diff --git a/.circleci/config.yml b/.circleci/config.yml index e5b1a2d0bf3..9761167ec13 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -53,7 +53,7 @@ jobs: local_testing: docker: - - image: cimg/python:3.11 + - image: cimg/python:3.12 auth: username: ${DOCKERHUB_USERNAME} password: ${DOCKERHUB_PASSWORD} @@ -79,7 +79,7 @@ jobs: pip install "pytest-retry==1.6.3" pip install "pytest-asyncio==0.21.1" pip install "pytest-cov==5.0.0" - pip install "mypy==1.15.0" + pip install "mypy==1.18.2" pip install "google-generativeai==0.3.2" pip install "google-cloud-aiplatform==1.43.0" pip install pyarrow @@ -144,14 +144,8 @@ jobs: name: Linting Testing command: | cd litellm - pip install "cryptography<40.0.0" - python -m pip install types-requests types-setuptools types-redis types-PyYAML - if ! python -m mypy . \ - --config-file mypy.ini \ - --ignore-missing-imports; then - echo "mypy detected errors" - exit 1 - fi + # Use the same simple approach that works in GitHub Actions + python -m mypy . --ignore-missing-imports --show-traceback cd .. # Run pytest and generate JUnit XML report @@ -204,7 +198,7 @@ jobs: pip install "pytest-retry==1.6.3" pip install "pytest-asyncio==0.21.1" pip install "pytest-cov==5.0.0" - pip install mypy + pip install "mypy==1.18.2" pip install "google-generativeai==0.3.2" pip install "google-cloud-aiplatform==1.43.0" pip install pyarrow @@ -311,7 +305,7 @@ jobs: pip install "pytest-retry==1.6.3" pip install "pytest-asyncio==0.21.1" pip install "pytest-cov==5.0.0" - pip install mypy + pip install "mypy==1.18.2" pip install "google-generativeai==0.3.2" pip install "google-cloud-aiplatform==1.43.0" pip install pyarrow @@ -439,6 +433,7 @@ jobs: paths: - auth_ui_unit_tests_coverage.xml - auth_ui_unit_tests_coverage + litellm_router_testing: # Runs all tests with the "router" keyword docker: - image: cimg/python:3.11 @@ -469,7 +464,55 @@ jobs: command: | pwd ls - python -m pytest tests/local_testing tests/router_unit_tests --cov=litellm --cov-report=xml -vv -k "router" -x -v --junitxml=test-results/junit.xml --durations=5 + python -m pytest tests/local_testing --cov=litellm --cov-report=xml -vv -k "router" -x -v --junitxml=test-results/junit.xml --durations=5 + no_output_timeout: 120m + - run: + name: Rename the coverage files + command: | + mv coverage.xml litellm_router_coverage.xml + mv .coverage litellm_router_coverage + # Store test results + - store_test_results: + path: test-results + + - persist_to_workspace: + root: . + paths: + - litellm_router_coverage.xml + - litellm_router_coverage + + litellm_router_unit_testing: # Runs all tests with the "router" keyword + docker: + - image: cimg/python:3.11 + auth: + username: ${DOCKERHUB_USERNAME} + password: ${DOCKERHUB_PASSWORD} + working_directory: ~/project + + steps: + - checkout + - setup_google_dns + - run: + name: Install Dependencies + command: | + python -m pip install --upgrade pip + python -m pip install -r requirements.txt + pip install "pytest==7.3.1" + pip install "respx==0.22.0" + pip install "pytest-cov==5.0.0" + pip install "pytest-retry==1.6.3" + pip install "pytest-asyncio==0.21.1" + pip install semantic_router --no-deps + pip install aurelio_sdk --no-deps + pip install "pytest-xdist==3.6.1" + # Run pytest and generate JUnit XML report + - setup_litellm_enterprise_pip + - run: + name: Run tests + command: | + pwd + ls + python -m pytest -vv tests/router_unit_tests --cov=litellm --cov-report=xml -x -s -v --junitxml=test-results/junit.xml --durations=5 no_output_timeout: 120m - run: name: Rename the coverage files @@ -486,11 +529,9 @@ jobs: - litellm_router_coverage.xml - litellm_router_coverage litellm_security_tests: - docker: - - image: cimg/python:3.11 - auth: - username: ${DOCKERHUB_USERNAME} - password: ${DOCKERHUB_PASSWORD} + machine: + image: ubuntu-2204:2023.10.1 + resource_class: xlarge working_directory: ~/project steps: - checkout @@ -499,32 +540,67 @@ jobs: name: Show git commit hash command: | echo "Git commit hash: $CIRCLE_SHA1" + - run: + name: Install Docker CLI (In case it's not already installed) + command: | + sudo apt-get update + sudo apt-get install -y docker-ce docker-ce-cli containerd.io + - run: + name: Install Python 3.13 + command: | + curl https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh --output miniconda.sh + bash miniconda.sh -b -p $HOME/miniconda + export PATH="$HOME/miniconda/bin:$PATH" + conda init bash + source ~/.bashrc + conda create -n myenv python=3.13 -y + conda activate myenv + python --version - run: name: Install Dependencies command: | + pip install "pytest==7.3.1" + pip install "pytest-asyncio==0.21.1" + pip install aiohttp python -m pip install --upgrade pip python -m pip install -r requirements.txt pip install "pytest==7.3.1" pip install "pytest-retry==1.6.3" + pip install "pytest-mock==3.12.0" pip install "pytest-asyncio==0.21.1" + 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 "langfuse>=2.0.0" + pip install "logfire==0.29.0" + pip install numpydoc + pip install prisma + pip install fastapi + pip install jsonschema + pip install "httpx==0.24.1" + pip install "gunicorn==21.2.0" + pip install "anyio==3.7.1" + pip install "aiodynamo==23.10.1" + pip install "asyncio==3.4.3" + pip install "PyGithub==1.59.1" + pip install "openai==1.100.1" pip install "pytest-cov==5.0.0" + pip install "apscheduler" - run: - name: Install Trivy + name: Install dockerize command: | - sudo apt-get update - sudo apt-get install wget apt-transport-https gnupg lsb-release - 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 + 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: Run Trivy scan on LiteLLM Docs + name: Run Security Scans command: | - trivy fs --scanners vuln --dependency-tree --exit-code 1 --severity HIGH,CRITICAL,MEDIUM ./docs/ - - run: - name: Run Trivy scan on LiteLLM UI - command: | - trivy fs --scanners vuln --dependency-tree --exit-code 1 --severity HIGH,CRITICAL,MEDIUM ./ui/ + chmod +x ci_cd/security_scans.sh + ./ci_cd/security_scans.sh - run: name: Run prisma ./docker/entrypoint.sh command: | @@ -586,9 +662,10 @@ jobs: pip install "pytest-retry==1.6.3" pip install "pytest-asyncio==0.21.1" pip install "pytest-cov==5.0.0" - pip install mypy + 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" @@ -967,6 +1044,51 @@ jobs: ls python -m pytest -vv tests/test_litellm --cov=litellm --cov-report=xml -x -s -v --junitxml=test-results/junit-litellm.xml --durations=10 -n 8 no_output_timeout: 120m + - run: + name: Rename the coverage files + command: | + mv coverage.xml litellm_mapped_tests_coverage.xml + mv .coverage litellm_mapped_tests_coverage + + # Store test results + - store_test_results: + path: test-results + - persist_to_workspace: + root: . + paths: + - litellm_mapped_tests_coverage.xml + - litellm_mapped_tests_coverage + litellm_mapped_enterprise_tests: + docker: + - image: cimg/python:3.11 + auth: + username: ${DOCKERHUB_USERNAME} + password: ${DOCKERHUB_PASSWORD} + working_directory: ~/project + + steps: + - checkout + - setup_google_dns + - run: + name: Install Dependencies + command: | + python -m pip install --upgrade pip + python -m pip install -r requirements.txt + pip install "pytest-mock==3.12.0" + pip install "pytest==7.3.1" + pip install "pytest-retry==1.6.3" + pip install "pytest-cov==5.0.0" + pip install "pytest-asyncio==0.21.1" + pip install "respx==0.22.0" + pip install "hypercorn==0.17.3" + pip install "pydantic==2.10.2" + pip install "mcp==1.10.1" + pip install "requests-mock>=1.12.1" + pip install "responses==0.25.7" + pip install "pytest-xdist==3.6.1" + pip install "semantic_router==0.1.10" + pip install "fastapi-offline==1.7.3" + - setup_litellm_enterprise_pip - run: name: Run enterprise tests command: | @@ -1242,6 +1364,7 @@ jobs: pip install jinja2 pip install "tokenizers==0.20.0" pip install "uvloop==0.21.0" + pip install "fastuuid==0.12.0" pip install jsonschema - setup_litellm_enterprise_pip - run: @@ -1374,6 +1497,8 @@ jobs: # - run: python ./tests/documentation_tests/test_general_setting_keys.py - run: python ./tests/code_coverage_tests/check_licenses.py - run: python ./tests/code_coverage_tests/router_code_coverage.py + - run: python ./tests/code_coverage_tests/test_chat_completion_imports.py + - run: python ./tests/code_coverage_tests/info_log_check.py - run: python ./tests/code_coverage_tests/test_ban_set_verbose.py - run: python ./tests/code_coverage_tests/code_qa_check_tests.py - run: python ./tests/code_coverage_tests/test_proxy_types_import.py @@ -1390,6 +1515,7 @@ jobs: - run: python ./tests/code_coverage_tests/prevent_key_leaks_in_exceptions.py - run: python ./tests/code_coverage_tests/check_unsafe_enterprise_import.py - run: python ./tests/code_coverage_tests/ban_copy_deepcopy_kwargs.py + - run: python ./tests/code_coverage_tests/check_fastuuid_usage.py - run: helm lint ./deploy/charts/litellm-helm db_migration_disable_update_check: @@ -1427,6 +1553,7 @@ jobs: docker run -d \ -p 4000:4000 \ -e DATABASE_URL=$PROXY_DATABASE_URL \ + -e DEFAULT_NUM_WORKERS_LITELLM_PROXY=1 \ -e DISABLE_SCHEMA_UPDATE="True" \ -v $(pwd)/litellm/proxy/example_config_yaml/bad_schema.prisma:/app/schema.prisma \ -v $(pwd)/litellm/proxy/example_config_yaml/bad_schema.prisma:/app/litellm/proxy/schema.prisma \ @@ -1503,7 +1630,7 @@ jobs: pip install "pytest-retry==1.6.3" pip install "pytest-mock==3.12.0" pip install "pytest-asyncio==0.21.1" - pip install mypy + pip install "mypy==1.18.2" pip install "google-generativeai==0.3.2" pip install "google-cloud-aiplatform==1.43.0" pip install pyarrow @@ -1542,23 +1669,6 @@ jobs: - run: name: Wait for PostgreSQL to be ready command: dockerize -wait tcp://localhost:5432 -timeout 1m - - run: - name: Install Grype - command: | - curl -sSfL https://raw.githubusercontent.com/anchore/grype/main/install.sh | sudo sh -s -- -b /usr/local/bin - - run: - name: Build and Scan Docker Images - command: | - # Build and scan Dockerfile.database - echo "Building and scanning Dockerfile.database..." - docker build -t litellm-database:latest -f ./docker/Dockerfile.database . - grype litellm-database:latest --fail-on critical - - - # Build and scan main Dockerfile - echo "Building and scanning main Dockerfile..." - docker build -t litellm:latest . - grype litellm:latest --fail-on critical - run: name: Build Docker image command: docker build -t my-app:latest -f ./docker/Dockerfile.database . @@ -1658,7 +1768,7 @@ jobs: pip install "pytest-retry==1.6.3" pip install "pytest-mock==3.12.0" pip install "pytest-asyncio==0.21.1" - pip install mypy + pip install "mypy==1.18.2" pip install "jsonlines==4.0.0" pip install "google-generativeai==0.3.2" pip install "google-cloud-aiplatform==1.43.0" @@ -1709,8 +1819,8 @@ jobs: docker run -d \ -p 4000:4000 \ -e DATABASE_URL=postgresql://postgres:postgres@host.docker.internal:5432/circle_test \ - -e AZURE_API_KEY=$AZURE_BATCHES_API_KEY \ - -e AZURE_API_BASE=$AZURE_BATCHES_API_BASE \ + -e AZURE_API_KEY=$AZURE_API_KEY \ + -e AZURE_API_BASE=$AZURE_API_BASE \ -e AZURE_API_VERSION="2024-05-01-preview" \ -e REDIS_HOST=$REDIS_HOST \ -e REDIS_PASSWORD=$REDIS_PASSWORD \ @@ -1800,7 +1910,7 @@ jobs: pip install "pytest-retry==1.6.3" pip install "pytest-mock==3.12.0" pip install "pytest-asyncio==0.21.1" - pip install mypy + pip install "mypy==1.18.2" pip install "google-generativeai==0.3.2" pip install "google-cloud-aiplatform==1.43.0" pip install pyarrow @@ -1862,6 +1972,7 @@ jobs: -e APORIA_API_BASE_1=$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 \ -e USE_DDTRACE=True \ -e DD_API_KEY=$DD_API_KEY \ -e DD_SITE=$DD_SITE \ @@ -2302,7 +2413,7 @@ jobs: pip install "pytest-retry==1.6.3" pip install "pytest-mock==3.12.0" pip install "pytest-asyncio==0.21.1" - pip install mypy + pip install "mypy==1.18.2" - run: name: Build Docker image command: | @@ -2407,7 +2518,7 @@ jobs: pip install "pytest-mock==3.12.0" pip install "pytest-asyncio==0.21.1" pip install "boto3==1.36.0" - pip install mypy + pip install "mypy==1.18.2" pip install pyarrow pip install numpydoc pip install prisma @@ -2755,8 +2866,8 @@ jobs: source "$NVM_DIR/bash_completion" # Install and use Node version - nvm install v18.17.0 - nvm use v18.17.0 + nvm install v20 + nvm use v20 cd ui/litellm-dashboard @@ -2796,7 +2907,7 @@ jobs: pip install "pytest==7.3.1" pip install "pytest-mock==3.12.0" pip install "pytest-asyncio==0.21.1" - pip install mypy + pip install "mypy==1.18.2" pip install pyarrow pip install numpydoc pip install prisma @@ -2809,7 +2920,26 @@ jobs: name: Install Playwright Browsers command: | npx playwright install + - run: + name: Run UI unit tests (Vitest) + command: | + # Use Node 20 (several deps require >=20) + export NVM_DIR="/opt/circleci/.nvm" + source "$NVM_DIR/nvm.sh" + nvm install 20 + nvm use 20 + cd ui/litellm-dashboard + npm ci || npm install + + # CI run, with both LCOV (Codecov) and HTML (artifact you can click) + CI=true npm run test -- --run --coverage \ + --coverage.provider=v8 \ + --coverage.reporter=lcov \ + --coverage.reporter=html \ + --coverage.reportsDirectory=coverage/html + + - run: name: Build Docker image command: docker build -t my-app:latest -f ./docker/Dockerfile.database . @@ -2912,6 +3042,7 @@ jobs: command: | docker run --name my-app \ -p 4000:4000 \ + -e DEFAULT_NUM_WORKERS_LITELLM_PROXY=1 \ -e DATABASE_URL="postgresql://wrong:wrong@wrong:5432/wrong" \ myapp:latest \ --port 4000 > docker_output.log 2>&1 || true @@ -2982,6 +3113,12 @@ workflows: only: - main - /litellm_.*/ + - litellm_router_unit_testing: + filters: + branches: + only: + - main + - /litellm_.*/ - check_code_and_doc_quality: filters: branches: @@ -3078,6 +3215,12 @@ workflows: only: - main - /litellm_.*/ + - litellm_mapped_enterprise_tests: + filters: + branches: + only: + - main + - /litellm_.*/ - litellm_mapped_tests: filters: branches: @@ -3122,12 +3265,14 @@ workflows: - guardrails_testing - llm_responses_api_testing - litellm_mapped_tests + - litellm_mapped_enterprise_tests - batches_testing - litellm_utils_testing - pass_through_unit_testing - image_gen_testing - logging_testing - litellm_router_testing + - litellm_router_unit_testing - caching_unit_tests - litellm_proxy_unit_testing - litellm_security_tests @@ -3181,12 +3326,14 @@ workflows: - google_generate_content_endpoint_testing - llm_responses_api_testing - litellm_mapped_tests + - litellm_mapped_enterprise_tests - batches_testing - litellm_utils_testing - pass_through_unit_testing - image_gen_testing - logging_testing - litellm_router_testing + - litellm_router_unit_testing - caching_unit_tests - langfuse_logging_unit_tests - litellm_assistants_api_testing diff --git a/.circleci/requirements.txt b/.circleci/requirements.txt index f41a5291e50..8e0f1dfe7e9 100644 --- a/.circleci/requirements.txt +++ b/.circleci/requirements.txt @@ -14,4 +14,5 @@ google-cloud-iam==2.19.1 fastapi-sso==0.16.0 uvloop==0.21.0 mcp==1.10.1 # for MCP server -semantic_router==0.1.10 # for auto-routing with litellm \ No newline at end of file +semantic_router==0.1.10 # for auto-routing with litellm +fastuuid==0.12.0 \ No newline at end of file diff --git a/.github/workflows/auto_update_price_and_context_window_file.py b/.github/workflows/auto_update_price_and_context_window_file.py index 3e0731b94bd..461d8d347d9 100644 --- a/.github/workflows/auto_update_price_and_context_window_file.py +++ b/.github/workflows/auto_update_price_and_context_window_file.py @@ -43,8 +43,8 @@ def write_to_file(file_path, data): # Print an error message if writing to file fails print("Error updating JSON file:", e) -# Update the existing models and add the missing models -def transform_remote_data(data): +# Update the existing models and add the missing models for OpenRouter +def transform_openrouter_data(data): transformed = {} for row in data: # Add the fields 'max_tokens' and 'input_cost_per_token' @@ -81,6 +81,34 @@ def transform_remote_data(data): return transformed +# Update the existing models and add the missing models for Vercel AI Gateway +def transform_vercel_ai_gateway_data(data): + transformed = {} + for row in data: + obj = { + "max_tokens": row["context_window"], + "input_cost_per_token": float(row["pricing"]["input"]), + "output_cost_per_token": float(row["pricing"]["output"]), + 'max_output_tokens': row['max_tokens'], + 'max_input_tokens': row["context_window"], + } + + # Handle cache pricing if available + if "pricing" in row: + if "input_cache_read" in row["pricing"] and row["pricing"]["input_cache_read"] is not None: + obj['cache_read_input_token_cost'] = float(f"{float(row['pricing']['input_cache_read']):e}") + + if "input_cache_write" in row["pricing"] and row["pricing"]["input_cache_write"] is not None: + obj['cache_creation_input_token_cost'] = float(f"{float(row['pricing']['input_cache_write']):e}") + + mode = "embedding" if "embedding" in row["id"].lower() else "chat" + + obj.update({"litellm_provider": "vercel_ai_gateway", "mode": mode}) + + transformed[f'vercel_ai_gateway/{row["id"]}'] = obj + + return transformed + # Load local data from a specified file def load_local_data(file_path): @@ -100,22 +128,32 @@ def load_local_data(file_path): def main(): local_file_path = "model_prices_and_context_window.json" # Path to the local data file - url = "https://openrouter.ai/api/v1/models" # URL to fetch remote data + openrouter_url = "https://openrouter.ai/api/v1/models" # URL to fetch OpenRouter data + vercel_ai_gateway_url = "https://ai-gateway.vercel.sh/v1/models" # URL to fetch Vercel AI Gateway data # Load local data from file local_data = load_local_data(local_file_path) - # Fetch remote data asynchronously - remote_data = asyncio.run(fetch_data(url)) - # Transform the fetched remote data - remote_data = transform_remote_data(remote_data) + + # Fetch OpenRouter data + openrouter_data = asyncio.run(fetch_data(openrouter_url)) + # Transform the fetched OpenRouter data + openrouter_data = transform_openrouter_data(openrouter_data) + + # Fetch Vercel AI Gateway data + vercel_data = asyncio.run(fetch_data(vercel_ai_gateway_url)) + # Transform the fetched Vercel AI Gateway data + vercel_data = transform_vercel_ai_gateway_data(vercel_data) + + # Combine both datasets + all_remote_data = {**openrouter_data, **vercel_data} - # If both local and remote data are available, synchronize and save - if local_data and remote_data: - sync_local_data_with_remote(local_data, remote_data) + # If both local and openrouter data are available, synchronize and save + if local_data and all_remote_data: + sync_local_data_with_remote(local_data, all_remote_data) write_to_file(local_file_path, local_data) else: print("Failed to fetch model data from either local file or URL.") # Entry point of the script if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/.github/workflows/test-litellm.yml b/.github/workflows/test-litellm.yml index 7e67aee8d73..0d3a9f2b5d4 100644 --- a/.github/workflows/test-litellm.yml +++ b/.github/workflows/test-litellm.yml @@ -31,6 +31,7 @@ jobs: 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" - name: Setup litellm-enterprise as local package run: | diff --git a/.github/workflows/test-mcp.yml b/.github/workflows/test-mcp.yml new file mode 100644 index 00000000000..2da6980951a --- /dev/null +++ b/.github/workflows/test-mcp.yml @@ -0,0 +1,48 @@ +name: LiteLLM MCP Tests (folder - tests/mcp_tests) + +on: + pull_request: + branches: [ main ] + +jobs: + test: + runs-on: ubuntu-latest + timeout-minutes: 25 + + steps: + - uses: actions/checkout@v4 + + - 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: Install Poetry + uses: snok/install-poetry@v1 + + - name: Install dependencies + run: | + 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==2.10.2" + poetry run pip install "mcp==1.10.1" + poetry run pip install pytest-xdist + + - name: Setup litellm-enterprise as local package + run: | + cd enterprise + python -m pip install -e . + cd .. + + - 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/.gitignore b/.gitignore index ed8c88c8990..c2ac5137cbe 100644 --- a/.gitignore +++ b/.gitignore @@ -95,4 +95,5 @@ test.py litellm_config.yaml .cursor .vscode/launch.json -litellm/proxy/to_delete_loadtest_work/* \ No newline at end of file +litellm/proxy/to_delete_loadtest_work/* +update_model_cost_map.py diff --git a/Dockerfile b/Dockerfile index addc109e10c..6ab78d85e33 100644 --- a/Dockerfile +++ b/Dockerfile @@ -15,7 +15,7 @@ USER root RUN apk add --no-cache gcc python3-dev openssl openssl-dev -RUN pip install --upgrade pip && \ +RUN pip install --upgrade pip>=24.3.1 && \ pip install build # Copy the current directory contents into the container at /app @@ -41,9 +41,6 @@ RUN pip uninstall jwt -y RUN pip uninstall PyJWT -y RUN pip install PyJWT==2.9.0 --no-cache-dir -# Build Admin UI -RUN chmod +x docker/build_admin_ui.sh && ./docker/build_admin_ui.sh - # Runtime stage FROM $LITELLM_RUNTIME_IMAGE AS runtime @@ -53,6 +50,9 @@ USER root # Install runtime dependencies RUN apk add --no-cache openssl tzdata +# Upgrade pip to fix CVE-2025-8869 +RUN pip install --upgrade pip>=24.3.1 + WORKDIR /app # Copy the current directory contents into the container at /app COPY . . diff --git a/Makefile b/Makefile index edeb27bac3f..159fe4fa2ef 100644 --- a/Makefile +++ b/Makefile @@ -48,7 +48,7 @@ install-test-deps: install-proxy-dev cd enterprise && python -m pip install -e . && cd .. install-helm-unittest: - helm plugin install https://github.com/helm-unittest/helm-unittest --version v0.4.4 + helm plugin install https://github.com/helm-unittest/helm-unittest --version v0.4.4 || echo "ignore error if plugin exists" # Formatting format: install-dev diff --git a/README.md b/README.md index 45f0bbe1395..0918d2b1fa4 100644 --- a/README.md +++ b/README.md @@ -25,7 +25,7 @@ Discord - + Slack @@ -37,7 +37,7 @@ LiteLLM manages: - Retry/fallback logic across multiple deployments (e.g. Azure/OpenAI) - [Router](https://docs.litellm.ai/docs/routing) - Set Budgets & Rate limits per project, api key, model [LiteLLM Proxy Server (LLM Gateway)](https://docs.litellm.ai/docs/simple_proxy) -[**Jump to LiteLLM Proxy (LLM Gateway) Docs**](https://github.com/BerriAI/litellm?tab=readme-ov-file#openai-proxy---docs)
+[**Jump to LiteLLM Proxy (LLM Gateway) Docs**](https://github.com/BerriAI/litellm?tab=readme-ov-file#litellm-proxy-server-llm-gateway---docs)
[**Jump to Supported LLM Providers**](https://github.com/BerriAI/litellm?tab=readme-ov-file#supported-providers-docs) 🚨 **Stable Release:** Use docker images with the `-stable` tag. These have undergone 12 hour load tests, before being published. [More information about the release cycle here](https://docs.litellm.ai/docs/proxy/release_cycle) @@ -316,6 +316,7 @@ curl 'http://0.0.0.0:4000/key/generate' \ | [google AI Studio - gemini](https://docs.litellm.ai/docs/providers/gemini) | ✅ | ✅ | ✅ | ✅ | | | | [mistral ai api](https://docs.litellm.ai/docs/providers/mistral) | ✅ | ✅ | ✅ | ✅ | ✅ | | | [cloudflare AI Workers](https://docs.litellm.ai/docs/providers/cloudflare_workers) | ✅ | ✅ | ✅ | ✅ | | | +| [CompactifAI](https://docs.litellm.ai/docs/providers/compactifai) | ✅ | ✅ | ✅ | ✅ | | | | [cohere](https://docs.litellm.ai/docs/providers/cohere) | ✅ | ✅ | ✅ | ✅ | ✅ | | | [anthropic](https://docs.litellm.ai/docs/providers/anthropic) | ✅ | ✅ | ✅ | ✅ | | | | [empower](https://docs.litellm.ai/docs/providers/empower) | ✅ | ✅ | ✅ | ✅ | @@ -344,16 +345,26 @@ curl 'http://0.0.0.0:4000/key/generate' \ | [Novita AI](https://novita.ai/models/llm?utm_source=github_litellm&utm_medium=github_readme&utm_campaign=github_link) | ✅ | ✅ | ✅ | ✅ | | | | [Featherless AI](https://docs.litellm.ai/docs/providers/featherless_ai) | ✅ | ✅ | ✅ | ✅ | | | | [Nebius AI Studio](https://docs.litellm.ai/docs/providers/nebius) | ✅ | ✅ | ✅ | ✅ | ✅ | | +| [Heroku](https://docs.litellm.ai/docs/providers/heroku) | ✅ | ✅ | | | | | +| [OVHCloud AI Endpoints](https://docs.litellm.ai/docs/providers/ovhcloud) | ✅ | ✅ | | | | | [**Read the Docs**](https://docs.litellm.ai/docs/) -## Contributing +## Run in Developer mode +### Services +1. Setup .env file in root +2. Run dependant services `docker-compose up db prometheus` -Interested in contributing? Contributions to LiteLLM Python SDK, Proxy Server, and LLM integrations are both accepted and highly encouraged! +### Backend +1. (In root) create virtual environment `python -m venv .venv` +2. Activate virtual environment `source .venv/bin/activate` +3. Install dependencies `pip install -e ".[all]"` +4. Start proxy backend `python litellm/proxy_cli.py` -**Quick start:** `git clone` → `make install-dev` → `make format` → `make lint` → `make test-unit` - -See our comprehensive [Contributing Guide (CONTRIBUTING.md)](CONTRIBUTING.md) for detailed instructions. +### Frontend +1. Navigate to `ui/litellm-dashboard` +2. Install dependencies `npm install` +3. Run `npm run dev` to start the dashboard # Enterprise For companies that need better security, user management and professional support @@ -407,7 +418,7 @@ 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://join.slack.com/share/enQtOTE0ODczMzk2Nzk4NC01YjUxNjY2YjBlYTFmNDRiZTM3NDFiYTM3MzVkODFiMDVjOGRjMmNmZTZkZTMzOWQzZGQyZWIwYjQ0MWExYmE3) +- [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 @@ -431,18 +442,3 @@ All these checks must pass before your PR can be merged. -## Run in Developer mode -### Services -1. Setup .env file in root -2. Run dependant services `docker-compose up db prometheus` - -### Backend -1. (In root) create virtual environment `python -m venv .venv` -2. Activate virtual environment `source .venv/bin/activate` -3. Install dependencies `pip install -e ".[all]"` -4. Start proxy backend `python3 /path/to/litellm/proxy_cli.py` - -### Frontend -1. Navigate to `ui/litellm-dashboard` -2. Install dependencies `npm install` -3. Run `npm run dev` to start the dashboard diff --git a/ci_cd/security_scans.sh b/ci_cd/security_scans.sh new file mode 100755 index 00000000000..897589c0a41 --- /dev/null +++ b/ci_cd/security_scans.sh @@ -0,0 +1,105 @@ +#!/bin/bash + +# Security Scans Script for LiteLLM +# This script runs comprehensive security scans including Trivy and Grype + +set -e + +echo "Starting security scans for LiteLLM..." + +# Function to install Trivy and required tools +install_trivy() { + echo "Installing Trivy and required tools..." + sudo apt-get update + sudo apt-get install -y wget apt-transport-https gnupg lsb-release jq curl + 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" +} + +# Function to install Grype +install_grype() { + echo "Installing Grype..." + curl -sSfL https://raw.githubusercontent.com/anchore/grype/main/install.sh | sudo sh -s -- -b /usr/local/bin + echo "Grype installed successfully" +} + +# Function to run Trivy scans +run_trivy_scans() { + echo "Running Trivy scans..." + + echo "Scanning LiteLLM Docs..." + trivy fs --scanners vuln --dependency-tree --exit-code 1 --severity HIGH,CRITICAL,MEDIUM ./docs/ + + echo "Scanning LiteLLM UI..." + trivy fs --scanners vuln --dependency-tree --exit-code 1 --severity HIGH,CRITICAL,MEDIUM ./ui/ + + echo "Trivy scans completed successfully" +} + +# Function to build and scan Docker images with Grype +run_grype_scans() { + echo "Running Grype scans..." + + # Temporarily add wheel files to .dockerignore for security scans + echo "Temporarily modifying .dockerignore to exclude problematic wheel files..." + cp .dockerignore .dockerignore.backup 2>/dev/null || touch .dockerignore.backup + echo "/*.whl" >> .dockerignore + + # Build and scan Dockerfile.database + echo "Building and scanning Dockerfile.database..." + docker build --no-cache -t litellm-database:latest -f ./docker/Dockerfile.database . + grype litellm-database:latest --fail-on critical + + # Build and scan main Dockerfile + echo "Building and scanning main Dockerfile..." + docker build --no-cache -t litellm:latest . + grype litellm:latest --fail-on critical + + # Restore original .dockerignore + echo "Restoring original .dockerignore..." + mv .dockerignore.backup .dockerignore + + # Scan the locally built LiteLLM image for vulnerabilities with CVSS >= 4.0 + echo "Scanning locally built LiteLLM image for high-severity vulnerabilities..." + echo "Using locally built image: litellm:latest" + + # Run grype scan and check for vulnerabilities with CVSS >= 4.0 + echo "Checking for vulnerabilities with CVSS score >= 4.0..." + HIGH_SEVERITY_COUNT=$(grype litellm:latest -o json | jq -r '.matches[] | select(.vulnerability.cvss[]?.metrics.baseScore >= 4.0) | .vulnerability.id' | wc -l) + + if [ "$HIGH_SEVERITY_COUNT" -gt 0 ]; then + echo "ERROR: Found $HIGH_SEVERITY_COUNT vulnerabilities with CVSS score >= 4.0 in litellm:latest" + echo "Detailed vulnerability report:" + grype litellm:latest -o json | jq -r ' + ["Package", "Version", "Vulnerability ID", "CVSS Score", "Severity", "Fix Version", "Description"], + (.matches[] | select(.vulnerability.cvss[]?.metrics.baseScore >= 4.0) | + [.artifact.name, .artifact.version, .vulnerability.id, .vulnerability.cvss[0].metrics.baseScore, .vulnerability.severity, (.vulnerability.fix.versions[0] // "No fix available"), .vulnerability.description]) | + @tsv' | column -t -s $'\t' + exit 1 + else + echo "No high-severity vulnerabilities (CVSS >= 4.0) found in litellm:latest" + fi + + echo "Grype scans completed successfully" +} + +# Main execution +main() { + echo "Installing security scanning tools..." + install_trivy + install_grype + + echo "Running filesystem vulnerability scans..." + run_trivy_scans + + echo "Running Docker image vulnerability scans..." + run_grype_scans + + echo "All security scans completed successfully!" +} + +# Execute main function +main "$@" diff --git a/ci_cd/security_scans_readme.md b/ci_cd/security_scans_readme.md new file mode 100644 index 00000000000..dd64b01c296 --- /dev/null +++ b/ci_cd/security_scans_readme.md @@ -0,0 +1,9 @@ +# Security Scans + +## Scans that run: + +- Trivy scan on `./docs/` (HIGH/CRITICAL/MEDIUM) +- Trivy scan on `./ui/` (HIGH/CRITICAL/MEDIUM) +- Grype scan on `Dockerfile.database` (fails on CRITICAL) +- Grype scan on main `Dockerfile` (fails on CRITICAL) +- Grype CVSS ≥ 4.0 scan on main `Dockerfile` (fails any vulnerabilities with CVSS ≥ 4.0) diff --git a/cookbook/litellm_proxy_server/batch_api/bedrock/bedrock.py b/cookbook/litellm_proxy_server/batch_api/bedrock/bedrock.py new file mode 100644 index 00000000000..615baa422eb --- /dev/null +++ b/cookbook/litellm_proxy_server/batch_api/bedrock/bedrock.py @@ -0,0 +1,25 @@ +from openai import OpenAI + +client = OpenAI( + base_url="http://0.0.0.0:4000", + api_key="sk-1234", +) + +BEDROCK_BATCH_MODEL = "bedrock/batch-anthropic.claude-3-5-sonnet-20240620-v1:0" + +# Upload file +batch_input_file = client.files.create( + file=open("./bedrock_batch_completions.jsonl", "rb"), + purpose="batch", + extra_body={"target_model_names": BEDROCK_BATCH_MODEL} +) +print(batch_input_file) + +# Create batch +batch = client.batches.create( + input_file_id=batch_input_file.id, + endpoint="/v1/chat/completions", + completion_window="24h", + metadata={"description": "Test batch job"}, +) +print(batch) \ No newline at end of file diff --git a/cookbook/litellm_proxy_server/batch_api/bedrock/bedrock_batch_completions.jsonl b/cookbook/litellm_proxy_server/batch_api/bedrock/bedrock_batch_completions.jsonl new file mode 100644 index 00000000000..adef9ac2dd5 --- /dev/null +++ b/cookbook/litellm_proxy_server/batch_api/bedrock/bedrock_batch_completions.jsonl @@ -0,0 +1,128 @@ +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": 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"url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} diff --git a/cookbook/litellm_proxy_server/cli_token_usage.py b/cookbook/litellm_proxy_server/cli_token_usage.py new file mode 100644 index 00000000000..6ee5555695e --- /dev/null +++ b/cookbook/litellm_proxy_server/cli_token_usage.py @@ -0,0 +1,62 @@ +#!/usr/bin/env python3 +""" +Example: Using CLI token with LiteLLM SDK + +This example shows how to use the CLI authentication token +in your Python scripts after running `litellm-proxy login`. +""" + +from textwrap import indent +import litellm +LITELLM_BASE_URL = "http://localhost:4000/" + + +def main(): + """Using CLI token with LiteLLM SDK""" + print("🚀 Using CLI Token with LiteLLM SDK") + print("=" * 40) + #litellm._turn_on_debug() + + # Get the CLI token + api_key = litellm.get_litellm_gateway_api_key() + + if not api_key: + print("❌ No CLI token found. Please run 'litellm-proxy login' first.") + return + + print("✅ Found CLI token.") + + available_models = litellm.get_valid_models( + check_provider_endpoint=True, + custom_llm_provider="litellm_proxy", + api_key=api_key, + api_base=LITELLM_BASE_URL + ) + + print("✅ Available models:") + if available_models: + for i, model in enumerate(available_models, 1): + print(f" {i:2d}. {model}") + else: + print(" No models available") + + # Use with LiteLLM + try: + response = litellm.completion( + model="litellm_proxy/gemini/gemini-2.5-flash", + messages=[{"role": "user", "content": "Hello from CLI token!"}], + api_key=api_key, + base_url=LITELLM_BASE_URL + ) + print(f"✅ LLM Response: {response.model_dump_json(indent=4)}") + except Exception as e: + print(f"❌ Error: {e}") + + +if __name__ == "__main__": + main() + + print("\n💡 Tips:") + print("1. Run 'litellm-proxy login' to authenticate first") + print("2. Replace 'https://your-proxy.com' with your actual proxy URL") + print("3. The token is stored locally at ~/.litellm/token.json") diff --git a/cookbook/litellm_proxy_server/mcp/mcp_with_litellm_proxy.py b/cookbook/litellm_proxy_server/mcp/mcp_with_litellm_proxy.py new file mode 100644 index 00000000000..351b0920eb8 --- /dev/null +++ b/cookbook/litellm_proxy_server/mcp/mcp_with_litellm_proxy.py @@ -0,0 +1,36 @@ +""" +Use LiteLLM Proxy MCP Gateway to call MCP tools. + +When using LiteLLM Proxy, you can use the same MCP tools across all your LLM providers. +""" +import openai + +client = openai.OpenAI( + api_key="sk-1234", # paste your litellm proxy api key here + base_url="http://localhost:4000" # paste your litellm proxy base url here +) +print("Making API request to Responses API with MCP tools") + +response = client.responses.create( + model="gpt-5", + input=[ + { + "role": "user", + "content": "give me TLDR of what BerriAI/litellm repo is about", + "type": "message" + } + ], + tools=[ + { + "type": "mcp", + "server_label": "litellm", + "server_url": "litellm_proxy", + "require_approval": "never" + } + ], + stream=True, + tool_choice="required" +) + +for chunk in response: + print("response chunk: ", chunk) diff --git a/cookbook/litellm_router_load_test/memory_usage/router_endpoint.py b/cookbook/litellm_router_load_test/memory_usage/router_endpoint.py index 689f105bc5f..1dc2d914857 100644 --- a/cookbook/litellm_router_load_test/memory_usage/router_endpoint.py +++ b/cookbook/litellm_router_load_test/memory_usage/router_endpoint.py @@ -5,7 +5,7 @@ import os import litellm from litellm import Router from dotenv import load_dotenv -import uuid +from litellm._uuid import uuid load_dotenv() diff --git a/cookbook/litellm_router_load_test/memory_usage/router_memory_usage copy.py b/cookbook/litellm_router_load_test/memory_usage/router_memory_usage copy.py index a8aa506e8a2..76d5d3913f5 100644 --- a/cookbook/litellm_router_load_test/memory_usage/router_memory_usage copy.py +++ b/cookbook/litellm_router_load_test/memory_usage/router_memory_usage copy.py @@ -12,7 +12,7 @@ sys.path.insert( import litellm from litellm import Router from dotenv import load_dotenv -import uuid +from litellm._uuid import uuid load_dotenv() diff --git a/cookbook/litellm_router_load_test/memory_usage/router_memory_usage.py b/cookbook/litellm_router_load_test/memory_usage/router_memory_usage.py index a8aa506e8a2..76d5d3913f5 100644 --- a/cookbook/litellm_router_load_test/memory_usage/router_memory_usage.py +++ b/cookbook/litellm_router_load_test/memory_usage/router_memory_usage.py @@ -12,7 +12,7 @@ sys.path.insert( import litellm from litellm import Router from dotenv import load_dotenv -import uuid +from litellm._uuid import uuid load_dotenv() diff --git a/cookbook/misc/RELEASE_NOTES_GENERATION_INSTRUCTIONS.md b/cookbook/misc/RELEASE_NOTES_GENERATION_INSTRUCTIONS.md new file mode 100644 index 00000000000..b0a24ef28e9 --- /dev/null +++ b/cookbook/misc/RELEASE_NOTES_GENERATION_INSTRUCTIONS.md @@ -0,0 +1,320 @@ +# LiteLLM Release Notes Generation Instructions + +This document provides comprehensive instructions for AI agents to generate release notes for LiteLLM following the established format and style. + +## Required Inputs + +1. **Release Version** (e.g., `v1.77.3-stable`) +2. **PR Diff/Changelog** - List of PRs with titles and contributors +3. **Previous Version Commit Hash** - To compare model pricing changes +4. **Reference Release Notes** - Use recent stable releases (v1.76.3-stable, v1.77.2-stable) as templates for consistent formatting + +## Step-by-Step Process + +### 1. Initial Setup and Analysis + +```bash +# Check git diff for model pricing changes +git diff HEAD -- model_prices_and_context_window.json +``` + +**Key Analysis Points:** +- New models added (look for new entries) +- Deprecated models removed (look for deleted entries) +- Pricing updates (look for cost changes) +- Feature support changes (tool calling, reasoning, etc.) + +### 2. Release Notes Structure + +Follow this exact structure based on recent stable releases (v1.76.3-stable, v1.77.2-stable): + +```markdown +--- +title: "v1.77.X-stable - [Key Theme]" +slug: "v1-77-X" +date: YYYY-MM-DDTHH:mm:ss +authors: [standard author block] +hide_table_of_contents: false +--- + +## Deploy this version +[Docker and pip installation tabs] + +## Key Highlights +[3-5 bullet points of major features] + +## New Models / Updated Models +#### New Model Support +[Model pricing table] + +#### Features +[Provider-specific features organized by provider] + +### Bug Fixes +[Provider-specific bug fixes organized by provider] + +#### New Provider Support +[New provider integrations] + +## LLM API Endpoints +#### Features +[API-specific features organized by API type] + +#### Bugs +[General bug fixes] + +## Management Endpoints / UI +#### Features +[UI and management features] + +#### Bugs +[Management-related bug fixes] + +## Logging / Guardrail Integrations +#### Features +[Organized by integration provider with proper doc links] + +#### Guardrails +[Guardrail-specific features and fixes] + +#### New Integration +[Major new integrations] + +## Performance / Loadbalancing / Reliability improvements +[Infrastructure improvements] + +## General Proxy Improvements +[Other proxy-related changes] + +## New Contributors +[List of first-time contributors] + +## Full Changelog +[Link to GitHub comparison] +``` + +### 3. Categorization Rules + +**Performance Improvements:** +- RPS improvements +- Memory optimizations +- CPU usage optimizations +- Timeout controls +- Worker configuration + +**New Models/Updated Models:** +- Extract from model_prices_and_context_window.json diff +- Create tables with: Provider, Model, Context Window, Input Cost, Output Cost, Features +- **Structure:** + - `#### New Model Support` - pricing table + - `#### Features` - organized by provider with documentation links + - `### Bug Fixes` - provider-specific bug fixes + - `#### New Provider Support` - major new provider integrations +- Group by provider with proper doc links: `**[Provider Name](../../docs/providers/[provider])**` +- Use bullet points under each provider for multiple features +- Separate features from bug fixes clearly + +**LLM API Endpoints:** +- **Structure:** + - `#### Features` - organized by API type (Responses API, Batch API, etc.) + - `#### Bugs` - general bug fixes under **General** category +- **API Categories:** + - Responses API + - Batch API + - CountTokens API + - Images API + - Video Generation (if applicable) + - General (miscellaneous improvements) +- Use proper documentation links for each API type + +**UI/Management:** +- Authentication changes +- Dashboard improvements +- Team management +- Key management + +**Logging / Guardrail Integrations:** +- **Structure:** + - `#### Features` - organized by integration provider with proper doc links + - `#### Guardrails` - guardrail-specific features and fixes + - `#### New Integration` - major new integrations +- **Integration Categories:** + - **[DataDog](../../docs/proxy/logging#datadog)** - group all DataDog-related changes + - **[Langfuse](../../docs/proxy/logging#langfuse)** - Langfuse-specific features + - **[Prometheus](../../docs/proxy/logging#prometheus)** - monitoring improvements + - **[PostHog](../../docs/observability/posthog)** - observability integration + - Other logging providers with proper doc links +- Use bullet points under each provider for multiple features +- Separate logging features from guardrails clearly + +### 4. Documentation Linking Strategy + +**Link to docs when:** +- New provider support added +- Significant feature additions +- API endpoint changes +- Integration additions + +**Link format:** `../../docs/[category]/[specific_doc]` + +**Common doc paths:** +- `../../docs/providers/[provider]` - Provider-specific docs +- `../../docs/image_generation` - Image generation +- `../../docs/video_generation` - Video generation (if exists) +- `../../docs/response_api` - Responses API +- `../../docs/proxy/logging` - Logging integrations +- `../../docs/proxy/guardrails` - Guardrails +- `../../docs/pass_through/[provider]` - Passthrough endpoints + +### 5. Model Table Generation + +From git diff analysis, create tables like: + +```markdown +| Provider | Model | Context Window | Input ($/1M tokens) | Output ($/1M tokens) | Features | +| -------- | ----- | -------------- | ------------------- | -------------------- | -------- | +| OpenRouter | `openrouter/openai/gpt-4.1` | 1M | $2.00 | $8.00 | Chat completions with vision | +``` + +**Extract from JSON:** +- `max_input_tokens` → Context Window +- `input_cost_per_token` × 1,000,000 → Input cost +- `output_cost_per_token` × 1,000,000 → Output cost +- `supports_*` fields → Features +- Special pricing fields (per image, per second) for generation models + +### 6. PR Categorization Logic + +**By Keywords in PR Title:** +- `[Perf]`, `Performance`, `RPS` → Performance Improvements +- `[Bug]`, `[Bug Fix]`, `Fix` → Bug Fixes section +- `[Feat]`, `[Feature]`, `Add support` → Features section +- `[Docs]` → Documentation (usually exclude from main sections) +- Provider names (Gemini, OpenAI, etc.) → Group under provider + +**By PR Content Analysis:** +- New model additions → New Models section +- UI changes → Management Endpoints/UI +- Logging/observability → Logging/Guardrail Integrations +- Rate limiting/budgets → Performance/Reliability +- Authentication → Management Endpoints + +### 7. Writing Style Guidelines + +**Tone:** +- Professional but accessible +- Focus on user impact +- Highlight breaking changes clearly +- Use active voice + +**Formatting:** +- Use consistent markdown formatting +- Include PR links: `[PR #XXXXX](https://github.com/BerriAI/litellm/pull/XXXXX)` +- Use code blocks for configuration examples +- Bold important terms and section headers + +**Warnings/Notes:** +- Add warning boxes for breaking changes +- Include migration instructions when needed +- Provide override options for default changes + +### 8. Quality Checks + +**Before finalizing:** +- Verify all PR links work +- Check documentation links are valid +- Ensure model pricing is accurate +- Confirm provider names are consistent +- Review for typos and formatting issues + +### 9. Common Patterns to Follow + +**Performance Changes:** +```markdown +- **+400 RPS Performance Boost** - Description - [PR #XXXXX](link) +``` + +**New Models:** +Always include pricing table and feature highlights + +**Breaking Changes:** +```markdown +:::warning +This release has a known issue... +::: +``` + +**Provider Features (New Models / Updated Models section):** +```markdown +#### Features + +- **[Provider Name](../../docs/providers/provider)** + - Feature description - [PR #XXXXX](link) + - Another feature description - [PR #YYYYY](link) +``` + +**API Features (LLM API Endpoints section):** +```markdown +#### Features + +- **[API Name](../../docs/api_path)** + - Feature description - [PR #XXXXX](link) + - Another feature - [PR #YYYYY](link) +- **General** + - Miscellaneous improvements - [PR #ZZZZZ](link) +``` + +**Integration Features (Logging / Guardrail Integrations section):** +```markdown +#### Features + +- **[Integration Name](../../docs/proxy/logging#integration)** + - Feature description - [PR #XXXXX](link) + - Bug fix description - [PR #YYYYY](link) +``` + +**Bug Fixes Pattern:** +```markdown +### Bug Fixes + +- **[Provider/Component Name](../../docs/providers/provider)** + - Bug fix description - [PR #XXXXX](link) +``` + +### 10. Missing Documentation Check + +**Review for missing docs:** +- New providers without documentation +- New API endpoints without examples +- Complex features without guides +- Integration setup instructions + +**Flag for documentation needs:** +- New provider integrations +- Significant API changes +- Complex configuration options +- Migration requirements + +## Example Command Workflow + +```bash +# 1. Get model changes +git diff HEAD -- model_prices_and_context_window.json + +# 2. Analyze PR list for categorization +# 3. Create release notes following template +# 4. Link to appropriate documentation +# 5. Review for missing documentation needs +``` + +## Output Requirements + +- Follow exact markdown structure from reference +- Include all PR links and contributors +- Provide accurate model pricing tables +- Link to relevant documentation +- Highlight breaking changes with warnings +- Include deployment instructions +- End with full changelog link + +This process ensures consistent, comprehensive release notes that help users understand changes and upgrade smoothly. diff --git a/cookbook/veo_video_generation.py b/cookbook/veo_video_generation.py new file mode 100644 index 00000000000..64a7207feb1 --- /dev/null +++ b/cookbook/veo_video_generation.py @@ -0,0 +1,311 @@ +#!/usr/bin/env python3 +""" +Complete example for Veo video generation through LiteLLM proxy. + +This script demonstrates how to: +1. Generate videos using Google's Veo model +2. Poll for completion status +3. Download the generated video file + +Requirements: +- LiteLLM proxy running with Google AI Studio pass-through configured +- Google AI Studio API key with Veo access +""" + +import json +import os +import time +import requests +from typing import Optional + + +class VeoVideoGenerator: + """Complete Veo video generation client using LiteLLM proxy.""" + + def __init__(self, base_url: str = "http://localhost:4000/gemini/v1beta", + api_key: str = "sk-1234"): + """ + Initialize the Veo video generator. + + Args: + base_url: Base URL for the LiteLLM proxy with Gemini pass-through + api_key: API key for LiteLLM proxy authentication + """ + self.base_url = base_url + self.api_key = api_key + self.headers = { + "x-goog-api-key": api_key, + "Content-Type": "application/json" + } + + def generate_video(self, prompt: str) -> Optional[str]: + """ + Initiate video generation with Veo. + + Args: + prompt: Text description of the video to generate + + Returns: + Operation name if successful, None otherwise + """ + print(f"🎬 Generating video with prompt: '{prompt}'") + + url = f"{self.base_url}/models/veo-3.0-generate-preview:predictLongRunning" + payload = { + "instances": [{ + "prompt": prompt + }] + } + + try: + response = requests.post(url, headers=self.headers, json=payload) + response.raise_for_status() + + data = response.json() + operation_name = data.get("name") + + if operation_name: + print(f"✅ Video generation started: {operation_name}") + return operation_name + else: + print("❌ No operation name returned") + print(f"Response: {json.dumps(data, indent=2)}") + return None + + except requests.RequestException as e: + print(f"❌ Failed to start video generation: {e}") + if hasattr(e, 'response') and e.response is not None: + try: + error_data = e.response.json() + print(f"Error details: {json.dumps(error_data, indent=2)}") + except: + print(f"Error response: {e.response.text}") + return None + + def wait_for_completion(self, operation_name: str, max_wait_time: int = 600) -> Optional[str]: + """ + Poll operation status until video generation is complete. + + Args: + operation_name: Name of the operation to monitor + max_wait_time: Maximum time to wait in seconds (default: 10 minutes) + + Returns: + Video URI if successful, None otherwise + """ + print("⏳ Waiting for video generation to complete...") + + operation_url = f"{self.base_url}/{operation_name}" + start_time = time.time() + poll_interval = 10 # Start with 10 seconds + + while time.time() - start_time < max_wait_time: + try: + print(f"🔍 Polling status... ({int(time.time() - start_time)}s elapsed)") + + response = requests.get(operation_url, headers=self.headers) + response.raise_for_status() + + data = response.json() + + # Check for errors + if "error" in data: + print("❌ Error in video generation:") + print(json.dumps(data["error"], indent=2)) + return None + + # Check if operation is complete + is_done = data.get("done", False) + + if is_done: + print("🎉 Video generation complete!") + + try: + # Extract video URI from nested response + video_uri = data["response"]["generateVideoResponse"]["generatedSamples"][0]["video"]["uri"] + print(f"📹 Video URI: {video_uri}") + return video_uri + except KeyError as e: + print(f"❌ Could not extract video URI: {e}") + print("Full response:") + print(json.dumps(data, indent=2)) + return None + + # Wait before next poll, with exponential backoff + time.sleep(poll_interval) + poll_interval = min(poll_interval * 1.2, 30) # Cap at 30 seconds + + except requests.RequestException as e: + print(f"❌ Error polling operation status: {e}") + time.sleep(poll_interval) + + print(f"⏰ Timeout after {max_wait_time} seconds") + return None + + def download_video(self, video_uri: str, output_filename: str = "generated_video.mp4") -> bool: + """ + Download the generated video file. + + Args: + video_uri: URI of the video to download (from Google's response) + output_filename: Local filename to save the video + + Returns: + True if download successful, False otherwise + """ + print(f"⬇️ Downloading video...") + print(f"Original URI: {video_uri}") + + # Convert Google URI to LiteLLM proxy URI + # Example: files/abc123 -> /gemini/v1beta/files/abc123:download?alt=media + if video_uri.startswith("files/"): + download_path = f"{video_uri}:download?alt=media" + else: + download_path = video_uri + + litellm_download_url = f"{self.base_url}/{download_path}" + print(f"Download URL: {litellm_download_url}") + + try: + # Download with streaming and redirect handling + response = requests.get( + litellm_download_url, + headers=self.headers, + stream=True, + allow_redirects=True # Handle redirects automatically + ) + response.raise_for_status() + + # Save video file + with open(output_filename, 'wb') as f: + downloaded_size = 0 + for chunk in response.iter_content(chunk_size=8192): + if chunk: + f.write(chunk) + downloaded_size += len(chunk) + + # Progress indicator for large files + if downloaded_size % (1024 * 1024) == 0: # Every MB + print(f"📦 Downloaded {downloaded_size / (1024*1024):.1f} MB...") + + # Verify file was created and has content + if os.path.exists(output_filename): + file_size = os.path.getsize(output_filename) + if file_size > 0: + print(f"✅ Video downloaded successfully!") + print(f"📁 Saved as: {output_filename}") + print(f"📏 File size: {file_size / (1024*1024):.2f} MB") + return True + else: + print("❌ Downloaded file is empty") + os.remove(output_filename) + return False + else: + print("❌ File was not created") + return False + + except requests.RequestException as e: + print(f"❌ Download failed: {e}") + if hasattr(e, 'response') and e.response is not None: + print(f"Status code: {e.response.status_code}") + print(f"Response headers: {dict(e.response.headers)}") + return False + + def generate_and_download(self, prompt: str, output_filename: str = None) -> bool: + """ + Complete workflow: generate video and download it. + + Args: + prompt: Text description for video generation + output_filename: Output filename (auto-generated if None) + + Returns: + True if successful, False otherwise + """ + # Auto-generate filename if not provided + if output_filename is None: + timestamp = int(time.time()) + safe_prompt = "".join(c for c in prompt[:30] if c.isalnum() or c in (' ', '-', '_')).rstrip() + output_filename = f"veo_video_{safe_prompt.replace(' ', '_')}_{timestamp}.mp4" + + print("=" * 60) + print("🎬 VEO VIDEO GENERATION WORKFLOW") + print("=" * 60) + + # Step 1: Generate video + operation_name = self.generate_video(prompt) + if not operation_name: + return False + + # Step 2: Wait for completion + video_uri = self.wait_for_completion(operation_name) + if not video_uri: + return False + + # Step 3: Download video + success = self.download_video(video_uri, output_filename) + + if success: + print("=" * 60) + print("🎉 SUCCESS! Video generation complete!") + print(f"📁 Video saved as: {output_filename}") + print("=" * 60) + else: + print("=" * 60) + print("❌ FAILED! Video generation or download failed") + print("=" * 60) + + return success + + +def main(): + """ + Example usage of the VeoVideoGenerator. + + Configure these environment variables: + - LITELLM_BASE_URL: Your LiteLLM proxy URL (default: http://localhost:4000/gemini/v1beta) + - LITELLM_API_KEY: Your LiteLLM API key (default: sk-1234) + """ + + # Configuration from environment or defaults + base_url = os.getenv("LITELLM_BASE_URL", "http://localhost:4000/gemini/v1beta") + api_key = os.getenv("LITELLM_API_KEY", "sk-1234") + + print("🚀 Starting Veo Video Generation Example") + print(f"📡 Using LiteLLM proxy at: {base_url}") + + # Initialize generator + generator = VeoVideoGenerator(base_url=base_url, api_key=api_key) + + # Example prompts - try different ones! + example_prompts = [ + "A cat playing with a ball of yarn in a sunny garden", + "Ocean waves crashing against rocky cliffs at sunset", + "A bustling city street with people walking and cars passing by", + "A peaceful forest with sunlight filtering through the trees" + ] + + # Use first example or get from user + prompt = example_prompts[0] + print(f"🎬 Using prompt: '{prompt}'") + + # Generate and download video + success = generator.generate_and_download(prompt) + + if success: + print("\n✅ Example completed successfully!") + print("💡 Try modifying the prompt in the script for different videos!") + else: + print("\n❌ Example failed!") + print("🔧 Check your LiteLLM proxy configuration and Google AI Studio API key") + + # Troubleshooting tips + print("\n🔍 Troubleshooting:") + print("1. Ensure LiteLLM proxy is running with Google AI Studio pass-through") + print("2. Verify your Google AI Studio API key has Veo access") + print("3. Check that your prompt meets Veo's content guidelines") + print("4. Review the LiteLLM proxy logs for detailed error information") + + +if __name__ == "__main__": + main() diff --git a/deploy/charts/litellm-helm/Chart.yaml b/deploy/charts/litellm-helm/Chart.yaml index b6ac264a228..e361ee226b7 100644 --- a/deploy/charts/litellm-helm/Chart.yaml +++ b/deploy/charts/litellm-helm/Chart.yaml @@ -18,7 +18,7 @@ type: application # This is the chart version. This version number should be incremented each time you make changes # to the chart and its templates, including the app version. # Versions are expected to follow Semantic Versioning (https://semver.org/) -version: 0.4.5 +version: 0.4.6 # This is the version number of the application being deployed. This version number should be # incremented each time you make changes to the application. Versions are not expected to diff --git a/deploy/charts/litellm-helm/README.md b/deploy/charts/litellm-helm/README.md index 73b722b64c6..352c3e9ddff 100644 --- a/deploy/charts/litellm-helm/README.md +++ b/deploy/charts/litellm-helm/README.md @@ -36,11 +36,50 @@ If `db.useStackgresOperator` is used (not yet implemented): | `service.port` | TCP port that the Kubernetes Service will listen on. Also the TCP port within the Pod that the proxy will listen on. | `4000` | | `service.loadBalancerClass` | Optional LoadBalancer implementation class (only used when `service.type` is `LoadBalancer`) | `""` | | `ingress.*` | See [values.yaml](./values.yaml) for example settings | N/A | -| `proxy_config.*` | See [values.yaml](./values.yaml) for default settings. See [example_config_yaml](../../../litellm/proxy/example_config_yaml/) for configuration examples. | N/A | -| `extraContainers[]` | An array of additional containers to be deployed as sidecars alongside the LiteLLM Proxy. | `[]` | +| `proxyConfigMap.create` | When `true`, render a ConfigMap from `.Values.proxy_config` and mount it. | `true` | +| `proxyConfigMap.name` | When `create=false`, name of the existing ConfigMap to mount. | `""` | +| `proxyConfigMap.key` | Key in the ConfigMap that contains the proxy config file. | `"config.yaml"` | +| `proxy_config.*` | See [values.yaml](./values.yaml) for default settings. Rendered into the ConfigMap’s `config.yaml` only when `proxyConfigMap.create=true`. See [example_config_yaml](../../../litellm/proxy/example_config_yaml/) for configuration examples. | `N/A` | +| `extraContainers[]` | An array of additional containers to be deployed as sidecars alongside the LiteLLM Proxy. +| `pdb.enabled` | Enable a PodDisruptionBudget for the LiteLLM proxy Deployment | `false` | +| `pdb.minAvailable` | Minimum number/percentage of pods that must be available during **voluntary** disruptions (choose **one** of minAvailable/maxUnavailable) | `null` | +| `pdb.maxUnavailable` | Maximum number/percentage of pods that can be unavailable during **voluntary** disruptions (choose **one** of minAvailable/maxUnavailable) | `null` | +| `pdb.annotations` | Extra metadata annotations to add to the PDB | `{}` | +| `pdb.labels` | Extra metadata labels to add to the PDB | `{}` | + +#### Example `proxy_config` ConfigMap from values (default): + + +``` +proxyConfigMap: + create: true + key: "config.yaml" + +proxy_config: + general_settings: + master_key: os.environ/PROXY_MASTER_KEY + model_list: + - model_name: gpt-3.5-turbo + litellm_params: + model: gpt-3.5-turbo + api_key: eXaMpLeOnLy +``` + +#### Example using existing `proxyConfigMap` instead of creating it: + + +``` +proxyConfigMap: + create: false + name: my-litellm-config + key: config.yaml + +# proxy_config is ignored in this mode +``` #### Example `environmentSecrets` Secret + ``` apiVersion: v1 kind: Secret diff --git a/deploy/charts/litellm-helm/templates/NOTES.txt b/deploy/charts/litellm-helm/templates/NOTES.txt index e72c9916080..017bbfa78bd 100644 --- a/deploy/charts/litellm-helm/templates/NOTES.txt +++ b/deploy/charts/litellm-helm/templates/NOTES.txt @@ -20,3 +20,4 @@ echo "Visit http://127.0.0.1:8080 to use your application" kubectl --namespace {{ .Release.Namespace }} port-forward $POD_NAME 8080:$CONTAINER_PORT {{- end }} +PDB: {{ if .Values.pdb.enabled }}enabled{{ else }}disabled{{ end }}. Configure via .Values.pdb.* \ No newline at end of file diff --git a/deploy/charts/litellm-helm/templates/configmap-litellm.yaml b/deploy/charts/litellm-helm/templates/configmap-litellm.yaml index 4598054a9d0..cf35917da03 100644 --- a/deploy/charts/litellm-helm/templates/configmap-litellm.yaml +++ b/deploy/charts/litellm-helm/templates/configmap-litellm.yaml @@ -1,7 +1,9 @@ +{{- if .Values.proxyConfigMap.create }} apiVersion: v1 kind: ConfigMap metadata: name: {{ include "litellm.fullname" . }}-config data: config.yaml: | -{{ .Values.proxy_config | toYaml | indent 6 }} \ No newline at end of file +{{ .Values.proxy_config | toYaml | indent 6 }} +{{- end }} \ No newline at end of file diff --git a/deploy/charts/litellm-helm/templates/deployment.yaml b/deploy/charts/litellm-helm/templates/deployment.yaml index b30b8829325..6a5a6e87577 100644 --- a/deploy/charts/litellm-helm/templates/deployment.yaml +++ b/deploy/charts/litellm-helm/templates/deployment.yaml @@ -16,7 +16,9 @@ spec: template: metadata: annotations: + {{- if .Values.proxyConfigMap.create }} checksum/config: {{ include (print $.Template.BasePath "/configmap-litellm.yaml") . | sha256sum }} + {{- end }} {{- with .Values.podAnnotations }} {{- toYaml . | nindent 8 }} {{- end }} @@ -183,9 +185,13 @@ spec: {{- end }} - name: litellm-config configMap: + {{- if .Values.proxyConfigMap.create }} name: {{ include "litellm.fullname" . }}-config + {{- else }} + name: {{ .Values.proxyConfigMap.name }} + {{- end }} items: - - key: "config.yaml" + - key: {{ .Values.proxyConfigMap.key | default "config.yaml" }} path: "config.yaml" {{- with .Values.volumes }} {{- toYaml . | nindent 8 }} diff --git a/deploy/charts/litellm-helm/templates/migrations-job.yaml b/deploy/charts/litellm-helm/templates/migrations-job.yaml index 4c8925564af..7a6893f28f1 100644 --- a/deploy/charts/litellm-helm/templates/migrations-job.yaml +++ b/deploy/charts/litellm-helm/templates/migrations-job.yaml @@ -61,7 +61,7 @@ spec: value: {{ .Values.db.database }} - name: DATABASE_URL value: {{ .Values.db.url | quote }} - {{- else }} + {{- else if .Values.db.deployStandalone }} - name: DATABASE_URL value: postgresql://{{ .Values.postgresql.auth.username }}:{{ .Values.postgresql.auth.password }}@{{ .Release.Name }}-postgresql/{{ .Values.postgresql.auth.database }} {{- end }} diff --git a/deploy/charts/litellm-helm/templates/poddisruptionbudget.yaml b/deploy/charts/litellm-helm/templates/poddisruptionbudget.yaml new file mode 100644 index 00000000000..1715b94c1f6 --- /dev/null +++ b/deploy/charts/litellm-helm/templates/poddisruptionbudget.yaml @@ -0,0 +1,33 @@ +{{- /* +PodDisruptionBudget for LiteLLM proxy +Controlled via .Values.pdb.enabled and .Values.pdb.{minAvailable|maxUnavailable} +Only one of minAvailable / maxUnavailable should be set. If both are set, minAvailable wins. +*/ -}} +{{- if .Values.pdb.enabled }} +apiVersion: policy/v1 +kind: PodDisruptionBudget +metadata: + name: {{ include "litellm.fullname" . }} + labels: + {{- include "litellm.labels" . | nindent 4 }} + {{- with .Values.pdb.labels }} + {{- toYaml . | nindent 4 }} + {{- end }} + {{- with .Values.pdb.annotations }} + annotations: + {{- toYaml . | nindent 4 }} + {{- end }} +spec: + selector: + matchLabels: + {{- /* Match the Deployment selector to target the same pod set */ -}} + {{- include "litellm.selectorLabels" . | nindent 6 }} + {{- if .Values.pdb.minAvailable }} + minAvailable: {{ .Values.pdb.minAvailable }} + {{- else if .Values.pdb.maxUnavailable }} + maxUnavailable: {{ .Values.pdb.maxUnavailable }} + {{- else }} + # Safe default if enabled but not configured + maxUnavailable: 1 + {{- end }} +{{- end }} diff --git a/deploy/charts/litellm-helm/tests/deployment_tests.yaml b/deploy/charts/litellm-helm/tests/deployment_tests.yaml index b71f91377f1..f9c83966696 100644 --- a/deploy/charts/litellm-helm/tests/deployment_tests.yaml +++ b/deploy/charts/litellm-helm/tests/deployment_tests.yaml @@ -115,3 +115,25 @@ tests: content: name: EXTRA_ENV_VAR value: EXTRA_ENV_VAR_VALUE + - it: should mount existing configmap when create=false + template: deployment.yaml + set: + proxyConfigMap: + create: false + name: my-litellm-config + key: custom.yaml + asserts: + - contains: + path: spec.template.spec.volumes + content: + name: litellm-config + configMap: + name: my-litellm-config + items: + - key: custom.yaml + path: config.yaml + - contains: + path: spec.template.spec.containers[0].volumeMounts + content: + name: litellm-config + mountPath: /etc/litellm/ \ No newline at end of file diff --git a/deploy/charts/litellm-helm/tests/migrations-job_tests.yaml b/deploy/charts/litellm-helm/tests/migrations-job_tests.yaml index 686d20efa55..3a7bfa5eb0c 100644 --- a/deploy/charts/litellm-helm/tests/migrations-job_tests.yaml +++ b/deploy/charts/litellm-helm/tests/migrations-job_tests.yaml @@ -110,4 +110,18 @@ tests: path: spec.template.spec.containers[0].env content: name: CUSTOM_VAR - value: "custom_value" \ No newline at end of file + value: "custom_value" + + - it: should not include DATABASE_URL when deployStandalone is false + template: migrations-job.yaml + set: + migrationJob: + enabled: true + db: + deployStandalone: false + useExisting: false + asserts: + - notContains: + path: spec.template.spec.containers[0].env + content: + name: DATABASE_URL \ No newline at end of file diff --git a/deploy/charts/litellm-helm/tests/pdb_tests.yaml b/deploy/charts/litellm-helm/tests/pdb_tests.yaml new file mode 100644 index 00000000000..5e042e80bd3 --- /dev/null +++ b/deploy/charts/litellm-helm/tests/pdb_tests.yaml @@ -0,0 +1,45 @@ +suite: "pdb enabled" +templates: + - poddisruptionbudget.yaml +tests: + - it: "renders a PDB with maxUnavailable=1" + set: + pdb.enabled: true + pdb.maxUnavailable: 1 + asserts: + - hasDocuments: { count: 1 } + - isKind: { of: PodDisruptionBudget } + - equal: { path: apiVersion, value: policy/v1 } + - equal: { path: spec.maxUnavailable, value: 1 } + - equal: + path: spec.selector.matchLabels + value: + app.kubernetes.io/name: litellm + app.kubernetes.io/instance: RELEASE-NAME + +--- +suite: "pdb disabled" +templates: + - poddisruptionbudget.yaml +tests: + - it: "does not render when disabled" + set: + pdb.enabled: false + asserts: + - hasDocuments: { count: 0 } + +--- +suite: "pdb minAvailable precedence" +templates: + - poddisruptionbudget.yaml +tests: + - it: "uses minAvailable when both are set" + set: + pdb.enabled: true + pdb.minAvailable: "50%" + pdb.maxUnavailable: 1 + asserts: + - isKind: { of: PodDisruptionBudget } + - equal: { path: apiVersion, value: policy/v1 } + - equal: { path: spec.minAvailable, value: "50%" } + - isNull: { path: spec.maxUnavailable } diff --git a/deploy/charts/litellm-helm/values.yaml b/deploy/charts/litellm-helm/values.yaml index 0bd95003c10..c1792497d29 100644 --- a/deploy/charts/litellm-helm/values.yaml +++ b/deploy/charts/litellm-helm/values.yaml @@ -93,6 +93,14 @@ masterkeySecretName: "" # if set, use this secret key for the master key; otherwise, use the default key masterkeySecretKey: "" +proxyConfigMap: + # when true, creates a new configmap + create: true + # if create is false and name is set, use existing ConfigMap + # create: false + # name: "" + # key: "config.yaml" + # The elements within proxy_config are rendered as config.yaml for the proxy # Examples: https://github.com/BerriAI/litellm/tree/main/litellm/proxy/example_config_yaml # Reference: https://docs.litellm.ai/docs/proxy/configs @@ -232,4 +240,11 @@ extraEnvVars: { # value: EXTRA_ENV_VAR_VALUE } - +# Pod Disruption Budget +pdb: + enabled: false + # Set exactly one of the following. If both are set, minAvailable takes precedence. + minAvailable: null # e.g. "50%" or 1 + maxUnavailable: null # e.g. 1 or "20%" + annotations: {} + labels: {} diff --git a/dist/litellm-1.57.6.tar.gz b/dist/litellm-1.57.6.tar.gz deleted file mode 100644 index 01a039cf6ee..00000000000 Binary files a/dist/litellm-1.57.6.tar.gz and /dev/null differ diff --git a/docker/Dockerfile.non_root b/docker/Dockerfile.non_root index bc2bf169c28..4178724e6e4 100644 --- a/docker/Dockerfile.non_root +++ b/docker/Dockerfile.non_root @@ -33,11 +33,12 @@ WORKDIR /app # Install runtime dependencies USER root RUN apk upgrade --no-cache && \ - apk add --no-cache bash libstdc++ ca-certificates openssl + apk add --no-cache bash libstdc++ ca-certificates openssl supervisor # Copy only necessary artifacts from builder stage for runtime COPY . . COPY --from=builder /app/docker/entrypoint.sh /app/docker/prod_entrypoint.sh /app/docker/ +COPY --from=builder /app/docker/supervisord.conf /etc/supervisord.conf COPY --from=builder /app/schema.prisma /app/schema.prisma COPY --from=builder /app/dist/*.whl . COPY --from=builder /wheels/ /wheels/ diff --git a/docs/my-website/docs/batches.md b/docs/my-website/docs/batches.md index d5fbc53c080..1bd4c700ae7 100644 --- a/docs/my-website/docs/batches.md +++ b/docs/my-website/docs/batches.md @@ -7,7 +7,7 @@ Covers Batches, Files | Feature | Supported | Notes | |-------|-------|-------| -| Supported Providers | OpenAI, Azure, Vertex | - | +| Supported Providers | OpenAI, Azure, Vertex, Bedrock | - | | ✨ Cost Tracking | ✅ | LiteLLM Enterprise only | | Logging | ✅ | Works across all logging integrations | @@ -178,6 +178,7 @@ print("list_batches_response=", list_batches_response) ### [Azure OpenAI](./providers/azure#azure-batches-api) ### [OpenAI](#quick-start) ### [Vertex AI](./providers/vertex#batch-apis) +### [Bedrock](./providers/bedrock_batches) ## How Cost Tracking for Batches API Works diff --git a/docs/my-website/docs/completion/http_handler_config.md b/docs/my-website/docs/completion/http_handler_config.md new file mode 100644 index 00000000000..d4a25ce2043 --- /dev/null +++ b/docs/my-website/docs/completion/http_handler_config.md @@ -0,0 +1,145 @@ +# Custom HTTP Handler + +Configure custom aiohttp sessions for better performance and control in LiteLLM completions. + +## Overview + +You can now inject custom `aiohttp.ClientSession` instances into LiteLLM for: +- Custom connection pooling and timeouts +- Corporate proxy and SSL configurations +- Performance optimization +- Request monitoring + +## Basic Usage + +### Default (No Changes Required) +```python +import litellm + +# Works exactly as before +response = await litellm.acompletion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "Hello!"}] +) +``` + +### Custom Session +```python +import aiohttp +import litellm +from litellm.llms.custom_httpx.aiohttp_handler import BaseLLMAIOHTTPHandler + +# Create optimized session +session = aiohttp.ClientSession( + timeout=aiohttp.ClientTimeout(total=180), + connector=aiohttp.TCPConnector(limit=300, limit_per_host=75) +) + +# Replace global handler +litellm.base_llm_aiohttp_handler = BaseLLMAIOHTTPHandler(client_session=session) + +# All completions now use your session +response = await litellm.acompletion(model="gpt-3.5-turbo", messages=[...]) +``` + +## Common Patterns + +### FastAPI Integration +```python +from contextlib import asynccontextmanager +from fastapi import FastAPI +import aiohttp +import litellm + +@asynccontextmanager +async def lifespan(app: FastAPI): + # Startup + session = aiohttp.ClientSession( + timeout=aiohttp.ClientTimeout(total=180), + connector=aiohttp.TCPConnector(limit=300) + ) + litellm.base_llm_aiohttp_handler = BaseLLMAIOHTTPHandler( + client_session=session + ) + yield + # Shutdown + await session.close() + +app = FastAPI(lifespan=lifespan) + +@app.post("/chat") +async def chat(messages: list[dict]): + return await litellm.acompletion(model="gpt-3.5-turbo", messages=messages) +``` + +### Corporate Proxy +```python +import ssl + +# Custom SSL context +ssl_context = ssl.create_default_context() +ssl_context.load_cert_chain('cert.pem', 'key.pem') + +# Proxy session +session = aiohttp.ClientSession( + connector=aiohttp.TCPConnector(ssl=ssl_context), + trust_env=True # Use environment proxy settings +) + +litellm.base_llm_aiohttp_handler = BaseLLMAIOHTTPHandler(client_session=session) +``` + +### High Performance +```python +# Optimized for high throughput +session = aiohttp.ClientSession( + timeout=aiohttp.ClientTimeout(total=300), + connector=aiohttp.TCPConnector( + limit=1000, # High connection limit + limit_per_host=200, # Per host limit + ttl_dns_cache=600, # DNS cache + keepalive_timeout=60, # Keep connections alive + enable_cleanup_closed=True + ) +) + +litellm.base_llm_aiohttp_handler = BaseLLMAIOHTTPHandler(client_session=session) +``` + +## Constructor Options + +```python +BaseLLMAIOHTTPHandler( + client_session=None, # Custom aiohttp.ClientSession + transport=None, # Advanced transport control + connector=None, # Custom aiohttp.BaseConnector +) +``` + +## Resource Management + +- **User sessions**: You manage the lifecycle (call `await session.close()`) +- **Auto-created sessions**: Automatically cleaned up by the handler +- **100% backward compatible**: Existing code works unchanged + +## Configuration Tips + +### Development +```python +session = aiohttp.ClientSession( + timeout=aiohttp.ClientTimeout(total=60), + connector=aiohttp.TCPConnector(limit=50) +) +``` + +### Production +```python +session = aiohttp.ClientSession( + timeout=aiohttp.ClientTimeout(total=300), + connector=aiohttp.TCPConnector( + limit=1000, + limit_per_host=200, + keepalive_timeout=60 + ) +) +``` \ No newline at end of file diff --git a/docs/my-website/docs/completion/image_generation_chat.md b/docs/my-website/docs/completion/image_generation_chat.md new file mode 100644 index 00000000000..58ae70e2fff --- /dev/null +++ b/docs/my-website/docs/completion/image_generation_chat.md @@ -0,0 +1,232 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Image Generation in Chat Completions, Responses API + +This guide covers how to generate images when using the `chat/completions`. Note - if you want this on Responses API please file a Feature Request [here](https://github.com/BerriAI/litellm/issues/new). + +:::info + +Requires LiteLLM v1.76.1+ + +::: + +Supported Providers: +- Google AI Studio (`gemini`) +- Vertex AI (`vertex_ai/`) + +LiteLLM will standardize the `image` response in the assistant message for models that support image generation during chat completions. + +```python title="Example response from litellm" +"message": { + ... + "content": "Here's the image you requested:", + "image": { + "url": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAA...", + "detail": "auto" + } +} +``` + +## Quick Start + + + + +```python showLineNumbers title="Image generation with chat completion" +from litellm import completion +import os + +os.environ["GEMINI_API_KEY"] = "your-api-key" + +response = completion( + model="gemini/gemini-2.5-flash-image-preview", + messages=[ + {"role": "user", "content": "Generate an image of a banana wearing a costume that says LiteLLM"} + ], +) + +print(response.choices[0].message.content) # Text response +print(response.choices[0].message.image) # Image data +``` + + + + +1. Setup config.yaml + +```yaml showLineNumbers title="config.yaml" +model_list: + - model_name: gemini-image-gen + litellm_params: + model: gemini/gemini-2.5-flash-image-preview + api_key: os.environ/GEMINI_API_KEY +``` + +2. Run proxy server + +```bash showLineNumbers title="Start the proxy" +litellm --config config.yaml + +# RUNNING on http://0.0.0.0:4000 +``` + +3. Test it! + +```bash showLineNumbers title="Make request" +curl http://0.0.0.0:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer $LITELLM_KEY" \ + -d '{ + "model": "gemini-image-gen", + "messages": [ + { + "role": "user", + "content": "Generate an image of a banana wearing a costume that says LiteLLM" + } + ] + }' +``` + + + + +**Expected Response** + +```bash +{ + "id": "chatcmpl-3b66124d79a708e10c603496b363574c", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Here's the image you requested:", + "role": "assistant", + "image": { + "url": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAA...", + "detail": "auto" + } + } + } + ], + "created": 1723323084, + "model": "gemini/gemini-2.5-flash-image-preview", + "object": "chat.completion", + "usage": { + "completion_tokens": 12, + "prompt_tokens": 16, + "total_tokens": 28 + } +} +``` + +## Streaming Support + + + + +```python showLineNumbers title="Streaming image generation" +from litellm import completion +import os + +os.environ["GEMINI_API_KEY"] = "your-api-key" + +response = completion( + model="gemini/gemini-2.5-flash-image-preview", + messages=[ + {"role": "user", "content": "Generate an image of a banana wearing a costume that says LiteLLM"} + ], + stream=True, +) + +for chunk in response: + if hasattr(chunk.choices[0].delta, "image") and chunk.choices[0].delta.image is not None: + print("Generated image:", chunk.choices[0].delta.image["url"]) + break +``` + + + + +```bash showLineNumbers title="Streaming request" +curl http://0.0.0.0:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer $LITELLM_KEY" \ + -d '{ + "model": "gemini-image-gen", + "messages": [ + { + "role": "user", + "content": "Generate an image of a banana wearing a costume that says LiteLLM" + } + ], + "stream": true + }' +``` + + + + +**Expected Streaming Response** + +```bash +data: {"id":"chatcmpl-123","object":"chat.completion.chunk","created":1723323084,"model":"gemini/gemini-2.5-flash-image-preview","choices":[{"index":0,"delta":{"role":"assistant"},"finish_reason":null}]} + +data: {"id":"chatcmpl-123","object":"chat.completion.chunk","created":1723323084,"model":"gemini/gemini-2.5-flash-image-preview","choices":[{"index":0,"delta":{"content":"Here's the image you requested:"},"finish_reason":null}]} + +data: {"id":"chatcmpl-123","object":"chat.completion.chunk","created":1723323084,"model":"gemini/gemini-2.5-flash-image-preview","choices":[{"index":0,"delta":{"image":{"url":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAA...","detail":"auto"}},"finish_reason":null}]} + +data: {"id":"chatcmpl-123","object":"chat.completion.chunk","created":1723323084,"model":"gemini/gemini-2.5-flash-image-preview","choices":[{"index":0,"delta":{},"finish_reason":"stop"}]} + +data: [DONE] +``` + +## Async Support + +```python showLineNumbers title="Async image generation" +from litellm import acompletion +import asyncio +import os + +os.environ["GEMINI_API_KEY"] = "your-api-key" + +async def generate_image(): + response = await acompletion( + model="gemini/gemini-2.5-flash-image-preview", + messages=[ + {"role": "user", "content": "Generate an image of a banana wearing a costume that says LiteLLM"} + ], + ) + + print(response.choices[0].message.content) # Text response + print(response.choices[0].message.image) # Image data + + return response + +# Run the async function +asyncio.run(generate_image()) +``` + +## Supported Models + +| Provider | Model | +|----------|--------| +| Google AI Studio | `gemini/gemini-2.5-flash-image-preview` | +| Vertex AI | `vertex_ai/gemini-2.5-flash-image-preview` | + +## Spec + +The `image` field in the response follows this structure: + +```python +"image": { + "url": "data:image/png;base64,", + "detail": "auto" +} +``` + +- `url` - str: Base64 encoded image data in data URI format +- `detail` - str: Image detail level (always "auto" for generated images) + +The image is returned as a base64-encoded data URI that can be directly used in HTML `` tags or saved to a file. diff --git a/docs/my-website/docs/completion/input.md b/docs/my-website/docs/completion/input.md index 26629a0b8f8..91d9cc72cf3 100644 --- a/docs/my-website/docs/completion/input.md +++ b/docs/my-website/docs/completion/input.md @@ -65,6 +65,7 @@ Use `litellm.get_supported_openai_params()` for an updated list of params for ea | Github | ✅| ✅ | ✅ | ✅| ✅ | ✅ | ✅ | ✅| ✅ | ✅| ✅|| || ✅ | ✅ (model dependent) | ✅ (model dependent) || || | Novita AI| ✅| ✅ || ✅| ✅ | ✅ | ✅ | ✅| ✅ | ✅| || ✅||| |||| || | Bytez | ✅| ✅ || ✅| ✅ | | | ✅|| || || || || || || +| OVHCloud AI Endpoints | ✅ | | ✅ | ✅ | ✅ | ✅ | ✅ | | | | | | | | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | :::note @@ -106,6 +107,7 @@ def completion( parallel_tool_calls: Optional[bool] = None, logprobs: Optional[bool] = None, top_logprobs: Optional[int] = None, + safety_identifier: Optional[str] = None, deployment_id=None, # soon to be deprecated params by OpenAI functions: Optional[List] = None, @@ -196,6 +198,8 @@ def completion( - `top_logprobs`: *int (optional)* - An integer between 0 and 5 specifying the number of most likely tokens to return at each token position, each with an associated log probability. `logprobs` must be set to true if this parameter is used. +- `safety_identifier`: *string (optional)* - A unique identifier for tracking and managing safety-related requests. This parameter helps with safety monitoring and compliance tracking. + - `headers`: *dict (optional)* - A dictionary of headers to be sent with the request. - `extra_headers`: *dict (optional)* - Alternative to `headers`, used to send extra headers in LLM API request. diff --git a/docs/my-website/docs/completion/provider_specific_params.md b/docs/my-website/docs/completion/provider_specific_params.md index a8307fc8a20..250b410c9c4 100644 --- a/docs/my-website/docs/completion/provider_specific_params.md +++ b/docs/my-website/docs/completion/provider_specific_params.md @@ -423,7 +423,7 @@ model_list: curl -X POST 'http://0.0.0.0:4000/chat/completions' \ -H 'Content-Type: application/json' \ -H 'Authorization: Bearer sk-1234' \ --D '{ +-d '{ "model": "llama-3-8b-instruct", "messages": [ { @@ -431,6 +431,56 @@ curl -X POST 'http://0.0.0.0:4000/chat/completions' \ "content": "What'\''s the weather like in Boston today?" } ], - "adapater_id": "my-special-adapter-id" # 👈 PROVIDER-SPECIFIC PARAM - }' -``` \ No newline at end of file + "adapater_id": "my-special-adapter-id" +}' +``` + +## Provider-Specific Metadata Parameters + +| Provider | Parameter | Use Case | +|----------|-----------|----------| +| **AWS Bedrock** | `requestMetadata` | Cost attribution, logging | +| **Gemini/Vertex AI** | `labels` | Resource labeling | +| **Anthropic** | `metadata` | User identification | + + + + +```python +import litellm + +response = litellm.completion( + model="bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0", + messages=[{"role": "user", "content": "Hello!"}], + requestMetadata={"cost_center": "engineering"} +) +``` + + + + +```python +import litellm + +response = litellm.completion( + model="vertex_ai/gemini-pro", + messages=[{"role": "user", "content": "Hello!"}], + labels={"environment": "production"} +) +``` + + + + +```python +import litellm + +response = litellm.completion( + model="anthropic/claude-3-sonnet-20240229", + messages=[{"role": "user", "content": "Hello!"}], + metadata={"user_id": "user123"} +) +``` + + + \ No newline at end of file diff --git a/docs/my-website/docs/completion/shared_session.md b/docs/my-website/docs/completion/shared_session.md new file mode 100644 index 00000000000..ff3da37f34f --- /dev/null +++ b/docs/my-website/docs/completion/shared_session.md @@ -0,0 +1,213 @@ +# Shared Session Support + +## Overview + +LiteLLM now supports sharing `aiohttp.ClientSession` instances across multiple API calls to avoid creating unnecessary new sessions. This improves performance and resource utilization. + +## Usage + +### Basic Usage + +```python +import asyncio +from aiohttp import ClientSession +from litellm import acompletion + +async def main(): + # Create a shared session + async with ClientSession() as shared_session: + # Use the same session for multiple calls + response1 = await acompletion( + model="gpt-4o", + messages=[{"role": "user", "content": "Hello"}], + shared_session=shared_session + ) + + response2 = await acompletion( + model="gpt-4o", + messages=[{"role": "user", "content": "How are you?"}], + shared_session=shared_session + ) + + # Both calls reuse the same session! + +asyncio.run(main()) +``` + +### Without Shared Session (Default) + +```python +import asyncio +from litellm import acompletion + +async def main(): + # Each call creates a new session + response1 = await acompletion( + model="gpt-4o", + messages=[{"role": "user", "content": "Hello"}] + ) + + response2 = await acompletion( + model="gpt-4o", + messages=[{"role": "user", "content": "How are you?"}] + ) + # Two separate sessions created + +asyncio.run(main()) +``` + +## Benefits + +- **Performance**: Reuse HTTP connections across multiple calls +- **Resource Efficiency**: Reduce memory and connection overhead +- **Better Control**: Manage session lifecycle explicitly +- **Debugging**: Easy to trace which calls use which sessions + +## Debug Logging + +Enable debug logging to see session reuse in action: + +```python +import os +import litellm + +# Enable debug logging +os.environ['LITELLM_LOG'] = 'DEBUG' + +# You'll see logs like: +# 🔄 SHARED SESSION: acompletion called with shared_session (ID: 12345) +# ✅ SHARED SESSION: Reusing existing ClientSession (ID: 12345) +``` + +## Common Patterns + +### FastAPI Integration + +```python +from fastapi import FastAPI +import aiohttp +import litellm + +app = FastAPI() + +@app.post("/chat") +async def chat(messages: list[dict]): + # Create session per request + async with aiohttp.ClientSession() as session: + return await litellm.acompletion( + model="gpt-4o", + messages=messages, + shared_session=session + ) +``` + +### Batch Processing + +```python +import asyncio +from aiohttp import ClientSession +from litellm import acompletion + +async def process_batch(messages_list): + async with ClientSession() as shared_session: + tasks = [] + for messages in messages_list: + task = acompletion( + model="gpt-4o", + messages=messages, + shared_session=shared_session + ) + tasks.append(task) + + # All tasks use the same session + results = await asyncio.gather(*tasks) + return results +``` + +### Custom Session Configuration + +```python +import aiohttp +import litellm + +# Create optimized session +async with aiohttp.ClientSession( + timeout=aiohttp.ClientTimeout(total=180), + connector=aiohttp.TCPConnector(limit=300, limit_per_host=75) +) as shared_session: + + response = await litellm.acompletion( + model="gpt-4o", + messages=[{"role": "user", "content": "Hello"}], + shared_session=shared_session + ) +``` + +## Implementation Details + +The `shared_session` parameter is threaded through the entire LiteLLM call chain: + +1. **`acompletion()`** - Accepts `shared_session` parameter +2. **`BaseLLMHTTPHandler`** - Passes session to HTTP client creation +3. **`AsyncHTTPHandler`** - Uses existing session if provided +4. **`LiteLLMAiohttpTransport`** - Reuses the session for HTTP requests + +## Backward Compatibility + +- **100% backward compatible** - Existing code works unchanged +- **Optional parameter** - `shared_session=None` by default +- **No breaking changes** - All existing functionality preserved + +## Testing + +Test the shared session functionality: + +```python +import asyncio +from aiohttp import ClientSession +from litellm import acompletion + +async def test_shared_session(): + async with ClientSession() as session: + print(f"✅ Created session: {id(session)}") + + try: + response = await acompletion( + model="gpt-4o", + messages=[{"role": "user", "content": "Hello"}], + shared_session=session, + api_key="your-api-key" + ) + print(f"Response: {response.choices[0].message.content}") + except Exception as e: + print(f"✅ Expected error: {type(e).__name__}") + + print("✅ Session control working!") + +asyncio.run(test_shared_session()) +``` + +## Files Modified + +The shared session functionality was added to these files: + +- `litellm/main.py` - Added `shared_session` parameter to `acompletion()` and `completion()` +- `litellm/llms/custom_httpx/http_handler.py` - Core session reuse logic +- `litellm/llms/custom_httpx/llm_http_handler.py` - HTTP handler integration +- `litellm/llms/openai/openai.py` - OpenAI provider integration +- `litellm/llms/openai/common_utils.py` - OpenAI client creation +- `litellm/llms/azure/chat/o_series_handler.py` - Azure O Series handler + +## Troubleshooting + +### Session Not Being Reused + +1. **Check debug logs**: Enable `LITELLM_LOG=DEBUG` to see session reuse messages +2. **Verify session is not closed**: Ensure the session is still active when making calls +3. **Check parameter passing**: Make sure `shared_session` is passed to all `acompletion()` calls + +### Performance Issues + +1. **Session configuration**: Tune `aiohttp.ClientSession` parameters for your use case +2. **Connection limits**: Adjust `limit` and `limit_per_host` in `TCPConnector` +3. **Timeout settings**: Configure appropriate timeouts for your environment diff --git a/docs/my-website/docs/completion/usage.md b/docs/my-website/docs/completion/usage.md index 2a9eab941ea..c388e5bfee1 100644 --- a/docs/my-website/docs/completion/usage.md +++ b/docs/my-website/docs/completion/usage.md @@ -26,6 +26,7 @@ response = completion( print(response.usage) ``` +> **Note:** LiteLLM supports endpoint bridging—if a model does not natively support a requested endpoint, LiteLLM will automatically route the call to the correct supported endpoint (such as bridging `/chat/completions` to `/responses` or vice versa) based on the model's `mode`set in `model_prices_and_context_window`. ## Streaming Usage diff --git a/docs/my-website/docs/completion/web_fetch.md b/docs/my-website/docs/completion/web_fetch.md new file mode 100644 index 00000000000..30a15e44495 --- /dev/null +++ b/docs/my-website/docs/completion/web_fetch.md @@ -0,0 +1,294 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Web Fetch + +The web fetch tool allows LLMs to retrieve full content from specified web pages and PDF documents. This enables AI models to access real-time information from the internet and incorporate web content into their responses. + +## Web Fetch vs Web Search + +**Web Fetch** retrieves the full content from specific web pages that you provide URLs for, while **Web Search** performs internet searches to find relevant information based on your queries. + +| Feature | Web Fetch | Web Search | +|---------|-----------|------------| +| **Purpose** | Retrieve content from specific URLs | Search the internet for information | +| **Input** | You provide exact URLs to fetch | You provide search queries/questions | +| **Output** | Full page content from specified URLs | Search results with relevant information | +| **Use Cases** | - Analyzing specific articles
- Comparing content from known websites
- Extracting data from particular pages | - Finding current news/events
- Researching topics
- Getting real-time information | + + +**Example Web Fetch**: "Fetch the content from https://example.com/pricing and summarize it" +**Example Web Search**: "What are the latest AI developments this week?" + +**Supported Providers:** +- Anthropic API (`anthropic/`) + +**Supported Tool Types:** +- `web_fetch_20250910` - Web content retrieval tool with usage limits, domain filtering, and citation support + + +## Quick Start + +### LiteLLM Python SDK + +```python +import os +from litellm import completion + +os.environ["ANTHROPIC_API_KEY"] = "your-api-key" + +# Web fetch tool +tools = [ + { + "type": "web_fetch_20250910", + "name": "web_fetch", + "max_uses": 5, + } +] + +messages = [ + { + "role": "user", + "content": "Please analyze the content at https://example.com/article and summarize the main points" + } +] + +response = completion( + model="anthropic/claude-3-5-sonnet-latest", + messages=messages, + tools=tools, +) + +print(response) +``` + +### LiteLLM Proxy + +1. Define web fetch models on config.yaml + +```yaml +model_list: + - model_name: claude-3-5-sonnet-latest # Anthropic claude-3-5-sonnet-latest + litellm_params: + model: anthropic/claude-3-5-sonnet-latest + api_key: os.environ/ANTHROPIC_API_KEY +``` + +2. Run proxy server + +```bash +litellm --config config.yaml +``` + +3. Test it using the OpenAI Python SDK + +```python +import os +from openai import OpenAI + +client = OpenAI( + api_key="sk-1234", # your litellm proxy api key + base_url="http://0.0.0.0:4000" +) + +response = client.chat.completions.create( + model="claude-3-5-sonnet-latest", + messages=[ + { + "role": "user", + "content": "Please fetch and analyze the content from https://news.ycombinator.com and tell me about the top stories" + } + ], + tools=[ + { + "type": "web_fetch_20250910", + "name": "web_fetch", + "max_uses": 5, + } + ] +) + +print(response) +``` + +## Supported Models + +Web fetch is available on the following Anthropic API models: + +- `claude-opus-4-1-20250805` (Claude Opus 4.1) +- `claude-opus-4-20250514` (Claude Opus 4) +- `claude-sonnet-4-20250514` (Claude Sonnet 4) +- `claude-3-7-sonnet-20250219` (Claude Sonnet 3.7) +- `claude-3-5-sonnet-latest` (Claude Sonnet 3.5 v2 - deprecated) +- `claude-3-5-haiku-latest` (Claude Haiku 3.5) + +:::note +The web fetch tool currently does not support websites dynamically rendered via JavaScript. +::: + +## Usage Examples + +### Basic Web Content Retrieval + +```python +import os +from litellm import completion + +os.environ["ANTHROPIC_API_KEY"] = "your-api-key" + +tools = [ + { + "type": "web_fetch_20250910", + "name": "web_fetch", + "max_uses": 3, + } +] + +messages = [ + { + "role": "user", + "content": "Fetch the latest news from https://techcrunch.com and summarize the top 3 articles" + } +] + +response = completion( + model="anthropic/claude-3-5-sonnet-latest", + messages=messages, + tools=tools, +) + +print(response) +``` + +### Research and Analysis + +```python +import os +from litellm import completion + +os.environ["ANTHROPIC_API_KEY"] = "your-api-key" + +tools = [ + { + "type": "web_fetch_20250910", + "name": "web_fetch", + "max_uses": 10, + } +] + +messages = [ + { + "role": "user", + "content": "Research the latest developments in AI by fetching content from multiple tech news websites and provide a comprehensive analysis" + } +] + +response = completion( + model="anthropic/claude-3-5-sonnet-latest", + messages=messages, + tools=tools, +) + +print(response) +``` + +### Content Comparison + +```python +import os +from litellm import completion + +os.environ["ANTHROPIC_API_KEY"] = "your-api-key" + +tools = [ + { + "type": "web_fetch_20250910", + "name": "web_fetch", + "max_uses": 5, + } +] + +messages = [ + { + "role": "user", + "content": "Compare the pricing information from https://openai.com/pricing and https://anthropic.com/pricing and create a comparison table" + } +] + +response = completion( + model="anthropic/claude-3-5-sonnet-latest", + messages=messages, + tools=tools, +) + +print(response) +``` + +## Advanced Usage with Multiple Tools + +You can combine web fetch with other tools like computer use or text editor: + +```python +import os +from litellm import completion + +os.environ["ANTHROPIC_API_KEY"] = "your-api-key" + +tools = [ + { + "type": "web_fetch_20250910", + "name": "web_fetch", + "max_uses": 5, + }, + { + "type": "text_editor_20250124", + "name": "str_replace_editor" + } +] + +messages = [ + { + "role": "user", + "content": "Fetch the latest AI research papers from arXiv, analyze them, and create a detailed report file with your findings" + } +] + +response = completion( + model="anthropic/claude-3-5-sonnet-latest", + messages=messages, + tools=tools, +) + +print(response) +``` + +## Spec + +### Web Fetch Tool (`web_fetch_20250910`) + +The web fetch tool supports the following parameters: + +```json +{ + "type": "web_fetch_20250910", + "name": "web_fetch", + + // Optional: Limit the number of fetches per request + "max_uses": 10, + + // Optional: Only fetch from these domains + "allowed_domains": ["example.com", "docs.example.com"], + + // Optional: Never fetch from these domains + "blocked_domains": ["private.example.com"], + + // Optional: Enable citations for fetched content + "citations": { + "enabled": true + }, + + // Optional: Maximum content length in tokens + "max_content_tokens": 100000 +} +``` + diff --git a/docs/my-website/docs/completion/web_search.md b/docs/my-website/docs/completion/web_search.md index fe49be852a7..b0d8fcdf4c0 100644 --- a/docs/my-website/docs/completion/web_search.md +++ b/docs/my-website/docs/completion/web_search.md @@ -1,17 +1,32 @@ import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; -# Using Web Search +# Web Search Use web search with litellm | Feature | Details | |---------|---------| | Supported Endpoints | - `/chat/completions`
- `/responses` | -| Supported Providers | `openai`, `xai`, `vertex_ai`, `gemini`, `perplexity` | +| Supported Providers | `openai`, `xai`, `vertex_ai`, `anthropic`, `gemini`, `perplexity` | | LiteLLM Cost Tracking | ✅ Supported | | LiteLLM Version | `v1.71.0+` | +## Which Search Engine is Used? + +Each provider uses their own search backend: + +| Provider | Search Engine | Notes | +|----------|---------------|-------| +| **OpenAI** (`gpt-4o-search-preview`) | OpenAI's internal search | Real-time web data | +| **xAI** (`grok-3`) | xAI's search + X/Twitter | Real-time social media data | +| **Google AI/Vertex** (`gemini-2.0-flash`) | **Google Search** | Uses actual Google search results | +| **Anthropic** (`claude-3-5-sonnet`) | Anthropic's web search | Real-time web data | +| **Perplexity** | Perplexity's search engine | AI-powered search and reasoning | + +:::info +**Anthropic Web Search Models**: Claude models that support web search: `claude-3-5-sonnet-latest`, `claude-3-5-sonnet-20241022`, `claude-3-5-haiku-latest`, `claude-3-5-haiku-20241022`, `claude-3-7-sonnet-20250219` +::: ## `/chat/completions` (litellm.completion) @@ -56,6 +71,12 @@ model_list: model: xai/grok-3 api_key: os.environ/XAI_API_KEY + # Anthropic + - model_name: claude-3-5-sonnet-latest + litellm_params: + model: anthropic/claude-3-5-sonnet-latest + api_key: os.environ/ANTHROPIC_API_KEY + # VertexAI - model_name: gemini-2-flash litellm_params: @@ -143,6 +164,31 @@ response = completion( ) ``` +**Anthropic (using web_search_options)** +```python showLineNumbers +from litellm import completion + +# Customize search context size for Anthropic +response = completion( + model="anthropic/claude-3-5-sonnet-latest", + messages=[ + { + "role": "user", + "content": "What was a positive news story from today?", + } + ], + web_search_options={ + "search_context_size": "medium", # Options: "low", "medium" (default), "high" + "user_location": { + "type": "approximate", + "approximate": { + "city": "San Francisco", + }, + } + } +) +``` + **VertexAI/Gemini (using web_search_options)** ```python showLineNumbers from litellm import completion @@ -375,6 +421,9 @@ assert litellm.supports_web_search(model="openai/gpt-4o-search-preview") == True # Check xAI models assert litellm.supports_web_search(model="xai/grok-3") == True +# Check Anthropic models +assert litellm.supports_web_search(model="anthropic/claude-3-5-sonnet-latest") == True + # Check VertexAI models assert litellm.supports_web_search(model="gemini-2.0-flash") == True @@ -405,6 +454,14 @@ model_list: model_info: supports_web_search: True + # Anthropic + - model_name: claude-3-5-sonnet-latest + litellm_params: + model: anthropic/claude-3-5-sonnet-latest + api_key: os.environ/ANTHROPIC_API_KEY + model_info: + supports_web_search: True + # VertexAI - model_name: gemini-2-flash litellm_params: diff --git a/docs/my-website/docs/contributing.md b/docs/my-website/docs/contributing.md index 8fc64b8f287..8768e0b4c4d 100644 --- a/docs/my-website/docs/contributing.md +++ b/docs/my-website/docs/contributing.md @@ -14,6 +14,11 @@ git clone https://github.com/BerriAI/litellm.git Tell the proxy where the UI is located ```bash export PROXY_BASE_URL="http://localhost:3000/" + +### ALSO ### - set the basic env variables +DATABASE_URL = "postgresql://:@:/" +LITELLM_MASTER_KEY = "sk-1234" +STORE_MODEL_IN_DB = "True" ``` ```bash diff --git a/docs/my-website/docs/enterprise.md b/docs/my-website/docs/enterprise.md index 9101d8e3751..cc3466fc103 100644 --- a/docs/my-website/docs/enterprise.md +++ b/docs/my-website/docs/enterprise.md @@ -1,6 +1,11 @@ import Image from '@theme/IdealImage'; # Enterprise + +:::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) +::: + For companies that need SSO, user management and professional support for LiteLLM Proxy :::info diff --git a/docs/my-website/docs/exception_mapping.md b/docs/my-website/docs/exception_mapping.md index 13eda5b405a..2342f444e17 100644 --- a/docs/my-website/docs/exception_mapping.md +++ b/docs/my-website/docs/exception_mapping.md @@ -12,6 +12,7 @@ All exceptions can be imported from `litellm` - e.g. `from litellm import BadReq | 400 | UnsupportedParamsError | litellm.BadRequestError | Raised when unsupported params are passed | | 400 | ContextWindowExceededError| litellm.BadRequestError | Special error type for context window exceeded error messages - enables context window fallbacks | | 400 | ContentPolicyViolationError| litellm.BadRequestError | Special error type for content policy violation error messages - enables content policy fallbacks | +| 400 | ImageFetchError | litellm.BadRequestError | Raised when there are errors fetching or processing images | | 400 | InvalidRequestError | openai.BadRequestError | Deprecated error, use BadRequestError instead | | 401 | AuthenticationError | openai.AuthenticationError | | 403 | PermissionDeniedError | openai.PermissionDeniedError | diff --git a/docs/my-website/docs/extras/gemini_img_migration.md b/docs/my-website/docs/extras/gemini_img_migration.md new file mode 100644 index 00000000000..a29f301e382 --- /dev/null +++ b/docs/my-website/docs/extras/gemini_img_migration.md @@ -0,0 +1,220 @@ +# Gemini Image Generation Migration Guide + +## Who is impacted by this change? + +Anyone using the following models with /chat/completions: +- `gemini/gemini-2.0-flash-exp-image-generation` +- `vertex_ai/gemini-2.0-flash-exp-image-generation` + +## Key Change + +:::info +From v1.77.0, LiteLLM will return the List of images in `response.choices[0].message.images` instead of a single image in `response.choices[0].message.image`. +::: + +Gemini models now support image generation through chat completions. Images are returned in `response.choices[0].message.images` with base64 data URLs. + +## Before and After + +### Before +```python +from litellm import completion + +response = completion( + model="gemini/gemini-2.0-flash-exp-image-generation", + messages=[{"role": "user", "content": "Generate an image of a cat"}], + modalities=["image", "text"], +) + + +base_64_image_data = response.choices[0].message.content +``` + +### After +```python +from litellm import completion + +response = completion( + model="gemini/gemini-2.0-flash-exp-image-generation", + messages=[{"role": "user", "content": "Generate an image of a cat"}], + modalities=["image", "text"], +) + +# Image is now available in the response +image_url = response.choices[0].message.images[0]["image_url"]["url"] # "data:image/png;base64,..." +``` + +### Why the change? + +Because the newer `gemini-2.5-flash-image-preview` model sends both text and image responses in the same response. This interface allows a developer to explicitly access the image or text components of the response. Before a developer would have needed to search through the message content to find the image generated by the model. + +**Why the change from `image` to `images`?** +This is to be consistent with the OpenRouter API, making sure we are using simple, well-known interfaces where possible. + +## Usage + +### Using the Python SDK + +**Key Change:** +```diff +# Before +-- base_64_image_data = response.choices[0].message.content + +# After +++ image_url = response.choices[0].message.images[0]["image_url"]["url"] +``` + +#### Basic Image Generation + +```python +from litellm import completion +import os + +# Set your API key +os.environ["GEMINI_API_KEY"] = "your-api-key" + +# Generate an image +response = completion( + model="gemini/gemini-2.0-flash-exp-image-generation", + messages=[{"role": "user", "content": "Generate an image of a cat"}], + modalities=["image", "text"], +) + +# Access the generated image +print(response.choices[0].message.content) # Text response (if any) +print(response.choices[0].message.images[0]) # Image data +``` + +#### Response Format + +The image is returned in the `message.images` field: + +```python +{ + "image_url": { + "url": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAA...", + "detail": "auto" + }, + "index": 0, + "type": "image_url" +} +``` + +### Using the LiteLLM Proxy Server + +**Key Change:** +```diff +# Before +-- "content": "base64-image-data..." + +# After +++ "images": [{ +++ "image_url": { +++ "url": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAA...", +++ "detail": "auto" +++ }, +++ "index": 0, +++ "type": "image_url" +++ }] +``` + +#### Configuration Setup + +1. **Configure your models in `config.yaml`:** + +```yaml +model_list: + - model_name: gemini-image-gen + litellm_params: + model: gemini/gemini-2.0-flash-exp-image-generation + api_key: os.environ/GEMINI_API_KEY + - model_name: vertex-image-gen + litellm_params: + model: vertex_ai/gemini-2.5-flash-image-preview + vertex_project: your-project-id + vertex_location: us-central1 + +general_settings: + master_key: sk-1234 # Your proxy API key +``` + +2. **Start the proxy server:** + +```bash +litellm --config /path/to/config.yaml + +# RUNNING on http://0.0.0.0:4000 +``` + +#### Making Requests + +**Using OpenAI SDK:** + +```python +from openai import OpenAI + +# Point to your proxy server +client = OpenAI( + api_key="sk-1234", # Your proxy API key + base_url="http://0.0.0.0:4000" +) + +response = client.chat.completions.create( + model="gemini-image-gen", + messages=[{"role": "user", "content": "Generate an image of a cat"}], + extra_body={"modalities": ["image", "text"]} +) + +# Access the generated image +print(response.choices[0].message.content) # Text response (if any) +print(response.choices[0].message.image) # Image data +``` + +**Using curl:** + +```bash +curl -X POST 'http://0.0.0.0:4000/v1/chat/completions' \ +-H 'Content-Type: application/json' \ +-H 'Authorization: Bearer sk-1234' \ +-d '{ + "model": "gemini-image-gen", + "messages": [ + { + "role": "user", + "content": "Generate an image of a cat" + } + ], + "modalities": ["image", "text"] +}' +``` + +**Response format from proxy:** + +```json +{ + "id": "chatcmpl-123", + "object": "chat.completion", + "created": 1704089632, + "model": "gemini-image-gen", + "choices": [ + { + "index": 0, + "message": { + "role": "assistant", + "content": "Here's an image of a cat for you!", + "images": [{ + "url": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAA...", + "detail": "auto" + } + }, + "finish_reason": "stop" + } + ], + "usage": { + "prompt_tokens": 10, + "completion_tokens": 8, + "total_tokens": 18 + } +} +``` + diff --git a/docs/my-website/docs/fine_tuning.md b/docs/my-website/docs/fine_tuning.md index f9a9297e062..f3f955cb01d 100644 --- a/docs/my-website/docs/fine_tuning.md +++ b/docs/my-website/docs/fine_tuning.md @@ -13,6 +13,8 @@ This is an Enterprise only endpoint [Get Started with Enterprise here](https://c | Feature | Supported | Notes | |-------|-------|-------| | Supported Providers | OpenAI, Azure OpenAI, Vertex AI | - | + +#### ⚡️See an exhaustive list of supported models and providers at [models.litellm.ai](https://models.litellm.ai/) | Cost Tracking | 🟡 | [Let us know if you need this](https://github.com/BerriAI/litellm/issues) | | Logging | ✅ | Works across all logging integrations | diff --git a/docs/my-website/docs/getting_started.md b/docs/my-website/docs/getting_started.md index 15ee00a7273..6b2c1fd531e 100644 --- a/docs/my-website/docs/getting_started.md +++ b/docs/my-website/docs/getting_started.md @@ -32,7 +32,8 @@ Next Steps 👉 [Call all supported models - e.g. Claude-2, Llama2-70b, etc.](./ More details 👉 - [Completion() function details](./completion/) -- [All supported models / providers on LiteLLM](./providers/) +- [Overview of supported models / providers on LiteLLM](./providers/) +- [Search all models / providers](https://models.litellm.ai/) - [Build your own OpenAI proxy](https://github.com/BerriAI/liteLLM-proxy/tree/main) ## streaming diff --git a/docs/my-website/docs/image_edits.md b/docs/my-website/docs/image_edits.md index f0254032964..84dddd5e4ad 100644 --- a/docs/my-website/docs/image_edits.md +++ b/docs/my-website/docs/image_edits.md @@ -4,7 +4,7 @@ import TabItem from '@theme/TabItem'; # /images/edits -LiteLLM provides image editing functionality that maps to OpenAI's `/images/edits` API endpoint. +LiteLLM provides image editing functionality that maps to OpenAI's `/images/edits` API endpoint. Now supports both single and multiple image editing. | Feature | Supported | Notes | |---------|-----------|--------| @@ -13,11 +13,14 @@ LiteLLM provides image editing functionality that maps to OpenAI's `/images/edit | End-user Tracking | ✅ | | | Fallbacks | ✅ | Works between supported models | | Loadbalancing | ✅ | Works between supported models | -| Supported operations | Create image edits | | +| Supported operations | Create image edits | Single and multiple images supported | | Supported LiteLLM SDK Versions | 1.63.8+ | | | Supported LiteLLM Proxy Versions | 1.71.1+ | | | Supported LLM providers | **OpenAI** | Currently only `openai` is supported | + #### ⚡️See all supported models and providers at [models.litellm.ai](https://models.litellm.ai/) + + ## Usage ### LiteLLM Python SDK @@ -41,6 +44,26 @@ response = litellm.image_edit( print(response) ``` +#### Multiple Images Edit +```python showLineNumbers title="OpenAI Multiple Images Edit" +import litellm + +# Edit multiple images with a prompt +response = litellm.image_edit( + model="gpt-image-1", + image=[ + open("image1.png", "rb"), + open("image2.png", "rb"), + open("image3.png", "rb") + ], + prompt="Apply vintage filter to all images", + n=1, + size="1024x1024" +) + +print(response) +``` + #### Image Edit with Mask ```python showLineNumbers title="OpenAI Image Edit with Mask" import litellm @@ -80,6 +103,30 @@ response = asyncio.run(edit_image()) print(response) ``` +#### Async Multiple Images Edit +```python showLineNumbers title="Async OpenAI Multiple Images Edit" +import litellm +import asyncio + +async def edit_multiple_images(): + response = await litellm.aimage_edit( + model="gpt-image-1", + image=[ + open("portrait1.png", "rb"), + open("portrait2.png", "rb") + ], + prompt="Add professional lighting to the portraits", + n=1, + size="1024x1024", + response_format="url" + ) + return response + +# Run the async function +response = asyncio.run(edit_multiple_images()) +print(response) +``` + #### Image Edit with Custom Parameters ```python showLineNumbers title="OpenAI Image Edit with Custom Parameters" import litellm @@ -163,6 +210,20 @@ curl -X POST "http://localhost:4000/v1/images/edits" \ -F "response_format=url" ``` +#### cURL Multiple Images Example +```bash showLineNumbers title="cURL Multiple Images Edit Request" +curl -X POST "http://localhost:4000/v1/images/edits" \ + -H "Authorization: Bearer your-api-key" \ + -F "model=gpt-image-1" \ + -F "image=@image1.png" \ + -F "image=@image2.png" \ + -F "image=@image3.png" \ + -F "prompt=Apply artistic filter to all images" \ + -F "n=1" \ + -F "size=1024x1024" \ + -F "response_format=url" +``` + diff --git a/docs/my-website/docs/image_generation.md b/docs/my-website/docs/image_generation.md index 60a6356f012..8cd5803aa6c 100644 --- a/docs/my-website/docs/image_generation.md +++ b/docs/my-website/docs/image_generation.md @@ -124,8 +124,6 @@ Any non-openai params, will be treated as provider-specific params, and sent in - `size`: *string (optional)* The size of the generated images. Must be one of `1024x1024`, `1536x1024` (landscape), `1024x1536` (portrait), or `auto` (default value) for `gpt-image-1`, one of `256x256`, `512x512`, or `1024x1024` for `dall-e-2`, and one of `1024x1024`, `1792x1024`, or `1024x1792` for `dall-e-3`. -- `input_fidelity`: *string (optional)* Controls how closely the model follows the input prompt. Supported for `gpt-image-1` model. Higher fidelity may improve prompt adherence but could affect generation speed. - - `timeout`: *integer* - The maximum time, in seconds, to wait for the API to respond. Defaults to 600 seconds (10 minutes). - `user`: *string (optional)* A unique identifier representing your end-user, @@ -281,6 +279,8 @@ print(f"response: {response}") ## Supported Providers +#### ⚡️See all supported models and providers at [models.litellm.ai](https://models.litellm.ai/) + | Provider | Documentation Link | |----------|-------------------| | OpenAI | [OpenAI Image Generation →](./providers/openai) | diff --git a/docs/my-website/docs/index.md b/docs/my-website/docs/index.md index 58cabc81b48..11d2963b7a3 100644 --- a/docs/my-website/docs/index.md +++ b/docs/my-website/docs/index.md @@ -226,6 +226,23 @@ response = completion( + + +```python +from litellm import completion +import os + +## set ENV variables. Visit https://vercel.com/docs/ai-gateway#using-the-ai-gateway-with-an-api-key for insturctions on obtaining a key +os.environ["VERCEL_AI_GATEWAY_API_KEY"] = "your-vercel-api-key" + +response = completion( + model="vercel_ai_gateway/openai/gpt-4o", + messages=[{ "content": "Hello, how are you?","role": "user"}] +) +``` + + + ### Response Format (OpenAI Format) @@ -234,7 +251,7 @@ response = completion( { "id": "chatcmpl-565d891b-a42e-4c39-8d14-82a1f5208885", "created": 1734366691, - "model": "claude-3-sonnet-20240229", + "model": "gpt-4o-2024-08-06", "object": "chat.completion", "system_fingerprint": null, "choices": [ @@ -446,6 +463,24 @@ response = completion( + + +```python +from litellm import completion +import os + +## set ENV variables. Visit https://vercel.com/docs/ai-gateway#using-the-ai-gateway-with-an-api-key for insturctions on obtaining a key +os.environ["VERCEL_AI_GATEWAY_API_KEY"] = "your-vercel-api-key" + +response = completion( + model="vercel_ai_gateway/openai/gpt-4o", + messages = [{ "content": "Hello, how are you?","role": "user"}], + stream=True, +) +``` + + + ### Streaming Response Format (OpenAI Format) @@ -489,6 +524,15 @@ try: except OpenAIError as e: print(e) ``` +### See How LiteLLM Transforms Your Requests + +Want to understand how LiteLLM parses and normalizes your LLM API requests? Use the `/utils/transform_request` endpoint to see exactly how your request is transformed internally. + +You can try it out now directly on our Demo App! +Go to the [LiteLLM API docs for transform_request](https://litellm-api.up.railway.app/#/llm%20utils/transform_request_utils_transform_request_post) + +LiteLLM will show you the normalized, provider-agnostic version of your request. This is useful for debugging, learning, and understanding how LiteLLM handles different providers and options. + ### Logging Observability - Log LLM Input/Output ([Docs](https://docs.litellm.ai/docs/observability/callbacks)) LiteLLM exposes pre defined callbacks to send data to Lunary, MLflow, Langfuse, Helicone, Promptlayer, Traceloop, Slack diff --git a/docs/my-website/docs/integrations/index.md b/docs/my-website/docs/integrations/index.md index 9731db6e751..95c922cce89 100644 --- a/docs/my-website/docs/integrations/index.md +++ b/docs/my-website/docs/integrations/index.md @@ -2,4 +2,17 @@ This section covers integrations with various tools and services that can be used with LiteLLM (either Proxy or SDK). +## AI Agent Frameworks +- **[Letta](./letta.md)** - Build stateful LLM agents with persistent memory using LiteLLM Proxy + +## Development Tools +- **[OpenWebUI](../tutorials/openweb_ui.md)** - Self-hosted ChatGPT-style interface + +## Observability & Monitoring +- **[Langfuse](../observability/langfuse_integration.md)** - LLM observability and analytics +- **[Prometheus](../proxy/prometheus.md)** - Metrics collection and monitoring +- **[PagerDuty](../proxy/pagerduty.md)** - Incident response and alerting +- **[Datadog](../observability/datadog.md)** + + Click into each section to learn more about the integrations. \ No newline at end of file diff --git a/docs/my-website/docs/integrations/letta.md b/docs/my-website/docs/integrations/letta.md new file mode 100644 index 00000000000..2afb82542f2 --- /dev/null +++ b/docs/my-website/docs/integrations/letta.md @@ -0,0 +1,928 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Letta Integration + +[Letta](https://github.com/letta-ai/letta) (formerly MemGPT) is a framework for building stateful LLM agents with persistent memory. This guide shows how to integrate both LiteLLM SDK and LiteLLM Proxy with Letta to leverage multiple LLM providers while building memory-enabled agents. + +## What is Letta? + +Letta allows you to build LLM agents that can: +- Maintain long-term memory across conversations +- Use function calling for tool interactions +- Handle large context windows efficiently +- Persist agent state and memory + +## Prerequisites + +```bash +pip install letta litellm +``` + +## Quick Start + + + + +### 1. Start LiteLLM Proxy + +First, create a configuration file for your LiteLLM proxy: + +```yaml +# config.yaml +model_list: + - model_name: gpt-4 + litellm_params: + model: openai/gpt-4 + api_key: os.environ/OPENAI_API_KEY + + - model_name: claude-3-sonnet + litellm_params: + model: anthropic/claude-3-sonnet-20240229 + api_key: os.environ/ANTHROPIC_API_KEY + + - model_name: gpt-3.5-turbo + litellm_params: + model: azure/gpt-35-turbo + api_key: os.environ/AZURE_API_KEY + api_base: os.environ/AZURE_API_BASE + api_version: "2023-07-01-preview" +``` + +Start the proxy: + +```bash +litellm --config config.yaml --port 4000 +``` + +### 2. Configure Letta with LiteLLM Proxy + +Configure Letta to use your LiteLLM proxy endpoint: + +```python +import letta +from letta import create_client + +# Configure Letta to use LiteLLM proxy +client = create_client() + +# Configure the LLM endpoint +client.set_default_llm_config( + model="gpt-4", # This should match a model from your LiteLLM config + model_endpoint_type="openai", + model_endpoint="http://localhost:4000", # Your LiteLLM proxy URL + context_window=8192 +) + +# Configure embedding endpoint (optional) +client.set_default_embedding_config( + embedding_endpoint_type="openai", + embedding_endpoint="http://localhost:4000", + embedding_model="text-embedding-ada-002" +) +``` + + + + +### 1. Configure LiteLLM SDK + +Set up your API keys and configure LiteLLM: + +```python +import os +import litellm + +# Set your API keys +os.environ["OPENAI_API_KEY"] = "your-openai-key" +os.environ["ANTHROPIC_API_KEY"] = "your-anthropic-key" + +# Optional: Configure default settings +litellm.set_verbose = True # For debugging +``` + +### 2. Create Custom LLM Wrapper for Letta + +Create a custom LLM wrapper that uses LiteLLM SDK: + +```python +import letta +from letta import create_client +from letta.llm_api.llm_api_base import LLMConfig +import litellm +from typing import List, Dict, Any + +class LiteLLMWrapper: + def __init__(self, model: str): + self.model = model + + def chat_completions_create(self, messages: List[Dict], **kwargs): + # Use LiteLLM SDK for completion + response = litellm.completion( + model=self.model, + messages=messages, + **kwargs + ) + return response + +# Configure Letta with custom LiteLLM wrapper +client = create_client() + +# Set up LLM configuration using direct SDK integration +llm_config = LLMConfig( + model="gpt-4", # or "claude-3-sonnet", "azure/gpt-35-turbo", etc. + model_endpoint_type="openai", + context_window=8192 +) + +client.set_default_llm_config(llm_config) +``` + + + + +### 3. Create and Use a Letta Agent + + + + +```python +import letta +from letta import create_client + +# Create Letta client +client = create_client() + +# Create a new agent +agent_state = client.create_agent( + name="my-assistant", + system="You are a helpful assistant with persistent memory.", + llm_config=client.get_default_llm_config(), + embedding_config=client.get_default_embedding_config() +) + +# Send a message to the agent +response = client.user_message( + agent_id=agent_state.id, + message="Hi! My name is Alice and I love reading science fiction books." +) + +print(f"Agent response: {response.messages[-1].text}") + +# Send another message - the agent will remember previous context +response = client.user_message( + agent_id=agent_state.id, + message="What did I tell you about my interests?" +) + +print(f"Agent response: {response.messages[-1].text}") +``` + + + + +```python +import letta +from letta import create_client +import litellm +import os + +# Set up environment variables +os.environ["OPENAI_API_KEY"] = "your-openai-key" + +# Create Letta client with LiteLLM integration +client = create_client() + +# Create a new agent +agent_state = client.create_agent( + name="my-assistant", + system="You are a helpful assistant with persistent memory.", + llm_config=client.get_default_llm_config(), + embedding_config=client.get_default_embedding_config() +) + +# Send a message to the agent +response = client.user_message( + agent_id=agent_state.id, + message="Hi! My name is Alice and I love reading science fiction books." +) + +print(f"Agent response: {response.messages[-1].text}") + +# Send another message - the agent will remember previous context +response = client.user_message( + agent_id=agent_state.id, + message="What did I tell you about my interests?" +) + +print(f"Agent response: {response.messages[-1].text}") +``` + + + + +## Advanced Configuration + +### Using Different Models for Different Agents + + + + +```python +from letta import LLMConfig, EmbeddingConfig + +# Create different LLM configurations pointing to your proxy +gpt4_config = LLMConfig( + model="gpt-4", + model_endpoint_type="openai", + model_endpoint="http://localhost:4000", + context_window=8192 +) + +claude_config = LLMConfig( + model="claude-3-sonnet", + model_endpoint_type="openai", # Using OpenAI-compatible endpoint + model_endpoint="http://localhost:4000", + context_window=200000 +) + +# Create agents with different configurations +research_agent = client.create_agent( + name="research-agent", + system="You are a research assistant specialized in analysis.", + llm_config=claude_config # Use Claude for research tasks +) + +creative_agent = client.create_agent( + name="creative-agent", + system="You are a creative writing assistant.", + llm_config=gpt4_config # Use GPT-4 for creative tasks +) +``` + + + + +```python +import os +import litellm +from letta import LLMConfig, EmbeddingConfig + +# Set up API keys for different providers +os.environ["OPENAI_API_KEY"] = "your-openai-key" +os.environ["ANTHROPIC_API_KEY"] = "your-anthropic-key" + +# Create different LLM configurations for direct SDK usage +gpt4_config = LLMConfig( + model="openai/gpt-4", # Using LiteLLM model format + model_endpoint_type="openai", + context_window=8192 +) + +claude_config = LLMConfig( + model="anthropic/claude-3-sonnet-20240229", # Using LiteLLM model format + model_endpoint_type="openai", + context_window=200000 +) + +# Create agents with different configurations +research_agent = client.create_agent( + name="research-agent", + system="You are a research assistant specialized in analysis.", + llm_config=claude_config # Use Claude for research tasks +) + +creative_agent = client.create_agent( + name="creative-agent", + system="You are a creative writing assistant.", + llm_config=gpt4_config # Use GPT-4 for creative tasks +) +``` + + + + +### Function Calling with Tools + + + + +```python +# Define custom tools for your agent +def search_web(query: str) -> str: + """Search the web for information""" + # Your web search implementation + return f"Search results for: {query}" + +def save_note(content: str) -> str: + """Save a note to persistent storage""" + # Your note saving implementation + return f"Note saved: {content}" + +# Create agent with tools (using proxy endpoint) +agent_state = client.create_agent( + name="research-assistant", + system="You are a research assistant that can search the web and save notes.", + llm_config=client.get_default_llm_config(), + embedding_config=client.get_default_embedding_config(), + tools=[search_web, save_note] +) + +# The agent can now use these tools +response = client.user_message( + agent_id=agent_state.id, + message="Search for recent developments in AI and save important findings." +) +``` + + + + +```python +import litellm +import os + +# Set up API keys +os.environ["OPENAI_API_KEY"] = "your-openai-key" + +# Define custom tools for your agent +def search_web(query: str) -> str: + """Search the web for information""" + # Your web search implementation + return f"Search results for: {query}" + +def save_note(content: str) -> str: + """Save a note to persistent storage""" + # Your note saving implementation + return f"Note saved: {content}" + +# Create agent with tools (using LiteLLM SDK directly) +agent_state = client.create_agent( + name="research-assistant", + system="You are a research assistant that can search the web and save notes.", + llm_config=LLMConfig( + model="openai/gpt-4", # Direct model specification + model_endpoint_type="openai", + context_window=8192 + ), + embedding_config=client.get_default_embedding_config(), + tools=[search_web, save_note] +) + +# The agent can now use these tools +response = client.user_message( + agent_id=agent_state.id, + message="Search for recent developments in AI and save important findings." +) +``` + + + + +## Authentication + + + + +If your LiteLLM proxy requires authentication: + +```python +import os +from letta import LLMConfig + +# Set up authenticated configuration +llm_config = LLMConfig( + model="gpt-4", + model_endpoint_type="openai", + model_endpoint="http://localhost:4000", + model_wrapper="openai", + context_window=8192 +) + +# If using API keys with your proxy +os.environ["OPENAI_API_KEY"] = "your-litellm-proxy-api-key" + +client = create_client() +client.set_default_llm_config(llm_config) +``` + +For proxy with authentication enabled: + +```yaml +# config.yaml with auth +general_settings: + master_key: "your-master-key" + +model_list: + - model_name: gpt-4 + litellm_params: + model: openai/gpt-4 + api_key: os.environ/OPENAI_API_KEY +``` + +```python +# Configure Letta with authenticated proxy +llm_config = LLMConfig( + model="gpt-4", + model_endpoint_type="openai", + model_endpoint="http://localhost:4000", + context_window=8192, + api_key="your-master-key" # Proxy master key +) +``` + + + + +With LiteLLM SDK, set up your provider API keys directly: + +```python +import os +import litellm + +# Set up API keys for different providers +os.environ["OPENAI_API_KEY"] = "your-openai-api-key" +os.environ["ANTHROPIC_API_KEY"] = "your-anthropic-api-key" +os.environ["AZURE_API_KEY"] = "your-azure-api-key" +os.environ["AZURE_API_BASE"] = "https://your-resource.openai.azure.com" +os.environ["AZURE_API_VERSION"] = "2023-07-01-preview" + +# Optional: Configure default settings +litellm.api_key = os.environ.get("OPENAI_API_KEY") # Default key +litellm.set_verbose = True # For debugging + +# Use in Letta configuration +from letta import LLMConfig + +llm_config = LLMConfig( + model="openai/gpt-4", # Will use OPENAI_API_KEY automatically + model_endpoint_type="openai", + context_window=8192 +) + +# Or for Azure +azure_config = LLMConfig( + model="azure/gpt-35-turbo", + model_endpoint_type="openai", + context_window=4096 +) +``` + + + + +## Load Balancing and Fallbacks + + + + +LiteLLM proxy's load balancing and fallback features work seamlessly with Letta: + +```yaml +# config.yaml with fallbacks +model_list: + - model_name: gpt-4 + litellm_params: + model: openai/gpt-4 + api_key: os.environ/OPENAI_API_KEY + tpm: 40000 + rpm: 500 + + - model_name: gpt-4 # Same model name for fallback + litellm_params: + model: azure/gpt-4 + api_key: os.environ/AZURE_API_KEY + api_base: os.environ/AZURE_API_BASE + api_version: "2023-07-01-preview" + tpm: 80000 + rpm: 800 + +router_settings: + routing_strategy: "usage-based-routing" + fallbacks: [{"gpt-4": ["azure/gpt-4"]}] +``` + +The proxy handles all routing, load balancing, and fallbacks transparently for Letta. + + + + +With LiteLLM SDK, you can set up routing and fallbacks programmatically: + +```python +import litellm +from litellm import Router + +# Configure router with multiple models +router = Router( + model_list=[ + { + "model_name": "gpt-4", + "litellm_params": { + "model": "openai/gpt-4", + "api_key": os.environ["OPENAI_API_KEY"] + }, + "tpm": 40000, + "rpm": 500 + }, + { + "model_name": "gpt-4", # Same name for fallback + "litellm_params": { + "model": "azure/gpt-4", + "api_key": os.environ["AZURE_API_KEY"], + "api_base": os.environ["AZURE_API_BASE"], + "api_version": "2023-07-01-preview" + }, + "tpm": 80000, + "rpm": 800 + } + ], + fallbacks=[{"gpt-4": ["azure/gpt-4"]}], + routing_strategy="usage-based-routing" +) + +# Create custom completion function for Letta +def custom_completion(messages, model="gpt-4", **kwargs): + return router.completion( + model=model, + messages=messages, + **kwargs + ) + +# Use with Letta by monkey-patching or custom wrapper +litellm.completion = custom_completion +``` + + + + +## Monitoring and Observability + + + + +Enable logging to track your Letta agents' LLM usage through the proxy: + +```yaml +# config.yaml with logging +model_list: + # ... your models + +litellm_settings: + success_callback: ["langfuse"] # or other observability tools + +environment_variables: + LANGFUSE_PUBLIC_KEY: "your-key" + LANGFUSE_SECRET_KEY: "your-secret" +``` + +View metrics in the proxy dashboard: +```bash +# Start proxy with UI +litellm --config config.yaml --port 4000 --detailed_debug +``` + + + + +Set up observability directly in your SDK integration: + +```python +import litellm +import os + +# Configure observability callbacks +os.environ["LANGFUSE_PUBLIC_KEY"] = "your-key" +os.environ["LANGFUSE_SECRET_KEY"] = "your-secret" + +# Set global callbacks +litellm.success_callback = ["langfuse"] +litellm.failure_callback = ["langfuse"] + +# Optional: Set up custom logging +litellm.set_verbose = True + +# Create custom completion wrapper with logging +def logged_completion(messages, model="gpt-4", **kwargs): + try: + response = litellm.completion( + model=model, + messages=messages, + **kwargs + ) + # Custom logging logic here if needed + return response + except Exception as e: + # Custom error handling + print(f"LLM call failed: {e}") + raise + +# Use in Letta configuration +litellm.completion = logged_completion +``` + + + + +## Example: Multi-Agent System + + + + +```python +import letta +from letta import create_client, LLMConfig + +client = create_client() + +# Create specialized agents using proxy endpoints +agents = {} + +# Research agent using Claude for analysis +agents['researcher'] = client.create_agent( + name="researcher", + system="You are a research specialist. Analyze information thoroughly.", + llm_config=LLMConfig( + model="claude-3-sonnet", + model_endpoint="http://localhost:4000", + model_endpoint_type="openai" + ) +) + +# Writer agent using GPT-4 for content creation +agents['writer'] = client.create_agent( + name="writer", + system="You are a content writer. Create engaging, well-structured content.", + llm_config=LLMConfig( + model="gpt-4", + model_endpoint="http://localhost:4000", + model_endpoint_type="openai" + ) +) + +# Coordinator workflow +def research_and_write_workflow(topic: str): + # Research phase + research_response = client.user_message( + agent_id=agents['researcher'].id, + message=f"Research the topic: {topic}. Provide key insights and data." + ) + + research_results = research_response.messages[-1].text + + # Writing phase + write_response = client.user_message( + agent_id=agents['writer'].id, + message=f"Based on this research: {research_results}\n\nWrite an article about {topic}." + ) + + return write_response.messages[-1].text + +# Execute workflow +article = research_and_write_workflow("The future of AI in healthcare") +print(article) +``` + + + + +```python +import letta +from letta import create_client, LLMConfig +import litellm +import os + +# Set up environment +os.environ["OPENAI_API_KEY"] = "your-openai-key" +os.environ["ANTHROPIC_API_KEY"] = "your-anthropic-key" + +client = create_client() + +# Create specialized agents using direct SDK models +agents = {} + +# Research agent using Claude for analysis +agents['researcher'] = client.create_agent( + name="researcher", + system="You are a research specialist. Analyze information thoroughly.", + llm_config=LLMConfig( + model="anthropic/claude-3-sonnet-20240229", + model_endpoint_type="openai" + ) +) + +# Writer agent using GPT-4 for content creation +agents['writer'] = client.create_agent( + name="writer", + system="You are a content writer. Create engaging, well-structured content.", + llm_config=LLMConfig( + model="openai/gpt-4", + model_endpoint_type="openai" + ) +) + +# Cost-conscious agent using GPT-3.5 +agents['reviewer'] = client.create_agent( + name="reviewer", + system="You are an editor. Review and improve content quality.", + llm_config=LLMConfig( + model="openai/gpt-3.5-turbo", + model_endpoint_type="openai" + ) +) + +# Enhanced workflow with multiple agents +def enhanced_workflow(topic: str): + # Research phase + research_response = client.user_message( + agent_id=agents['researcher'].id, + message=f"Research the topic: {topic}. Provide key insights and data." + ) + + research_results = research_response.messages[-1].text + + # Writing phase + write_response = client.user_message( + agent_id=agents['writer'].id, + message=f"Based on this research: {research_results}\n\nWrite an article about {topic}." + ) + + draft_article = write_response.messages[-1].text + + # Review phase + review_response = client.user_message( + agent_id=agents['reviewer'].id, + message=f"Please review and improve this article:\n\n{draft_article}" + ) + + return review_response.messages[-1].text + +# Execute enhanced workflow +article = enhanced_workflow("The future of AI in healthcare") +print(article) +``` + + + + +## Best Practices + + + + +1. **Model Selection**: Use appropriate models for different tasks: + - Claude for analysis and reasoning + - GPT-4 for creative tasks + - GPT-3.5-turbo for simple interactions + +2. **Proxy Configuration**: + - Set appropriate rate limits and timeouts + - Use fallbacks for reliability + - Enable authentication for production + +3. **Memory Management**: Letta handles memory automatically, but monitor usage with large contexts + +4. **Cost Optimization**: + - Use the proxy's budgeting features to control costs + - Set up rate limiting per user/team + - Monitor token usage through proxy dashboard + +5. **Monitoring**: Enable observability to track agent performance and token usage + + + + +1. **Model Selection**: Choose models based on task requirements: + - Use `openai/gpt-4` for complex reasoning + - Use `anthropic/claude-3-sonnet-20240229` for analysis + - Use `openai/gpt-3.5-turbo` for cost-effective simple tasks + +2. **Error Handling**: Implement robust error handling with retries: + ```python + import litellm + from litellm import completion + + # Set up retry logic + litellm.num_retries = 3 + litellm.request_timeout = 60 + + # Custom error handling + def safe_completion(**kwargs): + try: + return completion(**kwargs) + except Exception as e: + print(f"LLM call failed: {e}") + # Implement fallback logic + return completion(model="openai/gpt-3.5-turbo", **kwargs) + ``` + +3. **Cost Management**: + - Use cheaper models for non-critical tasks + - Implement token counting and budgets + - Cache responses when appropriate + +4. **Performance**: + - Use async operations for concurrent requests + - Implement connection pooling + - Monitor response times + +5. **Security**: + - Store API keys securely (environment variables) + - Rotate keys regularly + - Implement rate limiting + + + + +## Troubleshooting + + + + +### Connection Issues +```bash +# Test your LiteLLM proxy +curl -X POST http://localhost:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -d '{ + "model": "gpt-4", + "messages": [{"role": "user", "content": "Hello"}] + }' +``` + +### Configuration Debugging +```python +# Enable verbose logging +import logging +logging.basicConfig(level=logging.DEBUG) + +# Test Letta configuration +client = create_client() +print(client.get_default_llm_config()) +``` + +### Common Proxy Issues +- **Port conflicts**: Make sure port 4000 isn't in use +- **Model not found**: Verify model names match your config.yaml +- **Authentication errors**: Check master key configuration +- **Rate limiting**: Monitor proxy logs for rate limit hits + + + + +### API Key Issues +```python +import os +import litellm + +# Check if API keys are set +print("OpenAI Key:", os.environ.get("OPENAI_API_KEY", "Not set")) +print("Anthropic Key:", os.environ.get("ANTHROPIC_API_KEY", "Not set")) + +# Test direct LiteLLM call +try: + response = litellm.completion( + model="openai/gpt-3.5-turbo", + messages=[{"role": "user", "content": "Hello"}] + ) + print("LiteLLM working:", response.choices[0].message.content) +except Exception as e: + print("LiteLLM error:", e) +``` + +### Configuration Debugging +```python +# Enable verbose logging +litellm.set_verbose = True + +# Test model availability +models = ["openai/gpt-4", "anthropic/claude-3-sonnet-20240229"] +for model in models: + try: + response = litellm.completion( + model=model, + messages=[{"role": "user", "content": "Test"}], + max_tokens=10 + ) + print(f"✓ {model} working") + except Exception as e: + print(f"✗ {model} failed: {e}") +``` + +### Common SDK Issues +- **Import errors**: Ensure `pip install litellm letta` is run +- **Model format**: Use `provider/model` format (e.g., `openai/gpt-4`) +- **API key format**: Different providers have different key formats +- **Rate limits**: Implement exponential backoff for retries + + + + +## Resources + +- [Letta Documentation](https://docs.letta.ai/) +- [LiteLLM Proxy Documentation](../proxy/quick_start.md) +- [LiteLLM SDK Documentation](../completion/input.md) +- [Function Calling Guide](../completion/function_call.md) +- [Observability Setup](../observability/langfuse_integration.md) +- [Router Configuration](../routing.md) \ No newline at end of file diff --git a/docs/my-website/docs/langchain/langchain.md b/docs/my-website/docs/langchain/langchain.md index 78425a73b99..c67375ce1be 100644 --- a/docs/my-website/docs/langchain/langchain.md +++ b/docs/my-website/docs/langchain/langchain.md @@ -162,3 +162,321 @@ Get more details [here](../observability/lunary_integration.md) ## Use LangChain ChatLiteLLM + Langfuse Checkout this section [here](../observability/langfuse_integration#use-langchain-chatlitellm--langfuse) for more details on how to integrate Langfuse with ChatLiteLLM. + +## Using Tags with LangChain and LiteLLM + +Tags are a powerful feature in LiteLLM that allow you to categorize, filter, and track your LLM requests. When using LangChain with LiteLLM, you can pass tags through the `extra_body` parameter in the metadata. + +### Basic Tag Usage + + + + +```python +import os +from langchain_openai import ChatOpenAI +from langchain_core.messages import HumanMessage, SystemMessage + +os.environ['OPENAI_API_KEY'] = "sk-your-key-here" + +chat = ChatOpenAI( + model="gpt-4o", + temperature=0.7, + extra_body={ + "metadata": { + "tags": ["production", "customer-support", "high-priority"] + } + } +) + +messages = [ + SystemMessage(content="You are a helpful customer support assistant."), + HumanMessage(content="How do I reset my password?") +] + +response = chat.invoke(messages) +print(response) +``` + + + + + +```python +import os +from langchain_openai import ChatOpenAI +from langchain_core.messages import HumanMessage, SystemMessage + +os.environ['ANTHROPIC_API_KEY'] = "sk-ant-your-key-here" + +chat = ChatOpenAI( + model="claude-3-sonnet-20240229", + temperature=0.7, + extra_body={ + "metadata": { + "tags": ["research", "analysis", "claude-model"] + } + } +) + +messages = [ + SystemMessage(content="You are a research analyst."), + HumanMessage(content="Analyze this market trend...") +] + +response = chat.invoke(messages) +print(response) +``` + + + + + +```python +import os +from langchain_openai import ChatOpenAI +from langchain_core.messages import HumanMessage, SystemMessage + +# No API key needed when using proxy +chat = ChatOpenAI( + openai_api_base="http://localhost:4000", # Your proxy URL + model="gpt-4o", + temperature=0.7, + extra_body={ + "metadata": { + "tags": ["proxy", "team-alpha", "feature-flagged"], + "generation_name": "customer-onboarding", + "trace_user_id": "user-12345" + } + } +) + +messages = [ + SystemMessage(content="You are an onboarding assistant."), + HumanMessage(content="Welcome our new customer!") +] + +response = chat.invoke(messages) +print(response) +``` + + + + +### Advanced Tag Patterns + +#### Dynamic Tags Based on Context + +```python +import os +from langchain_openai import ChatOpenAI +from langchain_core.messages import HumanMessage, SystemMessage + +def create_chat_with_tags(user_type: str, feature: str): + """Create a chat instance with dynamic tags based on context""" + + # Build tags dynamically + tags = ["langchain-integration"] + + if user_type == "premium": + tags.extend(["premium-user", "high-priority"]) + elif user_type == "enterprise": + tags.extend(["enterprise", "custom-sla"]) + else: + tags.append("standard-user") + + # Add feature-specific tags + if feature == "code-review": + tags.extend(["development", "code-analysis"]) + elif feature == "content-gen": + tags.extend(["marketing", "content-creation"]) + + return ChatOpenAI( + openai_api_base="http://localhost:4000", + model="gpt-4o", + temperature=0.7, + extra_body={ + "metadata": { + "tags": tags, + "user_type": user_type, + "feature": feature, + "trace_user_id": f"user-{user_type}-{feature}" + } + } + ) + +# Usage examples +premium_chat = create_chat_with_tags("premium", "code-review") +enterprise_chat = create_chat_with_tags("enterprise", "content-gen") + +messages = [HumanMessage(content="Help me with this task")] +response = premium_chat.invoke(messages) +``` + +#### Tags for Cost Tracking and Analytics + +```python +import os +from langchain_openai import ChatOpenAI +from langchain_core.messages import HumanMessage, SystemMessage + +# Tags for cost tracking +cost_tracking_chat = ChatOpenAI( + openai_api_base="http://localhost:4000", + model="gpt-4o", + temperature=0.7, + extra_body={ + "metadata": { + "tags": [ + "cost-center-marketing", + "budget-q4-2024", + "project-launch-campaign", + "high-cost-model" # Flag for expensive models + ], + "department": "marketing", + "project_id": "campaign-2024-q4", + "cost_threshold": "high" + } + } +) + +messages = [ + SystemMessage(content="You are a marketing copywriter."), + HumanMessage(content="Create compelling ad copy for our new product launch.") +] + +response = cost_tracking_chat.invoke(messages) +``` + +#### Tags for A/B Testing + +```python +import os +from langchain_openai import ChatOpenAI +from langchain_core.messages import HumanMessage, SystemMessage +import random + +def create_ab_test_chat(test_variant: str = None): + """Create chat instance for A/B testing with appropriate tags""" + + if test_variant is None: + test_variant = random.choice(["variant-a", "variant-b"]) + + return ChatOpenAI( + openai_api_base="http://localhost:4000", + model="gpt-4o", + temperature=0.7 if test_variant == "variant-a" else 0.9, # Different temp for variants + extra_body={ + "metadata": { + "tags": [ + "ab-test-experiment-1", + f"variant-{test_variant}", + "temperature-test", + "user-experience" + ], + "experiment_id": "ab-test-001", + "variant": test_variant, + "test_group": "temperature-optimization" + } + } + ) + +# Run A/B test +variant_a_chat = create_ab_test_chat("variant-a") +variant_b_chat = create_ab_test_chat("variant-b") + +test_message = [HumanMessage(content="Explain quantum computing in simple terms")] + +response_a = variant_a_chat.invoke(test_message) +response_b = variant_b_chat.invoke(test_message) +``` + +### Tag Best Practices + +#### 1. **Consistent Naming Convention** +```python +# ✅ Good: Consistent, descriptive tags +tags = ["production", "api-v2", "customer-support", "urgent"] + +# ❌ Avoid: Inconsistent or unclear tags +tags = ["prod", "v2", "support", "urgent123"] +``` + +#### 2. **Hierarchical Tags** +```python +# ✅ Good: Hierarchical structure +tags = ["env:production", "team:backend", "service:api", "priority:high"] + +# This allows for easy filtering and grouping +``` + +#### 3. **Include Context Information** +```python +extra_body={ + "metadata": { + "tags": ["production", "user-onboarding"], + "user_id": "user-12345", + "session_id": "session-abc123", + "feature_flag": "new-onboarding-flow", + "environment": "production" + } +} +``` + +#### 4. **Tag Categories** +Consider organizing tags into categories: +- **Environment**: `production`, `staging`, `development` +- **Team/Service**: `backend`, `frontend`, `api`, `worker` +- **Feature**: `authentication`, `payment`, `notification` +- **Priority**: `critical`, `high`, `medium`, `low` +- **User Type**: `premium`, `enterprise`, `free` + +### Using Tags with LiteLLM Proxy + +When using tags with LiteLLM Proxy, you can: + +1. **Filter requests** based on tags +2. **Track costs** by tags in spend reports +3. **Apply routing rules** based on tags +4. **Monitor usage** with tag-based analytics + +#### Example Proxy Configuration with Tags + +```yaml +# config.yaml +model_list: + - model_name: gpt-4o + litellm_params: + model: gpt-4o + api_key: your-key + +# Tag-based routing rules +tag_routing: + - tags: ["premium", "high-priority"] + models: ["gpt-4o", "claude-3-opus"] + - tags: ["standard"] + models: ["gpt-3.5-turbo", "claude-3-haiku"] +``` + +### Monitoring and Analytics + +Tags enable powerful analytics capabilities: + +```python +# Example: Get spend reports by tags +import requests + +response = requests.get( + "http://localhost:4000/global/spend/report", + headers={"Authorization": "Bearer sk-your-key"}, + params={ + "start_date": "2024-01-01", + "end_date": "2024-12-31", + "group_by": "tags" + } +) + +spend_by_tags = response.json() +``` + +This documentation covers the essential patterns for using tags effectively with LangChain and LiteLLM, enabling better organization, tracking, and analytics of your LLM requests. diff --git a/docs/my-website/docs/load_test_advanced.md b/docs/my-website/docs/load_test_advanced.md index 0b3d38f3fcc..3171bc33594 100644 --- a/docs/my-website/docs/load_test_advanced.md +++ b/docs/my-website/docs/load_test_advanced.md @@ -27,13 +27,13 @@ Tutorial on how to get to 1K+ RPS with LiteLLM Proxy on locust **Use this config for testing:** -**Note:** we're currently migrating to aiohttp which has 10x higher throughput. We recommend using the `aiohttp_openai/` provider for load testing. +**Note:** we're currently migrating to aiohttp which has 10x higher throughput. We recommend using the `openai/` provider for load testing. ```yaml model_list: - model_name: "fake-openai-endpoint" litellm_params: - model: aiohttp_openai/any + model: openai/any api_base: https://your-fake-openai-endpoint.com/chat/completions api_key: "test" ``` @@ -58,7 +58,7 @@ litellm provides a hosted `fake-openai-endpoint` you can load test against model_list: - model_name: fake-openai-endpoint litellm_params: - model: aiohttp_openai/fake + model: openai/fake api_key: fake-key api_base: https://exampleopenaiendpoint-production.up.railway.app/ diff --git a/docs/my-website/docs/load_test_rpm.md b/docs/my-website/docs/load_test_rpm.md index 0954ffcdfac..b7621a76468 100644 --- a/docs/my-website/docs/load_test_rpm.md +++ b/docs/my-website/docs/load_test_rpm.md @@ -53,8 +53,8 @@ model_list = [ }, ] -router_1 = Router(model_list=model_list, num_retries=0, enable_pre_call_checks=True, routing_strategy="usage-based-routing-v2", redis_host=os.getenv("REDIS_HOST"), redis_port=os.getenv("REDIS_PORT"), redis_password=os.getenv("REDIS_PASSWORD")) -router_2 = Router(model_list=model_list, num_retries=0, routing_strategy="usage-based-routing-v2", enable_pre_call_checks=True, redis_host=os.getenv("REDIS_HOST"), redis_port=os.getenv("REDIS_PORT"), redis_password=os.getenv("REDIS_PASSWORD")) +router_1 = Router(model_list=model_list, num_retries=0, enable_pre_call_checks=True, routing_strategy="simple-shuffle", redis_host=os.getenv("REDIS_HOST"), redis_port=os.getenv("REDIS_PORT"), redis_password=os.getenv("REDIS_PASSWORD")) +router_2 = Router(model_list=model_list, num_retries=0, routing_strategy="simple-shuffle", enable_pre_call_checks=True, redis_host=os.getenv("REDIS_HOST"), redis_port=os.getenv("REDIS_PORT"), redis_password=os.getenv("REDIS_PASSWORD")) @@ -142,7 +142,7 @@ router_settings: redis_host: os.environ/REDIS_HOST ## 👈 IMPORTANT! Setup the proxy w/ redis redis_password: os.environ/REDIS_PASSWORD redis_port: os.environ/REDIS_PORT - routing_strategy: usage-based-routing-v2 + routing_strategy: simple-shuffle # recommended for best performance ``` ### 2. Start proxy 2 instances diff --git a/docs/my-website/docs/mcp.md b/docs/my-website/docs/mcp.md index 684c2e6ca74..80b4c32d0ab 100644 --- a/docs/my-website/docs/mcp.md +++ b/docs/my-website/docs/mcp.md @@ -40,7 +40,28 @@ LiteLLM supports the following MCP transports: style={{width: '80%', display: 'block', margin: '0'}} /> -### Adding a stdio MCP Server +
+
+ +### Add HTTP MCP Server + +This video walks through adding and using an HTTP MCP server on LiteLLM UI and using it in Cursor IDE. + + + +
+
+ +### Add SSE MCP Server + +This video walks through adding and using an SSE MCP server on LiteLLM UI and using it in Cursor IDE. + + + +
+
+ +### Add STDIO MCP Server For stdio MCP servers, select "Standard Input/Output (stdio)" as the transport type and provide the stdio configuration in JSON format: @@ -92,7 +113,7 @@ mcp_servers: transport: "http" description: "My custom MCP server" auth_type: "api_key" - spec_version: "2025-03-26" + auth_value: "abc123" ``` **Configuration Options:** @@ -107,8 +128,42 @@ mcp_servers: - **Args**: Array of arguments to pass to the command (optional for stdio) - **Env**: Environment variables to set for the stdio process (optional for stdio) - **Description**: Optional description for the server -- **Auth Type**: Optional authentication type -- **Spec Version**: Optional MCP specification version (defaults to `2025-03-26`) +- **Auth Type**: Optional authentication type. Supported values: + + | Value | Header sent | + |-------|-------------| + | `api_key` | `X-API-Key: ` | + | `bearer_token` | `Authorization: Bearer ` | + | `basic` | `Authorization: Basic ` | + | `authorization` | `Authorization: ` | + +- **Spec Version**: Optional MCP specification version (defaults to `2025-06-18`) + +Examples for each auth type: + +```yaml title="MCP auth examples (config.yaml)" showLineNumbers +mcp_servers: + api_key_example: + url: "https://my-mcp-server.com/mcp" + auth_type: "api_key" + auth_value: "abc123" # headers={"X-API-Key": "abc123"} + + bearer_example: + url: "https://my-mcp-server.com/mcp" + auth_type: "bearer_token" + auth_value: "abc123" # headers={"Authorization": "Bearer abc123"} + + basic_example: + url: "https://my-mcp-server.com/mcp" + auth_type: "basic" + auth_value: "dXNlcjpwYXNz" # headers={"Authorization": "Basic dXNlcjpwYXNz"} + + custom_auth_example: + url: "https://my-mcp-server.com/mcp" + auth_type: "authorization" + auth_value: "Token example123" # headers={"Authorization": "Token example123"} +``` + ### MCP Aliases @@ -139,70 +194,169 @@ litellm_settings: ## Using your MCP +### Use on LiteLLM UI + +Follow this walkthrough to use your MCP on LiteLLM UI + + + +### Use with Responses API + +Replace `http://localhost:4000` with your LiteLLM Proxy base URL. + +Demo Video Using Responses API with LiteLLM Proxy: [Demo video here](https://www.loom.com/share/34587e618c5c47c0b0d67b4e4d02718f?sid=2caf3d45-ead4-4490-bcc1-8d6dd6041c02) + + - - -#### Connect via OpenAI Responses API - -Use the OpenAI Responses API to connect to your LiteLLM MCP server: + ```bash title="cURL Example" showLineNumbers -curl --location 'https://api.openai.com/v1/responses' \ +curl --location 'http://localhost:4000/v1/responses' \ --header 'Content-Type: application/json' \ ---header "Authorization: Bearer $OPENAI_API_KEY" \ +--header "Authorization: Bearer sk-1234" \ --data '{ - "model": "gpt-4o", + "model": "gpt-5", + "input": [ + { + "role": "user", + "content": "give me TLDR of what BerriAI/litellm repo is about", + "type": "message" + } + ], "tools": [ { "type": "mcp", "server_label": "litellm", "server_url": "litellm_proxy", - "require_approval": "never", - "headers": { - "x-litellm-api-key": "Bearer YOUR_LITELLM_API_KEY" - } + "require_approval": "never" } ], - "input": "Run available tools", + "stream": true, "tool_choice": "required" }' ``` + - +```python title="Python SDK Example" showLineNumbers +""" +Use LiteLLM Proxy MCP Gateway to call MCP tools. -#### Connect via LiteLLM Proxy Responses API +When using LiteLLM Proxy, you can use the same MCP tools across all your LLM providers. +""" +import openai -Use this when calling LiteLLM Proxy for LLM API requests to `/v1/responses` endpoint. +client = openai.OpenAI( + api_key="sk-1234", # paste your litellm proxy api key here + base_url="http://localhost:4000" # paste your litellm proxy base url here +) +print("Making API request to Responses API with MCP tools") -```bash title="cURL Example" showLineNumbers -curl --location '/v1/responses' \ ---header 'Content-Type: application/json' \ ---header "Authorization: Bearer $LITELLM_API_KEY" \ ---data '{ - "model": "gpt-4o", - "tools": [ +response = client.responses.create( + model="gpt-5", + input=[ + { + "role": "user", + "content": "give me TLDR of what BerriAI/litellm repo is about", + "type": "message" + } + ], + tools=[ { "type": "mcp", "server_label": "litellm", "server_url": "litellm_proxy", - "require_approval": "never", - "headers": { - "x-litellm-api-key": "Bearer YOUR_LITELLM_API_KEY" - } + "require_approval": "never" } ], - "input": "Run available tools", + stream=True, + tool_choice="required" +) + +for chunk in response: + print("response chunk: ", chunk) +``` + + + + +#### Specifying MCP Tools + +You can specify which MCP tools are available by using the `allowed_tools` parameter. This allows you to restrict access to specific tools within an MCP server. + +To get the list of allowed tools when using LiteLLM MCP Gateway, you can naigate to the LiteLLM UI on MCP Servers > MCP Tools > Click the Tool > Copy Tool Name. + + + + +```bash title="cURL Example with allowed_tools" showLineNumbers +curl --location 'http://localhost:4000/v1/responses' \ +--header 'Content-Type: application/json' \ +--header "Authorization: Bearer sk-1234" \ +--data '{ + "model": "gpt-5", + "input": [ + { + "role": "user", + "content": "give me TLDR of what BerriAI/litellm repo is about", + "type": "message" + } + ], + "tools": [ + { + "type": "mcp", + "server_label": "litellm", + "server_url": "litellm_proxy/mcp", + "require_approval": "never", + "allowed_tools": ["GitMCP-fetch_litellm_documentation"] + } + ], + "stream": true, "tool_choice": "required" }' ``` + - +```python title="Python SDK Example with allowed_tools" showLineNumbers +import openai -#### Connect via Cursor IDE +client = openai.OpenAI( + api_key="sk-1234", + base_url="http://localhost:4000" +) + +response = client.responses.create( + model="gpt-5", + input=[ + { + "role": "user", + "content": "give me TLDR of what BerriAI/litellm repo is about", + "type": "message" + } + ], + tools=[ + { + "type": "mcp", + "server_label": "litellm", + "server_url": "litellm_proxy/mcp", + "require_approval": "never", + "allowed_tools": ["GitMCP-fetch_litellm_documentation"] + } + ], + stream=True, + tool_choice="required" +) + +print(response) +``` + + + + +### Use with Cursor IDE Use tools directly from Cursor IDE with LiteLLM MCP: @@ -225,9 +379,6 @@ Use tools directly from Cursor IDE with LiteLLM MCP: } ``` - - - #### How it works when server_url="litellm_proxy" When server_url="litellm_proxy", LiteLLM bridges non-MCP providers to your MCP tools. @@ -564,7 +715,6 @@ mcp_servers: url: https://mcp.deepwiki.com/mcp transport: "http" auth_type: "none" - spec_version: "2025-03-26" access_groups: ["dev_group"] ``` diff --git a/docs/my-website/docs/moderation.md b/docs/my-website/docs/moderation.md index 95fe8b2856d..f9c2810bc8a 100644 --- a/docs/my-website/docs/moderation.md +++ b/docs/my-website/docs/moderation.md @@ -130,6 +130,8 @@ Here's the exact json output and type you can expect from all moderation calls: ## **Supported Providers** +#### ⚡️See all supported models and providers at [models.litellm.ai](https://models.litellm.ai/) + | Provider | |-------------| | OpenAI | diff --git a/docs/my-website/docs/observability/braintrust.md b/docs/my-website/docs/observability/braintrust.md index eb26680b18a..e6b4fe769bc 100644 --- a/docs/my-website/docs/observability/braintrust.md +++ b/docs/my-website/docs/observability/braintrust.md @@ -71,6 +71,10 @@ curl -X POST 'http://0.0.0.0:4000/chat/completions' \ It is recommended that you include the `project_id` or `project_name` to ensure your traces are being written out to the correct Braintrust project. +### Custom Span Names + +You can customize the span name in Braintrust logging by passing `span_name` in the metadata. By default, the span name is set to "Chat Completion". + @@ -84,7 +88,9 @@ response = litellm.completion( "project_id": "1234", # passing project_name will try to find a project with that name, or create one if it doesn't exist # if both project_id and project_name are passed, project_id will be used - # "project_name": "my-special-project" + # "project_name": "my-special-project", + # custom span name for this operation (default: "Chat Completion") + "span_name": "User Greeting Handler" } ) ``` @@ -99,6 +105,7 @@ response = litellm.completion( ], metadata={ "project_id": "1234", + "span_name": "Custom Operation", "item1": "an item", "item2": "another item" } @@ -121,7 +128,8 @@ curl -X POST 'http://0.0.0.0:4000/chat/completions' \ { "role": "user", "content": "What time is it now? Use your tool"} ], "metadata": { - "project_id": "my-special-project" + "project_id": "my-special-project", + "span_name": "Tool Usage Request" } }' ``` @@ -146,7 +154,8 @@ response = client.chat.completions.create( ], extra_body={ # pass in any provider-specific param, if not supported by openai, https://docs.litellm.ai/docs/completion/input#provider-specific-params "metadata": { # 👈 use for logging additional params (e.g. to braintrust) - "project_id": "my-special-project" + "project_id": "my-special-project", + "span_name": "Poetry Generation" } } ) @@ -168,3 +177,7 @@ Here's everything you can pass in metadata for a braintrust request `braintrust_*` - If you are adding metadata from _proxy request headers_, any metadata field starting with `braintrust_` will be passed as metadata to the logging request. If you are using the SDK, just pass your metadata like normal (e.g., `metadata={"project_name": "my-test-project", "item1": "an item", "item2": "another item"}`) `project_id` - Set the project id for a braintrust call. Default is `litellm`. + +`project_name` - Set the project name for a braintrust call. Will try to find a project with that name, or create one if it doesn't exist. If both `project_id` and `project_name` are passed, `project_id` will be used. + +`span_name` - Set a custom span name for the operation. Default is `"Chat Completion"`. Use this to provide more descriptive names for different types of operations in your application (e.g., "User Query", "Document Summary", "Code Generation"). diff --git a/docs/my-website/docs/observability/callbacks.md b/docs/my-website/docs/observability/callbacks.md index 69cb0d053ee..b752bdc2764 100644 --- a/docs/my-website/docs/observability/callbacks.md +++ b/docs/my-website/docs/observability/callbacks.md @@ -4,9 +4,16 @@ liteLLM provides `input_callbacks`, `success_callbacks` and `failure_callbacks`, making it easy for you to send data to a particular provider depending on the status of your responses. -liteLLM supports: +:::tip +**New to LiteLLM Callbacks?** + +- For proxy/server logging and observability, see the [Proxy Logging Guide](https://docs.litellm.ai/docs/proxy/logging). +- To write your own callback logic, see the [Custom Callbacks Guide](https://docs.litellm.ai/docs/observability/custom_callback). +::: + + +### Supported Callback Integrations -- [Custom Callback Functions](https://docs.litellm.ai/docs/observability/custom_callback) - [Lunary](https://lunary.ai/docs) - [Langfuse](https://langfuse.com/docs) - [LangSmith](https://www.langchain.com/langsmith) @@ -16,9 +23,20 @@ liteLLM supports: - [Sentry](https://docs.sentry.io/platforms/python/) - [PostHog](https://posthog.com/docs/libraries/python) - [Slack](https://slack.dev/bolt-python/concepts) +- [Arize](https://docs.arize.com/) +- [PromptLayer](https://docs.promptlayer.com/) This is **not** an extensive list. Please check the dropdown for all logging integrations. +### Related Cookbooks +Try out our cookbooks for code snippets and interactive demos: + +- [Langfuse Callback Example (Colab)](https://colab.research.google.com/github/BerriAI/litellm/blob/main/cookbook/logging_observability/LiteLLM_Langfuse.ipynb) +- [Lunary Callback Example (Colab)](https://colab.research.google.com/github/BerriAI/litellm/blob/main/cookbook/logging_observability/LiteLLM_Lunary.ipynb) +- [Arize Callback Example (Colab)](https://colab.research.google.com/github/BerriAI/litellm/blob/main/cookbook/logging_observability/LiteLLM_Arize.ipynb) +- [Proxy + Langfuse Callback Example (Colab)](https://colab.research.google.com/github/BerriAI/litellm/blob/main/cookbook/logging_observability/LiteLLM_Proxy_Langfuse.ipynb) +- [PromptLayer Callback Example (Colab)](https://colab.research.google.com/github/BerriAI/litellm/blob/main/cookbook/LiteLLM_PromptLayer.ipynb) + ### Quick Start ```python diff --git a/docs/my-website/docs/observability/cloudzero.md b/docs/my-website/docs/observability/cloudzero.md new file mode 100644 index 00000000000..f213ef64e13 --- /dev/null +++ b/docs/my-website/docs/observability/cloudzero.md @@ -0,0 +1,209 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# CloudZero Integration + +LiteLLM provides an integration with CloudZero's AnyCost API, allowing you to export your LLM usage data to CloudZero for cost tracking analysis. + +## Overview + +| Property | Details | +|----------|---------| +| Description | Export LiteLLM usage data to CloudZero AnyCost API for cost tracking and analysis | +| callback name | `cloudzero`| +| Supported Operations | • Automatic hourly data export
• Manual data export
• Dry run testing
