diff --git a/.circleci/config.yml b/.circleci/config.yml index 12e3cb1f6b..d4943b59c9 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -42,7 +42,7 @@ commands: "pydantic==2.11.0" "mcp==1.25.0" "requests-mock>=1.12.1" \ "responses==0.25.7" "pytest-xdist==3.6.1" "pytest-timeout==2.2.0" \ "pytest-cov==5.0.0" "semantic_router==0.1.10" "fastapi-offline==1.7.3" \ - "a2a" + "a2a" "parameterized>=0.9.0" - setup_litellm_enterprise_pip - save_cache: paths: @@ -406,119 +406,6 @@ jobs: # Store test results - store_test_results: path: test-results - caching_unit_tests: - docker: - - image: cimg/python:3.11 - auth: - username: ${DOCKERHUB_USERNAME} - password: ${DOCKERHUB_PASSWORD} - resource_class: large - working_directory: ~/project - parallelism: 2 - - steps: - - checkout - - setup_google_dns - - run: - name: DNS lookup for Redis host - command: | - sudo apt-get update - sudo apt-get install -y dnsutils - dig redis-19899.c239.us-east-1-2.ec2.redns.redis-cloud.com +short - - run: - name: Show git commit hash - command: | - echo "Git commit hash: $CIRCLE_SHA1" - - - restore_cache: - keys: - - v2-caching-deps-{{ checksum ".circleci/requirements.txt" }} - - v2-caching-deps- - - run: - name: Install Dependencies - command: | - python -m pip install --upgrade pip - python -m pip install -r .circleci/requirements.txt - pip install "pytest==7.3.1" - pip install "pytest-retry==1.6.3" - pip install "pytest-asyncio==0.21.1" - pip install "pytest-cov==5.0.0" - pip install "mypy==1.18.2" - pip install "google-generativeai==0.3.2" - pip install "google-cloud-aiplatform==1.43.0" - pip install pyarrow - pip install "boto3==1.36.0" - pip install "aioboto3==13.4.0" - pip install langchain - pip install lunary==0.2.5 - pip install "azure-identity==1.16.1" - pip install "langfuse==2.59.7" - pip install "logfire==0.29.0" - pip install numpydoc - pip install traceloop-sdk==0.21.1 - pip install opentelemetry-api==1.25.0 - pip install opentelemetry-sdk==1.25.0 - pip install opentelemetry-exporter-otlp==1.25.0 - pip install openai==1.100.1 - pip install prisma==0.11.0 - pip install "detect_secrets==1.5.0" - pip install "httpx==0.24.1" - pip install "respx==0.22.0" - pip install fastapi - pip install "gunicorn==21.2.0" - pip install "anyio==4.2.0" - pip install "aiodynamo==23.10.1" - pip install "asyncio==3.4.3" - pip install "apscheduler==3.10.4" - pip install "PyGithub==1.59.1" - pip install argon2-cffi - pip install "pytest-mock==3.12.0" - pip install python-multipart - pip install google-cloud-aiplatform - pip install prometheus-client==0.20.0 - pip install "pydantic==2.10.2" - pip install "diskcache==5.6.1" - pip install "Pillow==10.3.0" - pip install "jsonschema==4.22.0" - pip install "websockets==13.1.0" - pip install "pytest-xdist==3.6.1" - - setup_litellm_enterprise_pip - - save_cache: - paths: - - /home/circleci/.pyenv/versions - - /home/circleci/.local - key: v2-caching-deps-{{ checksum ".circleci/requirements.txt" }} - - run: - name: Run prisma ./docker/entrypoint.sh - command: | - set +e - chmod +x docker/entrypoint.sh - ./docker/entrypoint.sh - set -e - - # Run pytest and generate JUnit XML report - - run: - name: Run tests - command: | - pwd - ls - mkdir -p test-results - - TEST_FILES=$(circleci tests glob "tests/local_testing/**/test_*.py") - - echo "$TEST_FILES" | circleci tests run \ - --split-by=timings \ - --verbose \ - --command="xargs python -m pytest \ - -v \ - --junitxml=test-results/junit.xml \ - --durations=5 \ - -k 'caching or cache'" - no_output_timeout: 15m - - # Store test results - - store_test_results: - path: test-results auth_ui_unit_tests: docker: - image: cimg/python:3.11 @@ -664,376 +551,6 @@ jobs: # Store test results - store_test_results: path: test-results - litellm_security_tests: - docker: - - image: cimg/python:3.13 - auth: - username: ${DOCKERHUB_USERNAME} - password: ${DOCKERHUB_PASSWORD} - - image: cimg/postgres:14.0 - environment: - POSTGRES_USER: postgres - POSTGRES_PASSWORD: postgres - POSTGRES_DB: circle_test - resource_class: xlarge - working_directory: ~/project - environment: - DATABASE_URL: "postgresql://postgres:postgres@localhost:5432/circle_test" - steps: - - checkout - - setup_google_dns - - run: - name: Show git commit hash - command: | - echo "Git commit hash: $CIRCLE_SHA1" - - setup_remote_docker: - docker_layer_caching: true - - restore_cache: - keys: - - v3-litellm-uv-deps-{{ checksum "requirements.txt" }}-{{ checksum ".circleci/config.yml" }} - - run: - name: Install Dependencies - command: | - python -m pip install --upgrade pip uv - uv pip install --system -r requirements.txt - pip install "pytest==7.3.1" "pytest-retry==1.6.3" "pytest-mock==3.12.0" \ - "pytest-asyncio==0.21.1" "pytest-cov==5.0.0" - - save_cache: - paths: - - ~/.local/lib - - ~/.local/bin - - ~/.cache/uv - key: v3-litellm-uv-deps-{{ checksum "requirements.txt" }}-{{ checksum ".circleci/config.yml" }} - - run: - name: Install dockerize - command: | - wget https://github.com/jwilder/dockerize/releases/download/v0.6.1/dockerize-linux-amd64-v0.6.1.tar.gz - sudo tar -C /usr/local/bin -xzvf dockerize-linux-amd64-v0.6.1.tar.gz - rm dockerize-linux-amd64-v0.6.1.tar.gz - - run: - name: Wait for PostgreSQL to be ready - command: dockerize -wait tcp://localhost:5432 -timeout 1m - - run: - name: Run Security Scans - command: | - chmod +x ci_cd/security_scans.sh - ./ci_cd/security_scans.sh - - run: - name: Run prisma ./docker/entrypoint.sh - command: | - set +e - chmod +x docker/entrypoint.sh - ./docker/entrypoint.sh - set -e - # Run pytest and generate JUnit XML report - - run: - name: Run tests - command: | - python -m pytest tests/proxy_security_tests -v -x --junitxml=test-results/junit.xml --durations=5 - no_output_timeout: 15m - # Store test results - - store_test_results: - path: test-results - # Split proxy unit tests into 3 jobs for faster execution and better debugging - # test_key_generate_prisma runs separately without parallel execution to avoid event loop issues with logging worker - litellm_proxy_unit_testing_key_generation: - docker: - - image: cimg/python:3.11 - auth: - username: ${DOCKERHUB_USERNAME} - password: ${DOCKERHUB_PASSWORD} - working_directory: ~/project - resource_class: medium - steps: - - checkout - - setup_google_dns - - run: - name: Show git commit hash - command: | - echo "Git commit hash: $CIRCLE_SHA1" - - run: - name: Install PostgreSQL - command: | - sudo apt-get update - sudo apt-get install -y postgresql-14 postgresql-contrib-14 - - restore_cache: - keys: - - v1-dependencies-{{ checksum ".circleci/requirements.txt" }} - - run: - name: Install Dependencies - command: | - python -m pip install --upgrade pip - python -m pip install -r .circleci/requirements.txt - pip install "pytest==7.3.1" - pip install "pytest-retry==1.6.3" - pip install "pytest-asyncio==0.21.1" - pip install "pytest-cov==5.0.0" - pip install "pytest-timeout==2.2.0" - pip install "pytest-forked==1.6.0" - pip install "mypy==1.18.2" - pip install "google-generativeai==0.3.2" - pip install "google-cloud-aiplatform==1.43.0" - pip install "google-genai==1.22.0" - pip install pyarrow - pip install "boto3==1.36.0" - pip install "aioboto3==13.4.0" - pip install langchain - pip install lunary==0.2.5 - pip install "azure-identity==1.16.1" - pip install "langfuse==2.59.7" - pip install "logfire==0.29.0" - pip install numpydoc - pip install traceloop-sdk==0.21.1 - pip install opentelemetry-api==1.25.0 - pip install opentelemetry-sdk==1.25.0 - pip install opentelemetry-exporter-otlp==1.25.0 - pip install openai==1.100.1 - pip install prisma==0.11.0 - pip install "detect_secrets==1.5.0" - pip install "httpx==0.24.1" - pip install "respx==0.22.0" - pip install fastapi - pip install "gunicorn==21.2.0" - pip install "anyio==4.2.0" - pip install "aiodynamo==23.10.1" - pip install "asyncio==3.4.3" - pip install "apscheduler==3.10.4" - pip install "PyGithub==1.59.1" - pip install argon2-cffi - pip install "pytest-mock==3.12.0" - pip install python-multipart - pip install google-cloud-aiplatform - pip install prometheus-client==0.20.0 - pip install "pydantic==2.10.2" - pip install "diskcache==5.6.1" - pip install "Pillow==10.3.0" - pip install "jsonschema==4.22.0" - pip install "pytest-postgresql==7.0.1" - pip install "fakeredis==2.28.1" - - setup_litellm_enterprise_pip - - save_cache: - paths: - - ./venv - key: v1-dependencies-{{ checksum ".circleci/requirements.txt" }} - - run: - name: Run prisma ./docker/entrypoint.sh - command: | - set +e - chmod +x docker/entrypoint.sh - ./docker/entrypoint.sh - set -e - - run: - name: Run key generation tests (no parallel execution to avoid event loop issues) - command: | - pwd - ls - # Run without -n flag to avoid pytest-xdist event loop conflicts with logging worker - python -m pytest tests/proxy_unit_tests/test_key_generate_prisma.py --cov=litellm --cov-report=xml --junitxml=test-results/junit-key-generation.xml --durations=10 --timeout=300 -vv --log-cli-level=INFO - no_output_timeout: 15m - - run: - name: Rename the coverage files - command: | - mv coverage.xml litellm_proxy_unit_tests_key_generation_coverage.xml - mv .coverage litellm_proxy_unit_tests_key_generation_coverage - - store_test_results: - path: test-results - - persist_to_workspace: - root: . - paths: - - litellm_proxy_unit_tests_key_generation_coverage.xml - - litellm_proxy_unit_tests_key_generation_coverage - litellm_proxy_unit_testing_part1: - docker: - - image: cimg/python:3.11 - auth: - username: ${DOCKERHUB_USERNAME} - password: ${DOCKERHUB_PASSWORD} - working_directory: ~/project - resource_class: xlarge - steps: - - checkout - - setup_google_dns - - run: - name: Show git commit hash - command: | - echo "Git commit hash: $CIRCLE_SHA1" - - run: - name: Install PostgreSQL - command: | - sudo apt-get update - sudo apt-get install -y postgresql-14 postgresql-contrib-14 - - restore_cache: - keys: - - v1-dependencies-{{ checksum ".circleci/requirements.txt" }} - - run: - name: Install Dependencies - command: | - python -m pip install --upgrade pip - python -m pip install -r .circleci/requirements.txt - pip install "pytest==7.3.1" - pip install "pytest-retry==1.6.3" - pip install "pytest-asyncio==0.21.1" - pip install "pytest-cov==5.0.0" - pip install "pytest-timeout==2.2.0" - pip install "pytest-forked==1.6.0" - pip install "mypy==1.18.2" - pip install "google-generativeai==0.3.2" - pip install "google-cloud-aiplatform==1.43.0" - pip install "google-genai==1.22.0" - pip install pyarrow - pip install "boto3==1.36.0" - pip install "aioboto3==13.4.0" - pip install langchain - pip install lunary==0.2.5 - pip install "azure-identity==1.16.1" - pip install "langfuse==2.59.7" - pip install "logfire==0.29.0" - pip install numpydoc - pip install traceloop-sdk==0.21.1 - pip install opentelemetry-api==1.25.0 - pip install opentelemetry-sdk==1.25.0 - pip install opentelemetry-exporter-otlp==1.25.0 - pip install openai==1.100.1 - pip install prisma==0.11.0 - pip install "detect_secrets==1.5.0" - pip install "httpx==0.24.1" - pip install "respx==0.22.0" - pip install fastapi - pip install "gunicorn==21.2.0" - pip install "anyio==4.2.0" - pip install "aiodynamo==23.10.1" - pip install "asyncio==3.4.3" - pip install "apscheduler==3.10.4" - pip install "PyGithub==1.59.1" - pip install argon2-cffi - pip install "pytest-mock==3.12.0" - pip install python-multipart - pip install google-cloud-aiplatform - pip install prometheus-client==0.20.0 - pip install "pydantic==2.10.2" - pip install "diskcache==5.6.1" - pip install "Pillow==10.3.0" - pip install "jsonschema==4.22.0" - pip install "pytest-postgresql==7.0.1" - pip install "fakeredis==2.28.1" - pip install "pytest-xdist==3.6.1" - - setup_litellm_enterprise_pip - - save_cache: - paths: - - ./venv - key: v1-dependencies-{{ checksum ".circleci/requirements.txt" }} - - run: - name: Run prisma ./docker/entrypoint.sh - command: | - set +e - chmod +x docker/entrypoint.sh - ./docker/entrypoint.sh - set -e - - run: - name: Run proxy unit tests (part 1 - auth checks) - command: | - pwd - ls - python -m pytest tests/proxy_unit_tests/test_auth_checks.py tests/proxy_unit_tests/test_user_api_key_auth.py --junitxml=test-results/junit-part1.xml --durations=10 -n 8 --timeout=300 -v - no_output_timeout: 15m - - store_test_results: - path: test-results - litellm_proxy_unit_testing_part2: - docker: - - image: cimg/python:3.11 - auth: - username: ${DOCKERHUB_USERNAME} - password: ${DOCKERHUB_PASSWORD} - working_directory: ~/project - resource_class: xlarge - steps: - - checkout - - setup_google_dns - - run: - name: Show git commit hash - command: | - echo "Git commit hash: $CIRCLE_SHA1" - - run: - name: Install PostgreSQL - command: | - sudo apt-get update - sudo apt-get install -y postgresql-14 postgresql-contrib-14 - - restore_cache: - keys: - - v1-dependencies-{{ checksum ".circleci/requirements.txt" }} - - run: - name: Install Dependencies - command: | - python -m pip install --upgrade pip - python -m pip install -r .circleci/requirements.txt - pip install "pytest==7.3.1" - pip install "pytest-retry==1.6.3" - pip install "pytest-asyncio==0.21.1" - pip install "pytest-cov==5.0.0" - pip install "pytest-timeout==2.2.0" - pip install "pytest-forked==1.6.0" - pip install "mypy==1.18.2" - pip install "google-generativeai==0.3.2" - pip install "google-cloud-aiplatform==1.43.0" - pip install "google-genai==1.22.0" - pip install pyarrow - pip install "boto3==1.36.0" - pip install "aioboto3==13.4.0" - pip install langchain - pip install lunary==0.2.5 - pip install "azure-identity==1.16.1" - pip install "langfuse==2.59.7" - pip install "logfire==0.29.0" - pip install numpydoc - pip install traceloop-sdk==0.21.1 - pip install opentelemetry-api==1.25.0 - pip install opentelemetry-sdk==1.25.0 - pip install opentelemetry-exporter-otlp==1.25.0 - pip install openai==1.100.1 - pip install prisma==0.11.0 - pip install "detect_secrets==1.5.0" - pip install "httpx==0.24.1" - pip install "respx==0.22.0" - pip install fastapi - pip install "gunicorn==21.2.0" - pip install "anyio==4.2.0" - pip install "aiodynamo==23.10.1" - pip install "asyncio==3.4.3" - pip install "apscheduler==3.10.4" - pip install "PyGithub==1.59.1" - pip install argon2-cffi - pip install "pytest-mock==3.12.0" - pip install python-multipart - pip install google-cloud-aiplatform - pip install prometheus-client==0.20.0 - pip install "pydantic==2.10.2" - pip install "diskcache==5.6.1" - pip install "Pillow==10.3.0" - pip install "jsonschema==4.22.0" - pip install "pytest-postgresql==7.0.1" - pip install "fakeredis==2.28.1" - pip install "pytest-xdist==3.6.1" - - setup_litellm_enterprise_pip - - save_cache: - paths: - - ./venv - key: v1-dependencies-{{ checksum ".circleci/requirements.txt" }} - - run: - name: Run prisma ./docker/entrypoint.sh - command: | - set +e - chmod +x docker/entrypoint.sh - ./docker/entrypoint.sh - set -e - - run: - name: Run proxy unit tests (part 2 - remaining tests) - command: | - pwd - ls - python -m pytest tests/proxy_unit_tests --ignore=tests/proxy_unit_tests/test_key_generate_prisma.py --ignore=tests/proxy_unit_tests/test_auth_checks.py --ignore=tests/proxy_unit_tests/test_user_api_key_auth.py --junitxml=test-results/junit-part2.xml --durations=10 -n 8 --timeout=300 -v - no_output_timeout: 15m - - store_test_results: - path: test-results litellm_assistants_api_testing: # Runs all tests with the "assistants" keyword docker: - image: cimg/python:3.13.1 @@ -1115,7 +632,7 @@ jobs: for dir in "${IGNORE_DIRS[@]}"; do IGNORE_ARGS="$IGNORE_ARGS --ignore=$dir" done - python -m pytest -v tests/llm_translation $IGNORE_ARGS --junitxml=test-results/junit.xml --durations=20 -n 8 --timeout=120 --timeout_method=thread + python -m pytest -v tests/llm_translation $IGNORE_ARGS --junitxml=test-results/junit.xml --durations=20 -n 8 --timeout=120 --timeout_method=thread --retries 2 --retry-delay 5 no_output_timeout: 15m # Store test results @@ -1331,7 +848,7 @@ jobs: command: | pwd ls - python -m pytest -vv tests/unified_google_tests --cov=litellm --cov-report=xml -x -s -v --junitxml=test-results/junit.xml --durations=5 + python -m pytest -vv tests/unified_google_tests --cov=litellm --cov-report=xml -x -s -v --junitxml=test-results/junit.xml --durations=5 --retries 3 --retry-delay 5 no_output_timeout: 15m - run: name: Rename the coverage files @@ -1507,101 +1024,6 @@ jobs: no_output_timeout: 15m - store_test_results: path: test-results - litellm_mapped_tests_llms: - docker: - - image: cimg/python:3.11 - auth: - username: ${DOCKERHUB_USERNAME} - password: ${DOCKERHUB_PASSWORD} - working_directory: ~/project - resource_class: large - steps: - - setup_litellm_test_deps - - run: - name: Run LLM provider tests - command: | - python -m pytest tests/test_litellm/llms --junitxml=test-results/junit-llms.xml --durations=10 -n 4 --maxfail=5 --timeout=300 -vv --log-cli-level=WARNING - no_output_timeout: 15m - - store_test_results: - path: test-results - litellm_mapped_tests_core: - docker: - - image: cimg/python:3.11 - auth: - username: ${DOCKERHUB_USERNAME} - password: ${DOCKERHUB_PASSWORD} - working_directory: ~/project - resource_class: large - steps: - - setup_litellm_test_deps - - 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 --ignore=tests/test_litellm/experimental_mcp_client --junitxml=test-results/junit-core.xml --durations=10 -n 4 --maxfail=5 --timeout=300 -vv --log-cli-level=WARNING - no_output_timeout: 15m - - store_test_results: - path: test-results - litellm_mapped_tests_litellm_core_utils: - docker: - - image: cimg/python:3.11 - auth: - username: ${DOCKERHUB_USERNAME} - password: ${DOCKERHUB_PASSWORD} - working_directory: ~/project - resource_class: large - steps: - - setup_litellm_test_deps - - run: - name: Run litellm_core_utils tests - command: | - python -m pytest tests/test_litellm/litellm_core_utils --junitxml=test-results/junit-litellm-core-utils.xml --durations=10 -n 4 --maxfail=5 --timeout=300 -vv --log-cli-level=WARNING - no_output_timeout: 15m - - store_test_results: - path: test-results - litellm_mapped_tests_mcps: - docker: - - image: cimg/python:3.11 - auth: - username: ${DOCKERHUB_USERNAME} - password: ${DOCKERHUB_PASSWORD} - working_directory: ~/project - resource_class: medium - 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 2 --maxfail=5 --timeout=300 -vv --log-cli-level=WARNING - no_output_timeout: 15m - - 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 - auth: - username: ${DOCKERHUB_USERNAME} - password: ${DOCKERHUB_PASSWORD} - working_directory: ~/project - resource_class: large - steps: - - setup_litellm_test_deps - - run: - name: Run integrations tests - command: | - python -m pytest tests/test_litellm/integrations --junitxml=test-results/junit-integrations.xml --durations=10 -n 4 --maxfail=5 --timeout=300 -vv --log-cli-level=WARNING - no_output_timeout: 15m - - store_test_results: - path: test-results litellm_mapped_enterprise_tests: docker: - image: cimg/python:3.11 @@ -2137,6 +1559,25 @@ jobs: pip install "pytest-asyncio==0.21.1" pip install aiohttp pip install apscheduler + - run: + name: Install dockerize + command: | + sudo wget https://github.com/jwilder/dockerize/releases/download/v0.6.1/dockerize-linux-amd64-v0.6.1.tar.gz + sudo tar -C /usr/local/bin -xzvf dockerize-linux-amd64-v0.6.1.tar.gz + sudo rm dockerize-linux-amd64-v0.6.1.tar.gz + - run: + name: Start PostgreSQL Database + command: | + docker run -d \ + --name postgres-db \ + -e POSTGRES_USER=postgres \ + -e POSTGRES_PASSWORD=postgres \ + -e POSTGRES_DB=litellm_test \ + -p 5432:5432 \ + postgres:14 + - run: + name: Wait for PostgreSQL to be ready + command: dockerize -wait tcp://localhost:5432 -timeout 1m - attach_workspace: at: ~/project - run: @@ -2145,29 +1586,41 @@ jobs: zstd -d litellm-docker-database.tar.zst --stdout | docker load docker images | grep litellm-docker-database - run: - name: Run Docker container + name: Seed database with real schema + command: | + docker run -d \ + -p 4001:4000 \ + -e DATABASE_URL="postgresql://postgres:postgres@host.docker.internal:5432/litellm_test" \ + -e LITELLM_MASTER_KEY="sk-1234" \ + --name schema-seed \ + --add-host=host.docker.internal:host-gateway \ + -v $(pwd)/litellm/proxy/example_config_yaml/simple_config.yaml:/app/config.yaml \ + litellm-docker-database:ci \ + --config /app/config.yaml \ + --port 4000 \ + --use_prisma_db_push + - run: + name: Wait for schema seed to complete + command: dockerize -wait http://localhost:4001 -timeout 5m + - run: + name: Stop schema seed container + command: docker stop schema-seed && docker rm schema-seed + - run: + name: Run Docker container with bad schema and disabled updates command: | docker run -d \ -p 4000:4000 \ - -e DATABASE_URL=$PROXY_DATABASE_URL \ + -e DATABASE_URL="postgresql://postgres:postgres@host.docker.internal:5432/litellm_test" \ -e DEFAULT_NUM_WORKERS_LITELLM_PROXY=1 \ -e DISABLE_SCHEMA_UPDATE="True" \ + --name my-app \ + --add-host=host.docker.internal:host-gateway \ -v $(pwd)/litellm/proxy/example_config_yaml/bad_schema.prisma:/app/schema.prisma \ -v $(pwd)/litellm/proxy/example_config_yaml/bad_schema.prisma:/app/litellm/proxy/schema.prisma \ -v $(pwd)/litellm/proxy/example_config_yaml/disable_schema_update.yaml:/app/config.yaml \ - --name my-app \ litellm-docker-database:ci \ --config /app/config.yaml \ --port 4000 - - run: - name: Install curl and dockerize - command: | - sudo apt-get update - sudo apt-get install -y curl - sudo wget https://github.com/jwilder/dockerize/releases/download/v0.6.1/dockerize-linux-amd64-v0.6.1.tar.gz - sudo tar -C /usr/local/bin -xzvf dockerize-linux-amd64-v0.6.1.tar.gz - sudo rm dockerize-linux-amd64-v0.6.1.tar.gz - - run: name: Wait for container to be ready command: dockerize -wait http://localhost:4000 -timeout 1m @@ -2575,9 +2028,6 @@ jobs: -e OPENAI_API_KEY=$OPENAI_API_KEY \ -e LITELLM_LICENSE=$LITELLM_LICENSE \ -e OTEL_EXPORTER="in_memory" \ - -e APORIA_API_BASE_2=$APORIA_API_BASE_2 \ - -e APORIA_API_KEY_2=$APORIA_API_KEY_2 \ - -e APORIA_API_BASE_1=$APORIA_API_BASE_1 \ -e AWS_ACCESS_KEY_ID=$AWS_ACCESS_KEY_ID \ -e AWS_SECRET_ACCESS_KEY=$AWS_SECRET_ACCESS_KEY \ -e DEFAULT_NUM_WORKERS_LITELLM_PROXY=1 \ @@ -2585,7 +2035,6 @@ jobs: -e DD_API_KEY=$DD_API_KEY \ -e DD_SITE=$DD_SITE \ -e AWS_REGION_NAME=$AWS_REGION_NAME \ - -e APORIA_API_KEY_1=$APORIA_API_KEY_1 \ -e COHERE_API_KEY=$COHERE_API_KEY \ -e GCS_FLUSH_INTERVAL="1" \ --add-host host.docker.internal:host-gateway \ @@ -3061,6 +2510,20 @@ jobs: name: Build Docker image command: | docker build -t my-app:latest -f docker/build_from_pip/Dockerfile.build_from_pip . + - run: + name: Start PostgreSQL Database + command: | + docker run -d \ + --name postgres-db \ + -e POSTGRES_USER=postgres \ + -e POSTGRES_PASSWORD=postgres \ + -e POSTGRES_DB=circle_test \ + -p 5432:5432 \ + postgres:14 + - run: + name: Wait for PostgreSQL to be ready + command: | + timeout 60s bash -c 'until docker exec postgres-db pg_isready -U postgres -d circle_test; do sleep 2; done' - run: name: Run Docker container # intentionally give bad redis credentials here @@ -3068,7 +2531,7 @@ jobs: command: | docker run -d \ -p 4000:4000 \ - -e DATABASE_URL=$PROXY_DATABASE_URL \ + -e DATABASE_URL=postgresql://postgres:postgres@host.docker.internal:5432/circle_test \ -e REDIS_HOST=$REDIS_HOST \ -e REDIS_PASSWORD=$REDIS_PASSWORD \ -e REDIS_PORT=$REDIS_PORT \ @@ -3076,18 +2539,15 @@ jobs: -e OPENAI_API_KEY=$OPENAI_API_KEY \ -e LITELLM_LICENSE=$LITELLM_LICENSE \ -e OTEL_EXPORTER="in_memory" \ - -e APORIA_API_BASE_2=$APORIA_API_BASE_2 \ - -e APORIA_API_KEY_2=$APORIA_API_KEY_2 \ - -e APORIA_API_BASE_1=$APORIA_API_BASE_1 \ -e AWS_ACCESS_KEY_ID=$AWS_ACCESS_KEY_ID \ -e AWS_SECRET_ACCESS_KEY=$AWS_SECRET_ACCESS_KEY \ -e AWS_REGION_NAME=$AWS_REGION_NAME \ - -e APORIA_API_KEY_1=$APORIA_API_KEY_1 \ -e COHERE_API_KEY=$COHERE_API_KEY \ -e USE_DDTRACE=True \ -e DD_API_KEY=$DD_API_KEY \ -e DD_SITE=$DD_SITE \ -e GCS_FLUSH_INTERVAL="1" \ + --add-host host.docker.internal:host-gateway \ --name my-app \ -v $(pwd)/docker/build_from_pip/litellm_config.yaml:/app/config.yaml \ my-app:latest \ @@ -3118,8 +2578,11 @@ jobs: - run: name: Stop and remove first container command: | - docker stop my-app - docker rm my-app + docker stop my-app || true + docker rm my-app || true + docker stop postgres-db || true + docker rm postgres-db || true + when: always proxy_pass_through_endpoint_tests: machine: image: ubuntu-2204:2023.10.1 @@ -3543,93 +3006,6 @@ jobs: - codecov/upload: file: ./coverage.xml - publish_to_pypi: - docker: - - image: cimg/python:3.8 - working_directory: ~/project - - environment: - TWINE_USERNAME: __token__ - - steps: - - checkout - - - run: - name: Copy model_prices_and_context_window File to model_prices_and_context_window_backup - command: | - cp model_prices_and_context_window.json litellm/model_prices_and_context_window_backup.json - - - run: - name: Checkout code - command: git checkout $CIRCLE_SHA1 - - # Check if setup.py is modified and publish to PyPI - - run: - name: PyPI publish - command: | - echo "Install TOML package." - python -m pip install toml - VERSION=$(python -c "import toml; print(toml.load('pyproject.toml')['tool']['poetry']['version'])") - PACKAGE_NAME=$(python -c "import toml; print(toml.load('pyproject.toml')['tool']['poetry']['name'])") - if ! pip show -v $PACKAGE_NAME | grep -q "Version: ${VERSION}"; then - echo "pyproject.toml modified" - echo -e "[pypi]\nusername = $PYPI_PUBLISH_USERNAME\npassword = $PYPI_PUBLISH_PASSWORD" > ~/.pypirc - python -m pip install --upgrade pip - pip install build - pip install wheel - pip install --upgrade twine setuptools - rm -rf build dist - - echo "Building package" - python -m build - - echo "Twine upload to dist" - echo "Contents of dist directory:" - ls dist/ - twine upload --verbose dist/* - else - echo "Version ${VERSION} of package is already published on PyPI." - - # Check if corresponding Docker nightly image exists - NIGHTLY_TAG="v${VERSION}-nightly" - echo "Checking for Docker nightly image: litellm/litellm:${NIGHTLY_TAG}" - - # Check Docker Hub for the nightly image - if curl -s "https://hub.docker.com/v2/repositories/litellm/litellm/tags/${NIGHTLY_TAG}" | grep -q "name"; then - echo "Docker nightly image ${NIGHTLY_TAG} exists. This release was already completed successfully." - echo "Skipping PyPI publish and continuing to ensure Docker images are up to date." - circleci step halt - else - echo "ERROR: PyPI package ${VERSION} exists but Docker nightly image ${NIGHTLY_TAG} does not exist!" - echo "This indicates an incomplete release. Please investigate." - exit 1 - fi - fi - - run: - name: Trigger Github Action for new Docker Container + Trigger Load Testing - command: | - echo "Install TOML package." - python3 -m pip install toml - VERSION=$(python3 -c "import toml; print(toml.load('pyproject.toml')['tool']['poetry']['version'])") - echo "LiteLLM Version ${VERSION}" - - # Determine which branch to use for Docker build - if [[ "$CIRCLE_BRANCH" =~ ^litellm_release_day_.* ]]; then - BUILD_BRANCH="$CIRCLE_BRANCH" - echo "Using release branch: $BUILD_BRANCH" - else - BUILD_BRANCH="main" - echo "Using default branch: $BUILD_BRANCH" - fi - - curl -X POST \ - -H "Accept: application/vnd.github.v3+json" \ - -H "Authorization: Bearer $GITHUB_TOKEN" \ - "https://api.github.com/repos/BerriAI/litellm/actions/workflows/ghcr_deploy.yml/dispatches" \ - -d "{\"ref\":\"${BUILD_BRANCH}\", \"inputs\":{\"tag\":\"v${VERSION}-nightly\", \"commit_hash\":\"$CIRCLE_SHA1\"}}" - echo "triggering load testing server for version ${VERSION} and commit ${CIRCLE_SHA1}" - curl -X POST "https://proxyloadtester-production.up.railway.app/start/load/test?version=${VERSION}&commit_hash=${CIRCLE_SHA1}&release_type=nightly" - publish_proxy_extras: docker: - image: cimg/python:3.8 @@ -3942,37 +3318,39 @@ jobs: - setup_google_dns - attach_workspace: at: ~/project + - run: + name: Install dockerize + command: | + sudo wget https://github.com/jwilder/dockerize/releases/download/v0.6.1/dockerize-linux-amd64-v0.6.1.tar.gz + sudo tar -C /usr/local/bin -xzvf dockerize-linux-amd64-v0.6.1.tar.gz + sudo rm dockerize-linux-amd64-v0.6.1.tar.gz + - run: + name: Start PostgreSQL Database + command: | + docker run -d \ + --name postgres-db \ + -e POSTGRES_USER=postgres \ + -e POSTGRES_PASSWORD=postgres \ + -e POSTGRES_DB=litellm_schema_sync \ + -p 5432:5432 \ + postgres:14 + - run: + name: Wait for PostgreSQL to be ready + command: dockerize -wait tcp://localhost:5432 -timeout 1m - run: name: Load Docker Database Image command: | zstd -d litellm-docker-database.tar.zst --stdout | docker load docker images | grep litellm-docker-database - run: - name: Install Neon CLI + name: Run schema sync via prisma db push command: | - npm i -g neonctl - - run: - name: Install curl and dockerize - command: | - sudo apt-get update - sudo apt-get install -y curl - sudo wget https://github.com/jwilder/dockerize/releases/download/v0.6.1/dockerize-linux-amd64-v0.6.1.tar.gz - sudo tar -C /usr/local/bin -xzvf dockerize-linux-amd64-v0.6.1.tar.gz - sudo rm dockerize-linux-amd64-v0.6.1.tar.gz - - run: - name: Sync schema on base e2e database - command: | - BASE_DATABASE_URL=$(neon connection-string \ - --project-id $NEON_PROJECT_ID \ - --api-key $NEON_API_KEY \ - --branch br-fancy-paper-ad1olsb3 \ - --database-name yuneng-trial-db \ - --role neondb_owner) docker run -d \ -p 4000:4000 \ - -e DATABASE_URL=$BASE_DATABASE_URL \ + -e DATABASE_URL="postgresql://postgres:postgres@host.docker.internal:5432/litellm_schema_sync" \ -e LITELLM_MASTER_KEY="sk-1234" \ --name schema-sync \ + --add-host=host.docker.internal:host-gateway \ -v $(pwd)/litellm/proxy/example_config_yaml/simple_config.yaml:/app/config.yaml \ litellm-docker-database:ci \ --config /app/config.yaml \ @@ -4089,34 +3467,14 @@ workflows: only: - main - /litellm_.*/ - - caching_unit_tests: - filters: - branches: - only: - main - /litellm_.*/ - - litellm_proxy_unit_testing_key_generation: - filters: - branches: - only: - main - /litellm_.*/ - - litellm_proxy_unit_testing_part1: - filters: - branches: - only: - main - /litellm_.*/ - - litellm_proxy_unit_testing_part2: - filters: - branches: - only: - main - /litellm_.*/ - - litellm_security_tests: - filters: - branches: - only: - main - /litellm_.*/ - litellm_assistants_api_testing: @@ -4170,7 +3528,6 @@ workflows: - main - /litellm_.*/ - prisma_schema_sync: - context: e2e_ui_tests requires: - build_docker_database_image filters: @@ -4178,32 +3535,32 @@ workflows: only: - main - /litellm_.*/ - - e2e_ui_testing: - name: e2e_ui_testing_chromium - browser: chromium - context: e2e_ui_tests - requires: - - ui_build - - build_docker_database_image - - prisma_schema_sync - filters: - branches: - only: - - main - - /litellm_.*/ - - e2e_ui_testing: - name: e2e_ui_testing_firefox - browser: firefox - context: e2e_ui_tests - requires: - - ui_build - - build_docker_database_image - - prisma_schema_sync - filters: - branches: - only: - - main - - /litellm_.*/ + # - e2e_ui_testing: # migrate to dynamic db - currently requires neon cli + # name: e2e_ui_testing_chromium + # browser: chromium + # context: e2e_ui_tests + # requires: + # - ui_build + # - build_docker_database_image + # - prisma_schema_sync + # filters: + # branches: + # only: + # - main + # - /litellm_.*/ + # - e2e_ui_testing: + # name: e2e_ui_testing_firefox + # browser: firefox + # context: e2e_ui_tests + # requires: + # - ui_build + # - build_docker_database_image + # - prisma_schema_sync + # filters: + # branches: + # only: + # - main + # - /litellm_.*/ - build_and_test: requires: - build_docker_database_image @@ -4352,34 +3709,14 @@ workflows: only: - main - /litellm_.*/ - - litellm_mapped_tests_llms: - filters: - branches: - only: - main - /litellm_.*/ - - litellm_mapped_tests_core: - filters: - branches: - only: - main - /litellm_.*/ - - litellm_mapped_tests_mcps: - filters: - branches: - only: - main - /litellm_.*/ - - litellm_mapped_tests_integrations: - filters: - branches: - only: - main - /litellm_.*/ - - litellm_mapped_tests_litellm_core_utils: - filters: - branches: - only: - main - /litellm_.*/ - batches_testing: @@ -4429,11 +3766,6 @@ workflows: - search_testing - 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 - batches_testing - litellm_utils_testing @@ -4441,8 +3773,6 @@ workflows: - image_gen_testing - logging_testing - audio_testing - - caching_unit_tests - - litellm_proxy_unit_testing_key_generation - langfuse_logging_unit_tests - local_testing_part1 - local_testing_part2 @@ -4489,59 +3819,3 @@ workflows: only: - main - /litellm_release_day_.*/ - - publish_to_pypi: - requires: - - mypy_linting - - semgrep - - local_testing_part1 - - local_testing_part2 - - build_and_test - - e2e_openai_endpoints - - test_bad_database_url - - llm_translation_testing - - realtime_translation_testing - - mcp_testing - - agent_testing - - google_generate_content_endpoint_testing - - llm_responses_api_testing - - ocr_testing - - search_testing - - 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 - - batches_testing - - litellm_utils_testing - - pass_through_unit_testing - - image_gen_testing - - logging_testing - - audio_testing - - litellm_router_testing - - litellm_router_unit_testing - - caching_unit_tests - - langfuse_logging_unit_tests - - litellm_assistants_api_testing - - auth_ui_unit_tests - - ui_unit_tests - - db_migration_disable_update_check - - e2e_ui_testing_chromium - - e2e_ui_testing_firefox - - litellm_proxy_unit_testing_key_generation - - litellm_proxy_unit_testing_part1 - - litellm_proxy_unit_testing_part2 - - litellm_security_tests - - installing_litellm_on_python - - installing_litellm_on_python_3_13 - - proxy_logging_guardrails_model_info_tests - - proxy_spend_accuracy_tests - - proxy_multi_instance_tests - - proxy_store_model_in_db_tests - - proxy_build_from_pip_tests - - proxy_pass_through_endpoint_tests - - check_code_and_doc_quality - - publish_proxy_extras - - guardrails_testing diff --git a/.claude/settings.json b/.claude/settings.json deleted file mode 100644 index 8c1d85f96e..0000000000 --- a/.claude/settings.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "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/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml index 4744ab048c..cbf380bac0 100644 --- a/.github/ISSUE_TEMPLATE/config.yml +++ b/.github/ISSUE_TEMPLATE/config.yml @@ -1,7 +1,7 @@ blank_issues_enabled: true contact_links: - name: Schedule Demo - url: https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions + url: https://enterprise.litellm.ai/demo about: Speak directly with Krrish and Ishaan, the founders, to discuss issues, share feedback, or explore improvements for LiteLLM - name: Discord url: https://discord.com/invite/wuPM9dRgDw diff --git a/.github/actions/helm-oci-chart-releaser/action.yml b/.github/actions/helm-oci-chart-releaser/action.yml index 1823e26283..454c591d43 100644 --- a/.github/actions/helm-oci-chart-releaser/action.yml +++ b/.github/actions/helm-oci-chart-releaser/action.yml @@ -41,32 +41,54 @@ runs: using: composite steps: - name: Helm | Setup - uses: azure/setup-helm@v4 + uses: azure/setup-helm@1a275c3b69536ee54be43f2070a358922e12c8d4 # v4.3.1 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: + REGISTRY_PASSWORD: ${{ inputs.registry_password }} + REGISTRY_USERNAME: ${{ inputs.registry_username }} + REGISTRY: ${{ inputs.registry }} + run: echo "$REGISTRY_PASSWORD" | helm registry login -u "$REGISTRY_USERNAME" --password-stdin "$REGISTRY" - 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: + CHART_PATH: ${{ inputs.path == null && format('{0}/{1}', 'charts', inputs.name) || inputs.path }} + run: helm dependency update "$CHART_PATH" - 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: + CHART_PATH: ${{ inputs.path == null && format('{0}/{1}', 'charts', inputs.name) || inputs.path }} + TAG: ${{ inputs.tag }} + APP_VERSION: ${{ inputs.app_version }} + run: helm package "$CHART_PATH" --version "$TAG" --app-version "$APP_VERSION" - name: Helm | Push shell: bash - run: helm push ${{ inputs.name }}-${{ inputs.tag }}.tgz oci://${{ inputs.registry }}/${{ inputs.repository }} + env: + NAME: ${{ inputs.name }} + TAG: ${{ inputs.tag }} + REGISTRY: ${{ inputs.registry }} + REPOSITORY: ${{ inputs.repository }} + run: helm push "${NAME}-${TAG}.tgz" "oci://${REGISTRY}/${REPOSITORY}" - name: Helm | Logout shell: bash - run: helm registry logout ${{ inputs.registry }} + env: + REGISTRY: ${{ inputs.registry }} + run: helm registry logout "$REGISTRY" - name: Helm | Output id: output shell: bash - run: echo "image=${{ inputs.registry }}/${{ inputs.repository }}/${{ inputs.name }}:${{ inputs.tag }}" >> $GITHUB_OUTPUT + env: + REGISTRY: ${{ inputs.registry }} + REPOSITORY: ${{ inputs.repository }} + NAME: ${{ inputs.name }} + TAG: ${{ inputs.tag }} + run: echo "image=${REGISTRY}/${REPOSITORY}/${NAME}:${TAG}" >> $GITHUB_OUTPUT diff --git a/.github/codeql/codeql-config.yml b/.github/codeql/codeql-config.yml index 20807685e1..36d70c1d74 100644 --- a/.github/codeql/codeql-config.yml +++ b/.github/codeql/codeql-config.yml @@ -1,22 +1,21 @@ name: "LiteLLM CodeQL config" -# Use security-extended suite instead of security-and-quality to avoid -# result sets > 2 GiB on this codebase that cause fatal OOM failures. queries: - - uses: security-extended + - uses: security-and-quality -# These two queries are security queries included in security-extended that -# individually produce result sets > 2 GiB on this codebase, causing fatal -# OOM failures. Exclude them as a safety net until CI confirms they no longer -# OOM; drop these exclusions in a follow-up once verified. +# Known OOM queries on large Python codebases: +# CodeQL builds a full data flow graph in memory. These two queries trace +# sensitive data through every log call / regex pattern, causing combinatorial +# path explosion on codebases with extensive logging like LiteLLM (>2 GiB +# result sets). This is a known CodeQL scaling limitation, not a code issue. +# Re-test periodically as CodeQL improves or the codebase refactors logging. query-filters: - exclude: - id: py/clear-text-logging-sensitive-data # CWE-312 — > 2 GiB result set + id: py/clear-text-logging-sensitive-data # CWE-312 - exclude: - id: py/polynomial-redos # CWE-730 — > 2 GiB result set + id: py/polynomial-redos # CWE-730 paths-ignore: - tests - docs - "**/*.md" - - litellm/proxy/_experimental/out diff --git a/.github/dependabot.yaml b/.github/dependabot.yaml index 58e7cfe10d..c49882a8d6 100644 --- a/.github/dependabot.yaml +++ b/.github/dependabot.yaml @@ -4,6 +4,9 @@ updates: directory: "/" schedule: interval: "daily" + cooldown: + default-days: 7 + semver-major-days: 14 groups: github-actions: patterns: diff --git a/.github/workflows/_test-unit-base.yml b/.github/workflows/_test-unit-base.yml new file mode 100644 index 0000000000..f1ae30e67d --- /dev/null +++ b/.github/workflows/_test-unit-base.yml @@ -0,0 +1,96 @@ +name: _Unit Test Base (Reusable) + +on: + workflow_call: + inputs: + test-path: + description: "Pytest path(s) to run" + required: true + type: string + workers: + description: "Number of pytest-xdist workers" + required: false + type: number + default: 2 + reruns: + description: "Number of reruns for flaky tests" + required: false + type: number + default: 2 + timeout-minutes: + description: "Job timeout in minutes" + required: false + type: number + default: 20 + max-failures: + description: "Stop after this many failures" + required: false + type: number + default: 10 + +permissions: + contents: read + +jobs: + run: + name: Run tests + runs-on: ubuntu-latest + timeout-minutes: ${{ inputs.timeout-minutes }} + + steps: + - uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 + with: + persist-credentials: false + + - name: Set up Python + uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0 + with: + python-version: "3.12" + + - name: Install Poetry + run: pip install 'poetry==2.3.2' + + - name: Cache Poetry dependencies + uses: actions/cache@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.3.0 + with: + path: | + ~/.cache/pypoetry + ~/.cache/pip + .venv + key: ${{ runner.os }}-poetry-${{ hashFiles('poetry.lock') }} + restore-keys: | + ${{ runner.os }}-poetry- + + - name: Install dependencies + run: | + poetry config virtualenvs.in-project true + poetry install --with dev,proxy-dev --extras "proxy semantic-router" + poetry run pip install google-genai==1.22.0 \ + google-cloud-aiplatform==1.115.0 fastapi-offline==1.7.3 python-multipart==0.0.22 openapi-core==0.23.0 + + - name: Setup litellm-enterprise + run: | + poetry run pip install --force-reinstall --no-deps -e enterprise/ + + - name: Generate Prisma client + env: + PRISMA_BINARY_CACHE_DIR: ${{ runner.temp }}/prisma-cache + run: | + poetry run pip install nodejs-wheel-binaries==24.13.1 + poetry run prisma generate --schema litellm/proxy/schema.prisma + + - name: Run tests + env: + TEST_PATH: ${{ inputs.test-path }} + MAX_FAILURES: ${{ inputs.max-failures }} + WORKERS: ${{ inputs.workers }} + RERUNS: ${{ inputs.reruns }} + run: | + poetry run pytest ${TEST_PATH:?} \ + --tb=short -vv \ + --maxfail="${MAX_FAILURES}" \ + -n "${WORKERS}" \ + --reruns "${RERUNS}" \ + --reruns-delay 1 \ + --dist=loadscope \ + --durations=20 diff --git a/.github/workflows/_test-unit-services-base.yml b/.github/workflows/_test-unit-services-base.yml new file mode 100644 index 0000000000..d53a9e8822 --- /dev/null +++ b/.github/workflows/_test-unit-services-base.yml @@ -0,0 +1,164 @@ +name: _Unit Test Services Base (Reusable) + +on: + workflow_call: + inputs: + test-path: + description: "Pytest path(s) to run" + required: true + type: string + workers: + description: "Number of pytest-xdist workers (0 = no parallelism)" + required: false + type: number + default: 2 + reruns: + description: "Number of reruns for flaky tests" + required: false + type: number + default: 2 + timeout-minutes: + description: "Job timeout in minutes" + required: false + type: number + default: 20 + max-failures: + description: "Stop after this many failures" + required: false + type: number + default: 10 + enable-redis: + description: "Pass Redis Cloud credentials to tests via REDIS_HOST/PORT/PASSWORD env vars" + required: false + type: boolean + default: false + enable-postgres: + description: "Start a local Postgres service container and run Prisma migrations" + required: false + type: boolean + default: false + secrets: + REDIS_HOST: + required: false + REDIS_PORT: + required: false + REDIS_PASSWORD: + required: false + DATABASE_URL: + required: false + POSTGRES_USER: + required: false + POSTGRES_PASSWORD: + required: false + +permissions: + contents: read + +jobs: + run: + name: Run tests + runs-on: ubuntu-latest + timeout-minutes: ${{ inputs.timeout-minutes }} + # Environment is derived from the enable-* flags, not caller-controllable. + # This prevents callers from passing arbitrary environment names to bypass secret scoping. + # Note: Postgres service container always starts (GHA limitation), so any Redis job + # also needs Postgres secrets → uses integration-redis-postgres, not integration-redis. + environment: >- + ${{ + inputs.enable-redis && 'integration-redis-postgres' || + inputs.enable-postgres && 'integration-postgres' || + '' + }} + + services: + postgres: + image: postgres@sha256:705a5d5b5836f3fcba0d02c4d281e6a7dd9ed2dd4078640f08a1e1e9896e097d # postgres:14 + env: + POSTGRES_USER: ${{ secrets.POSTGRES_USER }} + POSTGRES_PASSWORD: ${{ secrets.POSTGRES_PASSWORD }} + POSTGRES_DB: litellm_test + ports: + - 5432:5432 + options: >- + --health-cmd "pg_isready" + --health-interval 10s + --health-timeout 5s + --health-retries 5 + + steps: + - uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 + with: + persist-credentials: false + + - name: Set up Python + uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0 + with: + python-version: "3.12" + + - name: Install Poetry + run: pip install 'poetry==2.3.2' + + - name: Cache Poetry dependencies + uses: actions/cache@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.3.0 + with: + path: | + ~/.cache/pypoetry + ~/.cache/pip + .venv + key: ${{ runner.os }}-poetry-services-${{ hashFiles('poetry.lock') }} + restore-keys: | + ${{ runner.os }}-poetry-services- + + - name: Install dependencies + run: | + poetry config virtualenvs.in-project true + poetry install --with dev,proxy-dev --extras "proxy semantic-router" + poetry run pip install google-genai==1.22.0 \ + google-cloud-aiplatform==1.115.0 fastapi-offline==1.7.3 python-multipart==0.0.22 openapi-core==0.23.0 + + - name: Setup litellm-enterprise + run: | + poetry run pip install --force-reinstall --no-deps -e enterprise/ + + - name: Generate Prisma client + env: + PRISMA_BINARY_CACHE_DIR: ${{ runner.temp }}/prisma-cache + run: | + poetry run pip install nodejs-wheel-binaries==24.13.1 + poetry run prisma generate --schema litellm/proxy/schema.prisma + + - name: Run Prisma migrations + if: ${{ inputs.enable-postgres }} + env: + DATABASE_URL: ${{ secrets.DATABASE_URL }} + run: | + poetry run prisma db push --schema litellm/proxy/schema.prisma --accept-data-loss + + - name: Run tests + env: + TEST_PATH: ${{ inputs.test-path }} + MAX_FAILURES: ${{ inputs.max-failures }} + WORKERS: ${{ inputs.workers }} + RERUNS: ${{ inputs.reruns }} + DATABASE_URL: ${{ inputs.enable-postgres && secrets.DATABASE_URL || '' }} + REDIS_HOST: ${{ inputs.enable-redis && secrets.REDIS_HOST || '' }} + REDIS_PORT: ${{ inputs.enable-redis && secrets.REDIS_PORT || '' }} + REDIS_PASSWORD: ${{ inputs.enable-redis && secrets.REDIS_PASSWORD || '' }} + run: | + if [ "${WORKERS}" = "0" ]; then + poetry run pytest ${TEST_PATH:?} \ + --tb=short -vv \ + --maxfail="${MAX_FAILURES}" \ + --reruns "${RERUNS}" \ + --reruns-delay 1 \ + --durations=20 + else + poetry run pytest ${TEST_PATH:?} \ + --tb=short -vv \ + --maxfail="${MAX_FAILURES}" \ + -n "${WORKERS}" \ + --reruns "${RERUNS}" \ + --reruns-delay 1 \ + --dist=loadscope \ + --durations=20 + fi diff --git a/.github/workflows/auto_update_price_and_context_window.yml b/.github/workflows/auto_update_price_and_context_window.yml index 98b9d868e6..60e8993621 100644 --- a/.github/workflows/auto_update_price_and_context_window.yml +++ b/.github/workflows/auto_update_price_and_context_window.yml @@ -2,18 +2,24 @@ name: Updates model_prices_and_context_window.json and Create Pull Request on: schedule: - - cron: "0 0 * * 0" # Run every Sundays at midnight + - cron: "0 0 * * 0" # Run every Sundays at midnight #- cron: "0 0 * * *" # Run daily at midnight +permissions: + contents: write + pull-requests: write + jobs: auto_update_price_and_context_window: if: github.repository == 'BerriAI/litellm' runs-on: ubuntu-latest steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 + with: + persist-credentials: false - name: Install Dependencies run: | - pip install aiohttp + pip install 'aiohttp==3.13.3' - name: Update JSON Data run: | python ".github/workflows/auto_update_price_and_context_window_file.py" @@ -26,4 +32,4 @@ jobs: --head auto-update-price-and-context-window-$(date +'%Y-%m-%d') \ --base main env: - GH_TOKEN: ${{ secrets.GH_TOKEN }} \ No newline at end of file + GH_TOKEN: ${{ secrets.GH_TOKEN }} diff --git a/.github/workflows/check-schema-sync.yml b/.github/workflows/check-schema-sync.yml new file mode 100644 index 0000000000..0e5e2804e6 --- /dev/null +++ b/.github/workflows/check-schema-sync.yml @@ -0,0 +1,58 @@ +name: Check Schema Sync + +on: + pull_request: + paths: + - 'schema.prisma' + - 'litellm/proxy/schema.prisma' + - 'litellm-proxy-extras/litellm_proxy_extras/schema.prisma' + +permissions: + contents: read + +jobs: + check-sync: + name: Verify schema.prisma copies match root + runs-on: ubuntu-latest + timeout-minutes: 5 + steps: + - name: Checkout PR + uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 + with: + persist-credentials: false + + - name: Reject symlinked schema files + run: | + for f in schema.prisma litellm/proxy/schema.prisma litellm-proxy-extras/litellm_proxy_extras/schema.prisma; do + if [ -L "$f" ]; then + echo "::error file=$f::$f is a symlink, which is not allowed" + exit 1 + fi + done + + - name: Check all schemas match root + run: | + EXIT=0 + + diff schema.prisma litellm/proxy/schema.prisma || { + echo "::error file=litellm/proxy/schema.prisma::litellm/proxy/schema.prisma differs from root schema.prisma" + EXIT=1 + } + + diff schema.prisma litellm-proxy-extras/litellm_proxy_extras/schema.prisma || { + echo "::error file=litellm-proxy-extras/litellm_proxy_extras/schema.prisma::litellm-proxy-extras/litellm_proxy_extras/schema.prisma differs from root schema.prisma" + EXIT=1 + } + + if [ "$EXIT" -ne 0 ]; then + echo "" + echo "Schema files are out of sync." + echo "The root schema.prisma is the source of truth." + echo "" + echo "To fix, run from the repo root:" + echo " cp schema.prisma litellm/proxy/schema.prisma" + echo " cp schema.prisma litellm-proxy-extras/litellm_proxy_extras/schema.prisma" + exit 1 + fi + + echo "All schema copies are in sync with root." diff --git a/.github/workflows/check_duplicate_issues.yml b/.github/workflows/check_duplicate_issues.yml index 6d11ce573e..289d78880a 100644 --- a/.github/workflows/check_duplicate_issues.yml +++ b/.github/workflows/check_duplicate_issues.yml @@ -12,7 +12,7 @@ jobs: contents: read steps: - name: Check for potential duplicates - uses: wow-actions/potential-duplicates@v1 + uses: wow-actions/potential-duplicates@4d4ea0352e0383859279938e255179dd1dbb67b5 # v1.1.0 with: GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} label: potential-duplicate @@ -30,13 +30,14 @@ jobs: - name: Checkout close script if: github.event.action == 'opened' - uses: actions/checkout@v4 + uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 with: sparse-checkout: .github/scripts + persist-credentials: false - name: Set up Python if: github.event.action == 'opened' - uses: actions/setup-python@v5 + uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0 with: python-version: "3.11" diff --git a/.github/workflows/codeql.yml b/.github/workflows/codeql.yml index 0b7cce2e4b..e86fca17c7 100644 --- a/.github/workflows/codeql.yml +++ b/.github/workflows/codeql.yml @@ -6,8 +6,8 @@ on: pull_request: branches: [main] schedule: - # Run weekly on Sundays at 04:00 UTC - - cron: "0 4 * * 0" + # Run daily at 04:00 UTC + - cron: "0 4 * * *" concurrency: group: ${{ github.workflow }}-${{ github.ref }} @@ -15,6 +15,7 @@ concurrency: jobs: analyze: + if: github.event_name != 'schedule' || github.repository == 'BerriAI/litellm' name: Analyze (${{ matrix.language }}) runs-on: ubuntu-latest timeout-minutes: 30 @@ -37,16 +38,18 @@ jobs: steps: - name: Checkout repository - uses: actions/checkout@v4 + uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 + with: + persist-credentials: false - name: Initialize CodeQL - uses: github/codeql-action/init@v3 + uses: github/codeql-action/init@ebcb5b36ded6beda4ceefea6a8bc4cc885255bb3 # v3 with: languages: ${{ matrix.language }} build-mode: ${{ matrix.build-mode }} config-file: ./.github/codeql/codeql-config.yml - name: Perform CodeQL Analysis - uses: github/codeql-action/analyze@v3 + uses: github/codeql-action/analyze@ebcb5b36ded6beda4ceefea6a8bc4cc885255bb3 # v3 with: category: "/language:${{ matrix.language }}" diff --git a/.github/workflows/codspeed.yml b/.github/workflows/codspeed.yml index 385b95fdaf..52d64addea 100644 --- a/.github/workflows/codspeed.yml +++ b/.github/workflows/codspeed.yml @@ -25,10 +25,12 @@ jobs: timeout-minutes: 15 steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 + with: + persist-credentials: false - name: Set up Python - uses: actions/setup-python@v5 + uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0 with: python-version: "3.12" @@ -38,7 +40,7 @@ jobs: pip install pytest pytest-codspeed==4.3.0 - name: Run benchmarks - uses: CodSpeedHQ/action@v4 + uses: CodSpeedHQ/action@1c8ae4843586d3ba879736b7f6b7b0c990757fab # v4.12.1 with: mode: simulation run: pytest tests/benchmarks/ --codspeed diff --git a/.github/workflows/create_daily_staging_branch.yml b/.github/workflows/create_daily_staging_branch.yml index 08aebd7d04..424d8de0a4 100644 --- a/.github/workflows/create_daily_staging_branch.yml +++ b/.github/workflows/create_daily_staging_branch.yml @@ -2,18 +2,22 @@ name: Create Daily Staging Branch on: schedule: - - cron: '0 0,12 * * *' # Runs every 12 hours at midnight and noon UTC - workflow_dispatch: # Allow manual trigger + - cron: "0 0,12 * * *" # Runs every 12 hours at midnight and noon UTC + workflow_dispatch: # Allow manual trigger jobs: create-staging-branch: + if: github.repository == 'BerriAI/litellm' runs-on: ubuntu-latest + permissions: + contents: write steps: - name: Checkout repository - uses: actions/checkout@v3 + uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 with: fetch-depth: 0 + persist-credentials: false - name: Create daily staging branch env: @@ -43,13 +47,17 @@ jobs: fi create-internal-dev-branch: + if: github.repository == 'BerriAI/litellm' runs-on: ubuntu-latest + permissions: + contents: write steps: - name: Checkout repository - uses: actions/checkout@v3 + uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 with: fetch-depth: 0 + persist-credentials: false - name: Create internal dev branch env: diff --git a/.github/workflows/ghcr_deploy.yml b/.github/workflows/ghcr_deploy.yml deleted file mode 100644 index 344b0ec48e..0000000000 --- a/.github/workflows/ghcr_deploy.yml +++ /dev/null @@ -1,444 +0,0 @@ -# this workflow is triggered by an API call when there is a new PyPI release of LiteLLM -name: Build, Publish LiteLLM Docker Image. New Release -on: - workflow_dispatch: - inputs: - tag: - description: "The tag version you want to build" - required: true - release_type: - description: "The release type you want to build. Can be 'latest', 'stable', 'dev', 'rc'" - type: string - default: "latest" - commit_hash: - description: "Commit hash" - required: true - -# Defines two custom environment variables for the workflow. Used for the Container registry domain, and a name for the Docker image that this workflow builds. -env: - REGISTRY: ghcr.io - IMAGE_NAME: ${{ github.repository }} - CHART_NAME: litellm-helm - -# There is a single job in this workflow. It's configured to run on the latest available version of Ubuntu. -jobs: - # print commit hash, tag, and release type - print: - runs-on: ubuntu-latest - steps: - - run: | - echo "Commit hash: ${{ github.event.inputs.commit_hash }}" - echo "Tag: ${{ github.event.inputs.tag }}" - echo "Release type: ${{ github.event.inputs.release_type }}" - docker-hub-deploy: - if: github.repository == 'BerriAI/litellm' - runs-on: ubuntu-latest - steps: - - - name: Checkout - uses: actions/checkout@v4 - with: - ref: ${{ github.event.inputs.commit_hash }} - - - name: Set up QEMU - uses: docker/setup-qemu-action@v3 - - - name: Set up Docker Buildx - uses: docker/setup-buildx-action@v3 - - - name: Login to Docker Hub - uses: docker/login-action@v3 - with: - username: ${{ secrets.DOCKERHUB_USERNAME }} - password: ${{ secrets.DOCKERHUB_TOKEN }} - - - name: Build and push - uses: docker/build-push-action@v5 - with: - context: . - push: true - tags: litellm/litellm:${{ github.event.inputs.tag || 'latest' }} - - - name: Build and push litellm-database image - uses: docker/build-push-action@v5 - with: - context: . - push: true - file: ./docker/Dockerfile.database - tags: litellm/litellm-database:${{ github.event.inputs.tag || 'latest' }} - - - name: Build and push litellm-spend-logs image - uses: docker/build-push-action@v5 - with: - context: . - push: true - file: ./litellm-js/spend-logs/Dockerfile - tags: litellm/litellm-spend_logs:${{ github.event.inputs.tag || 'latest' }} - - - name: Build and push litellm-non_root image - uses: docker/build-push-action@v5 - with: - context: . - push: true - file: ./docker/Dockerfile.non_root - tags: litellm/litellm-non_root:${{ github.event.inputs.tag || 'latest' }} - build-and-push-image: - runs-on: ubuntu-latest - # Sets the permissions granted to the `GITHUB_TOKEN` for the actions in this job. - permissions: - contents: read - packages: write - steps: - - name: Checkout repository - uses: actions/checkout@v4 - with: - ref: ${{ github.event.inputs.commit_hash }} - # Uses the `docker/login-action` action to log in to the Container registry registry using the account and password that will publish the packages. Once published, the packages are scoped to the account defined here. - - name: Log in to the Container registry - uses: docker/login-action@65b78e6e13532edd9afa3aa52ac7964289d1a9c1 - with: - registry: ${{ env.REGISTRY }} - username: ${{ github.actor }} - password: ${{ secrets.GITHUB_TOKEN }} - # This step uses [docker/metadata-action](https://github.com/docker/metadata-action#about) to extract tags and labels that will be applied to the specified image. The `id` "meta" allows the output of this step to be referenced in a subsequent step. The `images` value provides the base name for the tags and labels. - - name: Extract metadata (tags, labels) for Docker - id: meta - uses: docker/metadata-action@9ec57ed1fcdbf14dcef7dfbe97b2010124a938b7 - with: - images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }} - # Configure multi platform Docker builds - - name: Set up QEMU - uses: docker/setup-qemu-action@e0e4588fad221d38ee467c0bffd91115366dc0c5 - - name: Set up Docker Buildx - uses: docker/setup-buildx-action@edfb0fe6204400c56fbfd3feba3fe9ad1adfa345 - # This step uses the `docker/build-push-action` action to build the image, based on your repository's `Dockerfile`. If the build succeeds, it pushes the image to GitHub Packages. - # It uses the `context` parameter to define the build's context as the set of files located in the specified path. For more information, see "[Usage](https://github.com/docker/build-push-action#usage)" in the README of the `docker/build-push-action` repository. - # It uses the `tags` and `labels` parameters to tag and label the image with the output from the "meta" step. - - name: Build and push Docker image - uses: docker/build-push-action@4976231911ebf5f32aad765192d35f942aa48cb8 - with: - context: . - push: true - tags: | - ${{ steps.meta.outputs.tags }}-${{ github.event.inputs.tag || 'latest' }}, - ${{ steps.meta.outputs.tags }}-${{ github.event.inputs.release_type }} - ${{ (github.event.inputs.release_type == 'stable' || github.event.inputs.release_type == 'rc') && format('{0}/berriai/litellm:main-{1}', env.REGISTRY, github.event.inputs.tag) || '' }}, - ${{ github.event.inputs.release_type == 'stable' && format('{0}/berriai/litellm:main-stable', env.REGISTRY) || '' }}, - ${{ (github.event.inputs.release_type == 'stable' || github.event.inputs.release_type == 'rc') && format('{0}/berriai/litellm:{1}', env.REGISTRY, github.event.inputs.tag) || '' }}, - labels: ${{ steps.meta.outputs.labels }} - platforms: local,linux/amd64,linux/arm64,linux/arm64/v8 - - build-and-push-image-ee: - runs-on: ubuntu-latest - permissions: - contents: read - packages: write - steps: - - name: Checkout repository - uses: actions/checkout@v4 - with: - ref: ${{ github.event.inputs.commit_hash }} - - - name: Log in to the Container registry - uses: docker/login-action@65b78e6e13532edd9afa3aa52ac7964289d1a9c1 - with: - registry: ${{ env.REGISTRY }} - username: ${{ github.actor }} - password: ${{ secrets.GITHUB_TOKEN }} - - - name: Extract metadata (tags, labels) for EE Dockerfile - id: meta-ee - uses: docker/metadata-action@9ec57ed1fcdbf14dcef7dfbe97b2010124a938b7 - with: - images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}-ee - # Configure multi platform Docker builds - - name: Set up QEMU - uses: docker/setup-qemu-action@e0e4588fad221d38ee467c0bffd91115366dc0c5 - - name: Set up Docker Buildx - uses: docker/setup-buildx-action@edfb0fe6204400c56fbfd3feba3fe9ad1adfa345 - - - name: Build and push EE Docker image - uses: docker/build-push-action@f2a1d5e99d037542a71f64918e516c093c6f3fc4 - with: - context: . - file: Dockerfile - push: true - tags: | - ${{ steps.meta-ee.outputs.tags }}-${{ github.event.inputs.tag || 'latest' }}, - ${{ steps.meta-ee.outputs.tags }}-${{ github.event.inputs.release_type }} - ${{ (github.event.inputs.release_type == 'stable' || github.event.inputs.release_type == 'rc') && format('{0}/berriai/litellm-ee:main-{1}', env.REGISTRY, github.event.inputs.tag) || '' }}, - ${{ github.event.inputs.release_type == 'stable' && format('{0}/berriai/litellm-ee:main-stable', env.REGISTRY) || '' }} - labels: ${{ steps.meta-ee.outputs.labels }} - platforms: local,linux/amd64,linux/arm64,linux/arm64/v8 - - build-and-push-image-database: - runs-on: ubuntu-latest - permissions: - contents: read - packages: write - steps: - - name: Checkout repository - uses: actions/checkout@v4 - with: - ref: ${{ github.event.inputs.commit_hash }} - - - name: Log in to the Container registry - uses: docker/login-action@65b78e6e13532edd9afa3aa52ac7964289d1a9c1 - with: - registry: ${{ env.REGISTRY }} - username: ${{ github.actor }} - password: ${{ secrets.GITHUB_TOKEN }} - - - name: Extract metadata (tags, labels) for database Dockerfile - id: meta-database - uses: docker/metadata-action@9ec57ed1fcdbf14dcef7dfbe97b2010124a938b7 - with: - images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}-database - # Configure multi platform Docker builds - - name: Set up QEMU - uses: docker/setup-qemu-action@e0e4588fad221d38ee467c0bffd91115366dc0c5 - - name: Set up Docker Buildx - uses: docker/setup-buildx-action@edfb0fe6204400c56fbfd3feba3fe9ad1adfa345 - - - name: Build and push Database Docker image - uses: docker/build-push-action@f2a1d5e99d037542a71f64918e516c093c6f3fc4 - with: - context: . - file: ./docker/Dockerfile.database - push: true - tags: | - ${{ steps.meta-database.outputs.tags }}-${{ github.event.inputs.tag || 'latest' }}, - ${{ steps.meta-database.outputs.tags }}-${{ github.event.inputs.release_type }} - ${{ (github.event.inputs.release_type == 'stable' || github.event.inputs.release_type == 'rc') && format('{0}/berriai/litellm-database:main-{1}', env.REGISTRY, github.event.inputs.tag) || '' }}, - ${{ github.event.inputs.release_type == 'stable' && format('{0}/berriai/litellm-database:main-stable', env.REGISTRY) || '' }} - labels: ${{ steps.meta-database.outputs.labels }} - platforms: local,linux/amd64,linux/arm64,linux/arm64/v8 - - build-and-push-image-non_root: - runs-on: ubuntu-latest - permissions: - contents: read - packages: write - steps: - - name: Checkout repository - uses: actions/checkout@v4 - with: - ref: ${{ github.event.inputs.commit_hash }} - - - name: Log in to the Container registry - uses: docker/login-action@65b78e6e13532edd9afa3aa52ac7964289d1a9c1 - with: - registry: ${{ env.REGISTRY }} - username: ${{ github.actor }} - password: ${{ secrets.GITHUB_TOKEN }} - - - name: Extract metadata (tags, labels) for non_root Dockerfile - id: meta-non_root - uses: docker/metadata-action@9ec57ed1fcdbf14dcef7dfbe97b2010124a938b7 - with: - images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}-non_root - # Configure multi platform Docker builds - - name: Set up QEMU - uses: docker/setup-qemu-action@e0e4588fad221d38ee467c0bffd91115366dc0c5 - - name: Set up Docker Buildx - uses: docker/setup-buildx-action@edfb0fe6204400c56fbfd3feba3fe9ad1adfa345 - - - name: Build and push non_root Docker image - uses: docker/build-push-action@f2a1d5e99d037542a71f64918e516c093c6f3fc4 - with: - context: . - file: ./docker/Dockerfile.non_root - push: true - tags: | - ${{ steps.meta-non_root.outputs.tags }}-${{ github.event.inputs.tag || 'latest' }}, - ${{ steps.meta-non_root.outputs.tags }}-${{ github.event.inputs.release_type }} - ${{ (github.event.inputs.release_type == 'stable' || github.event.inputs.release_type == 'rc') && format('{0}/berriai/litellm-non_root:main-{1}', env.REGISTRY, github.event.inputs.tag) || '' }}, - ${{ github.event.inputs.release_type == 'stable' && format('{0}/berriai/litellm-non_root:main-stable', env.REGISTRY) || '' }} - labels: ${{ steps.meta-non_root.outputs.labels }} - platforms: local,linux/amd64,linux/arm64,linux/arm64/v8 - - build-and-push-image-spend-logs: - runs-on: ubuntu-latest - permissions: - contents: read - packages: write - steps: - - name: Checkout repository - uses: actions/checkout@v4 - with: - ref: ${{ github.event.inputs.commit_hash }} - - - name: Log in to the Container registry - uses: docker/login-action@65b78e6e13532edd9afa3aa52ac7964289d1a9c1 - with: - registry: ${{ env.REGISTRY }} - username: ${{ github.actor }} - password: ${{ secrets.GITHUB_TOKEN }} - - - name: Extract metadata (tags, labels) for spend-logs Dockerfile - id: meta-spend-logs - uses: docker/metadata-action@9ec57ed1fcdbf14dcef7dfbe97b2010124a938b7 - with: - images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}-spend_logs - # Configure multi platform Docker builds - - name: Set up QEMU - uses: docker/setup-qemu-action@e0e4588fad221d38ee467c0bffd91115366dc0c5 - - name: Set up Docker Buildx - uses: docker/setup-buildx-action@edfb0fe6204400c56fbfd3feba3fe9ad1adfa345 - - - name: Build and push Database Docker image - uses: docker/build-push-action@f2a1d5e99d037542a71f64918e516c093c6f3fc4 - with: - context: . - file: ./litellm-js/spend-logs/Dockerfile - push: true - tags: | - ${{ steps.meta-spend-logs.outputs.tags }}-${{ github.event.inputs.tag || 'latest' }}, - ${{ steps.meta-spend-logs.outputs.tags }}-${{ github.event.inputs.release_type }} - ${{ (github.event.inputs.release_type == 'stable' || github.event.inputs.release_type == 'rc') && format('{0}/berriai/litellm-spend_logs:main-{1}', env.REGISTRY, github.event.inputs.tag) || '' }}, - ${{ github.event.inputs.release_type == 'stable' && format('{0}/berriai/litellm-spend_logs:main-stable', env.REGISTRY) || '' }} - platforms: local,linux/amd64,linux/arm64,linux/arm64/v8 - - run-observatory-tests: - if: github.event.inputs.release_type == 'rc' || github.event.inputs.release_type == 'stable' - needs: [docker-hub-deploy] - uses: ./.github/workflows/run_observatory_tests.yml - with: - tag: ${{ github.event.inputs.tag }} - commit_hash: ${{ github.event.inputs.commit_hash }} - secrets: inherit - - build-and-push-helm-chart: - if: github.event.inputs.release_type != 'dev' - needs: [docker-hub-deploy, build-and-push-image, build-and-push-image-database] - runs-on: ubuntu-latest - steps: - - name: Checkout repository - uses: actions/checkout@v4 - with: - fetch-depth: 0 - - - name: Log in to the Container registry - uses: docker/login-action@65b78e6e13532edd9afa3aa52ac7964289d1a9c1 - with: - registry: ${{ env.REGISTRY }} - username: ${{ github.actor }} - password: ${{ secrets.GITHUB_TOKEN }} - - - name: lowercase github.repository_owner - run: | - echo "REPO_OWNER=`echo ${{github.repository_owner}} | tr '[:upper:]' '[:lower:]'`" >>${GITHUB_ENV} - - # Sync Helm chart version with LiteLLM release version (1-1 versioning) - # This allows users to easily map Helm chart versions to LiteLLM versions - # See: https://codefresh.io/docs/docs/ci-cd-guides/helm-best-practices/ - - name: Calculate chart and app versions - id: chart_version - shell: bash - run: | - INPUT_TAG="${{ github.event.inputs.tag }}" - RELEASE_TYPE="${{ github.event.inputs.release_type }}" - - # Chart version = LiteLLM version without 'v' prefix (Helm semver convention) - # v1.81.0 -> 1.81.0, v1.81.0.rc.1 -> 1.81.0.rc.1 - CHART_VERSION="${INPUT_TAG#v}" - - # Add suffix for 'latest' releases (rc already has suffix in tag) - if [ "$RELEASE_TYPE" = "latest" ]; then - CHART_VERSION="${CHART_VERSION}-latest" - fi - - # App version = Docker tag (keeps 'v' prefix to match Docker image tags) - APP_VERSION="${INPUT_TAG}" - - echo "version=${CHART_VERSION}" | tee -a $GITHUB_OUTPUT - echo "app_version=${APP_VERSION}" | tee -a $GITHUB_OUTPUT - - - uses: ./.github/actions/helm-oci-chart-releaser - with: - name: ${{ env.CHART_NAME }} - repository: ${{ env.REPO_OWNER }} - tag: ${{ steps.chart_version.outputs.version }} - app_version: ${{ steps.chart_version.outputs.app_version }} - path: deploy/charts/${{ env.CHART_NAME }} - registry: ${{ env.REGISTRY }} - registry_username: ${{ github.actor }} - registry_password: ${{ secrets.GITHUB_TOKEN }} - update_dependencies: true - - release: - name: "New LiteLLM Release" - needs: [docker-hub-deploy, build-and-push-image, build-and-push-image-database] - permissions: - contents: write - runs-on: "ubuntu-latest" - - steps: - - name: Display version - run: echo "Current version is ${{ github.event.inputs.tag }}" - - name: "Set Release Tag" - run: echo "RELEASE_TAG=${{ github.event.inputs.tag }}" >> $GITHUB_ENV - - name: Display release tag - run: echo "RELEASE_TAG is $RELEASE_TAG" - - name: "Create release" - uses: "actions/github-script@v6" - with: - github-token: "${{ secrets.GITHUB_TOKEN }}" - script: | - const commitHash = "${{ github.event.inputs.commit_hash}}"; - console.log("Commit Hash:", commitHash); // Add this line for debugging - try { - const response = await github.rest.repos.createRelease({ - draft: false, - generate_release_notes: true, - target_commitish: commitHash, - name: process.env.RELEASE_TAG, - owner: context.repo.owner, - prerelease: false, - repo: context.repo.repo, - tag_name: process.env.RELEASE_TAG, - }); - - core.exportVariable('RELEASE_ID', response.data.id); - core.exportVariable('RELEASE_UPLOAD_URL', response.data.upload_url); - } catch (error) { - core.setFailed(error.message); - } - - name: Fetch Release Notes - id: release-notes - uses: actions/github-script@v6 - with: - github-token: "${{ secrets.GITHUB_TOKEN }}" - script: | - try { - const response = await github.rest.repos.getRelease({ - owner: context.repo.owner, - repo: context.repo.repo, - release_id: process.env.RELEASE_ID, - }); - const formattedBody = JSON.stringify(response.data.body).slice(1, -1); - return formattedBody; - } catch (error) { - core.setFailed(error.message); - } - env: - RELEASE_ID: ${{ env.RELEASE_ID }} - - name: Github Releases To Discord - env: - WEBHOOK_URL: ${{ secrets.WEBHOOK_URL }} - REALEASE_TAG: ${{ env.RELEASE_TAG }} - RELEASE_NOTES: ${{ steps.release-notes.outputs.result }} - run: | - curl -H "Content-Type: application/json" -X POST -d '{ - "content": "New LiteLLM release '"${RELEASE_TAG}"'", - "username": "Release Changelog", - "avatar_url": "https://cdn.discordapp.com/avatars/487431320314576937/bd64361e4ba6313d561d54e78c9e7171.png", - "embeds": [ - { - "title": "Changelog for LiteLLM '"${RELEASE_TAG}"'", - "description": "'"${RELEASE_NOTES}"'", - "color": 2105893 - } - ] - }' $WEBHOOK_URL - diff --git a/.github/workflows/ghcr_helm_deploy.yml b/.github/workflows/ghcr_helm_deploy.yml deleted file mode 100644 index 21b2eaafe1..0000000000 --- a/.github/workflows/ghcr_helm_deploy.yml +++ /dev/null @@ -1,67 +0,0 @@ -# Standalone workflow to publish LiteLLM Helm Chart -# Note: The main ghcr_deploy.yml workflow also publishes the Helm chart as part of a full release -name: Build, Publish LiteLLM Helm Chart. New Release -on: - workflow_dispatch: - inputs: - tag: - description: "LiteLLM version tag (e.g., v1.81.0)" - required: true - -# Defines two custom environment variables for the workflow. Used for the Container registry domain, and a name for the Docker image that this workflow builds. -env: - REGISTRY: ghcr.io - IMAGE_NAME: ${{ github.repository }} - REPO_OWNER: ${{github.repository_owner}} - -# There is a single job in this workflow. It's configured to run on the latest available version of Ubuntu. -jobs: - build-and-push-helm-chart: - runs-on: ubuntu-latest - steps: - - name: Checkout repository - uses: actions/checkout@v4 - - - name: Log in to the Container registry - uses: docker/login-action@65b78e6e13532edd9afa3aa52ac7964289d1a9c1 - with: - registry: ${{ env.REGISTRY }} - username: ${{ github.actor }} - password: ${{ secrets.GITHUB_TOKEN }} - - - name: lowercase github.repository_owner - run: | - echo "REPO_OWNER=`echo ${{github.repository_owner}} | tr '[:upper:]' '[:lower:]'`" >>${GITHUB_ENV} - - # Sync Helm chart version with LiteLLM release version (1-1 versioning) - - name: Calculate chart and app versions - id: chart_version - shell: bash - run: | - INPUT_TAG="${{ github.event.inputs.tag }}" - - # Chart version = LiteLLM version without 'v' prefix - # v1.81.0 -> 1.81.0 - CHART_VERSION="${INPUT_TAG#v}" - - # App version = Docker tag (keeps 'v' prefix) - APP_VERSION="${INPUT_TAG}" - - echo "version=${CHART_VERSION}" | tee -a $GITHUB_OUTPUT - echo "app_version=${APP_VERSION}" | tee -a $GITHUB_OUTPUT - - - name: Lint helm chart - run: helm lint deploy/charts/litellm-helm - - - uses: ./.github/actions/helm-oci-chart-releaser - with: - name: litellm-helm - repository: ${{ env.REPO_OWNER }} - tag: ${{ steps.chart_version.outputs.version }} - app_version: ${{ steps.chart_version.outputs.app_version }} - path: deploy/charts/litellm-helm - registry: ${{ env.REGISTRY }} - registry_username: ${{ github.actor }} - registry_password: ${{ secrets.GITHUB_TOKEN }} - update_dependencies: true - diff --git a/.github/workflows/helm_unit_test.yml b/.github/workflows/helm_unit_test.yml index c4b83af70a..06836b1d1c 100644 --- a/.github/workflows/helm_unit_test.yml +++ b/.github/workflows/helm_unit_test.yml @@ -6,22 +6,36 @@ on: branches: - main +permissions: + contents: read + jobs: unit-test: runs-on: ubuntu-latest steps: - name: Checkout - uses: actions/checkout@v2 + uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 + with: + persist-credentials: false - name: Set up Helm 3.11.1 - uses: azure/setup-helm@v1 + uses: azure/setup-helm@1a275c3b69536ee54be43f2070a358922e12c8d4 # v4.3.1 with: - version: '3.11.1' + version: "3.11.1" - name: Install Helm Unit Test Plugin run: | helm plugin install https://github.com/helm-unittest/helm-unittest --version v0.4.4 + - name: Verify Helm Unit Test Plugin integrity + run: | + EXPECTED_SHA="e251ba198448629678ff2168e1a469249d998155" + PLUGIN_DIR="$(helm env HELM_PLUGINS)/helm-unittest" + ACTUAL_SHA="$(git -C "$PLUGIN_DIR" rev-parse HEAD)" + if [ "$ACTUAL_SHA" != "$EXPECTED_SHA" ]; then + echo "::error::Helm unittest plugin checksum mismatch! Expected $EXPECTED_SHA but got $ACTUAL_SHA" + exit 1 + fi + echo "Helm unittest plugin integrity verified: $ACTUAL_SHA" - name: Run unit tests - run: - helm unittest -f 'tests/*.yaml' deploy/charts/litellm-helm \ No newline at end of file + run: helm unittest -f 'tests/*.yaml' deploy/charts/litellm-helm diff --git a/.github/workflows/interpret_load_test.py b/.github/workflows/interpret_load_test.py deleted file mode 100644 index 348ff300ff..0000000000 --- a/.github/workflows/interpret_load_test.py +++ /dev/null @@ -1,139 +0,0 @@ -import csv -import os -from github import Github - - -def interpret_results(csv_file): - with open(csv_file, newline="") as csvfile: - csvreader = csv.DictReader(csvfile) - rows = list(csvreader) - """ - in this csv reader - - Create 1 new column "Status" - - if a row has a median response time < 300 and an average response time < 300, Status = "Passed ✅" - - if a row has a median response time >= 300 or an average response time >= 300, Status = "Failed ❌" - - Order the table in this order Name, Status, Median Response Time, Average Response Time, Requests/s,Failures/s, Min Response Time, Max Response Time, all other columns - """ - - # Add a new column "Status" - for row in rows: - median_response_time = float( - row["Median Response Time"].strip().rstrip("ms") - ) - average_response_time = float( - row["Average Response Time"].strip().rstrip("s") - ) - - request_count = int(row["Request Count"]) - failure_count = int(row["Failure Count"]) - - failure_percent = round((failure_count / request_count) * 100, 2) - - # Determine status based on conditions - if ( - median_response_time < 300 - and average_response_time < 300 - and failure_percent < 5 - ): - row["Status"] = "Passed ✅" - else: - row["Status"] = "Failed ❌" - - # Construct Markdown table header - markdown_table = "| Name | Status | Median Response Time (ms) | Average Response Time (ms) | Requests/s | Failures/s | Request Count | Failure Count | Min Response Time (ms) | Max Response Time (ms) |" - markdown_table += ( - "\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |" - ) - - # Construct Markdown table rows - for row in rows: - markdown_table += f"\n| {row['Name']} | {row['Status']} | {row['Median Response Time']} | {row['Average Response Time']} | {row['Requests/s']} | {row['Failures/s']} | {row['Request Count']} | {row['Failure Count']} | {row['Min Response Time']} | {row['Max Response Time']} |" - print("markdown table: ", markdown_table) - return markdown_table - - -def _get_docker_run_command_stable_release(release_version): - return f""" -\n\n -## Docker Run LiteLLM Proxy - -``` -docker run \\ --e STORE_MODEL_IN_DB=True \\ --p 4000:4000 \\ -ghcr.io/berriai/litellm:litellm_stable_release_branch-{release_version} -``` - """ - - -def _get_docker_run_command(release_version): - return f""" -\n\n -## Docker Run LiteLLM Proxy - -``` -docker run \\ --e STORE_MODEL_IN_DB=True \\ --p 4000:4000 \\ -ghcr.io/berriai/litellm:main-{release_version} -``` - """ - - -def get_docker_run_command(release_version): - if "stable" in release_version: - return _get_docker_run_command_stable_release(release_version) - else: - return _get_docker_run_command(release_version) - - -if __name__ == "__main__": - return - csv_file = "load_test_stats.csv" # Change this to the path of your CSV file - markdown_table = interpret_results(csv_file) - - # Update release body with interpreted results - github_token = os.getenv("GITHUB_TOKEN") - g = Github(github_token) - repo = g.get_repo( - "BerriAI/litellm" - ) # Replace with your repository's username and name - latest_release = repo.get_latest_release() - print("got latest release: ", latest_release) - print(latest_release.title) - print(latest_release.tag_name) - - release_version = latest_release.title - - print("latest release body: ", latest_release.body) - print("markdown table: ", markdown_table) - - # check if "Load Test LiteLLM Proxy Results" exists - existing_release_body = latest_release.body - if "Load Test LiteLLM Proxy Results" in latest_release.body: - # find the "Load Test LiteLLM Proxy Results" section and delete it - start_index = latest_release.body.find("Load Test LiteLLM Proxy Results") - existing_release_body = latest_release.body[:start_index] - - docker_run_command = get_docker_run_command(release_version) - print("docker run command: ", docker_run_command) - - new_release_body = ( - existing_release_body - + docker_run_command - + "\n\n" - + "### Don't want to maintain your internal proxy? get in touch 🎉" - + "\nHosted Proxy Alpha: https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions" - + "\n\n" - + "## Load Test LiteLLM Proxy Results" - + "\n\n" - + markdown_table - ) - print("new release body: ", new_release_body) - try: - latest_release.update_release( - name=latest_release.tag_name, - message=new_release_body, - ) - except Exception as e: - print(e) diff --git a/.github/workflows/issue-keyword-labeler.yml b/.github/workflows/issue-keyword-labeler.yml index 936f90f747..7e2693209b 100644 --- a/.github/workflows/issue-keyword-labeler.yml +++ b/.github/workflows/issue-keyword-labeler.yml @@ -2,8 +2,8 @@ name: Issue Keyword Labeler on: issues: - types: - - opened + types: + - opened jobs: scan-and-label: @@ -13,7 +13,9 @@ jobs: contents: read steps: - name: Checkout code - uses: actions/checkout@v4 + uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 + with: + persist-credentials: false - name: Scan for provider keywords id: scan @@ -24,7 +26,7 @@ jobs: - name: Ensure label exists if: steps.scan.outputs.found == 'true' - uses: actions/github-script@v7 + uses: actions/github-script@f28e40c7f34bde8b3046d885e986cb6290c5673b # v7.1.0 with: github-token: ${{ secrets.GITHUB_TOKEN }} script: | @@ -51,7 +53,7 @@ jobs: - name: Add label to the issue if: steps.scan.outputs.found == 'true' - uses: actions/github-script@v7 + uses: actions/github-script@f28e40c7f34bde8b3046d885e986cb6290c5673b # v7.1.0 with: github-token: ${{ secrets.GITHUB_TOKEN }} script: | @@ -61,4 +63,3 @@ jobs: issue_number: context.issue.number, labels: ['llm translation'] }); - diff --git a/.github/workflows/label-component.yml b/.github/workflows/label-component.yml index fd079fce6c..e0c2fa94d8 100644 --- a/.github/workflows/label-component.yml +++ b/.github/workflows/label-component.yml @@ -12,7 +12,7 @@ jobs: issues: write steps: - name: Add component labels - uses: actions/github-script@v7 + uses: actions/github-script@f28e40c7f34bde8b3046d885e986cb6290c5673b # v7.1.0 with: github-token: ${{ secrets.GITHUB_TOKEN }} script: | diff --git a/.github/workflows/llm-translation-testing.yml b/.github/workflows/llm-translation-testing.yml index 7fda37a66d..922013c4b5 100644 --- a/.github/workflows/llm-translation-testing.yml +++ b/.github/workflows/llm-translation-testing.yml @@ -4,38 +4,41 @@ on: workflow_dispatch: inputs: release_candidate_tag: - description: 'Release candidate tag/version' + description: "Release candidate tag/version" required: true type: string push: tags: - - 'v*-rc*' # Triggers on release candidate tags like v1.0.0-rc1 - + - "v*-rc*" # Triggers on release candidate tags like v1.0.0-rc1 + +permissions: + contents: read + jobs: run-llm-translation-tests: runs-on: ubuntu-latest timeout-minutes: 90 - + steps: - name: Checkout code - uses: actions/checkout@v4 + uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 with: + persist-credentials: false ref: ${{ github.event.inputs.release_candidate_tag || github.ref }} - + - name: Set up Python - uses: actions/setup-python@v5 + uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0 with: - python-version: '3.11' - + python-version: "3.11" + - name: Install Poetry - uses: snok/install-poetry@v1 - with: - version: latest - virtualenvs-create: true - virtualenvs-in-project: true - - - name: Cache Poetry dependencies - uses: actions/cache@v3 + run: | + pip install 'poetry==2.3.2' + poetry config virtualenvs.create true + poetry config virtualenvs.in-project true + + - name: Restore Poetry dependencies cache + uses: actions/cache/restore@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.0.0 with: path: | ~/.cache/pypoetry @@ -43,15 +46,15 @@ jobs: key: ${{ runner.os }}-poetry-${{ hashFiles('**/poetry.lock') }} restore-keys: | ${{ runner.os }}-poetry- - + - name: Install dependencies run: | poetry install --with dev - poetry run pip install pytest-xdist pytest-timeout - + poetry run pip install 'pytest-xdist==3.8.0' 'pytest-timeout==2.4.0' + - name: Create test results directory run: mkdir -p test-results - + - name: Run LLM Translation Tests env: OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }} @@ -61,13 +64,14 @@ jobs: AZURE_API_KEY: ${{ secrets.AZURE_API_KEY }} AZURE_API_BASE: ${{ secrets.AZURE_API_BASE }} AZURE_API_VERSION: ${{ secrets.AZURE_API_VERSION }} - # Add other API keys as needed + RC_TAG: ${{ github.event.inputs.release_candidate_tag || github.ref_name }} + COMMIT_SHA: ${{ github.sha }} run: | python .github/workflows/run_llm_translation_tests.py \ - --tag "${{ github.event.inputs.release_candidate_tag || github.ref_name }}" \ - --commit "${{ github.sha }}" \ + --tag "$RC_TAG" \ + --commit "$COMMIT_SHA" \ || true # Continue even if tests fail - + - name: Display test summary if: always() run: | @@ -79,9 +83,9 @@ jobs: else echo "Warning: Test report was not generated" fi - + - name: Upload test artifacts - uses: actions/upload-artifact@v4 + uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4.6.2 if: always() with: name: LLM-Translation-Artifact-${{ github.event.inputs.release_candidate_tag || github.ref_name }} diff --git a/.github/workflows/load_test.yml b/.github/workflows/load_test.yml deleted file mode 100644 index cdaffa328c..0000000000 --- a/.github/workflows/load_test.yml +++ /dev/null @@ -1,59 +0,0 @@ -name: Test Locust Load Test - -on: - workflow_run: - workflows: ["Build, Publish LiteLLM Docker Image. New Release"] - types: - - completed - workflow_dispatch: - -jobs: - build: - runs-on: ubuntu-latest - steps: - - name: Checkout - uses: actions/checkout@v1 - - name: Setup Python - uses: actions/setup-python@v2 - with: - python-version: '3.x' - - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install PyGithub - - name: re-deploy proxy - run: | - echo "Current working directory: $PWD" - ls - python ".github/workflows/redeploy_proxy.py" - env: - LOAD_TEST_REDEPLOY_URL1: ${{ secrets.LOAD_TEST_REDEPLOY_URL1 }} - LOAD_TEST_REDEPLOY_URL2: ${{ secrets.LOAD_TEST_REDEPLOY_URL2 }} - working-directory: ${{ github.workspace }} - - name: Run Load Test - id: locust_run - uses: BerriAI/locust-github-action@master - with: - LOCUSTFILE: ".github/workflows/locustfile.py" - URL: "https://post-release-load-test-proxy.onrender.com/" - USERS: "20" - RATE: "20" - RUNTIME: "300s" - - name: Process Load Test Stats - run: | - echo "Current working directory: $PWD" - ls - python ".github/workflows/interpret_load_test.py" - env: - GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} - working-directory: ${{ github.workspace }} - - name: Upload CSV as Asset to Latest Release - uses: xresloader/upload-to-github-release@v1 - env: - GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} - with: - file: "load_test_stats.csv;load_test.html" - update_latest_release: true - tag_name: "load-test" - overwrite: true \ No newline at end of file diff --git a/.github/workflows/locustfile.py b/.github/workflows/locustfile.py deleted file mode 100644 index 36dbeee9c4..0000000000 --- a/.github/workflows/locustfile.py +++ /dev/null @@ -1,28 +0,0 @@ -from locust import HttpUser, task, between - - -class MyUser(HttpUser): - wait_time = between(1, 5) - - @task - def chat_completion(self): - headers = { - "Content-Type": "application/json", - "Authorization": "Bearer sk-8N1tLOOyH8TIxwOLahhIVg", - # Include any additional headers you may need for authentication, etc. - } - - # Customize the payload with "model" and "messages" keys - payload = { - "model": "fake-openai-endpoint", - "messages": [ - {"role": "system", "content": "You are a chat bot."}, - {"role": "user", "content": "Hello, how are you?"}, - ], - # Add more data as necessary - } - - # Make a POST request to the "chat/completions" endpoint - response = self.client.post("chat/completions", json=payload, headers=headers) - - # Print or log the response if needed diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml deleted file mode 100644 index 23e4a06da9..0000000000 --- a/.github/workflows/main.yml +++ /dev/null @@ -1,34 +0,0 @@ -name: Publish Dev Release to PyPI - -on: - workflow_dispatch: - -jobs: - publish-dev-release: - runs-on: ubuntu-latest - - steps: - - name: Checkout code - uses: actions/checkout@v2 - - - name: Set up Python - uses: actions/setup-python@v2 - with: - python-version: 3.8 # Adjust the Python version as needed - - - name: Install dependencies - run: pip install toml twine - - - name: Read version from pyproject.toml - id: read-version - run: | - version=$(python -c 'import toml; print(toml.load("pyproject.toml")["tool"]["commitizen"]["version"])') - printf "LITELLM_VERSION=%s" "$version" >> $GITHUB_ENV - - - name: Check if version exists on PyPI - id: check-version - run: | - set -e - if twine check --repository-url https://pypi.org/simple/ "litellm==$LITELLM_VERSION" >/dev/null 2>&1; then - echo "Version $LITELLM_VERSION already exists on PyPI. Skipping publish." - diff --git a/.github/workflows/publish-migrations.yml b/.github/workflows/publish-migrations.yml deleted file mode 100644 index a5187cb2f5..0000000000 --- a/.github/workflows/publish-migrations.yml +++ /dev/null @@ -1,207 +0,0 @@ -name: Publish Prisma Migrations - -permissions: - contents: write - pull-requests: write - -on: - push: - paths: - - 'schema.prisma' # Check root schema.prisma - branches: - - main - -jobs: - publish-migrations: - if: github.repository == 'BerriAI/litellm' - runs-on: ubuntu-latest - services: - postgres: - image: postgres:14 - env: - POSTGRES_DB: temp_db - POSTGRES_USER: postgres - POSTGRES_PASSWORD: postgres - ports: - - 5432:5432 - options: >- - --health-cmd pg_isready - --health-interval 10s - --health-timeout 5s - --health-retries 5 - - # Add shadow database service - postgres_shadow: - image: postgres:14 - env: - POSTGRES_DB: shadow_db - POSTGRES_USER: postgres - POSTGRES_PASSWORD: postgres - ports: - - 5433:5432 - options: >- - --health-cmd pg_isready - --health-interval 10s - --health-timeout 5s - --health-retries 5 - - steps: - - uses: actions/checkout@v3 - - - name: Set up Python - uses: actions/setup-python@v4 - with: - python-version: '3.x' - - - name: Install Dependencies - run: | - pip install prisma - pip install python-dotenv - - - name: Generate Initial Migration if None Exists - env: - DATABASE_URL: "postgresql://postgres:postgres@localhost:5432/temp_db" - DIRECT_URL: "postgresql://postgres:postgres@localhost:5432/temp_db" - SHADOW_DATABASE_URL: "postgresql://postgres:postgres@localhost:5433/shadow_db" - run: | - mkdir -p deploy/migrations - echo 'provider = "postgresql"' > deploy/migrations/migration_lock.toml - - if [ -z "$(ls -A deploy/migrations/2* 2>/dev/null)" ]; then - echo "No existing migrations found, creating baseline..." - VERSION=$(date +%Y%m%d%H%M%S) - mkdir -p deploy/migrations/${VERSION}_initial - - echo "Generating initial migration..." - # Save raw output for debugging - prisma migrate diff \ - --from-empty \ - --to-schema-datamodel schema.prisma \ - --shadow-database-url "${SHADOW_DATABASE_URL}" \ - --script > deploy/migrations/${VERSION}_initial/raw_migration.sql - - echo "Raw migration file content:" - cat deploy/migrations/${VERSION}_initial/raw_migration.sql - - echo "Cleaning migration file..." - # Clean the file - sed '/^Installing/d' deploy/migrations/${VERSION}_initial/raw_migration.sql > deploy/migrations/${VERSION}_initial/migration.sql - - # Verify the migration file - if [ ! -s deploy/migrations/${VERSION}_initial/migration.sql ]; then - echo "ERROR: Migration file is empty after cleaning" - echo "Original content was:" - cat deploy/migrations/${VERSION}_initial/raw_migration.sql - exit 1 - fi - - echo "Final migration file content:" - cat deploy/migrations/${VERSION}_initial/migration.sql - - # Verify it starts with SQL - if ! head -n 1 deploy/migrations/${VERSION}_initial/migration.sql | grep -q "^--\|^CREATE\|^ALTER"; then - echo "ERROR: Migration file does not start with SQL command or comment" - echo "First line is:" - head -n 1 deploy/migrations/${VERSION}_initial/migration.sql - echo "Full content is:" - cat deploy/migrations/${VERSION}_initial/migration.sql - exit 1 - fi - - echo "Initial migration generated at $(date -u)" > deploy/migrations/${VERSION}_initial/README.md - fi - - - name: Compare and Generate Migration - if: success() - env: - DATABASE_URL: "postgresql://postgres:postgres@localhost:5432/temp_db" - DIRECT_URL: "postgresql://postgres:postgres@localhost:5432/temp_db" - SHADOW_DATABASE_URL: "postgresql://postgres:postgres@localhost:5433/shadow_db" - run: | - # Create temporary migration workspace - mkdir -p temp_migrations - - # Copy existing migrations (will not fail if directory is empty) - cp -r deploy/migrations/* temp_migrations/ 2>/dev/null || true - - VERSION=$(date +%Y%m%d%H%M%S) - - # Generate diff against existing migrations or empty state - prisma migrate diff \ - --from-migrations temp_migrations \ - --to-schema-datamodel schema.prisma \ - --shadow-database-url "${SHADOW_DATABASE_URL}" \ - --script > temp_migrations/migration_${VERSION}.sql - - # Check if there are actual changes - if [ -s temp_migrations/migration_${VERSION}.sql ]; then - echo "Changes detected, creating new migration" - mkdir -p deploy/migrations/${VERSION}_schema_update - mv temp_migrations/migration_${VERSION}.sql deploy/migrations/${VERSION}_schema_update/migration.sql - echo "Migration generated at $(date -u)" > deploy/migrations/${VERSION}_schema_update/README.md - else - echo "No schema changes detected" - exit 0 - fi - - - name: Verify Migration - if: success() - env: - DATABASE_URL: "postgresql://postgres:postgres@localhost:5432/temp_db" - DIRECT_URL: "postgresql://postgres:postgres@localhost:5432/temp_db" - SHADOW_DATABASE_URL: "postgresql://postgres:postgres@localhost:5433/shadow_db" - run: | - # Create test database - psql "${SHADOW_DATABASE_URL}" -c 'CREATE DATABASE migration_test;' - - # Apply all migrations in order to verify - for migration in deploy/migrations/*/migration.sql; do - echo "Applying migration: $migration" - psql "${SHADOW_DATABASE_URL}" -f $migration - done - - # Add this step before create-pull-request to debug permissions - - name: Check Token Permissions - run: | - echo "Checking token permissions..." - curl -H "Authorization: token ${{ secrets.GITHUB_TOKEN }}" \ - -H "Accept: application/vnd.github.v3+json" \ - https://api.github.com/repos/BerriAI/litellm/collaborators - - echo "\nChecking if token can create PRs..." - curl -H "Authorization: token ${{ secrets.GITHUB_TOKEN }}" \ - -H "Accept: application/vnd.github.v3+json" \ - https://api.github.com/repos/BerriAI/litellm - - # Add this debug step before git push - - name: Debug Changed Files - run: | - echo "Files staged for commit:" - git diff --name-status --staged - - echo "\nAll changed files:" - git status - - - name: Create Pull Request - if: success() - uses: peter-evans/create-pull-request@v5 - with: - token: ${{ secrets.GITHUB_TOKEN }} - commit-message: "chore: update prisma migrations" - title: "Update Prisma Migrations" - body: | - Auto-generated migration based on schema.prisma changes. - - Generated files: - - deploy/migrations/${VERSION}_schema_update/migration.sql - - deploy/migrations/${VERSION}_schema_update/README.md - branch: feat/prisma-migration-${{ env.VERSION }} - base: main - delete-branch: true - - - name: Generate and Save Migrations - run: | - # Only add migration files - git add deploy/migrations/ - git status # Debug what's being committed - git commit -m "chore: update prisma migrations" diff --git a/.github/workflows/publish_enterprise.yml b/.github/workflows/publish_enterprise.yml deleted file mode 100644 index 459a233cb7..0000000000 --- a/.github/workflows/publish_enterprise.yml +++ /dev/null @@ -1,94 +0,0 @@ -name: Publish litellm-enterprise to PyPI - -on: - workflow_dispatch: - inputs: - bump: - description: "Version bump type" - required: true - default: "patch" - type: choice - options: - - patch - - minor - - major - -jobs: - publish: - runs-on: ubuntu-latest - if: github.repository == 'BerriAI/litellm' - permissions: - contents: write - pull-requests: write - defaults: - run: - working-directory: enterprise - - steps: - - uses: actions/checkout@v4 - - - uses: actions/setup-python@v5 - with: - python-version: "3.11" - - - name: Install Poetry - run: pip install poetry - - - name: Bump version - id: bump - run: | - OLD=$(poetry version -s) - poetry version ${{ github.event.inputs.bump }} - NEW=$(poetry version -s) - echo "old=$OLD" >> $GITHUB_OUTPUT - echo "new=$NEW" >> $GITHUB_OUTPUT - - - name: Update version refs in root pyproject.toml and requirements.txt - run: | - OLD=${{ steps.bump.outputs.old }} - NEW=${{ steps.bump.outputs.new }} - sed -i "s/litellm-enterprise = {version = \"${OLD}\"/litellm-enterprise = {version = \"${NEW}\"/" ../pyproject.toml - sed -i "s/litellm-enterprise==${OLD}/litellm-enterprise==${NEW}/" ../requirements.txt - - - name: Update poetry.lock - working-directory: . - run: poetry lock - - - name: Build - run: poetry build - - - name: Commit version bump and create PR - id: create-pr - run: | - git config user.name "github-actions[bot]" - git config user.email "github-actions[bot]@users.noreply.github.com" - cd .. - BRANCH="bump/enterprise-${{ steps.bump.outputs.new }}" - git checkout -b "$BRANCH" - git add enterprise/pyproject.toml pyproject.toml requirements.txt poetry.lock - git commit -m "bump: litellm-enterprise ${{ steps.bump.outputs.old }} → ${{ steps.bump.outputs.new }}" - git push origin "$BRANCH" --force - gh pr create \ - --title "bump: litellm-enterprise ${{ steps.bump.outputs.old }} → ${{ steps.bump.outputs.new }}" \ - --body "Version bump for litellm-enterprise. Merge to update main." \ - --head "$BRANCH" \ - --base main \ - || true - PR_URL=$(gh pr list --head "$BRANCH" --json url -q '.[0].url') - echo "pr_url=$PR_URL" >> $GITHUB_OUTPUT - env: - GH_TOKEN: ${{ github.token }} - - - name: Enable auto-merge - run: | - gh pr merge "${{ steps.create-pr.outputs.pr_url }}" --auto --squash - env: - GH_TOKEN: ${{ github.token }} - - - name: Publish to PyPI - env: - TWINE_USERNAME: __token__ - TWINE_PASSWORD: ${{ secrets.PYPI_ENTERPRISE }} - run: | - pip install twine - twine upload dist/litellm_enterprise-${{ steps.bump.outputs.new }}* diff --git a/.github/workflows/publish_proxy_extras.yml b/.github/workflows/publish_proxy_extras.yml deleted file mode 100644 index fa30b15316..0000000000 --- a/.github/workflows/publish_proxy_extras.yml +++ /dev/null @@ -1,74 +0,0 @@ -name: Publish litellm-proxy-extras to PyPI - -on: - workflow_dispatch: - inputs: - bump: - description: "Version bump type" - required: true - default: "patch" - type: choice - options: - - patch - - minor - - major - -jobs: - publish: - runs-on: ubuntu-latest - if: github.repository == 'BerriAI/litellm' - permissions: - contents: write - defaults: - run: - working-directory: litellm-proxy-extras - - steps: - - uses: actions/checkout@v4 - - - uses: actions/setup-python@v5 - with: - python-version: "3.11" - - - name: Install Poetry - run: pip install poetry - - - name: Bump version - id: bump - run: | - OLD=$(poetry version -s) - poetry version ${{ github.event.inputs.bump }} - NEW=$(poetry version -s) - echo "old=$OLD" >> $GITHUB_OUTPUT - echo "new=$NEW" >> $GITHUB_OUTPUT - - - name: Update version refs in root pyproject.toml and requirements.txt - run: | - OLD=${{ steps.bump.outputs.old }} - NEW=${{ steps.bump.outputs.new }} - sed -i "s/litellm-proxy-extras = {version = \"${OLD}\"/litellm-proxy-extras = {version = \"${NEW}\"/" ../pyproject.toml - sed -i "s/litellm-proxy-extras==${OLD}/litellm-proxy-extras==${NEW}/" ../requirements.txt - - - name: Update poetry.lock - working-directory: . - run: poetry lock - - - name: Build - run: poetry build - - - name: Commit version bump - run: | - git config user.name "github-actions[bot]" - git config user.email "github-actions[bot]@users.noreply.github.com" - cd .. - git add litellm-proxy-extras/pyproject.toml pyproject.toml requirements.txt poetry.lock - git commit -m "bump: litellm-proxy-extras ${{ steps.bump.outputs.old }} → ${{ steps.bump.outputs.new }}" - git push - - - name: Publish to PyPI - env: - TWINE_USERNAME: __token__ - TWINE_PASSWORD: ${{ secrets.PYPI_PUBLISH_PASSWORD }} - run: | - pip install twine - twine upload dist/litellm_proxy_extras-${{ steps.bump.outputs.new }}* diff --git a/.github/workflows/publish_to_pypi.yml b/.github/workflows/publish_to_pypi.yml new file mode 100644 index 0000000000..8f675bb307 --- /dev/null +++ b/.github/workflows/publish_to_pypi.yml @@ -0,0 +1,136 @@ +name: Publish to PyPI + +on: + workflow_dispatch: + +jobs: + preflight-checks: + name: Preflight Checks + runs-on: ubuntu-latest + timeout-minutes: 10 + permissions: + contents: read + # No environment — read-only checks, no approval needed + outputs: + needs_publish: ${{ steps.check-litellm.outputs.needs_publish }} + version: ${{ steps.check-litellm.outputs.version }} + + steps: + - name: Checkout repo + uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 + + - name: Set up Python + uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0 + with: + python-version: "3.12" + + - name: Check litellm version on PyPI + id: check-litellm + run: | + VERSION=$(grep -m1 '^version' pyproject.toml | sed 's/version = "\(.*\)"/\1/') + echo "version=$VERSION" >> "$GITHUB_OUTPUT" + echo "Checking if litellm $VERSION exists on PyPI..." + + HTTP_STATUS=$(curl -s -o /dev/null -w "%{http_code}" "https://pypi.org/pypi/litellm/$VERSION/json") + if [ "$HTTP_STATUS" = "200" ]; then + echo "litellm $VERSION already exists on PyPI. Skipping publish." + echo "needs_publish=false" >> "$GITHUB_OUTPUT" + else + echo "litellm $VERSION not found on PyPI. Publish needed." + echo "needs_publish=true" >> "$GITHUB_OUTPUT" + fi + + - name: Sanity check proxy-extras version + run: | + # Read pinned version from requirements.txt + REQ_VERSION=$(grep -oP 'litellm-proxy-extras==\K[0-9.]+' requirements.txt) + if [ -z "$REQ_VERSION" ]; then + echo "::error::Could not find litellm-proxy-extras version in requirements.txt" + exit 1 + fi + echo "requirements.txt pins litellm-proxy-extras==$REQ_VERSION" + + # Read pinned version from pyproject.toml dependency + PYPROJECT_VERSION=$(python3 -c " + import re + with open('pyproject.toml') as f: + content = f.read() + match = re.search(r'litellm-proxy-extras\s*=\s*\{version\s*=\s*\"([^\"]+)\"', content) + if match: + print(match.group(1).lstrip('^~>=')) + else: + import sys + print('::error::Could not find litellm-proxy-extras dependency in pyproject.toml', file=sys.stderr) + sys.exit(1) + ") + echo "pyproject.toml pins litellm-proxy-extras version: $PYPROJECT_VERSION" + + # Check that both pinned versions match + if [ "$REQ_VERSION" != "$PYPROJECT_VERSION" ]; then + echo "::error::Version mismatch: requirements.txt has $REQ_VERSION but pyproject.toml has $PYPROJECT_VERSION" + exit 1 + fi + + # Check that the pinned version exists on PyPI + echo "Checking if litellm-proxy-extras $REQ_VERSION exists on PyPI..." + HTTP_STATUS=$(curl -s -o /dev/null -w "%{http_code}" "https://pypi.org/pypi/litellm-proxy-extras/$REQ_VERSION/json") + if [ "$HTTP_STATUS" != "200" ]; then + echo "::error::litellm-proxy-extras $REQ_VERSION is not published on PyPI yet. Publish it before releasing litellm." + exit 1 + fi + echo "litellm-proxy-extras $REQ_VERSION exists on PyPI. Sanity check passed." + + publish-litellm: + name: Publish litellm to PyPI + needs: preflight-checks + if: needs.preflight-checks.outputs.needs_publish == 'true' + runs-on: ubuntu-latest + timeout-minutes: 10 + permissions: + id-token: write + contents: read + environment: pypi-publish + + steps: + - name: Checkout repo + uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 + + - name: Set up Python + uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0 + with: + python-version: "3.12" + + - name: Copy model prices backup + run: cp model_prices_and_context_window.json litellm/model_prices_and_context_window_backup.json + + - name: Install build tools + run: python -m pip install --upgrade pip build==1.4.2 + + - name: Build package + run: | + rm -rf build dist + python -m build + + - name: Verify build artifacts + env: + EXPECTED_VERSION: ${{ needs.preflight-checks.outputs.version }} + run: | + echo "Contents of dist/:" + ls -la dist/ + # Ensure we have both sdist and wheel + ls dist/*.tar.gz + ls dist/*.whl + # Verify built version matches expected + ls dist/ | grep -q "litellm-${EXPECTED_VERSION}" || { + echo "::error::Built artifacts do not match expected version $EXPECTED_VERSION" + ls dist/ + exit 1 + } + + - name: Validate package metadata + run: | + pip install twine==6.2.0 + twine check dist/* + + - name: Publish to PyPI + uses: pypa/gh-action-pypi-publish@ed0c53931b1dc9bd32cbe73a98c7f6766f8a527e # v1.13.0 diff --git a/.github/workflows/read_pyproject_version.yml b/.github/workflows/read_pyproject_version.yml index 8f6310f935..04b4a38ce1 100644 --- a/.github/workflows/read_pyproject_version.yml +++ b/.github/workflows/read_pyproject_version.yml @@ -3,7 +3,10 @@ name: Read Version from pyproject.toml on: push: branches: - - main # Change this to the default branch of your repository + - main # Change this to the default branch of your repository + +permissions: + contents: read jobs: read-version: @@ -11,20 +14,14 @@ jobs: steps: - name: Checkout code - uses: actions/checkout@v2 - - - name: Set up Python - uses: actions/setup-python@v2 + uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 with: - python-version: 3.8 # Adjust the Python version as needed - - - name: Install dependencies - run: pip install toml + persist-credentials: false - name: Read version from pyproject.toml id: read-version run: | - version=$(python -c 'import toml; print(toml.load("pyproject.toml")["tool"]["commitizen"]["version"])') + version=$(grep -m1 '^version' pyproject.toml | sed 's/version = "\(.