, WeaveOtelLogger) + and callback.callback_name == "weave_otel" + ): + return callback # type: ignore + _otel_logger = WeaveOtelLogger( + config=otel_config, callback_name="weave_otel" + ) + _in_memory_loggers.append(_otel_logger) + return _otel_logger # type: ignore + elif logging_integration == "pagerduty": + for callback in _in_memory_loggers: + if isinstance(callback, PagerDutyAlerting): + return callback + pagerduty_logger = PagerDutyAlerting(**custom_logger_init_args) + _in_memory_loggers.append(pagerduty_logger) + return pagerduty_logger # type: ignore + elif logging_integration == "anthropic_cache_control_hook": + for callback in _in_memory_loggers: + if isinstance(callback, AnthropicCacheControlHook): + return callback + anthropic_cache_control_hook = AnthropicCacheControlHook() + _in_memory_loggers.append(anthropic_cache_control_hook) + return anthropic_cache_control_hook # type: ignore + elif logging_integration == "vector_store_pre_call_hook": + from litellm.integrations.vector_store_integrations.vector_store_pre_call_hook import ( + VectorStorePreCallHook, + ) + + for callback in _in_memory_loggers: + if isinstance(callback, VectorStorePreCallHook): + return callback + vector_store_pre_call_hook = VectorStorePreCallHook() + _in_memory_loggers.append(vector_store_pre_call_hook) + return vector_store_pre_call_hook # type: ignore + elif logging_integration == "gcs_pubsub": + for callback in _in_memory_loggers: + if isinstance(callback, GcsPubSubLogger): + return callback + _gcs_pubsub_logger = GcsPubSubLogger() + _in_memory_loggers.append(_gcs_pubsub_logger) + return _gcs_pubsub_logger # type: ignore + elif logging_integration == "generic_api": + for callback in _in_memory_loggers: + if isinstance(callback, GenericAPILogger): + return callback + generic_api_logger = GenericAPILogger() + _in_memory_loggers.append(generic_api_logger) + return generic_api_logger # type: ignore + elif logging_integration == "resend_email": + for callback in _in_memory_loggers: + if isinstance(callback, ResendEmailLogger): + return callback + resend_email_logger = ResendEmailLogger() + _in_memory_loggers.append(resend_email_logger) + return resend_email_logger # type: ignore + elif logging_integration == "sendgrid_email": + for callback in _in_memory_loggers: + if isinstance(callback, SendGridEmailLogger): + return callback + sendgrid_email_logger = SendGridEmailLogger() + _in_memory_loggers.append(sendgrid_email_logger) + return sendgrid_email_logger # type: ignore + elif logging_integration == "smtp_email": + for callback in _in_memory_loggers: + if isinstance(callback, SMTPEmailLogger): + return callback + smtp_email_logger = SMTPEmailLogger() + _in_memory_loggers.append(smtp_email_logger) + return smtp_email_logger # type: ignore + elif logging_integration == "humanloop": + for callback in _in_memory_loggers: + if isinstance(callback, HumanloopLogger): + return callback + + humanloop_logger = HumanloopLogger() + _in_memory_loggers.append(humanloop_logger) + return humanloop_logger # type: ignore + elif logging_integration == "dotprompt": + for callback in _in_memory_loggers: + if isinstance(callback, DotpromptManager): + return callback + + dotprompt_logger = DotpromptManager() + _in_memory_loggers.append(dotprompt_logger) + return dotprompt_logger # type: ignore + elif logging_integration == "bitbucket": + from litellm.integrations.bitbucket.bitbucket_prompt_manager import ( + BitBucketPromptManager, + ) + + for callback in _in_memory_loggers: + if isinstance(callback, BitBucketPromptManager): + return callback + + # Get global BitBucket config + bitbucket_config = getattr(litellm, "global_bitbucket_config", None) + if bitbucket_config is None: + raise ValueError( + "BitBucket configuration not found. Please set litellm.global_bitbucket_config first." + ) + + bitbucket_logger = BitBucketPromptManager(bitbucket_config=bitbucket_config) + _in_memory_loggers.append(bitbucket_logger) + return bitbucket_logger # type: ignore + elif logging_integration == "gitlab": + from litellm.integrations.gitlab.gitlab_prompt_manager import ( + GitLabPromptManager, + ) + + for callback in _in_memory_loggers: + if isinstance(callback, GitLabPromptManager): + return callback + + # Get global BitBucket config + gitlab_config = getattr(litellm, "global_gitlab_config", None) + if gitlab_config is None: + raise ValueError( + "Gitlab configuration not found. Please set litellm.global_gitlab_config first." + ) + + gitlab_logger = GitLabPromptManager(gitlab_config=gitlab_config) + _in_memory_loggers.append(gitlab_logger) + return gitlab_logger # type: ignore + return None + except Exception as e: + verbose_logger.exception( + f"[Non-Blocking Error] Error initializing custom logger: {e}" + ) + return None + return None + + +def get_custom_logger_compatible_class( # noqa: PLR0915 + logging_integration: _custom_logger_compatible_callbacks_literal, +) -> Optional[CustomLogger]: + try: + if logging_integration == "lago": + for callback in _in_memory_loggers: + if isinstance(callback, LagoLogger): + return callback + elif logging_integration == "openmeter": + for callback in _in_memory_loggers: + if isinstance(callback, OpenMeterLogger): + return callback + elif logging_integration == "braintrust": + from litellm.integrations.braintrust_logging import BraintrustLogger + + for callback in _in_memory_loggers: + if isinstance(callback, BraintrustLogger): + return callback + elif logging_integration == "galileo": + for callback in _in_memory_loggers: + if isinstance(callback, GalileoObserve): + return callback + elif logging_integration == "cloudzero": + from litellm.integrations.cloudzero.cloudzero import CloudZeroLogger + + for callback in _in_memory_loggers: + if isinstance(callback, CloudZeroLogger): + return callback + elif logging_integration == "focus": + from litellm.integrations.focus.focus_logger import FocusLogger + + for callback in _in_memory_loggers: + if isinstance(callback, FocusLogger): + return callback + elif logging_integration == "deepeval": + for callback in _in_memory_loggers: + if isinstance(callback, DeepEvalLogger): + return callback + elif logging_integration == "langsmith": + for callback in _in_memory_loggers: + if isinstance(callback, LangsmithLogger): + return callback + elif logging_integration == "argilla": + for callback in _in_memory_loggers: + if isinstance(callback, ArgillaLogger): + return callback + elif logging_integration == "literalai": + for callback in _in_memory_loggers: + if isinstance(callback, LiteralAILogger): + return callback + elif logging_integration == "prometheus": + PrometheusLogger = _get_cached_prometheus_logger() + for callback in _in_memory_loggers: + if isinstance(callback, PrometheusLogger): + return callback + elif logging_integration == "datadog": + for callback in _in_memory_loggers: + if isinstance(callback, DataDogLogger): + return callback + elif logging_integration == "datadog_llm_observability": + for callback in _in_memory_loggers: + if isinstance(callback, DataDogLLMObsLogger): + return callback + elif logging_integration == "azure_sentinel": + for callback in _in_memory_loggers: + if isinstance(callback, AzureSentinelLogger): + return callback + elif logging_integration == "gcs_bucket": + for callback in _in_memory_loggers: + if isinstance(callback, GCSBucketLogger): + return callback + elif logging_integration == "s3_v2": + for callback in _in_memory_loggers: + if isinstance(callback, S3V2Logger): + return callback + elif logging_integration == "aws_sqs": + for callback in _in_memory_loggers: + if isinstance(callback, SQSLogger): + return callback + _aws_sqs_logger = SQSLogger() + _in_memory_loggers.append(_aws_sqs_logger) + return _aws_sqs_logger # type: ignore + elif logging_integration == "azure_storage": + for callback in _in_memory_loggers: + if isinstance(callback, AzureBlobStorageLogger): + return callback + elif logging_integration == "opik": + for callback in _in_memory_loggers: + if isinstance(callback, OpikLogger): + return callback + elif logging_integration == "langfuse": + for callback in _in_memory_loggers: + if isinstance(callback, LangfusePromptManagement): + return callback + elif logging_integration == "otel": + from litellm.integrations.opentelemetry import OpenTelemetry + + for callback in _in_memory_loggers: + if isinstance(callback, OpenTelemetry): + return callback + elif logging_integration == "arize": + if "ARIZE_API_KEY" not in os.environ: + raise ValueError("ARIZE_API_KEY not found in environment variables") + for callback in _in_memory_loggers: + if ( + isinstance(callback, ArizeLogger) + and callback.callback_name == "arize" + ): + return callback + elif logging_integration == "logfire": + if "LOGFIRE_TOKEN" not in os.environ: + raise ValueError("LOGFIRE_TOKEN not found in environment variables") + from litellm.integrations.opentelemetry import OpenTelemetry + + for callback in _in_memory_loggers: + if isinstance(callback, OpenTelemetry): + return callback # type: ignore + + elif logging_integration == "dynamic_rate_limiter": + from litellm.proxy.hooks.dynamic_rate_limiter import ( + _PROXY_DynamicRateLimitHandler, + ) + + for callback in _in_memory_loggers: + if isinstance(callback, _PROXY_DynamicRateLimitHandler): + return callback # type: ignore + elif logging_integration == "dynamic_rate_limiter_v3": + from litellm.proxy.hooks.dynamic_rate_limiter_v3 import ( + _PROXY_DynamicRateLimitHandlerV3, + ) + + for callback in _in_memory_loggers: + if isinstance(callback, _PROXY_DynamicRateLimitHandlerV3): + return callback # type: ignore + + elif logging_integration == "langtrace": + from litellm.integrations.opentelemetry import OpenTelemetry + + if "LANGTRACE_API_KEY" not in os.environ: + raise ValueError("LANGTRACE_API_KEY not found in environment variables") + + for callback in _in_memory_loggers: + if ( + isinstance(callback, OpenTelemetry) + and callback.callback_name == "langtrace" + ): + return callback + + elif logging_integration == "mlflow": + for callback in _in_memory_loggers: + if isinstance(callback, MlflowLogger): + return callback + elif logging_integration == "pagerduty": + for callback in _in_memory_loggers: + if isinstance(callback, PagerDutyAlerting): + return callback + elif logging_integration == "anthropic_cache_control_hook": + for callback in _in_memory_loggers: + if isinstance(callback, AnthropicCacheControlHook): + return callback + elif logging_integration == "vector_store_pre_call_hook": + from litellm.integrations.vector_store_integrations.vector_store_pre_call_hook import ( + VectorStorePreCallHook, + ) + + for callback in _in_memory_loggers: + if isinstance(callback, VectorStorePreCallHook): + return callback + elif logging_integration == "gcs_pubsub": + for callback in _in_memory_loggers: + if isinstance(callback, GcsPubSubLogger): + return callback + elif logging_integration == "generic_api": + for callback in _in_memory_loggers: + if isinstance(callback, GenericAPILogger): + return callback + elif logging_integration == "resend_email": + for callback in _in_memory_loggers: + if isinstance(callback, ResendEmailLogger): + return callback + elif logging_integration == "sendgrid_email": + for callback in _in_memory_loggers: + if isinstance(callback, SendGridEmailLogger): + return callback + elif logging_integration == "smtp_email": + for callback in _in_memory_loggers: + if isinstance(callback, SMTPEmailLogger): + return callback + return None + + except Exception as e: + verbose_logger.exception( + f"[Non-Blocking Error] Error getting custom logger: {e}" + ) + return None + + +def _get_custom_logger_settings_from_proxy_server(callback_name: str) -> Dict: + """ + Get the settings for a custom logger from the proxy server config.yaml + + Proxy server config.yaml defines callback_settings as: + + callback_settings: + otel: + message_logging: False + """ + if litellm.callback_settings: + return dict(litellm.callback_settings.get(callback_name, {})) + return {} + + +def use_custom_pricing_for_model(litellm_params: Optional[dict]) -> bool: + """ + Check if the model uses custom pricing + + Returns True if any of `SPECIAL_MODEL_INFO_PARAMS` are present in `litellm_params` or `model_info` + """ + if litellm_params is None: + return False + + # Check litellm_params using set intersection (only check keys that exist in both) + matching_keys = _CUSTOM_PRICING_KEYS & litellm_params.keys() + for key in matching_keys: + if litellm_params.get(key) is not None: + return True + + # Check model_info + metadata: dict = litellm_params.get("metadata", {}) or {} + model_info: dict = metadata.get("model_info", {}) or {} + + if model_info: + matching_keys = _CUSTOM_PRICING_KEYS & model_info.keys() + for key in matching_keys: + if model_info.get(key) is not None: + return True + + return False + + +def is_valid_sha256_hash(value: str) -> bool: + # Check if the value is a valid SHA-256 hash (64 hexadecimal characters) + return bool(re.fullmatch(r"[a-fA-F0-9]{64}", value)) + + +class StandardLoggingPayloadSetup: + @staticmethod + def cleanup_timestamps( + start_time: Union[dt_object, float], + end_time: Union[dt_object, float], + completion_start_time: Union[dt_object, float], + ) -> Tuple[float, float, float]: + """ + Convert datetime objects to floats + + Args: + start_time: Union[dt_object, float] + end_time: Union[dt_object, float] + completion_start_time: Union[dt_object, float] + + Returns: + Tuple[float, float, float]: A tuple containing the start time, end time, and completion start time as floats. + """ + + if isinstance(start_time, datetime.datetime): + start_time_float = start_time.timestamp() + elif isinstance(start_time, float): + start_time_float = start_time + else: + raise ValueError( + f"start_time is required, got={start_time} of type {type(start_time)}" + ) + + if isinstance(end_time, datetime.datetime): + end_time_float = end_time.timestamp() + elif isinstance(end_time, float): + end_time_float = end_time + else: + raise ValueError( + f"end_time is required, got={end_time} of type {type(end_time)}" + ) + + if isinstance(completion_start_time, datetime.datetime): + completion_start_time_float = completion_start_time.timestamp() + elif isinstance(completion_start_time, float): + completion_start_time_float = completion_start_time + else: + completion_start_time_float = end_time_float + + return start_time_float, end_time_float, completion_start_time_float + + @staticmethod + def append_system_prompt_messages( + kwargs: Optional[Dict] = None, messages: Optional[Any] = None + ): + """ + Append system prompt messages to the messages + """ + if kwargs is not None: + if kwargs.get("system") is not None and isinstance( + kwargs.get("system"), str + ): + if messages is None: + return [{"role": "system", "content": kwargs.get("system")}] + elif isinstance(messages, list): + if len(messages) == 0: + return [{"role": "system", "content": kwargs.get("system")}] + # check for duplicates + if messages[0].get("role") == "system" and messages[0].get( + "content" + ) == kwargs.get("system"): + return messages + messages = [ + {"role": "system", "content": kwargs.get("system")} + ] + messages + elif isinstance(messages, str): + messages = [ + {"role": "system", "content": kwargs.get("system")}, + {"role": "user", "content": messages}, + ] + return messages + + return messages + + @staticmethod + def merge_litellm_metadata(litellm_params: dict) -> dict: + """ + Merge both litellm_metadata and metadata from litellm_params. + + litellm_metadata contains model-related fields, metadata contains user API key fields. + We need both for complete standard logging payload. + + Args: + litellm_params: Dictionary containing metadata and litellm_metadata + + Returns: + dict: Merged metadata with user API key fields taking precedence + """ + merged_metadata: dict = {} + + # Start with metadata (user API key fields) - but skip non-serializable objects + if litellm_params.get("metadata") and isinstance( + litellm_params.get("metadata"), dict + ): + for key, value in litellm_params["metadata"].items(): + # Skip non-serializable objects like UserAPIKeyAuth + if key == "user_api_key_auth": + continue + merged_metadata[key] = value + + # Then merge litellm_metadata (model-related fields) - this will NOT overwrite existing keys + if litellm_params.get("litellm_metadata") and isinstance( + litellm_params.get("litellm_metadata"), dict + ): + for key, value in litellm_params["litellm_metadata"].items(): + if ( + key not in merged_metadata + ): # Don't overwrite existing keys from metadata + merged_metadata[key] = value + + return merged_metadata + + @staticmethod + def get_standard_logging_metadata( + metadata: Optional[Dict[str, Any]], + litellm_params: Optional[dict] = None, + prompt_integration: Optional[str] = None, + applied_guardrails: Optional[List[str]] = None, + mcp_tool_call_metadata: Optional[StandardLoggingMCPToolCall] = None, + vector_store_request_metadata: Optional[ + List[StandardLoggingVectorStoreRequest] + ] = None, + usage_object: Optional[dict] = None, + proxy_server_request: Optional[dict] = None, + start_time: Optional[dt_object] = None, + response_id: Optional[str] = None, + ) -> StandardLoggingMetadata: + """ + Clean and filter the metadata dictionary to include only the specified keys in StandardLoggingMetadata. + + Args: + metadata (Optional[Dict[str, Any]]): The original metadata dictionary. + + Returns: + StandardLoggingMetadata: A StandardLoggingMetadata object containing the cleaned metadata. + + Note: + - If the input metadata is None or not a dictionary, an empty StandardLoggingMetadata object is returned. + - If 'user_api_key' is present in metadata and is a valid SHA256 hash, it's stored as 'user_api_key_hash'. + """ + + prompt_management_metadata: Optional[ + StandardLoggingPromptManagementMetadata + ] = None + if litellm_params is not None: + prompt_id = cast(Optional[str], litellm_params.get("prompt_id", None)) + prompt_variables = cast( + Optional[dict], litellm_params.get("prompt_variables", None) + ) + + if prompt_id is not None and prompt_integration is not None: + prompt_management_metadata = StandardLoggingPromptManagementMetadata( + prompt_id=prompt_id, + prompt_variables=prompt_variables, + prompt_integration=prompt_integration, + ) + + # Initialize with default values + clean_metadata = StandardLoggingMetadata( + user_api_key_hash=None, + user_api_key_alias=None, + user_api_key_spend=None, + user_api_key_max_budget=None, + user_api_key_budget_reset_at=None, + user_api_key_team_id=None, + user_api_key_org_id=None, + user_api_key_project_id=None, + user_api_key_user_id=None, + user_api_key_team_alias=None, + user_api_key_user_email=None, + user_api_key_end_user_id=None, + user_api_key_request_route=None, + spend_logs_metadata=None, + requester_ip_address=None, + user_agent=None, + requester_metadata=None, + prompt_management_metadata=prompt_management_metadata, + applied_guardrails=applied_guardrails, + mcp_tool_call_metadata=mcp_tool_call_metadata, + vector_store_request_metadata=vector_store_request_metadata, + usage_object=usage_object, + requester_custom_headers=None, + cold_storage_object_key=None, + user_api_key_auth_metadata=None, + team_alias=None, + team_id=None, + ) + if isinstance(metadata, dict): + for key in metadata.keys() & _STANDARD_LOGGING_METADATA_KEYS: + clean_metadata[key] = metadata[key] # type: ignore + + user_api_key = metadata.get("user_api_key") + if ( + user_api_key + and isinstance(user_api_key, str) + and is_valid_sha256_hash(user_api_key) + ): + clean_metadata["user_api_key_hash"] = user_api_key + _potential_requester_metadata = metadata.get( + "metadata", None + ) # check if user passed metadata in the sdk request - e.g. metadata for langsmith logging - https://docs.litellm.ai/docs/observability/langsmith_integration#set-langsmith-fields + if ( + clean_metadata["requester_metadata"] is None + and _potential_requester_metadata is not None + and isinstance(_potential_requester_metadata, dict) + ): + clean_metadata["requester_metadata"] = _potential_requester_metadata + + if ( + EnterpriseStandardLoggingPayloadSetupVAR + and proxy_server_request is not None + ): + clean_metadata = EnterpriseStandardLoggingPayloadSetupVAR.apply_enterprise_specific_metadata( + standard_logging_metadata=clean_metadata, + proxy_server_request=proxy_server_request, + ) + + # Generate cold storage object key if cold storage is configured + if start_time is not None and response_id is not None: + cold_storage_object_key = ( + StandardLoggingPayloadSetup._generate_cold_storage_object_key( + start_time=start_time, + response_id=response_id, + team_alias=clean_metadata.get("user_api_key_team_alias"), + ) + ) + if cold_storage_object_key: + clean_metadata["cold_storage_object_key"] = cold_storage_object_key + + return clean_metadata + + @staticmethod + def get_usage_from_response_obj( + response_obj: Optional[dict], combined_usage_object: Optional[Usage] = None + ) -> Usage: + ## BASE CASE ## + if combined_usage_object is not None: + return combined_usage_object + if response_obj is None: + return Usage( + prompt_tokens=0, + completion_tokens=0, + total_tokens=0, + ) + + usage = response_obj.get("usage", None) or {} + if usage is None or ( + not isinstance(usage, dict) and not isinstance(usage, Usage) + ): + return Usage( + prompt_tokens=0, + completion_tokens=0, + total_tokens=0, + ) + elif isinstance(usage, Usage): + return usage + elif isinstance(usage, ResponseAPIUsage): + return ResponseAPILoggingUtils._transform_response_api_usage_to_chat_usage( + usage + ) + elif isinstance(usage, dict): + if ResponseAPILoggingUtils._is_response_api_usage(usage): + return ( + ResponseAPILoggingUtils._transform_response_api_usage_to_chat_usage( + usage + ) + ) + return Usage(**usage) + + raise ValueError(f"usage is required, got={usage} of type {type(usage)}") + + @staticmethod + def get_usage_as_dict( + response_obj: Optional[dict], + combined_usage_object: Optional[Usage] = None, + ) -> dict: + """ + Like get_usage_from_response_obj but returns a plain dict, skipping + the Pydantic Usage construction on the hot path. + """ + _empty: dict = {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0} + if combined_usage_object is not None: + return combined_usage_object.model_dump() + if not response_obj: + return _empty + _raw = response_obj.get("usage", None) + if _raw is None: + return _empty + if isinstance(_raw, ResponseAPIUsage): + return ResponseAPILoggingUtils._transform_response_api_usage_to_chat_usage( + _raw + ).model_dump() + if isinstance(_raw, dict): + if ResponseAPILoggingUtils._is_response_api_usage(_raw): + return ResponseAPILoggingUtils._transform_response_api_usage_to_chat_usage( + _raw + ).model_dump() + return _raw + if isinstance(_raw, Usage): + return _raw.model_dump() + return _empty + + @staticmethod + def get_model_cost_information( + base_model: Optional[str], + custom_pricing: Optional[bool], + custom_llm_provider: Optional[str], + init_response_obj: Union[Any, BaseModel, dict], + ) -> StandardLoggingModelInformation: + model_cost_name = _select_model_name_for_cost_calc( + model=None, + completion_response=init_response_obj, # type: ignore + base_model=base_model, + custom_pricing=custom_pricing, + ) + if model_cost_name is None: + model_cost_information = StandardLoggingModelInformation( + model_map_key="", model_map_value=None + ) + else: + try: + _model_cost_information = litellm.get_model_info( + model=model_cost_name, custom_llm_provider=custom_llm_provider + ) + model_cost_information = StandardLoggingModelInformation( + model_map_key=model_cost_name, + model_map_value=_model_cost_information, + ) + except Exception: + verbose_logger.debug( # keep in debug otherwise it will trigger on every call + "Model={} is not mapped in model cost map. Defaulting to None model_cost_information for standard_logging_payload".format( + model_cost_name + ) + ) + model_cost_information = StandardLoggingModelInformation( + model_map_key=model_cost_name, model_map_value=None + ) + return model_cost_information + + @staticmethod + def get_final_response_obj( + response_obj: dict, init_response_obj: Union[Any, BaseModel, dict], kwargs: dict + ) -> Optional[Union[dict, str, list]]: + """ + Get final response object after redacting the message input/output from logging + """ + if response_obj: + final_response_obj: Optional[Union[dict, str, list]] = response_obj + elif isinstance(init_response_obj, list) or isinstance(init_response_obj, str): + final_response_obj = init_response_obj + else: + final_response_obj = {} + + modified_final_response_obj = redact_message_input_output_from_logging( + model_call_details=kwargs, + result=final_response_obj, + ) + + if modified_final_response_obj is not None and isinstance( + modified_final_response_obj, BaseModel + ): + final_response_obj = modified_final_response_obj.model_dump() + else: + final_response_obj = modified_final_response_obj + + return final_response_obj + + @staticmethod + def get_additional_headers( + additiona_headers: Optional[dict], + ) -> Optional[StandardLoggingAdditionalHeaders]: + if additiona_headers is None: + return None + + additional_logging_headers: StandardLoggingAdditionalHeaders = {} + + for key in StandardLoggingAdditionalHeaders.__annotations__.keys(): + _key = key.lower() + _key = _key.replace("_", "-") + if _key in additiona_headers: + try: + additional_logging_headers[key] = int(additiona_headers[_key]) # type: ignore + except (ValueError, TypeError): + verbose_logger.debug( + f"Could not convert {additiona_headers[_key]} to int for key {key}." + ) + return additional_logging_headers + + @staticmethod + def get_hidden_params( + hidden_params: Optional[dict], + ) -> StandardLoggingHiddenParams: + clean_hidden_params = StandardLoggingHiddenParams( + model_id=None, + cache_key=None, + api_base=None, + response_cost=None, + additional_headers=None, + litellm_overhead_time_ms=None, + batch_models=None, + litellm_model_name=None, + usage_object=None, + ) + if hidden_params is not None: + for key in StandardLoggingHiddenParams.__annotations__.keys(): + if key in hidden_params: + if key == "additional_headers": + clean_hidden_params[ + "additional_headers" + ] = StandardLoggingPayloadSetup.get_additional_headers( + hidden_params[key] + ) + else: + clean_hidden_params[key] = hidden_params[key] # type: ignore + return clean_hidden_params + + @staticmethod + def strip_trailing_slash(api_base: Optional[str]) -> Optional[str]: + if api_base: + if api_base.endswith("//"): + return api_base.rstrip("/") + if api_base[-1] == "/": + return api_base[:-1] + return api_base + + @staticmethod + def _generate_cold_storage_object_key( + start_time: dt_object, + response_id: str, + team_alias: Optional[str] = None, + ) -> Optional[str]: + """ + Generate cold storage object key in the same format as S3Logger. + + Args: + start_time: The start time of the request + response_id: The response ID + team_alias: Optional team alias for team-based prefixing + + Returns: + Optional[str]: The generated object key or None if cold storage not configured + """ + # Generate object key in same format as S3Logger + from litellm.integrations.s3 import get_s3_object_key + + # Only generate object key if cold storage is configured + cold_storage_custom_logger = litellm.cold_storage_custom_logger + if cold_storage_custom_logger is None: + return None + + try: + # Generate file name in same format as litellm.utils.get_logging_id + s3_file_name = f"time-{start_time.strftime('%H-%M-%S-%f')}_{response_id}" + + # Get the actual s3_path from the configured cold storage logger instance + s3_path = "" # default value + + # Try to get the actual logger instance from the logger name + try: + custom_logger = litellm.logging_callback_manager.get_active_custom_logger_for_callback_name( + cold_storage_custom_logger + ) + if ( + custom_logger + and hasattr(custom_logger, "s3_path") + and getattr(custom_logger, "s3_path") + ): + s3_path = getattr(custom_logger, "s3_path") + except Exception: + # If any error occurs in getting the logger instance, use default empty s3_path + pass + + s3_object_key = get_s3_object_key( + s3_path=s3_path, # Use actual s3_path from logger configuration + prefix="", # Don't split by team alias for cold storage + start_time=start_time, + s3_file_name=s3_file_name, + ) + + return s3_object_key + except Exception: + # If any error occurs in generating the key, return None + return None + + @staticmethod + def get_error_information( + original_exception: Optional[Exception], + traceback_str: Optional[str] = None, + ) -> StandardLoggingPayloadErrorInformation: + from litellm.constants import MAXIMUM_TRACEBACK_LINES_TO_LOG + + # Check for 'code' first (used by ProxyException), then fall back to 'status_code' (used by LiteLLM exceptions) + # Ensure error_code is always a string for Prisma Python JSON field compatibility + error_code_attr = getattr(original_exception, "code", None) + if error_code_attr is not None and str(error_code_attr) not in ("", "None"): + error_status: str = str(error_code_attr) + else: + status_code_attr = getattr(original_exception, "status_code", None) + error_status = str(status_code_attr) if status_code_attr is not None else "" + error_class: str = ( + str(original_exception.__class__.__name__) if original_exception else "" + ) + _llm_provider_in_exception = getattr(original_exception, "llm_provider", "") + + # Get traceback information (first 100 lines) + traceback_info = traceback_str or "" + if original_exception: + tb = getattr(original_exception, "__traceback__", None) + if tb: + tb_lines = traceback.format_tb(tb) + traceback_info += "".join( + tb_lines[:MAXIMUM_TRACEBACK_LINES_TO_LOG] + ) # Limit to first 100 lines + + # Get additional error details + error_message = str(original_exception) + + return StandardLoggingPayloadErrorInformation( + error_code=error_status, + error_class=error_class, + llm_provider=_llm_provider_in_exception, + traceback=traceback_info, + error_message=error_message if original_exception else "", + ) + + @staticmethod + def get_response_time( + start_time_float: float, + end_time_float: float, + completion_start_time_float: float, + stream: bool, + ) -> float: + """ + Get the response time for the LLM response + + Args: + start_time_float: float - start time of the LLM call + end_time_float: float - end time of the LLM call + completion_start_time_float: float - time to first token of the LLM response (for streaming responses) + stream: bool - True when a stream response is returned + + Returns: + float: The response time for the LLM response + """ + if stream is True: + return completion_start_time_float - start_time_float + else: + return end_time_float - start_time_float + + @staticmethod + def _get_standard_logging_payload_trace_id( + logging_obj: Logging, + litellm_params: dict, + ) -> str: + """ + Returns the `litellm_trace_id` for this request + + This helps link sessions when multiple requests are made in a single session + """ + dynamic_litellm_session_id = litellm_params.get("litellm_session_id") + dynamic_litellm_trace_id = litellm_params.get("litellm_trace_id") + + # Note: we recommend using `litellm_session_id` for session tracking + # `litellm_trace_id` is an internal litellm param + if dynamic_litellm_session_id: + return str(dynamic_litellm_session_id) + elif dynamic_litellm_trace_id: + return str(dynamic_litellm_trace_id) + else: + return logging_obj.litellm_trace_id + + @staticmethod + def _get_user_agent_tags(proxy_server_request: dict) -> Optional[List[str]]: + """ + Return the user agent tags from the proxy server request for spend tracking + """ + if litellm.disable_add_user_agent_to_request_tags is True: + return None + user_agent_tags: Optional[List[str]] = None + headers = proxy_server_request.get("headers", {}) + if headers is not None and isinstance(headers, dict): + if "user-agent" in headers: + user_agent = headers["user-agent"] + if user_agent is not None: + if user_agent_tags is None: + user_agent_tags = [] + user_agent_part: Optional[str] = None + if "/" in user_agent: + user_agent_part = user_agent.split("/")[0] + if user_agent_part is not None: + user_agent_tags.append("User-Agent: " + user_agent_part) + if user_agent is not None: + user_agent_tags.append("User-Agent: " + user_agent) + return user_agent_tags + + @staticmethod + def _get_extra_header_tags(proxy_server_request: dict) -> Optional[List[str]]: + """ + Extract additional header tags for spend tracking based on config. + """ + extra_headers: List[str] = ( + getattr(litellm, "extra_spend_tag_headers", None) or [] + ) + if not extra_headers: + return None + + headers = proxy_server_request.get("headers", {}) + if not isinstance(headers, dict): + return None + + header_tags = [] + for header_name in extra_headers: + header_value = headers.get(header_name) + if header_value: + header_tags.append(f"{header_name}: {header_value}") + + return header_tags if header_tags else None + + @staticmethod + def _get_request_tags( + litellm_params: dict, proxy_server_request: dict + ) -> List[str]: + # check for 'tags' in both 'metadata' and 'litellm_metadata' + metadata = litellm_params.get("metadata") or {} + litellm_metadata = litellm_params.get("litellm_metadata") or {} + if metadata.get("tags", []): + request_tags = metadata.get("tags", []).copy() + elif litellm_metadata.get("tags", []): + request_tags = litellm_metadata.get("tags", []).copy() + else: + request_tags = [] + user_agent_tags = StandardLoggingPayloadSetup._get_user_agent_tags( + proxy_server_request + ) + additional_header_tags = StandardLoggingPayloadSetup._get_extra_header_tags( + proxy_server_request + ) + if user_agent_tags is not None: + request_tags.extend(user_agent_tags) + if additional_header_tags is not None: + request_tags.extend(additional_header_tags) + return request_tags + + +def _get_status_fields( + status: StandardLoggingPayloadStatus, + guardrail_information: Optional[List[dict]], + error_str: Optional[str], +) -> "StandardLoggingPayloadStatusFields": + """ + Determine status fields based on request status and guardrail information. + + Args: + status: Overall request status ("success" or "failure") + guardrail_information: Guardrail information from metadata + error_str: Error string if any + + Returns: + StandardLoggingPayloadStatusFields with llm_api_status and guardrail_status + """ + # Mapping for legacy guardrail status values to new GuardrailStatus values + GUARDRAIL_STATUS_MAP: Dict[str, GuardrailStatus] = { + "success": "success", + "blocked": "guardrail_intervened", # legacy + "guardrail_intervened": "guardrail_intervened", # direct + "failure": "guardrail_failed_to_respond", # legacy + "guardrail_failed_to_respond": "guardrail_failed_to_respond", # direct + "not_run": "not_run", + } + + # Set LLM API status + llm_api_status: StandardLoggingPayloadStatus = status + + ######################################################### + # Map - guardrail_information.guardrail_status to guardrail_status + ######################################################### + guardrail_status: GuardrailStatus = "not_run" + if guardrail_information and isinstance(guardrail_information, list): + for information in guardrail_information: + if isinstance(information, dict): + raw_status = information.get("guardrail_status", "not_run") + if raw_status != "not_run": + guardrail_status = GUARDRAIL_STATUS_MAP.get(raw_status, "not_run") + break + + return StandardLoggingPayloadStatusFields( + llm_api_status=llm_api_status, guardrail_status=guardrail_status + ) + + +def _extract_response_obj_and_hidden_params( + init_response_obj: Union[Any, BaseModel, dict], + original_exception: Optional[Exception], +) -> Tuple[dict, Optional[dict]]: + """Extract response_obj and hidden_params from init_response_obj.""" + hidden_params: Optional[dict] = None + if init_response_obj is None: + response_obj = {} + elif isinstance(init_response_obj, BaseModel): + response_obj = init_response_obj.model_dump() + hidden_params = getattr(init_response_obj, "_hidden_params", None) + elif isinstance(init_response_obj, dict): + response_obj = init_response_obj + else: + response_obj = {} + + if original_exception is not None and hidden_params is None: + response_headers = _get_response_headers(original_exception) + if response_headers is not None: + hidden_params = dict( + StandardLoggingHiddenParams( + additional_headers=StandardLoggingPayloadSetup.get_additional_headers( + dict(response_headers) + ), + model_id=None, + cache_key=None, + api_base=None, + response_cost=None, + litellm_overhead_time_ms=None, + batch_models=None, + litellm_model_name=None, + usage_object=None, + ) + ) + + return response_obj, hidden_params + + +def get_standard_logging_object_payload( + kwargs: Optional[dict], + init_response_obj: Union[Any, BaseModel, dict], + start_time: dt_object, + end_time: dt_object, + logging_obj: Logging, + status: StandardLoggingPayloadStatus, + error_str: Optional[str] = None, + original_exception: Optional[Exception] = None, + standard_built_in_tools_params: Optional[StandardBuiltInToolsParams] = None, +) -> Optional[StandardLoggingPayload]: + try: + kwargs = kwargs or {} + + response_obj, hidden_params = _extract_response_obj_and_hidden_params( + init_response_obj, original_exception + ) + + # standardize this function to be used across, s3, dynamoDB, langfuse logging + litellm_params = kwargs.get("litellm_params", {}) or {} + proxy_server_request = litellm_params.get("proxy_server_request") or {} + + # Merge both litellm_metadata and metadata to get complete metadata + metadata: dict = StandardLoggingPayloadSetup.merge_litellm_metadata( + litellm_params + ) + + completion_start_time = kwargs.get("completion_start_time", end_time) + call_type = kwargs.get("call_type") + cache_hit = kwargs.get("cache_hit", False) + # Extract usage as a plain dict, avoiding Pydantic round-trip + usage_dict = StandardLoggingPayloadSetup.get_usage_as_dict( + response_obj=response_obj, + combined_usage_object=cast( + Optional[Usage], kwargs.get("combined_usage_object") + ), + ) + + id = response_obj.get("id", kwargs.get("litellm_call_id")) + + _model_id = metadata.get("model_info", {}).get("id", "") + _model_group = metadata.get("model_group", "") + + request_tags = StandardLoggingPayloadSetup._get_request_tags( + litellm_params=litellm_params, proxy_server_request=proxy_server_request + ) + + # cleanup timestamps + ( + start_time_float, + end_time_float, + completion_start_time_float, + ) = StandardLoggingPayloadSetup.cleanup_timestamps( + start_time=start_time, + end_time=end_time, + completion_start_time=completion_start_time, + ) + response_time = StandardLoggingPayloadSetup.get_response_time( + start_time_float=start_time_float, + end_time_float=end_time_float, + completion_start_time_float=completion_start_time_float, + stream=kwargs.get("stream", False), + ) + # clean up litellm hidden params + clean_hidden_params = StandardLoggingPayloadSetup.get_hidden_params( + hidden_params + ) + + # clean up litellm metadata + clean_metadata = StandardLoggingPayloadSetup.get_standard_logging_metadata( + metadata=metadata, + litellm_params=litellm_params, + prompt_integration=kwargs.get("prompt_integration", None), + applied_guardrails=kwargs.get("applied_guardrails", None), + mcp_tool_call_metadata=kwargs.get("mcp_tool_call_metadata", None), + vector_store_request_metadata=kwargs.get( + "vector_store_request_metadata", None + ), + usage_object=usage_dict, + proxy_server_request=proxy_server_request, + start_time=start_time, + response_id=id, + ) + _request_body = proxy_server_request.get("body", {}) + end_user_id = clean_metadata["user_api_key_end_user_id"] or _request_body.get( + "user", None + ) # maintain backwards compatibility with old request body check + + saved_cache_cost: float = 0.0 + if cache_hit is True: + id = f"{id}_cache_hit{time.time()}" # do not duplicate the request id + saved_cache_cost = ( + logging_obj._response_cost_calculator( + result=init_response_obj, cache_hit=False # type: ignore + ) + or 0.0 + ) + + ## Get model cost information ## + base_model = _get_base_model_from_metadata(model_call_details=kwargs) + custom_pricing = use_custom_pricing_for_model(litellm_params=litellm_params) + + model_cost_information = StandardLoggingPayloadSetup.get_model_cost_information( + base_model=base_model, + custom_pricing=custom_pricing, + custom_llm_provider=kwargs.get("custom_llm_provider"), + init_response_obj=init_response_obj, + ) + response_cost: float = kwargs.get("response_cost", 0) or 0.0 + + error_information = StandardLoggingPayloadSetup.get_error_information( + original_exception=original_exception, + ) + + ## get final response object ## + final_response_obj = StandardLoggingPayloadSetup.get_final_response_obj( + response_obj=response_obj, + init_response_obj=init_response_obj, + kwargs=kwargs, + ) + + stream: Optional[bool] = None + if ( + kwargs.get("complete_streaming_response") is not None + or kwargs.get("async_complete_streaming_response") is not None + ) and kwargs.get("stream") is True: + stream = True + + # Reconstruct full model name with provider prefix for logging + # This ensures Bedrock models like "us.anthropic.claude-3-5-sonnet-20240620-v1:0" + # are logged as "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0" + custom_llm_provider = cast(Optional[str], kwargs.get("custom_llm_provider")) + model_name = reconstruct_model_name( + kwargs.get("model", "") or "", custom_llm_provider, metadata + ) + + payload: StandardLoggingPayload = StandardLoggingPayload( + id=str(id), + trace_id=StandardLoggingPayloadSetup._get_standard_logging_payload_trace_id( + logging_obj=logging_obj, + litellm_params=litellm_params, + ), + call_type=call_type or "", + cache_hit=cache_hit, + stream=stream, + status=status, + status_fields=_get_status_fields( + status=status, + guardrail_information=metadata.get( + "standard_logging_guardrail_information", None + ), + error_str=error_str, + ), + custom_llm_provider=custom_llm_provider, + saved_cache_cost=saved_cache_cost, + startTime=start_time_float, + endTime=end_time_float, + completionStartTime=completion_start_time_float, + response_time=response_time, + model=model_name, + metadata=clean_metadata, + cache_key=clean_hidden_params["cache_key"], + response_cost=response_cost, + cost_breakdown=logging_obj.cost_breakdown, + total_tokens=usage_dict.get("total_tokens", 0), + prompt_tokens=usage_dict.get("prompt_tokens", 0), + completion_tokens=usage_dict.get("completion_tokens", 0), + request_tags=request_tags, + end_user=end_user_id or "", + api_base=StandardLoggingPayloadSetup.strip_trailing_slash( + litellm_params.get("api_base", "") + ) + or "", + model_group=_model_group, + model_id=_model_id, + requester_ip_address=clean_metadata.get("requester_ip_address", None), + user_agent=clean_metadata.get("user_agent", None), + messages=truncate_base64_in_messages( + StandardLoggingPayloadSetup.append_system_prompt_messages( + kwargs=kwargs, messages=kwargs.get("messages") + ) + ), + response=final_response_obj, + model_parameters=ModelParamHelper.get_standard_logging_model_parameters( + kwargs.get("optional_params", None) or {} + ), + hidden_params=clean_hidden_params, + model_map_information=model_cost_information, + error_str=error_str, + error_information=error_information, + response_cost_failure_debug_info=kwargs.get( + "response_cost_failure_debug_information" + ), + guardrail_information=metadata.get( + "standard_logging_guardrail_information", None + ), + standard_built_in_tools_params=standard_built_in_tools_params, + ) + + # emit_standard_logging_payload(payload) - Moved to success_handler to prevent double emitting + + return payload + except Exception as e: + verbose_logger.exception( + "Error creating standard logging object - {}".format(str(e)) + ) + return None + + +def emit_standard_logging_payload(payload: StandardLoggingPayload): + if os.getenv("LITELLM_PRINT_STANDARD_LOGGING_PAYLOAD"): + print(json.dumps(payload, indent=4)) # noqa + + +def get_standard_logging_metadata( + metadata: Optional[Dict[str, Any]], +) -> StandardLoggingMetadata: + """ + Clean and filter the metadata dictionary to include only the specified keys in StandardLoggingMetadata. + + Args: + metadata (Optional[Dict[str, Any]]): The original metadata dictionary. + + Returns: + StandardLoggingMetadata: A StandardLoggingMetadata object containing the cleaned metadata. + + Note: + - If the input metadata is None or not a dictionary, an empty StandardLoggingMetadata object is returned. + - If 'user_api_key' is present in metadata and is a valid SHA256 hash, it's stored as 'user_api_key_hash'. + """ + # Initialize with default values + clean_metadata = StandardLoggingMetadata( + user_api_key_hash=None, + user_api_key_alias=None, + user_api_key_spend=None, + user_api_key_max_budget=None, + user_api_key_budget_reset_at=None, + user_api_key_team_id=None, + user_api_key_org_id=None, + user_api_key_project_id=None, + user_api_key_user_id=None, + user_api_key_user_email=None, + user_api_key_team_alias=None, + spend_logs_metadata=None, + requester_ip_address=None, + user_agent=None, + requester_metadata=None, + user_api_key_end_user_id=None, + prompt_management_metadata=None, + applied_guardrails=None, + mcp_tool_call_metadata=None, + vector_store_request_metadata=None, + usage_object=None, + requester_custom_headers=None, + user_api_key_request_route=None, + cold_storage_object_key=None, + user_api_key_auth_metadata=None, + team_alias=None, + team_id=None, + ) + if isinstance(metadata, dict): + # Update the clean_metadata with values from input metadata that match StandardLoggingMetadata fields + for key in StandardLoggingMetadata.__annotations__.keys(): + if key in metadata: + clean_metadata[key] = metadata[key] # type: ignore + + if metadata.get("user_api_key") is not None: + if is_valid_sha256_hash(str(metadata.get("user_api_key"))): + clean_metadata["user_api_key_hash"] = metadata.get( + "user_api_key" + ) # this is the hash + return clean_metadata + + +def scrub_sensitive_keys_in_metadata(litellm_params: Optional[dict]): + if litellm_params is None: + litellm_params = {} + + metadata = litellm_params.get("metadata", {}) or {} + + ## Extract provider-specific callable values (like langfuse_masking_function) + ## Store them separately so only the intended logger can access them + ## This prevents callables from leaking to other logging integrations + if "langfuse_masking_function" in metadata: + masking_fn = metadata.pop("langfuse_masking_function", None) + if callable(masking_fn): + litellm_params["_langfuse_masking_function"] = masking_fn + litellm_params["metadata"] = metadata + + ## check user_api_key_metadata for sensitive logging keys + cleaned_user_api_key_metadata = {} + if "user_api_key_metadata" in metadata and isinstance( + metadata["user_api_key_metadata"], dict + ): + for k, v in metadata["user_api_key_metadata"].items(): + if k == "logging": # prevent logging user logging keys + cleaned_user_api_key_metadata[ + k + ] = "scrubbed_by_litellm_for_sensitive_keys" + else: + cleaned_user_api_key_metadata[k] = v + + metadata["user_api_key_metadata"] = cleaned_user_api_key_metadata + litellm_params["metadata"] = metadata + + return litellm_params + + +# integration helper function +def modify_integration(integration_name, integration_params): + global supabaseClient + if integration_name == "supabase": + if "table_name" in integration_params: + Supabase.supabase_table_name = integration_params["table_name"] + + +@lru_cache(maxsize=16) +def _get_traceback_str_for_error(error_str: str) -> str: + """ + function wrapped with lru_cache to limit the number of times `traceback.format_exc()` is called + """ + return traceback.format_exc() + + +from decimal import Decimal + +# used for unit testing +from typing import Any, Dict, List, Optional, Union + + +def create_dummy_standard_logging_payload() -> StandardLoggingPayload: + # First create the nested objects with proper typing + model_info = StandardLoggingModelInformation( + model_map_key="gpt-3.5-turbo", model_map_value=None + ) + + metadata = StandardLoggingMetadata( # type: ignore + user_api_key_hash=str("test_hash"), + user_api_key_alias=str("test_alias"), + user_api_key_team_id=str("test_team"), + user_api_key_user_id=str("test_user"), + user_api_key_team_alias=str("test_team_alias"), + user_api_key_org_id=None, + spend_logs_metadata=None, + requester_ip_address=str("127.0.0.1"), + requester_metadata=None, + user_api_key_end_user_id=str("test_end_user"), + ) + + hidden_params = StandardLoggingHiddenParams( + model_id=None, + cache_key=None, + api_base=None, + response_cost=None, + additional_headers=None, + litellm_overhead_time_ms=None, + batch_models=None, + litellm_model_name=None, + usage_object=None, + ) + + # Convert numeric values to appropriate types + response_cost = Decimal("0.1") + start_time = Decimal("1234567890.0") + end_time = Decimal("1234567891.0") + completion_start_time = Decimal("1234567890.5") + saved_cache_cost = Decimal("0.0") + + # Create messages and response with proper typing + messages: List[Dict[str, str]] = [{"role": "user", "content": "Hello, world!"}] + response: Dict[str, List[Dict[str, Dict[str, str]]]] = { + "choices": [{"message": {"content": "Hi there!"}}] + } + + # Main payload initialization + return StandardLoggingPayload( # type: ignore + id=str("test_id"), + call_type=str("completion"), + stream=bool(False), + response_cost=response_cost, + response_cost_failure_debug_info=None, + status=str("success"), + total_tokens=int( + DEFAULT_MOCK_RESPONSE_PROMPT_TOKEN_COUNT + + DEFAULT_MOCK_RESPONSE_COMPLETION_TOKEN_COUNT + ), + prompt_tokens=int(DEFAULT_MOCK_RESPONSE_PROMPT_TOKEN_COUNT), + completion_tokens=int(DEFAULT_MOCK_RESPONSE_COMPLETION_TOKEN_COUNT), + startTime=start_time, + endTime=end_time, + completionStartTime=completion_start_time, + model_map_information=model_info, + model=str("gpt-3.5-turbo"), + model_id=str("model-123"), + model_group=str("openai-gpt"), + custom_llm_provider=str("openai"), + api_base=str("https://api.openai.com"), + metadata=metadata, + cache_hit=bool(False), + cache_key=None, + saved_cache_cost=saved_cache_cost, + request_tags=[], + end_user=None, + requester_ip_address=str("127.0.0.1"), + messages=messages, + response=response, + error_str=None, + model_parameters={"stream": True}, + hidden_params=hidden_params, + ) + diff --git a/litellm/litellm_core_utils/llm_cost_calc/utils.py b/litellm/litellm_core_utils/llm_cost_calc/utils.py index 2308dc7bec..7c41e1bbe6 100644 --- a/litellm/litellm_core_utils/llm_cost_calc/utils.py +++ b/litellm/litellm_core_utils/llm_cost_calc/utils.py @@ -16,6 +16,15 @@ from litellm.types.utils import ( ) from litellm.utils import get_model_info +# Pre-resolved CallTypes enum values for fast membership checks +_IMAGE_RESPONSE_CALL_TYPES = frozenset({ + CallTypes.image_generation.value, + CallTypes.aimage_generation.value, + PassthroughCallTypes.passthrough_image_generation.value, + CallTypes.image_edit.value, + CallTypes.aimage_edit.value, +}) + def _is_above_128k(tokens: float) -> bool: if tokens > 128000: @@ -189,9 +198,25 @@ def _get_token_base_cost( cache_read_cost = cast(float, _get_cost_per_unit(model_info, cache_read_cost_key)) ## CHECK IF ABOVE THRESHOLD + # Optimization: collect threshold keys first to avoid sorting all model_info keys. + # Most models don't have threshold pricing, so we can return early. + threshold_keys = [ + k for k in model_info if k.startswith("input_cost_per_token_above_") + ] + if not threshold_keys: + return ( + prompt_base_cost, + completion_base_cost, + cache_creation_cost, + cache_creation_cost_above_1hr, + cache_read_cost, + ) + + # Only sort the threshold keys (typically 1-2 keys instead of 66+) threshold: Optional[float] = None - for key, value in sorted(model_info.items(), reverse=True): - if key.startswith("input_cost_per_token_above_") and value is not None: + for key in sorted(threshold_keys, reverse=True): + value = model_info.get(key) + if value is not None: try: # Handle both formats: _above_128k_tokens and _above_128_tokens threshold_str = key.split("_above_")[1].split("_tokens")[0] @@ -502,47 +527,52 @@ def _calculate_input_cost( prompt_cost += float(prompt_tokens_details["cache_hit_tokens"]) * cache_read_cost ### AUDIO COST - prompt_cost += calculate_cost_component( - model_info, "input_cost_per_audio_token", prompt_tokens_details["audio_tokens"] - ) + if prompt_tokens_details["audio_tokens"]: + prompt_cost += calculate_cost_component( + model_info, "input_cost_per_audio_token", prompt_tokens_details["audio_tokens"] + ) ### IMAGE TOKEN COST - # For image token costs: - # First check if input_cost_per_image_token is available. If not, default to generic input_cost_per_token. - image_token_cost_key = "input_cost_per_image_token" - if model_info.get(image_token_cost_key) is None: - image_token_cost_key = "input_cost_per_token" - prompt_cost += calculate_cost_component( - model_info, image_token_cost_key, prompt_tokens_details["image_tokens"] - ) + if prompt_tokens_details["image_tokens"]: + # For image token costs: + # First check if input_cost_per_image_token is available. If not, default to generic input_cost_per_token. + image_token_cost_key = "input_cost_per_image_token" + if model_info.get(image_token_cost_key) is None: + image_token_cost_key = "input_cost_per_token" + prompt_cost += calculate_cost_component( + model_info, image_token_cost_key, prompt_tokens_details["image_tokens"] + ) ### CACHE WRITING COST - Now uses tiered pricing - prompt_cost += calculate_cache_writing_cost( - cache_creation_tokens=prompt_tokens_details["cache_creation_tokens"], - cache_creation_token_details=prompt_tokens_details[ - "cache_creation_token_details" - ], - cache_creation_cost_above_1hr=cache_creation_cost_above_1hr, - cache_creation_cost=cache_creation_cost, - ) + if prompt_tokens_details["cache_creation_tokens"] or prompt_tokens_details["cache_creation_token_details"] is not None: + prompt_cost += calculate_cache_writing_cost( + cache_creation_tokens=prompt_tokens_details["cache_creation_tokens"], + cache_creation_token_details=prompt_tokens_details[ + "cache_creation_token_details" + ], + cache_creation_cost_above_1hr=cache_creation_cost_above_1hr, + cache_creation_cost=cache_creation_cost, + ) ### CHARACTER COST - - prompt_cost += calculate_cost_component( - model_info, "input_cost_per_character", prompt_tokens_details["character_count"] - ) + if prompt_tokens_details["character_count"]: + prompt_cost += calculate_cost_component( + model_info, "input_cost_per_character", prompt_tokens_details["character_count"] + ) ### IMAGE COUNT COST - prompt_cost += calculate_cost_component( - model_info, "input_cost_per_image", prompt_tokens_details["image_count"] - ) + if prompt_tokens_details["image_count"]: + prompt_cost += calculate_cost_component( + model_info, "input_cost_per_image", prompt_tokens_details["image_count"] + ) ### VIDEO LENGTH COST - prompt_cost += calculate_cost_component( - model_info, - "input_cost_per_video_per_second", - prompt_tokens_details["video_length_seconds"], - ) + if prompt_tokens_details["video_length_seconds"]: + prompt_cost += calculate_cost_component( + model_info, + "input_cost_per_video_per_second", + prompt_tokens_details["video_length_seconds"], + ) return prompt_cost @@ -602,7 +632,7 @@ def generic_cost_per_token( # noqa: PLR0915 total_details = text_tokens + cache_hit + audio_tokens + cache_creation + image_tokens has_double_counting = cache_hit > 0 and total_details > usage.prompt_tokens - if text_tokens == 0 or has_double_counting: + if (text_tokens == 0 and prompt_tokens_details["image_count"] == 0) or has_double_counting: text_tokens = ( usage.prompt_tokens - cache_hit @@ -667,18 +697,11 @@ def generic_cost_per_token( # noqa: PLR0915 ## TEXT COST completion_cost = float(text_tokens) * completion_base_cost - _output_cost_per_audio_token = _get_cost_per_unit( - model_info, "output_cost_per_audio_token", None - ) - _output_cost_per_reasoning_token = _get_cost_per_unit( - model_info, "output_cost_per_reasoning_token", None - ) - _output_cost_per_image_token = _get_cost_per_unit( - model_info, "output_cost_per_image_token", None - ) - ## AUDIO COST if not is_text_tokens_total and audio_tokens is not None and audio_tokens > 0: + _output_cost_per_audio_token = _get_cost_per_unit( + model_info, "output_cost_per_audio_token", None + ) _output_cost_per_audio_token = ( _output_cost_per_audio_token if _output_cost_per_audio_token is not None @@ -688,6 +711,9 @@ def generic_cost_per_token( # noqa: PLR0915 ## REASONING COST if not is_text_tokens_total and reasoning_tokens and reasoning_tokens > 0: + _output_cost_per_reasoning_token = _get_cost_per_unit( + model_info, "output_cost_per_reasoning_token", None + ) _output_cost_per_reasoning_token = ( _output_cost_per_reasoning_token if _output_cost_per_reasoning_token is not None @@ -697,6 +723,9 @@ def generic_cost_per_token( # noqa: PLR0915 ## IMAGE COST if not is_text_tokens_total and image_tokens and image_tokens > 0: + _output_cost_per_image_token = _get_cost_per_unit( + model_info, "output_cost_per_image_token", None + ) _output_cost_per_image_token = ( _output_cost_per_image_token if _output_cost_per_image_token is not None @@ -718,18 +747,7 @@ class CostCalculatorUtils: - Image Edit - Passthrough Image Generation """ - if call_type in [ - # image generation - CallTypes.image_generation.value, - CallTypes.aimage_generation.value, - # passthrough image generation - PassthroughCallTypes.passthrough_image_generation.value, - # image edit - CallTypes.image_edit.value, - CallTypes.aimage_edit.value, - ]: - return True - return False + return call_type in _IMAGE_RESPONSE_CALL_TYPES @staticmethod def route_image_generation_cost_calculator( diff --git a/litellm/litellm_core_utils/llm_response_utils/convert_dict_to_response.py b/litellm/litellm_core_utils/llm_response_utils/convert_dict_to_response.py index 0f7779e8b5..a2b03d0eb6 100644 --- a/litellm/litellm_core_utils/llm_response_utils/convert_dict_to_response.py +++ b/litellm/litellm_core_utils/llm_response_utils/convert_dict_to_response.py @@ -550,10 +550,13 @@ def convert_to_model_response_object( # noqa: PLR0915 message = litellm.Message(content=json_mode_content_str) finish_reason = "stop" if message is None: - provider_specific_fields = { - f: choice["message"][f] - for f in choice["message"].keys() - _MESSAGE_FIELDS - } + # Preserve provider_specific_fields if already present + # in the response (e.g. from proxy passthrough) + provider_specific_fields = dict( + choice["message"].get("provider_specific_fields", None) or {} + ) + for f in choice["message"].keys() - _MESSAGE_FIELDS: + provider_specific_fields[f] = choice["message"][f] # Handle reasoning models that display `reasoning_content` within `content` reasoning_content, content = _extract_reasoning_content( diff --git a/litellm/litellm_core_utils/llm_response_utils/response_metadata.py b/litellm/litellm_core_utils/llm_response_utils/response_metadata.py index ccfdcfeb2e..06933a6fbc 100644 --- a/litellm/litellm_core_utils/llm_response_utils/response_metadata.py +++ b/litellm/litellm_core_utils/llm_response_utils/response_metadata.py @@ -1,6 +1,7 @@ import datetime from typing import Any, Optional, Union +from litellm.constants import LITELLM_DETAILED_TIMING from litellm.litellm_core_utils.core_helpers import process_response_headers from litellm.litellm_core_utils.llm_response_utils.get_api_base import get_api_base from litellm.litellm_core_utils.logging_utils import LiteLLMLoggingObject @@ -108,7 +109,18 @@ class ResponseMetadata: ) ######################################################### - # 3. Add duration for reading from cache + # 3. Add callback processing duration + ######################################################### + callback_duration_ms = getattr(logging_obj, "callback_duration_ms", None) + if callback_duration_ms is not None: + self._update_hidden_params( + { + "callback_duration_ms": round(callback_duration_ms, 4), + } + ) + + ######################################################### + # 4. Add duration for reading from cache # In this case overhead from litellm is the difference between the cache read duration and the total response time ######################################################### if ( @@ -128,6 +140,31 @@ class ResponseMetadata: } ) + ######################################################### + # 5. Detailed per-phase timing (opt-in via env var) + ######################################################### + if LITELLM_DETAILED_TIMING and llm_api_duration_ms is not None: + detailed: dict = { + "timing_llm_api_ms": round(llm_api_duration_ms, 4), + } + + # message copy time from Logging.