• Cost and token usage tracking | +| Data Format | CloudZero Billing Format (CBF) with proper resource tagging | +| Export Frequency | Hourly (configurable via `CLOUDZERO_EXPORT_INTERVAL_MINUTES`) | + +## Environment Variables + +| Variable | Required | Description | Example | +|----------|----------|-------------|---------| +| `CLOUDZERO_API_KEY` | Yes | Your CloudZero API key | `cz_api_xxxxxxxxxx` | +| `CLOUDZERO_CONNECTION_ID` | Yes | CloudZero connection ID for data submission | `conn_xxxxxxxxxx` | +| `CLOUDZERO_TIMEZONE` | No | Timezone for date handling (default: UTC) | `America/New_York` | +| `CLOUDZERO_EXPORT_INTERVAL_MINUTES` | No | Export frequency in minutes (default: 60) | `60` | + +## Setup + +### End to End Video Walkthrough +This video walks through the entire process of setting up LiteLLM with CloudZero integration and viewing LiteLLM exported usage data in CloudZero. + + + +### Step 1: Configure Environment Variables + +Set your CloudZero credentials in your environment: + +```bash +export CLOUDZERO_API_KEY="cz_api_xxxxxxxxxx" +export CLOUDZERO_CONNECTION_ID="conn_xxxxxxxxxx" +export CLOUDZERO_TIMEZONE="UTC" # Optional, defaults to UTC +``` + +### Step 2: Enable CloudZero Integration + +Add the CloudZero callback to your LiteLLM configuration YAML file: + + +```yaml +model_list: + - model_name: gpt-4o + litellm_params: + model: openai/gpt-4o + api_key: sk-xxxxxxx + +litellm_settings: + callbacks: ["cloudzero"] # Enable CloudZero integration +``` + +### Step 3: Start LiteLLM Proxy + +Start your LiteLLM proxy with the configuration: + +```bash +litellm --config /path/to/config.yaml +``` + +## Testing Your Setup + +### Dry Run Export + +Call the dry run endpoint to test your CloudZero configuration without sending data to CloudZero. This endpoint will not send any data to CloudZero, but will return the data that would be exported. + +```bash +curl -X POST "http://localhost:4000/cloudzero/dry-run" \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer sk-1234" \ + -d '{ + "limit": 10 + }' | jq +``` + +**Expected Response:** +```json +{ + "message": "CloudZero dry run export completed successfully.", + "status": "success", + "dry_run_data": { + "usage_data": [...], + "cbf_data": [...], + "summary": { + "total_cost": 0.05, + "total_tokens": 1250, + "total_records": 10 + } + } +} +``` + +### Manual Export + +Call the export endpoint to send data immediately to CloudZero. We suggest setting a small `limit` to test the export. This will only export the last 10 records to CloudZero. Note: Cloudzero can take up to 15 minutes to process the exported data. + +```bash +curl -X POST "http://localhost:4000/cloudzero/export" \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer sk-1234" \ + -d '{ + "limit": 10 + }' | jq +``` + +**Expected Response:** +```json +{ + "message": "CloudZero export completed successfully", + "status": "success" +} +``` + +## Data Export Details + +### Automatic Export Schedule + +- **Frequency**: Every 60 minutes (configurable via `CLOUDZERO_EXPORT_INTERVAL_MINUTES`) +- **Data Processing**: LiteLLM automatically processes and exports usage data hourly +- **CloudZero Processing**: CloudZero typically takes 10-15 minutes to process data from LiteLLM + +### Data Format + +LiteLLM exports data in CloudZero Billing Format (CBF) with the following structure: + +```json +{ + "time/usage_start": "2024-01-15T14:00:00Z", + "cost/cost": 0.002, + "usage/amount": 150, + "usage/units": "tokens", + "resource/id": "czrn:litellm:openai:cross-region:team-123:llm-usage:gpt-4o", + "resource/service": "litellm", + "resource/account": "team-123", + "resource/region": "cross-region", + "resource/usage_family": "llm-usage", + "resource/tag:provider": "openai", + "resource/tag:model": "gpt-4o", + "resource/tag:prompt_tokens": "100", + "resource/tag:completion_tokens": "50" +} +``` + +### Resource Tagging + +LiteLLM automatically creates comprehensive resource tags for cost attribution: + +- **Provider Tags**: `openai`, `anthropic`, `azure`, etc. +- **Model Tags**: Specific model names like `gpt-4o`, `claude-3-sonnet` +- **Team/User Tags**: Team IDs and user IDs for cost allocation +- **Token Breakdown**: Separate tracking of prompt and completion tokens +- **Usage Metrics**: Total tokens consumed per request + +## Advanced Configuration + +### Custom Export Frequency + +Change the export frequency (not recommended to go below 60 minutes): + +```bash +export CLOUDZERO_EXPORT_INTERVAL_MINUTES=120 # Export every 2 hours +``` + +### Custom Time Range Export + +Export data for a specific time range: + +```bash +curl -X POST "http://localhost:4000/cloudzero/export" \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer sk-1234" \ + -d '{ + "start_time_utc": "2024-01-15T00:00:00Z", + "end_time_utc": "2024-01-15T23:59:59Z", + "operation": "replace_hourly" + }' | jq +``` + +## Troubleshooting + +### Common Issues + +1. **Missing Credentials Error** + ``` + CloudZero configuration missing. Please set CLOUDZERO_API_KEY and CLOUDZERO_CONNECTION_ID environment variables. + ``` + **Solution**: Ensure both environment variables are set with valid values. + +2. **Connection Issues** + - Verify your CloudZero API key is valid + - Check that the connection ID exists in your CloudZero account + - Ensure your proxy has internet access to reach CloudZero's API + +3. **No Data in CloudZero** + - CloudZero can take 10-15 minutes to process data + - Check that your LiteLLM proxy is generating usage data + - Use the dry-run endpoint to verify data is being formatted correctly + +## Related Links + +- [CloudZero Documentation](https://docs.cloudzero.com/) +- [CloudZero AnyCost API](https://docs.cloudzero.com/reference/anycost-api) diff --git a/docs/my-website/docs/observability/custom_callback.md b/docs/my-website/docs/observability/custom_callback.md index cc586b2e5d9..cfe97ca42c0 100644 --- a/docs/my-website/docs/observability/custom_callback.md +++ b/docs/my-website/docs/observability/custom_callback.md @@ -4,7 +4,6 @@ **For PROXY** [Go Here](../proxy/logging.md#custom-callback-class-async) ::: - ## Callback Class You can create a custom callback class to precisely log events as they occur in litellm. @@ -57,6 +56,34 @@ def async completion(): asyncio.run(completion()) ``` +## Common Hooks + +- `async_log_success_event` - Log successful API calls +- `async_log_failure_event` - Log failed API calls +- `log_pre_api_call` - Log before API call +- `log_post_api_call` - Log after API call + +**Proxy-only hooks** (only work with LiteLLM Proxy): +- `async_post_call_success_hook` - Access user data + modify responses +- `async_pre_call_hook` - Modify requests before sending + +### Example: Modifying the Response in async_post_call_success_hook + +You can use `async_post_call_success_hook` to add custom headers or metadata to the response before it is returned to the client. For example: + +```python +async def async_post_call_success_hook(data, user_api_key_dict, response): + # Add a custom header to the response + additional_headers = getattr(response, "_hidden_params", {}).get("additional_headers", {}) or {} + additional_headers["x-litellm-custom-header"] = "my-value" + if not hasattr(response, "_hidden_params"): + response._hidden_params = {} + response._hidden_params["additional_headers"] = additional_headers + return response +``` + +This allows you to inject custom metadata or headers into the response for downstream consumers. You can use this pattern to pass information to clients, proxies, or observability tools. + ## Callback Functions If you just want to log on a specific event (e.g. on input) - you can use callback functions. @@ -174,260 +201,87 @@ async def test_chat_openai(): asyncio.run(test_chat_openai()) ``` -:::info +## What's Available in kwargs? -We're actively trying to expand this to other event types. [Tell us if you need this!](https://github.com/BerriAI/litellm/issues/1007) -::: - -## What's in kwargs? - -Notice we pass in a kwargs argument to custom callback. -```python -def custom_callback( - kwargs, # kwargs to completion - completion_response, # response from completion - start_time, end_time # start/end time -): - # Your custom code here - print("LITELLM: in custom callback function") - print("kwargs", kwargs) - print("completion_response", completion_response) - print("start_time", start_time) - print("end_time", end_time) -``` - -This is a dictionary containing all the model-call details (the params we receive, the values we send to the http endpoint, the response we receive, stacktrace in case of errors, etc.). - -This is all logged in the [model_call_details via our Logger](https://github.com/BerriAI/litellm/blob/fc757dc1b47d2eb9d0ea47d6ad224955b705059d/litellm/utils.py#L246). - -Here's exactly what you can expect in the kwargs dictionary: -```shell -### DEFAULT PARAMS ### -"model": self.model, -"messages": self.messages, -"optional_params": self.optional_params, # model-specific params passed in -"litellm_params": self.litellm_params, # litellm-specific params passed in (e.g. metadata passed to completion call) -"start_time": self.start_time, # datetime object of when call was started - -### PRE-API CALL PARAMS ### (check via kwargs["log_event_type"]="pre_api_call") -"input" = input # the exact prompt sent to the LLM API -"api_key" = api_key # the api key used for that LLM API -"additional_args" = additional_args # any additional details for that API call (e.g. contains optional params sent) - -### POST-API CALL PARAMS ### (check via kwargs["log_event_type"]="post_api_call") -"original_response" = original_response # the original http response received (saved via response.text) - -### ON-SUCCESS PARAMS ### (check via kwargs["log_event_type"]="successful_api_call") -"complete_streaming_response" = complete_streaming_response # the complete streamed response (only set if `completion(..stream=True)`) -"end_time" = end_time # datetime object of when call was completed - -### ON-FAILURE PARAMS ### (check via kwargs["log_event_type"]="failed_api_call") -"exception" = exception # the Exception raised -"traceback_exception" = traceback_exception # the traceback generated via `traceback.format_exc()` -"end_time" = end_time # datetime object of when call was completed -``` - - -### Cache hits - -Cache hits are logged in success events as `kwarg["cache_hit"]`. - -Here's an example of accessing it: - - ```python - import litellm -from litellm.integrations.custom_logger import CustomLogger -from litellm import completion, acompletion, Cache - -class MyCustomHandler(CustomLogger): - async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): - print(f"On Success") - print(f"Value of Cache hit: {kwargs['cache_hit']"}) - -async def test_async_completion_azure_caching(): - customHandler_caching = MyCustomHandler() - litellm.cache = Cache(type="redis", host=os.environ['REDIS_HOST'], port=os.environ['REDIS_PORT'], password=os.environ['REDIS_PASSWORD']) - litellm.callbacks = [customHandler_caching] - unique_time = time.time() - response1 = await litellm.acompletion(model="azure/chatgpt-v-2", - messages=[{ - "role": "user", - "content": f"Hi 👋 - i'm async azure {unique_time}" - }], - caching=True) - await asyncio.sleep(1) - print(f"customHandler_caching.states pre-cache hit: {customHandler_caching.states}") - response2 = await litellm.acompletion(model="azure/chatgpt-v-2", - messages=[{ - "role": "user", - "content": f"Hi 👋 - i'm async azure {unique_time}" - }], - caching=True) - await asyncio.sleep(1) # success callbacks are done in parallel - print(f"customHandler_caching.states post-cache hit: {customHandler_caching.states}") - assert len(customHandler_caching.errors) == 0 - assert len(customHandler_caching.states) == 4 # pre, post, success, success - ``` - -### Get complete streaming response - -LiteLLM will pass you the complete streaming response in the final streaming chunk as part of the kwargs for your custom callback function. +The kwargs dictionary contains all the details about your API call: ```python -# litellm.set_verbose = False - def custom_callback( - kwargs, # kwargs to completion - completion_response, # response from completion - start_time, end_time # start/end time - ): - # print(f"streaming response: {completion_response}") - if "complete_streaming_response" in kwargs: - print(f"Complete Streaming Response: {kwargs['complete_streaming_response']}") - - # Assign the custom callback function - litellm.success_callback = [custom_callback] - - response = completion(model="claude-instant-1", messages=messages, stream=True) - for idx, chunk in enumerate(response): - pass -``` - - -### Log additional metadata - -LiteLLM accepts a metadata dictionary in the completion call. You can pass additional metadata into your completion call via `completion(..., metadata={"key": "value"})`. - -Since this is a [litellm-specific param](https://github.com/BerriAI/litellm/blob/b6a015404eed8a0fa701e98f4581604629300ee3/litellm/main.py#L235), it's accessible via kwargs["litellm_params"] - -```python -from litellm import completion -import os, litellm - -## set ENV variables -os.environ["OPENAI_API_KEY"] = "your-api-key" - -messages = [{ "content": "Hello, how are you?","role": "user"}] - -def custom_callback( - kwargs, # kwargs to completion - completion_response, # response from completion - start_time, end_time # start/end time -): - print(kwargs["litellm_params"]["metadata"]) +def custom_callback(kwargs, completion_response, start_time, end_time): + # Access common data + model = kwargs.get("model") + messages = kwargs.get("messages", []) + cost = kwargs.get("response_cost", 0) + cache_hit = kwargs.get("cache_hit", False) - -# Assign the custom callback function -litellm.success_callback = [custom_callback] - -response = litellm.completion(model="gpt-3.5-turbo", messages=messages, metadata={"hello": "world"}) + # Access metadata you passed in + metadata = kwargs.get("litellm_params", {}).get("metadata", {}) ``` -## Examples +**Key fields in kwargs:** +- `model` - The model name +- `messages` - Input messages +- `response_cost` - Calculated cost +- `cache_hit` - Whether response was cached +- `litellm_params.metadata` - Your custom metadata -### Custom Callback to track costs for Streaming + Non-Streaming -By default, the response cost is accessible in the logging object via `kwargs["response_cost"]` on success (sync + async) +## Practical Examples + +### Track API Costs ```python +def track_cost_callback(kwargs, completion_response, start_time, end_time): + cost = kwargs["response_cost"] # litellm calculates this for you + print(f"Request cost: ${cost}") -# Step 1. Write your custom callback function -def track_cost_callback( - kwargs, # kwargs to completion - completion_response, # response from completion - start_time, end_time # start/end time -): - try: - response_cost = kwargs["response_cost"] # litellm calculates response cost for you - print("regular response_cost", response_cost) - except: - pass - -# Step 2. Assign the custom callback function litellm.success_callback = [track_cost_callback] -# Step 3. Make litellm.completion call -response = completion( - model="gpt-3.5-turbo", - messages=[ - { - "role": "user", - "content": "Hi 👋 - i'm openai" - } - ] -) - -print(response) +response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hello"}]) ``` -### Custom Callback to log transformed Input to LLMs +### Log Inputs to LLMs ```python -def get_transformed_inputs( - kwargs, -): +def get_transformed_inputs(kwargs): params_to_model = kwargs["additional_args"]["complete_input_dict"] print("params to model", params_to_model) litellm.input_callback = [get_transformed_inputs] -def test_chat_openai(): - try: - response = completion(model="claude-2", - messages=[{ - "role": "user", - "content": "Hi 👋 - i'm openai" - }]) - - print(response) - - except Exception as e: - print(e) - pass +response = completion(model="claude-2", messages=[{"role": "user", "content": "Hello"}]) ``` -#### Output -```shell -params to model {'model': 'claude-2', 'prompt': "\n\nHuman: Hi 👋 - i'm openai\n\nAssistant: ", 'max_tokens_to_sample': 256} +### Send to External Service +```python +import requests + +def send_to_analytics(kwargs, completion_response, start_time, end_time): + data = { + "model": kwargs.get("model"), + "cost": kwargs.get("response_cost", 0), + "duration": (end_time - start_time).total_seconds() + } + requests.post("https://your-analytics.com/api", json=data) + +litellm.success_callback = [send_to_analytics] ``` -### Custom Callback to write to Mixpanel +## Common Issues + +### Callback Not Called +Make sure you: +1. Register callbacks correctly: `litellm.callbacks = [MyHandler()]` +2. Use the right hook names (check spelling) +3. Don't use proxy-only hooks in library mode + +### Performance Issues +- Use async hooks for I/O operations +- Don't block in callback functions +- Handle exceptions properly: ```python -import mixpanel -import litellm -from litellm import completion - -def custom_callback( - kwargs, # kwargs to completion - completion_response, # response from completion - start_time, end_time # start/end time -): - # Your custom code here - mixpanel.track("LLM Response", {"llm_response": completion_response}) - - -# Assign the custom callback function -litellm.success_callback = [custom_callback] - -response = completion( - model="gpt-3.5-turbo", - messages=[ - { - "role": "user", - "content": "Hi 👋 - i'm openai" - } - ] -) - -print(response) - +class SafeHandler(CustomLogger): + async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): + try: + await external_service(response_obj) + except Exception as e: + print(f"Callback error: {e}") # Log but don't break the flow ``` - - - - - - - - - - - diff --git a/docs/my-website/docs/observability/helicone_integration.md b/docs/my-website/docs/observability/helicone_integration.md index 9b807b8d0f6..22ea051f7cd 100644 --- a/docs/my-website/docs/observability/helicone_integration.md +++ b/docs/my-website/docs/observability/helicone_integration.md @@ -1,3 +1,6 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + # Helicone - OSS LLM Observability Platform :::tip @@ -9,9 +12,68 @@ https://github.com/BerriAI/litellm [Helicone](https://helicone.ai/) is an open source observability platform that proxies your LLM requests and provides key insights into your usage, spend, latency and more. -## Using Helicone with LiteLLM +## Quick Start -LiteLLM provides `success_callbacks` and `failure_callbacks`, allowing you to easily log data to Helicone based on the status of your responses. + + + +Use just 1 line of code to instantly log your responses **across all providers** with Helicone: + +```python +import os +from litellm import completion + +## Set env variables +os.environ["HELICONE_API_KEY"] = "your-helicone-key" +os.environ["OPENAI_API_KEY"] = "your-openai-key" + +# Set callbacks +litellm.success_callback = ["helicone"] + +# OpenAI call +response = completion( + model="gpt-4o", + messages=[{"role": "user", "content": "Hi 👋 - I'm OpenAI"}], +) + +print(response) +``` + + + + +Add Helicone to your LiteLLM proxy configuration: + +```yaml title="config.yaml" +model_list: + - model_name: gpt-4 + litellm_params: + model: gpt-4 + api_key: os.environ/OPENAI_API_KEY + +# Add Helicone callback +litellm_settings: + success_callback: ["helicone"] + +# Set Helicone API key +environment_variables: + HELICONE_API_KEY: "your-helicone-key" +``` + +Start the proxy: +```bash +litellm --config config.yaml +``` + + + + +## Integration Methods + +There are two main approaches to integrate Helicone with LiteLLM: + +1. **Callbacks**: Log to Helicone while using any provider +2. **Proxy Mode**: Use Helicone as a proxy for advanced features ### Supported LLM Providers @@ -26,27 +88,16 @@ Helicone can log requests across [various LLM providers](https://docs.helicone.a - Replicate - And more -### Integration Methods +## Method 1: Using Callbacks -There are two main approaches to integrate Helicone with LiteLLM: +Log requests to Helicone while using any LLM provider directly. -1. Using callbacks -2. Using Helicone as a proxy - -Let's explore each method in detail. - -### Approach 1: Use Callbacks - -Use just 1 line of code to instantly log your responses **across all providers** with Helicone: - -```python -litellm.success_callback = ["helicone"] -``` - -Complete Code + + ```python import os +import litellm from litellm import completion ## Set env variables @@ -66,28 +117,78 @@ response = completion( print(response) ``` -### Approach 2: Use Helicone as a proxy + + + +```yaml title="config.yaml" +model_list: + - model_name: gpt-4 + litellm_params: + model: gpt-4 + api_key: os.environ/OPENAI_API_KEY + - model_name: claude-3 + litellm_params: + model: anthropic/claude-3-sonnet-20240229 + api_key: os.environ/ANTHROPIC_API_KEY + +# Add Helicone logging +litellm_settings: + success_callback: ["helicone"] + +# Environment variables +environment_variables: + HELICONE_API_KEY: "your-helicone-key" + OPENAI_API_KEY: "your-openai-key" + ANTHROPIC_API_KEY: "your-anthropic-key" +``` + +Start the proxy: +```bash +litellm --config config.yaml +``` + +Make requests to your proxy: +```python +import openai + +client = openai.OpenAI( + api_key="anything", # proxy doesn't require real API key + base_url="http://localhost:4000" +) + +response = client.chat.completions.create( + model="gpt-4", # This gets logged to Helicone + messages=[{"role": "user", "content": "Hello!"}] +) +``` + + + + +## Method 2: Using Helicone as a Proxy Helicone's proxy provides [advanced functionality](https://docs.helicone.ai/getting-started/proxy-vs-async) like caching, rate limiting, LLM security through [PromptArmor](https://promptarmor.com/) and more. -To use Helicone as a proxy for your LLM requests: + + -1. Set Helicone as your base URL via: litellm.api_base -2. Pass in Helicone request headers via: litellm.metadata - -Complete Code: +Set Helicone as your base URL and pass authentication headers: ```python import os import litellm from litellm import completion +# Configure LiteLLM to use Helicone proxy litellm.api_base = "https://oai.hconeai.com/v1" litellm.headers = { - "Helicone-Auth": f"Bearer {os.getenv('HELICONE_API_KEY')}", # Authenticate to send requests to Helicone API + "Helicone-Auth": f"Bearer {os.getenv('HELICONE_API_KEY')}", } -response = litellm.completion( +# Set your OpenAI API key +os.environ["OPENAI_API_KEY"] = "your-openai-key" + +response = completion( model="gpt-3.5-turbo", messages=[{"role": "user", "content": "How does a court case get to the Supreme Court?"}] ) @@ -136,36 +237,119 @@ litellm.metadata = { } ``` -### Session Tracking and Tracing + + + +## Session Tracking and Tracing Track multi-step and agentic LLM interactions using session IDs and paths: -```python -litellm.metadata = { - "Helicone-Auth": f"Bearer {os.getenv('HELICONE_API_KEY')}", # Authenticate to send requests to Helicone API - "Helicone-Session-Id": "session-abc-123", # The session ID you want to track - "Helicone-Session-Path": "parent-trace/child-trace", # The path of the session -} -``` - -- `Helicone-Session-Id`: Use this to specify the unique identifier for the session you want to track. This allows you to group related requests together. -- `Helicone-Session-Path`: This header defines the path of the session, allowing you to represent parent and child traces. For example, "parent/child" represents a child trace of a parent trace. - -By using these two headers, you can effectively group and visualize multi-step LLM interactions, gaining insights into complex AI workflows. - -### Retry and Fallback Mechanisms - -Set up retry mechanisms and fallback options: + + ```python +import litellm + +litellm.api_base = "https://oai.hconeai.com/v1" litellm.metadata = { - "Helicone-Auth": f"Bearer {os.getenv('HELICONE_API_KEY')}", # Authenticate to send requests to Helicone API - "Helicone-Retry-Enabled": "true", # Enable retry mechanism - "helicone-retry-num": "3", # Set number of retries - "helicone-retry-factor": "2", # Set exponential backoff factor - "Helicone-Fallbacks": '["gpt-3.5-turbo", "gpt-4"]', # Set fallback models + "Helicone-Auth": f"Bearer {os.getenv('HELICONE_API_KEY')}", + "Helicone-Session-Id": "session-abc-123", + "Helicone-Session-Path": "parent-trace/child-trace", } + +response = litellm.completion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "Start a conversation"}] +) ``` + + + +```python +import openai + +client = openai.OpenAI( + api_key="anything", + base_url="http://localhost:4000" +) + +# First request in session +response1 = client.chat.completions.create( + model="gpt-4", + messages=[{"role": "user", "content": "Hello"}], + extra_headers={ + "Helicone-Session-Id": "session-abc-123", + "Helicone-Session-Path": "conversation/greeting" + } +) + +# Follow-up request in same session +response2 = client.chat.completions.create( + model="gpt-4", + messages=[{"role": "user", "content": "Tell me more"}], + extra_headers={ + "Helicone-Session-Id": "session-abc-123", + "Helicone-Session-Path": "conversation/follow-up" + } +) +``` + + + + +- `Helicone-Session-Id`: Unique identifier for the session to group related requests +- `Helicone-Session-Path`: Hierarchical path to represent parent/child traces (e.g., "parent/child") + +## Retry and Fallback Mechanisms + + + + +```python +import litellm + +litellm.api_base = "https://oai.hconeai.com/v1" +litellm.metadata = { + "Helicone-Auth": f"Bearer {os.getenv('HELICONE_API_KEY')}", + "Helicone-Retry-Enabled": "true", + "helicone-retry-num": "3", + "helicone-retry-factor": "2", # Exponential backoff + "Helicone-Fallbacks": '["gpt-3.5-turbo", "gpt-4"]', +} + +response = litellm.completion( + model="gpt-4", + messages=[{"role": "user", "content": "Hello"}] +) +``` + + + + +```yaml title="config.yaml" +model_list: + - model_name: gpt-4 + litellm_params: + model: gpt-4 + api_key: os.environ/OPENAI_API_KEY + api_base: "https://oai.hconeai.com/v1" + +default_litellm_params: + headers: + Helicone-Auth: "Bearer ${HELICONE_API_KEY}" + Helicone-Retry-Enabled: "true" + helicone-retry-num: "3" + helicone-retry-factor: "2" + Helicone-Fallbacks: '["gpt-3.5-turbo", "gpt-4"]' + +environment_variables: + HELICONE_API_KEY: "your-helicone-key" + OPENAI_API_KEY: "your-openai-key" +``` + + + + > **Supported Headers** - For a full list of supported Helicone headers and their descriptions, please refer to the [Helicone documentation](https://docs.helicone.ai/getting-started/quick-start). > By utilizing these headers and metadata options, you can gain deeper insights into your LLM usage, optimize performance, and better manage your AI workflows with Helicone and LiteLLM. diff --git a/docs/my-website/docs/observability/langfuse_otel_integration.md b/docs/my-website/docs/observability/langfuse_otel_integration.md index 4801fa8e1b0..b4c9a2bd1ad 100644 --- a/docs/my-website/docs/observability/langfuse_otel_integration.md +++ b/docs/my-website/docs/observability/langfuse_otel_integration.md @@ -35,14 +35,14 @@ The Langfuse OpenTelemetry integration allows you to send LiteLLM traces and obs |----------|----------|-------------|---------| | `LANGFUSE_PUBLIC_KEY` | Yes | Your Langfuse public key | `pk-lf-...` | | `LANGFUSE_SECRET_KEY` | Yes | Your Langfuse secret key | `sk-lf-...` | -| `LANGFUSE_HOST` | No | Langfuse host URL | `https://us.cloud.langfuse.com` (default) | +| `LANGFUSE_OTEL_HOST` | No | OTEL endpoint host | `https://otel.my-langfuse.com` | ### Endpoint Resolution -The integration automatically constructs the OTEL endpoint from the `LANGFUSE_HOST`: +The integration automatically constructs the OTEL endpoint from `LANGFUSE_OTEL_HOST` - **Default (US)**: `https://us.cloud.langfuse.com/api/public/otel` - **EU Region**: `https://cloud.langfuse.com/api/public/otel` -- **Self-hosted**: `{LANGFUSE_HOST}/api/public/otel` +- **Self-hosted**: `{LANGFUSE_OTEL_HOST}/api/public/otel` ## Usage @@ -77,11 +77,11 @@ os.environ["LANGFUSE_PUBLIC_KEY"] = "pk-lf-..." os.environ["LANGFUSE_SECRET_KEY"] = "sk-lf-..." # Use EU region -os.environ["LANGFUSE_HOST"] = "https://cloud.langfuse.com" # EU region -# os.environ["LANGFUSE_HOST"] = "https://us.cloud.langfuse.com" # US region (default) +os.environ["LANGFUSE_OTEL_HOST"] = "https://cloud.langfuse.com" # EU region +# os.environ["LANGFUSE_OTEL_HOST"] = "https://otel.my-langfuse.company.com" # custom OTEL endpoint # Or use self-hosted instance -# os.environ["LANGFUSE_HOST"] = "https://my-langfuse.company.com" +# os.environ["LANGFUSE_OTEL_HOST"] = "https://my-langfuse.company.com" litellm.callbacks = ["langfuse_otel"] ``` @@ -98,14 +98,16 @@ import litellm # Get keys for your project from the project settings page: https://cloud.langfuse.com os.environ["LANGFUSE_PUBLIC_KEY"] = "pk-lf-..." os.environ["LANGFUSE_SECRET_KEY"] = "sk-lf-..." -os.environ["LANGFUSE_HOST"] = "https://cloud.langfuse.com" # EU region -# os.environ["LANGFUSE_HOST"] = "https://us.cloud.langfuse.com" # US region +os.environ["LANGFUSE_OTEL_HOST"] = "https://cloud.langfuse.com" # EU region +# os.environ["LANGFUSE_OTEL_HOST"] = "https://us.cloud.langfuse.com" # US region +# os.environ["LANGFUSE_OTEL_HOST"] = "https://otel.my-langfuse.company.com" # custom OTEL endpoint LANGFUSE_AUTH = base64.b64encode( f"{os.environ.get('LANGFUSE_PUBLIC_KEY')}:{os.environ.get('LANGFUSE_SECRET_KEY')}".encode() ).decode() -os.environ["OTEL_EXPORTER_OTLP_ENDPOINT"] = os.environ.get("LANGFUSE_HOST") + "/api/public/otel" +host = os.environ.get("LANGFUSE_OTEL_HOST") +os.environ["OTEL_EXPORTER_OTLP_ENDPOINT"] = host + "/api/public/otel" os.environ["OTEL_EXPORTER_OTLP_HEADERS"] = f"Authorization=Basic {LANGFUSE_AUTH}" litellm.callbacks = ["langfuse_otel"] @@ -120,7 +122,8 @@ Add the integration to your proxy configuration: ```bash export LANGFUSE_PUBLIC_KEY="pk-lf-..." export LANGFUSE_SECRET_KEY="sk-lf-..." -export LANGFUSE_HOST="https://us.cloud.langfuse.com" # Default US region +export LANGFUSE_OTEL_HOST="https://us.cloud.langfuse.com" # Default US region +# export LANGFUSE_OTEL_HOST="https://otel.my-langfuse.company.com" # custom OTEL endpoint ``` 2. Setup config.yaml diff --git a/docs/my-website/docs/observability/opik_integration.md b/docs/my-website/docs/observability/opik_integration.md index b4bcef53937..1ba1c2de210 100644 --- a/docs/my-website/docs/observability/opik_integration.md +++ b/docs/my-website/docs/observability/opik_integration.md @@ -140,6 +140,7 @@ These can be passed inside metadata with the `opik` key. - `project_name` - Name of the Opik project to send data to. - `current_span_data` - The current span data to be used for tracing. - `tags` - Tags to be used for tracing. +- `thread_id` - The thread id to group together multiple related traces. ### Usage @@ -159,8 +160,10 @@ response = litellm.completion( messages=messages, metadata = { "opik": { + "project_name": "your-opik-project-name", "current_span_data": get_current_span_data(), "tags": ["streaming-test"], + "thread_id": "your-thread-id" }, } ) @@ -174,7 +177,7 @@ curl -L -X POST 'http://0.0.0.0:4000/v1/chat/completions' \ -H 'Content-Type: application/json' \ -H 'Authorization: Bearer sk-1234' \ -d '{ - "model": "gpt-3.5-turbo-testing", + "model": "gpt-3.5-turbo", "messages": [ { "role": "user", @@ -183,8 +186,10 @@ curl -L -X POST 'http://0.0.0.0:4000/v1/chat/completions' \ ], "metadata": { "opik": { + "project_name": "your-opik-project-name", "current_span_data": "...", "tags": ["streaming-test"], + "thread_id": "your-thread-id" }, } }' @@ -195,12 +200,25 @@ curl -L -X POST 'http://0.0.0.0:4000/v1/chat/completions' \ +You can also pass the fields as part of the request header with a `opik_*` prefix: - - - - - +```shell +curl --location --request POST 'http://0.0.0.0:4000/chat/completions' \ + --header 'Content-Type: application/json' \ + --header 'Authorization: Bearer sk-1234' \ + --header 'opik_project_name: your-opik-project-name' \ + --header 'opik_thread_id: your-thread-id' \ + --header 'opik_tags: ["streaming-test"]' \ + --data '{ + "model": "gpt-3.5-turbo", + "messages": [ + { + "role": "user", + "content": "What's the weather like in Boston today?" + } + ] +}' +``` diff --git a/docs/my-website/docs/observability/posthog_integration.md b/docs/my-website/docs/observability/posthog_integration.md new file mode 100644 index 00000000000..7e6a0e1076b --- /dev/null +++ b/docs/my-website/docs/observability/posthog_integration.md @@ -0,0 +1,216 @@ +# PostHog - Tracking LLM Usage Analytics + +## What is PostHog? + +PostHog is an open-source product analytics platform that helps you track and analyze how users interact with your product. For LLM applications, PostHog provides specialized AI features to track model usage, performance, and user interactions with your AI features. + +## Usage with LiteLLM Proxy (LLM Gateway) + +**Step 1**: Create a `config.yaml` file and set `litellm_settings`: `success_callback` + +```yaml +model_list: + - model_name: gpt-3.5-turbo + litellm_params: + model: gpt-3.5-turbo + +litellm_settings: + success_callback: ["posthog"] + failure_callback: ["posthog"] +``` + +**Step 2**: Set required environment variables + +```shell +export POSTHOG_API_KEY="your-posthog-api-key" +# Optional, defaults to https://app.posthog.com +export POSTHOG_API_URL="https://app.posthog.com" # optional +``` + +**Step 3**: Start the proxy, make a test request + +Start proxy + +```shell +litellm --config config.yaml --debug +``` + +Test Request + +```shell +curl --location 'http://0.0.0.0:4000/chat/completions' \ + --header 'Content-Type: application/json' \ + --data '{ + "model": "gpt-3.5-turbo", + "messages": [ + { + "role": "user", + "content": "what llm are you" + } + ], + "metadata": { + "user_id": "user-123", + "custom_field": "custom_value" + } +}' +``` + +## Usage with LiteLLM Python SDK + +### Quick Start + +Use just 2 lines of code, to instantly log your responses **across all providers** with PostHog: + +```python +litellm.success_callback = ["posthog"] +litellm.failure_callback = ["posthog"] # logs errors to posthog +``` +```python +import litellm +import os + +# from PostHog +os.environ["POSTHOG_API_KEY"] = "" +# Optional, defaults to https://app.posthog.com +os.environ["POSTHOG_API_URL"] = "" # optional + +# LLM API Keys +os.environ['OPENAI_API_KEY']="" + +# set posthog as a callback, litellm will send the data to posthog +litellm.success_callback = ["posthog"] + +# openai call +response = litellm.completion( + model="gpt-3.5-turbo", + messages=[ + {"role": "user", "content": "Hi - i'm openai"} + ], + metadata = { + "user_id": "user-123", # set posthog user ID + } +) +``` + +### Advanced + +#### Set User ID and Custom Metadata + +Pass `user_id` in `metadata` to associate events with specific users in PostHog: + +**With LiteLLM Python SDK:** + +```python +import litellm + +litellm.success_callback = ["posthog"] + +response = litellm.completion( + model="gpt-3.5-turbo", + messages=[ + {"role": "user", "content": "Hello world"} + ], + metadata={ + "user_id": "user-123", # Add user ID for PostHog tracking + "custom_field": "custom_value" # Add custom metadata + } +) +``` + +**With LiteLLM Proxy using OpenAI Python SDK:** + +```python +import openai + +client = openai.OpenAI( + api_key="sk-1234", # Your LiteLLM Proxy API key + base_url="http://0.0.0.0:4000" # Your LiteLLM Proxy URL +) + +response = client.chat.completions.create( + model="gpt-3.5-turbo", + messages=[ + {"role": "user", "content": "Hello world"} + ], + extra_body={ + "metadata": { + "user_id": "user-123", # Add user ID for PostHog tracking + "project_name": "my-project", # Add custom metadata + "environment": "production" + } + } +) +``` + +#### Disable Logging for Specific Calls + +Use the `no-log` flag to prevent logging for specific calls: + +```python +import litellm + +litellm.success_callback = ["posthog"] + +response = litellm.completion( + model="gpt-3.5-turbo", + messages=[ + {"role": "user", "content": "This won't be logged"} + ], + metadata={"no-log": True} +) +``` + +## What's Logged to PostHog? + +When LiteLLM logs to PostHog, it captures detailed information about your LLM usage: + +### For Completion Calls +- **Model Information**: Provider, model name, model parameters +- **Usage Metrics**: Input tokens, output tokens, total cost +- **Performance**: Latency, completion time +- **Content**: Input messages, model responses (respects privacy settings) +- **Metadata**: Custom fields, user ID, trace information + +### For Embedding Calls +- **Model Information**: Provider, model name +- **Usage Metrics**: Input tokens, total cost +- **Performance**: Latency +- **Content**: Input text (respects privacy settings) +- **Metadata**: Custom fields, user ID, trace information + +### For Errors +- **Error Details**: Error type, error message, stack trace +- **Context**: Model, provider, input that caused the error +- **Timing**: When the error occurred, request duration + +## Environment Variables + +| Variable | Required | Description | +|----------|----------|-------------| +| `POSTHOG_API_KEY` | Yes | Your PostHog project API key | +| `POSTHOG_API_URL` | No | PostHog API URL (defaults to https://app.posthog.com) | + +## Troubleshooting + +### 1. Missing API Key +``` +Error: POSTHOG_API_KEY is not set +``` + +Set your PostHog API key: +```python +import os +os.environ["POSTHOG_API_KEY"] = "your-api-key" +``` + +### 2. Custom PostHog Instance +If you're using a self-hosted PostHog instance: +```python +import os +os.environ["POSTHOG_API_URL"] = "https://your-posthog-instance.com" +``` + +### 3. Events Not Appearing +- Check that your API key is correct +- Verify network connectivity to PostHog +- Events may take a few minutes to appear in PostHog dashboard \ No newline at end of file diff --git a/docs/my-website/docs/pass_through/google_ai_studio.md b/docs/my-website/docs/pass_through/google_ai_studio.md index c3671f58d36..3de7c54aa7a 100644 --- a/docs/my-website/docs/pass_through/google_ai_studio.md +++ b/docs/my-website/docs/pass_through/google_ai_studio.md @@ -230,6 +230,13 @@ curl -X POST "https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5 ``` +## **Example 4: Video Generation with Veo** + +Generate videos using Google's Veo model through LiteLLM pass-through routes. + +[**→ Complete Veo Video Generation Guide**](../proxy/veo_video_generation.md) + + ## Advanced Pre-requisites diff --git a/docs/my-website/docs/pass_through/intro.md b/docs/my-website/docs/pass_through/intro.md index 3d6286afcc5..38218224f11 100644 --- a/docs/my-website/docs/pass_through/intro.md +++ b/docs/my-website/docs/pass_through/intro.md @@ -11,3 +11,43 @@ These endpoints are useful for 2 scenarios: ## How is your request handled? The request is passed through to the provider's endpoint. The response is then passed back to the client. **No translation is done.** + +### Request Forwarding Process + +1. **Request Reception**: LiteLLM receives your request at `/provider/endpoint` +2. **Authentication**: Your LiteLLM API key is validated and mapped to the provider's API key +3. **Request Transformation**: Request is reformatted for the target provider's API +4. **Forwarding**: Request is sent to the actual provider endpoint +5. **Response Handling**: Provider response is returned directly to you + +### Authentication Flow + +```mermaid +graph LR + A[Client Request] --> B[LiteLLM Proxy] + B --> C[Validate LiteLLM API Key] + C --> D[Map to Provider API Key] + D --> E[Forward to Provider] + E --> F[Return Response] +``` + +**Key Points:** +- Use your **LiteLLM API key** in requests, not the provider's key +- LiteLLM handles the provider authentication internally +- Same authentication works across all passthrough endpoints + +### Error Handling + +**Provider Errors**: Forwarded directly to you with original error codes and messages + +**LiteLLM Errors**: +- `401`: Invalid LiteLLM API key +- `404`: Provider or endpoint not supported +- `500`: Internal routing/forwarding errors + +### Benefits + +- **Unified Authentication**: One API key for all providers +- **Centralized Logging**: All requests logged through LiteLLM +- **Cost Tracking**: Usage tracked across all endpoints +- **Access Control**: Same permissions apply to passthrough endpoints diff --git a/docs/my-website/docs/providers/aiml.md b/docs/my-website/docs/providers/aiml.md index 1343cbf8d8e..9d763daf7d7 100644 --- a/docs/my-website/docs/providers/aiml.md +++ b/docs/my-website/docs/providers/aiml.md @@ -1,5 +1,23 @@ # AI/ML API +https://aimlapi.com/ +## Overview + +| Property | Details | +|-------|-------| +| Description | AI/ML API provides access to state-of-the-art AI models including flux-pro/v1.1 for high-quality image generation. | +| Provider Route on LiteLLM | `aiml/` | +| Link to Provider Doc | [AI/ML API ↗](https://docs.aimlapi.com/) | +| Supported Operations | [`/chat/completions`], [`/images/generations`](#image-generation) | + +LiteLLM supports AI/ML API Image Generation calls. + +## API Base, Key +```python +# env variable +os.environ['AIML_API_KEY'] = "your-api-key" +os.environ['AIML_API_BASE'] = "https://api.aimlapi.com" # [optional] +``` Getting started with the AI/ML API is simple. Follow these steps to set up your integration: ### 1. Get Your API Key @@ -24,7 +42,7 @@ You can choose from LLama, Qwen, Flux, and 200+ other open and closed-source mod import litellm response = litellm.completion( - model="openai/meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo", # The model name must include prefix "openai" + the model name from ai/ml api + model="aiml/meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo", # The model name must include prefix "openai" + the model name from ai/ml api api_key="", # your aiml api-key api_base="https://api.aimlapi.com/v2", messages=[ @@ -42,7 +60,7 @@ response = litellm.completion( import litellm response = litellm.completion( - model="openai/Qwen/Qwen2-72B-Instruct", # The model name must include prefix "openai" + the model name from ai/ml api + model="aiml/Qwen/Qwen2-72B-Instruct", # The model name must include prefix "openai" + the model name from ai/ml api api_key="", # your aiml api-key api_base="https://api.aimlapi.com/v2", messages=[ @@ -67,7 +85,7 @@ import litellm async def main(): response = await litellm.acompletion( - model="openai/anthropic/claude-3-5-haiku", # The model name must include prefix "openai" + the model name from ai/ml api + model="aiml/anthropic/claude-3-5-haiku", # The model name must include prefix "openai" + the model name from ai/ml api api_key="", # your aiml api-key api_base="https://api.aimlapi.com/v2", messages=[ @@ -97,7 +115,7 @@ async def main(): try: print("test acompletion + streaming") response = await litellm.acompletion( - model="openai/nvidia/Llama-3.1-Nemotron-70B-Instruct-HF", # The model name must include prefix "openai" + the model name from ai/ml api + model="aiml/nvidia/Llama-3.1-Nemotron-70B-Instruct-HF", # The model name must include prefix "openai" + the model name from ai/ml api api_key="", # your aiml api-key api_base="https://api.aimlapi.com/v2", messages=[{"content": "Hey, how's it going?", "role": "user"}], @@ -125,7 +143,7 @@ import litellm async def main(): response = await litellm.aembedding( - model="openai/text-embedding-3-small", # The model name must include prefix "openai" + the model name from ai/ml api + model="aiml/text-embedding-3-small", # The model name must include prefix "openai" + the model name from ai/ml api api_key="", # your aiml api-key api_base="https://api.aimlapi.com/v1", # 👈 the URL has changed from v2 to v1 input="Your text string", @@ -147,7 +165,7 @@ import litellm async def main(): response = await litellm.aimage_generation( - model="openai/dall-e-3", # The model name must include prefix "openai" + the model name from ai/ml api + model="aiml/dall-e-3", # The model name must include prefix "openai" + the model name from ai/ml api api_key="", # your aiml api-key api_base="https://api.aimlapi.com/v1", # 👈 the URL has changed from v2 to v1 prompt="A cute baby sea otter", diff --git a/docs/my-website/docs/providers/anthropic.md b/docs/my-website/docs/providers/anthropic.md index a7a9dc30013..820c2906bf0 100644 --- a/docs/my-website/docs/providers/anthropic.md +++ b/docs/my-website/docs/providers/anthropic.md @@ -55,8 +55,29 @@ import os os.environ["ANTHROPIC_API_KEY"] = "your-api-key" # os.environ["ANTHROPIC_API_BASE"] = "" # [OPTIONAL] or 'ANTHROPIC_BASE_URL' +# os.environ["LITELLM_ANTHROPIC_DISABLE_URL_SUFFIX"] = "true" # [OPTIONAL] Disable automatic URL suffix appending ``` +### Custom API Base + +When using a custom API base for Anthropic (e.g., a proxy or custom endpoint), LiteLLM automatically appends the appropriate suffix (`/v1/messages` or `/v1/complete`) to your base URL. + +If your custom endpoint already includes the full path or doesn't follow Anthropic's standard URL structure, you can disable this automatic suffix appending: + +```python +import os + +os.environ["ANTHROPIC_API_BASE"] = "https://my-custom-endpoint.com/custom/path" +os.environ["LITELLM_ANTHROPIC_DISABLE_URL_SUFFIX"] = "true" # Prevents automatic suffix +``` + +Without `LITELLM_ANTHROPIC_DISABLE_URL_SUFFIX`: +- Base URL `https://my-proxy.com` → `https://my-proxy.com/v1/messages` +- Base URL `https://my-proxy.com/api` → `https://my-proxy.com/api/v1/messages` + +With `LITELLM_ANTHROPIC_DISABLE_URL_SUFFIX=true`: +- Base URL `https://my-proxy.com/custom/path` → `https://my-proxy.com/custom/path` (unchanged) + ## Usage ```python diff --git a/docs/my-website/docs/providers/azure_ai_img_edit.md b/docs/my-website/docs/providers/azure_ai_img_edit.md new file mode 100644 index 00000000000..0d5408f0af4 --- /dev/null +++ b/docs/my-website/docs/providers/azure_ai_img_edit.md @@ -0,0 +1,260 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Azure AI Image Editing + +Azure AI provides powerful image editing capabilities using FLUX models from Black Forest Labs to modify existing images based on text descriptions. + +## Overview + +| Property | Details | +|----------|---------| +| Description | Azure AI Image Editing uses FLUX models to modify existing images based on text prompts. | +| Provider Route on LiteLLM | `azure_ai/` | +| Provider Doc | [Azure AI FLUX Models ↗](https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/black-forest-labs-flux-1-kontext-pro-and-flux1-1-pro-now-available-in-azure-ai-f/4434659) | +| Supported Operations | [`/images/edits`](#image-editing) | + +## Setup + +### API Key & Base URL & API Version + +```python showLineNumbers +# Set your Azure AI API credentials +import os +os.environ["AZURE_AI_API_KEY"] = "your-api-key-here" +os.environ["AZURE_AI_API_BASE"] = "your-azure-ai-endpoint" # e.g., https://your-endpoint.eastus2.inference.ai.azure.com/ +os.environ["AZURE_AI_API_VERSION"] = "2025-04-01-preview" # Example API version +``` + +Get your API key and endpoint from [Azure AI Studio](https://ai.azure.com/). + +## Supported Models + +| Model Name | Description | Cost per Image | +|------------|-------------|----------------| +| `azure_ai/FLUX.1-Kontext-pro` | FLUX 1 Kontext Pro model with enhanced context understanding for editing | $0.04 | + +## Image Editing + +### Usage - LiteLLM Python SDK + + + + +```python showLineNumbers title="Basic Image Editing" +import os +import base64 +from pathlib import Path + +import litellm + +# Set your API credentials +os.environ["AZURE_AI_API_KEY"] = "your-api-key-here" +os.environ["AZURE_AI_API_BASE"] = "your-azure-ai-endpoint" +os.environ["AZURE_AI_API_VERSION"] = "2025-04-01-preview" + +# Edit an image with a prompt +response = litellm.image_edit( + model="azure_ai/FLUX.1-Kontext-pro", + image=open("path/to/your/image.png", "rb"), + prompt="Add a winter theme with snow and cold colors", + api_base=os.environ["AZURE_AI_API_BASE"], + api_key=os.environ["AZURE_AI_API_KEY"], + api_version=os.environ["AZURE_AI_API_VERSION"] +) + +img_base64 = response.data[0].get("b64_json") +img_bytes = base64.b64decode(img_base64) +path = Path("edited_image.png") +path.write_bytes(img_bytes) +``` + + + + + +```python showLineNumbers title="Async Image Editing" +import os +import base64 +from pathlib import Path + +import litellm +import asyncio + +# Set your API credentials +os.environ["AZURE_AI_API_KEY"] = "your-api-key-here" +os.environ["AZURE_AI_API_BASE"] = "your-azure-ai-endpoint" +os.environ["AZURE_AI_API_VERSION"] = "2025-04-01-preview" + +async def edit_image(): + # Edit image asynchronously + response = await litellm.aimage_edit( + model="azure_ai/FLUX.1-Kontext-pro", + image=open("path/to/your/image.png", "rb"), + prompt="Make this image look like a watercolor painting", + api_base=os.environ["AZURE_AI_API_BASE"], + api_key=os.environ["AZURE_AI_API_KEY"], + api_version=os.environ["AZURE_AI_API_VERSION"] + ) + img_base64 = response.data[0].get("b64_json") + img_bytes = base64.b64decode(img_base64) + path = Path("async_edited_image.png") + path.write_bytes(img_bytes) + +# Run the async function +asyncio.run(edit_image()) +``` + + + + + +```python showLineNumbers title="Advanced Image Editing with Parameters" +import os +import base64 +from pathlib import Path + +import litellm + +# Set your API credentials +os.environ["AZURE_AI_API_KEY"] = "your-api-key-here" +os.environ["AZURE_AI_API_BASE"] = "your-azure-ai-endpoint" +os.environ["AZURE_AI_API_VERSION"] = "2025-04-01-preview" + +# Edit image with additional parameters +response = litellm.image_edit( + model="azure_ai/FLUX.1-Kontext-pro", + image=open("path/to/your/image.png", "rb"), + prompt="Add magical elements like floating crystals and mystical lighting", + api_base=os.environ["AZURE_AI_API_BASE"], + api_key=os.environ["AZURE_AI_API_KEY"], + api_version=os.environ["AZURE_AI_API_VERSION"], + n=1 +) +img_base64 = response.data[0].get("b64_json") +img_bytes = base64.b64decode(img_base64) +path = Path("advanced_edited_image.png") +path.write_bytes(img_bytes) +``` + + + + +### Usage - LiteLLM Proxy Server + +#### 1. Configure your config.yaml + +```yaml showLineNumbers title="Azure AI Image Editing Configuration" +model_list: + - model_name: azure-flux-kontext-edit + litellm_params: + model: azure_ai/FLUX.1-Kontext-pro + api_key: os.environ/AZURE_AI_API_KEY + api_base: os.environ/AZURE_AI_API_BASE + api_version: os.environ/AZURE_AI_API_VERSION + model_info: + mode: image_edit + +general_settings: + master_key: sk-1234 +``` + +#### 2. Start LiteLLM Proxy Server + +```bash showLineNumbers title="Start LiteLLM Proxy Server" +litellm --config /path/to/config.yaml + +# RUNNING on http://0.0.0.0:4000 +``` + +#### 3. Make image editing requests with OpenAI Python SDK + + + + +```python showLineNumbers title="Azure AI Image Editing via Proxy - OpenAI SDK" +from openai import OpenAI + +# Initialize client with your proxy URL +client = OpenAI( + base_url="http://localhost:4000", # Your proxy URL + api_key="sk-1234" # Your proxy API key +) + +# Edit image with FLUX Kontext Pro +response = client.images.edit( + model="azure-flux-kontext-edit", + image=open("path/to/your/image.png", "rb"), + prompt="Transform this image into a beautiful oil painting style", +) + +img_base64 = response.data[0].b64_json +img_bytes = base64.b64decode(img_base64) +path = Path("proxy_edited_image.png") +path.write_bytes(img_bytes) +``` + + + + + +```python showLineNumbers title="Azure AI Image Editing via Proxy - LiteLLM SDK" +import litellm + +# Edit image through proxy +response = litellm.image_edit( + model="litellm_proxy/azure-flux-kontext-edit", + image=open("path/to/your/image.png", "rb"), + prompt="Add a mystical forest background with magical creatures", + api_base="http://localhost:4000", + api_key="sk-1234" +) + +img_base64 = response.data[0].b64_json +img_bytes = base64.b64decode(img_base64) +path = Path("proxy_edited_image.png") +path.write_bytes(img_bytes) +``` + + + + + +```bash showLineNumbers title="Azure AI Image Editing via Proxy - cURL" +curl --location 'http://localhost:4000/v1/images/edits' \ +--header 'Authorization: Bearer sk-1234' \ +--form 'model="azure-flux-kontext-edit"' \ +--form 'prompt="Convert this image to a vintage sepia tone with old-fashioned effects"' \ +--form 'image=@"path/to/your/image.png"' +``` + + + + +## Supported Parameters + +Azure AI Image Editing supports the following OpenAI-compatible parameters: + +| Parameter | Type | Description | Default | Example | +|-----------|------|-------------|---------|---------| +| `image` | file | The image file to edit | Required | File object or binary data | +| `prompt` | string | Text description of the desired changes | Required | `"Add snow and winter elements"` | +| `model` | string | The FLUX model to use for editing | Required | `"azure_ai/FLUX.1-Kontext-pro"` | +| `n` | integer | Number of edited images to generate (You can specify only 1) | `1` | `1` | +| `api_base` | string | Your Azure AI endpoint URL | Required | `"https://your-endpoint.eastus2.inference.ai.azure.com/"` | +| `api_key` | string | Your Azure AI API key | Required | Environment variable or direct value | +| `api_version` | string | API version for Azure AI | Required | `"2025-04-01-preview"` | + +## Getting Started + +1. Create an account at [Azure AI Studio](https://ai.azure.com/) +2. Deploy a FLUX model in your Azure AI Studio workspace +3. Get your API key and endpoint from the deployment details +4. Set your `AZURE_AI_API_KEY`, `AZURE_AI_API_BASE` and `AZURE_AI_API_VERSION` environment variables +5. Prepare your source image +6. Use `litellm.image_edit()` to modify your images with text instructions + +## Additional Resources + +- [Azure AI Studio Documentation](https://docs.microsoft.com/en-us/azure/ai-services/) +- [FLUX Models Announcement](https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/black-forest-labs-flux-1-kontext-pro-and-flux1-1-pro-now-available-in-azure-ai-f/4434659) \ No newline at end of file diff --git a/docs/my-website/docs/providers/bedrock.md b/docs/my-website/docs/providers/bedrock.md index 1356ec1744e..fe996099145 100644 --- a/docs/my-website/docs/providers/bedrock.md +++ b/docs/my-website/docs/providers/bedrock.md @@ -308,6 +308,65 @@ print(response) +## Usage - Request Metadata + +Attach metadata to Bedrock requests for logging and cost attribution. + + + + +```python +import os +from litellm import completion + +os.environ["AWS_ACCESS_KEY_ID"] = "" +os.environ["AWS_SECRET_ACCESS_KEY"] = "" +os.environ["AWS_REGION_NAME"] = "" + +response = completion( + model="bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0", + messages=[{"role": "user", "content": "Hello, how are you?"}], + requestMetadata={ + "cost_center": "engineering", + "user_id": "user123" + } +) +``` + + + +**Set on yaml** + +```yaml +model_list: + - model_name: bedrock-claude-v1 + litellm_params: + model: bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0 + requestMetadata: + cost_center: "engineering" +``` + +**Set on request** + +```python +import openai +client = openai.OpenAI( + api_key="anything", + base_url="http://0.0.0.0:4000" +) + +response = client.chat.completions.create( + model="bedrock-claude-v1", + messages=[{"role": "user", "content": "Hello"}], + extra_body={ + "requestMetadata": {"cost_center": "engineering"} + } +) +``` + + + + ## Usage - Function Calling / Tool calling LiteLLM supports tool calling via Bedrock's Converse and Invoke API's. @@ -467,7 +526,7 @@ print(f"\nResponse: {resp}") ## Usage - 'thinking' / 'reasoning content' -This is currently only supported for Anthropic's Claude 3.7 Sonnet + Deepseek R1. +This is currently only supported for Anthropic's Claude 3.7 Sonnet + Deepseek R1 + GPT-OSS models. Works on v1.61.20+. @@ -889,6 +948,19 @@ curl http://0.0.0.0:4000/v1/chat/completions \ Example of using [Bedrock Guardrails with LiteLLM](https://docs.aws.amazon.com/bedrock/latest/userguide/guardrails-use-converse-api.html) +### Selective Content Moderation with `guarded_text` + +LiteLLM supports selective content moderation using the `guarded_text` content type. This allows you to wrap only specific content that should be moderated by Bedrock Guardrails, rather than evaluating the entire conversation. + +**How it works:** +- Content with `type: "guarded_text"` gets automatically wrapped in `guardrailConverseContent` blocks +- Only the wrapped content is evaluated by Bedrock Guardrails +- Regular content with `type: "text"` bypasses guardrail evaluation + +:::note +If `guarded_text` is not used, the entire conversation history will be sent to the guardrail for evaluation, which can increase latency and costs. +::: + @@ -915,6 +987,24 @@ response = completion( "trace": "disabled", # The trace behavior for the guardrail. Can either be "disabled" or "enabled" }, ) + +# Selective guardrail usage with guarded_text - only specific content is evaluated +response_guard = completion( + model="anthropic.claude-v2", + messages=[ + { + "role": "user", + "content": [ + {"type": "text", "text": "What is the main topic of this legal document?"}, + {"type": "guarded_text", "text": "This document contains sensitive legal information that should be moderated by guardrails."} + ] + } + ], + guardrailConfig={ + "guardrailIdentifier": "gr-abc123", + "guardrailVersion": "DRAFT" + } +) ``` @@ -993,7 +1083,20 @@ response = client.chat.completions.create(model="bedrock-claude-v1", messages = temperature=0.7 ) -print(response) +# For adding selective guardrail usage with guarded_text +response_guard = client.chat.completions.create(model="bedrock-claude-v1", messages = [ + { + "role": "user", + "content": [ + {"type": "text", "text": "What is the main topic of this legal document?"}, + {"type": "guarded_text", "text": "This document contains sensitive legal information that should be moderated by guardrails."} + ] + } +], +temperature=0.7 +) + +print(response_guard) ``` @@ -1777,6 +1880,7 @@ Here's an example of using a bedrock model with LiteLLM. For a complete list, re | Mistral 7B Instruct | `completion(model='bedrock/mistral.mistral-7b-instruct-v0:2', messages=messages)` | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']` | | Mixtral 8x7B Instruct | `completion(model='bedrock/mistral.mixtral-8x7b-instruct-v0:1', messages=messages)` | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']` | + ## Bedrock Embedding ### API keys @@ -1798,11 +1902,29 @@ response = embedding( print(response) ``` +#### Titan V2 - encoding_format support +```python +from litellm import embedding +# Float format (default) +response = embedding( + model="bedrock/amazon.titan-embed-text-v2:0", + input=["good morning from litellm"], + encoding_format="float" # Returns float array +) + +# Binary format +response = embedding( + model="bedrock/amazon.titan-embed-text-v2:0", + input=["good morning from litellm"], + encoding_format="base64" # Returns base64 encoded binary +) +``` + ## Supported AWS Bedrock Embedding Models | Model Name | Usage | Supported Additional OpenAI params | |----------------------|---------------------------------------------|-----| -| Titan Embeddings V2 | `embedding(model="bedrock/amazon.titan-embed-text-v2:0", input=input)` | [here](https://github.com/BerriAI/litellm/blob/f5905e100068e7a4d61441d7453d7cf5609c2121/litellm/llms/bedrock/embed/amazon_titan_v2_transformation.py#L59) | +| Titan Embeddings V2 | `embedding(model="bedrock/amazon.titan-embed-text-v2:0", input=input)` | `dimensions`, `encoding_format` | | Titan Embeddings - V1 | `embedding(model="bedrock/amazon.titan-embed-text-v1", input=input)` | [here](https://github.com/BerriAI/litellm/blob/f5905e100068e7a4d61441d7453d7cf5609c2121/litellm/llms/bedrock/embed/amazon_titan_g1_transformation.py#L53) | Titan Multimodal Embeddings | `embedding(model="bedrock/amazon.titan-embed-image-v1", input=input)` | [here](https://github.com/BerriAI/litellm/blob/f5905e100068e7a4d61441d7453d7cf5609c2121/litellm/llms/bedrock/embed/amazon_titan_multimodal_transformation.py#L28) | | Cohere Embeddings - English | `embedding(model="bedrock/cohere.embed-english-v3", input=input)` | [here](https://github.com/BerriAI/litellm/blob/f5905e100068e7a4d61441d7453d7cf5609c2121/litellm/llms/bedrock/embed/cohere_transformation.py#L18) @@ -1891,6 +2013,39 @@ curl -L -X POST 'http://0.0.0.0:4000/v1/images/generations' \ +### Using Inference Profiles with Image Generation + +For AWS Bedrock Application Inference Profiles with image generation, use the `model_id` parameter to specify the inference profile ARN: + + + + +```python +from litellm import image_generation + +response = image_generation( + model="bedrock/amazon.nova-canvas-v1:0", + model_id="arn:aws:bedrock:eu-west-1:000000000000:application-inference-profile/a0a0a0a0a0a0", + prompt="A cute baby sea otter" +) +print(f"response: {response}") +``` + + + + +```yaml +model_list: + - model_name: nova-canvas-inference-profile + litellm_params: + model: bedrock/amazon.nova-canvas-v1:0 + model_id: arn:aws:bedrock:eu-west-1:000000000000:application-inference-profile/a0a0a0a0a0a0 + aws_region_name: "eu-west-1" +``` + + + + ## Supported AWS Bedrock Image Generation Models | Model Name | Function Call | @@ -2185,6 +2340,39 @@ response = completion( Make the bedrock completion call +--- + +### Required AWS IAM Policy for AssumeRole + +To use `aws_role_name` (STS AssumeRole) with LiteLLM, your IAM user or role **must** have permission to call `sts:AssumeRole` on the target role. If you see an error like: + +``` +An error occurred (AccessDenied) when calling the AssumeRole operation: User: arn:aws:sts::...:assumed-role/litellm-ecs-task-role/... is not authorized to perform: sts:AssumeRole on resource: arn:aws:iam::...:role/Enterprise/BedrockCrossAccountConsumer +``` + +This means the IAM identity running LiteLLM does **not** have permission to assume the target role. You must update your IAM policy to allow this action. + +#### Example IAM Policy + +Replace `` with the ARN of the role you want to assume (e.g., `arn:aws:iam::123456789012:role/Enterprise/BedrockCrossAccountConsumer`). + +```json +{ + "Version": "2012-10-17", + "Statement": [ + { + "Effect": "Allow", + "Action": "sts:AssumeRole", + "Resource": "" + } + ] +} +``` + +**Note:** The target role itself must also trust the calling IAM identity (via its trust policy) for AssumeRole to succeed. See [AWS AssumeRole docs](https://docs.aws.amazon.com/IAM/latest/UserGuide/id_roles_use_switch-role-api.html) for more details. + +--- + diff --git a/docs/my-website/docs/providers/bedrock_batches.md b/docs/my-website/docs/providers/bedrock_batches.md new file mode 100644 index 00000000000..57487f7d2c9 --- /dev/null +++ b/docs/my-website/docs/providers/bedrock_batches.md @@ -0,0 +1,180 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Bedrock Batches + +Use Amazon Bedrock Batch Inference API through LiteLLM. + +| Property | Details | +|----------|---------| +| Description | Amazon Bedrock Batch Inference allows you to run inference on large datasets asynchronously | +| Provider Doc | [AWS Bedrock Batch Inference ↗](https://docs.aws.amazon.com/bedrock/latest/userguide/batch-inference.html) | + +## Overview + +Use this to: + +- Run batch inference on large datasets with Bedrock models +- Control batch model access by key/user/team (same as chat completion models) +- Manage S3 storage for batch input/output files + +## (Proxy Admin) Usage + +Here's how to give developers access to your Bedrock Batch models. + +### 1. Setup config.yaml + +- Specify `mode: batch` for each model: Allows developers to know this is a batch model +- Configure S3 bucket and AWS credentials for batch operations + +```yaml showLineNumbers title="litellm_config.yaml" +model_list: + - model_name: "bedrock-batch-claude" + litellm_params: + model: bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0 + ######################################################### + ########## batch specific params ######################## + s3_bucket_name: litellm-proxy + s3_region_name: us-west-2 + s3_access_key_id: os.environ/AWS_ACCESS_KEY_ID + s3_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY + aws_batch_role_arn: arn:aws:iam::888602223428:role/service-role/AmazonBedrockExecutionRoleForAgents_BB9HNW6V4CV + model_info: + mode: batch # 👈 SPECIFY MODE AS BATCH, to tell user this is a batch model +``` + +**Required Parameters:** + +| Parameter | Description | +|-----------|-------------| +| `s3_bucket_name` | S3 bucket for batch input/output files | +| `s3_region_name` | AWS region for S3 bucket | +| `s3_access_key_id` | AWS access key for S3 bucket | +| `s3_secret_access_key` | AWS secret key for S3 bucket | +| `aws_batch_role_arn` | IAM role ARN for Bedrock batch operations. Bedrock Batch APIs require an IAM role ARN to be set. | +| `mode: batch` | Indicates to LiteLLM this is a batch model | + +### 2. Create Virtual Key + +```bash showLineNumbers title="create_virtual_key.sh" +curl -L -X POST 'https://{PROXY_BASE_URL}/key/generate' \ +-H 'Authorization: Bearer ${PROXY_API_KEY}' \ +-H 'Content-Type: application/json' \ +-d '{"models": ["bedrock-batch-claude"]}' +``` + +You can now use the virtual key to access the batch models (See Developer flow). + +## (Developer) Usage + +Here's how to create a LiteLLM managed file and execute Bedrock Batch CRUD operations with the file. + +### 1. Create request.jsonl + +- Check models available via `/model_group/info` +- See all models with `mode: batch` +- Set `model` in .jsonl to the model from `/model_group/info` + +```json showLineNumbers title="bedrock_batch_completions.jsonl" +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock-batch-claude", "messages": [{"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Hello world!"}], "max_tokens": 1000}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock-batch-claude", "messages": [{"role": "system", "content": "You are an unhelpful assistant."}, {"role": "user", "content": "Hello world!"}], "max_tokens": 1000}} +``` + +Expectation: + +- LiteLLM translates this to the bedrock deployment specific value (e.g. `bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0`) + +### 2. Upload File + +Specify `target_model_names: ""` to enable LiteLLM managed files and request validation. + +model-name should be the same as the model-name in the request.jsonl + + + + +```python showLineNumbers title="bedrock_batch.py" +from openai import OpenAI + +client = OpenAI( + base_url="http://0.0.0.0:4000", + api_key="sk-1234", +) + +# Upload file +batch_input_file = client.files.create( + file=open("./bedrock_batch_completions.jsonl", "rb"), # {"model": "bedrock-batch-claude"} <-> {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0"} + purpose="batch", + extra_body={"target_model_names": "bedrock-batch-claude"} +) +print(batch_input_file) +``` + + + + +```bash showLineNumbers title="Upload File" +curl http://localhost:4000/v1/files \ + -H "Authorization: Bearer sk-1234" \ + -F purpose="batch" \ + -F file="@bedrock_batch_completions.jsonl" \ + -F extra_body='{"target_model_names": "bedrock-batch-claude"}' +``` + + + + +**Where is the file written?**: + +The file is written to S3 bucket specified in your config and prepared for Bedrock batch inference. + +### 3. Create the batch + + + + +```python showLineNumbers title="bedrock_batch.py" +... +# Create batch +batch = client.batches.create( + input_file_id=batch_input_file.id, + endpoint="/v1/chat/completions", + completion_window="24h", + metadata={"description": "Test batch job"}, +) +print(batch) +``` + + + + +```bash showLineNumbers title="Create Batch Request" +curl http://localhost:4000/v1/batches \ + -H "Authorization: Bearer sk-1234" \ + -H "Content-Type: application/json" \ + -d '{ + "input_file_id": "file-abc123", + "endpoint": "/v1/chat/completions", + "completion_window": "24h", + "metadata": {"description": "Test batch job"} + }' +``` + + + + +## FAQ + +### Where are my files written? + +When a `target_model_names` is specified, the file is written to the S3 bucket configured in your Bedrock batch model configuration. + +### What models are supported? + +LiteLLM only supports Bedrock Anthropic Models for Batch API. If you want other bedrock models file an issue [here](https://github.com/BerriAI/litellm/issues/new/choose). + +## Further Reading + +- [AWS Bedrock Batch Inference Documentation](https://docs.aws.amazon.com/bedrock/latest/userguide/batch-inference.html) +- [LiteLLM Managed Batches](../proxy/managed_batches) +- [LiteLLM Authentication to Bedrock](https://docs.litellm.ai/docs/providers/bedrock#boto3---authentication) diff --git a/docs/my-website/docs/providers/bedrock_embedding.md b/docs/my-website/docs/providers/bedrock_embedding.md new file mode 100644 index 00000000000..95ee8d3d228 --- /dev/null +++ b/docs/my-website/docs/providers/bedrock_embedding.md @@ -0,0 +1,95 @@ +# Bedrock Embedding + +## Supported Embedding Models + +| Provider | LiteLLM Route | AWS Documentation | +|----------|---------------|-------------------| +| Amazon Titan | `bedrock/amazon.*` | [Amazon Titan Embeddings](https://docs.aws.amazon.com/bedrock/latest/userguide/titan-embedding-models.html) | +| Cohere | `bedrock/cohere.*` | [Cohere Embeddings](https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-cohere-embed.html) | +| TwelveLabs | `bedrock/us.twelvelabs.*` | [TwelveLabs](https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-twelvelabs.html) | + +### API keys +This can be set as env variables or passed as **params to litellm.embedding()** +```python +import os +os.environ["AWS_ACCESS_KEY_ID"] = "" # Access key +os.environ["AWS_SECRET_ACCESS_KEY"] = "" # Secret access key +os.environ["AWS_REGION_NAME"] = "" # us-east-1, us-east-2, us-west-1, us-west-2 +``` + +## Usage +### LiteLLM Python SDK +```python +from litellm import embedding +response = embedding( + model="bedrock/amazon.titan-embed-text-v1", + input=["good morning from litellm"], +) +print(response) +``` + +### LiteLLM Proxy Server + +#### 1. Setup config.yaml +```yaml +model_list: + - model_name: titan-embed-v1 + litellm_params: + model: bedrock/amazon.titan-embed-text-v1 + aws_access_key_id: os.environ/AWS_ACCESS_KEY_ID + aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY + aws_region_name: us-east-1 + - model_name: titan-embed-v2 + litellm_params: + model: bedrock/amazon.titan-embed-text-v2:0 + aws_access_key_id: os.environ/AWS_ACCESS_KEY_ID + aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY + aws_region_name: us-east-1 +``` + +#### 2. Start Proxy +```bash +litellm --config /path/to/config.yaml +``` + +#### 3. Use with OpenAI Python SDK +```python +import openai +client = openai.OpenAI( + api_key="anything", + base_url="http://0.0.0.0:4000" +) + +response = client.embeddings.create( + input=["good morning from litellm"], + model="titan-embed-v1" +) +print(response) +``` + +#### 4. Use with LiteLLM Python SDK +```python +import litellm +response = litellm.embedding( + model="titan-embed-v1", # model alias from config.yaml + input=["good morning from litellm"], + api_base="http://0.0.0.0:4000", + api_key="anything" +) +print(response) +``` + +## Supported AWS Bedrock Embedding Models + +| Model Name | Usage | Supported Additional OpenAI params | +|----------------------|---------------------------------------------|-----| +| Titan Embeddings V2 | `embedding(model="bedrock/amazon.titan-embed-text-v2:0", input=input)` | [here](https://github.com/BerriAI/litellm/blob/f5905e100068e7a4d61441d7453d7cf5609c2121/litellm/llms/bedrock/embed/amazon_titan_v2_transformation.py#L59) | +| Titan Embeddings - V1 | `embedding(model="bedrock/amazon.titan-embed-text-v1", input=input)` | [here](https://github.com/BerriAI/litellm/blob/f5905e100068e7a4d61441d7453d7cf5609c2121/litellm/llms/bedrock/embed/amazon_titan_g1_transformation.py#L53) +| Titan Multimodal Embeddings | `embedding(model="bedrock/amazon.titan-embed-image-v1", input=input)` | [here](https://github.com/BerriAI/litellm/blob/f5905e100068e7a4d61441d7453d7cf5609c2121/litellm/llms/bedrock/embed/amazon_titan_multimodal_transformation.py#L28) | +| TwelveLabs Marengo Embed 2.7 | `embedding(model="bedrock/us.twelvelabs.marengo-embed-2-7-v1:0", input=input)` | Supports multimodal input (text, video, audio, image) | +| Cohere Embeddings - English | `embedding(model="bedrock/cohere.embed-english-v3", input=input)` | [here](https://github.com/BerriAI/litellm/blob/f5905e100068e7a4d61441d7453d7cf5609c2121/litellm/llms/bedrock/embed/cohere_transformation.py#L18) +| Cohere Embeddings - Multilingual | `embedding(model="bedrock/cohere.embed-multilingual-v3", input=input)` | [here](https://github.com/BerriAI/litellm/blob/f5905e100068e7a4d61441d7453d7cf5609c2121/litellm/llms/bedrock/embed/cohere_transformation.py#L18) + +### Advanced - [Drop Unsupported Params](https://docs.litellm.ai/docs/completion/drop_params#openai-proxy-usage) + +### Advanced - [Pass model/provider-specific Params](https://docs.litellm.ai/docs/completion/provider_specific_params#proxy-usage) \ No newline at end of file diff --git a/docs/my-website/docs/providers/cometapi.md b/docs/my-website/docs/providers/cometapi.md new file mode 100644 index 00000000000..1245bacfad4 --- /dev/null +++ b/docs/my-website/docs/providers/cometapi.md @@ -0,0 +1,144 @@ +# CometAPI +LiteLLM supports all AI models from [CometAPI](https://www.cometapi.com/). CometAPI provides access to 500+ AI models through a unified API interface, including cutting-edge models like GPT-5, Claude Opus 4.1, and various other state-of-the-art language models. + +## Authentication + +To use CometAPI models, you need to obtain an API key from [CometAPI Token Console](https://api.cometapi.com/console/token). CometAPI offers free tokens for new users - you can get your free API key instantly by registering. + +## Usage + +Set your CometAPI key as an environment variable and use the completion function: + +```python +import os +from litellm import completion + +# Set API key +os.environ["COMETAPI_KEY"] = "your_comet_api_key_here" + +# Define messages +messages = [{"content": "Hello, how are you?", "role": "user"}] + +# Method 1: Using environment variable (recommended) +response = completion( + model="cometapi/gpt-5", + messages=messages +) + +print(response.choices[0].message.content) +``` + +### Alternative Usage - Explicit API Key + +You can also pass the API key explicitly: + +```python +import os +from litellm import completion + +# Define messages +messages = [{"content": "Hello, how are you?", "role": "user"}] + +# Method 2: Explicitly passing API key +response = completion( + model="cometapi/gpt-4o", + messages=messages, + api_key="your_comet_api_key_here" +) + +print(response.choices[0].message.content) +``` + +## Usage - Streaming + +Just set `stream=True` when calling completion: + +```python +import os +from litellm import completion + +os.environ["COMETAPI_KEY"] = "your_comet_api_key_here" + +messages = [{"content": "Hello, how are you?", "role": "user"}] + +response = completion( + model="cometapi/gpt-5", + messages=messages, + stream=True +) + +for chunk in response: + print(chunk.choices[0].delta.content or "", end="") +``` + +## Usage - Async Streaming + +For async streaming, use `acompletion`: + +```python +from litellm import acompletion +import asyncio, os, traceback + +async def completion_call(): + try: + os.environ["COMETAPI_KEY"] = "your_comet_api_key_here" + + print("test acompletion + streaming") + response = await acompletion( + model="cometapi/chatgpt-4o-latest", + messages=[{"content": "Hello, how are you?", "role": "user"}], + stream=True + ) + print(f"response: {response}") + async for chunk in response: + print(chunk) + except: + print(f"error occurred: {traceback.format_exc()}") + pass + +# Run the async function +await completion_call() +``` + +## CometAPI Models + +CometAPI offers access to 500+ AI models through a unified API. Some popular models include: + +| Model Name | Function Call | +|------------|---------------| +| cometapi/gpt-5 | `completion('cometapi/gpt-5', messages)` | +| cometapi/gpt-5-mini | `completion('cometapi/gpt-5-mini', messages)` | +| cometapi/gpt-5-nano | `completion('cometapi/gpt-5-nano', messages)` | +| cometapi/gpt-oss-20b | `completion('cometapi/gpt-oss-20b', messages)` | +| cometapi/gpt-oss-120b | `completion('cometapi/gpt-oss-120b', messages)` | +| cometapi/chatgpt-4o-latest | `completion('cometapi/chatgpt-4o-latest', messages)` | + +For a complete list of available models, visit the [CometAPI Models page](https://www.cometapi.com/model/). + +## Environment Variables + +| Variable | Description | Required | +|----------|-------------|----------| +| `COMETAPI_KEY` | Your CometAPI API key | Yes | + +## Error Handling + +```python +import os +from litellm import completion + +try: + os.environ["COMETAPI_KEY"] = "your_comet_api_key_here" + + messages = [{"content": "Hello, how are you?", "role": "user"}] + + response = completion( + model="cometapi/gpt-5", + messages=messages + ) + + print(response.choices[0].message.content) + +except Exception as e: + print(f"Error: {e}") +``` diff --git a/docs/my-website/docs/providers/compactifai.md b/docs/my-website/docs/providers/compactifai.md new file mode 100644 index 00000000000..1aa81463071 --- /dev/null +++ b/docs/my-website/docs/providers/compactifai.md @@ -0,0 +1,223 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# CompactifAI +https://docs.compactif.ai/ + +CompactifAI offers highly compressed versions of leading language models, delivering up to **70% lower inference costs**, **4x throughput gains**, and **low-latency inference** with minimal quality loss (under 5%). CompactifAI's OpenAI-compatible API makes integration straightforward, enabling developers to build ultra-efficient, scalable AI applications with superior concurrency and resource efficiency. + +| Property | Details | +|-------|-------| +| Description | CompactifAI offers compressed versions of leading language models with up to 70% cost reduction and 4x throughput gains | +| Provider Route on LiteLLM | `compactifai/` (add this prefix to the model name - e.g. `compactifai/cai-llama-3-1-8b-slim`) | +| Provider Doc | [CompactifAI ↗](https://docs.compactif.ai/) | +| API Endpoint for Provider | https://api.compactif.ai/v1 | +| Supported Endpoints | `/chat/completions`, `/completions` | + +## Supported OpenAI Parameters + +CompactifAI is fully OpenAI-compatible and supports the following parameters: + +``` +"stream", +"stop", +"temperature", +"top_p", +"max_tokens", +"presence_penalty", +"frequency_penalty", +"logit_bias", +"user", +"response_format", +"seed", +"tools", +"tool_choice", +"parallel_tool_calls", +"extra_headers" +``` + +## API Key Setup + +CompactifAI API keys are available through AWS Marketplace subscription: + +1. Subscribe via [AWS Marketplace](https://aws.amazon.com/marketplace) +2. Complete subscription verification (24-hour review process) +3. Access MultiverseIAM dashboard with provided credentials +4. Retrieve your API key from the dashboard + +```python +import os + +os.environ["COMPACTIFAI_API_KEY"] = "your-api-key" +``` + +## Usage + + + + +```python +from litellm import completion +import os + +os.environ['COMPACTIFAI_API_KEY'] = "your-api-key" + +response = completion( + model="compactifai/cai-llama-3-1-8b-slim", + messages=[ + {"role": "user", "content": "Hello from LiteLLM!"} + ], +) +print(response) +``` + + + + +```yaml +model_list: + - model_name: llama-2-compressed + litellm_params: + model: compactifai/cai-llama-3-1-8b-slim + api_key: os.environ/COMPACTIFAI_API_KEY +``` + + + + +## Streaming + +```python +from litellm import completion +import os + +os.environ['COMPACTIFAI_API_KEY'] = "your-api-key" + +response = completion( + model="compactifai/cai-llama-3-1-8b-slim", + messages=[ + {"role": "user", "content": "Write a short story"} + ], + stream=True +) + +for chunk in response: + print(chunk) +``` + +## Advanced Usage + +### Custom Parameters + +```python +from litellm import completion + +response = completion( + model="compactifai/cai-llama-3-1-8b-slim", + messages=[{"role": "user", "content": "Explain quantum computing"}], + temperature=0.7, + max_tokens=500, + top_p=0.9, + stop=["Human:", "AI:"] +) +``` + +### Function Calling + +CompactifAI supports OpenAI-compatible function calling: + +```python +from litellm import completion + +functions = [ + { + "name": "get_weather", + "description": "Get current weather information", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state" + } + }, + "required": ["location"] + } + } +] + +response = completion( + model="compactifai/cai-llama-3-1-8b-slim", + messages=[{"role": "user", "content": "What's the weather in San Francisco?"}], + tools=[{"type": "function", "function": f} for f in functions], + tool_choice="auto" +) +``` + +### Async Usage + +```python +import asyncio +from litellm import acompletion + +async def async_call(): + response = await acompletion( + model="compactifai/cai-llama-3-1-8b-slim", + messages=[{"role": "user", "content": "Hello async world!"}] + ) + return response + +# Run async function +response = asyncio.run(async_call()) +print(response) +``` + +## Available Models + +CompactifAI offers compressed versions of popular models. Use the `/models` endpoint to get the latest list: + +```python +import httpx + +headers = {"Authorization": f"Bearer {your_api_key}"} +response = httpx.get("https://api.compactif.ai/v1/models", headers=headers) +models = response.json() +``` + +Common model formats: +- `compactifai/cai-llama-3-1-8b-slim` +- `compactifai/mistral-7b-compressed` +- `compactifai/codellama-7b-compressed` + +## Benefits + +- **Cost Efficient**: Up to 70% lower inference costs compared to standard models +- **High Performance**: 4x throughput gains with minimal quality loss (under 5%) +- **Low Latency**: Optimized for fast response times +- **Drop-in Replacement**: Full OpenAI API compatibility +- **Scalable**: Superior concurrency and resource efficiency + +## Error Handling + +CompactifAI returns standard OpenAI-compatible error responses: + +```python +from litellm import completion +from litellm.exceptions import AuthenticationError, RateLimitError + +try: + response = completion( + model="compactifai/cai-llama-3-1-8b-slim", + messages=[{"role": "user", "content": "Hello"}] + ) +except AuthenticationError: + print("Invalid API key") +except RateLimitError: + print("Rate limit exceeded") +``` + +## Support + +- Documentation: https://docs.compactif.ai/ +- LinkedIn: [MultiverseComputing](https://www.linkedin.com/company/multiversecomputing) +- Analysis: [Artificial Analysis Provider Comparison](https://artificialanalysis.ai/providers/compactifai) \ No newline at end of file diff --git a/docs/my-website/docs/providers/dashscope.md b/docs/my-website/docs/providers/dashscope.md index eb18fa32a47..565776d6c4c 100644 --- a/docs/my-website/docs/providers/dashscope.md +++ b/docs/my-website/docs/providers/dashscope.md @@ -1,4 +1,4 @@ -# Dashscope +# Dashscope (Qwen API) https://dashscope.console.aliyun.com/ **We support ALL Qwen models, just set `dashscope/` as a prefix when sending completion requests** diff --git a/docs/my-website/docs/providers/databricks.md b/docs/my-website/docs/providers/databricks.md index 8631cbfdad9..921b06a17b7 100644 --- a/docs/my-website/docs/providers/databricks.md +++ b/docs/my-website/docs/providers/databricks.md @@ -282,6 +282,11 @@ ModelResponse( ) ``` +### Citations + +Anthropic models served through Databricks can return citation metadata. LiteLLM +exposes these via `response.choices[0].message.provider_specific_fields["citations"]`. + ### Pass `thinking` to Anthropic models You can also pass the `thinking` parameter to Anthropic models. diff --git a/docs/my-website/docs/providers/datarobot.md b/docs/my-website/docs/providers/datarobot.md new file mode 100644 index 00000000000..3f4a0f71ac4 --- /dev/null +++ b/docs/my-website/docs/providers/datarobot.md @@ -0,0 +1,43 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# DataRobot +LiteLLM supports all models from [DataRobot](https://datarobot.com). Select `datarobot` as the provider to route your request through the `datarobot` OpenAI-compatible endpoint using the upstream [official OpenAI Python API library](https://github.com/openai/openai-python/blob/main/README.md). + +## Usage + +### Environment variables +```python +import os +from litellm import completion +os.environ["DATAROBOT_API_KEY"] = "" +os.environ["DATAROBOT_API_BASE"] = "" # [OPTIONAL] defaults to https://app.datarobot.com + +response = completion( + model="datarobot/openai/gpt-4o-mini", + messages=messages, + ) + + +### Completion +```python +import litellm +import os + +response = litellm.completion( + model="datarobot/openai/gpt-4o-mini", # add `datarobot/` prefix to model so litellm knows to route through DataRobot + messages=[ + { + "role": "user", + "content": "Hey, how's it going?", + } + ], +) +print(response) +``` + +## DataRobot completion models + +🚨 LiteLLM supports _all_ DataRobot LLM gateway models. To get a list for your installation and user account, send the following CURL command: +`curl -X GET -H "Authorization: Bearer $DATAROBOT_API_TOKEN" "$DATAROBOT_ENDPOINT/genai/llmgw/catalog/" | jq | grep 'model":'DATAROBOT_ENDPOINT/genai/llmgw/catalog/` + diff --git a/docs/my-website/docs/providers/gemini.md b/docs/my-website/docs/providers/gemini.md index 9376144cc85..40d64656528 100644 --- a/docs/my-website/docs/providers/gemini.md +++ b/docs/my-website/docs/providers/gemini.md @@ -1199,6 +1199,10 @@ response = litellm.completion( | gemini-2.0-flash | `completion(model='gemini/gemini-2.0-flash', messages)` | `os.environ['GEMINI_API_KEY']` | | gemini-2.0-flash-exp | `completion(model='gemini/gemini-2.0-flash-exp', messages)` | `os.environ['GEMINI_API_KEY']` | | gemini-2.0-flash-lite-preview-02-05 | `completion(model='gemini/gemini-2.0-flash-lite-preview-02-05', messages)` | `os.environ['GEMINI_API_KEY']` | +| gemini-2.5-flash-preview-09-2025 | `completion(model='gemini/gemini-2.5-flash-preview-09-2025', messages)` | `os.environ['GEMINI_API_KEY']` | +| gemini-2.5-flash-lite-preview-09-2025 | `completion(model='gemini/gemini-2.5-flash-lite-preview-09-2025', messages)` | `os.environ['GEMINI_API_KEY']` | +| gemini-flash-latest | `completion(model='gemini/gemini-flash-latest', messages)` | `os.environ['GEMINI_API_KEY']` | +| gemini-flash-lite-latest | `completion(model='gemini/gemini-flash-lite-latest', messages)` | `os.environ['GEMINI_API_KEY']` | diff --git a/docs/my-website/docs/providers/google_ai_studio/image_gen.md b/docs/my-website/docs/providers/google_ai_studio/image_gen.md index f4e96d5225a..31b1766e450 100644 --- a/docs/my-website/docs/providers/google_ai_studio/image_gen.md +++ b/docs/my-website/docs/providers/google_ai_studio/image_gen.md @@ -42,7 +42,7 @@ os.environ["GEMINI_API_KEY"] = "your-api-key-here" # Generate a single image response = litellm.image_generation( - model="gemini/imagen-4.0-generate-preview-06-06", + model="gemini/imagen-4.0-generate-001", prompt="A cute baby sea otter swimming in crystal clear water" ) @@ -64,7 +64,7 @@ async def generate_image(): # Generate image asynchronously response = await litellm.aimage_generation( - model="gemini/imagen-4.0-generate-preview-06-06", + model="gemini/imagen-4.0-generate-001", prompt="A beautiful sunset over mountains with vibrant colors", n=1, ) @@ -89,7 +89,7 @@ os.environ["GEMINI_API_KEY"] = "your-api-key-here" # Generate image with additional parameters response = litellm.image_generation( - model="gemini/imagen-4.0-generate-preview-06-06", + model="gemini/imagen-4.0-generate-001", prompt="A futuristic cityscape at night with neon lights", n=1, size="1024x1024", @@ -112,7 +112,7 @@ for image in response.data: model_list: - model_name: google-imagen litellm_params: - model: gemini/imagen-4.0-generate-preview-06-06 + model: gemini/imagen-4.0-generate-001 api_key: os.environ/GEMINI_API_KEY model_info: mode: image_generation @@ -198,7 +198,7 @@ Google AI Studio Image Generation supports the following OpenAI-compatible param | Parameter | Type | Description | Default | Example | |-----------|------|-------------|---------|---------| | `prompt` | string | Text description of the image to generate | Required | `"A sunset over the ocean"` | -| `model` | string | The model to use for generation | Required | `"gemini/imagen-4.0-generate-preview-06-06"` | +| `model` | string | The model to use for generation | Required | `"gemini/imagen-4.0-generate-001"` | | `n` | integer | Number of images to generate (1-4) | `1` | `2` | | `size` | string | Image dimensions | `"1024x1024"` | `"512x512"`, `"1024x1024"` | diff --git a/docs/my-website/docs/providers/heroku.md b/docs/my-website/docs/providers/heroku.md new file mode 100644 index 00000000000..bf37ed64b19 --- /dev/null +++ b/docs/my-website/docs/providers/heroku.md @@ -0,0 +1,76 @@ +# Heroku + +## Provision a Model + +To use Heroku with LiteLLM, [configure a Heroku app and attach a supported model](https://devcenter.heroku.com/articles/heroku-inference#provision-access-to-an-ai-model-resource). + + +## Supported Models + +Heroku for LiteLLM supports various [chat](https://devcenter.heroku.com/articles/heroku-inference-api-v1-chat-completions) models: + +| Model | Region | +|-----------------------------------|---------| +| [`heroku/claude-sonnet-4`](https://devcenter.heroku.com/articles/heroku-inference-api-model-claude-4-sonnet) | US, EU | +| [`heroku/claude-3-7-sonnet`](https://devcenter.heroku.com/articles/heroku-inference-api-model-claude-3-7-sonnet) | US, EU | +| [`heroku/claude-3-5-sonnet-latest`](https://devcenter.heroku.com/articles/heroku-inference-api-model-claude-3-5-sonnet-latest) | US | +| [`heroku/claude-3-5-haiku`](https://devcenter.heroku.com/articles/heroku-inference-api-model-claude-3-5-haiku) | US | +| [`heroku/claude-3`](https://devcenter.heroku.com/articles/heroku-inference-api-model-claude-3-haiku) | EU | + +## Environment Variables + +When you attach a model to a Heroku app, three config variables are set: + +- `INFERENCE_KEY`: The API key used for authenticating requests to the model. +- `INFERENCE_MODEL_ID`: The name of the model, for example`claude-3-5-haiku`. +- `INFERENCE_URL`: The base URL for calling the model. + +Both `INFERENCE_KEY` and `INFERENCE_URL` are required to make calls to your model. + +For more information on these variables, see the [Heroku documentation](https://devcenter.heroku.com/articles/heroku-inference#model-resource-config-vars). + +## Usage Examples +### Using Config Variables + +Heroku uses the following LiteLLM API config variables: + +- `HEROKU_API_KEY`: This value corresponds to [LiteLLM's `api_key` param](https://docs.litellm.ai/docs/set_keys#litellmapi_key). Set this variable to the value of Heroku's `INFERENCE_KEY` config variable. +- `HEROKU_API_BASE`: This value corresponds to [LiteLLM's `api_base` param](https://docs.litellm.ai/docs/set_keys#litellmapi_base). Set this variable to the value of Heroku's `INFERENCE_URL` config variable. + +In this example, we don't explicitly pass the `api_key` and `api_base` variables. Instead, we set the config variables which Heroku will use: + +```python +import os +from litellm import completion + +os.environ["HEROKU_API_BASE"] = "https://us.inference.heroku.com" +os.environ["HEROKU_API_KEY"] = "fake-heroku-key" + +response = completion( + model="heroku/claude-3-5-haiku", + messages=[ + {"role": "user", "content": "write code for saying hey from LiteLLM"} + ] +) + +print(response) +``` + +> Include the `heroku/` prefix in the model name so LiteLLM knows the model provider to use. + +### Explicitly Setting `api_key` and `api_base` + +```python +from litellm import completion + +response = completion( + model="heroku/claude-sonnet-4", + api_key="fake-heroku-key", + api_base="https://us.inference.heroku.com", + messages=[ + {"role": "user", "content": "write code for saying hey from LiteLLM"} + ], +) +``` + +> Include the `heroku/` prefix in the model name so LiteLLM knows the model provider to use. diff --git a/docs/my-website/docs/providers/oci.md b/docs/my-website/docs/providers/oci.md index 28beb71094a..6fc1835154a 100644 --- a/docs/my-website/docs/providers/oci.md +++ b/docs/my-website/docs/providers/oci.md @@ -44,7 +44,11 @@ response = completion( oci_user=, oci_fingerprint=, oci_tenancy=, + # Provide either the private key string OR the path to the key file: + # Option 1: pass the private key as a string oci_key=, + # Option 2: pass the private key file path + # oci_key_file="", oci_compartment_id=, ) print(response) @@ -67,7 +71,11 @@ response = completion( oci_user=, oci_fingerprint=, oci_tenancy=, + # Provide either the private key string OR the path to the key file: + # Option 1: pass the private key as a string oci_key=, + # Option 2: pass the private key file path + # oci_key_file="", oci_compartment_id=, ) for chunk in response: diff --git a/docs/my-website/docs/providers/ovhcloud.md b/docs/my-website/docs/providers/ovhcloud.md new file mode 100644 index 00000000000..6c42208f2cc --- /dev/null +++ b/docs/my-website/docs/providers/ovhcloud.md @@ -0,0 +1,380 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# 🆕 OVHCloud AI Endpoints +Leading French Cloud provider in Europe with data sovereignty and privacy. + +You can explore the last models we made available in our [catalog](https://endpoints.ai.cloud.ovh.net/catalog). + +:::tip + +We support ALL OVHCloud AI Endpoints models, just set `model=ovhcloud/` as a prefix when sending litellm requests. +For the complete models catalog, visit https://endpoints.ai.cloud.ovh.net/catalog. ** + +::: + +## Sample usage +### Chat completion +You can define your API key by setting the `OVHCLOUD_API_KEY` environment variable or by overriding the `api_key` parameter. You can generate a key on the [OVHCloud Manager](https://www.ovh.com/manager). + +```python +from litellm import completion +import os + +# Our API is free but ratelimited for calls without an API key. +os.environ['OVHCLOUD_API_KEY'] = "your-api-key" + +response = completion( + model = "ovhcloud/Meta-Llama-3_3-70B-Instruct", + messages = [ + { + "role": "user", + "content": "Hello, how are you?", + } + ], + max_tokens = 10, + stop = [], + temperature = 0.2, + top_p = 0.9, + user = "user", + api_key = "your-api-key" # Optional if set through the enviromnent variable. +) + +print(response) +``` + +### Streaming +Set the parameter `stream` to `True` to stream a response. +```python +from litellm import completion +import os + +os.environ['OVHCLOUD_API_KEY'] = "your-api-key" + +response = completion( + model = "ovhcloud/Meta-Llama-3_3-70B-Instruct", + messages = [ + { + "role": "user", + "content": "Hello, how are you?", + } + ], + max_tokens = 10, + stop = [], + temperature = 0.2, + top_p = 0.9, + user = "user", + api_key = "your-api-key" # Optional if set through the enviromnent variable, + stream = True +) + +for part in response: + print(response) +``` + +### Tool Calling + +```python +from litellm import completion +import json + +def get_current_weather(location, unit="celsius"): + if unit == "celsius": + return {"location": location, "temperature": "22", "unit": "celsius"} + else: + return {"location": location, "temperature": "72", "unit": "fahrenheit"} + +def print_message(role, content, is_tool_call=False, function_name=None): + if role == "user": + print(f"🧑 User: {content}") + elif role == "assistant": + if is_tool_call: + print(f"🤖 Assistant: I will call the function '{function_name}' to get some informations.") + else: + print(f"🤖 Assistant: {content}") + elif role == "tool": + print(f"🔧 Tool ({function_name}): {content}") + print() + +messages = [{"role": "user", "content": "What's the weather like in Paris?"}] +model = "ovhcloud/Meta-Llama-3_3-70B-Instruct" + +tools = [ + { + "type": "function", + "function": { + "name": "get_current_weather", + "description": "Get the current weather in a given location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and country, e.g. Montréal, Canada", + }, + "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, + }, + "required": ["location"], + }, + }, + } +] + +print("🌟 Beginning of the conversation") + +# Initial user message +print_message("user", messages[0]["content"]) + +# First request to the model +print("📡 Sending first request to the model...") +response = completion( + model=model, + messages=messages, + tools=tools, + tool_choice="auto", +) + +response_message = response.choices[0].message +tool_calls = response_message.tool_calls + +if tool_calls: + available_functions = { + "get_current_weather": get_current_weather, + } + + # Display the tool calls suggested by the model + for tool_call in tool_calls: + print_message("assistant", "", is_tool_call=True, function_name=tool_call.function.name) + print(f" 📋 Arguments: {tool_call.function.arguments}") + print() + + # Add assistant message with tool calls to the conversation history + assistant_message = { + "role": "assistant", + "content": response_message.content, + "tool_calls": [ + { + "id": tool_call.id, + "type": "function", + "function": { + "name": tool_call.function.name, + "arguments": tool_call.function.arguments + } + } for tool_call in tool_calls + ] + } + + messages.append(assistant_message) + + # Execute each tool call and add the results to the conversation history + for tool_call in tool_calls: + function_name = tool_call.function.name + function_to_call = available_functions[function_name] + function_args = json.loads(tool_call.function.arguments) + + print(f"🔧 Executing function '{function_name}'...") + function_response = function_to_call( + location=function_args.get("location"), + unit=function_args.get("unit"), + ) + + # Display tool response + print_message("tool", json.dumps(function_response, indent=2), function_name=function_name) + + messages.append({ + "tool_call_id": tool_call.id, + "role": "tool", + "name": function_name, + "content": json.dumps(function_response), + }) + + print("📡 Sending second request to the model with results...") + + # Second request with function results + second_response = completion( + model=model, + messages=messages + ) + + # Display final response + final_content = second_response.choices[0].message.content + print_message("assistant", final_content) + +else: + print("❌ No function call detected") + print_message("assistant", response_message.content) +``` + +### Vision Example + +```python +from base64 import b64encode +from mimetypes import guess_type +import litellm + +# Auxiliary function to get b64 images +def data_url_from_image(file_path): + mime_type, _ = guess_type(file_path) + if mime_type is None: + raise ValueError("Could not determine MIME type of the file") + + with open(file_path, "rb") as image_file: + encoded_string = b64encode(image_file.read()).decode("utf-8") + + data_url = f"data:{mime_type};base64,{encoded_string}" + return data_url + +response = litellm.completion( + model = "ovhcloud/Mistral-Small-3.2-24B-Instruct-2506", + messages=[ + { + "role": "user", + "content": [ + { + "type": "text", + "text": "What's in this image?" + }, + { + "type": "image_url", + "image_url": { + "url": data_url_from_image("your_image.jpg"), + "format": "image/jpeg" + } + } + ] + } + ], + stream=False +) + +print(response.choices[0].message.content) +``` + + +### Structured Output + +```python +from litellm import completion + +response = completion( + model="ovhcloud/Meta-Llama-3_3-70B-Instruct", + messages=[ + { + "role": "system", + "content": ( + "You are a specialist in extracting structured data from unstructured text. " + "Your task is to identify relevant entities and categories, then format them " + "according to the requested structure." + ), + }, + { + "role": "user", + "content": "Room 12 contains books, a desk, and a lamp." + }, + ], + response_format={ + "type": "json_schema", + "json_schema": { + "title": "data", + "name": "data_extraction", + "schema": { + "type": "object", + "properties": { + "section": {"type": "string"}, + "products": { + "type": "array", + "items": {"type": "string"} + } + }, + "required": ["section", "products"], + "additionalProperties": False + }, + "strict": False + } + }, + stream=False +) + +print(response.choices[0].message.content) +``` + +### Embeddings + +```python +from litellm import embedding + +response = embedding( + model="ovhcloud/BGE-M3", + input=["sample text to embed", "another sample text to embed"] +) + +print(response.data) +``` + +## Usage with LiteLLM Proxy Server + +Here's how to call a OVHCloud AI Endpoints model with the LiteLLM Proxy Server + +1. Modify the config.yaml + + ```yaml + model_list: + - model_name: my-model + litellm_params: + model: ovhcloud/ # add ovhcloud/ prefix to route as OVHCloud provider + api_key: api-key # api key to send your model + ``` + + +2. Start the proxy + + ```bash + $ litellm --config /path/to/config.yaml + ``` + +3. Send Request to LiteLLM Proxy Server + + + + + + ```python + import openai + client = openai.OpenAI( + api_key="sk-1234", # pass litellm proxy key, if you're using virtual keys + base_url="http://0.0.0.0:4000" # litellm-proxy-base url + ) + + response = client.chat.completions.create( + model="my-model", + messages = [ + { + "role": "user", + "content": "what llm are you" + } + ], + ) + + print(response) + ``` + + + + + ```shell + curl --location 'http://0.0.0.0:4000/chat/completions' \ + --header 'Authorization: Bearer sk-1234' \ + --header 'Content-Type: application/json' \ + --data '{ + "model": "my-model", + "messages": [ + { + "role": "user", + "content": "what llm are you" + } + ], + }' + ``` + + + diff --git a/docs/my-website/docs/providers/vercel_ai_gateway.md b/docs/my-website/docs/providers/vercel_ai_gateway.md new file mode 100644 index 00000000000..91f0a18ea1c --- /dev/null +++ b/docs/my-website/docs/providers/vercel_ai_gateway.md @@ -0,0 +1,219 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Vercel AI Gateway + +## Overview + +| Property | Details | +|-------|-------| +| Description | Vercel AI Gateway provides a unified interface to access multiple AI providers through a single endpoint, with built-in caching, rate limiting, and analytics. | +| Provider Route on LiteLLM | `vercel_ai_gateway/` | +| Link to Provider Doc | [Vercel AI Gateway Documentation ↗](https://vercel.com/docs/ai-gateway) | +| Base URL | `https://ai-gateway.vercel.sh/v1` | +| Supported Operations | `/chat/completions`, `/models` | + +
+
+ +https://vercel.com/docs/ai-gateway + +**We support ALL models available through Vercel AI Gateway, just set `vercel_ai_gateway/` as a prefix when sending completion requests** + +## Required Variables + +```python showLineNumbers title="Environment Variables" +os.environ["VERCEL_AI_GATEWAY_API_KEY"] = "" # your Vercel AI Gateway API key +# OR +os.environ["VERCEL_OIDC_TOKEN"] = "" # your Vercel OIDC token for authentication +``` + +## Optional Variables + +```python showLineNumbers title="Environment Variables" +os.environ["VERCEL_SITE_URL"] = "" # your site url +# OR +os.environ["VERCEL_APP_NAME"] = "" # your app name +``` + +Note: see the [Vercel AI Gateway docs](https://vercel.com/docs/ai-gateway#using-the-ai-gateway-with-an-api-key) for instructions on obtaining a key. + +## Usage - LiteLLM Python SDK + +### Non-streaming + +```python showLineNumbers title="Vercel AI Gateway Non-streaming Completion" +import os +import litellm +from litellm import completion + +os.environ["VERCEL_AI_GATEWAY_API_KEY"] = "your-api-key" + +messages = [{"content": "Hello, how are you?", "role": "user"}] + +# Vercel AI Gateway call +response = completion( + model="vercel_ai_gateway/openai/gpt-4o", + messages=messages +) + +print(response) +``` + +### Streaming + +```python showLineNumbers title="Vercel AI Gateway Streaming Completion" +import os +import litellm +from litellm import completion + +os.environ["VERCEL_AI_GATEWAY_API_KEY"] = "your-api-key" + +messages = [{"content": "Hello, how are you?", "role": "user"}] + +# Vercel AI Gateway call with streaming +response = completion( + model="vercel_ai_gateway/openai/gpt-4o", + messages=messages, + stream=True +) + +for chunk in response: + print(chunk) +``` + +## Usage - LiteLLM Proxy + +Add the following to your LiteLLM Proxy configuration file: + +```yaml showLineNumbers title="config.yaml" +model_list: + - model_name: gpt-4o-gateway + litellm_params: + model: vercel_ai_gateway/openai/gpt-4o + api_key: os.environ/VERCEL_AI_GATEWAY_API_KEY + + - model_name: claude-4-sonnet-gateway + litellm_params: + model: vercel_ai_gateway/anthropic/claude-4-sonnet + api_key: os.environ/VERCEL_AI_GATEWAY_API_KEY +``` + +Start your LiteLLM Proxy server: + +```bash showLineNumbers title="Start LiteLLM Proxy" +litellm --config config.yaml + +# RUNNING on http://0.0.0.0:4000 +``` + + + + +```python showLineNumbers title="Vercel AI Gateway via Proxy - Non-streaming" +from openai import OpenAI + +# Initialize client with your proxy URL +client = OpenAI( + base_url="http://localhost:4000", # Your proxy URL + api_key="your-proxy-api-key" # Your proxy API key +) + +# Non-streaming response +response = client.chat.completions.create( + model="gpt-4o-gateway", + messages=[{"role": "user", "content": "Hello, how are you?"}] +) + +print(response.choices[0].message.content) +``` + +```python showLineNumbers title="Vercel AI Gateway via Proxy - Streaming" +from openai import OpenAI + +# Initialize client with your proxy URL +client = OpenAI( + base_url="http://localhost:4000", # Your proxy URL + api_key="your-proxy-api-key" # Your proxy API key +) + +# Streaming response +response = client.chat.completions.create( + model="gpt-4o-gateway", + messages=[{"role": "user", "content": "Hello, how are you?"}], + stream=True +) + +for chunk in response: + if chunk.choices[0].delta.content is not None: + print(chunk.choices[0].delta.content, end="") +``` + + + + + +```python showLineNumbers title="Vercel AI Gateway via Proxy - LiteLLM SDK" +import litellm + +# Configure LiteLLM to use your proxy +response = litellm.completion( + model="litellm_proxy/gpt-4o-gateway", + messages=[{"role": "user", "content": "Hello, how are you?"}], + api_base="http://localhost:4000", + api_key="your-proxy-api-key" +) + +print(response.choices[0].message.content) +``` + +```python showLineNumbers title="Vercel AI Gateway via Proxy - LiteLLM SDK Streaming" +import litellm + +# Configure LiteLLM to use your proxy with streaming +response = litellm.completion( + model="litellm_proxy/gpt-4o-gateway", + messages=[{"role": "user", "content": "Hello, how are you?"}], + api_base="http://localhost:4000", + api_key="your-proxy-api-key", + stream=True +) + +for chunk in response: + if hasattr(chunk.choices[0], 'delta') and chunk.choices[0].delta.content is not None: + print(chunk.choices[0].delta.content, end="") +``` + + + + + +```bash showLineNumbers title="Vercel AI Gateway via Proxy - cURL" +curl http://localhost:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer your-proxy-api-key" \ + -d '{ + "model": "gpt-4o-gateway", + "messages": [{"role": "user", "content": "Hello, how are you?"}] + }' +``` + +```bash showLineNumbers title="Vercel AI Gateway via Proxy - cURL Streaming" +curl http://localhost:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer your-proxy-api-key" \ + -d '{ + "model": "gpt-4o-gateway", + "messages": [{"role": "user", "content": "Hello, how are you?"}], + "stream": true + }' +``` + + + + +For more detailed information on using the LiteLLM Proxy, see the [LiteLLM Proxy documentation](../providers/litellm_proxy). + +## Additional Resources + +- [Vercel AI Gateway Documentation](https://vercel.com/docs/ai-gateway) diff --git a/docs/my-website/docs/providers/vertex.md b/docs/my-website/docs/providers/vertex.md index fda0cee8626..9a969876432 100644 --- a/docs/my-website/docs/providers/vertex.md +++ b/docs/my-website/docs/providers/vertex.md @@ -45,7 +45,7 @@ vertex_credentials_json = json.dumps(vertex_credentials) ## COMPLETION CALL response = completion( - model="vertex_ai/gemini-pro", + model="vertex_ai/gemini-2.5-pro", messages=[{ "content": "Hello, how are you?","role": "user"}], vertex_credentials=vertex_credentials_json ) @@ -69,7 +69,7 @@ vertex_credentials_json = json.dumps(vertex_credentials) response = completion( - model="vertex_ai/gemini-pro", + model="vertex_ai/gemini-2.5-pro", messages=[{"content": "You are a good bot.","role": "system"}, {"content": "Hello, how are you?","role": "user"}], vertex_credentials=vertex_credentials_json ) @@ -189,13 +189,26 @@ print(json.loads(completion.choices[0].message.content)) 1. Add model to config.yaml ```yaml model_list: - - model_name: gemini-pro + - model_name: gemini-2.5-pro litellm_params: model: vertex_ai/gemini-1.5-pro vertex_project: "project-id" vertex_location: "us-central1" vertex_credentials: "/path/to/service_account.json" # [OPTIONAL] Do this OR `!gcloud auth application-default login` - run this to add vertex credentials to your env ``` +or +```yaml +model_list: + - model_name: gemini-pro + litellm_params: + model: vertex_ai/gemini-1.5-pro + litellm_credential_name: vertex-global + vertex_project: project-name-here + vertex_location: global + base_model: gemini + model_info: + provider: Vertex +``` 2. Start Proxy @@ -210,7 +223,7 @@ curl -X POST 'http://0.0.0.0:4000/chat/completions' \ -H 'Content-Type: application/json' \ -H 'Authorization: Bearer sk-1234' \ -D '{ - "model": "gemini-pro", + "model": "gemini-2.5-pro", "messages": [ {"role": "user", "content": "List 5 popular cookie recipes."} ], @@ -262,7 +275,7 @@ except JSONSchemaValidationError as e: 1. Add model to config.yaml ```yaml model_list: - - model_name: gemini-pro + - model_name: gemini-2.5-pro litellm_params: model: vertex_ai/gemini-1.5-pro vertex_project: "project-id" @@ -283,7 +296,7 @@ curl -X POST 'http://0.0.0.0:4000/chat/completions' \ -H 'Content-Type: application/json' \ -H 'Authorization: Bearer sk-1234' \ -D '{ - "model": "gemini-pro", + "model": "gemini-2.5-pro", "messages": [ {"role": "user", "content": "List 5 popular cookie recipes."} ], @@ -391,7 +404,7 @@ client = OpenAI( ) response = client.chat.completions.create( - model="gemini-pro", + model="gemini-2.5-pro", messages=[{"role": "user", "content": "Who won the world cup?"}], tools=[{"googleSearch": {}}], ) @@ -406,7 +419,7 @@ curl http://localhost:4000/v1/chat/completions \ -H "Content-Type: application/json" \ -H "Authorization: Bearer sk-1234" \ -d '{ - "model": "gemini-pro", + "model": "gemini-2.5-pro", "messages": [ {"role": "user", "content": "Who won the world cup?"} ], @@ -527,7 +540,7 @@ client = OpenAI( ) response = client.chat.completions.create( - model="gemini-pro", + model="gemini-2.5-pro", messages=[{"role": "user", "content": "Who won the world cup?"}], tools=[{"enterpriseWebSearch": {}}], ) @@ -542,7 +555,7 @@ curl http://localhost:4000/v1/chat/completions \ -H "Content-Type: application/json" \ -H "Authorization: Bearer sk-1234" \ -d '{ - "model": "gemini-pro", + "model": "gemini-2.5-pro", "messages": [ {"role": "user", "content": "Who won the world cup?"} ], @@ -815,6 +828,77 @@ Use Vertex AI context caching is supported by calling provider api directly. (Un [**Go straight to provider**](../pass_through/vertex_ai.md#context-caching) +#### 1. Create the Cache + +First, create the cache by sending a `POST` request to the `cachedContents` endpoint via the LiteLLM proxy. + + + + +```bash +curl http://0.0.0.0:4000/vertex_ai/v1/projects/{project_id}/locations/{location}/cachedContents \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer $LITELLM_KEY" \ + -d '{ + "model": "projects/{project_id}/locations/{location}/publishers/google/models/gemini-2.5-flash", + "displayName": "example_cache", + "contents": [{ + "role": "user", + "parts": [{ + "text": ".... a long book to be cached" + }] + }] + }' +``` + + + + +#### 2. Get the Cache Name from the Response + +Vertex AI will return a response containing the `name` of the cached content. This name is the identifier for your cached data. + +```json +{ + "name": "projects/12341234/locations/{location}/cachedContents/123123123123123", + "model": "projects/{project_id}/locations/{location}/publishers/google/models/gemini-2.5-flash", + "createTime": "2025-09-23T19:13:50.674976Z", + "updateTime": "2025-09-23T19:13:50.674976Z", + "expireTime": "2025-09-23T20:13:50.655988Z", + "displayName": "example_cache", + "usageMetadata": { + "totalTokenCount": 1246, + "textCount": 5132 + } +} +``` + +#### 3. Use the Cached Content + +Use the `name` from the response as `cachedContent` or `cached_content` in subsequent API calls to reuse the cached information. This is passed in the body of your request to `/chat/completions`. + + + + +```bash + +curl http://0.0.0.0:4000/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer $LITELLM_KEY" \ + -d '{ + "cachedContent": "projects/545201925769/locations/us-central1/cachedContents/4511135542628319232", + "model": "gemini-2.5-flash", + "messages": [ + { + "role": "user", + "content": "what is the book about?" + } + ] + }' +``` + + + ## Pre-requisites * `pip install google-cloud-aiplatform` (pre-installed on proxy docker image) @@ -835,7 +919,7 @@ import litellm litellm.vertex_project = "hardy-device-38811" # Your Project ID litellm.vertex_location = "us-central1" # proj location -response = litellm.completion(model="gemini-pro", messages=[{"role": "user", "content": "write code for saying hi from LiteLLM"}]) +response = litellm.completion(model="gemini-2.5-pro", messages=[{"role": "user", "content": "write code for saying hi from LiteLLM"}]) ``` ## Usage with LiteLLM Proxy Server @@ -876,9 +960,9 @@ Here's how to use Vertex AI with the LiteLLM Proxy Server vertex_location: "us-central1" # proj location model_list: - -model_name: team1-gemini-pro + -model_name: team1-gemini-2.5-pro litellm_params: - model: gemini-pro + model: gemini-2.5-pro ``` @@ -905,7 +989,7 @@ Here's how to use Vertex AI with the LiteLLM Proxy Server ) response = client.chat.completions.create( - model="team1-gemini-pro", + model="team1-gemini-2.5-pro", messages = [ { "role": "user", @@ -925,7 +1009,7 @@ Here's how to use Vertex AI with the LiteLLM Proxy Server --header 'Authorization: Bearer sk-1234' \ --header 'Content-Type: application/json' \ --data '{ - "model": "team1-gemini-pro", + "model": "team1-gemini-2.5-pro", "messages": [ { "role": "user", @@ -975,7 +1059,7 @@ vertex_credentials_json = json.dumps(vertex_credentials) response = completion( - model="vertex_ai/gemini-pro", + model="vertex_ai/gemini-2.5-pro", messages=[{"content": "You are a good bot.","role": "system"}, {"content": "Hello, how are you?","role": "user"}], vertex_credentials=vertex_credentials_json, vertex_project="my-special-project", @@ -1039,7 +1123,7 @@ In certain use-cases you may need to make calls to the models and pass [safety s ```python response = completion( - model="vertex_ai/gemini-pro", + model="vertex_ai/gemini-2.5-pro", messages=[{"role": "user", "content": "write code for saying hi from LiteLLM"}] safety_settings=[ { @@ -1153,7 +1237,7 @@ litellm.vertex_ai_safety_settings = [ }, ] response = completion( - model="vertex_ai/gemini-pro", + model="vertex_ai/gemini-2.5-pro", messages=[{"role": "user", "content": "write code for saying hi from LiteLLM"}] ) ``` @@ -1212,7 +1296,11 @@ litellm.vertex_location = "us-central1 # Your Location ## Gemini Pro | Model Name | Function Call | |------------------|--------------------------------------| -| gemini-pro | `completion('gemini-pro', messages)`, `completion('vertex_ai/gemini-pro', messages)` | +| gemini-2.5-pro | `completion('gemini-2.5-pro', messages)`, `completion('vertex_ai/gemini-2.5-pro', messages)` | +| gemini-2.5-flash-preview-09-2025 | `completion('gemini-2.5-flash-preview-09-2025', messages)`, `completion('vertex_ai/gemini-2.5-flash-preview-09-2025', messages)` | +| gemini-2.5-flash-lite-preview-09-2025 | `completion('gemini-2.5-flash-lite-preview-09-2025', messages)`, `completion('vertex_ai/gemini-2.5-flash-lite-preview-09-2025', messages)` | +| gemini-flash-latest | `completion('gemini-flash-latest', messages)`, `completion('vertex_ai/gemini-flash-latest', messages)` | +| gemini-flash-lite-latest | `completion('gemini-flash-lite-latest', messages)`, `completion('vertex_ai/gemini-flash-lite-latest', messages)` | ## Fine-tuned Models @@ -1307,7 +1395,7 @@ curl --location 'https://0.0.0.0:4000/v1/chat/completions' \ ## Gemini Pro Vision | Model Name | Function Call | |------------------|--------------------------------------| -| gemini-pro-vision | `completion('gemini-pro-vision', messages)`, `completion('vertex_ai/gemini-pro-vision', messages)`| +| gemini-2.5-pro-vision | `completion('gemini-2.5-pro-vision', messages)`, `completion('vertex_ai/gemini-2.5-pro-vision', messages)`| ## Gemini 1.5 Pro (and Vision) | Model Name | Function Call | @@ -1321,7 +1409,7 @@ curl --location 'https://0.0.0.0:4000/v1/chat/completions' \ #### Using Gemini Pro Vision -Call `gemini-pro-vision` in the same input/output format as OpenAI [`gpt-4-vision`](https://docs.litellm.ai/docs/providers/openai#openai-vision-models) +Call `gemini-2.5-pro-vision` in the same input/output format as OpenAI [`gpt-4-vision`](https://docs.litellm.ai/docs/providers/openai#openai-vision-models) LiteLLM Supports the following image types passed in `url` - Images with Cloud Storage URIs - gs://cloud-samples-data/generative-ai/image/boats.jpeg @@ -1339,7 +1427,7 @@ LiteLLM Supports the following image types passed in `url` import litellm response = litellm.completion( - model = "vertex_ai/gemini-pro-vision", + model = "vertex_ai/gemini-2.5-pro-vision", messages=[ { "role": "user", @@ -1377,7 +1465,7 @@ image_path = "cached_logo.jpg" # Getting the base64 string base64_image = encode_image(image_path) response = litellm.completion( - model="vertex_ai/gemini-pro-vision", + model="vertex_ai/gemini-2.5-pro-vision", messages=[ { "role": "user", @@ -1433,7 +1521,7 @@ tools = [ messages = [{"role": "user", "content": "What's the weather like in Boston today?"}] response = completion( - model="vertex_ai/gemini-pro-vision", + model="vertex_ai/gemini-2.5-pro-vision", messages=messages, tools=tools, ) @@ -2509,150 +2597,6 @@ print("response from proxy", response) -## **Batch APIs** - -Just add the following Vertex env vars to your environment. - -```bash -# GCS Bucket settings, used to store batch prediction files in -export GCS_BUCKET_NAME = "litellm-testing-bucket" # the bucket you want to store batch prediction files in -export GCS_PATH_SERVICE_ACCOUNT="/path/to/service_account.json" # path to your service account json file - -# Vertex /batch endpoint settings, used for LLM API requests -export GOOGLE_APPLICATION_CREDENTIALS="/path/to/service_account.json" # path to your service account json file -export VERTEXAI_LOCATION="us-central1" # can be any vertex location -export VERTEXAI_PROJECT="my-test-project" -``` - -### Usage - - -#### 1. Create a file of batch requests for vertex - -LiteLLM expects the file to follow the **[OpenAI batches files format](https://platform.openai.com/docs/guides/batch)** - -Each `body` in the file should be an **OpenAI API request** - -Create a file called `vertex_batch_completions.jsonl` in the current working directory, the `model` should be the Vertex AI model name -``` -{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "gemini-1.5-flash-001", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} -{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "gemini-1.5-flash-001", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} -``` - - -#### 2. Upload a File of batch requests - -For `vertex_ai` litellm will upload the file to the provided `GCS_BUCKET_NAME` - -```python -import os -oai_client = OpenAI( - api_key="sk-1234", # litellm proxy API key - base_url="http://localhost:4000" # litellm proxy base url -) -file_name = "vertex_batch_completions.jsonl" # -_current_dir = os.path.dirname(os.path.abspath(__file__)) -file_path = os.path.join(_current_dir, file_name) -file_obj = oai_client.files.create( - file=open(file_path, "rb"), - purpose="batch", - extra_body={"custom_llm_provider": "vertex_ai"}, # tell litellm to use vertex_ai for this file upload -) -``` - -**Expected Response** - -```json -{ - "id": "gs://litellm-testing-bucket/litellm-vertex-files/publishers/google/models/gemini-1.5-flash-001/d3f198cd-c0d1-436d-9b1e-28e3f282997a", - "bytes": 416, - "created_at": 1733392026, - "filename": "litellm-vertex-files/publishers/google/models/gemini-1.5-flash-001/d3f198cd-c0d1-436d-9b1e-28e3f282997a", - "object": "file", - "purpose": "batch", - "status": "uploaded", - "status_details": null -} -``` - - - -#### 3. Create a batch - -```python -batch_input_file_id = file_obj.id # use `file_obj` from step 2 -create_batch_response = oai_client.batches.create( - completion_window="24h", - endpoint="/v1/chat/completions", - input_file_id=batch_input_file_id, # example input_file_id = "gs://litellm-testing-bucket/litellm-vertex-files/publishers/google/models/gemini-1.5-flash-001/c2b1b785-252b-448c-b180-033c4c63b3ce" - extra_body={"custom_llm_provider": "vertex_ai"}, # tell litellm to use `vertex_ai` for this batch request -) -``` - -**Expected Response** - -```json -{ - "id": "3814889423749775360", - "completion_window": "24hrs", - "created_at": 1733392026, - "endpoint": "", - "input_file_id": "gs://litellm-testing-bucket/litellm-vertex-files/publishers/google/models/gemini-1.5-flash-001/d3f198cd-c0d1-436d-9b1e-28e3f282997a", - "object": "batch", - "status": "validating", - "cancelled_at": null, - "cancelling_at": null, - "completed_at": null, - "error_file_id": null, - "errors": null, - "expired_at": null, - "expires_at": null, - "failed_at": null, - "finalizing_at": null, - "in_progress_at": null, - "metadata": null, - "output_file_id": "gs://litellm-testing-bucket/litellm-vertex-files/publishers/google/models/gemini-1.5-flash-001", - "request_counts": null -} -``` - -#### 4. Retrieve a batch - -```python -retrieved_batch = oai_client.batches.retrieve( - batch_id=create_batch_response.id, - extra_body={"custom_llm_provider": "vertex_ai"}, # tell litellm to use `vertex_ai` for this batch request -) -``` - -**Expected Response** - -```json -{ - "id": "3814889423749775360", - "completion_window": "24hrs", - "created_at": 1736500100, - "endpoint": "", - "input_file_id": "gs://example-bucket-1-litellm/litellm-vertex-files/publishers/google/models/gemini-1.5-flash-001/7b2e47f5-3dd4-436d-920f-f9155bbdc952", - "object": "batch", - "status": "completed", - "cancelled_at": null, - "cancelling_at": null, - "completed_at": null, - "error_file_id": null, - "errors": null, - "expired_at": null, - "expires_at": null, - "failed_at": null, - "finalizing_at": null, - "in_progress_at": null, - "metadata": null, - "output_file_id": "gs://example-bucket-1-litellm/litellm-vertex-files/publishers/google/models/gemini-1.5-flash-001", - "request_counts": null -} -``` - - ## **Fine Tuning APIs** @@ -2758,6 +2702,44 @@ curl http://localhost:4000/v1/fine_tuning/jobs \ +## Labels + + +Google enables you to add custom metadata to its `generateContent` and `streamGenerateContent` calls. +This mechanism is useful in Vertex AI because it allows costs and usage tracking over multiple +different applications or users. + + +### Usage + +You can use that feature through LiteLLM by sending `labels` or `metadata` field in your requests. + +If the client sets the `labels` field in the request to the LiteLLM, +the LiteLLM will pass the `labels` field to the Vertex AI backend. + +If the client sets the `metadata` field in the request to the LiteLLM and the `labels` field is not set, +the LiteLLM will create the `labels` field filled with `metadata` key/value pairs for all string values and +pass it to the Vertex AI backend. + + +Here is an example JSON request demonstrating the labels usage: + +```json +{ + "model": "gemini-2.0-flash-lite", + "messages": [ + { "role": "user", "content": "respond in 20 words. who are you?" } + ], + "labels": { + "client_app": "acme_comp_financial_app", + "department": "finance", + "project": "acme_ai" + } +} +``` + + + ## Extra ### Using `GOOGLE_APPLICATION_CREDENTIALS` @@ -2830,7 +2812,3 @@ Once that's done, when you deploy the new container in the Google Cloud Run serv s/o @[Darien Kindlund](https://www.linkedin.com/in/kindlund/) for this tutorial - - - - diff --git a/docs/my-website/docs/providers/vertex_batch.md b/docs/my-website/docs/providers/vertex_batch.md new file mode 100644 index 00000000000..4eaa0d69d4b --- /dev/null +++ b/docs/my-website/docs/providers/vertex_batch.md @@ -0,0 +1,264 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +## **Batch APIs** + +Just add the following Vertex env vars to your environment. + +```bash +# GCS Bucket settings, used to store batch prediction files in +export GCS_BUCKET_NAME="my-batch-bucket" # the bucket you want to store batch prediction files in +export GCS_PATH_SERVICE_ACCOUNT="/path/to/service_account.json" # path to your service account json file + +# Vertex /batch endpoint settings, used for LLM API requests +export GOOGLE_APPLICATION_CREDENTIALS="/path/to/service_account.json" # path to your service account json file +export VERTEXAI_LOCATION="us-central1" # can be any vertex location +export VERTEXAI_PROJECT="my-project" +``` + +### Usage + +Follow this complete workflow: create JSONL file → upload file → create batch → retrieve batch status → get file content + +#### 1. Create a JSONL file of batch requests + +LiteLLM expects the file to follow the **[OpenAI batches files format](https://platform.openai.com/docs/guides/batch)**. + +Each `body` in the file should be an **OpenAI API request**. + +Create a file called `batch_requests.jsonl` with your requests: +```jsonl +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "gemini-2.5-flash-lite", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "gemini-2.5-flash-lite", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +``` + +#### 2. Upload the file + +Upload your JSONL file. For `vertex_ai`, the file will be stored in your configured GCS bucket provided by `GCS_BUCKET_NAME`. + + + + +```python showLineNumbers title="upload_file.py" +from openai import OpenAI + +oai_client = OpenAI( + api_key="sk-1234", # litellm proxy API key + base_url="http://localhost:4000" # litellm proxy base url +) + +file_obj = oai_client.files.create( + file=open("batch_requests.jsonl", "rb"), + purpose="batch", + extra_body={"custom_llm_provider": "vertex_ai"} +) + +print(f"File uploaded with ID: {file_obj.id}") +``` + + + + +```bash showLineNumbers title="Upload File" +curl --request POST \ + --url http://localhost:4000/v1/files \ + --header 'Content-Type: multipart/form-data' \ + --form purpose=batch \ + --form file=@batch_requests.jsonl \ + --form custom_llm_provider=vertex_ai +``` + + + + +**Expected Response:** + +```json +{ + "id": "gs://my-batch-bucket/litellm-vertex-files/publishers/google/models/gemini-2.5-flash-lite/abc123-def4-5678-9012-34567890abcd", + "bytes": 416, + "created_at": 1758303684, + "filename": "litellm-vertex-files/publishers/google/models/gemini-2.5-flash-lite/abc123-def4-5678-9012-34567890abcd", + "object": "file", + "purpose": "batch", + "status": "uploaded", + "expires_at": null, + "status_details": null +} +``` + +#### 3. Create a batch + +Create a batch job using the uploaded file ID. + + + + +```python showLineNumbers title="create_batch.py" +batch_input_file_id = file_obj.id # from step 2 +create_batch_response = oai_client.batches.create( + completion_window="24h", + endpoint="/v1/chat/completions", + input_file_id=batch_input_file_id, # e.g. "gs://my-batch-bucket/litellm-vertex-files/publishers/google/models/gemini-2.5-flash-lite/abc123-def4-5678-9012-34567890abcd" + extra_body={"custom_llm_provider": "vertex_ai"} +) + +print(f"Batch created with ID: {create_batch_response.id}") +``` + + + + +```bash showLineNumbers title="Create Batch Request" +curl --request POST \ + --url http://localhost:4000/v1/batches \ + --header 'Content-Type: application/json' \ + --data '{ + "input_file_id": "gs://my-batch-bucket/litellm-vertex-files/publishers/google/models/gemini-2.5-flash-lite/abc123-def4-5678-9012-34567890abcd", + "endpoint": "/v1/chat/completions", + "completion_window": "24h", + "custom_llm_provider": "vertex_ai" +}' +``` + + + + +**Expected Response:** + +```json +{ + "id": "7814463557919047680", + "completion_window": "24hrs", + "created_at": 1758328011, + "endpoint": "", + "input_file_id": "gs://my-batch-bucket/litellm-vertex-files/publishers/google/models/gemini-2.5-flash-lite/abc123-def4-5678-9012-34567890abcd", + "object": "batch", + "status": "validating", + "cancelled_at": null, + "cancelling_at": null, + "completed_at": null, + "error_file_id": null, + "errors": null, + "expired_at": null, + "expires_at": null, + "failed_at": null, + "finalizing_at": null, + "in_progress_at": null, + "metadata": null, + "output_file_id": "gs://my-batch-bucket/litellm-vertex-files/publishers/google/models/gemini-2.5-flash-lite", + "request_counts": null, + "usage": null +} +``` + +#### 4. Retrieve batch status + +Check the status of your batch job. The batch will progress through states: `validating` → `in_progress` → `completed`. + + + + +```python showLineNumbers title="retrieve_batch.py" +retrieved_batch = oai_client.batches.retrieve( + batch_id=create_batch_response.id, # Created batch id, e.g. 7814463557919047680 + extra_body={"custom_llm_provider": "vertex_ai"} +) + +print(f"Batch status: {retrieved_batch.status}") +if retrieved_batch.status == "completed": + print(f"Output file: {retrieved_batch.output_file_id}") +``` + + + + +```bash showLineNumbers title="Retrieve Batch Status" +curl --request GET \ + --url 'http://localhost:4000/batches/7814463557919047680?provider=vertex_ai' \ + --header 'Authorization: Bearer sk-1234' +``` + + + + +**Expected Response (when completed):** + +```json +{ + "id": "7814463557919047680", + "completion_window": "24hrs", + "created_at": 1758328011, + "endpoint": "", + "input_file_id": "gs://my-batch-bucket/litellm-vertex-files/publishers/google/models/gemini-2.5-flash-lite/abc123-def4-5678-9012-34567890abcd", + "object": "batch", + "status": "completed", + "cancelled_at": null, + "cancelling_at": null, + "completed_at": null, + "error_file_id": null, + "errors": null, + "expired_at": null, + "expires_at": null, + "failed_at": null, + "finalizing_at": null, + "in_progress_at": null, + "metadata": null, + "output_file_id": "gs://my-batch-bucket/litellm-vertex-files/publishers/google/models/gemini-2.5-flash-lite/prediction-model-2025-09-19T21:26:51.569037Z/predictions.jsonl", + "request_counts": null, + "usage": null +} +``` + +#### 5. Get file content + +Once the batch is completed, retrieve the results using the `output_file_id` from the batch response. + +**Important:** The `output_file_id` must be URL encoded when used in the request path. + + + + +```python showLineNumbers title="get_file_content.py" +import urllib.parse +import json + +output_file_id = retrieved_batch.output_file_id +# URL encode the file ID +encoded_file_id = urllib.parse.quote_plus(output_file_id) + +# Get file content +file_content = oai_client.files.content( + file_id=encoded_file_id, + extra_body={"custom_llm_provider": "vertex_ai"} +) + +# Process the results +for line in file_content.text.strip().split('\n'): + result = json.loads(line) + print(f"Request: {result['request']}") + print(f"Response: {result['response']}") + print("---") +``` + + + + +```bash showLineNumbers title="Get File Content" +# Note: The file ID must be URL encoded +curl --request GET \ + --url 'http://localhost:4000/files/gs%253A%252F%252Fmy-batch-bucket%252Flitellm-vertex-files%252Fpublishers%252Fgoogle%252Fmodels%252Fgemini-2.5-flash-lite%252Fprediction-model-2025-09-19T21%253A26%253A51.569037Z%252Fpredictions.jsonl/content?provider=vertex_ai' \ + --header 'Authorization: Bearer sk-1234' +``` + + + + +**Expected Response:** + +The response contains JSONL format with one result per line: + +```jsonl +{"status":"","processed_time":"2025-09-19T21:29:47.352+00:00","request":{"contents":[{"parts":[{"text":"Hello world!"}],"role":"user"}],"generationConfig":{"max_output_tokens":10},"system_instruction":{"parts":[{"text":"You are a helpful assistant."}]}},"response":{"candidates":[{"avgLogprobs":-0.48079710006713866,"content":{"parts":[{"text":"Hello there! It's nice to meet you"}],"role":"model"},"finishReason":"MAX_TOKENS"}],"createTime":"2025-09-19T21:29:47.484619Z","modelVersion":"gemini-2.5-flash-lite","responseId":"S8vNaIvKHdvshMIP_aOtuAg","usageMetadata":{"candidatesTokenCount":10,"candidatesTokensDetails":[{"modality":"TEXT","tokenCount":10}],"promptTokenCount":9,"promptTokensDetails":[{"modality":"TEXT","tokenCount":9}],"totalTokenCount":19,"trafficType":"ON_DEMAND"}}} +{"status":"","processed_time":"2025-09-19T21:29:47.358+00:00","request":{"contents":[{"parts":[{"text":"Hello world!"}],"role":"user"}],"generationConfig":{"max_output_tokens":10},"system_instruction":{"parts":[{"text":"You are an unhelpful assistant."}]}},"response":{"candidates":[{"avgLogprobs":-0.6168075137668185,"content":{"parts":[{"text":"I am unable to assist with this request."}],"role":"model"},"finishReason":"STOP"}],"createTime":"2025-09-19T21:29:47.470889Z","modelVersion":"gemini-2.5-flash-lite","responseId":"S8vNaOneHISShMIP28nA8QQ","usageMetadata":{"candidatesTokenCount":9,"candidatesTokensDetails":[{"modality":"TEXT","tokenCount":9}],"promptTokenCount":9,"promptTokensDetails":[{"modality":"TEXT","tokenCount":9}],"totalTokenCount":18,"trafficType":"ON_DEMAND"}}} +``` diff --git a/docs/my-website/docs/providers/vertex_image.md b/docs/my-website/docs/providers/vertex_image.md index 2434c3a9a57..27e584cb222 100644 --- a/docs/my-website/docs/providers/vertex_image.md +++ b/docs/my-website/docs/providers/vertex_image.md @@ -18,7 +18,7 @@ import litellm # Generate a single image response = await litellm.aimage_generation( prompt="An olympic size swimming pool with crystal clear water and modern architecture", - model="vertex_ai/imagen-4.0-generate-preview-06-06", + model="vertex_ai/imagen-4.0-generate-001", vertex_ai_project="your-project-id", vertex_ai_location="us-central1", ) @@ -34,7 +34,7 @@ print(response.data[0].url) model_list: - model_name: vertex-imagen litellm_params: - model: vertex_ai/imagen-4.0-generate-preview-06-06 + model: vertex_ai/imagen-4.0-generate-001 vertex_ai_project: "your-project-id" vertex_ai_location: "us-central1" vertex_ai_credentials: "path/to/service-account.json" # Optional if using environment auth diff --git a/docs/my-website/docs/providers/vertex_partner.md b/docs/my-website/docs/providers/vertex_partner.md index cf780e35dbd..856f054b8e6 100644 --- a/docs/my-website/docs/providers/vertex_partner.md +++ b/docs/my-website/docs/providers/vertex_partner.md @@ -15,6 +15,7 @@ import TabItem from '@theme/TabItem'; | Mistral | `vertex_ai/mistral-*` | [Vertex AI - Mistral Models](https://cloud.google.com/vertex-ai/generative-ai/docs/partner-models/mistral) | | AI21 (Jamba) | `vertex_ai/jamba-*` | [Vertex AI - AI21 Models](https://cloud.google.com/vertex-ai/generative-ai/docs/partner-models/ai21) | | Qwen | `vertex_ai/qwen/*` | [Vertex AI - Qwen Models](https://cloud.google.com/vertex-ai/generative-ai/docs/maas/qwen) | +| OpenAI (GPT-OSS) | `vertex_ai/openai/gpt-oss-*` | [Vertex AI - GPT-OSS Models](https://console.cloud.google.com/vertex-ai/publishers/openai/model-garden/) | | Model Garden | `vertex_ai/openai/{MODEL_ID}` or `vertex_ai/{MODEL_ID}` | [Vertex Model Garden](https://cloud.google.com/model-garden?hl=en) | ## Vertex AI - Anthropic (Claude) @@ -658,6 +659,141 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \ +## VertexAI GPT-OSS Models + +| Property | Details | +|----------|---------| +| Provider Route | `vertex_ai/openai/{MODEL}` | +| Vertex Documentation | [Vertex AI - GPT-OSS Models](https://console.cloud.google.com/vertex-ai/publishers/openai/model-garden/) | + +**LiteLLM Supports all Vertex AI GPT-OSS Models.** Ensure you use the `vertex_ai/openai/` prefix for all Vertex AI GPT-OSS models. + +| Model Name | Usage | +|------------------|------------------------------| +| vertex_ai/openai/gpt-oss-20b-maas | `completion('vertex_ai/openai/gpt-oss-20b-maas', messages)` | + +#### Usage + + + + +```python +from litellm import completion +import os + +os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "" + +model = "openai/gpt-oss-20b-maas" + +vertex_ai_project = "your-vertex-project" # can also set this as os.environ["VERTEXAI_PROJECT"] +vertex_ai_location = "your-vertex-location" # can also set this as os.environ["VERTEXAI_LOCATION"] + +response = completion( + model="vertex_ai/" + model, + messages=[{"role": "user", "content": "hi"}], + vertex_ai_project=vertex_ai_project, + vertex_ai_location=vertex_ai_location, +) +print("\nModel Response", response) +``` + + + +**1. Add to config** + +```yaml +model_list: + - model_name: gpt-oss + litellm_params: + model: vertex_ai/openai/gpt-oss-20b-maas + vertex_ai_project: "my-test-project" + vertex_ai_location: "us-central1" +``` + +**2. Start proxy** + +```bash +litellm --config /path/to/config.yaml + +# RUNNING at http://0.0.0.0:4000 +``` + +**3. Test it!** + +```bash +curl --location 'http://0.0.0.0:4000/chat/completions' \ + --header 'Authorization: Bearer sk-1234' \ + --header 'Content-Type: application/json' \ + --data '{ + "model": "gpt-oss", # 👈 the 'model_name' in config + "messages": [ + { + "role": "user", + "content": "what llm are you" + } + ], + }' +``` + + + + +#### Usage - `reasoning_effort` + +GPT-OSS models support the `reasoning_effort` parameter for enhanced reasoning capabilities. + + + + +```python +from litellm import completion + +response = completion( + model="vertex_ai/openai/gpt-oss-20b-maas", + messages=[{"role": "user", "content": "Solve this complex problem step by step"}], + reasoning_effort="low", # Options: "minimal", "low", "medium", "high" + vertex_ai_project="your-vertex-project", + vertex_ai_location="us-central1", +) +``` + + + + + +1. Setup config.yaml + +```yaml +model_list: +- model_name: gpt-oss + litellm_params: + model: vertex_ai/openai/gpt-oss-20b-maas + vertex_ai_project: "my-test-project" + vertex_ai_location: "us-central1" +``` + +2. Start proxy + +```bash +litellm --config /path/to/config.yaml +``` + +3. Test it! + +```bash +curl http://0.0.0.0:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer " \ + -d '{ + "model": "gpt-oss", + "messages": [{"role": "user", "content": "Solve this complex problem step by step"}], + "reasoning_effort": "low" + }' +``` + + + + ## Model Garden :::tip diff --git a/docs/my-website/docs/providers/vllm.md b/docs/my-website/docs/providers/vllm.md index 5472f0602f4..1a37f2f10e7 100644 --- a/docs/my-website/docs/providers/vllm.md +++ b/docs/my-website/docs/providers/vllm.md @@ -8,9 +8,9 @@ LiteLLM supports all models on VLLM. | Property | Details | |-------|-------| | Description | vLLM is a fast and easy-to-use library for LLM inference and serving. [Docs](https://docs.vllm.ai/en/latest/index.html) | -| Provider Route on LiteLLM | `hosted_vllm/` (for OpenAI compatible server), `vllm/` (for vLLM sdk usage) | +| Provider Route on LiteLLM | `hosted_vllm/` (for OpenAI compatible server), `vllm/` ([DEPRECATED] for vLLM sdk usage) | | Provider Doc | [vLLM ↗](https://docs.vllm.ai/en/latest/index.html) | -| Supported Endpoints | `/chat/completions`, `/embeddings`, `/completions`, `/rerank` | +| Supported Endpoints | `/chat/completions`, `/embeddings`, `/completions`, `/rerank`, `/audio/transcriptions` | # Quick Start diff --git a/docs/my-website/docs/providers/volcano.md b/docs/my-website/docs/providers/volcano.md index 1742a43d819..efd1e02b60b 100644 --- a/docs/my-website/docs/providers/volcano.md +++ b/docs/my-website/docs/providers/volcano.md @@ -3,7 +3,7 @@ https://www.volcengine.com/docs/82379/1263482 :::tip -**We support ALL Volcengine NIM models, just set `model=volcengine/` as a prefix when sending litellm requests** +**We support ALL Volcengine models including Chat and Embeddings, just set `model=volcengine/` as a prefix when sending litellm requests** ::: @@ -11,6 +11,8 @@ https://www.volcengine.com/docs/82379/1263482 ```python # env variable os.environ['VOLCENGINE_API_KEY'] +# or +os.environ['ARK_API_KEY'] ``` ## Sample Usage @@ -64,9 +66,42 @@ for chunk in response: print(chunk) ``` +## Sample Usage - Embedding +```python +from litellm import embedding +import os -## Supported Models - 💥 ALL Volcengine NIM Models Supported! -We support ALL `volcengine` models, just set `volcengine/` as a prefix when sending completion requests +os.environ['VOLCENGINE_API_KEY'] = "" +response = embedding( + model="volcengine/doubao-embedding-text-240715", + input=["hello world", "good morning"] +) +print(response) +``` + +### Supported Embedding Models +- `doubao-embedding-large` (2048 dimensions) +- `doubao-embedding-large-text-250515` (2048 dimensions) +- `doubao-embedding-large-text-240915` (4096 dimensions) +- `doubao-embedding` (2560 dimensions) +- `doubao-embedding-text-240715` (2560 dimensions) + +### Embedding Parameters +```python +from litellm import embedding + +response = embedding( + model="volcengine/doubao-embedding-text-240715", + input=["sample text"], + encoding_format="float", # optional: "float" (default), "base64" + user="user-123", # optional: user identifier for tracking +) +``` + +## Supported Models - 💥 ALL Volcengine Models Supported! +We support ALL `volcengine` models for both chat completions and embeddings: +- **Chat Models**: Set `volcengine/` as a prefix when sending completion requests +- **Embedding Models**: Use the specific model names listed above (e.g., `volcengine/doubao-embedding-text-240715`) ## Sample Usage - LiteLLM Proxy @@ -74,14 +109,21 @@ We support ALL `volcengine` models, just set `volcengine/` as a ```yaml model_list: + # Chat model - model_name: volcengine-model litellm_params: model: volcengine/ api_key: os.environ/VOLCENGINE_API_KEY + # Embedding model + - model_name: volcengine-embedding + litellm_params: + model: volcengine/doubao-embedding-text-240715 + api_key: os.environ/VOLCENGINE_API_KEY ``` ### Send Request +#### Chat Completion ```shell curl --location 'http://localhost:4000/chat/completions' \ --header 'Authorization: Bearer sk-1234' \ @@ -95,4 +137,15 @@ curl --location 'http://localhost:4000/chat/completions' \ } ] }' +``` + +#### Embedding +```shell +curl --location 'http://localhost:4000/embeddings' \ + --header 'Authorization: Bearer sk-1234' \ + --header 'Content-Type: application/json' \ + --data '{ + "model": "volcengine-embedding", + "input": ["hello world", "good morning"] +}' ``` \ No newline at end of file diff --git a/docs/my-website/docs/proxy/access_control.md b/docs/my-website/docs/proxy/access_control.md index 69b8a3ff6de..4ca3eb119d6 100644 --- a/docs/my-website/docs/proxy/access_control.md +++ b/docs/my-website/docs/proxy/access_control.md @@ -4,7 +4,7 @@ Role-based access control (RBAC) is based on Organizations, Teams and Internal U - `Organizations` are the top-level entities that contain Teams. - `Team` - A Team is a collection of multiple `Internal Users` -- `Internal Users` - users that can create keys, make LLM API calls, view usage on LiteLLM +- `Internal Users` - users that can create keys, make LLM API calls, view usage on LiteLLM. Users can be on multiple teams. - `Roles` define the permissions of an `Internal User` - `Virtual Keys` - Keys are used for authentication to the LiteLLM API. Keys are tied to a `Internal User` and `Team` diff --git a/docs/my-website/docs/proxy/admin_ui_sso.md b/docs/my-website/docs/proxy/admin_ui_sso.md index 86cb6b0bf8c..32bf97410cb 100644 --- a/docs/my-website/docs/proxy/admin_ui_sso.md +++ b/docs/my-website/docs/proxy/admin_ui_sso.md @@ -4,6 +4,10 @@ import TabItem from '@theme/TabItem'; # ✨ SSO for Admin UI +:::info +From v1.76.0, SSO is now Free for up to 5 users. +::: + :::info ✨ SSO is on LiteLLM Enterprise @@ -235,6 +239,13 @@ Example setting a local image (on your container) ```shell UI_LOGO_PATH="ui_images/logo.jpg" ``` + +#### Or set your logo directly from Admin UI: +
+ + +
+ #### Set Custom Color Theme - Navigate to [/enterprise/enterprise_ui](https://github.com/BerriAI/litellm/blob/main/enterprise/enterprise_ui/_enterprise_colors.json) - Inside the `enterprise_ui` directory, rename `_enterprise_colors.json` to `enterprise_colors.json` diff --git a/docs/my-website/docs/proxy/budget_reset_and_tz.md b/docs/my-website/docs/proxy/budget_reset_and_tz.md index 541ff6a2f0a..340e33afe18 100644 --- a/docs/my-website/docs/proxy/budget_reset_and_tz.md +++ b/docs/my-website/docs/proxy/budget_reset_and_tz.md @@ -29,5 +29,6 @@ Common timezone values: - `US/Pacific` - Pacific Time - `Europe/London` - UK Time - `Asia/Kolkata` - Indian Standard Time (IST) +- `Asia/Bangkok` - Indochina Time (ICT) - `Asia/Tokyo` - Japan Standard Time - `Australia/Sydney` - Australian Eastern Time diff --git a/docs/my-website/docs/proxy/caching.md b/docs/my-website/docs/proxy/caching.md index 1fb7385f689..617609cf08a 100644 --- a/docs/my-website/docs/proxy/caching.md +++ b/docs/my-website/docs/proxy/caching.md @@ -958,6 +958,19 @@ curl http://localhost:4000/v1/chat/completions \ + +## Redis max_connections + +You can set the `max_connections` parameter in your `cache_params` for Redis. This is passed directly to the Redis client and controls the maximum number of simultaneous connections in the pool. If you see errors like `No connection available`, try increasing this value: + +```yaml +litellm_settings: + cache: true + cache_params: + type: redis + max_connections: 100 +``` + ## Supported `cache_params` on proxy config.yaml ```yaml @@ -966,6 +979,7 @@ cache_params: ttl: Optional[float] default_in_memory_ttl: Optional[float] default_in_redis_ttl: Optional[float] + max_connections: Optional[Int] # Type of cache (options: "local", "redis", "s3") type: s3 diff --git a/docs/my-website/docs/proxy/call_hooks.md b/docs/my-website/docs/proxy/call_hooks.md index b4e22027d19..aef33f8c708 100644 --- a/docs/my-website/docs/proxy/call_hooks.md +++ b/docs/my-website/docs/proxy/call_hooks.md @@ -6,6 +6,10 @@ import Image from '@theme/IdealImage'; - Reject data before making llm api calls / before returning the response - Enforce 'user' param for all openai endpoint calls +:::tip +**Understanding Callback Hooks?** Check out our [Callback Management Guide](../observability/callback_management.md) to understand the differences between proxy-specific hooks like `async_pre_call_hook` and general logging hooks like `async_log_success_event`. +::: + See a complete example with our [parallel request rate limiter](https://github.com/BerriAI/litellm/blob/main/litellm/proxy/hooks/parallel_request_limiter.py) ## Quick Start diff --git a/docs/my-website/docs/proxy/config_settings.md b/docs/my-website/docs/proxy/config_settings.md index 73b16af2f95..ad3afd59a02 100644 --- a/docs/my-website/docs/proxy/config_settings.md +++ b/docs/my-website/docs/proxy/config_settings.md @@ -21,7 +21,7 @@ litellm_settings: failure_callback: ["sentry"] # list of failure callbacks callbacks: ["otel"] # list of callbacks - runs on success and failure service_callbacks: ["datadog", "prometheus"] # logs redis, postgres failures on datadog, prometheus - turn_off_message_logging: boolean # prevent the messages and responses from being logged to on your callbacks, but request metadata will still be logged. + turn_off_message_logging: boolean # prevent the messages and responses from being logged to on your callbacks, but request metadata will still be logged. Useful for privacy/compliance when handling sensitive data. redact_user_api_key_info: boolean # Redact information about the user api key (hashed token, user_id, team id, etc.), from logs. Currently supported for Langfuse, OpenTelemetry, Logfire, ArizeAI logging. langfuse_default_tags: ["cache_hit", "cache_key", "proxy_base_url", "user_api_key_alias", "user_api_key_user_id", "user_api_key_user_email", "user_api_key_team_alias", "semantic-similarity", "proxy_base_url"] # default tags for Langfuse Logging @@ -50,6 +50,7 @@ litellm_settings: port: 6379 # The port number for the Redis cache. Required if type is "redis". password: "your_password" # The password for the Redis cache. Required if type is "redis". namespace: "litellm.caching.caching" # namespace for redis cache + max_connections: 100 # [OPTIONAL] Set Maximum number of Redis connections. Passed directly to redis-py. # Optional - Redis Cluster Settings redis_startup_nodes: [{"host": "127.0.0.1", "port": "7001"}] @@ -93,6 +94,8 @@ callback_settings: general_settings: completion_model: string + store_prompts_in_spend_logs: boolean + forward_client_headers_to_llm_api: boolean disable_spend_logs: boolean # turn off writing each transaction to the db disable_master_key_return: boolean # turn off returning master key on UI (checked on '/user/info' endpoint) disable_retry_on_max_parallel_request_limit_error: boolean # turn off retries when max parallel request limit is reached @@ -121,6 +124,35 @@ general_settings: alerting: ["slack", "email"] alerting_threshold: 0 use_client_credentials_pass_through_routes: boolean # use client credentials for all pass through routes like "/vertex-ai", /bedrock/. When this is True Virtual Key auth will not be applied on these endpoints + +router_settings: + routing_strategy: simple-shuffle # Literal["simple-shuffle", "least-busy", "usage-based-routing","latency-based-routing"], default="simple-shuffle" - RECOMMENDED for best performance + redis_host: # string + redis_password: # string + redis_port: # string + enable_pre_call_checks: true # bool - Before call is made check if a call is within model context window + allowed_fails: 3 # cooldown model if it fails > 1 call in a minute. + cooldown_time: 30 # (in seconds) how long to cooldown model if fails/min > allowed_fails + disable_cooldowns: True # bool - Disable cooldowns for all models + enable_tag_filtering: True # bool - Use tag based routing for requests + retry_policy: { # Dict[str, int]: retry policy for different types of exceptions + "AuthenticationErrorRetries": 3, + "TimeoutErrorRetries": 3, + "RateLimitErrorRetries": 3, + "ContentPolicyViolationErrorRetries": 4, + "InternalServerErrorRetries": 4 + } + allowed_fails_policy: { + "BadRequestErrorAllowedFails": 1000, # Allow 1000 BadRequestErrors before cooling down a deployment + "AuthenticationErrorAllowedFails": 10, # int + "TimeoutErrorAllowedFails": 12, # int + "RateLimitErrorAllowedFails": 10000, # int + "ContentPolicyViolationErrorAllowedFails": 15, # int + "InternalServerErrorAllowedFails": 20, # int + } + content_policy_fallbacks=[{"claude-2": ["my-fallback-model"]}] # List[Dict[str, List[str]]]: Fallback model for content policy violations + fallbacks=[{"claude-2": ["my-fallback-model"]}] # List[Dict[str, List[str]]]: Fallback model for all errors + ``` ### litellm_settings - Reference @@ -131,7 +163,7 @@ general_settings: | failure_callback | array of strings | List of failure callbacks [Doc Proxy logging callbacks](logging), [Doc Metrics](prometheus) | | callbacks | array of strings | List of callbacks - runs on success and failure [Doc Proxy logging callbacks](logging), [Doc Metrics](prometheus) | | service_callbacks | array of strings | System health monitoring - Logs redis, postgres failures on specified services (e.g. datadog, prometheus) [Doc Metrics](prometheus) | -| turn_off_message_logging | boolean | If true, prevents messages and responses from being logged to callbacks, but request metadata will still be logged [Proxy Logging](logging) | +| turn_off_message_logging | boolean | If true, prevents messages and responses from being logged to callbacks, but request metadata will still be logged. Useful for privacy/compliance when handling sensitive data [Proxy Logging](logging) | | modify_params | boolean | If true, allows modifying the parameters of the request before it is sent to the LLM provider | | enable_preview_features | boolean | If true, enables preview features - e.g. Azure O1 Models with streaming support.| | redact_user_api_key_info | boolean | If true, redacts information about the user api key from logs [Proxy Logging](logging#redacting-userapikeyinfo) | @@ -236,7 +268,7 @@ Most values can also be set via `litellm_settings`. If you see overlapping value ```yaml router_settings: - routing_strategy: usage-based-routing-v2 # Literal["simple-shuffle", "least-busy", "usage-based-routing","latency-based-routing"], default="simple-shuffle" + routing_strategy: simple-shuffle # Literal["simple-shuffle", "least-busy", "usage-based-routing","latency-based-routing"], default="simple-shuffle" - RECOMMENDED for best performance redis_host: # string redis_password: # string redis_port: # string @@ -335,12 +367,15 @@ router_settings: | ANTHROPIC_API_KEY | API key for Anthropic service | ANTHROPIC_API_BASE | Base URL for Anthropic API. Default is https://api.anthropic.com | AWS_ACCESS_KEY_ID | Access Key ID for AWS services +| AWS_BATCH_ROLE_ARN | ARN of the AWS IAM role for batch operations | AWS_DEFAULT_REGION | Default AWS region for service interactions when AWS_REGION is not set | AWS_PROFILE_NAME | AWS CLI profile name to be used | AWS_REGION | AWS region for service interactions (takes precedence over AWS_DEFAULT_REGION) | AWS_REGION_NAME | Default AWS region for service interactions | AWS_ROLE_ARN | ARN of the AWS IAM role to assume for authentication | AWS_ROLE_NAME | Role name for AWS IAM usage +| AWS_S3_BUCKET_NAME | Name of the AWS S3 bucket for file operations +| AWS_S3_OUTPUT_BUCKET_NAME | Name of the AWS S3 output bucket for batch operations | AWS_SECRET_ACCESS_KEY | Secret Access Key for AWS services | AWS_SESSION_NAME | Name for AWS session | AWS_WEB_IDENTITY_TOKEN | Web identity token for AWS @@ -380,6 +415,8 @@ router_settings: | CIRCLE_OIDC_TOKEN_V2 | Version 2 of the OpenID Connect token for CircleCI | CLOUDZERO_API_KEY | CloudZero API key for authentication | CLOUDZERO_CONNECTION_ID | CloudZero connection ID for data submission +| CLOUDZERO_EXPORT_INTERVAL_MINUTES | Interval in minutes for CloudZero data export operations +| CLOUDZERO_MAX_FETCHED_DATA_RECORDS | Maximum number of data records to fetch from CloudZero | CLOUDZERO_TIMEZONE | Timezone for date handling (default: UTC) | CONFIG_FILE_PATH | File path for configuration file | CONFIDENT_API_KEY | API key for DeepEval integration @@ -412,6 +449,7 @@ router_settings: | DEFAULT_ALLOWED_FAILS | Maximum failures allowed before cooling down a model. Default is 3 | DEFAULT_ANTHROPIC_CHAT_MAX_TOKENS | Default maximum tokens for Anthropic chat completions. Default is 4096 | DEFAULT_BATCH_SIZE | Default batch size for operations. Default is 512 +| DEFAULT_CLIENT_DISCONNECT_CHECK_TIMEOUT_SECONDS | Timeout in seconds for checking client disconnection. Default is 1 | DEFAULT_COOLDOWN_TIME_SECONDS | Duration in seconds to cooldown a model after failures. Default is 5 | DEFAULT_CRON_JOB_LOCK_TTL_SECONDS | Time-to-live for cron job locks in seconds. Default is 60 (1 minute) | DEFAULT_FAILURE_THRESHOLD_PERCENT | Threshold percentage of failures to cool down a deployment. Default is 0.5 (50%) @@ -431,12 +469,17 @@ router_settings: | DEFAULT_MOCK_RESPONSE_COMPLETION_TOKEN_COUNT | Default token count for mock response completions. Default is 20 | DEFAULT_MOCK_RESPONSE_PROMPT_TOKEN_COUNT | Default token count for mock response prompts. Default is 10 | DEFAULT_MODEL_CREATED_AT_TIME | Default creation timestamp for models. Default is 1677610602 +| DEFAULT_NUM_WORKERS_LITELLM_PROXY | Default number of workers for LiteLLM proxy. Default is 4. **We strongly recommend setting NUM Workers to Number of vCPUs available** | DEFAULT_PROMPT_INJECTION_SIMILARITY_THRESHOLD | Default threshold for prompt injection similarity. Default is 0.7 | DEFAULT_POLLING_INTERVAL | Default polling interval for schedulers in seconds. Default is 0.03 | DEFAULT_REASONING_EFFORT_DISABLE_THINKING_BUDGET | Default reasoning effort disable thinking budget. Default is 0 | DEFAULT_REASONING_EFFORT_HIGH_THINKING_BUDGET | Default high reasoning effort thinking budget. Default is 4096 | DEFAULT_REASONING_EFFORT_LOW_THINKING_BUDGET | Default low reasoning effort thinking budget. Default is 1024 | DEFAULT_REASONING_EFFORT_MEDIUM_THINKING_BUDGET | Default medium reasoning effort thinking budget. Default is 2048 +| DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET | Default minimal reasoning effort thinking budget. Default is 512 +| DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_FLASH | Default minimal reasoning effort thinking budget for Gemini 2.5 Flash. Default is 512 +| DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_FLASH_LITE | Default minimal reasoning effort thinking budget for Gemini 2.5 Flash Lite. Default is 512 +| DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_PRO | Default minimal reasoning effort thinking budget for Gemini 2.5 Pro. Default is 512 | DEFAULT_REDIS_SYNC_INTERVAL | Default Redis synchronization interval in seconds. Default is 1 | DEFAULT_REPLICATE_GPU_PRICE_PER_SECOND | Default price per second for Replicate GPU. Default is 0.001400 | DEFAULT_REPLICATE_POLLING_DELAY_SECONDS | Default delay in seconds for Replicate polling. Default is 1 @@ -512,6 +555,8 @@ router_settings: | GOOGLE_KMS_RESOURCE_NAME | Name of the resource in Google KMS | GUARDRAILS_AI_API_BASE | Base URL for Guardrails AI API | HEALTH_CHECK_TIMEOUT_SECONDS | Timeout in seconds for health checks. Default is 60 +| HEROKU_API_BASE | Base URL for Heroku API +| HEROKU_API_KEY | API key for Heroku services | HF_API_BASE | Base URL for Hugging Face API | HCP_VAULT_ADDR | Address for [Hashicorp Vault Secret Manager](../secret.md#hashicorp-vault) | HCP_VAULT_CLIENT_CERT | Path to client certificate for [Hashicorp Vault Secret Manager](../secret.md#hashicorp-vault) @@ -555,9 +600,11 @@ router_settings: | LASSO_USER_ID | User ID for Lasso service | LASSO_CONVERSATION_ID | Conversation ID for Lasso service | LENGTH_OF_LITELLM_GENERATED_KEY | Length of keys generated by LiteLLM. Default is 16 +| LEGACY_MULTI_INSTANCE_RATE_LIMITING | Flag to enable legacy multi-instance rate limiting. **Default is False** | LITERAL_API_KEY | API key for Literal integration | LITERAL_API_URL | API URL for Literal service | LITERAL_BATCH_SIZE | Batch size for Literal operations +| LITELLM_ANTHROPIC_DISABLE_URL_SUFFIX | Disable automatic URL suffix appending for Anthropic API base URLs. When set to `true`, prevents LiteLLM from automatically adding `/v1/messages` or `/v1/complete` to custom Anthropic API endpoints | LITELLM_DONT_SHOW_FEEDBACK_BOX | Flag to hide feedback box in LiteLLM UI | LITELLM_DROP_PARAMS | Parameters to drop in LiteLLM requests | LITELLM_MODIFY_PARAMS | Parameters to modify in LiteLLM requests @@ -567,9 +614,16 @@ router_settings: | LITELLM_MIGRATION_DIR | Custom migrations directory for prisma migrations, used for baselining db in read-only file systems. | LITELLM_HOSTED_UI | URL of the hosted UI for LiteLLM | LITELM_ENVIRONMENT | Environment of LiteLLM Instance, used by logging services. Currently only used by DeepEval. +| LITELLM_KEY_ROTATION_ENABLED | Enable auto-key rotation for LiteLLM (boolean). Default is false. +| LITELLM_KEY_ROTATION_CHECK_INTERVAL_SECONDS | Interval in seconds for how often to run job that auto-rotates keys. Default is 86400 (24 hours). | LITELLM_LICENSE | License key for LiteLLM usage | LITELLM_LOCAL_MODEL_COST_MAP | Local configuration for model cost mapping in LiteLLM | LITELLM_LOG | Enable detailed logging for LiteLLM +| LITELLM_LOG_FILE | File path to write LiteLLM logs to. When set, logs will be written to both console and the specified file +| LITELLM_LOGGER_NAME | Name for OTEL logger +| LITELLM_METER_NAME | Name for OTEL Meter +| LITELLM_OTEL_INTEGRATION_ENABLE_EVENTS | Optionally enable semantic logs for OTEL +| LITELLM_OTEL_INTEGRATION_ENABLE_METRICS | Optionally enable emantic metrics for OTEL | LITELLM_MASTER_KEY | Master key for proxy authentication | LITELLM_MODE | Operating mode for LiteLLM (e.g., production, development) | LITELLM_RATE_LIMIT_WINDOW_SIZE | Rate limit window size for LiteLLM. Default is 60 @@ -580,6 +634,7 @@ router_settings: | LITELM_ENVIRONMENT | Environment for LiteLLM Instance. This is currently only logged to DeepEval to determine the environment for DeepEval integration. | LOGFIRE_TOKEN | Token for Logfire logging service | MAX_EXCEPTION_MESSAGE_LENGTH | Maximum length for exception messages. Default is 2000 +| MAX_STRING_LENGTH_PROMPT_IN_DB | Maximum length for strings in spend logs when sanitizing request bodies. Strings longer than this will be truncated. Default is 1000 | MAX_IN_MEMORY_QUEUE_FLUSH_COUNT | Maximum count for in-memory queue flush operations. Default is 1000 | MAX_LONG_SIDE_FOR_IMAGE_HIGH_RES | Maximum length for the long side of high-resolution images. Default is 2000 | MAX_REDIS_BUFFER_DEQUEUE_COUNT | Maximum count for Redis buffer dequeue operations. Default is 100 @@ -638,6 +693,8 @@ router_settings: | PILLAR_API_KEY | API key for Pillar API Guardrails | PILLAR_ON_FLAGGED_ACTION | Action to take when content is flagged ('block' or 'monitor') | POD_NAME | Pod name for the server, this will be [emitted to `datadog` logs](https://docs.litellm.ai/docs/proxy/logging#datadog) as `POD_NAME` +| POSTHOG_API_KEY | API key for PostHog analytics integration +| POSTHOG_API_URL | Base URL for PostHog API (defaults to https://us.i.posthog.com) | PREDIBASE_API_BASE | Base URL for Predibase API | PRESIDIO_ANALYZER_API_BASE | Base URL for Presidio Analyzer service | PRESIDIO_ANONYMIZER_API_BASE | Base URL for Presidio Anonymizer service @@ -718,3 +775,4 @@ router_settings: | WEBHOOK_URL | URL for receiving webhooks from external services | SPEND_LOG_RUN_LOOPS | Constant for setting how many runs of 1000 batch deletes should spend_log_cleanup task run | | SPEND_LOG_CLEANUP_BATCH_SIZE | Number of logs deleted per batch during cleanup. Default is 1000 | +| COROUTINE_CHECKER_MAX_SIZE_IN_MEMORY | Maximum size for CoroutineChecker in-memory cache. Default is 1000 | \ No newline at end of file diff --git a/docs/my-website/docs/proxy/cost_tracking.md b/docs/my-website/docs/proxy/cost_tracking.md index 19e3344f21b..35db752cbb6 100644 --- a/docs/my-website/docs/proxy/cost_tracking.md +++ b/docs/my-website/docs/proxy/cost_tracking.md @@ -17,7 +17,6 @@ LiteLLM automatically tracks spend for all known models. See our [model cost map **Step2** Send `/chat/completions` request - ```python @@ -505,11 +504,11 @@ litellm_settings: ### Disable user-agent tracking -You can disable user-agent tracking by setting `litellm_settings.disable_user_agent_tracking` to `true`. +You can disable user-agent tracking by setting `litellm_settings.disable_add_user_agent_to_request_tags` to `true`. ```yaml litellm_settings: - disable_user_agent_tracking: true + disable_add_user_agent_to_request_tags: true ``` ## ✨ (Enterprise) Generate Spend Reports @@ -860,6 +859,303 @@ Log specific key,value pairs as part of the metadata for a spend log :::info -Logging specific key,value pairs in spend logs metadata is an enterprise feature. [See here](./enterprise.md#tracking-spend-with-custom-metadata) +Logging specific key,value pairs in spend logs metadata is an enterprise feature. ::: + +Requirements: + +- Virtual Keys & a database should be set up, see [virtual keys](https://docs.litellm.ai/docs/proxy/virtual_keys) + +#### Usage - /chat/completions requests with special spend logs metadata + + + + + +```bash +curl -L -X POST 'http://0.0.0.0:4000/key/generate' \ +-H 'Authorization: Bearer sk-1234' \ +-H 'Content-Type: application/json' \ +-d '{ + "metadata": { + "spend_logs_metadata": { + "hello": "world" + } + } +} + +' +``` + + + + +```bash +curl -L -X POST 'http://0.0.0.0:4000/team/new' \ +-H 'Authorization: Bearer sk-1234' \ +-H 'Content-Type: application/json' \ +-d '{ + "metadata": { + "spend_logs_metadata": { + "hello": "world" + } + } +} + +' +``` + + + + + +Set `extra_body={"metadata": { }}` to `metadata` you want to pass + +```python +import openai +client = openai.OpenAI( + api_key="anything", + base_url="http://0.0.0.0:4000" +) + +# request sent to model set on litellm proxy, `litellm --model` +response = client.chat.completions.create( + model="gpt-3.5-turbo", + messages = [ + { + "role": "user", + "content": "this is a test request, write a short poem" + } + ], + extra_body={ + "metadata": { + "spend_logs_metadata": { + "hello": "world" + } + } + } +) + +print(response) +``` + +**Using Headers:** + +```python +import openai +client = openai.OpenAI( + api_key="sk-1234", + base_url="http://0.0.0.0:4000" +) + +# Pass spend logs metadata via headers +response = client.chat.completions.create( + model="gpt-3.5-turbo", + messages = [ + { + "role": "user", + "content": "this is a test request, write a short poem" + } + ], + extra_headers={ + "x-litellm-spend-logs-metadata": '{"user_id": "12345", "project_id": "proj_abc", "request_type": "chat_completion"}' + } +) + +print(response) +``` + + + + + + +```js +const openai = require('openai'); + +async function runOpenAI() { + const client = new openai.OpenAI({ + apiKey: 'sk-1234', + baseURL: 'http://0.0.0.0:4000' + }); + + try { + const response = await client.chat.completions.create({ + model: 'gpt-3.5-turbo', + messages: [ + { + role: 'user', + content: "this is a test request, write a short poem" + }, + ], + metadata: { + spend_logs_metadata: { // 👈 Key Change + hello: "world" + } + } + }); + console.log(response); + } catch (error) { + console.log("got this exception from server"); + console.error(error); + } +} + +// Call the asynchronous function +runOpenAI(); +``` + +**Using Headers:** + +```js +const openai = require('openai'); + +async function runOpenAI() { + const client = new openai.OpenAI({ + apiKey: 'sk-1234', + baseURL: 'http://0.0.0.0:4000' + }); + + try { + const response = await client.chat.completions.create({ + model: 'gpt-3.5-turbo', + messages: [ + { + role: 'user', + content: "this is a test request, write a short poem" + }, + ] + }, { + headers: { + 'x-litellm-spend-logs-metadata': '{"user_id": "12345", "project_id": "proj_abc", "request_type": "chat_completion"}' + } + }); + console.log(response); + } catch (error) { + console.log("got this exception from server"); + console.error(error); + } +} + +// Call the asynchronous function +runOpenAI(); +``` + + + + + +Pass `metadata` as part of the request body + +```shell +curl --location 'http://0.0.0.0:4000/chat/completions' \ + --header 'Content-Type: application/json' \ + --data '{ + "model": "gpt-3.5-turbo", + "messages": [ + { + "role": "user", + "content": "what llm are you" + } + ], + "metadata": { + "spend_logs_metadata": { + "hello": "world" + } + } +}' +``` + + + + + +Pass `x-litellm-spend-logs-metadata` as a request header with JSON string + +```shell +curl --location 'http://0.0.0.0:4000/chat/completions' \ + --header 'Content-Type: application/json' \ + --header 'Authorization: Bearer sk-1234' \ + --header 'x-litellm-spend-logs-metadata: {"user_id": "12345", "project_id": "proj_abc", "request_type": "chat_completion"}' \ + --data '{ + "model": "gpt-3.5-turbo", + "messages": [ + { + "role": "user", + "content": "what llm are you" + } + ] +}' +``` + + + + +```python +from langchain.chat_models import ChatOpenAI +from langchain.prompts.chat import ( + ChatPromptTemplate, + HumanMessagePromptTemplate, + SystemMessagePromptTemplate, +) +from langchain.schema import HumanMessage, SystemMessage + +chat = ChatOpenAI( + openai_api_base="http://0.0.0.0:4000", + model = "gpt-3.5-turbo", + temperature=0.1, + extra_body={ + "metadata": { + "spend_logs_metadata": { + "hello": "world" + } + } + } +) + +messages = [ + SystemMessage( + content="You are a helpful assistant that im using to make a test request to." + ), + HumanMessage( + content="test from litellm. tell me why it's amazing in 1 sentence" + ), +] +response = chat(messages) + +print(response) +``` + + + + + +#### Viewing Spend w/ custom metadata + +#### `/spend/logs` Request Format + +```bash +curl -X GET "http://0.0.0.0:4000/spend/logs?request_id= UserAPIKeyAuth: raise Exception ``` +## UserAPIKeyAuth Fields Reference + +The `UserAPIKeyAuth` object supports the following fields for comprehensive auth configuration: + +### Core Authentication Fields +```python +UserAPIKeyAuth( + # Basic auth fields + api_key: Optional[str] = None, # The API key (will be hashed automatically) + token: Optional[str] = None, # Hashed token for internal use + key_name: Optional[str] = None, # Human-readable key name + key_alias: Optional[str] = None, # Key alias for identification + + # User identification + user_id: Optional[str] = None, # Unique user identifier + user_email: Optional[str] = None, # User email address + user_role: Optional[LitellmUserRoles] = None, # User role (PROXY_ADMIN, INTERNAL_USER, etc.) + + # Team/Organization + team_id: Optional[str] = None, # Team identifier + team_alias: Optional[str] = None, # Team display name + org_id: Optional[str] = None, # Organization identifier +) +``` + +### Budget and Spend Tracking +```python +UserAPIKeyAuth( + # User budgets + max_budget: Optional[float] = None, # Maximum budget for the key + spend: float = 0.0, # Current spend amount + soft_budget: Optional[float] = None, # Soft budget limit (warnings) + model_max_budget: Dict = {}, # Per-model budget limits + model_spend: Dict = {}, # Per-model spend tracking + + # Team budgets + team_max_budget: Optional[float] = None, # Team's maximum budget + team_spend: Optional[float] = None, # Team's current spend + team_member_spend: Optional[float] = None, # This user's spend within the team + + # Budget timing + budget_duration: Optional[str] = None, # Budget reset period + budget_reset_at: Optional[datetime] = None, # When budget resets +) +``` + +### Rate Limiting +```python +UserAPIKeyAuth( + # User limits + tpm_limit: Optional[int] = None, # Tokens per minute limit + rpm_limit: Optional[int] = None, # Requests per minute limit + user_tpm_limit: Optional[int] = None, # User-specific TPM limit + user_rpm_limit: Optional[int] = None, # User-specific RPM limit + + # Team limits + team_tpm_limit: Optional[int] = None, # Team TPM limit + team_rpm_limit: Optional[int] = None, # Team RPM limit + team_member_tpm_limit: Optional[int] = None, # Per-member TPM limit + team_member_rpm_limit: Optional[int] = None, # Per-member RPM limit + + # Per-model limits + rpm_limit_per_model: Optional[Dict[str, int]] = None, # RPM limits by model + tpm_limit_per_model: Optional[Dict[str, int]] = None, # TPM limits by model +) +``` + +### End User Tracking +```python +UserAPIKeyAuth( + # End user identification and limits + end_user_id: Optional[str] = None, # End user identifier + end_user_tpm_limit: Optional[int] = None, # End user TPM limit + end_user_rpm_limit: Optional[int] = None, # End user RPM limit + end_user_max_budget: Optional[float] = None, # End user budget limit +) +``` + +### Model and Route Access +```python +UserAPIKeyAuth( + # Model access control + models: List = [], # Allowed models list + team_models: List = [], # Team's allowed models + aliases: Dict = {}, # Model aliases + + # Route permissions + allowed_routes: Optional[list] = [], # Allowed API routes + allowed_cache_controls: Optional[list] = [], # Cache control permissions + permissions: Dict = {}, # General permissions +) +``` + +### Advanced Configuration +```python +UserAPIKeyAuth( + # Request handling + max_parallel_requests: Optional[int] = None, # Concurrent request limit + allowed_model_region: Optional[AllowedModelRegion] = None, # Geographic restrictions + + # Expiration and status + expires: Optional[Union[str, datetime]] = None, # Key expiration + blocked: Optional[bool] = None, # Whether key is blocked + + # Metadata and configuration + metadata: Dict = {}, # Custom metadata + config: Dict = {}, # Configuration settings + team_metadata: Optional[Dict] = None, # Team metadata + + # Internal tracking + request_route: Optional[str] = None, # Current request route + last_refreshed_at: Optional[float] = None, # Cache refresh timestamp +) +``` + +### Complete Example + +```python +from datetime import datetime, timedelta +from litellm.proxy._types import UserAPIKeyAuth, LitellmUserRoles + +async def user_api_key_auth(request: Request, api_key: str) -> UserAPIKeyAuth: + try: + # Example: Comprehensive auth configuration + if api_key.startswith("sk-admin-"): + return UserAPIKeyAuth( + api_key=api_key, + user_id="admin_user_123", + user_email="admin@company.com", + user_role=LitellmUserRoles.PROXY_ADMIN, + team_id="admin_team", + team_alias="Administrative Team", + max_budget=1000.0, + soft_budget=800.0, + tpm_limit=10000, + rpm_limit=100, + models=["gpt-4", "claude-3-sonnet", "gpt-3.5-turbo"], + allowed_routes=["/chat/completions", "/embeddings"], + expires=datetime.now() + timedelta(days=30), + metadata={"department": "engineering", "cost_center": "ai_ops"} + ) + elif api_key.startswith("sk-team-"): + return UserAPIKeyAuth( + api_key=api_key, + user_id="team_user_456", + user_email="user@company.com", + user_role=LitellmUserRoles.INTERNAL_USER, + team_id="dev_team", + team_alias="Development Team", + max_budget=100.0, + tpm_limit=1000, + rpm_limit=20, + models=["gpt-3.5-turbo", "claude-3-haiku"], + team_member_tpm_limit=500, # Limit within team + end_user_tpm_limit=100, # Per end-user limit + metadata={"project": "chatbot_v2"} + ) + else: + raise Exception("Invalid API key") + except Exception: + raise Exception("Authentication failed") +``` + #### 2. Pass the filepath (relative to the config.yaml) Pass the filepath to the config.yaml diff --git a/docs/my-website/docs/proxy/custom_pricing.md b/docs/my-website/docs/proxy/custom_pricing.md index e2df7721bfb..fc7312b92ac 100644 --- a/docs/my-website/docs/proxy/custom_pricing.md +++ b/docs/my-website/docs/proxy/custom_pricing.md @@ -83,6 +83,24 @@ model_list: cache_read_input_token_cost: 0.0000006 ``` +### Additional Cost Keys + +There are other keys you can use to specify costs for different scenarios and modalities: + +- `input_cost_per_token_above_200k_tokens` - Cost for input tokens when context exceeds 200k tokens +- `output_cost_per_token_above_200k_tokens` - Cost for output tokens when context exceeds 200k tokens +- `cache_creation_input_token_cost_above_200k_tokens` - Cache creation cost for large contexts +- `cache_read_input_token_cost_above_200k_token` - Cache read cost for large contexts +- `input_cost_per_image` - Cost per image in multimodal requests +- `output_cost_per_reasoning_token` - Cost for reasoning tokens (e.g., OpenAI o1 models) +- `input_cost_per_audio_token` - Cost for audio input tokens +- `output_cost_per_audio_token` - Cost for audio output tokens +- `input_cost_per_video_per_second` - Cost per second of video input +- `input_cost_per_video_per_second_above_128k_tokens` - Video cost for large contexts +- `input_cost_per_character` - Character-based pricing for some providers + +These keys evolve based on how new models handle multimodality. The latest version can be found at [https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json](https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json). + ## Set 'base_model' for Cost Tracking (e.g. Azure deployments) **Problem**: Azure returns `gpt-4` in the response when `azure/gpt-4-1106-preview` is used. This leads to inaccurate cost tracking diff --git a/docs/my-website/docs/proxy/custom_sso.md b/docs/my-website/docs/proxy/custom_sso.md index 8e869a11393..bbd7f41bee1 100644 --- a/docs/my-website/docs/proxy/custom_sso.md +++ b/docs/my-website/docs/proxy/custom_sso.md @@ -1,9 +1,7 @@ # ✨ Event Hooks for SSO Login :::info - -✨ This is an Enterprise only feature [Get Started with Enterprise here](https://www.litellm.ai/enterprise) - +✨ 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) ::: ## Overview diff --git a/docs/my-website/docs/proxy/db_deadlocks.md b/docs/my-website/docs/proxy/db_deadlocks.md index 0eee928fa64..ef9d31d6232 100644 --- a/docs/my-website/docs/proxy/db_deadlocks.md +++ b/docs/my-website/docs/proxy/db_deadlocks.md @@ -84,3 +84,29 @@ LiteLLM emits the following prometheus metrics to monitor the health/status of t | `litellm_in_memory_spend_update_queue_size` | In-memory aggregate spend values for keys, users, teams, team members, etc.| In-Memory | | `litellm_redis_spend_update_queue_size` | Redis aggregate spend values for keys, users, teams, etc. | Redis | + +## Troubleshooting: Redis Connection Errors + +You may see errors like: + +``` +LiteLLM Redis Caching: async async_increment() - Got exception from REDIS No connection available., Writing value=21 +LiteLLM Redis Caching: async set_cache_pipeline() - Got exception from REDIS No connection available., Writing value=None +``` + +This means all available Redis connections are in use, and LiteLLM cannot obtain a new connection from the pool. This can happen under high load or with many concurrent proxy requests. + +**Solution:** + +- Increase the `max_connections` parameter in your Redis config section in `proxy_config.yaml` to allow more simultaneous connections. For example: + +```yaml +litellm_settings: + cache: True + cache_params: + type: redis + max_connections: 100 # Increase as needed for your traffic +``` + +Adjust this value based on your expected concurrency and Redis server capacity. + diff --git a/docs/my-website/docs/proxy/debugging.md b/docs/my-website/docs/proxy/debugging.md index 5cca6541763..fbcac24a4d6 100644 --- a/docs/my-website/docs/proxy/debugging.md +++ b/docs/my-website/docs/proxy/debugging.md @@ -11,13 +11,13 @@ The proxy also supports json logs. [See here](#json-logs) **via cli** -```bash +```bash showLineNumbers $ litellm --debug ``` **via env** -```python +```python showLineNumbers os.environ["LITELLM_LOG"] = "INFO" ``` @@ -25,25 +25,25 @@ os.environ["LITELLM_LOG"] = "INFO" **via cli** -```bash +```bash showLineNumbers $ litellm --detailed_debug ``` **via env** -```python +```python showLineNumbers os.environ["LITELLM_LOG"] = "DEBUG" ``` ### Debug Logs Run the proxy with `--detailed_debug` to view detailed debug logs -```shell +```shell showLineNumbers litellm --config /path/to/config.yaml --detailed_debug ``` When making requests you should see the POST request sent by LiteLLM to the LLM on the Terminal output -```shell +```shell showLineNumbers POST Request Sent from LiteLLM: curl -X POST \ https://api.openai.com/v1/chat/completions \ @@ -51,25 +51,63 @@ https://api.openai.com/v1/chat/completions \ -d '{"model": "gpt-3.5-turbo", "messages": [{"role": "user", "content": "this is a test request, write a short poem"}]}' ``` +## Debug single request + +Pass in `litellm_request_debug=True` in the request body + +```bash showLineNumbers +curl -L -X POST 'http://0.0.0.0:4000/chat/completions' \ +-H 'Content-Type: application/json' \ +-H 'Authorization: Bearer sk-1234' \ +-d '{ + "model":"fake-openai-endpoint", + "messages": [{"role": "user","content": "How many r in the word strawberry?"