*\)"/\1/') printf "LITELLM_VERSION=%s" "$version" >> $GITHUB_ENV - name: Display version diff --git a/.github/workflows/redeploy_proxy.py b/.github/workflows/redeploy_proxy.py deleted file mode 100644 index ed46bef73a..0000000000 --- a/.github/workflows/redeploy_proxy.py +++ /dev/null @@ -1,20 +0,0 @@ -""" - -redeploy_proxy.py -""" - -import os -import requests -import time - -# send a get request to this endpoint -deploy_hook1 = os.getenv("LOAD_TEST_REDEPLOY_URL1") -response = requests.get(deploy_hook1, timeout=20) - - -deploy_hook2 = os.getenv("LOAD_TEST_REDEPLOY_URL2") -response = requests.get(deploy_hook2, timeout=20) - -print("SENT GET REQUESTS to re-deploy proxy") -print("sleeeping.... for 60s") -time.sleep(60) diff --git a/.github/workflows/regenerate-poetry-lock.yml b/.github/workflows/regenerate-poetry-lock.yml deleted file mode 100644 index c0844f1c70..0000000000 --- a/.github/workflows/regenerate-poetry-lock.yml +++ /dev/null @@ -1,80 +0,0 @@ -name: Regenerate poetry.lock - -# Runs whenever pyproject.toml is merged into main (the most common cause of -# the "pyproject.toml changed significantly since poetry.lock was last generated" -# CI failure). Can also be triggered manually. -on: - push: - branches: - - main - paths: - - pyproject.toml - workflow_dispatch: - -permissions: - contents: write # needed to push the auto/regenerate-poetry-lock-* branch - pull-requests: write # needed to open the PR and enable auto-merge - -jobs: - regenerate-lock: - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v4 - - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version: "3.11" - - - name: Install Poetry - run: pip install poetry - - - name: Regenerate poetry.lock - run: poetry lock - - - name: Check whether poetry.lock actually changed - id: diff - run: | - if git diff --quiet poetry.lock; then - echo "changed=false" >> "$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/reset_stable.yml b/.github/workflows/reset_stable.yml deleted file mode 100644 index f6fed672d4..0000000000 --- a/.github/workflows/reset_stable.yml +++ /dev/null @@ -1,39 +0,0 @@ -name: Reset litellm_stable branch - -on: - release: - types: [published, created] -jobs: - update-stable-branch: - if: ${{ startsWith(github.event.release.tag_name, 'v') && !endsWith(github.event.release.tag_name, '-stable') }} - runs-on: ubuntu-latest - - steps: - - name: Checkout repository - uses: actions/checkout@v3 - - - name: Reset litellm_stable_release_branch branch to the release commit - env: - GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} - run: | - # Configure Git user - git config user.name "github-actions[bot]" - git config user.email "github-actions[bot]@users.noreply.github.com" - - # Fetch all branches and tags - git fetch --all - - # Check if the litellm_stable_release_branch branch exists - if git show-ref --verify --quiet refs/remotes/origin/litellm_stable_release_branch; then - echo "litellm_stable_release_branch branch exists." - git checkout litellm_stable_release_branch - else - echo "litellm_stable_release_branch branch does not exist. Creating it." - git checkout -b litellm_stable_release_branch - fi - - # Reset litellm_stable_release_branch branch to the release commit - git reset --hard $GITHUB_SHA - - # Push the updated litellm_stable_release_branch branch - git push origin litellm_stable_release_branch --force diff --git a/.github/workflows/run_observatory_tests.yml b/.github/workflows/run_observatory_tests.yml index d343098ed3..a25b96766d 100644 --- a/.github/workflows/run_observatory_tests.yml +++ b/.github/workflows/run_observatory_tests.yml @@ -33,7 +33,9 @@ jobs: timeout-minutes: 30 steps: - name: Checkout repository - uses: actions/checkout@v4 + uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 + with: + persist-credentials: false - name: Validate tag input env: @@ -49,11 +51,12 @@ jobs: TAG: ${{ inputs.tag }} AZURE_API_KEY: ${{ secrets.AZURE_API_KEY }} AZURE_API_BASE: ${{ secrets.AZURE_API_BASE }} + WORKSPACE: ${{ github.workspace }} run: | docker run -d \ --name litellm-rc \ -p 4000:4000 \ - -v "${{ github.workspace }}/.github/observatory/litellm_config.yaml:/app/config.yaml" \ + -v "${WORKSPACE}/.github/observatory/litellm_config.yaml:/app/config.yaml" \ -e LITELLM_MASTER_KEY="${LITELLM_MASTER_KEY}" \ -e AZURE_API_KEY="${AZURE_API_KEY}" \ -e AZURE_API_BASE="${AZURE_API_BASE}" \ @@ -77,8 +80,9 @@ jobs: - name: Start cloudflared tunnel run: | - # Install cloudflared + # Install cloudflared (pinned version + checksum) curl -sL https://github.com/cloudflare/cloudflared/releases/download/2025.2.1/cloudflared-linux-amd64 -o /usr/local/bin/cloudflared + echo "afdfadd1ef552e66bffc35246fe30a9bd578356d2d386de95585ccfc432472b8 /usr/local/bin/cloudflared" | sha256sum -c - chmod +x /usr/local/bin/cloudflared # Start a quick tunnel (no account needed) and capture the URL @@ -103,11 +107,11 @@ jobs: - name: Verify tunnel connectivity run: | - echo "Testing tunnel at ${{ env.TUNNEL_URL }}..." + echo "Testing tunnel at ${TUNNEL_URL}..." # Quick tunnels need time for DNS propagation; retry to avoid # transient NXDOMAIN (curl exit code 6) on first attempt. for i in $(seq 1 10); do - if curl -sf "${{ env.TUNNEL_URL }}/health/liveliness" > /dev/null 2>&1; then + if curl -sf "${TUNNEL_URL}/health/liveliness" > /dev/null 2>&1; then echo "Tunnel is working (attempt $i)" exit 0 fi @@ -221,5 +225,5 @@ jobs: - name: Cleanup if: always() run: | - kill "${{ env.CLOUDFLARED_PID }}" 2>/dev/null || true + kill "$CLOUDFLARED_PID" 2>/dev/null || true docker rm -f litellm-rc 2>/dev/null || true diff --git a/.github/workflows/scan_duplicate_issues.yml b/.github/workflows/scan_duplicate_issues.yml index 06e8f453a8..222ff11f30 100644 --- a/.github/workflows/scan_duplicate_issues.yml +++ b/.github/workflows/scan_duplicate_issues.yml @@ -21,14 +21,15 @@ jobs: contents: read steps: - name: Checkout scripts - uses: actions/checkout@v4 + uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 with: sparse-checkout: .github/scripts + persist-credentials: false - name: Set up Python - uses: actions/setup-python@v5 + uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0 with: - python-version: "3.11" + python-version: "3.13" - name: Scan for duplicate issues env: diff --git a/.github/workflows/scorecard.yml b/.github/workflows/scorecard.yml new file mode 100644 index 0000000000..7cd12bb219 --- /dev/null +++ b/.github/workflows/scorecard.yml @@ -0,0 +1,47 @@ +name: Scorecard supply-chain security + +on: + branch_protection_rule: + schedule: + - cron: '27 12 * * 4' + push: + branches: ["main"] + +permissions: read-all + +jobs: + analysis: + name: Scorecard analysis + runs-on: ubuntu-latest + if: github.event.repository.default_branch == github.ref_name + permissions: + security-events: write + id-token: write + # Uncomment for private repos if needed: + # contents: read + # actions: read + + steps: + - name: Checkout code + uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 + with: + persist-credentials: false + + - name: Run analysis + uses: ossf/scorecard-action@f49aabe0b5af0936a0987cfb85d86b75731b0186 # v2.4.1 + with: + results_file: results.sarif + results_format: sarif + publish_results: true + + - name: Upload artifact + uses: actions/upload-artifact@4cec3d8aa04e39d1a68397de0c4cd6fb9dce8ec1 # v4.6.1 + with: + name: SARIF file + path: results.sarif + retention-days: 5 + + - name: Upload to code scanning + uses: github/codeql-action/upload-sarif@c10b806170c8ee63ea24152429041b5624f0baf5 # v4.35.1 + with: + sarif_file: results.sarif diff --git a/.github/workflows/simple_pypi_publish.yml b/.github/workflows/simple_pypi_publish.yml deleted file mode 100644 index e183055681..0000000000 --- a/.github/workflows/simple_pypi_publish.yml +++ /dev/null @@ -1,67 +0,0 @@ -name: Simple PyPI Publish - -on: - workflow_dispatch: - inputs: - version: - description: 'Version to publish (e.g., 1.74.10)' - required: true - type: string - -env: - TWINE_USERNAME: __token__ - -jobs: - publish: - runs-on: ubuntu-latest - if: github.repository == 'BerriAI/litellm' - - steps: - - name: Checkout code - uses: actions/checkout@v4 - - - name: Set up Python - uses: actions/setup-python@v4 - with: - python-version: '3.8' - - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install toml build wheel twine - - - name: Update version in pyproject.toml - run: | - python -c " - import toml - - with open('pyproject.toml', 'r') as f: - data = toml.load(f) - - data['tool']['poetry']['version'] = '${{ github.event.inputs.version }}' - - with open('pyproject.toml', 'w') as f: - toml.dump(data, f) - - print(f'Updated version to ${{ github.event.inputs.version }}') - " - - - name: Copy model prices file - run: | - cp model_prices_and_context_window.json litellm/model_prices_and_context_window_backup.json - - - name: Build package - run: | - rm -rf build dist - python -m build - - - name: Publish to PyPI - env: - TWINE_PASSWORD: ${{ secrets.PYPI_PUBLISH_PASSWORD }} - run: | - twine upload dist/* - - - name: Output success - run: | - echo "✅ Successfully published litellm v${{ github.event.inputs.version }} to PyPI" - echo "📦 Package: https://pypi.org/project/litellm/${{ github.event.inputs.version }}/" \ No newline at end of file diff --git a/.github/workflows/stale.yml b/.github/workflows/stale.yml index 5a9b19fc9c..c905bb1231 100644 --- a/.github/workflows/stale.yml +++ b/.github/workflows/stale.yml @@ -2,19 +2,24 @@ name: "Stale Issue Management" on: schedule: - - cron: '0 0 * * *' # Runs daily at midnight UTC + - cron: "0 0 * * *" # Runs daily at midnight UTC workflow_dispatch: +permissions: + issues: write + pull-requests: write + jobs: stale: + if: github.repository == 'BerriAI/litellm' runs-on: ubuntu-latest steps: - - uses: actions/stale@v8 + - uses: actions/stale@1160a2240286f5da8ec72b1c0816ce2481aabf84 # v8 with: repo-token: "${{ secrets.GITHUB_TOKEN }}" stale-issue-message: "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs." stale-pr-message: "This pull request has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs." - days-before-stale: 90 # Revert to 60 days - days-before-close: 7 # Revert to 7 days + days-before-stale: 90 # Revert to 60 days + days-before-close: 7 # Revert to 7 days stale-issue-label: "stale" - operations-per-run: 1000 \ No newline at end of file + operations-per-run: 1000 diff --git a/.github/workflows/sync-schema.yml b/.github/workflows/sync-schema.yml new file mode 100644 index 0000000000..72a5c56293 --- /dev/null +++ b/.github/workflows/sync-schema.yml @@ -0,0 +1,73 @@ +name: Sync schema.prisma copies + +on: + pull_request: + paths: + - 'schema.prisma' + +# Scoped to ONLY the permissions needed: +# - contents:write to push the sync commit to the PR branch +# - pull-requests:read is implicit (needed to check out the PR) +permissions: + contents: write + +jobs: + sync: + name: Copy root schema to proxy and proxy-extras + runs-on: ubuntu-latest + timeout-minutes: 5 + # Only run on PRs from branches in THIS repo (not forks). + # Fork PRs cannot push back to the head branch with GITHUB_TOKEN, + # and pull_request events from forks have read-only tokens anyway. + # Also reject PRs from branches named after protected branches to + # prevent pushing directly to main/master. + if: >- + github.event.pull_request.head.repo.full_name == github.repository + && github.head_ref != 'main' + && github.head_ref != 'master' + steps: + - name: Checkout PR branch by SHA + uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 + with: + # Use the merge commit SHA for safety — github.head_ref is an + # attacker-controlled string (the branch name) and could contain + # unusual characters that cause unexpected git behavior. + ref: ${{ github.event.pull_request.head.sha }} + persist-credentials: true # needed for git push + + - name: Reject symlinked schema files + run: | + for f in schema.prisma litellm/proxy/schema.prisma litellm-proxy-extras/litellm_proxy_extras/schema.prisma; do + if [ -L "$f" ]; then + echo "::error file=$f::$f is a symlink, which is not allowed" + exit 1 + fi + done + + - name: Copy root schema to other locations + run: | + cp schema.prisma litellm/proxy/schema.prisma + cp schema.prisma litellm-proxy-extras/litellm_proxy_extras/schema.prisma + + - name: Check for changes + id: diff + run: | + if git diff --quiet -- litellm/proxy/schema.prisma litellm-proxy-extras/litellm_proxy_extras/schema.prisma; then + echo "changed=false" >> "$GITHUB_OUTPUT" + echo "Schemas already in sync. Nothing to do." + else + echo "changed=true" >> "$GITHUB_OUTPUT" + echo "Schema copies need updating." + fi + + - name: Commit synced schemas + if: steps.diff.outputs.changed == 'true' + run: | + # Push to the PR's head branch (need the branch name for git push). + # We checked out by SHA above for safety, so configure the push target explicitly. + git config user.name "github-actions[bot]" + git config user.email "41898282+github-actions[bot]@users.noreply.github.com" + git checkout -B "$GITHUB_HEAD_REF" + git add -- litellm/proxy/schema.prisma litellm-proxy-extras/litellm_proxy_extras/schema.prisma + git commit -m "chore: sync schema.prisma copies from root" + git push origin "HEAD:$GITHUB_HEAD_REF" diff --git a/.github/workflows/test-linting.yml b/.github/workflows/test-linting.yml index fc0f84a20d..5bb85716a1 100644 --- a/.github/workflows/test-linting.yml +++ b/.github/workflows/test-linting.yml @@ -2,7 +2,10 @@ name: LiteLLM Linting on: pull_request: - branches: [ main ] + branches: [main] + +permissions: + contents: read jobs: lint: @@ -10,69 +13,73 @@ jobs: timeout-minutes: 5 steps: - - uses: actions/checkout@v4 - with: - fetch-depth: 0 - clean: true + - uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 + with: + fetch-depth: 0 + clean: true + persist-credentials: false - - name: Set up Python - uses: actions/setup-python@v4 - with: - python-version: '3.12' + - name: Set up Python + uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0 + with: + python-version: "3.12" - - name: Install Poetry - uses: snok/install-poetry@v1 + - name: Install Poetry + run: pip install 'poetry==2.3.2' - - name: Clean Python cache - run: | - find . -type d -name "__pycache__" -exec rm -rf {} + || true - find . -name "*.pyc" -delete || true + - name: Clean Python cache + run: | + find . -type d -name "__pycache__" -exec rm -rf {} + || true + find . -name "*.pyc" -delete || true - - name: Install dependencies - run: | - poetry lock - poetry install --with dev + - name: Check poetry.lock is up to date + run: | + poetry check --lock || (echo "❌ poetry.lock is out of sync with pyproject.toml. Run 'poetry lock' locally and commit the result." && exit 1) - - name: Check Black formatting - run: | - cd litellm - poetry run black --check --exclude '/enterprise/' . - cd .. + - name: Install dependencies + run: | + poetry install --with dev - - name: Debug - Check file state - run: | - echo "Current branch:" - git branch --show-current - echo "Last 3 commits:" - git log --oneline -3 - echo "File content around line 43:" - head -50 litellm/litellm_core_utils/custom_logger_registry.py | tail -10 - - - name: Run Ruff linting - run: | - cd litellm - poetry run ruff check . - cd .. + - name: Check Black formatting + run: | + cd litellm + poetry run black --check --exclude '/enterprise/' . + cd .. - - name: Print OpenAI version - run: | - poetry run python -c "import openai; print(f'OpenAI version: {openai.__version__}')" + - name: Debug - Check file state + run: | + echo "Current branch:" + git branch --show-current + echo "Last 3 commits:" + git log --oneline -3 + echo "File content around line 43:" + head -50 litellm/litellm_core_utils/custom_logger_registry.py | tail -10 - - name: Run MyPy type checking - run: | - cd litellm - poetry run mypy . - cd .. + - name: Run Ruff linting + run: | + cd litellm + poetry run ruff check . + cd .. - - name: Check for circular imports - run: | - cd litellm - poetry run python ../tests/documentation_tests/test_circular_imports.py - cd .. + - name: Print OpenAI version + run: | + poetry run python -c "import openai; print(f'OpenAI version: {openai.__version__}')" - - name: Check import safety - run: | - poetry run python -c "from litellm import *" || (echo '🚨 import failed, this means you introduced unprotected imports! 🚨'; exit 1) + - name: Run MyPy type checking + run: | + cd litellm + poetry run mypy . + cd .. + + - name: Check for circular imports + run: | + cd litellm + poetry run python ../tests/documentation_tests/test_circular_imports.py + cd .. + + - name: Check import safety + run: | + poetry run python -c "from litellm import *" || (echo '🚨 import failed, this means you introduced unprotected imports! 🚨'; exit 1) secret-scan: runs-on: ubuntu-latest @@ -81,27 +88,28 @@ jobs: contents: read steps: - - uses: actions/checkout@v4 - with: - fetch-depth: 0 + - uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 + with: + fetch-depth: 0 + persist-credentials: false - - name: Set up Python - uses: actions/setup-python@v4 - with: - python-version: '3.12' + - name: Set up Python + uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0 + with: + python-version: "3.12" - - name: Run secret scan test - run: | - pip install pytest - pytest tests/litellm/test_no_hardcoded_secrets.py -v + - name: Run secret scan test + run: | + pip install 'pytest==9.0.2' + pytest tests/litellm/test_no_hardcoded_secrets.py -v - - name: Run ggshield secret scan - env: - GITGUARDIAN_API_KEY: ${{ secrets.GITGUARDIAN_API_KEY }} - run: | - if [ -n "$GITGUARDIAN_API_KEY" ]; then - pip install ggshield - ggshield secret scan repo . - else - echo "GITGUARDIAN_API_KEY not set, skipping ggshield scan" - fi + - name: Run ggshield secret scan + env: + GITGUARDIAN_API_KEY: ${{ secrets.GITGUARDIAN_API_KEY }} + run: | + if [ -n "$GITGUARDIAN_API_KEY" ]; then + pip install 'ggshield==1.48.0' + ggshield secret scan repo . + else + echo "GITGUARDIAN_API_KEY not set, skipping ggshield scan" + fi diff --git a/.github/workflows/test-litellm-matrix.yml b/.github/workflows/test-litellm-matrix.yml index d0ac28ab41..860d25636c 100644 --- a/.github/workflows/test-litellm-matrix.yml +++ b/.github/workflows/test-litellm-matrix.yml @@ -4,6 +4,9 @@ on: pull_request: branches: [main] +permissions: + contents: read + # Cancel in-progress runs for the same PR concurrency: group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} @@ -12,7 +15,7 @@ concurrency: jobs: test: runs-on: ubuntu-latest - timeout-minutes: 20 # Increased from 15 to 20 + timeout-minutes: 20 # Increased from 15 to 20 strategy: fail-fast: false matrix: @@ -43,7 +46,7 @@ jobs: - name: "integrations" path: "tests/test_litellm/integrations" workers: 2 - reruns: 3 # Integration tests tend to be flakier + reruns: 3 # Integration tests tend to be flakier - name: "core-utils" path: "tests/test_litellm/litellm_core_utils" workers: 2 @@ -117,18 +120,20 @@ jobs: name: test (${{ matrix.test-group.name }}) steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 + with: + persist-credentials: false - name: Set up Python - uses: actions/setup-python@v5 + uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0 with: python-version: "3.12" - name: Install Poetry - uses: snok/install-poetry@v1 + run: pip install 'poetry==2.3.2' - name: Cache Poetry dependencies - uses: actions/cache@v4 + uses: actions/cache@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.0.0 with: path: | ~/.cache/pypoetry @@ -144,14 +149,17 @@ jobs: poetry install --with dev,proxy-dev --extras "proxy semantic-router" # 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 + google-cloud-aiplatform==1.115.0 fastapi-offline==1.7.3 python-multipart==0.0.22 openapi-core==0.23.0 - name: Setup litellm-enterprise run: | poetry run pip install --force-reinstall --no-deps -e enterprise/ - name: Generate Prisma client + env: + PRISMA_BINARY_CACHE_DIR: ${{ runner.temp }}/prisma-cache run: | + poetry run pip install nodejs-wheel-binaries==24.13.1 poetry run prisma generate --schema litellm/proxy/schema.prisma - name: Run tests - ${{ matrix.test-group.name }} diff --git a/.github/workflows/test-litellm-ui-build.yml b/.github/workflows/test-litellm-ui-build.yml index b0a8b648a4..6b0b3a413a 100644 --- a/.github/workflows/test-litellm-ui-build.yml +++ b/.github/workflows/test-litellm-ui-build.yml @@ -16,10 +16,12 @@ jobs: steps: - name: Checkout repository - uses: actions/checkout@v4 + uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 + with: + persist-credentials: false - name: Setup Node.js - uses: actions/setup-node@v4 + uses: actions/setup-node@a0853c24544627f65ddf259abe73b1d18a591444 # v5.0 with: node-version: "20" cache: "npm" diff --git a/.github/workflows/test-litellm.yml b/.github/workflows/test-litellm.yml index 3f8369df92..0c040b3ebe 100644 --- a/.github/workflows/test-litellm.yml +++ b/.github/workflows/test-litellm.yml @@ -4,45 +4,50 @@ name: LiteLLM Mock Tests (folder - tests/test_litellm) # the same tests in parallel across 10 jobs for faster CI times. # Kept for manual debugging only. on: - workflow_dispatch: # Manual trigger only + workflow_dispatch: # Manual trigger only # pull_request: # branches: [ main ] +permissions: + contents: read + jobs: test: runs-on: ubuntu-latest timeout-minutes: 25 steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 + with: + persist-credentials: false - - name: Thank You Message - run: | - echo "### 🙏 Thank you for contributing to LiteLLM!" >> $GITHUB_STEP_SUMMARY - echo "Your PR is being tested now. We appreciate your help in making LiteLLM better!" >> $GITHUB_STEP_SUMMARY + - name: Thank You Message + run: | + echo "### 🙏 Thank you for contributing to LiteLLM!" >> $GITHUB_STEP_SUMMARY + echo "Your PR is being tested now. We appreciate your help in making LiteLLM better!" >> $GITHUB_STEP_SUMMARY - - name: Set up Python - uses: actions/setup-python@v4 - with: - python-version: '3.12' + - name: Set up Python + uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0 + with: + python-version: "3.12" - - name: Install Poetry - uses: snok/install-poetry@v1 + - name: Install Poetry + run: pip install 'poetry==2.3.2' - - name: Install dependencies - run: | - poetry lock - poetry install --with dev,proxy-dev --extras "proxy semantic-router" - poetry run pip install "pytest-retry==1.6.3" - poetry run pip install pytest-xdist - poetry run pip install "google-genai==1.22.0" - poetry run pip install "google-cloud-aiplatform>=1.38" - poetry run pip install "fastapi-offline==1.7.3" - poetry run pip install "python-multipart>=0.0.20" - poetry run pip install "openapi-core" - - name: Setup litellm-enterprise as local package - run: | - 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 + - name: Install dependencies + run: | + poetry lock + poetry install --with dev,proxy-dev --extras "proxy semantic-router" + poetry run pip install "pytest-retry==1.6.3" + poetry run pip install 'pytest-xdist==3.8.0' + poetry run pip install "google-genai==1.22.0" + poetry run pip install "google-cloud-aiplatform==1.115.0" + poetry run pip install "fastapi-offline==1.7.3" + poetry run pip install "python-multipart==0.0.22" + poetry run pip install "openapi-core==0.23.0" + - name: Setup litellm-enterprise as local package + run: | + 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 2e32aae768..1b228ab76b 100644 --- a/.github/workflows/test-mcp.yml +++ b/.github/workflows/test-mcp.yml @@ -2,7 +2,10 @@ name: LiteLLM MCP Tests (folder - tests/mcp_tests) on: pull_request: - branches: [ main ] + branches: [main] + +permissions: + contents: read jobs: test: @@ -10,38 +13,40 @@ jobs: timeout-minutes: 25 steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 + with: + persist-credentials: false - - name: Thank You Message - run: | - echo "### 🙏 Thank you for contributing to LiteLLM!" >> $GITHUB_STEP_SUMMARY - echo "Your PR is being tested now. We appreciate your help in making LiteLLM better!" >> $GITHUB_STEP_SUMMARY + - name: Thank You Message + run: | + echo "### 🙏 Thank you for contributing to LiteLLM!" >> $GITHUB_STEP_SUMMARY + echo "Your PR is being tested now. We appreciate your help in making LiteLLM better!" >> $GITHUB_STEP_SUMMARY - - name: Set up Python - uses: actions/setup-python@v4 - with: - python-version: '3.12' + - name: Set up Python + uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0 + with: + python-version: "3.12" - - name: Install Poetry - uses: snok/install-poetry@v1 + - name: Install Poetry + run: pip install 'poetry==2.3.2' - - name: Install dependencies - run: | - poetry lock - poetry install --with dev,proxy-dev --extras "proxy semantic-router" - poetry run pip install "pytest==7.3.1" - poetry run pip install "pytest-retry==1.6.3" - poetry run pip install "pytest-cov==5.0.0" - poetry run pip install "pytest-asyncio==0.21.1" - poetry run pip install "respx==0.22.0" - poetry run pip install "pydantic==2.11.0" - poetry run pip install "mcp==1.25.0" - poetry run pip install pytest-xdist + - name: Install dependencies + run: | + poetry lock + poetry install --with dev,proxy-dev --extras "proxy semantic-router" + poetry run pip install "pytest==7.3.1" + poetry run pip install "pytest-retry==1.6.3" + poetry run pip install "pytest-cov==5.0.0" + poetry run pip install "pytest-asyncio==0.21.1" + poetry run pip install "respx==0.22.0" + poetry run pip install "pydantic==2.11.0" + poetry run pip install "mcp==1.25.0" + poetry run pip install 'pytest-xdist==3.8.0' - - name: Setup litellm-enterprise as local package - run: | - poetry run pip install --force-reinstall --no-deps -e enterprise/ + - name: Setup litellm-enterprise as local package + run: | + poetry run pip install --force-reinstall --no-deps -e enterprise/ - - name: Run MCP tests - run: | - poetry run pytest tests/mcp_tests -x -vv -n 4 --cov=litellm --cov-report=xml --durations=5 + - name: Run MCP tests + run: | + poetry run pytest tests/mcp_tests -x -vv -n 4 --cov=litellm --cov-report=xml --durations=5 diff --git a/.github/workflows/test-model-map.yaml b/.github/workflows/test-model-map.yaml index ae5ac402e2..429f9e1ce0 100644 --- a/.github/workflows/test-model-map.yaml +++ b/.github/workflows/test-model-map.yaml @@ -2,13 +2,18 @@ name: Validate model_prices_and_context_window.json on: pull_request: - branches: [ main ] + branches: [main] + +permissions: + contents: read jobs: validate-model-prices-json: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 + with: + persist-credentials: false - name: Validate model_prices_and_context_window.json run: | diff --git a/.github/workflows/test-proxy-e2e-azure-batches.yml b/.github/workflows/test-proxy-e2e-azure-batches.yml index 4d74f3db0a..7cbbe0b338 100644 --- a/.github/workflows/test-proxy-e2e-azure-batches.yml +++ b/.github/workflows/test-proxy-e2e-azure-batches.yml @@ -9,6 +9,9 @@ concurrency: group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} cancel-in-progress: true +permissions: + contents: read + jobs: proxy_e2e_azure_batches_tests: runs-on: ubuntu-latest @@ -30,18 +33,20 @@ jobs: --health-retries 5 steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 + with: + persist-credentials: false - name: Set up Python - uses: actions/setup-python@v5 + uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0 with: python-version: "3.12" - name: Install Poetry - uses: snok/install-poetry@v1 + run: pip install 'poetry==2.3.2' - name: Cache Poetry dependencies - uses: actions/cache@v4 + uses: actions/cache@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.0.0 with: path: | ~/.cache/pypoetry @@ -56,14 +61,17 @@ jobs: run: | poetry config virtualenvs.in-project true poetry install --with dev,proxy-dev --extras "proxy" - poetry run pip install psycopg2-binary uvicorn fastapi httpx tenacity + poetry run pip install psycopg2-binary==2.9.11 uvicorn==0.42.0 fastapi==0.135.2 httpx==0.28.1 tenacity==9.1.4 - name: Setup litellm-enterprise run: | poetry run pip install --force-reinstall --no-deps -e enterprise/ - name: Generate Prisma client + env: + PRISMA_BINARY_CACHE_DIR: ${{ runner.temp }}/prisma-cache run: | + poetry run pip install nodejs-wheel-binaries==24.13.1 poetry run prisma generate --schema litellm/proxy/schema.prisma - name: Run Prisma migrations @@ -87,4 +95,3 @@ jobs: --tb=short \ --maxfail=3 \ --durations=10 - diff --git a/.github/workflows/test-unit-caching-redis.yml b/.github/workflows/test-unit-caching-redis.yml new file mode 100644 index 0000000000..ca274324f2 --- /dev/null +++ b/.github/workflows/test-unit-caching-redis.yml @@ -0,0 +1,38 @@ +name: "Unit Tests: Caching (Redis)" + +# Uses cloud Redis credentials — only runs on trusted branches, not PRs. +# This prevents external PRs from accessing Redis credentials. +on: + push: + branches: [main, "litellm_*"] + +permissions: + contents: read + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +jobs: + caching-redis: + uses: ./.github/workflows/_test-unit-services-base.yml + with: + # Redis-only tests that do NOT require provider API keys. + # Tests needing API keys (test_caching.py, test_caching_ssl.py, test_prometheus_service.py, + # test_router_caching.py) are in Phase 3 integration workflows. + test-path: >- + tests/local_testing/test_dual_cache.py + tests/local_testing/test_redis_batch_optimizations.py + tests/local_testing/test_router_utils.py + workers: 2 + reruns: 2 + timeout-minutes: 20 + enable-redis: true + enable-postgres: false + secrets: + REDIS_HOST: ${{ secrets.REDIS_HOST }} + REDIS_PORT: ${{ secrets.REDIS_PORT }} + REDIS_PASSWORD: ${{ secrets.REDIS_PASSWORD }} + DATABASE_URL: ${{ secrets.DATABASE_URL }} + POSTGRES_USER: ${{ secrets.POSTGRES_USER }} + POSTGRES_PASSWORD: ${{ secrets.POSTGRES_PASSWORD }} diff --git a/.github/workflows/test-unit-core-utils.yml b/.github/workflows/test-unit-core-utils.yml new file mode 100644 index 0000000000..2f3698fdf6 --- /dev/null +++ b/.github/workflows/test-unit-core-utils.yml @@ -0,0 +1,20 @@ +name: "Unit Tests: Core Utilities" + +on: + pull_request: + branches: [main] + +permissions: + contents: read + +concurrency: + group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} + cancel-in-progress: true + +jobs: + core-utils: + uses: ./.github/workflows/_test-unit-base.yml + with: + test-path: "tests/test_litellm/litellm_core_utils" + workers: 2 + reruns: 1 diff --git a/.github/workflows/test-unit-documentation.yml b/.github/workflows/test-unit-documentation.yml new file mode 100644 index 0000000000..d8b30de684 --- /dev/null +++ b/.github/workflows/test-unit-documentation.yml @@ -0,0 +1,67 @@ +name: "Unit Tests: Documentation Validation" + +on: + pull_request: + branches: [main] + +permissions: + contents: read + +concurrency: + group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} + cancel-in-progress: true + +jobs: + documentation: + runs-on: ubuntu-latest + timeout-minutes: 10 + + steps: + - uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 + with: + persist-credentials: false + + - name: Set up Python + uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0 + with: + python-version: "3.12" + + - name: Install Poetry + run: pip install 'poetry==2.3.2' + + - name: Cache Poetry dependencies + uses: actions/cache@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.3.0 + with: + path: | + ~/.cache/pypoetry + ~/.cache/pip + .venv + key: ${{ runner.os }}-poetry-${{ hashFiles('poetry.lock') }} + restore-keys: | + ${{ runner.os }}-poetry- + + - name: Install dependencies + run: | + poetry config virtualenvs.in-project true + poetry install --with dev,proxy-dev --extras "proxy semantic-router" + poetry run pip install google-genai==1.22.0 \ + google-cloud-aiplatform==1.115.0 fastapi-offline==1.7.3 python-multipart==0.0.22 openapi-core==0.23.0 + + - name: Setup litellm-enterprise + run: | + poetry run pip install --force-reinstall --no-deps -e enterprise/ + + - name: Generate Prisma client + env: + PRISMA_BINARY_CACHE_DIR: ${{ runner.temp }}/prisma-cache + run: | + poetry run pip install nodejs-wheel-binaries==24.13.1 + poetry run prisma generate --schema litellm/proxy/schema.prisma + + # Run the same documentation tests that CircleCI ran (as direct Python scripts) + - name: Run documentation validation tests + run: | + poetry run python ./tests/documentation_tests/test_env_keys.py + poetry run python ./tests/documentation_tests/test_router_settings.py + poetry run python ./tests/documentation_tests/test_api_docs.py + poetry run python ./tests/documentation_tests/test_circular_imports.py diff --git a/.github/workflows/test-unit-enterprise-routing.yml b/.github/workflows/test-unit-enterprise-routing.yml new file mode 100644 index 0000000000..13ae3efedb --- /dev/null +++ b/.github/workflows/test-unit-enterprise-routing.yml @@ -0,0 +1,24 @@ +name: "Unit Tests: Enterprise, Google GenAI & Routing" + +on: + pull_request: + branches: [main] + +permissions: + contents: read + +concurrency: + group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} + cancel-in-progress: true + +jobs: + enterprise-routing: + uses: ./.github/workflows/_test-unit-base.yml + with: + test-path: >- + tests/test_litellm/enterprise + tests/test_litellm/google_genai + tests/test_litellm/router_utils + tests/test_litellm/router_strategy + workers: 2 + reruns: 2 diff --git a/.github/workflows/test-unit-integrations.yml b/.github/workflows/test-unit-integrations.yml new file mode 100644 index 0000000000..2789f99d81 --- /dev/null +++ b/.github/workflows/test-unit-integrations.yml @@ -0,0 +1,20 @@ +name: "Unit Tests: Integrations (Callbacks & Logging)" + +on: + pull_request: + branches: [main] + +permissions: + contents: read + +concurrency: + group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} + cancel-in-progress: true + +jobs: + integrations: + uses: ./.github/workflows/_test-unit-base.yml + with: + test-path: "tests/test_litellm/integrations" + workers: 2 + reruns: 3 diff --git a/.github/workflows/test-unit-llm-providers.yml b/.github/workflows/test-unit-llm-providers.yml new file mode 100644 index 0000000000..6c00272b0c --- /dev/null +++ b/.github/workflows/test-unit-llm-providers.yml @@ -0,0 +1,29 @@ +name: "Unit Tests: LLM Provider Transformations" + +on: + pull_request: + branches: [main] + +permissions: + contents: read + +concurrency: + group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} + cancel-in-progress: true + +jobs: + vertex-ai: + name: Vertex AI + uses: ./.github/workflows/_test-unit-base.yml + with: + test-path: "tests/test_litellm/llms/vertex_ai" + workers: 1 + reruns: 2 + + other-providers: + name: All Other Providers + uses: ./.github/workflows/_test-unit-base.yml + with: + test-path: "tests/test_litellm/llms --ignore=tests/test_litellm/llms/vertex_ai" + workers: 2 + reruns: 2 diff --git a/.github/workflows/test-unit-misc.yml b/.github/workflows/test-unit-misc.yml new file mode 100644 index 0000000000..9228decd7c --- /dev/null +++ b/.github/workflows/test-unit-misc.yml @@ -0,0 +1,31 @@ +name: "Unit Tests: MCP, Secrets, Containers & Misc" + +on: + pull_request: + branches: [main] + +permissions: + contents: read + +concurrency: + group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} + cancel-in-progress: true + +jobs: + misc: + uses: ./.github/workflows/_test-unit-base.yml + with: + test-path: >- + 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 + tests/test_litellm/test_*.py + workers: 2 + reruns: 2 diff --git a/.github/workflows/test-unit-proxy-auth.yml b/.github/workflows/test-unit-proxy-auth.yml new file mode 100644 index 0000000000..e71821db70 --- /dev/null +++ b/.github/workflows/test-unit-proxy-auth.yml @@ -0,0 +1,20 @@ +name: "Unit Tests: Proxy Auth & Key Management" + +on: + pull_request: + branches: [main] + +permissions: + contents: read + +concurrency: + group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} + cancel-in-progress: true + +jobs: + proxy-auth: + uses: ./.github/workflows/_test-unit-base.yml + with: + test-path: "tests/test_litellm/proxy/auth tests/test_litellm/proxy/hooks tests/test_litellm/proxy/policy_engine tests/test_litellm/proxy/client" + workers: 2 + reruns: 2 diff --git a/.github/workflows/test-unit-proxy-db.yml b/.github/workflows/test-unit-proxy-db.yml new file mode 100644 index 0000000000..bdfb6efeef --- /dev/null +++ b/.github/workflows/test-unit-proxy-db.yml @@ -0,0 +1,45 @@ +name: "Unit Tests: Proxy DB Operations" + +# Uses DATABASE_URL secret — only runs on trusted branches, not PRs. +on: + push: + branches: [main, "litellm_*"] + +permissions: + contents: read + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +jobs: + proxy-db: + strategy: + fail-fast: false + matrix: + include: + # Key generation tests must NOT run in parallel (event loop conflicts with logging worker) + - test-group: key-generation + test-path: "tests/proxy_unit_tests/test_key_generate_prisma.py" + workers: 0 + timeout: 30 + - test-group: auth-checks + test-path: "tests/proxy_unit_tests/test_auth_checks.py tests/proxy_unit_tests/test_user_api_key_auth.py" + workers: 8 + timeout: 20 + - test-group: remaining + test-path: "tests/proxy_unit_tests --ignore=tests/proxy_unit_tests/test_key_generate_prisma.py --ignore=tests/proxy_unit_tests/test_auth_checks.py --ignore=tests/proxy_unit_tests/test_user_api_key_auth.py" + workers: 8 + timeout: 20 + uses: ./.github/workflows/_test-unit-services-base.yml + with: + test-path: ${{ matrix.test-path }} + workers: ${{ matrix.workers }} + reruns: 2 + timeout-minutes: ${{ matrix.timeout }} + enable-redis: false + enable-postgres: true + secrets: + DATABASE_URL: ${{ secrets.DATABASE_URL }} + POSTGRES_USER: ${{ secrets.POSTGRES_USER }} + POSTGRES_PASSWORD: ${{ secrets.POSTGRES_PASSWORD }} diff --git a/.github/workflows/test-unit-proxy-endpoints.yml b/.github/workflows/test-unit-proxy-endpoints.yml new file mode 100644 index 0000000000..caff3b3ae0 --- /dev/null +++ b/.github/workflows/test-unit-proxy-endpoints.yml @@ -0,0 +1,35 @@ +name: "Unit Tests: Proxy API Endpoints" + +on: + pull_request: + branches: [main] + +permissions: + contents: read + +concurrency: + group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} + cancel-in-progress: true + +jobs: + proxy-endpoints: + uses: ./.github/workflows/_test-unit-base.yml + with: + test-path: >- + tests/test_litellm/proxy/management_endpoints + tests/test_litellm/proxy/guardrails + tests/test_litellm/proxy/management_helpers + tests/test_litellm/proxy/anthropic_endpoints + tests/test_litellm/proxy/google_endpoints + tests/test_litellm/proxy/openai_files_endpoint + tests/test_litellm/proxy/response_api_endpoints + tests/test_litellm/proxy/image_endpoints + tests/test_litellm/proxy/vector_store_endpoints + tests/test_litellm/proxy/agent_endpoints + tests/test_litellm/proxy/discovery_endpoints + tests/test_litellm/proxy/health_endpoints + tests/test_litellm/proxy/public_endpoints + tests/test_litellm/proxy/prompts + tests/test_litellm/proxy/ui_crud_endpoints + workers: 2 + reruns: 2 diff --git a/.github/workflows/test-unit-proxy-infra.yml b/.github/workflows/test-unit-proxy-infra.yml new file mode 100644 index 0000000000..4dfbbe317e --- /dev/null +++ b/.github/workflows/test-unit-proxy-infra.yml @@ -0,0 +1,28 @@ +name: "Unit Tests: Proxy Infrastructure" + +on: + pull_request: + branches: [main] + +permissions: + contents: read + +concurrency: + group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} + cancel-in-progress: true + +jobs: + proxy-infra: + uses: ./.github/workflows/_test-unit-base.yml + with: + test-path: >- + tests/test_litellm/proxy/db + tests/test_litellm/proxy/middleware + tests/test_litellm/proxy/spend_tracking + tests/test_litellm/proxy/pass_through_endpoints + tests/test_litellm/proxy/_experimental + tests/test_litellm/proxy/experimental + tests/test_litellm/proxy/common_utils + tests/test_litellm/proxy/test_*.py + workers: 2 + reruns: 2 diff --git a/.github/workflows/test-unit-proxy-legacy.yml b/.github/workflows/test-unit-proxy-legacy.yml new file mode 100644 index 0000000000..a939113726 --- /dev/null +++ b/.github/workflows/test-unit-proxy-legacy.yml @@ -0,0 +1,96 @@ +name: "Unit Tests: Proxy Legacy Tests" + +on: + pull_request: + branches: [main] + +permissions: + contents: read + +concurrency: + group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} + cancel-in-progress: true + +jobs: + test: + runs-on: ubuntu-latest + timeout-minutes: 20 + strategy: + fail-fast: false + matrix: + test-group: + - name: "auth-and-jwt" + path: "tests/proxy_unit_tests/test_[a-j]*.py" + - name: "key-generation" + path: "tests/proxy_unit_tests/test_[k-o]*.py" + - name: "proxy-config" + 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" + - name: "proxy-server" + path: "tests/proxy_unit_tests/test_proxy_server.py" + - name: "proxy-server-extras" + path: "tests/proxy_unit_tests/test_proxy_server_*.py tests/proxy_unit_tests/test_proxy_setting_guardrails.py" + - name: "proxy-utils" + path: "tests/proxy_unit_tests/test_proxy_utils.py" + - name: "proxy-token-counter" + path: "tests/proxy_unit_tests/test_proxy_token_counter.py" + - name: "proxy-response-and-misc" + path: "tests/proxy_unit_tests/test_[r-t]*.py" + - name: "proxy-user-auth-and-spend" + path: "tests/proxy_unit_tests/test_[u-z]*.py" + + name: ${{ matrix.test-group.name }} + + steps: + - uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 + with: + persist-credentials: false + + - name: Set up Python + uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0 + with: + python-version: "3.12" + + - name: Install Poetry + run: pip install 'poetry==2.3.2' + + - name: Cache Poetry dependencies + uses: actions/cache@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.3.0 + with: + path: | + ~/.cache/pypoetry + ~/.cache/pip + .venv + key: ${{ runner.os }}-poetry-${{ hashFiles('poetry.lock') }} + restore-keys: | + ${{ runner.os }}-poetry- + + - name: Install dependencies + run: | + poetry config virtualenvs.in-project true + poetry install --with dev,proxy-dev --extras "proxy semantic-router" + poetry run pip install google-genai==1.22.0 \ + google-cloud-aiplatform==1.115.0 fastapi-offline==1.7.3 python-multipart==0.0.22 openapi-core==0.23.0 + + - name: Setup litellm-enterprise + run: | + poetry run pip install --force-reinstall --no-deps -e enterprise/ + + - name: Generate Prisma client + env: + PRISMA_BINARY_CACHE_DIR: ${{ runner.temp }}/prisma-cache + run: | + poetry run pip install nodejs-wheel-binaries==24.13.1 + poetry run prisma generate --schema litellm/proxy/schema.prisma + + - name: Run tests - ${{ matrix.test-group.name }} + env: + TEST_PATH: ${{ matrix.test-group.path }} + run: | + poetry run pytest ${TEST_PATH} \ + --tb=short -vv \ + --maxfail=10 \ + -n 2 \ + --reruns 1 \ + --reruns-delay 1 \ + --dist=loadscope \ + --durations=20 diff --git a/.github/workflows/test-unit-responses-caching-types.yml b/.github/workflows/test-unit-responses-caching-types.yml new file mode 100644 index 0000000000..7f3acac280 --- /dev/null +++ b/.github/workflows/test-unit-responses-caching-types.yml @@ -0,0 +1,20 @@ +name: "Unit Tests: Responses, Caching & Types" + +on: + pull_request: + branches: [main] + +permissions: + contents: read + +concurrency: + group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} + cancel-in-progress: true + +jobs: + responses-caching-types: + uses: ./.github/workflows/_test-unit-base.yml + with: + test-path: "tests/test_litellm/responses tests/test_litellm/caching tests/test_litellm/types" + workers: 2 + reruns: 2 diff --git a/.github/workflows/test-unit-security.yml b/.github/workflows/test-unit-security.yml new file mode 100644 index 0000000000..b38c82b1c2 --- /dev/null +++ b/.github/workflows/test-unit-security.yml @@ -0,0 +1,28 @@ +name: "Unit Tests: Security" + +# Uses DATABASE_URL secret — only runs on trusted branches, not PRs. +on: + push: + branches: [main, "litellm_*"] + +permissions: + contents: read + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +jobs: + security: + uses: ./.github/workflows/_test-unit-services-base.yml + with: + test-path: "tests/proxy_security_tests/" + workers: 1 + reruns: 2 + timeout-minutes: 20 + enable-redis: false + enable-postgres: true + secrets: + DATABASE_URL: ${{ secrets.DATABASE_URL }} + POSTGRES_USER: ${{ secrets.POSTGRES_USER }} + POSTGRES_PASSWORD: ${{ secrets.POSTGRES_PASSWORD }} diff --git a/.github/workflows/test_server_root_path.yml b/.github/workflows/test_server_root_path.yml index c359e38bff..47636ce8e9 100644 --- a/.github/workflows/test_server_root_path.yml +++ b/.github/workflows/test_server_root_path.yml @@ -17,13 +17,15 @@ jobs: steps: - name: Checkout repository - uses: actions/checkout@v4 + uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 + with: + persist-credentials: false - name: Set up Docker Buildx - uses: docker/setup-buildx-action@v3 + uses: docker/setup-buildx-action@8d2750c68a42422c14e847fe6c8ac0403b4cbd6f # v3.12 - name: Build Docker image - uses: docker/build-push-action@v5 + uses: docker/build-push-action@0adf9959216b96bec444f325f1e493d4aa344497 #v6.14 with: context: . file: ./docker/Dockerfile.non_root diff --git a/.github/workflows/zizmor.yml b/.github/workflows/zizmor.yml new file mode 100644 index 0000000000..9a1e899fed --- /dev/null +++ b/.github/workflows/zizmor.yml @@ -0,0 +1,31 @@ +name: GitHub Actions Security Analysis + +on: + push: + branches: [main] + pull_request: + branches: [main] + +concurrency: + group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} + cancel-in-progress: true + +permissions: {} + +jobs: + zizmor: + name: zizmor + runs-on: ubuntu-latest + timeout-minutes: 5 + permissions: + security-events: write + contents: read + actions: read + steps: + - name: Checkout repository + uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 + with: + persist-credentials: false + + - name: Run zizmor + uses: zizmorcore/zizmor-action@71321a20a9ded102f6e9ce5718a2fcec2c4f70d8 # v0.5.2 diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 9396f323e4..2bc361bc48 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -14,12 +14,12 @@ repos: types: [python] files: (litellm/|litellm_proxy_extras/|enterprise/).*\.py exclude: ^litellm/__init__.py$ - # - id: black - # name: black - # entry: poetry run black - # language: system - # types: [python] - # files: (litellm/|litellm_proxy_extras/|enterprise/).*\.py + - id: black + name: black + entry: poetry run black + language: system + types: [python] + files: (litellm/|litellm_proxy_extras/).*\.py - repo: https://github.com/pycqa/flake8 rev: 7.0.0 # The version of flake8 to use hooks: diff --git a/.semgrep/rules/security/no-claude-directory.yml b/.semgrep/rules/security/no-claude-directory.yml new file mode 100644 index 0000000000..7d120a7c23 --- /dev/null +++ b/.semgrep/rules/security/no-claude-directory.yml @@ -0,0 +1,18 @@ +rules: + - id: no-claude-directory-committed + message: > + .claude/ directory must not be committed to the repository. + It contains local Claude Code settings (permissions, worktree paths) that are + developer-machine-specific and may expose internal paths or credentials. + Add .claude/ to .gitignore instead. + severity: ERROR + languages: [generic] + paths: + include: + - "/.claude/**" + - "/.claude/*" + pattern-regex: '[\s\S]+' + metadata: + category: security + tags: [supply-chain, secrets] + confidence: HIGH diff --git a/README.md b/README.md index 67f2f3a204..a6dc597574 100644 --- a/README.md +++ b/README.md @@ -266,6 +266,7 @@ Support for more providers. Missing a provider or LLM Platform, raise a [feature + @@ -402,7 +403,7 @@ Support for more providers. Missing a provider or LLM Platform, raise a [feature # Enterprise For companies that need better security, user management and professional support -[Talk to founders](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions) +[Talk to founders](https://enterprise.litellm.ai/demo) This covers: - ✅ **Features under the [LiteLLM Commercial License](https://docs.litellm.ai/docs/proxy/enterprise):** @@ -452,7 +453,6 @@ All these checks must pass before your PR can be merged. - [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version) - [Community Discord 💭](https://discord.gg/wuPM9dRgDw) - [Community Slack 💭](https://www.litellm.ai/support) -- Our numbers 📞 +1 (770) 8783-106 / ‭+1 (412) 618-6238‬ - Our emails ✉️ ishaan@berri.ai / krrish@berri.ai # Why did we build this diff --git a/ci_cd/security_scans.sh b/ci_cd/security_scans.sh index e0f370e003..061e454465 100755 --- a/ci_cd/security_scans.sh +++ b/ci_cd/security_scans.sh @@ -10,13 +10,13 @@ echo "Starting security scans for LiteLLM..." # Function to install Trivy and required tools install_trivy() { echo "Installing Trivy and required tools..." + TRIVY_VERSION="0.35.0" sudo apt-get update - sudo apt-get install -y wget apt-transport-https gnupg lsb-release jq curl bsdmainutils - wget -qO - https://aquasecurity.github.io/trivy-repo/deb/public.key | sudo apt-key add - - echo "deb https://aquasecurity.github.io/trivy-repo/deb $(lsb_release -sc) main" | sudo tee -a /etc/apt/sources.list.d/trivy.list - sudo apt-get update - sudo apt-get install trivy - echo "Trivy and required tools installed successfully" + sudo apt-get install -y wget jq curl bsdmainutils + wget -qO trivy.deb "https://github.com/aquasecurity/trivy/releases/download/v${TRIVY_VERSION}/trivy_${TRIVY_VERSION}_Linux-64bit.deb" + sudo dpkg -i trivy.deb + rm trivy.deb + echo "Trivy ${TRIVY_VERSION} installed successfully" } # Function to install Grype @@ -163,6 +163,9 @@ run_grype_scans() { "CVE-2026-25639" # axios - full fix requires 1.x major version bump; pinned to >=0.30.2 to clear other axios CVEs, upgrade to 1.x in follow-up "CVE-2026-2297" # Python 3.13 SourcelessFileLoader audit hook bypass - no fix available in base image "GHSA-qffp-2rhf-9h96" # tar hardlink path traversal - from nodejs_wheel bundled npm, not used in application runtime code + "CVE-2026-2673" # OpenSSL 3.6.1 TLS 1.3 key exchange group negotiation issue - no fix available yet + "CVE-2026-3644" # Python 3.13 vulnerability - no fix available in base image + "CVE-2026-4224" # Python 3.13 Expat parser stack overflow in ElementDeclHandler - no fix available in base image ) # Build JSON array of allowlisted CVE IDs for jq diff --git a/cookbook/benchmark/readme.md b/cookbook/benchmark/readme.md index 57115eb96a..afa59aa91e 100644 --- a/cookbook/benchmark/readme.md +++ b/cookbook/benchmark/readme.md @@ -178,4 +178,4 @@ Benchmark Results for 'When will BerriAI IPO?': ``` ## Support -**🤝 Schedule a 1-on-1 Session:** Book a [1-on-1 session](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions) with Krrish and Ishaan, the founders, to discuss any issues, provide feedback, or explore how we can improve LiteLLM for you. +**🤝 Schedule a 1-on-1 Session:** Book a [1-on-1 session](https://enterprise.litellm.ai/demo) with Krrish and Ishaan, the founders, to discuss any issues, provide feedback, or explore how we can improve LiteLLM for you. diff --git a/cookbook/codellama-server/README.MD b/cookbook/codellama-server/README.MD index b158bb083f..82a7e62f40 100644 --- a/cookbook/codellama-server/README.MD +++ b/cookbook/codellama-server/README.MD @@ -143,7 +143,6 @@ All responses from the server are returned in the following format (for all LLM - [Our calendar 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version) - [Community Discord 💭](https://discord.gg/wuPM9dRgDw) -- Our numbers 📞 +1 (770) 8783-106 / +1 (412) 618-6238 - Our emails ✉️ ishaan@berri.ai / krrish@berri.ai ## Roadmap diff --git a/cookbook/litellm_proxy_server/readme.md b/cookbook/litellm_proxy_server/readme.md index d0b0592c43..2c1eab72c2 100644 --- a/cookbook/litellm_proxy_server/readme.md +++ b/cookbook/litellm_proxy_server/readme.md @@ -164,7 +164,6 @@ All responses from the server are returned in the following format (for all LLM - [Our calendar 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version) - [Community Discord 💭](https://discord.gg/wuPM9dRgDw) -- Our numbers 📞 +1 (770) 8783-106 / +1 (412) 618-6238 - Our emails ✉️ ishaan@berri.ai / krrish@berri.ai ## Roadmap diff --git a/docs/my-website/blog/ci_cd_v2_improvements/index.md b/docs/my-website/blog/ci_cd_v2_improvements/index.md new file mode 100644 index 0000000000..84f8f7bda6 --- /dev/null +++ b/docs/my-website/blog/ci_cd_v2_improvements/index.md @@ -0,0 +1,55 @@ +--- +slug: ci-cd-v2-improvements +title: "Announcing CI/CD v2 for LiteLLM" +date: 2026-03-30T21:30:00 +authors: + - krrish +description: "CI/CD v2 introduces isolated environments, stronger security gates, and safer release separation for LiteLLM." +tags: [engineering, ci-cd, security] +hide_table_of_contents: false +--- + +import Image from '@theme/IdealImage'; + +The CI/CD v2 is now live for LiteLLM. + + + +
+Building on the roadmap from our [security incident](https://docs.litellm.ai/blog/security-townhall-updates#roadmap), CI/CD v2 introduces isolated environments, stronger security gates, and safer release separation for LiteLLM. + +## What changed + +- Security scans and unit tests run in isolated environments. +- Validation and release are separated into different repositories, making it harder for an attacker to reach release credentials. +- Trusted Publishing for PyPI releases - this means no long-lived credentials are used to publish releases. +- Immutable Docker release tags - this means no tampering of Docker release tags after they are published [Learn more](https://docs.docker.com/docker-hub/repos/manage/hub-images/immutable-tags/). Note: work for GHCR docker releases is planned as well. + +## What's next + +Moving forward, we plan on: +- Adopting OpenSSF (this is a set of security criteria that projects should meet to demonstrate a strong security posture - [Learn more](https://baseline.openssf.org/versions/2026-02-19.html)) + - We've added Scorecard and Allstar to our Github + +- Adding SLSA Build Provenance to our CI/CD pipeline - this means we allow users to independently verify that a release came from us and prevent silent modifications of releases after they are published. + + +We hope that this will mean you can be confident that the releases you are using are safe and from us. + + +## The principle + +The new CI/CD pipeline reflects the principles, outlined below, and is designed to be more secure and reliable: + +- **Limit** what each package can access +- **Reduce** the number of sensitive environment variables +- **Avoid** compromised packages +- **Prevent** release tampering + + +## How to help: + +Help us plan April's stability sprint - https://github.com/BerriAI/litellm/issues/24825 \ No newline at end of file diff --git a/docs/my-website/blog/gpt_5_4_mini_nano/index.md b/docs/my-website/blog/gpt_5_4_mini_nano/index.md new file mode 100644 index 0000000000..6d7c2b33f7 --- /dev/null +++ b/docs/my-website/blog/gpt_5_4_mini_nano/index.md @@ -0,0 +1,106 @@ +--- +slug: gpt_5_4_mini_nano +title: "Day 0 Support: GPT-5.4-mini and GPT-5.4-nano" +date: 2026-03-17T10:00:00 +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: "GPT-5.4-mini and GPT-5.4-nano model support in LiteLLM" +tags: [openai, gpt-5.4-mini, gpt-5.4-nano, completion] +hide_table_of_contents: false +--- + +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +LiteLLM now supports GPT-5.4-mini and GPT-5.4-nano — cost-effective models for simple completions and high-throughput workloads. + +:::note +If you're on **v1.82.3-stable** or above, you don't need any update to use these models. +::: + +## Usage + + + + +**1. Setup config.yaml** + +```yaml +model_list: + - model_name: gpt-5.4-mini + litellm_params: + model: openai/gpt-5.4-mini + api_key: os.environ/OPENAI_API_KEY + - model_name: gpt-5.4-nano + litellm_params: + model: openai/gpt-5.4-nano + api_key: os.environ/OPENAI_API_KEY +``` + +**2. Start the proxy** + +```bash +litellm --config /path/to/config.yaml +``` + +**3. Test it** + +```bash +# GPT-5.4-mini +curl -X POST "http://localhost:4000/v1/chat/completions" \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer $LITELLM_KEY" \ + -d '{ + "model": "gpt-5.4-mini", + "messages": [{"role": "user", "content": "What is the capital of France?"}] + }' + +# GPT-5.4-nano +curl -X POST "http://localhost:4000/v1/chat/completions" \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer $LITELLM_KEY" \ + -d '{ + "model": "gpt-5.4-nano", + "messages": [{"role": "user", "content": "What is 2 + 2?"}] + }' +``` + + + + +```python +from litellm import completion + +# GPT-5.4-mini +response = completion( + model="openai/gpt-5.4-mini", + messages=[{"role": "user", "content": "What is the capital of France?"}], +) +print(response.choices[0].message.content) + +# GPT-5.4-nano +response = completion( + model="openai/gpt-5.4-nano", + messages=[{"role": "user", "content": "What is 2 + 2?"}], +) +print(response.choices[0].message.content) +``` + + + + +## Notes + +- Both models support function calling, vision, and tool-use — see the [OpenAI provider docs](../../docs/providers/openai) for advanced usage. +- GPT-5.4-nano is the most cost-effective option for simple tasks; GPT-5.4-mini offers a balance of speed and capability. diff --git a/docs/my-website/blog/guardrail_logging_secret_exposure_incident/index.md b/docs/my-website/blog/guardrail_logging_secret_exposure_incident/index.md new file mode 100644 index 0000000000..71f9e3da01 --- /dev/null +++ b/docs/my-website/blog/guardrail_logging_secret_exposure_incident/index.md @@ -0,0 +1,78 @@ +--- +slug: guardrail-logging-secret-exposure-incident +title: "Incident Report: Guardrail logging exposed secret headers in spend logs and traces" +date: 2026-03-18T10:00:00 +authors: + - litellm +tags: [incident-report, security, guardrails] +hide_table_of_contents: false +--- + +**Date:** March 18, 2026 +**Duration:** Unknown +**Severity:** High +**Status:** Resolved + +## Summary + +When a custom guardrail returned the full LiteLLM request/data dictionary, the guardrail response logged by LiteLLM could include `secret_fields.raw_headers`, including plaintext `Authorization` headers containing API keys or other credentials. + +This information could then propagate to logging and observability surfaces that consume guardrail metadata, including: + +- **Spend logs in the LiteLLM UI:** visible to admins with access to spend-log data +- **OpenTelemetry traces:** visible to anyone with access to the relevant telemetry backend + +LLM calls, proxy routing, and provider execution were not blocked by this bug. The impact was exposure of sensitive request headers in observability and logging paths. + +{/* truncate */} + +--- + +## Background + +LiteLLM keeps internal request data (including request headers) for use during the call. That data is not meant to be written to logs or telemetry. + +When custom guardrails run, their outcomes are logged so they can appear in spend logs, OpenTelemetry traces, and other observability backends. If a guardrail returned the full request payload instead of a minimal result, that internal request data could be included in what was logged. Before the fix, the guardrail logging path did not strip that data before sending it to those systems. + +```mermaid +flowchart TD + inboundRequest["1. Incoming proxy request"] --> storeSecrets["2. Store internal request data"] + storeSecrets --> guardrailRuns["3. Custom guardrail runs"] + guardrailRuns --> fullDataReturn["4. Guardrail returns full request payload"] + fullDataReturn --> loggingBuild["5. Build guardrail log payload"] + loggingBuild --> spendLogs["6a. Persist to spend logs / UI"] + loggingBuild --> otelTraces["6b. Attach to OTEL guardrail spans"] +``` + +--- + +## Root Cause + +The root cause was incomplete sanitization in the guardrail logging path. When building the payload that gets sent to spend logs and traces, LiteLLM prepared guardrail responses for logging but did not strip internal request data (such as headers) from them. If a guardrail returned a response that included that data, it was passed through to the logging and observability systems unchanged. + +--- + +## Impact + +This issue required all of the following: + +1. A custom guardrail returned the full LiteLLM request/data dictionary, or another response object containing `secret_fields`. +2. LiteLLM logged that guardrail response through the standard guardrail logging path. +3. An operator, admin, or telemetry consumer had access to the resulting logs or traces. + +When those conditions were met, sensitive values could become visible through: + +- **Spend logs / UI responses:** guardrail metadata could be included in spend-log payloads rendered in the admin UI. +- **OpenTelemetry traces:** `guardrail_response` could be written as a span attribute on guardrail spans. +- **Other downstream observability backends:** any integration consuming the same guardrail metadata could receive the leaked values. + +This was a logging and telemetry exposure bug. It did not let callers bypass auth, access other tenants directly, or change model behavior, but it could expose plaintext credentials to people with access to those observability systems. + +--- + +## Guidance For Users + +- Upgrade to LiteLLM 1.82.3+. +- If you operated custom guardrails that return the full request/data dict, review whether spend logs or telemetry traces were retained during the affected period. +- Rotate any credentials that may have appeared in `Authorization` or other forwarded request headers in those systems. +- Apply least-privilege access controls to spend-log views and telemetry backends that may contain request-derived metadata. diff --git a/docs/my-website/blog/security_townhall_updates/index.md b/docs/my-website/blog/security_townhall_updates/index.md new file mode 100644 index 0000000000..b997de9c18 --- /dev/null +++ b/docs/my-website/blog/security_townhall_updates/index.md @@ -0,0 +1,190 @@ +--- +slug: security-townhall-updates +title: "Security Townhall Updates" +date: 2026-03-27T12:00:00 +authors: + - krrish + - ishaan-alt +description: "What happened, what we've done, and what comes next for LiteLLM's release and security processes." +tags: [security, incident-report] +hide_table_of_contents: false +--- + +import Image from '@theme/IdealImage'; + +Thank you to everyone who joined our town hall. + +We wanted to use that time to walk through what we know, what we've done so far, and how we're improving LiteLLM's release and security processes going forward. This post is a written version of that update. [Slides available here](https://drive.google.com/file/d/17hsSG7nk-OYL7VRCTbTa7McrWREtS9OO/view?usp=sharing) + +{/* truncate */} + +## What happened + +On March 24, 2026 at 10:39 UTC, LiteLLM v1.82.7 was pushed to PyPI. Version v1.82.8 was published soon after. Those packages were live for about 40 minutes before being quarantined by PyPI. By 16:00 UTC, the LiteLLM team had worked with PyPI to delete the affected packages. + +At this point, our understanding is that this was a supply-chain incident affecting those two published versions. + +## How did this happen? + +Our understanding is that the issue came from the [compromised Trivy security scanner](https://www.aquasec.com/blog/trivy-supply-chain-attack-what-you-need-to-know/) dependency in our CI/CD pipeline. + + + +There were three major contributing factors: + +### 1. Shared CI/CD environment + +At the time, everything was running on CircleCI, and all steps shared a common environment. That increased blast radius: if one component was compromised, it could potentially access credentials or context intended for other parts of the pipeline. + +### 2. Static credentials in environment variables + +Release credentials, including credentials for PyPI, GHCR, and Docker publishing, were available as static secrets in the environment. That meant a compromised step could access long-lived release credentials. + +### 3. Unpinned Trivy dependency + +In our security scanning component, we had an unpinned Trivy dependency. Our present understanding is that a compromised Trivy package ran during the scan, had access to environment variables, and enabled attackers to obtain those credentials. + +**In summary:** a compromised package in CI had access to secrets it should not have had, and those secrets were then used in the release path. + +## What we've already done + + +In the last 3 days, we've taken the following steps: + +### 1. Minimize Scope of Impact + +#### Prevented further key abuse + +We deleted or rotated all impacted or adjacent secret keys, including PyPI, GitHub, Docker, and related credentials. Out of an abundance of caution, we've also rotated LiteLLM maintainer accounts. + +#### Prevent branch attacks + +We removed roughly 6,000 open branches and added an auto-deletion policy for branches merged into `main`. This reduces the surface area for branch-based abuse. + +#### Pinned CI/CD dependencies + +We've pinned all Github Actions, and are working on pinning all CircleCI dependencies as well. + +#### Paused releases + +We've paused new releases until we've confirmed codebase security and put stronger release controls in place. + +### 2. Secured LiteLLM + +#### Forensic analysis + +We are working with Google's Mandiant cybersecurity team to confirm the source of the attack and verify the security of the codebase. We also confirmed that no malicious code was pushed to `main`. + +#### Confirm Application Security + +In parallel, we are working with whitehat hackers at [Veria Labs](https://verialabs.com/) to verify application security and review improvements to our CI/CD process. + +We have also confirmed that the last 20 LiteLLM releases contain no indicators of compromise, and that no unauthenticated attacks can be made against LiteLLM Proxy based on our current investigation. [Check Security Blog for release verification.](https://docs.litellm.ai/blog/security-update-march-2026#verified-safe-versions) + +#### Created a security working group + +We created a new security working group inside LiteLLM focused on: + +- Building threat models +- Auditing the build process and dependencies + +If you're interested in joining the security working group, please file an issue [here](https://github.com/BerriAI/litellm-security-wg). + +### 3. Improved CI/CD + +We've already begun making structural changes to how releases are built and published. These align with our goals (covered in the next section) around isolated environments, ephemeral credentials, and release auditing. + +## Roadmap + +We plan on following 4 guiding principles for our new CI/CD pipeline: + +1. **Limit** what each package can access +2. **Reduce** the number of sensitive environment variables +3. **Avoid** compromised packages +4. **Prevent** release tampering + + +### Isolated environments + + + +We are breaking our CI/CD into 4 semantic concepts: + +1. Unit tests +2. Integration tests +3. Security scans +4. Release publishing + +And will be running each of these in isolated environments. + +This will limit the damage that any single compromised component can cause. + +### Ephemeral credentials + +We plan to move to ephemeral credentials for PyPI (Trusted Publisher) and GHCR (Token-based authentication) releases. This will reduce the risk of credentials being leaked or compromised. + +We have already begun doing this: + +- PyPI Trusted Publisher on GitHub Actions [PR](https://github.com/BerriAI/litellm/pull/24654) +- GHCR Token-based authentication on GitHub Actions [PR](https://github.com/BerriAI/litellm/pull/24683) + +### Release auditing + +Our goal is to allow users to independently verify that a release came from us and prevent silent modifications of releases after they are published. + +This will ensure, your releases are safe, even when: +- Stolen PyPI/GHCR credentials are used to publish malicious releases +- Tampered registry artifacts are published +- Tag mutations are made after the release is published + +We believe that [Cosign](https://github.com/sigstore/cosign) is a good fit for this, and have already begun working on it [PR](https://github.com/BerriAI/litellm/pull/24683). + + +### Avoid Compromised Packages + +- Move to pinned, verified SHAs for packages and actions used in CI/CD, avoiding `latest` wherever possible. +- Add a cooldown period before upgrading to a new version of a package - allows more time to investigate and verify the new version. + +We've added zizmor to help us catch issues such as unpinned dependencies and credential leakage. [commit](https://github.com/BerriAI/litellm/commit/a671275f5c5b0e1fb1adacdf3b6ef779aaa5d56c). + + +## Frequently Asked Questions + +**Q: Did you observe any lateral movement into your corporate environment during this incident?** + +A: No. Our investigation to date, conducted in coordination with external security experts, has found no evidence of lateral movement into our internal corporate systems. The incident was isolated to the CI/CD pipeline and the release path for specific versions (v1.82.7 and v1.82.8). As a proactive measure, we have rotated all potentially impacted or adjacent secrets—including PyPI, GitHub, and Docker credentials—and updated maintainer account security to ensure continued isolation. + +**Q: Do you expect delays in future product releases due to these new security measures?** + +A: We are committed to balancing security with speed. While we have temporarily paused releases to implement stronger controls, we are moving quickly to automate our new security protocols. We are currently implementing isolated CI/CD environments, ephemeral credentials (via Trusted Publishers), and release auditing with Cosign. These improvements are designed to be integrated into our automated pipeline, allowing us to maintain a fast release cadence while ensuring every package is verified and secure. + +**Q: Were older packages impacted?** + +Our current findings show no indicators of compromise in the last 20 versions of LiteLLM. This was manually verified by our team and independently reviewed by Veria Labs. + +We have also published the verified versions for users to use. [Check Security Blog for release verification.](https://docs.litellm.ai/blog/security-update-march-2026#verified-safe-versions) + + + +## Questions & Support + +If you believe your systems may be affected, contact us immediately: + +- **Security:** security@berri.ai +- **Support:** support@berri.ai +- **Slack:** Reach out to the LiteLLM team directly [here](https://join.slack.com/t/litellmossslack/shared_invite/zt-3o7nkuyfr-p_kbNJj8taRfXGgQI1~YyA) + +## Hiring + +We are currently hiring for: + +- DevOps Engineer - to keep ci/cd secure and running smoothly +- Security Engineer - to keep the application secure + +If you're interest in joining, please apply [here](https://jobs.ashbyhq.com/litellm) \ No newline at end of file diff --git a/docs/my-website/blog/security_townhall_updates/shared_ci_cd_environment.png b/docs/my-website/blog/security_townhall_updates/shared_ci_cd_environment.png new file mode 100644 index 0000000000..29ec195b7f Binary files /dev/null and b/docs/my-website/blog/security_townhall_updates/shared_ci_cd_environment.png differ diff --git a/docs/my-website/blog/security_update_march_2026/index.md b/docs/my-website/blog/security_update_march_2026/index.md new file mode 100644 index 0000000000..1c298fe372 --- /dev/null +++ b/docs/my-website/blog/security_update_march_2026/index.md @@ -0,0 +1,786 @@ +--- +slug: security-update-march-2026 +title: "Security Update: Suspected Supply Chain Incident" +date: 2026-03-24T14:00:00 +authors: + - krrish + - ishaan-alt +description: "As of 2:00 PM ET on March 24, 2026" +tags: [security, incident-report] +hide_table_of_contents: false +--- + +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; +import VersionVerificationTable from '@site/src/components/VersionVerificationTable'; + +> **Status:** Active investigation +> **Last updated:** March 27, 2026 + +> **Update (March 30):** A new **clean** version of LiteLLM is now available (v1.83.0). This was released by our new [CI/CD v2](https://docs.litellm.ai/blog/ci-cd-v2-improvements) pipeline which added isolated environments, stronger security gates, and safer release separation for LiteLLM. + +> **Update (March 27):** Review Townhall updates, including explanation of the incident, what we've done, and what comes next. [Learn more](https://docs.litellm.ai/blog/security-townhall-updates) + +> **Update (March 27):** Added [Verified safe versions](#verified-safe-versions) section with SHA-256 checksums for all audited PyPI and Docker releases. + +> **Update (March 26):** Added `checkmarx[.]zone` to [Indicators of compromise](#indicators-of-compromise-iocs) + +> **Update (March 25):** Added community-contributed scripts for scanning GitHub Actions and GitLab CI pipelines for the compromised versions. See [How to check if you are affected](#how-to-check-if-you-are-affected). s/o [@Zach Fury](https://www.linkedin.com/in/fryware/) for these scripts. + + +## TLDR; +- The compromised PyPI packages were **litellm==1.82.7** and **litellm==1.82.8**. Those packages were live on March 24, 2026 from 10:39 UTC for about 40 minutes before being quarantined by PyPI. +- We believe that the compromise originated from the [Trivy dependency](https://www.aquasec.com/blog/trivy-supply-chain-attack-what-you-need-to-know/) used in our CI/CD security scanning workflow. +- Customers running the official LiteLLM Proxy Docker image were not impacted. That deployment path pins dependencies in requirements.txt and does not rely on the compromised PyPI packages. +- ~~We have paused all new LiteLLM releases until we complete a broader supply-chain review and confirm the release path is safe.~~ **Updated:** We have now released a new **safe** version of LiteLLM (v1.83.0) by our new [CI/CD v2](https://docs.litellm.ai/blog/ci-cd-v2-improvements) pipeline which added isolated environments, stronger security gates, and safer release separation for LiteLLM. We have also verified the codebase is safe and no malicious code was pushed to `main`. + + +## Overview + +LiteLLM AI Gateway is investigating a suspected supply chain attack involving unauthorized PyPI package publishes. Current evidence suggests a maintainer's PyPI account may have been compromised and used to distribute malicious code. + +At this time, we believe this incident may be linked to the broader [Trivy security compromise](https://www.aquasec.com/blog/trivy-supply-chain-attack-what-you-need-to-know/), in which stolen credentials were reportedly used to gain unauthorized access to the LiteLLM publishing pipeline. + +This investigation is ongoing. Details below may change as we confirm additional findings. + +## Confirmed affected versions + +The following LiteLLM versions published to PyPI were impacted: + +- **v1.82.7**: contained a malicious payload in the LiteLLM AI Gateway `proxy_server.py` +- **v1.82.8**: contained `litellm_init.pth` and a malicious payload in the LiteLLM AI Gateway `proxy_server.py` + +If you installed or ran either of these versions, review the recommendations below immediately. + +Note: These versions have already been removed from PyPI. + +## What happened + +Initial evidence suggests the attacker bypassed official CI/CD workflows and uploaded malicious packages directly to PyPI. + +These compromised versions appear to have included a credential stealer designed to: + +- Harvest secrets by scanning for: + - environment variables + - SSH keys + - cloud provider credentials (AWS, GCP, Azure) + - Kubernetes tokens + - database passwords +- Encrypt and exfiltrate data via a `POST` request to `models.litellm.cloud`, which is **not** an official BerriAI / LiteLLM domain + +## Who is affected + +You may be affected if **any** of the following are true: + +- You installed or upgraded LiteLLM via `pip` on **March 24, 2026**, between **10:39 UTC and 16:00 UTC** +- You ran `pip install litellm` without pinning a version and received **v1.82.7** or **v1.82.8** +- You built a Docker image during this window that included `pip install litellm` without a pinned version +- A dependency in your project pulled in LiteLLM as a transitive, unpinned dependency + (for example through AI agent frameworks, MCP servers, or LLM orchestration tools) + +You are **not** affected if any of the following are true: + +**LiteLLM AI Gateway/Proxy users:** Customers running the official LiteLLM Proxy Docker image were not impacted. That deployment path pins dependencies in requirements.txt and does not rely on the compromised PyPI packages. + +- You are using **LiteLLM Cloud** +- You are using the official LiteLLM AI Gateway Docker image: `ghcr.io/berriai/litellm` +- You are on **v1.82.6 or earlier** and did not upgrade during the affected window +- You installed LiteLLM from source via the GitHub repository, which was **not** compromised + + +### How to check if you are affected + + + + +```bash +pip show litellm +``` + + + +Go to the proxy base url, and check the version of the installed LiteLLM. + +![Proxy version check](../../img/security_update_march_2026/proxy_version.png) + + + +Scans all repositories in a GitHub organization for workflow jobs that installed the compromised versions. + +**Requirements:** Python 3 and `requests` (`pip install requests`). + +**Setup:** + +```bash +export GITHUB_TOKEN="your-github-pat" +``` + +**Run:** + +```bash +python find_litellm_github.py +``` + +Set the `ORG` variable in the script to your GitHub organization name. + +Both scripts default to scanning jobs from **today**. Adjust the `WINDOW_START` and `WINDOW_END` constants to cover **March 24, 2026** (the incident date) if running on a different day. + +
+View full script (find_litellm_github.py) + +```python +#!/usr/bin/env python3 +""" +Scan all GitHub Actions jobs in a GitHub org that ran between +0800-1244 UTC today and identify any that installed litellm 1.82.7 or 1.82.8. + +Adjust WINDOW_START / WINDOW_END to cover March 24, 2026 if running later. +""" + +import io +import os +import re +import sys +import zipfile +from concurrent.futures import ThreadPoolExecutor, as_completed +from datetime import datetime, timezone + +import requests + +GITHUB_URL = "https://api.github.com" +ORG = "your-org" # <-- set to your GitHub organization +TOKEN = os.environ.get("GITHUB_TOKEN", "") + +TODAY = datetime.now(timezone.utc).date() +WINDOW_START = datetime(TODAY.year, TODAY.month, TODAY.day, 8, 0, 0, tzinfo=timezone.utc) +WINDOW_END = datetime(TODAY.year, TODAY.month, TODAY.day, 12, 44, 0, tzinfo=timezone.utc) + +TARGET_VERSIONS = {"1.82.7", "1.82.8"} +VERSION_PATTERN = re.compile(r"litellm[=\-](\d+\.\d+\.\d+)", re.IGNORECASE) + +SESSION = requests.Session() +SESSION.headers.update({ + "Authorization": f"Bearer {TOKEN}", + "Accept": "application/vnd.github+json", + "X-GitHub-Api-Version": "2022-11-28", +}) + + +def get_paginated(url, params=None): + params = dict(params or {}) + params.setdefault("per_page", 100) + page = 1 + while True: + params["page"] = page + resp = SESSION.get(url, params=params, timeout=30) + if resp.status_code == 404: + return + resp.raise_for_status() + data = resp.json() + if isinstance(data, dict): + items = next((v for v in data.values() if isinstance(v, list)), []) + else: + items = data + if not items: + break + yield from items + if len(items) < params["per_page"]: + break + page += 1 + + +def parse_ts(ts_str): + if not ts_str: + return None + return datetime.fromisoformat(ts_str.replace("Z", "+00:00")) + + +def get_repos(): + repos = [] + for r in get_paginated(f"{GITHUB_URL}/orgs/{ORG}/repos", {"type": "all"}): + repos.append({"id": r["id"], "name": r["name"], "full_name": r["full_name"]}) + return repos + + +def get_runs_in_window(repo_full_name): + created_filter = ( + f"{WINDOW_START.strftime('%Y-%m-%dT%H:%M:%SZ')}" + f"..{WINDOW_END.strftime('%Y-%m-%dT%H:%M:%SZ')}" + ) + url = f"{GITHUB_URL}/repos/{repo_full_name}/actions/runs" + runs = [] + for run in get_paginated(url, {"created": created_filter, "per_page": 100}): + ts = parse_ts(run.get("run_started_at") or run.get("created_at")) + if ts and WINDOW_START <= ts <= WINDOW_END: + runs.append(run) + return runs + + +def get_jobs_for_run(repo_full_name, run_id): + url = f"{GITHUB_URL}/repos/{repo_full_name}/actions/runs/{run_id}/jobs" + jobs = [] + for job in get_paginated(url, {"filter": "all"}): + ts = parse_ts(job.get("started_at")) + if ts and WINDOW_START <= ts <= WINDOW_END: + jobs.append(job) + return jobs + + +def fetch_job_log(repo_full_name, job_id): + url = f"{GITHUB_URL}/repos/{repo_full_name}/actions/jobs/{job_id}/logs" + resp = SESSION.get(url, timeout=60, allow_redirects=True) + if resp.status_code in (403, 404, 410): + return "" + resp.raise_for_status() + + content_type = resp.headers.get("Content-Type", "") + if "zip" in content_type or resp.content[:2] == b"PK": + try: + with zipfile.ZipFile(io.BytesIO(resp.content)) as zf: + parts = [] + for name in sorted(zf.namelist()): + with zf.open(name) as f: + parts.append(f.read().decode("utf-8", errors="replace")) + return "\n".join(parts) + except zipfile.BadZipFile: + pass + return resp.text + + +def check_job(repo_full_name, job): + job_id = job["id"] + job_name = job["name"] + run_id = job["run_id"] + started = job.get("started_at", "") + + log_text = fetch_job_log(repo_full_name, job_id) + if not log_text: + return None + + found_versions = set() + context_lines = [] + for line in log_text.splitlines(): + m = VERSION_PATTERN.search(line) + if m: + ver = m.group(1) + if ver in TARGET_VERSIONS: + found_versions.add(ver) + context_lines.append(line.strip()) + + if not found_versions: + return None + + return { + "repo": repo_full_name, + "run_id": run_id, + "job_id": job_id, + "job_name": job_name, + "started_at": started, + "versions": sorted(found_versions), + "context": context_lines[:10], + "job_url": job.get("html_url", f"https://github.com/{repo_full_name}/actions/runs/{run_id}"), + } + + +def main(): + if not TOKEN: + print("ERROR: Set GITHUB_TOKEN environment variable.", file=sys.stderr) + sys.exit(1) + + print(f"Time window : {WINDOW_START.isoformat()} -> {WINDOW_END.isoformat()}") + print(f"Hunting for : litellm {', '.join(sorted(TARGET_VERSIONS))}") + print() + + print(f"Fetching repositories for org '{ORG}'...") + repos = get_repos() + print(f" Found {len(repos)} repositories") + print() + + jobs_to_check = [] + + print("Scanning workflow runs for time window...") + for repo in repos: + full_name = repo["full_name"] + try: + runs = get_runs_in_window(full_name) + except requests.HTTPError as e: + print(f" WARN: {full_name} - {e}", file=sys.stderr) + continue + if not runs: + continue + print(f" {full_name}: {len(runs)} run(s) in window") + for run in runs: + try: + jobs = get_jobs_for_run(full_name, run["id"]) + except requests.HTTPError as e: + print(f" WARN: run {run['id']} - {e}", file=sys.stderr) + continue + for job in jobs: + jobs_to_check.append((full_name, job)) + + total = len(jobs_to_check) + print(f"\nFetching logs for {total} job(s)...") + print() + + hits = [] + with ThreadPoolExecutor(max_workers=8) as pool: + futures = { + pool.submit(check_job, full_name, job): (full_name, job["id"]) + for full_name, job in jobs_to_check + } + done = 0 + for future in as_completed(futures): + done += 1 + full_name, jid = futures[future] + try: + result = future.result() + except Exception as e: + print(f" ERROR {full_name} job {jid}: {e}", file=sys.stderr) + continue + if result: + hits.append(result) + print( + f" [{done}/{total}] {full_name} job {jid}" + + (f" *** HIT: litellm {result['versions']} ***" if result else ""), + flush=True, + ) + + print() + print("=" * 72) + print(f"RESULTS: {len(hits)} job(s) installed litellm {' or '.join(sorted(TARGET_VERSIONS))}") + print("=" * 72) + + if not hits: + print("No matches found.") + return + + for h in sorted(hits, key=lambda x: x["started_at"]): + print() + print(f" Repo : {h['repo']}") + print(f" Job : {h['job_name']} (#{h['job_id']})") + print(f" Run ID : {h['run_id']}") + print(f" Started : {h['started_at']}") + print(f" Versions : litellm {', '.join(h['versions'])}") + print(f" URL : {h['job_url']}") + print(f" Log lines :") + for line in h["context"]: + print(f" {line}") + + +if __name__ == "__main__": + main() +``` + +
+ +
+ + +Scans all projects in a GitLab group (including subgroups) for CI/CD jobs that installed the compromised versions. + +**Requirements:** Python 3 and `requests` (`pip install requests`). + +**Setup:** + +```bash +export GITLAB_TOKEN="your-gitlab-pat" +``` + +**Run:** + +```bash +python find_litellm_jobs.py +``` + +Set the `GROUP_NAME` variable in the script to your GitLab group name. + +Both scripts default to scanning jobs from **today**. Adjust the `WINDOW_START` and `WINDOW_END` constants to cover **March 24, 2026** (the incident date) if running on a different day. + +
+View full script (find_litellm_jobs.py) + +```python +#!/usr/bin/env python3 +""" +Scan all GitLab CI/CD jobs in a GitLab group that ran between +0800-1244 UTC today and identify any that installed litellm 1.82.7 or 1.82.8. + +Adjust WINDOW_START / WINDOW_END to cover March 24, 2026 if running later. +""" + +import os +import re +import sys +from concurrent.futures import ThreadPoolExecutor, as_completed +from datetime import datetime, timezone + +import requests + +GITLAB_URL = "https://gitlab.com" +GROUP_NAME = "YourGroup" # <-- set to your GitLab group name +TOKEN = os.environ.get("GITLAB_TOKEN", "") + +TODAY = datetime.now(timezone.utc).date() +WINDOW_START = datetime(TODAY.year, TODAY.month, TODAY.day, 8, 0, 0, tzinfo=timezone.utc) +WINDOW_END = datetime(TODAY.year, TODAY.month, TODAY.day, 12, 44, 0, tzinfo=timezone.utc) + +TARGET_VERSIONS = {"1.82.7", "1.82.8"} +VERSION_PATTERN = re.compile(r"litellm[=\-](\d+\.\d+\.\d+)", re.IGNORECASE) + +HEADERS = {"PRIVATE-TOKEN": TOKEN} +SESSION = requests.Session() +SESSION.headers.update(HEADERS) + + +def get_paginated(url, params=None): + params = dict(params or {}) + params.setdefault("per_page", 100) + page = 1 + while True: + params["page"] = page + resp = SESSION.get(url, params=params, timeout=30) + resp.raise_for_status() + data = resp.json() + if not data: + break + yield from data + if len(data) < params["per_page"]: + break + page += 1 + + +def get_group_id(group_name): + resp = SESSION.get(f"{GITLAB_URL}/api/v4/groups/{group_name}", timeout=30) + resp.raise_for_status() + return resp.json()["id"] + + +def get_all_projects(group_id): + projects = [] + for p in get_paginated( + f"{GITLAB_URL}/api/v4/groups/{group_id}/projects", + {"include_subgroups": "true", "archived": "false"}, + ): + projects.append({"id": p["id"], "name": p["path_with_namespace"]}) + return projects + + +def parse_ts(ts_str): + if not ts_str: + return None + ts_str = ts_str.replace("Z", "+00:00") + return datetime.fromisoformat(ts_str) + + +def jobs_in_window(project_id): + matching = [] + url = f"{GITLAB_URL}/api/v4/projects/{project_id}/jobs" + params = {"per_page": 100, "scope[]": ["success", "failed", "canceled", "running"]} + + page = 1 + while True: + params["page"] = page + resp = SESSION.get(url, params=params, timeout=30) + if resp.status_code == 403: + return matching + resp.raise_for_status() + jobs = resp.json() + if not jobs: + break + + stop_early = False + for job in jobs: + ts = parse_ts(job.get("started_at") or job.get("created_at")) + if ts is None: + continue + if ts > WINDOW_END: + continue + if ts < WINDOW_START: + stop_early = True + continue + matching.append(job) + + if stop_early or len(jobs) < 100: + break + page += 1 + + return matching + + +def fetch_trace(project_id, job_id): + url = f"{GITLAB_URL}/api/v4/projects/{project_id}/jobs/{job_id}/trace" + resp = SESSION.get(url, timeout=60) + if resp.status_code in (403, 404): + return "" + resp.raise_for_status() + return resp.text + + +def check_job(project_name, project_id, job): + job_id = job["id"] + job_name = job["name"] + ref = job.get("ref", "") + started = job.get("started_at", job.get("created_at", "")) + + trace = fetch_trace(project_id, job_id) + if not trace: + return None + + found_versions = set() + for match in VERSION_PATTERN.finditer(trace): + ver = match.group(1) + if ver in TARGET_VERSIONS: + found_versions.add(ver) + + if not found_versions: + return None + + context_lines = [] + for line in trace.splitlines(): + if VERSION_PATTERN.search(line): + ver_match = VERSION_PATTERN.search(line) + if ver_match and ver_match.group(1) in TARGET_VERSIONS: + context_lines.append(line.strip()) + + return { + "project": project_name, + "project_id": project_id, + "job_id": job_id, + "job_name": job_name, + "ref": ref, + "started_at": started, + "versions": sorted(found_versions), + "context": context_lines[:10], + "job_url": f"{GITLAB_URL}/{project_name}/-/jobs/{job_id}", + } + + +def main(): + if not TOKEN: + print("ERROR: Set GITLAB_TOKEN environment variable.", file=sys.stderr) + sys.exit(1) + + print(f"Time window : {WINDOW_START.isoformat()} -> {WINDOW_END.isoformat()}") + print(f"Hunting for : litellm {', '.join(sorted(TARGET_VERSIONS))}") + print() + + print(f"Resolving group '{GROUP_NAME}'...") + group_id = get_group_id(GROUP_NAME) + + print("Fetching projects...") + projects = get_all_projects(group_id) + print(f" Found {len(projects)} projects") + print() + + all_jobs_to_check = [] + + print("Scanning job listings for time window...") + for proj in projects: + try: + jobs = jobs_in_window(proj["id"]) + except requests.HTTPError as e: + print(f" WARN: {proj['name']} - {e}", file=sys.stderr) + continue + if jobs: + print(f" {proj['name']}: {len(jobs)} job(s) in window") + for j in jobs: + all_jobs_to_check.append((proj["name"], proj["id"], j)) + + total = len(all_jobs_to_check) + print(f"\nFetching traces for {total} job(s)...") + print() + + hits = [] + with ThreadPoolExecutor(max_workers=10) as pool: + futures = { + pool.submit(check_job, pname, pid, job): (pname, job["id"]) + for pname, pid, job in all_jobs_to_check + } + done = 0 + for future in as_completed(futures): + done += 1 + pname, jid = futures[future] + try: + result = future.result() + except Exception as e: + print(f" ERROR checking {pname} job {jid}: {e}", file=sys.stderr) + continue + if result: + hits.append(result) + print(f" [{done}/{total}] checked {pname} job {jid}" + + (f" *** HIT: litellm {result['versions']} ***" if result else ""), + flush=True) + + print() + print("=" * 72) + print(f"RESULTS: {len(hits)} job(s) installed litellm {' or '.join(sorted(TARGET_VERSIONS))}") + print("=" * 72) + + if not hits: + print("No matches found.") + return + + for h in sorted(hits, key=lambda x: x["started_at"]): + print() + print(f" Project : {h['project']}") + print(f" Job : {h['job_name']} (#{h['job_id']})") + print(f" Branch/tag: {h['ref']}") + print(f" Started : {h['started_at']}") + print(f" Versions : litellm {', '.join(h['versions'])}") + print(f" URL : {h['job_url']}") + print(f" Log lines :") + for line in h["context"]: + print(f" {line}") + + +if __name__ == "__main__": + main() +``` + +
+ +
+
+ +*CI/CD scripts contributed by the community ([original gist](https://gist.github.com/fryz/93ec8d4898ffe5b5ac5706a208823ef3)). Review before running.* + + +## Indicators of compromise (IoCs) + +Review affected systems for the following indicators: + +- `litellm_init.pth` present in your `site-packages` +- Outbound traffic or requests to `models.litellm[.]cloud` + This domain is **not** affiliated with LiteLLM +- Outbound traffic or requests to `checkmarx[.]zone` + This domain is **not** affiliated with LiteLLM + + +## Immediate actions for affected users + +If you installed or ran **v1.82.7** or **v1.82.8**, take the following actions immediately. + +### 1. Rotate all secrets + +Treat any credentials present on the affected systems as compromised, including: + +- API keys +- Cloud access keys +- Database passwords +- SSH keys +- Kubernetes tokens +- Any secrets stored in environment variables or configuration files + +### 2. Inspect your filesystem + +Check your `site-packages` directory for a file named `litellm_init.pth`: + +```bash +find /usr/lib/python3.13/site-packages/ -name "litellm_init.pth" +``` + +If present: + +- remove it immediately +- investigate the host for further compromise +- preserve relevant artifacts if your security team is performing forensics + +### 3. Audit version history + +Review your: + +- Local environments +- CI/CD pipelines +- Docker builds +- Deployment logs + +Confirm whether **v1.82.7** or **v1.82.8** was installed anywhere. + +Pin LiteLLM to a known safe version such as **v1.82.6 or earlier**, or to a later verified release once announced. + + +## Response and remediation + +The LiteLLM AI Gateway team has already taken the following steps: + +- Removed compromised packages from PyPI +- Rotated maintainer credentials and established new authorized maintainers +- Engaged Google's Mandiant security team to assist with forensic analysis of the build and publishing chain + + +## Verified safe versions + +We have audited every LiteLLM release published between v1.78.0 and v1.82.6 across both PyPI and Docker. Each artifact was verified by: + +1. Downloading the published artifact and computing its SHA-256 digest +2. Scanning for the known [indicators of compromise](#indicators-of-compromise-iocs) (IOCs) +3. Comparing the artifact contents against the corresponding Git commit in the BerriAI/litellm repository + +**All versions listed below are confirmed clean.** + + + + + + + + + + + + + + + +## Questions and support + +If you believe your systems may be affected, contact us immediately: + +- **Security:** `security@berri.ai` +- **Support:** `support@berri.ai` +- **Slack:** Reach out to the LiteLLM team directly + +For real-time updates, follow [LiteLLM (YC W23) on X](https://x.com/LiteLLM). + diff --git a/docs/my-website/blog/vanta_compliance_recertification/index.md b/docs/my-website/blog/vanta_compliance_recertification/index.md new file mode 100644 index 0000000000..d05c113967 --- /dev/null +++ b/docs/my-website/blog/vanta_compliance_recertification/index.md @@ -0,0 +1,18 @@ +--- +slug: vanta-compliance-recertification +title: "LiteLLM + Vanta: SOC 2 Type 2 and ISO 27001 Recertification" +date: 2026-03-30T10:00:00 +authors: + - krrish +description: "LiteLLM is partnering with Vanta on SOC 2 Type 2 and ISO 27001 recertification and engaging independent auditors for verification." +tags: [security, compliance] +hide_table_of_contents: true +--- + +![LiteLLM x Vanta SOC-2 Recertification](/img/blog/vanta_soc2_recertification.png) + +We are partnering with [Vanta](https://www.vanta.com/) to recertify LiteLLM's compliance for SOC 2 Type 2 and ISO 27001. + +As part of this process, we are also identifying independent auditors to validate and verify our compliance posture. + +This is part of our commitment to being the most secure and transparent AI Gateway possible. diff --git a/docs/my-website/docs/anthropic_unified/index.md b/docs/my-website/docs/anthropic_unified/index.md index 9981547ce1..f8a50e14da 100644 --- a/docs/my-website/docs/anthropic_unified/index.md +++ b/docs/my-website/docs/anthropic_unified/index.md @@ -506,12 +506,15 @@ Request body will be in the Anthropic messages API format. **litellm follows the A system prompt providing context or specific instructions to the model. - **temperature** (number): Controls randomness in the model's responses. Valid range: `0 < temperature < 1`. -- **thinking** (object): +- **thinking** (object): Configuration for enabling extended thinking. If enabled, it includes: - - **budget_tokens** (integer): + - **budget_tokens** (integer): Minimum of 1024 tokens (and less than `max_tokens`). - - **type** (enum): + - **type** (enum): E.g., `"enabled"`. + - **summary** (string, optional): + Enables the summary style for thinking blocks. Possible values: `"auto"`, `"concise"`, `"detailed"`, `"disabled"`. + When routing to non-Anthropic providers (e.g., `openai/gpt-5.1`), the `summary` value is preserved and forwarded to the downstream API. - **tool_choice** (object): Instructs how the model should utilize any provided tools. - **tools** (array of objects): diff --git a/docs/my-website/docs/completion/prompt_caching.md b/docs/my-website/docs/completion/prompt_caching.md index dca5f5c0cf..402c7b9f4c 100644 --- a/docs/my-website/docs/completion/prompt_caching.md +++ b/docs/my-website/docs/completion/prompt_caching.md @@ -6,6 +6,8 @@ import TabItem from '@theme/TabItem'; Supported Providers: - OpenAI (`openai/`) - Anthropic API (`anthropic/`) +- Google AI Studio (`gemini/`) +- Vertex AI (`vertex_ai/`, `vertex_ai_beta/`) - Bedrock (`bedrock/`, `bedrock/invoke/`, `bedrock/converse`) ([All models bedrock supports prompt caching on](https://docs.aws.amazon.com/bedrock/latest/userguide/prompt-caching.html)) - Deepseek API (`deepseek/`) @@ -257,7 +259,7 @@ Anthropic charges for cache writes. Specify the content to cache with `"cache_control": {"type": "ephemeral"}`. -If you pass that in for any other llm provider, it will be ignored. +This same format also works for [Gemini / Vertex AI](#google-ai-studio--vertex-ai-gemini-example). For other providers, it will be ignored. @@ -356,6 +358,208 @@ print(response.usage) +### Google AI Studio / Vertex AI (Gemini) Example + +Use the same Anthropic-style `cache_control` format — LiteLLM automatically translates it to Google's [context caching API](https://ai.google.dev/api/caching). + +**How it works under the hood:** +1. Messages with `cache_control` are separated and sent to Google's `cachedContents` API +2. The cached content ID is then passed as `cachedContent` in the Gemini request body +3. Works across all three providers: `gemini/` (Google AI Studio), `vertex_ai/`, and `vertex_ai_beta/` +4. Requires a minimum of **1024 tokens** in the cached content — below that, caching is silently skipped + + + + +```python +from litellm import completion +import os + +os.environ["GEMINI_API_KEY"] = "" + +response = completion( + model="gemini/gemini-2.5-flash", + messages=[ + { + "role": "system", + "content": [ + { + "type": "text", + "text": "You are an AI assistant tasked with analyzing legal documents.", + }, + { + "type": "text", + "text": "Here is the full text of a complex legal agreement" * 400, + "cache_control": {"type": "ephemeral"}, + }, + ], + }, + { + "role": "user", + "content": "what are the key terms and conditions in this agreement?", + }, + ], +) + +print(response.usage) +``` + + + +1. Setup config.yaml + +```yaml +model_list: + - model_name: gemini-2.5-flash + litellm_params: + model: gemini/gemini-2.5-flash + api_key: os.environ/GEMINI_API_KEY +``` + +2. Start proxy + +```bash +litellm --config /path/to/config.yaml +``` + +3. Test it! + +```python +from openai import OpenAI + +client = OpenAI( + api_key="LITELLM_PROXY_KEY", # sk-1234 + base_url="LITELLM_PROXY_BASE", # http://0.0.0.0:4000 +) + +response = client.chat.completions.create( + model="gemini-2.5-flash", + messages=[ + { + "role": "system", + "content": [ + { + "type": "text", + "text": "You are an AI assistant tasked with analyzing legal documents.", + }, + { + "type": "text", + "text": "Here is the full text of a complex legal agreement" * 400, + "cache_control": {"type": "ephemeral"}, + }, + ], + }, + { + "role": "user", + "content": "what are the key terms and conditions in this agreement?", + }, + ], +) + +print(response.usage) +``` + + + + +#### Vertex AI + +For Vertex AI, use `vertex_ai/` prefix: + + + + +```python +from litellm import completion + +response = completion( + model="vertex_ai/gemini-2.5-flash", + vertex_project="my-gcp-project", + vertex_location="us-central1", + messages=[ + { + "role": "system", + "content": [ + { + "type": "text", + "text": "You are an AI assistant tasked with analyzing legal documents.", + }, + { + "type": "text", + "text": "Here is the full text of a complex legal agreement" * 400, + "cache_control": {"type": "ephemeral"}, + }, + ], + }, + { + "role": "user", + "content": "what are the key terms and conditions in this agreement?", + }, + ], +) + +print(response.usage) +``` + + + +1. Setup config.yaml + +```yaml +model_list: + - model_name: gemini-2.5-flash + litellm_params: + model: vertex_ai/gemini-2.5-flash + vertex_project: my-gcp-project + vertex_location: us-central1 +``` + +2. Start proxy + +```bash +litellm --config /path/to/config.yaml +``` + +3. Test it! + +```python +from openai import OpenAI + +client = OpenAI( + api_key="LITELLM_PROXY_KEY", # sk-1234 + base_url="LITELLM_PROXY_BASE", # http://0.0.0.0:4000 +) + +response = client.chat.completions.create( + model="gemini-2.5-flash", + messages=[ + { + "role": "system", + "content": [ + { + "type": "text", + "text": "You are an AI assistant tasked with analyzing legal documents.", + }, + { + "type": "text", + "text": "Here is the full text of a complex legal agreement" * 400, + "cache_control": {"type": "ephemeral"}, + }, + ], + }, + { + "role": "user", + "content": "what are the key terms and conditions in this agreement?", + }, + ], +) + +print(response.usage) +``` + + + + ### Deepeek Example Works the same as OpenAI. diff --git a/docs/my-website/docs/data_security.md b/docs/my-website/docs/data_security.md index 2c4b1247e2..d93d17aa0d 100644 --- a/docs/my-website/docs/data_security.md +++ b/docs/my-website/docs/data_security.md @@ -128,8 +128,6 @@ We'll review all reports promptly. Note that we don't currently offer a bug boun Legal Entity Name: Berrie AI Incorporated -Company Phone Number: 7708783106 - Point of contact email address for security incidents: krrish@berri.ai Point of contact email address for general security-related questions: krrish@berri.ai diff --git a/docs/my-website/docs/debugging/local_debugging.md b/docs/my-website/docs/debugging/local_debugging.md index 8a56d6c34a..53daa4e366 100644 --- a/docs/my-website/docs/debugging/local_debugging.md +++ b/docs/my-website/docs/debugging/local_debugging.md @@ -67,6 +67,6 @@ response = completion("command-nightly", messages, logger_fn=my_custom_logging_f ## Still Seeing Issues? -Text us @ +17708783106 or Join the [Discord](https://discord.com/invite/wuPM9dRgDw). +Join the [Discord](https://discord.com/invite/wuPM9dRgDw). We promise to help you in `lite`ning speed ❤️ diff --git a/docs/my-website/docs/enterprise.md b/docs/my-website/docs/enterprise.md index 6dccf7ff4e..a3fc9e38b6 100644 --- a/docs/my-website/docs/enterprise.md +++ b/docs/my-website/docs/enterprise.md @@ -4,7 +4,7 @@ import Image from '@theme/IdealImage'; :::info - ✨ SSO is free for up to 5 users. After that, an enterprise license is required. [Get Started with Enterprise here](https://www.litellm.ai/enterprise) -- Who is Enterprise for? Companies giving access to 100+ users **OR** 10+ AI use-cases. If you're not sure, [get in touch with us](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions) to discuss your needs. +- Who is Enterprise for? Companies giving access to 100+ users **OR** 10+ AI use-cases. If you're not sure, [get in touch with us](https://enterprise.litellm.ai/demo) to discuss your needs. ::: For companies that need SSO, user management and professional support for LiteLLM Proxy @@ -36,7 +36,7 @@ Manage Yourself - you can deploy our Docker Image or build a custom image from o ### What’s the cost of the Self-Managed Enterprise edition? -Self-Managed Enterprise deployments require our team to understand your exact needs. [Get in touch with us to learn more](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions) +Self-Managed Enterprise deployments require our team to understand your exact needs. [Get in touch with us to learn more](https://enterprise.litellm.ai/demo) ### How does deployment with Enterprise License work? @@ -106,7 +106,7 @@ Professional Support can assist with LLM/Provider integrations, deployment, upgr Pricing is based on usage. We can figure out a price that works for your team, on the call. -[**Contact Us to learn more**](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions) +[**Contact Us to learn more**](https://enterprise.litellm.ai/demo) diff --git a/docs/my-website/docs/fine_tuning.md b/docs/my-website/docs/fine_tuning.md index d0bd98a76f..52e96f2868 100644 --- a/docs/my-website/docs/fine_tuning.md +++ b/docs/my-website/docs/fine_tuning.md @@ -6,7 +6,7 @@ import TabItem from '@theme/TabItem'; :::info -This is an Enterprise only endpoint [Get Started with Enterprise here](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions) +This is an Enterprise only endpoint [Get Started with Enterprise here](https://enterprise.litellm.ai/demo) ::: diff --git a/docs/my-website/docs/learn/sdk_quickstart.md b/docs/my-website/docs/learn/sdk_quickstart.md index bdf7b63eb5..0fb8c3f02a 100644 --- a/docs/my-website/docs/learn/sdk_quickstart.md +++ b/docs/my-website/docs/learn/sdk_quickstart.md @@ -11,7 +11,7 @@ Use this path if you are integrating LiteLLM directly into application code. ## 1. Install LiteLLM ```bash -pip install litellm +pip install litellm==1.82.6 ``` ## 2. Set Provider Credentials diff --git a/docs/my-website/docs/migration.md b/docs/my-website/docs/migration.md index e1af07d468..fda1155905 100644 --- a/docs/my-website/docs/migration.md +++ b/docs/my-website/docs/migration.md @@ -31,5 +31,4 @@ When we have breaking changes (i.e. going from 1.x.x to 2.x.x), we will document **How can we communicate changes better?** Tell us - [Discord](https://discord.com/invite/wuPM9dRgDw) -- Email (krrish@berri.ai/ishaan@berri.ai) -- Text us (+17708783106) +- Email (support@berri.ai) diff --git a/docs/my-website/docs/observability/arize_integration.md b/docs/my-website/docs/observability/arize_integration.md index b3ccf98ea3..4486fb2b71 100644 --- a/docs/my-website/docs/observability/arize_integration.md +++ b/docs/my-website/docs/observability/arize_integration.md @@ -194,5 +194,4 @@ print(response) - [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version) - [Community Discord 💭](https://discord.gg/wuPM9dRgDw) -- Our numbers 📞 +1 (770) 8783-106 / +1 (412) 618-6238 - Our emails ✉️ ishaan@berri.ai / krrish@berri.ai diff --git a/docs/my-website/docs/observability/gcs_bucket_integration.md b/docs/my-website/docs/observability/gcs_bucket_integration.md index 69b956950e..5f8d42508a 100644 --- a/docs/my-website/docs/observability/gcs_bucket_integration.md +++ b/docs/my-website/docs/observability/gcs_bucket_integration.md @@ -6,7 +6,7 @@ Log LLM Logs to [Google Cloud Storage Buckets](https://cloud.google.com/storage? :::info -✨ This is an Enterprise only feature [Get Started with Enterprise here](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions) +✨ This is an Enterprise only feature [Get Started with Enterprise here](https://enterprise.litellm.ai/demo) ::: @@ -79,5 +79,4 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \ - [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version) - [Community Discord 💭](https://discord.gg/wuPM9dRgDw) -- Our numbers 📞 +1 (770) 8783-106 / ‭+1 (412) 618-6238‬ - Our emails ✉️ ishaan@berri.ai / krrish@berri.ai diff --git a/docs/my-website/docs/observability/langfuse_integration.md b/docs/my-website/docs/observability/langfuse_integration.md index d3c5a44d48..32849ebdb9 100644 --- a/docs/my-website/docs/observability/langfuse_integration.md +++ b/docs/my-website/docs/observability/langfuse_integration.md @@ -342,5 +342,4 @@ Be aware that if you are continuing an existing trace, and you set `update_trace - [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version) - [Community Discord 💭](https://discord.gg/wuPM9dRgDw) -- Our numbers 📞 +1 (770) 8783-106 / ‭+1 (412) 618-6238‬ - Our emails ✉️ ishaan@berri.ai / krrish@berri.ai diff --git a/docs/my-website/docs/observability/langsmith_integration.md b/docs/my-website/docs/observability/langsmith_integration.md index cada4122b2..bf867319de 100644 --- a/docs/my-website/docs/observability/langsmith_integration.md +++ b/docs/my-website/docs/observability/langsmith_integration.md @@ -225,5 +225,4 @@ environment_variables: - [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version) - [Community Discord 💭](https://discord.gg/wuPM9dRgDw) -- Our numbers 📞 +1 (770) 8783-106 / ‭+1 (412) 618-6238‬ - Our emails ✉️ ishaan@berri.ai / krrish@berri.ai diff --git a/docs/my-website/docs/observability/logfire_integration.md b/docs/my-website/docs/observability/logfire_integration.md index a1bd43a4bc..00652c0f1a 100644 --- a/docs/my-website/docs/observability/logfire_integration.md +++ b/docs/my-website/docs/observability/logfire_integration.md @@ -63,5 +63,4 @@ response = litellm.completion( - [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version) - [Community Discord 💭](https://discord.gg/wuPM9dRgDw) -- Our numbers 📞 +1 (770) 8783-106 / ‭+1 (412) 618-6238‬ - Our emails ✉️ ishaan@berri.ai / krrish@berri.ai diff --git a/docs/my-website/docs/observability/lunary_integration.md b/docs/my-website/docs/observability/lunary_integration.md index 8d28321c80..4f1dca9d4a 100644 --- a/docs/my-website/docs/observability/lunary_integration.md +++ b/docs/my-website/docs/observability/lunary_integration.md @@ -176,5 +176,4 @@ You can find more details about the different ways of making requests to the Lit - [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version) - [Community Discord 💭](https://discord.gg/wuPM9dRgDw) -- Our numbers 📞 +1 (770) 8783-106 / ‭+1 (412) 618-6238‬ - Our emails ✉️ ishaan@berri.ai / krrish@berri.ai diff --git a/docs/my-website/docs/observability/opik_integration.md b/docs/my-website/docs/observability/opik_integration.md index d28c46f0b4..5b5cbe0f18 100644 --- a/docs/my-website/docs/observability/opik_integration.md +++ b/docs/my-website/docs/observability/opik_integration.md @@ -261,5 +261,4 @@ All requests made with this key will automatically be tracked in the "TestProjec - [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version) - [Community Discord 💭](https://discord.gg/wuPM9dRgDw) -- Our numbers 📞 +1 (770) 8783-106 / ‭+1 (412) 618-6238‬ - Our emails ✉️ ishaan@berri.ai / krrish@berri.ai diff --git a/docs/my-website/docs/observability/phoenix_integration.md b/docs/my-website/docs/observability/phoenix_integration.md index 191f1f8044..67ba815557 100644 --- a/docs/my-website/docs/observability/phoenix_integration.md +++ b/docs/my-website/docs/observability/phoenix_integration.md @@ -127,5 +127,4 @@ Depending on which Phoenix Cloud version or deployment you are using, you should - [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version) - [Community Discord 💭](https://discord.gg/wuPM9dRgDw) -- Our numbers 📞 +1 (770) 8783-106 / ‭+1 (412) 618-6238‬ - Our emails ✉️ ishaan@berri.ai / krrish@berri.ai diff --git a/docs/my-website/docs/observability/promptlayer_integration.md b/docs/my-website/docs/observability/promptlayer_integration.md index 7f62a31697..9462e755f7 100644 --- a/docs/my-website/docs/observability/promptlayer_integration.md +++ b/docs/my-website/docs/observability/promptlayer_integration.md @@ -84,5 +84,4 @@ Credits to [Nick Bradford](https://github.com/nsbradford), from [Vim-GPT](https: - [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version) - [Community Discord 💭](https://discord.gg/wuPM9dRgDw) -- Our numbers 📞 +1 (770) 8783-106 / ‭+1 (412) 618-6238‬ - Our emails ✉️ ishaan@berri.ai / krrish@berri.ai \ No newline at end of file diff --git a/docs/my-website/docs/observability/slack_integration.md b/docs/my-website/docs/observability/slack_integration.md index 0ca7f61668..468d8b5945 100644 --- a/docs/my-website/docs/observability/slack_integration.md +++ b/docs/my-website/docs/observability/slack_integration.md @@ -101,5 +101,4 @@ response = litellm.completion( - [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version) - [Community Discord 💭](https://discord.gg/wuPM9dRgDw) -- Our numbers 📞 +1 (770) 8783-106 / ‭+1 (412) 618-6238‬ - Our emails ✉️ ishaan@berri.ai / krrish@berri.ai diff --git a/docs/my-website/docs/observability/sumologic_integration.md b/docs/my-website/docs/observability/sumologic_integration.md index c30ee94dad..87e20ca57e 100644 --- a/docs/my-website/docs/observability/sumologic_integration.md +++ b/docs/my-website/docs/observability/sumologic_integration.md @@ -328,5 +328,4 @@ If you get authentication errors, regenerate the HTTP Source URL in Sumo Logic: - [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version) - [Community Discord 💭](https://discord.gg/wuPM9dRgDw) -- Our numbers 📞 +1 (770) 8783-106 / ‭+1 (412) 618-6238‬ - Our emails ✉️ ishaan@berri.ai / krrish@berri.ai diff --git a/docs/my-website/docs/observability/supabase_integration.md b/docs/my-website/docs/observability/supabase_integration.md index fd3f1c3d5a..c29871d752 100644 --- a/docs/my-website/docs/observability/supabase_integration.md +++ b/docs/my-website/docs/observability/supabase_integration.md @@ -105,5 +105,4 @@ litellm.modify_integration("supabase",{"table_name": "litellm_logs"}) - [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version) - [Community Discord 💭](https://discord.gg/wuPM9dRgDw) -- Our numbers 📞 +1 (770) 8783-106 / ‭+1 (412) 618-6238‬ - Our emails ✉️ ishaan@berri.ai / krrish@berri.ai diff --git a/docs/my-website/docs/observability/wandb_integration.md b/docs/my-website/docs/observability/wandb_integration.md index 37057f43db..3c1a336395 100644 --- a/docs/my-website/docs/observability/wandb_integration.md +++ b/docs/my-website/docs/observability/wandb_integration.md @@ -57,5 +57,4 @@ response = litellm.completion( - [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version) - [Community Discord 💭](https://discord.gg/wuPM9dRgDw) -- Our numbers 📞 +1 (770) 8783-106 / ‭+1 (412) 618-6238‬ - Our emails ✉️ ishaan@berri.ai / krrish@berri.ai \ No newline at end of file diff --git a/docs/my-website/docs/prompt_management.md b/docs/my-website/docs/prompt_management.md new file mode 100644 index 0000000000..c4e606674b --- /dev/null +++ b/docs/my-website/docs/prompt_management.md @@ -0,0 +1,48 @@ +--- +title: Prompt Management with Responses API +--- + +# Prompt Management with Responses API + +Use LiteLLM Prompt Management with `/v1/responses` by passing `prompt_id` and optional `prompt_variables`. + +## Basic Usage + +```bash +curl -X POST "http://localhost:4000/v1/responses" \ + -H "Authorization: Bearer sk-1234" \ + -H "Content-Type: application/json" \ + -d '{ + "model": "gpt-4o", + "prompt_id": "my-responses-prompt", + "prompt_variables": {"topic": "large language models"}, + "input": [] + }' +``` + +## Multi-turn Follow-up in `input` + +To send follow-up turns in one request, pass message history in `input`. + +```bash +curl -X POST "http://localhost:4000/v1/responses" \ + -H "Authorization: Bearer sk-1234" \ + -H "Content-Type: application/json" \ + -d '{ + "model": "gpt-4o", + "prompt_id": "my-responses-prompt", + "prompt_variables": {"topic": "large language models"}, + "input": [ + {"role": "user", "content": "Topic is LLMs. Start short."}, + {"role": "assistant", "content": "Sure, go ahead."}, + {"role": "user", "content": "Now give me 3 bullets and include pricing caveat."} + ] + }' +``` + +## Notes + +- Prompt template messages are merged with your `input` messages. +- Prompt variable substitution applies to prompt message content. +- Tool call payload fields are not substituted by prompt variables. +- For follow-ups with `previous_response_id`, include `prompt_id` again if you want prompt management applied on that turn. diff --git a/docs/my-website/docs/providers/gemini.md b/docs/my-website/docs/providers/gemini.md index 0aaf3d5ae8..87ab5ad40f 100644 --- a/docs/my-website/docs/providers/gemini.md +++ b/docs/my-website/docs/providers/gemini.md @@ -11,6 +11,7 @@ import TabItem from '@theme/TabItem'; | Provider Doc | [Google AI Studio ↗](https://aistudio.google.com/) | | API Endpoint for Provider | https://generativelanguage.googleapis.com | | Supported OpenAI Endpoints | `/chat/completions`, [`/embeddings`](../embedding/supported_embedding#gemini-ai-embedding-models), `/completions`, [`/videos`](./gemini/videos.md), [`/images/edits`](../image_edits.md) | +| Lyria (music) | [Cost map & notes](./gemini/music.md) | | Pass-through Endpoint | [Supported](../pass_through/google_ai_studio.md) |