__init__() + msg_copy_ms = getattr(logging_obj, "message_copy_duration_ms", None) + if msg_copy_ms is not None: + detailed["timing_message_copy_ms"] = round(msg_copy_ms, 4) + + # pre-processing = time from request start to LLM API call start + api_call_start = logging_obj.model_call_details.get("api_call_start_time") + if api_call_start is not None and start_time is not None: + pre_ms = (api_call_start - start_time).total_seconds() * 1000 + detailed["timing_pre_processing_ms"] = round(pre_ms, 4) + + # post-processing = total - pre - llm_api + post_ms = total_response_time_ms - pre_ms - llm_api_duration_ms + detailed["timing_post_processing_ms"] = round(max(post_ms, 0), 4) + + self._update_hidden_params(detailed) + def apply(self) -> None: """Apply metadata to the response object""" if hasattr(self.result, "_hidden_params"): diff --git a/litellm/litellm_core_utils/logging_callback_manager.py b/litellm/litellm_core_utils/logging_callback_manager.py index 34d2581737..38da11e777 100644 --- a/litellm/litellm_core_utils/logging_callback_manager.py +++ b/litellm/litellm_core_utils/logging_callback_manager.py @@ -25,13 +25,31 @@ class LoggingCallbackManager: - Keep a reasonable MAX_CALLBACKS limit (this ensures callbacks don't exponentially grow and consume CPU Resources) """ - def add_litellm_input_callback(self, callback: Union[CustomLogger, str]): + # healthy maximum number of callbacks - unlikely someone needs more than 20 + MAX_CALLBACKS = 30 + + def _is_async_callable(self, callback) -> bool: + """Check if a callback is async. Used to auto-route callbacks to the correct list.""" + try: + from litellm.litellm_core_utils.coroutine_checker import coroutine_checker + + return coroutine_checker.is_async_callable(callback) + except Exception: + return False + + def add_litellm_input_callback(self, callback: Union[CustomLogger, str, Callable]): """ - Add a input callback to litellm.input_callback + Add a input callback to litellm.input_callback. + Auto-routes async callbacks to litellm._async_input_callback. """ - self._safe_add_callback_to_list( - callback=callback, parent_list=litellm.input_callback - ) + if not isinstance(callback, str) and self._is_async_callable(callback): + self._safe_add_callback_to_list( + callback=callback, parent_list=litellm._async_input_callback + ) + else: + self._safe_add_callback_to_list( + callback=callback, parent_list=litellm.input_callback + ) def add_litellm_service_callback( self, callback: Union[CustomLogger, str, Callable] @@ -57,21 +75,38 @@ class LoggingCallbackManager: self, callback: Union[CustomLogger, str, Callable] ): """ - Add a success callback to `litellm.success_callback` + Add a success callback to `litellm.success_callback`. + Auto-routes async callbacks to litellm._async_success_callback. + Special-cases 'dynamodb' and 'openmeter' as async callbacks. """ - self._safe_add_callback_to_list( - callback=callback, parent_list=litellm.success_callback - ) + if isinstance(callback, str) and callback in ("dynamodb", "openmeter"): + self._safe_add_callback_to_list( + callback=callback, parent_list=litellm._async_success_callback + ) + elif not isinstance(callback, str) and self._is_async_callable(callback): + self._safe_add_callback_to_list( + callback=callback, parent_list=litellm._async_success_callback + ) + else: + self._safe_add_callback_to_list( + callback=callback, parent_list=litellm.success_callback + ) def add_litellm_failure_callback( self, callback: Union[CustomLogger, str, Callable] ): """ - Add a failure callback to `litellm.failure_callback` + Add a failure callback to `litellm.failure_callback`. + Auto-routes async callbacks to litellm._async_failure_callback. """ - self._safe_add_callback_to_list( - callback=callback, parent_list=litellm.failure_callback - ) + if not isinstance(callback, str) and self._is_async_callable(callback): + self._safe_add_callback_to_list( + callback=callback, parent_list=litellm._async_failure_callback + ) + else: + self._safe_add_callback_to_list( + callback=callback, parent_list=litellm.failure_callback + ) def add_litellm_async_success_callback( self, callback: Union[CustomLogger, Callable, str] diff --git a/litellm/litellm_core_utils/logging_utils.py b/litellm/litellm_core_utils/logging_utils.py index bf43519afc..4b2b740935 100644 --- a/litellm/litellm_core_utils/logging_utils.py +++ b/litellm/litellm_core_utils/logging_utils.py @@ -1,10 +1,13 @@ import asyncio import functools +import inspect +import re import time from datetime import datetime from typing import TYPE_CHECKING, Any, List, Optional, Union from litellm._logging import verbose_logger +from litellm.constants import MAX_BASE64_LENGTH_FOR_LOGGING from litellm.types.utils import ( ModelResponse, ModelResponseStream, @@ -33,6 +36,110 @@ import litellm Helper utils used for logging callbacks """ +_BYTES_PER_KIB = 1024 +_BYTES_PER_MIB = 1024 * 1024 + +# Regex matching data-URI base64 content: "data:;base64," +# Captures: group(1)=mime_type, group(2)=base64_payload +_DATA_URI_RE = re.compile(r"data:([^;]+);base64,([A-Za-z0-9+/=]+)") + +# Maximum nesting depth for _truncate_base64_in_value to guard against +# pathological payloads. OpenAI message format is typically 3-4 levels deep. +_MAX_TRUNCATION_DEPTH = 20 + + +def _format_base64_size(num_chars: int) -> str: + """Return a human-readable byte-size estimate from a base64 character count.""" + num_bytes = num_chars * 3 / 4 + if num_bytes >= _BYTES_PER_MIB: + return f"{num_bytes / _BYTES_PER_MIB:.2f}MB" + if num_bytes >= _BYTES_PER_KIB: + return f"{num_bytes / _BYTES_PER_KIB:.1f}KB" + return f"{int(num_bytes)}B" + + +def _base64_data_uri_replacer(match: re.Match) -> str: + """Replace a single base64 data-URI match with a size placeholder if too long.""" + mime_type = match.group(1) + payload = match.group(2) + if len(payload) <= MAX_BASE64_LENGTH_FOR_LOGGING: + return match.group(0) + size_str = _format_base64_size(len(payload)) + return f"data:{mime_type};base64,[base64_data truncated: {size_str}]" + + +def _truncate_base64_in_string(value: str) -> str: + """Replace long base64 data-URI payloads in a string with a size placeholder.""" + if MAX_BASE64_LENGTH_FOR_LOGGING <= 0: + return value + return _DATA_URI_RE.sub(_base64_data_uri_replacer, value) + + +def _truncate_base64_in_value(value: Any) -> Any: + """Iteratively truncate base64 data URIs in a JSON-like value (str/list/dict). + + Uses an explicit stack instead of recursion to satisfy the project's + recursive-function detector and avoid stack-overflow on deep payloads. + """ + # Stack entries: (source_value, depth, parent_container, key_or_index) + # We mutate *copies* of dicts/lists in-place via parent references. + if isinstance(value, str): + return _truncate_base64_in_string(value) + if not isinstance(value, (dict, list)): + return value + + # Shallow-copy the root so we don't mutate the caller's data. + root = {k: v for k, v in value.items()} if isinstance(value, dict) else list(value) + stack: list = [(root, 0)] + + while stack: + container, depth = stack.pop() + if depth > _MAX_TRUNCATION_DEPTH: + continue + if isinstance(container, dict): + for k, v in container.items(): + if isinstance(v, str): + container[k] = _truncate_base64_in_string(v) + elif isinstance(v, dict): + copy: Union[dict, list] = {ck: cv for ck, cv in v.items()} + container[k] = copy + stack.append((copy, depth + 1)) + elif isinstance(v, list): + copy = list(v) + container[k] = copy + stack.append((copy, depth + 1)) + elif isinstance(container, list): + for i, v in enumerate(container): + if isinstance(v, str): + container[i] = _truncate_base64_in_string(v) + elif isinstance(v, dict): + copy = {ck: cv for ck, cv in v.items()} + container[i] = copy + stack.append((copy, depth + 1)) + elif isinstance(v, list): + copy = list(v) + container[i] = copy + stack.append((copy, depth + 1)) + + return root + + +def truncate_base64_in_messages( + messages: Optional[Union[str, list, dict]], +) -> Optional[Union[str, list, dict]]: + """ + Return a copy of *messages* with long base64 data-URI payloads replaced + by human-readable size placeholders. + """ + if messages is None or MAX_BASE64_LENGTH_FOR_LOGGING <= 0: + return messages + try: + return _truncate_base64_in_value(messages) + except Exception as e: + verbose_logger.debug("Failed to truncate base64 in messages: %s", e) + return messages + + # Global service logger instance to avoid recreating it _service_logger = None @@ -270,7 +377,7 @@ def track_llm_api_timing(): verbose_logger.debug(f"Error in service logging: {str(e)}") # Check if the function is async or sync - if asyncio.iscoroutinefunction(func): + if inspect.iscoroutinefunction(func): return async_wrapper return sync_wrapper diff --git a/litellm/litellm_core_utils/prompt_templates/factory.py b/litellm/litellm_core_utils/prompt_templates/factory.py index c907ed32b9..7b485501f6 100644 --- a/litellm/litellm_core_utils/prompt_templates/factory.py +++ b/litellm/litellm_core_utils/prompt_templates/factory.py @@ -2018,6 +2018,235 @@ def anthropic_process_openai_file_message( ) +def _sanitize_empty_text_content( + message: AllMessageValues, +) -> AllMessageValues: + """ + Case C: Sanitize empty text content + - Replace empty or whitespace-only text content with a placeholder message. + + Returns: + The message with sanitized content if needed, otherwise the original message + """ + if message.get("role") in ["user", "assistant"]: + content = message.get("content") + if isinstance(content, str): + if not content or not content.strip(): + message = cast(AllMessageValues, dict(message)) # Make a copy + message["content"] = "[System: Empty message content sanitised to satisfy protocol]" + verbose_logger.debug( + f"_sanitize_empty_text_content: Replaced empty text content in {message.get('role')} message" + ) + return message + + +def _add_missing_tool_results( # noqa: PLR0915 + current_message: AllMessageValues, + messages: List[AllMessageValues], + current_index: int, +) -> Tuple[List[AllMessageValues], int]: + """ + Case A: Missing tool_result for tool_use (orphaned tool calls) + - If an assistant message has tool_calls but no corresponding tool result follows, + add a dummy tool result message indicating the user did not provide the result. + + Returns: + A tuple of: + - List containing the assistant message, followed by existing tool results, + followed by any dummy tool results needed + - Number of original messages consumed (to adjust iteration index) + """ + result_messages: List[AllMessageValues] = [] + tool_calls = current_message.get("tool_calls") + + if not tool_calls or len(cast(list, tool_calls)) == 0: + return ([current_message], 0) + + # Collect all tool_call_ids from this assistant message + expected_tool_call_ids = set() + for tool_call in cast(list, tool_calls): + tool_call_id = None + if isinstance(tool_call, dict): + tool_call_id = tool_call.get("id") + else: + tool_call_id = getattr(tool_call, "id", None) + if tool_call_id: + expected_tool_call_ids.add(tool_call_id) + + # Collect actual tool result messages that follow this assistant message + found_tool_call_ids = set() + actual_tool_results: List[AllMessageValues] = [] + j = current_index + 1 + + while j < len(messages): + next_msg = messages[j] + next_role = next_msg.get("role") + + if next_role == "assistant": + break + + if next_role in ["tool", "function"]: + tool_call_id = next_msg.get("tool_call_id") + if tool_call_id and tool_call_id in expected_tool_call_ids: + found_tool_call_ids.add(tool_call_id) + actual_tool_results.append(next_msg) + + j += 1 + + # Find missing tool results + missing_tool_call_ids = expected_tool_call_ids - found_tool_call_ids + + if missing_tool_call_ids: + verbose_logger.debug( + f"_add_missing_tool_results: Found {len(missing_tool_call_ids)} orphaned tool calls. Adding dummy tool results." + ) + + result_messages.append(current_message) + + # Add existing tool results FIRST + result_messages.extend(actual_tool_results) + + # Then add dummy tool results for missing ones + for tool_call_id in missing_tool_call_ids: + tool_name = "unknown_tool" + for tool_call in cast(list, tool_calls): + tc_id = None + if isinstance(tool_call, dict): + tc_id = tool_call.get("id") + else: + tc_id = getattr(tool_call, "id", None) + + if tc_id == tool_call_id: + if isinstance(tool_call, dict): + function = tool_call.get("function", {}) + if isinstance(function, dict): + tool_name = function.get("name", "unknown_tool") + else: + tool_name = getattr(function, "name", "unknown_tool") + else: + function = getattr(tool_call, "function", None) + if function: + tool_name = getattr(function, "name", "unknown_tool") + break + + dummy_tool_result: ChatCompletionToolMessage = { + "role": "tool", + "tool_call_id": tool_call_id, + "content": f"[System: Tool execution skipped/interrupted by user. No result provided for tool '{tool_name}'.]", + } + result_messages.append(dummy_tool_result) + + # Return the messages and the number of original messages to skip + return (result_messages, len(actual_tool_results)) + + return ([current_message], 0) + + +def _is_orphaned_tool_result( + current_message: AllMessageValues, + sanitized_messages: List[AllMessageValues], +) -> bool: + """ + Case B: Orphaned tool_result (unexpected result) + - Check if a tool message references a tool_call_id that doesn't exist in the previous + assistant message. + + Returns: + True if this is an orphaned tool result that should be removed, False otherwise + """ + if current_message.get("role") not in ["tool", "function"]: + return False + + tool_call_id = current_message.get("tool_call_id") + + if not tool_call_id: + return False + + # Look back to find the most recent assistant message with tool_calls + found_matching_tool_call = False + + for j in range(len(sanitized_messages) - 1, -1, -1): + prev_msg = sanitized_messages[j] + if prev_msg.get("role") == "assistant": + tool_calls = prev_msg.get("tool_calls") + if tool_calls: + for tool_call in cast(list, tool_calls): + tc_id = None + if isinstance(tool_call, dict): + tc_id = tool_call.get("id") + else: + tc_id = getattr(tool_call, "id", None) + + if tc_id == tool_call_id: + found_matching_tool_call = True + break + + break + + if not found_matching_tool_call: + verbose_logger.debug( + "_is_orphaned_tool_result: Found orphaned tool result with redacted tool_call_id" + ) + return True + + return False + + +def sanitize_messages_for_tool_calling( + messages: List[AllMessageValues], +) -> List[AllMessageValues]: + """ + Sanitize messages for tool calling to handle common issues when modify_params=True: + + Case A: Missing tool_result for tool_use (orphaned tool calls) + - If an assistant message has tool_calls but no corresponding tool result follows, + add a dummy tool result message indicating the user did not provide the result. + + Case B: Orphaned tool_result (unexpected result) + - If a tool message references a tool_call_id that doesn't exist in the previous + assistant message, remove that tool message. + + Case C: Empty text content + - Replace empty or whitespace-only text content with a placeholder message. + + This function operates on OpenAI format messages before they are converted to + provider-specific formats. + """ + if not litellm.modify_params: + return messages + + sanitized_messages: List[AllMessageValues] = [] + i = 0 + + while i < len(messages): + current_message = messages[i] + + # Case C: Sanitize empty text content + current_message = _sanitize_empty_text_content(current_message) + + # Case A: Check if assistant message has tool_calls without following tool results + if current_message.get("role") == "assistant": + result_messages, messages_consumed = _add_missing_tool_results(current_message, messages, i) + + # If dummy tool results were added, extend sanitized_messages and skip consumed messages + if len(result_messages) > 1: + sanitized_messages.extend(result_messages) + # Skip the assistant message and any actual tool results that were included + i += 1 + messages_consumed + continue + + # Case B: Check for orphaned tool results + if _is_orphaned_tool_result(current_message, sanitized_messages): + i += 1 + continue # Skip this orphaned tool result + + # Add the message to sanitized list + sanitized_messages.append(current_message) + i += 1 + + return sanitized_messages + + def anthropic_messages_pt( # noqa: PLR0915 messages: List[AllMessageValues], model: str, @@ -2037,6 +2266,9 @@ def anthropic_messages_pt( # noqa: PLR0915 5. System messages are a separate param to the Messages API 6. Ensure we only accept role, content. (message.name is not supported) """ + # Sanitize messages for tool calling issues when modify_params=True + messages = sanitize_messages_for_tool_calling(messages) + # add role=tool support to allow function call result/error submission user_message_types = {"user", "tool", "function"} # reformat messages to ensure user/assistant are alternating, if there's either 2 consecutive 'user' messages or 2 consecutive 'assistant' message, merge them. diff --git a/litellm/litellm_core_utils/redact_messages.py b/litellm/litellm_core_utils/redact_messages.py index 5d6d1fbc1c..ad68f3851a 100644 --- a/litellm/litellm_core_utils/redact_messages.py +++ b/litellm/litellm_core_utils/redact_messages.py @@ -9,6 +9,7 @@ import asyncio import copy +import inspect from typing import TYPE_CHECKING, Any, Optional import litellm @@ -101,8 +102,8 @@ def perform_redaction(model_call_details: dict, result): # Redact result if result is not None: # Check if result is a coroutine, async generator, or other async object - these cannot be deepcopied - if (asyncio.iscoroutine(result) or - asyncio.iscoroutinefunction(result) or + if (asyncio.iscoroutine(result) or + inspect.iscoroutinefunction(result) or hasattr(result, '__aiter__') or # async generator hasattr(result, '__anext__')): # async iterator # For async objects, return a simple redacted response without deepcopy diff --git a/litellm/litellm_core_utils/safe_json_dumps.py b/litellm/litellm_core_utils/safe_json_dumps.py index 8b50e41a79..051aa2f27a 100644 --- a/litellm/litellm_core_utils/safe_json_dumps.py +++ b/litellm/litellm_core_utils/safe_json_dumps.py @@ -1,6 +1,8 @@ import json from typing import Any, Union +from pydantic import BaseModel + from litellm.constants import DEFAULT_MAX_RECURSE_DEPTH @@ -41,6 +43,11 @@ def safe_dumps(data: Any, max_depth: int = DEFAULT_MAX_RECURSE_DEPTH) -> str: result = sorted([_serialize(item, seen, depth + 1) for item in obj]) seen.remove(id(obj)) return result + elif isinstance(obj, BaseModel): + dumped = obj.model_dump() + result = _serialize(dumped, seen, depth + 1) + seen.remove(id(obj)) + return result else: # Fall back to string conversion for non-serializable objects. try: @@ -49,4 +56,4 @@ def safe_dumps(data: Any, max_depth: int = DEFAULT_MAX_RECURSE_DEPTH) -> str: return "Unserializable Object" safe_data = _serialize(data, set(), 0) - return json.dumps(safe_data, default=str) \ No newline at end of file + return json.dumps(safe_data, default=str) diff --git a/litellm/litellm_core_utils/sensitive_data_masker.py b/litellm/litellm_core_utils/sensitive_data_masker.py index 8b6ae74463..3ec34e6d9e 100644 --- a/litellm/litellm_core_utils/sensitive_data_masker.py +++ b/litellm/litellm_core_utils/sensitive_data_masker.py @@ -8,6 +8,7 @@ class SensitiveDataMasker: def __init__( self, sensitive_patterns: Optional[Set[str]] = None, + non_sensitive_overrides: Optional[Set[str]] = None, visible_prefix: int = 4, visible_suffix: int = 4, mask_char: str = "*", @@ -26,6 +27,10 @@ class SensitiveDataMasker: "fingerprint", "tenancy", } + # If any key segment matches one of these, the key is not considered sensitive + # even if it also matches a sensitive pattern. For example, "input_cost_per_token" + # contains "token" but "cost" overrides that — it's a pricing field, not a secret. + self.non_sensitive_overrides = non_sensitive_overrides or {"cost"} self.visible_prefix = visible_prefix self.visible_suffix = visible_suffix @@ -56,6 +61,13 @@ class SensitiveDataMasker: # This avoids false positives like "max_tokens" matching "token" # but still catches "api_key", "access_token", etc. key_segments = key_lower.replace("-", "_").split("_") + + # If any segment matches a non-sensitive override, the key is not sensitive. + # For example, "input_cost_per_token" contains "token" but also "cost", + # so it should not be masked — it's a pricing field, not a secret. + if any(override in key_segments for override in self.non_sensitive_overrides): + return False + result = any(pattern in key_segments for pattern in self.sensitive_patterns) return result diff --git a/litellm/litellm_core_utils/streaming_chunk_builder_utils.py b/litellm/litellm_core_utils/streaming_chunk_builder_utils.py index 76c7246b87..143d87ebf3 100644 --- a/litellm/litellm_core_utils/streaming_chunk_builder_utils.py +++ b/litellm/litellm_core_utils/streaming_chunk_builder_utils.py @@ -41,10 +41,29 @@ class ChunkProcessor: def _sort_chunks(self, chunks: list) -> list: if not chunks: return [] - if chunks[0]._hidden_params.get("created_at"): - return sorted( - chunks, key=lambda x: x._hidden_params.get("created_at", float("inf")) - ) + + first_chunk = chunks[0] + first_hidden_params: Dict[str, Any] = {} + if isinstance(first_chunk, dict): + candidate = first_chunk.get("_hidden_params", {}) + if isinstance(candidate, dict): + first_hidden_params = candidate + else: + candidate = getattr(first_chunk, "_hidden_params", {}) + if isinstance(candidate, dict): + first_hidden_params = candidate + + if first_hidden_params.get("created_at"): + def _created_at(chunk: Any) -> Union[int, float]: + if isinstance(chunk, dict): + params = chunk.get("_hidden_params", {}) + else: + params = getattr(chunk, "_hidden_params", {}) + if isinstance(params, dict): + return cast(Union[int, float], params.get("created_at", float("inf"))) + return float("inf") + + return sorted(chunks, key=_created_at) return chunks def update_model_response_with_hidden_params( diff --git a/litellm/litellm_core_utils/streaming_handler.py b/litellm/litellm_core_utils/streaming_handler.py index c6f0f67976..1f739a60b4 100644 --- a/litellm/litellm_core_utils/streaming_handler.py +++ b/litellm/litellm_core_utils/streaming_handler.py @@ -2,11 +2,13 @@ import asyncio import collections.abc import datetime import json +import logging import threading import time import traceback from typing import Any, Callable, Dict, List, Optional, Union, cast +import anyio import httpx from pydantic import BaseModel @@ -155,6 +157,27 @@ class CustomStreamWrapper: def __aiter__(self): return self + async def aclose(self): + if self.completion_stream is not None: + stream_to_close = self.completion_stream + self.completion_stream = None + # Shield from anyio cancellation so cleanup awaits can complete. + # Without this, CancelledError is thrown into every await during + # task group cancellation, preventing HTTP connection release. + with anyio.CancelScope(shield=True): + try: + if hasattr(stream_to_close, "aclose"): + await stream_to_close.aclose() + elif hasattr(stream_to_close, "close"): + result = stream_to_close.close() + if result is not None: + await result + except BaseException as e: + verbose_logger.debug( + "CustomStreamWrapper.aclose: error closing completion_stream: %s", + e, + ) + def check_send_stream_usage(self, stream_options: Optional[dict]): return ( stream_options is not None @@ -435,7 +458,7 @@ class CustomStreamWrapper: def handle_openai_chat_completion_chunk(self, chunk): try: - print_verbose(f"\nRaw OpenAI Chunk\n{chunk}\n") + str_line = chunk text = "" is_finished = False @@ -485,7 +508,7 @@ class CustomStreamWrapper: def handle_azure_text_completion_chunk(self, chunk): try: - print_verbose(f"\nRaw OpenAI Chunk\n{chunk}\n") + text = "" is_finished = False finish_reason = None @@ -506,7 +529,7 @@ class CustomStreamWrapper: def handle_openai_text_completion_chunk(self, chunk): try: - print_verbose(f"\nRaw OpenAI Chunk\n{chunk}\n") + text = "" is_finished = False finish_reason = None @@ -870,9 +893,6 @@ class CustomStreamWrapper: preserve_upstream_non_openai_attributes, ) - print_verbose( - f"completion_obj: {completion_obj}, model_response.choices[0]: {model_response.choices[0]}, response_obj: {response_obj}" - ) is_chunk_non_empty = self.is_chunk_non_empty( completion_obj, model_response, response_obj ) @@ -899,11 +919,9 @@ class CustomStreamWrapper: choice_json.pop( "finish_reason", None ) # for mistral etc. which return a value in their last chunk (not-openai compatible). - print_verbose(f"choice_json: {choice_json}") choices.append(StreamingChoices(**choice_json)) except Exception: choices.append(StreamingChoices()) - print_verbose(f"choices in streaming: {choices}") setattr(model_response, "choices", choices) else: return @@ -921,9 +939,11 @@ class CustomStreamWrapper: ) model_response = self.strip_role_from_delta(model_response) - verbose_logger.debug( - f"model_response.choices[0].delta inside is_chunk_non_empty: {model_response.choices[0].delta}" - ) + if verbose_logger.isEnabledFor(logging.DEBUG): + verbose_logger.debug( + "model_response.choices[0].delta: %s", + model_response.choices[0].delta, + ) else: ## else completion_obj["content"] = model_response_str @@ -1370,9 +1390,6 @@ class CustomStreamWrapper: ) model_response.model = self.model - print_verbose( - f"model_response finish reason 3: {self.received_finish_reason}; response_obj={response_obj}" - ) ## FUNCTION CALL PARSING original_chunk = ( response_obj.get("original_chunk") if response_obj is not None else None @@ -1432,7 +1449,6 @@ class CustomStreamWrapper: ): t.function.arguments = "" _json_delta = delta.model_dump() - print_verbose(f"_json_delta: {_json_delta}") if "role" not in _json_delta or _json_delta["role"] is None: _json_delta[ "role" @@ -1466,11 +1482,7 @@ class CustomStreamWrapper: if original_chunk.choices[0].delta is None else dict(original_chunk.choices[0].delta) ) - print_verbose(f"original delta: {delta}") model_response.choices[0].delta = Delta(**delta) - print_verbose( - f"new delta: {model_response.choices[0].delta}" - ) except Exception: model_response.choices[0].delta = Delta() else: @@ -1480,11 +1492,6 @@ class CustomStreamWrapper: ): return model_response return - print_verbose( - f"model_response.choices[0].delta: {model_response.choices[0].delta}; completion_obj: {completion_obj}" - ) - print_verbose(f"self.sent_first_chunk: {self.sent_first_chunk}") - ## CHECK FOR TOOL USE if "tool_calls" in completion_obj and len(completion_obj["tool_calls"]) > 0: @@ -1915,18 +1922,9 @@ class CustomStreamWrapper: and len(chunk.parts) == 0 ): continue - # chunk_creator() does logging/stream chunk building. We need to let it know its being called in_async_func, so we don't double add chunks. - # __anext__ also calls async_success_handler, which does logging - verbose_logger.debug( - f"PROCESSED ASYNC CHUNK PRE CHUNK CREATOR: {chunk}" - ) - processed_chunk: Optional[ModelResponseStream] = self.chunk_creator( chunk=chunk ) - verbose_logger.debug( - f"PROCESSED ASYNC CHUNK POST CHUNK CREATOR: {processed_chunk}" - ) if processed_chunk is None: continue @@ -1943,31 +1941,33 @@ class CustomStreamWrapper: self.rules.post_call_rules( input=self.response_uptil_now, model=self.model ) - self.chunks.append(processed_chunk) - + # Store a shallow copy so usage stripping below + # does not mutate the stored chunk. + self.chunks.append(processed_chunk.model_copy()) + # Add mcp_list_tools to first chunk if present if not self.sent_first_chunk: processed_chunk = self._add_mcp_list_tools_to_first_chunk(processed_chunk) self.sent_first_chunk = True - if hasattr( - processed_chunk, "usage" - ): # remove usage from chunk, only send on final chunk - # Convert the object to a dictionary + if ( + hasattr(processed_chunk, "usage") + and getattr(processed_chunk, "usage", None) is not None + ): + # Strip usage from the outgoing chunk so it's not sent twice + # (once in the chunk, once in _hidden_params). + # Create a new object without usage, matching sync behavior. + # The copy in self.chunks retains usage for calculate_total_usage(). obj_dict = processed_chunk.model_dump() - - # Remove an attribute (e.g., 'attr2') if "usage" in obj_dict: del obj_dict["usage"] - - # Create a new object without the removed attribute - processed_chunk = self.model_response_creator(chunk=obj_dict) + processed_chunk = self.model_response_creator( + chunk=obj_dict, hidden_params=processed_chunk._hidden_params + ) is_empty = is_model_response_stream_empty( model_response=cast(ModelResponseStream, processed_chunk) ) - if is_empty: continue - print_verbose(f"final returned processed chunk: {processed_chunk}") # add usage as hidden param if self.sent_last_chunk is True and self.stream_options is None: @@ -1982,7 +1982,7 @@ class CustomStreamWrapper: ) ) # Add MCP metadata to final chunk if present (after hooks) - processed_chunk = self._add_mcp_metadata_to_final_chunk(processed_chunk) + processed_chunk = self._add_mcp_metadata_to_final_chunk(processed_chunk) # type: ignore[reportArgumentType] return processed_chunk raise StopAsyncIteration @@ -1996,13 +1996,9 @@ class CustomStreamWrapper: else: chunk = next(self.completion_stream) if chunk is not None and chunk != b"": - print_verbose(f"PROCESSED CHUNK PRE CHUNK CREATOR: {chunk}") processed_chunk: Optional[ ModelResponseStream ] = self.chunk_creator(chunk=chunk) - print_verbose( - f"PROCESSED CHUNK POST CHUNK CREATOR: {processed_chunk}" - ) if processed_chunk is None: continue @@ -2193,7 +2189,7 @@ def calculate_total_usage(chunks: List[ModelResponse]) -> Usage: prompt_tokens: int = 0 completion_tokens: int = 0 for chunk in chunks: - if "usage" in chunk: + if "usage" in chunk and chunk["usage"] is not None: if "prompt_tokens" in chunk["usage"]: prompt_tokens = chunk["usage"].get("prompt_tokens", 0) or 0 if "completion_tokens" in chunk["usage"]: diff --git a/litellm/litellm_core_utils/token_counter.py b/litellm/litellm_core_utils/token_counter.py index 6b9e51034c..da357e51c2 100644 --- a/litellm/litellm_core_utils/token_counter.py +++ b/litellm/litellm_core_utils/token_counter.py @@ -726,10 +726,12 @@ def _count_content_list( if thinking_text: num_tokens += count_function(thinking_text) else: + content_type = ( + c.get("type", type(c).__name__) if isinstance(c, dict) else type(c).__name__ + ) raise ValueError( - f"Invalid content item type: {type(c).__name__}. " - f"Expected str or dict with 'type' field. " - f"Value: {c!r}" + f"Invalid content item type: {content_type}. " + f"Expected str or dict with 'type' field (text, image_url, tool_use, tool_result, thinking)." ) return num_tokens except Exception as e: diff --git a/litellm/llms/a2a/chat/guardrail_translation/handler.py b/litellm/llms/a2a/chat/guardrail_translation/handler.py index 770453f2de..fbd1da749c 100644 --- a/litellm/llms/a2a/chat/guardrail_translation/handler.py +++ b/litellm/llms/a2a/chat/guardrail_translation/handler.py @@ -10,6 +10,7 @@ A2A Protocol Format: - Output: JSON-RPC 2.0 with result containing message/artifact parts """ +import json from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union from litellm._logging import verbose_proxy_logger @@ -206,6 +207,118 @@ class A2AGuardrailHandler(BaseTranslation): response["result"] = result return response + async def process_output_streaming_response( + self, + responses_so_far: List[Any], + guardrail_to_apply: "CustomGuardrail", + litellm_logging_obj: Optional["LiteLLMLoggingObj"] = None, + user_api_key_dict: Optional["UserAPIKeyAuth"] = None, + ) -> List[Any]: + """ + Process A2A streaming output by applying guardrails to accumulated text. + + responses_so_far can be a list of JSON-RPC 2.0 objects (dict or NDJSON str), e.g.: + - task with history, status-update, artifact-update (with result.artifact.parts), + - then status-update (final). Text is extracted from result.artifact.parts, + result.message.parts, result.parts, etc., concatenated in order, guardrailed once, + then the combined guardrailed text is written into the first chunk that had text + and all other text parts in other chunks are cleared (in-place). + """ + from litellm.llms.a2a.common_utils import extract_text_from_a2a_response + + # Parse each item; keep alignment with responses_so_far (None where unparseable) + parsed: List[Optional[Dict[str, Any]]] = [None] * len(responses_so_far) + for i, item in enumerate(responses_so_far): + if isinstance(item, dict): + obj = item + elif isinstance(item, str): + try: + obj = json.loads(item.strip()) + except (json.JSONDecodeError, TypeError): + continue + else: + continue + if isinstance(obj.get("result"), dict): + parsed[i] = obj + + valid_parsed = [(i, obj) for i, obj in enumerate(parsed) if obj is not None] + if not valid_parsed: + return responses_so_far + + # Collect text from each chunk in order (by original index in responses_so_far) + text_parts: List[str] = [] + chunk_indices_with_text: List[int] = [] # indices into valid_parsed + for idx, (orig_i, obj) in enumerate(valid_parsed): + t = extract_text_from_a2a_response(obj) + if t: + text_parts.append(t) + chunk_indices_with_text.append(orig_i) + + combined_text = "".join(text_parts) + if not combined_text: + return responses_so_far + + request_data: dict = {"responses_so_far": responses_so_far} + user_metadata = self.transform_user_api_key_dict_to_metadata(user_api_key_dict) + if user_metadata: + request_data["litellm_metadata"] = user_metadata + + inputs = GenericGuardrailAPIInputs(texts=[combined_text]) + guardrailed_inputs = await guardrail_to_apply.apply_guardrail( + inputs=inputs, + request_data=request_data, + input_type="response", + logging_obj=litellm_logging_obj, + ) + guardrailed_texts = guardrailed_inputs.get("texts", []) + if not guardrailed_texts: + return responses_so_far + guardrailed_text = guardrailed_texts[0] + + # Find first chunk (by original index) that has text; put full guardrailed text there and clear rest + first_chunk_with_text: Optional[int] = ( + chunk_indices_with_text[0] if chunk_indices_with_text else None + ) + + for orig_i, obj in valid_parsed: + result = obj.get("result", {}) + if not isinstance(result, dict): + continue + texts_in_chunk: List[str] = [] + mappings: List[Tuple[Tuple[str, ...], int]] = [] + self._extract_texts_from_result( + result=result, + texts_to_check=texts_in_chunk, + task_mappings=mappings, + ) + if not mappings: + continue + if orig_i == first_chunk_with_text: + # Put full guardrailed text in first text part; clear others + for task_idx, (path, part_idx) in enumerate(mappings): + text = guardrailed_text if task_idx == 0 else "" + self._apply_text_to_path( + result=result, + path=path, + part_idx=part_idx, + text=text, + ) + else: + for path, part_idx in mappings: + self._apply_text_to_path( + result=result, + path=path, + part_idx=part_idx, + text="", + ) + + # Write back to responses_so_far where we had NDJSON strings + for i, item in enumerate(responses_so_far): + if isinstance(item, str) and parsed[i] is not None: + responses_so_far[i] = json.dumps(parsed[i]) + "\n" + + return responses_so_far + def _extract_texts_from_result( self, result: Dict[str, Any], diff --git a/litellm/llms/anthropic/chat/guardrail_translation/handler.py b/litellm/llms/anthropic/chat/guardrail_translation/handler.py index a14e7d118e..98650a238e 100644 --- a/litellm/llms/anthropic/chat/guardrail_translation/handler.py +++ b/litellm/llms/anthropic/chat/guardrail_translation/handler.py @@ -124,6 +124,9 @@ class AnthropicMessagesHandler(BaseTranslation): ) guardrailed_texts = guardrailed_inputs.get("texts", []) + guardrailed_tools = guardrailed_inputs.get("tools") + if guardrailed_tools is not None: + data["tools"] = guardrailed_tools # Step 3: Map guardrail responses back to original message structure await self._apply_guardrail_responses_to_input( @@ -194,7 +197,7 @@ class AnthropicMessagesHandler(BaseTranslation): openai_tools = self.adapter.translate_anthropic_tools_to_openai( tools=cast(List[AllAnthropicToolsValues], tools) ) - tools_to_check.extend(openai_tools) + tools_to_check.extend(openai_tools) # type: ignore async def _apply_guardrail_responses_to_input( self, diff --git a/litellm/llms/anthropic/chat/transformation.py b/litellm/llms/anthropic/chat/transformation.py index 9938cd7979..0f613ceb50 100644 --- a/litellm/llms/anthropic/chat/transformation.py +++ b/litellm/llms/anthropic/chat/transformation.py @@ -171,9 +171,22 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): return tool_call @staticmethod - def _is_claude_opus_4_6(model: str) -> bool: - """Check if the model is Claude Opus 4.5.""" - return "opus-4-6" in model.lower() or "opus_4_6" in model.lower() + def _is_claude_4_6_model(model: str) -> bool: + """Check if the model is a Claude 4.6 model that uses adaptive thinking.""" + model_lower = model.lower() + return any( + model_variant in model_lower + for model_variant in ( + "opus-4-6", + "opus_4_6", + "opus-4.6", + "opus_4.6", + "sonnet-4-6", + "sonnet_4_6", + "sonnet-4.6", + "sonnet_4.6", + ) + ) def get_supported_openai_params(self, model: str): params = [ @@ -191,11 +204,16 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): "user", "web_search_options", "speed", + "context_management", ] - if "claude-3-7-sonnet" in model or supports_reasoning( - model=model, - custom_llm_provider=self.custom_llm_provider, + if ( + "claude-3-7-sonnet" in model + or AnthropicConfig._is_claude_4_6_model(model) + or supports_reasoning( + model=model, + custom_llm_provider=self.custom_llm_provider, + ) ): params.append("thinking") params.append("reasoning_effort") @@ -206,31 +224,74 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): def filter_anthropic_output_schema(schema: Dict[str, Any]) -> Dict[str, Any]: """ Filter out unsupported fields from JSON schema for Anthropic's output_format API. - + Anthropic's output_format doesn't support certain JSON schema properties: - - maxItems: Not supported for array types - - minItems: Not supported for array types - - This function recursively removes these unsupported fields while preserving - all other valid schema properties. - + - maxItems/minItems: Not supported for array types + - minimum/maximum: Not supported for numeric types + - minLength/maxLength: Not supported for string types + + This mirrors the transformation done by the Anthropic Python SDK. + See: https://platform.claude.com/docs/en/build-with-claude/structured-outputs#how-sdk-transformation-works + + The SDK approach: + 1. Remove unsupported constraints from schema + 2. Add constraint info to description (e.g., "Must be at least 100") + 3. Validate responses against original schema Args: schema: The JSON schema dictionary to filter - + Returns: - A new dictionary with unsupported fields removed - - Related issue: https://github.com/BerriAI/litellm/issues/19444 + A new dictionary with unsupported fields removed and descriptions updated + + Related issues: + - https://github.com/BerriAI/litellm/issues/19444 """ if not isinstance(schema, dict): return schema - unsupported_fields = {"maxItems", "minItems"} + # All numeric/string/array constraints not supported by Anthropic + unsupported_fields = { + "maxItems", "minItems", # array constraints + "minimum", "maximum", # numeric constraints + "exclusiveMinimum", "exclusiveMaximum", # numeric constraints + "minLength", "maxLength", # string constraints + } + + # Build description additions from removed constraints + constraint_descriptions: list = [] + constraint_labels = { + "minItems": "minimum number of items: {}", + "maxItems": "maximum number of items: {}", + "minimum": "minimum value: {}", + "maximum": "maximum value: {}", + "exclusiveMinimum": "exclusive minimum value: {}", + "exclusiveMaximum": "exclusive maximum value: {}", + "minLength": "minimum length: {}", + "maxLength": "maximum length: {}", + } + for field in unsupported_fields: + if field in schema: + constraint_descriptions.append( + constraint_labels[field].format(schema[field]) + ) result: Dict[str, Any] = {} + + # Update description with removed constraint info + if constraint_descriptions: + existing_desc = schema.get("description", "") + constraint_note = "Note: " + ", ".join(constraint_descriptions) + "." + if existing_desc: + result["description"] = existing_desc + " " + constraint_note + else: + result["description"] = constraint_note + for key, value in schema.items(): if key in unsupported_fields: continue + if key == "description" and "description" in result: + # Already handled above + continue if key == "properties" and isinstance(value, dict): result[key] = { @@ -661,12 +722,12 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): @staticmethod def _map_reasoning_effort( - reasoning_effort: Optional[Union[REASONING_EFFORT, str]], + reasoning_effort: Optional[Union[REASONING_EFFORT, str]], model: str, ) -> Optional[AnthropicThinkingParam]: if reasoning_effort is None or reasoning_effort == "none": return None - if AnthropicConfig._is_claude_opus_4_6(model): + if AnthropicConfig._is_claude_4_6_model(model): return AnthropicThinkingParam( type="adaptive", ) @@ -714,10 +775,10 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): ) if json_schema is None: return None - + # Filter out unsupported fields for Anthropic's output_format API filtered_schema = self.filter_anthropic_output_schema(json_schema) - + return AnthropicOutputSchema( type="json_schema", schema=filtered_schema, @@ -781,6 +842,62 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): return hosted_web_search_tool + @staticmethod + def map_openai_context_management_to_anthropic( + context_management: Union[List[Dict[str, Any]], Dict[str, Any]] + ) -> Optional[Dict[str, Any]]: + """ + OpenAI format: [{"type": "compaction", "compact_threshold": 200000}] + Anthropic format: { + "edits": [ + { + "type": "compact_20260112", + "trigger": {"type": "input_tokens", "value": 150000} + } + ] + } + + Args: + context_management: OpenAI or Anthropic context_management parameter + + Returns: + Anthropic-formatted context_management dict, or None if invalid + """ + # If already in Anthropic format (dict with 'edits'), pass through + if isinstance(context_management, dict) and "edits" in context_management: + return context_management + + # If in OpenAI format (list), transform to Anthropic format + if isinstance(context_management, list): + anthropic_edits = [] + for entry in context_management: + if not isinstance(entry, dict): + continue + + entry_type = entry.get("type") + if entry_type == "compaction": + anthropic_edit: Dict[str, Any] = { + "type": "compact_20260112" + } + compact_threshold = entry.get("compact_threshold") + # Rewrite to 'trigger' with correct nesting if threshold exists + if compact_threshold is not None and isinstance(compact_threshold, (int, float)): + anthropic_edit["trigger"] = { + "type": "input_tokens", + "value": int(compact_threshold) + } + # Map any other keys by passthrough except handled ones + for k in entry: + if k not in {"type", "compact_threshold"}: # only passthrough other keys + anthropic_edit[k] = entry[k] + + anthropic_edits.append(anthropic_edit) + + if anthropic_edits: + return {"edits": anthropic_edits} + + return None + def map_openai_params( # noqa: PLR0915 self, non_default_params: dict, @@ -837,6 +954,10 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): "opus-4-5", "opus-4.6", "opus-4-6", + "sonnet-4.6", + "sonnet-4-6", + "sonnet_4.6", + "sonnet_4_6", } ): _output_format = ( @@ -883,9 +1004,12 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): ) elif param == "extra_headers": optional_params["extra_headers"] = value - elif param == "context_management" and isinstance(value, dict): - # Pass through Anthropic-specific context_management parameter - optional_params["context_management"] = value + elif param == "context_management": + # Supports both OpenAI list format and Anthropic dict format + if isinstance(value, (list, dict)): + anthropic_context_management = self.map_openai_context_management_to_anthropic(value) + if anthropic_context_management is not None: + optional_params["context_management"] = anthropic_context_management elif param == "speed" and isinstance(value, str): # Pass through Anthropic-specific speed parameter for fast mode optional_params["speed"] = value @@ -940,7 +1064,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): anthropic_system_message_list: List[AnthropicSystemMessageContent] = [] for idx, message in enumerate(messages): if message["role"] == "system": - valid_content: bool = False + system_prompt_indices.append(idx) system_message_block = ChatCompletionSystemMessage(**message) if isinstance(system_message_block["content"], str): # Skip empty text blocks - Anthropic API raises errors for empty text @@ -960,7 +1084,6 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): anthropic_system_message_list.append( anthropic_system_message_content ) - valid_content = True elif isinstance(message["content"], list): for _content in message["content"]: # Skip empty text blocks - Anthropic API raises errors for empty text @@ -984,10 +1107,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): anthropic_system_message_list.append( anthropic_system_message_content ) - valid_content = True - if valid_content: - system_prompt_indices.append(idx) if len(system_prompt_indices) > 0: for idx in reversed(system_prompt_indices): messages.pop(idx) @@ -1032,7 +1152,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): """ Ensure a beta header value is present in the anthropic-beta header. Merges with existing values instead of overriding them. - + Args: headers: Dictionary of headers to update beta_value: The beta header value to add @@ -1046,32 +1166,39 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): headers["anthropic-beta"] = f"{existing_beta}, {beta_value}" def _ensure_context_management_beta_header( - self, headers: dict, context_management: dict + self, headers: dict, context_management: object ) -> None: """ Add appropriate beta headers based on context_management edits. - - If any edit has type "compact_20260112", add compact-2026-01-12 header - - For all other edits, add context-management-2025-06-27 header """ - edits = context_management.get("edits", []) - + edits = [] + # If anthropic format (dict with "edits" key) + if isinstance(context_management, dict) and "edits" in context_management: + edits = context_management.get("edits", []) + # If OpenAI format: list of context management entries + elif isinstance(context_management, list): + edits = context_management + # Defensive: ignore/fallback if context_management not valid + else: + return + has_compact = False has_other = False - + for edit in edits: edit_type = edit.get("type", "") - if edit_type == "compact_20260112": + if edit_type == "compact_20260112" or edit_type == "compaction": has_compact = True else: has_other = True - - # Add compact header if any compact edits exist + + # Add compact header if any compact edits/entries exist if has_compact: self._ensure_beta_header( headers, ANTHROPIC_BETA_HEADER_VALUES.COMPACT_2026_01_12.value ) - - # Add context management header if any other edits exist + + # Add context management header if any other edits/entries exist if has_other: self._ensure_beta_header( headers, ANTHROPIC_BETA_HEADER_VALUES.CONTEXT_MANAGEMENT_2025_06_27.value @@ -1081,7 +1208,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): self, headers: dict, optional_params: dict ) -> dict: """Update headers with optional anthropic beta.""" - + # Skip adding beta headers for Vertex requests # Vertex AI handles these headers differently is_vertex_request = optional_params.get("is_vertex_request", False) @@ -1238,9 +1365,13 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): output_config = optional_params.get("output_config") if output_config and isinstance(output_config, dict): effort = output_config.get("effort") - if effort and effort not in ["high", "medium", "low"]: + if effort and effort not in ["high", "medium", "low", "max"]: raise ValueError( - f"Invalid effort value: {effort}. Must be one of: 'high', 'medium', 'low'" + f"Invalid effort value: {effort}. Must be one of: 'high', 'medium', 'low', 'max'" + ) + if effort == "max" and not self._is_claude_4_6_model(model): + raise ValueError( + f"effort='max' is only supported by Claude 4.6 models (Opus 4.6, Sonnet 4.6). Got model: {model}" ) data["output_config"] = output_config @@ -1316,7 +1447,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): elif content["type"] == "web_fetch_tool_result": if web_search_results is None: web_search_results = [] - web_search_results.append(content) + web_search_results.append(content) else: # All other tool results (bash_code_execution_tool_result, text_editor_code_execution_tool_result, etc.) if tool_results is None: @@ -1333,7 +1464,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): thinking_blocks.append( cast(ChatCompletionRedactedThinkingBlock, content) ) - + ## COMPACTION elif content["type"] == "compaction": if compaction_blocks is None: @@ -1541,7 +1672,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): provider_specific_fields["container"] = container if compaction_blocks is not None: provider_specific_fields["compaction_blocks"] = compaction_blocks - + _message = litellm.Message( tool_calls=tool_calls, content=text_content or None, diff --git a/litellm/llms/anthropic/common_utils.py b/litellm/llms/anthropic/common_utils.py index cb23d21fbc..0cceddd9ac 100644 --- a/litellm/llms/anthropic/common_utils.py +++ b/litellm/llms/anthropic/common_utils.py @@ -22,6 +22,15 @@ from litellm.types.llms.anthropic import ( from litellm.types.llms.openai import AllMessageValues +def is_anthropic_oauth_key(value: Optional[str]) -> bool: + """Check if a value contains an Anthropic OAuth token (sk-ant-oat*).""" + if value is None: + return False + # Handle both raw token and "Bearer " format + if value.startswith("Bearer "): + value = value[7:] + return value.startswith(ANTHROPIC_OAUTH_TOKEN_PREFIX) + def optionally_handle_anthropic_oauth( headers: dict, api_key: Optional[str] ) -> tuple[dict, Optional[str]]: @@ -38,9 +47,18 @@ def optionally_handle_anthropic_oauth( Returns: Tuple of (updated headers, api_key) """ + # Check Authorization header (passthrough / forwarded requests) auth_header = headers.get("authorization", "") if auth_header and auth_header.startswith(f"Bearer {ANTHROPIC_OAUTH_TOKEN_PREFIX}"): api_key = auth_header.replace("Bearer ", "") + headers.pop("x-api-key", None) + headers["anthropic-beta"] = ANTHROPIC_OAUTH_BETA_HEADER + headers["anthropic-dangerous-direct-browser-access"] = "true" + return headers, api_key + # Check api_key directly (standard chat/completion flow) + if api_key and api_key.startswith(ANTHROPIC_OAUTH_TOKEN_PREFIX): + headers.pop("x-api-key", None) + headers["authorization"] = f"Bearer {api_key}" headers["anthropic-beta"] = ANTHROPIC_OAUTH_BETA_HEADER headers["anthropic-dangerous-direct-browser-access"] = "true" return headers, api_key @@ -108,7 +126,9 @@ class AnthropicModelInfo(BaseLLMModelInfo): if tools is None: return False for tool in tools: - if "type" in tool and tool["type"].startswith(ANTHROPIC_HOSTED_TOOLS.WEB_SEARCH.value): + if "type" in tool and tool["type"].startswith( + ANTHROPIC_HOSTED_TOOLS.WEB_SEARCH.value + ): return True return False @@ -134,111 +154,126 @@ class AnthropicModelInfo(BaseLLMModelInfo): """ if not tools: return False - + for tool in tools: tool_type = tool.get("type", "") - if tool_type in ["tool_search_tool_regex_20251119", "tool_search_tool_bm25_20251119"]: + if tool_type in [ + "tool_search_tool_regex_20251119", + "tool_search_tool_bm25_20251119", + ]: return True return False - + def is_programmatic_tool_calling_used(self, tools: Optional[List]) -> bool: """ Check if programmatic tool calling is being used (tools with allowed_callers field). - + Returns True if any tool has allowed_callers containing 'code_execution_20250825'. """ if not tools: return False - + for tool in tools: # Check top-level allowed_callers allowed_callers = tool.get("allowed_callers", None) if allowed_callers and isinstance(allowed_callers, list): if "code_execution_20250825" in allowed_callers: return True - + # Check function.allowed_callers for OpenAI format tools function = tool.get("function", {}) if isinstance(function, dict): function_allowed_callers = function.get("allowed_callers", None) - if function_allowed_callers and isinstance(function_allowed_callers, list): + if function_allowed_callers and isinstance( + function_allowed_callers, list + ): if "code_execution_20250825" in function_allowed_callers: return True - + return False - + def is_input_examples_used(self, tools: Optional[List]) -> bool: """ Check if input_examples is being used in any tools. - + Returns True if any tool has input_examples field. """ if not tools: return False - + for tool in tools: # Check top-level input_examples input_examples = tool.get("input_examples", None) - if input_examples and isinstance(input_examples, list) and len(input_examples) > 0: + if ( + input_examples + and isinstance(input_examples, list) + and len(input_examples) > 0 + ): return True - + # Check function.input_examples for OpenAI format