}], + "litellm_request_debug": true +}' +``` + +This will emit the raw request sent by LiteLLM to the API Provider and raw response received from the API Provider for **just** this request in the logs. + + +```bash showLineNumbers +INFO: Uvicorn running on http://0.0.0.0:4000 (Press CTRL+C to quit) +20:14:06 - LiteLLM:WARNING: litellm_logging.py:938 - + +POST Request Sent from LiteLLM: +curl -X POST \ +https://exampleopenaiendpoint-production.up.railway.app/chat/completions \ +-H 'Authorization: Be****ey' -H 'Content-Type: application/json' \ +-d '{'model': 'fake', 'messages': [{'role': 'user', 'content': 'How many r in the word strawberry?'}], 'stream': False}' + + +20:14:06 - LiteLLM:WARNING: litellm_logging.py:1015 - RAW RESPONSE: +{"id":"chatcmpl-817fc08f0d6c451485d571dab39b26a1","object":"chat.completion","created":1677652288,"model":"gpt-3.5-turbo-0301","system_fingerprint":"fp_44709d6fcb","choices":[{"index":0,"message":{"role":"assistant","content":"\n\nHello there, how may I assist you today?"},"logprobs":null,"finish_reason":"stop"}],"usage":{"prompt_tokens":9,"completion_tokens":12,"total_tokens":21}} + + +INFO: 127.0.0.1:56155 - "POST /chat/completions HTTP/1.1" 200 OK + +``` + + ## JSON LOGS Set `JSON_LOGS="True"` in your env: -```bash +```bash showLineNumbers export JSON_LOGS="True" ``` **OR** Set `json_logs: true` in your yaml: -```yaml +```yaml showLineNumbers litellm_settings: json_logs: true ``` Start proxy -```bash +```bash showLineNumbers $ litellm ``` @@ -80,7 +118,7 @@ The proxy will now all logs in json format. Turn off fastapi's default 'INFO' logs 1. Turn on 'json logs' -```yaml +```yaml showLineNumbers litellm_settings: json_logs: true ``` @@ -89,20 +127,20 @@ litellm_settings: Only get logs if an error occurs. -```bash +```bash showLineNumbers LITELLM_LOG="ERROR" ``` 3. Start proxy -```bash +```bash showLineNumbers $ litellm ``` Expected Output: -```bash +```bash showLineNumbers # no info statements ``` @@ -119,14 +157,14 @@ This can be caused due to all your models hitting rate limit errors, causing the How to control this? - Adjust the cooldown time -```yaml +```yaml showLineNumbers router_settings: cooldown_time: 0 # 👈 KEY CHANGE ``` - Disable Cooldowns [NOT RECOMMENDED] -```yaml +```yaml showLineNumbers router_settings: disable_cooldowns: True ``` diff --git a/docs/my-website/docs/proxy/deploy.md b/docs/my-website/docs/proxy/deploy.md index 701fc492232..6a11d069fb0 100644 --- a/docs/my-website/docs/proxy/deploy.md +++ b/docs/my-website/docs/proxy/deploy.md @@ -12,10 +12,8 @@ To start using Litellm, run the following commands in a shell: ```bash # Get the code -git clone https://github.com/BerriAI/litellm - -# Go to folder -cd litellm +curl -O https://raw.githubusercontent.com/BerriAI/litellm/main/docker-compose.yml +curl -O https://raw.githubusercontent.com/BerriAI/litellm/main/prometheus.yml # Add the master key - you can change this after setup echo 'LITELLM_MASTER_KEY="sk-1234"' > .env @@ -1010,5 +1008,13 @@ User-agent: * Disallow: / ``` +## Deployment FAQ + +**Q: Is Postgres the only supported database, or do you support other ones (like Mongo)?** + +A: We explored MySQL but that was hard to maintain and led to bugs for customers. Currently, PostgreSQL is our primary supported database for production deployments. +**Q: If there is Postgres downtime, how does LiteLLM react? Does it fail-open or is there API downtime?** + +A: You can gracefully handle DB unavailability if it's on your VPC. See our production guide for more details: [Gracefully Handle DB Unavailability](https://docs.litellm.ai/docs/proxy/prod#6-if-running-litellm-on-vpc-gracefully-handle-db-unavailability) \ No newline at end of file diff --git a/docs/my-website/docs/proxy/docker_quick_start.md b/docs/my-website/docs/proxy/docker_quick_start.md index 99bf618b5a4..1bb5150dc21 100644 --- a/docs/my-website/docs/proxy/docker_quick_start.md +++ b/docs/my-website/docs/proxy/docker_quick_start.md @@ -2,7 +2,7 @@ import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; -# Getting Started - E2E Tutorial +# E2E Tutorial End-to-End tutorial for LiteLLM Proxy to: - Add an Azure OpenAI model @@ -13,7 +13,7 @@ End-to-End tutorial for LiteLLM Proxy to: ## Pre-Requisites -- Install LiteLLM Docker Image ** OR ** LiteLLM CLI (pip package) +- Install LiteLLM Docker Image **OR** LiteLLM CLI (pip package) @@ -35,6 +35,30 @@ $ pip install 'litellm[proxy]' + + +Use this docker compose to spin up the proxy with a postgres database running locally. + +```bash +# Get the docker compose file +curl -O https://raw.githubusercontent.com/BerriAI/litellm/main/docker-compose.yml + +# Add the master key - you can change this after setup +echo 'LITELLM_MASTER_KEY="sk-1234"' > .env + +# Add the litellm salt key - you cannot change this after adding a model +# It is used to encrypt / decrypt your LLM API Key credentials +# We recommend - https://1password.com/password-generator/ +# password generator to get a random hash for litellm salt key +echo 'LITELLM_SALT_KEY="sk-1234"' >> .env + +source .env + +# Start +docker-compose up +``` + + ## 1. Add a model @@ -43,6 +67,8 @@ Control LiteLLM Proxy with a config.yaml file. Setup your config.yaml with your azure model. +Note: When using the proxy with a database, you can also **just add models via UI** (UI is available on `/ui` route). + ```yaml model_list: - model_name: gpt-4o @@ -252,15 +278,15 @@ See All General Settings [here](http://localhost:3000/docs/proxy/configs#all-set - **Description**: - Set a `master key`, this is your Proxy Admin key - you can use this to create other keys (🚨 must start with `sk-`). - **Usage**: - - ** Set on config.yaml** set your master key under `general_settings:master_key`, example - + - **Set on config.yaml** set your master key under `general_settings:master_key`, example - `master_key: sk-1234` - - ** Set env variable** set `LITELLM_MASTER_KEY` + - **Set env variable** set `LITELLM_MASTER_KEY` 2. **`database_url`** (str) - **Description**: - Set a `database_url`, this is the connection to your Postgres DB, which is used by litellm for generating keys, users, teams. - **Usage**: - - ** Set on config.yaml** set your `database_url` under `general_settings:database_url`, example - + - **Set on config.yaml** set your `database_url` under `general_settings:database_url`, example - `database_url: "postgresql://..."` - Set `DATABASE_URL=postgresql://:@:/` in your env diff --git a/docs/my-website/docs/proxy/dynamic_rate_limit.md b/docs/my-website/docs/proxy/dynamic_rate_limit.md new file mode 100644 index 00000000000..b3aed6a359e --- /dev/null +++ b/docs/my-website/docs/proxy/dynamic_rate_limit.md @@ -0,0 +1,255 @@ + +# Dynamic TPM/RPM Allocation + +Prevent projects from gobbling too much tpm/rpm. + +Dynamically allocate TPM/RPM quota to api keys, based on active keys in that minute. [**See Code**](https://github.com/BerriAI/litellm/blob/9bffa9a48e610cc6886fc2dce5c1815aeae2ad46/litellm/proxy/hooks/dynamic_rate_limiter.py#L125) + +## Quick Start Usage + +1. Setup config.yaml + +```yaml showLineNumbers title="config.yaml" +model_list: + - model_name: my-fake-model + litellm_params: + model: gpt-3.5-turbo + api_key: my-fake-key + mock_response: hello-world + tpm: 60 + +litellm_settings: + callbacks: ["dynamic_rate_limiter_v3"] + +general_settings: + master_key: sk-1234 # OR set `LITELLM_MASTER_KEY=".."` in your .env + database_url: postgres://.. # OR set `DATABASE_URL=".."` in your .env +``` + +2. Start proxy + +```bash +litellm --config /path/to/config.yaml +``` + +3. Test it! + +```python showLineNumbers title="test.py" +""" +- Run 2 concurrent teams calling same model +- model has 60 TPM +- Mock response returns 30 total tokens / request +- Each team will only be able to make 1 request per minute +""" + +import requests +from openai import OpenAI, RateLimitError + +def create_key(api_key: str, base_url: str): + response = requests.post( + url="{}/key/generate".format(base_url), + json={}, + headers={ + "Authorization": "Bearer {}".format(api_key) + } + ) + + _response = response.json() + + return _response["key"] + +key_1 = create_key(api_key="sk-1234", base_url="http://0.0.0.0:4000") +key_2 = create_key(api_key="sk-1234", base_url="http://0.0.0.0:4000") + +# call proxy with key 1 - works +openai_client_1 = OpenAI(api_key=key_1, base_url="http://0.0.0.0:4000") + +response = openai_client_1.chat.completions.with_raw_response.create( + model="my-fake-model", messages=[{"role": "user", "content": "Hello world!"}], +) + +print("Headers for call 1 - {}".format(response.headers)) +_response = response.parse() +print("Total tokens for call - {}".format(_response.usage.total_tokens)) + + +# call proxy with key 2 - works +openai_client_2 = OpenAI(api_key=key_2, base_url="http://0.0.0.0:4000") + +response = openai_client_2.chat.completions.with_raw_response.create( + model="my-fake-model", messages=[{"role": "user", "content": "Hello world!"}], +) + +print("Headers for call 2 - {}".format(response.headers)) +_response = response.parse() +print("Total tokens for call - {}".format(_response.usage.total_tokens)) +# call proxy with key 2 - fails +try: + openai_client_2.chat.completions.with_raw_response.create(model="my-fake-model", messages=[{"role": "user", "content": "Hey, how's it going?"}]) + raise Exception("This should have failed!") +except RateLimitError as e: + print("This was rate limited b/c - {}".format(str(e))) + +``` + +**Expected Response** + +``` +This was rate limited b/c - Error code: 429 - {'error': {'message': {'error': 'Key= over available TPM=0. Model TPM=0, Active keys=2'}, 'type': 'None', 'param': 'None', 'code': 429}} +``` + + +## [BETA] Set Priority / Reserve Quota + +Reserve TPM/RPM capacity for different environments or use cases. This ensures critical production workloads always have guaranteed capacity, while development or lower-priority tasks use remaining quota. + +**Use Cases:** +- Production vs Development environments +- Real-time applications vs batch processing +- Critical services vs experimental features + +:::tip + +Reserving TPM/RPM on keys based on priority is a premium feature. Please [get an enterprise license](./enterprise.md) for it. +::: + +### How Priority Reservation Works + +Priority reservation allocates a percentage of your model's total TPM/RPM to specific priority levels. Keys with higher priority get guaranteed access to their reserved quota first. + +**Example Scenario:** +- Model has 10 RPM total capacity +- Priority reservation: `{"prod": 0.9, "dev": 0.1}` +- Result: Production keys get 9 RPM guaranteed, Development keys get 1 RPM guaranteed + +### Configuration + +#### 1. Setup config.yaml + +```yaml showLineNumbers title="config.yaml" +model_list: + - model_name: gpt-3.5-turbo + litellm_params: + model: "gpt-3.5-turbo" + api_key: os.environ/OPENAI_API_KEY + rpm: 10 # Total model capacity + +litellm_settings: + callbacks: ["dynamic_rate_limiter_v3"] + priority_reservation: + "prod": 0.9 # 90% reserved for production (9 RPM) + "dev": 0.1 # 10% reserved for development (1 RPM) + priority_reservation_settings: + default_priority: 0 # Weight (0%) assigned to keys without explicit priority metadata + +general_settings: + master_key: sk-1234 # OR set `LITELLM_MASTER_KEY=".."` in your .env + database_url: postgres://.. # OR set `DATABASE_URL=".."` in your.env +``` + +**Configuration Details:** + +`priority_reservation`: Dict[str, float] +- **Key (str)**: Priority level name (can be any string like "prod", "dev", "critical", etc.) +- **Value (float)**: Percentage of total TPM/RPM to reserve (0.0 to 1.0) +- **Note**: Values should sum to 1.0 or less + +`priority_reservation_settings`: Object (Optional) +- **default_priority (float)**: Weight/percentage (0.0 to 1.0) assigned to API keys that have no priority metadata set (defaults to 0.5) + +**Start Proxy** + +```bash +litellm --config /path/to/config.yaml +``` + +#### 2. Create Keys with Priority Levels + +**Production Key:** +```bash +curl -X POST 'http://0.0.0.0:4000/key/generate' \ +-H 'Authorization: Bearer sk-1234' \ +-H 'Content-Type: application/json' \ +-d '{ + "metadata": {"priority": "prod"} +}' +``` + +**Development Key:** +```bash +curl -X POST 'http://0.0.0.0:4000/key/generate' \ +-H 'Authorization: Bearer sk-1234' \ +-H 'Content-Type: application/json' \ +-d '{ + "metadata": {"priority": "dev"} +}' +``` + +**Key Without Priority (uses default_priority weight):** +```bash +curl -X POST 'http://0.0.0.0:4000/key/generate' \ +-H 'Authorization: Bearer sk-1234' \ +-H 'Content-Type: application/json' \ +-d '{}' +``` + +**Expected Response for both:** +```json +{ + "key": "sk-...", + "metadata": {"priority": "prod"}, // or "dev" + ... +} +``` + +#### 3. Test Priority Allocation + +**Test Production Key (should get 9 RPM):** +```bash +curl -X POST 'http://0.0.0.0:4000/chat/completions' \ + -H 'Content-Type: application/json' \ + -H 'Authorization: Bearer sk-prod-key' \ + -d '{ + "model": "gpt-3.5-turbo", + "messages": [{"role": "user", "content": "Hello from prod"}] + }' +``` + +**Test Development Key (should get 1 RPM):** +```bash +curl -X POST 'http://0.0.0.0:4000/chat/completions' \ + -H 'Content-Type: application/json' \ + -H 'Authorization: Bearer sk-dev-key' \ + -d '{ + "model": "gpt-3.5-turbo", + "messages": [{"role": "user", "content": "Hello from dev"}] + }' +``` + +### Expected Behavior + +With the configuration above: + +1. **Production keys** can make up to 9 requests per minute (90% of 10 RPM) +2. **Development keys** can make up to 1 request per minute (10% of 10 RPM) +3. **Keys without explicit priority** get the default_priority weight (0 = 0%), which allocates 0 requests per minute (0% of 10 RPM) +4. Named priorities in `priority_reservation` and keys with `default_priority` operate independently + +**Rate Limit Error Example:** +```json +{ + "error": { + "message": "Key=sk-dev-... over available RPM=0. Model RPM=10, Reserved RPM for priority 'dev'=1, Active keys=1", + "type": "rate_limit_exceeded", + "code": 429 + } +} +``` + +### Demo Video + +This video walks through setting up dynamic rate limiting with priority reservation and locust tests to validate the behavior. + + + diff --git a/docs/my-website/docs/proxy/enterprise.md b/docs/my-website/docs/proxy/enterprise.md index 468bcad2cf8..42677264ff6 100644 --- a/docs/my-website/docs/proxy/enterprise.md +++ b/docs/my-website/docs/proxy/enterprise.md @@ -357,221 +357,13 @@ curl -X GET "http://0.0.0.0:4000/spend/tags" \ "total_spend": 0.000224 } ] - ``` +:::tip +For comprehensive spend tracking features including budgets, alerts, and detailed analytics, check out [Spend Tracking](https://docs.litellm.ai/docs/proxy/cost_tracking). -### Tracking Spend with custom metadata +::: -Requirements: - -- Virtual Keys & a database should be set up, see [virtual keys](https://docs.litellm.ai/docs/proxy/virtual_keys) - -#### Usage - /chat/completions requests with special spend logs metadata - - - - - -```bash -curl -L -X POST 'http://0.0.0.0:4000/key/generate' \ --H 'Authorization: Bearer sk-1234' \ --H 'Content-Type: application/json' \ --d '{ - "metadata": { - "spend_logs_metadata": { - "hello": "world" - } - } -} - -' -``` - - - - -```bash -curl -L -X POST 'http://0.0.0.0:4000/team/new' \ --H 'Authorization: Bearer sk-1234' \ --H 'Content-Type: application/json' \ --d '{ - "metadata": { - "spend_logs_metadata": { - "hello": "world" - } - } -} - -' -``` - - - - - -Set `extra_body={"metadata": { }}` to `metadata` you want to pass - -```python -import openai -client = openai.OpenAI( - api_key="anything", - base_url="http://0.0.0.0:4000" -) - -# request sent to model set on litellm proxy, `litellm --model` -response = client.chat.completions.create( - model="gpt-3.5-turbo", - messages = [ - { - "role": "user", - "content": "this is a test request, write a short poem" - } - ], - extra_body={ - "metadata": { - "spend_logs_metadata": { - "hello": "world" - } - } - } -) - -print(response) -``` - - - - - -```js -const openai = require('openai'); - -async function runOpenAI() { - const client = new openai.OpenAI({ - apiKey: 'sk-1234', - baseURL: 'http://0.0.0.0:4000' - }); - - try { - const response = await client.chat.completions.create({ - model: 'gpt-3.5-turbo', - messages: [ - { - role: 'user', - content: "this is a test request, write a short poem" - }, - ], - metadata: { - spend_logs_metadata: { // 👈 Key Change - hello: "world" - } - } - }); - console.log(response); - } catch (error) { - console.log("got this exception from server"); - console.error(error); - } -} - -// Call the asynchronous function -runOpenAI(); -``` - - - - -Pass `metadata` as part of the request body - -```shell -curl --location 'http://0.0.0.0:4000/chat/completions' \ - --header 'Content-Type: application/json' \ - --data '{ - "model": "gpt-3.5-turbo", - "messages": [ - { - "role": "user", - "content": "what llm are you" - } - ], - "metadata": { - "spend_logs_metadata": { - "hello": "world" - } - } -}' -``` - - - -```python -from langchain.chat_models import ChatOpenAI -from langchain.prompts.chat import ( - ChatPromptTemplate, - HumanMessagePromptTemplate, - SystemMessagePromptTemplate, -) -from langchain.schema import HumanMessage, SystemMessage - -chat = ChatOpenAI( - openai_api_base="http://0.0.0.0:4000", - model = "gpt-3.5-turbo", - temperature=0.1, - extra_body={ - "metadata": { - "spend_logs_metadata": { - "hello": "world" - } - } - } -) - -messages = [ - SystemMessage( - content="You are a helpful assistant that im using to make a test request to." - ), - HumanMessage( - content="test from litellm. tell me why it's amazing in 1 sentence" - ), -] -response = chat(messages) - -print(response) -``` - - - - - -#### Viewing Spend w/ custom metadata - -#### `/spend/logs` Request Format - -```bash -curl -X GET "http://0.0.0.0:4000/spend/logs?request_id= + + +Expect this to fail since the request contains harmful content: + +```shell showLineNumbers title="Curl Request" +curl -i http://0.0.0.0:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -d '{ + "model": "gpt-4o-mini", + "messages": [ + {"role": "user", "content": "Tell me how to hack into someone's email account"} + ] + }' +``` + +Expected response on failure: + +```json +{ + "error": { + "message": "{\n \"error\": \"Request blocked by Noma guardrail\",\n \"details\": {\n \"prompt\": {\n \"harmfulContent\": {\n \"result\": true,\n \"confidence\": 0.95\n }\n }\n }\n }", + "type": "None", + "param": "None", + "code": "400" + } +} +``` + + + + + +```shell showLineNumbers title="Curl Request" +curl -i http://0.0.0.0:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -d '{ + "model": "gpt-4o-mini", + "messages": [ + {"role": "user", "content": "What is the capital of France?"} + ] + }' +``` + +Expected response: + +```json +{ + "id": "chatcmpl-123", + "object": "chat.completion", + "created": 1677652288, + "model": "gpt-4o-mini", + "choices": [ + { + "index": 0, + "message": { + "role": "assistant", + "content": "The capital of France is Paris." + }, + "finish_reason": "stop" + } + ], + "usage": { + "prompt_tokens": 9, + "completion_tokens": 12, + "total_tokens": 21 + } +} +``` + + + + +## Supported Params + +```yaml +guardrails: + - guardrail_name: "noma-guard" + litellm_params: + guardrail: noma + mode: "pre_call" + api_key: os.environ/NOMA_API_KEY + api_base: os.environ/NOMA_API_BASE + ### OPTIONAL ### + # application_id: "my-app" + # monitor_mode: false + # block_failures: true + # anonymize_input: false +``` + +### Required Parameters + +- **`api_key`**: Your Noma Security API key (set as `os.environ/NOMA_API_KEY` in YAML config) + +### Optional Parameters + +- **`api_base`**: Noma API base URL (defaults to `https://api.noma.security/`) +- **`application_id`**: Your application identifier (defaults to `"litellm"`) +- **`monitor_mode`**: If `true`, logs violations without blocking (defaults to `false`) +- **`block_failures`**: If `true`, blocks requests when guardrail API failures occur (defaults to `true`) +- **`anonymize_input`**: If `true`, replaces sensitive content with anonymized version (defaults to `false`) + +## Environment Variables + +You can set these environment variables instead of hardcoding values in your config: + +```shell +export NOMA_API_KEY="your-api-key-here" +export NOMA_API_BASE="https://api.noma.security/" # Optional +export NOMA_APPLICATION_ID="my-app" # Optional +export NOMA_MONITOR_MODE="false" # Optional +export NOMA_BLOCK_FAILURES="true" # Optional +export NOMA_ANONYMIZE_INPUT="false" # Optional +``` + +## Advanced Configuration + +### Monitor Mode + +Use monitor mode to test your guardrails without blocking requests: + +```yaml +guardrails: + - guardrail_name: "noma-monitor" + litellm_params: + guardrail: noma + mode: "pre_call" + api_key: os.environ/NOMA_API_KEY + monitor_mode: true # Log violations but don't block +``` + +### Handling API Failures + +Control behavior when the Noma API is unavailable: + +```yaml +guardrails: + - guardrail_name: "noma-failopen" + litellm_params: + guardrail: noma + mode: "pre_call" + api_key: os.environ/NOMA_API_KEY + block_failures: false # Allow requests to proceed if guardrail API fails +``` + +### Content Anonymization + +Enable anonymization to replace sensitive content instead of blocking: + +```yaml +guardrails: + - guardrail_name: "noma-anonymize" + litellm_params: + guardrail: noma + mode: "pre_call" + api_key: os.environ/NOMA_API_KEY + anonymize_input: true # Replace sensitive data with anonymized version +``` + +### Multiple Guardrails + +Apply different configurations for input and output: + +```yaml +guardrails: + - guardrail_name: "noma-strict-input" + litellm_params: + guardrail: noma + mode: "pre_call" + api_key: os.environ/NOMA_API_KEY + block_failures: true + + - guardrail_name: "noma-monitor-output" + litellm_params: + guardrail: noma + mode: "post_call" + api_key: os.environ/NOMA_API_KEY + monitor_mode: true +``` + +## ✨ Pass Additional Parameters + +Use `extra_body` to pass additional parameters to the Noma Security API call, such as dynamically setting the application ID for specific requests. + + + + +```python +import openai +client = openai.OpenAI( + api_key="your-api-key", + base_url="http://0.0.0.0:4000" +) + +response = client.chat.completions.create( + model="gpt-4o-mini", + messages=[{"role": "user", "content": "Hello, how are you?"}], + extra_body={ + "guardrails": { + "noma-guard": { + "extra_body": { + "application_id": "my-specific-app-id" + } + } + } + } +) +``` + + + + +```shell +curl 'http://0.0.0.0:4000/v1/chat/completions' \ + -H 'Content-Type: application/json' \ + -d '{ + "model": "gpt-4o-mini", + "messages": [ + { + "role": "user", + "content": "Hello, how are you?" + } + ], + "guardrails": { + "noma-guard": { + "extra_body": { + "application_id": "my-specific-app-id" + } + } + } +}' +``` + + + +This allows you to override the default `application_id` parameter for specific requests, which is useful for tracking usage across different applications or components. + +## Response Details + +When content is blocked, Noma provides detailed information about the violations as JSON inside the `message` field, with the following structure: + +```json +{ + "error": "Request blocked by Noma guardrail", + "details": { + "prompt": { + "harmfulContent": { + "result": true, + "confidence": 0.95 + }, + "sensitiveData": { + "email": { + "result": true, + "entities": ["user@example.com"] + } + }, + "bannedTopics": { + "violence": { + "result": true, + "confidence": 0.88 + } + } + } + } +} +``` diff --git a/docs/my-website/docs/proxy/guardrails/tool_permission.md b/docs/my-website/docs/proxy/guardrails/tool_permission.md new file mode 100644 index 00000000000..9ed05ed46a8 --- /dev/null +++ b/docs/my-website/docs/proxy/guardrails/tool_permission.md @@ -0,0 +1,153 @@ +import Image from '@theme/IdealImage'; +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Tool Permission Guardrail + +LiteLLM provides a Tool Permission Guardrail that lets you control which **tool calls** a model is allowed to invoke, using configurable allow/deny rules. This offers fine-grained, provider-agnostic control over tool execution (e.g., OpenAI Chat Completions `tool_calls`, Anthropic Messages `tool_use`, MCP tools). + +## Quick Start +### 1. Define Guardrails on your LiteLLM config.yaml + +Define your guardrails under the `guardrails` section +```yaml +guardrails: + - guardrail_name: "tool-permission-guardrail" + litellm_params: + guardrail: tool_permission + mode: "post_call" + rules: + - id: "allow_bash" + tool_name: "Bash" + decision: "allow" + - id: "allow_github_mcp" + tool_name: "mcp__github_*" + decision: "allow" + - id: "allow_aws_documentation" + tool_name: "mcp__aws-documentation_*_documentation" + decision: "allow" + - id: "deny_read_commands" + tool_name: "Read" + decision: "Deny" + default_action: "deny" # Fallback when no rule matches: "allow" or "deny" + on_disallowed_action: "block" # How to handle disallowed tools: "block" or "rewrite" +``` + +#### Rule Structure + +```yaml +- id: "unique_rule_id" # Unique identifier for the rule + tool_name: "pattern" # Tool name or pattern to match + decision: "allow" # "allow" or "deny" +``` + +#### Supported values for `mode` + +- `pre_call` Run **before** LLM call, on **input** +- `post_call` Run **after** LLM call, on **input & output** + +### 2. Start the Proxy + +```shell +litellm --config config.yaml --port 4000 +``` + +## Examples + + + + +**Block requset** + +```bash +# Test +curl -X POST "http://localhost:4000/v1/chat/completions" \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer your-master-key-here" \ + -d '{ + "model": "gpt-5-mini", + "messages": [{"role": "user","content": "What is the weather like in Tokyo today?"}], + "tools": [ + { + "type":"function", + "function": { + "name":"get_current_weather", + "description": "Get the current weather in a given location" + } + } + ] + }' +``` + +**Expected response (Denied):** + +```json +{ + "error": + { + "message": "Guardrail raised an exception, Guardrail: tool-permission-guardrail, Message: Tool 'get_current_weather' denied by default action", + "type": "None", + "param": "None", + "code": "500" + } +} +``` + + + + +**Rewrite requset** + +```bash +# Test +curl -X POST "http://localhost:4000/v1/chat/completions" \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer your-master-key-here" \ + -d '{ + "model": "gpt-5-mini", + "messages": [{"role": "user","content": "What is the weather like in Tokyo today?"}], + "tools": [ + { + "type":"function", + "function": { + "name":"get_current_weather", + "description": "Get the current weather in a given location" + } + } + ] + }' +``` + +**Expected response:** + +```json +{ + "id": "chatcmpl-xxxxxxxxxxxxxxx", + "created": 1757716050, + "model": "gpt-5-mini-2025-08-07", + "object": "chat.completion", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "I can’t fetch live weather — I don’t have real‑time internet access.", + "role": "assistant", + "annotations": [] + }, + "provider_specific_fields": {} + } + ], + "usage": { + "prompt_tokens": 112, + "total_tokens": 735, + "completion_tokens_details": { + "reasoning_tokens": 384, + }, + }, + "service_tier": "default" +} +``` + + + diff --git a/docs/my-website/docs/proxy/health.md b/docs/my-website/docs/proxy/health.md index 5cd6b5d18a7..7e627846b1d 100644 --- a/docs/my-website/docs/proxy/health.md +++ b/docs/my-website/docs/proxy/health.md @@ -128,8 +128,11 @@ model_list: api_key: "os.environ/OPENAI_API_KEY" model_info: mode: audio_speech + health_check_voice: alloy ``` +You can specify a `health_check_voice` if you need to use a voice other than "alloy". + ### Rerank Models To run rerank health checks, specify the mode as "rerank" in your config for the relevant model. diff --git a/docs/my-website/docs/proxy/load_balancing.md b/docs/my-website/docs/proxy/load_balancing.md index fd95b57c1ba..54c917bbbca 100644 --- a/docs/my-website/docs/proxy/load_balancing.md +++ b/docs/my-website/docs/proxy/load_balancing.md @@ -13,6 +13,23 @@ For more details on routing strategies / params, see [Routing](../routing.md) ::: +## How Load Balancing Works + +LiteLLM automatically distributes requests across multiple deployments of the same model using its built-in router. the proxy routes traffic to optimize performance and reliability. + +"simple-shuffle" routing strategy is used by default + +### Routing Strategies + +| Strategy | Description | When to Use | +|----------|-------------|-------------| +| **simple-shuffle** (recommended) | Randomly distributes requests | General purpose, good for even load distribution | +| **least-busy** | Routes to deployment with fewest active requests | High concurrency scenarios | +| **usage-based-routing** (bad for perf) | Routes to deployment with lowest current usage (RPM/TPM) | When you want to respect rate limits evenly | +| **latency-based-routing** | Routes to fastest responding deployment | Latency-critical applications | +| **cost-based-routing** | Routes to deployment with lowest cost | Cost-sensitive applications | + + ## Quick Start - Load Balancing #### Step 1 - Set deployments on config @@ -106,49 +123,14 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \ ] }' ``` - - - -```python -from langchain.chat_models import ChatOpenAI -from langchain.prompts.chat import ( - ChatPromptTemplate, - HumanMessagePromptTemplate, - SystemMessagePromptTemplate, -) -from langchain.schema import HumanMessage, SystemMessage -import os - -os.environ["OPENAI_API_KEY"] = "anything" - -chat = ChatOpenAI( - openai_api_base="http://0.0.0.0:4000", - model="gpt-3.5-turbo", -) - -messages = [ - SystemMessage( - content="You are a helpful assistant that im using to make a test request to." - ), - HumanMessage( - content="test from litellm. tell me why it's amazing in 1 sentence" - ), -] -response = chat(messages) - -print(response) -``` - - - ### Test - Loadbalancing In this request, the following will occur: 1. A rate limit exception will be raised -2. LiteLLM proxy will retry the request on the model group (default is 3). +2. LiteLLM proxy will retry the request on the model group (default retries are 3). ```bash curl -X POST 'http://0.0.0.0:4000/chat/completions' \ @@ -190,6 +172,9 @@ router_settings: redis_host: redis_password: redis_port: 1992 + cache_params: + type: redis + max_connections: 100 # maximum Redis connections in the pool; tune based on expected concurrency/load ``` ## Router settings on config - routing_strategy, model_group_alias @@ -256,4 +241,16 @@ model_group_alias: Optional[Dict[str, Union[str, RouterModelGroupAliasItem]]] = class RouterModelGroupAliasItem(TypedDict): model: str hidden: bool # if 'True', don't return on `/v1/models`, `/v1/model/info`, `/v1/model_group/info` -``` \ No newline at end of file +``` + +### When You'll See Load Balancing in Action + +**Immediate Effects:** + +- Different deployments serve subsequent requests (visible in logs) +- Better response times during high traffic + +**Observable Benefits:** +- **Higher throughput**: More requests handled simultaneously across deployments +- **Improved reliability**: If one deployment fails, traffic automatically routes to healthy ones +- **Better resource utilization**: Load spread evenly across all available deployments diff --git a/docs/my-website/docs/proxy/logging.md b/docs/my-website/docs/proxy/logging.md index 5d3f8417222..ff2591daad2 100644 --- a/docs/my-website/docs/proxy/logging.md +++ b/docs/my-website/docs/proxy/logging.md @@ -60,7 +60,7 @@ components in your system, including in logging tools. ### Redact Messages, Response Content -Set `litellm.turn_off_message_logging=True` This will prevent the messages and responses from being logged to your logging provider, but request metadata - e.g. spend, will still be tracked. +Set `litellm.turn_off_message_logging=True` This will prevent the messages and responses from being logged to your logging provider, but request metadata - e.g. spend, will still be tracked. Useful for privacy/compliance when handling sensitive data. diff --git a/docs/my-website/docs/proxy/logging_spec.md b/docs/my-website/docs/proxy/logging_spec.md index a39a62318e7..205282428ee 100644 --- a/docs/my-website/docs/proxy/logging_spec.md +++ b/docs/my-website/docs/proxy/logging_spec.md @@ -11,6 +11,7 @@ Found under `kwargs["standard_logging_object"]`. This is a standard payload, log | `trace_id` | `str` | Trace multiple LLM calls belonging to same overall request | | `call_type` | `str` | Type of call | | `response_cost` | `float` | Cost of the response in USD ($) | +| `cost_breakdown` | `Optional[CostBreakdown]` | Detailed cost breakdown object | | `response_cost_failure_debug_info` | `StandardLoggingModelCostFailureDebugInformation` | Debug information if cost tracking fails | | `status` | `StandardLoggingPayloadStatus` | Status of the payload | | `total_tokens` | `int` | Total number of tokens | @@ -39,6 +40,29 @@ Found under `kwargs["standard_logging_object"]`. This is a standard payload, log | `model_parameters` | `dict` | Model parameters | | `hidden_params` | `StandardLoggingHiddenParams` | Hidden parameters | +## Cost Breakdown + +The `cost_breakdown` field provides detailed cost breakdown for completion requests as a `CostBreakdown` object containing: + +- **`input_cost`**: Cost of input/prompt tokens including cache creation tokens +- **`output_cost`**: Cost of output/completion tokens (including reasoning tokens if applicable) +- **`tool_usage_cost`**: Cost of built-in tools usage (e.g., web search, code interpreter) +- **`total_cost`**: Total cost of input + output + tool usage + +**Note**: This field is populated for all call types. For non-completion calls, `input_cost` and `output_cost` may be 0. + +The total cost relationship is: `response_cost = cost_breakdown.total_cost` + +### CostBreakdown Type + +```python +class CostBreakdown(TypedDict, total=False): + input_cost: float # Cost of input/prompt tokens in USD + output_cost: float # Cost of output/completion tokens in USD (includes reasoning) + tool_usage_cost: float # Cost of built-in tools usage in USD + total_cost: float # Total cost in USD +``` + ## StandardLoggingUserAPIKeyMetadata | Field | Type | Description | @@ -61,6 +85,11 @@ Inherits from `StandardLoggingUserAPIKeyMetadata` and adds: | `requester_metadata` | `Optional[dict]` | Additional requester metadata | | `vector_store_request_metadata` | `Optional[List[StandardLoggingVectorStoreRequest]]` | Vector store request metadata | | `requester_custom_headers` | Dict[str, str] | Any custom (`x-`) headers sent by the client to the proxy. | +| `prompt_management_metadata` | `Optional[StandardLoggingPromptManagementMetadata]` | Prompt management and versioning metadata | +| `mcp_tool_call_metadata` | `Optional[StandardLoggingMCPToolCall]` | MCP (Model Context Protocol) tool call information and cost tracking | +| `applied_guardrails` | `Optional[List[str]]` | List of applied guardrail names | +| `usage_object` | `Optional[dict]` | Raw usage object from the LLM provider | +| `cold_storage_object_key` | `Optional[str]` | S3/GCS object key for cold storage retrieval | | `guardrail_information` | `Optional[StandardLoggingGuardrailInformation]` | Guardrail information | @@ -145,4 +174,82 @@ A literal type with two possible values: | `duration` | `Optional[float]` | Duration of the guardrail in seconds | | `masked_entity_count` | `Optional[Dict[str, int]]` | Count of masked entities | +## StandardLoggingPromptManagementMetadata +Used for tracking prompt versioning and management information. + +| Field | Type | Description | +|-------|------|-------------| +| `prompt_id` | `str` | **Required**. Unique identifier for the prompt template or version | +| `prompt_variables` | `Optional[dict]` | Variables/parameters used in the prompt template (e.g., `{"user_name": "John", "context": "support"}`) | +| `prompt_integration` | `str` | **Required**. Integration or system managing the prompt (e.g., `"langfuse"`, `"promptlayer"`, `"custom"`) | + +## StandardLoggingMCPToolCall + +Used to track Model Context Protocol (MCP) tool calls within LiteLLM requests. This provides detailed logging for external tool integrations. + +| Field | Type | Description | +|-------|------|-------------| +| `name` | `str` | **Required**. The name of the tool being called (e.g., `"get_weather"`, `"search_database"`) | +| `arguments` | `dict` | **Required**. Arguments passed to the tool as key-value pairs | +| `result` | `Optional[dict]` | The response/result returned by the tool execution (populated by custom logging hooks) | +| `mcp_server_name` | `Optional[str]` | Name of the MCP server that handled the tool call (e.g., `"weather-service"`, `"database-connector"`) | +| `mcp_server_logo_url` | `Optional[str]` | URL for the MCP server's logo (used for UI display in LiteLLM dashboard) | +| `namespaced_tool_name` | `Optional[str]` | Fully qualified tool name including server prefix (e.g., `"deepwiki-mcp/get_page_content"`, `"github-mcp/create_issue"`) | +| `mcp_server_cost_info` | `Optional[MCPServerCostInfo]` | Cost tracking information for the tool call | + +### MCPServerCostInfo + +Cost tracking structure for MCP server tool calls: + +| Field | Type | Description | +|-------|------|-------------| +| `default_cost_per_query` | `Optional[float]` | Default cost in USD for any tool call to this MCP server | +| `tool_name_to_cost_per_query` | `Optional[Dict[str, float]]` | Per-tool cost mapping for granular pricing (e.g., `{"search": 0.01, "create": 0.05}`) | + +### Usage + +```python +# Basic MCP tool call metadata +mcp_tool_call = { + "name": "search_documents", + "arguments": { + "query": "machine learning tutorials", + "limit": 10, + "filter": "type:pdf" + }, + "mcp_server_name": "document-search-service", + "namespaced_tool_name": "docs-mcp/search_documents", + "mcp_server_cost_info": { + "default_cost_per_query": 0.02, + "tool_name_to_cost_per_query": { + "search_documents": 0.02, + "get_document": 0.01 + } + } +} + +# optional result field (via custom logging hooks) +mcp_tool_call_with_result = { + "name": "search_documents", + "arguments": { + "query": "machine learning tutorials", + "limit": 10, + "filter": "type:pdf" + }, + "result": { + "documents": [...], + "total_found": 42, + "search_time_ms": 150 + }, + "mcp_server_name": "document-search-service", + "namespaced_tool_name": "docs-mcp/search_documents", + "mcp_server_cost_info": { + "default_cost_per_query": 0.02, + "tool_name_to_cost_per_query": { + "search_documents": 0.02, + "get_document": 0.01 + } + } +} +``` \ No newline at end of file diff --git a/docs/my-website/docs/proxy/native_litellm_prompt.md b/docs/my-website/docs/proxy/native_litellm_prompt.md index 1e1df999db9..ea326d00690 100644 --- a/docs/my-website/docs/proxy/native_litellm_prompt.md +++ b/docs/my-website/docs/proxy/native_litellm_prompt.md @@ -1,4 +1,3 @@ -import Image from '@theme/IdealImage'; import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; @@ -6,6 +5,11 @@ import TabItem from '@theme/TabItem'; Store prompts as `.prompt` files in your repository and use them directly with LiteLLM. No external services required. +## Supported Integrations + +- **File System**: Store `.prompt` files locally +- **BitBucket**: Store `.prompt` files in BitBucket repositories with team-based access control + ## Quick Start @@ -41,6 +45,50 @@ response = litellm.completion( ) ``` + + + +**1. Create a .prompt file in BitBucket** + +Create `prompts/hello.prompt` in your BitBucket repository: + +```yaml +--- +model: gpt-4 +temperature: 0.7 +--- +System: You are a helpful assistant. + +User: {{user_message}} +``` + +**2. Configure BitBucket access** + +```python +import litellm + +# Configure BitBucket access +bitbucket_config = { + "workspace": "your-workspace", + "repository": "your-repo", + "access_token": "your-access-token", + "branch": "main" +} + +# Set global BitBucket configuration +litellm.set_global_bitbucket_config(bitbucket_config) +``` + +**3. Use with LiteLLM** + +```python +response = litellm.completion( + model="bitbucket/gpt-4", + prompt_id="hello", + prompt_variables={"user_message": "What is the capital of France?"} +) +``` + @@ -70,6 +118,12 @@ model_list: litellm_settings: global_prompt_directory: "./prompts" + # Or use BitBucket for team-based prompt management + global_bitbucket_config: + workspace: "your-workspace" + repository: "your-repo" + access_token: "your-access-token" + branch: "main" ``` **3. Start the proxy** @@ -142,21 +196,43 @@ User: {{user_message}} ### API Reference -For dotprompt integration, use these parameters: +For prompt integrations, use these parameters: +**File System (dotprompt):** ``` model: dotprompt/ # required (e.g., dotprompt/gpt-4) prompt_id: str # required - the .prompt filename without extension prompt_variables: Optional[dict] # optional - variables for template rendering ``` -**Example API call:** +**BitBucket:** +``` +model: bitbucket/ # required (e.g., bitbucket/gpt-4) +prompt_id: str # required - the .prompt filename without extension +prompt_variables: Optional[dict] # optional - variables for template rendering +bitbucket_config: Optional[dict] # optional - BitBucket configuration (if not set globally) +``` + +**Example API calls:** ```python +# File system integration response = litellm.completion( model="dotprompt/gpt-4", prompt_id="hello", prompt_variables={"user_message": "Hello world"}, messages=[{"role": "user", "content": "This will be ignored"}] ) + +# BitBucket integration +response = litellm.completion( + model="bitbucket/gpt-4", + prompt_id="hello", + prompt_variables={"user_message": "Hello world"}, + bitbucket_config={ + "workspace": "your-workspace", + "repository": "your-repo", + "access_token": "your-token" + } +) ``` diff --git a/docs/my-website/docs/proxy/prod.md b/docs/my-website/docs/proxy/prod.md index fb2acf230c1..a45474f39e8 100644 --- a/docs/my-website/docs/proxy/prod.md +++ b/docs/my-website/docs/proxy/prod.md @@ -90,7 +90,7 @@ Recommended to do this for prod: ```yaml router_settings: - routing_strategy: usage-based-routing-v2 + routing_strategy: simple-shuffle # (default) - recommended for best performance # redis_url: "os.environ/REDIS_URL" redis_host: os.environ/REDIS_HOST redis_port: os.environ/REDIS_PORT @@ -105,6 +105,9 @@ litellm_settings: password: os.environ/REDIS_PASSWORD ``` +> **WARNING** +**Usage-based routing is not recommended for production due to performance impacts.** Use `simple-shuffle` (default) for optimal performance in high-traffic scenarios. + ## 5. Disable 'load_dotenv' Set `export LITELLM_MODE="PRODUCTION"` diff --git a/docs/my-website/docs/proxy/prometheus.md b/docs/my-website/docs/proxy/prometheus.md index dc7030949bd..8bbf737540d 100644 --- a/docs/my-website/docs/proxy/prometheus.md +++ b/docs/my-website/docs/proxy/prometheus.md @@ -63,7 +63,7 @@ Use this for for tracking per [user, key, team, etc.](virtual_keys) | Metric Name | Description | |----------------------|--------------------------------------| -| `litellm_spend_metric` | Total Spend, per `"user", "key", "model", "team", "end-user"` | +| `litellm_spend_metric` | Total Spend, per `"end_user", "hashed_api_key", "api_key_alias", "model", "team", "team_alias", "user"` | | `litellm_total_tokens_metric` | input + output tokens per `"end_user", "hashed_api_key", "api_key_alias", "requested_model", "team", "team_alias", "user", "model"` | | `litellm_input_tokens_metric` | input tokens per `"end_user", "hashed_api_key", "api_key_alias", "requested_model", "team", "team_alias", "user", "model"` | | `litellm_output_tokens_metric` | output tokens per `"end_user", "hashed_api_key", "api_key_alias", "requested_model", "team", "team_alias", "user", "model"` | @@ -73,9 +73,9 @@ Use this for for tracking per [user, key, team, etc.](virtual_keys) | Metric Name | Description | |----------------------|--------------------------------------| -| `litellm_team_max_budget_metric` | Max Budget for Team Labels: `"team_id", "team_alias"`| -| `litellm_remaining_team_budget_metric` | Remaining Budget for Team (A team created on LiteLLM) Labels: `"team_id", "team_alias"`| -| `litellm_team_budget_remaining_hours_metric` | Hours before the team budget is reset Labels: `"team_id", "team_alias"`| +| `litellm_team_max_budget_metric` | Max Budget for Team Labels: `"team", "team_alias"`| +| `litellm_remaining_team_budget_metric` | Remaining Budget for Team (A team created on LiteLLM) Labels: `"team", "team_alias"`| +| `litellm_team_budget_remaining_hours_metric` | Hours before the team budget is reset Labels: `"team", "team_alias"`| ### Virtual Key - Budget @@ -119,8 +119,8 @@ Use this to track overall LiteLLM Proxy usage. | Metric Name | Description | |----------------------|--------------------------------------| -| `litellm_proxy_failed_requests_metric` | Total number of failed responses from proxy - the client did not get a success response from litellm proxy. Labels: `"end_user", "hashed_api_key", "api_key_alias", "requested_model", "team", "team_alias", "user", "exception_status", "exception_class"` | -| `litellm_proxy_total_requests_metric` | Total number of requests made to the proxy server - track number of client side requests. Labels: `"end_user", "hashed_api_key", "api_key_alias", "requested_model", "team", "team_alias", "user", "status_code"` | +| `litellm_proxy_failed_requests_metric` | Total number of failed responses from proxy - the client did not get a success response from litellm proxy. Labels: `"end_user", "hashed_api_key", "api_key_alias", "requested_model", "team", "team_alias", "user", "exception_status", "exception_class", "route"` | +| `litellm_proxy_total_requests_metric` | Total number of requests made to the proxy server - track number of client side requests. Labels: `"end_user", "hashed_api_key", "api_key_alias", "requested_model", "team", "team_alias", "user", "status_code", "user_email", "route"` | ## LLM Provider Metrics @@ -155,7 +155,7 @@ Use this for LLM API Error monitoring and tracking remaining rate limits and tok | Metric Name | Description | |----------------------|--------------------------------------| | `litellm_remaining_requests_metric` | Track `x-ratelimit-remaining-requests` returned from LLM API Deployment. Labels: `"model_group", "api_provider", "api_base", "litellm_model_name", "hashed_api_key", "api_key_alias"` | -| `litellm_remaining_tokens` | Track `x-ratelimit-remaining-tokens` return from LLM API Deployment. Labels: `"model_group", "api_provider", "api_base", "litellm_model_name", "hashed_api_key", "api_key_alias"` | +| `litellm_remaining_tokens_metric` | Track `x-ratelimit-remaining-tokens` return from LLM API Deployment. Labels: `"model_group", "api_provider", "api_base", "litellm_model_name", "hashed_api_key", "api_key_alias"` | ### Deployment State | Metric Name | Description | @@ -167,16 +167,22 @@ Use this for LLM API Error monitoring and tracking remaining rate limits and tok | Metric Name | Description | |----------------------|--------------------------------------| -| `litellm_deployment_cooled_down` | Number of times a deployment has been cooled down by LiteLLM load balancing logic. Labels: `"litellm_model_name", "model_id", "api_base", "api_provider", "exception_status"` | +| `litellm_deployment_cooled_down` | Number of times a deployment has been cooled down by LiteLLM load balancing logic. Labels: `"litellm_model_name", "model_id", "api_base", "api_provider"` | | `litellm_deployment_successful_fallbacks` | Number of successful fallback requests from primary model -> fallback model. Labels: `"requested_model", "fallback_model", "hashed_api_key", "api_key_alias", "team", "team_alias", "exception_status", "exception_class"` | | `litellm_deployment_failed_fallbacks` | Number of failed fallback requests from primary model -> fallback model. Labels: `"requested_model", "fallback_model", "hashed_api_key", "api_key_alias", "team", "team_alias", "exception_status", "exception_class"` | +## Request Counting Metrics + +| Metric Name | Description | +|----------------------|--------------------------------------| +| `litellm_requests_metric` | Total number of requests tracked per endpoint. Labels: `"end_user", "hashed_api_key", "api_key_alias", "model", "team", "team_alias", "user", "user_email"` | + ## Request Latency Metrics | Metric Name | Description | |----------------------|--------------------------------------| | `litellm_request_total_latency_metric` | Total latency (seconds) for a request to LiteLLM Proxy Server - tracked for labels "end_user", "hashed_api_key", "api_key_alias", "requested_model", "team", "team_alias", "user", "model" | -| `litellm_overhead_latency_metric` | Latency overhead (seconds) added by LiteLLM processing - tracked for labels "end_user", "hashed_api_key", "api_key_alias", "requested_model", "team", "team_alias", "user", "model" | +| `litellm_overhead_latency_metric` | Latency overhead (seconds) added by LiteLLM processing - tracked for labels "model_group", "api_provider", "api_base", "litellm_model_name", "hashed_api_key", "api_key_alias" | | `litellm_llm_api_latency_metric` | Latency (seconds) for just the LLM API call - tracked for labels "model", "hashed_api_key", "api_key_alias", "team", "team_alias", "requested_model", "end_user", "user" | | `litellm_llm_api_time_to_first_token_metric` | Time to first token for LLM API call - tracked for labels `model`, `hashed_api_key`, `api_key_alias`, `team`, `team_alias` [Note: only emitted for streaming requests] | @@ -486,7 +492,6 @@ Here is a screenshot of the metrics you can monitor with the LiteLLM Grafana Das | Metric Name | Description | |----------------------|--------------------------------------| | `litellm_llm_api_failed_requests_metric` | **deprecated** use `litellm_proxy_failed_requests_metric` | -| `litellm_requests_metric` | **deprecated** use `litellm_proxy_total_requests_metric` | diff --git a/docs/my-website/docs/proxy/quick_start.md b/docs/my-website/docs/proxy/quick_start.md index 8f8de2a9fae..a343bb00e9b 100644 --- a/docs/my-website/docs/proxy/quick_start.md +++ b/docs/my-website/docs/proxy/quick_start.md @@ -2,8 +2,9 @@ import Image from '@theme/IdealImage'; import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; -# Quick Start -Quick start CLI, Config, Docker +# CLI - Quick Start + +Setup LiteLLM Proxy quickly via CLI. LiteLLM Server (LLM Gateway) manages: diff --git a/docs/my-website/docs/proxy/request_headers.md b/docs/my-website/docs/proxy/request_headers.md index 246d917d00c..090c201f884 100644 --- a/docs/my-website/docs/proxy/request_headers.md +++ b/docs/my-website/docs/proxy/request_headers.md @@ -2,26 +2,38 @@ Special headers that are supported by LiteLLM. +## Header Forwarding + +By default, LiteLLM does not forward client headers to LLM provider APIs. However, you can selectively enable header forwarding for specific model groups. [Learn more about configuring header forwarding](./forward_client_headers.md). + ## LiteLLM Headers `x-litellm-timeout` Optional[float]: The timeout for the request in seconds. +`x-litellm-stream-timeout` Optional[float]: The timeout for getting the first chunk of the response in seconds (only applies for streaming requests). [Demo Video](https://www.loom.com/share/8da67e4845ce431a98c901d4e45db0e5) + `x-litellm-enable-message-redaction`: Optional[bool]: Don't log the message content to logging integrations. Just track spend. [Learn More](./logging#redact-messages-response-content) `x-litellm-tags`: Optional[str]: A comma separated list (e.g. `tag1,tag2,tag3`) of tags to use for [tag-based routing](./tag_routing) **OR** [spend-tracking](./enterprise.md#tracking-spend-for-custom-tags). `x-litellm-num-retries`: Optional[int]: The number of retries for the request. +`x-litellm-spend-logs-metadata`: Optional[str]: JSON string containing custom metadata to include in spend logs. Example: `{"user_id": "12345", "project_id": "proj_abc", "request_type": "chat_completion"}`. [Learn More](../proxy/enterprise#tracking-spend-with-custom-metadata) + ## Anthropic Headers `anthropic-version` Optional[str]: The version of the Anthropic API to use. `anthropic-beta` Optional[str]: The beta version of the Anthropic API to use. - For `/v1/messages` endpoint, this will always be forward the header to the underlying model. - - For `/chat/completions` endpoint, this will only be forwarded if `forward_client_headers_to_llm_api` is true. + - For `/chat/completions` endpoint, this will only be forwarded if the model is configured in `forward_client_headers_to_llm_api`. [Learn more](./forward_client_headers.md) ## OpenAI Headers `openai-organization` Optional[str]: The organization to use for the OpenAI API. (currently needs to be enabled via `general_settings::forward_openai_org_id: true`) +## Custom Headers + +Custom headers starting with `x-` can be forwarded to LLM provider APIs when the model is configured in `forward_client_headers_to_llm_api`. [Learn more about header forwarding configuration](./forward_client_headers.md). + diff --git a/docs/my-website/docs/proxy/self_serve.md b/docs/my-website/docs/proxy/self_serve.md index 815231b59a2..b54344c1d05 100644 --- a/docs/my-website/docs/proxy/self_serve.md +++ b/docs/my-website/docs/proxy/self_serve.md @@ -227,7 +227,7 @@ export PROXY_LOGOUT_URL="https://www.google.com" -### Set max budget for internal users +### Set default max budget for internal users Automatically apply budget per internal user when they sign up. By default the table will be checked every 10 minutes, for users to reset. To modify this, [see this](./users.md#reset-budgets) @@ -239,6 +239,10 @@ litellm_settings: This sets a max budget of $10 USD for internal users when they sign up. +You can also manage these settings visually in the UI: + + + This budget only applies to personal keys created by that user - seen under `Default Team` on the UI. @@ -309,6 +313,37 @@ curl -X POST '/team/new' \ +### Team Member Rate Limits + +Set a default tpm/rpm limit for an individual team member. + +You can do this when creating a new team, or by updating an existing team. + + + + + + + + + + +```bash +curl -X POST '/team/new' \ +-H 'Authorization: Bearer ' \ +-H 'Content-Type: application/json' \ +-D '{ + "team_alias": "team_1", + "team_member_rpm_limit": 100, + "team_member_tpm_limit": 1000 +}' +``` + + + + + + ### Set default params for new teams When you connect litellm to your SSO provider, litellm can auto-create teams. Use this to set the default `models`, `max_budget`, `budget_duration` for these auto-created teams. diff --git a/docs/my-website/docs/proxy/tag_routing.md b/docs/my-website/docs/proxy/tag_routing.md index 23715e77f81..838b2a09d76 100644 --- a/docs/my-website/docs/proxy/tag_routing.md +++ b/docs/my-website/docs/proxy/tag_routing.md @@ -5,6 +5,12 @@ This is useful for - Implementing free / paid tiers for users - Controlling model access per team, example Team A can access gpt-4 deployment A, Team B can access gpt-4 deployment B (LLM Access Control For Teams ) +:::info +## See here for spend tags +- [Track spend per tag](cost_tracking#-custom-tags) +- [Setup Budgets per Virtual Key, Team](users) +::: + ## Quick Start ### 1. Define tags on config.yaml @@ -324,7 +330,4 @@ Here's how to set up and use team-based tag routing using curl commands: By following these steps and using these curl commands, you can implement and test team-based tag routing in your LiteLLM Proxy setup, ensuring that different teams are routed to the appropriate models or deployments based on their assigned tags. -## Other Tag Based Features -- [Track spend per tag](cost_tracking#-custom-tags) -- [Setup Budgets per Virtual Key, Team](users) diff --git a/docs/my-website/docs/proxy/team_budgets.md b/docs/my-website/docs/proxy/team_budgets.md index 66ba679c65e..03d18797133 100644 --- a/docs/my-website/docs/proxy/team_budgets.md +++ b/docs/my-website/docs/proxy/team_budgets.md @@ -10,8 +10,30 @@ import TabItem from '@theme/TabItem'; - You must set up a Postgres database (e.g. Supabase, Neon, etc.) - To enable team member rate limits, set the environment variable `EXPERIMENTAL_MULTI_INSTANCE_RATE_LIMITING=true` **before starting the proxy server**. Without this, team member rate limits will not be enforced. + +## Default Budget for Auto-Generated JWT Teams + +When using JWT authentication with `team_id_upsert: true`, you can automatically assign a default budget to any newly created team. + +This is configured in `default_team_settings` in your `config.yaml`. + +**Example:** +```yaml +# in your config.yaml + +litellm_jwtauth: + team_id_upsert: true + team_id_jwt_field: "team_id" + # ... other jwt settings + +litellm_settings: + default_team_settings: + - team_id: "default-settings" + max_budget: 100.0 +``` Track spend, set budgets for your Internal Team + ## Setting Monthly Team Budgets ### 1. Create a team @@ -156,188 +178,3 @@ Expect to see this metric on prometheus to track the Remaining Budget for the te ```shell litellm_remaining_team_budget_metric{team_alias="QA Prod Bot",team_id="de35b29e-6ca8-4f47-b804-2b79d07aa99a"} 9.699999999999992e-06 ``` - - -### Dynamic TPM/RPM Allocation - -Prevent projects from gobbling too much tpm/rpm. - -Dynamically allocate TPM/RPM quota to api keys, based on active keys in that minute. [**See Code**](https://github.com/BerriAI/litellm/blob/9bffa9a48e610cc6886fc2dce5c1815aeae2ad46/litellm/proxy/hooks/dynamic_rate_limiter.py#L125) - -1. Setup config.yaml - -```yaml -model_list: - - model_name: my-fake-model - litellm_params: - model: gpt-3.5-turbo - api_key: my-fake-key - mock_response: hello-world - tpm: 60 - -litellm_settings: - callbacks: ["dynamic_rate_limiter"] - -general_settings: - master_key: sk-1234 # OR set `LITELLM_MASTER_KEY=".."` in your .env - database_url: postgres://.. # OR set `DATABASE_URL=".."` in your .env -``` - -2. Start proxy - -```bash -litellm --config /path/to/config.yaml -``` - -3. Test it! - -```python -""" -- Run 2 concurrent teams calling same model -- model has 60 TPM -- Mock response returns 30 total tokens / request -- Each team will only be able to make 1 request per minute -""" - -import requests -from openai import OpenAI, RateLimitError - -def create_key(api_key: str, base_url: str): - response = requests.post( - url="{}/key/generate".format(base_url), - json={}, - headers={ - "Authorization": "Bearer {}".format(api_key) - } - ) - - _response = response.json() - - return _response["key"] - -key_1 = create_key(api_key="sk-1234", base_url="http://0.0.0.0:4000") -key_2 = create_key(api_key="sk-1234", base_url="http://0.0.0.0:4000") - -# call proxy with key 1 - works -openai_client_1 = OpenAI(api_key=key_1, base_url="http://0.0.0.0:4000") - -response = openai_client_1.chat.completions.with_raw_response.create( - model="my-fake-model", messages=[{"role": "user", "content": "Hello world!"}], -) - -print("Headers for call 1 - {}".format(response.headers)) -_response = response.parse() -print("Total tokens for call - {}".format(_response.usage.total_tokens)) - - -# call proxy with key 2 - works -openai_client_2 = OpenAI(api_key=key_2, base_url="http://0.0.0.0:4000") - -response = openai_client_2.chat.completions.with_raw_response.create( - model="my-fake-model", messages=[{"role": "user", "content": "Hello world!"}], -) - -print("Headers for call 2 - {}".format(response.headers)) -_response = response.parse() -print("Total tokens for call - {}".format(_response.usage.total_tokens)) -# call proxy with key 2 - fails -try: - openai_client_2.chat.completions.with_raw_response.create(model="my-fake-model", messages=[{"role": "user", "content": "Hey, how's it going?"}]) - raise Exception("This should have failed!") -except RateLimitError as e: - print("This was rate limited b/c - {}".format(str(e))) - -``` - -**Expected Response** - -``` -This was rate limited b/c - Error code: 429 - {'error': {'message': {'error': 'Key= over available TPM=0. Model TPM=0, Active keys=2'}, 'type': 'None', 'param': 'None', 'code': 429}} -``` - - -#### ✨ [BETA] Set Priority / Reserve Quota - -Reserve tpm/rpm capacity for projects in prod. - -:::tip - -Reserving tpm/rpm on keys based on priority is a premium feature. Please [get an enterprise license](./enterprise.md) for it. -::: - - -1. Setup config.yaml - -```yaml -model_list: - - model_name: gpt-3.5-turbo - litellm_params: - model: "gpt-3.5-turbo" - api_key: os.environ/OPENAI_API_KEY - rpm: 100 - -litellm_settings: - callbacks: ["dynamic_rate_limiter"] - priority_reservation: {"dev": 0, "prod": 1} - -general_settings: - master_key: sk-1234 # OR set `LITELLM_MASTER_KEY=".."` in your .env - database_url: postgres://.. # OR set `DATABASE_URL=".."` in your .env -``` - - -priority_reservation: -- Dict[str, float] - - str: can be any string - - float: from 0 to 1. Specify the % of tpm/rpm to reserve for keys of this priority. - -**Start Proxy** - -``` -litellm --config /path/to/config.yaml -``` - -2. Create a key with that priority - -```bash -curl -X POST 'http://0.0.0.0:4000/key/generate' \ --H 'Authorization: Bearer ' \ --H 'Content-Type: application/json' \ --D '{ - "metadata": {"priority": "dev"} # 👈 KEY CHANGE -}' -``` - -**Expected Response** - -``` -{ - ... - "key": "sk-.." -} -``` - - -3. Test it! - -```bash -curl -X POST 'http://0.0.0.0:4000/chat/completions' \ - -H 'Content-Type: application/json' \ - -H 'Authorization: sk-...' \ # 👈 key from step 2. - -d '{ - "model": "gpt-3.5-turbo", - "messages": [ - { - "role": "user", - "content": "what llm are you" - } - ], -}' -``` - -**Expected Response** - -``` -Key=... over available RPM=0. Model RPM=100, Active keys=None -``` - diff --git a/docs/my-website/docs/proxy/timeout.md b/docs/my-website/docs/proxy/timeout.md index 85428ae53e2..52cb160cf76 100644 --- a/docs/my-website/docs/proxy/timeout.md +++ b/docs/my-website/docs/proxy/timeout.md @@ -38,9 +38,15 @@ $ litellm --config /path/to/config.yaml -### Custom Timeouts, Stream Timeouts - Per Model -For each model you can set `timeout` & `stream_timeout` under `litellm_params` +### Custom Timeouts & Stream Timeouts (Per Model) +For each model, you can set `timeout` and `stream_timeout` under `litellm_params`: + +- **`timeout`** → maximum time for the *complete response*. + Use this to cap long-running completions. + +- **`stream_timeout`** → maximum time to wait for the *first chunk* (i.e., first token) in a streaming response. + Use this to abort “hanging” providers (e.g., Bedrock slow start) and retry another model. diff --git a/docs/my-website/docs/proxy/user_keys.md b/docs/my-website/docs/proxy/user_keys.md index ecf6f2d0532..21e1d3dbf40 100644 --- a/docs/my-website/docs/proxy/user_keys.md +++ b/docs/my-website/docs/proxy/user_keys.md @@ -357,6 +357,106 @@ assert user.age == 25 +## Using Tags for Categorization and Tracking + +Tags allow you to categorize, filter, and track your LLM requests. Add tags to your metadata for better organization and analytics. + + + + +```python +import openai +client = openai.OpenAI( + api_key="anything", + base_url="http://0.0.0.0:4000" +) + +response = client.chat.completions.create( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "Hello!"}], + extra_body={ + "metadata": { + "tags": ["production", "customer-support", "urgent"], + "generation_name": "support-bot", + "trace_user_id": "user-123" + } + } +) +``` + + + + + +```python +from langchain_openai import ChatOpenAI +from langchain_core.messages import HumanMessage + +chat = ChatOpenAI( + openai_api_base="http://0.0.0.0:4000", + model="gpt-4o", + extra_body={ + "metadata": { + "tags": ["langchain-integration", "content-gen"], + "trace_user_id": "user-456" + } + } +) + +response = chat.invoke([HumanMessage(content="Generate a blog post")]) +``` + + + + + +```bash +curl --location 'http://0.0.0.0:4000/chat/completions' \ + --header 'Content-Type: application/json' \ + --data '{ + "model": "gpt-3.5-turbo", + "messages": [{"role": "user", "content": "Hello!"}], + "metadata": { + "tags": ["api-test", "development"], + "trace_user_id": "test-user" + } +}' +``` + + + + + +```js +const { OpenAI } = require('openai'); + +const openai = new OpenAI({ + apiKey: "sk-1234", + baseURL: "http://0.0.0.0:4000" +}); + +async function main() { + const response = await openai.chat.completions.create({ + messages: [{ role: 'user', content: 'Hello!' }], + model: 'gpt-3.5-turbo', + metadata: { + tags: ["javascript-client", "api-test"], + trace_user_id: "js-user-789" + } + }); +} +``` + + + + +### Tag Benefits + +- **Cost Tracking**: Monitor spending by project/team/feature +- **Analytics**: Filter requests by tags in logs and dashboards +- **Routing**: Use tags for conditional model routing +- **Debugging**: Easier troubleshooting with categorized requests + ### Response Format ```json diff --git a/docs/my-website/docs/proxy/user_management_heirarchy.md b/docs/my-website/docs/proxy/user_management_heirarchy.md index 3565c9d257d..cb5cc0dd7a2 100644 --- a/docs/my-website/docs/proxy/user_management_heirarchy.md +++ b/docs/my-website/docs/proxy/user_management_heirarchy.md @@ -9,5 +9,5 @@ LiteLLM supports a hierarchy of users, teams, organizations, and budgets. - Organizations can have multiple teams. [API Reference](https://litellm-api.up.railway.app/#/organization%20management) - Teams can have multiple users. [API Reference](https://litellm-api.up.railway.app/#/team%20management) -- Users can have multiple keys. [API Reference](https://litellm-api.up.railway.app/#/budget%20management) +- Users can have multiple keys, and be on multiple teams. [API Reference](https://litellm-api.up.railway.app/#/budget%20management) - Keys can belong to either a team or a user. [API Reference](https://litellm-api.up.railway.app/#/end-user%20management) diff --git a/docs/my-website/docs/proxy/user_onboarding.md b/docs/my-website/docs/proxy/user_onboarding.md new file mode 100644 index 00000000000..baa241d6cdf --- /dev/null +++ b/docs/my-website/docs/proxy/user_onboarding.md @@ -0,0 +1,82 @@ +# User Onboarding Guide + +A step-by-step guide to help admins onboard users to your LiteLLM proxy instance and help users get started with their API key. + +--- + +## For Administrators + +### Step 1: Create a User Account + +You can create a user account via the Admin UI or using the API. + +#### Admin UI +- Go to the (`/ui` endpoint) +- Navigate to the Internal Users section +- Click "Add User" and fill in the required details + +#### API +```bash +curl -X POST http://localhost:4000/user/new \ + -H "Authorization: Bearer " \ + -H "Content-Type: application/json" \ + -d '{"user_email": "user@example.com"}' +``` + +--- + +### Step 2: Grant Access & Permissions + +- Assign the user to a team (optional) +- Set budgets, rate limits, and allowed models as needed +- Generate an API key for the user (via UI or API) + +#### **Generate API Key (API Example)** +```bash +curl -X POST http://localhost:4000/key/generate \ + -H "Authorization: Bearer " \ + -H "Content-Type: application/json" \ + -d '{"user_id": "", "max_budget": 100}' +``` + +--- + +## For End Users + +### Step 3: Validate Your API Key + +Before making LLM calls, validate your key works by calling the `/v1/models` endpoint: + +```bash +curl -X GET http://localhost:4000/v1/models \ + -H "Authorization: Bearer " +``` +- If your key is valid, you'll get a list of available models. +- If invalid, you'll get a 401 error. + +--- + +### Step 4: Hello World - Make Your First LLM Call + +```bash +curl -X POST http://localhost:4000/v1/chat/completions \ + -H "Authorization: Bearer " \ + -H "Content-Type: application/json" \ + -d '{ + "model": "gpt-3.5-turbo", + "messages": [{"role": "user", "content": "Hello!"}] + }' +``` + +--- + +## Troubleshooting +- If you get a 401 error, check with your admin that your key is active and you have access to the requested model. +- Use the `/v1/models` endpoint to quickly check if your key is valid without consuming LLM tokens. + +--- + +## See Also +- [Proxy Quick Start](./quick_start.md) +- [User Management](./users.md) +- [Key Management](./key_management.md) diff --git a/docs/my-website/docs/proxy/veo_video_generation.md b/docs/my-website/docs/proxy/veo_video_generation.md new file mode 100644 index 00000000000..14c263bf847 --- /dev/null +++ b/docs/my-website/docs/proxy/veo_video_generation.md @@ -0,0 +1,163 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Veo Video Generation with Google AI Studio + +Generate videos using Google's Veo model through LiteLLM's pass-through endpoints. + +## Quick Start + +LiteLLM allows you to use Google AI Studio's Veo video generation API through pass-through routes with zero configuration. + +### 1. Add Google AI Studio API Key to your environment + +```bash +export GEMINI_API_KEY="your_google_ai_studio_api_key" +``` + +### 2. Start LiteLLM Proxy + +```bash +litellm + +# RUNNING on http://0.0.0.0:4000 +``` + +### 3. Generate Video + + + + +```python +import requests +import time +import json + +# Configuration +BASE_URL = "http://localhost:4000/gemini/v1beta" +API_KEY = "anything" # Use "anything" as the key + +headers = { + "x-goog-api-key": API_KEY, + "Content-Type": "application/json" +} + +# Step 1: Initiate video generation +def generate_video(prompt): + url = f"{BASE_URL}/models/veo-3.0-generate-preview:predictLongRunning" + payload = { + "instances": [{ + "prompt": prompt + }] + } + + response = requests.post(url, headers=headers, json=payload) + response.raise_for_status() + + data = response.json() + return data.get("name") # Operation name + +# Step 2: Poll for completion +def wait_for_completion(operation_name): + operation_url = f"{BASE_URL}/{operation_name}" + + while True: + response = requests.get(operation_url, headers=headers) + response.raise_for_status() + + data = response.json() + + if data.get("done", False): + # Extract video URI + video_uri = data["response"]["generateVideoResponse"]["generatedSamples"][0]["video"]["uri"] + return video_uri + + time.sleep(10) # Wait 10 seconds before next poll + +# Step 3: Download video +def download_video(video_uri, filename="generated_video.mp4"): + # Replace Google URL with LiteLLM proxy URL + litellm_url = video_uri.replace( + "https://generativelanguage.googleapis.com/v1beta", + BASE_URL + ) + + response = requests.get(litellm_url, headers=headers, stream=True) + response.raise_for_status() + + with open(filename, 'wb') as f: + for chunk in response.iter_content(chunk_size=8192): + if chunk: + f.write(chunk) + + return filename + +# Complete workflow +prompt = "A cat playing with a ball of yarn in a sunny garden" + +print("Generating video...") +operation_name = generate_video(prompt) + +print("Waiting for completion...") +video_uri = wait_for_completion(operation_name) + +print("Downloading video...") +filename = download_video(video_uri) + +print(f"Video saved as: {filename}") +``` + + + + + +```bash +# Step 1: Initiate video generation +curl -X POST "http://localhost:4000/gemini/v1beta/models/veo-3.0-generate-preview:predictLongRunning" \ + -H "x-goog-api-key: anything" \ + -H "Content-Type: application/json" \ + -d '{ + "instances": [{ + "prompt": "A cat playing with a ball of yarn in a sunny garden" + }] + }' + +# Response will include operation name: +# {"name": "operations/generate_12345"} + +# Step 2: Poll for completion +curl -X GET "http://localhost:4000/gemini/v1beta/operations/generate_12345" \ + -H "x-goog-api-key: anything" + +# Step 3: Download video (when done=true) +curl -X GET "http://localhost:4000/gemini/v1beta/files/VIDEO_ID:download?alt=media" \ + -H "x-goog-api-key: anything" \ + --output generated_video.mp4 +``` + + + + +## Complete Example + +For a full working example with error handling and logging, see our [Veo Video Generation Cookbook](https://github.com/BerriAI/litellm/blob/main/cookbook/veo_video_generation.py). + +## How It Works + +1. **Video Generation Request**: Send a prompt to Veo's `predictLongRunning` endpoint +2. **Operation Polling**: Monitor the long-running operation until completion +3. **File Download**: Download the generated video through LiteLLM's pass-through with automatic redirect handling + +LiteLLM handles: +- ✅ Authentication with Google AI Studio +- ✅ Request routing and proxying +- ✅ Automatic redirect handling for file downloads + +## Configuration Options + +### Environment Variables + +```bash +export GEMINI_API_KEY="your_google_ai_studio_api_key" +``` + diff --git a/docs/my-website/docs/proxy_api.md b/docs/my-website/docs/proxy_api.md index 89bfacbe19f..7612645fb54 100644 --- a/docs/my-website/docs/proxy_api.md +++ b/docs/my-website/docs/proxy_api.md @@ -27,7 +27,7 @@ Email us @ krrish@berri.ai ## Supported Models for LiteLLM Key These are the models that currently work with the "sk-litellm-.." keys. -For a complete list of models/providers that you can call with LiteLLM, [check out our provider list](./providers/) +For a complete list of models/providers that you can call with LiteLLM, [check out our provider list](./providers/) or check out [models.litellm.ai](https://models.litellm.ai/) * OpenAI models - [OpenAI docs](./providers/openai.md) * gpt-4 diff --git a/docs/my-website/docs/reasoning_content.md b/docs/my-website/docs/reasoning_content.md index f9cab01639d..12db17325d4 100644 --- a/docs/my-website/docs/reasoning_content.md +++ b/docs/my-website/docs/reasoning_content.md @@ -12,7 +12,7 @@ Requires LiteLLM v1.63.0+ Supported Providers: - Deepseek (`deepseek/`) - Anthropic API (`anthropic/`) -- Bedrock (Anthropic + Deepseek) (`bedrock/`) +- Bedrock (Anthropic + Deepseek + GPT-OSS) (`bedrock/`) - Vertex AI (Anthropic) (`vertexai/`) - OpenRouter (`openrouter/`) - XAI (`xai/`) @@ -20,6 +20,7 @@ Supported Providers: - Vertex AI (`vertex_ai/`) - Perplexity (`perplexity/`) - Mistral AI (Magistral models) (`mistral/`) +- Groq (`groq/`) LiteLLM will standardize the `reasoning_content` in the response and `thinking_blocks` in the assistant message. diff --git a/docs/my-website/docs/rerank.md b/docs/my-website/docs/rerank.md index c57eacbb224..cad64718384 100644 --- a/docs/my-website/docs/rerank.md +++ b/docs/my-website/docs/rerank.md @@ -109,6 +109,8 @@ curl http://0.0.0.0:4000/rerank \ ## **Supported Providers** +#### ⚡️See all supported models and providers at [models.litellm.ai](https://models.litellm.ai/) + | Provider | Link to Usage | |-------------|--------------------| | Cohere (v1 + v2 clients) | [Usage](#quick-start) | diff --git a/docs/my-website/docs/response_api.md b/docs/my-website/docs/response_api.md index 94d7c73be05..8bb10bbe36a 100644 --- a/docs/my-website/docs/response_api.md +++ b/docs/my-website/docs/response_api.md @@ -3,8 +3,11 @@ import TabItem from '@theme/TabItem'; # /responses [Beta] + LiteLLM provides a BETA endpoint in the spec of [OpenAI's `/responses` API](https://platform.openai.com/docs/api-reference/responses) +Requests to /chat/completions may be bridged here automatically when the provider lacks support for that endpoint. The model’s default `mode` determines how bridging works.(see `model_prices_and_context_window`) + | Feature | Supported | Notes | |---------|-----------|--------| | Cost Tracking | ✅ | Works with all supported models | @@ -78,6 +81,43 @@ print(retrieved_response) # retrieved_response = await litellm.aget_responses(response_id=response_id) ``` +#### CANCEL a Response +You can cancel an in-progress response (if supported by the provider): + +```python showLineNumbers title="Cancel Response by ID" +import litellm + +# First, create a response +response = litellm.responses( + model="openai/o1-pro", + input="Tell me a three sentence bedtime story about a unicorn.", + max_output_tokens=100 +) + +# Get the response ID +response_id = response.id + +# Cancel the response by ID +cancel_response = litellm.cancel_responses( + response_id=response_id +) + +print(cancel_response) + +# For async usage +# cancel_response = await litellm.acancel_responses(response_id=response_id) +``` + + +**REST API:** +```bash +curl -X POST http://localhost:4000/v1/responses/response_id/cancel \ + -H "Authorization: Bearer sk-1234" +``` + +This will attempt to cancel the in-progress response with the given ID. +**Note:** Not all providers support response cancellation. If unsupported, an error will be raised. + #### DELETE a Response ```python showLineNumbers title="Delete Response by ID" import litellm @@ -795,9 +835,9 @@ curl http://localhost:4000/v1/responses \ -## Session Management - Non-OpenAI Models +## Session Management -LiteLLM Proxy supports session management for non-OpenAI models. This allows you to store and fetch conversation history (state) in LiteLLM Proxy. +LiteLLM Proxy supports session management for all supported models. This allows you to store and fetch conversation history (state) in LiteLLM Proxy. #### Usage diff --git a/docs/my-website/docs/routing.md b/docs/my-website/docs/routing.md index fbb069895d8..971427806ed 100644 --- a/docs/my-website/docs/routing.md +++ b/docs/my-website/docs/routing.md @@ -154,11 +154,153 @@ curl -X POST 'http://0.0.0.0:4000/chat/completions' \ ## Advanced - Routing Strategies ⭐️ #### Routing Strategies - Weighted Pick, Rate Limit Aware, Least Busy, Latency Based, Cost Based -Router provides 4 strategies for routing your calls across multiple deployments: +Router provides multiple strategies for routing your calls across multiple deployments. **We recommend using `simple-shuffle` (default) for best performance in production.** + + +**Default and Recommended for Production** - Best performance with minimal latency overhead. + +Picks a deployment based on the provided **Requests per minute (rpm) or Tokens per minute (tpm)** + +If `rpm` or `tpm` is not provided, it randomly picks a deployment + +You can also set a `weight` param, to specify which model should get picked when. + + + + +##### **LiteLLM Proxy Config.yaml** + +```yaml +model_list: + - model_name: gpt-3.5-turbo + litellm_params: + model: azure/chatgpt-v-2 + api_key: os.environ/AZURE_API_KEY + api_version: os.environ/AZURE_API_VERSION + api_base: os.environ/AZURE_API_BASE + rpm: 900 + - model_name: gpt-3.5-turbo + litellm_params: + model: azure/chatgpt-functioncalling + api_key: os.environ/AZURE_API_KEY + api_version: os.environ/AZURE_API_VERSION + api_base: os.environ/AZURE_API_BASE + rpm: 10 +``` + +##### **Python SDK** + +```python +from litellm import Router +import asyncio + +model_list = [{ # list of model deployments + "model_name": "gpt-3.5-turbo", # model alias + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-v-2", # actual model name + "api_key": os.getenv("AZURE_API_KEY"), + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE"), + "rpm": 900, # requests per minute for this API + } +}, { + "model_name": "gpt-3.5-turbo", + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-functioncalling", + "api_key": os.getenv("AZURE_API_KEY"), + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE"), + "rpm": 10, + } +},] + +# init router +router = Router(model_list=model_list, routing_strategy="simple-shuffle") +async def router_acompletion(): + response = await router.acompletion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "Hey, how's it going?"}] + ) + print(response) + return response + +asyncio.run(router_acompletion()) +``` + + + + +##### **LiteLLM Proxy Config.yaml** + +```yaml +model_list: + - model_name: gpt-3.5-turbo + litellm_params: + model: azure/chatgpt-v-2 + api_key: os.environ/AZURE_API_KEY + api_version: os.environ/AZURE_API_VERSION + api_base: os.environ/AZURE_API_BASE + weight: 9 + - model_name: gpt-3.5-turbo + litellm_params: + model: azure/chatgpt-functioncalling + api_key: os.environ/AZURE_API_KEY + api_version: os.environ/AZURE_API_VERSION + api_base: os.environ/AZURE_API_BASE + weight: 1 +``` + +##### **Python SDK** + +```python +from litellm import Router +import asyncio + +model_list = [{ + "model_name": "gpt-3.5-turbo", # model alias + "litellm_params": { + "model": "azure/chatgpt-v-2", # actual model name + "api_key": os.getenv("AZURE_API_KEY"), + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE"), + "weight": 9, # pick this 90% of the time + } +}, { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "azure/chatgpt-functioncalling", + "api_key": os.getenv("AZURE_API_KEY"), + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE"), + "weight": 1, + } +}] + +# init router +router = Router(model_list=model_list, routing_strategy="simple-shuffle") +async def router_acompletion(): + response = await router.acompletion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "Hey, how's it going?"}] + ) + print(response) + return response + +asyncio.run(router_acompletion()) +``` + + + + + +> [!WARNING] +**Usage-based routing is not recommended for production due to performance impacts.** Use `simple-shuffle` (default) for optimal performance in high-traffic scenarios. Usage-based routing adds significant latency due to Redis operations for tracking usage across deployments. + + **🎉 NEW** This is an async implementation of usage-based-routing. **Filters out deployment if tpm/rpm limit exceeded** - If you pass in the deployment's tpm/rpm limits. @@ -209,7 +351,7 @@ router = Router(model_list=model_list, redis_host=os.environ["REDIS_HOST"], redis_password=os.environ["REDIS_PASSWORD"], redis_port=os.environ["REDIS_PORT"], - routing_strategy="usage-based-routing-v2" # 👈 KEY CHANGE + routing_strategy="simple-shuffle" # 👈 RECOMMENDED - best performance enable_pre_call_checks=True, # enables router rate limits for concurrent calls ) @@ -241,7 +383,7 @@ model_list: rpm: 1000 router_settings: - routing_strategy: usage-based-routing-v2 # 👈 KEY CHANGE + routing_strategy: simple-shuffle # 👈 RECOMMENDED - best performance redis_host: redis_password: redis_port: @@ -365,143 +507,7 @@ router_settings: ``` - -**Default** Picks a deployment based on the provided **Requests per minute (rpm) or Tokens per minute (tpm)** - -If `rpm` or `tpm` is not provided, it randomly picks a deployment - -You can also set a `weight` param, to specify which model should get picked when. - - - - -##### **LiteLLM Proxy Config.yaml** - -```yaml -model_list: - - model_name: gpt-3.5-turbo - litellm_params: - model: azure/chatgpt-v-2 - api_key: os.environ/AZURE_API_KEY - api_version: os.environ/AZURE_API_VERSION - api_base: os.environ/AZURE_API_BASE - rpm: 900 - - model_name: gpt-3.5-turbo - litellm_params: - model: azure/chatgpt-functioncalling - api_key: os.environ/AZURE_API_KEY - api_version: os.environ/AZURE_API_VERSION - api_base: os.environ/AZURE_API_BASE - rpm: 10 -``` - -##### **Python SDK** - -```python -from litellm import Router -import asyncio - -model_list = [{ # list of model deployments - "model_name": "gpt-3.5-turbo", # model alias - "litellm_params": { # params for litellm completion/embedding call - "model": "azure/chatgpt-v-2", # actual model name - "api_key": os.getenv("AZURE_API_KEY"), - "api_version": os.getenv("AZURE_API_VERSION"), - "api_base": os.getenv("AZURE_API_BASE"), - "rpm": 900, # requests per minute for this API - } -}, { - "model_name": "gpt-3.5-turbo", - "litellm_params": { # params for litellm completion/embedding call - "model": "azure/chatgpt-functioncalling", - "api_key": os.getenv("AZURE_API_KEY"), - "api_version": os.getenv("AZURE_API_VERSION"), - "api_base": os.getenv("AZURE_API_BASE"), - "rpm": 10, - } -},] - -# init router -router = Router(model_list=model_list, routing_strategy="simple-shuffle") -async def router_acompletion(): - response = await router.acompletion( - model="gpt-3.5-turbo", - messages=[{"role": "user", "content": "Hey, how's it going?"}] - ) - print(response) - return response - -asyncio.run(router_acompletion()) -``` - - - - -##### **LiteLLM Proxy Config.yaml** - -```yaml -model_list: - - model_name: gpt-3.5-turbo - litellm_params: - model: azure/chatgpt-v-2 - api_key: os.environ/AZURE_API_KEY - api_version: os.environ/AZURE_API_VERSION - api_base: os.environ/AZURE_API_BASE - weight: 9 - - model_name: gpt-3.5-turbo - litellm_params: - model: azure/chatgpt-functioncalling - api_key: os.environ/AZURE_API_KEY - api_version: os.environ/AZURE_API_VERSION - api_base: os.environ/AZURE_API_BASE - weight: 1 -``` - - -##### **Python SDK** - -```python -from litellm import Router -import asyncio - -model_list = [{ - "model_name": "gpt-3.5-turbo", # model alias - "litellm_params": { - "model": "azure/chatgpt-v-2", # actual model name - "api_key": os.getenv("AZURE_API_KEY"), - "api_version": os.getenv("AZURE_API_VERSION"), - "api_base": os.getenv("AZURE_API_BASE"), - "weight": 9, # pick this 90% of the time - } -}, { - "model_name": "gpt-3.5-turbo", - "litellm_params": { - "model": "azure/chatgpt-functioncalling", - "api_key": os.getenv("AZURE_API_KEY"), - "api_version": os.getenv("AZURE_API_VERSION"), - "api_base": os.getenv("AZURE_API_BASE"), - "weight": 1, - } -}] - -# init router -router = Router(model_list=model_list, routing_strategy="simple-shuffle") -async def router_acompletion(): - response = await router.acompletion( - model="gpt-3.5-turbo", - messages=[{"role": "user", "content": "Hey, how's it going?"}] - ) - print(response) - return response - -asyncio.run(router_acompletion()) -``` - - - - - This will route to the deployment with the lowest TPM usage for that minute. diff --git a/docs/my-website/docs/scheduler.md b/docs/my-website/docs/scheduler.md index 2b0a582626c..9b84c374e3b 100644 --- a/docs/my-website/docs/scheduler.md +++ b/docs/my-website/docs/scheduler.md @@ -41,7 +41,7 @@ router = Router( }, ], timeout=2, # timeout request if takes > 2s - routing_strategy="usage-based-routing-v2", + routing_strategy="simple-shuffle", # recommended for best performance polling_interval=0.03 # poll queue every 3ms if no healthy deployments ) diff --git a/docs/my-website/docs/simple_proxy_old_doc.md b/docs/my-website/docs/simple_proxy_old_doc.md deleted file mode 100644 index 730fd0aab42..00000000000 --- a/docs/my-website/docs/simple_proxy_old_doc.md +++ /dev/null @@ -1,1353 +0,0 @@ -import Image from '@theme/IdealImage'; -import Tabs from '@theme/Tabs'; -import TabItem from '@theme/TabItem'; - -# 💥 LiteLLM Proxy Server - -LiteLLM Server manages: - -* **Unified Interface**: Calling 100+ LLMs [Huggingface/Bedrock/TogetherAI/etc.](#other-supported-models) in the OpenAI `ChatCompletions` & `Completions` format -* **Load Balancing**: between [Multiple Models](#multiple-models---quick-start) + [Deployments of the same model](#multiple-instances-of-1-model) - LiteLLM proxy can handle 1.5k+ requests/second during load tests. -* **Cost tracking**: Authentication & Spend Tracking [Virtual Keys](#managing-auth---virtual-keys) - -[**See LiteLLM Proxy code**](https://github.com/BerriAI/litellm/tree/main/litellm/proxy) - -## Quick Start -View all the supported args for the Proxy CLI [here](https://docs.litellm.ai/docs/simple_proxy#proxy-cli-arguments) - -```shell -$ pip install 'litellm[proxy]' -``` - -```shell -$ litellm --model huggingface/bigcode/starcoder - -#INFO: Proxy running on http://0.0.0.0:4000 -``` - -### Test -In a new shell, run, this will make an `openai.chat.completions` request. Ensure you're using openai v1.0.0+ -```shell -litellm --test -``` - -This will now automatically route any requests for gpt-3.5-turbo to bigcode starcoder, hosted on huggingface inference endpoints. - -### Using LiteLLM Proxy - Curl Request, OpenAI Package - - - - -```shell -curl --location 'http://0.0.0.0:4000/chat/completions' \ ---header 'Content-Type: application/json' \ ---data ' { - "model": "gpt-3.5-turbo", - "messages": [ - { - "role": "user", - "content": "what llm are you" - } - ], - } -' -``` - - - -```python -import openai -client = openai.OpenAI( - api_key="anything", - base_url="http://0.0.0.0:4000" -) - -# request sent to model set on litellm proxy, `litellm --model` -response = client.chat.completions.create(model="gpt-3.5-turbo", messages = [ - { - "role": "user", - "content": "this is a test request, write a short poem" - } -]) - -print(response) - -``` - - - - -### Server Endpoints -- POST `/chat/completions` - chat completions endpoint to call 100+ LLMs -- POST `/completions` - completions endpoint -- POST `/embeddings` - embedding endpoint for Azure, OpenAI, Huggingface endpoints -- GET `/models` - available models on server -- POST `/key/generate` - generate a key to access the proxy - -### Supported LLMs -All LiteLLM supported LLMs are supported on the Proxy. Seel all [supported llms](https://docs.litellm.ai/docs/providers) - - - -```shell -$ export AWS_ACCESS_KEY_ID= -$ export AWS_REGION_NAME= -$ export AWS_SECRET_ACCESS_KEY= -``` - -```shell -$ litellm --model bedrock/anthropic.claude-v2 -``` - - - -```shell -$ export AZURE_API_KEY=my-api-key -$ export AZURE_API_BASE=my-api-base -``` -``` -$ litellm --model azure/my-deployment-name -``` - - - - -```shell -$ export OPENAI_API_KEY=my-api-key -``` - -```shell -$ litellm --model gpt-3.5-turbo -``` - - - -```shell -$ export HUGGINGFACE_API_KEY=my-api-key #[OPTIONAL] -``` -```shell -$ litellm --model huggingface/ --api_base https://k58ory32yinf1ly0.us-east-1.aws.endpoints.huggingface.cloud -``` - - - - -```shell -$ litellm --model huggingface/ --api_base http://0.0.0.0:8001 -``` - - - - -```shell -export AWS_ACCESS_KEY_ID= -export AWS_REGION_NAME= -export AWS_SECRET_ACCESS_KEY= -``` - -```shell -$ litellm --model sagemaker/jumpstart-dft-meta-textgeneration-llama-2-7b -``` - - - - -```shell -$ export ANTHROPIC_API_KEY=my-api-key -``` -```shell -$ litellm --model claude-instant-1 -``` - - - -Assuming you're running vllm locally - -```shell -$ litellm --model vllm/facebook/opt-125m -``` - - - -```shell -$ export TOGETHERAI_API_KEY=my-api-key -``` -```shell -$ litellm --model together_ai/lmsys/vicuna-13b-v1.5-16k -``` - - - - - -```shell -$ export REPLICATE_API_KEY=my-api-key -``` -```shell -$ litellm \ - --model replicate/meta/llama-2-70b-chat:02e509c789964a7ea8736978a43525956ef40397be9033abf9fd2badfe68c9e3 -``` - - - - - -```shell -$ litellm --model petals/meta-llama/Llama-2-70b-chat-hf -``` - - - - - -```shell -$ export PALM_API_KEY=my-palm-key -``` -```shell -$ litellm --model palm/chat-bison -``` - - - - - -```shell -$ export AI21_API_KEY=my-api-key -``` - -```shell -$ litellm --model j2-light -``` - - - - - -```shell -$ export COHERE_API_KEY=my-api-key -``` - -```shell -$ litellm --model command-nightly -``` - - - - - - -## Using with OpenAI compatible projects -Set `base_url` to the LiteLLM Proxy server - - - - -```python -import openai -client = openai.OpenAI( - api_key="anything", - base_url="http://0.0.0.0:4000" -) - -# request sent to model set on litellm proxy, `litellm --model` -response = client.chat.completions.create(model="gpt-3.5-turbo", messages = [ - { - "role": "user", - "content": "this is a test request, write a short poem" - } -]) - -print(response) - -``` - - - -#### Start the LiteLLM proxy -```shell -litellm --model gpt-3.5-turbo - -#INFO: Proxy running on http://0.0.0.0:4000 -``` - -#### 1. Clone the repo - -```shell -git clone https://github.com/danny-avila/LibreChat.git -``` - - -#### 2. Modify Librechat's `docker-compose.yml` -LiteLLM Proxy is running on port `4000`, set `4000` as the proxy below -```yaml -OPENAI_REVERSE_PROXY=http://host.docker.internal:4000/v1/chat/completions -``` - -#### 3. Save fake OpenAI key in Librechat's `.env` - -Copy Librechat's `.env.example` to `.env` and overwrite the default OPENAI_API_KEY (by default it requires the user to pass a key). -```env -OPENAI_API_KEY=sk-1234 -``` - -#### 4. Run LibreChat: -```shell -docker compose up -``` - - - - -Continue-Dev brings ChatGPT to VSCode. See how to [install it here](https://continue.dev/docs/quickstart). - -In the [config.py](https://continue.dev/docs/reference/Models/openai) set this as your default model. -```python - default=OpenAI( - api_key="IGNORED", - model="fake-model-name", - context_length=2048, # customize if needed for your model - api_base="http://localhost:4000" # your proxy server url - ), -``` - -Credits [@vividfog](https://github.com/ollama/ollama/issues/305#issuecomment-1751848077) for this tutorial. - - - - -```shell -$ pip install aider - -$ aider --openai-api-base http://0.0.0.0:4000 --openai-api-key fake-key -``` - - - -```python -pip install pyautogen -``` - -```python -from autogen import AssistantAgent, UserProxyAgent, oai -config_list=[ - { - "model": "my-fake-model", - "api_base": "http://localhost:4000", #litellm compatible endpoint - "api_type": "open_ai", - "api_key": "NULL", # just a placeholder - } -] - -response = oai.Completion.create(config_list=config_list, prompt="Hi") -print(response) # works fine - -llm_config={ - "config_list": config_list, -} - -assistant = AssistantAgent("assistant", llm_config=llm_config) -user_proxy = UserProxyAgent("user_proxy") -user_proxy.initiate_chat(assistant, message="Plot a chart of META and TESLA stock price change YTD.", config_list=config_list) -``` - -Credits [@victordibia](https://github.com/microsoft/autogen/issues/45#issuecomment-1749921972) for this tutorial. - - - -A guidance language for controlling large language models. -https://github.com/guidance-ai/guidance - -**NOTE:** Guidance sends additional params like `stop_sequences` which can cause some models to fail if they don't support it. - -**Fix**: Start your proxy using the `--drop_params` flag - -```shell -litellm --model ollama/codellama --temperature 0.3 --max_tokens 2048 --drop_params -``` - -```python -import guidance - -# set api_base to your proxy -# set api_key to anything -gpt4 = guidance.llms.OpenAI("gpt-4", api_base="http://0.0.0.0:4000", api_key="anything") - -experts = guidance(''' -{{#system~}} -You are a helpful and terse assistant. -{{~/system}} - -{{#user~}} -I want a response to the following question: -{{query}} -Name 3 world-class experts (past or present) who would be great at answering this? -Don't answer the question yet. -{{~/user}} - -{{#assistant~}} -{{gen 'expert_names' temperature=0 max_tokens=300}} -{{~/assistant}} -''', llm=gpt4) - -result = experts(query='How can I be more productive?') -print(result) -``` - - - -## Proxy Configs -The Config allows you to set the following params - -| Param Name | Description | -|----------------------|---------------------------------------------------------------| -| `model_list` | List of supported models on the server, with model-specific configs | -| `litellm_settings` | litellm Module settings, example `litellm.drop_params=True`, `litellm.set_verbose=True`, `litellm.api_base`, `litellm.cache` | -| `general_settings` | Server settings, example setting `master_key: sk-my_special_key` | -| `environment_variables` | Environment Variables example, `REDIS_HOST`, `REDIS_PORT` | - -#### Example Config -```yaml -model_list: - - model_name: gpt-3.5-turbo - litellm_params: - model: azure/gpt-turbo-small-eu - api_base: https://my-endpoint-europe-berri-992.openai.azure.com/ - api_key: - rpm: 6 # Rate limit for this deployment: in requests per minute (rpm) - - model_name: gpt-3.5-turbo - litellm_params: - model: azure/gpt-turbo-small-ca - api_base: https://my-endpoint-canada-berri992.openai.azure.com/ - api_key: - rpm: 6 - - model_name: gpt-3.5-turbo - litellm_params: - model: azure/gpt-turbo-large - api_base: https://openai-france-1234.openai.azure.com/ - api_key: - rpm: 1440 - -litellm_settings: - drop_params: True - set_verbose: True - -general_settings: - master_key: sk-1234 # [OPTIONAL] Only use this if you to require all calls to contain this key (Authorization: Bearer sk-1234) - - -environment_variables: - OPENAI_API_KEY: sk-123 - REPLICATE_API_KEY: sk-cohere-is-okay - REDIS_HOST: redis-16337.c322.us-east-1-2.ec2.cloud.redislabs.com - REDIS_PORT: "16337" - REDIS_PASSWORD: -``` - -### Config for Multiple Models - GPT-4, Claude-2 - -Here's how you can use multiple llms with one proxy `config.yaml`. - -#### Step 1: Setup Config -```yaml -model_list: - - model_name: zephyr-alpha # the 1st model is the default on the proxy - litellm_params: # params for litellm.completion() - https://docs.litellm.ai/docs/completion/input#input---request-body - model: huggingface/HuggingFaceH4/zephyr-7b-alpha - api_base: http://0.0.0.0:8001 - - model_name: gpt-4 - litellm_params: - model: gpt-4 - api_key: sk-1233 - - model_name: claude-2 - litellm_params: - model: claude-2 - api_key: sk-claude -``` - -:::info - -The proxy uses the first model in the config as the default model - in this config the default model is `zephyr-alpha` -::: - - -#### Step 2: Start Proxy with config - -```shell -$ litellm --config /path/to/config.yaml -``` - -#### Step 3: Use proxy -Curl Command -```shell -curl --location 'http://0.0.0.0:4000/chat/completions' \ ---header 'Content-Type: application/json' \ ---data ' { - "model": "zephyr-alpha", - "messages": [ - { - "role": "user", - "content": "what llm are you" - } - ], - } -' -``` - -### Load Balancing - Multiple Instances of 1 model -Use this config to load balance between multiple instances of the same model. The proxy will handle routing requests (using LiteLLM's Router). **Set `rpm` in the config if you want maximize throughput** - -#### Example config -requests with `model=gpt-3.5-turbo` will be routed across multiple instances of `azure/gpt-3.5-turbo` -```yaml -model_list: - - model_name: gpt-3.5-turbo - litellm_params: - model: azure/gpt-turbo-small-eu - api_base: https://my-endpoint-europe-berri-992.openai.azure.com/ - api_key: - rpm: 6 # Rate limit for this deployment: in requests per minute (rpm) - - model_name: gpt-3.5-turbo - litellm_params: - model: azure/gpt-turbo-small-ca - api_base: https://my-endpoint-canada-berri992.openai.azure.com/ - api_key: - rpm: 6 - - model_name: gpt-3.5-turbo - litellm_params: - model: azure/gpt-turbo-large - api_base: https://openai-france-1234.openai.azure.com/ - api_key: - rpm: 1440 -``` - -#### Step 2: Start Proxy with config - -```shell -$ litellm --config /path/to/config.yaml -``` - -#### Step 3: Use proxy -Curl Command -```shell -curl --location 'http://0.0.0.0:4000/chat/completions' \ ---header 'Content-Type: application/json' \ ---data ' { - "model": "gpt-3.5-turbo", - "messages": [ - { - "role": "user", - "content": "what llm are you" - } - ], - } -' -``` - -### Fallbacks + Cooldowns + Retries + Timeouts - -If a call fails after num_retries, fall back to another model group. - -If the error is a context window exceeded error, fall back to a larger model group (if given). - -[**See Code**](https://github.com/BerriAI/litellm/blob/main/litellm/router.py) - -**Set via config** -```yaml -model_list: - - model_name: zephyr-beta - litellm_params: - model: huggingface/HuggingFaceH4/zephyr-7b-beta - api_base: http://0.0.0.0:8001 - - model_name: zephyr-beta - litellm_params: - model: huggingface/HuggingFaceH4/zephyr-7b-beta - api_base: http://0.0.0.0:8002 - - model_name: zephyr-beta - litellm_params: - model: huggingface/HuggingFaceH4/zephyr-7b-beta - api_base: http://0.0.0.0:8003 - - model_name: gpt-3.5-turbo - litellm_params: - model: gpt-3.5-turbo - api_key: - - model_name: gpt-3.5-turbo-16k - litellm_params: - model: gpt-3.5-turbo-16k - api_key: - -litellm_settings: - num_retries: 3 # retry call 3 times on each model_name (e.g. zephyr-beta) - request_timeout: 10 # raise Timeout error if call takes longer than 10s - fallbacks: [{"zephyr-beta": ["gpt-3.5-turbo"]}] # fallback to gpt-3.5-turbo if call fails num_retries - context_window_fallbacks: [{"zephyr-beta": ["gpt-3.5-turbo-16k"]}, {"gpt-3.5-turbo": ["gpt-3.5-turbo-16k"]}] # fallback to gpt-3.5-turbo-16k if context window error - allowed_fails: 3 # cooldown model if it fails > 1 call in a minute. -``` - -**Set dynamically** - -```bash -curl --location 'http://0.0.0.0:4000/chat/completions' \ ---header 'Content-Type: application/json' \ ---data ' { - "model": "zephyr-beta", - "messages": [ - { - "role": "user", - "content": "what llm are you" - } - ], - "fallbacks": [{"zephyr-beta": ["gpt-3.5-turbo"]}], - "context_window_fallbacks": [{"zephyr-beta": ["gpt-3.5-turbo"]}], - "num_retries": 2, - "request_timeout": 10 - } -' -``` - -### Config for Embedding Models - xorbitsai/inference - -Here's how you can use multiple llms with one proxy `config.yaml`. -Here is how [LiteLLM calls OpenAI Compatible Embedding models](https://docs.litellm.ai/docs/embedding/supported_embedding#openai-compatible-embedding-models) - -#### Config -```yaml -model_list: - - model_name: custom_embedding_model - litellm_params: - model: openai/custom_embedding # the `openai/` prefix tells litellm it's openai compatible - api_base: http://0.0.0.0:4000/ - - model_name: custom_embedding_model - litellm_params: - model: openai/custom_embedding # the `openai/` prefix tells litellm it's openai compatible - api_base: http://0.0.0.0:8001/ -``` - -Run the proxy using this config -```shell -$ litellm --config /path/to/config.yaml -``` - - -### Managing Auth - Virtual Keys - -Grant other's temporary access to your proxy, with keys that expire after a set duration. - -Requirements: - -- Need to a postgres database (e.g. [Supabase](https://supabase.com/), [Neon](https://neon.tech/), etc) - -You can then generate temporary keys by hitting the `/key/generate` endpoint. - -[**See code**](https://github.com/BerriAI/litellm/blob/7a669a36d2689c7f7890bc9c93e04ff3c2641299/litellm/proxy/proxy_server.py#L672) - -**Step 1: Save postgres db url** - -```yaml -model_list: - - model_name: gpt-4 - litellm_params: - model: ollama/llama2 - - model_name: gpt-3.5-turbo - litellm_params: - model: ollama/llama2 - -general_settings: - master_key: sk-1234 # [OPTIONAL] if set all calls to proxy will require either this key or a valid generated token - database_url: "postgresql://:@:/" -``` - -**Step 2: Start litellm** - -```shell -litellm --config /path/to/config.yaml -``` - -**Step 3: Generate temporary keys** - -```shell -curl 'http://0.0.0.0:4000/key/generate' \ ---h 'Authorization: Bearer sk-1234' \ ---d '{"models": ["gpt-3.5-turbo", "gpt-4", "claude-2"], "duration": "20m"}' -``` - -- `models`: *list or null (optional)* - Specify the models a token has access too. If null, then token has access to all models on server. - -- `duration`: *str or null (optional)* Specify the length of time the token is valid for. If null, default is set to 1 hour. You can set duration as seconds ("30s"), minutes ("30m"), hours ("30h"), days ("30d"). - -Expected response: - -```python -{ - "key": "sk-kdEXbIqZRwEeEiHwdg7sFA", # Bearer token - "expires": "2023-11-19T01:38:25.838000+00:00" # datetime object -} -``` - -### Managing Auth - Upgrade/Downgrade Models - -If a user is expected to use a given model (i.e. gpt3-5), and you want to: - -- try to upgrade the request (i.e. GPT4) -- or downgrade it (i.e. Mistral) -- OR rotate the API KEY (i.e. open AI) -- OR access the same model through different end points (i.e. openAI vs openrouter vs Azure) - -Here's how you can do that: - -**Step 1: Create a model group in config.yaml (save model name, api keys, etc.)** - -```yaml -model_list: - - model_name: my-free-tier - litellm_params: - model: huggingface/HuggingFaceH4/zephyr-7b-beta - api_base: http://0.0.0.0:8001 - - model_name: my-free-tier - litellm_params: - model: huggingface/HuggingFaceH4/zephyr-7b-beta - api_base: http://0.0.0.0:8002 - - model_name: my-free-tier - litellm_params: - model: huggingface/HuggingFaceH4/zephyr-7b-beta - api_base: http://0.0.0.0:8003 - - model_name: my-paid-tier - litellm_params: - model: gpt-4 - api_key: my-api-key -``` - -**Step 2: Generate a user key - enabling them access to specific models, custom model aliases, etc.** - -```bash -curl -X POST "https://0.0.0.0:4000/key/generate" \ --H "Authorization: Bearer sk-1234" \ --H "Content-Type: application/json" \ --d '{ - "models": ["my-free-tier"], - "aliases": {"gpt-3.5-turbo": "my-free-tier"}, - "duration": "30min" -}' -``` - -- **How to upgrade / downgrade request?** Change the alias mapping -- **How are routing between diff keys/api bases done?** litellm handles this by shuffling between different models in the model list with the same model_name. [**See Code**](https://github.com/BerriAI/litellm/blob/main/litellm/router.py) - -### Managing Auth - Tracking Spend - -You can get spend for a key by using the `/key/info` endpoint. - -```bash -curl 'http://0.0.0.0:4000/key/info?key=' \ - -X GET \ - -H 'Authorization: Bearer ' -``` - -This is automatically updated (in USD) when calls are made to /completions, /chat/completions, /embeddings using litellm's completion_cost() function. [**See Code**](https://github.com/BerriAI/litellm/blob/1a6ea20a0bb66491968907c2bfaabb7fe45fc064/litellm/utils.py#L1654). - -**Sample response** - -```python -{ - "key": "sk-tXL0wt5-lOOVK9sfY2UacA", - "info": { - "token": "sk-tXL0wt5-lOOVK9sfY2UacA", - "spend": 0.0001065, - "expires": "2023-11-24T23:19:11.131000Z", - "models": [ - "gpt-3.5-turbo", - "gpt-4", - "claude-2" - ], - "aliases": { - "mistral-7b": "gpt-3.5-turbo" - }, - "config": {} - } -} -``` - -### Save Model-specific params (API Base, API Keys, Temperature, Headers etc.) -You can use the config to save model-specific information like api_base, api_key, temperature, max_tokens, etc. - -**Step 1**: Create a `config.yaml` file -```yaml -model_list: - - model_name: gpt-4-team1 - litellm_params: # params for litellm.completion() - https://docs.litellm.ai/docs/completion/input#input---request-body - model: azure/chatgpt-v-2 - api_base: https://openai-gpt-4-test-v-1.openai.azure.com/ - api_version: "2023-05-15" - azure_ad_token: eyJ0eXAiOiJ - - model_name: gpt-4-team2 - litellm_params: - model: azure/gpt-4 - api_key: sk-123 - api_base: https://openai-gpt-4-test-v-2.openai.azure.com/ - - model_name: mistral-7b - litellm_params: - model: ollama/mistral - api_base: your_ollama_api_base -``` - -**Step 2**: Start server with config - -```shell -$ litellm --config /path/to/config.yaml -``` - -### Load API Keys from Vault - -If you have secrets saved in Azure Vault, etc. and don't want to expose them in the config.yaml, here's how to load model-specific keys from the environment. - -```python -os.environ["AZURE_NORTH_AMERICA_API_KEY"] = "your-azure-api-key" -``` - -```yaml -model_list: - - model_name: gpt-4-team1 - litellm_params: # params for litellm.completion() - https://docs.litellm.ai/docs/completion/input#input---request-body - model: azure/chatgpt-v-2 - api_base: https://openai-gpt-4-test-v-1.openai.azure.com/ - api_version: "2023-05-15" - api_key: os.environ/AZURE_NORTH_AMERICA_API_KEY -``` - -[**See Code**](https://github.com/BerriAI/litellm/blob/c12d6c3fe80e1b5e704d9846b246c059defadce7/litellm/utils.py#L2366) - -s/o to [@David Manouchehri](https://www.linkedin.com/in/davidmanouchehri/) for helping with this. - -### Config for setting Model Aliases - -Set a model alias for your deployments. - -In the `config.yaml` the model_name parameter is the user-facing name to use for your deployment. - -In the config below requests with `model=gpt-4` will route to `ollama/llama2` - -```yaml -model_list: - - model_name: text-davinci-003 - litellm_params: - model: ollama/zephyr - - model_name: gpt-4 - litellm_params: - model: ollama/llama2 - - model_name: gpt-3.5-turbo - litellm_params: - model: ollama/llama2 -``` -### Caching Responses -Caching can be enabled by adding the `cache` key in the `config.yaml` -#### Step 1: Add `cache` to the config.yaml -```yaml -model_list: - - model_name: gpt-3.5-turbo - litellm_params: - model: gpt-3.5-turbo - -litellm_settings: - set_verbose: True - cache: # init cache - type: redis # tell litellm to use redis caching -``` - -#### Step 2: Add Redis Credentials to .env -LiteLLM requires the following REDIS credentials in your env to enable caching - - ```shell - REDIS_HOST = "" # REDIS_HOST='redis-18841.c274.us-east-1-3.ec2.cloud.redislabs.com' - REDIS_PORT = "" # REDIS_PORT='18841' - REDIS_PASSWORD = "" # REDIS_PASSWORD='liteLlmIsAmazing' - ``` -#### Step 3: Run proxy with config -```shell -$ litellm --config /path/to/config.yaml -``` - -#### Using Caching -Send the same request twice: -```shell -curl http://0.0.0.0:4000/v1/chat/completions \ - -H "Content-Type: application/json" \ - -d '{ - "model": "gpt-3.5-turbo", - "messages": [{"role": "user", "content": "write a poem about litellm!"}], - "temperature": 0.7 - }' - -curl http://0.0.0.0:4000/v1/chat/completions \ - -H "Content-Type: application/json" \ - -d '{ - "model": "gpt-3.5-turbo", - "messages": [{"role": "user", "content": "write a poem about litellm!"}], - "temperature": 0.7 - }' -``` - -#### Control caching per completion request -Caching can be switched on/off per `/chat/completions` request -- Caching **on** for completion - pass `caching=True`: - ```shell - curl http://0.0.0.0:4000/v1/chat/completions \ - -H "Content-Type: application/json" \ - -d '{ - "model": "gpt-3.5-turbo", - "messages": [{"role": "user", "content": "write a poem about litellm!"}], - "temperature": 0.7, - "caching": true - }' - ``` -- Caching **off** for completion - pass `caching=False`: - ```shell - curl http://0.0.0.0:4000/v1/chat/completions \ - -H "Content-Type: application/json" \ - -d '{ - "model": "gpt-3.5-turbo", - "messages": [{"role": "user", "content": "write a poem about litellm!"}], - "temperature": 0.7, - "caching": false - }' - ``` - -### Set Custom Prompt Templates - -LiteLLM by default checks if a model has a [prompt template and applies it](./completion/prompt_formatting.md) (e.g. if a huggingface model has a saved chat template in it's tokenizer_config.json). However, you can also set a custom prompt template on your proxy in the `config.yaml`: - -**Step 1**: Save your prompt template in a `config.yaml` -```yaml -# Model-specific parameters -model_list: - - model_name: mistral-7b # model alias - litellm_params: # actual params for litellm.completion() - model: "huggingface/mistralai/Mistral-7B-Instruct-v0.1" - api_base: "" - api_key: "" # [OPTIONAL] for hf inference endpoints - initial_prompt_value: "\n" - roles: {"system":{"pre_message":"<|im_start|>system\n", "post_message":"<|im_end|>"}, "assistant":{"pre_message":"<|im_start|>assistant\n","post_message":"<|im_end|>"}, "user":{"pre_message":"<|im_start|>user\n","post_message":"<|im_end|>"}} - final_prompt_value: "\n" - bos_token: "" - eos_token: "" - max_tokens: 4096 -``` - -**Step 2**: Start server with config - -```shell -$ litellm --config /path/to/config.yaml -``` - -## Debugging Proxy -Run the proxy with `--debug` to easily view debug logs -```shell -litellm --model gpt-3.5-turbo --debug -``` - -### Detailed Debug Logs - -Run the proxy with `--detailed_debug` to view detailed debug logs -```shell -litellm --model gpt-3.5-turbo --detailed_debug -``` - -When making requests you should see the POST request sent by LiteLLM to the LLM on the Terminal output -```shell -POST Request Sent from LiteLLM: -curl -X POST \ -https://api.openai.com/v1/chat/completions \ --H 'content-type: application/json' -H 'Authorization: Bearer sk-qnWGUIW9****************************************' \ --d '{"model": "gpt-3.5-turbo", "messages": [{"role": "user", "content": "this is a test request, write a short poem"}]}' -``` - -## Health Check LLMs on Proxy -Use this to health check all LLMs defined in your config.yaml -#### Request -```shell -curl --location 'http://0.0.0.0:4000/health' -``` - -You can also run `litellm -health` it makes a `get` request to `http://0.0.0.0:4000/health` for you -``` -litellm --health -``` -#### Response -```shell -{ - "healthy_endpoints": [ - { - "model": "azure/gpt-35-turbo", - "api_base": "https://my-endpoint-canada-berri992.openai.azure.com/" - }, - { - "model": "azure/gpt-35-turbo", - "api_base": "https://my-endpoint-europe-berri-992.openai.azure.com/" - } - ], - "unhealthy_endpoints": [ - { - "model": "azure/gpt-35-turbo", - "api_base": "https://openai-france-1234.openai.azure.com/" - } - ] -} -``` - -## Logging Proxy Input/Output - OpenTelemetry - -### Step 1 Start OpenTelemetry Collector Docker Container -This container sends logs to your selected destination - -#### Install OpenTelemetry Collector Docker Image -```shell -docker pull otel/opentelemetry-collector:0.90.0 -docker run -p 127.0.0.1:4317:4317 -p 127.0.0.1:55679:55679 otel/opentelemetry-collector:0.90.0 -``` - -#### Set Destination paths on OpenTelemetry Collector - -Here's the OpenTelemetry yaml config to use with Elastic Search -```yaml -receivers: - otlp: - protocols: - grpc: - endpoint: 0.0.0.0:4317 - -processors: - batch: - timeout: 1s - send_batch_size: 1024 - -exporters: - logging: - loglevel: debug - otlphttp/elastic: - endpoint: "" - headers: - Authorization: "Bearer " - -service: - pipelines: - metrics: - receivers: [otlp] - exporters: [logging, otlphttp/elastic] - traces: - receivers: [otlp] - exporters: [logging, otlphttp/elastic] - logs: - receivers: [otlp] - exporters: [logging,otlphttp/elastic] -``` - -#### Start the OpenTelemetry container with config -Run the following command to start your docker container. We pass `otel_config.yaml` from the previous step - -```shell -docker run -p 4317:4317 \ - -v $(pwd)/otel_config.yaml:/etc/otel-collector-config.yaml \ - otel/opentelemetry-collector:latest \ - --config=/etc/otel-collector-config.yaml -``` - -### Step 2 Configure LiteLLM proxy to log on OpenTelemetry - -#### Pip install opentelemetry -```shell -pip install opentelemetry-api opentelemetry-sdk opentelemetry-exporter-otlp -U -``` - -#### Set (OpenTelemetry) `otel=True` on the proxy `config.yaml` -**Example config.yaml** - -```yaml -model_list: - - model_name: gpt-3.5-turbo - litellm_params: - model: azure/gpt-turbo-small-eu - api_base: https://my-endpoint-europe-berri-992.openai.azure.com/ - api_key: - rpm: 6 # Rate limit for this deployment: in requests per minute (rpm) - -general_settings: - otel: True # set OpenTelemetry=True, on litellm Proxy - -``` - -#### Set OTEL collector endpoint -LiteLLM will read the `OTEL_ENDPOINT` environment variable to send data to your OTEL collector - -```python -os.environ['OTEL_ENDPOINT'] # defaults to 127.0.0.1:4317 if not provided -``` - -#### Start LiteLLM Proxy -```shell -litellm -config config.yaml -``` - -#### Run a test request to Proxy -```shell -curl --location 'http://0.0.0.0:4000/chat/completions' \ - --header 'Authorization: Bearer sk-1244' \ - --data ' { - "model": "gpt-3.5-turbo", - "messages": [ - { - "role": "user", - "content": "request from LiteLLM testing" - } - ] - }' -``` - - -#### Test & View Logs on OpenTelemetry Collector -On successful logging you should be able to see this log on your `OpenTelemetry Collector` Docker Container -```shell -Events: -SpanEvent #0 - -> Name: LiteLLM: Request Input - -> Timestamp: 2023-12-02 05:05:53.71063 +0000 UTC - -> DroppedAttributesCount: 0 - -> Attributes:: - -> type: Str(http) - -> asgi: Str({'version': '3.0', 'spec_version': '2.3'}) - -> http_version: Str(1.1) - -> server: Str(('127.0.0.1', 8000)) - -> client: Str(('127.0.0.1', 62796)) - -> scheme: Str(http) - -> method: Str(POST) - -> root_path: Str() - -> path: Str(/chat/completions) - -> raw_path: Str(b'/chat/completions') - -> query_string: Str(b'') - -> headers: Str([(b'host', b'0.0.0.0:8000'), (b'user-agent', b'curl/7.88.1'), (b'accept', b'*/*'), (b'authorization', b'Bearer sk-1244'), (b'content-length', b'147'), (b'content-type', b'application/x-www-form-urlencoded')]) - -> state: Str({}) - -> app: Str() - -> fastapi_astack: Str() - -> router: Str() - -> endpoint: Str() - -> path_params: Str({}) - -> route: Str(APIRoute(path='/chat/completions', name='chat_completion', methods=['POST'])) -SpanEvent #1 - -> Name: LiteLLM: Request Headers - -> Timestamp: 2023-12-02 05:05:53.710652 +0000 UTC - -> DroppedAttributesCount: 0 - -> Attributes:: - -> host: Str(0.0.0.0:8000) - -> user-agent: Str(curl/7.88.1) - -> accept: Str(*/*) - -> authorization: Str(Bearer sk-1244) - -> content-length: Str(147) - -> content-type: Str(application/x-www-form-urlencoded) -SpanEvent #2 -``` - -### View Log on Elastic Search -Here's the log view on Elastic Search. You can see the request `input`, `output` and `headers` - - - -## Logging Proxy Input/Output - Langfuse -We will use the `--config` to set `litellm.success_callback = ["langfuse"]` this will log all successful LLM calls to langfuse - -**Step 1** Install langfuse - -```shell -pip install langfuse -``` - -**Step 2**: Create a `config.yaml` file and set `litellm_settings`: `success_callback` -```yaml -model_list: - - model_name: gpt-3.5-turbo - litellm_params: - model: gpt-3.5-turbo -litellm_settings: - success_callback: ["langfuse"] -``` - -**Step 3**: Start the proxy, make a test request - -Start proxy -```shell -litellm --config config.yaml --debug -``` - -Test Request -``` -litellm --test -``` - -Expected output on Langfuse - - - -## Deploying LiteLLM Proxy - -### Deploy on Render https://render.com/ - - - -## LiteLLM Proxy Performance - -### Throughput - 30% Increase -LiteLLM proxy + Load Balancer gives **30% increase** in throughput compared to Raw OpenAI API - - -### Latency Added - 0.00325 seconds -LiteLLM proxy adds **0.00325 seconds** latency as compared to using the Raw OpenAI API - - - - - -## Proxy CLI Arguments - -#### --host - - **Default:** `'0.0.0.0'` - - The host for the server to listen on. - - **Usage:** - ```shell - litellm --host 127.0.0.1 - ``` - -#### --port - - **Default:** `4000` - - The port to bind the server to. - - **Usage:** - ```shell - litellm --port 8080 - ``` - -#### --num_workers - - **Default:** `1` - - The number of uvicorn workers to spin up. - - **Usage:** - ```shell - litellm --num_workers 4 - ``` - -#### --api_base - - **Default:** `None` - - The API base for the model litellm should call. - - **Usage:** - ```shell - litellm --model huggingface/tinyllama --api_base https://k58ory32yinf1ly0.us-east-1.aws.endpoints.huggingface.cloud - ``` - -#### --api_version - - **Default:** `None` - - For Azure services, specify the API version. - - **Usage:** - ```shell - litellm --model azure/gpt-deployment --api_version 2023-08-01 --api_base https://" - ``` - -#### --model or -m - - **Default:** `None` - - The model name to pass to Litellm. - - **Usage:** - ```shell - litellm --model gpt-3.5-turbo - ``` - -#### --test - - **Type:** `bool` (Flag) - - Proxy chat completions URL to make a test request. - - **Usage:** - ```shell - litellm --test - ``` - -#### --health - - **Type:** `bool` (Flag) - - Runs a health check on all models in config.yaml - - **Usage:** - ```shell - litellm --health - ``` - -#### --alias - - **Default:** `None` - - An alias for the model, for user-friendly reference. - - **Usage:** - ```shell - litellm --alias my-gpt-model - ``` - -#### --debug - - **Default:** `False` - - **Type:** `bool` (Flag) - - Enable debugging mode for the input. - - **Usage:** - ```shell - litellm --debug - ``` -#### --detailed_debug - - **Default:** `False` - - **Type:** `bool` (Flag) - - Enable debugging mode for the input. - - **Usage:** - ```shell - litellm --detailed_debug - ``` - -#### --temperature - - **Default:** `None` - - **Type:** `float` - - Set the temperature for the model. - - **Usage:** - ```shell - litellm --temperature 0.7 - ``` - -#### --max_tokens - - **Default:** `None` - - **Type:** `int` - - Set the maximum number of tokens for the model output. - - **Usage:** - ```shell - litellm --max_tokens 50 - ``` - -#### --request_timeout - - **Default:** `6000` - - **Type:** `int` - - Set the timeout in seconds for completion calls. - - **Usage:** - ```shell - litellm --request_timeout 300 - ``` - -#### --drop_params - - **Type:** `bool` (Flag) - - Drop any unmapped params. - - **Usage:** - ```shell - litellm --drop_params - ``` - -#### --add_function_to_prompt - - **Type:** `bool` (Flag) - - If a function passed but unsupported, pass it as a part of the prompt. - - **Usage:** - ```shell - litellm --add_function_to_prompt - ``` - -#### --config - - Configure Litellm by providing a configuration file path. - - **Usage:** - ```shell - litellm --config path/to/config.yaml - ``` - -#### --telemetry - - **Default:** `True` - - **Type:** `bool` - - Help track usage of this feature. - - **Usage:** - ```shell - litellm --telemetry False - ``` diff --git a/docs/my-website/docs/troubleshoot.md b/docs/my-website/docs/troubleshoot.md index b6a9c6a6b92..9d2b3757ee2 100644 --- a/docs/my-website/docs/troubleshoot.md +++ b/docs/my-website/docs/troubleshoot.md @@ -2,7 +2,7 @@ [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://join.slack.com/share/enQtOTE0ODczMzk2Nzk4NC01YjUxNjY2YjBlYTFmNDRiZTM3NDFiYTM3MzVkODFiMDVjOGRjMmNmZTZkZTMzOWQzZGQyZWIwYjQ0MWExYmE3) +[Community Slack 💭](https://litellmossslack.slack.com/) Our numbers 📞 +1 (770) 8783-106 / ‭+1 (412) 618-6238‬ diff --git a/docs/my-website/docs/tutorials/claude_responses_api.md b/docs/my-website/docs/tutorials/claude_responses_api.md index a06be87409b..5000161a520 100644 --- a/docs/my-website/docs/tutorials/claude_responses_api.md +++ b/docs/my-website/docs/tutorials/claude_responses_api.md @@ -4,14 +4,20 @@ import TabItem from '@theme/TabItem'; # Claude Code -This tutorial shows how to call the Responses API models like `codex-mini` and `o3-pro` from the Claude Code endpoint on LiteLLM. +This tutorial shows how to call Claude models through LiteLLM proxy from Claude Code. :::info -This tutorial is based on [Anthropic's official LiteLLM configuration documentation](https://docs.anthropic.com/en/docs/claude-code/llm-gateway#litellm-configuration). This integration allows you to use any LiteLLM supported model through Claude Code. +This tutorial is based on [Anthropic's official LiteLLM configuration documentation](https://docs.anthropic.com/en/docs/claude-code/llm-gateway#litellm-configuration). This integration allows you to use any LiteLLM supported model through Claude Code with centralized authentication, usage tracking, and cost controls. ::: +
+ +### Video Walkthrough + + + ## Prerequisites - [Claude Code](https://docs.anthropic.com/en/docs/claude-code/overview) installed @@ -31,19 +37,18 @@ Create a secure configuration using environment variables: ```yaml model_list: - # Responses API models - - model_name: codex-mini + # Claude models + - model_name: claude-3-5-sonnet-20241022 litellm_params: - model: openai/codex-mini - api_key: os.environ/OPENAI_API_KEY - api_base: https://api.openai.com/v1 + model: anthropic/claude-3-5-sonnet-20241022 + api_key: os.environ/ANTHROPIC_API_KEY - - model_name: o3-pro + - model_name: claude-3-5-haiku-20241022 litellm_params: - model: openai/o3-pro - api_key: os.environ/OPENAI_API_KEY - api_base: https://api.openai.com/v1 + model: anthropic/claude-3-5-haiku-20241022 + api_key: os.environ/ANTHROPIC_API_KEY + litellm_settings: master_key: os.environ/LITELLM_MASTER_KEY ``` @@ -51,7 +56,7 @@ litellm_settings: Set your environment variables: ```bash -export OPENAI_API_KEY="your-openai-api-key" +export ANTHROPIC_API_KEY="your-anthropic-api-key" export LITELLM_MASTER_KEY="sk-1234567890" # Generate a secure key ``` @@ -72,31 +77,49 @@ curl -X POST http://0.0.0.0:4000/v1/messages \ -H "Authorization: Bearer $LITELLM_MASTER_KEY" \ -H "Content-Type: application/json" \ -d '{ - "model": "codex-mini", + "model": "claude-3-5-sonnet-20241022", + "max_tokens": 1000, "messages": [{"role": "user", "content": "What is the capital of France?"}] }' ``` ### 4. Configure Claude Code -Setup Claude Code to use your LiteLLM proxy: +#### Method 1: Unified Endpoint (Recommended) + +Configure Claude Code to use LiteLLM's unified endpoint: + +Either a virtual key / master key can be used here ```bash export ANTHROPIC_BASE_URL="http://0.0.0.0:4000" export ANTHROPIC_AUTH_TOKEN="$LITELLM_MASTER_KEY" ``` -### 5. Use Claude Code +:::tip +LITELLM_MASTER_KEY gives claude access to all proxy models, whereas a virtual key would be limited to the models set in UI +::: -Start Claude Code with any configured model: +#### Method 2: Provider-specific Pass-through Endpoint + +Alternatively, use the Anthropic pass-through endpoint: ```bash -# Use Responses API models -claude --model codex-mini -claude --model o3-pro +export ANTHROPIC_BASE_URL="http://0.0.0.0:4000/anthropic" +export ANTHROPIC_AUTH_TOKEN="$LITELLM_MASTER_KEY" +``` -# Or use the latest model alias -claude --model codex-mini-latest +### 5. Use Claude Code + +Start Claude Code and it will automatically use your configured models: + +```bash +# Claude Code will use the models configured in your LiteLLM proxy +claude + +# Or specify a model if you have multiple configured +claude --model claude-3-5-sonnet-20241022 +claude --model claude-3-5-haiku-20241022 ``` Example conversation: @@ -112,7 +135,8 @@ Common issues and solutions: **Authentication errors:** - Verify your environment variables are set: `echo $LITELLM_MASTER_KEY` -- Check that your OpenAI API key is valid and has sufficient credits +- Check that your API keys are valid and have sufficient credits +- Ensure the `ANTHROPIC_AUTH_TOKEN` matches your LiteLLM master key **Model not found:** - Ensure the model name in Claude Code matches exactly with your `config.yaml` @@ -123,33 +147,47 @@ Common issues and solutions: Expand your configuration to support multiple providers and models: - + ```yaml model_list: - # Responses API models + # OpenAI models - model_name: codex-mini - litellm_params: + litellm_params: model: openai/codex-mini api_key: os.environ/OPENAI_API_KEY api_base: https://api.openai.com/v1 - + - model_name: o3-pro litellm_params: model: openai/o3-pro api_key: os.environ/OPENAI_API_KEY api_base: https://api.openai.com/v1 - # Standard models - model_name: gpt-4o litellm_params: model: openai/gpt-4o api_key: os.environ/OPENAI_API_KEY + api_base: https://api.openai.com/v1 - - model_name: claude-3-5-sonnet + # Anthropic models + - model_name: claude-3-5-sonnet-20241022 litellm_params: model: anthropic/claude-3-5-sonnet-20241022 api_key: os.environ/ANTHROPIC_API_KEY + + - model_name: claude-3-5-haiku-20241022 + litellm_params: + model: anthropic/claude-3-5-haiku-20241022 + api_key: os.environ/ANTHROPIC_API_KEY + + # AWS Bedrock + - model_name: claude-bedrock + litellm_params: + model: bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0 + aws_access_key_id: os.environ/AWS_ACCESS_KEY_ID + aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY + aws_region_name: us-east-1 litellm_settings: master_key: os.environ/LITELLM_MASTER_KEY @@ -158,13 +196,14 @@ litellm_settings: Switch between models seamlessly: ```bash -# Use Responses API models for advanced reasoning -claude --model o3-pro -claude --model codex-mini +# Use Claude for complex reasoning +claude --model claude-3-5-sonnet-20241022 -# Use standard models for general tasks -claude --model gpt-4o -claude --model claude-3-5-sonnet +# Use Haiku for fast responses +claude --model claude-3-5-haiku-20241022 + +# Use Bedrock deployment +claude --model claude-bedrock ``` diff --git a/docs/my-website/docs/tutorials/openweb_ui.md b/docs/my-website/docs/tutorials/openweb_ui.md index ecf1e289da3..38f1ec38260 100644 --- a/docs/my-website/docs/tutorials/openweb_ui.md +++ b/docs/my-website/docs/tutorials/openweb_ui.md @@ -89,16 +89,20 @@ To track spend and usage for each Open WebUI user, configure both Open WebUI and 2. **Configure LiteLLM to Parse User Headers** - Add the following to your LiteLLM `config.yaml` to specify a header to use for user tracking: + Add the following to your LiteLLM `config.yaml` to specify the request header mapping for user tracking: ```yaml general_settings: - user_header_name: X-OpenWebUI-User-Id + user_header_mappings: + - header_name: X-OpenWebUI-User-Id + litellm_user_role: internal_user + - header_name: X-OpenWebUI-User-Email + litellm_user_role: customer ``` ⓘ Available tracking options - You can use any of the following headers for `user_header_name`: + You can use any of the following headers in `header_name` in `user_header_mappings` : - `X-OpenWebUI-User-Id` - `X-OpenWebUI-User-Email` - `X-OpenWebUI-User-Name` @@ -109,6 +113,12 @@ To track spend and usage for each Open WebUI user, configure both Open WebUI and - Users can modify their own usernames - Administrators can modify both usernames and emails of any account +This video walks through on how we can map the openweb ui headers to LiteLLM user roles + + + +
+
## Render `thinking` content on Open WebUI diff --git a/docs/my-website/docs/tutorials/scim_litellm.md b/docs/my-website/docs/tutorials/scim_litellm.md index 851379610b0..f7168531f80 100644 --- a/docs/my-website/docs/tutorials/scim_litellm.md +++ b/docs/my-website/docs/tutorials/scim_litellm.md @@ -72,6 +72,7 @@ On the LiteLLM UI, Navigate to `Teams`, You should see the new team `Production +> **Note:** When a user is removed from your organization via SCIM, all API keys and access tokens associated with that user will be automatically deleted from LiteLLM. This ensures that removed users lose all access immediately and securely. diff --git a/docs/my-website/img/admin_settings_ui_theme.png b/docs/my-website/img/admin_settings_ui_theme.png new file mode 100644 index 00000000000..81e6d761e17 Binary files /dev/null and b/docs/my-website/img/admin_settings_ui_theme.png differ diff --git a/docs/my-website/img/admin_settings_ui_theme_logo.png b/docs/my-website/img/admin_settings_ui_theme_logo.png new file mode 100644 index 00000000000..38f36e61602 Binary files /dev/null and b/docs/my-website/img/admin_settings_ui_theme_logo.png differ diff --git a/docs/my-website/img/create_team_member_rate_limits.png b/docs/my-website/img/create_team_member_rate_limits.png new file mode 100644 index 00000000000..0c5eba04461 Binary files /dev/null and b/docs/my-website/img/create_team_member_rate_limits.png differ diff --git a/docs/my-website/img/default_user_settings_admin_ui.png b/docs/my-website/img/default_user_settings_admin_ui.png new file mode 100644 index 00000000000..5910154cd51 Binary files /dev/null and b/docs/my-website/img/default_user_settings_admin_ui.png differ diff --git a/docs/my-website/img/mcp_tools.png b/docs/my-website/img/mcp_tools.png new file mode 100644 index 00000000000..825dbf6ed8c Binary files /dev/null and b/docs/my-website/img/mcp_tools.png differ diff --git a/docs/my-website/img/release_notes/team_member_rate_limits.png b/docs/my-website/img/release_notes/team_member_rate_limits.png new file mode 100644 index 00000000000..ec0affb1271 Binary files /dev/null and b/docs/my-website/img/release_notes/team_member_rate_limits.png differ diff --git a/docs/my-website/package-lock.json b/docs/my-website/package-lock.json index 4b37e2be11d..b71a15cc8e6 100644 --- a/docs/my-website/package-lock.json +++ b/docs/my-website/package-lock.json @@ -17120,9 +17120,10 @@ } }, "node_modules/prebuild-install/node_modules/tar-fs": { - "version": "2.1.3", - "resolved": "https://registry.npmjs.org/tar-fs/-/tar-fs-2.1.3.tgz", - "integrity": "sha512-090nwYJDmlhwFwEW3QQl+vaNnxsO2yVsd45eTKRBzSzu+hlb1w2K9inVq5b0ngXuLVqQ4ApvsUHHnu/zQNkWAg==", + "version": "2.1.4", + "resolved": "https://registry.npmjs.org/tar-fs/-/tar-fs-2.1.4.tgz", + "integrity": "sha512-mDAjwmZdh7LTT6pNleZ05Yt65HC3E+NiQzl672vQG38jIrehtJk/J3mNwIg+vShQPcLF/LV7CMnDW6vjj6sfYQ==", + "license": "MIT", "dependencies": { "chownr": "^1.1.1", "mkdirp-classic": "^0.5.2", @@ -19295,9 +19296,10 @@ } }, "node_modules/tar-fs": { - "version": "3.0.10", - "resolved": "https://registry.npmjs.org/tar-fs/-/tar-fs-3.0.10.tgz", - "integrity": "sha512-C1SwlQGNLe/jPNqapK8epDsXME7CAJR5RL3GcE6KWx1d9OUByzoHVcbu1VPI8tevg9H8Alae0AApHHFGzrD5zA==", + "version": "3.1.1", + "resolved": "https://registry.npmjs.org/tar-fs/-/tar-fs-3.1.1.tgz", + "integrity": "sha512-LZA0oaPOc2fVo82Txf3gw+AkEd38szODlptMYejQUhndHMLQ9M059uXR+AfS7DNo0NpINvSqDsvyaCrBVkptWg==", + "license": "MIT", "dependencies": { "pump": "^3.0.0", "tar-stream": "^3.1.5" diff --git a/docs/my-website/release_notes/v1.67.4-stable/index.md b/docs/my-website/release_notes/v1.67.4-stable/index.md index 6750ced47c7..93a27155d2b 100644 --- a/docs/my-website/release_notes/v1.67.4-stable/index.md +++ b/docs/my-website/release_notes/v1.67.4-stable/index.md @@ -106,7 +106,7 @@ This release allow you to group requests to LiteLLM proxy into a session. If you 1. Added support for max_completion_tokens parameter [Get Started](https://docs.litellm.ai/docs/providers/sagemaker), [PR](https://github.com/BerriAI/litellm/pull/10300) - **Responses API** 1. Added support for GET and DELETE operations - `/v1/responses/{response_id}` [Get Started](../../docs/response_api) - 2. Added session management support for non-OpenAI models [PR](https://github.com/BerriAI/litellm/pull/10321) + 2. Added session management support for all supported models [PR](https://github.com/BerriAI/litellm/pull/10321) 3. Added routing affinity to maintain model consistency within sessions [Get Started](https://docs.litellm.ai/docs/response_api#load-balancing-with-routing-affinity), [PR](https://github.com/BerriAI/litellm/pull/10193) diff --git a/docs/my-website/release_notes/v1.74.15-stable/index.md b/docs/my-website/release_notes/v1.74.15-stable/index.md index dd748f18ffa..9807a00b7e7 100644 --- a/docs/my-website/release_notes/v1.74.15-stable/index.md +++ b/docs/my-website/release_notes/v1.74.15-stable/index.md @@ -86,9 +86,9 @@ This is great to central AI Platform teams looking to track how they are helping | Provider | Model | Context Window | Input ($/1M tokens) | Output ($/1M tokens) | Cost per Image | | ----------- | -------------------------------------- | -------------- | ------------------- | -------------------- | -------------- | | OpenRouter | `openrouter/x-ai/grok-4` | 256k | $3 | $15 | N/A | -| Google AI Studio | `gemini/imagen-4.0-generate-preview-06-06` | N/A | N/A | N/A | $0.04 | -| Google AI Studio | `gemini/imagen-4.0-ultra-generate-preview-06-06` | N/A | N/A | N/A | $0.06 | -| Google AI Studio | `gemini/imagen-4.0-fast-generate-preview-06-06` | N/A | N/A | N/A | $0.02 | +| Google AI Studio | `gemini/imagen-4.0-generate-001` | N/A | N/A | N/A | $0.04 | +| Google AI Studio | `gemini/imagen-4.0-ultra-generate-001` | N/A | N/A | N/A | $0.06 | +| Google AI Studio | `gemini/imagen-4.0-fast-generate-001` | N/A | N/A | N/A | $0.02 | | Google AI Studio | `gemini/imagen-3.0-generate-002` | N/A | N/A | N/A | $0.04 | | Google AI Studio | `gemini/imagen-3.0-generate-001` | N/A | N/A | N/A | $0.04 | | Google AI Studio | `gemini/imagen-3.0-fast-generate-001` | N/A | N/A | N/A | $0.02 | diff --git a/docs/my-website/release_notes/v1.74.7/index.md b/docs/my-website/release_notes/v1.74.7/index.md index e3a2ac0aa00..7d7a568e13f 100644 --- a/docs/my-website/release_notes/v1.74.7/index.md +++ b/docs/my-website/release_notes/v1.74.7/index.md @@ -148,7 +148,6 @@ Starting with this release, you can run health endpoints on an isolated process - New provider integration for v0.dev - [PR #12751](https://github.com/BerriAI/litellm/pull/12751), [Get Started](../../docs/providers/v0) - **[OpenAI](../../docs/providers/openai)** - Use OpenAI DeepResearch models with `litellm.completion` (`/chat/completions`) - [PR #12627](https://github.com/BerriAI/litellm/pull/12627) **DOC NEEDED** - - Add `input_fidelity` parameter for OpenAI image generation - [PR #12662](https://github.com/BerriAI/litellm/pull/12662), [Get Started](../../docs/image_generation) - **[Azure OpenAI](../../docs/providers/azure_openai)** - Use Azure OpenAI DeepResearch models with `litellm.completion` (`/chat/completions`) - [PR #12627](https://github.com/BerriAI/litellm/pull/12627) **DOC NEEDED** - Added `response_format` support for openai gpt-4.1 models - [PR #12745](https://github.com/BerriAI/litellm/pull/12745) diff --git a/docs/my-website/release_notes/v1.75.8/index.md b/docs/my-website/release_notes/v1.75.8/index.md index 2e90f26c3ad..d7d4f37c4ee 100644 --- a/docs/my-website/release_notes/v1.75.8/index.md +++ b/docs/my-website/release_notes/v1.75.8/index.md @@ -1,5 +1,5 @@ --- -title: "[PRE-RELEASE]v1.75.8" +title: "v1.75.8-stable - Team Member Rate Limits" slug: "v1-75-8" date: 2025-08-16T10:00:00 authors: @@ -28,7 +28,7 @@ import TabItem from '@theme/TabItem'; docker run \ -e STORE_MODEL_IN_DB=True \ -p 4000:4000 \ -ghcr.io/berriai/litellm:v1.75.8 +ghcr.io/berriai/litellm:v1.75.8-stable ``` @@ -52,6 +52,22 @@ pip install litellm==1.75.8 --- +## Team Member Rate Limits + + +

+ LiteLLM MCP Architecture: Use MCP tools with all LiteLLM supported models +

+ + +This release adds support for setting rate limits on individual members (including machine users) within a team. Teams can now give each agent its own rate limits—so that heavy-traffic agents don’t impact other agents or human users. + +Agents can authenticate with LiteLLM using JWT and the same team role as human users, while still enforcing per-agent rate limits. + + ## New Models / Updated Models #### New Model Support diff --git a/docs/my-website/release_notes/v1.76.0-stable/index.md b/docs/my-website/release_notes/v1.76.0-stable/index.md new file mode 100644 index 00000000000..d93568d49dc --- /dev/null +++ b/docs/my-website/release_notes/v1.76.0-stable/index.md @@ -0,0 +1,189 @@ +--- +title: "v1.76.0-stable - RPS Improvements" +slug: "v1-76-0" +date: 2025-08-23T10:00:00 +authors: + - name: Krrish Dholakia + title: CEO, LiteLLM + url: https://www.linkedin.com/in/krish-d/ + image_url: https://pbs.twimg.com/profile_images/1298587542745358340/DZv3Oj-h_400x400.jpg + - name: Ishaan Jaffer + title: CTO, LiteLLM + url: https://www.linkedin.com/in/reffajnaahsi/ + image_url: https://pbs.twimg.com/profile_images/1613813310264340481/lz54oEiB_400x400.jpg + +hide_table_of_contents: false +--- + +import Image from '@theme/IdealImage'; +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +:::info + +LiteLLM is hiring a **Founding Backend Engineer**, in San Francisco. + +[Apply here](https://www.ycombinator.com/companies/litellm/jobs/6uvoBp3-founding-backend-engineer) if you're interested! +::: + + + + + +## Deploy this version + +:::info + +This release is not live yet. +::: + + +--- + +## New Models / Updated Models + +#### Bugs +- **[OpenAI](../../docs/providers/openai)** + - Gpt-5 chat: clarify does not support function calling [PR #13612](https://github.com/BerriAI/litellm/pull/13612), s/o  @[superpoussin22](https://github.com/superpoussin22) +- **[VertexAI](../../docs/providers/vertex)** + - fix vertexai batch file format by @[thiagosalvatore](https://github.com/thiagosalvatore) in [PR #13576](https://github.com/BerriAI/litellm/pull/13576) +- **[LiteLLM Proxy](../../docs/providers/litellm_proxy)** + - Add support for calling image_edits + image_generations via SDK to Proxy - [PR #13735](https://github.com/BerriAI/litellm/pull/13735) +- **[OpenRouter](../../docs/providers/openrouter)** + - Fix max_output_tokens value for anthropic Claude 4 - [PR #13526](https://github.com/BerriAI/litellm/pull/13526) +- **[Gemini](../../docs/providers/gemini)** + - Fix prompt caching cost calculation - [PR #13742](https://github.com/BerriAI/litellm/pull/13742) +- **[Azure](../../docs/providers/azure)** + - Support `../openai/v1/respones` api base - [PR #13526](https://github.com/BerriAI/litellm/pull/13526) + - Fix azure/gpt-5-chat max_input_tokens - [PR #13660](https://github.com/BerriAI/litellm/pull/13660) +- **[Groq](../../docs/providers/groq)** + - streaming ASCII encoding issue - [PR #13675](https://github.com/BerriAI/litellm/pull/13675) +- **[Baseten](../../docs/providers/baseten)** + - Refactored integration to use new openai-compatible endpoints - [PR #13783](https://github.com/BerriAI/litellm/pull/13783) +- **[Bedrock](../../docs/providers/bedrock)** + - fix application inference profile for pass-through endpoints for bedrock - [PR #13881](https://github.com/BerriAI/litellm/pull/13881) +- **[DataRobot](../../docs/providers/datarobot)** + - Updated URL handling for DataRobot provider URL - [PR #13880](https://github.com/BerriAI/litellm/pull/13880) + +#### Features +- **[Together AI](../../docs/providers/together)** + - Added Qwen3, Deepseek R1 0528 Throughput, GLM 4.5 and GPT-OSS models cost tracking - [PR #13637](https://github.com/BerriAI/litellm/pull/13637), s/o  @[Tasmay-Tibrewal](https://github.com/Tasmay-Tibrewal) +- **[Fireworks AI](../../docs/providers/fireworks_ai)** + - add fireworks_ai/accounts/fireworks/models/deepseek-v3-0324 - [PR #13821](https://github.com/BerriAI/litellm/pull/13821) +- **[VertexAI](../../docs/providers/vertex)** + - Add VertexAI qwen API Service - [PR #13828](https://github.com/BerriAI/litellm/pull/13828) + - Add new VertexAI image models vertex_ai/imagen-4.0-generate-001, vertex_ai/imagen-4.0-ultra-generate-001, vertex_ai/imagen-4.0-fast-generate-001  - [PR #13874](https://github.com/BerriAI/litellm/pull/13874) +- **[Anthropic](../../docs/providers/anthropic)** + - Add long context support w/ cost tracking - [PR #13759](https://github.com/BerriAI/litellm/pull/13759) +- **[DeepInfra](../../docs/providers/deepinfra)** + - Add rerank endpoint support for deepinfra - [PR #13820](https://github.com/BerriAI/litellm/pull/13820) + - Add new models for cost tracking - [PR #13883](https://github.com/BerriAI/litellm/pull/13883), s/o  @[Toy-97](https://github.com/Toy-97) +- **[Bedrock](../../docs/providers/bedrock)** + - Add tool prompt caching on async calls - [PR #13803](https://github.com/BerriAI/litellm/pull/13803), s/o  @[UlookEE](https://github.com/UlookEE) + - role chaining and session name with webauthentication for aws bedrock - [PR #13753](https://github.com/BerriAI/litellm/pull/13753), s/o @[RichardoC](https://github.com/RichardoC) +- **[Ollama](../../docs/providers/ollama)** + - Handle Ollama null response when using tool calling with non-tool trained models - [PR #13902](https://github.com/BerriAI/litellm/pull/13902) +- **[OpenRouter](../../docs/providers/openrouter)** + - Add deepseek/deepseek-chat-v3.1 support - [PR #13897](https://github.com/BerriAI/litellm/pull/13897) +- **[Mistral](../../docs/providers/mistral)** + - Add support for calling mistral files via chat completions - [PR #13866](https://github.com/BerriAI/litellm/pull/13866), s/o  @[jinskjoy](https://github.com/jinskjoy) + - Handle empty assistant content - [PR #13671](https://github.com/BerriAI/litellm/pull/13671) + - Support new ‘thinking’ response block - [PR #13671](https://github.com/BerriAI/litellm/pull/13671) +- **[Databricks](../../docs/providers/databricks)** + - remove deprecated dbrx models (dbrx-instruct, llama 3.1) - [PR #13843](https://github.com/BerriAI/litellm/pull/13843) +- **[AI/ML API](../../docs/providers/ai_ml_api)** + - Image gen api support - [PR #13893](https://github.com/BerriAI/litellm/pull/13893) + + +## LLM API Endpoints +#### Bugs +- **[Responses API](../../docs/response_api)** + - add default api version for openai responses api calls - [PR #13526](https://github.com/BerriAI/litellm/pull/13526) + - support allowed_openai_params - [PR #13671](https://github.com/BerriAI/litellm/pull/13671) + + +## MCP Gateway +#### Bugs +- fix StreamableHTTPSessionManager .run() error - [PR #13666](https://github.com/BerriAI/litellm/pull/13666) + +## Vector Stores +#### Bugs +- **[Bedrock](../../docs/providers/bedrock)** + - Using LiteLLM Managed Credentials for Query - [PR #13787](https://github.com/BerriAI/litellm/pull/13787) + +## Management Endpoints / UI +#### Bugs +- **[Passthrough](../../docs/pass_through/intro)** + - Fix query passthrough deletion - [PR #13622](https://github.com/BerriAI/litellm/pull/13622) + +#### Features +- **Models** + - Add Search Functionality for Public Model Names in Model Dashboard - [PR #13687](https://github.com/BerriAI/litellm/pull/13687) + - Auto-Add `azure/` to deployment Name in UI - [PR #13685](https://github.com/BerriAI/litellm/pull/13685) + - Models page row UI restructure - [PR #13771](https://github.com/BerriAI/litellm/pull/13771) +- **Notifications** + - Add new notifications toast UI everywhere - [PR #13813](https://github.com/BerriAI/litellm/pull/13813) +- **Keys** + - Fix key edit settings after regenerating a key - [PR #13815](https://github.com/BerriAI/litellm/pull/13815) + - Require team_id when creating service account keys - [PR #13873](https://github.com/BerriAI/litellm/pull/13873) + - Filter - show all options on filter option click - [PR #13858](https://github.com/BerriAI/litellm/pull/13858) +- **Usage** + - Fix ‘Cannot read properties of undefined’ exception on user agent activity tab - [PR #13892](https://github.com/BerriAI/litellm/pull/13892) +- **SSO** + - Free SSO usage for up to 5 users - [PR #13843](https://github.com/BerriAI/litellm/pull/13843) + +## Logging / Guardrail Integrations +#### Bugs +- **[Bedrock Guardrails](../../docs/proxy/guardrails/bedrock)** + - Add bedrock api key support - [PR #13835](https://github.com/BerriAI/litellm/pull/13835) +#### Features +- **[Datadog LLM Observability](../../docs/integrations/datadog)** + - Add support for Failure Logging [PR #13726](https://github.com/BerriAI/litellm/pull/13726) + - Add time to first token, litellm overhead, guardrail overhead latency metrics - [PR #13734](https://github.com/BerriAI/litellm/pull/13734) + - Add support for tracing guardrail input/output - [PR #13767](https://github.com/BerriAI/litellm/pull/13767) +- **[Langfuse OTEL](../../docs/integrations/langfuse)** + - Allow using Key/Team Based Logging - [PR #13791](https://github.com/BerriAI/litellm/pull/13791) +- **[AIM](../../docs/integrations/aim)** + - Migrate to new firewall API - [PR #13748](https://github.com/BerriAI/litellm/pull/13748) +- **[OTEL](../../docs/observability/opentelemetry_integration)** + - Add OTEL tracing for actual LLM API call - [PR #13836](https://github.com/BerriAI/litellm/pull/13836) +- **[MLFlow](../../docs/observability/mlflow_integration)** + - Include predicted output in MLflow tracing - [PR #13795](https://github.com/BerriAI/litellm/pull/13795), s/o @TomeHirata  + + +## Performance / Loadbalancing / Reliability improvements +#### Bugs +- **[Cooldowns](../../docs/routing#how-cooldowns-work)** + - don't return raw Azure Exceptions to client (can contain prompt leakage) - [PR #13529](https://github.com/BerriAI/litellm/pull/13529) +- **[Auto-router](../../docs/proxy/auto_routing)** + - Ensures the relevant dependencies for auto router existing on LiteLLM Docker - [PR #13788](https://github.com/BerriAI/litellm/pull/13788) +- **Model Alias** + - Fix calling key with access to model alias - [PR #13830](https://github.com/BerriAI/litellm/pull/13830) + +#### Features +- **[S3 Caching](../../docs/proxy/caching)** + - Use namespace as prefix for s3 cache - [PR #13704](https://github.com/BerriAI/litellm/pull/13704) + - Async S3 Caching support (4x RPS improvement) - [PR #13852](https://github.com/BerriAI/litellm/pull/13852), s/o @[michal-otmianowski](https://github.com/michal-otmianowski) +- **Model Group header forwarding** + - reuse same logic as global header forwarding - [PR #13741](https://github.com/BerriAI/litellm/pull/13741) + - add support for hosted_vllm on UI - [PR #13885](https://github.com/BerriAI/litellm/pull/13885) +- **Performance** + - Improve LiteLLM Python SDK RPS by +200 RPS (braintrust import + aiohttp transport fixes) - [PR #13839](https://github.com/BerriAI/litellm/pull/13839) + - Use O(1) Set lookups for model routing - [PR #13879](https://github.com/BerriAI/litellm/pull/13879) + - Reduce Significant CPU overhead from litellm_logging.py - [PR #13895](https://github.com/BerriAI/litellm/pull/13895) + - Improvements for Async Success Handler (Logging Callbacks) - Approx +130 RPS - [PR #13905](https://github.com/BerriAI/litellm/pull/13905) + + +## General Proxy Improvements +#### Bugs + +- **SDK** + - Fix litellm compatibility with newest release of openAI (>v1.100.0) - [PR #13728](https://github.com/BerriAI/litellm/pull/13728) +- **Helm** + - Add possibility to configure resources for migrations-job - [PR #13617](https://github.com/BerriAI/litellm/pull/13617) + - Ensure Helm chart auto generated master keys follow sk-xxxx format - [PR #13871](https://github.com/BerriAI/litellm/pull/13871) + - Enhance database configuration: add support for optional endpointKey - [PR #13763](https://github.com/BerriAI/litellm/pull/13763) +- **Rate Limits** + - fixing descriptor/response size mismatch on parallel_request_limiter_v3 - [PR #13863](https://github.com/BerriAI/litellm/pull/13863), s/o  @[luizrennocosta](https://github.com/luizrennocosta) +- **Non-root** + - fix permission access on prisma migrate in non-root image - [PR #13848](https://github.com/BerriAI/litellm/pull/13848), s/o @[Ithanil](https://github.com/Ithanil) \ No newline at end of file diff --git a/docs/my-website/release_notes/v1.76.1-stable/index.md b/docs/my-website/release_notes/v1.76.1-stable/index.md new file mode 100644 index 00000000000..4437b7f5799 --- /dev/null +++ b/docs/my-website/release_notes/v1.76.1-stable/index.md @@ -0,0 +1,269 @@ +--- +title: "v1.76.1-stable - Gemini 2.5 Flash Image" +slug: "v1-76-1" +date: 2025-08-30T10:00:00 +authors: + - name: Krrish Dholakia + title: CEO, LiteLLM + url: https://www.linkedin.com/in/krish-d/ + image_url: https://pbs.twimg.com/profile_images/1298587542745358340/DZv3Oj-h_400x400.jpg + - name: Ishaan Jaffer + title: CTO, LiteLLM + url: https://www.linkedin.com/in/reffajnaahsi/ + image_url: https://pbs.twimg.com/profile_images/1613813310264340481/lz54oEiB_400x400.jpg + +hide_table_of_contents: false +--- + +import Image from '@theme/IdealImage'; +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +## Deploy this version + + + + +``` showLineNumbers title="docker run litellm" +docker run \ +-e STORE_MODEL_IN_DB=True \ +-p 4000:4000 \ +ghcr.io/berriai/litellm:v1.76.1 +``` + + + + +``` showLineNumbers title="pip install litellm" +pip install litellm==1.76.1 +``` + + + + +--- + +## Key Highlights + +- **Major Performance Improvements** - 6.5x faster LiteLLM Python SDK completion with fastuuid integration. +- **New Model Support** - Gemini 2.5 Flash Image Preview, Grok Code Fast, and GPT Realtime models +- **Enhanced Provider Support** - DeepSeek-v3.1 pricing on Fireworks AI, Vercel AI Gateway, and improved Anthropic/GitHub Copilot integration +- **MCP Improvements** - Better connection testing and SSE MCP tools bug fixes + +## Major Changes +- Added support for using Gemini 2.5 Flash Image Preview with /chat/completions. **🚨 Warning** If you were using `gemini-2.0-flash-exp-image-generation` please follow this migration guide. + [Gemini Image Generation Migration Guide](../../docs/extras/gemini_img_migration) +--- + +## Performance Improvements + +This release includes significant performance optimizations: + +- **6.5x faster LiteLLM Python SDK Completion** - Major performance boost for completion operations - [PR #13990](https://github.com/BerriAI/litellm/pull/13990) +- **fastuuid Integration** - 2.1x faster UUID generation with +80 RPS improvement for /chat/completions and other LLM endpoints - [PR #13992](https://github.com/BerriAI/litellm/pull/13992), [PR #14016](https://github.com/BerriAI/litellm/pull/14016) +- **Optimized Request Logging** - Don't print request params by default for +50 RPS improvement - [PR #14015](https://github.com/BerriAI/litellm/pull/14015) +- **Cache Performance** - 21% speedup in InMemoryCache.evict_cache and 45% speedup in `_is_debugging_on` function - [PR #14012](https://github.com/BerriAI/litellm/pull/14012), [PR #13988](https://github.com/BerriAI/litellm/pull/13988) + +--- + +## New Models / Updated Models + +#### New Model Support + +| Provider | Model | Context Window | Input ($/1M tokens) | Output ($/1M tokens) | Features | +| ----------- | -------------------------------------- | -------------- | ------------------- | -------------------- | -------- | +| Google | `gemini-2.5-flash-image-preview` | 1M | $0.30 | $2.50 | Chat completions + image generation ($0.039/image) | +| X.AI | `xai/grok-code-fast` | 256K | $0.20 | $1.50 | Code generation | +| OpenAI | `gpt-realtime` | 32K | $4.00 | $16.00 | Real-time conversation + audio | +| Vercel AI Gateway | `vercel_ai_gateway/openai/o3` | 200K | $2.00 | $8.00 | Advanced reasoning | +| Vercel AI Gateway | `vercel_ai_gateway/openai/o3-mini` | 200K | $1.10 | $4.40 | Efficient reasoning | +| Vercel AI Gateway | `vercel_ai_gateway/openai/o4-mini` | 200K | $1.10 | $4.40 | Latest mini model | +| DeepInfra | `deepinfra/zai-org/GLM-4.5` | 131K | $0.55 | $2.00 | Chat completions | +| Perplexity | `perplexity/codellama-34b-instruct` | 16K | $0.35 | $1.40 | Code generation | +| Fireworks AI | `fireworks_ai/accounts/fireworks/models/deepseek-v3p1` | 128K | $0.56 | $1.68 | Chat completions | + +**Additional Models Added:** Various other Vercel AI Gateway models were added too. See [models.litellm.ai](https://models.litellm.ai) for the full list. + +#### Features + +- **[Google Gemini](../../docs/providers/gemini)** + - Added support for `gemini-2.5-flash-image-preview` with image return capability - [PR #13979](https://github.com/BerriAI/litellm/pull/13979), [PR #13983](https://github.com/BerriAI/litellm/pull/13983) + - Support for requests with only system prompt - [PR #14010](https://github.com/BerriAI/litellm/pull/14010) + - Fixed invalid model name error for Gemini Imagen models - [PR #13991](https://github.com/BerriAI/litellm/pull/13991) +- **[X.AI](../../docs/providers/xai)** + - Added `xai/grok-code-fast` model family support - [PR #14054](https://github.com/BerriAI/litellm/pull/14054) + - Fixed frequency_penalty parameter for grok-4 models - [PR #14078](https://github.com/BerriAI/litellm/pull/14078) +- **[OpenAI](../../docs/providers/openai)** + - Added support for gpt-realtime models - [PR #14082](https://github.com/BerriAI/litellm/pull/14082) + - Support for reasoning and reasoning_effort parameters by default - [PR #12865](https://github.com/BerriAI/litellm/pull/12865) +- **[Fireworks AI](../../docs/providers/fireworks_ai)** + - Added DeepSeek-v3.1 pricing - [PR #13958](https://github.com/BerriAI/litellm/pull/13958) +- **[DeepInfra](../../docs/providers/deepinfra)** + - Fixed reasoning_effort setting for DeepSeek-V3.1 - [PR #14053](https://github.com/BerriAI/litellm/pull/14053) +- **[GitHub Copilot](../../docs/providers/github_copilot)** + - Added support for thinking and reasoning_effort parameters - [PR #13691](https://github.com/BerriAI/litellm/pull/13691) + - Added image headers support - [PR #13955](https://github.com/BerriAI/litellm/pull/13955) +- **[Anthropic](../../docs/providers/anthropic)** + - Support for custom Anthropic-compatible API endpoints - [PR #13945](https://github.com/BerriAI/litellm/pull/13945) + - Fixed /messages fallback from Anthropic API to Bedrock API - [PR #13946](https://github.com/BerriAI/litellm/pull/13946) +- **[Nebius](../../docs/providers/nebius)** + - Expanded provider models and normalized model IDs - [PR #13965](https://github.com/BerriAI/litellm/pull/13965) +- **[Vertex AI](../../docs/providers/vertex)** + - Fixed Vertex Mistral streaming issues - [PR #13952](https://github.com/BerriAI/litellm/pull/13952) + - Fixed anyOf corner cases for Gemini tool calls - [PR #12797](https://github.com/BerriAI/litellm/pull/12797) +- **[Bedrock](../../docs/providers/bedrock)** + - Fixed structure output issues - [PR #14005](https://github.com/BerriAI/litellm/pull/14005) +- **[OpenRouter](../../docs/providers/openrouter)** + - Added GPT-5 family models pricing - [PR #13536](https://github.com/BerriAI/litellm/pull/13536) + +#### New Provider Support + +- **[Vercel AI Gateway](../../docs/providers/vercel_ai_gateway)** + - New provider support added - [PR #13144](https://github.com/BerriAI/litellm/pull/13144) +- **[DataRobot](../../docs/providers/datarobot)** + - Added provider documentation - [PR #14038](https://github.com/BerriAI/litellm/pull/14038), [PR #14074](https://github.com/BerriAI/litellm/pull/14074) + +--- + +## LLM API Endpoints + +#### Features + +- **[Images API](../../docs/image_generation)** + - Support for multiple images in OpenAI images/edits endpoint - [PR #13916](https://github.com/BerriAI/litellm/pull/13916) + - Allow using dynamic `api_key` for image generation requests - [PR #14007](https://github.com/BerriAI/litellm/pull/14007) +- **[Responses API](../../docs/response_api)** + - Fixed `/responses` endpoint ignoring extra_headers in GitHub Copilot - [PR #13775](https://github.com/BerriAI/litellm/pull/13775) + - Added support for new web_search tool - [PR #14083](https://github.com/BerriAI/litellm/pull/14083) +- **[Azure Passthrough](../../docs/providers/azure/azure)** + - Fixed Azure Passthrough request with streaming - [PR #13831](https://github.com/BerriAI/litellm/pull/13831) + +#### Bugs + +- **General** + - Fixed handling of None metadata in batch requests - [PR #13996](https://github.com/BerriAI/litellm/pull/13996) + - Fixed token_counter with special token input - [PR #13374](https://github.com/BerriAI/litellm/pull/13374) + - Removed incorrect web search support for azure/gpt-4.1 family - [PR #13566](https://github.com/BerriAI/litellm/pull/13566) + +--- + +## [MCP Gateway](../../docs/mcp) + +#### Features + +- **SSE MCP Tools** + - Bug fix for adding SSE MCP tools - improved connection testing when adding MCPs - [PR #14048](https://github.com/BerriAI/litellm/pull/14048) + +[Read More](../../docs/mcp) + +--- + +## Management Endpoints / UI + +#### Features + +- **Team Management** + - Allow setting Team Member RPM/TPM limits when creating a team - [PR #13943](https://github.com/BerriAI/litellm/pull/13943) +- **UI Improvements** + - Fixed Next.js Security Vulnerabilities in UI Dashboard - [PR #14084](https://github.com/BerriAI/litellm/pull/14084) + - Fixed collapsible navbar design - [PR #14075](https://github.com/BerriAI/litellm/pull/14075) + +#### Bugs + +- **Authentication** + - Fixed Virtual keys with llm_api type causing Internal Server Error for /anthropic/* and other LLM passthrough routes - [PR #14046](https://github.com/BerriAI/litellm/pull/14046) + +--- + +## Logging / Guardrail Integrations + +#### Features + +- **[Langfuse OTEL](../../docs/proxy/logging#langfuse)** + - Allow using LANGFUSE_OTEL_HOST for configuring host - [PR #14013](https://github.com/BerriAI/litellm/pull/14013) +- **[Braintrust](../../docs/proxy/logging#braintrust)** + - Added span name metadata feature - [PR #13573](https://github.com/BerriAI/litellm/pull/13573) + - Fixed tests to reference moved attributes in `braintrust_logging` module - [PR #13978](https://github.com/BerriAI/litellm/pull/13978) +- **[OpenMeter](../../docs/proxy/logging#openmeter)** + - Set user from token user_id for OpenMeter integration - [PR #13152](https://github.com/BerriAI/litellm/pull/13152) + +#### New Guardrail Support + +- **[Noma Security](../../docs/proxy/guardrails)** + - Added Noma Security guardrail support - [PR #13572](https://github.com/BerriAI/litellm/pull/13572) +- **[Pangea](../../docs/proxy/guardrails)** + - Updated Pangea Guardrail to support new AIDR endpoint - [PR #13160](https://github.com/BerriAI/litellm/pull/13160) + +--- + +## Performance / Loadbalancing / Reliability improvements + +#### Features + +- **Caching** + - Verify if cache entry has expired prior to serving it to client - [PR #13933](https://github.com/BerriAI/litellm/pull/13933) + - Fixed error saving latency as timedelta on Redis - [PR #14040](https://github.com/BerriAI/litellm/pull/14040) +- **Router** + - Refactored router to choose weights by 'weight', 'rpm', 'tpm' in one loop for simple_shuffle - [PR #13562](https://github.com/BerriAI/litellm/pull/13562) +- **Logging** + - Fixed LoggingWorker graceful shutdown to prevent CancelledError warnings - [PR #14050](https://github.com/BerriAI/litellm/pull/14050) + - Enhanced logging for containers to log on files both with usual format and json format - [PR #13394](https://github.com/BerriAI/litellm/pull/13394) + +#### Bugs + +- **Dependencies** + - Bumped `orjson` version to "3.11.2" - [PR #13969](https://github.com/BerriAI/litellm/pull/13969) + +--- + +## General Proxy Improvements + +#### Features + +- **AWS** + - Add support for AWS assume_role with a session token - [PR #13919](https://github.com/BerriAI/litellm/pull/13919) +- **OCI Provider** + - Added oci_key_file as an optional_parameter - [PR #14036](https://github.com/BerriAI/litellm/pull/14036) +- **Configuration** + - Allow configuration to set threshold before request entry in spend log gets truncated - [PR #14042](https://github.com/BerriAI/litellm/pull/14042) + - Enhanced proxy_config configuration: add support for existing configmap in Helm charts - [PR #14041](https://github.com/BerriAI/litellm/pull/14041) +- **Docker** + - Added back supervisor to non-root image - [PR #13922](https://github.com/BerriAI/litellm/pull/13922) + + +--- + +## New Contributors +* @ArthurRenault made their first contribution in [PR #13922](https://github.com/BerriAI/litellm/pull/13922) +* @stevenmanton made their first contribution in [PR #13919](https://github.com/BerriAI/litellm/pull/13919) +* @uc4w6c made their first contribution in [PR #13914](https://github.com/BerriAI/litellm/pull/13914) +* @nielsbosma made their first contribution in [PR #13573](https://github.com/BerriAI/litellm/pull/13573) +* @Yuki-Imajuku made their first contribution in [PR #13567](https://github.com/BerriAI/litellm/pull/13567) +* @codeflash-ai[bot] made their first contribution in [PR #13988](https://github.com/BerriAI/litellm/pull/13988) +* @ColeFrench made their first contribution in [PR #13978](https://github.com/BerriAI/litellm/pull/13978) +* @dttran-glo made their first contribution in [PR #13969](https://github.com/BerriAI/litellm/pull/13969) +* @manascb1344 made their first contribution in [PR #13965](https://github.com/BerriAI/litellm/pull/13965) +* @DorZion made their first contribution in [PR #13572](https://github.com/BerriAI/litellm/pull/13572) +* @edwardsamuel made their first contribution in [PR #13536](https://github.com/BerriAI/litellm/pull/13536) +* @blahgeek made their first contribution in [PR #13374](https://github.com/BerriAI/litellm/pull/13374) +* @Deviad made their first contribution in [PR #13394](https://github.com/BerriAI/litellm/pull/13394) +* @XSAM made their first contribution in [PR #13775](https://github.com/BerriAI/litellm/pull/13775) +* @KRRT7 made their first contribution in [PR #14012](https://github.com/BerriAI/litellm/pull/14012) +* @ikaadil made their first contribution in [PR #13991](https://github.com/BerriAI/litellm/pull/13991) +* @timelfrink made their first contribution in [PR #13691](https://github.com/BerriAI/litellm/pull/13691) +* @qidu made their first contribution in [PR #13562](https://github.com/BerriAI/litellm/pull/13562) +* @nagyv made their first contribution in [PR #13243](https://github.com/BerriAI/litellm/pull/13243) +* @xywei made their first contribution in [PR #12885](https://github.com/BerriAI/litellm/pull/12885) +* @ericgtkb made their first contribution in [PR #12797](https://github.com/BerriAI/litellm/pull/12797) +* @NoWall57 made their first contribution in [PR #13945](https://github.com/BerriAI/litellm/pull/13945) +* @lmwang9527 made their first contribution in [PR #14050](https://github.com/BerriAI/litellm/pull/14050) +* @WilsonSunBritten made their first contribution in [PR #14042](https://github.com/BerriAI/litellm/pull/14042) +* @Const-antine made their first contribution in [PR #14041](https://github.com/BerriAI/litellm/pull/14041) +* @dmvieira made their first contribution in [PR #14040](https://github.com/BerriAI/litellm/pull/14040) +* @gotsysdba made their first contribution in [PR #14036](https://github.com/BerriAI/litellm/pull/14036) +* @moshemorad made their first contribution in [PR #14005](https://github.com/BerriAI/litellm/pull/14005) +* @joshualipman123 made their first contribution in [PR #13144](https://github.com/BerriAI/litellm/pull/13144) + +--- + +## **[Full Changelog](https://github.com/BerriAI/litellm/compare/v1.76.0-nightly...v1.76.1)** diff --git a/docs/my-website/release_notes/v1.76.3-stable/index.md b/docs/my-website/release_notes/v1.76.3-stable/index.md new file mode 100644 index 00000000000..6b40e4f5b35 --- /dev/null +++ b/docs/my-website/release_notes/v1.76.3-stable/index.md @@ -0,0 +1,289 @@ +--- +title: "v1.76.3-stable - Performance, Video Generation & CloudZero Integration" +slug: "v1-76-3" +date: 2025-09-06T10:00:00 +authors: + - name: Krrish Dholakia + title: CEO, LiteLLM + url: https://www.linkedin.com/in/krish-d/ + image_url: https://pbs.twimg.com/profile_images/1298587542745358340/DZv3Oj-h_400x400.jpg + - name: Ishaan Jaffer + title: CTO, LiteLLM + url: https://www.linkedin.com/in/reffajnaahsi/ + image_url: https://pbs.twimg.com/profile_images/1613813310264340481/lz54oEiB_400x400.jpg + +hide_table_of_contents: false +--- + +import Image from '@theme/IdealImage'; +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +:::warning + +This release has a known issue where startup is leading to Out of Memory errors when deploying on Kubernetes. We recommend waiting before upgrading to this version. + +::: + + +## Deploy this version + + + + +``` showLineNumbers title="docker run litellm" +docker run \ +-e STORE_MODEL_IN_DB=True \ +-p 4000:4000 \ +ghcr.io/berriai/litellm:v1.76.3 +``` + + + + +``` showLineNumbers title="pip install litellm" +pip install litellm==1.76.3 +``` + + + + +--- + +## Key Highlights + +- **Major Performance Improvements** +400 RPS when using correct amount of workers + CPU cores combination +- **Video Generation Support** - Added Google AI Studio and Vertex AI Veo Video Generation through LiteLLM Pass through routes +- **CloudZero Integration** - New cost tracking integration for exporting LiteLLM Usage and Spend data to CloudZero. + +## Major Changes +- **Performance Optimization**: LiteLLM Proxy now achieves +400 RPS when using correct amount of CPU cores - [PR #14153](https://github.com/BerriAI/litellm/pull/14153), [PR #14242](https://github.com/BerriAI/litellm/pull/14242) + + By default, LiteLLM will now use `num_workers = os.cpu_count()` to achieve optimal performance. + + **Override Options:** + + Set environment variable: + ```bash + DEFAULT_NUM_WORKERS_LITELLM_PROXY=1 + ``` + + Or start LiteLLM Proxy with: + ```bash + litellm --num_workers 1 + ``` + +- **Security Fix**: Fixed memory_usage_in_mem_cache cache endpoint vulnerability - [PR #14229](https://github.com/BerriAI/litellm/pull/14229) + +--- + +## Performance Improvements + +This release includes significant performance optimizations. On our internal benchmarks we saw 1 instance get +400 RPS when using correct amount of workers + CPU cores combination. + +- **+400 RPS Performance Boost** - LiteLLM Proxy now uses correct amount of CPU cores for optimal performance - [PR #14153](https://github.com/BerriAI/litellm/pull/14153) +- **Default CPU Workers** - Changed DEFAULT_NUM_WORKERS_LITELLM_PROXY default to number of CPUs - [PR #14242](https://github.com/BerriAI/litellm/pull/14242) + + +--- + +## New Models / Updated Models + +#### New Model Support + +| Provider | Model | Context Window | Input ($/1M tokens) | Output ($/1M tokens) | Features | +| ----------- | -------------------------------------- | -------------- | ------------------- | -------------------- | -------- | +| OpenRouter | `openrouter/openai/gpt-4.1` | 1M | $2.00 | $8.00 | Chat completions with vision | +| OpenRouter | `openrouter/openai/gpt-4.1-mini` | 1M | $0.40 | $1.60 | Efficient chat completions | +| OpenRouter | `openrouter/openai/gpt-4.1-nano` | 1M | $0.10 | $0.40 | Ultra-efficient chat | +| Vertex AI | `vertex_ai/openai/gpt-oss-20b-maas` | 131K | $0.075 | $0.30 | Reasoning support | +| Vertex AI | `vertex_ai/openai/gpt-oss-120b-maas` | 131K | $0.15 | $0.60 | Advanced reasoning | +| Gemini | `gemini/veo-3.0-generate-preview` | 1K | - | $0.75/sec | Video generation | +| Gemini | `gemini/veo-3.0-fast-generate-preview` | 1K | - | $0.40/sec | Fast video generation | +| Gemini | `gemini/veo-2.0-generate-001` | 1K | - | $0.35/sec | Video generation | +| Volcengine | `doubao-embedding-large` | 4K | Free | Free | 2048-dim embeddings | +| Together AI | `together_ai/deepseek-ai/DeepSeek-V3.1` | 128K | $0.60 | $1.70 | Reasoning support | + +#### Features + +- **[Google Gemini](../../docs/providers/gemini)** + - Added 'thoughtSignature' support via 'thinking_blocks' - [PR #14122](https://github.com/BerriAI/litellm/pull/14122) + - Added support for reasoning_effort='minimal' for Gemini models - [PR #14262](https://github.com/BerriAI/litellm/pull/14262) +- **[OpenRouter](../../docs/providers/openrouter)** + - Added GPT-4.1 model family - [PR #14101](https://github.com/BerriAI/litellm/pull/14101) +- **[Groq](../../docs/providers/groq)** + - Added support for reasoning_effort parameter - [PR #14207](https://github.com/BerriAI/litellm/pull/14207) +- **[X.AI](../../docs/providers/xai)** + - Fixed XAI cost calculation - [PR #14127](https://github.com/BerriAI/litellm/pull/14127) +- **[Vertex AI](../../docs/providers/vertex)** + - Added support for GPT-OSS models on Vertex AI - [PR #14184](https://github.com/BerriAI/litellm/pull/14184) + - Added additionalProperties to Vertex AI Schema definition - [PR #14252](https://github.com/BerriAI/litellm/pull/14252) +- **[VLLM](../../docs/providers/vllm)** + - Handle output parsing responses API output - [PR #14121](https://github.com/BerriAI/litellm/pull/14121) +- **[Ollama](../../docs/providers/ollama)** + - Added unified 'thinking' param support via `reasoning_content` - [PR #14121](https://github.com/BerriAI/litellm/pull/14121) +- **[Anthropic](../../docs/providers/anthropic)** + - Added supported text field to anthropic citation response - [PR #14126](https://github.com/BerriAI/litellm/pull/14126) +- **[OCI Provider](../../docs/providers/oci)** + - Handle assistant messages with both content and tool_calls - [PR #14171](https://github.com/BerriAI/litellm/pull/14171) +- **[Bedrock](../../docs/providers/bedrock)** + - Fixed structure output - [PR #14130](https://github.com/BerriAI/litellm/pull/14130) + - Added initial support for Bedrock Batches API - [PR #14190](https://github.com/BerriAI/litellm/pull/14190) +- **[Databricks](../../docs/providers/databricks)** + - Added support for anthropic citation API in Databricks - [PR #14077](https://github.com/BerriAI/litellm/pull/14077) + +### Bug Fixes +- **[Google Gemini (Google AI Studio + Vertex AI)](../../docs/providers/gemini)** + - Fixed Gemini 2.5 Pro schema validation with OpenAI-style type arrays in tools - [PR #14154](https://github.com/BerriAI/litellm/pull/14154) + - Fixed Gemini Tool Calling empty enum property - [PR #14155](https://github.com/BerriAI/litellm/pull/14155) + +#### New Provider Support + +- **[Volcengine](../../docs/providers/volcengine)** + - Added Volcengine embedding module with handler and transformation logic - [PR #14028](https://github.com/BerriAI/litellm/pull/14028) + +--- + +## LLM API Endpoints + +#### Features + +- **[Images API](../../docs/image_generation)** + - Added pass through image generation and image editing on OpenAI - [PR #14292](https://github.com/BerriAI/litellm/pull/14292) + - Support extra_body parameter for image generation - [PR #14211](https://github.com/BerriAI/litellm/pull/14211) +- **[Responses API](../../docs/response_api)** + - Fixed response API for reasoning item in input for litellm proxy - [PR #14200](https://github.com/BerriAI/litellm/pull/14200) + - Added structured output for SDK - [PR #14206](https://github.com/BerriAI/litellm/pull/14206) +- **[Bedrock Passthrough](../../docs/pass_through/bedrock)** + - Support AWS_BEDROCK_RUNTIME_ENDPOINT on bedrock passthrough - [PR #14156](https://github.com/BerriAI/litellm/pull/14156) +- **[Google AI Studio Passthrough](../../docs/pass_through/google_ai_studio)** + - Allow using Veo Video Generation through LiteLLM Pass through routes - [PR #14228](https://github.com/BerriAI/litellm/pull/14228) +- **General** + - Added support for safety_identifier parameter in chat.completions.create - [PR #14174](https://github.com/BerriAI/litellm/pull/14174) + - Fixed misclassified 500 error on invalid image_url in /chat/completions request - [PR #14149](https://github.com/BerriAI/litellm/pull/14149) + - Fixed token count error for Gemini CLI - [PR #14133](https://github.com/BerriAI/litellm/pull/14133) + +#### Bugs + +- **General** + - Remove "/" or ":" from model name when being used as h11 header name - [PR #14191](https://github.com/BerriAI/litellm/pull/14191) + - Bug fix for openai.gpt-oss when using reasoning_effort parameter - [PR #14300](https://github.com/BerriAI/litellm/pull/14300) + +--- + +## Spend Tracking, Budgets and Rate Limiting + +### Features + - Added header support for spend_logs_metadata - [PR #14186](https://github.com/BerriAI/litellm/pull/14186) + - Litellm passthrough cost tracking for chat completion - [PR #14256](https://github.com/BerriAI/litellm/pull/14256) + +### Bug Fixes + - Fixed TPM Rate Limit Bug - [PR #14237](https://github.com/BerriAI/litellm/pull/14237) + - Fixed Key Budget not resets at expectable times - [PR #14241](https://github.com/BerriAI/litellm/pull/14241) + + + +## Management Endpoints / UI + +#### Features + +- **UI Improvements** + - Logs page screen size fixed - [PR #14135](https://github.com/BerriAI/litellm/pull/14135) + - Create Organization Tooltip added on Success - [PR #14132](https://github.com/BerriAI/litellm/pull/14132) + - Back to Keys should say Back to Logs - [PR #14134](https://github.com/BerriAI/litellm/pull/14134) + - Add client side pagination on All Models table - [PR #14136](https://github.com/BerriAI/litellm/pull/14136) + - Model Filters UI improvement - [PR #14131](https://github.com/BerriAI/litellm/pull/14131) + - Remove table filter on user info page - [PR #14169](https://github.com/BerriAI/litellm/pull/14169) + - Team name badge added on the User Details - [PR #14003](https://github.com/BerriAI/litellm/pull/14003) + - Fix: Log page parameter passing error - [PR #14193](https://github.com/BerriAI/litellm/pull/14193) +- **Authentication & Authorization** + - Support for ES256/ES384/ES512 and EdDSA JWT verification - [PR #14118](https://github.com/BerriAI/litellm/pull/14118) + - Ensure `team_id` is a required field for generating service account keys - [PR #14270](https://github.com/BerriAI/litellm/pull/14270) + +#### Bugs + +- **General** + - Validate store model in db setting - [PR #14269](https://github.com/BerriAI/litellm/pull/14269) + +--- + +## Logging / Guardrail Integrations + +#### Features + +- **[Datadog](../../docs/proxy/logging#datadog)** + - Ensure `apm_id` is set on DD LLM Observability traces - [PR #14272](https://github.com/BerriAI/litellm/pull/14272) +- **[Braintrust](../../docs/proxy/logging#braintrust)** + - Fix logging when OTEL is enabled - [PR #14122](https://github.com/BerriAI/litellm/pull/14122) +- **[OTEL](../../docs/proxy/logging#otel)** + - Optional Metrics and Logs following semantic conventions - [PR #14179](https://github.com/BerriAI/litellm/pull/14179) +- **[Slack Alerting](../../docs/proxy/alerting)** + - Added alert type to alert message to slack for easier handling - [PR #14176](https://github.com/BerriAI/litellm/pull/14176) + +#### Guardrails + - Added guardrail to the Anthropic API endpoint - [PR #14107](https://github.com/BerriAI/litellm/pull/14107) + +#### New Integration + +- **[CloudZero](../../docs/proxy/cost_tracking)** + - LiteLLM x CloudZero Integration for Cost Tracking - [PR #14296](https://github.com/BerriAI/litellm/pull/14296) + +--- + +## Performance / Loadbalancing / Reliability improvements + +#### Features + +- **Performance** + - LiteLLM Proxy: +400 RPS when using correct amount of CPU cores - [PR #14153](https://github.com/BerriAI/litellm/pull/14153) + - Allow using `x-litellm-stream-timeout` header for stream timeout in requests - [PR #14147](https://github.com/BerriAI/litellm/pull/14147) + - Change DEFAULT_NUM_WORKERS_LITELLM_PROXY default to number CPUs - [PR #14242](https://github.com/BerriAI/litellm/pull/14242) +- **Monitoring** + - Added Prometheus missing metrics - [PR #14139](https://github.com/BerriAI/litellm/pull/14139) +- **Timeout** + - **Stream Timeout Control** - Allow using `x-litellm-stream-timeout` header for stream timeout in requests - [PR #14147](https://github.com/BerriAI/litellm/pull/14147) +- **Routing** + - Fixed x-litellm-tags not routing with Responses API - [PR #14289](https://github.com/BerriAI/litellm/pull/14289) + +#### Bugs + +- **Security** + - Fixed memory_usage_in_mem_cache cache endpoint vulnerability - [PR #14229](https://github.com/BerriAI/litellm/pull/14229) + +--- + +## General Proxy Improvements + +#### Features + +- **SCIM Support** + - Added better SCIM debugging - [PR #14221](https://github.com/BerriAI/litellm/pull/14221) + - Bug fixes for handling SCIM Group Memberships - [PR #14226](https://github.com/BerriAI/litellm/pull/14226) +- **Kubernetes** + - Added optional PodDisruptionBudget for litellm proxy - [PR #14093](https://github.com/BerriAI/litellm/pull/14093) +- **Error Handling** + - Add model to azure error message - [PR #14294](https://github.com/BerriAI/litellm/pull/14294) + +--- + +## New Contributors +* @iabhi4 made their first contribution in [PR #14093](https://github.com/BerriAI/litellm/pull/14093) +* @zainhas made their first contribution in [PR #14087](https://github.com/BerriAI/litellm/pull/14087) +* @LifeDJIK made their first contribution in [PR #14146](https://github.com/BerriAI/litellm/pull/14146) +* @retanoj made their first contribution in [PR #14133](https://github.com/BerriAI/litellm/pull/14133) +* @zhxlp made their first contribution in [PR #14193](https://github.com/BerriAI/litellm/pull/14193) +* @kayoch1n made their first contribution in [PR #14191](https://github.com/BerriAI/litellm/pull/14191) +* @kutsushitaneko made their first contribution in [PR #14171](https://github.com/BerriAI/litellm/pull/14171) +* @mjmendo made their first contribution in [PR #14176](https://github.com/BerriAI/litellm/pull/14176) +* @HarshavardhanK made their first contribution in [PR #14213](https://github.com/BerriAI/litellm/pull/14213) +* @eycjur made their first contribution in [PR #14207](https://github.com/BerriAI/litellm/pull/14207) +* @22mSqRi made their first contribution in [PR #14241](https://github.com/BerriAI/litellm/pull/14241) +* @onlylhf made their first contribution in [PR #14028](https://github.com/BerriAI/litellm/pull/14028) +* @btpemercier made their first contribution in [PR #11319](https://github.com/BerriAI/litellm/pull/11319) +* @tremlin made their first contribution in [PR #14287](https://github.com/BerriAI/litellm/pull/14287) +* @TobiMayr made their first contribution in [PR #14262](https://github.com/BerriAI/litellm/pull/14262) +* @Eitan1112 made their first contribution in [PR #14252](https://github.com/BerriAI/litellm/pull/14252) + +--- + +## **[Full Changelog](https://github.com/BerriAI/litellm/compare/v1.76.1-nightly...v1.76.3-nightly)** diff --git a/docs/my-website/release_notes/v1.77.2-stable/index.md b/docs/my-website/release_notes/v1.77.2-stable/index.md new file mode 100644 index 00000000000..fdd80693d05 --- /dev/null +++ b/docs/my-website/release_notes/v1.77.2-stable/index.md @@ -0,0 +1,156 @@ +--- +title: "v1.77.2-stable - Bedrock Batches API" +slug: "v1-77-2" +date: 2025-09-13T10:00:00 +authors: + - name: Krrish Dholakia + title: CEO, LiteLLM + url: https://www.linkedin.com/in/krish-d/ + image_url: https://pbs.twimg.com/profile_images/1298587542745358340/DZv3Oj-h_400x400.jpg + - name: Ishaan Jaffer + title: CTO, LiteLLM + url: https://www.linkedin.com/in/reffajnaahsi/ + image_url: https://pbs.twimg.com/profile_images/1613813310264340481/lz54oEiB_400x400.jpg + +hide_table_of_contents: false +--- + +import Image from '@theme/IdealImage'; +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +## Deploy this version + + + + +``` showLineNumbers title="docker run litellm" +docker run \ +-e STORE_MODEL_IN_DB=True \ +-p 4000:4000 \ +ghcr.io/berriai/litellm:main-v1.77.2-stable +``` + + + + + +``` showLineNumbers title="pip install litellm" +pip install litellm==1.77.2.post1 +``` + + + + +--- + +## Key Highlights + +- **Bedrock Batches API** - Support for creating Batch Inference Jobs on Bedrock using LiteLLM's unified batch API (OpenAI compatible) +- **Qwen API Tiered Pricing** - Cost tracking support for Dashscope (Qwen) models with multiple pricing tiers + +## New Models / Updated Models + +#### New Model Support + +| Provider | Model | Context Window | Pricing ($/1M tokens) | Features | +| ----------- | ------------------------------- | -------------- | --------------------- | -------- | +| DeepInfra | `deepinfra/deepseek-ai/DeepSeek-R1` | 164K | **Input:** $0.70