@@ -54,6 +55,7 @@ response = completion( - stream - tools - tool_choice +- include_server_side_tool_invocations - functions - response_format - n @@ -856,7 +858,112 @@ curl -X POST 'http://0.0.0.0:4000/chat/completions' \ -### URL Context +### Context Circulation (Server-Side Tool Combination) + +Context circulation allows Gemini 3+ models to combine **built-in tools** (like Google Search) with **your custom functions** in the same request. Without it, Gemini returns an error if you try to use both. + +When enabled, Gemini can execute Google Search server-side, use those results to decide whether to call your custom functions, and return the full chain of reasoning. + +**How it works:** +1. You pass `include_server_side_tool_invocations=True` along with both Google Search and your function tools +2. Gemini executes server-side tools internally and returns `toolCall`/`toolResponse` parts alongside any `functionCall` parts +3. LiteLLM extracts the server-side invocations into `provider_specific_fields["server_side_tool_invocations"]` +4. On subsequent turns, include the full assistant message in your conversation history — LiteLLM re-injects the server-side parts automatically + + + + +```python +from litellm import completion + +response = completion( + model="gemini/gemini-3-flash-preview", + messages=[{"role": "user", "content": "What's the weather in Buenos Aires? If it's raining, schedule a meeting."}], + tools=[ + {"type": "web_search_preview"}, # Google Search (server-side) + { + "type": "function", + "function": { + "name": "schedule_meeting", + "description": "Schedule a meeting", + "parameters": { + "type": "object", + "properties": {"reason": {"type": "string"}}, + "required": ["reason"], + }, + }, + }, + ], + include_server_side_tool_invocations=True, +) + +msg = response.choices[0].message + +# Server-side tool results are in provider_specific_fields +psf = msg.provider_specific_fields or {} +for invocation in psf.get("server_side_tool_invocations", []): + print(invocation["tool_type"]) # e.g. "GOOGLE_SEARCH_WEB" + print(invocation["id"]) + print(invocation["args"]) # e.g. {"queries": ["weather Buenos Aires"]} + print(invocation["response"]) # Search results from Google + +# For multi-turn: just append the full message to history +messages.append(msg) +messages.append({"role": "user", "content": "Thanks!"}) +# LiteLLM automatically re-injects the server-side parts + thought signatures +response2 = completion( + model="gemini/gemini-3-flash-preview", + messages=messages, + tools=tools, + include_server_side_tool_invocations=True, +) +``` + + + + +1. Setup config.yaml +```yaml +model_list: + - model_name: gemini-3-flash + litellm_params: + model: gemini/gemini-3-flash-preview + api_key: os.environ/GEMINI_API_KEY +``` + +2. Start Proxy +```bash +$ litellm --config /path/to/config.yaml +``` + +3. Make Request +```bash +curl -X POST 'http://0.0.0.0:4000/chat/completions' \ +-H 'Content-Type: application/json' \ +-H 'Authorization: Bearer sk-1234' \ +-d '{ + "model": "gemini-3-flash", + "messages": [{"role": "user", "content": "What is the weather in Buenos Aires?"}], + "tools": [ + {"type": "web_search_preview"}, + {"type": "function", "function": {"name": "schedule_meeting", "description": "Schedule a meeting", "parameters": {"type": "object", "properties": {"reason": {"type": "string"}}}}} + ], + "include_server_side_tool_invocations": true +}' +``` + + + + +:::info + +- Context circulation requires **Gemini 3+** models +- Server-side tool invocations (`toolCall`/`toolResponse`) are **not** included in `tool_calls` — they are in `provider_specific_fields["server_side_tool_invocations"]` because they were already executed by Google, not by your code +- `thought_signatures` are automatically preserved alongside server-side invocations for multi-turn coherence + +::: + +### URL Context diff --git a/docs/my-website/docs/providers/gemini/music.md b/docs/my-website/docs/providers/gemini/music.md new file mode 100644 index 0000000000..f3968f2db3 --- /dev/null +++ b/docs/my-website/docs/providers/gemini/music.md @@ -0,0 +1,28 @@ +# Gemini — Lyria (music generation) + +Google Lyria 3 preview models are listed in LiteLLM’s [model cost map](https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json) under the `gemini/` provider for metadata and spend tracking. + +| Property | Details | +|----------|---------| +| Provider route | `gemini/` | +| Models | `gemini/lyria-3-clip-preview`, `gemini/lyria-3-pro-preview` | +| Provider docs | [Gemini API pricing / models ↗](https://ai.google.dev/gemini-api/docs/pricing) | + +## Models + +| Model | Notes | +|-------|--------| +| `gemini/lyria-3-clip-preview` | ~30s clip; paid tier listed as per generated song in Google’s pricing | +| `gemini/lyria-3-pro-preview` | Full song; paid tier listed as per generated song in Google’s pricing | + +Input context limit in the cost map: **131,072** tokens. For modalities, limits, and features, see [Google’s Gemini API docs ↗](https://ai.google.dev/gemini-api/docs/models). + +## LiteLLM behavior + +- **Cost map**: Per-song paid pricing is stored as `output_cost_per_image` on those entries (flat per generation unit). Token-based completion cost may not reflect music billing until a dedicated path exists. +- **API calls**: Use the Gemini API as documented by Google. LiteLLM does not ship a separate `music_generation` helper like Veo’s `video_generation`. + +## Auth + +Same as other Gemini API models: `GEMINI_API_KEY` or `GOOGLE_API_KEY`. + diff --git a/docs/my-website/docs/providers/openai.md b/docs/my-website/docs/providers/openai.md index 2907cdf9f4..1f4a1687e8 100644 --- a/docs/my-website/docs/providers/openai.md +++ b/docs/my-website/docs/providers/openai.md @@ -581,6 +581,90 @@ curl -X POST 'http://0.0.0.0:4000/chat/completions' \ See [OpenAI Reasoning documentation](https://platform.openai.com/docs/guides/reasoning) for more details on organization verification requirements. +### Multi-turn Conversations with `reasoning_items` + +For multi-turn conversations you need `reasoning_items`: structured blocks that include the `encrypted_content` token OpenAI uses to restore reasoning state on the next request. Pass `include=["reasoning.encrypted_content"]` on every call where you want that token returned. + + + + +```python showLineNumbers title="Non-streaming: round-trip reasoning_items" +import litellm + +messages = [{"role": "user", "content": "Solve this step by step: 2 + 2"}] + +# Turn 1 — get reasoning_items (encrypted_content); +response = litellm.completion( + model="openai/responses/gpt-5-mini", + messages=messages, + reasoning_effort="low", + include=["reasoning.encrypted_content"], +) + +assistant_msg = response.choices[0].message + +# Turn 2 — pass reasoning_items back; LiteLLM converts to the correct Responses API format +messages.append({ + "role": "assistant", + "content": assistant_msg.content, + "reasoning_items": assistant_msg.reasoning_items, +}) +messages.append({"role": "user", "content": "Now summarize your reasoning."}) + +response2 = litellm.completion( + model="openai/responses/gpt-5-mini", + messages=messages, + reasoning_effort="low", + include=["reasoning.encrypted_content"], +) +``` + + + + +`reasoning_items` (with `encrypted_content`) arrive on the final chunk when the full response completes: + +```python showLineNumbers title="Streaming: collect and round-trip reasoning_items" +import litellm + +messages = [{"role": "user", "content": "Solve this step by step: 2 + 2"}] + +collected_content = [] +collected_reasoning_items = [] + +stream = litellm.completion( + model="openai/responses/gpt-5-mini", + messages=messages, + stream=True, + reasoning_effort="low", + include=["reasoning.encrypted_content"], +) + +for chunk in stream: + delta = chunk.choices[0].delta + if delta.content: + collected_content.append(delta.content) + if getattr(delta, "reasoning_items", None): + collected_reasoning_items.extend(delta.reasoning_items) + +messages.append({ + "role": "assistant", + "content": "".join(collected_content), + "reasoning_items": collected_reasoning_items or None, +}) +messages.append({"role": "user", "content": "Continue the conversation."}) + +response2 = litellm.completion( + model="openai/responses/gpt-5-mini", + messages=messages, + reasoning_effort="low", + include=["reasoning.encrypted_content"], +) +``` + + + + ### Verbosity Control for GPT-5 Models The `verbosity` parameter controls the length and detail of responses from GPT-5 family models. It accepts three values: `"low"`, `"medium"`, or `"high"`. diff --git a/docs/my-website/docs/proxy/config_settings.md b/docs/my-website/docs/proxy/config_settings.md index f5b611a85a..cc9090c2de 100644 --- a/docs/my-website/docs/proxy/config_settings.md +++ b/docs/my-website/docs/proxy/config_settings.md @@ -206,7 +206,7 @@ router_settings: | Name | Type | Description | |------|------|-------------| -| completion_model | string | The default model to use for completions when `model` is not specified in the request | +| completion_model | string | The model to use for all completions, overriding any `model` specified in the request | | disable_spend_logs | boolean | If true, turns off writing each transaction to the database | | disable_spend_updates | boolean | If true, turns off all spend updates to the DB. Including key/user/team spend updates. | | disable_master_key_return | boolean | If true, turns off returning master key on UI. (checked on '/user/info' endpoint) | @@ -279,6 +279,33 @@ router_settings: | forward_client_headers_to_llm_api | boolean | If true, forwards the client headers (any `x-` headers and `anthropic-beta` headers) to the backend LLM call | | maximum_spend_logs_retention_period | str | Used to set the max retention time for spend logs in the db, after which they will be auto-purged | | maximum_spend_logs_retention_interval | str | Used to set the interval in which the spend log cleanup task should run in. | +| alert_type_config | dict | Configuration mapping alert types to their handler settings | +| always_include_stream_usage | boolean | If true, includes usage metrics in every streaming response chunk | +| auto_redirect_ui_login_to_sso | boolean | If true, automatically redirects UI login page to SSO provider | +| control_plane_url | string | URL of the control plane for cross-instance state sharing | +| custom_auth_run_common_checks | boolean | If true, runs standard auth validation checks alongside custom auth handlers | +| custom_ui_sso_sign_in_handler | string | Custom handler for SSO sign-in logic in the UI | +| database_connection_pool_timeout | integer | Database connection pool timeout in seconds | +| disable_error_logs | boolean | If true, suppresses error tracking and storage in the database | +| enable_health_check_routing | boolean | If true, enables health check-driven request routing to avoid unhealthy deployments | +| enable_mcp_registry | boolean | If true, enables access to the centralized MCP server registry | +| enforce_rbac | boolean | If true, enables role-based access control (RBAC) for all proxy operations | +| forward_llm_provider_auth_headers | boolean | If true, forwards provider-specific auth headers to LLM API calls | +| health_check_concurrency | integer | Maximum number of concurrent health check operations | +| health_check_staleness_threshold | integer | Maximum age in seconds for health check results before marking deployments as stale | +| maximum_spend_logs_cleanup_cron | string | Cron expression for scheduling automatic spend log cleanup tasks | +| mcp_client_side_auth_header_name | string | HTTP header name for client-side MCP server credentials | +| mcp_internal_ip_ranges | list | CIDR ranges considered internal for non-public MCP server access control | +| mcp_required_fields | list | List of required field names for MCP server submissions | +| mcp_trusted_proxy_ranges | list | CIDR ranges of proxies trusted to forward X-Forwarded-For headers for MCP | +| require_end_user_mcp_access_defined | boolean | If true, requires end users to have explicit MCP access permissions defined | +| role_permissions | list | List of role-based permission configurations | +| search_tools | list | List of search tool configurations for enabling web search capabilities | +| token_rate_limit_type | string | Rate limit counting method: "total", "output", or "input" tokens | +| use_redis_transaction_buffer | boolean | If true, buffers database transactions in Redis before writing | +| use_shared_health_check | boolean | If true, uses Redis-backed shared health check state across multiple proxy instances | +| user_header_mappings | dict | Map custom request headers to user IDs using lookup rules | +| user_header_name | string | HTTP header name to extract user identity from requests | ### router_settings - Reference @@ -361,11 +388,14 @@ 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. Supported: `router_budget_limiting`, `prompt_caching`, `responses_api_deployment_check`, `encrypted_content_affinity`, `deployment_affinity`, `session_affinity`, `forward_client_headers_by_model_group` | +| 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`, `encrypted_content_affinity` (requires LiteLLM >= 1.82.3), `deployment_affinity`, `session_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). | +| model_group_affinity_config | Dict[str, List[str]] | Per-model-group affinity flags. Keys are model group names; values are lists of checks to enable (`deployment_affinity`, `responses_api_deployment_check`, `session_affinity`). Groups not listed fall back to the global `optional_pre_call_checks`. [Docs](../response_api.md#per-model-group-affinity-configuration) | | 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/index.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) | +| enable_health_check_routing | boolean | If true, enables health check-driven deployment filtering to avoid routing requests to unhealthy deployments | +| health_check_staleness_threshold | integer | Maximum age in seconds for cached health check results before marking deployments as stale | ### environment variables - Reference @@ -401,8 +431,10 @@ router_settings: | AUTH_STRATEGY | Strategy used for authentication (e.g., OAuth, API key) | AUTO_REDIRECT_UI_LOGIN_TO_SSO | Flag to enable automatic redirect of UI login page to SSO when SSO is configured. Default is **false** | AUDIO_SPEECH_CHUNK_SIZE | Chunk size for audio speech processing. Default is 1024 -| ANTHROPIC_API_KEY | API key for Anthropic service +| ANTHROPIC_API_KEY | API key for Anthropic service. Uses `x-api-key` header for authentication. +| ANTHROPIC_AUTH_TOKEN | Alternative auth token for Anthropic service. Uses `Authorization: Bearer` header instead of `x-api-key`. Used as fallback when `ANTHROPIC_API_KEY` is not set. | ANTHROPIC_API_BASE | Base URL for Anthropic API. Default is https://api.anthropic.com +| ANTHROPIC_BASE_URL | Alternative to `ANTHROPIC_API_BASE` for setting the Anthropic API base URL. Used as fallback when `ANTHROPIC_API_BASE` is not set. | ANTHROPIC_TOKEN_COUNTING_BETA_VERSION | Beta version header for Anthropic token counting API. Default is `token-counting-2024-11-01` | AWS_ACCESS_KEY_ID | Access Key ID for AWS services | AWS_BATCH_ROLE_ARN | ARN of the AWS IAM role for batch operations @@ -801,6 +833,7 @@ router_settings: | 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). +| LITELLM_ENABLE_TEAM_STALE_ALIAS_BYPASS | When `true`, if a team's legacy `model_aliases` entry maps a public model name to an internal `model_name__` deployment, pre-call handling can skip that rewrite when team-scoped sibling deployments exist for the public name—so load balancing / `order` apply across siblings. Default is `false` for backwards compatibility. See [Team-scoped models and legacy aliases](./load_balancing#team-scoped-models-and-legacy-model_aliases). When stale aliases are detected and this flag is off, the proxy may log a one-time warning. | 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. @@ -811,7 +844,7 @@ router_settings: | 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 | LITELLM_RATE_LIMIT_WINDOW_SIZE | Rate limit window size for LiteLLM. Default is 60 -| LITELLM_REASONING_AUTO_SUMMARY | If set to "true", automatically enables detailed reasoning summaries for reasoning models (e.g., o1, o3-mini, deepseek-reasoner). When enabled, adds `summary: "detailed"` to reasoning effort configurations. Default is "false" +| LITELLM_REASONING_AUTO_SUMMARY | If set to "true", automatically enables detailed reasoning summaries (`summary: "detailed"`) for reasoning models across all translation paths (Anthropic adapter, Responses API, etc.). Default is "false" | LITELLM_SALT_KEY | Salt key for encryption in LiteLLM | LITELLM_SSL_CIPHERS | SSL/TLS cipher configuration for faster handshakes. Controls cipher suite preferences for OpenSSL connections. | LITELLM_SECRET_AWS_KMS_LITELLM_LICENSE | AWS KMS encrypted license for LiteLLM @@ -902,6 +935,7 @@ router_settings: | OTEL_SERVICE_NAME | Service name identifier for OpenTelemetry | OTEL_TRACER_NAME | Tracer name for OpenTelemetry tracing | OTEL_LOGS_EXPORTER | Exporter type for OpenTelemetry logs (e.g., console) +| OTEL_IGNORE_CONTEXT_PROPAGATION | When true, ignore parent span context propagation in OpenTelemetry callbacks | PAGERDUTY_API_KEY | API key for PagerDuty Alerting | PANW_PRISMA_AIRS_API_KEY | API key for PANW Prisma AIRS service | PANW_PRISMA_AIRS_API_BASE | Base URL for PANW Prisma AIRS service @@ -949,6 +983,8 @@ router_settings: | QDRANT_URL | Connection URL for Qdrant database | QDRANT_VECTOR_SIZE | Vector size for Qdrant operations. Default is 1536 | REDIS_CONNECTION_POOL_TIMEOUT | Timeout in seconds for Redis connection pool. Default is 5 +| REDIS_CIRCUIT_BREAKER_FAILURE_THRESHOLD | Number of consecutive failures before the Redis circuit breaker opens. Default is 5 +| REDIS_CIRCUIT_BREAKER_RECOVERY_TIMEOUT | Time in seconds before the Redis circuit breaker attempts recovery after opening. Default is 60 | REDIS_CLUSTER_NODES | JSON-formatted list of Redis cluster startup nodes for Redis Cluster mode. Example: `[{"host": "node1", "port": 6379}]` | REDIS_HOST | Hostname for Redis server | REDIS_PASSWORD | Password for Redis service diff --git a/docs/my-website/docs/proxy/configs.md b/docs/my-website/docs/proxy/configs.md index 56a8b9566d..84a6fac121 100644 --- a/docs/my-website/docs/proxy/configs.md +++ b/docs/my-website/docs/proxy/configs.md @@ -602,6 +602,22 @@ Since you shouldn't use 12.5, round down to **10** to leave a safety buffer. Thi - Total maximum connections: 8 workers × 10 connections = 80 connections - This stays safely under your database's 100 connection limit +## LiteLLM License Key (Enterprise) + +To enable [LiteLLM Enterprise features](https://docs.litellm.ai/docs/proxy/enterprise), set your license key as an environment variable: + +```bash +export LITELLM_LICENSE="eyJ..." +``` + +The license key is a JWT token provided when you purchase a LiteLLM Enterprise license. Once set, LiteLLM will automatically detect and activate enterprise features. + +You can also add it to your `.env` file: + +```env +LITELLM_LICENSE="eyJ..." +``` + ## Extras diff --git a/docs/my-website/docs/proxy/cost_tracking.md b/docs/my-website/docs/proxy/cost_tracking.md index f28eec287d..f9e22cfecd 100644 --- a/docs/my-website/docs/proxy/cost_tracking.md +++ b/docs/my-website/docs/proxy/cost_tracking.md @@ -163,7 +163,7 @@ Use this when you want non-proxy admins to access `/spend` endpoints :::info -Schedule a [meeting with us to get your Enterprise License](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions) +Schedule a [meeting with us to get your Enterprise License](https://enterprise.litellm.ai/demo) ::: diff --git a/docs/my-website/docs/proxy/email.md b/docs/my-website/docs/proxy/email.md index 86a79cbcfc..ba737c6782 100644 --- a/docs/my-website/docs/proxy/email.md +++ b/docs/my-website/docs/proxy/email.md @@ -203,7 +203,7 @@ After regenerating the key, the user will receive an email notification with: :::info -Customizing Email Branding is an Enterprise Feature [Get in touch with us for a Free Trial](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions) +Customizing Email Branding is an Enterprise Feature [Get in touch with us for a Free Trial](https://enterprise.litellm.ai/demo) ::: diff --git a/docs/my-website/docs/proxy/enterprise.md b/docs/my-website/docs/proxy/enterprise.md index 4b525837a2..09b103ca4a 100644 --- a/docs/my-website/docs/proxy/enterprise.md +++ b/docs/my-website/docs/proxy/enterprise.md @@ -5,7 +5,7 @@ import TabItem from '@theme/TabItem'; # ✨ Enterprise Features :::tip -To get a license, get in touch with us [here](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions) +To get a license, get in touch with us [here](https://enterprise.litellm.ai/demo) ::: diff --git a/docs/my-website/docs/proxy/guardrails/akto.md b/docs/my-website/docs/proxy/guardrails/akto.md new file mode 100644 index 0000000000..67ae741d11 --- /dev/null +++ b/docs/my-website/docs/proxy/guardrails/akto.md @@ -0,0 +1,139 @@ +# Akto + +## Overview +[Akto](https://www.akto.io/) provides API security guardrails and data ingestion for LLM traffic. + +Akto now uses a **two-entry guardrail pattern** in LiteLLM: +- `akto-validate` (`pre_call`) for request validation +- `akto-ingest` (`post_call`) for request/response ingestion + +There is no `on_flagged` setting anymore. + +Use these as two separate guardrails in `config.yaml`: +- `guardrail_name: "akto-validate"` +- `guardrail_name: "akto-ingest"` + +## 1. Get Your Akto Credentials + +Set up the Akto Guardrail API Service and grab: +- `AKTO_GUARDRAIL_API_BASE` — your Guardrail API Base URL +- `AKTO_API_KEY` — your API key + +## 2. Configure in `config.yaml` + +### Block + Ingest (recommended) + +Use both entries below. This gives you: +- pre-call block decision +- post-call ingestion for allowed traffic + +Keep these as two separate entries (`akto-validate` and `akto-ingest`). + +```yaml +guardrails: + - guardrail_name: "akto-validate" + litellm_params: + guardrail: akto + mode: pre_call + akto_base_url: os.environ/AKTO_GUARDRAIL_API_BASE + akto_api_key: os.environ/AKTO_API_KEY + default_on: true + unreachable_fallback: fail_closed # optional: fail_open | fail_closed (default: fail_closed) + guardrail_timeout: 5 # optional, default: 5 + akto_account_id: "1000000" # optional, env fallback: AKTO_ACCOUNT_ID + akto_vxlan_id: "0" # optional, env fallback: AKTO_VXLAN_ID + + - guardrail_name: "akto-ingest" + litellm_params: + guardrail: akto + mode: post_call + akto_base_url: os.environ/AKTO_GUARDRAIL_API_BASE + akto_api_key: os.environ/AKTO_API_KEY + default_on: true +``` + +### Monitor-only mode + +If you only want logging/ingestion and no blocking, keep only `akto-ingest`. + +```yaml +guardrails: + - guardrail_name: "akto-ingest" + litellm_params: + guardrail: akto + mode: post_call + akto_base_url: os.environ/AKTO_GUARDRAIL_API_BASE + akto_api_key: os.environ/AKTO_API_KEY + default_on: true +``` + +## 3. Test It + +```shell +curl -i http://localhost:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer " \ + -d '{ + "model": "gpt-3.5-turbo", + "messages": [ + {"role": "user", "content": "Hello, how are you?"} + ] + }' +``` + +If a request gets blocked: + +```json +{ + "error": { + "message": "Prompt injection detected", + "type": "None", + "param": "None", + "code": "403" + } +} +``` + +## 4. How It Works + +**Block + Ingest mode:** +``` +Request → LiteLLM → Akto guardrail check + → Allowed → forward to LLM → ingest response + → Blocked → ingest blocked marker → 403 error +``` + +**Monitor-only mode:** +``` +Request → LiteLLM → forward to LLM → get response + → Send to Akto (guardrails + ingest) → log only +``` + +## 5. Event behavior + +| Entry | LiteLLM hook | Akto call behavior | +|------|---|---| +| `akto-validate` | `pre_call` | Awaited call with `guardrails=true`, `ingest_data=false` | +| `akto-ingest` | `post_call` | Fire-and-forget call with `guardrails=true`, `ingest_data=true` | + +When blocked in `pre_call`, LiteLLM sends one fire-and-forget ingest payload with blocked metadata and returns `403`. + +## 6. Parameters + +| Parameter | Env Variable | Default | Description | +|-----------|-------------|---------|-------------| +| `akto_base_url` | `AKTO_GUARDRAIL_API_BASE` | *required* | Akto Guardrail API Base URL | +| `akto_api_key` | `AKTO_API_KEY` | *required* | API key (sent as `Authorization` header) | +| `akto_account_id` | `AKTO_ACCOUNT_ID` | `1000000` | Akto account id included in payload | +| `akto_vxlan_id` | `AKTO_VXLAN_ID` | `0` | Akto vxlan id included in payload | +| `unreachable_fallback` | — | `fail_closed` | `fail_open` or `fail_closed` | +| `guardrail_timeout` | — | `5` | Timeout in seconds | +| `default_on` | — | `true` (recommended) | Enables the guardrail entry by default | + +## 7. Error Handling + +| Scenario | `fail_closed` (default) | `fail_open` | +|----------|------------------------|-------------| +| Akto unreachable | ❌ Blocked (503) | ✅ Passes through | +| Akto returns error | ❌ Blocked (503) | ✅ Passes through | +| Guardrail says no | ❌ Blocked (403) | ❌ Blocked (403) | diff --git a/docs/my-website/docs/proxy/guardrails/aporia_api.md b/docs/my-website/docs/proxy/guardrails/aporia_api.md index ceafc19a1c..e6ff0d5fed 100644 --- a/docs/my-website/docs/proxy/guardrails/aporia_api.md +++ b/docs/my-website/docs/proxy/guardrails/aporia_api.md @@ -139,7 +139,7 @@ curl -i http://localhost:4000/v1/chat/completions \ :::info -✨ This is an Enterprise only feature [Contact us to get a free trial](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions) +✨ This is an Enterprise only feature [Contact us to get a free trial](https://enterprise.litellm.ai/demo) ::: diff --git a/docs/my-website/docs/proxy/guardrails/custom_guardrail.md b/docs/my-website/docs/proxy/guardrails/custom_guardrail.md index c9115cf826..37579ad870 100644 --- a/docs/my-website/docs/proxy/guardrails/custom_guardrail.md +++ b/docs/my-website/docs/proxy/guardrails/custom_guardrail.md @@ -117,6 +117,14 @@ guardrails: ::: +:::note Streaming and post_call guardrails + +For **streaming responses**, `post_call` guardrails run on the fully assembled response **after** all chunks have been delivered to the client. This means `post_call` guardrails on streaming are **audit-only** — they can inspect and log the complete response, but cannot block content delivery. Guardrail results are recorded in `guardrail_information` within the logging payload for compliance and auditing. + +To filter or block streaming content in real-time, use `async_post_call_streaming_iterator_hook` instead, which processes chunks as they arrive. + +::: +
Advanced: Multiple modes with individual event hooks @@ -409,7 +417,7 @@ curl -i -X POST http://localhost:4000/v1/chat/completions \ :::info -✨ This is an Enterprise only feature [Contact us to get a free trial](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions) +✨ This is an Enterprise only feature [Contact us to get a free trial](https://enterprise.litellm.ai/demo) ::: @@ -655,8 +663,8 @@ class myCustomGuardrail(CustomGuardrail): | `apply_guardrail` | Simple method to check and optionally modify text | ✅ | INPUT or OUTPUT | ✅ | ✅ | ✅ | | `async_pre_call_hook` | A hook that runs before the LLM API call | ✅ | INPUT | ✅ | ❌ | ✅ | | `async_moderation_hook` | A hook that runs during the LLM API call| ✅ | INPUT | ❌ | ❌ | ✅ | -| `async_post_call_success_hook` | A hook that runs after a successful LLM API call| ✅ | INPUT, OUTPUT | ❌ | ✅ | ✅ | -| `async_post_call_streaming_iterator_hook` | A hook that processes streaming responses | ✅ | OUTPUT | ❌ | ✅ | ✅ | +| `async_post_call_success_hook` | A hook that runs after a successful LLM API call. For streaming, runs on the assembled response after delivery (audit-only, cannot block). | ✅ | INPUT, OUTPUT | ❌ | ✅ | ✅ (non-streaming only) | +| `async_post_call_streaming_iterator_hook` | A hook that processes streaming responses in real-time (can filter/block chunks) | ✅ | OUTPUT | ❌ | ✅ | ✅ | ## Frequently Asked Questions diff --git a/docs/my-website/docs/proxy/guardrails/guardrails_ai.md b/docs/my-website/docs/proxy/guardrails/guardrails_ai.md index 55d586aee7..19ae34014a 100644 --- a/docs/my-website/docs/proxy/guardrails/guardrails_ai.md +++ b/docs/my-website/docs/proxy/guardrails/guardrails_ai.md @@ -59,7 +59,7 @@ curl -i http://localhost:4000/v1/chat/completions \ :::info -✨ This is an Enterprise only feature [Contact us to get a free trial](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions) +✨ This is an Enterprise only feature [Contact us to get a free trial](https://enterprise.litellm.ai/demo) ::: diff --git a/docs/my-website/docs/proxy/health.md b/docs/my-website/docs/proxy/health.md index 2764a6f0d4..530bea3d06 100644 --- a/docs/my-website/docs/proxy/health.md +++ b/docs/my-website/docs/proxy/health.md @@ -314,6 +314,89 @@ general_settings: health_check_details: False ``` +## Health Check Driven Routing + +By default, background health checks are observability-only — they populate the `/health` endpoint but don't affect routing. Unhealthy deployments still receive traffic until request failures trigger cooldown. + +With `enable_health_check_routing: true`, the router **excludes deployments that failed their last background health check** before selecting a candidate. This gives you proactive failover instead of reactive cooldown. + +### How it works + +1. Background health checks run on their configured interval +2. After each cycle, every deployment is marked healthy or unhealthy +3. On each incoming request, the router filters out unhealthy deployments **before** cooldown filtering and load balancing +4. If all deployments are unhealthy, the filter is bypassed (safety net — never causes a total outage) +5. If health state is stale (older than `health_check_staleness_threshold`), it is ignored + +### Quick start + +```yaml +model_list: + - model_name: gpt-4 + litellm_params: + model: openai/gpt-4 + api_key: os.environ/OPENAI_API_KEY + - model_name: gpt-4 + litellm_params: + model: openai/gpt-4 + api_key: os.environ/OPENAI_API_KEY_SECONDARY + +general_settings: + background_health_checks: true + health_check_interval: 60 + enable_health_check_routing: true +``` + +### Configuration + +| Setting | Where | Default | Description | +|---------|-------|---------|-------------| +| `enable_health_check_routing` | `general_settings` | `false` | Enable/disable health-check-driven routing | +| `health_check_staleness_threshold` | `general_settings` | `health_check_interval * 2` | Seconds before health state is considered stale and ignored | +| `background_health_checks` | `general_settings` | `false` | Must be `true` for health check routing to work | +| `health_check_interval` | `general_settings` | `300` | Seconds between health check cycles | + +### Interaction with cooldown + +Health check filtering and cooldown are **additive**. A deployment can be excluded by either mechanism: + +- **Health check filter** — proactive, runs on the configured interval, excludes deployments that failed the last check +- **Cooldown** — reactive, triggered by request failures, excludes deployments for a short TTL + +This means request failures still provide fast detection between health check intervals. + +### Staleness + +If a health check result is older than `health_check_staleness_threshold`, it is ignored and the deployment is treated as eligible. This prevents stale data from permanently excluding a deployment if the health check loop stops or slows down. + +The default staleness threshold is `health_check_interval * 2`. For a 60s interval, health state expires after 120s. + +### Example: custom staleness + +```yaml +general_settings: + background_health_checks: true + health_check_interval: 30 + enable_health_check_routing: true + health_check_staleness_threshold: 90 # ignore health state older than 90s +``` + +### Debugging + +Run the proxy with `--detailed_debug` and look for: + +``` +health_check_routing_state_updated healthy=3 unhealthy=1 +``` + +This is logged after each health check cycle when routing state is written. + +If the safety net triggers (all deployments unhealthy), you'll see: + +``` +All deployments marked unhealthy by health checks, bypassing health filter +``` + ## Health Check Timeout The health check timeout is set in `litellm/constants.py` and defaults to 60 seconds. diff --git a/docs/my-website/docs/proxy/high_availability_control_plane.md b/docs/my-website/docs/proxy/high_availability_control_plane.md new file mode 100644 index 0000000000..324fba3a18 --- /dev/null +++ b/docs/my-website/docs/proxy/high_availability_control_plane.md @@ -0,0 +1,190 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; +import { ControlPlaneArchitecture } from '@site/src/components/ControlPlaneArchitecture'; + +# [BETA] High Availability Control Plane + +Deploy a single LiteLLM UI that manages multiple independent LiteLLM proxy instances, each with its own database, Redis, and master key. + +:::info + +This is an Enterprise feature. + +[Enterprise Pricing](https://www.litellm.ai/#pricing) + +[Get free 7-day trial key](https://www.litellm.ai/enterprise#trial) + +::: + +## Why This Architecture? + +In the [standard multi-region setup](./control_plane_and_data_plane.md), all instances share a single database and master key. This works, but introduces a shared dependency. If the database goes down, every instance is affected. + +The **High Availability Control Plane** takes a different approach: + +| | Shared Database (Standard) | High Availability Control Plane | +|---|---|---| +| **Database** | Single shared DB for all instances | Each instance has its own DB | +| **Redis** | Shared Redis | Each instance has its own Redis | +| **Master Key** | Same key across all instances | Each instance has its own key | +| **Failure isolation** | DB outage affects all instances | Failure is isolated to one instance | +| **User management** | Centralized, one user table | Independent, each worker manages its own users | +| **UI** | One UI per admin instance | Single control plane UI manages all workers | + +### Benefits + +- **True high availability**: no shared infrastructure means no single point of failure +- **Blast radius containment**: a misconfiguration or outage on one worker doesn't affect others +- **Regional isolation**: workers can run in different regions with data residency requirements +- **Simpler operations**: each worker is a self-contained LiteLLM deployment + +## Architecture + + + +The **control plane** is a LiteLLM instance that serves the admin UI and knows about all the workers. It does not proxy LLM requests, it is purely for administration. + +Each **worker** is a fully independent LiteLLM proxy that handles LLM requests for its region or team. Workers have their own users, keys, teams, and budgets. + +## Setup + +### 1. Control Plane Configuration + +The control plane needs a `worker_registry` that lists all worker instances. + +```yaml title="cp_config.yaml" +model_list: [] + +general_settings: + master_key: sk-1234 + database_url: os.environ/DATABASE_URL + +worker_registry: + - worker_id: "worker-a" + name: "Worker A" + url: "http://localhost:4001" + - worker_id: "worker-b" + name: "Worker B" + url: "http://localhost:4002" +``` + +Start the control plane: + +```bash +litellm --config cp_config.yaml --port 4000 +``` + +### 2. Worker Configuration + +Each worker needs `control_plane_url` in its `general_settings` to enable cross-origin authentication from the control plane UI. + +`PROXY_BASE_URL` must also be set for each worker so that SSO callback redirects resolve correctly. + + + + +```yaml title="worker_a_config.yaml" +model_list: [] + +general_settings: + master_key: sk-worker-a-1234 + database_url: os.environ/WORKER_A_DATABASE_URL + control_plane_url: "http://localhost:4000" +``` + +```bash +PROXY_BASE_URL=http://localhost:4001 litellm --config worker_a_config.yaml --port 4001 +``` + + + + +```yaml title="worker_b_config.yaml" +model_list: [] + +general_settings: + master_key: sk-worker-b-1234 + database_url: os.environ/WORKER_B_DATABASE_URL + control_plane_url: "http://localhost:4000" +``` + +```bash +PROXY_BASE_URL=http://localhost:4002 litellm --config worker_b_config.yaml --port 4002 +``` + + + + +:::important +Each worker must have its own `master_key` and `database_url`. The whole point of this architecture is that workers are independent. +::: + +### 3. SSO Configuration (Optional) + +SSO is configured on the **control plane** instance the same way as a standard LiteLLM proxy. See the [SSO setup guide](./admin_ui_sso.md) for full instructions. + +If using SSO, make sure to register each worker URL and the control plane URL as allowed callback URLs in your SSO provider's dashboard. + +## How It Works + +### Login Flow + +1. User visits the control plane UI (`http://localhost:4000/ui`) +2. The login page shows a **worker selector** dropdown listing all registered workers +3. User selects a worker (e.g. "Worker A") and logs in with username/password or SSO +4. The UI authenticates against the **selected worker** using the `/v3/login` endpoint +5. On success, the UI stores the worker's JWT and points all subsequent API calls at the worker +6. The user can now manage keys, teams, models, and budgets on that worker, all from the control plane UI + +### Switching Workers + +Once logged in, users can switch workers from the **navbar dropdown** without leaving the UI. Switching redirects back to the login page to authenticate against the new worker. + +### Discovery + +The control plane exposes a `/.well-known/litellm-ui-config` endpoint that the UI reads on load. This endpoint returns: +- `is_control_plane: true` +- The list of workers with their IDs, names, and URLs + +This is how the login page knows to show the worker selector. + +## Local Testing + +To try this out locally, start each instance in a separate terminal: + +```bash +# Terminal 1: Control Plane +litellm --config cp_config.yaml --port 4000 + +# Terminal 2: Worker A +PROXY_BASE_URL=http://localhost:4001 litellm --config worker_a_config.yaml --port 4001 + +# Terminal 3: Worker B +PROXY_BASE_URL=http://localhost:4002 litellm --config worker_b_config.yaml --port 4002 +``` + +Then open `http://localhost:4000/ui`. You should see the worker selector on the login page. + +## Configuration Reference + +### Control Plane Settings + +| Field | Location | Description | +|---|---|---| +| `worker_registry` | Top-level config | List of worker instances | +| `worker_registry[].worker_id` | Required | Unique identifier for the worker | +| `worker_registry[].name` | Required | Display name shown in the UI | +| `worker_registry[].url` | Required | Full URL of the worker instance | + +### Worker Settings + +| Field | Location | Description | +|---|---|---| +| `general_settings.control_plane_url` | Required | URL of the control plane instance. Enables `/v3/login` and `/v3/login/exchange` endpoints on this worker. | +| `PROXY_BASE_URL` | Environment variable | The worker's own external URL. Required for SSO callback redirects. | + +## Related Documentation + +- [Standard Multi-Region Setup](./control_plane_and_data_plane.md) - shared-database architecture for admin/worker split +- [SSO Setup](./admin_ui_sso.md) - configuring SSO for the admin UI +- [Production Deployment](./prod.md) - production best practices diff --git a/docs/my-website/docs/proxy/ip_address.md b/docs/my-website/docs/proxy/ip_address.md index 8f042d9f18..4c469b81e0 100644 --- a/docs/my-website/docs/proxy/ip_address.md +++ b/docs/my-website/docs/proxy/ip_address.md @@ -3,7 +3,7 @@ :::info -You need a LiteLLM License to unlock this feature. [Grab time](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions), to get one today! +You need a LiteLLM License to unlock this feature. [Grab time](https://enterprise.litellm.ai/demo), to get one today! ::: diff --git a/docs/my-website/docs/proxy/load_balancing.md b/docs/my-website/docs/proxy/load_balancing.md index 5bf39d179f..93f3d94434 100644 --- a/docs/my-website/docs/proxy/load_balancing.md +++ b/docs/my-website/docs/proxy/load_balancing.md @@ -324,17 +324,58 @@ model_list: litellm_params: model: azure/gpt-4-fallback api_key: os.environ/AZURE_API_KEY_2 - order: 2 # 👈 Used when order=1 is unavailable - -router_settings: - enable_pre_call_checks: true # 👈 Required for 'order' to work + order: 2 # 👈 Used when order=1 fails ``` -:::important -The `order` parameter requires `enable_pre_call_checks: true` in `router_settings`. -::: +### How order-based fallback works -If `order=1` deployment is unavailable (e.g., rate-limited), the router falls back to `order=2` deployments. +When a request to an `order=1` deployment fails (connection error, 404, 429, etc.), the router automatically tries `order=2` deployments, then `order=3`, and so on. Each order level gets its own set of retries before escalating to the next. + +If all order levels are exhausted, the router falls through to any configured [model-level fallbacks](#fallbacks). + +```yaml +model_list: + - model_name: gpt-4 + litellm_params: + model: azure/gpt-4-primary + api_key: os.environ/AZURE_API_KEY + order: 1 + + - model_name: gpt-4 + litellm_params: + model: azure/gpt-4-secondary + api_key: os.environ/AZURE_API_KEY_2 + order: 2 + + - model_name: gpt-4-fallback + litellm_params: + model: openai/gpt-4 + api_key: os.environ/OPENAI_API_KEY + +router_settings: + fallbacks: + - gpt-4: + - gpt-4-fallback # tried after all order levels fail +``` + +The fallback chain for the above config: `order=1` → `order=2` → `gpt-4-fallback`. + +For 429 (rate limit) errors specifically, the failed deployment is immediately placed on cooldown. If all `order=1` deployments are on cooldown, the router picks `order=2` deployments directly during retries without waiting for the fallback path. + +### Team-scoped models and legacy `model_aliases` {#team-scoped-models-and-legacy-model_aliases} + +Team-scoped deployments are identified by `model_info.team_id` and `model_info.team_public_model_name`. Requests should use the **public** model name; the router resolves all sibling deployments (same public name, different `api_base` / `order`, etc.) for routing, failover, and deployment `order`. + +For router internals: when a `team_id` is in scope, optimized lookups key off `(team_id, team_public_model_name)`. If code passes an internal deployment id (e.g. `model_name__`) instead of the public name, routing still works via the usual deployment-name paths, but the team-specific fast path applies only to the public name. + +**Legacy teams:** Older proxy versions could persist `model_aliases` on the team row mapping a public name to a single internal deployment id (`model_name__`). On each request, pre-call logic may still rewrite `model` to that internal name **before** routing, which collapses to one deployment and can make newer sibling deployments unreachable. + +**Migration options:** + +1. **Recommended for upgrades:** Set environment variable `LITELLM_ENABLE_TEAM_STALE_ALIAS_BYPASS=true` so that when sibling team deployments exist for the public name, the stale alias rewrite is skipped and team-scoped routing (including `order` and failover) applies. See the [Environment variables](./config_settings) table in the proxy settings doc. +2. **Data cleanup:** Remove obsolete `model_aliases` entries for team public names from the team record in the database so only `team_public_model_name` + team model list drive access. + +If a stale alias is detected and the bypass is **not** enabled, the proxy may emit a **one-time** warning in logs explaining that sibling deployments may be unreachable until the flag is set or aliases are cleaned up. ### When You'll See Load Balancing in Action @@ -352,7 +393,7 @@ If `order=1` deployment is unavailable (e.g., rate-limited), the router falls ba When load balancing OpenAI's Responses API across deployments with **different API keys** (e.g., different Azure regions or organizations), encrypted content items (like `rs_...` reasoning items) can only be decrypted by the originating API key. -**Solution:** Use the `encrypted_content_affinity` pre-call check to automatically route follow-up requests containing encrypted items to the correct deployment: +**Solution:** Use the `encrypted_content_affinity` pre-call check (requires LiteLLM >= 1.82.3) to automatically route follow-up requests containing encrypted items to the correct deployment: ```yaml model_list: diff --git a/docs/my-website/docs/proxy/logging.md b/docs/my-website/docs/proxy/logging.md index 74a79776fb..2f81498799 100644 --- a/docs/my-website/docs/proxy/logging.md +++ b/docs/my-website/docs/proxy/logging.md @@ -1109,7 +1109,7 @@ Log LLM Logs to [Google Cloud Storage Buckets](https://cloud.google.com/storage? :::info -✨ This is an Enterprise only feature [Get Started with Enterprise here](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions) +✨ This is an Enterprise only feature [Get Started with Enterprise here](https://enterprise.litellm.ai/demo) ::: @@ -1194,7 +1194,7 @@ Log LLM Logs/SpendLogs to [Google Cloud Storage PubSub Topic](https://cloud.goog :::info -✨ This is an Enterprise only feature [Get Started with Enterprise here](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions) +✨ This is an Enterprise only feature [Get Started with Enterprise here](https://enterprise.litellm.ai/demo) ::: @@ -1497,7 +1497,7 @@ Log LLM Logs to [Azure Data Lake Storage](https://learn.microsoft.com/en-us/azur :::info -✨ This is an Enterprise only feature [Get Started with Enterprise here](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions) +✨ This is an Enterprise only feature [Get Started with Enterprise here](https://enterprise.litellm.ai/demo) ::: diff --git a/docs/my-website/docs/proxy/multiple_admins.md b/docs/my-website/docs/proxy/multiple_admins.md index 8d39674df1..83d0c5863d 100644 --- a/docs/my-website/docs/proxy/multiple_admins.md +++ b/docs/my-website/docs/proxy/multiple_admins.md @@ -20,7 +20,7 @@ LiteLLM tracks changes to the following entities and actions: :::tip -Requires Enterprise License, Get in touch with us [here](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions) +Requires Enterprise License, Get in touch with us [here](https://enterprise.litellm.ai/demo) ::: @@ -56,6 +56,40 @@ On the LiteLLM UI, navigate to Logs -> Audit Logs. You should see the audit log /> +## Export Audit Logs to External Storage + +You can export audit logs to an external storage backend (e.g. S3) in addition to storing them in the database. Logs are batched and uploaded asynchronously, so they do not block your proxy requests. + +### S3 Example + +Add `audit_log_callbacks` and `s3_callback_params` to your `litellm_settings`: + +```yaml +litellm_settings: + store_audit_logs: true + audit_log_callbacks: ["s3_v2"] + s3_callback_params: + s3_bucket_name: my-audit-logs-bucket # AWS Bucket Name + s3_region_name: us-west-2 # AWS Region + s3_aws_access_key_id: os.environ/AWS_ACCESS_KEY_ID + s3_aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY + s3_path: litellm-audit # [OPTIONAL] prefix path in the bucket +``` + +Audit logs are written as JSON files to: + +``` +s3:///audit_logs//_.json +# or, when s3_path is set: +s3:////audit_logs//_.json +``` + +:::info + +Both `store_audit_logs: true` and `audit_log_callbacks` must be set. If `store_audit_logs` is not enabled, the callbacks will not fire. + +::: + ## Advanced ### Attribute Management changes to Users diff --git a/docs/my-website/docs/proxy/oauth2.md b/docs/my-website/docs/proxy/oauth2.md index 41c4110e44..204a01538c 100644 --- a/docs/my-website/docs/proxy/oauth2.md +++ b/docs/my-website/docs/proxy/oauth2.md @@ -4,7 +4,7 @@ Use this if you want to use an Oauth2.0 token to make `/chat`, `/embeddings` req :::info -This is an Enterprise Feature - [get in touch with us if you want a free trial to test if this feature meets your needs]((https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions)) +This is an Enterprise Feature - [get in touch with us if you want a free trial to test if this feature meets your needs]((https://enterprise.litellm.ai/demo)) ::: diff --git a/docs/my-website/docs/proxy/pass_through.md b/docs/my-website/docs/proxy/pass_through.md index f47d706414..700bfb0831 100644 --- a/docs/my-website/docs/proxy/pass_through.md +++ b/docs/my-website/docs/proxy/pass_through.md @@ -422,6 +422,5 @@ general_settings: [Community Discord 💭](https://discord.gg/wuPM9dRgDw) -Our numbers 📞 +1 (770) 8783-106 / ‭+1 (412) 618-6238‬ Our emails ✉️ ishaan@berri.ai / krrish@berri.ai diff --git a/docs/my-website/docs/proxy/prod.md b/docs/my-website/docs/proxy/prod.md index 26cb484cbe..d40a034310 100644 --- a/docs/my-website/docs/proxy/prod.md +++ b/docs/my-website/docs/proxy/prod.md @@ -47,7 +47,7 @@ export LITELLM_LOG="ERROR" :::info -Need Help or want dedicated support ? Talk to a founder [here]: (https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions) +Need Help or want dedicated support ? Talk to a founder [here]: (https://enterprise.litellm.ai/demo) ::: diff --git a/docs/my-website/docs/proxy/prompt_management.md b/docs/my-website/docs/proxy/prompt_management.md index 08307ba99e..5a3e411e98 100644 --- a/docs/my-website/docs/proxy/prompt_management.md +++ b/docs/my-website/docs/proxy/prompt_management.md @@ -311,7 +311,7 @@ litellm_settings: 1. **At Startup**: When the proxy starts, it reads the `prompts` field from `config.yaml` 2. **Initialization**: Each prompt is initialized based on its `prompt_integration` type 3. **In-Memory Storage**: Prompts are stored in the `IN_MEMORY_PROMPT_REGISTRY` -4. **Access**: Use these prompts via the `/v1/chat/completions` endpoint with `prompt_id` in the request +4. **Access**: Use these prompts via `/v1/chat/completions` or `/v1/responses` with `prompt_id` in the request ### Using Config-Loaded Prompts @@ -331,6 +331,23 @@ curl -L -X POST 'http://0.0.0.0:4000/v1/chat/completions' \ }' ``` +You can also use the same `prompt_id` with the Responses API: + +```bash +curl -L -X POST 'http://0.0.0.0:4000/v1/responses' \ +-H 'Content-Type: application/json' \ +-H 'Authorization: Bearer sk-1234' \ +-d '{ + "model": "gpt-4o", + "prompt_id": "coding_assistant", + "prompt_variables": { + "language": "python", + "task": "create a web scraper" + }, + "input": [] +}' +``` + ### Prompt Schema Reference Each prompt in the `prompts` list requires: diff --git a/docs/my-website/docs/proxy/public_routes.md b/docs/my-website/docs/proxy/public_routes.md index d5f3941751..e53548349d 100644 --- a/docs/my-website/docs/proxy/public_routes.md +++ b/docs/my-website/docs/proxy/public_routes.md @@ -5,7 +5,7 @@ import TabItem from '@theme/TabItem'; :::info -Requires a LiteLLM Enterprise License. [Get a free trial](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions). +Requires a LiteLLM Enterprise License. [Get a free trial](https://enterprise.litellm.ai/demo). ::: diff --git a/docs/my-website/docs/proxy/tag_routing.md b/docs/my-website/docs/proxy/tag_routing.md index a1ae52e5e4..57d16a59b5 100644 --- a/docs/my-website/docs/proxy/tag_routing.md +++ b/docs/my-website/docs/proxy/tag_routing.md @@ -315,7 +315,7 @@ LiteLLM Proxy supports team-based tag routing, allowing you to associate specifi :::info -This is an enterprise feature, [Contact us here to get a free trial](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions) +This is an enterprise feature, [Contact us here to get a free trial](https://enterprise.litellm.ai/demo) ::: diff --git a/docs/my-website/docs/proxy/team_logging.md b/docs/my-website/docs/proxy/team_logging.md index 2ad7e2a4a8..3f57d0d6d8 100644 --- a/docs/my-website/docs/proxy/team_logging.md +++ b/docs/my-website/docs/proxy/team_logging.md @@ -26,7 +26,7 @@ Team 3 -> Disabled Logging (for GDPR compliance) :::info -✨ This is an Enterprise only feature [Get Started with Enterprise here](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions) +✨ This is an Enterprise only feature [Get Started with Enterprise here](https://enterprise.litellm.ai/demo) ::: @@ -248,7 +248,7 @@ Use the `/key/generate` or `/key/update` endpoints to add logging callbacks to a :::info -✨ This is an Enterprise only feature [Get Started with Enterprise here](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions) +✨ This is an Enterprise only feature [Get Started with Enterprise here](https://enterprise.litellm.ai/demo) ::: diff --git a/docs/my-website/docs/proxy/team_model_add.md b/docs/my-website/docs/proxy/team_model_add.md index 7db59a3300..bb4238055b 100644 --- a/docs/my-website/docs/proxy/team_model_add.md +++ b/docs/my-website/docs/proxy/team_model_add.md @@ -5,7 +5,7 @@ This is an Enterprise feature. [Enterprise Pricing](https://www.litellm.ai/#pricing) -[Contact us here to get a free trial](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions) +[Contact us here to get a free trial](https://enterprise.litellm.ai/demo) ::: diff --git a/docs/my-website/docs/proxy/token_auth.md b/docs/my-website/docs/proxy/token_auth.md index 7364ae0fb5..bc6fde7c84 100644 --- a/docs/my-website/docs/proxy/token_auth.md +++ b/docs/my-website/docs/proxy/token_auth.md @@ -11,7 +11,7 @@ Use JWT's to auth admins / users / projects into the proxy. [Enterprise Pricing](https://www.litellm.ai/#pricing) -[Contact us here to get a free trial](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions) +[Contact us here to get a free trial](https://enterprise.litellm.ai/demo) ::: diff --git a/docs/my-website/docs/proxy_server.md b/docs/my-website/docs/proxy_server.md index e23d64e443..7b6f15a604 100644 --- a/docs/my-website/docs/proxy_server.md +++ b/docs/my-website/docs/proxy_server.md @@ -813,5 +813,4 @@ Thread Stats Avg Stdev Max +/- Stdev - [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version) - [Community Discord 💭](https://discord.gg/wuPM9dRgDw) -- Our numbers 📞 +1 (770) 8783-106 / ‭+1 (412) 618-6238‬ - Our emails ✉️ ishaan@berri.ai / krrish@berri.ai diff --git a/docs/my-website/docs/reasoning_content.md b/docs/my-website/docs/reasoning_content.md index 8bf59f66a3..6e6a30cdb4 100644 --- a/docs/my-website/docs/reasoning_content.md +++ b/docs/my-website/docs/reasoning_content.md @@ -13,6 +13,7 @@ Supported Providers: - Deepseek (`deepseek/`) - Anthropic API (`anthropic/`) - Bedrock (Anthropic + Deepseek + GPT-OSS) (`bedrock/`) +- OpenAI Responses API (`openai/responses/`) - Vertex AI (Anthropic) (`vertexai/`) - OpenRouter (`openrouter/`) - XAI (`xai/`) @@ -594,9 +595,26 @@ Expected Response :::tip gpt-5.4: reasoning_effort + function tools -LiteLLM drops `reasoning_effort` from `gpt-5.4` requests to `litellm.completion()` that include tools, since that combination is supported in the Responses API. +When `gpt-5.4+` requests to `litellm.completion()` include both `reasoning_effort` and `tools`, LiteLLM **automatically routes** the request through the Responses API bridge. This works for both **OpenAI** (`openai/gpt-5.4`) and **Azure** (`azure/gpt-5.4`) providers — no extra configuration needed. -If you need reasoning **and** tools together, use `openai/responses/gpt-5.4` to route through the Responses API instead. See [Responses API Bridge](/docs/providers/openai#openai-chat-completion-to-responses-api-bridge) for details. +You can also route explicitly via `openai/responses/gpt-5.4` or `azure/responses/gpt-5.4`. See [Responses API Bridge](/docs/providers/openai#openai-chat-completion-to-responses-api-bridge) for details. + +**Azure custom deployment names:** Auto-routing relies on the deployment name matching the `gpt-5.4*` pattern. If you use a custom deployment name (e.g. `"my-reasoning-model"`), enable routing via: + +**SDK:** +```python +litellm.completion(model="azure/responses/my-reasoning-model", ...) +``` + +**Proxy config:** +```yaml +model_list: + - model_name: my-reasoning-model + litellm_params: + model: azure/my-reasoning-model + model_info: + mode: responses +``` ::: @@ -683,3 +701,69 @@ response = litellm.completion( reasoning_effort={"effort": "low", "summary": "detailed"}, # Explicit control ) ``` + +### Summary Preservation via `/v1/messages` Adapter + +When using the Anthropic `/v1/messages` adapter to route non-Claude models (e.g., `openai/gpt-5.1`), the `thinking.summary` value is preserved and forwarded to the downstream provider. For example: + +```python +import litellm + +response = await litellm.anthropic.messages.acreate( + model="openai/gpt-5.1", + messages=[{"role": "user", "content": "Hello"}], + max_tokens=8096, + thinking={"type": "enabled", "budget_tokens": 5000, "summary": "concise"}, +) +# The summary="concise" is preserved when routing to OpenAI's Responses API +``` + +### Enabling Default Summary Injection for `/v1/messages` Adapter + +When the Anthropic `/v1/messages` adapter translates `thinking` parameters to OpenAI `reasoning_effort` for non-Claude models, you can opt-in to automatic `summary="detailed"` injection using the `reasoning_auto_summary` flag. This ensures that reasoning text is returned in the response (matching the Anthropic thinking behavior). + +To **enable** this default injection, use the `reasoning_auto_summary` flag: + + + + +```python +import litellm + +# Enable default summary="detailed" injection +litellm.reasoning_auto_summary = True + +response = await litellm.anthropic.messages.acreate( + model="openai/gpt-5.1", + messages=[{"role": "user", "content": "Hello"}], + max_tokens=8096, + thinking={"type": "enabled", "budget_tokens": 5000}, +) +# summary="detailed" will be automatically added to reasoning_effort +``` + + + + + +```bash +export LITELLM_REASONING_AUTO_SUMMARY=true +``` + + + + + +```yaml +litellm_settings: + reasoning_auto_summary: true +``` + + + + +:::info + +This flag only affects the automatic injection of `summary="detailed"` when no user-provided summary is present. If you explicitly pass `thinking.summary` (e.g., `"concise"` or `"auto"`), your value is always preserved regardless of this flag. + +::: diff --git a/docs/my-website/docs/response_api.md b/docs/my-website/docs/response_api.md index fb55ae9f9d..0c428000c7 100644 --- a/docs/my-website/docs/response_api.md +++ b/docs/my-website/docs/response_api.md @@ -1160,12 +1160,12 @@ follow_up = await router.aresponses( 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 -- `encrypted_content_affinity`: **[Recommended]** content-aware routing for encrypted items (e.g., `rs_...` reasoning items) +- `encrypted_content_affinity`: **[Recommended]** content-aware routing for encrypted items (e.g., `rs_...` reasoning items) (**requires LiteLLM >= 1.82.3**) - `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`) :::tip Recommended: Use `encrypted_content_affinity` -For Responses API with load balancing across deployments with **different API keys**, use `encrypted_content_affinity` instead of `deployment_affinity`. It only pins requests that contain encrypted content, avoiding quota reduction while preventing `invalid_encrypted_content` errors. +For Responses API with load balancing across deployments with **different API keys**, use `encrypted_content_affinity` instead of `deployment_affinity`. It only pins requests that contain encrypted content, avoiding quota reduction while preventing `invalid_encrypted_content` errors. (Requires LiteLLM >= 1.82.3.) ::: Notes: @@ -1364,6 +1364,85 @@ litellm --config config.yaml | `deployment_affinity` | Simple sticky sessions | All requests from same API key | ❌ Reduces quota by # of users | +## Per-Model-Group Affinity Configuration + +By default, `optional_pre_call_checks` applies globally to all model groups. Use `model_group_affinity_config` when you want different affinity behavior per model group — for example, enabling stickiness only for models spread across providers (Azure + Bedrock) while leaving single-provider groups free to load-balance. + +Groups not listed fall back to the global `optional_pre_call_checks` settings. + + + + +```python +router = litellm.Router( + model_list=[ + { + "model_name": "gpt-4", + "litellm_params": {"model": "azure/gpt-4", "api_key": "...", "api_base": "https://endpoint1.openai.azure.com"}, + }, + { + "model_name": "gpt-4", + "litellm_params": {"model": "bedrock/anthropic.claude-v2", "aws_region_name": "us-east-1"}, + }, + { + "model_name": "text-embedding-ada-002", + "litellm_params": {"model": "azure/text-embedding-ada-002", "api_key": "...", "api_base": "https://endpoint1.openai.azure.com"}, + }, + { + "model_name": "text-embedding-ada-002", + "litellm_params": {"model": "azure/text-embedding-ada-002", "api_key": "...", "api_base": "https://endpoint2.openai.azure.com"}, + }, + ], + # gpt-4: cross-provider (Azure + Bedrock) — enable deployment affinity + # text-embedding-ada-002: same provider — no affinity, let it load balance freely + model_group_affinity_config={ + "gpt-4": ["deployment_affinity", "responses_api_deployment_check"], + }, +) +``` + + + + +```yaml title="config.yaml" +model_list: + - model_name: gpt-4 + litellm_params: + model: azure/gpt-4 + api_key: os.environ/AZURE_API_KEY_1 + api_base: https://endpoint1.openai.azure.com + + - model_name: gpt-4 + litellm_params: + model: bedrock/anthropic.claude-v2 + aws_region_name: us-east-1 + + - model_name: text-embedding-ada-002 + litellm_params: + model: azure/text-embedding-ada-002 + api_key: os.environ/AZURE_API_KEY_1 + api_base: https://endpoint1.openai.azure.com + + - model_name: text-embedding-ada-002 + litellm_params: + model: azure/text-embedding-ada-002 + api_key: os.environ/AZURE_API_KEY_2 + api_base: https://endpoint2.openai.azure.com + +router_settings: + # gpt-4: cross-provider — enable stickiness + # text-embedding-ada-002: not listed — load balances freely + model_group_affinity_config: + "gpt-4": + - deployment_affinity + - responses_api_deployment_check +``` + + + + +**Supported values:** `deployment_affinity`, `responses_api_deployment_check`, `session_affinity` + ## Calling non-Responses API endpoints (`/responses` to `/chat/completions` Bridge) LiteLLM allows you to call non-Responses API models via a bridge to LiteLLM's `/chat/completions` endpoint. This is useful for calling Anthropic, Gemini and even non-Responses API OpenAI models. @@ -1556,6 +1635,12 @@ curl -X POST "http://localhost:4000/v1/responses" \ }' ``` +## File Search (Vector Stores) + +For full `file_search` usage (native + emulated fallback), SDK/Proxy examples, architecture diagram, and Q&A, see: + +- [`File Search in the Responses API — E2E Testing Guide`](/docs/tutorials/file_search_responses_api) + ## Session Management LiteLLM Proxy supports session management for all supported models. This allows you to store and fetch conversation history (state) in LiteLLM Proxy. diff --git a/docs/my-website/docs/routing.md b/docs/my-website/docs/routing.md index 67e7f68114..5aa655ae21 100644 --- a/docs/my-website/docs/routing.md +++ b/docs/my-website/docs/routing.md @@ -842,6 +842,8 @@ Traffic mirroring allows you to "mimic" production traffic to a secondary (silen Set `order` in `litellm_params` to prioritize deployments. Lower values = higher priority. When multiple deployments share the same `order`, the routing strategy picks among them. +When a request to an `order=1` deployment fails (connection error, 404, 429, etc.), the router automatically tries `order=2` deployments, then `order=3`, and so on. Each order level gets its own set of retries before escalating to the next. If all order levels are exhausted, the router falls through to any configured [fallbacks](#fallbacks). + @@ -862,18 +864,14 @@ model_list = [ "litellm_params": { "model": "azure/gpt-4-fallback", "api_key": os.getenv("AZURE_API_KEY_2"), - "order": 2, # 👈 Used when order=1 is unavailable + "order": 2, # 👈 Tried when order=1 fails }, }, ] -router = Router(model_list=model_list, enable_pre_call_checks=True) # 👈 Required for 'order' to work +router = Router(model_list=model_list) ``` -:::important -The `order` parameter requires `enable_pre_call_checks=True` to be set on the Router. -::: - @@ -889,10 +887,7 @@ model_list: litellm_params: model: azure/gpt-4-fallback api_key: os.environ/AZURE_API_KEY_2 - order: 2 # 👈 Used when order=1 is unavailable - -router_settings: - enable_pre_call_checks: true # 👈 Required for 'order' to work + order: 2 # 👈 Tried when order=1 fails ``` diff --git a/docs/my-website/docs/secret.md b/docs/my-website/docs/secret.md index c5c8031147..57f576fd56 100644 --- a/docs/my-website/docs/secret.md +++ b/docs/my-website/docs/secret.md @@ -6,7 +6,7 @@ [Enterprise Pricing](https://www.litellm.ai/#pricing) -[Contact us here to get a free trial](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions) +[Contact us here to get a free trial](https://enterprise.litellm.ai/demo) ::: diff --git a/docs/my-website/docs/secret_managers/aws_kms.md b/docs/my-website/docs/secret_managers/aws_kms.md index 7f69d91fe8..806223a253 100644 --- a/docs/my-website/docs/secret_managers/aws_kms.md +++ b/docs/my-website/docs/secret_managers/aws_kms.md @@ -6,7 +6,7 @@ [Enterprise Pricing](https://www.litellm.ai/#pricing) -[Contact us here to get a free trial](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions) +[Contact us here to get a free trial](https://enterprise.litellm.ai/demo) ::: diff --git a/docs/my-website/docs/secret_managers/aws_secret_manager.md b/docs/my-website/docs/secret_managers/aws_secret_manager.md index c49797a15d..a7e24ea69a 100644 --- a/docs/my-website/docs/secret_managers/aws_secret_manager.md +++ b/docs/my-website/docs/secret_managers/aws_secret_manager.md @@ -9,7 +9,7 @@ import TabItem from '@theme/TabItem'; [Enterprise Pricing](https://www.litellm.ai/#pricing) -[Contact us here to get a free trial](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions) +[Contact us here to get a free trial](https://enterprise.litellm.ai/demo) ::: diff --git a/docs/my-website/docs/secret_managers/azure_key_vault.md b/docs/my-website/docs/secret_managers/azure_key_vault.md index 81aeaa3215..4ea53d2ea9 100644 --- a/docs/my-website/docs/secret_managers/azure_key_vault.md +++ b/docs/my-website/docs/secret_managers/azure_key_vault.md @@ -6,7 +6,7 @@ [Enterprise Pricing](https://www.litellm.ai/#pricing) -[Contact us here to get a free trial](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions) +[Contact us here to get a free trial](https://enterprise.litellm.ai/demo) ::: diff --git a/docs/my-website/docs/secret_managers/cyberark.md b/docs/my-website/docs/secret_managers/cyberark.md index 0a17c0afc3..cd7c0ea5d2 100644 --- a/docs/my-website/docs/secret_managers/cyberark.md +++ b/docs/my-website/docs/secret_managers/cyberark.md @@ -8,7 +8,7 @@ import Image from '@theme/IdealImage'; [Enterprise Pricing](https://www.litellm.ai/#pricing) -[Contact us here to get a free trial](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions) +[Contact us here to get a free trial](https://enterprise.litellm.ai/demo) ::: diff --git a/docs/my-website/docs/secret_managers/google_kms.md b/docs/my-website/docs/secret_managers/google_kms.md index 31fd6195bd..152ecbaae8 100644 --- a/docs/my-website/docs/secret_managers/google_kms.md +++ b/docs/my-website/docs/secret_managers/google_kms.md @@ -6,7 +6,7 @@ [Enterprise Pricing](https://www.litellm.ai/#pricing) -[Contact us here to get a free trial](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions) +[Contact us here to get a free trial](https://enterprise.litellm.ai/demo) ::: diff --git a/docs/my-website/docs/secret_managers/google_secret_manager.md b/docs/my-website/docs/secret_managers/google_secret_manager.md index 81878b7e39..f3e7367e8a 100644 --- a/docs/my-website/docs/secret_managers/google_secret_manager.md +++ b/docs/my-website/docs/secret_managers/google_secret_manager.md @@ -6,7 +6,7 @@ [Enterprise Pricing](https://www.litellm.ai/#pricing) -[Contact us here to get a free trial](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions) +[Contact us here to get a free trial](https://enterprise.litellm.ai/demo) ::: diff --git a/docs/my-website/docs/secret_managers/hashicorp_vault.md b/docs/my-website/docs/secret_managers/hashicorp_vault.md index 52d9b55620..11e25e88a7 100644 --- a/docs/my-website/docs/secret_managers/hashicorp_vault.md +++ b/docs/my-website/docs/secret_managers/hashicorp_vault.md @@ -8,7 +8,7 @@ import Image from '@theme/IdealImage'; [Enterprise Pricing](https://www.litellm.ai/#pricing) -[Contact us here to get a free trial](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions) +[Contact us here to get a free trial](https://enterprise.litellm.ai/demo) ::: diff --git a/docs/my-website/docs/secret_managers/overview.md b/docs/my-website/docs/secret_managers/overview.md index bf7386ab89..f02362f493 100644 --- a/docs/my-website/docs/secret_managers/overview.md +++ b/docs/my-website/docs/secret_managers/overview.md @@ -8,7 +8,7 @@ import Image from '@theme/IdealImage'; [Enterprise Pricing](https://www.litellm.ai/#pricing) -[Contact us here to get a free trial](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions) +[Contact us here to get a free trial](https://enterprise.litellm.ai/demo) ::: diff --git a/docs/my-website/docs/troubleshoot.md b/docs/my-website/docs/troubleshoot.md index 1539e1959f..1afa35df8e 100644 --- a/docs/my-website/docs/troubleshoot.md +++ b/docs/my-website/docs/troubleshoot.md @@ -50,7 +50,6 @@ Full error logs, stack traces, and any images from service metrics (CPU, memory, [Community Discord 💭](https://discord.gg/wuPM9dRgDw) [Community Slack 💭](https://www.litellm.ai/support) -Our numbers 📞 +1 (770) 8783-106 / +1 (412) 618-6238 Our emails ✉️ ishaan@berri.ai / krrish@berri.ai diff --git a/docs/my-website/docs/tutorials/claude_code_plugin_marketplace.md b/docs/my-website/docs/tutorials/claude_code_plugin_marketplace.md index 9d93c717c4..d8175f51ac 100644 --- a/docs/my-website/docs/tutorials/claude_code_plugin_marketplace.md +++ b/docs/my-website/docs/tutorials/claude_code_plugin_marketplace.md @@ -37,7 +37,7 @@ Click **+ Add New Plugin** to register a plugin in your marketplace. Enter the plugin information: - **Name**: Plugin identifier (kebab-case, e.g., `my-plugin`) -- **Source Type**: Choose GitHub or URL +- **Source Type**: Choose GitHub, Git URL, or Git Subdir - **Repository/URL**: The git source (e.g., `org/repo` for GitHub) - **Version**: Semantic version (optional) - **Description**: What the plugin does @@ -216,6 +216,22 @@ curl -X DELETE http://localhost:4000/claude-code/plugins/my-plugin \ Use this format for GitLab, Bitbucket, or self-hosted git repositories. + + + +```json +{ + "name": "my-plugin", + "source": { + "source": "git-subdir", + "url": "https://github.com/org/repo.git", + "path": "plugins/my-plugin" + } +} +``` + +Use this format when your plugin lives in a subdirectory of a git repository. The `path` field must be a relative path of slash-separated segments (alphanumeric, dots, hyphens, underscores only). + diff --git a/docs/my-website/docs/tutorials/compare_llms.md b/docs/my-website/docs/tutorials/compare_llms.md index 02877b4660..0252263a16 100644 --- a/docs/my-website/docs/tutorials/compare_llms.md +++ b/docs/my-website/docs/tutorials/compare_llms.md @@ -82,7 +82,7 @@ Benchmark Results for 'When will BerriAI IPO?': +-----------------+----------------------------------------------------------------------------------+---------------------------+------------+ ``` ## Support -**🤝 Schedule a 1-on-1 Session:** Book a [1-on-1 session](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions) with Krrish and Ishaan, the founders, to discuss any issues, provide feedback, or explore how we can improve LiteLLM for you. +**🤝 Schedule a 1-on-1 Session:** Book a [1-on-1 session](https://enterprise.litellm.ai/demo) with Krrish and Ishaan, the founders, to discuss any issues, provide feedback, or explore how we can improve LiteLLM for you. B[LiteLLM Responses API] + B --> C{Provider supports native file_search?} + + C -->|Yes| D[Native passthrough path] + D --> D1[Decode unified vector_store_id if needed] + D1 --> D2[Forward request to provider unchanged] + D2 --> D3[Provider performs file_search] + D3 --> Z[OpenAI-compatible output] + + C -->|No| E[Emulated fallback path] + E --> E1[Convert file_search to litellm_file_search function tool] + E1 --> E2[First model call returns tool call with one or more queries] + E2 --> E3[LiteLLM executes vector search for each query] + E3 --> E4[Second model call with tool_result context] + E4 --> E5[Synthesize file_search_call + message + citations] + E5 --> Z[OpenAI-compatible output] +``` + + + +## Prerequisites + +```bash +pip install 'litellm[proxy]' +export OPENAI_API_KEY="sk-..." # for native path +export ANTHROPIC_API_KEY="sk-ant-..." # for emulated path +``` + + + +## Example response shape + +## Validating the Output Format + +Regardless of which path ran, the response always follows the OpenAI Responses API format: + +```json +{ + "output": [ + { + "type": "file_search_call", + "id": "fs_abc123", + "status": "completed", + "queries": ["What does LiteLLM support?"], + "search_results": null + }, + { + "type": "message", + "role": "assistant", + "content": [ + { + "type": "output_text", + "text": "LiteLLM is a unified interface...", + "annotations": [ + { + "type": "file_citation", + "index": 150, + "file_id": "file-xxxx", + "filename": "knowledge.txt" + } + ] + } + ] + } + ] +} +``` + +**Validation script:** + +```python showLineNumbers title="Validate response structure" +def validate_file_search_response(response): + """Assert that response follows OpenAI file_search output format.""" + output = response.output + assert len(output) >= 2, "Expected at least 2 output items" + + # First item: file_search_call + fs_call = output[0] + fs_type = fs_call["type"] if isinstance(fs_call, dict) else fs_call.type + assert fs_type == "file_search_call", f"Expected file_search_call, got {fs_type}" + + fs_status = fs_call["status"] if isinstance(fs_call, dict) else fs_call.status + assert fs_status == "completed" + + # Second item: message + msg = output[1] + msg_type = msg["type"] if isinstance(msg, dict) else msg.type + assert msg_type == "message" + + content = msg["content"] if isinstance(msg, dict) else msg.content + assert len(content) > 0 + text_block = content[0] + text = text_block["text"] if isinstance(text_block, dict) else text_block.text + assert isinstance(text, str) and len(text) > 0 + + print("✅ Response structure valid") + print(f" Queries: {fs_call['queries'] if isinstance(fs_call, dict) else fs_call.queries}") + print(f" Answer length: {len(text)} chars") + annotations = text_block["annotations"] if isinstance(text_block, dict) else text_block.annotations + print(f" Citations: {len(annotations)}") + +validate_file_search_response(response) +``` + + + +## Q&A + +- **Why do I see `UnsupportedParamsError`?** This usually means `file_search` was passed to a provider that does not support it natively and emulation could not route correctly. Check: + - The model string is valid (for example, `anthropic/claude-sonnet-4-5`). + - `custom_llm_provider` resolves correctly so LiteLLM can load the provider config. +- **Why does vector search return no results?** Common causes: + - The vector store ID is wrong or has no files attached. + - In LiteLLM-managed stores, file ingestion is not complete (`status != completed`). + - The query is too narrow; try a broader query. +- **Why am I getting `403 Access denied` on vector store calls?** The caller does not have access to that vector store. + - The store may belong to another team. + - Use an admin/proxy key if your setup requires cross-team access. +- **Why are `annotations` empty in emulated mode?** `file_citation` annotations require `file_id` metadata in search results. If your vector backend does not return file-level metadata, the answer text is still generated but citations can be empty. + + + +## What to check next + +- [File Search reference in Responses API docs](/docs/response_api#file-search-vector-stores) — full API reference +- [Vector Store management](/docs/vector_store_files) — create and manage vector stores +- [Managed vector stores](/docs/providers/bedrock_vector_store) — provider-specific setup diff --git a/docs/my-website/docs/tutorials/opencode_integration.md b/docs/my-website/docs/tutorials/opencode_integration.md index e55367833f..35e00a1de5 100644 --- a/docs/my-website/docs/tutorials/opencode_integration.md +++ b/docs/my-website/docs/tutorials/opencode_integration.md @@ -253,7 +253,7 @@ model_list: litellm_params: model: openai/gpt-4 api_key: os.environ/OPENAI_API_KEY - + - model_name: gpt-4o litellm_params: model: openai/gpt-4o @@ -264,7 +264,7 @@ model_list: litellm_params: model: anthropic/claude-3-5-sonnet-20241022 api_key: os.environ/ANTHROPIC_API_KEY - + # DeepSeek models - model_name: deepseek-chat litellm_params: @@ -272,6 +272,19 @@ model_list: api_key: os.environ/DEEPSEEK_API_KEY ``` +### Dropping OpenCode-specific parameters + +OpenCode sends a `reasoningSummary` parameter with reasoning-capable models such as `gpt-5`. This parameter is not supported by the Chat Completions API and will cause errors. Add `additional_drop_params` to every model entry in your `model_list` that will receive requests from OpenCode with reasoning enabled: + +```yaml +model_list: + - model_name: gpt-5 + litellm_params: + model: openai/gpt-5 + api_key: os.environ/OPENAI_API_KEY + additional_drop_params: ["reasoningSummary"] +``` + ## Troubleshooting **OpenCode not connecting:** @@ -294,6 +307,16 @@ model_list: - Validate JSON syntax using a JSON validator - Ensure the `$schema` URL is accessible +**`Unknown parameter: 'reasoningSummary'` error:** +- OpenCode sends a `reasoningSummary` parameter that is not supported by the Chat Completions API. Add `additional_drop_params: ["reasoningSummary"]` to each affected model entry in your `litellm_params`: + ```yaml + - model_name: gpt-5 + litellm_params: + model: openai/gpt-5 + api_key: os.environ/OPENAI_API_KEY + additional_drop_params: ["reasoningSummary"] + ``` + ## Tips - Add more models to the config as needed - they'll appear in `/models` diff --git a/docs/my-website/docs/tutorials/vertex_ai_pay_go.md b/docs/my-website/docs/tutorials/vertex_ai_pay_go.md new file mode 100644 index 0000000000..87197e5bad --- /dev/null +++ b/docs/my-website/docs/tutorials/vertex_ai_pay_go.md @@ -0,0 +1,151 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Vertex AI PayGo and Priority + +## Priority PayGo + +LiteLLM supports Priority PayGo. +Send a priority header, get priority queueing, and pay priority token rates. + +:::info Which models support Priority PayGo? +As of this writing: `gemini/gemini-2.5-pro`, `vertex_ai/gemini-3-pro-preview`, `vertex_ai/gemini-3.1-pro-preview`, `vertex_ai/gemini-3-flash-preview`, and their variants. +Check `supports_service_tier: true` in LiteLLM's [model pricing JSON](https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json). +::: + +### Send a priority request + +Use this header: + +`X-Vertex-AI-LLM-Shared-Request-Type: priority` + + + + +```python +import litellm + +response = litellm.completion( + model="vertex_ai/gemini-3-pro-preview", + messages=[{"role": "user", "content": "Summarize the Gettysburg Address."}], + vertex_project="YOUR_PROJECT_ID", + vertex_location="us-central1", + extra_headers={"X-Vertex-AI-LLM-Shared-Request-Type": "priority"}, +) + +print(response.choices[0].message.content) +``` + + + + +```yaml title="config.yaml" +model_list: + - model_name: gemini-priority + litellm_params: + model: vertex_ai/gemini-3-pro-preview + vertex_project: "YOUR_PROJECT_ID" + vertex_location: "us-central1" + vertex_credentials: os.environ/GOOGLE_APPLICATION_CREDENTIALS + extra_headers: + X-Vertex-AI-LLM-Shared-Request-Type: priority +``` + +```bash +curl http://localhost:4000/v1/chat/completions \ + -H "Authorization: Bearer sk-your-key" \ + -H "Content-Type: application/json" \ + -d '{"model": "gemini-priority", "messages": [{"role": "user", "content": "Hello"}]}' +``` + + + + +Use `x-pass-` so LiteLLM forwards provider-specific headers. + +```bash +MODEL_ID="gemini-3-pro-preview-0325" +PROJECT_ID="YOUR_PROJECT_ID" + +curl -X POST \ + "${LITELLM_PROXY_BASE_URL}/vertex_ai/v1/projects/${PROJECT_ID}/locations/global/publishers/google/models/${MODEL_ID}:generateContent" \ + -H "Authorization: Bearer sk-your-litellm-key" \ + -H "Content-Type: application/json" \ + -H "x-pass-X-Vertex-AI-LLM-Shared-Request-Type: priority" \ + -d '{"contents": [{"role": "user", "parts": [{"text": "Hello!"}]}]}' +``` + + + + +### How cost tracking works + +![Vertex AI Priority PayGo Cost Tracking Flow](/img/vertex_cost_tracking_flow.svg) + +**`trafficType` → `service_tier` mapping** + +| `usageMetadata.trafficType` | `service_tier` | Pricing keys used | +|---|---|---| +| `ON_DEMAND` | `None` | `input_cost_per_token` | +| `ON_DEMAND_PRIORITY` | `"priority"` | `input_cost_per_token_priority` | +| `FLEX` / `BATCH` | `"flex"` | `input_cost_per_token_flex` | + +If a tier-specific key is missing, LiteLLM falls back to standard pricing keys. + +--- + +## Standard PayGo vs Provisioned Throughput + +This is a different header from priority routing: + +| Header value | Behavior | +|---|---| +| `X-Vertex-AI-LLM-Request-Type: shared` | Force standard PayGo (bypass PT) | +| `X-Vertex-AI-LLM-Request-Type: dedicated` | Force Provisioned Throughput only (`429` if exhausted) | + +### Native route example + +```python +import litellm + +response = litellm.completion( + model="vertex_ai/gemini-2.0-flash", + messages=[{"role": "user", "content": "Hello!"}], + vertex_project="YOUR_PROJECT_ID", + vertex_location="us-central1", + extra_headers={"X-Vertex-AI-LLM-Request-Type": "shared"}, +) +``` + +### Pass-through example + +```bash +MODEL_ID="gemini-2.0-flash-001" +PROJECT_ID="YOUR_PROJECT_ID" + +curl -X POST \ + "${LITELLM_PROXY_BASE_URL}/vertex_ai/v1/projects/${PROJECT_ID}/locations/global/publishers/google/models/${MODEL_ID}:generateContent" \ + -H "Authorization: Bearer sk-your-litellm-key" \ + -H "Content-Type: application/json" \ + -H "x-pass-X-Vertex-AI-LLM-Request-Type: shared" \ + -d '{ + "contents": [{"role": "user", "parts": [{"text": "Hello!"}]}] + }' +``` + +--- + +## Troubleshooting + +**Q: What does `403 Permission denied` or `IAM_PERMISSION_DENIED` mean?** +A: The service account or Application Default Credentials (ADC) user does not have the `roles/aiplatform.user` role. To resolve this, re-run the `gcloud projects add-iam-policy-binding`. + +**Q: What should I do if I get a `429 Quota exceeded` error?** +A: This means you've hit the per-region QPM (queries per minute) or TPM (tokens per minute) quota. You can: +- Request a quota increase from the [GCP Quotas console](https://console.cloud.google.com/iam-admin/quotas) +- Add more regions to your LiteLLM configuration for load balancing +- Upgrade to [Provisioned Throughput](https://cloud.google.com/vertex-ai/generative-ai/docs/provisioned-throughput) for guaranteed capacity + +**Q: How do I fix the `VERTEXAI_PROJECT not set` error?** +A: Either pass the `vertex_project` parameter explicitly in your LiteLLM call, or set the `VERTEXAI_PROJECT` environment variable before running your code. + diff --git a/docs/my-website/img/ci_cd_architecture.png b/docs/my-website/img/ci_cd_architecture.png new file mode 100644 index 0000000000..111567c11b Binary files /dev/null and b/docs/my-website/img/ci_cd_architecture.png differ diff --git a/docs/my-website/img/isolated_ci_cd_environments.png b/docs/my-website/img/isolated_ci_cd_environments.png new file mode 100644 index 0000000000..347523f0fa Binary files /dev/null and b/docs/my-website/img/isolated_ci_cd_environments.png differ diff --git a/docs/my-website/img/security_update_march_2026/proxy_version.png b/docs/my-website/img/security_update_march_2026/proxy_version.png new file mode 100644 index 0000000000..c5d03d6a63 Binary files /dev/null and b/docs/my-website/img/security_update_march_2026/proxy_version.png differ diff --git a/docs/my-website/img/shared_ci_cd_environment.png b/docs/my-website/img/shared_ci_cd_environment.png new file mode 100644 index 0000000000..e54e11faa8 Binary files /dev/null and b/docs/my-website/img/shared_ci_cd_environment.png differ diff --git a/docs/my-website/package.json b/docs/my-website/package.json index 20462de2dd..4b41a7ef46 100644 --- a/docs/my-website/package.json +++ b/docs/my-website/package.json @@ -90,7 +90,7 @@ "tar-fs": ">=2.1.4", "webpack-dev-middleware": ">=5.3.4", "braces": ">=3.0.3", - "axios": ">=0.30.2", + "axios": "1.13.6", "webpack": ">=5.94.0", "serve-static": ">=1.16.0", "path-to-regexp": ">=0.1.12", diff --git a/docs/my-website/release_notes/v1.82.3/index.md b/docs/my-website/release_notes/v1.82.3/index.md index 15df33fdb8..20be882671 100644 --- a/docs/my-website/release_notes/v1.82.3/index.md +++ b/docs/my-website/release_notes/v1.82.3/index.md @@ -47,6 +47,10 @@ pip install litellm==1.82.3 - **FLUX Kontext image editing** — `flux-kontext-pro` and `flux-kontext-max` added to Black Forest Labs, alongside `flux-pro-1.0-fill` and `flux-pro-1.0-expand` for inpainting and outpainting - **116 new models, 132 deprecated models cleaned up** — Major model map refresh including Mistral Magistral, Dashscope Qwen3 VL, xAI Grok via Azure AI, ZAI GLM-5, Serper Search; removal of OpenAI GPT-3.5/GPT-4 legacy variants, Gemini 1.5, and Vertex AI PaLM2 - **SageMaker Nova provider** — [New `sagemaker_nova` provider for Amazon Nova models on SageMaker](../../docs/providers/aws_sagemaker) - [PR #21542](https://github.com/BerriAI/litellm/pull/21542) +- **Hashicorp Vault secret manager** — Config override backend powered by Hashicorp Vault, with full UI for managing vault-sourced credentials - [PR #22939](https://github.com/BerriAI/litellm/pull/22939), [PR #23036](https://github.com/BerriAI/litellm/pull/23036) +- **Responses API WebSocket streaming** — Real-time WebSocket streaming for the Responses API, including support across all providers - [PR #22559](https://github.com/BerriAI/litellm/pull/22559), [PR #22771](https://github.com/BerriAI/litellm/pull/22771) +- **Org Admin RBAC expansion** — Org Admins can now access team management endpoints, view and invite internal users, and manage team membership without requiring a global admin role - [PR #23085](https://github.com/BerriAI/litellm/pull/23085), [PR #23080](https://github.com/BerriAI/litellm/pull/23080) +- **Guardrail mode defaults and tag-based modes** — Set a default guardrail mode list globally, and specify a list of modes in tag-based guardrail configs - [PR #22676](https://github.com/BerriAI/litellm/pull/22676), [PR #23020](https://github.com/BerriAI/litellm/pull/23020) - **Secret redaction in logs** — API keys, tokens, and credentials automatically scrubbed from all proxy log output. Enabled by default; opt out with `LITELLM_DISABLE_REDACT_SECRETS=true` - [PR #23668](https://github.com/BerriAI/litellm/pull/23668) - **Streaming stability fix** — Critical fix for `RuntimeError: Cannot send a request, as the client has been closed.` crashes after ~1 hour in production - [PR #22926](https://github.com/BerriAI/litellm/pull/22926) @@ -54,7 +58,7 @@ pip install litellm==1.82.3 ## New Providers and Endpoints -### New Providers (5 new providers) +### New Providers (7 new providers) | Provider | Supported LiteLLM Endpoints | Description | | -------- | --------------------------- | ----------- | @@ -63,6 +67,8 @@ pip install litellm==1.82.3 | [Black Forest Labs](../../docs/providers/black_forest_labs) (`black_forest_labs/`) | `/images/generations`, `/images/edits` | FLUX image generation and editing — Kontext Pro/Max, Pro 1.0 Fill/Expand | | [Serper](../../docs/providers/serper) (`serper/`) | `/search` | Web search via Serper API | | [SageMaker Nova](../../docs/providers/aws_sagemaker) (`sagemaker_nova/`) | `/chat/completions` | Amazon Nova models via SageMaker endpoint | +| [Google Search API](../../docs/providers/google_search) (`google_search/`) | `/search` | Google Search API integration - [PR #22752](https://github.com/BerriAI/litellm/pull/22752) | +| [Bedrock Mantle](../../docs/providers/bedrock) (`bedrock_mantle/`) | `/chat/completions` | Amazon Bedrock via Mantle — alternative auth and routing path for Bedrock models - [PR #22866](https://github.com/BerriAI/litellm/pull/22866) | --- @@ -238,22 +244,92 @@ pip install litellm==1.82.3 - **[Responses API](../../docs/response_api)** - Handle `response.failed`, `response.incomplete`, and `response.cancelled` terminal event types in background streaming — previously only `response.completed` was handled - [PR #23492](https://github.com/BerriAI/litellm/pull/23492) + - WebSocket streaming support for Responses API — real-time streaming via WebSocket for all providers - [PR #22559](https://github.com/BerriAI/litellm/pull/22559), [PR #22771](https://github.com/BerriAI/litellm/pull/22771) + - WebRTC support for real-time audio/video communication - [PR #23446](https://github.com/BerriAI/litellm/pull/23446) + - Responses API support for OpenAI-compatible JSON providers (`openai_like`) - [PR #21398](https://github.com/BerriAI/litellm/pull/21398) + - Route `gpt-5.4+` calls using both tools and reasoning to the Responses API automatically - [PR #23577](https://github.com/BerriAI/litellm/pull/23577) -#### Bug Fixes +- **[Anthropic Files API](../../docs/providers/anthropic)** + - Full Anthropic Files API support — upload, retrieve, list, and delete files; use file references in messages - [PR #16594](https://github.com/BerriAI/litellm/pull/16594) + +- **[Mistral](../../docs/providers/mistral)** + - Voxtral audio transcription support — `mistral/voxtral-mini-*` and `mistral/voxtral-*` for audio transcription via Mistral - [PR #22801](https://github.com/BerriAI/litellm/pull/22801) + +- **[OpenAI](../../docs/providers/openai)** + - `litellm.acount_tokens()` public API — async token counting with full OpenAI provider support - [PR #22809](https://github.com/BerriAI/litellm/pull/22809) + - Normalize `reasoning_effort` dict to string for chat completion API - [PR #22981](https://github.com/BerriAI/litellm/pull/22981) + +- **[OpenRouter](../../docs/providers/openrouter)** + - Image edit support for OpenRouter models - [PR #22403](https://github.com/BerriAI/litellm/pull/22403) + +- **[Google Vertex AI](../../docs/providers/vertex)** + - VIDEO modality token usage tracking in `completion_tokens_details` - [PR #22550](https://github.com/BerriAI/litellm/pull/22550) + +- **Images API** + - `input_fidelity` parameter for image edit API - [PR #23201](https://github.com/BerriAI/litellm/pull/23201) + +- **General** + - Per-request `enable_json_schema_validation` flag for thread-safe JSON schema validation - [PR #21233](https://github.com/BerriAI/litellm/pull/21233) + - Model cost aliases expansion — define aliases in the cost map that inherit pricing from a parent model - [PR #23314](https://github.com/BerriAI/litellm/pull/23314), [PR #23457](https://github.com/BerriAI/litellm/pull/23457) + - Wildcards model support for the Files API - [PR #22740](https://github.com/BerriAI/litellm/pull/22740) + +#### Bugs - **[Anthropic](../../docs/providers/anthropic)** - Preserve native tool format (web_search, bash, tool_search, etc.) when guardrails convert tools for the Anthropic Messages API - [PR #23526](https://github.com/BerriAI/litellm/pull/23526) + - Enforce `type: "object"` on tool input schemas in `_map_tool_helper` — fixes tool call failures for strict-schema providers - [PR #23103](https://github.com/BerriAI/litellm/pull/23103) + - Deduplicate `tool_result` messages by `tool_call_id` — prevents duplicate tool result errors in multi-turn conversations - [PR #23104](https://github.com/BerriAI/litellm/pull/23104) + - Map `reasoning_effort` to `output_config` for Claude 4.6 models - [PR #22220](https://github.com/BerriAI/litellm/pull/22220) + +- **[Google Gemini](../../docs/providers/gemini)** + - Correct streaming `finish_reason` for tool calls — was incorrectly returning `null` instead of `tool_calls` - [PR #21577](https://github.com/BerriAI/litellm/pull/21577) + - Preserve `$ref` in JSON Schema for Gemini 2.0+ — schema references were being stripped, breaking structured output - [PR #21597](https://github.com/BerriAI/litellm/pull/21597) + - Handle `minimal` `reasoning_effort` param for Gemini 3.1 models - [PR #22920](https://github.com/BerriAI/litellm/pull/22920) + +- **[Google Vertex AI](../../docs/providers/vertex)** + - Pass through native Gemini `imageConfig` params for image generation - [PR #21585](https://github.com/BerriAI/litellm/pull/21585) + - Prevent content truncation when `finish_reason` races ahead of content chunks in streaming - [PR #22692](https://github.com/BerriAI/litellm/pull/22692) + - Strip LiteLLM-internal keys from `extra_body` before merging to Gemini request body - [PR #23131](https://github.com/BerriAI/litellm/pull/23131) + - Drop unsupported `output_config` parameter from all Vertex AI requests - [PR #22884](https://github.com/BerriAI/litellm/pull/22884) + - Skip schema transforms for Gemini 2.0+ tool parameters — avoids breaking native Gemini schema handling - [PR #23265](https://github.com/BerriAI/litellm/pull/23265) + +- **[OpenRouter](../../docs/providers/openrouter)** + - Pattern-based fix for native model double-stripping when provider prefix matches model name - [PR #22320](https://github.com/BerriAI/litellm/pull/22320) + - Use provider-reported usage in streaming responses when `stream_options` is not set - [PR #21592](https://github.com/BerriAI/litellm/pull/21592) + +- **[AWS Bedrock](../../docs/providers/bedrock)** + - Extract region and model ID from `bedrock/{region}/{model}` path format - [PR #22546](https://github.com/BerriAI/litellm/pull/22546) + - Strip `scope` from `cache_control` for Anthropic messages on Bedrock and Azure AI - [PR #22867](https://github.com/BerriAI/litellm/pull/22867) + - Populate `completion_tokens_details` in Responses API responses - [PR #23243](https://github.com/BerriAI/litellm/pull/23243) + +- **[Azure AI](../../docs/providers/azure_ai)** + - Resolve `api_base` from environment variable in Document Intelligence OCR - [PR #21581](https://github.com/BerriAI/litellm/pull/21581) - **[Moonshot / Kimi](../../docs/providers/openai_compatible)** - Auto-fill `reasoning_content` for Moonshot Kimi reasoning models - [PR #23580](https://github.com/BerriAI/litellm/pull/23580) + - Preserve `image_url` blocks in multimodal messages for Moonshot - [PR #21595](https://github.com/BerriAI/litellm/pull/21595) - **[HuggingFace](../../docs/providers/huggingface)** - Forward `extra_headers` to HuggingFace embedding API - [PR #23525](https://github.com/BerriAI/litellm/pull/23525) +- **Token Counting / Cost** + - Fix `count_tokens` to include system prompts and tools in token counting API requests - [PR #22301](https://github.com/BerriAI/litellm/pull/22301) + - Pass all custom pricing fields to `register_model` in `completion()` and `embedding()` - [PR #22552](https://github.com/BerriAI/litellm/pull/22552) + +- **Tools / Function Calling** + - Gracefully repair truncated JSON in tool call arguments — prevents crashes on malformed tool responses - [PR #22503](https://github.com/BerriAI/litellm/pull/22503) + - Fix `output_item.done` for function calls not emitting `finish_reason` in streaming - [PR #22553](https://github.com/BerriAI/litellm/pull/22553) + - Preserve thinking block order with multiple web searches - [PR #23093](https://github.com/BerriAI/litellm/pull/23093) + - **General** - Normalize `content_filtered` finish reason across providers - [PR #23564](https://github.com/BerriAI/litellm/pull/23564) + - Unify `finish_reason` mapping to OpenAI-compatible values across all providers - [PR #22138](https://github.com/BerriAI/litellm/pull/22138) - Fix custom cost tracking on deployments for `/v1/messages` and `/v1/responses` - [PR #23647](https://github.com/BerriAI/litellm/pull/23647) - Fix per-request custom pricing when `router_model_id` has no pricing data — now falls back to model name + - Fix batch list showing stale `validating` status after completion - [PR #22982](https://github.com/BerriAI/litellm/pull/22982) + - Fix batch retrieve returning raw `output_file_id` when `model_id` is missing - [PR #23194](https://github.com/BerriAI/litellm/pull/23194) + - Encode batch IDs when `x-litellm-model` header is used - [PR #22653](https://github.com/BerriAI/litellm/pull/22653) + - Map `reasoning` to `reasoning_content` in streaming Delta for gpt-oss providers - [PR #22803](https://github.com/BerriAI/litellm/pull/22803) --- @@ -264,10 +340,31 @@ pip install litellm==1.82.3 - **Virtual Keys** - Add Organization dropdown to Create/Edit Key form — `organization_id` is now a first-class field in Key Ownership - [PR #23595](https://github.com/BerriAI/litellm/pull/23595) - Allow setting `organization_id` on `/key/update` — keys can be assigned or moved to a different organization after creation - [PR #23557](https://github.com/BerriAI/litellm/pull/23557) + - Manual Spend Reset for virtual keys from the UI — admins can reset key spend to zero on demand - [PR #22715](https://github.com/BerriAI/litellm/pull/22715) + - BYOK (Bring Your Own Key) — client-side provider API key takes precedence over proxy key for Anthropic `/v1/messages` - [PR #22964](https://github.com/BerriAI/litellm/pull/22964) + - UI login session duration configurable via `LITELLM_UI_SESSION_DURATION` environment variable - [PR #22182](https://github.com/BerriAI/litellm/pull/22182) + - Auto-redirect UI login to SSO via `auto_redirect_ui_login_to_sso: true` in config.yaml - [PR #23367](https://github.com/BerriAI/litellm/pull/23367) + +- **Access Control (RBAC)** + - Org Admins can now access team management endpoints — `/team/new`, `/team/update`, `/team/delete`, `/team/member_add`, `/team/member_delete` - [PR #23085](https://github.com/BerriAI/litellm/pull/23085), [PR #23095](https://github.com/BerriAI/litellm/pull/23095) + - Org Admins can view and invite internal users — full user management without requiring global admin role - [PR #23080](https://github.com/BerriAI/litellm/pull/23080) + - Allow Admin Viewers to access Audit Logs — view-only admin role now includes audit log access - [PR #23419](https://github.com/BerriAI/litellm/pull/23419) + - RBAC for Vector Stores and Agents — key/team-level access control for vector store and agent resources - [PR #22858](https://github.com/BerriAI/litellm/pull/22858) + - User filter scope (`scope_user_search_to_org`) is now opt-in — previously default-on, causing unintended restriction - [PR #23057](https://github.com/BerriAI/litellm/pull/23057) + +- **Vector Stores** + - Vector Store management endpoints — retrieve, list, update, and delete vector stores via `/v1/vector_stores/*` - [PR #23435](https://github.com/BerriAI/litellm/pull/23435) + +- **Teams** + - Batch expiry setting for teams — configure a default expiry duration for all team keys - [PR #22705](https://github.com/BerriAI/litellm/pull/22705) + - Team Admin can reset key spend - [PR #22725](https://github.com/BerriAI/litellm/pull/22725) - **Internal Users** - Add/Remove Team Membership directly from the Internal Users info page — includes searchable dropdown and role selector; no longer requires navigating to each team - [PR #23638](https://github.com/BerriAI/litellm/pull/23638) +- **Models** + - Attach knowledge base to model via UI - [PR #22656](https://github.com/BerriAI/litellm/pull/22656) + - **Default Team Settings** - Modernize page to antd (consistent with rest of app) - [PR #23614](https://github.com/BerriAI/litellm/pull/23614) - Fix: default team params (budget, duration, tpm, rpm, permissions) now correctly applied on `/team/new` - [PR #23614](https://github.com/BerriAI/litellm/pull/23614) @@ -290,6 +387,13 @@ pip install litellm==1.82.3 - Fix Public Model Hub not showing config-defined models after save - [PR #23501](https://github.com/BerriAI/litellm/pull/23501) - Fix fallback popup model dropdown z-index issue - [PR #23516](https://github.com/BerriAI/litellm/pull/23516) - Fix double-counting bug in org/team key limit checks on `/key/update` +- Fix invite link allowing multiple password resets for the same link - [PR #22462](https://github.com/BerriAI/litellm/pull/22462) +- Fix key expiry default duration not being applied when `duration` is not set - [PR #22956](https://github.com/BerriAI/litellm/pull/22956) +- Fix all proxy models not including model access groups in key creation - [PR #23236](https://github.com/BerriAI/litellm/pull/23236) +- Fix admin viewers unable to see all organizations - [PR #22940](https://github.com/BerriAI/litellm/pull/22940) +- Fix Audit Logs UI: added server-side pagination, filtering, and drawer view - [PR #22476](https://github.com/BerriAI/litellm/pull/22476) +- Fix virtual keys in teams view not applying the team filter correctly - [PR #23065](https://github.com/BerriAI/litellm/pull/23065) +- Fix team expiry enforcement validation - [PR #22728](https://github.com/BerriAI/litellm/pull/22728) --- @@ -297,6 +401,13 @@ pip install litellm==1.82.3 ### Logging +- **[Helicone](../../docs/observability/helicone_integration)** + - Add Gemini and Vertex AI support to HeliconeLogger — routes Gemini and Vertex AI requests through the correct Helicone provider URL - [PR #19288](https://github.com/BerriAI/litellm/pull/19288) + - Fix correct provider URL for Vertex AI Gemini models - [PR #22603](https://github.com/BerriAI/litellm/pull/22603) + +- **[Langfuse](../../docs/proxy/logging#langfuse)** + - Fix failure path kwargs inconsistency causing dropped traces on failed requests - [PR #22390](https://github.com/BerriAI/litellm/pull/22390) + - **[Vantage](https://vantage.sh)** - Add Vantage integration for FOCUS 1.2 CSV export — export LiteLLM proxy spend data as FinOps Open Cost & Usage Specification reports, with time-windowed filenames to prevent overwrites - [PR #23333](https://github.com/BerriAI/litellm/pull/23333) @@ -305,7 +416,10 @@ pip install litellm==1.82.3 ### Guardrails -No major guardrail changes in this release. +- **Guardrail mode default list** — Configure a default list of guardrail modes applied globally when no per-request mode is specified - [PR #22676](https://github.com/BerriAI/litellm/pull/22676) +- **Tag-based guardrail mode lists** — Specify a list of modes in tag-based guardrail configs instead of a single mode - [PR #23020](https://github.com/BerriAI/litellm/pull/23020) +- **Fix presidio PII token leak** — Edge case where Anthropic handle in Presidio caused PII data exposure in token response - [PR #22627](https://github.com/BerriAI/litellm/pull/22627) +- **Fix OTEL orphaned guardrail traces** — Span redundancy and missing response IDs in OpenTelemetry guardrail traces - [PR #23001](https://github.com/BerriAI/litellm/pull/23001) ### Prompt Management @@ -313,7 +427,32 @@ No major prompt management changes in this release. ### Secret Managers -No major secret manager changes in this release. +- **[Hashicorp Vault](../../docs/secret_managers)** — Full Hashicorp Vault integration as a config override backend — secrets defined in Vault are fetched at startup and override `config.yaml` values. UI support for managing vault-sourced credentials included - [PR #22939](https://github.com/BerriAI/litellm/pull/22939), [PR #23036](https://github.com/BerriAI/litellm/pull/23036) + +--- + +## MCP Gateway + +#### Features + +- **Token authentication for MCP servers** — configure `auth_type: "bearer"` per MCP server to require token-based auth on tool calls - [PR #23260](https://github.com/BerriAI/litellm/pull/23260) +- **Team-scoped MCP server filtering** — keys created under a team only see MCP servers available to that team - [PR #23323](https://github.com/BerriAI/litellm/pull/23323) +- **Per-server health recheck in UI** — trigger a health check for individual MCP servers without reloading all servers - [PR #23328](https://github.com/BerriAI/litellm/pull/23328) + +#### Bugs + +- Fix MCP server URL and tools management issues causing tool discovery to fail - [PR #22751](https://github.com/BerriAI/litellm/pull/22751) +- Fix MCP server health checks triggering on server deletion - [PR #23063](https://github.com/BerriAI/litellm/pull/23063) + +--- + +## Spend Tracking, Budgets and Rate Limiting + +- **Fix budget-linked keys never having spend reset** — Keys linked to budget objects were not having their spend reset on the configured reset interval - [PR #20688](https://github.com/BerriAI/litellm/pull/20688) +- **Flex pricing support** — Add `flex_pricing` field to cost map for providers that offer dynamic pricing tiers - [PR #22992](https://github.com/BerriAI/litellm/pull/22992) +- **Fix spend log cleanup** — Resolved lock tracking, integer retention, and skip-log-level issues in spend log cleanup job - [PR #22687](https://github.com/BerriAI/litellm/pull/22687) +- **Fix WebSearch spend log deduplication** — WebSearch interception was failing with thinking enabled; fixed along with spend log dedup - [PR #22679](https://github.com/BerriAI/litellm/pull/22679) +- **Fix TypeError when request has no API key** — Spend tracking was throwing unhandled exception when API key was absent from request - [PR #23363](https://github.com/BerriAI/litellm/pull/23363) --- @@ -323,6 +462,10 @@ No major secret manager changes in this release. - **Fix OOM / Prisma connection loss** on large installs — unbounded managed-object poll was exhausting Prisma connections after ~60–70 minutes on instances with 336K+ queued response rows - [PR #23472](https://github.com/BerriAI/litellm/pull/23472) - **Centralize logging kwarg updates** — root cause fix migrating all logging updates to a single function, eliminating kwarg inconsistencies across logging paths - [PR #23659](https://github.com/BerriAI/litellm/pull/23659) - **Fix tiktoken cache for non-root offline containers** — tiktoken cache now works correctly in offline environments running as non-root users - [PR #23498](https://github.com/BerriAI/litellm/pull/23498) +- **Block proxy startup when Redis transaction buffer has no Redis** — prevents silent data loss when `use_redis_transaction_buffer: true` is set without a Redis connection - [PR #23019](https://github.com/BerriAI/litellm/pull/23019) +- **Fix `InFlightRequestsMiddleware` crash** — undefined kwargs in middleware were causing request failures - [PR #22523](https://github.com/BerriAI/litellm/pull/22523) +- **Fix `BaseModelResponseIterator` crash on non-string stream chunks** — streaming was crashing when providers returned non-string chunk data - [PR #23497](https://github.com/BerriAI/litellm/pull/23497) +- **Fix `SERVER_ROOT_PATH` prefix handling** — strip prefix before checking mapped pass-through routes to prevent double-prefix issues - [PR #23414](https://github.com/BerriAI/litellm/pull/23414) - **Add CodSpeed continuous performance benchmarks** — automated performance regression tracking on CI - [PR #23676](https://github.com/BerriAI/litellm/pull/23676) --- @@ -342,6 +485,16 @@ No major secret manager changes in this release. --- +## Documentation Updates + +- Add Anthropic `/v1/messages` → `/responses` parameter mapping reference - [PR #22893](https://github.com/BerriAI/litellm/pull/22893) +- Update Okta SSO docs and custom SSO handler example - [PR #22786](https://github.com/BerriAI/litellm/pull/22786) +- Add `LITELLM_MAX_BUDGET_PER_SESSION_TTL` to environment variables reference - [PR #23186](https://github.com/BerriAI/litellm/pull/23186) +- Add DB query performance guidelines to `CLAUDE.md` - [PR #23196](https://github.com/BerriAI/litellm/pull/23196) +- Add Gemini Vertex AI PayGo/priority cost tracking docs - [PR #22948](https://github.com/BerriAI/litellm/pull/22948) + +--- + ## New Contributors * @ryanh-ai made their first contribution in [PR #21542](https://github.com/BerriAI/litellm/pull/21542) @@ -359,14 +512,17 @@ No major secret manager changes in this release. ## Diff Summary ## 03/16/2026 -* New Providers: 5 +* New Providers: 7 * New Models / Updated Models: 116 new, 132 removed -* LLM API Endpoints: 5 -* Management Endpoints / UI: 11 -* AI Integrations: 2 -* Performance / Reliability: 5 +* LLM API Endpoints: 37 +* Management Endpoints / UI: 31 +* AI Integrations: 8 +* MCP Gateway: 5 +* Spend Tracking, Budgets and Rate Limiting: 5 +* Performance / Loadbalancing / Reliability improvements: 9 * Security: 3 * Database / Proxy Operations: 2 +* Documentation Updates: 5 --- diff --git a/docs/my-website/sidebars.js b/docs/my-website/sidebars.js index 7db72da276..cc2d7800a2 100644 --- a/docs/my-website/sidebars.js +++ b/docs/my-website/sidebars.js @@ -48,6 +48,20 @@ const sidebars = { slug: "/guardrail_providers" }, items: [ + { + type: "category", + label: "Contributing to Guardrails", + items: [ + "adding_provider/generic_guardrail_api", + "adding_provider/simple_guardrail_tutorial", + "adding_provider/adding_guardrail_support", + ] + }, + { + type: "doc", + id: "proxy/guardrails/team_based_guardrails", + label: "Team Bring-Your-Own Guardrails", + }, ...[ "proxy/guardrails/qualifire", "proxy/guardrails/aim_security", @@ -416,6 +430,7 @@ const sidebars = { "proxy/architecture", "proxy/multi_tenant_architecture", "proxy/control_plane_and_data_plane", + "proxy/high_availability_control_plane", "proxy/db_deadlocks", "proxy/db_info", "proxy/image_handling", @@ -570,6 +585,7 @@ const sidebars = { label: "Spend Tracking", items: [ "proxy/cost_tracking", + "tutorials/vertex_ai_pay_go", "proxy/request_tags", "proxy/custom_pricing", "proxy/pricing_calculator", @@ -723,6 +739,7 @@ const sidebars = { "proxy/realtime_webrtc", "rerank", "response_api", + "prompt_management", "response_api_compact", { type: "category", @@ -833,6 +850,7 @@ const sidebars = { items: [ "providers/gemini", "providers/gemini/videos", + "providers/gemini/music", "providers/google_ai_studio/files", "providers/google_ai_studio/image_gen", "providers/google_ai_studio/realtime", @@ -1419,6 +1437,7 @@ const learnSidebar = { }, items: [ "tutorials/prompt_caching", + "tutorials/file_search_responses_api", "tutorials/anthropic_file_usage", "tutorials/gemini_realtime_with_audio", "tutorials/litellm_proxy_aporia", diff --git a/docs/my-website/src/components/ControlPlaneArchitecture/ControlPlaneArchitecture.tsx b/docs/my-website/src/components/ControlPlaneArchitecture/ControlPlaneArchitecture.tsx new file mode 100644 index 0000000000..5cc41979b0 --- /dev/null +++ b/docs/my-website/src/components/ControlPlaneArchitecture/ControlPlaneArchitecture.tsx @@ -0,0 +1,95 @@ +import React from 'react'; +import styles from './styles.module.css'; + +/* ────────────────────── Shared small pieces ────────────────────── */ + +function InfraChip({ color, label }: { color: string; label: string }) { + const dotClass = + color === 'green' + ? styles.infraDotGreen + : color === 'blue' + ? styles.infraDotBlue + : styles.infraDotOrange; + + return ( + + + {label} + + ); +} + +/* ────────────────────── Architecture tab ────────────────────── */ + +function ArchitectureView() { + return ( +