tools function = tool.get("function", {}) if isinstance(function, dict): function_input_examples = function.get("input_examples", None) - if function_input_examples and isinstance(function_input_examples, list) and len(function_input_examples) > 0: + if ( + function_input_examples + and isinstance(function_input_examples, list) + and len(function_input_examples) > 0 + ): return True - + return False - - def is_effort_used(self, optional_params: Optional[dict], model: Optional[str] = None) -> bool: + + def is_effort_used( + self, optional_params: Optional[dict], model: Optional[str] = None + ) -> bool: """ Check if effort parameter is being used. - + Returns True if effort-related parameters are present. """ if not optional_params: return False - + # Check if reasoning_effort is provided for Claude Opus 4.5 if model and ("opus-4-5" in model.lower() or "opus_4_5" in model.lower()): reasoning_effort = optional_params.get("reasoning_effort") if reasoning_effort and isinstance(reasoning_effort, str): return True - + # Check if output_config is directly provided output_config = optional_params.get("output_config") if output_config and isinstance(output_config, dict): effort = output_config.get("effort") if effort and isinstance(effort, str): return True - + return False def is_code_execution_tool_used(self, tools: Optional[List]) -> bool: """ Check if code execution tool is being used. - + Returns True if any tool has type "code_execution_20250825". """ if not tools: return False - + for tool in tools: tool_type = tool.get("type", "") if tool_type == "code_execution_20250825": return True return False - + def is_container_with_skills_used(self, optional_params: Optional[dict]) -> bool: """ Check if container with skills is being used. - + Returns True if optional_params contains container with skills. """ if not optional_params: return False - + container = optional_params.get("container") if container and isinstance(container, dict): skills = container.get("skills") @@ -256,10 +291,10 @@ class AnthropicModelInfo(BaseLLMModelInfo): def get_computer_tool_beta_header(self, computer_tool_version: str) -> str: """ Get the appropriate beta header for a given computer tool version. - + Args: computer_tool_version: The computer tool version (e.g., 'computer_20250124', 'computer_20241022') - + Returns: The corresponding beta header string """ @@ -282,37 +317,37 @@ class AnthropicModelInfo(BaseLLMModelInfo): ) -> List[str]: """ Get list of common beta headers based on the features that are active. - + Returns: List of beta header strings """ from litellm.types.llms.anthropic import ( ANTHROPIC_EFFORT_BETA_HEADER, ) - + betas = [] - + # Detect features effort_used = self.is_effort_used(optional_params, model) - + if effort_used: betas.append(ANTHROPIC_EFFORT_BETA_HEADER) # effort-2025-11-24 - + if computer_tool_used: beta_header = self.get_computer_tool_beta_header(computer_tool_used) betas.append(beta_header) - + # Anthropic no longer requires the prompt-caching beta header # Prompt caching now works automatically when cache_control is used in messages # Reference: https://docs.anthropic.com/en/docs/build-with-claude/prompt-caching - + if file_id_used: betas.append("files-api-2025-04-14") betas.append("code-execution-2025-05-22") - + if mcp_server_used: betas.append("mcp-client-2025-04-04") - + return list(set(betas)) def get_anthropic_headers( @@ -351,27 +386,35 @@ class AnthropicModelInfo(BaseLLMModelInfo): # Tool search, programmatic tool calling, and input_examples all use the same beta header if tool_search_used or programmatic_tool_calling_used or input_examples_used: from litellm.types.llms.anthropic import ANTHROPIC_TOOL_SEARCH_BETA_HEADER + betas.add(ANTHROPIC_TOOL_SEARCH_BETA_HEADER) - + # Effort parameter uses a separate beta header if effort_used: from litellm.types.llms.anthropic import ANTHROPIC_EFFORT_BETA_HEADER + betas.add(ANTHROPIC_EFFORT_BETA_HEADER) - + # Code execution tool uses a separate beta header if code_execution_tool_used: betas.add("code-execution-2025-08-25") - + # Container with skills uses a separate beta header if container_with_skills_used: betas.add("skills-2025-10-02") + _is_oauth = api_key and api_key.startswith(ANTHROPIC_OAUTH_TOKEN_PREFIX) headers = { "anthropic-version": anthropic_version or "2023-06-01", - "x-api-key": api_key, "accept": "application/json", "content-type": "application/json", } + if _is_oauth: + headers["authorization"] = f"Bearer {api_key}" + headers["anthropic-dangerous-direct-browser-access"] = "true" + betas.add(ANTHROPIC_OAUTH_BETA_HEADER) + else: + headers["x-api-key"] = api_key if user_anthropic_beta_headers is not None: betas.update(user_anthropic_beta_headers) @@ -381,7 +424,10 @@ class AnthropicModelInfo(BaseLLMModelInfo): # Vertex AI requires web search beta header for web search to work if web_search_tool_used: from litellm.types.llms.anthropic import ANTHROPIC_BETA_HEADER_VALUES - headers["anthropic-beta"] = ANTHROPIC_BETA_HEADER_VALUES.WEB_SEARCH_2025_03_05.value + + headers[ + "anthropic-beta" + ] = ANTHROPIC_BETA_HEADER_VALUES.WEB_SEARCH_2025_03_05.value elif len(betas) > 0: headers["anthropic-beta"] = ",".join(betas) @@ -398,7 +444,9 @@ class AnthropicModelInfo(BaseLLMModelInfo): api_base: Optional[str] = None, ) -> Dict: # Check for Anthropic OAuth token in headers - headers, api_key = optionally_handle_anthropic_oauth(headers=headers, api_key=api_key) + headers, api_key = optionally_handle_anthropic_oauth( + headers=headers, api_key=api_key + ) if api_key is None: raise litellm.AuthenticationError( message="Missing Anthropic API Key - A call is being made to anthropic but no key is set either in the environment variables or via params. Please set `ANTHROPIC_API_KEY` in your environment vars", @@ -416,11 +464,15 @@ class AnthropicModelInfo(BaseLLMModelInfo): file_id_used = self.is_file_id_used(messages=messages) web_search_tool_used = self.is_web_search_tool_used(tools=tools) tool_search_used = self.is_tool_search_used(tools=tools) - programmatic_tool_calling_used = self.is_programmatic_tool_calling_used(tools=tools) + programmatic_tool_calling_used = self.is_programmatic_tool_calling_used( + tools=tools + ) input_examples_used = self.is_input_examples_used(tools=tools) effort_used = self.is_effort_used(optional_params=optional_params, model=model) code_execution_tool_used = self.is_code_execution_tool_used(tools=tools) - container_with_skills_used = self.is_container_with_skills_used(optional_params=optional_params) + container_with_skills_used = self.is_container_with_skills_used( + optional_params=optional_params + ) user_anthropic_beta_headers = self._get_user_anthropic_beta_headers( anthropic_beta_header=headers.get("anthropic-beta") ) @@ -499,7 +551,7 @@ class AnthropicModelInfo(BaseLLMModelInfo): def get_token_counter(self) -> Optional[BaseTokenCounter]: """ Factory method to create an Anthropic token counter. - + Returns: AnthropicTokenCounter instance for this provider. """ diff --git a/litellm/llms/anthropic/experimental_pass_through/adapters/handler.py b/litellm/llms/anthropic/experimental_pass_through/adapters/handler.py index c6caaddf98..73e74c228b 100644 --- a/litellm/llms/anthropic/experimental_pass_through/adapters/handler.py +++ b/litellm/llms/anthropic/experimental_pass_through/adapters/handler.py @@ -19,6 +19,7 @@ from litellm.types.llms.anthropic_messages.anthropic_response import ( AnthropicMessagesResponse, ) from litellm.types.utils import ModelResponse +from litellm.utils import get_model_info if TYPE_CHECKING: pass @@ -63,6 +64,14 @@ class LiteLLMMessagesToCompletionTransformationHandler: return model = completion_kwargs.get("model") + try: + model_info = get_model_info(model=cast(str, model), custom_llm_provider=custom_llm_provider) + if model_info and model_info.get("supports_reasoning") is False: + # Model doesn't support reasoning/responses API, don't route + return + except Exception: + pass + if isinstance(model, str) and model and not model.startswith("responses/"): # Prefix model with "responses/" to route to OpenAI Responses API completion_kwargs["model"] = f"responses/{model}" diff --git a/litellm/llms/anthropic/experimental_pass_through/adapters/streaming_iterator.py b/litellm/llms/anthropic/experimental_pass_through/adapters/streaming_iterator.py index a86820f82e..de634ff9ec 100644 --- a/litellm/llms/anthropic/experimental_pass_through/adapters/streaming_iterator.py +++ b/litellm/llms/anthropic/experimental_pass_through/adapters/streaming_iterator.py @@ -239,8 +239,13 @@ class AnthropicStreamWrapper(AdapterCompletionStreamWrapper): merged_chunk["delta"] = {} # Add usage to the held chunk + uncached_input_tokens = chunk.usage.prompt_tokens or 0 + if hasattr(chunk.usage, "prompt_tokens_details") and chunk.usage.prompt_tokens_details: + cached_tokens = getattr(chunk.usage.prompt_tokens_details, "cached_tokens", 0) or 0 + uncached_input_tokens -= cached_tokens + usage_dict: UsageDelta = { - "input_tokens": chunk.usage.prompt_tokens or 0, + "input_tokens": uncached_input_tokens, "output_tokens": chunk.usage.completion_tokens or 0, } # Add cache tokens if available (for prompt caching support) @@ -412,6 +417,7 @@ class AnthropicStreamWrapper(AdapterCompletionStreamWrapper): if block_type == "tool_use": # Type narrowing: content_block_start is ToolUseBlock when block_type is "tool_use" from typing import cast + from litellm.types.llms.anthropic import ToolUseBlock tool_block = cast(ToolUseBlock, content_block_start) @@ -430,6 +436,7 @@ class AnthropicStreamWrapper(AdapterCompletionStreamWrapper): # if we get a function name since it signals a new tool call if block_type == "tool_use": from typing import cast + from litellm.types.llms.anthropic import ToolUseBlock tool_block = cast(ToolUseBlock, content_block_start) diff --git a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py index 169b138a5f..8b21569546 100644 --- a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py +++ b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py @@ -299,6 +299,26 @@ class LiteLLMAnthropicMessagesAdapter: """ return ["messages", "metadata", "system", "tool_choice", "tools", "thinking", "output_format"] + def _is_web_search_tool(self, tool: Dict[str, Any]) -> bool: + """ + Check if a tool is an Anthropic web search tool. + + Anthropic web search tools have: + - type starting with "web_search" (e.g., "web_search_20260209") + - name = "web_search" + + Args: + tool: Tool definition dict + + Returns: + True if this is a web search tool + """ + tool_type = tool.get("type", "") + tool_name = tool.get("name", "") + return ( + isinstance(tool_type, str) and tool_type.startswith("web_search") + ) or tool_name == "web_search" + def translate_anthropic_messages_to_openai( # noqa: PLR0915 self, messages: List[ @@ -872,10 +892,25 @@ class LiteLLMAnthropicMessagesAdapter: if "tools" in anthropic_message_request: tools = anthropic_message_request["tools"] if tools: - new_kwargs["tools"], tool_name_mapping = self.translate_anthropic_tools_to_openai( - tools=cast(List[AllAnthropicToolsValues], tools), - model=new_kwargs.get("model"), - ) + # Separate web search tools from regular tools + web_search_tools = [] + regular_tools = [] + for tool in tools: + if self._is_web_search_tool(cast(Dict[str, Any], tool)): + web_search_tools.append(tool) + else: + regular_tools.append(tool) + + # If web search tools are present, add web_search_options parameter + if web_search_tools: + new_kwargs["web_search_options"] = {} # type: ignore + + # Only translate regular tools (non-web-search) + if regular_tools: + new_kwargs["tools"], tool_name_mapping = self.translate_anthropic_tools_to_openai( + tools=cast(List[AllAnthropicToolsValues], regular_tools), + model=new_kwargs.get("model"), + ) ## CONVERT THINKING if "thinking" in anthropic_message_request: @@ -1070,8 +1105,13 @@ class LiteLLMAnthropicMessagesAdapter: ) # extract usage usage: Usage = getattr(response, "usage") + uncached_input_tokens = usage.prompt_tokens or 0 + if hasattr(usage, "prompt_tokens_details") and usage.prompt_tokens_details: + cached_tokens = getattr(usage.prompt_tokens_details, "cached_tokens", 0) or 0 + uncached_input_tokens -= cached_tokens + anthropic_usage = AnthropicUsage( - input_tokens=usage.prompt_tokens or 0, + input_tokens=uncached_input_tokens, output_tokens=usage.completion_tokens or 0, ) # Add cache tokens if available (for prompt caching support) @@ -1230,8 +1270,13 @@ class LiteLLMAnthropicMessagesAdapter: else: litellm_usage_chunk = None if litellm_usage_chunk is not None: + uncached_input_tokens = litellm_usage_chunk.prompt_tokens or 0 + if hasattr(litellm_usage_chunk, "prompt_tokens_details") and litellm_usage_chunk.prompt_tokens_details: + cached_tokens = getattr(litellm_usage_chunk.prompt_tokens_details, "cached_tokens", 0) or 0 + uncached_input_tokens -= cached_tokens + usage_delta = UsageDelta( - input_tokens=litellm_usage_chunk.prompt_tokens or 0, + input_tokens=uncached_input_tokens, output_tokens=litellm_usage_chunk.completion_tokens or 0, ) # Add cache tokens if available (for prompt caching support) diff --git a/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py b/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py index 8f2f3bf354..e8d7a0383f 100644 --- a/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py +++ b/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py @@ -49,15 +49,15 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig): # TODO: Add Anthropic `metadata` support # "metadata", ] - + @staticmethod def _filter_billing_headers_from_system(system_param): """ Filter out x-anthropic-billing-header metadata from system parameter. - + Args: system_param: Can be a string or a list of system message content blocks - + Returns: Filtered system parameter (string or list), or None if all content was filtered """ @@ -74,7 +74,9 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig): text = content_block.get("text", "") content_type = content_block.get("type", "") # Skip text blocks that start with billing header - if content_type == "text" and text.startswith("x-anthropic-billing-header:"): + if content_type == "text" and text.startswith( + "x-anthropic-billing-header:" + ): continue filtered_list.append(content_block) else: @@ -111,11 +113,13 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig): import os # Check for Anthropic OAuth token in Authorization header - headers, api_key = optionally_handle_anthropic_oauth(headers=headers, api_key=api_key) + headers, api_key = optionally_handle_anthropic_oauth( + headers=headers, api_key=api_key + ) if api_key is None: api_key = os.getenv("ANTHROPIC_API_KEY") - if "x-api-key" not in headers and api_key: + if "x-api-key" not in headers and "authorization" not in headers and api_key: headers["x-api-key"] = api_key if "anthropic-version" not in headers: headers["anthropic-version"] = DEFAULT_ANTHROPIC_API_VERSION @@ -149,7 +153,7 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig): message="max_tokens is required for Anthropic /v1/messages API", status_code=400, ) - + # Filter out x-anthropic-billing-header from system messages system_param = anthropic_messages_optional_request_params.get("system") if system_param is not None: @@ -159,7 +163,18 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig): else: # Remove system parameter if all content was filtered out anthropic_messages_optional_request_params.pop("system", None) - + + # Transform context_management from OpenAI format to Anthropic format if needed + context_management_param = anthropic_messages_optional_request_params.get("context_management") + if context_management_param is not None: + from litellm.llms.anthropic.chat.transformation import AnthropicConfig + + transformed_context_management = AnthropicConfig.map_openai_context_management_to_anthropic( + context_management_param + ) + if transformed_context_management is not None: + anthropic_messages_optional_request_params["context_management"] = transformed_context_management + ####### get required params for all anthropic messages requests ###### verbose_logger.debug(f"TRANSFORMATION DEBUG - Messages: {messages}") anthropic_messages_request: AnthropicMessagesRequest = AnthropicMessagesRequest( @@ -244,25 +259,29 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig): edits = context_management_param.get("edits", []) has_compact = False has_other = False - + for edit in edits: edit_type = edit.get("type", "") if edit_type == "compact_20260112": has_compact = True else: has_other = True - + # Add compact header if any compact edits exist if has_compact: beta_values.add(ANTHROPIC_BETA_HEADER_VALUES.COMPACT_2026_01_12.value) - + # Add context management header if any other edits exist if has_other: - beta_values.add(ANTHROPIC_BETA_HEADER_VALUES.CONTEXT_MANAGEMENT_2025_06_27.value) + beta_values.add( + ANTHROPIC_BETA_HEADER_VALUES.CONTEXT_MANAGEMENT_2025_06_27.value + ) # Check for structured outputs if optional_params.get("output_format") is not None: - beta_values.add(ANTHROPIC_BETA_HEADER_VALUES.STRUCTURED_OUTPUT_2025_09_25.value) + beta_values.add( + ANTHROPIC_BETA_HEADER_VALUES.STRUCTURED_OUTPUT_2025_09_25.value + ) # Check for fast mode if optional_params.get("speed") == "fast": diff --git a/litellm/llms/azure/chat/gpt_transformation.py b/litellm/llms/azure/chat/gpt_transformation.py index 18dad503a5..69eda95be1 100644 --- a/litellm/llms/azure/chat/gpt_transformation.py +++ b/litellm/llms/azure/chat/gpt_transformation.py @@ -106,6 +106,7 @@ class AzureOpenAIConfig(BaseConfig): "audio", "web_search_options", "prompt_cache_key", + "store", ] def _is_response_format_supported_model(self, model: str) -> bool: @@ -158,7 +159,6 @@ class AzureOpenAIConfig(BaseConfig): api_version: str = "", ) -> dict: supported_openai_params = self.get_supported_openai_params(model) - api_version_times = api_version.split("-") if len(api_version_times) >= 3: @@ -245,7 +245,6 @@ class AzureOpenAIConfig(BaseConfig): optional_params["tools"].extend(value) elif param in supported_openai_params: optional_params[param] = value - return optional_params def transform_request( diff --git a/litellm/llms/azure/responses/transformation.py b/litellm/llms/azure/responses/transformation.py index 44ce368fd4..78631d3800 100644 --- a/litellm/llms/azure/responses/transformation.py +++ b/litellm/llms/azure/responses/transformation.py @@ -1,5 +1,5 @@ -from typing import TYPE_CHECKING, Any, Dict, List, Literal, Optional, Tuple, Union from copy import deepcopy +from typing import TYPE_CHECKING, Any, Dict, List, Literal, Optional, Tuple, Union import httpx from openai.types.responses import ResponseReasoningItem @@ -21,10 +21,25 @@ else: class AzureOpenAIResponsesAPIConfig(OpenAIResponsesAPIConfig): + + # Parameters not supported by Azure Responses API + AZURE_UNSUPPORTED_PARAMS = ["context_management"] + @property def custom_llm_provider(self) -> LlmProviders: return LlmProviders.AZURE + def get_supported_openai_params(self, model: str) -> list: + """ + Azure Responses API does not support context_management (compaction). + """ + base_supported_params = super().get_supported_openai_params(model) + return [ + param + for param in base_supported_params + if param not in self.AZURE_UNSUPPORTED_PARAMS + ] + def validate_environment( self, headers: dict, model: str, litellm_params: Optional[GenericLiteLLMParams] ) -> dict: diff --git a/litellm/llms/base_llm/evals/__init__.py b/litellm/llms/base_llm/evals/__init__.py new file mode 100644 index 0000000000..948ed5364e --- /dev/null +++ b/litellm/llms/base_llm/evals/__init__.py @@ -0,0 +1,7 @@ +""" +Base configuration for Evals API +""" + +from .transformation import BaseEvalsAPIConfig + +__all__ = ["BaseEvalsAPIConfig"] diff --git a/litellm/llms/base_llm/evals/transformation.py b/litellm/llms/base_llm/evals/transformation.py new file mode 100644 index 0000000000..54dc2f7aae --- /dev/null +++ b/litellm/llms/base_llm/evals/transformation.py @@ -0,0 +1,542 @@ +""" +Base configuration class for Evals API +""" + +from abc import ABC, abstractmethod +from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple + +import httpx + +from litellm.llms.base_llm.chat.transformation import BaseLLMException +from litellm.types.llms.openai_evals import ( + CancelEvalResponse, + CancelRunResponse, + CreateEvalRequest, + CreateRunRequest, + DeleteEvalResponse, + Eval, + ListEvalsParams, + ListEvalsResponse, + ListRunsParams, + ListRunsResponse, + Run, + RunDeleteResponse, + UpdateEvalRequest, +) +from litellm.types.router import GenericLiteLLMParams +from litellm.types.utils import LlmProviders + +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj + + LiteLLMLoggingObj = _LiteLLMLoggingObj +else: + LiteLLMLoggingObj = Any + + +class BaseEvalsAPIConfig(ABC): + """Base configuration for Evals API providers""" + + def __init__(self): + pass + + @property + @abstractmethod + def custom_llm_provider(self) -> LlmProviders: + pass + + @abstractmethod + def validate_environment( + self, headers: dict, litellm_params: Optional[GenericLiteLLMParams] + ) -> dict: + """ + Validate and update headers with provider-specific requirements + + Args: + headers: Base headers dictionary + litellm_params: LiteLLM parameters + + Returns: + Updated headers dictionary + """ + return headers + + @abstractmethod + def get_complete_url( + self, + api_base: Optional[str], + endpoint: str, + eval_id: Optional[str] = None, + ) -> str: + """ + Get the complete URL for the API request + + Args: + api_base: Base API URL + endpoint: API endpoint (e.g., 'evals', 'evals/{id}') + eval_id: Optional eval ID for specific eval operations + + Returns: + Complete URL + """ + if api_base is None: + raise ValueError("api_base is required") + return f"{api_base}/v1/{endpoint}" + + @abstractmethod + def transform_create_eval_request( + self, + create_request: CreateEvalRequest, + litellm_params: GenericLiteLLMParams, + headers: dict, + ) -> Dict: + """ + Transform create eval request to provider-specific format + + Args: + create_request: Eval creation parameters + litellm_params: LiteLLM parameters + headers: Request headers + + Returns: + Provider-specific request body + """ + pass + + @abstractmethod + def transform_create_eval_response( + self, + raw_response: httpx.Response, + logging_obj: LiteLLMLoggingObj, + ) -> Eval: + """ + Transform provider response to Eval object + + Args: + raw_response: Raw HTTP response + logging_obj: Logging object + + Returns: + Eval object + """ + pass + + @abstractmethod + def transform_list_evals_request( + self, + list_params: ListEvalsParams, + litellm_params: GenericLiteLLMParams, + headers: dict, + ) -> Tuple[str, Dict]: + """ + Transform list evals request parameters + + Args: + list_params: List parameters (pagination, filters) + litellm_params: LiteLLM parameters + headers: Request headers + + Returns: + Tuple of (url, query_params) + """ + pass + + @abstractmethod + def transform_list_evals_response( + self, + raw_response: httpx.Response, + logging_obj: LiteLLMLoggingObj, + ) -> ListEvalsResponse: + """ + Transform provider response to ListEvalsResponse + + Args: + raw_response: Raw HTTP response + logging_obj: Logging object + + Returns: + ListEvalsResponse object + """ + pass + + @abstractmethod + def transform_get_eval_request( + self, + eval_id: str, + api_base: str, + litellm_params: GenericLiteLLMParams, + headers: dict, + ) -> Tuple[str, Dict]: + """ + Transform get eval request + + Args: + eval_id: Eval ID + api_base: Base API URL + litellm_params: LiteLLM parameters + headers: Request headers + + Returns: + Tuple of (url, headers) + """ + pass + + @abstractmethod + def transform_get_eval_response( + self, + raw_response: httpx.Response, + logging_obj: LiteLLMLoggingObj, + ) -> Eval: + """ + Transform provider response to Eval object + + Args: + raw_response: Raw HTTP response + logging_obj: Logging object + + Returns: + Eval object + """ + pass + + @abstractmethod + def transform_update_eval_request( + self, + eval_id: str, + update_request: UpdateEvalRequest, + api_base: str, + litellm_params: GenericLiteLLMParams, + headers: dict, + ) -> Tuple[str, Dict, Dict]: + """ + Transform update eval request + + Args: + eval_id: Eval ID + update_request: Update parameters + api_base: Base API URL + litellm_params: LiteLLM parameters + headers: Request headers + + Returns: + Tuple of (url, headers, body) + """ + pass + + @abstractmethod + def transform_update_eval_response( + self, + raw_response: httpx.Response, + logging_obj: LiteLLMLoggingObj, + ) -> Eval: + """ + Transform provider response to Eval object + + Args: + raw_response: Raw HTTP response + logging_obj: Logging object + + Returns: + Eval object + """ + pass + + @abstractmethod + def transform_delete_eval_request( + self, + eval_id: str, + api_base: str, + litellm_params: GenericLiteLLMParams, + headers: dict, + ) -> Tuple[str, Dict]: + """ + Transform delete eval request + + Args: + eval_id: Eval ID + api_base: Base API URL + litellm_params: LiteLLM parameters + headers: Request headers + + Returns: + Tuple of (url, headers) + """ + pass + + @abstractmethod + def transform_delete_eval_response( + self, + raw_response: httpx.Response, + logging_obj: LiteLLMLoggingObj, + ) -> DeleteEvalResponse: + """ + Transform provider response to DeleteEvalResponse + + Args: + raw_response: Raw HTTP response + logging_obj: Logging object + + Returns: + DeleteEvalResponse object + """ + pass + + @abstractmethod + def transform_cancel_eval_request( + self, + eval_id: str, + api_base: str, + litellm_params: GenericLiteLLMParams, + headers: dict, + ) -> Tuple[str, Dict, Dict]: + """ + Transform cancel eval request + + Args: + eval_id: Eval ID + api_base: Base API URL + litellm_params: LiteLLM parameters + headers: Request headers + + Returns: + Tuple of (url, headers, body) + """ + pass + + @abstractmethod + def transform_cancel_eval_response( + self, + raw_response: httpx.Response, + logging_obj: LiteLLMLoggingObj, + ) -> CancelEvalResponse: + """ + Transform provider response to CancelEvalResponse + + Args: + raw_response: Raw HTTP response + logging_obj: Logging object + + Returns: + CancelEvalResponse object + """ + pass + + # Run API Transformations + @abstractmethod + def transform_create_run_request( + self, + eval_id: str, + create_request: CreateRunRequest, + litellm_params: GenericLiteLLMParams, + headers: dict, + ) -> Tuple[str, Dict]: + """ + Transform create run request to provider-specific format + + Args: + eval_id: Eval ID + create_request: Run creation parameters + litellm_params: LiteLLM parameters + headers: Request headers + + Returns: + Tuple of (url, request_body) + """ + pass + + @abstractmethod + def transform_create_run_response( + self, + raw_response: httpx.Response, + logging_obj: LiteLLMLoggingObj, + ) -> Run: + """ + Transform provider response to Run object + + Args: + raw_response: Raw HTTP response + logging_obj: Logging object + + Returns: + Run object + """ + pass + + @abstractmethod + def transform_list_runs_request( + self, + eval_id: str, + list_params: ListRunsParams, + litellm_params: GenericLiteLLMParams, + headers: dict, + ) -> Tuple[str, Dict]: + """ + Transform list runs request parameters + + Args: + eval_id: Eval ID + list_params: List parameters (pagination, filters) + litellm_params: LiteLLM parameters + headers: Request headers + + Returns: + Tuple of (url, query_params) + """ + pass + + @abstractmethod + def transform_list_runs_response( + self, + raw_response: httpx.Response, + logging_obj: LiteLLMLoggingObj, + ) -> ListRunsResponse: + """ + Transform provider response to ListRunsResponse + + Args: + raw_response: Raw HTTP response + logging_obj: Logging object + + Returns: + ListRunsResponse object + """ + pass + + @abstractmethod + def transform_get_run_request( + self, + eval_id: str, + run_id: str, + api_base: str, + litellm_params: GenericLiteLLMParams, + headers: dict, + ) -> Tuple[str, Dict]: + """ + Transform get run request + + Args: + eval_id: Eval ID + run_id: Run ID + api_base: Base API URL + litellm_params: LiteLLM parameters + headers: Request headers + + Returns: + Tuple of (url, headers) + """ + pass + + @abstractmethod + def transform_get_run_response( + self, + raw_response: httpx.Response, + logging_obj: LiteLLMLoggingObj, + ) -> Run: + """ + Transform provider response to Run object + + Args: + raw_response: Raw HTTP response + logging_obj: Logging object + + Returns: + Run object + """ + pass + + @abstractmethod + def transform_cancel_run_request( + self, + eval_id: str, + run_id: str, + api_base: str, + litellm_params: GenericLiteLLMParams, + headers: dict, + ) -> Tuple[str, Dict, Dict]: + """ + Transform cancel run request + + Args: + eval_id: Eval ID + run_id: Run ID + api_base: Base API URL + litellm_params: LiteLLM parameters + headers: Request headers + + Returns: + Tuple of (url, headers, body) + """ + pass + + @abstractmethod + def transform_cancel_run_response( + self, + raw_response: httpx.Response, + logging_obj: LiteLLMLoggingObj, + ) -> CancelRunResponse: + """ + Transform provider response to CancelRunResponse + + Args: + raw_response: Raw HTTP response + logging_obj: Logging object + + Returns: + CancelRunResponse object + """ + pass + + @abstractmethod + def transform_delete_run_request( + self, + eval_id: str, + run_id: str, + api_base: str, + litellm_params: GenericLiteLLMParams, + headers: dict, + ) -> Tuple[str, Dict, Dict]: + """ + Transform delete run request + + Args: + eval_id: Eval ID + run_id: Run ID + api_base: Base API URL + litellm_params: LiteLLM parameters + headers: Request headers + + Returns: + Tuple of (url, headers, body) + """ + pass + + @abstractmethod + def transform_delete_run_response( + self, + raw_response: httpx.Response, + logging_obj: LiteLLMLoggingObj, + ) -> "RunDeleteResponse": + """ + Transform provider response to RunDeleteResponse + + Args: + raw_response: Raw HTTP response + logging_obj: Logging object + + Returns: + RunDeleteResponse object + """ + pass + + def get_error_class( + self, + error_message: str, + status_code: int, + headers: dict, + ) -> Exception: + """Get appropriate error class for the provider.""" + return BaseLLMException( + status_code=status_code, + message=error_message, + headers=headers, + ) diff --git a/litellm/llms/base_llm/managed_resources/__init__.py b/litellm/llms/base_llm/managed_resources/__init__.py new file mode 100644 index 0000000000..5eb9b46f89 --- /dev/null +++ b/litellm/llms/base_llm/managed_resources/__init__.py @@ -0,0 +1,41 @@ +""" +Managed Resources Module + +This module provides base classes and utilities for managing resources +(files, vector stores, etc.) with target_model_names support. + +The BaseManagedResource class provides common functionality for: +- Storing unified resource IDs with model mappings +- Retrieving resources by unified ID +- Deleting resources across multiple models +- Creating resources for multiple models +- Filtering deployments based on model mappings +""" + +from .base_managed_resource import BaseManagedResource +from .utils import ( + decode_unified_id, + encode_unified_id, + extract_model_id_from_unified_id, + extract_provider_resource_id_from_unified_id, + extract_resource_type_from_unified_id, + extract_target_model_names_from_unified_id, + extract_unified_uuid_from_unified_id, + generate_unified_id_string, + is_base64_encoded_unified_id, + parse_unified_id, +) + +__all__ = [ + "BaseManagedResource", + "is_base64_encoded_unified_id", + "extract_target_model_names_from_unified_id", + "extract_resource_type_from_unified_id", + "extract_unified_uuid_from_unified_id", + "extract_model_id_from_unified_id", + "extract_provider_resource_id_from_unified_id", + "generate_unified_id_string", + "encode_unified_id", + "decode_unified_id", + "parse_unified_id", +] diff --git a/litellm/llms/base_llm/managed_resources/base_managed_resource.py b/litellm/llms/base_llm/managed_resources/base_managed_resource.py new file mode 100644 index 0000000000..3c8ce748ad --- /dev/null +++ b/litellm/llms/base_llm/managed_resources/base_managed_resource.py @@ -0,0 +1,605 @@ +# What is this? +## Base class for managing resources (files, vector stores, etc.) with target_model_names support +## This provides common functionality for creating, retrieving, and managing resources across multiple models + +import base64 +import json +from abc import ABC, abstractmethod +from typing import ( + TYPE_CHECKING, + Any, + Dict, + Generic, + List, + Optional, + TypeVar, + Union, + cast, +) + +from litellm import verbose_logger +from litellm.proxy._types import UserAPIKeyAuth +from litellm.types.utils import SpecialEnums + +if TYPE_CHECKING: + from opentelemetry.trace import Span as _Span + + from litellm.proxy.utils import InternalUsageCache as _InternalUsageCache + from litellm.proxy.utils import PrismaClient as _PrismaClient + from litellm.router import Router as _Router + + Span = Union[_Span, Any] + InternalUsageCache = _InternalUsageCache + PrismaClient = _PrismaClient + Router = _Router +else: + Span = Any + InternalUsageCache = Any + PrismaClient = Any + Router = Any + +# Generic type for resource objects +ResourceObjectType = TypeVar('ResourceObjectType') + + +class BaseManagedResource(ABC, Generic[ResourceObjectType]): + """ + Base class for managing resources with target_model_names support. + + This class provides common functionality for: + - Storing unified resource IDs with model mappings + - Retrieving resources by unified ID + - Deleting resources across multiple models + - Creating resources for multiple models + - Filtering deployments based on model mappings + + Subclasses should implement: + - resource_type: str property + - table_name: str property + - create_resource_for_model: method to create resource on a specific model + - get_unified_resource_id_format: method to generate unified ID format + """ + + def __init__( + self, + internal_usage_cache: InternalUsageCache, + prisma_client: PrismaClient, + ): + self.internal_usage_cache = internal_usage_cache + self.prisma_client = prisma_client + + # ============================================================================ + # ABSTRACT METHODS + # ============================================================================ + + @property + @abstractmethod + def resource_type(self) -> str: + """ + Return the resource type identifier (e.g., 'file', 'vector_store', 'vector_store_file'). + Used for logging and unified ID generation. + """ + pass + + @property + @abstractmethod + def table_name(self) -> str: + """ + Return the database table name for this resource type. + Example: 'litellm_managedfiletable', 'litellm_managedvectorstoretable' + """ + pass + + @abstractmethod + def get_unified_resource_id_format( + self, + resource_object: ResourceObjectType, + target_model_names_list: List[str], + ) -> str: + """ + Generate the format string for the unified resource ID. + + This should return a string that will be base64 encoded. + Example for files: + "litellm_proxy:application/json;unified_id,{uuid};target_model_names,{models};..." + + Args: + resource_object: The resource object returned from the provider + target_model_names_list: List of target model names + + Returns: + Format string to be base64 encoded + """ + pass + + @abstractmethod + async def create_resource_for_model( + self, + llm_router: Router, + model: str, + request_data: Dict[str, Any], + litellm_parent_otel_span: Span, + ) -> ResourceObjectType: + """ + Create a resource for a specific model. + + Args: + llm_router: LiteLLM router instance + model: Model name to create resource for + request_data: Request data for resource creation + litellm_parent_otel_span: OpenTelemetry span for tracing + + Returns: + Resource object from the provider + """ + pass + + # ============================================================================ + # COMMON STORAGE OPERATIONS + # ============================================================================ + + async def store_unified_resource_id( + self, + unified_resource_id: str, + resource_object: Optional[ResourceObjectType], + litellm_parent_otel_span: Optional[Span], + model_mappings: Dict[str, str], + user_api_key_dict: UserAPIKeyAuth, + additional_db_fields: Optional[Dict[str, Any]] = None, + ) -> None: + """ + Store unified resource ID with model mappings in cache and database. + + Args: + unified_resource_id: The unified resource ID (base64 encoded) + resource_object: The resource object to store (can be None) + litellm_parent_otel_span: OpenTelemetry span for tracing + model_mappings: Dictionary mapping model_id -> provider_resource_id + user_api_key_dict: User API key authentication details + additional_db_fields: Additional fields to store in database + """ + verbose_logger.info( + f"Storing LiteLLM Managed {self.resource_type} with id={unified_resource_id} in cache" + ) + + # Prepare cache data + cache_data = { + "unified_resource_id": unified_resource_id, + "resource_object": resource_object, + "model_mappings": model_mappings, + "flat_model_resource_ids": list(model_mappings.values()), + "created_by": user_api_key_dict.user_id, + "updated_by": user_api_key_dict.user_id, + } + + # Add additional fields if provided + if additional_db_fields: + cache_data.update(additional_db_fields) + + # Store in cache + if resource_object is not None: + await self.internal_usage_cache.async_set_cache( + key=unified_resource_id, + value=cache_data, + litellm_parent_otel_span=litellm_parent_otel_span, + ) + + # Prepare database data + db_data = { + "unified_resource_id": unified_resource_id, + "model_mappings": json.dumps(model_mappings), + "flat_model_resource_ids": list(model_mappings.values()), + "created_by": user_api_key_dict.user_id, + "updated_by": user_api_key_dict.user_id, + } + + # Add resource object if available + if resource_object is not None: + # Handle both dict and Pydantic models + if hasattr(resource_object, "model_dump_json"): + db_data["resource_object"] = resource_object.model_dump_json() # type: ignore + elif isinstance(resource_object, dict): + db_data["resource_object"] = json.dumps(resource_object) + + # Extract storage metadata from hidden params if present + hidden_params = getattr(resource_object, "_hidden_params", {}) or {} + if "storage_backend" in hidden_params: + db_data["storage_backend"] = hidden_params["storage_backend"] + if "storage_url" in hidden_params: + db_data["storage_url"] = hidden_params["storage_url"] + + # Add additional fields to database + if additional_db_fields: + db_data.update(additional_db_fields) + + # Store in database + table = getattr(self.prisma_client.db, self.table_name) + result = await table.create(data=db_data) + + verbose_logger.debug( + f"LiteLLM Managed {self.resource_type} with id={unified_resource_id} stored in db: {result}" + ) + + async def get_unified_resource_id( + self, + unified_resource_id: str, + litellm_parent_otel_span: Optional[Span] = None, + ) -> Optional[Dict[str, Any]]: + """ + Retrieve unified resource by ID from cache or database. + + Args: + unified_resource_id: The unified resource ID to retrieve + litellm_parent_otel_span: OpenTelemetry span for tracing + + Returns: + Dictionary containing resource data or None if not found + """ + # Check cache first + result = cast( + Optional[dict], + await self.internal_usage_cache.async_get_cache( + key=unified_resource_id, + litellm_parent_otel_span=litellm_parent_otel_span, + ), + ) + + if result: + return result + + # Check database + table = getattr(self.prisma_client.db, self.table_name) + db_object = await table.find_first( + where={"unified_resource_id": unified_resource_id} + ) + + if db_object: + return db_object.model_dump() + + return None + + async def delete_unified_resource_id( + self, + unified_resource_id: str, + litellm_parent_otel_span: Optional[Span] = None, + ) -> Optional[ResourceObjectType]: + """ + Delete unified resource from cache and database. + + Args: + unified_resource_id: The unified resource ID to delete + litellm_parent_otel_span: OpenTelemetry span for tracing + + Returns: + The deleted resource object or None if not found + """ + # Get old value from database + table = getattr(self.prisma_client.db, self.table_name) + initial_value = await table.find_first( + where={"unified_resource_id": unified_resource_id} + ) + + if initial_value is None: + raise Exception( + f"LiteLLM Managed {self.resource_type} with id={unified_resource_id} not found" + ) + + # Delete from cache + await self.internal_usage_cache.async_set_cache( + key=unified_resource_id, + value=None, + litellm_parent_otel_span=litellm_parent_otel_span, + ) + + # Delete from database + await table.delete(where={"unified_resource_id": unified_resource_id}) + + return initial_value.resource_object + + async def can_user_access_unified_resource_id( + self, + unified_resource_id: str, + user_api_key_dict: UserAPIKeyAuth, + litellm_parent_otel_span: Optional[Span] = None, + ) -> bool: + """ + Check if user has access to the unified resource ID. + + Uses get_unified_resource_id() which checks cache first before hitting the database, + avoiding direct DB queries in the critical request path. + + Args: + unified_resource_id: The unified resource ID to check + user_api_key_dict: User API key authentication details + litellm_parent_otel_span: OpenTelemetry span for tracing + + Returns: + True if user has access, False otherwise + """ + user_id = user_api_key_dict.user_id + + # Use cached method instead of direct DB query + resource = await self.get_unified_resource_id( + unified_resource_id, litellm_parent_otel_span + ) + + if resource: + return resource.get("created_by") == user_id + + return False + + # ============================================================================ + # MODEL MAPPING OPERATIONS + # ============================================================================ + + async def get_model_resource_id_mapping( + self, + resource_ids: List[str], + litellm_parent_otel_span: Span, + ) -> Dict[str, Dict[str, str]]: + """ + Get model-specific resource IDs for a list of unified resource IDs. + + Args: + resource_ids: List of unified resource IDs + litellm_parent_otel_span: OpenTelemetry span for tracing + + Returns: + Dictionary mapping unified_resource_id -> model_id -> provider_resource_id + + Example: + { + "unified_resource_id_1": { + "model_id_1": "provider_resource_id_1", + "model_id_2": "provider_resource_id_2" + } + } + """ + resource_id_mapping: Dict[str, Dict[str, str]] = {} + + for resource_id in resource_ids: + # Get unified resource from cache/db + unified_resource_object = await self.get_unified_resource_id( + resource_id, litellm_parent_otel_span + ) + + if unified_resource_object: + model_mappings = unified_resource_object.get("model_mappings", {}) + + # Handle both JSON string and dict + if isinstance(model_mappings, str): + model_mappings = json.loads(model_mappings) + + resource_id_mapping[resource_id] = model_mappings + + return resource_id_mapping + + # ============================================================================ + # RESOURCE CREATION OPERATIONS + # ============================================================================ + + async def create_resource_for_each_model( + self, + llm_router: Router, + request_data: Dict[str, Any], + target_model_names_list: List[str], + litellm_parent_otel_span: Span, + ) -> List[ResourceObjectType]: + """ + Create a resource for each model in the target list. + + Args: + llm_router: LiteLLM router instance + request_data: Request data for resource creation + target_model_names_list: List of target model names + litellm_parent_otel_span: OpenTelemetry span for tracing + + Returns: + List of resource objects created for each model + """ + if llm_router is None: + raise Exception("LLM Router not initialized. Ensure models added to proxy.") + + responses = [] + for model in target_model_names_list: + individual_response = await self.create_resource_for_model( + llm_router=llm_router, + model=model, + request_data=request_data, + litellm_parent_otel_span=litellm_parent_otel_span, + ) + responses.append(individual_response) + return responses + + def generate_unified_resource_id( + self, + resource_objects: List[ResourceObjectType], + target_model_names_list: List[str], + ) -> str: + """ + Generate a unified resource ID from multiple resource objects. + + Args: + resource_objects: List of resource objects from different models + target_model_names_list: List of target model names + + Returns: + Base64 encoded unified resource ID + """ + # Use the first resource object to generate the format + unified_id_format = self.get_unified_resource_id_format( + resource_object=resource_objects[0], + target_model_names_list=target_model_names_list, + ) + + # Convert to URL-safe base64 and strip padding + base64_unified_id = ( + base64.urlsafe_b64encode(unified_id_format.encode()).decode().rstrip("=") + ) + + return base64_unified_id + + def extract_model_mappings_from_responses( + self, + resource_objects: List[ResourceObjectType], + ) -> Dict[str, str]: + """ + Extract model mappings from resource objects. + + Args: + resource_objects: List of resource objects from different models + + Returns: + Dictionary mapping model_id -> provider_resource_id + """ + model_mappings: Dict[str, str] = {} + + for resource_object in resource_objects: + # Get hidden params if available + hidden_params = getattr(resource_object, "_hidden_params", {}) or {} + model_resource_id_mapping = hidden_params.get("model_resource_id_mapping") + + if model_resource_id_mapping and isinstance(model_resource_id_mapping, dict): + model_mappings.update(model_resource_id_mapping) + + return model_mappings + + # ============================================================================ + # DEPLOYMENT FILTERING + # ============================================================================ + + async def async_filter_deployments( + self, + model: str, + healthy_deployments: List, + request_kwargs: Optional[Dict] = None, + parent_otel_span: Optional[Span] = None, + resource_id_key: str = "resource_id", + ) -> List[Dict]: + """ + Filter deployments based on model mappings for a resource. + + This is used by the router to select only deployments that have + the resource available. + + Args: + model: Model name + healthy_deployments: List of healthy deployments + request_kwargs: Request kwargs containing resource_id and mappings + parent_otel_span: OpenTelemetry span for tracing + resource_id_key: Key to use for resource ID in request_kwargs + + Returns: + Filtered list of deployments + """ + if request_kwargs is None: + return healthy_deployments + + resource_id = cast(Optional[str], request_kwargs.get(resource_id_key)) + model_resource_id_mapping = cast( + Optional[Dict[str, Dict[str, str]]], + request_kwargs.get("model_resource_id_mapping"), + ) + + allowed_model_ids = [] + if resource_id and model_resource_id_mapping: + model_id_dict = model_resource_id_mapping.get(resource_id, {}) + allowed_model_ids = list(model_id_dict.keys()) + + if len(allowed_model_ids) == 0: + return healthy_deployments + + return [ + deployment + for deployment in healthy_deployments + if deployment.get("model_info", {}).get("id") in allowed_model_ids + ] + + # ============================================================================ + # UTILITY METHODS + # ============================================================================ + + def get_unified_id_prefix(self) -> str: + """ + Get the prefix for unified IDs for this resource type. + + Returns: + Prefix string (e.g., "litellm_proxy:") + """ + return SpecialEnums.LITELM_MANAGED_FILE_ID_PREFIX.value + + async def list_user_resources( + self, + user_api_key_dict: UserAPIKeyAuth, + limit: Optional[int] = None, + after: Optional[str] = None, + additional_filters: Optional[Dict[str, Any]] = None, + ) -> Dict[str, Any]: + """ + List resources created by a user. + + Args: + user_api_key_dict: User API key authentication details + limit: Maximum number of resources to return + after: Cursor for pagination + additional_filters: Additional filters to apply + + Returns: + Dictionary with list of resources and pagination info + """ + where_clause: Dict[str, Any] = {} + + # Filter by user who created the resource + if user_api_key_dict.user_id: + where_clause["created_by"] = user_api_key_dict.user_id + + if after: + where_clause["id"] = {"gt": after} + + # Add additional filters + if additional_filters: + where_clause.update(additional_filters) + + # Fetch resources + fetch_limit = limit or 20 + table = getattr(self.prisma_client.db, self.table_name) + resources = await table.find_many( + where=where_clause, + take=fetch_limit, + order={"created_at": "desc"}, + ) + + resource_objects: List[Any] = [] + for resource in resources: + try: + # Stop once we have enough + if len(resource_objects) >= (limit or 20): + break + + # Parse resource object + resource_data = resource.resource_object + if isinstance(resource_data, str): + resource_data = json.loads(resource_data) + + # Set unified ID + if hasattr(resource_data, "id"): + resource_data.id = resource.unified_resource_id + elif isinstance(resource_data, dict): + resource_data["id"] = resource.unified_resource_id + + resource_objects.append(resource_data) + + except Exception as e: + verbose_logger.warning( + f"Failed to parse {self.resource_type} object " + f"{resource.unified_resource_id}: {e}" + ) + continue + + return { + "object": "list", + "data": resource_objects, + "first_id": resource_objects[0].id if resource_objects else None, + "last_id": resource_objects[-1].id if resource_objects else None, + "has_more": len(resource_objects) == (limit or 20), + } diff --git a/litellm/llms/base_llm/managed_resources/utils.py b/litellm/llms/base_llm/managed_resources/utils.py new file mode 100644 index 0000000000..0d843b6d12 --- /dev/null +++ b/litellm/llms/base_llm/managed_resources/utils.py @@ -0,0 +1,364 @@ +""" +Utility functions for managed resources. + +This module provides common utility functions that can be used across +different managed resource types (files, vector stores, etc.). +""" + +import base64 +import re +from typing import List, Optional, Union, Literal + + +def is_base64_encoded_unified_id( + resource_id: str, + prefix: str = "litellm_proxy:", +) -> Union[str, Literal[False]]: + """ + Check if a resource ID is a base64 encoded unified ID. + + Args: + resource_id: The resource ID to check + prefix: The expected prefix for unified IDs + + Returns: + Decoded string if valid unified ID, False otherwise + """ + # Ensure resource_id is a string + if not isinstance(resource_id, str): + return False + + # Add padding back if needed + padded = resource_id + "=" * (-len(resource_id) % 4) + + # Decode from base64 + try: + decoded = base64.urlsafe_b64decode(padded).decode() + if decoded.startswith(prefix): + return decoded + else: + return False + except Exception: + return False + + +def extract_target_model_names_from_unified_id( + unified_id: str, +) -> List[str]: + """ + Extract target model names from a unified resource ID. + + Args: + unified_id: The unified resource ID (decoded or encoded) + + Returns: + List of target model names + + Example: + unified_id = "litellm_proxy:vector_store;unified_id,uuid;target_model_names,gpt-4,gemini-2.0" + returns: ["gpt-4", "gemini-2.0"] + """ + try: + # Ensure unified_id is a string + if not isinstance(unified_id, str): + return [] + + # Decode if it's base64 encoded + decoded_id = is_base64_encoded_unified_id(unified_id) + if decoded_id: + unified_id = decoded_id + + # Extract model names using regex + match = re.search(r"target_model_names,([^;]+)", unified_id) + if match: + # Split on comma and strip whitespace from each model name + return [model.strip() for model in match.group(1).split(",")] + + return [] + except Exception: + return [] + + +def extract_resource_type_from_unified_id( + unified_id: str, +) -> Optional[str]: + """ + Extract resource type from a unified resource ID. + + Args: + unified_id: The unified resource ID (decoded or encoded) + + Returns: + Resource type string or None + + Example: + unified_id = "litellm_proxy:vector_store;unified_id,uuid;..." + returns: "vector_store" + """ + try: + # Ensure unified_id is a string + if not isinstance(unified_id, str): + return None + + # Decode if it's base64 encoded + decoded_id = is_base64_encoded_unified_id(unified_id) + if decoded_id: + unified_id = decoded_id + + # Extract resource type (comes after prefix and before first semicolon) + match = re.search(r"litellm_proxy:([^;]+)", unified_id) + if match: + return match.group(1).strip() + + return None + except Exception: + return None + + +def extract_unified_uuid_from_unified_id( + unified_id: str, +) -> Optional[str]: + """ + Extract the UUID from a unified resource ID. + + Args: + unified_id: The unified resource ID (decoded or encoded) + + Returns: + UUID string or None + + Example: + unified_id = "litellm_proxy:vector_store;unified_id,abc-123;..." + returns: "abc-123" + """ + try: + # Ensure unified_id is a string + if not isinstance(unified_id, str): + return None + + # Decode if it's base64 encoded + decoded_id = is_base64_encoded_unified_id(unified_id) + if decoded_id: + unified_id = decoded_id + + # Extract UUID + match = re.search(r"unified_id,([^;]+)", unified_id) + if match: + return match.group(1).strip() + + return None + except Exception: + return None + + +def extract_model_id_from_unified_id( + unified_id: str, +) -> Optional[str]: + """ + Extract model ID from a unified resource ID. + + Args: + unified_id: The unified resource ID (decoded or encoded) + + Returns: + Model ID string or None + + Example: + unified_id = "litellm_proxy:vector_store;...;model_id,gpt-4-model-id;..." + returns: "gpt-4-model-id" + """ + try: + # Ensure unified_id is a string + if not isinstance(unified_id, str): + return None + + # Decode if it's base64 encoded + decoded_id = is_base64_encoded_unified_id(unified_id) + if decoded_id: + unified_id = decoded_id + + # Extract model ID + match = re.search(r"model_id,([^;]+)", unified_id) + if match: + return match.group(1).strip() + + return None + except Exception: + return None + + +def extract_provider_resource_id_from_unified_id( + unified_id: str, +) -> Optional[str]: + """ + Extract provider resource ID from a unified resource ID. + + Args: + unified_id: The unified resource ID (decoded or encoded) + + Returns: + Provider resource ID string or None + + Example: + unified_id = "litellm_proxy:vector_store;...;resource_id,vs_abc123;..." + returns: "vs_abc123" + """ + try: + # Ensure unified_id is a string + if not isinstance(unified_id, str): + return None + + # Decode if it's base64 encoded + decoded_id = is_base64_encoded_unified_id(unified_id) + if decoded_id: + unified_id = decoded_id + + # Extract resource ID (try multiple patterns for different resource types) + patterns = [ + r"resource_id,([^;]+)", + r"vector_store_id,([^;]+)", + r"file_id,([^;]+)", + ] + + for pattern in patterns: + match = re.search(pattern, unified_id) + if match: + return match.group(1).strip() + + return None + except Exception: + return None + + +def generate_unified_id_string( + resource_type: str, + unified_uuid: str, + target_model_names: List[str], + provider_resource_id: str, + model_id: str, + additional_fields: Optional[dict] = None, +) -> str: + """ + Generate a unified ID string (before base64 encoding). + + Args: + resource_type: Type of resource (e.g., "vector_store", "file") + unified_uuid: UUID for this unified resource + target_model_names: List of target model names + provider_resource_id: Resource ID from the provider + model_id: Model ID from the router + additional_fields: Additional fields to include in the ID + + Returns: + Unified ID string (not yet base64 encoded) + + Example: + generate_unified_id_string( + resource_type="vector_store", + unified_uuid="abc-123", + target_model_names=["gpt-4", "gemini"], + provider_resource_id="vs_xyz", + model_id="model-id-123", + ) + returns: "litellm_proxy:vector_store;unified_id,abc-123;target_model_names,gpt-4,gemini;resource_id,vs_xyz;model_id,model-id-123" + """ + # Build the unified ID string + parts = [ + f"litellm_proxy:{resource_type}", + f"unified_id,{unified_uuid}", + f"target_model_names,{','.join(target_model_names)}", + f"resource_id,{provider_resource_id}", + f"model_id,{model_id}", + ] + + # Add additional fields if provided + if additional_fields: + for key, value in additional_fields.items(): + parts.append(f"{key},{value}") + + return ";".join(parts) + + +def encode_unified_id(unified_id_string: str) -> str: + """ + Encode a unified ID string to base64. + + Args: + unified_id_string: The unified ID string to encode + + Returns: + Base64 encoded unified ID (URL-safe, padding stripped) + """ + return ( + base64.urlsafe_b64encode(unified_id_string.encode()) + .decode() + .rstrip("=") + ) + + +def decode_unified_id(encoded_unified_id: str) -> Optional[str]: + """ + Decode a base64 encoded unified ID. + + Args: + encoded_unified_id: The base64 encoded unified ID + + Returns: + Decoded unified ID string or None if invalid + """ + try: + # Add padding back if needed + padded = encoded_unified_id + "=" * (-len(encoded_unified_id) % 4) + + # Decode from base64 + decoded = base64.urlsafe_b64decode(padded).decode() + + # Verify it starts with the expected prefix + if decoded.startswith("litellm_proxy:"): + return decoded + + return None + except Exception: + return None + + +def parse_unified_id( + unified_id: str, +) -> Optional[dict]: + """ + Parse a unified ID into its components. + + Args: + unified_id: The unified ID (encoded or decoded) + + Returns: + Dictionary with parsed components or None if invalid + + Example: + { + "resource_type": "vector_store", + "unified_uuid": "abc-123", + "target_model_names": ["gpt-4", "gemini"], + "provider_resource_id": "vs_xyz", + "model_id": "model-id-123" + } + """ + try: + # Decode if needed + decoded_id = decode_unified_id(unified_id) + if not decoded_id: + # Maybe it's already decoded + if unified_id.startswith("litellm_proxy:"): + decoded_id = unified_id + else: + return None + + return { + "resource_type": extract_resource_type_from_unified_id(decoded_id), + "unified_uuid": extract_unified_uuid_from_unified_id(decoded_id), + "target_model_names": extract_target_model_names_from_unified_id(decoded_id), + "provider_resource_id": extract_provider_resource_id_from_unified_id(decoded_id), + "model_id": extract_model_id_from_unified_id(decoded_id), + } + except Exception: + return None diff --git a/litellm/llms/bedrock/base_aws_llm.py b/litellm/llms/bedrock/base_aws_llm.py index 304c707fa0..dfaddb3c2b 100644 --- a/litellm/llms/bedrock/base_aws_llm.py +++ b/litellm/llms/bedrock/base_aws_llm.py @@ -384,6 +384,14 @@ class BaseAWSLLM: model_id = BaseAWSLLM._get_model_id_from_model_with_spec( model_id, spec="moonshot" ) + elif "nova-2/" in model_id: + model_id = BaseAWSLLM._get_model_id_from_model_with_spec( + model_id, spec="nova-2" + ) + elif "nova/" in model_id: + model_id = BaseAWSLLM._get_model_id_from_model_with_spec( + model_id, spec="nova" + ) return model_id @staticmethod diff --git a/litellm/llms/bedrock/chat/agentcore/transformation.py b/litellm/llms/bedrock/chat/agentcore/transformation.py index 94e845e309..9ae850ad4c 100644 --- a/litellm/llms/bedrock/chat/agentcore/transformation.py +++ b/litellm/llms/bedrock/chat/agentcore/transformation.py @@ -114,6 +114,11 @@ class AmazonAgentCoreConfig(BaseConfig, BaseAWSLLM): stream: Optional[bool] = None, fake_stream: Optional[bool] = None, ) -> Tuple[dict, Optional[bytes]]: + # Set Accept header required by MCP servers on AgentCore + # Per MCP spec (Streamable HTTP transport): client MUST include Accept header + # listing both application/json and text/event-stream as supported content types + headers["Accept"] = "application/json, text/event-stream" + # Check if api_key (bearer token) is provided for Cognito authentication # Priority: api_key parameter first, then optional_params jwt_token = api_key or optional_params.get("api_key") diff --git a/litellm/llms/bedrock/chat/converse_handler.py b/litellm/llms/bedrock/chat/converse_handler.py index 25af852e09..60a93b169c 100644 --- a/litellm/llms/bedrock/chat/converse_handler.py +++ b/litellm/llms/bedrock/chat/converse_handler.py @@ -272,7 +272,18 @@ class BedrockConverseLLM(BaseAWSLLM): if unencoded_model_id is not None: modelId = self.encode_model_id(model_id=unencoded_model_id) else: - modelId = self.encode_model_id(model_id=model) + # Strip nova spec prefixes before encoding model ID for API URL + _model_for_id = model + _stripped = _model_for_id + for rp in ["bedrock/converse/", "bedrock/", "converse/"]: + if _stripped.startswith(rp): + _stripped = _stripped[len(rp):] + break + for _nova_prefix in ["nova-2/", "nova/"]: + if _stripped.startswith(_nova_prefix): + _model_for_id = _model_for_id.replace(_nova_prefix, "", 1) + break + modelId = self.encode_model_id(model_id=_model_for_id) fake_stream = litellm.AmazonConverseConfig().should_fake_stream( fake_stream=fake_stream, diff --git a/litellm/llms/bedrock/chat/converse_transformation.py b/litellm/llms/bedrock/chat/converse_transformation.py index efa755d515..d4fd060630 100644 --- a/litellm/llms/bedrock/chat/converse_transformation.py +++ b/litellm/llms/bedrock/chat/converse_transformation.py @@ -3,6 +3,7 @@ Translating between OpenAI's `/chat/completion` format and Amazon's `/converse` """ import copy +import json import time import types from typing import List, Literal, Optional, Tuple, Union, cast, overload @@ -11,7 +12,10 @@ import httpx import litellm from litellm._logging import verbose_logger -from litellm.constants import RESPONSE_FORMAT_TOOL_NAME +from litellm.constants import ( + BEDROCK_MIN_THINKING_BUDGET_TOKENS, + RESPONSE_FORMAT_TOOL_NAME, +) from litellm.litellm_core_utils.core_helpers import ( filter_exceptions_from_params, filter_internal_params, @@ -82,9 +86,37 @@ BEDROCK_COMPUTER_USE_TOOLS = [ UNSUPPORTED_BEDROCK_CONVERSE_BETA_PATTERNS = [ "advanced-tool-use", # Bedrock Converse doesn't support advanced-tool-use beta headers "prompt-caching", # Prompt caching not supported in Converse API - "compact-2026-01-12", # The compact beta feature is not currently supported on the Converse and ConverseStream APIs + "compact-2026-01-12", # The compact beta feature is not currently supported on the Converse and ConverseStream APIs ] +# Models that support Bedrock's native structured outputs API (outputConfig.textFormat) +# Uses substring matching against the Bedrock model ID +# Ref: https://docs.aws.amazon.com/bedrock/latest/userguide/structured-output.html +BEDROCK_NATIVE_STRUCTURED_OUTPUT_MODELS = { + # Anthropic Claude 4.5+ + "claude-haiku-4-5", + "claude-sonnet-4-5", + "claude-opus-4-5", + "claude-opus-4-6", + # Qwen3 + "qwen3", + # DeepSeek + "deepseek-v3.1", + # Gemma 3 + "gemma-3", + # MiniMax + "minimax-m2", + # Mistral (magistral-small excluded: broken constrained decoding on Bedrock) + "ministral", + "mistral-large-3", + "voxtral", + # Moonshot + "kimi-k2", + # NVIDIA + "nemotron-nano", + # OpenAI (gpt-oss excluded: broken constrained decoding, works via tool-call fallback) +} + class AmazonConverseConfig(BaseConfig): """ @@ -267,45 +299,56 @@ class AmazonConverseConfig(BaseConfig): llm_provider="bedrock", ) - def _is_nova_lite_2_model(self, model: str) -> bool: + def _is_nova_2_model(self, model: str) -> bool: """ - Check if the model is a Nova Lite 2 model that supports reasoningConfig. + Check if the model is a Nova 2 model that supports reasoningConfig. - Nova Lite 2 models use a different reasoning configuration structure compared to + Nova 2 models use a different reasoning configuration structure compared to Anthropic's thinking parameter and GPT-OSS's reasoning_effort parameter. Supported models: - amazon.nova-2-lite-v1:0 + - amazon.nova-2-pro-preview-20251202-v1:0 - us.amazon.nova-2-lite-v1:0 - eu.amazon.nova-2-lite-v1:0 - apac.amazon.nova-2-lite-v1:0 + - (and other regional variants) Args: model: The model identifier Returns: - True if the model is a Nova Lite 2 model, False otherwise + True if the model is a Nova 2 model, False otherwise Examples: >>> config = AmazonConverseConfig() - >>> config._is_nova_lite_2_model("amazon.nova-2-lite-v1:0") + >>> config._is_nova_2_model("amazon.nova-2-lite-v1:0") True - >>> config._is_nova_lite_2_model("us.amazon.nova-2-lite-v1:0") + >>> config._is_nova_2_model("us.amazon.nova-2-lite-v1:0") True - >>> config._is_nova_lite_2_model("amazon.nova-pro-1-5-v1:0") + >>> config._is_nova_2_model("us.amazon.nova-2-pro-preview-20251202-v1:0") + True + >>> config._is_nova_2_model("amazon.nova-pro-1-5-v1:0") False - >>> config._is_nova_lite_2_model("amazon.nova-pro-v1:0") + >>> config._is_nova_2_model("amazon.nova-pro-v1:0") False """ - # Remove regional prefix if present (us., eu., apac.) + # Remove provider routing prefix if present (bedrock/converse/, bedrock/, converse/) model_without_region = model - for prefix in ["us.", "eu.", "apac."]: - if model.startswith(prefix): - model_without_region = model[len(prefix) :] + for routing_prefix in ["bedrock/converse/", "bedrock/", "converse/"]: + if model_without_region.startswith(routing_prefix): + model_without_region = model_without_region[len(routing_prefix) :] break - # Check if the model is specifically Nova Lite 2 - return "nova-2-lite" in model_without_region + # Remove regional prefix if present (us., eu., apac.) + for prefix in ["us.", "eu.", "apac."]: + if model_without_region.startswith(prefix): + model_without_region = model_without_region[len(prefix) :] + break + + # Check if the model is a Nova 2 model (matches nova-2-lite, nova-2-pro, etc.) + # Also check for nova-2/ spec prefix for imported models + return model_without_region.startswith("amazon.nova-2-") or model_without_region.startswith("nova-2/") def _map_web_search_options( self, web_search_options: dict, model: str @@ -393,7 +436,7 @@ class AmazonConverseConfig(BaseConfig): Different model families handle reasoning effort differently: - GPT-OSS models: Keep reasoning_effort as-is (passed to additionalModelRequestFields) - - Nova Lite 2 models: Transform to reasoningConfig structure + - Nova 2 models: Transform to reasoningConfig structure - Other models (Anthropic, etc.): Convert to thinking parameter Args: @@ -422,8 +465,8 @@ class AmazonConverseConfig(BaseConfig): # GPT-OSS models: keep reasoning_effort as-is # It will be passed through to additionalModelRequestFields optional_params["reasoning_effort"] = reasoning_effort - elif self._is_nova_lite_2_model(model): - # Nova Lite 2 models: transform to reasoningConfig + elif self._is_nova_2_model(model): + # Nova 2 models: transform to reasoningConfig reasoning_config = self._transform_reasoning_effort_to_reasoning_config( reasoning_effort ) @@ -434,6 +477,25 @@ class AmazonConverseConfig(BaseConfig): reasoning_effort=reasoning_effort, model=model ) + @staticmethod + def _clamp_thinking_budget_tokens(optional_params: dict) -> None: + """ + Clamp thinking.budget_tokens to the Bedrock minimum (1024). + + Bedrock returns a 400 error if budget_tokens < 1024. + """ + thinking = optional_params.get("thinking") + if isinstance(thinking, dict): + budget = thinking.get("budget_tokens") + if isinstance(budget, int) and budget < BEDROCK_MIN_THINKING_BUDGET_TOKENS: + verbose_logger.debug( + "Bedrock requires thinking.budget_tokens >= %d, got %d. " + "Clamping to minimum.", + BEDROCK_MIN_THINKING_BUDGET_TOKENS, + budget, + ) + thinking["budget_tokens"] = BEDROCK_MIN_THINKING_BUDGET_TOKENS + def get_supported_openai_params(self, model: str) -> List[str]: from litellm.utils import supports_function_calling @@ -449,6 +511,7 @@ class AmazonConverseConfig(BaseConfig): "response_format", "requestMetadata", "service_tier", + "parallel_tool_calls", ] if ( @@ -458,6 +521,9 @@ class AmazonConverseConfig(BaseConfig): supported_params.append("tool_choice") supported_params.append("thinking") supported_params.append("reasoning_effort") + # For nova imported models, also add web_search_options + if "nova" in model.lower(): + supported_params.append("web_search_options") return supported_params ## Filter out 'cross-region' from model name @@ -492,8 +558,8 @@ class AmazonConverseConfig(BaseConfig): if "gpt-oss" in model: supported_params.append("reasoning_effort") - elif self._is_nova_lite_2_model(model): - # Nova Lite 2 models support reasoning_effort (transformed to reasoningConfig) + elif self._is_nova_2_model(model): + # Nova 2 models support reasoning_effort (transformed to reasoningConfig) # These models use a different reasoning structure than Anthropic's thinking parameter supported_params.append("reasoning_effort") elif ( @@ -692,6 +758,100 @@ class AmazonConverseConfig(BaseConfig): ) return _tool + @staticmethod + def _supports_native_structured_outputs(model: str) -> bool: + """Check if the Bedrock model supports native structured outputs (outputConfig.textFormat).""" + return any( + substring in model + for substring in BEDROCK_NATIVE_STRUCTURED_OUTPUT_MODELS + ) + + @staticmethod + def _add_additional_properties_to_schema(schema: dict) -> dict: + """ + Recursively ensure all object types in a JSON schema have + ``"additionalProperties": false``. + + Bedrock's native structured-outputs API requires this field to be + explicitly set on every object node, otherwise it returns a + validation error. + """ + if not isinstance(schema, dict): + return schema + + result = dict(schema) + + if result.get("type") == "object" and "additionalProperties" not in result: + result["additionalProperties"] = False + + # Recurse into nested schemas + if "properties" in result and isinstance(result["properties"], dict): + result["properties"] = { + k: AmazonConverseConfig._add_additional_properties_to_schema(v) + for k, v in result["properties"].items() + } + if "items" in result and isinstance(result["items"], dict): + result["items"] = AmazonConverseConfig._add_additional_properties_to_schema( + result["items"] + ) + for defs_key in ("$defs", "definitions"): + if defs_key in result and isinstance(result[defs_key], dict): + result[defs_key] = { + k: AmazonConverseConfig._add_additional_properties_to_schema(v) + for k, v in result[defs_key].items() + } + for key in ("anyOf", "allOf", "oneOf"): + if key in result and isinstance(result[key], list): + result[key] = [ + AmazonConverseConfig._add_additional_properties_to_schema(item) + for item in result[key] + ] + + return result + + @staticmethod + def _create_output_config_for_response_format( + json_schema: Optional[dict] = None, + name: Optional[str] = None, + description: Optional[str] = None, + ) -> "OutputConfigBlock": + """ + Build an outputConfig block for Bedrock's native structured outputs API. + + The Converse API expects: + { + "outputConfig": { + "textFormat": { + "type": "json_schema", + "structure": { + "jsonSchema": { + "schema": "", + "name": "optional", + "description": "optional" + } + } + } + } + } + """ + if json_schema is not None: + json_schema = AmazonConverseConfig._add_additional_properties_to_schema( + json_schema + ) + schema_str = json.dumps(json_schema) if json_schema is not None else "{}" + json_schema_def: JsonSchemaDefinition = {"schema": schema_str} + if name is not None: + json_schema_def["name"] = name + if description is not None: + json_schema_def["description"] = description + + return OutputConfigBlock( + textFormat=OutputFormat( + type="json_schema", + structure=OutputFormatStructure(jsonSchema=json_schema_def), + ) + ) + def _apply_tool_call_transformation( self, tools: List[OpenAIChatCompletionToolParam], @@ -754,6 +914,13 @@ class AmazonConverseConfig(BaseConfig): ) if _tool_choice_value is not None: optional_params["tool_choice"] = _tool_choice_value + if param == "parallel_tool_calls": + disable_parallel = not value + optional_params["_parallel_tool_use_config"] = { + "tool_choice": { + "disable_parallel_tool_use": disable_parallel + } + } if param == "thinking": optional_params["thinking"] = value elif param == "reasoning_effort" and isinstance(value, str): @@ -765,14 +932,7 @@ class AmazonConverseConfig(BaseConfig): self._validate_request_metadata(value) # type: ignore optional_params["requestMetadata"] = value if param == "service_tier" and isinstance(value, str): - # Map OpenAI service_tier (string) to Bedrock serviceTier (object) - # OpenAI values: "auto", "default", "flex", "priority" - # Bedrock values: "default", "flex", "priority" (no "auto") - bedrock_tier = value - if value == "auto": - bedrock_tier = "default" # Bedrock doesn't support "auto" - if bedrock_tier in ("default", "flex", "priority"): - optional_params["serviceTier"] = {"type": bedrock_tier} + self._map_service_tier_param(value, optional_params) if param == "web_search_options" and isinstance(value, dict): # Note: we use `isinstance(value, dict)` instead of `value and isinstance(value, dict)` @@ -784,8 +944,8 @@ class AmazonConverseConfig(BaseConfig): ) # Only update thinking tokens for non-GPT-OSS models and non-Nova-Lite-2 models - # Nova Lite 2 handles token budgeting differently through reasoningConfig - if "gpt-oss" not in model and not self._is_nova_lite_2_model(model): + # Nova 2 handles token budgeting differently through reasoningConfig + if "gpt-oss" not in model and not self._is_nova_2_model(model): self.update_optional_params_with_thinking_tokens( non_default_params=non_default_params, optional_params=optional_params ) @@ -803,6 +963,18 @@ class AmazonConverseConfig(BaseConfig): return optional_params + def _map_service_tier_param(self, value: str, optional_params: dict) -> None: + """Map OpenAI service_tier (string) to Bedrock serviceTier (object). + + OpenAI values: "auto", "default", "flex", "priority" + Bedrock values: "default", "flex", "priority" (no "auto") + """ + bedrock_tier = value + if value == "auto": + bedrock_tier = "default" # Bedrock doesn't support "auto" + if bedrock_tier in ("default", "flex", "priority"): + optional_params["serviceTier"] = {"type": bedrock_tier} + def _translate_response_format_param( self, value: dict, @@ -821,45 +993,53 @@ class AmazonConverseConfig(BaseConfig): return optional_params json_schema: Optional[dict] = None + name: Optional[str] = None description: Optional[str] = None if "response_schema" in value: json_schema = value["response_schema"] elif "json_schema" in value: json_schema = value["json_schema"]["schema"] + name = value["json_schema"].get("name") description = value["json_schema"].get("description") if "type" in value and value["type"] == "text": return optional_params - """ - Follow similar approach to anthropic - translate to a single tool call. - - When using tools in this way: - https://docs.anthropic.com/en/docs/build-with-claude/tool-use#json-mode - - You usually want to provide a single tool - - You should set tool_choice (see Forcing tool use) to instruct the model to explicitly use that tool - - Remember that the model will pass the input to the tool, so the name of the tool and description should be from the model’s perspective. - """ - _tool = self._create_json_tool_call_for_response_format( - json_schema=json_schema, - description=description, - ) - optional_params = self._add_tools_to_optional_params( - optional_params=optional_params, tools=[_tool] - ) - - if ( - litellm.utils.supports_tool_choice( - model=model, custom_llm_provider=self.custom_llm_provider + if self._supports_native_structured_outputs(model) and json_schema is not None: + # Use Bedrock's native structured outputs API (outputConfig.textFormat) + # No synthetic tool injection, no fake_stream needed. + # Requires an explicit schema — json_object with no schema falls through + # to the tool-call path below. + output_config = self._create_output_config_for_response_format( + json_schema=json_schema, + name=name, + description=description, ) - and not is_thinking_enabled - ): - optional_params["tool_choice"] = ToolChoiceValuesBlock( - tool=SpecificToolChoiceBlock(name=RESPONSE_FORMAT_TOOL_NAME) + optional_params["outputConfig"] = output_config + else: + # Fallback: translate to a synthetic tool call + # https://docs.anthropic.com/en/docs/build-with-claude/tool-use#json-mode + _tool = self._create_json_tool_call_for_response_format( + json_schema=json_schema, + description=description, ) + optional_params = self._add_tools_to_optional_params( + optional_params=optional_params, tools=[_tool] + ) + + if ( + litellm.utils.supports_tool_choice( + model=model, custom_llm_provider=self.custom_llm_provider + ) + and not is_thinking_enabled + ): + optional_params["tool_choice"] = ToolChoiceValuesBlock( + tool=SpecificToolChoiceBlock(name=RESPONSE_FORMAT_TOOL_NAME) + ) + if non_default_params.get("stream", False) is True: + optional_params["fake_stream"] = True + optional_params["json_mode"] = True - if non_default_params.get("stream", False) is True: - optional_params["fake_stream"] = True - return optional_params def update_optional_params_with_thinking_tokens( @@ -871,9 +1051,14 @@ class AmazonConverseConfig(BaseConfig): Checks 'non_default_params' for 'thinking' and 'max_tokens' if 'thinking' is enabled and 'max_tokens' is not specified, set 'max_tokens' to the thinking token budget + DEFAULT_MAX_TOKENS + + Also clamps thinking.budget_tokens to the Bedrock minimum (1024) to + prevent 400 errors from the Bedrock API. """ from litellm.constants import DEFAULT_MAX_TOKENS + self._clamp_thinking_budget_tokens(optional_params) + is_thinking_enabled = self.is_thinking_enabled(optional_params) is_max_tokens_in_request = self.is_max_tokens_in_request(non_default_params) if is_thinking_enabled and not is_max_tokens_in_request: @@ -997,7 +1182,7 @@ class AmazonConverseConfig(BaseConfig): def _prepare_request_params( self, optional_params: dict, model: str - ) -> Tuple[dict, dict, dict]: + ) -> Tuple[dict, dict, dict, Optional[OutputConfigBlock]]: """Prepare and separate request parameters.""" # Filter out exception objects before deepcopy to prevent deepcopy failures # Exceptions should not be stored in optional_params (this is a defensive fix) @@ -1020,6 +1205,8 @@ class AmazonConverseConfig(BaseConfig): if request_metadata is not None: self._validate_request_metadata(request_metadata) + output_config: Optional[OutputConfigBlock] = inference_params.pop("outputConfig", None) + # keep supported params in 'inference_params', and set all model-specific params in 'additional_request_params' additional_request_params = { k: v for k, v in inference_params.items() if k not in total_supported_params @@ -1028,6 +1215,17 @@ class AmazonConverseConfig(BaseConfig): k: v for k, v in inference_params.items() if k in total_supported_params } + # Handle parallel_tool_calls configuration + parallel_tool_use_config = additional_request_params.pop("_parallel_tool_use_config", None) + if parallel_tool_use_config is not None: + # Merge the tool_choice config from parallel_tool_calls into additional_request_params + for key, value in parallel_tool_use_config.items(): + if key in additional_request_params and isinstance(additional_request_params[key], dict) and isinstance(value, dict): + # Merge dictionaries + additional_request_params[key].update(value) + else: + additional_request_params[key] = value + # Only set the topK value in for models that support it additional_request_params.update( self._handle_top_k_value(model, inference_params) @@ -1044,7 +1242,12 @@ class AmazonConverseConfig(BaseConfig): additional_request_params ) - return inference_params, additional_request_params, request_metadata + return ( + inference_params, + additional_request_params, + request_metadata, + output_config, + ) def _process_tools_and_beta( self, @@ -1098,22 +1301,44 @@ class AmazonConverseConfig(BaseConfig): # "computer-use-2025-01-24" for Claude Sonnet 4.5, Haiku 4.5, Opus 4.1, Sonnet 4, Opus 4, and Sonnet 3.7 # "computer-use-2024-10-22" for older models model_lower = model.lower() - if "opus-4.6" in model_lower or "opus_4.6" in model_lower or "opus-4-6" in model_lower or "opus_4_6" in model_lower: + if "opus-4.6" in model_lower or "opus_4.6" in model_lower or "opus-4-6" in model_lower or "opus_4_6" in model_lower or "sonnet-4.6" in model_lower or "sonnet_4.6" in model_lower or "sonnet-4-6" in model_lower or "sonnet_4_6" in model_lower: computer_use_header = "computer-use-2025-11-24" - elif "opus-4.5" in model_lower or "opus_4.5" in model_lower or "opus-4-5" in model_lower or "opus_4_5" in model_lower: + elif ( + "opus-4.5" in model_lower + or "opus_4.5" in model_lower + or "opus-4-5" in model_lower + or "opus_4_5" in model_lower + ): computer_use_header = "computer-use-2025-11-24" - elif any(pattern in model_lower for pattern in [ - "sonnet-4.5", "sonnet_4.5", "sonnet-4-5", "sonnet_4_5", - "haiku-4.5", "haiku_4.5", "haiku-4-5", "haiku_4_5", - "opus-4.1", "opus_4.1", "opus-4-1", "opus_4_1", - "sonnet-4", "sonnet_4", - "opus-4", "opus_4", - "sonnet-3.7", "sonnet_3.7", "sonnet-3-7", "sonnet_3_7" - ]): + elif any( + pattern in model_lower + for pattern in [ + "sonnet-4.5", + "sonnet_4.5", + "sonnet-4-5", + "sonnet_4_5", + "haiku-4.5", + "haiku_4.5", + "haiku-4-5", + "haiku_4_5", + "opus-4.1", + "opus_4.1", + "opus-4-1", + "opus_4_1", + "sonnet-4", + "sonnet_4", + "opus-4", + "opus_4", + "sonnet-3.7", + "sonnet_3.7", + "sonnet-3-7", + "sonnet_3_7", + ] + ): computer_use_header = "computer-use-2025-01-24" else: computer_use_header = "computer-use-2024-10-22" - + anthropic_beta_list.append(computer_use_header) # Transform computer use tools to proper Bedrock format transformed_computer_tools = self._transform_computer_use_tools( @@ -1187,6 +1412,7 @@ class AmazonConverseConfig(BaseConfig): inference_params, additional_request_params, request_metadata, + output_config, ) = self._prepare_request_params(optional_params, model) original_tools = inference_params.pop("tools", []) @@ -1229,6 +1455,9 @@ class AmazonConverseConfig(BaseConfig): if request_metadata is not None: data["requestMetadata"] = request_metadata + if output_config is not None: + data["outputConfig"] = output_config + return data async def _async_transform_request( @@ -1477,9 +1706,7 @@ class AmazonConverseConfig(BaseConfig): return message, returned_finish_reason - def _translate_message_content( - self, content_blocks: List[ContentBlock] - ) -> Tuple[ + def _translate_message_content(self, content_blocks: List[ContentBlock]) -> Tuple[ str, List[ChatCompletionToolCallChunk], Optional[List[BedrockConverseReasoningContentBlock]], @@ -1496,9 +1723,9 @@ class AmazonConverseConfig(BaseConfig): """ content_str = "" tools: List[ChatCompletionToolCallChunk] = [] - reasoningContentBlocks: Optional[ - List[BedrockConverseReasoningContentBlock] - ] = None + reasoningContentBlocks: Optional[List[BedrockConverseReasoningContentBlock]] = ( + None + ) citationsContentBlocks: Optional[List[CitationsContentBlock]] = None for idx, content in enumerate(content_blocks): """ @@ -1625,9 +1852,9 @@ class AmazonConverseConfig(BaseConfig): chat_completion_message: ChatCompletionResponseMessage = {"role": "assistant"} content_str = "" tools: List[ChatCompletionToolCallChunk] = [] - reasoningContentBlocks: Optional[ - List[BedrockConverseReasoningContentBlock] - ] = None + reasoningContentBlocks: Optional[List[BedrockConverseReasoningContentBlock]] = ( + None + ) citationsContentBlocks: Optional[List[CitationsContentBlock]] = None if message is not None: @@ -1646,17 +1873,17 @@ class AmazonConverseConfig(BaseConfig): provider_specific_fields["citationsContent"] = citationsContentBlocks if provider_specific_fields: - chat_completion_message[ - "provider_specific_fields" - ] = provider_specific_fields + chat_completion_message["provider_specific_fields"] = ( + provider_specific_fields + ) if reasoningContentBlocks is not None: - chat_completion_message[ - "reasoning_content" - ] = self._transform_reasoning_content(reasoningContentBlocks) - chat_completion_message[ - "thinking_blocks" - ] = self._transform_thinking_blocks(reasoningContentBlocks) + chat_completion_message["reasoning_content"] = ( + self._transform_reasoning_content(reasoningContentBlocks) + ) + chat_completion_message["thinking_blocks"] = ( + self._transform_thinking_blocks(reasoningContentBlocks) + ) chat_completion_message["content"] = content_str if ( json_mode is True @@ -1669,8 +1896,6 @@ class AmazonConverseConfig(BaseConfig): ) json_mode_content_str: Optional[str] = tools[0]["function"].get("arguments") if json_mode_content_str is not None: - import json - # Bedrock returns the response wrapped in a "properties" object # We need to extract the actual content from this wrapper try: @@ -1689,7 +1914,7 @@ class AmazonConverseConfig(BaseConfig): pass chat_completion_message["content"] = json_mode_content_str - else: + elif tools: chat_completion_message["tool_calls"] = tools ## CALCULATING USAGE - bedrock returns usage in the headers diff --git a/litellm/llms/bedrock/common_utils.py b/litellm/llms/bedrock/common_utils.py index 4c87f6fa99..b779c892c6 100644 --- a/litellm/llms/bedrock/common_utils.py +++ b/litellm/llms/bedrock/common_utils.py @@ -404,7 +404,7 @@ def extract_model_name_from_bedrock_arn(model: str) -> str: def strip_bedrock_routing_prefix(model: str) -> str: """Strip LiteLLM routing prefixes from model name.""" - for prefix in ["bedrock/", "converse/", "invoke/", "openai/"]: + for prefix in ["bedrock/", "converse/", "invoke/", "openai/", "nova-2/", "nova/"]: if model.startswith(prefix): model = model.split("/", 1)[1] return model @@ -427,7 +427,20 @@ def get_bedrock_base_model(model: str) -> str: - "us.meta.llama3-2-11b-instruct-v1:0" -> "meta.llama3-2-11b-instruct-v1" - "bedrock/converse/model" -> "model" - "anthropic.claude-3-5-sonnet-20241022-v2:0:51k" -> "anthropic.claude-3-5-sonnet-20241022-v2:0" + - "bedrock/nova-2/arn:aws:..." -> "amazon.nova-2-custom" + - "bedrock/nova/arn:aws:..." -> "amazon.nova-custom" """ + # Detect nova spec prefixes before stripping them + stripped = model + for rp in ["bedrock/converse/", "bedrock/", "converse/"]: + if stripped.startswith(rp): + stripped = stripped[len(rp):] + break + if stripped.startswith("nova-2/"): + return "amazon.nova-2-custom" + elif stripped.startswith("nova/"): + return "amazon.nova-custom" + model = strip_bedrock_routing_prefix(model) model = extract_model_name_from_bedrock_arn(model) model = strip_bedrock_throughput_suffix(model) @@ -465,6 +478,14 @@ def is_claude_4_5_on_bedrock(model: str) -> bool: "opus_4.5", "opus-4-5", "opus_4_5", + "sonnet-4.6", + "sonnet_4.6", + "sonnet-4-6", + "sonnet_4_6", + "opus-4.6", + "opus_4.6", + "opus-4-6", + "opus_4_6", ] return any(pattern in model_lower for pattern in claude_4_5_patterns) @@ -594,6 +615,11 @@ class BedrockModelInfo(BaseLLMModelInfo): if prefix in model: return route_type + # Check for nova spec prefixes (nova/ and nova-2/) + _model_after_bedrock = model.replace("bedrock/", "", 1) + if _model_after_bedrock.startswith("nova-2/") or _model_after_bedrock.startswith("nova/"): + return "converse" + base_model = BedrockModelInfo.get_base_model(model) alt_model = BedrockModelInfo.get_non_litellm_routing_model_name(model=model) if ( diff --git a/litellm/llms/bedrock/cost_calculation.py b/litellm/llms/bedrock/cost_calculation.py index b20350d732..ac99d4e36e 100644 --- a/litellm/llms/bedrock/cost_calculation.py +++ b/litellm/llms/bedrock/cost_calculation.py @@ -3,7 +3,7 @@ Helper util for handling bedrock-specific cost calculation - e.g.: prompt caching """ -from typing import TYPE_CHECKING, Tuple +from typing import TYPE_CHECKING, Optional, Tuple from litellm.litellm_core_utils.llm_cost_calc.utils import generic_cost_per_token @@ -11,12 +11,17 @@ if TYPE_CHECKING: from litellm.types.utils import Usage -def cost_per_token(model: str, usage: "Usage") -> Tuple[float, float]: +def cost_per_token( + model: str, usage: "Usage", service_tier: Optional[str] = None +) -> Tuple[float, float]: """ Calculates the cost per token for a given model, prompt tokens, and completion tokens. Follows the same logic as Anthropic's cost per token calculation. """ return generic_cost_per_token( - model=model, usage=usage, custom_llm_provider="bedrock" - ) \ No newline at end of file + model=model, + usage=usage, + custom_llm_provider="bedrock", + service_tier=service_tier, + ) diff --git a/litellm/llms/bedrock/embed/amazon_nova_transformation.py b/litellm/llms/bedrock/embed/amazon_nova_transformation.py index 3e5686c46f..40d2a21e1c 100644 --- a/litellm/llms/bedrock/embed/amazon_nova_transformation.py +++ b/litellm/llms/bedrock/embed/amazon_nova_transformation.py @@ -14,7 +14,7 @@ Docs - https://docs.aws.amazon.com/bedrock/latest/userguide/nova-embed.html from typing import List, Optional -from litellm.types.utils import Embedding, EmbeddingResponse, Usage +from litellm.types.utils import Embedding, EmbeddingResponse, PromptTokensDetailsWrapper, Usage class AmazonNovaEmbeddingConfig: @@ -244,11 +244,14 @@ class AmazonNovaEmbeddingConfig: } def _transform_response( - self, response_list: List[dict], model: str + self, + response_list: List[dict], + model: str, + batch_data: Optional[List[dict]] = None, ) -> EmbeddingResponse: """ Transform Nova response to OpenAI format. - + Nova response format: { "embeddings": [ @@ -262,7 +265,7 @@ class AmazonNovaEmbeddingConfig: """ embeddings: List[Embedding] = [] total_tokens = 0 - + for response in response_list: # Nova response has an "embeddings" array if "embeddings" in response and isinstance(response["embeddings"], list): @@ -274,7 +277,7 @@ class AmazonNovaEmbeddingConfig: object="embedding", ) embeddings.append(embedding) - + # Estimate token count # For text, use truncatedCharLength if available if "truncatedCharLength" in item: @@ -291,9 +294,31 @@ class AmazonNovaEmbeddingConfig: ) embeddings.append(embedding) total_tokens += len(response["embedding"]) // 4 - - usage = Usage(prompt_tokens=total_tokens, total_tokens=total_tokens) - + + # Count images from original requests for cost calculation + image_count = 0 + if batch_data: + for request_data in batch_data: + # Nova wraps params in singleEmbeddingParams or segmentedEmbeddingParams + params = request_data.get( + "singleEmbeddingParams", + request_data.get("segmentedEmbeddingParams", {}), + ) + if "image" in params: + image_count += 1 + + prompt_tokens_details: Optional[PromptTokensDetailsWrapper] = None + if image_count > 0: + prompt_tokens_details = PromptTokensDetailsWrapper( + image_count=image_count, + ) + + usage = Usage( + prompt_tokens=total_tokens, + total_tokens=total_tokens, + prompt_tokens_details=prompt_tokens_details, + ) + return EmbeddingResponse(data=embeddings, model=model, usage=usage) def _transform_async_invoke_response( diff --git a/litellm/llms/bedrock/embed/amazon_titan_multimodal_transformation.py b/litellm/llms/bedrock/embed/amazon_titan_multimodal_transformation.py index 338029adc3..e59d3cbf77 100644 --- a/litellm/llms/bedrock/embed/amazon_titan_multimodal_transformation.py +++ b/litellm/llms/bedrock/embed/amazon_titan_multimodal_transformation.py @@ -6,14 +6,14 @@ Why separate file? Make it easy to see how transformation works Docs - https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-titan-embed-mm.html """ -from typing import List +from typing import List, Optional from litellm.types.llms.bedrock import ( AmazonTitanMultimodalEmbeddingConfig, AmazonTitanMultimodalEmbeddingRequest, AmazonTitanMultimodalEmbeddingResponse, ) -from litellm.types.utils import Embedding, EmbeddingResponse, Usage +from litellm.types.utils import Embedding, EmbeddingResponse, PromptTokensDetailsWrapper, Usage from litellm.utils import get_base64_str, is_base64_encoded @@ -56,7 +56,10 @@ class AmazonTitanMultimodalEmbeddingG1Config: return transformed_request def _transform_response( - self, response_list: List[dict], model: str + self, + response_list: List[dict], + model: str, + batch_data: Optional[List[dict]] = None, ) -> EmbeddingResponse: total_prompt_tokens = 0 transformed_responses: List[Embedding] = [] @@ -71,9 +74,23 @@ class AmazonTitanMultimodalEmbeddingG1Config: ) total_prompt_tokens += _parsed_response["inputTextTokenCount"] + # Count images from original requests for cost calculation + image_count = 0 + if batch_data: + for request_data in batch_data: + if "inputImage" in request_data: + image_count += 1 + + prompt_tokens_details: Optional[PromptTokensDetailsWrapper] = None + if image_count > 0: + prompt_tokens_details = PromptTokensDetailsWrapper( + image_count=image_count, + ) + usage = Usage( prompt_tokens=total_prompt_tokens, completion_tokens=0, total_tokens=total_prompt_tokens, + prompt_tokens_details=prompt_tokens_details, ) return EmbeddingResponse(model=model, usage=usage, data=transformed_responses) diff --git a/litellm/llms/bedrock/embed/embedding.py b/litellm/llms/bedrock/embed/embedding.py index 56900d296a..783345d78d 100644 --- a/litellm/llms/bedrock/embed/embedding.py +++ b/litellm/llms/bedrock/embed/embedding.py @@ -158,6 +158,7 @@ class BedrockEmbedding(BaseAWSLLM): model: str, provider: BEDROCK_EMBEDDING_PROVIDERS_LITERAL, is_async_invoke: Optional[bool] = False, + batch_data: Optional[List[dict]] = None, ) -> Optional[EmbeddingResponse]: """ Transforms the response from the Bedrock embedding provider to the OpenAI format. @@ -212,7 +213,7 @@ class BedrockEmbedding(BaseAWSLLM): if model == "amazon.titan-embed-image-v1": returned_response = ( AmazonTitanMultimodalEmbeddingG1Config()._transform_response( - response_list=response_list, model=model + response_list=response_list, model=model, batch_data=batch_data ) ) elif model == "amazon.titan-embed-text-v1": @@ -231,7 +232,7 @@ class BedrockEmbedding(BaseAWSLLM): ) elif provider == "nova": returned_response = AmazonNovaEmbeddingConfig()._transform_response( - response_list=response_list, model=model + response_list=response_list, model=model, batch_data=batch_data ) ########################################################## @@ -310,6 +311,7 @@ class BedrockEmbedding(BaseAWSLLM): model=model, provider=provider, is_async_invoke=is_async_invoke, + batch_data=batch_data, ) async def _async_single_func_embeddings( @@ -379,6 +381,7 @@ class BedrockEmbedding(BaseAWSLLM): model=model, provider=provider, is_async_invoke=is_async_invoke, + batch_data=batch_data, ) def embeddings( # noqa: PLR0915 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 477fa3316d..03885ff208 100644 --- a/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py +++ b/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py @@ -180,6 +180,14 @@ class AmazonAnthropicClaudeMessagesConfig( "opus_4", # Opus 4 "sonnet-4", "sonnet_4", # Sonnet 4 + "sonnet-4.6", + "sonnet_4.6", + "sonnet-4-6", + "sonnet_4_6", + "opus-4.6", + "opus_4.6", + "opus-4-6", + "opus_4_6", ] return any(pattern in model_lower for pattern in supported_patterns) @@ -251,6 +259,11 @@ class AmazonAnthropicClaudeMessagesConfig( "opus_4.6", "opus-4-6", "opus_4_6", + #sonnet 4.6 + "sonnet-4.6", + "sonnet_4.6", + "sonnet-4-6", + "sonnet_4_6", ] return any(pattern in model_lower for pattern in supported_patterns) @@ -285,7 +298,7 @@ class AmazonAnthropicClaudeMessagesConfig( programmatic_tool_calling_used or input_examples_used ): beta_set.discard(ANTHROPIC_TOOL_SEARCH_BETA_HEADER) - if "opus-4" in model.lower() or "opus_4" in model.lower(): + if self._supports_tool_search_on_bedrock(model): beta_set.add("tool-search-tool-2025-10-19") def _convert_output_format_to_inline_schema( @@ -420,10 +433,8 @@ class AmazonAnthropicClaudeMessagesConfig( beta_set=beta_set, ) - # --- Custom logic: if tool-search-tool-2025-10-19 is present, add tool-examples-2025-10-29 --- if "tool-search-tool-2025-10-19" in beta_set: beta_set.add("tool-examples-2025-10-29") - # ------------------------------------------------------------------------------ if beta_set: anthropic_messages_request["anthropic_beta"] = list(beta_set) diff --git a/litellm/llms/chatgpt/responses/transformation.py b/litellm/llms/chatgpt/responses/transformation.py index 0ce24f63a8..bcb6edd39f 100644 --- a/litellm/llms/chatgpt/responses/transformation.py +++ b/litellm/llms/chatgpt/responses/transformation.py @@ -73,10 +73,6 @@ class ChatGPTResponsesAPIConfig(OpenAIResponsesAPIConfig): litellm_params, headers, ) - request.pop("max_output_tokens", None) - request.pop("max_tokens", None) - request.pop("max_completion_tokens", None) - request.pop("metadata", None) base_instructions = get_chatgpt_default_instructions() existing_instructions = request.get("instructions") if existing_instructions: @@ -92,7 +88,22 @@ class ChatGPTResponsesAPIConfig(OpenAIResponsesAPIConfig): if "reasoning.encrypted_content" not in include: include.append("reasoning.encrypted_content") request["include"] = include - return request + + allowed_keys = { + "model", + "input", + "instructions", + "stream", + "store", + "include", + "tools", + "tool_choice", + "reasoning", + "previous_response_id", + "truncation", + } + + return {k: v for k, v in request.items() if k in allowed_keys} def transform_response_api_response( self, diff --git a/litellm/llms/custom_httpx/aiohttp_transport.py b/litellm/llms/custom_httpx/aiohttp_transport.py index 1b03ec4764..60f34a2a82 100644 --- a/litellm/llms/custom_httpx/aiohttp_transport.py +++ b/litellm/llms/custom_httpx/aiohttp_transport.py @@ -4,7 +4,7 @@ import os import ssl import typing import urllib.request -from typing import Callable, Dict, Optional, Union +from typing import Any, Callable, Dict, Optional, Union import aiohttp import aiohttp.client_exceptions @@ -119,8 +119,13 @@ class AiohttpResponseStream(httpx.AsyncByteStream): class AiohttpTransport(httpx.AsyncBaseTransport): - def __init__(self, client: Union[ClientSession, Callable[[], ClientSession]]) -> None: + def __init__( + self, + client: Union[ClientSession, Callable[[], ClientSession]], + owns_session: bool = True, + ) -> None: self.client = client + self._owns_session = owns_session ######################################################### # Class variables for proxy settings @@ -128,7 +133,7 @@ class AiohttpTransport(httpx.AsyncBaseTransport): self.proxy_cache: Dict[str, Optional[str]] = {} async def aclose(self) -> None: - if isinstance(self.client, ClientSession): + if self._owns_session and isinstance(self.client, ClientSession): await self.client.close() @@ -144,10 +149,11 @@ class LiteLLMAiohttpTransport(AiohttpTransport): self, client: Union[ClientSession, Callable[[], ClientSession]], ssl_verify: Optional[Union[bool, ssl.SSLContext]] = None, + owns_session: bool = True, ): self.client = client self._ssl_verify = ssl_verify # Store for per-request SSL override - super().__init__(client=client) + super().__init__(client=client, owns_session=owns_session) # Store the client factory for recreating sessions when needed if callable(client): self._client_factory = client @@ -248,26 +254,25 @@ class LiteLLMAiohttpTransport(AiohttpTransport): # Only pass ssl kwarg when explicitly configured, to avoid # overriding the session/connector defaults with None (which is # not a valid value for aiohttp's ssl parameter). - ssl_kwargs: Dict[str, Union[bool, ssl.SSLContext]] = {} - if ssl_verify is not None: - ssl_kwargs["ssl"] = ssl_verify - - response = await client_session.request( - method=request.method, - url=YarlURL(str(request.url), encoded=True), - headers=request.headers, - data=data, - allow_redirects=False, - auto_decompress=False, - timeout=ClientTimeout( + request_kwargs: Dict[str, Any] = { + "method": request.method, + "url": YarlURL(str(request.url), encoded=True), + "headers": request.headers, + "data": data, + "allow_redirects": False, + "auto_decompress": False, + "timeout": ClientTimeout( sock_connect=timeout.get("connect"), sock_read=timeout.get("read"), connect=timeout.get("pool"), ), - proxy=proxy, - server_hostname=sni_hostname, - **ssl_kwargs, - ).__aenter__() + "proxy": proxy, + "server_hostname": sni_hostname, + } + if ssl_verify is not None: + request_kwargs["ssl"] = ssl_verify + + response = await client_session.request(**request_kwargs).__aenter__() return response @@ -325,7 +330,7 @@ class LiteLLMAiohttpTransport(AiohttpTransport): return httpx.Response( status_code=response.status, headers=response.headers, - content=AiohttpResponseStream(response), + stream=AiohttpResponseStream(response), request=request, ) diff --git a/litellm/llms/custom_httpx/http_handler.py b/litellm/llms/custom_httpx/http_handler.py index 95f411c397..3789f546d7 100644 --- a/litellm/llms/custom_httpx/http_handler.py +++ b/litellm/llms/custom_httpx/http_handler.py @@ -866,6 +866,7 @@ class AsyncHTTPHandler: return LiteLLMAiohttpTransport( client=shared_session, ssl_verify=ssl_for_transport, + owns_session=False, ) # Create new session only if none provided or existing one is invalid diff --git a/litellm/llms/custom_httpx/llm_http_handler.py b/litellm/llms/custom_httpx/llm_http_handler.py index a97ebd8e74..0a5364bfcf 100644 --- a/litellm/llms/custom_httpx/llm_http_handler.py +++ b/litellm/llms/custom_httpx/llm_http_handler.py @@ -21,6 +21,9 @@ import litellm.litellm_core_utils import litellm.types import litellm.types.utils from litellm._logging import verbose_logger +from litellm.anthropic_beta_headers_manager import ( + update_headers_with_filtered_beta, +) from litellm.constants import REALTIME_WEBSOCKET_MAX_MESSAGE_SIZE_BYTES from litellm.litellm_core_utils.realtime_streaming import RealTimeStreaming from litellm.llms.base_llm.anthropic_messages.transformation import ( @@ -34,6 +37,7 @@ from litellm.llms.base_llm.batches.transformation import BaseBatchesConfig from litellm.llms.base_llm.chat.transformation import BaseConfig from litellm.llms.base_llm.containers.transformation import BaseContainerConfig from litellm.llms.base_llm.embedding.transformation import BaseEmbeddingConfig +from litellm.llms.base_llm.evals.transformation import BaseEvalsAPIConfig from litellm.llms.base_llm.files.transformation import BaseFilesConfig from litellm.llms.base_llm.google_genai.transformation import ( BaseGoogleGenAIGenerateContentConfig, @@ -81,9 +85,6 @@ from litellm.types.llms.anthropic_skills import ( ListSkillsResponse, Skill, ) -from litellm.anthropic_beta_headers_manager import ( - update_headers_with_filtered_beta, - ) from litellm.types.llms.openai import ( CreateBatchRequest, CreateFileRequest, @@ -133,6 +134,16 @@ if TYPE_CHECKING: from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj from litellm.llms.base_llm.passthrough.transformation import BasePassthroughConfig + from litellm.types.llms.openai_evals import ( + CancelEvalResponse, + CancelRunResponse, + DeleteEvalResponse, + Eval, + ListEvalsResponse, + ListRunsResponse, + Run, + RunDeleteResponse, + ) LiteLLMLoggingObj = _LiteLLMLoggingObj else: @@ -4599,6 +4610,7 @@ class BaseLLMHTTPHandler: BaseSkillsAPIConfig, "BasePassthroughConfig", "BaseContainerConfig", + BaseEvalsAPIConfig, ], ): status_code = getattr(e, "status_code", 500) @@ -9316,3 +9328,1209 @@ class BaseLLMHTTPHandler: raw_response=response, logging_obj=logging_obj, ) + + # =================================== + # Evals API Handlers + # =================================== + + def create_eval_handler( + self, + url: str, + request_body: Dict, + evals_api_provider_config: "BaseEvalsAPIConfig", + custom_llm_provider: str, + litellm_params: GenericLiteLLMParams, + logging_obj: LiteLLMLoggingObj, + extra_headers: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None, + _is_async: bool = False, + shared_session: Optional["ClientSession"] = None, + ) -> Union["Eval", Coroutine[Any, Any, "Eval"]]: + """Create an eval""" + if _is_async: + return self.async_create_eval_handler( + url=url, + request_body=request_body, + evals_api_provider_config=evals_api_provider_config, + custom_llm_provider=custom_llm_provider, + litellm_params=litellm_params, + logging_obj=logging_obj, + extra_headers=extra_headers, + timeout=timeout, + client=client, + shared_session=shared_session, + ) + + if client is None or not isinstance(client, HTTPHandler): + sync_httpx_client = _get_httpx_client( + params={"ssl_verify": litellm_params.get("ssl_verify", None)} + ) + else: + sync_httpx_client = client + + headers = extra_headers or {} + + logging_obj.pre_call( + input=request_body.get("display_name", ""), + api_key="", + additional_args={ + "complete_input_dict": request_body, + "api_base": url, + "headers": headers, + }, + ) + + try: + response = sync_httpx_client.post( + url=url, headers=headers, json=request_body, timeout=timeout + ) + except Exception as e: + raise self._handle_error( + e=e, + provider_config=evals_api_provider_config, + ) + + return evals_api_provider_config.transform_create_eval_response( + raw_response=response, + logging_obj=logging_obj, + ) + + async def async_create_eval_handler( + self, + url: str, + request_body: Dict, + evals_api_provider_config: "BaseEvalsAPIConfig", + custom_llm_provider: str, + litellm_params: GenericLiteLLMParams, + logging_obj: LiteLLMLoggingObj, + extra_headers: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None, + shared_session: Optional["ClientSession"] = None, + ) -> "Eval": + """Async create an eval""" + if client is None or not isinstance(client, AsyncHTTPHandler): + async_httpx_client = get_async_httpx_client( + llm_provider=litellm.LlmProviders(custom_llm_provider), + params={"ssl_verify": litellm_params.get("ssl_verify", None)}, + ) + else: + async_httpx_client = client + + headers = extra_headers or {} + + logging_obj.pre_call( + input=request_body.get("name", ""), + api_key="", + additional_args={ + "complete_input_dict": request_body, + "api_base": url, + "headers": headers, + }, + ) + + try: + response = await async_httpx_client.post( + url=url, headers=headers, json=request_body, timeout=timeout + ) + except Exception as e: + raise self._handle_error( + e=e, + provider_config=evals_api_provider_config, + ) + + return evals_api_provider_config.transform_create_eval_response( + raw_response=response, + logging_obj=logging_obj, + ) + + def list_evals_handler( + self, + url: str, + query_params: Dict, + evals_api_provider_config: "BaseEvalsAPIConfig", + custom_llm_provider: str, + litellm_params: GenericLiteLLMParams, + logging_obj: LiteLLMLoggingObj, + extra_headers: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None, + _is_async: bool = False, + shared_session: Optional["ClientSession"] = None, + ) -> Union["ListEvalsResponse", Coroutine[Any, Any, "ListEvalsResponse"]]: + """List evals""" + if _is_async: + return self.async_list_evals_handler( + url=url, + query_params=query_params, + evals_api_provider_config=evals_api_provider_config, + custom_llm_provider=custom_llm_provider, + litellm_params=litellm_params, + logging_obj=logging_obj, + extra_headers=extra_headers, + timeout=timeout, + client=client, + shared_session=shared_session, + ) + + if client is None or not isinstance(client, HTTPHandler): + sync_httpx_client = _get_httpx_client( + params={"ssl_verify": litellm_params.get("ssl_verify", None)} + ) + else: + sync_httpx_client = client + + headers = extra_headers or {} + + logging_obj.pre_call( + input="", + api_key="", + additional_args={ + "complete_input_dict": query_params, + "api_base": url, + "headers": headers, + }, + ) + + try: + response = sync_httpx_client.get( + url=url, headers=headers, params=query_params + ) + except Exception as e: + raise self._handle_error( + e=e, + provider_config=evals_api_provider_config, + ) + + return evals_api_provider_config.transform_list_evals_response( + raw_response=response, + logging_obj=logging_obj, + ) + + async def async_list_evals_handler( + self, + url: str, + query_params: Dict, + evals_api_provider_config: "BaseEvalsAPIConfig", + custom_llm_provider: str, + litellm_params: GenericLiteLLMParams, + logging_obj: LiteLLMLoggingObj, + extra_headers: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None, + shared_session: Optional["ClientSession"] = None, + ) -> "ListEvalsResponse": + """Async list evals""" + if client is None or not isinstance(client, AsyncHTTPHandler): + async_httpx_client = get_async_httpx_client( + llm_provider=litellm.LlmProviders(custom_llm_provider), + params={"ssl_verify": litellm_params.get("ssl_verify", None)}, + ) + else: + async_httpx_client = client + + headers = extra_headers or {} + + logging_obj.pre_call( + input="", + api_key="", + additional_args={ + "complete_input_dict": query_params, + "api_base": url, + "headers": headers, + }, + ) + + try: + response = await async_httpx_client.get( + url=url, headers=headers, params=query_params + ) + except Exception as e: + raise self._handle_error( + e=e, + provider_config=evals_api_provider_config, + ) + + return evals_api_provider_config.transform_list_evals_response( + raw_response=response, + logging_obj=logging_obj, + ) + + def get_eval_handler( + self, + url: str, + evals_api_provider_config: "BaseEvalsAPIConfig", + custom_llm_provider: str, + litellm_params: GenericLiteLLMParams, + logging_obj: LiteLLMLoggingObj, + extra_headers: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None, + _is_async: bool = False, + shared_session: Optional["ClientSession"] = None, + ) -> Union["Eval", Coroutine[Any, Any, "Eval"]]: + """Get an eval""" + if _is_async: + return self.async_get_eval_handler( + url=url, + evals_api_provider_config=evals_api_provider_config, + custom_llm_provider=custom_llm_provider, + litellm_params=litellm_params, + logging_obj=logging_obj, + extra_headers=extra_headers, + timeout=timeout, + client=client, + shared_session=shared_session, + ) + + if client is None or not isinstance(client, HTTPHandler): + sync_httpx_client = _get_httpx_client( + params={"ssl_verify": litellm_params.get("ssl_verify", None)} + ) + else: + sync_httpx_client = client + + headers = extra_headers or {} + + logging_obj.pre_call( + input="", + api_key="", + additional_args={ + "api_base": url, + "headers": headers, + }, + ) + + try: + response = sync_httpx_client.get(url=url, headers=headers) + except Exception as e: + raise self._handle_error( + e=e, + provider_config=evals_api_provider_config, + ) + + return evals_api_provider_config.transform_get_eval_response( + raw_response=response, + logging_obj=logging_obj, + ) + + async def async_get_eval_handler( + self, + url: str, + evals_api_provider_config: "BaseEvalsAPIConfig", + custom_llm_provider: str, + litellm_params: GenericLiteLLMParams, + logging_obj: LiteLLMLoggingObj, + extra_headers: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None, + shared_session: Optional["ClientSession"] = None, + ) -> "Eval": + """Async get an eval""" + if client is None or not isinstance(client, AsyncHTTPHandler): + async_httpx_client = get_async_httpx_client( + llm_provider=litellm.LlmProviders(custom_llm_provider), + params={"ssl_verify": litellm_params.get("ssl_verify", None)}, + ) + else: + async_httpx_client = client + + headers = extra_headers or {} + + logging_obj.pre_call( + input="", + api_key="", + additional_args={ + "api_base": url, + "headers": headers, + }, + ) + + try: + response = await async_httpx_client.get( + url=url, headers=headers + ) + except Exception as e: + raise self._handle_error( + e=e, + provider_config=evals_api_provider_config, + ) + + return evals_api_provider_config.transform_get_eval_response( + raw_response=response, + logging_obj=logging_obj, + ) + + def update_eval_handler( + self, + url: str, + request_body: Dict, + evals_api_provider_config: "BaseEvalsAPIConfig", + custom_llm_provider: str, + litellm_params: GenericLiteLLMParams, + logging_obj: LiteLLMLoggingObj, + extra_headers: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None, + _is_async: bool = False, + shared_session: Optional["ClientSession"] = None, + ) -> Union["Eval", Coroutine[Any, Any, "Eval"]]: + """Update an eval""" + if _is_async: + return self.async_update_eval_handler( + url=url, + request_body=request_body, + evals_api_provider_config=evals_api_provider_config, + custom_llm_provider=custom_llm_provider, + litellm_params=litellm_params, + logging_obj=logging_obj, + extra_headers=extra_headers, + timeout=timeout, + client=client, + shared_session=shared_session, + ) + + if client is None or not isinstance(client, HTTPHandler): + sync_httpx_client = _get_httpx_client( + params={"ssl_verify": litellm_params.get("ssl_verify", None)} + ) + else: + sync_httpx_client = client + + headers = extra_headers or {} + + logging_obj.pre_call( + input=request_body.get("display_name", ""), + api_key="", + additional_args={ + "complete_input_dict": request_body, + "api_base": url, + "headers": headers, + }, + ) + + try: + response = sync_httpx_client.post( + url=url, headers=headers, json=request_body, timeout=timeout + ) + except Exception as e: + raise self._handle_error( + e=e, + provider_config=evals_api_provider_config, + ) + + return evals_api_provider_config.transform_update_eval_response( + raw_response=response, + logging_obj=logging_obj, + ) + + async def async_update_eval_handler( + self, + url: str, + request_body: Dict, + evals_api_provider_config: "BaseEvalsAPIConfig", + custom_llm_provider: str, + litellm_params: GenericLiteLLMParams, + logging_obj: LiteLLMLoggingObj, + extra_headers: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None, + shared_session: Optional["ClientSession"] = None, + ) -> "Eval": + """Async update an eval""" + if client is None or not isinstance(client, AsyncHTTPHandler): + async_httpx_client = get_async_httpx_client( + llm_provider=litellm.LlmProviders(custom_llm_provider), + params={"ssl_verify": litellm_params.get("ssl_verify", None)}, + ) + else: + async_httpx_client = client + + headers = extra_headers or {} + + logging_obj.pre_call( + input=request_body.get("display_name", ""), + api_key="", + additional_args={ + "complete_input_dict": request_body, + "api_base": url, + "headers": headers, + }, + ) + + try: + response = await async_httpx_client.post( + url=url, headers=headers, json=request_body, timeout=timeout + ) + except Exception as e: + raise self._handle_error( + e=e, + provider_config=evals_api_provider_config, + ) + + return evals_api_provider_config.transform_update_eval_response( + raw_response=response, + logging_obj=logging_obj, + ) + + def delete_eval_handler( + self, + url: str, + evals_api_provider_config: "BaseEvalsAPIConfig", + custom_llm_provider: str, + litellm_params: GenericLiteLLMParams, + logging_obj: LiteLLMLoggingObj, + extra_headers: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None, + _is_async: bool = False, + shared_session: Optional["ClientSession"] = None, + ) -> Union["DeleteEvalResponse", Coroutine[Any, Any, "DeleteEvalResponse"]]: + """Delete an eval""" + if _is_async: + return self.async_delete_eval_handler( + url=url, + evals_api_provider_config=evals_api_provider_config, + custom_llm_provider=custom_llm_provider, + litellm_params=litellm_params, + logging_obj=logging_obj, + extra_headers=extra_headers, + timeout=timeout, + client=client, + shared_session=shared_session, + ) + + if client is None or not isinstance(client, HTTPHandler): + sync_httpx_client = _get_httpx_client( + params={"ssl_verify": litellm_params.get("ssl_verify", None)} + ) + else: + sync_httpx_client = client + + headers = extra_headers or {} + + logging_obj.pre_call( + input="", + api_key="", + additional_args={ + "api_base": url, + "headers": headers, + }, + ) + + try: + response = sync_httpx_client.delete( + url=url, headers=headers, timeout=timeout + ) + except Exception as e: + raise self._handle_error( + e=e, + provider_config=evals_api_provider_config, + ) + + return evals_api_provider_config.transform_delete_eval_response( + raw_response=response, + logging_obj=logging_obj, + ) + + async def async_delete_eval_handler( + self, + url: str, + evals_api_provider_config: "BaseEvalsAPIConfig", + custom_llm_provider: str, + litellm_params: GenericLiteLLMParams, + logging_obj: LiteLLMLoggingObj, + extra_headers: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None, + shared_session: Optional["ClientSession"] = None, + ) -> "DeleteEvalResponse": + """Async delete an eval""" + if client is None or not isinstance(client, AsyncHTTPHandler): + async_httpx_client = get_async_httpx_client( + llm_provider=litellm.LlmProviders(custom_llm_provider), + params={"ssl_verify": litellm_params.get("ssl_verify", None)}, + ) + else: + async_httpx_client = client + + headers = extra_headers or {} + + logging_obj.pre_call( + input="", + api_key="", + additional_args={ + "api_base": url, + "headers": headers, + }, + ) + + try: + response = await async_httpx_client.delete( + url=url, headers=headers, timeout=timeout + ) + except Exception as e: + raise self._handle_error( + e=e, + provider_config=evals_api_provider_config, + ) + + return evals_api_provider_config.transform_delete_eval_response( + raw_response=response, + logging_obj=logging_obj, + ) + + def cancel_eval_handler( + self, + url: str, + evals_api_provider_config: "BaseEvalsAPIConfig", + custom_llm_provider: str, + litellm_params: GenericLiteLLMParams, + logging_obj: LiteLLMLoggingObj, + extra_headers: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None, + _is_async: bool = False, + shared_session: Optional["ClientSession"] = None, + ) -> Union["CancelEvalResponse", Coroutine[Any, Any, "CancelEvalResponse"]]: + """Cancel an eval""" + if _is_async: + return self.async_cancel_eval_handler( + url=url, + evals_api_provider_config=evals_api_provider_config, + custom_llm_provider=custom_llm_provider, + litellm_params=litellm_params, + logging_obj=logging_obj, + extra_headers=extra_headers, + timeout=timeout, + client=client, + shared_session=shared_session, + ) + + if client is None or not isinstance(client, HTTPHandler): + sync_httpx_client = _get_httpx_client( + params={"ssl_verify": litellm_params.get("ssl_verify", None)} + ) + else: + sync_httpx_client = client + + headers = extra_headers or {} + + logging_obj.pre_call( + input="", + api_key="", + additional_args={ + "api_base": url, + "headers": headers, + }, + ) + + try: + response = sync_httpx_client.post( + url=url, headers=headers, json={}, timeout=timeout + ) + except Exception as e: + raise self._handle_error( + e=e, + provider_config=evals_api_provider_config, + ) + + return evals_api_provider_config.transform_cancel_eval_response( + raw_response=response, + logging_obj=logging_obj, + ) + + async def async_cancel_eval_handler( + self, + url: str, + evals_api_provider_config: "BaseEvalsAPIConfig", + custom_llm_provider: str, + litellm_params: GenericLiteLLMParams, + logging_obj: LiteLLMLoggingObj, + extra_headers: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None, + shared_session: Optional["ClientSession"] = None, + ) -> "CancelEvalResponse": + """Async cancel an eval""" + if client is None or not isinstance(client, AsyncHTTPHandler): + async_httpx_client = get_async_httpx_client( + llm_provider=litellm.LlmProviders(custom_llm_provider), + params={"ssl_verify": litellm_params.get("ssl_verify", None)}, + ) + else: + async_httpx_client = client + + headers = extra_headers or {} + + logging_obj.pre_call( + input="", + api_key="", + additional_args={ + "api_base": url, + "headers": headers, + }, + ) + + try: + response = await async_httpx_client.post( + url=url, headers=headers, json={}, timeout=timeout + ) + except Exception as e: + raise self._handle_error( + e=e, + provider_config=evals_api_provider_config, + ) + + return evals_api_provider_config.transform_cancel_eval_response( + raw_response=response, + logging_obj=logging_obj, + ) + + # =================================== + # Eval Runs API Handlers + # =================================== + + def create_run_handler( + self, + url: str, + request_body: Dict, + evals_api_provider_config: "BaseEvalsAPIConfig", + custom_llm_provider: str, + litellm_params: GenericLiteLLMParams, + logging_obj: LiteLLMLoggingObj, + extra_headers: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None, + _is_async: bool = False, + shared_session: Optional["ClientSession"] = None, + ) -> Union["Run", Coroutine[Any, Any, "Run"]]: + """Create a run""" + if _is_async: + return self.async_create_run_handler( + url=url, + request_body=request_body, + evals_api_provider_config=evals_api_provider_config, + custom_llm_provider=custom_llm_provider, + litellm_params=litellm_params, + logging_obj=logging_obj, + extra_headers=extra_headers, + timeout=timeout, + client=client, + shared_session=shared_session, + ) + + if client is None or not isinstance(client, HTTPHandler): + sync_httpx_client = _get_httpx_client( + params={"ssl_verify": litellm_params.get("ssl_verify", None)} + ) + else: + sync_httpx_client = client + + headers = extra_headers or {} + + logging_obj.pre_call( + input=request_body.get("name", ""), + api_key="", + additional_args={ + "complete_input_dict": request_body, + "api_base": url, + "headers": headers, + }, + ) + + try: + response = sync_httpx_client.post( + url=url, headers=headers, json=request_body, timeout=timeout + ) + except Exception as e: + raise self._handle_error( + e=e, + provider_config=evals_api_provider_config, + ) + + return evals_api_provider_config.transform_create_run_response( + raw_response=response, + logging_obj=logging_obj, + ) + + async def async_create_run_handler( + self, + url: str, + request_body: Dict, + evals_api_provider_config: "BaseEvalsAPIConfig", + custom_llm_provider: str, + litellm_params: GenericLiteLLMParams, + logging_obj: LiteLLMLoggingObj, + extra_headers: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None, + shared_session: Optional["ClientSession"] = None, + ) -> "Run": + """Async create a run""" + if client is None or not isinstance(client, AsyncHTTPHandler): + async_httpx_client = get_async_httpx_client( + llm_provider=litellm.LlmProviders(custom_llm_provider), + params={"ssl_verify": litellm_params.get("ssl_verify", None)}, + ) + else: + async_httpx_client = client + + headers = extra_headers or {} + + logging_obj.pre_call( + input=request_body.get("name", ""), + api_key="", + additional_args={ + "complete_input_dict": request_body, + "api_base": url, + "headers": headers, + }, + ) + + try: + response = await async_httpx_client.post( + url=url, headers=headers, json=request_body, timeout=timeout + ) + except Exception as e: + raise self._handle_error( + e=e, + provider_config=evals_api_provider_config, + ) + + return evals_api_provider_config.transform_create_run_response( + raw_response=response, + logging_obj=logging_obj, + ) + + def list_runs_handler( + self, + url: str, + query_params: Dict, + evals_api_provider_config: "BaseEvalsAPIConfig", + custom_llm_provider: str, + litellm_params: GenericLiteLLMParams, + logging_obj: LiteLLMLoggingObj, + extra_headers: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None, + _is_async: bool = False, + shared_session: Optional["ClientSession"] = None, + ) -> Union["ListRunsResponse", Coroutine[Any, Any, "ListRunsResponse"]]: + """List runs""" + if _is_async: + return self.async_list_runs_handler( + url=url, + query_params=query_params, + evals_api_provider_config=evals_api_provider_config, + custom_llm_provider=custom_llm_provider, + litellm_params=litellm_params, + logging_obj=logging_obj, + extra_headers=extra_headers, + timeout=timeout, + client=client, + shared_session=shared_session, + ) + + if client is None or not isinstance(client, HTTPHandler): + sync_httpx_client = _get_httpx_client( + params={"ssl_verify": litellm_params.get("ssl_verify", None)} + ) + else: + sync_httpx_client = client + + headers = extra_headers or {} + + logging_obj.pre_call( + input="", + api_key="", + additional_args={ + "api_base": url, + "headers": headers, + "params": query_params, + }, + ) + + try: + response = sync_httpx_client.get( + url=url, headers=headers, params=query_params + ) + except Exception as e: + raise self._handle_error( + e=e, + provider_config=evals_api_provider_config, + ) + + return evals_api_provider_config.transform_list_runs_response( + raw_response=response, + logging_obj=logging_obj, + ) + + async def async_list_runs_handler( + self, + url: str, + query_params: Dict, + evals_api_provider_config: "BaseEvalsAPIConfig", + custom_llm_provider: str, + litellm_params: GenericLiteLLMParams, + logging_obj: LiteLLMLoggingObj, + extra_headers: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None, + shared_session: Optional["ClientSession"] = None, + ) -> "ListRunsResponse": + """Async list runs""" + if client is None or not isinstance(client, AsyncHTTPHandler): + async_httpx_client = get_async_httpx_client( + llm_provider=litellm.LlmProviders(custom_llm_provider), + params={"ssl_verify": litellm_params.get("ssl_verify", None)}, + ) + else: + async_httpx_client = client + + headers = extra_headers or {} + + logging_obj.pre_call( + input="", + api_key="", + additional_args={ + "api_base": url, + "headers": headers, + "params": query_params, + }, + ) + + try: + response = await async_httpx_client.get( + url=url, headers=headers, params=query_params + ) + except Exception as e: + raise self._handle_error( + e=e, + provider_config=evals_api_provider_config, + ) + + return evals_api_provider_config.transform_list_runs_response( + raw_response=response, + logging_obj=logging_obj, + ) + + def get_run_handler( + self, + url: str, + evals_api_provider_config: "BaseEvalsAPIConfig", + custom_llm_provider: str, + litellm_params: GenericLiteLLMParams, + logging_obj: LiteLLMLoggingObj, + extra_headers: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None, + _is_async: bool = False, + shared_session: Optional["ClientSession"] = None, + ) -> Union["Run", Coroutine[Any, Any, "Run"]]: + """Get a run""" + if _is_async: + return self.async_get_run_handler( + url=url, + evals_api_provider_config=evals_api_provider_config, + custom_llm_provider=custom_llm_provider, + litellm_params=litellm_params, + logging_obj=logging_obj, + extra_headers=extra_headers, + timeout=timeout, + client=client, + shared_session=shared_session, + ) + + if client is None or not isinstance(client, HTTPHandler): + sync_httpx_client = _get_httpx_client( + params={"ssl_verify": litellm_params.get("ssl_verify", None)} + ) + else: + sync_httpx_client = client + + headers = extra_headers or {} + + logging_obj.pre_call( + input="", + api_key="", + additional_args={ + "api_base": url, + "headers": headers, + }, + ) + + try: + response = sync_httpx_client.get(url=url, headers=headers) + except Exception as e: + raise self._handle_error( + e=e, + provider_config=evals_api_provider_config, + ) + + return evals_api_provider_config.transform_get_run_response( + raw_response=response, + logging_obj=logging_obj, + ) + + async def async_get_run_handler( + self, + url: str, + evals_api_provider_config: "BaseEvalsAPIConfig", + custom_llm_provider: str, + litellm_params: GenericLiteLLMParams, + logging_obj: LiteLLMLoggingObj, + extra_headers: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None, + shared_session: Optional["ClientSession"] = None, + ) -> "Run": + """Async get a run""" + if client is None or not isinstance(client, AsyncHTTPHandler): + async_httpx_client = get_async_httpx_client( + llm_provider=litellm.LlmProviders(custom_llm_provider), + params={"ssl_verify": litellm_params.get("ssl_verify", None)}, + ) + else: + async_httpx_client = client + + headers = extra_headers or {} + + logging_obj.pre_call( + input="", + api_key="", + additional_args={ + "api_base": url, + "headers": headers, + }, + ) + + try: + response = await async_httpx_client.get( + url=url, headers=headers + ) + except Exception as e: + raise self._handle_error( + e=e, + provider_config=evals_api_provider_config, + ) + + return evals_api_provider_config.transform_get_run_response( + raw_response=response, + logging_obj=logging_obj, + ) + + def cancel_run_handler( + self, + url: str, + evals_api_provider_config: "BaseEvalsAPIConfig", + custom_llm_provider: str, + litellm_params: GenericLiteLLMParams, + logging_obj: LiteLLMLoggingObj, + extra_headers: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None, + _is_async: bool = False, + shared_session: Optional["ClientSession"] = None, + ) -> Union["CancelRunResponse", Coroutine[Any, Any, "CancelRunResponse"]]: + """Cancel a run""" + if _is_async: + return self.async_cancel_run_handler( + url=url, + evals_api_provider_config=evals_api_provider_config, + custom_llm_provider=custom_llm_provider, + litellm_params=litellm_params, + logging_obj=logging_obj, + extra_headers=extra_headers, + timeout=timeout, + client=client, + shared_session=shared_session, + ) + + if client is None or not isinstance(client, HTTPHandler): + sync_httpx_client = _get_httpx_client( + params={"ssl_verify": litellm_params.get("ssl_verify", None)} + ) + else: + sync_httpx_client = client + + headers = extra_headers or {} + + logging_obj.pre_call( + input="", + api_key="", + additional_args={ + "api_base": url, + "headers": headers, + }, + ) + + try: + response = sync_httpx_client.post( + url=url, headers=headers, json={}, timeout=timeout + ) + except Exception as e: + raise self._handle_error( + e=e, + provider_config=evals_api_provider_config, + ) + + return evals_api_provider_config.transform_cancel_run_response( + raw_response=response, + logging_obj=logging_obj, + ) + + async def async_cancel_run_handler( + self, + url: str, + evals_api_provider_config: "BaseEvalsAPIConfig", + custom_llm_provider: str, + litellm_params: GenericLiteLLMParams, + logging_obj: LiteLLMLoggingObj, + extra_headers: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None, + shared_session: Optional["ClientSession"] = None, + ) -> "CancelRunResponse": + """Async cancel a run""" + if client is None or not isinstance(client, AsyncHTTPHandler): + async_httpx_client = get_async_httpx_client( + llm_provider=litellm.LlmProviders(custom_llm_provider), + params={"ssl_verify": litellm_params.get("ssl_verify", None)}, + ) + else: + async_httpx_client = client + + headers = extra_headers or {} + + logging_obj.pre_call( + input="", + api_key="", + additional_args={ + "api_base": url, + "headers": headers, + }, + ) + + try: + response = await async_httpx_client.post( + url=url, headers=headers, json={}, timeout=timeout + ) + except Exception as e: + raise self._handle_error( + e=e, + provider_config=evals_api_provider_config, + ) + + return evals_api_provider_config.transform_cancel_run_response( + raw_response=response, + logging_obj=logging_obj, + ) + + def delete_run_handler( + self, + url: str, + evals_api_provider_config: "BaseEvalsAPIConfig", + custom_llm_provider: str, + litellm_params: GenericLiteLLMParams, + logging_obj: LiteLLMLoggingObj, + extra_headers: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None, + _is_async: bool = False, + shared_session: Optional["ClientSession"] = None, + ) -> Union["RunDeleteResponse", Coroutine[Any, Any, "RunDeleteResponse"]]: + """Delete a run""" + if _is_async: + return self.async_delete_run_handler( + url=url, + evals_api_provider_config=evals_api_provider_config, + custom_llm_provider=custom_llm_provider, + litellm_params=litellm_params, + logging_obj=logging_obj, + extra_headers=extra_headers, + timeout=timeout, + client=client, + shared_session=shared_session, + ) + + if client is None or not isinstance(client, HTTPHandler): + sync_httpx_client = _get_httpx_client( + params={"ssl_verify": litellm_params.get("ssl_verify", None)} + ) + else: + sync_httpx_client = client + + headers = extra_headers or {} + + logging_obj.pre_call( + input="", + api_key="", + additional_args={ + "api_base": url, + "headers": headers, + }, + ) + + try: + response = sync_httpx_client.delete( + url=url, headers=headers, timeout=timeout + ) + except Exception as e: + raise self._handle_error( + e=e, + provider_config=evals_api_provider_config, + ) + + return evals_api_provider_config.transform_delete_run_response( + raw_response=response, + logging_obj=logging_obj, + ) + + async def async_delete_run_handler( + self, + url: str, + evals_api_provider_config: "BaseEvalsAPIConfig", + custom_llm_provider: str, + litellm_params: GenericLiteLLMParams, + logging_obj: LiteLLMLoggingObj, + extra_headers: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None, + shared_session: Optional["ClientSession"] = None, + ) -> "RunDeleteResponse": + """Async delete a run""" + if client is None or not isinstance(client, AsyncHTTPHandler): + async_httpx_client = get_async_httpx_client( + llm_provider=litellm.LlmProviders(custom_llm_provider), + params={"ssl_verify": litellm_params.get("ssl_verify", None)}, + ) + else: + async_httpx_client = client + + headers = extra_headers or {} + + logging_obj.pre_call( + input="", + api_key="", + additional_args={ + "api_base": url, + "headers": headers, + }, + ) + + try: + response = await async_httpx_client.delete( + url=url, headers=headers, timeout=timeout + ) + except Exception as e: + raise self._handle_error( + e=e, + provider_config=evals_api_provider_config, + ) + + return evals_api_provider_config.transform_delete_run_response( + raw_response=response, + logging_obj=logging_obj, + ) diff --git a/litellm/llms/dashscope/chat/transformation.py b/litellm/llms/dashscope/chat/transformation.py index 155d8c9ec2..cc5cf99182 100644 --- a/litellm/llms/dashscope/chat/transformation.py +++ b/litellm/llms/dashscope/chat/transformation.py @@ -4,9 +4,6 @@ Translates from OpenAI's `/v1/chat/completions` to DashScope's `/v1/chat/complet from typing import Any, Coroutine, List, Literal, Optional, Tuple, Union, overload -from litellm.litellm_core_utils.prompt_templates.common_utils import ( - handle_messages_with_content_list_to_str_conversion, -) from litellm.secret_managers.main import get_secret_str from litellm.types.llms.openai import AllMessageValues @@ -32,10 +29,6 @@ class DashScopeChatConfig(OpenAIGPTConfig): def _transform_messages( self, messages: List[AllMessageValues], model: str, is_async: bool = False ) -> Union[List[AllMessageValues], Coroutine[Any, Any, List[AllMessageValues]]]: - """ - DashScope does not support content in list format. - """ - messages = handle_messages_with_content_list_to_str_conversion(messages) if is_async: return super()._transform_messages( messages=messages, model=model, is_async=True diff --git a/ui/litellm-dashboard/src/components/playground/llm_calls/NonOpenAIChatCompletion.tsx b/litellm/llms/databricks/responses/__init__.py similarity index 100% rename from ui/litellm-dashboard/src/components/playground/llm_calls/NonOpenAIChatCompletion.tsx rename to litellm/llms/databricks/responses/__init__.py diff --git a/litellm/llms/databricks/responses/transformation.py b/litellm/llms/databricks/responses/transformation.py new file mode 100644 index 0000000000..0d9f433bfd --- /dev/null +++ b/litellm/llms/databricks/responses/transformation.py @@ -0,0 +1,100 @@ +""" +Databricks Responses API configuration. + +Inherits from OpenAIResponsesAPIConfig since Databricks' Responses API +is compatible with OpenAI's for GPT models. + +Reference: https://docs.databricks.com/aws/en/machine-learning/foundation-model-apis/api-reference +""" + +import os +from typing import TYPE_CHECKING, Any, Dict, Optional, Union + +from litellm.llms.databricks.common_utils import DatabricksBase +from litellm.llms.openai.responses.transformation import OpenAIResponsesAPIConfig +from litellm.types.llms.openai import ResponseInputParam +from litellm.types.router import GenericLiteLLMParams +from litellm.types.utils import LlmProviders + +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj + + LiteLLMLoggingObj = _LiteLLMLoggingObj +else: + LiteLLMLoggingObj = Any + + +class DatabricksResponsesAPIConfig(DatabricksBase, OpenAIResponsesAPIConfig): + """ + Configuration for Databricks Responses API. + + Inherits from OpenAIResponsesAPIConfig since Databricks' Responses API + is largely compatible with OpenAI's for GPT models. + + Note: The Responses API on Databricks is only compatible with OpenAI GPT models. + """ + + @property + def custom_llm_provider(self) -> LlmProviders: + return LlmProviders.DATABRICKS + + def validate_environment( + self, + headers: dict, + model: str, + litellm_params: Optional[GenericLiteLLMParams], + ) -> dict: + litellm_params = litellm_params or GenericLiteLLMParams() + api_key = litellm_params.api_key or os.getenv("DATABRICKS_API_KEY") + api_base = litellm_params.api_base or os.getenv("DATABRICKS_API_BASE") + + # Reuse Databricks auth logic (OAuth M2M, PAT, SDK fallback). + # custom_endpoint=False allows SDK auth fallback; the appended + # /chat/completions suffix is harmless since we discard api_base + # here and build the URL separately in get_complete_url(). + _, headers = self.databricks_validate_environment( + api_key=api_key, + api_base=api_base, + endpoint_type="chat_completions", + custom_endpoint=False, + headers=headers, + ) + + headers["Content-Type"] = "application/json" + return headers + + def get_complete_url( + self, + api_base: Optional[str], + litellm_params: dict, + ) -> str: + api_base = api_base or os.getenv("DATABRICKS_API_BASE") + api_base = self._get_api_base(api_base) + api_base = api_base.rstrip("/") + return f"{api_base}/responses" + + def transform_responses_api_request( + self, + model: str, + input: Union[str, ResponseInputParam], + response_api_optional_request_params: Dict, + litellm_params: GenericLiteLLMParams, + headers: dict, + ) -> Dict: + """ + Transform request for Databricks Responses API. + + Strips the 'databricks/' prefix from model name if present, + then delegates to OpenAI's transformation. + """ + # Strip provider prefix if present (e.g., "databricks/databricks-gpt-5-nano" -> "databricks-gpt-5-nano") + if model.startswith("databricks/"): + model = model[len("databricks/") :] + + return super().transform_responses_api_request( + model=model, + input=input, + response_api_optional_request_params=response_api_optional_request_params, + litellm_params=litellm_params, + headers=headers, + ) diff --git a/litellm/llms/duckduckgo/search/__init__.py b/litellm/llms/duckduckgo/search/__init__.py new file mode 100644 index 0000000000..c001963783 --- /dev/null +++ b/litellm/llms/duckduckgo/search/__init__.py @@ -0,0 +1,6 @@ +""" +DuckDuckGo Search API module. +""" +from litellm.llms.duckduckgo.search.transformation import DuckDuckGoSearchConfig + +__all__ = ["DuckDuckGoSearchConfig"] diff --git a/litellm/llms/duckduckgo/search/transformation.py b/litellm/llms/duckduckgo/search/transformation.py new file mode 100644 index 0000000000..509d69041f --- /dev/null +++ b/litellm/llms/duckduckgo/search/transformation.py @@ -0,0 +1,252 @@ +""" +Calls DuckDuckGo's Instant Answer API to search the web. + +DuckDuckGo API Reference: https://duckduckgo.com/api +""" +from typing import Dict, List, Literal, Optional, TypedDict, Union +from urllib.parse import urlencode + +import httpx + +from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj +from litellm.llms.base_llm.search.transformation import ( + BaseSearchConfig, + SearchResponse, + SearchResult, +) +from litellm.secret_managers.main import get_secret_str + + +class _DuckDuckGoSearchRequestRequired(TypedDict): + """Required fields for DuckDuckGo Search API request.""" + q: str # Required - search query + + +class DuckDuckGoSearchRequest(_DuckDuckGoSearchRequestRequired, total=False): + """ + DuckDuckGo Instant Answer API request format. + Based on: https://duckduckgo.com/api + """ + format: str # Optional - output format ('json', 'xml'), default 'json' + pretty: int # Optional - pretty print (0 or 1), default 1 + no_redirect: int # Optional - skip HTTP redirects (0 or 1), default 0 + no_html: int # Optional - remove HTML from text (0 or 1), default 0 + skip_disambig: int # Optional - skip disambiguation results (0 or 1), default 0 + + +class DuckDuckGoSearchConfig(BaseSearchConfig): + DUCKDUCKGO_API_BASE = "https://api.duckduckgo.com" + + @staticmethod + def ui_friendly_name() -> str: + return "DuckDuckGo" + + def get_http_method(self) -> Literal["GET", "POST"]: + """ + Get HTTP method for search requests. + DuckDuckGo Instant Answer API uses GET requests. + + Returns: + HTTP method 'GET' + """ + return "GET" + + def validate_environment( + self, + headers: Dict, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + **kwargs, + ) -> Dict: + """ + Validate environment and return headers. + DuckDuckGo Instant Answer API does not require authentication. + """ + # DuckDuckGo API is free and doesn't require API key + headers["Content-Type"] = "application/json" + return headers + + def get_complete_url( + self, + api_base: Optional[str], + optional_params: dict, + data: Optional[Union[Dict, List[Dict]]] = None, + **kwargs, + ) -> str: + """ + Get complete URL for Search endpoint. + DuckDuckGo uses query parameters, so we construct the URL with the query. + """ + api_base = api_base or get_secret_str("DUCKDUCKGO_API_BASE") or self.DUCKDUCKGO_API_BASE + + # Build query parameters from the transformed request body + if data and isinstance(data, dict) and "_duckduckgo_params" in data: + params = data["_duckduckgo_params"] + query_string = urlencode(params, doseq=True) + return f"{api_base}/?{query_string}" + + return api_base + + + def transform_search_request( + self, + query: Union[str, List[str]], + optional_params: dict, + **kwargs, + ) -> Dict: + """ + Transform Search request to DuckDuckGo API format. + + Args: + query: Search query (string or list of strings). DuckDuckGo only supports single string queries. + optional_params: Optional parameters for the request + - max_results: Maximum number of search results (DuckDuckGo API doesn't directly support this, used for filtering) + - format: Output format ('json', 'xml') + - pretty: Pretty print (0 or 1) + - no_redirect: Skip HTTP redirects (0 or 1) + - no_html: Remove HTML from text (0 or 1) + - skip_disambig: Skip disambiguation results (0 or 1) + + Returns: + Dict with typed request data following DuckDuckGoSearchRequest spec + """ + if isinstance(query, list): + # DuckDuckGo only supports single string queries + query = " ".join(query) + + request_data: DuckDuckGoSearchRequest = { + "q": query, + "format": "json", # Always use JSON format + } + + # Convert to dict before dynamic key assignments + result_data = dict(request_data) + + if "max_results" in optional_params: + result_data["_max_results"] = optional_params["max_results"] + + # Pass through DuckDuckGo-specific parameters + ddg_params = ["pretty", "no_redirect", "no_html", "skip_disambig"] + for param in ddg_params: + if param in optional_params: + result_data[param] = optional_params[param] + + return { + "_duckduckgo_params": result_data, + } + + def transform_search_response( + self, + raw_response: httpx.Response, + logging_obj: LiteLLMLoggingObj, + **kwargs, + ) -> SearchResponse: + """ + Transform DuckDuckGo API response to LiteLLM unified SearchResponse format. + + DuckDuckGo → LiteLLM mappings: + - RelatedTopics[].Text → SearchResult.title + snippet + - RelatedTopics[].FirstURL → SearchResult.url + - RelatedTopics[].Text → SearchResult.snippet + - No date/last_updated fields in DuckDuckGo response (set to None) + + Args: + raw_response: Raw httpx response from DuckDuckGo API + logging_obj: Logging object for tracking + + Returns: + SearchResponse with standardized format + """ + response_json = raw_response.json() + + # Extract max_results from the request URL params + query_params = raw_response.request.url.params if raw_response.request else {} + max_results = None + if "_max_results" in query_params: + try: + max_results = int(query_params["_max_results"]) + except (ValueError, TypeError): + pass + + # Transform results to SearchResult objects + results = [] + + # DuckDuckGo can return results in different fields + # Priority: Abstract > Answer > RelatedTopics + + # Check if there's an Abstract with URL + if response_json.get("AbstractURL") and response_json.get("AbstractText"): + abstract_result = SearchResult( + title=response_json.get("Heading", ""), + url=response_json.get("AbstractURL", ""), + snippet=response_json.get("AbstractText", ""), + date=None, + last_updated=None, + ) + results.append(abstract_result) + + # Process RelatedTopics + related_topics = response_json.get("RelatedTopics", []) + for topic in related_topics: + # Stop if we've reached max_results + if max_results is not None and len(results) >= max_results: + break + + if isinstance(topic, dict): + # Check if it's a direct result + if "FirstURL" in topic and "Text" in topic: + text = topic.get("Text", "") + url = topic.get("FirstURL", "") + + # Try to split title and snippet + if " - " in text: + parts = text.split(" - ", 1) + title = parts[0] + snippet = parts[1] if len(parts) > 1 else text + else: + title = text[:50] + "..." if len(text) > 50 else text + snippet = text + + search_result = SearchResult( + title=title, + url=url, + snippet=snippet, + date=None, + last_updated=None, + ) + results.append(search_result) + + # Check if it contains nested topics + elif "Topics" in topic: + nested_topics = topic.get("Topics", []) + for nested_topic in nested_topics: + # Stop if we've reached max_results + if max_results is not None and len(results) >= max_results: + break + + if "FirstURL" in nested_topic and "Text" in nested_topic: + text = nested_topic.get("Text", "") + url = nested_topic.get("FirstURL", "") + + # Try to split title and snippet + if " - " in text: + parts = text.split(" - ", 1) + title = parts[0] + snippet = parts[1] if len(parts) > 1 else text + else: + title = text[:50] + "..." if len(text) > 50 else text + snippet = text + + search_result = SearchResult( + title=title, + url=url, + snippet=snippet, + date=None, + last_updated=None, + ) + results.append(search_result) + + return SearchResponse( + results=results, + object="search", + ) diff --git a/litellm/llms/hosted_vllm/chat/transformation.py b/litellm/llms/hosted_vllm/chat/transformation.py index e955800b94..35dfa8a385 100644 --- a/litellm/llms/hosted_vllm/chat/transformation.py +++ b/litellm/llms/hosted_vllm/chat/transformation.py @@ -137,10 +137,29 @@ class HostedVLLMChatConfig(OpenAIGPTConfig): self, messages: List[AllMessageValues], model: str, is_async: bool = False ) -> Union[List[AllMessageValues], Coroutine[Any, Any, List[AllMessageValues]]]: """ - Support translating video files from file_id or file_data to video_url + Support translating: + - video files from file_id or file_data to video_url + - thinking_blocks on assistant messages to content blocks """ for message in messages: - if message["role"] == "user": + if message["role"] == "assistant": + thinking_blocks = message.pop("thinking_blocks", None) # type: ignore + if thinking_blocks: + new_content: list = [ + {"type": block["type"], "thinking": block.get("thinking", "")} + if block.get("type") == "thinking" + else {"type": block["type"], "data": block.get("data", "")} + for block in thinking_blocks + ] + existing_content = message.get("content") + if isinstance(existing_content, str): + new_content.append( + {"type": "text", "text": existing_content} + ) + elif isinstance(existing_content, list): + new_content.extend(existing_content) + message["content"] = new_content # type: ignore + elif message["role"] == "user": message_content = message.get("content") if message_content and isinstance(message_content, list): replaced_content_items: List[ diff --git a/litellm/llms/openai/chat/gpt_transformation.py b/litellm/llms/openai/chat/gpt_transformation.py index 1636890707..aa8471a597 100644 --- a/litellm/llms/openai/chat/gpt_transformation.py +++ b/litellm/llms/openai/chat/gpt_transformation.py @@ -162,6 +162,7 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig): "service_tier", "safety_identifier", "prompt_cache_key", + "store", ] # works across all models model_specific_params = [] @@ -770,14 +771,39 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig): class OpenAIChatCompletionStreamingHandler(BaseModelResponseIterator): + def _map_reasoning_to_reasoning_content(self, choices: list) -> list: + """ + Map 'reasoning' field to 'reasoning_content' field in delta. + + Some OpenAI-compatible providers (e.g., GLM-5, hosted_vllm) return + delta.reasoning, but LiteLLM expects delta.reasoning_content. + + Args: + choices: List of choice objects from the streaming chunk + + Returns: + List of choices with reasoning field mapped to reasoning_content + """ + for choice in choices: + delta = choice.get("delta", {}) + if "reasoning" in delta: + delta["reasoning_content"] = delta.pop("reasoning") + return choices + def chunk_parser(self, chunk: dict) -> ModelResponseStream: try: - return ModelResponseStream( - id=chunk["id"], - object="chat.completion.chunk", - created=chunk.get("created"), - model=chunk.get("model"), - choices=chunk.get("choices", []), - ) + choices = chunk.get("choices", []) + choices = self._map_reasoning_to_reasoning_content(choices) + + kwargs = { + "id": chunk["id"], + "object": "chat.completion.chunk", + "created": chunk.get("created"), + "model": chunk.get("model"), + "choices": choices, + } + if "usage" in chunk and chunk["usage"] is not None: + kwargs["usage"] = chunk["usage"] + return ModelResponseStream(**kwargs) except Exception as e: raise e diff --git a/litellm/llms/openai/chat/guardrail_translation/handler.py b/litellm/llms/openai/chat/guardrail_translation/handler.py index c406f502b4..683e165c31 100644 --- a/litellm/llms/openai/chat/guardrail_translation/handler.py +++ b/litellm/llms/openai/chat/guardrail_translation/handler.py @@ -107,6 +107,9 @@ class OpenAIChatCompletionsHandler(BaseTranslation): guardrailed_texts = guardrailed_inputs.get("texts", []) guardrailed_tool_calls = guardrailed_inputs.get("tool_calls", []) + guardrailed_tools = guardrailed_inputs.get("tools") + if guardrailed_tools is not None: + data["tools"] = guardrailed_tools # Step 3: Map guardrail responses back to original message structure if guardrailed_texts and texts_to_check: diff --git a/litellm/llms/openai/common_utils.py b/litellm/llms/openai/common_utils.py index ce470f04ac..28de9f1303 100644 --- a/litellm/llms/openai/common_utils.py +++ b/litellm/llms/openai/common_utils.py @@ -3,9 +3,10 @@ Common helpers / utils across al OpenAI endpoints """ import hashlib +import inspect import json import ssl -from typing import Any, Dict, List, Literal, Optional, TYPE_CHECKING, Union +from typing import TYPE_CHECKING, Any, Dict, List, Literal, Optional, Tuple, Union import httpx import openai @@ -23,6 +24,15 @@ from litellm.llms.custom_httpx.http_handler import ( ) +def _get_client_init_params(cls: type) -> Tuple[str, ...]: + """Extract __init__ parameter names (excluding 'self') from a class.""" + return tuple(p for p in inspect.signature(cls.__init__).parameters if p != "self") # type: ignore[misc] + + +_OPENAI_INIT_PARAMS: Tuple[str, ...] = _get_client_init_params(OpenAI) +_AZURE_OPENAI_INIT_PARAMS: Tuple[str, ...] = _get_client_init_params(AzureOpenAI) + + class OpenAIError(BaseLLMException): def __init__( self, @@ -159,12 +169,12 @@ class BaseOpenAILLM: f"is_async={client_initialization_params.get('is_async')}", ] - LITELLM_CLIENT_SPECIFIC_PARAMS = [ + LITELLM_CLIENT_SPECIFIC_PARAMS = ( "timeout", "max_retries", "organization", "api_base", - ] + ) openai_client_fields = ( BaseOpenAILLM.get_openai_client_initialization_param_fields( client_type=client_type @@ -181,20 +191,12 @@ class BaseOpenAILLM: @staticmethod def get_openai_client_initialization_param_fields( client_type: Literal["openai", "azure"] - ) -> List[str]: - """Returns a list of fields that are used to initialize the OpenAI client""" - import inspect - - from openai import AzureOpenAI, OpenAI - + ) -> Tuple[str, ...]: + """Returns a tuple of fields that are used to initialize the OpenAI client""" if client_type == "openai": - signature = inspect.signature(OpenAI.__init__) + return _OPENAI_INIT_PARAMS else: - signature = inspect.signature(AzureOpenAI.__init__) - - # Extract parameter names, excluding 'self' - param_names = [param for param in signature.parameters if param != "self"] - return param_names + return _AZURE_OPENAI_INIT_PARAMS @staticmethod def _get_async_http_client( @@ -230,3 +232,5 @@ class BaseOpenAILLM: verify=ssl_config, follow_redirects=True, ) + + diff --git a/litellm/llms/openai/evals/__init__.py b/litellm/llms/openai/evals/__init__.py new file mode 100644 index 0000000000..b04d27622b --- /dev/null +++ b/litellm/llms/openai/evals/__init__.py @@ -0,0 +1,7 @@ +""" +OpenAI Evals API configuration +""" + +from .transformation import OpenAIEvalsConfig + +__all__ = ["OpenAIEvalsConfig"] diff --git a/litellm/llms/openai/evals/transformation.py b/litellm/llms/openai/evals/transformation.py new file mode 100644 index 0000000000..c24dbf8637 --- /dev/null +++ b/litellm/llms/openai/evals/transformation.py @@ -0,0 +1,426 @@ +""" +OpenAI Evals API configuration and transformations +""" + +from typing import Any, Dict, Optional, Tuple + +import httpx + +from litellm._logging import verbose_logger +from litellm.llms.base_llm.evals.transformation import ( + BaseEvalsAPIConfig, + LiteLLMLoggingObj, +) +from litellm.types.llms.openai_evals import ( + CancelEvalResponse, + CancelRunResponse, + CreateEvalRequest, + CreateRunRequest, + DeleteEvalResponse, + Eval, + ListEvalsParams, + ListEvalsResponse, + ListRunsParams, + ListRunsResponse, + Run, + RunDeleteResponse, + UpdateEvalRequest, +) +from litellm.types.router import GenericLiteLLMParams +from litellm.types.utils import LlmProviders + + +class OpenAIEvalsConfig(BaseEvalsAPIConfig): + """OpenAI-specific Evals API configuration""" + + @property + def custom_llm_provider(self) -> LlmProviders: + return LlmProviders.OPENAI + + def validate_environment( + self, headers: dict, litellm_params: Optional[GenericLiteLLMParams] + ) -> dict: + """Add OpenAI-specific headers""" + import litellm + from litellm.secret_managers.main import get_secret_str + + # Get API key following OpenAI pattern + api_key = None + if litellm_params: + api_key = litellm_params.api_key + + api_key = ( + api_key + or litellm.api_key + or litellm.openai_key + or get_secret_str("OPENAI_API_KEY") + ) + + if not api_key: + raise ValueError("OPENAI_API_KEY is required for Evals API") + + # Add required headers + headers["Authorization"] = f"Bearer {api_key}" + headers["Content-Type"] = "application/json" + + return headers + + def get_complete_url( + self, + api_base: Optional[str], + endpoint: str, + eval_id: Optional[str] = None, + ) -> str: + """Get complete URL for OpenAI Evals API""" + if api_base is None: + api_base = "https://api.openai.com" + + if eval_id: + return f"{api_base}/v1/evals/{eval_id}" + return f"{api_base}/v1/{endpoint}" + + def transform_create_eval_request( + self, + create_request: CreateEvalRequest, + litellm_params: GenericLiteLLMParams, + headers: dict, + ) -> Dict: + """Transform create eval request for OpenAI""" + verbose_logger.debug("Transforming create eval request: %s", create_request) + + # OpenAI expects the request body directly + request_body = {k: v for k, v in create_request.items() if v is not None} + + return request_body + + def transform_create_eval_response( + self, + raw_response: httpx.Response, + logging_obj: LiteLLMLoggingObj, + ) -> Eval: + """Transform OpenAI response to Eval object""" + response_json = raw_response.json() + verbose_logger.debug("Transforming create eval response: %s", response_json) + + return Eval(**response_json) + + def transform_list_evals_request( + self, + list_params: ListEvalsParams, + litellm_params: GenericLiteLLMParams, + headers: dict, + ) -> Tuple[str, Dict]: + """Transform list evals request for OpenAI""" + api_base = "https://api.openai.com" + if litellm_params and litellm_params.api_base: + api_base = litellm_params.api_base + + url = self.get_complete_url(api_base=api_base, endpoint="evals") + + # Build query parameters + query_params: Dict[str, Any] = {} + if "limit" in list_params and list_params["limit"]: + query_params["limit"] = list_params["limit"] + if "after" in list_params and list_params["after"]: + query_params["after"] = list_params["after"] + if "before" in list_params and list_params["before"]: + query_params["before"] = list_params["before"] + if "order" in list_params and list_params["order"]: + query_params["order"] = list_params["order"] + if "order_by" in list_params and list_params["order_by"]: + query_params["order_by"] = list_params["order_by"] + + verbose_logger.debug( + "List evals request made to OpenAI Evals endpoint with params: %s", + query_params, + ) + + return url, query_params + + def transform_list_evals_response( + self, + raw_response: httpx.Response, + logging_obj: LiteLLMLoggingObj, + ) -> ListEvalsResponse: + """Transform OpenAI response to ListEvalsResponse""" + response_json = raw_response.json() + verbose_logger.debug("Transforming list evals response: %s", response_json) + + return ListEvalsResponse(**response_json) + + def transform_get_eval_request( + self, + eval_id: str, + api_base: str, + litellm_params: GenericLiteLLMParams, + headers: dict, + ) -> Tuple[str, Dict]: + """Transform get eval request for OpenAI""" + url = self.get_complete_url( + api_base=api_base, endpoint="evals", eval_id=eval_id + ) + + verbose_logger.debug("Get eval request - URL: %s", url) + + return url, headers + + def transform_get_eval_response( + self, + raw_response: httpx.Response, + logging_obj: LiteLLMLoggingObj, + ) -> Eval: + """Transform OpenAI response to Eval object""" + response_json = raw_response.json() + verbose_logger.debug("Transforming get eval response: %s", response_json) + + return Eval(**response_json) + + def transform_update_eval_request( + self, + eval_id: str, + update_request: UpdateEvalRequest, + api_base: str, + litellm_params: GenericLiteLLMParams, + headers: dict, + ) -> Tuple[str, Dict, Dict]: + """Transform update eval request for OpenAI""" + url = self.get_complete_url( + api_base=api_base, endpoint="evals", eval_id=eval_id + ) + + # Build request body + request_body = {k: v for k, v in update_request.items() if v is not None} + + verbose_logger.debug( + "Update eval request - URL: %s, body: %s", url, request_body + ) + + return url, headers, request_body + + def transform_update_eval_response( + self, + raw_response: httpx.Response, + logging_obj: LiteLLMLoggingObj, + ) -> Eval: + """Transform OpenAI response to Eval object""" + response_json = raw_response.json() + verbose_logger.debug("Transforming update eval response: %s", response_json) + + return Eval(**response_json) + + def transform_delete_eval_request( + self, + eval_id: str, + api_base: str, + litellm_params: GenericLiteLLMParams, + headers: dict, + ) -> Tuple[str, Dict]: + """Transform delete eval request for OpenAI""" + url = self.get_complete_url( + api_base=api_base, endpoint="evals", eval_id=eval_id + ) + + verbose_logger.debug("Delete eval request - URL: %s", url) + + return url, headers + + def transform_delete_eval_response( + self, + raw_response: httpx.Response, + logging_obj: LiteLLMLoggingObj, + ) -> DeleteEvalResponse: + """Transform OpenAI response to DeleteEvalResponse""" + response_json = raw_response.json() + verbose_logger.debug("Transforming delete eval response: %s", response_json) + + return DeleteEvalResponse(**response_json) + + def transform_cancel_eval_request( + self, + eval_id: str, + api_base: str, + litellm_params: GenericLiteLLMParams, + headers: dict, + ) -> Tuple[str, Dict, Dict]: + """Transform cancel eval request for OpenAI""" + url = f"{self.get_complete_url(api_base=api_base, endpoint='evals', eval_id=eval_id)}/cancel" + + # Empty body for cancel request + request_body: Dict[str, Any] = {} + + verbose_logger.debug("Cancel eval request - URL: %s", url) + + return url, headers, request_body + + def transform_cancel_eval_response( + self, + raw_response: httpx.Response, + logging_obj: LiteLLMLoggingObj, + ) -> CancelEvalResponse: + """Transform OpenAI response to CancelEvalResponse""" + response_json = raw_response.json() + verbose_logger.debug("Transforming cancel eval response: %s", response_json) + + return CancelEvalResponse(**response_json) + + # Run API Transformations + def transform_create_run_request( + self, + eval_id: str, + create_request: CreateRunRequest, + litellm_params: GenericLiteLLMParams, + headers: dict, + ) -> Tuple[str, Dict]: + """Transform create run request for OpenAI""" + api_base = "https://api.openai.com" + if litellm_params and litellm_params.api_base: + api_base = litellm_params.api_base + + url = f"{api_base}/v1/evals/{eval_id}/runs" + + # Build request body + request_body = {k: v for k, v in create_request.items() if v is not None} + + verbose_logger.debug( + "Create run request - URL: %s, body: %s", url, request_body + ) + + return url, request_body + + def transform_create_run_response( + self, + raw_response: httpx.Response, + logging_obj: LiteLLMLoggingObj, + ) -> Run: + """Transform OpenAI response to Run object""" + response_json = raw_response.json() + verbose_logger.debug("Transforming create run response: %s", response_json) + + return Run(**response_json) + + def transform_list_runs_request( + self, + eval_id: str, + list_params: ListRunsParams, + litellm_params: GenericLiteLLMParams, + headers: dict, + ) -> Tuple[str, Dict]: + """Transform list runs request for OpenAI""" + api_base = "https://api.openai.com" + if litellm_params and litellm_params.api_base: + api_base = litellm_params.api_base + + url = f"{api_base}/v1/evals/{eval_id}/runs" + + # Build query parameters + query_params: Dict[str, Any] = {} + if "limit" in list_params and list_params["limit"]: + query_params["limit"] = list_params["limit"] + if "after" in list_params and list_params["after"]: + query_params["after"] = list_params["after"] + if "before" in list_params and list_params["before"]: + query_params["before"] = list_params["before"] + if "order" in list_params and list_params["order"]: + query_params["order"] = list_params["order"] + + verbose_logger.debug( + "List runs request made to OpenAI Evals endpoint with params: %s", + query_params, + ) + + return url, query_params + + def transform_list_runs_response( + self, + raw_response: httpx.Response, + logging_obj: LiteLLMLoggingObj, + ) -> ListRunsResponse: + """Transform OpenAI response to ListRunsResponse""" + response_json = raw_response.json() + verbose_logger.debug("Transforming list runs response: %s", response_json) + + return ListRunsResponse(**response_json) + + def transform_get_run_request( + self, + eval_id: str, + run_id: str, + api_base: str, + litellm_params: GenericLiteLLMParams, + headers: dict, + ) -> Tuple[str, Dict]: + """Transform get run request for OpenAI""" + url = f"{api_base}/v1/evals/{eval_id}/runs/{run_id}" + + verbose_logger.debug("Get run request - URL: %s", url) + + return url, headers + + def transform_get_run_response( + self, + raw_response: httpx.Response, + logging_obj: LiteLLMLoggingObj, + ) -> Run: + """Transform OpenAI response to Run object""" + response_json = raw_response.json() + verbose_logger.debug("Transforming get run response: %s", response_json) + + return Run(**response_json) + + def transform_cancel_run_request( + self, + eval_id: str, + run_id: str, + api_base: str, + litellm_params: GenericLiteLLMParams, + headers: dict, + ) -> Tuple[str, Dict, Dict]: + """Transform cancel run request for OpenAI""" + url = f"{api_base}/v1/evals/{eval_id}/runs/{run_id}/cancel" + + # Empty body for cancel request + request_body: Dict[str, Any] = {} + + verbose_logger.debug("Cancel run request - URL: %s", url) + + return url, headers, request_body + + def transform_cancel_run_response( + self, + raw_response: httpx.Response, + logging_obj: LiteLLMLoggingObj, + ) -> CancelRunResponse: + """Transform OpenAI response to CancelRunResponse""" + response_json = raw_response.json() + verbose_logger.debug("Transforming cancel run response: %s", response_json) + + return CancelRunResponse(**response_json) + + def transform_delete_run_request( + self, + eval_id: str, + run_id: str, + api_base: str, + litellm_params: GenericLiteLLMParams, + headers: dict, + ) -> Tuple[str, Dict, Dict]: + """Transform delete run request for OpenAI""" + url = f"{api_base}/v1/evals/{eval_id}/runs/{run_id}" + + # Empty body for delete request + request_body: Dict[str, Any] = {} + + verbose_logger.debug("Delete run request - URL: %s", url) + + return url, headers, request_body + + def transform_delete_run_response( + self, + raw_response: httpx.Response, + logging_obj: LiteLLMLoggingObj, + ) -> RunDeleteResponse: + """Transform OpenAI response to RunDeleteResponse""" + response_json = raw_response.json() + verbose_logger.debug("Transforming delete run response: %s", response_json) + + return RunDeleteResponse(**response_json) diff --git a/litellm/llms/openai/openai.py b/litellm/llms/openai/openai.py index da87852dff..c7524925bd 100644 --- a/litellm/llms/openai/openai.py +++ b/litellm/llms/openai/openai.py @@ -693,6 +693,7 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): organization=organization, drop_params=drop_params, stream_options=stream_options, + shared_session=shared_session, ) else: return self.acompletion( @@ -1063,6 +1064,7 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): headers=None, drop_params: Optional[bool] = None, stream_options: Optional[dict] = None, + shared_session: Optional["ClientSession"] = None, ): response = None data = provider_config.transform_request( @@ -1087,6 +1089,7 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): max_retries=max_retries, organization=organization, client=client, + shared_session=shared_session, ) ## LOGGING logging_obj.pre_call( diff --git a/litellm/llms/openai/responses/guardrail_translation/handler.py b/litellm/llms/openai/responses/guardrail_translation/handler.py index ad3d4c932d..6b092911d3 100644 --- a/litellm/llms/openai/responses/guardrail_translation/handler.py +++ b/litellm/llms/openai/responses/guardrail_translation/handler.py @@ -96,10 +96,11 @@ class OpenAIResponsesHandler(BaseTranslation): # Handle simple string input if isinstance(input_data, str): inputs = GenericGuardrailAPIInputs(texts=[input_data]) + original_tools: List[Dict[str, Any]] = [] # Extract and transform tools if present - if "tools" in data and data["tools"]: + original_tools = list(data["tools"]) self._extract_and_transform_tools(data["tools"], tools_to_check) if tools_to_check: inputs["tools"] = tools_to_check @@ -118,6 +119,9 @@ class OpenAIResponsesHandler(BaseTranslation): ) guardrailed_texts = guardrailed_inputs.get("texts", []) data["input"] = guardrailed_texts[0] if guardrailed_texts else input_data + self._apply_guardrailed_tools_to_data( + data, original_tools, guardrailed_inputs.get("tools") + ) verbose_proxy_logger.debug("OpenAI Responses API: Processed string input") return data @@ -128,8 +132,7 @@ class OpenAIResponsesHandler(BaseTranslation): texts_to_check: List[str] = [] images_to_check: List[str] = [] task_mappings: List[Tuple[int, Optional[int]]] = [] - # Track (message_index, content_index) for each text - # content_index is None for string content, int for list content + original_tools_list: List[Dict[str, Any]] = list(data.get("tools") or []) # Step 1: Extract all text content, images, and tools for msg_idx, message in enumerate(input_data): @@ -166,6 +169,11 @@ class OpenAIResponsesHandler(BaseTranslation): ) guardrailed_texts = guardrailed_inputs.get("texts", []) + self._apply_guardrailed_tools_to_data( + data, + original_tools_list, + guardrailed_inputs.get("tools"), + ) # Step 3: Map guardrail responses back to original input structure await self._apply_guardrail_responses_to_input( @@ -203,6 +211,53 @@ class OpenAIResponsesHandler(BaseTranslation): cast(List[ChatCompletionToolParam], transformed_tools) ) + def _remap_tools_to_responses_api_format( + self, guardrailed_tools: List[Any] + ) -> List[Dict[str, Any]]: + """ + Remap guardrail-returned tools (Chat Completion format) back to + Responses API request tool format. + """ + return LiteLLMCompletionResponsesConfig.transform_chat_completion_tool_params_to_responses_api_tools( + guardrailed_tools # type: ignore + ) + + def _merge_tools_after_guardrail( + self, + original_tools: List[Dict[str, Any]], + remapped: List[Dict[str, Any]], + ) -> List[Dict[str, Any]]: + """ + Merge remapped guardrailed tools with original tools that were not sent + to the guardrail (e.g. web_search, web_search_preview), preserving order. + """ + if not original_tools: + return remapped + result: List[Dict[str, Any]] = [] + j = 0 + for tool in original_tools: + if isinstance(tool, dict) and tool.get("type") in ( + "web_search", + "web_search_preview", + ): + result.append(tool) + else: + if j < len(remapped): + result.append(remapped[j]) + j += 1 + return result + + def _apply_guardrailed_tools_to_data( + self, + data: dict, + original_tools: List[Dict[str, Any]], + guardrailed_tools: Optional[List[Any]], + ) -> None: + """Remap guardrailed tools to Responses API format and merge with original, then set data['tools'].""" + if guardrailed_tools is not None: + remapped = self._remap_tools_to_responses_api_format(guardrailed_tools) + data["tools"] = self._merge_tools_after_guardrail(original_tools, remapped) + def _extract_input_text_and_images( self, message: Any, # Can be Dict[str, Any] or ResponseInputParam @@ -407,7 +462,10 @@ class OpenAIResponsesHandler(BaseTranslation): List[ChatCompletionToolCallChunk], tool_calls ) # Include model information if available - if hasattr(model_response_stream, "model") and model_response_stream.model: + if ( + hasattr(model_response_stream, "model") + and model_response_stream.model + ): inputs["model"] = model_response_stream.model _guardrailed_inputs = await guardrail_to_apply.apply_guardrail( inputs=inputs, @@ -448,7 +506,9 @@ class OpenAIResponsesHandler(BaseTranslation): ) return responses_so_far else: - verbose_proxy_logger.debug("Skipping output guardrail - model response has no choices") + verbose_proxy_logger.debug( + "Skipping output guardrail - model response has no choices" + ) # model_response_stream = OpenAiResponsesToChatCompletionStreamIterator.translate_responses_chunk_to_openai_stream(final_chunk) # tool_calls = model_response_stream.choices[0].tool_calls # convert openai response to model response @@ -456,7 +516,11 @@ class OpenAIResponsesHandler(BaseTranslation): inputs = GenericGuardrailAPIInputs(texts=[string_so_far]) # Try to get model from the final chunk if available if isinstance(final_chunk, dict): - response_model = final_chunk.get("response", {}).get("model") if isinstance(final_chunk.get("response"), dict) else None + response_model = ( + final_chunk.get("response", {}).get("model") + if isinstance(final_chunk.get("response"), dict) + else None + ) if response_model: inputs["model"] = response_model _guardrailed_inputs = await guardrail_to_apply.apply_guardrail( @@ -591,8 +655,8 @@ class OpenAIResponsesHandler(BaseTranslation): content = generic_response_output_item.content except Exception: # Try to extract content directly from output_item if validation fails - if hasattr(output_item, "content") and output_item.content: - content = output_item.content + if hasattr(output_item, "content") and output_item.content: # type: ignore + content = output_item.content # type: ignore else: return elif isinstance(output_item, dict): @@ -669,10 +733,10 @@ class OpenAIResponsesHandler(BaseTranslation): if isinstance(content_item, OutputText): content_item.text = guardrail_response # Update the original response output - if hasattr(output_item, "content") and output_item.content: - original_content = output_item.content[content_idx] + if hasattr(output_item, "content") and output_item.content: # type: ignore + original_content = output_item.content[content_idx] # type: ignore if hasattr(original_content, "text"): - original_content.text = guardrail_response + original_content.text = guardrail_response # type: ignore except Exception: pass elif isinstance(output_item, dict): diff --git a/litellm/llms/openai/responses/transformation.py b/litellm/llms/openai/responses/transformation.py index 5c87071106..3e08968209 100644 --- a/litellm/llms/openai/responses/transformation.py +++ b/litellm/llms/openai/responses/transformation.py @@ -2,7 +2,7 @@ from typing import TYPE_CHECKING, Any, Dict, Optional, Union, cast, get_type_hin import httpx from openai.types.responses import ResponseReasoningItem -from pydantic import BaseModel +from pydantic import BaseModel, ValidationError import litellm from litellm._logging import verbose_logger @@ -249,7 +249,17 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): parsed_chunk["error"]["code"] = "unknown_error" except Exception: verbose_logger.debug("Failed to coalesce error.code in parsed_chunk") - return event_pydantic_model(**parsed_chunk) + + try: + return event_pydantic_model(**parsed_chunk) + except ValidationError: + verbose_logger.debug( + "Pydantic validation failed for %s with chunk %s, " + "falling back to model_construct", + event_pydantic_model.