**Output:** $2.40 | Chat completions, tool calling | +| Heroku | `heroku/claude-4-sonnet` | 8K | Contact provider for pricing | Function calling, tool choice | +| Heroku | `heroku/claude-3-7-sonnet` | 8K | Contact provider for pricing | Function calling, tool choice | +| Heroku | `heroku/claude-3-5-sonnet-latest` | 8K | Contact provider for pricing | Function calling, tool choice | +| Heroku | `heroku/claude-3-5-haiku` | 4K | Contact provider for pricing | Function calling, tool choice | +| Dashscope | `dashscope/qwen-plus-latest` | 1M | **Tiered Pricing:**
• 0-256K tokens: $0.40 / $1.20
• 256K-1M tokens: $1.20 / $3.60 | Function calling, reasoning | +| Dashscope | `dashscope/qwen3-max-preview` | 262K | **Tiered Pricing:**
• 0-32K tokens: $1.20 / $6.00
• 32K-128K tokens: $2.40 / $12.00
• 128K-252K tokens: $3.00 / $15.00 | Function calling, reasoning | +| Dashscope | `dashscope/qwen-flash` | 1M | **Tiered Pricing:**
• 0-256K tokens: $0.05 / $0.40
• 256K-1M tokens: $0.25 / $2.00 | Function calling, reasoning | +| Dashscope | `dashscope/qwen3-coder-plus` | 1M | **Tiered Pricing:**
• 0-32K tokens: $1.00 / $5.00
• 32K-128K tokens: $1.80 / $9.00
• 128K-256K tokens: $3.00 / $15.00
• 256K-1M tokens: $6.00 / $60.00 | Function calling, reasoning, caching | +| Dashscope | `dashscope/qwen3-coder-flash` | 1M | **Tiered Pricing:**
• 0-32K tokens: $0.30 / $1.50
• 32K-128K tokens: $0.50 / $2.50
• 128K-256K tokens: $0.80 / $4.00
• 256K-1M tokens: $1.60 / $9.60 | Function calling, reasoning, caching | + +--- + +#### Features + +- **[Bedrock](../../docs/providers/bedrock_batches)** + - Bedrock Batches API - batch processing support with file upload and request transformation - [PR #14518](https://github.com/BerriAI/litellm/pull/14518), [PR #14522](https://github.com/BerriAI/litellm/pull/14522) +- **[VLLM](../../docs/providers/vllm)** + - Added transcription endpoint support - [PR #14523](https://github.com/BerriAI/litellm/pull/14523) +- **[Ollama](../../docs/providers/ollama)** + - `ollama_chat/` - images, thinking, and content as list handling - [PR #14523](https://github.com/BerriAI/litellm/pull/14523) +- **General** + - New debug flag for detailed request/response logging [PR #14482](https://github.com/BerriAI/litellm/pull/14482) + +#### Bug Fixes + +- **[Azure OpenAI](../../docs/providers/azure)** + - Fixed extra_body injection causing payload rejection in image generation - [PR #14475](https://github.com/BerriAI/litellm/pull/14475) +- **[LM Studio](../../docs/providers/lm-studio)** + - Resolved illegal Bearer header value issue - [PR #14512](https://github.com/BerriAI/litellm/pull/14512) + +--- + +## LLM API Endpoints + +#### Bug Fixes + +- **[/messages](../../docs/anthropic_unified)** + - Don't send content block after message w/ finish reason + usage block - [PR #14477](https://github.com/BerriAI/litellm/pull/14477) +- **[/generateContent](../../docs/generateContent)** + - Gemini CLI Integration - Fixed token count errors - [PR #14451](https://github.com/BerriAI/litellm/pull/14451), [PR #14417](https://github.com/BerriAI/litellm/pull/14417) + +--- + +## Spend Tracking, Budgets and Rate Limiting + +#### Features + +- **[Qwen API Tiered Pricing](../../docs/providers/dashscope)** - Added comprehensive tiered cost tracking for Dashscope/Qwen models - [PR #14471](https://github.com/BerriAI/litellm/pull/14471), [PR #14479](https://github.com/BerriAI/litellm/pull/14479) + +#### Bug Fixes + +- **Provider Budgets** - Fixed provider budget calculations - [PR #14459](https://github.com/BerriAI/litellm/pull/14459) + +--- + +## Management Endpoints / UI + +#### Features + +- **User Headers Mapping** - New X-LiteLLM Users mapping feature for enhanced user tracking - [PR #14485](https://github.com/BerriAI/litellm/pull/14485) +- **Key Unblocking** - Support for hashed tokens in `/key/unblock` endpoint - [PR #14477](https://github.com/BerriAI/litellm/pull/14477) +- **Model Group Header Forwarding** - Enhanced wildcard model support with documentation - [PR #14528](https://github.com/BerriAI/litellm/pull/14528) + +#### Bug Fixes + +- **Log Tab Key Alias** - Fixed filtering inaccuracies for failed logs - [PR #14469](https://github.com/BerriAI/litellm/pull/14469), [PR #14529](https://github.com/BerriAI/litellm/pull/14529) + +--- + +## Logging / Guardrail Integrations + +#### Features + +- **Noma Integration** - Added non-blocking monitor mode with anonymize input support - [PR #14401](https://github.com/BerriAI/litellm/pull/14401) + +--- + +## Performance / Loadbalancing / Reliability improvements + +#### Performance +- Removed dynamic creation of static values - [PR #14538](https://github.com/BerriAI/litellm/pull/14538) +- Using `_PROXY_MaxParallelRequestsHandler_v3` by default for optimal throughput - [PR #14450](https://github.com/BerriAI/litellm/pull/14450) +- Improved execution context propagation into logging tasks - [PR #14455](https://github.com/BerriAI/litellm/pull/14455) + +--- + + + +## New Contributors +* @Sameerlite made their first contribution in [PR #14460](https://github.com/BerriAI/litellm/pull/14460) +* @holzman made their first contribution in [PR #14459](https://github.com/BerriAI/litellm/pull/14459) +* @sashank5644 made their first contribution in [PR #14469](https://github.com/BerriAI/litellm/pull/14469) +* @TomAlon made their first contribution in [PR #14401](https://github.com/BerriAI/litellm/pull/14401) +* @AlexsanderHamir made their first contribution in [PR #14538](https://github.com/BerriAI/litellm/pull/14538) + +--- + +## **[Full Changelog](https://github.com/BerriAI/litellm/compare/v1.77.1.dev.2...v1.77.2.dev)** diff --git a/docs/my-website/release_notes/v1.77.3-stable/index.md b/docs/my-website/release_notes/v1.77.3-stable/index.md new file mode 100644 index 00000000000..adea34f9782 --- /dev/null +++ b/docs/my-website/release_notes/v1.77.3-stable/index.md @@ -0,0 +1,258 @@ +--- +title: "[Preview] v1.77.3-stable - Priority Based Rate Limiting" +slug: "v1-77-3" +date: 2025-09-21T10:00:00 +authors: + - name: Krrish Dholakia + title: CEO, LiteLLM + url: https://www.linkedin.com/in/krish-d/ + image_url: https://pbs.twimg.com/profile_images/1298587542745358340/DZv3Oj-h_400x400.jpg + - name: Ishaan Jaff + title: CTO, LiteLLM + url: https://www.linkedin.com/in/reffajnaahsi/ + image_url: https://pbs.twimg.com/profile_images/1613813310264340481/lz54oEiB_400x400.jpg + +hide_table_of_contents: false +--- + +import Image from '@theme/IdealImage'; +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +## Deploy this version + + + + +``` showLineNumbers title="docker run litellm" +docker run \ +-e STORE_MODEL_IN_DB=True \ +-p 4000:4000 \ +ghcr.io/berriai/litellm:main-v1.77.3.rc.1 +``` + + + + + +``` showLineNumbers title="pip install litellm" +pip install litellm==1.77.3 +``` + + + + +--- + +## Key Highlights + +- **+550 RPS Performance Improvements** - Optimizations in request handling and object initialization. +- **Priority Quota Reservation** - Proxy admins can now reserve TPM/RPM capacity for specific keys. + +## Priority Quota Reservation + +This release adds support for priority quota reservation. This allows **Proxy Admins** to reserve TPM/RPM capacity for keys based on metadata priority levels, ensuring critical production workloads get guaranteed access regardless of development traffic volume. + +Get started [here](../../docs/proxy/dynamic_rate_limit#priority-quota-reservation) + + + + +## New Models / Updated Models + +#### New Model Support + +| Provider | Model | Context Window | Input ($/1M tokens) | Output ($/1M tokens) | Features | +| -------- | ----- | -------------- | ------------------- | -------------------- | -------- | +| SambaNova | `sambanova/deepseek-v3.1` | 128K | $0.90 | $0.90 | Chat completions | +| SambaNova | `sambanova/gpt-oss-120b` | 128K | $0.72 | $0.72 | Chat completions | +| OVHCloud | Various models | Varies | Contact provider | Contact provider | Chat completions | +| CompactifAI | Various models | Varies | Contact provider | Contact provider | Chat completions | +| TwelveLabs | `twelvelabs/marengo-embed-2.7` | 32K | $0.12 | $0.00 | Embeddings | + +#### Features + +- **[OVHCloud AI Endpoints](../../docs/providers/ovhcloud)** + - New provider support with comprehensive model catalog - [PR #14494](https://github.com/BerriAI/litellm/pull/14494) +- **[CompactifAI](../../docs/providers/compactifai)** + - New provider integration - [PR #14532](https://github.com/BerriAI/litellm/pull/14532) +- **[SambaNova](../../docs/providers/sambanova)** + - Added DeepSeek v3.1 and GPT-OSS-120B models - [PR #14500](https://github.com/BerriAI/litellm/pull/14500) +- **[Bedrock](../../docs/providers/bedrock)** + - Cross-region inference profile cost calculation - [PR #14566](https://github.com/BerriAI/litellm/pull/14566) + - AWS external ID parameter support for authentication - [PR #14582](https://github.com/BerriAI/litellm/pull/14582) + - CountTokens API implementation - [PR #14557](https://github.com/BerriAI/litellm/pull/14557) + - Titan V2 encoding_format parameter support - [PR #14687](https://github.com/BerriAI/litellm/pull/14687) + - Nova Canvas image generation inference profiles - [PR #14578](https://github.com/BerriAI/litellm/pull/14578) + - Bedrock Batches API - batch processing support with file upload and request transformation - [PR #14618](https://github.com/BerriAI/litellm/pull/14618) + - Bedrock Twelve Labs embedding provider support - [PR #14697](https://github.com/BerriAI/litellm/pull/14697) +- **[Vertex AI](../../docs/providers/vertex)** + - Gemini labels field provider-aware filtering - [PR #14563](https://github.com/BerriAI/litellm/pull/14563) + - Gemini Batch API support - [PR #14733](https://github.com/BerriAI/litellm/pull/14733) +- **[Volcengine](../../docs/providers/volcengine)** + - Fixed thinking parameters when disabled - [PR #14569](https://github.com/BerriAI/litellm/pull/14569) +- **[Cohere](../../docs/providers/cohere)** + - Handle Generate API deprecation, default to chat endpoints - [PR #14676](https://github.com/BerriAI/litellm/pull/14676) +- **[TwelveLabs](../../docs/providers/twelvelabs)** + - Added Marengo Embed 2.7 embedding support - [PR #14674](https://github.com/BerriAI/litellm/pull/14674) + +### Bug Fixes + +- **[Bedrock](../../docs/providers/bedrock)** + - Empty arguments handling in tool call invocation - [PR #14583](https://github.com/BerriAI/litellm/pull/14583) +- **[Vertex AI](../../docs/providers/vertex)** + - Avoid deepcopy crash with non-pickleables in Gemini/Vertex - [PR #14418](https://github.com/BerriAI/litellm/pull/14418) +- **[XAI](../../docs/providers/xai)** + - Fix unsupported stop parameter for grok-code models - [PR #14565](https://github.com/BerriAI/litellm/pull/14565) +- **[Gemini](../../docs/providers/gemini)** + - Updated error message for Gemini API - [PR #14589](https://github.com/BerriAI/litellm/pull/14589) + - Fixed 2.5 Flash Image Preview model routing - [PR #14715](https://github.com/BerriAI/litellm/pull/14715) + - API key passing for token counting endpoints - [PR #14744](https://github.com/BerriAI/litellm/pull/14744) + +#### New Provider Support + +- **[OVHCloud AI Endpoints](../../docs/providers/ovhcloud)** + - Complete provider integration with model catalog and authentication - [PR #14494](https://github.com/BerriAI/litellm/pull/14494) +- **[CompactifAI](../../docs/providers/compactifai)** + - New provider support with documentation - [PR #14532](https://github.com/BerriAI/litellm/pull/14532) + +--- + +## LLM API Endpoints + +#### Features + +- **[/responses](../../docs/response_api)** + - Added cancel endpoint support for non-admin users - [PR #14594](https://github.com/BerriAI/litellm/pull/14594) + - Improved response session handling and cold storage configuration with s3 - [PR #14534](https://github.com/BerriAI/litellm/pull/14534) + - Added OpenAI & Azure /responses/cancel endpoint support - [PR #14561](https://github.com/BerriAI/litellm/pull/14561) +- **General** + - Enhanced rate limit error messages with details - [PR #14736](https://github.com/BerriAI/litellm/pull/14736) + - Middle-truncation for spend log payloads - [PR #14637](https://github.com/BerriAI/litellm/pull/14637) + +#### Bugs + +- **[/chat/completions](../../docs/completion/input)** + - Fixed completion chat ID handling - [PR #14548](https://github.com/BerriAI/litellm/pull/14548) + - Prevent AttributeError for _get_tags_from_request_kwargs - [PR #14735](https://github.com/BerriAI/litellm/pull/14735) +- **[/responses](../../docs/response_api)** + - Fixed cost calculation - [PR #14675](https://github.com/BerriAI/litellm/pull/14675) +- **General** + - Rate limiter AttributeError fix - [PR #14609](https://github.com/BerriAI/litellm/pull/14609) + +--- + +## Spend Tracking, Budgets and Rate Limiting + +- **Responses API Cost Calculation** fix - [PR #14675](https://github.com/BerriAI/litellm/pull/14675) +- **Anthropic Cache Token Pricing** - Separate 1-hour vs 5-minute cache creation costs - [PR #14620](https://github.com/BerriAI/litellm/pull/14620), [PR #14652](https://github.com/BerriAI/litellm/pull/14652) +- **Indochina Time Timezone** support for budget resets - [PR #14666](https://github.com/BerriAI/litellm/pull/14666) +- **Soft Budget Alert Cache Issues** - Resolved soft budget alert cache issues - [PR #14491](https://github.com/BerriAI/litellm/pull/14491) +- **Dynamic Rate Limiter v3** - Priority routing improvements - [PR #14734](https://github.com/BerriAI/litellm/pull/14734) +- **Enhanced Rate Limit Errors** - More detailed error messages - [PR #14736](https://github.com/BerriAI/litellm/pull/14736) + +--- + +## Management Endpoints / UI + +#### Features + +- **Team Member Service Account Keys** - Allow team members to view keys they create - [PR #14619](https://github.com/BerriAI/litellm/pull/14619) +- **Default Budget for JWT Teams** - Auto-assign budgets to generated teams - [PR #14514](https://github.com/BerriAI/litellm/pull/14514) +- **SSO Access Control Groups** - Enhanced token info endpoint integration - [PR #14738](https://github.com/BerriAI/litellm/pull/14738) +- **Health Test Connect Protection** - Restrict access based on model creation permissions - [PR #14650](https://github.com/BerriAI/litellm/pull/14650) +- **Amazon Bedrock Guardrail Info View** - Enhanced logging visualization - [PR #14696](https://github.com/BerriAI/litellm/pull/14696) + +#### Bug Fixes + +- **SCIM v2** - Fix group PUSH and PUT operations for non-existent members - [PR #14581](https://github.com/BerriAI/litellm/pull/14581) +- **Guardrail View/Edit/Delete** behavior fixes - [PR #14622](https://github.com/BerriAI/litellm/pull/14622) +- **In-Memory Guardrail** update failures - [PR #14653](https://github.com/BerriAI/litellm/pull/14653) + +--- + +## Logging / Guardrail Integrations + +#### Features + +- **[DataDog](../../docs/proxy/logging#datadog)** + - Enhanced spend tracking metrics - [PR #14555](https://github.com/BerriAI/litellm/pull/14555) + - Stream support with is_streamed_request parameter - [PR #14673](https://github.com/BerriAI/litellm/pull/14673) + - Fixed tool calls metadata passing - [PR #14531](https://github.com/BerriAI/litellm/pull/14531) +- **[Langfuse](../../docs/proxy/logging#langfuse)** + - Added logging support for Responses API - [PR #14597](https://github.com/BerriAI/litellm/pull/14597) +- **[Langsmith](../../docs/proxy/logging#langsmith)** + - Langsmith Sampling Rate - Key/Team-level tracing configuration - [PR #14740](https://github.com/BerriAI/litellm/pull/14740) +- **[Prometheus](../../docs/proxy/logging#prometheus)** + - Multi-worker support improvements - [PR #14530](https://github.com/BerriAI/litellm/pull/14530) + - User email labels in monitoring - [PR #14520](https://github.com/BerriAI/litellm/pull/14520) +- **[Opik](../../docs/proxy/logging#opik)** + - Fixed timezone issue - [PR #14708](https://github.com/BerriAI/litellm/pull/14708) + +### Bug Fixes + +- **[S3](../../docs/proxy/logging#s3-buckets)** + - Fixed 404 error when using s3_endpoint_url - [PR #14559](https://github.com/BerriAI/litellm/pull/14559) + +#### Guardrails + +- **Tool Permission Guardrail** - Fine-grained tool access control - [PR #14519](https://github.com/BerriAI/litellm/pull/14519) +- **Bedrock Guardrails** - Selective guarding support with runtime endpoint configuration - [PR #14575](https://github.com/BerriAI/litellm/pull/14575), [PR #14650](https://github.com/BerriAI/litellm/pull/14650) +- **Default Last Message** in guardrails - [PR #14640](https://github.com/BerriAI/litellm/pull/14640) +- **AWS exceptions handling despite 200 response** - [PR #14658](https://github.com/BerriAI/litellm/pull/14658) +#### New Integration + +- **[PostHog](../../docs/observability/posthog)** - Complete observability integration for LiteLLM usage tracking and analytics - [PR #14610](https://github.com/BerriAI/litellm/pull/14610) + +--- + + +## MCP Gateway + +- **MCP Server Alias Parsing** - Multi-part URL path support - [PR #14558](https://github.com/BerriAI/litellm/pull/14558) +- **MCP Filter Recomputation** - After server deletion - [PR #14542](https://github.com/BerriAI/litellm/pull/14542) +- **MCP Gateway Tools List** improvements - [PR #14695](https://github.com/BerriAI/litellm/pull/14695) + +--- + +## Performance / Loadbalancing / Reliability improvements + +- **+500 RPS Performance Boost** when sending the `user` field - [PR #14616](https://github.com/BerriAI/litellm/pull/14616) +- **+50 RPS** by removing iscoroutine from hot path - [PR #14649](https://github.com/BerriAI/litellm/pull/14649) +- **7% reduction** in __init__ overhead - [PR #14689](https://github.com/BerriAI/litellm/pull/14689) +- **Generic Object Pool** implementation for better resource management - [PR #14702](https://github.com/BerriAI/litellm/pull/14702) + +--- + +## General Proxy Improvements + +- **Middle-Truncation** for spend log payloads - [PR #14637](https://github.com/BerriAI/litellm/pull/14637) + +#### Security + +- **Security Update** - Bump aiohttp==3.12.14, fix CVE-2025-53643 - [PR #14638](https://github.com/BerriAI/litellm/pull/14638) + +--- + +## New Contributors + +* @luisfucros made their first contribution in [PR #14500](https://github.com/BerriAI/litellm/pull/14500) +* @hanakannzashi made their first contribution in [PR #14548](https://github.com/BerriAI/litellm/pull/14548) +* @eliasto made their first contribution in [PR #14494](https://github.com/BerriAI/litellm/pull/14494) +* @Rasmusafj made their first contribution in [PR #14491](https://github.com/BerriAI/litellm/pull/14491) +* @LingXuanYin made their first contribution in [PR #14569](https://github.com/BerriAI/litellm/pull/14569) +* @ronaldpereira made their first contribution in [PR #14613](https://github.com/BerriAI/litellm/pull/14613) +* @hula-la made their first contribution in [PR #14534](https://github.com/BerriAI/litellm/pull/14534) +* @carlos-marchal-ph made their first contribution in [PR #14610](https://github.com/BerriAI/litellm/pull/14610) +* @akraines made their first contribution in [PR #14637](https://github.com/BerriAI/litellm/pull/14637) +* @mrFranklin made their first contribution in [PR #14708](https://github.com/BerriAI/litellm/pull/14708) +* @tcx4c70 made their first contribution in [PR #14675](https://github.com/BerriAI/litellm/pull/14675) +* @michaeltansg made their first contribution in [PR #14666](https://github.com/BerriAI/litellm/pull/14666) +* @tosi29 made their first contribution in [PR #14725](https://github.com/BerriAI/litellm/pull/14725) +* @gmdfalk made their first contribution in [PR #14735](https://github.com/BerriAI/litellm/pull/14735) +* @FelipeRodriguesGare made their first contribution in [PR #14733](https://github.com/BerriAI/litellm/pull/14733) +* @mritunjaysharma394 made their first contribution in [PR #14678](https://github.com/BerriAI/litellm/pull/14678) + +--- + +## **[Full Changelog](https://github.com/BerriAI/litellm/compare/v1.77.2.rc.1...v1.77.3.rc.1)** diff --git a/docs/my-website/sidebars.js b/docs/my-website/sidebars.js index f81ecda3916..cb68e9024d6 100644 --- a/docs/my-website/sidebars.js +++ b/docs/my-website/sidebars.js @@ -40,6 +40,7 @@ const sidebars = { "proxy/guardrails/guardrails_ai", "proxy/guardrails/lakera_ai", "proxy/guardrails/model_armor", + "proxy/guardrails/noma_security", "proxy/guardrails/openai_moderation", "proxy/guardrails/pangea", "proxy/guardrails/pillar_security", @@ -48,6 +49,7 @@ const sidebars = { "proxy/guardrails/secret_detection", "proxy/guardrails/custom_guardrail", "proxy/guardrails/prompt_injection", + "proxy/guardrails/tool_permission", ].sort(), ], }, @@ -55,31 +57,31 @@ const sidebars = { type: "category", label: "Alerting & Monitoring", items: [ - "proxy/prometheus", "proxy/alerting", - "proxy/pagerduty" - ].sort() + "proxy/pagerduty", + "proxy/prometheus" + ] }, { type: "category", label: "[Beta] Prompt Management", items: [ - "proxy/prompt_management", + "proxy/custom_prompt_management", "proxy/native_litellm_prompt", - "proxy/custom_prompt_management" - ].sort() + "proxy/prompt_management" + ] }, { type: "category", label: "AI Tools (OpenWebUI, Claude Code, etc.)", items: [ - "tutorials/openweb_ui", - "tutorials/openai_codex", - "tutorials/litellm_gemini_cli", - "tutorials/litellm_qwen_code_cli", - "tutorials/github_copilot_integration", "tutorials/claude_responses_api", "tutorials/cost_tracking_coding", + "tutorials/github_copilot_integration", + "tutorials/litellm_gemini_cli", + "tutorials/litellm_qwen_code_cli", + "tutorials/openai_codex", + "tutorials/openweb_ui" ] }, @@ -108,40 +110,64 @@ const sidebars = { type: "category", label: "Setup & Deployment", items: [ - "proxy/deploy", - "proxy/prod", + "proxy/quick_start", "proxy/cli", - "proxy/release_cycle", - "proxy/model_management", - "proxy/health", "proxy/debugging", + "proxy/deploy", + "proxy/health", "proxy/master_key_rotations", + "proxy/model_management", + "proxy/prod", + "proxy/release_cycle", ], }, "proxy/demo", + { + type: "category", + label: "Admin UI", + items: [ + "proxy/admin_ui_sso", + "proxy/custom_root_ui", + "proxy/custom_sso", + "proxy/model_hub", + "proxy/public_teams", + "proxy/self_serve", + "proxy/ui", + "proxy/ui/bulk_edit_users", + "proxy/ui_credentials", + "tutorials/scim_litellm", + { + type: "category", + label: "UI Logs", + items: [ + "proxy/ui_logs", + "proxy/ui_logs_sessions" + ] + } + ], + }, { type: "category", label: "Architecture", - items: ["proxy/architecture", "proxy/control_plane_and_data_plane", "proxy/db_info", "proxy/db_deadlocks", "router_architecture", "proxy/user_management_heirarchy", "proxy/jwt_auth_arch", "proxy/image_handling", "proxy/spend_logs_deletion"], + items: [ + "proxy/architecture", + "proxy/control_plane_and_data_plane", + "proxy/db_deadlocks", + "proxy/db_info", + "proxy/image_handling", + "proxy/jwt_auth_arch", + "proxy/spend_logs_deletion", + "proxy/user_management_heirarchy", + "router_architecture" + ], }, { type: "link", label: "All Endpoints (Swagger)", href: "https://litellm-api.up.railway.app/", }, - "proxy/enterprise", - "proxy/management_cli", - { - type: "category", - label: "Making LLM Requests", - items: [ - "proxy/user_keys", - "proxy/clientside_auth", - "proxy/request_headers", - "proxy/response_headers", - "proxy/model_discovery", - ], - }, + "proxy/enterprise", + "proxy/management_cli", { type: "category", label: "Authentication", @@ -159,45 +185,25 @@ const sidebars = { }, { type: "category", - label: "Model Access", + label: "Budgets + Rate Limits", items: [ - "proxy/model_access", - "proxy/team_model_add" - ] - }, - { - type: "category", - label: "Admin UI", - items: [ - "proxy/ui", - "proxy/admin_ui_sso", - "proxy/custom_root_ui", - "proxy/model_hub", - "proxy/self_serve", - "proxy/public_teams", - "tutorials/scim_litellm", - "proxy/custom_sso", - "proxy/ui_credentials", - "proxy/ui/bulk_edit_users", - { - type: "category", - label: "UI Logs", - items: [ - "proxy/ui_logs", - "proxy/ui_logs_sessions" - ] - } + "proxy/customers", + "proxy/dynamic_rate_limit", + "proxy/rate_limit_tiers", + "proxy/team_budgets", + "proxy/temporary_budget_increase", + "proxy/users" ], }, + "proxy/caching", { type: "category", - label: "Spend Tracking", - items: ["proxy/cost_tracking", "proxy/custom_pricing", "proxy/billing",], - }, - { - type: "category", - label: "Budgets + Rate Limits", - items: ["proxy/users", "proxy/temporary_budget_increase", "proxy/rate_limit_tiers", "proxy/team_budgets", "proxy/customers"], + label: "Create Custom Plugins", + description: "Modify requests, responses, and more", + items: [ + "proxy/call_hooks", + "proxy/rules", + ] }, { type: "link", @@ -208,13 +214,32 @@ const sidebars = { type: "category", label: "Logging, Alerting, Metrics", items: [ + "proxy/dynamic_logging", "proxy/logging", "proxy/logging_spec", - "proxy/team_logging", - "proxy/dynamic_logging" + "proxy/team_logging" ], }, - + { + type: "category", + label: "Making LLM Requests", + items: [ + "proxy/user_keys", + "proxy/clientside_auth", + "proxy/request_headers", + "proxy/response_headers", + "proxy/forward_client_headers", + "proxy/model_discovery", + ], + }, + { + type: "category", + label: "Model Access", + items: [ + "proxy/model_access", + "proxy/team_model_add" + ] + }, { type: "category", label: "Secret Managers", @@ -225,14 +250,13 @@ const sidebars = { }, { type: "category", - label: "Create Custom Plugins", - description: "Modify requests, responses, and more", + label: "Spend Tracking", items: [ - "proxy/call_hooks", - "proxy/rules", - ] + "proxy/billing", + "proxy/cost_tracking", + "proxy/custom_pricing" + ], }, - "proxy/caching", ] }, { @@ -246,6 +270,23 @@ const sidebars = { slug: "/supported_endpoints", }, items: [ + "assistants", + { + type: "category", + label: "/audio", + items: [ + "audio_transcription", + "text_to_speech", + ] + }, + { + type: "category", + label: "/batches", + items: [ + "batches", + "proxy/managed_batches", + ] + }, { type: "category", label: "/chat/completions", @@ -259,59 +300,11 @@ const sidebars = { "completion/input", "completion/output", "completion/usage", + "completion/http_handler_config", ], }, - "response_api", "text_completion", "embedding/supported_embedding", - "anthropic_unified", - "mcp", - "generateContent", - { - type: "category", - label: "/images", - items: [ - "image_generation", - "image_edits", - "image_variations", - ] - }, - { - type: "category", - label: "/audio", - "items": [ - "audio_transcription", - "text_to_speech", - ] - }, - { - type: "category", - label: "/vector_stores", - items: [ - "vector_stores/search", - ] - }, - { - type: "category", - label: "Pass-through Endpoints (Anthropic SDK, etc.)", - items: [ - "pass_through/intro", - "pass_through/vertex_ai", - "pass_through/google_ai_studio", - "pass_through/cohere", - "pass_through/vllm", - "pass_through/mistral", - "pass_through/openai_passthrough", - "pass_through/anthropic_completion", - "pass_through/bedrock", - "pass_through/assembly_ai", - "pass_through/langfuse", - "proxy/pass_through", - ], - }, - "rerank", - "assistants", - { type: "category", label: "/files", @@ -320,15 +313,6 @@ const sidebars = { "proxy/litellm_managed_files", ], }, - { - type: "category", - label: "/batches", - items: [ - "batches", - "proxy/managed_batches", - ] - }, - "realtime", { type: "category", label: "/fine_tuning", @@ -337,8 +321,48 @@ const sidebars = { "proxy/managed_finetuning", ] }, + "generateContent", + "apply_guardrail", + { + type: "category", + label: "/images", + items: [ + "image_edits", + "image_generation", + "image_variations", + ] + }, + "mcp", "moderation", - "apply_guardrail", + { + type: "category", + label: "Pass-through Endpoints (Anthropic SDK, etc.)", + items: [ + "pass_through/intro", + "pass_through/anthropic_completion", + "pass_through/assembly_ai", + "pass_through/bedrock", + "pass_through/cohere", + "pass_through/google_ai_studio", + "pass_through/langfuse", + "pass_through/mistral", + "pass_through/openai_passthrough", + "pass_through/vertex_ai", + "pass_through/vllm", + "proxy/pass_through" + ] + }, + "realtime", + "rerank", + "response_api", + "anthropic_unified", + { + type: "category", + label: "/vector_stores", + items: [ + "vector_stores/search", + ] + }, ], }, { @@ -387,6 +411,7 @@ const sidebars = { "providers/vertex", "providers/vertex_partner", "providers/vertex_image", + "providers/vertex_batch", ] }, { @@ -406,7 +431,9 @@ const sidebars = { label: "Bedrock", items: [ "providers/bedrock", + "providers/bedrock_embedding", "providers/bedrock_agents", + "providers/bedrock_batches", "providers/bedrock_vector_store", ] }, @@ -447,6 +474,7 @@ const sidebars = { "providers/elevenlabs", "providers/fireworks_ai", "providers/clarifai", + "providers/compactifai", "providers/vllm", "providers/llamafile", "providers/infinity", @@ -462,6 +490,7 @@ const sidebars = { "providers/replicate", "providers/togetherai", "providers/v0", + "providers/vercel_ai_gateway", "providers/morph", "providers/lambda_ai", "providers/novita", @@ -479,38 +508,49 @@ const sidebars = { "providers/nebius", "providers/dashscope", "providers/bytez", + "providers/heroku", "providers/oci", + "providers/datarobot", + "providers/ovhcloud", ], }, { type: "category", label: "Guides", items: [ - "exception_mapping", + { + type: "category", + label: "Tools", + items: [ + "completion/computer_use", + "completion/web_search", + "completion/web_fetch", + "completion/function_call", + ] + }, + "completion/audio", + "completion/document_understanding", + "completion/drop_params", + "completion/image_generation_chat", + "completion/json_mode", + "completion/knowledgebase", + "completion/message_trimming", + "completion/model_alias", + "completion/mock_requests", + "completion/predict_outputs", + "completion/prefix", + "completion/prompt_caching", + "completion/prompt_formatting", + "completion/reliable_completions", + "completion/stream", "completion/provider_specific_params", + "completion/vision", + "exception_mapping", + "completion/batching", "guides/finetuned_models", "guides/security_settings", - "completion/audio", - "completion/web_search", - "completion/document_understanding", - "completion/vision", - "completion/json_mode", - "reasoning_content", - "completion/computer_use", - "completion/prompt_caching", - "completion/predict_outputs", - "completion/knowledgebase", - "completion/prefix", - "completion/drop_params", - "completion/prompt_formatting", - "completion/stream", - "completion/message_trimming", - "completion/function_call", - "completion/model_alias", - "completion/batching", - "completion/mock_requests", - "completion/reliable_completions", - + "proxy/veo_video_generation", + "reasoning_content" ] }, @@ -523,25 +563,35 @@ const sidebars = { description: "Learn how to load balance, route, and set fallbacks for your LLM requests", slug: "/routing-load-balancing", }, - items: ["routing", "scheduler", "proxy/load_balancing", "proxy/reliability", "proxy/timeout", "proxy/auto_routing", "proxy/tag_routing", "proxy/provider_budget_routing", "wildcard_routing"], + items: [ + "routing", + "scheduler", + "proxy/auto_routing", + "proxy/load_balancing", + "proxy/provider_budget_routing", + "proxy/reliability", + "proxy/tag_routing", + "proxy/timeout", + "wildcard_routing" + ], }, { type: "category", label: "LiteLLM Python SDK", items: [ "set_keys", - "completion/token_usage", - "sdk_custom_pricing", - "embedding/async_embedding", - "embedding/moderation", "budget_manager", "caching/all_caches", + "completion/token_usage", + "embedding/async_embedding", + "embedding/moderation", "migration", + "sdk_custom_pricing", { type: "category", label: "LangChain, LlamaIndex, Instructor Integration", items: ["langchain/langchain", "tutorials/instructor"], - }, + } ], }, diff --git a/docs/my-website/static/llms-full.txt b/docs/my-website/static/llms-full.txt index c64d4170968..203dfd12bab 100644 --- a/docs/my-website/static/llms-full.txt +++ b/docs/my-website/static/llms-full.txt @@ -1699,7 +1699,7 @@ This release allow you to group requests to LiteLLM proxy into a session. If you 1. Added support for max\_completion\_tokens parameter [Get Started](https://docs.litellm.ai/docs/providers/sagemaker), [PR](https://github.com/BerriAI/litellm/pull/10300) - **Responses API** 1. Added support for GET and DELETE operations - `/v1/responses/{response_id}` [Get Started](https://docs.litellm.ai/docs/response_api) -2. Added session management support for non-OpenAI models [PR](https://github.com/BerriAI/litellm/pull/10321) +2. Added session management support for all supported models [PR](https://github.com/BerriAI/litellm/pull/10321) 3. Added routing affinity to maintain model consistency within sessions [Get Started](https://docs.litellm.ai/docs/response_api#load-balancing-with-routing-affinity), [PR](https://github.com/BerriAI/litellm/pull/10193) ## Spend Tracking Improvements [​](https://docs.litellm.ai/release_notes\#spend-tracking-improvements "Direct link to Spend Tracking Improvements") @@ -7736,7 +7736,7 @@ This release allow you to group requests to LiteLLM proxy into a session. If you 1. Added support for max\_completion\_tokens parameter [Get Started](https://docs.litellm.ai/docs/providers/sagemaker), [PR](https://github.com/BerriAI/litellm/pull/10300) - **Responses API** 1. Added support for GET and DELETE operations - `/v1/responses/{response_id}` [Get Started](https://docs.litellm.ai/docs/response_api) -2. Added session management support for non-OpenAI models [PR](https://github.com/BerriAI/litellm/pull/10321) +2. Added session management support for all supported models [PR](https://github.com/BerriAI/litellm/pull/10321) 3. Added routing affinity to maintain model consistency within sessions [Get Started](https://docs.litellm.ai/docs/response_api#load-balancing-with-routing-affinity), [PR](https://github.com/BerriAI/litellm/pull/10193) ## Spend Tracking Improvements [​](https://docs.litellm.ai/release_notes/tags/responses-api\#spend-tracking-improvements "Direct link to Spend Tracking Improvements") @@ -8295,7 +8295,7 @@ This release allow you to group requests to LiteLLM proxy into a session. If you 1. Added support for max\_completion\_tokens parameter [Get Started](https://docs.litellm.ai/docs/providers/sagemaker), [PR](https://github.com/BerriAI/litellm/pull/10300) - **Responses API** 1. Added support for GET and DELETE operations - `/v1/responses/{response_id}` [Get Started](https://docs.litellm.ai/docs/response_api) -2. Added session management support for non-OpenAI models [PR](https://github.com/BerriAI/litellm/pull/10321) +2. Added session management support for all supported models [PR](https://github.com/BerriAI/litellm/pull/10321) 3. Added routing affinity to maintain model consistency within sessions [Get Started](https://docs.litellm.ai/docs/response_api#load-balancing-with-routing-affinity), [PR](https://github.com/BerriAI/litellm/pull/10193) ## Spend Tracking Improvements [​](https://docs.litellm.ai/release_notes/tags/security\#spend-tracking-improvements "Direct link to Spend Tracking Improvements") @@ -8821,7 +8821,7 @@ This release allow you to group requests to LiteLLM proxy into a session. If you 1. Added support for max\_completion\_tokens parameter [Get Started](https://docs.litellm.ai/docs/providers/sagemaker), [PR](https://github.com/BerriAI/litellm/pull/10300) - **Responses API** 1. Added support for GET and DELETE operations - `/v1/responses/{response_id}` [Get Started](https://docs.litellm.ai/docs/response_api) -2. Added session management support for non-OpenAI models [PR](https://github.com/BerriAI/litellm/pull/10321) +2. Added session management support for all supported models [PR](https://github.com/BerriAI/litellm/pull/10321) 3. Added routing affinity to maintain model consistency within sessions [Get Started](https://docs.litellm.ai/docs/response_api#load-balancing-with-routing-affinity), [PR](https://github.com/BerriAI/litellm/pull/10193) ## Spend Tracking Improvements [​](https://docs.litellm.ai/release_notes/tags/session-management\#spend-tracking-improvements "Direct link to Spend Tracking Improvements") diff --git a/enterprise/litellm_enterprise/enterprise_callbacks/generic_api_callback.py b/enterprise/litellm_enterprise/enterprise_callbacks/generic_api_callback.py index d239be41257..7e259d4e19d 100644 --- a/enterprise/litellm_enterprise/enterprise_callbacks/generic_api_callback.py +++ b/enterprise/litellm_enterprise/enterprise_callbacks/generic_api_callback.py @@ -9,7 +9,7 @@ Callback to log events to a Generic API Endpoint import asyncio import os import traceback -import uuid +from litellm._uuid import uuid from typing import Dict, List, Optional, Union import litellm diff --git a/enterprise/litellm_enterprise/enterprise_callbacks/llm_guard.py b/enterprise/litellm_enterprise/enterprise_callbacks/llm_guard.py index 6735998960b..e290013248d 100644 --- a/enterprise/litellm_enterprise/enterprise_callbacks/llm_guard.py +++ b/enterprise/litellm_enterprise/enterprise_callbacks/llm_guard.py @@ -63,7 +63,7 @@ class _ENTERPRISE_LLMGuard(CustomLogger): analyze_url, json=analyze_payload ) as response: redacted_text = await response.json() - verbose_proxy_logger.info( + verbose_proxy_logger.debug( f"LLM Guard: Received response - {redacted_text}" ) if redacted_text is not None: diff --git a/enterprise/litellm_enterprise/enterprise_callbacks/pagerduty/pagerduty.py b/enterprise/litellm_enterprise/enterprise_callbacks/pagerduty/pagerduty.py index 1028a443a42..d4964b9667e 100644 --- a/enterprise/litellm_enterprise/enterprise_callbacks/pagerduty/pagerduty.py +++ b/enterprise/litellm_enterprise/enterprise_callbacks/pagerduty/pagerduty.py @@ -109,6 +109,9 @@ class PagerDutyAlerting(SlackAlerting): error_llm_provider=error_info.get("llm_provider"), user_api_key_hash=_meta.get("user_api_key_hash"), user_api_key_alias=_meta.get("user_api_key_alias"), + user_api_key_spend=_meta.get("user_api_key_spend"), + user_api_key_max_budget=_meta.get("user_api_key_max_budget"), + user_api_key_budget_reset_at=_meta.get("user_api_key_budget_reset_at"), user_api_key_org_id=_meta.get("user_api_key_org_id"), user_api_key_team_id=_meta.get("user_api_key_team_id"), user_api_key_user_id=_meta.get("user_api_key_user_id"), @@ -191,6 +194,9 @@ class PagerDutyAlerting(SlackAlerting): error_llm_provider="HangingRequest", user_api_key_hash=user_api_key_dict.api_key, user_api_key_alias=user_api_key_dict.key_alias, + user_api_key_spend=user_api_key_dict.spend, + user_api_key_max_budget=user_api_key_dict.max_budget, + user_api_key_budget_reset_at=user_api_key_dict.budget_reset_at.isoformat() if user_api_key_dict.budget_reset_at else None, user_api_key_org_id=user_api_key_dict.org_id, user_api_key_team_id=user_api_key_dict.team_id, user_api_key_user_id=user_api_key_dict.user_id, diff --git a/enterprise/litellm_enterprise/integrations/prometheus.py b/enterprise/litellm_enterprise/integrations/prometheus.py index a2d781fa1c4..4451d76bed0 100644 --- a/enterprise/litellm_enterprise/integrations/prometheus.py +++ b/enterprise/litellm_enterprise/integrations/prometheus.py @@ -95,13 +95,16 @@ class PrometheusLogger(CustomLogger): self.litellm_llm_api_time_to_first_token_metric = self._histogram_factory( "litellm_llm_api_time_to_first_token_metric", "Time to first token for a models LLM API call", - labelnames=[ - "model", - "hashed_api_key", - "api_key_alias", - "team", - "team_alias", - ], + # labelnames=[ + # "model", + # "hashed_api_key", + # "api_key_alias", + # "team", + # "team_alias", + # ], + labelnames=self.get_labels_for_metric( + "litellm_llm_api_time_to_first_token_metric" + ), buckets=LATENCY_BUCKETS, ) @@ -109,15 +112,7 @@ class PrometheusLogger(CustomLogger): self.litellm_spend_metric = self._counter_factory( "litellm_spend_metric", "Total spend on LLM requests", - labelnames=[ - "end_user", - "hashed_api_key", - "api_key_alias", - "model", - "team", - "team_alias", - "user", - ], + labelnames=self.get_labels_for_metric("litellm_spend_metric"), ) # Counter for total_output_tokens @@ -243,25 +238,18 @@ class PrometheusLogger(CustomLogger): labelnames=["api_provider"], ) - # Get all keys - _logged_llm_labels = [ - UserAPIKeyLabelNames.v2_LITELLM_MODEL_NAME.value, - UserAPIKeyLabelNames.MODEL_ID.value, - UserAPIKeyLabelNames.API_BASE.value, - UserAPIKeyLabelNames.API_PROVIDER.value, - ] - # Metric for deployment state self.litellm_deployment_state = self._gauge_factory( "litellm_deployment_state", "LLM Deployment Analytics - The state of the deployment: 0 = healthy, 1 = partial outage, 2 = complete outage", - labelnames=_logged_llm_labels, + labelnames=self.get_labels_for_metric("litellm_deployment_state"), ) self.litellm_deployment_cooled_down = self._counter_factory( "litellm_deployment_cooled_down", "LLM Deployment Analytics - Number of times a deployment has been cooled down by LiteLLM load balancing logic. exception_status is the status of the exception that caused the deployment to be cooled down", - labelnames=_logged_llm_labels + [EXCEPTION_STATUS], + # labelnames=_logged_llm_labels + [EXCEPTION_STATUS], + labelnames=self.get_labels_for_metric("litellm_deployment_cooled_down"), ) self.litellm_deployment_success_responses = self._counter_factory( @@ -327,6 +315,7 @@ class PrometheusLogger(CustomLogger): documentation="deprecated - use litellm_proxy_total_requests_metric. Total number of LLM calls to litellm - track total per API Key, team, user", labelnames=self.get_labels_for_metric("litellm_requests_metric"), ) + except Exception as e: print_verbose(f"Got exception on init prometheus client {str(e)}") raise e @@ -1052,20 +1041,12 @@ class PrometheusLogger(CustomLogger): _labels = prometheus_label_factory( supported_enum_labels=self.get_labels_for_metric( - metric_name="litellm_proxy_total_requests_metric" + metric_name="litellm_spend_metric" ), enum_values=enum_values, ) - self.litellm_spend_metric.labels( - end_user_id, - user_api_key, - user_api_key_alias, - model, - user_api_team, - user_api_team_alias, - user_id, - ).inc(response_cost) + self.litellm_spend_metric.labels(**_labels).inc(response_cost) def _set_virtual_key_rate_limit_metrics( self, @@ -2293,7 +2274,9 @@ def get_custom_labels_from_metadata(metadata: dict) -> Dict[str, str]: return result -def _tag_matches_wildcard_configured_pattern(tags: List[str], configured_tag: str) -> bool: +def _tag_matches_wildcard_configured_pattern( + tags: List[str], configured_tag: str +) -> bool: """ Check if any of the request tags matches a wildcard configured pattern @@ -2318,6 +2301,7 @@ def _tag_matches_wildcard_configured_pattern(tags: List[str], configured_tag: st import re from litellm.router_utils.pattern_match_deployments import PatternMatchRouter + pattern_router = PatternMatchRouter() regex_pattern = pattern_router._pattern_to_regex(configured_tag) return any(re.match(pattern=regex_pattern, string=tag) for tag in tags) @@ -2326,11 +2310,11 @@ def _tag_matches_wildcard_configured_pattern(tags: List[str], configured_tag: st def get_custom_labels_from_tags(tags: List[str]) -> Dict[str, str]: """ Get custom labels from tags based on admin configuration. - + Supports both exact matches and wildcard patterns: - Exact match: "prod" matches "prod" exactly - - Wildcard pattern: "User-Agent: curl/*" matches "User-Agent: curl/7.68.0" - + - Wildcard pattern: "User-Agent: curl/*" matches "User-Agent: curl/7.68.0" + Reuses PatternMatchRouter for wildcard pattern matching. Returns dict of label_name: "true" if the tag matches the configured tag, "false" otherwise @@ -2344,7 +2328,6 @@ def get_custom_labels_from_tags(tags: List[str]) -> Dict[str, str]: "tag_Service_web_app_v1": "false", } """ - import re from litellm.router_utils.pattern_match_deployments import PatternMatchRouter from litellm.types.integrations.prometheus import _sanitize_prometheus_label_name @@ -2358,17 +2341,19 @@ def get_custom_labels_from_tags(tags: List[str]) -> Dict[str, str]: for configured_tag in configured_tags: label_name = _sanitize_prometheus_label_name(f"tag_{configured_tag}") - + # Check for exact match first (backwards compatibility) if configured_tag in tags: result[label_name] = "true" continue - + # Use PatternMatchRouter for wildcard pattern matching - if "*" in configured_tag and _tag_matches_wildcard_configured_pattern(tags=tags, configured_tag=configured_tag): + if "*" in configured_tag and _tag_matches_wildcard_configured_pattern( + tags=tags, configured_tag=configured_tag + ): result[label_name] = "true" continue - + # No match found result[label_name] = "false" 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 6edd198cd8e..4b1bb024ac6 100644 --- a/enterprise/litellm_enterprise/proxy/common_utils/check_batch_cost.py +++ b/enterprise/litellm_enterprise/proxy/common_utils/check_batch_cost.py @@ -2,7 +2,7 @@ Polls LiteLLM_ManagedObjectTable to check if the batch job is complete, and if the cost has been tracked. """ -import uuid +from litellm._uuid import uuid from datetime import datetime from typing import TYPE_CHECKING, Optional, cast diff --git a/enterprise/litellm_enterprise/proxy/hooks/managed_files.py b/enterprise/litellm_enterprise/proxy/hooks/managed_files.py index e069a89b9c5..e2963f8fb87 100644 --- a/enterprise/litellm_enterprise/proxy/hooks/managed_files.py +++ b/enterprise/litellm_enterprise/proxy/hooks/managed_files.py @@ -4,7 +4,7 @@ import asyncio import base64 import json -import uuid +from litellm._uuid import uuid from typing import TYPE_CHECKING, Any, Dict, List, Literal, Optional, Union, cast from fastapi import HTTPException diff --git a/enterprise/litellm_enterprise/proxy/management_endpoints/internal_user_endpoints.py b/enterprise/litellm_enterprise/proxy/management_endpoints/internal_user_endpoints.py index d17946171bb..2f53f9e9281 100644 --- a/enterprise/litellm_enterprise/proxy/management_endpoints/internal_user_endpoints.py +++ b/enterprise/litellm_enterprise/proxy/management_endpoints/internal_user_endpoints.py @@ -2,6 +2,7 @@ Enterprise internal user management endpoints """ + from fastapi import APIRouter, Depends, HTTPException from litellm.proxy._types import UserAPIKeyAuth @@ -21,7 +22,7 @@ async def available_enterprise_users( """ For keys with `max_users` set, return the list of users that are allowed to use the key. """ - from litellm.proxy._types import CommonProxyErrors + from litellm.proxy._types import CommonProxyErrors, EnterpriseLicenseData from litellm.proxy.proxy_server import ( premium_user, premium_user_data, @@ -34,10 +35,14 @@ async def available_enterprise_users( detail={"error": CommonProxyErrors.db_not_connected_error.value}, ) - if premium_user is None: - raise HTTPException( - status_code=500, detail={"error": CommonProxyErrors.not_premium_user.value} - ) + if not premium_user: + # check if SSO is enabled - show 5 user limit + from litellm.proxy.auth.auth_utils import _has_user_setup_sso + + if _has_user_setup_sso(): + premium_user_data = EnterpriseLicenseData( + max_users=5, + ) # Count number of rows in LiteLLM_UserTable user_count = await prisma_client.db.litellm_usertable.count() diff --git a/enterprise/litellm_enterprise/proxy/vector_stores/endpoints.py b/enterprise/litellm_enterprise/proxy/vector_stores/endpoints.py index 43bdfa3844f..bb4b546b8d3 100644 --- a/enterprise/litellm_enterprise/proxy/vector_stores/endpoints.py +++ b/enterprise/litellm_enterprise/proxy/vector_stores/endpoints.py @@ -11,7 +11,7 @@ All /vector_store management endpoints import copy from typing import List, Optional -from fastapi import APIRouter, Depends, HTTPException, Request, Response +from fastapi import APIRouter, Depends, HTTPException import litellm from litellm._logging import verbose_proxy_logger diff --git a/enterprise/litellm_enterprise/types/proxy/proxy_server.py b/enterprise/litellm_enterprise/types/proxy/proxy_server.py index 497be59c4b9..f1a1f2639ed 100644 --- a/enterprise/litellm_enterprise/types/proxy/proxy_server.py +++ b/enterprise/litellm_enterprise/types/proxy/proxy_server.py @@ -1,4 +1,6 @@ -from typing import Literal, TypedDict +from typing import Literal + +from typing_extensions import TypedDict class CustomAuthSettings(TypedDict): diff --git a/enterprise/pyproject.toml b/enterprise/pyproject.toml index 217bb753f42..1d1fa64549c 100644 --- a/enterprise/pyproject.toml +++ b/enterprise/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "litellm-enterprise" -version = "0.1.19" +version = "0.1.20" 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 = "0.1.19" +version = "0.1.20" version_files = [ "pyproject.toml:version", "../requirements.txt:litellm-enterprise==", diff --git a/git_model_armor.py b/git_model_armor.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/litellm-js/spend-logs/package-lock.json b/litellm-js/spend-logs/package-lock.json index 2f9e2248351..95f8acdec3a 100644 --- a/litellm-js/spend-logs/package-lock.json +++ b/litellm-js/spend-logs/package-lock.json @@ -6,7 +6,7 @@ "": { "dependencies": { "@hono/node-server": "^1.10.1", - "hono": "^4.6.5" + "hono": "^4.9.7" }, "devDependencies": { "@types/node": "^20.11.17", @@ -463,9 +463,10 @@ } }, "node_modules/hono": { - "version": "4.6.5", - "resolved": "https://registry.npmjs.org/hono/-/hono-4.6.5.tgz", - "integrity": "sha512-qsmN3V5fgtwdKARGLgwwHvcdLKursMd+YOt69eGpl1dUCJb8mCd7hZfyZnBYjxCegBG7qkJRQRUy2oO25yHcyQ==", + "version": "4.9.7", + "resolved": "https://registry.npmjs.org/hono/-/hono-4.9.7.tgz", + "integrity": "sha512-t4Te6ERzIaC48W3x4hJmBwgNlLhmiEdEE5ViYb02ffw4ignHNHa5IBtPjmbKstmtKa8X6C35iWwK4HaqvrzG9w==", + "license": "MIT", "engines": { "node": ">=16.9.0" } diff --git a/litellm-js/spend-logs/package.json b/litellm-js/spend-logs/package.json index 9e51f1018a6..5370f7a0eca 100644 --- a/litellm-js/spend-logs/package.json +++ b/litellm-js/spend-logs/package.json @@ -4,7 +4,7 @@ }, "dependencies": { "@hono/node-server": "^1.10.1", - "hono": "^4.6.5" + "hono": "^4.9.7" }, "devDependencies": { "@types/node": "^20.11.17", diff --git a/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.18-py3-none-any.whl b/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.18-py3-none-any.whl new file mode 100644 index 00000000000..fca66b532ff Binary files /dev/null and b/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.18-py3-none-any.whl differ diff --git a/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.18.tar.gz b/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.18.tar.gz new file mode 100644 index 00000000000..ddd00e8439e Binary files /dev/null and b/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.18.tar.gz differ diff --git a/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.19-py3-none-any.whl b/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.19-py3-none-any.whl new file mode 100644 index 00000000000..c035bb44215 Binary files /dev/null and b/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.19-py3-none-any.whl differ diff --git a/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.19.tar.gz b/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.19.tar.gz new file mode 100644 index 00000000000..85069c622b0 Binary files /dev/null and b/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.19.tar.gz differ diff --git a/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.20-py3-none-any.whl b/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.20-py3-none-any.whl new file mode 100644 index 00000000000..0a94ef6ff62 Binary files /dev/null and b/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.20-py3-none-any.whl differ diff --git a/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.20.tar.gz b/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.20.tar.gz new file mode 100644 index 00000000000..1562aacc22b Binary files /dev/null and b/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.20.tar.gz differ diff --git a/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.21-py3-none-any.whl b/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.21-py3-none-any.whl new file mode 100644 index 00000000000..75baeb5c575 Binary files /dev/null and b/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.21-py3-none-any.whl differ diff --git a/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.21.tar.gz b/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.21.tar.gz new file mode 100644 index 00000000000..bc934024b3a Binary files /dev/null and b/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.21.tar.gz differ diff --git a/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.22-py3-none-any.whl b/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.22-py3-none-any.whl new file mode 100644 index 00000000000..0194c9148aa Binary files /dev/null and b/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.22-py3-none-any.whl differ diff --git a/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.22.tar.gz b/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.22.tar.gz new file mode 100644 index 00000000000..17cb242663c Binary files /dev/null and b/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.22.tar.gz differ diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20250918083359_drop_spec_version_column_from_mcp_table/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20250918083359_drop_spec_version_column_from_mcp_table/migration.sql new file mode 100644 index 00000000000..5686876b37c --- /dev/null +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20250918083359_drop_spec_version_column_from_mcp_table/migration.sql @@ -0,0 +1,8 @@ +/* + Warnings: + + - You are about to drop the column `spec_version` on the `LiteLLM_MCPServerTable` table. All the data in the column will be lost. + +*/ +-- AlterTable +ALTER TABLE "public"."LiteLLM_MCPServerTable" DROP COLUMN "spec_version"; diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20250926194702_unnamed_migration/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20250926194702_unnamed_migration/migration.sql new file mode 100644 index 00000000000..ea28db19662 --- /dev/null +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20250926194702_unnamed_migration/migration.sql @@ -0,0 +1,7 @@ +-- AlterTable +ALTER TABLE "LiteLLM_VerificationToken" ADD COLUMN "auto_rotate" BOOLEAN DEFAULT false, +ADD COLUMN "key_rotation_at" TIMESTAMP(3), +ADD COLUMN "last_rotation_at" TIMESTAMP(3), +ADD COLUMN "rotation_count" INTEGER DEFAULT 0, +ADD COLUMN "rotation_interval" TEXT; + diff --git a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma index b8f2201d6b5..766625145f6 100644 --- a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma +++ b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma @@ -171,7 +171,6 @@ model LiteLLM_MCPServerTable { description String? url String? transport String @default("sse") - spec_version String @default("2025-03-26") auth_type String? created_at DateTime? @default(now()) @map("created_at") created_by String? @@ -222,6 +221,11 @@ model LiteLLM_VerificationToken { created_by String? updated_at DateTime? @default(now()) @updatedAt @map("updated_at") updated_by String? + rotation_count Int? @default(0) // Number of times key has been rotated + auto_rotate Boolean? @default(false) // Whether this key should be auto-rotated + rotation_interval String? // How often to rotate (e.g., "30d", "90d") + last_rotation_at DateTime? // When this key was last rotated + key_rotation_at DateTime? // When this key should next be rotated litellm_budget_table LiteLLM_BudgetTable? @relation(fields: [budget_id], references: [budget_id]) litellm_organization_table LiteLLM_OrganizationTable? @relation(fields: [organization_id], references: [organization_id]) object_permission LiteLLM_ObjectPermissionTable? @relation(fields: [object_permission_id], references: [object_permission_id]) diff --git a/litellm-proxy-extras/migration_runbook.md b/litellm-proxy-extras/migration_runbook.md new file mode 100644 index 00000000000..93948f24b13 --- /dev/null +++ b/litellm-proxy-extras/migration_runbook.md @@ -0,0 +1,50 @@ +# Database Migration Runbook + +This is a runbook for creating and running database migrations for the LiteLLM proxy. For use for litellm engineers only. + +## Quick Start + +```bash +# Install deps (one time) +pip install testing.postgresql +brew install postgresql@14 # macOS + +# Add to PATH +export PATH="/opt/homebrew/opt/postgresql@14/bin:$PATH" + +# Run migration +python ci_cd/run_migration.py "your_migration_name" +``` + +## What It Does + +1. Creates temp PostgreSQL DB +2. Applies existing migrations +3. Compares with `schema.prisma` +4. Generates new migration if changes found + +## Common Fixes + +**Missing testing module:** +```bash +pip install testing.postgresql +``` + +**initdb not found:** +```bash +brew install postgresql@14 +export PATH="/opt/homebrew/opt/postgresql@14/bin:$PATH" +``` + +**Empty migration directory error:** +```bash +rm -rf litellm-proxy-extras/litellm_proxy_extras/migrations/[empty_dir] +``` + +## Rules + +- Update `schema.prisma` first +- Review generated SQL before committing +- Use descriptive migration names +- Never edit existing migration files +- Commit schema + migration together diff --git a/litellm-proxy-extras/pyproject.toml b/litellm-proxy-extras/pyproject.toml index b1de9c566a7..94e7f59bfa1 100644 --- a/litellm-proxy-extras/pyproject.toml +++ b/litellm-proxy-extras/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "litellm-proxy-extras" -version = "0.2.17" +version = "0.2.22" 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 = "0.2.17" +version = "0.2.22" version_files = [ "pyproject.toml:version", "../requirements.txt:litellm-proxy-extras==", diff --git a/litellm/__init__.py b/litellm/__init__.py index 44254bd259c..6df9d0780a4 100644 --- a/litellm/__init__.py +++ b/litellm/__init__.py @@ -17,6 +17,7 @@ from typing import ( TYPE_CHECKING, ) from litellm.types.integrations.datadog_llm_obs import DatadogLLMObsInitParams +from litellm.types.integrations.datadog import DatadogInitParams from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler from litellm.caching.caching import Cache, DualCache, RedisCache, InMemoryCache from litellm.caching.llm_caching_handler import LLMClientCache @@ -60,6 +61,7 @@ from litellm.constants import ( empower_models, together_ai_models, baseten_models, + WANDB_MODELS, REPEATED_STREAMING_CHUNK_LIMIT, request_timeout, open_ai_embedding_models, @@ -67,6 +69,8 @@ from litellm.constants import ( bedrock_embedding_models, known_tokenizer_config, BEDROCK_INVOKE_PROVIDERS_LITERAL, + BEDROCK_EMBEDDING_PROVIDERS_LITERAL, + BEDROCK_CONVERSE_MODELS, DEFAULT_MAX_TOKENS, DEFAULT_SOFT_BUDGET, DEFAULT_ALLOWED_FAILS, @@ -86,6 +90,7 @@ from litellm.types.proxy.management_endpoints.ui_sso import ( LiteLLM_UpperboundKeyGenerateParams, ) from litellm.types.utils import StandardKeyGenerationConfig, LlmProviders +from litellm.types.utils import PriorityReservationSettings from litellm.integrations.custom_logger import CustomLogger from litellm.litellm_core_utils.logging_callback_manager import LoggingCallbackManager import httpx @@ -115,6 +120,7 @@ _custom_logger_compatible_callbacks_literal = Literal[ "logfire", "literalai", "dynamic_rate_limiter", + "dynamic_rate_limiter_v3", "langsmith", "prometheus", "otel", @@ -145,8 +151,13 @@ _custom_logger_compatible_callbacks_literal = Literal[ "aws_sqs", "vector_store_pre_call_hook", "dotprompt", + "bitbucket", + "cloudzero", + "posthog", ] -configured_cold_storage_logger: Optional[_custom_logger_compatible_callbacks_literal] = None +configured_cold_storage_logger: Optional[ + _custom_logger_compatible_callbacks_literal +] = None logged_real_time_event_types: Optional[Union[List[str], Literal["*"]]] = None _known_custom_logger_compatible_callbacks: List = list( get_args(_custom_logger_compatible_callbacks_literal) @@ -226,6 +237,7 @@ vertex_location: Optional[str] = None predibase_tenant_id: Optional[str] = None togetherai_api_key: Optional[str] = None cloudflare_api_key: Optional[str] = None +vercel_ai_gateway_key: Optional[str] = None baseten_key: Optional[str] = None llama_api_key: Optional[str] = None aleph_alpha_key: Optional[str] = None @@ -234,7 +246,10 @@ novita_api_key: Optional[str] = None snowflake_key: Optional[str] = None gradient_ai_api_key: Optional[str] = None nebius_key: Optional[str] = None +wandb_key: Optional[str] = None +heroku_key: Optional[str] = None cometapi_key: Optional[str] = None +ovhcloud_key: Optional[str] = None common_cloud_provider_auth_params: dict = { "params": ["project", "region_name", "token"], "providers": ["vertex_ai", "bedrock", "watsonx", "azure", "vertex_ai_beta"], @@ -304,7 +319,6 @@ default_in_memory_ttl: Optional[float] = None default_redis_ttl: Optional[float] = None default_redis_batch_cache_expiry: Optional[float] = None model_alias_map: Dict[str, str] = {} -model_group_alias_map: Dict[str, str] = {} model_group_settings: Optional["ModelGroupSettings"] = None max_budget: float = 0.0 # set the max budget across all providers budget_duration: Optional[str] = ( @@ -331,6 +345,7 @@ suppress_debug_info = False dynamodb_table_name: Optional[str] = None s3_callback_params: Optional[Dict] = None datadog_llm_observability_params: Optional[Union[DatadogLLMObsInitParams, Dict]] = None +datadog_params: Optional[Union[DatadogInitParams, Dict]] = None aws_sqs_callback_params: Optional[Dict] = None generic_logger_headers: Optional[Dict] = None default_key_generate_params: Optional[Dict] = None @@ -362,6 +377,7 @@ public_model_groups: Optional[List[str]] = None public_model_groups_links: Dict[str, str] = {} #### REQUEST PRIORITIZATION ###### priority_reservation: Optional[Dict[str, float]] = None +priority_reservation_settings: "PriorityReservationSettings" = PriorityReservationSettings() ######## Networking Settings ######## @@ -432,43 +448,10 @@ organization = None project = None config_path = None vertex_ai_safety_settings: Optional[dict] = None -BEDROCK_CONVERSE_MODELS = [ - "openai.gpt-oss-20b-1:0", - "openai.gpt-oss-120b-1:0", - "anthropic.claude-opus-4-1-20250805-v1:0", - "anthropic.claude-opus-4-20250514-v1:0", - "anthropic.claude-sonnet-4-20250514-v1:0", - "anthropic.claude-3-7-sonnet-20250219-v1:0", - "anthropic.claude-3-5-haiku-20241022-v1:0", - "anthropic.claude-3-5-sonnet-20241022-v2:0", - "anthropic.claude-3-5-sonnet-20240620-v1:0", - "anthropic.claude-3-opus-20240229-v1:0", - "anthropic.claude-3-sonnet-20240229-v1:0", - "anthropic.claude-3-haiku-20240307-v1:0", - "anthropic.claude-v2", - "anthropic.claude-v2:1", - "anthropic.claude-v1", - "anthropic.claude-instant-v1", - "ai21.jamba-instruct-v1:0", - "ai21.jamba-1-5-mini-v1:0", - "ai21.jamba-1-5-large-v1:0", - "meta.llama3-70b-instruct-v1:0", - "meta.llama3-8b-instruct-v1:0", - "meta.llama3-1-8b-instruct-v1:0", - "meta.llama3-1-70b-instruct-v1:0", - "meta.llama3-1-405b-instruct-v1:0", - "meta.llama3-70b-instruct-v1:0", - "mistral.mistral-large-2407-v1:0", - "mistral.mistral-large-2402-v1:0", - "mistral.mistral-small-2402-v1:0", - "meta.llama3-2-1b-instruct-v1:0", - "meta.llama3-2-3b-instruct-v1:0", - "meta.llama3-2-11b-instruct-v1:0", - "meta.llama3-2-90b-instruct-v1:0", -] ####### COMPLETION MODELS ################### -from typing import Set +from typing import Set + open_ai_chat_completion_models: Set = set() open_ai_text_completion_models: Set = set() cohere_models: Set = set() @@ -483,6 +466,7 @@ vertex_vision_models: Set = set() vertex_chat_models: Set = set() vertex_code_chat_models: Set = set() vertex_ai_image_models: Set = set() +vertex_ai_video_models: Set = set() vertex_text_models: Set = set() vertex_code_text_models: Set = set() vertex_embedding_models: Set = set() @@ -491,6 +475,7 @@ vertex_llama3_models: Set = set() vertex_deepseek_models: Set = set() vertex_ai_ai21_models: Set = set() vertex_mistral_models: Set = set() +vertex_openai_models: Set = set() ai21_models: Set = set() ai21_chat_models: Set = set() nlp_cloud_models: Set = set() @@ -509,6 +494,7 @@ azure_ai_models: Set = set() jina_ai_models: Set = set() voyage_models: Set = set() infinity_models: Set = set() +heroku_models: Set = set() databricks_models: Set = set() cloudflare_models: Set = set() codestral_models: Set = set() @@ -531,6 +517,7 @@ llama_models: Set = set() nscale_models: Set = set() nebius_models: Set = set() nebius_embedding_models: Set = set() +aiml_models: Set = set() deepgram_models: Set = set() elevenlabs_models: Set = set() dashscope_models: Set = set() @@ -542,6 +529,11 @@ hyperbolic_models: Set = set() recraft_models: Set = set() cometapi_models: Set = set() oci_models: Set = set() +vercel_ai_gateway_models: Set = set() +volcengine_models: Set = set() +wandb_models: Set = set(WANDB_MODELS) +ovhcloud_models: Set = set() +ovhcloud_embedding_models: Set = set() def is_bedrock_pricing_only_model(key: str) -> bool: @@ -599,6 +591,8 @@ def add_known_models(): empower_models.add(key) elif value.get("litellm_provider") == "openrouter": openrouter_models.add(key) + elif value.get("litellm_provider") == "vercel_ai_gateway": + vercel_ai_gateway_models.add(key) elif value.get("litellm_provider") == "datarobot": datarobot_models.add(key) elif value.get("litellm_provider") == "vertex_ai-text-models": @@ -633,6 +627,12 @@ def add_known_models(): elif value.get("litellm_provider") == "vertex_ai-image-models": key = key.replace("vertex_ai/", "") vertex_ai_image_models.add(key) + elif value.get("litellm_provider") == "vertex_ai-video-models": + key = key.replace("vertex_ai/", "") + vertex_ai_video_models.add(key) + elif value.get("litellm_provider") == "vertex_ai-openai_models": + key = key.replace("vertex_ai/", "") + vertex_openai_models.add(key) elif value.get("litellm_provider") == "ai21": if value.get("mode") == "chat": ai21_chat_models.add(key) @@ -710,6 +710,8 @@ def add_known_models(): nebius_models.add(key) elif value.get("litellm_provider") == "nebius-embedding-models": nebius_embedding_models.add(key) + elif value.get("litellm_provider") == "aiml": + aiml_models.add(key) elif value.get("litellm_provider") == "assemblyai": assemblyai_models.add(key) elif value.get("litellm_provider") == "jina_ai": @@ -724,6 +726,8 @@ def add_known_models(): deepgram_models.add(key) elif value.get("litellm_provider") == "elevenlabs": elevenlabs_models.add(key) + elif value.get("litellm_provider") == "heroku": + heroku_models.add(key) elif value.get("litellm_provider") == "dashscope": dashscope_models.add(key) elif value.get("litellm_provider") == "moonshot": @@ -742,6 +746,14 @@ def add_known_models(): cometapi_models.add(key) elif value.get("litellm_provider") == "oci": oci_models.add(key) + elif value.get("litellm_provider") == "volcengine": + volcengine_models.add(key) + elif value.get("litellm_provider") == "wandb": + wandb_models.add(key) + elif value.get("litellm_provider") == "ovhcloud": + ovhcloud_models.add(key) + elif value.get("litellm_provider") == "ovhcloud-embedding-models": + ovhcloud_embedding_models.add(key) add_known_models() @@ -833,6 +845,11 @@ model_list = list( | recraft_models | cometapi_models | oci_models + | heroku_models + | vercel_ai_gateway_models + | volcengine_models + | wandb_models + | ovhcloud_models ) model_list_set = set(model_list) @@ -851,8 +868,14 @@ models_by_provider: dict = { "together_ai": together_ai_models, "baseten": baseten_models, "openrouter": openrouter_models, + "vercel_ai_gateway": vercel_ai_gateway_models, "datarobot": datarobot_models, - "vertex_ai": vertex_chat_models | vertex_text_models | vertex_anthropic_models | vertex_vision_models | vertex_language_models | vertex_deepseek_models, + "vertex_ai": vertex_chat_models + | vertex_text_models + | vertex_anthropic_models + | vertex_vision_models + | vertex_language_models + | vertex_deepseek_models, "ai21": ai21_models, "bedrock": bedrock_models | bedrock_converse_models, "petals": petals_models, @@ -887,6 +910,7 @@ models_by_provider: dict = { "sambanova": sambanova_models | sambanova_embedding_models, "novita": novita_models, "nebius": nebius_models | nebius_embedding_models, + "aiml": aiml_models, "assemblyai": assemblyai_models, "jina_ai": jina_ai_models, "snowflake": snowflake_models, @@ -896,6 +920,7 @@ models_by_provider: dict = { "featherless_ai": featherless_ai_models, "deepgram": deepgram_models, "elevenlabs": elevenlabs_models, + "heroku": heroku_models, "dashscope": dashscope_models, "moonshot": moonshot_models, "v0": v0_models, @@ -905,6 +930,9 @@ models_by_provider: dict = { "recraft": recraft_models, "cometapi": cometapi_models, "oci": oci_models, + "volcengine": volcengine_models, + "wandb": wandb_models, + "ovhcloud": ovhcloud_models | ovhcloud_embedding_models, } # mapping for those models which have larger equivalents @@ -939,6 +967,7 @@ all_embedding_models = ( | fireworks_ai_embedding_models | nebius_embedding_models | sambanova_embedding_models + | ovhcloud_embedding_models ) ####### IMAGE GENERATION MODELS ################### @@ -1009,6 +1038,7 @@ from .llms.openai_like.chat.handler import OpenAILikeChatConfig from .llms.aiohttp_openai.chat.transformation import AiohttpOpenAIChatConfig from .llms.galadriel.chat.transformation import GaladrielChatConfig from .llms.github.chat.transformation import GithubChatConfig +from .llms.compactifai.chat.transformation import CompactifAIChatConfig from .llms.empower.chat.transformation import EmpowerChatConfig from .llms.huggingface.chat.transformation import HuggingFaceChatConfig from .llms.huggingface.embedding.transformation import HuggingFaceEmbeddingConfig @@ -1029,7 +1059,6 @@ from .llms.databricks.chat.transformation import DatabricksConfig from .llms.databricks.embed.transformation import DatabricksEmbeddingConfig from .llms.predibase.chat.transformation import PredibaseConfig from .llms.replicate.chat.transformation import ReplicateConfig -from .llms.cohere.completion.transformation import CohereTextConfig as CohereConfig from .llms.snowflake.chat.transformation import SnowflakeConfig from .llms.cohere.rerank.transformation import CohereRerankConfig from .llms.cohere.rerank_v2.transformation import CohereRerankV2Config @@ -1148,13 +1177,17 @@ from .llms.topaz.image_variations.transformation import TopazImageVariationConfi from litellm.llms.openai.completion.transformation import OpenAITextCompletionConfig from .llms.groq.chat.transformation import GroqChatConfig from .llms.voyage.embedding.transformation import VoyageEmbeddingConfig -from .llms.voyage.embedding.transformation_contextual import VoyageContextualEmbeddingConfig +from .llms.voyage.embedding.transformation_contextual import ( + VoyageContextualEmbeddingConfig, +) from .llms.infinity.embedding.transformation import InfinityEmbeddingConfig from .llms.azure_ai.chat.transformation import AzureAIStudioConfig from .llms.mistral.chat.transformation import MistralConfig from .llms.openai.responses.transformation import OpenAIResponsesAPIConfig from .llms.azure.responses.transformation import AzureOpenAIResponsesAPIConfig -from .llms.azure.responses.o_series_transformation import AzureOpenAIOSeriesResponsesAPIConfig +from .llms.azure.responses.o_series_transformation import ( + AzureOpenAIOSeriesResponsesAPIConfig, +) from .llms.openai.chat.o_series_transformation import ( OpenAIOSeriesConfig as OpenAIO1Config, # maintain backwards compatibility OpenAIOSeriesConfig, @@ -1162,6 +1195,7 @@ from .llms.openai.chat.o_series_transformation import ( from .llms.snowflake.chat.transformation import SnowflakeConfig from .llms.gradient_ai.chat.transformation import GradientAIConfig + openaiOSeriesConfig = OpenAIOSeriesConfig() from .llms.openai.chat.gpt_transformation import ( OpenAIGPTConfig, @@ -1208,12 +1242,16 @@ from .llms.friendliai.chat.transformation import FriendliaiChatConfig from .llms.jina_ai.embedding.transformation import JinaAIEmbeddingConfig from .llms.xai.chat.transformation import XAIChatConfig from .llms.xai.common_utils import XAIModelInfo -from .llms.volcengine import VolcEngineConfig +from .llms.aiml.chat.transformation import AIMLChatConfig +from .llms.volcengine.chat.transformation import ( + VolcEngineChatConfig as VolcEngineConfig, +) from .llms.codestral.completion.transformation import CodestralTextCompletionConfig from .llms.azure.azure import ( AzureOpenAIError, AzureOpenAIAssistantsAPIConfig, ) +from .llms.heroku.chat.transformation import HerokuChatConfig from .llms.cometapi.chat.transformation import CometAPIConfig from .llms.azure.chat.gpt_transformation import AzureOpenAIConfig from .llms.azure.chat.gpt_5_transformation import AzureOpenAIGPT5Config @@ -1233,6 +1271,7 @@ from .llms.watsonx.chat.transformation import IBMWatsonXChatConfig from .llms.watsonx.embed.transformation import IBMWatsonXEmbeddingConfig from .llms.github_copilot.chat.transformation import GithubCopilotConfig from .llms.nebius.chat.transformation import NebiusConfig +from .llms.wandb.chat.transformation import WandbConfig from .llms.dashscope.chat.transformation import DashScopeChatConfig from .llms.moonshot.chat.transformation import MoonshotChatConfig from .llms.v0.chat.transformation import V0ChatConfig @@ -1240,6 +1279,9 @@ from .llms.oci.chat.transformation import OCIChatConfig from .llms.morph.chat.transformation import MorphChatConfig from .llms.lambda_ai.chat.transformation import LambdaAIChatConfig from .llms.hyperbolic.chat.transformation import HyperbolicChatConfig +from .llms.vercel_ai_gateway.chat.transformation import VercelAIGatewayConfig +from .llms.ovhcloud.chat.transformation import OVHCloudChatConfig +from .llms.ovhcloud.embedding.transformation import OVHCloudEmbeddingConfig from .main import * # type: ignore from .integrations import * from .llms.custom_httpx.async_client_cleanup import close_litellm_async_clients @@ -1247,6 +1289,7 @@ from .exceptions import ( AuthenticationError, InvalidRequestError, BadRequestError, + ImageFetchError, NotFoundError, RateLimitError, ServiceUnavailableError, @@ -1271,7 +1314,6 @@ from .router import Router from .assistants.main import * from .batches.main import * from .images.main import * -from .vector_stores import * from .batch_completion.main import * # type: ignore from .rerank_api.main import * from .llms.anthropic.experimental_pass_through.messages.handler import * @@ -1306,5 +1348,16 @@ disable_hf_tokenizer_download: Optional[bool] = ( ) global_disable_no_log_param: bool = False +### CLI UTILITIES ### +from litellm.litellm_core_utils.cli_token_utils import get_litellm_gateway_api_key + ### PASSTHROUGH ### from .passthrough import allm_passthrough_route, llm_passthrough_route + +### GLOBAL CONFIG ### +global_bitbucket_config: Optional[Dict[str, Any]] = None + +def set_global_bitbucket_config(config: Dict[str, Any]) -> None: + """Set global BitBucket configuration for prompt management.""" + global global_bitbucket_config + global_bitbucket_config = config diff --git a/litellm/_logging.py b/litellm/_logging.py index 8c23994f92a..73902d2fc5a 100644 --- a/litellm/_logging.py +++ b/litellm/_logging.py @@ -108,6 +108,7 @@ verbose_router_logger.addHandler(handler) verbose_proxy_logger.addHandler(handler) verbose_logger.addHandler(handler) + def _suppress_loggers(): """Suppress noisy loggers at INFO level""" # Suppress httpx request logging at INFO level @@ -120,6 +121,7 @@ def _suppress_loggers(): apscheduler_scheduler_logger = logging.getLogger("apscheduler.scheduler") apscheduler_scheduler_logger.setLevel(logging.WARNING) + # Call the suppression function _suppress_loggers() @@ -187,6 +189,4 @@ def _is_debugging_on() -> bool: """ Returns True if debugging is on """ - if verbose_logger.isEnabledFor(logging.DEBUG) or set_verbose is True: - return True - return False + return verbose_logger.isEnabledFor(logging.DEBUG) or set_verbose is True diff --git a/litellm/_redis.py b/litellm/_redis.py index 8371ef5bbc7..bcb305985fc 100644 --- a/litellm/_redis.py +++ b/litellm/_redis.py @@ -142,7 +142,10 @@ def create_gcp_iam_redis_connect_func( """ def iam_connect(self): """Initialize the connection and authenticate using GCP IAM""" - from redis.exceptions import AuthenticationError, AuthenticationWrongNumberOfArgsError + from redis.exceptions import ( + AuthenticationError, + AuthenticationWrongNumberOfArgsError, + ) from redis.utils import str_if_bytes self._parser.on_connect(self) @@ -395,7 +398,7 @@ def get_redis_async_client( # Handle GCP IAM authentication for async clusters redis_connect_func = cluster_kwargs.pop("redis_connect_func", None) from litellm import get_secret_str - + # Get GCP service account - first try from redis_connect_func, then from environment gcp_service_account = None if redis_connect_func and hasattr(redis_connect_func, '_gcp_service_account'): @@ -403,22 +406,22 @@ def get_redis_async_client( else: gcp_service_account = redis_kwargs.get("gcp_service_account") or get_secret_str("REDIS_GCP_SERVICE_ACCOUNT") - verbose_logger.info(f"DEBUG: Redis cluster kwargs: redis_connect_func={redis_connect_func is not None}, gcp_service_account_provided={gcp_service_account is not None}") + verbose_logger.debug(f"DEBUG: Redis cluster kwargs: redis_connect_func={redis_connect_func is not None}, gcp_service_account_provided={gcp_service_account is not None}") # If GCP IAM is configured (indicated by redis_connect_func), generate access token and use as password if redis_connect_func and gcp_service_account: - verbose_logger.info("DEBUG: Generating IAM token for service account (value not logged for security reasons)") + verbose_logger.debug("DEBUG: Generating IAM token for service account (value not logged for security reasons)") try: # Generate IAM access token using the helper function access_token = _generate_gcp_iam_access_token(gcp_service_account) cluster_kwargs["password"] = access_token - verbose_logger.info("DEBUG: Successfully generated GCP IAM access token for async Redis cluster") + verbose_logger.debug("DEBUG: Successfully generated GCP IAM access token for async Redis cluster") except Exception as e: verbose_logger.error(f"Failed to generate GCP IAM access token: {e}") from redis.exceptions import AuthenticationError raise AuthenticationError("Failed to generate GCP IAM access token") else: - verbose_logger.info(f"DEBUG: Not using GCP IAM auth - redis_connect_func={redis_connect_func is not None}, gcp_service_account={gcp_service_account}") + verbose_logger.debug(f"DEBUG: Not using GCP IAM auth - redis_connect_func={redis_connect_func is not None}, gcp_service_account_provided={gcp_service_account is not None}") new_startup_nodes: List[ClusterNode] = [] diff --git a/litellm/_uuid.py b/litellm/_uuid.py new file mode 100644 index 00000000000..52acf647dd8 --- /dev/null +++ b/litellm/_uuid.py @@ -0,0 +1,16 @@ +""" +Internal unified UUID helper. + +Always uses fastuuid for performance. +""" + +import fastuuid as _uuid # type: ignore + + +# Expose a module-like alias so callers can use: uuid.uuid4() +uuid = _uuid + + +def uuid4(): + """Return a UUID4 using the selected backend.""" + return uuid.uuid4() diff --git a/litellm/batches/main.py b/litellm/batches/main.py index 3ea0f95157f..37b9aff4efb 100644 --- a/litellm/batches/main.py +++ b/litellm/batches/main.py @@ -14,13 +14,16 @@ import asyncio import contextvars import os from functools import partial -from typing import Any, Coroutine, Dict, Literal, Optional, Union +from typing import Any, Coroutine, Dict, Literal, Optional, Union, cast import httpx import litellm +from litellm._logging import verbose_logger from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.llms.azure.batches.handler import AzureBatchesAPI +from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler +from litellm.llms.custom_httpx.llm_http_handler import BaseLLMHTTPHandler from litellm.llms.openai.openai import OpenAIBatchesAPI from litellm.llms.vertex_ai.batches.handler import VertexAIBatchPrediction from litellm.secret_managers.main import get_secret_str @@ -31,22 +34,68 @@ from litellm.types.llms.openai import ( RetrieveBatchRequest, ) from litellm.types.router import GenericLiteLLMParams -from litellm.types.utils import LiteLLMBatch -from litellm.utils import client, get_litellm_params, supports_httpx_timeout +from litellm.types.utils import LiteLLMBatch, LlmProviders +from litellm.utils import ( + ProviderConfigManager, + client, + get_litellm_params, + get_llm_provider, + supports_httpx_timeout, +) ####### ENVIRONMENT VARIABLES ################### openai_batches_instance = OpenAIBatchesAPI() azure_batches_instance = AzureBatchesAPI() vertex_ai_batches_instance = VertexAIBatchPrediction(gcs_bucket_name="") +base_llm_http_handler = BaseLLMHTTPHandler() ################################################# +def _resolve_timeout( + optional_params: GenericLiteLLMParams, + kwargs: Dict[str, Any], + custom_llm_provider: str, + default_timeout: float = 600.0, +) -> float: + """ + Resolve timeout value from various sources and handle httpx.Timeout objects. + + Args: + optional_params: GenericLiteLLMParams object containing timeout + kwargs: Additional kwargs that may contain request_timeout + custom_llm_provider: Provider name for httpx timeout support check + default_timeout: Default timeout value to use + + Returns: + Resolved timeout as float + """ + timeout = optional_params.timeout or kwargs.get("request_timeout", default_timeout) or default_timeout + + # Handle httpx.Timeout objects + if isinstance(timeout, httpx.Timeout): + if supports_httpx_timeout(custom_llm_provider) is False: + # Extract read timeout for providers that don't support httpx.Timeout + read_timeout = timeout.read or default_timeout + return float(read_timeout) + else: + # For providers that support httpx.Timeout, we still need to return a float + # This case might need to be handled differently based on the actual use case + return float(timeout.read or default_timeout) + + # Handle None case + if timeout is None: + return float(default_timeout) + + # Handle numeric values (int, float, string representations) + return float(timeout) + + @client async def acreate_batch( completion_window: Literal["24h"], endpoint: Literal["/v1/chat/completions", "/v1/embeddings", "/v1/completions"], input_file_id: str, - custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai", + custom_llm_provider: Literal["openai", "azure", "vertex_ai", "bedrock"] = "openai", metadata: Optional[Dict[str, str]] = None, extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, @@ -94,7 +143,7 @@ def create_batch( completion_window: Literal["24h"], endpoint: Literal["/v1/chat/completions", "/v1/embeddings", "/v1/completions"], input_file_id: str, - custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai", + custom_llm_provider: Literal["openai", "azure", "vertex_ai", "bedrock"] = "openai", metadata: Optional[Dict[str, str]] = None, extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, @@ -110,13 +159,23 @@ def create_batch( litellm_call_id = kwargs.get("litellm_call_id", None) proxy_server_request = kwargs.get("proxy_server_request", None) model_info = kwargs.get("model_info", None) + model: Optional[str] = kwargs.get("model", None) + try: + if model is not None: + model, _, _, _ = get_llm_provider( + model=model, + custom_llm_provider=None, + ) + except Exception as e: + verbose_logger.exception(f"litellm.batches.main.py::create_batch() - Error inferring custom_llm_provider - {str(e)}") + _is_async = kwargs.pop("acreate_batch", False) is True - litellm_params = get_litellm_params(**kwargs) - litellm_logging_obj: LiteLLMLoggingObj = kwargs.get("litellm_logging_obj", None) + litellm_params = dict(GenericLiteLLMParams(**kwargs)) + litellm_logging_obj: LiteLLMLoggingObj = cast(LiteLLMLoggingObj, kwargs.get("litellm_logging_obj", None)) ### TIMEOUT LOGIC ### - timeout = optional_params.timeout or kwargs.get("request_timeout", 600) or 600 + timeout = _resolve_timeout(optional_params, kwargs, custom_llm_provider) litellm_logging_obj.update_environment_variables( - model=None, + model=model, user=None, optional_params=optional_params.model_dump(), litellm_params={ @@ -130,18 +189,7 @@ def create_batch( }, custom_llm_provider=custom_llm_provider, ) - - if ( - timeout is not None - and isinstance(timeout, httpx.Timeout) - and supports_httpx_timeout(custom_llm_provider) is False - ): - read_timeout = timeout.read or 600 - timeout = read_timeout # default 10 min timeout - elif timeout is not None and not isinstance(timeout, httpx.Timeout): - timeout = float(timeout) # type: ignore - elif timeout is None: - timeout = 600.0 + _create_batch_request = CreateBatchRequest( completion_window=completion_window, @@ -151,6 +199,31 @@ def create_batch( extra_headers=extra_headers, extra_body=extra_body, ) + if model is not None: + provider_config = ProviderConfigManager.get_provider_batches_config( + model=model, + provider=LlmProviders(custom_llm_provider), + ) + else: + provider_config = None + if provider_config is not None: + response = base_llm_http_handler.create_batch( + provider_config=provider_config, + litellm_params=litellm_params, + create_batch_data=_create_batch_request, + headers=extra_headers or {}, + api_base=optional_params.api_base, + api_key=optional_params.api_key, + logging_obj=litellm_logging_obj, + _is_async=_is_async, + client=client + if client is not None + and isinstance(client, (HTTPHandler, AsyncHTTPHandler)) + else None, + timeout=timeout, + model=model, + ) + return response api_base: Optional[str] = None if custom_llm_provider == "openai": # for deepinfra/perplexity/anyscale/groq we check in get_llm_provider and pass in the api base from there @@ -267,7 +340,7 @@ def create_batch( @client async def aretrieve_batch( batch_id: str, - custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai", + custom_llm_provider: Literal["openai", "azure", "vertex_ai", "bedrock"] = "openai", metadata: Optional[Dict[str, str]] = None, extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, @@ -305,11 +378,129 @@ async def aretrieve_batch( except Exception as e: raise e +def _handle_retrieve_batch_providers_without_provider_config( + batch_id: str, + optional_params: GenericLiteLLMParams, + timeout: Union[float, httpx.Timeout], + litellm_params: dict, + _retrieve_batch_request: RetrieveBatchRequest, + _is_async: bool, + custom_llm_provider: Literal["openai", "azure", "vertex_ai", "bedrock"] = "openai", +): + api_base: Optional[str] = None + if custom_llm_provider == "openai": + # for deepinfra/perplexity/anyscale/groq we check in get_llm_provider and pass in the api base from there + api_base = ( + optional_params.api_base + or litellm.api_base + or os.getenv("OPENAI_BASE_URL") + or os.getenv("OPENAI_API_BASE") + or "https://api.openai.com/v1" + ) + organization = ( + optional_params.organization + or litellm.organization + or os.getenv("OPENAI_ORGANIZATION", None) + or None # default - https://github.com/openai/openai-python/blob/284c1799070c723c6a553337134148a7ab088dd8/openai/util.py#L105 + ) + # set API KEY + api_key = ( + optional_params.api_key + or litellm.api_key # for deepinfra/perplexity/anyscale we check in get_llm_provider and pass in the api key from there + or litellm.openai_key + or os.getenv("OPENAI_API_KEY") + ) + + response = openai_batches_instance.retrieve_batch( + _is_async=_is_async, + retrieve_batch_data=_retrieve_batch_request, + api_base=api_base, + api_key=api_key, + organization=organization, + timeout=timeout, + max_retries=optional_params.max_retries, + ) + elif custom_llm_provider == "azure": + api_base = ( + optional_params.api_base + or litellm.api_base + or get_secret_str("AZURE_API_BASE") + ) + api_version = ( + optional_params.api_version + or litellm.api_version + or get_secret_str("AZURE_API_VERSION") + ) + + api_key = ( + optional_params.api_key + or litellm.api_key + or litellm.azure_key + or get_secret_str("AZURE_OPENAI_API_KEY") + or get_secret_str("AZURE_API_KEY") + ) + + extra_body = optional_params.get("extra_body", {}) + if extra_body is not None: + extra_body.pop("azure_ad_token", None) + else: + get_secret_str("AZURE_AD_TOKEN") # type: ignore + + response = azure_batches_instance.retrieve_batch( + _is_async=_is_async, + api_base=api_base, + api_key=api_key, + api_version=api_version, + timeout=timeout, + max_retries=optional_params.max_retries, + retrieve_batch_data=_retrieve_batch_request, + litellm_params=litellm_params, + ) + elif custom_llm_provider == "vertex_ai": + api_base = optional_params.api_base or "" + 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.retrieve_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 'create_batch'. Only 'openai' is supported.".format( + custom_llm_provider + ), + model="n/a", + llm_provider=custom_llm_provider, + response=httpx.Response( + status_code=400, + content="Unsupported provider", + request=httpx.Request(method="create_thread", url="https://github.com/BerriAI/litellm"), # type: ignore + ), + ) + return response @client def retrieve_batch( batch_id: str, - custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai", + custom_llm_provider: Literal["openai", "azure", "vertex_ai", "bedrock"] = "openai", metadata: Optional[Dict[str, str]] = None, extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, @@ -322,20 +513,21 @@ def retrieve_batch( """ try: optional_params = GenericLiteLLMParams(**kwargs) - litellm_logging_obj: LiteLLMLoggingObj = kwargs.get("litellm_logging_obj", None) + litellm_logging_obj: Optional[LiteLLMLoggingObj] = kwargs.get("litellm_logging_obj", None) ### TIMEOUT LOGIC ### timeout = optional_params.timeout or kwargs.get("request_timeout", 600) or 600 litellm_params = get_litellm_params( custom_llm_provider=custom_llm_provider, **kwargs, ) - litellm_logging_obj.update_environment_variables( - model=None, - user=None, - optional_params=optional_params.model_dump(), - litellm_params=litellm_params, - custom_llm_provider=custom_llm_provider, - ) + if litellm_logging_obj is not None: + litellm_logging_obj.update_environment_variables( + model=None, + user=None, + optional_params=optional_params.model_dump(), + litellm_params=litellm_params, + custom_llm_provider=custom_llm_provider, + ) if ( timeout is not None @@ -356,115 +548,59 @@ def retrieve_batch( ) _is_async = kwargs.pop("aretrieve_batch", False) is True - api_base: Optional[str] = None - if custom_llm_provider == "openai": - # for deepinfra/perplexity/anyscale/groq we check in get_llm_provider and pass in the api base from there - api_base = ( - optional_params.api_base - or litellm.api_base - or os.getenv("OPENAI_BASE_URL") - or os.getenv("OPENAI_API_BASE") - or "https://api.openai.com/v1" - ) - organization = ( - optional_params.organization - or litellm.organization - or os.getenv("OPENAI_ORGANIZATION", None) - or None # default - https://github.com/openai/openai-python/blob/284c1799070c723c6a553337134148a7ab088dd8/openai/util.py#L105 - ) - # set API KEY - api_key = ( - optional_params.api_key - or litellm.api_key # for deepinfra/perplexity/anyscale we check in get_llm_provider and pass in the api key from there - or litellm.openai_key - or os.getenv("OPENAI_API_KEY") - ) - - response = openai_batches_instance.retrieve_batch( - _is_async=_is_async, - retrieve_batch_data=_retrieve_batch_request, - api_base=api_base, - api_key=api_key, - organization=organization, - timeout=timeout, - max_retries=optional_params.max_retries, - ) - elif custom_llm_provider == "azure": - api_base = ( - optional_params.api_base - or litellm.api_base - or get_secret_str("AZURE_API_BASE") - ) - api_version = ( - optional_params.api_version - or litellm.api_version - or get_secret_str("AZURE_API_VERSION") - ) - - api_key = ( - optional_params.api_key - or litellm.api_key - or litellm.azure_key - or get_secret_str("AZURE_OPENAI_API_KEY") - or get_secret_str("AZURE_API_KEY") - ) - - extra_body = optional_params.get("extra_body", {}) - if extra_body is not None: - extra_body.pop("azure_ad_token", None) - else: - get_secret_str("AZURE_AD_TOKEN") # type: ignore - - response = azure_batches_instance.retrieve_batch( - _is_async=_is_async, - api_base=api_base, - api_key=api_key, - api_version=api_version, - timeout=timeout, - max_retries=optional_params.max_retries, - retrieve_batch_data=_retrieve_batch_request, - litellm_params=litellm_params, - ) - elif custom_llm_provider == "vertex_ai": - api_base = optional_params.api_base or "" - 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.retrieve_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, + client = kwargs.get("client", None) + + # Try to use provider config first (for providers like bedrock) + model: Optional[str] = kwargs.get("model", None) + if model is not None: + provider_config = ProviderConfigManager.get_provider_batches_config( + model=model, + provider=LlmProviders(custom_llm_provider), ) else: - raise litellm.exceptions.BadRequestError( - message="LiteLLM doesn't support {} for 'create_batch'. Only 'openai' is supported.".format( - custom_llm_provider - ), - model="n/a", - llm_provider=custom_llm_provider, - response=httpx.Response( - status_code=400, - content="Unsupported provider", - request=httpx.Request(method="create_thread", url="https://github.com/BerriAI/litellm"), # type: ignore + provider_config = None + + if provider_config is not None: + response = base_llm_http_handler.retrieve_batch( + batch_id=batch_id, + provider_config=provider_config, + litellm_params=litellm_params, + headers=extra_headers or {}, + api_base=optional_params.api_base, + api_key=optional_params.api_key, + logging_obj=litellm_logging_obj or LiteLLMLoggingObj( + model=model or "bedrock/unknown", + messages=[], + stream=False, + call_type="batch_retrieve", + start_time=None, + litellm_call_id="batch_retrieve_" + batch_id, + function_id="batch_retrieve", ), + _is_async=_is_async, + client=client + if client is not None + and isinstance(client, (HTTPHandler, AsyncHTTPHandler)) + else None, + timeout=timeout, + model=model, ) - return response + return response + + + ######################################################### + # Handle providers without provider config + ######################################################### + return _handle_retrieve_batch_providers_without_provider_config( + batch_id=batch_id, + custom_llm_provider=custom_llm_provider, + optional_params=optional_params, + litellm_params=litellm_params, + _retrieve_batch_request=_retrieve_batch_request, + _is_async=_is_async, + timeout=timeout, + ) + except Exception as e: raise e diff --git a/litellm/caching/caching_handler.py b/litellm/caching/caching_handler.py index 1dcc0f1fdb2..9526c4a2f39 100644 --- a/litellm/caching/caching_handler.py +++ b/litellm/caching/caching_handler.py @@ -17,7 +17,6 @@ In each method it will call the appropriate method from caching.py import asyncio import datetime import inspect -import threading from typing import ( TYPE_CHECKING, Any, @@ -301,10 +300,12 @@ class LLMCachingHandler: is_async=False, ) - threading.Thread( - target=logging_obj.success_handler, - args=(cached_result, start_time, end_time, cache_hit), - ).start() + logging_obj.handle_sync_success_callbacks_for_async_calls( + result=cached_result, + start_time=start_time, + end_time=end_time, + cache_hit=cache_hit + ) cache_key = litellm.cache._get_preset_cache_key_from_kwargs( **kwargs ) @@ -530,15 +531,17 @@ class LLMCachingHandler: end_time (datetime): The end time of the operation. cache_hit (bool): Whether it was a cache hit. """ - asyncio.create_task( - logging_obj.async_success_handler( - cached_result, start_time, end_time, cache_hit + from litellm.litellm_core_utils.logging_worker import GLOBAL_LOGGING_WORKER + + GLOBAL_LOGGING_WORKER.ensure_initialized_and_enqueue( + async_coroutine=logging_obj.async_success_handler( + result=cached_result, start_time=start_time, end_time=end_time, cache_hit=cache_hit ) ) - threading.Thread( - target=logging_obj.success_handler, - args=(cached_result, start_time, end_time, cache_hit), - ).start() + + logging_obj.handle_sync_success_callbacks_for_async_calls( + result=cached_result, start_time=start_time, end_time=end_time, cache_hit=cache_hit + ) async def _retrieve_from_cache( self, call_type: str, kwargs: Dict[str, Any], args: Tuple[Any, ...] diff --git a/litellm/caching/in_memory_cache.py b/litellm/caching/in_memory_cache.py index 47f911894a3..082cac791f2 100644 --- a/litellm/caching/in_memory_cache.py +++ b/litellm/caching/in_memory_cache.py @@ -36,7 +36,7 @@ class InMemoryCache(BaseCache): max_size_in_memory [int]: Maximum number of items in cache. done to prevent memory leaks. Use 200 items as a default """ self.max_size_in_memory = ( - max_size_in_memory or 200 + max_size_in_memory if max_size_in_memory is not None else 200 ) # set an upper bound of 200 items in-memory self.default_ttl = default_ttl or 600 self.max_size_per_item = ( @@ -103,23 +103,36 @@ class InMemoryCache(BaseCache): def evict_cache(self): """ Eviction policy: - - check if any items in ttl_dict are expired -> remove them from ttl_dict and cache_dict + 1. First, remove expired items from ttl_dict and cache_dict + 2. If cache is still at or above max_size_in_memory, evict items with earliest expiration times This guarantees the following: - - 1. When item ttl not set: At minimumm each item will remain in memory for 5 minutes - - 2. When ttl is set: the item will remain in memory for at least that amount of time + - 1. When item ttl not set: At minimum each item will remain in memory for the default ttl + - 2. When ttl is set: the item will remain in memory for at least that amount of time, unless cache size requires eviction - 3. the size of in-memory cache is bounded """ - for key in list(self.ttl_dict.keys()): - if self._is_key_expired(key): + current_time = time.time() + + # Step 1: Remove expired items + expired_keys = [key for key, ttl in self.ttl_dict.items() if current_time > ttl] + for key in expired_keys: + self._remove_key(key) + + # Step 2: If cache is still full, evict items with earliest expiration times + if len(self.cache_dict) >= self.max_size_in_memory: + # Sort by expiration time (earliest first) and evict until we're under the limit + items_by_expiration = sorted(self.ttl_dict.items(), key=lambda x: x[1]) + keys_to_evict = items_by_expiration[:len(self.cache_dict) - self.max_size_in_memory + 1] + + for key, _ in keys_to_evict: self._remove_key(key) - # de-reference the removed item - # https://www.geeksforgeeks.org/diagnosing-and-fixing-memory-leaks-in-python/ - # One of the most common causes of memory leaks in Python is the retention of objects that are no longer being used. - # This can occur when an object is referenced by another object, but the reference is never removed. + # de-reference the removed item + # https://www.geeksforgeeks.org/diagnosing-and-fixing-memory-leaks-in-python/ + # One of the most common causes of memory leaks in Python is the retention of objects that are no longer being used. + # This can occur when an object is referenced by another object, but the reference is never removed. def allow_ttl_override(self, key: str) -> bool: """ @@ -134,6 +147,10 @@ class InMemoryCache(BaseCache): return False def set_cache(self, key, value, **kwargs): + # Handle the edge case where max_size_in_memory is 0 + if self.max_size_in_memory == 0: + return # Don't cache anything if max size is 0 + if len(self.cache_dict) >= self.max_size_in_memory: # only evict when cache is full self.evict_cache() diff --git a/litellm/caching/qdrant_semantic_cache.py b/litellm/caching/qdrant_semantic_cache.py index 32d4d8b0fdc..0e77b5a6c21 100644 --- a/litellm/caching/qdrant_semantic_cache.py +++ b/litellm/caching/qdrant_semantic_cache.py @@ -168,7 +168,7 @@ class QdrantSemanticCache(BaseCache): def set_cache(self, key, value, **kwargs): print_verbose(f"qdrant semantic-cache set_cache, kwargs: {kwargs}") - import uuid + from litellm._uuid import uuid # get the prompt messages = kwargs["messages"] @@ -279,7 +279,7 @@ class QdrantSemanticCache(BaseCache): pass async def async_set_cache(self, key, value, **kwargs): - import uuid + from litellm._uuid import uuid from litellm.proxy.proxy_server import llm_model_list, llm_router diff --git a/litellm/caching/redis_cache.py b/litellm/caching/redis_cache.py index 47bc0222ed5..af7468ba14c 100644 --- a/litellm/caching/redis_cache.py +++ b/litellm/caching/redis_cache.py @@ -19,6 +19,7 @@ from typing import TYPE_CHECKING, Any, List, Optional, Tuple, Union, cast import litellm from litellm._logging import print_verbose, verbose_logger 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 RedisPipelineIncrementOperation from litellm.types.services import ServiceTypes @@ -138,7 +139,7 @@ class RedisCache(BaseCache): self.redis_flush_size = redis_flush_size self.redis_version = "Unknown" try: - if not inspect.iscoroutinefunction(self.redis_client): + if not coroutine_checker.is_async_callable(self.redis_client): self.redis_version = self.redis_client.info()["redis_version"] # type: ignore except Exception: pass diff --git a/litellm/caching/s3_cache.py b/litellm/caching/s3_cache.py index 15f7a5c1e16..180964605f6 100644 --- a/litellm/caching/s3_cache.py +++ b/litellm/caching/s3_cache.py @@ -13,6 +13,7 @@ import asyncio import json from functools import partial from typing import Optional +from datetime import datetime, timezone, timedelta from litellm._logging import print_verbose, verbose_logger @@ -69,11 +70,9 @@ class S3Cache(BaseCache): if ttl is not None: cache_control = f"immutable, max-age={ttl}, s-maxage={ttl}" - import datetime # Calculate expiration time - expiration_time = datetime.datetime.now() + datetime.timedelta(seconds=ttl) - + expiration_time = datetime.now(timezone.utc) + timedelta(seconds=ttl) # Upload the data to S3 with the calculated expiration time self.s3_client.put_object( Bucket=self.bucket_name, @@ -126,6 +125,13 @@ class S3Cache(BaseCache): ) if cached_response is not None: + if "Expires" in cached_response: + expires_time = cached_response['Expires'] + current_time = datetime.now(expires_time.tzinfo) + + if current_time > expires_time: + return None + # cached_response is in `b{} convert it to ModelResponse cached_response = ( cached_response["Body"].read().decode("utf-8") diff --git a/litellm/completion_extras/litellm_responses_transformation/handler.py b/litellm/completion_extras/litellm_responses_transformation/handler.py index f2eeaf04554..6ec49ce0620 100644 --- a/litellm/completion_extras/litellm_responses_transformation/handler.py +++ b/litellm/completion_extras/litellm_responses_transformation/handler.py @@ -2,7 +2,9 @@ Handler for transforming /chat/completions api requests to litellm.responses requests """ -from typing import TYPE_CHECKING, Any, Coroutine, TypedDict, Union +from typing import TYPE_CHECKING, Any, Coroutine, Union + +from typing_extensions import TypedDict if TYPE_CHECKING: from litellm import CustomStreamWrapper, LiteLLMLoggingObj, ModelResponse diff --git a/litellm/constants.py b/litellm/constants.py index 365dfa84510..1ed9f237a29 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -14,6 +14,9 @@ DEFAULT_S3_BATCH_SIZE = int(os.getenv("DEFAULT_S3_BATCH_SIZE", 512)) DEFAULT_SQS_FLUSH_INTERVAL_SECONDS = int( os.getenv("DEFAULT_SQS_FLUSH_INTERVAL_SECONDS", 10) ) +DEFAULT_NUM_WORKERS_LITELLM_PROXY = int( + os.getenv("DEFAULT_NUM_WORKERS_LITELLM_PROXY", 1) +) DEFAULT_SQS_BATCH_SIZE = int(os.getenv("DEFAULT_SQS_BATCH_SIZE", 512)) SQS_SEND_MESSAGE_ACTION = "SendMessage" SQS_API_VERSION = "2012-11-05" @@ -48,6 +51,25 @@ SINGLE_DEPLOYMENT_TRAFFIC_FAILURE_THRESHOLD = int( DEFAULT_REASONING_EFFORT_DISABLE_THINKING_BUDGET = int( os.getenv("DEFAULT_REASONING_EFFORT_DISABLE_THINKING_BUDGET", 0) ) + +# Gemini model-specific minimal thinking budget constants +DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_FLASH = int( + os.getenv("DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_FLASH", 1) +) +DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_PRO = int( + os.getenv("DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_PRO", 128) +) +DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_FLASH_LITE = int( + os.getenv( + "DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_FLASH_LITE", 512 + ) +) + +# Generic fallback for unknown models +DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET = int( + os.getenv("DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET", 128) +) + DEFAULT_REASONING_EFFORT_LOW_THINKING_BUDGET = int( os.getenv("DEFAULT_REASONING_EFFORT_LOW_THINKING_BUDGET", 1024) ) @@ -157,6 +179,7 @@ NON_LLM_CONNECTION_TIMEOUT = int( os.getenv("NON_LLM_CONNECTION_TIMEOUT", 15) ) # timeout for adjacent services (e.g. jwt auth) MAX_EXCEPTION_MESSAGE_LENGTH = int(os.getenv("MAX_EXCEPTION_MESSAGE_LENGTH", 2000)) +MAX_STRING_LENGTH_PROMPT_IN_DB = int(os.getenv("MAX_STRING_LENGTH_PROMPT_IN_DB", 2048)) BEDROCK_MAX_POLICY_SIZE = int(os.getenv("BEDROCK_MAX_POLICY_SIZE", 75)) REPLICATE_POLLING_DELAY_SECONDS = float( os.getenv("REPLICATE_POLLING_DELAY_SECONDS", 0.5) @@ -285,9 +308,13 @@ LITELLM_CHAT_PROVIDERS = [ "dashscope", "moonshot", "v0", + "heroku", "oci", "morph", "lambda_ai", + "vercel_ai_gateway", + "wandb", + "ovhcloud", ] LITELLM_EMBEDDING_PROVIDERS_SUPPORTING_INPUT_ARRAY_OF_TOKENS = [ @@ -392,6 +419,7 @@ DEFAULT_CHAT_COMPLETION_PARAM_VALUES = { "reasoning_effort": None, "thinking": None, "web_search_options": None, + "safety_identifier": None, } openai_compatible_endpoints: List = [ @@ -420,6 +448,8 @@ openai_compatible_endpoints: List = [ "https://api.morphllm.com/v1", "https://api.lambda.ai/v1", "https://api.hyperbolic.xyz/v1", + "https://ai-gateway.vercel.sh/v1", + "https://api.inference.wandb.ai/v1", ] @@ -462,6 +492,9 @@ openai_compatible_providers: List = [ "morph", "lambda_ai", "hyperbolic", + "vercel_ai_gateway", + "aiml", + "wandb", ] openai_text_completion_compatible_providers: List = ( [ # providers that support `/v1/completions` @@ -477,6 +510,7 @@ openai_text_completion_compatible_providers: List = ( "v0", "lambda_ai", "hyperbolic", + "wandb", ] ) _openai_like_providers: List = [ @@ -485,190 +519,279 @@ _openai_like_providers: List = [ "watsonx", ] # private helper. similar to openai but require some custom auth / endpoint handling, so can't use the openai sdk # well supported replicate llms -replicate_models: set = set([ - # llama replicate supported LLMs - "replicate/llama-2-70b-chat:2796ee9483c3fd7aa2e171d38f4ca12251a30609463dcfd4cd76703f22e96cdf", - "a16z-infra/llama-2-13b-chat:2a7f981751ec7fdf87b5b91ad4db53683a98082e9ff7bfd12c8cd5ea85980a52", - "meta/codellama-13b:1c914d844307b0588599b8393480a3ba917b660c7e9dfae681542b5325f228db", - # Vicuna - "replicate/vicuna-13b:6282abe6a492de4145d7bb601023762212f9ddbbe78278bd6771c8b3b2f2a13b", - "joehoover/instructblip-vicuna13b:c4c54e3c8c97cd50c2d2fec9be3b6065563ccf7d43787fb99f84151b867178fe", - # Flan T-5 - "daanelson/flan-t5-large:ce962b3f6792a57074a601d3979db5839697add2e4e02696b3ced4c022d4767f", - # Others - "replicate/dolly-v2-12b:ef0e1aefc61f8e096ebe4db6b2bacc297daf2ef6899f0f7e001ec445893500e5", - "replit/replit-code-v1-3b:b84f4c074b807211cd75e3e8b1589b6399052125b4c27106e43d47189e8415ad", -]) +replicate_models: set = set( + [ + # llama replicate supported LLMs + "replicate/llama-2-70b-chat:2796ee9483c3fd7aa2e171d38f4ca12251a30609463dcfd4cd76703f22e96cdf", + "a16z-infra/llama-2-13b-chat:2a7f981751ec7fdf87b5b91ad4db53683a98082e9ff7bfd12c8cd5ea85980a52", + "meta/codellama-13b:1c914d844307b0588599b8393480a3ba917b660c7e9dfae681542b5325f228db", + # Vicuna + "replicate/vicuna-13b:6282abe6a492de4145d7bb601023762212f9ddbbe78278bd6771c8b3b2f2a13b", + "joehoover/instructblip-vicuna13b:c4c54e3c8c97cd50c2d2fec9be3b6065563ccf7d43787fb99f84151b867178fe", + # Flan T-5 + "daanelson/flan-t5-large:ce962b3f6792a57074a601d3979db5839697add2e4e02696b3ced4c022d4767f", + # Others + "replicate/dolly-v2-12b:ef0e1aefc61f8e096ebe4db6b2bacc297daf2ef6899f0f7e001ec445893500e5", + "replit/replit-code-v1-3b:b84f4c074b807211cd75e3e8b1589b6399052125b4c27106e43d47189e8415ad", + ] +) -clarifai_models: set = set([ - "clarifai/meta.Llama-3.Llama-3-8B-Instruct", - "clarifai/gcp.generate.gemma-1_1-7b-it", - "clarifai/mistralai.completion.mixtral-8x22B", - "clarifai/cohere.generate.command-r-plus", - "clarifai/databricks.drbx.dbrx-instruct", - "clarifai/mistralai.completion.mistral-large", - "clarifai/mistralai.completion.mistral-medium", - "clarifai/mistralai.completion.mistral-small", - "clarifai/mistralai.completion.mixtral-8x7B-Instruct-v0_1", - "clarifai/gcp.generate.gemma-2b-it", - "clarifai/gcp.generate.gemma-7b-it", - "clarifai/deci.decilm.deciLM-7B-instruct", - "clarifai/mistralai.completion.mistral-7B-Instruct", - "clarifai/gcp.generate.gemini-pro", - "clarifai/anthropic.completion.claude-v1", - "clarifai/anthropic.completion.claude-instant-1_2", - "clarifai/anthropic.completion.claude-instant", - "clarifai/anthropic.completion.claude-v2", - "clarifai/anthropic.completion.claude-2_1", - "clarifai/meta.Llama-2.codeLlama-70b-Python", - "clarifai/meta.Llama-2.codeLlama-70b-Instruct", - "clarifai/openai.completion.gpt-3_5-turbo-instruct", - "clarifai/meta.Llama-2.llama2-7b-chat", - "clarifai/meta.Llama-2.llama2-13b-chat", - "clarifai/meta.Llama-2.llama2-70b-chat", - "clarifai/openai.chat-completion.gpt-4-turbo", - "clarifai/microsoft.text-generation.phi-2", - "clarifai/meta.Llama-2.llama2-7b-chat-vllm", - "clarifai/upstage.solar.solar-10_7b-instruct", - "clarifai/openchat.openchat.openchat-3_5-1210", - "clarifai/togethercomputer.stripedHyena.stripedHyena-Nous-7B", - "clarifai/gcp.generate.text-bison", - "clarifai/meta.Llama-2.llamaGuard-7b", - "clarifai/fblgit.una-cybertron.una-cybertron-7b-v2", - "clarifai/openai.chat-completion.GPT-4", - "clarifai/openai.chat-completion.GPT-3_5-turbo", - "clarifai/ai21.complete.Jurassic2-Grande", - "clarifai/ai21.complete.Jurassic2-Grande-Instruct", - "clarifai/ai21.complete.Jurassic2-Jumbo-Instruct", - "clarifai/ai21.complete.Jurassic2-Jumbo", - "clarifai/ai21.complete.Jurassic2-Large", - "clarifai/cohere.generate.cohere-generate-command", - "clarifai/wizardlm.generate.wizardCoder-Python-34B", - "clarifai/wizardlm.generate.wizardLM-70B", - "clarifai/tiiuae.falcon.falcon-40b-instruct", - "clarifai/togethercomputer.RedPajama.RedPajama-INCITE-7B-Chat", - "clarifai/gcp.generate.code-gecko", - "clarifai/gcp.generate.code-bison", - "clarifai/mistralai.completion.mistral-7B-OpenOrca", - "clarifai/mistralai.completion.openHermes-2-mistral-7B", - "clarifai/wizardlm.generate.wizardLM-13B", - "clarifai/huggingface-research.zephyr.zephyr-7B-alpha", - "clarifai/wizardlm.generate.wizardCoder-15B", - "clarifai/microsoft.text-generation.phi-1_5", - "clarifai/databricks.Dolly-v2.dolly-v2-12b", - "clarifai/bigcode.code.StarCoder", - "clarifai/salesforce.xgen.xgen-7b-8k-instruct", - "clarifai/mosaicml.mpt.mpt-7b-instruct", - "clarifai/anthropic.completion.claude-3-opus", - "clarifai/anthropic.completion.claude-3-sonnet", - "clarifai/gcp.generate.gemini-1_5-pro", - "clarifai/gcp.generate.imagen-2", - "clarifai/salesforce.blip.general-english-image-caption-blip-2", -]) +clarifai_models: set = set( + [ + "clarifai/meta.Llama-3.Llama-3-8B-Instruct", + "clarifai/gcp.generate.gemma-1_1-7b-it", + "clarifai/mistralai.completion.mixtral-8x22B", + "clarifai/cohere.generate.command-r-plus", + "clarifai/databricks.drbx.dbrx-instruct", + "clarifai/mistralai.completion.mistral-large", + "clarifai/mistralai.completion.mistral-medium", + "clarifai/mistralai.completion.mistral-small", + "clarifai/mistralai.completion.mixtral-8x7B-Instruct-v0_1", + "clarifai/gcp.generate.gemma-2b-it", + "clarifai/gcp.generate.gemma-7b-it", + "clarifai/deci.decilm.deciLM-7B-instruct", + "clarifai/mistralai.completion.mistral-7B-Instruct", + "clarifai/gcp.generate.gemini-pro", + "clarifai/anthropic.completion.claude-v1", + "clarifai/anthropic.completion.claude-instant-1_2", + "clarifai/anthropic.completion.claude-instant", + "clarifai/anthropic.completion.claude-v2", + "clarifai/anthropic.completion.claude-2_1", + "clarifai/meta.Llama-2.codeLlama-70b-Python", + "clarifai/meta.Llama-2.codeLlama-70b-Instruct", + "clarifai/openai.completion.gpt-3_5-turbo-instruct", + "clarifai/meta.Llama-2.llama2-7b-chat", + "clarifai/meta.Llama-2.llama2-13b-chat", + "clarifai/meta.Llama-2.llama2-70b-chat", + "clarifai/openai.chat-completion.gpt-4-turbo", + "clarifai/microsoft.text-generation.phi-2", + "clarifai/meta.Llama-2.llama2-7b-chat-vllm", + "clarifai/upstage.solar.solar-10_7b-instruct", + "clarifai/openchat.openchat.openchat-3_5-1210", + "clarifai/togethercomputer.stripedHyena.stripedHyena-Nous-7B", + "clarifai/gcp.generate.text-bison", + "clarifai/meta.Llama-2.llamaGuard-7b", + "clarifai/fblgit.una-cybertron.una-cybertron-7b-v2", + "clarifai/openai.chat-completion.GPT-4", + "clarifai/openai.chat-completion.GPT-3_5-turbo", + "clarifai/ai21.complete.Jurassic2-Grande", + "clarifai/ai21.complete.Jurassic2-Grande-Instruct", + "clarifai/ai21.complete.Jurassic2-Jumbo-Instruct", + "clarifai/ai21.complete.Jurassic2-Jumbo", + "clarifai/ai21.complete.Jurassic2-Large", + "clarifai/cohere.generate.cohere-generate-command", + "clarifai/wizardlm.generate.wizardCoder-Python-34B", + "clarifai/wizardlm.generate.wizardLM-70B", + "clarifai/tiiuae.falcon.falcon-40b-instruct", + "clarifai/togethercomputer.RedPajama.RedPajama-INCITE-7B-Chat", + "clarifai/gcp.generate.code-gecko", + "clarifai/gcp.generate.code-bison", + "clarifai/mistralai.completion.mistral-7B-OpenOrca", + "clarifai/mistralai.completion.openHermes-2-mistral-7B", + "clarifai/wizardlm.generate.wizardLM-13B", + "clarifai/huggingface-research.zephyr.zephyr-7B-alpha", + "clarifai/wizardlm.generate.wizardCoder-15B", + "clarifai/microsoft.text-generation.phi-1_5", + "clarifai/databricks.Dolly-v2.dolly-v2-12b", + "clarifai/bigcode.code.StarCoder", + "clarifai/salesforce.xgen.xgen-7b-8k-instruct", + "clarifai/mosaicml.mpt.mpt-7b-instruct", + "clarifai/anthropic.completion.claude-3-opus", + "clarifai/anthropic.completion.claude-3-sonnet", + "clarifai/gcp.generate.gemini-1_5-pro", + "clarifai/gcp.generate.imagen-2", + "clarifai/salesforce.blip.general-english-image-caption-blip-2", + ] +) -huggingface_models: set = set([ - "meta-llama/Llama-2-7b-hf", - "meta-llama/Llama-2-7b-chat-hf", - "meta-llama/Llama-2-13b-hf", - "meta-llama/Llama-2-13b-chat-hf", - "meta-llama/Llama-2-70b-hf", - "meta-llama/Llama-2-70b-chat-hf", - "meta-llama/Llama-2-7b", - "meta-llama/Llama-2-7b-chat", - "meta-llama/Llama-2-13b", - "meta-llama/Llama-2-13b-chat", - "meta-llama/Llama-2-70b", - "meta-llama/Llama-2-70b-chat", -]) # these have been tested on extensively. But by default all text2text-generation and text-generation models are supported by liteLLM. - https://docs.litellm.ai/docs/providers -empower_models = set([ - "empower/empower-functions", - "empower/empower-functions-small", -]) +huggingface_models: set = set( + [ + "meta-llama/Llama-2-7b-hf", + "meta-llama/Llama-2-7b-chat-hf", + "meta-llama/Llama-2-13b-hf", + "meta-llama/Llama-2-13b-chat-hf", + "meta-llama/Llama-2-70b-hf", + "meta-llama/Llama-2-70b-chat-hf", + "meta-llama/Llama-2-7b", + "meta-llama/Llama-2-7b-chat", + "meta-llama/Llama-2-13b", + "meta-llama/Llama-2-13b-chat", + "meta-llama/Llama-2-70b", + "meta-llama/Llama-2-70b-chat", + ] +) # these have been tested on extensively. But by default all text2text-generation and text-generation models are supported by liteLLM. - https://docs.litellm.ai/docs/providers +empower_models = set( + [ + "empower/empower-functions", + "empower/empower-functions-small", + ] +) -together_ai_models: set = set([ - # llama llms - chat - "togethercomputer/llama-2-70b-chat", - # llama llms - language / instruct - "togethercomputer/llama-2-70b", - "togethercomputer/LLaMA-2-7B-32K", - "togethercomputer/Llama-2-7B-32K-Instruct", - "togethercomputer/llama-2-7b", - # falcon llms - "togethercomputer/falcon-40b-instruct", - "togethercomputer/falcon-7b-instruct", - # alpaca - "togethercomputer/alpaca-7b", - # chat llms - "HuggingFaceH4/starchat-alpha", - # code llms - "togethercomputer/CodeLlama-34b", - "togethercomputer/CodeLlama-34b-Instruct", - "togethercomputer/CodeLlama-34b-Python", - "defog/sqlcoder", - "NumbersStation/nsql-llama-2-7B", - "WizardLM/WizardCoder-15B-V1.0", - "WizardLM/WizardCoder-Python-34B-V1.0", - # language llms - "NousResearch/Nous-Hermes-Llama2-13b", - "Austism/chronos-hermes-13b", - "upstage/SOLAR-0-70b-16bit", - "WizardLM/WizardLM-70B-V1.0", -]) - # supports all together ai models, just pass in the model id e.g. completion(model="together_computer/replit_code_3b",...) +together_ai_models: set = set( + [ + # llama llms - chat + "togethercomputer/llama-2-70b-chat", + # llama llms - language / instruct + "togethercomputer/llama-2-70b", + "togethercomputer/LLaMA-2-7B-32K", + "togethercomputer/Llama-2-7B-32K-Instruct", + "togethercomputer/llama-2-7b", + # falcon llms + "togethercomputer/falcon-40b-instruct", + "togethercomputer/falcon-7b-instruct", + # alpaca + "togethercomputer/alpaca-7b", + # chat llms + "HuggingFaceH4/starchat-alpha", + # code llms + "togethercomputer/CodeLlama-34b", + "togethercomputer/CodeLlama-34b-Instruct", + "togethercomputer/CodeLlama-34b-Python", + "defog/sqlcoder", + "NumbersStation/nsql-llama-2-7B", + "WizardLM/WizardCoder-15B-V1.0", + "WizardLM/WizardCoder-Python-34B-V1.0", + # language llms + "NousResearch/Nous-Hermes-Llama2-13b", + "Austism/chronos-hermes-13b", + "upstage/SOLAR-0-70b-16bit", + "WizardLM/WizardLM-70B-V1.0", + ] +) +# supports all together ai models, just pass in the model id e.g. completion(model="together_computer/replit_code_3b",...) -baseten_models: set = set([ - "qvv0xeq", - "q841o8w", - "31dxrj3", -]) # FALCON 7B # WizardLM # Mosaic ML +baseten_models: set = set( + [ + "qvv0xeq", + "q841o8w", + "31dxrj3", + ] +) # FALCON 7B # WizardLM # Mosaic ML -featherless_ai_models: set = set([ - "featherless-ai/Qwerky-72B", - "featherless-ai/Qwerky-QwQ-32B", - "Qwen/Qwen2.5-72B-Instruct", - "all-hands/openhands-lm-32b-v0.1", - "Qwen/Qwen2.5-Coder-32B-Instruct", - "deepseek-ai/DeepSeek-V3-0324", - "mistralai/Mistral-Small-24B-Instruct-2501", - "mistralai/Mistral-Nemo-Instruct-2407", - "ProdeusUnity/Stellar-Odyssey-12b-v0.0", -]) +featherless_ai_models: set = set( + [ + "featherless-ai/Qwerky-72B", + "featherless-ai/Qwerky-QwQ-32B", + "Qwen/Qwen2.5-72B-Instruct", + "all-hands/openhands-lm-32b-v0.1", + "Qwen/Qwen2.5-Coder-32B-Instruct", + "deepseek-ai/DeepSeek-V3-0324", + "mistralai/Mistral-Small-24B-Instruct-2501", + "mistralai/Mistral-Nemo-Instruct-2407", + "ProdeusUnity/Stellar-Odyssey-12b-v0.0", + ] +) -nebius_models: set = set([ - "Qwen/Qwen3-235B-A22B", - "Qwen/Qwen3-30B-A3B-fast", - "Qwen/Qwen3-32B", - "Qwen/Qwen3-14B", - "nvidia/Llama-3_1-Nemotron-Ultra-253B-v1", - "deepseek-ai/DeepSeek-V3-0324", - "deepseek-ai/DeepSeek-V3-0324-fast", - "deepseek-ai/DeepSeek-R1", - "deepseek-ai/DeepSeek-R1-fast", - "meta-llama/Llama-3.3-70B-Instruct-fast", - "Qwen/Qwen2.5-32B-Instruct-fast", - "Qwen/Qwen2.5-Coder-32B-Instruct-fast", -]) +nebius_models: set = set( + [ + # deepseek models + "deepseek-ai/DeepSeek-R1-0528", + "deepseek-ai/DeepSeek-V3-0324", + "deepseek-ai/DeepSeek-V3", + "deepseek-ai/DeepSeek-R1", + "deepseek-ai/DeepSeek-R1-Distill-Llama-70B", + # google models + "google/gemma-2-2b-it", + "google/gemma-2-9b-it-fast", + # llama models + "meta-llama/Llama-3.3-70B-Instruct", + "meta-llama/Meta-Llama-3.1-70B-Instruct", + "meta-llama/Meta-Llama-3.1-8B-Instruct", + "meta-llama/Meta-Llama-3.1-405B-Instruct", + "NousResearch/Hermes-3-Llama-405B", + # microsoft models + "microsoft/phi-4", + # mistral models + "mistralai/Mistral-Nemo-Instruct-2407", + "mistralai/Devstral-Small-2505", + # moonshot models + "moonshotai/Kimi-K2-Instruct", + # nvidia models + "nvidia/Llama-3_1-Nemotron-Ultra-253B-v1", + "nvidia/Llama-3_3-Nemotron-Super-49B-v1", + # openai models + "openai/gpt-oss-120b", + "openai/gpt-oss-20b", + # qwen models + "Qwen/Qwen3-Coder-480B-A35B-Instruct", + "Qwen/Qwen3-235B-A22B-Instruct-2507", + "Qwen/Qwen3-235B-A22B", + "Qwen/Qwen3-30B-A3B", + "Qwen/Qwen3-32B", + "Qwen/Qwen3-14B", + "Qwen/Qwen3-4B-fast", + "Qwen/Qwen2.5-Coder-7B", + "Qwen/Qwen2.5-Coder-32B-Instruct", + "Qwen/Qwen2.5-72B-Instruct", + "Qwen/QwQ-32B", + "Qwen/Qwen3-30B-A3B-Thinking-2507", + "Qwen/Qwen3-30B-A3B-Instruct-2507", + # zai models + "zai-org/GLM-4.5", + "zai-org/GLM-4.5-Air", + # other models + "aaditya/Llama3-OpenBioLLM-70B", + "ProdeusUnity/Stellar-Odyssey-12b-v0.0", + "all-hands/openhands-lm-32b-v0.1", + ] +) -dashscope_models: set = set([ - "qwen-turbo", - "qwen-plus", - "qwen-max", - "qwen-turbo-latest", - "qwen-plus-latest", - "qwen-max-latest", - "qwq-32b", - "qwen3-235b-a22b", - "qwen3-32b", - "qwen3-30b-a3b", -]) +dashscope_models: set = set( + [ + "qwen-turbo", + "qwen-plus", + "qwen-max", + "qwen-turbo-latest", + "qwen-plus-latest", + "qwen-max-latest", + "qwq-32b", + "qwen3-235b-a22b", + "qwen3-32b", + "qwen3-30b-a3b", + ] +) -nebius_embedding_models: set = set([ - "BAAI/bge-en-icl", - "BAAI/bge-multilingual-gemma2", - "intfloat/e5-mistral-7b-instruct", -]) +nebius_embedding_models: set = set( + [ + "BAAI/bge-en-icl", + "BAAI/bge-multilingual-gemma2", + "intfloat/e5-mistral-7b-instruct", + ] +) + +WANDB_MODELS: set = set( + [ + # openai models + "openai/gpt-oss-120b", + "openai/gpt-oss-20b", + + # zai-org models + "zai-org/GLM-4.5", + + # Qwen models + "Qwen/Qwen3-235B-A22B-Instruct-2507", + "Qwen/Qwen3-Coder-480B-A35B-Instruct", + "Qwen/Qwen3-235B-A22B-Thinking-2507", + + # moonshotai + "moonshotai/Kimi-K2-Instruct", + + # meta models + "meta-llama/Llama-3.1-8B-Instruct", + "meta-llama/Llama-3.3-70B-Instruct", + "meta-llama/Llama-4-Scout-17B-16E-Instruct", + + # deepseek-ai + "deepseek-ai/DeepSeek-V3.1", + "deepseek-ai/DeepSeek-R1-0528", + "deepseek-ai/DeepSeek-V3-0324", + + # microsoft + "microsoft/Phi-4-mini-instruct", + ] +) BEDROCK_INVOKE_PROVIDERS_LITERAL = Literal[ "cohere", @@ -682,21 +805,73 @@ BEDROCK_INVOKE_PROVIDERS_LITERAL = Literal[ "deepseek_r1", ] +BEDROCK_EMBEDDING_PROVIDERS_LITERAL = Literal[ + "cohere", + "amazon", + "twelvelabs", +] + +BEDROCK_CONVERSE_MODELS = [ + "qwen.qwen3-coder-480b-a35b-v1:0", + "qwen.qwen3-235b-a22b-2507-v1:0", + "qwen.qwen3-coder-30b-a3b-v1:0", + "qwen.qwen3-32b-v1:0", + "deepseek.v3-v1:0", + "openai.gpt-oss-20b-1:0", + "openai.gpt-oss-120b-1:0", + "anthropic.claude-opus-4-1-20250805-v1:0", + "anthropic.claude-opus-4-20250514-v1:0", + "anthropic.claude-sonnet-4-20250514-v1:0", + "anthropic.claude-3-7-sonnet-20250219-v1:0", + "anthropic.claude-3-5-haiku-20241022-v1:0", + "anthropic.claude-3-5-sonnet-20241022-v2:0", + "anthropic.claude-3-5-sonnet-20240620-v1:0", + "anthropic.claude-3-opus-20240229-v1:0", + "anthropic.claude-3-sonnet-20240229-v1:0", + "anthropic.claude-3-haiku-20240307-v1:0", + "anthropic.claude-v2", + "anthropic.claude-v2:1", + "anthropic.claude-v1", + "anthropic.claude-instant-v1", + "ai21.jamba-instruct-v1:0", + "ai21.jamba-1-5-mini-v1:0", + "ai21.jamba-1-5-large-v1:0", + "meta.llama3-70b-instruct-v1:0", + "meta.llama3-8b-instruct-v1:0", + "meta.llama3-1-8b-instruct-v1:0", + "meta.llama3-1-70b-instruct-v1:0", + "meta.llama3-1-405b-instruct-v1:0", + "meta.llama3-70b-instruct-v1:0", + "mistral.mistral-large-2407-v1:0", + "mistral.mistral-large-2402-v1:0", + "mistral.mistral-small-2402-v1:0", + "meta.llama3-2-1b-instruct-v1:0", + "meta.llama3-2-3b-instruct-v1:0", + "meta.llama3-2-11b-instruct-v1:0", + "meta.llama3-2-90b-instruct-v1:0", +] + + open_ai_embedding_models: set = set(["text-embedding-ada-002"]) -cohere_embedding_models: set = set([ - "embed-v4.0", - "embed-english-v3.0", - "embed-english-light-v3.0", - "embed-multilingual-v3.0", - "embed-english-v2.0", - "embed-english-light-v2.0", - "embed-multilingual-v2.0", -]) -bedrock_embedding_models: set = set([ - "amazon.titan-embed-text-v1", - "cohere.embed-english-v3", - "cohere.embed-multilingual-v3", -]) +cohere_embedding_models: set = set( + [ + "embed-v4.0", + "embed-english-v3.0", + "embed-english-light-v3.0", + "embed-multilingual-v3.0", + "embed-english-v2.0", + "embed-english-light-v2.0", + "embed-multilingual-v2.0", + ] +) +bedrock_embedding_models: set = set( + [ + "amazon.titan-embed-text-v1", + "cohere.embed-english-v3", + "cohere.embed-multilingual-v3", + "twelvelabs.marengo-embed-2-7-v1:0", + ] +) known_tokenizer_config = { "mistralai/Mistral-7B-Instruct-v0.1": { @@ -767,6 +942,9 @@ AZURE_STORAGE_MSFT_VERSION = "2019-07-07" PROMETHEUS_BUDGET_METRICS_REFRESH_INTERVAL_MINUTES = int( os.getenv("PROMETHEUS_BUDGET_METRICS_REFRESH_INTERVAL_MINUTES", 5) ) +CLOUDZERO_EXPORT_INTERVAL_MINUTES = int( + os.getenv("CLOUDZERO_EXPORT_INTERVAL_MINUTES", 60) +) MCP_TOOL_NAME_PREFIX = "mcp_tool" MAXIMUM_TRACEBACK_LINES_TO_LOG = int(os.getenv("MAXIMUM_TRACEBACK_LINES_TO_LOG", 100)) @@ -810,7 +988,12 @@ HEALTH_CHECK_TIMEOUT_SECONDS = int( os.getenv("HEALTH_CHECK_TIMEOUT_SECONDS", 60) ) # 60 seconds LITTELM_INTERNAL_HEALTH_SERVICE_ACCOUNT_NAME = "litellm-internal-health-check" +LITTELM_CLI_SERVICE_ACCOUNT_NAME = "litellm-cli" +LITELLM_INTERNAL_JOBS_SERVICE_ACCOUNT_NAME = "litellm_internal_jobs" +# Key Rotation Constants +LITELLM_KEY_ROTATION_ENABLED = os.getenv("LITELLM_KEY_ROTATION_ENABLED", "false") +LITELLM_KEY_ROTATION_CHECK_INTERVAL_SECONDS = int(os.getenv("LITELLM_KEY_ROTATION_CHECK_INTERVAL_SECONDS", 86400)) # 24 hours default UI_SESSION_TOKEN_TEAM_ID = "litellm-dashboard" LITELLM_PROXY_ADMIN_NAME = "default_user_id" @@ -821,6 +1004,10 @@ LITELLM_CLI_SESSION_TOKEN_PREFIX = "litellm-session-token" ########################### DB CRON JOB NAMES ########################### DB_SPEND_UPDATE_JOB_NAME = "db_spend_update_job" PROMETHEUS_EMIT_BUDGET_METRICS_JOB_NAME = "prometheus_emit_budget_metrics" +CLOUDZERO_EXPORT_USAGE_DATA_JOB_NAME = "cloudzero_export_usage_data" +CLOUDZERO_MAX_FETCHED_DATA_RECORDS = int( + os.getenv("CLOUDZERO_MAX_FETCHED_DATA_RECORDS", 50000) +) SPEND_LOG_CLEANUP_JOB_NAME = "spend_log_cleanup" SPEND_LOG_RUN_LOOPS = int(os.getenv("SPEND_LOG_RUN_LOOPS", 500)) SPEND_LOG_CLEANUP_BATCH_SIZE = int(os.getenv("SPEND_LOG_CLEANUP_BATCH_SIZE", 1000)) @@ -890,6 +1077,7 @@ SENTRY_DENYLIST = [ "FIREWORKS_API_KEY", "FIREWORKS_AI_API_KEY", "FIREWORKSAI_API_KEY", + "OVHCLOUD_API_KEY", # Database and Connection Strings "database_url", "redis_url", @@ -928,3 +1116,8 @@ SENTRY_PII_DENYLIST = [ "SMTP_SENDER_EMAIL", "TEST_EMAIL_ADDRESS", ] + +# CoroutineChecker cache configuration +COROUTINE_CHECKER_MAX_SIZE_IN_MEMORY = int( + os.getenv("COROUTINE_CHECKER_MAX_SIZE_IN_MEMORY", 1000) +) diff --git a/litellm/cost_calculator.py b/litellm/cost_calculator.py index 6c6a09cd73e..94a9523facd 100644 --- a/litellm/cost_calculator.py +++ b/litellm/cost_calculator.py @@ -57,6 +57,7 @@ from litellm.llms.vertex_ai.cost_calculator import ( cost_per_token as google_cost_per_token, ) from litellm.llms.vertex_ai.cost_calculator import cost_router as google_cost_router +from litellm.llms.xai.cost_calculator import cost_per_token as xai_cost_per_token from litellm.responses.utils import ResponseAPILoggingUtils from litellm.types.llms.openai import ( HttpxBinaryResponseContent, @@ -147,6 +148,8 @@ def cost_per_token( # noqa: PLR0915 ### CALL TYPE ### call_type: CallTypesLiteral = "completion", audio_transcription_file_duration: float = 0.0, # for audio transcription calls - the file time in seconds + ### SERVICE TIER ### + service_tier: Optional[str] = None, # for OpenAI service tier pricing ) -> Tuple[float, float]: # type: ignore """ Calculates the cost per token for a given model, prompt tokens, and completion tokens. @@ -277,6 +280,7 @@ def cost_per_token( # noqa: PLR0915 model=model_without_prefix, usage=usage_block, custom_llm_provider=custom_llm_provider, + service_tier=service_tier, ) return prompt_cost, completion_cost @@ -326,7 +330,7 @@ def cost_per_token( # noqa: PLR0915 elif custom_llm_provider == "bedrock": return bedrock_cost_per_token(model=model, usage=usage_block) elif custom_llm_provider == "openai": - return openai_cost_per_token(model=model, usage=usage_block) + return openai_cost_per_token(model=model, usage=usage_block, service_tier=service_tier) elif custom_llm_provider == "databricks": return databricks_cost_per_token(model=model, usage=usage_block) elif custom_llm_provider == "fireworks_ai": @@ -341,6 +345,13 @@ def cost_per_token( # noqa: PLR0915 return deepseek_cost_per_token(model=model, usage=usage_block) elif custom_llm_provider == "perplexity": return perplexity_cost_per_token(model=model, usage=usage_block) + elif custom_llm_provider == "xai": + return xai_cost_per_token(model=model, usage=usage_block) + elif custom_llm_provider == "dashscope": + from litellm.llms.dashscope.cost_calculator import ( + cost_per_token as dashscope_cost_per_token, + ) + return dashscope_cost_per_token(model=model, usage=usage_block) else: model_info = _cached_get_model_info_helper( model=model, custom_llm_provider=custom_llm_provider @@ -573,6 +584,42 @@ def _infer_call_type( return call_type +def _store_cost_breakdown_in_logging_obj( + litellm_logging_obj: Optional[LitellmLoggingObject], + prompt_tokens_cost_usd_dollar: float, + completion_tokens_cost_usd_dollar: float, + cost_for_built_in_tools_cost_usd_dollar: float, + total_cost_usd_dollar: float, +) -> None: + """ + Helper function to store cost breakdown in the logging object. + + Args: + litellm_logging_obj: The logging object to store breakdown in + call_type: Type of call (completion, etc.) + prompt_tokens_cost_usd_dollar: Cost of input tokens + completion_tokens_cost_usd_dollar: Cost of completion tokens (includes reasoning if applicable) + cost_for_built_in_tools_cost_usd_dollar: Cost of built-in tools + total_cost_usd_dollar: Total cost of request + """ + if (litellm_logging_obj is None): + return + + try: + # Store the cost breakdown - reasoning cost is 0 since it's already included in completion cost + litellm_logging_obj.set_cost_breakdown( + input_cost=prompt_tokens_cost_usd_dollar, + output_cost=completion_tokens_cost_usd_dollar, + total_cost=total_cost_usd_dollar, + cost_for_built_in_tools_cost_usd_dollar=cost_for_built_in_tools_cost_usd_dollar + ) + + except Exception as breakdown_error: + verbose_logger.debug(f"Error storing cost breakdown: {str(breakdown_error)}") + # Don't fail the main cost calculation if breakdown storage fails + pass + + def completion_cost( # noqa: PLR0915 completion_response=None, model: Optional[str] = None, @@ -598,6 +645,8 @@ def completion_cost( # noqa: PLR0915 litellm_model_name: Optional[str] = None, router_model_id: Optional[str] = None, litellm_logging_obj: Optional[LitellmLoggingObject] = None, + ### SERVICE TIER ### + service_tier: Optional[str] = None, # for OpenAI service tier pricing ) -> float: """ Calculate the cost of a given completion call fot GPT-3.5-turbo, llama2, any litellm supported llm. @@ -650,6 +699,10 @@ def completion_cost( # noqa: PLR0915 completion_response=completion_response ) rerank_billed_units: Optional[RerankBilledUnits] = None + + # Extract service_tier from optional_params if not provided directly + if service_tier is None and optional_params is not None: + service_tier = optional_params.get("service_tier") selected_model = _select_model_name_for_cost_calc( model=model, @@ -675,9 +728,9 @@ def completion_cost( # noqa: PLR0915 or isinstance(completion_response, dict) ): # tts returns a custom class if isinstance(completion_response, dict): - usage_obj: Optional[Union[dict, Usage]] = ( - completion_response.get("usage", {}) - ) + usage_obj: Optional[ + Union[dict, Usage] + ] = completion_response.get("usage", {}) else: usage_obj = getattr(completion_response, "usage", {}) if isinstance(usage_obj, BaseModel) and not _is_known_usage_objects( @@ -901,11 +954,12 @@ def completion_cost( # noqa: PLR0915 call_type=cast(CallTypesLiteral, call_type), audio_transcription_file_duration=audio_transcription_file_duration, rerank_billed_units=rerank_billed_units, + service_tier=service_tier, ) _final_cost = ( prompt_tokens_cost_usd_dollar + completion_tokens_cost_usd_dollar ) - _final_cost += ( + cost_for_built_in_tools = ( StandardBuiltInToolCostTracking.get_cost_for_built_in_tools( model=model, response_object=completion_response, @@ -914,6 +968,17 @@ def completion_cost( # noqa: PLR0915 custom_llm_provider=custom_llm_provider, ) ) + _final_cost += cost_for_built_in_tools + + # Store cost breakdown in logging object if available + _store_cost_breakdown_in_logging_obj( + litellm_logging_obj=litellm_logging_obj, + prompt_tokens_cost_usd_dollar=prompt_tokens_cost_usd_dollar, + completion_tokens_cost_usd_dollar=completion_tokens_cost_usd_dollar, + cost_for_built_in_tools_cost_usd_dollar=cost_for_built_in_tools, + total_cost_usd_dollar=_final_cost + ) + return _final_cost except Exception as e: verbose_logger.debug( @@ -995,6 +1060,8 @@ def response_cost_calculator( litellm_model_name: Optional[str] = None, router_model_id: Optional[str] = None, litellm_logging_obj: Optional[LitellmLoggingObject] = None, + ### SERVICE TIER ### + service_tier: Optional[str] = None, # for OpenAI service tier pricing ) -> float: """ Returns @@ -1028,6 +1095,7 @@ def response_cost_calculator( litellm_model_name=litellm_model_name, router_model_id=router_model_id, litellm_logging_obj=litellm_logging_obj, + service_tier=service_tier, ) return response_cost except Exception as e: @@ -1279,7 +1347,9 @@ class BaseTokenUsageProcessor: not hasattr(combined, "completion_tokens_details") or not combined.completion_tokens_details ): - combined.completion_tokens_details = CompletionTokensDetailsWrapper() + combined.completion_tokens_details = ( + CompletionTokensDetailsWrapper() + ) # Check what keys exist in the model's completion_tokens_details for attr in usage.completion_tokens_details.model_fields: diff --git a/litellm/endpoints/speech/speech_to_completion_bridge/handler.py b/litellm/endpoints/speech/speech_to_completion_bridge/handler.py index 3035c5065c5..13af0a30fe0 100644 --- a/litellm/endpoints/speech/speech_to_completion_bridge/handler.py +++ b/litellm/endpoints/speech/speech_to_completion_bridge/handler.py @@ -2,7 +2,9 @@ Handler for transforming /chat/completions api requests to litellm.responses requests """ -from typing import TYPE_CHECKING, Optional, TypedDict, Union +from typing import TYPE_CHECKING, Optional, Union + +from typing_extensions import TypedDict if TYPE_CHECKING: from litellm import LiteLLMLoggingObj diff --git a/litellm/exceptions.py b/litellm/exceptions.py index 153230518cc..77fb9c1faef 100644 --- a/litellm/exceptions.py +++ b/litellm/exceptions.py @@ -153,6 +153,29 @@ class BadRequestError(openai.BadRequestError): # type: ignore _message += f", LiteLLM Max Retries: {self.max_retries}" return _message +class ImageFetchError(BadRequestError): + def __init__( + self, + message, + model=None, + llm_provider=None, + response: Optional[httpx.Response] = None, + litellm_debug_info: Optional[str] = None, + max_retries: Optional[int] = None, + num_retries: Optional[int] = None, + body: Optional[dict] = None, + ): + super().__init__( + message=message, + model=model, + llm_provider=llm_provider, + response=response, + litellm_debug_info=litellm_debug_info, + max_retries=max_retries, + num_retries=num_retries, + body=body, + ) + class UnprocessableEntityError(openai.UnprocessableEntityError): # type: ignore def __init__( diff --git a/litellm/experimental_mcp_client/client.py b/litellm/experimental_mcp_client/client.py index 185fe34a3fb..ecb58e18223 100644 --- a/litellm/experimental_mcp_client/client.py +++ b/litellm/experimental_mcp_client/client.py @@ -19,8 +19,6 @@ from litellm._logging import verbose_logger from litellm.types.mcp import ( MCPAuth, MCPAuthType, - MCPSpecVersion, - MCPSpecVersionType, MCPStdioConfig, MCPTransport, MCPTransportType, @@ -48,7 +46,6 @@ class MCPClient: auth_value: Optional[str] = None, timeout: float = 60.0, stdio_config: Optional[MCPStdioConfig] = None, - protocol_version: MCPSpecVersionType = MCPSpecVersion.jun_2025, ): self.server_url: str = server_url self.transport_type: MCPTransport = transport_type @@ -62,7 +59,6 @@ class MCPClient: self._session_ctx = None self._task: Optional[asyncio.Task] = None self.stdio_config: Optional[MCPStdioConfig] = stdio_config - self.protocol_version: MCPSpecVersionType = protocol_version # handle the basic auth value if provided if auth_value: @@ -84,22 +80,24 @@ class MCPClient: """Initialize the transport and session.""" if self._session: return # Already connected - + try: if self.transport_type == MCPTransport.stdio: # For stdio transport, use stdio_client with command-line parameters if not self.stdio_config: raise ValueError("stdio_config is required for stdio transport") - + server_params = StdioServerParameters( command=self.stdio_config.get("command", ""), args=self.stdio_config.get("args", []), - env=self.stdio_config.get("env", {}) + env=self.stdio_config.get("env", {}), ) - + self._transport_ctx = stdio_client(server_params) self._transport = await self._transport_ctx.__aenter__() - self._session_ctx = ClientSession(self._transport[0], self._transport[1]) + self._session_ctx = ClientSession( + self._transport[0], self._transport[1] + ) self._session = await self._session_ctx.__aenter__() await self._session.initialize() elif self.transport_type == MCPTransport.sse: @@ -110,7 +108,9 @@ class MCPClient: headers=headers, ) self._transport = await self._transport_ctx.__aenter__() - self._session_ctx = ClientSession(self._transport[0], self._transport[1]) + self._session_ctx = ClientSession( + self._transport[0], self._transport[1] + ) self._session = await self._session_ctx.__aenter__() await self._session.initialize() else: # http @@ -121,7 +121,9 @@ class MCPClient: headers=headers, ) self._transport = await self._transport_ctx.__aenter__() - self._session_ctx = ClientSession(self._transport[0], self._transport[1]) + self._session_ctx = ClientSession( + self._transport[0], self._transport[1] + ) self._session = await self._session_ctx.__aenter__() await self._session.initialize() except ValueError as e: @@ -184,8 +186,10 @@ class MCPClient: def _get_auth_headers(self) -> dict: """Generate authentication headers based on auth type.""" - headers = {} - + headers = { + "MCP-Protocol-Version": "2025-06-18" + } + if self._mcp_auth_value: if self.auth_type == MCPAuth.bearer_token: headers["Authorization"] = f"Bearer {self._mcp_auth_value}" @@ -193,19 +197,11 @@ class MCPClient: headers["Authorization"] = f"Basic {self._mcp_auth_value}" elif self.auth_type == MCPAuth.api_key: headers["X-API-Key"] = self._mcp_auth_value + elif self.auth_type == MCPAuth.authorization: + headers["Authorization"] = self._mcp_auth_value - # Handle protocol version - it might be a string or enum - if hasattr(self.protocol_version, 'value'): - # It's an enum - protocol_version_str = self.protocol_version.value - else: - # It's a string - protocol_version_str = str(self.protocol_version) - - headers["MCP-Protocol-Version"] = protocol_version_str return headers - async def list_tools(self) -> List[MCPTool]: """List available tools from the server.""" if not self._session: @@ -214,7 +210,7 @@ class MCPClient: except Exception as e: verbose_logger.warning(f"MCP client connection failed: {str(e)}") return [] - + if self._session is None: verbose_logger.warning("MCP client session is not initialized") return [] @@ -243,17 +239,20 @@ class MCPClient: except Exception as e: verbose_logger.warning(f"MCP client connection failed: {str(e)}") return MCPCallToolResult( - content=[TextContent(type="text", text=f"{str(e)}")], - isError=True + content=[TextContent(type="text", text=f"{str(e)}")], isError=True ) if self._session is None: verbose_logger.warning("MCP client session is not initialized") return MCPCallToolResult( - content=[TextContent(type="text", text="MCP client session is not initialized")], + content=[ + TextContent( + type="text", text="MCP client session is not initialized" + ) + ], isError=True, ) - + try: tool_result = await self._session.call_tool( name=call_tool_request_params.name, @@ -268,8 +267,8 @@ class MCPClient: await self.disconnect() # Return a default error result instead of raising return MCPCallToolResult( - content=[TextContent(type="text", text=f"{str(e)}")], # Empty content for error case + content=[ + TextContent(type="text", text=f"{str(e)}") + ], # Empty content for error case isError=True, ) - - diff --git a/litellm/experimental_mcp_client/tools.py b/litellm/experimental_mcp_client/tools.py index bfbd3f96a5c..b716e3171e7 100644 --- a/litellm/experimental_mcp_client/tools.py +++ b/litellm/experimental_mcp_client/tools.py @@ -17,22 +17,60 @@ from litellm.types.utils import ChatCompletionMessageToolCall ######################################################## def transform_mcp_tool_to_openai_tool(mcp_tool: MCPTool) -> ChatCompletionToolParam: """Convert an MCP tool to an OpenAI tool.""" + normalized_parameters = _normalize_mcp_input_schema(mcp_tool.inputSchema) + return ChatCompletionToolParam( type="function", function=FunctionDefinition( name=mcp_tool.name, description=mcp_tool.description or "", - parameters=mcp_tool.inputSchema, + parameters=normalized_parameters, strict=False, ), ) +def _normalize_mcp_input_schema(input_schema: dict) -> dict: + """ + Normalize MCP input schema to ensure it's valid for OpenAI function calling. + + OpenAI requires that function parameters have: + - type: 'object' + - properties: dict (can be empty) + - additionalProperties: false (recommended) + """ + if not input_schema: + return { + "type": "object", + "properties": {}, + "additionalProperties": False + } + + # Make a copy to avoid modifying the original + normalized_schema = dict(input_schema) + + # Ensure type is 'object' + if "type" not in normalized_schema: + normalized_schema["type"] = "object" + + # Ensure properties exists (can be empty) + if "properties" not in normalized_schema: + normalized_schema["properties"] = {} + + # Add additionalProperties if not present (recommended by OpenAI) + if "additionalProperties" not in normalized_schema: + normalized_schema["additionalProperties"] = False + + return normalized_schema + + def transform_mcp_tool_to_openai_responses_api_tool(mcp_tool: MCPTool) -> FunctionToolParam: """Convert an MCP tool to an OpenAI Responses API tool.""" + normalized_parameters = _normalize_mcp_input_schema(mcp_tool.inputSchema) + return FunctionToolParam( name=mcp_tool.name, - parameters=mcp_tool.inputSchema, + parameters=normalized_parameters, strict=False, type="function", description=mcp_tool.description or "", diff --git a/litellm/files/main.py b/litellm/files/main.py index 5d0dc05771a..18be2c702bf 100644 --- a/litellm/files/main.py +++ b/litellm/files/main.py @@ -50,7 +50,7 @@ vertex_ai_files_instance = VertexAIFilesHandler() async def acreate_file( file: FileTypes, purpose: Literal["assistants", "batch", "fine-tune"], - custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai", + custom_llm_provider: Literal["openai", "azure", "vertex_ai", "bedrock"] = "openai", extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, **kwargs, @@ -94,7 +94,7 @@ async def acreate_file( def create_file( file: FileTypes, purpose: Literal["assistants", "batch", "fine-tune"], - custom_llm_provider: Optional[Literal["openai", "azure", "vertex_ai"]] = None, + custom_llm_provider: Optional[Literal["openai", "azure", "vertex_ai", "bedrock"]] = None, extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, **kwargs, @@ -109,7 +109,7 @@ def create_file( try: _is_async = kwargs.pop("acreate_file", False) is True optional_params = GenericLiteLLMParams(**kwargs) - litellm_params_dict = get_litellm_params(**kwargs) + litellm_params_dict = dict(**kwargs) logging_obj = cast( Optional[LiteLLMLoggingObj], kwargs.get("litellm_logging_obj") ) @@ -731,7 +731,7 @@ def file_list( async def afile_content( file_id: str, - custom_llm_provider: Literal["openai", "azure"] = "openai", + custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai", extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, **kwargs, @@ -887,6 +887,32 @@ def file_content( client=client, litellm_params=litellm_params_dict, ) + elif custom_llm_provider == "vertex_ai": + api_base = optional_params.api_base or "" + 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_files_instance.file_content( + _is_async=_is_async, + file_content_request=_file_content_request, + api_base=api_base, + vertex_credentials=vertex_credentials, + vertex_project=vertex_ai_project, + vertex_location=vertex_ai_location, + timeout=timeout, + max_retries=optional_params.max_retries, + ) else: raise litellm.exceptions.BadRequestError( message="LiteLLM doesn't support {} for 'custom_llm_provider'. Supported providers are 'openai', 'azure', 'vertex_ai'.".format( diff --git a/litellm/files/utils.py b/litellm/files/utils.py new file mode 100644 index 00000000000..a56a29467d9 --- /dev/null +++ b/litellm/files/utils.py @@ -0,0 +1,27 @@ +from typing import Optional + +from litellm.types.llms.openai import CreateFileRequest +from litellm.types.utils import ExtractedFileData + + +class FilesAPIUtils: + """ + Utils for files API interface on litellm + """ + @staticmethod + def is_batch_jsonl_file(create_file_data: CreateFileRequest, extracted_file_data: ExtractedFileData) -> bool: + """ + Check if the file is a batch jsonl file + """ + return ( + create_file_data.get("purpose") == "batch" + and FilesAPIUtils.valid_content_type(extracted_file_data.get("content_type")) + and extracted_file_data.get("content") is not None + ) + + @staticmethod + def valid_content_type(content_type: Optional[str]) -> bool: + """ + Check if the content type is valid + """ + return content_type in set(["application/jsonl", "application/octet-stream"]) diff --git a/litellm/google_genai/adapters/handler.py b/litellm/google_genai/adapters/handler.py index 1f575f27591..dcf707ebd51 100644 --- a/litellm/google_genai/adapters/handler.py +++ b/litellm/google_genai/adapters/handler.py @@ -37,6 +37,10 @@ class GenerateContentToCompletionHandler: completion_kwargs: Dict[str, Any] = dict(completion_request) + # feed metadata for custom callback + if extra_kwargs is not None and "metadata" in extra_kwargs: + completion_kwargs["metadata"] = extra_kwargs["metadata"] + if stream: completion_kwargs["stream"] = stream diff --git a/litellm/google_genai/main.py b/litellm/google_genai/main.py index 87970885355..b480a85c85e 100644 --- a/litellm/google_genai/main.py +++ b/litellm/google_genai/main.py @@ -224,6 +224,9 @@ async def agenerate_content( loop = asyncio.get_event_loop() kwargs["agenerate_content"] = True + # Handle generationConfig parameter from kwargs for backward compatibility + if "generationConfig" in kwargs and config is None: + config = kwargs.pop("generationConfig") # get custom llm provider so we can use this for mapping exceptions if custom_llm_provider is None: _, custom_llm_provider, _, _ = litellm.get_llm_provider( @@ -288,6 +291,9 @@ def generate_content( try: _is_async = kwargs.pop("agenerate_content", False) is True + # Handle generationConfig parameter from kwargs for backward compatibility + if "generationConfig" in kwargs and config is None: + config = kwargs.pop("generationConfig") # Check for mock response first litellm_params = GenericLiteLLMParams(**kwargs) if litellm_params.mock_response and isinstance( @@ -374,6 +380,9 @@ async def agenerate_content_stream( try: kwargs["agenerate_content_stream"] = True + # Handle generationConfig parameter from kwargs for backward compatibility + if "generationConfig" in kwargs and config is None: + config = kwargs.pop("generationConfig") # get custom llm provider so we can use this for mapping exceptions if custom_llm_provider is None: _, custom_llm_provider, _, _ = litellm.get_llm_provider( @@ -461,6 +470,9 @@ def generate_content_stream( # Remove any async-related flags since this is the sync function _is_async = kwargs.pop("agenerate_content_stream", False) + # Handle generationConfig parameter from kwargs for backward compatibility + if "generationConfig" in kwargs and config is None: + config = kwargs.pop("generationConfig") # Setup the call setup_result = GenerateContentHelper.setup_generate_content_call( model=model, diff --git a/litellm/images/main.py b/litellm/images/main.py index 70d9eb41ddd..2a8b62bce24 100644 --- a/litellm/images/main.py +++ b/litellm/images/main.py @@ -1,7 +1,7 @@ import asyncio import contextvars from functools import partial -from typing import Any, Coroutine, Dict, Literal, Optional, Union, cast, overload +from typing import Any, Coroutine, Dict, List, Literal, Optional, Union, cast, overload import httpx @@ -90,12 +90,12 @@ async def aimage_generation(*args, **kwargs) -> ImageResponse: response = init_response elif asyncio.iscoroutine(init_response): response = await init_response # type: ignore - + if response is None: raise ValueError( "Unable to get Image Response. Please pass a valid llm_provider." ) - + return response except Exception as e: custom_llm_provider = custom_llm_provider or "openai" @@ -108,6 +108,8 @@ async def aimage_generation(*args, **kwargs) -> ImageResponse: ) +# fmt: off + # Overload for when aimg_generation=True (returns Coroutine) @overload def image_generation( @@ -119,7 +121,6 @@ def image_generation( size: Optional[str] = None, style: Optional[str] = None, user: Optional[str] = None, - input_fidelity: Optional[str] = None, timeout=600, # default to 10 minutes api_key: Optional[str] = None, api_base: Optional[str] = None, @@ -128,10 +129,11 @@ def image_generation( *, aimg_generation: Literal[True], **kwargs, -) -> Coroutine[Any, Any, ImageResponse]: +) -> Coroutine[Any, Any, ImageResponse]: ... + # Overload for when aimg_generation=False or not specified (returns ImageResponse) @overload def image_generation( @@ -143,7 +145,6 @@ def image_generation( size: Optional[str] = None, style: Optional[str] = None, user: Optional[str] = None, - input_fidelity: Optional[str] = None, timeout=600, # default to 10 minutes api_key: Optional[str] = None, api_base: Optional[str] = None, @@ -152,9 +153,11 @@ def image_generation( *, aimg_generation: Literal[False] = False, **kwargs, -) -> ImageResponse: +) -> ImageResponse: ... +# fmt: on + @client def image_generation( # noqa: PLR0915 @@ -166,7 +169,6 @@ def image_generation( # noqa: PLR0915 size: Optional[str] = None, style: Optional[str] = None, user: Optional[str] = None, - input_fidelity: Optional[str] = None, timeout=600, # default to 10 minutes api_key: Optional[str] = None, api_base: Optional[str] = None, @@ -174,9 +176,9 @@ def image_generation( # noqa: PLR0915 custom_llm_provider=None, **kwargs, ) -> Union[ - ImageResponse, - Coroutine[Any, Any, ImageResponse], - ]: + ImageResponse, + Coroutine[Any, Any, ImageResponse], +]: """ Maps the https://api.openai.com/v1/images/generations endpoint. @@ -227,7 +229,6 @@ def image_generation( # noqa: PLR0915 "quality", "size", "style", - "input_fidelity", ] litellm_params = all_litellm_params default_params = openai_params + litellm_params @@ -255,7 +256,6 @@ def image_generation( # noqa: PLR0915 size=size, style=style, user=user, - input_fidelity=input_fidelity, custom_llm_provider=custom_llm_provider, provider_config=image_generation_config, **non_default_params, @@ -335,8 +335,34 @@ def image_generation( # noqa: PLR0915 headers=headers, litellm_params=litellm_params_dict, ) + ######################################################### + # Providers using llm_http_handler + ######################################################### + elif custom_llm_provider in ( + litellm.LlmProviders.RECRAFT, + litellm.LlmProviders.AIML, + litellm.LlmProviders.GEMINI, + ): + if image_generation_config is None: + raise ValueError( + f"image generation config is not supported for {custom_llm_provider}" + ) + + return llm_http_handler.image_generation_handler( + api_key=api_key, + model=model, + prompt=prompt, + image_generation_provider_config=image_generation_config, + image_generation_optional_request_params=optional_params, + custom_llm_provider=custom_llm_provider, + litellm_params=litellm_params_dict, + logging_obj=litellm_logging_obj, + timeout=timeout, + client=client, + ) elif custom_llm_provider == "azure_ai": from litellm.llms.azure_ai.common_utils import AzureFoundryModelInfo + api_base = AzureFoundryModelInfo.get_api_base(api_base) api_key = AzureFoundryModelInfo.get_api_key(api_key) if extra_headers is not None: @@ -397,7 +423,7 @@ def image_generation( # noqa: PLR0915 aimg_generation=aimg_generation, client=client, api_base=api_base, - api_key=api_key + api_key=api_key, ) elif custom_llm_provider == "vertex_ai": vertex_ai_project = ( @@ -439,27 +465,6 @@ def image_generation( # noqa: PLR0915 api_base=api_base, client=client, ) - ######################################################### - # Providers using llm_http_handler - ######################################################### - elif custom_llm_provider in ( - litellm.LlmProviders.RECRAFT, - litellm.LlmProviders.GEMINI, - ): - if image_generation_config is None: - raise ValueError(f"image generation config is not supported for {custom_llm_provider}") - - return llm_http_handler.image_generation_handler( - model=model, - prompt=prompt, - image_generation_provider_config=image_generation_config, - image_generation_optional_request_params=optional_params, - custom_llm_provider=custom_llm_provider, - litellm_params=litellm_params_dict, - logging_obj=litellm_logging_obj, - timeout=timeout, - client=client, - ) elif ( custom_llm_provider in litellm._custom_providers ): # Assume custom LLM provider @@ -675,7 +680,7 @@ def image_variation( @client def image_edit( - image: FileTypes, + image: Union[FileTypes, List[FileTypes]], prompt: str, model: Optional[str] = None, mask: Optional[str] = None, @@ -703,6 +708,9 @@ def image_edit( litellm_call_id: Optional[str] = kwargs.get("litellm_call_id", None) _is_async = kwargs.pop("async_call", False) is True + # add images / or return a single image + images = image if isinstance(image, list) else [image] + # get llm provider logic litellm_params = GenericLiteLLMParams(**kwargs) model, custom_llm_provider, _, _ = get_llm_provider( @@ -711,11 +719,11 @@ def image_edit( ) # get provider config - image_edit_provider_config: Optional[ - BaseImageEditConfig - ] = ProviderConfigManager.get_provider_image_edit_config( - model=model, - provider=litellm.LlmProviders(custom_llm_provider), + image_edit_provider_config: Optional[BaseImageEditConfig] = ( + ProviderConfigManager.get_provider_image_edit_config( + model=model, + provider=litellm.LlmProviders(custom_llm_provider), + ) ) if image_edit_provider_config is None: @@ -751,7 +759,7 @@ def image_edit( # Call the handler with _is_async flag instead of directly calling the async handler return base_llm_http_handler.image_edit_handler( model=model, - image=image, + image=images, prompt=prompt, image_edit_provider_config=image_edit_provider_config, image_edit_optional_request_params=image_edit_request_params, @@ -777,7 +785,7 @@ def image_edit( @client async def aimage_edit( - image: FileTypes, + image: Union[FileTypes, List[FileTypes]], model: str, prompt: str, mask: Optional[str] = None, @@ -817,9 +825,11 @@ async def aimage_edit( model=model, api_base=local_vars.get("base_url", None) ) + images = image if isinstance(image, list) else [image] + func = partial( image_edit, - image=image, + image=images, prompt=prompt, mask=mask, model=model, diff --git a/litellm/integrations/SlackAlerting/budget_alert_types.py b/litellm/integrations/SlackAlerting/budget_alert_types.py index beebee8b6bf..1e9ad286e37 100644 --- a/litellm/integrations/SlackAlerting/budget_alert_types.py +++ b/litellm/integrations/SlackAlerting/budget_alert_types.py @@ -31,7 +31,7 @@ class SoftBudgetAlert(BaseBudgetAlertType): return "Soft Budget Crossed: " def get_id(self, user_info: CallInfo) -> str: - return "default_id" + return user_info.token or "default_id" class UserBudgetAlert(BaseBudgetAlertType): diff --git a/litellm/integrations/SlackAlerting/slack_alerting.py b/litellm/integrations/SlackAlerting/slack_alerting.py index 41db4a551bd..7da38e193b6 100644 --- a/litellm/integrations/SlackAlerting/slack_alerting.py +++ b/litellm/integrations/SlackAlerting/slack_alerting.py @@ -805,9 +805,9 @@ class SlackAlerting(CustomBatchLogger): ### UNIQUE CACHE KEY ### cache_key = provider + region_name - outage_value: Optional[ProviderRegionOutageModel] = ( - await self.internal_usage_cache.async_get_cache(key=cache_key) - ) + outage_value: Optional[ + ProviderRegionOutageModel + ] = await self.internal_usage_cache.async_get_cache(key=cache_key) if ( getattr(exception, "status_code", None) is None @@ -1367,12 +1367,13 @@ Model Info: # Get the current timestamp current_time = datetime.now().strftime("%H:%M:%S") _proxy_base_url = os.getenv("PROXY_BASE_URL", None) + # Use .name if it's an enum, otherwise use as is + alert_type_name = getattr(alert_type, 'name', alert_type) + alert_type_formatted = f"Alert type: `{alert_type_name}`" if alert_type == "daily_reports" or alert_type == "new_model_added": - formatted_message = message + formatted_message = alert_type_formatted + message else: - formatted_message = ( - f"Level: `{level}`\nTimestamp: `{current_time}`\n\nMessage: {message}" - ) + formatted_message = f"{alert_type_formatted}\nLevel: `{level}`\nTimestamp: `{current_time}`\n\nMessage: {message}" if kwargs: for key, value in kwargs.items(): @@ -1388,9 +1389,9 @@ Model Info: self.alert_to_webhook_url is not None and alert_type in self.alert_to_webhook_url ): - slack_webhook_url: Optional[Union[str, List[str]]] = ( - self.alert_to_webhook_url[alert_type] - ) + slack_webhook_url: Optional[ + Union[str, List[str]] + ] = self.alert_to_webhook_url[alert_type] elif self.default_webhook_url is not None: slack_webhook_url = self.default_webhook_url else: diff --git a/litellm/integrations/azure_storage/azure_storage.py b/litellm/integrations/azure_storage/azure_storage.py index 6ffb1e542fc..b4362665a4c 100644 --- a/litellm/integrations/azure_storage/azure_storage.py +++ b/litellm/integrations/azure_storage/azure_storage.py @@ -2,7 +2,7 @@ import asyncio import json import os import time -import uuid +from litellm._uuid import uuid from datetime import datetime, timedelta from typing import List, Optional diff --git a/litellm/integrations/bitbucket/README.md b/litellm/integrations/bitbucket/README.md new file mode 100644 index 00000000000..473beeea9e0 --- /dev/null +++ b/litellm/integrations/bitbucket/README.md @@ -0,0 +1,317 @@ +# LiteLLM BitBucket Prompt Management + +A powerful prompt management system for LiteLLM that fetches `.prompt` files from BitBucket repositories. This enables team-based prompt management with BitBucket's built-in access control and version control capabilities. + +## Features + +- **🏢 Team-based access control**: Leverage BitBucket's workspace and repository permissions +- **📁 Repository-based prompt storage**: Store prompts in BitBucket repositories +- **🔐 Multiple authentication methods**: Support for access tokens and basic auth +- **🎯 YAML frontmatter**: Define model, parameters, and schemas in file headers +- **🔧 Handlebars templating**: Use `{{variable}}` syntax with Jinja2 backend +- **✅ Input validation**: Automatic validation against defined schemas +- **🔗 LiteLLM integration**: Works seamlessly with `litellm.completion()` +- **💬 Smart message parsing**: Converts prompts to proper chat messages +- **⚙️ Parameter extraction**: Automatically applies model settings from prompts + +## Quick Start + +### 1. Set up BitBucket Repository + +Create a repository in your BitBucket workspace and add `.prompt` files: + +``` +your-repo/ +├── prompts/ +│ ├── chat_assistant.prompt +│ ├── code_reviewer.prompt +│ └── data_analyst.prompt +``` + +### 2. Create a `.prompt` file + +Create a file called `prompts/chat_assistant.prompt`: + +```yaml +--- +model: gpt-4 +temperature: 0.7 +max_tokens: 150 +input: + schema: + user_message: string + system_context?: string +--- + +{% if system_context %}System: {{system_context}} + +{% endif %}User: {{user_message}} +``` + +### 3. Configure BitBucket Access + +#### Option A: Access Token (Recommended) + +```python +import litellm + +# Configure BitBucket access +bitbucket_config = { + "workspace": "your-workspace", + "repository": "your-repo", + "access_token": "your-access-token", + "branch": "main" # optional, defaults to main +} + +# Set global BitBucket configuration +litellm.set_global_bitbucket_config(bitbucket_config) +``` + +#### Option B: Basic Authentication + +```python +import litellm + +# Configure BitBucket access with basic auth +bitbucket_config = { + "workspace": "your-workspace", + "repository": "your-repo", + "username": "your-username", + "access_token": "your-app-password", # Use app password for basic auth + "auth_method": "basic", + "branch": "main" +} + +litellm.set_global_bitbucket_config(bitbucket_config) +``` + +### 4. Use with LiteLLM + +```python +# Use with completion - the model prefix 'bitbucket/' tells LiteLLM to use BitBucket prompt management +response = litellm.completion( + model="bitbucket/gpt-4", # The actual model comes from the .prompt file + prompt_id="prompts/chat_assistant", # Location of the prompt file + prompt_variables={ + "user_message": "What is machine learning?", + "system_context": "You are a helpful AI tutor." + }, + # Any additional messages will be appended after the prompt + messages=[{"role": "user", "content": "Please explain it simply."