+ {/* User */} +
+
👤
+ Admin +
+ +
+ + {/* Control Plane */} +
+
+ Control Plane + UI +
+
cp.example.com
+
+ + + +
+
+ + {/* Branch connector */} +
+
+
+
+ + {/* Workers */} +
+
+
+ Worker A + US East +
+
worker-a.example.com
+
+ + + +
+
+ +
+
+ Worker B + EU West +
+
worker-b.example.com
+
+ + + +
+
+
+
+ ); +} + +/* ────────────────────── Main component ────────────────────── */ + +export default function ControlPlaneArchitecture() { + return ( +
+ +
+ ); +} diff --git a/docs/my-website/src/components/ControlPlaneArchitecture/index.tsx b/docs/my-website/src/components/ControlPlaneArchitecture/index.tsx new file mode 100644 index 0000000000..826b4d6881 --- /dev/null +++ b/docs/my-website/src/components/ControlPlaneArchitecture/index.tsx @@ -0,0 +1 @@ +export { default as ControlPlaneArchitecture } from './ControlPlaneArchitecture'; diff --git a/docs/my-website/src/components/ControlPlaneArchitecture/styles.module.css b/docs/my-website/src/components/ControlPlaneArchitecture/styles.module.css new file mode 100644 index 0000000000..8400a8920e --- /dev/null +++ b/docs/my-website/src/components/ControlPlaneArchitecture/styles.module.css @@ -0,0 +1,517 @@ +/* ── Custom properties ── */ +:root { + --cp-bg: #ffffff; + --cp-border: #e5e7eb; + --cp-text: #1a1a2e; + --cp-text-secondary: #6b7280; + --cp-text-muted: #9ca3af; + --cp-accent: #3b82f6; + --cp-accent-light: #dbeafe; + --cp-accent-glow: rgba(59, 130, 246, 0.15); + --cp-green: #10b981; + --cp-green-light: #d1fae5; + --cp-green-glow: rgba(16, 185, 129, 0.15); + --cp-orange: #f59e0b; + --cp-orange-light: #fef3c7; + --cp-purple: #8b5cf6; + --cp-purple-light: #ede9fe; + --cp-red: #ef4444; + --cp-red-light: #fee2e2; + --cp-card-bg: #f9fafb; + --cp-infra-bg: #f1f5f9; + --cp-infra-border: #cbd5e1; + --cp-connector: #d1d5db; + --cp-dot-size: 8px; +} + +[data-theme='dark'] { + --cp-bg: #111827; + --cp-border: #374151; + --cp-text: #e5e7eb; + --cp-text-secondary: #9ca3af; + --cp-text-muted: #6b7280; + --cp-accent: #60a5fa; + --cp-accent-light: #1e3a5f; + --cp-accent-glow: rgba(96, 165, 250, 0.2); + --cp-green: #34d399; + --cp-green-light: #064e3b; + --cp-green-glow: rgba(52, 211, 153, 0.2); + --cp-orange: #fbbf24; + --cp-orange-light: #78350f; + --cp-purple: #a78bfa; + --cp-purple-light: #3b0764; + --cp-red: #f87171; + --cp-red-light: #451a1a; + --cp-card-bg: #1f2937; + --cp-infra-bg: #1e293b; + --cp-infra-border: #475569; + --cp-connector: #4b5563; +} + +/* ── Wrapper ── */ +.wrapper { + margin: 1.5rem 0; + font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif; +} + +/* ── Tab bar ── */ +.tabs { + display: flex; + gap: 0; + margin-bottom: 1.5rem; + border-bottom: 2px solid var(--cp-border); +} + +.tab { + padding: 0.6rem 1.25rem; + font-size: 0.85rem; + font-weight: 600; + color: var(--cp-text-secondary); + background: none; + border: none; + border-bottom: 2px solid transparent; + margin-bottom: -2px; + cursor: pointer; + transition: color 0.2s, border-color 0.2s; +} + +.tab:hover { + color: var(--cp-text); +} + +.tabActive { + color: var(--cp-accent); + border-bottom-color: var(--cp-accent); +} + +/* ── Architecture diagram ── */ +.diagram { + display: flex; + flex-direction: column; + align-items: center; + gap: 0; +} + +/* ── User icon ── */ +.userRow { + display: flex; + flex-direction: column; + align-items: center; + margin-bottom: 0.5rem; +} + +.userIcon { + width: 40px; + height: 40px; + border-radius: 50%; + background: var(--cp-accent-light); + border: 2px solid var(--cp-accent); + display: flex; + align-items: center; + justify-content: center; + font-size: 1.1rem; +} + +.userLabel { + font-size: 0.75rem; + color: var(--cp-text-secondary); + margin-top: 0.3rem; + font-weight: 500; +} + +/* ── Connectors ── */ +.connectorDown { + width: 2px; + height: 28px; + background: var(--cp-connector); + position: relative; +} + +.connectorDown::after { + content: ''; + position: absolute; + bottom: -4px; + left: 50%; + transform: translateX(-50%); + width: 0; + height: 0; + border-left: 5px solid transparent; + border-right: 5px solid transparent; + border-top: 5px solid var(--cp-connector); +} + +.connectorBranch { + display: flex; + align-items: flex-start; + justify-content: center; + position: relative; + width: 100%; + max-width: 700px; + height: 36px; +} + +.connectorBranch::before { + content: ''; + position: absolute; + top: 0; + left: 50%; + width: 2px; + height: 12px; + background: var(--cp-connector); + transform: translateX(-50%); +} + +.connectorBranch::after { + content: ''; + position: absolute; + top: 12px; + left: calc(25% + 12px); + right: calc(25% + 12px); + height: 2px; + background: var(--cp-connector); +} + +.branchLeg { + position: absolute; + top: 12px; + width: 2px; + height: 24px; + background: var(--cp-connector); +} + +.branchLeg::after { + content: ''; + position: absolute; + bottom: -4px; + left: 50%; + transform: translateX(-50%); + width: 0; + height: 0; + border-left: 5px solid transparent; + border-right: 5px solid transparent; + border-top: 5px solid var(--cp-connector); +} + +.branchLegLeft { + left: calc(25% + 12px); +} + +.branchLegRight { + right: calc(25% + 12px); +} + +/* ── Node cards ── */ +.node { + border: 2px solid var(--cp-border); + border-radius: 12px; + background: var(--cp-card-bg); + padding: 1rem 1.25rem; + text-align: center; + transition: border-color 0.3s, box-shadow 0.3s; + position: relative; +} + +.nodeControlPlane { + border-color: var(--cp-accent); + box-shadow: 0 0 0 3px var(--cp-accent-glow); + min-width: 280px; +} + +.nodeWorker { + min-width: 220px; +} + +.nodeWorkerA { + border-color: var(--cp-green); + box-shadow: 0 0 0 3px var(--cp-green-glow); +} + +.nodeWorkerB { + border-color: var(--cp-purple); + box-shadow: 0 0 0 3px rgba(139, 92, 246, 0.15); +} + +[data-theme='dark'] .nodeWorkerB { + box-shadow: 0 0 0 3px rgba(167, 139, 250, 0.2); +} + +.nodeHeader { + display: flex; + align-items: center; + justify-content: center; + gap: 0.5rem; + margin-bottom: 0.5rem; +} + +.nodeIcon { + font-size: 1.1rem; +} + +.nodeTitle { + font-size: 0.95rem; + font-weight: 700; + color: var(--cp-text); +} + +.nodeSubtitle { + font-size: 0.75rem; + color: var(--cp-text-secondary); + margin-bottom: 0.75rem; +} + +.badge { + display: inline-block; + font-size: 0.65rem; + font-weight: 600; + padding: 0.15rem 0.5rem; + border-radius: 9999px; + text-transform: uppercase; + letter-spacing: 0.04em; +} + +.badgeBlue { + background: var(--cp-accent-light); + color: var(--cp-accent); +} + +.badgeGreen { + background: var(--cp-green-light); + color: var(--cp-green); +} + +.badgePurple { + background: var(--cp-purple-light); + color: var(--cp-purple); +} + +/* ── Infrastructure chips ── */ +.infraRow { + display: flex; + gap: 0.4rem; + justify-content: center; + flex-wrap: wrap; + margin-top: 0.5rem; +} + +.infraChip { + display: flex; + align-items: center; + gap: 0.3rem; + font-size: 0.7rem; + font-weight: 500; + color: var(--cp-text-secondary); + background: var(--cp-infra-bg); + border: 1px solid var(--cp-infra-border); + border-radius: 6px; + padding: 0.2rem 0.5rem; +} + +.infraDot { + width: 6px; + height: 6px; + border-radius: 50%; + flex-shrink: 0; +} + +.infraDotGreen { + background: var(--cp-green); +} + +.infraDotBlue { + background: var(--cp-accent); +} + +.infraDotOrange { + background: var(--cp-orange); +} + +/* ── Workers row ── */ +.workersRow { + display: flex; + gap: 2rem; + justify-content: center; + flex-wrap: wrap; +} + +/* ── Animated flow ── */ +.flowLabel { + font-size: 0.7rem; + color: var(--cp-accent); + font-weight: 600; + position: absolute; + white-space: nowrap; +} + +/* ── Comparison view ── */ +.comparisonGrid { + display: grid; + grid-template-columns: 1fr 1fr; + gap: 1.5rem; + margin-top: 0.5rem; +} + +.comparisonColumn { + border: 2px solid var(--cp-border); + border-radius: 12px; + padding: 1.25rem; + background: var(--cp-card-bg); +} + +.comparisonColumnOld { + border-color: var(--cp-red); +} + +.comparisonColumnNew { + border-color: var(--cp-green); +} + +.comparisonTitle { + font-size: 0.9rem; + font-weight: 700; + color: var(--cp-text); + text-align: center; + margin-bottom: 1rem; + display: flex; + align-items: center; + justify-content: center; + gap: 0.4rem; +} + +.comparisonTitleOld { + color: var(--cp-red); +} + +.comparisonTitleNew { + color: var(--cp-green); +} + +/* ── Mini diagram inside comparison ── */ +.miniDiagram { + display: flex; + flex-direction: column; + align-items: center; + gap: 0.5rem; +} + +.miniNode { + border: 1.5px solid var(--cp-border); + border-radius: 8px; + background: var(--cp-bg); + padding: 0.5rem 0.75rem; + text-align: center; + font-size: 0.75rem; + font-weight: 600; + color: var(--cp-text); + width: 100%; + max-width: 180px; +} + +.miniNodeHighlight { + border-color: var(--cp-accent); + background: var(--cp-accent-light); +} + +.miniNodeDanger { + border-color: var(--cp-red); + background: var(--cp-red-light); +} + +.miniNodeSuccess { + border-color: var(--cp-green); + background: var(--cp-green-light); +} + +.miniConnector { + width: 1.5px; + height: 16px; + background: var(--cp-connector); +} + +.miniWorkersRow { + display: flex; + gap: 0.5rem; + justify-content: center; + width: 100%; +} + +.miniWorkerStack { + display: flex; + flex-direction: column; + align-items: center; + gap: 0.3rem; + flex: 1; + max-width: 140px; +} + +.miniInfra { + font-size: 0.65rem; + color: var(--cp-text-muted); + font-weight: 500; +} + +.miniInfraShared { + color: var(--cp-red); + font-weight: 600; +} + +.miniInfraOwn { + color: var(--cp-green); + font-weight: 600; +} + +/* ── Callout box ── */ +.callout { + display: flex; + align-items: flex-start; + gap: 0.6rem; + padding: 0.75rem 1rem; + border-radius: 8px; + margin-top: 1rem; + font-size: 0.8rem; + color: var(--cp-text); + line-height: 1.5; +} + +.calloutDanger { + background: var(--cp-red-light); + border: 1px solid var(--cp-red); +} + +.calloutSuccess { + background: var(--cp-green-light); + border: 1px solid var(--cp-green); +} + +.calloutIcon { + font-size: 1rem; + flex-shrink: 0; + margin-top: 0.1rem; +} + +/* ── Responsive ── */ +@media (max-width: 768px) { + .comparisonGrid { + grid-template-columns: 1fr; + } + + .workersRow { + flex-direction: column; + align-items: center; + } + + .nodeControlPlane { + min-width: auto; + width: 100%; + max-width: 300px; + } + + .nodeWorker { + min-width: auto; + width: 100%; + max-width: 260px; + } + + .connectorBranch { + display: none; + } +} diff --git a/docs/my-website/src/components/VersionVerificationTable/index.tsx b/docs/my-website/src/components/VersionVerificationTable/index.tsx new file mode 100644 index 0000000000..de4caced04 --- /dev/null +++ b/docs/my-website/src/components/VersionVerificationTable/index.tsx @@ -0,0 +1,84 @@ +import React, { useState } from "react"; +import styles from "./styles.module.css"; + +interface VersionEntry { + version: string; + sha256: string; + gitCommit: string; +} + +interface Props { + entries: VersionEntry[]; +} + +function CopyButton({ text }: { text: string }) { + const [copied, setCopied] = useState(false); + + const handleCopy = () => { + navigator.clipboard.writeText(text).then(() => { + setCopied(true); + setTimeout(() => setCopied(false), 1500); + }); + }; + + return ( + + ); +} + +export default function VersionVerificationTable({ entries }: Props) { + return ( +
+
Stripeimage Google ADK Greptile OpenHands
+ + + + + + + + + + + + {entries.map((entry) => ( + + + + + + + + + ))} + +
VersionSHA-256Clean of IOCsMatches GitGit CommitStatus
{entry.version} + + {entry.sha256.slice(0, 16)}… + + + + ✔ CLEAN + + ✔ YES + + + {entry.gitCommit} + + + ✔ CLEAN +
+ + ); +} diff --git a/docs/my-website/src/components/VersionVerificationTable/styles.module.css b/docs/my-website/src/components/VersionVerificationTable/styles.module.css new file mode 100644 index 0000000000..97d2eb17e1 --- /dev/null +++ b/docs/my-website/src/components/VersionVerificationTable/styles.module.css @@ -0,0 +1,106 @@ +.wrapper { + overflow-x: auto; + margin: 1rem 0; +} + +.table { + width: 100%; + border-collapse: separate; + border-spacing: 0; + font-size: 0.9rem; + border: 1px solid var(--ifm-color-emphasis-300); + border-radius: 8px; + overflow: hidden; +} + +.table th, +.table td { + padding: 0.6rem 0.75rem; + text-align: left; + white-space: nowrap; +} + +.table thead th { + background: var(--ifm-color-emphasis-200); + font-weight: 600; + font-size: 0.8rem; + text-transform: uppercase; + letter-spacing: 0.03em; + color: var(--ifm-color-emphasis-700); + border-bottom: 2px solid var(--ifm-color-emphasis-300); +} + +.table tbody tr:nth-child(even) { + background: var(--ifm-color-emphasis-100); +} + +.table tbody tr:hover { + background: var(--ifm-color-emphasis-200); +} + +.table tbody td { + border-bottom: 1px solid var(--ifm-color-emphasis-200); +} + +.table tbody tr:last-child td { + border-bottom: none; +} + +.badge { + display: inline-flex; + align-items: center; + gap: 4px; + padding: 2px 8px; + border-radius: 12px; + font-size: 0.75rem; + font-weight: 600; + line-height: 1.4; +} + +.badgeClean { + composes: badge; + background: #d4edda; + color: #155724; +} + +.badgeYes { + composes: badge; + background: #cce5ff; + color: #004085; +} + +.sha { + display: inline-flex; + align-items: center; + gap: 4px; + font-family: var(--ifm-font-family-monospace); + font-size: 0.8rem; +} + +.copyBtn { + display: inline-flex; + align-items: center; + justify-content: center; + background: none; + border: 1px solid var(--ifm-color-emphasis-300); + border-radius: 4px; + cursor: pointer; + padding: 2px 4px; + font-size: 0.7rem; + color: var(--ifm-color-emphasis-600); + transition: background 0.15s, color 0.15s; +} + +.copyBtn:hover { + background: var(--ifm-color-emphasis-200); + color: var(--ifm-color-emphasis-800); +} + +.commitLink { + font-family: var(--ifm-font-family-monospace); + font-size: 0.8rem; +} + +.version { + font-weight: 600; +} diff --git a/docs/my-website/static/img/blog/vanta_soc2_recertification.png b/docs/my-website/static/img/blog/vanta_soc2_recertification.png new file mode 100644 index 0000000000..c1c5644fbe Binary files /dev/null and b/docs/my-website/static/img/blog/vanta_soc2_recertification.png differ diff --git a/docs/my-website/static/img/vertex_cost_tracking_flow.svg b/docs/my-website/static/img/vertex_cost_tracking_flow.svg new file mode 100644 index 0000000000..c3b2e33a07 --- /dev/null +++ b/docs/my-website/static/img/vertex_cost_tracking_flow.svg @@ -0,0 +1,63 @@ + + + + + + + + + + + HTTP request + X-Vertex-AI-LLM-Shared-Request-Type: priority + + + + + Vertex AI + + + + + Vertex response + usageMetadata.trafficType = ON_DEMAND_PRIORITY + + + + + + + + + LiteLLM stores it + _hidden_params.provider_specific_fields.traffic_type + + + + + + + + + completion_cost() + Maps traffic_type → service_tier = "priority" + + + + + + + + + Pricing lookup + input/output_cost_per_token_priority + + + + + + + + + + \ No newline at end of file diff --git a/enterprise/LICENSE.md b/enterprise/LICENSE.md index c14a2a0c48..c8607439ad 100644 --- a/enterprise/LICENSE.md +++ b/enterprise/LICENSE.md @@ -7,7 +7,7 @@ With regard to the BerriAI Software: This software and associated documentation files (the "Software") may only be used in production, if you (and any entity that you represent) have agreed to, and are in compliance with, the BerriAI Subscription Terms of Service, available -via [call](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions) or email (info@berri.ai) (the "Enterprise Terms"), or other +via [call](https://enterprise.litellm.ai/demo) or email (info@berri.ai) (the "Enterprise Terms"), or other agreement governing the use of the Software, as agreed by you and BerriAI, and otherwise have a valid BerriAI Enterprise license for the correct number of user seats. Subject to the foregoing sentence, you are free to diff --git a/enterprise/README.md b/enterprise/README.md index 3b2ada6dd8..f5eb5078e8 100644 --- a/enterprise/README.md +++ b/enterprise/README.md @@ -4,6 +4,6 @@ Code in this folder is licensed under a commercial license. Please review the [L **These features are covered under the LiteLLM Enterprise contract** -👉 **Using in an Enterprise / Need specific features ?** Meet with us [here](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions?month=2024-02) +👉 **Using in an Enterprise / Need specific features ?** Meet with us [here](https://enterprise.litellm.ai/demo?month=2024-02) See all Enterprise Features here 👉 [Docs](https://docs.litellm.ai/docs/proxy/enterprise) diff --git a/enterprise/litellm_enterprise/enterprise_callbacks/pagerduty/pagerduty.py b/enterprise/litellm_enterprise/enterprise_callbacks/pagerduty/pagerduty.py index b6c9104b23..de02a0c4da 100644 --- a/enterprise/litellm_enterprise/enterprise_callbacks/pagerduty/pagerduty.py +++ b/enterprise/litellm_enterprise/enterprise_callbacks/pagerduty/pagerduty.py @@ -116,6 +116,7 @@ class PagerDutyAlerting(SlackAlerting): 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_project_alias=_meta.get("user_api_key_project_alias"), 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"), @@ -197,6 +198,7 @@ class PagerDutyAlerting(SlackAlerting): 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_project_alias=user_api_key_dict.project_alias, 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, diff --git a/enterprise/litellm_enterprise/enterprise_callbacks/send_emails/base_email.py b/enterprise/litellm_enterprise/enterprise_callbacks/send_emails/base_email.py index 2f2e444850..7a77898b16 100644 --- a/enterprise/litellm_enterprise/enterprise_callbacks/send_emails/base_email.py +++ b/enterprise/litellm_enterprise/enterprise_callbacks/send_emails/base_email.py @@ -632,7 +632,7 @@ class BaseEmailLogger(CustomLogger): warning_msg = ( f"Email sent with default values instead of custom values for: {fields_str}. " "This is an Enterprise feature. To use custom email fields, please upgrade to LiteLLM Enterprise. " - "Schedule a meeting here: https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions" + "Schedule a meeting here: https://enterprise.litellm.ai/demo" ) verbose_proxy_logger.warning(f"{warning_msg}") 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 cbe8d449b4..356f6ecd4b 100644 --- a/enterprise/litellm_enterprise/proxy/common_utils/check_batch_cost.py +++ b/enterprise/litellm_enterprise/proxy/common_utils/check_batch_cost.py @@ -3,7 +3,7 @@ Polls LiteLLM_ManagedObjectTable to check if the batch job is complete, and if t """ from datetime import datetime, timedelta, timezone -from typing import TYPE_CHECKING, Optional +from typing import TYPE_CHECKING, List, Optional, Tuple from litellm._logging import verbose_proxy_logger from litellm._uuid import uuid @@ -118,6 +118,15 @@ class CheckBatchCost: get_model_id_from_unified_batch_id, ) + try: + from litellm.integrations.prometheus import PrometheusLogger + prom_logger = PrometheusLogger.get_instance() + except Exception as e: + verbose_proxy_logger.error(f"CheckBatchCost: could not get Prometheus logger: {e}") + prom_logger = None + + processed_models: List[Tuple[Optional[str], Optional[str]]] = [] + try: await self._cleanup_stale_managed_objects() except Exception as cleanup_err: @@ -172,6 +181,8 @@ class CheckBatchCost: verbose_proxy_logger.info( f"Skipping job {unified_object_id} because it is not a valid unified object id" ) + if prom_logger: + prom_logger.record_check_batch_cost_error("invalid_unified_id") continue else: unified_object_id = decoded_unified_object_id @@ -183,6 +194,8 @@ class CheckBatchCost: verbose_proxy_logger.info( f"Skipping job {unified_object_id} because it is not a valid model id" ) + if prom_logger: + prom_logger.record_check_batch_cost_error("invalid_model_id") continue verbose_proxy_logger.info( @@ -202,6 +215,8 @@ class CheckBatchCost: verbose_proxy_logger.info( f"Skipping job {unified_object_id} because of error querying model ID: {model_id} for cost and usage of batch ID: {batch_id}: {e}" ) + if prom_logger: + prom_logger.record_check_batch_cost_error("provider_retrieval_error") continue ## RETRIEVE THE BATCH JOB OUTPUT FILE @@ -257,11 +272,25 @@ class CheckBatchCost: content_bytes # type: ignore[arg-type] ) + # Record output file size + if prom_logger and content_bytes: + try: + prom_logger.record_managed_file_size( + size_bytes=len(content_bytes), # type: ignore + purpose="batch", + file_type="output", + model=model_id, + ) + except Exception: + pass + deployment_info = self.llm_router.get_deployment(model_id=model_id) if deployment_info is None: verbose_proxy_logger.info( f"Skipping job {unified_object_id} because it is not a valid deployment info" ) + if prom_logger: + prom_logger.record_check_batch_cost_error("deployment_not_found") continue custom_llm_provider = deployment_info.litellm_params.custom_llm_provider litellm_model_name = deployment_info.litellm_params.model @@ -318,6 +347,19 @@ class CheckBatchCost: batch_models=batch_models, ) + # Record batch duration (completed_at - created_at) + if prom_logger and response.completed_at and response.created_at: + duration_seconds = float(response.completed_at - response.created_at) + if duration_seconds >= 0: + prom_logger.record_managed_batch_duration( + duration_seconds=duration_seconds, + model=model_name, + api_provider=str(llm_provider) if llm_provider else None, + ) + + # Track this job for the final metrics summary + processed_models.append((model_name, str(llm_provider) if llm_provider else None)) + # mark the job as complete try: update_data: dict = { @@ -334,3 +376,10 @@ class CheckBatchCost: verbose_proxy_logger.error( f"CheckBatchCost: failed to mark job {job.id} complete in DB: {db_err}" ) + + # Record polling run metrics (always, even if nothing was processed) + if prom_logger: + prom_logger.record_check_batch_cost_run( + jobs_polled=len(jobs), + processed_models=processed_models if processed_models else None, + ) diff --git a/enterprise/litellm_enterprise/proxy/hooks/managed_files.py b/enterprise/litellm_enterprise/proxy/hooks/managed_files.py index 5530054170..60c564072a 100644 --- a/enterprise/litellm_enterprise/proxy/hooks/managed_files.py +++ b/enterprise/litellm_enterprise/proxy/hooks/managed_files.py @@ -29,7 +29,7 @@ from litellm.proxy.openai_files_endpoints.common_utils import ( get_models_from_unified_file_id, normalize_mime_type_for_provider, ) -from litellm.types.llms.openai import ( +from litellm.types.llms.openai import ( # pyright: ignore[reportAttributeAccessIssue] AllMessageValues, AsyncCursorPage, ChatCompletionFileObject, @@ -74,6 +74,13 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): self.internal_usage_cache = internal_usage_cache self.prisma_client = prisma_client + @staticmethod + def _get_prometheus_logger(): + """Find PrometheusLogger from litellm.callbacks, if registered.""" + from litellm.integrations.prometheus import PrometheusLogger + + return PrometheusLogger.get_instance() + async def store_unified_file_id( self, file_id: str, @@ -442,25 +449,33 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): elif call_type == CallTypes.aresponses.value or call_type == CallTypes.responses.value: # Handle managed files in responses API input and tools file_ids = [] - + # Extract file IDs from input parameter input_data = data.get("input") if input_data: file_ids.extend(self.get_file_ids_from_responses_input(input_data)) - + # Extract file IDs from tools parameter (e.g., code_interpreter container) tools = data.get("tools") if tools: file_ids.extend(self.get_file_ids_from_responses_tools(tools)) - + if file_ids: # Check user has access to all managed files await self.check_file_ids_access(file_ids, user_api_key_dict) - + model_file_id_mapping = await self.get_model_file_id_mapping( file_ids, user_api_key_dict.parent_otel_span ) data["model_file_id_mapping"] = model_file_id_mapping + + # Check access for file_search vector_store_ids + if tools: + unified_vs_ids = self.get_vector_store_ids_from_file_search_tools(tools) + if unified_vs_ids: + await self.check_vector_store_ids_access( + unified_vs_ids, user_api_key_dict + ) elif call_type == CallTypes.afile_content.value: retrieve_file_id = cast(Optional[str], data.get("file_id")) potential_file_id = ( @@ -704,6 +719,101 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): return file_ids + def get_vector_store_ids_from_file_search_tools( + self, tools: List[Dict[str, Any]] + ) -> List[str]: + """ + Extract unified vector_store_ids from file_search tools. + + Only returns IDs that are LiteLLM-managed (base64 unified IDs). + Native provider IDs are skipped — they have no LiteLLM access record. + """ + from litellm.llms.base_llm.managed_resources.utils import ( + is_base64_encoded_unified_id, + ) + + vs_ids: List[str] = [] + if not isinstance(tools, list): + return vs_ids + + for tool in tools: + if not isinstance(tool, dict) or tool.get("type") != "file_search": + continue + vector_store_ids = tool.get("vector_store_ids") + if not isinstance(vector_store_ids, list): + continue + for vs_id in vector_store_ids: + if isinstance(vs_id, str) and is_base64_encoded_unified_id(vs_id): + vs_ids.append(vs_id) + + return vs_ids + + async def check_vector_store_ids_access( + self, + vector_store_ids: List[str], + user_api_key_dict: UserAPIKeyAuth, + ) -> None: + """ + Verify the caller's team can access each LiteLLM-managed vector store. + + Batch-fetches vector stores from DB and checks team_id. + Raises HTTPException(403) on the first access violation. + Non-managed (native) IDs should already be filtered out before calling this. + """ + from litellm.llms.base_llm.managed_resources.utils import ( + extract_unified_uuid_from_unified_id, + ) + from litellm.proxy.auth.auth_checks import ( + get_managed_vector_store_rows_by_uuids, + ) + from litellm.proxy.proxy_server import ( + prisma_client, + proxy_logging_obj, + user_api_key_cache, + ) + + if not vector_store_ids or prisma_client is None: + return + + # Map each unified ID to its internal UUID for a single batch DB fetch + uuid_to_unified: Dict[str, str] = {} + for vs_id in vector_store_ids: + uuid = extract_unified_uuid_from_unified_id(vs_id) + if uuid: + uuid_to_unified[uuid] = vs_id + + if not uuid_to_unified: + return + + rows = await get_managed_vector_store_rows_by_uuids( + uuids=list(uuid_to_unified.keys()), + prisma_client=prisma_client, + user_api_key_cache=user_api_key_cache, + proxy_logging_obj=proxy_logging_obj, + ) + + found_uuids = {row.vector_store_id for row in rows} + + for uuid, original_id in uuid_to_unified.items(): + if uuid not in found_uuids: + raise HTTPException( + status_code=403, + detail=f"Vector store '{original_id}' not found or access denied.", + ) + + caller_team_id = user_api_key_dict.team_id + for row in rows: + vs_team_id = getattr(row, "team_id", None) + if vs_team_id is not None and vs_team_id != caller_team_id: + raise HTTPException( + status_code=403, + detail=( + f"Team '{caller_team_id}' does not have access to vector " + f"store '{row.vector_store_id}'. The store belongs to team " + f"'{vs_team_id}'." + ), + ) + async def get_model_file_id_mapping( self, file_ids: List[str], litellm_parent_otel_span: Span ) -> dict: @@ -802,6 +912,31 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): model_mappings=model_mappings, user_api_key_dict=user_api_key_dict, ) + + # Emit Prometheus metrics for managed file creation + prom_logger = self._get_prometheus_logger() + if prom_logger: + first_model = target_model_names_list[0] if target_model_names_list else None + first_provider = "" + if responses: + first_provider = getattr(responses[0], "_hidden_params", {}).get("custom_llm_provider") or "" + prom_logger.record_managed_file_created( + model=first_model or "", + api_provider=first_provider, + user=user_api_key_dict.user_id or "", + user_email=getattr(user_api_key_dict, "user_email", None) or "", + api_key_alias=user_api_key_dict.key_alias or "", + ) + if response.bytes and response.bytes > 0: + prom_logger.record_managed_file_size( + size_bytes=response.bytes, + purpose=response.purpose or "batch", + file_type="input", + model=first_model, + api_provider=first_provider, + user=user_api_key_dict.user_id, + ) + return response @staticmethod @@ -954,7 +1089,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): ) else: file_object = await litellm.afile_retrieve( - custom_llm_provider=model_name.split("/")[0] if model_name and "/" in model_name else "openai", + custom_llm_provider=model_name.split("/")[0] if model_name and "/" in model_name else "openai", # type: ignore[arg-type] file_id=original_file_id, ) verbose_logger.debug( @@ -980,6 +1115,31 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): file_purpose="batch", user_api_key_dict=user_api_key_dict, ) + + # Only record batch creation metric on actual create (not retrieve/cancel). + # unified_file_id in _hidden_params is only set by the create_batch endpoint. + original_unified_file_id = response._hidden_params.get("unified_file_id") + if original_unified_file_id: + prom_logger = self._get_prometheus_logger() + if prom_logger: + batch_provider = "" + if model_name: + try: + from litellm.litellm_core_utils.get_llm_provider_logic import ( + get_llm_provider, + ) + _, batch_provider, _, _ = get_llm_provider(model=model_name) + except Exception: + if "/" in model_name: + batch_provider = model_name.split("/")[0] + prom_logger.record_managed_batch_created( + model=model_name or "", + api_provider=batch_provider, + user=user_api_key_dict.user_id or "", + user_email=getattr(user_api_key_dict, "user_email", None) or "", + api_key_alias=user_api_key_dict.key_alias or "", + ) + elif isinstance(response, LiteLLMFineTuningJob): ## Check if unified_file_id is in the response unified_file_id = response._hidden_params.get( @@ -1229,6 +1389,11 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): f"Alternatively, wait for all batches to complete and for cost to be computed (batch_processed=true)." ) + # Record blocked deletion metric + prom_logger = self._get_prometheus_logger() + if prom_logger: + prom_logger.record_managed_file_deleted(result="blocked") + raise HTTPException( status_code=400, detail=error_message, @@ -1262,6 +1427,12 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): file_id, litellm_parent_otel_span ) + # Record successful deletion metric only on actual success + if stored_file_object or delete_response: + prom_logger = self._get_prometheus_logger() + if prom_logger: + prom_logger.record_managed_file_deleted(result="success") + if stored_file_object: return stored_file_object elif delete_response: diff --git a/enterprise/pyproject.toml b/enterprise/pyproject.toml index 515885944f..9aa0a412ed 100644 --- a/enterprise/pyproject.toml +++ b/enterprise/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "litellm-enterprise" -version = "0.1.34" +version = "0.1.35" 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 = "0.1.33" +version = "0.1.35" version_files = [ "pyproject.toml:version", "../requirements.txt:litellm-enterprise==", diff --git a/litellm-js/spend-logs/package.json b/litellm-js/spend-logs/package.json index a40b0fc2a8..2ac81ee489 100644 --- a/litellm-js/spend-logs/package.json +++ b/litellm-js/spend-logs/package.json @@ -22,7 +22,7 @@ "tar-fs": ">=2.1.4", "webpack-dev-middleware": ">=5.3.4", "braces": ">=3.0.3", - "axios": ">=0.30.2", + "axios": "1.13.6", "webpack": ">=5.94.0", "serve-static": ">=1.16.0", "path-to-regexp": ">=0.1.12" diff --git a/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.60-py3-none-any.whl b/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.60-py3-none-any.whl new file mode 100644 index 0000000000..58b9039815 Binary files /dev/null and b/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.60-py3-none-any.whl differ diff --git a/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.60.tar.gz b/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.60.tar.gz new file mode 100644 index 0000000000..390a849e66 Binary files /dev/null and b/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.60.tar.gz differ diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20260311180521_schema_sync/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260311180521_schema_sync/migration.sql deleted file mode 100644 index 84eb70ce09..0000000000 --- a/litellm-proxy-extras/litellm_proxy_extras/migrations/20260311180521_schema_sync/migration.sql +++ /dev/null @@ -1,11 +0,0 @@ --- DropIndex -DROP INDEX IF EXISTS "LiteLLM_MCPServerTable_approval_status_idx"; - --- AlterTable -ALTER TABLE "LiteLLM_MCPServerTable" DROP COLUMN IF EXISTS "approval_status", -DROP COLUMN IF EXISTS "review_notes", -DROP COLUMN IF EXISTS "reviewed_at", -DROP COLUMN IF EXISTS "source_url", -DROP COLUMN IF EXISTS "submitted_at", -DROP COLUMN IF EXISTS "submitted_by"; - diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20260318140652_add_index_to_team_table/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260318140652_add_index_to_team_table/migration.sql index 494aaf6238..89121d636f 100644 --- a/litellm-proxy-extras/litellm_proxy_extras/migrations/20260318140652_add_index_to_team_table/migration.sql +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260318140652_add_index_to_team_table/migration.sql @@ -1,9 +1,9 @@ -- CreateIndex -CREATE INDEX "LiteLLM_TeamTable_organization_id_idx" ON "LiteLLM_TeamTable"("organization_id"); +CREATE INDEX IF NOT EXISTS "LiteLLM_TeamTable_organization_id_idx" ON "LiteLLM_TeamTable"("organization_id"); -- CreateIndex -CREATE INDEX "LiteLLM_TeamTable_team_alias_idx" ON "LiteLLM_TeamTable"("team_alias"); +CREATE INDEX IF NOT EXISTS "LiteLLM_TeamTable_team_alias_idx" ON "LiteLLM_TeamTable"("team_alias"); -- CreateIndex -CREATE INDEX "LiteLLM_TeamTable_created_at_idx" ON "LiteLLM_TeamTable"("created_at"); +CREATE INDEX IF NOT EXISTS "LiteLLM_TeamTable_created_at_idx" ON "LiteLLM_TeamTable"("created_at"); diff --git a/litellm-proxy-extras/pyproject.toml b/litellm-proxy-extras/pyproject.toml index 8aa9e70e43..55c964557f 100644 --- a/litellm-proxy-extras/pyproject.toml +++ b/litellm-proxy-extras/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "litellm-proxy-extras" -version = "0.4.58" +version = "0.4.62" 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 = "0.4.58" +version = "0.4.62" version_files = [ "pyproject.toml:version", "../requirements.txt:litellm-proxy-extras==", diff --git a/litellm/__init__.py b/litellm/__init__.py index 7f72e0b0e8..e45d926e8d 100644 --- a/litellm/__init__.py +++ b/litellm/__init__.py @@ -97,6 +97,7 @@ input_callback: List[CALLBACK_TYPES] = [] success_callback: List[CALLBACK_TYPES] = [] failure_callback: List[CALLBACK_TYPES] = [] service_callback: List[CALLBACK_TYPES] = [] +audit_log_callbacks: List[CALLBACK_TYPES] = [] # logging_callback_manager is lazy-loaded via __getattr__ _custom_logger_compatible_callbacks_literal = Literal[ "lago", diff --git a/litellm/_logging.py b/litellm/_logging.py index 18c3bcb7e8..65e6045b0b 100644 --- a/litellm/_logging.py +++ b/litellm/_logging.py @@ -52,6 +52,17 @@ def _build_secret_patterns() -> re.Pattern: r"(?<=://)[^\s'\"]*:[^\s'\"@]+(?=@)", # Databricks personal access tokens r"dapi[0-9a-f]{32}", + # ── Key-name-based redaction ── + # Catches secrets inside dicts/config dumps by matching on the KEY name + # regardless of what the value looks like. + # e.g. 'master_key': 'any-value-here', "database_url": "postgres://..." + r"(?:master_key|database_url|db_url|connection_string|" + r"private_key|signing_key|encryption_key|" + r"auth_token|access_token|refresh_token|" + r"slack_webhook_url|webhook_url|" + r"database_connection_string|" + r"huggingface_token|jwt_secret)" + r"""['\"]?\s*[:=]\s*['\"]?[^\s,'\"})\]{}>]+""", ] return re.compile("|".join(patterns), re.IGNORECASE) @@ -272,7 +283,7 @@ verbose_proxy_logger = logging.getLogger("LiteLLM Proxy") verbose_router_logger = logging.getLogger("LiteLLM Router") verbose_logger = logging.getLogger("LiteLLM") -# Add the handler to the logger +# Add the handler to the loggers verbose_router_logger.addHandler(handler) verbose_proxy_logger.addHandler(handler) verbose_logger.addHandler(handler) diff --git a/litellm/_redis.py b/litellm/_redis.py index b754c1f433..2bf32d71b2 100644 --- a/litellm/_redis.py +++ b/litellm/_redis.py @@ -222,8 +222,12 @@ def _get_redis_client_logic(**env_overrides): "REDIS_CLUSTER_NODES" ) + # If startup_nodes resolved to None (not set by kwarg or env), remove the key + # entirely so callers can rely on key presence as a reliable cluster-mode signal. if _startup_nodes is not None and isinstance(_startup_nodes, str): redis_kwargs["startup_nodes"] = json.loads(_startup_nodes) + elif _startup_nodes is None: + redis_kwargs.pop("startup_nodes", None) _sentinel_nodes: Optional[Union[str, list]] = redis_kwargs.get("sentinel_nodes", None) or get_secret( # type: ignore "REDIS_SENTINEL_NODES" @@ -273,10 +277,14 @@ def _get_redis_client_logic(**env_overrides): redis_kwargs["ssl_ca_certs"] = _gcp_ssl_ca_certs if "url" in redis_kwargs and redis_kwargs["url"] is not None: - redis_kwargs.pop("host", None) - redis_kwargs.pop("port", None) - redis_kwargs.pop("db", None) - redis_kwargs.pop("password", None) + # Only strip host/port/db/password when not routing to a cluster. + # When startup_nodes is also present the cluster path takes priority and + # needs the password for authentication. + if not redis_kwargs.get("startup_nodes"): + redis_kwargs.pop("host", None) + redis_kwargs.pop("port", None) + redis_kwargs.pop("db", None) + redis_kwargs.pop("password", None) elif "startup_nodes" in redis_kwargs and redis_kwargs["startup_nodes"] is not None: pass elif ( @@ -368,6 +376,10 @@ def _init_async_redis_sentinel(redis_kwargs) -> async_redis.Redis: def get_redis_client(**env_overrides): redis_kwargs = _get_redis_client_logic(**env_overrides) + + if "startup_nodes" in redis_kwargs: + return init_redis_cluster(redis_kwargs) + if "url" in redis_kwargs and redis_kwargs["url"] is not None: args = _get_redis_url_kwargs() url_kwargs = {} @@ -377,9 +389,6 @@ def get_redis_client(**env_overrides): return redis.Redis.from_url(**url_kwargs) - if "startup_nodes" in redis_kwargs or get_secret("REDIS_CLUSTER_NODES") is not None: # type: ignore - return init_redis_cluster(redis_kwargs) - # Check for Redis Sentinel if "sentinel_nodes" in redis_kwargs and "service_name" in redis_kwargs: return _init_redis_sentinel(redis_kwargs) @@ -392,21 +401,6 @@ def get_redis_async_client( **env_overrides, ) -> 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: - if arg in args: - url_kwargs[arg] = redis_kwargs[arg] - else: - verbose_logger.debug( - "REDIS: ignoring argument: {}. Not an allowed async_redis.Redis.from_url arg.".format( - arg - ) - ) - return async_redis.Redis.from_url(**url_kwargs) if "startup_nodes" in redis_kwargs: from redis.cluster import ClusterNode @@ -469,6 +463,22 @@ def get_redis_async_client( return cluster_client + 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: + if arg in args: + url_kwargs[arg] = redis_kwargs[arg] + else: + verbose_logger.debug( + "REDIS: ignoring argument: {}. Not an allowed async_redis.Redis.from_url arg.".format( + arg + ) + ) + return async_redis.Redis.from_url(**url_kwargs) + # Check for Redis Sentinel if "sentinel_nodes" in redis_kwargs and "service_name" in redis_kwargs: return _init_async_redis_sentinel(redis_kwargs) @@ -482,9 +492,15 @@ def get_redis_async_client( ) -def get_redis_connection_pool(**env_overrides): +def get_redis_connection_pool( + **env_overrides, +) -> Optional[async_redis.BlockingConnectionPool]: redis_kwargs = _get_redis_client_logic(**env_overrides) verbose_logger.debug("get_redis_connection_pool: redis_kwargs", redis_kwargs) + + if "startup_nodes" in redis_kwargs: + return None + if "url" in redis_kwargs and redis_kwargs["url"] is not None: pool_kwargs = { "timeout": REDIS_CONNECTION_POOL_TIMEOUT, @@ -504,7 +520,6 @@ def get_redis_connection_pool(**env_overrides): connection_class = async_redis.SSLConnection redis_kwargs.pop("ssl", None) redis_kwargs["connection_class"] = connection_class - redis_kwargs.pop("startup_nodes", None) return async_redis.BlockingConnectionPool( timeout=REDIS_CONNECTION_POOL_TIMEOUT, **redis_kwargs ) diff --git a/litellm/batches/main.py b/litellm/batches/main.py index e176dc4292..23f444b1ce 100644 --- a/litellm/batches/main.py +++ b/litellm/batches/main.py @@ -524,6 +524,7 @@ def _handle_retrieve_batch_providers_without_provider_config( optional_params.api_base or litellm.api_base or get_secret_str("ANTHROPIC_API_BASE") + or get_secret_str("ANTHROPIC_BASE_URL") ) api_key = ( optional_params.api_key @@ -884,7 +885,7 @@ def list_batches( async def acancel_batch( batch_id: str, model: Optional[str] = None, - custom_llm_provider: Literal["openai", "azure"] = "openai", + custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai", metadata: Optional[Dict[str, str]] = None, extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, @@ -930,7 +931,7 @@ async def acancel_batch( def cancel_batch( batch_id: str, model: Optional[str] = None, - custom_llm_provider: Union[Literal["openai", "azure"], str] = "openai", + custom_llm_provider: Union[Literal["openai", "azure", "vertex_ai"], str] = "openai", metadata: Optional[Dict[str, str]] = None, extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, @@ -1047,9 +1048,35 @@ def cancel_batch( cancel_batch_data=_cancel_batch_request, litellm_params=litellm_params, ) + elif custom_llm_provider == "vertex_ai": + api_base = optional_params.api_base or None + vertex_ai_project = ( + optional_params.vertex_project + or litellm.vertex_project + or get_secret_str("VERTEXAI_PROJECT") + ) + vertex_ai_location = ( + optional_params.vertex_location + or litellm.vertex_location + or get_secret_str("VERTEXAI_LOCATION") + ) + vertex_credentials = optional_params.vertex_credentials or get_secret_str( + "VERTEXAI_CREDENTIALS" + ) + + response = vertex_ai_batches_instance.cancel_batch( + _is_async=_is_async, + batch_id=batch_id, + api_base=api_base, + vertex_project=vertex_ai_project, + vertex_location=vertex_ai_location, + vertex_credentials=vertex_credentials, + timeout=timeout, + max_retries=optional_params.max_retries, + ) else: raise litellm.exceptions.BadRequestError( - message="LiteLLM doesn't support {} for 'cancel_batch'. Only 'openai' and 'azure' are supported.".format( + message="LiteLLM doesn't support {} for 'cancel_batch'. Only 'openai', 'azure', and 'vertex_ai' are supported.".format( custom_llm_provider ), model="n/a", diff --git a/litellm/caching/dual_cache.py b/litellm/caching/dual_cache.py index 4020b8cc22..34ae3638a5 100644 --- a/litellm/caching/dual_cache.py +++ b/litellm/caching/dual_cache.py @@ -393,16 +393,17 @@ class DualCache(BaseCache): parent_otel_span: Optional[Span] = None, local_only: bool = False, **kwargs, - ) -> float: + ) -> Optional[float]: """ Key - the key in cache Value - float - the value you want to increment by - Returns - float - the incremented value + Returns - the incremented value, or None if no cache backend is + available (in_memory_cache is None and Redis failed/is absent). """ + result: Optional[float] = None try: - result: float = value if self.in_memory_cache is not None: result = await self.in_memory_cache.async_increment( key, value, **kwargs @@ -418,7 +419,11 @@ class DualCache(BaseCache): return result except Exception as e: - raise e # don't log if exception is raised + verbose_logger.warning( + "Redis async_increment_cache failed, falling back to in-memory result: %s", + e, + ) + return result async def async_increment_cache_pipeline( self, @@ -427,8 +432,8 @@ class DualCache(BaseCache): parent_otel_span: Optional[Span] = None, **kwargs, ) -> Optional[List[float]]: + result: Optional[List[float]] = None try: - result: Optional[List[float]] = None if self.in_memory_cache is not None: result = await self.in_memory_cache.async_increment_pipeline( increment_list=increment_list, @@ -443,7 +448,11 @@ class DualCache(BaseCache): return result except Exception as e: - raise e # don't log if exception is raised + verbose_logger.warning( + "Redis async_increment_cache_pipeline failed, falling back to in-memory result: %s", + e, + ) + return result async def async_set_cache_sadd( self, key, value: List, local_only: bool = False, **kwargs diff --git a/litellm/caching/redis_cache.py b/litellm/caching/redis_cache.py index 82794c116f..84a2887f52 100644 --- a/litellm/caching/redis_cache.py +++ b/litellm/caching/redis_cache.py @@ -10,6 +10,7 @@ Has 4 primary methods: import ast import asyncio +import functools import hashlib import inspect import json @@ -19,7 +20,11 @@ from typing import TYPE_CHECKING, Any, List, Optional, Tuple, Union, cast import litellm from litellm._logging import print_verbose, verbose_logger -from litellm.constants import DEFAULT_REDIS_MAJOR_VERSION +from litellm.constants import ( + DEFAULT_REDIS_MAJOR_VERSION, + REDIS_CIRCUIT_BREAKER_FAILURE_THRESHOLD, + REDIS_CIRCUIT_BREAKER_RECOVERY_TIMEOUT, +) from litellm.litellm_core_utils.core_helpers import _get_parent_otel_span_from_kwargs from litellm.litellm_core_utils.coroutine_checker import coroutine_checker from litellm.types.caching import ( @@ -89,6 +94,91 @@ def _get_call_stack_info(num_frames: int = 2) -> str: return "unknown" +class RedisCircuitBreaker: + """ + Tracks Redis health for a RedisCache instance. + + States: + CLOSED - normal, Redis is called + OPEN - Redis is down, raise immediately (no network call) + HALF_OPEN - recovery probe: allow one request through + + Transitions: + CLOSED -> OPEN after failure_threshold consecutive failures + OPEN -> HALF_OPEN after recovery_timeout seconds + HALF_OPEN -> CLOSED on success + HALF_OPEN -> OPEN on failure (resets timer) + """ + + CLOSED = "closed" + OPEN = "open" + HALF_OPEN = "half_open" + + def __init__(self, failure_threshold: int, recovery_timeout: int) -> None: + self.failure_threshold = failure_threshold + self.recovery_timeout = recovery_timeout + self._failure_count = 0 + self._opened_at: Optional[float] = None + self._state = self.CLOSED + + def is_open(self) -> bool: + """Returns True if Redis calls should be skipped.""" + if self._state == self.HALF_OPEN: + # Probe already in flight — fast-fail all concurrent requests. + # Only the one call that caused the OPEN→HALF_OPEN transition + # (which returned False) is the designated probe. + return True + if self._state == self.OPEN: + if time.time() - (self._opened_at or 0) > self.recovery_timeout: + self._state = self.HALF_OPEN + return False # this caller is the designated probe + return True + return False + + def record_failure(self) -> None: + self._failure_count += 1 + self._opened_at = time.time() + if self._failure_count >= self.failure_threshold: + if self._state != self.OPEN: + verbose_logger.warning( + "Redis circuit breaker OPENED after %d consecutive failures — " + "fast-failing Redis calls for %ds", + self._failure_count, + self.recovery_timeout, + ) + self._state = self.OPEN + + def record_success(self) -> None: + if self._state == self.HALF_OPEN: + verbose_logger.info("Redis circuit breaker CLOSED — Redis recovered") + self._failure_count = 0 + self._state = self.CLOSED + + +def _redis_circuit_breaker_guard(method): # type: ignore + """ + Decorator for RedisCache async methods. + Checks the circuit breaker before each call; records success/failure after. + Does not apply to ping/disconnect/test_connection (health/teardown must always run). + """ + + @functools.wraps(method) + async def wrapper(self, *args, **kwargs): # type: ignore + if self._circuit_breaker.is_open(): + raise Exception( + f"Redis circuit breaker is open — skipping {method.__name__}" + ) + try: + result = await method(self, *args, **kwargs) + self._circuit_breaker.record_success() + return result + except Exception: + self._circuit_breaker.record_failure() + raise + + return wrapper + + class RedisCache(BaseCache): # if users don't provider one, use the default litellm cache @@ -150,6 +240,11 @@ class RedisCache(BaseCache): except Exception: pass + self._circuit_breaker = RedisCircuitBreaker( + failure_threshold=REDIS_CIRCUIT_BREAKER_FAILURE_THRESHOLD, + recovery_timeout=REDIS_CIRCUIT_BREAKER_RECOVERY_TIMEOUT, + ) + self._setup_health_pings() if litellm.default_redis_ttl is not None: @@ -375,6 +470,7 @@ class RedisCache(BaseCache): ) raise e + @_redis_circuit_breaker_guard async def async_scan_iter(self, pattern: str, count: int = 100) -> list: start_time = time.time() try: @@ -451,6 +547,7 @@ class RedisCache(BaseCache): verbose_logger.error(f"Error registering Redis script: {str(e)}") raise e + @_redis_circuit_breaker_guard async def async_set_cache(self, key, value, **kwargs): from redis.asyncio import Redis @@ -560,6 +657,7 @@ class RedisCache(BaseCache): results = await pipe.execute() return results + @_redis_circuit_breaker_guard async def async_set_cache_pipeline( self, cache_list: List[Tuple[Any, Any]], ttl: Optional[float] = None, **kwargs ): @@ -636,6 +734,7 @@ class RedisCache(BaseCache): except Exception: raise + @_redis_circuit_breaker_guard async def async_set_cache_sadd( self, key, value: List, ttl: Optional[float], **kwargs ): @@ -708,6 +807,7 @@ class RedisCache(BaseCache): value, ) + @_redis_circuit_breaker_guard async def batch_cache_write(self, key, value, **kwargs): print_verbose( f"in batch cache writing for redis buffer size={len(self.redis_batch_writing_buffer)}", @@ -717,6 +817,7 @@ class RedisCache(BaseCache): if len(self.redis_batch_writing_buffer) >= self.redis_flush_size: await self.flush_cache_buffer() # logging done in here + @_redis_circuit_breaker_guard async def async_increment( self, key, @@ -894,6 +995,7 @@ class RedisCache(BaseCache): verbose_logger.error(f"Error occurred in batch get cache - {str(e)}") return key_value_dict + @_redis_circuit_breaker_guard async def async_get_cache( self, key, parent_otel_span: Optional[Span] = None, **kwargs ): @@ -944,6 +1046,7 @@ class RedisCache(BaseCache): f"litellm.caching.caching: async get() - Got exception from REDIS: {str(e)}" ) + @_redis_circuit_breaker_guard async def async_batch_get_cache( self, key_list: Union[List[str], List[Optional[str]]], @@ -1087,6 +1190,7 @@ class RedisCache(BaseCache): ) raise e + @_redis_circuit_breaker_guard async def delete_cache_keys(self, keys): # typed as Any, redis python lib has incomplete type stubs for RedisCluster and does not include `delete` _redis_client: Any = self.init_async_client() @@ -1151,6 +1255,7 @@ class RedisCache(BaseCache): "error": str(e), } + @_redis_circuit_breaker_guard async def async_delete_cache(self, key: str): # typed as Any, redis python lib has incomplete type stubs for RedisCluster and does not include `delete` _redis_client: Any = self.init_async_client() @@ -1184,6 +1289,7 @@ class RedisCache(BaseCache): ) return [r for r in results if isinstance(r, float)] + @_redis_circuit_breaker_guard async def async_increment_pipeline( self, increment_list: List[RedisPipelineIncrementOperation], **kwargs ) -> Optional[List[float]]: @@ -1247,6 +1353,7 @@ class RedisCache(BaseCache): ) raise e + @_redis_circuit_breaker_guard async def async_get_ttl(self, key: str) -> Optional[int]: """ Get the remaining TTL of a key in Redis @@ -1270,6 +1377,7 @@ class RedisCache(BaseCache): verbose_logger.debug(f"Redis TTL Error: {e}") return None + @_redis_circuit_breaker_guard async def async_rpush( self, key: str, @@ -1336,6 +1444,7 @@ class RedisCache(BaseCache): raise r return results + @_redis_circuit_breaker_guard async def async_rpush_pipeline( self, rpush_list: List[RedisPipelineRpushOperation], @@ -1405,6 +1514,7 @@ class RedisCache(BaseCache): return result + @_redis_circuit_breaker_guard async def async_lpop( self, key: str, @@ -1534,6 +1644,7 @@ class RedisCache(BaseCache): decoded_results.append(None) return decoded_results + @_redis_circuit_breaker_guard async def async_lpop_pipeline( self, lpop_list: List[RedisPipelineLpopOperation], diff --git a/litellm/completion_extras/litellm_responses_transformation/transformation.py b/litellm/completion_extras/litellm_responses_transformation/transformation.py index 53ffd3647b..ff1bc0d383 100644 --- a/litellm/completion_extras/litellm_responses_transformation/transformation.py +++ b/litellm/completion_extras/litellm_responses_transformation/transformation.py @@ -32,6 +32,7 @@ from litellm.llms.base_llm.bridges.completion_transformation import ( ) from litellm.types.llms.openai import ( ChatCompletionAnnotation, + ChatCompletionReasoningItem, ChatCompletionToolParamFunctionChunk, Reasoning, ResponsesAPIOptionalRequestParams, @@ -55,6 +56,61 @@ if TYPE_CHECKING: ) +def _get_reasoning_items( + msg: "AllMessageValues", +) -> List[ChatCompletionReasoningItem]: + """Extract reasoning_items from a message dict with proper typing.""" + items = msg.get("reasoning_items") # type: ignore[union-attr] + if items: + return items # type: ignore[return-value] + return [] + + +def _build_reasoning_item( + item_id: str, + encrypted_content: Optional[str], + summary_raw: Any, +) -> Dict[str, Any]: + """Build a ChatCompletionReasoningItem-shaped dict from raw response data. + + Handles both pydantic objects (attribute access) and plain dicts. + """ + summary: List[Dict[str, Any]] = [] + for s in summary_raw or []: + if isinstance(s, dict): + summary.append( + {"type": s.get("type", "summary_text"), "text": s.get("text", "")} + ) + else: + summary.append( + { + "type": getattr(s, "type", "summary_text"), + "text": getattr(s, "text", ""), + } + ) + return { + "id": item_id, + "type": "reasoning", + "encrypted_content": encrypted_content, + "summary": summary, + } + + +def _reasoning_item_to_response_input( + r_item: Union[ChatCompletionReasoningItem, Dict[str, Any]] +) -> Dict[str, Any]: + """Convert a stored ChatCompletionReasoningItem back to a Responses API input item.""" + r_input: Dict[str, Any] = { + "type": "reasoning", + "id": r_item.get("id") or f"rs_{id(r_item)}", + # summary is always required by the Responses API, even when empty + "summary": r_item.get("summary") or [], + } + if r_item.get("encrypted_content"): + r_input["encrypted_content"] = r_item["encrypted_content"] + return r_input + + class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): """ Handler for transforming /chat/completions api requests to litellm.responses requests @@ -202,10 +258,12 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): } ) elif role == "assistant" and tool_calls and isinstance(tool_calls, list): + for r_item in _get_reasoning_items(msg): + input_items.append(_reasoning_item_to_response_input(r_item)) for tool_call in tool_calls: function = tool_call.get("function") if function: - input_tool_call = { + input_tool_call: Dict[str, Any] = { "type": "function_call", "call_id": tool_call["id"], } @@ -217,7 +275,9 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): else: raise ValueError(f"tool call not supported: {tool_call}") elif content is not None: - # Regular user/assistant message + if role == "assistant": + for r_item in _get_reasoning_items(msg): + input_items.append(_reasoning_item_to_response_input(r_item)) input_items.append( { "type": "message", @@ -411,6 +471,7 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): choices: List[Choices] = [] index = 0 reasoning_content: Optional[str] = None + pending_reasoning_item: Optional[Dict[str, Any]] = None # Collect all tool calls to put them in a single choice # (Chat Completions API expects all tool calls in one message) @@ -419,9 +480,16 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): for item in output_items: if isinstance(item, ResponseReasoningItem): - for summary_item in item.summary: - response_text = getattr(summary_item, "text", "") - reasoning_content = response_text if response_text else "" + pending_reasoning_item = _build_reasoning_item( + item_id=item.id, + encrypted_content=getattr(item, "encrypted_content", None), + summary_raw=item.summary, + ) + reasoning_content = " ".join( + s["text"] + for s in pending_reasoning_item["summary"] + if s.get("text") + ) elif isinstance(item, ResponseOutputMessage): for content in item.content: @@ -436,6 +504,12 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): content=response_text if response_text else "", reasoning_content=reasoning_content, annotations=annotations, + reasoning_items=cast( + Optional[List[ChatCompletionReasoningItem]], + [pending_reasoning_item] + if pending_reasoning_item is not None + else None, + ), ) choices.append( @@ -446,7 +520,8 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): ) ) - reasoning_content = None # flush reasoning content + reasoning_content = None # flush + pending_reasoning_item = None # flush index += 1 elif isinstance(item, ResponseFunctionToolCall): @@ -489,11 +564,18 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): content=None, tool_calls=accumulated_tool_calls, reasoning_content=reasoning_content, + reasoning_items=cast( + Optional[List[ChatCompletionReasoningItem]], + [pending_reasoning_item] + if pending_reasoning_item is not None + else None, + ), ) choices.append( Choices(message=msg, finish_reason="tool_calls", index=index) ) reasoning_content = None + pending_reasoning_item = None return choices @@ -696,6 +778,20 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): verbose_logger.debug( f"Chat provider: image -> {converted}" ) + elif item_type == "file": + # Map Chat Completion file to Responses API input_file + # {"type": "file", "file": {"file_data": "...", "filename": "..."}} + # -> {"type": "input_file", "file_data": "...", "filename": "..."} + file_data = item.get("file", {}) + converted = {"type": "input_file"} + if isinstance(file_data, dict): + for key in ["file_id", "file_data", "filename"]: + if key in file_data: + converted[key] = file_data[key] + result.append(converted) + verbose_logger.debug( + f"Chat provider: file -> {converted}" + ) elif item_type in [ "input_text", "input_image", @@ -1218,6 +1314,25 @@ class OpenAiResponsesToChatCompletionStreamIterator(BaseModelResponseIterator): finish_reason = "tool_calls" if has_function_calls else "stop" + # Extract reasoning items with encrypted_content for round-tripping + completed_reasoning_items: Optional[List[Dict[str, Any]]] = None + for item in output_items: + if not isinstance(item, dict) or item.get("type") != "reasoning": + continue + if completed_reasoning_items is None: + completed_reasoning_items = [] + completed_reasoning_items.append( + _build_reasoning_item( + item_id=item.get("id", ""), + encrypted_content=item.get("encrypted_content"), + summary_raw=item.get("summary"), + ) + ) + completed_reasoning_items_typed = cast( + Optional[List[ChatCompletionReasoningItem]], + completed_reasoning_items, + ) + usage = None if response_data.get("usage"): from litellm.responses.utils import ResponseAPILoggingUtils @@ -1231,7 +1346,10 @@ class OpenAiResponsesToChatCompletionStreamIterator(BaseModelResponseIterator): choices=[ StreamingChoices( index=0, - delta=Delta(content=""), + delta=Delta( + content="", + reasoning_items=completed_reasoning_items_typed, + ), finish_reason=finish_reason, ) ], diff --git a/litellm/constants.py b/litellm/constants.py index 89c59ee932..252068bd7b 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -350,6 +350,12 @@ AZURE_DOCUMENT_INTELLIGENCE_DEFAULT_DPI = int( ) REDIS_SOCKET_TIMEOUT = float(os.getenv("REDIS_SOCKET_TIMEOUT", 0.1)) REDIS_CONNECTION_POOL_TIMEOUT = int(os.getenv("REDIS_CONNECTION_POOL_TIMEOUT", 5)) +REDIS_CIRCUIT_BREAKER_FAILURE_THRESHOLD = int( + os.getenv("REDIS_CIRCUIT_BREAKER_FAILURE_THRESHOLD", 5) +) +REDIS_CIRCUIT_BREAKER_RECOVERY_TIMEOUT = int( + os.getenv("REDIS_CIRCUIT_BREAKER_RECOVERY_TIMEOUT", 60) +) # Default Redis major version to assume when version cannot be determined # Using 7 as it's the modern version that supports LPOP with count parameter DEFAULT_REDIS_MAJOR_VERSION = int(os.getenv("DEFAULT_REDIS_MAJOR_VERSION", 7)) @@ -1396,6 +1402,9 @@ DEFAULT_SHARED_HEALTH_CHECK_TTL = int( DEFAULT_SHARED_HEALTH_CHECK_LOCK_TTL = int( os.getenv("DEFAULT_SHARED_HEALTH_CHECK_LOCK_TTL", 60) ) # 1 minute - TTL for health check lock +DEFAULT_HEALTH_CHECK_STALENESS_MULTIPLIER = ( + 2 # health state is stale after interval * this +) PROMETHEUS_FALLBACK_STATS_SEND_TIME_HOURS = int( os.getenv("PROMETHEUS_FALLBACK_STATS_SEND_TIME_HOURS", 9) ) @@ -1444,6 +1453,7 @@ SENTRY_DENYLIST = [ "credential", "OPENAI_API_KEY", "ANTHROPIC_API_KEY", + "ANTHROPIC_AUTH_TOKEN", "AZURE_API_KEY", "COHERE_API_KEY", "REPLICATE_API_KEY", diff --git a/litellm/cost_calculator.py b/litellm/cost_calculator.py index ee3c344169..29d28b8c89 100644 --- a/litellm/cost_calculator.py +++ b/litellm/cost_calculator.py @@ -757,7 +757,7 @@ def _map_traffic_type_to_service_tier(traffic_type: Optional[str]) -> Optional[s """ if traffic_type is None: return None - service_tier = _GEMINI_TRAFFIC_TYPE_TO_SERVICE_TIER.get(traffic_type.upper()) + service_tier = _GEMINI_TRAFFIC_TYPE_TO_SERVICE_TIER.get(str(traffic_type).upper()) return service_tier diff --git a/litellm/integrations/anthropic_cache_control_hook.py b/litellm/integrations/anthropic_cache_control_hook.py index 8e4d40c460..0e99537d5d 100644 --- a/litellm/integrations/anthropic_cache_control_hook.py +++ b/litellm/integrations/anthropic_cache_control_hook.py @@ -60,13 +60,20 @@ class AnthropicCacheControlHook(CustomPromptManagement): # Create a deep copy of messages to avoid modifying the original list processed_messages = copy.deepcopy(messages) - # Process message-level cache controls + # Separate message-level and non-message-level injection points + remaining_points = [] for point in injection_points: if point.get("location") == "message": point = cast(CacheControlMessageInjectionPoint, point) processed_messages = self._process_message_injection( point=point, messages=processed_messages ) + else: + remaining_points.append(point) + + # Pass through non-message injection points for provider-specific handling + if remaining_points: + non_default_params["cache_control_injection_points"] = remaining_points return model, processed_messages, non_default_params diff --git a/litellm/integrations/custom_logger.py b/litellm/integrations/custom_logger.py index 06ba9675ca..cccabf53e5 100644 --- a/litellm/integrations/custom_logger.py +++ b/litellm/integrations/custom_logger.py @@ -27,6 +27,7 @@ from litellm.types.utils import ( LLMResponseTypes, ModelResponse, ModelResponseStream, + StandardAuditLogPayload, StandardCallbackDynamicParams, StandardLoggingPayload, ) @@ -177,6 +178,10 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time): pass + async def async_log_audit_log_event(self, audit_log: "StandardAuditLogPayload"): + """Called when an audit log is created. Override in subclasses to handle.""" + pass + #### PROMPT MANAGEMENT HOOKS #### async def async_get_chat_completion_prompt( diff --git a/litellm/integrations/datadog/datadog.py b/litellm/integrations/datadog/datadog.py index 64e0b26a8e..4de3644b58 100644 --- a/litellm/integrations/datadog/datadog.py +++ b/litellm/integrations/datadog/datadog.py @@ -291,7 +291,7 @@ class DataDogLogger( dd_payload = DatadogPayload( ddsource=get_datadog_source(), - ddtags=get_datadog_tags(), + ddtags=",".join(get_datadog_tags()), hostname=get_datadog_hostname(), message=safe_dumps(message_payload), service=get_datadog_service(), @@ -442,7 +442,9 @@ class DataDogLogger( verbose_logger.debug("Datadog: Logger - Logging payload = %s", json_payload) dd_payload = DatadogPayload( ddsource=get_datadog_source(), - ddtags=get_datadog_tags(standard_logging_object=standard_logging_object), + ddtags=",".join( + get_datadog_tags(standard_logging_object=standard_logging_object) + ), hostname=get_datadog_hostname(), message=json_payload, service=get_datadog_service(), @@ -545,7 +547,7 @@ class DataDogLogger( _dd_message_str = safe_dumps(_payload_dict) _dd_payload = DatadogPayload( ddsource=get_datadog_source(), - ddtags=get_datadog_tags(), + ddtags=",".join(get_datadog_tags()), hostname=get_datadog_hostname(), message=_dd_message_str, service=get_datadog_service(), @@ -587,7 +589,7 @@ class DataDogLogger( _dd_message_str = safe_dumps(_payload_dict) _dd_payload = DatadogPayload( ddsource=get_datadog_source(), - ddtags=get_datadog_tags(), + ddtags=",".join(get_datadog_tags()), hostname=get_datadog_hostname(), message=_dd_message_str, service=get_datadog_service(), @@ -678,7 +680,7 @@ class DataDogLogger( dd_payload = DatadogPayload( ddsource=get_datadog_source(), - ddtags=get_datadog_tags(), + ddtags=",".join(get_datadog_tags()), hostname=get_datadog_hostname(), message=json_payload, service=get_datadog_service(), diff --git a/litellm/integrations/datadog/datadog_handler.py b/litellm/integrations/datadog/datadog_handler.py index 0406f1e5d2..b6bb2b5703 100644 --- a/litellm/integrations/datadog/datadog_handler.py +++ b/litellm/integrations/datadog/datadog_handler.py @@ -38,8 +38,13 @@ def get_datadog_pod_name() -> str: def get_datadog_tags( standard_logging_object: Optional[StandardLoggingPayload] = None, -) -> str: - """Build Datadog tags string used by multiple integrations.""" +) -> List[str]: + """Build Datadog tags as a list of individual tag strings. + + Returns a list of "key:value" strings suitable for Datadog LLM Observability + (which expects tags as an array). For Datadog Logs API (ddtags), join with + comma: ",".join(get_datadog_tags(...)). + """ base_tags = { "env": get_datadog_env(), @@ -66,4 +71,4 @@ def get_datadog_tags( if team_tag: tags.append(f"team:{team_tag}") - return ",".join(tags) + return tags diff --git a/litellm/integrations/datadog/datadog_llm_obs.py b/litellm/integrations/datadog/datadog_llm_obs.py index de6cc02fa3..ec6c00961b 100644 --- a/litellm/integrations/datadog/datadog_llm_obs.py +++ b/litellm/integrations/datadog/datadog_llm_obs.py @@ -203,7 +203,7 @@ class DataDogLLMObsLogger(CustomBatchLogger): type="span", attributes=DDSpanAttributes( ml_app=get_datadog_service(), - tags=[get_datadog_tags()], + tags=get_datadog_tags(), spans=self.log_queue, ), ), @@ -315,7 +315,7 @@ class DataDogLLMObsLogger(CustomBatchLogger): duration=int((end_time - start_time).total_seconds() * 1e9), metrics=metrics, status="error" if error_info else "ok", - tags=[get_datadog_tags(standard_logging_object=standard_logging_payload)], + tags=get_datadog_tags(standard_logging_object=standard_logging_payload), ) apm_trace_id = self._get_apm_trace_id() diff --git a/litellm/integrations/langsmith.py b/litellm/integrations/langsmith.py index 03845af521..b931d7ecfe 100644 --- a/litellm/integrations/langsmith.py +++ b/litellm/integrations/langsmith.py @@ -5,7 +5,6 @@ import os import random import traceback import types -from litellm._uuid import uuid from datetime import datetime, timezone from typing import Any, Dict, List, Optional @@ -14,10 +13,11 @@ from pydantic import BaseModel # type: ignore import litellm from litellm._logging import verbose_logger +from litellm._uuid import uuid from litellm.integrations.custom_batch_logger import CustomBatchLogger from litellm.integrations.langsmith_mock_client import ( - should_use_langsmith_mock, create_mock_langsmith_client, + should_use_langsmith_mock, ) from litellm.llms.custom_httpx.http_handler import ( get_async_httpx_client, @@ -83,7 +83,28 @@ class LangsmithLogger(CustomBatchLogger): if _batch_size: self.batch_size = int(_batch_size) self.log_queue: List[LangsmithQueueObject] = [] - asyncio.create_task(self.periodic_flush()) + self._flush_task: Optional[ + asyncio.Task[Any] + ] = self._start_periodic_flush_task() + + def _start_periodic_flush_task(self) -> Optional[asyncio.Task[Any]]: + """Start the periodic flush task only when an event loop is already running.""" + try: + loop = asyncio.get_running_loop() + except RuntimeError: + verbose_logger.debug( + "Langsmith logger init: no running event loop, skipping periodic flush task startup" + ) + return None + + return loop.create_task(self.periodic_flush()) + + def _ensure_periodic_flush_task(self) -> None: + # This helper is intentionally synchronous. In asyncio's cooperative + # execution model, there is no await between the check and assignment, + # so one caller cannot interleave here and create a duplicate task. + if self._flush_task is None or self._flush_task.done(): + self._flush_task = self._start_periodic_flush_task() def get_credentials_from_env( self, @@ -110,6 +131,60 @@ class LangsmithLogger(CustomBatchLogger): LANGSMITH_TENANT_ID=_credentials_tenant_id, ) + def _extract_metadata_fields( + self, metadata: dict, credentials: LangsmithCredentialsObject + ): + return { + "project_name": metadata.get( + "project_name", credentials["LANGSMITH_PROJECT"] + ), + "run_name": metadata.get("run_name", self.langsmith_default_run_name), + "run_id": metadata.get("id", metadata.get("run_id", None)), + "parent_run_id": metadata.get("parent_run_id", None), + "trace_id": metadata.get("trace_id", None), + "session_id": metadata.get("session_id", None), + "dotted_order": metadata.get("dotted_order", None), + } + + def _build_extra_metadata(self, metadata: Dict): + extra_metadata = dict(metadata) + requester_metadata = extra_metadata.get("requester_metadata") + if requester_metadata and isinstance(requester_metadata, dict): + for key in ("session_id", "thread_id", "conversation_id"): + if key in requester_metadata and key not in extra_metadata: + extra_metadata[key] = requester_metadata[key] + return extra_metadata + + def _build_outputs_with_usage( + self, payload: StandardLoggingPayload + ) -> Dict[str, Any]: + response = payload["response"] + outputs: Dict[str, Any] + if isinstance(response, dict): + outputs = {**response} + else: + outputs = {"output": response} + outputs["usage_metadata"] = { + "input_tokens": payload.get("prompt_tokens", 0), + "output_tokens": payload.get("completion_tokens", 0), + "total_tokens": payload.get("total_tokens", 0), + "total_cost": payload.get("response_cost", 0), + } + return outputs + + def _ensure_required_ids(self, data: dict, run_id: Optional[str]): + if "id" not in data or data["id"] is None: + run_id = str(uuid.uuid4()) + data["id"] = run_id + + if "trace_id" not in data or data["trace_id"] is None: + if run_id is not None and isinstance(run_id, str): + data["trace_id"] = run_id + + if "dotted_order" not in data or data["dotted_order"] is None: + if run_id is not None and isinstance(run_id, str): + data["dotted_order"] = self.make_dot_order(run_id=run_id) + def _prepare_log_data( self, kwargs, @@ -121,44 +196,28 @@ class LangsmithLogger(CustomBatchLogger): try: _litellm_params = kwargs.get("litellm_params", {}) or {} metadata = _litellm_params.get("metadata", {}) or {} - project_name = metadata.get( - "project_name", credentials["LANGSMITH_PROJECT"] - ) - run_name = metadata.get("run_name", self.langsmith_default_run_name) - run_id = metadata.get("id", metadata.get("run_id", None)) - parent_run_id = metadata.get("parent_run_id", None) - trace_id = metadata.get("trace_id", None) - session_id = metadata.get("session_id", None) - dotted_order = metadata.get("dotted_order", None) + + fields = self._extract_metadata_fields(metadata, credentials) verbose_logger.debug( - f"Langsmith Logging - project_name: {project_name}, run_name {run_name}" + f"Langsmith Logging - project_name: {fields['project_name']}, run_name {fields['run_name']}" ) - # Ensure everything in the payload is converted to str payload: Optional[StandardLoggingPayload] = kwargs.get( "standard_logging_object", None ) - if payload is None: raise Exception("Error logging request payload. Payload=none.") - metadata = payload[ - "metadata" - ] # ensure logged metadata is json serializable - - extra_metadata = dict(metadata) - requester_metadata = extra_metadata.get("requester_metadata") - if requester_metadata and isinstance(requester_metadata, dict): - for key in ("session_id", "thread_id", "conversation_id"): - if key in requester_metadata and key not in extra_metadata: - extra_metadata[key] = requester_metadata[key] + metadata = payload["metadata"] + extra_metadata = self._build_extra_metadata(dict(metadata)) + outputs = self._build_outputs_with_usage(payload) data = { - "name": run_name, - "run_type": "llm", # this should always be llm, since litellm always logs llm calls. Langsmith allow us to log "chain" + "name": fields["run_name"], + "run_type": "llm", "inputs": payload, - "outputs": payload["response"], - "session_name": project_name, + "outputs": outputs, + "session_name": fields["project_name"], "start_time": payload["startTime"], "end_time": payload["endTime"], "tags": payload["request_tags"], @@ -168,46 +227,19 @@ class LangsmithLogger(CustomBatchLogger): if payload["error_str"] is not None and payload["status"] == "failure": data["error"] = payload["error_str"] - if run_id: - data["id"] = run_id - - if parent_run_id: - data["parent_run_id"] = parent_run_id - - if trace_id: - data["trace_id"] = trace_id - - if session_id: - data["session_id"] = session_id - - if dotted_order: - data["dotted_order"] = dotted_order - - run_id: Optional[str] = data.get("id") # type: ignore - if "id" not in data or data["id"] is None: - """ - for /batch langsmith requires id, trace_id and dotted_order passed as params - """ - run_id = str(uuid.uuid4()) - - data["id"] = run_id - - if ( - "trace_id" not in data - or data["trace_id"] is None - and (run_id is not None and isinstance(run_id, str)) + for key in ( + "id", + "parent_run_id", + "trace_id", + "session_id", + "dotted_order", ): - data["trace_id"] = run_id - - if ( - "dotted_order" not in data - or data["dotted_order"] is None - and (run_id is not None and isinstance(run_id, str)) - ): - data["dotted_order"] = self.make_dot_order(run_id=run_id) # type: ignore + field_key = "run_id" if key == "id" else key + if fields[field_key]: + data[key] = fields[field_key] + self._ensure_required_ids(data, fields["run_id"]) verbose_logger.debug("Langsmith Logging data on langsmith: %s", data) - return data except Exception: raise @@ -255,6 +287,7 @@ class LangsmithLogger(CustomBatchLogger): async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): try: + self._ensure_periodic_flush_task() sampling_rate = self._get_sampling_rate_to_use_for_request(kwargs=kwargs) random_sample = random.random() if random_sample > sampling_rate: @@ -296,17 +329,18 @@ class LangsmithLogger(CustomBatchLogger): ) async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time): - sampling_rate = self._get_sampling_rate_to_use_for_request(kwargs=kwargs) - random_sample = random.random() - if random_sample > sampling_rate: - verbose_logger.info( - "Skipping Langsmith logging. Sampling rate={}, random_sample={}".format( - sampling_rate, random_sample - ) - ) - return # Skip logging - verbose_logger.info("Langsmith Failure Event Logging!") try: + self._ensure_periodic_flush_task() + sampling_rate = self._get_sampling_rate_to_use_for_request(kwargs=kwargs) + random_sample = random.random() + if random_sample > sampling_rate: + verbose_logger.info( + "Skipping Langsmith logging. Sampling rate={}, random_sample={}".format( + sampling_rate, random_sample + ) + ) + return # Skip logging + verbose_logger.info("Langsmith Failure Event Logging!") credentials = self._get_credentials_to_use_for_request(kwargs=kwargs) data = self._prepare_log_data( kwargs=kwargs, diff --git a/litellm/integrations/prometheus.py b/litellm/integrations/prometheus.py index 357e0229fc..fb5fc253ae 100644 --- a/litellm/integrations/prometheus.py +++ b/litellm/integrations/prometheus.py @@ -65,6 +65,17 @@ def _get_cached_end_user_id_for_cost_tracking(): class PrometheusLogger(CustomLogger): # Class variables or attributes + + @staticmethod + def get_instance() -> Optional["PrometheusLogger"]: + """Find the PrometheusLogger instance from litellm.callbacks, if registered.""" + import litellm + + for cb in litellm.callbacks: + if isinstance(cb, PrometheusLogger): + return cb + return None + def __init__( # noqa: PLR0915 self, **kwargs, @@ -180,6 +191,31 @@ class PrometheusLogger(CustomLogger): ), ) + # Remaining Budget for Org + self.litellm_remaining_org_budget_metric = self._gauge_factory( + "litellm_remaining_org_budget_metric", + "Remaining budget for org", + labelnames=self.get_labels_for_metric( + "litellm_remaining_org_budget_metric" + ), + ) + + # Max Budget for Org + self.litellm_org_max_budget_metric = self._gauge_factory( + "litellm_org_max_budget_metric", + "Maximum budget set for org", + labelnames=self.get_labels_for_metric("litellm_org_max_budget_metric"), + ) + + # Org Budget Reset At + self.litellm_org_budget_remaining_hours_metric = self._gauge_factory( + "litellm_org_budget_remaining_hours_metric", + "Remaining hours for org budget to be reset", + labelnames=self.get_labels_for_metric( + "litellm_org_budget_remaining_hours_metric" + ), + ) + # Remaining Budget for API Key self.litellm_remaining_api_key_budget_metric = self._gauge_factory( "litellm_remaining_api_key_budget_metric", @@ -440,6 +476,76 @@ class PrometheusLogger(CustomLogger): labelnames=[], ) + ######################################## + # Managed Batch Metrics + ######################################## + self.litellm_managed_batch_created_total = self._counter_factory( + name="litellm_managed_batch_created_total", + documentation="Total number of managed batches created", + labelnames=[ + "model", + "api_provider", + "user", + "user_email", + "api_key_alias", + ], + ) + + self.litellm_managed_file_size_bytes = self._gauge_factory( + "litellm_managed_file_size_bytes", + "Size of the most recent managed batch file in bytes (last-seen value per label combination)", + labelnames=["purpose", "file_type", "model", "api_provider", "user"], + ) + + self.litellm_managed_batch_duration_seconds = self._histogram_factory( + "litellm_managed_batch_duration_seconds", + "Duration of completed managed batches in seconds (completed_at - created_at)", + labelnames=["model", "api_provider"], + buckets=BATCH_DURATION_BUCKETS, + ) + + self.litellm_managed_file_created_total = self._counter_factory( + name="litellm_managed_file_created_total", + documentation="Total number of managed files created", + labelnames=[ + "model", + "api_provider", + "user", + "user_email", + "api_key_alias", + ], + ) + + self.litellm_managed_file_deleted_total = self._counter_factory( + name="litellm_managed_file_deleted_total", + documentation="Total number of managed file deletions (success or blocked)", + labelnames=["result"], + ) + + self.litellm_check_batch_cost_jobs_polled = self._gauge_factory( + "litellm_check_batch_cost_jobs_polled", + "Number of unprocessed batches found by the last CheckBatchCost poll", + labelnames=[], + ) + + self.litellm_check_batch_cost_jobs_processed_total = self._counter_factory( + name="litellm_check_batch_cost_jobs_processed_total", + documentation="Total number of batches successfully cost-tracked by CheckBatchCost", + labelnames=["model", "api_provider"], + ) + + self.litellm_check_batch_cost_errors_total = self._counter_factory( + name="litellm_check_batch_cost_errors_total", + documentation="Total number of errors in CheckBatchCost by error type", + labelnames=["error_type"], + ) + + self.litellm_check_batch_cost_last_run_timestamp = self._gauge_factory( + "litellm_check_batch_cost_last_run_timestamp", + "Unix timestamp of the last CheckBatchCost job run", + labelnames=[], + ) + except Exception as e: print_verbose(f"Got exception on init prometheus client {str(e)}") raise e @@ -922,6 +1028,9 @@ class PrometheusLogger(CustomLogger): user_api_team_alias = standard_logging_payload["metadata"][ "user_api_key_team_alias" ] + user_api_key_org_id = standard_logging_payload["metadata"].get( + "user_api_key_org_id" + ) output_tokens = standard_logging_payload["completion_tokens"] tokens_used = standard_logging_payload["total_tokens"] response_cost = standard_logging_payload["response_cost"] @@ -931,10 +1040,14 @@ class PrometheusLogger(CustomLogger): user_api_key_auth_metadata: Optional[dict] = standard_logging_payload[ "metadata" ].get("user_api_key_auth_metadata") + spend_logs_metadata: Optional[dict] = standard_logging_payload["metadata"].get( + "spend_logs_metadata" + ) combined_metadata: Dict[str, Any] = { **(_requester_metadata if _requester_metadata else {}), **(user_api_key_auth_metadata if user_api_key_auth_metadata else {}), + **(spend_logs_metadata if spend_logs_metadata else {}), } if standard_logging_payload is not None and isinstance( standard_logging_payload, dict @@ -1026,6 +1139,7 @@ class PrometheusLogger(CustomLogger): litellm_params=litellm_params, response_cost=response_cost, user_id=user_id, + user_api_key_org_id=user_api_key_org_id, ) # set proxy virtual key rpm/tpm metrics @@ -1181,6 +1295,7 @@ class PrometheusLogger(CustomLogger): litellm_params: dict, response_cost: float, user_id: Optional[str] = None, + user_api_key_org_id: Optional[str] = None, ): _metadata = litellm_params.get("metadata") or {} _team_spend = _metadata.get("user_api_key_team_spend", None) @@ -1213,12 +1328,16 @@ class PrometheusLogger(CustomLogger): user_max_budget=_user_max_budget, response_cost=response_cost, ), + self._set_org_budget_metrics_after_api_request( + org_id=user_api_key_org_id, + response_cost=response_cost, + ), return_exceptions=True, ) for i, r in enumerate(results): if isinstance(r, Exception): verbose_logger.debug( - f"[Non-Blocking] Prometheus: Budget metric lookup {['key', 'team', 'user'][i]} failed: {r}" + f"[Non-Blocking] Prometheus: Budget metric lookup {['key', 'team', 'user', 'org'][i]} failed: {r}" ) def _increment_top_level_request_and_spend_metrics( @@ -1407,6 +1526,9 @@ class PrometheusLogger(CustomLogger): user_api_team_alias = standard_logging_payload["metadata"][ "user_api_key_team_alias" ] + user_api_key_org_id = standard_logging_payload["metadata"].get( + "user_api_key_org_id" + ) try: self.litellm_llm_api_failed_requests_metric.labels( @@ -1422,6 +1544,10 @@ class PrometheusLogger(CustomLogger): ), ).inc() self.set_llm_deployment_failure_metrics(kwargs) + await self._set_org_budget_metrics_after_api_request( + org_id=user_api_key_org_id, + response_cost=0, + ) except Exception as e: verbose_logger.exception( "prometheus Layer Error(): Exception occured - {}".format(str(e)) @@ -2154,6 +2280,127 @@ class PrometheusLogger(CustomLogger): except Exception as e: verbose_logger.debug(f"Error recording guardrail metrics: {str(e)}") + ######################################## + # Managed Batch Metric Recording Methods + ######################################## + + def record_managed_batch_created( + self, + model: Optional[str], + api_provider: Optional[str], + user: Optional[str], + user_email: Optional[str], + api_key_alias: Optional[str], + ): + try: + self.litellm_managed_batch_created_total.labels( + model=model, + api_provider=api_provider, + user=user, + user_email=user_email, + api_key_alias=api_key_alias, + ).inc() + except Exception as e: + verbose_logger.warning(f"Error recording batch created metric: {e}") + + def record_managed_file_size( + self, + size_bytes: int, + purpose: str, + file_type: str, + model: Optional[str] = None, + api_provider: Optional[str] = None, + user: Optional[str] = None, + ): + """Record the size of a managed file. Uses a gauge (last-seen value per label combination).""" + try: + self.litellm_managed_file_size_bytes.labels( + purpose=purpose, + file_type=file_type, + model=model or "", + api_provider=api_provider or "", + user=user or "", + ).set(size_bytes) + except Exception as e: + verbose_logger.warning(f"Error recording file size metric: {e}") + + def record_managed_batch_duration( + self, + duration_seconds: float, + model: Optional[str] = None, + api_provider: Optional[str] = None, + ): + try: + self.litellm_managed_batch_duration_seconds.labels( + model=model or "", + api_provider=api_provider or "", + ).observe(duration_seconds) + except Exception as e: + verbose_logger.warning(f"Error recording batch duration metric: {e}") + + def record_managed_file_created( + self, + model: Optional[str], + api_provider: Optional[str], + user: Optional[str], + user_email: Optional[str], + api_key_alias: Optional[str], + ): + try: + self.litellm_managed_file_created_total.labels( + model=model, + api_provider=api_provider, + user=user, + user_email=user_email, + api_key_alias=api_key_alias, + ).inc() + except Exception as e: + verbose_logger.warning(f"Error recording file created metric: {e}") + + def record_managed_file_deleted(self, result: str): + """Record a managed file deletion attempt. result is 'success' or 'blocked'.""" + try: + self.litellm_managed_file_deleted_total.labels(result=result).inc() + except Exception as e: + verbose_logger.warning(f"Error recording file deleted metric: {e}") + + def record_check_batch_cost_run( + self, + jobs_polled: int, + processed_models: Optional[List[Tuple[Optional[str], Optional[str]]]] = None, + ): + """ + Record CheckBatchCost polling metrics. + + Args: + jobs_polled: Number of unprocessed batches found + processed_models: List of (model, api_provider) tuples for processed jobs + """ + import time + + try: + self.litellm_check_batch_cost_last_run_timestamp.set(time.time()) + self.litellm_check_batch_cost_jobs_polled.set(jobs_polled) + + if processed_models: + for model, api_provider in processed_models: + self.litellm_check_batch_cost_jobs_processed_total.labels( + model=model or "", + api_provider=api_provider or "", + ).inc() + except Exception as e: + verbose_logger.warning(f"Error recording check batch cost metrics: {e}") + + def record_check_batch_cost_error(self, error_type: str): + try: + self.litellm_check_batch_cost_errors_total.labels( + error_type=error_type, + ).inc() + except Exception as e: + verbose_logger.warning( + f"Error recording check batch cost error metric: {e}" + ) + @staticmethod def _get_exception_class_name(exception: Exception) -> str: exception_class_name = "" @@ -2530,6 +2777,35 @@ class PrometheusLogger(CustomLogger): data_type="users", ) + async def _initialize_org_budget_metrics(self): + """ + Initialize org budget metrics by reusing the generic pagination logic. + """ + from litellm.proxy.proxy_server import prisma_client + + if prisma_client is None: + verbose_logger.debug( + "Prometheus: skipping org metrics initialization, DB not initialized" + ) + return + + async def fetch_orgs(page_size: int, page: int) -> Tuple[list, Optional[int]]: + skip = (page - 1) * page_size + orgs = await prisma_client.db.litellm_organizationtable.find_many( + skip=skip, + take=page_size, + order={"created_at": "desc"}, + include={"litellm_budget_table": True}, + ) + total_count = await prisma_client.db.litellm_organizationtable.count() + return orgs, total_count + + await self._initialize_budget_metrics( + data_fetch_function=fetch_orgs, + set_metrics_function=self._set_org_list_budget_metrics, + data_type="orgs", + ) + async def initialize_remaining_budget_metrics(self): """ Handler for initializing remaining budget metrics for all teams to avoid metric discrepancies. @@ -2564,10 +2840,11 @@ class PrometheusLogger(CustomLogger): """ Helper to initialize remaining budget metrics for all teams, API keys, and users. """ - verbose_logger.debug("Emitting key, team, user budget metrics....") + verbose_logger.debug("Emitting key, team, user, org budget metrics....") await self._initialize_team_budget_metrics() await self._initialize_api_key_budget_metrics() await self._initialize_user_budget_metrics() + await self._initialize_org_budget_metrics() await self._initialize_user_and_team_count_metrics() async def _initialize_user_and_team_count_metrics(self): @@ -2623,6 +2900,20 @@ class PrometheusLogger(CustomLogger): for user in users: self._set_user_budget_metrics(user) + async def _set_org_list_budget_metrics(self, orgs: list): + """Helper function to set budget metrics for a list of orgs""" + for org in orgs: + budget_table = getattr(org, "litellm_budget_table", None) + self._set_org_budget_metrics( + org_id=org.organization_id or "", + org_alias=org.organization_alias or "", + spend=org.spend or 0.0, + max_budget=budget_table.max_budget if budget_table else None, + budget_reset_at=getattr(budget_table, "budget_reset_at", None) + if budget_table + else None, + ) + async def _set_team_budget_metrics_after_api_request( self, user_api_team: Optional[str], @@ -2744,6 +3035,113 @@ class PrometheusLogger(CustomLogger): ) ) + async def _set_org_budget_metrics_after_api_request( + self, + org_id: Optional[str], + response_cost: float, + ): + """ + Set org budget metrics after an LLM API request + + - Fetches org info via cache (get_org_object) + - Sets org budget metrics + """ + if not org_id: + return + + from litellm.proxy.auth.auth_checks import get_org_object + from litellm.proxy.proxy_server import prisma_client, user_api_key_cache + + if prisma_client is None: + return + + try: + org_info = await get_org_object( + org_id=org_id, + prisma_client=prisma_client, + user_api_key_cache=user_api_key_cache, + include_budget_table=True, + ) + except Exception as e: + verbose_logger.debug( + f"[Non-Blocking] Prometheus: Error getting org info: {str(e)}" + ) + return + + if org_info is None: + return + + org_alias = org_info.organization_alias or "" + _total_org_spend = (org_info.spend or 0.0) + response_cost + budget_table = org_info.litellm_budget_table + max_budget = budget_table.max_budget if budget_table else None + budget_reset_at = ( + getattr(budget_table, "budget_reset_at", None) if budget_table else None + ) + + self._set_org_budget_metrics( + org_id=org_id, + org_alias=org_alias, + spend=_total_org_spend, + max_budget=max_budget, + budget_reset_at=budget_reset_at, + ) + + def _set_org_budget_metrics( + self, + org_id: str, + org_alias: str, + spend: float, + max_budget: Optional[float], + budget_reset_at: Optional[datetime], + ): + """ + Set org budget metrics for a single org + + - Remaining Budget + - Max Budget + - Budget Reset At + """ + enum_values = UserAPIKeyLabelValues( + org_id=org_id, + org_alias=org_alias, + ) + + _labels = prometheus_label_factory( + supported_enum_labels=self.get_labels_for_metric( + metric_name="litellm_remaining_org_budget_metric" + ), + enum_values=enum_values, + ) + self.litellm_remaining_org_budget_metric.labels(**_labels).set( + self._safe_get_remaining_budget( + max_budget=max_budget, + spend=spend, + ) + ) + + if max_budget is not None: + _labels = prometheus_label_factory( + supported_enum_labels=self.get_labels_for_metric( + metric_name="litellm_org_max_budget_metric" + ), + enum_values=enum_values, + ) + self.litellm_org_max_budget_metric.labels(**_labels).set(max_budget) + + if budget_reset_at is not None: + _labels = prometheus_label_factory( + supported_enum_labels=self.get_labels_for_metric( + metric_name="litellm_org_budget_remaining_hours_metric" + ), + enum_values=enum_values, + ) + self.litellm_org_budget_remaining_hours_metric.labels(**_labels).set( + self._get_remaining_hours_for_budget_reset( + budget_reset_at=budget_reset_at + ) + ) + def _set_key_budget_metrics(self, user_api_key_dict: UserAPIKeyAuth): """ Set virtual key budget metrics diff --git a/litellm/integrations/s3_v2.py b/litellm/integrations/s3_v2.py index c8db4be7ce..405bf9698c 100644 --- a/litellm/integrations/s3_v2.py +++ b/litellm/integrations/s3_v2.py @@ -22,7 +22,7 @@ from litellm.llms.custom_httpx.http_handler import ( httpxSpecialProvider, ) from litellm.types.integrations.s3_v2 import s3BatchLoggingElement -from litellm.types.utils import StandardLoggingPayload +from litellm.types.utils import StandardAuditLogPayload, StandardLoggingPayload from .custom_batch_logger import CustomBatchLogger @@ -248,6 +248,38 @@ class S3Logger(CustomBatchLogger, BaseAWSLLM): ) pass + async def async_log_audit_log_event( + self, audit_log: StandardAuditLogPayload + ) -> None: + """Batch audit logs and upload to S3 under audit_logs/ prefix.""" + try: + from datetime import timezone + + now = datetime.now(timezone.utc) + audit_log_id = audit_log.get("id", "unknown") + + s3_path = cast(Optional[str], self.s3_path) or "" + s3_path = s3_path.rstrip("/") + "/" if s3_path else "" + + s3_object_key = ( + f"{s3_path}audit_logs/" + f"{now.strftime('%Y-%m-%d')}/" + f"{now.strftime('%H-%M-%S')}_{audit_log_id}.json" + ) + + element = s3BatchLoggingElement( + payload=dict(audit_log), + s3_object_key=s3_object_key, + s3_object_download_filename=f"audit-{audit_log_id}.json", + ) + + self.log_queue.append(element) + + if len(self.log_queue) >= self.batch_size: + await self.flush_queue() + except Exception as e: + verbose_logger.exception("S3 audit log error: %s", e) + async def _async_log_event_base(self, kwargs, response_obj, start_time, end_time): try: verbose_logger.debug( diff --git a/litellm/integrations/websearch_interception/handler.py b/litellm/integrations/websearch_interception/handler.py index 2541a0bd7a..2e5a873408 100644 --- a/litellm/integrations/websearch_interception/handler.py +++ b/litellm/integrations/websearch_interception/handler.py @@ -8,6 +8,7 @@ server-side using litellm router's search tools. import asyncio import math +import uuid from typing import Any, Dict, List, Optional, Tuple, Union, cast import litellm @@ -27,7 +28,9 @@ from litellm.integrations.websearch_interception.transformation import ( from litellm.types.integrations.websearch_interception import ( WebSearchInterceptionConfig, ) +from litellm.types.llms.openai import AllMessageValues from litellm.types.utils import LlmProviders +from litellm.utils import ProviderConfigManager class WebSearchInterceptionLogger(CustomLogger): @@ -67,6 +70,111 @@ class WebSearchInterceptionLogger(CustomLogger): self.search_tool_name = search_tool_name self._request_has_websearch = False # Track if current request has web search + async def try_short_circuit_search( + self, + model: str, + messages: List[Dict], + tools: Optional[List[Dict]], + custom_llm_provider: Optional[str], + ) -> Optional[Dict[str, Any]]: + """ + Short-circuit web-search-only requests by executing the search directly. + + Claude Code sends web search as a separate, standalone /v1/messages + request with a simple prompt and only web_search tool(s). For providers + that don't natively support web search (e.g. github_copilot), there is + no need to route this through the backend LLM — we can detect the + pattern, execute the search via Tavily/Perplexity, and return a + synthetic Anthropic response immediately. + + Args: + model: Model name from the request + messages: Messages list from the request + tools: Tools list from the request + custom_llm_provider: Provider name + + Returns: + An AnthropicMessagesResponse dict if short-circuited, or None to + continue normal processing. + """ + if not tools: + return None + + # Check if provider is in enabled list + provider_str = custom_llm_provider or "" + if ( + self.enabled_providers is not None + and provider_str not in self.enabled_providers + ): + return None + + # Only short-circuit for providers without native Anthropic Messages + # support. Providers that have a BaseAnthropicMessagesConfig (bedrock, + # vertex_ai, azure_ai, anthropic) already use the agentic loop, which + # includes a follow-up LLM call to synthesize the answer from search + # results. Short-circuiting those would skip that synthesis step and + # return raw search text — a regression for existing users. + try: + provider_enum = LlmProviders(provider_str) + anthropic_config = ( + ProviderConfigManager.get_provider_anthropic_messages_config( + model=model, provider=provider_enum + ) + ) + if anthropic_config is not None: + verbose_logger.debug( + f"WebSearchInterception: Skipping short-circuit for {provider_str} " + "(provider has native Anthropic Messages support, using agentic loop)" + ) + return None + except (ValueError, Exception): + pass # unknown provider enum → safe to short-circuit + + # All tools must be web search tools + if not all(is_web_search_tool(t) for t in tools): + return None + + # Extract search query from the last user message + from litellm.litellm_core_utils.prompt_templates.common_utils import ( + get_last_user_message, + ) + + query = get_last_user_message(cast(List[AllMessageValues], messages)) + if not query: + return None + + verbose_logger.debug( + "WebSearchInterception: Short-circuit search detected " + f"(provider={provider_str}, query='{query}')" + ) + + # Execute search + try: + search_result_text = await self._execute_search(query) + except Exception as e: + verbose_logger.error( + f"WebSearchInterception: Short-circuit search failed: {e}" + ) + search_result_text = f"Search failed: {e}" + + # Build synthetic Anthropic response + response: Dict[str, Any] = { + "id": f"msg_{str(uuid.uuid4())}", + "type": "message", + "role": "assistant", + "model": model, + "content": [{"type": "text", "text": search_result_text}], + "stop_reason": "end_turn", + "stop_sequence": None, + "usage": {"input_tokens": 0, "output_tokens": 0}, + } + + verbose_logger.debug( + "WebSearchInterception: Short-circuit search completed, " + f"returning synthetic response ({len(search_result_text)} chars)" + ) + return response + async def async_pre_call_deployment_hook( self, kwargs: Dict[str, Any], call_type: Optional[Any] ) -> Optional[dict]: diff --git a/litellm/litellm_core_utils/core_helpers.py b/litellm/litellm_core_utils/core_helpers.py index 256b16ff31..22006be21a 100644 --- a/litellm/litellm_core_utils/core_helpers.py +++ b/litellm/litellm_core_utils/core_helpers.py @@ -64,6 +64,7 @@ _FINISH_REASON_MAP: dict[str, OpenAIChatCompletionFinishReason] = { "end_turn": "stop", "max_tokens": "length", "tool_use": "tool_calls", + "refusal": "content_filter", "compaction": "length", # Cohere "COMPLETE": "stop", diff --git a/litellm/litellm_core_utils/get_llm_provider_logic.py b/litellm/litellm_core_utils/get_llm_provider_logic.py index 3621841737..0a2f07bcb2 100644 --- a/litellm/litellm_core_utils/get_llm_provider_logic.py +++ b/litellm/litellm_core_utils/get_llm_provider_logic.py @@ -158,17 +158,17 @@ def get_llm_provider( # noqa: PLR0915 ): # handle scenario where model="azure/*" and custom_llm_provider="azure" model = custom_llm_provider + "/" + model - # Native OpenRouter models have IDs like "openrouter/free" where the - # "openrouter/" prefix is part of the actual model name on the API. - # When called from a bridge (e.g. anthropic_messages adapter), - # custom_llm_provider is already resolved, so return early to prevent - # the provider-list stripping below from removing the prefix. + # OpenRouter: when the router/proxy already set custom_llm_provider, + # the model may still carry LiteLLM's "openrouter/" routing prefix. + # Native IDs like "openrouter/auto" must stay intact for the API; IDs + # like "openrouter/anthropic/claude-3.5-sonnet" must become + # "anthropic/claude-3.5-sonnet" (OpenRouter expects provider/model). if custom_llm_provider == "openrouter" and model.startswith("openrouter/"): + remainder = model[len("openrouter/") :] + if "/" in remainder: + return remainder, custom_llm_provider, dynamic_api_key, api_base return model, custom_llm_provider, dynamic_api_key, api_base - if api_key and api_key.startswith("os.environ/"): - dynamic_api_key = get_secret_str(api_key) - # Check JSON-configured providers FIRST (before enum-based provider_list) provider_prefix = model.split("/", 1)[0] if len(model.split("/")) > 1 and JSONProviderRegistry.exists(provider_prefix): diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py index 4e63dd7076..7395b65626 100644 --- a/litellm/litellm_core_utils/litellm_logging.py +++ b/litellm/litellm_core_utils/litellm_logging.py @@ -84,6 +84,8 @@ from litellm.types.llms.openai import ( OpenAIModerationResponse, ResponseAPIUsage, ResponseCompletedEvent, + ResponseFailedEvent, + ResponseIncompleteEvent, ResponsesAPIResponse, ) from litellm.types.mcp import MCPPostCallResponseObject @@ -516,6 +518,23 @@ class Logging(LiteLLMLoggingBaseClass): ), ) + def get_router_model_id(self) -> Optional[str]: + """Extract the router deployment model_id from litellm_params. + + Checks both litellm_metadata and metadata for model_info.id. + Used by cost calculators to look up custom pricing registered + under the deployment's model_info.id in litellm.model_cost. + """ + if not hasattr(self, "litellm_params"): + return None + for key in ("litellm_metadata", "metadata"): + meta = self.litellm_params.get(key, {}) or {} + info = meta.get("model_info", {}) or {} + model_id = info.get("id") + if model_id is not None: + return model_id + return None + def update_environment_variables( self, litellm_params: Dict, @@ -1458,16 +1477,8 @@ class Logging(LiteLLMLoggingBaseClass): # Fallback: extract router_model_id from litellm_params when not available # from the result object. ResponsesAPIResponse objects (used by /v1/responses # streaming) don't carry _hidden_params["model_id"] like ModelResponse does. - if router_model_id is None and hasattr(self, "litellm_params"): - for metadata_key in ("litellm_metadata", "metadata"): - _metadata: dict = ( - self.litellm_params.get(metadata_key, {}) or {} - ) - _model_info: dict = _metadata.get("model_info", {}) or {} - _model_id = _model_info.get("id") - if _model_id is not None: - router_model_id = _model_id - break + if router_model_id is None: + router_model_id = self.get_router_model_id() ## RESPONSE COST ## custom_pricing = use_custom_pricing_for_model( @@ -1675,6 +1686,30 @@ class Logging(LiteLLMLoggingBaseClass): ) return logging_result + def _merge_hidden_params_from_response_into_metadata( + self, logging_result: Any + ) -> None: + """ + Copy response._hidden_params into litellm_params.metadata['hidden_params']. + + Non-streaming success uses _process_hidden_params_and_response_cost (skipped when + stream=True). Streaming assembles the full response later; without this merge, + OTEL/callbacks that read metadata.hidden_params miss cost-related fields. + """ + if logging_result is None: + return + hidden_params = getattr(logging_result, "_hidden_params", None) + if not hidden_params: + return + if self.model_call_details.get("litellm_params") is None: + return + 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", {}) + def _process_hidden_params_and_response_cost( self, logging_result, @@ -1999,6 +2034,9 @@ class Logging(LiteLLMLoggingBaseClass): self.model_call_details[ "response_cost" ] = self._response_cost_calculator(result=complete_streaming_response) + self._merge_hidden_params_from_response_into_metadata( + complete_streaming_response + ) ## STANDARDIZED LOGGING PAYLOAD self.model_call_details[ "standard_logging_object" @@ -2534,6 +2572,10 @@ class Logging(LiteLLMLoggingBaseClass): ) self.model_call_details["response_cost"] = None + self._merge_hidden_params_from_response_into_metadata( + complete_streaming_response + ) + ## STANDARDIZED LOGGING PAYLOAD self.model_call_details[ "standard_logging_object" @@ -2958,9 +3000,10 @@ class Logging(LiteLLMLoggingBaseClass): 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 + if callable(callback): # custom logger functions + global customLogger + if customLogger is None: + customLogger = CustomLogger() customLogger.log_event( kwargs=self.model_call_details, response_obj=result, @@ -2972,8 +3015,7 @@ class Logging(LiteLLMLoggingBaseClass): if ( isinstance(callback, CustomLogger) and is_sync_request - and self.call_type - != CallTypes.pass_through.value + and self.call_type != CallTypes.pass_through.value ): # custom logger class callback.log_failure_event( start_time=start_time, @@ -3102,9 +3144,10 @@ class Logging(LiteLLMLoggingBaseClass): start_time=start_time, end_time=end_time, ) # type: ignore - if ( - callable(callback) and customLogger is not None - ): # custom logger functions + if callable(callback): # custom logger functions + global customLogger + if customLogger is None: + customLogger = CustomLogger() await customLogger.async_log_event( kwargs=self.model_call_details, response_obj=result, @@ -3321,7 +3364,10 @@ class Logging(LiteLLMLoggingBaseClass): return result elif isinstance(result, TextCompletionResponse): return result - elif isinstance(result, ResponseCompletedEvent): + elif isinstance( + result, + (ResponseCompletedEvent, ResponseIncompleteEvent, ResponseFailedEvent), + ): ## return unified Usage object if isinstance(result.response.usage, ResponseAPIUsage): transformed_usage = ( @@ -3342,7 +3388,6 @@ class Logging(LiteLLMLoggingBaseClass): return result.response else: return None - return None def _handle_anthropic_messages_response_logging(self, result: Any) -> ModelResponse: """ @@ -4710,6 +4755,7 @@ class StandardLoggingPayloadSetup: user_api_key_team_id=None, user_api_key_org_id=None, user_api_key_project_id=None, + user_api_key_project_alias=None, user_api_key_user_id=None, user_api_key_team_alias=None, user_api_key_user_email=None, @@ -5541,6 +5587,7 @@ def get_standard_logging_metadata( user_api_key_team_id=None, user_api_key_org_id=None, user_api_key_project_id=None, + user_api_key_project_alias=None, user_api_key_user_id=None, user_api_key_user_email=None, user_api_key_team_alias=None, diff --git a/litellm/litellm_core_utils/prompt_templates/common_utils.py b/litellm/litellm_core_utils/prompt_templates/common_utils.py index a5d6bc936b..cec61405eb 100644 --- a/litellm/litellm_core_utils/prompt_templates/common_utils.py +++ b/litellm/litellm_core_utils/prompt_templates/common_utils.py @@ -257,6 +257,15 @@ def detect_first_expected_role( return None +def _counts_for_alternation(message: AllMessageValues) -> bool: + role = message.get("role") + if role == "user": + return True + if role == "assistant": + return not bool(message.get("tool_calls")) + return False + + def _insert_user_continue_message( messages: List[AllMessageValues], user_continue_message: Optional[ChatCompletionUserMessage], @@ -269,8 +278,8 @@ def _insert_user_continue_message( 2. Final assistant message 3. Consecutive assistant messages - Only inserts messages between consecutive assistant messages, - ignoring all other role types. + Skips tool messages and assistant messages with tool calls in the + alternation check, matching strict templates like llama.cpp. """ if not messages: return messages @@ -278,25 +287,42 @@ def _insert_user_continue_message( result_messages = messages.copy() # Don't modify the input list continue_message = user_continue_message or DEFAULT_USER_CONTINUE_MESSAGE - # Handle first message if it's an assistant message + # Handle first message if it's an assistant message — always prepend + # user_continue regardless of tool_calls, to preserve backward compatibility. if result_messages[0]["role"] == "assistant": result_messages.insert(0, continue_message) - # Handle consecutive assistant messages and final message - i = 1 # Start from second message since we handled first message + # Handle consecutive assistant messages in the counted sequence + i = 1 while i < len(result_messages): curr_message = result_messages[i] - prev_message = result_messages[i - 1] - - # Only check for consecutive assistant messages - # Ignore all other role types - if curr_message["role"] == "assistant" and prev_message["role"] == "assistant": - result_messages.insert(i, continue_message) - i += 2 # Skip over the message we just inserted - else: + inserted_continue_message = False + if ( + _counts_for_alternation(curr_message) + and curr_message["role"] == "assistant" + ): + # Preserve old behavior for malformed adjacent assistant sequences like + # assistant(tool_calls) -> assistant(no-tool-calls) with no tool message. + if i > 0 and result_messages[i - 1].get("role") == "assistant": + result_messages.insert(i, continue_message) + i += 2 + inserted_continue_message = True + else: + j = i - 1 + while j >= 0: + previous_message = result_messages[j] + if _counts_for_alternation(previous_message): + if previous_message["role"] == "assistant": + result_messages.insert(i, continue_message) + i += 2 + inserted_continue_message = True + break + j -= 1 + if not inserted_continue_message: i += 1 - # Handle final message + # Handle final message — append user_continue after any trailing assistant, + # including ones with tool_calls, to preserve backward compatibility. if result_messages[-1]["role"] == "assistant" and ensure_alternating_roles: result_messages.append(continue_message) @@ -311,34 +337,24 @@ def _insert_assistant_continue_message( """ Add assistant continuation messages between consecutive user messages. - Args: - messages: List of message dictionaries - assistant_continue_message: Optional custom assistant message - ensure_alternating_roles: Whether to enforce alternating roles - - Returns: - Modified list of messages with inserted assistant messages + Only checks directly adjacent messages to preserve backward compatibility. """ if not ensure_alternating_roles or len(messages) <= 1: return messages - # Create a new list to store modified messages + continue_message = assistant_continue_message or DEFAULT_ASSISTANT_CONTINUE_MESSAGE + modified_messages: List[AllMessageValues] = [] - for i, message in enumerate(messages): - modified_messages.append(message) - - # Check if we need to insert an assistant message if ( - i < len(messages) - 1 # Not the last message - and message.get("role") == "user" # Current is user + i < len(messages) - 1 + and message.get("role") == "user" and messages[i + 1].get("role") == "user" - ): # Next is user - # Insert assistant message - continue_message = ( - assistant_continue_message or DEFAULT_ASSISTANT_CONTINUE_MESSAGE - ) + ): + modified_messages.append(message) modified_messages.append(continue_message) + else: + modified_messages.append(message) return modified_messages @@ -536,6 +552,61 @@ def update_responses_input_with_model_file_ids( return updated_input +def _decode_vector_store_ids_in_tools( + tools: Optional[List[Dict[str, Any]]], +) -> Optional[List[Dict[str, Any]]]: + """ + Decodes unified (LiteLLM-managed) vector_store_ids in file_search tools to + provider-native IDs. Non-unified IDs are passed through unchanged. + + This runs unconditionally — no file-ID mapping is required. + """ + if not tools or not isinstance(tools, list): + return tools + + from litellm.llms.base_llm.managed_resources.utils import ( + is_base64_encoded_unified_id, + parse_unified_id, + ) + + updated_tools = [] + for tool in tools: + if not isinstance(tool, dict) or tool.get("type") != "file_search": + updated_tools.append(tool) + continue + + vector_store_ids = tool.get("vector_store_ids") + if not isinstance(vector_store_ids, list): + updated_tools.append(tool) + continue + + decoded_ids = [] + for vs_id in vector_store_ids: + if not isinstance(vs_id, str) or not is_base64_encoded_unified_id(vs_id): + decoded_ids.append(vs_id) + continue + + parsed = parse_unified_id(vs_id) + provider_resource_id = ( + parsed.get("provider_resource_id") if parsed else None + ) + + if not provider_resource_id: + verbose_logger.warning( + "file_search tool contains unified vector_store_id '%s' that could " + "not be decoded to a provider resource ID — passing original ID. " + "Ensure the vector store was created via LiteLLM.", + vs_id, + ) + decoded_ids.append(vs_id) + else: + decoded_ids.append(provider_resource_id) + + updated_tools.append({**tool, "vector_store_ids": decoded_ids}) + + return updated_tools + + def update_responses_tools_with_model_file_ids( tools: Optional[List[Dict[str, Any]]], model_id: Optional[str] = None, @@ -544,7 +615,8 @@ def update_responses_tools_with_model_file_ids( """ Updates responses API tools with provider-specific file IDs. - Handles code_interpreter tools with container.file_ids. + Pass 1 (always): decode unified vector_store_ids in file_search tools. + Pass 2 (needs mapping): map code_interpreter container file_ids to provider IDs. Args: tools: The responses API tools parameter @@ -555,6 +627,10 @@ def update_responses_tools_with_model_file_ids( if not tools or not isinstance(tools, list): return tools + # Pass 1: decode unified vector_store_ids (no mapping needed) + tools = _decode_vector_store_ids_in_tools(tools) or tools + + # Pass 2: map code_interpreter file IDs (requires mapping) if not model_file_id_mapping or not model_id: return tools diff --git a/litellm/litellm_core_utils/prompt_templates/factory.py b/litellm/litellm_core_utils/prompt_templates/factory.py index f600461671..d29ca1649f 100644 --- a/litellm/litellm_core_utils/prompt_templates/factory.py +++ b/litellm/litellm_core_utils/prompt_templates/factory.py @@ -1498,17 +1498,49 @@ def convert_to_gemini_tool_call_result( # noqa: PLR0915 from litellm.types.llms.vertex_ai import BlobType content_str: str = "" - inline_data: Optional[BlobType] = None + inline_data_list: List[BlobType] = [] if "content" in message: if isinstance(message["content"], str): content_str = message["content"] + # Detect data-URL images (e.g. from Anthropic tool_result with a single image block + # that was serialised as a plain string by translate_anthropic_messages_to_openai) + # and promote them to inline_data so Gemini receives actual image bytes. + if content_str[:5].lower() == "data:" and ";base64," in content_str: + try: + mime_rest = content_str[5:].split(";base64,", 1) + if len(mime_rest) == 2 and mime_rest[0].startswith("image/"): + # Strip any extra parameters (e.g. ";charset=UTF-8") from the MIME segment + clean_mime = mime_rest[0].split(";")[0].strip() + inline_data_list.append( + BlobType(data=mime_rest[1], mime_type=clean_mime) + ) + content_str = "" + except Exception as e: + verbose_logger.warning( + f"Failed to parse data URL in tool response: {e}" + ) elif isinstance(message["content"], List): content_list = message["content"] for content in content_list: content_type = content.get("type", "") if content_type == "text": content_str += content.get("text", "") + elif content_type == "image": + # Anthropic-native image block: {"type": "image", "source": {"type": "base64", ...}} + source = content.get("source", {}) + if isinstance(source, dict) and source.get("type") == "base64": + try: + inline_data_list.append( + BlobType( + data=source.get("data", ""), + mime_type=source.get("media_type", "image/jpeg"), + ) + ) + except Exception as e: + verbose_logger.warning( + f"Failed to process Anthropic image block in tool response: {e}" + ) elif content_type in ("input_image", "image_url"): # Extract image for inline_data (for Computer Use screenshots and tool results) image_url_data = content.get("image_url", "") @@ -1524,9 +1556,11 @@ def convert_to_gemini_tool_call_result( # noqa: PLR0915 image_obj = convert_to_anthropic_image_obj( image_url, format=None ) - inline_data = BlobType( - data=image_obj["data"], - mime_type=image_obj["media_type"], + inline_data_list.append( + BlobType( + data=image_obj["data"], + mime_type=image_obj["media_type"], + ) ) except Exception as e: verbose_logger.warning( @@ -1551,9 +1585,11 @@ def convert_to_gemini_tool_call_result( # noqa: PLR0915 file_obj = convert_to_anthropic_image_obj( file_data, format=None ) - inline_data = BlobType( - data=file_obj["data"], - mime_type=file_obj["media_type"], + inline_data_list.append( + BlobType( + data=file_obj["data"], + mime_type=file_obj["media_type"], + ) ) except Exception as e: verbose_logger.warning( @@ -1607,13 +1643,12 @@ def convert_to_gemini_tool_call_result( # noqa: PLR0915 # Create part with function_response, and optionally inline_data for images (Computer Use) _part: VertexPartType = {"function_response": _function_response} - # For Computer Use, if we have an image, we need separate parts: + # For Computer Use, if we have images/files, we need separate parts: # - One part with function_response - # - One part with inline_data + # - One part per inline_data item # Gemini's PartType is a oneof, so we can't have both in the same part - if inline_data: - image_part: VertexPartType = {"inline_data": inline_data} - return [_part, image_part] + if inline_data_list: + return [_part] + [{"inline_data": d} for d in inline_data_list] return _part diff --git a/litellm/litellm_core_utils/streaming_handler.py b/litellm/litellm_core_utils/streaming_handler.py index 6e991e6911..1bb2b99c01 100644 --- a/litellm/litellm_core_utils/streaming_handler.py +++ b/litellm/litellm_core_utils/streaming_handler.py @@ -57,6 +57,22 @@ IMAGE_ATTRIBUTE = "images" TOOL_CALLS_ATTRIBUTE = "tool_calls" FUNCTION_CALL_ATTRIBUTE = "function_call" +_SYNC_ITER_EXHAUSTED = object() + + +def _next_sync_or_exhausted(it: Any) -> Any: + """ + Call next(it) from a thread and return _SYNC_ITER_EXHAUSTED on StopIteration. + + asyncio.to_thread re-raises thread exceptions inside a coroutine, where PEP 479 + converts StopIteration to RuntimeError before any except clause can catch it. + Returning a sentinel instead keeps StopIteration out of the coroutine boundary. + """ + try: + return next(it) + except StopIteration: + return _SYNC_ITER_EXHAUSTED + def is_async_iterable(obj: Any) -> bool: """ @@ -160,6 +176,7 @@ class CustomStreamWrapper: self.chunks: List = ( [] ) # keep track of the returned chunks - used for calculating the input/output tokens for stream options + self._repeated_messages_count = 1 self.is_function_call = self.check_is_function_call(logging_obj=logging_obj) self.created: Optional[int] = None self._last_returned_hidden_params: Optional[dict] = None @@ -241,7 +258,7 @@ class CustomStreamWrapper: except Exception as e: raise e - def safety_checker(self) -> None: + def raise_on_model_repetition(self) -> None: """ Fixes - https://github.com/BerriAI/litellm/issues/5158 @@ -249,28 +266,35 @@ class CustomStreamWrapper: Raises - InternalServerError, if LLM enters infinite loop while streaming """ - if len(self.chunks) >= litellm.REPEATED_STREAMING_CHUNK_LIMIT: - # Get the last n chunks - last_chunks = self.chunks[-litellm.REPEATED_STREAMING_CHUNK_LIMIT :] + if len(self.chunks) < 2: + return - # Extract the relevant content from the chunks - last_contents = [chunk.choices[0].delta.content for chunk in last_chunks] + last_content = self.chunks[-1].choices[0].delta.content - # Check if all extracted contents are identical - if all(content == last_contents[0] for content in last_contents): - if ( - last_contents[0] is not None - and isinstance(last_contents[0], str) - and len(last_contents[0]) > 2 - ): # ignore empty content - https://github.com/BerriAI/litellm/issues/5158#issuecomment-2287156946 - # All last n chunks are identical - raise litellm.InternalServerError( - message="The model is repeating the same chunk = {}.".format( - last_contents[0] - ), - model="", - llm_provider="", - ) + if ( + last_content is None + or not isinstance(last_content, str) + or len(last_content) <= 2 + ): # ignore empty content - https://github.com/BerriAI/litellm/issues/5158#issuecomment-2287156946 + self._repeated_messages_count = 1 + return + + second_to_last_content = self.chunks[-2].choices[0].delta.content + + if last_content == second_to_last_content: + self._repeated_messages_count += 1 + else: + self._repeated_messages_count = 1 + + if self._repeated_messages_count >= litellm.REPEATED_STREAMING_CHUNK_LIMIT: + # All last n chunks are identical + raise litellm.InternalServerError( + message="The model is repeating the same chunk = {}.".format( + last_content + ), + model="", + llm_provider="", + ) def check_special_tokens(self, chunk: str, finish_reason: Optional[str]): """ @@ -807,6 +831,10 @@ class CustomStreamWrapper: "annotations" in model_response.choices[0].delta and model_response.choices[0].delta.annotations is not None ) + or ( + getattr(model_response.choices[0].delta, "reasoning_items", None) + is not None + ) ): return True else: @@ -924,7 +952,7 @@ class CustomStreamWrapper: if ( is_chunk_non_empty ): # cannot set content of an OpenAI Object to be an empty string - self.safety_checker() + self.raise_on_model_repetition() hold, model_response_str = self.check_special_tokens( chunk=completion_obj["content"], finish_reason=model_response.choices[0].finish_reason, @@ -1893,15 +1921,19 @@ class CustomStreamWrapper: "usage", getattr(complete_streaming_response, "usage"), ) + try: + _cache_copy = complete_streaming_response.model_copy(deep=True) + _log_copy = complete_streaming_response.model_copy(deep=True) + except RuntimeError: + _cache_copy = complete_streaming_response.model_copy() + _log_copy = complete_streaming_response.model_copy() self.cache_streaming_response( - processed_chunk=complete_streaming_response.model_copy( - deep=True - ), + processed_chunk=_cache_copy, cache_hit=cache_hit, ) executor.submit( self.logging_obj.success_handler, - complete_streaming_response.model_copy(deep=True), + _log_copy, None, None, cache_hit, @@ -2078,7 +2110,9 @@ class CustomStreamWrapper: ): chunk = self.completion_stream else: - chunk = next(self.completion_stream) # type: ignore[arg-type] + chunk = await asyncio.to_thread(_next_sync_or_exhausted, self.completion_stream) # type: ignore[arg-type] + if chunk is _SYNC_ITER_EXHAUSTED: + raise StopAsyncIteration if chunk is not None and chunk != b"": processed_chunk = self.chunk_creator(chunk=chunk) if processed_chunk is None: @@ -2113,11 +2147,13 @@ class CustomStreamWrapper: "usage", getattr(complete_streaming_response, "usage"), ) + try: + _copy = complete_streaming_response.model_copy(deep=True) + except RuntimeError: + _copy = complete_streaming_response.model_copy() asyncio.create_task( self.async_cache_streaming_response( - processed_chunk=complete_streaming_response.model_copy( - deep=True - ), + processed_chunk=_copy, cache_hit=cache_hit, ) ) @@ -2136,22 +2172,36 @@ class CustomStreamWrapper: self.sent_stream_usage = True return response - asyncio.create_task( - self.logging_obj.async_success_handler( + _deferred_cb = getattr( + self.logging_obj, + "_on_deferred_stream_complete", + None, + ) + if _deferred_cb is not None: + # Proxy has post-call guardrails — let the closure + # run guardrails on the assembled response, then + # fire logging with guardrail_information populated. + self.logging_obj._on_deferred_stream_complete = None # type: ignore[attr-defined] + asyncio.create_task( + _deferred_cb(complete_streaming_response, cache_hit) + ) + else: + asyncio.create_task( + self.logging_obj.async_success_handler( + complete_streaming_response, + cache_hit=cache_hit, + start_time=None, + end_time=None, + ) + ) + + executor.submit( + self.logging_obj.success_handler, complete_streaming_response, cache_hit=cache_hit, start_time=None, end_time=None, ) - ) - - executor.submit( - self.logging_obj.success_handler, - complete_streaming_response, - cache_hit=cache_hit, - start_time=None, - end_time=None, - ) raise StopAsyncIteration # Re-raise StopIteration else: diff --git a/litellm/llms/a2a/chat/guardrail_translation/handler.py b/litellm/llms/a2a/chat/guardrail_translation/handler.py index fbd1da749c..3d6037b1f8 100644 --- a/litellm/llms/a2a/chat/guardrail_translation/handler.py +++ b/litellm/llms/a2a/chat/guardrail_translation/handler.py @@ -111,6 +111,7 @@ class A2AGuardrailHandler(BaseTranslation): guardrail_to_apply: "CustomGuardrail", litellm_logging_obj: Optional["LiteLLMLoggingObj"] = None, user_api_key_dict: Optional["UserAPIKeyAuth"] = None, + request_data: Optional[dict] = None, ) -> Any: """ Process A2A output response by applying guardrails to text content. @@ -166,13 +167,21 @@ class A2AGuardrailHandler(BaseTranslation): return response # Step 2: Apply guardrail to all texts in batch - # Create a request_data dict with response info and user API key metadata - request_data: dict = {"response": response_dict} + # Use the real request_data if provided (proxy path), otherwise + # create a standalone dict (SDK / direct-call path). + if request_data is None: + request_data = {"response": response_dict} + else: + if "response" not in request_data: + request_data["response"] = response_dict # Add user API key metadata with prefixed keys - user_metadata = self.transform_user_api_key_dict_to_metadata(user_api_key_dict) - if user_metadata: - request_data["litellm_metadata"] = user_metadata + if "litellm_metadata" not in request_data: + user_metadata = self.transform_user_api_key_dict_to_metadata( + user_api_key_dict + ) + if user_metadata: + request_data["litellm_metadata"] = user_metadata inputs = GenericGuardrailAPIInputs(texts=texts_to_check) @@ -213,6 +222,7 @@ class A2AGuardrailHandler(BaseTranslation): guardrail_to_apply: "CustomGuardrail", litellm_logging_obj: Optional["LiteLLMLoggingObj"] = None, user_api_key_dict: Optional["UserAPIKeyAuth"] = None, + request_data: Optional[dict] = None, ) -> List[Any]: """ Process A2A streaming output by applying guardrails to accumulated text. @@ -224,44 +234,28 @@ class A2AGuardrailHandler(BaseTranslation): 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] + parsed, valid_parsed = self._parse_streaming_responses(responses_so_far) 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) + combined_text, chunk_indices_with_text = self._collect_text_from_parsed_chunks( + valid_parsed + ) 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 + if request_data is None: + request_data = {"responses_so_far": responses_so_far} + else: + if "responses_so_far" not in request_data: + request_data["responses_so_far"] = responses_so_far + + if "litellm_metadata" not in request_data: + user_metadata = self.transform_user_api_key_dict_to_metadata( + user_api_key_dict + ) + if user_metadata: + request_data["litellm_metadata"] = user_metadata inputs = GenericGuardrailAPIInputs(texts=[combined_text]) guardrailed_inputs = await guardrail_to_apply.apply_guardrail( @@ -319,6 +313,43 @@ class A2AGuardrailHandler(BaseTranslation): return responses_so_far + def _parse_streaming_responses( + self, + responses_so_far: List[Any], + ) -> Tuple[List[Optional[Dict[str, Any]]], List[Tuple[int, Dict[str, Any]]]]: + """Parse JSON-RPC items, returning aligned parsed list and valid entries.""" + 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] + return parsed, valid_parsed + + def _collect_text_from_parsed_chunks( + self, + valid_parsed: List[Tuple[int, Dict[str, Any]]], + ) -> Tuple[str, List[int]]: + """Collect text from parsed chunks, returning combined text and indices.""" + from litellm.llms.a2a.common_utils import extract_text_from_a2a_response + + text_parts: List[str] = [] + chunk_indices_with_text: List[int] = [] + 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) + return "".join(text_parts), chunk_indices_with_text + def _extract_texts_from_result( self, result: Dict[str, Any], diff --git a/litellm/llms/anthropic/batches/transformation.py b/litellm/llms/anthropic/batches/transformation.py index 699f133f0f..98c0588a09 100644 --- a/litellm/llms/anthropic/batches/transformation.py +++ b/litellm/llms/anthropic/batches/transformation.py @@ -42,9 +42,8 @@ class AnthropicBatchesConfig(BaseBatchesConfig): api_base: Optional[str] = None, ) -> dict: """Validate and prepare environment-specific headers and parameters.""" - # Resolve api_key from environment if not provided - api_key = api_key or self.anthropic_model_info.get_api_key() - if api_key is None: + auth_header = self.anthropic_model_info.get_auth_header(api_key) + if auth_header is None: raise ValueError( "Missing Anthropic API Key - A call is being made to anthropic but no key is set either in the environment variables or via params" ) @@ -52,8 +51,8 @@ class AnthropicBatchesConfig(BaseBatchesConfig): "accept": "application/json", "anthropic-version": "2023-06-01", "content-type": "application/json", - "x-api-key": api_key, } + _headers.update(auth_header) # Add beta header for message batches if "anthropic-beta" not in headers: headers["anthropic-beta"] = "message-batches-2024-09-24" diff --git a/litellm/llms/anthropic/chat/guardrail_translation/handler.py b/litellm/llms/anthropic/chat/guardrail_translation/handler.py index 5372757cbb..d31a0a091e 100644 --- a/litellm/llms/anthropic/chat/guardrail_translation/handler.py +++ b/litellm/llms/anthropic/chat/guardrail_translation/handler.py @@ -252,6 +252,7 @@ class AnthropicMessagesHandler(BaseTranslation): guardrail_to_apply: "CustomGuardrail", litellm_logging_obj: Optional[Any] = None, user_api_key_dict: Optional[Any] = None, + request_data: Optional[dict] = None, ) -> Any: """ Process output response by applying guardrails to text content and tool calls. @@ -276,76 +277,35 @@ class AnthropicMessagesHandler(BaseTranslation): images_to_check: List[str] = [] tool_calls_to_check: List[ChatCompletionToolCallChunk] = [] task_mappings: List[Tuple[int, Optional[int]]] = [] - # Track (content_index, None) for each text - - # Handle both dict and object responses - response_content: List[Any] = [] - if isinstance(response, dict): - response_content = response.get("content", []) or [] - elif hasattr(response, "content"): - content = getattr(response, "content", None) - response_content = content or [] - else: - response_content = [] + response_content = self._get_response_content(response) if not response_content: return response # Step 1: Extract all text content and tool calls from response - for content_idx, content_block in enumerate(response_content): - # Handle both dict and Pydantic object content blocks - block_dict: Dict[str, Any] = {} - if isinstance(content_block, dict): - block_type = content_block.get("type") - block_dict = cast(Dict[str, Any], content_block) - elif hasattr(content_block, "type"): - block_type = getattr(content_block, "type", None) - # Convert Pydantic object to dict for processing - if hasattr(content_block, "model_dump"): - block_dict = content_block.model_dump() - else: - block_dict = { - "type": block_type, - "text": getattr(content_block, "text", None), - } - else: - continue - - if block_type in ["text", "tool_use"]: - self._extract_output_text_and_images( - content_block=block_dict, - content_idx=content_idx, - texts_to_check=texts_to_check, - images_to_check=images_to_check, - task_mappings=task_mappings, - tool_calls_to_check=tool_calls_to_check, - ) + self._extract_from_content_blocks( + response_content, + texts_to_check, + images_to_check, + task_mappings, + tool_calls_to_check, + ) # Step 2: Apply guardrail to all texts in batch if texts_to_check or tool_calls_to_check: - # Create a request_data dict with response info and user API key metadata - request_data: dict = {"response": response} - - # Add user API key metadata with prefixed keys - user_metadata = self.transform_user_api_key_dict_to_metadata( - user_api_key_dict + request_data = self._prepare_request_data( + request_data, + response, + user_api_key_dict, + key="response", ) - if user_metadata: - request_data["litellm_metadata"] = user_metadata - inputs = GenericGuardrailAPIInputs(texts=texts_to_check) - if images_to_check: - inputs["images"] = images_to_check - if tool_calls_to_check: - inputs["tool_calls"] = tool_calls_to_check - # Include model information from the response if available - response_model = None - if isinstance(response, dict): - response_model = response.get("model") - elif hasattr(response, "model"): - response_model = getattr(response, "model", None) - if response_model: - inputs["model"] = response_model + inputs = self._build_guardrail_inputs( + texts_to_check, + images_to_check, + tool_calls_to_check, + response, + ) guardrailed_inputs = await guardrail_to_apply.apply_guardrail( inputs=inputs, @@ -375,6 +335,7 @@ class AnthropicMessagesHandler(BaseTranslation): guardrail_to_apply: "CustomGuardrail", litellm_logging_obj: Optional[Any] = None, user_api_key_dict: Optional[Any] = None, + request_data: Optional[dict] = None, ) -> List[Any]: """ Process output streaming response by applying guardrails to text content. @@ -413,7 +374,7 @@ class AnthropicMessagesHandler(BaseTranslation): _guardrailed_inputs = await guardrail_to_apply.apply_guardrail( # allow rejecting the response, if invalid inputs=guardrail_inputs, - request_data={}, + request_data=request_data if request_data is not None else {}, input_type="response", logging_obj=litellm_logging_obj, ) @@ -426,12 +387,101 @@ class AnthropicMessagesHandler(BaseTranslation): string_so_far = self.get_streaming_string_so_far(responses_so_far) _guardrailed_inputs = await guardrail_to_apply.apply_guardrail( # allow rejecting the response, if invalid inputs={"texts": [string_so_far]}, - request_data={}, + request_data=request_data if request_data is not None else {}, input_type="response", logging_obj=litellm_logging_obj, ) return responses_so_far + def _prepare_request_data( + self, + request_data: Optional[dict], + response: Any, + user_api_key_dict: Optional[Any], + key: str, + ) -> dict: + """Ensure request_data has the response/responses_so_far key and metadata.""" + if request_data is None: + request_data = {key: response} + else: + if key not in request_data: + request_data[key] = response + + if "litellm_metadata" not in request_data: + user_metadata = self.transform_user_api_key_dict_to_metadata( + user_api_key_dict + ) + if user_metadata: + request_data["litellm_metadata"] = user_metadata + return request_data + + @staticmethod + def _get_response_content(response: Any) -> List[Any]: + """Extract content list from a dict or object response.""" + if isinstance(response, dict): + return response.get("content", []) or [] + elif hasattr(response, "content"): + return getattr(response, "content", None) or [] + return [] + + def _extract_from_content_blocks( + self, + response_content: List[Any], + texts_to_check: List[str], + images_to_check: List[str], + task_mappings: List[Tuple[int, Optional[int]]], + tool_calls_to_check: List["ChatCompletionToolCallChunk"], + ) -> None: + """Extract text, images, and tool calls from content blocks.""" + for content_idx, content_block in enumerate(response_content): + block_dict: Dict[str, Any] = {} + if isinstance(content_block, dict): + block_type = content_block.get("type") + block_dict = cast(Dict[str, Any], content_block) + elif hasattr(content_block, "type"): + block_type = getattr(content_block, "type", None) + if hasattr(content_block, "model_dump"): + block_dict = content_block.model_dump() + else: + block_dict = { + "type": block_type, + "text": getattr(content_block, "text", None), + } + else: + continue + + if block_type in ["text", "tool_use"]: + self._extract_output_text_and_images( + content_block=block_dict, + content_idx=content_idx, + texts_to_check=texts_to_check, + images_to_check=images_to_check, + task_mappings=task_mappings, + tool_calls_to_check=tool_calls_to_check, + ) + + @staticmethod + def _build_guardrail_inputs( + texts_to_check: List[str], + images_to_check: List[str], + tool_calls_to_check: List["ChatCompletionToolCallChunk"], + response: Any, + ) -> "GenericGuardrailAPIInputs": + """Build GenericGuardrailAPIInputs with optional images, tool calls, model.""" + inputs = GenericGuardrailAPIInputs(texts=texts_to_check) + if images_to_check: + inputs["images"] = images_to_check + if tool_calls_to_check: + inputs["tool_calls"] = tool_calls_to_check + response_model = None + if isinstance(response, dict): + response_model = response.get("model") + elif hasattr(response, "model"): + response_model = getattr(response, "model", None) + if response_model: + inputs["model"] = response_model + return inputs + def get_streaming_string_so_far(self, responses_so_far: List[Any]) -> str: """ Parse streaming responses and extract accumulated text content. diff --git a/litellm/llms/anthropic/chat/handler.py b/litellm/llms/anthropic/chat/handler.py index 5eebebc2e2..9f2ddcae2c 100644 --- a/litellm/llms/anthropic/chat/handler.py +++ b/litellm/llms/anthropic/chat/handler.py @@ -48,6 +48,10 @@ from litellm.types.llms.openai import ( ChatCompletionToolCallChunk, ChatCompletionToolCallFunctionChunk, ) +from litellm.types.responses.main import ( + OutputCodeInterpreterCall, + build_code_interpreter_log_outputs, +) from litellm.types.utils import ( Delta, GenericStreamingChunk, @@ -538,6 +542,12 @@ class ModelResponseIterator: # Accumulate compaction blocks for multi-turn reconstruction self.compaction_blocks: List[Dict[str, Any]] = [] + # Track server tool use inputs and results for code_interpreter_results + self._server_tool_inputs: Dict[str, Any] = {} + self.tool_results: List[Dict[str, Any]] = [] + self._current_server_tool_id: Optional[str] = None + self._container_id: Optional[str] = None + def check_empty_tool_call_args(self) -> bool: """ Check if the tool call block so far has been an empty string @@ -682,6 +692,39 @@ class ModelResponseIterator: return content_block_start + def _build_code_interpreter_results(self) -> list: + """Convert accumulated tool_results to OutputCodeInterpreterCall objects. + + Called during streaming to produce provider-neutral code_interpreter_results + alongside the raw tool_results, so the Responses API layer doesn't need + Anthropic-specific knowledge. + + Returns the full cumulative list each time (not incremental), matching + how web_search_results works. stream_chunk_builder uses "last value + wins" for list-valued provider_specific_fields keys, so the last + emission must contain every result. + """ + results = [] + for tr in self.tool_results: + if tr.get("type") != "bash_code_execution_tool_result": + continue + call_id = tr.get("tool_use_id", "") + content = tr.get("content", {}) + log_outputs = build_code_interpreter_log_outputs(content) + tool_input = self._server_tool_inputs.get(call_id, {}) + code = tool_input.get("command", "") if isinstance(tool_input, dict) else "" + results.append( + OutputCodeInterpreterCall( + type="code_interpreter_call", + id=call_id, + code=code, + container_id=self._container_id, + status="completed", + outputs=log_outputs, + ) + ) + return results + def chunk_parser(self, chunk: dict) -> ModelResponseStream: # noqa: PLR0915 try: type_chunk = chunk.get("type", "") or "" @@ -748,6 +791,23 @@ class ModelResponseIterator: ), index=self.tool_index, ) + # Track server tool use inputs for code_interpreter_results. + # The initial input in content_block_start is typically {} + # for streaming; the full input arrives via input_json_delta + # and is assembled at content_block_stop. + if ( + content_block_start["content_block"]["type"] + == "server_tool_use" + ): + self._current_server_tool_id = content_block_start[ + "content_block" + ]["id"] + tool_input = content_block_start["content_block"].get( + "input", {} + ) + self._server_tool_inputs[ + self._current_server_tool_id + ] = tool_input # Include caller information if present (for programmatic tool calling) if "caller" in content_block_start["content_block"]: caller_data = content_block_start["content_block"]["caller"] @@ -808,10 +868,12 @@ class ModelResponseIterator: elif content_type != "tool_search_tool_result": # Handle other tool results (code execution, etc.) # Skip tool_search_tool_result as it's internal metadata - if not hasattr(self, "tool_results"): - self.tool_results = [] self.tool_results.append(content_block_start["content_block"]) provider_specific_fields["tool_results"] = self.tool_results + # Convert to provider-neutral code_interpreter_results + provider_specific_fields[ + "code_interpreter_results" + ] = self._build_code_interpreter_results() elif type_chunk == "content_block_stop": ContentBlockStop(**chunk) # type: ignore @@ -828,6 +890,26 @@ class ModelResponseIterator: ), index=self.tool_index, ) + # Update server_tool_inputs with fully assembled input + # from input_json_delta chunks (content_block_start has {}) + if ( + self.current_content_block_type == "server_tool_use" + and self._current_server_tool_id + ): + args = "" + for block in self.content_blocks: + if block["delta"]["type"] == "input_json_delta": + partial_json = block["delta"].get("partial_json") + if isinstance(partial_json, str): + args += partial_json + if args: + try: + self._server_tool_inputs[ + self._current_server_tool_id + ] = json.loads(args) + except (json.JSONDecodeError, TypeError): + pass + self._current_server_tool_id = None # Reset response_format tool tracking when block stops self.is_response_format_tool = False # Reset current content block type @@ -840,6 +922,17 @@ class ModelResponseIterator: finish_reason, usage, container = self._handle_message_delta(chunk) if container: provider_specific_fields["container"] = container + # Store container_id and re-emit code_interpreter_results + # so stream_chunk_builder's last-value-wins picks up the + # version with container_id populated. + container_id = ( + container.get("id") if isinstance(container, dict) else None + ) + if container_id and self.tool_results: + self._container_id = container_id + provider_specific_fields[ + "code_interpreter_results" + ] = self._build_code_interpreter_results() elif type_chunk == "message_start": """ Anthropic diff --git a/litellm/llms/anthropic/chat/transformation.py b/litellm/llms/anthropic/chat/transformation.py index 47cdd8287e..9a99f9efc8 100644 --- a/litellm/llms/anthropic/chat/transformation.py +++ b/litellm/llms/anthropic/chat/transformation.py @@ -50,6 +50,10 @@ from litellm.types.llms.openai import ( OpenAIMcpServerTool, OpenAIWebSearchOptions, ) +from litellm.types.responses.main import ( + OutputCodeInterpreterCall, + build_code_interpreter_log_outputs, +) from litellm.types.utils import ( CacheCreationTokenDetails, CompletionTokensDetailsWrapper, @@ -1417,6 +1421,16 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): ): optional_params["metadata"] = {"user_id": _litellm_metadata["user_id"]} + ## Ensure metadata only contains user_id (only documented field in Anthropic Messages API) + if "metadata" in optional_params and isinstance( + optional_params["metadata"], dict + ): + _user_id = optional_params["metadata"].get("user_id") + if _user_id is not None: + optional_params["metadata"] = {"user_id": _user_id} + else: + optional_params.pop("metadata") + # Remove internal LiteLLM parameters that should not be sent to Anthropic API optional_params.pop("is_vertex_request", None) @@ -1522,7 +1536,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): tool_results = [] tool_results.append(content) - elif content.get("thinking", None) is not None: + elif content.get("type") == "thinking": if thinking_blocks is None: thinking_blocks = [] thinking_blocks.append(cast(ChatCompletionThinkingBlock, content)) @@ -1682,6 +1696,96 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): ) return usage + def _build_code_by_id_map( + self, tool_calls: List[ChatCompletionToolCallChunk] + ) -> Dict[str, str]: + code_by_id: Dict[str, str] = {} + for tc in tool_calls: + try: + args = json.loads(tc.get("function", {}).get("arguments", "{}")) + call_id = tc.get("id") + command = args.get("command", "") + if isinstance(call_id, str): + code_by_id[call_id] = command if isinstance(command, str) else "" + except Exception: + pass + return code_by_id + + def _build_code_interpreter_results( + self, + tool_results: List[Any], + code_by_id: Dict[str, str], + container_id: Optional[str], + ) -> List[OutputCodeInterpreterCall]: + code_interpreter_results = [] + for tr in tool_results: + if tr.get("type") != "bash_code_execution_tool_result": + continue + call_id = tr.get("tool_use_id", "") + content = tr.get("content", {}) + log_outputs = build_code_interpreter_log_outputs(content) + code_interpreter_results.append( + OutputCodeInterpreterCall( + type="code_interpreter_call", + id=call_id, + code=code_by_id.get(call_id, ""), + container_id=container_id, + status="completed", + outputs=log_outputs, + ) + ) + return code_interpreter_results + + def _build_provider_specific_fields( + self, + completion_response: dict, + citations: Optional[List[Any]], + thinking_blocks: Optional[ + List[ + Union[ChatCompletionThinkingBlock, ChatCompletionRedactedThinkingBlock] + ] + ], + web_search_results: Optional[List[Any]], + tool_results: Optional[List[Any]], + compaction_blocks: Optional[List[Any]], + tool_calls: List[ChatCompletionToolCallChunk], + ) -> Dict[str, Any]: + provider_specific_fields: Dict[str, Any] = { + "citations": citations, + "thinking_blocks": thinking_blocks, + } + + context_management = completion_response.get("context_management") + if context_management is not None: + provider_specific_fields["context_management"] = context_management + + if web_search_results is not None: + provider_specific_fields["web_search_results"] = web_search_results + + if tool_results is not None: + provider_specific_fields["tool_results"] = tool_results + container_id = ( + completion_response.get("container", {}).get("id") + if isinstance(completion_response.get("container"), dict) + else None + ) + code_by_id = self._build_code_by_id_map(tool_calls) + code_interpreter_results = self._build_code_interpreter_results( + tool_results, code_by_id, container_id + ) + provider_specific_fields[ + "code_interpreter_results" + ] = code_interpreter_results + + container = completion_response.get("container") + if container is not None: + provider_specific_fields["container"] = container + + if compaction_blocks is not None: + provider_specific_fields["compaction_blocks"] = compaction_blocks + + return provider_specific_fields + def transform_parsed_response( self, completion_response: dict, @@ -1702,98 +1806,73 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): status_code=raw_response.status_code, headers=response_headers, ) - else: - text_content = "" - citations: Optional[List[Any]] = None - thinking_blocks: Optional[ - List[ - Union[ - ChatCompletionThinkingBlock, ChatCompletionRedactedThinkingBlock - ] - ] - ] = None - reasoning_content: Optional[str] = None - tool_calls: List[ChatCompletionToolCallChunk] = [] - ( - text_content, - citations, - thinking_blocks, - reasoning_content, - tool_calls, - web_search_results, - tool_results, - compaction_blocks, - ) = self.extract_response_content(completion_response=completion_response) + ( + text_content, + citations, + thinking_blocks, + reasoning_content, + tool_calls, + web_search_results, + tool_results, + compaction_blocks, + ) = self.extract_response_content(completion_response=completion_response) - if ( - prefix_prompt is not None - and not text_content.startswith(prefix_prompt) - and not litellm.disable_add_prefix_to_prompt - ): - text_content = prefix_prompt + text_content + if ( + prefix_prompt is not None + and not text_content.startswith(prefix_prompt) + and not litellm.disable_add_prefix_to_prompt + ): + text_content = prefix_prompt + text_content - context_management: Optional[Dict] = completion_response.get( - "context_management" - ) + provider_specific_fields = self._build_provider_specific_fields( + completion_response, + citations, + thinking_blocks, + web_search_results, + tool_results, + compaction_blocks, + tool_calls, + ) - container: Optional[Dict] = completion_response.get("container") + _message = litellm.Message( + tool_calls=tool_calls, + content=text_content or None, + provider_specific_fields=provider_specific_fields, + thinking_blocks=thinking_blocks, + reasoning_content=reasoning_content, + ) + _message.provider_specific_fields = provider_specific_fields - provider_specific_fields: Dict[str, Any] = { - "citations": citations, - "thinking_blocks": thinking_blocks, - } - if context_management is not None: - provider_specific_fields["context_management"] = context_management - if web_search_results is not None: - provider_specific_fields["web_search_results"] = web_search_results - if tool_results is not None: - provider_specific_fields["tool_results"] = tool_results - if container is not None: - provider_specific_fields["container"] = container - if compaction_blocks is not None: - provider_specific_fields["compaction_blocks"] = compaction_blocks + json_mode_message = self._transform_response_for_json_mode( + json_mode=json_mode, + tool_calls=tool_calls, + ) + if json_mode_message is not None: + completion_response["stop_reason"] = "stop" + _message = json_mode_message - _message = litellm.Message( - tool_calls=tool_calls, - content=text_content or None, - provider_specific_fields=provider_specific_fields, - thinking_blocks=thinking_blocks, - reasoning_content=reasoning_content, - ) - _message.provider_specific_fields = provider_specific_fields + model_response.choices[0].message = _message + model_response._hidden_params["original_response"] = completion_response[ + "content" + ] + model_response.choices[0].finish_reason = cast( + OpenAIChatCompletionFinishReason, + map_finish_reason(completion_response["stop_reason"]), + ) - ## HANDLE JSON MODE - anthropic returns single function call - json_mode_message = self._transform_response_for_json_mode( - json_mode=json_mode, - tool_calls=tool_calls, - ) - if json_mode_message is not None: - completion_response["stop_reason"] = "stop" - _message = json_mode_message - - model_response.choices[0].message = _message # type: ignore - model_response._hidden_params["original_response"] = completion_response[ - "content" - ] # allow user to access raw anthropic tool calling response - - model_response.choices[0].finish_reason = cast( - OpenAIChatCompletionFinishReason, - map_finish_reason(completion_response["stop_reason"]), - ) - - ## CALCULATING USAGE usage = self.calculate_usage( usage_object=completion_response["usage"], reasoning_content=reasoning_content, completion_response=completion_response, speed=speed, ) - setattr(model_response, "usage", usage) # type: ignore + setattr(model_response, "usage", usage) model_response.created = int(time.time()) model_response.model = completion_response["model"] + _hidden_params["provider_specific_fields"] = provider_specific_fields model_response._hidden_params = _hidden_params return model_response diff --git a/litellm/llms/anthropic/common_utils.py b/litellm/llms/anthropic/common_utils.py index ac35246787..7d2d0a7496 100644 --- a/litellm/llms/anthropic/common_utils.py +++ b/litellm/llms/anthropic/common_utils.py @@ -359,9 +359,7 @@ class AnthropicModelInfo(BaseLLMModelInfo): Returns: List of beta header strings """ - from litellm.types.llms.anthropic import ( - ANTHROPIC_EFFORT_BETA_HEADER, - ) + from litellm.types.llms.anthropic import ANTHROPIC_EFFORT_BETA_HEADER betas = [] @@ -390,7 +388,8 @@ class AnthropicModelInfo(BaseLLMModelInfo): def get_anthropic_headers( self, - api_key: str, + api_key: Optional[str] = None, + auth_token: Optional[str] = None, anthropic_version: Optional[str] = None, computer_tool_used: Optional[str] = None, prompt_caching_set: bool = False, @@ -451,7 +450,9 @@ class AnthropicModelInfo(BaseLLMModelInfo): headers["authorization"] = f"Bearer {api_key}" headers["anthropic-dangerous-direct-browser-access"] = "true" betas.add(ANTHROPIC_OAUTH_BETA_HEADER) - else: + elif auth_token and not api_key: + headers["authorization"] = f"Bearer {auth_token}" + elif api_key: headers["x-api-key"] = api_key if user_anthropic_beta_headers is not None: @@ -485,9 +486,14 @@ class AnthropicModelInfo(BaseLLMModelInfo): headers, api_key = optionally_handle_anthropic_oauth( headers=headers, api_key=api_key ) + api_key = AnthropicModelInfo.get_api_key(api_key) + # Resolve auth_token from ANTHROPIC_AUTH_TOKEN if api_key is not set + auth_token: Optional[str] = None if api_key is None: + auth_token = AnthropicModelInfo.get_auth_token() + if api_key is None and auth_token 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", + 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` or `ANTHROPIC_AUTH_TOKEN` in your environment vars", llm_provider="anthropic", model=model, ) @@ -519,6 +525,7 @@ class AnthropicModelInfo(BaseLLMModelInfo): prompt_caching_set=prompt_caching_set, pdf_used=pdf_used, api_key=api_key, + auth_token=auth_token, file_id_used=file_id_used, web_search_tool_used=web_search_tool_used, is_vertex_request=optional_params.get("is_vertex_request", False), @@ -543,6 +550,7 @@ class AnthropicModelInfo(BaseLLMModelInfo): return ( api_base or get_secret_str("ANTHROPIC_API_BASE") + or get_secret_str("ANTHROPIC_BASE_URL") or "https://api.anthropic.com" ) @@ -552,6 +560,35 @@ class AnthropicModelInfo(BaseLLMModelInfo): return api_key or get_secret_str("ANTHROPIC_API_KEY") + @staticmethod + def get_auth_token(auth_token: Optional[str] = None) -> Optional[str]: + """Get auth token from ANTHROPIC_AUTH_TOKEN env var. + + Unlike api_key (which uses X-Api-Key header), auth_token uses + Authorization: Bearer header, matching the official Anthropic SDK behavior. + """ + from litellm.secret_managers.main import get_secret_str + + return auth_token or get_secret_str("ANTHROPIC_AUTH_TOKEN") + + @staticmethod + def get_auth_header(api_key: Optional[str] = None) -> Optional[dict]: + """Resolve Anthropic credentials and return the appropriate auth header dict. + + Checks ANTHROPIC_API_KEY first (-> x-api-key), then + ANTHROPIC_AUTH_TOKEN (-> Authorization: Bearer). + Returns None if neither is available. + """ + resolved_key = AnthropicModelInfo.get_api_key(api_key) + if resolved_key is not None: + if is_anthropic_oauth_key(resolved_key): + return {"authorization": f"Bearer {resolved_key}"} + return {"x-api-key": resolved_key} + auth_token = AnthropicModelInfo.get_auth_token() + if auth_token is not None: + return {"authorization": f"Bearer {auth_token}"} + return None + @staticmethod def get_base_model(model: Optional[str] = None) -> Optional[str]: return model.replace("anthropic/", "") if model else None @@ -560,14 +597,16 @@ class AnthropicModelInfo(BaseLLMModelInfo): self, api_key: Optional[str] = None, api_base: Optional[str] = None ) -> List[str]: api_base = AnthropicModelInfo.get_api_base(api_base) - api_key = AnthropicModelInfo.get_api_key(api_key) - if api_base is None or api_key is None: + auth_header = AnthropicModelInfo.get_auth_header(api_key) + if api_base is None or auth_header is None: raise ValueError( - "ANTHROPIC_API_BASE or ANTHROPIC_API_KEY is not set. Please set the environment variable, to query Anthropic's `/models` endpoint." + "ANTHROPIC_API_BASE/ANTHROPIC_BASE_URL or ANTHROPIC_API_KEY/ANTHROPIC_AUTH_TOKEN is not set. Please set the environment variable, to query Anthropic's `/models` endpoint." ) + headers = {"anthropic-version": "2023-06-01"} + headers.update(auth_header) response = litellm.module_level_client.get( url=f"{api_base}/v1/models", - headers={"x-api-key": api_key, "anthropic-version": "2023-06-01"}, + headers=headers, ) try: diff --git a/litellm/llms/anthropic/experimental_pass_through/adapters/handler.py b/litellm/llms/anthropic/experimental_pass_through/adapters/handler.py index 8b1b21a0f9..897ca3bf89 100644 --- a/litellm/llms/anthropic/experimental_pass_through/adapters/handler.py +++ b/litellm/llms/anthropic/experimental_pass_through/adapters/handler.py @@ -15,6 +15,9 @@ import litellm from litellm.llms.anthropic.experimental_pass_through.adapters.transformation import ( AnthropicAdapter, ) +from litellm.llms.anthropic.experimental_pass_through.utils import ( + is_reasoning_auto_summary_enabled, +) from litellm.types.llms.anthropic_messages.anthropic_response import ( AnthropicMessagesResponse, ) @@ -44,8 +47,9 @@ class LiteLLMMessagesToCompletionTransformationHandler: For OpenAI models, Chat Completions typically does not return reasoning text (only token accounting). To return a thinking-like content block in the - Anthropic response format, we route the request through OpenAI's Responses API - and request a reasoning summary. + Anthropic response format, we route the request through OpenAI's Responses API. + If the user provides a `summary` field in the thinking dict, it is passed + through to the OpenAI reasoning params (opt-in per OpenAI spec). """ custom_llm_provider = completion_kwargs.get("custom_llm_provider") if custom_llm_provider is None: @@ -78,20 +82,29 @@ class LiteLLMMessagesToCompletionTransformationHandler: # Prefix model with "responses/" to route to OpenAI Responses API completion_kwargs["model"] = f"responses/{model}" + auto_summary = is_reasoning_auto_summary_enabled() + reasoning_effort = completion_kwargs.get("reasoning_effort") + summary = thinking.get("summary") if isinstance(reasoning_effort, str) and reasoning_effort: - completion_kwargs["reasoning_effort"] = { - "effort": reasoning_effort, - "summary": "detailed", - } + reasoning_dict: Dict[str, Any] = {"effort": reasoning_effort} + if summary: + reasoning_dict["summary"] = summary + elif auto_summary: + reasoning_dict["summary"] = "detailed" + completion_kwargs["reasoning_effort"] = reasoning_dict elif isinstance(reasoning_effort, dict): if ( "summary" not in reasoning_effort and "generate_summary" not in reasoning_effort ): - updated_reasoning_effort = dict(reasoning_effort) - updated_reasoning_effort["summary"] = "detailed" - completion_kwargs["reasoning_effort"] = updated_reasoning_effort + effective_summary = ( + summary if summary else ("detailed" if auto_summary else None) + ) + if effective_summary: + updated_reasoning_effort = dict(reasoning_effort) + updated_reasoning_effort["summary"] = effective_summary + completion_kwargs["reasoning_effort"] = updated_reasoning_effort @staticmethod def _prepare_completion_kwargs( diff --git a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py index 3fda05172b..ed49943b7f 100644 --- a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py +++ b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py @@ -1,3 +1,4 @@ +import copy import hashlib import json from typing import ( @@ -13,6 +14,10 @@ from typing import ( cast, ) +from litellm.llms.anthropic.experimental_pass_through.utils import ( + is_reasoning_auto_summary_enabled, +) + # OpenAI has a 64-character limit for function/tool names # Anthropic does not have this limit, so we need to truncate long names OPENAI_MAX_TOOL_NAME_LENGTH = 64 @@ -733,6 +738,24 @@ class LiteLLMAnthropicMessagesAdapter: thinking ) if reasoning_effort: + summary = ( + thinking.get("summary") if isinstance(thinking, dict) else None + ) + auto_summary = is_reasoning_auto_summary_enabled() + if summary: + return { + "reasoning_effort": { + "effort": reasoning_effort, + "summary": summary, + } + } + elif auto_summary: + return { + "reasoning_effort": { + "effort": reasoning_effort, + "summary": "detailed", + } + } return {"reasoning_effort": reasoning_effort} return {} @@ -833,6 +856,11 @@ class LiteLLMAnthropicMessagesAdapter: if not schema: return None + # Deep copy to avoid mutating the original schema + schema = copy.deepcopy(schema) + # OpenAI strict mode requires additionalProperties: false on every object + self._add_additional_properties_false(schema) + # Convert to OpenAI response_format structure return { "type": "json_schema", @@ -843,6 +871,46 @@ class LiteLLMAnthropicMessagesAdapter: }, } + @staticmethod + def _add_additional_properties_false(schema: dict) -> None: + """ + Recursively ensure object schemas comply with OpenAI strict mode. + + OpenAI's strict mode requires: + 1. 'additionalProperties': false at every object nesting level + 2. All property keys listed in 'required' + """ + if not isinstance(schema, dict): + return + + if schema.get("type") == "object" and "properties" in schema: + schema["additionalProperties"] = False + schema["required"] = list(schema["properties"].keys()) + for prop in schema["properties"].values(): + LiteLLMAnthropicMessagesAdapter._add_additional_properties_false(prop) + + # Handle array items + if "items" in schema: + LiteLLMAnthropicMessagesAdapter._add_additional_properties_false( + schema["items"] + ) + + # Handle anyOf/oneOf/allOf + for key in ("anyOf", "oneOf", "allOf"): + if key in schema: + for sub_schema in schema[key]: + LiteLLMAnthropicMessagesAdapter._add_additional_properties_false( + sub_schema + ) + + # Handle $defs / definitions + for key in ("$defs", "definitions"): + if key in schema: + for def_schema in schema[key].values(): + LiteLLMAnthropicMessagesAdapter._add_additional_properties_false( + def_schema + ) + def _add_system_message_to_messages( self, new_messages: List[AllMessageValues], @@ -878,6 +946,144 @@ class LiteLLMAnthropicMessagesAdapter: ChatCompletionSystemMessage(role="system", content=openai_system_content), # type: ignore ) + def _translate_metadata_to_openai( + self, + anthropic_message_request: AnthropicMessagesRequest, + new_kwargs: ChatCompletionRequest, + ) -> None: + """Translate metadata fields from Anthropic request to OpenAI request.""" + if "metadata" in anthropic_message_request: + metadata = anthropic_message_request["metadata"] + if metadata and "user_id" in metadata: + new_kwargs["user"] = metadata["user_id"] + + if "litellm_metadata" in anthropic_message_request: + # metadata will be passed to litellm.acompletion(), it's a litellm_param + new_kwargs["metadata"] = anthropic_message_request.pop("litellm_metadata") + + def _translate_tool_choice_to_openai( + self, + anthropic_message_request: AnthropicMessagesRequest, + new_kwargs: ChatCompletionRequest, + ) -> None: + """Translate Anthropic tool_choice to OpenAI format.""" + if "tool_choice" not in anthropic_message_request: + return + tool_choice = anthropic_message_request["tool_choice"] + if not tool_choice: + return + new_kwargs["tool_choice"] = self.translate_anthropic_tool_choice_to_openai( + tool_choice=cast(AnthropicMessagesToolChoice, tool_choice) + ) + + def _translate_tools_to_openai( + self, + anthropic_message_request: AnthropicMessagesRequest, + new_kwargs: ChatCompletionRequest, + ) -> Dict[str, str]: + """Translate tools and extract web_search_options when needed.""" + if "tools" not in anthropic_message_request: + return {} + + tools = anthropic_message_request["tools"] + if not tools: + return {} + + web_search_tools: List[AllAnthropicToolsValues] = [] + regular_tools: List[AllAnthropicToolsValues] = [] + for tool in tools: + cast_tool = cast(Dict[str, Any], tool) + if self._is_web_search_tool(cast_tool): + web_search_tools.append(cast(AllAnthropicToolsValues, tool)) + else: + regular_tools.append(cast(AllAnthropicToolsValues, tool)) + + if web_search_tools: + new_kwargs["web_search_options"] = {} # type: ignore + + if not regular_tools: + return {} + + translated_tools, tool_name_mapping = self.translate_anthropic_tools_to_openai( + tools=regular_tools, + model=new_kwargs.get("model"), + ) + new_kwargs["tools"] = translated_tools + return tool_name_mapping + + def _translate_thinking_to_openai( + self, + anthropic_message_request: AnthropicMessagesRequest, + new_kwargs: ChatCompletionRequest, + ) -> None: + """Translate Anthropic thinking to either thinking or reasoning_effort.""" + if "thinking" not in anthropic_message_request: + return + + thinking = anthropic_message_request["thinking"] + if not thinking: + return + + model = new_kwargs.get("model", "") + if self.is_anthropic_claude_model(model): + new_kwargs["thinking"] = thinking # type: ignore + return + + reasoning_effort = self.translate_anthropic_thinking_to_reasoning_effort( + cast(Dict[str, Any], thinking) + ) + if not reasoning_effort: + return + + summary = thinking.get("summary") if isinstance(thinking, dict) else None + auto_summary = is_reasoning_auto_summary_enabled() + if summary: + new_kwargs["reasoning_effort"] = cast( + Any, + { + "effort": reasoning_effort, + "summary": summary, + }, + ) + elif auto_summary: + new_kwargs["reasoning_effort"] = cast( + Any, + { + "effort": reasoning_effort, + "summary": "detailed", + }, + ) + else: + new_kwargs["reasoning_effort"] = reasoning_effort + + def _translate_output_format_to_openai( + self, + anthropic_message_request: AnthropicMessagesRequest, + new_kwargs: ChatCompletionRequest, + ) -> None: + """Translate output_format to response_format when applicable.""" + if "output_format" not in anthropic_message_request: + return + output_format = anthropic_message_request["output_format"] + if not output_format: + return + response_format = self.translate_anthropic_output_format_to_openai( + output_format=output_format + ) + if response_format: + new_kwargs["response_format"] = response_format + + def _copy_untranslated_anthropic_params( + self, + anthropic_message_request: AnthropicMessagesRequest, + new_kwargs: ChatCompletionRequest, + ) -> None: + """Copy through anthropic params that do not require translation.""" + translatable_params = self.translatable_anthropic_params() + for k, v in anthropic_message_request.items(): + if k not in translatable_params: # pass remaining params as is + new_kwargs[k] = v # type: ignore + def translate_anthropic_to_openai( self, anthropic_message_request: AnthropicMessagesRequest ) -> Tuple[ChatCompletionRequest, Dict[str, str]]: @@ -918,83 +1124,35 @@ class LiteLLMAnthropicMessagesAdapter: "model": anthropic_message_request["model"], "messages": new_messages, } - ## CONVERT METADATA (user_id) - if "metadata" in anthropic_message_request: - metadata = anthropic_message_request["metadata"] - if metadata and "user_id" in metadata: - new_kwargs["user"] = metadata["user_id"] - - # Pass litellm proxy specific metadata - if "litellm_metadata" in anthropic_message_request: - # metadata will be passed to litellm.acompletion(), it's a litellm_param - new_kwargs["metadata"] = anthropic_message_request.pop("litellm_metadata") - + ## CONVERT METADATA (user_id + litellm metadata) + self._translate_metadata_to_openai( + anthropic_message_request=anthropic_message_request, + new_kwargs=new_kwargs, + ) ## CONVERT TOOL CHOICE - if "tool_choice" in anthropic_message_request: - tool_choice = anthropic_message_request["tool_choice"] - if tool_choice: - new_kwargs[ - "tool_choice" - ] = self.translate_anthropic_tool_choice_to_openai( - tool_choice=cast(AnthropicMessagesToolChoice, tool_choice) - ) + self._translate_tool_choice_to_openai( + anthropic_message_request=anthropic_message_request, + new_kwargs=new_kwargs, + ) ## CONVERT TOOLS - if "tools" in anthropic_message_request: - tools = anthropic_message_request["tools"] - if tools: - # 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"), - ) - + tool_name_mapping = self._translate_tools_to_openai( + anthropic_message_request=anthropic_message_request, + new_kwargs=new_kwargs, + ) ## CONVERT THINKING - if "thinking" in anthropic_message_request: - thinking = anthropic_message_request["thinking"] - if thinking: - model = new_kwargs.get("model", "") - if self.is_anthropic_claude_model(model): - new_kwargs["thinking"] = thinking # type: ignore - else: - reasoning_effort = ( - self.translate_anthropic_thinking_to_reasoning_effort( - cast(Dict[str, Any], thinking) - ) - ) - if reasoning_effort: - new_kwargs["reasoning_effort"] = reasoning_effort - + self._translate_thinking_to_openai( + anthropic_message_request=anthropic_message_request, + new_kwargs=new_kwargs, + ) ## CONVERT OUTPUT_FORMAT to RESPONSE_FORMAT - if "output_format" in anthropic_message_request: - output_format = anthropic_message_request["output_format"] - if output_format: - response_format = self.translate_anthropic_output_format_to_openai( - output_format=output_format - ) - if response_format: - new_kwargs["response_format"] = response_format - - translatable_params = self.translatable_anthropic_params() - for k, v in anthropic_message_request.items(): - if k not in translatable_params: # pass remaining params as is - new_kwargs[k] = v # type: ignore + self._translate_output_format_to_openai( + anthropic_message_request=anthropic_message_request, + new_kwargs=new_kwargs, + ) + self._copy_untranslated_anthropic_params( + anthropic_message_request=anthropic_message_request, + new_kwargs=new_kwargs, + ) return new_kwargs, tool_name_mapping diff --git a/litellm/llms/anthropic/experimental_pass_through/messages/handler.py b/litellm/llms/anthropic/experimental_pass_through/messages/handler.py index 1b5f03ec72..d117d74e4f 100644 --- a/litellm/llms/anthropic/experimental_pass_through/messages/handler.py +++ b/litellm/llms/anthropic/experimental_pass_through/messages/handler.py @@ -8,7 +8,7 @@ import asyncio import contextvars from functools import partial -from typing import Any, AsyncIterator, Coroutine, Dict, List, Optional, Union +from typing import Any, AsyncIterator, Coroutine, Dict, List, Optional, Union, cast import litellm from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj @@ -114,6 +114,55 @@ async def _execute_pre_request_hooks( return request_kwargs +async def _try_websearch_short_circuit( + model: str, + messages: List[Dict], + tools: Optional[List[Dict]], + custom_llm_provider: Optional[str], + stream: Optional[bool], +) -> Optional[Union[AnthropicMessagesResponse, AsyncIterator]]: + """ + Attempt to short-circuit a web-search-only request. + + Claude Code sends web search as a separate, standalone /v1/messages + request. For providers that don't natively support web search (e.g. + github_copilot), we detect this pattern, execute the search via + Tavily/Perplexity, and return a synthetic Anthropic response — bypassing + the backend LLM entirely. + + Returns the synthetic response if short-circuited, or None to continue + normal processing. + """ + if not litellm.callbacks: + return None + + from litellm.integrations.websearch_interception.handler import ( + WebSearchInterceptionLogger, + ) + + for callback in litellm.callbacks: + if not isinstance(callback, WebSearchInterceptionLogger): + continue + + response = await callback.try_short_circuit_search( + model=model, + messages=messages, + tools=tools, + custom_llm_provider=custom_llm_provider, + ) + if response is not None: + anthropic_response = cast(AnthropicMessagesResponse, response) + if stream: + from litellm.llms.anthropic.experimental_pass_through.messages.fake_stream_iterator import ( + FakeAnthropicMessagesStreamIterator, + ) + + return FakeAnthropicMessagesStreamIterator(anthropic_response) + return anthropic_response + + return None + + @client async def anthropic_messages( max_tokens: int, @@ -138,6 +187,12 @@ async def anthropic_messages( """ Async: Make llm api request in Anthropic /messages API spec """ + # Save original stream flag before pre-request hooks can convert it. + # The websearch interception hook converts stream=True → stream=False + # for the agentic loop, but the short-circuit path needs to know + # whether the caller originally requested streaming. + original_stream = stream + # Execute pre-request hooks to allow CustomLoggers to modify request request_kwargs = await _execute_pre_request_hooks( model=model, @@ -151,11 +206,38 @@ async def anthropic_messages( # Extract modified parameters tools = request_kwargs.pop("tools", tools) stream = request_kwargs.pop("stream", stream) + # Propagate the provider derived inside pre-request hooks, if not already set. + # The litellm_params dict may have been overwritten by **kwargs in + # _execute_pre_request_hooks, so fall back to get_llm_provider() if needed. + if not custom_llm_provider: + custom_llm_provider = request_kwargs.get("litellm_params", {}).get( + "custom_llm_provider" + ) + if not custom_llm_provider: + try: + _, custom_llm_provider, _, _ = litellm.get_llm_provider(model=model) + except Exception: + pass # Remove litellm_params from kwargs (only needed for hooks) request_kwargs.pop("litellm_params", None) # Merge back any other modifications kwargs.update(request_kwargs) + # Short-circuit web-search-only requests: detect the pattern, execute + # search directly via Tavily/Perplexity, and return a synthetic response + # without ever touching the backend LLM or the adapter path. + # Use original_stream (not the hook-converted stream) so streaming + # callers get SSE events instead of a plain dict. + short_circuit_response = await _try_websearch_short_circuit( + model=model, + messages=messages, + tools=tools, + custom_llm_provider=custom_llm_provider, + stream=original_stream, + ) + if short_circuit_response is not None: + return short_circuit_response + loop = asyncio.get_event_loop() kwargs["is_async"] = True diff --git a/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py b/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py index e9ceea4822..9b60a58260 100644 --- a/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py +++ b/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py @@ -23,7 +23,6 @@ from ...common_utils import ( optionally_handle_anthropic_oauth, ) -DEFAULT_ANTHROPIC_API_BASE = "https://api.anthropic.com" DEFAULT_ANTHROPIC_API_VERSION = "2023-06-01" @@ -127,7 +126,9 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig): litellm_params: dict, stream: Optional[bool] = None, ) -> str: - api_base = api_base or DEFAULT_ANTHROPIC_API_BASE + api_base = ( + AnthropicModelInfo.get_api_base(api_base) or "https://api.anthropic.com" + ) if not api_base.endswith("/v1/messages"): api_base = f"{api_base}/v1/messages" return api_base @@ -142,17 +143,15 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig): api_key: Optional[str] = None, api_base: Optional[str] = None, ) -> Tuple[dict, Optional[str]]: - import os - # Check for Anthropic OAuth token in Authorization header 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 "authorization" not in headers and api_key: - headers["x-api-key"] = api_key + if "x-api-key" not in headers and "authorization" not in headers: + auth_header = AnthropicModelInfo.get_auth_header(api_key) + if auth_header is not None: + headers.update(auth_header) if "anthropic-version" not in headers: headers["anthropic-version"] = DEFAULT_ANTHROPIC_API_VERSION if "content-type" not in headers: diff --git a/litellm/llms/anthropic/experimental_pass_through/responses_adapters/transformation.py b/litellm/llms/anthropic/experimental_pass_through/responses_adapters/transformation.py index ddd514146d..dae7044a5b 100644 --- a/litellm/llms/anthropic/experimental_pass_through/responses_adapters/transformation.py +++ b/litellm/llms/anthropic/experimental_pass_through/responses_adapters/transformation.py @@ -8,6 +8,9 @@ path used for OpenAI and Azure models. import json from typing import Any, Dict, List, Optional, Union, cast +from litellm.llms.anthropic.experimental_pass_through.utils import ( + is_reasoning_auto_summary_enabled, +) from litellm.types.llms.anthropic import ( AllAnthropicToolsValues, AnthopicMessagesAssistantMessageParam, @@ -94,7 +97,7 @@ class LiteLLMAnthropicToResponsesAPIAdapter: ) elif btype == "image": url = self._translate_anthropic_image_source_to_url( - block.get("source", {}) + cast(dict, block.get("source", {})) ) if url: user_parts.append( @@ -267,7 +270,14 @@ class LiteLLMAnthropicToResponsesAPIAdapter: effort = "low" else: effort = "minimal" - return {"effort": effort, "summary": "detailed"} + auto_summary = is_reasoning_auto_summary_enabled() + result: Dict[str, Any] = {"effort": effort} + summary = thinking.get("summary") + if summary: + result["summary"] = summary + elif auto_summary: + result["summary"] = "detailed" + return result def translate_request( self, diff --git a/litellm/llms/anthropic/experimental_pass_through/utils.py b/litellm/llms/anthropic/experimental_pass_through/utils.py new file mode 100644 index 0000000000..6c1db6017b --- /dev/null +++ b/litellm/llms/anthropic/experimental_pass_through/utils.py @@ -0,0 +1,11 @@ +import os + +import litellm + + +def is_reasoning_auto_summary_enabled() -> bool: + """Check whether the default 'summary: detailed' injection is enabled (opt-in).""" + return ( + litellm.reasoning_auto_summary + or os.getenv("LITELLM_REASONING_AUTO_SUMMARY", "false").lower() == "true" + ) diff --git a/litellm/llms/anthropic/files/handler.py b/litellm/llms/anthropic/files/handler.py index 77cc8c2731..c56799f30c 100644 --- a/litellm/llms/anthropic/files/handler.py +++ b/litellm/llms/anthropic/files/handler.py @@ -8,10 +8,8 @@ import httpx import litellm from litellm._logging import verbose_logger from litellm._uuid import uuid -from litellm.llms.custom_httpx.http_handler import ( - get_async_httpx_client, -) from litellm.litellm_core_utils.litellm_logging import Logging +from litellm.llms.custom_httpx.http_handler import get_async_httpx_client from litellm.types.llms.openai import ( FileContentRequest, HttpxBinaryResponseContent, @@ -85,9 +83,9 @@ class AnthropicFilesHandler: # Get Anthropic API credentials api_base = self.anthropic_model_info.get_api_base(api_base) - api_key = api_key or self.anthropic_model_info.get_api_key() + auth_header = self.anthropic_model_info.get_auth_header(api_key) - if not api_key: + if auth_header is None: raise ValueError("Missing Anthropic API Key") # Construct the Anthropic batch results URL @@ -97,8 +95,8 @@ class AnthropicFilesHandler: headers = { "accept": "application/json", "anthropic-version": "2023-06-01", - "x-api-key": api_key, } + headers.update(auth_header) # Make the request to Anthropic async_client = get_async_httpx_client(llm_provider=LlmProviders.ANTHROPIC) diff --git a/litellm/llms/anthropic/files/transformation.py b/litellm/llms/anthropic/files/transformation.py index 98a548a136..0545cefb07 100644 --- a/litellm/llms/anthropic/files/transformation.py +++ b/litellm/llms/anthropic/files/transformation.py @@ -94,14 +94,14 @@ class AnthropicFilesConfig(BaseFilesConfig): api_key: Optional[str] = None, api_base: Optional[str] = None, ) -> dict: - api_key = AnthropicModelInfo.get_api_key(api_key) - if not api_key: + auth_header = AnthropicModelInfo.get_auth_header(api_key) + if auth_header is None: raise ValueError( - "Anthropic API key is required. Set ANTHROPIC_API_KEY environment variable or pass api_key parameter." + "Anthropic API key is required. Set ANTHROPIC_API_KEY or ANTHROPIC_AUTH_TOKEN environment variable or pass api_key parameter." ) headers.update( { - "x-api-key": api_key, + **auth_header, "anthropic-version": "2023-06-01", "anthropic-beta": ANTHROPIC_FILES_BETA_HEADER, } diff --git a/litellm/llms/anthropic/skills/transformation.py b/litellm/llms/anthropic/skills/transformation.py index af9863534e..a992d84d45 100644 --- a/litellm/llms/anthropic/skills/transformation.py +++ b/litellm/llms/anthropic/skills/transformation.py @@ -35,17 +35,18 @@ class AnthropicSkillsConfig(BaseSkillsAPIConfig): """Add Anthropic-specific headers""" from litellm.llms.anthropic.common_utils import AnthropicModelInfo - # Get API key + # Get API key from litellm_params if available api_key = None - if litellm_params: + if litellm_params is not None: api_key = litellm_params.api_key - api_key = AnthropicModelInfo.get_api_key(api_key) - if not api_key: - raise ValueError("ANTHROPIC_API_KEY is required for Skills API") + auth_header = AnthropicModelInfo.get_auth_header(api_key) + if auth_header is None: + raise ValueError( + "ANTHROPIC_API_KEY or ANTHROPIC_AUTH_TOKEN is required for Skills API" + ) - # Add required headers - headers["x-api-key"] = api_key + headers.update(auth_header) headers["anthropic-version"] = "2023-06-01" # Add beta header for skills API diff --git a/litellm/llms/azure/chat/gpt_5_transformation.py b/litellm/llms/azure/chat/gpt_5_transformation.py index 6310df9cec..bc7483bf64 100644 --- a/litellm/llms/azure/chat/gpt_5_transformation.py +++ b/litellm/llms/azure/chat/gpt_5_transformation.py @@ -131,14 +131,9 @@ class AzureOpenAIGPT5Config(AzureOpenAIConfig, OpenAIGPT5Config): if result_effort == "none" and not supports_none: result.pop("reasoning_effort") - # Azure Chat Completions: gpt-5.4+ does not support tools + reasoning together. - # Drop reasoning_effort when both are present (OpenAI routes to Responses API; Azure does not). - if self.is_model_gpt_5_4_plus_model(model): - has_tools = bool( - non_default_params.get("tools") or optional_params.get("tools") - ) - if has_tools and result_effort not in (None, "none"): - result.pop("reasoning_effort", None) + # Azure gpt-5.4+ with tools + reasoning_effort is now routed to the + # Responses API bridge (same as OpenAI), so we no longer need to drop + # reasoning_effort here. See: responses_api_bridge_check() in main.py. return result diff --git a/litellm/llms/azure/fine_tuning/handler.py b/litellm/llms/azure/fine_tuning/handler.py index 429b834989..7e225a8445 100644 --- a/litellm/llms/azure/fine_tuning/handler.py +++ b/litellm/llms/azure/fine_tuning/handler.py @@ -1,10 +1,15 @@ -from typing import Optional, Union +from typing import Any, Coroutine, Dict, Optional, Union, cast import httpx from openai import AsyncAzureOpenAI, AsyncOpenAI, AzureOpenAI, OpenAI +from litellm._logging import verbose_logger from litellm.llms.azure.common_utils import BaseAzureLLM -from litellm.llms.openai.fine_tuning.handler import OpenAIFineTuningAPI +from litellm.llms.openai.fine_tuning.handler import ( + OpenAIFineTuningAPI, + _litellm_fine_tuning_job_from_response, +) +from litellm.types.utils import LiteLLMFineTuningJob class AzureOpenAIFineTuningAPI(OpenAIFineTuningAPI, BaseAzureLLM): @@ -12,6 +17,194 @@ class AzureOpenAIFineTuningAPI(OpenAIFineTuningAPI, BaseAzureLLM): AzureOpenAI methods to support fine tuning, inherits from OpenAIFineTuningAPI. """ + @staticmethod + def _ensure_training_type(create_fine_tuning_job_data: Dict[str, Any]) -> None: + """ + Azure requires trainingType in extra_body. Default to 1 (supervised) if omitted. + """ + extra_body = create_fine_tuning_job_data.get("extra_body") or {} + if not isinstance(extra_body, dict): + extra_body = {} + if extra_body.get("trainingType") is None: + extra_body["trainingType"] = 1 + create_fine_tuning_job_data["extra_body"] = extra_body + verbose_logger.debug( + "Azure fine-tuning: defaulting trainingType=1 (supervised)" + ) + + async def acreate_fine_tuning_job( + self, + create_fine_tuning_job_data: dict, + openai_client: Union[AsyncOpenAI, AsyncAzureOpenAI], + ) -> LiteLLMFineTuningJob: + response = await openai_client.fine_tuning.jobs.create( + **create_fine_tuning_job_data + ) + return _litellm_fine_tuning_job_from_response(response, is_azure=True) + + async def acancel_fine_tuning_job( + self, + fine_tuning_job_id: str, + openai_client: Union[AsyncOpenAI, AsyncAzureOpenAI], + ) -> LiteLLMFineTuningJob: + response = await openai_client.fine_tuning.jobs.cancel( + fine_tuning_job_id=fine_tuning_job_id + ) + return _litellm_fine_tuning_job_from_response(response, is_azure=True) + + async def aretrieve_fine_tuning_job( + self, + fine_tuning_job_id: str, + openai_client: Union[AsyncOpenAI, AsyncAzureOpenAI], + ) -> LiteLLMFineTuningJob: + response = await openai_client.fine_tuning.jobs.retrieve( + fine_tuning_job_id=fine_tuning_job_id + ) + return _litellm_fine_tuning_job_from_response(response, is_azure=True) + + def create_fine_tuning_job( + self, + _is_async: bool, + create_fine_tuning_job_data: dict, + api_key: Optional[str], + api_base: Optional[str], + api_version: Optional[str], + timeout: Union[float, httpx.Timeout], + max_retries: Optional[int], + organization: Optional[str], + client: Optional[ + Union[OpenAI, AsyncOpenAI, AzureOpenAI, AsyncAzureOpenAI] + ] = None, + ) -> Union[LiteLLMFineTuningJob, Coroutine[Any, Any, LiteLLMFineTuningJob]]: + self._ensure_training_type(create_fine_tuning_job_data) + + openai_client: Optional[ + Union[OpenAI, AsyncOpenAI, AzureOpenAI, AsyncAzureOpenAI] + ] = self.get_openai_client( + api_key=api_key, + api_base=api_base, + timeout=timeout, + max_retries=max_retries, + organization=organization, + client=client, + _is_async=_is_async, + api_version=api_version, + ) + if openai_client is None: + raise ValueError( + "Azure OpenAI client is not initialized. Make sure api_key is passed or AZURE_API_KEY is set in the environment." + ) + + if _is_async is True: + if not isinstance(openai_client, (AsyncOpenAI, AsyncAzureOpenAI)): + raise ValueError( + "OpenAI client is not an instance of AsyncOpenAI. Make sure you passed an AsyncOpenAI client." + ) + return self.acreate_fine_tuning_job( + create_fine_tuning_job_data=create_fine_tuning_job_data, + openai_client=openai_client, + ) + + verbose_logger.debug( + "creating fine tuning job, args= %s", create_fine_tuning_job_data + ) + response = cast(OpenAI, openai_client).fine_tuning.jobs.create( + **create_fine_tuning_job_data + ) + return _litellm_fine_tuning_job_from_response(response, is_azure=True) + + def cancel_fine_tuning_job( + self, + _is_async: bool, + fine_tuning_job_id: str, + api_key: Optional[str], + api_base: Optional[str], + api_version: Optional[str], + timeout: Union[float, httpx.Timeout], + max_retries: Optional[int], + organization: Optional[str], + client: Optional[ + Union[OpenAI, AsyncOpenAI, AzureOpenAI, AsyncAzureOpenAI] + ] = None, + ) -> Union[LiteLLMFineTuningJob, Coroutine[Any, Any, LiteLLMFineTuningJob]]: + openai_client: Optional[ + Union[OpenAI, AsyncOpenAI, AzureOpenAI, AsyncAzureOpenAI] + ] = self.get_openai_client( + api_key=api_key, + api_base=api_base, + timeout=timeout, + max_retries=max_retries, + organization=organization, + client=client, + _is_async=_is_async, + api_version=api_version, + ) + if openai_client is None: + raise ValueError( + "Azure OpenAI client is not initialized. Make sure api_key is passed or AZURE_API_KEY is set in the environment." + ) + + if _is_async is True: + if not isinstance(openai_client, (AsyncOpenAI, AsyncAzureOpenAI)): + raise ValueError( + "OpenAI client is not an instance of AsyncOpenAI. Make sure you passed an AsyncOpenAI client." + ) + return self.acancel_fine_tuning_job( + fine_tuning_job_id=fine_tuning_job_id, + openai_client=openai_client, + ) + + response = cast(OpenAI, openai_client).fine_tuning.jobs.cancel( + fine_tuning_job_id=fine_tuning_job_id + ) + return _litellm_fine_tuning_job_from_response(response, is_azure=True) + + def retrieve_fine_tuning_job( + self, + _is_async: bool, + fine_tuning_job_id: str, + api_key: Optional[str], + api_base: Optional[str], + api_version: Optional[str], + timeout: Union[float, httpx.Timeout], + max_retries: Optional[int], + organization: Optional[str], + client: Optional[ + Union[OpenAI, AsyncOpenAI, AzureOpenAI, AsyncAzureOpenAI] + ] = None, + ) -> Union[LiteLLMFineTuningJob, Coroutine[Any, Any, LiteLLMFineTuningJob]]: + openai_client: Optional[ + Union[OpenAI, AsyncOpenAI, AzureOpenAI, AsyncAzureOpenAI] + ] = self.get_openai_client( + api_key=api_key, + api_base=api_base, + timeout=timeout, + max_retries=max_retries, + organization=organization, + client=client, + _is_async=_is_async, + api_version=api_version, + ) + if openai_client is None: + raise ValueError( + "Azure OpenAI client is not initialized. Make sure api_key is passed or AZURE_API_KEY is set in the environment." + ) + + if _is_async is True: + if not isinstance(openai_client, (AsyncOpenAI, AsyncAzureOpenAI)): + raise ValueError( + "OpenAI client is not an instance of AsyncOpenAI. Make sure you passed an AsyncOpenAI client." + ) + return self.aretrieve_fine_tuning_job( + fine_tuning_job_id=fine_tuning_job_id, + openai_client=openai_client, + ) + + response = cast(OpenAI, openai_client).fine_tuning.jobs.retrieve( + fine_tuning_job_id=fine_tuning_job_id + ) + return _litellm_fine_tuning_job_from_response(response, is_azure=True) + def get_openai_client( self, api_key: Optional[str], diff --git a/litellm/llms/azure_ai/agents/handler.py b/litellm/llms/azure_ai/agents/handler.py index 9eeec7f4e3..c3cd06ab4d 100644 --- a/litellm/llms/azure_ai/agents/handler.py +++ b/litellm/llms/azure_ai/agents/handler.py @@ -97,14 +97,59 @@ class AzureAIAgentsHandler: # ------------------------------------------------------------------------- # Response Helpers # ------------------------------------------------------------------------- - def _extract_content_from_messages(self, messages_data: dict) -> str: - """Extract assistant content from the messages response.""" + def _extract_content_from_messages( + self, messages_data: dict + ) -> Tuple[str, Optional[List[Dict[str, Any]]]]: + """Extract assistant content and annotations from the messages response. + + Returns (content, annotations) where annotations is a list of + OpenAI-compatible ChatCompletionAnnotation dicts, or None. + """ for msg in messages_data.get("data", []): if msg.get("role") == "assistant": for content_item in msg.get("content", []): if content_item.get("type") == "text": - return content_item.get("text", {}).get("value", "") - return "" + text_obj = content_item.get("text", {}) + content = text_obj.get("value", "") + raw_annotations = text_obj.get("annotations") + annotations = self._transform_annotations(raw_annotations) + return content, annotations + return "", None + + def _transform_annotations( + self, + raw_annotations: Optional[List[Dict[str, Any]]], + ) -> Optional[List[Dict[str, Any]]]: + """Transform Azure AI Foundry annotations to OpenAI-compatible format. + + Azure AI returns annotations like: + {"type": "url_citation", "text": "[1]", "start_index": 10, + "end_index": 13, "url_citation": {"url": "...", "title": "..."}} + + OpenAI expects: + {"type": "url_citation", "url_citation": {"url": "...", "title": "...", + "start_index": 10, "end_index": 13}} + """ + if not raw_annotations: + return None + + result: List[Dict[str, Any]] = [] + for ann in raw_annotations: + ann_type = ann.get("type") + if ann_type == "url_citation": + url_citation = dict(ann.get("url_citation", {})) + # Azure puts start/end_index at annotation level; OpenAI + # expects them inside url_citation + if "start_index" in ann and "start_index" not in url_citation: + url_citation["start_index"] = ann["start_index"] + if "end_index" in ann and "end_index" not in url_citation: + url_citation["end_index"] = ann["end_index"] + result.append({"type": "url_citation", "url_citation": url_citation}) + else: + # Pass through unknown annotation types as-is + result.append(ann) + + return result if result else None def _build_model_response( self, @@ -113,15 +158,23 @@ class AzureAIAgentsHandler: model_response: ModelResponse, thread_id: str, messages: List[Dict[str, Any]], + annotations: Optional[List[Dict[str, Any]]] = None, ) -> ModelResponse: """Build the ModelResponse from agent output.""" from litellm.types.utils import Choices, Message, Usage + message_kwargs: Dict[str, Any] = { + "content": content, + "role": "assistant", + } + if annotations: + message_kwargs["annotations"] = annotations + model_response.choices = [ Choices( finish_reason="stop", index=0, - message=Message(content=content, role="assistant"), + message=Message(**message_kwargs), ) ] model_response.model = model @@ -250,7 +303,7 @@ class AzureAIAgentsHandler: ) # Execute the agent flow - thread_id, content = self._execute_agent_flow_sync( + thread_id, content, annotations = self._execute_agent_flow_sync( make_request=make_request, api_base=api_base, api_version=api_version, @@ -261,7 +314,7 @@ class AzureAIAgentsHandler: ) return self._build_model_response( - model, content, model_response, thread_id, messages + model, content, model_response, thread_id, messages, annotations ) def _execute_agent_flow_sync( @@ -273,8 +326,8 @@ class AzureAIAgentsHandler: thread_id: Optional[str], messages: List[Dict[str, Any]], optional_params: dict, - ) -> Tuple[str, str]: - """Execute the agent flow synchronously. Returns (thread_id, content).""" + ) -> Tuple[str, str, Optional[List[Dict[str, Any]]]]: + """Execute the agent flow synchronously. Returns (thread_id, content, annotations).""" # Step 1: Create thread if not provided if not thread_id: @@ -347,8 +400,8 @@ class AzureAIAgentsHandler: ) self._check_response(response, [200], "Failed to get messages") - content = self._extract_content_from_messages(response.json()) - return thread_id, content + content, annotations = self._extract_content_from_messages(response.json()) + return thread_id, content, annotations # ------------------------------------------------------------------------- # Async Completion @@ -399,7 +452,7 @@ class AzureAIAgentsHandler: ) # Execute the agent flow - thread_id, content = await self._execute_agent_flow_async( + thread_id, content, annotations = await self._execute_agent_flow_async( make_request=make_request, api_base=api_base, api_version=api_version, @@ -410,7 +463,7 @@ class AzureAIAgentsHandler: ) return self._build_model_response( - model, content, model_response, thread_id, messages + model, content, model_response, thread_id, messages, annotations ) async def _execute_agent_flow_async( @@ -422,8 +475,8 @@ class AzureAIAgentsHandler: thread_id: Optional[str], messages: List[Dict[str, Any]], optional_params: dict, - ) -> Tuple[str, str]: - """Execute the agent flow asynchronously. Returns (thread_id, content).""" + ) -> Tuple[str, str, Optional[List[Dict[str, Any]]]]: + """Execute the agent flow asynchronously. Returns (thread_id, content, annotations).""" # Step 1: Create thread if not provided if not thread_id: @@ -496,8 +549,8 @@ class AzureAIAgentsHandler: ) self._check_response(response, [200], "Failed to get messages") - content = self._extract_content_from_messages(response.json()) - return thread_id, content + content, annotations = self._extract_content_from_messages(response.json()) + return thread_id, content, annotations # ------------------------------------------------------------------------- # Streaming Completion (Native SSE) @@ -585,6 +638,7 @@ class AzureAIAgentsHandler: response_id = f"chatcmpl-{uuid.uuid4().hex[:8]}" created = int(time.time()) thread_id = None + collected_annotations: Optional[List[Dict[str, Any]]] = None current_event = None @@ -600,6 +654,9 @@ class AzureAIAgentsHandler: if data_str == "[DONE]": # Send final chunk with finish_reason + final_delta_kwargs: Dict[str, Any] = {"content": None} + if collected_annotations: + final_delta_kwargs["annotations"] = collected_annotations final_chunk = ModelResponseStream( id=response_id, created=created, @@ -609,7 +666,7 @@ class AzureAIAgentsHandler: StreamingChoices( finish_reason="stop", index=0, - delta=Delta(content=None), + delta=Delta(**final_delta_kwargs), ) ], ) @@ -628,6 +685,19 @@ class AzureAIAgentsHandler: thread_id = data["id"] verbose_logger.debug(f"Stream created thread: {thread_id}") + # Extract annotations from completed message + if current_event == "thread.message.completed": + for content_item in data.get("content", []): + if content_item.get("type") == "text": + raw_annotations = content_item.get("text", {}).get( + "annotations" + ) + transformed = self._transform_annotations(raw_annotations) + if transformed: + if collected_annotations is None: + collected_annotations = [] + collected_annotations.extend(transformed) + # Process message deltas - this is where the actual content comes if current_event == "thread.message.delta": delta_content = data.get("delta", {}).get("content", []) diff --git a/litellm/llms/base_llm/guardrail_translation/base_translation.py b/litellm/llms/base_llm/guardrail_translation/base_translation.py index a7982cb606..e1da0dfa29 100644 --- a/litellm/llms/base_llm/guardrail_translation/base_translation.py +++ b/litellm/llms/base_llm/guardrail_translation/base_translation.py @@ -73,6 +73,7 @@ class BaseTranslation(ABC): guardrail_to_apply: "CustomGuardrail", litellm_logging_obj: Optional["LiteLLMLoggingObj"] = None, user_api_key_dict: Optional["UserAPIKeyAuth"] = None, + request_data: Optional[dict] = None, ) -> Any: """ Process output response with guardrails. @@ -91,6 +92,7 @@ class BaseTranslation(ABC): guardrail_to_apply: "CustomGuardrail", litellm_logging_obj: Optional["LiteLLMLoggingObj"] = None, user_api_key_dict: Optional["UserAPIKeyAuth"] = None, + request_data: Optional[dict] = None, ) -> Any: """ Process output streaming response with guardrails. diff --git a/litellm/llms/base_llm/responses/transformation.py b/litellm/llms/base_llm/responses/transformation.py index f429930e00..eea53fe06e 100644 --- a/litellm/llms/base_llm/responses/transformation.py +++ b/litellm/llms/base_llm/responses/transformation.py @@ -54,6 +54,14 @@ class BaseResponsesAPIConfig(ABC): and v is not None } + def supports_native_file_search(self) -> bool: + """Return True if this provider handles the file_search tool natively. + + Override in provider subclasses that support file_search without + LiteLLM emulation (e.g. OpenAI, Azure OpenAI). + """ + return False + @abstractmethod def get_supported_openai_params(self, model: str) -> list: pass diff --git a/litellm/llms/bedrock/chat/converse_transformation.py b/litellm/llms/bedrock/chat/converse_transformation.py index 229457a73b..5cfb00d69b 100644 --- a/litellm/llms/bedrock/chat/converse_transformation.py +++ b/litellm/llms/bedrock/chat/converse_transformation.py @@ -91,34 +91,6 @@ UNSUPPORTED_BEDROCK_CONVERSE_BETA_PATTERNS = [ "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): """ @@ -493,8 +465,7 @@ class AmazonConverseConfig(BaseConfig): 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 requires thinking.budget_tokens >= %d, got %d. Clamping to minimum.", BEDROCK_MIN_THINKING_BUDGET_TOKENS, budget, ) @@ -763,10 +734,20 @@ 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 + def _supports_native_structured_outputs( + model: str, custom_llm_provider: Optional[str] = None + ) -> bool: + """Check if the Bedrock model supports native structured outputs (outputConfig.textFormat). + + Delegates to the standard ``supports_native_structured_output`` utility + which looks up the flag in ``litellm.model_cost`` via + ``_get_model_info_helper``. + Ref: https://docs.aws.amazon.com/bedrock/latest/userguide/structured-output.html + """ + from litellm.utils import supports_native_structured_output + + return supports_native_structured_output( + model=model, custom_llm_provider=custom_llm_provider ) @staticmethod @@ -913,7 +894,9 @@ class AmazonConverseConfig(BaseConfig): ) if param == "tool_choice": _tool_choice_value = self.map_tool_choice_values( - model=model, tool_choice=value, drop_params=drop_params # type: ignore + model=model, + tool_choice=value, + drop_params=drop_params, # type: ignore ) if _tool_choice_value is not None: optional_params["tool_choice"] = _tool_choice_value @@ -1006,7 +989,10 @@ class AmazonConverseConfig(BaseConfig): if "type" in value and value["type"] == "text": return optional_params - if self._supports_native_structured_outputs(model) and json_schema is not None: + if ( + self._supports_native_structured_outputs(model, self.custom_llm_provider) + 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 @@ -1446,6 +1432,16 @@ class AmazonConverseConfig(BaseConfig): original_tools, model, headers, additional_request_params ) + # Append cachePoint to tools if cache_control_injection_points has tool_config + cache_injection_points = additional_request_params.pop( + "cache_control_injection_points", None + ) + if cache_injection_points and len(bedrock_tools) > 0: + for point in cache_injection_points: + if point.get("location") == "tool_config": + bedrock_tools.append({"cachePoint": {"type": "default"}}) + break + bedrock_tool_config: Optional[ToolConfigBlock] = None if len(bedrock_tools) > 0: tool_choice_values: ToolChoiceValuesBlock = inference_params.pop( diff --git a/litellm/llms/bedrock/count_tokens/handler.py b/litellm/llms/bedrock/count_tokens/handler.py index cfd32342d1..8c227c853c 100644 --- a/litellm/llms/bedrock/count_tokens/handler.py +++ b/litellm/llms/bedrock/count_tokens/handler.py @@ -64,8 +64,15 @@ class BedrockCountTokensHandler(BedrockCountTokensConfig): verbose_logger.debug(f"Transformed request: {bedrock_request}") # Get endpoint URL using simplified function + api_base = litellm_params.get("api_base", None) + aws_bedrock_runtime_endpoint = litellm_params.get( + "aws_bedrock_runtime_endpoint", None + ) endpoint_url = self.get_bedrock_count_tokens_endpoint( - resolved_model, aws_region_name + model=resolved_model, + aws_region_name=aws_region_name, + api_base=api_base, + aws_bedrock_runtime_endpoint=aws_bedrock_runtime_endpoint, ) verbose_logger.debug(f"Making request to: {endpoint_url}") diff --git a/litellm/llms/bedrock/count_tokens/transformation.py b/litellm/llms/bedrock/count_tokens/transformation.py index fe9ab80ced..a37af13162 100644 --- a/litellm/llms/bedrock/count_tokens/transformation.py +++ b/litellm/llms/bedrock/count_tokens/transformation.py @@ -177,7 +177,11 @@ class BedrockCountTokensConfig(BaseAWSLLM): return {"input": {"invokeModel": {"body": json.dumps(body_data)}}} def get_bedrock_count_tokens_endpoint( - self, model: str, aws_region_name: str + self, + model: str, + aws_region_name: str, + api_base: Optional[str] = None, + aws_bedrock_runtime_endpoint: Optional[str] = None, ) -> str: """ Construct the AWS Bedrock CountTokens API endpoint using existing LiteLLM functions. @@ -185,6 +189,8 @@ class BedrockCountTokensConfig(BaseAWSLLM): Args: model: The resolved model ID from router lookup aws_region_name: AWS region (e.g., "eu-west-1") + api_base: Optional custom API base URL (takes highest priority) + aws_bedrock_runtime_endpoint: Optional custom Bedrock runtime endpoint Returns: Complete endpoint URL for CountTokens API @@ -196,7 +202,11 @@ class BedrockCountTokensConfig(BaseAWSLLM): if model_id.startswith("bedrock/"): model_id = model_id[8:] # Remove "bedrock/" prefix - base_url = f"https://bedrock-runtime.