__name__, + parsed_chunk, + ) + return event_pydantic_model.model_construct(**parsed_chunk) @staticmethod def get_event_model_class(event_type: str) -> Any: diff --git a/litellm/llms/openai_like/dynamic_config.py b/litellm/llms/openai_like/dynamic_config.py index 1e7866bebb..a2ce6b9a53 100644 --- a/litellm/llms/openai_like/dynamic_config.py +++ b/litellm/llms/openai_like/dynamic_config.py @@ -4,6 +4,7 @@ Dynamic configuration class generator for JSON-based providers. from typing import Any, Coroutine, List, Literal, Optional, Tuple, Union, overload +from litellm._logging import verbose_logger from litellm.litellm_core_utils.prompt_templates.common_utils import ( handle_messages_with_content_list_to_str_conversion, ) @@ -96,8 +97,27 @@ def create_config_class(provider: SimpleProviderConfig): return api_base def get_supported_openai_params(self, model: str) -> list: - """Get supported OpenAI params from base class""" - return super().get_supported_openai_params(model=model) + """Get supported OpenAI params, excluding tool-related params for models + that don't support function calling.""" + from litellm.utils import supports_function_calling + + supported_params = super().get_supported_openai_params(model=model) + + _supports_fc = supports_function_calling( + model=model, custom_llm_provider=provider.slug + ) + + if not _supports_fc: + tool_params = ["tools", "tool_choice", "function_call", "functions", "parallel_tool_calls"] + for param in tool_params: + if param in supported_params: + supported_params.remove(param) + verbose_logger.debug( + f"Model {model} on provider {provider.slug} does not support " + f"function calling — removed tool-related params from supported params." + ) + + return supported_params def map_openai_params( self, diff --git a/litellm/llms/openai_like/providers.json b/litellm/llms/openai_like/providers.json index b4f9cbe42d..1b1b1c2f8c 100644 --- a/litellm/llms/openai_like/providers.json +++ b/litellm/llms/openai_like/providers.json @@ -26,6 +26,10 @@ "max_completion_tokens": "max_tokens" } }, + "scaleway": { + "base_url": "https://api.scaleway.ai/v1", + "api_key_env": "SCW_SECRET_KEY" + }, "synthetic": { "base_url": "https://api.synthetic.new/openai/v1", "api_key_env": "SYNTHETIC_API_KEY", diff --git a/litellm/llms/vertex_ai/gemini/transformation.py b/litellm/llms/vertex_ai/gemini/transformation.py index 00b461dcda..5d39729789 100644 --- a/litellm/llms/vertex_ai/gemini/transformation.py +++ b/litellm/llms/vertex_ai/gemini/transformation.py @@ -529,6 +529,18 @@ def _gemini_convert_messages_with_history( # noqa: PLR0915 raise e +def _pop_and_merge_extra_body(data: RequestBody, optional_params: dict) -> None: + """Pop extra_body from optional_params and shallow-merge into data, deep-merging dict values.""" + extra_body: Optional[dict] = optional_params.pop("extra_body", None) + if extra_body is not None: + data_dict: dict = data # type: ignore[assignment] + for k, v in extra_body.items(): + if k in data_dict and isinstance(data_dict[k], dict) and isinstance(v, dict): + data_dict[k].update(v) + else: + data_dict[k] = v + + def _transform_request_body( messages: List[AllMessageValues], model: str, @@ -619,6 +631,7 @@ def _transform_request_body( # Only add labels for Vertex AI endpoints (not Google GenAI/AI Studio) and only if non-empty if labels and custom_llm_provider != LlmProviders.GEMINI: data["labels"] = labels + _pop_and_merge_extra_body(data, optional_params) except Exception as e: raise e diff --git a/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py b/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py index 04ae4b6beb..d248d2862e 100644 --- a/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py +++ b/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py @@ -480,7 +480,10 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): tool = {VertexToolName.COMPUTER_USE.value: computer_use_config} # Handle OpenAI-style web_search and web_search_preview tools # Transform them to Gemini's googleSearch tool - elif "type" in tool and tool["type"] in ("web_search", "web_search_preview"): + elif "type" in tool and tool["type"] in ( + "web_search", + "web_search_preview", + ): verbose_logger.info( f"Gemini: Transforming OpenAI-style '{tool['type']}' tool to googleSearch" ) @@ -756,6 +759,9 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): "gemini-3-flash-preview" in model.lower() or "gemini-3-flash" in model.lower() ) + is_gemini31pro = model and ( + "gemini-3.1-pro-preview" in model.lower() + ) if reasoning_effort == "minimal": if is_gemini3flash: return {"thinkingLevel": "minimal", "includeThoughts": True} @@ -764,14 +770,10 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): elif reasoning_effort == "low": return {"thinkingLevel": "low", "includeThoughts": True} elif reasoning_effort == "medium": - # For gemini-3-flash-preview, medium maps to "medium", otherwise "high" - if is_gemini3flash: + if is_gemini31pro or is_gemini3flash: return {"thinkingLevel": "medium", "includeThoughts": True} else: - return { - "thinkingLevel": "high", - "includeThoughts": True, - } # medium is not out yet for other models + return {"thinkingLevel": "high", "includeThoughts": True} elif reasoning_effort == "high": return {"thinkingLevel": "high", "includeThoughts": True} elif reasoning_effort == "disable": @@ -1069,7 +1071,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): elif param == "modalities" and isinstance(value, list): response_modalities = self.map_response_modalities(value) optional_params["responseModalities"] = response_modalities - elif param == "web_search_options" and value and isinstance(value, dict): + elif param == "web_search_options" and isinstance(value, dict): _tools = self._map_web_search_options(value) optional_params = self._add_tools_to_optional_params( optional_params, [_tools] @@ -1196,6 +1198,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): "PROHIBITED_CONTENT": "The token generation was stopped as the response was flagged for the prohibited contents.", "SPII": "The token generation was stopped as the response was flagged for Sensitive Personally Identifiable Information (SPII) contents.", "IMAGE_SAFETY": "The token generation was stopped as the response was flagged for image safety reasons.", + "IMAGE_PROHIBITED_CONTENT": "The token generation was stopped as the response was flagged for prohibited image content.", } @staticmethod @@ -1218,6 +1221,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): "SPII": "content_filter", "MALFORMED_FUNCTION_CALL": "malformed_function_call", # openai doesn't have a way of representing this "IMAGE_SAFETY": "content_filter", + "IMAGE_PROHIBITED_CONTENT": "content_filter", } def translate_exception_str(self, exception_string: str): @@ -1630,7 +1634,9 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): completion_image_tokens = response_tokens_details.image_tokens or 0 completion_audio_tokens = response_tokens_details.audio_tokens or 0 calculated_text_tokens = ( - candidates_token_count - completion_image_tokens - completion_audio_tokens + candidates_token_count + - completion_image_tokens + - completion_audio_tokens ) response_tokens_details.text_tokens = calculated_text_tokens ######################################################### @@ -2248,6 +2254,13 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): citation_metadata # older approach - maintaining to prevent regressions ) + ## ADD TRAFFIC TYPE ## + traffic_type = completion_response.get("usageMetadata", {}).get( + "trafficType" + ) + if traffic_type: + model_response._hidden_params.setdefault("provider_specific_fields", {})["traffic_type"] = traffic_type + except Exception as e: raise VertexAIError( message="Received={}, Error converting to valid response block={}. File an issue if litellm error - https://github.com/BerriAI/litellm/issues".format( @@ -2906,6 +2919,12 @@ class ModelResponseIterator: PromptTokensDetailsWrapper, usage.prompt_tokens_details ).web_search_requests = web_search_requests + traffic_type = processed_chunk.get("usageMetadata", {}).get( + "trafficType" + ) + if traffic_type: + model_response._hidden_params.setdefault("provider_specific_fields", {})["traffic_type"] = traffic_type + setattr(model_response, "usage", usage) # type: ignore model_response._hidden_params["is_finished"] = False diff --git a/litellm/llms/vertex_ai/vector_stores/rag_api/transformation.py b/litellm/llms/vertex_ai/vector_stores/rag_api/transformation.py index 08b93145e5..1be9cd820a 100644 --- a/litellm/llms/vertex_ai/vector_stores/rag_api/transformation.py +++ b/litellm/llms/vertex_ai/vector_stores/rag_api/transformation.py @@ -115,8 +115,13 @@ class VertexVectorStoreConfig(BaseVectorStoreConfig, VertexBase): vertex_project = self.get_vertex_ai_project(litellm_params) vertex_location = self.get_vertex_ai_location(litellm_params) - # Construct full rag corpus path - full_rag_corpus = f"projects/{vertex_project}/locations/{vertex_location}/ragCorpora/{vector_store_id}" + # Handle both full corpus path and just corpus ID + if vector_store_id.startswith("projects/"): + # Already a full path + full_rag_corpus = vector_store_id + else: + # Just the corpus ID, construct full path + full_rag_corpus = f"projects/{vertex_project}/locations/{vertex_location}/ragCorpora/{vector_store_id}" # Build the request body for Vertex AI RAG API request_body: Dict[str, Any] = { diff --git a/litellm/llms/vertex_ai/vertex_ai_non_gemini.py b/litellm/llms/vertex_ai/vertex_ai_non_gemini.py index 8933729233..54cb83bb0b 100644 --- a/litellm/llms/vertex_ai/vertex_ai_non_gemini.py +++ b/litellm/llms/vertex_ai/vertex_ai_non_gemini.py @@ -247,7 +247,7 @@ def completion( # noqa: PLR0915 instances = [optional_params.copy()] instances[0]["prompt"] = prompt instances = [ - json_format.ParseDict(instance_dict, Value()) + json_format.ParseDict(instance_dict, Value()) # type: ignore[misc] for instance_dict in instances ] # Will determine the API used based on async parameter @@ -375,7 +375,7 @@ def completion( # noqa: PLR0915 ) llm_model = aiplatform.gapic.PredictionServiceClient( client_options=client_options, - credentials=creds, + credentials=creds, # type: ignore[arg-type] ) request_str += f"llm_model = aiplatform.gapic.PredictionServiceClient(client_options={client_options}, credentials=...)\n" endpoint_path = llm_model.endpoint_path( @@ -441,7 +441,7 @@ def completion( # noqa: PLR0915 model_response.model = model ## CALCULATING USAGE if model in litellm.vertex_language_models and response_obj is not None: - model_response.choices[0].finish_reason = map_finish_reason( + model_response.choices[0].finish_reason = map_finish_reason( # type: ignore[assignment] response_obj.candidates[0].finish_reason.name ) usage = Usage( @@ -614,7 +614,7 @@ async def async_completion( # noqa: PLR0915 model_response.model = model ## CALCULATING USAGE if model in litellm.vertex_language_models and response_obj is not None: - model_response.choices[0].finish_reason = map_finish_reason( + model_response.choices[0].finish_reason = map_finish_reason( # type: ignore[assignment] response_obj.candidates[0].finish_reason.name ) usage = Usage( diff --git a/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/experimental_pass_through/transformation.py b/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/experimental_pass_through/transformation.py index 54c3f9e047..e05e64988d 100644 --- a/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/experimental_pass_through/transformation.py +++ b/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/experimental_pass_through/transformation.py @@ -31,10 +31,12 @@ class VertexAIPartnerModelsAnthropicMessagesConfig(AnthropicMessagesConfig, Vert Validate the environment for the request """ + vertex_ai_project = VertexBase.safe_get_vertex_ai_project(litellm_params) + vertex_ai_location = VertexBase.safe_get_vertex_ai_location(litellm_params) + + project_id: Optional[str] = None if "Authorization" not in headers: - vertex_ai_project = VertexBase.get_vertex_ai_project(litellm_params) - vertex_credentials = VertexBase.get_vertex_ai_credentials(litellm_params) - vertex_ai_location = VertexBase.get_vertex_ai_location(litellm_params) + vertex_credentials = VertexBase.safe_get_vertex_ai_credentials(litellm_params) access_token, project_id = self._ensure_access_token( credentials=vertex_credentials, @@ -43,12 +45,17 @@ class VertexAIPartnerModelsAnthropicMessagesConfig(AnthropicMessagesConfig, Vert ) headers["Authorization"] = f"Bearer {access_token}" + else: + # Authorization already in headers, but we still need project_id + project_id = vertex_ai_project + # Always calculate api_base if not provided, regardless of Authorization header + if api_base is None: api_base = self.get_complete_vertex_url( custom_api_base=api_base, vertex_location=vertex_ai_location, vertex_project=vertex_ai_project, - project_id=project_id, + project_id=project_id or "", partner=VertexPartnerProvider.claude, stream=optional_params.get("stream", False), model=model, diff --git a/litellm/llms/vertex_ai/vertex_ai_partner_models/llama3/transformation.py b/litellm/llms/vertex_ai/vertex_ai_partner_models/llama3/transformation.py index 748a5f5fb4..51310e4fa8 100644 --- a/litellm/llms/vertex_ai/vertex_ai_partner_models/llama3/transformation.py +++ b/litellm/llms/vertex_ai/vertex_ai_partner_models/llama3/transformation.py @@ -1,12 +1,21 @@ import types -from typing import Any, List, Optional +from typing import Any, AsyncIterator, Iterator, List, Optional, Union import httpx from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj -from litellm.llms.openai.chat.gpt_transformation import OpenAIGPTConfig +from litellm.llms.openai.chat.gpt_transformation import ( + OpenAIChatCompletionStreamingHandler, + OpenAIGPTConfig, +) from litellm.types.llms.openai import AllMessageValues, OpenAIChatCompletionResponse -from litellm.types.utils import ModelResponse, Usage +from litellm.types.utils import ( + Delta, + ModelResponse, + ModelResponseStream, + StreamingChoices, + Usage, +) from ...common_utils import VertexAIError @@ -79,6 +88,18 @@ class VertexAILlama3Config(OpenAIGPTConfig): drop_params=drop_params, ) + def get_model_response_iterator( + self, + streaming_response: Union[Iterator[str], AsyncIterator[str], ModelResponse], + sync_stream: bool, + json_mode: Optional[bool] = False, + ) -> Any: + return VertexAILlama3StreamingHandler( + streaming_response=streaming_response, + sync_stream=sync_stream, + json_mode=json_mode, + ) + def transform_response( self, model: str, @@ -124,3 +145,80 @@ class VertexAILlama3Config(OpenAIGPTConfig): ) return model_response + + +class VertexAILlama3StreamingHandler(OpenAIChatCompletionStreamingHandler): + """ + Vertex AI Llama models may not include role in streaming chunk deltas. + This handler ensures the first chunk always has role="assistant". + + When Vertex AI returns a single chunk with both role and finish_reason (empty response), + this handler splits it into two chunks: + 1. First chunk: role="assistant", content="", finish_reason=None + 2. Second chunk: role=None, content=None, finish_reason="stop" + + This matches OpenAI's streaming format where the first chunk has role and + the final chunk has finish_reason but no role. + """ + + def __init__(self, **kwargs): + super().__init__(**kwargs) + self.sent_role = False + self._pending_chunk: Optional[ModelResponseStream] = None + + def chunk_parser(self, chunk: dict) -> ModelResponseStream: + result = super().chunk_parser(chunk) + if not self.sent_role and result.choices: + delta = result.choices[0].delta + finish_reason = result.choices[0].finish_reason + + # If this is both the first chunk AND the final chunk (has finish_reason), + # we need to split it into two chunks to match OpenAI format + if finish_reason is not None: + # Create a pending final chunk with finish_reason but no role + self._pending_chunk = ModelResponseStream( + id=result.id, + object="chat.completion.chunk", + created=result.created, + model=result.model, + choices=[ + StreamingChoices( + index=0, + delta=Delta(content=None, role=None), + finish_reason=finish_reason, + ) + ], + ) + # Modify current chunk to be the first chunk with role but no finish_reason + result.choices[0].finish_reason = None + delta.role = "assistant" + # Ensure content is empty string for first chunk, not None + if delta.content is None: + delta.content = "" + # Prevent downstream stream wrapper from dropping this chunk + # (it drops empty-content chunks unless special fields are present) + if delta.provider_specific_fields is None: + delta.provider_specific_fields = {} + elif delta.role is None: + delta.role = "assistant" + # If the first chunk has empty content, ensure it's still emitted + if (delta.content == "" or delta.content is None) and delta.provider_specific_fields is None: + delta.provider_specific_fields = {} + self.sent_role = True + return result + + def __next__(self): + # First return any pending chunk from a previous split + if self._pending_chunk is not None: + chunk = self._pending_chunk + self._pending_chunk = None + return chunk + return super().__next__() + + async def __anext__(self): + # First return any pending chunk from a previous split + if self._pending_chunk is not None: + chunk = self._pending_chunk + self._pending_chunk = None + return chunk + return await super().__anext__() diff --git a/litellm/llms/watsonx/__init__.py b/litellm/llms/watsonx/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/litellm/llms/watsonx/chat/__init__.py b/litellm/llms/watsonx/chat/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/litellm/llms/watsonx/completion/__init__.py b/litellm/llms/watsonx/completion/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/litellm/llms/watsonx/embed/__init__.py b/litellm/llms/watsonx/embed/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/litellm/llms/watsonx/rerank/__init__.py b/litellm/llms/watsonx/rerank/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/litellm/llms/watsonx/rerank/transformation.py b/litellm/llms/watsonx/rerank/transformation.py new file mode 100644 index 0000000000..7b4c2a07c3 --- /dev/null +++ b/litellm/llms/watsonx/rerank/transformation.py @@ -0,0 +1,204 @@ +""" +Transformation logic for IBM watsonx.ai's /ml/v1/text/rerank endpoint. + +Docs - https://cloud.ibm.com/apidocs/watsonx-ai#text-rerank +""" + +import uuid +from typing import Any, Dict, List, Optional, Union, cast + +import httpx + +from litellm.llms.base_llm.chat.transformation import LiteLLMLoggingObj +from litellm.llms.base_llm.rerank.transformation import BaseRerankConfig +from litellm.secret_managers.main import get_secret_str +from litellm.types.llms.watsonx import ( + WatsonXAIEndpoint, +) +from litellm.types.rerank import ( + RerankResponse, + RerankResponseMeta, + RerankTokens, +) + +from ..common_utils import IBMWatsonXMixin, _generate_watsonx_token, _get_api_params + + +class IBMWatsonXRerankConfig(IBMWatsonXMixin, BaseRerankConfig): + """ + IBM watsonx.ai Rerank API configuration + """ + + def get_complete_url( + self, + api_base: Optional[str], + model: str, + optional_params: Optional[dict] = None, + ) -> str: + base_url = self._get_base_url(api_base=api_base) + endpoint = WatsonXAIEndpoint.RERANK.value + + url = base_url.rstrip("/") + endpoint + + params = optional_params or {} + + complete_url = self._add_api_version_to_url(url=url, api_version=(params.get("api_version", None))) + return complete_url + + def get_supported_cohere_rerank_params(self, model: str) -> list: + return [ + "query", + "documents", + "top_n", + "return_documents", + "max_tokens_per_doc", + ] + + def validate_environment( # type: ignore[override] + self, + headers: dict, + model: str, + api_key: Optional[str] = None, + optional_params: Optional[dict] = None, + ) -> Dict: + optional_params = optional_params or {} + + default_headers = { + "Content-Type": "application/json", + "Accept": "application/json", + } + + if "Authorization" in headers: + return {**default_headers, **headers} + token = cast( + Optional[str], + optional_params.pop("token", None) or get_secret_str("WATSONX_TOKEN"), + ) + zen_api_key = cast( + Optional[str], + optional_params.pop("zen_api_key", None) or get_secret_str("WATSONX_ZENAPIKEY"), + ) + if token: + headers["Authorization"] = f"Bearer {token}" + elif zen_api_key: + headers["Authorization"] = f"ZenApiKey {zen_api_key}" + else: + token = _generate_watsonx_token(api_key=api_key, token=token) + # build auth headers + headers["Authorization"] = f"Bearer {token}" + return {**default_headers, **headers} + + def map_cohere_rerank_params( + self, + non_default_params: Optional[dict], + model: str, + drop_params: bool, + query: str, + documents: List[Union[str, Dict[str, Any]]], + custom_llm_provider: Optional[str] = None, + top_n: Optional[int] = None, + rank_fields: Optional[List[str]] = None, + return_documents: Optional[bool] = True, + max_chunks_per_doc: Optional[int] = None, + max_tokens_per_doc: Optional[int] = None, + ) -> Dict: + """ + Map Cohere rerank params to IBM watsonx.ai rerank params + """ + optional_rerank_params = {} + if non_default_params is not None: + for k, v in non_default_params.items(): + if k == "query" and v is not None: + optional_rerank_params["query"] = v + elif k == "documents" and v is not None: + optional_rerank_params["inputs"] = [ + {"text": el} if isinstance(el, str) else el for el in v + ] + elif k == "top_n" and v is not None: + optional_rerank_params.setdefault("parameters", {}).setdefault("return_options", {})["top_n"] = v + elif k == "return_documents" and v is not None and isinstance(v, bool): + optional_rerank_params.setdefault("parameters", {}).setdefault("return_options", {})["inputs"] = v + elif k == "max_tokens_per_doc" and v is not None: + optional_rerank_params.setdefault("parameters", {})["truncate_input_tokens"] = v + + # IBM watsonx.ai require one of below parameters + elif k == "project_id" and v is not None: + optional_rerank_params["project_id"] = v + elif k == "space_id" and v is not None: + optional_rerank_params["space_id"] = v + + return dict(optional_rerank_params) + + def transform_rerank_request( + self, + model: str, + optional_rerank_params: Dict, + headers: dict, + ) -> dict: + """ + Transform request to IBM watsonx.ai rerank format + """ + watsonx_api_params = _get_api_params(params=optional_rerank_params, model=model) + watsonx_auth_payload = self._prepare_payload( + model=model, + api_params=watsonx_api_params, + ) + + return optional_rerank_params | watsonx_auth_payload + + def transform_rerank_response( + self, + model: str, + raw_response: httpx.Response, + model_response: RerankResponse, + logging_obj: LiteLLMLoggingObj, + api_key: Optional[str] = None, + request_data: dict = {}, + optional_params: dict = {}, + litellm_params: dict = {}, + ) -> RerankResponse: + """ + Transform IBM watsonx.ai rerank response to LiteLLM RerankResponse format + """ + try: + raw_response_json = raw_response.json() + except Exception as e: + raise self.get_error_class( + error_message=f"Failed to parse response: {str(e)}", + status_code=raw_response.status_code, + headers=raw_response.headers, + ) + + _results: Optional[List[dict]] = raw_response_json.get("results") + if _results is None: + raise ValueError(f"No results found in the response={raw_response_json}") + + transformed_results = [] + + for result in _results: + transformed_result: Dict[str, Any] = { + "index": result["index"], + "relevance_score": result["score"], + } + + if "input" in result: + if isinstance(result["input"], str): + transformed_result["document"] = {"text": result["input"]} + else: + transformed_result["document"] = result["input"] + + transformed_results.append(transformed_result) + + response_id = raw_response_json.get("id") or raw_response_json.get("model_id") or str(uuid.uuid4()) + + # Extract usage information + _tokens = RerankTokens( + input_tokens=raw_response_json.get("input_token_count", 0), + ) + rerank_meta = RerankResponseMeta(tokens=_tokens) + + return RerankResponse( + id=response_id, + results=transformed_results, # type: ignore + meta=rerank_meta, + ) diff --git a/litellm/llms/xai/chat/transformation.py b/litellm/llms/xai/chat/transformation.py index 21782fc6fb..aa2dee354c 100644 --- a/litellm/llms/xai/chat/transformation.py +++ b/litellm/llms/xai/chat/transformation.py @@ -1,4 +1,4 @@ -from typing import List, Optional, Tuple +from typing import Any, AsyncIterator, Iterator, List, Optional, Tuple, Union import httpx @@ -11,9 +11,18 @@ from litellm.litellm_core_utils.prompt_templates.common_utils import ( ) from litellm.secret_managers.main import get_secret_str from litellm.types.llms.openai import AllMessageValues -from litellm.types.utils import Choices, ModelResponse, Usage, PromptTokensDetailsWrapper +from litellm.types.utils import ( + Choices, + ModelResponse, + ModelResponseStream, + PromptTokensDetailsWrapper, + Usage, +) -from ...openai.chat.gpt_transformation import OpenAIGPTConfig +from ...openai.chat.gpt_transformation import ( + OpenAIChatCompletionStreamingHandler, + OpenAIGPTConfig, +) class XAIChatConfig(OpenAIGPTConfig): @@ -119,6 +128,18 @@ class XAIChatConfig(OpenAIGPTConfig): optional_params[param] = value return optional_params + def get_model_response_iterator( + self, + streaming_response: Union[Iterator[str], AsyncIterator[str], ModelResponse], + sync_stream: bool, + json_mode: Optional[bool] = False, + ) -> Any: + return XAIChatCompletionStreamingHandler( + streaming_response=streaming_response, + sync_stream=sync_stream, + json_mode=json_mode, + ) + def transform_request( self, model: str, @@ -225,3 +246,25 @@ class XAIChatConfig(OpenAIGPTConfig): usage.prompt_tokens_details.web_search_requests = int(num_sources_used) setattr(usage, "num_sources_used", int(num_sources_used)) verbose_logger.debug(f"X.AI web search sources used: {num_sources_used}") + + +class XAIChatCompletionStreamingHandler(OpenAIChatCompletionStreamingHandler): + def chunk_parser(self, chunk: dict) -> ModelResponseStream: + """ + Handle xAI-specific streaming behavior. + + xAI Grok sends a final chunk with empty choices array but with usage data + when stream_options={"include_usage": True} is set. + + Example from xAI API: + {"id":"...","object":"chat.completion.chunk","created":...,"model":"grok-4-1-fast-non-reasoning", + "choices":[],"usage":{"prompt_tokens":171,"completion_tokens":2,"total_tokens":173,...}} + """ + # Handle chunks with empty choices but with usage data + choices = chunk.get("choices", []) + if len(choices) == 0 and "usage" in chunk: + # xAI sends usage in a chunk with empty choices array + # Add a dummy choice with empty delta to ensure proper processing + chunk["choices"] = [{"index": 0, "delta": {}, "finish_reason": None}] + + return super().chunk_parser(chunk) diff --git a/litellm/main.py b/litellm/main.py index bca023e65e..356ca7ecf1 100644 --- a/litellm/main.py +++ b/litellm/main.py @@ -2506,10 +2506,10 @@ def completion( # type: ignore # noqa: PLR0915 # Add GitHub Copilot headers (same as /responses endpoint does) if custom_llm_provider == "github_copilot": + from litellm.llms.github_copilot.authenticator import Authenticator from litellm.llms.github_copilot.common_utils import ( get_copilot_default_headers, ) - from litellm.llms.github_copilot.authenticator import Authenticator copilot_auth = Authenticator() copilot_api_key = copilot_auth.get_api_key() @@ -7230,6 +7230,71 @@ def stream_chunk_builder( # noqa: PLR0915 # Initialize the response dictionary response = processor.build_base_response(chunks) + # Fast path for the common text-only streaming case: + # avoid repeated multi-pass list scans over chunks. + simple_content_parts: List[str] = [] + is_simple_text_stream = True + for chunk in chunks: + if len(chunk["choices"]) == 0: + continue + + choice = chunk["choices"][0] + delta_obj = choice.get("delta", {}) if isinstance(choice, dict) else getattr(choice, "delta", {}) + if isinstance(delta_obj, dict): + delta = delta_obj + elif hasattr(delta_obj, "model_dump"): + delta = cast(Dict[str, Any], delta_obj.model_dump()) + else: + delta = {} + + if ( + delta.get("tool_calls") is not None + or delta.get("function_call") is not None + or delta.get("reasoning_content") is not None + or delta.get("thinking_blocks") is not None + or delta.get("annotations") is not None + or delta.get("audio") is not None + or delta.get("images") is not None + or delta.get("provider_specific_fields") is not None + ): + is_simple_text_stream = False + break + + content = delta.get("content") + if isinstance(content, str) and content: + simple_content_parts.append(content) + + if is_simple_text_stream: + if simple_content_parts: + response["choices"][0]["message"]["content"] = "".join(simple_content_parts) + completion_output = get_content_from_model_response(response) + usage = processor.calculate_usage( + chunks=chunks, + model=model, + completion_output=completion_output, + messages=messages, + reasoning_tokens=0, + ) + setattr(response, "usage", usage) + + # Propagate provider_specific_fields from chunk hidden params when present. + for chunk in reversed(chunks): + if isinstance(chunk, dict): + hidden = chunk.get("_hidden_params") + else: + hidden = getattr(chunk, "_hidden_params", None) + if isinstance(hidden, dict) and "provider_specific_fields" in hidden: + response._hidden_params.setdefault( + "provider_specific_fields", {} + ).update(hidden["provider_specific_fields"]) + break + + if litellm.include_cost_in_streaming_usage and logging_obj is not None: + setattr( + usage, "cost", logging_obj._response_cost_calculator(result=response) + ) + return response + tool_call_chunks = [ chunk for chunk in chunks @@ -7383,6 +7448,19 @@ def stream_chunk_builder( # noqa: PLR0915 setattr(response, "usage", usage) + # Propagate provider_specific_fields from the last chunk (contains provider + # metadata like traffic_type set during streaming) + for chunk in reversed(chunks): + if isinstance(chunk, dict): + hidden = chunk.get("_hidden_params") + else: + hidden = getattr(chunk, "_hidden_params", None) + if isinstance(hidden, dict) and "provider_specific_fields" in hidden: + response._hidden_params.setdefault( + "provider_specific_fields", {} + ).update(hidden["provider_specific_fields"]) + break + # Add cost to usage object if include_cost_in_streaming_usage is True if litellm.include_cost_in_streaming_usage and logging_obj is not None: setattr( diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 7d0225af59..e54eaf89d7 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -1083,7 +1083,7 @@ "supports_vision": true, "tool_use_system_prompt_tokens": 346 }, - "apac.anthropic.claude-opus-4-6-v1": { + "au.anthropic.claude-opus-4-6-v1": { "cache_creation_input_token_cost": 6.875e-06, "cache_creation_input_token_cost_above_200k_tokens": 1.375e-05, "cache_read_input_token_cost": 5.5e-07, @@ -1113,6 +1113,156 @@ "supports_vision": true, "tool_use_system_prompt_tokens": 346 }, + "anthropic.claude-sonnet-4-6": { + "cache_creation_input_token_cost": 3.75e-06, + "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, + "input_cost_per_token": 3e-06, + "input_cost_per_token_above_200k_tokens": 6e-06, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 200000, + "max_output_tokens": 64000, + "max_tokens": 64000, + "mode": "chat", + "output_cost_per_token": 1.5e-05, + "output_cost_per_token_above_200k_tokens": 2.25e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_assistant_prefill": true, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true, + "tool_use_system_prompt_tokens": 346 + }, + "global.anthropic.claude-sonnet-4-6": { + "cache_creation_input_token_cost": 3.75e-06, + "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, + "input_cost_per_token": 3e-06, + "input_cost_per_token_above_200k_tokens": 6e-06, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 200000, + "max_output_tokens": 64000, + "max_tokens": 64000, + "mode": "chat", + "output_cost_per_token": 1.5e-05, + "output_cost_per_token_above_200k_tokens": 2.25e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_assistant_prefill": true, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true, + "tool_use_system_prompt_tokens": 346 + }, + "us.anthropic.claude-sonnet-4-6": { + "cache_creation_input_token_cost": 4.125e-06, + "cache_creation_input_token_cost_above_200k_tokens": 8.25e-06, + "cache_read_input_token_cost": 3.3e-07, + "cache_read_input_token_cost_above_200k_tokens": 6.6e-07, + "input_cost_per_token": 3.3e-06, + "input_cost_per_token_above_200k_tokens": 6.6e-06, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 200000, + "max_output_tokens": 64000, + "max_tokens": 64000, + "mode": "chat", + "output_cost_per_token": 1.65e-05, + "output_cost_per_token_above_200k_tokens": 2.475e-05, + 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"cache_read_input_token_cost": 3e-07, + "input_cost_per_token": 3e-06, + "litellm_provider": "azure_ai", + "max_input_tokens": 200000, + "max_output_tokens": 64000, + "max_tokens": 64000, + "mode": "chat", + "output_cost_per_token": 1.5e-05, + "supports_assistant_prefill": true, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true, + "tool_use_system_prompt_tokens": 346 + }, "azure/computer-use-preview": { "input_cost_per_token": 3e-06, "litellm_provider": "azure", @@ -5859,6 +6031,7 @@ "source": "https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/kimi-k2-5-now-in-microsoft-foundry/4492321", "supports_function_calling": true, "supports_tool_choice": true, + "supports_video_input": true, "supports_vision": true }, "azure_ai/ministral-3b": { @@ -6104,6 +6277,32 @@ "output_cost_per_token": 2.4e-05, "supports_tool_choice": true }, + "bedrock/ap-northeast-1/deepseek.v3.2": { + "input_cost_per_token": 7.4e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 163840, + "max_output_tokens": 163840, + "max_tokens": 163840, + "mode": "chat", + "output_cost_per_token": 2.22e-06, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true, + "source": "https://aws.amazon.com/bedrock/pricing/" + }, + "bedrock/ap-northeast-1/minimax.minimax-m2.1": { + "input_cost_per_token": 3.6e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 196000, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 1.44e-06, + "supports_function_calling": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "source": "https://aws.amazon.com/bedrock/pricing/" + }, "bedrock/ap-northeast-1/moonshotai.kimi-k2-thinking": { "input_cost_per_token": 7.3e-07, "litellm_provider": "bedrock", @@ -6115,6 +6314,33 @@ "supports_function_calling": true, "supports_reasoning": true }, + "bedrock/ap-northeast-1/moonshotai.kimi-k2.5": { + "input_cost_per_token": 7.2e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 3.6e-06, + "supports_function_calling": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_vision": true, + "source": "https://aws.amazon.com/bedrock/pricing/" + }, + "bedrock/ap-northeast-1/qwen.qwen3-coder-next": { + "input_cost_per_token": 6e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 262144, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 1.44e-06, + "supports_function_calling": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "source": "https://aws.amazon.com/bedrock/pricing/" + }, "bedrock/moonshotai.kimi-k2-thinking": { "input_cost_per_token": 7.3e-07, "litellm_provider": "bedrock", @@ -6127,15 +6353,20 @@ "supports_reasoning": true }, "bedrock/moonshotai.kimi-k2.5": { - "input_cost_per_token": 7.3e-07, + "input_cost_per_token": 6e-07, "litellm_provider": "bedrock", "max_input_tokens": 262144, "max_output_tokens": 262144, "max_tokens": 262144, "mode": "chat", "output_cost_per_token": 3.03e-06, + "source": "https://platform.moonshot.ai/docs/guide/kimi-k2-5-quickstart", "supports_function_calling": true, - "supports_reasoning": true + "supports_reasoning": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_video_input": true, + "supports_vision": true }, "bedrock/ap-south-1/meta.llama3-70b-instruct-v1:0": { "input_cost_per_token": 3.18e-06, @@ -6155,6 +6386,32 @@ "mode": "chat", "output_cost_per_token": 7.2e-07 }, + "bedrock/ap-south-1/deepseek.v3.2": { + "input_cost_per_token": 7.4e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 163840, + "max_output_tokens": 163840, + "max_tokens": 163840, + "mode": "chat", + "output_cost_per_token": 2.22e-06, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true, + "source": "https://aws.amazon.com/bedrock/pricing/" + }, + "bedrock/ap-south-1/minimax.minimax-m2.1": { + "input_cost_per_token": 3.6e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 196000, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 1.44e-06, + "supports_function_calling": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "source": "https://aws.amazon.com/bedrock/pricing/" + }, "bedrock/ap-south-1/moonshotai.kimi-k2-thinking": { "input_cost_per_token": 7.1e-07, "litellm_provider": "bedrock", @@ -6166,6 +6423,86 @@ "supports_function_calling": true, "supports_reasoning": true }, + "bedrock/ap-south-1/moonshotai.kimi-k2.5": { + "input_cost_per_token": 7.2e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 3.6e-06, + "supports_function_calling": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_vision": true, + "source": "https://aws.amazon.com/bedrock/pricing/" + }, + "bedrock/ap-south-1/qwen.qwen3-coder-next": { + "input_cost_per_token": 6e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 262144, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 1.44e-06, + "supports_function_calling": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "source": "https://aws.amazon.com/bedrock/pricing/" + }, + "bedrock/ap-southeast-3/deepseek.v3.2": { + "input_cost_per_token": 7.4e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 163840, + "max_output_tokens": 163840, + "max_tokens": 163840, + "mode": "chat", + "output_cost_per_token": 2.22e-06, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true, + "source": "https://aws.amazon.com/bedrock/pricing/" + }, + "bedrock/ap-southeast-3/minimax.minimax-m2.1": { + "input_cost_per_token": 3.6e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 196000, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 1.44e-06, + "supports_function_calling": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "source": "https://aws.amazon.com/bedrock/pricing/" + }, + "bedrock/ap-southeast-3/moonshotai.kimi-k2.5": { + "input_cost_per_token": 7.2e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 3.6e-06, + "supports_function_calling": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_vision": true, + "source": "https://aws.amazon.com/bedrock/pricing/" + }, + "bedrock/ap-southeast-3/qwen.qwen3-coder-next": { + "input_cost_per_token": 6e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 262144, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 1.44e-06, + "supports_function_calling": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "source": "https://aws.amazon.com/bedrock/pricing/" + }, "bedrock/ca-central-1/meta.llama3-70b-instruct-v1:0": { "input_cost_per_token": 3.05e-06, "litellm_provider": "bedrock", @@ -6184,6 +6521,46 @@ "mode": "chat", "output_cost_per_token": 6.9e-07 }, + "bedrock/eu-north-1/deepseek.v3.2": { + "input_cost_per_token": 7.4e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 163840, + "max_output_tokens": 163840, + "max_tokens": 163840, + "mode": "chat", + "output_cost_per_token": 2.22e-06, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true, + "source": "https://aws.amazon.com/bedrock/pricing/" + }, + "bedrock/eu-north-1/minimax.minimax-m2.1": { + "input_cost_per_token": 3.6e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 196000, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 1.44e-06, + "supports_function_calling": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "source": "https://aws.amazon.com/bedrock/pricing/" + }, + "bedrock/eu-north-1/moonshotai.kimi-k2.5": { + "input_cost_per_token": 7.2e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 3.6e-06, + "supports_function_calling": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_vision": true, + "source": "https://aws.amazon.com/bedrock/pricing/" + }, "bedrock/eu-central-1/1-month-commitment/anthropic.claude-instant-v1": { "input_cost_per_second": 0.01635, "litellm_provider": "bedrock", @@ -6271,6 +6648,32 @@ "output_cost_per_token": 2.4e-05, "supports_tool_choice": true }, + "bedrock/eu-central-1/minimax.minimax-m2.1": { + "input_cost_per_token": 3.6e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 196000, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 1.44e-06, + "supports_function_calling": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "source": "https://aws.amazon.com/bedrock/pricing/" + }, + "bedrock/eu-central-1/qwen.qwen3-coder-next": { + "input_cost_per_token": 6e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 262144, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 1.44e-06, + "supports_function_calling": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "source": "https://aws.amazon.com/bedrock/pricing/" + }, "bedrock/eu-west-1/meta.llama3-70b-instruct-v1:0": { "input_cost_per_token": 2.86e-06, "litellm_provider": "bedrock", @@ -6289,6 +6692,32 @@ "mode": "chat", "output_cost_per_token": 6.5e-07 }, + "bedrock/eu-west-1/minimax.minimax-m2.1": { + "input_cost_per_token": 3.6e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 196000, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 1.44e-06, + "supports_function_calling": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "source": "https://aws.amazon.com/bedrock/pricing/" + }, + "bedrock/eu-west-1/qwen.qwen3-coder-next": { + "input_cost_per_token": 6e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 262144, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 1.44e-06, + "supports_function_calling": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "source": "https://aws.amazon.com/bedrock/pricing/" + }, "bedrock/eu-west-2/meta.llama3-70b-instruct-v1:0": { "input_cost_per_token": 3.45e-06, "litellm_provider": "bedrock", @@ -6307,6 +6736,32 @@ "mode": "chat", "output_cost_per_token": 7.8e-07 }, + "bedrock/eu-west-2/minimax.minimax-m2.1": { + "input_cost_per_token": 4.7e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 196000, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 1.86e-06, + "supports_function_calling": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "source": "https://aws.amazon.com/bedrock/pricing/" + }, + "bedrock/eu-west-2/qwen.qwen3-coder-next": { + "input_cost_per_token": 7.8e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 262144, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 1.86e-06, + "supports_function_calling": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "source": "https://aws.amazon.com/bedrock/pricing/" + }, "bedrock/eu-west-3/mistral.mistral-7b-instruct-v0:2": { "input_cost_per_token": 2e-07, "litellm_provider": "bedrock", @@ -6337,6 +6792,32 @@ "output_cost_per_token": 9.1e-07, "supports_tool_choice": true }, + "bedrock/eu-south-1/minimax.minimax-m2.1": { + "input_cost_per_token": 3.6e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 196000, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 1.44e-06, + "supports_function_calling": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "source": "https://aws.amazon.com/bedrock/pricing/" + }, + "bedrock/eu-south-1/qwen.qwen3-coder-next": { + "input_cost_per_token": 6e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 262144, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 1.44e-06, + "supports_function_calling": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "source": "https://aws.amazon.com/bedrock/pricing/" + }, "bedrock/invoke/anthropic.claude-3-5-sonnet-20240620-v1:0": { "input_cost_per_token": 3e-06, "litellm_provider": "bedrock", @@ -6371,6 +6852,32 @@ "mode": "chat", "output_cost_per_token": 1.01e-06 }, + "bedrock/sa-east-1/deepseek.v3.2": { + "input_cost_per_token": 7.4e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 163840, + "max_output_tokens": 163840, + "max_tokens": 163840, + "mode": "chat", + "output_cost_per_token": 2.22e-06, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true, + "source": "https://aws.amazon.com/bedrock/pricing/" + }, + "bedrock/sa-east-1/minimax.minimax-m2.1": { + "input_cost_per_token": 3.6e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 196000, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 1.44e-06, + "supports_function_calling": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "source": "https://aws.amazon.com/bedrock/pricing/" + }, "bedrock/sa-east-1/moonshotai.kimi-k2-thinking": { "input_cost_per_token": 7.3e-07, "litellm_provider": "bedrock", @@ -6382,6 +6889,33 @@ "supports_function_calling": true, "supports_reasoning": true }, + "bedrock/sa-east-1/moonshotai.kimi-k2.5": { + "input_cost_per_token": 7.2e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 3.6e-06, + "supports_function_calling": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_vision": true, + "source": "https://aws.amazon.com/bedrock/pricing/" + }, + "bedrock/sa-east-1/qwen.qwen3-coder-next": { + "input_cost_per_token": 6e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 262144, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 1.44e-06, + "supports_function_calling": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "source": "https://aws.amazon.com/bedrock/pricing/" + }, "bedrock/us-east-1/1-month-commitment/anthropic.claude-instant-v1": { "input_cost_per_second": 0.011, "litellm_provider": "bedrock", @@ -6518,6 +7052,32 @@ "output_cost_per_token": 7e-07, "supports_tool_choice": true }, + "bedrock/us-east-1/deepseek.v3.2": { + "input_cost_per_token": 6.2e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 163840, + "max_output_tokens": 163840, + "max_tokens": 163840, + "mode": "chat", + "output_cost_per_token": 1.85e-06, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true, + "source": "https://aws.amazon.com/bedrock/pricing/" + }, + "bedrock/us-east-1/minimax.minimax-m2.1": { + "input_cost_per_token": 3e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 196000, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 1.2e-06, + "supports_function_calling": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "source": "https://aws.amazon.com/bedrock/pricing/" + }, "bedrock/us-east-1/moonshotai.kimi-k2-thinking": { "input_cost_per_token": 6e-07, "litellm_provider": "bedrock", @@ -6529,6 +7089,59 @@ "supports_function_calling": true, "supports_reasoning": true }, + "bedrock/us-east-1/moonshotai.kimi-k2.5": { + "input_cost_per_token": 6e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 3e-06, + "supports_function_calling": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_vision": true, + "source": "https://aws.amazon.com/bedrock/pricing/" + }, + "bedrock/us-east-1/qwen.qwen3-coder-next": { + "input_cost_per_token": 5e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 262144, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 1.2e-06, + "supports_function_calling": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "source": "https://aws.amazon.com/bedrock/pricing/" + }, + "bedrock/us-east-2/deepseek.v3.2": { + "input_cost_per_token": 6.2e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 163840, + "max_output_tokens": 163840, + "max_tokens": 163840, + "mode": "chat", + "output_cost_per_token": 1.85e-06, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true, + "source": "https://aws.amazon.com/bedrock/pricing/" + }, + "bedrock/us-east-2/minimax.minimax-m2.1": { + "input_cost_per_token": 3e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 196000, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 1.2e-06, + "supports_function_calling": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "source": "https://aws.amazon.com/bedrock/pricing/" + }, "bedrock/us-east-2/moonshotai.kimi-k2-thinking": { "input_cost_per_token": 6e-07, "litellm_provider": "bedrock", @@ -6540,6 +7153,33 @@ "supports_function_calling": true, "supports_reasoning": true }, + "bedrock/us-east-2/moonshotai.kimi-k2.5": { + "input_cost_per_token": 6e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 3e-06, + "supports_function_calling": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_vision": true, + "source": "https://aws.amazon.com/bedrock/pricing/" + }, + "bedrock/us-east-2/qwen.qwen3-coder-next": { + "input_cost_per_token": 5e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 262144, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 1.2e-06, + "supports_function_calling": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "source": "https://aws.amazon.com/bedrock/pricing/" + }, "bedrock/us-gov-east-1/amazon.nova-pro-v1:0": { "input_cost_per_token": 9.6e-07, "litellm_provider": "bedrock", @@ -6946,6 +7586,32 @@ "output_cost_per_token": 7e-07, "supports_tool_choice": true }, + "bedrock/us-west-2/deepseek.v3.2": { + "input_cost_per_token": 6.2e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 163840, + "max_output_tokens": 163840, + "max_tokens": 163840, + "mode": "chat", + "output_cost_per_token": 1.85e-06, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true, + "source": "https://aws.amazon.com/bedrock/pricing/" + }, + "bedrock/us-west-2/minimax.minimax-m2.1": { + "input_cost_per_token": 3e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 196000, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 1.2e-06, + "supports_function_calling": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "source": "https://aws.amazon.com/bedrock/pricing/" + }, "bedrock/us-west-2/moonshotai.kimi-k2-thinking": { "input_cost_per_token": 6e-07, "litellm_provider": "bedrock", @@ -6957,6 +7623,33 @@ "supports_function_calling": true, "supports_reasoning": true }, + "bedrock/us-west-2/moonshotai.kimi-k2.5": { + "input_cost_per_token": 6e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 3e-06, + "supports_function_calling": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_vision": true, + "source": "https://aws.amazon.com/bedrock/pricing/" + }, + "bedrock/us-west-2/qwen.qwen3-coder-next": { + "input_cost_per_token": 5e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 262144, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 1.2e-06, + "supports_function_calling": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "source": "https://aws.amazon.com/bedrock/pricing/" + }, "bedrock/us.anthropic.claude-3-5-haiku-20241022-v1:0": { "cache_creation_input_token_cost": 1e-06, "cache_read_input_token_cost": 8e-08, @@ -7508,6 +8201,7 @@ "supports_response_schema": true, "supports_tool_choice": true, "supports_vision": true, + "supports_web_search": true, "tool_use_system_prompt_tokens": 159 }, "claude-sonnet-4-5": { @@ -7571,6 +8265,67 @@ "supports_web_search": true, "tool_use_system_prompt_tokens": 346 }, + "claude-sonnet-4-6": { + "cache_creation_input_token_cost": 3.75e-06, + "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, + "input_cost_per_token": 3e-06, + "input_cost_per_token_above_200k_tokens": 6e-06, + "litellm_provider": "anthropic", + "max_input_tokens": 200000, + "max_output_tokens": 64000, + "max_tokens": 64000, + "mode": "chat", + "output_cost_per_token": 1.5e-05, + "output_cost_per_token_above_200k_tokens": 2.25e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_assistant_prefill": true, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true, + "tool_use_system_prompt_tokens": 346 + }, + "us/claude-sonnet-4-6": { + "cache_creation_input_token_cost": 4.125e-06, + "cache_creation_input_token_cost_above_200k_tokens": 8.25e-06, + "cache_read_input_token_cost": 3.3e-07, + "cache_read_input_token_cost_above_200k_tokens": 6.6e-07, + "input_cost_per_token": 3.3e-06, + "input_cost_per_token_above_200k_tokens": 6.6e-06, + "litellm_provider": "anthropic", + "max_input_tokens": 200000, + "max_output_tokens": 64000, + "max_tokens": 64000, + "mode": "chat", + "output_cost_per_token": 1.65e-05, + "output_cost_per_token_above_200k_tokens": 2.475e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_assistant_prefill": true, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true, + "tool_use_system_prompt_tokens": 346, + "inference_geo": "us" + }, "claude-sonnet-4-5-20250929-v1:0": { "cache_creation_input_token_cost": 3.75e-06, "cache_read_input_token_cost": 3e-07, @@ -10870,6 +11625,19 @@ "supports_reasoning": true, "supports_tool_choice": true }, + "deepseek.v3.2": { + "input_cost_per_token": 6.2e-07, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 163840, + "max_output_tokens": 163840, + "max_tokens": 163840, + "mode": "chat", + "output_cost_per_token": 1.85e-06, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true, + "source": "https://aws.amazon.com/bedrock/pricing/" + }, "dolphin": { "input_cost_per_token": 5e-07, "litellm_provider": "nlp_cloud", @@ -11854,6 +12622,21 @@ "supports_response_schema": true, "supports_tool_choice": true }, + "fireworks_ai/accounts/fireworks/models/glm-4p7": { + "cache_read_input_token_cost": 3e-07, + "input_cost_per_token": 6e-07, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 202800, + "max_output_tokens": 202800, + "max_tokens": 202800, + "mode": "chat", + "output_cost_per_token": 2.2e-06, + "source": "https://fireworks.ai/models/fireworks/glm-4p7", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, "fireworks_ai/accounts/fireworks/models/gpt-oss-120b": { "input_cost_per_token": 1.5e-07, "litellm_provider": "fireworks_ai", @@ -11922,6 +12705,20 @@ "supports_tool_choice": true, "supports_web_search": true }, + "fireworks_ai/accounts/fireworks/models/kimi-k2p5": { + "cache_read_input_token_cost": 1e-07, + "input_cost_per_token": 6e-07, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 3e-06, + "source": "https://fireworks.ai/pricing", + "supports_function_calling": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, "fireworks_ai/accounts/fireworks/models/llama-v3p1-405b-instruct": { "input_cost_per_token": 3e-06, "litellm_provider": "fireworks_ai", @@ -12025,6 +12822,20 @@ "supports_response_schema": true, "supports_tool_choice": false }, + "fireworks_ai/accounts/fireworks/models/minimax-m2p1": { + "cache_read_input_token_cost": 3e-08, + "input_cost_per_token": 3e-07, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 204800, + "max_output_tokens": 204800, + "max_tokens": 204800, + "mode": "chat", + "output_cost_per_token": 1.2e-06, + "source": "https://fireworks.ai/models/fireworks/minimax-m2p1", + "supports_function_calling": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, "fireworks_ai/accounts/fireworks/models/mixtral-8x22b-instruct-hf": { "input_cost_per_token": 1.2e-06, "litellm_provider": "fireworks_ai", @@ -12077,6 +12888,49 @@ "supports_response_schema": true, "supports_tool_choice": false }, + "fireworks_ai/glm-4p7": { + "cache_read_input_token_cost": 3e-07, + "input_cost_per_token": 6e-07, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 