}] +) + +print(response.choices[0].message.content) +``` + +## Proxy Server Configuration + +### 1. Create a `.prompt` file + +Create `prompts/hello.prompt`: + +```yaml +--- +model: gpt-4 +temperature: 0.7 +--- +System: You are a helpful assistant. + +User: {{user_message}} +``` + +### 2. Setup config.yaml + +```yaml +model_list: + - model_name: my-bitbucket-model + litellm_params: + model: bitbucket/gpt-4 + prompt_id: "prompts/hello" + api_key: os.environ/OPENAI_API_KEY + +litellm_settings: + global_bitbucket_config: + workspace: "your-workspace" + repository: "your-repo" + access_token: "your-access-token" + branch: "main" +``` + +### 3. Start the proxy + +```bash +litellm --config config.yaml --detailed_debug +``` + +### 4. Test it! + +```bash +curl -L -X POST 'http://0.0.0.0:4000/v1/chat/completions' \ +-H 'Content-Type: application/json' \ +-H 'Authorization: Bearer sk-1234' \ +-d '{ + "model": "my-bitbucket-model", + "messages": [{"role": "user", "content": "IGNORED"}], + "prompt_variables": { + "user_message": "What is the capital of France?" + } +}' +``` + +## Prompt File Format + +### Basic Structure + +```yaml +--- +# Model configuration +model: gpt-4 +temperature: 0.7 +max_tokens: 500 + +# Input schema (optional) +input: + schema: + user_message: string + system_context?: string +--- + +System: You are a helpful {{role}} assistant. + +User: {{user_message}} +``` + +### Advanced Features + +**Multi-role conversations:** + +```yaml +--- +model: gpt-4 +temperature: 0.3 +--- +System: You are a helpful coding assistant. + +User: {{user_question}} +``` + +**Dynamic model selection:** + +```yaml +--- +model: "{{preferred_model}}" # Model can be a variable +temperature: 0.7 +--- +System: You are a helpful assistant specialized in {{domain}}. + +User: {{user_message}} +``` + +## Team-Based Access Control + +BitBucket's built-in permission system provides team-based access control: + +1. **Workspace-level permissions**: Control access to entire workspaces +2. **Repository-level permissions**: Control access to specific repositories +3. **Branch-level permissions**: Control access to specific branches +4. **User and group management**: Manage team members and their access levels + +### Setting up Team Access + +1. **Create workspaces for each team**: + ``` + team-a-prompts/ + team-b-prompts/ + team-c-prompts/ + ``` + +2. **Configure repository permissions**: + - Grant read access to team members + - Grant write access to prompt maintainers + - Use branch protection rules for production prompts + +3. **Use different access tokens**: + - Each team can have their own access token + - Tokens can be scoped to specific repositories + - Use app passwords for additional security + +## API Reference + +### BitBucket Configuration + +```python +bitbucket_config = { + "workspace": str, # Required: BitBucket workspace name + "repository": str, # Required: Repository name + "access_token": str, # Required: BitBucket access token or app password + "branch": str, # Optional: Branch to fetch from (default: "main") + "base_url": str, # Optional: Custom BitBucket API URL + "auth_method": str, # Optional: "token" or "basic" (default: "token") + "username": str, # Optional: Username for basic auth + "base_url" : str # Optional: Incase where the base url is not https://api.bitbucket.org/2.0 +} +``` + +### LiteLLM Integration + +```python +response = litellm.completion( + model="bitbucket/", # required (e.g., bitbucket/gpt-4) + prompt_id=str, # required - the .prompt filename without extension + prompt_variables=dict, # optional - variables for template rendering + bitbucket_config=dict, # optional - BitBucket configuration (if not set globally) + messages=list, # optional - additional messages +) +``` + +## Error Handling + +The BitBucket integration provides detailed error messages for common issues: + +- **Authentication errors**: Invalid access tokens or credentials +- **Permission errors**: Insufficient access to workspace/repository +- **File not found**: Missing .prompt files +- **Network errors**: Connection issues with BitBucket API + +## Security Considerations + +1. **Access Token Security**: Store access tokens securely using environment variables or secret management systems +2. **Repository Permissions**: Use BitBucket's permission system to control access +3. **Branch Protection**: Protect main branches from unauthorized changes +4. **Audit Logging**: BitBucket provides audit logs for all repository access + +## Troubleshooting + +### Common Issues + +1. **"Access denied" errors**: Check your BitBucket permissions for the workspace and repository +2. **"Authentication failed" errors**: Verify your access token or credentials +3. **"File not found" errors**: Ensure the .prompt file exists in the specified branch +4. **Template rendering errors**: Check your Handlebars syntax in the .prompt file + +### Debug Mode + +Enable debug logging to troubleshoot issues: + +```python +import litellm +litellm.set_verbose = True + +# Your BitBucket prompt calls will now show detailed logs +response = litellm.completion( + model="bitbucket/gpt-4", + prompt_id="your_prompt", + prompt_variables={"key": "value"} +) +``` + +## Migration from File-Based Prompts + +If you're currently using file-based prompts with the dotprompt integration, you can easily migrate to BitBucket: + +1. **Upload your .prompt files** to a BitBucket repository +2. **Update your configuration** to use BitBucket instead of local files +3. **Set up team access** using BitBucket's permission system +4. **Update your code** to use `bitbucket/` model prefix instead of `dotprompt/` + +This provides better collaboration, version control, and team-based access control for your prompts. diff --git a/litellm/integrations/bitbucket/__init__.py b/litellm/integrations/bitbucket/__init__.py new file mode 100644 index 00000000000..111d38f78a4 --- /dev/null +++ b/litellm/integrations/bitbucket/__init__.py @@ -0,0 +1,66 @@ +from typing import TYPE_CHECKING, Optional + +if TYPE_CHECKING: + from .bitbucket_prompt_manager import BitBucketPromptManager + from litellm.types.prompts.init_prompts import PromptLiteLLMParams, PromptSpec + from litellm.integrations.custom_prompt_management import CustomPromptManagement + +from litellm.types.prompts.init_prompts import SupportedPromptIntegrations + +from .bitbucket_prompt_manager import BitBucketPromptManager + +# Global instances +global_bitbucket_config: Optional[dict] = None + + +def set_global_bitbucket_config(config: dict) -> None: + """ + Set the global BitBucket configuration for prompt management. + + Args: + config: Dictionary containing BitBucket configuration + - workspace: BitBucket workspace name + - repository: Repository name + - access_token: BitBucket access token + - branch: Branch to fetch prompts from (default: main) + """ + import litellm + + litellm.global_bitbucket_config = config # type: ignore + + +def prompt_initializer( + litellm_params: "PromptLiteLLMParams", prompt_spec: "PromptSpec" +) -> "CustomPromptManagement": + """ + Initialize a prompt from a BitBucket repository. + """ + bitbucket_config = getattr(litellm_params, "bitbucket_config", None) + prompt_id = getattr(litellm_params, "prompt_id", None) + + if not bitbucket_config: + raise ValueError( + "bitbucket_config is required for BitBucket prompt integration" + ) + + try: + bitbucket_prompt_manager = BitBucketPromptManager( + bitbucket_config=bitbucket_config, + prompt_id=prompt_id, + ) + + return bitbucket_prompt_manager + except Exception as e: + raise e + + +prompt_initializer_registry = { + SupportedPromptIntegrations.BITBUCKET.value: prompt_initializer, +} + +# Export public API +__all__ = [ + "BitBucketPromptManager", + "set_global_bitbucket_config", + "global_bitbucket_config", +] diff --git a/litellm/integrations/bitbucket/bitbucket_client.py b/litellm/integrations/bitbucket/bitbucket_client.py new file mode 100644 index 00000000000..0502422cf8b --- /dev/null +++ b/litellm/integrations/bitbucket/bitbucket_client.py @@ -0,0 +1,241 @@ +""" +BitBucket API client for fetching .prompt files from BitBucket repositories. +""" + +import base64 +from typing import Any, Dict, List, Optional + +from litellm.llms.custom_httpx.http_handler import HTTPHandler + + +class BitBucketClient: + """ + Client for interacting with BitBucket API to fetch .prompt files. + + Supports: + - Authentication with access tokens + - Fetching file contents from repositories + - Team-based access control through BitBucket permissions + - Branch-specific file fetching + """ + + def __init__(self, config: Dict[str, Any]): + """ + Initialize the BitBucket client. + + Args: + config: Dictionary containing: + - workspace: BitBucket workspace name + - repository: Repository name + - access_token: BitBucket access token (or app password) + - branch: Branch to fetch from (default: main) + - base_url: Custom BitBucket API base URL (optional) + - auth_method: Authentication method ('token' or 'basic', default: 'token') + - username: Username for basic auth (optional) + """ + self.workspace = config.get("workspace") + self.repository = config.get("repository") + self.access_token = config.get("access_token") + self.branch = config.get("branch", "main") + self.base_url = config.get("", "https://api.bitbucket.org/2.0") + self.auth_method = config.get("auth_method", "token") + self.username = config.get("username") + + if not all([self.workspace, self.repository, self.access_token]): + raise ValueError("workspace, repository, and access_token are required") + + # Set up authentication headers + self.headers = { + "Accept": "application/json", + "Content-Type": "application/json", + } + + if self.auth_method == "basic" and self.username: + # Use basic auth with username and app password + credentials = f"{self.username}:{self.access_token}" + encoded_credentials = base64.b64encode(credentials.encode()).decode() + self.headers["Authorization"] = f"Basic {encoded_credentials}" + else: + # Use token-based authentication (default) + self.headers["Authorization"] = f"Bearer {self.access_token}" + + # Initialize HTTPHandler + self.http_handler = HTTPHandler() + + def get_file_content(self, file_path: str) -> Optional[str]: + """ + Fetch the content of a file from the BitBucket repository. + + Args: + file_path: Path to the file in the repository + + Returns: + File content as string, or None if file not found + """ + url = f"{self.base_url}/repositories/{self.workspace}/{self.repository}/src/{self.branch}/{file_path}" + + try: + response = self.http_handler.get(url, headers=self.headers) + response.raise_for_status() + + # BitBucket returns file content as base64 encoded + if response.headers.get("content-type", "").startswith("text/"): + return response.text + else: + # For binary files or when content-type is not text, try to decode as base64 + try: + return base64.b64decode(response.content).decode("utf-8") + except Exception: + return response.text + + except Exception as e: + # Check if it's an HTTP error + if hasattr(e, "response") and hasattr(e.response, "status_code"): + if e.response.status_code == 404: + return None + elif e.response.status_code == 403: + raise Exception( + f"Access denied to file '{file_path}'. Check your BitBucket permissions for workspace '{self.workspace}' and repository '{self.repository}'." + ) + elif e.response.status_code == 401: + raise Exception( + "Authentication failed. Check your BitBucket access token and permissions." + ) + else: + raise Exception(f"Failed to fetch file '{file_path}': {e}") + else: + raise Exception(f"Error fetching file '{file_path}': {e}") + + def list_files( + self, directory_path: str = "", file_extension: str = ".prompt" + ) -> List[str]: + """ + List files in a directory with a specific extension. + + Args: + directory_path: Directory path in the repository (empty for root) + file_extension: File extension to filter by (default: .prompt) + + Returns: + List of file paths + """ + url = f"{self.base_url}/repositories/{self.workspace}/{self.repository}/src/{self.branch}/{directory_path}" + + try: + response = self.http_handler.get(url, headers=self.headers) + response.raise_for_status() + + data = response.json() + files = [] + + for item in data.get("values", []): + if item.get("type") == "commit_file": + file_path = item.get("path", "") + if file_path.endswith(file_extension): + files.append(file_path) + + return files + + except Exception as e: + # Check if it's an HTTP error + if hasattr(e, "response") and hasattr(e.response, "status_code"): + if e.response.status_code == 404: + return [] + elif e.response.status_code == 403: + raise Exception( + f"Access denied to directory '{directory_path}'. Check your BitBucket permissions for workspace '{self.workspace}' and repository '{self.repository}'." + ) + elif e.response.status_code == 401: + raise Exception( + "Authentication failed. Check your BitBucket access token and permissions." + ) + else: + raise Exception(f"Failed to list files in '{directory_path}': {e}") + else: + raise Exception(f"Error listing files in '{directory_path}': {e}") + + def get_repository_info(self) -> Dict[str, Any]: + """ + Get information about the repository. + + Returns: + Dictionary containing repository information + """ + url = f"{self.base_url}/repositories/{self.workspace}/{self.repository}" + + try: + response = self.http_handler.get(url, headers=self.headers) + response.raise_for_status() + return response.json() + except Exception as e: + raise Exception(f"Failed to get repository info: {e}") + + def test_connection(self) -> bool: + """ + Test the connection to the BitBucket repository. + + Returns: + True if connection is successful, False otherwise + """ + try: + self.get_repository_info() + return True + except Exception: + return False + + def get_branches(self) -> List[Dict[str, Any]]: + """ + Get list of branches in the repository. + + Returns: + List of branch information dictionaries + """ + url = f"{self.base_url}/repositories/{self.workspace}/{self.repository}/refs/branches" + + try: + response = self.http_handler.get(url, headers=self.headers) + response.raise_for_status() + + data = response.json() + return data.get("values", []) + except Exception as e: + raise Exception(f"Failed to get branches: {e}") + + def get_file_metadata(self, file_path: str) -> Optional[Dict[str, Any]]: + """ + Get metadata about a file (size, last modified, etc.). + + Args: + file_path: Path to the file in the repository + + Returns: + Dictionary containing file metadata, or None if file not found + """ + url = f"{self.base_url}/repositories/{self.workspace}/{self.repository}/src/{self.branch}/{file_path}" + + try: + # Use GET with Range header to get just the headers (HEAD equivalent) + headers = self.headers.copy() + headers["Range"] = "bytes=0-0" # Request only first byte to get headers + + response = self.http_handler.get(url, headers=headers) + response.raise_for_status() + + return { + "content_type": response.headers.get("content-type"), + "content_length": response.headers.get("content-length"), + "last_modified": response.headers.get("last-modified"), + } + except Exception as e: + # Check if it's an HTTP error + if hasattr(e, "response") and hasattr(e.response, "status_code"): + if e.response.status_code == 404: + return None + raise Exception(f"Failed to get file metadata for '{file_path}': {e}") + else: + raise Exception(f"Error getting file metadata for '{file_path}': {e}") + + def close(self): + """Close the HTTP handler to free resources.""" + if hasattr(self, "http_handler"): + self.http_handler.close() diff --git a/litellm/integrations/bitbucket/bitbucket_prompt_manager.py b/litellm/integrations/bitbucket/bitbucket_prompt_manager.py new file mode 100644 index 00000000000..d683fa3a0d4 --- /dev/null +++ b/litellm/integrations/bitbucket/bitbucket_prompt_manager.py @@ -0,0 +1,508 @@ +""" +BitBucket prompt manager that integrates with LiteLLM's prompt management system. +Fetches .prompt files from BitBucket repositories and provides team-based access control. +""" + +from typing import Any, Dict, List, Optional, Tuple, Union + +from jinja2 import DictLoader, Environment, select_autoescape + +from litellm.integrations.custom_prompt_management import CustomPromptManagement +from litellm.integrations.prompt_management_base import ( + PromptManagementBase, + PromptManagementClient, +) +from litellm.types.llms.openai import AllMessageValues +from litellm.types.utils import StandardCallbackDynamicParams + +from .bitbucket_client import BitBucketClient + + +class BitBucketPromptTemplate: + """ + Represents a prompt template loaded from BitBucket. + """ + + def __init__( + self, + template_id: str, + content: str, + metadata: Dict[str, Any], + model: Optional[str] = None, + ): + self.template_id = template_id + self.content = content + self.metadata = metadata + self.model = model or metadata.get("model") + self.temperature = metadata.get("temperature") + self.max_tokens = metadata.get("max_tokens") + self.input_schema = metadata.get("input", {}).get("schema", {}) + self.optional_params = { + k: v for k, v in metadata.items() if k not in ["model", "input", "content"] + } + + def __repr__(self): + return f"BitBucketPromptTemplate(id='{self.template_id}', model='{self.model}')" + + +class BitBucketTemplateManager: + """ + Manager for loading and rendering .prompt files from BitBucket repositories. + + Supports: + - Fetching .prompt files from BitBucket repositories + - Team-based access control through BitBucket permissions + - YAML frontmatter for metadata + - Handlebars-style templating (using Jinja2) + - Input/output schema validation + - Model configuration + """ + + def __init__( + self, + bitbucket_config: Dict[str, Any], + prompt_id: Optional[str] = None, + ): + self.bitbucket_config = bitbucket_config + self.prompt_id = prompt_id + self.prompts: Dict[str, BitBucketPromptTemplate] = {} + self.bitbucket_client = BitBucketClient(bitbucket_config) + + self.jinja_env = Environment( + loader=DictLoader({}), + autoescape=select_autoescape(["html", "xml"]), + # Use Handlebars-style delimiters to match Dotprompt spec + variable_start_string="{{", + variable_end_string="}}", + block_start_string="{%", + block_end_string="%}", + comment_start_string="{#", + comment_end_string="#}", + ) + + # Load prompts from BitBucket if prompt_id is provided + if self.prompt_id: + self._load_prompt_from_bitbucket(self.prompt_id) + + def _load_prompt_from_bitbucket(self, prompt_id: str) -> None: + """Load a specific .prompt file from BitBucket.""" + try: + # Fetch the .prompt file from BitBucket + prompt_content = self.bitbucket_client.get_file_content( + f"{prompt_id}.prompt" + ) + + if prompt_content: + template = self._parse_prompt_file(prompt_content, prompt_id) + self.prompts[prompt_id] = template + except Exception as e: + raise Exception(f"Failed to load prompt '{prompt_id}' from BitBucket: {e}") + + def _parse_prompt_file( + self, content: str, prompt_id: str + ) -> BitBucketPromptTemplate: + """Parse a .prompt file content and extract metadata and template.""" + # Split frontmatter and content + if content.startswith("---"): + parts = content.split("---", 2) + if len(parts) >= 3: + frontmatter_str = parts[1].strip() + template_content = parts[2].strip() + else: + frontmatter_str = "" + template_content = content + else: + frontmatter_str = "" + template_content = content + + # Parse YAML frontmatter + metadata: Dict[str, Any] = {} + if frontmatter_str: + try: + import yaml + + metadata = yaml.safe_load(frontmatter_str) or {} + except ImportError: + # Fallback to basic parsing if PyYAML is not available + metadata = self._parse_yaml_basic(frontmatter_str) + except Exception: + metadata = {} + + return BitBucketPromptTemplate( + template_id=prompt_id, + content=template_content, + metadata=metadata, + ) + + def _parse_yaml_basic(self, yaml_str: str) -> Dict[str, Any]: + """Basic YAML parser for simple cases when PyYAML is not available.""" + result: Dict[str, Any] = {} + for line in yaml_str.split("\n"): + line = line.strip() + if ":" in line and not line.startswith("#"): + key, value = line.split(":", 1) + key = key.strip() + value = value.strip() + + # Try to parse value as appropriate type + if value.lower() in ["true", "false"]: + result[key] = value.lower() == "true" + elif value.isdigit(): + result[key] = int(value) + elif value.replace(".", "").isdigit(): + result[key] = float(value) + else: + result[key] = value.strip("\"'") + return result + + def render_template( + self, template_id: str, variables: Optional[Dict[str, Any]] = None + ) -> str: + """Render a template with the given variables.""" + if template_id not in self.prompts: + raise ValueError(f"Template '{template_id}' not found") + + template = self.prompts[template_id] + jinja_template = self.jinja_env.from_string(template.content) + + return jinja_template.render(**(variables or {})) + + def get_template(self, template_id: str) -> Optional[BitBucketPromptTemplate]: + """Get a template by ID.""" + return self.prompts.get(template_id) + + def list_templates(self) -> List[str]: + """List all available template IDs.""" + return list(self.prompts.keys()) + + +class BitBucketPromptManager(CustomPromptManagement): + """ + BitBucket prompt manager that integrates with LiteLLM's prompt management system. + + This class enables using .prompt files from BitBucket repositories with the + litellm completion() function by implementing the PromptManagementBase interface. + + Usage: + # Configure BitBucket access + bitbucket_config = { + "workspace": "your-workspace", + "repository": "your-repo", + "access_token": "your-token", + "branch": "main" # optional, defaults to main + } + + # Use with completion + response = litellm.completion( + model="bitbucket/gpt-4", + prompt_id="my_prompt", + prompt_variables={"variable": "value"}, + bitbucket_config=bitbucket_config, + messages=[{"role": "user", "content": "This will be combined with the prompt"}] + ) + """ + + def __init__( + self, + bitbucket_config: Dict[str, Any], + prompt_id: Optional[str] = None, + ): + self.bitbucket_config = bitbucket_config + self.prompt_id = prompt_id + self._prompt_manager: Optional[BitBucketTemplateManager] = None + + @property + def integration_name(self) -> str: + """Integration name used in model names like 'bitbucket/gpt-4'.""" + return "bitbucket" + + @property + def prompt_manager(self) -> BitBucketTemplateManager: + """Get or create the prompt manager instance.""" + if self._prompt_manager is None: + self._prompt_manager = BitBucketTemplateManager( + bitbucket_config=self.bitbucket_config, + prompt_id=self.prompt_id, + ) + return self._prompt_manager + + def get_prompt_template( + self, + prompt_id: str, + prompt_variables: Optional[Dict[str, Any]] = None, + ) -> Tuple[str, Dict[str, Any]]: + """ + Get a prompt template and render it with variables. + + Args: + prompt_id: The ID of the prompt template + prompt_variables: Variables to substitute in the template + + Returns: + Tuple of (rendered_prompt, metadata) + """ + template = self.prompt_manager.get_template(prompt_id) + if not template: + raise ValueError(f"Prompt template '{prompt_id}' not found") + + # Render the template + rendered_prompt = self.prompt_manager.render_template( + prompt_id, prompt_variables or {} + ) + + # Extract metadata + metadata = { + "model": template.model, + "temperature": template.temperature, + "max_tokens": template.max_tokens, + **template.optional_params, + } + + return rendered_prompt, metadata + + def pre_call_hook( + self, + user_id: Optional[str], + messages: List[AllMessageValues], + function_call: Optional[Union[Dict[str, Any], str]] = None, + litellm_params: Optional[Dict[str, Any]] = None, + prompt_id: Optional[str] = None, + prompt_variables: Optional[Dict[str, Any]] = None, + **kwargs, + ) -> Tuple[List[AllMessageValues], Optional[Dict[str, Any]]]: + """ + Pre-call hook that processes the prompt template before making the LLM call. + """ + if not prompt_id: + return messages, litellm_params + + try: + # Get the rendered prompt and metadata + rendered_prompt, prompt_metadata = self.get_prompt_template( + prompt_id, prompt_variables + ) + + # Parse the rendered prompt into messages + parsed_messages = self._parse_prompt_to_messages(rendered_prompt) + + # Merge with existing messages + if parsed_messages: + # If we have parsed messages, use them instead of the original messages + final_messages: List[AllMessageValues] = parsed_messages + else: + # If no messages were parsed, prepend the prompt to existing messages + final_messages = [ + {"role": "user", "content": rendered_prompt} # type: ignore + ] + messages + + # Update litellm_params with prompt metadata + if litellm_params is None: + litellm_params = {} + + # Apply model and parameters from prompt metadata + if prompt_metadata.get("model"): + litellm_params["model"] = prompt_metadata["model"] + + for param in [ + "temperature", + "max_tokens", + "top_p", + "frequency_penalty", + "presence_penalty", + ]: + if param in prompt_metadata: + litellm_params[param] = prompt_metadata[param] + + return final_messages, litellm_params + + except Exception as e: + # Log error but don't fail the call + import litellm + + litellm._logging.verbose_proxy_logger.error( + f"Error in BitBucket prompt pre_call_hook: {e}" + ) + return messages, litellm_params + + def _parse_prompt_to_messages(self, prompt_content: str) -> List[AllMessageValues]: + """ + Parse prompt content into a list of messages. + Handles both simple prompts and multi-role conversations. + """ + messages = [] + lines = prompt_content.strip().split("\n") + current_role = None + current_content = [] + + for line in lines: + line = line.strip() + if not line: + continue + + # Check for role indicators + if line.lower().startswith("system:"): + if current_role and current_content: + messages.append( + { + "role": current_role, + "content": "\n".join(current_content).strip(), + } # type: ignore + ) + current_role = "system" + current_content = [line[7:].strip()] # Remove "System:" prefix + elif line.lower().startswith("user:"): + if current_role and current_content: + messages.append( + { + "role": current_role, + "content": "\n".join(current_content).strip(), + } # type: ignore + ) + current_role = "user" + current_content = [line[5:].strip()] # Remove "User:" prefix + elif line.lower().startswith("assistant:"): + if current_role and current_content: + messages.append( + { + "role": current_role, + "content": "\n".join(current_content).strip(), + } # type: ignore + ) + current_role = "assistant" + current_content = [line[10:].strip()] # Remove "Assistant:" prefix + else: + # Continue building current message + current_content.append(line) + + # Add the last message + if current_role and current_content: + messages.append( + {"role": current_role, "content": "\n".join(current_content).strip()} + ) + + # If no role indicators found, treat as a single user message + if not messages and prompt_content.strip(): + messages = [{"role": "user", "content": prompt_content.strip()}] # type: ignore + + return messages # type: ignore + + def post_call_hook( + self, + user_id: Optional[str], + response: Any, + input_messages: List[AllMessageValues], + function_call: Optional[Union[Dict[str, Any], str]] = None, + litellm_params: Optional[Dict[str, Any]] = None, + prompt_id: Optional[str] = None, + prompt_variables: Optional[Dict[str, Any]] = None, + **kwargs, + ) -> Any: + """ + Post-call hook for any post-processing after the LLM call. + """ + return response + + def get_available_prompts(self) -> List[str]: + """Get list of available prompt IDs.""" + return self.prompt_manager.list_templates() + + def reload_prompts(self) -> None: + """Reload prompts from BitBucket.""" + if self.prompt_id: + self._prompt_manager = None # Reset to force reload + self.prompt_manager # This will trigger reload + + def should_run_prompt_management( + self, + prompt_id: str, + dynamic_callback_params: StandardCallbackDynamicParams, + ) -> bool: + """ + Determine if prompt management should run based on the prompt_id. + + For BitBucket, we always return True and handle the prompt loading + in the _compile_prompt_helper method. + """ + return True + + def _compile_prompt_helper( + self, + prompt_id: str, + prompt_variables: Optional[dict], + dynamic_callback_params: StandardCallbackDynamicParams, + prompt_label: Optional[str] = None, + prompt_version: Optional[int] = None, + ) -> PromptManagementClient: + """ + Compile a BitBucket prompt template into a PromptManagementClient structure. + + This method: + 1. Loads the prompt template from BitBucket + 2. Renders it with the provided variables + 3. Converts the rendered text into chat messages + 4. Extracts model and optional parameters from metadata + """ + try: + # Load the prompt from BitBucket if not already loaded + if prompt_id not in self.prompt_manager.prompts: + self.prompt_manager._load_prompt_from_bitbucket(prompt_id) + + # Get the rendered prompt and metadata + rendered_prompt, prompt_metadata = self.get_prompt_template( + prompt_id, prompt_variables + ) + + # Convert rendered content to chat messages + messages = self._parse_prompt_to_messages(rendered_prompt) + + # Extract model from metadata (if specified) + template_model = prompt_metadata.get("model") + + # Extract optional parameters from metadata + optional_params = {} + for param in [ + "temperature", + "max_tokens", + "top_p", + "frequency_penalty", + "presence_penalty", + ]: + if param in prompt_metadata: + optional_params[param] = prompt_metadata[param] + + return PromptManagementClient( + prompt_id=prompt_id, + prompt_template=messages, + prompt_template_model=template_model, + prompt_template_optional_params=optional_params, + completed_messages=None, + ) + + except Exception as e: + raise ValueError(f"Error compiling prompt '{prompt_id}': {e}") + + def get_chat_completion_prompt( + self, + model: str, + messages: List[AllMessageValues], + non_default_params: dict, + prompt_id: Optional[str], + prompt_variables: Optional[dict], + dynamic_callback_params: StandardCallbackDynamicParams, + prompt_label: Optional[str] = None, + prompt_version: Optional[int] = None, + ) -> Tuple[str, List[AllMessageValues], dict]: + """ + Get chat completion prompt from BitBucket and return processed model, messages, and parameters. + """ + return PromptManagementBase.get_chat_completion_prompt( + self, + model, + messages, + non_default_params, + prompt_id, + prompt_variables, + dynamic_callback_params, + prompt_label, + prompt_version, + ) diff --git a/litellm/integrations/braintrust_logging.py b/litellm/integrations/braintrust_logging.py index 5cf403adee4..5bc6afb6dbc 100644 --- a/litellm/integrations/braintrust_logging.py +++ b/litellm/integrations/braintrust_logging.py @@ -1,13 +1,11 @@ # What is this? ## Log success + failure events to Braintrust -import copy import os from datetime import datetime from typing import Dict, Optional import httpx -from pydantic import BaseModel import litellm from litellm import verbose_logger @@ -24,7 +22,6 @@ API_BASE = "https://api.braintrustdata.com/v1" def get_utc_datetime(): import datetime as dt - from datetime import datetime if hasattr(dt, "UTC"): return datetime.now(dt.UTC) # type: ignore @@ -45,9 +42,9 @@ class BraintrustLogger(CustomLogger): "Authorization": "Bearer " + self.api_key, "Content-Type": "application/json", } - self._project_id_cache: Dict[ - str, str - ] = {} # Cache mapping project names to IDs + self._project_id_cache: Dict[str, str] = ( + {} + ) # Cache mapping project names to IDs self.global_braintrust_http_handler = get_async_httpx_client( llm_provider=httpxSpecialProvider.LoggingCallback ) @@ -108,43 +105,6 @@ class BraintrustLogger(CustomLogger): except httpx.HTTPStatusError as e: raise Exception(f"Failed to register project: {e.response.text}") - @staticmethod - def add_metadata_from_header(litellm_params: dict, metadata: dict) -> dict: - """ - Adds metadata from proxy request headers to Braintrust logging if keys start with "braintrust_" - and overwrites litellm_params.metadata if already included. - - For example if you want to append your trace to an existing `trace_id` via header, send - `headers: { ..., langfuse_existing_trace_id: your-existing-trace-id }` via proxy request. - """ - if litellm_params is None: - return metadata - - if litellm_params.get("proxy_server_request") is None: - return metadata - - if metadata is None: - metadata = {} - - proxy_headers = ( - litellm_params.get("proxy_server_request", {}).get("headers", {}) or {} - ) - - for metadata_param_key in proxy_headers: - if metadata_param_key.startswith("braintrust"): - trace_param_key = metadata_param_key.replace("braintrust", "", 1) - if trace_param_key in metadata: - verbose_logger.warning( - f"Overwriting Braintrust `{trace_param_key}` from request header" - ) - else: - verbose_logger.debug( - f"Found Braintrust `{trace_param_key}` in request header" - ) - metadata[trace_param_key] = proxy_headers.get(metadata_param_key) - - return metadata - async def create_default_project_and_experiment(self): project = await self.global_braintrust_http_handler.post( f"{self.api_base}/project", headers=self.headers, json={"name": "litellm"} @@ -169,7 +129,9 @@ class BraintrustLogger(CustomLogger): verbose_logger.debug("REACHES BRAINTRUST SUCCESS") try: litellm_call_id = kwargs.get("litellm_call_id") + standard_logging_object = kwargs.get("standard_logging_object", {}) prompt = {"messages": kwargs.get("messages")} + output = None choices = [] if response_obj is not None and ( @@ -192,33 +154,13 @@ class BraintrustLogger(CustomLogger): ): output = response_obj["data"] - litellm_params = kwargs.get("litellm_params", {}) - metadata = ( - litellm_params.get("metadata", {}) or {} - ) # if litellm_params['metadata'] == None - metadata = self.add_metadata_from_header(litellm_params, metadata) - clean_metadata = {} - try: - metadata = copy.deepcopy( - metadata - ) # Avoid modifying the original metadata - except Exception: - new_metadata = {} - for key, value in metadata.items(): - if ( - isinstance(value, list) - or isinstance(value, dict) - or isinstance(value, str) - or isinstance(value, int) - or isinstance(value, float) - ): - new_metadata[key] = copy.deepcopy(value) - metadata = new_metadata + litellm_params = kwargs.get("litellm_params", {}) or {} + dynamic_metadata = litellm_params.get("metadata", {}) or {} # Get project_id from metadata or create default if needed - project_id = metadata.get("project_id") + project_id = dynamic_metadata.get("project_id") if project_id is None: - project_name = metadata.get("project_name") + project_name = dynamic_metadata.get("project_name") project_id = ( self.get_project_id_sync(project_name) if project_name else None ) @@ -229,8 +171,9 @@ class BraintrustLogger(CustomLogger): project_id = self.default_project_id tags = [] - if isinstance(metadata, dict): - for key, value in metadata.items(): + + if isinstance(dynamic_metadata, dict): + for key, value in dynamic_metadata.items(): # generate langfuse tags - Default Tags sent to Langfuse from LiteLLM Proxy if ( litellm.langfuse_default_tags is not None @@ -239,25 +182,12 @@ class BraintrustLogger(CustomLogger): ): tags.append(f"{key}:{value}") - # clean litellm metadata before logging - if key in [ - "headers", - "endpoint", - "caching_groups", - "previous_models", - ]: - continue - else: - clean_metadata[key] = value + if ( + isinstance(value, str) and key not in standard_logging_object + ): # support logging dynamic metadata to braintrust + standard_logging_object[key] = value cost = kwargs.get("response_cost", None) - if cost is not None: - clean_metadata["litellm_response_cost"] = cost - - # metadata.model is required for braintrust to calculate the "Estimated cost" metric - litellm_model = kwargs.get("model", None) - if litellm_model is not None: - clean_metadata["model"] = litellm_model metrics: Optional[dict] = None usage_obj = getattr(response_obj, "usage", None) @@ -274,12 +204,15 @@ class BraintrustLogger(CustomLogger): "end": end_time.timestamp(), } + # Allow metadata override for span name + span_name = dynamic_metadata.get("span_name", "Chat Completion") + request_data = { "id": litellm_call_id, "input": prompt["messages"], - "metadata": clean_metadata, + "metadata": standard_logging_object, "tags": tags, - "span_attributes": {"name": "Chat Completion", "type": "llm"}, + "span_attributes": {"name": span_name, "type": "llm"}, } if choices is not None: request_data["output"] = [choice.dict() for choice in choices] @@ -309,6 +242,7 @@ class BraintrustLogger(CustomLogger): verbose_logger.debug("REACHES BRAINTRUST SUCCESS") try: litellm_call_id = kwargs.get("litellm_call_id") + standard_logging_object = kwargs.get("standard_logging_object", {}) prompt = {"messages": kwargs.get("messages")} output = None choices = [] @@ -333,32 +267,12 @@ class BraintrustLogger(CustomLogger): output = response_obj["data"] litellm_params = kwargs.get("litellm_params", {}) - metadata = ( - litellm_params.get("metadata", {}) or {} - ) # if litellm_params['metadata'] == None - metadata = self.add_metadata_from_header(litellm_params, metadata) - clean_metadata = {} - new_metadata = {} - for key, value in metadata.items(): - if ( - isinstance(value, list) - or isinstance(value, str) - or isinstance(value, int) - or isinstance(value, float) - ): - new_metadata[key] = value - elif isinstance(value, BaseModel): - new_metadata[key] = value.model_dump_json() - elif isinstance(value, dict): - for k, v in value.items(): - if isinstance(v, datetime): - value[k] = v.isoformat() - new_metadata[key] = value + dynamic_metadata = litellm_params.get("metadata", {}) or {} # Get project_id from metadata or create default if needed - project_id = metadata.get("project_id") + project_id = dynamic_metadata.get("project_id") if project_id is None: - project_name = metadata.get("project_name") + project_name = dynamic_metadata.get("project_name") project_id = ( await self.get_project_id_async(project_name) if project_name @@ -371,8 +285,9 @@ class BraintrustLogger(CustomLogger): project_id = self.default_project_id tags = [] - if isinstance(metadata, dict): - for key, value in metadata.items(): + + if isinstance(dynamic_metadata, dict): + for key, value in dynamic_metadata.items(): # generate langfuse tags - Default Tags sent to Langfuse from LiteLLM Proxy if ( litellm.langfuse_default_tags is not None @@ -381,25 +296,12 @@ class BraintrustLogger(CustomLogger): ): tags.append(f"{key}:{value}") - # clean litellm metadata before logging - if key in [ - "headers", - "endpoint", - "caching_groups", - "previous_models", - ]: - continue - else: - clean_metadata[key] = value + if ( + isinstance(value, str) and key not in standard_logging_object + ): # support logging dynamic metadata to braintrust + standard_logging_object[key] = value cost = kwargs.get("response_cost", None) - if cost is not None: - clean_metadata["litellm_response_cost"] = cost - - # metadata.model is required for braintrust to calculate the "Estimated cost" metric - litellm_model = kwargs.get("model", None) - if litellm_model is not None: - clean_metadata["model"] = litellm_model metrics: Optional[dict] = None usage_obj = getattr(response_obj, "usage", None) @@ -426,13 +328,16 @@ class BraintrustLogger(CustomLogger): - api_call_start_time.timestamp() ) + # Allow metadata override for span name + span_name = dynamic_metadata.get("span_name", "Chat Completion") + request_data = { "id": litellm_call_id, "input": prompt["messages"], "output": output, - "metadata": clean_metadata, + "metadata": standard_logging_object, "tags": tags, - "span_attributes": {"name": "Chat Completion", "type": "llm"}, + "span_attributes": {"name": span_name, "type": "llm"}, } if choices is not None: request_data["output"] = [choice.dict() for choice in choices] diff --git a/litellm/integrations/cloudzero/cloudzero.py b/litellm/integrations/cloudzero/cloudzero.py index 85aa1679732..ca15962b72a 100644 --- a/litellm/integrations/cloudzero/cloudzero.py +++ b/litellm/integrations/cloudzero/cloudzero.py @@ -1,14 +1,15 @@ -import asyncio import os -from datetime import datetime, timedelta -from typing import Optional +from datetime import datetime +from typing import TYPE_CHECKING, Any, List, Optional, cast +import litellm from litellm._logging import verbose_logger from litellm.integrations.custom_logger import CustomLogger -from .cz_stream_api import CloudZeroStreamer -from .database import LiteLLMDatabase -from .transform import CBFTransformer +if TYPE_CHECKING: + from apscheduler.schedulers.asyncio import AsyncIOScheduler +else: + AsyncIOScheduler = Any class CloudZeroLogger(CustomLogger): @@ -29,20 +30,80 @@ class CloudZeroLogger(CustomLogger): self.api_key = api_key or os.getenv("CLOUDZERO_API_KEY") self.connection_id = connection_id or os.getenv("CLOUDZERO_CONNECTION_ID") self.timezone = timezone or os.getenv("CLOUDZERO_TIMEZONE", "UTC") + verbose_logger.debug(f"CloudZero Logger initialized with connection ID: {self.connection_id}, timezone: {self.timezone}") - async def export_usage_data(self, target_hour: datetime, limit: Optional[int] = 1000, operation: str = "replace_hourly"): + async def initialize_cloudzero_export_job(self): """ - Exports the usage data for a specific hour to CloudZero. + Handler for initializing CloudZero export job. - - Reads spend logs from the DB for the specified hour + Runs when CloudZero logger starts up. + + - If redis cache is available, we use the pod lock manager to acquire a lock and export the data. + - Ensures only one pod exports the data at a time. + - If redis cache is not available, we export the data directly. + """ + from litellm.constants import ( + CLOUDZERO_EXPORT_USAGE_DATA_JOB_NAME, + ) + from litellm.proxy.proxy_server import proxy_logging_obj + pod_lock_manager = proxy_logging_obj.db_spend_update_writer.pod_lock_manager + + # if using redis, ensure only one pod exports the data at a time + if pod_lock_manager and pod_lock_manager.redis_cache: + if await pod_lock_manager.acquire_lock( + cronjob_id=CLOUDZERO_EXPORT_USAGE_DATA_JOB_NAME + ): + try: + await self._hourly_usage_data_export() + finally: + await pod_lock_manager.release_lock( + cronjob_id=CLOUDZERO_EXPORT_USAGE_DATA_JOB_NAME + ) + else: + # if not using redis, export the data directly + await self._hourly_usage_data_export() + + async def _hourly_usage_data_export(self): + """ + Exports the hourly usage data to CloudZero. + + Start time: 1 hour ago + End time: current time + """ + from datetime import timedelta, timezone + + from litellm.constants import CLOUDZERO_MAX_FETCHED_DATA_RECORDS + current_time_utc = datetime.now(timezone.utc) + one_hour_ago_utc = current_time_utc - timedelta(hours=1) + await self.export_usage_data( + limit=CLOUDZERO_MAX_FETCHED_DATA_RECORDS, + operation="replace_hourly", + start_time_utc=one_hour_ago_utc, + end_time_utc=current_time_utc + ) + + + async def export_usage_data( + self, + limit: Optional[int] = None, + operation: str = "replace_hourly", + start_time_utc: Optional[datetime] = None, + end_time_utc: Optional[datetime] = None + ): + """ + Exports the usage data to CloudZero. + + - Reads data from the DB - Transforms the data to the CloudZero format - Sends the data to CloudZero Args: - target_hour: The specific hour to export data for - limit: Optional limit on number of records to export (default: 1000) + limit: Optional limit on number of records to export operation: CloudZero operation type ("replace_hourly" or "sum") """ + from litellm.integrations.cloudzero.cz_stream_api import CloudZeroStreamer + from litellm.integrations.cloudzero.database import LiteLLMDatabase + from litellm.integrations.cloudzero.transform import CBFTransformer try: verbose_logger.debug("CloudZero Logger: Starting usage data export") @@ -52,11 +113,27 @@ class CloudZeroLogger(CustomLogger): "CloudZero configuration missing. Please set CLOUDZERO_API_KEY and CLOUDZERO_CONNECTION_ID environment variables." ) - # Fetch and transform data using helper - cbf_data = await self._fetch_cbf_data_for_hour(target_hour, limit) + # Initialize database connection and load data + database = LiteLLMDatabase() + verbose_logger.debug("CloudZero Logger: Loading usage data from database") + data = await database.get_usage_data( + limit=limit, + start_time_utc=start_time_utc, + end_time_utc=end_time_utc + ) + + if data.is_empty(): + verbose_logger.debug("CloudZero Logger: No usage data found to export") + return + + verbose_logger.debug(f"CloudZero Logger: Processing {len(data)} records") + + # Transform data to CloudZero CBF format + transformer = CBFTransformer() + cbf_data = transformer.transform(data) if cbf_data.is_empty(): - verbose_logger.info("CloudZero Logger: No usage data found to export") + verbose_logger.warning("CloudZero Logger: No valid data after transformation") return # Send data to CloudZero @@ -69,65 +146,91 @@ class CloudZeroLogger(CustomLogger): verbose_logger.debug(f"CloudZero Logger: Transmitting {len(cbf_data)} records to CloudZero") streamer.send_batched(cbf_data, operation=operation) - verbose_logger.info(f"CloudZero Logger: Successfully exported {len(cbf_data)} records to CloudZero") + verbose_logger.debug(f"CloudZero Logger: Successfully exported {len(cbf_data)} records to CloudZero") except Exception as e: verbose_logger.error(f"CloudZero Logger: Error exporting usage data: {str(e)}") raise - async def _fetch_cbf_data_for_hour(self, target_hour: datetime, limit: Optional[int] = 1000): + async def dry_run_export_usage_data(self, limit: Optional[int] = 10000): """ - Helper method to fetch usage data for a specific hour and transform it to CloudZero CBF format. + Returns the data that would be exported to CloudZero without actually sending it. Args: - target_hour: The specific hour to fetch data for - limit: Optional limit on number of records to fetch (default: 1000) + limit: Limit number of records to display (default: 10000) Returns: - CBF formatted data ready for CloudZero ingestion - """ - # Initialize database connection and load data - database = LiteLLMDatabase() - verbose_logger.debug(f"CloudZero Logger: Loading spend logs for hour {target_hour}") - data = await database.get_usage_data_for_hour(target_hour=target_hour, limit=limit) - - if data.is_empty(): - verbose_logger.info("CloudZero Logger: No usage data found for the specified hour") - return data # Return empty data - - verbose_logger.debug(f"CloudZero Logger: Processing {len(data)} records") - - # Transform data to CloudZero CBF format - transformer = CBFTransformer() - cbf_data = transformer.transform(data) - - if cbf_data.is_empty(): - verbose_logger.warning("CloudZero Logger: No valid data after transformation") - - return cbf_data - - async def dry_run_export_usage_data(self, target_hour: datetime, limit: Optional[int] = 1000): - """ - Only prints the spend logs data for a specific hour that would be exported to CloudZero. - - Args: - target_hour: The specific hour to export data for - limit: Limit number of records to display (default: 1000) + dict: Contains usage_data, cbf_data, and summary statistics """ + from litellm.integrations.cloudzero.database import LiteLLMDatabase + from litellm.integrations.cloudzero.transform import CBFTransformer try: verbose_logger.debug("CloudZero Logger: Starting dry run export") - # Fetch and transform data using helper - cbf_data = await self._fetch_cbf_data_for_hour(target_hour, limit) + # Initialize database connection and load data + database = LiteLLMDatabase() + verbose_logger.debug("CloudZero Logger: Loading usage data for dry run") + data = await database.get_usage_data(limit=limit) + + if data.is_empty(): + verbose_logger.warning("CloudZero Dry Run: No usage data found") + return { + "usage_data": [], + "cbf_data": [], + "summary": { + "total_records": 0, + "total_cost": 0, + "total_tokens": 0, + "unique_accounts": 0, + "unique_services": 0 + } + } + + verbose_logger.debug(f"CloudZero Dry Run: Processing {len(data)} records...") + + # Convert usage data to dict format for response + usage_data_sample = data.head(50).to_dicts() # Return first 50 rows + + # Transform data to CloudZero CBF format + transformer = CBFTransformer() + cbf_data = transformer.transform(data) if cbf_data.is_empty(): - verbose_logger.warning("CloudZero Dry Run: No usage data found") - return + verbose_logger.warning("CloudZero Dry Run: No valid data after transformation") + return { + "usage_data": usage_data_sample, + "cbf_data": [], + "summary": { + "total_records": len(usage_data_sample), + "total_cost": sum(row.get('spend', 0) for row in usage_data_sample), + "total_tokens": sum(row.get('prompt_tokens', 0) + row.get('completion_tokens', 0) for row in usage_data_sample), + "unique_accounts": 0, + "unique_services": 0 + } + } - # Display the transformed data on screen - self._display_cbf_data_on_screen(cbf_data) + # Convert CBF data to dict format for response + cbf_data_dict = cbf_data.to_dicts() - verbose_logger.info(f"CloudZero Logger: Dry run completed for {len(cbf_data)} records") + # Calculate summary statistics + total_cost = sum(record.get('cost/cost', 0) for record in cbf_data_dict) + unique_accounts = len(set(record.get('resource/account', '') for record in cbf_data_dict if record.get('resource/account'))) + unique_services = len(set(record.get('resource/service', '') for record in cbf_data_dict if record.get('resource/service'))) + total_tokens = sum(record.get('usage/amount', 0) for record in cbf_data_dict) + + verbose_logger.debug(f"CloudZero Logger: Dry run completed for {len(cbf_data)} records") + + return { + "usage_data": usage_data_sample, + "cbf_data": cbf_data_dict, + "summary": { + "total_records": len(cbf_data_dict), + "total_cost": total_cost, + "total_tokens": total_tokens, + "unique_accounts": unique_accounts, + "unique_services": unique_services + } + } except Exception as e: verbose_logger.error(f"CloudZero Logger: Error in dry run export: {str(e)}") @@ -155,6 +258,11 @@ class CloudZeroLogger(CustomLogger): cbf_table = Table(show_header=True, header_style="bold cyan", box=SIMPLE, padding=(0, 1)) cbf_table.add_column("time/usage_start", style="blue", no_wrap=False) cbf_table.add_column("cost/cost", style="green", justify="right", no_wrap=False) + cbf_table.add_column("entity_type", style="magenta", justify="right", no_wrap=False) + cbf_table.add_column("entity_id", style="magenta", justify="right", no_wrap=False) + cbf_table.add_column("team_id", style="cyan", no_wrap=False) + cbf_table.add_column("team_alias", style="cyan", no_wrap=False) + cbf_table.add_column("api_key_alias", style="yellow", no_wrap=False) cbf_table.add_column("usage/amount", style="yellow", justify="right", no_wrap=False) cbf_table.add_column("resource/id", style="magenta", no_wrap=False) cbf_table.add_column("resource/service", style="cyan", no_wrap=False) @@ -170,10 +278,20 @@ class CloudZeroLogger(CustomLogger): resource_service = str(record.get('resource/service', 'N/A')) resource_account = str(record.get('resource/account', 'N/A')) resource_region = str(record.get('resource/region', 'N/A')) + entity_type = str(record.get('entity_type', 'N/A')) + entity_id = str(record.get('entity_id', 'N/A')) + team_id = str(record.get('resource/tag:team_id', 'N/A')) + team_alias = str(record.get('resource/tag:team_alias', 'N/A')) + api_key_alias = str(record.get('resource/tag:api_key_alias', 'N/A')) cbf_table.add_row( time_usage_start, cost_cost, + entity_type, + entity_id, + team_id, + team_alias, + api_key_alias, usage_amount, resource_id, resource_service, @@ -199,55 +317,33 @@ class CloudZeroLogger(CustomLogger): console.print(f" Unique Services: {unique_services}") console.print("\n[dim]💡 This is the CloudZero CBF format ready for AnyCost ingestion[/dim]") + + @staticmethod + async def init_cloudzero_background_job(scheduler: AsyncIOScheduler): + """ + Initialize the CloudZero background job. - async def init_background_job(self, redis_cache=None): + Starts the background job that exports the usage data to CloudZero every hour. """ - Initialize a background job that exports usage data every hour. - Uses PodLockManager to ensure only one instance runs the export at a time. + from litellm.constants import CLOUDZERO_EXPORT_INTERVAL_MINUTES + from litellm.integrations.custom_logger import CustomLogger - Args: - redis_cache: Redis cache instance for pod locking - """ - from litellm.proxy.db.db_transaction_queue.pod_lock_manager import ( - PodLockManager, + + prometheus_loggers: List[CustomLogger] = ( + litellm.logging_callback_manager.get_custom_loggers_for_type( + callback_type=CloudZeroLogger + ) ) - - lock_manager = PodLockManager(redis_cache=redis_cache) - cronjob_id = "cloudzero_hourly_export" - - async def hourly_export_task(): - while True: - try: - # Calculate the previous completed hour - now = datetime.utcnow() - target_hour = now.replace(minute=0, second=0, microsecond=0) - # Export data for the previous hour to ensure all data is available - target_hour = target_hour - timedelta(hours=1) - - # Try to acquire lock - lock_acquired = await lock_manager.acquire_lock(cronjob_id) - - if lock_acquired: - try: - verbose_logger.info(f"CloudZero Background Job: Starting export for hour {target_hour}") - await self.export_usage_data(target_hour) - verbose_logger.info(f"CloudZero Background Job: Completed export for hour {target_hour}") - finally: - # Always release the lock - await lock_manager.release_lock(cronjob_id) - else: - verbose_logger.debug("CloudZero Background Job: Another instance is already running the export") - - # Wait until the next hour - next_hour = (datetime.utcnow() + timedelta(hours=1)).replace(minute=0, second=0, microsecond=0) - sleep_seconds = (next_hour - datetime.utcnow()).total_seconds() - await asyncio.sleep(sleep_seconds) - - except Exception as e: - verbose_logger.error(f"CloudZero Background Job: Error in hourly export task: {str(e)}") - # Sleep for 5 minutes before retrying on error - await asyncio.sleep(300) - - # Start the background task - asyncio.create_task(hourly_export_task()) - verbose_logger.debug("CloudZero Background Job: Initialized hourly export task") \ No newline at end of file + # we need to get the initialized prometheus logger instance(s) and call logger.initialize_remaining_budget_metrics() on them + verbose_logger.debug("found %s cloudzero loggers", len(prometheus_loggers)) + if len(prometheus_loggers) > 0: + cloudzero_logger = cast(CloudZeroLogger, prometheus_loggers[0]) + verbose_logger.debug( + "Initializing remaining budget metrics as a cron job executing every %s minutes" + % CLOUDZERO_EXPORT_INTERVAL_MINUTES + ) + scheduler.add_job( + cloudzero_logger.initialize_cloudzero_export_job, + "interval", + minutes=CLOUDZERO_EXPORT_INTERVAL_MINUTES + ) \ No newline at end of file diff --git a/litellm/integrations/cloudzero/cz_resource_names.py b/litellm/integrations/cloudzero/cz_resource_names.py index 44147f9c210..f1098d20381 100644 --- a/litellm/integrations/cloudzero/cz_resource_names.py +++ b/litellm/integrations/cloudzero/cz_resource_names.py @@ -17,11 +17,16 @@ """CloudZero Resource Names (CZRN) generation and validation for LiteLLM resources.""" import re +from enum import Enum from typing import Any, cast import litellm +class CZEntityType(str, Enum): + TEAM = "team" + + class CZRNGenerator: """Generate CloudZero Resource Names (CZRNs) for LiteLLM resources.""" @@ -49,8 +54,8 @@ class CZRNGenerator: region = 'cross-region' # Use the actual entity_id (team_id or user_id) as the owner account - entity_id = row.get('entity_id', 'unknown') - owner_account_id = self._normalize_component(entity_id) + team_id = row.get('team_id', 'unknown') + owner_account_id = self._normalize_component(team_id) resource_type = 'llm-usage' diff --git a/litellm/integrations/cloudzero/database.py b/litellm/integrations/cloudzero/database.py index 6d12c5cfbd9..71b4125ed75 100644 --- a/litellm/integrations/cloudzero/database.py +++ b/litellm/integrations/cloudzero/database.py @@ -12,14 +12,13 @@ # See the License for the specific language governing permissions and # limitations under the License. # -# CHANGELOG: 2025-07-23 - Added support for using LiteLLM_SpendLogs table for CBF mapping (ishaan-jaff) # CHANGELOG: 2025-01-19 - Refactored to use daily spend tables for proper CBF mapping (erik.peterson) # CHANGELOG: 2025-01-19 - Migrated from pandas to polars for database operations (erik.peterson) # CHANGELOG: 2025-01-19 - Initial database module for LiteLLM data extraction (erik.peterson) """Database connection and data extraction for LiteLLM.""" -from datetime import datetime, timedelta +from datetime import datetime from typing import Any, Dict, Optional import polars as pl @@ -37,61 +36,88 @@ class LiteLLMDatabase: ) return prisma_client - async def get_usage_data_for_hour(self, target_hour: datetime, limit: Optional[int] = 1000) -> pl.DataFrame: - """Retrieve spend logs for a specific hour from LiteLLM_SpendLogs table with batching.""" + async def get_usage_data( + self, + limit: Optional[int] = None, + start_time_utc: Optional[datetime] = None, + end_time_utc: Optional[datetime] = None + ) -> pl.DataFrame: + """Retrieve usage data from LiteLLM daily user spend table.""" client = self._ensure_prisma_client() - # Calculate hour range - hour_start = target_hour.replace(minute=0, second=0, microsecond=0) - hour_end = hour_start + timedelta(hours=1) + # Build WHERE clause for time filtering + where_conditions = [] + if start_time_utc: + where_conditions.append(f"dus.created_at >= '{start_time_utc.isoformat()}'") + if end_time_utc: + where_conditions.append(f"dus.created_at <= '{end_time_utc.isoformat()}'") - # Convert datetime objects to ISO format strings for PostgreSQL compatibility - hour_start_str = hour_start.isoformat() - hour_end_str = hour_end.isoformat() + where_clause = "" + if where_conditions: + where_clause = "WHERE " + " AND ".join(where_conditions) - # Query to get spend logs for the specific hour - query = """ - SELECT * - FROM "LiteLLM_SpendLogs" - WHERE "startTime" >= $1::timestamp - AND "startTime" < $2::timestamp - ORDER BY "startTime" ASC + # Query to get user spend data with team information + query = f""" + SELECT + dus.id, + dus.date, + dus.user_id, + dus.api_key, + dus.model, + dus.model_group, + dus.custom_llm_provider, + dus.prompt_tokens, + dus.completion_tokens, + dus.spend, + dus.api_requests, + dus.successful_requests, + dus.failed_requests, + dus.cache_creation_input_tokens, + dus.cache_read_input_tokens, + dus.created_at, + dus.updated_at, + vt.team_id, + vt.key_alias as api_key_alias, + tt.team_alias + FROM "LiteLLM_DailyUserSpend" dus + LEFT JOIN "LiteLLM_VerificationToken" vt ON dus.api_key = vt.token + LEFT JOIN "LiteLLM_TeamTable" tt ON vt.team_id = tt.team_id + {where_clause} + ORDER BY dus.date DESC, dus.created_at DESC """ if limit: query += f" LIMIT {limit}" try: - db_response = await client.db.query_raw(query, hour_start_str, hour_end_str) - # Convert the response to polars DataFrame - return pl.DataFrame(db_response) if db_response else pl.DataFrame() + db_response = await client.db.query_raw(query) + # Convert the response to polars DataFrame with full schema inference + # This prevents schema mismatch errors when data types vary across rows + return pl.DataFrame(db_response, infer_schema_length=None) except Exception as e: - raise Exception(f"Error retrieving spend logs for hour {target_hour}: {str(e)}") - + raise Exception(f"Error retrieving usage data: {str(e)}") async def get_table_info(self) -> Dict[str, Any]: - """Get information about the LiteLLM_SpendLogs table.""" + """Get information about the daily user spend table.""" client = self._ensure_prisma_client() try: - # Get row count from SpendLogs table - spend_logs_count = await self._get_table_row_count('LiteLLM_SpendLogs') + # Get row count from user spend table + user_count = await self._get_table_row_count('LiteLLM_DailyUserSpend') - # Get column structure from spend logs table + # Get column structure from user spend table query = """ SELECT column_name, data_type, is_nullable FROM information_schema.columns - WHERE table_name = 'LiteLLM_SpendLogs' + WHERE table_name = 'LiteLLM_DailyUserSpend' ORDER BY ordinal_position; """ columns_response = await client.db.query_raw(query) return { 'columns': columns_response, - 'row_count': spend_logs_count, - 'table_breakdown': { - 'spend_logs': spend_logs_count - } + 'row_count': user_count, + 'table_name': 'LiteLLM_DailyUserSpend' } except Exception as e: raise Exception(f"Error getting table info: {str(e)}") diff --git a/litellm/integrations/cloudzero/transform.py b/litellm/integrations/cloudzero/transform.py index 7091ea26b95..e0263295388 100644 --- a/litellm/integrations/cloudzero/transform.py +++ b/litellm/integrations/cloudzero/transform.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # -# CHANGELOG: 2025-01-19 - Updated CBF transformation for LiteLLM_SpendLogs with hourly aggregation and team_id focus (ishaan-jaff) +# CHANGELOG: 2025-01-19 - Updated CBF transformation for daily spend tables and proper CloudZero mapping (erik.peterson) # CHANGELOG: 2025-01-19 - Migrated from pandas to polars for data transformation (erik.peterson) # CHANGELOG: 2025-01-19 - Initial CBF transformation module (erik.peterson) @@ -24,7 +24,7 @@ from typing import Any, Optional import polars as pl from ...types.integrations.cloudzero import CBFRecord -from .cz_resource_names import CZRNGenerator +from .cz_resource_names import CZEntityType, CZRNGenerator class CBFTransformer: @@ -35,160 +35,99 @@ class CBFTransformer: self.czrn_generator = CZRNGenerator() def transform(self, data: pl.DataFrame) -> pl.DataFrame: - """Transform LiteLLM SpendLogs data to hourly aggregated CBF format.""" + """Transform LiteLLM data to CBF format, dropping records with zero successful_requests or invalid CZRNs.""" if data.is_empty(): return pl.DataFrame() - # Filter out records with zero spend or invalid team_id + # Filter out records with zero successful_requests first original_count = len(data) - filtered_data = data.filter( - (pl.col('spend') > 0) & - (pl.col('team_id').is_not_null()) & - (pl.col('team_id') != "") - ) - filtered_count = len(filtered_data) - zero_spend_dropped = original_count - filtered_count + if 'successful_requests' in data.columns: + filtered_data = data.filter(pl.col('successful_requests') > 0) + zero_requests_dropped = original_count - len(filtered_data) + else: + filtered_data = data + zero_requests_dropped = 0 - if filtered_data.is_empty(): - from rich.console import Console - console = Console() - console.print(f"[yellow]⚠️ Dropped all {original_count:,} records due to zero spend or missing team_id[/yellow]") - return pl.DataFrame() - - # Aggregate data to hourly level - hourly_aggregated = self._aggregate_to_hourly(filtered_data) - - # Transform aggregated data to CBF format cbf_data = [] czrn_dropped_count = 0 - - for row in hourly_aggregated.iter_rows(named=True): + filtered_count = len(filtered_data) + + for row in filtered_data.iter_rows(named=True): try: cbf_record = self._create_cbf_record(row) + # Only include the record if CZRN generation was successful cbf_data.append(cbf_record) except Exception: # Skip records that fail CZRN generation czrn_dropped_count += 1 continue - # Print summary of transformations + # Print summary of dropped records if any from rich.console import Console console = Console() - if zero_spend_dropped > 0: - console.print(f"[yellow]⚠️ Dropped {zero_spend_dropped:,} of {original_count:,} records with zero spend or missing team_id[/yellow]") + if zero_requests_dropped > 0: + console.print(f"[yellow]⚠️ Dropped {zero_requests_dropped:,} of {original_count:,} records with zero successful_requests[/yellow]") if czrn_dropped_count > 0: - console.print(f"[yellow]⚠️ Dropped {czrn_dropped_count:,} of {len(hourly_aggregated):,} aggregated records due to invalid CZRNs[/yellow]") + console.print(f"[yellow]⚠️ Dropped {czrn_dropped_count:,} of {filtered_count:,} filtered records due to invalid CZRNs[/yellow]") if len(cbf_data) > 0: - console.print(f"[green]✓ Successfully transformed {len(cbf_data):,} hourly aggregated records[/green]") + console.print(f"[green]✓ Successfully transformed {len(cbf_data):,} records[/green]") return pl.DataFrame(cbf_data) - def _aggregate_to_hourly(self, data: pl.DataFrame) -> pl.DataFrame: - """Aggregate spend logs to hourly level by team_id, key_name, model, and tags.""" - - # Extract hour from startTime, skip tags and metadata for now - data_with_hour = data.with_columns([ - pl.col('startTime').str.to_datetime().dt.truncate('1h').alias('usage_hour'), - pl.lit([]).cast(pl.List(pl.String)).alias('parsed_tags'), # Empty tags list for now - pl.lit("").alias('key_name') # Empty key name for now - ]) - - # Skip tag explosion for now - just add a null tag column - all_data = data_with_hour.with_columns([ - pl.lit(None, dtype=pl.String).alias('tag') - ]) - - # Group by hour, team_id, key_name, model, provider, and tag - aggregated = all_data.group_by([ - 'usage_hour', - 'team_id', - 'key_name', - 'model', - 'model_group', - 'custom_llm_provider', - 'tag' - ]).agg([ - pl.col('spend').sum().alias('total_spend'), - pl.col('total_tokens').sum().alias('total_tokens'), - pl.col('prompt_tokens').sum().alias('total_prompt_tokens'), - pl.col('completion_tokens').sum().alias('total_completion_tokens'), - pl.col('request_id').count().alias('request_count'), - pl.col('api_key').first().alias('api_key_sample'), # Keep one for reference - pl.col('status').filter(pl.col('status') == 'success').count().alias('successful_requests'), - pl.col('status').filter(pl.col('status') != 'success').count().alias('failed_requests') - ]) - return aggregated - - def _create_cbf_record(self, row: dict[str, Any]) -> CBFRecord: - """Create a single CBF record from aggregated hourly spend data.""" + """Create a single CBF record from LiteLLM daily spend row.""" - # Helper function to extract scalar values from polars data - def extract_scalar(value): - if hasattr(value, 'item') and not isinstance(value, (str, int, float, bool)): - return value.item() if value is not None else None - return value + # Parse date (daily spend tables use date strings like '2025-04-19') + usage_date = self._parse_date(row.get('date')) - # Use the aggregated hour as usage time - usage_time = self._parse_datetime(extract_scalar(row.get('usage_hour'))) - - # Use team_id as the primary entity_id - entity_id = str(extract_scalar(row.get('team_id', ''))) - key_name = str(extract_scalar(row.get('key_name', ''))) - model = str(extract_scalar(row.get('model', ''))) - model_group = str(extract_scalar(row.get('model_group', ''))) - provider = str(extract_scalar(row.get('custom_llm_provider', ''))) - tag = extract_scalar(row.get('tag')) - - # Calculate aggregated metrics - total_spend = float(extract_scalar(row.get('total_spend', 0.0)) or 0.0) - total_tokens = int(extract_scalar(row.get('total_tokens', 0)) or 0) - total_prompt_tokens = int(extract_scalar(row.get('total_prompt_tokens', 0)) or 0) - total_completion_tokens = int(extract_scalar(row.get('total_completion_tokens', 0)) or 0) - request_count = int(extract_scalar(row.get('request_count', 0)) or 0) - successful_requests = int(extract_scalar(row.get('successful_requests', 0)) or 0) - failed_requests = int(extract_scalar(row.get('failed_requests', 0)) or 0) + # Calculate total tokens + prompt_tokens = int(row.get('prompt_tokens', 0)) + completion_tokens = int(row.get('completion_tokens', 0)) + total_tokens = prompt_tokens + completion_tokens # Create CloudZero Resource Name (CZRN) as resource_id - # Create a mock row for CZRN generation with team_id as entity_id - czrn_row = { - 'entity_id': entity_id, - 'entity_type': 'team', - 'model': model, - 'custom_llm_provider': provider, - 'api_key': str(extract_scalar(row.get('api_key_sample', ''))) - } - resource_id = self.czrn_generator.create_from_litellm_data(czrn_row) + resource_id = self.czrn_generator.create_from_litellm_data(row) - # Build dimensions for CloudZero tracking - dimensions = { - 'entity_type': 'team', - 'entity_id': entity_id, - 'key_name': key_name, - 'model': model, - 'model_group': model_group, - 'provider': provider, - 'request_count': str(request_count), - 'successful_requests': str(successful_requests), - 'failed_requests': str(failed_requests), - } + # Build dimensions for CloudZero + model = str(row.get('model', '')) + api_key_hash = str(row.get('api_key', ''))[:8] # First 8 chars for identification - # Add tag if present - if tag is not None and str(tag) not in ['', 'null', 'None']: - dimensions['tag'] = str(tag) + # Handle team information with fallbacks + team_id = row.get('team_id') + team_alias = row.get('team_alias') + + # Use team_alias if available, otherwise team_id, otherwise fallback to 'unknown' + entity_id = str(team_alias) if team_alias else (str(team_id) if team_id else 'unknown') + + dimensions = { + 'entity_type': CZEntityType.TEAM.value, + 'entity_id': entity_id, + 'team_id': str(team_id) if team_id else 'unknown', + 'team_alias': str(team_alias) if team_alias else 'unknown', + 'model': model, + 'model_group': str(row.get('model_group', '')), + 'provider': str(row.get('custom_llm_provider', '')), + 'api_key_prefix': api_key_hash, + 'api_key_alias': str(row.get('api_key_alias', '')), + 'api_requests': str(row.get('api_requests', 0)), + 'successful_requests': str(row.get('successful_requests', 0)), + 'failed_requests': str(row.get('failed_requests', 0)), + 'cache_creation_tokens': str(row.get('cache_creation_input_tokens', 0)), + 'cache_read_tokens': str(row.get('cache_read_input_tokens', 0)), + } # Extract CZRN components to populate corresponding CBF columns czrn_components = self.czrn_generator.extract_components(resource_id) - service_type, provider_czrn, region, owner_account_id, resource_type, cloud_local_id = czrn_components + service_type, provider, region, owner_account_id, resource_type, cloud_local_id = czrn_components # CloudZero CBF format with proper column names cbf_record = { # Required CBF fields - 'time/usage_start': usage_time.isoformat() if usage_time else None, # Required: ISO-formatted UTC datetime - 'cost/cost': total_spend, # Required: billed cost + 'time/usage_start': usage_date.isoformat() if usage_date else None, # Required: ISO-formatted UTC datetime + 'cost/cost': float(row.get('spend', 0.0)), # Required: billed cost 'resource/id': resource_id, # Required when resource tags are present # Usage metrics for token consumption @@ -206,41 +145,42 @@ class CBFTransformer: } # Add CZRN components that don't have direct CBF column mappings as resource tags - cbf_record['resource/tag:provider'] = provider_czrn # CZRN provider component + cbf_record['resource/tag:provider'] = provider # CZRN provider component cbf_record['resource/tag:model'] = cloud_local_id # CZRN cloud-local-id component (model) - + # Add resource tags for all dimensions (using resource/tag: format) for key, value in dimensions.items(): - # Ensure value is a scalar and not empty - if hasattr(value, 'item') and not isinstance(value, str): - value = value.item() if value is not None else None - if value is not None and str(value) not in ['', 'N/A', 'None', 'null']: # Only add non-empty tags + if value and value != 'N/A' and value != 'unknown': # Only add meaningful tags cbf_record[f'resource/tag:{key}'] = str(value) # Add token breakdown as resource tags for analysis - if total_prompt_tokens > 0: - cbf_record['resource/tag:prompt_tokens'] = str(total_prompt_tokens) - if total_completion_tokens > 0: - cbf_record['resource/tag:completion_tokens'] = str(total_completion_tokens) + if prompt_tokens > 0: + cbf_record['resource/tag:prompt_tokens'] = str(prompt_tokens) + if completion_tokens > 0: + cbf_record['resource/tag:completion_tokens'] = str(completion_tokens) if total_tokens > 0: cbf_record['resource/tag:total_tokens'] = str(total_tokens) return CBFRecord(cbf_record) - def _parse_datetime(self, datetime_obj) -> Optional[datetime]: - """Parse datetime object to ensure proper format.""" - if datetime_obj is None: + def _parse_date(self, date_str) -> Optional[datetime]: + """Parse date string from daily spend tables (e.g., '2025-04-19').""" + if date_str is None: return None - if isinstance(datetime_obj, datetime): - return datetime_obj + if isinstance(date_str, datetime): + return date_str - if isinstance(datetime_obj, str): + if isinstance(date_str, str): try: - # Try to parse ISO format - return pl.Series([datetime_obj]).str.to_datetime().item() + # Parse date string and set to midnight UTC for daily aggregation + return pl.Series([date_str]).str.to_datetime("%Y-%m-%d").item() except Exception: - return None + try: + # Fallback: try ISO format parsing + return pl.Series([date_str]).str.to_datetime().item() + except Exception: + return None return None diff --git a/litellm/integrations/custom_guardrail.py b/litellm/integrations/custom_guardrail.py index 501185b207e..6b77557cd3d 100644 --- a/litellm/integrations/custom_guardrail.py +++ b/litellm/integrations/custom_guardrail.py @@ -119,11 +119,8 @@ class CustomGuardrail(CustomLogger): """ if "guardrails" in data: return data["guardrails"] - metadata = data.get("metadata") or {} - requested_guardrails = metadata.get("guardrails") or [] - if requested_guardrails: - return requested_guardrails - return requested_guardrails + metadata = data.get("litellm_metadata") or data.get("metadata", {}) + return metadata.get("guardrails") or [] def _guardrail_is_in_requested_guardrails( self, @@ -355,11 +352,12 @@ class CustomGuardrail(CustomLogger): self, guardrail_json_response: Union[Exception, str, dict, List[dict]], request_data: dict, - guardrail_status: Literal["success", "failure"], + guardrail_status: Literal["success", "failure", "blocked"], start_time: Optional[float] = None, end_time: Optional[float] = None, duration: Optional[float] = None, masked_entity_count: Optional[Dict[str, int]] = None, + guardrail_provider: Optional[str] = None, ) -> None: """ Builds `StandardLoggingGuardrailInformation` and adds it to the request metadata so it can be used for logging to DataDog, Langfuse, etc. @@ -370,6 +368,7 @@ class CustomGuardrail(CustomLogger): slg = StandardLoggingGuardrailInformation( guardrail_name=self.guardrail_name, + guardrail_provider=guardrail_provider, guardrail_mode=( GuardrailMode(**self.event_hook.model_dump()) # type: ignore if isinstance(self.event_hook, Mode) @@ -490,7 +489,8 @@ class CustomGuardrail(CustomLogger): """ Update the guardrails litellm params in memory """ - pass + for key, value in vars(litellm_params).items(): + setattr(self, key, value) def log_guardrail_information(func): diff --git a/litellm/integrations/datadog/datadog.py b/litellm/integrations/datadog/datadog.py index 1fa651ec71c..0c62667f749 100644 --- a/litellm/integrations/datadog/datadog.py +++ b/litellm/integrations/datadog/datadog.py @@ -17,9 +17,9 @@ import asyncio import datetime import os import traceback -import uuid +from litellm._uuid import uuid from datetime import datetime as datetimeObj -from typing import Any, List, Optional, Union +from typing import Any, Dict, List, Optional, Union import httpx from httpx import Response @@ -71,6 +71,13 @@ class DataDogLogger( raise Exception("DD_API_KEY is not set, set 'DD_API_KEY=<>") if os.getenv("DD_SITE", None) is None: raise Exception("DD_SITE is not set in .env, set 'DD_SITE=<>") + + ######################################################### + # Handle datadog_params set as litellm.datadog_params + ######################################################### + dict_datadog_params = self._get_datadog_params() + kwargs.update(dict_datadog_params) + self.async_client = get_async_httpx_client( llm_provider=httpxSpecialProvider.LoggingCallback ) @@ -101,6 +108,21 @@ class DataDogLogger( ) raise e + def _get_datadog_params(self) -> Dict: + """ + Get the datadog_params from litellm.datadog_params + + These are params specific to initializing the DataDogLogger e.g. turn_off_message_logging + """ + dict_datadog_params: Dict = {} + if litellm.datadog_params is not None: + if isinstance(litellm.datadog_params, DatadogInitParams): + dict_datadog_params = litellm.datadog_params.model_dump() + elif isinstance(litellm.datadog_params, Dict): + # only allow params that are of DatadogInitParams + dict_datadog_params = DatadogInitParams(**litellm.datadog_params).model_dump() + return dict_datadog_params + async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): """ Async Log success events to Datadog @@ -458,6 +480,7 @@ class DataDogLogger( else: clean_metadata[key] = value + # Build the initial payload payload = { "id": id, diff --git a/litellm/integrations/datadog/datadog_llm_obs.py b/litellm/integrations/datadog/datadog_llm_obs.py index 4f9c6409770..fc3cf4b9ff2 100644 --- a/litellm/integrations/datadog/datadog_llm_obs.py +++ b/litellm/integrations/datadog/datadog_llm_obs.py @@ -9,7 +9,7 @@ API Reference: https://docs.datadoghq.com/llm_observability/setup/api/?tab=examp import asyncio import json import os -import uuid +from litellm._uuid import uuid from datetime import datetime from typing import Any, Dict, List, Literal, Optional, Union @@ -19,6 +19,7 @@ import litellm from litellm._logging import verbose_logger from litellm.integrations.custom_batch_logger import CustomBatchLogger from litellm.integrations.datadog.datadog import DataDogLogger +from litellm.litellm_core_utils.dd_tracing import tracer from litellm.litellm_core_utils.prompt_templates.common_utils import ( handle_any_messages_to_chat_completion_str_messages_conversion, ) @@ -63,7 +64,7 @@ class DataDogLLMObsLogger(DataDogLogger, CustomBatchLogger): asyncio.create_task(self.periodic_flush()) self.flush_lock = asyncio.Lock() self.log_queue: List[LLMObsPayload] = [] - + ######################################################### # Handle datadog_llm_observability_params set as litellm.datadog_llm_observability_params ######################################################### @@ -82,22 +83,25 @@ class DataDogLLMObsLogger(DataDogLogger, CustomBatchLogger): """ dict_datadog_llm_obs_params: Dict = {} if litellm.datadog_llm_observability_params is not None: - if isinstance(litellm.datadog_llm_observability_params, DatadogLLMObsInitParams): - dict_datadog_llm_obs_params = litellm.datadog_llm_observability_params.model_dump() + if isinstance( + litellm.datadog_llm_observability_params, DatadogLLMObsInitParams + ): + dict_datadog_llm_obs_params = ( + litellm.datadog_llm_observability_params.model_dump() + ) elif isinstance(litellm.datadog_llm_observability_params, Dict): # only allow params that are of DatadogLLMObsInitParams - dict_datadog_llm_obs_params = DatadogLLMObsInitParams(**litellm.datadog_llm_observability_params).model_dump() + dict_datadog_llm_obs_params = DatadogLLMObsInitParams( + **litellm.datadog_llm_observability_params + ).model_dump() return dict_datadog_llm_obs_params - async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): try: verbose_logger.debug( f"DataDogLLMObs: Logging success event for model {kwargs.get('model', 'unknown')}" ) - payload = self.create_llm_obs_payload( - kwargs, start_time, end_time - ) + payload = self.create_llm_obs_payload(kwargs, start_time, end_time) verbose_logger.debug(f"DataDogLLMObs: Payload: {payload}") self.log_queue.append(payload) @@ -107,15 +111,13 @@ class DataDogLLMObsLogger(DataDogLogger, CustomBatchLogger): verbose_logger.exception( f"DataDogLLMObs: Error logging success event - {str(e)}" ) - + async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time): try: verbose_logger.debug( f"DataDogLLMObs: Logging failure event for model {kwargs.get('model', 'unknown')}" ) - payload = self.create_llm_obs_payload( - kwargs, start_time, end_time - ) + payload = self.create_llm_obs_payload(kwargs, start_time, end_time) verbose_logger.debug(f"DataDogLLMObs: Payload: {payload}") self.log_queue.append(payload) @@ -146,10 +148,22 @@ class DataDogLLMObsLogger(DataDogLogger, CustomBatchLogger): ), ), } - verbose_logger.debug("payload %s", json.dumps(payload, indent=4)) + + # serialize datetime objects - for budget reset time in spend metrics + from litellm.litellm_core_utils.safe_json_dumps import safe_dumps + + try: + verbose_logger.debug("payload %s", safe_dumps(payload)) + except Exception as debug_error: + verbose_logger.debug( + "payload serialization failed: %s", str(debug_error) + ) + + json_payload = safe_dumps(payload) + response = await self.async_client.post( url=self.intake_url, - json=payload, + content=json_payload, headers={ "DD-API-KEY": self.DD_API_KEY, "Content-Type": "application/json", @@ -183,7 +197,6 @@ class DataDogLLMObsLogger(DataDogLogger, CustomBatchLogger): messages = standard_logging_payload["messages"] messages = self._ensure_string_content(messages=messages) - response_obj = standard_logging_payload.get("response") metadata = kwargs.get("litellm_params", {}).get("metadata", {}) @@ -192,10 +205,12 @@ class DataDogLLMObsLogger(DataDogLogger, CustomBatchLogger): messages ) ) - output_meta = OutputMeta(messages=self._get_response_messages( - response_obj=response_obj, - call_type=standard_logging_payload.get("call_type") - )) + output_meta = OutputMeta( + messages=self._get_response_messages( + standard_logging_payload=standard_logging_payload, + call_type=standard_logging_payload.get("call_type"), + ) + ) error_info = self._assemble_error_info(standard_logging_payload) @@ -213,10 +228,12 @@ class DataDogLLMObsLogger(DataDogLogger, CustomBatchLogger): output_tokens=float(standard_logging_payload.get("completion_tokens", 0)), total_tokens=float(standard_logging_payload.get("total_tokens", 0)), total_cost=float(standard_logging_payload.get("response_cost", 0)), - time_to_first_token=self._get_time_to_first_token_seconds(standard_logging_payload), + time_to_first_token=self._get_time_to_first_token_seconds( + standard_logging_payload + ), ) - return LLMObsPayload( + payload: LLMObsPayload = LLMObsPayload( parent_id=metadata.get("parent_id", "undefined"), trace_id=standard_logging_payload.get("trace_id", str(uuid.uuid4())), span_id=metadata.get("span_id", str(uuid.uuid4())), @@ -230,27 +247,55 @@ class DataDogLLMObsLogger(DataDogLogger, CustomBatchLogger): self._get_datadog_tags(standard_logging_object=standard_logging_payload) ], ) - - def _assemble_error_info(self, standard_logging_payload: StandardLoggingPayload) -> Optional[DDLLMObsError]: + + apm_trace_id = self._get_apm_trace_id() + if apm_trace_id is not None: + payload["apm_id"] = apm_trace_id + + return payload + + def _get_apm_trace_id(self) -> Optional[str]: + """Retrieve the current APM trace ID if available.""" + try: + current_span_fn = getattr(tracer, "current_span", None) + if callable(current_span_fn): + current_span = current_span_fn() + if current_span is not None: + trace_id = getattr(current_span, "trace_id", None) + if trace_id is not None: + return str(trace_id) + except Exception: + pass + return None + + def _assemble_error_info( + self, standard_logging_payload: StandardLoggingPayload + ) -> Optional[DDLLMObsError]: """ Assemble error information for failure cases according to DD LLM Obs API spec """ # Handle error information for failure cases according to DD LLM Obs API spec error_info: Optional[DDLLMObsError] = None - + if standard_logging_payload.get("status") == "failure": # Try to get structured error information first - error_information: Optional[StandardLoggingPayloadErrorInformation] = standard_logging_payload.get("error_information") - + error_information: Optional[ + StandardLoggingPayloadErrorInformation + ] = standard_logging_payload.get("error_information") + if error_information: error_info = DDLLMObsError( - message=error_information.get("error_message") or standard_logging_payload.get("error_str") or "Unknown error", + message=error_information.get("error_message") + or standard_logging_payload.get("error_str") + or "Unknown error", type=error_information.get("error_class"), - stack=error_information.get("traceback") + stack=error_information.get("traceback"), ) return error_info - def _get_time_to_first_token_seconds(self, standard_logging_payload: StandardLoggingPayload) -> float: + def _get_time_to_first_token_seconds( + self, standard_logging_payload: StandardLoggingPayload + ) -> float: """ Get the time to first token in seconds @@ -259,7 +304,9 @@ class DataDogLLMObsLogger(DataDogLogger, CustomBatchLogger): For non streaming calls, CompletionStartTime is time we get the response back """ start_time: Optional[float] = standard_logging_payload.get("startTime") - completion_start_time: Optional[float] = standard_logging_payload.get("completionStartTime") + completion_start_time: Optional[float] = standard_logging_payload.get( + "completionStartTime" + ) end_time: Optional[float] = standard_logging_payload.get("endTime") if completion_start_time is not None and start_time is not None: @@ -269,19 +316,43 @@ class DataDogLLMObsLogger(DataDogLogger, CustomBatchLogger): else: return 0.0 - def _get_response_messages( - self, response_obj: Any, call_type: Optional[str] + self, standard_logging_payload: StandardLoggingPayload, call_type: Optional[str] ) -> List[Any]: """ Get the messages from the response object for now this handles logging /chat/completions responses """ + + response_obj = standard_logging_payload.get("response") if response_obj is None: return [] - - if call_type in [CallTypes.completion.value, CallTypes.acompletion.value]: + + # edge case: handle response_obj is a string representation of a dict + if isinstance(response_obj, str): + try: + import ast + + response_obj = ast.literal_eval(response_obj) + except (ValueError, SyntaxError): + try: + # fallback to json parsing + response_obj = json.loads(str(response_obj)) + except json.JSONDecodeError: + return [] + + if call_type in [ + CallTypes.completion.value, + CallTypes.acompletion.value, + CallTypes.text_completion.value, + CallTypes.atext_completion.value, + CallTypes.generate_content.value, + CallTypes.agenerate_content.value, + CallTypes.generate_content_stream.value, + CallTypes.agenerate_content_stream.value, + CallTypes.anthropic_messages.value, + ]: try: # Safely extract message from response_obj, handle failure cases if isinstance(response_obj, dict) and "choices" in response_obj: @@ -294,102 +365,104 @@ class DataDogLLMObsLogger(DataDogLogger, CustomBatchLogger): return [] return [] - def _get_datadog_span_kind(self, call_type: Optional[str]) -> Literal["llm", "tool", "task", "embedding", "retrieval"]: + def _get_datadog_span_kind( + self, call_type: Optional[str] + ) -> Literal["llm", "tool", "task", "embedding", "retrieval"]: """ Map liteLLM call_type to appropriate DataDog LLM Observability span kind. - + Available DataDog span kinds: "llm", "tool", "task", "embedding", "retrieval" """ if call_type is None: return "llm" - + # Embedding operations if call_type in [CallTypes.embedding.value, CallTypes.aembedding.value]: return "embedding" - - # LLM completion operations + + # LLM completion operations if call_type in [ - CallTypes.completion.value, + CallTypes.completion.value, CallTypes.acompletion.value, - CallTypes.text_completion.value, + CallTypes.text_completion.value, CallTypes.atext_completion.value, - CallTypes.generate_content.value, + CallTypes.generate_content.value, CallTypes.agenerate_content.value, - CallTypes.generate_content_stream.value, + CallTypes.generate_content_stream.value, CallTypes.agenerate_content_stream.value, - CallTypes.anthropic_messages.value + CallTypes.anthropic_messages.value, ]: return "llm" - + # Tool operations if call_type in [CallTypes.call_mcp_tool.value]: return "tool" - + # Retrieval operations if call_type in [ - CallTypes.get_assistants.value, + CallTypes.get_assistants.value, CallTypes.aget_assistants.value, - CallTypes.get_thread.value, + CallTypes.get_thread.value, CallTypes.aget_thread.value, - CallTypes.get_messages.value, + CallTypes.get_messages.value, CallTypes.aget_messages.value, - CallTypes.afile_retrieve.value, + CallTypes.afile_retrieve.value, CallTypes.file_retrieve.value, - CallTypes.afile_list.value, + CallTypes.afile_list.value, CallTypes.file_list.value, - CallTypes.afile_content.value, + CallTypes.afile_content.value, CallTypes.file_content.value, - CallTypes.retrieve_batch.value, + CallTypes.retrieve_batch.value, CallTypes.aretrieve_batch.value, - CallTypes.retrieve_fine_tuning_job.value, + CallTypes.retrieve_fine_tuning_job.value, CallTypes.aretrieve_fine_tuning_job.value, - CallTypes.responses.value, + CallTypes.responses.value, CallTypes.aresponses.value, - CallTypes.alist_input_items.value + CallTypes.alist_input_items.value, ]: return "retrieval" - + # Task operations (batch, fine-tuning, file operations, etc.) if call_type in [ - CallTypes.create_batch.value, + CallTypes.create_batch.value, CallTypes.acreate_batch.value, - CallTypes.create_fine_tuning_job.value, + CallTypes.create_fine_tuning_job.value, CallTypes.acreate_fine_tuning_job.value, - CallTypes.cancel_fine_tuning_job.value, + CallTypes.cancel_fine_tuning_job.value, CallTypes.acancel_fine_tuning_job.value, - CallTypes.list_fine_tuning_jobs.value, + CallTypes.list_fine_tuning_jobs.value, CallTypes.alist_fine_tuning_jobs.value, - CallTypes.create_assistants.value, + CallTypes.create_assistants.value, CallTypes.acreate_assistants.value, - CallTypes.delete_assistant.value, + CallTypes.delete_assistant.value, CallTypes.adelete_assistant.value, - CallTypes.create_thread.value, + CallTypes.create_thread.value, CallTypes.acreate_thread.value, - CallTypes.add_message.value, + CallTypes.add_message.value, CallTypes.a_add_message.value, - CallTypes.run_thread.value, + CallTypes.run_thread.value, CallTypes.arun_thread.value, - CallTypes.run_thread_stream.value, + CallTypes.run_thread_stream.value, CallTypes.arun_thread_stream.value, - CallTypes.file_delete.value, + CallTypes.file_delete.value, CallTypes.afile_delete.value, - CallTypes.create_file.value, + CallTypes.create_file.value, CallTypes.acreate_file.value, - CallTypes.image_generation.value, + CallTypes.image_generation.value, CallTypes.aimage_generation.value, - CallTypes.image_edit.value, + CallTypes.image_edit.value, CallTypes.aimage_edit.value, - CallTypes.moderation.value, + CallTypes.moderation.value, CallTypes.amoderation.value, - CallTypes.transcription.value, + CallTypes.transcription.value, CallTypes.atranscription.value, - CallTypes.speech.value, + CallTypes.speech.value, CallTypes.aspeech.value, - CallTypes.rerank.value, - CallTypes.arerank.value + CallTypes.rerank.value, + CallTypes.arerank.value, ]: return "task" - + # Default fallback for unknown or passthrough operations return "llm" @@ -422,7 +495,10 @@ class DataDogLLMObsLogger(DataDogLogger, CustomBatchLogger): "cache_hit": standard_logging_payload.get("cache_hit", "unknown"), "cache_key": standard_logging_payload.get("cache_key", "unknown"), "saved_cache_cost": standard_logging_payload.get("saved_cache_cost", 0), - "guardrail_information": standard_logging_payload.get("guardrail_information", None), + "guardrail_information": standard_logging_payload.get( + "guardrail_information", None + ), + "is_streamed_request": self._get_stream_value_from_payload(standard_logging_payload), } ######################################################### @@ -431,22 +507,38 @@ class DataDogLLMObsLogger(DataDogLogger, CustomBatchLogger): latency_metrics = self._get_latency_metrics(standard_logging_payload) _metadata.update({"latency_metrics": dict(latency_metrics)}) + ######################################################### + # Add spend metrics to metadata + ######################################################### + spend_metrics = self._get_spend_metrics(standard_logging_payload) + _metadata.update({"spend_metrics": dict(spend_metrics)}) + + ## extract tool calls and add to metadata + tool_call_metadata = self._extract_tool_call_metadata(standard_logging_payload) + _metadata.update(tool_call_metadata) + _standard_logging_metadata: dict = ( dict(standard_logging_payload.get("metadata", {})) or {} ) _metadata.update(_standard_logging_metadata) return _metadata - def _get_latency_metrics(self, standard_logging_payload: StandardLoggingPayload) -> DDLLMObsLatencyMetrics: + def _get_latency_metrics( + self, standard_logging_payload: StandardLoggingPayload + ) -> DDLLMObsLatencyMetrics: """ Get the latency metrics from the standard logging payload """ latency_metrics: DDLLMObsLatencyMetrics = DDLLMObsLatencyMetrics() # Add latency metrics to metadata # Time to first token (convert from seconds to milliseconds for consistency) - time_to_first_token_seconds = self._get_time_to_first_token_seconds(standard_logging_payload) + time_to_first_token_seconds = self._get_time_to_first_token_seconds( + standard_logging_payload + ) if time_to_first_token_seconds > 0: - latency_metrics["time_to_first_token_ms"] = time_to_first_token_seconds * 1000 + latency_metrics["time_to_first_token_ms"] = ( + time_to_first_token_seconds * 1000 + ) # LiteLLM overhead time hidden_params = standard_logging_payload.get("hidden_params", {}) @@ -455,11 +547,233 @@ class DataDogLLMObsLogger(DataDogLogger, CustomBatchLogger): latency_metrics["litellm_overhead_time_ms"] = litellm_overhead_ms # Guardrail overhead latency - guardrail_info: Optional[StandardLoggingGuardrailInformation] = standard_logging_payload.get("guardrail_information") + guardrail_info: Optional[ + StandardLoggingGuardrailInformation + ] = standard_logging_payload.get("guardrail_information") if guardrail_info is not None: - _guardrail_duration_seconds: Optional[float] = guardrail_info.get("duration") + _guardrail_duration_seconds: Optional[float] = guardrail_info.get( + "duration" + ) if _guardrail_duration_seconds is not None: # Convert from seconds to milliseconds for consistency - latency_metrics["guardrail_overhead_time_ms"] = _guardrail_duration_seconds * 1000 - - return latency_metrics \ No newline at end of file + latency_metrics["guardrail_overhead_time_ms"] = ( + _guardrail_duration_seconds * 1000 + ) + + return latency_metrics + + def _get_stream_value_from_payload(self, standard_logging_payload: StandardLoggingPayload) -> bool: + """ + Extract the stream value from standard logging payload. + + The stream field in StandardLoggingPayload is only set to True for completed streaming responses. + For non-streaming requests, it's None. The original stream parameter is in model_parameters. + + Returns: + bool: True if this was a streaming request, False otherwise + """ + # Check top-level stream field first (only True for completed streaming) + stream_value = standard_logging_payload.get("stream") + if stream_value is True: + return True + + # Fallback to model_parameters.stream for original request parameters + model_params = standard_logging_payload.get("model_parameters", {}) + if isinstance(model_params, dict): + stream_value = model_params.get("stream") + if stream_value is True: + return True + + # Default to False for non-streaming requests + return False + + def _get_spend_metrics( + self, standard_logging_payload: StandardLoggingPayload + ) -> DDLLMObsSpendMetrics: + """ + Get the spend metrics from the standard logging payload + """ + spend_metrics: DDLLMObsSpendMetrics = DDLLMObsSpendMetrics() + + # send response cost + spend_metrics["response_cost"] = standard_logging_payload.get( + "response_cost", 0.0 + ) + + # Get budget information from metadata + metadata = standard_logging_payload.get("metadata", {}) + + # API key max budget + user_api_key_max_budget = metadata.get("user_api_key_max_budget") + if user_api_key_max_budget is not None: + spend_metrics["user_api_key_max_budget"] = float(user_api_key_max_budget) + + # API key spend + user_api_key_spend = metadata.get("user_api_key_spend") + if user_api_key_spend is not None: + try: + spend_metrics["user_api_key_spend"] = float(user_api_key_spend) + except (ValueError, TypeError): + verbose_logger.debug( + f"Invalid user_api_key_spend value: {user_api_key_spend}" + ) + + # API key budget reset datetime + user_api_key_budget_reset_at = metadata.get("user_api_key_budget_reset_at") + if user_api_key_budget_reset_at is not None: + try: + from datetime import datetime, timezone + + budget_reset_at = None + if isinstance(user_api_key_budget_reset_at, str): + # Handle ISO format strings that might have 'Z' suffix + iso_string = user_api_key_budget_reset_at.replace("Z", "+00:00") + budget_reset_at = datetime.fromisoformat(iso_string) + elif isinstance(user_api_key_budget_reset_at, datetime): + budget_reset_at = user_api_key_budget_reset_at + + if budget_reset_at is not None: + # Preserve timezone info if already present + if budget_reset_at.tzinfo is None: + budget_reset_at = budget_reset_at.replace(tzinfo=timezone.utc) + + # Convert to ISO string format for JSON serialization + # This prevents circular reference issues and ensures proper timezone representation + iso_string = budget_reset_at.isoformat() + spend_metrics["user_api_key_budget_reset_at"] = iso_string + + # Debug logging to verify the conversion + verbose_logger.debug( + f"Converted budget_reset_at to ISO format: {iso_string}" + ) + except Exception as e: + verbose_logger.debug(f"Error processing budget reset datetime: {e}") + verbose_logger.debug(f"Original value: {user_api_key_budget_reset_at}") + + return spend_metrics + + def _process_input_messages_preserving_tool_calls( + self, messages: List[Any] + ) -> List[Dict[str, Any]]: + """ + Process input messages while preserving tool_calls and tool message types. + + This bypasses the lossy string conversion when tool calls are present, + allowing complex nested tool_calls objects to be preserved for Datadog. + """ + processed = [] + for msg in messages: + if isinstance(msg, dict): + # Preserve messages with tool_calls or tool role as-is + if "tool_calls" in msg or msg.get("role") == "tool": + processed.append(msg) + else: + # For regular messages, still apply string conversion + converted = ( + handle_any_messages_to_chat_completion_str_messages_conversion( + [msg] + ) + ) + processed.extend(converted) + else: + # For non-dict messages, apply string conversion + converted = ( + handle_any_messages_to_chat_completion_str_messages_conversion( + [msg] + ) + ) + processed.extend(converted) + return processed + + @staticmethod + def _tool_calls_kv_pair(tool_calls: List[Dict[str, Any]]) -> Dict[str, Any]: + """ + Extract tool call information into key-value pairs for Datadog metadata. + + Similar to OpenTelemetry's implementation but adapted for Datadog's format. + """ + kv_pairs: Dict[str, Any] = {} + for idx, tool_call in enumerate(tool_calls): + try: + # Extract tool call ID + tool_id = tool_call.get("id") + if tool_id: + kv_pairs[f"tool_calls.{idx}.id"] = tool_id + + # Extract tool call type + tool_type = tool_call.get("type") + if tool_type: + kv_pairs[f"tool_calls.{idx}.type"] = tool_type + + # Extract function information + function = tool_call.get("function") + if function: + function_name = function.get("name") + if function_name: + kv_pairs[f"tool_calls.{idx}.function.name"] = function_name + + function_arguments = function.get("arguments") + if function_arguments: + # Store arguments as JSON string for Datadog + if isinstance(function_arguments, str): + kv_pairs[ + f"tool_calls.{idx}.function.arguments" + ] = function_arguments + else: + import json + + kv_pairs[ + f"tool_calls.{idx}.function.arguments" + ] = json.dumps(function_arguments) + except (KeyError, TypeError, ValueError) as e: + verbose_logger.debug( + f"DataDogLLMObs: Error processing tool call {idx}: {str(e)}" + ) + continue + + return kv_pairs + + def _extract_tool_call_metadata( + self, standard_logging_payload: StandardLoggingPayload + ) -> Dict[str, Any]: + """ + Extract tool call information from both input messages and response for Datadog metadata. + """ + tool_call_metadata: Dict[str, Any] = {} + + try: + # Extract tool calls from input messages + messages = standard_logging_payload.get("messages", []) + if messages and isinstance(messages, list): + for message in messages: + if isinstance(message, dict) and "tool_calls" in message: + tool_calls = message.get("tool_calls") + if tool_calls: + input_tool_calls_kv = self._tool_calls_kv_pair(tool_calls) + # Prefix with "input_" to distinguish from response tool calls + for key, value in input_tool_calls_kv.items(): + tool_call_metadata[f"input_{key}"] = value + + # Extract tool calls from response + response_obj = standard_logging_payload.get("response") + if response_obj and isinstance(response_obj, dict): + choices = response_obj.get("choices", []) + for choice in choices: + if isinstance(choice, dict): + message = choice.get("message") + if message and isinstance(message, dict): + tool_calls = message.get("tool_calls") + if tool_calls: + response_tool_calls_kv = self._tool_calls_kv_pair( + tool_calls + ) + # Prefix with "output_" to distinguish from input tool calls + for key, value in response_tool_calls_kv.items(): + tool_call_metadata[f"output_{key}"] = value + + except Exception as e: + verbose_logger.debug( + f"DataDogLLMObs: Error extracting tool call metadata: {str(e)}" + ) + + return tool_call_metadata diff --git a/litellm/integrations/deepeval/deepeval.py b/litellm/integrations/deepeval/deepeval.py index f548ff50d73..972843e120a 100644 --- a/litellm/integrations/deepeval/deepeval.py +++ b/litellm/integrations/deepeval/deepeval.py @@ -1,5 +1,5 @@ import os -import uuid +from litellm._uuid import uuid from litellm.integrations.custom_logger import CustomLogger from litellm.integrations.deepeval.api import Api, Endpoints, HttpMethods from litellm.integrations.deepeval.types import ( diff --git a/litellm/integrations/dynamodb.py b/litellm/integrations/dynamodb.py index 2c527ea8aa9..dfc05ae1f32 100644 --- a/litellm/integrations/dynamodb.py +++ b/litellm/integrations/dynamodb.py @@ -3,7 +3,7 @@ import os import traceback -import uuid +from litellm._uuid import uuid from typing import Any import litellm diff --git a/litellm/integrations/gcs_bucket/gcs_bucket.py b/litellm/integrations/gcs_bucket/gcs_bucket.py index 972a0236666..9190f921d50 100644 --- a/litellm/integrations/gcs_bucket/gcs_bucket.py +++ b/litellm/integrations/gcs_bucket/gcs_bucket.py @@ -1,7 +1,7 @@ import asyncio import json import os -import uuid +from litellm._uuid import uuid from datetime import datetime, timedelta, timezone from typing import TYPE_CHECKING, Any, Dict, List, Optional from urllib.parse import quote diff --git a/litellm/integrations/humanloop.py b/litellm/integrations/humanloop.py index 9f43d806266..8e60d3736e0 100644 --- a/litellm/integrations/humanloop.py +++ b/litellm/integrations/humanloop.py @@ -4,9 +4,10 @@ Humanloop integration https://humanloop.com/ """ -from typing import Any, Dict, List, Optional, Tuple, TypedDict, Union, cast +from typing import Any, Dict, List, Optional, Tuple, Union, cast import httpx +from typing_extensions import TypedDict import litellm from litellm.caching import DualCache diff --git a/litellm/integrations/lago.py b/litellm/integrations/lago.py index 5dfb1ce097d..b881193e869 100644 --- a/litellm/integrations/lago.py +++ b/litellm/integrations/lago.py @@ -3,7 +3,7 @@ import json import os -import uuid +from litellm._uuid import uuid from typing import Literal, Optional import httpx diff --git a/litellm/integrations/langfuse/langfuse.py b/litellm/integrations/langfuse/langfuse.py index 9c3f07fa1a5..69943a0fe4d 100644 --- a/litellm/integrations/langfuse/langfuse.py +++ b/litellm/integrations/langfuse/langfuse.py @@ -15,7 +15,7 @@ from litellm.litellm_core_utils.redact_messages import redact_user_api_key_info from litellm.llms.custom_httpx.http_handler import _get_httpx_client from litellm.secret_managers.main import str_to_bool from litellm.types.integrations.langfuse import * -from litellm.types.llms.openai import HttpxBinaryResponseContent +from litellm.types.llms.openai import HttpxBinaryResponseContent, ResponsesAPIResponse from litellm.types.utils import ( EmbeddingResponse, ImageResponse, @@ -196,6 +196,7 @@ class LangFuseLogger: TranscriptionResponse, RerankResponse, HttpxBinaryResponseContent, + ResponsesAPIResponse, ], start_time: Optional[datetime] = None, end_time: Optional[datetime] = None, @@ -305,6 +306,7 @@ class LangFuseLogger: TranscriptionResponse, RerankResponse, HttpxBinaryResponseContent, + ResponsesAPIResponse, ], prompt: dict, level: str, @@ -369,6 +371,11 @@ class LangFuseLogger: ): input = prompt output = response_obj.results + elif response_obj is not None and isinstance( + response_obj, litellm.ResponsesAPIResponse + ): + input = prompt + output = self._get_responses_api_content_for_langfuse(response_obj) elif ( kwargs.get("call_type") is not None and kwargs.get("call_type") == "_arealtime" @@ -664,6 +671,7 @@ class LangFuseLogger: generation_id = None usage = None + usage_details = None if response_obj is not None: if ( hasattr(response_obj, "id") @@ -680,6 +688,11 @@ class LangFuseLogger: "completion_tokens": _usage_obj.completion_tokens, "total_cost": cost if self._supports_costs() else None, } + usage_details = LangfuseUsageDetails(input=_usage_obj.prompt_tokens, + output=_usage_obj.completion_tokens, + cache_creation_input_tokens=_usage_obj.get('cache_creation_input_tokens', 0), + cache_read_input_tokens=_usage_obj.get('cache_read_input_tokens', 0)) + generation_name = clean_metadata.pop("generation_name", None) if generation_name is None: # if `generation_name` is None, use sensible default values @@ -712,6 +725,7 @@ class LangFuseLogger: "input": input if not mask_input else "redacted-by-litellm", "output": output if not mask_output else "redacted-by-litellm", "usage": usage, + "usage_details": usage_details, "metadata": log_requester_metadata(clean_metadata), "level": level, "version": clean_metadata.pop("version", None), @@ -768,6 +782,19 @@ class LangFuseLogger: else: return None + @staticmethod + def _get_responses_api_content_for_langfuse( + response_obj: ResponsesAPIResponse, + ): + """ + Get the responses API content for Langfuse logging + """ + if hasattr(response_obj, 'output') and response_obj.output: + # ResponsesAPIResponse.output is a list of strings + return response_obj.output + else: + return None + @staticmethod def _get_langfuse_tags( standard_logging_object: Optional[StandardLoggingPayload], diff --git a/litellm/integrations/langfuse/langfuse_otel.py b/litellm/integrations/langfuse/langfuse_otel.py index 8b90e123371..fbe480be95f 100644 --- a/litellm/integrations/langfuse/langfuse_otel.py +++ b/litellm/integrations/langfuse/langfuse_otel.py @@ -141,6 +141,17 @@ class LangfuseOtelLogger(OpenTelemetry): value = str(value) safe_set_attribute(span, enum_attr.value, value) + @staticmethod + def _get_langfuse_otel_host() -> Optional[str]: + """ + Returns the Langfuse OTEL host based on environment variables. + + Returned in the following order of precedence: + 1. LANGFUSE_OTEL_HOST + 2. LANGFUSE_HOST + """ + return os.environ.get("LANGFUSE_OTEL_HOST") or os.environ.get("LANGFUSE_HOST") + @staticmethod def get_langfuse_otel_config() -> LangfuseOtelConfig: """ @@ -166,7 +177,7 @@ class LangfuseOtelLogger(OpenTelemetry): ) # Determine endpoint - default to US cloud - langfuse_host = os.environ.get("LANGFUSE_HOST", None) + langfuse_host = LangfuseOtelLogger._get_langfuse_otel_host() if langfuse_host: # If LANGFUSE_HOST is provided, construct OTEL endpoint from it diff --git a/litellm/integrations/langsmith.py b/litellm/integrations/langsmith.py index 7035aa3a819..cc9b361b69d 100644 --- a/litellm/integrations/langsmith.py +++ b/litellm/integrations/langsmith.py @@ -5,7 +5,7 @@ import os import random import traceback import types -import uuid +from litellm._uuid import uuid from datetime import datetime, timezone from typing import Any, Dict, List, Optional @@ -39,6 +39,7 @@ class LangsmithLogger(CustomBatchLogger): langsmith_api_key: Optional[str] = None, langsmith_project: Optional[str] = None, langsmith_base_url: Optional[str] = None, + langsmith_sampling_rate: Optional[float] = None, **kwargs, ): self.flush_lock = asyncio.Lock() @@ -49,7 +50,8 @@ class LangsmithLogger(CustomBatchLogger): langsmith_base_url=langsmith_base_url, ) self.sampling_rate: float = ( - float(os.getenv("LANGSMITH_SAMPLING_RATE")) # type: ignore + langsmith_sampling_rate + or float(os.getenv("LANGSMITH_SAMPLING_RATE")) # type: ignore if os.getenv("LANGSMITH_SAMPLING_RATE") is not None and os.getenv("LANGSMITH_SAMPLING_RATE").strip().isdigit() # type: ignore else 1.0 @@ -76,26 +78,14 @@ class LangsmithLogger(CustomBatchLogger): langsmith_base_url: Optional[str] = None, ) -> LangsmithCredentialsObject: _credentials_api_key = langsmith_api_key or os.getenv("LANGSMITH_API_KEY") - if _credentials_api_key is None: - raise Exception( - "Invalid Langsmith API Key given. _credentials_api_key=None." - ) _credentials_project = ( langsmith_project or os.getenv("LANGSMITH_PROJECT") or "litellm-completion" ) - if _credentials_project is None: - raise Exception( - "Invalid Langsmith API Key given. _credentials_project=None." - ) _credentials_base_url = ( langsmith_base_url or os.getenv("LANGSMITH_BASE_URL") or "https://api.smith.langchain.com" ) - if _credentials_base_url is None: - raise Exception( - "Invalid Langsmith API Key given. _credentials_base_url=None." - ) return LangsmithCredentialsObject( LANGSMITH_API_KEY=_credentials_api_key, @@ -200,12 +190,7 @@ class LangsmithLogger(CustomBatchLogger): def log_success_event(self, kwargs, response_obj, start_time, end_time): try: - sampling_rate = ( - float(os.getenv("LANGSMITH_SAMPLING_RATE")) # type: ignore - if os.getenv("LANGSMITH_SAMPLING_RATE") is not None - and os.getenv("LANGSMITH_SAMPLING_RATE").strip().isdigit() # type: ignore - else 1.0 - ) + sampling_rate = self._get_sampling_rate_to_use_for_request(kwargs=kwargs) random_sample = random.random() if random_sample > sampling_rate: verbose_logger.info( @@ -219,6 +204,7 @@ class LangsmithLogger(CustomBatchLogger): kwargs, response_obj, ) + credentials = self._get_credentials_to_use_for_request(kwargs=kwargs) data = self._prepare_log_data( kwargs=kwargs, @@ -245,7 +231,7 @@ class LangsmithLogger(CustomBatchLogger): async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): try: - sampling_rate = self.sampling_rate + sampling_rate = self._get_sampling_rate_to_use_for_request(kwargs=kwargs) random_sample = random.random() if random_sample > sampling_rate: verbose_logger.info( @@ -286,7 +272,7 @@ class LangsmithLogger(CustomBatchLogger): ) async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time): - sampling_rate = self.sampling_rate + sampling_rate = self._get_sampling_rate_to_use_for_request(kwargs=kwargs) random_sample = random.random() if random_sample > sampling_rate: verbose_logger.info( @@ -417,6 +403,17 @@ class LangsmithLogger(CustomBatchLogger): for queue_object in self.log_queue: credentials = queue_object["credentials"] + # if credential missing, skip - log warning + if ( + credentials["LANGSMITH_API_KEY"] is None + or credentials["LANGSMITH_PROJECT"] is None + ): + verbose_logger.warning( + "Langsmith Logging - credentials missing - api_key: %s, project: %s", + credentials["LANGSMITH_API_KEY"], + credentials["LANGSMITH_PROJECT"], + ) + continue key = CredentialsKey( api_key=credentials["LANGSMITH_API_KEY"], project=credentials["LANGSMITH_PROJECT"], @@ -432,6 +429,19 @@ class LangsmithLogger(CustomBatchLogger): return log_queue_by_credentials + def _get_sampling_rate_to_use_for_request(self, kwargs: Dict[str, Any]) -> float: + standard_callback_dynamic_params: Optional[StandardCallbackDynamicParams] = ( + kwargs.get("standard_callback_dynamic_params", None) + ) + sampling_rate: float = self.sampling_rate + if standard_callback_dynamic_params is not None: + _sampling_rate = standard_callback_dynamic_params.get( + "langsmith_sampling_rate" + ) + if _sampling_rate is not None: + sampling_rate = float(_sampling_rate) + return sampling_rate + def _get_credentials_to_use_for_request( self, kwargs: Dict[str, Any] ) -> LangsmithCredentialsObject: @@ -442,9 +452,9 @@ class LangsmithLogger(CustomBatchLogger): Otherwise, use the default credentials. """ - standard_callback_dynamic_params: Optional[ - StandardCallbackDynamicParams - ] = kwargs.get("standard_callback_dynamic_params", None) + standard_callback_dynamic_params: Optional[StandardCallbackDynamicParams] = ( + kwargs.get("standard_callback_dynamic_params", None) + ) if standard_callback_dynamic_params is not None: credentials = self.get_credentials_from_env( langsmith_api_key=standard_callback_dynamic_params.get( diff --git a/litellm/integrations/literal_ai.py b/litellm/integrations/literal_ai.py index 5bf9afd7eb4..042779ba844 100644 --- a/litellm/integrations/literal_ai.py +++ b/litellm/integrations/literal_ai.py @@ -2,7 +2,7 @@ # This file contains the LiteralAILogger class which is used to log steps to the LiteralAI observability platform. import asyncio import os -import uuid +from litellm._uuid import uuid from typing import List, Optional import httpx diff --git a/litellm/integrations/logfire_logger.py b/litellm/integrations/logfire_logger.py index 516bd4a8e28..2345dc869c6 100644 --- a/litellm/integrations/logfire_logger.py +++ b/litellm/integrations/logfire_logger.py @@ -3,7 +3,7 @@ import os import traceback -import uuid +from litellm._uuid import uuid from enum import Enum from typing import Any, Dict, NamedTuple diff --git a/litellm/integrations/openmeter.py b/litellm/integrations/openmeter.py index 19010daf831..b8fb64ec287 100644 --- a/litellm/integrations/openmeter.py +++ b/litellm/integrations/openmeter.py @@ -66,8 +66,18 @@ class OpenMeterLogger(CustomLogger): } user_param = kwargs.get("user", None) # end-user passed in via 'user' param + + # If no user provided directly, try to get it from token user_id if user_param is None: - raise Exception("OpenMeter: user is required") + # Check if user_id is available from the API key metadata + litellm_params = kwargs.get("litellm_params", {}) + metadata = litellm_params.get("metadata", {}) + user_api_key_user_id = metadata.get("user_api_key_user_id", None) + + if user_api_key_user_id is not None: + user_param = user_api_key_user_id + else: + raise Exception("OpenMeter: user is required") # Ensure subject is always a string for OpenMeter API subject = str(user_param) diff --git a/litellm/integrations/opentelemetry.py b/litellm/integrations/opentelemetry.py index 22ab3092901..e6f265ded58 100644 --- a/litellm/integrations/opentelemetry.py +++ b/litellm/integrations/opentelemetry.py @@ -15,6 +15,8 @@ from litellm.types.utils import ( StandardLoggingPayload, ) +# OpenTelemetry imports moved to individual functions to avoid import errors when not installed + if TYPE_CHECKING: from opentelemetry.sdk.trace.export import SpanExporter as _SpanExporter from opentelemetry.trace import Context as _Context @@ -41,6 +43,8 @@ else: Context = Any LITELLM_TRACER_NAME = os.getenv("OTEL_TRACER_NAME", "litellm") +LITELLM_METER_NAME = os.getenv("LITELLM_METER_NAME", "litellm") +LITELLM_LOGGER_NAME = os.getenv("LITELLM_LOGGER_NAME", "litellm") # Remove the hardcoded LITELLM_RESOURCE dictionary - we'll create it properly later RAW_REQUEST_SPAN_NAME = "raw_gen_ai_request" LITELLM_REQUEST_SPAN_NAME = "litellm_request" @@ -83,6 +87,8 @@ class OpenTelemetryConfig: exporter: Union[str, SpanExporter] = "console" endpoint: Optional[str] = None headers: Optional[str] = None + enable_metrics: bool = False + enable_events: bool = False @classmethod def from_env(cls): @@ -104,6 +110,14 @@ class OpenTelemetryConfig: headers = os.getenv( "OTEL_EXPORTER_OTLP_HEADERS", os.getenv("OTEL_HEADERS") ) # example: OTEL_HEADERS=x-honeycomb-team=B85YgLm96***" + enable_metrics: bool = ( + os.getenv("LITELLM_OTEL_INTEGRATION_ENABLE_METRICS", "false").lower() + == "true" + ) + enable_events: bool = ( + os.getenv("LITELLM_OTEL_INTEGRATION_ENABLE_EVENTS", "false").lower() + == "true" + ) if exporter == "in_memory": return cls(exporter=InMemorySpanExporter()) @@ -111,6 +125,8 @@ class OpenTelemetryConfig: exporter=exporter, endpoint=endpoint, headers=headers, # example: OTEL_HEADERS=x-honeycomb-team=B85YgLm96***" + enable_metrics=enable_metrics, + enable_events=enable_events, ) @@ -119,27 +135,22 @@ class OpenTelemetry(CustomLogger): self, config: Optional[OpenTelemetryConfig] = None, callback_name: Optional[str] = None, + # injection points for testing + tracer_provider: Optional[Any] = None, + logger_provider: Optional[Any] = None, + meter_provider: Optional[Any] = None, **kwargs, ): - from opentelemetry import trace - from opentelemetry.sdk.trace import TracerProvider - from opentelemetry.trace import SpanKind if config is None: config = OpenTelemetryConfig.from_env() self.config = config + self.callback_name = callback_name self.OTEL_EXPORTER = self.config.exporter self.OTEL_ENDPOINT = self.config.endpoint self.OTEL_HEADERS = self.config.headers - provider = TracerProvider(resource=_get_litellm_resource()) - provider.add_span_processor(self._get_span_processor()) - self.callback_name = callback_name - - trace.set_tracer_provider(provider) - self.tracer = trace.get_tracer(LITELLM_TRACER_NAME) - - self.span_kind = SpanKind + self._init_tracing(tracer_provider) _debug_otel = str(os.getenv("DEBUG_OTEL", "False")).lower() @@ -156,6 +167,8 @@ class OpenTelemetry(CustomLogger): # init CustomLogger params super().__init__(**kwargs) + self._init_metrics(meter_provider) + self._init_logs(logger_provider) self._init_otel_logger_on_litellm_proxy() def _init_otel_logger_on_litellm_proxy(self): @@ -178,14 +191,109 @@ class OpenTelemetry(CustomLogger): litellm.service_callback.append("otel") setattr(proxy_server, "open_telemetry_logger", self) + def _init_tracing(self, tracer_provider): + from opentelemetry import trace + from opentelemetry.sdk.trace import TracerProvider + from opentelemetry.trace import SpanKind + + # use provided tracer or create a new one + if tracer_provider is None: + tracer_provider = TracerProvider(resource=_get_litellm_resource()) + # Only add OTLP span processor if we created the tracer provider ourselves + tracer_provider.add_span_processor(self._get_span_processor()) + + # register global provider and grab our tracer + trace.set_tracer_provider(tracer_provider) + self.tracer = trace.get_tracer(LITELLM_TRACER_NAME) + self.span_kind = SpanKind + + def _init_metrics(self, meter_provider): + if not self.config.enable_metrics: + self._operation_duration_histogram = None + self._token_usage_histogram = None + self._cost_histogram = None + return + + from opentelemetry import metrics + from opentelemetry.sdk.metrics import Histogram, MeterProvider + + # Only create OTLP infrastructure if no custom meter provider is provided + if meter_provider is None: + from opentelemetry.exporter.otlp.proto.grpc.metric_exporter import ( + OTLPMetricExporter, + ) + from opentelemetry.sdk.metrics.export import ( + AggregationTemporality, + PeriodicExportingMetricReader, + ) + + _metric_exporter = OTLPMetricExporter( + endpoint=self.config.endpoint, + headers=OpenTelemetry._get_headers_dictionary(self.config.headers), + preferred_temporality={Histogram: AggregationTemporality.DELTA}, + ) + _metric_reader = PeriodicExportingMetricReader( + _metric_exporter, export_interval_millis=10000 + ) + + meter_provider = MeterProvider( + metric_readers=[_metric_reader], resource=_get_litellm_resource() + ) + meter = meter_provider.get_meter(__name__) + else: + # Use the provided meter provider as-is, without creating additional OTLP infrastructure + meter = meter_provider.get_meter(__name__) + + metrics.set_meter_provider(meter_provider) + + self._operation_duration_histogram = meter.create_histogram( + name="gen_ai.client.operation.duration", # Replace with semconv constant in otel 1.38 + description="GenAI operation duration", + unit="s", + ) + self._token_usage_histogram = meter.create_histogram( + name="gen_ai.client.token.usage", # Replace with semconv constant in otel 1.38 + description="GenAI token usage", + unit="{token}", + ) + self._cost_histogram = meter.create_histogram( + name="gen_ai.client.token.cost", + description="GenAI request cost", + unit="USD", + ) + + def _init_logs(self, logger_provider): + # nothing to do if events disabled + if not self.config.enable_events: + return + + from opentelemetry._logs import set_logger_provider + from opentelemetry.exporter.otlp.proto.grpc._log_exporter import OTLPLogExporter + from opentelemetry.sdk._logs import LoggerProvider as OTLoggerProvider + from opentelemetry.sdk._logs.export import BatchLogRecordProcessor + + # set up log pipeline + if logger_provider is None: + logger_provider = OTLoggerProvider() + # Only add OTLP exporter if we created the logger provider ourselves + logger_provider.add_log_record_processor( + BatchLogRecordProcessor( + OTLPLogExporter( + endpoint=self.config.endpoint, + headers=self._get_headers_dictionary(self.config.headers), + ) + ) + ) + set_logger_provider(logger_provider) + def log_success_event(self, kwargs, response_obj, start_time, end_time): - self._handle_sucess(kwargs, response_obj, start_time, end_time) + self._handle_success(kwargs, response_obj, start_time, end_time) def log_failure_event(self, kwargs, response_obj, start_time, end_time): self._handle_failure(kwargs, response_obj, start_time, end_time) async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): - self._handle_sucess(kwargs, response_obj, start_time, end_time) + self._handle_success(kwargs, response_obj, start_time, end_time) async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time): self._handle_failure(kwargs, response_obj, start_time, end_time) @@ -372,9 +480,9 @@ class OpenTelemetry(CustomLogger): def _get_dynamic_otel_headers_from_kwargs(self, kwargs) -> Optional[dict]: """Extract dynamic headers from kwargs if available.""" - standard_callback_dynamic_params: Optional[StandardCallbackDynamicParams] = ( - kwargs.get("standard_callback_dynamic_params") - ) + standard_callback_dynamic_params: Optional[ + StandardCallbackDynamicParams + ] = kwargs.get("standard_callback_dynamic_params") if not standard_callback_dynamic_params: return None @@ -414,50 +522,185 @@ class OpenTelemetry(CustomLogger): # End of Team/Key Based Logging Control Flow ######################################################### - def _handle_sucess(self, kwargs, response_obj, start_time, end_time): - from opentelemetry import trace - from opentelemetry.trace import Status, StatusCode + def _handle_success(self, kwargs, response_obj, start_time, end_time): verbose_logger.debug( "OpenTelemetry Logger: Logging kwargs: %s, OTEL config settings=%s", kwargs, self.config, ) + ctx, parent_span = self._get_span_context(kwargs) + + # 1. Primary span + span = self._start_primary_span(kwargs, response_obj, start_time, end_time, ctx) + + # 2. Raw‐request sub-span (if enabled) + self._maybe_log_raw_request(kwargs, response_obj, start_time, end_time, span) + + # 3. Guardrail span + self._create_guardrail_span(kwargs=kwargs, context=ctx) + + # 4. Metrics & cost recording + self._record_metrics(kwargs, response_obj, start_time, end_time) + + # 5. Semantic logs. + if self.config.enable_events: + self._emit_semantic_logs(kwargs, response_obj, span) + + # 6. End parent span + if parent_span is not None: + parent_span.end(end_time=self._to_ns(datetime.now())) + + def _start_primary_span(self, kwargs, response_obj, start_time, end_time, context): + from opentelemetry.trace import Status, StatusCode - _parent_context, parent_otel_span = self._get_span_context(kwargs) - # Span 1: Request sent to litellm SDK otel_tracer: Tracer = self.get_tracer_to_use_for_request(kwargs) span = otel_tracer.start_span( name=self._get_span_name(kwargs), start_time=self._to_ns(start_time), - context=_parent_context, + context=context, ) span.set_status(Status(StatusCode.OK)) self.set_attributes(span, kwargs, response_obj) + span.end(end_time=self._to_ns(end_time)) + return span - if litellm.turn_off_message_logging is True: - pass - elif self.message_logging is not True: - pass - else: - # Span 2: Raw Request / Response to LLM - raw_request_span = otel_tracer.start_span( - name=RAW_REQUEST_SPAN_NAME, - start_time=self._to_ns(start_time), - context=trace.set_span_in_context(span), + def _maybe_log_raw_request( + self, kwargs, response_obj, start_time, end_time, parent_span + ): + from opentelemetry import trace + from opentelemetry.trace import Status, StatusCode + + # only log raw LLM request/response if message_logging is on and not globally turned off + if litellm.turn_off_message_logging or not self.message_logging: + return + + otel_tracer: Tracer = self.get_tracer_to_use_for_request(kwargs) + raw_span = otel_tracer.start_span( + name=RAW_REQUEST_SPAN_NAME, + start_time=self._to_ns(start_time), + context=trace.set_span_in_context(parent_span), + ) + raw_span.set_status(Status(StatusCode.OK)) + self.set_raw_request_attributes(raw_span, kwargs, response_obj) + raw_span.end(end_time=self._to_ns(end_time)) + + def _record_metrics(self, kwargs, response_obj, start_time, end_time): + duration_s = (end_time - start_time).total_seconds() + params = kwargs.get("litellm_params") or {} + provider = params.get("custom_llm_provider", "Unknown") + + common_attrs = { + "gen_ai.operation.name": "chat", + "gen_ai.system": provider, + "gen_ai.request.model": kwargs.get("model"), + "gen_ai.framework": "litellm", + } + + std_log = kwargs.get("standard_logging_object") + md = getattr(std_log, "metadata", None) or (std_log or {}).get("metadata", {}) + for key in [ + "user_api_key_hash", + "user_api_key_alias", + "user_api_key_team_id", + "user_api_key_org_id", + "user_api_key_user_id", + "user_api_key_team_alias", + "user_api_key_user_email", + "spend_logs_metadata", + "requester_ip_address", + "requester_metadata", + "user_api_key_end_user_id", + "prompt_management_metadata", + "applied_guardrails", + "mcp_tool_call_metadata", + "vector_store_request_metadata", + ]: + if md.get(key) is not None: + common_attrs[f"metadata.{key}"] = str(md[key]) + + if self._operation_duration_histogram: + self._operation_duration_histogram.record( + duration_s, attributes=common_attrs + ) + if ( + response_obj + and (usage := response_obj.get("usage")) + and self._token_usage_histogram + ): + in_attrs = {**common_attrs, "gen_ai.token.type": "input"} + out_attrs = {**common_attrs, "gen_ai.token.type": "completion"} + self._token_usage_histogram.record( + usage.get("prompt_tokens", 0), attributes=in_attrs + ) + self._token_usage_histogram.record( + usage.get("completion_tokens", 0), attributes=out_attrs + ) + + cost = kwargs.get("response_cost") + if self._cost_histogram and cost: + self._cost_histogram.record(cost, attributes=common_attrs) + + def _emit_semantic_logs(self, kwargs, response_obj, span: Span): + if not self.config.enable_events: + return + + from opentelemetry._logs import get_logger, LogRecord + otel_logger = get_logger(LITELLM_LOGGER_NAME) + + parent_ctx = span.get_span_context() + provider = (kwargs.get("litellm_params") or {}).get( + "custom_llm_provider", "Unknown" + ) + + # per-message events + for msg in kwargs.get("messages", []): + role = msg.get("role", "user") + attrs = {"event_name": "gen_ai.content.prompt", "gen_ai.system": provider} + if role == "tool" and msg.get("id"): + attrs["id"] = msg["id"] + if self.message_logging and msg.get("content"): + attrs["gen_ai.prompt"] = msg["content"] + + otel_logger.emit( + LogRecord( + attributes=attrs, + body=msg.copy(), + trace_id=parent_ctx.trace_id, + span_id=parent_ctx.span_id, + trace_flags=parent_ctx.trace_flags, + ) ) - raw_request_span.set_status(Status(StatusCode.OK)) - self.set_raw_request_attributes(raw_request_span, kwargs, response_obj) - raw_request_span.end(end_time=self._to_ns(end_time)) + # per-choice events + for idx, choice in enumerate(response_obj.get("choices", [])): + attrs = { + "event_name": "gen_ai.content.completion", + "gen_ai.system": provider, + "index": idx, + "finish_reason": choice.get("finish_reason"), + } + body_msg = choice.get("message", {}) + if self.message_logging and body_msg.get("content"): + attrs["message.content"] = body_msg["content"] + body = { + "index": idx, + "finish_reason": choice.get("finish_reason"), + "message": {"role": body_msg.get("role", "assistant")}, + } + if self.message_logging and body_msg.get("content"): + body["message"]["content"] = body_msg["content"] - span.end(end_time=self._to_ns(end_time)) + otel_logger.emit( + LogRecord( + attributes=attrs, + body=body, + trace_id=parent_ctx.trace_id, + span_id=parent_ctx.span_id, + trace_flags=parent_ctx.trace_flags, + ) + ) - # Create span for guardrail information - self._create_guardrail_span(kwargs=kwargs, context=_parent_context) - - if parent_otel_span is not None: - parent_otel_span.end(end_time=self._to_ns(datetime.now())) def _create_guardrail_span( self, kwargs: Optional[dict], context: Optional[Context] diff --git a/litellm/integrations/opik/opik.py b/litellm/integrations/opik/opik.py index 8cbfb9e6535..9fa3482f663 100644 --- a/litellm/integrations/opik/opik.py +++ b/litellm/integrations/opik/opik.py @@ -3,6 +3,7 @@ Opik Logger that logs LLM events to an Opik server """ import asyncio +from datetime import timezone import json import traceback from typing import Dict, List @@ -191,9 +192,25 @@ class OpikLogger(CustomBatchLogger): # Extract opik metadata litellm_opik_metadata = litellm_params_metadata.get("opik", {}) + + # Use standard_logging_object to create metadata and input/output data + standard_logging_object = kwargs.get("standard_logging_object", None) + if standard_logging_object is None: + verbose_logger.debug( + "OpikLogger skipping event; no standard_logging_object found" + ) + return [] + + # Update litellm_opik_metadata with opik metadata from requester + standard_logging_metadata = standard_logging_object.get("metadata", {}) or {} + requester_metadata = standard_logging_metadata.get("requester_metadata", {}) or {} + requester_opik_metadata = requester_metadata.get("opik", {}) or {} + litellm_opik_metadata.update(requester_opik_metadata) + verbose_logger.debug( f"litellm_opik_metadata - {json.dumps(litellm_opik_metadata, default=str)}" ) + project_name = litellm_opik_metadata.get("project_name", self.opik_project_name) # Extract trace_id and parent_span_id @@ -207,19 +224,33 @@ class OpikLogger(CustomBatchLogger): else: trace_id = None parent_span_id = None + # Create Opik tags opik_tags = litellm_opik_metadata.get("tags", []) if kwargs.get("custom_llm_provider"): opik_tags.append(kwargs["custom_llm_provider"]) + + # Get thread_id if present + thread_id = litellm_opik_metadata.get("thread_id", None) - # Use standard_logging_object to create metadata and input/output data - standard_logging_object = kwargs.get("standard_logging_object", None) - if standard_logging_object is None: - verbose_logger.debug( - "OpikLogger skipping event; no standard_logging_object found" - ) - return [] - + # Override with any opik_ headers from proxy request + proxy_server_request = _litellm_params.get("proxy_server_request", {}) or {} + proxy_headers = proxy_server_request.get("headers", {}) or {} + for key, value in proxy_headers.items(): + if key.startswith("opik_"): + param_key = key.replace("opik_", "", 1) + if param_key == "project_name" and value: + project_name = value + elif param_key == "thread_id" and value: + thread_id = value + elif param_key == "tags" and value: + try: + parsed_tags = json.loads(value) + if isinstance(parsed_tags, list): + opik_tags.extend(parsed_tags) + except (json.JSONDecodeError, TypeError): + pass + # Create input and output data input_data = standard_logging_object.get("messages", {}) output_data = standard_logging_object.get("response", {}) @@ -242,7 +273,7 @@ class OpikLogger(CustomBatchLogger): del metadata["current_span_data"] metadata["created_from"] = "litellm" - metadata.update(standard_logging_object.get("metadata", {})) + metadata.update(standard_logging_metadata) if "call_type" in standard_logging_object: metadata["type"] = standard_logging_object["call_type"] if "status" in standard_logging_object: @@ -285,20 +316,20 @@ class OpikLogger(CustomBatchLogger): verbose_logger.debug( f"OpikLogger creating payload for trace with id {trace_id}" ) - - payload.append( - { - "project_name": project_name, - "id": trace_id, - "name": trace_name, - "start_time": start_time.isoformat() + "Z", - "end_time": end_time.isoformat() + "Z", - "input": input_data, - "output": output_data, - "metadata": metadata, - "tags": opik_tags, - } - ) + payload.append( + { + "project_name": project_name, + "id": trace_id, + "name": trace_name, + "start_time": start_time.astimezone(timezone.utc).isoformat().replace("+00:00", "Z"), + "end_time": end_time.astimezone(timezone.utc).isoformat().replace("+00:00", "Z"), + "input": input_data, + "output": output_data, + "metadata": metadata, + "tags": opik_tags, + "thread_id": thread_id, + } + ) span_id = create_uuid7() verbose_logger.debug( @@ -312,12 +343,13 @@ class OpikLogger(CustomBatchLogger): "parent_span_id": parent_span_id, "name": span_name, "type": "llm", - "start_time": start_time.isoformat() + "Z", - "end_time": end_time.isoformat() + "Z", + "start_time": start_time.astimezone(timezone.utc).isoformat().replace("+00:00", "Z"), + "end_time": end_time.astimezone(timezone.utc).isoformat().replace("+00:00", "Z"), "input": input_data, "output": output_data, "metadata": metadata, "tags": opik_tags, + "thread_id": thread_id, "usage": usage, } ) diff --git a/litellm/integrations/posthog.py b/litellm/integrations/posthog.py new file mode 100644 index 00000000000..5298e538a72 --- /dev/null +++ b/litellm/integrations/posthog.py @@ -0,0 +1,333 @@ +""" +PostHog Integration - sends LLM analytics events to PostHog + +Follows PostHog's LLM Analytics format: https://posthog.com/docs/llm-analytics/manual-capture + +async_log_success_event: stores batch of events in memory and flushes to PostHog +async_log_failure_event: logs failed LLM calls with error information + +For batching specific details see CustomBatchLogger class +""" + +import asyncio +import os +from litellm._uuid import uuid +from typing import Any, Dict, Optional + + +from litellm._logging import verbose_logger +from litellm.integrations.custom_batch_logger import CustomBatchLogger +from litellm.llms.custom_httpx.http_handler import ( + _get_httpx_client, + get_async_httpx_client, + httpxSpecialProvider, +) +from litellm.types.integrations.posthog import ( + POSTHOG_MAX_BATCH_SIZE, + PostHogEventPayload, +) +from litellm.types.utils import StandardLoggingPayload + + +class PostHogLogger(CustomBatchLogger): + def __init__(self, **kwargs): + """ + Initializes the PostHog logger, checks if the correct env variables are set + + Required environment variables: + `POSTHOG_API_KEY` - your PostHog API key + `POSTHOG_API_URL` - your PostHog API URL (defaults to https://app.posthog.com) + """ + try: + verbose_logger.debug("PostHog: in init posthog logger") + if os.getenv("POSTHOG_API_KEY", None) is None: + raise Exception("POSTHOG_API_KEY is not set, set 'POSTHOG_API_KEY=<>'") + + self.async_client = get_async_httpx_client( + llm_provider=httpxSpecialProvider.LoggingCallback + ) + self.sync_client = _get_httpx_client() + + self.POSTHOG_API_KEY = os.getenv("POSTHOG_API_KEY") + posthog_api_url = os.getenv("POSTHOG_API_URL", "https://us.i.posthog.com") + self.posthog_host = posthog_api_url.rstrip('/') + self.capture_url = f"{self.posthog_host}/batch/" + + self._async_initialized = False + self.flush_lock = None + self.log_queue = [] + + super().__init__( + **kwargs, flush_lock=None, batch_size=POSTHOG_MAX_BATCH_SIZE + ) + + except Exception as e: + verbose_logger.exception( + f"PostHog: Got exception on init PostHog client {str(e)}" + ) + raise e + + def log_success_event(self, kwargs, response_obj, start_time, end_time): + try: + verbose_logger.debug( + "PostHog: Sync logging - Enters logging function for model %s", kwargs + ) + + event_payload = self.create_posthog_event_payload(kwargs) + + headers = { + "Content-Type": "application/json", + } + + payload = self._create_posthog_payload([event_payload]) + + response = self.sync_client.post( + url=self.capture_url, + json=payload, + headers=headers, + ) + response.raise_for_status() + + if response.status_code != 200: + raise Exception( + f"Response from PostHog API status_code: {response.status_code}, text: {response.text}" + ) + + verbose_logger.debug("PostHog: Sync event successfully sent") + + except Exception as e: + verbose_logger.exception(f"PostHog Sync Layer Error - {str(e)}") + + async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): + try: + verbose_logger.debug( + "PostHog: Async logging - Enters logging function for model %s", kwargs + ) + self._ensure_async_setup() # Lazy initialization + await self._log_async_event(kwargs, response_obj, start_time, end_time) + except Exception as e: + verbose_logger.exception(f"PostHog Layer Error - {str(e)}") + pass + + async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time): + try: + verbose_logger.debug( + "PostHog: Async logging - Enters logging function for model %s", kwargs + ) + self._ensure_async_setup() # Lazy initialization + await self._log_async_event(kwargs, response_obj, start_time, end_time) + except Exception as e: + verbose_logger.exception(f"PostHog Layer Error - {str(e)}") + pass + + async def _log_async_event(self, kwargs, response_obj=None, start_time=0.0, end_time=0.0): + # Note: response_obj, start_time, end_time not used - all data comes from kwargs + event_payload = self.create_posthog_event_payload(kwargs) + + self.log_queue.append(event_payload) + verbose_logger.debug( + f"PostHog, event added to queue. Will flush in {self.flush_interval} seconds..." + ) + + if len(self.log_queue) >= self.batch_size: + await self.flush_queue() + + def create_posthog_event_payload(self, kwargs: Dict[str, Any]) -> PostHogEventPayload: + """ + Helper function to create a PostHog event payload for logging + + Args: + kwargs (Dict[str, Any]): request kwargs containing standard_logging_object + + Returns: + PostHogEventPayload: defined in types.py + """ + standard_logging_object: Optional[StandardLoggingPayload] = kwargs.get( + "standard_logging_object", None + ) + if standard_logging_object is None: + raise ValueError("standard_logging_object not found in kwargs") + + call_type = standard_logging_object.get("call_type", "") + event_name = "$ai_embedding" if call_type == "embedding" else "$ai_generation" + + properties = self._create_posthog_properties( + standard_logging_object=standard_logging_object, + kwargs=kwargs, + event_name=event_name, + ) + + distinct_id = self._get_distinct_id(standard_logging_object, kwargs) + + return PostHogEventPayload( + event=event_name, + properties=properties, + distinct_id=distinct_id, + ) + + def _create_posthog_properties( + self, + standard_logging_object: StandardLoggingPayload, + kwargs: Dict[str, Any], + event_name: str, + ) -> Dict[str, Any]: + """Create PostHog properties following LLM Analytics spec""" + properties = {} + + # Core model information + properties["$ai_model"] = self._safe_get(standard_logging_object, "model", "") + properties["$ai_provider"] = self._safe_get(standard_logging_object, "custom_llm_provider", "") + + # Input/Output data + messages = self._safe_get(standard_logging_object, "messages") + if messages is not None: + properties["$ai_input"] = messages + + if event_name == "$ai_generation": + response = self._safe_get(standard_logging_object, "response") + if response is not None: + properties["$ai_output_choices"] = response + + # Token information + properties["$ai_input_tokens"] = self._safe_get(standard_logging_object, "prompt_tokens", 0) + if event_name == "$ai_generation": + properties["$ai_output_tokens"] = self._safe_get(standard_logging_object, "completion_tokens", 0) + + # Cost and performance + response_cost = self._safe_get(standard_logging_object, "response_cost") + if response_cost is not None: + properties["$ai_total_cost_usd"] = response_cost + + properties["$ai_latency"] = self._safe_get(standard_logging_object, "response_time", 0.0) + + # Error handling + if self._safe_get(standard_logging_object, "status") == "failure": + properties["$ai_is_error"] = True + error_str = self._safe_get(standard_logging_object, "error_str") + if error_str is not None: + properties["$ai_error"] = error_str + + # Add trace properties + self._add_trace_properties(properties, kwargs) + + # Add custom metadata fields + self._add_custom_metadata_properties(properties, kwargs) + + return properties + + def _add_trace_properties(self, properties: Dict[str, Any], kwargs: Dict[str, Any]): + standard_logging_object = self._safe_get(kwargs, "standard_logging_object", {}) + + trace_id = self._safe_get(standard_logging_object, "trace_id", self._safe_uuid()) + properties["$ai_trace_id"] = trace_id + + span_id = self._safe_get(standard_logging_object, "id", self._safe_uuid()) + properties["$ai_span_id"] = span_id + + metadata = self._extract_metadata(kwargs) + parent_id = metadata.get("parent_run_id") or metadata.get("parent_id") + if parent_id: + properties["$ai_parent_id"] = parent_id + + def _add_custom_metadata_properties(self, properties: Dict[str, Any], kwargs: Dict[str, Any]): + """Add custom metadata fields to PostHog properties""" + metadata = self._extract_metadata(kwargs) + if not isinstance(metadata, dict): + return + + litellm_internal_fields = { + "endpoint", "caching_groups", "user_api_key_hash", "user_api_key_alias", + "user_api_key_team_id", "user_api_key_user_id", "user_api_key_org_id", + "user_api_key_team_alias", "user_api_key_end_user_id", "user_api_key_user_email", + "user_api_key", "user_api_end_user_max_budget", "litellm_api_version", + "global_max_parallel_requests", "user_api_key_team_max_budget", "user_api_key_team_spend", + "user_api_key_spend", "user_api_key_max_budget", "user_api_key_model_max_budget", + "user_api_key_metadata", "headers", "litellm_parent_otel_span", "requester_ip_address", + "model_group", "model_group_size", "deployment", "model_info", "api_base", + "caching_groups", "hidden_params", "parent_run_id", "parent_id", "user_id" + } + + for key, value in metadata.items(): + if key not in litellm_internal_fields: + properties[key] = value + + def _get_distinct_id( + self, standard_logging_object: StandardLoggingPayload, kwargs: Dict[str, Any] + ) -> str: + metadata = self._extract_metadata(kwargs) + user_id = self._safe_get(metadata, "user_id") + if user_id: + return str(user_id) + end_user = self._safe_get(standard_logging_object, "end_user") + if end_user: + return str(end_user) + trace_id = self._safe_get(standard_logging_object, "trace_id") + if trace_id: + return str(trace_id) + + return self._safe_uuid() + + async def async_send_batch(self): + """ + Sends the in memory logs queue to PostHog API + + Raises: + Raises a NON Blocking verbose_logger.exception if an error occurs + """ + try: + if not self.log_queue: + return + + verbose_logger.debug( + f"PostHog: Sending batch of {len(self.log_queue)} events" + ) + + headers = { + "Content-Type": "application/json", + } + + payload = self._create_posthog_payload(list(self.log_queue)) + + response = await self.async_client.post( + url=self.capture_url, + json=payload, + headers=headers, + ) + response.raise_for_status() + + if response.status_code != 200: + raise Exception( + f"Response from PostHog API status_code: {response.status_code}, text: {response.text}" + ) + + verbose_logger.debug( + f"PostHog: Batch of {len(self.log_queue)} events successfully sent" + ) + except Exception as e: + verbose_logger.exception(f"PostHog Error sending batch API - {str(e)}") + + def _ensure_async_setup(self): + if not self._async_initialized: + try: + self.flush_lock = asyncio.Lock() + asyncio.create_task(self.periodic_flush()) + self._async_initialized = True + verbose_logger.debug("PostHog: Async components initialized") + except Exception as e: + verbose_logger.error(f"PostHog: Failed to initialize async components: {str(e)}") + raise + + def _extract_metadata(self, kwargs: Dict[str, Any]) -> Dict[str, Any]: + litellm_params = kwargs.get("litellm_params", {}) or {} + return litellm_params.get("metadata", {}) or {} + + def _safe_uuid(self) -> str: + return str(uuid.uuid4()) + + def _create_posthog_payload(self, events: list) -> Dict[str, Any]: + return {"api_key": self.POSTHOG_API_KEY, "batch": events} + + def _safe_get(self, obj: Any, key: str, default: Any = None) -> Any: + if obj is None or not hasattr(obj, 'get'): + return default + return obj.get(key, default) diff --git a/litellm/integrations/prompt_management_base.py b/litellm/integrations/prompt_management_base.py index 34b4455f564..7754ca435ca 100644 --- a/litellm/integrations/prompt_management_base.py +++ b/litellm/integrations/prompt_management_base.py @@ -1,5 +1,7 @@ from abc import ABC, abstractmethod -from typing import Any, Dict, List, Optional, Tuple, TypedDict +from typing import Any, Dict, List, Optional, Tuple + +from typing_extensions import TypedDict from litellm.types.llms.openai import AllMessageValues from litellm.types.utils import StandardCallbackDynamicParams diff --git a/litellm/integrations/s3_v2.py b/litellm/integrations/s3_v2.py index efe18cb68ad..a65500c80dc 100644 --- a/litellm/integrations/s3_v2.py +++ b/litellm/integrations/s3_v2.py @@ -203,7 +203,7 @@ class S3Logger(CustomBatchLogger, BaseAWSLLM): start_time=start_time, end_time=end_time, ) - + async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time): await self._async_log_event_base( kwargs=kwargs, @@ -212,7 +212,6 @@ class S3Logger(CustomBatchLogger, BaseAWSLLM): end_time=end_time, ) pass - async def _async_log_event_base(self, kwargs, response_obj, start_time, end_time): try: @@ -242,7 +241,6 @@ class S3Logger(CustomBatchLogger, BaseAWSLLM): verbose_logger.exception(f"s3 Layer Error - {str(e)}") pass - async def async_upload_data_to_s3( self, batch_logging_element: s3BatchLoggingElement ): @@ -277,8 +275,14 @@ class S3Logger(CustomBatchLogger, BaseAWSLLM): # Prepare the URL url = f"https://{self.s3_bucket_name}.s3.{self.s3_region_name}.amazonaws.com/{batch_logging_element.s3_object_key}" - if self.s3_endpoint_url: - url = self.s3_endpoint_url + "/" + batch_logging_element.s3_object_key + if self.s3_endpoint_url and self.s3_bucket_name: + url = ( + self.s3_endpoint_url + + "/" + + self.s3_bucket_name + + "/" + + batch_logging_element.s3_object_key + ) # Convert JSON to string json_string = safe_dumps(batch_logging_element.payload) @@ -420,8 +424,14 @@ class S3Logger(CustomBatchLogger, BaseAWSLLM): # Prepare the URL url = f"https://{self.s3_bucket_name}.s3.{self.s3_region_name}.amazonaws.com/{batch_logging_element.s3_object_key}" - if self.s3_endpoint_url: - url = self.s3_endpoint_url + "/" + batch_logging_element.s3_object_key + if self.s3_endpoint_url and self.s3_bucket_name: + url = ( + self.s3_endpoint_url + + "/" + + self.s3_bucket_name + + "/" + + batch_logging_element.s3_object_key + ) # Convert JSON to string json_string = safe_dumps(batch_logging_element.payload) @@ -462,14 +472,13 @@ class S3Logger(CustomBatchLogger, BaseAWSLLM): except Exception as e: verbose_logger.exception(f"Error uploading to s3: {str(e)}") - async def _download_object_from_s3(self, s3_object_key: str) -> Optional[dict]: """ Download and parse JSON object from S3. - + Args: s3_object_key: The S3 object key to download - + Returns: Optional[dict]: The parsed JSON object or None if not found/error """ @@ -481,7 +490,7 @@ class S3Logger(CustomBatchLogger, BaseAWSLLM): from botocore.awsrequest import AWSRequest except ImportError: raise ImportError("Missing boto3 to call S3. Run 'pip install boto3'.") - + try: from litellm.litellm_core_utils.asyncify import asyncify @@ -506,8 +515,14 @@ class S3Logger(CustomBatchLogger, BaseAWSLLM): # Prepare the URL url = f"https://{self.s3_bucket_name}.s3.