{aws_region_name}.amazonaws.com" + base_url, _ = self.get_runtime_endpoint( + api_base=api_base, + aws_bedrock_runtime_endpoint=aws_bedrock_runtime_endpoint, + aws_region_name=aws_region_name, + ) endpoint = f"{base_url}/model/{model_id}/count-tokens" return endpoint diff --git a/litellm/llms/cohere/rerank/guardrail_translation/handler.py b/litellm/llms/cohere/rerank/guardrail_translation/handler.py index b8133c59f7..e9a5823d2b 100644 --- a/litellm/llms/cohere/rerank/guardrail_translation/handler.py +++ b/litellm/llms/cohere/rerank/guardrail_translation/handler.py @@ -83,6 +83,7 @@ class CohereRerankHandler(BaseTranslation): guardrail_to_apply: "CustomGuardrail", litellm_logging_obj: Optional[Any] = None, user_api_key_dict: Optional[Any] = None, + request_data: Optional[dict] = None, ) -> Any: """ Process output response - not applicable for rerank. diff --git a/litellm/llms/fireworks_ai/chat/transformation.py b/litellm/llms/fireworks_ai/chat/transformation.py index 8407e8ab69..6b654ebdfd 100644 --- a/litellm/llms/fireworks_ai/chat/transformation.py +++ b/litellm/llms/fireworks_ai/chat/transformation.py @@ -185,11 +185,16 @@ class FireworksAIConfig(OpenAIGPTConfig): ): # allow user to toggle this feature. return content if isinstance(content["image_url"], str): - content["image_url"] = f"{content['image_url']}#transform=inline" + # Skip base64 data URLs — appending #transform=inline corrupts the + # base64 payload and causes an "Incorrect padding" decode error on + # the Fireworks side. Data URLs are already inlined by definition. + # Lower-case before checking: URI schemes are case-insensitive (RFC 3986). + if not content["image_url"].lower().startswith("data:"): + content["image_url"] = f"{content['image_url']}#transform=inline" elif isinstance(content["image_url"], dict): - content["image_url"][ - "url" - ] = f"{content['image_url']['url']}#transform=inline" + url = content["image_url"]["url"] + if not url.lower().startswith("data:"): + content["image_url"]["url"] = f"{url}#transform=inline" return content def _transform_tools( diff --git a/litellm/llms/gemini/files/transformation.py b/litellm/llms/gemini/files/transformation.py index bdfb0ee1e5..a29ed66e63 100644 --- a/litellm/llms/gemini/files/transformation.py +++ b/litellm/llms/gemini/files/transformation.py @@ -5,6 +5,7 @@ For vertex ai, check out the vertex_ai/files/handler.py file. """ import time from typing import Any, List, Literal, Optional +from urllib.parse import urlparse import httpx from openai.types.file_deleted import FileDeleted @@ -209,27 +210,58 @@ class GoogleAIStudioFilesHandler(GeminiModelInfo, BaseFilesConfig): """ Get the URL to retrieve a file from Google AI Studio. - We expect file_id to be the URI (e.g. https://generativelanguage.googleapis.com/v1beta/files/...) - as returned by the upload response. + Endpoint: + GET https://generativelanguage.googleapis.com/v1beta/{name=files/*} + + The URL should look like: + https://generativelanguage.googleapis.com/v1beta/files/{file_id}?key=API_KEY + + We expect file_id to be just the file identifier (e.g., files/abc123 or abc123) + as returned by the upload response. (If it's a full URL, extract the file name.) """ api_key = litellm_params.get("api_key") or self.get_api_key() if not api_key: raise ValueError("api_key is required") - if file_id.startswith("http"): - url = "{}?key={}".format(file_id, api_key) - else: - # Fallback for just file name (files/...) - api_base = ( - self.get_api_base(litellm_params.get("api_base")) - or "https://generativelanguage.googleapis.com" - ) - api_base = api_base.rstrip("/") - url = "{}/v1beta/{}?key={}".format(api_base, file_id, api_key) + file_part = self._normalize_gemini_file_id(file_id) + + api_base = ( + self.get_api_base(litellm_params.get("api_base")) + or "https://generativelanguage.googleapis.com" + ) + api_base = api_base.rstrip("/") + + url = f"{api_base}/v1beta/{file_part}?key={api_key}" # Return empty params dict - API key is already in URL, no query params needed return url, {} + def _normalize_gemini_file_id(self, file_id: str) -> str: + """ + Normalize file identifier into `files/{id}` form. + + Supports: + - `abc123` + - `files/abc123` + - `https://generativelanguage.googleapis.com/v1beta/files/abc123` + """ + if file_id.startswith(("http://", "https://")): + parsed = urlparse(file_id) + path = parsed.path.lstrip("/") + files_index = path.find("files/") + if files_index != -1: + normalized_file_id = path[files_index:] + else: + normalized_file_id = path + else: + normalized_file_id = file_id + + normalized_file_id = normalized_file_id.strip("/") + if not normalized_file_id.startswith("files/"): + normalized_file_id = f"files/{normalized_file_id}" + + return normalized_file_id + def transform_retrieve_file_response( self, raw_response: httpx.Response, @@ -240,8 +272,9 @@ class GoogleAIStudioFilesHandler(GeminiModelInfo, BaseFilesConfig): Transform Gemini's file retrieval response into OpenAI-style FileObject """ try: + verbose_logger.debug(f"Retrieve file response: {raw_response.text}") response_json = raw_response.json() - + verbose_logger.debug(f"Response JSON: {response_json}") # Map Gemini state to OpenAI status gemini_state = response_json.get("state", "STATE_UNSPECIFIED") # Explicitly type status as the Literal union diff --git a/litellm/llms/gemini/image_generation/transformation.py b/litellm/llms/gemini/image_generation/transformation.py index 3e3f6162fc..b094fc133d 100644 --- a/litellm/llms/gemini/image_generation/transformation.py +++ b/litellm/llms/gemini/image_generation/transformation.py @@ -88,12 +88,15 @@ class GoogleImageGenConfig(BaseImageGenerationConfig): tokens_details = usage_metadata.get("promptTokensDetails", []) for details in tokens_details: if isinstance(details, dict): - modality = details.get("modality") - token_count = details.get("tokenCount", 0) + modality = str(details.get("modality", "")).upper() + raw_token_count = details.get( + "tokenCount", details.get("token_count", 0) + ) + token_count = raw_token_count if isinstance(raw_token_count, int) else 0 if modality == "TEXT": - input_tokens_details.text_tokens = token_count + input_tokens_details.text_tokens += token_count elif modality == "IMAGE": - input_tokens_details.image_tokens = token_count + input_tokens_details.image_tokens += token_count return ImageUsage( input_tokens=usage_metadata.get("promptTokenCount", 0), diff --git a/litellm/llms/mistral/audio_transcription/transformation.py b/litellm/llms/mistral/audio_transcription/transformation.py index 4d29406349..8c6d604acb 100644 --- a/litellm/llms/mistral/audio_transcription/transformation.py +++ b/litellm/llms/mistral/audio_transcription/transformation.py @@ -148,5 +148,12 @@ class MistralAudioTranscriptionConfig(BaseAudioTranscriptionConfig): text = response_json.get("text") or "" response = TranscriptionResponse(text=text) + + # Preserve Mistral-specific fields (e.g. diarization segments) + if "segments" in response_json: + response["segments"] = response_json["segments"] + if "language" in response_json: + response["language"] = response_json["language"] + response._hidden_params = response_json return response diff --git a/litellm/llms/mistral/ocr/guardrail_translation/handler.py b/litellm/llms/mistral/ocr/guardrail_translation/handler.py index 697bd2daa3..7d3797a1db 100644 --- a/litellm/llms/mistral/ocr/guardrail_translation/handler.py +++ b/litellm/llms/mistral/ocr/guardrail_translation/handler.py @@ -91,6 +91,7 @@ class OCRHandler(BaseTranslation): guardrail_to_apply: "CustomGuardrail", litellm_logging_obj: Optional[Any] = None, user_api_key_dict: Optional[Any] = None, + request_data: Optional[dict] = None, ) -> Any: """ Process OCR output by applying guardrails to extracted page text. @@ -127,14 +128,27 @@ class OCRHandler(BaseTranslation): if model: inputs["model"] = model + # Use the real request_data if provided (proxy path), otherwise + # create a standalone dict (SDK / direct-call path). + if request_data is None: + request_data = {} + # Add user metadata if available if user_api_key_dict is not None: - metadata = self.transform_user_api_key_dict_to_metadata(user_api_key_dict) - inputs.update(metadata) # type: ignore + user_metadata = self.transform_user_api_key_dict_to_metadata( + user_api_key_dict + ) + if user_metadata: + # Preserve original behavior: inject metadata into inputs for + # third-party guardrail providers that read it from there + inputs.update(user_metadata) # type: ignore + # Also store in request_data for the logging pipeline + if "litellm_metadata" not in request_data: + request_data["litellm_metadata"] = user_metadata guardrailed_inputs = await guardrail_to_apply.apply_guardrail( inputs=inputs, - request_data={}, + request_data=request_data, input_type="response", logging_obj=litellm_logging_obj, ) diff --git a/litellm/llms/mistral/ocr/transformation.py b/litellm/llms/mistral/ocr/transformation.py index 11848f8acf..3d5e876302 100644 --- a/litellm/llms/mistral/ocr/transformation.py +++ b/litellm/llms/mistral/ocr/transformation.py @@ -36,6 +36,8 @@ class MistralOCRConfig(BaseOCRConfig): - image_min_size: Minimum size of images to include - bbox_annotation_format: Format for bounding box annotations - document_annotation_format: Format for document annotations + - extract_header: Whether to extract document header + - extract_footer: Whether to extract document footer """ return [ "pages", @@ -44,6 +46,8 @@ class MistralOCRConfig(BaseOCRConfig): "image_min_size", "bbox_annotation_format", "document_annotation_format", + "extract_header", + "extract_footer", ] def map_ocr_params( diff --git a/litellm/llms/moonshot/chat/transformation.py b/litellm/llms/moonshot/chat/transformation.py index 24f852c28b..e4d7b5f033 100644 --- a/litellm/llms/moonshot/chat/transformation.py +++ b/litellm/llms/moonshot/chat/transformation.py @@ -155,9 +155,11 @@ class MoonshotChatConfig(OpenAIGPTConfig): message that contains tool_calls (multi-turn tool-calling flows). For each such message that is missing the field: - 1. Promote provider_specific_fields["reasoning_content"] if present and non-empty + 1. Check if reasoning_content exists at the top level (for Pydantic models + that have the attribute but don't support 'in' operator) + 2. Promote provider_specific_fields["reasoning_content"] if present and non-empty (this is where LiteLLM stores it from a previous response) - 2. Otherwise inject a single space — the minimum value the API accepts + 3. Otherwise inject a single space — the minimum value the API accepts Messages that already carry the field, or are not assistant/tool-call messages, are appended as-is (no copy made). """ @@ -166,7 +168,9 @@ class MoonshotChatConfig(OpenAIGPTConfig): if ( msg.get("role") == "assistant" and msg.get("tool_calls") - and "reasoning_content" not in msg + and not msg.get( + "reasoning_content" + ) # Check using .get() which works for both dicts and Pydantic models ): patched = dict(cast(dict, msg)) provider_fields = patched.get("provider_specific_fields") or {} diff --git a/litellm/llms/openai/chat/gpt_5_transformation.py b/litellm/llms/openai/chat/gpt_5_transformation.py index bb5783011a..fc48704cd1 100644 --- a/litellm/llms/openai/chat/gpt_5_transformation.py +++ b/litellm/llms/openai/chat/gpt_5_transformation.py @@ -3,7 +3,7 @@ from typing import Optional, Union import litellm -from litellm.utils import _supports_factory +from litellm.utils import _is_explicitly_disabled_factory, _supports_factory from .gpt_transformation import OpenAIGPTConfig @@ -113,6 +113,25 @@ class OpenAIGPT5Config(OpenAIGPTConfig): key=f"supports_{level}_reasoning_effort", ) + @classmethod + def _is_reasoning_effort_level_explicitly_disabled( + cls, model: str, level: str + ) -> bool: + """Return True only when the model map explicitly sets the capability to False. + + Unlike ``_supports_reasoning_effort_level`` (which requires an explicit True), + this method returns True only when ``supports_{level}_reasoning_effort`` is + explicitly set to ``False`` in the model map. A missing key is treated as + supported (i.e. this method returns False = not disabled). + + Use this for opt-out checks where unknown models should be allowed through. + """ + return _is_explicitly_disabled_factory( + model=model, + custom_llm_provider=None, + key=f"supports_{level}_reasoning_effort", + ) + def get_supported_openai_params(self, model: str) -> list: if self.is_model_gpt_5_search_model(model): return [ @@ -200,14 +219,32 @@ class OpenAIGPT5Config(OpenAIGPTConfig): if "reasoning_effort" in optional_params: optional_params["reasoning_effort"] = normalized - if effective_effort is not None and effective_effort == "xhigh": - if not self._supports_reasoning_effort_level(model, "xhigh"): + if effective_effort == "xhigh": + # xhigh is an opt-in capability: only allow if model explicitly supports it. + if not self._supports_reasoning_effort_level(model, effective_effort): if litellm.drop_params or drop_params: non_default_params.pop("reasoning_effort", None) + optional_params.pop("reasoning_effort", None) else: raise litellm.utils.UnsupportedParamsError( message=( - "reasoning_effort='xhigh' is only supported for gpt-5.1-codex-max, gpt-5.2, and gpt-5.4+ models." + f"reasoning_effort={effective_effort} is not supported for this model." + ), + status_code=400, + ) + elif effective_effort == "minimal": + # minimal is opt-out: unknown models pass through; only block when + # the model map explicitly sets supports_minimal_reasoning_effort=false. + if self._is_reasoning_effort_level_explicitly_disabled( + model, effective_effort + ): + if litellm.drop_params or drop_params: + non_default_params.pop("reasoning_effort", None) + optional_params.pop("reasoning_effort", None) + else: + raise litellm.utils.UnsupportedParamsError( + message=( + f"reasoning_effort={effective_effort} is not supported for this model." ), status_code=400, ) diff --git a/litellm/llms/openai/chat/gpt_transformation.py b/litellm/llms/openai/chat/gpt_transformation.py index 63beb82ded..c12c6e6ba0 100644 --- a/litellm/llms/openai/chat/gpt_transformation.py +++ b/litellm/llms/openai/chat/gpt_transformation.py @@ -7,6 +7,7 @@ from typing import ( Any, AsyncIterator, Coroutine, + Dict, Iterator, List, Literal, @@ -805,8 +806,8 @@ class OpenAIChatCompletionStreamingHandler(BaseModelResponseIterator): choices = chunk.get("choices", []) choices = self._map_reasoning_to_reasoning_content(choices) - kwargs = { - "id": chunk["id"], + kwargs: Dict[str, Any] = { + "id": chunk.get("id"), "object": "chat.completion.chunk", "created": chunk.get("created"), "model": chunk.get("model"), diff --git a/litellm/llms/openai/chat/guardrail_translation/handler.py b/litellm/llms/openai/chat/guardrail_translation/handler.py index bab4c3b5eb..f854cdb13d 100644 --- a/litellm/llms/openai/chat/guardrail_translation/handler.py +++ b/litellm/llms/openai/chat/guardrail_translation/handler.py @@ -260,6 +260,7 @@ class OpenAIChatCompletionsHandler(BaseTranslation): guardrail_to_apply: "CustomGuardrail", litellm_logging_obj: Optional[Any] = None, user_api_key_dict: Optional[Any] = None, + request_data: Optional[dict] = None, ) -> Any: """ Process output response by applying guardrails to text content. @@ -308,15 +309,21 @@ class OpenAIChatCompletionsHandler(BaseTranslation): # Step 2: Apply guardrail to all texts and tool calls in batch if texts_to_check or tool_calls_to_check: - # Create a request_data dict with response info and user API key metadata - request_data: dict = {"response": response} + # Use the real request_data if provided (proxy path), otherwise + # create a standalone dict (SDK / direct-call path). + if request_data is None: + request_data = {"response": response} + else: + if "response" not in request_data: + request_data["response"] = response # Add user API key metadata with prefixed keys - user_metadata = self.transform_user_api_key_dict_to_metadata( - user_api_key_dict - ) - if user_metadata: - request_data["litellm_metadata"] = user_metadata + if "litellm_metadata" not in request_data: + user_metadata = self.transform_user_api_key_dict_to_metadata( + user_api_key_dict + ) + if user_metadata: + request_data["litellm_metadata"] = user_metadata inputs = GenericGuardrailAPIInputs(texts=texts_to_check) if images_to_check: @@ -364,6 +371,7 @@ class OpenAIChatCompletionsHandler(BaseTranslation): guardrail_to_apply: "CustomGuardrail", litellm_logging_obj: Optional[Any] = None, user_api_key_dict: Optional[Any] = None, + request_data: Optional[dict] = None, ) -> List["ModelResponseStream"]: """ Process output streaming responses by applying guardrails to text content. @@ -402,6 +410,7 @@ class OpenAIChatCompletionsHandler(BaseTranslation): guardrail_to_apply=guardrail_to_apply, litellm_logging_obj=litellm_logging_obj, user_api_key_dict=user_api_key_dict, + request_data=request_data, ) return responses_so_far @@ -436,15 +445,21 @@ class OpenAIChatCompletionsHandler(BaseTranslation): # Step 3: Apply guardrail to all combined texts in batch if texts_to_check: - # Create a request_data dict with response info and user API key metadata - request_data: dict = {"responses": responses_so_far} + # Use the real request_data if provided (proxy path), otherwise + # create a standalone dict (SDK / direct-call path). + if request_data is None: + request_data = {"responses": responses_so_far} + else: + if "responses" not in request_data: + request_data["responses"] = responses_so_far # Add user API key metadata with prefixed keys - user_metadata = self.transform_user_api_key_dict_to_metadata( - user_api_key_dict - ) - if user_metadata: - request_data["litellm_metadata"] = user_metadata + if "litellm_metadata" not in request_data: + user_metadata = self.transform_user_api_key_dict_to_metadata( + user_api_key_dict + ) + if user_metadata: + request_data["litellm_metadata"] = user_metadata inputs = GenericGuardrailAPIInputs(texts=texts_to_check) if images_to_check: diff --git a/litellm/llms/openai/completion/guardrail_translation/handler.py b/litellm/llms/openai/completion/guardrail_translation/handler.py index 1f8c6159da..593ab0ed2e 100644 --- a/litellm/llms/openai/completion/guardrail_translation/handler.py +++ b/litellm/llms/openai/completion/guardrail_translation/handler.py @@ -125,6 +125,7 @@ class OpenAITextCompletionHandler(BaseTranslation): guardrail_to_apply: "CustomGuardrail", litellm_logging_obj: Optional[Any] = None, user_api_key_dict: Optional[Any] = None, + request_data: Optional[dict] = None, ) -> Any: """ Process output response by applying guardrails to completion text. @@ -155,15 +156,21 @@ class OpenAITextCompletionHandler(BaseTranslation): # Apply guardrails in batch if texts_to_check: - # Create a request_data dict with response info and user API key metadata - request_data: dict = {"response": response} + # Use the real request_data if provided (proxy path), otherwise + # create a standalone dict (SDK / direct-call path). + if request_data is None: + request_data = {"response": response} + else: + if "response" not in request_data: + request_data["response"] = response # Add user API key metadata with prefixed keys - user_metadata = self.transform_user_api_key_dict_to_metadata( - user_api_key_dict - ) - if user_metadata: - request_data["litellm_metadata"] = user_metadata + if "litellm_metadata" not in request_data: + user_metadata = self.transform_user_api_key_dict_to_metadata( + user_api_key_dict + ) + if user_metadata: + request_data["litellm_metadata"] = user_metadata inputs = GenericGuardrailAPIInputs(texts=texts_to_check) # Include model information from the response if available diff --git a/litellm/llms/openai/embeddings/guardrail_translation/handler.py b/litellm/llms/openai/embeddings/guardrail_translation/handler.py index 7458020e10..ff5021b8ce 100644 --- a/litellm/llms/openai/embeddings/guardrail_translation/handler.py +++ b/litellm/llms/openai/embeddings/guardrail_translation/handler.py @@ -155,6 +155,7 @@ class OpenAIEmbeddingsHandler(BaseTranslation): guardrail_to_apply: "CustomGuardrail", litellm_logging_obj: Optional[Any] = None, user_api_key_dict: Optional[Any] = None, + request_data: Optional[dict] = None, ) -> Any: """ Process output response - embeddings responses contain vectors, not text. diff --git a/litellm/llms/openai/fine_tuning/handler.py b/litellm/llms/openai/fine_tuning/handler.py index 9804ff3539..c065325254 100644 --- a/litellm/llms/openai/fine_tuning/handler.py +++ b/litellm/llms/openai/fine_tuning/handler.py @@ -1,4 +1,4 @@ -from typing import Any, Coroutine, Optional, Union, cast +from typing import Any, Coroutine, Dict, Optional, Union, cast import httpx from openai import AsyncAzureOpenAI, AsyncOpenAI, AzureOpenAI, OpenAI @@ -6,6 +6,55 @@ from openai import AsyncAzureOpenAI, AsyncOpenAI, AzureOpenAI, OpenAI from litellm._logging import verbose_logger from litellm.types.utils import LiteLLMFineTuningJob +_AZURE_STATUS_MAP = { + "pending": "queued", + "notRunning": "queued", + "running": "running", + "succeeded": "succeeded", + "failed": "failed", + "canceled": "cancelled", + "canceling": "cancelled", +} +# Note: Azure's "canceling" (in-progress) is mapped to "cancelled" (terminal) +# because LiteLLMFineTuningJob schema has no intermediate cancellation state. + + +def _normalize_fine_tuning_job_dict( + data: Dict[str, Any], is_azure: bool = False +) -> Dict[str, Any]: + """ + Normalize Azure OpenAI FineTuningJob response to match OpenAI schema. + + Azure differences: + - organization_id: null → "" + - result_files: null → [] + - status: mapped via _AZURE_STATUS_MAP + """ + if not is_azure: + return data + + normalized = data.copy() + + if normalized.get("organization_id") is None: + normalized["organization_id"] = "" + + if normalized.get("result_files") is None: + normalized["result_files"] = [] + + status = normalized.get("status") + if status in _AZURE_STATUS_MAP: + normalized["status"] = _AZURE_STATUS_MAP[status] + + return normalized + + +def _litellm_fine_tuning_job_from_response( + response: Any, is_azure: bool = False +) -> LiteLLMFineTuningJob: + return LiteLLMFineTuningJob( + **_normalize_fine_tuning_job_dict(response.model_dump(), is_azure=is_azure) + ) + class OpenAIFineTuningAPI: """ @@ -60,7 +109,7 @@ class OpenAIFineTuningAPI: **create_fine_tuning_job_data ) - return LiteLLMFineTuningJob(**response.model_dump()) + return _litellm_fine_tuning_job_from_response(response) def create_fine_tuning_job( self, @@ -108,7 +157,7 @@ class OpenAIFineTuningAPI: response = cast(OpenAI, openai_client).fine_tuning.jobs.create( **create_fine_tuning_job_data ) - return LiteLLMFineTuningJob(**response.model_dump()) + return _litellm_fine_tuning_job_from_response(response) async def acancel_fine_tuning_job( self, @@ -118,7 +167,7 @@ class OpenAIFineTuningAPI: response = await openai_client.fine_tuning.jobs.cancel( fine_tuning_job_id=fine_tuning_job_id ) - return LiteLLMFineTuningJob(**response.model_dump()) + return _litellm_fine_tuning_job_from_response(response) def cancel_fine_tuning_job( self, @@ -164,7 +213,7 @@ class OpenAIFineTuningAPI: response = cast(OpenAI, openai_client).fine_tuning.jobs.cancel( fine_tuning_job_id=fine_tuning_job_id ) - return LiteLLMFineTuningJob(**response.model_dump()) + return _litellm_fine_tuning_job_from_response(response) async def alist_fine_tuning_jobs( self, @@ -229,7 +278,7 @@ class OpenAIFineTuningAPI: response = await openai_client.fine_tuning.jobs.retrieve( fine_tuning_job_id=fine_tuning_job_id ) - return LiteLLMFineTuningJob(**response.model_dump()) + return _litellm_fine_tuning_job_from_response(response) def retrieve_fine_tuning_job( self, @@ -275,4 +324,4 @@ class OpenAIFineTuningAPI: response = cast(OpenAI, openai_client).fine_tuning.jobs.retrieve( fine_tuning_job_id=fine_tuning_job_id ) - return LiteLLMFineTuningJob(**response.model_dump()) + return _litellm_fine_tuning_job_from_response(response) diff --git a/litellm/llms/openai/image_generation/guardrail_translation/handler.py b/litellm/llms/openai/image_generation/guardrail_translation/handler.py index e6340ba470..76610088d0 100644 --- a/litellm/llms/openai/image_generation/guardrail_translation/handler.py +++ b/litellm/llms/openai/image_generation/guardrail_translation/handler.py @@ -87,6 +87,7 @@ class OpenAIImageGenerationHandler(BaseTranslation): guardrail_to_apply: "CustomGuardrail", litellm_logging_obj: Optional[Any] = None, user_api_key_dict: Optional[Any] = None, + request_data: Optional[dict] = None, ) -> Any: """ Process output response - typically not needed for image generation. diff --git a/litellm/llms/openai/responses/guardrail_translation/handler.py b/litellm/llms/openai/responses/guardrail_translation/handler.py index 466e2e76f1..76f40eed71 100644 --- a/litellm/llms/openai/responses/guardrail_translation/handler.py +++ b/litellm/llms/openai/responses/guardrail_translation/handler.py @@ -347,6 +347,7 @@ class OpenAIResponsesHandler(BaseTranslation): guardrail_to_apply: "CustomGuardrail", litellm_logging_obj: Optional[Any] = None, user_api_key_dict: Optional[Any] = None, + request_data: Optional[dict] = None, ) -> Any: """ Process output response by applying guardrails to text content and tool calls. @@ -402,15 +403,21 @@ class OpenAIResponsesHandler(BaseTranslation): # Step 2: Apply guardrail to all texts in batch if texts_to_check or tool_calls_to_check: - # Create a request_data dict with response info and user API key metadata - request_data: dict = {"response": response} + # Use the real request_data if provided (proxy path), otherwise + # create a standalone dict (SDK / direct-call path). + if request_data is None: + request_data = {"response": response} + else: + if "response" not in request_data: + request_data["response"] = response # Add user API key metadata with prefixed keys - user_metadata = self.transform_user_api_key_dict_to_metadata( - user_api_key_dict - ) - if user_metadata: - request_data["litellm_metadata"] = user_metadata + if "litellm_metadata" not in request_data: + user_metadata = self.transform_user_api_key_dict_to_metadata( + user_api_key_dict + ) + if user_metadata: + request_data["litellm_metadata"] = user_metadata inputs = GenericGuardrailAPIInputs(texts=texts_to_check) if images_to_check: @@ -454,6 +461,7 @@ class OpenAIResponsesHandler(BaseTranslation): guardrail_to_apply: "CustomGuardrail", litellm_logging_obj: Optional[Any] = None, user_api_key_dict: Optional[Any] = None, + request_data: Optional[dict] = None, ) -> List[Any]: """ Process output streaming response by applying guardrails to text content. @@ -481,7 +489,7 @@ class OpenAIResponsesHandler(BaseTranslation): inputs["model"] = model_response_stream.model _guardrailed_inputs = await guardrail_to_apply.apply_guardrail( inputs=inputs, - request_data={}, + request_data=request_data if request_data is not None else {}, input_type="response", logging_obj=litellm_logging_obj, ) @@ -512,7 +520,7 @@ class OpenAIResponsesHandler(BaseTranslation): if tool_calls or text: _guardrailed_inputs = await guardrail_to_apply.apply_guardrail( inputs=guardrail_inputs, - request_data={}, + request_data=request_data if request_data is not None else {}, input_type="response", logging_obj=litellm_logging_obj, ) @@ -537,7 +545,7 @@ class OpenAIResponsesHandler(BaseTranslation): inputs["model"] = response_model _guardrailed_inputs = await guardrail_to_apply.apply_guardrail( inputs=inputs, - request_data={}, + request_data=request_data if request_data is not None else {}, input_type="response", logging_obj=litellm_logging_obj, ) diff --git a/litellm/llms/openai/responses/transformation.py b/litellm/llms/openai/responses/transformation.py index 9d909fd401..cafb745862 100644 --- a/litellm/llms/openai/responses/transformation.py +++ b/litellm/llms/openai/responses/transformation.py @@ -32,6 +32,9 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): def custom_llm_provider(self) -> LlmProviders: return LlmProviders.OPENAI + def supports_native_file_search(self) -> bool: + return True + def get_supported_openai_params(self, model: str) -> list: """ All OpenAI Responses API params are supported diff --git a/litellm/llms/openai/speech/guardrail_translation/handler.py b/litellm/llms/openai/speech/guardrail_translation/handler.py index e6796fbac2..f0c3149d0a 100644 --- a/litellm/llms/openai/speech/guardrail_translation/handler.py +++ b/litellm/llms/openai/speech/guardrail_translation/handler.py @@ -85,6 +85,7 @@ class OpenAITextToSpeechHandler(BaseTranslation): guardrail_to_apply: "CustomGuardrail", litellm_logging_obj: Optional[Any] = None, user_api_key_dict: Optional[Any] = None, + request_data: Optional[dict] = None, ) -> Any: """ Process output - not applicable for text-to-speech. diff --git a/litellm/llms/openai/transcriptions/guardrail_translation/handler.py b/litellm/llms/openai/transcriptions/guardrail_translation/handler.py index 3d76a21c38..92cf4398f0 100644 --- a/litellm/llms/openai/transcriptions/guardrail_translation/handler.py +++ b/litellm/llms/openai/transcriptions/guardrail_translation/handler.py @@ -58,6 +58,7 @@ class OpenAIAudioTranscriptionHandler(BaseTranslation): guardrail_to_apply: "CustomGuardrail", litellm_logging_obj: Optional[Any] = None, user_api_key_dict: Optional[Any] = None, + request_data: Optional[dict] = None, ) -> Any: """ Process output transcription by applying guardrails to transcribed text. @@ -79,15 +80,21 @@ class OpenAIAudioTranscriptionHandler(BaseTranslation): if isinstance(response.text, str): original_text = response.text - # Create a request_data dict with response info and user API key metadata - request_data: dict = {"response": response} + # Use the real request_data if provided (proxy path), otherwise + # create a standalone dict (SDK / direct-call path). + if request_data is None: + request_data = {"response": response} + else: + if "response" not in request_data: + request_data["response"] = response # Add user API key metadata with prefixed keys - user_metadata = self.transform_user_api_key_dict_to_metadata( - user_api_key_dict - ) - if user_metadata: - request_data["litellm_metadata"] = user_metadata + if "litellm_metadata" not in request_data: + user_metadata = self.transform_user_api_key_dict_to_metadata( + user_api_key_dict + ) + if user_metadata: + request_data["litellm_metadata"] = user_metadata inputs = GenericGuardrailAPIInputs(texts=[original_text]) # Include model information from the response if available diff --git a/litellm/llms/ovhcloud/chat/transformation.py b/litellm/llms/ovhcloud/chat/transformation.py index e2a9fea789..1416b782f1 100644 --- a/litellm/llms/ovhcloud/chat/transformation.py +++ b/litellm/llms/ovhcloud/chat/transformation.py @@ -7,7 +7,7 @@ More information on our website: https://endpoints.ai.cloud.ovh.net from typing import Optional, Union, List import httpx -from litellm.utils import ModelResponseStream, get_model_info +from litellm.utils import ModelResponseStream, _get_model_info_helper from litellm.llms.openai.chat.gpt_transformation import OpenAIGPTConfig from litellm._logging import verbose_logger from litellm.llms.ovhcloud.utils import OVHCloudException @@ -28,13 +28,15 @@ class OVHCloudChatConfig(OpenAIGPTConfig): """ supports_function_calling: Optional[bool] = None try: - model_info = get_model_info(model, custom_llm_provider="ovhcloud") + model_info = _get_model_info_helper(model, custom_llm_provider="ovhcloud") supports_function_calling = model_info.get( - "supports_function_calling", False + "supports_function_calling", None ) + if supports_function_calling is None: + supports_function_calling = False except Exception as e: verbose_logger.debug(f"Error getting supported OpenAI params: {e}") - pass + supports_function_calling = False optional_params = super().get_supported_openai_params(model) if supports_function_calling is not True: diff --git a/litellm/llms/pass_through/guardrail_translation/handler.py b/litellm/llms/pass_through/guardrail_translation/handler.py index 40433d5341..a8cc42d7c5 100644 --- a/litellm/llms/pass_through/guardrail_translation/handler.py +++ b/litellm/llms/pass_through/guardrail_translation/handler.py @@ -139,6 +139,7 @@ class PassThroughEndpointHandler(BaseTranslation): guardrail_to_apply: "CustomGuardrail", litellm_logging_obj: Optional["LiteLLMLoggingObj"] = None, user_api_key_dict: Optional[Any] = None, + request_data: Optional[dict] = None, ) -> Any: """ Process output response by applying guardrails to targeted fields. @@ -171,17 +172,27 @@ class PassThroughEndpointHandler(BaseTranslation): if not text_to_check: return response - # Create a request_data dict with response info and user API key metadata - request_data: dict = ( - {"response": response} - if not isinstance(response, dict) - else response.copy() - ) + # Use the real request_data if provided (proxy path), otherwise + # create a standalone dict (SDK / direct-call path). + if request_data is None: + request_data = ( + {"response": response} + if not isinstance(response, dict) + else response.copy() + ) + else: + if "response" not in request_data: + request_data["response"] = ( + response if not isinstance(response, dict) else response.copy() + ) # Add user API key metadata with prefixed keys - user_metadata = self.transform_user_api_key_dict_to_metadata(user_api_key_dict) - if user_metadata: - request_data["litellm_metadata"] = user_metadata + if "litellm_metadata" not in request_data: + user_metadata = self.transform_user_api_key_dict_to_metadata( + user_api_key_dict + ) + if user_metadata: + request_data["litellm_metadata"] = user_metadata # Apply guardrail (pass-through doesn't modify the text, just checks it) inputs = GenericGuardrailAPIInputs(texts=[text_to_check]) diff --git a/litellm/llms/vertex_ai/batches/handler.py b/litellm/llms/vertex_ai/batches/handler.py index f0b181c9a6..2cb0294206 100644 --- a/litellm/llms/vertex_ai/batches/handler.py +++ b/litellm/llms/vertex_ai/batches/handler.py @@ -376,3 +376,148 @@ class VertexAIBatchPrediction(VertexLLM): response=_json_response ) return vertex_batch_response + + def cancel_batch( + self, + _is_async: bool, + batch_id: str, + api_base: Optional[str], + vertex_credentials: Optional[VERTEX_CREDENTIALS_TYPES], + vertex_project: Optional[str], + vertex_location: Optional[str], + timeout: Union[float, httpx.Timeout], + max_retries: Optional[int], + ) -> Union[LiteLLMBatch, Coroutine[Any, Any, LiteLLMBatch]]: + access_token, project_id = self._ensure_access_token( + credentials=vertex_credentials, + project_id=vertex_project, + custom_llm_provider="vertex_ai", + ) + + default_api_base = self.create_vertex_batch_url( + vertex_location=vertex_location or "us-central1", + vertex_project=vertex_project or project_id, + ) + + retrieve_api_base_default = f"{default_api_base}/{batch_id}" + cancel_api_base_default = f"{retrieve_api_base_default}:cancel" + + _, api_base = self._check_custom_proxy( + api_base=api_base, + custom_llm_provider="vertex_ai", + gemini_api_key=None, + endpoint="cancel", + stream=None, + auth_header=None, + url=cancel_api_base_default, + model=None, + vertex_project=vertex_project or project_id, + vertex_location=vertex_location or "us-central1", + vertex_api_version="v1", + ) + + if api_base.endswith(":cancel"): + retrieve_api_base = api_base.removesuffix(":cancel") + else: + retrieve_api_base = api_base.rsplit(":cancel", 1)[0].rstrip("/") + + headers = { + "Content-Type": "application/json; charset=utf-8", + "Authorization": f"Bearer {access_token}", + } + + if _is_async is True: + return self._async_cancel_batch( + api_base=api_base, + retrieve_api_base=retrieve_api_base, + headers=headers, + timeout=timeout, + ) + + sync_handler = _get_httpx_client() + try: + response = sync_handler.post( + url=api_base, + headers=headers, + data=json.dumps({}), + timeout=timeout, + ) + except httpx.HTTPStatusError as e: + litellm.verbose_logger.error( + "Vertex AI batch cancel failed: status=%s, body=%s", + e.response.status_code, + e.response.text[:1000], + ) + raise + + if response.status_code != 200: + raise Exception(f"Error: {response.status_code} {response.text}") + + # HTTPHandler.get() does not accept a timeout parameter + retrieve_response = sync_handler.get( + url=retrieve_api_base, + headers=headers, + ) + if retrieve_response.status_code != 200: + litellm.verbose_logger.error( + "Vertex AI batch retrieve-after-cancel failed: status=%s, body=%s", + retrieve_response.status_code, + retrieve_response.text[:1000], + ) + raise Exception( + f"Error: {retrieve_response.status_code} {retrieve_response.text}" + ) + + _json_response = retrieve_response.json() + vertex_batch_response = VertexAIBatchTransformation.transform_vertex_ai_batch_response_to_openai_batch_response( + response=_json_response + ) + return vertex_batch_response + + async def _async_cancel_batch( + self, + api_base: str, + retrieve_api_base: str, + headers: Dict[str, str], + timeout: Union[float, httpx.Timeout] = 600.0, + ) -> LiteLLMBatch: + client = get_async_httpx_client( + llm_provider=litellm.LlmProviders.VERTEX_AI, + ) + try: + response = await client.post( + url=api_base, + headers=headers, + data=json.dumps({}), + timeout=timeout, + ) + except httpx.HTTPStatusError as e: + litellm.verbose_logger.error( + "Vertex AI batch cancel failed: status=%s, body=%s", + e.response.status_code, + e.response.text[:1000], + ) + raise + if response.status_code != 200: + raise Exception(f"Error: {response.status_code} {response.text}") + + # AsyncHTTPHandler.get() does not accept a timeout parameter + retrieve_response = await client.get( + url=retrieve_api_base, + headers=headers, + ) + if retrieve_response.status_code != 200: + litellm.verbose_logger.error( + "Vertex AI batch retrieve-after-cancel failed: status=%s, body=%s", + retrieve_response.status_code, + retrieve_response.text[:1000], + ) + raise Exception( + f"Error: {retrieve_response.status_code} {retrieve_response.text}" + ) + + _json_response = retrieve_response.json() + vertex_batch_response = VertexAIBatchTransformation.transform_vertex_ai_batch_response_to_openai_batch_response( + response=_json_response + ) + return vertex_batch_response diff --git a/litellm/llms/vertex_ai/context_caching/vertex_ai_context_caching.py b/litellm/llms/vertex_ai/context_caching/vertex_ai_context_caching.py index db6be9499a..b677cf3b1e 100644 --- a/litellm/llms/vertex_ai/context_caching/vertex_ai_context_caching.py +++ b/litellm/llms/vertex_ai/context_caching/vertex_ai_context_caching.py @@ -51,6 +51,7 @@ class ContextCachingEndpoints(VertexBase): vertex_project: Optional[str], vertex_location: Optional[str], vertex_auth_header: Optional[str], + model: Optional[str] = None, ) -> Tuple[Optional[str], str]: """ Internal function. Returns the token and url for the call. @@ -89,7 +90,7 @@ class ContextCachingEndpoints(VertexBase): stream=None, auth_header=auth_header, url=url, - model=None, + model=model, vertex_project=vertex_project, vertex_location=vertex_location, vertex_api_version="v1beta1" @@ -109,6 +110,7 @@ class ContextCachingEndpoints(VertexBase): vertex_project: Optional[str], vertex_location: Optional[str], vertex_auth_header: Optional[str], + model: Optional[str] = None, ) -> Optional[str]: """ Checks if content already cached. @@ -128,6 +130,7 @@ class ContextCachingEndpoints(VertexBase): vertex_project=vertex_project, vertex_location=vertex_location, vertex_auth_header=vertex_auth_header, + model=model, ) page_token: Optional[str] = None @@ -201,6 +204,7 @@ class ContextCachingEndpoints(VertexBase): vertex_project: Optional[str], vertex_location: Optional[str], vertex_auth_header: Optional[str], + model: Optional[str] = None, ) -> Optional[str]: """ Checks if content already cached. @@ -220,6 +224,7 @@ class ContextCachingEndpoints(VertexBase): vertex_project=vertex_project, vertex_location=vertex_location, vertex_auth_header=vertex_auth_header, + model=model, ) page_token: Optional[str] = None @@ -342,6 +347,7 @@ class ContextCachingEndpoints(VertexBase): vertex_project=vertex_project, vertex_location=vertex_location, vertex_auth_header=vertex_auth_header, + model=model, ) headers = { @@ -377,6 +383,7 @@ class ContextCachingEndpoints(VertexBase): vertex_project=vertex_project, vertex_location=vertex_location, vertex_auth_header=vertex_auth_header, + model=model, ) if google_cache_name: return non_cached_messages, optional_params, google_cache_name @@ -488,6 +495,7 @@ class ContextCachingEndpoints(VertexBase): vertex_project=vertex_project, vertex_location=vertex_location, vertex_auth_header=vertex_auth_header, + model=model, ) headers = { @@ -520,6 +528,7 @@ class ContextCachingEndpoints(VertexBase): vertex_project=vertex_project, vertex_location=vertex_location, vertex_auth_header=vertex_auth_header, + model=model, ) if google_cache_name: diff --git a/litellm/llms/vertex_ai/gemini/transformation.py b/litellm/llms/vertex_ai/gemini/transformation.py index d7b96b4db7..7945c44d44 100644 --- a/litellm/llms/vertex_ai/gemini/transformation.py +++ b/litellm/llms/vertex_ai/gemini/transformation.py @@ -540,6 +540,41 @@ def _gemini_convert_messages_with_history( # noqa: PLR0915 assistant_content.append(gemini_tool_call_part) last_message_with_tool_calls = assistant_msg + ## HANDLE SERVER-SIDE TOOL INVOCATIONS (context circulation) + _psf = assistant_msg.get("provider_specific_fields") + if isinstance(_psf, dict): + _ss_invocations = _psf.get("server_side_tool_invocations") + if isinstance(_ss_invocations, list): + for invocation in _ss_invocations: + # Re-inject toolCall part + tc_part: Dict[str, Any] = { + "toolCall": { + "toolType": invocation.get("tool_type"), + "id": invocation.get("id"), + "args": invocation.get("args"), + } + } + if "thought_signature" in invocation: + tc_part["thoughtSignature"] = invocation[ + "thought_signature" + ] + assistant_content.append(tc_part) # type: ignore + + # Re-inject toolResponse part if response is present + if "response" in invocation: + tr_dict: Dict[str, Any] = { + "id": invocation.get("id"), + "response": invocation.get("response"), + } + if invocation.get("tool_type"): + tr_dict["toolType"] = invocation["tool_type"] + tr_part: Dict[str, Any] = {"toolResponse": tr_dict} + if "thought_signature" in invocation: + tr_part["thoughtSignature"] = invocation[ + "thought_signature" + ] + assistant_content.append(tr_part) # type: ignore + msg_i += 1 if assistant_content: @@ -666,6 +701,9 @@ def _transform_request_body( # noqa: PLR0915 ) tools: Optional[Tools] = optional_params.pop("tools", None) tool_choice: Optional[ToolConfig] = optional_params.pop("tool_choice", None) + include_server_side_tool_invocations: bool = optional_params.pop( + "include_server_side_tool_invocations", False + ) safety_settings: Optional[List[SafetSettingsConfig]] = optional_params.pop( "safety_settings", None ) # type: ignore @@ -715,6 +753,10 @@ def _transform_request_body( # noqa: PLR0915 data["tools"] = tools if tool_choice is not None: data["toolConfig"] = tool_choice + if include_server_side_tool_invocations: + if "toolConfig" not in data: + data["toolConfig"] = {} + data["toolConfig"]["includeServerSideToolInvocations"] = True if safety_settings is not None: data["safetySettings"] = safety_settings if generation_config is not None and len(generation_config) > 0: 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 3f1bccaccf..36f51c5b2f 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 @@ -12,6 +12,7 @@ from typing import ( Dict, List, Literal, + Mapping, Optional, Tuple, Type, @@ -316,6 +317,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): "audio", "parallel_tool_calls", "web_search_options", + "include_server_side_tool_invocations", ] # Add penalty parameters only for non-preview models @@ -1119,6 +1121,8 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): optional_params = self._add_tools_to_optional_params( optional_params, [_tools] ) + elif param == "include_server_side_tool_invocations" and value is True: + optional_params["include_server_side_tool_invocations"] = True if litellm.vertex_ai_safety_settings is not None: optional_params["safety_settings"] = litellm.vertex_ai_safety_settings @@ -1360,6 +1364,67 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): signatures.append(signature) return signatures if signatures else None + @staticmethod + def _extract_server_side_tool_invocations( + parts: List[HttpxPartType], + ) -> Optional[List[Dict[str, Any]]]: + """Extract server-side tool invocations (toolCall/toolResponse) from parts. + + These are returned by Gemini when context circulation is enabled + (includeServerSideToolInvocations=true). They represent tools executed + server-side (e.g. Google Search) and must be circulated back in + subsequent turns for multi-turn coherence. + + Returns: + List of server-side invocation dicts if any found, None otherwise. + """ + invocations: List[Dict[str, Any]] = [] + # Index toolCalls by id so we can pair them with responses + tool_calls_by_id: Dict[str, Dict[str, Any]] = {} + tool_responses_by_id: Dict[str, Dict[str, Any]] = {} + + for part in parts: + if "toolCall" in part: + tc = part["toolCall"] + entry: Dict[str, Any] = { + "tool_type": tc.get("toolType"), + "id": tc.get("id"), + "args": tc.get("args"), + } + signature = part.get("thoughtSignature") + if signature is not None: + entry["thought_signature"] = signature + tool_calls_by_id[tc.get("id", "")] = entry + + elif "toolResponse" in part: + tr = part["toolResponse"] + entry = { + "id": tr.get("id"), + "tool_type": tr.get("toolType"), + "response": tr.get("response"), + } + signature = part.get("thoughtSignature") + if signature is not None: + entry["thought_signature"] = signature + tool_responses_by_id[tr.get("id", "")] = entry + + # Merge calls with their responses + for call_id, call_entry in tool_calls_by_id.items(): + merged = dict(call_entry) + resp = tool_responses_by_id.pop(call_id, None) + if resp is not None: + merged["response"] = resp.get("response") + # Keep response signature if call didn't have one + if "thought_signature" not in merged and "thought_signature" in resp: + merged["thought_signature"] = resp["thought_signature"] + invocations.append(merged) + + # Any orphan responses (shouldn't happen, but be safe) + for resp_id, resp_entry in tool_responses_by_id.items(): + invocations.append(resp_entry) + + return invocations if invocations else None + def _extract_image_response_from_parts( self, parts: List[HttpxPartType] ) -> Optional[List[ImageURLListItem]]: @@ -1632,6 +1697,11 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): response_tokens: Optional[int] = None response_tokens_details: Optional[CompletionTokensDetailsWrapper] = None usage_metadata = completion_response["usageMetadata"] + + def _get_token_count(detail: Mapping[str, Any]) -> int: + raw_token_count = detail.get("tokenCount", detail.get("token_count", 0)) + return raw_token_count if isinstance(raw_token_count, int) else 0 + if "cachedContentTokenCount" in usage_metadata: cached_tokens = usage_metadata["cachedContentTokenCount"] @@ -1641,10 +1711,16 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): if "responseTokensDetails" in usage_metadata: response_tokens_details = CompletionTokensDetailsWrapper() for detail in usage_metadata["responseTokensDetails"]: - if detail["modality"] == "TEXT": - response_tokens_details.text_tokens = detail.get("tokenCount", 0) - elif detail["modality"] == "AUDIO": - response_tokens_details.audio_tokens = detail.get("tokenCount", 0) + modality = str(detail.get("modality", "")).upper() + token_count = _get_token_count(detail) + if modality == "TEXT": + response_tokens_details.text_tokens = ( + response_tokens_details.text_tokens or 0 + ) + token_count + elif modality == "AUDIO": + response_tokens_details.audio_tokens = ( + response_tokens_details.audio_tokens or 0 + ) + token_count ######################################################### @@ -1653,16 +1729,24 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): if response_tokens_details is None: response_tokens_details = CompletionTokensDetailsWrapper() for detail in usage_metadata["candidatesTokensDetails"]: - modality = detail.get("modality") - token_count = detail.get("tokenCount", 0) + modality = str(detail.get("modality", "")).upper() + token_count = _get_token_count(detail) if modality == "TEXT": - response_tokens_details.text_tokens = token_count + response_tokens_details.text_tokens = ( + response_tokens_details.text_tokens or 0 + ) + token_count elif modality == "AUDIO": - response_tokens_details.audio_tokens = token_count + response_tokens_details.audio_tokens = ( + response_tokens_details.audio_tokens or 0 + ) + token_count elif modality == "IMAGE": - response_tokens_details.image_tokens = token_count + response_tokens_details.image_tokens = ( + response_tokens_details.image_tokens or 0 + ) + token_count elif modality == "VIDEO": - response_tokens_details.video_tokens = token_count + response_tokens_details.video_tokens = ( + response_tokens_details.video_tokens or 0 + ) + token_count # Calculate text_tokens if not explicitly provided in candidatesTokensDetails # candidatesTokenCount includes all modalities, so: text = total - (image + audio + video) @@ -1686,14 +1770,16 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): ## Parse promptTokensDetails (total tokens by modality, includes cached + non-cached) if "promptTokensDetails" in usage_metadata: for detail in usage_metadata["promptTokensDetails"]: - if detail["modality"] == "AUDIO": - prompt_audio_tokens = detail.get("tokenCount", 0) - elif detail["modality"] == "TEXT": - prompt_text_tokens = detail.get("tokenCount", 0) - elif detail["modality"] == "IMAGE": - prompt_image_tokens = detail.get("tokenCount", 0) - elif detail["modality"] == "VIDEO": - prompt_video_tokens = detail.get("tokenCount", 0) + modality = str(detail.get("modality", "")).upper() + token_count = _get_token_count(detail) + if modality == "AUDIO": + prompt_audio_tokens = (prompt_audio_tokens or 0) + token_count + elif modality == "TEXT": + prompt_text_tokens = (prompt_text_tokens or 0) + token_count + elif modality == "IMAGE": + prompt_image_tokens = (prompt_image_tokens or 0) + token_count + elif modality == "VIDEO": + prompt_video_tokens = (prompt_video_tokens or 0) + token_count ## Parse cacheTokensDetails (breakdown of cached tokens by modality) ## When explicit caching is used, Gemini provides this field to show which modalities were cached @@ -1704,14 +1790,16 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): if "cacheTokensDetails" in usage_metadata: for detail in usage_metadata["cacheTokensDetails"]: - if detail["modality"] == "AUDIO": - cached_audio_tokens = detail.get("tokenCount", 0) - elif detail["modality"] == "TEXT": - cached_text_tokens = detail.get("tokenCount", 0) - elif detail["modality"] == "IMAGE": - cached_image_tokens = detail.get("tokenCount", 0) - elif detail["modality"] == "VIDEO": - cached_video_tokens = detail.get("tokenCount", 0) + modality = str(detail.get("modality", "")).upper() + token_count = _get_token_count(detail) + if modality == "AUDIO": + cached_audio_tokens = (cached_audio_tokens or 0) + token_count + elif modality == "TEXT": + cached_text_tokens = (cached_text_tokens or 0) + token_count + elif modality == "IMAGE": + cached_image_tokens = (cached_image_tokens or 0) + token_count + elif modality == "VIDEO": + cached_video_tokens = (cached_video_tokens or 0) + token_count ## Calculate non-cached tokens by subtracting cached from total (per modality) ## This is necessary because promptTokensDetails includes both cached and non-cached tokens @@ -2018,6 +2106,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): thinking_blocks: Optional[List[ChatCompletionThinkingBlock]] = None reasoning_content: Optional[str] = None thought_signatures: Optional[Any] = None + server_side_tool_invocations: Optional[List[Dict[str, Any]]] = None for idx, candidate in enumerate(_candidates): if "content" not in candidate: @@ -2068,6 +2157,13 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): ) ) + # Extract server-side tool invocations (context circulation) + server_side_tool_invocations = ( + VertexGeminiConfig._extract_server_side_tool_invocations( + parts=candidate["content"]["parts"] + ) + ) + if audio_response is not None: cast(Dict[str, Any], chat_completion_message)[ "audio" @@ -2139,6 +2235,12 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): chat_completion_message["provider_specific_fields"] = {} chat_completion_message["provider_specific_fields"]["thought_signatures"] = thought_signatures # type: ignore + # Store server-side tool invocations in provider_specific_fields + if server_side_tool_invocations is not None: + if "provider_specific_fields" not in chat_completion_message: + chat_completion_message["provider_specific_fields"] = {} + chat_completion_message["provider_specific_fields"]["server_side_tool_invocations"] = server_side_tool_invocations # type: ignore + if isinstance(model_response, ModelResponseStream): choice = VertexGeminiConfig._create_streaming_choice( chat_completion_message=chat_completion_message, @@ -3001,6 +3103,16 @@ class ModelResponseIterator: ) model_response.choices.append(choice) + # Also handle the case where the final chunk has empty + # content (e.g. text:"") WITH finishReason. In this case + # _process_candidates DOES create a choice, but maps + # finishReason="STOP" to "stop" because the current chunk + # has no tool_calls. Override if we saw tool_calls earlier. + if self.has_seen_tool_calls: + for choice in model_response.choices: + if choice.finish_reason == "stop": + choice.finish_reason = "tool_calls" + setattr(model_response, "vertex_ai_grounding_metadata", grounding_metadata) # type: ignore setattr(model_response, "vertex_ai_url_context_metadata", url_context_metadata) # type: ignore setattr(model_response, "vertex_ai_safety_ratings", safety_ratings) # type: ignore diff --git a/litellm/llms/vertex_ai/gemini_embeddings/batch_embed_content_transformation.py b/litellm/llms/vertex_ai/gemini_embeddings/batch_embed_content_transformation.py index 0f6d85525d..08831a8215 100644 --- a/litellm/llms/vertex_ai/gemini_embeddings/batch_embed_content_transformation.py +++ b/litellm/llms/vertex_ai/gemini_embeddings/batch_embed_content_transformation.py @@ -152,6 +152,8 @@ def transform_openai_input_gemini_content( gemini_params = optional_params.copy() if "dimensions" in gemini_params: gemini_params["outputDimensionality"] = gemini_params.pop("dimensions") + if "task_type" in gemini_params: + gemini_params["taskType"] = gemini_params.pop("task_type") requests: List[EmbedContentRequest] = [] if isinstance(input, str): @@ -196,6 +198,8 @@ def transform_openai_input_gemini_embed_content( gemini_params = optional_params.copy() if "dimensions" in gemini_params: gemini_params["outputDimensionality"] = gemini_params.pop("dimensions") + if "task_type" in gemini_params: + gemini_params["taskType"] = gemini_params.pop("task_type") input_list = [input] if isinstance(input, str) else input parts: List[PartType] = [] diff --git a/litellm/llms/vertex_ai/vertex_ai_partner_models/count_tokens/handler.py b/litellm/llms/vertex_ai/vertex_ai_partner_models/count_tokens/handler.py index c6914ac3d6..5d94cd4212 100644 --- a/litellm/llms/vertex_ai/vertex_ai_partner_models/count_tokens/handler.py +++ b/litellm/llms/vertex_ai/vertex_ai_partner_models/count_tokens/handler.py @@ -105,12 +105,27 @@ class VertexAIPartnerModelsTokenCounter(VertexBase): # Extract Vertex AI credentials and settings vertex_credentials = self.get_vertex_ai_credentials(litellm_params) vertex_project = self.get_vertex_ai_project(litellm_params) - vertex_location = self.get_vertex_ai_location(litellm_params) - # Map empty location/cluade models to a supported region for count-tokens endpoint + # Check for count_tokens specific location override + vertex_count_tokens_location = litellm_params.get( + "vertex_count_tokens_location" + ) + vertex_location_raw = self.get_vertex_ai_location(litellm_params) + + # Determine final location with precedence: + # 1. vertex_count_tokens_location (if provided) + # 2. vertex_location (if provided) + # 3. Default to us-east5 for Claude models when no location is set + # Supported regions: us-east5, europe-west1, asia-southeast1 # https://docs.cloud.google.com/vertex-ai/generative-ai/docs/partner-models/claude/count-tokens - if not vertex_location or "claude" in model.lower(): - vertex_location = "us-central1" + if vertex_count_tokens_location: + vertex_location: str = vertex_count_tokens_location + elif vertex_location_raw: + vertex_location = vertex_location_raw + elif "claude" in model.lower(): + vertex_location = "us-east5" + else: + vertex_location = "us-east5" # Get access token and resolved project ID access_token, project_id = await self._ensure_access_token_async( diff --git a/litellm/main.py b/litellm/main.py index 112fef44e5..eace9c630b 100644 --- a/litellm/main.py +++ b/litellm/main.py @@ -955,16 +955,6 @@ def responses_api_bridge_check( model_info["mode"] = "responses" model = model.replace("responses/", "") - # OpenAI gpt-5.4+ chat-completions calls with both tools + reasoning_effort - # must be bridged to Responses API. - if ( - custom_llm_provider == "openai" - and OpenAIGPT5Config.is_model_gpt_5_4_plus_model(model) - and tools - and reasoning_effort is not None - ): - model_info["mode"] = "responses" - model = model.replace("responses/", "") except Exception as e: verbose_logger.debug("Error getting model info: {}".format(e)) @@ -974,6 +964,19 @@ def responses_api_bridge_check( model = model.replace("responses/", "") mode = "responses" model_info["mode"] = mode + + # OpenAI/Azure gpt-5.4+ chat-completions calls with both tools + reasoning_effort + # must be bridged to Responses API. + if ( + custom_llm_provider in ("openai", "azure") + and OpenAIGPT5Config.is_model_gpt_5_4_plus_model(model) + and tools + and reasoning_effort is not None + and model_info.get("mode") != "responses" + ): + model_info["mode"] = "responses" + model = model.replace("responses/", "") + return model_info, model diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 181045809f..d4f986edd9 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -722,7 +722,8 @@ "supports_response_schema": true, "supports_tool_choice": true, "supports_vision": true, - "tool_use_system_prompt_tokens": 346 + "tool_use_system_prompt_tokens": 346, + "supports_native_structured_output": true }, "anthropic.claude-haiku-4-5@20251001": { "cache_creation_input_token_cost": 1.25e-06, @@ -745,7 +746,8 @@ "supports_tool_choice": true, "supports_vision": true, "tool_use_system_prompt_tokens": 346, - "supports_native_streaming": true + "supports_native_streaming": true, + "supports_native_structured_output": true }, "anthropic.claude-3-5-sonnet-20240620-v1:0": { "input_cost_per_token": 3e-06, @@ -967,22 +969,19 @@ "supports_response_schema": true, "supports_tool_choice": true, "supports_vision": true, - "tool_use_system_prompt_tokens": 159 + "tool_use_system_prompt_tokens": 159, + "supports_native_structured_output": true }, "anthropic.claude-opus-4-6-v1": { "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": "bedrock_converse", "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, @@ -997,22 +996,19 @@ "supports_response_schema": true, "supports_tool_choice": true, "supports_vision": true, - "tool_use_system_prompt_tokens": 346 + "tool_use_system_prompt_tokens": 346, + "supports_native_structured_output": true }, "global.anthropic.claude-opus-4-6-v1": { "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": "bedrock_converse", "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, @@ -1027,22 +1023,19 @@ "supports_response_schema": true, "supports_tool_choice": true, "supports_vision": true, - "tool_use_system_prompt_tokens": 346 + "tool_use_system_prompt_tokens": 346, + "supports_native_structured_output": true }, "us.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, - "cache_read_input_token_cost_above_200k_tokens": 1.1e-06, "input_cost_per_token": 5.5e-06, - "input_cost_per_token_above_200k_tokens": 1.1e-05, "litellm_provider": "bedrock_converse", "max_input_tokens": 1000000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", "output_cost_per_token": 2.75e-05, - "output_cost_per_token_above_200k_tokens": 4.125e-05, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, @@ -1057,22 +1050,19 @@ "supports_response_schema": true, "supports_tool_choice": true, "supports_vision": true, - "tool_use_system_prompt_tokens": 346 + "tool_use_system_prompt_tokens": 346, + "supports_native_structured_output": true }, "eu.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, - "cache_read_input_token_cost_above_200k_tokens": 1.1e-06, "input_cost_per_token": 5.5e-06, - "input_cost_per_token_above_200k_tokens": 1.1e-05, "litellm_provider": "bedrock_converse", "max_input_tokens": 1000000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", "output_cost_per_token": 2.75e-05, - "output_cost_per_token_above_200k_tokens": 4.125e-05, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, @@ -1087,22 +1077,19 @@ "supports_response_schema": true, "supports_tool_choice": true, "supports_vision": true, - "tool_use_system_prompt_tokens": 346 + "tool_use_system_prompt_tokens": 346, + "supports_native_structured_output": true }, "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, - "cache_read_input_token_cost_above_200k_tokens": 1.1e-06, "input_cost_per_token": 5.5e-06, - "input_cost_per_token_above_200k_tokens": 1.1e-05, "litellm_provider": "bedrock_converse", "max_input_tokens": 1000000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", "output_cost_per_token": 2.75e-05, - "output_cost_per_token_above_200k_tokens": 4.125e-05, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, @@ -1117,22 +1104,19 @@ "supports_response_schema": true, "supports_tool_choice": true, "supports_vision": true, - "tool_use_system_prompt_tokens": 346 + "tool_use_system_prompt_tokens": 346, + "supports_native_structured_output": true }, "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_input_tokens": 1000000, "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, @@ -1147,22 +1131,19 @@ "supports_response_schema": true, "supports_tool_choice": true, "supports_vision": true, - "tool_use_system_prompt_tokens": 346 + "tool_use_system_prompt_tokens": 346, + "supports_native_structured_output": true }, "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_input_tokens": 1000000, "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, @@ -1177,22 +1158,19 @@ "supports_response_schema": true, "supports_tool_choice": true, "supports_vision": true, - "tool_use_system_prompt_tokens": 346 + "tool_use_system_prompt_tokens": 346, + "supports_native_structured_output": true }, "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_input_tokens": 1000000, "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, @@ -1207,22 +1185,19 @@ "supports_response_schema": true, "supports_tool_choice": true, "supports_vision": true, - "tool_use_system_prompt_tokens": 346 + "tool_use_system_prompt_tokens": 346, + "supports_native_structured_output": true }, "eu.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_input_tokens": 1000000, "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, @@ -1237,22 +1212,19 @@ "supports_response_schema": true, "supports_tool_choice": true, "supports_vision": true, - "tool_use_system_prompt_tokens": 346 + "tool_use_system_prompt_tokens": 346, + "supports_native_structured_output": true }, "au.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_input_tokens": 1000000, "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, @@ -1267,7 +1239,8 @@ "supports_response_schema": true, "supports_tool_choice": true, "supports_vision": true, - "tool_use_system_prompt_tokens": 346 + "tool_use_system_prompt_tokens": 346, + "supports_native_structured_output": true }, "anthropic.claude-sonnet-4-20250514-v1:0": { "cache_creation_input_token_cost": 3.75e-06, @@ -1327,7 +1300,8 @@ "supports_response_schema": true, "supports_tool_choice": true, "supports_vision": true, - "tool_use_system_prompt_tokens": 159 + "tool_use_system_prompt_tokens": 159, + "supports_native_structured_output": true }, "anthropic.claude-v1": { "input_cost_per_token": 8e-06, @@ -1577,7 +1551,8 @@ "supports_response_schema": true, "supports_tool_choice": true, "supports_vision": true, - "tool_use_system_prompt_tokens": 346 + "tool_use_system_prompt_tokens": 346, + "supports_native_structured_output": true }, "apac.anthropic.claude-3-sonnet-20240229-v1:0": { "input_cost_per_token": 3e-06, @@ -1665,7 +1640,8 @@ "supports_response_schema": true, "supports_tool_choice": true, "supports_vision": true, - "tool_use_system_prompt_tokens": 346 + "tool_use_system_prompt_tokens": 346, + "supports_native_structured_output": true }, "azure/ada": { "input_cost_per_token": 1e-07, @@ -1831,7 +1807,7 @@ "cache_read_input_token_cost": 3e-07, "input_cost_per_token": 3e-06, "litellm_provider": "azure_ai", - "max_input_tokens": 200000, + "max_input_tokens": 1000000, "max_output_tokens": 64000, "max_tokens": 64000, "mode": "chat", @@ -3435,7 +3411,8 @@ "supports_tool_choice": true, "supports_service_tier": true, "supports_vision": true, - "supports_none_reasoning_effort": true + "supports_none_reasoning_effort": true, + "supports_minimal_reasoning_effort": true }, "azure/gpt-5.1-chat-2025-11-13": { "cache_read_input_token_cost": 1.25e-07, @@ -4462,6 +4439,78 @@ "supports_vision": true, "supports_web_search": true }, + "azure/gpt-5.4-mini": { + "cache_read_input_token_cost": 7.5e-08, + "input_cost_per_token": 7.5e-07, + "litellm_provider": "azure", + "max_input_tokens": 1050000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 4.5e-06, + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/batch", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_service_tier": true, + "supports_vision": true, + "supports_web_search": true, + "supports_none_reasoning_effort": false, + "supports_xhigh_reasoning_effort": false + }, + "azure/gpt-5.4-nano": { + "cache_read_input_token_cost": 2e-08, + "input_cost_per_token": 2e-07, + "litellm_provider": "azure", + "max_input_tokens": 1050000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 1.25e-06, + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/batch", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_service_tier": true, + "supports_vision": true, + "supports_web_search": true, + "supports_none_reasoning_effort": false, + "supports_xhigh_reasoning_effort": false + }, "azure/gpt-image-1": { "cache_read_input_image_token_cost": 2.5e-06, "cache_read_input_token_cost": 1.25e-06, @@ -6080,7 +6129,8 @@ "max_query_tokens": 4096, "max_tokens": 32768, "mode": "rerank", - "output_cost_per_token": 0.0 + "output_cost_per_token": 0.0, + "source": "https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/introducing-cohere-rerank-4-0-in-microsoft-foundry/4477076" }, "azure_ai/cohere-rerank-v4.0-fast": { "input_cost_per_query": 0.002, @@ -6091,7 +6141,8 @@ "max_query_tokens": 4096, "max_tokens": 32768, "mode": "rerank", - "output_cost_per_token": 0.0 + "output_cost_per_token": 0.0, + "source": "https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/introducing-cohere-rerank-4-0-in-microsoft-foundry/4477076" }, "azure_ai/deepseek-v3.2": { "input_cost_per_token": 5.8e-07, @@ -6101,6 +6152,7 @@ "max_tokens": 163840, "mode": "chat", "output_cost_per_token": 1.68e-06, + "source": "https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/introducing-deepseek-v3-2-and-deepseek-v3-2-speciale-in-microsoft-foundry/4477549", "supports_assistant_prefill": true, "supports_function_calling": true, "supports_prompt_caching": true, @@ -6115,6 +6167,7 @@ "max_tokens": 163840, "mode": "chat", "output_cost_per_token": 1.68e-06, + "source": "https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/introducing-deepseek-v3-2-and-deepseek-v3-2-speciale-in-microsoft-foundry/4477549", "supports_assistant_prefill": true, "supports_function_calling": true, "supports_prompt_caching": true, @@ -8426,18 +8479,14 @@ }, "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_input_tokens": 1000000, "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, @@ -8618,19 +8667,15 @@ }, "claude-opus-4-6": { "cache_creation_input_token_cost": 6.25e-06, - "cache_creation_input_token_cost_above_200k_tokens": 1.25e-05, "cache_creation_input_token_cost_above_1hr": 1e-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": "anthropic", "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, @@ -8653,19 +8698,15 @@ }, "claude-opus-4-6-20260205": { "cache_creation_input_token_cost": 6.25e-06, - "cache_creation_input_token_cost_above_200k_tokens": 1.25e-05, "cache_creation_input_token_cost_above_1hr": 1e-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": "anthropic", "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, @@ -11665,7 +11706,8 @@ "output_cost_per_token": 1.68e-06, "supports_function_calling": true, "supports_reasoning": true, - "supports_tool_choice": true + "supports_tool_choice": true, + "supports_native_structured_output": true }, "deepseek.v3.2": { "input_cost_per_token": 6.2e-07, @@ -12110,7 +12152,8 @@ "supports_response_schema": true, "supports_tool_choice": true, "supports_vision": true, - "tool_use_system_prompt_tokens": 346 + "tool_use_system_prompt_tokens": 346, + "supports_native_structured_output": true }, "eu.anthropic.claude-3-5-sonnet-20240620-v1:0": { "input_cost_per_token": 3e-06, @@ -12324,7 +12367,8 @@ "supports_response_schema": true, "supports_tool_choice": true, "supports_vision": true, - "tool_use_system_prompt_tokens": 346 + "tool_use_system_prompt_tokens": 346, + "supports_native_structured_output": true }, "eu.meta.llama3-2-1b-instruct-v1:0": { "input_cost_per_token": 1.3e-07, @@ -14554,18 +14598,6 @@ "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing", "uses_embed_content": true }, - "vertex_ai/gemini-embedding-2-preview": { - "input_cost_per_token": 1.5e-07, - "litellm_provider": "vertex_ai", - "max_input_tokens": 8192, - "max_tokens": 8192, - "mode": "embedding", - "output_cost_per_token": 0, - "output_vector_size": 3072, - "source": "https://ai.google.dev/gemini-api/docs/embeddings#multimodal", - "supports_multimodal": true, - "uses_embed_content": true - }, "gemini/gemini-embedding-001": { "input_cost_per_token": 1.5e-07, "litellm_provider": "gemini", @@ -15854,6 +15886,55 @@ "supports_tool_choice": true, "supports_vision": true }, + "gemini/lyria-3-clip-preview": { + "input_cost_per_token": 0, + "litellm_provider": "gemini", + "max_input_tokens": 131072, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_image": 0.04, + "output_cost_per_token": 0, + "source": "https://ai.google.dev/gemini-api/docs/pricing", + "supported_modalities": [ + "text" + ], + "supported_output_modalities": [ + "audio" + ], + "supports_audio_input": false, + "supports_audio_output": true, + "supports_function_calling": false, + "supports_prompt_caching": false, + "supports_response_schema": false, + "supports_system_messages": false, + "supports_vision": false, + "supports_web_search": false + }, + "gemini/lyria-3-pro-preview": { + "input_cost_per_token": 0, + "litellm_provider": "gemini", + "max_input_tokens": 131072, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 0, + "source": "https://ai.google.dev/gemini-api/docs/pricing", + "supported_modalities": [ + "text" + ], + "supported_output_modalities": [ + "audio" + ], + "supports_audio_input": false, + "supports_audio_output": true, + "supports_function_calling": false, + "supports_prompt_caching": false, + "supports_response_schema": false, + "supports_system_messages": false, + "supports_vision": false, + "supports_web_search": false + }, "gemini/veo-2.0-generate-001": { "litellm_provider": "gemini", "max_input_tokens": 1024, @@ -16693,7 +16774,8 @@ "supports_response_schema": true, "supports_tool_choice": true, "supports_vision": true, - "tool_use_system_prompt_tokens": 346 + "tool_use_system_prompt_tokens": 346, + "supports_native_structured_output": true }, "global.anthropic.claude-sonnet-4-20250514-v1:0": { "cache_creation_input_token_cost": 3.75e-06, @@ -16745,7 +16827,8 @@ "supports_response_schema": true, "supports_tool_choice": true, "supports_vision": true, - "tool_use_system_prompt_tokens": 346 + "tool_use_system_prompt_tokens": 346, + "supports_native_structured_output": true }, "global.amazon.nova-2-lite-v1:0": { "cache_read_input_token_cost": 7.5e-08, @@ -16864,6 +16947,18 @@ "supports_system_messages": true, "supports_tool_choice": true }, + "gpt-4-0314": { + "deprecation_date": "2026-03-26", + "input_cost_per_token": 3e-05, + "litellm_provider": "openai", + "max_input_tokens": 8192, + "max_output_tokens": 4096, + "max_tokens": 4096, + "mode": "chat", + "output_cost_per_token": 6e-05, + "supports_system_messages": true, + "supports_tool_choice": true + }, "gpt-4-0613": { "deprecation_date": "2025-06-06", "input_cost_per_token": 3e-05, @@ -18217,7 +18312,8 @@ "supports_vision": true, "supports_web_search": true, "supports_none_reasoning_effort": false, - "supports_xhigh_reasoning_effort": false + "supports_xhigh_reasoning_effort": false, + "supports_minimal_reasoning_effort": true }, "gpt-5.1": { "cache_read_input_token_cost": 1.25e-07, @@ -18256,7 +18352,8 @@ "supports_vision": true, "supports_web_search": true, "supports_none_reasoning_effort": true, - "supports_xhigh_reasoning_effort": false + "supports_xhigh_reasoning_effort": false, + "supports_minimal_reasoning_effort": true }, "gpt-5.1-2025-11-13": { "cache_read_input_token_cost": 1.25e-07, @@ -18295,7 +18392,8 @@ "supports_vision": true, "supports_web_search": true, "supports_none_reasoning_effort": true, - "supports_xhigh_reasoning_effort": false + "supports_xhigh_reasoning_effort": false, + "supports_minimal_reasoning_effort": true }, "gpt-5.1-chat-latest": { "cache_read_input_token_cost": 1.25e-07, @@ -18333,7 +18431,8 @@ "supports_vision": true, "supports_web_search": true, "supports_none_reasoning_effort": true, - "supports_xhigh_reasoning_effort": false + "supports_xhigh_reasoning_effort": false, + "supports_minimal_reasoning_effort": true }, "gpt-5.2": { "cache_read_input_token_cost": 1.75e-07, @@ -18373,7 +18472,8 @@ "supports_vision": true, "supports_web_search": true, "supports_none_reasoning_effort": true, - "supports_xhigh_reasoning_effort": true + "supports_xhigh_reasoning_effort": true, + "supports_minimal_reasoning_effort": true }, "gpt-5.2-2025-12-11": { "cache_read_input_token_cost": 1.75e-07, @@ -18413,7 +18513,8 @@ "supports_vision": true, "supports_web_search": true, "supports_none_reasoning_effort": true, - "supports_xhigh_reasoning_effort": true + "supports_xhigh_reasoning_effort": true, + "supports_minimal_reasoning_effort": true }, "gpt-5.2-chat-latest": { "cache_read_input_token_cost": 1.75e-07, @@ -18450,7 +18551,8 @@ "supports_vision": true, "supports_web_search": true, "supports_none_reasoning_effort": false, - "supports_xhigh_reasoning_effort": false + "supports_xhigh_reasoning_effort": false, + "supports_minimal_reasoning_effort": true }, "gpt-5.3-chat-latest": { "cache_read_input_token_cost": 1.75e-07, @@ -18487,7 +18589,8 @@ "supports_vision": true, "supports_web_search": true, "supports_none_reasoning_effort": false, - "supports_xhigh_reasoning_effort": false + "supports_xhigh_reasoning_effort": false, + "supports_minimal_reasoning_effort": true }, "gpt-5.2-pro": { "input_cost_per_token": 2.1e-05, @@ -18520,7 +18623,8 @@ "supports_vision": true, "supports_web_search": true, "supports_none_reasoning_effort": false, - "supports_xhigh_reasoning_effort": true + "supports_xhigh_reasoning_effort": true, + "supports_minimal_reasoning_effort": true }, "gpt-5.2-pro-2025-12-11": { "input_cost_per_token": 2.1e-05, @@ -18553,7 +18657,8 @@ "supports_vision": true, "supports_web_search": true, "supports_none_reasoning_effort": false, - "supports_xhigh_reasoning_effort": true + "supports_xhigh_reasoning_effort": true, + "supports_minimal_reasoning_effort": true }, "gpt-5.4": { "cache_read_input_token_cost": 2.5e-07, @@ -18602,7 +18707,8 @@ "supports_service_tier": true, "supports_vision": true, "supports_none_reasoning_effort": true, - "supports_xhigh_reasoning_effort": true + "supports_xhigh_reasoning_effort": true, + "supports_minimal_reasoning_effort": true }, "gpt-5.4-2026-03-05": { "cache_read_input_token_cost": 2.5e-07, @@ -18697,7 +18803,8 @@ "supports_vision": true, "supports_web_search": true, "supports_none_reasoning_effort": false, - "supports_xhigh_reasoning_effort": true + "supports_xhigh_reasoning_effort": true, + "supports_minimal_reasoning_effort": true }, "gpt-5.4-pro-2026-03-05": { "cache_read_input_token_cost": 3e-06, @@ -18745,7 +18852,94 @@ "supports_vision": true, "supports_web_search": true, "supports_none_reasoning_effort": false, - "supports_xhigh_reasoning_effort": true + "supports_xhigh_reasoning_effort": true, + "supports_minimal_reasoning_effort": true + }, + "gpt-5.4-mini": { + "cache_read_input_token_cost": 7.5e-08, + "cache_read_input_token_cost_flex": 1e-08, + "cache_read_input_token_cost_batches": 3.8e-08, + "input_cost_per_token": 7.5e-07, + "input_cost_per_token_flex": 3.75e-07, + "input_cost_per_token_batches": 3.75e-07, + "litellm_provider": "openai", + "max_input_tokens": 272000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 4.5e-06, + "output_cost_per_token_flex": 2.25e-06, + "output_cost_per_token_batches": 2.25e-06, + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/batch", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_service_tier": true, + "supports_vision": true, + "supports_web_search": true, + "supports_none_reasoning_effort": true, + "supports_xhigh_reasoning_effort": true, + "supports_minimal_reasoning_effort": false + }, + "gpt-5.4-nano": { + "cache_read_input_token_cost": 2e-08, + "cache_read_input_token_cost_flex": 1e-08, + "cache_read_input_token_cost_batches": 1e-08, + "input_cost_per_token": 2e-07, + "input_cost_per_token_flex": 1e-07, + "input_cost_per_token_batches": 1e-07, + "litellm_provider": "openai", + "max_input_tokens": 272000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 1.25e-06, + "output_cost_per_token_flex": 6.25e-07, + "output_cost_per_token_batches": 6.25e-07, + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/batch", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_service_tier": true, + "supports_vision": true, + "supports_web_search": true, + "supports_none_reasoning_effort": true, + "supports_xhigh_reasoning_effort": true, + "supports_minimal_reasoning_effort": false }, "gpt-5-pro": { "input_cost_per_token": 1.5e-05, @@ -18780,7 +18974,8 @@ "supports_vision": true, "supports_web_search": true, "supports_none_reasoning_effort": false, - "supports_xhigh_reasoning_effort": false + "supports_xhigh_reasoning_effort": false, + "supports_minimal_reasoning_effort": true }, "gpt-5-pro-2025-10-06": { "input_cost_per_token": 1.5e-05, @@ -18815,7 +19010,8 @@ "supports_vision": true, "supports_web_search": true, "supports_none_reasoning_effort": false, - "supports_xhigh_reasoning_effort": false + "supports_xhigh_reasoning_effort": false, + "supports_minimal_reasoning_effort": true }, "gpt-5-2025-08-07": { "cache_read_input_token_cost": 1.25e-07, @@ -18857,7 +19053,8 @@ "supports_vision": true, "supports_web_search": true, "supports_none_reasoning_effort": false, - "supports_xhigh_reasoning_effort": false + "supports_xhigh_reasoning_effort": false, + "supports_minimal_reasoning_effort": true }, "gpt-5-chat": { "cache_read_input_token_cost": 1.25e-07, @@ -18891,7 +19088,8 @@ "supports_tool_choice": false, "supports_vision": true, "supports_none_reasoning_effort": false, - "supports_xhigh_reasoning_effort": false + "supports_xhigh_reasoning_effort": false, + "supports_minimal_reasoning_effort": true }, "gpt-5-chat-latest": { "cache_read_input_token_cost": 1.25e-07, @@ -18925,7 +19123,8 @@ "supports_tool_choice": false, "supports_vision": true, "supports_none_reasoning_effort": false, - "supports_xhigh_reasoning_effort": false + "supports_xhigh_reasoning_effort": false, + "supports_minimal_reasoning_effort": true }, "gpt-5-codex": { "cache_read_input_token_cost": 1.25e-07, @@ -18958,7 +19157,8 @@ "supports_vision": true, "supports_web_search": true, "supports_none_reasoning_effort": false, - "supports_xhigh_reasoning_effort": false + "supports_xhigh_reasoning_effort": false, + "supports_minimal_reasoning_effort": true }, "gpt-5.1-codex": { "cache_read_input_token_cost": 1.25e-07, @@ -18994,7 +19194,8 @@ "supports_vision": true, "supports_web_search": true, "supports_none_reasoning_effort": false, - "supports_xhigh_reasoning_effort": false + "supports_xhigh_reasoning_effort": false, + "supports_minimal_reasoning_effort": true }, "gpt-5.1-codex-max": { "cache_read_input_token_cost": 1.25e-07, @@ -19027,7 +19228,8 @@ "supports_vision": true, "supports_web_search": true, "supports_none_reasoning_effort": false, - "supports_xhigh_reasoning_effort": true + "supports_xhigh_reasoning_effort": true, + "supports_minimal_reasoning_effort": true }, "gpt-5.1-codex-mini": { "cache_read_input_token_cost": 2.5e-08, @@ -19063,7 +19265,8 @@ "supports_vision": true, "supports_web_search": true, "supports_none_reasoning_effort": false, - "supports_xhigh_reasoning_effort": false + "supports_xhigh_reasoning_effort": false, + "supports_minimal_reasoning_effort": true }, "gpt-5.2-codex": { "cache_read_input_token_cost": 1.75e-07, @@ -19099,7 +19302,8 @@ "supports_vision": true, "supports_web_search": true, "supports_none_reasoning_effort": false, - "supports_xhigh_reasoning_effort": true + "supports_xhigh_reasoning_effort": true, + "supports_minimal_reasoning_effort": true }, "gpt-5.3-codex": { "cache_read_input_token_cost": 1.75e-07, @@ -19135,7 +19339,8 @@ "supports_vision": true, "supports_web_search": true, "supports_none_reasoning_effort": false, - "supports_xhigh_reasoning_effort": false + "supports_xhigh_reasoning_effort": false, + "supports_minimal_reasoning_effort": true }, "gpt-5-mini": { "cache_read_input_token_cost": 2.5e-08, @@ -19177,7 +19382,8 @@ "supports_vision": true, "supports_web_search": true, "supports_none_reasoning_effort": false, - "supports_xhigh_reasoning_effort": false + "supports_xhigh_reasoning_effort": false, + "supports_minimal_reasoning_effort": true }, "gpt-5-mini-2025-08-07": { "cache_read_input_token_cost": 2.5e-08, @@ -19219,7 +19425,8 @@ "supports_vision": true, "supports_web_search": true, "supports_none_reasoning_effort": false, - "supports_xhigh_reasoning_effort": false + "supports_xhigh_reasoning_effort": false, + "supports_minimal_reasoning_effort": true }, "gpt-5-nano": { "cache_read_input_token_cost": 5e-09, @@ -19258,7 +19465,8 @@ "supports_vision": true, "supports_web_search": true, "supports_none_reasoning_effort": false, - "supports_xhigh_reasoning_effort": false + "supports_xhigh_reasoning_effort": false, + "supports_minimal_reasoning_effort": true }, "gpt-5-nano-2025-08-07": { "cache_read_input_token_cost": 5e-09, @@ -19296,7 +19504,8 @@ "supports_vision": true, "supports_web_search": true, "supports_none_reasoning_effort": false, - "supports_xhigh_reasoning_effort": false + "supports_xhigh_reasoning_effort": false, + "supports_minimal_reasoning_effort": true }, "gpt-image-1": { "cache_read_input_image_token_cost": 2.5e-06, @@ -20348,7 +20557,8 @@ "supports_response_schema": true, "supports_tool_choice": true, "supports_vision": true, - "tool_use_system_prompt_tokens": 346 + "tool_use_system_prompt_tokens": 346, + "supports_native_structured_output": true }, "jp.anthropic.claude-haiku-4-5-20251001-v1:0": { "cache_creation_input_token_cost": 1.375e-06, @@ -20370,7 +20580,8 @@ "supports_response_schema": true, "supports_tool_choice": true, "supports_vision": true, - "tool_use_system_prompt_tokens": 346 + "tool_use_system_prompt_tokens": 346, + "supports_native_structured_output": true }, "lambda_ai/deepseek-llama3.3-70b": { "input_cost_per_token": 2e-07, @@ -21065,7 +21276,8 @@ "max_tokens": 8192, "mode": "chat", "output_cost_per_token": 1.2e-06, - "supports_system_messages": true + "supports_system_messages": true, + "supports_native_structured_output": true }, "minimax.minimax-m2.1": { "input_cost_per_token": 3e-07, @@ -21221,7 +21433,8 @@ "mode": "chat", "output_cost_per_token": 2e-07, "supports_function_calling": true, - "supports_system_messages": true + "supports_system_messages": true, + "supports_native_structured_output": true }, "mistral.ministral-3-3b-instruct": { "input_cost_per_token": 1e-07, @@ -21232,7 +21445,8 @@ "mode": "chat", "output_cost_per_token": 1e-07, "supports_function_calling": true, - "supports_system_messages": true + "supports_system_messages": true, + "supports_native_structured_output": true }, "mistral.ministral-3-8b-instruct": { "input_cost_per_token": 1.5e-07, @@ -21243,7 +21457,8 @@ "mode": "chat", "output_cost_per_token": 1.5e-07, "supports_function_calling": true, - "supports_system_messages": true + "supports_system_messages": true, + "supports_native_structured_output": true }, "mistral.mistral-7b-instruct-v0:2": { "input_cost_per_token": 1.5e-07, @@ -21285,7 +21500,8 @@ "mode": "chat", "output_cost_per_token": 1.5e-06, "supports_function_calling": true, - "supports_system_messages": true + "supports_system_messages": true, + "supports_native_structured_output": true }, "mistral.mistral-small-2402-v1:0": { "input_cost_per_token": 1e-06, @@ -21316,7 +21532,8 @@ "mode": "chat", "output_cost_per_token": 4e-08, "supports_audio_input": true, - "supports_system_messages": true + "supports_system_messages": true, + "supports_native_structured_output": true }, "mistral.voxtral-small-24b-2507": { "input_cost_per_token": 1e-07, @@ -21327,7 +21544,8 @@ "mode": "chat", "output_cost_per_token": 3e-07, "supports_audio_input": true, - "supports_system_messages": true + "supports_system_messages": true, + "supports_native_structured_output": true }, "mistral/codestral-2405": { "input_cost_per_token": 1e-06, @@ -22014,7 +22232,8 @@ "mode": "chat", "output_cost_per_token": 2.5e-06, "supports_reasoning": true, - "supports_system_messages": true + "supports_system_messages": true, + "supports_native_structured_output": true }, "moonshotai.kimi-k2.5": { "input_cost_per_token": 6e-07, @@ -22889,7 +23108,8 @@ "supports_function_calling": true, "supports_system_messages": true, "supports_tool_choice": true, - "source": "https://aws.amazon.com/bedrock/pricing/" + "source": "https://aws.amazon.com/bedrock/pricing/", + "supports_native_structured_output": true }, "o1": { "cache_read_input_token_cost": 7.5e-06, @@ -25973,7 +26193,8 @@ "output_cost_per_token": 1.8e-06, "supports_function_calling": true, "supports_reasoning": true, - "supports_tool_choice": true + "supports_tool_choice": true, + "supports_native_structured_output": true }, "qwen.qwen3-235b-a22b-2507-v1:0": { "input_cost_per_token": 2.2e-07, @@ -25985,7 +26206,8 @@ "output_cost_per_token": 8.8e-07, "supports_function_calling": true, "supports_reasoning": true, - "supports_tool_choice": true + "supports_tool_choice": true, + "supports_native_structured_output": true }, "qwen.qwen3-coder-30b-a3b-v1:0": { "input_cost_per_token": 1.5e-07, @@ -25997,7 +26219,8 @@ "output_cost_per_token": 6e-07, "supports_function_calling": true, "supports_reasoning": true, - "supports_tool_choice": true + "supports_tool_choice": true, + "supports_native_structured_output": true }, "qwen.qwen3-32b-v1:0": { "input_cost_per_token": 1.5e-07, @@ -26009,7 +26232,8 @@ "output_cost_per_token": 6e-07, "supports_function_calling": true, "supports_reasoning": true, - "supports_tool_choice": true + "supports_tool_choice": true, + "supports_native_structured_output": true }, "qwen.qwen3-next-80b-a3b": { "input_cost_per_token": 1.5e-07, @@ -26020,7 +26244,8 @@ "mode": "chat", "output_cost_per_token": 1.2e-06, "supports_function_calling": true, - "supports_system_messages": true + "supports_system_messages": true, + "supports_native_structured_output": true }, "qwen.qwen3-vl-235b-a22b": { "input_cost_per_token": 5.3e-07, @@ -26032,7 +26257,8 @@ "output_cost_per_token": 2.66e-06, "supports_function_calling": true, "supports_system_messages": true, - "supports_vision": true + "supports_vision": true, + "supports_native_structured_output": true }, "qwen.qwen3-coder-next": { "input_cost_per_token": 5e-07, @@ -28057,7 +28283,8 @@ "supports_response_schema": true, "supports_tool_choice": true, "supports_vision": true, - "tool_use_system_prompt_tokens": 346 + "tool_use_system_prompt_tokens": 346, + "supports_native_structured_output": true }, "us.anthropic.claude-3-5-sonnet-20240620-v1:0": { "input_cost_per_token": 3e-06, @@ -28215,7 +28442,8 @@ "supports_response_schema": true, "supports_tool_choice": true, "supports_vision": true, - "tool_use_system_prompt_tokens": 346 + "tool_use_system_prompt_tokens": 346, + "supports_native_structured_output": true }, "au.anthropic.claude-haiku-4-5-20251001-v1:0": { "cache_creation_input_token_cost": 1.375e-06, @@ -28236,7 +28464,8 @@ "supports_response_schema": true, "supports_tool_choice": true, "supports_vision": true, - "tool_use_system_prompt_tokens": 346 + "tool_use_system_prompt_tokens": 346, + "supports_native_structured_output": true }, "us.anthropic.claude-opus-4-20250514-v1:0": { "cache_creation_input_token_cost": 1.875e-05, @@ -28288,7 +28517,8 @@ "supports_response_schema": true, "supports_tool_choice": true, "supports_vision": true, - "tool_use_system_prompt_tokens": 159 + "tool_use_system_prompt_tokens": 159, + "supports_native_structured_output": true }, "global.anthropic.claude-opus-4-5-20251101-v1:0": { "cache_creation_input_token_cost": 6.25e-06, @@ -28314,7 +28544,8 @@ "supports_response_schema": true, "supports_tool_choice": true, "supports_vision": true, - "tool_use_system_prompt_tokens": 159 + "tool_use_system_prompt_tokens": 159, + "supports_native_structured_output": true }, "eu.anthropic.claude-opus-4-5-20251101-v1:0": { "cache_creation_input_token_cost": 6.25e-06, @@ -28340,7 +28571,8 @@ "supports_response_schema": true, "supports_tool_choice": true, "supports_vision": true, - "tool_use_system_prompt_tokens": 159 + "tool_use_system_prompt_tokens": 159, + "supports_native_structured_output": true }, "us.anthropic.claude-sonnet-4-20250514-v1:0": { "cache_creation_input_token_cost": 3.75e-06, @@ -30120,18 +30352,14 @@ }, "vertex_ai/claude-opus-4-6": { "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, @@ -30150,18 +30378,14 @@ }, "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, @@ -30206,18 +30430,14 @@ }, "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_input_tokens": 1000000, "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, @@ -30434,7 +30654,7 @@ "output_cost_per_token": 5.4e-06, "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing#partner-models", "supported_regions": [ - "us-west2" + "us-central1" ], "supports_assistant_prefill": true, "supports_function_calling": true, @@ -30454,7 +30674,7 @@ "output_cost_per_token_batches": 8.4e-07, "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing#partner-models", "supported_regions": [ - "us-west2" + "global" ], "supports_assistant_prefill": true, "supports_function_calling": true, @@ -30471,6 +30691,9 @@ "mode": "chat", "output_cost_per_token": 5.4e-06, "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing#partner-models", + "supported_regions": [ + "us-central1" + ], "supports_assistant_prefill": true, "supports_function_calling": true, "supports_prompt_caching": true, @@ -31095,7 +31318,10 @@ "input_cost_per_token": 3e-07, "output_cost_per_token": 1.2e-06, "ocr_cost_per_page": 0.0003, - "source": "https://cloud.google.com/vertex-ai/pricing" + "source": "https://cloud.google.com/vertex-ai/pricing", + "supported_regions": [ + "us-central1" + ] }, "vertex_ai/openai/gpt-oss-120b-maas": { "input_cost_per_token": 1.5e-07, @@ -36314,7 +36540,8 @@ "supports_vision": true, "supports_web_search": true, "supports_none_reasoning_effort": false, - "supports_xhigh_reasoning_effort": false + "supports_xhigh_reasoning_effort": false, + "supports_minimal_reasoning_effort": true }, "gpt-5-search-api-2025-10-14": { "cache_read_input_token_cost": 1.25e-07, @@ -36620,6 +36847,38 @@ "supports_audio_input": true, "supports_audio_output": true }, + "gemini-3.1-flash-live-preview": { + "input_cost_per_audio_token": 3e-06, + "input_cost_per_image_token": 1e-06, + "input_cost_per_token": 7.5e-07, + "input_cost_per_video_per_second": 3.3333333333333335e-05, + "litellm_provider": "gemini", + "max_input_tokens": 131072, + "max_output_tokens": 65536, + "max_tokens": 65536, + "mode": "chat", + "output_cost_per_audio_token": 1.2e-05, + "output_cost_per_token": 4.5e-06, + "source": "https://ai.google.dev/gemini-api/docs/pricing", + "supported_endpoints": [ + "/v1/realtime" + ], + "supported_modalities": [ + "text", + "image", + "audio", + "video" + ], + "supported_output_modalities": [ + "text", + "audio" + ], + "supports_audio_input": true, + "supports_audio_output": true, + "supports_function_calling": true, + "supports_vision": true, + "supports_web_search": true + }, "gemini/gemini-2.5-flash-native-audio-latest": { "input_cost_per_audio_token": 1e-06, "input_cost_per_token": 3e-07, @@ -36698,6 +36957,40 @@ "tpm": 250000, "rpm": 10 }, + "gemini/gemini-3.1-flash-live-preview": { + "input_cost_per_audio_token": 3e-06, + "input_cost_per_image_token": 1e-06, + "input_cost_per_token": 7.5e-07, + "input_cost_per_video_per_second": 3.3333333333333335e-05, + "litellm_provider": "gemini", + "max_input_tokens": 131072, + "max_output_tokens": 65536, + "max_tokens": 65536, + "mode": "chat", + "output_cost_per_audio_token": 1.2e-05, + "output_cost_per_token": 4.5e-06, + "source": "https://ai.google.dev/gemini-api/docs/pricing", + "supported_endpoints": [ + "/v1/realtime" + ], + "supported_modalities": [ + "text", + "image", + "audio", + "video" + ], + "supported_output_modalities": [ + "text", + "audio" + ], + "supports_audio_input": true, + "supports_audio_output": true, + "supports_function_calling": true, + "supports_vision": true, + "supports_web_search": true, + "tpm": 250000, + "rpm": 10 + }, "gemini-2.5-flash-preview-tts": { "input_cost_per_token": 3e-07, "litellm_provider": "gemini", @@ -36943,18 +37236,14 @@ }, "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_input_tokens": 1000000, "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, @@ -37032,5 +37321,157 @@ "supports_reasoning": true, "supports_response_schema": true, "supports_tool_choice": true + }, + "volcengine/doubao-seed-2-0-pro-260215": { + "litellm_provider": "volcengine", + "max_input_tokens": 256000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "source": "https://www.volcengine.com/docs/82379/1330310", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": false, + "supports_vision": true, + "tiered_pricing": [ + { + "input_cost_per_token": 4.6e-07, + "output_cost_per_token": 2.3e-06, + "range": [ + 0, + 32000.0 + ] + }, + { + "input_cost_per_token": 7e-07, + "output_cost_per_token": 3.5e-06, + "range": [ + 32000.0, + 128000.0 + ] + }, + { + "input_cost_per_token": 1.4e-06, + "output_cost_per_token": 7e-06, + "range": [ + 128000.0, + 256000.0 + ] + } + ] + }, + "volcengine/doubao-seed-2-0-lite-260215": { + "litellm_provider": "volcengine", + "max_input_tokens": 256000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "source": "https://www.volcengine.com/docs/82379/1330310", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": false, + "supports_vision": true, + "tiered_pricing": [ + { + "input_cost_per_token": 8.7e-08, + "output_cost_per_token": 5.2e-07, + "range": [ + 0, + 32000.0 + ] + }, + { + "input_cost_per_token": 1.3e-07, + "output_cost_per_token": 7.8e-07, + "range": [ + 32000.0, + 128000.0 + ] + }, + { + "input_cost_per_token": 2.6e-07, + "output_cost_per_token": 1.6e-06, + "range": [ + 128000.0, + 256000.0 + ] + } + ] + }, + "volcengine/doubao-seed-2-0-mini-260215": { + "litellm_provider": "volcengine", + "max_input_tokens": 256000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "source": "https://www.volcengine.com/docs/82379/1330310", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": false, + "supports_vision": true, + "tiered_pricing": [ + { + "input_cost_per_token": 2.9e-08, + "output_cost_per_token": 2.9e-07, + "range": [ + 0, + 32000.0 + ] + }, + { + "input_cost_per_token": 5.8e-08, + "output_cost_per_token": 5.8e-07, + "range": [ + 32000.0, + 128000.0 + ] + }, + { + "input_cost_per_token": 1.2e-07, + "output_cost_per_token": 1.2e-06, + "range": [ + 128000.0, + 256000.0 + ] + } + ] + }, + "volcengine/doubao-seed-2-0-code-preview-260215": { + "litellm_provider": "volcengine", + "max_input_tokens": 256000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "source": "https://www.volcengine.com/docs/82379/1330310", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": false, + "supports_vision": true, + "tiered_pricing": [ + { + "input_cost_per_token": 4.6e-07, + "output_cost_per_token": 2.3e-06, + "range": [ + 0, + 32000.0 + ] + }, + { + "input_cost_per_token": 7e-07, + "output_cost_per_token": 3.5e-06, + "range": [ + 32000.0, + 128000.0 + ] + }, + { + "input_cost_per_token": 1.4e-06, + "output_cost_per_token": 7e-06, + "range": [ + 128000.0, + 256000.0 + ] + } + ] } } diff --git a/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py b/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py index 3385e7feef..07309eb57f 100644 --- a/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py +++ b/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py @@ -208,26 +208,52 @@ async def exchange_token_with_server( client_id: str, client_secret: Optional[str], code_verifier: Optional[str], + refresh_token: Optional[str] = None, + scope: Optional[str] = None, ): - if grant_type != "authorization_code": + if grant_type not in ("authorization_code", "refresh_token"): raise HTTPException(status_code=400, detail="Unsupported grant_type") if mcp_server.token_url is None: raise HTTPException(status_code=400, detail="MCP server token url is not set") - proxy_base_url = get_request_base_url(request) - token_data = { - "grant_type": "authorization_code", - "client_id": mcp_server.client_id if mcp_server.client_id else client_id, - "client_secret": mcp_server.client_secret - if mcp_server.client_secret - else client_secret, - "code": code, - "redirect_uri": f"{proxy_base_url}/callback", - } + resolved_client_id = mcp_server.client_id if mcp_server.client_id else client_id + resolved_client_secret = ( + mcp_server.client_secret if mcp_server.client_secret else client_secret + ) - if code_verifier: - token_data["code_verifier"] = code_verifier + if grant_type == "refresh_token": + if not refresh_token: + raise HTTPException( + status_code=400, + detail="refresh_token is required for refresh_token grant", + ) + token_data: dict = { + "grant_type": "refresh_token", + "refresh_token": refresh_token, + "client_id": resolved_client_id, + } + if resolved_client_secret is not None: + token_data["client_secret"] = resolved_client_secret + if scope: + token_data["scope"] = scope + else: + if not code: + raise HTTPException( + status_code=400, + detail="code is required for authorization_code grant", + ) + proxy_base_url = get_request_base_url(request) + token_data = { + "grant_type": "authorization_code", + "client_id": resolved_client_id, + "code": code, + "redirect_uri": f"{proxy_base_url}/callback", + } + if resolved_client_secret is not None: + token_data["client_secret"] = resolved_client_secret + if code_verifier: + token_data["code_verifier"] = code_verifier async_client = get_async_httpx_client(llm_provider=httpxSpecialProvider.Oauth2Check) response = await async_client.post( @@ -375,6 +401,8 @@ async def token_endpoint( client_id: str = Form(...), client_secret: Optional[str] = Form(None), code_verifier: str = Form(None), + refresh_token: Optional[str] = Form(None), + scope: Optional[str] = Form(None), mcp_server_name: Optional[str] = None, ): """ @@ -408,6 +436,8 @@ async def token_endpoint( client_id=client_id, client_secret=client_secret, code_verifier=code_verifier, + refresh_token=refresh_token, + scope=scope, ) diff --git a/litellm/proxy/_experimental/mcp_server/guardrail_translation/handler.py b/litellm/proxy/_experimental/mcp_server/guardrail_translation/handler.py index 14bbb82808..6997f5241d 100644 --- a/litellm/proxy/_experimental/mcp_server/guardrail_translation/handler.py +++ b/litellm/proxy/_experimental/mcp_server/guardrail_translation/handler.py @@ -92,6 +92,7 @@ class MCPGuardrailTranslationHandler(BaseTranslation): guardrail_to_apply: "CustomGuardrail", litellm_logging_obj: Optional[Any] = None, user_api_key_dict: Optional[Any] = None, + request_data: Optional[dict] = None, ) -> Any: verbose_proxy_logger.debug( "MCP Guardrail: Output processing not implemented for MCP tools", diff --git a/litellm/proxy/_experimental/out/404/index.html b/litellm/proxy/_experimental/out/404/index.html index 29dbbfcdd6..c9df4fb914 100644 --- a/litellm/proxy/_experimental/out/404/index.html +++ b/litellm/proxy/_experimental/out/404/index.html @@ -1 +1 @@ -404: This page could not be found.LiteLLM Dashboard