202800, + "max_output_tokens": 202800, + "max_tokens": 202800, + "mode": "chat", + "output_cost_per_token": 2.2e-06, + "source": "https://fireworks.ai/models/fireworks/glm-4p7", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "fireworks_ai/kimi-k2p5": { + "cache_read_input_token_cost": 1e-07, + "input_cost_per_token": 6e-07, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 3e-06, + "source": "https://fireworks.ai/pricing", + "supports_function_calling": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "fireworks_ai/minimax-m2p1": { + "cache_read_input_token_cost": 3e-08, + "input_cost_per_token": 3e-07, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 204800, + "max_output_tokens": 204800, + "max_tokens": 204800, + "mode": "chat", + "output_cost_per_token": 1.2e-06, + "source": "https://fireworks.ai/models/fireworks/minimax-m2p1", + "supports_function_calling": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, "fireworks_ai/nomic-ai/nomic-embed-text-v1": { "input_cost_per_token": 8e-09, "litellm_provider": "fireworks_ai-embedding-models", @@ -13916,6 +14770,108 @@ "supports_web_search": true, "supports_native_streaming": true }, + "gemini-3.1-pro-preview": { + "cache_read_input_token_cost": 2e-07, + "cache_read_input_token_cost_above_200k_tokens": 4e-07, + "cache_creation_input_token_cost_above_200k_tokens": 2.5e-07, + "input_cost_per_token": 2e-06, + "input_cost_per_token_above_200k_tokens": 4e-06, + "input_cost_per_token_batches": 1e-06, + "litellm_provider": "vertex_ai-language-models", + "max_audio_length_hours": 8.4, + "max_audio_per_prompt": 1, + "max_images_per_prompt": 3000, + "max_input_tokens": 1048576, + "max_output_tokens": 65536, + "max_pdf_size_mb": 30, + "max_tokens": 65536, + "max_video_length": 1, + "max_videos_per_prompt": 10, + "mode": "chat", + "output_cost_per_token": 1.2e-05, + "output_cost_per_token_above_200k_tokens": 1.8e-05, + "output_cost_per_token_batches": 6e-06, + "output_cost_per_image": 0.00012, + "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", + "audio", + "video" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_input": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_video_input": true, + "supports_vision": true, + "supports_web_search": true, + "supports_url_context": true, + "supports_native_streaming": true + }, + "gemini-3.1-pro-preview-customtools": { + "cache_read_input_token_cost": 2e-07, + "cache_read_input_token_cost_above_200k_tokens": 4e-07, + "cache_creation_input_token_cost_above_200k_tokens": 2.5e-07, + "input_cost_per_token": 2e-06, + "input_cost_per_token_above_200k_tokens": 4e-06, + "input_cost_per_token_batches": 1e-06, + "litellm_provider": "vertex_ai-language-models", + "max_audio_length_hours": 8.4, + "max_audio_per_prompt": 1, + "max_images_per_prompt": 3000, + "max_input_tokens": 1048576, + "max_output_tokens": 65536, + "max_pdf_size_mb": 30, + "max_tokens": 65536, + "max_video_length": 1, + "max_videos_per_prompt": 10, + "mode": "chat", + "output_cost_per_token": 1.2e-05, + "output_cost_per_token_above_200k_tokens": 1.8e-05, + "output_cost_per_token_batches": 6e-06, + "output_cost_per_image": 0.00012, + "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", + "audio", + "video" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_input": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_video_input": true, + "supports_vision": true, + "supports_web_search": true, + "supports_url_context": true, + "supports_native_streaming": true + }, "vertex_ai/gemini-3-pro-preview": { "cache_read_input_token_cost": 2e-07, "cache_read_input_token_cost_above_200k_tokens": 4e-07, @@ -14009,6 +14965,108 @@ "supports_web_search": true, "supports_native_streaming": true }, + "vertex_ai/gemini-3.1-pro-preview": { + "cache_read_input_token_cost": 2e-07, + "cache_read_input_token_cost_above_200k_tokens": 4e-07, + "cache_creation_input_token_cost_above_200k_tokens": 2.5e-07, + "input_cost_per_token": 2e-06, + "input_cost_per_token_above_200k_tokens": 4e-06, + "input_cost_per_token_batches": 1e-06, + "litellm_provider": "vertex_ai", + "max_audio_length_hours": 8.4, + "max_audio_per_prompt": 1, + "max_images_per_prompt": 3000, + "max_input_tokens": 1048576, + "max_output_tokens": 65536, + "max_pdf_size_mb": 30, + "max_tokens": 65536, + "max_video_length": 1, + "max_videos_per_prompt": 10, + "mode": "chat", + "output_cost_per_token": 1.2e-05, + "output_cost_per_token_above_200k_tokens": 1.8e-05, + "output_cost_per_token_batches": 6e-06, + "output_cost_per_image": 0.00012, + "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", + "audio", + "video" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_input": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_video_input": true, + "supports_vision": true, + "supports_web_search": true, + "supports_url_context": true, + "supports_native_streaming": true + }, + "vertex_ai/gemini-3.1-pro-preview-customtools": { + "cache_read_input_token_cost": 2e-07, + "cache_read_input_token_cost_above_200k_tokens": 4e-07, + "cache_creation_input_token_cost_above_200k_tokens": 2.5e-07, + "input_cost_per_token": 2e-06, + "input_cost_per_token_above_200k_tokens": 4e-06, + "input_cost_per_token_batches": 1e-06, + "litellm_provider": "vertex_ai", + "max_audio_length_hours": 8.4, + "max_audio_per_prompt": 1, + "max_images_per_prompt": 3000, + "max_input_tokens": 1048576, + "max_output_tokens": 65536, + "max_pdf_size_mb": 30, + "max_tokens": 65536, + "max_video_length": 1, + "max_videos_per_prompt": 10, + "mode": "chat", + "output_cost_per_token": 1.2e-05, + "output_cost_per_token_above_200k_tokens": 1.8e-05, + "output_cost_per_token_batches": 6e-06, + "output_cost_per_image": 0.00012, + "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", + "audio", + "video" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_input": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_video_input": true, + "supports_vision": true, + "supports_web_search": true, + "supports_url_context": true, + "supports_native_streaming": true + }, "gemini-2.5-pro-exp-03-25": { "cache_read_input_token_cost": 1.25e-07, "cache_read_input_token_cost_above_200k_tokens": 2.5e-07, @@ -14303,7 +15361,9 @@ "supports_tool_choice": true, "supports_url_context": true, "supports_vision": true, - "supports_web_search": true + "supports_web_search": true, + "tpm": 250000, + "rpm": 10 }, "gemini-2.5-computer-use-preview-10-2025": { "input_cost_per_token": 1.25e-06, @@ -15784,44 +16844,16 @@ "tpm": 250000 }, "gemini/gemini-2.5-flash-preview-tts": { - "cache_read_input_token_cost": 3.75e-08, - "input_cost_per_audio_token": 1e-06, - "input_cost_per_token": 1.5e-07, + "input_cost_per_token": 3e-07, "litellm_provider": "gemini", - "max_audio_length_hours": 8.4, - "max_audio_per_prompt": 1, - "max_images_per_prompt": 3000, - "max_input_tokens": 1048576, - "max_output_tokens": 65535, - "max_pdf_size_mb": 30, - "max_tokens": 65535, - "max_video_length": 1, - "max_videos_per_prompt": 10, - "mode": "chat", - "output_cost_per_reasoning_token": 3.5e-06, - "output_cost_per_token": 6e-07, - "rpm": 10, - "source": "https://ai.google.dev/gemini-api/docs/models#gemini-2.5-flash-preview", + "mode": "audio_speech", + "output_cost_per_token": 2.5e-06, + "source": "https://ai.google.dev/pricing", "supported_endpoints": [ - "/v1/chat/completions", - "/v1/completions" + "/v1/audio/speech" ], - "supported_modalities": [ - "text" - ], - "supported_output_modalities": [ - "audio" - ], - "supports_audio_output": false, - "supports_function_calling": true, - "supports_prompt_caching": true, - "supports_reasoning": true, - "supports_response_schema": true, - "supports_system_messages": true, - "supports_tool_choice": true, - "supports_vision": true, - "supports_web_search": true, - "tpm": 250000 + "tpm": 4000000, + "rpm": 10 }, "gemini/gemini-2.5-pro": { "cache_read_input_token_cost": 1.25e-07, @@ -15997,6 +17029,108 @@ "supports_native_streaming": true, "tpm": 800000 }, + "gemini/gemini-3.1-pro-preview": { + "cache_read_input_token_cost": 2e-07, + "cache_read_input_token_cost_above_200k_tokens": 4e-07, + "input_cost_per_token": 2e-06, + "input_cost_per_token_above_200k_tokens": 4e-06, + "input_cost_per_token_batches": 1e-06, + "litellm_provider": "gemini", + "max_audio_length_hours": 8.4, + "max_audio_per_prompt": 1, + "max_images_per_prompt": 3000, + "max_input_tokens": 1048576, + "max_output_tokens": 65536, + "max_pdf_size_mb": 30, + "max_tokens": 65536, + "max_video_length": 1, + "max_videos_per_prompt": 10, + "mode": "chat", + "output_cost_per_token": 1.2e-05, + "output_cost_per_token_above_200k_tokens": 1.8e-05, + "output_cost_per_token_batches": 6e-06, + "rpm": 2000, + "source": "https://ai.google.dev/gemini-api/docs/models#gemini-3.1-pro-preview", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/completions", + "/v1/batch" + ], + "supported_modalities": [ + "text", + "image", + "audio", + "video" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_input": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_video_input": true, + "supports_vision": true, + "supports_web_search": true, + "supports_url_context": true, + "supports_native_streaming": true, + "tpm": 800000 + }, + "gemini/gemini-3.1-pro-preview-customtools": { + "cache_read_input_token_cost": 2e-07, + "cache_read_input_token_cost_above_200k_tokens": 4e-07, + "input_cost_per_token": 2e-06, + "input_cost_per_token_above_200k_tokens": 4e-06, + "input_cost_per_token_batches": 1e-06, + "litellm_provider": "gemini", + "max_audio_length_hours": 8.4, + "max_audio_per_prompt": 1, + "max_images_per_prompt": 3000, + "max_input_tokens": 1048576, + "max_output_tokens": 65536, + "max_pdf_size_mb": 30, + "max_tokens": 65536, + "max_video_length": 1, + "max_videos_per_prompt": 10, + "mode": "chat", + "output_cost_per_token": 1.2e-05, + "output_cost_per_token_above_200k_tokens": 1.8e-05, + "output_cost_per_token_batches": 6e-06, + "rpm": 2000, + "source": "https://ai.google.dev/gemini-api/docs/models#gemini-3.1-pro-preview", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/completions", + "/v1/batch" + ], + "supported_modalities": [ + "text", + "image", + "audio", + "video" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_input": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_video_input": true, + "supports_vision": true, + "supports_web_search": true, + "supports_url_context": true, + "supports_native_streaming": true, + "tpm": 800000 + }, "gemini-3-flash-preview": { "cache_read_input_token_cost": 5e-08, "input_cost_per_audio_token": 1e-06, @@ -16319,7 +17453,9 @@ "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", "supports_function_calling": true, "supports_tool_choice": true, - "supports_vision": true + "supports_vision": true, + "tpm": 250000, + "rpm": 10 }, "gemini/gemini-gemma-2-9b-it": { "input_cost_per_token": 3.5e-07, @@ -16331,7 +17467,9 @@ "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", "supports_function_calling": true, "supports_tool_choice": true, - "supports_vision": true + "supports_vision": true, + "tpm": 250000, + "rpm": 10 }, "gemini/gemini-pro": { "input_cost_per_token": 3.5e-07, @@ -16587,6 +17725,19 @@ "supports_parallel_function_calling": true, "supports_vision": true }, + "github_copilot/claude-opus-4.6-fast": { + "litellm_provider": "github_copilot", + "max_input_tokens": 128000, + "max_output_tokens": 16000, + "max_tokens": 16000, + "mode": "chat", + "supported_endpoints": [ + "/v1/chat/completions" + ], + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_vision": true + }, "github_copilot/claude-opus-41": { "litellm_provider": "github_copilot", "max_input_tokens": 80000, @@ -16838,6 +17989,20 @@ "supports_response_schema": true, "supports_vision": true }, + "github_copilot/gpt-5.3-codex": { + "litellm_provider": "github_copilot", + "max_input_tokens": 128000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "responses", + "supported_endpoints": [ + "/v1/responses" + ], + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_response_schema": true, + "supports_vision": true + }, "github_copilot/text-embedding-3-small": { "litellm_provider": "github_copilot", "max_input_tokens": 8191, @@ -21370,6 +22535,19 @@ "output_cost_per_token": 1.2e-06, "supports_system_messages": true }, + "minimax.minimax-m2.1": { + "input_cost_per_token": 3e-07, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 196000, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 1.2e-06, + "supports_function_calling": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "source": "https://aws.amazon.com/bedrock/pricing/" + }, "minimax/speech-02-hd": { "input_cost_per_character": 0.0001, "litellm_provider": "minimax", @@ -21698,6 +22876,20 @@ "supports_response_schema": true, "supports_tool_choice": true }, + "mistral/devstral-small-latest": { + "input_cost_per_token": 1e-07, + "litellm_provider": "mistral", + "max_input_tokens": 256000, + "max_output_tokens": 256000, + "max_tokens": 256000, + "mode": "chat", + "output_cost_per_token": 3e-07, + "source": "https://docs.mistral.ai/models/devstral-small-2-25-12", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, "mistral/labs-devstral-small-2512": { "input_cost_per_token": 1e-07, "litellm_provider": "mistral", @@ -21712,6 +22904,34 @@ "supports_response_schema": true, "supports_tool_choice": true }, + "mistral/devstral-latest": { + "input_cost_per_token": 4e-07, + "litellm_provider": "mistral", + "max_input_tokens": 256000, + "max_output_tokens": 256000, + "max_tokens": 256000, + "mode": "chat", + "output_cost_per_token": 2e-06, + "source": "https://mistral.ai/news/devstral-2-vibe-cli", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "mistral/devstral-medium-latest": { + "input_cost_per_token": 4e-07, + "litellm_provider": "mistral", + "max_input_tokens": 256000, + "max_output_tokens": 256000, + "max_tokens": 256000, + "mode": "chat", + "output_cost_per_token": 2e-06, + "source": "https://mistral.ai/news/devstral-2-vibe-cli", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, "mistral/devstral-2512": { "input_cost_per_token": 4e-07, "litellm_provider": "mistral", @@ -22126,6 +23346,20 @@ "supports_reasoning": true, "supports_system_messages": true }, + "moonshotai.kimi-k2.5": { + "input_cost_per_token": 6e-07, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 3e-06, + "supports_function_calling": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_vision": true, + "source": "https://aws.amazon.com/bedrock/pricing/" + }, "moonshot/kimi-k2-0711-preview": { "cache_read_input_token_cost": 1.5e-07, "input_cost_per_token": 6e-07, @@ -22177,9 +23411,10 @@ "max_tokens": 262144, "mode": "chat", "output_cost_per_token": 3e-06, - "source": "https://platform.moonshot.ai/docs/pricing/chat", + "source": "https://platform.moonshot.ai/docs/guide/kimi-k2-5-quickstart", "supports_function_calling": true, "supports_tool_choice": true, + "supports_video_input": true, "supports_vision": true }, "moonshot/kimi-latest": { @@ -22641,6 +23876,19 @@ "output_cost_per_token": 2.3e-07, "supports_system_messages": true }, + "nvidia.nemotron-nano-3-30b": { + "input_cost_per_token": 6e-08, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 262144, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 2.4e-07, + "supports_function_calling": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "source": "https://aws.amazon.com/bedrock/pricing/" + }, "o1": { "cache_read_input_token_cost": 7.5e-06, "input_cost_per_token": 1.5e-05, @@ -22651,7 +23899,7 @@ "mode": "chat", "output_cost_per_token": 6e-05, "supports_function_calling": true, - "supports_parallel_function_calling": true, + "supports_parallel_function_calling": false, "supports_pdf_input": true, "supports_prompt_caching": true, "supports_reasoning": true, @@ -23206,7 +24454,7 @@ "max_output_tokens": 131072, "max_tokens": 131072, "mode": "chat", - "output_cost_per_token": 1.5e-07, + "output_cost_per_token": 1.5e-05, "source": "https://www.oracle.com/artificial-intelligence/generative-ai/generative-ai-service/pricing", "supports_function_calling": true, "supports_response_schema": false @@ -23254,7 +24502,7 @@ "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", - "output_cost_per_token": 1.5e-07, + "output_cost_per_token": 1.5e-05, "source": "https://www.oracle.com/artificial-intelligence/generative-ai/generative-ai-service/pricing", "supports_function_calling": true, "supports_response_schema": false @@ -23646,36 +24894,6 @@ "output_cost_per_token": 2e-07, "supports_system_messages": true }, - "openrouter/anthropic/claude-2": { - "input_cost_per_token": 1.102e-05, - "litellm_provider": "openrouter", - "max_output_tokens": 8191, - "max_tokens": 8191, - "mode": "chat", - "output_cost_per_token": 3.268e-05, - "supports_tool_choice": true - }, - "openrouter/anthropic/claude-3-5-haiku": { - "input_cost_per_token": 1e-06, - "litellm_provider": "openrouter", - "max_tokens": 200000, - "mode": "chat", - "output_cost_per_token": 5e-06, - "supports_function_calling": true, - "supports_tool_choice": true - }, - "openrouter/anthropic/claude-3-5-haiku-20241022": { - "input_cost_per_token": 1e-06, - "litellm_provider": "openrouter", - "max_input_tokens": 200000, - "max_output_tokens": 8192, - "max_tokens": 8192, - "mode": "chat", - "output_cost_per_token": 5e-06, - "supports_function_calling": true, - "supports_tool_choice": true, - "tool_use_system_prompt_tokens": 264 - }, "openrouter/anthropic/claude-3-haiku": { "input_cost_per_image": 0.0004, "input_cost_per_token": 2.5e-07, @@ -23687,43 +24905,6 @@ "supports_tool_choice": true, "supports_vision": true }, - "openrouter/anthropic/claude-3-haiku-20240307": { - "input_cost_per_token": 2.5e-07, - "litellm_provider": "openrouter", - "max_input_tokens": 200000, - "max_output_tokens": 4096, - "max_tokens": 4096, - "mode": "chat", - "output_cost_per_token": 1.25e-06, - "supports_function_calling": true, - "supports_tool_choice": true, - "supports_vision": true, - "tool_use_system_prompt_tokens": 264 - }, - "openrouter/anthropic/claude-3-opus": { - "input_cost_per_token": 1.5e-05, - "litellm_provider": "openrouter", - "max_input_tokens": 200000, - "max_output_tokens": 4096, - "max_tokens": 4096, - "mode": "chat", - "output_cost_per_token": 7.5e-05, - "supports_function_calling": true, - "supports_tool_choice": true, - "supports_vision": true, - "tool_use_system_prompt_tokens": 395 - }, - "openrouter/anthropic/claude-3-sonnet": { - "input_cost_per_image": 0.0048, - "input_cost_per_token": 3e-06, - "litellm_provider": "openrouter", - "max_tokens": 200000, - "mode": "chat", - "output_cost_per_token": 1.5e-05, - "supports_function_calling": true, - "supports_tool_choice": true, - "supports_vision": true - }, "openrouter/anthropic/claude-3.5-sonnet": { "input_cost_per_token": 3e-06, "litellm_provider": "openrouter", @@ -23739,20 +24920,6 @@ "supports_vision": true, "tool_use_system_prompt_tokens": 159 }, - "openrouter/anthropic/claude-3.5-sonnet:beta": { - "input_cost_per_token": 3e-06, - "litellm_provider": "openrouter", - "max_input_tokens": 200000, - "max_output_tokens": 8192, - "max_tokens": 8192, - "mode": "chat", - "output_cost_per_token": 1.5e-05, - "supports_computer_use": true, - "supports_function_calling": true, - "supports_tool_choice": true, - "supports_vision": true, - "tool_use_system_prompt_tokens": 159 - }, "openrouter/anthropic/claude-3.7-sonnet": { "input_cost_per_image": 0.0048, "input_cost_per_token": 3e-06, @@ -23770,31 +24937,6 @@ "supports_vision": true, "tool_use_system_prompt_tokens": 159 }, - "openrouter/anthropic/claude-3.7-sonnet:beta": { - "input_cost_per_image": 0.0048, - "input_cost_per_token": 3e-06, - "litellm_provider": "openrouter", - "max_input_tokens": 200000, - "max_output_tokens": 128000, - "max_tokens": 128000, - "mode": "chat", - "output_cost_per_token": 1.5e-05, - "supports_computer_use": true, - "supports_function_calling": true, - "supports_reasoning": true, - "supports_tool_choice": true, - "supports_vision": true, - "tool_use_system_prompt_tokens": 159 - }, - "openrouter/anthropic/claude-instant-v1": { - "input_cost_per_token": 1.63e-06, - "litellm_provider": "openrouter", - "max_output_tokens": 8191, - "max_tokens": 8191, - "mode": "chat", - "output_cost_per_token": 5.51e-06, - "supports_tool_choice": true - }, "openrouter/anthropic/claude-opus-4": { "input_cost_per_image": 0.0048, "cache_creation_input_token_cost": 1.875e-05, @@ -23933,30 +25075,6 @@ "source": "https://openrouter.ai/api/v1/models/bytedance/ui-tars-1.5-7b", "supports_tool_choice": true }, - "openrouter/cognitivecomputations/dolphin-mixtral-8x7b": { - "input_cost_per_token": 5e-07, - "litellm_provider": "openrouter", - "max_tokens": 32769, - "mode": "chat", - "output_cost_per_token": 5e-07, - "supports_tool_choice": true - }, - "openrouter/cohere/command-r-plus": { - "input_cost_per_token": 3e-06, - "litellm_provider": "openrouter", - "max_tokens": 128000, - "mode": "chat", - "output_cost_per_token": 1.5e-05, - "supports_tool_choice": true - }, - "openrouter/databricks/dbrx-instruct": { - "input_cost_per_token": 6e-07, - "litellm_provider": "openrouter", - "max_tokens": 32768, - "mode": "chat", - "output_cost_per_token": 6e-07, - "supports_tool_choice": true - }, "openrouter/deepseek/deepseek-chat": { "input_cost_per_token": 1.4e-07, "litellm_provider": "openrouter", @@ -24024,17 +25142,6 @@ "supports_reasoning": false, "supports_tool_choice": true }, - "openrouter/deepseek/deepseek-coder": { - "input_cost_per_token": 1.4e-07, - "litellm_provider": "openrouter", - "max_input_tokens": 66000, - "max_output_tokens": 4096, - "max_tokens": 4096, - "mode": "chat", - "output_cost_per_token": 2.8e-07, - "supports_prompt_caching": true, - "supports_tool_choice": true - }, "openrouter/deepseek/deepseek-r1": { "input_cost_per_token": 5.5e-07, "input_cost_per_token_cache_hit": 1.4e-07, @@ -24065,14 +25172,6 @@ "supports_reasoning": true, "supports_tool_choice": true }, - "openrouter/fireworks/firellava-13b": { - "input_cost_per_token": 2e-07, - "litellm_provider": "openrouter", - "max_tokens": 4096, - "mode": "chat", - "output_cost_per_token": 2e-07, - "supports_tool_choice": true - }, "openrouter/google/gemini-2.0-flash-001": { "deprecation_date": "2026-03-31", "input_cost_per_audio_token": 7e-07, @@ -24234,46 +25333,6 @@ "supports_web_search": true, "tpm": 800000 }, - "openrouter/google/gemini-pro-1.5": { - "input_cost_per_image": 0.00265, - "input_cost_per_token": 2.5e-06, - "litellm_provider": "openrouter", - "max_input_tokens": 1000000, - "max_output_tokens": 8192, - "max_tokens": 8192, - "mode": "chat", - "output_cost_per_token": 7.5e-06, - "supports_function_calling": true, - "supports_tool_choice": true, - "supports_vision": true - }, - "openrouter/google/gemini-pro-vision": { - "input_cost_per_image": 0.0025, - "input_cost_per_token": 1.25e-07, - "litellm_provider": "openrouter", - "max_tokens": 45875, - "mode": "chat", - "output_cost_per_token": 3.75e-07, - "supports_function_calling": true, - "supports_tool_choice": true, - "supports_vision": true - }, - "openrouter/google/palm-2-chat-bison": { - "input_cost_per_token": 5e-07, - "litellm_provider": "openrouter", - "max_tokens": 25804, - "mode": "chat", - "output_cost_per_token": 5e-07, - "supports_tool_choice": true - }, - "openrouter/google/palm-2-codechat-bison": { - "input_cost_per_token": 5e-07, - "litellm_provider": "openrouter", - "max_tokens": 20070, - "mode": "chat", - "output_cost_per_token": 5e-07, - "supports_tool_choice": true - }, "openrouter/gryphe/mythomax-l2-13b": { "input_cost_per_token": 1.875e-06, "litellm_provider": "openrouter", @@ -24282,14 +25341,6 @@ "output_cost_per_token": 1.875e-06, "supports_tool_choice": true }, - "openrouter/jondurbin/airoboros-l2-70b-2.1": { - "input_cost_per_token": 1.3875e-05, - "litellm_provider": "openrouter", - "max_tokens": 4096, - "mode": "chat", - "output_cost_per_token": 1.3875e-05, - "supports_tool_choice": true - }, "openrouter/mancer/weaver": { "input_cost_per_token": 5.625e-06, "litellm_provider": "openrouter", @@ -24298,30 +25349,6 @@ "output_cost_per_token": 5.625e-06, "supports_tool_choice": true }, - "openrouter/meta-llama/codellama-34b-instruct": { - "input_cost_per_token": 5e-07, - "litellm_provider": "openrouter", - "max_tokens": 8192, - "mode": "chat", - "output_cost_per_token": 5e-07, - "supports_tool_choice": true - }, - "openrouter/meta-llama/llama-2-13b-chat": { - "input_cost_per_token": 2e-07, - "litellm_provider": "openrouter", - "max_tokens": 4096, - "mode": "chat", - "output_cost_per_token": 2e-07, - "supports_tool_choice": true - }, - "openrouter/meta-llama/llama-2-70b-chat": { - "input_cost_per_token": 1.5e-06, - "litellm_provider": "openrouter", - "max_tokens": 4096, - "mode": "chat", - "output_cost_per_token": 1.5e-06, - "supports_tool_choice": true - }, "openrouter/meta-llama/llama-3-70b-instruct": { "input_cost_per_token": 5.9e-07, "litellm_provider": "openrouter", @@ -24330,38 +25357,6 @@ "output_cost_per_token": 7.9e-07, "supports_tool_choice": true }, - "openrouter/meta-llama/llama-3-70b-instruct:nitro": { - "input_cost_per_token": 9e-07, - "litellm_provider": "openrouter", - "max_tokens": 8192, - "mode": "chat", - "output_cost_per_token": 9e-07, - "supports_tool_choice": true - }, - "openrouter/meta-llama/llama-3-8b-instruct:extended": { - "input_cost_per_token": 2.25e-07, - "litellm_provider": "openrouter", - "max_tokens": 16384, - "mode": "chat", - "output_cost_per_token": 2.25e-06, - "supports_tool_choice": true - }, - "openrouter/meta-llama/llama-3-8b-instruct:free": { - "input_cost_per_token": 0.0, - "litellm_provider": "openrouter", - "max_tokens": 8192, - "mode": "chat", - "output_cost_per_token": 0.0, - "supports_tool_choice": true - }, - "openrouter/microsoft/wizardlm-2-8x22b:nitro": { - "input_cost_per_token": 1e-06, - "litellm_provider": "openrouter", - "max_tokens": 65536, - "mode": "chat", - "output_cost_per_token": 1e-06, - "supports_tool_choice": true - }, "openrouter/minimax/minimax-m2": { "input_cost_per_token": 2.55e-07, "litellm_provider": "openrouter", @@ -24375,20 +25370,6 @@ "supports_reasoning": true, "supports_tool_choice": true }, - "openrouter/mistralai/devstral-2512:free": { - "input_cost_per_image": 0, - "input_cost_per_token": 0, - "litellm_provider": "openrouter", - "max_input_tokens": 262144, - "max_output_tokens": 262144, - "max_tokens": 262144, - "mode": "chat", - "output_cost_per_token": 0, - "supports_function_calling": true, - "supports_prompt_caching": false, - "supports_tool_choice": true, - "supports_vision": false - }, "openrouter/mistralai/devstral-2512": { "input_cost_per_image": 0, "input_cost_per_token": 1.5e-07, @@ -24467,14 +25448,6 @@ "output_cost_per_token": 1.3e-07, "supports_tool_choice": true }, - "openrouter/mistralai/mistral-7b-instruct:free": { - "input_cost_per_token": 0.0, - "litellm_provider": "openrouter", - "max_tokens": 8192, - "mode": "chat", - "output_cost_per_token": 0.0, - "supports_tool_choice": true - }, "openrouter/mistralai/mistral-large": { "input_cost_per_token": 8e-06, "litellm_provider": "openrouter", @@ -24519,16 +25492,9 @@ "source": "https://openrouter.ai/moonshotai/kimi-k2.5", "supports_function_calling": true, "supports_tool_choice": true, + "supports_video_input": true, "supports_vision": true }, - "openrouter/nousresearch/nous-hermes-llama2-13b": { - "input_cost_per_token": 2e-07, - "litellm_provider": "openrouter", - "max_tokens": 4096, - "mode": "chat", - "output_cost_per_token": 2e-07, - "supports_tool_choice": true - }, "openrouter/openai/gpt-3.5-turbo": { "input_cost_per_token": 1.5e-06, "litellm_provider": "openrouter", @@ -24553,17 +25519,6 @@ "output_cost_per_token": 6e-05, "supports_tool_choice": true }, - "openrouter/openai/gpt-4-vision-preview": { - "input_cost_per_image": 0.01445, - "input_cost_per_token": 1e-05, - "litellm_provider": "openrouter", - "max_tokens": 130000, - "mode": "chat", - "output_cost_per_token": 3e-05, - "supports_function_calling": true, - "supports_tool_choice": true, - "supports_vision": true - }, "openrouter/openai/gpt-4.1": { "cache_read_input_token_cost": 5e-07, "input_cost_per_token": 2e-06, @@ -24581,23 +25536,6 @@ "supports_tool_choice": true, "supports_vision": true }, - "openrouter/openai/gpt-4.1-2025-04-14": { - "cache_read_input_token_cost": 5e-07, - "input_cost_per_token": 2e-06, - "litellm_provider": "openrouter", - "max_input_tokens": 1047576, - "max_output_tokens": 32768, - "max_tokens": 32768, - "mode": "chat", - "output_cost_per_token": 8e-06, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_prompt_caching": true, - "supports_response_schema": true, - "supports_system_messages": true, - "supports_tool_choice": true, - "supports_vision": true - }, "openrouter/openai/gpt-4.1-mini": { "cache_read_input_token_cost": 1e-07, "input_cost_per_token": 4e-07, @@ -24615,23 +25553,6 @@ "supports_tool_choice": true, "supports_vision": true }, - "openrouter/openai/gpt-4.1-mini-2025-04-14": { - "cache_read_input_token_cost": 1e-07, - "input_cost_per_token": 4e-07, - "litellm_provider": "openrouter", - "max_input_tokens": 1047576, - "max_output_tokens": 32768, - "max_tokens": 32768, - "mode": "chat", - "output_cost_per_token": 1.6e-06, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_prompt_caching": true, - "supports_response_schema": true, - "supports_system_messages": true, - "supports_tool_choice": true, - "supports_vision": true - }, "openrouter/openai/gpt-4.1-nano": { "cache_read_input_token_cost": 2.5e-08, "input_cost_per_token": 1e-07, @@ -24649,23 +25570,6 @@ "supports_tool_choice": true, "supports_vision": true }, - "openrouter/openai/gpt-4.1-nano-2025-04-14": { - "cache_read_input_token_cost": 2.5e-08, - "input_cost_per_token": 1e-07, - "litellm_provider": "openrouter", - "max_input_tokens": 1047576, - "max_output_tokens": 32768, - "max_tokens": 32768, - "mode": "chat", - "output_cost_per_token": 4e-07, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_prompt_caching": true, - "supports_response_schema": true, - "supports_system_messages": true, - "supports_tool_choice": true, - "supports_vision": true - }, "openrouter/openai/gpt-4o": { "input_cost_per_token": 2.5e-06, "litellm_provider": "openrouter", @@ -24737,11 +25641,8 @@ "max_input_tokens": 272000, "max_output_tokens": 128000, "max_tokens": 128000, - "mode": "responses", + "mode": "chat", "output_cost_per_token": 1.4e-05, - "supported_endpoints": [ - "/v1/responses" - ], "supported_modalities": [ "text", "image" @@ -24902,58 +25803,6 @@ "supports_tool_choice": true, "supports_vision": true }, - "openrouter/openai/o1-mini": { - "input_cost_per_token": 3e-06, - "litellm_provider": "openrouter", - "max_input_tokens": 128000, - "max_output_tokens": 65536, - "max_tokens": 65536, - "mode": "chat", - "output_cost_per_token": 1.2e-05, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_tool_choice": true, - "supports_vision": false - }, - "openrouter/openai/o1-mini-2024-09-12": { - "input_cost_per_token": 3e-06, - "litellm_provider": "openrouter", - "max_input_tokens": 128000, - "max_output_tokens": 65536, - "max_tokens": 65536, - "mode": "chat", - "output_cost_per_token": 1.2e-05, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_tool_choice": true, - "supports_vision": false - }, - "openrouter/openai/o1-preview": { - "input_cost_per_token": 1.5e-05, - "litellm_provider": "openrouter", - "max_input_tokens": 128000, - "max_output_tokens": 32768, - "max_tokens": 32768, - "mode": "chat", - "output_cost_per_token": 6e-05, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_tool_choice": true, - "supports_vision": false - }, - "openrouter/openai/o1-preview-2024-09-12": { - "input_cost_per_token": 1.5e-05, - "litellm_provider": "openrouter", - "max_input_tokens": 128000, - "max_output_tokens": 32768, - "max_tokens": 32768, - "mode": "chat", - "output_cost_per_token": 6e-05, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_tool_choice": true, - "supports_vision": false - }, "openrouter/openai/o3-mini": { "input_cost_per_token": 1.1e-06, "litellm_provider": "openrouter", @@ -24982,14 +25831,6 @@ "supports_tool_choice": true, "supports_vision": false }, - "openrouter/pygmalionai/mythalion-13b": { - "input_cost_per_token": 1.875e-06, - "litellm_provider": "openrouter", - "max_tokens": 4096, - "mode": "chat", - "output_cost_per_token": 1.875e-06, - "supports_tool_choice": true - }, "openrouter/qwen/qwen-2.5-coder-32b-instruct": { "input_cost_per_token": 1.8e-07, "litellm_provider": "openrouter", @@ -25081,20 +25922,6 @@ "supports_tool_choice": true, "supports_web_search": true }, - "openrouter/x-ai/grok-4-fast:free": { - "input_cost_per_token": 0, - "litellm_provider": "openrouter", - "max_input_tokens": 2000000, - "max_output_tokens": 30000, - "max_tokens": 30000, - "mode": "chat", - "output_cost_per_token": 0, - "source": "https://openrouter.ai/x-ai/grok-4-fast:free", - "supports_function_calling": true, - "supports_reasoning": true, - "supports_tool_choice": true, - "supports_web_search": false - }, "openrouter/z-ai/glm-4.6": { "input_cost_per_token": 4e-07, "litellm_provider": "openrouter", @@ -25189,6 +26016,23 @@ "supports_prompt_caching": false, "supports_computer_use": false }, + "openrouter/minimax/minimax-m2.5": { + "input_cost_per_token": 3e-07, + "output_cost_per_token": 1.1e-06, + "cache_read_input_token_cost": 1.5e-07, + "litellm_provider": "openrouter", + "max_input_tokens": 196608, + "max_output_tokens": 65536, + "max_tokens": 65536, + "mode": "chat", + "source": "https://openrouter.ai/minimax/minimax-m2.5", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_reasoning": true, + "supports_vision": false, + "supports_prompt_caching": true, + "supports_computer_use": false + }, "ovhcloud/DeepSeek-R1-Distill-Llama-70B": { "input_cost_per_token": 6.7e-07, "litellm_provider": "ovhcloud", @@ -25967,6 +26811,19 @@ "supports_system_messages": true, "supports_vision": true }, + "qwen.qwen3-coder-next": { + "input_cost_per_token": 5e-07, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 262144, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 1.2e-06, + "supports_function_calling": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "source": "https://aws.amazon.com/bedrock/pricing/" + }, "recraft/recraftv2": { "litellm_provider": "recraft", "mode": "image_generation", @@ -28403,6 +29260,30 @@ "supports_reasoning": true, "supports_tool_choice": false }, + "us.deepseek.v3.2": { + "input_cost_per_token": 6.2e-07, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 163840, + "max_output_tokens": 163840, + "max_tokens": 163840, + "mode": "chat", + "output_cost_per_token": 1.85e-06, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, + "eu.deepseek.v3.2": { + "input_cost_per_token": 7.4e-07, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 163840, + "max_output_tokens": 163840, + "max_tokens": 163840, + "mode": "chat", + "output_cost_per_token": 2.22e-06, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, "us.meta.llama3-1-405b-instruct-v1:0": { "input_cost_per_token": 5.32e-06, "litellm_provider": "bedrock", @@ -30173,6 +31054,36 @@ "supports_vision": true, "tool_use_system_prompt_tokens": 346 }, + "vertex_ai/claude-opus-4-6@default": { + "cache_creation_input_token_cost": 6.25e-06, + "cache_creation_input_token_cost_above_200k_tokens": 1.25e-05, + "cache_read_input_token_cost": 5e-07, + "cache_read_input_token_cost_above_200k_tokens": 1e-06, + "input_cost_per_token": 5e-06, + "input_cost_per_token_above_200k_tokens": 1e-05, + "litellm_provider": "vertex_ai-anthropic_models", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 2.5e-05, + "output_cost_per_token_above_200k_tokens": 3.75e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true, + "tool_use_system_prompt_tokens": 346 + }, "vertex_ai/claude-sonnet-4-5": { "cache_creation_input_token_cost": 3.75e-06, "cache_read_input_token_cost": 3e-07, @@ -30199,6 +31110,36 @@ "supports_tool_choice": true, "supports_vision": true }, + "vertex_ai/claude-sonnet-4-6": { + "cache_creation_input_token_cost": 3.75e-06, + "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, + "input_cost_per_token": 3e-06, + "input_cost_per_token_above_200k_tokens": 6e-06, + "litellm_provider": "vertex_ai-anthropic_models", + "max_input_tokens": 200000, + "max_output_tokens": 64000, + "max_tokens": 64000, + "mode": "chat", + "output_cost_per_token": 1.5e-05, + "output_cost_per_token_above_200k_tokens": 2.25e-05, + "supports_assistant_prefill": true, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true, + "tool_use_system_prompt_tokens": 346, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + } + }, "vertex_ai/claude-sonnet-4-5@20250929": { "cache_creation_input_token_cost": 3.75e-06, "cache_read_input_token_cost": 3e-07, @@ -30829,6 +31770,21 @@ "supports_reasoning": true, "supports_tool_choice": true }, + "vertex_ai/zai-org/glm-5-maas": { + "cache_read_input_token_cost": 1e-07, + "input_cost_per_token": 1e-06, + "litellm_provider": "vertex_ai-zai_models", + "max_input_tokens": 200000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 3.2e-06, + "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing#glm-models", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, "vertex_ai/mistral-medium-3": { "input_cost_per_token": 4e-07, "litellm_provider": "vertex_ai-mistral_models", @@ -32332,6 +33288,20 @@ "supports_vision": true, "supports_web_search": true }, + "zai.glm-4.7": { + "input_cost_per_token": 6e-07, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 200000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 2.2e-06, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "source": "https://aws.amazon.com/bedrock/pricing/" + }, "zai/glm-4.7": { "cache_creation_input_token_cost": 0, "cache_read_input_token_cost": 1.1e-07, @@ -32484,6 +33454,23 @@ "1280x720" ] }, + "openai/sora-2-pro-high-res": { + "litellm_provider": "openai", + "mode": "video_generation", + "output_cost_per_video_per_second": 0.5, + "source": "https://platform.openai.com/docs/api-reference/videos", + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "video" + ], + "supported_resolutions": [ + "1024x1792", + "1792x1024" + ] + }, "azure/sora-2": { "litellm_provider": "azure", "mode": "video_generation", @@ -36005,5 +36992,761 @@ "mode": "chat", "output_cost_per_token": 0, "supports_reasoning": true + }, + "tts-1-1106": { + "input_cost_per_character": 1.5e-05, + "litellm_provider": "openai", + "mode": "audio_speech", + "supported_endpoints": [ + "/v1/audio/speech" + ] + }, + "tts-1-hd-1106": { + "input_cost_per_character": 3e-05, + "litellm_provider": "openai", + "mode": "audio_speech", + "supported_endpoints": [ + "/v1/audio/speech" + ] + }, + "gpt-4o-mini-tts-2025-03-20": { + "input_cost_per_token": 2.5e-06, + "litellm_provider": "openai", + "mode": "audio_speech", + "output_cost_per_audio_token": 1.2e-05, + "output_cost_per_second": 0.00025, + "output_cost_per_token": 1e-05, + "supported_endpoints": [ + "/v1/audio/speech" + ], + "supported_modalities": [ + "text", + "audio" + ], + "supported_output_modalities": [ + "audio" + ] + }, + "gpt-4o-mini-tts-2025-12-15": { + "input_cost_per_token": 2.5e-06, + "litellm_provider": "openai", + "mode": "audio_speech", + "output_cost_per_audio_token": 1.2e-05, + "output_cost_per_second": 0.00025, + "output_cost_per_token": 1e-05, + "supported_endpoints": [ + "/v1/audio/speech" + ], + "supported_modalities": [ + "text", + "audio" + ], + "supported_output_modalities": [ + "audio" + ] + }, + "gpt-4o-mini-transcribe-2025-03-20": { + "input_cost_per_audio_token": 3e-06, + "input_cost_per_token": 1.25e-06, + "litellm_provider": "openai", + "max_input_tokens": 16000, + "max_output_tokens": 2000, + "mode": "audio_transcription", + "output_cost_per_token": 5e-06, + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "gpt-4o-mini-transcribe-2025-12-15": { + "input_cost_per_audio_token": 3e-06, + "input_cost_per_token": 1.25e-06, + "litellm_provider": "openai", + "max_input_tokens": 16000, + "max_output_tokens": 2000, + "mode": "audio_transcription", + "output_cost_per_token": 5e-06, + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "gpt-5-search-api": { + "cache_read_input_token_cost": 1.25e-07, + "input_cost_per_token": 1.25e-06, + "litellm_provider": "openai", + "max_input_tokens": 272000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 1e-05, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_vision": true, + "supports_web_search": true + }, + "gpt-5-search-api-2025-10-14": { + "cache_read_input_token_cost": 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true + }, + "gemini-2.5-flash-native-audio-preview-12-2025": { + "input_cost_per_audio_token": 1e-06, + "input_cost_per_token": 3e-07, + "litellm_provider": "gemini", + "max_input_tokens": 1048576, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 2.5e-06, + "source": "https://ai.google.dev/pricing", + "supported_endpoints": [ + "/v1/realtime" + ], + "supported_modalities": [ + "text", + "audio" + ], + "supported_output_modalities": [ + "text", + "audio" + ], + "supports_audio_input": true, + "supports_audio_output": true + }, + "gemini/gemini-2.5-flash-native-audio-latest": { + "input_cost_per_audio_token": 1e-06, + "input_cost_per_token": 3e-07, + "litellm_provider": "gemini", + "max_input_tokens": 1048576, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 2.5e-06, + "source": "https://ai.google.dev/pricing", + "supported_endpoints": [ + "/v1/realtime" + ], + "supported_modalities": [ + 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"cache_read_input_token_cost": 1e-08, + "input_cost_per_audio_token": 3e-07, + "input_cost_per_token": 1e-07, + "litellm_provider": "gemini", + "max_audio_length_hours": 8.4, + "max_audio_per_prompt": 1, + "max_images_per_prompt": 3000, + "max_input_tokens": 1048576, + "max_output_tokens": 65535, + "max_pdf_size_mb": 30, + "max_tokens": 65535, + "max_video_length": 1, + "max_videos_per_prompt": 10, + "mode": "chat", + "output_cost_per_reasoning_token": 4e-07, + "output_cost_per_token": 4e-07, + "rpm": 15, + "source": "https://ai.google.dev/gemini-api/docs/models#gemini-2.5-flash-lite", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/completions", + "/v1/batch" + ], + "supported_modalities": [ + "text", + "image", + "audio", + "video" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_output": false, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_url_context": true, + "supports_vision": true, + "supports_web_search": true, + "tpm": 250000 + }, + "gemini-pro-latest": { + "cache_read_input_token_cost": 1.25e-07, + "cache_read_input_token_cost_above_200k_tokens": 2.5e-07, + "input_cost_per_token": 1.25e-06, + "input_cost_per_token_above_200k_tokens": 2.5e-06, + "litellm_provider": "gemini", + "max_audio_length_hours": 8.4, + "max_audio_per_prompt": 1, + "max_images_per_prompt": 3000, + "max_input_tokens": 1048576, + "max_output_tokens": 65535, + "max_pdf_size_mb": 30, + "max_tokens": 65535, + "max_video_length": 1, + "max_videos_per_prompt": 10, + "mode": "chat", + "output_cost_per_token": 1e-05, + "output_cost_per_token_above_200k_tokens": 1.5e-05, + "rpm": 2000, + "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/completions" + ], + "supported_modalities": [ + "text", + "image", + "audio", + "video" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_input": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_video_input": true, + "supports_vision": true, + "supports_web_search": true, + "tpm": 800000 + }, + "gemini/gemini-pro-latest": { + "cache_read_input_token_cost": 1.25e-07, + "cache_read_input_token_cost_above_200k_tokens": 2.5e-07, + "input_cost_per_token": 1.25e-06, + "input_cost_per_token_above_200k_tokens": 2.5e-06, + "litellm_provider": "gemini", + "max_audio_length_hours": 8.4, + "max_audio_per_prompt": 1, + "max_images_per_prompt": 3000, + "max_input_tokens": 1048576, + "max_output_tokens": 65535, + "max_pdf_size_mb": 30, + "max_tokens": 65535, + "max_video_length": 1, + "max_videos_per_prompt": 10, + "mode": "chat", + "output_cost_per_token": 1e-05, + "output_cost_per_token_above_200k_tokens": 1.5e-05, + "rpm": 2000, + "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/completions" + ], + "supported_modalities": [ + "text", + "image", + "audio", + "video" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_input": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_video_input": true, + "supports_vision": true, + "supports_web_search": true, + "tpm": 800000 + }, + "gemini-exp-1206": { + "cache_read_input_token_cost": 3e-08, + "input_cost_per_audio_token": 1e-06, + "input_cost_per_token": 3e-07, + "litellm_provider": "gemini", + 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"supports_url_context": true, + "supports_vision": true, + "supports_web_search": true, + "tpm": 8000000 + }, + "vertex_ai/claude-sonnet-4-6@default": { + "cache_creation_input_token_cost": 3.75e-06, + "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, + "input_cost_per_token": 3e-06, + "input_cost_per_token_above_200k_tokens": 6e-06, + "litellm_provider": "vertex_ai-anthropic_models", + "max_input_tokens": 200000, + "max_output_tokens": 64000, + "max_tokens": 64000, + "mode": "chat", + "output_cost_per_token": 1.5e-05, + "output_cost_per_token_above_200k_tokens": 2.25e-05, + "supports_assistant_prefill": true, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true, + "tool_use_system_prompt_tokens": 346, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + } + }, + "duckduckgo/search": { + "litellm_provider": "duckduckgo", + "mode": "search", + "input_cost_per_query": 0.0, + "metadata": { + "notes": "DuckDuckGo Instant Answer API is free and does not require an API key." + } } } diff --git a/litellm/passthrough/utils.py b/litellm/passthrough/utils.py index fbbf9cd258..fe1ecad96c 100644 --- a/litellm/passthrough/utils.py +++ b/litellm/passthrough/utils.py @@ -1,4 +1,4 @@ -from typing import Dict, List, Optional, Union +from typing import Dict, List, Mapping, Optional, Union from urllib.parse import parse_qs import httpx @@ -9,7 +9,9 @@ from litellm.constants import PASS_THROUGH_HEADER_PREFIX class BasePassthroughUtils: @staticmethod def get_merged_query_parameters( - existing_url: httpx.URL, request_query_params: Dict[str, Union[str, list]] + existing_url: httpx.URL, + request_query_params: Mapping[str, Union[str, list]], + default_query_params: Optional[Dict[str, Union[str, list]]] = None ) -> Dict[str, Union[str, List[str]]]: # Get the existing query params from the target URL existing_query_string = existing_url.query.decode("utf-8") @@ -19,8 +21,19 @@ class BasePassthroughUtils: updated_existing_query_params = { k: v[0] if len(v) == 1 else v for k, v in existing_query_params.items() } - # Merge the query params, giving priority to the existing ones - return {**request_query_params, **updated_existing_query_params} + + # Start with default query params (lowest priority) + merged_params = {} + if default_query_params: + merged_params.update(default_query_params) + + # Override with existing URL query params (medium priority) + merged_params.update(updated_existing_query_params) + + # Override with request query params (highest priority - client can override anything) + merged_params.update(request_query_params) + + return merged_params @staticmethod def forward_headers_from_request( diff --git a/litellm/policy_templates_backup.json b/litellm/policy_templates_backup.json new file mode 100644 index 0000000000..be2352866b --- /dev/null +++ b/litellm/policy_templates_backup.json @@ -0,0 +1,2458 @@ +[ + { + "id": "advanced-au-pii-protection", + "title": "Advanced PII Protection (Australia)", + "description": "Protects Australian-specific identifiers, international employee data, financial information, credentials, protected class information, and industry-specific sensitive data.", + "example_sentences": [ + "My TFN is 123 456 789, can you check it?", + "Here is my ABN 51 824 753 556 for the invoice", + "Medicare number 2123 45670 1", + "My passport number is PA1234567" + ], + "icon": "ShieldCheckIcon", + "iconColor": "text-purple-500", + "iconBg": "bg-purple-50", + "guardrails": [ + "au-pii-tax-identifiers", + "au-pii-passports", + "international-pii-identifiers", + "contact-information-pii", + "financial-pii", + "credentials-api-keys", + "network-infrastructure-pii", + "protected-class-information" + ], + "complexity": "High", + "guardrailDefinitions": [ + { + "guardrail_name": "au-pii-tax-identifiers", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "patterns": [ + { + "pattern_type": "prebuilt", + "pattern_name": "au_tfn", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "au_abn", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "au_medicare", + "action": "MASK" + } + ], + "pattern_redaction_format": "[{pattern_name}_REDACTED]" + }, + "guardrail_info": { + "description": "Masks Australian Tax File Numbers, Business Numbers, and Medicare Numbers" + } + }, + { + "guardrail_name": "au-pii-passports", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "patterns": [ + { + "pattern_type": "prebuilt", + "pattern_name": "passport_australia", + "action": "MASK" + } + ], + "pattern_redaction_format": "[PASSPORT_REDACTED]" + }, + "guardrail_info": { + "description": "Masks Australian passport numbers" + } + }, + { + "guardrail_name": "international-pii-identifiers", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "patterns": [ + { + "pattern_type": "prebuilt", + "pattern_name": "us_ssn", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "us_ssn_no_dash", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "passport_us", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "passport_uk", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "passport_germany", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "passport_france", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "passport_netherlands", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "nl_bsn_contextual", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "passport_china", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "passport_india", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "passport_japan", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "passport_canada", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "br_cpf", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "br_cpf_unformatted", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "br_rg", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "br_cnpj", + "action": "MASK" + } + ], + "pattern_redaction_format": "[{pattern_name}_REDACTED]" + }, + "guardrail_info": { + "description": "Masks international PII identifiers including passports and national IDs" + } + }, + { + "guardrail_name": "contact-information-pii", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "patterns": [ + { + "pattern_type": "prebuilt", + "pattern_name": "email", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "us_phone", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "br_phone_landline", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "br_phone_mobile", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "street_address", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "br_cep", + "action": "MASK" + } + ], + "pattern_redaction_format": "[{pattern_name}_REDACTED]" + }, + "guardrail_info": { + "description": "Masks contact information including emails, phone numbers, and addresses" + } + }, + { + "guardrail_name": "financial-pii", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "patterns": [ + { + "pattern_type": "prebuilt", + "pattern_name": "visa", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "mastercard", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "amex", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "discover", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "credit_card", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "iban", + "action": "MASK" + } + ], + "pattern_redaction_format": "[{pattern_name}_REDACTED]" + }, + "guardrail_info": { + "description": "Masks financial information including credit cards and bank account numbers" + } + }, + { + "guardrail_name": "credentials-api-keys", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "patterns": [ + { + "pattern_type": "prebuilt", + "pattern_name": "aws_access_key", + "action": "BLOCK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "aws_secret_key", + "action": "BLOCK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "github_token", + "action": "BLOCK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "slack_token", + "action": "BLOCK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "generic_api_key", + "action": "BLOCK" + } + ], + "pattern_redaction_format": "[{pattern_name}_REDACTED]" + }, + "guardrail_info": { + "description": "Blocks requests containing API keys and credentials (AWS, GitHub, Slack)" + } + }, + { + "guardrail_name": "network-infrastructure-pii", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "patterns": [ + { + "pattern_type": "prebuilt", + "pattern_name": "ipv4", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "ipv6", + "action": "MASK" + } + ], + "pattern_redaction_format": "[INTERNAL_IP_REDACTED]" + }, + "guardrail_info": { + "description": "Masks IP addresses in requests" + } + }, + { + "guardrail_name": "protected-class-information", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "patterns": [ + { + "pattern_type": "prebuilt", + "pattern_name": "gender_sexual_orientation", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "race_ethnicity_national_origin", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "religion", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "age_discrimination", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "disability", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "marital_family_status", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "military_status", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "public_assistance", + "action": "MASK" + } + ], + "pattern_redaction_format": "[PROTECTED_CLASS_INFO_REDACTED]" + }, + "guardrail_info": { + "description": "Masks protected class information for HR compliance and anti-discrimination" + } + } + ], + "templateData": { + "policy_name": "advanced-pii-protection-australia", + "description": "Comprehensive PII detection and masking policy for Australia. Protects Australian-specific identifiers, international employee data, financial information, credentials, protected class information, and industry-specific sensitive data.", + "guardrails_add": [ + "au-pii-tax-identifiers", + "au-pii-passports", + "international-pii-identifiers", + "contact-information-pii", + "financial-pii", + "credentials-api-keys", + "network-infrastructure-pii", + "protected-class-information" + ], + "guardrails_remove": [] + }, + "tags": [ + "PII Protection", + "Australia" + ], + "estimated_latency_ms": 1 + }, + { + "id": "baseline-pii-protection", + "title": "Baseline PII Protection", + "description": "Baseline PII protection for internal tools and testing. Focuses on credentials and high-risk identifiers only. Suitable for non-sensitive internal use.", + "example_sentences": [ + "My AWS secret key is AKIAIOSFODNN7EXAMPLE", + "Here is my API token: sk-abc123def456", + "My password is hunter2, can you help me reset it?", + "Credit card number is 4111-1111-1111-1111" + ], + "icon": "ShieldCheckIcon", + "iconColor": "text-blue-500", + "iconBg": "bg-blue-50", + "guardrails": [ + "au-pii-tax-identifiers", + "credentials-api-keys", + "financial-pii" + ], + "complexity": "Low", + "guardrailDefinitions": [ + { + "guardrail_name": "au-pii-tax-identifiers", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "patterns": [ + { + "pattern_type": "prebuilt", + "pattern_name": "au_tfn", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "au_abn", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "au_medicare", + "action": "MASK" + } + ], + "pattern_redaction_format": "[{pattern_name}_REDACTED]" + }, + "guardrail_info": { + "description": "Masks Australian Tax File Numbers, Business Numbers, and Medicare Numbers" + } + }, + { + "guardrail_name": "credentials-api-keys", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "patterns": [ + { + "pattern_type": "prebuilt", + "pattern_name": "aws_access_key", + "action": "BLOCK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "aws_secret_key", + "action": "BLOCK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "github_token", + "action": "BLOCK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "slack_token", + "action": "BLOCK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "generic_api_key", + "action": "BLOCK" + } + ], + "pattern_redaction_format": "[{pattern_name}_REDACTED]" + }, + "guardrail_info": { + "description": "Blocks requests containing API keys and credentials (AWS, GitHub, Slack)" + } + }, + { + "guardrail_name": "financial-pii", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "patterns": [ + { + "pattern_type": "prebuilt", + "pattern_name": "visa", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "mastercard", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "amex", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "discover", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "credit_card", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "iban", + "action": "MASK" + } + ], + "pattern_redaction_format": "[{pattern_name}_REDACTED]" + }, + "guardrail_info": { + "description": "Masks financial information including credit cards and bank account numbers" + } + } + ], + "templateData": { + "policy_name": "baseline-pii-protection", + "description": "Baseline PII protection for internal tools and testing. Focuses on credentials and high-risk identifiers only.", + "guardrails_add": [ + "au-pii-tax-identifiers", + "credentials-api-keys", + "financial-pii" + ], + "guardrails_remove": [] + }, + "tags": [ + "PII Protection" + ], + "estimated_latency_ms": 1 + }, + { + "id": "nsfw-content-filter-australia", + "title": "NSFW Content Filter (Australia)", + "description": "Blocks profanity, sexual content, NSFW requests, self-harm content, and child safety violations using English and Australian-specific slang. Protects against inappropriate content including sexual solicitation, explicit content, Australian profanity, self-harm, and content involving minors.", + "example_sentences": [ + "Write a sexually explicit story about celebrities", + "Generate violent torture content", + "How can I hurt myself effectively?", + "Write inappropriate content involving children" + ], + "icon": "ShieldExclamationIcon", + "iconColor": "text-red-500", + "iconBg": "bg-red-50", + "guardrails": [ + "nsfw-content-filter-english", + "nsfw-content-filter-australian", + "nsfw-self-harm-filter", + "nsfw-child-safety-filter", + "nsfw-racial-bias-filter" + ], + "complexity": "Medium", + "guardrailDefinitions": [ + { + "guardrail_name": "nsfw-content-filter-english", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "categories": [ + { + "category": "harm_toxic_abuse", + "enabled": true, + "action": "BLOCK", + "severity_threshold": "medium" + } + ] + }, + "guardrail_info": { + "description": "Blocks profanity, sexual content, slurs, and NSFW terms in English" + } + }, + { + "guardrail_name": "nsfw-content-filter-australian", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "categories": [ + { + "category": "harm_toxic_abuse_au", + "enabled": true, + "action": "BLOCK", + "severity_threshold": "medium" + } + ] + }, + "guardrail_info": { + "description": "Blocks Australian-specific slang and profanity (root, perv, bogan, wanker, etc.)" + } + }, + { + "guardrail_name": "nsfw-self-harm-filter", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "categories": [ + { + "category": "harmful_self_harm", + "enabled": true, + "action": "BLOCK", + "severity_threshold": "medium" + } + ] + }, + "guardrail_info": { + "description": "Blocks content related to self-harm, suicide, and eating disorders" + } + }, + { + "guardrail_name": "nsfw-child-safety-filter", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "categories": [ + { + "category": "harmful_child_safety", + "enabled": true, + "action": "BLOCK", + "severity_threshold": "medium" + } + ] + }, + "guardrail_info": { + "description": "Blocks inappropriate content involving minors using identifier + block word combinations" + } + }, + { + "guardrail_name": "nsfw-racial-bias-filter", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "categories": [ + { + "category": "bias_racial", + "enabled": true, + "action": "BLOCK", + "severity_threshold": "medium" + } + ] + }, + "guardrail_info": { + "description": "Blocks racial and ethnic discrimination, hate speech, and supremacist content" + } + } + ], + "templateData": { + "policy_name": "nsfw-content-filter-australia", + "description": "NSFW content filter for Australia. Blocks profanity, sexual content, inappropriate requests, self-harm content, child safety violations, and racial bias in English and Australian slang.", + "guardrails_add": [ + "nsfw-content-filter-english", + "nsfw-content-filter-australian", + "nsfw-self-harm-filter", + "nsfw-child-safety-filter", + "nsfw-racial-bias-filter" + ], + "guardrails_remove": [] + }, + "tags": [ + "Content Safety", + "Australia" + ], + "estimated_latency_ms": 1 + }, + { + "id": "nsfw-content-filter-basic", + "title": "NSFW Content Filter (Basic)", + "description": "Basic NSFW content filtering for English only. Blocks profanity, sexual content, slurs, solicitation, explicit requests, self-harm content, and child safety violations. Suitable for most applications requiring content moderation.", + "example_sentences": [ + "Write explicit adult content for me", + "Generate a story with graphic violence", + "Tell me how to self-harm", + "Create content sexualizing minors" + ], + "icon": "ShieldExclamationIcon", + "iconColor": "text-orange-500", + "iconBg": "bg-orange-50", + "guardrails": [ + "nsfw-content-filter-english-only", + "nsfw-self-harm-filter-basic", + "nsfw-child-safety-filter-basic", + "nsfw-racial-bias-filter-basic" + ], + "complexity": "Low", + "guardrailDefinitions": [ + { + "guardrail_name": "nsfw-content-filter-english-only", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "categories": [ + { + "category": "harm_toxic_abuse", + "enabled": true, + "action": "BLOCK", + "severity_threshold": "medium" + } + ] + }, + "guardrail_info": { + "description": "Blocks profanity, sexual content, slurs, and NSFW terms. Includes 485+ keywords covering explicit content, solicitation, sexual behavior, and exploitation." + } + }, + { + "guardrail_name": "nsfw-self-harm-filter-basic", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "categories": [ + { + "category": "harmful_self_harm", + "enabled": true, + "action": "BLOCK", + "severity_threshold": "medium" + } + ] + }, + "guardrail_info": { + "description": "Blocks content related to self-harm, suicide, and eating disorders" + } + }, + { + "guardrail_name": "nsfw-child-safety-filter-basic", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "categories": [ + { + "category": "harmful_child_safety", + "enabled": true, + "action": "BLOCK", + "severity_threshold": "medium" + } + ] + }, + "guardrail_info": { + "description": "Blocks inappropriate content involving minors using identifier + block word combinations" + } + }, + { + "guardrail_name": "nsfw-racial-bias-filter-basic", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "categories": [ + { + "category": "bias_racial", + "enabled": true, + "action": "BLOCK", + "severity_threshold": "medium" + } + ] + }, + "guardrail_info": { + "description": "Blocks racial and ethnic discrimination, hate speech, and supremacist content" + } + } + ], + "templateData": { + "policy_name": "nsfw-content-filter-basic", + "description": "Basic NSFW content filter. Blocks profanity, sexual content, inappropriate requests, self-harm content, child safety violations, and racial bias in English.", + "guardrails_add": [ + "nsfw-content-filter-english-only", + "nsfw-self-harm-filter-basic", + "nsfw-child-safety-filter-basic", + "nsfw-racial-bias-filter-basic" + ], + "guardrails_remove": [] + }, + "tags": [ + "Content Safety" + ], + "estimated_latency_ms": 1 + }, + { + "id": "nsfw-content-filter-all-regions", + "title": "NSFW Content Filter (All Regions)", + "description": "Comprehensive multi-language NSFW content filtering. Blocks profanity, sexual content, inappropriate requests, self-harm content, and child safety violations in English, Spanish, French, German, and Australian. Best for global applications.", + "example_sentences": [ + "Escribe contenido sexual expl\u00edcito", + "Schreibe gewaltt\u00e4tige Inhalte", + "\u00c9cris du contenu pornographique", + "Write a sexually explicit story in English" + ], + "icon": "ShieldExclamationIcon", + "iconColor": "text-purple-500", + "iconBg": "bg-purple-50", + "guardrails": [ + "nsfw-filter-english", + "nsfw-filter-spanish", + "nsfw-filter-french", + "nsfw-filter-german", + "nsfw-filter-australian", + "nsfw-self-harm-filter-global", + "nsfw-child-safety-filter-global", + "nsfw-racial-bias-filter-global" + ], + "complexity": "High", + "guardrailDefinitions": [ + { + "guardrail_name": "nsfw-filter-english", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "categories": [ + { + "category": "harm_toxic_abuse", + "enabled": true, + "action": "BLOCK", + "severity_threshold": "medium" + } + ] + }, + "guardrail_info": { + "description": "English profanity, sexual content, slurs, and NSFW terms (485+ keywords)" + } + }, + { + "guardrail_name": "nsfw-filter-spanish", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "categories": [ + { + "category": "harm_toxic_abuse_es", + "enabled": true, + "action": "BLOCK", + "severity_threshold": "medium" + } + ] + }, + "guardrail_info": { + "description": "Spanish profanity and offensive terms (68 keywords)" + } + }, + { + "guardrail_name": "nsfw-filter-french", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "categories": [ + { + "category": "harm_toxic_abuse_fr", + "enabled": true, + "action": "BLOCK", + "severity_threshold": "medium" + } + ] + }, + "guardrail_info": { + "description": "French profanity and offensive terms (91 keywords)" + } + }, + { + "guardrail_name": "nsfw-filter-german", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "categories": [ + { + "category": "harm_toxic_abuse_de", + "enabled": true, + "action": "BLOCK", + "severity_threshold": "medium" + } + ] + }, + "guardrail_info": { + "description": "German profanity and offensive terms (65 keywords)" + } + }, + { + "guardrail_name": "nsfw-filter-australian", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "categories": [ + { + "category": "harm_toxic_abuse_au", + "enabled": true, + "action": "BLOCK", + "severity_threshold": "medium" + } + ] + }, + "guardrail_info": { + "description": "Australian slang and profanity (32 keywords: root, perv, bogan, wanker, etc.)" + } + }, + { + "guardrail_name": "nsfw-self-harm-filter-global", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "categories": [ + { + "category": "harmful_self_harm", + "enabled": true, + "action": "BLOCK", + "severity_threshold": "medium" + } + ] + }, + "guardrail_info": { + "description": "Blocks content related to self-harm, suicide, and eating disorders" + } + }, + { + "guardrail_name": "nsfw-child-safety-filter-global", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "categories": [ + { + "category": "harmful_child_safety", + "enabled": true, + "action": "BLOCK", + "severity_threshold": "medium" + } + ] + }, + "guardrail_info": { + "description": "Blocks inappropriate content involving minors using identifier + block word combinations" + } + }, + { + "guardrail_name": "nsfw-racial-bias-filter-global", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "categories": [ + { + "category": "bias_racial", + "enabled": true, + "action": "BLOCK", + "severity_threshold": "medium" + } + ] + }, + "guardrail_info": { + "description": "Blocks racial and ethnic discrimination, hate speech, and supremacist content" + } + } + ], + "templateData": { + "policy_name": "nsfw-content-filter-all-regions", + "description": "Comprehensive multi-language NSFW content filter. Blocks profanity, inappropriate content, self-harm, child safety violations, and racial bias in English, Spanish, French, German, and Australian. Total coverage: 741+ keywords across all languages plus self-harm, child safety, and racial bias protection.", + "guardrails_add": [ + "nsfw-filter-english", + "nsfw-filter-spanish", + "nsfw-filter-french", + "nsfw-filter-german", + "nsfw-filter-australian", + "nsfw-self-harm-filter-global", + "nsfw-child-safety-filter-global", + "nsfw-racial-bias-filter-global" + ], + "guardrails_remove": [] + }, + "tags": [ + "Content Safety" + ], + "estimated_latency_ms": 1 + }, + { + "id": "gdpr-eu-pii-protection", + "title": "GDPR Art. 32 \u2014 EU PII Protection", + "description": "GDPR Article 32 compliance for EU personal data protection. Masks French national IDs (NIR/INSEE), EU IBANs, French phone numbers, EU VAT numbers, EU passport numbers, and email addresses. Suitable for applications processing EU citizen data requiring GDPR compliance.", + "example_sentences": [ + "My French NIR number is 1 85 12 75 108 123 45", + "IBAN DE89 3704 0044 0532 0130 00", + "My EU passport number is FR1234567", + "VAT number is DE123456789" + ], + "icon": "ShieldCheckIcon", + "iconColor": "text-indigo-500", + "iconBg": "bg-indigo-50", + "guardrails": [ + "gdpr-eu-national-identifiers", + "gdpr-eu-financial-data", + "gdpr-eu-contact-information", + "gdpr-eu-business-identifiers" + ], + "complexity": "Medium", + "guardrailDefinitions": [ + { + "guardrail_name": "gdpr-eu-national-identifiers", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "patterns": [ + { + "pattern_type": "prebuilt", + "pattern_name": "fr_nir", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "eu_passport_generic", + "action": "MASK" + } + ], + "pattern_redaction_format": "[{pattern_name}_REDACTED]" + }, + "guardrail_info": { + "description": "Masks EU national identification numbers including French NIR/INSEE and EU passport numbers for GDPR compliance" + } + }, + { + "guardrail_name": "gdpr-eu-financial-data", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "patterns": [ + { + "pattern_type": "prebuilt", + "pattern_name": "eu_iban_enhanced", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "iban", + "action": "MASK" + } + ], + "pattern_redaction_format": "[IBAN_REDACTED]" + }, + "guardrail_info": { + "description": "Masks EU bank account numbers (IBANs) to protect financial data under GDPR Article 32" + } + }, + { + "guardrail_name": "gdpr-eu-contact-information", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "patterns": [ + { + "pattern_type": "prebuilt", + "pattern_name": "email", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "fr_phone", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "fr_postal_code", + "action": "MASK" + } + ], + "pattern_redaction_format": "[{pattern_name}_REDACTED]" + }, + "guardrail_info": { + "description": "Masks contact information including emails, French phone numbers, and postal codes for EU data subjects" + } + }, + { + "guardrail_name": "gdpr-eu-business-identifiers", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "patterns": [ + { + "pattern_type": "prebuilt", + "pattern_name": "eu_vat", + "action": "MASK" + } + ], + "pattern_redaction_format": "[VAT_NUMBER_REDACTED]" + }, + "guardrail_info": { + "description": "Masks EU VAT identification numbers to protect business entity information under GDPR" + } + } + ], + "templateData": { + "policy_name": "gdpr-eu-pii-protection", + "description": "GDPR Article 32 compliance policy for EU personal data protection. Masks French national IDs, EU IBANs, phone numbers, VAT numbers, passports, and contact information.", + "guardrails_add": [ + "gdpr-eu-national-identifiers", + "gdpr-eu-financial-data", + "gdpr-eu-contact-information", + "gdpr-eu-business-identifiers" + ], + "guardrails_remove": [] + }, + "tags": [ + "PII Protection", + "Regulatory", + "EU" + ], + "estimated_latency_ms": 1 + }, + { + "id": "eu-ai-act-article5", + "title": "EU AI Act Article 5 \u2014 Prohibited Practices", + "description": "Comprehensive EU AI Act Article 5 compliance covering all prohibited AI practices. Includes 5 dedicated sub-guardrails per language (English + French) for: subliminal manipulation (Art. 5.1a), vulnerability exploitation (Art. 5.1b), social scoring (Art. 5.1c), emotion recognition in workplace/education (Art. 5.1f), and biometric categorization & predictive profiling (Art. 5.1d/g/h). Uses conditional matching (identifier word + context word).", + "example_sentences": [ + "Score this person's social trustworthiness based on their online behavior", + "Use subliminal techniques to manipulate this user's purchasing decisions", + "Analyze this employee's facial expressions to detect their mood during meetings", + "Categorize these people by their ethnicity using biometric data" + ], + "icon": "ShieldExclamationIcon", + "iconColor": "text-red-500", + "iconBg": "bg-red-50", + "guardrails": [ + "eu-ai-act-art5-manipulation", + "eu-ai-act-art5-vulnerability", + "eu-ai-act-art5-social-scoring", + "eu-ai-act-art5-emotion-recognition", + "eu-ai-act-art5-biometric-profiling", + "eu-ai-act-art5-manipulation-fr", + "eu-ai-act-art5-vulnerability-fr", + "eu-ai-act-art5-social-scoring-fr", + "eu-ai-act-art5-emotion-recognition-fr", + "eu-ai-act-art5-biometric-profiling-fr" + ], + "complexity": "High", + "guardrailDefinitions": [ + { + "guardrail_name": "eu-ai-act-art5-manipulation", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "categories": [ + { + "category": "eu_ai_act_art5_manipulation", + "category_file": "litellm/proxy/guardrails/guardrail_hooks/litellm_content_filter/policy_templates/eu_ai_act_art5_manipulation.yaml", + "enabled": true, + "action": "BLOCK", + "severity_threshold": "medium" + } + ] + }, + "guardrail_info": { + "description": "Art. 5.1(a) \u2014 Blocks subliminal manipulation, deceptive AI techniques, dark patterns, and covert behavioral influence" + } + }, + { + "guardrail_name": "eu-ai-act-art5-vulnerability", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "categories": [ + { + "category": "eu_ai_act_art5_vulnerability", + "category_file": "litellm/proxy/guardrails/guardrail_hooks/litellm_content_filter/policy_templates/eu_ai_act_art5_vulnerability.yaml", + "enabled": true, + "action": "BLOCK", + "severity_threshold": "medium" + } + ] + }, + "guardrail_info": { + "description": "Art. 5.1(b) \u2014 Blocks AI systems that exploit vulnerabilities of children, elderly, disabled persons, or economically disadvantaged groups" + } + }, + { + "guardrail_name": "eu-ai-act-art5-social-scoring", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "categories": [ + { + "category": "eu_ai_act_art5_social_scoring", + "category_file": "litellm/proxy/guardrails/guardrail_hooks/litellm_content_filter/policy_templates/eu_ai_act_art5_social_scoring.yaml", + "enabled": true, + "action": "BLOCK", + "severity_threshold": "medium" + } + ] + }, + "guardrail_info": { + "description": "Art. 5.1(c) \u2014 Blocks social credit systems, citizen scoring, trustworthiness classification, and behavioral reputation scoring" + } + }, + { + "guardrail_name": "eu-ai-act-art5-emotion-recognition", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "categories": [ + { + "category": "eu_ai_act_art5_emotion_recognition", + "category_file": "litellm/proxy/guardrails/guardrail_hooks/litellm_content_filter/policy_templates/eu_ai_act_art5_emotion_recognition.yaml", + "enabled": true, + "action": "BLOCK", + "severity_threshold": "medium" + } + ] + }, + "guardrail_info": { + "description": "Art. 5.1(f) \u2014 Blocks emotion recognition, mood tracking, and sentiment analysis in workplace and educational settings" + } + }, + { + "guardrail_name": "eu-ai-act-art5-biometric-profiling", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "categories": [ + { + "category": "eu_ai_act_art5_biometric_profiling", + "category_file": "litellm/proxy/guardrails/guardrail_hooks/litellm_content_filter/policy_templates/eu_ai_act_art5_biometric_profiling.yaml", + "enabled": true, + "action": "BLOCK", + "severity_threshold": "medium" + } + ] + }, + "guardrail_info": { + "description": "Art. 5.1(d)(g)(h) \u2014 Blocks biometric categorization by race/ethnicity/religion/politics, facial recognition database scraping, and predictive policing" + } + }, + { + "guardrail_name": "eu-ai-act-art5-manipulation-fr", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "categories": [ + { + "category": "eu_ai_act_art5_manipulation_fr", + "category_file": "litellm/proxy/guardrails/guardrail_hooks/litellm_content_filter/policy_templates/eu_ai_act_art5_manipulation_fr.yaml", + "enabled": true, + "action": "BLOCK", + "severity_threshold": "medium" + } + ] + }, + "guardrail_info": { + "description": "Art. 5.1(a) FR \u2014 Bloque la manipulation subliminale, les techniques d'IA trompeuses et les dark patterns (fran\u00e7ais)" + } + }, + { + "guardrail_name": "eu-ai-act-art5-vulnerability-fr", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "categories": [ + { + "category": "eu_ai_act_art5_vulnerability_fr", + "category_file": "litellm/proxy/guardrails/guardrail_hooks/litellm_content_filter/policy_templates/eu_ai_act_art5_vulnerability_fr.yaml", + "enabled": true, + "action": "BLOCK", + "severity_threshold": "medium" + } + ] + }, + "guardrail_info": { + "description": "Art. 5.1(b) FR \u2014 Bloque l'exploitation des vuln\u00e9rabilit\u00e9s des enfants, personnes \u00e2g\u00e9es et handicap\u00e9es (fran\u00e7ais)" + } + }, + { + "guardrail_name": "eu-ai-act-art5-social-scoring-fr", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "categories": [ + { + "category": "eu_ai_act_art5_social_scoring_fr", + "category_file": "litellm/proxy/guardrails/guardrail_hooks/litellm_content_filter/policy_templates/eu_ai_act_art5_social_scoring_fr.yaml", + "enabled": true, + "action": "BLOCK", + "severity_threshold": "medium" + } + ] + }, + "guardrail_info": { + "description": "Art. 5.1(c) FR \u2014 Bloque les syst\u00e8mes de cr\u00e9dit social, notation des citoyens et classification de fiabilit\u00e9 (fran\u00e7ais)" + } + }, + { + "guardrail_name": "eu-ai-act-art5-emotion-recognition-fr", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "categories": [ + { + "category": "eu_ai_act_art5_emotion_recognition_fr", + "category_file": "litellm/proxy/guardrails/guardrail_hooks/litellm_content_filter/policy_templates/eu_ai_act_art5_emotion_recognition_fr.yaml", + "enabled": true, + "action": "BLOCK", + "severity_threshold": "medium" + } + ] + }, + "guardrail_info": { + "description": "Art. 5.1(f) FR \u2014 Bloque la reconnaissance des \u00e9motions et l'analyse des sentiments au travail et dans l'\u00e9ducation (fran\u00e7ais)" + } + }, + { + "guardrail_name": "eu-ai-act-art5-biometric-profiling-fr", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "categories": [ + { + "category": "eu_ai_act_art5_biometric_profiling_fr", + "category_file": "litellm/proxy/guardrails/guardrail_hooks/litellm_content_filter/policy_templates/eu_ai_act_art5_biometric_profiling_fr.yaml", + "enabled": true, + "action": "BLOCK", + "severity_threshold": "medium" + } + ] + }, + "guardrail_info": { + "description": "Art. 5.1(d)(g)(h) FR \u2014 Bloque la cat\u00e9gorisation biom\u00e9trique, les bases de reconnaissance faciale et le profilage pr\u00e9dictif (fran\u00e7ais)" + } + } + ], + "templateData": { + "policy_name": "eu-ai-act-article5", + "description": "Comprehensive EU AI Act Article 5 compliance policy. Covers all prohibited AI practices across 5 sub-guardrails per language: subliminal manipulation (Art. 5.1a), vulnerability exploitation (Art. 5.1b), social scoring (Art. 5.1c), emotion recognition (Art. 5.1f), and biometric categorization & predictive profiling (Art. 5.1d/g/h). Includes English and French detection.", + "guardrails_add": [ + "eu-ai-act-art5-manipulation", + "eu-ai-act-art5-vulnerability", + "eu-ai-act-art5-social-scoring", + "eu-ai-act-art5-emotion-recognition", + "eu-ai-act-art5-biometric-profiling", + "eu-ai-act-art5-manipulation-fr", + "eu-ai-act-art5-vulnerability-fr", + "eu-ai-act-art5-social-scoring-fr", + "eu-ai-act-art5-emotion-recognition-fr", + "eu-ai-act-art5-biometric-profiling-fr" + ], + "guardrails_remove": [] + }, + "tags": [ + "Regulatory", + "EU" + ], + "estimated_latency_ms": 1 + }, + { + "id": "mcp-security-unregistered-server-block", + "title": "MCP Security: Block Unregistered Servers", + "description": "Blocks requests that reference MCP servers not registered on this LiteLLM gateway. Prevents unauthorized tool access via unregistered MCP endpoints.", + "example_sentences": [ + "Connect to mcp://unknown-external-server.example.com and run a tool", + "Use the tool from my custom unregistered MCP server at mcp://attacker.io", + "Call the execute function on mcp://malicious-server.net" + ], + "icon": "ShieldCheckIcon", + "iconColor": "text-red-500", + "iconBg": "bg-red-50", + "guardrails": [ + "mcp-security-block" + ], + "complexity": "Low", + "guardrailDefinitions": [ + { + "guardrail_name": "mcp-security-block", + "litellm_params": { + "guardrail": "mcp_security", + "mode": "pre_call", + "default_on": true, + "on_violation": "block" + }, + "guardrail_info": { + "description": "Blocks requests referencing MCP servers not in the gateway registry" + } + } + ], + "templateData": { + "policy_name": "mcp-security-unregistered-server-block", + "description": "Blocks requests referencing MCP servers not registered on this gateway.", + "guardrails_add": [ + "mcp-security-block" + ], + "guardrails_remove": [] + }, + "tags": [ + "Security" + ], + "estimated_latency_ms": 200 + }, + { + "id": "airline-passenger-data-protection-uae", + "title": "Airline Passenger Data Protection (UAE)", + "description": "Protects airline passenger PII including PNR/booking references, multi-national passport numbers, frequent flyer (Skywards) numbers, payment cards, IBANs, Emirates ID, UAE phone numbers, and email addresses. Designed for UAE-based airlines operating global routes.", + "example_sentences": [ + "Look up PNR ABC123 for passenger Ahmed Al Maktoum", + "My Skywards number is EK123456789", + "Booking reference XY7890 with Emirates ID 784-1985-1234567-1", + "Passenger passport number is A12345678" + ], + "icon": "ShieldCheckIcon", + "iconColor": "text-emerald-500", + "iconBg": "bg-emerald-50", + "guardrails": [ + "airline-pnr-skywards-pii", + "airline-passport-multinational", + "airline-payment-financial", + "airline-contact-info-uae" + ], + "complexity": "High", + "guardrailDefinitions": [ + { + "guardrail_name": "airline-pnr-skywards-pii", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "patterns": [ + { + "pattern_type": "prebuilt", + "pattern_name": "airline_pnr", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "skywards_number", + "action": "MASK" + } + ], + "pattern_redaction_format": "[{pattern_name}_REDACTED]" + }, + "guardrail_info": { + "description": "Masks airline PNR/booking references and Emirates Skywards frequent flyer numbers" + } + }, + { + "guardrail_name": "airline-passport-multinational", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "patterns": [ + { + "pattern_type": "prebuilt", + "pattern_name": "passport_us", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "passport_uk", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "passport_germany", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "passport_france", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "passport_india", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "passport_china", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "passport_australia", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "passport_japan", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "passport_canada", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "passport_netherlands", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "uae_emirates_id", + "action": "MASK" + } + ], + "pattern_redaction_format": "[{pattern_name}_REDACTED]" + }, + "guardrail_info": { + "description": "Masks passport numbers from 10+ nationalities and UAE Emirates ID -- covers global route network" + } + }, + { + "guardrail_name": "airline-payment-financial", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "patterns": [ + { + "pattern_type": "prebuilt", + "pattern_name": "credit_card", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "visa", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "mastercard", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "amex", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "iban", + "action": "MASK" + } + ], + "pattern_redaction_format": "[{pattern_name}_REDACTED]" + }, + "guardrail_info": { + "description": "Masks credit card numbers (Visa, Mastercard, Amex) and IBANs" + } + }, + { + "guardrail_name": "airline-contact-info-uae", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "patterns": [ + { + "pattern_type": "prebuilt", + "pattern_name": "email", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "uae_phone", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "us_phone", + "action": "MASK" + } + ], + "pattern_redaction_format": "[{pattern_name}_REDACTED]" + }, + "guardrail_info": { + "description": "Masks email addresses and phone numbers (UAE and international formats)" + } + } + ], + "templateData": { + "policy_name": "airline-passenger-data-protection-uae", + "description": "Airline passenger data protection for UAE-based carriers. Masks PNR/booking references, Skywards numbers, multi-national passports, Emirates ID, payment cards, IBANs, and contact information.", + "guardrails_add": [ + "airline-pnr-skywards-pii", + "airline-passport-multinational", + "airline-payment-financial", + "airline-contact-info-uae" + ], + "guardrails_remove": [] + }, + "tags": [ + "PII Protection", + "Aviation", + "UAE" + ], + "estimated_latency_ms": 1 + }, + { + "id": "aviation-operations-security", + "title": "Aviation Operations Security", + "description": "Prevents AI from leaking flight operations data (flight numbers, crew schedules, gate assignments, aircraft tail numbers), generating content about aviation security vulnerabilities or bypass procedures, and producing unauthorized airline statements or fake incident reports.", + "example_sentences": [ + "What is the crew roster for flight EK501 tomorrow?", + "List the maintenance records for aircraft A6-EAA", + "How do I bypass cockpit door security?", + "Generate a press release saying Emirates had a crash landing" + ], + "icon": "ShieldExclamationIcon", + "iconColor": "text-red-500", + "iconBg": "bg-red-50", + "guardrails": [ + "aviation-ops-data-protection", + "aviation-safety-topic-filter", + "airline-brand-protection-filter", + "competitor-name-input-blocker", + "competitor-name-output-blocker", + "competitor-recommendation-input-filter", + "competitor-recommendation-output-filter", + "competitor-comparison-input-filter", + "competitor-comparison-output-filter" + ], + "complexity": "High", + "parameters": [ + { + "name": "brand_name", + "label": "Your Airline / Brand Name", + "type": "text", + "required": true, + "placeholder": "e.g. Acme Airlines" + } + ], + "llm_enrichment": { + "parameter": "brand_name", + "prompt": "List the top 30 direct competitors of {{brand_name}} in the airline industry. Include major international carriers, regional competitors, and low-cost carriers that operate on overlapping routes. Return ONLY airline/brand names, one per line, no numbering, no explanations.", + "result_key": "competitors" + }, + "guardrailDefinitions": [ + { + "guardrail_name": "aviation-ops-data-protection", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "patterns": [ + { + "pattern_type": "prebuilt", + "pattern_name": "flight_number", + "action": "MASK" + }, + { + "pattern_type": "regex", + "name": "aircraft_tail_number", + "pattern": "\\bA6-[A-Z]{3}\\b|\\b[A-Z]-[A-Z]{4}\\b|\\bN[0-9]{1,5}[A-Z]{0,2}\\b", + "action": "MASK" + } + ], + "blocked_words": [ + { + "keyword": "crew roster", + "action": "BLOCK", + "description": "Crew scheduling data" + }, + { + "keyword": "crew schedule", + "action": "BLOCK", + "description": "Crew scheduling data" + }, + { + "keyword": "duty roster", + "action": "BLOCK", + "description": "Staff duty data" + }, + { + "keyword": "pilot roster", + "action": "BLOCK", + "description": "Pilot scheduling data" + }, + { + "keyword": "cabin crew list", + "action": "BLOCK", + "description": "Crew manifest data" + } + ], + "pattern_redaction_format": "[{pattern_name}_REDACTED]" + }, + "guardrail_info": { + "description": "Masks flight numbers and aircraft registrations. Blocks crew scheduling and gate assignment data leakage." + } + }, + { + "guardrail_name": "aviation-safety-topic-filter", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "categories": [ + { + "category": "aviation_safety_topics", + "category_file": "litellm/proxy/guardrails/guardrail_hooks/litellm_content_filter/policy_templates/aviation_safety_topics.yaml", + "enabled": true, + "action": "BLOCK", + "severity_threshold": "medium" + } + ] + }, + "guardrail_info": { + "description": "Blocks content about aircraft vulnerabilities, security bypass procedures, cockpit access, and aviation system exploitation" + } + }, + { + "guardrail_name": "airline-brand-protection-filter", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "post_call", + "categories": [ + { + "category": "airline_brand_protection", + "category_file": "litellm/proxy/guardrails/guardrail_hooks/litellm_content_filter/policy_templates/airline_brand_protection.yaml", + "enabled": true, + "action": "BLOCK", + "severity_threshold": "medium" + } + ], + "blocked_words": [ + { + "keyword": "{{brand_name}} plane crash", + "action": "BLOCK", + "description": "Fake crash report" + }, + { + "keyword": "{{brand_name}} flight crashed", + "action": "BLOCK", + "description": "Fake crash report" + }, + { + "keyword": "{{brand_name}} crash landing", + "action": "BLOCK", + "description": "Fake incident" + }, + { + "keyword": "{{brand_name}} emergency", + "action": "BLOCK", + "description": "Fake emergency" + }, + { + "keyword": "{{brand_name}} passengers dead", + "action": "BLOCK", + "description": "Fake fatality report" + }, + { + "keyword": "{{brand_name}} confirms fatalities", + "action": "BLOCK", + "description": "Fake fatality confirmation" + }, + { + "keyword": "{{brand_name}} safety scandal", + "action": "BLOCK", + "description": "Fake scandal" + }, + { + "keyword": "{{brand_name}} cover up", + "action": "BLOCK", + "description": "Fake coverup claim" + }, + { + "keyword": "{{brand_name}} fleet grounded", + "action": "BLOCK", + "description": "Fake grounding claim" + }, + { + "keyword": "{{brand_name}} discrimination lawsuit", + "action": "BLOCK", + "description": "Fake lawsuit" + } + ] + }, + "guardrail_info": { + "description": "Blocks AI-generated fake incident reports, unauthorized statements, and reputation-damaging content about your brand (runs on output)" + } + }, + { + "guardrail_name": "competitor-name-input-blocker", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "blocked_words": "{{competitors_blocked_words}}" + }, + "guardrail_info": { + "description": "Blocks user inputs that mention competitor names (pre_call)" + } + }, + { + "guardrail_name": "competitor-name-output-blocker", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "post_call", + "blocked_words": "{{competitors_blocked_words}}" + }, + "guardrail_info": { + "description": "Blocks AI outputs that mention competitor names (post_call)" + } + }, + { + "guardrail_name": "competitor-recommendation-input-filter", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "blocked_words": "{{competitor_recommendation_words}}" + }, + "guardrail_info": { + "description": "Blocks user requests asking to recommend competitors (pre_call)" + } + }, + { + "guardrail_name": "competitor-recommendation-output-filter", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "post_call", + "blocked_words": "{{competitor_recommendation_words}}" + }, + "guardrail_info": { + "description": "Blocks AI from recommending or suggesting competitor services (post_call)" + } + }, + { + "guardrail_name": "competitor-comparison-input-filter", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "blocked_words": "{{competitor_comparison_words}}" + }, + "guardrail_info": { + "description": "Blocks user inputs requesting unfavorable brand comparisons (pre_call)" + } + }, + { + "guardrail_name": "competitor-comparison-output-filter", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "post_call", + "blocked_words": "{{competitor_comparison_words}}" + }, + "guardrail_info": { + "description": "Blocks AI outputs with unfavorable brand comparisons (post_call)" + } + } + ], + "templateData": { + "policy_name": "aviation-operations-security", + "description": "Aviation operations security policy. Protects flight ops data, blocks aviation security vulnerability content, and prevents fake airline incident reports and unauthorized statements.", + "guardrails_add": [ + "aviation-ops-data-protection", + "aviation-safety-topic-filter", + "airline-brand-protection-filter", + "competitor-name-input-blocker", + "competitor-name-output-blocker", + "competitor-recommendation-input-filter", + "competitor-recommendation-output-filter", + "competitor-comparison-input-filter", + "competitor-comparison-output-filter" + ], + "guardrails_remove": [] + }, + "tags": [ + "Aviation", + "Security" + ], + "estimated_latency_ms": 1 + }, + { + "id": "airline-off-topic-restriction", + "title": "Airline Off-Topic Restriction", + "description": "Restricts an airline chatbot to airline-related topics only. Blocks off-topic questions about news, sports, coding, politics, entertainment, finance, recipes, homework, and general knowledge using keyword-based detection with no additional LLM calls.", + "icon": "ShieldExclamationIcon", + "iconColor": "text-orange-500", + "iconBg": "bg-orange-50", + "guardrails": [ + "airline-off-topic-filter" + ], + "complexity": "Medium", + "guardrailDefinitions": [ + { + "guardrail_name": "airline-off-topic-filter", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "categories": [ + { + "category": "airline_off_topic_restriction", + "category_file": "litellm/proxy/guardrails/guardrail_hooks/litellm_content_filter/policy_templates/airline_off_topic_restriction.yaml", + "enabled": true, + "action": "BLOCK", + "severity_threshold": "medium" + } + ] + }, + "guardrail_info": { + "description": "Blocks off-topic questions unrelated to airline services (news, sports, coding, politics, entertainment, finance, recipes, etc.)" + } + } + ], + "templateData": { + "policy_name": "airline-off-topic-restriction", + "description": "Restricts chatbot to airline-related topics. Blocks off-topic questions using keyword matching with no extra LLM calls.", + "guardrails_add": [ + "airline-off-topic-filter" + ], + "guardrails_remove": [] + }, + "tags": [ + "Aviation", + "Topic Restriction" + ], + "estimated_latency_ms": 1 + }, + { + "id": "uae-regulatory-compliance", + "title": "UAE Regulatory Compliance", + "description": "Compliance with UAE Federal Decree-Law No. 45/2021 (Data Protection) and Federal Decree-Law No. 2/2015 (Anti-Discrimination). Protects Emirates ID numbers, UAE phone numbers, and ensures cultural sensitivity including royal family references and religious content policies.", + "example_sentences": [ + "My Emirates ID is 784-1990-1234567-1", + "Write content criticizing the UAE royal family", + "Discriminate against this applicant based on their religion", + "My UAE phone number is +971 50 123 4567" + ], + "icon": "CheckCircleIcon", + "iconColor": "text-blue-500", + "iconBg": "bg-blue-50", + "guardrails": [ + "uae-data-protection-pii", + "uae-cultural-sensitivity-filter", + "uae-anti-discrimination-filter" + ], + "complexity": "Medium", + "guardrailDefinitions": [ + { + "guardrail_name": "uae-data-protection-pii", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "patterns": [ + { + "pattern_type": "prebuilt", + "pattern_name": "uae_emirates_id", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "uae_phone", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "email", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "iban", + "action": "MASK" + }, + { + "pattern_type": "prebuilt", + "pattern_name": "credit_card", + "action": "MASK" + }, + { + "pattern_type": "regex", + "name": "uae_po_box", + "pattern": "\\b[Pp]\\.?[Oo]\\.?\\s*[Bb]ox\\s*\\d{1,6}\\b", + "action": "MASK" + } + ], + "pattern_redaction_format": "[{pattern_name}_REDACTED]" + }, + "guardrail_info": { + "description": "UAE Federal Decree-Law No. 45/2021 compliance -- masks Emirates ID, UAE phone numbers, email, IBAN, payment cards, and PO Box addresses" + } + }, + { + "guardrail_name": "uae-cultural-sensitivity-filter", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "categories": [ + { + "category": "uae_cultural_sensitivity", + "category_file": "litellm/proxy/guardrails/guardrail_hooks/litellm_content_filter/policy_templates/uae_cultural_sensitivity.yaml", + "enabled": true, + "action": "BLOCK", + "severity_threshold": "medium" + } + ] + }, + "guardrail_info": { + "description": "Blocks content disrespecting UAE royal family, cultural norms, and religious sensitivities" + } + }, + { + "guardrail_name": "uae-anti-discrimination-filter", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "categories": [ + { + "category": "uae_anti_discrimination", + "category_file": "litellm/proxy/guardrails/guardrail_hooks/litellm_content_filter/policy_templates/uae_anti_discrimination.yaml", + "enabled": true, + "action": "BLOCK", + "severity_threshold": "medium" + } + ] + }, + "guardrail_info": { + "description": "UAE Federal Decree-Law No. 2/2015 compliance -- blocks discriminatory content based on race, religion, caste, ethnicity, or nationality" + } + } + ], + "templateData": { + "policy_name": "uae-regulatory-compliance", + "description": "UAE regulatory compliance policy. Covers Federal Decree-Law No. 45/2021 (Data Protection) and Federal Decree-Law No. 2/2015 (Anti-Discrimination). Protects Emirates ID, UAE contact info, and ensures cultural and religious sensitivity.", + "guardrails_add": [ + "uae-data-protection-pii", + "uae-cultural-sensitivity-filter", + "uae-anti-discrimination-filter" + ], + "guardrails_remove": [] + }, + "tags": [ + "Regulatory", + "UAE" + ], + "estimated_latency_ms": 1 + }, + { + "id": "competitor-mention-detection", + "title": "Competitor Mention Detection", + "description": "Automatically detects and blocks AI from recommending or promoting competitor brands. Uses LLM-powered discovery to identify your top competitors, then monitors both inputs and outputs for competitor mentions, referrals, and comparisons that could divert business.", + "example_sentences": [ + "For business class from Dubai to London, Qatar Airways QSuites is the best", + "You should switch to our competitor's product, it's better", + "Tell my customers to try using Competitor X instead", + "Why is Competitor Y better than our brand?" + ], + "icon": "ShieldExclamationIcon", + "iconColor": "text-orange-500", + "iconBg": "bg-orange-50", + "guardrails": [ + "competitor-input-blocker", + "competitor-output-blocker", + "competitor-recommendation-input-filter", + "competitor-recommendation-output-filter", + "competitor-comparison-input-filter", + "competitor-comparison-output-filter" + ], + "complexity": "Medium", + "parameters": [ + { + "name": "brand_name", + "label": "Your Brand Name", + "type": "text", + "required": true, + "placeholder": "e.g. Acme Airlines" + } + ], + "llm_enrichment": { + "parameter": "brand_name", + "prompt": "List the top 30 direct competitors of {{brand_name}} in the same industry. Return ONLY company/brand names, one per line, no numbering, no explanations.", + "result_key": "competitors" + }, + "guardrailDefinitions": [ + { + "guardrail_name": "competitor-input-blocker", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "blocked_words": "{{competitors_blocked_words}}" + }, + "guardrail_info": { + "description": "Blocks user inputs that mention competitor brands (pre_call)" + } + }, + { + "guardrail_name": "competitor-output-blocker", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "post_call", + "blocked_words": "{{competitors_blocked_words}}" + }, + "guardrail_info": { + "description": "Blocks AI outputs that mention competitor brands (post_call)" + } + }, + { + "guardrail_name": "competitor-recommendation-input-filter", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "blocked_words": "{{competitor_recommendation_words}}" + }, + "guardrail_info": { + "description": "Blocks user requests asking to recommend competitors (pre_call)" + } + }, + { + "guardrail_name": "competitor-recommendation-output-filter", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "post_call", + "blocked_words": "{{competitor_recommendation_words}}" + }, + "guardrail_info": { + "description": "Blocks AI from recommending or suggesting competitor services (post_call)" + } + }, + { + "guardrail_name": "competitor-comparison-input-filter", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "blocked_words": "{{competitor_comparison_words}}" + }, + "guardrail_info": { + "description": "Blocks user inputs requesting unfavorable brand comparisons (pre_call)" + } + }, + { + "guardrail_name": "competitor-comparison-output-filter", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "post_call", + "blocked_words": "{{competitor_comparison_words}}" + }, + "guardrail_info": { + "description": "Blocks AI outputs with unfavorable brand comparisons (post_call)" + } + } + ], + "templateData": { + "policy_name": "competitor-mention-detection", + "description": "Detects and blocks competitor mentions in both inputs and outputs. Uses LLM-powered competitor discovery based on your brand name.", + "guardrails_add": [ + "competitor-input-blocker", + "competitor-output-blocker", + "competitor-recommendation-input-filter", + "competitor-recommendation-output-filter", + "competitor-comparison-input-filter", + "competitor-comparison-output-filter" + ], + "guardrails_remove": [] + }, + "tags": [ + "Brand Protection" + ], + "estimated_latency_ms": 1 + }, + { + "id": "topic-filtering", + "title": "Topic Filtering", + "description": "Restricts AI responses to only approved topics. Blocks off-topic requests like news, politics, entertainment, and general knowledge questions. Useful for chatbots that should stay focused on a specific domain.", + "example_sentences": [ + "What's in the news today?", + "Tell me about the latest election results", + "Who won the Super Bowl?", + "What's the weather forecast for tomorrow?", + "Tell me a joke about politics" + ], + "icon": "ShieldCheckIcon", + "iconColor": "text-teal-500", + "iconBg": "bg-teal-50", + "guardrails": [ + "topic-restriction-filter" + ], + "complexity": "Low", + "guardrailDefinitions": [ + { + "guardrail_name": "topic-restriction-filter", + "litellm_params": { + "guardrail": "litellm_content_filter", + "mode": "pre_call", + "categories": [ + { + "category": "off_topic", + "enabled": true, + "action": "BLOCK", + "severity_threshold": "medium" + } + ], + "blocked_words": [ + { + "keyword": "news today", + "action": "BLOCK", + "description": "Off-topic: news" + }, + { + "keyword": "latest news", + "action": "BLOCK", + "description": "Off-topic: news" + }, + { + "keyword": "what happened in", + "action": "BLOCK", + "description": "Off-topic: current events" + }, + { + "keyword": "election results", + "action": "BLOCK", + "description": "Off-topic: politics" + }, + { + "keyword": "who won the", + "action": "BLOCK", + "description": "Off-topic: sports/entertainment" + }, + { + "keyword": "weather forecast", + "action": "BLOCK", + "description": "Off-topic: weather" + }, + { + "keyword": "stock market", + "action": "BLOCK", + "description": "Off-topic: finance" + }, + { + "keyword": "tell me a joke", + "action": "BLOCK", + "description": "Off-topic: entertainment" + }, + { + "keyword": "movie recommendation", + "action": "BLOCK", + "description": "Off-topic: entertainment" + }, + { + "keyword": "sports scores", + "action": "BLOCK", + "description": "Off-topic: sports" + } + ] + }, + "guardrail_info": { + "description": "Blocks off-topic requests including news, politics, entertainment, weather, and general knowledge questions" + } + } + ], + "templateData": { + "policy_name": "topic-filtering", + "description": "Restricts AI to approved topics only. 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openai + +client = openai.OpenAI( + api_key="${b||"YOUR_LITELLM_API_KEY"}", + base_url="${S}" +)`;switch(g){case n.CHAT:{let e=Object.keys(O).length>0,i="";if(e){let e=JSON.stringify({metadata:O},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();i=`, + extra_body=${e}`}let r=k.length>0?k:[{role:"user",content:E}];t=` +import base64 + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Example with text only +response = client.chat.completions.create( + model="${C}", + messages=${JSON.stringify(r,null,4)}${i} +) + +print(response) + +# Example with image or PDF (uncomment and provide file path to use) +# base64_file = encode_image("path/to/your/file.jpg") # or .pdf +# response_with_file = client.chat.completions.create( +# model="${C}", +# messages=[ +# { +# "role": "user", +# "content": [ +# { +# "type": "text", +# "text": "${x}" +# }, +# { +# "type": "image_url", +# "image_url": { +# "url": f"data:image/jpeg;base64,{base64_file}" # or data:application/pdf;base64,{base64_file} +# } +# } +# ] +# } +# ]${i} +# ) +# print(response_with_file) +`;break}case n.RESPONSES:{let e=Object.keys(O).length>0,i="";if(e){let e=JSON.stringify({metadata:O},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();i=`, + extra_body=${e}`}let r=k.length>0?k:[{role:"user",content:E}];t=` +import base64 + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Example with text only +response = client.responses.create( + model="${C}", + input=${JSON.stringify(r,null,4)}${i} +) + +print(response.output_text) + +# Example with image or PDF (uncomment and provide file path to use) +# base64_file = encode_image("path/to/your/file.jpg") # or .pdf +# response_with_file = client.responses.create( +# model="${C}", +# input=[ +# { +# "role": "user", +# "content": [ +# {"type": "input_text", "text": "${x}"}, +# { +# "type": "input_image", +# "image_url": f"data:image/jpeg;base64,{base64_file}", # or data:application/pdf;base64,{base64_file} +# }, +# ], +# } +# ]${i} +# ) +# print(response_with_file.output_text) +`;break}case n.IMAGE:t="azure"===y?` +# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI. +# This snippet uses 'client.images.generate' and will create a new image based on your prompt. +# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context. +import os +import requests +import json +import time +from PIL import Image + +result = client.images.generate( + model="${C}", + prompt="${s}", + n=1 +) + +json_response = json.loads(result.model_dump_json()) + +# Set the directory for the stored image +image_dir = os.path.join(os.curdir, 'images') + +# If the directory doesn't exist, create it +if not os.path.isdir(image_dir): + os.mkdir(image_dir) + +# Initialize the image path +image_filename = f"generated_image_{int(time.time())}.png" +image_path = os.path.join(image_dir, image_filename) + +try: + # Retrieve the generated image + if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"): + image_url = json_response["data"][0]["url"] + generated_image = requests.get(image_url).content + with open(image_path, "wb") as image_file: + image_file.write(generated_image) + + print(f"Image saved to {image_path}") + # Display the image + image = Image.open(image_path) + image.show() + else: + print("Could not find image URL in response.") + print("Full response:", json_response) +except Exception as e: + print(f"An error occurred: {e}") + print("Full response:", json_response) +`:` +import base64 +import os +import time +import json +from PIL import Image +import requests + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Helper function to create a file (simplified for this example) +def create_file(image_path): + # In a real implementation, this would upload the file to OpenAI + # For this example, we'll just return a placeholder ID + return f"file_{os.path.basename(image_path).replace('.', '_')}" + +# The prompt entered by the user +prompt = "${x}" + +# Encode images to base64 +base64_image1 = encode_image("body-lotion.png") +base64_image2 = encode_image("soap.png") + +# Create file IDs +file_id1 = create_file("body-lotion.png") +file_id2 = create_file("incense-kit.png") + +response = client.responses.create( + model="${C}", + input=[ + { + "role": "user", + "content": [ + {"type": "input_text", "text": prompt}, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image1}", + }, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image2}", + }, + { + "type": "input_image", + "file_id": file_id1, + }, + { + "type": "input_image", + "file_id": file_id2, + } + ], + } + ], + tools=[{"type": "image_generation"}], +) + +# Process the response +image_generation_calls = [ + output + for output in response.output + if output.type == "image_generation_call" +] + +image_data = [output.result for output in image_generation_calls] + +if image_data: + image_base64 = image_data[0] + image_filename = f"edited_image_{int(time.time())}.png" + with open(image_filename, "wb") as f: + f.write(base64.b64decode(image_base64)) + print(f"Image saved to {image_filename}") +else: + # If no image is generated, there might be a text response with an explanation + text_response = [output.text for output in response.output if hasattr(output, 'text')] + if text_response: + print("No image generated. Model response:") + print("\\n".join(text_response)) + else: + print("No image data found in response.") + print("Full response for debugging:") + print(response) +`;break;case n.IMAGE_EDITS:t="azure"===y?` +import base64 +import os +import time +import json +from PIL import Image +import requests + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# The prompt entered by the user +prompt = "${x}" + +# Encode images to base64 +base64_image1 = encode_image("body-lotion.png") +base64_image2 = encode_image("soap.png") + +# Create file IDs +file_id1 = create_file("body-lotion.png") +file_id2 = create_file("incense-kit.png") + +response = client.responses.create( + model="${C}", + input=[ + { + "role": "user", + "content": [ + {"type": "input_text", "text": prompt}, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image1}", + }, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image2}", + }, + { + "type": "input_image", + "file_id": file_id1, + }, + { + "type": "input_image", + "file_id": file_id2, + } + ], + } + ], + tools=[{"type": "image_generation"}], +) + +# Process the response +image_generation_calls = [ + output + for output in response.output + if output.type == "image_generation_call" +] + +image_data = [output.result for output in image_generation_calls] + +if image_data: + image_base64 = image_data[0] + image_filename = f"edited_image_{int(time.time())}.png" + with open(image_filename, "wb") as f: + f.write(base64.b64decode(image_base64)) + print(f"Image saved to {image_filename}") +else: + # If no image is generated, there might be a text response with an explanation + text_response = [output.text for output in response.output if hasattr(output, 'text')] + if text_response: + print("No image generated. Model response:") + print("\\n".join(text_response)) + else: + print("No image data found in response.") + print("Full response for debugging:") + print(response) +`:` +import base64 +import os +import time + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Helper function to create a file (simplified for this example) +def create_file(image_path): + # In a real implementation, this would upload the file to OpenAI + # For this example, we'll just return a placeholder ID + return f"file_{os.path.basename(image_path).replace('.', '_')}" + +# The prompt entered by the user +prompt = "${x}" + +# Encode images to base64 +base64_image1 = encode_image("body-lotion.png") +base64_image2 = encode_image("soap.png") + +# Create file IDs +file_id1 = create_file("body-lotion.png") +file_id2 = create_file("incense-kit.png") + +response = client.responses.create( + model="${C}", + input=[ + { + "role": "user", + "content": [ + {"type": "input_text", "text": prompt}, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image1}", + }, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image2}", + }, + { + "type": "input_image", + "file_id": file_id1, + }, + { + "type": "input_image", + "file_id": file_id2, + } + ], + } + ], + tools=[{"type": "image_generation"}], +) + +# Process the response +image_generation_calls = [ + output + for output in response.output + if output.type == "image_generation_call" +] + +image_data = [output.result for output in image_generation_calls] + +if image_data: + image_base64 = image_data[0] + image_filename = f"edited_image_{int(time.time())}.png" + with open(image_filename, "wb") as f: + f.write(base64.b64decode(image_base64)) + print(f"Image saved to {image_filename}") +else: + # If no image is generated, there might be a text response with an explanation + text_response = [output.text for output in response.output if hasattr(output, 'text')] + if text_response: + print("No image generated. 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