{self.s3_region_name}.amazonaws.com/{s3_object_key}" - if self.s3_endpoint_url: - url = self.s3_endpoint_url + "/" + s3_object_key + if self.s3_endpoint_url and self.s3_bucket_name: + url = ( + self.s3_endpoint_url + + "/" + + self.s3_bucket_name + + "/" + + s3_object_key + ) # Prepare the request for GET operation # For GET requests, we need x-amz-content-sha256 with hash of empty string @@ -533,12 +548,14 @@ class S3Logger(CustomBatchLogger, BaseAWSLLM): response = await self.async_httpx_client.get(url, headers=signed_headers) if response.status_code != 200: - verbose_logger.exception("S3 object not found, saw response=", response.text) + verbose_logger.exception( + "S3 object not found, saw response=", response.text + ) return None - + # Parse JSON response return response.json() - + except Exception as e: verbose_logger.exception(f"Error downloading from S3: {str(e)}") return None @@ -551,11 +568,11 @@ class S3Logger(CustomBatchLogger, BaseAWSLLM): Get the proxy server request from cold storage Allows fetching a dict of the proxy server request from s3 or GCS bucket. - + Args: request_id: The unique request ID to search for start_time: The start time of the request (datetime or ISO string) - + Returns: Optional[dict]: The request data dictionary or None if not found """ @@ -564,5 +581,7 @@ class S3Logger(CustomBatchLogger, BaseAWSLLM): downloaded_object = await self._download_object_from_s3(object_key) return downloaded_object except Exception as e: - verbose_logger.exception(f"Error retrieving object {object_key} from cold storage: {str(e)}") - return None \ No newline at end of file + verbose_logger.exception( + f"Error retrieving object {object_key} from cold storage: {str(e)}" + ) + return None diff --git a/litellm/integrations/vector_store_integrations/vector_store_pre_call_hook.py b/litellm/integrations/vector_store_integrations/vector_store_pre_call_hook.py index 59c378f8204..8ef160dd783 100644 --- a/litellm/integrations/vector_store_integrations/vector_store_pre_call_hook.py +++ b/litellm/integrations/vector_store_integrations/vector_store_pre_call_hook.py @@ -8,6 +8,7 @@ It searches the vector store for relevant context and appends it to the messages from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, cast import litellm +import litellm.vector_stores from litellm._logging import verbose_logger from litellm.integrations.custom_logger import CustomLogger from litellm.types.llms.openai import AllMessageValues, ChatCompletionUserMessage @@ -192,4 +193,4 @@ class VectorStorePreCallHook(CustomLogger): modified_messages.insert(-1, cast(AllMessageValues, context_message)) return modified_messages - return messages \ No newline at end of file + return messages diff --git a/litellm/integrations/weights_biases.py b/litellm/integrations/weights_biases.py index 63d87c9bd90..0d011e26aef 100644 --- a/litellm/integrations/weights_biases.py +++ b/litellm/integrations/weights_biases.py @@ -44,7 +44,7 @@ try: request, response, time_elapsed ) else: - logger.info(f"Unknown OpenAI response object: {response['object']}") + logger.debug(f"Unknown OpenAI response object: {response['object']}") except Exception as e: logger.warning(f"Failed to resolve request/response: {e}") return None diff --git a/litellm/litellm_core_utils/cached_imports.py b/litellm/litellm_core_utils/cached_imports.py new file mode 100644 index 00000000000..c3ab292e9c5 --- /dev/null +++ b/litellm/litellm_core_utils/cached_imports.py @@ -0,0 +1,56 @@ +""" +Cached imports module for LiteLLM. + +This module provides cached import functionality to avoid repeated imports +inside functions that are critical to performance. +""" + +from typing import TYPE_CHECKING, Callable, Optional, Type + +# Type annotations for cached imports +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging + from litellm.litellm_core_utils.coroutine_checker import CoroutineChecker + +# Global cache variables +_LiteLLMLogging: Optional[Type["Logging"]] = None +_coroutine_checker: Optional["CoroutineChecker"] = None +_set_callbacks: Optional[Callable] = None + + +def get_litellm_logging_class() -> Type["Logging"]: + """Get the cached LiteLLM Logging class, initializing if needed.""" + global _LiteLLMLogging + if _LiteLLMLogging is not None: + return _LiteLLMLogging + from litellm.litellm_core_utils.litellm_logging import Logging + _LiteLLMLogging = Logging + return _LiteLLMLogging + + +def get_coroutine_checker() -> "CoroutineChecker": + """Get the cached coroutine checker instance, initializing if needed.""" + global _coroutine_checker + if _coroutine_checker is not None: + return _coroutine_checker + from litellm.litellm_core_utils.coroutine_checker import coroutine_checker + _coroutine_checker = coroutine_checker + return _coroutine_checker + + +def get_set_callbacks() -> Callable: + """Get the cached set_callbacks function, initializing if needed.""" + global _set_callbacks + if _set_callbacks is not None: + return _set_callbacks + from litellm.litellm_core_utils.litellm_logging import set_callbacks + _set_callbacks = set_callbacks + return _set_callbacks + + +def clear_cached_imports() -> None: + """Clear all cached imports. Useful for testing or memory management.""" + global _LiteLLMLogging, _coroutine_checker, _set_callbacks + _LiteLLMLogging = None + _coroutine_checker = None + _set_callbacks = None diff --git a/litellm/litellm_core_utils/cli_token_utils.py b/litellm/litellm_core_utils/cli_token_utils.py new file mode 100644 index 00000000000..2aedb1c19d2 --- /dev/null +++ b/litellm/litellm_core_utils/cli_token_utils.py @@ -0,0 +1,58 @@ +""" +CLI Token Utilities + +SDK-level utilities for reading CLI authentication tokens. +This module has no dependencies on proxy code and can be safely imported at the SDK level. +""" + +import json +import os +from pathlib import Path +from typing import Optional + + +def get_cli_token_file_path() -> str: + """Get the path to the CLI token file""" + home_dir = Path.home() + config_dir = home_dir / ".litellm" + return str(config_dir / "token.json") + + +def load_cli_token() -> Optional[dict]: + """Load CLI token data from file""" + token_file = get_cli_token_file_path() + if not os.path.exists(token_file): + return None + + try: + with open(token_file, 'r') as f: + return json.load(f) + except (json.JSONDecodeError, IOError): + return None + + +def get_litellm_gateway_api_key() -> Optional[str]: + """ + Get the stored CLI API key for use with LiteLLM SDK. + + This function reads the token file created by `litellm-proxy login` + and returns the API key for use in Python scripts. + + Returns: + str: The API key if found, None otherwise + + Example: + >>> import litellm + >>> api_key = litellm.get_litellm_gateway_api_key() + >>> if api_key: + >>> response = litellm.completion( + >>> model="gpt-3.5-turbo", + >>> messages=[{"role": "user", "content": "Hello"}], + >>> api_key=api_key, + >>> base_url="https://your-proxy.com/v1" + >>> ) + """ + token_data = load_cli_token() + if token_data and 'key' in token_data: + return token_data['key'] + return None diff --git a/litellm/litellm_core_utils/core_helpers.py b/litellm/litellm_core_utils/core_helpers.py index 4aeb9d4d640..7423e55b626 100644 --- a/litellm/litellm_core_utils/core_helpers.py +++ b/litellm/litellm_core_utils/core_helpers.py @@ -228,9 +228,11 @@ def safe_deep_copy(data): """ Safe Deep Copy - The LiteLLM Request has some object that can-not be pickled / deep copied - - Use this function to safely deep copy the LiteLLM Request + The LiteLLM request may contain objects that cannot be pickled/deep-copied + (e.g., tracing spans, locks, clients). + + This helper deep-copies each top-level key independently; on failure keeps + original ref """ import copy @@ -255,9 +257,22 @@ def safe_deep_copy(data): "litellm_parent_otel_span" ) data["litellm_metadata"]["litellm_parent_otel_span"] = "placeholder" - new_data = copy.deepcopy(data) - # Step 2: re-add the litellm_parent_otel_span after doing a deep copy + # Step 2: Per-key deepcopy with fallback + if isinstance(data, dict): + new_data = {} + for k, v in data.items(): + try: + new_data[k] = copy.deepcopy(v) + except Exception: + new_data[k] = v + else: + try: + new_data = copy.deepcopy(data) + except Exception: + new_data = data + + # Step 3: re-add the litellm_parent_otel_span after doing a deep copy if isinstance(data, dict) and litellm_parent_otel_span is not None: if "metadata" in data and "litellm_parent_otel_span" in data["metadata"]: data["metadata"]["litellm_parent_otel_span"] = litellm_parent_otel_span @@ -268,4 +283,4 @@ def safe_deep_copy(data): data["litellm_metadata"][ "litellm_parent_otel_span" ] = litellm_parent_otel_span - return new_data + return new_data \ No newline at end of file diff --git a/litellm/litellm_core_utils/coroutine_checker.py b/litellm/litellm_core_utils/coroutine_checker.py new file mode 100644 index 00000000000..368aee62ed0 --- /dev/null +++ b/litellm/litellm_core_utils/coroutine_checker.py @@ -0,0 +1,63 @@ +# CoroutineChecker utility for checking if functions/callables are coroutines or coroutine functions + +import inspect +from typing import Any +from weakref import WeakKeyDictionary +from litellm.constants import ( + COROUTINE_CHECKER_MAX_SIZE_IN_MEMORY, +) + + +class CoroutineChecker: + """Utility class for checking coroutine status of functions and callables. + + Simple bounded cache using WeakKeyDictionary to avoid memory leaks. + """ + + def __init__(self): + self._cache = WeakKeyDictionary() + self._max_size = COROUTINE_CHECKER_MAX_SIZE_IN_MEMORY + + def is_async_callable(self, callback: Any) -> bool: + """Fast, cached check for whether a callback is an async function. + Falls back gracefully if the object cannot be weak-referenced or cached. + 2.59x speedup. + """ + # Fast path: check cache first (most common case) + try: + cached = self._cache.get(callback) + if cached is not None: + return cached + except Exception: + pass + + # Determine target - optimized path for common cases + target = callback + if not inspect.isfunction(target) and not inspect.ismethod(target): + try: + call_attr = getattr(target, "__call__", None) + if call_attr is not None: + target = call_attr + except Exception: + pass + + # Compute result + try: + result = inspect.iscoroutinefunction(target) + except Exception: + result = False + + # Cache the result with size enforcement + try: + # Simple size enforcement: clear cache if it gets too large + if len(self._cache) >= self._max_size: + self._cache.clear() + + self._cache[callback] = result + except Exception: + pass + + return result + +# Global instance for backward compatibility and convenience +coroutine_checker = CoroutineChecker() diff --git a/litellm/litellm_core_utils/custom_logger_registry.py b/litellm/litellm_core_utils/custom_logger_registry.py index fd82ecdf2b2..fbf685c3700 100644 --- a/litellm/litellm_core_utils/custom_logger_registry.py +++ b/litellm/litellm_core_utils/custom_logger_registry.py @@ -33,11 +33,14 @@ from litellm.integrations.mlflow import MlflowLogger from litellm.integrations.openmeter import OpenMeterLogger from litellm.integrations.opentelemetry import OpenTelemetry from litellm.integrations.opik.opik import OpikLogger +from litellm.integrations.posthog import PostHogLogger try: from litellm_enterprise.integrations.prometheus import PrometheusLogger except Exception: PrometheusLogger = None +from litellm.integrations.bitbucket import BitBucketPromptManager +from litellm.integrations.cloudzero.cloudzero import CloudZeroLogger from litellm.integrations.dotprompt import DotpromptManager from litellm.integrations.s3_v2 import S3Logger from litellm.integrations.sqs import SQSLogger @@ -45,6 +48,7 @@ from litellm.integrations.vector_store_integrations.vector_store_pre_call_hook i VectorStorePreCallHook, ) from litellm.proxy.hooks.dynamic_rate_limiter import _PROXY_DynamicRateLimitHandler +from litellm.proxy.hooks.dynamic_rate_limiter_v3 import _PROXY_DynamicRateLimitHandlerV3 class CustomLoggerRegistry: @@ -84,8 +88,12 @@ class CustomLoggerRegistry: "s3_v2": S3Logger, "aws_sqs": SQSLogger, "dynamic_rate_limiter": _PROXY_DynamicRateLimitHandler, + "dynamic_rate_limiter_v3": _PROXY_DynamicRateLimitHandlerV3, "vector_store_pre_call_hook": VectorStorePreCallHook, "dotprompt": DotpromptManager, + "bitbucket": BitBucketPromptManager, + "cloudzero": CloudZeroLogger, + "posthog": PostHogLogger, } try: @@ -151,7 +159,6 @@ class CustomLoggerRegistry: if callback_class == class_type: callback_strs.append(callback_str) return callback_strs - @classmethod def get_class_type_for_custom_logger_name( diff --git a/litellm/litellm_core_utils/duration_parser.py b/litellm/litellm_core_utils/duration_parser.py index 08f1d4c82d0..9a317cfcf0d 100644 --- a/litellm/litellm_core_utils/duration_parser.py +++ b/litellm/litellm_core_utils/duration_parser.py @@ -1,7 +1,7 @@ """ Helper utilities for parsing durations - 1s, 1d, 10d, 30d, 1mo, 2mo -duration_in_seconds is used in diff parts of the code base, example +duration_in_seconds is used in diff parts of the code base, example - Router - Provider budget routing - Proxy - Key, Team Generation """ @@ -158,6 +158,7 @@ def _setup_timezone( "US/Eastern": timezone(timedelta(hours=-4)), # EDT "US/Pacific": timezone(timedelta(hours=-7)), # PDT "Asia/Kolkata": timezone(timedelta(hours=5, minutes=30)), # IST + "Asia/Bangkok": timezone(timedelta(hours=7)), # ICT (Indochina Time) "Europe/London": timezone(timedelta(hours=1)), # BST "UTC": timezone.utc, } @@ -192,6 +193,10 @@ def _handle_day_reset( current_time: datetime, base_midnight: datetime, value: int, timezone: timezone ) -> datetime: """Handle day-based reset times.""" + # Handle zero value - immediate expiration + if value == 0: + return current_time + if value == 1: # Daily reset at midnight return base_midnight + timedelta(days=1) elif value == 7: # Weekly reset on Monday at midnight @@ -234,6 +239,10 @@ def _handle_hour_reset( current_time: datetime, base_midnight: datetime, value: int ) -> datetime: """Handle hour-based reset times.""" + # Handle zero value - immediate expiration + if value == 0: + return current_time + current_hour = current_time.hour current_minute = current_time.minute current_second = current_time.second @@ -266,6 +275,10 @@ def _handle_minute_reset( current_time: datetime, base_midnight: datetime, value: int ) -> datetime: """Handle minute-based reset times.""" + # Handle zero value - immediate expiration + if value == 0: + return current_time + current_hour = current_time.hour current_minute = current_time.minute current_second = current_time.second @@ -306,6 +319,10 @@ def _handle_second_reset( current_time: datetime, base_midnight: datetime, value: int ) -> datetime: """Handle second-based reset times.""" + # Handle zero value - immediate expiration + if value == 0: + return current_time + current_hour = current_time.hour current_minute = current_time.minute current_second = current_time.second diff --git a/litellm/litellm_core_utils/exception_mapping_utils.py b/litellm/litellm_core_utils/exception_mapping_utils.py index 25ae0269ab3..409f5ebe0bc 100644 --- a/litellm/litellm_core_utils/exception_mapping_utils.py +++ b/litellm/litellm_core_utils/exception_mapping_utils.py @@ -6,6 +6,7 @@ import httpx import litellm from litellm._logging import verbose_logger +from litellm.types.utils import LlmProviders from ..exceptions import ( APIConnectionError, @@ -556,7 +557,7 @@ def exception_type( # type: ignore # noqa: PLR0915 model=model, llm_provider="anthropic", ) - elif "overloaded_error" in error_str: + elif "overloaded_error" in error_str or "Overloaded" in error_str: exception_mapping_worked = True raise InternalServerError( message="AnthropicError - {}".format(error_str), @@ -762,7 +763,7 @@ def exception_type( # type: ignore # noqa: PLR0915 error_str += "XXXXXXX" + '"' raise AuthenticationError( - message=f"{custom_llm_provider}Exception: Authentication Error - {error_str}", + message=f"{custom_llm_provider.capitalize()}Exception: Authentication Error - {error_str}", llm_provider=custom_llm_provider, model=model, response=getattr(original_exception, "response", None), @@ -771,14 +772,14 @@ def exception_type( # type: ignore # noqa: PLR0915 elif "model's maximum context limit" in error_str: exception_mapping_worked = True raise ContextWindowExceededError( - message=f"{custom_llm_provider}Exception: Context Window Error - {error_str}", + message=f"{custom_llm_provider.capitalize()}Exception: Context Window Error - {error_str}", model=model, llm_provider=custom_llm_provider, ) elif "token_quota_reached" in error_str: exception_mapping_worked = True raise RateLimitError( - message=f"{custom_llm_provider}Exception: Rate Limit Errror - {error_str}", + message=f"{custom_llm_provider.capitalize()}Exception: Rate Limit Errror - {error_str}", llm_provider=custom_llm_provider, model=model, response=getattr(original_exception, "response", None), @@ -789,14 +790,14 @@ def exception_type( # type: ignore # noqa: PLR0915 ): exception_mapping_worked = True raise litellm.InternalServerError( - message=f"{custom_llm_provider}Exception - {original_exception.message}", + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", llm_provider=custom_llm_provider, model=model, ) elif "model_no_support_for_function" in error_str: exception_mapping_worked = True raise BadRequestError( - message=f"{custom_llm_provider}Exception - Use 'watsonx_text' route instead. IBM WatsonX does not support `/text/chat` endpoint. - {error_str}", + message=f"{custom_llm_provider.capitalize()}Exception - Use 'watsonx_text' route instead. IBM WatsonX does not support `/text/chat` endpoint. - {error_str}", llm_provider=custom_llm_provider, model=model, ) @@ -804,7 +805,7 @@ def exception_type( # type: ignore # noqa: PLR0915 if original_exception.status_code == 500: exception_mapping_worked = True raise litellm.InternalServerError( - message=f"{custom_llm_provider}Exception - {original_exception.message}", + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", llm_provider=custom_llm_provider, model=model, ) @@ -814,28 +815,28 @@ def exception_type( # type: ignore # noqa: PLR0915 ): exception_mapping_worked = True raise AuthenticationError( - message=f"{custom_llm_provider}Exception - {original_exception.message}", + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", llm_provider=custom_llm_provider, model=model, ) elif original_exception.status_code == 400: exception_mapping_worked = True raise BadRequestError( - message=f"{custom_llm_provider}Exception - {original_exception.message}", + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", llm_provider=custom_llm_provider, model=model, ) elif original_exception.status_code == 404: exception_mapping_worked = True raise NotFoundError( - message=f"{custom_llm_provider}Exception - {original_exception.message}", + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", llm_provider=custom_llm_provider, model=model, ) elif original_exception.status_code == 408: exception_mapping_worked = True raise Timeout( - message=f"{custom_llm_provider}Exception - {original_exception.message}", + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", model=model, llm_provider=custom_llm_provider, litellm_debug_info=extra_information, @@ -846,7 +847,7 @@ def exception_type( # type: ignore # noqa: PLR0915 ): exception_mapping_worked = True raise BadRequestError( - message=f"{custom_llm_provider}Exception - {original_exception.message}", + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", model=model, llm_provider=custom_llm_provider, litellm_debug_info=extra_information, @@ -854,7 +855,7 @@ def exception_type( # type: ignore # noqa: PLR0915 elif original_exception.status_code == 429: exception_mapping_worked = True raise RateLimitError( - message=f"{custom_llm_provider}Exception - {original_exception.message}", + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", model=model, llm_provider=custom_llm_provider, litellm_debug_info=extra_information, @@ -862,7 +863,7 @@ def exception_type( # type: ignore # noqa: PLR0915 elif original_exception.status_code == 503: exception_mapping_worked = True raise ServiceUnavailableError( - message=f"{custom_llm_provider}Exception - {original_exception.message}", + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", model=model, llm_provider=custom_llm_provider, litellm_debug_info=extra_information, @@ -870,7 +871,7 @@ def exception_type( # type: ignore # noqa: PLR0915 elif original_exception.status_code == 504: # gateway timeout error exception_mapping_worked = True raise Timeout( - message=f"{custom_llm_provider}Exception - {original_exception.message}", + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", model=model, llm_provider=custom_llm_provider, litellm_debug_info=extra_information, @@ -1168,9 +1169,9 @@ def exception_type( # type: ignore # noqa: PLR0915 exception_status_code=original_exception.status_code, ) elif ( - custom_llm_provider == "vertex_ai" - or custom_llm_provider == "vertex_ai_beta" - or custom_llm_provider == "gemini" + custom_llm_provider == LlmProviders.VERTEX_AI + or custom_llm_provider == LlmProviders.VERTEX_AI_BETA + or custom_llm_provider == LlmProviders.GEMINI ): if ( "Vertex AI API has not been used in project" in error_str @@ -1178,9 +1179,9 @@ def exception_type( # type: ignore # noqa: PLR0915 ): exception_mapping_worked = True raise BadRequestError( - message=f"litellm.BadRequestError: VertexAIException - {error_str}", + message=f"litellm.BadRequestError: {custom_llm_provider}Exception - {error_str}", model=model, - llm_provider="vertex_ai", + llm_provider=custom_llm_provider, response=httpx.Response( status_code=400, request=httpx.Request( @@ -1193,7 +1194,7 @@ def exception_type( # type: ignore # noqa: PLR0915 if "400 Request payload size exceeds" in error_str: exception_mapping_worked = True raise ContextWindowExceededError( - message=f"VertexException - {error_str}", + message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", model=model, llm_provider=custom_llm_provider, ) @@ -1203,9 +1204,9 @@ def exception_type( # type: ignore # noqa: PLR0915 ): exception_mapping_worked = True raise litellm.InternalServerError( - message=f"litellm.InternalServerError: VertexAIException - {error_str}", + message=f"litellm.InternalServerError: {custom_llm_provider}Exception - {error_str}", model=model, - llm_provider="vertex_ai", + llm_provider=custom_llm_provider, response=httpx.Response( status_code=500, content=str(original_exception), @@ -1216,7 +1217,7 @@ def exception_type( # type: ignore # noqa: PLR0915 elif "API key not valid." in error_str: exception_mapping_worked = True raise AuthenticationError( - message=f"{custom_llm_provider}Exception - {error_str}", + message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", model=model, llm_provider=custom_llm_provider, litellm_debug_info=extra_information, @@ -1224,9 +1225,9 @@ def exception_type( # type: ignore # noqa: PLR0915 elif "403" in error_str: exception_mapping_worked = True raise BadRequestError( - message=f"VertexAIException BadRequestError - {error_str}", + message=f"{custom_llm_provider.capitalize()}Exception BadRequestError - {error_str}", model=model, - llm_provider="vertex_ai", + llm_provider=custom_llm_provider, response=httpx.Response( status_code=403, request=httpx.Request( @@ -1243,9 +1244,9 @@ def exception_type( # type: ignore # noqa: PLR0915 ): exception_mapping_worked = True raise ContentPolicyViolationError( - message=f"VertexAIException ContentPolicyViolationError - {error_str}", + message=f"{custom_llm_provider.capitalize()}Exception ContentPolicyViolationError - {error_str}", model=model, - llm_provider="vertex_ai", + llm_provider=custom_llm_provider, litellm_debug_info=extra_information, response=httpx.Response( status_code=400, @@ -1264,9 +1265,9 @@ def exception_type( # type: ignore # noqa: PLR0915 ): exception_mapping_worked = True raise RateLimitError( - message=f"litellm.RateLimitError: VertexAIException - {error_str}", + message=f"litellm.RateLimitError: {custom_llm_provider}Exception - {error_str}", model=model, - llm_provider="vertex_ai", + llm_provider=custom_llm_provider, litellm_debug_info=extra_information, response=httpx.Response( status_code=429, @@ -1282,18 +1283,18 @@ def exception_type( # type: ignore # noqa: PLR0915 ): exception_mapping_worked = True raise litellm.InternalServerError( - message=f"litellm.InternalServerError: VertexAIException - {error_str}", + message=f"litellm.InternalServerError: {custom_llm_provider}Exception - {error_str}", model=model, - llm_provider="vertex_ai", + llm_provider=custom_llm_provider, litellm_debug_info=extra_information, ) if hasattr(original_exception, "status_code"): if original_exception.status_code == 400: exception_mapping_worked = True raise BadRequestError( - message=f"VertexAIException BadRequestError - {error_str}", + message=f"{custom_llm_provider.capitalize()}Exception BadRequestError - {error_str}", model=model, - llm_provider="vertex_ai", + llm_provider=custom_llm_provider, litellm_debug_info=extra_information, response=httpx.Response( status_code=400, @@ -1306,21 +1307,35 @@ def exception_type( # type: ignore # noqa: PLR0915 if original_exception.status_code == 401: exception_mapping_worked = True raise AuthenticationError( - message=f"VertexAIException - {original_exception.message}", + message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", llm_provider=custom_llm_provider, model=model, ) + if original_exception.status_code == 403: + exception_mapping_worked = True + raise PermissionDeniedError( + message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", + llm_provider=custom_llm_provider, + model=model, + response=httpx.Response( + status_code=403, + request=httpx.Request( + method="POST", + url="https://cloud.google.com/vertex-ai/", + ), + ), + ) if original_exception.status_code == 404: exception_mapping_worked = True raise NotFoundError( - message=f"VertexAIException - {original_exception.message}", + message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", llm_provider=custom_llm_provider, model=model, ) if original_exception.status_code == 408: exception_mapping_worked = True raise Timeout( - message=f"VertexAIException - {original_exception.message}", + message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", llm_provider=custom_llm_provider, model=model, ) @@ -1328,9 +1343,9 @@ def exception_type( # type: ignore # noqa: PLR0915 if original_exception.status_code == 429: exception_mapping_worked = True raise RateLimitError( - message=f"litellm.RateLimitError: VertexAIException - {error_str}", + message=f"litellm.RateLimitError: {custom_llm_provider.capitalize()}Exception - {error_str}", model=model, - llm_provider="vertex_ai", + llm_provider=custom_llm_provider, litellm_debug_info=extra_information, response=httpx.Response( status_code=429, @@ -1343,9 +1358,9 @@ def exception_type( # type: ignore # noqa: PLR0915 if original_exception.status_code == 500: exception_mapping_worked = True raise litellm.InternalServerError( - message=f"VertexAIException InternalServerError - {error_str}", + message=f"{custom_llm_provider.capitalize()}Exception InternalServerError - {error_str}", model=model, - llm_provider="vertex_ai", + llm_provider=custom_llm_provider, litellm_debug_info=extra_information, response=httpx.Response( status_code=500, @@ -1353,71 +1368,20 @@ def exception_type( # type: ignore # noqa: PLR0915 request=httpx.Request(method="completion", url="https://github.com/BerriAI/litellm"), # type: ignore ), ) - if original_exception.status_code == 503: + if original_exception.status_code == 502: exception_mapping_worked = True - raise ServiceUnavailableError( - message=f"VertexAIException - {original_exception.message}", + raise APIConnectionError( + message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", llm_provider=custom_llm_provider, model=model, ) - elif custom_llm_provider == "palm" or custom_llm_provider == "gemini": - if "503 Getting metadata" in error_str: - # auth errors look like this - # 503 Getting metadata from plugin failed with error: Reauthentication is needed. Please run `gcloud auth application-default login` to reauthenticate. - exception_mapping_worked = True - raise BadRequestError( - message="GeminiException - Invalid api key", - model=model, - llm_provider="palm", - response=getattr(original_exception, "response", None), - ) - if ( - "504 Deadline expired before operation could complete." in error_str - or "504 Deadline Exceeded" in error_str - ): - exception_mapping_worked = True - raise Timeout( - message=f"GeminiException - {original_exception.message}", - model=model, - llm_provider="palm", - exception_status_code=original_exception.status_code, - ) - if "400 Request payload size exceeds" in error_str: - exception_mapping_worked = True - raise ContextWindowExceededError( - message=f"GeminiException - {error_str}", - model=model, - llm_provider="palm", - response=getattr(original_exception, "response", None), - ) - if ( - "500 An internal error has occurred." in error_str - or "list index out of range" in error_str - ): - exception_mapping_worked = True - raise APIError( - status_code=getattr(original_exception, "status_code", 500), - message=f"GeminiException - {original_exception.message}", - llm_provider="palm", - model=model, - request=httpx.Response( - status_code=429, - request=httpx.Request( - method="POST", - url=" https://cloud.google.com/vertex-ai/", - ), - ), - ) - if hasattr(original_exception, "status_code"): - if original_exception.status_code == 400: + if original_exception.status_code == 503: exception_mapping_worked = True - raise BadRequestError( - message=f"GeminiException - {error_str}", + raise ServiceUnavailableError( + message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", + llm_provider=custom_llm_provider, model=model, - llm_provider="palm", - response=getattr(original_exception, "response", None), ) - # Dailed: Error occurred: 400 Request payload size exceeds the limit: 20000 bytes elif custom_llm_provider == "cloudflare": if "Authentication error" in error_str: exception_mapping_worked = True @@ -1449,6 +1413,14 @@ def exception_type( # type: ignore # noqa: PLR0915 model=model, response=getattr(original_exception, "response", None), ) + elif "invalid type: parameter" in error_str: + exception_mapping_worked = True + raise BadRequestError( + message=f"CohereException - {original_exception.message}", + llm_provider="cohere", + model=model, + response=getattr(original_exception, "response", None), + ) elif "too many tokens" in error_str: exception_mapping_worked = True raise ContextWindowExceededError( diff --git a/litellm/litellm_core_utils/fallback_utils.py b/litellm/litellm_core_utils/fallback_utils.py index a5b0c85c816..7ce53862089 100644 --- a/litellm/litellm_core_utils/fallback_utils.py +++ b/litellm/litellm_core_utils/fallback_utils.py @@ -1,4 +1,4 @@ -import uuid +from litellm._uuid import uuid from typing import Optional import litellm diff --git a/litellm/litellm_core_utils/get_litellm_params.py b/litellm/litellm_core_utils/get_litellm_params.py index c354dea0241..c167c202e5d 100644 --- a/litellm/litellm_core_utils/get_litellm_params.py +++ b/litellm/litellm_core_utils/get_litellm_params.py @@ -62,6 +62,7 @@ def get_litellm_params( use_litellm_proxy: Optional[bool] = None, api_version: Optional[str] = None, max_retries: Optional[int] = None, + litellm_request_debug: Optional[bool] = None, **kwargs, ) -> dict: litellm_params = { @@ -118,5 +119,6 @@ def get_litellm_params( "vertex_credentials": kwargs.get("vertex_credentials"), "vertex_project": kwargs.get("vertex_project"), "use_litellm_proxy": use_litellm_proxy, + "litellm_request_debug": litellm_request_debug, } return litellm_params diff --git a/litellm/litellm_core_utils/get_llm_provider_logic.py b/litellm/litellm_core_utils/get_llm_provider_logic.py index 02b542ed165..69c996d8139 100644 --- a/litellm/litellm_core_utils/get_llm_provider_logic.py +++ b/litellm/litellm_core_utils/get_llm_provider_logic.py @@ -249,6 +249,12 @@ def get_llm_provider( # noqa: PLR0915 elif endpoint == "https://api.hyperbolic.xyz/v1": custom_llm_provider = "hyperbolic" dynamic_api_key = get_secret_str("HYPERBOLIC_API_KEY") + elif endpoint == "https://ai-gateway.vercel.sh/v1": + custom_llm_provider = "vercel_ai_gateway" + dynamic_api_key = get_secret_str("VERCEL_AI_GATEWAY_API_KEY") + elif endpoint == "https://api.inference.wandb.ai/v1": + custom_llm_provider = "wandb" + dynamic_api_key = get_secret_str("WANDB_API_KEY") if api_base is not None and not isinstance(api_base, str): raise Exception( @@ -317,6 +323,7 @@ def get_llm_provider( # noqa: PLR0915 or model in litellm.vertex_embedding_models or model in litellm.vertex_vision_models or model in litellm.vertex_ai_image_models + or model in litellm.vertex_ai_video_models ): custom_llm_provider = "vertex_ai" ## ai21 @@ -361,11 +368,17 @@ def get_llm_provider( # noqa: PLR0915 # bytez models elif model.startswith("bytez/"): custom_llm_provider = "bytez" + elif model.startswith("heroku/"): + custom_llm_provider = "heroku" # cometapi models elif model.startswith("cometapi/"): custom_llm_provider = "cometapi" elif model.startswith("oci/"): custom_llm_provider = "oci" + elif model.startswith("compactifai/"): + custom_llm_provider = "compactifai" + elif model.startswith("ovhcloud/"): + custom_llm_provider = "ovhcloud" if not custom_llm_provider: if litellm.suppress_debug_info is False: print() # noqa @@ -486,7 +499,7 @@ def _get_openai_compatible_provider_info( # noqa: PLR0915 if api_base is None: api_base = litellm.BasetenConfig.get_api_base_for_model(model) else: - api_base = api_base or get_secret("BASETEN_API_BASE") or "https://inference.baseten.co/v1" + api_base = api_base or get_secret_str("BASETEN_API_BASE") or "https://inference.baseten.co/v1" dynamic_api_key = api_key or get_secret_str("BASETEN_API_KEY") elif custom_llm_provider == "sambanova": api_base = ( @@ -700,6 +713,13 @@ def _get_openai_compatible_provider_info( # noqa: PLR0915 ) = litellm.NscaleConfig()._get_openai_compatible_provider_info( api_base=api_base, api_key=api_key ) + elif custom_llm_provider == "heroku": + ( + api_base, + dynamic_api_key, + ) = litellm.HerokuChatConfig()._get_openai_compatible_provider_info( + api_base, api_key + ) elif custom_llm_provider == "dashscope": ( api_base, @@ -742,6 +762,27 @@ def _get_openai_compatible_provider_info( # noqa: PLR0915 ) = litellm.HyperbolicChatConfig()._get_openai_compatible_provider_info( api_base, api_key ) + elif custom_llm_provider == "vercel_ai_gateway": + ( + api_base, + dynamic_api_key, + ) = litellm.VercelAIGatewayConfig()._get_openai_compatible_provider_info( + api_base, api_key + ) + elif custom_llm_provider == "aiml": + ( + api_base, + dynamic_api_key, + ) = litellm.AIMLChatConfig()._get_openai_compatible_provider_info( + api_base, api_key + ) + elif custom_llm_provider == "wandb": + api_base = ( + api_base + or get_secret("WANDB_API_BASE") + or "https://api.inference.wandb.ai/v1" + ) # type: ignore + dynamic_api_key = api_key or get_secret_str("WANDB_API_KEY") if api_base is not None and not isinstance(api_base, str): raise Exception("api base needs to be a string. api_base={}".format(api_base)) diff --git a/litellm/litellm_core_utils/get_provider_specific_headers.py b/litellm/litellm_core_utils/get_provider_specific_headers.py new file mode 100644 index 00000000000..cf9165cfda9 --- /dev/null +++ b/litellm/litellm_core_utils/get_provider_specific_headers.py @@ -0,0 +1,23 @@ +from typing import Dict, Optional + +from litellm.types.utils import ProviderSpecificHeader + + +class ProviderSpecificHeaderUtils: + @staticmethod + def get_provider_specific_headers( + provider_specific_header: Optional[ProviderSpecificHeader], + custom_llm_provider: Optional[str], + ) -> Dict: + """ + Get the provider specific headers for the given custom llm provider + + Returns: + Optional[Dict]: The provider specific headers for the given custom llm provider + """ + if ( + provider_specific_header is not None + and provider_specific_header.get("custom_llm_provider") == custom_llm_provider + ): + return provider_specific_header.get("extra_headers", {}) + return {} \ No newline at end of file diff --git a/litellm/litellm_core_utils/get_supported_openai_params.py b/litellm/litellm_core_utils/get_supported_openai_params.py index 6584defe5b7..06e650f938d 100644 --- a/litellm/litellm_core_utils/get_supported_openai_params.py +++ b/litellm/litellm_core_utils/get_supported_openai_params.py @@ -94,9 +94,7 @@ def get_supported_openai_params( # noqa: PLR0915 return litellm.VLLMConfig().get_supported_openai_params(model=model) elif custom_llm_provider == "deepseek": return litellm.DeepSeekChatConfig().get_supported_openai_params(model=model) - elif custom_llm_provider == "cohere": - return litellm.CohereConfig().get_supported_openai_params(model=model) - elif custom_llm_provider == "cohere_chat": + elif custom_llm_provider == "cohere_chat" or custom_llm_provider == "cohere": return litellm.CohereChatConfig().get_supported_openai_params(model=model) elif custom_llm_provider == "maritalk": return litellm.MaritalkConfig().get_supported_openai_params(model=model) @@ -131,6 +129,8 @@ def get_supported_openai_params( # noqa: PLR0915 return litellm.AzureOpenAIConfig().get_supported_openai_params(model=model) elif custom_llm_provider == "openrouter": return litellm.OpenrouterConfig().get_supported_openai_params(model=model) + elif custom_llm_provider == "vercel_ai_gateway": + return litellm.VercelAIGatewayConfig().get_supported_openai_params(model=model) elif custom_llm_provider == "mistral" or custom_llm_provider == "codestral": # mistal and codestral api have the exact same params if request_type == "chat_completion": @@ -149,6 +149,9 @@ def get_supported_openai_params( # noqa: PLR0915 elif custom_llm_provider == "nebius": if request_type == "chat_completion": return litellm.NebiusConfig().get_supported_openai_params(model=model) + elif custom_llm_provider == "wandb": + if request_type == "chat_completion": + return litellm.WandbConfig().get_supported_openai_params(model=model) elif custom_llm_provider == "replicate": return litellm.ReplicateConfig().get_supported_openai_params(model=model) elif custom_llm_provider == "huggingface": @@ -268,10 +271,9 @@ def get_supported_openai_params( # noqa: PLR0915 from litellm.llms.elevenlabs.audio_transcription.transformation import ( ElevenLabsAudioTranscriptionConfig, ) - return ( - ElevenLabsAudioTranscriptionConfig().get_supported_openai_params( - model=model - ) + + return ElevenLabsAudioTranscriptionConfig().get_supported_openai_params( + model=model ) elif custom_llm_provider in litellm._custom_providers: if request_type == "chat_completion": diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py index 7bc7702684d..ea45f45d1c9 100644 --- a/litellm/litellm_core_utils/litellm_logging.py +++ b/litellm/litellm_core_utils/litellm_logging.py @@ -10,7 +10,6 @@ import subprocess import sys import time import traceback -import uuid from datetime import datetime as dt_object from functools import lru_cache from typing import ( @@ -38,6 +37,7 @@ from litellm import ( turn_off_message_logging, ) from litellm._logging import _is_debugging_on, verbose_logger +from litellm._uuid import uuid from litellm.batches.batch_utils import _handle_completed_batch from litellm.caching.caching import DualCache, InMemoryCache from litellm.caching.caching_handler import LLMCachingHandler @@ -84,6 +84,7 @@ from litellm.types.rerank import RerankResponse from litellm.types.router import CustomPricingLiteLLMParams from litellm.types.utils import ( CallTypes, + CostBreakdown, CostResponseTypes, DynamicPromptManagementParamLiteral, EmbeddingResponse, @@ -138,6 +139,7 @@ from ..integrations.logfire_logger import LogfireLevel, LogfireLogger from ..integrations.lunary import LunaryLogger from ..integrations.openmeter import OpenMeterLogger from ..integrations.opik.opik import OpikLogger +from ..integrations.posthog import PostHogLogger from ..integrations.prompt_layer import PromptLayerLogger from ..integrations.s3 import S3Logger from ..integrations.s3_v2 import S3Logger as S3V2Logger @@ -193,7 +195,6 @@ _in_memory_loggers: List[Any] = [] sentry_sdk_instance = None capture_exception = None add_breadcrumb = None -posthog = None slack_app = None alerts_channel = None heliconeLogger = None @@ -245,6 +246,7 @@ class Logging(LiteLLMLoggingBaseClass): global supabaseClient, promptLayerLogger, weightsBiasesLogger, logfireLogger, capture_exception, add_breadcrumb, lunaryLogger, logfireLogger, prometheusLogger, slack_app custom_pricing: bool = False stream_options = None + litellm_request_debug: bool = False def __init__( self, @@ -299,9 +301,9 @@ class Logging(LiteLLMLoggingBaseClass): self.litellm_trace_id: str = litellm_trace_id or str(uuid.uuid4()) self.function_id = function_id self.streaming_chunks: List[Any] = [] # for generating complete stream response - self.sync_streaming_chunks: List[Any] = ( - [] - ) # for generating complete stream response + self.sync_streaming_chunks: List[ + Any + ] = [] # for generating complete stream response self.log_raw_request_response = log_raw_request_response # Initialize dynamic callbacks @@ -342,6 +344,9 @@ class Logging(LiteLLMLoggingBaseClass): litellm_params = scrub_sensitive_keys_in_metadata(litellm_params) self.litellm_params = litellm_params + + # Initialize cost breakdown field + self.cost_breakdown: Optional[CostBreakdown] = None self.model_call_details: Dict[str, Any] = { "litellm_trace_id": litellm_trace_id, @@ -470,6 +475,7 @@ class Logging(LiteLLMLoggingBaseClass): **self.litellm_params, **scrub_sensitive_keys_in_metadata(litellm_params), } + self.litellm_request_debug = litellm_params.get("litellm_request_debug", False) self.logger_fn = litellm_params.get("logger_fn", None) verbose_logger.debug(f"self.optional_params: {self.optional_params}") @@ -670,24 +676,23 @@ class Logging(LiteLLMLoggingBaseClass): if anthropic_cache_control_logger := AnthropicCacheControlHook.get_custom_logger_for_anthropic_cache_control_hook( non_default_params ): - self.model_call_details["prompt_integration"] = ( - anthropic_cache_control_logger.__class__.__name__ - ) + self.model_call_details[ + "prompt_integration" + ] = anthropic_cache_control_logger.__class__.__name__ return anthropic_cache_control_logger ######################################################### # Vector Store / Knowledge Base hooks ######################################################### if litellm.vector_store_registry is not None: - vector_store_custom_logger = _init_custom_logger_compatible_class( logging_integration="vector_store_pre_call_hook", internal_usage_cache=None, llm_router=None, ) - self.model_call_details["prompt_integration"] = ( - vector_store_custom_logger.__class__.__name__ - ) + self.model_call_details[ + "prompt_integration" + ] = vector_store_custom_logger.__class__.__name__ return vector_store_custom_logger return None @@ -739,9 +744,9 @@ class Logging(LiteLLMLoggingBaseClass): model ): # if model name was changes pre-call, overwrite the initial model call name with the new one self.model_call_details["model"] = model - self.model_call_details["litellm_params"]["api_base"] = ( - self._get_masked_api_base(additional_args.get("api_base", "")) - ) + self.model_call_details["litellm_params"][ + "api_base" + ] = self._get_masked_api_base(additional_args.get("api_base", "")) def pre_call(self, input, api_key, model=None, additional_args={}): # noqa: PLR0915 # Log the exact input to the LLM API @@ -770,10 +775,10 @@ class Logging(LiteLLMLoggingBaseClass): try: # [Non-blocking Extra Debug Information in metadata] if turn_off_message_logging is True: - _metadata["raw_request"] = ( - "redacted by litellm. \ + _metadata[ + "raw_request" + ] = "redacted by litellm. \ 'litellm.turn_off_message_logging=True'" - ) else: curl_command = self._get_request_curl_command( api_base=additional_args.get("api_base", ""), @@ -784,32 +789,32 @@ class Logging(LiteLLMLoggingBaseClass): _metadata["raw_request"] = str(curl_command) # split up, so it's easier to parse in the UI - self.model_call_details["raw_request_typed_dict"] = ( - RawRequestTypedDict( - raw_request_api_base=str( - additional_args.get("api_base") or "" - ), - raw_request_body=self._get_raw_request_body( - additional_args.get("complete_input_dict", {}) - ), - raw_request_headers=self._get_masked_headers( - additional_args.get("headers", {}) or {}, - ignore_sensitive_headers=True, - ), - error=None, - ) + self.model_call_details[ + "raw_request_typed_dict" + ] = RawRequestTypedDict( + raw_request_api_base=str( + additional_args.get("api_base") or "" + ), + raw_request_body=self._get_raw_request_body( + additional_args.get("complete_input_dict", {}) + ), + raw_request_headers=self._get_masked_headers( + additional_args.get("headers", {}) or {}, + ignore_sensitive_headers=True, + ), + error=None, ) except Exception as e: - self.model_call_details["raw_request_typed_dict"] = ( - RawRequestTypedDict( - error=str(e), - ) + self.model_call_details[ + "raw_request_typed_dict" + ] = RawRequestTypedDict( + error=str(e), ) - _metadata["raw_request"] = ( - "Unable to Log \ + _metadata[ + "raw_request" + ] = "Unable to Log \ raw request: {}".format( - str(e) - ) + str(e) ) if getattr(self, "logger_fn", None) and callable(self.logger_fn): try: @@ -907,13 +912,19 @@ class Logging(LiteLLMLoggingBaseClass): Prints the RAW curl command sent from LiteLLM """ - if _is_debugging_on(): + if _is_debugging_on() or self.litellm_request_debug: if json_logs: masked_headers = self._get_masked_headers(headers) - verbose_logger.debug( - "POST Request Sent from LiteLLM", - extra={"api_base": {api_base}, **masked_headers}, - ) + if self.litellm_request_debug: + verbose_logger.warning( # .warning ensures this shows up in all environments + "POST Request Sent from LiteLLM", + extra={"api_base": {api_base}, **masked_headers}, + ) + else: + verbose_logger.debug( + "POST Request Sent from LiteLLM", + extra={"api_base": {api_base}, **masked_headers}, + ) else: headers = additional_args.get("headers", {}) if headers is None: @@ -926,7 +937,12 @@ class Logging(LiteLLMLoggingBaseClass): additional_args=additional_args, data=data, ) - verbose_logger.debug(f"\033[92m{curl_command}\033[0m\n") + if self.litellm_request_debug: + verbose_logger.warning( + f"\033[92m{curl_command}\033[0m\n" + ) # .warning ensures this shows up in all environments + else: + verbose_logger.debug(f"\033[92m{curl_command}\033[0m\n") def _get_request_body(self, data: dict) -> str: return str(data) @@ -983,8 +999,14 @@ class Logging(LiteLLMLoggingBaseClass): self.model_call_details["additional_args"] = additional_args self.model_call_details["log_event_type"] = "post_api_call" + if self.litellm_request_debug: + attr = "warning" + else: + attr = "debug" + if json_logs: - verbose_logger.debug( + callattr = getattr(verbose_logger, attr) + callattr( "RAW RESPONSE:\n{}\n\n".format( self.model_call_details.get( "original_response", self.model_call_details @@ -992,7 +1014,8 @@ class Logging(LiteLLMLoggingBaseClass): ), ) else: - print_verbose( + callattr = getattr(verbose_logger, attr) + callattr( "RAW RESPONSE:\n{}\n\n".format( self.model_call_details.get( "original_response", self.model_call_details @@ -1092,13 +1115,13 @@ class Logging(LiteLLMLoggingBaseClass): for callback in callbacks: try: if isinstance(callback, CustomLogger): - response: Optional[MCPPostCallResponseObject] = ( - await callback.async_post_mcp_tool_call_hook( - kwargs=kwargs, - response_obj=post_mcp_tool_call_response_obj, - start_time=start_time, - end_time=end_time, - ) + response: Optional[ + MCPPostCallResponseObject + ] = await callback.async_post_mcp_tool_call_hook( + kwargs=kwargs, + response_obj=post_mcp_tool_call_response_obj, + start_time=start_time, + end_time=end_time, ) ###################################################################### # if any of the callbacks modify the response, use the modified response @@ -1136,6 +1159,33 @@ class Logging(LiteLLMLoggingBaseClass): - self.model_call_details.get("start_time", datetime.datetime.now()) ).total_seconds() * 1000 + def set_cost_breakdown( + self, + input_cost: float, + output_cost: float, + total_cost: float, + cost_for_built_in_tools_cost_usd_dollar: float, + ) -> None: + """ + Helper method to store cost breakdown in the logging object. + + Args: + input_cost: Cost of input/prompt tokens + output_cost: Cost of output/completion tokens + cost_for_built_in_tools_cost_usd_dollar: Cost of built-in tools + total_cost: Total cost of request + """ + + self.cost_breakdown = CostBreakdown( + input_cost=input_cost, + output_cost=output_cost, + total_cost=total_cost, + tool_usage_cost=cost_for_built_in_tools_cost_usd_dollar + ) + verbose_logger.debug( + f"Cost breakdown set - input: {input_cost}, output: {output_cost}, cost_for_built_in_tools_cost_usd_dollar: {cost_for_built_in_tools_cost_usd_dollar}, total: {total_cost}" + ) + def _response_cost_calculator( self, result: Union[ @@ -1164,7 +1214,6 @@ class Logging(LiteLLMLoggingBaseClass): used for consistent cost calculation across response headers + logging integrations. """ - if isinstance(result, BaseModel) and hasattr(result, "_hidden_params"): hidden_params = getattr(result, "_hidden_params", {}) if ( @@ -1210,6 +1259,9 @@ class Logging(LiteLLMLoggingBaseClass): "standard_built_in_tools_params": self.standard_built_in_tools_params, "router_model_id": router_model_id, "litellm_logging_obj": self, + "service_tier": self.optional_params.get("service_tier") + if self.optional_params + else None, } except Exception as e: # error creating kwargs for cost calculation debug_info = StandardLoggingModelCostFailureDebugInformation( @@ -1219,9 +1271,9 @@ class Logging(LiteLLMLoggingBaseClass): verbose_logger.debug( f"response_cost_failure_debug_information: {debug_info}" ) - self.model_call_details["response_cost_failure_debug_information"] = ( - debug_info - ) + self.model_call_details[ + "response_cost_failure_debug_information" + ] = debug_info return None try: @@ -1246,9 +1298,9 @@ class Logging(LiteLLMLoggingBaseClass): verbose_logger.debug( f"response_cost_failure_debug_information: {debug_info}" ) - self.model_call_details["response_cost_failure_debug_information"] = ( - debug_info - ) + self.model_call_details[ + "response_cost_failure_debug_information" + ] = debug_info return None @@ -1392,9 +1444,9 @@ class Logging(LiteLLMLoggingBaseClass): end_time = datetime.datetime.now() if self.completion_start_time is None: self.completion_start_time = end_time - self.model_call_details["completion_start_time"] = ( - self.completion_start_time - ) + self.model_call_details[ + "completion_start_time" + ] = self.completion_start_time self.model_call_details["log_event_type"] = "successful_api_call" self.model_call_details["end_time"] = end_time self.model_call_details["cache_hit"] = cache_hit @@ -1447,39 +1499,39 @@ class Logging(LiteLLMLoggingBaseClass): "response_cost" ] else: - self.model_call_details["response_cost"] = ( - self._response_cost_calculator(result=logging_result) - ) + self.model_call_details[ + "response_cost" + ] = self._response_cost_calculator(result=logging_result) ## STANDARDIZED LOGGING PAYLOAD - self.model_call_details["standard_logging_object"] = ( - get_standard_logging_object_payload( - kwargs=self.model_call_details, - init_response_obj=logging_result, - start_time=start_time, - end_time=end_time, - logging_obj=self, - status="success", - standard_built_in_tools_params=self.standard_built_in_tools_params, - ) + self.model_call_details[ + "standard_logging_object" + ] = get_standard_logging_object_payload( + kwargs=self.model_call_details, + init_response_obj=logging_result, + start_time=start_time, + end_time=end_time, + logging_obj=self, + status="success", + standard_built_in_tools_params=self.standard_built_in_tools_params, ) elif isinstance(result, dict) or isinstance(result, list): ## STANDARDIZED LOGGING PAYLOAD - self.model_call_details["standard_logging_object"] = ( - get_standard_logging_object_payload( - kwargs=self.model_call_details, - init_response_obj=result, - start_time=start_time, - end_time=end_time, - logging_obj=self, - status="success", - standard_built_in_tools_params=self.standard_built_in_tools_params, - ) + self.model_call_details[ + "standard_logging_object" + ] = get_standard_logging_object_payload( + kwargs=self.model_call_details, + init_response_obj=result, + start_time=start_time, + end_time=end_time, + logging_obj=self, + status="success", + standard_built_in_tools_params=self.standard_built_in_tools_params, ) elif standard_logging_object is not None: - self.model_call_details["standard_logging_object"] = ( - standard_logging_object - ) + self.model_call_details[ + "standard_logging_object" + ] = standard_logging_object else: # streaming chunks + image gen. self.model_call_details["response_cost"] = None @@ -1578,7 +1630,6 @@ class Logging(LiteLLMLoggingBaseClass): ) if complete_streaming_response is not None: - self.success_handler(result=complete_streaming_response) return @@ -1631,23 +1682,23 @@ class Logging(LiteLLMLoggingBaseClass): verbose_logger.debug( "Logging Details LiteLLM-Success Call streaming complete" ) - self.model_call_details["complete_streaming_response"] = ( - complete_streaming_response - ) - self.model_call_details["response_cost"] = ( - self._response_cost_calculator(result=complete_streaming_response) - ) + self.model_call_details[ + "complete_streaming_response" + ] = complete_streaming_response + self.model_call_details[ + "response_cost" + ] = self._response_cost_calculator(result=complete_streaming_response) ## STANDARDIZED LOGGING PAYLOAD - self.model_call_details["standard_logging_object"] = ( - get_standard_logging_object_payload( - kwargs=self.model_call_details, - init_response_obj=complete_streaming_response, - start_time=start_time, - end_time=end_time, - logging_obj=self, - status="success", - standard_built_in_tools_params=self.standard_built_in_tools_params, - ) + self.model_call_details[ + "standard_logging_object" + ] = get_standard_logging_object_payload( + kwargs=self.model_call_details, + init_response_obj=complete_streaming_response, + start_time=start_time, + end_time=end_time, + logging_obj=self, + status="success", + standard_built_in_tools_params=self.standard_built_in_tools_params, ) callbacks = self.get_combined_callback_list( dynamic_success_callbacks=self.dynamic_success_callbacks, @@ -1715,8 +1766,15 @@ class Logging(LiteLLMLoggingBaseClass): response_obj=result, start_time=start_time, end_time=end_time, - litellm_call_id=litellm_params.get( - "litellm_call_id", str(uuid.uuid4()) + litellm_call_id=( + current_call_id + if ( + current_call_id := litellm_params.get( + "litellm_call_id" + ) + ) + is not None + else str(uuid.uuid4()) ), print_verbose=print_verbose, ) @@ -1968,10 +2026,10 @@ class Logging(LiteLLMLoggingBaseClass): ) else: if self.stream and complete_streaming_response: - self.model_call_details["complete_response"] = ( - self.model_call_details.get( - "complete_streaming_response", {} - ) + self.model_call_details[ + "complete_response" + ] = self.model_call_details.get( + "complete_streaming_response", {} ) result = self.model_call_details["complete_response"] openMeterLogger.log_success_event( @@ -2010,10 +2068,10 @@ class Logging(LiteLLMLoggingBaseClass): ) else: if self.stream and complete_streaming_response: - self.model_call_details["complete_response"] = ( - self.model_call_details.get( - "complete_streaming_response", {} - ) + self.model_call_details[ + "complete_response" + ] = self.model_call_details.get( + "complete_streaming_response", {} ) result = self.model_call_details["complete_response"] @@ -2115,10 +2173,12 @@ class Logging(LiteLLMLoggingBaseClass): result.usage = batch_usage elif not is_base64_unified_file_id: # only run for non-unified file ids - response_cost, batch_usage, batch_models = ( - await _handle_completed_batch( - batch=result, custom_llm_provider=self.custom_llm_provider - ) + ( + response_cost, + batch_usage, + batch_models, + ) = await _handle_completed_batch( + batch=result, custom_llm_provider=self.custom_llm_provider ) result._hidden_params["response_cost"] = response_cost @@ -2149,9 +2209,9 @@ class Logging(LiteLLMLoggingBaseClass): if complete_streaming_response is not None: print_verbose("Async success callbacks: Got a complete streaming response") - self.model_call_details["async_complete_streaming_response"] = ( - complete_streaming_response - ) + self.model_call_details[ + "async_complete_streaming_response" + ] = complete_streaming_response try: if self.model_call_details.get("cache_hit", False) is True: @@ -2162,10 +2222,10 @@ class Logging(LiteLLMLoggingBaseClass): model_call_details=self.model_call_details ) # base_model defaults to None if not set on model_info - self.model_call_details["response_cost"] = ( - self._response_cost_calculator( - result=complete_streaming_response - ) + self.model_call_details[ + "response_cost" + ] = self._response_cost_calculator( + result=complete_streaming_response ) verbose_logger.debug( @@ -2178,16 +2238,16 @@ class Logging(LiteLLMLoggingBaseClass): self.model_call_details["response_cost"] = None ## STANDARDIZED LOGGING PAYLOAD - self.model_call_details["standard_logging_object"] = ( - get_standard_logging_object_payload( - kwargs=self.model_call_details, - init_response_obj=complete_streaming_response, - start_time=start_time, - end_time=end_time, - logging_obj=self, - status="success", - standard_built_in_tools_params=self.standard_built_in_tools_params, - ) + self.model_call_details[ + "standard_logging_object" + ] = get_standard_logging_object_payload( + kwargs=self.model_call_details, + init_response_obj=complete_streaming_response, + start_time=start_time, + end_time=end_time, + logging_obj=self, + status="success", + standard_built_in_tools_params=self.standard_built_in_tools_params, ) callbacks = self.get_combined_callback_list( dynamic_success_callbacks=self.dynamic_async_success_callbacks, @@ -2400,18 +2460,18 @@ class Logging(LiteLLMLoggingBaseClass): ## STANDARDIZED LOGGING PAYLOAD - self.model_call_details["standard_logging_object"] = ( - get_standard_logging_object_payload( - kwargs=self.model_call_details, - init_response_obj={}, - start_time=start_time, - end_time=end_time, - logging_obj=self, - status="failure", - error_str=str(exception), - original_exception=exception, - standard_built_in_tools_params=self.standard_built_in_tools_params, - ) + self.model_call_details[ + "standard_logging_object" + ] = get_standard_logging_object_payload( + kwargs=self.model_call_details, + init_response_obj={}, + start_time=start_time, + end_time=end_time, + logging_obj=self, + status="failure", + error_str=str(exception), + original_exception=exception, + standard_built_in_tools_params=self.standard_built_in_tools_params, ) return start_time, end_time @@ -2775,6 +2835,7 @@ class Logging(LiteLLMLoggingBaseClass): result: Any, start_time: datetime.datetime, end_time: datetime.datetime, + cache_hit: Optional[Any] = None, ) -> None: """ Handles calling success callbacks for Async calls. @@ -2789,6 +2850,7 @@ class Logging(LiteLLMLoggingBaseClass): result, start_time, end_time, + cache_hit, ) def _should_run_sync_callbacks_for_async_calls(self) -> bool: @@ -3040,7 +3102,7 @@ def set_callbacks(callback_list, function_id=None): # noqa: PLR0915 """ Globally sets the callback client """ - global sentry_sdk_instance, capture_exception, add_breadcrumb, posthog, slack_app, alerts_channel, traceloopLogger, athinaLogger, heliconeLogger, supabaseClient, lunaryLogger, promptLayerLogger, langFuseLogger, customLogger, weightsBiasesLogger, logfireLogger, dynamoLogger, s3Logger, dataDogLogger, prometheusLogger, greenscaleLogger, openMeterLogger, deepevalLogger + global sentry_sdk_instance, capture_exception, add_breadcrumb, slack_app, alerts_channel, traceloopLogger, athinaLogger, heliconeLogger, supabaseClient, lunaryLogger, promptLayerLogger, langFuseLogger, customLogger, weightsBiasesLogger, logfireLogger, dynamoLogger, s3Logger, dataDogLogger, prometheusLogger, greenscaleLogger, openMeterLogger, deepevalLogger try: for callback in callback_list: @@ -3079,19 +3141,6 @@ def set_callbacks(callback_list, function_id=None): # noqa: PLR0915 ) capture_exception = sentry_sdk_instance.capture_exception add_breadcrumb = sentry_sdk_instance.add_breadcrumb - elif callback == "posthog": - try: - from posthog import Posthog - except ImportError: - print_verbose("Package 'posthog' is missing. Installing it...") - subprocess.check_call( - [sys.executable, "-m", "pip", "install", "posthog"] - ) - from posthog import Posthog - posthog = Posthog( - project_api_key=os.environ.get("POSTHOG_API_KEY"), - host=os.environ.get("POSTHOG_API_URL"), - ) elif callback == "slack": try: from slack_bolt import App @@ -3186,6 +3235,14 @@ def _init_custom_logger_compatible_class( # noqa: PLR0915 _openmeter_logger = OpenMeterLogger() _in_memory_loggers.append(_openmeter_logger) return _openmeter_logger # type: ignore + elif logging_integration == "posthog": + for callback in _in_memory_loggers: + if isinstance(callback, PostHogLogger): + return callback # type: ignore + + _posthog_logger = PostHogLogger() + _in_memory_loggers.append(_posthog_logger) + return _posthog_logger # type: ignore elif logging_integration == "braintrust": from litellm.integrations.braintrust_logging import BraintrustLogger @@ -3298,9 +3355,9 @@ def _init_custom_logger_compatible_class( # noqa: PLR0915 endpoint=arize_config.endpoint, ) - os.environ["OTEL_EXPORTER_OTLP_TRACES_HEADERS"] = ( - f"space_id={arize_config.space_key},api_key={arize_config.api_key}" - ) + os.environ[ + "OTEL_EXPORTER_OTLP_TRACES_HEADERS" + ] = f"space_id={arize_config.space_key},api_key={arize_config.api_key}" for callback in _in_memory_loggers: if ( isinstance(callback, ArizeLogger) @@ -3324,9 +3381,9 @@ def _init_custom_logger_compatible_class( # noqa: PLR0915 # auth can be disabled on local deployments of arize phoenix if arize_phoenix_config.otlp_auth_headers is not None: - os.environ["OTEL_EXPORTER_OTLP_TRACES_HEADERS"] = ( - arize_phoenix_config.otlp_auth_headers - ) + os.environ[ + "OTEL_EXPORTER_OTLP_TRACES_HEADERS" + ] = arize_phoenix_config.otlp_auth_headers for callback in _in_memory_loggers: if ( @@ -3361,7 +3418,15 @@ def _init_custom_logger_compatible_class( # noqa: PLR0915 galileo_logger = GalileoObserve() _in_memory_loggers.append(galileo_logger) return galileo_logger # type: ignore + elif logging_integration == "cloudzero": + from litellm.integrations.cloudzero.cloudzero import CloudZeroLogger + for callback in _in_memory_loggers: + if isinstance(callback, CloudZeroLogger): + return callback # type: ignore + cloudzero_logger = CloudZeroLogger() + _in_memory_loggers.append(cloudzero_logger) + return cloudzero_logger # type: ignore elif logging_integration == "deepeval": for callback in _in_memory_loggers: if isinstance(callback, DeepEvalLogger): @@ -3413,6 +3478,30 @@ def _init_custom_logger_compatible_class( # noqa: PLR0915 dynamic_rate_limiter_obj.update_variables(llm_router=llm_router) _in_memory_loggers.append(dynamic_rate_limiter_obj) return dynamic_rate_limiter_obj # type: ignore + elif logging_integration == "dynamic_rate_limiter_v3": + from litellm.proxy.hooks.dynamic_rate_limiter_v3 import ( + _PROXY_DynamicRateLimitHandlerV3, + ) + + for callback in _in_memory_loggers: + if isinstance(callback, _PROXY_DynamicRateLimitHandlerV3): + return callback # type: ignore + + if internal_usage_cache is None: + raise Exception( + "Internal Error: Cache cannot be empty - internal_usage_cache={}".format( + internal_usage_cache + ) + ) + + dynamic_rate_limiter_obj_v3 = _PROXY_DynamicRateLimitHandlerV3( + internal_usage_cache=internal_usage_cache + ) + + if llm_router is not None and isinstance(llm_router, litellm.Router): + dynamic_rate_limiter_obj_v3.update_variables(llm_router=llm_router) + _in_memory_loggers.append(dynamic_rate_limiter_obj_v3) + return dynamic_rate_limiter_obj_v3 # type: ignore elif logging_integration == "langtrace": if "LANGTRACE_API_KEY" not in os.environ: raise ValueError("LANGTRACE_API_KEY not found in environment variables") @@ -3426,9 +3515,9 @@ def _init_custom_logger_compatible_class( # noqa: PLR0915 exporter="otlp_http", endpoint="https://langtrace.ai/api/trace", ) - os.environ["OTEL_EXPORTER_OTLP_TRACES_HEADERS"] = ( - f"api_key={os.getenv('LANGTRACE_API_KEY')}" - ) + os.environ[ + "OTEL_EXPORTER_OTLP_TRACES_HEADERS" + ] = f"api_key={os.getenv('LANGTRACE_API_KEY')}" for callback in _in_memory_loggers: if ( isinstance(callback, OpenTelemetry) @@ -3550,6 +3639,25 @@ def _init_custom_logger_compatible_class( # noqa: PLR0915 dotprompt_logger = DotpromptManager() _in_memory_loggers.append(dotprompt_logger) return dotprompt_logger # type: ignore + elif logging_integration == "bitbucket": + from litellm.integrations.bitbucket.bitbucket_prompt_manager import ( + BitBucketPromptManager, + ) + + for callback in _in_memory_loggers: + if isinstance(callback, BitBucketPromptManager): + return callback + + # Get global BitBucket config + bitbucket_config = getattr(litellm, "global_bitbucket_config", None) + if bitbucket_config is None: + raise ValueError( + "BitBucket configuration not found. Please set litellm.global_bitbucket_config first." + ) + + bitbucket_logger = BitBucketPromptManager(bitbucket_config=bitbucket_config) + _in_memory_loggers.append(bitbucket_logger) + return bitbucket_logger # type: ignore return None except Exception as e: verbose_logger.exception( @@ -3581,6 +3689,12 @@ def get_custom_logger_compatible_class( # noqa: PLR0915 for callback in _in_memory_loggers: if isinstance(callback, GalileoObserve): return callback + elif logging_integration == "cloudzero": + from litellm.integrations.cloudzero.cloudzero import CloudZeroLogger + + for callback in _in_memory_loggers: + if isinstance(callback, CloudZeroLogger): + return callback elif logging_integration == "deepeval": for callback in _in_memory_loggers: if isinstance(callback, DeepEvalLogger): @@ -3670,6 +3784,14 @@ def get_custom_logger_compatible_class( # noqa: PLR0915 for callback in _in_memory_loggers: if isinstance(callback, _PROXY_DynamicRateLimitHandler): return callback # type: ignore + elif logging_integration == "dynamic_rate_limiter_v3": + from litellm.proxy.hooks.dynamic_rate_limiter_v3 import ( + _PROXY_DynamicRateLimitHandlerV3, + ) + + for callback in _in_memory_loggers: + if isinstance(callback, _PROXY_DynamicRateLimitHandlerV3): + return callback # type: ignore elif logging_integration == "langtrace": from litellm.integrations.opentelemetry import OpenTelemetry @@ -3868,22 +3990,25 @@ class StandardLoggingPayloadSetup: clean_metadata = StandardLoggingMetadata( user_api_key_hash=None, user_api_key_alias=None, + user_api_key_spend=None, + user_api_key_max_budget=None, + user_api_key_budget_reset_at=None, user_api_key_team_id=None, user_api_key_org_id=None, user_api_key_user_id=None, user_api_key_team_alias=None, user_api_key_user_email=None, + user_api_key_end_user_id=None, + user_api_key_request_route=None, spend_logs_metadata=None, requester_ip_address=None, requester_metadata=None, - user_api_key_end_user_id=None, prompt_management_metadata=prompt_management_metadata, applied_guardrails=applied_guardrails, mcp_tool_call_metadata=mcp_tool_call_metadata, vector_store_request_metadata=vector_store_request_metadata, usage_object=usage_object, requester_custom_headers=None, - user_api_key_request_route=None, cold_storage_object_key=None, ) if isinstance(metadata, dict): @@ -4072,10 +4197,10 @@ class StandardLoggingPayloadSetup: for key in StandardLoggingHiddenParams.__annotations__.keys(): if key in hidden_params: if key == "additional_headers": - clean_hidden_params["additional_headers"] = ( - StandardLoggingPayloadSetup.get_additional_headers( - hidden_params[key] - ) + clean_hidden_params[ + "additional_headers" + ] = StandardLoggingPayloadSetup.get_additional_headers( + hidden_params[key] ) else: clean_hidden_params[key] = hidden_params[key] # type: ignore @@ -4108,15 +4233,34 @@ class StandardLoggingPayloadSetup: from litellm.integrations.s3 import get_s3_object_key # Only generate object key if cold storage is configured - if litellm.configured_cold_storage_logger is None: + configured_cold_storage_logger = litellm.configured_cold_storage_logger + if configured_cold_storage_logger is None: return None try: # Generate file name in same format as litellm.utils.get_logging_id s3_file_name = f"time-{start_time.strftime('%H-%M-%S-%f')}_{response_id}" + # Get the actual s3_path from the configured cold storage logger instance + s3_path = "" # default value + + # Try to get the actual logger instance from the logger name + try: + custom_logger = litellm.logging_callback_manager.get_active_custom_logger_for_callback_name( + configured_cold_storage_logger + ) + if ( + custom_logger + and hasattr(custom_logger, "s3_path") + and custom_logger.s3_path + ): + s3_path = custom_logger.s3_path + except Exception: + # If any error occurs in getting the logger instance, use default empty s3_path + pass + s3_object_key = get_s3_object_key( - s3_path="", # Use empty path as default + s3_path=s3_path, # Use actual s3_path from logger configuration team_alias_prefix="", # Don't split by team alias for cold storage start_time=start_time, s3_file_name=s3_file_name, @@ -4447,6 +4591,7 @@ def get_standard_logging_object_payload( metadata=clean_metadata, cache_key=clean_hidden_params["cache_key"], response_cost=response_cost, + cost_breakdown=logging_obj.cost_breakdown, total_tokens=usage.total_tokens, prompt_tokens=usage.prompt_tokens, completion_tokens=usage.completion_tokens, @@ -4488,7 +4633,7 @@ def get_standard_logging_object_payload( def emit_standard_logging_payload(payload: StandardLoggingPayload): if os.getenv("LITELLM_PRINT_STANDARD_LOGGING_PAYLOAD"): - verbose_logger.info(json.dumps(payload, indent=4)) + print(json.dumps(payload, indent=4)) # noqa def get_standard_logging_metadata( @@ -4511,6 +4656,9 @@ def get_standard_logging_metadata( clean_metadata = StandardLoggingMetadata( user_api_key_hash=None, user_api_key_alias=None, + user_api_key_spend=None, + user_api_key_max_budget=None, + user_api_key_budget_reset_at=None, user_api_key_team_id=None, user_api_key_org_id=None, user_api_key_user_id=None, @@ -4530,14 +4678,10 @@ def get_standard_logging_metadata( cold_storage_object_key=None, ) if isinstance(metadata, dict): - # Filter the metadata dictionary to include only the specified keys - clean_metadata = StandardLoggingMetadata( - **{ # type: ignore - key: metadata[key] - for key in StandardLoggingMetadata.__annotations__.keys() - if key in metadata - } - ) + # Update the clean_metadata with values from input metadata that match StandardLoggingMetadata fields + for key in StandardLoggingMetadata.__annotations__.keys(): + if key in metadata: + clean_metadata[key] = metadata[key] # type: ignore if metadata.get("user_api_key") is not None: if is_valid_sha256_hash(str(metadata.get("user_api_key"))): @@ -4560,9 +4704,9 @@ def scrub_sensitive_keys_in_metadata(litellm_params: Optional[dict]): ): for k, v in metadata["user_api_key_metadata"].items(): if k == "logging": # prevent logging user logging keys - cleaned_user_api_key_metadata[k] = ( - "scrubbed_by_litellm_for_sensitive_keys" - ) + cleaned_user_api_key_metadata[ + k + ] = "scrubbed_by_litellm_for_sensitive_keys" else: cleaned_user_api_key_metadata[k] = v diff --git a/litellm/litellm_core_utils/llm_cost_calc/tool_call_cost_tracking.py b/litellm/litellm_core_utils/llm_cost_calc/tool_call_cost_tracking.py index 75bb699292e..21ff44ab082 100644 --- a/litellm/litellm_core_utils/llm_cost_calc/tool_call_cost_tracking.py +++ b/litellm/litellm_core_utils/llm_cost_calc/tool_call_cost_tracking.py @@ -580,7 +580,9 @@ class StandardBuiltInToolCostTracking: return WebSearchOptions(**kwargs.get("web_search_options", {})) tools = StandardBuiltInToolCostTracking._get_tools_from_kwargs( - kwargs, "web_search_preview" + kwargs=kwargs, tool_type="web_search_preview" + ) or StandardBuiltInToolCostTracking._get_tools_from_kwargs( + kwargs=kwargs, tool_type="web_search" ) if tools: # Look for web search tool in the tools array @@ -612,6 +614,8 @@ class StandardBuiltInToolCostTracking: def _is_web_search_tool_call(tool: Dict) -> bool: if tool.get("type", None) == "web_search_preview": return True + if tool.get("type", None) == "web_search": + return True if "search_context_size" in tool: return True return False diff --git a/litellm/litellm_core_utils/llm_cost_calc/utils.py b/litellm/litellm_core_utils/llm_cost_calc/utils.py index fe747788243..626a3f3625f 100644 --- a/litellm/litellm_core_utils/llm_cost_calc/utils.py +++ b/litellm/litellm_core_utils/llm_cost_calc/utils.py @@ -1,16 +1,18 @@ # What is this? ## Helper utilities for cost_per_token() -from typing import Any, Literal, Optional, Tuple, cast +from typing import Any, Literal, Optional, Tuple, TypedDict, cast import litellm from litellm._logging import verbose_logger from litellm.types.utils import ( + CacheCreationTokenDetails, CallTypes, ImageResponse, ModelInfo, PassthroughCallTypes, Usage, + ServiceTier, ) from litellm.utils import get_model_info @@ -113,20 +115,62 @@ def _generic_cost_per_character( return prompt_cost, completion_cost -def _get_token_base_cost(model_info: ModelInfo, usage: Usage) -> Tuple[float, float, float, float]: +def _get_service_tier_cost_key(base_key: str, service_tier: Optional[str]) -> str: + """ + Get the appropriate cost key based on service tier. + + Args: + base_key: The base cost key (e.g., "input_cost_per_token") + service_tier: The service tier ("flex", "priority", or None for standard) + + Returns: + str: The cost key to use (e.g., "input_cost_per_token_flex" or "input_cost_per_token") + """ + if service_tier is None: + return base_key + + # Only use service tier specific keys for "flex" and "priority" + if service_tier.lower() in [ServiceTier.FLEX.value, ServiceTier.PRIORITY.value]: + return f"{base_key}_{service_tier.lower()}" + + # For any other service tier, use standard pricing + return base_key + + +def _get_token_base_cost( + model_info: ModelInfo, usage: Usage, service_tier: Optional[str] = None +) -> Tuple[float, float, float, float, float]: """ Return prompt cost, completion cost, and cache costs for a given model and usage. If input_tokens > threshold and `input_cost_per_token_above_[x]k_tokens` or `input_cost_per_token_above_[x]_tokens` is set, then we use the corresponding threshold cost for all token types. - + Returns: Tuple[float, float, float, float] - (prompt_cost, completion_cost, cache_creation_cost, cache_read_cost) """ - prompt_base_cost = cast(float, _get_cost_per_unit(model_info, "input_cost_per_token")) - completion_base_cost = cast(float, _get_cost_per_unit(model_info, "output_cost_per_token")) - cache_creation_cost = cast(float, _get_cost_per_unit(model_info, "cache_creation_input_token_cost")) - cache_read_cost = cast(float, _get_cost_per_unit(model_info, "cache_read_input_token_cost")) + # Get service tier aware cost keys + input_cost_key = _get_service_tier_cost_key("input_cost_per_token", service_tier) + output_cost_key = _get_service_tier_cost_key("output_cost_per_token", service_tier) + cache_creation_cost_key = _get_service_tier_cost_key("cache_creation_input_token_cost", service_tier) + cache_read_cost_key = _get_service_tier_cost_key("cache_read_input_token_cost", service_tier) + + prompt_base_cost = cast( + float, _get_cost_per_unit(model_info, input_cost_key) + ) + completion_base_cost = cast( + float, _get_cost_per_unit(model_info, output_cost_key) + ) + cache_creation_cost = cast( + float, _get_cost_per_unit(model_info, cache_creation_cost_key) + ) + cache_creation_cost_above_1hr = cast( + float, + _get_cost_per_unit(model_info, "cache_creation_input_token_cost_above_1hr"), + ) + cache_read_cost = cast( + float, _get_cost_per_unit(model_info, cache_read_cost_key) + ) ## CHECK IF ABOVE THRESHOLD threshold: Optional[float] = None @@ -140,34 +184,57 @@ def _get_token_base_cost(model_info: ModelInfo, usage: Usage) -> Tuple[float, fl ) if usage.prompt_tokens > threshold: - prompt_base_cost = cast(float, _get_cost_per_unit(model_info, key, prompt_base_cost)) - completion_base_cost = cast(float, _get_cost_per_unit( - model_info, - f"output_cost_per_token_above_{threshold_str}_tokens", - completion_base_cost, - )) - + prompt_base_cost = cast( + float, _get_cost_per_unit(model_info, key, prompt_base_cost) + ) + completion_base_cost = cast( + float, + _get_cost_per_unit( + model_info, + f"output_cost_per_token_above_{threshold_str}_tokens", + completion_base_cost, + ), + ) + # Apply tiered pricing to cache costs - cache_creation_tiered_key = f"cache_creation_input_token_cost_above_{threshold_str}_tokens" - cache_read_tiered_key = f"cache_read_input_token_cost_above_{threshold_str}_tokens" - + cache_creation_tiered_key = ( + f"cache_creation_input_token_cost_above_{threshold_str}_tokens" + ) + cache_read_tiered_key = ( + f"cache_read_input_token_cost_above_{threshold_str}_tokens" + ) + if cache_creation_tiered_key in model_info: - cache_creation_cost = cast(float, _get_cost_per_unit( - model_info, cache_creation_tiered_key, cache_creation_cost - )) - + cache_creation_cost = cast( + float, + _get_cost_per_unit( + model_info, + cache_creation_tiered_key, + cache_creation_cost, + ), + ) + if cache_read_tiered_key in model_info: - cache_read_cost = cast(float, _get_cost_per_unit( - model_info, cache_read_tiered_key, cache_read_cost - )) - + cache_read_cost = cast( + float, + _get_cost_per_unit( + model_info, cache_read_tiered_key, cache_read_cost + ), + ) + break except (IndexError, ValueError): continue except Exception: continue - return prompt_base_cost, completion_base_cost, cache_creation_cost, cache_read_cost + return ( + prompt_base_cost, + completion_base_cost, + cache_creation_cost, + cache_creation_cost_above_1hr, + cache_read_cost, + ) def calculate_cost_component( @@ -195,7 +262,9 @@ def calculate_cost_component( return 0.0 -def _get_cost_per_unit(model_info: ModelInfo, cost_key: str, default_value: Optional[float] = 0.0) -> Optional[float]: +def _get_cost_per_unit( + model_info: ModelInfo, cost_key: str, default_value: Optional[float] = 0.0 +) -> Optional[float]: # Sometimes the cost per unit is a string (e.g.: If a value like "3e-7" was read from the config.yaml) cost_per_unit = model_info.get(cost_key) if isinstance(cost_per_unit, float): @@ -209,12 +278,224 @@ def _get_cost_per_unit(model_info: ModelInfo, cost_key: str, default_value: Opti verbose_logger.exception( f"litellm.litellm_core_utils.llm_cost_calc.utils.py::calculate_cost_per_component(): Exception occured - {cost_per_unit}\nDefaulting to 0.0" ) - return default_value + # If the service tier key doesn't exist or is None, try to fall back to the standard key + if cost_per_unit is None: + # Check if any service tier suffix exists in the cost key using ServiceTier enum + for service_tier in ServiceTier: + suffix = f"_{service_tier.value}" + if suffix in cost_key: + # Extract the base key by removing the matched suffix + base_key = cost_key.replace(suffix, '') + fallback_cost = model_info.get(base_key) + if isinstance(fallback_cost, float): + return fallback_cost + if isinstance(fallback_cost, int): + return float(fallback_cost) + if isinstance(fallback_cost, str): + try: + return float(fallback_cost) + except ValueError: + verbose_logger.exception( + f"litellm.litellm_core_utils.llm_cost_calc.utils.py::_get_cost_per_unit(): Exception occured - {fallback_cost}\nDefaulting to 0.0" + ) + break # Only try the first matching suffix + + return default_value + + +def calculate_cache_writing_cost( + cache_creation_tokens: int, + cache_creation_token_details: Optional[CacheCreationTokenDetails], + cache_creation_cost_above_1hr: float, + cache_creation_cost: float, +) -> float: + """ + Adjust cost of cache creation tokens based on the cache creation token details. + """ + total_cost: float = 0.0 + if cache_creation_token_details is not None: + # get the number of 5m and 1h cache creation tokens + cache_creation_tokens_5m = ( + cache_creation_token_details.ephemeral_5m_input_tokens + ) + cache_creation_tokens_1h = ( + cache_creation_token_details.ephemeral_1h_input_tokens + ) + # add the number of 5m and 1h cache creation tokens to the cache creation tokens + total_cost += ( + cache_creation_tokens_5m * cache_creation_cost + if cache_creation_tokens_5m is not None + else 0.0 + ) + total_cost += ( + cache_creation_tokens_1h * cache_creation_cost_above_1hr + if cache_creation_tokens_1h is not None + else 0.0 + ) + else: + total_cost += cache_creation_tokens * cache_creation_cost + return total_cost + + +class PromptTokensDetailsResult(TypedDict): + cache_hit_tokens: int + cache_creation_tokens: int + cache_creation_token_details: Optional[CacheCreationTokenDetails] + text_tokens: int + audio_tokens: int + character_count: int + image_count: int + video_length_seconds: int + + +def _parse_prompt_tokens_details(usage: Usage) -> PromptTokensDetailsResult: + cache_hit_tokens = ( + cast(Optional[int], getattr(usage.prompt_tokens_details, "cached_tokens", 0)) + or 0 + ) + cache_creation_tokens = ( + cast( + Optional[int], + getattr(usage.prompt_tokens_details, "cache_creation_tokens", 0), + ) + or 0 + ) + cache_creation_token_details = ( + cast( + Optional[CacheCreationTokenDetails], + getattr(usage.prompt_tokens_details, "cache_creation_token_details", None), + ) + or None + ) + text_tokens = ( + cast(Optional[int], getattr(usage.prompt_tokens_details, "text_tokens", None)) + or 0 # default to prompt tokens, if this field is not set + ) + audio_tokens = ( + cast(Optional[int], getattr(usage.prompt_tokens_details, "audio_tokens", 0)) + or 0 + ) + character_count = ( + cast( + Optional[int], + getattr(usage.prompt_tokens_details, "character_count", 0), + ) + or 0 + ) + image_count = ( + cast(Optional[int], getattr(usage.prompt_tokens_details, "image_count", 0)) or 0 + ) + video_length_seconds = ( + cast( + Optional[int], + getattr(usage.prompt_tokens_details, "video_length_seconds", 0), + ) + or 0 + ) + + return PromptTokensDetailsResult( + cache_hit_tokens=cache_hit_tokens, + cache_creation_tokens=cache_creation_tokens, + cache_creation_token_details=cache_creation_token_details, + text_tokens=text_tokens, + audio_tokens=audio_tokens, + character_count=character_count, + image_count=image_count, + video_length_seconds=video_length_seconds, + ) + + +class CompletionTokensDetailsResult(TypedDict): + audio_tokens: int + text_tokens: int + reasoning_tokens: int + + +def _parse_completion_tokens_details(usage: Usage) -> CompletionTokensDetailsResult: + audio_tokens = ( + cast( + Optional[int], + getattr(usage.completion_tokens_details, "audio_tokens", 0), + ) + or 0 + ) + text_tokens = ( + cast( + Optional[int], + getattr(usage.completion_tokens_details, "text_tokens", None), + ) + or 0 # default to completion tokens, if this field is not set + ) + reasoning_tokens = ( + cast( + Optional[int], + getattr(usage.completion_tokens_details, "reasoning_tokens", 0), + ) + or 0 + ) + + return CompletionTokensDetailsResult( + audio_tokens=audio_tokens, + text_tokens=text_tokens, + reasoning_tokens=reasoning_tokens, + ) + + +def _calculate_input_cost( + prompt_tokens_details: PromptTokensDetailsResult, + model_info: ModelInfo, + prompt_base_cost: float, + cache_read_cost: float, + cache_creation_cost: float, + cache_creation_cost_above_1hr: float, +) -> float: + """ + Calculates the input cost for a given model, prompt tokens, and completion tokens. + """ + prompt_cost = float(prompt_tokens_details["text_tokens"]) * prompt_base_cost + + ### CACHE READ COST - Now uses tiered pricing + prompt_cost += float(prompt_tokens_details["cache_hit_tokens"]) * cache_read_cost + + ### AUDIO COST + prompt_cost += calculate_cost_component( + model_info, "input_cost_per_audio_token", prompt_tokens_details["audio_tokens"] + ) + + ### CACHE WRITING COST - Now uses tiered pricing + prompt_cost += calculate_cache_writing_cost( + cache_creation_tokens=prompt_tokens_details["cache_creation_tokens"], + cache_creation_token_details=prompt_tokens_details[ + "cache_creation_token_details" + ], + cache_creation_cost_above_1hr=cache_creation_cost_above_1hr, + cache_creation_cost=cache_creation_cost, + ) + + ### CHARACTER COST + + prompt_cost += calculate_cost_component( + model_info, "input_cost_per_character", prompt_tokens_details["character_count"] + ) + + ### IMAGE COUNT COST + prompt_cost += calculate_cost_component( + model_info, "input_cost_per_image", prompt_tokens_details["image_count"] + ) + + ### VIDEO LENGTH COST + prompt_cost += calculate_cost_component( + model_info, + "input_cost_per_video_per_second", + prompt_tokens_details["video_length_seconds"], + ) + + return prompt_cost def generic_cost_per_token( - model: str, usage: Usage, custom_llm_provider: str + model: str, usage: Usage, custom_llm_provider: str, service_tier: Optional[str] = None ) -> Tuple[float, float]: """ Calculates the cost per token for a given model, prompt tokens, and completion tokens. @@ -236,83 +517,45 @@ def generic_cost_per_token( ### Cost of processing (non-cache hit + cache hit) + Cost of cache-writing (cache writing) prompt_cost = 0.0 ### PROCESSING COST - text_tokens = usage.prompt_tokens - cache_hit_tokens = 0 - audio_tokens = 0 - character_count = 0 - image_count = 0 - video_length_seconds = 0 + prompt_tokens_details = PromptTokensDetailsResult( + cache_hit_tokens=0, + cache_creation_tokens=0, + cache_creation_token_details=None, + text_tokens=usage.prompt_tokens, + audio_tokens=0, + character_count=0, + image_count=0, + video_length_seconds=0, + ) if usage.prompt_tokens_details: - cache_hit_tokens = ( - cast( - Optional[int], getattr(usage.prompt_tokens_details, "cached_tokens", 0) - ) - or 0 - ) - text_tokens = ( - cast( - Optional[int], getattr(usage.prompt_tokens_details, "text_tokens", None) - ) - or 0 # default to prompt tokens, if this field is not set - ) - audio_tokens = ( - cast(Optional[int], getattr(usage.prompt_tokens_details, "audio_tokens", 0)) - or 0 - ) - character_count = ( - cast( - Optional[int], - getattr(usage.prompt_tokens_details, "character_count", 0), - ) - or 0 - ) - image_count = ( - cast(Optional[int], getattr(usage.prompt_tokens_details, "image_count", 0)) - or 0 - ) - video_length_seconds = ( - cast( - Optional[int], - getattr(usage.prompt_tokens_details, "video_length_seconds", 0), - ) - or 0 - ) + prompt_tokens_details = _parse_prompt_tokens_details(usage) ## EDGE CASE - text tokens not set inside PromptTokensDetails - if text_tokens == 0: - text_tokens = usage.prompt_tokens - cache_hit_tokens - audio_tokens - prompt_base_cost, completion_base_cost, cache_creation_cost, cache_read_cost = _get_token_base_cost( - model_info=model_info, usage=usage - ) + if prompt_tokens_details["text_tokens"] == 0: + text_tokens = ( + usage.prompt_tokens + - prompt_tokens_details["cache_hit_tokens"] + - prompt_tokens_details["audio_tokens"] + - prompt_tokens_details["cache_creation_tokens"] + ) + prompt_tokens_details["text_tokens"] = text_tokens - prompt_cost = float(text_tokens) * prompt_base_cost + ( + prompt_base_cost, + completion_base_cost, + cache_creation_cost, + cache_creation_cost_above_1hr, + cache_read_cost, + ) = _get_token_base_cost(model_info=model_info, usage=usage, service_tier=service_tier) - ### CACHE READ COST - Now uses tiered pricing - prompt_cost += float(cache_hit_tokens) * cache_read_cost - - ### AUDIO COST - prompt_cost += calculate_cost_component( - model_info, "input_cost_per_audio_token", audio_tokens - ) - - ### CACHE WRITING COST - Now uses tiered pricing - prompt_cost += float(usage._cache_creation_input_tokens or 0) * cache_creation_cost - - ### CHARACTER COST - - prompt_cost += calculate_cost_component( - model_info, "input_cost_per_character", character_count - ) - - ### IMAGE COUNT COST - prompt_cost += calculate_cost_component( - model_info, "input_cost_per_image", image_count - ) - - ### VIDEO LENGTH COST - prompt_cost += calculate_cost_component( - model_info, "input_cost_per_video_per_second", video_length_seconds + prompt_cost = _calculate_input_cost( + prompt_tokens_details=prompt_tokens_details, + model_info=model_info, + prompt_base_cost=prompt_base_cost, + cache_read_cost=cache_read_cost, + cache_creation_cost=cache_creation_cost, + cache_creation_cost_above_1hr=cache_creation_cost_above_1hr, ) ## CALCULATE OUTPUT COST @@ -321,27 +564,10 @@ def generic_cost_per_token( reasoning_tokens = 0 is_text_tokens_total = False if usage.completion_tokens_details is not None: - audio_tokens = ( - cast( - Optional[int], - getattr(usage.completion_tokens_details, "audio_tokens", 0), - ) - or 0 - ) - text_tokens = ( - cast( - Optional[int], - getattr(usage.completion_tokens_details, "text_tokens", None), - ) - or 0 # default to completion tokens, if this field is not set - ) - reasoning_tokens = ( - cast( - Optional[int], - getattr(usage.completion_tokens_details, "reasoning_tokens", 0), - ) - or 0 - ) + completion_tokens_details = _parse_completion_tokens_details(usage) + audio_tokens = completion_tokens_details["audio_tokens"] + text_tokens = completion_tokens_details["text_tokens"] + reasoning_tokens = completion_tokens_details["reasoning_tokens"] if text_tokens == 0: text_tokens = usage.completion_tokens @@ -350,8 +576,12 @@ def generic_cost_per_token( ## TEXT COST completion_cost = float(text_tokens) * completion_base_cost - _output_cost_per_audio_token = _get_cost_per_unit(model_info, "output_cost_per_audio_token", None) - _output_cost_per_reasoning_token = _get_cost_per_unit(model_info, "output_cost_per_reasoning_token", None) + _output_cost_per_audio_token = _get_cost_per_unit( + model_info, "output_cost_per_audio_token", None + ) + _output_cost_per_reasoning_token = _get_cost_per_unit( + model_info, "output_cost_per_reasoning_token", None + ) ## AUDIO COST if not is_text_tokens_total and audio_tokens is not None and audio_tokens > 0: @@ -397,7 +627,7 @@ class CostCalculatorUtils: ]: return True return False - + @staticmethod def route_image_generation_cost_calculator( model: str, @@ -454,6 +684,15 @@ class CostCalculatorUtils: model=model, image_response=completion_response, ) + elif custom_llm_provider == litellm.LlmProviders.AIML.value: + from litellm.llms.aiml.image_generation.cost_calculator import ( + cost_calculator as aiml_image_cost_calculator, + ) + + return aiml_image_cost_calculator( + model=model, + image_response=completion_response, + ) elif custom_llm_provider == litellm.LlmProviders.GEMINI.value: from litellm.llms.gemini.image_generation.cost_calculator import ( cost_calculator as gemini_image_cost_calculator, diff --git a/litellm/litellm_core_utils/llm_response_utils/convert_dict_to_response.py b/litellm/litellm_core_utils/llm_response_utils/convert_dict_to_response.py index 54adef9c958..2def52ed7e6 100644 --- a/litellm/litellm_core_utils/llm_response_utils/convert_dict_to_response.py +++ b/litellm/litellm_core_utils/llm_response_utils/convert_dict_to_response.py @@ -1,14 +1,16 @@ import asyncio import json -import re import time import traceback -import uuid +from litellm._uuid import uuid from typing import Dict, Iterable, List, Literal, Optional, Tuple, Union import litellm from litellm._logging import verbose_logger from litellm.constants import RESPONSE_FORMAT_TOOL_NAME +from litellm.litellm_core_utils.prompt_templates.common_utils import ( + _extract_reasoning_content, +) from litellm.types.llms.databricks import DatabricksTool from litellm.types.llms.openai import ( ChatCompletionThinkingBlock, @@ -43,13 +45,13 @@ from .get_headers import get_response_headers def _safe_convert_created_field(created_value) -> int: """ Safely convert a 'created' field value to an integer. - - Some providers (like SambaNova) return the 'created' field as a float + + Some providers (like SambaNova) return the 'created' field as a float (Unix timestamp with fractional seconds), but LiteLLM expects an integer. - + Args: created_value: The value from response_object["created"] - + Returns: int: Unix timestamp as integer """ @@ -161,7 +163,9 @@ async def convert_to_streaming_response_async(response_object: Optional[dict] = model_response_object.id = response_object["id"] if "created" in response_object: - model_response_object.created = _safe_convert_created_field(response_object["created"]) + model_response_object.created = _safe_convert_created_field( + response_object["created"] + ) if "system_fingerprint" in response_object: model_response_object.system_fingerprint = response_object["system_fingerprint"] @@ -209,7 +213,9 @@ def convert_to_streaming_response(response_object: Optional[dict] = None): model_response_object.id = response_object["id"] if "created" in response_object: - model_response_object.created = _safe_convert_created_field(response_object["created"]) + model_response_object.created = _safe_convert_created_field( + response_object["created"] + ) if "system_fingerprint" in response_object: model_response_object.system_fingerprint = response_object["system_fingerprint"] @@ -270,49 +276,6 @@ def _handle_invalid_parallel_tool_calls( return tool_calls -def _parse_content_for_reasoning( - message_text: Optional[str], -) -> Tuple[Optional[str], Optional[str]]: - """ - Parse the content for reasoning - - Returns: - - reasoning_content: The content of the reasoning - - content: The content of the message - """ - if not message_text: - return None, message_text - - reasoning_match = re.match( - r"<(?:think|thinking)>(.*?)(.*)", message_text, re.DOTALL - ) - - if reasoning_match: - return reasoning_match.group(1), reasoning_match.group(2) - - return None, message_text - - -def _extract_reasoning_content(message: dict) -> Tuple[Optional[str], Optional[str]]: - """ - Extract reasoning content and main content from a message. - - Args: - message (dict): The message dictionary that may contain reasoning_content - - Returns: - tuple[Optional[str], Optional[str]]: A tuple of (reasoning_content, content) - """ - message_content = message.get("content") - if "reasoning_content" in message: - return message["reasoning_content"], message["content"] - elif "reasoning" in message: - return message["reasoning"], message["content"] - elif isinstance(message_content, str): - return _parse_content_for_reasoning(message_content) - return None, message_content - - class LiteLLMResponseObjectHandler: @staticmethod def convert_to_image_response( @@ -557,9 +520,9 @@ def convert_to_model_response_object( # noqa: PLR0915 provider_specific_fields["thinking_blocks"] = thinking_blocks if reasoning_content: - provider_specific_fields[ - "reasoning_content" - ] = reasoning_content + provider_specific_fields["reasoning_content"] = ( + reasoning_content + ) message = Message( content=content, @@ -571,6 +534,7 @@ def convert_to_model_response_object( # noqa: PLR0915 reasoning_content=reasoning_content, thinking_blocks=thinking_blocks, annotations=choice["message"].get("annotations", None), + images=choice["message"].get("images", None), ) finish_reason = choice.get("finish_reason", None) if finish_reason is None: @@ -606,7 +570,9 @@ def convert_to_model_response_object( # noqa: PLR0915 usage_object = litellm.Usage(**response_object["usage"]) setattr(model_response_object, "usage", usage_object) if "created" in response_object: - model_response_object.created = _safe_convert_created_field(response_object["created"]) + model_response_object.created = _safe_convert_created_field( + response_object["created"] + ) if "id" in response_object: model_response_object.id = response_object["id"] or str(uuid.uuid4()) diff --git a/litellm/litellm_core_utils/logging_worker.py b/litellm/litellm_core_utils/logging_worker.py new file mode 100644 index 00000000000..3c475f133a8 --- /dev/null +++ b/litellm/litellm_core_utils/logging_worker.py @@ -0,0 +1,159 @@ +import asyncio +import contextlib +import contextvars +from typing import Coroutine, Optional + +from typing_extensions import TypedDict + +from litellm._logging import verbose_logger + + +class LoggingTask(TypedDict): + """ + A logging task with its associated context to ensure logging is executed in + the original task's context. + """ + + coroutine: Coroutine + context: contextvars.Context + + +class LoggingWorker: + """ + A simple, async logging worker that processes log coroutines in the background. + Designed to be best-effort with bounded queues to prevent backpressure. + + This leads to a +200 RPS performance improvement when using LiteLLM Python SDK or Proxy Server. + - Use this to queue coroutine tasks that are not critical to the main flow of the application. e.g Success/Error callbacks, logging, etc. + """ + + LOGGING_WORKER_MAX_QUEUE_SIZE = 50_000 + LOGGING_WORKER_MAX_TIME_PER_COROUTINE = 20.0 + + MAX_ITERATIONS_TO_CLEAR_QUEUE = 200 + MAX_TIME_TO_CLEAR_QUEUE = 5.0 + + def __init__( + self, + timeout: float = LOGGING_WORKER_MAX_TIME_PER_COROUTINE, + max_queue_size: int = LOGGING_WORKER_MAX_QUEUE_SIZE, + ): + self.timeout = timeout + self.max_queue_size = max_queue_size + self._queue: Optional[asyncio.Queue[LoggingTask]] = None + self._worker_task: Optional[asyncio.Task] = None + + def _ensure_queue(self) -> None: + """Initialize the queue if it doesn't exist.""" + if self._queue is None: + self._queue = asyncio.Queue(maxsize=self.max_queue_size) + + def start(self) -> None: + """Start the logging worker. Idempotent - safe to call multiple times.""" + self._ensure_queue() + if self._worker_task is None or self._worker_task.done(): + self._worker_task = asyncio.create_task(self._worker_loop()) + + async def _worker_loop(self) -> None: + """Main worker loop that processes log coroutines sequentially.""" + try: + if self._queue is None: + return + + while True: + # Process one coroutine at a time to keep event loop load predictable + task = await self._queue.get() + try: + # Run the coroutine in its original context + await asyncio.wait_for( + task["context"].run(asyncio.create_task, task["coroutine"]), + timeout=self.timeout, + ) + except Exception as e: + verbose_logger.exception(f"LoggingWorker error: {e}") + pass + finally: + self._queue.task_done() + + except asyncio.CancelledError: + verbose_logger.debug("LoggingWorker cancelled during shutdown") + # Attempt to clear remaining items to prevent "never awaited" warnings + await self.clear_queue() + + def enqueue(self, coroutine: Coroutine) -> None: + """ + Add a coroutine to the logging queue. + Hot path: never blocks, drops logs if queue is full. + """ + if self._queue is None: + return + + try: + # Capture the current context when enqueueing + task = LoggingTask(coroutine=coroutine, context=contextvars.copy_context()) + self._queue.put_nowait(task) + except asyncio.QueueFull as e: + verbose_logger.exception(f"LoggingWorker queue is full: {e}") + # Drop logs on overload to protect request throughput + pass + + def ensure_initialized_and_enqueue(self, async_coroutine: Coroutine): + """ + Ensure the logging worker is initialized and enqueue the coroutine. + """ + self.start() + self.enqueue(async_coroutine) + + async def stop(self) -> None: + """Stop the logging worker and clean up resources.""" + if self._worker_task: + self._worker_task.cancel() + with contextlib.suppress(Exception): + await self._worker_task + self._worker_task = None + + async def flush(self) -> None: + """Flush the logging queue.""" + if self._queue is None: + return + while not self._queue.empty(): + await self._queue.join() + + async def clear_queue(self): + """ + Clear the queue with a maximum time limit. + """ + if self._queue is None: + return + + start_time = asyncio.get_event_loop().time() + + for _ in range(self.MAX_ITERATIONS_TO_CLEAR_QUEUE): + # Check if we've exceeded the maximum time + if ( + asyncio.get_event_loop().time() - start_time + >= self.MAX_TIME_TO_CLEAR_QUEUE + ): + verbose_logger.warning( + f"clear_queue exceeded max_time of {self.MAX_TIME_TO_CLEAR_QUEUE}s, stopping early" + ) + break + + try: + task = self._queue.get_nowait() + # Await the coroutine to properly execute and avoid "never awaited" warnings + try: + await asyncio.wait_for( + task["context"].run(asyncio.create_task, task["coroutine"]), + timeout=self.timeout, + ) + except Exception: + # Suppress errors during cleanup + pass + self._queue.task_done() # If you're using join() elsewhere + except asyncio.QueueEmpty: + break + + +# Global instance for backward compatibility +GLOBAL_LOGGING_WORKER = LoggingWorker() diff --git a/litellm/litellm_core_utils/prompt_templates/common_utils.py b/litellm/litellm_core_utils/prompt_templates/common_utils.py index a99883ef7b6..19d5932ff28 100644 --- a/litellm/litellm_core_utils/prompt_templates/common_utils.py +++ b/litellm/litellm_core_utils/prompt_templates/common_utils.py @@ -14,6 +14,7 @@ from typing import ( Literal, Mapping, Optional, + Tuple, Union, cast, ) @@ -869,3 +870,63 @@ def convert_prefix_message_to_non_prefix_messages( else: new_messages.append(message) return new_messages + + +def _extract_reasoning_content(message: dict) -> Tuple[Optional[str], Optional[str]]: + """ + Extract reasoning content and main content from a message. + + Args: + message (dict): The message dictionary that may contain reasoning_content + + Returns: + tuple[Optional[str], Optional[str]]: A tuple of (reasoning_content, content) + """ + message_content = message.get("content") + if "reasoning_content" in message: + return message["reasoning_content"], message["content"] + elif "reasoning" in message: + return message["reasoning"], message["content"] + elif isinstance(message_content, str): + return _parse_content_for_reasoning(message_content) + return None, message_content + + +def _parse_content_for_reasoning( + message_text: Optional[str], +) -> Tuple[Optional[str], Optional[str]]: + """ + Parse the content for reasoning + + Returns: + - reasoning_content: The content of the reasoning + - content: The content of the message + """ + if not message_text: + return None, message_text + + reasoning_match = re.match( + r"<(?:think|thinking)>(.*?)(.*)", message_text, re.DOTALL + ) + + if reasoning_match: + return reasoning_match.group(1), reasoning_match.group(2) + + return None, message_text + + +def extract_images_from_message(message: AllMessageValues) -> List[str]: + """ + Extract images from a message + """ + images = [] + message_content = message.get("content") + if isinstance(message_content, list): + for m in message_content: + image_url = m.get("image_url") + if image_url: + if isinstance(image_url, str): + images.append(image_url) + elif isinstance(image_url, dict) and "url" in image_url: + images.append(image_url["url"]) + return images diff --git a/litellm/litellm_core_utils/prompt_templates/factory.py b/litellm/litellm_core_utils/prompt_templates/factory.py index 77cbe4c9a8e..c1d8a1cd41b 100644 --- a/litellm/litellm_core_utils/prompt_templates/factory.py +++ b/litellm/litellm_core_utils/prompt_templates/factory.py @@ -2,7 +2,7 @@ import copy import json import mimetypes import re -import uuid +from litellm._uuid import uuid import xml.etree.ElementTree as ET from enum import Enum from typing import Any, List, Optional, Tuple, cast, overload @@ -16,8 +16,8 @@ from litellm import verbose_logger from litellm.llms.custom_httpx.http_handler import HTTPHandler, get_async_httpx_client from litellm.types.files import get_file_extension_from_mime_type from litellm.types.llms.anthropic import * -from litellm.types.llms.bedrock import MessageBlock as BedrockMessageBlock from litellm.types.llms.bedrock import CachePointBlock +from litellm.types.llms.bedrock import MessageBlock as BedrockMessageBlock from litellm.types.llms.custom_http import httpxSpecialProvider from litellm.types.llms.ollama import OllamaVisionModelObject from litellm.types.llms.openai import ( @@ -1067,10 +1067,10 @@ def convert_to_gemini_tool_call_invoke( if tool_calls is not None: for tool in tool_calls: if "function" in tool: - gemini_function_call: Optional[VertexFunctionCall] = ( - _gemini_tool_call_invoke_helper( - function_call_params=tool["function"] - ) + gemini_function_call: Optional[ + VertexFunctionCall + ] = _gemini_tool_call_invoke_helper( + function_call_params=tool["function"] ) if gemini_function_call is not None: _parts_list.append( @@ -1589,9 +1589,9 @@ def anthropic_messages_pt( # noqa: PLR0915 ) if "cache_control" in _content_element: - _anthropic_content_element["cache_control"] = ( - _content_element["cache_control"] - ) + _anthropic_content_element[ + "cache_control" + ] = _content_element["cache_control"] user_content.append(_anthropic_content_element) elif m.get("type", "") == "text": m = cast(ChatCompletionTextObject, m) @@ -1629,9 +1629,9 @@ def anthropic_messages_pt( # noqa: PLR0915 ) if "cache_control" in _content_element: - _anthropic_content_text_element["cache_control"] = ( - _content_element["cache_control"] - ) + _anthropic_content_text_element[ + "cache_control" + ] = _content_element["cache_control"] user_content.append(_anthropic_content_text_element) @@ -2482,8 +2482,7 @@ class BedrockImageProcessor: if is_document: return BedrockImageProcessor._get_document_format( - mime_type=mime_type, - supported_doc_formats=supported_doc_formats + mime_type=mime_type, supported_doc_formats=supported_doc_formats ) else: @@ -2495,12 +2494,9 @@ class BedrockImageProcessor: f"Unsupported image format: {image_format}. Supported formats: {supported_image_and_video_formats}" ) return image_format - + @staticmethod - def _get_document_format( - mime_type: str, - supported_doc_formats: List[str] - ) -> str: + def _get_document_format(mime_type: str, supported_doc_formats: List[str]) -> str: """ Get the document format from the mime type @@ -2519,13 +2515,9 @@ class BedrockImageProcessor: The document format """ valid_extensions: Optional[List[str]] = None - potential_extensions = mimetypes.guess_all_extensions( - mime_type, strict=False - ) + potential_extensions = mimetypes.guess_all_extensions(mime_type, strict=False) valid_extensions = [ - ext[1:] - for ext in potential_extensions - if ext[1:] in supported_doc_formats + ext[1:] for ext in potential_extensions if ext[1:] in supported_doc_formats ] # Fallback to types/files.py if mimetypes doesn't return valid extensions @@ -2680,16 +2672,21 @@ def _convert_to_bedrock_tool_call_invoke( id = tool["id"] name = tool["function"].get("name", "") arguments = tool["function"].get("arguments", "") - arguments_dict = json.loads(arguments) if arguments else {} + if not arguments or not arguments.strip(): + arguments_dict = {} + else: + arguments_dict = json.loads(arguments) bedrock_tool = BedrockToolUseBlock( input=arguments_dict, name=name, toolUseId=id ) bedrock_content_block = BedrockContentBlock(toolUse=bedrock_tool) _parts_list.append(bedrock_content_block) - + # Check for cache_control and add a separate cachePoint block if tool.get("cache_control", None) is not None: - cache_point_block = BedrockContentBlock(cachePoint=CachePointBlock(type="default")) + cache_point_block = BedrockContentBlock( + cachePoint=CachePointBlock(type="default") + ) _parts_list.append(cache_point_block) return _parts_list except Exception as e: @@ -2751,7 +2748,7 @@ def _convert_to_bedrock_tool_call_result( for content in content_list: if content["type"] == "text": content_str += content["text"] - + message.get("name", "") id = str(message.get("tool_call_id", str(uuid.uuid4()))) @@ -2760,7 +2757,7 @@ def _convert_to_bedrock_tool_call_result( content=[tool_result_content_block], toolUseId=id, ) - + content_block = BedrockContentBlock(toolResult=tool_result) return content_block @@ -3125,6 +3122,12 @@ class BedrockConverseMessagesProcessor: if element["type"] == "text": _part = BedrockContentBlock(text=element["text"]) _parts.append(_part) + elif element["type"] == "guarded_text": + # Wrap guarded_text in guardContent block + _part = BedrockContentBlock( + guardContent={"text": {"text": element["text"]}} + ) + _parts.append(_part) elif element["type"] == "image_url": format: Optional[str] = None if isinstance(element["image_url"], dict): @@ -3196,26 +3199,29 @@ class BedrockConverseMessagesProcessor: current_message = messages[msg_i] tool_call_result = _convert_to_bedrock_tool_call_result(current_message) tool_content.append(tool_call_result) - + # Check if we need to add a separate cachePoint block has_cache_control = False - + # Check for message-level cache_control if current_message.get("cache_control", None) is not None: has_cache_control = True # Check for content-level cache_control in list content elif isinstance(current_message.get("content"), list): for content_element in current_message["content"]: - if (isinstance(content_element, dict) and - content_element.get("cache_control", None) is not None): + if ( + isinstance(content_element, dict) + and content_element.get("cache_control", None) is not None + ): has_cache_control = True break - + # Add a separate cachePoint block if cache_control is present if has_cache_control: - cache_point_block = BedrockContentBlock(cachePoint=CachePointBlock(type="default")) + cache_point_block = BedrockContentBlock( + cachePoint=CachePointBlock(type="default") + ) tool_content.append(cache_point_block) - msg_i += 1 if tool_content: @@ -3296,7 +3302,7 @@ class BedrockConverseMessagesProcessor: image_url=image_url ) assistants_parts.append(assistants_part) - # Add cache point block for assistant content elements + # Add cache point block for assistant content elements _cache_point_block = ( litellm.AmazonConverseConfig()._get_cache_point_block( message_block=cast( @@ -3308,8 +3314,12 @@ class BedrockConverseMessagesProcessor: if _cache_point_block is not None: assistants_parts.append(_cache_point_block) assistant_content.extend(assistants_parts) - elif _assistant_content is not None and isinstance(_assistant_content, str): - assistant_content.append(BedrockContentBlock(text=_assistant_content)) + elif _assistant_content is not None and isinstance( + _assistant_content, str + ): + assistant_content.append( + BedrockContentBlock(text=_assistant_content) + ) # Add cache point block for assistant string content _cache_point_block = ( litellm.AmazonConverseConfig()._get_cache_point_block( @@ -3493,6 +3503,12 @@ def _bedrock_converse_messages_pt( # noqa: PLR0915 if element["type"] == "text": _part = BedrockContentBlock(text=element["text"]) _parts.append(_part) + elif element["type"] == "guarded_text": + # Wrap guarded_text in guardContent block + _part = BedrockContentBlock( + guardContent={"text": {"text": element["text"]}} + ) + _parts.append(_part) elif element["type"] == "image_url": format: Optional[str] = None if isinstance(element["image_url"], dict): @@ -3562,29 +3578,33 @@ def _bedrock_converse_messages_pt( # noqa: PLR0915 while msg_i < len(messages) and messages[msg_i]["role"] == "tool": tool_call_result = _convert_to_bedrock_tool_call_result(messages[msg_i]) current_message = messages[msg_i] - + # Add the tool result first tool_content.append(tool_call_result) - + # Check if we need to add a separate cachePoint block has_cache_control = False - + # Check for message-level cache_control if current_message.get("cache_control", None) is not None: has_cache_control = True # Check for content-level cache_control in list content elif isinstance(current_message.get("content"), list): for content_element in current_message["content"]: - if (isinstance(content_element, dict) and - content_element.get("cache_control", None) is not None): + if ( + isinstance(content_element, dict) + and content_element.get("cache_control", None) is not None + ): has_cache_control = True break - + # Add a separate cachePoint block if cache_control is present if has_cache_control: - cache_point_block = BedrockContentBlock(cachePoint=CachePointBlock(type="default")) + cache_point_block = BedrockContentBlock( + cachePoint=CachePointBlock(type="default") + ) tool_content.append(cache_point_block) - + msg_i += 1 if tool_content: # if last message was a 'user' message, then add a blank assistant message (bedrock requires alternating roles) @@ -3846,7 +3866,12 @@ def function_call_prompt(messages: list, functions: list): function_added_to_prompt = False for message in messages: if "system" in message["role"]: - message["content"] += f""" {function_prompt}""" + if isinstance(message["content"], str): + message["content"] += f""" {function_prompt}""" + else: + message["content"].append( + {"type": "text", "text": f""" {function_prompt}"""} + ) function_added_to_prompt = True if function_added_to_prompt is False: diff --git a/litellm/litellm_core_utils/prompt_templates/image_handling.py b/litellm/litellm_core_utils/prompt_templates/image_handling.py index a9ff14d6c82..4fa10e42111 100644 --- a/litellm/litellm_core_utils/prompt_templates/image_handling.py +++ b/litellm/litellm_core_utils/prompt_templates/image_handling.py @@ -17,7 +17,7 @@ in_memory_cache = InMemoryCache(max_size_in_memory=MAX_IMGS_IN_MEMORY) def _process_image_response(response: Response, url: str) -> str: if response.status_code != 200: - raise Exception( + raise litellm.ImageFetchError( f"Error: Unable to fetch image from URL. Status code: {response.status_code}, url={url}" ) @@ -57,9 +57,11 @@ async def async_convert_url_to_base64(url: str) -> str: try: response = await client.get(url, follow_redirects=True) return _process_image_response(response, url) + except litellm.ImageFetchError: + raise except Exception: pass - raise Exception( + raise litellm.ImageFetchError( f"Error: Unable to fetch image from URL after 3 attempts. url={url}" ) @@ -74,10 +76,11 @@ def convert_url_to_base64(url: str) -> str: try: response = client.get(url, follow_redirects=True) return _process_image_response(response, url) + except litellm.ImageFetchError: + raise except Exception as e: verbose_logger.exception(e) - # print(e) pass - raise Exception( - f"Error: Unable to fetch image from URL after 3 attempts. url={url}" + raise litellm.ImageFetchError( + f"Error: Unable to fetch image from URL after 3 attempts. url={url}", ) diff --git a/litellm/litellm_core_utils/rules.py b/litellm/litellm_core_utils/rules.py index beeb012d032..717ff55ab22 100644 --- a/litellm/litellm_core_utils/rules.py +++ b/litellm/litellm_core_utils/rules.py @@ -23,6 +23,11 @@ class Rules: def __init__(self) -> None: pass + @staticmethod + def has_pre_call_rules() -> bool: + """Check if any pre-call rules are configured""" + return len(litellm.pre_call_rules) > 0 + def pre_call_rules(self, input: str, model: str): for rule in litellm.pre_call_rules: if callable(rule): diff --git a/litellm/litellm_core_utils/safe_json_dumps.py b/litellm/litellm_core_utils/safe_json_dumps.py index 7ad0038ecb2..c714e36b5f9 100644 --- a/litellm/litellm_core_utils/safe_json_dumps.py +++ b/litellm/litellm_core_utils/safe_json_dumps.py @@ -1,5 +1,6 @@ import json from typing import Any, Union + from litellm.constants import DEFAULT_MAX_RECURSE_DEPTH diff --git a/litellm/litellm_core_utils/streaming_handler.py b/litellm/litellm_core_utils/streaming_handler.py index b9433239271..b2f6c621407 100644 --- a/litellm/litellm_core_utils/streaming_handler.py +++ b/litellm/litellm_core_utils/streaming_handler.py @@ -5,7 +5,7 @@ import json import threading import time import traceback -import uuid +from litellm._uuid import uuid from typing import Any, Callable, Dict, List, Optional, Union, cast import httpx @@ -35,6 +35,12 @@ from .exception_mapping_utils import exception_type from .llm_response_utils.get_api_base import get_api_base from .rules import Rules +# Constants for special delta attribute names +AUDIO_ATTRIBUTE = "audio" +IMAGE_ATTRIBUTE = "images" +TOOL_CALLS_ATTRIBUTE = "tool_calls" +FUNCTION_CALL_ATTRIBUTE = "function_call" + def is_async_iterable(obj: Any) -> bool: """ @@ -766,6 +772,83 @@ class CustomStreamWrapper: model_response.choices[0].delta = Delta(**_initial_delta) return model_response + def _has_special_delta_content(self, model_response: ModelResponseStream) -> bool: + """ + Check if the delta contains special content types (tool_calls, function_call, audio, or image). + """ + if len(model_response.choices) == 0: + return False + + delta = model_response.choices[0].delta + + # Check for tool_calls or function_call + if ( + getattr(delta, TOOL_CALLS_ATTRIBUTE, None) is not None + or getattr(delta, FUNCTION_CALL_ATTRIBUTE, None) is not None + ): + return True + + # Check for audio + if ( + hasattr(delta, AUDIO_ATTRIBUTE) + and getattr(delta, AUDIO_ATTRIBUTE, None) is not None + ): + return True + + # Check for image + if ( + hasattr(delta, IMAGE_ATTRIBUTE) + and getattr(delta, IMAGE_ATTRIBUTE, None) is not None + ): + return True + + return False + + def _handle_special_delta_content( + self, model_response: ModelResponseStream + ) -> ModelResponseStream: + """ + Handle special delta content types by stripping role and returning the response. + """ + return self.strip_role_from_delta(model_response) + + def _has_special_delta_attribute(self, delta, attribute_name: str) -> bool: + """ + Check if delta has a specific attribute and it's not None. + """ + return delta is not None and getattr(delta, attribute_name, None) is not None + + def _copy_delta_attribute( + self, source_delta, target_delta, attribute_name: str + ) -> None: + """ + Copy a specific attribute from source delta to target delta. + """ + setattr(target_delta, attribute_name, getattr(source_delta, attribute_name)) + + def _has_any_special_delta_attributes(self, delta) -> bool: + """ + Check if delta has any special attributes (audio, image). + """ + special_attributes = [AUDIO_ATTRIBUTE, IMAGE_ATTRIBUTE] + for attribute in special_attributes: + if self._has_special_delta_attribute(delta, attribute): + return True + return False + + def _handle_special_delta_attributes( + self, delta, model_response: "ModelResponseStream" + ) -> None: + """ + Handle special delta attributes (audio, image) by copying them to model_response. + """ + special_attributes = [AUDIO_ATTRIBUTE, IMAGE_ATTRIBUTE] + for attribute in special_attributes: + if self._has_special_delta_attribute(delta, attribute): + self._copy_delta_attribute( + delta, model_response.choices[0].delta, attribute + ) + def return_processed_chunk_logic( # noqa self, completion_obj: Dict[str, Any], @@ -888,20 +971,8 @@ class CustomStreamWrapper: self.sent_last_chunk = True return model_response - elif ( - model_response.choices[0].delta.tool_calls is not None - or model_response.choices[0].delta.function_call is not None - ): - model_response = self.strip_role_from_delta(model_response) - - return model_response - elif ( - len(model_response.choices) > 0 - and hasattr(model_response.choices[0].delta, "audio") - and model_response.choices[0].delta.audio is not None - ): - model_response = self.strip_role_from_delta(model_response) - return model_response + elif self._has_special_delta_content(model_response): + return self._handle_special_delta_content(model_response) else: if hasattr(model_response, "usage"): self.chunks.append(model_response) @@ -953,6 +1024,8 @@ class CustomStreamWrapper: return def chunk_creator(self, chunk: Any): # type: ignore # noqa: PLR0915 + if hasattr(chunk, 'id'): + self.response_id = chunk.id model_response = self.model_response_creator() response_obj: Dict[str, Any] = {} try: @@ -1374,10 +1447,8 @@ class CustomStreamWrapper: ) ) model_response.choices[0].delta = Delta() - elif ( - delta is not None and getattr(delta, "audio", None) is not None - ): - model_response.choices[0].delta.audio = delta.audio + elif self._has_any_special_delta_attributes(delta): + self._handle_special_delta_attributes(delta, model_response) else: try: delta = ( @@ -1548,11 +1619,12 @@ class CustomStreamWrapper: completion_start_time=datetime.datetime.now() ) ## LOGGING - executor.submit( - self.run_success_logging_and_cache_storage, - response, - cache_hit, - ) # log response + if not litellm.disable_streaming_logging: + executor.submit( + self.run_success_logging_and_cache_storage, + response, + cache_hit, + ) # log response choice = response.choices[0] if isinstance(choice, StreamingChoices): self.response_uptil_now += choice.delta.get("content", "") or "" @@ -1868,7 +1940,7 @@ class CustomStreamWrapper: ) ## Map to OpenAI Exception try: - exception_type( + raise exception_type( model=self.model, custom_llm_provider=self.custom_llm_provider, original_exception=e, diff --git a/litellm/litellm_core_utils/token_counter.py b/litellm/litellm_core_utils/token_counter.py index 4df944edbaa..fab2c1e76ee 100644 --- a/litellm/litellm_core_utils/token_counter.py +++ b/litellm/litellm_core_utils/token_counter.py @@ -529,7 +529,7 @@ def _get_count_function( encoding = tiktoken.get_encoding("cl100k_base") def count_tokens(text: str) -> int: - return len(encoding.encode(text)) + return len(encoding.encode(text, disallowed_special=())) else: raise ValueError("Unsupported tokenizer type") diff --git a/litellm/llms/aiml/__init__.py b/litellm/llms/aiml/__init__.py new file mode 100644 index 00000000000..42482760cda --- /dev/null +++ b/litellm/llms/aiml/__init__.py @@ -0,0 +1,5 @@ +from .image_generation import get_aiml_image_generation_config + +__all__ = [ + "get_aiml_image_generation_config", +] diff --git a/litellm/llms/aiml/chat/transformation.py b/litellm/llms/aiml/chat/transformation.py new file mode 100644 index 00000000000..0f3e333343d --- /dev/null +++ b/litellm/llms/aiml/chat/transformation.py @@ -0,0 +1,23 @@ +from typing import Optional, Tuple + +from litellm.llms.openai.chat.gpt_transformation import OpenAIGPTConfig +from litellm.secret_managers.main import get_secret_str + + +class AIMLChatConfig(OpenAIGPTConfig): + @property + def custom_llm_provider(self) -> Optional[str]: + return "aiml" + + def _get_openai_compatible_provider_info( + self, api_base: Optional[str], api_key: Optional[str] + ) -> Tuple[Optional[str], Optional[str]]: + # AIML is openai compatible, we just need to set the api_base + api_base = ( + api_base + or get_secret_str("AIML_API_BASE") + or "https://api.aimlapi.com/v1" # Default AIML API base URL + ) # type: ignore + dynamic_api_key = api_key or get_secret_str("AIML_API_KEY") + return api_base, dynamic_api_key + pass \ No newline at end of file diff --git a/litellm/llms/aiml/image_generation/__init__.py b/litellm/llms/aiml/image_generation/__init__.py new file mode 100644 index 00000000000..4548bd1b3f8 --- /dev/null +++ b/litellm/llms/aiml/image_generation/__init__.py @@ -0,0 +1,13 @@ +from litellm.llms.base_llm.image_generation.transformation import ( + BaseImageGenerationConfig, +) + +from .transformation import AimlImageGenerationConfig + +__all__ = [ + "AimlImageGenerationConfig", +] + + +def get_aiml_image_generation_config(model: str) -> BaseImageGenerationConfig: + return AimlImageGenerationConfig() diff --git a/litellm/llms/aiml/image_generation/cost_calculator.py b/litellm/llms/aiml/image_generation/cost_calculator.py new file mode 100644 index 00000000000..1fecfb6a9a5 --- /dev/null +++ b/litellm/llms/aiml/image_generation/cost_calculator.py @@ -0,0 +1,25 @@ +from typing import Any + +import litellm +from litellm.types.utils import ImageResponse + + +def cost_calculator( + model: str, + image_response: Any, +) -> float: + """ + AI/ML flux image generation cost calculator + """ + _model_info = litellm.get_model_info( + model=model, + custom_llm_provider=litellm.LlmProviders.AIML.value, + ) + output_cost_per_image: float = _model_info.get("output_cost_per_image") or 0.0 + num_images: int = 0 + if isinstance(image_response, ImageResponse): + if image_response.data: + num_images = len(image_response.data) + return output_cost_per_image * num_images + else: + raise ValueError(f"image_response must be of type ImageResponse got type={type(image_response)}") diff --git a/litellm/llms/aiml/image_generation/transformation.py b/litellm/llms/aiml/image_generation/transformation.py new file mode 100644 index 00000000000..3b586689ea7 --- /dev/null +++ b/litellm/llms/aiml/image_generation/transformation.py @@ -0,0 +1,204 @@ +from typing import TYPE_CHECKING, Any, List, Optional + +import httpx + +from litellm.llms.base_llm.image_generation.transformation import ( + BaseImageGenerationConfig, +) +from litellm.secret_managers.main import get_secret_str +from litellm.types.llms.aiml import AimlImageGenerationRequestParams +from litellm.types.llms.openai import ( + AllMessageValues, + OpenAIImageGenerationOptionalParams, +) +from litellm.types.utils import ImageObject, ImageResponse + +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj + + LiteLLMLoggingObj = _LiteLLMLoggingObj +else: + LiteLLMLoggingObj = Any + + +class AimlImageGenerationConfig(BaseImageGenerationConfig): + DEFAULT_BASE_URL: str = "https://api.aimlapi.com" + IMAGE_GENERATION_ENDPOINT: str = "v1/images/generations" + + def get_supported_openai_params( + self, model: str + ) -> List[OpenAIImageGenerationOptionalParams]: + """ + https://api.aimlapi.com/v1/images/generations + """ + return [ + "n", + "response_format", + "size" + ] + + def map_openai_params( + self, + non_default_params: dict, + optional_params: dict, + model: str, + drop_params: bool, + ) -> dict: + supported_params = self.get_supported_openai_params(model) + + for k in non_default_params.keys(): + if k not in optional_params.keys(): + if k in supported_params: + # Map OpenAI params to AI/ML params + if k == "n": + optional_params["num_images"] = non_default_params[k] + elif k == "response_format": + optional_params["output_format"] = non_default_params[k] + elif k == "size": + # Map OpenAI size format to AI/ML image_size + size_value = non_default_params[k] + if isinstance(size_value, str): + # Handle standard OpenAI sizes like "1024x1024" + if "x" in size_value: + width, height = map(int, size_value.split("x")) + optional_params["image_size"] = {"width": width, "height": height} + else: + # Pass through predefined sizes + optional_params["image_size"] = size_value + else: + optional_params["image_size"] = size_value + else: + optional_params[k] = non_default_params[k] + elif drop_params: + pass + else: + raise ValueError( + f"Parameter {k} is not supported for model {model}. Supported parameters are {supported_params}. Set drop_params=True to drop unsupported parameters." + ) + + return optional_params + + def get_complete_url( + self, + api_base: Optional[str], + api_key: Optional[str], + model: str, + optional_params: dict, + litellm_params: dict, + stream: Optional[bool] = None, + ) -> str: + """ + Get the complete url for the request + """ + complete_url: str = ( + api_base + or get_secret_str("AIML_API_BASE") + or self.DEFAULT_BASE_URL + ) + + complete_url = complete_url.rstrip("/") + complete_url = f"{complete_url}/{self.IMAGE_GENERATION_ENDPOINT}" + return complete_url + + def validate_environment( + self, + headers: dict, + model: str, + messages: List[AllMessageValues], + optional_params: dict, + litellm_params: dict, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + ) -> dict: + final_api_key: Optional[str] = ( + api_key or + get_secret_str("AIML_API_KEY") or + get_secret_str("AIMLAPI_KEY") # Alternative name + ) + if not final_api_key: + raise ValueError("AIML_API_KEY or AIMLAPI_KEY is not set") + + headers["Authorization"] = f"Bearer {final_api_key}" + headers["Content-Type"] = "application/json" + return headers + + def transform_image_generation_request( + self, + model: str, + prompt: str, + optional_params: dict, + litellm_params: dict, + headers: dict, + ) -> dict: + """ + Transform the image generation request to the AI/ML flux image generation request body + + https://api.aimlapi.com/v1/images/generations + """ + aiml_image_generation_request_body: AimlImageGenerationRequestParams = AimlImageGenerationRequestParams( + prompt=prompt, + model=model, + **optional_params, + ) + return dict(aiml_image_generation_request_body) + + def transform_image_generation_response( + self, + model: str, + raw_response: httpx.Response, + model_response: ImageResponse, + logging_obj: LiteLLMLoggingObj, + request_data: dict, + optional_params: dict, + litellm_params: dict, + encoding: Any, + api_key: Optional[str] = None, + json_mode: Optional[bool] = None, + ) -> ImageResponse: + """ + Transform the image generation response to the litellm image response + + https://api.aimlapi.com/v1/images/generations + """ + try: + response_data = raw_response.json() + except Exception as e: + raise self.get_error_class( + error_message=f"Error transforming image generation response: {e}", + status_code=raw_response.status_code, + headers=raw_response.headers, + ) + + if not model_response.data: + model_response.data = [] + + # AI/ML API can return images in two different formats: + # 1. output.choices array with image_base64 + # 2. images array with url (and optional width, height, content_type) + + if "output" in response_data and "choices" in response_data["output"]: + for choice in response_data["output"]["choices"]: + if "image_base64" in choice: + model_response.data.append(ImageObject( + b64_json=choice["image_base64"], + url=None, # AI/ML API returns base64, not URLs + )) + elif "url" in choice: + model_response.data.append(ImageObject( + b64_json=None, + url=choice["url"], + )) + elif "images" in response_data: + # Handle alternative format: {"images": [{"url": "...", "width": 1024, "height": 768, "content_type": "image/jpeg"}]} + for image in response_data["images"]: + if "url" in image: + model_response.data.append(ImageObject( + b64_json=None, + url=image["url"], + )) + elif "image_base64" in image: + model_response.data.append(ImageObject( + b64_json=image["image_base64"], + url=None, + )) + return model_response diff --git a/litellm/llms/anthropic/chat/transformation.py b/litellm/llms/anthropic/chat/transformation.py index ce874bfde9a..c7f0a6da61e 100644 --- a/litellm/llms/anthropic/chat/transformation.py +++ b/litellm/llms/anthropic/chat/transformation.py @@ -18,6 +18,8 @@ from litellm.litellm_core_utils.core_helpers import map_finish_reason from litellm.llms.base_llm.base_utils import type_to_response_format_param from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMException from litellm.types.llms.anthropic import ( + ANTHROPIC_BETA_HEADER_VALUES, + ANTHROPIC_HOSTED_TOOLS, AllAnthropicMessageValues, AllAnthropicToolsValues, AnthropicCodeExecutionTool, @@ -45,9 +47,15 @@ from litellm.types.llms.openai import ( OpenAIMcpServerTool, OpenAIWebSearchOptions, ) -from litellm.types.utils import CompletionTokensDetailsWrapper +from litellm.types.utils import ( + CacheCreationTokenDetails, + CompletionTokensDetailsWrapper, +) from litellm.types.utils import Message as LitellmMessage -from litellm.types.utils import PromptTokensDetailsWrapper, ServerToolUse +from litellm.types.utils import ( + PromptTokensDetailsWrapper, + ServerToolUse, +) from litellm.utils import ( ModelResponse, Usage, @@ -67,9 +75,6 @@ else: LoggingClass = Any -ANTHROPIC_HOSTED_TOOLS = ["web_search", "bash", "text_editor", "code_execution"] - - class AnthropicConfig(AnthropicModelInfo, BaseConfig): """ Reference: https://docs.anthropic.com/claude/reference/messages_post @@ -200,8 +205,12 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): ) _allowed_properties = set(AnthropicInputSchema.__annotations__.keys()) - input_schema_filtered = {k: v for k, v in _input_schema.items() if k in _allowed_properties} - input_anthropic_schema: AnthropicInputSchema = AnthropicInputSchema(**input_schema_filtered) + input_schema_filtered = { + k: v for k, v in _input_schema.items() if k in _allowed_properties + } + input_anthropic_schema: AnthropicInputSchema = AnthropicInputSchema( + **input_schema_filtered + ) _tool = AnthropicMessagesTool( name=tool["function"]["name"], @@ -632,6 +641,14 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): ) ) return tools + + def update_headers_with_optional_anthropic_beta(self, headers: dict, optional_params: dict) -> dict: + """Update headers with optional anthropic beta.""" + _tools = optional_params.get("tools", []) + for tool in _tools: + if tool.get("type", None) and tool.get("type").startswith(ANTHROPIC_HOSTED_TOOLS.WEB_FETCH.value): + headers["anthropic-beta"] = ANTHROPIC_BETA_HEADER_VALUES.WEB_FETCH_2025_09_10.value + return headers def transform_request( self, @@ -668,6 +685,8 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): llm_provider="anthropic", ) + headers = self.update_headers_with_optional_anthropic_beta(headers=headers, optional_params=optional_params) + # Separate system prompt from rest of message anthropic_system_message_list = self.translate_system_message(messages=messages) # Handling anthropic API Prompt Caching @@ -797,7 +816,15 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): if content.get("citations") is not None: if citations is None: citations = [] - citations.append(content["citations"]) + citations.append( + [ + { + **citation, + "supported_text": content.get("text", ""), + } + for citation in content["citations"] + ] + ) if thinking_blocks is not None: reasoning_content = "" for block in thinking_blocks: @@ -816,12 +843,14 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): _usage = usage_object cache_creation_input_tokens: int = 0 cache_read_input_tokens: int = 0 + cache_creation_token_details: Optional[CacheCreationTokenDetails] = None web_search_requests: Optional[int] = None if ( "cache_creation_input_tokens" in _usage and _usage["cache_creation_input_tokens"] is not None ): cache_creation_input_tokens = _usage["cache_creation_input_tokens"] + prompt_tokens += cache_creation_input_tokens if ( "cache_read_input_tokens" in _usage and _usage["cache_read_input_tokens"] is not None @@ -837,8 +866,20 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): int, _usage["server_tool_use"]["web_search_requests"] ) + if "cache_creation" in _usage and _usage["cache_creation"] is not None: + cache_creation_token_details = CacheCreationTokenDetails( + ephemeral_5m_input_tokens=_usage["cache_creation"].get( + "ephemeral_5m_input_tokens" + ), + ephemeral_1h_input_tokens=_usage["cache_creation"].get( + "ephemeral_1h_input_tokens" + ), + ) + prompt_tokens_details = PromptTokensDetailsWrapper( cached_tokens=cache_read_input_tokens, + cache_creation_tokens=cache_read_input_tokens, + cache_creation_token_details=cache_creation_token_details, ) completion_token_details = ( CompletionTokensDetailsWrapper( diff --git a/litellm/llms/anthropic/experimental_pass_through/adapters/streaming_iterator.py b/litellm/llms/anthropic/experimental_pass_through/adapters/streaming_iterator.py index aa95183bb6c..306bcd9bb2c 100644 --- a/litellm/llms/anthropic/experimental_pass_through/adapters/streaming_iterator.py +++ b/litellm/llms/anthropic/experimental_pass_through/adapters/streaming_iterator.py @@ -2,7 +2,7 @@ ## Translates OpenAI call to Anthropic `/v1/messages` format import json import traceback -import uuid +from litellm._uuid import uuid from collections import deque from typing import TYPE_CHECKING, Any, AsyncIterator, Iterator, Literal, Optional @@ -28,10 +28,6 @@ class AnthropicStreamWrapper(AdapterCompletionStreamWrapper): TextBlock, ) - def __init__(self, completion_stream: Any, model: str): - super().__init__(completion_stream) - self.model = model - sent_first_chunk: bool = False sent_content_block_start: bool = False sent_content_block_finish: bool = False @@ -39,6 +35,7 @@ class AnthropicStreamWrapper(AdapterCompletionStreamWrapper): sent_last_message: bool = False holding_chunk: Optional[Any] = None holding_stop_reason_chunk: Optional[Any] = None + queued_usage_chunk: bool = False current_content_block_index: int = 0 current_content_block_start: ContentBlockContentBlockDict = TextBlock( type="text", @@ -47,6 +44,10 @@ class AnthropicStreamWrapper(AdapterCompletionStreamWrapper): pending_new_content_block: bool = False chunk_queue: deque = deque() # Queue for buffering multiple chunks + def __init__(self, completion_stream: Any, model: str): + super().__init__(completion_stream) + self.model = model + def __next__(self): from .transformation import LiteLLMAnthropicMessagesAdapter @@ -217,77 +218,83 @@ class AnthropicStreamWrapper(AdapterCompletionStreamWrapper): # Queue the merged chunk and reset self.chunk_queue.append(merged_chunk) + self.queued_usage_chunk = True self.holding_stop_reason_chunk = None return self.chunk_queue.popleft() # Check if this processed chunk has a stop_reason - hold it for next chunk - if should_start_new_block and not self.sent_content_block_finish: - # Queue the sequence: content_block_stop -> content_block_start -> current_chunk + if not self.queued_usage_chunk: + if should_start_new_block and not self.sent_content_block_finish: + # Queue the sequence: content_block_stop -> content_block_start -> current_chunk - # 1. Stop current content block - self.chunk_queue.append( - { - "type": "content_block_stop", - "index": max(self.current_content_block_index - 1, 0), - } - ) + # 1. Stop current content block + self.chunk_queue.append( + { + "type": "content_block_stop", + "index": max(self.current_content_block_index - 1, 0), + } + ) - # 2. Start new content block - self.chunk_queue.append( - { - "type": "content_block_start", - "index": self.current_content_block_index, - "content_block": self.current_content_block_start, - } - ) + # 2. Start new content block + self.chunk_queue.append( + { + "type": "content_block_start", + "index": self.current_content_block_index, + "content_block": self.current_content_block_start, + } + ) - # 3. Queue the current chunk (don't lose it!) - self.chunk_queue.append(processed_chunk) - - # Reset state for new block - self.sent_content_block_finish = False - - # Return the first queued item - return self.chunk_queue.popleft() - - if ( - processed_chunk["type"] == "message_delta" - and self.sent_content_block_finish is False - ): - # Queue both the content_block_stop and the holding chunk - self.chunk_queue.append( - { - "type": "content_block_stop", - "index": self.current_content_block_index, - } - ) - self.sent_content_block_finish = True - if processed_chunk.get("delta", {}).get("stop_reason") is not None: - - self.holding_stop_reason_chunk = processed_chunk - else: + # 3. Queue the current chunk (don't lose it!) self.chunk_queue.append(processed_chunk) - return self.chunk_queue.popleft() - elif self.holding_chunk is not None: - # Queue both chunks - self.chunk_queue.append(self.holding_chunk) - self.chunk_queue.append(processed_chunk) - self.holding_chunk = None - return self.chunk_queue.popleft() - else: - # Queue the current chunk - self.chunk_queue.append(processed_chunk) - return self.chunk_queue.popleft() + + # Reset state for new block + self.sent_content_block_finish = False + + # Return the first queued item + return self.chunk_queue.popleft() + + if ( + processed_chunk["type"] == "message_delta" + and self.sent_content_block_finish is False + ): + # Queue both the content_block_stop and the holding chunk + self.chunk_queue.append( + { + "type": "content_block_stop", + "index": self.current_content_block_index, + } + ) + self.sent_content_block_finish = True + if ( + processed_chunk.get("delta", {}).get("stop_reason") + is not None + ): + + self.holding_stop_reason_chunk = processed_chunk + else: + self.chunk_queue.append(processed_chunk) + return self.chunk_queue.popleft() + elif self.holding_chunk is not None: + # Queue both chunks + self.chunk_queue.append(self.holding_chunk) + self.chunk_queue.append(processed_chunk) + self.holding_chunk = None + return self.chunk_queue.popleft() + else: + # Queue the current chunk + self.chunk_queue.append(processed_chunk) + return self.chunk_queue.popleft() # Handle any remaining held chunks after stream ends - if self.holding_stop_reason_chunk is not None: - self.chunk_queue.append(self.holding_stop_reason_chunk) - self.holding_stop_reason_chunk = None + if not self.queued_usage_chunk: + if self.holding_stop_reason_chunk is not None: + self.chunk_queue.append(self.holding_stop_reason_chunk) + self.holding_stop_reason_chunk = None - if self.holding_chunk is not None: - self.chunk_queue.append(self.holding_chunk) - self.holding_chunk = None + if self.holding_chunk is not None: + self.chunk_queue.append(self.holding_chunk) + self.holding_chunk = None if not self.sent_last_message: self.sent_last_message = True diff --git a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py index d38e7adc231..7de2a1e1c66 100644 --- a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py +++ b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py @@ -458,7 +458,7 @@ class LiteLLMAnthropicMessagesAdapter: Literal["text", "tool_use"], "ContentBlockContentBlockDict", ]: - import uuid + from litellm._uuid import uuid from litellm.types.llms.anthropic import TextBlock, ToolUseBlock diff --git a/litellm/llms/azure/audio_transcriptions.py b/litellm/llms/azure/audio_transcriptions.py index 1f09ac7574a..8519b1c35a5 100644 --- a/litellm/llms/azure/audio_transcriptions.py +++ b/litellm/llms/azure/audio_transcriptions.py @@ -1,4 +1,4 @@ -import uuid +from litellm._uuid import uuid from typing import Any, Coroutine, Optional, Union from openai import AsyncAzureOpenAI, AzureOpenAI diff --git a/litellm/llms/azure/batches/handler.py b/litellm/llms/azure/batches/handler.py index 7fc6388ba87..c038f62561c 100644 --- a/litellm/llms/azure/batches/handler.py +++ b/litellm/llms/azure/batches/handler.py @@ -62,7 +62,7 @@ class AzureBatchesAPI(BaseAzureLLM): ) if azure_client is None: raise ValueError( - "OpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment." + "Azure OpenAI client is not initialized. Make sure api_key is passed or AZURE_API_KEY/AZURE_OPENAI_API_KEY is set in the environment." ) if _is_async is True: @@ -108,7 +108,7 @@ class AzureBatchesAPI(BaseAzureLLM): ) if azure_client is None: raise ValueError( - "OpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment." + "Azure OpenAI client is not initialized. Make sure api_key is passed or AZURE_API_KEY/AZURE_OPENAI_API_KEY is set in the environment." ) if _is_async is True: @@ -156,7 +156,7 @@ class AzureBatchesAPI(BaseAzureLLM): ) if azure_client is None: raise ValueError( - "OpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment." + "Azure OpenAI client is not initialized. Make sure api_key is passed or AZURE_API_KEY/AZURE_OPENAI_API_KEY is set in the environment." ) response = azure_client.batches.cancel(**cancel_batch_data) return response @@ -195,7 +195,7 @@ class AzureBatchesAPI(BaseAzureLLM): ) if azure_client is None: raise ValueError( - "OpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment." + "Azure OpenAI client is not initialized. Make sure api_key is passed or AZURE_API_KEY/AZURE_OPENAI_API_KEY is set in the environment." ) if _is_async is True: diff --git a/litellm/llms/azure/chat/o_series_handler.py b/litellm/llms/azure/chat/o_series_handler.py index 2f3e9e63996..d0f5153b0eb 100644 --- a/litellm/llms/azure/chat/o_series_handler.py +++ b/litellm/llms/azure/chat/o_series_handler.py @@ -4,7 +4,7 @@ Handler file for calls to Azure OpenAI's o1/o3 family of models Written separately to handle faking streaming for o1 and o3 models. """ -from typing import Any, Callable, Optional, Union +from typing import TYPE_CHECKING, Any, Callable, Optional, Union import httpx @@ -13,6 +13,9 @@ from litellm.types.utils import ModelResponse from ...openai.openai import OpenAIChatCompletion from ..common_utils import BaseAzureLLM +if TYPE_CHECKING: + from aiohttp import ClientSession + class AzureOpenAIO1ChatCompletion(BaseAzureLLM, OpenAIChatCompletion): def completion( @@ -38,6 +41,7 @@ class AzureOpenAIO1ChatCompletion(BaseAzureLLM, OpenAIChatCompletion): organization: Optional[str] = None, custom_llm_provider: Optional[str] = None, drop_params: Optional[bool] = None, + shared_session: Optional["ClientSession"] = None, ): client = self.get_azure_openai_client( litellm_params=litellm_params, @@ -69,4 +73,5 @@ class AzureOpenAIO1ChatCompletion(BaseAzureLLM, OpenAIChatCompletion): organization=organization, custom_llm_provider=custom_llm_provider, drop_params=drop_params, + shared_session=shared_session, ) diff --git a/litellm/llms/azure/common_utils.py b/litellm/llms/azure/common_utils.py index 09b1888e04d..15569581eee 100644 --- a/litellm/llms/azure/common_utils.py +++ b/litellm/llms/azure/common_utils.py @@ -576,8 +576,19 @@ class BaseAzureLLM(BaseOpenAILLM): verbose_logger.debug( f"Initializing Azure OpenAI Client for {model_name}, Api Base: {str(api_base)}, Api Key:{_api_key}" ) + + # Extract API key from multiple sources with proper precedence + resolved_api_key = ( + api_key + or litellm_params.get("api_key") + or litellm.api_key + or litellm.azure_key + or get_secret_str("AZURE_OPENAI_API_KEY") + or get_secret_str("AZURE_API_KEY") + ) + azure_client_params = { - "api_key": api_key, + "api_key": resolved_api_key, "azure_endpoint": api_base, "api_version": api_version, "azure_ad_token": azure_ad_token, diff --git a/litellm/llms/azure/files/handler.py b/litellm/llms/azure/files/handler.py index 50c122ccf2c..d7b16d304d4 100644 --- a/litellm/llms/azure/files/handler.py +++ b/litellm/llms/azure/files/handler.py @@ -58,7 +58,7 @@ class AzureOpenAIFilesAPI(BaseAzureLLM): ) if openai_client is None: raise ValueError( - "AzureOpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment." + "Azure OpenAI client is not initialized. Make sure api_key is passed or AZURE_API_KEY/AZURE_OPENAI_API_KEY is set in the environment." ) if _is_async is True: @@ -106,7 +106,7 @@ class AzureOpenAIFilesAPI(BaseAzureLLM): ) if openai_client is None: raise ValueError( - "AzureOpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment." + "Azure OpenAI client is not initialized. Make sure api_key is passed or AZURE_API_KEY/AZURE_OPENAI_API_KEY is set in the environment." ) if _is_async is True: @@ -156,7 +156,7 @@ class AzureOpenAIFilesAPI(BaseAzureLLM): ) if openai_client is None: raise ValueError( - "AzureOpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment." + "Azure OpenAI client is not initialized. Make sure api_key is passed or AZURE_API_KEY/AZURE_OPENAI_API_KEY is set in the environment." ) if _is_async is True: @@ -208,7 +208,7 @@ class AzureOpenAIFilesAPI(BaseAzureLLM): ) if openai_client is None: raise ValueError( - "AzureOpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment." + "Azure OpenAI client is not initialized. Make sure api_key is passed or AZURE_API_KEY/AZURE_OPENAI_API_KEY is set in the environment." ) if _is_async is True: @@ -262,7 +262,7 @@ class AzureOpenAIFilesAPI(BaseAzureLLM): ) if openai_client is None: raise ValueError( - "AzureOpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment." + "Azure OpenAI client is not initialized. Make sure api_key is passed or AZURE_API_KEY/AZURE_OPENAI_API_KEY is set in the environment." ) if _is_async is True: diff --git a/litellm/llms/azure/responses/transformation.py b/litellm/llms/azure/responses/transformation.py index 063d1af9c33..0050bd163d1 100644 --- a/litellm/llms/azure/responses/transformation.py +++ b/litellm/llms/azure/responses/transformation.py @@ -1,11 +1,14 @@ from typing import TYPE_CHECKING, Any, Dict, Literal, Optional, Tuple +import httpx + from litellm._logging import verbose_logger from litellm.llms.azure.common_utils import BaseAzureLLM from litellm.llms.openai.responses.transformation import OpenAIResponsesAPIConfig from litellm.types.llms.openai import * from litellm.types.responses.main import * from litellm.types.router import GenericLiteLLMParams +from litellm.types.utils import LlmProviders if TYPE_CHECKING: from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj @@ -16,6 +19,10 @@ else: class AzureOpenAIResponsesAPIConfig(OpenAIResponsesAPIConfig): + @property + def custom_llm_provider(self) -> LlmProviders: + return LlmProviders.AZURE + def validate_environment( self, headers: dict, model: str, litellm_params: Optional[GenericLiteLLMParams] ) -> dict: @@ -189,3 +196,66 @@ class AzureOpenAIResponsesAPIConfig(OpenAIResponsesAPIConfig): params["order"] = order verbose_logger.debug(f"list input items url={url}") return url, params + + ######################################################### + ########## CANCEL RESPONSE API TRANSFORMATION ########## + ######################################################### + def transform_cancel_response_api_request( + self, + response_id: str, + api_base: str, + litellm_params: GenericLiteLLMParams, + headers: dict, + ) -> Tuple[str, Dict]: + """ + Transform the cancel response API request into a URL and data + + Azure OpenAI API expects the following request: + - POST /openai/responses/{response_id}/cancel?api-version=xxx + + This function handles URLs with query parameters by inserting the response_id + at the correct location (before any query parameters). + """ + from urllib.parse import urlparse, urlunparse + + # Parse the URL to separate its components + parsed_url = urlparse(api_base) + + # Insert the response_id and /cancel at the end of the path component + # Remove trailing slash if present to avoid double slashes + path = parsed_url.path.rstrip("/") + new_path = f"{path}/{response_id}/cancel" + + # Reconstruct the URL with all original components but with the modified path + cancel_url = urlunparse( + ( + parsed_url.scheme, # http, https + parsed_url.netloc, # domain name, port + new_path, # path with response_id and /cancel added + parsed_url.params, # parameters + parsed_url.query, # query string + parsed_url.fragment, # fragment + ) + ) + + data: Dict = {} + verbose_logger.debug(f"cancel response url={cancel_url}") + return cancel_url, data + + def transform_cancel_response_api_response( + self, + raw_response: httpx.Response, + logging_obj: LiteLLMLoggingObj, + ) -> ResponsesAPIResponse: + """ + Transform the cancel response API response into a ResponsesAPIResponse + """ + try: + raw_response_json = raw_response.json() + except Exception: + from litellm.llms.azure.chat.gpt_transformation import AzureOpenAIError + + raise AzureOpenAIError( + message=raw_response.text, status_code=raw_response.status_code + ) + return ResponsesAPIResponse(**raw_response_json) diff --git a/litellm/llms/azure_ai/image_edit/__init__.py b/litellm/llms/azure_ai/image_edit/__init__.py new file mode 100644 index 00000000000..e0e57bec403 --- /dev/null +++ b/litellm/llms/azure_ai/image_edit/__init__.py @@ -0,0 +1,15 @@ +from litellm.llms.base_llm.image_edit.transformation import BaseImageEditConfig + +from .transformation import AzureFoundryFluxImageEditConfig + +__all__ = ["AzureFoundryFluxImageEditConfig"] + + +def get_azure_ai_image_edit_config(model: str) -> BaseImageEditConfig: + model = model.lower() + model = model.replace("-", "") + model = model.replace("_", "") + if model == "" or "flux" in model: # empty model is flux + return AzureFoundryFluxImageEditConfig() + else: + raise ValueError(f"Model {model} is not supported for Azure AI image editing.") diff --git a/litellm/llms/azure_ai/image_edit/transformation.py b/litellm/llms/azure_ai/image_edit/transformation.py new file mode 100644 index 00000000000..47f612912ce --- /dev/null +++ b/litellm/llms/azure_ai/image_edit/transformation.py @@ -0,0 +1,99 @@ +from typing import Optional + +import httpx + +import litellm +from litellm.llms.azure_ai.common_utils import AzureFoundryModelInfo +from litellm.llms.openai.image_edit.transformation import OpenAIImageEditConfig +from litellm.secret_managers.main import get_secret_str +from litellm.utils import _add_path_to_api_base + + +class AzureFoundryFluxImageEditConfig(OpenAIImageEditConfig): + """ + Azure AI Foundry FLUX image edit config + + Supports FLUX models including FLUX-1-kontext-pro for image editing. + + Azure AI Foundry FLUX models handle image editing through the /images/edits endpoint, + same as standard Azure OpenAI models. The request format uses multipart/form-data + with image files and prompt. + """ + + def validate_environment( + self, + headers: dict, + model: str, + api_key: Optional[str] = None, + ) -> dict: + """ + Validate Azure AI Foundry environment and set up authentication + Uses Api-Key header format + """ + api_key = AzureFoundryModelInfo.get_api_key(api_key) + + if not api_key: + raise ValueError( + f"Azure AI API key is required for model {model}. Set AZURE_AI_API_KEY environment variable or pass api_key parameter." + ) + + headers.update( + { + "Api-Key": api_key, # Azure AI Foundry uses Api-Key header format + } + ) + return headers + + def get_complete_url( + self, + model: str, + api_base: Optional[str], + litellm_params: dict, + ) -> str: + """ + Constructs a complete URL for Azure AI Foundry image edits API request. + + Azure AI Foundry FLUX models handle image editing through the /images/edits + endpoint. + + Args: + - model: Model name (deployment name for Azure AI Foundry) + - api_base: Base URL for Azure AI endpoint + - litellm_params: Additional parameters including api_version + + Returns: + - Complete URL for the image edits endpoint + """ + api_base = AzureFoundryModelInfo.get_api_base(api_base) + + if api_base is None: + raise ValueError( + "Azure AI API base is required. Set AZURE_AI_API_BASE environment variable or pass api_base parameter." + ) + + api_version = (litellm_params.get("api_version") or litellm.api_version + or get_secret_str("AZURE_AI_API_VERSION") + ) + if api_version is None: + # API version is mandatory for Azure AI Foundry + raise ValueError( + "Azure API version is required. Set AZURE_AI_API_VERSION environment variable or pass api_version parameter." + ) + + # Add the path to the base URL using the model as deployment name + # Azure AI Foundry FLUX models use /images/edits for editing + if "/openai/deployments/" in api_base: + new_url = _add_path_to_api_base( + api_base=api_base, + ending_path="/images/edits", + ) + else: + new_url = _add_path_to_api_base( + api_base=api_base, + ending_path=f"/openai/deployments/{model}/images/edits", + ) + + # Use the new query_params dictionary + final_url = httpx.URL(new_url).copy_with(params={"api-version": api_version}) + + return str(final_url) diff --git a/litellm/llms/base_llm/__init__.py b/litellm/llms/base_llm/__init__.py index 187c985fd67..665e242969c 100644 --- a/litellm/llms/base_llm/__init__.py +++ b/litellm/llms/base_llm/__init__.py @@ -1,5 +1,6 @@ from .anthropic_messages.transformation import BaseAnthropicMessagesConfig from .audio_transcription.transformation import BaseAudioTranscriptionConfig +from .batches.transformation import BaseBatchesConfig from .chat.transformation import BaseConfig from .embedding.transformation import BaseEmbeddingConfig from .image_edit.transformation import BaseImageEditConfig @@ -12,4 +13,5 @@ __all__ = [ "BaseAnthropicMessagesConfig", "BaseEmbeddingConfig", "BaseImageEditConfig", + "BaseBatchesConfig", ] diff --git a/litellm/llms/base_llm/audio_transcription/transformation.py b/litellm/llms/base_llm/audio_transcription/transformation.py index 179b8d0fb02..3574996e48e 100644 --- a/litellm/llms/base_llm/audio_transcription/transformation.py +++ b/litellm/llms/base_llm/audio_transcription/transformation.py @@ -1,6 +1,6 @@ from abc import ABC, abstractmethod from dataclasses import dataclass -from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union +from typing import TYPE_CHECKING, Any, List, Optional, Union import httpx @@ -23,12 +23,13 @@ else: class AudioTranscriptionRequestData: """ Structured data for audio transcription requests. - + Attributes: data: The request data (form data for multipart, json data for regular requests) files: Optional files dict for multipart form data content_type: Optional content type override """ + data: Union[dict, bytes] files: Optional[dict] = None content_type: Optional[str] = None @@ -66,13 +67,11 @@ class BaseAudioTranscriptionConfig(BaseConfig, ABC): audio_file: FileTypes, optional_params: dict, litellm_params: dict, - ) -> Union[AudioTranscriptionRequestData, Dict]: + ) -> AudioTranscriptionRequestData: raise NotImplementedError( "AudioTranscriptionConfig needs a request transformation for audio transcription models" ) - - def transform_audio_transcription_response( self, raw_response: httpx.Response, @@ -110,7 +109,6 @@ class BaseAudioTranscriptionConfig(BaseConfig, ABC): raise NotImplementedError( "AudioTranscriptionConfig does not need a response transformation for audio transcription models" ) - def get_provider_specific_params( self, @@ -141,7 +139,7 @@ class BaseAudioTranscriptionConfig(BaseConfig, ABC): provider_specific_params[key] = value return provider_specific_params - + def _should_exclude_param( self, param_name: str, diff --git a/litellm/llms/base_llm/batches/transformation.py b/litellm/llms/base_llm/batches/transformation.py new file mode 100644 index 00000000000..9e67689fcd9 --- /dev/null +++ b/litellm/llms/base_llm/batches/transformation.py @@ -0,0 +1,218 @@ +import types +from abc import ABC, abstractmethod +from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union + +import httpx +from httpx import Headers + +from litellm.types.llms.openai import ( + AllMessageValues, + CreateBatchRequest, +) +from litellm.types.utils import LiteLLMBatch, LlmProviders + +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj + + from ..chat.transformation import BaseLLMException as _BaseLLMException + + LiteLLMLoggingObj = _LiteLLMLoggingObj + BaseLLMException = _BaseLLMException +else: + LiteLLMLoggingObj = Any + BaseLLMException = Any + + +class BaseBatchesConfig(ABC): + """ + Abstract base class for batch processing configurations across different LLM providers. + + This class defines the interface that all provider-specific batch configurations + must implement to work with LiteLLM's unified batch processing system. + """ + + def __init__(self): + pass + + @property + @abstractmethod + def custom_llm_provider(self) -> LlmProviders: + """Return the LLM provider type for this configuration.""" + pass + + @classmethod + def get_config(cls): + """Get configuration dictionary for this class.""" + return { + k: v + for k, v in cls.__dict__.items() + if not k.startswith("__") + and not k.startswith("_abc") + and not isinstance( + v, + ( + types.FunctionType, + types.BuiltinFunctionType, + classmethod, + staticmethod, + ), + ) + and v is not None + } + + @abstractmethod + def validate_environment( + self, + headers: dict, + model: str, + messages: List[AllMessageValues], + optional_params: dict, + litellm_params: dict, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + ) -> dict: + """ + Validate and prepare environment-specific headers and parameters. + + Args: + headers: HTTP headers dictionary + model: Model name + messages: List of messages + optional_params: Optional parameters + litellm_params: LiteLLM parameters + api_key: API key + api_base: API base URL + + Returns: + Updated headers dictionary + """ + pass + + @abstractmethod + def get_complete_batch_url( + self, + api_base: Optional[str], + api_key: Optional[str], + model: str, + optional_params: Dict, + litellm_params: Dict, + data: CreateBatchRequest, + ) -> str: + """ + Get the complete URL for batch creation request. + + Args: + api_base: Base API URL + api_key: API key + model: Model name + optional_params: Optional parameters + litellm_params: LiteLLM parameters + data: Batch creation request data + + Returns: + Complete URL for the batch request + """ + pass + + @abstractmethod + def transform_create_batch_request( + self, + model: str, + create_batch_data: CreateBatchRequest, + optional_params: dict, + litellm_params: dict, + ) -> Union[bytes, str, Dict[str, Any]]: + """ + Transform the batch creation request to provider-specific format. + + Args: + model: Model name + create_batch_data: Batch creation request data + optional_params: Optional parameters + litellm_params: LiteLLM parameters + + Returns: + Transformed request data + """ + pass + + @abstractmethod + def transform_create_batch_response( + self, + model: Optional[str], + raw_response: httpx.Response, + logging_obj: LiteLLMLoggingObj, + litellm_params: dict, + ) -> LiteLLMBatch: + """ + Transform provider-specific batch response to LiteLLM format. + + Args: + model: Model name + raw_response: Raw HTTP response + logging_obj: Logging object + litellm_params: LiteLLM parameters + + Returns: + LiteLLM batch object + """ + pass + + @abstractmethod + def transform_retrieve_batch_request( + self, + batch_id: str, + optional_params: dict, + litellm_params: dict, + ) -> Union[bytes, str, Dict[str, Any]]: + """ + Transform the batch retrieval request to provider-specific format. + + Args: + batch_id: Batch ID to retrieve + optional_params: Optional parameters + litellm_params: LiteLLM parameters + + Returns: + Transformed request data + """ + pass + + @abstractmethod + def transform_retrieve_batch_response( + self, + model: Optional[str], + raw_response: httpx.Response, + logging_obj: LiteLLMLoggingObj, + litellm_params: dict, + ) -> LiteLLMBatch: + """ + Transform provider-specific batch retrieval response to LiteLLM format. + + Args: + model: Model name + raw_response: Raw HTTP response + logging_obj: Logging object + litellm_params: LiteLLM parameters + + Returns: + LiteLLM batch object + """ + pass + + @abstractmethod + def get_error_class( + self, error_message: str, status_code: int, headers: Union[Dict, Headers] + ) -> "BaseLLMException": + """ + Get the appropriate error class for this provider. + + Args: + error_message: Error message + status_code: HTTP status code + headers: Response headers + + Returns: + Provider-specific exception class + """ + pass diff --git a/litellm/llms/base_llm/files/transformation.py b/litellm/llms/base_llm/files/transformation.py index 5c37a8b7547..35b76479cdc 100644 --- a/litellm/llms/base_llm/files/transformation.py +++ b/litellm/llms/base_llm/files/transformation.py @@ -35,6 +35,16 @@ class BaseFilesConfig(BaseConfig): def custom_llm_provider(self) -> LlmProviders: pass + @property + def file_upload_http_method(self) -> str: + """ + HTTP method to use for file uploads. + Override this in provider configs if they need different methods. + Default is POST (used by most providers like OpenAI, Anthropic). + S3-based providers like Bedrock should return "PUT". + """ + return "POST" + @abstractmethod def get_supported_openai_params( self, model: str diff --git a/litellm/llms/base_llm/passthrough/transformation.py b/litellm/llms/base_llm/passthrough/transformation.py index 60d89c1610f..f925e6819dc 100644 --- a/litellm/llms/base_llm/passthrough/transformation.py +++ b/litellm/llms/base_llm/passthrough/transformation.py @@ -31,30 +31,26 @@ class BasePassthroughConfig(BaseLLMModelInfo): Args: endpoint: str - the endpoint to add to the url base_target_url: str - the base url to add the endpoint to - request_query_params: dict - the query params to add to the url + request_query_params: Optional[dict] - the query params to add to the url Returns: - str - the formatted url + httpx.URL - the formatted url """ from urllib.parse import urlencode import httpx - encoded_endpoint = httpx.URL(endpoint).path + base = base_target_url.rstrip('/') + endpoint = endpoint.lstrip('/') + full_url = f"{base}/{endpoint}" - # Ensure endpoint starts with '/' for proper URL construction - if not encoded_endpoint.startswith("/"): - encoded_endpoint = "/" + encoded_endpoint - - # Construct the full target URL using httpx - base_url = httpx.URL(base_target_url) - updated_url = base_url.copy_with(path=encoded_endpoint) + url = httpx.URL(full_url) if request_query_params: - # Create a new URL with the merged query params - updated_url = updated_url.copy_with( + url = url.copy_with( query=urlencode(request_query_params).encode("ascii") ) - return updated_url + + return url @abstractmethod def get_complete_url( diff --git a/litellm/llms/base_llm/responses/transformation.py b/litellm/llms/base_llm/responses/transformation.py index e2f89da5e86..facabbda72a 100644 --- a/litellm/llms/base_llm/responses/transformation.py +++ b/litellm/llms/base_llm/responses/transformation.py @@ -12,6 +12,7 @@ from litellm.types.llms.openai import ( ) from litellm.types.responses.main import * from litellm.types.router import GenericLiteLLMParams +from litellm.types.utils import LlmProviders if TYPE_CHECKING: from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj @@ -29,6 +30,11 @@ class BaseResponsesAPIConfig(ABC): def __init__(self): pass + @property + @abstractmethod + def custom_llm_provider(self) -> LlmProviders: + pass + @classmethod def get_config(cls): return { @@ -211,3 +217,28 @@ class BaseResponsesAPIConfig(ABC): ) -> bool: """Returns True if litellm should fake a stream for the given model and stream value""" return False + + ######################################################### + ########## CANCEL RESPONSE API TRANSFORMATION ########## + ######################################################### + @abstractmethod + def transform_cancel_response_api_request( + self, + response_id: str, + api_base: str, + litellm_params: GenericLiteLLMParams, + headers: dict, + ) -> Tuple[str, Dict]: + pass + + @abstractmethod + def transform_cancel_response_api_response( + self, + raw_response: httpx.Response, + logging_obj: LiteLLMLoggingObj, + ) -> ResponsesAPIResponse: + pass + + ######################################################### + ########## END CANCEL RESPONSE API TRANSFORMATION ####### + ######################################################### diff --git a/litellm/llms/bedrock/base_aws_llm.py b/litellm/llms/bedrock/base_aws_llm.py index 55aad673ced..8211addaf95 100644 --- a/litellm/llms/bedrock/base_aws_llm.py +++ b/litellm/llms/bedrock/base_aws_llm.py @@ -20,7 +20,11 @@ from pydantic import BaseModel from litellm._logging import verbose_logger from litellm.caching.caching import DualCache -from litellm.constants import BEDROCK_INVOKE_PROVIDERS_LITERAL, BEDROCK_MAX_POLICY_SIZE +from litellm.constants import ( + BEDROCK_EMBEDDING_PROVIDERS_LITERAL, + BEDROCK_INVOKE_PROVIDERS_LITERAL, + BEDROCK_MAX_POLICY_SIZE, +) from litellm.litellm_core_utils.dd_tracing import tracer from litellm.secret_managers.main import get_secret, get_secret_str @@ -66,6 +70,7 @@ class BaseAWSLLM: "aws_web_identity_token", "aws_sts_endpoint", "aws_bedrock_runtime_endpoint", + "aws_external_id", ] def get_cache_key(self, credential_args: Dict[str, Optional[str]]) -> str: @@ -88,6 +93,7 @@ class BaseAWSLLM: aws_role_name: Optional[str] = None, aws_web_identity_token: Optional[str] = None, aws_sts_endpoint: Optional[str] = None, + aws_external_id: Optional[str] = None, ): """ Return a boto3.Credentials object @@ -103,6 +109,7 @@ class BaseAWSLLM: aws_role_name, aws_web_identity_token, aws_sts_endpoint, + aws_external_id, ] # Iterate over parameters and update if needed @@ -127,6 +134,7 @@ class BaseAWSLLM: aws_role_name, aws_web_identity_token, aws_sts_endpoint, + aws_external_id, ) = params_to_check verbose_logger.debug( @@ -139,7 +147,8 @@ class BaseAWSLLM: "aws_profile_name=%s\n" "aws_role_name=%s\n" "aws_web_identity_token=%s\n" - "aws_sts_endpoint=%s", + "aws_sts_endpoint=%s\n" + "aws_external_id=%s", aws_access_key_id, aws_secret_access_key, aws_session_token, @@ -149,6 +158,7 @@ class BaseAWSLLM: aws_role_name, aws_web_identity_token, aws_sts_endpoint, + aws_external_id, ) # create cache key for non-expiring auth flows @@ -177,33 +187,45 @@ class BaseAWSLLM: aws_session_name=aws_session_name, aws_region_name=aws_region_name, aws_sts_endpoint=aws_sts_endpoint, + aws_external_id=aws_external_id, ) elif aws_role_name is not None: # Check if we're in IRSA and trying to assume the same role we already have current_role_arn = os.getenv("AWS_ROLE_ARN") web_identity_token_file = os.getenv("AWS_WEB_IDENTITY_TOKEN_FILE") - + # In IRSA environments, we should skip role assumption if we're already running as the target role # This is true when: # 1. We have AWS_ROLE_ARN set (current role) # 2. We have AWS_WEB_IDENTITY_TOKEN_FILE set (IRSA environment) # 3. The current role matches the requested role - if (current_role_arn and web_identity_token_file and - current_role_arn == aws_role_name): - verbose_logger.debug("Using IRSA same-role optimization: calling _auth_with_env_vars") + if ( + current_role_arn + and web_identity_token_file + and current_role_arn == aws_role_name + ): + verbose_logger.debug( + "Using IRSA same-role optimization: calling _auth_with_env_vars" + ) # We're already running as this role via IRSA, no need to assume it again # Use the default boto3 credentials (which will use the IRSA credentials) credentials, _cache_ttl = self._auth_with_env_vars() else: - verbose_logger.debug("Using role assumption: calling _auth_with_aws_role") + verbose_logger.debug( + "Using role assumption: calling _auth_with_aws_role" + ) # If aws_session_name is not provided, generate a default one if aws_session_name is None: - aws_session_name = f"litellm-session-{int(datetime.now().timestamp())}" + aws_session_name = ( + f"litellm-session-{int(datetime.now().timestamp())}" + ) credentials, _cache_ttl = self._auth_with_aws_role( aws_access_key_id=aws_access_key_id, aws_secret_access_key=aws_secret_access_key, + aws_session_token=aws_session_token, aws_role_name=aws_role_name, aws_session_name=aws_session_name, + aws_external_id=aws_external_id, ) elif aws_profile_name is not None: ### CHECK SESSION ### @@ -309,6 +331,40 @@ class BaseAWSLLM: return provider return None + @staticmethod + def get_bedrock_embedding_provider( + model: str, + ) -> Optional[BEDROCK_EMBEDDING_PROVIDERS_LITERAL]: + """ + Helper function to get the bedrock embedding provider from the model + + Handles scenarios like: + 1. model=cohere.embed-english-v3:0 -> Returns `cohere` + 2. model=amazon.titan-embed-text-v1 -> Returns `amazon` + 3. model=us.twelvelabs.marengo-embed-2-7-v1:0 -> Returns `twelvelabs` + 4. model=twelvelabs.marengo-embed-2-7-v1:0 -> Returns `twelvelabs` + """ + # Handle regional models like us.twelvelabs.marengo-embed-2-7-v1:0 + if "." in model: + parts = model.split(".") + # Check if the second part (after potential region) is a known provider + if len(parts) >= 2: + potential_provider = parts[1] # e.g., "twelvelabs" from "us.twelvelabs.marengo-embed-2-7-v1:0" + if potential_provider in get_args(BEDROCK_EMBEDDING_PROVIDERS_LITERAL): + return cast(BEDROCK_EMBEDDING_PROVIDERS_LITERAL, potential_provider) + + # Check if the first part is a known provider (standard format) + potential_provider = parts[0] # e.g., "cohere" from "cohere.embed-english-v3:0" + if potential_provider in get_args(BEDROCK_EMBEDDING_PROVIDERS_LITERAL): + return cast(BEDROCK_EMBEDDING_PROVIDERS_LITERAL, potential_provider) + + # Fallback: check if any provider name appears in the model string + for provider in get_args(BEDROCK_EMBEDDING_PROVIDERS_LITERAL): + if provider in model: + return cast(BEDROCK_EMBEDDING_PROVIDERS_LITERAL, provider) + + return None + def _get_aws_region_name( self, optional_params: dict, @@ -405,6 +461,7 @@ class BaseAWSLLM: aws_session_name: str, aws_region_name: Optional[str], aws_sts_endpoint: Optional[str], + aws_external_id: Optional[str] = None, ) -> Tuple[Credentials, Optional[int]]: """ Authenticate with AWS Web Identity Token @@ -437,13 +494,19 @@ class BaseAWSLLM: # https://docs.aws.amazon.com/STS/latest/APIReference/API_AssumeRoleWithWebIdentity.html # https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sts/client/assume_role_with_web_identity.html - sts_response = sts_client.assume_role_with_web_identity( - RoleArn=aws_role_name, - RoleSessionName=aws_session_name, - WebIdentityToken=oidc_token, - DurationSeconds=3600, - Policy='{"Version":"2012-10-17","Statement":[{"Sid":"BedrockLiteLLM","Effect":"Allow","Action":["bedrock:InvokeModel","bedrock:InvokeModelWithResponseStream"],"Resource":"*","Condition":{"Bool":{"aws:SecureTransport":"true"},"StringLike":{"aws:UserAgent":"litellm/*"}}}]}', - ) + assume_role_params = { + "RoleArn": aws_role_name, + "RoleSessionName": aws_session_name, + "WebIdentityToken": oidc_token, + "DurationSeconds": 3600, + "Policy": '{"Version":"2012-10-17","Statement":[{"Sid":"BedrockLiteLLM","Effect":"Allow","Action":["bedrock:InvokeModel","bedrock:InvokeModelWithResponseStream"],"Resource":"*","Condition":{"Bool":{"aws:SecureTransport":"true"},"StringLike":{"aws:UserAgent":"litellm/*"}}}]}', + } + + # Add ExternalId parameter if provided + if aws_external_id is not None: + assume_role_params["ExternalId"] = aws_external_id + + sts_response = sts_client.assume_role_with_web_identity(**assume_role_params) iam_creds_dict = { "aws_access_key_id": sts_response["Credentials"]["AccessKeyId"], @@ -463,90 +526,131 @@ class BaseAWSLLM: iam_creds = session.get_credentials() return iam_creds, self._get_default_ttl_for_boto3_credentials() - def _handle_irsa_cross_account(self, irsa_role_arn: str, aws_role_name: str, - aws_session_name: str, region: str, web_identity_token_file: str) -> dict: + def _handle_irsa_cross_account( + self, + irsa_role_arn: str, + aws_role_name: str, + aws_session_name: str, + region: str, + web_identity_token_file: str, + aws_external_id: Optional[str] = None, + ) -> dict: """Handle cross-account role assumption for IRSA.""" import boto3 - + verbose_logger.debug("Cross-account role assumption detected") - + # Read the web identity token - with open(web_identity_token_file, 'r') as f: + with open(web_identity_token_file, "r") as f: web_identity_token = f.read().strip() - + # Create an STS client without credentials with tracer.trace("boto3.client(sts) for manual IRSA"): - sts_client = boto3.client('sts', region_name=region) - + sts_client = boto3.client("sts", region_name=region) + # Manually assume the IRSA role with the session name - verbose_logger.debug(f"Manually assuming IRSA role {irsa_role_arn} with session {aws_session_name}") + verbose_logger.debug( + f"Manually assuming IRSA role {irsa_role_arn} with session {aws_session_name}" + ) irsa_response = sts_client.assume_role_with_web_identity( RoleArn=irsa_role_arn, RoleSessionName=aws_session_name, - WebIdentityToken=web_identity_token + WebIdentityToken=web_identity_token, ) - + # Extract the credentials from the IRSA assumption irsa_creds = irsa_response["Credentials"] - + # Create a new STS client with the IRSA credentials with tracer.trace("boto3.client(sts) with manual IRSA credentials"): sts_client_with_creds = boto3.client( - 'sts', + "sts", region_name=region, aws_access_key_id=irsa_creds["AccessKeyId"], aws_secret_access_key=irsa_creds["SecretAccessKey"], - aws_session_token=irsa_creds["SessionToken"] + aws_session_token=irsa_creds["SessionToken"], ) - + # Get current caller identity for debugging try: caller_identity = sts_client_with_creds.get_caller_identity() - verbose_logger.debug(f"Current identity after manual IRSA assumption: {caller_identity.get('Arn', 'unknown')}") + verbose_logger.debug( + f"Current identity after manual IRSA assumption: {caller_identity.get('Arn', 'unknown')}" + ) except Exception as e: verbose_logger.debug(f"Failed to get caller identity: {e}") - - # Now assume the target role - verbose_logger.debug(f"Attempting to assume target role: {aws_role_name} with session: {aws_session_name}") - return sts_client_with_creds.assume_role( - RoleArn=aws_role_name, RoleSessionName=aws_session_name - ) - def _handle_irsa_same_account(self, aws_role_name: str, aws_session_name: str, region: str) -> dict: + # Now assume the target role + verbose_logger.debug( + f"Attempting to assume target role: {aws_role_name} with session: {aws_session_name}" + ) + assume_role_params = { + "RoleArn": aws_role_name, + "RoleSessionName": aws_session_name, + } + + # Add ExternalId parameter if provided + if aws_external_id is not None: + assume_role_params["ExternalId"] = aws_external_id + + return sts_client_with_creds.assume_role(**assume_role_params) + + def _handle_irsa_same_account( + self, + aws_role_name: str, + aws_session_name: str, + region: str, + aws_external_id: Optional[str] = None, + ) -> dict: """Handle same-account role assumption for IRSA.""" import boto3 - + verbose_logger.debug("Same account role assumption, using automatic IRSA") with tracer.trace("boto3.client(sts) with automatic IRSA"): sts_client = boto3.client("sts", region_name=region) - + # Get current caller identity for debugging try: caller_identity = sts_client.get_caller_identity() - verbose_logger.debug(f"Current IRSA identity: {caller_identity.get('Arn', 'unknown')}") + verbose_logger.debug( + f"Current IRSA identity: {caller_identity.get('Arn', 'unknown')}" + ) except Exception as e: verbose_logger.debug(f"Failed to get caller identity: {e}") - - # Assume the role - verbose_logger.debug(f"Attempting to assume role: {aws_role_name} with session: {aws_session_name}") - return sts_client.assume_role( - RoleArn=aws_role_name, RoleSessionName=aws_session_name - ) - def _extract_credentials_and_ttl(self, sts_response: dict) -> Tuple[Credentials, Optional[int]]: + # Assume the role + verbose_logger.debug( + f"Attempting to assume role: {aws_role_name} with session: {aws_session_name}" + ) + assume_role_params = { + "RoleArn": aws_role_name, + "RoleSessionName": aws_session_name, + } + + # Add ExternalId parameter if provided + if aws_external_id is not None: + assume_role_params["ExternalId"] = aws_external_id + + return sts_client.assume_role(**assume_role_params) + + def _extract_credentials_and_ttl( + self, sts_response: dict + ) -> Tuple[Credentials, Optional[int]]: """Extract credentials and TTL from STS response.""" from botocore.credentials import Credentials - + sts_credentials = sts_response["Credentials"] credentials = Credentials( access_key=sts_credentials["AccessKeyId"], secret_key=sts_credentials["SecretAccessKey"], token=sts_credentials["SessionToken"], ) - + expiration_time = sts_credentials["Expiration"] - ttl = int((expiration_time - datetime.now(expiration_time.tzinfo)).total_seconds()) - + ttl = int( + (expiration_time - datetime.now(expiration_time.tzinfo)).total_seconds() + ) + return credentials, ttl @tracer.wrap() @@ -554,8 +658,10 @@ class BaseAWSLLM: self, aws_access_key_id: Optional[str], aws_secret_access_key: Optional[str], + aws_session_token: Optional[str], aws_role_name: str, aws_session_name: str, + aws_external_id: Optional[str] = None, ) -> Tuple[Credentials, Optional[int]]: """ Authenticate with AWS Role @@ -566,34 +672,51 @@ class BaseAWSLLM: # Check if we're in an EKS/IRSA environment web_identity_token_file = os.getenv("AWS_WEB_IDENTITY_TOKEN_FILE") irsa_role_arn = os.getenv("AWS_ROLE_ARN") - + # If we have IRSA environment variables and no explicit credentials, # we need to use the web identity token flow - if (web_identity_token_file and irsa_role_arn and - aws_access_key_id is None and aws_secret_access_key is None): + if ( + web_identity_token_file + and irsa_role_arn + and aws_access_key_id is None + and aws_secret_access_key is None + ): # For cross-account role assumption with specific session names, # we need to manually assume the IRSA role first with the correct session name - verbose_logger.debug(f"IRSA detected: using web identity token from {web_identity_token_file}") - + verbose_logger.debug( + f"IRSA detected: using web identity token from {web_identity_token_file}" + ) + try: # Get region from environment - region = os.getenv("AWS_REGION") or os.getenv("AWS_DEFAULT_REGION") or "us-east-1" - + region = ( + os.getenv("AWS_REGION") + or os.getenv("AWS_DEFAULT_REGION") + or "us-east-1" + ) + # Check if we need to do cross-account role assumption if aws_role_name != irsa_role_arn: sts_response = self._handle_irsa_cross_account( - irsa_role_arn, aws_role_name, aws_session_name, region, web_identity_token_file + irsa_role_arn, + aws_role_name, + aws_session_name, + region, + web_identity_token_file, + aws_external_id, ) else: sts_response = self._handle_irsa_same_account( - aws_role_name, aws_session_name, region + aws_role_name, aws_session_name, region, aws_external_id ) - + return self._extract_credentials_and_ttl(sts_response) - + except Exception as e: verbose_logger.debug(f"Failed to assume role via IRSA: {e}") - if "AccessDenied" in str(e) and "is not authorized to perform: sts:AssumeRole" in str(e): + if "AccessDenied" in str( + e + ) and "is not authorized to perform: sts:AssumeRole" in str(e): # Provide a more helpful error message for trust policy issues verbose_logger.error( f"Access denied when trying to assume role {aws_role_name}. " @@ -602,7 +725,7 @@ class BaseAWSLLM: ) # Re-raise the exception instead of falling through raise - + # In EKS/IRSA environments, use ambient credentials (no explicit keys needed) # This allows the web identity token to work automatically if aws_access_key_id is None and aws_secret_access_key is None: @@ -614,11 +737,19 @@ class BaseAWSLLM: "sts", aws_access_key_id=aws_access_key_id, aws_secret_access_key=aws_secret_access_key, + aws_session_token=aws_session_token, ) - sts_response = sts_client.assume_role( - RoleArn=aws_role_name, RoleSessionName=aws_session_name - ) + assume_role_params = { + "RoleArn": aws_role_name, + "RoleSessionName": aws_session_name, + } + + # Add ExternalId parameter if provided + if aws_external_id is not None: + assume_role_params["ExternalId"] = aws_external_id + + sts_response = sts_client.assume_role(**assume_role_params) # Extract the credentials from the response and convert to Session Credentials sts_credentials = sts_response["Credentials"] @@ -740,14 +871,14 @@ class BaseAWSLLM: ) # Determine proxy_endpoint_url - if env_aws_bedrock_runtime_endpoint and isinstance( - env_aws_bedrock_runtime_endpoint, str - ): - proxy_endpoint_url = env_aws_bedrock_runtime_endpoint - elif aws_bedrock_runtime_endpoint is not None and isinstance( + if aws_bedrock_runtime_endpoint is not None and isinstance( aws_bedrock_runtime_endpoint, str ): proxy_endpoint_url = aws_bedrock_runtime_endpoint + elif env_aws_bedrock_runtime_endpoint and isinstance( + env_aws_bedrock_runtime_endpoint, str + ): + proxy_endpoint_url = env_aws_bedrock_runtime_endpoint else: proxy_endpoint_url = endpoint_url @@ -797,6 +928,7 @@ class BaseAWSLLM: aws_bedrock_runtime_endpoint = optional_params.pop( "aws_bedrock_runtime_endpoint", None ) # https://bedrock-runtime.{region_name}.amazonaws.com + aws_external_id = optional_params.pop("aws_external_id", None) credentials: Credentials = self.get_credentials( aws_access_key_id=aws_access_key_id, @@ -808,6 +940,7 @@ class BaseAWSLLM: aws_role_name=aws_role_name, aws_web_identity_token=aws_web_identity_token, aws_sts_endpoint=aws_sts_endpoint, + aws_external_id=aws_external_id, ) return Boto3CredentialsInfo( @@ -912,6 +1045,7 @@ class BaseAWSLLM: aws_profile_name = optional_params.get("aws_profile_name", None) aws_web_identity_token = optional_params.get("aws_web_identity_token", None) aws_sts_endpoint = optional_params.get("aws_sts_endpoint", None) + aws_external_id = optional_params.get("aws_external_id", None) aws_region_name = self._get_aws_region_name( optional_params=optional_params, model=model ) @@ -926,6 +1060,7 @@ class BaseAWSLLM: aws_role_name=aws_role_name, aws_web_identity_token=aws_web_identity_token, aws_sts_endpoint=aws_sts_endpoint, + aws_external_id=aws_external_id, ) sigv4 = SigV4Auth(credentials, service_name, aws_region_name) diff --git a/litellm/llms/bedrock/batches/transformation.py b/litellm/llms/bedrock/batches/transformation.py new file mode 100644 index 00000000000..2f3d00dddda --- /dev/null +++ b/litellm/llms/bedrock/batches/transformation.py @@ -0,0 +1,452 @@ +import os +import time +from typing import Any, Dict, List, Literal, Optional, Union, cast + +from httpx import Headers, Response + +from litellm.llms.base_llm.batches.transformation import BaseBatchesConfig +from litellm.llms.base_llm.chat.transformation import BaseLLMException +from litellm.types.llms.bedrock import ( + BedrockCreateBatchRequest, + BedrockCreateBatchResponse, + BedrockInputDataConfig, + BedrockOutputDataConfig, + BedrockS3InputDataConfig, + BedrockS3OutputDataConfig, +) +from litellm.types.llms.openai import ( + AllMessageValues, + CreateBatchRequest, +) +from litellm.types.utils import LiteLLMBatch, LlmProviders + +from ..base_aws_llm import BaseAWSLLM +from ..common_utils import CommonBatchFilesUtils + + +class BedrockBatchesConfig(BaseAWSLLM, BaseBatchesConfig): + """ + Config for Bedrock Batches - handles batch job creation and management for Bedrock + """ + + def __init__(self): + super().__init__() + self.common_utils = CommonBatchFilesUtils() + + @property + def custom_llm_provider(self) -> LlmProviders: + return LlmProviders.BEDROCK + + def validate_environment( + self, + headers: dict, + model: str, + messages: List[AllMessageValues], + optional_params: dict, + litellm_params: dict, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + ) -> dict: + """ + Validate and prepare environment for Bedrock batch requests. + AWS credentials are handled by BaseAWSLLM. + """ + # Add any Bedrock-specific headers if needed + return headers + + def get_complete_batch_url( + self, + api_base: Optional[str], + api_key: Optional[str], + model: str, + optional_params: Dict, + litellm_params: Dict, + data: CreateBatchRequest, + ) -> str: + """ + Get the complete URL for Bedrock batch creation. + Bedrock batch jobs are created via the model invocation job API. + """ + aws_region_name = self._get_aws_region_name(optional_params, model) + + # Bedrock model invocation job endpoint + # Format: https://bedrock.{region}.amazonaws.com/model-invocation-job + bedrock_endpoint = f"https://bedrock.{aws_region_name}.amazonaws.com/model-invocation-job" + + return bedrock_endpoint + + + + + + + + def transform_create_batch_request( + self, + model: str, + create_batch_data: CreateBatchRequest, + optional_params: dict, + litellm_params: dict, + ) -> Dict[str, Any]: + """ + Transform the batch creation request to Bedrock format. + + Bedrock batch inference requires: + - modelId: The Bedrock model ID + - jobName: Unique name for the batch job + - inputDataConfig: Configuration for input data (S3 location) + - outputDataConfig: Configuration for output data (S3 location) + - roleArn: IAM role ARN for the batch job + """ + # Get required parameters + input_file_id = create_batch_data.get("input_file_id") + if not input_file_id: + raise ValueError("input_file_id is required for Bedrock batch creation") + + # Extract S3 information from file ID using common utility + input_bucket, input_key = self.common_utils.parse_s3_uri(input_file_id) + + # Get output S3 configuration + output_bucket = litellm_params.get("s3_output_bucket_name") or os.getenv("AWS_S3_OUTPUT_BUCKET_NAME") + if not output_bucket: + # Use same bucket as input if no output bucket specified + output_bucket = input_bucket + + # Get IAM role ARN + role_arn = ( + litellm_params.get("aws_batch_role_arn") + or optional_params.get("aws_batch_role_arn") + or os.getenv("AWS_BATCH_ROLE_ARN") + ) + if not role_arn: + raise ValueError( + "AWS IAM role ARN is required for Bedrock batch jobs. " + "Set 'aws_batch_role_arn' in litellm_params or AWS_BATCH_ROLE_ARN env var" + ) + + + if not model: + raise ValueError("Could not determine Bedrock model ID. Please pass `model` in your request body.") + + # Generate job name with the correct model ID using common utility + job_name = self.common_utils.generate_unique_job_name(model, prefix="litellm") + output_key = f"litellm-batch-outputs/{job_name}/" + + # Build input data config + input_data_config: BedrockInputDataConfig = { + "s3InputDataConfig": BedrockS3InputDataConfig( + s3Uri=f"s3://{input_bucket}/{input_key}" + ) + } + + # Build output data config + output_data_config: BedrockOutputDataConfig = { + "s3OutputDataConfig": BedrockS3OutputDataConfig( + s3Uri=f"s3://{output_bucket}/{output_key}" + ) + } + + # Create Bedrock batch request with proper typing + bedrock_request: BedrockCreateBatchRequest = { + "modelId": model, + "jobName": job_name, + "inputDataConfig": input_data_config, + "outputDataConfig": output_data_config, + "roleArn": role_arn + } + + # Add optional parameters if provided + completion_window = create_batch_data.get("completion_window") + if completion_window: + # Map OpenAI completion window to Bedrock timeout + # OpenAI uses "24h", Bedrock expects timeout in hours + if completion_window == "24h": + bedrock_request["timeoutDurationInHours"] = 24 + + # For Bedrock, we need to return a pre-signed request with AWS auth headers + # Use common utility for AWS signing + endpoint_url = f"https://bedrock.