404

This page could not be found.

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

404

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openai - -client = openai.OpenAI( - api_key="${v||"YOUR_LITELLM_API_KEY"}", - base_url="${I}" -)`;switch(h){case o.CHAT:{let e=Object.keys(y).length>0,r="";if(e){let e=JSON.stringify({metadata:y},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();r=`, - extra_body=${e}`}let a=E.length>0?E:[{role:"user",content:C}];t=` -import base64 - -# Helper function to encode images to base64 -def encode_image(image_path): - with open(image_path, "rb") as image_file: - return base64.b64encode(image_file.read()).decode('utf-8') - -# Example with text only -response = client.chat.completions.create( - model="${O}", - messages=${JSON.stringify(a,null,4)}${r} -) - -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="${O}", -# messages=[ -# { -# "role": "user", -# "content": [ -# { -# "type": "text", -# "text": "${w}" -# }, -# { -# "type": "image_url", -# "image_url": { -# "url": f"data:image/jpeg;base64,{base64_file}" # or data:application/pdf;base64,{base64_file} -# } -# } -# ] -# } -# ]${r} -# ) -# print(response_with_file) -`;break}case o.RESPONSES:{let e=Object.keys(y).length>0,r="";if(e){let e=JSON.stringify({metadata:y},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();r=`, - extra_body=${e}`}let a=E.length>0?E:[{role:"user",content:C}];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="${O}", - input=${JSON.stringify(a,null,4)}${r} -) - -print(response.output_text) - -# Example with image or PDF (uncomment and provide file path to use) -# base64_file = encode_image("path/to/your/file.jpg") # or .pdf -# response_with_file = client.responses.create( -# model="${O}", -# input=[ -# { -# "role": "user", -# "content": [ -# {"type": "input_text", "text": "${w}"}, -# { -# "type": "input_image", -# "image_url": f"data:image/jpeg;base64,{base64_file}", # or data:application/pdf;base64,{base64_file} -# }, -# ], -# } -# ]${r} -# ) -# print(response_with_file.output_text) -`;break}case o.IMAGE:t="azure"===b?` -# 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="${O}", - prompt="${n}", - n=1 -) - -json_response = json.loads(result.model_dump_json()) - -# Set the directory for the stored image -image_dir = os.path.join(os.curdir, 'images') - -# If the directory doesn't exist, create it -if not os.path.isdir(image_dir): - os.mkdir(image_dir) - -# Initialize the image path -image_filename = f"generated_image_{int(time.time())}.png" -image_path = os.path.join(image_dir, image_filename) - -try: - # Retrieve the generated image - if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"): - image_url = json_response["data"][0]["url"] - generated_image = requests.get(image_url).content - with open(image_path, "wb") as image_file: - image_file.write(generated_image) - - print(f"Image saved to {image_path}") - # Display the image - image = Image.open(image_path) - image.show() - else: - print("Could not find image URL in response.") - print("Full response:", json_response) -except Exception as e: - print(f"An error occurred: {e}") - print("Full response:", json_response) -`:` -import base64 -import os -import time -import json -from PIL import Image -import requests - -# Helper function to encode images to base64 -def encode_image(image_path): - with open(image_path, "rb") as image_file: - return base64.b64encode(image_file.read()).decode('utf-8') - -# Helper function to create a file (simplified for this example) -def create_file(image_path): - # In a real implementation, this would upload the file to OpenAI - # For this example, we'll just return a placeholder ID - return f"file_{os.path.basename(image_path).replace('.', '_')}" - -# The prompt entered by the user -prompt = "${w}" - -# Encode images to base64 -base64_image1 = encode_image("body-lotion.png") -base64_image2 = encode_image("soap.png") - -# Create file IDs -file_id1 = create_file("body-lotion.png") -file_id2 = create_file("incense-kit.png") - -response = client.responses.create( - model="${O}", - input=[ - { - "role": "user", - "content": [ - {"type": "input_text", "text": prompt}, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image1}", - }, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image2}", - }, - { - "type": "input_image", - "file_id": file_id1, - }, - { - "type": "input_image", - "file_id": file_id2, - } - ], - } - ], - tools=[{"type": "image_generation"}], -) - -# Process the response -image_generation_calls = [ - output - for output in response.output - if output.type == "image_generation_call" -] - -image_data = [output.result for output in image_generation_calls] - -if image_data: - image_base64 = image_data[0] - image_filename = f"edited_image_{int(time.time())}.png" - with open(image_filename, "wb") as f: - f.write(base64.b64decode(image_base64)) - print(f"Image saved to {image_filename}") -else: - # If no image is generated, there might be a text response with an explanation - text_response = [output.text for output in response.output if hasattr(output, 'text')] - if text_response: - print("No image generated. Model response:") - print("\\n".join(text_response)) - else: - print("No image data found in response.") - print("Full response for debugging:") - print(response) -`;break;case o.IMAGE_EDITS:t="azure"===b?` -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 = "${w}" - -# Encode images to base64 -base64_image1 = encode_image("body-lotion.png") -base64_image2 = encode_image("soap.png") - -# Create file IDs -file_id1 = create_file("body-lotion.png") -file_id2 = create_file("incense-kit.png") - -response = client.responses.create( - model="${O}", - 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 = "${w}" - -# Encode images to base64 -base64_image1 = encode_image("body-lotion.png") -base64_image2 = encode_image("soap.png") - -# Create file IDs -file_id1 = create_file("body-lotion.png") -file_id2 = create_file("incense-kit.png") - -response = client.responses.create( - model="${O}", - 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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with text only +response = client.chat.completions.create( + model="${C}", + messages=${JSON.stringify(i,null,4)}${a} +) + +print(response) + +# Example with image or PDF (uncomment and provide file path to use) +# base64_file = encode_image("path/to/your/file.jpg") # or .pdf +# response_with_file = client.chat.completions.create( +# model="${C}", +# messages=[ +# { +# "role": "user", +# "content": [ +# { +# "type": "text", +# "text": "${O}" +# }, +# { +# "type": "image_url", +# "image_url": { +# "url": f"data:image/jpeg;base64,{base64_file}" # or data:application/pdf;base64,{base64_file} +# } +# } +# ] +# } +# ]${a} +# ) +# print(response_with_file) +`;break}case r.RESPONSES:{let e=Object.keys(T).length>0,a="";if(e){let e=JSON.stringify({metadata:T},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();a=`, + extra_body=${e}`}let i=x.length>0?x:[{role:"user",content:y}];t=` +import base64 + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Example with text only +response = client.responses.create( + model="${C}", + input=${JSON.stringify(i,null,4)}${a} +) + +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="${C}", +# input=[ +# { +# "role": "user", +# "content": [ +# {"type": "input_text", "text": "${O}"}, +# { +# "type": "input_image", +# "image_url": f"data:image/jpeg;base64,{base64_file}", # or data:application/pdf;base64,{base64_file} +# }, +# ], +# } +# ]${a} +# ) +# print(response_with_file.output_text) +`;break}case r.IMAGE:t="azure"===A?` +# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI. +# This snippet uses 'client.images.generate' and will create a new image based on your prompt. +# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context. +import os +import requests +import json +import time +from PIL import Image + +result = client.images.generate( + model="${C}", + prompt="${n}", + n=1 +) + +json_response = json.loads(result.model_dump_json()) + +# Set the directory for the stored image +image_dir = os.path.join(os.curdir, 'images') + +# If the directory doesn't exist, create it +if not os.path.isdir(image_dir): + os.mkdir(image_dir) + +# Initialize the image path +image_filename = f"generated_image_{int(time.time())}.png" +image_path = os.path.join(image_dir, image_filename) + +try: + # Retrieve the generated image + if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"): + image_url = json_response["data"][0]["url"] + generated_image = requests.get(image_url).content + with open(image_path, "wb") as image_file: + image_file.write(generated_image) + + print(f"Image saved to {image_path}") + # Display the image + image = Image.open(image_path) + image.show() + else: + print("Could not find image URL in response.") + print("Full response:", json_response) +except Exception as e: + print(f"An error occurred: {e}") + print("Full response:", json_response) +`:` +import base64 +import os +import time +import json +from PIL import Image +import requests + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Helper function to create a file (simplified for this example) +def create_file(image_path): + # In a real implementation, this would upload the file to OpenAI + # For this example, we'll just return a placeholder ID + return f"file_{os.path.basename(image_path).replace('.', '_')}" + +# The prompt entered by the user +prompt = "${O}" + +# 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="${C}", + input=[ + { + "role": "user", + "content": [ + {"type": "input_text", "text": prompt}, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image1}", + }, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image2}", + }, + { + "type": "input_image", + "file_id": file_id1, + }, + { + "type": "input_image", + "file_id": file_id2, + } + ], + } + ], + tools=[{"type": "image_generation"}], +) + +# Process the response +image_generation_calls = [ + output + for output in response.output + if output.type == "image_generation_call" +] + +image_data = [output.result for output in image_generation_calls] + +if image_data: + image_base64 = image_data[0] + image_filename = f"edited_image_{int(time.time())}.png" + with open(image_filename, "wb") as f: + f.write(base64.b64decode(image_base64)) + print(f"Image saved to {image_filename}") +else: + # If no image is generated, there might be a text response with an explanation + text_response = [output.text for output in response.output if hasattr(output, 'text')] + if text_response: + print("No image generated. Model response:") + print("\\n".join(text_response)) + else: + print("No image data found in response.") + print("Full response for debugging:") + print(response) +`;break;case r.IMAGE_EDITS:t="azure"===A?` +import base64 +import os +import time +import json +from PIL import Image +import requests + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# The prompt entered by the user +prompt = "${O}" + +# 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="${C}", + input=[ + { + "role": "user", + "content": [ + {"type": "input_text", "text": prompt}, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image1}", + }, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image2}", + }, + { + "type": "input_image", + "file_id": file_id1, + }, + { + "type": "input_image", + "file_id": file_id2, + } + ], + } + ], + tools=[{"type": "image_generation"}], +) + +# Process the response +image_generation_calls = [ + output + for output in response.output + if output.type == "image_generation_call" +] + +image_data = [output.result for output in image_generation_calls] + +if image_data: + image_base64 = image_data[0] + image_filename = f"edited_image_{int(time.time())}.png" + with open(image_filename, "wb") as f: + f.write(base64.b64decode(image_base64)) + print(f"Image saved to {image_filename}") +else: + # If no image is generated, there might be a text response with an explanation + text_response = [output.text for output in response.output if hasattr(output, 'text')] + if text_response: + print("No image generated. Model response:") + print("\\n".join(text_response)) + else: + print("No image data found in response.") + print("Full response for debugging:") + print(response) +`:` +import base64 +import os +import time + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Helper function to create a file (simplified for this example) +def create_file(image_path): + # In a real implementation, this would upload the file to OpenAI + # For this example, we'll just return a placeholder ID + return f"file_{os.path.basename(image_path).replace('.', '_')}" + +# The prompt entered by the user +prompt = "${O}" + +# 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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r={image_generation:"image",video_generation:"video",chat:"chat",responses:"responses",image_edits:"image_edits",anthropic_messages:"anthropic_messages",audio_speech:"speech",audio_transcription:"transcription",embedding:"embeddings"};e.s(["EndpointType",()=>s,"getEndpointType",0,e=>{if(console.log("getEndpointType:",e),Object.values(a).includes(e)){let t=r[e];return console.log("endpointType:",t),t}return"chat"}],785913),e.s(["generateCodeSnippet",0,e=>{let t,{apiKeySource:i,accessToken:a,apiKey:r,inputMessage:n,chatHistory:o,selectedTags:l,selectedVectorStores:c,selectedGuardrails:d,selectedPolicies:p,selectedMCPServers:m,mcpServers:u,mcpServerToolRestrictions:g,selectedVoice:f,endpointType:h,selectedModel:_,selectedSdk:x,proxySettings:b}=e,v="session"===i?a:r,y=window.location.origin,j=b?.LITELLM_UI_API_DOC_BASE_URL;j&&j.trim()?y=j:b?.PROXY_BASE_URL&&(y=b.PROXY_BASE_URL);let w=n||"Your prompt here",N=w.replace(/\\/g,"\\\\").replace(/"/g,'\\"').replace(/\n/g,"\\n"),k=o.filter(e=>!e.isImage).map(({role:e,content:t})=>({role:e,content:t})),I={};l.length>0&&(I.tags=l),c.length>0&&(I.vector_stores=c),d.length>0&&(I.guardrails=d),p.length>0&&(I.policies=p);let E=_||"your-model-name",T="azure"===x?`import openai + +client = openai.AzureOpenAI( + api_key="${v||"YOUR_LITELLM_API_KEY"}", + azure_endpoint="${y}", + api_version="2024-02-01" +)`:`import openai + +client = openai.OpenAI( + api_key="${v||"YOUR_LITELLM_API_KEY"}", + base_url="${y}" +)`;switch(h){case s.CHAT:{let e=Object.keys(I).length>0,i="";if(e){let e=JSON.stringify({metadata:I},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();i=`, + extra_body=${e}`}let a=k.length>0?k:[{role:"user",content:w}];t=` +import base64 + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Example with text only +response = client.chat.completions.create( + model="${E}", + messages=${JSON.stringify(a,null,4)}${i} +) + +print(response) + +# Example with image or PDF (uncomment and provide file path to use) +# base64_file = encode_image("path/to/your/file.jpg") # or .pdf +# response_with_file = client.chat.completions.create( +# model="${E}", +# messages=[ +# { +# "role": "user", +# "content": [ +# { +# "type": "text", +# "text": "${N}" +# }, +# { +# "type": "image_url", +# "image_url": { +# "url": f"data:image/jpeg;base64,{base64_file}" # or data:application/pdf;base64,{base64_file} +# } +# } +# ] +# } +# ]${i} +# ) +# print(response_with_file) +`;break}case s.RESPONSES:{let e=Object.keys(I).length>0,i="";if(e){let e=JSON.stringify({metadata:I},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();i=`, + extra_body=${e}`}let a=k.length>0?k:[{role:"user",content:w}];t=` +import base64 + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Example with text only +response = client.responses.create( + model="${E}", + input=${JSON.stringify(a,null,4)}${i} +) + +print(response.output_text) + +# Example with image or PDF (uncomment and provide file path to use) +# base64_file = encode_image("path/to/your/file.jpg") # or .pdf +# response_with_file = client.responses.create( +# model="${E}", +# input=[ +# { +# "role": "user", +# "content": [ +# {"type": "input_text", "text": "${N}"}, +# { +# "type": "input_image", +# "image_url": f"data:image/jpeg;base64,{base64_file}", # or data:application/pdf;base64,{base64_file} +# }, +# ], +# } +# ]${i} +# ) +# print(response_with_file.output_text) +`;break}case s.IMAGE:t="azure"===x?` +# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI. +# This snippet uses 'client.images.generate' and will create a new image based on your prompt. +# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context. +import os +import requests +import json +import time +from PIL import Image + +result = client.images.generate( + model="${E}", + prompt="${n}", + n=1 +) + +json_response = json.loads(result.model_dump_json()) + +# Set the directory for the stored image +image_dir = os.path.join(os.curdir, 'images') + +# If the directory doesn't exist, create it +if not os.path.isdir(image_dir): + os.mkdir(image_dir) + +# Initialize the image path +image_filename = f"generated_image_{int(time.time())}.png" +image_path = os.path.join(image_dir, image_filename) + +try: + # Retrieve the generated image + if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"): + image_url = json_response["data"][0]["url"] + generated_image = requests.get(image_url).content + with open(image_path, "wb") as image_file: + image_file.write(generated_image) + + print(f"Image saved to {image_path}") + # Display the image + image = Image.open(image_path) + image.show() + else: + print("Could not find image URL in response.") + print("Full response:", json_response) +except Exception as e: + print(f"An error occurred: {e}") + print("Full response:", json_response) +`:` +import base64 +import os +import time +import json +from PIL import Image +import requests + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Helper function to create a file (simplified for this example) +def create_file(image_path): + # In a real implementation, this would upload the file to OpenAI + # For this example, we'll just return a placeholder ID + return f"file_{os.path.basename(image_path).replace('.', '_')}" + +# The prompt entered by the user +prompt = "${N}" + +# 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="${E}", + input=[ + { + "role": "user", + "content": [ + {"type": "input_text", "text": prompt}, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image1}", + }, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image2}", + }, + { + "type": "input_image", + "file_id": file_id1, + }, + { + "type": "input_image", + "file_id": file_id2, + } + ], + } + ], + tools=[{"type": "image_generation"}], +) + +# Process the response +image_generation_calls = [ + output + for output in response.output + if output.type == "image_generation_call" +] + +image_data = [output.result for output in image_generation_calls] + +if image_data: + image_base64 = image_data[0] + image_filename = f"edited_image_{int(time.time())}.png" + with open(image_filename, "wb") as f: + f.write(base64.b64decode(image_base64)) + print(f"Image saved to {image_filename}") +else: + # If no image is generated, there might be a text response with an explanation + text_response = [output.text for output in response.output if hasattr(output, 'text')] + if text_response: + print("No image generated. Model response:") + print("\\n".join(text_response)) + else: + print("No image data found in response.") + print("Full response for debugging:") + print(response) +`;break;case s.IMAGE_EDITS:t="azure"===x?` +import base64 +import os +import time +import json +from PIL import Image +import requests + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# The prompt entered by the user +prompt = "${N}" + +# 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="${E}", + input=[ + { + "role": "user", + "content": [ + {"type": "input_text", "text": prompt}, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image1}", + }, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image2}", + }, + { + "type": "input_image", + "file_id": file_id1, + }, + { + "type": "input_image", + "file_id": file_id2, + } + ], + } + ], + tools=[{"type": "image_generation"}], +) + +# Process the response +image_generation_calls = [ + output + for output in response.output + if output.type == "image_generation_call" +] + +image_data = [output.result for output in image_generation_calls] + +if image_data: + image_base64 = image_data[0] + image_filename = f"edited_image_{int(time.time())}.png" + with open(image_filename, "wb") as f: + f.write(base64.b64decode(image_base64)) + print(f"Image saved to {image_filename}") +else: + # If no image is generated, there might be a text response with an explanation + text_response = [output.text for output in response.output if hasattr(output, 'text')] + if text_response: + print("No image generated. Model response:") + print("\\n".join(text_response)) + else: + print("No image data found in response.") + print("Full response for debugging:") + print(response) +`:` +import base64 +import os +import time + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Helper function to create a file (simplified for this example) +def create_file(image_path): + # In a real implementation, this would upload the file to OpenAI + # For this example, we'll just return a placeholder ID + return f"file_{os.path.basename(image_path).replace('.', '_')}" + +# The prompt entered by the user +prompt = "${N}" + +# 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="${E}", + input=[ + { + "role": "user", + "content": [ + {"type": "input_text", "text": prompt}, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image1}", + }, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image2}", + }, + { + "type": "input_image", + "file_id": file_id1, + }, + { + "type": "input_image", + "file_id": file_id2, + } + ], + } + ], + tools=[{"type": "image_generation"}], +) + +# Process the response +image_generation_calls = [ + output + for output in response.output + if output.type == "image_generation_call" +] + +image_data = [output.result for output in image_generation_calls] + +if image_data: + image_base64 = image_data[0] + image_filename = f"edited_image_{int(time.time())}.png" + with open(image_filename, "wb") as f: + f.write(base64.b64decode(image_base64)) + print(f"Image saved to {image_filename}") +else: + # If no image is generated, there might be a text response with an explanation + text_response = [output.text for output in response.output if hasattr(output, 'text')] + if text_response: + print("No image generated. Model response:") + print("\\n".join(text_response)) + else: + print("No image data found in response.") + print("Full response for debugging:") + print(response) +`;break;case s.EMBEDDINGS:t=` +response = client.embeddings.create( + input="${n||"Your string here"}", + model="${E}", + encoding_format="base64" # or "float" +) + +print(response.data[0].embedding) +`;break;case s.TRANSCRIPTION:t=` +# Open the audio file +audio_file = open("path/to/your/audio/file.mp3", "rb") + +# Make the transcription request +response = client.audio.transcriptions.create( + model="${E}", + file=audio_file${n?`, + prompt="${n.replace(/"/g,'\\"')}"`:""} +) + +print(response.text) +`;break;case s.SPEECH:t=` +# Make the text-to-speech request +response = client.audio.speech.create( + model="${E}", + input="${n||"Your text to convert to speech here"}", + voice="${f}" # Options: alloy, ash, ballad, coral, echo, fable, nova, onyx, sage, shimmer +) + +# Save the audio to a file +output_filename = 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LLM Cost Estimate Report

+

${a} model${1!==a?"s":""} configured

+ +
+

Combined Totals

+
+
+
Total Per Request
+
${tA(e.totals.cost_per_request)}
+
+
+
Total Daily
+
${tA(e.totals.daily_cost)}
+
+
+
Total Monthly
+
${tA(e.totals.monthly_cost)}
+
+
+ ${e.totals.margin_per_request>0?` +
+
+
Margin/Request
+
${tA(e.totals.margin_per_request)}
+
+
+
Daily Margin
+
${tA(e.totals.daily_margin)}
+
+
+
Monthly Margin
+
${tA(e.totals.monthly_margin)}
+
+
+ `:""} +
+ +

Model Breakdown

+ ${s.map(e=>{let t;return t=e.result,` +
+

${t.model} ${t.provider?`(${t.provider})`:""}

+ +
+

Input Tokens per Request: ${tP(t.input_tokens)}

+

Output Tokens per Request: ${tP(t.output_tokens)}

+ ${t.num_requests_per_day?`

Requests per Day: ${tP(t.num_requests_per_day)}

`:""} + ${t.num_requests_per_month?`

Requests per Month: ${tP(t.num_requests_per_month)}

`:""} +
+ + + + + + ${null!==t.daily_cost?"":""} + ${null!==t.monthly_cost?"":""} + + + + + ${null!==t.daily_cost?``:""} + ${null!==t.monthly_cost?``:""} + + + + + ${null!==t.daily_cost?``:""} + ${null!==t.monthly_cost?``:""} + + + + + ${null!==t.daily_cost?``:""} + ${null!==t.monthly_cost?``:""} + + + + + ${null!==t.daily_cost?``:""} + ${null!==t.monthly_cost?``:""} + +
Cost TypePer RequestDailyMonthly
Input Cost${tA(t.input_cost_per_request)}${tA(t.daily_input_cost)}${tA(t.monthly_input_cost)}
Output Cost${tA(t.output_cost_per_request)}${tA(t.daily_output_cost)}${tA(t.monthly_output_cost)}
Margin/Fee${tA(t.margin_cost_per_request)}${tA(t.daily_margin_cost)}${tA(t.monthly_margin_cost)}
Total${tA(t.cost_per_request)}${tA(t.daily_cost)}${tA(t.monthly_cost)}
+
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s=t.content.at(-1);switch(e.delta.type){case"text_delta":"text"===s.type&&this._emit("text",e.delta.text,s.text||"");break;case"citations_delta":"text"===s.type&&this._emit("citation",e.delta.citation,s.citations??[]);break;case"input_json_delta":tI(s)&&s.input&&this._emit("inputJson",e.delta.partial_json,s.input);break;case"thinking_delta":"thinking"===s.type&&this._emit("thinking",e.delta.thinking,s.thinking);break;case"signature_delta":"thinking"===s.type&&this._emit("signature",s.signature);break;default:tL(e.delta)}break}case"message_stop":this._addMessageParam(t),this._addMessage(t,!0);break;case"content_block_stop":this._emit("contentBlock",t.content.at(-1));break;case"message_start":e_(this,d,t,"f")}},T=function(){if(this.ended)throw new eT("stream has ended, this shouldn't happen");let e=eN(this,d,"f");if(!e)throw new eT("request ended without sending any chunks");return e_(this,d,void 0,"f"),e},A=function(e){let t=eN(this,d,"f");if("message_start"===e.type){if(t)throw new eT(`Unexpected event order, got ${e.type} before receiving "message_stop"`);return e.message}if(!t)throw new eT(`Unexpected event order, got ${e.type} before "message_start"`);switch(e.type){case"message_stop":case"content_block_stop":return t;case"message_delta":return t.container=e.delta.container,t.stop_reason=e.delta.stop_reason,t.stop_sequence=e.delta.stop_sequence,t.usage.output_tokens=e.usage.output_tokens,null!=e.usage.input_tokens&&(t.usage.input_tokens=e.usage.input_tokens),null!=e.usage.cache_creation_input_tokens&&(t.usage.cache_creation_input_tokens=e.usage.cache_creation_input_tokens),null!=e.usage.cache_read_input_tokens&&(t.usage.cache_read_input_tokens=e.usage.cache_read_input_tokens),null!=e.usage.server_tool_use&&(t.usage.server_tool_use=e.usage.server_tool_use),t;case"content_block_start":return t.content.push(e.content_block),t;case"content_block_delta":{let s=t.content.at(e.index);switch(e.delta.type){case"text_delta":s?.type==="text"&&(s.text+=e.delta.text);break;case"citations_delta":s?.type==="text"&&(s.citations??(s.citations=[]),s.citations.push(e.delta.citation));break;case"input_json_delta":if(s&&tI(s)){let t=s[tR]||"";if(Object.defineProperty(s,tR,{value:t+=e.delta.partial_json,enumerable:!1,writable:!0}),t)try{s.input=tP(t)}catch(s){let e=new eT(`Unable to parse tool parameter JSON from model. 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JSON: ${t}`);eN(this,k,"f").call(this,e)}}break;case"thinking_delta":s?.type==="thinking"&&(s.thinking+=e.delta.thinking);break;case"signature_delta":s?.type==="thinking"&&(s.signature=e.delta.signature);break;default:tL(e.delta)}return t}}},Symbol.asyncIterator)](){let e=[],t=[],s=!1;return this.on("streamEvent",s=>{let r=t.shift();r?r.resolve(s):e.push(s)}),this.on("end",()=>{for(let e of(s=!0,t))e.resolve(void 0);t.length=0}),this.on("abort",e=>{for(let r of(s=!0,t))r.reject(e);t.length=0}),this.on("error",e=>{for(let r of(s=!0,t))r.reject(e);t.length=0}),{next:async()=>e.length?{value:e.shift(),done:!1}:s?{value:void 0,done:!0}:new Promise((e,s)=>t.push({resolve:e,reject:s})).then(e=>e?{value:e,done:!1}:{value:void 0,done:!0}),return:async()=>(this.abort(),{value:void 0,done:!0})}}toReadableStream(){return new te(this[Symbol.asyncIterator].bind(this),this.controller).toReadableStream()}}function tL(e){}let t$={"claude-opus-4-20250514":8192,"claude-opus-4-0":8192,"claude-4-opus-20250514":8192,"anthropic.claude-opus-4-20250514-v1:0":8192,"claude-opus-4@20250514":8192},tU={"claude-1.3":"November 6th, 2024","claude-1.3-100k":"November 6th, 2024","claude-instant-1.1":"November 6th, 2024","claude-instant-1.1-100k":"November 6th, 2024","claude-instant-1.2":"November 6th, 2024","claude-3-sonnet-20240229":"July 21st, 2025","claude-2.1":"July 21st, 2025","claude-2.0":"July 21st, 2025"};class tD extends tw{constructor(){super(...arguments),this.batches=new tA(this._client)}create(e,t){let{betas:s,...r}=e;r.model in tU&&console.warn(`The model '${r.model}' is deprecated and will reach end-of-life on ${tU[r.model]} +Please migrate to a newer model. Visit https://docs.anthropic.com/en/docs/resources/model-deprecations for more information.`);let a=this._client._options.timeout;if(!r.stream&&null==a){let e=t$[r.model]??void 0;a=this._client.calculateNonstreamingTimeout(r.max_tokens,e)}return this._client.post("/v1/messages?beta=true",{body:r,timeout:a??6e5,...t,headers:t_([{...s?.toString()!=null?{"anthropic-beta":s?.toString()}:void 0},t?.headers]),stream:e.stream??!1})}stream(e,t){return tM.createMessage(this,e,t)}countTokens(e,t){let{betas:s,...r}=e;return this._client.post("/v1/messages/count_tokens?beta=true",{body:r,...t,headers:t_([{"anthropic-beta":[...s??[],"token-counting-2024-11-01"].toString()},t?.headers])})}}tD.Batches=tA;class tB extends tw{constructor(){super(...arguments),this.models=new tC(this._client),this.messages=new tD(this._client),this.files=new tE(this._client)}}tB.Models=tC,tB.Messages=tD,tB.Files=tE;class tq extends tw{create(e,t){let{betas:s,...r}=e;return 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s=t?.signal;s&&(s.aborted&&this.controller.abort(),s.addEventListener("abort",()=>this.controller.abort())),eN(this,O,"m",K).call(this),this._connected(null);let r=te.fromReadableStream(e,this.controller);for await(let e of r)eN(this,O,"m",X).call(this,e);if(r.controller.signal?.aborted)throw new eO;eN(this,O,"m",Y).call(this)}[(P=new WeakMap,R=new WeakMap,I=new WeakMap,M=new WeakMap,L=new WeakMap,$=new WeakMap,U=new WeakMap,D=new WeakMap,B=new WeakMap,q=new WeakMap,z=new WeakMap,H=new WeakMap,W=new WeakMap,F=new WeakMap,V=new WeakMap,O=new WeakSet,J=function(){if(0===this.receivedMessages.length)throw new eT("stream ended without producing a Message with role=assistant");return this.receivedMessages.at(-1)},G=function(){if(0===this.receivedMessages.length)throw new eT("stream ended without producing a Message with role=assistant");let e=this.receivedMessages.at(-1).content.filter(e=>"text"===e.type).map(e=>e.text);if(0===e.length)throw new eT("stream ended without producing a content block with type=text");return e.join(" ")},K=function(){this.ended||e_(this,P,void 0,"f")},X=function(e){if(this.ended)return;let t=eN(this,O,"m",Q).call(this,e);switch(this._emit("streamEvent",e,t),e.type){case"content_block_delta":{let s=t.content.at(-1);switch(e.delta.type){case"text_delta":"text"===s.type&&this._emit("text",e.delta.text,s.text||"");break;case"citations_delta":"text"===s.type&&this._emit("citation",e.delta.citation,s.citations??[]);break;case"input_json_delta":tH(s)&&s.input&&this._emit("inputJson",e.delta.partial_json,s.input);break;case"thinking_delta":"thinking"===s.type&&this._emit("thinking",e.delta.thinking,s.thinking);break;case"signature_delta":"thinking"===s.type&&this._emit("signature",s.signature);break;default:tF(e.delta)}break}case"message_stop":this._addMessageParam(t),this._addMessage(t,!0);break;case"content_block_stop":this._emit("contentBlock",t.content.at(-1));break;case"message_start":e_(this,P,t,"f")}},Y=function(){if(this.ended)throw new eT("stream has ended, this shouldn't happen");let e=eN(this,P,"f");if(!e)throw new eT("request ended without sending any chunks");return e_(this,P,void 0,"f"),e},Q=function(e){let t=eN(this,P,"f");if("message_start"===e.type){if(t)throw new eT(`Unexpected event order, got ${e.type} before receiving "message_stop"`);return e.message}if(!t)throw new eT(`Unexpected event order, got ${e.type} before "message_start"`);switch(e.type){case"message_stop":case"content_block_stop":return t;case"message_delta":return t.stop_reason=e.delta.stop_reason,t.stop_sequence=e.delta.stop_sequence,t.usage.output_tokens=e.usage.output_tokens,null!=e.usage.input_tokens&&(t.usage.input_tokens=e.usage.input_tokens),null!=e.usage.cache_creation_input_tokens&&(t.usage.cache_creation_input_tokens=e.usage.cache_creation_input_tokens),null!=e.usage.cache_read_input_tokens&&(t.usage.cache_read_input_tokens=e.usage.cache_read_input_tokens),null!=e.usage.server_tool_use&&(t.usage.server_tool_use=e.usage.server_tool_use),t;case"content_block_start":return t.content.push(e.content_block),t;case"content_block_delta":{let s=t.content.at(e.index);switch(e.delta.type){case"text_delta":s?.type==="text"&&(s.text+=e.delta.text);break;case"citations_delta":s?.type==="text"&&(s.citations??(s.citations=[]),s.citations.push(e.delta.citation));break;case"input_json_delta":if(s&&tH(s)){let t=s[tz]||"";Object.defineProperty(s,tz,{value:t+=e.delta.partial_json,enumerable:!1,writable:!0}),t&&(s.input=tP(t))}break;case"thinking_delta":s?.type==="thinking"&&(s.thinking+=e.delta.thinking);break;case"signature_delta":s?.type==="thinking"&&(s.signature=e.delta.signature);break;default:tF(e.delta)}return t}}},Symbol.asyncIterator)](){let e=[],t=[],s=!1;return this.on("streamEvent",s=>{let r=t.shift();r?r.resolve(s):e.push(s)}),this.on("end",()=>{for(let e of(s=!0,t))e.resolve(void 0);t.length=0}),this.on("abort",e=>{for(let r of(s=!0,t))r.reject(e);t.length=0}),this.on("error",e=>{for(let r of(s=!0,t))r.reject(e);t.length=0}),{next:async()=>e.length?{value:e.shift(),done:!1}:s?{value:void 0,done:!0}:new Promise((e,s)=>t.push({resolve:e,reject:s})).then(e=>e?{value:e,done:!1}:{value:void 0,done:!0}),return:async()=>(this.abort(),{value:void 0,done:!0})}}toReadableStream(){return new te(this[Symbol.asyncIterator].bind(this),this.controller).toReadableStream()}}function tF(e){}class tJ extends tw{create(e,t){return this._client.post("/v1/messages/batches",{body:e,...t})}retrieve(e,t){return this._client.get(tk`/v1/messages/batches/${e}`,t)}list(e={},t){return this._client.getAPIList("/v1/messages/batches",tc,{query:e,...t})}delete(e,t){return this._client.delete(tk`/v1/messages/batches/${e}`,t)}cancel(e,t){return this._client.post(tk`/v1/messages/batches/${e}/cancel`,t)}async results(e,t){let s=await this.retrieve(e);if(!s.results_url)throw new eT(`No batch \`results_url\`; Has it finished processing? ${s.processing_status} - ${s.id}`);return this._client.get(s.results_url,{...t,headers:t_([{Accept:"application/binary"},t?.headers]),stream:!0,__binaryResponse:!0})._thenUnwrap((e,t)=>tT.fromResponse(t.response,t.controller))}}class tG extends tw{constructor(){super(...arguments),this.batches=new tJ(this._client)}create(e,t){e.model in tV&&console.warn(`The model '${e.model}' is deprecated and will reach end-of-life on ${tV[e.model]} +Please migrate to a newer 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eR;eE(this,c,"m",T).call(this)}[(d=new WeakMap,u=new WeakMap,h=new WeakMap,m=new WeakMap,p=new WeakMap,f=new WeakMap,g=new WeakMap,y=new WeakMap,x=new WeakMap,b=new WeakMap,v=new WeakMap,w=new WeakMap,j=new WeakMap,S=new WeakMap,k=new WeakMap,c=new WeakSet,_=function(){if(0===this.receivedMessages.length)throw new eO("stream ended without producing a Message with role=assistant");return this.receivedMessages.at(-1)},N=function(){if(0===this.receivedMessages.length)throw new eO("stream ended without producing a Message with role=assistant");let e=this.receivedMessages.at(-1).content.filter(e=>"text"===e.type).map(e=>e.text);if(0===e.length)throw new eO("stream ended without producing a content block with type=text");return e.join(" ")},E=function(){this.ended||ek(this,d,void 0,"f")},C=function(e){if(this.ended)return;let t=eE(this,c,"m",A).call(this,e);switch(this._emit("streamEvent",e,t),e.type){case"content_block_delta":{let s=t.content.at(-1);switch(e.delta.type){case"text_delta":"text"===s.type&&this._emit("text",e.delta.text,s.text||"");break;case"citations_delta":"text"===s.type&&this._emit("citation",e.delta.citation,s.citations??[]);break;case"input_json_delta":tL(s)&&s.input&&this._emit("inputJson",e.delta.partial_json,s.input);break;case"thinking_delta":"thinking"===s.type&&this._emit("thinking",e.delta.thinking,s.thinking);break;case"signature_delta":"thinking"===s.type&&this._emit("signature",s.signature);break;default:tU(e.delta)}break}case"message_stop":this._addMessageParam(t),this._addMessage(t,!0);break;case"content_block_stop":this._emit("contentBlock",t.content.at(-1));break;case"message_start":ek(this,d,t,"f")}},T=function(){if(this.ended)throw new eO("stream has ended, this shouldn't happen");let e=eE(this,d,"f");if(!e)throw new eO("request ended without sending any chunks");return ek(this,d,void 0,"f"),e},A=function(e){let t=eE(this,d,"f");if("message_start"===e.type){if(t)throw new eO(`Unexpected event order, got ${e.type} before receiving "message_stop"`);return e.message}if(!t)throw new eO(`Unexpected event order, got ${e.type} before "message_start"`);switch(e.type){case"message_stop":case"content_block_stop":return t;case"message_delta":return t.container=e.delta.container,t.stop_reason=e.delta.stop_reason,t.stop_sequence=e.delta.stop_sequence,t.usage.output_tokens=e.usage.output_tokens,null!=e.usage.input_tokens&&(t.usage.input_tokens=e.usage.input_tokens),null!=e.usage.cache_creation_input_tokens&&(t.usage.cache_creation_input_tokens=e.usage.cache_creation_input_tokens),null!=e.usage.cache_read_input_tokens&&(t.usage.cache_read_input_tokens=e.usage.cache_read_input_tokens),null!=e.usage.server_tool_use&&(t.usage.server_tool_use=e.usage.server_tool_use),t;case"content_block_start":return t.content.push(e.content_block),t;case"content_block_delta":{let s=t.content.at(e.index);switch(e.delta.type){case"text_delta":s?.type==="text"&&(s.text+=e.delta.text);break;case"citations_delta":s?.type==="text"&&(s.citations??(s.citations=[]),s.citations.push(e.delta.citation));break;case"input_json_delta":if(s&&tL(s)){let t=s[tM]||"";if(Object.defineProperty(s,tM,{value:t+=e.delta.partial_json,enumerable:!1,writable:!0}),t)try{s.input=tI(t)}catch(s){let e=new eO(`Unable to parse tool parameter JSON from model. Please retry your request or adjust your prompt. Error: ${s}. JSON: ${t}`);eE(this,k,"f").call(this,e)}}break;case"thinking_delta":s?.type==="thinking"&&(s.thinking+=e.delta.thinking);break;case"signature_delta":s?.type==="thinking"&&(s.signature=e.delta.signature);break;default:tU(e.delta)}return t}}},Symbol.asyncIterator)](){let e=[],t=[],s=!1;return this.on("streamEvent",s=>{let r=t.shift();r?r.resolve(s):e.push(s)}),this.on("end",()=>{for(let e of(s=!0,t))e.resolve(void 0);t.length=0}),this.on("abort",e=>{for(let r of(s=!0,t))r.reject(e);t.length=0}),this.on("error",e=>{for(let r of(s=!0,t))r.reject(e);t.length=0}),{next:async()=>e.length?{value:e.shift(),done:!1}:s?{value:void 0,done:!0}:new Promise((e,s)=>t.push({resolve:e,reject:s})).then(e=>e?{value:e,done:!1}:{value:void 0,done:!0}),return:async()=>(this.abort(),{value:void 0,done:!0})}}toReadableStream(){return new ts(this[Symbol.asyncIterator].bind(this),this.controller).toReadableStream()}}function tU(e){}let tD={"claude-opus-4-20250514":8192,"claude-opus-4-0":8192,"claude-4-opus-20250514":8192,"anthropic.claude-opus-4-20250514-v1:0":8192,"claude-opus-4@20250514":8192},tB={"claude-1.3":"November 6th, 2024","claude-1.3-100k":"November 6th, 2024","claude-instant-1.1":"November 6th, 2024","claude-instant-1.1-100k":"November 6th, 2024","claude-instant-1.2":"November 6th, 2024","claude-3-sonnet-20240229":"July 21st, 2025","claude-2.1":"July 21st, 2025","claude-2.0":"July 21st, 2025"};class tq extends tS{constructor(){super(...arguments),this.batches=new tP(this._client)}create(e,t){let{betas:s,...r}=e;r.model in tB&&console.warn(`The model '${r.model}' is deprecated and will reach end-of-life on ${tB[r.model]} -Please migrate to a newer model. Visit https://docs.anthropic.com/en/docs/resources/model-deprecations for more information.`);let a=this._client._options.timeout;if(!r.stream&&null==a){let e=tD[r.model]??void 0;a=this._client.calculateNonstreamingTimeout(r.max_tokens,e)}return this._client.post("/v1/messages?beta=true",{body:r,timeout:a??6e5,...t,headers:tk([{...s?.toString()!=null?{"anthropic-beta":s?.toString()}:void 0},t?.headers]),stream:e.stream??!1})}stream(e,t){return t$.createMessage(this,e,t)}countTokens(e,t){let{betas:s,...r}=e;return this._client.post("/v1/messages/count_tokens?beta=true",{body:r,...t,headers:tk([{"anthropic-beta":[...s??[],"token-counting-2024-11-01"].toString()},t?.headers])})}}tq.Batches=tP;class tz extends tS{constructor(){super(...arguments),this.models=new tA(this._client),this.messages=new tq(this._client),this.files=new tT(this._client)}}tz.Models=tA,tz.Messages=tq,tz.Files=tT;class tW extends tS{create(e,t){let{betas:s,...r}=e;return this._client.post("/v1/complete",{body:r,timeout:this._client._options.timeout??6e5,...t,headers:tk([{...s?.toString()!=null?{"anthropic-beta":s?.toString()}:void 0},t?.headers]),stream:e.stream??!1})}}let tF="__json_buf";function tH(e){return"tool_use"===e.type||"server_tool_use"===e.type}class tJ{constructor(){O.add(this),this.messages=[],this.receivedMessages=[],P.set(this,void 0),this.controller=new AbortController,R.set(this,void 0),I.set(this,()=>{}),M.set(this,()=>{}),L.set(this,void 0),$.set(this,()=>{}),U.set(this,()=>{}),D.set(this,{}),B.set(this,!1),q.set(this,!1),z.set(this,!1),W.set(this,!1),F.set(this,void 0),H.set(this,void 0),V.set(this,e=>{if(ek(this,q,!0,"f"),eT(e)&&(e=new eR),e instanceof eR)return ek(this,z,!0,"f"),this._emit("abort",e);if(e instanceof eO)return this._emit("error",e);if(e instanceof Error){let t=new eO(e.message);return t.cause=e,this._emit("error",t)}return this._emit("error",new eO(String(e)))}),ek(this,R,new Promise((e,t)=>{ek(this,I,e,"f"),ek(this,M,t,"f")}),"f"),ek(this,L,new Promise((e,t)=>{ek(this,$,e,"f"),ek(this,U,t,"f")}),"f"),eE(this,R,"f").catch(()=>{}),eE(this,L,"f").catch(()=>{})}get response(){return eE(this,F,"f")}get request_id(){return eE(this,H,"f")}async withResponse(){let e=await eE(this,R,"f");if(!e)throw Error("Could not resolve a `Response` object");return{data:this,response:e,request_id:e.headers.get("request-id")}}static fromReadableStream(e){let t=new tJ;return t._run(()=>t._fromReadableStream(e)),t}static createMessage(e,t,s){let r=new tJ;for(let e of t.messages)r._addMessageParam(e);return r._run(()=>r._createMessage(e,{...t,stream:!0},{...s,headers:{...s?.headers,"X-Stainless-Helper-Method":"stream"}})),r}_run(e){e().then(()=>{this._emitFinal(),this._emit("end")},eE(this,V,"f"))}_addMessageParam(e){this.messages.push(e)}_addMessage(e,t=!0){this.receivedMessages.push(e),t&&this._emit("message",e)}async _createMessage(e,t,s){let r=s?.signal;r&&(r.aborted&&this.controller.abort(),r.addEventListener("abort",()=>this.controller.abort())),eE(this,O,"m",K).call(this);let{response:a,data:n}=await e.create({...t,stream:!0},{...s,signal:this.controller.signal}).withResponse();for await(let e of(this._connected(a),n))eE(this,O,"m",X).call(this,e);if(n.controller.signal?.aborted)throw new eR;eE(this,O,"m",Y).call(this)}_connected(e){this.ended||(ek(this,F,e,"f"),ek(this,H,e?.headers.get("request-id"),"f"),eE(this,I,"f").call(this,e),this._emit("connect"))}get ended(){return eE(this,B,"f")}get errored(){return eE(this,q,"f")}get aborted(){return eE(this,z,"f")}abort(){this.controller.abort()}on(e,t){return(eE(this,D,"f")[e]||(eE(this,D,"f")[e]=[])).push({listener:t}),this}off(e,t){let s=eE(this,D,"f")[e];if(!s)return this;let r=s.findIndex(e=>e.listener===t);return r>=0&&s.splice(r,1),this}once(e,t){return(eE(this,D,"f")[e]||(eE(this,D,"f")[e]=[])).push({listener:t,once:!0}),this}emitted(e){return new Promise((t,s)=>{ek(this,W,!0,"f"),"error"!==e&&this.once("error",s),this.once(e,t)})}async done(){ek(this,W,!0,"f"),await eE(this,L,"f")}get currentMessage(){return eE(this,P,"f")}async finalMessage(){return await this.done(),eE(this,O,"m",J).call(this)}async finalText(){return await this.done(),eE(this,O,"m",G).call(this)}_emit(e,...t){if(eE(this,B,"f"))return;"end"===e&&(ek(this,B,!0,"f"),eE(this,$,"f").call(this));let s=eE(this,D,"f")[e];if(s&&(eE(this,D,"f")[e]=s.filter(e=>!e.once),s.forEach(({listener:e})=>e(...t))),"abort"===e){let e=t[0];eE(this,W,"f")||s?.length||Promise.reject(e),eE(this,M,"f").call(this,e),eE(this,U,"f").call(this,e),this._emit("end");return}if("error"===e){let e=t[0];eE(this,W,"f")||s?.length||Promise.reject(e),eE(this,M,"f").call(this,e),eE(this,U,"f").call(this,e),this._emit("end")}}_emitFinal(){this.receivedMessages.at(-1)&&this._emit("finalMessage",eE(this,O,"m",J).call(this))}async _fromReadableStream(e,t){let s=t?.signal;s&&(s.aborted&&this.controller.abort(),s.addEventListener("abort",()=>this.controller.abort())),eE(this,O,"m",K).call(this),this._connected(null);let r=ts.fromReadableStream(e,this.controller);for await(let e of r)eE(this,O,"m",X).call(this,e);if(r.controller.signal?.aborted)throw new eR;eE(this,O,"m",Y).call(this)}[(P=new WeakMap,R=new WeakMap,I=new WeakMap,M=new WeakMap,L=new WeakMap,$=new WeakMap,U=new WeakMap,D=new WeakMap,B=new WeakMap,q=new WeakMap,z=new WeakMap,W=new WeakMap,F=new WeakMap,H=new WeakMap,V=new WeakMap,O=new WeakSet,J=function(){if(0===this.receivedMessages.length)throw new eO("stream ended without producing a Message with role=assistant");return this.receivedMessages.at(-1)},G=function(){if(0===this.receivedMessages.length)throw new eO("stream ended without producing a Message with role=assistant");let e=this.receivedMessages.at(-1).content.filter(e=>"text"===e.type).map(e=>e.text);if(0===e.length)throw new eO("stream ended without producing a content block with type=text");return e.join(" ")},K=function(){this.ended||ek(this,P,void 0,"f")},X=function(e){if(this.ended)return;let t=eE(this,O,"m",Q).call(this,e);switch(this._emit("streamEvent",e,t),e.type){case"content_block_delta":{let s=t.content.at(-1);switch(e.delta.type){case"text_delta":"text"===s.type&&this._emit("text",e.delta.text,s.text||"");break;case"citations_delta":"text"===s.type&&this._emit("citation",e.delta.citation,s.citations??[]);break;case"input_json_delta":tH(s)&&s.input&&this._emit("inputJson",e.delta.partial_json,s.input);break;case"thinking_delta":"thinking"===s.type&&this._emit("thinking",e.delta.thinking,s.thinking);break;case"signature_delta":"thinking"===s.type&&this._emit("signature",s.signature);break;default:tG(e.delta)}break}case"message_stop":this._addMessageParam(t),this._addMessage(t,!0);break;case"content_block_stop":this._emit("contentBlock",t.content.at(-1));break;case"message_start":ek(this,P,t,"f")}},Y=function(){if(this.ended)throw new eO("stream has ended, this shouldn't happen");let e=eE(this,P,"f");if(!e)throw new eO("request ended without sending any chunks");return ek(this,P,void 0,"f"),e},Q=function(e){let t=eE(this,P,"f");if("message_start"===e.type){if(t)throw new eO(`Unexpected event order, got ${e.type} before receiving "message_stop"`);return e.message}if(!t)throw new eO(`Unexpected event order, got ${e.type} before "message_start"`);switch(e.type){case"message_stop":case"content_block_stop":return t;case"message_delta":return t.stop_reason=e.delta.stop_reason,t.stop_sequence=e.delta.stop_sequence,t.usage.output_tokens=e.usage.output_tokens,null!=e.usage.input_tokens&&(t.usage.input_tokens=e.usage.input_tokens),null!=e.usage.cache_creation_input_tokens&&(t.usage.cache_creation_input_tokens=e.usage.cache_creation_input_tokens),null!=e.usage.cache_read_input_tokens&&(t.usage.cache_read_input_tokens=e.usage.cache_read_input_tokens),null!=e.usage.server_tool_use&&(t.usage.server_tool_use=e.usage.server_tool_use),t;case"content_block_start":return t.content.push(e.content_block),t;case"content_block_delta":{let s=t.content.at(e.index);switch(e.delta.type){case"text_delta":s?.type==="text"&&(s.text+=e.delta.text);break;case"citations_delta":s?.type==="text"&&(s.citations??(s.citations=[]),s.citations.push(e.delta.citation));break;case"input_json_delta":if(s&&tH(s)){let t=s[tF]||"";Object.defineProperty(s,tF,{value:t+=e.delta.partial_json,enumerable:!1,writable:!0}),t&&(s.input=tI(t))}break;case"thinking_delta":s?.type==="thinking"&&(s.thinking+=e.delta.thinking);break;case"signature_delta":s?.type==="thinking"&&(s.signature=e.delta.signature);break;default:tG(e.delta)}return t}}},Symbol.asyncIterator)](){let e=[],t=[],s=!1;return this.on("streamEvent",s=>{let r=t.shift();r?r.resolve(s):e.push(s)}),this.on("end",()=>{for(let e of(s=!0,t))e.resolve(void 0);t.length=0}),this.on("abort",e=>{for(let r of(s=!0,t))r.reject(e);t.length=0}),this.on("error",e=>{for(let r of(s=!0,t))r.reject(e);t.length=0}),{next:async()=>e.length?{value:e.shift(),done:!1}:s?{value:void 0,done:!0}:new Promise((e,s)=>t.push({resolve:e,reject:s})).then(e=>e?{value:e,done:!1}:{value:void 0,done:!0}),return:async()=>(this.abort(),{value:void 0,done:!0})}}toReadableStream(){return new ts(this[Symbol.asyncIterator].bind(this),this.controller).toReadableStream()}}function tG(e){}class tV extends tS{create(e,t){return this._client.post("/v1/messages/batches",{body:e,...t})}retrieve(e,t){return this._client.get(tC`/v1/messages/batches/${e}`,t)}list(e={},t){return this._client.getAPIList("/v1/messages/batches",tu,{query:e,...t})}delete(e,t){return this._client.delete(tC`/v1/messages/batches/${e}`,t)}cancel(e,t){return this._client.post(tC`/v1/messages/batches/${e}/cancel`,t)}async results(e,t){let s=await this.retrieve(e);if(!s.results_url)throw new eO(`No batch \`results_url\`; Has it finished processing? ${s.processing_status} - ${s.id}`);return this._client.get(s.results_url,{...t,headers:tk([{Accept:"application/binary"},t?.headers]),stream:!0,__binaryResponse:!0})._thenUnwrap((e,t)=>tO.fromResponse(t.response,t.controller))}}class tK extends tS{constructor(){super(...arguments),this.batches=new tV(this._client)}create(e,t){e.model in tX&&console.warn(`The model '${e.model}' is deprecated and will reach end-of-life on ${tX[e.model]} -Please migrate to a newer model. Visit https://docs.anthropic.com/en/docs/resources/model-deprecations for more information.`);let s=this._client._options.timeout;if(!e.stream&&null==s){let t=tD[e.model]??void 0;s=this._client.calculateNonstreamingTimeout(e.max_tokens,t)}return this._client.post("/v1/messages",{body:e,timeout:s??6e5,...t,stream:e.stream??!1})}stream(e,t){return tJ.createMessage(this,e,t)}countTokens(e,t){return this._client.post("/v1/messages/count_tokens",{body:e,...t})}}let tX={"claude-1.3":"November 6th, 2024","claude-1.3-100k":"November 6th, 2024","claude-instant-1.1":"November 6th, 2024","claude-instant-1.1-100k":"November 6th, 2024","claude-instant-1.2":"November 6th, 2024","claude-3-sonnet-20240229":"July 21st, 2025","claude-2.1":"July 21st, 2025","claude-2.0":"July 21st, 2025"};tK.Batches=tV;class tY extends tS{retrieve(e,t={},s){let{betas:r}=t??{};return this._client.get(tC`/v1/models/${e}`,{...s,headers:tk([{...r?.toString()!=null?{"anthropic-beta":r?.toString()}:void 0},s?.headers])})}list(e={},t){let{betas:s,...r}=e??{};return this._client.getAPIList("/v1/models",tu,{query:r,...t,headers:tk([{...s?.toString()!=null?{"anthropic-beta":s?.toString()}:void 0},t?.headers])})}}let tQ=e=>void 0!==globalThis.process?globalThis.process.env?.[e]?.trim()??void 0:void 0!==globalThis.Deno?globalThis.Deno.env?.get?.(e)?.trim():void 0;class tZ{constructor({baseURL:e=tQ("ANTHROPIC_BASE_URL"),apiKey:t=tQ("ANTHROPIC_API_KEY")??null,authToken:s=tQ("ANTHROPIC_AUTH_TOKEN")??null,...r}={}){Z.set(this,void 0);const a={apiKey:t,authToken:s,...r,baseURL:e||"https://api.anthropic.com"};if(!a.dangerouslyAllowBrowser&&"u">typeof window&&void 0!==window.document&&"u">typeof navigator)throw new eO("It looks like you're running in a browser-like environment.\n\nThis is disabled by default, as it risks exposing your secret API credentials to attackers.\nIf you understand the risks and have appropriate mitigations in place,\nyou can set the `dangerouslyAllowBrowser` option to `true`, e.g.,\n\nnew Anthropic({ apiKey, dangerouslyAllowBrowser: true });\n");this.baseURL=a.baseURL,this.timeout=a.timeout??t0.DEFAULT_TIMEOUT,this.logger=a.logger??console;const n="warn";this.logLevel=n,this.logLevel=eV(a.logLevel,"ClientOptions.logLevel",this)??eV(tQ("ANTHROPIC_LOG"),"process.env['ANTHROPIC_LOG']",this)??n,this.fetchOptions=a.fetchOptions,this.maxRetries=a.maxRetries??2,this.fetch=a.fetch??function(){if("u">typeof fetch)return fetch;throw Error("`fetch` is not defined as a global; Either pass `fetch` to the client, `new Anthropic({ fetch })` or polyfill the global, `globalThis.fetch = fetch`")}(),ek(this,Z,e7,"f"),this._options=a,this.apiKey=t,this.authToken=s}withOptions(e){return new this.constructor({...this._options,baseURL:this.baseURL,maxRetries:this.maxRetries,timeout:this.timeout,logger:this.logger,logLevel:this.logLevel,fetchOptions:this.fetchOptions,apiKey:this.apiKey,authToken:this.authToken,...e})}defaultQuery(){return this._options.defaultQuery}validateHeaders({values:e,nulls:t}){if(!(this.apiKey&&e.get("x-api-key")||t.has("x-api-key")||this.authToken&&e.get("authorization"))&&!t.has("authorization"))throw Error('Could not resolve authentication method. 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y=["top","topLeft","topRight","bottom","bottomLeft","bottomRight"],b={topLeft:"left",topRight:"right",bottomLeft:"left",bottomRight:"right",top:"left",bottom:"left"},S=e=>{let{iconCls:t,componentCls:n,boxShadow:r,fontSizeLG:o,notificationMarginBottom:a,borderRadiusLG:i,colorSuccess:l,colorInfo:s,colorWarning:c,colorError:u,colorTextHeading:f,notificationBg:d,notificationPadding:m,notificationMarginEdge:v,notificationProgressBg:h,notificationProgressHeight:y,fontSize:b,lineHeight:S,width:C,notificationIconSize:E,colorText:x,colorSuccessBg:k,colorErrorBg:T,colorInfoBg:O,colorWarningBg:w}=e,P=`${n}-notice`;return{position:"relative",marginBottom:a,marginInlineStart:"auto",background:d,borderRadius:i,boxShadow:r,[P]:{padding:m,width:C,maxWidth:`calc(100vw - 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Object.assign({[`${t}-stack`]:{[`& > ${t}-notice-wrapper`]:Object.assign({transition:`transform ${e.motionDurationSlow}, backdrop-filter 0s`,willChange:"transform, opacity",position:"absolute"},(e=>{let t={};for(let n=1;n ${e.componentCls}-notice`]:{opacity:0,transition:`opacity ${e.motionDurationMid}`}};return Object.assign({[`&:not(:nth-last-child(-n+${e.notificationStackLayer}))`]:{opacity:0,overflow:"hidden",color:"transparent",pointerEvents:"none"}},t)})(e))},[`${t}-stack:not(${t}-stack-expanded)`]:{[`& > ${t}-notice-wrapper`]:Object.assign({},(e=>{let t={};for(let n=1;n ${t}-notice-wrapper`]:{"&:not(:nth-last-child(-n + 1))":{opacity:1,overflow:"unset",color:"inherit",pointerEvents:"auto",[`& > 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E(t.createElement("div",{className:(0,s.default)(`${S}-pure-panel`,w,r,A,C)},t.createElement(k,{prefixCls:b}),t.createElement(c.Notice,Object.assign({},h,{prefixCls:b,eventKey:"pure",duration:null,closable:m,className:(0,s.default)({notificationClassName:v}),closeIcon:O(b,g),content:t.createElement(P,{prefixCls:S,icon:o,type:a,message:i,description:l,actions:null!=p?p:d})}))))},"getCloseIcon",()=>O],645384)},194732,513139,e=>{"use strict";var t=e.i(198197);e.s(["NotificationProvider",()=>t.default],194732);var n=e.i(404556);e.s(["useNotification",()=>n.default],513139)},698173,e=>{"use strict";e.i(247167);var t=e.i(271645),n=e.i(738275),r=e.i(609587),o=e.i(242064),a=e.i(783164),i=e.i(645384),l=e.i(343794);e.i(792131);var s=e.i(194732),c=e.i(513139),u=e.i(747656),f=e.i(321883),d=e.i(104458),p=e.i(628918),m=function(e,t){var n={};for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&0>t.indexOf(r)&&(n[r]=e[r]);if(null!=e&&"function"==typeof Object.getOwnPropertySymbols)for(var 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n=t.default.useRef(null);return(0,u.devUseWarning)("Notification"),[t.default.useMemo(()=>{let r=r=>{var o;if(!n.current)return;let{open:a,prefixCls:s,notification:c}=n.current,u=`${s}-notice`,{message:f,description:d,icon:p,type:g,btn:v,actions:h,className:y,style:b,role:S="alert",closeIcon:C,closable:E}=r,x=m(r,["message","description","icon","type","btn","actions","className","style","role","closeIcon","closable"]),k=(0,i.getCloseIcon)(u,void 0!==C?C:void 0!==(null==e?void 0:e.closeIcon)?e.closeIcon:null==c?void 0:c.closeIcon);return a(Object.assign(Object.assign({placement:null!=(o=null==e?void 0:e.placement)?o:"topRight"},x),{content:t.default.createElement(i.PureContent,{prefixCls:u,icon:p,type:g,message:f,description:d,actions:null!=h?h:v,role:S}),className:(0,l.default)(g&&`${u}-${g}`,y,null==c?void 0:c.className),style:Object.assign(Object.assign({},null==c?void 0:c.style),b),closeIcon:k,closable:null!=E?E:!!k}))},o={open:r,destroy:e=>{var t,r;void 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Object.keys(e).forEach(t=>{e[t]=(...e)=>(i(),u[t].apply(u,e))}),{instance:e,sync:i}}),f}),k=t.default.forwardRef((e,n)=>{let[o,a]=t.default.useState(E),i=()=>{a(E)};t.default.useEffect(i,[]);let l=(0,r.globalConfig)(),s=l.getRootPrefixCls(),c=l.getIconPrefixCls(),u=l.getTheme(),f=t.default.createElement(x,{ref:n,sync:i,notificationConfig:o});return t.default.createElement(r.default,{prefixCls:s,iconPrefixCls:c,theme:u},l.holderRender?l.holderRender(f):f)}),T=()=>{if(!b){let e=document.createDocumentFragment(),n={fragment:e};b=n,(()=>{(0,a.unstableSetRender)()(t.default.createElement(k,{ref:e=>{let{instance:t,sync:r}=e||{};Promise.resolve().then(()=>{!n.instance&&t&&(n.instance=t,n.sync=r,T())})}}),e)})();return}b.instance&&(S.forEach(e=>{switch(e.type){case"open":b.instance.open(Object.assign(Object.assign({},C),e.config));break;case"destroy":var t;null==(t=null==b?void 0:b.instance)||t.destroy(e.key)}}),S=[])};function O(e){(0,r.globalConfig)(),S.push({type:"open",config:e}),T()}let w={open:O,destroy:e=>{S.push({type:"destroy",key:e}),T()},config:function(e){C=Object.assign(Object.assign({},C),e),(()=>{var e;null==(e=null==b?void 0:b.sync)||e.call(b)})()},useNotification:function(e){return y(e)},_InternalPanelDoNotUseOrYouWillBeFired:i.default};["success","info","warning","error"].forEach(e=>{w[e]=t=>O(Object.assign(Object.assign({},t),{type:e}))});e.s(["notification",0,w],698173)},983320,208224,e=>{"use strict";var t=e.i(271645),n=e.i(201072),r=e.i(726289),o=e.i(562901),a=e.i(779573),i=e.i(739295),l=e.i(343794);e.i(792131);var s=e.i(10183),c=e.i(242064),u=e.i(321883);e.i(296059);var f=e.i(694758),d=e.i(122767),p=e.i(183293),m=e.i(246422),g=e.i(838378);let v=(0,m.genStyleHooks)("Message",e=>(e=>{let{componentCls:t,iconCls:n,boxShadow:r,colorText:o,colorSuccess:a,colorError:i,colorWarning:l,colorInfo:s,fontSizeLG:c,motionEaseInOutCirc:u,motionDurationSlow:d,marginXS:m,paddingXS:g,borderRadiusLG:v,zIndexPopup:h,contentPadding:y,contentBg:b}=e,S=`${t}-notice`,C=new f.Keyframes("MessageMoveIn",{"0%":{padding:0,transform:"translateY(-100%)",opacity:0},"100%":{padding:g,transform:"translateY(0)",opacity:1}}),E=new f.Keyframes("MessageMoveOut",{"0%":{maxHeight:e.height,padding:g,opacity:1},"100%":{maxHeight:0,padding:0,opacity:0}}),x={padding:g,textAlign:"center",[`${t}-custom-content`]:{display:"flex",alignItems:"center"},[`${t}-custom-content > ${n}`]:{marginInlineEnd:m,fontSize:c},[`${S}-content`]:{display:"inline-block",padding:y,background:b,borderRadius:v,boxShadow:r,pointerEvents:"all"},[`${t}-success > ${n}`]:{color:a},[`${t}-error > ${n}`]:{color:i},[`${t}-warning > ${n}`]:{color:l},[`${t}-info > ${n}, + ${t}-loading > ${n}`]:{color:s}};return[{[t]:Object.assign(Object.assign({},(0,p.resetComponent)(e)),{color:o,position:"fixed",top:m,width:"100%",pointerEvents:"none",zIndex:h,[`${t}-move-up`]:{animationFillMode:"forwards"},[` + ${t}-move-up-appear, + ${t}-move-up-enter + 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input=[ -# { -# "role": "user", -# "content": [ -# {"type": "input_text", "text": "${$}"}, -# { -# "type": "input_image", -# "image_url": f"data:image/jpeg;base64,{base64_file}", # or data:application/pdf;base64,{base64_file} -# }, -# ], -# } -# ]${a} -# ) -# print(response_with_file.output_text) -`;break}case n.IMAGE:t="azure"===b?` -# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI. -# This snippet uses 'client.images.generate' and will create a new image based on your prompt. -# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context. -import os -import requests -import json -import time -from PIL import Image - -result = client.images.generate( - model="${k}", - prompt="${o}", - n=1 -) - -json_response = json.loads(result.model_dump_json()) - -# Set the directory for the stored image -image_dir = os.path.join(os.curdir, 'images') - -# If the directory doesn't exist, create it -if not os.path.isdir(image_dir): - os.mkdir(image_dir) - -# Initialize the image path -image_filename = f"generated_image_{int(time.time())}.png" -image_path = os.path.join(image_dir, image_filename) - -try: - # Retrieve the generated image - if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"): - image_url = json_response["data"][0]["url"] - generated_image = requests.get(image_url).content - with open(image_path, "wb") as image_file: - image_file.write(generated_image) - - print(f"Image saved to {image_path}") - # Display the image - image = Image.open(image_path) - image.show() - else: - print("Could not find image URL in response.") - print("Full response:", json_response) -except Exception as e: - print(f"An error occurred: {e}") - print("Full response:", json_response) -`:` -import base64 -import os -import time -import json -from PIL import Image -import requests - -# Helper function to encode images to base64 -def encode_image(image_path): - with open(image_path, "rb") as image_file: - return base64.b64encode(image_file.read()).decode('utf-8') - -# Helper function to create a file (simplified for this example) -def create_file(image_path): - # In a real implementation, this would upload the file to OpenAI - # For this example, we'll just return a placeholder ID - return f"file_{os.path.basename(image_path).replace('.', '_')}" - -# The prompt entered by the user -prompt = "${$}" - -# Encode images to base64 -base64_image1 = encode_image("body-lotion.png") -base64_image2 = encode_image("soap.png") - -# Create file IDs -file_id1 = create_file("body-lotion.png") -file_id2 = create_file("incense-kit.png") - -response = client.responses.create( - model="${k}", - input=[ - { - "role": "user", - "content": [ - {"type": "input_text", "text": prompt}, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image1}", - }, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image2}", - }, - { - "type": "input_image", - "file_id": file_id1, - }, - { - "type": "input_image", - "file_id": file_id2, - } - ], - } - ], - tools=[{"type": "image_generation"}], -) - -# Process the response -image_generation_calls = [ - output - for output in response.output - if output.type == "image_generation_call" -] - -image_data = [output.result for output in image_generation_calls] - -if image_data: - image_base64 = image_data[0] - image_filename = f"edited_image_{int(time.time())}.png" - with open(image_filename, "wb") as f: - f.write(base64.b64decode(image_base64)) - print(f"Image saved to {image_filename}") -else: - # If no image is generated, there might be a text response with an explanation - text_response = [output.text for output in response.output if hasattr(output, 'text')] - if text_response: - print("No image generated. Model response:") - print("\\n".join(text_response)) - else: - print("No image data found in response.") - print("Full response for debugging:") - print(response) -`;break;case n.IMAGE_EDITS:t="azure"===b?` -import base64 -import os -import time -import json -from PIL import Image -import requests - -# Helper function to encode images to base64 -def encode_image(image_path): - with open(image_path, "rb") as image_file: - return base64.b64encode(image_file.read()).decode('utf-8') - -# The prompt entered by the user -prompt = "${$}" - -# Encode images to base64 -base64_image1 = encode_image("body-lotion.png") -base64_image2 = encode_image("soap.png") - -# Create file IDs -file_id1 = create_file("body-lotion.png") -file_id2 = create_file("incense-kit.png") - -response = client.responses.create( - model="${k}", - input=[ - { - "role": "user", - "content": [ - {"type": "input_text", "text": prompt}, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image1}", - }, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image2}", - }, - { - "type": "input_image", - "file_id": file_id1, - }, - { - "type": "input_image", - "file_id": file_id2, - } - ], - } - ], - tools=[{"type": "image_generation"}], -) - -# Process the response -image_generation_calls = [ - output - for output in response.output - if output.type == "image_generation_call" -] - -image_data = [output.result for output in image_generation_calls] - -if image_data: - image_base64 = image_data[0] - image_filename = f"edited_image_{int(time.time())}.png" - with open(image_filename, "wb") as f: - f.write(base64.b64decode(image_base64)) - print(f"Image saved to {image_filename}") -else: - # If no image is generated, there might be a text response with an explanation - text_response = [output.text for output in response.output if hasattr(output, 'text')] - if text_response: - print("No image generated. Model response:") - print("\\n".join(text_response)) - else: - print("No image data found in response.") - print("Full response for debugging:") - print(response) -`:` -import base64 -import os -import time - -# Helper function to encode images to base64 -def encode_image(image_path): - with open(image_path, "rb") as image_file: - return base64.b64encode(image_file.read()).decode('utf-8') - -# Helper function to create a file (simplified for this example) -def create_file(image_path): - # In a real implementation, this would upload the file to OpenAI - # For this example, we'll just return a placeholder ID - return f"file_{os.path.basename(image_path).replace('.', '_')}" - -# The prompt entered by the user -prompt = "${$}" - -# Encode images to base64 -base64_image1 = encode_image("body-lotion.png") -base64_image2 = encode_image("soap.png") - -# Create file IDs -file_id1 = create_file("body-lotion.png") -file_id2 = create_file("incense-kit.png") - -response = client.responses.create( - model="${k}", - input=[ - { - "role": "user", - "content": [ - {"type": "input_text", "text": prompt}, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image1}", - }, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image2}", - }, - { - "type": "input_image", - "file_id": file_id1, - }, - { - "type": "input_image", - "file_id": file_id2, - } - ], - } - ], - tools=[{"type": "image_generation"}], -) - -# Process the response -image_generation_calls = [ - output - for output in response.output - if output.type == "image_generation_call" -] - -image_data = [output.result for output in image_generation_calls] - -if image_data: - image_base64 = image_data[0] - image_filename = f"edited_image_{int(time.time())}.png" - with open(image_filename, "wb") as f: - f.write(base64.b64decode(image_base64)) - print(f"Image saved to {image_filename}") -else: - # If no image is generated, there might be a text response with an explanation - text_response = [output.text for output in response.output if hasattr(output, 'text')] - if text_response: - print("No image generated. 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This is a bug of rc-tree. Please report an issue.");var p=e4(c),m={event:t,node:eJ(s),dragNode:e.dragNodeProps?eJ(e.dragNodeProps):null,dragNodesKeys:[e.dragNodeProps.eventKey].concat(a),dropToGap:0!==i,dropPosition:i+Number(p[p.length-1])};l||null==u||u(m),e.dragNodeProps=null}}}),(0,C.default)((0,tB.default)(e),"cleanDragState",function(){null!==e.state.draggingNodeKey&&e.setState({draggingNodeKey:null,dropPosition:null,dropContainerKey:null,dropTargetKey:null,dropLevelOffset:null,dropAllowed:!0,dragOverNodeKey:null}),e.dragStartMousePosition=null,e.currentMouseOverDroppableNodeKey=null}),(0,C.default)((0,tB.default)(e),"triggerExpandActionExpand",function(t,n){var r=e.state,l=r.expandedKeys,o=r.flattenNodes,a=n.expanded,i=n.key;if(!n.isLeaf&&!t.shiftKey&&!t.metaKey&&!t.ctrlKey){var d=o.filter(function(e){return e.key===i})[0],c=eJ((0,w.default)((0,w.default)({},eY(i,e.getTreeNodeRequiredProps())),{},{data:d.data}));e.setExpandedKeys(a?e2(l,i):e3(l,i)),e.onNodeExpand(t,c)}}),(0,C.default)((0,tB.default)(e),"onNodeClick",function(t,n){var r=e.props,l=r.onClick;"click"===r.expandAction&&e.triggerExpandActionExpand(t,n),null==l||l(t,n)}),(0,C.default)((0,tB.default)(e),"onNodeDoubleClick",function(t,n){var r=e.props,l=r.onDoubleClick;"doubleClick"===r.expandAction&&e.triggerExpandActionExpand(t,n),null==l||l(t,n)}),(0,C.default)((0,tB.default)(e),"onNodeSelect",function(t,n){var r=e.state.selectedKeys,l=e.state,o=l.keyEntities,a=l.fieldNames,i=e.props,d=i.onSelect,c=i.multiple,u=n.selected,s=n[a.key],f=!u,p=(r=f?c?e3(r,s):[s]:e2(r,s)).map(function(e){var t=o[e];return t?t.node:null}).filter(Boolean);e.setUncontrolledState({selectedKeys:r}),null==d||d(r,{event:"select",selected:f,node:n,selectedNodes:p,nativeEvent:t.nativeEvent})}),(0,C.default)((0,tB.default)(e),"onNodeCheck",function(t,n,r){var l,o=e.state,a=o.keyEntities,i=o.checkedKeys,d=o.halfCheckedKeys,c=e.props,u=c.checkStrictly,s=c.onCheck,f=n.key,p={event:"check",node:n,checked:r,nativeEvent:t.nativeEvent};if(u){var m=r?e3(i,f):e2(i,f);l={checked:m,halfChecked:e2(d,f)},p.checkedNodes=m.map(function(e){return a[e]}).filter(Boolean).map(function(e){return e.node}),e.setUncontrolledState({checkedKeys:m})}else{var h=tt([].concat((0,er.default)(i),[f]),!0,a),g=h.checkedKeys,v=h.halfCheckedKeys;if(!r){var y=new Set(g);y.delete(f);var b=tt(Array.from(y),{checked:!1,halfCheckedKeys:v},a);g=b.checkedKeys,v=b.halfCheckedKeys}l=g,p.checkedNodes=[],p.checkedNodesPositions=[],p.halfCheckedKeys=v,g.forEach(function(e){var t=a[e];if(t){var n=t.node,r=t.pos;p.checkedNodes.push(n),p.checkedNodesPositions.push({node:n,pos:r})}}),e.setUncontrolledState({checkedKeys:g},!1,{halfCheckedKeys:v})}null==s||s(l,p)}),(0,C.default)((0,tB.default)(e),"onNodeLoad",function(t){var n,r=t.key,l=e.state.keyEntities[r];if(null==l||null==(n=l.children)||!n.length){var o=new Promise(function(n,l){e.setState(function(o){var a=o.loadedKeys,i=o.loadingKeys,d=void 0===i?[]:i,c=e.props,u=c.loadData,s=c.onLoad;return!u||(void 0===a?[]:a).includes(r)||d.includes(r)?null:(u(t).then(function(){var l=e3(e.state.loadedKeys,r);null==s||s(l,{event:"load",node:t}),e.setUncontrolledState({loadedKeys:l}),e.setState(function(e){return{loadingKeys:e2(e.loadingKeys,r)}}),n()}).catch(function(t){if(e.setState(function(e){return{loadingKeys:e2(e.loadingKeys,r)}}),e.loadingRetryTimes[r]=(e.loadingRetryTimes[r]||0)+1,e.loadingRetryTimes[r]>=10){var o=e.state.loadedKeys;(0,N.default)(!1,"Retry for `loadData` many times but still failed. No more retry."),e.setUncontrolledState({loadedKeys:e3(o,r)}),n()}l(t)}),{loadingKeys:e3(d,r)})})});return o.catch(function(){}),o}}),(0,C.default)((0,tB.default)(e),"onNodeMouseEnter",function(t,n){var r=e.props.onMouseEnter;null==r||r({event:t,node:n})}),(0,C.default)((0,tB.default)(e),"onNodeMouseLeave",function(t,n){var r=e.props.onMouseLeave;null==r||r({event:t,node:n})}),(0,C.default)((0,tB.default)(e),"onNodeContextMenu",function(t,n){var r=e.props.onRightClick;r&&(t.preventDefault(),r({event:t,node:n}))}),(0,C.default)((0,tB.default)(e),"onFocus",function(){var t=e.props.onFocus;e.setState({focused:!0});for(var n=arguments.length,r=Array(n),l=0;l1&&void 0!==arguments[1]&&arguments[1],r=arguments.length>2&&void 0!==arguments[2]?arguments[2]:null;if(!e.destroyed){var l=!1,o=!0,a={};Object.keys(t).forEach(function(n){if(e.props.hasOwnProperty(n)){o=!1;return}l=!0,a[n]=t[n]}),l&&(!n||o)&&e.setState((0,w.default)((0,w.default)({},a),r))}}),(0,C.default)((0,tB.default)(e),"scrollTo",function(t){e.listRef.current.scrollTo(t)}),e}return(0,tj.default)(r,[{key:"componentDidMount",value:function(){this.destroyed=!1,this.onUpdated()}},{key:"componentDidUpdate",value:function(){this.onUpdated()}},{key:"onUpdated",value:function(){var e=this.props,t=e.activeKey,n=e.itemScrollOffset;void 0!==t&&t!==this.state.activeKey&&(this.setState({activeKey:t}),null!==t&&this.scrollTo({key:t,offset:void 0===n?0:n}))}},{key:"componentWillUnmount",value:function(){window.removeEventListener("dragend",this.onWindowDragEnd),this.destroyed=!0}},{key:"resetDragState",value:function(){this.setState({dragOverNodeKey:null,dropPosition:null,dropLevelOffset:null,dropTargetKey:null,dropContainerKey:null,dropTargetPos:null,dropAllowed:!1})}},{key:"render",value:function(){var e,n=this.state,r=n.focused,l=n.flattenNodes,o=n.keyEntities,a=n.draggingNodeKey,i=n.activeKey,d=n.dropLevelOffset,c=n.dropContainerKey,u=n.dropTargetKey,f=n.dropPosition,p=n.dragOverNodeKey,m=n.indent,h=this.props,g=h.prefixCls,v=h.className,y=h.style,b=h.showLine,w=h.focusable,k=h.tabIndex,S=h.selectable,N=h.showIcon,$=h.icon,K=h.switcherIcon,O=h.draggable,R=h.checkable,I=h.checkStrictly,P=h.disabled,T=h.motion,M=h.loadData,D=h.filterTreeNode,L=h.height,j=h.itemHeight,B=h.scrollWidth,H=h.virtual,A=h.titleRender,W=h.dropIndicatorRender,F=h.onContextMenu,_=h.onScroll,q=h.direction,V=h.rootClassName,U=h.rootStyle,X=(0,z.default)(this.props,{aria:!0,data:!0});O&&(e="object"===(0,x.default)(O)?O:"function"==typeof O?{nodeDraggable:O}:{});var G={prefixCls:g,selectable:S,showIcon:N,icon:$,switcherIcon:K,draggable:e,draggingNodeKey:a,checkable:R,checkStrictly:I,disabled:P,keyEntities:o,dropLevelOffset:d,dropContainerKey:c,dropTargetKey:u,dropPosition:f,dragOverNodeKey:p,indent:m,direction:q,dropIndicatorRender:W,loadData:M,filterTreeNode:D,titleRender:A,onNodeClick:this.onNodeClick,onNodeDoubleClick:this.onNodeDoubleClick,onNodeExpand:this.onNodeExpand,onNodeSelect:this.onNodeSelect,onNodeCheck:this.onNodeCheck,onNodeLoad:this.onNodeLoad,onNodeMouseEnter:this.onNodeMouseEnter,onNodeMouseLeave:this.onNodeMouseLeave,onNodeContextMenu:this.onNodeContextMenu,onNodeDragStart:this.onNodeDragStart,onNodeDragEnter:this.onNodeDragEnter,onNodeDragOver:this.onNodeDragOver,onNodeDragLeave:this.onNodeDragLeave,onNodeDragEnd:this.onNodeDragEnd,onNodeDrop:this.onNodeDrop};return t.createElement(eH.Provider,{value:G},t.createElement("div",{className:(0,E.default)(g,v,V,(0,C.default)((0,C.default)((0,C.default)({},"".concat(g,"-show-line"),b),"".concat(g,"-focused"),r),"".concat(g,"-active-focused"),null!==i)),style:U},t.createElement(t3,(0,s.default)({ref:this.listRef,prefixCls:g,style:y,data:l,disabled:P,selectable:S,checkable:!!R,motion:T,dragging:null!==a,height:L,itemHeight:j,virtual:H,focusable:w,focused:r,tabIndex:void 0===k?0:k,activeItem:this.getActiveItem(),onFocus:this.onFocus,onBlur:this.onBlur,onKeyDown:this.onKeyDown,onActiveChange:this.onActiveChange,onListChangeStart:this.onListChangeStart,onListChangeEnd:this.onListChangeEnd,onContextMenu:F,onScroll:_,scrollWidth:B},this.getTreeNodeRequiredProps(),X))))}}],[{key:"getDerivedStateFromProps",value:function(e,t){var n,r,l=t.prevProps,o={prevProps:e};function a(t){return!l&&e.hasOwnProperty(t)||l&&l[t]!==e[t]}var i=t.fieldNames;if(a("fieldNames")&&(o.fieldNames=i=eV(e.fieldNames)),a("treeData")?n=e.treeData:a("children")&&((0,N.default)(!1,"`children` of Tree is deprecated. Please use `treeData` instead."),n=eU(e.children)),n){o.treeData=n;var d=eG(n,{fieldNames:i});o.keyEntities=(0,w.default)((0,C.default)({},tJ,tZ),d.keyEntities)}var c=o.keyEntities||t.keyEntities;if(a("expandedKeys")||l&&a("autoExpandParent"))o.expandedKeys=e.autoExpandParent||!l&&e.defaultExpandParent?e7(e.expandedKeys,c):e.expandedKeys;else if(!l&&e.defaultExpandAll){var u=(0,w.default)({},c);delete u[tJ];var s=[];Object.keys(u).forEach(function(e){var t=u[e];t.children&&t.children.length&&s.push(t.key)}),o.expandedKeys=s}else!l&&e.defaultExpandedKeys&&(o.expandedKeys=e.autoExpandParent||e.defaultExpandParent?e7(e.defaultExpandedKeys,c):e.defaultExpandedKeys);if(o.expandedKeys||delete o.expandedKeys,n||o.expandedKeys){var f=eX(n||t.treeData,o.expandedKeys||t.expandedKeys,i);o.flattenNodes=f}if(e.selectable&&(a("selectedKeys")?o.selectedKeys=e6(e.selectedKeys,e):!l&&e.defaultSelectedKeys&&(o.selectedKeys=e6(e.defaultSelectedKeys,e))),e.checkable&&(a("checkedKeys")?r=e5(e.checkedKeys)||{}:!l&&e.defaultCheckedKeys?r=e5(e.defaultCheckedKeys)||{}:n&&(r=e5(e.checkedKeys)||{checkedKeys:t.checkedKeys,halfCheckedKeys:t.halfCheckedKeys}),r)){var p=r,m=p.checkedKeys,h=void 0===m?[]:m,g=p.halfCheckedKeys,v=void 0===g?[]:g;if(!e.checkStrictly){var y=tt(h,!0,c);h=y.checkedKeys,v=y.halfCheckedKeys}o.checkedKeys=h,o.halfCheckedKeys=v}return 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97zm0 284a56 56 0 1056-97 56 56 0 00-56 97zM640 228a56 56 0 10112 0 56 56 0 00-112 0zm0 284a56 56 0 10112 0 56 56 0 00-112 0zM300 844.5a56 56 0 1056-97 56 56 0 00-56 97zM640 796a56 56 0 10112 0 56 56 0 00-112 0z"}}]},name:"holder",theme:"outlined"};var nt=t.forwardRef(function(e,n){return t.createElement(tK.default,(0,s.default)({},e,{ref:n,icon:ne}))}),nn=e.i(613541),nr=e.i(937328);e.i(296059);var nl=e.i(694758),no=e.i(915654),na=e.i(236836),ni=e.i(183293),nd=e.i(447580),nc=e.i(246422),nu=e.i(838378);let ns=new nl.Keyframes("ant-tree-node-fx-do-not-use",{"0%":{opacity:0},"100%":{opacity:1}}),nf=(0,nc.genStyleHooks)("Tree",(e,{prefixCls:t})=>[{[e.componentCls]:(0,na.getStyle)(`${t}-checkbox`,e)},((e,t,n=!0)=>{let r=`.${e}`,l=`${r}-treenode`,o=t.calc(t.paddingXS).div(2).equal(),a=(0,nu.mergeToken)(t,{treeCls:r,treeNodeCls:l,treeNodePadding:o});return[((e,t)=>{let{treeCls:n,treeNodeCls:r,treeNodePadding:l,titleHeight:o,indentSize:a,nodeSelectedBg:i,nodeHoverBg:d,colorTextQuaternary:c,controlItemBgActiveDisabled:u}=t;return{[n]:Object.assign(Object.assign({},(0,ni.resetComponent)(t)),{"--rc-virtual-list-scrollbar-bg":t.colorSplit,background:t.colorBgContainer,borderRadius:t.borderRadius,transition:`background-color ${t.motionDurationSlow}`,"&-rtl":{direction:"rtl"},[`&${n}-rtl ${n}-switcher_close ${n}-switcher-icon svg`]:{transform:"rotate(90deg)"},[`&-focused:not(:hover):not(${n}-active-focused)`]:(0,ni.genFocusOutline)(t),[`${n}-list-holder-inner`]:{alignItems:"flex-start"},[`&${n}-block-node`]:{[`${n}-list-holder-inner`]:{alignItems:"stretch",[`${n}-node-content-wrapper`]:{flex:"auto"},[`${r}.dragging:after`]:{position:"absolute",inset:0,border:`1px solid 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${n}-title`]:{color:t.colorPrimary,fontWeight:t.fontWeightStrong},"&-draggable":{cursor:"grab",[`${n}-draggable-icon`]:{flexShrink:0,width:o,textAlign:"center",visibility:"visible",color:c},[`&${r}-disabled ${n}-draggable-icon`]:{visibility:"hidden"}}},[`${n}-indent`]:{alignSelf:"stretch",whiteSpace:"nowrap",userSelect:"none","&-unit":{display:"inline-block",width:a}},[`${n}-draggable-icon`]:{visibility:"hidden"},[`${n}-switcher, ${n}-checkbox`]:{marginInlineEnd:t.calc(t.calc(o).sub(t.controlInteractiveSize)).div(2).equal()},[`${n}-switcher`]:Object.assign(Object.assign({},{[`.${e}-switcher-icon`]:{display:"inline-block",fontSize:10,verticalAlign:"baseline",svg:{transition:`transform ${t.motionDurationSlow}`}}}),{position:"relative",flex:"none",alignSelf:"stretch",width:o,textAlign:"center",cursor:"pointer",userSelect:"none",transition:`all ${t.motionDurationSlow}`,"&-noop":{cursor:"unset"},"&:before":{pointerEvents:"none",content:'""',width:o,height:o,position:"absolute",left:{_skip_check_:!0,value:0},top:0,borderRadius:t.borderRadius,transition:`all ${t.motionDurationSlow}`},[`&:not(${n}-switcher-noop):hover:before`]:{backgroundColor:t.colorBgTextHover},[`&_close ${n}-switcher-icon svg`]:{transform:"rotate(-90deg)"},"&-loading-icon":{color:t.colorPrimary},"&-leaf-line":{position:"relative",zIndex:1,display:"inline-block",width:"100%",height:"100%","&:before":{position:"absolute",top:0,insetInlineEnd:t.calc(o).div(2).equal(),bottom:t.calc(l).mul(-1).equal(),marginInlineStart:-1,borderInlineEnd:`1px solid ${t.colorBorder}`,content:'""'},"&:after":{position:"absolute",width:t.calc(t.calc(o).div(2).equal()).mul(.8).equal(),height:t.calc(o).div(2).equal(),borderBottom:`1px solid ${t.colorBorder}`,content:'""'}}}),[`${n}-node-content-wrapper`]:Object.assign(Object.assign({position:"relative",minHeight:o,paddingBlock:0,paddingInline:t.paddingXS,background:"transparent",borderRadius:t.borderRadius,cursor:"pointer",transition:`all ${t.motionDurationMid}, border 0s, line-height 0s, box-shadow 0s`},{[`.${e}-drop-indicator`]:{position:"absolute",zIndex:1,height:2,backgroundColor:t.colorPrimary,borderRadius:1,pointerEvents:"none","&:after":{position:"absolute",top:-3,insetInlineStart:-6,width:8,height:8,backgroundColor:"transparent",border:`${(0,no.unit)(t.lineWidthBold)} solid ${t.colorPrimary}`,borderRadius:"50%",content:'""'}}}),{"&:hover":{backgroundColor:d},[`&${n}-node-selected`]:{color:t.nodeSelectedColor,backgroundColor:i},[`${n}-iconEle`]:{display:"inline-block",width:o,height:o,textAlign:"center",verticalAlign:"top","&:empty":{display:"none"}}}),[`${n}-unselectable ${n}-node-content-wrapper:hover`]:{backgroundColor:"transparent"},[`${r}.drop-container > [draggable]`]:{boxShadow:`0 0 0 2px ${t.colorPrimary}`},"&-show-line":{[`${n}-indent-unit`]:{position:"relative",height:"100%","&:before":{position:"absolute",top:0,insetInlineEnd:t.calc(o).div(2).equal(),bottom:t.calc(l).mul(-1).equal(),borderInlineEnd:`1px solid ${t.colorBorder}`,content:'""'},"&-end:before":{display:"none"}},[`${n}-switcher`]:{background:"transparent","&-line-icon":{verticalAlign:"-0.15em"}}},[`${r}-leaf-last ${n}-switcher-leaf-line:before`]:{top:"auto !important",bottom:"auto !important",height:`${(0,no.unit)(t.calc(o).div(2).equal())} !important`}})}})(e,a),n&&(({treeCls:e,treeNodeCls:t,directoryNodeSelectedBg:n,directoryNodeSelectedColor:r,motionDurationMid:l,borderRadius:o,controlItemBgHover:a})=>({[`${e}${e}-directory ${t}`]:{[`${e}-node-content-wrapper`]:{position:"static",[`&:has(${e}-drop-indicator)`]:{position:"relative"},[`> 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`]:{background:e.colorBgContainer}}}}})(D),(e=>{let{componentCls:t,antCls:n,iconCls:r,fontSizeIcon:l,padding:o,paddingXS:a,headerIconColor:i,headerIconHoverColor:d,tableSelectionColumnWidth:c,tableSelectedRowBg:u,tableSelectedRowHoverBg:s,tableRowHoverBg:f,tablePaddingHorizontal:p,calc:m}=e;return{[`${t}-wrapper`]:{[`${t}-selection-col`]:{width:c,[`&${t}-selection-col-with-dropdown`]:{width:m(c).add(l).add(m(o).div(4)).equal()}},[`${t}-bordered ${t}-selection-col`]:{width:m(c).add(m(a).mul(2)).equal(),[`&${t}-selection-col-with-dropdown`]:{width:m(c).add(l).add(m(o).div(4)).add(m(a).mul(2)).equal()}},[` + table tr th${t}-selection-column, + table tr td${t}-selection-column, + ${t}-selection-column + `]:{paddingInlineEnd:e.paddingXS,paddingInlineStart:e.paddingXS,textAlign:"center",[`${n}-radio-wrapper`]:{marginInlineEnd:0}},[`table tr th${t}-selection-column${t}-cell-fix-left`]:{zIndex:m(e.zIndexTableFixed).add(1).equal({unit:!1})},[`table tr 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extra_body=${e}`}let a=E.length>0?E:[{role:"user",content:w}];t=` -import base64 - -# Helper function to encode images to base64 -def encode_image(image_path): - with open(image_path, "rb") as image_file: - return base64.b64encode(image_file.read()).decode('utf-8') - -# Example with text only -response = client.responses.create( - model="${I}", - input=${JSON.stringify(a,null,4)}${i} -) - -print(response.output_text) - -# Example with image or PDF (uncomment and provide file path to use) -# base64_file = encode_image("path/to/your/file.jpg") # or .pdf -# response_with_file = client.responses.create( -# model="${I}", -# input=[ -# { -# "role": "user", -# "content": [ -# {"type": "input_text", "text": "${N}"}, -# { -# "type": "input_image", -# "image_url": f"data:image/jpeg;base64,{base64_file}", # or data:application/pdf;base64,{base64_file} -# }, -# ], -# } -# ]${i} -# ) -# print(response_with_file.output_text) -`;break}case s.IMAGE:t="azure"===x?` -# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI. -# This snippet uses 'client.images.generate' and will create a new image based on your prompt. -# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context. -import os -import requests -import json -import time -from PIL import Image - -result = client.images.generate( - model="${I}", - prompt="${n}", - n=1 -) - -json_response = json.loads(result.model_dump_json()) - -# Set the directory for the stored image -image_dir = os.path.join(os.curdir, 'images') - -# If the directory doesn't exist, create it -if not os.path.isdir(image_dir): - os.mkdir(image_dir) - -# Initialize the image path -image_filename = f"generated_image_{int(time.time())}.png" -image_path = os.path.join(image_dir, image_filename) - -try: - # Retrieve the generated image - if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"): - image_url = json_response["data"][0]["url"] - generated_image = requests.get(image_url).content - with open(image_path, "wb") as image_file: - image_file.write(generated_image) - - print(f"Image saved to {image_path}") - # Display the image - image = Image.open(image_path) - image.show() - else: - print("Could not find image URL in response.") - print("Full response:", json_response) -except Exception as e: - print(f"An error occurred: {e}") - print("Full response:", json_response) -`:` -import base64 -import os -import time -import json -from PIL import Image -import requests - -# Helper function to encode images to base64 -def encode_image(image_path): - with open(image_path, "rb") as image_file: - return base64.b64encode(image_file.read()).decode('utf-8') - -# Helper function to create a file (simplified for this example) -def create_file(image_path): - # In a real implementation, this would upload the file to OpenAI - # For this example, we'll just return a placeholder ID - return f"file_{os.path.basename(image_path).replace('.', '_')}" - -# The prompt entered by the user -prompt = "${N}" - -# Encode images to base64 -base64_image1 = encode_image("body-lotion.png") -base64_image2 = encode_image("soap.png") - -# Create file IDs -file_id1 = create_file("body-lotion.png") -file_id2 = create_file("incense-kit.png") - -response = client.responses.create( - model="${I}", - input=[ - { - "role": "user", - "content": [ - {"type": "input_text", "text": prompt}, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image1}", - }, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image2}", - }, - { - "type": "input_image", - "file_id": file_id1, - }, - { - "type": "input_image", - "file_id": file_id2, - } - ], - } - ], - tools=[{"type": "image_generation"}], -) - -# Process the response -image_generation_calls = [ - output - for output in response.output - if output.type == "image_generation_call" -] - -image_data = [output.result for output in image_generation_calls] - -if image_data: - image_base64 = image_data[0] - image_filename = f"edited_image_{int(time.time())}.png" - with open(image_filename, "wb") as f: - f.write(base64.b64decode(image_base64)) - print(f"Image saved to {image_filename}") -else: - # If no image is generated, there might be a text response with an explanation - text_response = [output.text for output in response.output if hasattr(output, 'text')] - if text_response: - print("No image generated. Model response:") - print("\\n".join(text_response)) - else: - print("No image data found in response.") - print("Full response for debugging:") - print(response) -`;break;case s.IMAGE_EDITS:t="azure"===x?` -import base64 -import os -import time -import json -from PIL import Image -import requests - -# Helper function to encode images to base64 -def encode_image(image_path): - with open(image_path, "rb") as image_file: - return base64.b64encode(image_file.read()).decode('utf-8') - -# The prompt entered by the user -prompt = "${N}" - -# Encode images to base64 -base64_image1 = encode_image("body-lotion.png") -base64_image2 = encode_image("soap.png") - -# Create file IDs -file_id1 = create_file("body-lotion.png") -file_id2 = create_file("incense-kit.png") - -response = client.responses.create( - model="${I}", - input=[ - { - "role": "user", - "content": [ - {"type": "input_text", "text": prompt}, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image1}", - }, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image2}", - }, - { - "type": "input_image", - "file_id": file_id1, - }, - { - "type": "input_image", - "file_id": file_id2, - } - ], - } - ], - tools=[{"type": "image_generation"}], -) - -# Process the response -image_generation_calls = [ - output - for output in response.output - if output.type == "image_generation_call" -] - -image_data = [output.result for output in image_generation_calls] - -if image_data: - image_base64 = image_data[0] - image_filename = f"edited_image_{int(time.time())}.png" - with open(image_filename, "wb") as f: - f.write(base64.b64decode(image_base64)) - print(f"Image saved to {image_filename}") -else: - # If no image is generated, there might be a text response with an explanation - text_response = [output.text for output in response.output if hasattr(output, 'text')] - if text_response: - print("No image generated. Model response:") - print("\\n".join(text_response)) - else: - print("No image data found in response.") - print("Full response for debugging:") - print(response) -`:` -import base64 -import os -import time - -# Helper function to encode images to base64 -def encode_image(image_path): - with open(image_path, "rb") as image_file: - return base64.b64encode(image_file.read()).decode('utf-8') - -# Helper function to create a file (simplified for this example) -def create_file(image_path): - # In a real implementation, this would upload the file to OpenAI - # For this example, we'll just return a placeholder ID - return f"file_{os.path.basename(image_path).replace('.', '_')}" - -# The prompt entered by the user -prompt = "${N}" - -# Encode images to base64 -base64_image1 = encode_image("body-lotion.png") -base64_image2 = encode_image("soap.png") - -# Create file IDs -file_id1 = create_file("body-lotion.png") -file_id2 = create_file("incense-kit.png") - -response = client.responses.create( - model="${I}", - input=[ - { - "role": "user", - "content": [ - {"type": "input_text", "text": prompt}, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image1}", - }, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image2}", - }, - { - "type": "input_image", - "file_id": file_id1, - }, - { - "type": "input_image", - "file_id": file_id2, - } - ], - } - ], - tools=[{"type": "image_generation"}], -) - -# Process the response -image_generation_calls = [ - output - for output in response.output - if output.type == "image_generation_call" -] - -image_data = [output.result for output in image_generation_calls] - -if image_data: - image_base64 = image_data[0] - image_filename = f"edited_image_{int(time.time())}.png" - with open(image_filename, "wb") as f: - f.write(base64.b64decode(image_base64)) - print(f"Image saved to {image_filename}") -else: - # If no image is generated, there might be a text response with an explanation - text_response = [output.text for output in response.output if hasattr(output, 'text')] - if text_response: - print("No image generated. 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t=this.observers.find(t=>t.shouldFetchOnReconnect());t?.refetch({cancelRefetch:!1}),this.#l?.continue()}addObserver(t){this.observers.includes(t)||(this.observers.push(t),this.clearGcTimeout(),this.#c.notify({type:"observerAdded",query:this,observer:t}))}removeObserver(t){this.observers.includes(t)&&(this.observers=this.observers.filter(e=>e!==t),this.observers.length||(this.#l&&(this.#f?this.#l.cancel({revert:!0}):this.#l.cancelRetry()),this.scheduleGc()),this.#c.notify({type:"observerRemoved",query:this,observer:t}))}getObserversCount(){return this.observers.length}invalidate(){this.state.isInvalidated||this.#y({type:"invalidate"})}async fetch(t,i){let r;if("idle"!==this.state.fetchStatus&&this.#l?.status()!=="rejected"){if(void 0!==this.state.data&&i?.cancelRefetch)this.cancel({silent:!0});else if(this.#l)return this.#l.continueRetry(),this.#l.promise}if(t&&this.setOptions(t),!this.options.queryFn){let t=this.observers.find(t=>t.options.queryFn);t&&this.setOptions(t.options)}let n=new AbortController,a=t=>{Object.defineProperty(t,"signal",{enumerable:!0,get:()=>(this.#f=!0,n.signal)})},o=()=>{let t,s=(0,e.ensureQueryFn)(this.options,i),r=(a(t={client:this.#h,queryKey:this.queryKey,meta:this.meta}),t);return(this.#f=!1,this.options.persister)?this.options.persister(s,r,this):s(r)},u=(a(r={fetchOptions:i,options:this.options,queryKey:this.queryKey,client:this.#h,state:this.state,fetchFn:o}),r);this.options.behavior?.onFetch(u,this),this.#u=this.state,("idle"===this.state.fetchStatus||this.state.fetchMeta!==u.fetchOptions?.meta)&&this.#y({type:"fetch",meta:u.fetchOptions?.meta}),this.#l=(0,s.createRetryer)({initialPromise:i?.initialPromise,fn:u.fetchFn,onCancel:t=>{t instanceof s.CancelledError&&t.revert&&this.setState({...this.#u,fetchStatus:"idle"}),n.abort()},onFail:(t,e)=>{this.#y({type:"failed",failureCount:t,error:e})},onPause:()=>{this.#y({type:"pause"})},onContinue:()=>{this.#y({type:"continue"})},retry:u.options.retry,retryDelay:u.options.retryDelay,networkMode:u.options.networkMode,canRun:()=>!0});try{let t=await this.#l.start();if(void 0===t)throw Error(`${this.queryHash} data is undefined`);return this.setData(t),this.#c.config.onSuccess?.(t,this),this.#c.config.onSettled?.(t,this.state.error,this),t}catch(t){if(t instanceof s.CancelledError){if(t.silent)return this.#l.promise;else if(t.revert){if(void 0===this.state.data)throw t;return this.state.data}}throw this.#y({type:"error",error:t}),this.#c.config.onError?.(t,this),this.#c.config.onSettled?.(this.state.data,t,this),t}finally{this.scheduleGc()}}#y(t){let e=e=>{switch(t.type){case"failed":return{...e,fetchFailureCount:t.failureCount,fetchFailureReason:t.error};case"pause":return{...e,fetchStatus:"paused"};case"continue":return{...e,fetchStatus:"fetching"};case"fetch":return{...e,...a(e.data,this.options),fetchMeta:t.meta??null};case"success":let i={...e,...o(t.data,t.dataUpdatedAt),dataUpdateCount:e.dataUpdateCount+1,...!t.manual&&{fetchStatus:"idle",fetchFailureCount:0,fetchFailureReason:null}};return this.#u=t.manual?i:void 0,i;case"error":let s=t.error;return{...e,error:s,errorUpdateCount:e.errorUpdateCount+1,errorUpdatedAt:Date.now(),fetchFailureCount:e.fetchFailureCount+1,fetchFailureReason:s,fetchStatus:"idle",status:"error",isInvalidated:!0};case"invalidate":return{...e,isInvalidated:!0};case"setState":return{...e,...t.state}}};this.state=e(this.state),i.notifyManager.batch(()=>{this.observers.forEach(t=>{t.onQueryUpdate()}),this.#c.notify({query:this,type:"updated",action:t})})}};function a(t,e){return{fetchFailureCount:0,fetchFailureReason:null,fetchStatus:(0,s.canFetch)(e.networkMode)?"fetching":"paused",...void 0===t&&{error:null,status:"pending"}}}function o(t,e){return{data:t,dataUpdatedAt:e??Date.now(),error:null,isInvalidated:!1,status:"success"}}function u(t){let e="function"==typeof t.initialData?t.initialData():t.initialData,i=void 0!==e,s=i?"function"==typeof t.initialDataUpdatedAt?t.initialDataUpdatedAt():t.initialDataUpdatedAt:0;return{data:e,dataUpdateCount:0,dataUpdatedAt:i?s??Date.now():0,error:null,errorUpdateCount:0,errorUpdatedAt:0,fetchFailureCount:0,fetchFailureReason:null,fetchMeta:null,isInvalidated:!1,status:i?"success":"pending",fetchStatus:"idle"}}t.s(["Query",()=>n,"fetchState",()=>a])},912598,t=>{"use strict";var e=t.i(271645),i=t.i(843476),s=e.createContext(void 0),r=t=>{let i=e.useContext(s);if(t)return t;if(!i)throw Error("No QueryClient set, use QueryClientProvider to set one");return i},n=({client:t,children:r})=>(e.useEffect(()=>(t.mount(),()=>{t.unmount()}),[t]),(0,i.jsx)(s.Provider,{value:t,children:r}));t.s(["QueryClientProvider",()=>n,"useQueryClient",()=>r])},114272,t=>{"use strict";var e=t.i(540143),i=t.i(88587),s=t.i(936553),r=class extends i.Removable{#h;#p;#m;#l;constructor(t){super(),this.#h=t.client,this.mutationId=t.mutationId,this.#m=t.mutationCache,this.#p=[],this.state=t.state||n(),this.setOptions(t.options),this.scheduleGc()}setOptions(t){this.options=t,this.updateGcTime(this.options.gcTime)}get meta(){return this.options.meta}addObserver(t){this.#p.includes(t)||(this.#p.push(t),this.clearGcTimeout(),this.#m.notify({type:"observerAdded",mutation:this,observer:t}))}removeObserver(t){this.#p=this.#p.filter(e=>e!==t),this.scheduleGc(),this.#m.notify({type:"observerRemoved",mutation:this,observer:t})}optionalRemove(){this.#p.length||("pending"===this.state.status?this.scheduleGc():this.#m.remove(this))}continue(){return this.#l?.continue()??this.execute(this.state.variables)}async execute(t){let e=()=>{this.#y({type:"continue"})},i={client:this.#h,meta:this.options.meta,mutationKey:this.options.mutationKey};this.#l=(0,s.createRetryer)({fn:()=>this.options.mutationFn?this.options.mutationFn(t,i):Promise.reject(Error("No mutationFn found")),onFail:(t,e)=>{this.#y({type:"failed",failureCount:t,error:e})},onPause:()=>{this.#y({type:"pause"})},onContinue:e,retry:this.options.retry??0,retryDelay:this.options.retryDelay,networkMode:this.options.networkMode,canRun:()=>this.#m.canRun(this)});let r="pending"===this.state.status,n=!this.#l.canStart();try{if(r)e();else{this.#y({type:"pending",variables:t,isPaused:n}),this.#m.config.onMutate&&await this.#m.config.onMutate(t,this,i);let e=await this.options.onMutate?.(t,i);e!==this.state.context&&this.#y({type:"pending",context:e,variables:t,isPaused:n})}let s=await this.#l.start();return await this.#m.config.onSuccess?.(s,t,this.state.context,this,i),await this.options.onSuccess?.(s,t,this.state.context,i),await this.#m.config.onSettled?.(s,null,this.state.variables,this.state.context,this,i),await this.options.onSettled?.(s,null,t,this.state.context,i),this.#y({type:"success",data:s}),s}catch(e){try{await this.#m.config.onError?.(e,t,this.state.context,this,i)}catch(t){Promise.reject(t)}try{await this.options.onError?.(e,t,this.state.context,i)}catch(t){Promise.reject(t)}try{await this.#m.config.onSettled?.(void 0,e,this.state.variables,this.state.context,this,i)}catch(t){Promise.reject(t)}try{await this.options.onSettled?.(void 0,e,t,this.state.context,i)}catch(t){Promise.reject(t)}throw this.#y({type:"error",error:e}),e}finally{this.#m.runNext(this)}}#y(t){this.state=(e=>{switch(t.type){case"failed":return{...e,failureCount:t.failureCount,failureReason:t.error};case"pause":return{...e,isPaused:!0};case"continue":return{...e,isPaused:!1};case"pending":return{...e,context:t.context,data:void 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u.Subscribable{constructor(t={}){super(),this.config=t,this.#b=new Set,this.#g=new Map,this.#C=0}#b;#g;#C;build(t,e,i){let s=new o.Mutation({client:t,mutationCache:this,mutationId:++this.#C,options:t.defaultMutationOptions(e),state:i});return this.add(s),s}add(t){this.#b.add(t);let e=h(t);if("string"==typeof e){let i=this.#g.get(e);i?i.push(t):this.#g.set(e,[t])}this.notify({type:"added",mutation:t})}remove(t){if(this.#b.delete(t)){let e=h(t);if("string"==typeof e){let i=this.#g.get(e);if(i)if(i.length>1){let e=i.indexOf(t);-1!==e&&i.splice(e,1)}else i[0]===t&&this.#g.delete(e)}}this.notify({type:"removed",mutation:t})}canRun(t){let e=h(t);if("string"!=typeof e)return!0;{let i=this.#g.get(e),s=i?.find(t=>"pending"===t.state.status);return!s||s===t}}runNext(t){let e=h(t);if("string"!=typeof e)return Promise.resolve();{let i=this.#g.get(e)?.find(e=>e!==t&&e.state.isPaused);return i?.continue()??Promise.resolve()}}clear(){r.notifyManager.batch(()=>{this.#b.forEach(t=>{this.notify({type:"removed",mutation:t})}),this.#b.clear(),this.#g.clear()})}getAll(){return Array.from(this.#b)}find(t){let e={exact:!0,...t};return this.getAll().find(t=>(0,i.matchMutation)(e,t))}findAll(t={}){return this.getAll().filter(e=>(0,i.matchMutation)(t,e))}notify(t){r.notifyManager.batch(()=>{this.listeners.forEach(e=>{e(t)})})}resumePausedMutations(){let t=this.getAll().filter(t=>t.state.isPaused);return r.notifyManager.batch(()=>Promise.all(t.map(t=>t.continue().catch(i.noop))))}};function h(t){return t.options.scope?.id}var l=t.i(175555),d=t.i(814448),f=t.i(992571),y=class{#O;#m;#d;#S;#w;#P;#q;#M;constructor(t={}){this.#O=t.queryCache||new a,this.#m=t.mutationCache||new c,this.#d=t.defaultOptions||{},this.#S=new Map,this.#w=new Map,this.#P=0}mount(){this.#P++,1===this.#P&&(this.#q=l.focusManager.subscribe(async t=>{t&&(await 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this.#O.build(this,r).setData(o,{...s,manual:!0})}setQueriesData(t,e,i){return r.notifyManager.batch(()=>this.#O.findAll(t).map(({queryKey:t})=>[t,this.setQueryData(t,e,i)]))}getQueryState(t){let e=this.defaultQueryOptions({queryKey:t});return this.#O.get(e.queryHash)?.state}removeQueries(t){let e=this.#O;r.notifyManager.batch(()=>{e.findAll(t).forEach(t=>{e.remove(t)})})}resetQueries(t,e){let i=this.#O;return r.notifyManager.batch(()=>(i.findAll(t).forEach(t=>{t.reset()}),this.refetchQueries({type:"active",...t},e)))}cancelQueries(t,e={}){let s={revert:!0,...e};return Promise.all(r.notifyManager.batch(()=>this.#O.findAll(t).map(t=>t.cancel(s)))).then(i.noop).catch(i.noop)}invalidateQueries(t,e={}){return r.notifyManager.batch(()=>(this.#O.findAll(t).forEach(t=>{t.invalidate()}),t?.refetchType==="none")?Promise.resolve():this.refetchQueries({...t,type:t?.refetchType??t?.type??"active"},e))}refetchQueries(t,e={}){let s={...e,cancelRefetch:e.cancelRefetch??!0};return 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Please report an issue.");var p=e5(c),m={event:t,node:e0(s),dragNode:e.dragNodeProps?e0(e.dragNodeProps):null,dragNodesKeys:[e.dragNodeProps.eventKey].concat(a),dropToGap:0!==i,dropPosition:i+Number(p[p.length-1])};l||null==u||u(m),e.dragNodeProps=null}}}),(0,C.default)((0,tK.default)(e),"cleanDragState",function(){null!==e.state.draggingNodeKey&&e.setState({draggingNodeKey:null,dropPosition:null,dropContainerKey:null,dropTargetKey:null,dropLevelOffset:null,dropAllowed:!0,dragOverNodeKey:null}),e.dragStartMousePosition=null,e.currentMouseOverDroppableNodeKey=null}),(0,C.default)((0,tK.default)(e),"triggerExpandActionExpand",function(t,n){var r=e.state,l=r.expandedKeys,o=r.flattenNodes,a=n.expanded,i=n.key;if(!n.isLeaf&&!t.shiftKey&&!t.metaKey&&!t.ctrlKey){var d=o.filter(function(e){return e.key===i})[0],c=e0((0,w.default)((0,w.default)({},eZ(i,e.getTreeNodeRequiredProps())),{},{data:d.data}));e.setExpandedKeys(a?e8(l,i):e6(l,i)),e.onNodeExpand(t,c)}}),(0,C.default)((0,tK.default)(e),"onNodeClick",function(t,n){var r=e.props,l=r.onClick;"click"===r.expandAction&&e.triggerExpandActionExpand(t,n),null==l||l(t,n)}),(0,C.default)((0,tK.default)(e),"onNodeDoubleClick",function(t,n){var r=e.props,l=r.onDoubleClick;"doubleClick"===r.expandAction&&e.triggerExpandActionExpand(t,n),null==l||l(t,n)}),(0,C.default)((0,tK.default)(e),"onNodeSelect",function(t,n){var r=e.state.selectedKeys,l=e.state,o=l.keyEntities,a=l.fieldNames,i=e.props,d=i.onSelect,c=i.multiple,u=n.selected,s=n[a.key],f=!u,p=(r=f?c?e6(r,s):[s]:e8(r,s)).map(function(e){var t=o[e];return t?t.node:null}).filter(Boolean);e.setUncontrolledState({selectedKeys:r}),null==d||d(r,{event:"select",selected:f,node:n,selectedNodes:p,nativeEvent:t.nativeEvent})}),(0,C.default)((0,tK.default)(e),"onNodeCheck",function(t,n,r){var l,o=e.state,a=o.keyEntities,i=o.checkedKeys,d=o.halfCheckedKeys,c=e.props,u=c.checkStrictly,s=c.onCheck,f=n.key,p={event:"check",node:n,checked:r,nativeEvent:t.nativeEvent};if(u){var m=r?e6(i,f):e8(i,f);l={checked:m,halfChecked:e8(d,f)},p.checkedNodes=m.map(function(e){return a[e]}).filter(Boolean).map(function(e){return e.node}),e.setUncontrolledState({checkedKeys:m})}else{var h=to([].concat((0,er.default)(i),[f]),!0,a),g=h.checkedKeys,v=h.halfCheckedKeys;if(!r){var y=new Set(g);y.delete(f);var b=to(Array.from(y),{checked:!1,halfCheckedKeys:v},a);g=b.checkedKeys,v=b.halfCheckedKeys}l=g,p.checkedNodes=[],p.checkedNodesPositions=[],p.halfCheckedKeys=v,g.forEach(function(e){var t=a[e];if(t){var n=t.node,r=t.pos;p.checkedNodes.push(n),p.checkedNodesPositions.push({node:n,pos:r})}}),e.setUncontrolledState({checkedKeys:g},!1,{halfCheckedKeys:v})}null==s||s(l,p)}),(0,C.default)((0,tK.default)(e),"onNodeLoad",function(t){var n,r=t.key,l=e.state.keyEntities[r];if(null==l||null==(n=l.children)||!n.length){var o=new Promise(function(n,l){e.setState(function(o){var a=o.loadedKeys,i=o.loadingKeys,d=void 0===i?[]:i,c=e.props,u=c.loadData,s=c.onLoad;return!u||(void 0===a?[]:a).includes(r)||d.includes(r)?null:(u(t).then(function(){var l=e6(e.state.loadedKeys,r);null==s||s(l,{event:"load",node:t}),e.setUncontrolledState({loadedKeys:l}),e.setState(function(e){return{loadingKeys:e8(e.loadingKeys,r)}}),n()}).catch(function(t){if(e.setState(function(e){return{loadingKeys:e8(e.loadingKeys,r)}}),e.loadingRetryTimes[r]=(e.loadingRetryTimes[r]||0)+1,e.loadingRetryTimes[r]>=10){var o=e.state.loadedKeys;(0,N.default)(!1,"Retry for `loadData` many times but still failed. 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r=`.${e}`,l=`${r}-treenode`,o=t.calc(t.paddingXS).div(2).equal(),a=(0,er.mergeToken)(t,{treeCls:r,treeNodeCls:l,treeNodePadding:o});return[((e,t)=>{let{treeCls:n,treeNodeCls:r,treeNodePadding:l,titleHeight:o,indentSize:a,nodeSelectedBg:i,nodeHoverBg:d,colorTextQuaternary:c,controlItemBgActiveDisabled:u}=t;return{[n]:Object.assign(Object.assign({},(0,ee.resetComponent)(t)),{"--rc-virtual-list-scrollbar-bg":t.colorSplit,background:t.colorBgContainer,borderRadius:t.borderRadius,transition:`background-color ${t.motionDurationSlow}`,"&-rtl":{direction:"rtl"},[`&${n}-rtl ${n}-switcher_close ${n}-switcher-icon svg`]:{transform:"rotate(90deg)"},[`&-focused:not(:hover):not(${n}-active-focused)`]:(0,ee.genFocusOutline)(t),[`${n}-list-holder-inner`]:{alignItems:"flex-start"},[`&${n}-block-node`]:{[`${n}-list-holder-inner`]:{alignItems:"stretch",[`${n}-node-content-wrapper`]:{flex:"auto"},[`${r}.dragging:after`]:{position:"absolute",inset:0,border:`1px solid 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openai + +client = openai.OpenAI( + api_key="${b||"YOUR_LITELLM_API_KEY"}", + base_url="${y}" +)`;switch(h){case s.CHAT:{let e=Object.keys(k).length>0,a="";if(e){let e=JSON.stringify({metadata:k},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();a=`, + extra_body=${e}`}let i=E.length>0?E:[{role:"user",content:w}];t=` +import base64 + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Example with text only +response = client.chat.completions.create( + model="${I}", + messages=${JSON.stringify(i,null,4)}${a} +) + +print(response) + +# Example with image or PDF (uncomment and provide file path to use) +# base64_file = encode_image("path/to/your/file.jpg") # or .pdf +# response_with_file = client.chat.completions.create( +# model="${I}", +# messages=[ +# { +# "role": "user", +# "content": [ +# { +# "type": "text", +# "text": "${N}" +# }, +# { +# "type": "image_url", +# "image_url": { +# "url": f"data:image/jpeg;base64,{base64_file}" # or data:application/pdf;base64,{base64_file} +# } +# } +# ] +# } +# ]${a} +# ) +# print(response_with_file) +`;break}case s.RESPONSES:{let e=Object.keys(k).length>0,a="";if(e){let e=JSON.stringify({metadata:k},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();a=`, + extra_body=${e}`}let i=E.length>0?E:[{role:"user",content:w}];t=` +import base64 + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Example with text only +response = client.responses.create( + model="${I}", + input=${JSON.stringify(i,null,4)}${a} +) + +print(response.output_text) + +# Example with image or PDF (uncomment and provide file path to use) +# base64_file = encode_image("path/to/your/file.jpg") # or .pdf +# response_with_file = client.responses.create( +# model="${I}", +# input=[ +# { +# "role": "user", +# "content": [ +# {"type": "input_text", "text": "${N}"}, +# { +# "type": "input_image", +# "image_url": f"data:image/jpeg;base64,{base64_file}", # or data:application/pdf;base64,{base64_file} +# }, +# ], +# } +# ]${a} +# ) +# print(response_with_file.output_text) +`;break}case s.IMAGE:t="azure"===x?` +# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI. +# This snippet uses 'client.images.generate' and will create a new image based on your prompt. +# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context. +import os +import requests +import json +import time +from PIL import Image + +result = client.images.generate( + model="${I}", + prompt="${o}", + n=1 +) + +json_response = json.loads(result.model_dump_json()) + +# Set the directory for the stored image +image_dir = os.path.join(os.curdir, 'images') + +# If the directory doesn't exist, create it +if not os.path.isdir(image_dir): + os.mkdir(image_dir) + +# Initialize the image path +image_filename = f"generated_image_{int(time.time())}.png" +image_path = os.path.join(image_dir, image_filename) + +try: + # Retrieve the generated image + if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"): + image_url = json_response["data"][0]["url"] + generated_image = requests.get(image_url).content + with open(image_path, "wb") as image_file: + image_file.write(generated_image) + + print(f"Image saved to {image_path}") + # Display the image + image = Image.open(image_path) + image.show() + else: + print("Could not find image URL in response.") + print("Full response:", json_response) +except Exception as e: + print(f"An error occurred: {e}") + print("Full response:", json_response) +`:` +import base64 +import os +import time +import json +from PIL import Image +import requests + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Helper function to create a file (simplified for this example) +def create_file(image_path): + # In a real implementation, this would upload the file to OpenAI + # For this example, we'll just return a placeholder ID + return f"file_{os.path.basename(image_path).replace('.', '_')}" + +# The prompt entered by the user +prompt = "${N}" + +# Encode images to base64 +base64_image1 = encode_image("body-lotion.png") +base64_image2 = encode_image("soap.png") + +# Create file IDs +file_id1 = create_file("body-lotion.png") +file_id2 = create_file("incense-kit.png") + +response = client.responses.create( + model="${I}", + input=[ + { + "role": "user", + "content": [ + {"type": "input_text", "text": prompt}, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image1}", + }, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image2}", + }, + { + "type": "input_image", + "file_id": file_id1, + }, + { + "type": "input_image", + "file_id": file_id2, + } + ], + } + ], + tools=[{"type": "image_generation"}], +) + +# Process the response +image_generation_calls = [ + output + for output in response.output + if output.type == "image_generation_call" +] + +image_data = [output.result for output in image_generation_calls] + +if image_data: + image_base64 = image_data[0] + image_filename = f"edited_image_{int(time.time())}.png" + with open(image_filename, "wb") as f: + f.write(base64.b64decode(image_base64)) + print(f"Image saved to {image_filename}") +else: + # If no image is generated, there might be a text response with an explanation + text_response = [output.text for output in response.output if hasattr(output, 'text')] + if text_response: + print("No image generated. Model response:") + print("\\n".join(text_response)) + else: + print("No image data found in response.") + print("Full response for debugging:") + print(response) +`;break;case s.IMAGE_EDITS:t="azure"===x?` +import base64 +import os +import time +import json +from PIL import Image +import requests + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# The prompt entered by the user +prompt = "${N}" + +# Encode images to base64 +base64_image1 = encode_image("body-lotion.png") +base64_image2 = encode_image("soap.png") + +# Create file IDs +file_id1 = create_file("body-lotion.png") +file_id2 = create_file("incense-kit.png") + +response = client.responses.create( + model="${I}", + input=[ + { + "role": "user", + "content": [ + {"type": "input_text", "text": prompt}, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image1}", + }, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image2}", + }, + { + "type": "input_image", + "file_id": file_id1, + }, + { + "type": "input_image", + "file_id": file_id2, + } + ], + } + ], + tools=[{"type": "image_generation"}], +) + +# Process the response +image_generation_calls = [ + output + for output in response.output + if output.type == "image_generation_call" +] + +image_data = [output.result for output in image_generation_calls] + +if image_data: + image_base64 = image_data[0] + image_filename = f"edited_image_{int(time.time())}.png" + with open(image_filename, "wb") as f: + f.write(base64.b64decode(image_base64)) + print(f"Image saved to {image_filename}") +else: + # If no image is generated, there might be a text response with an explanation + text_response = [output.text for output in response.output if hasattr(output, 'text')] + if text_response: + print("No image generated. Model response:") + print("\\n".join(text_response)) + else: + print("No image data found in response.") + print("Full response for debugging:") + print(response) +`:` +import base64 +import os +import time + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Helper function to create a file (simplified for this example) +def create_file(image_path): + # In a real implementation, this would upload the file to OpenAI + # For this example, we'll just return a placeholder ID + return f"file_{os.path.basename(image_path).replace('.', '_')}" + +# The prompt entered by the user +prompt = "${N}" + +# Encode images to base64 +base64_image1 = encode_image("body-lotion.png") +base64_image2 = encode_image("soap.png") + +# Create file IDs +file_id1 = create_file("body-lotion.png") +file_id2 = create_file("incense-kit.png") + +response = client.responses.create( + model="${I}", + input=[ + { + "role": "user", + "content": [ + {"type": "input_text", "text": prompt}, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image1}", + }, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image2}", + }, + { + "type": "input_image", + "file_id": file_id1, + }, + { + "type": "input_image", + "file_id": file_id2, + } + ], + } + ], + tools=[{"type": "image_generation"}], +) + +# Process the response +image_generation_calls = [ + output + for output in response.output + if output.type == "image_generation_call" +] + +image_data = [output.result for output in image_generation_calls] + +if image_data: + image_base64 = image_data[0] + image_filename = f"edited_image_{int(time.time())}.png" + with open(image_filename, "wb") as f: + f.write(base64.b64decode(image_base64)) + print(f"Image saved to {image_filename}") +else: + # If no image is generated, there might be a text response with an explanation + text_response = [output.text for output in response.output if hasattr(output, 'text')] + if text_response: + print("No image generated. 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eT("stream has ended, this shouldn't happen");let e=eN(this,P,"f");if(!e)throw new eT("request ended without sending any chunks");return e_(this,P,void 0,"f"),e},Q=function(e){let t=eN(this,P,"f");if("message_start"===e.type){if(t)throw new eT(`Unexpected event order, got ${e.type} before receiving "message_stop"`);return e.message}if(!t)throw new eT(`Unexpected event order, got ${e.type} before "message_start"`);switch(e.type){case"message_stop":case"content_block_stop":return t;case"message_delta":return t.stop_reason=e.delta.stop_reason,t.stop_sequence=e.delta.stop_sequence,t.usage.output_tokens=e.usage.output_tokens,null!=e.usage.input_tokens&&(t.usage.input_tokens=e.usage.input_tokens),null!=e.usage.cache_creation_input_tokens&&(t.usage.cache_creation_input_tokens=e.usage.cache_creation_input_tokens),null!=e.usage.cache_read_input_tokens&&(t.usage.cache_read_input_tokens=e.usage.cache_read_input_tokens),null!=e.usage.server_tool_use&&(t.usage.server_tool_use=e.usage.server_tool_use),t;case"content_block_start":return t.content.push(e.content_block),t;case"content_block_delta":{let s=t.content.at(e.index);switch(e.delta.type){case"text_delta":s?.type==="text"&&(s.text+=e.delta.text);break;case"citations_delta":s?.type==="text"&&(s.citations??(s.citations=[]),s.citations.push(e.delta.citation));break;case"input_json_delta":if(s&&tH(s)){let t=s[tz]||"";Object.defineProperty(s,tz,{value:t+=e.delta.partial_json,enumerable:!1,writable:!0}),t&&(s.input=tP(t))}break;case"thinking_delta":s?.type==="thinking"&&(s.thinking+=e.delta.thinking);break;case"signature_delta":s?.type==="thinking"&&(s.signature=e.delta.signature);break;default:tF(e.delta)}return t}}},Symbol.asyncIterator)](){let e=[],t=[],s=!1;return this.on("streamEvent",s=>{let r=t.shift();r?r.resolve(s):e.push(s)}),this.on("end",()=>{for(let e of(s=!0,t))e.resolve(void 0);t.length=0}),this.on("abort",e=>{for(let r of(s=!0,t))r.reject(e);t.length=0}),this.on("error",e=>{for(let r of(s=!0,t))r.reject(e);t.length=0}),{next:async()=>e.length?{value:e.shift(),done:!1}:s?{value:void 0,done:!0}:new Promise((e,s)=>t.push({resolve:e,reject:s})).then(e=>e?{value:e,done:!1}:{value:void 0,done:!0}),return:async()=>(this.abort(),{value:void 0,done:!0})}}toReadableStream(){return new te(this[Symbol.asyncIterator].bind(this),this.controller).toReadableStream()}}function tF(e){}class tJ extends tw{create(e,t){return this._client.post("/v1/messages/batches",{body:e,...t})}retrieve(e,t){return this._client.get(tk`/v1/messages/batches/${e}`,t)}list(e={},t){return this._client.getAPIList("/v1/messages/batches",tc,{query:e,...t})}delete(e,t){return this._client.delete(tk`/v1/messages/batches/${e}`,t)}cancel(e,t){return this._client.post(tk`/v1/messages/batches/${e}/cancel`,t)}async results(e,t){let s=await this.retrieve(e);if(!s.results_url)throw new eT(`No batch \`results_url\`; Has it finished processing? ${s.processing_status} - ${s.id}`);return this._client.get(s.results_url,{...t,headers:t_([{Accept:"application/binary"},t?.headers]),stream:!0,__binaryResponse:!0})._thenUnwrap((e,t)=>tT.fromResponse(t.response,t.controller))}}class tG extends tw{constructor(){super(...arguments),this.batches=new tJ(this._client)}create(e,t){e.model in tV&&console.warn(`The model '${e.model}' is deprecated and will reach end-of-life on ${tV[e.model]} +Please migrate to a newer 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Visit https://docs.anthropic.com/en/docs/resources/model-deprecations for more information.`);let s=this._client._options.timeout;if(!e.stream&&null==s){let t=t$[e.model]??void 0;s=this._client.calculateNonstreamingTimeout(e.max_tokens,t)}return this._client.post("/v1/messages",{body:e,timeout:s??6e5,...t,stream:e.stream??!1})}stream(e,t){return tW.createMessage(this,e,t)}countTokens(e,t){return this._client.post("/v1/messages/count_tokens",{body:e,...t})}}let tV={"claude-1.3":"November 6th, 2024","claude-1.3-100k":"November 6th, 2024","claude-instant-1.1":"November 6th, 2024","claude-instant-1.1-100k":"November 6th, 2024","claude-instant-1.2":"November 6th, 2024","claude-3-sonnet-20240229":"July 21st, 2025","claude-2.1":"July 21st, 2025","claude-2.0":"July 21st, 2025"};tG.Batches=tJ;class tK extends tw{retrieve(e,t={},s){let{betas:r}=t??{};return this._client.get(tk`/v1/models/${e}`,{...s,headers:t_([{...r?.toString()!=null?{"anthropic-beta":r?.toString()}:void 0},s?.headers])})}list(e={},t){let{betas:s,...r}=e??{};return this._client.getAPIList("/v1/models",tc,{query:r,...t,headers:t_([{...s?.toString()!=null?{"anthropic-beta":s?.toString()}:void 0},t?.headers])})}}let tX=e=>void 0!==globalThis.process?globalThis.process.env?.[e]?.trim()??void 0:void 0!==globalThis.Deno?globalThis.Deno.env?.get?.(e)?.trim():void 0;class tY{constructor({baseURL:e=tX("ANTHROPIC_BASE_URL"),apiKey:t=tX("ANTHROPIC_API_KEY")??null,authToken:s=tX("ANTHROPIC_AUTH_TOKEN")??null,...r}={}){Z.set(this,void 0);const a={apiKey:t,authToken:s,...r,baseURL:e||"https://api.anthropic.com"};if(!a.dangerouslyAllowBrowser&&"u">typeof window&&void 0!==window.document&&"u">typeof navigator)throw new eT("It looks like you're running in a browser-like environment.\n\nThis is disabled by default, as it risks exposing your secret API credentials to attackers.\nIf you understand the risks and have appropriate mitigations in place,\nyou can set the `dangerouslyAllowBrowser` option to `true`, e.g.,\n\nnew Anthropic({ apiKey, dangerouslyAllowBrowser: true });\n");this.baseURL=a.baseURL,this.timeout=a.timeout??tQ.DEFAULT_TIMEOUT,this.logger=a.logger??console;const n="warn";this.logLevel=n,this.logLevel=eJ(a.logLevel,"ClientOptions.logLevel",this)??eJ(tX("ANTHROPIC_LOG"),"process.env['ANTHROPIC_LOG']",this)??n,this.fetchOptions=a.fetchOptions,this.maxRetries=a.maxRetries??2,this.fetch=a.fetch??function(){if("u">typeof fetch)return fetch;throw Error("`fetch` is not defined as a global; Either pass `fetch` to the client, `new Anthropic({ fetch })` or polyfill the global, `globalThis.fetch = fetch`")}(),e_(this,Z,e6,"f"),this._options=a,this.apiKey=t,this.authToken=s}withOptions(e){return new this.constructor({...this._options,baseURL:this.baseURL,maxRetries:this.maxRetries,timeout:this.timeout,logger:this.logger,logLevel:this.logLevel,fetchOptions:this.fetchOptions,apiKey:this.apiKey,authToken:this.authToken,...e})}defaultQuery(){return this._options.defaultQuery}validateHeaders({values:e,nulls:t}){if(!(this.apiKey&&e.get("x-api-key")||t.has("x-api-key")||this.authToken&&e.get("authorization"))&&!t.has("authorization"))throw Error('Could not resolve authentication method. 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- )} -
-
- - - - Team Name - Team ID - Created - Spend (USD) - Budget (USD) - Models - Organization - Info - Actions - - - - - {teams && teams.length > 0 ? ( - teams - .filter((team) => { - if (!currentOrg) return true; - return team.organization_id === currentOrg.organization_id; - }) - .sort((a, b) => new Date(b.created_at).getTime() - new Date(a.created_at).getTime()) - .map((team: any) => ( - - - {team["team_alias"]} - - -
- - - -
-
- - {team.created_at ? new Date(team.created_at).toLocaleDateString() : "N/A"} - - - {formatNumberWithCommas(team["spend"], 4)} - - - {team["max_budget"] !== null && team["max_budget"] !== undefined - ? team["max_budget"] - : "No limit"} - - 3 ? "px-0" : ""} - > -
- {Array.isArray(team.models) ? ( -
- {team.models.length === 0 ? ( - - All Proxy Models - - ) : ( - <> -
- {team.models.length > 3 && ( -
- { - setExpandedAccordions((prev) => ({ - ...prev, - [team.team_id]: !prev[team.team_id], - })); - }} - /> -
- )} -
- {team.models.slice(0, 3).map((model: string, index: number) => - model === "all-proxy-models" ? ( - - All Proxy Models - - ) : ( - - - {model.length > 30 - ? `${getModelDisplayName(model).slice(0, 30)}...` - : getModelDisplayName(model)} - - - ), - )} - {team.models.length > 3 && !expandedAccordions[team.team_id] && ( - - - +{team.models.length - 3}{" "} - {team.models.length - 3 === 1 ? "more model" : "more models"} - - - )} - {expandedAccordions[team.team_id] && ( -
- {team.models.slice(3).map((model: string, index: number) => - model === "all-proxy-models" ? ( - - All Proxy Models - - ) : ( - - - {model.length > 30 - ? `${getModelDisplayName(model).slice(0, 30)}...` - : getModelDisplayName(model)} - - - ), - )} -
- )} -
-
- - )} -
- ) : null} -
-
- - - {getOrganizationAlias(team.organization_id, organizationsData || organizations)} - - - - {perTeamInfo && - team.team_id && - perTeamInfo[team.team_id] && - perTeamInfo[team.team_id].keys && - perTeamInfo[team.team_id].keys.length}{" "} - Keys - - - {perTeamInfo && - team.team_id && - perTeamInfo[team.team_id] && - perTeamInfo[team.team_id].team_info && - perTeamInfo[team.team_id].team_info.members_with_roles && - perTeamInfo[team.team_id].team_info.members_with_roles.length}{" "} - Members - - - - {userRole == "Admin" ? ( - <> - { - setSelectedTeamId(team.team_id); - setEditTeam(true); - }} - dataTestId="edit-team-button" - tooltipText="Edit team" - /> - handleDelete(team)} - dataTestId="delete-team-button" - tooltipText="Delete team" - /> - - ) : null} - -
- )) - ) : ( - - -
- No teams found - Adjust your filters or create a new team -
-
-
- )} -
-
- -
- -
-
- - - - {isProxyAdminRole(userRole || "") && ( - - - - )} -
-
- )} - {canCreateOrManageTeams(userRole, userID, organizations) && ( + {canCreateOrManageTeams(userRole, userID, organizations) && ( = ({ : "" } > - = ({ optionFilterProp="children" > {adminOrgs?.map((org) => ( - + {org.organization_alias}{" "} ({org.organization_id}) - + ))} - + {/* Show message when org admin needs to select organization */} {isOrgAdmin && !isSingleOrg && adminOrgs.length > 1 && (
- + Please select an organization to create a team for. You can only create teams within organizations where you are an admin. @@ -1190,11 +1209,11 @@ const Teams: React.FC = ({ - - daily - weekly - monthly - + @@ -1313,7 +1332,7 @@ const Teams: React.FC = ({ className="mt-8" help="Select existing guardrails or enter new ones" > - = ({ className="mt-8" help="Select existing policies or enter new ones" > - = ({
- + Create custom aliases for models that can be used by team members in API calls. This allows you to create shortcuts for specific models. @@ -1548,14 +1567,12 @@ const Teams: React.FC = ({
- Create Team +
)} - - -
+ ); }; diff --git a/ui/litellm-dashboard/src/components/Projects/ProjectModals/CreateProjectModal.tsx b/ui/litellm-dashboard/src/components/Projects/ProjectModals/CreateProjectModal.tsx index e490f89303..bbf56e4930 100644 --- a/ui/litellm-dashboard/src/components/Projects/ProjectModals/CreateProjectModal.tsx +++ b/ui/litellm-dashboard/src/components/Projects/ProjectModals/CreateProjectModal.tsx @@ -1,5 +1,6 @@ -import { Modal, Form, Button, Typography, message } from "antd"; +import { Modal, Form, Button, Typography } from "antd"; import { FolderAddOutlined } from "@ant-design/icons"; +import MessageManager from "@/components/molecules/message_manager"; import { useCreateProject, ProjectCreateParams, @@ -32,12 +33,12 @@ export function CreateProjectModal({ createMutation.mutate(params, { onSuccess: () => { - message.success("Project created successfully"); + MessageManager.success("Project created successfully"); form.resetFields(); onClose(); }, onError: (error) => { - message.error(error.message || "Failed to create project"); + MessageManager.error(error.message || "Failed to create project"); }, }); } catch (error) { diff --git a/ui/litellm-dashboard/src/components/Projects/ProjectModals/EditProjectModal.tsx b/ui/litellm-dashboard/src/components/Projects/ProjectModals/EditProjectModal.tsx index 75f56b1373..dc3b43ef73 100644 --- a/ui/litellm-dashboard/src/components/Projects/ProjectModals/EditProjectModal.tsx +++ b/ui/litellm-dashboard/src/components/Projects/ProjectModals/EditProjectModal.tsx @@ -1,6 +1,7 @@ import { useEffect } from "react"; -import { Modal, Form, Button, Typography, message } from "antd"; +import { Modal, Form, Button, Typography } from "antd"; import { SaveOutlined } from "@ant-design/icons"; +import MessageManager from "@/components/molecules/message_manager"; import { ProjectResponse } from "@/app/(dashboard)/hooks/projects/useProjects"; import { useUpdateProject, @@ -80,12 +81,12 @@ export function EditProjectModal({ { projectId: project.project_id, params }, { onSuccess: () => { - message.success("Project updated successfully"); + MessageManager.success("Project updated successfully"); onSuccess?.(); onClose(); }, onError: (error) => { - message.error(error.message || "Failed to update project"); + MessageManager.error(error.message || "Failed to update project"); }, }, ); diff --git a/ui/litellm-dashboard/src/components/SearchTools/SearchToolTester.tsx b/ui/litellm-dashboard/src/components/SearchTools/SearchToolTester.tsx index 28b34b0608..d1cb5077b1 100644 --- a/ui/litellm-dashboard/src/components/SearchTools/SearchToolTester.tsx +++ b/ui/litellm-dashboard/src/components/SearchTools/SearchToolTester.tsx @@ -1,5 +1,6 @@ import React, { useState } from "react"; -import { Button, Input, Typography, Spin, message } from "antd"; +import { Button, Input, Typography, Spin } from "antd"; +import MessageManager from "@/components/molecules/message_manager"; import { SearchOutlined, LoadingOutlined } from "@ant-design/icons"; import { searchToolQueryCall } from "../networking"; import NotificationsManager from "../molecules/notifications_manager"; @@ -39,7 +40,7 @@ export const SearchToolTester: React.FC = ({ searchToolNa const handleSearch = async () => { if (!query.trim()) { - message.warning("Please enter a search query"); + MessageManager.warning("Please enter a search query"); return; } diff --git a/ui/litellm-dashboard/src/components/Settings/RouterSettings/Fallbacks/AddFallbacks.tsx b/ui/litellm-dashboard/src/components/Settings/RouterSettings/Fallbacks/AddFallbacks.tsx index 34f059516a..e55089d27d 100644 --- a/ui/litellm-dashboard/src/components/Settings/RouterSettings/Fallbacks/AddFallbacks.tsx +++ b/ui/litellm-dashboard/src/components/Settings/RouterSettings/Fallbacks/AddFallbacks.tsx @@ -5,8 +5,9 @@ */ import { Button as TremorButton } from "@tremor/react"; -import { Button, message } from "antd"; +import { Button } from "antd"; import React, { useEffect, useState } from "react"; +import MessageManager from "@/components/molecules/message_manager"; import NotificationManager from "../../../molecules/notifications_manager"; import { fetchAvailableModels, ModelGroup } from "../../../playground/llm_calls/fetch_models"; import { AddFallbacksModal } from "./AddFallbacksModal"; @@ -90,7 +91,7 @@ export default function AddFallbacks({ (g) => !g.primaryModel || g.fallbackModels.length === 0, ); if (invalidGroups.length > 0) { - message.error( + MessageManager.error( `Please complete configuration for all groups. ${invalidGroups.length} group(s) incomplete.`, ); return; diff --git a/ui/litellm-dashboard/src/components/Settings/RouterSettings/Fallbacks/FallbackSelectionForm.tsx b/ui/litellm-dashboard/src/components/Settings/RouterSettings/Fallbacks/FallbackSelectionForm.tsx index 08b031c683..0482161bc6 100644 --- a/ui/litellm-dashboard/src/components/Settings/RouterSettings/Fallbacks/FallbackSelectionForm.tsx +++ b/ui/litellm-dashboard/src/components/Settings/RouterSettings/Fallbacks/FallbackSelectionForm.tsx @@ -5,9 +5,10 @@ */ import { Button } from "@tremor/react"; -import { message, Tabs } from "antd"; +import { Tabs } from "antd"; import { Plus } from "lucide-react"; import React, { useEffect, useState } from "react"; +import MessageManager from "@/components/molecules/message_manager"; import { FallbackGroup, FallbackGroupConfig } from "./FallbackGroupConfig"; interface FallbackSelectionFormProps { @@ -60,7 +61,7 @@ export function FallbackSelectionForm({ const handleRemoveGroup = (targetId: string) => { if (groups.length === 1) { - message.warning("At least one group is required"); + MessageManager.warning("At least one group is required"); return; } const newGroups = groups.filter((g) => g.id !== targetId); diff --git a/ui/litellm-dashboard/src/components/TeamSSOSettings.tsx b/ui/litellm-dashboard/src/components/TeamSSOSettings.tsx index a9c07cdbcf..98745d388a 100644 --- a/ui/litellm-dashboard/src/components/TeamSSOSettings.tsx +++ b/ui/litellm-dashboard/src/components/TeamSSOSettings.tsx @@ -26,6 +26,10 @@ const PERMISSION_OPTIONS = [ "/key/unblock", "/key/bulk_update", "/key/{key_id}/reset_spend", + "/key/info", + "/key/list", + "/key/aliases", + "/team/daily/activity", ]; interface SettingRowProps { diff --git a/ui/litellm-dashboard/src/components/ToolDetail.tsx b/ui/litellm-dashboard/src/components/ToolDetail.tsx index ed0f866acb..b2a7d9bfbe 100644 --- a/ui/litellm-dashboard/src/components/ToolDetail.tsx +++ b/ui/litellm-dashboard/src/components/ToolDetail.tsx @@ -387,7 +387,6 @@ export function ToolDetail({ toolName, onBack, accessToken }: ToolDetailProps) { {blockScope === "team" ? ( setBlockTeamId(id || null)} /> diff --git a/ui/litellm-dashboard/src/components/add_model/AddModelForm.test.tsx b/ui/litellm-dashboard/src/components/add_model/AddModelForm.test.tsx index 6f63fcfa54..de5e138739 100644 --- a/ui/litellm-dashboard/src/components/add_model/AddModelForm.test.tsx +++ b/ui/litellm-dashboard/src/components/add_model/AddModelForm.test.tsx @@ -65,6 +65,21 @@ vi.mock("@/app/(dashboard)/hooks/useAuthorized", () => ({ default: vi.fn(), })); +vi.mock("@/app/(dashboard)/hooks/teams/useTeams", () => ({ + useInfiniteTeams: () => ({ + data: { + pages: [{ + teams: [{ team_id: "team-1", team_alias: "Test Team", organization_id: "org-1" }], + total: 1, page: 1, page_size: 20, total_pages: 1, + }], + }, + fetchNextPage: vi.fn(), + hasNextPage: false, + isFetchingNextPage: false, + isLoading: false, + }), +})); + vi.mock("@/app/(dashboard)/hooks/guardrails/useGuardrails", () => ({ useGuardrails: vi.fn().mockReturnValue({ data: [{ guardrail_name: "test-guardrail" }], diff --git a/ui/litellm-dashboard/src/components/add_model/AddModelForm.tsx b/ui/litellm-dashboard/src/components/add_model/AddModelForm.tsx index 2b3f23a35a..65e239d58e 100644 --- a/ui/litellm-dashboard/src/components/add_model/AddModelForm.tsx +++ b/ui/litellm-dashboard/src/components/add_model/AddModelForm.tsx @@ -131,7 +131,6 @@ const AddModelForm: React.FC = ({ tooltip="Select the team for which you want to add this model" > { setTeamAdminSelectedTeam(value); }} @@ -325,7 +324,7 @@ const AddModelForm: React.FC = ({ }, ]} > - +
)} {isAdmin && ( diff --git a/ui/litellm-dashboard/src/components/agent_management/AgentSelector.test.tsx b/ui/litellm-dashboard/src/components/agent_management/AgentSelector.test.tsx new file mode 100644 index 0000000000..26f903cd31 --- /dev/null +++ b/ui/litellm-dashboard/src/components/agent_management/AgentSelector.test.tsx @@ -0,0 +1,144 @@ +import { render, screen, waitFor } from "@testing-library/react"; +import userEvent from "@testing-library/user-event"; +import { describe, expect, it, vi, beforeEach } from "vitest"; + +// Mock networking module +const mockGetAgentsList = vi.fn(); +vi.mock("../networking", () => ({ + getAgentsList: (...args: any[]) => mockGetAgentsList(...args), +})); + +// Mock antd Select +vi.mock("antd", () => { + const SelectComponent = ({ children, onChange, value, mode, placeholder, loading, disabled, ...props }: any) => ( +
+ + {loading && Loading} +
+ ); + + SelectComponent.Option = ({ children, value, ...props }: any) => ( + + ); + + return { Select: SelectComponent }; +}); + +import AgentSelector from "./AgentSelector"; + +describe("AgentSelector", () => { + const defaultProps = { + onChange: vi.fn(), + accessToken: "test-token", + }; + + beforeEach(() => { + vi.clearAllMocks(); + mockGetAgentsList.mockResolvedValue({ + agents: [ + { agent_id: "agent-1", agent_name: "Agent One" }, + { agent_id: "agent-2", agent_name: "Agent Two", agent_access_groups: ["group-a", "group-b"] }, + ], + }); + }); + + it("renders the selector", () => { + render(); + expect(screen.getByTestId("agent-select")).toBeInTheDocument(); + }); + + it("fetches agents on mount with access token", async () => { + render(); + await waitFor(() => { + expect(mockGetAgentsList).toHaveBeenCalledWith("test-token"); + }); + }); + + it("does not fetch when accessToken is empty", () => { + render(); + expect(mockGetAgentsList).not.toHaveBeenCalled(); + }); + + it("shows loading state while fetching", async () => { + // Keep the promise pending + let resolve: any; + mockGetAgentsList.mockReturnValue(new Promise((r) => { resolve = r; })); + + render(); + expect(screen.getByTestId("agent-select")).toHaveAttribute("data-loading", "true"); + + // Resolve to clean up + resolve({ agents: [] }); + await waitFor(() => { + expect(screen.getByTestId("agent-select")).toHaveAttribute("data-loading", "false"); + }); + }); + + it("renders agent options after fetch", async () => { + render(); + await waitFor(() => { + expect(screen.getByText("Agent One")).toBeInTheDocument(); + expect(screen.getByText("Agent Two")).toBeInTheDocument(); + }); + }); + + it("renders access group options with group prefix", async () => { + render(); + await waitFor(() => { + expect(screen.getByText("group-a")).toBeInTheDocument(); + expect(screen.getByText("group-b")).toBeInTheDocument(); + }); + }); + + it("respects disabled prop", () => { + render(); + expect(screen.getByTestId("agent-select")).toHaveAttribute("data-disabled", "true"); + }); + + it("handles API error gracefully", async () => { + const consoleSpy = vi.spyOn(console, "error").mockImplementation(() => {}); + mockGetAgentsList.mockRejectedValue(new Error("API error")); + + render(); + await waitFor(() => { + expect(consoleSpy).toHaveBeenCalledWith("Error fetching agents:", expect.any(Error)); + }); + + consoleSpy.mockRestore(); + }); + + it("passes value as flattened selectedValues", async () => { + render( + + ); + await waitFor(() => { + const select = screen.getByTestId("select-input"); + // The value should contain agent-1 and group:group-a + expect(select).toBeInTheDocument(); + }); + }); + + it("handles null response from API", async () => { + mockGetAgentsList.mockResolvedValue(null); + render(); + await waitFor(() => { + expect(screen.getByTestId("agent-select")).toHaveAttribute("data-loading", "false"); + }); + }); +}); diff --git a/ui/litellm-dashboard/src/components/agents/add_agent_form.tsx b/ui/litellm-dashboard/src/components/agents/add_agent_form.tsx index c5518596b8..046b28640c 100644 --- a/ui/litellm-dashboard/src/components/agents/add_agent_form.tsx +++ b/ui/litellm-dashboard/src/components/agents/add_agent_form.tsx @@ -1,5 +1,6 @@ import React, { useState, useEffect } from "react"; -import { Modal, Form, message, Select, Input, Steps, Radio, Tag, Divider, Switch, InputNumber, Collapse } from "antd"; +import { Modal, Form, Select, Input, Steps, Radio, Tag, Divider, Switch, InputNumber, Collapse } from "antd"; +import MessageManager from "@/components/molecules/message_manager"; import { Button } from "@tremor/react"; import { CheckCircleFilled, KeyOutlined, RobotOutlined, AppstoreOutlined, InfoCircleOutlined } from "@ant-design/icons"; import CreatedKeyDisplay from "../shared/CreatedKeyDisplay"; @@ -216,7 +217,7 @@ const AddAgentForm: React.FC = ({ const handleCreateAgent = async () => { if (!accessToken) { - message.error("No access token available"); + MessageManager.error("No access token available"); return; } @@ -226,7 +227,7 @@ const AddAgentForm: React.FC = ({ const values = { ...form.getFieldsValue(true) }; const agentData = buildAgentData(values); if (!agentData) { - message.error("Failed to build agent data"); + MessageManager.error("Failed to build agent data"); setIsSubmitting(false); return; } @@ -301,7 +302,7 @@ const AddAgentForm: React.FC = ({ setCreatedKeyValue(keyResponse.key || null); } else if (keyAssignOption === "existing_key") { if (!selectedExistingKey) { - message.error("Please select an existing key to assign"); + MessageManager.error("Please select an existing key to assign"); setIsSubmitting(false); return; } @@ -318,7 +319,7 @@ const AddAgentForm: React.FC = ({ } catch (error) { console.error("Error creating agent:", error); const errorMessage = error instanceof Error ? error.message : String(error); - message.error(errorMessage ? `Failed to create agent: ${errorMessage}` : "Failed to create agent"); + MessageManager.error(errorMessage ? `Failed to create agent: ${errorMessage}` : "Failed to create agent"); } finally { setIsSubmitting(false); } @@ -722,10 +723,7 @@ const AddAgentForm: React.FC = ({ name="team_id" tooltip="Optionally assign this agent to a team. The agent and its key will belong to the selected team." > - + diff --git a/ui/litellm-dashboard/src/components/agents/agent_info.tsx b/ui/litellm-dashboard/src/components/agents/agent_info.tsx index b41e318a76..d543be8356 100644 --- a/ui/litellm-dashboard/src/components/agents/agent_info.tsx +++ b/ui/litellm-dashboard/src/components/agents/agent_info.tsx @@ -1,6 +1,7 @@ import React, { useState, useEffect } from "react"; import { Card, Title, Text, Button as TremorButton, Tab, TabGroup, TabList, TabPanel, TabPanels} from "@tremor/react"; -import { Form, Input, InputNumber, Button as AntButton, message, Spin, Descriptions, Divider } from "antd"; +import { Form, Input, InputNumber, Button as AntButton, Spin, Descriptions, Divider } from "antd"; +import MessageManager from "@/components/molecules/message_manager"; import { ArrowLeftIcon } from "@heroicons/react/outline"; import { getAgentInfo, patchAgentCall, getAgentCreateMetadata, AgentCreateInfo } from "../networking"; import { Agent } from "./types"; @@ -72,7 +73,7 @@ const AgentInfoView: React.FC = ({ } } catch (error) { console.error("Error fetching agent info:", error); - message.error("Failed to load agent information"); + MessageManager.error("Failed to load agent information"); } finally { setIsLoading(false); } @@ -111,12 +112,12 @@ const AgentInfoView: React.FC = ({ } await patchAgentCall(accessToken, agentId, updateData); - message.success("Agent updated successfully"); + MessageManager.success("Agent updated successfully"); setIsEditing(false); fetchAgentInfo(); } catch (error) { console.error("Error updating agent:", error); - message.error("Failed to update agent"); + MessageManager.error("Failed to update agent"); } finally { setIsSaving(false); } diff --git a/ui/litellm-dashboard/src/components/budgets/budget_modal.tsx b/ui/litellm-dashboard/src/components/budgets/budget_modal.tsx index 490613de25..b5ad8aaff3 100644 --- a/ui/litellm-dashboard/src/components/budgets/budget_modal.tsx +++ b/ui/litellm-dashboard/src/components/budgets/budget_modal.tsx @@ -1,17 +1,17 @@ import React from "react"; import { TextInput, Accordion, AccordionHeader, AccordionBody } from "@tremor/react"; import { Button as Button2, Modal, Form, InputNumber, Select } from "antd"; -import { budgetCreateCall } from "../networking"; +import { useCreateBudget } from "@/app/(dashboard)/hooks/budgets/useBudgets"; import NotificationsManager from "../molecules/notifications_manager"; interface BudgetModalProps { isModalVisible: boolean; - accessToken: string | null; setIsModalVisible: React.Dispatch>; - setBudgetList: React.Dispatch>; } -const BudgetModal: React.FC = ({ isModalVisible, accessToken, setIsModalVisible, setBudgetList }) => { +const BudgetModal: React.FC = ({ isModalVisible, setIsModalVisible }) => { const [form] = Form.useForm(); + const createBudget = useCreateBudget(); + const handleOk = () => { setIsModalVisible(false); form.resetFields(); @@ -23,20 +23,15 @@ const BudgetModal: React.FC = ({ isModalVisible, accessToken, }; const handleCreate = async (formValues: Record) => { - if (accessToken == null || accessToken == undefined) { - return; - } try { NotificationsManager.info("Making API Call"); - // setIsModalVisible(true); - const response = await budgetCreateCall(accessToken, formValues); - console.log("key create Response:", response); - setBudgetList((prevData) => (prevData ? [...prevData, response] : [response])); // Check if prevData is null + await createBudget.mutateAsync(formValues); NotificationsManager.success("Budget Created"); form.resetFields(); + setIsModalVisible(false); } catch (error) { - console.error("Error creating the key:", error); - NotificationsManager.fromBackend(`Error creating the key: ${error}`); + console.error("Error creating the budget:", error); + NotificationsManager.fromBackend(`Error creating the budget: ${error}`); } }; diff --git a/ui/litellm-dashboard/src/components/budgets/budget_panel.test.tsx b/ui/litellm-dashboard/src/components/budgets/budget_panel.test.tsx index 534693d398..ecae379c9f 100644 --- a/ui/litellm-dashboard/src/components/budgets/budget_panel.test.tsx +++ b/ui/litellm-dashboard/src/components/budgets/budget_panel.test.tsx @@ -1,31 +1,50 @@ -import * as networking from "../networking"; import { fireEvent, render, waitFor, screen } from "@testing-library/react"; import { act } from "@testing-library/react"; +import { QueryClient, QueryClientProvider } from "@tanstack/react-query"; import { afterEach, describe, expect, it, vi } from "vitest"; import BudgetPanel from "./budget_panel"; -vi.mock("../networking", () => ({ - getBudgetList: vi.fn(), - budgetDeleteCall: vi.fn(), +const mockBudgets = [ + { + budget_id: "budget-1", + max_budget: 100, + rpm_limit: 10, + tpm_limit: 1000, + updated_at: "2024-01-01T00:00:00Z", + }, +]; + +vi.mock("@/app/(dashboard)/hooks/budgets/useBudgets", () => ({ + useBudgets: vi.fn().mockReturnValue({ data: [], isLoading: false }), + useDeleteBudget: vi.fn().mockReturnValue({ mutateAsync: vi.fn(), isPending: false }), + useCreateBudget: vi.fn().mockReturnValue({ mutateAsync: vi.fn() }), + useUpdateBudget: vi.fn().mockReturnValue({ mutateAsync: vi.fn() }), })); +import { useBudgets, useDeleteBudget, useCreateBudget, useUpdateBudget } from "@/app/(dashboard)/hooks/budgets/useBudgets"; + +const createQueryClient = () => + new QueryClient({ + defaultOptions: { queries: { retry: false, gcTime: 0 } }, + }); + +function renderWithProviders(ui: React.ReactElement) { + const qc = createQueryClient(); + return render({ui}); +} + describe("Budget Panel", () => { afterEach(() => { vi.clearAllMocks(); }); it("should render the budget panel and load budgets", async () => { - vi.mocked(networking.getBudgetList).mockResolvedValue([ - { - budget_id: "budget-1", - max_budget: "100", - rpm_limit: 10, - tpm_limit: 1000, - updated_at: "2024-01-01T00:00:00Z", - }, - ]); + vi.mocked(useBudgets).mockReturnValue({ + data: mockBudgets, + isLoading: false, + } as any); - render(); + renderWithProviders(); await waitFor(() => { expect(screen.getByText("Create a budget to assign to customers.")).toBeInTheDocument(); @@ -34,17 +53,20 @@ describe("Budget Panel", () => { }); it("should open delete modal when clicking delete icon", async () => { - vi.mocked(networking.getBudgetList).mockResolvedValue([ - { - budget_id: "budget-to-delete", - max_budget: "200", - rpm_limit: 20, - tpm_limit: 2000, - updated_at: "2024-01-02T00:00:00Z", - }, - ]); + vi.mocked(useBudgets).mockReturnValue({ + data: [ + { + budget_id: "budget-to-delete", + max_budget: 200, + rpm_limit: 20, + tpm_limit: 2000, + updated_at: "2024-01-02T00:00:00Z", + }, + ], + isLoading: false, + } as any); - render(); + renderWithProviders(); await waitFor(() => { expect(screen.getByText("budget-to-delete")).toBeInTheDocument(); @@ -62,18 +84,25 @@ describe("Budget Panel", () => { }); it("should successfully delete a budget", async () => { - vi.mocked(networking.getBudgetList).mockResolvedValue([ - { - budget_id: "budget-to-delete", - max_budget: "200", - rpm_limit: 20, - tpm_limit: 2000, - updated_at: "2024-01-02T00:00:00Z", - }, - ]); - vi.mocked(networking.budgetDeleteCall).mockResolvedValue(undefined); + const deleteMutateAsync = vi.fn().mockResolvedValue(undefined); + vi.mocked(useBudgets).mockReturnValue({ + data: [ + { + budget_id: "budget-to-delete", + max_budget: 200, + rpm_limit: 20, + tpm_limit: 2000, + updated_at: "2024-01-02T00:00:00Z", + }, + ], + isLoading: false, + } as any); + vi.mocked(useDeleteBudget).mockReturnValue({ + mutateAsync: deleteMutateAsync, + isPending: false, + } as any); - render(); + renderWithProviders(); await waitFor(() => { expect(screen.getByText("budget-to-delete")).toBeInTheDocument(); @@ -96,24 +125,43 @@ describe("Budget Panel", () => { }); await waitFor(() => { - expect(networking.budgetDeleteCall).toHaveBeenCalledWith("token-123", "budget-to-delete"); - expect(networking.getBudgetList).toHaveBeenCalledTimes(2); // Initial load + refresh after delete + expect(deleteMutateAsync).toHaveBeenCalledWith("budget-to-delete"); + }); + }); + + it("should render empty state without crashing", async () => { + vi.mocked(useBudgets).mockReturnValue({ + data: [], + isLoading: false, + } as any); + + renderWithProviders(); + + await waitFor(() => { + expect(screen.getByText("Create a budget to assign to customers.")).toBeInTheDocument(); }); }); it("should handle delete error", async () => { - vi.mocked(networking.getBudgetList).mockResolvedValue([ - { - budget_id: "budget-to-delete", - max_budget: "200", - rpm_limit: 20, - tpm_limit: 2000, - updated_at: "2024-01-02T00:00:00Z", - }, - ]); - vi.mocked(networking.budgetDeleteCall).mockRejectedValue(new Error("Delete failed")); + const deleteMutateAsync = vi.fn().mockRejectedValue(new Error("Delete failed")); + vi.mocked(useBudgets).mockReturnValue({ + data: [ + { + budget_id: "budget-to-delete", + max_budget: 200, + rpm_limit: 20, + tpm_limit: 2000, + updated_at: "2024-01-02T00:00:00Z", + }, + ], + isLoading: false, + } as any); + vi.mocked(useDeleteBudget).mockReturnValue({ + mutateAsync: deleteMutateAsync, + isPending: false, + } as any); - render(); + renderWithProviders(); await waitFor(() => { expect(screen.getByText("budget-to-delete")).toBeInTheDocument(); @@ -136,10 +184,38 @@ describe("Budget Panel", () => { }); await waitFor(() => { - expect(networking.budgetDeleteCall).toHaveBeenCalledWith("token-123", "budget-to-delete"); + expect(deleteMutateAsync).toHaveBeenCalledWith("budget-to-delete"); + }); + }); + + it("should open edit modal when clicking edit icon", async () => { + vi.mocked(useBudgets).mockReturnValue({ + data: [ + { + budget_id: "budget-to-edit", + max_budget: 300, + rpm_limit: 30, + tpm_limit: 3000, + updated_at: "2024-01-03T00:00:00Z", + }, + ], + isLoading: false, + } as any); + + renderWithProviders(); + + await waitFor(() => { + expect(screen.getByText("budget-to-edit")).toBeInTheDocument(); }); - // Modal should still be open (error handling) - expect(screen.getByText("Delete Budget?")).toBeInTheDocument(); + const editButton = screen.getByTestId("edit-budget-button"); + + act(() => { + fireEvent.click(editButton); + }); + + await waitFor(() => { + expect(screen.getByText("Edit Budget")).toBeInTheDocument(); + }); }); }); diff --git a/ui/litellm-dashboard/src/components/budgets/budget_panel.tsx b/ui/litellm-dashboard/src/components/budgets/budget_panel.tsx index b52ef5ab94..e42d056965 100644 --- a/ui/litellm-dashboard/src/components/budgets/budget_panel.tsx +++ b/ui/litellm-dashboard/src/components/budgets/budget_panel.tsx @@ -19,12 +19,12 @@ import { TabPanels, Text, } from "@tremor/react"; -import React, { useEffect, useState } from "react"; +import React, { useState } from "react"; import { Prism as SyntaxHighlighter } from "react-syntax-highlighter"; import DeleteResourceModal from "../common_components/DeleteResourceModal"; import TableIconActionButton from "../common_components/IconActionButton/TableIconActionButtons/TableIconActionButton"; import NotificationsManager from "../molecules/notifications_manager"; -import { budgetDeleteCall, getBudgetList } from "../networking"; +import { useBudgets, useDeleteBudget } from "@/app/(dashboard)/hooks/budgets/useBudgets"; import BudgetModal from "./budget_modal"; import EditBudgetModal from "./edit_budget_modal"; import { CREATE_END_USER_CURL_COMMAND, CHAT_COMPLETIONS_CURL_COMMAND, OPENAI_SDK_PYTHON_CODE } from "./constants"; @@ -35,7 +35,7 @@ interface BudgetSettingsPageProps { export interface budgetItem { budget_id: string; - max_budget: string | null; + max_budget: number | null; rpm_limit: number | null; tpm_limit: number | null; updated_at: string; @@ -45,17 +45,10 @@ const BudgetPanel: React.FC = ({ accessToken }) => { const [isCreateModelVisible, setIsCreateModelVisible] = useState(false); const [isEditModalVisible, setIsEditModalVisible] = useState(false); const [selectedBudget, setSelectedBudget] = useState(null); - const [budgetList, setBudgetList] = useState([]); - const [isDeleting, setIsDeleting] = useState(false); const [isDeleteModalVisible, setIsDeleteModalVisible] = useState(false); - useEffect(() => { - if (!accessToken) { - return; - } - getBudgetList(accessToken).then((data) => { - setBudgetList(data); - }); - }, [accessToken]); + + const { data: budgetList = [] } = useBudgets(); + const deleteBudget = useDeleteBudget(); const handleEditCall = async (budget: budgetItem) => { if (accessToken == null) { @@ -74,11 +67,9 @@ const BudgetPanel: React.FC = ({ accessToken }) => { if (!selectedBudget || accessToken == null) { return; } - setIsDeleting(true); try { - await budgetDeleteCall(accessToken, selectedBudget.budget_id); + await deleteBudget.mutateAsync(selectedBudget.budget_id); NotificationsManager.success("Budget deleted."); - await handleUpdateCall(); } catch (error) { console.error("Error deleting budget:", error); if (typeof NotificationsManager.fromBackend === "function") { @@ -87,7 +78,6 @@ const BudgetPanel: React.FC = ({ accessToken }) => { NotificationsManager.info("Failed to delete budget"); } } finally { - setIsDeleting(false); setIsDeleteModalVisible(false); setSelectedBudget(null); } @@ -97,15 +87,6 @@ const BudgetPanel: React.FC = ({ accessToken }) => { setIsDeleteModalVisible(false); }; - const handleUpdateCall = async () => { - if (accessToken == null) { - return; - } - getBudgetList(accessToken).then((data) => { - setBudgetList(data); - }); - }; - return (