{self._get_aws_region_name(optional_params, model)}.amazonaws.com/model-invocation-job" + signed_headers, signed_data = self.common_utils.sign_aws_request( + service_name="bedrock", + data=bedrock_request, + endpoint_url=endpoint_url, + optional_params=optional_params, + method="POST" + ) + + # Return a pre-signed request format that the HTTP handler can use + return { + "method": "POST", + "url": endpoint_url, + "headers": signed_headers, + "data": signed_data.decode('utf-8') + } + + def transform_create_batch_response( + self, + model: Optional[str], + raw_response: Response, + logging_obj: Any, + litellm_params: dict, + ) -> LiteLLMBatch: + """ + Transform Bedrock batch creation response to LiteLLM format. + """ + try: + response_data: BedrockCreateBatchResponse = raw_response.json() + except Exception as e: + raise ValueError(f"Failed to parse Bedrock batch response: {e}") + + # Extract information from typed Bedrock response + job_arn = response_data.get("jobArn", "") + status_str: str = str(response_data.get("status", "Submitted")) + + # Map Bedrock status to OpenAI-compatible status + status_mapping: Dict[str, str] = { + "Submitted": "validating", + "Validating": "validating", + "Scheduled": "in_progress", + "InProgress": "in_progress", + "PartiallyCompleted": "completed", + "Completed": "completed", + "Failed": "failed", + "Stopping": "cancelling", + "Stopped": "cancelled", + "Expired": "expired", + } + + openai_status = cast(Literal["validating", "failed", "in_progress", "finalizing", "completed", "expired", "cancelling", "cancelled"], status_mapping.get(status_str, "validating")) + + # Get original request data from litellm_params if available + original_request = litellm_params.get("original_batch_request", {}) + + # Create LiteLLM batch object + return LiteLLMBatch( + id=job_arn, # Use ARN as the batch ID + object="batch", + endpoint=original_request.get("endpoint", "/v1/chat/completions"), + errors=None, + input_file_id=original_request.get("input_file_id", ""), + completion_window=original_request.get("completion_window", "24h"), + status=openai_status, + output_file_id=None, # Will be populated when job completes + error_file_id=None, + created_at=int(time.time()), + in_progress_at=int(time.time()) if status_str == "InProgress" else None, + expires_at=None, + finalizing_at=None, + completed_at=None, + failed_at=None, + expired_at=None, + cancelling_at=None, + cancelled_at=None, + request_counts=None, + metadata=original_request.get("metadata", {}), + ) + + def transform_retrieve_batch_request( + self, + batch_id: str, + optional_params: dict, + litellm_params: dict, + ) -> Dict[str, Any]: + """ + Transform batch retrieval request for Bedrock. + + Args: + batch_id: Bedrock job ARN + optional_params: Optional parameters + litellm_params: LiteLLM parameters + + Returns: + Transformed request data for Bedrock GetModelInvocationJob API + """ + # For Bedrock, batch_id should be the full job ARN + # The GetModelInvocationJob API expects the full ARN as the identifier + if not batch_id.startswith("arn:aws:bedrock:"): + raise ValueError(f"Invalid batch_id format. Expected ARN, got: {batch_id}") + + # Extract the job identifier from the ARN - use the full ARN path part + # ARN format: arn:aws:bedrock:region:account:model-invocation-job/job-name + arn_parts = batch_id.split(":") + if len(arn_parts) < 6: + raise ValueError(f"Invalid ARN format: {batch_id}") + + region = arn_parts[3] + # arn_parts[5] contains "model-invocation-job/{jobId}" + + # Build the endpoint URL for GetModelInvocationJob + # AWS API format: GET /model-invocation-job/{jobIdentifier} + # Use the FULL ARN as jobIdentifier and URL-encode it (includes ':' and '/') + import urllib.parse as _ul + encoded_arn = _ul.quote(batch_id, safe="") + endpoint_url = f"https://bedrock.{region}.amazonaws.com/model-invocation-job/{encoded_arn}" + + # Use common utility for AWS signing + signed_headers, _ = self.common_utils.sign_aws_request( + service_name="bedrock", + data={}, # GET request has no body + endpoint_url=endpoint_url, + optional_params=optional_params, + method="GET" + ) + + # Return pre-signed request format + return { + "method": "GET", + "url": endpoint_url, + "headers": signed_headers, + "data": None + } + + def _parse_timestamps_and_status(self, response_data, status_str: str): + """Helper to parse timestamps based on status.""" + import datetime + def parse_timestamp(ts_str: Optional[str]) -> Optional[int]: + if not ts_str: + return None + try: + dt = datetime.datetime.fromisoformat(ts_str.replace('Z', '+00:00')) + return int(dt.timestamp()) + except Exception: + return None + + created_at = parse_timestamp(str(response_data.get("submitTime")) if response_data.get("submitTime") is not None else None) + in_progress_states = {"InProgress", "Validating", "Scheduled"} + in_progress_at = ( + parse_timestamp(str(response_data.get("lastModifiedTime")) if response_data.get("lastModifiedTime") is not None else None) + if status_str in in_progress_states + else None + ) + completed_at = parse_timestamp(str(response_data.get("endTime")) if response_data.get("endTime") is not None else None) if status_str in {"Completed", "PartiallyCompleted"} else None + failed_at = parse_timestamp(str(response_data.get("endTime")) if response_data.get("endTime") is not None else None) if status_str == "Failed" else None + cancelled_at = parse_timestamp(str(response_data.get("endTime")) if response_data.get("endTime") is not None else None) if status_str == "Stopped" else None + expires_at = parse_timestamp(str(response_data.get("jobExpirationTime")) if response_data.get("jobExpirationTime") is not None else None) + + return created_at, in_progress_at, completed_at, failed_at, cancelled_at, expires_at + + def _extract_file_configs(self, response_data): + """Helper to extract input and output file configurations.""" + # Extract input file ID + input_file_id = "" + input_data_config = response_data.get("inputDataConfig", {}) + if isinstance(input_data_config, dict): + s3_input_config = input_data_config.get("s3InputDataConfig", {}) + if isinstance(s3_input_config, dict): + input_file_id = s3_input_config.get("s3Uri", "") + + # Extract output file ID + output_file_id = None + output_data_config = response_data.get("outputDataConfig", {}) + if isinstance(output_data_config, dict): + s3_output_config = output_data_config.get("s3OutputDataConfig", {}) + if isinstance(s3_output_config, dict): + output_file_id = s3_output_config.get("s3Uri", "") + + return input_file_id, output_file_id + + def _extract_errors_and_metadata(self, response_data, raw_response): + """Helper to extract errors and enriched metadata.""" + # Extract errors + message = response_data.get("message") + errors = None + if message: + from openai.types.batch import Errors + from openai.types.batch_error import BatchError + errors = Errors( + data=[BatchError(message=message, code=str(raw_response.status_code))], + object="list" + ) + + # Enrich metadata with useful Bedrock fields + enriched_metadata_raw: Dict[str, Any] = { + "jobName": response_data.get("jobName"), + "clientRequestToken": response_data.get("clientRequestToken"), + "modelId": response_data.get("modelId"), + "roleArn": response_data.get("roleArn"), + "timeoutDurationInHours": response_data.get("timeoutDurationInHours"), + "vpcConfig": response_data.get("vpcConfig"), + } + import json as _json + enriched_metadata: Dict[str, str] = {} + for _k, _v in enriched_metadata_raw.items(): + if _v is None: + continue + if isinstance(_v, (dict, list)): + try: + enriched_metadata[_k] = _json.dumps(_v) + except Exception: + enriched_metadata[_k] = str(_v) + else: + enriched_metadata[_k] = str(_v) + + return errors, enriched_metadata + + def transform_retrieve_batch_response( + self, + model: Optional[str], + raw_response: Response, + logging_obj: Any, + litellm_params: dict, + ) -> LiteLLMBatch: + """ + Transform Bedrock batch retrieval response to LiteLLM format. + """ + from litellm.types.llms.bedrock import BedrockGetBatchResponse + try: + response_data: BedrockGetBatchResponse = raw_response.json() + except Exception as e: + raise ValueError(f"Failed to parse Bedrock batch response: {e}") + + job_arn = response_data.get("jobArn", "") + status_str: str = str(response_data.get("status", "Submitted")) + + # Map Bedrock status to OpenAI-compatible status + status_mapping: Dict[str, str] = { + "Submitted": "validating", "Validating": "validating", "Scheduled": "in_progress", + "InProgress": "in_progress", "PartiallyCompleted": "completed", "Completed": "completed", + "Failed": "failed", "Stopping": "cancelling", "Stopped": "cancelled", "Expired": "expired" + } + openai_status = cast(Literal["validating", "failed", "in_progress", "finalizing", "completed", "expired", "cancelling", "cancelled"], status_mapping.get(status_str, "validating")) + + # Parse timestamps + created_at, in_progress_at, completed_at, failed_at, cancelled_at, expires_at = self._parse_timestamps_and_status(response_data, status_str) + + # Extract file configurations + input_file_id, output_file_id = self._extract_file_configs(response_data) + + # Extract errors and metadata + errors, enriched_metadata = self._extract_errors_and_metadata(response_data, raw_response) + + return LiteLLMBatch( + id=job_arn, + object="batch", + endpoint="/v1/chat/completions", + errors=errors, + input_file_id=input_file_id, + completion_window="24h", + status=openai_status, + output_file_id=output_file_id, + error_file_id=None, + created_at=created_at or int(time.time()), + in_progress_at=in_progress_at, + expires_at=expires_at, + finalizing_at=None, + completed_at=completed_at, + failed_at=failed_at, + expired_at=None, + cancelling_at=None, + cancelled_at=cancelled_at, + request_counts=None, + metadata=enriched_metadata, + ) + + def get_error_class( + self, error_message: str, status_code: int, headers: Union[Dict, Headers] + ) -> BaseLLMException: + """ + Get Bedrock-specific error class using common utility. + """ + return self.common_utils.get_error_class(error_message, status_code, headers) + + diff --git a/litellm/llms/bedrock/chat/converse_handler.py b/litellm/llms/bedrock/chat/converse_handler.py index 15a5002f0e4..54c603e5960 100644 --- a/litellm/llms/bedrock/chat/converse_handler.py +++ b/litellm/llms/bedrock/chat/converse_handler.py @@ -307,6 +307,7 @@ class BedrockConverseLLM(BaseAWSLLM): ) # https://bedrock-runtime.{region_name}.amazonaws.com aws_web_identity_token = optional_params.pop("aws_web_identity_token", None) aws_sts_endpoint = optional_params.pop("aws_sts_endpoint", None) + aws_external_id = optional_params.pop("aws_external_id", None) optional_params.pop("aws_region_name", None) litellm_params[ @@ -323,6 +324,7 @@ class BedrockConverseLLM(BaseAWSLLM): aws_role_name=aws_role_name, aws_web_identity_token=aws_web_identity_token, aws_sts_endpoint=aws_sts_endpoint, + aws_external_id=aws_external_id, ) ### SET RUNTIME ENDPOINT ### diff --git a/litellm/llms/bedrock/chat/converse_transformation.py b/litellm/llms/bedrock/chat/converse_transformation.py index b93ca94bed4..a61cfa39e47 100644 --- a/litellm/llms/bedrock/chat/converse_transformation.py +++ b/litellm/llms/bedrock/chat/converse_transformation.py @@ -10,9 +10,11 @@ from typing import List, Literal, Optional, Tuple, Union, cast, overload import httpx import litellm +from litellm._logging import verbose_logger +from litellm.constants import RESPONSE_FORMAT_TOOL_NAME from litellm.litellm_core_utils.core_helpers import map_finish_reason from litellm.litellm_core_utils.litellm_logging import Logging -from litellm.litellm_core_utils.llm_response_utils.convert_dict_to_response import ( +from litellm.litellm_core_utils.prompt_templates.common_utils import ( _parse_content_for_reasoning, ) from litellm.litellm_core_utils.prompt_templates.factory import ( @@ -48,14 +50,19 @@ from litellm.types.utils import ( ) from litellm.utils import add_dummy_tool, has_tool_call_blocks, supports_reasoning -from ..common_utils import BedrockError, BedrockModelInfo, get_bedrock_tool_name, get_anthropic_beta_from_headers +from ..common_utils import ( + BedrockError, + BedrockModelInfo, + get_anthropic_beta_from_headers, + get_bedrock_tool_name, +) # Computer use tool prefixes supported by Bedrock BEDROCK_COMPUTER_USE_TOOLS = [ "computer_use_preview", "computer_", "bash_", - "text_editor_" + "text_editor_", ] @@ -95,6 +102,61 @@ class AmazonConverseConfig(BaseConfig): "performanceConfig": PerformanceConfigBlock, } + @staticmethod + def _convert_consecutive_user_messages_to_guarded_text( + messages: List[AllMessageValues], optional_params: dict + ) -> List[AllMessageValues]: + """ + Convert consecutive user messages at the end to guarded_text type if guardrailConfig is present + and no guarded_text is already present in those messages. + """ + # Check if guardrailConfig is present + if "guardrailConfig" not in optional_params: + return messages + + # Find all consecutive user messages at the end + consecutive_user_message_indices = [] + for i in range(len(messages) - 1, -1, -1): + if messages[i].get("role") == "user": + consecutive_user_message_indices.append(i) + else: + break + + if not consecutive_user_message_indices: + return messages + + # Process each consecutive user message + messages_copy = copy.deepcopy(messages) + for user_message_index in consecutive_user_message_indices: + user_message = messages_copy[user_message_index] + content = user_message.get("content", []) + + if isinstance(content, list): + has_guarded_text = any( + isinstance(item, dict) and item.get("type") == "guarded_text" + for item in content + ) + if has_guarded_text: + continue # Skip this message if it already has guarded_text + + # Convert text elements to guarded_text + new_content = [] + for item in content: + if isinstance(item, dict) and item.get("type") == "text": + new_item = {"type": "guarded_text", "text": item["text"]} # type: ignore + new_content.append(new_item) + else: + new_content.append(item) + + messages_copy[user_message_index]["content"] = new_content # type: ignore + elif isinstance(content, str): + # If content is a string, convert it to guarded_text + messages_copy[user_message_index]["content"] = [ # type: ignore + {"type": "guarded_text", "text": content} # type: ignore + ] + + return messages_copy + @classmethod def get_config(cls): return { @@ -113,6 +175,77 @@ class AmazonConverseConfig(BaseConfig): and v is not None } + def _validate_request_metadata(self, metadata: dict) -> None: + """ + Validate requestMetadata according to AWS Bedrock Converse API constraints. + + Constraints: + - Maximum of 16 items + - Keys: 1-256 characters, pattern [a-zA-Z0-9\\s:_@$#=/+,-.]{1,256} + - Values: 0-256 characters, pattern [a-zA-Z0-9\\s:_@$#=/+,-.]{0,256} + """ + import re + + if not isinstance(metadata, dict): + raise litellm.exceptions.BadRequestError( + message="requestMetadata must be a dictionary", + model="bedrock", + llm_provider="bedrock", + ) + + if len(metadata) > 16: + raise litellm.exceptions.BadRequestError( + message="requestMetadata can contain a maximum of 16 items", + model="bedrock", + llm_provider="bedrock", + ) + + key_pattern = re.compile(r"^[a-zA-Z0-9\s:_@$#=/+,.-]{1,256}$") + value_pattern = re.compile(r"^[a-zA-Z0-9\s:_@$#=/+,.-]{0,256}$") + + for key, value in metadata.items(): + if not isinstance(key, str): + raise litellm.exceptions.BadRequestError( + message="requestMetadata keys must be strings", + model="bedrock", + llm_provider="bedrock", + ) + + if not isinstance(value, str): + raise litellm.exceptions.BadRequestError( + message="requestMetadata values must be strings", + model="bedrock", + llm_provider="bedrock", + ) + + if len(key) == 0 or len(key) > 256: + raise litellm.exceptions.BadRequestError( + message="requestMetadata key length must be 1-256 characters", + model="bedrock", + llm_provider="bedrock", + ) + + if len(value) > 256: + raise litellm.exceptions.BadRequestError( + message="requestMetadata value length must be 0-256 characters", + model="bedrock", + llm_provider="bedrock", + ) + + if not key_pattern.match(key): + raise litellm.exceptions.BadRequestError( + message=f"requestMetadata key '{key}' contains invalid characters. Allowed: [a-zA-Z0-9\\s:_@$#=/+,.-]", + model="bedrock", + llm_provider="bedrock", + ) + + if not value_pattern.match(value): + raise litellm.exceptions.BadRequestError( + message=f"requestMetadata value '{value}' contains invalid characters. Allowed: [a-zA-Z0-9\\s:_@$#=/+,.-]", + model="bedrock", + llm_provider="bedrock", + ) + def get_supported_openai_params(self, model: str) -> List[str]: from litellm.utils import supports_function_calling @@ -126,6 +259,7 @@ class AmazonConverseConfig(BaseConfig): "top_p", "extra_headers", "response_format", + "requestMetadata", ] if ( @@ -163,7 +297,9 @@ class AmazonConverseConfig(BaseConfig): # only anthropic and mistral support tool choice config. otherwise (E.g. cohere) will fail the call - https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_ToolChoice.html supported_params.append("tool_choice") - if ( + if "gpt-oss" in model: + supported_params.append("reasoning_effort") + elif ( "claude-3-7" in model or "claude-sonnet-4" in model or "claude-opus-4" in model @@ -233,7 +369,7 @@ class AmazonConverseConfig(BaseConfig): """Check if computer use tools are being used in the request.""" if tools is None: return False - + for tool in tools: if "type" in tool: tool_type = tool["type"] @@ -247,17 +383,17 @@ class AmazonConverseConfig(BaseConfig): ) -> List[dict]: """Transform computer use tools to Bedrock format.""" transformed_tools: List[dict] = [] - + for tool in computer_use_tools: tool_type = tool.get("type", "") - + # Check if this is a computer use tool with the startswith method is_computer_use_tool = False for computer_use_prefix in BEDROCK_COMPUTER_USE_TOOLS: if tool_type.startswith(computer_use_prefix): is_computer_use_tool = True break - + transformed_tool: dict = {} if is_computer_use_tool: if tool_type.startswith("computer_") and "function" in tool: @@ -266,7 +402,7 @@ class AmazonConverseConfig(BaseConfig): transformed_tool = { "type": tool_type, "name": func.get("name", "computer"), - **func.get("parameters", {}) + **func.get("parameters", {}), } else: # Direct tools - just need to ensure name is present @@ -279,27 +415,29 @@ class AmazonConverseConfig(BaseConfig): else: # Pass through other tools as-is transformed_tool = dict(tool) - + transformed_tools.append(transformed_tool) - + return transformed_tools def _separate_computer_use_tools( self, tools: List[OpenAIChatCompletionToolParam], model: str - ) -> Tuple[List[OpenAIChatCompletionToolParam], List[OpenAIChatCompletionToolParam]]: + ) -> Tuple[ + List[OpenAIChatCompletionToolParam], List[OpenAIChatCompletionToolParam] + ]: """ Separate computer use tools from regular function tools. - + Args: tools: List of tools to separate model: The model name to check if it supports computer use - + Returns: Tuple of (computer_use_tools, regular_tools) """ computer_use_tools = [] regular_tools = [] - + for tool in tools: if "type" in tool: tool_type = tool["type"] @@ -314,15 +452,12 @@ class AmazonConverseConfig(BaseConfig): regular_tools.append(tool) else: regular_tools.append(tool) - + return computer_use_tools, regular_tools - - def _create_json_tool_call_for_response_format( self, json_schema: Optional[dict] = None, - schema_name: str = "json_tool_call", description: Optional[str] = None, ) -> ChatCompletionToolParam: """ @@ -344,10 +479,12 @@ class AmazonConverseConfig(BaseConfig): "properties": {}, } else: + # Use the schema as-is for Bedrock + # Bedrock requires the tool schema to be of type "object" and doesn't need unwrapping _input_schema = json_schema tool_param_function_chunk = ChatCompletionToolParamFunctionChunk( - name=schema_name, parameters=_input_schema + name=RESPONSE_FORMAT_TOOL_NAME, parameters=_input_schema ) if description: tool_param_function_chunk["description"] = description @@ -386,56 +523,13 @@ class AmazonConverseConfig(BaseConfig): for param, value in non_default_params.items(): if param == "response_format" and isinstance(value, dict): - ignore_response_format_types = ["text"] - if value["type"] in ignore_response_format_types: # value is a no-op - continue - - json_schema: Optional[dict] = None - schema_name: str = "" - description: Optional[str] = None - if "response_schema" in value: - json_schema = value["response_schema"] - schema_name = "json_tool_call" - elif "json_schema" in value: - json_schema = value["json_schema"]["schema"] - schema_name = value["json_schema"]["name"] - description = value["json_schema"].get("description") - - if "type" in value and value["type"] == "text": - continue - - """ - Follow similar approach to anthropic - translate to a single tool call. - - When using tools in this way: - https://docs.anthropic.com/en/docs/build-with-claude/tool-use#json-mode - - You usually want to provide a single tool - - You should set tool_choice (see Forcing tool use) to instruct the model to explicitly use that tool - - Remember that the model will pass the input to the tool, so the name of the tool and description should be from the model’s perspective. - """ - _tool = self._create_json_tool_call_for_response_format( - json_schema=json_schema, - schema_name=schema_name if schema_name != "" else "json_tool_call", - description=description, + optional_params = self._translate_response_format_param( + value=value, + model=model, + optional_params=optional_params, + non_default_params=non_default_params, + is_thinking_enabled=is_thinking_enabled, ) - optional_params = self._add_tools_to_optional_params( - optional_params=optional_params, tools=[_tool] - ) - - if ( - litellm.utils.supports_tool_choice( - model=model, custom_llm_provider=self.custom_llm_provider - ) - and not is_thinking_enabled - ): - - optional_params["tool_choice"] = ToolChoiceValuesBlock( - tool=SpecificToolChoiceBlock( - name=schema_name if schema_name != "" else "json_tool_call" - ) - ) - optional_params["json_mode"] = True - if non_default_params.get("stream", False) is True: - optional_params["fake_stream"] = True if param == "max_tokens" or param == "max_completion_tokens": optional_params["maxTokens"] = value if param == "stream": @@ -466,13 +560,84 @@ class AmazonConverseConfig(BaseConfig): if param == "thinking": optional_params["thinking"] = value elif param == "reasoning_effort" and isinstance(value, str): - optional_params["thinking"] = AnthropicConfig._map_reasoning_effort( - value - ) + if "gpt-oss" in model: + # GPT-OSS models: keep reasoning_effort as-is + # It will be passed through to additionalModelRequestFields + optional_params["reasoning_effort"] = value + else: + # Anthropic and other models: convert to thinking parameter + optional_params["thinking"] = AnthropicConfig._map_reasoning_effort( + value + ) + if param == "requestMetadata": + if value is not None and isinstance(value, dict): + self._validate_request_metadata(value) # type: ignore + optional_params["requestMetadata"] = value - self.update_optional_params_with_thinking_tokens( - non_default_params=non_default_params, optional_params=optional_params + # Only update thinking tokens for non-GPT-OSS models + if "gpt-oss" not in model: + self.update_optional_params_with_thinking_tokens( + non_default_params=non_default_params, optional_params=optional_params + ) + + return optional_params + + def _translate_response_format_param( + self, + value: dict, + model: str, + optional_params: dict, + non_default_params: dict, + is_thinking_enabled: bool, + ) -> dict: + """ + Handles translation of response_format parameter to Bedrock format. + + Returns `optional_params` with the translated response_format parameter. + """ + ignore_response_format_types = ["text"] + if value["type"] in ignore_response_format_types: # value is a no-op + return optional_params + + json_schema: Optional[dict] = None + description: Optional[str] = None + if "response_schema" in value: + json_schema = value["response_schema"] + elif "json_schema" in value: + json_schema = value["json_schema"]["schema"] + description = value["json_schema"].get("description") + + if "type" in value and value["type"] == "text": + return optional_params + + """ + Follow similar approach to anthropic - translate to a single tool call. + + When using tools in this way: - https://docs.anthropic.com/en/docs/build-with-claude/tool-use#json-mode + - You usually want to provide a single tool + - You should set tool_choice (see Forcing tool use) to instruct the model to explicitly use that tool + - Remember that the model will pass the input to the tool, so the name of the tool and description should be from the model’s perspective. + """ + _tool = self._create_json_tool_call_for_response_format( + json_schema=json_schema, + description=description, ) + optional_params = self._add_tools_to_optional_params( + optional_params=optional_params, tools=[_tool] + ) + + if ( + litellm.utils.supports_tool_choice( + model=model, custom_llm_provider=self.custom_llm_provider + ) + and not is_thinking_enabled + ): + optional_params["tool_choice"] = ToolChoiceValuesBlock( + tool=SpecificToolChoiceBlock(name=RESPONSE_FORMAT_TOOL_NAME) + ) + optional_params["json_mode"] = True + if non_default_params.get("stream", False) is True: + optional_params["fake_stream"] = True return optional_params @@ -597,6 +762,93 @@ class AmazonConverseConfig(BaseConfig): return {} + def _prepare_request_params( + self, optional_params: dict, model: str + ) -> Tuple[dict, dict, dict]: + """Prepare and separate request parameters.""" + inference_params = copy.deepcopy(optional_params) + supported_converse_params = list( + AmazonConverseConfig.__annotations__.keys() + ) + ["top_k"] + supported_tool_call_params = ["tools", "tool_choice"] + supported_config_params = list(self.get_config_blocks().keys()) + total_supported_params = ( + supported_converse_params + + supported_tool_call_params + + supported_config_params + ) + inference_params.pop("json_mode", None) # used for handling json_schema + + # Extract requestMetadata before processing other parameters + request_metadata = inference_params.pop("requestMetadata", None) + if request_metadata is not None: + self._validate_request_metadata(request_metadata) + + # keep supported params in 'inference_params', and set all model-specific params in 'additional_request_params' + additional_request_params = { + k: v for k, v in inference_params.items() if k not in total_supported_params + } + inference_params = { + k: v for k, v in inference_params.items() if k in total_supported_params + } + + # Only set the topK value in for models that support it + additional_request_params.update( + self._handle_top_k_value(model, inference_params) + ) + + return inference_params, additional_request_params, request_metadata + + def _process_tools_and_beta( + self, + original_tools: list, + model: str, + headers: Optional[dict], + additional_request_params: dict, + ) -> Tuple[List[ToolBlock], list]: + """Process tools and collect anthropic_beta values.""" + bedrock_tools: List[ToolBlock] = [] + + # Collect anthropic_beta values from user headers + anthropic_beta_list = [] + if headers: + user_betas = get_anthropic_beta_from_headers(headers) + anthropic_beta_list.extend(user_betas) + + # Only separate tools if computer use tools are actually present + if original_tools and self.is_computer_use_tool_used(original_tools, model): + # Separate computer use tools from regular function tools + computer_use_tools, regular_tools = self._separate_computer_use_tools( + original_tools, model + ) + + # Process regular function tools using existing logic + bedrock_tools = _bedrock_tools_pt(regular_tools) + + # Add computer use tools and anthropic_beta if needed (only when computer use tools are present) + if computer_use_tools: + anthropic_beta_list.append("computer-use-2024-10-22") + # Transform computer use tools to proper Bedrock format + transformed_computer_tools = self._transform_computer_use_tools( + computer_use_tools + ) + additional_request_params["tools"] = transformed_computer_tools + else: + # No computer use tools, process all tools as regular tools + bedrock_tools = _bedrock_tools_pt(original_tools) + + # Set anthropic_beta in additional_request_params if we have any beta features + if anthropic_beta_list: + # Remove duplicates while preserving order + unique_betas = [] + seen = set() + for beta in anthropic_beta_list: + if beta not in seen: + unique_betas.append(beta) + seen.add(beta) + additional_request_params["anthropic_beta"] = unique_betas + + return bedrock_tools, anthropic_beta_list def _transform_request_helper( self, @@ -626,74 +878,18 @@ class AmazonConverseConfig(BaseConfig): llm_provider="bedrock", ) - inference_params = copy.deepcopy(optional_params) - supported_converse_params = list( - AmazonConverseConfig.__annotations__.keys() - ) + ["top_k"] - supported_tool_call_params = ["tools", "tool_choice"] - supported_config_params = list(self.get_config_blocks().keys()) - total_supported_params = ( - supported_converse_params - + supported_tool_call_params - + supported_config_params - ) - inference_params.pop("json_mode", None) # used for handling json_schema - - # keep supported params in 'inference_params', and set all model-specific params in 'additional_request_params' - additional_request_params = { - k: v for k, v in inference_params.items() if k not in total_supported_params - } - inference_params = { - k: v for k, v in inference_params.items() if k in total_supported_params - } - - # Only set the topK value in for models that support it - additional_request_params.update( - self._handle_top_k_value(model, inference_params) + # Prepare and separate parameters + inference_params, additional_request_params, request_metadata = ( + self._prepare_request_params(optional_params, model) ) original_tools = inference_params.pop("tools", []) - - # Initialize bedrock_tools - bedrock_tools: List[ToolBlock] = [] - - # Collect anthropic_beta values from user headers - anthropic_beta_list = [] - if headers: - user_betas = get_anthropic_beta_from_headers(headers) - anthropic_beta_list.extend(user_betas) - - # Only separate tools if computer use tools are actually present - if original_tools and self.is_computer_use_tool_used(original_tools, model): - # Separate computer use tools from regular function tools - computer_use_tools, regular_tools = self._separate_computer_use_tools( - original_tools, model - ) - - # Process regular function tools using existing logic - bedrock_tools = _bedrock_tools_pt(regular_tools) - - # Add computer use tools and anthropic_beta if needed (only when computer use tools are present) - if computer_use_tools: - anthropic_beta_list.append("computer-use-2024-10-22") - # Transform computer use tools to proper Bedrock format - transformed_computer_tools = self._transform_computer_use_tools(computer_use_tools) - additional_request_params["tools"] = transformed_computer_tools - else: - # No computer use tools, process all tools as regular tools - bedrock_tools = _bedrock_tools_pt(original_tools) - - # Set anthropic_beta in additional_request_params if we have any beta features - if anthropic_beta_list: - # Remove duplicates while preserving order - unique_betas = [] - seen = set() - for beta in anthropic_beta_list: - if beta not in seen: - unique_betas.append(beta) - seen.add(beta) - additional_request_params["anthropic_beta"] = unique_betas - + + # Process tools and collect beta values + bedrock_tools, anthropic_beta_list = self._process_tools_and_beta( + original_tools, model, headers, additional_request_params + ) + bedrock_tool_config: Optional[ToolConfigBlock] = None if len(bedrock_tools) > 0: tool_choice_values: ToolChoiceValuesBlock = inference_params.pop( @@ -723,6 +919,10 @@ class AmazonConverseConfig(BaseConfig): if bedrock_tool_config is not None: data["toolConfig"] = bedrock_tool_config + # Request Metadata (top-level field) + if request_metadata is not None: + data["requestMetadata"] = request_metadata + return data async def _async_transform_request( @@ -734,6 +934,11 @@ class AmazonConverseConfig(BaseConfig): headers: Optional[dict] = None, ) -> RequestObject: messages, system_content_blocks = self._transform_system_message(messages) + + # Convert last user message to guarded_text if guardrailConfig is present + messages = self._convert_consecutive_user_messages_to_guarded_text( + messages, optional_params + ) ## TRANSFORMATION ## _data: CommonRequestObject = self._transform_request_helper( @@ -786,6 +991,11 @@ class AmazonConverseConfig(BaseConfig): ) -> RequestObject: messages, system_content_blocks = self._transform_system_message(messages) + # Convert last user message to guarded_text if guardrailConfig is present + messages = self._convert_consecutive_user_messages_to_guarded_text( + messages, optional_params + ) + _data: CommonRequestObject = self._transform_request_helper( model=model, system_content_blocks=system_content_blocks, @@ -1119,10 +1329,36 @@ class AmazonConverseConfig(BaseConfig): self._transform_thinking_blocks(reasoningContentBlocks) ) chat_completion_message["content"] = content_str - if json_mode is True and tools is not None and len(tools) == 1: - # to support 'json_schema' logic on bedrock models + if ( + json_mode is True + and tools is not None + and len(tools) == 1 + and tools[0]["function"].get("name") == RESPONSE_FORMAT_TOOL_NAME + ): + verbose_logger.debug( + "Processing JSON tool call response for response_format" + ) json_mode_content_str: Optional[str] = tools[0]["function"].get("arguments") if json_mode_content_str is not None: + import json + + # Bedrock returns the response wrapped in a "properties" object + # We need to extract the actual content from this wrapper + try: + response_data = json.loads(json_mode_content_str) + + # If Bedrock wrapped the response in "properties", extract the content + if ( + isinstance(response_data, dict) + and "properties" in response_data + and len(response_data) == 1 + ): + response_data = response_data["properties"] + json_mode_content_str = json.dumps(response_data) + except json.JSONDecodeError: + # If parsing fails, use the original response + pass + chat_completion_message["content"] = json_mode_content_str else: chat_completion_message["tool_calls"] = tools @@ -1182,7 +1418,6 @@ class AmazonConverseConfig(BaseConfig): if api_key: headers["Authorization"] = f"Bearer {api_key}" return headers - def should_fake_stream( self, diff --git a/litellm/llms/bedrock/chat/invoke_agent/transformation.py b/litellm/llms/bedrock/chat/invoke_agent/transformation.py index e4ff6d398ea..4a7c33ed204 100644 --- a/litellm/llms/bedrock/chat/invoke_agent/transformation.py +++ b/litellm/llms/bedrock/chat/invoke_agent/transformation.py @@ -5,7 +5,7 @@ https://docs.aws.amazon.com/bedrock/latest/APIReference/API_agent-runtime_Invoke """ import base64 import json -import uuid +from litellm._uuid import uuid from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union import httpx diff --git a/litellm/llms/bedrock/chat/invoke_handler.py b/litellm/llms/bedrock/chat/invoke_handler.py index 42cdb34fc1a..0d1b4102c94 100644 --- a/litellm/llms/bedrock/chat/invoke_handler.py +++ b/litellm/llms/bedrock/chat/invoke_handler.py @@ -7,7 +7,7 @@ import json import time import types import urllib.parse -import uuid +from litellm._uuid import uuid from functools import partial from typing import ( Any, diff --git a/litellm/llms/bedrock/chat/invoke_transformations/amazon_deepseek_transformation.py b/litellm/llms/bedrock/chat/invoke_transformations/amazon_deepseek_transformation.py index d7ceec1f1c1..0fe84b0ce0c 100644 --- a/litellm/llms/bedrock/chat/invoke_transformations/amazon_deepseek_transformation.py +++ b/litellm/llms/bedrock/chat/invoke_transformations/amazon_deepseek_transformation.py @@ -3,7 +3,7 @@ from typing import Any, List, Optional, cast from httpx import Response from litellm import verbose_logger -from litellm.litellm_core_utils.llm_response_utils.convert_dict_to_response import ( +from litellm.litellm_core_utils.prompt_templates.common_utils import ( _parse_content_for_reasoning, ) from litellm.llms.base_llm.base_model_iterator import BaseModelResponseIterator diff --git a/litellm/llms/bedrock/common_utils.py b/litellm/llms/bedrock/common_utils.py index c76fc0a80c3..2b111cde600 100644 --- a/litellm/llms/bedrock/common_utils.py +++ b/litellm/llms/bedrock/common_utils.py @@ -6,6 +6,9 @@ import json import os from typing import TYPE_CHECKING, Dict, List, Literal, Optional, Union +if TYPE_CHECKING: + from litellm.types.llms.bedrock import BedrockCreateBatchRequest + import httpx import litellm @@ -608,3 +611,223 @@ def get_anthropic_beta_from_headers(headers: dict) -> List[str]: # Split comma-separated values and strip whitespace return [beta.strip() for beta in anthropic_beta_header.split(",")] + + +class CommonBatchFilesUtils: + """ + Common utilities for Bedrock batch and file operations. + Provides shared functionality to reduce code duplication between batches and files. + """ + + def __init__(self): + # Import here to avoid circular imports + from .base_aws_llm import BaseAWSLLM + self._base_aws = BaseAWSLLM() + + def get_bedrock_model_id_from_litellm_model(self, model: str) -> str: + """ + Extract the actual Bedrock model ID from LiteLLM model name. + + Args: + model: LiteLLM model name (e.g., "bedrock/anthropic.claude-3-sonnet-20240229-v1:0") + + Returns: + Bedrock model ID (e.g., "anthropic.claude-3-sonnet-20240229-v1:0") + """ + if model.startswith("bedrock/"): + return model[8:] # Remove "bedrock/" prefix + return model + + def parse_s3_uri(self, s3_uri: str) -> tuple: + """ + Parse S3 URI into bucket and key components. + + Args: + s3_uri: S3 URI (e.g., "s3://bucket/key/path") + + Returns: + Tuple of (bucket, key) + + Raises: + ValueError: If URI format is invalid + """ + if not s3_uri.startswith("s3://"): + raise ValueError(f"Invalid S3 URI format: {s3_uri}") + + s3_parts = s3_uri[5:].split("/", 1) # Remove "s3://" and split on first "/" + if len(s3_parts) != 2: + raise ValueError(f"Invalid S3 URI format: {s3_uri}") + + return s3_parts[0], s3_parts[1] # bucket, key + + def extract_model_from_s3_file_path(self, s3_uri: str, optional_params: dict) -> str: + """ + Extract model ID from S3 file path. + + The Bedrock file transformation creates S3 objects with the model name embedded: + Format: s3://bucket/litellm-bedrock-files-{model}-{uuid}.jsonl + """ + # Check if model is provided in optional_params first + if "model" in optional_params and optional_params["model"]: + return self.get_bedrock_model_id_from_litellm_model(optional_params["model"]) + + # Extract model from S3 URI path + # Expected format: s3://bucket/litellm-bedrock-files-{model}-{uuid}.jsonl + try: + bucket, object_key = self.parse_s3_uri(s3_uri) + + # Extract model from object key if it follows our naming pattern + if object_key.startswith("litellm-bedrock-files-"): + # Remove prefix and suffix to get model part + model_part = object_key[22:] # Remove "litellm-bedrock-files-" + # Find the last dash before the UUID + parts = model_part.split("-") + if len(parts) > 1: + # Reconstruct model name (everything except the last UUID part and .jsonl) + model_name = "-".join(parts[:-1]) + if model_name.endswith(".jsonl"): + model_name = model_name[:-6] # Remove .jsonl + return model_name + except Exception: + pass + + # Fallback to default model + return "anthropic.claude-3-5-sonnet-20240620-v1:0" + + def sign_aws_request( + self, + service_name: str, + data: Union[str, dict, "BedrockCreateBatchRequest"], + endpoint_url: str, + optional_params: dict, + method: str = "POST", + ) -> tuple: + """ + Sign AWS request using Signature Version 4. + + Args: + service_name: AWS service name ("bedrock" or "s3") + data: Request data (string or dict) + endpoint_url: Full endpoint URL + optional_params: Optional parameters containing AWS credentials + method: HTTP method (default: POST) + + Returns: + Tuple of (signed_headers, signed_data) + """ + try: + from botocore.auth import SigV4Auth + from botocore.awsrequest import AWSRequest + except ImportError: + raise ImportError("Missing boto3 to call bedrock. Run 'pip install boto3'.") + + # Get AWS credentials using existing methods + aws_region_name = self._base_aws._get_aws_region_name( + optional_params=optional_params, model="" + ) + credentials = self._base_aws.get_credentials( + aws_access_key_id=optional_params.get("aws_access_key_id"), + aws_secret_access_key=optional_params.get("aws_secret_access_key"), + aws_session_token=optional_params.get("aws_session_token"), + aws_region_name=aws_region_name, + aws_session_name=optional_params.get("aws_session_name"), + aws_profile_name=optional_params.get("aws_profile_name"), + aws_role_name=optional_params.get("aws_role_name"), + aws_web_identity_token=optional_params.get("aws_web_identity_token"), + aws_sts_endpoint=optional_params.get("aws_sts_endpoint"), + ) + + # Prepare the request data + method_upper = method.upper() + if method_upper == "GET": + # GET requests should be signed with an empty payload + request_data = "" + headers = {} + else: + if isinstance(data, dict): + import json + request_data = json.dumps(data) + else: + request_data = data + # Prepare headers for non-GET requests + headers = {"Content-Type": "application/json"} + + # Create AWS request and sign it + sigv4 = SigV4Auth(credentials, service_name, aws_region_name) + request = AWSRequest( + method=method_upper, url=endpoint_url, data=request_data, headers=headers + ) + sigv4.add_auth(request) + prepped = request.prepare() + + return dict(prepped.headers), request_data.encode('utf-8') if isinstance(request_data, str) else request_data + + def generate_unique_job_name(self, model: str, prefix: str = "litellm") -> str: + """ + Generate a unique job name for AWS services. + AWS services often have length limits, so this creates a concise name. + + Args: + model: Model name to include in the job name + prefix: Prefix for the job name + + Returns: + Unique job name (≤ 63 characters for Bedrock compatibility) + """ + from litellm._uuid import uuid + unique_id = str(uuid.uuid4())[:8] + # Format: {prefix}-batch-{model}-{uuid} + # Example: litellm-batch-claude-266c398e + job_name = f"{prefix}-batch-{unique_id}" + + return job_name + + def get_s3_bucket_and_key_from_config( + self, + litellm_params: dict, + optional_params: dict, + bucket_env_var: str = "AWS_S3_BUCKET_NAME", + key_prefix: str = "litellm" + ) -> tuple: + """ + Get S3 bucket and generate a unique key from configuration. + + Args: + litellm_params: LiteLLM parameters + optional_params: Optional parameters + bucket_env_var: Environment variable name for bucket + key_prefix: Prefix for the S3 key + + Returns: + Tuple of (bucket_name, object_key) + """ + import time + from litellm._uuid import uuid + + # Get bucket name + bucket_name = ( + litellm_params.get("s3_bucket_name") + or optional_params.get("s3_bucket_name") + or os.getenv(bucket_env_var) + ) + if not bucket_name: + raise ValueError(f"S3 bucket name is required. Set 's3_bucket_name' parameter or {bucket_env_var} env var") + + # Generate unique object key + timestamp = int(time.time()) + unique_id = str(uuid.uuid4())[:8] + object_key = f"{key_prefix}-{timestamp}-{unique_id}" + + return bucket_name, object_key + + def get_error_class( + self, error_message: str, status_code: int, headers: Union[Dict, httpx.Headers] + ) -> BaseLLMException: + """ + Get Bedrock-specific error class. + """ + return BedrockError( + status_code=status_code, + message=error_message, + headers=headers + ) diff --git a/litellm/llms/bedrock/count_tokens/handler.py b/litellm/llms/bedrock/count_tokens/handler.py new file mode 100644 index 00000000000..d4355c0c360 --- /dev/null +++ b/litellm/llms/bedrock/count_tokens/handler.py @@ -0,0 +1,123 @@ +""" +AWS Bedrock CountTokens API handler. + +Simplified handler leveraging existing LiteLLM Bedrock infrastructure. +""" + +from typing import Any, Dict + +from fastapi import HTTPException + +import litellm +from litellm._logging import verbose_logger +from litellm.llms.bedrock.count_tokens.transformation import BedrockCountTokensConfig +from litellm.llms.custom_httpx.http_handler import get_async_httpx_client + + +class BedrockCountTokensHandler(BedrockCountTokensConfig): + """ + Simplified handler for AWS Bedrock CountTokens API requests. + + Uses existing LiteLLM infrastructure for authentication and request handling. + """ + + async def handle_count_tokens_request( + self, + request_data: Dict[str, Any], + litellm_params: Dict[str, Any], + resolved_model: str, + ) -> Dict[str, Any]: + """ + Handle a CountTokens request using existing LiteLLM patterns. + + Args: + request_data: The incoming request payload + litellm_params: LiteLLM configuration parameters + resolved_model: The actual model ID resolved from router + + Returns: + Dictionary containing token count response + """ + try: + # Validate the request + self.validate_count_tokens_request(request_data) + + verbose_logger.debug( + f"Processing CountTokens request for resolved model: {resolved_model}" + ) + + # Get AWS region using existing LiteLLM function + aws_region_name = self._get_aws_region_name( + optional_params=litellm_params, + model=resolved_model, + model_id=None, + ) + + verbose_logger.debug(f"Retrieved AWS region: {aws_region_name}") + + # Transform request to Bedrock format (supports both Converse and InvokeModel) + bedrock_request = self.transform_anthropic_to_bedrock_count_tokens( + request_data=request_data + ) + + verbose_logger.debug(f"Transformed request: {bedrock_request}") + + # Get endpoint URL using simplified function + endpoint_url = self.get_bedrock_count_tokens_endpoint( + resolved_model, aws_region_name + ) + + verbose_logger.debug(f"Making request to: {endpoint_url}") + + # Use existing _sign_request method from BaseAWSLLM + headers = {"Content-Type": "application/json"} + signed_headers, signed_body = self._sign_request( + service_name="bedrock", + headers=headers, + optional_params=litellm_params, + request_data=bedrock_request, + api_base=endpoint_url, + model=resolved_model, + ) + + async_client = get_async_httpx_client(llm_provider=litellm.LlmProviders.BEDROCK) + + response = await async_client.post( + endpoint_url, + headers=signed_headers, + data=signed_body, + timeout=30.0, + ) + + verbose_logger.debug(f"Response status: {response.status_code}") + + if response.status_code != 200: + error_text = response.text + verbose_logger.error(f"AWS Bedrock error: {error_text}") + raise HTTPException( + status_code=400, + detail={"error": f"AWS Bedrock error: {error_text}"}, + ) + + bedrock_response = response.json() + + verbose_logger.debug(f"Bedrock response: {bedrock_response}") + + # Transform response back to expected format + final_response = self.transform_bedrock_response_to_anthropic( + bedrock_response + ) + + verbose_logger.debug(f"Final response: {final_response}") + + return final_response + + except HTTPException: + # Re-raise HTTP exceptions as-is + raise + except Exception as e: + verbose_logger.error(f"Error in CountTokens handler: {str(e)}") + raise HTTPException( + status_code=500, + detail={"error": f"CountTokens processing error: {str(e)}"}, + ) diff --git a/litellm/llms/bedrock/count_tokens/transformation.py b/litellm/llms/bedrock/count_tokens/transformation.py new file mode 100644 index 00000000000..d46ed3aa452 --- /dev/null +++ b/litellm/llms/bedrock/count_tokens/transformation.py @@ -0,0 +1,213 @@ +""" +AWS Bedrock CountTokens API transformation logic. + +This module handles the transformation of requests from Anthropic Messages API format +to AWS Bedrock's CountTokens API format and vice versa. +""" + +from typing import Any, Dict, List + +from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM +from litellm.llms.bedrock.common_utils import BedrockModelInfo + + +class BedrockCountTokensConfig(BaseAWSLLM): + """ + Configuration and transformation logic for AWS Bedrock CountTokens API. + + AWS Bedrock CountTokens API Specification: + - Endpoint: POST /model/{modelId}/count-tokens + - Input formats: 'invokeModel' or 'converse' + - Response: {"inputTokens": } + """ + + def _detect_input_type(self, request_data: Dict[str, Any]) -> str: + """ + Detect whether to use 'converse' or 'invokeModel' input format. + + Args: + request_data: The original request data + + Returns: + 'converse' or 'invokeModel' + """ + # If the request has messages in the expected Anthropic format, use converse + if "messages" in request_data and isinstance(request_data["messages"], list): + return "converse" + + # For raw text or other formats, use invokeModel + # This handles cases where the input is prompt-based or already in raw Bedrock format + return "invokeModel" + + def transform_anthropic_to_bedrock_count_tokens( + self, + request_data: Dict[str, Any], + ) -> Dict[str, Any]: + """ + Transform request to Bedrock CountTokens format. + Supports both Converse and InvokeModel input types. + + Input (Anthropic format): + { + "model": "claude-3-5-sonnet", + "messages": [{"role": "user", "content": "Hello!"}] + } + + Output (Bedrock CountTokens format for Converse): + { + "input": { + "converse": { + "messages": [...], + "system": [...] (if present) + } + } + } + + Output (Bedrock CountTokens format for InvokeModel): + { + "input": { + "invokeModel": { + "body": "{...raw model input...}" + } + } + } + """ + input_type = self._detect_input_type(request_data) + + if input_type == "converse": + return self._transform_to_converse_format(request_data.get("messages", [])) + else: + return self._transform_to_invoke_model_format(request_data) + + def _transform_to_converse_format( + self, messages: List[Dict[str, Any]] + ) -> Dict[str, Any]: + """Transform to Converse input format.""" + # Extract system messages if present + system_messages = [] + user_messages = [] + + for message in messages: + if message.get("role") == "system": + system_messages.append({"text": message.get("content", "")}) + else: + # Transform message content to Bedrock format + transformed_message: Dict[str, Any] = {"role": message.get("role"), "content": []} + + # Handle content - ensure it's in the correct array format + content = message.get("content", "") + if isinstance(content, str): + # String content -> convert to text block + transformed_message["content"].append({"text": content}) + elif isinstance(content, list): + # Already in blocks format - use as is + transformed_message["content"] = content + + user_messages.append(transformed_message) + + # Build the converse input format + converse_input = {"messages": user_messages} + + # Add system messages if present + if system_messages: + converse_input["system"] = system_messages + + # Build the complete request + return {"input": {"converse": converse_input}} + + def _transform_to_invoke_model_format( + self, request_data: Dict[str, Any] + ) -> Dict[str, Any]: + """Transform to InvokeModel input format.""" + import json + + # For InvokeModel, we need to provide the raw body that would be sent to the model + # Remove the 'model' field from the body as it's not part of the model input + body_data = {k: v for k, v in request_data.items() if k != "model"} + + return {"input": {"invokeModel": {"body": json.dumps(body_data)}}} + + def get_bedrock_count_tokens_endpoint( + self, model: str, aws_region_name: str + ) -> str: + """ + Construct the AWS Bedrock CountTokens API endpoint using existing LiteLLM functions. + + Args: + model: The resolved model ID from router lookup + aws_region_name: AWS region (e.g., "eu-west-1") + + Returns: + Complete endpoint URL for CountTokens API + """ + # Use existing LiteLLM function to get the base model ID (removes region prefix) + model_id = BedrockModelInfo.get_base_model(model) + + # Remove bedrock/ prefix if present + if model_id.startswith("bedrock/"): + model_id = model_id[8:] # Remove "bedrock/" prefix + + base_url = f"https://bedrock-runtime.{aws_region_name}.amazonaws.com" + endpoint = f"{base_url}/model/{model_id}/count-tokens" + + return endpoint + + def transform_bedrock_response_to_anthropic( + self, bedrock_response: Dict[str, Any] + ) -> Dict[str, Any]: + """ + Transform Bedrock CountTokens response to Anthropic format. + + Input (Bedrock response): + { + "inputTokens": 123 + } + + Output (Anthropic format): + { + "input_tokens": 123 + } + """ + input_tokens = bedrock_response.get("inputTokens", 0) + + return {"input_tokens": input_tokens} + + def validate_count_tokens_request(self, request_data: Dict[str, Any]) -> None: + """ + Validate the incoming count tokens request. + Supports both Converse and InvokeModel input formats. + + Args: + request_data: The request payload + + Raises: + ValueError: If the request is invalid + """ + if not request_data.get("model"): + raise ValueError("model parameter is required") + + input_type = self._detect_input_type(request_data) + + if input_type == "converse": + # Validate Converse format (messages-based) + messages = request_data.get("messages", []) + if not messages: + raise ValueError("messages parameter is required for Converse input") + + if not isinstance(messages, list): + raise ValueError("messages must be a list") + + for i, message in enumerate(messages): + if not isinstance(message, dict): + raise ValueError(f"Message {i} must be a dictionary") + + if "role" not in message: + raise ValueError(f"Message {i} must have a 'role' field") + + if "content" not in message: + raise ValueError(f"Message {i} must have a 'content' field") + else: + # For InvokeModel format, we need at least some content to count tokens + # The content structure varies by model, so we do minimal validation + if len(request_data) <= 1: # Only has 'model' field + raise ValueError("Request must contain content to count tokens") diff --git a/litellm/llms/bedrock/embed/amazon_titan_v2_transformation.py b/litellm/llms/bedrock/embed/amazon_titan_v2_transformation.py index 8056e9e9b2c..ff748b58e8e 100644 --- a/litellm/llms/bedrock/embed/amazon_titan_v2_transformation.py +++ b/litellm/llms/bedrock/embed/amazon_titan_v2_transformation.py @@ -10,7 +10,7 @@ Docs - https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-tit """ import types -from typing import List, Optional +from typing import List, Optional, Union from litellm.types.llms.bedrock import ( AmazonTitanV2EmbeddingRequest, @@ -30,9 +30,7 @@ class AmazonTitanV2Config: normalize: Optional[bool] = None dimensions: Optional[int] = None - def __init__( - self, normalize: Optional[bool] = None, dimensions: Optional[int] = None - ) -> None: + def __init__(self, normalize: Optional[bool] = None, dimensions: Optional[int] = None) -> None: locals_ = locals().copy() for key, value in locals_.items(): if key != "self" and value is not None: @@ -57,32 +55,56 @@ class AmazonTitanV2Config: } def get_supported_openai_params(self) -> List[str]: - return ["dimensions"] + return ["dimensions", "encoding_format"] - def map_openai_params( - self, non_default_params: dict, optional_params: dict - ) -> dict: + def map_openai_params(self, non_default_params: dict, optional_params: dict) -> dict: for k, v in non_default_params.items(): if k == "dimensions": optional_params["dimensions"] = v + elif k == "encoding_format": + # Map OpenAI encoding_format to AWS embeddingTypes + if v == "float": + optional_params["embeddingTypes"] = ["float"] + elif v == "base64": + # base64 maps to binary format in AWS + optional_params["embeddingTypes"] = ["binary"] + else: + # For any other encoding format, default to float + optional_params["embeddingTypes"] = ["float"] return optional_params - def _transform_request( - self, input: str, inference_params: dict - ) -> AmazonTitanV2EmbeddingRequest: + def _transform_request(self, input: str, inference_params: dict) -> AmazonTitanV2EmbeddingRequest: return AmazonTitanV2EmbeddingRequest(inputText=input, **inference_params) # type: ignore - def _transform_response( - self, response_list: List[dict], model: str - ) -> EmbeddingResponse: + def _transform_response(self, response_list: List[dict], model: str) -> EmbeddingResponse: total_prompt_tokens = 0 transformed_responses: List[Embedding] = [] for index, response in enumerate(response_list): _parsed_response = AmazonTitanV2EmbeddingResponse(**response) # type: ignore + + # According to AWS docs, embeddingsByType is always present + # If binary was requested (encoding_format="base64"), use binary data + # Otherwise, use float data from embeddingsByType or fallback to embedding field + embedding_data: Union[List[float], List[int]] + + if ("embeddingsByType" in _parsed_response and + "binary" in _parsed_response["embeddingsByType"]): + # Use binary data if available (for encoding_format="base64") + embedding_data = _parsed_response["embeddingsByType"]["binary"] + elif ("embeddingsByType" in _parsed_response and + "float" in _parsed_response["embeddingsByType"]): + # Use float data from embeddingsByType + embedding_data = _parsed_response["embeddingsByType"]["float"] + elif "embedding" in _parsed_response: + # Fallback to legacy embedding field + embedding_data = _parsed_response["embedding"] + else: + raise ValueError(f"No embedding data found in response: {response}") + transformed_responses.append( Embedding( - embedding=_parsed_response["embedding"], + embedding=embedding_data, index=index, object="embedding", ) diff --git a/litellm/llms/bedrock/embed/embedding.py b/litellm/llms/bedrock/embed/embedding.py index 91c71e86f1a..d4dd716a1f4 100644 --- a/litellm/llms/bedrock/embed/embedding.py +++ b/litellm/llms/bedrock/embed/embedding.py @@ -4,11 +4,13 @@ Handles embedding calls to Bedrock's `/invoke` endpoint import copy import json -from typing import Any, Callable, List, Optional, Tuple, Union +import urllib.parse +from typing import Any, Callable, List, Optional, Tuple, Union, get_args import httpx import litellm +from litellm.constants import BEDROCK_EMBEDDING_PROVIDERS_LITERAL from litellm.llms.cohere.embed.handler import embedding as cohere_embedding from litellm.llms.custom_httpx.http_handler import ( AsyncHTTPHandler, @@ -17,7 +19,11 @@ from litellm.llms.custom_httpx.http_handler import ( get_async_httpx_client, ) from litellm.secret_managers.main import get_secret -from litellm.types.llms.bedrock import AmazonEmbeddingRequest, CohereEmbeddingRequest +from litellm.types.llms.bedrock import ( + AmazonEmbeddingRequest, + CohereEmbeddingRequest, + TwelveLabsMarengoEmbeddingRequest, +) from litellm.types.utils import EmbeddingResponse from ..base_aws_llm import BaseAWSLLM @@ -28,6 +34,7 @@ from .amazon_titan_multimodal_transformation import ( ) from .amazon_titan_v2_transformation import AmazonTitanV2Config from .cohere_transformation import BedrockCohereEmbeddingConfig +from .twelvelabs_marengo_transformation import TwelveLabsMarengoEmbeddingConfig class BedrockEmbedding(BaseAWSLLM): @@ -144,6 +151,44 @@ class BedrockEmbedding(BaseAWSLLM): raise BedrockError(status_code=408, message="Timeout error occurred.") return response.json() + + def _transform_response( + self, response_list: List[dict], model: str, provider: BEDROCK_EMBEDDING_PROVIDERS_LITERAL + ) -> Optional[EmbeddingResponse]: + """ + Transforms the response from the Bedrock embedding provider to the OpenAI format. + """ + returned_response: Optional[EmbeddingResponse] = None + if model == "amazon.titan-embed-image-v1": + returned_response = ( + AmazonTitanMultimodalEmbeddingG1Config()._transform_response( + response_list=response_list, model=model + ) + ) + elif model == "amazon.titan-embed-text-v1": + returned_response = AmazonTitanG1Config()._transform_response( + response_list=response_list, model=model + ) + elif model == "amazon.titan-embed-text-v2:0": + returned_response = AmazonTitanV2Config()._transform_response( + response_list=response_list, model=model + ) + elif provider == "twelvelabs": + returned_response = TwelveLabsMarengoEmbeddingConfig()._transform_response( + response_list=response_list, model=model + ) + + + ########################################################## + # Validate returned response + ########################################################## + if returned_response is None: + raise Exception( + "Unable to map model response to known provider format. model={}".format( + model + ) + ) + return returned_response def _single_func_embeddings( self, @@ -156,6 +201,7 @@ class BedrockEmbedding(BaseAWSLLM): aws_region_name: str, model: str, logging_obj: Any, + provider: BEDROCK_EMBEDDING_PROVIDERS_LITERAL, api_key: Optional[str] = None, ): responses: List[dict] = [] @@ -163,16 +209,16 @@ class BedrockEmbedding(BaseAWSLLM): headers = {"Content-Type": "application/json"} if extra_headers is not None: headers = {"Content-Type": "application/json", **extra_headers} - + prepped = self.get_request_headers( - credentials=credentials, - aws_region_name=aws_region_name, - extra_headers=extra_headers, - endpoint_url=endpoint_url, - data=json.dumps(data), - headers=headers, - api_key=api_key - ) + credentials=credentials, + aws_region_name=aws_region_name, + extra_headers=extra_headers, + endpoint_url=endpoint_url, + data=json.dumps(data), + headers=headers, + api_key=api_key, + ) ## LOGGING logging_obj.pre_call( @@ -202,32 +248,9 @@ class BedrockEmbedding(BaseAWSLLM): responses.append(response) - returned_response: Optional[EmbeddingResponse] = None - - ## TRANSFORM RESPONSE ## - if model == "amazon.titan-embed-image-v1": - returned_response = ( - AmazonTitanMultimodalEmbeddingG1Config()._transform_response( - response_list=responses, model=model - ) - ) - elif model == "amazon.titan-embed-text-v1": - returned_response = AmazonTitanG1Config()._transform_response( - response_list=responses, model=model - ) - elif model == "amazon.titan-embed-text-v2:0": - returned_response = AmazonTitanV2Config()._transform_response( - response_list=responses, model=model - ) - - if returned_response is None: - raise Exception( - "Unable to map model response to known provider format. model={}".format( - model - ) - ) - - return returned_response + return self._transform_response( + response_list=responses, model=model, provider=provider + ) async def _async_single_func_embeddings( self, @@ -240,6 +263,7 @@ class BedrockEmbedding(BaseAWSLLM): aws_region_name: str, model: str, logging_obj: Any, + provider: BEDROCK_EMBEDDING_PROVIDERS_LITERAL, api_key: Optional[str] = None, ): responses: List[dict] = [] @@ -247,16 +271,16 @@ class BedrockEmbedding(BaseAWSLLM): headers = {"Content-Type": "application/json"} if extra_headers is not None: headers = {"Content-Type": "application/json", **extra_headers} - + prepped = self.get_request_headers( - credentials=credentials, - aws_region_name=aws_region_name, - extra_headers=extra_headers, - endpoint_url=endpoint_url, - data=json.dumps(data), - headers=headers, - api_key=api_key, - ) + credentials=credentials, + aws_region_name=aws_region_name, + extra_headers=extra_headers, + endpoint_url=endpoint_url, + data=json.dumps(data), + headers=headers, + api_key=api_key, + ) ## LOGGING logging_obj.pre_call( @@ -285,33 +309,10 @@ class BedrockEmbedding(BaseAWSLLM): ) responses.append(response) - - returned_response: Optional[EmbeddingResponse] = None - ## TRANSFORM RESPONSE ## - if model == "amazon.titan-embed-image-v1": - returned_response = ( - AmazonTitanMultimodalEmbeddingG1Config()._transform_response( - response_list=responses, model=model - ) - ) - elif model == "amazon.titan-embed-text-v1": - returned_response = AmazonTitanG1Config()._transform_response( - response_list=responses, model=model - ) - elif model == "amazon.titan-embed-text-v2:0": - returned_response = AmazonTitanV2Config()._transform_response( - response_list=responses, model=model - ) - - if returned_response is None: - raise Exception( - "Unable to map model response to known provider format. model={}".format( - model - ) - ) - - return returned_response + return self._transform_response( + response_list=responses, model=model, provider=provider + ) def embeddings( self, @@ -333,7 +334,22 @@ class BedrockEmbedding(BaseAWSLLM): credentials, aws_region_name = self._load_credentials(optional_params) ### TRANSFORMATION ### - provider = model.split(".")[0] + unencoded_model_id = ( + optional_params.pop("model_id", None) or model + ) # default to model if not passed + modelId = urllib.parse.quote(unencoded_model_id, safe="") + aws_region_name = self._get_aws_region_name( + optional_params=optional_params, + model=model, + model_id=unencoded_model_id, + ) + + provider = self.get_bedrock_embedding_provider(model) + if provider is None: + raise Exception( + f"Unable to determine bedrock embedding provider for model: {model}. " + f"Supported providers: {list(get_args(BEDROCK_EMBEDDING_PROVIDERS_LITERAL))}" + ) inference_params = copy.deepcopy(optional_params) inference_params = { k: v @@ -343,9 +359,6 @@ class BedrockEmbedding(BaseAWSLLM): inference_params.pop( "user", None ) # make sure user is not passed in for bedrock call - modelId = ( - optional_params.pop("model_id", None) or model - ) # default to model if not passed data: Optional[CohereEmbeddingRequest] = None batch_data: Optional[List] = None @@ -386,6 +399,15 @@ class BedrockEmbedding(BaseAWSLLM): ) ) batch_data.append(transformed_request) + elif provider == "twelvelabs": + batch_data = [] + for i in input: + twelvelabs_request: ( + TwelveLabsMarengoEmbeddingRequest + ) = TwelveLabsMarengoEmbeddingConfig()._transform_request( + input=i, inference_params=inference_params + ) + batch_data.append(twelvelabs_request) ### SET RUNTIME ENDPOINT ### endpoint_url, proxy_endpoint_url = self.get_runtime_endpoint( @@ -414,8 +436,9 @@ class BedrockEmbedding(BaseAWSLLM): model=model, logging_obj=logging_obj, api_key=api_key, + provider=provider, ) - return self._single_func_embeddings( + returned_response = self._single_func_embeddings( client=( client if client is not None and isinstance(client, HTTPHandler) @@ -430,14 +453,18 @@ class BedrockEmbedding(BaseAWSLLM): model=model, logging_obj=logging_obj, api_key=api_key, + provider=provider, ) + if returned_response is None: + raise Exception("Unable to map Bedrock request to provider") + return returned_response elif data is None: raise Exception("Unable to map Bedrock request to provider") headers = {"Content-Type": "application/json"} if extra_headers is not None: headers = {"Content-Type": "application/json", **extra_headers} - + prepped = self.get_request_headers( credentials=credentials, aws_region_name=aws_region_name, diff --git a/litellm/llms/bedrock/embed/twelvelabs_marengo_transformation.py b/litellm/llms/bedrock/embed/twelvelabs_marengo_transformation.py new file mode 100644 index 00000000000..fdad8a65043 --- /dev/null +++ b/litellm/llms/bedrock/embed/twelvelabs_marengo_transformation.py @@ -0,0 +1,140 @@ +""" +Transformation logic from OpenAI /v1/embeddings format to Bedrock TwelveLabs Marengo /invoke format. + +Why separate file? Make it easy to see how transformation works + +Docs - https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-marengo.html +""" + +from typing import List + +from litellm.types.llms.bedrock import ( + TwelveLabsMarengoEmbeddingRequest, +) +from litellm.types.utils import Embedding, EmbeddingResponse, Usage +from litellm.utils import get_base64_str, is_base64_encoded + + +class TwelveLabsMarengoEmbeddingConfig: + """ + Reference - https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-marengo.html + + Supports text and image inputs for Phase 1. + Video and audio support will be added in Phase 2. + """ + + def __init__(self) -> None: + pass + + def get_supported_openai_params(self) -> List[str]: + return ["encoding_format", "textTruncate", "embeddingOption"] + + def map_openai_params( + self, non_default_params: dict, optional_params: dict + ) -> dict: + for k, v in non_default_params.items(): + if k == "encoding_format": + # TwelveLabs doesn't have encoding_format, but we can map it to embeddingOption + if v == "float": + optional_params["embeddingOption"] = ["visual-text", "visual-image"] + elif k == "textTruncate": + optional_params["textTruncate"] = v + elif k == "embeddingOption": + optional_params["embeddingOption"] = v + return optional_params + + def _transform_request( + self, input: str, inference_params: dict + ) -> TwelveLabsMarengoEmbeddingRequest: + """ + Transform OpenAI-style input to TwelveLabs Marengo format. + Phase 1: Supports text and image inputs only. + """ + # Check if input is base64 encoded image + is_encoded = is_base64_encoded(input) + + if is_encoded: + # Image input + b64_str = get_base64_str(input) + transformed_request = TwelveLabsMarengoEmbeddingRequest( + inputType="image", mediaSource={"base64String": b64_str} + ) + else: + # Text input + transformed_request = TwelveLabsMarengoEmbeddingRequest( + inputType="text", inputText=input + ) + + # Set default textTruncate if not specified + if "textTruncate" not in inference_params: + transformed_request["textTruncate"] = "end" + + # Apply any additional inference parameters + for k, v in inference_params.items(): + if k not in [ + "inputType", + "inputText", + "mediaSource", + ]: # Don't override core fields + transformed_request[k] = v # type: ignore + + return transformed_request + + def _transform_response( + self, response_list: List[dict], model: str + ) -> EmbeddingResponse: + """ + Transform TwelveLabs response to OpenAI format. + Handles the actual TwelveLabs response format: {"data": [{"embedding": [...]}]} + """ + embeddings: List[Embedding] = [] + total_tokens = 0 + + for response in response_list: + # TwelveLabs response format has a "data" field containing the embeddings + if "data" in response and isinstance(response["data"], list): + for item in response["data"]: + if "embedding" in item: + # Single embedding response + embedding = Embedding( + embedding=item["embedding"], + index=len(embeddings), + object="embedding", + ) + embeddings.append(embedding) + + # Estimate token count (rough approximation) + if "inputTextTokenCount" in item: + total_tokens += item["inputTextTokenCount"] + else: + # Rough estimate: 1 token per 4 characters for text, or use embedding size + total_tokens += len(item["embedding"]) // 4 + elif "embedding" in response: + # Direct embedding response (fallback for other formats) + embedding = Embedding( + embedding=response["embedding"], + index=len(embeddings), + object="embedding", + ) + embeddings.append(embedding) + + # Estimate token count (rough approximation) + if "inputTextTokenCount" in response: + total_tokens += response["inputTextTokenCount"] + else: + # Rough estimate: 1 token per 4 characters for text + total_tokens += len(response.get("inputText", "")) // 4 + elif "embeddings" in response: + # Multiple embeddings response (from video/audio) + for i, emb in enumerate(response["embeddings"]): + embedding = Embedding( + embedding=emb["embedding"], + index=len(embeddings), + object="embedding", + ) + embeddings.append(embedding) + total_tokens += len(emb["embedding"]) // 4 # Rough estimate + + usage = Usage(prompt_tokens=total_tokens, total_tokens=total_tokens) + + return EmbeddingResponse(data=embeddings, model=model, usage=usage) diff --git a/litellm/llms/bedrock/files/transformation.py b/litellm/llms/bedrock/files/transformation.py new file mode 100644 index 00000000000..0a95cf9168f --- /dev/null +++ b/litellm/llms/bedrock/files/transformation.py @@ -0,0 +1,662 @@ +import json +import os +import time +from litellm._uuid import uuid +from typing import Any, Dict, List, Optional, Tuple, Union + +from httpx import Headers, Response + +from litellm._logging import verbose_logger +from litellm.files.utils import FilesAPIUtils +from litellm.litellm_core_utils.prompt_templates.common_utils import extract_file_data +from litellm.llms.base_llm.chat.transformation import BaseLLMException +from litellm.llms.base_llm.files.transformation import ( + BaseFilesConfig, + LiteLLMLoggingObj, +) +from litellm.types.llms.openai import ( + AllMessageValues, + CreateFileRequest, + FileTypes, + OpenAICreateFileRequestOptionalParams, + OpenAIFileObject, + PathLike, +) +from litellm.types.utils import ExtractedFileData, LlmProviders +from litellm.utils import get_llm_provider + +from ..base_aws_llm import BaseAWSLLM +from ..common_utils import BedrockError + + +class BedrockFilesConfig(BaseAWSLLM, BaseFilesConfig): + """ + Config for Bedrock Files - handles S3 uploads for Bedrock batch processing + """ + + def __init__(self): + self.jsonl_transformation = BedrockJsonlFilesTransformation() + super().__init__() + + @property + def custom_llm_provider(self) -> LlmProviders: + return LlmProviders.BEDROCK + + @property + def file_upload_http_method(self) -> str: + """ + Bedrock files are uploaded to S3, which requires PUT requests + """ + return "PUT" + + def validate_environment( + self, + headers: dict, + model: str, + messages: List[AllMessageValues], + optional_params: dict, + litellm_params: dict, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + ) -> dict: + # No additional headers needed for S3 uploads - AWS credentials handled by BaseAWSLLM + return headers + + + + def _get_content_from_openai_file(self, openai_file_content: FileTypes) -> str: + """ + Helper to extract content from various OpenAI file types and return as string. + + Handles: + - Direct content (str, bytes, IO[bytes]) + - Tuple formats: (filename, content, [content_type], [headers]) + - PathLike objects + """ + content: Union[str, bytes] = b"" + # Extract file content from tuple if necessary + if isinstance(openai_file_content, tuple): + # Take the second element which is always the file content + file_content = openai_file_content[1] + else: + file_content = openai_file_content + + # Handle different file content types + if isinstance(file_content, str): + # String content can be used directly + content = file_content + elif isinstance(file_content, bytes): + # Bytes content can be decoded + content = file_content + elif isinstance(file_content, PathLike): # PathLike + with open(str(file_content), "rb") as f: + content = f.read() + elif hasattr(file_content, "read"): # IO[bytes] + # File-like objects need to be read + content = file_content.read() + + # Ensure content is string + if isinstance(content, bytes): + content = content.decode("utf-8") + + return content + + def _get_s3_object_name_from_batch_jsonl( + self, + openai_jsonl_content: List[Dict[str, Any]], + ) -> str: + """ + Gets a unique S3 object name for the Bedrock batch processing job + + named as: litellm-bedrock-files/{model}/{uuid} + """ + _model = openai_jsonl_content[0].get("body", {}).get("model", "") + # Remove bedrock/ prefix if present + if _model.startswith("bedrock/"): + _model = _model[8:] + + # Replace colons with hyphens for Bedrock S3 URI compliance + _model = _model.replace(":", "-") + + object_name = f"litellm-bedrock-files-{_model}-{uuid.uuid4()}.jsonl" + return object_name + + def get_object_name( + self, extracted_file_data: ExtractedFileData, purpose: str + ) -> str: + """ + Get the object name for the request + """ + extracted_file_data_content = extracted_file_data.get("content") + + if extracted_file_data_content is None: + raise ValueError("file content is required") + + if purpose == "batch": + ## 1. If jsonl, check if there's a model name + file_content = self._get_content_from_openai_file( + extracted_file_data_content + ) + + # Split into lines and parse each line as JSON + openai_jsonl_content = [ + json.loads(line) for line in file_content.splitlines() if line.strip() + ] + if len(openai_jsonl_content) > 0: + return self._get_s3_object_name_from_batch_jsonl(openai_jsonl_content) + + ## 2. If not jsonl, return the filename + filename = extracted_file_data.get("filename") + if filename: + return filename + ## 3. If no file name, return timestamp + return str(int(time.time())) + + def get_complete_file_url( + self, + api_base: Optional[str], + api_key: Optional[str], + model: str, + optional_params: Dict, + litellm_params: Dict, + data: CreateFileRequest, + ) -> str: + """ + Get the complete S3 URL for the file upload request + """ + bucket_name = litellm_params.get("s3_bucket_name") or os.getenv("AWS_S3_BUCKET_NAME") + if not bucket_name: + raise ValueError("S3 bucket_name is required. Set 's3_bucket_name' in litellm_params or AWS_S3_BUCKET_NAME env var") + + aws_region_name = self._get_aws_region_name(optional_params, model) + + file_data = data.get("file") + purpose = data.get("purpose") + if file_data is None: + raise ValueError("file is required") + if purpose is None: + raise ValueError("purpose is required") + extracted_file_data = extract_file_data(file_data) + object_name = self.get_object_name(extracted_file_data, purpose) + + # S3 endpoint URL format + s3_endpoint_url = optional_params.get("s3_endpoint_url") or f"https://s3.{aws_region_name}.amazonaws.com" + + return f"{s3_endpoint_url}/{bucket_name}/{object_name}" + + def get_supported_openai_params( + self, model: str + ) -> List[OpenAICreateFileRequestOptionalParams]: + return [] + + def map_openai_params( + self, + non_default_params: dict, + optional_params: dict, + model: str, + drop_params: bool, + ) -> dict: + return optional_params + + + def _map_openai_to_bedrock_params( + self, + openai_request_body: Dict[str, Any], + provider: Optional[str] = None, + ) -> Dict[str, Any]: + """ + Transform OpenAI request body to Bedrock-compatible modelInput parameters using existing transformation logic + """ + from litellm.types.utils import LlmProviders + _model = openai_request_body.get("model", "") + messages = openai_request_body.get("messages", []) + + # Use existing Anthropic transformation logic for Anthropic models + if provider == LlmProviders.ANTHROPIC: + from litellm.llms.bedrock.chat.invoke_transformations.anthropic_claude3_transformation import ( + AmazonAnthropicClaudeConfig, + ) + + anthropic_config = AmazonAnthropicClaudeConfig() + + # Extract optional params (everything except model and messages) + optional_params = {k: v for k, v in openai_request_body.items() if k not in ["model", "messages"]} + mapped_params = anthropic_config.map_openai_params( + non_default_params={}, + optional_params=optional_params, + model=_model, + drop_params=False + ) + + # Transform using existing Anthropic logic + bedrock_params = anthropic_config.transform_request( + model=_model, + messages=messages, + optional_params=mapped_params, + litellm_params={}, + headers={} + ) + + return bedrock_params + else: + # For other providers, use basic mapping + bedrock_params = { + "messages": messages, + **{k: v for k, v in openai_request_body.items() if k not in ["model", "messages"]} + } + return bedrock_params + + def _transform_openai_jsonl_content_to_bedrock_jsonl_content( + self, openai_jsonl_content: List[Dict[str, Any]] + ) -> List[Dict[str, Any]]: + """ + Transforms OpenAI JSONL content to Bedrock batch format + + Bedrock batch format: { "recordId": "alphanumeric string", "modelInput": {JSON body} } + Example: + { + "recordId": "CALL0000001", + "modelInput": { + "anthropic_version": "bedrock-2023-05-31", + "max_tokens": 1024, + "messages": [ + { + "role": "user", + "content": [{"type": "text", "text": "Hello"}] + } + ] + } + } + """ + + bedrock_jsonl_content = [] + for idx, _openai_jsonl_content in enumerate(openai_jsonl_content): + # Extract the request body from OpenAI format + openai_body = _openai_jsonl_content.get("body", {}) + model = openai_body.get("model", "") + + try: + model, _, _, _ = get_llm_provider( + model=model, + custom_llm_provider=None, + ) + except Exception as e: + verbose_logger.exception(f"litellm.llms.bedrock.files.transformation.py::_transform_openai_jsonl_content_to_bedrock_jsonl_content() - Error inferring custom_llm_provider - {str(e)}") + + # Determine provider from model name + provider = self.get_bedrock_invoke_provider(model) + + # Transform to Bedrock modelInput format + model_input = self._map_openai_to_bedrock_params( + openai_request_body=openai_body, + provider=provider + ) + + # Create Bedrock batch record + record_id = _openai_jsonl_content.get("custom_id", f"CALL{str(idx).zfill(7)}") + bedrock_record = { + "recordId": record_id, + "modelInput": model_input + } + + bedrock_jsonl_content.append(bedrock_record) + return bedrock_jsonl_content + + def transform_create_file_request( + self, + model: str, + create_file_data: CreateFileRequest, + optional_params: dict, + litellm_params: dict, + ) -> Union[bytes, str, dict]: + """ + Transform file request and return a pre-signed request for S3. + This keeps the HTTP handler clean by doing all the signing here. + """ + file_data = create_file_data.get("file") + if file_data is None: + raise ValueError("file is required") + extracted_file_data = extract_file_data(file_data) + extracted_file_data_content = extracted_file_data.get("content") + + if extracted_file_data_content is None: + raise ValueError("file content is required") + + # Get and transform the file content + if FilesAPIUtils.is_batch_jsonl_file( + create_file_data=create_file_data, + extracted_file_data=extracted_file_data, + ): + ## Transform JSONL content to Bedrock format + original_file_content = self._get_content_from_openai_file( + extracted_file_data_content + ) + openai_jsonl_content = [ + json.loads(line) for line in original_file_content.splitlines() if line.strip() + ] + bedrock_jsonl_content = ( + self._transform_openai_jsonl_content_to_bedrock_jsonl_content( + openai_jsonl_content + ) + ) + file_content = "\n".join(json.dumps(item) for item in bedrock_jsonl_content) + elif isinstance(extracted_file_data_content, bytes): + file_content = extracted_file_data_content.decode('utf-8') + elif isinstance(extracted_file_data_content, str): + file_content = extracted_file_data_content + else: + raise ValueError("Unsupported file content type") + + # Get the S3 URL for upload + api_base = self.get_complete_file_url( + api_base=None, + api_key=None, + model=model, + optional_params=optional_params, + litellm_params=litellm_params, + data=create_file_data, + ) + + # Sign the request and return a pre-signed request object + signed_headers, signed_body = self._sign_s3_request( + content=file_content, + api_base=api_base, + optional_params=optional_params, + ) + + litellm_params["upload_url"] = api_base + + # Return a dict that tells the HTTP handler exactly what to do + return { + "method": "PUT", + "url": api_base, + "headers": signed_headers, + "data": signed_body or file_content, + } + + def _sign_s3_request( + self, + content: str, + api_base: str, + optional_params: dict, + ) -> Tuple[dict, str]: + """ + Sign S3 PUT request using the same proven logic as S3Logger. + Reuses the exact pattern from litellm/integrations/s3_v2.py + """ + try: + import hashlib + + import requests + from botocore.auth import SigV4Auth + from botocore.awsrequest import AWSRequest + except ImportError: + raise ImportError("Missing boto3 to call bedrock. Run 'pip install boto3'.") + + # Get AWS credentials using existing methods + aws_region_name = self._get_aws_region_name( + optional_params=optional_params, model="" + ) + credentials = self.get_credentials( + aws_access_key_id=optional_params.get("aws_access_key_id"), + aws_secret_access_key=optional_params.get("aws_secret_access_key"), + aws_session_token=optional_params.get("aws_session_token"), + aws_region_name=aws_region_name, + aws_session_name=optional_params.get("aws_session_name"), + aws_profile_name=optional_params.get("aws_profile_name"), + aws_role_name=optional_params.get("aws_role_name"), + aws_web_identity_token=optional_params.get("aws_web_identity_token"), + aws_sts_endpoint=optional_params.get("aws_sts_endpoint"), + ) + + # Calculate SHA256 hash of the content (REQUIRED for S3) + content_hash = hashlib.sha256(content.encode("utf-8")).hexdigest() + + # Prepare headers with required S3 headers (same as s3_v2.py) + request_headers = { + "Content-Type": "application/json", # JSONL files are JSON content + "x-amz-content-sha256": content_hash, # REQUIRED by S3 + "Content-Language": "en", + "Cache-Control": "private, immutable, max-age=31536000, s-maxage=0", + } + + # Use requests.Request to prepare the request (same pattern as s3_v2.py) + req = requests.Request("PUT", api_base, data=content, headers=request_headers) + prepped = req.prepare() + + # Sign the request with S3 service + aws_request = AWSRequest( + method=prepped.method, + url=prepped.url, + data=prepped.body, + headers=prepped.headers, + ) + + # Get region name for non-LLM API calls (same as s3_v2.py) + signing_region = self.get_aws_region_name_for_non_llm_api_calls( + aws_region_name=aws_region_name + ) + + SigV4Auth(credentials, "s3", signing_region).add_auth(aws_request) + + # Return signed headers and body + signed_body = aws_request.body + if isinstance(signed_body, bytes): + signed_body = signed_body.decode('utf-8') + elif signed_body is None: + signed_body = content # Fallback to original content + + return dict(aws_request.headers), signed_body + + def _convert_https_url_to_s3_uri(self, https_url: str) -> tuple[str, str]: + """ + Convert HTTPS S3 URL to s3:// URI format. + + Args: + https_url: HTTPS S3 URL (e.g., "https://s3.us-west-2.amazonaws.com/bucket/key") + + Returns: + Tuple of (s3_uri, filename) + + Example: + Input: "https://s3.us-west-2.amazonaws.com/litellm-proxy/file.jsonl" + Output: ("s3://litellm-proxy/file.jsonl", "file.jsonl") + """ + import re + + # Match HTTPS S3 URL patterns + # Pattern 1: https://s3.region.amazonaws.com/bucket/key + # Pattern 2: https://bucket.s3.region.amazonaws.com/key + + pattern1 = r"https://s3\.([^.]+)\.amazonaws\.com/([^/]+)/(.+)" + pattern2 = r"https://([^.]+)\.s3\.([^.]+)\.amazonaws\.com/(.+)" + + match1 = re.match(pattern1, https_url) + match2 = re.match(pattern2, https_url) + + if match1: + # Pattern: https://s3.region.amazonaws.com/bucket/key + region, bucket, key = match1.groups() + s3_uri = f"s3://{bucket}/{key}" + elif match2: + # Pattern: https://bucket.s3.region.amazonaws.com/key + bucket, region, key = match2.groups() + s3_uri = f"s3://{bucket}/{key}" + else: + # Fallback: try to extract bucket and key from URL path + from urllib.parse import urlparse + parsed = urlparse(https_url) + path_parts = parsed.path.lstrip('/').split('/', 1) + if len(path_parts) >= 2: + bucket, key = path_parts[0], path_parts[1] + s3_uri = f"s3://{bucket}/{key}" + else: + raise ValueError(f"Unable to parse S3 URL: {https_url}") + + # Extract filename from key + filename = key.split("/")[-1] if "/" in key else key + + return s3_uri, filename + + def transform_create_file_response( + self, + model: Optional[str], + raw_response: Response, + logging_obj: LiteLLMLoggingObj, + litellm_params: dict, + ) -> OpenAIFileObject: + """ + Transform S3 File upload response into OpenAI-style FileObject + """ + # For S3 uploads, we typically get an ETag and other metadata + response_headers = raw_response.headers + # Extract S3 object information from the response + # S3 PUT object returns ETag and other metadata in headers + content_length = response_headers.get("Content-Length", "0") + + # Use the actual upload URL that was used for the S3 upload + upload_url = litellm_params.get("upload_url") + file_id: str = "" + filename: str = "" + if upload_url: + # Convert HTTPS S3 URL to s3:// URI format + file_id, filename = self._convert_https_url_to_s3_uri(upload_url) + + return OpenAIFileObject( + purpose="batch", # Default purpose for Bedrock files + id=file_id, + filename=filename, + created_at=int(time.time()), # Current timestamp + status="uploaded", + bytes=int(content_length) if content_length.isdigit() else 0, + object="file", + ) + + def get_error_class( + self, error_message: str, status_code: int, headers: Union[Dict, Headers] + ) -> BaseLLMException: + return BedrockError( + status_code=status_code, message=error_message, headers=headers + ) + + +class BedrockJsonlFilesTransformation: + """ + Transforms OpenAI /v1/files/* requests to Bedrock S3 file uploads for batch processing + """ + + def transform_openai_file_content_to_bedrock_file_content( + self, openai_file_content: Optional[FileTypes] = None + ) -> Tuple[str, str]: + """ + Transforms OpenAI FileContentRequest to Bedrock S3 file format + """ + + if openai_file_content is None: + raise ValueError("contents of file are None") + # Read the content of the file + file_content = self._get_content_from_openai_file(openai_file_content) + + # Split into lines and parse each line as JSON + openai_jsonl_content = [ + json.loads(line) for line in file_content.splitlines() if line.strip() + ] + bedrock_jsonl_content = ( + self._transform_openai_jsonl_content_to_bedrock_jsonl_content( + openai_jsonl_content + ) + ) + bedrock_jsonl_string = "\n".join( + json.dumps(item) for item in bedrock_jsonl_content + ) + object_name = self._get_s3_object_name( + openai_jsonl_content=openai_jsonl_content + ) + return bedrock_jsonl_string, object_name + + def _transform_openai_jsonl_content_to_bedrock_jsonl_content( + self, openai_jsonl_content: List[Dict[str, Any]] + ): + """ + Delegate to the main BedrockFilesConfig transformation method + """ + config = BedrockFilesConfig() + return config._transform_openai_jsonl_content_to_bedrock_jsonl_content(openai_jsonl_content) + + def _get_s3_object_name( + self, + openai_jsonl_content: List[Dict[str, Any]], + ) -> str: + """ + Gets a unique S3 object name for the Bedrock batch processing job + + named as: litellm-bedrock-files-{model}-{uuid} + """ + _model = openai_jsonl_content[0].get("body", {}).get("model", "") + # Remove bedrock/ prefix if present + if _model.startswith("bedrock/"): + _model = _model[8:] + object_name = f"litellm-bedrock-files-{_model}-{uuid.uuid4()}.jsonl" + return object_name + + + + def _get_content_from_openai_file(self, openai_file_content: FileTypes) -> str: + """ + Helper to extract content from various OpenAI file types and return as string. + + Handles: + - Direct content (str, bytes, IO[bytes]) + - Tuple formats: (filename, content, [content_type], [headers]) + - PathLike objects + """ + content: Union[str, bytes] = b"" + # Extract file content from tuple if necessary + if isinstance(openai_file_content, tuple): + # Take the second element which is always the file content + file_content = openai_file_content[1] + else: + file_content = openai_file_content + + # Handle different file content types + if isinstance(file_content, str): + # String content can be used directly + content = file_content + elif isinstance(file_content, bytes): + # Bytes content can be decoded + content = file_content + elif isinstance(file_content, PathLike): # PathLike + with open(str(file_content), "rb") as f: + content = f.read() + elif hasattr(file_content, "read"): # IO[bytes] + # File-like objects need to be read + content = file_content.read() + + # Ensure content is string + if isinstance(content, bytes): + content = content.decode("utf-8") + + return content + + def transform_s3_bucket_response_to_openai_file_object( + self, create_file_data: CreateFileRequest, s3_upload_response: Dict[str, Any] + ) -> OpenAIFileObject: + """ + Transforms S3 Bucket upload file response to OpenAI FileObject + """ + # S3 response typically contains ETag, key, etc. + object_key = s3_upload_response.get("Key", "") + bucket_name = s3_upload_response.get("Bucket", "") + + # Extract filename from object key + filename = object_key.split("/")[-1] if "/" in object_key else object_key + + return OpenAIFileObject( + purpose=create_file_data.get("purpose", "batch"), + id=f"s3://{bucket_name}/{object_key}", + filename=filename, + created_at=int(time.time()), # Current timestamp + status="uploaded", + bytes=s3_upload_response.get("ContentLength", 0), + object="file", + ) diff --git a/litellm/llms/bedrock/image/amazon_nova_canvas_transformation.py b/litellm/llms/bedrock/image/amazon_nova_canvas_transformation.py index 3ef7a40e9a9..cd33e62af16 100644 --- a/litellm/llms/bedrock/image/amazon_nova_canvas_transformation.py +++ b/litellm/llms/bedrock/image/amazon_nova_canvas_transformation.py @@ -7,12 +7,12 @@ from litellm.types.llms.bedrock import ( AmazonNovaCanvasColorGuidedGenerationParams, AmazonNovaCanvasColorGuidedRequest, AmazonNovaCanvasImageGenerationConfig, + AmazonNovaCanvasInpaintingParams, + AmazonNovaCanvasInpaintingRequest, AmazonNovaCanvasRequestBase, AmazonNovaCanvasTextToImageParams, AmazonNovaCanvasTextToImageRequest, AmazonNovaCanvasTextToImageResponse, - AmazonNovaCanvasInpaintingParams, - AmazonNovaCanvasInpaintingRequest, ) from litellm.types.utils import ImageResponse @@ -67,6 +67,11 @@ class AmazonNovaCanvasConfig: """ task_type = optional_params.pop("taskType", "TEXT_IMAGE") image_generation_config = optional_params.pop("imageGenerationConfig", {}) + + # Extract model_id parameter to prevent "extraneous key" error from Bedrock API + # Following the same pattern as chat completions and embeddings + unencoded_model_id = optional_params.pop("model_id", None) # noqa: F841 + image_generation_config = {**image_generation_config, **optional_params} if task_type == "TEXT_IMAGE": text_to_image_params: Dict[str, Any] = image_generation_config.pop( diff --git a/litellm/llms/bedrock/image/image_handler.py b/litellm/llms/bedrock/image/image_handler.py index 55d94675d14..0103f190d36 100644 --- a/litellm/llms/bedrock/image/image_handler.py +++ b/litellm/llms/bedrock/image/image_handler.py @@ -233,7 +233,17 @@ class BedrockImageGeneration(BaseAWSLLM): Returns: dict: The request body to use for the Bedrock Image Generation API """ - provider = model.split(".")[0] + # Use the existing ARN-aware provider detection method + bedrock_provider = self.get_bedrock_invoke_provider(model) + + if bedrock_provider == "amazon" or bedrock_provider == "nova": + # Handle Amazon Nova Canvas models + provider = "amazon" + elif bedrock_provider == "stability": + provider = "stability" + else: + # Fallback to original logic for backward compatibility + provider = model.split(".")[0] inference_params = copy.deepcopy(optional_params) inference_params.pop( "user", None diff --git a/litellm/llms/bedrock/passthrough/transformation.py b/litellm/llms/bedrock/passthrough/transformation.py index d7221ff4b7a..5791bfb8013 100644 --- a/litellm/llms/bedrock/passthrough/transformation.py +++ b/litellm/llms/bedrock/passthrough/transformation.py @@ -41,9 +41,15 @@ class BedrockPassthroughConfig( model_id=None, ) - api_base = f"https://bedrock-runtime.{aws_region_name}.amazonaws.com" + aws_bedrock_runtime_endpoint = optional_params.get("aws_bedrock_runtime_endpoint") + endpoint_url, _ = self.get_runtime_endpoint( + api_base=api_base, + aws_bedrock_runtime_endpoint=aws_bedrock_runtime_endpoint, + aws_region_name=aws_region_name, + endpoint_type="runtime", + ) - return self.format_url(endpoint, api_base, request_query_params or {}), api_base + return self.format_url(endpoint, endpoint_url, request_query_params or {}), endpoint_url def sign_request( self, diff --git a/litellm/llms/bedrock/rerank/transformation.py b/litellm/llms/bedrock/rerank/transformation.py index be8250a9671..b5d33eda49f 100644 --- a/litellm/llms/bedrock/rerank/transformation.py +++ b/litellm/llms/bedrock/rerank/transformation.py @@ -4,7 +4,7 @@ Translates from Cohere's `/v1/rerank` input format to Bedrock's `/rerank` input Why separate file? Make it easy to see how transformation works """ -import uuid +from litellm._uuid import uuid from typing import List, Optional, Union from litellm.types.llms.bedrock import ( diff --git a/litellm/llms/cohere/completion/handler.py b/litellm/llms/cohere/completion/handler.py deleted file mode 100644 index 6a77951146f..00000000000 --- a/litellm/llms/cohere/completion/handler.py +++ /dev/null @@ -1,5 +0,0 @@ -""" -Cohere /generate API - uses `llm_http_handler.py` to make httpx requests - -Request/Response transformation is handled in `transformation.py` -""" diff --git a/litellm/llms/cohere/completion/transformation.py b/litellm/llms/cohere/completion/transformation.py deleted file mode 100644 index f96ef89d3c5..00000000000 --- a/litellm/llms/cohere/completion/transformation.py +++ /dev/null @@ -1,265 +0,0 @@ -import time -from typing import TYPE_CHECKING, Any, AsyncIterator, Iterator, List, Optional, Union - -import httpx - -import litellm -from litellm.litellm_core_utils.prompt_templates.common_utils import ( - convert_content_list_to_str, -) -from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMException -from litellm.types.llms.openai import AllMessageValues -from litellm.types.utils import Choices, Message, ModelResponse, Usage - -from ..common_utils import CohereError -from ..common_utils import ModelResponseIterator as CohereModelResponseIterator -from ..common_utils import validate_environment as cohere_validate_environment - -if TYPE_CHECKING: - from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj - - LiteLLMLoggingObj = _LiteLLMLoggingObj -else: - LiteLLMLoggingObj = Any - - -class CohereTextConfig(BaseConfig): - """ - Reference: https://docs.cohere.com/reference/generate - - The class `CohereConfig` provides configuration for the Cohere's API interface. Below are the parameters: - - - `num_generations` (integer): Maximum number of generations returned. Default is 1, with a minimum value of 1 and a maximum value of 5. - - - `max_tokens` (integer): Maximum number of tokens the model will generate as part of the response. Default value is 20. - - - `truncate` (string): Specifies how the API handles inputs longer than maximum token length. Options include NONE, START, END. Default is END. - - - `temperature` (number): A non-negative float controlling the randomness in generation. Lower temperatures result in less random generations. Default is 0.75. - - - `preset` (string): Identifier of a custom preset, a combination of parameters such as prompt, temperature etc. - - - `end_sequences` (array of strings): The generated text gets cut at the beginning of the earliest occurrence of an end sequence, which will be excluded from the text. - - - `stop_sequences` (array of strings): The generated text gets cut at the end of the earliest occurrence of a stop sequence, which will be included in the text. - - - `k` (integer): Limits generation at each step to top `k` most likely tokens. Default is 0. - - - `p` (number): Limits generation at each step to most likely tokens with total probability mass of `p`. Default is 0. - - - `frequency_penalty` (number): Reduces repetitiveness of generated tokens. Higher values apply stronger penalties to previously occurred tokens. - - - `presence_penalty` (number): Reduces repetitiveness of generated tokens. Similar to frequency_penalty, but this penalty applies equally to all tokens that have already appeared. - - - `return_likelihoods` (string): Specifies how and if token likelihoods are returned with the response. Options include GENERATION, ALL and NONE. - - - `logit_bias` (object): Used to prevent the model from generating unwanted tokens or to incentivize it to include desired tokens. e.g. {"hello_world": 1233} - """ - - num_generations: Optional[int] = None - max_tokens: Optional[int] = None - truncate: Optional[str] = None - temperature: Optional[int] = None - preset: Optional[str] = None - end_sequences: Optional[list] = None - stop_sequences: Optional[list] = None - k: Optional[int] = None - p: Optional[int] = None - frequency_penalty: Optional[int] = None - presence_penalty: Optional[int] = None - return_likelihoods: Optional[str] = None - logit_bias: Optional[dict] = None - - def __init__( - self, - num_generations: Optional[int] = None, - max_tokens: Optional[int] = None, - truncate: Optional[str] = None, - temperature: Optional[int] = None, - preset: Optional[str] = None, - end_sequences: Optional[list] = None, - stop_sequences: Optional[list] = None, - k: Optional[int] = None, - p: Optional[int] = None, - frequency_penalty: Optional[int] = None, - presence_penalty: Optional[int] = None, - return_likelihoods: Optional[str] = None, - logit_bias: Optional[dict] = None, - ) -> None: - locals_ = locals().copy() - for key, value in locals_.items(): - if key != "self" and value is not None: - setattr(self.__class__, key, value) - - @classmethod - def get_config(cls): - return super().get_config() - - def validate_environment( - self, - headers: dict, - model: str, - messages: List[AllMessageValues], - optional_params: dict, - litellm_params: dict, - api_key: Optional[str] = None, - api_base: Optional[str] = None, - ) -> dict: - return cohere_validate_environment( - headers=headers, - model=model, - messages=messages, - optional_params=optional_params, - api_key=api_key, - ) - - def get_error_class( - self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers] - ) -> BaseLLMException: - return CohereError(status_code=status_code, message=error_message) - - def get_supported_openai_params(self, model: str) -> List: - return [ - "stream", - "temperature", - "max_tokens", - "logit_bias", - "top_p", - "frequency_penalty", - "presence_penalty", - "stop", - "n", - "extra_headers", - ] - - def map_openai_params( - self, - non_default_params: dict, - optional_params: dict, - model: str, - drop_params: bool, - ) -> dict: - for param, value in non_default_params.items(): - if param == "stream": - optional_params["stream"] = value - elif param == "temperature": - optional_params["temperature"] = value - elif param == "max_tokens": - optional_params["max_tokens"] = value - elif param == "n": - optional_params["num_generations"] = value - elif param == "logit_bias": - optional_params["logit_bias"] = value - elif param == "top_p": - optional_params["p"] = value - elif param == "frequency_penalty": - optional_params["frequency_penalty"] = value - elif param == "presence_penalty": - optional_params["presence_penalty"] = value - elif param == "stop": - optional_params["stop_sequences"] = value - return optional_params - - def transform_request( - self, - model: str, - messages: List[AllMessageValues], - optional_params: dict, - litellm_params: dict, - headers: dict, - ) -> dict: - prompt = " ".join( - convert_content_list_to_str(message=message) for message in messages - ) - - ## Load Config - config = litellm.CohereConfig.get_config() - for k, v in config.items(): - if ( - k not in optional_params - ): # completion(top_k=3) > cohere_config(top_k=3) <- allows for dynamic variables to be passed in - optional_params[k] = v - - ## Handle Tool Calling - if "tools" in optional_params: - _is_function_call = True - tool_calling_system_prompt = self._construct_cohere_tool_for_completion_api( - tools=optional_params["tools"] - ) - optional_params["tools"] = tool_calling_system_prompt - - data = { - "model": model, - "prompt": prompt, - **optional_params, - } - - return data - - def transform_response( - self, - model: str, - raw_response: httpx.Response, - model_response: ModelResponse, - logging_obj: LiteLLMLoggingObj, - request_data: dict, - messages: List[AllMessageValues], - optional_params: dict, - litellm_params: dict, - encoding: Any, - api_key: Optional[str] = None, - json_mode: Optional[bool] = None, - ) -> ModelResponse: - prompt = " ".join( - convert_content_list_to_str(message=message) for message in messages - ) - completion_response = raw_response.json() - choices_list = [] - for idx, item in enumerate(completion_response["generations"]): - if len(item["text"]) > 0: - message_obj = Message(content=item["text"]) - else: - message_obj = Message(content=None) - choice_obj = Choices( - finish_reason=item["finish_reason"], - index=idx + 1, - message=message_obj, - ) - choices_list.append(choice_obj) - model_response.choices = choices_list # type: ignore - - ## CALCULATING USAGE - prompt_tokens = len(encoding.encode(prompt)) - completion_tokens = len( - encoding.encode(model_response["choices"][0]["message"].get("content", "")) - ) - - model_response.created = int(time.time()) - model_response.model = model - usage = Usage( - prompt_tokens=prompt_tokens, - completion_tokens=completion_tokens, - total_tokens=prompt_tokens + completion_tokens, - ) - setattr(model_response, "usage", usage) - return model_response - - def _construct_cohere_tool_for_completion_api( - self, - tools: Optional[List] = None, - ) -> dict: - if tools is None: - tools = [] - return {"tools": tools} - - def get_model_response_iterator( - self, - streaming_response: Union[Iterator[str], AsyncIterator[str], ModelResponse], - sync_stream: bool, - json_mode: Optional[bool] = False, - ): - return CohereModelResponseIterator( - streaming_response=streaming_response, - sync_stream=sync_stream, - json_mode=json_mode, - ) diff --git a/litellm/llms/compactifai/__init__.py b/litellm/llms/compactifai/__init__.py new file mode 100644 index 00000000000..16b0c04cdab --- /dev/null +++ b/litellm/llms/compactifai/__init__.py @@ -0,0 +1 @@ +# CompactifAI provider for LiteLLM \ No newline at end of file diff --git a/litellm/llms/compactifai/chat/__init__.py b/litellm/llms/compactifai/chat/__init__.py new file mode 100644 index 00000000000..d1a4463166b --- /dev/null +++ b/litellm/llms/compactifai/chat/__init__.py @@ -0,0 +1 @@ +# CompactifAI chat completions \ No newline at end of file diff --git a/litellm/llms/compactifai/chat/transformation.py b/litellm/llms/compactifai/chat/transformation.py new file mode 100644 index 00000000000..5cb8cd9a4ab --- /dev/null +++ b/litellm/llms/compactifai/chat/transformation.py @@ -0,0 +1,100 @@ +""" +CompactifAI chat completion transformation +""" + +from typing import TYPE_CHECKING, Any, List, Optional, Tuple, Union + +import httpx + +from litellm.secret_managers.main import get_secret_str +from litellm.types.utils import ModelResponse +from litellm.llms.openai.common_utils import OpenAIError +from litellm.llms.base_llm.chat.transformation import BaseLLMException + +from ...openai.chat.gpt_transformation import OpenAIGPTConfig + +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj + + LiteLLMLoggingObj = _LiteLLMLoggingObj +else: + LiteLLMLoggingObj = Any + + +class CompactifAIChatConfig(OpenAIGPTConfig): + """ + Configuration class for CompactifAI chat completions. + Since CompactifAI is OpenAI-compatible, we extend OpenAIGPTConfig. + """ + + def _get_openai_compatible_provider_info( + self, + api_base: Optional[str], + api_key: Optional[str], + ) -> Tuple[Optional[str], Optional[str]]: + """ + Get API base and key for CompactifAI provider. + """ + api_base = api_base or "https://api.compactif.ai/v1" + dynamic_api_key = api_key or get_secret_str("COMPACTIFAI_API_KEY") or "" + return api_base, dynamic_api_key + + def transform_response( + self, + model: str, + raw_response: httpx.Response, + model_response: ModelResponse, + logging_obj: LiteLLMLoggingObj, + request_data: dict, + messages: List, + optional_params: dict, + litellm_params: dict, + encoding: Any, + api_key: Optional[str] = None, + json_mode: Optional[bool] = None, + ) -> ModelResponse: + """ + Transform CompactifAI response to LiteLLM format. + Since CompactifAI is OpenAI-compatible, we can use the standard OpenAI transformation. + """ + ## LOGGING + logging_obj.post_call( + input=messages, + api_key=api_key, + original_response=raw_response.text, + additional_args={"complete_input_dict": request_data}, + ) + + ## RESPONSE OBJECT + response_json = raw_response.json() + + # Handle JSON mode if needed + if json_mode: + for choice in response_json["choices"]: + message = choice.get("message") + if message and message.get("tool_calls"): + # Convert tool calls to content for JSON mode + tool_calls = message.get("tool_calls", []) + if len(tool_calls) == 1: + message["content"] = tool_calls[0]["function"].get("arguments", "") + message["tool_calls"] = None + + returned_response = ModelResponse(**response_json) + + # Set model name with provider prefix + returned_response.model = f"compactifai/{model}" + + return returned_response + + def get_error_class( + self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers] + ) -> BaseLLMException: + """ + Get the appropriate error class for CompactifAI errors. + Since CompactifAI is OpenAI-compatible, we use OpenAI error handling. + """ + return OpenAIError( + status_code=status_code, + message=error_message, + headers=headers, + ) \ No newline at end of file diff --git a/litellm/llms/custom_httpx/aiohttp_handler.py b/litellm/llms/custom_httpx/aiohttp_handler.py index d9fc85877c3..c7a04a49fc2 100644 --- a/litellm/llms/custom_httpx/aiohttp_handler.py +++ b/litellm/llms/custom_httpx/aiohttp_handler.py @@ -17,6 +17,7 @@ from litellm.llms.custom_httpx.http_handler import ( HTTPHandler, _get_httpx_client, ) +from litellm.llms.custom_httpx.aiohttp_transport import LiteLLMAiohttpTransport from litellm.types.llms.openai import FileTypes from litellm.types.utils import HttpHandlerRequestFields, ImageResponse, LlmProviders from litellm.utils import CustomStreamWrapper, ModelResponse, ProviderConfigManager @@ -32,8 +33,71 @@ DEFAULT_TIMEOUT = 600 class BaseLLMAIOHTTPHandler: - def __init__(self): - self.client_session: Optional[aiohttp.ClientSession] = None + def __init__( + self, + client_session: Optional[aiohttp.ClientSession] = None, + transport: Optional[LiteLLMAiohttpTransport] = None, + connector: Optional[aiohttp.BaseConnector] = None, + ): + self.client_session = client_session + self._owns_session = ( + client_session is None + ) # Track if we own the session for cleanup + + self.transport = transport + self._owns_transport = ( + transport is None + ) # Track if we own the transport for cleanup + + self.connector = connector + self._owns_connector = ( + connector is None + ) # Track if we own the connector for cleanup + + def _get_or_create_transport(self) -> Optional[LiteLLMAiohttpTransport]: + """Get existing transport or create a new one if needed.""" + if self.transport: + return self.transport + + # Create a transport using AsyncHTTPHandler's logic + try: + self.transport = AsyncHTTPHandler._create_aiohttp_transport() + self._owns_transport = True + return self.transport + except Exception: + # If transport creation fails, return None (will use direct session) + return None + + def _get_connector(self) -> Optional[aiohttp.BaseConnector]: + """Get or create a connector for the client session.""" + if self.connector: + return self.connector + elif self.transport and hasattr(self.transport, "client"): + # Extract connector from transport if available + client = self.transport.client + if callable(client): + # If client is a factory, we can't extract connector directly + return None + elif hasattr(client, "connector"): + return client.connector + return None + + def _create_client_session_with_transport(self) -> ClientSession: + """Create a new client session using transport or connector configuration.""" + connector = self._get_connector() + + if self.transport and hasattr(self.transport, "_get_valid_client_session"): + # Use transport's session creation if available + session = self.transport._get_valid_client_session() + return session + elif connector: + # Use provided connector + session = aiohttp.ClientSession(connector=connector) + return session + else: + # Default session creation + session = aiohttp.ClientSession() + return session def _get_async_client_session( self, dynamic_client_session: Optional[ClientSession] = None @@ -43,15 +107,33 @@ class BaseLLMAIOHTTPHandler: elif self.client_session: return self.client_session else: - # init client session, and then return new session - self.client_session = aiohttp.ClientSession() + # Create client session using transport/connector if available + self.client_session = self._create_client_session_with_transport() + self._owns_session = True # We created this session, so we own it return self.client_session async def close(self): - """Close the aiohttp client session if it exists.""" - if self.client_session and not self.client_session.closed: + """Close the aiohttp client session and transport if we own them.""" + # Close client session if we own it + if ( + self.client_session + and not self.client_session.closed + and self._owns_session + ): await self.client_session.close() + # Close transport if we own it + if ( + self.transport + and self._owns_transport + and hasattr(self.transport, "aclose") + ): + try: + await self.transport.aclose() + except Exception: + # Ignore errors during transport cleanup + pass + async def _make_common_async_call( self, async_client_session: Optional[ClientSession], diff --git a/litellm/llms/custom_httpx/http_handler.py b/litellm/llms/custom_httpx/http_handler.py index 4d8781fff2a..3a67d5127b7 100644 --- a/litellm/llms/custom_httpx/http_handler.py +++ b/litellm/llms/custom_httpx/http_handler.py @@ -167,6 +167,7 @@ class AsyncHTTPHandler: concurrent_limit=1000, client_alias: Optional[str] = None, # name for client in logs ssl_verify: Optional[VerifyTypes] = None, + shared_session: Optional["ClientSession"] = None, ): self.timeout = timeout self.event_hooks = event_hooks @@ -175,6 +176,7 @@ class AsyncHTTPHandler: concurrent_limit=concurrent_limit, event_hooks=event_hooks, ssl_verify=ssl_verify, + shared_session=shared_session, ) self.client_alias = client_alias @@ -184,6 +186,7 @@ class AsyncHTTPHandler: concurrent_limit: int, event_hooks: Optional[Mapping[str, List[Callable[..., Any]]]], ssl_verify: Optional[VerifyTypes] = None, + shared_session: Optional["ClientSession"] = None, ) -> httpx.AsyncClient: # Get unified SSL configuration ssl_config = get_ssl_configuration(ssl_verify) @@ -199,6 +202,7 @@ class AsyncHTTPHandler: transport = AsyncHTTPHandler._create_async_transport( ssl_context=ssl_config if isinstance(ssl_config, ssl.SSLContext) else None, ssl_verify=ssl_config if isinstance(ssl_config, bool) else None, + shared_session=shared_session, ) return httpx.AsyncClient( @@ -212,6 +216,7 @@ class AsyncHTTPHandler: verify=ssl_config, cert=cert, headers=headers, + follow_redirects=True, ) async def close(self): @@ -259,7 +264,6 @@ class AsyncHTTPHandler: files: Optional[RequestFiles] = None, content: Any = None, ): - start_time = time.time() try: if timeout is None: @@ -522,7 +526,9 @@ class AsyncHTTPHandler: @staticmethod def _create_async_transport( - ssl_context: Optional[ssl.SSLContext] = None, ssl_verify: Optional[bool] = None + ssl_context: Optional[ssl.SSLContext] = None, + ssl_verify: Optional[bool] = None, + shared_session: Optional["ClientSession"] = None, ) -> Optional[Union[LiteLLMAiohttpTransport, AsyncHTTPTransport]]: """ - Creates a transport for httpx.AsyncClient @@ -543,7 +549,9 @@ class AsyncHTTPHandler: ######################################################### if AsyncHTTPHandler._should_use_aiohttp_transport(): return AsyncHTTPHandler._create_aiohttp_transport( - ssl_context=ssl_context, ssl_verify=ssl_verify + ssl_context=ssl_context, + ssl_verify=ssl_verify, + shared_session=shared_session, ) ######################################################### @@ -611,6 +619,7 @@ class AsyncHTTPHandler: def _create_aiohttp_transport( ssl_verify: Optional[bool] = None, ssl_context: Optional[ssl.SSLContext] = None, + shared_session: Optional["ClientSession"] = None, ) -> LiteLLMAiohttpTransport: """ Creates an AiohttpTransport with RequestNotRead error handling @@ -634,6 +643,18 @@ class AsyncHTTPHandler: trust_env = True verbose_logger.debug("Creating AiohttpTransport...") + + # Use shared session if provided and valid + if shared_session is not None and not shared_session.closed: + verbose_logger.debug( + f"SHARED SESSION: Reusing existing ClientSession (ID: {id(shared_session)})" + ) + return LiteLLMAiohttpTransport(client=shared_session) + + # Create new session only if none provided or existing one is invalid + verbose_logger.debug( + "NEW SESSION: Creating new ClientSession (no shared session provided)" + ) return LiteLLMAiohttpTransport( client=lambda: ClientSession( connector=TCPConnector(**connector_kwargs), @@ -687,6 +708,7 @@ class HTTPHandler: verify=ssl_config, cert=cert, headers=headers, + follow_redirects=True, ) else: self.client = client @@ -913,12 +935,13 @@ class HTTPHandler: if litellm.force_ipv4: return HTTPTransport(local_address="0.0.0.0") else: - return None + return getattr(litellm, 'sync_transport', None) def get_async_httpx_client( llm_provider: Union[LlmProviders, httpxSpecialProvider], params: Optional[dict] = None, + shared_session: Optional["ClientSession"] = None, ) -> AsyncHTTPHandler: """ Retrieves the async HTTP client from the cache @@ -940,10 +963,12 @@ def get_async_httpx_client( return _cached_client if params is not None: + params["shared_session"] = shared_session _new_client = AsyncHTTPHandler(**params) else: _new_client = AsyncHTTPHandler( - timeout=httpx.Timeout(timeout=600.0, connect=5.0) + timeout=httpx.Timeout(timeout=600.0, connect=5.0), + shared_session=shared_session, ) litellm.in_memory_llm_clients_cache.set_cache( diff --git a/litellm/llms/custom_httpx/llm_http_handler.py b/litellm/llms/custom_httpx/llm_http_handler.py index c9d70088d04..f8a92c2ac96 100644 --- a/litellm/llms/custom_httpx/llm_http_handler.py +++ b/litellm/llms/custom_httpx/llm_http_handler.py @@ -28,6 +28,7 @@ from litellm.llms.base_llm.audio_transcription.transformation import ( BaseAudioTranscriptionConfig, ) from litellm.llms.base_llm.base_model_iterator import MockResponseIterator +from litellm.llms.base_llm.batches.transformation import BaseBatchesConfig from litellm.llms.base_llm.chat.transformation import BaseConfig from litellm.llms.base_llm.embedding.transformation import BaseEmbeddingConfig from litellm.llms.base_llm.files.transformation import BaseFilesConfig @@ -58,6 +59,7 @@ from litellm.types.llms.anthropic_messages.anthropic_response import ( AnthropicMessagesResponse, ) from litellm.types.llms.openai import ( + CreateBatchRequest, CreateFileRequest, OpenAIFileObject, ResponseInputParam, @@ -66,7 +68,12 @@ from litellm.types.llms.openai import ( from litellm.types.rerank import OptionalRerankParams, RerankResponse from litellm.types.responses.main import DeleteResponseResult from litellm.types.router import GenericLiteLLMParams -from litellm.types.utils import EmbeddingResponse, FileTypes, TranscriptionResponse +from litellm.types.utils import ( + EmbeddingResponse, + FileTypes, + LiteLLMBatch, + TranscriptionResponse, +) from litellm.types.vector_stores import ( VectorStoreCreateOptionalRequestParams, VectorStoreCreateResponse, @@ -81,6 +88,7 @@ from litellm.utils import ( ) if TYPE_CHECKING: + from aiohttp import ClientSession from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj from litellm.llms.base_llm.passthrough.transformation import BasePassthroughConfig @@ -111,7 +119,6 @@ class BaseLLMHTTPHandler: response: Optional[httpx.Response] = None for i in range(max(max_retry_on_unprocessable_entity_error, 1)): try: - response = await async_httpx_client.post( url=api_base, headers=headers, @@ -230,11 +237,16 @@ class BaseLLMHTTPHandler: client: Optional[AsyncHTTPHandler] = None, json_mode: bool = False, signed_json_body: Optional[bytes] = None, + shared_session: Optional["ClientSession"] = None, ): if client is None: + verbose_logger.debug( + f"Creating HTTP client with shared_session: {id(shared_session) if shared_session else None}" + ) async_httpx_client = get_async_httpx_client( llm_provider=litellm.LlmProviders(custom_llm_provider), params={"ssl_verify": litellm_params.get("ssl_verify", None)}, + shared_session=shared_session, ) else: async_httpx_client = client @@ -284,6 +296,7 @@ class BaseLLMHTTPHandler: headers: Optional[Dict[str, Any]] = None, client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None, provider_config: Optional[BaseConfig] = None, + shared_session: Optional["ClientSession"] = None, ): json_mode: bool = optional_params.pop("json_mode", False) extra_body: Optional[dict] = optional_params.pop("extra_body", None) @@ -463,7 +476,7 @@ class BaseLLMHTTPHandler: if client is None or not isinstance(client, HTTPHandler): sync_httpx_client = _get_httpx_client( - params={"ssl_verify": litellm_params.get("ssl_verify", None)} + params={"ssl_verify": litellm_params.get("ssl_verify", None)}, ) else: sync_httpx_client = client @@ -1257,6 +1270,10 @@ class BaseLLMHTTPHandler: stream: Optional[bool] = False, kwargs: Optional[Dict[str, Any]] = None, ) -> Union[AnthropicMessagesResponse, AsyncIterator]: + from litellm.litellm_core_utils.get_provider_specific_headers import ( + ProviderSpecificHeaderUtils, + ) + if client is None or not isinstance(client, AsyncHTTPHandler): async_httpx_client = get_async_httpx_client( llm_provider=litellm.LlmProviders.ANTHROPIC @@ -1270,10 +1287,9 @@ class BaseLLMHTTPHandler: Optional[litellm.types.utils.ProviderSpecificHeader], kwargs.get("provider_specific_header", None), ) - extra_headers = ( - provider_specific_header.get("extra_headers", {}) - if provider_specific_header - else {} + extra_headers = ProviderSpecificHeaderUtils.get_provider_specific_headers( + provider_specific_header=provider_specific_header, + custom_llm_provider=custom_llm_provider, ) ( headers, @@ -2209,15 +2225,40 @@ class BaseLLMHTTPHandler: else: sync_httpx_client = client - if isinstance(transformed_request, str) or isinstance( - transformed_request, bytes - ): - upload_response = sync_httpx_client.post( - url=api_base, - headers=headers, - data=transformed_request, + if isinstance(transformed_request, dict) and "method" in transformed_request: + # Handle pre-signed requests (e.g., from Bedrock S3 uploads) + upload_response = getattr( + sync_httpx_client, transformed_request["method"].lower() + )( + url=transformed_request["url"], + headers=transformed_request["headers"], + data=transformed_request["data"], timeout=timeout, ) + elif isinstance(transformed_request, str) or isinstance( + transformed_request, bytes + ): + # Handle traditional file uploads + # Ensure transformed_request is a string for httpx compatibility + if isinstance(transformed_request, bytes): + transformed_request = transformed_request.decode("utf-8") + + # Use the HTTP method specified by the provider config + http_method = provider_config.file_upload_http_method.upper() + if http_method == "PUT": + upload_response = sync_httpx_client.put( + url=api_base, + headers=headers, + data=transformed_request, + timeout=timeout, + ) + else: # Default to POST + upload_response = sync_httpx_client.post( + url=api_base, + headers=headers, + data=transformed_request, + timeout=timeout, + ) else: try: # Step 1: Initial request to get upload URL @@ -2250,11 +2291,15 @@ class BaseLLMHTTPHandler: provider_config=provider_config, ) + # Store the upload URL in litellm_params for the transformation method + litellm_params_with_url = dict(litellm_params) + litellm_params_with_url["upload_url"] = api_base + return provider_config.transform_create_file_response( model=None, raw_response=upload_response, logging_obj=logging_obj, - litellm_params=litellm_params, + litellm_params=litellm_params_with_url, ) async def async_create_file( @@ -2278,15 +2323,53 @@ class BaseLLMHTTPHandler: else: async_httpx_client = client - if isinstance(transformed_request, str) or isinstance( - transformed_request, bytes - ): - upload_response = await async_httpx_client.post( - url=api_base, - headers=headers, - data=transformed_request, + ######################################################### + # Debug Logging + ######################################################### + logging_obj.pre_call( + input="", + api_key="", + additional_args={ + "complete_input_dict": transformed_request, + "api_base": api_base, + "headers": headers, + }, + ) + + if isinstance(transformed_request, dict) and "method" in transformed_request: + # Handle pre-signed requests (e.g., from Bedrock S3 uploads) + upload_response = await getattr( + async_httpx_client, transformed_request["method"].lower() + )( + url=transformed_request["url"], + headers=transformed_request["headers"], + data=transformed_request["data"], timeout=timeout, ) + elif isinstance(transformed_request, str) or isinstance( + transformed_request, bytes + ): + # Handle traditional file uploads + # Ensure transformed_request is a string for httpx compatibility + if isinstance(transformed_request, bytes): + transformed_request = transformed_request.decode("utf-8") + + # Use the HTTP method specified by the provider config + http_method = provider_config.file_upload_http_method.upper() + if http_method == "PUT": + upload_response = await async_httpx_client.put( + url=api_base, + headers=headers, + data=transformed_request, + timeout=timeout, + ) + else: # Default to POST + upload_response = await async_httpx_client.post( + url=api_base, + headers=headers, + data=transformed_request, + timeout=timeout, + ) else: try: # Step 1: Initial request to get upload URL @@ -2327,6 +2410,536 @@ class BaseLLMHTTPHandler: litellm_params=litellm_params, ) + def create_batch( + self, + create_batch_data: "CreateBatchRequest", + litellm_params: dict, + provider_config: "BaseBatchesConfig", + headers: dict, + api_base: Optional[str], + api_key: Optional[str], + logging_obj: "LiteLLMLoggingObj", + _is_async: bool = False, + client: Optional[Union["HTTPHandler", "AsyncHTTPHandler"]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + model: Optional[str] = None, + ) -> Union["LiteLLMBatch", Coroutine[Any, Any, "LiteLLMBatch"]]: + """ + Creates a batch using provider-specific batch creation process + """ + # get config from model, custom llm provider + if model is None: + raise ValueError("model is required for create_batch") + + headers = provider_config.validate_environment( + api_key=api_key, + headers=headers, + model=model, + messages=[], + optional_params={}, + litellm_params=litellm_params, + ) + + api_base = provider_config.get_complete_batch_url( + api_base=api_base, + api_key=api_key, + model=model, + optional_params={}, + litellm_params=litellm_params, + data=create_batch_data, + ) + if api_base is None: + raise ValueError("api_base is required for create_batch") + + # Get the transformed request data + transformed_request = provider_config.transform_create_batch_request( + model=model, + create_batch_data=create_batch_data, + litellm_params=litellm_params, + optional_params={}, + ) + + if _is_async: + return self.async_create_batch( + transformed_request=transformed_request, + litellm_params=litellm_params, + provider_config=provider_config, + headers=headers, + api_base=api_base, + logging_obj=logging_obj, + client=client, + timeout=timeout, + create_batch_data=create_batch_data, + ) + + if client is None or not isinstance(client, HTTPHandler): + sync_httpx_client = _get_httpx_client() + else: + sync_httpx_client = client + + try: + if ( + isinstance(transformed_request, dict) + and "method" in transformed_request + ): + # Handle pre-signed requests (e.g., from Bedrock with AWS auth) + batch_response = getattr( + sync_httpx_client, transformed_request["method"].lower() + )( + url=transformed_request["url"], + headers=transformed_request["headers"], + data=transformed_request["data"], + timeout=timeout, + ) + elif isinstance(transformed_request, dict): + # For other providers that use JSON requests + batch_response = sync_httpx_client.post( + url=api_base, + headers={**headers, "Content-Type": "application/json"}, + json=transformed_request, + timeout=timeout, + ) + else: + # Handle other request types if needed + batch_response = sync_httpx_client.post( + url=api_base, + headers=headers, + data=transformed_request, + timeout=timeout, + ) + except Exception as e: + verbose_logger.exception(f"Error creating batch: {e}") + raise self._handle_error( + e=e, + provider_config=provider_config, + ) + + # Store original request for response transformation + litellm_params_with_request = { + **litellm_params, + "original_batch_request": create_batch_data, + } + + return provider_config.transform_create_batch_response( + model=model, + raw_response=batch_response, + logging_obj=logging_obj, + litellm_params=litellm_params_with_request, + ) + + def retrieve_batch( + self, + batch_id: str, + litellm_params: dict, + provider_config: "BaseBatchesConfig", + headers: dict, + api_base: Optional[str], + api_key: Optional[str], + logging_obj: "LiteLLMLoggingObj", + _is_async: bool = False, + client: Optional[Union["HTTPHandler", "AsyncHTTPHandler"]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + model: Optional[str] = None, + ) -> Union["LiteLLMBatch", Coroutine[Any, Any, "LiteLLMBatch"]]: + """ + Retrieve a batch using provider-specific configuration. + """ + # Transform the request using provider config + transformed_request = provider_config.transform_retrieve_batch_request( + batch_id=batch_id, + optional_params=litellm_params, + litellm_params=litellm_params, + ) + + if _is_async: + return self.async_retrieve_batch( + transformed_request=transformed_request, + litellm_params=litellm_params, + provider_config=provider_config, + headers=headers, + api_base=api_base, + logging_obj=logging_obj, + client=client, + timeout=timeout, + batch_id=batch_id, + model=model, + ) + + if client is None or not isinstance(client, HTTPHandler): + sync_httpx_client = _get_httpx_client() + else: + sync_httpx_client = client + + try: + if ( + isinstance(transformed_request, dict) + and "method" in transformed_request + ): + # Handle pre-signed requests (e.g., from Bedrock with AWS auth) + method = transformed_request["method"].lower() + request_kwargs = { + "url": transformed_request["url"], + "headers": transformed_request["headers"], + } + + # Only add data for non-GET requests + if method != "get" and transformed_request.get("data") is not None: + request_kwargs["data"] = transformed_request["data"] + + batch_response = getattr(sync_httpx_client, method)(**request_kwargs) + elif isinstance(transformed_request, dict) and api_base: + # For other providers that use JSON requests + batch_response = sync_httpx_client.get( + url=api_base, + headers={**headers, "Content-Type": "application/json"}, + params=transformed_request, + ) + else: + # Handle other request types if needed + if not api_base: + raise ValueError("api_base is required for non-pre-signed requests") + batch_response = sync_httpx_client.get( + url=api_base, + headers=headers, + ) + except Exception as e: + verbose_logger.exception(f"Error retrieving batch: {e}") + raise self._handle_error( + e=e, + provider_config=provider_config, + ) + + return provider_config.transform_retrieve_batch_response( + model=model, + raw_response=batch_response, + logging_obj=logging_obj, + litellm_params=litellm_params, + ) + + async def async_create_batch( + self, + transformed_request: Union[bytes, str, dict], + litellm_params: dict, + provider_config: "BaseBatchesConfig", + headers: dict, + api_base: str, + logging_obj: "LiteLLMLoggingObj", + client: Optional[Union["HTTPHandler", "AsyncHTTPHandler"]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + create_batch_data: Optional["CreateBatchRequest"] = None, + model: Optional[str] = None, + ): + """ + Async version of create_batch + """ + if client is None or not isinstance(client, AsyncHTTPHandler): + async_httpx_client = get_async_httpx_client( + llm_provider=provider_config.custom_llm_provider + ) + else: + async_httpx_client = client + + ######################################################### + # Debug Logging + ######################################################### + logging_obj.pre_call( + input="", + api_key="", + additional_args={ + "complete_input_dict": transformed_request, + "api_base": api_base, + "headers": headers, + }, + ) + + try: + if ( + isinstance(transformed_request, dict) + and "method" in transformed_request + ): + # Handle pre-signed requests (e.g., from Bedrock with AWS auth) + batch_response = await getattr( + async_httpx_client, transformed_request["method"].lower() + )( + url=transformed_request["url"], + headers=transformed_request["headers"], + data=transformed_request["data"], + timeout=timeout, + ) + elif isinstance(transformed_request, dict): + # For other providers that use JSON requests + batch_response = await async_httpx_client.post( + url=api_base, + headers={**headers, "Content-Type": "application/json"}, + json=transformed_request, + timeout=timeout, + ) + else: + # Handle other request types if needed + batch_response = await async_httpx_client.post( + url=api_base, + headers=headers, + data=transformed_request, + timeout=timeout, + ) + except Exception as e: + verbose_logger.exception(f"Error creating batch: {e}") + raise self._handle_error( + e=e, + provider_config=provider_config, + ) + + # Store original request for response transformation (for async version) + litellm_params_with_request = { + **litellm_params, + "original_batch_request": create_batch_data or {}, + } + + return provider_config.transform_create_batch_response( + model=model, + raw_response=batch_response, + logging_obj=logging_obj, + litellm_params=litellm_params_with_request, + ) + + async def async_retrieve_batch( + self, + transformed_request: Union[bytes, str, dict], + litellm_params: dict, + provider_config: "BaseBatchesConfig", + headers: dict, + api_base: Optional[str], + logging_obj: "LiteLLMLoggingObj", + client: Optional[Union["HTTPHandler", "AsyncHTTPHandler"]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + batch_id: Optional[str] = None, + model: Optional[str] = None, + ): + """ + Async version of retrieve_batch + """ + if client is None or not isinstance(client, AsyncHTTPHandler): + async_httpx_client = get_async_httpx_client( + llm_provider=provider_config.custom_llm_provider + ) + else: + async_httpx_client = client + + ######################################################### + # Debug Logging + ######################################################### + logging_obj.pre_call( + input="", + api_key="", + additional_args={ + "complete_input_dict": transformed_request, + "api_base": api_base, + "headers": headers, + "batch_id": batch_id, + }, + ) + + try: + if ( + isinstance(transformed_request, dict) + and "method" in transformed_request + ): + # Handle pre-signed requests (e.g., from Bedrock with AWS auth) + method = transformed_request["method"].lower() + request_kwargs = { + "url": transformed_request["url"], + "headers": transformed_request["headers"], + } + + # Only add data for non-GET requests + if method != "get" and transformed_request.get("data") is not None: + request_kwargs["data"] = transformed_request["data"] + + batch_response = await getattr(async_httpx_client, method)( + **request_kwargs + ) + elif isinstance(transformed_request, dict) and api_base: + # For other providers that use JSON requests + batch_response = await async_httpx_client.get( + url=api_base, + headers={**headers, "Content-Type": "application/json"}, + params=transformed_request, + ) + else: + # Handle other request types if needed + if not api_base: + raise ValueError("api_base is required for non-pre-signed requests") + batch_response = await async_httpx_client.get( + url=api_base, + headers=headers, + ) + except Exception as e: + verbose_logger.exception(f"Error retrieving batch: {e}") + raise self._handle_error( + e=e, + provider_config=provider_config, + ) + + return provider_config.transform_retrieve_batch_response( + model=model, + raw_response=batch_response, + logging_obj=logging_obj, + litellm_params=litellm_params, + ) + + def cancel_response_api_handler( + self, + response_id: str, + responses_api_provider_config: BaseResponsesAPIConfig, + litellm_params: GenericLiteLLMParams, + logging_obj: LiteLLMLoggingObj, + custom_llm_provider: Optional[str], + extra_headers: Optional[Dict[str, Any]] = None, + extra_body: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None, + _is_async: bool = False, + ) -> Union[ResponsesAPIResponse, Coroutine[Any, Any, ResponsesAPIResponse]]: + """ + Async version of the responses API handler. + Uses async HTTP client to make requests. + """ + if _is_async: + return self.async_cancel_response_api_handler( + response_id=response_id, + responses_api_provider_config=responses_api_provider_config, + litellm_params=litellm_params, + logging_obj=logging_obj, + custom_llm_provider=custom_llm_provider, + extra_headers=extra_headers, + extra_body=extra_body, + timeout=timeout, + client=client, + ) + if client is None or not isinstance(client, HTTPHandler): + sync_httpx_client = _get_httpx_client( + params={"ssl_verify": litellm_params.get("ssl_verify", None)} + ) + else: + sync_httpx_client = client + + headers = responses_api_provider_config.validate_environment( + headers=extra_headers or {}, model="None", litellm_params=litellm_params + ) + + if extra_headers: + headers.update(extra_headers) + + api_base = responses_api_provider_config.get_complete_url( + api_base=litellm_params.api_base, + litellm_params=dict(litellm_params), + ) + + url, data = responses_api_provider_config.transform_cancel_response_api_request( + response_id=response_id, + api_base=api_base, + litellm_params=litellm_params, + headers=headers, + ) + + ## LOGGING + logging_obj.pre_call( + input=response_id, + api_key="", + additional_args={ + "complete_input_dict": data, + "api_base": url, + "headers": headers, + }, + ) + + try: + response = sync_httpx_client.post( + url=url, headers=headers, json=data, timeout=timeout + ) + + except Exception as e: + raise self._handle_error( + e=e, + provider_config=responses_api_provider_config, + ) + + return responses_api_provider_config.transform_cancel_response_api_response( + raw_response=response, + logging_obj=logging_obj, + ) + + async def async_cancel_response_api_handler( + self, + response_id: str, + responses_api_provider_config: BaseResponsesAPIConfig, + litellm_params: GenericLiteLLMParams, + logging_obj: LiteLLMLoggingObj, + custom_llm_provider: Optional[str], + extra_headers: Optional[Dict[str, Any]] = None, + extra_body: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None, + _is_async: bool = False, + ) -> ResponsesAPIResponse: + """ + Async version of the cancel response API handler. + Uses async HTTP client to make requests. + """ + if client is None or not isinstance(client, AsyncHTTPHandler): + async_httpx_client = get_async_httpx_client( + llm_provider=litellm.LlmProviders(custom_llm_provider), + params={"ssl_verify": litellm_params.get("ssl_verify", None)}, + ) + else: + async_httpx_client = client + + headers = responses_api_provider_config.validate_environment( + headers=extra_headers or {}, model="None", litellm_params=litellm_params + ) + + if extra_headers: + headers.update(extra_headers) + + api_base = responses_api_provider_config.get_complete_url( + api_base=litellm_params.api_base, + litellm_params=dict(litellm_params), + ) + + url, data = responses_api_provider_config.transform_cancel_response_api_request( + response_id=response_id, + api_base=api_base, + litellm_params=litellm_params, + headers=headers, + ) + + ## LOGGING + logging_obj.pre_call( + input=response_id, + api_key="", + additional_args={ + "complete_input_dict": data, + "api_base": url, + "headers": headers, + }, + ) + + try: + response = await async_httpx_client.post( + url=url, headers=headers, json=data, timeout=timeout + ) + + except Exception as e: + raise self._handle_error( + e=e, + provider_config=responses_api_provider_config, + ) + + return responses_api_provider_config.transform_cancel_response_api_response( + raw_response=response, + logging_obj=logging_obj, + ) + def list_files(self): """ Lists all files @@ -2378,6 +2991,7 @@ class BaseLLMHTTPHandler: BaseVectorStoreConfig, BaseGoogleGenAIGenerateContentConfig, BaseAnthropicMessagesConfig, + BaseBatchesConfig, "BasePassthroughConfig", ], ): @@ -2486,10 +3100,7 @@ class BaseLLMHTTPHandler: _is_async: bool = False, fake_stream: bool = False, litellm_metadata: Optional[Dict[str, Any]] = None, - ) -> Union[ - ImageResponse, - Coroutine[Any, Any, ImageResponse], - ]: + ) -> Union[ImageResponse, Coroutine[Any, Any, ImageResponse],]: """ Handles image edit requests. @@ -2678,10 +3289,8 @@ class BaseLLMHTTPHandler: _is_async: bool = False, fake_stream: bool = False, litellm_metadata: Optional[Dict[str, Any]] = None, - ) -> Union[ - ImageResponse, - Coroutine[Any, Any, ImageResponse], - ]: + api_key: Optional[str] = None, + ) -> Union[ImageResponse, Coroutine[Any, Any, ImageResponse],]: """ Handles image generation requests. When _is_async=True, returns a coroutine instead of making the call directly. @@ -2702,6 +3311,7 @@ class BaseLLMHTTPHandler: client=client if isinstance(client, AsyncHTTPHandler) else None, fake_stream=fake_stream, litellm_metadata=litellm_metadata, + api_key=api_key, ) if client is None or not isinstance(client, HTTPHandler): @@ -2712,7 +3322,7 @@ class BaseLLMHTTPHandler: sync_httpx_client = client headers = image_generation_provider_config.validate_environment( - api_key=litellm_params.get("api_key", None), + api_key=api_key, headers=image_generation_optional_request_params.get("extra_headers", {}) or {}, model=model, @@ -2795,6 +3405,7 @@ class BaseLLMHTTPHandler: client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None, fake_stream: bool = False, litellm_metadata: Optional[Dict[str, Any]] = None, + api_key: Optional[str] = None, ) -> ImageResponse: """ Async version of the image generation handler. @@ -2809,7 +3420,7 @@ class BaseLLMHTTPHandler: async_httpx_client = client headers = image_generation_provider_config.validate_environment( - api_key=litellm_params.get("api_key", None), + api_key=api_key, headers=image_generation_optional_request_params.get("extra_headers", {}) or {}, model=model, @@ -2913,15 +3524,16 @@ class BaseLLMHTTPHandler: litellm_params=dict(litellm_params), ) - url, request_body = ( - vector_store_provider_config.transform_search_vector_store_request( - vector_store_id=vector_store_id, - query=query, - vector_store_search_optional_params=vector_store_search_optional_params, - api_base=api_base, - litellm_logging_obj=logging_obj, - litellm_params=dict(litellm_params), - ) + ( + url, + request_body, + ) = vector_store_provider_config.transform_search_vector_store_request( + vector_store_id=vector_store_id, + query=query, + vector_store_search_optional_params=vector_store_search_optional_params, + api_base=api_base, + litellm_logging_obj=logging_obj, + litellm_params=dict(litellm_params), ) all_optional_params: Dict[str, Any] = dict(litellm_params) all_optional_params.update(vector_store_search_optional_params or {}) @@ -3012,15 +3624,16 @@ class BaseLLMHTTPHandler: litellm_params=dict(litellm_params), ) - url, request_body = ( - vector_store_provider_config.transform_search_vector_store_request( - vector_store_id=vector_store_id, - query=query, - vector_store_search_optional_params=vector_store_search_optional_params, - api_base=api_base, - litellm_logging_obj=logging_obj, - litellm_params=dict(litellm_params), - ) + ( + url, + request_body, + ) = vector_store_provider_config.transform_search_vector_store_request( + vector_store_id=vector_store_id, + query=query, + vector_store_search_optional_params=vector_store_search_optional_params, + api_base=api_base, + litellm_logging_obj=logging_obj, + litellm_params=dict(litellm_params), ) all_optional_params: Dict[str, Any] = dict(litellm_params) @@ -3094,11 +3707,12 @@ class BaseLLMHTTPHandler: litellm_params=dict(litellm_params), ) - url, request_body = ( - vector_store_provider_config.transform_create_vector_store_request( - vector_store_create_optional_params=vector_store_create_optional_params, - api_base=api_base, - ) + ( + url, + request_body, + ) = vector_store_provider_config.transform_create_vector_store_request( + vector_store_create_optional_params=vector_store_create_optional_params, + api_base=api_base, ) logging_obj.pre_call( @@ -3169,11 +3783,12 @@ class BaseLLMHTTPHandler: litellm_params=dict(litellm_params), ) - url, request_body = ( - vector_store_provider_config.transform_create_vector_store_request( - vector_store_create_optional_params=vector_store_create_optional_params, - api_base=api_base, - ) + ( + url, + request_body, + ) = vector_store_provider_config.transform_create_vector_store_request( + vector_store_create_optional_params=vector_store_create_optional_params, + api_base=api_base, ) logging_obj.pre_call( @@ -3252,13 +3867,14 @@ class BaseLLMHTTPHandler: sync_httpx_client = client # Get headers and URL from the provider config - headers, api_base = ( - generate_content_provider_config.sync_get_auth_token_and_url( - api_base=litellm_params.api_base, - model=model, - litellm_params=dict(litellm_params), - stream=stream, - ) + ( + headers, + api_base, + ) = generate_content_provider_config.sync_get_auth_token_and_url( + api_base=litellm_params.api_base, + model=model, + litellm_params=dict(litellm_params), + stream=stream, ) if extra_headers: @@ -3358,13 +3974,14 @@ class BaseLLMHTTPHandler: async_httpx_client = client # Get headers and URL from the provider config - headers, api_base = ( - await generate_content_provider_config.get_auth_token_and_url( - model=model, - litellm_params=dict(litellm_params), - stream=stream, - api_base=litellm_params.api_base, - ) + ( + headers, + api_base, + ) = await generate_content_provider_config.get_auth_token_and_url( + model=model, + litellm_params=dict(litellm_params), + stream=stream, + api_base=litellm_params.api_base, ) if extra_headers: diff --git a/litellm/llms/dashscope/cost_calculator.py b/litellm/llms/dashscope/cost_calculator.py index 0f4490cb3df..107eb7f5adf 100644 --- a/litellm/llms/dashscope/cost_calculator.py +++ b/litellm/llms/dashscope/cost_calculator.py @@ -1,21 +1,155 @@ """ -Cost calculator for DeepSeek Chat models. +Cost calculator for Dashscope Chat models. -Handles prompt caching scenario. +Handles tiered pricing and prompt caching scenarios. """ -from typing import Tuple +from dataclasses import dataclass +from typing import List, Optional, Tuple -from litellm.litellm_core_utils.llm_cost_calc.utils import generic_cost_per_token -from litellm.types.utils import Usage +from litellm.types.utils import ModelInfo, Usage +from litellm.utils import get_model_info + + +@dataclass +class TokenBreakdown: + """Token breakdown for cost calculation.""" + text_tokens: int + cached_tokens: int + completion_tokens: int + reasoning_tokens: int + + +def _extract_token_breakdown(usage: Usage) -> TokenBreakdown: + """Extract token counts from usage, handling cached and reasoning tokens.""" + cached_tokens = 0 + if usage.prompt_tokens_details and hasattr(usage.prompt_tokens_details, "cached_tokens"): + cached_tokens = usage.prompt_tokens_details.cached_tokens or 0 + + text_tokens = usage.prompt_tokens - cached_tokens + + reasoning_tokens = 0 + if (hasattr(usage, "completion_tokens_details") and + usage.completion_tokens_details and + hasattr(usage.completion_tokens_details, "reasoning_tokens")): + reasoning_tokens = usage.completion_tokens_details.reasoning_tokens or 0 + + completion_tokens = (usage.completion_tokens or 0) - reasoning_tokens + + return TokenBreakdown(text_tokens, cached_tokens, completion_tokens, reasoning_tokens) + + +def _calculate_tiered_cost( + tokens: int, + tiered_pricing: List[dict], + cost_key: str, + fallback_cost_key: Optional[str] = None +) -> float: + """Calculate cost using tiered pricing structure. + + Finds the appropriate tier based on token count and applies that tier's rate to all tokens. + """ + if not tiered_pricing or tokens <= 0: + return 0.0 + + # Find the appropriate tier for the token count + for tier in tiered_pricing: + tier_range = tier.get("range", []) + if len(tier_range) != 2: + continue + + range_start, range_end = tier_range + + # Check if tokens fall within this tier's range + if range_start <= tokens <= range_end: + cost_per_token = tier.get(cost_key) or tier.get(fallback_cost_key, 0) + return tokens * cost_per_token + + # If no tier matches, use the last tier (highest tier) + if tiered_pricing: + last_tier = tiered_pricing[-1] + cost_per_token = last_tier.get(cost_key) or last_tier.get(fallback_cost_key, 0) + return tokens * cost_per_token + + return 0.0 + + +def _calculate_flat_cost(tokens: int, cost_per_token: float) -> float: + """Calculate cost using flat pricing.""" + return tokens * cost_per_token + + +def _calculate_prompt_cost(breakdown: TokenBreakdown, model_info: ModelInfo, tiered_pricing: Optional[List[dict]]) -> float: + """Calculate total prompt cost including cached tokens.""" + if tiered_pricing: + text_cost = _calculate_tiered_cost( + tokens=breakdown.text_tokens, + tiered_pricing=tiered_pricing, + cost_key="input_cost_per_token" + ) + cache_cost = _calculate_tiered_cost( + tokens=breakdown.cached_tokens, + tiered_pricing=tiered_pricing, + cost_key="cache_read_input_token_cost" + ) + return text_cost + cache_cost + + input_cost = model_info.get("input_cost_per_token", 0.0) + cache_cost = model_info.get("cache_read_input_token_cost", input_cost) or input_cost + + return (_calculate_flat_cost(tokens=breakdown.text_tokens, cost_per_token=input_cost) + + _calculate_flat_cost(tokens=breakdown.cached_tokens, cost_per_token=cache_cost)) + + +def _calculate_completion_cost(breakdown: TokenBreakdown, model_info: ModelInfo, tiered_pricing: Optional[List[dict]]) -> float: + """Calculate total completion cost including reasoning tokens.""" + if tiered_pricing: + completion_cost = _calculate_tiered_cost( + tokens=breakdown.completion_tokens, + tiered_pricing=tiered_pricing, + cost_key="output_cost_per_token" + ) + reasoning_cost = _calculate_tiered_cost( + tokens=breakdown.reasoning_tokens, + tiered_pricing=tiered_pricing, + cost_key="output_cost_per_reasoning_token", + fallback_cost_key="output_cost_per_token" + ) + return completion_cost + reasoning_cost + + output_cost = model_info.get("output_cost_per_token", 0.0) + reasoning_cost = model_info.get("output_cost_per_reasoning_token", output_cost) or output_cost + + return (_calculate_flat_cost(tokens=breakdown.completion_tokens, cost_per_token=output_cost) + + _calculate_flat_cost(tokens=breakdown.reasoning_tokens, cost_per_token=reasoning_cost)) def cost_per_token(model: str, usage: Usage) -> Tuple[float, float]: """ - Calculates the cost per token for a given model, prompt tokens, and completion tokens. - - Follows the same logic as Anthropic's cost per token calculation. + Calculate cost per token for Dashscope models. + + Supports both tiered and flat pricing with cached and reasoning tokens. + + Args: + model: Model name without provider prefix + usage: LiteLLM Usage block + + Returns: + Tuple[float, float] - (prompt_cost_in_usd, completion_cost_in_usd) """ - return generic_cost_per_token( - model=model, usage=usage, custom_llm_provider="deepseek" + model_info = get_model_info(model=model, custom_llm_provider="dashscope") + breakdown = _extract_token_breakdown(usage) + tiered_pricing = model_info.get("tiered_pricing") if isinstance(model_info.get("tiered_pricing"), list) else None + + prompt_cost = _calculate_prompt_cost( + breakdown=breakdown, + model_info=model_info, + tiered_pricing=tiered_pricing ) + completion_cost = _calculate_completion_cost( + breakdown=breakdown, + model_info=model_info, + tiered_pricing=tiered_pricing + ) + + return prompt_cost, completion_cost diff --git a/litellm/llms/databricks/chat/transformation.py b/litellm/llms/databricks/chat/transformation.py index 908419f7193..a1370074238 100644 --- a/litellm/llms/databricks/chat/transformation.py +++ b/litellm/llms/databricks/chat/transformation.py @@ -26,7 +26,6 @@ from litellm.litellm_core_utils.llm_response_utils.convert_dict_to_response impo _should_convert_tool_call_to_json_mode, ) from litellm.litellm_core_utils.prompt_templates.common_utils import ( - handle_messages_with_content_list_to_str_conversion, strip_name_from_messages, ) from litellm.llms.base_llm.base_model_iterator import BaseModelResponseIterator @@ -170,12 +169,20 @@ class DatabricksConfig(DatabricksBase, OpenAILikeChatConfig, AnthropicConfig): if tool is None: return None + # Build DatabricksFunction explicitly to avoid parameter conflicts + function_params: DatabricksFunction = { + "name": tool["name"], + "parameters": cast(dict, tool.get("input_schema") or {}) + } + + # Only add description if it exists + description = tool.get("description") + if description is not None: + function_params["description"] = cast(Union[dict, str], description) + return DatabricksTool( type="function", - function=DatabricksFunction( - name=tool["name"], - parameters=cast(dict, tool.get("input_schema") or {}), - ), + function=function_params, ) def _map_openai_to_dbrx_tool(self, model: str, tools: List) -> List[DatabricksTool]: @@ -301,7 +308,6 @@ class DatabricksConfig(DatabricksBase, OpenAILikeChatConfig, AnthropicConfig): ) -> Union[List[AllMessageValues], Coroutine[Any, Any, List[AllMessageValues]]]: """ Databricks does not support: - - content in list format. - 'name' in user message. """ new_messages = [] @@ -311,7 +317,6 @@ class DatabricksConfig(DatabricksBase, OpenAILikeChatConfig, AnthropicConfig): else: _message = message new_messages.append(_message) - new_messages = handle_messages_with_content_list_to_str_conversion(new_messages) new_messages = strip_name_from_messages(new_messages) if is_async: @@ -334,8 +339,9 @@ class DatabricksConfig(DatabricksBase, OpenAILikeChatConfig, AnthropicConfig): elif isinstance(content, list): content_str = "" for item in content: - if item["type"] == "text": - content_str += item["text"] + if item.get("type") == "text": + text_value = item.get("text", "") + content_str += str(text_value) if text_value is not None else "" return content_str else: raise Exception(f"Unsupported content type: {type(content)}") @@ -364,21 +370,42 @@ class DatabricksConfig(DatabricksBase, OpenAILikeChatConfig, AnthropicConfig): reasoning_content: Optional[str] = None if isinstance(content, list): for item in content: - if item["type"] == "reasoning": - for sum in item["summary"]: - if reasoning_content is None: - reasoning_content = "" - reasoning_content += sum["text"] - thinking_block = ChatCompletionThinkingBlock( - type="thinking", - thinking=sum.get("text", ""), - signature=sum.get("signature", ""), - ) - if thinking_blocks is None: - thinking_blocks = [] - thinking_blocks.append(thinking_block) + if item.get("type") == "reasoning": + summary_list = item.get("summary", []) + if isinstance(summary_list, list): + for sum in summary_list: + if reasoning_content is None: + reasoning_content = "" + reasoning_content += sum["text"] + thinking_block = ChatCompletionThinkingBlock( + type="thinking", + thinking=sum.get("text", ""), + signature=sum.get("signature", ""), + ) + if thinking_blocks is None: + thinking_blocks = [] + thinking_blocks.append(thinking_block) return reasoning_content, thinking_blocks + @staticmethod + def extract_citations( + content: Optional[AllDatabricksContentValues], + ) -> Optional[List[Any]]: + if content is None: + return None + citations = [] + if isinstance(content, list): + for item in content: + text = item.get("text", None) + if citations_item := item.get("citations"): + citations.append( + [ + {**citation, "supported_text": text} + for citation in citations_item + ] + ) + return citations or None + def _transform_dbrx_choices( self, choices: List[DatabricksChoice], json_mode: Optional[bool] = None ) -> List[Choices]: @@ -427,12 +454,19 @@ class DatabricksConfig(DatabricksBase, OpenAILikeChatConfig, AnthropicConfig): choice["message"].get("content") ) + citations = DatabricksConfig.extract_citations( + choice["message"].get("content") + ) + translated_message = Message( role="assistant", content=content_str, reasoning_content=reasoning_content, thinking_blocks=thinking_blocks, tool_calls=choice["message"].get("tool_calls"), + provider_specific_fields={"citations": citations} + if citations is not None + else None, ) if finish_reason is None: @@ -561,6 +595,17 @@ class DatabricksChatResponseIterator(BaseModelResponseIterator): for _tc in tool_calls: if _tc.get("function", {}).get("arguments") == "{}": _tc["function"]["arguments"] = "" # avoid invalid json + if isinstance(choice["delta"]["content"], list) and ( + content := choice["delta"]["content"] + ): + if citations := content[0].get("citations"): + # TODO: Databricks delta does not include supported text or chunk type. + # Add either here once Databricks supports it to enable citation linkage. + choice["delta"].setdefault("provider_specific_fields", {})[ + "citation" + ] = citations[ + 0 + ] # Databricks Content item always has citation as a list of list # extract the content str content_str = DatabricksConfig.extract_content_str( choice["delta"].get("content") diff --git a/litellm/llms/deepinfra/chat/transformation.py b/litellm/llms/deepinfra/chat/transformation.py index 0d446d39b92..09cdabcdd82 100644 --- a/litellm/llms/deepinfra/chat/transformation.py +++ b/litellm/llms/deepinfra/chat/transformation.py @@ -12,6 +12,9 @@ class DeepInfraConfig(OpenAIGPTConfig): The class `DeepInfra` provides configuration for the DeepInfra's Chat Completions API interface. Below are the parameters: """ + @property + def custom_llm_provider(self) -> Optional[str]: + return "deepinfra" frequency_penalty: Optional[int] = None function_call: Optional[Union[str, dict]] = None @@ -53,7 +56,7 @@ class DeepInfraConfig(OpenAIGPTConfig): return super().get_config() def get_supported_openai_params(self, model: str): - return [ + supported_openai_params = [ "stream", "frequency_penalty", "function_call", @@ -68,9 +71,16 @@ class DeepInfraConfig(OpenAIGPTConfig): "top_p", "response_format", "tools", - "tool_choice", + "tool_choice" ] + if litellm.supports_reasoning( + model=model, + custom_llm_provider=self.custom_llm_provider, + ): + supported_openai_params.append("reasoning_effort") + return supported_openai_params + def map_openai_params( self, non_default_params: dict, diff --git a/litellm/llms/deepinfra/rerank/transformation.py b/litellm/llms/deepinfra/rerank/transformation.py index 8259c6075bb..6a3244a3c88 100644 --- a/litellm/llms/deepinfra/rerank/transformation.py +++ b/litellm/llms/deepinfra/rerank/transformation.py @@ -2,7 +2,7 @@ Translate between Cohere's `/rerank` format and Deepinfra's `/rerank` format. """ -import uuid +from litellm._uuid import uuid from typing import Any, Dict, List, Optional, Union import httpx diff --git a/litellm/llms/fireworks_ai/chat/transformation.py b/litellm/llms/fireworks_ai/chat/transformation.py index 31d749032b4..524b1c97145 100644 --- a/litellm/llms/fireworks_ai/chat/transformation.py +++ b/litellm/llms/fireworks_ai/chat/transformation.py @@ -1,5 +1,5 @@ import json -import uuid +from litellm._uuid import uuid from typing import Any, List, Literal, Optional, Tuple, Union, cast import httpx diff --git a/litellm/llms/gemini/chat/transformation.py b/litellm/llms/gemini/chat/transformation.py index 37217ebfaab..e889126883c 100644 --- a/litellm/llms/gemini/chat/transformation.py +++ b/litellm/llms/gemini/chat/transformation.py @@ -1,10 +1,13 @@ -from typing import List, Optional +from typing import List, Optional, cast from litellm.litellm_core_utils.prompt_templates.factory import ( convert_generic_image_chunk_to_openai_image_obj, convert_to_anthropic_image_obj, ) -from litellm.types.llms.openai import AllMessageValues +from litellm.litellm_core_utils.prompt_templates.image_handling import ( + convert_url_to_base64, +) +from litellm.types.llms.openai import AllMessageValues, ChatCompletionFileObject from litellm.types.llms.vertex_ai import ContentType, PartType from litellm.utils import supports_reasoning @@ -99,7 +102,8 @@ class GoogleAIStudioGeminiConfig(VertexGeminiConfig): self, messages: List[AllMessageValues] ) -> List[ContentType]: """ - Google AI Studio Gemini does not support image urls in messages. + Google AI Studio Gemini does not support HTTP/HTTPS URLs for files. + Convert them to base64 data instead. """ for message in messages: _message_content = message.get("content") @@ -124,4 +128,16 @@ class GoogleAIStudioGeminiConfig(VertexGeminiConfig): image_obj ) ) + elif element.get("type") == "file": + file_element = cast(ChatCompletionFileObject, element) + file_id = file_element["file"].get("file_id") + if file_id and ("http://" in file_id or "https://" in file_id): + # Convert HTTP/HTTPS file URL to base64 data + try: + base64_data = convert_url_to_base64(file_id) + file_element["file"]["file_data"] = base64_data # type: ignore + file_element["file"].pop("file_id", None) # type: ignore + except Exception: + # If conversion fails, leave as is and let the API handle it + pass return _gemini_convert_messages_with_history(messages=messages) diff --git a/litellm/llms/gemini/count_tokens/handler.py b/litellm/llms/gemini/count_tokens/handler.py index bcc8ab9553d..4d6c7fd8864 100644 --- a/litellm/llms/gemini/count_tokens/handler.py +++ b/litellm/llms/gemini/count_tokens/handler.py @@ -11,7 +11,39 @@ if TYPE_CHECKING: else: GenerateContentContentListUnionDict = Any + class GoogleAIStudioTokenCounter: + def _clean_contents_for_gemini_api(self, contents: Any) -> Any: + """ + Clean up contents to remove unsupported fields for the Gemini API. + + The Google Gemini API doesn't recognize the 'id' field in function responses, + so we need to remove it to prevent 400 Bad Request errors. + + Args: + contents: The contents to clean up + + Returns: + Cleaned contents with unsupported fields removed + """ + import copy + + from google.genai.types import FunctionResponse + + cleaned_contents = copy.deepcopy(contents) + + for content in cleaned_contents: + parts = content["parts"] + for part in parts: + if "functionResponse" in part: + function_response_data = part["functionResponse"] + function_response_part = FunctionResponse(**function_response_data) + function_response_part.id = None + part["functionResponse"] = function_response_part.model_dump( + exclude_none=True + ) + + return cleaned_contents def _construct_url(self, model: str, api_base: Optional[str] = None) -> str: """ @@ -20,7 +52,6 @@ class GoogleAIStudioTokenCounter: base_url = api_base or "https://generativelanguage.googleapis.com" return f"{base_url}/v1beta/models/{model}:countTokens" - async def validate_environment( self, api_base: Optional[str] = None, @@ -33,7 +64,8 @@ class GoogleAIStudioTokenCounter: Returns a Tuple of headers and url for the Google Gen AI Studio countTokens endpoint. """ from litellm.llms.gemini.google_genai.transformation import GoogleGenAIConfig - headers = GoogleGenAIConfig().validate_environment( + + headers = GoogleGenAIConfig().validate_environment( api_key=api_key, headers=headers, model=model, @@ -54,7 +86,7 @@ class GoogleAIStudioTokenCounter: ) -> Dict[str, Any]: """ Count tokens using Google Gen AI Studio countTokens endpoint. - + Args: contents: The content to count tokens for (Google Gen AI format) Example: [{"parts": [{"text": "Hello world"}]}] @@ -63,7 +95,7 @@ class GoogleAIStudioTokenCounter: api_base: Optional API base URL (defaults to Google Gen AI Studio) timeout: Optional timeout for the request **kwargs: Additional parameters - + Returns: Dict containing token count information from Google Gen AI Studio API. Example response: @@ -77,14 +109,13 @@ class GoogleAIStudioTokenCounter: } ] } - + Raises: ValueError: If API key is missing litellm.APIError: If the API call fails litellm.APIConnectionError: If the connection fails Exception: For any other unexpected errors """ - # Set up API base URL # Prepare headers headers, url = await self.validate_environment( @@ -94,46 +125,40 @@ class GoogleAIStudioTokenCounter: model=model, litellm_params=kwargs, ) - - # Prepare request body - request_body = { - "contents": contents - } - + + # Prepare request body - clean up contents to remove unsupported fields + cleaned_contents = self._clean_contents_for_gemini_api(contents) + request_body = {"contents": cleaned_contents} + async_httpx_client = get_async_httpx_client( llm_provider=LlmProviders.GEMINI, ) try: response = await async_httpx_client.post( - url=url, - headers=headers, - json=request_body + url=url, headers=headers, json=request_body ) - + # Check for HTTP errors response.raise_for_status() - + # Parse response result = response.json() return result - + except httpx.HTTPStatusError as e: error_msg = f"Google Gen AI Studio API error: {e.response.status_code} - {e.response.text}" raise litellm.APIError( message=error_msg, llm_provider="gemini", model=model, - status_code=e.response.status_code + status_code=e.response.status_code, ) from e except httpx.RequestError as e: error_msg = f"Request to Google Gen AI Studio failed: {str(e)}" raise litellm.APIConnectionError( - message=error_msg, - llm_provider="gemini", - model=model + message=error_msg, llm_provider="gemini", model=model ) from e except Exception as e: error_msg = f"Unexpected error during token counting: {str(e)}" raise Exception(error_msg) from e - diff --git a/litellm/llms/gemini/google_genai/transformation.py b/litellm/llms/gemini/google_genai/transformation.py index 28142f72739..f38c772e355 100644 --- a/litellm/llms/gemini/google_genai/transformation.py +++ b/litellm/llms/gemini/google_genai/transformation.py @@ -121,7 +121,8 @@ class GoogleGenAIConfig(BaseGoogleGenAIGenerateContentConfig, VertexLLM): default_headers = { "Content-Type": "application/json", } - gemini_api_key = self._get_google_ai_studio_api_key(dict(litellm_params or {})) + # Use the passed api_key first, then fall back to litellm_params and environment + gemini_api_key = api_key or self._get_google_ai_studio_api_key(dict(litellm_params or {})) if gemini_api_key is not None: default_headers[self.XGOOGLE_API_KEY] = gemini_api_key if headers is not None: diff --git a/litellm/llms/gemini/image_generation/transformation.py b/litellm/llms/gemini/image_generation/transformation.py index e57364fd288..f136bd0a404 100644 --- a/litellm/llms/gemini/image_generation/transformation.py +++ b/litellm/llms/gemini/image_generation/transformation.py @@ -85,17 +85,25 @@ class GoogleImageGenConfig(BaseImageGenerationConfig): ) -> str: """ Get the complete url for the request - - Google AI API format: https://generativelanguage.googleapis.com/v1beta/models/{model}:predict + + Gemini 2.5 Flash Image Preview: :generateContent + Other Imagen models: :predict """ complete_url: str = ( - api_base - or get_secret_str("GEMINI_API_BASE") + api_base + or get_secret_str("GEMINI_API_BASE") or self.DEFAULT_BASE_URL ) complete_url = complete_url.rstrip("/") - complete_url = f"{complete_url}/models/{model}:predict" + + # Gemini 2.5 Flash Image Preview uses generateContent endpoint + if "2.5-flash-image-preview" in model: + complete_url = f"{complete_url}/models/{model}:generateContent" + else: + # All other Imagen models use predict endpoint + complete_url = f"{complete_url}/models/{model}:predict" + return complete_url def validate_environment( @@ -128,35 +136,52 @@ class GoogleImageGenConfig(BaseImageGenerationConfig): headers: dict, ) -> dict: """ - Transform the image generation request to Google AI Imagen format - - Google AI API format: + Transform the image generation request to Gemini format + + For Gemini 2.5 Flash Image Preview, use the standard Gemini format with response_modalities: { - "instances": [ + "contents": [ { - "prompt": "Robot holding a red skateboard" + "parts": [ + {"text": "Generate an image of..."} + ] } ], - "parameters": { - "sampleCount": 4, - "aspectRatio": "1:1", - "personGeneration": "allow_adult" + "generationConfig": { + "response_modalities": ["IMAGE", "TEXT"] } } """ - from litellm.types.llms.gemini import ( - GeminiImageGenerationInstance, - GeminiImageGenerationParameters, - ) - request_body: GeminiImageGenerationRequest = GeminiImageGenerationRequest( - instances=[ - GeminiImageGenerationInstance( - prompt=prompt - ) - ], - parameters=GeminiImageGenerationParameters(**optional_params) - ) - return request_body.model_dump(exclude_none=True) + # For Gemini 2.5 Flash Image Preview, use standard Gemini format + if "2.5-flash-image-preview" in model: + request_body: dict = { + "contents": [ + { + "parts": [ + {"text": prompt} + ] + } + ], + "generationConfig": { + "response_modalities": ["IMAGE", "TEXT"] + } + } + return request_body + else: + # For other Imagen models, use the original Imagen format + from litellm.types.llms.gemini import ( + GeminiImageGenerationInstance, + GeminiImageGenerationParameters, + ) + request_body_obj: GeminiImageGenerationRequest = GeminiImageGenerationRequest( + instances=[ + GeminiImageGenerationInstance( + prompt=prompt + ) + ], + parameters=GeminiImageGenerationParameters(**optional_params) + ) + return request_body_obj.model_dump(exclude_none=True) def transform_image_generation_response( self, @@ -185,14 +210,30 @@ class GoogleImageGenConfig(BaseImageGenerationConfig): if not model_response.data: model_response.data = [] - - # Google AI returns predictions with generated images - predictions = response_data.get("predictions", []) - for prediction in predictions: - # Google AI returns base64 encoded images in the prediction - model_response.data.append(ImageObject( - b64_json=prediction.get("bytesBase64Encoded", None), - url=None, # Google AI returns base64, not URLs - )) - + + # Handle different response formats based on model + if "2.5-flash-image-preview" in model: + # Gemini 2.5 Flash Image Preview returns in candidates format + candidates = response_data.get("candidates", []) + for candidate in candidates: + content = candidate.get("content", {}) + parts = content.get("parts", []) + for part in parts: + # Look for inlineData with image + if "inlineData" in part: + inline_data = part["inlineData"] + if "data" in inline_data: + model_response.data.append(ImageObject( + b64_json=inline_data["data"], + url=None, + )) + else: + # Original Imagen format - predictions with generated images + predictions = response_data.get("predictions", []) + for prediction in predictions: + # Google AI returns base64 encoded images in the prediction + model_response.data.append(ImageObject( + b64_json=prediction.get("bytesBase64Encoded", None), + url=None, # Google AI returns base64, not URLs + )) return model_response \ No newline at end of file diff --git a/litellm/llms/gemini/realtime/transformation.py b/litellm/llms/gemini/realtime/transformation.py index f32a404c9e8..e1dd6f146f3 100644 --- a/litellm/llms/gemini/realtime/transformation.py +++ b/litellm/llms/gemini/realtime/transformation.py @@ -3,7 +3,7 @@ This file contains the transformation logic for the Gemini realtime API. """ import json -import uuid +from litellm._uuid import uuid from typing import Any, Dict, List, Optional, Union, cast from litellm import verbose_logger diff --git a/litellm/llms/github_copilot/chat/transformation.py b/litellm/llms/github_copilot/chat/transformation.py index 4526e6247b4..66227ac21d8 100644 --- a/litellm/llms/github_copilot/chat/transformation.py +++ b/litellm/llms/github_copilot/chat/transformation.py @@ -75,8 +75,36 @@ class GithubCopilotConfig(OpenAIConfig): initiator = self._determine_initiator(messages) validated_headers["X-Initiator"] = initiator + # Add Copilot-Vision-Request header if request contains images + if self._has_vision_content(messages): + validated_headers["Copilot-Vision-Request"] = "true" + return validated_headers + def get_supported_openai_params(self, model: str) -> list: + """ + Get supported OpenAI parameters for GitHub Copilot. + + For Claude models that support extended thinking (Claude 4 family and Claude 3-7), includes thinking and reasoning_effort parameters. + For other models, returns standard OpenAI parameters (which may include reasoning_effort for o-series models). + """ + from litellm.utils import supports_reasoning + + # Get base OpenAI parameters + base_params = super().get_supported_openai_params(model) + + # Add Claude-specific parameters for models that support extended thinking + if "claude" in model.lower() and supports_reasoning( + model=model.lower(), + ): + if "thinking" not in base_params: + base_params.append("thinking") + # reasoning_effort is not included by parent for Claude models, so add it + if "reasoning_effort" not in base_params: + base_params.append("reasoning_effort") + + return base_params + def _determine_initiator(self, messages: List[AllMessageValues]) -> str: """ Determine if request is user or agent initiated based on message roles. @@ -87,3 +115,27 @@ class GithubCopilotConfig(OpenAIConfig): if role in ["tool", "assistant"]: return "agent" return "user" + + def _has_vision_content(self, messages: List[AllMessageValues]) -> bool: + """ + Check if any message contains vision content (images). + Returns True if any message has content with vision-related types, otherwise False. + + Checks for: + - image_url content type (OpenAI format) + - Content items with type 'image_url' + """ + for message in messages: + content = message.get("content") + if isinstance(content, list): + # Check if any content item indicates vision content + for content_item in content: + if isinstance(content_item, dict): + # Check for image_url field (direct image URL) + if "image_url" in content_item: + return True + # Check for type field indicating image content + content_type = content_item.get("type") + if content_type == "image_url": + return True + return False diff --git a/litellm/llms/github_copilot/common_utils.py b/litellm/llms/github_copilot/common_utils.py index 4c9a4b6dad0..86fbb706e52 100644 --- a/litellm/llms/github_copilot/common_utils.py +++ b/litellm/llms/github_copilot/common_utils.py @@ -28,7 +28,6 @@ class GithubCopilotError(BaseLLMException): ) - class GetDeviceCodeError(GithubCopilotError): pass diff --git a/litellm/llms/groq/chat/transformation.py b/litellm/llms/groq/chat/transformation.py index 86fa323f9e3..165301efb5c 100644 --- a/litellm/llms/groq/chat/transformation.py +++ b/litellm/llms/groq/chat/transformation.py @@ -6,6 +6,8 @@ from typing import Any, Coroutine, List, Literal, Optional, Tuple, Union, cast, import httpx from pydantic import BaseModel +import litellm +from litellm._logging import verbose_logger from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.secret_managers.main import get_secret_str from litellm.types.llms.openai import ( @@ -55,6 +57,10 @@ class GroqChatConfig(OpenAILikeChatConfig): if key != "self" and value is not None: setattr(self.__class__, key, value) + @property + def custom_llm_provider(self) -> Optional[str]: + return "groq" + @classmethod def get_config(cls): return super().get_config() @@ -65,6 +71,15 @@ class GroqChatConfig(OpenAILikeChatConfig): base_params.remove("max_retries") except ValueError: pass + + try: + if litellm.supports_reasoning( + model=model, custom_llm_provider=self.custom_llm_provider + ): + base_params.append("reasoning_effort") + except Exception as e: + verbose_logger.debug(f"Error checking if model supports reasoning: {e}") + return base_params @overload diff --git a/litellm/llms/heroku/chat/transformation.py b/litellm/llms/heroku/chat/transformation.py new file mode 100644 index 00000000000..a64d8afe63a --- /dev/null +++ b/litellm/llms/heroku/chat/transformation.py @@ -0,0 +1,67 @@ +""" +Heroku Chat Completions API + +this is OpenAI compatible - no translation needed / occurs +""" +import os + +from typing import Optional, List, Tuple, Union, Coroutine, Any, Literal, overload +from litellm.litellm_core_utils.prompt_templates.common_utils import ( + handle_messages_with_content_list_to_str_conversion, +) +from litellm.types.llms.openai import AllMessageValues +from litellm.llms.openai.chat.gpt_transformation import OpenAIGPTConfig + +# Base error class for Heroku +class HerokuError(Exception): + pass + +class HerokuChatConfig(OpenAIGPTConfig): + @overload + def _transform_messages( + self, messages: List[AllMessageValues], model: str, is_async: Literal[True] + ) -> Coroutine[Any, Any, List[AllMessageValues]]: + ... + + @overload + def _transform_messages( + self, + messages: List[AllMessageValues], + model: str, + is_async: Literal[False] = False, + ) -> List[AllMessageValues]: + ... + + def _transform_messages( + self, messages: List[AllMessageValues], model: str, is_async: bool = False + ) -> Union[List[AllMessageValues], Coroutine[Any, Any, List[AllMessageValues]]]: + """ + Heroku does not support content in list format. + See: https://devcenter.heroku.com/articles/heroku-inference-api-v1-chat-completions#content-object + """ + messages = handle_messages_with_content_list_to_str_conversion(messages) + if is_async: + return super()._transform_messages( + messages=messages, model=model, is_async=True + ) + else: + return super()._transform_messages( + messages=messages, model=model, is_async=False + ) + + def _get_openai_compatible_provider_info(self, api_base: Optional[str], api_key: Optional[str]) -> Tuple[Optional[str], Optional[str]]: + api_base = api_base or os.getenv("HEROKU_API_BASE") + api_key = api_key or os.getenv("HEROKU_API_KEY") + + return api_base, api_key + + def get_complete_url(self, api_base: Optional[str], api_key: Optional[str], model: str, optional_params: dict, litellm_params: dict, stream: Optional[bool] = None) -> str: + api_base, _ = self._get_openai_compatible_provider_info(api_base, api_key) + + if not api_base: + raise HerokuError("No api base was set. Please provide an api_base, or set the HEROKU_API_BASE environment variable.") + + if not api_base.endswith("/v1/chat/completions"): + api_base = f"{api_base}/v1/chat/completions" + + return api_base \ No newline at end of file diff --git a/litellm/llms/hosted_vllm/rerank/transformation.py b/litellm/llms/hosted_vllm/rerank/transformation.py index 419327d9d5c..c0a57c43534 100644 --- a/litellm/llms/hosted_vllm/rerank/transformation.py +++ b/litellm/llms/hosted_vllm/rerank/transformation.py @@ -2,7 +2,7 @@ Transformation logic for Hosted VLLM rerank """ -import uuid +from litellm._uuid import uuid from typing import Any, Dict, List, Optional, Union from litellm.types.rerank import ( diff --git a/litellm/llms/hosted_vllm/transcriptions/transformation.py b/litellm/llms/hosted_vllm/transcriptions/transformation.py new file mode 100644 index 00000000000..5eeb892d846 --- /dev/null +++ b/litellm/llms/hosted_vllm/transcriptions/transformation.py @@ -0,0 +1,72 @@ +""" +Transformation logic for Hosted VLLM rerank +""" + +from typing import Optional, Union + +import httpx + +from litellm.llms.base_llm.audio_transcription.transformation import ( + AudioTranscriptionRequestData, +) +from litellm.llms.base_llm.chat.transformation import BaseLLMException +from litellm.llms.openai.transcriptions.whisper_transformation import ( + OpenAIWhisperAudioTranscriptionConfig, +) +from litellm.types.utils import FileTypes + + +class HostedVLLMAudioTranscriptionError(BaseLLMException): + def __init__( + self, + status_code: int, + message: str, + headers: Optional[Union[dict, httpx.Headers]] = None, + ): + super().__init__(status_code=status_code, message=message, headers=headers) + + +class HostedVLLMAudioTranscriptionConfig(OpenAIWhisperAudioTranscriptionConfig): + def __init__(self) -> None: + pass + + def get_complete_url( + self, + api_base: Optional[str], + api_key: Optional[str], + model: str, + optional_params: dict, + litellm_params: dict, + stream: Optional[bool] = None, + ) -> str: + if api_base: + # Remove trailing slashes and ensure clean base URL + api_base = api_base.rstrip("/") + if not api_base.endswith("/v1/audio/transcriptions"): + api_base = f"{api_base}/v1/audio/transcriptions" + return api_base + raise ValueError("api_base must be provided for Hosted VLLM rerank") + + def transform_audio_transcription_request( + self, + model: str, + audio_file: FileTypes, + optional_params: dict, + litellm_params: dict, + ) -> AudioTranscriptionRequestData: + """ + Transform the audio transcription request + """ + + data = {"model": model, "file": audio_file, **optional_params} + + if "response_format" not in data or ( + data["response_format"] == "text" or data["response_format"] == "json" + ): + data["response_format"] = ( + "verbose_json" # ensures 'duration' is received - used for cost calculation + ) + + return AudioTranscriptionRequestData( + data=data, + ) diff --git a/litellm/llms/huggingface/rerank/transformation.py b/litellm/llms/huggingface/rerank/transformation.py index 3f5c44fec05..aa0f37bc6ba 100644 --- a/litellm/llms/huggingface/rerank/transformation.py +++ b/litellm/llms/huggingface/rerank/transformation.py @@ -1,8 +1,9 @@ import os -import uuid -from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, TypedDict, Union +from litellm._uuid import uuid +from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union import httpx +from typing_extensions import TypedDict import litellm from litellm.llms.base_llm.chat.transformation import BaseLLMException diff --git a/litellm/llms/infinity/rerank/transformation.py b/litellm/llms/infinity/rerank/transformation.py index 4b75fa121b2..6259d445aec 100644 --- a/litellm/llms/infinity/rerank/transformation.py +++ b/litellm/llms/infinity/rerank/transformation.py @@ -4,7 +4,7 @@ Transformation logic from Cohere's /v1/rerank format to Infinity's `/v1/rerank` Why separate file? Make it easy to see how transformation works """ -import uuid +from litellm._uuid import uuid from typing import List, Optional import httpx diff --git a/litellm/llms/jina_ai/rerank/transformation.py b/litellm/llms/jina_ai/rerank/transformation.py index 8d0a9b1431c..d8569b01b83 100644 --- a/litellm/llms/jina_ai/rerank/transformation.py +++ b/litellm/llms/jina_ai/rerank/transformation.py @@ -6,7 +6,7 @@ Why separate file? Make it easy to see how transformation works Docs - https://jina.ai/reranker """ -import uuid +from litellm._uuid import uuid from typing import Any, Dict, List, Optional, Tuple, Union from httpx import URL, Response diff --git a/litellm/llms/lm_studio/chat/transformation.py b/litellm/llms/lm_studio/chat/transformation.py index f7a2cc0f28a..7b188ff33f8 100644 --- a/litellm/llms/lm_studio/chat/transformation.py +++ b/litellm/llms/lm_studio/chat/transformation.py @@ -15,8 +15,8 @@ class LMStudioChatConfig(OpenAIGPTConfig): ) -> Tuple[Optional[str], Optional[str]]: api_base = api_base or get_secret_str("LM_STUDIO_API_BASE") # type: ignore dynamic_api_key = ( - api_key or get_secret_str("LM_STUDIO_API_KEY") or " " - ) # vllm does not require an api key + api_key or get_secret_str("LM_STUDIO_API_KEY") or "fake-api-key" + ) # LM Studio does not require an api key, but OpenAI client requires non-None value return api_base, dynamic_api_key def map_openai_params( diff --git a/litellm/llms/mistral/chat/transformation.py b/litellm/llms/mistral/chat/transformation.py index af4ded0983b..51fa65244a0 100644 --- a/litellm/llms/mistral/chat/transformation.py +++ b/litellm/llms/mistral/chat/transformation.py @@ -6,7 +6,18 @@ Why separate file? Make it easy to see how transformation works Docs - https://docs.mistral.ai/api/ """ -from typing import Any, Coroutine, List, Literal, Optional, Tuple, Union, cast, overload +from typing import ( + Any, + Coroutine, + List, + Literal, + Optional, + Tuple, + Union, + cast, + get_type_hints, + overload, +) import httpx @@ -17,7 +28,7 @@ from litellm.litellm_core_utils.prompt_templates.common_utils import ( ) from litellm.llms.openai.chat.gpt_transformation import OpenAIGPTConfig from litellm.secret_managers.main import get_secret_str -from litellm.types.llms.mistral import MistralToolCallMessage +from litellm.types.llms.mistral import MistralThinkingBlock, MistralToolCallMessage from litellm.types.llms.openai import AllMessageValues from litellm.types.utils import ModelResponse from litellm.utils import convert_to_model_response_object @@ -145,7 +156,9 @@ class MistralConfig(OpenAIGPTConfig): for param, value in non_default_params.items(): if param == "max_tokens": optional_params["max_tokens"] = value - if param == "max_completion_tokens": # max_completion_tokens should take priority + if ( + param == "max_completion_tokens" + ): # max_completion_tokens should take priority optional_params["max_tokens"] = value if param == "tools": # Clean tools to remove problematic schema fields for Mistral API @@ -159,7 +172,9 @@ class MistralConfig(OpenAIGPTConfig): if param == "stop": optional_params["stop"] = value if param == "tool_choice" and isinstance(value, str): - optional_params["tool_choice"] = self._map_tool_choice(tool_choice=value) + optional_params["tool_choice"] = self._map_tool_choice( + tool_choice=value + ) if param == "seed": optional_params["extra_body"] = {"random_seed": value} if param == "response_format": @@ -185,7 +200,9 @@ class MistralConfig(OpenAIGPTConfig): ) # type: ignore # if api_base does not end with /v1 we add it - if api_base is not None and not api_base.endswith("/v1"): # Mistral always needs a /v1 at the end + if api_base is not None and not api_base.endswith( + "/v1" + ): # Mistral always needs a /v1 at the end api_base = api_base + "/v1" dynamic_api_key = ( api_key @@ -194,10 +211,12 @@ class MistralConfig(OpenAIGPTConfig): ) return api_base, dynamic_api_key + # fmt: off + @overload def _transform_messages( self, messages: List[AllMessageValues], model: str, is_async: Literal[True] - ) -> Coroutine[Any, Any, List[AllMessageValues]]: + ) -> Coroutine[Any, Any, List[AllMessageValues]]: ... @overload @@ -206,8 +225,9 @@ class MistralConfig(OpenAIGPTConfig): messages: List[AllMessageValues], model: str, is_async: Literal[False] = False, - ) -> List[AllMessageValues]: + ) -> List[AllMessageValues]: ... + # fmt: on def _transform_messages( self, messages: List[AllMessageValues], model: str, is_async: bool = False @@ -241,6 +261,8 @@ class MistralConfig(OpenAIGPTConfig): for m in messages: m = MistralConfig._handle_name_in_message(m) m = MistralConfig._handle_tool_call_message(m) + if MistralConfig._is_empty_assistant_message(m): + continue m = strip_none_values_from_message(m) # prevents 'extra_forbidden' error new_messages.append(m) @@ -316,20 +338,30 @@ class MistralConfig(OpenAIGPTConfig): # Handle both string and list content, preserving original format if isinstance(existing_content, str): # String content - prepend reasoning prompt - new_content: Union[str, list] = f"{reasoning_prompt}\n\n{existing_content}" + new_content: Union[str, list] = ( + f"{reasoning_prompt}\n\n{existing_content}" + ) elif isinstance(existing_content, list): # List content - prepend reasoning prompt as text block - new_content = [{"type": "text", "text": reasoning_prompt + "\n\n"}] + existing_content + new_content = [ + {"type": "text", "text": reasoning_prompt + "\n\n"} + ] + existing_content else: # Fallback for any other type - convert to string new_content = f"{reasoning_prompt}\n\n{str(existing_content)}" - messages[i] = cast(AllMessageValues, {**msg, "content": new_content}) + messages[i] = cast( + AllMessageValues, {**msg, "content": new_content} + ) break else: # Add new system message with reasoning instructions reasoning_message: AllMessageValues = cast( - AllMessageValues, {"role": "system", "content": self._get_mistral_reasoning_system_prompt()} + AllMessageValues, + { + "role": "system", + "content": self._get_mistral_reasoning_system_prompt(), + }, ) messages = [reasoning_message] + messages @@ -341,32 +373,34 @@ class MistralConfig(OpenAIGPTConfig): def _clean_tool_schema_for_mistral(cls, tools: list) -> list: """ Clean tool schemas to remove fields that cause issues with Mistral API. - + Removes: - $id and $schema fields (cause grammar validation errors) - additionalProperties=False (causes OpenAI API schema errors) - strict field (not supported by Mistral) - + Args: tools: List of tool definitions max_depth: Maximum recursion depth for schema cleaning (default: 10) - + Returns: Cleaned tools list """ if not tools: return tools - + import copy from litellm.constants import DEFAULT_MAX_RECURSE_DEPTH from litellm.utils import _remove_json_schema_refs cleaned_tools = copy.deepcopy(tools) - + # Apply all cleaning functions with max_depth protection - cleaned_tools = _remove_json_schema_refs(cleaned_tools, max_depth=DEFAULT_MAX_RECURSE_DEPTH) - + cleaned_tools = _remove_json_schema_refs( + cleaned_tools, max_depth=DEFAULT_MAX_RECURSE_DEPTH + ) + return cleaned_tools @classmethod @@ -407,6 +441,25 @@ class MistralConfig(OpenAIGPTConfig): message["tool_calls"] = mistral_tool_calls # type: ignore return message + @classmethod + def _is_empty_assistant_message(cls, message: AllMessageValues) -> bool: + """ + Mistral API does not support empty string in assistant content. + """ + from litellm.types.llms.openai import ChatCompletionAssistantMessage + + set_keys = get_type_hints(ChatCompletionAssistantMessage).keys() + + all_expected_values_are_empty = True + for key in set_keys: + if key != "role" and message.get(key) is not None: + if key == "content" and message.get(key) == "": + continue + else: + all_expected_values_are_empty = False + break + return all_expected_values_are_empty + @staticmethod def _handle_empty_content_response(response_data: dict) -> dict: """ @@ -427,6 +480,58 @@ class MistralConfig(OpenAIGPTConfig): choice["message"]["content"] = None return response_data + @staticmethod + def _convert_thinking_block_to_reasoning_content( + thinking_blocks: MistralThinkingBlock, + ) -> str: + """ + Convert Mistral thinking blocks to reasoning content. + """ + return "\n".join( + [block.get("text", "") for block in thinking_blocks["thinking"]] + ) + + @staticmethod + def _handle_content_list_to_str_conversion(response_data: dict) -> dict: + """ + Handle Mistral's content list format and extract thinking content. + + Map mistral's content list to string and extract thinking blocks: + - Thinking block -> reasoning_content field + - Text block -> content field + """ + + if response_data.get("choices") and len(response_data["choices"]) > 0: + for choice in response_data["choices"]: + if choice.get("message") and choice["message"].get("content"): + content = choice["message"]["content"] + + # Only process if content is a list + if isinstance(content, list): + thinking_content = "" + text_content = "" + + # Process each content block + for block in content: + if block.get("type") == "thinking": + thinking_blocks = block.get("thinking", []) + thinking_texts = [] + for thinking_block in thinking_blocks: + if thinking_block.get("type") == "text": + thinking_texts.append( + thinking_block.get("text", "") + ) + thinking_content = "\n".join(thinking_texts) + elif block.get("type") == "text": + text_content = block.get("text", "") + + # Set the extracted content + choice["message"]["content"] = text_content + if thinking_content: + choice["message"]["reasoning_content"] = thinking_content + + return response_data + def transform_request( self, model: str, @@ -443,8 +548,12 @@ class MistralConfig(OpenAIGPTConfig): dict: The transformed request. Sent as the body of the API call. """ # Add reasoning system prompt if needed (for magistral models) - if "magistral" in model.lower() and optional_params.get("_add_reasoning_prompt", False): - messages = self._add_reasoning_system_prompt_if_needed(messages, optional_params) + if "magistral" in model.lower() and optional_params.get( + "_add_reasoning_prompt", False + ): + messages = self._add_reasoning_system_prompt_if_needed( + messages, optional_params + ) # Call parent transform_request which handles _transform_messages return super().transform_request( @@ -471,14 +580,16 @@ class MistralConfig(OpenAIGPTConfig): ) -> ModelResponse: """ Transform the raw response from Mistral API. - Handles Mistral-specific behavior like converting empty string content to None. + Handles Mistral-specific behavior like converting empty string content to None + and extracting thinking content from content lists. """ logging_obj.post_call(original_response=raw_response.text) logging_obj.model_call_details["response_headers"] = raw_response.headers - # Handle Mistral-specific empty string content conversion to None + # Handle Mistral-specific response transformations response_data = raw_response.json() response_data = self._handle_empty_content_response(response_data) + response_data = self._handle_content_list_to_str_conversion(response_data) final_response_obj = cast( ModelResponse, diff --git a/litellm/llms/oci/chat/transformation.py b/litellm/llms/oci/chat/transformation.py index 3e05473630d..6755cab22e0 100644 --- a/litellm/llms/oci/chat/transformation.py +++ b/litellm/llms/oci/chat/transformation.py @@ -92,6 +92,22 @@ def load_private_key_from_str(key_str: str): return key +def load_private_key_from_file(file_path: str): + """Loads a private key from a file path""" + try: + with open(file_path, "r", encoding="utf-8") as f: + key_str = f.read().strip() + except FileNotFoundError: + raise FileNotFoundError(f"Private key file not found: {file_path}") + except OSError as e: + raise OSError(f"Failed to read private key file '{file_path}': {e}") from e + + if not key_str: + raise ValueError(f"Private key file is empty: {file_path}") + + return load_private_key_from_str(key_str) + + def get_vendor_from_model(model: str) -> OCIVendors: """ Extracts the vendor from the model name. @@ -237,10 +253,17 @@ class OCIChatConfig(BaseConfig): oci_fingerprint = optional_params.get("oci_fingerprint") oci_tenancy = optional_params.get("oci_tenancy") oci_key = optional_params.get("oci_key") + oci_key_file = optional_params.get("oci_key_file") - if not oci_user or not oci_fingerprint or not oci_tenancy or not oci_key: + if ( + not oci_user + or not oci_fingerprint + or not oci_tenancy + or not (oci_key or oci_key_file) + ): raise Exception( - "Missing one of the following parameters: oci_user, oci_fingerprint, oci_tenancy, oci_key" + "Missing required parameters: oci_user, oci_fingerprint, oci_tenancy, " + "and at least one of oci_key or oci_key_file." ) method = str(optional_params.get("method", "POST")).upper() @@ -283,7 +306,17 @@ class OCIChatConfig(BaseConfig): "Please install it with: pip install cryptography" ) from e - private_key = load_private_key_from_str(oci_key) + private_key = ( + load_private_key_from_str(oci_key) + if oci_key + else load_private_key_from_file(oci_key_file) if oci_key_file else None + ) + + if private_key is None: + raise Exception( + "Private key is required for OCI authentication. Please provide either oci_key or oci_key_file." + ) + signature = private_key.sign( signing_string.encode("utf-8"), padding.PKCS1v15(), @@ -334,17 +367,19 @@ class OCIChatConfig(BaseConfig): oci_fingerprint = optional_params.get("oci_fingerprint") oci_tenancy = optional_params.get("oci_tenancy") oci_key = optional_params.get("oci_key") + oci_key_file = optional_params.get("oci_key_file") oci_compartment_id = optional_params.get("oci_compartment_id") if ( not oci_user or not oci_fingerprint or not oci_tenancy - or not oci_key + or not (oci_key or oci_key_file) or not oci_compartment_id ): raise Exception( - "Missing one of the following parameters: oci_user, oci_fingerprint, oci_tenancy, oci_key, oci_compartment_id" + "Missing required parameters: oci_user, oci_fingerprint, oci_tenancy, oci_compartment_id " + "and at least one of oci_key or oci_key_file." ) if not api_base: @@ -737,7 +772,14 @@ def adapt_messages_to_generic_oci_standard( tool_calls = message.get("tool_calls") tool_call_id = message.get("tool_call_id") - if role in ["system", "user", "assistant"] and content is not None: + if role == "assistant" and tool_calls is not None: + if not isinstance(tool_calls, list): + raise Exception("Prop `tool_calls` must be a list of tool calls") + new_messages.append( + adapt_messages_to_generic_oci_standard_tool_call(role, tool_calls) + ) + + elif role in ["system", "user", "assistant"] and content is not None: if not isinstance(content, (str, list)): raise Exception( "Prop `content` must be a string or a list of content items" @@ -746,13 +788,6 @@ def adapt_messages_to_generic_oci_standard( adapt_messages_to_generic_oci_standard_content_message(role, content) ) - elif role == "assistant" and tool_calls is not None: - if not isinstance(tool_calls, list): - raise Exception("Prop `tool_calls` must be a list of tool calls") - new_messages.append( - adapt_messages_to_generic_oci_standard_tool_call(role, tool_calls) - ) - elif role == "tool": if not isinstance(tool_call_id, str): raise Exception("Prop `tool_call_id` is required and must be a string") diff --git a/litellm/llms/ollama/chat/transformation.py b/litellm/llms/ollama/chat/transformation.py index d4ce4052a7e..3b755e79330 100644 --- a/litellm/llms/ollama/chat/transformation.py +++ b/litellm/llms/ollama/chat/transformation.py @@ -1,6 +1,6 @@ import json import time -import uuid +from litellm._uuid import uuid from typing import ( TYPE_CHECKING, Any, @@ -16,9 +16,18 @@ from httpx._models import Headers, Response from pydantic import BaseModel import litellm +from litellm.litellm_core_utils.prompt_templates.common_utils import ( + _extract_reasoning_content, + convert_content_list_to_str, + extract_images_from_message, +) from litellm.llms.base_llm.base_model_iterator import BaseModelResponseIterator from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMException -from litellm.types.llms.ollama import OllamaToolCall, OllamaToolCallFunction +from litellm.types.llms.ollama import ( + OllamaChatCompletionMessage, + OllamaToolCall, + OllamaToolCallFunction, +) from litellm.types.llms.openai import ( AllMessageValues, ChatCompletionAssistantToolCall, @@ -137,6 +146,7 @@ class OllamaChatConfig(BaseConfig): "tool_choice", "functions", "response_format", + "reasoning_effort", ] def map_openai_params( @@ -175,6 +185,8 @@ class OllamaChatConfig(BaseConfig): if value.get("json_schema") and value["json_schema"].get("schema"): optional_params["format"] = value["json_schema"]["schema"] ### FUNCTION CALLING LOGIC ### + if param == "reasoning_effort" and value is not None: + optional_params["think"] = True if param == "tools": ## CHECK IF MODEL SUPPORTS TOOL CALLING ## try: @@ -212,9 +224,9 @@ class OllamaChatConfig(BaseConfig): litellm.add_function_to_prompt = ( True # so that main.py adds the function call to the prompt ) - optional_params[ - "functions_unsupported_model" - ] = non_default_params.get("functions") + optional_params["functions_unsupported_model"] = ( + non_default_params.get("functions") + ) non_default_params.pop("tool_choice", None) # causes ollama requests to hang non_default_params.pop("functions", None) # causes ollama requests to hang return optional_params @@ -229,6 +241,8 @@ class OllamaChatConfig(BaseConfig): api_key: Optional[str] = None, api_base: Optional[str] = None, ) -> dict: + if api_key is not None and "Authorization" not in headers: + headers["Authorization"] = f"Bearer {api_key}" return headers def get_complete_url( @@ -294,7 +308,23 @@ class OllamaChatConfig(BaseConfig): ) new_tools.append(ollama_tool_call) cast(dict, m)["tool_calls"] = new_tools - new_messages.append(m) + reasoning_content, parsed_content = _extract_reasoning_content( + cast(dict, m) + ) + content_str = convert_content_list_to_str(cast(AllMessageValues, m)) + images = extract_images_from_message(cast(AllMessageValues, m)) + + ollama_message = OllamaChatCompletionMessage( + role=cast(str, m.get("role")), + ) + if reasoning_content is not None: + ollama_message["thinking"] = reasoning_content + if content_str is not None: + ollama_message["content"] = content_str + if images is not None: + ollama_message["images"] = images + + new_messages.append(ollama_message) # Load Config config = self.get_config() @@ -346,11 +376,31 @@ class OllamaChatConfig(BaseConfig): ## RESPONSE OBJECT model_response.choices[0].finish_reason = "stop" + response_json_message = response_json.get("message") + if response_json_message is not None: + if "thinking" in response_json_message: + # remap 'thinking' to 'reasoning_content' + response_json_message["reasoning_content"] = response_json_message[ + "thinking" + ] + del response_json_message["thinking"] + elif response_json_message.get("content") is not None: + # parse reasoning content from content + from litellm.litellm_core_utils.prompt_templates.common_utils import ( + _parse_content_for_reasoning, + ) + + reasoning_content, content = _parse_content_for_reasoning( + response_json_message["content"] + ) + response_json_message["reasoning_content"] = reasoning_content + response_json_message["content"] = content + if ( request_data.get("format", "") == "json" and litellm_params.get("function_name") is not None ): - function_call = json.loads(response_json["message"]["content"]) + function_call = json.loads(response_json_message["content"]) message = litellm.Message( content=None, tool_calls=[ @@ -367,11 +417,13 @@ class OllamaChatConfig(BaseConfig): "type": "function", } ], + reasoning_content=response_json_message.get("reasoning_content"), ) model_response.choices[0].message = message # type: ignore model_response.choices[0].finish_reason = "tool_calls" else: - _message = litellm.Message(**response_json["message"]) + + _message = litellm.Message(**response_json_message) model_response.choices[0].message = _message # type: ignore model_response.created = int(time.time()) model_response.model = "ollama_chat/" + model @@ -412,6 +464,9 @@ class OllamaChatConfig(BaseConfig): class OllamaChatCompletionResponseIterator(BaseModelResponseIterator): + started_reasoning_content: bool = False + finished_reasoning_content: bool = False + def _is_function_call_complete(self, function_args: Union[str, dict]) -> bool: if isinstance(function_args, dict): return True @@ -465,8 +520,38 @@ class OllamaChatCompletionResponseIterator(BaseModelResponseIterator): if is_function_call_complete: tool_call["id"] = str(uuid.uuid4()) + # PROCESS REASONING CONTENT + reasoning_content: Optional[str] = None + content: Optional[str] = None + if chunk["message"].get("thinking") is not None: + if self.started_reasoning_content is False: + reasoning_content = chunk["message"].get("thinking") + self.started_reasoning_content = True + elif self.finished_reasoning_content is False: + reasoning_content = chunk["message"].get("thinking") + self.finished_reasoning_content = True + elif chunk["message"].get("content") is not None: + message_content = chunk["message"].get("content") + if "" in message_content: + message_content = message_content.replace("", "") + + self.started_reasoning_content = True + + if "" in message_content and self.started_reasoning_content: + message_content = message_content.replace("", "") + self.finished_reasoning_content = True + + if ( + self.started_reasoning_content + and not self.finished_reasoning_content + ): + reasoning_content = message_content + else: + content = message_content + delta = Delta( - content=chunk["message"].get("content", ""), + content=content, + reasoning_content=reasoning_content, tool_calls=tool_calls, ) diff --git a/litellm/llms/ollama/common_utils.py b/litellm/llms/ollama/common_utils.py index daff7a12065..166ceee27fc 100644 --- a/litellm/llms/ollama/common_utils.py +++ b/litellm/llms/ollama/common_utils.py @@ -57,8 +57,20 @@ class OllamaModelInfo(BaseLLMModelInfo): """ @staticmethod - def get_api_key(api_key=None) -> None: - return None # Ollama does not use an API key by default + def get_api_key(api_key=None) -> Optional[str]: + """Get API key from environment variables or litellm configuration""" + import os + + import litellm + from litellm.secret_managers.main import get_secret_str + + return ( + os.environ.get("OLLAMA_API_KEY") + or litellm.api_key + or litellm.openai_key + or get_secret_str("OLLAMA_API_KEY") + ) + @staticmethod def get_api_base(api_base: Optional[str] = None) -> str: @@ -73,9 +85,12 @@ class OllamaModelInfo(BaseLLMModelInfo): """ base = self.get_api_base(api_base) + api_key = self.get_api_key() + headers = { "Authorization": f"Bearer {api_key}" } if api_key else {} + names: set[str] = set() try: - resp = httpx.get(f"{base}/api/tags") + resp = httpx.get(f"{base}/api/tags", headers=headers) resp.raise_for_status() data = resp.json() # Expecting a dict with a 'models' list diff --git a/litellm/llms/ollama/completion/transformation.py b/litellm/llms/ollama/completion/transformation.py index 4f7be507cc2..981a987ec91 100644 --- a/litellm/llms/ollama/completion/transformation.py +++ b/litellm/llms/ollama/completion/transformation.py @@ -1,6 +1,6 @@ import json import time -import uuid +from litellm._uuid import uuid from typing import TYPE_CHECKING, Any, AsyncIterator, Iterator, List, Optional, Union from httpx._models import Headers, Response @@ -19,13 +19,13 @@ from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMExcepti from litellm.secret_managers.main import get_secret_str from litellm.types.llms.openai import AllMessageValues, ChatCompletionUsageBlock from litellm.types.utils import ( + Delta, GenericStreamingChunk, ModelInfoBase, ModelResponse, ModelResponseStream, ProviderField, StreamingChoices, - Delta, ) from ..common_utils import OllamaError, _convert_image @@ -92,9 +92,9 @@ class OllamaConfig(BaseConfig): repeat_penalty: Optional[float] = None temperature: Optional[float] = None seed: Optional[int] = None - stop: Optional[ - list - ] = None # stop is a list based on this - https://github.com/ollama/ollama/pull/442 + stop: Optional[list] = ( + None # stop is a list based on this - https://github.com/ollama/ollama/pull/442 + ) tfs_z: Optional[float] = None num_predict: Optional[int] = None top_k: Optional[int] = None @@ -154,6 +154,7 @@ class OllamaConfig(BaseConfig): "stop", "response_format", "max_completion_tokens", + "reasoning_effort", ] def map_openai_params( @@ -166,19 +167,21 @@ class OllamaConfig(BaseConfig): for param, value in non_default_params.items(): if param == "max_tokens" or param == "max_completion_tokens": optional_params["num_predict"] = value - if param == "stream": + elif param == "stream": optional_params["stream"] = value - if param == "temperature": + elif param == "temperature": optional_params["temperature"] = value - if param == "seed": + elif param == "seed": optional_params["seed"] = value - if param == "top_p": + elif param == "top_p": optional_params["top_p"] = value - if param == "frequency_penalty": + elif param == "frequency_penalty": optional_params["frequency_penalty"] = value - if param == "stop": + elif param == "stop": optional_params["stop"] = value - if param == "response_format" and isinstance(value, dict): + elif param == "reasoning_effort" and value is not None: + optional_params["think"] = True + elif param == "response_format" and isinstance(value, dict): if value["type"] == "json_object": optional_params["format"] = "json" elif value["type"] == "json_schema": @@ -201,6 +204,21 @@ class OllamaConfig(BaseConfig): return v return None + @staticmethod + def get_api_key() -> Optional[str]: + """Get API key from environment variables or litellm configuration""" + import os + + import litellm + from litellm.secret_managers.main import get_secret_str + + return ( + os.environ.get("OLLAMA_API_KEY") + or litellm.api_key + or litellm.openai_key + or get_secret_str("OLLAMA_API_KEY") + ) + def get_model_info(self, model: str) -> ModelInfoBase: """ curl http://localhost:11434/api/show -d '{ @@ -210,11 +228,14 @@ class OllamaConfig(BaseConfig): if model.startswith("ollama/") or model.startswith("ollama_chat/"): model = model.split("/", 1)[1] api_base = get_secret_str("OLLAMA_API_BASE") or "http://localhost:11434" + api_key = self.get_api_key() + headers = {"Authorization": f"Bearer {api_key}"} if api_key else {} try: response = litellm.module_level_client.post( url=f"{api_base}/api/show", json={"name": model}, + headers=headers, ) except Exception as e: raise Exception( @@ -258,12 +279,17 @@ class OllamaConfig(BaseConfig): api_key: Optional[str] = None, json_mode: Optional[bool] = None, ) -> ModelResponse: + from litellm.litellm_core_utils.prompt_templates.common_utils import ( + _parse_content_for_reasoning, + ) + response_json = raw_response.json() ## RESPONSE OBJECT model_response.choices[0].finish_reason = "stop" if request_data.get("format", "") == "json": # Check if response field exists and is not empty before parsing JSON response_text = response_json.get("response", "") + if not response_text or not response_text.strip(): # Handle empty response gracefully - set empty content message = litellm.Message(content="") @@ -288,7 +314,9 @@ class OllamaConfig(BaseConfig): "id": f"call_{str(uuid.uuid4())}", "function": { "name": function_call["name"], - "arguments": json.dumps(function_call["arguments"]), + "arguments": json.dumps( + function_call["arguments"] + ), }, "type": "function", } @@ -305,11 +333,28 @@ class OllamaConfig(BaseConfig): model_response.choices[0].finish_reason = "stop" except json.JSONDecodeError: # If JSON parsing fails, treat as regular text response - message = litellm.Message(content=response_text) + ## output parse reasoning content from response_text + reasoning_content: Optional[str] = None + content: Optional[str] = None + if response_text is not None: + reasoning_content, content = _parse_content_for_reasoning( + response_text + ) + message = litellm.Message( + content=content, reasoning_content=reasoning_content + ) model_response.choices[0].message = message # type: ignore model_response.choices[0].finish_reason = "stop" else: - model_response.choices[0].message.content = response_json["response"] # type: ignore + response_text = response_json.get("response", "") + content = None + reasoning_content = None + if response_text is not None and isinstance(response_text, str): + reasoning_content, content = _parse_content_for_reasoning(response_text) + else: + content = response_text # type: ignore + model_response.choices[0].message.content = content # type: ignore + model_response.choices[0].message.reasoning_content = reasoning_content # type: ignore model_response.created = int(time.time()) model_response.model = "ollama/" + model _prompt = request_data.get("prompt", "") @@ -434,12 +479,21 @@ class OllamaConfig(BaseConfig): class OllamaTextCompletionResponseIterator(BaseModelResponseIterator): + def __init__( + self, streaming_response, sync_stream: bool, json_mode: Optional[bool] = False + ): + super().__init__(streaming_response, sync_stream, json_mode) + self.started_reasoning_content: bool = False + self.finished_reasoning_content: bool = False + def _handle_string_chunk( self, str_line: str ) -> Union[GenericStreamingChunk, ModelResponseStream]: return self.chunk_parser(json.loads(str_line)) - def chunk_parser(self, chunk: dict) -> Union[GenericStreamingChunk, ModelResponseStream]: + def chunk_parser( + self, chunk: dict + ) -> Union[GenericStreamingChunk, ModelResponseStream]: try: if "error" in chunk: raise Exception(f"Ollama Error - {chunk}") @@ -469,12 +523,42 @@ class OllamaTextCompletionResponseIterator(BaseModelResponseIterator): ) elif chunk["response"]: text = chunk["response"] - return GenericStreamingChunk( - text=text, - is_finished=is_finished, - finish_reason="stop", + reasoning_content: Optional[str] = None + content: Optional[str] = None + if text is not None: + if "" in text: + text = text.replace("", "") + self.started_reasoning_content = True + elif "" in text: + text = text.replace("", "") + self.finished_reasoning_content = True + + if ( + self.started_reasoning_content + and not self.finished_reasoning_content + ): + reasoning_content = text + else: + content = text + + return ModelResponseStream( + choices=[ + StreamingChoices( + index=0, + delta=Delta( + reasoning_content=reasoning_content, content=content + ), + ) + ], + finish_reason=finish_reason, usage=None, ) + # return GenericStreamingChunk( + # text=text, + # is_finished=is_finished, + # finish_reason="stop", + # usage=None, + # ) elif "thinking" in chunk and not chunk["response"]: # Return reasoning content as ModelResponseStream so UIs can render it thinking_content = chunk.get("thinking") or "" diff --git a/litellm/llms/ollama_chat.py b/litellm/llms/ollama_chat.py index d46e7145194..082312d28f2 100644 --- a/litellm/llms/ollama_chat.py +++ b/litellm/llms/ollama_chat.py @@ -1,6 +1,6 @@ import json import time -import uuid +from litellm._uuid import uuid from typing import Any, List, Optional, Union import aiohttp diff --git a/litellm/llms/openai/chat/gpt_5_transformation.py b/litellm/llms/openai/chat/gpt_5_transformation.py index 9a8bb74d447..3902304a3b4 100644 --- a/litellm/llms/openai/chat/gpt_5_transformation.py +++ b/litellm/llms/openai/chat/gpt_5_transformation.py @@ -28,7 +28,18 @@ class OpenAIGPT5Config(OpenAIGPTConfig): base_gpt_series_params.extend(gpt_5_only_params) if not supports_tool_choice(model=model): base_gpt_series_params.remove("tool_choice") - return base_gpt_series_params + + non_supported_params = [ + "logprobs", + "top_p", + "presence_penalty", + "frequency_penalty", + "top_logprobs", + ] + + return [ + param for param in base_gpt_series_params if param not in non_supported_params + ] def map_openai_params( self, diff --git a/litellm/llms/openai/chat/gpt_transformation.py b/litellm/llms/openai/chat/gpt_transformation.py index f043ef40521..204916e3a48 100644 --- a/litellm/llms/openai/chat/gpt_transformation.py +++ b/litellm/llms/openai/chat/gpt_transformation.py @@ -158,6 +158,7 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig): "parallel_tool_calls", "audio", "web_search_options", + "safety_identifier", ] # works across all models model_specific_params = [] @@ -348,6 +349,7 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig): for message in messages: message_content = message.get("content") message_role = message.get("role") + if ( message_role == "user" and message_content diff --git a/litellm/llms/openai/common_utils.py b/litellm/llms/openai/common_utils.py index aa670df0531..00197a67e95 100644 --- a/litellm/llms/openai/common_utils.py +++ b/litellm/llms/openai/common_utils.py @@ -5,12 +5,15 @@ Common helpers / utils across al OpenAI endpoints import hashlib import json import ssl -from typing import Any, Dict, List, Literal, Optional, Union +from typing import Any, Dict, List, Literal, Optional, TYPE_CHECKING, Union import httpx import openai from openai import AsyncAzureOpenAI, AsyncOpenAI, AzureOpenAI, OpenAI +if TYPE_CHECKING: + from aiohttp import ClientSession + import litellm from litellm.llms.base_llm.chat.transformation import BaseLLMException from litellm.llms.custom_httpx.http_handler import ( @@ -194,7 +197,9 @@ class BaseOpenAILLM: return param_names @staticmethod - def _get_async_http_client() -> Optional[httpx.AsyncClient]: + def _get_async_http_client( + shared_session: Optional["ClientSession"] = None, + ) -> Optional[httpx.AsyncClient]: if litellm.aclient_session is not None: return litellm.aclient_session @@ -205,8 +210,11 @@ class BaseOpenAILLM: limits=httpx.Limits(max_connections=1000, max_keepalive_connections=100), verify=ssl_config, transport=AsyncHTTPHandler._create_async_transport( - ssl_context=ssl_config if isinstance(ssl_config, ssl.SSLContext) else None, + ssl_context=ssl_config + if isinstance(ssl_config, ssl.SSLContext) + else None, ssl_verify=ssl_config if isinstance(ssl_config, bool) else None, + shared_session=shared_session, ), follow_redirects=True, ) @@ -215,10 +223,10 @@ class BaseOpenAILLM: def _get_sync_http_client() -> Optional[httpx.Client]: if litellm.client_session is not None: return litellm.client_session - + # Get unified SSL configuration ssl_config = get_ssl_configuration() - + return httpx.Client( limits=httpx.Limits(max_connections=1000, max_keepalive_connections=100), verify=ssl_config, diff --git a/litellm/llms/openai/cost_calculation.py b/litellm/llms/openai/cost_calculation.py index 304c444e37a..229f75f2657 100644 --- a/litellm/llms/openai/cost_calculation.py +++ b/litellm/llms/openai/cost_calculation.py @@ -18,7 +18,7 @@ def cost_router(call_type: CallTypes) -> Literal["cost_per_token", "cost_per_sec return "cost_per_token" -def cost_per_token(model: str, usage: Usage) -> Tuple[float, float]: +def cost_per_token(model: str, usage: Usage, service_tier: Optional[str] = None) -> Tuple[float, float]: """ Calculates the cost per token for a given model, prompt tokens, and completion tokens. @@ -31,7 +31,7 @@ def cost_per_token(model: str, usage: Usage) -> Tuple[float, float]: """ ## CALCULATE INPUT COST return generic_cost_per_token( - model=model, usage=usage, custom_llm_provider="openai" + model=model, usage=usage, custom_llm_provider="openai", service_tier=service_tier ) # ### Non-cached text tokens # non_cached_text_tokens = usage.prompt_tokens diff --git a/litellm/llms/openai/image_edit/transformation.py b/litellm/llms/openai/image_edit/transformation.py index c8a1e8f0e1c..f6898c3a762 100644 --- a/litellm/llms/openai/image_edit/transformation.py +++ b/litellm/llms/openai/image_edit/transformation.py @@ -80,24 +80,47 @@ class OpenAIImageEditConfig(BaseImageEditConfig): request_dict = cast(Dict, request) ######################################################### - # Separate images as `files` and send other parameters as `data` + # Separate images and masks as `files` and send other parameters as `data` ######################################################### - _images = request_dict.get("image") or [] - data_without_images = {k: v for k, v in request_dict.items() if k != "image"} + _image_list = request_dict.get("image") + _mask = request_dict.get("mask") + data_without_files = { + k: v for k, v in request_dict.items() if k not in ["image", "mask"] + } files_list: List[Tuple[str, Any]] = [] - for _image in _images: - image_content_type: str = ImageEditRequestUtils.get_image_content_type( - _image - ) - if isinstance(_image, BufferedReader): - files_list.append( - ("image[]", (_image.name, _image, image_content_type)) + + # Handle image parameter + if _image_list is not None: + image_list = [_image_list] if not isinstance(_image_list, list) else _image_list + for _image in image_list: + if _image is not None: + image_content_type: str = ImageEditRequestUtils.get_image_content_type( + _image + ) + if isinstance(_image, BufferedReader): + files_list.append( + ("image", (_image.name, _image, image_content_type)) + ) + else: + files_list.append( + ("image", ("image.png", _image, image_content_type)) + ) + + # Handle mask parameter if provided + if _mask is not None: + # Handle case where mask can be a list (extract first mask) + if isinstance(_mask, list): + _mask = _mask[0] if _mask else None + + if _mask is not None: + mask_content_type: str = ImageEditRequestUtils.get_image_content_type( + _mask ) - else: - files_list.append( - ("image[]", ("image.png", _image, image_content_type)) - ) - return data_without_images, files_list + if isinstance(_mask, BufferedReader): + files_list.append(("mask", (_mask.name, _mask, mask_content_type))) + else: + files_list.append(("mask", ("mask.png", _mask, mask_content_type))) + return data_without_files, files_list def transform_image_edit_response( self, diff --git a/litellm/llms/openai/image_generation/gpt_transformation.py b/litellm/llms/openai/image_generation/gpt_transformation.py index 150cffba21c..1cee13784e7 100644 --- a/litellm/llms/openai/image_generation/gpt_transformation.py +++ b/litellm/llms/openai/image_generation/gpt_transformation.py @@ -16,7 +16,6 @@ class GPTImageGenerationConfig(BaseImageGenerationConfig): ) -> List[OpenAIImageGenerationOptionalParams]: return [ "background", - "input_fidelity", "moderation", "n", "output_compression", diff --git a/litellm/llms/openai/openai.py b/litellm/llms/openai/openai.py index 1f3cf24457d..3347e533242 100644 --- a/litellm/llms/openai/openai.py +++ b/litellm/llms/openai/openai.py @@ -10,12 +10,16 @@ from typing import ( List, Literal, Optional, + TYPE_CHECKING, Union, cast, ) from urllib.parse import urlparse import httpx + +if TYPE_CHECKING: + from aiohttp import ClientSession import openai from openai import AsyncOpenAI, OpenAI from openai.types.beta.assistant_deleted import AssistantDeleted @@ -355,6 +359,7 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): max_retries: Optional[int] = DEFAULT_MAX_RETRIES, organization: Optional[str] = None, client: Optional[Union[OpenAI, AsyncOpenAI]] = None, + shared_session: Optional["ClientSession"] = None, ) -> Optional[Union[OpenAI, AsyncOpenAI]]: client_initialization_params: Dict = locals() if client is None: @@ -379,7 +384,9 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): _new_client: Union[OpenAI, AsyncOpenAI] = AsyncOpenAI( api_key=api_key, base_url=api_base, - http_client=OpenAIChatCompletion._get_async_http_client(), + http_client=OpenAIChatCompletion._get_async_http_client( + shared_session=shared_session + ), timeout=timeout, max_retries=max_retries, organization=organization, @@ -522,8 +529,9 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): organization: Optional[str] = None, custom_llm_provider: Optional[str] = None, drop_params: Optional[bool] = None, + shared_session: Optional["ClientSession"] = None, ): - super().completion() + super().completion(shared_session=shared_session) try: fake_stream: bool = False inference_params = optional_params.copy() @@ -606,6 +614,7 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): organization=organization, drop_params=drop_params, fake_stream=fake_stream, + shared_session=shared_session, ) data = provider_config.transform_request( @@ -771,6 +780,7 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): drop_params: Optional[bool] = None, stream_options: Optional[dict] = None, fake_stream: bool = False, + shared_session: Optional["ClientSession"] = None, ): response = None data = await provider_config.async_transform_request( @@ -793,6 +803,7 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): max_retries=max_retries, organization=organization, client=client, + shared_session=shared_session, ) ## LOGGING diff --git a/litellm/llms/openai/responses/transformation.py b/litellm/llms/openai/responses/transformation.py index 501941fdc59..25078267571 100644 --- a/litellm/llms/openai/responses/transformation.py +++ b/litellm/llms/openai/responses/transformation.py @@ -1,6 +1,15 @@ -from typing import TYPE_CHECKING, Any, Dict, Optional, Union, cast +from typing import ( + TYPE_CHECKING, + Any, + Dict, + Optional, + Union, + cast, + get_type_hints, +) import httpx +from openai.types.responses import ResponseReasoningItem from pydantic import BaseModel import litellm @@ -13,6 +22,7 @@ from litellm.secret_managers.main import get_secret_str from litellm.types.llms.openai import * from litellm.types.responses.main import * from litellm.types.router import GenericLiteLLMParams +from litellm.types.utils import LlmProviders from ..common_utils import OpenAIError @@ -25,38 +35,28 @@ else: class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): + @property + def custom_llm_provider(self) -> LlmProviders: + return LlmProviders.OPENAI + def get_supported_openai_params(self, model: str) -> list: """ All OpenAI Responses API params are supported """ - return [ - "input", - "model", - "include", - "instructions", - "max_output_tokens", - "metadata", - "parallel_tool_calls", - "previous_response_id", - "reasoning", - "store", - "background", - "stream", - "prompt", - "temperature", - "text", - "tool_choice", - "tools", - "top_p", - "truncation", - "user", - "service_tier", - "safety_identifier", - "extra_headers", - "extra_query", - "extra_body", - "timeout", - ] + supported_params = get_type_hints(ResponsesAPIRequestParams).keys() + return list( + set( + [ + "input", + "model", + "extra_headers", + "extra_query", + "extra_body", + "timeout", + ] + + list(supported_params) + ) + ) def map_openai_params( self, @@ -85,8 +85,10 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): ) return final_request_params - - def _validate_input_param(self, input: Union[str, ResponseInputParam]) -> Union[str, ResponseInputParam]: + + def _validate_input_param( + self, input: Union[str, ResponseInputParam] + ) -> Union[str, ResponseInputParam]: """ Ensure all input fields if pydantic are converted to dict @@ -99,12 +101,67 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): # if it's pydantic, convert to dict if isinstance(item, BaseModel): validated_input.append(item.model_dump(exclude_none=True)) + elif isinstance(item, dict): + # Handle reasoning items specifically to filter out status=None + verbose_logger.debug(f"Handling reasoning item: {item}") + if item.get("type") == "reasoning": + # Type assertion since we know it's a dict at this point + dict_item = cast(Dict[str, Any], item) + filtered_item = self._handle_reasoning_item(dict_item) + else: + # For other dict items, just pass through + filtered_item = cast(Dict[str, Any], item) + validated_input.append(filtered_item) else: validated_input.append(item) - return validated_input + return validated_input # type: ignore # Input is expected to be either str or List, no single BaseModel expected return input + def _handle_reasoning_item(self, item: Dict[str, Any]) -> Dict[str, Any]: + """ + Handle reasoning items specifically to filter out status=None using OpenAI's model. + Issue: https://github.com/BerriAI/litellm/issues/13484 + OpenAI API does not accept ReasoningItem(status=None), so we need to: + 1. Check if the item is a reasoning type + 2. Create a ResponseReasoningItem object with the item data + 3. Convert it back to dict with exclude_none=True to filter None values + """ + verbose_logger.debug(f"Handling reasoning item: {item}") + if item.get("type") == "reasoning": + try: + # Ensure required fields are present for ResponseReasoningItem + item_data = dict(item) + if "id" not in item_data: + item_data["id"] = f"reasoning_{hash(str(item_data))}" + if "summary" not in item_data: + item_data["summary"] = ( + item_data.get("reasoning_content", "")[:100] + "..." + if len(item_data.get("reasoning_content", "")) > 100 + else item_data.get("reasoning_content", "") + ) + + # Create ResponseReasoningItem object from the item data + reasoning_item = ResponseReasoningItem(**item_data) + + # Convert back to dict with exclude_none=True to exclude None fields + dict_reasoning_item = reasoning_item.model_dump(exclude_none=True) + + return dict_reasoning_item + except Exception as e: + verbose_logger.debug( + f"Failed to create ResponseReasoningItem, falling back to manual filtering: {e}" + ) + # Fallback: manually filter out known None fields + filtered_item = { + k: v + for k, v in item.items() + if v is not None + or k not in {"status", "content", "encrypted_content"} + } + return filtered_item + return item + def transform_response_api_response( self, model: str, @@ -114,7 +171,9 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): """No transform applied since outputs are in OpenAI spec already""" try: raw_response_json = raw_response.json() - raw_response_json["created_at"] = _safe_convert_created_field(raw_response_json["created_at"]) + raw_response_json["created_at"] = _safe_convert_created_field( + raw_response_json["created_at"] + ) except Exception: raise OpenAIError( message=raw_response.text, status_code=raw_response.status_code @@ -213,6 +272,14 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): ResponsesAPIStreamEvents.WEB_SEARCH_CALL_IN_PROGRESS: WebSearchCallInProgressEvent, ResponsesAPIStreamEvents.WEB_SEARCH_CALL_SEARCHING: WebSearchCallSearchingEvent, ResponsesAPIStreamEvents.WEB_SEARCH_CALL_COMPLETED: WebSearchCallCompletedEvent, + ResponsesAPIStreamEvents.MCP_LIST_TOOLS_IN_PROGRESS: MCPListToolsInProgressEvent, + ResponsesAPIStreamEvents.MCP_LIST_TOOLS_COMPLETED: MCPListToolsCompletedEvent, + ResponsesAPIStreamEvents.MCP_LIST_TOOLS_FAILED: MCPListToolsFailedEvent, + ResponsesAPIStreamEvents.MCP_CALL_IN_PROGRESS: MCPCallInProgressEvent, + ResponsesAPIStreamEvents.MCP_CALL_ARGUMENTS_DELTA: MCPCallArgumentsDeltaEvent, + ResponsesAPIStreamEvents.MCP_CALL_ARGUMENTS_DONE: MCPCallArgumentsDoneEvent, + ResponsesAPIStreamEvents.MCP_CALL_COMPLETED: MCPCallCompletedEvent, + ResponsesAPIStreamEvents.MCP_CALL_FAILED: MCPCallFailedEvent, ResponsesAPIStreamEvents.ERROR: ErrorEvent, } @@ -358,3 +425,39 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): raise OpenAIError( message=raw_response.text, status_code=raw_response.status_code ) + + ######################################################### + ########## CANCEL RESPONSE API TRANSFORMATION ########## + ######################################################### + def transform_cancel_response_api_request( + self, + response_id: str, + api_base: str, + litellm_params: GenericLiteLLMParams, + headers: dict, + ) -> Tuple[str, Dict]: + """ + Transform the cancel response API request into a URL and data + + OpenAI API expects the following request + - POST /v1/responses/{response_id}/cancel + """ + url = f"{api_base}/{response_id}/cancel" + data: Dict = {} + return url, data + + def transform_cancel_response_api_response( + self, + raw_response: httpx.Response, + logging_obj: LiteLLMLoggingObj, + ) -> ResponsesAPIResponse: + """ + Transform the cancel response API response into a ResponsesAPIResponse + """ + try: + raw_response_json = raw_response.json() + except Exception: + raise OpenAIError( + message=raw_response.text, status_code=raw_response.status_code + ) + return ResponsesAPIResponse(**raw_response_json) diff --git a/litellm/llms/openai/transcriptions/gpt_transformation.py b/litellm/llms/openai/transcriptions/gpt_transformation.py index 796e10f5153..34621c44e22 100644 --- a/litellm/llms/openai/transcriptions/gpt_transformation.py +++ b/litellm/llms/openai/transcriptions/gpt_transformation.py @@ -1,5 +1,8 @@ from typing import List +from litellm.llms.base_llm.audio_transcription.transformation import ( + AudioTranscriptionRequestData, +) from litellm.types.llms.openai import OpenAIAudioTranscriptionOptionalParams from litellm.types.utils import FileTypes @@ -27,8 +30,12 @@ class OpenAIGPTAudioTranscriptionConfig(OpenAIWhisperAudioTranscriptionConfig): audio_file: FileTypes, optional_params: dict, litellm_params: dict, - ) -> dict: + ) -> AudioTranscriptionRequestData: """ Transform the audio transcription request """ - return {"model": model, "file": audio_file, **optional_params} + data = {"model": model, "file": audio_file, **optional_params} + + return AudioTranscriptionRequestData( + data=data, + ) diff --git a/litellm/llms/openai/transcriptions/handler.py b/litellm/llms/openai/transcriptions/handler.py index 4fe48dd3c6c..19b303bb968 100644 --- a/litellm/llms/openai/transcriptions/handler.py +++ b/litellm/llms/openai/transcriptions/handler.py @@ -1,4 +1,4 @@ -from typing import Optional, Union +from typing import Optional, Union, cast import httpx from openai import AsyncOpenAI, OpenAI @@ -34,6 +34,7 @@ class OpenAIAudioTranscription(OpenAIChatCompletion): - call openai_aclient.audio.transcriptions.create by default """ try: + raw_response = ( await openai_aclient.audio.transcriptions.with_raw_response.create( **data, timeout=timeout @@ -93,15 +94,14 @@ class OpenAIAudioTranscription(OpenAIChatCompletion): Handle audio transcription request """ if provider_config is not None: - data = provider_config.transform_audio_transcription_request( + transformed_data = provider_config.transform_audio_transcription_request( model=model, audio_file=audio_file, optional_params=optional_params, litellm_params=litellm_params, ) - if not isinstance(data, dict): - raise ValueError("OpenAI transformation route requires a dict") + data = cast(dict, transformed_data.data) else: data = {"model": model, "file": audio_file, **optional_params} diff --git a/litellm/llms/openai/transcriptions/whisper_transformation.py b/litellm/llms/openai/transcriptions/whisper_transformation.py index c0ccc71579f..fa507e1bc26 100644 --- a/litellm/llms/openai/transcriptions/whisper_transformation.py +++ b/litellm/llms/openai/transcriptions/whisper_transformation.py @@ -1,8 +1,9 @@ from typing import List, Optional, Union -from httpx import Headers +from httpx import Headers, Response from litellm.llms.base_llm.audio_transcription.transformation import ( + AudioTranscriptionRequestData, BaseAudioTranscriptionConfig, ) from litellm.llms.base_llm.chat.transformation import BaseLLMException @@ -11,12 +12,40 @@ from litellm.types.llms.openai import ( AllMessageValues, OpenAIAudioTranscriptionOptionalParams, ) -from litellm.types.utils import FileTypes +from litellm.types.utils import FileTypes, TranscriptionResponse from ..common_utils import OpenAIError class OpenAIWhisperAudioTranscriptionConfig(BaseAudioTranscriptionConfig): + def get_complete_url( + self, + api_base: Optional[str], + api_key: Optional[str], + model: str, + optional_params: dict, + litellm_params: dict, + stream: Optional[bool] = None, + ) -> str: + """ + OPTIONAL + + Get the complete url for the request + + Some providers need `model` in `api_base` + """ + ## get the api base, attach the endpoint - v1/audio/transcriptions + # strip trailing slash if present + api_base = api_base.rstrip("/") if api_base else "" + + # if endswith "/v1" + if api_base and api_base.endswith("/v1"): + api_base = f"{api_base}/audio/transcriptions" + else: + api_base = f"{api_base}/v1/audio/transcriptions" + + return api_base or "" + def get_supported_openai_params( self, model: str ) -> List[OpenAIAudioTranscriptionOptionalParams]: @@ -72,21 +101,22 @@ class OpenAIWhisperAudioTranscriptionConfig(BaseAudioTranscriptionConfig): audio_file: FileTypes, optional_params: dict, litellm_params: dict, - ) -> dict: + ) -> AudioTranscriptionRequestData: """ Transform the audio transcription request """ - data = {"model": model, "file": audio_file, **optional_params} if "response_format" not in data or ( data["response_format"] == "text" or data["response_format"] == "json" ): - data[ - "response_format" - ] = "verbose_json" # ensures 'duration' is received - used for cost calculation + data["response_format"] = ( + "verbose_json" # ensures 'duration' is received - used for cost calculation + ) - return data + return AudioTranscriptionRequestData( + data=data, + ) def get_error_class( self, error_message: str, status_code: int, headers: Union[dict, Headers] @@ -96,3 +126,25 @@ class OpenAIWhisperAudioTranscriptionConfig(BaseAudioTranscriptionConfig): message=error_message, headers=headers, ) + + def transform_audio_transcription_response( + self, + raw_response: Response, + ) -> TranscriptionResponse: + try: + raw_response_json = raw_response.json() + except Exception as e: + raise ValueError( + f"Error transforming response to json: {str(e)}\nResponse: {raw_response.text}" + ) + + if any( + key in raw_response_json + for key in TranscriptionResponse.model_fields.keys() + ): + return TranscriptionResponse(**raw_response_json) + else: + raise ValueError( + "Invalid response format. Received response does not match the expected format. Got: ", + raw_response_json, + ) diff --git a/litellm/llms/openai_like/chat/handler.py b/litellm/llms/openai_like/chat/handler.py index ae0a3cc3551..821fc9b7f15 100644 --- a/litellm/llms/openai_like/chat/handler.py +++ b/litellm/llms/openai_like/chat/handler.py @@ -49,15 +49,8 @@ async def make_call( model_response = ModelResponse(**response.json()) completion_stream = MockResponseIterator(model_response=model_response) else: - # Use aiter_text with explicit UTF-8 encoding to avoid ASCII encoding errors - async def utf8_aiter_lines(): - async for line in response.aiter_text(encoding='utf-8'): - for line_part in line.splitlines(keepends=True): - if line_part.strip(): - yield line_part.rstrip('\r\n') - completion_stream = ModelResponseIterator( - streaming_response=utf8_aiter_lines(), sync_stream=False + streaming_response=response.aiter_lines(), sync_stream=False ) # LOGGING logging_obj.post_call( @@ -100,15 +93,8 @@ def make_sync_call( model_response = ModelResponse(**response.json()) completion_stream = MockResponseIterator(model_response=model_response) else: - # Use iter_text with explicit UTF-8 encoding to avoid ASCII encoding errors - def utf8_iter_lines(): - for line in response.iter_text(encoding='utf-8'): - for line_part in line.splitlines(keepends=True): - if line_part.strip(): - yield line_part.rstrip('\r\n') - completion_stream = ModelResponseIterator( - streaming_response=utf8_iter_lines(), sync_stream=True + streaming_response=response.iter_lines(), sync_stream=True ) # LOGGING diff --git a/litellm/llms/ovhcloud/chat/transformation.py b/litellm/llms/ovhcloud/chat/transformation.py new file mode 100644 index 00000000000..6bdc28620ff --- /dev/null +++ b/litellm/llms/ovhcloud/chat/transformation.py @@ -0,0 +1,141 @@ +""" +Support for OVHCloud AI Endpoints `/v1/chat/completions` endpoint. + +Our unified API follows the OpenAI standard. +More information on our website: https://endpoints.ai.cloud.ovh.net +""" +from typing import Optional, Union, List + +import httpx +from litellm import ModelResponseStream, OpenAIGPTConfig, get_model_info, verbose_logger +from litellm.llms.ovhcloud.utils import OVHCloudException +from litellm.llms.base_llm.base_model_iterator import BaseModelResponseIterator +from litellm.llms.base_llm.chat.transformation import BaseLLMException +from litellm.types.llms.openai import AllMessageValues + +class OVHCloudChatConfig(OpenAIGPTConfig): + @property + def custom_llm_provider(self) -> Optional[str]: + return "ovhcloud" + + def get_supported_openai_params(self, model: str) -> list: + """ + Details about function calling support can be found here: + https://help.ovhcloud.com/csm/en-gb-public-cloud-ai-endpoints-function-calling?id=kb_article_view&sysparm_article=KB0071907 + """ + supports_function_calling: Optional[bool] = None + try: + model_info = get_model_info(model, custom_llm_provider="ovhcloud") + supports_function_calling = model_info.get( + "supports_function_calling", False + ) + except Exception as e: + verbose_logger.debug(f"Error getting supported OpenAI params: {e}") + pass + + optional_params = super().get_supported_openai_params(model) + if supports_function_calling is not True: + verbose_logger.debug( + "You can see our models supporting function_calling in our catalog: https://endpoints.ai.cloud.ovh.net/catalog " + ) + optional_params.remove("tools") + optional_params.remove("tool_choice") + optional_params.remove("function_call") + optional_params.remove("response_format") + return optional_params + + def get_complete_url( + self, + api_base: Optional[str], + api_key: Optional[str], + model: str, + optional_params: dict, + litellm_params: dict, + stream: Optional[bool] = None, + ) -> str: + api_base = "https://oai.endpoints.kepler.ai.cloud.ovh.net/v1" if api_base is None else api_base.rstrip("/") + complete_url = f"{api_base}/chat/completions" + return complete_url + + def get_error_class( + self, + error_message: str, + status_code: int, + headers: Union[dict, httpx.Headers] + ) -> BaseLLMException: + return OVHCloudException( + message=error_message, + status_code=status_code, + headers=headers, + ) + + def map_openai_params( + self, + non_default_params: dict, + optional_params: dict, + model: str, + drop_params: bool, + ) -> dict: + mapped_openai_params = super().map_openai_params( + non_default_params, optional_params, model, drop_params + ) + return mapped_openai_params + + def transform_request( + self, + model: str, + messages: List[AllMessageValues], + optional_params: dict, + litellm_params: dict, + headers: dict, + ) -> dict: + extra_body = optional_params.pop("extra_body", {}) + response = super().transform_request( + model, messages, optional_params, litellm_params, headers + ) + response.update(extra_body) + return response + +class OVHCloudChatCompletionStreamingHandler(BaseModelResponseIterator): + """ + Handler for OVHCloud AI Endpoints streaming chat completion responses + """ + + def chunk_parser(self, chunk: dict) -> ModelResponseStream: + """ + Parse individual chunks from streaming response + """ + try: + if "error" in chunk: + error_chunk = chunk["error"] + error_message = "OVHCloud Error: {}".format( + error_chunk.get("message", "Unknown error") + ) + raise OVHCloudException( + message=error_message, + status_code=error_chunk.get("code", 400), + headers={"Content-Type": "application/json"}, + ) + + new_choices = [] + for choice in chunk["choices"]: + if "delta" in choice and "reasoning" in choice["delta"]: + choice["delta"]["reasoning_content"] = choice["delta"].get("reasoning") + new_choices.append(choice) + + return ModelResponseStream( + id=chunk["id"], + object="chat.completion.chunk", + created=chunk["created"], + usage=chunk.get("usage"), + model=chunk["model"], + choices=new_choices, + ) + except KeyError as e: + raise OVHCloudException( + message=f"KeyError: {e}, Got unexpected response from CometAPI: {chunk}", + status_code=400, + headers={"Content-Type": "application/json"}, + ) + except Exception as e: + raise e \ No newline at end of file diff --git a/litellm/llms/ovhcloud/embedding/transformation.py b/litellm/llms/ovhcloud/embedding/transformation.py new file mode 100644 index 00000000000..1266f74c0a2 --- /dev/null +++ b/litellm/llms/ovhcloud/embedding/transformation.py @@ -0,0 +1,122 @@ +""" +This is OpenAI compatible - no transformation is applied + +""" +from typing import List, Optional, Union + +import httpx + +from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj +from litellm.llms.base_llm.chat.transformation import BaseLLMException +from litellm.llms.base_llm.embedding.transformation import BaseEmbeddingConfig +from litellm.secret_managers.main import get_secret_str +from litellm.types.llms.openai import AllEmbeddingInputValues, AllMessageValues +from litellm.types.utils import EmbeddingResponse, Usage + +from ..utils import OVHCloudException + + +class OVHCloudEmbeddingConfig(BaseEmbeddingConfig): + def __init__(self) -> None: + pass + + def get_complete_url( + self, + api_base: Optional[str], + api_key: Optional[str], + model: str, + optional_params: dict, + litellm_params: dict, + stream: Optional[bool] = None, + ) -> str: + api_base = "https://oai.endpoints.kepler.ai.cloud.ovh.net/v1" if api_base is None else api_base.rstrip("/") + complete_url = f"{api_base}/embeddings" + return complete_url + + def validate_environment( + self, + headers: dict, + model: str, + messages: List[AllMessageValues], + optional_params: dict, + litellm_params: dict, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + ) -> dict: + if api_key is None: + api_key = get_secret_str("OVHCLOUD_API_KEY") + + default_headers = { + "Authorization": f"Bearer {api_key}", + "accept": "application/json", + "Content-Type": "application/json", + } + + if "Authorization" in headers: + default_headers["Authorization"] = headers["Authorization"] + + return {**default_headers, **headers} + + def get_supported_openai_params(self, model: str): + return [] + + def map_openai_params( + self, + non_default_params: dict, + optional_params: dict, + model: str, + drop_params: bool, + ): + supported_openai_params = self.get_supported_openai_params(model) + for param, value in non_default_params.items(): + if param in supported_openai_params: + optional_params[param] = value + return optional_params + + def transform_embedding_request( + self, + model: str, + input: AllEmbeddingInputValues, + optional_params: dict, + headers: dict, + ) -> dict: + return {"input": input, "model": model, **optional_params} + + def transform_embedding_response( + self, + model: str, + raw_response: httpx.Response, + model_response: EmbeddingResponse, + logging_obj: LiteLLMLoggingObj, + api_key: Optional[str], + request_data: dict, + optional_params: dict, + litellm_params: dict, + ) -> EmbeddingResponse: + try: + raw_response_json = raw_response.json() + except Exception: + raise OVHCloudException( + message=raw_response.text, + status_code=raw_response.status_code, + headers=raw_response.headers, + ) + + model_response.model = raw_response_json.get("model") + model_response.data = raw_response_json.get("data") + model_response.object = raw_response_json.get("object") + + usage = Usage( + prompt_tokens=raw_response_json.get("usage", {}).get("prompt_tokens", 0), + total_tokens=raw_response_json.get("usage", {}).get("total_tokens", 0), + ) + + model_response.usage = usage + return model_response + + def get_error_class( + self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers] + ) -> BaseLLMException: + return OVHCloudException( + message=error_message, status_code=status_code, headers=headers + ) diff --git a/litellm/llms/ovhcloud/utils.py b/litellm/llms/ovhcloud/utils.py new file mode 100644 index 00000000000..9ae4dfb1efd --- /dev/null +++ b/litellm/llms/ovhcloud/utils.py @@ -0,0 +1,6 @@ +from litellm.llms.base_llm.chat.transformation import BaseLLMException + + +class OVHCloudException(BaseLLMException): + """OVHCloud AI Endpoints exception handling class""" + pass \ No newline at end of file diff --git a/litellm/llms/together_ai/rerank/transformation.py b/litellm/llms/together_ai/rerank/transformation.py index 1fdb772adde..63b593dfe42 100644 --- a/litellm/llms/together_ai/rerank/transformation.py +++ b/litellm/llms/together_ai/rerank/transformation.py @@ -4,7 +4,7 @@ Transformation logic from Cohere's /v1/rerank format to Together AI's `/v1/rera Why separate file? Make it easy to see how transformation works """ -import uuid +from litellm._uuid import uuid from typing import List, Optional from litellm.types.rerank import ( diff --git a/litellm/llms/vercel_ai_gateway/chat/transformation.py b/litellm/llms/vercel_ai_gateway/chat/transformation.py new file mode 100644 index 00000000000..13a88377489 --- /dev/null +++ b/litellm/llms/vercel_ai_gateway/chat/transformation.py @@ -0,0 +1,112 @@ +""" +Support for OpenAI's `/v1/chat/completions` endpoint. + +Calls done in OpenAI/openai.py as Vercel AI Gateway is openai-compatible. + +Docs: https://vercel.com/docs/ai-gateway +""" + +from typing import List, Optional, Tuple, Union + +import httpx + +from litellm.llms.base_llm.chat.transformation import BaseLLMException +from litellm.types.llms.openai import AllMessageValues +from litellm.secret_managers.main import get_secret_str +import litellm + +from ...openai.chat.gpt_transformation import OpenAIGPTConfig +from ..common_utils import VercelAIGatewayException + + +class VercelAIGatewayConfig(OpenAIGPTConfig): + @property + def custom_llm_provider(self) -> Optional[str]: + return "vercel_ai_gateway" + + def get_supported_openai_params(self, model: str) -> list: + base_params = super().get_supported_openai_params(model) + if "extra_body" not in base_params: + base_params.append("extra_body") + return base_params + + def _get_openai_compatible_provider_info( + self, api_base: Optional[str], api_key: Optional[str] + ) -> Tuple[Optional[str], Optional[str]]: + + api_base = ( + api_base + or get_secret_str("VERCEL_AI_GATEWAY_API_BASE") + or "https://ai-gateway.vercel.sh/v1" + ) + user_api_key = ( + api_key + or get_secret_str("VERCEL_AI_GATEWAY_API_KEY") + or get_secret_str("VERCEL_OIDC_TOKEN") + ) + return api_base, user_api_key + + def map_openai_params( + self, + non_default_params: dict, + optional_params: dict, + model: str, + drop_params: bool, + ) -> dict: + mapped_openai_params = super().map_openai_params( + non_default_params, optional_params, model, drop_params + ) + + # Vercel AI Gateway-only parameters + extra_body = {} + provider_options = non_default_params.pop("providerOptions", None) + + if provider_options is not None: + extra_body["providerOptions"] = provider_options + + mapped_openai_params["extra_body"] = extra_body # openai client supports `extra_body` param + return mapped_openai_params + + def transform_request( + self, + model: str, + messages: List[AllMessageValues], + optional_params: dict, + litellm_params: dict, + headers: dict, + ) -> dict: + """ + Transform the overall request to be sent to the API. + + Returns: + dict: The transformed request. Sent as the body of the API call. + """ + return super().transform_request( + model, messages, optional_params, litellm_params, headers + ) + + def get_error_class( + self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers] + ) -> BaseLLMException: + return VercelAIGatewayException( + message=error_message, + status_code=status_code, + headers=headers, + ) + + def get_models( + self, api_key: Optional[str] = None, api_base: Optional[str] = None + ) -> List[str]: + api_base, _ = self._get_openai_compatible_provider_info(api_base, api_key) + + if api_base is None: + api_base = "https://ai-gateway.vercel.sh/v1" + + models_url = f"{api_base}/models" + response = litellm.module_level_client.get(url=models_url) + + if response.status_code != 200: + raise Exception(f"Failed to get models: {response.text}") + + models = response.json()["data"] + return [model["id"] for model in models] diff --git a/litellm/llms/vercel_ai_gateway/common_utils.py b/litellm/llms/vercel_ai_gateway/common_utils.py new file mode 100644 index 00000000000..93e792be05e --- /dev/null +++ b/litellm/llms/vercel_ai_gateway/common_utils.py @@ -0,0 +1,5 @@ +from litellm.llms.base_llm.chat.transformation import BaseLLMException + + +class VercelAIGatewayException(BaseLLMException): + pass diff --git a/litellm/llms/vertex_ai/batches/transformation.py b/litellm/llms/vertex_ai/batches/transformation.py index a97f312d486..22cd0bd402a 100644 --- a/litellm/llms/vertex_ai/batches/transformation.py +++ b/litellm/llms/vertex_ai/batches/transformation.py @@ -1,4 +1,4 @@ -import uuid +from litellm._uuid import uuid from typing import Dict from litellm.llms.vertex_ai.common_utils import ( @@ -114,7 +114,14 @@ class VertexAIBatchTransformation: """ Gets the output file id from the Vertex AI Batch response """ - output_file_id: str = "" + + output_file_id: str = ( + response.get("outputInfo", OutputInfo()).get("gcsOutputDirectory", "") + + "/predictions.jsonl" + ) + if output_file_id != "/predictions.jsonl": + return output_file_id + output_config = response.get("outputConfig") if output_config is None: return output_file_id diff --git a/litellm/llms/vertex_ai/common_utils.py b/litellm/llms/vertex_ai/common_utils.py index 4931631d75d..8588c3efa27 100644 --- a/litellm/llms/vertex_ai/common_utils.py +++ b/litellm/llms/vertex_ai/common_utils.py @@ -187,6 +187,25 @@ def _check_text_in_content(parts: List[PartType]) -> bool: return has_text_param +def _fix_enum_empty_strings(schema, depth=0): + """Fix empty strings in enum values by replacing them with None. Gemini doesn't accept empty strings in enums.""" + if depth > DEFAULT_MAX_RECURSE_DEPTH: + raise ValueError(f"Max depth of {DEFAULT_MAX_RECURSE_DEPTH} exceeded while processing schema.") + + if "enum" in schema and isinstance(schema["enum"], list): + schema["enum"] = [None if value == "" else value for value in schema["enum"]] + + # Reuse existing recursion pattern from convert_anyof_null_to_nullable + properties = schema.get("properties", None) + if properties is not None: + for _, value in properties.items(): + _fix_enum_empty_strings(value, depth=depth + 1) + + items = schema.get("items", None) + if items is not None: + _fix_enum_empty_strings(items, depth=depth + 1) + + def _build_vertex_schema(parameters: dict, add_property_ordering: bool = False): """ This is a modified version of https://github.com/google-gemini/generative-ai-python/blob/8f77cc6ac99937cd3a81299ecf79608b91b06bbb/google/generativeai/types/content_types.py#L419 @@ -215,6 +234,11 @@ def _build_vertex_schema(parameters: dict, add_property_ordering: bool = False): # * https://github.com/pydantic/pydantic/discussions/4872 convert_anyof_null_to_nullable(parameters) + _convert_schema_types(parameters) + + # Handle empty strings in enum values - Gemini doesn't accept empty strings in enums + _fix_enum_empty_strings(parameters) + # Handle empty items objects process_items(parameters) add_object_type(parameters) @@ -254,9 +278,7 @@ def _filter_anyof_fields(schema_dict: Dict[str, Any]) -> Dict[str, Any]: item["title"] = title if description: item["description"] = description - return {"anyOf": any_of} - else: - return schema_dict + return {"anyOf": any_of} return schema_dict @@ -441,6 +463,47 @@ def _convert_vertex_datetime_to_openai_datetime(vertex_datetime: str) -> int: return int(dt.timestamp()) +def _convert_schema_types(schema, depth=0): + """ + Convert type arrays and lowercase types for Vertex AI compatibility. + + Transforms OpenAI-style schemas to Vertex AI format by converting type arrays + like ["string", "number"] to anyOf format and converting all types to uppercase. + """ + if depth > DEFAULT_MAX_RECURSE_DEPTH: + raise ValueError( + f"Max depth of {DEFAULT_MAX_RECURSE_DEPTH} exceeded while processing schema. Please check the schema for excessive nesting." + ) + + if not isinstance(schema, dict): + return + + + # Handle type field + if "type" in schema: + type_val = schema["type"] + if isinstance(type_val, list) and len(type_val) > 1: + # Convert ["string", "number"] -> {"anyOf": [{"type": "STRING"}, {"type": "NUMBER"}]} + schema["anyOf"] = [{"type": t} for t in type_val if isinstance(t, str)] + schema.pop("type") + elif isinstance(type_val, list) and len(type_val) == 1: + schema["type"] = type_val[0] + elif isinstance(type_val, str): + schema["type"] = type_val + + # Recursively process nested properties, items, and anyOf + for key in ["properties", "items", "anyOf"]: + if key in schema: + value = schema[key] + if key == "properties" and isinstance(value, dict): + for prop_schema in value.values(): + _convert_schema_types(prop_schema, depth + 1) + elif key == "items": + _convert_schema_types(value, depth + 1) + elif key == "anyOf" and isinstance(value, list): + for anyof_schema in value: + _convert_schema_types(anyof_schema, depth + 1) + def get_vertex_project_id_from_url(url: str) -> Optional[str]: """ Get the vertex project id from the url diff --git a/litellm/llms/vertex_ai/files/handler.py b/litellm/llms/vertex_ai/files/handler.py index a666a2c37fb..6636bccd6a3 100644 --- a/litellm/llms/vertex_ai/files/handler.py +++ b/litellm/llms/vertex_ai/files/handler.py @@ -1,5 +1,6 @@ import asyncio -from typing import Any, Coroutine, Optional, Union +import urllib.parse +from typing import Any, Coroutine, Optional, Tuple, Union import httpx @@ -9,7 +10,12 @@ from litellm.integrations.gcs_bucket.gcs_bucket_base import ( GCSLoggingConfig, ) from litellm.llms.custom_httpx.http_handler import get_async_httpx_client -from litellm.types.llms.openai import CreateFileRequest, OpenAIFileObject +from litellm.types.llms.openai import ( + CreateFileRequest, + FileContentRequest, + HttpxBinaryResponseContent, + OpenAIFileObject, +) from litellm.types.llms.vertex_ai import VERTEX_CREDENTIALS_TYPES from .transformation import VertexAIJsonlFilesTransformation @@ -105,3 +111,136 @@ class VertexAIFilesHandler(GCSBucketBase): max_retries=max_retries, ) ) + + def _extract_bucket_and_object_from_file_id(self, file_id: str) -> Tuple[str, str]: + """ + Extract bucket name and object path from URL-encoded file_id. + + Expected format: gs%3A%2F%2Fbucket-name%2Fpath%2Fto%2Ffile + Which decodes to: gs://bucket-name/path/to/file + + Returns: + tuple: (bucket_name, url_encoded_object_path) + - bucket_name: "bucket-name" + - url_encoded_object_path: "path%2Fto%2Ffile" + """ + decoded_path = urllib.parse.unquote(file_id) + + if decoded_path.startswith("gs://"): + full_path = decoded_path[5:] # Remove 'gs://' prefix + else: + full_path = decoded_path + + if "/" in full_path: + bucket_name, object_path = full_path.split("/", 1) + else: + bucket_name = full_path + object_path = "" + + encoded_object_path = urllib.parse.quote(object_path, safe="") + + return bucket_name, encoded_object_path + + async def afile_content( + self, + file_content_request: FileContentRequest, + vertex_credentials: Optional[VERTEX_CREDENTIALS_TYPES], + vertex_project: Optional[str], + vertex_location: Optional[str], + timeout: Union[float, httpx.Timeout], + max_retries: Optional[int], + ) -> HttpxBinaryResponseContent: + """ + Download file content from GCS bucket for VertexAI files. + + Args: + file_content_request: Contains file_id (URL-encoded GCS path) + vertex_credentials: VertexAI credentials + vertex_project: VertexAI project ID + vertex_location: VertexAI location + timeout: Request timeout + max_retries: Max retry attempts + + Returns: + HttpxBinaryResponseContent: Binary content wrapped in compatible response format + """ + file_id = file_content_request.get("file_id") + if not file_id: + raise ValueError("file_id is required in file_content_request") + + bucket_name, encoded_object_path = self._extract_bucket_and_object_from_file_id( + file_id + ) + + download_kwargs = { + "standard_callback_dynamic_params": {"gcs_bucket_name": bucket_name} + } + + file_content = await self.download_gcs_object( + object_name=encoded_object_path, **download_kwargs + ) + + if file_content is None: + decoded_path = urllib.parse.unquote(file_id) + raise ValueError(f"Failed to download file from GCS: {decoded_path}") + + decoded_path = urllib.parse.unquote(file_id) + mock_response = httpx.Response( + status_code=200, + content=file_content, + headers={"content-type": "application/octet-stream"}, + request=httpx.Request(method="GET", url=decoded_path), + ) + + return HttpxBinaryResponseContent(response=mock_response) + + def file_content( + self, + _is_async: bool, + file_content_request: FileContentRequest, + 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[ + HttpxBinaryResponseContent, Coroutine[Any, Any, HttpxBinaryResponseContent] + ]: + """ + Download file content from GCS bucket for VertexAI files. + Supports both sync and async operations. + + Args: + _is_async: Whether to run asynchronously + file_content_request: Contains file_id (URL-encoded GCS path) + api_base: API base (unused for GCS operations) + vertex_credentials: VertexAI credentials + vertex_project: VertexAI project ID + vertex_location: VertexAI location + timeout: Request timeout + max_retries: Max retry attempts + + Returns: + HttpxBinaryResponseContent or Coroutine: Binary content wrapped in compatible response format + """ + if _is_async: + return self.afile_content( + file_content_request=file_content_request, + vertex_credentials=vertex_credentials, + vertex_project=vertex_project, + vertex_location=vertex_location, + timeout=timeout, + max_retries=max_retries, + ) + else: + return asyncio.run( + self.afile_content( + file_content_request=file_content_request, + vertex_credentials=vertex_credentials, + vertex_project=vertex_project, + vertex_location=vertex_location, + timeout=timeout, + max_retries=max_retries, + ) + ) diff --git a/litellm/llms/vertex_ai/files/transformation.py b/litellm/llms/vertex_ai/files/transformation.py index c795367e486..01f6c86fd4d 100644 --- a/litellm/llms/vertex_ai/files/transformation.py +++ b/litellm/llms/vertex_ai/files/transformation.py @@ -1,11 +1,12 @@ import json import os import time -import uuid +from litellm._uuid import uuid from typing import Any, Dict, List, Optional, Tuple, Union from httpx import Headers, Response +from litellm.files.utils import FilesAPIUtils from litellm.litellm_core_utils.prompt_templates.common_utils import extract_file_data from litellm.llms.base_llm.chat.transformation import BaseLLMException from litellm.llms.base_llm.files.transformation import ( @@ -260,10 +261,13 @@ class VertexAIFilesConfig(VertexBase, BaseFilesConfig): raise ValueError("file is required") extracted_file_data = extract_file_data(file_data) extracted_file_data_content = extracted_file_data.get("content") - if ( - create_file_data.get("purpose") == "batch" - and extracted_file_data.get("content_type") == "application/jsonl" - and extracted_file_data_content is not None + + if extracted_file_data_content is None: + raise ValueError("file content is required") + + if FilesAPIUtils.is_batch_jsonl_file( + create_file_data=create_file_data, + extracted_file_data=extracted_file_data, ): ## 1. If jsonl, check if there's a model name file_content = self._get_content_from_openai_file( @@ -279,7 +283,7 @@ class VertexAIFilesConfig(VertexBase, BaseFilesConfig): openai_jsonl_content ) ) - return json.dumps(vertex_jsonl_content) + return "\n".join(json.dumps(item) for item in vertex_jsonl_content) elif isinstance(extracted_file_data_content, bytes): return extracted_file_data_content else: diff --git a/litellm/llms/vertex_ai/gemini/transformation.py b/litellm/llms/vertex_ai/gemini/transformation.py index 85e3f15364b..ccaf28e5906 100644 --- a/litellm/llms/vertex_ai/gemini/transformation.py +++ b/litellm/llms/vertex_ai/gemini/transformation.py @@ -28,6 +28,7 @@ from litellm.types.files import ( get_file_type_from_extension, is_gemini_1_5_accepted_file_type, ) +from litellm.types.utils import LlmProviders from litellm.types.llms.openai import ( AllMessageValues, ChatCompletionAssistantMessage, @@ -35,6 +36,7 @@ from litellm.types.llms.openai import ( ChatCompletionFileObject, ChatCompletionImageObject, ChatCompletionTextObject, + ChatCompletionUserMessage, ) from litellm.types.llms.vertex_ai import * from litellm.types.llms.vertex_ai import ( @@ -104,6 +106,64 @@ def _process_gemini_image(image_url: str, format: Optional[str] = None) -> PartT raise e +def _snake_to_camel(snake_str: str) -> str: + """Convert snake_case to camelCase""" + components = snake_str.split("_") + return components[0] + "".join(x.capitalize() for x in components[1:]) + + +def _camel_to_snake(camel_str: str) -> str: + """Convert camelCase to snake_case""" + import re + + return re.sub(r"(? Optional[str]: + """ + Get the equivalent key from available keys, checking both camelCase and snake_case variants + """ + if key in available_keys: + return key + + # Try camelCase version + camel_key = _snake_to_camel(key) + if camel_key in available_keys: + return camel_key + + # Try snake_case version + snake_key = _camel_to_snake(key) + if snake_key in available_keys: + return snake_key + + return None + + +def check_if_part_exists_in_parts( + parts: List[PartType], part: PartType, excluded_keys: List[str] = [] +) -> bool: + """ + Check if a part exists in a list of parts + Handles both camelCase and snake_case key variations (e.g., function_call vs functionCall) + """ + keys_to_compare = set(part.keys()) - set(excluded_keys) + for p in parts: + p_keys = set(p.keys()) + # Check if all keys in part have equivalent values in p + match_found = True + for key in keys_to_compare: + equivalent_key = _get_equivalent_key(key, p_keys) + if equivalent_key is None or p.get(equivalent_key, None) != part.get( + key, None + ): + match_found = False + break + + if match_found: + return True + return False + + def _gemini_convert_messages_with_history( # noqa: PLR0915 messages: List[AllMessageValues], ) -> List[ContentType]: @@ -235,10 +295,33 @@ def _gemini_convert_messages_with_history( # noqa: PLR0915 assistant_msg = ChatCompletionAssistantMessage(**msg_dict) # type: ignore _message_content = assistant_msg.get("content", None) reasoning_content = assistant_msg.get("reasoning_content", None) + thinking_blocks = assistant_msg.get("thinking_blocks") if reasoning_content is not None: assistant_content.append( PartType(thought=True, text=reasoning_content) ) + if thinking_blocks is not None: + for block in thinking_blocks: + block_thinking_str = block.get("thinking") + block_signature = block.get("signature") + if ( + block_thinking_str is not None + and block_signature is not None + ): + try: + assistant_content.append( + PartType( + thoughtSignature=block_signature, + **json.loads(block_thinking_str), + ) + ) + except Exception: + assistant_content.append( + PartType( + thoughtSignature=block_signature, + text=block_thinking_str, + ) + ) if _message_content is not None and isinstance(_message_content, list): _parts = [] for element in _message_content: @@ -261,9 +344,17 @@ def _gemini_convert_messages_with_history( # noqa: PLR0915 assistant_msg.get("tool_calls", []) is not None or assistant_msg.get("function_call") is not None ): # support assistant tool invoke conversion - assistant_content.extend( - convert_to_gemini_tool_call_invoke(assistant_msg) + gemini_tool_call_parts = convert_to_gemini_tool_call_invoke( + assistant_msg ) + ## check if gemini_tool_call already exists in assistant_content + for gemini_tool_call_part in gemini_tool_call_parts: + if not check_if_part_exists_in_parts( + assistant_content, + gemini_tool_call_part, + excluded_keys=["thoughtSignature"], + ): + assistant_content.append(gemini_tool_call_part) last_message_with_tool_calls = assistant_msg msg_i += 1 @@ -297,6 +388,19 @@ def _gemini_convert_messages_with_history( # noqa: PLR0915 ) if len(tool_call_responses) > 0: contents.append(ContentType(parts=tool_call_responses)) + + if len(contents) == 0: + verbose_logger.warning( + """ + No contents in messages. Contents are required. See + https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.publishers.models/generateContent#request-body. + If the original request did not comply to OpenAI API requirements it should have failed by now, + but LiteLLM does not check for missing messages. + Setting an empty content to prevent an 400 error. + Relevant Issue - https://github.com/BerriAI/litellm/issues/9733 + """ + ) + contents.append(ContentType(role="user", parts=[PartType(text=" ")])) return contents except Exception as e: raise e @@ -358,6 +462,17 @@ def _transform_request_body( ) # type: ignore config_fields = GenerationConfig.__annotations__.keys() + # If the LiteLLM client sends Gemini-supported parameter "labels", add it + # as "labels" field to the request sent to the Gemini backend. + labels: Optional[dict[str, str]] = optional_params.pop("labels", None) + # If the LiteLLM client sends OpenAI-supported parameter "metadata", add it + # as "labels" field to the request sent to the Gemini backend. + if labels is None and "metadata" in litellm_params: + metadata = litellm_params["metadata"] + if metadata is not None and "requester_metadata" in metadata: + rm = metadata["requester_metadata"] + labels = {k: v for k, v in rm.items() if isinstance(v, str)} + filtered_params = { k: v for k, v in optional_params.items() if k in config_fields } @@ -378,6 +493,9 @@ def _transform_request_body( data["generationConfig"] = generation_config if cached_content is not None: data["cachedContent"] = cached_content + # Only add labels for Vertex AI endpoints (not Google GenAI/AI Studio) and only if non-empty + if labels and custom_llm_provider != LlmProviders.GEMINI: + data["labels"] = labels except Exception as e: raise e @@ -402,22 +520,28 @@ def sync_transform_request_body( context_caching_endpoints = ContextCachingEndpoints() if gemini_api_key is not None: - messages, optional_params, cached_content = ( - context_caching_endpoints.check_and_create_cache( - messages=messages, - optional_params=optional_params, - api_key=gemini_api_key, - api_base=api_base, - model=model, - client=client, - timeout=timeout, - extra_headers=extra_headers, - cached_content=optional_params.pop("cached_content", None), - logging_obj=logging_obj, - ) + ( + messages, + optional_params, + cached_content, + ) = context_caching_endpoints.check_and_create_cache( + messages=messages, + optional_params=optional_params, + api_key=gemini_api_key, + api_base=api_base, + model=model, + client=client, + timeout=timeout, + extra_headers=extra_headers, + cached_content=optional_params.pop("cached_content", None), + logging_obj=logging_obj, ) else: # [TODO] implement context caching for gemini as well - cached_content = optional_params.pop("cached_content", None) + cached_content = None + if "cached_content" in optional_params: + cached_content = optional_params.pop("cached_content") + elif "cachedContent" in optional_params: + cached_content = optional_params.pop("cachedContent") return _transform_request_body( messages=messages, @@ -464,7 +588,11 @@ async def async_transform_request_body( logging_obj=logging_obj, ) else: # [TODO] implement context caching for gemini as well - cached_content = optional_params.pop("cached_content", None) + cached_content = None + if "cached_content" in optional_params: + cached_content = optional_params.pop("cached_content") + elif "cachedContent" in optional_params: + cached_content = optional_params.pop("cachedContent") return _transform_request_body( messages=messages, @@ -476,6 +604,15 @@ async def async_transform_request_body( ) +def _default_user_message_when_system_message_passed() -> ChatCompletionUserMessage: + """ + Returns a default user message when a "system" message is passed in gemini fails. + + This adds a blank user message to the messages list, to ensure that gemini doesn't fail the request. + """ + return ChatCompletionUserMessage(content=".", role="user") + + def _transform_system_message( supports_system_message: bool, messages: List[AllMessageValues] ) -> Tuple[Optional[SystemInstructions], List[AllMessageValues]]: @@ -510,6 +647,13 @@ def _transform_system_message( messages.pop(idx) if len(system_content_blocks) > 0: + ######################################################### + # If no messages are passed in, add a blank user message + # Relevant Issue - https://github.com/BerriAI/litellm/issues/13769 + ######################################################### + if len(messages) == 0: + messages.append(_default_user_message_when_system_message_passed()) + ######################################################### return SystemInstructions(parts=system_content_blocks), messages return None, messages 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 c5c4f46c92f..dc3a6cf15e5 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 @@ -3,7 +3,7 @@ ## Initial implementation - covers gemini + image gen calls import json import time -import uuid +from litellm._uuid import uuid from copy import deepcopy from functools import partial from typing import ( @@ -30,6 +30,10 @@ from litellm.constants import ( DEFAULT_REASONING_EFFORT_HIGH_THINKING_BUDGET, DEFAULT_REASONING_EFFORT_LOW_THINKING_BUDGET, DEFAULT_REASONING_EFFORT_MEDIUM_THINKING_BUDGET, + DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET, + DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_FLASH, + DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_PRO, + DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_FLASH_LITE, ) from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMException from litellm.llms.custom_httpx.http_handler import ( @@ -43,9 +47,12 @@ from litellm.types.llms.gemini import BidiGenerateContentServerMessage from litellm.types.llms.openai import ( AllMessageValues, ChatCompletionResponseMessage, + ChatCompletionThinkingBlock, ChatCompletionToolCallChunk, ChatCompletionToolCallFunctionChunk, ChatCompletionToolParamFunctionChunk, + ImageURLListItem, + ImageURLObject, OpenAIChatCompletionFinishReason, ) from litellm.types.llms.vertex_ai import ( @@ -89,11 +96,12 @@ from .transformation import ( if TYPE_CHECKING: from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj - from litellm.types.utils import ModelResponseStream + from litellm.types.utils import ModelResponseStream, StreamingChoices LoggingClass = LiteLLMLoggingObj else: LoggingClass = Any + StreamingChoices = Any class VertexAIBaseConfig: @@ -419,8 +427,25 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): @staticmethod def _map_reasoning_effort_to_thinking_budget( reasoning_effort: str, + model: Optional[str] = None, ) -> GeminiThinkingConfig: - if reasoning_effort == "low": + if reasoning_effort == "minimal": + # Use model-specific minimum thinking budget or fallback + # Check for exact matches first, then partial matches + if model and "gemini-2.5-flash-lite" in model.lower(): + budget = DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_FLASH_LITE + elif model and "gemini-2.5-pro" in model.lower(): + budget = DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_PRO + elif model and "gemini-2.5-flash" in model.lower(): + budget = DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_FLASH + else: + budget = DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET + + return { + "thinkingBudget": budget, + "includeThoughts": True, + } + elif reasoning_effort == "low": return { "thinkingBudget": DEFAULT_REASONING_EFFORT_LOW_THINKING_BUDGET, "includeThoughts": True, @@ -461,7 +486,6 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): params["includeThoughts"] = True if thinking_budget is not None and isinstance(thinking_budget, int): params["thinkingBudget"] = thinking_budget - return params def map_response_modalities(self, value: list) -> list: @@ -598,7 +622,9 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): optional_params["seed"] = value elif param == "reasoning_effort" and isinstance(value, str): optional_params["thinkingConfig"] = ( - VertexGeminiConfig._map_reasoning_effort_to_thinking_budget(value) + VertexGeminiConfig._map_reasoning_effort_to_thinking_budget( + value, model + ) ) elif param == "thinking": optional_params["thinkingConfig"] = ( @@ -774,8 +800,9 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): elif "inlineData" in part: mime_type = part["inlineData"]["mimeType"] data = part["inlineData"]["data"] - # Check if inline data is audio - if so, exclude from text content - if mime_type.startswith("audio/"): + # Check if inline data is audio or image - if so, exclude from text content + # Images and audio are now handled separately in their respective response fields + if mime_type.startswith("audio/") or mime_type.startswith("image/"): continue _content_str += "data:{};base64,{}".format(mime_type, data) @@ -791,6 +818,45 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): return content_str, reasoning_content_str + def _extract_thinking_blocks_from_parts( + self, parts: List[HttpxPartType] + ) -> List[ChatCompletionThinkingBlock]: + """Extract thinking blocks from parts if present""" + thinking_blocks: List[ChatCompletionThinkingBlock] = [] + for part in parts: + if "thoughtSignature" in part: + part_copy = part.copy() + part_copy.pop("thoughtSignature") + thinking_blocks.append( + ChatCompletionThinkingBlock( + type="thinking", + thinking=json.dumps(part_copy), + signature=part["thoughtSignature"], + ) + ) + return thinking_blocks + + def _extract_image_response_from_parts( + self, parts: List[HttpxPartType] + ) -> Optional[List[ImageURLListItem]]: + """Extract image response from parts if present""" + images: List[ImageURLListItem] = [] + for part in parts: + if "inlineData" in part: + mime_type = part["inlineData"]["mimeType"] + data = part["inlineData"]["data"] + if mime_type.startswith("image/"): + # Convert base64 data to data URI format + data_uri = f"data:{mime_type};base64,{data}" + images.append( + ImageURLListItem( + image_url=ImageURLObject(url=data_uri, detail="auto"), + index=0, + type="image_url", + ) + ) + return images + def _extract_audio_response_from_parts( self, parts: List[HttpxPartType] ) -> Optional[ChatCompletionAudioResponse]: @@ -1109,6 +1175,80 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): web_search_requests = len(grounding_metadata) return web_search_requests + @staticmethod + def _create_streaming_choice( + chat_completion_message: ChatCompletionResponseMessage, + candidate: Candidates, + idx: int, + tools: Optional[List[ChatCompletionToolCallChunk]], + functions: Optional[ChatCompletionToolCallFunctionChunk], + chat_completion_logprobs: Optional[ChoiceLogprobs], + image_response: Optional[List[ImageURLListItem]], + ) -> StreamingChoices: + """ + Helper method to create a streaming choice object for Vertex AI + """ + from litellm.types.utils import Delta, StreamingChoices + + # create a streaming choice object + choice = StreamingChoices( + finish_reason=VertexGeminiConfig._check_finish_reason( + chat_completion_message, candidate.get("finishReason") + ), + index=candidate.get("index", idx), + delta=Delta( + content=chat_completion_message.get("content"), + reasoning_content=chat_completion_message.get("reasoning_content"), + tool_calls=tools, + images=image_response, + function_call=functions, + ), + logprobs=chat_completion_logprobs, + enhancements=None, + ) + return choice + + @staticmethod + def _extract_candidate_metadata( + candidate: Candidates, + ) -> Tuple[List[dict], List[dict], List, List]: + """ + Extract metadata from a single candidate response. + + Returns: + grounding_metadata: List[dict] + url_context_metadata: List[dict] + safety_ratings: List + citation_metadata: List + """ + grounding_metadata: List[dict] = [] + url_context_metadata: List[dict] = [] + safety_ratings: List = [] + citation_metadata: List = [] + + if "groundingMetadata" in candidate: + if isinstance(candidate["groundingMetadata"], list): + grounding_metadata.extend(candidate["groundingMetadata"]) # type: ignore + else: + grounding_metadata.append(candidate["groundingMetadata"]) # type: ignore + + if "safetyRatings" in candidate: + safety_ratings.append(candidate["safetyRatings"]) + + if "citationMetadata" in candidate: + citation_metadata.append(candidate["citationMetadata"]) + + if "urlContextMetadata" in candidate: + # Add URL context metadata to grounding metadata + url_context_metadata.append(cast(dict, candidate["urlContextMetadata"])) + + return ( + grounding_metadata, + url_context_metadata, + safety_ratings, + citation_metadata, + ) + @staticmethod def _process_candidates( _candidates: List[Candidates], @@ -1131,6 +1271,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): grounding_metadata: List[dict] = [] url_context_metadata: List[dict] = [] + image_response: Optional[List[ImageURLListItem]] = None safety_ratings: List = [] citation_metadata: List = [] chat_completion_message: ChatCompletionResponseMessage = {"role": "assistant"} @@ -1138,26 +1279,24 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): tools: Optional[List[ChatCompletionToolCallChunk]] = [] functions: Optional[ChatCompletionToolCallFunctionChunk] = None cumulative_tool_call_index: int = 0 + thinking_blocks: Optional[List[ChatCompletionThinkingBlock]] = None for idx, candidate in enumerate(_candidates): if "content" not in candidate: continue - if "groundingMetadata" in candidate: - if isinstance(candidate["groundingMetadata"], list): - grounding_metadata.extend(candidate["groundingMetadata"]) # type: ignore - else: - grounding_metadata.append(candidate["groundingMetadata"]) # type: ignore + # Extract metadata using helper function + ( + candidate_grounding_metadata, + candidate_url_context_metadata, + candidate_safety_ratings, + candidate_citation_metadata, + ) = VertexGeminiConfig._extract_candidate_metadata(candidate) - if "safetyRatings" in candidate: - safety_ratings.append(candidate["safetyRatings"]) - - if "citationMetadata" in candidate: - citation_metadata.append(candidate["citationMetadata"]) - - if "urlContextMetadata" in candidate: - # Add URL context metadata to grounding metadata - url_context_metadata.append(cast(dict, candidate["urlContextMetadata"])) + grounding_metadata.extend(candidate_grounding_metadata) + url_context_metadata.extend(candidate_url_context_metadata) + safety_ratings.extend(candidate_safety_ratings) + citation_metadata.extend(candidate_citation_metadata) if "parts" in candidate["content"]: ( @@ -1172,18 +1311,33 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): parts=candidate["content"]["parts"] ) ) + image_response = ( + VertexGeminiConfig()._extract_image_response_from_parts( + parts=candidate["content"]["parts"] + ) + ) + + thinking_blocks = ( + VertexGeminiConfig()._extract_thinking_blocks_from_parts( + parts=candidate["content"]["parts"] + ) + ) if audio_response is not None: cast(Dict[str, Any], chat_completion_message)[ "audio" ] = audio_response chat_completion_message["content"] = None # OpenAI spec - elif content is not None: + if image_response is not None: + # Handle image response - combine with text content into structured format + cast(Dict[str, Any], chat_completion_message)[ + "images" + ] = image_response + if content is not None: chat_completion_message["content"] = content if reasoning_content is not None: chat_completion_message["reasoning_content"] = reasoning_content - ( functions, tools, @@ -1205,25 +1359,18 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): if functions is not None: chat_completion_message["function_call"] = functions - if isinstance(model_response, ModelResponseStream): - from litellm.types.utils import Delta, StreamingChoices + if thinking_blocks is not None: + chat_completion_message["thinking_blocks"] = thinking_blocks # type: ignore - # create a streaming choice object - choice = StreamingChoices( - finish_reason=VertexGeminiConfig._check_finish_reason( - chat_completion_message, candidate.get("finishReason") - ), - index=candidate.get("index", idx), - delta=Delta( - content=chat_completion_message.get("content"), - reasoning_content=chat_completion_message.get( - "reasoning_content" - ), - tool_calls=tools, - function_call=functions, - ), - logprobs=chat_completion_logprobs, - enhancements=None, + if isinstance(model_response, ModelResponseStream): + choice = VertexGeminiConfig._create_streaming_choice( + chat_completion_message=chat_completion_message, + candidate=candidate, + idx=idx, + tools=tools, + functions=functions, + chat_completion_logprobs=chat_completion_logprobs, + image_response=image_response, ) model_response.choices.append(choice) elif isinstance(model_response, ModelResponse): diff --git a/litellm/llms/vertex_ai/google_genai/transformation.py b/litellm/llms/vertex_ai/google_genai/transformation.py index 47933811196..574000e6bca 100644 --- a/litellm/llms/vertex_ai/google_genai/transformation.py +++ b/litellm/llms/vertex_ai/google_genai/transformation.py @@ -1,7 +1,7 @@ """ Transformation for Calling Google models in their native format. """ -from typing import Literal, Optional, Union +from typing import Dict, Literal, Optional, Union from litellm.llms.gemini.google_genai.transformation import GoogleGenAIConfig from litellm.types.router import GenericLiteLLMParams @@ -11,20 +11,20 @@ class VertexAIGoogleGenAIConfig(GoogleGenAIConfig): """ Configuration for calling Google models in their native format. """ + HEADER_NAME = "Authorization" BEARER_PREFIX = "Bearer" - + @property def custom_llm_provider(self) -> Literal["gemini", "vertex_ai"]: return "vertex_ai" - def validate_environment( - self, + self, api_key: Optional[str], headers: Optional[dict], model: str, - litellm_params: Optional[Union[GenericLiteLLMParams, dict]] + litellm_params: Optional[Union[GenericLiteLLMParams, dict]], ) -> dict: default_headers = { "Content-Type": "application/json", @@ -36,4 +36,65 @@ class VertexAIGoogleGenAIConfig(GoogleGenAIConfig): default_headers.update(headers) return default_headers - \ No newline at end of file + + def _camel_to_snake(self, camel_str: str) -> str: + """Convert camelCase to snake_case""" + import re + + return re.sub(r"(? dict: + """ + Transform the generate content request for Vertex AI. + Since Vertex AI natively supports Google GenAI format, we can pass most fields directly. + """ + # Build the request in Google GenAI format that Vertex AI expects + result = { + "model": model, + "contents": contents, + } + + # Add tools if provided + if tools: + result["tools"] = tools + + # Add systemInstruction if provided + if system_instruction: + result["systemInstruction"] = system_instruction + + # Handle generationConfig - Vertex AI expects it in the same format + if generate_content_config_dict: + result["generationConfig"] = generate_content_config_dict + + return result diff --git a/litellm/llms/vertex_ai/text_to_speech/text_to_speech_handler.py b/litellm/llms/vertex_ai/text_to_speech/text_to_speech_handler.py index 18bc72db46a..9d9015c2b91 100644 --- a/litellm/llms/vertex_ai/text_to_speech/text_to_speech_handler.py +++ b/litellm/llms/vertex_ai/text_to_speech/text_to_speech_handler.py @@ -1,6 +1,7 @@ -from typing import Optional, TypedDict, Union +from typing import Optional, Union import httpx +from typing_extensions import TypedDict import litellm from litellm.llms.custom_httpx.http_handler import ( diff --git a/litellm/llms/vertex_ai/vertex_ai_partner_models/gpt_oss/transformation.py b/litellm/llms/vertex_ai/vertex_ai_partner_models/gpt_oss/transformation.py new file mode 100644 index 00000000000..86e36e802ed --- /dev/null +++ b/litellm/llms/vertex_ai/vertex_ai_partner_models/gpt_oss/transformation.py @@ -0,0 +1,27 @@ +import litellm +from litellm.llms.openai.chat.gpt_transformation import OpenAIGPTConfig + + +class VertexAIGPTOSSTransformation(OpenAIGPTConfig): + """ + Transformation for GPT-OSS model from VertexAI + + https://console.cloud.google.com/vertex-ai/publishers/openai/model-garden/gpt-oss-120b-maas?hl=id + """ + def __init__(self): + super().__init__() + + def get_supported_openai_params(self, model: str) -> list: + base_gpt_series_params = super().get_supported_openai_params(model=model) + gpt_oss_only_params = ["reasoning_effort"] + base_gpt_series_params.extend(gpt_oss_only_params) + + ######################################################### + # VertexAI - GPT-OSS does not support tool calls + ######################################################### + if litellm.supports_function_calling(model=model) is False: + TOOL_CALLING_PARAMS_TO_REMOVE = ["tool", "tool_choice", "function_call", "functions"] + base_gpt_series_params = [param for param in base_gpt_series_params if param not in TOOL_CALLING_PARAMS_TO_REMOVE] + + return base_gpt_series_params + diff --git a/litellm/llms/vertex_ai/vertex_ai_partner_models/main.py b/litellm/llms/vertex_ai/vertex_ai_partner_models/main.py index f281cab3b58..ea29970f0aa 100644 --- a/litellm/llms/vertex_ai/vertex_ai_partner_models/main.py +++ b/litellm/llms/vertex_ai/vertex_ai_partner_models/main.py @@ -1,5 +1,6 @@ # What is this? ## API Handler for calling Vertex AI Partner Models +from enum import Enum from typing import Callable, Optional, Union import httpx # type: ignore @@ -27,6 +28,16 @@ class VertexAIError(Exception): self.message ) # Call the base class constructor with the parameters it needs +class PartnerModelPrefixes(str, Enum): + META_PREFIX = "meta/" + DEEPSEEK_PREFIX = "deepseek-ai" + MISTRAL_PREFIX = "mistral" + CODERESTAL_PREFIX = "codestral" + JAMBA_PREFIX = "jamba" + CLAUDE_PREFIX = "claude" + QWEN_PREFIX = "qwen" + GPT_OSS_PREFIX = "openai/gpt-oss-" + class VertexAIPartnerModels(VertexBase): def __init__(self) -> None: @@ -42,13 +53,14 @@ class VertexAIPartnerModels(VertexBase): bool: True if the model string is a Vertex AI Partner Model, False otherwise """ if ( - model.startswith("meta/") - or model.startswith("deepseek-ai") - or model.startswith("mistral") - or model.startswith("codestral") - or model.startswith("jamba") - or model.startswith("claude") - or model.startswith("qwen") + model.startswith(PartnerModelPrefixes.META_PREFIX) + or model.startswith(PartnerModelPrefixes.DEEPSEEK_PREFIX) + or model.startswith(PartnerModelPrefixes.MISTRAL_PREFIX) + or model.startswith(PartnerModelPrefixes.CODERESTAL_PREFIX) + or model.startswith(PartnerModelPrefixes.JAMBA_PREFIX) + or model.startswith(PartnerModelPrefixes.CLAUDE_PREFIX) + or model.startswith(PartnerModelPrefixes.QWEN_PREFIX) + or model.startswith(PartnerModelPrefixes.GPT_OSS_PREFIX) ): return True return False @@ -57,8 +69,9 @@ class VertexAIPartnerModels(VertexBase): def should_use_openai_handler(model: str): OPENAI_LIKE_VERTEX_PROVIDERS = [ "llama", - "deepseek-ai", - "qwen", + PartnerModelPrefixes.DEEPSEEK_PREFIX, + PartnerModelPrefixes.QWEN_PREFIX, + PartnerModelPrefixes.GPT_OSS_PREFIX, ] if any(provider in model for provider in OPENAI_LIKE_VERTEX_PROVIDERS): return True diff --git a/litellm/llms/vertex_ai/vertex_embeddings/types.py b/litellm/llms/vertex_ai/vertex_embeddings/types.py index c0c53b170c4..7f85ea46f31 100644 --- a/litellm/llms/vertex_ai/vertex_embeddings/types.py +++ b/litellm/llms/vertex_ai/vertex_embeddings/types.py @@ -3,7 +3,9 @@ Types for Vertex Embeddings Requests """ from enum import Enum -from typing import List, Optional, TypedDict, Union +from typing import List, Optional, Union + +from typing_extensions import TypedDict class TaskType(str, Enum): diff --git a/litellm/llms/vertex_ai/vertex_llm_base.py b/litellm/llms/vertex_ai/vertex_llm_base.py index ae43e1fd167..6d194d41add 100644 --- a/litellm/llms/vertex_ai/vertex_llm_base.py +++ b/litellm/llms/vertex_ai/vertex_llm_base.py @@ -239,6 +239,7 @@ class VertexBase: stream=stream, auth_header=None, url=default_api_base, + model=model, ) return api_base @@ -270,17 +271,11 @@ class VertexBase: def is_using_v1beta1_features(self, optional_params: dict) -> bool: """ - VertexAI only supports ContextCaching on v1beta1 - use this helper to decide if request should be sent to v1 or v1beta1 - Returns v1beta1 if context caching is enabled - Returns v1 in all other cases + Returns true if any beta feature is enabled + Returns false in all other cases """ - if "cached_content" in optional_params: - return True - if "CachedContent" in optional_params: - return True return False def _check_custom_proxy( @@ -292,6 +287,7 @@ class VertexBase: stream: Optional[bool], auth_header: Optional[str], url: str, + model: Optional[str] = None, ) -> Tuple[Optional[str], str]: """ for cloudflare ai gateway - https://github.com/BerriAI/litellm/issues/4317 @@ -301,7 +297,12 @@ class VertexBase: """ if api_base: if custom_llm_provider == "gemini": - url = "{}:{}".format(api_base, endpoint) + # For Gemini (Google AI Studio), construct the full path like other providers + if model is None: + raise ValueError( + "Model parameter is required for Gemini custom API base URLs" + ) + url = "{}/models/{}:{}".format(api_base, model, endpoint) if gemini_api_key is None: raise ValueError( "Missing gemini_api_key, please set `GEMINI_API_KEY`" @@ -373,12 +374,63 @@ class VertexBase: endpoint=endpoint, stream=stream, url=url, + model=model, ) + def _handle_reauthentication( + self, + credentials: Optional[VERTEX_CREDENTIALS_TYPES], + project_id: Optional[str], + credential_cache_key: Tuple, + error: Exception, + ) -> Tuple[str, str]: + """ + Handle reauthentication when credentials refresh fails. + + This method clears the cached credentials and attempts to reload them once. + It should only be called when "Reauthentication is needed" error occurs. + + Args: + credentials: The original credentials + project_id: The project ID + credential_cache_key: The cache key to clear + error: The original error that triggered reauthentication + + Returns: + Tuple of (access_token, project_id) + + Raises: + The original error if reauthentication fails + """ + verbose_logger.debug( + f"Handling reauthentication for project_id: {project_id}. " + f"Clearing cache and retrying once." + ) + + # Clear the cached credentials + if credential_cache_key in self._credentials_project_mapping: + del self._credentials_project_mapping[credential_cache_key] + + # Retry once with _retry_reauth=True to prevent infinite recursion + try: + return self.get_access_token( + credentials=credentials, + project_id=project_id, + _retry_reauth=True, + ) + except Exception as retry_error: + verbose_logger.error( + f"Reauthentication retry failed for project_id: {project_id}. " + f"Original error: {str(error)}. Retry error: {str(retry_error)}" + ) + # Re-raise the original error for better context + raise error + def get_access_token( self, credentials: Optional[VERTEX_CREDENTIALS_TYPES], project_id: Optional[str], + _retry_reauth: bool = False, ) -> Tuple[str, str]: """ Get access token and project id @@ -388,6 +440,14 @@ class VertexBase: 3. Check if loaded credentials have expired 4. If expired, refresh credentials 5. Return access token and project id + + Args: + credentials: The credentials to use for authentication + project_id: The Google Cloud project ID + _retry_reauth: Internal flag to prevent infinite recursion during reauthentication + + Returns: + Tuple of (access_token, project_id) """ # Convert dict credentials to string for caching @@ -469,14 +529,26 @@ class VertexBase: raise ValueError("Credentials are None after loading") if _credentials.expired: - verbose_logger.debug( - f"Credentials expired, refreshing for project_id: {project_id}" - ) - self.refresh_auth(_credentials) - self._credentials_project_mapping[credential_cache_key] = ( - _credentials, - credential_project_id, - ) + try: + verbose_logger.debug( + f"Credentials expired, refreshing for project_id: {project_id}" + ) + self.refresh_auth(_credentials) + self._credentials_project_mapping[credential_cache_key] = ( + _credentials, + credential_project_id, + ) + except Exception as e: + # if refresh fails, it's possible the user has re-authenticated via `gcloud auth application-default login` + # in this case, we should try to reload the credentials by clearing the cache and retrying + if "Reauthentication is needed" in str(e) and not _retry_reauth: + return self._handle_reauthentication( + credentials=credentials, + project_id=project_id, + credential_cache_key=credential_cache_key, + error=e, + ) + raise e ## VALIDATION STEP if _credentials.token is None or not isinstance(_credentials.token, str): diff --git a/litellm/llms/vllm/common_utils.py b/litellm/llms/vllm/common_utils.py index 8dca3e1de25..e2ed0daafe4 100644 --- a/litellm/llms/vllm/common_utils.py +++ b/litellm/llms/vllm/common_utils.py @@ -11,7 +11,21 @@ from litellm.utils import _add_path_to_api_base class VLLMError(BaseLLMException): - pass + def __init__( + self, + status_code: int, + message: str, + request: Optional[httpx.Request] = None, + response: Optional[httpx.Response] = None, + headers: Optional[Union[httpx.Headers, dict]] = None, + ): + super().__init__( + status_code=status_code, + message=message, + request=request, + response=response, + headers=headers, + ) class VLLMModelInfo(BaseLLMModelInfo): @@ -25,7 +39,8 @@ class VLLMModelInfo(BaseLLMModelInfo): api_key: Optional[str] = None, api_base: Optional[str] = None, ) -> dict: - """Google AI Studio sends api key in query params""" + if api_key is not None: + headers["x-api-key"] = api_key return headers @staticmethod @@ -53,7 +68,7 @@ class VLLMModelInfo(BaseLLMModelInfo): endpoint = "/v1/models" if api_base is None or api_key is None: raise ValueError( - "GEMINI_API_BASE or GEMINI_API_KEY is not set. Please set the environment variable, to query Gemini's `/models` endpoint." + "VLLM_API_BASE or VLLM_API_KEY is not set. Please set the environment variable, to query VLLM's `/models` endpoint." ) url = _add_path_to_api_base(api_base, endpoint) diff --git a/litellm/llms/volcengine/__init__.py b/litellm/llms/volcengine/__init__.py new file mode 100644 index 00000000000..0887937bed5 --- /dev/null +++ b/litellm/llms/volcengine/__init__.py @@ -0,0 +1,24 @@ +""" +Volcengine LLM Provider +Support for Volcengine (ByteDance) chat and embedding models +""" + +from .chat.transformation import VolcEngineChatConfig +from .common_utils import ( + VolcEngineError, + get_volcengine_base_url, + get_volcengine_headers, +) +from .embedding import VolcEngineEmbeddingConfig + +# For backward compatibility, keep the old class name +VolcEngineConfig = VolcEngineChatConfig + +__all__ = [ + "VolcEngineChatConfig", + "VolcEngineConfig", # backward compatibility + "VolcEngineEmbeddingConfig", + "VolcEngineError", + "get_volcengine_base_url", + "get_volcengine_headers", +] diff --git a/litellm/llms/volcengine.py b/litellm/llms/volcengine/chat/transformation.py similarity index 77% rename from litellm/llms/volcengine.py rename to litellm/llms/volcengine/chat/transformation.py index c878aaf933c..6df1cd38267 100644 --- a/litellm/llms/volcengine.py +++ b/litellm/llms/volcengine/chat/transformation.py @@ -3,7 +3,10 @@ from typing import Optional, Union from litellm.llms.openai_like.chat.transformation import OpenAILikeChatConfig -class VolcEngineConfig(OpenAILikeChatConfig): +class VolcEngineChatConfig(OpenAILikeChatConfig): + """ + Reference: https://www.volcengine.com/docs/82379/1494384 + """ frequency_penalty: Optional[int] = None function_call: Optional[Union[str, dict]] = None functions: Optional[list] = None @@ -81,18 +84,22 @@ class VolcEngineConfig(OpenAILikeChatConfig): ) if "thinking" in optional_params: + """ + The `thinking` parameters of VolcEngine model has different default values. + See the docs for details. + Refrence: https://www.volcengine.com/docs/82379/1449737#0002 + """ thinking_value = optional_params.pop("thinking") - - # Handle disabled thinking case - don't add to extra_body if disabled - if ( - thinking_value is not None - and isinstance(thinking_value, dict) - and thinking_value.get("type") == "disabled" - ): - # Skip adding thinking parameter when it's disabled - pass - else: - # Add thinking parameter to extra_body for all other cases - optional_params.setdefault("extra_body", {})["thinking"] = thinking_value + # Handle using thinking params case - add to extra_body if value is legal + if ( + thinking_value is not None + and isinstance(thinking_value, dict) + and thinking_value.get("type", None) in ["enabled", "disabled", "auto"] # legal values, see docs + ): + # Add thinking parameter to extra_body for all legal cases + optional_params.setdefault("extra_body", {})["thinking"] = thinking_value + else: + # Skip adding thinking parameter when it's not set or has invalid value + pass return optional_params diff --git a/litellm/llms/volcengine/common_utils.py b/litellm/llms/volcengine/common_utils.py new file mode 100644 index 00000000000..0c8d3daebdc --- /dev/null +++ b/litellm/llms/volcengine/common_utils.py @@ -0,0 +1,62 @@ +""" +Common utilities for Volcengine LLM provider +""" + +from typing import Optional + +import httpx + +from litellm.llms.base_llm.chat.transformation import BaseLLMException + + +class VolcEngineError(BaseLLMException): + """ + Custom exception class for Volcengine provider errors. + """ + + def __init__( + self, status_code: int, message: str, headers: Optional[httpx.Headers] = None + ): + self.status_code = status_code + self.message = message + self.headers = headers or httpx.Headers() + super().__init__( + status_code=status_code, message=message, headers=dict(self.headers) + ) + + +def get_volcengine_base_url(api_base: Optional[str] = None) -> str: + """ + Get the base URL for Volcengine API calls. + + Args: + api_base: Optional custom API base URL + + Returns: + The base URL to use for API calls + """ + if api_base: + return api_base + return "https://ark.cn-beijing.volces.com" + + +def get_volcengine_headers(api_key: str, extra_headers: Optional[dict] = None) -> dict: + """ + Get headers for Volcengine API calls. + + Args: + api_key: The API key for authentication + extra_headers: Optional additional headers + + Returns: + Dictionary of headers + """ + headers = { + "Content-Type": "application/json", + "Authorization": f"Bearer {api_key}", + } + + if extra_headers: + headers.update(extra_headers) + + return headers diff --git a/litellm/llms/volcengine/embedding/__init__.py b/litellm/llms/volcengine/embedding/__init__.py new file mode 100644 index 00000000000..7b3efc4f961 --- /dev/null +++ b/litellm/llms/volcengine/embedding/__init__.py @@ -0,0 +1,7 @@ +""" +Volcengine Embedding Module +""" + +from .transformation import VolcEngineEmbeddingConfig + +__all__ = ["VolcEngineEmbeddingConfig"] diff --git a/litellm/llms/volcengine/embedding/transformation.py b/litellm/llms/volcengine/embedding/transformation.py new file mode 100644 index 00000000000..20747b76725 --- /dev/null +++ b/litellm/llms/volcengine/embedding/transformation.py @@ -0,0 +1,211 @@ +""" +Volcengine Embedding Transformation +Transforms OpenAI embedding requests to Volcengine format +""" + +from typing import List, Optional, Union, Dict, Any +import httpx +from litellm.types.llms.openai import AllEmbeddingInputValues, AllMessageValues +from litellm.types.utils import EmbeddingResponse +from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj +from litellm.llms.base_llm.embedding.transformation import BaseEmbeddingConfig +from litellm.llms.base_llm.chat.transformation import BaseLLMException +from ..common_utils import get_volcengine_base_url, get_volcengine_headers + + +class VolcEngineEmbeddingConfig(BaseEmbeddingConfig): + """ + Configuration class for Volcengine embedding models. + Reference: https://ark.cn-beijing.volces.com/api/v3/embeddings + """ + + def __init__( + self, + encoding_format: Optional[str] = None, + ) -> None: + locals_ = locals().copy() + for key, value in locals_.items(): + if key != "self" and value is not None: + setattr(self.__class__, key, value) + + @classmethod + def get_config(cls): + return super().get_config() + + def get_supported_openai_params(self, model: str) -> List[str]: + """ + Get the list of OpenAI parameters supported by Volcengine embedding models. + + Args: + model: The model name + + Returns: + List of supported parameter names + """ + return [ + "encoding_format", + "user", + "extra_headers", + ] + + def get_complete_url( + self, + api_base: Optional[str], + api_key: Optional[str], + model: str, + optional_params: dict, + litellm_params: dict, + stream: Optional[bool] = None, + ) -> str: + """ + Get the complete URL for volcengine embedding API calls. + + Args: + api_base: Optional custom API base URL + api_key: API key (not used for URL construction) + model: Model name (not used for URL construction) + optional_params: Optional parameters (not used for URL construction) + litellm_params: LiteLLM parameters (not used for URL construction) + stream: Stream parameter (not used for URL construction) + + Returns: + Complete URL for the embedding API endpoint + """ + base_url = get_volcengine_base_url(api_base) + # Construct the complete URL with /embeddings endpoint + if base_url.endswith("/api/v3"): + return f"{base_url}/embeddings" + else: + return f"{base_url}/api/v3/embeddings" + + def map_openai_params( + self, + non_default_params: Dict[str, Any], + optional_params: Dict[str, Any], + model: str, + drop_params: bool, + ) -> Dict[str, Any]: + """ + Map OpenAI embedding parameters to Volcengine format. + + Args: + non_default_params: Parameters that are not default values + optional_params: Optional parameters dict to update + model: The model name + drop_params: Whether to drop unsupported parameters + + Returns: + Updated optional_params dict + """ + for param, value in non_default_params.items(): + if param == "encoding_format": + # Volcengine supports: float, base64, null + if value in ["float", "base64", None]: + optional_params["encoding_format"] = value + else: + if not drop_params: + raise ValueError( + f"Unsupported encoding_format: {value}. Volcengine supports: float, base64, null" + ) + elif param == "user": + # Keep user parameter as-is + optional_params["user"] = value + elif param in self.get_supported_openai_params(model): + optional_params[param] = value + elif not drop_params: + raise ValueError(f"Unsupported parameter for Volcengine: {param}") + + return optional_params + + + + def transform_embedding_request( + self, + model: str, + input: AllEmbeddingInputValues, + optional_params: dict, + headers: dict, + ) -> dict: + """Transform embedding request to Volcengine format""" + # Prepare request data (only the JSON body, not the full request) + data = { + "model": model, + "input": input if isinstance(input, list) else [input], + } + + # Add optional parameters from optional_params + if "encoding_format" in optional_params: + encoding_format = optional_params["encoding_format"] + if encoding_format is not None: + data["encoding_format"] = encoding_format + + if "user" in optional_params: + user = optional_params["user"] + if user is not None: + data["user"] = user + + return data + + def transform_embedding_response( + self, + model: str, + raw_response: httpx.Response, + model_response: EmbeddingResponse, + logging_obj: LiteLLMLoggingObj, + api_key: Optional[str], + request_data: dict, + optional_params: dict, + litellm_params: dict, + ) -> EmbeddingResponse: + """Transform Volcengine response to EmbeddingResponse""" + try: + response_json = raw_response.json() + except Exception as e: + raise ValueError(f"Failed to parse Volcengine response as JSON: {str(e)}") + + # Volcengine response format matches OpenAI format closely + # Just need to ensure all required fields are present + transformed_response = { + "object": "list", + "data": response_json.get("data", []), + "model": response_json.get("model", model), + "usage": response_json.get("usage", {}), + } + + # Add id if present + if "id" in response_json: + transformed_response["id"] = response_json["id"] + + # Create EmbeddingResponse from transformed data + return EmbeddingResponse(**transformed_response) + + def validate_environment( + self, + headers: dict, + model: str, + messages: List[AllMessageValues], + optional_params: dict, + litellm_params: dict, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + ) -> dict: + """Validate environment and return headers""" + # Get Volcengine headers + if api_key is None: + raise ValueError("api_key is required for Volcengine authentication") + volcengine_headers = get_volcengine_headers(api_key) + return {**headers, **volcengine_headers} + + def get_error_class( + self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers] + ) -> BaseLLMException: + """Get error class for Volcengine errors""" + from ..common_utils import VolcEngineError + # Convert dict to httpx.Headers if needed + if isinstance(headers, dict): + headers = httpx.Headers(headers) + return VolcEngineError( + status_code=status_code, + message=error_message, + headers=headers, + ) diff --git a/litellm/llms/wandb/__init__.py b/litellm/llms/wandb/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/litellm/llms/wandb/chat/__init__.py b/litellm/llms/wandb/chat/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/litellm/llms/wandb/chat/transformation.py b/litellm/llms/wandb/chat/transformation.py new file mode 100644 index 00000000000..1cb2ab492bc --- /dev/null +++ b/litellm/llms/wandb/chat/transformation.py @@ -0,0 +1,27 @@ +""" +Wandb Chat Completions API - Transformation + +This is OpenAI compatible - no translation needed / occurs +""" + +from litellm.llms.openai.chat.gpt_transformation import OpenAIGPTConfig + + +class WandbConfig(OpenAIGPTConfig): + def map_openai_params( + self, + non_default_params: dict, + optional_params: dict, + model: str, + drop_params: bool, + ) -> dict: + """ + map max_completion_tokens param to max_tokens + """ + supported_openai_params = self.get_supported_openai_params(model=model) + for param, value in non_default_params.items(): + if param == "max_completion_tokens": + optional_params["max_tokens"] = value + elif param in supported_openai_params: + optional_params[param] = value + return optional_params diff --git a/litellm/llms/xai/chat/transformation.py b/litellm/llms/xai/chat/transformation.py index 5a488876cd9..b01f6c18466 100644 --- a/litellm/llms/xai/chat/transformation.py +++ b/litellm/llms/xai/chat/transformation.py @@ -31,7 +31,6 @@ class XAIChatConfig(OpenAIGPTConfig): def get_supported_openai_params(self, model: str) -> list: base_openai_params = [ - "frequency_penalty", "logit_bias", "logprobs", "max_tokens", @@ -50,8 +49,22 @@ class XAIChatConfig(OpenAIGPTConfig): "web_search_options", ] # for some reason, grok-3-mini does not support stop tokens + ######################################################### + # stop tokens check + ######################################################### if self._supports_stop_reason(model): base_openai_params.append("stop") + + + ######################################################### + # frequency penalty check + ######################################################### + if self._supports_frequency_penalty(model): + base_openai_params.append("frequency_penalty") + + ######################################################### + # reasoning check + ######################################################### try: if litellm.supports_reasoning( model=model, custom_llm_provider=self.custom_llm_provider @@ -67,6 +80,20 @@ class XAIChatConfig(OpenAIGPTConfig): return False elif "grok-4" in model: return False + elif "grok-code-fast" in model: + return False + return True + + def _supports_frequency_penalty(self, model: str) -> bool: + """ + From manual testing grok-4 does not support `frequency_penalty` + + When sent the model fails from xAI API + """ + if "grok-4" in model: + return False + if "grok-code-fast" in model: + return False return True def map_openai_params( diff --git a/litellm/llms/xai/cost_calculator.py b/litellm/llms/xai/cost_calculator.py new file mode 100644 index 00000000000..62a48080d1c --- /dev/null +++ b/litellm/llms/xai/cost_calculator.py @@ -0,0 +1,54 @@ +""" +Helper util for handling XAI-specific cost calculation +- e.g.: reasoning tokens for grok models +""" + +from typing import Tuple, Union + +from litellm.types.utils import Usage +from litellm.utils import get_model_info + + +def cost_per_token(model: str, usage: Usage) -> Tuple[float, float]: + """ + Calculates the cost per token for a given XAI model, prompt tokens, and completion tokens. + + Input: + - model: str, the model name without provider prefix + - usage: LiteLLM Usage block, containing XAI-specific usage information + + Returns: + Tuple[float, float] - prompt_cost_in_usd, completion_cost_in_usd + """ + ## GET MODEL INFO + model_info = get_model_info(model=model, custom_llm_provider="xai") + + def _safe_float_cast( + value: Union[str, int, float, None, object], default: float = 0.0 + ) -> float: + """Safely cast a value to float with proper type handling for mypy.""" + if value is None: + return default + try: + return float(value) # type: ignore + except (ValueError, TypeError): + return default + + ## CALCULATE INPUT COST + input_cost_per_token = _safe_float_cast(model_info.get("input_cost_per_token")) + prompt_cost: float = (usage.prompt_tokens or 0) * input_cost_per_token + + ## CALCULATE OUTPUT COST + output_cost_per_token = _safe_float_cast(model_info.get("output_cost_per_token")) + + # For XAI models, completion is billed as (visible completion tokens + reasoning tokens) + completion_tokens = int(getattr(usage, "completion_tokens", 0) or 0) + reasoning_tokens = 0 + if hasattr(usage, "completion_tokens_details") and usage.completion_tokens_details: + reasoning_tokens = int( + getattr(usage.completion_tokens_details, "reasoning_tokens", 0) or 0 + ) + + completion_cost = (completion_tokens + reasoning_tokens) * output_cost_per_token + + return prompt_cost, completion_cost diff --git a/litellm/main.py b/litellm/main.py index c8442d483e9..f0b1820cbb5 100644 --- a/litellm/main.py +++ b/litellm/main.py @@ -17,7 +17,7 @@ import random import sys import time import traceback -import uuid +from litellm._uuid import uuid from concurrent import futures from concurrent.futures import FIRST_COMPLETED, ThreadPoolExecutor, wait from copy import deepcopy @@ -36,8 +36,12 @@ from typing import ( Union, cast, get_args, + TYPE_CHECKING, ) +if TYPE_CHECKING: + from aiohttp import ClientSession + import dotenv import httpx import openai @@ -61,6 +65,9 @@ from litellm.exceptions import LiteLLMUnknownProvider from litellm.integrations.custom_logger import CustomLogger from litellm.litellm_core_utils.audio_utils.utils import get_audio_file_for_health_check from litellm.litellm_core_utils.dd_tracing import tracer +from litellm.litellm_core_utils.get_provider_specific_headers import ( + ProviderSpecificHeaderUtils, +) from litellm.litellm_core_utils.health_check_utils import ( _create_health_check_response, _filter_model_params, @@ -113,6 +120,7 @@ from litellm.utils import ( from ._logging import verbose_logger from .caching.caching import disable_cache, enable_cache, update_cache +from .litellm_core_utils.core_helpers import safe_deep_copy from .litellm_core_utils.fallback_utils import ( async_completion_with_fallbacks, completion_with_fallbacks, @@ -149,6 +157,7 @@ from .llms.databricks.embed.handler import DatabricksEmbeddingHandler from .llms.deprecated_providers import aleph_alpha, palm from .llms.gemini.common_utils import get_api_key_from_env from .llms.groq.chat.handler import GroqChatCompletion +from .llms.heroku.chat.transformation import HerokuChatConfig from .llms.huggingface.embedding.handler import HuggingFaceEmbedding from .llms.nlp_cloud.chat.handler import completion as nlp_cloud_chat_completion from .llms.oci.chat.transformation import OCIChatConfig @@ -160,6 +169,7 @@ from .llms.openai.openai import OpenAIChatCompletion from .llms.openai.transcriptions.handler import OpenAIAudioTranscription from .llms.openai_like.chat.handler import OpenAILikeChatHandler from .llms.openai_like.embedding.handler import OpenAILikeEmbeddingHandler +from .llms.ovhcloud.chat.transformation import OVHCloudChatConfig from .llms.petals.completion import handler as petals_handler from .llms.predibase.chat.handler import PredibaseChatCompletion from .llms.replicate.chat.handler import completion as replicate_chat_completion @@ -253,7 +263,9 @@ base_llm_http_handler = BaseLLMHTTPHandler() base_llm_aiohttp_handler = BaseLLMAIOHTTPHandler() sagemaker_chat_completion = SagemakerChatHandler() bytez_transformation = BytezChatConfig() +heroku_transformation = HerokuChatConfig() oci_transformation = OCIChatConfig() +ovhcloud_transformation = OVHCloudChatConfig() ####### COMPLETION ENDPOINTS ################ @@ -353,7 +365,10 @@ async def acompletion( logprobs: Optional[bool] = None, top_logprobs: Optional[int] = None, deployment_id=None, - reasoning_effort: Optional[Literal["minimal", "low", "medium", "high"]] = None, + reasoning_effort: Optional[ + Literal["none", "minimal", "low", "medium", "high", "default"] + ] = None, + safety_identifier: Optional[str] = None, # set api_base, api_version, api_key base_url: Optional[str] = None, api_version: Optional[str] = None, @@ -363,6 +378,8 @@ async def acompletion( # Optional liteLLM function params thinking: Optional[AnthropicThinkingParam] = None, web_search_options: Optional[OpenAIWebSearchOptions] = None, + # Session management + shared_session: Optional["ClientSession"] = None, **kwargs, ) -> Union[ModelResponse, CustomStreamWrapper]: """ @@ -455,6 +472,16 @@ async def acompletion( ######################################################### ######################################################### + # Log shared session usage + if shared_session is not None: + verbose_logger.debug( + f"🔄 SHARED SESSION: acompletion called with shared_session (ID: {id(shared_session)})" + ) + else: + verbose_logger.debug( + "🔄 NO SHARED SESSION: acompletion called without shared_session" + ) + # Adjusted to use explicit arguments instead of *args and **kwargs completion_kwargs = { "model": model, @@ -490,14 +517,18 @@ async def acompletion( "api_key": api_key, "model_list": model_list, "reasoning_effort": reasoning_effort, + "safety_identifier": safety_identifier, "extra_headers": extra_headers, "acompletion": True, # assuming this is a required parameter "thinking": thinking, "web_search_options": web_search_options, + "shared_session": shared_session, } if custom_llm_provider is None: _, custom_llm_provider, _, _ = get_llm_provider( - model=model, api_base=completion_kwargs.get("base_url", None) + model=model, + custom_llm_provider=custom_llm_provider, + api_base=completion_kwargs.get("base_url", None), ) fallbacks = fallbacks or litellm.model_fallbacks @@ -892,7 +923,9 @@ def completion( # type: ignore # noqa: PLR0915 logit_bias: Optional[dict] = None, user: Optional[str] = None, # openai v1.0+ new params - reasoning_effort: Optional[Literal["minimal", "low", "medium", "high"]] = None, + reasoning_effort: Optional[ + Literal["none", "minimal", "low", "medium", "high", "default"] + ] = None, response_format: Optional[Union[dict, Type[BaseModel]]] = None, seed: Optional[int] = None, tools: Optional[List] = None, @@ -903,6 +936,7 @@ def completion( # type: ignore # noqa: PLR0915 web_search_options: Optional[OpenAIWebSearchOptions] = None, deployment_id=None, extra_headers: Optional[dict] = None, + safety_identifier: Optional[str] = None, # soon to be deprecated params by OpenAI functions: Optional[List] = None, function_call: Optional[str] = None, @@ -913,6 +947,8 @@ def completion( # type: ignore # noqa: PLR0915 model_list: Optional[list] = None, # pass in a list of api_base,keys, etc. # Optional liteLLM function params thinking: Optional[AnthropicThinkingParam] = None, + # Session management + shared_session: Optional["ClientSession"] = None, **kwargs, ) -> Union[ModelResponse, CustomStreamWrapper]: """ @@ -1107,11 +1143,13 @@ def completion( # type: ignore # noqa: PLR0915 api_key=api_key, ) - if ( - provider_specific_header is not None - and provider_specific_header["custom_llm_provider"] == custom_llm_provider - ): - headers.update(provider_specific_header["extra_headers"]) + if provider_specific_header is not None: + headers.update( + ProviderSpecificHeaderUtils.get_provider_specific_headers( + provider_specific_header=provider_specific_header, + custom_llm_provider=custom_llm_provider, + ) + ) if model_response is not None and hasattr(model_response, "_hidden_params"): model_response._hidden_params["custom_llm_provider"] = custom_llm_provider @@ -1240,6 +1278,7 @@ def completion( # type: ignore # noqa: PLR0915 "reasoning_effort": reasoning_effort, "thinking": thinking, "web_search_options": web_search_options, + "safety_identifier": safety_identifier, "allowed_openai_params": kwargs.get("allowed_openai_params"), } optional_params = get_optional_params( @@ -1253,6 +1292,7 @@ def completion( # type: ignore # noqa: PLR0915 additional_drop_params=kwargs.get("additional_drop_params"), remove_sensitive_keys=True, add_provider_specific_params=True, + provider_config=provider_config, ) if litellm.add_function_to_prompt and optional_params.get( @@ -1315,6 +1355,7 @@ def completion( # type: ignore # noqa: PLR0915 azure_scope=kwargs.get("azure_scope"), max_retries=max_retries, timeout=timeout, + litellm_request_debug=kwargs.get("litellm_request_debug", False), ) cast(LiteLLMLoggingObj, logging).update_environment_variables( model=model, @@ -1574,6 +1615,7 @@ def completion( # type: ignore # noqa: PLR0915 logging_obj=logging, optional_params=optional_params, litellm_params=litellm_params, + shared_session=shared_session, timeout=timeout, # type: ignore client=client, custom_llm_provider=custom_llm_provider, @@ -1620,6 +1662,7 @@ def completion( # type: ignore # noqa: PLR0915 logging_obj=logging, optional_params=optional_params, litellm_params=litellm_params, + shared_session=shared_session, timeout=timeout, # type: ignore client=client, # pass AsyncOpenAI, OpenAI client custom_llm_provider=custom_llm_provider, @@ -1749,6 +1792,7 @@ def completion( # type: ignore # noqa: PLR0915 logging_obj=logging, optional_params=optional_params, litellm_params=litellm_params, + shared_session=shared_session, timeout=timeout, # type: ignore client=client, custom_llm_provider=custom_llm_provider, @@ -1765,6 +1809,36 @@ def completion( # type: ignore # noqa: PLR0915 additional_args={"headers": headers}, ) raise e + elif custom_llm_provider == "heroku": + try: + response = base_llm_http_handler.completion( + model=model, + messages=messages, + headers=headers, + model_response=model_response, + api_key=api_key, + api_base=api_base, + acompletion=acompletion, + logging_obj=logging, + optional_params=optional_params, + litellm_params=litellm_params, + shared_session=shared_session, + timeout=timeout, + client=client, + custom_llm_provider=custom_llm_provider, + encoding=encoding, + stream=stream, + provider_config=provider_config, + ) + except Exception as e: + logging.post_call( + input=messages, + api_key=api_key, + original_response=str(e), + additional_args={"headers": headers}, + ) + raise e + elif custom_llm_provider == "xai": ## COMPLETION CALL try: @@ -1779,6 +1853,7 @@ def completion( # type: ignore # noqa: PLR0915 logging_obj=logging, optional_params=optional_params, litellm_params=litellm_params, + shared_session=shared_session, timeout=timeout, # type: ignore client=client, custom_llm_provider=custom_llm_provider, @@ -1830,6 +1905,7 @@ def completion( # type: ignore # noqa: PLR0915 model_response=model_response, optional_params=optional_params, litellm_params=litellm_params, + shared_session=shared_session, custom_llm_provider=custom_llm_provider, timeout=timeout, headers=headers, @@ -1903,6 +1979,7 @@ def completion( # type: ignore # noqa: PLR0915 logging_obj=logging, optional_params=optional_params, litellm_params=litellm_params, + shared_session=shared_session, timeout=timeout, client=client, custom_llm_provider=custom_llm_provider, @@ -1929,6 +2006,7 @@ def completion( # type: ignore # noqa: PLR0915 or custom_llm_provider == "openai" or custom_llm_provider == "together_ai" or custom_llm_provider == "nebius" + or custom_llm_provider == "wandb" or custom_llm_provider in litellm.openai_compatible_providers or "ft:gpt-3.5-turbo" in model # finetune gpt-3.5-turbo ): # allow user to make an openai call with a custom base @@ -1993,6 +2071,7 @@ def completion( # type: ignore # noqa: PLR0915 optional_params=optional_params, timeout=timeout, litellm_params=litellm_params, + shared_session=shared_session, acompletion=acompletion, stream=stream, api_key=api_key, @@ -2019,6 +2098,7 @@ def completion( # type: ignore # noqa: PLR0915 client=client, # pass AsyncOpenAI, OpenAI client organization=organization, custom_llm_provider=custom_llm_provider, + shared_session=shared_session, ) except Exception as e: ## LOGGING - log the original exception returned @@ -2059,6 +2139,7 @@ def completion( # type: ignore # noqa: PLR0915 optional_params=optional_params, timeout=timeout, litellm_params=litellm_params, + shared_session=shared_session, acompletion=acompletion, stream=stream, api_key=api_key, @@ -2146,6 +2227,7 @@ def completion( # type: ignore # noqa: PLR0915 model_response=model_response, optional_params=optional_params, litellm_params=litellm_params, + shared_session=shared_session, custom_llm_provider="clarifai", timeout=timeout, headers=headers, @@ -2169,8 +2251,18 @@ def completion( # type: ignore # noqa: PLR0915 or "https://api.anthropic.com/v1/complete" ) - if api_base is not None and not api_base.endswith("/v1/complete"): + # Check if we should disable automatic URL suffix appending + disable_url_suffix = get_secret_bool("LITELLM_ANTHROPIC_DISABLE_URL_SUFFIX") + if ( + api_base is not None + and not disable_url_suffix + and not api_base.endswith("/v1/complete") + ): api_base += "/v1/complete" + elif disable_url_suffix: + verbose_logger.debug( + "LITELLM_ANTHROPIC_DISABLE_URL_SUFFIX is set, skipping /v1/complete suffix" + ) response = base_llm_http_handler.completion( model=model, @@ -2181,6 +2273,7 @@ def completion( # type: ignore # noqa: PLR0915 model_response=model_response, optional_params=optional_params, litellm_params=litellm_params, + shared_session=shared_session, custom_llm_provider="anthropic_text", timeout=timeout, headers=headers, @@ -2206,8 +2299,18 @@ def completion( # type: ignore # noqa: PLR0915 or "https://api.anthropic.com/v1/messages" ) - if api_base is not None and not api_base.endswith("/v1/messages"): + # Check if we should disable automatic URL suffix appending + disable_url_suffix = get_secret_bool("LITELLM_ANTHROPIC_DISABLE_URL_SUFFIX") + if ( + api_base is not None + and not disable_url_suffix + and not api_base.endswith("/v1/messages") + ): api_base += "/v1/messages" + elif disable_url_suffix: + verbose_logger.debug( + "LITELLM_ANTHROPIC_DISABLE_URL_SUFFIX is set, skipping /v1/messages suffix" + ) response = anthropic_chat_completions.completion( model=model, @@ -2324,47 +2427,7 @@ def completion( # type: ignore # noqa: PLR0915 ) return response response = model_response - elif custom_llm_provider == "cohere": - cohere_key = ( - api_key - or litellm.cohere_key - or get_secret("COHERE_API_KEY") - or get_secret("CO_API_KEY") - or litellm.api_key - ) - - api_base = ( - api_base - or litellm.api_base - or get_secret("COHERE_API_BASE") - or "https://api.cohere.ai/v1/generate" - ) - - headers = headers or litellm.headers or {} - if headers is None: - headers = {} - - if extra_headers is not None: - headers.update(extra_headers) - - response = base_llm_http_handler.completion( - model=model, - stream=stream, - messages=messages, - acompletion=acompletion, - api_base=api_base, - model_response=model_response, - optional_params=optional_params, - litellm_params=litellm_params, - custom_llm_provider="cohere", - timeout=timeout, - headers=headers, - encoding=encoding, - api_key=cohere_key, - logging_obj=logging, # model call logging done inside the class as we make need to modify I/O to fit aleph alpha's requirements - client=client, - ) - elif custom_llm_provider == "cohere_chat": + elif custom_llm_provider == "cohere_chat" or custom_llm_provider == "cohere": cohere_key = ( api_key or litellm.cohere_key @@ -2396,6 +2459,7 @@ def completion( # type: ignore # noqa: PLR0915 model_response=model_response, optional_params=optional_params, litellm_params=litellm_params, + shared_session=shared_session, custom_llm_provider="cohere_chat", timeout=timeout, headers=headers, @@ -2479,6 +2543,37 @@ def completion( # type: ignore # noqa: PLR0915 encoding=encoding, stream=stream, ) + elif custom_llm_provider == "compactifai": + api_key = ( + api_key + or get_secret_str("COMPACTIFAI_API_KEY") + or litellm.api_key + ) + + api_base = ( + api_base + or "https://api.compactif.ai/v1" + ) + + ## COMPLETION CALL + response = base_llm_http_handler.completion( + model=model, + messages=messages, + headers=headers, + model_response=model_response, + api_key=api_key, + api_base=api_base, + acompletion=acompletion, + logging_obj=logging, + optional_params=optional_params, + litellm_params=litellm_params, + timeout=timeout, + client=client, + custom_llm_provider=custom_llm_provider, + encoding=encoding, + stream=stream, + provider_config=provider_config, + ) elif custom_llm_provider == "oobabooga": custom_llm_provider = "oobabooga" model_response = oobabooga.completion( @@ -2632,6 +2727,7 @@ def completion( # type: ignore # noqa: PLR0915 model_response=model_response, optional_params=optional_params, litellm_params=litellm_params, + shared_session=shared_session, custom_llm_provider="openrouter", timeout=timeout, headers=headers, @@ -2644,6 +2740,69 @@ def completion( # type: ignore # noqa: PLR0915 logging.post_call( input=messages, api_key=openai.api_key, original_response=response ) + elif custom_llm_provider == "vercel_ai_gateway": + api_base = ( + api_base + or litellm.api_base + or get_secret_str("VERCEL_AI_GATEWAY_API_BASE") + or "https://ai-gateway.vercel.sh/v1" + ) + + api_key = ( + api_key or litellm.api_key or get_secret("VERCEL_AI_GATEWAY_API_KEY") + ) + + vercel_site_url = get_secret("VERCEL_SITE_URL") or "https://litellm.ai" + vercel_app_name = get_secret("VERCEL_APP_NAME") or "liteLLM" + + vercel_headers = { + "http-referer": vercel_site_url, + "x-title": vercel_app_name, + } + + _headers = headers or litellm.headers + if _headers: + vercel_headers.update(_headers) + + headers = vercel_headers + + ## Load Config + config = litellm.VercelAIGatewayConfig.get_config() + for k, v in config.items(): + if k == "extra_body": + # we use openai 'extra_body' to pass vercel specific params - providerOptions + if "extra_body" in optional_params: + optional_params[k].update(v) + else: + optional_params[k] = v + elif k not in optional_params: + optional_params[k] = v + + data = {"model": model, "messages": messages, **optional_params} + + ## COMPLETION CALL + response = base_llm_http_handler.completion( + model=model, + stream=stream, + messages=messages, + acompletion=acompletion, + api_base=api_base, + model_response=model_response, + optional_params=optional_params, + litellm_params=litellm_params, + shared_session=shared_session, + custom_llm_provider="vercel_ai_gateway", + timeout=timeout, + headers=headers, + encoding=encoding, + api_key=api_key, + logging_obj=logging, # model call logging done inside the class as we make need to modify I/O to fit aleph alpha's requirements + client=client, + ) + ## LOGGING + logging.post_call( + input=messages, api_key=openai.api_key, original_response=response + ) elif ( custom_llm_provider == "together_ai" or ("togethercomputer" in model) @@ -2684,8 +2843,7 @@ def completion( # type: ignore # noqa: PLR0915 ) api_base = api_base or litellm.api_base or get_secret("GEMINI_API_BASE") - - new_params = deepcopy(optional_params) + new_params = safe_deep_copy(optional_params or {}) response = vertex_chat_completion.completion( # type: ignore model=model, messages=messages, @@ -2729,7 +2887,7 @@ def completion( # type: ignore # noqa: PLR0915 api_base = api_base or litellm.api_base or get_secret("VERTEXAI_API_BASE") - new_params = deepcopy(optional_params) + new_params = safe_deep_copy(optional_params or {}) if vertex_partner_models_chat_completion.is_vertex_partner_model(model): model_response = vertex_partner_models_chat_completion.completion( model=model, @@ -3119,6 +3277,7 @@ def completion( # type: ignore # noqa: PLR0915 model_response=model_response, optional_params=optional_params, litellm_params=litellm_params, + shared_session=shared_session, custom_llm_provider="watsonx_text", timeout=timeout, headers=headers, @@ -3172,6 +3331,7 @@ def completion( # type: ignore # noqa: PLR0915 model_response=model_response, optional_params=optional_params, litellm_params=litellm_params, + shared_session=shared_session, custom_llm_provider="ollama", timeout=timeout, headers=headers, @@ -3205,6 +3365,7 @@ def completion( # type: ignore # noqa: PLR0915 model_response=model_response, optional_params=optional_params, litellm_params=litellm_params, + shared_session=shared_session, custom_llm_provider="ollama_chat", timeout=timeout, headers=headers, @@ -3225,6 +3386,7 @@ def completion( # type: ignore # noqa: PLR0915 model_response=model_response, optional_params=optional_params, litellm_params=litellm_params, + shared_session=shared_session, custom_llm_provider=custom_llm_provider, timeout=timeout, headers=headers, @@ -3257,6 +3419,7 @@ def completion( # type: ignore # noqa: PLR0915 model_response=model_response, optional_params=optional_params, litellm_params=litellm_params, + shared_session=shared_session, custom_llm_provider="cloudflare", timeout=timeout, headers=headers, @@ -3310,6 +3473,7 @@ def completion( # type: ignore # noqa: PLR0915 logging_obj=logging, optional_params=optional_params, litellm_params=litellm_params, + shared_session=shared_session, timeout=timeout, # type: ignore client=client, custom_llm_provider=custom_llm_provider, @@ -3338,6 +3502,7 @@ def completion( # type: ignore # noqa: PLR0915 model_response=model_response, optional_params=optional_params, litellm_params=litellm_params, + shared_session=shared_session, custom_llm_provider="gradient_ai", timeout=timeout, headers=headers, @@ -3375,6 +3540,42 @@ def completion( # type: ignore # noqa: PLR0915 pass + elif custom_llm_provider == "ovhcloud" or model in litellm.ovhcloud_models: + api_key = ( + api_key + or litellm.ovhcloud_key + or get_secret_str("OVHCLOUD_API_KEY") + or litellm.api_key + ) + + api_base = ( + api_base + or litellm.api_base + or get_secret_str("OVHCLOUD_API_BASE") + or "https://oai.endpoints.kepler.ai.cloud.ovh.net/v1" + ) + + response = base_llm_http_handler.completion( + model=model, + messages=messages, + headers=headers, + model_response=model_response, + api_key=api_key, + api_base=api_base, + acompletion=acompletion, + logging_obj=logging, + optional_params=optional_params, + litellm_params=litellm_params, + timeout=timeout, # type: ignore + client=client, + custom_llm_provider=custom_llm_provider, + encoding=encoding, + stream=stream, + provider_config=ovhcloud_transformation, + ) + + pass + elif custom_llm_provider == "custom": url = litellm.api_base or api_base or "" if url is None or url == "": @@ -3455,7 +3656,7 @@ def completion( # type: ignore # noqa: PLR0915 async_fn=acompletion, stream=stream, custom_llm=custom_handler ) - headers = headers or litellm.headers + headers = headers or litellm.headers or {} ## CALL FUNCTION response = handler_fn( @@ -3579,7 +3780,7 @@ async def aembedding(*args, **kwargs) -> EmbeddingResponse: model = args[0] if len(args) > 0 else kwargs["model"] ### PASS ARGS TO Embedding ### kwargs["aembedding"] = True - custom_llm_provider = None + custom_llm_provider = kwargs.get("custom_llm_provider", None) try: # Use a partial function to pass your keyword arguments func = partial(embedding, *args, **kwargs) @@ -3589,7 +3790,9 @@ async def aembedding(*args, **kwargs) -> EmbeddingResponse: func_with_context = partial(ctx.run, func) _, custom_llm_provider, _, _ = get_llm_provider( - model=model, api_base=kwargs.get("api_base", None) + model=model, + custom_llm_provider=custom_llm_provider, + api_base=kwargs.get("api_base", None), ) # Await normally @@ -3731,7 +3934,7 @@ def embedding( # noqa: PLR0915 max_retries = kwargs.get("max_retries", None) litellm_logging_obj: LiteLLMLoggingObj = kwargs.get("litellm_logging_obj") # type: ignore mock_response: Optional[List[float]] = kwargs.get("mock_response", None) # type: ignore - azure_ad_token_provider = kwargs.pop("azure_ad_token_provider", None) + azure_ad_token_provider = kwargs.get("azure_ad_token_provider", None) aembedding = kwargs.get("aembedding", None) extra_headers = kwargs.get("extra_headers", None) headers = kwargs.get("headers", None) @@ -4251,6 +4454,27 @@ def embedding( # noqa: PLR0915 or "api.studio.nebius.ai/v1" ) + response = openai_chat_completions.embedding( + model=model, + input=input, + api_base=api_base, + api_key=api_key, + logging_obj=logging, + timeout=timeout, + model_response=EmbeddingResponse(), + optional_params=optional_params, + client=client, + aembedding=aembedding, + ) + elif custom_llm_provider == "wandb": + api_key = api_key or litellm.api_key or get_secret_str("WANDB_API_KEY") + api_base = ( + api_base + or litellm.api_base + or get_secret_str("WANDB_API_BASE") + or "https://api.inference.wandb.ai/v1" + ) + response = openai_chat_completions.embedding( model=model, input=input, @@ -4411,6 +4635,58 @@ def embedding( # noqa: PLR0915 client=client, aembedding=aembedding, ) + elif custom_llm_provider == "volcengine": + volcengine_key = ( + api_key + or litellm.api_key + or get_secret_str("ARK_API_KEY") + or get_secret_str("VOLCENGINE_API_KEY") + ) + if volcengine_key is None: + raise ValueError( + "Missing API key for Volcengine. Set ARK_API_KEY or VOLCENGINE_API_KEY environment variable or pass api_key parameter." + ) + if extra_headers is not None and isinstance(extra_headers, dict): + headers = extra_headers + else: + headers = {} + response = base_llm_http_handler.embedding( + model=model, + input=input, + timeout=timeout, + custom_llm_provider=custom_llm_provider, + logging_obj=logging, + api_base=api_base, + optional_params=optional_params, + litellm_params={}, + model_response=EmbeddingResponse(), + api_key=volcengine_key, + client=client, + aembedding=aembedding, + headers=headers, + ) + elif custom_llm_provider == "ovhcloud": + api_key = api_key or litellm.api_key or get_secret_str("OVHCLOUD_API_KEY") + api_base = ( + api_base + or litellm.api_base + or get_secret_str("OVHCLOUD_API_BASE") + or "https://oai.endpoints.kepler.ai.cloud.ovh.net/v1" + ) + response = base_llm_http_handler.embedding( + model=model, + input=input, + custom_llm_provider=custom_llm_provider, + api_base=api_base, + api_key=api_key, + logging_obj=logging, + timeout=timeout, + model_response=EmbeddingResponse(), + optional_params=optional_params, + client=client, + aembedding=aembedding, + litellm_params={}, + ) elif custom_llm_provider in litellm._custom_providers: custom_handler: Optional[CustomLLM] = None for item in litellm.custom_provider_map: @@ -5105,7 +5381,10 @@ def transcription( model_response = litellm.utils.TranscriptionResponse() model, custom_llm_provider, dynamic_api_key, api_base = get_llm_provider( - model=model, custom_llm_provider=custom_llm_provider, api_base=api_base + model=model, + custom_llm_provider=custom_llm_provider, + api_base=api_base, + api_key=api_key, ) # type: ignore if dynamic_api_key is not None: @@ -5121,6 +5400,7 @@ def transcription( custom_llm_provider=custom_llm_provider, **non_default_params, ) + litellm_params_dict = get_litellm_params(**kwargs) litellm_logging_obj.update_environment_variables( @@ -5185,9 +5465,8 @@ def transcription( max_retries=max_retries, litellm_params=litellm_params_dict, ) - elif ( - custom_llm_provider == "openai" - or custom_llm_provider in litellm.openai_compatible_providers + elif custom_llm_provider == "openai" or ( + custom_llm_provider in litellm.openai_compatible_providers ): api_base = ( api_base @@ -5202,6 +5481,7 @@ def transcription( or None # default - https://github.com/openai/openai-python/blob/284c1799070c723c6a553337134148a7ab088dd8/openai/util.py#L105 ) # set API KEY + api_key = api_key or litellm.api_key or litellm.openai_key or get_secret("OPENAI_API_KEY") # type: ignore response = openai_audio_transcriptions.audio_transcriptions( model=model, @@ -5218,10 +5498,7 @@ def transcription( provider_config=provider_config, litellm_params=litellm_params_dict, ) - elif custom_llm_provider in [ - LlmProviders.DEEPGRAM.value, - LlmProviders.ELEVENLABS.value, - ]: + elif provider_config is not None: response = base_llm_http_handler.audio_transcriptions( model=model, audio_file=file, @@ -5627,9 +5904,15 @@ async def ahealth_check( input=input or ["test"], ), "audio_speech": lambda: litellm.aspeech( - **_filter_model_params(model_params), + **{ + **_filter_model_params(model_params), + **( + {"voice": "alloy"} + if "voice" not in _filter_model_params(model_params) + else {} + ), + }, input=prompt or "test", - voice="alloy", ), "audio_transcription": lambda: litellm.atranscription( **_filter_model_params(model_params), diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index b1dbe72a5b5..e04c75655db 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -1,2505 +1,889 @@ { - "sample_spec": { - "max_tokens": "LEGACY parameter. set to max_output_tokens if provider specifies it. IF not set to max_input_tokens, if provider specifies it.", - "max_input_tokens": "max input tokens, if the provider specifies it. if not default to max_tokens", - "max_output_tokens": "max output tokens, if the provider specifies it. if not default to max_tokens", - "input_cost_per_token": 0.0, - "output_cost_per_token": 0.0, - "output_cost_per_reasoning_token": 0.0, - "litellm_provider": "one of https://docs.litellm.ai/docs/providers", - "mode": "one of: chat, embedding, completion, image_generation, audio_transcription, audio_speech, image_generation, moderation, rerank", - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_vision": true, - "supports_audio_input": true, - "supports_audio_output": true, - "supports_prompt_caching": true, - "supports_response_schema": true, - "supports_system_messages": true, - "supports_reasoning": true, - "supports_web_search": true, - "search_context_cost_per_query": { - "search_context_size_low": 0.0, - "search_context_size_medium": 0.0, - "search_context_size_high": 0.0 - }, - "file_search_cost_per_1k_calls": 0.0, - "file_search_cost_per_gb_per_day": 0.0, - "vector_store_cost_per_gb_per_day": 0.0, - "computer_use_input_cost_per_1k_tokens": 0.0, - "computer_use_output_cost_per_1k_tokens": 0.0, - "code_interpreter_cost_per_session": 0.0, - "supported_regions": [ - "global", - "us-west-2", - "eu-west-1", - "ap-southeast-1", - "ap-northeast-1" - ], - "deprecation_date": "date when the model becomes deprecated in the format YYYY-MM-DD" - }, - "omni-moderation-latest": { - "max_tokens": 32768, - "max_input_tokens": 32768, - "max_output_tokens": 0, - "input_cost_per_token": 0.0, - "output_cost_per_token": 0.0, - "litellm_provider": "openai", - "mode": "moderation" - }, - "omni-moderation-latest-intents": { - "max_tokens": 32768, - "max_input_tokens": 32768, - "max_output_tokens": 0, - "input_cost_per_token": 0.0, - "output_cost_per_token": 0.0, - "litellm_provider": "openai", - "mode": "moderation" - }, - "omni-moderation-2024-09-26": { - "max_tokens": 32768, - "max_input_tokens": 32768, - "max_output_tokens": 0, - "input_cost_per_token": 0.0, - "output_cost_per_token": 0.0, - "litellm_provider": "openai", - "mode": "moderation" - }, - "gpt-4": { - "max_tokens": 4096, - "max_input_tokens": 8192, - "max_output_tokens": 4096, - "input_cost_per_token": 3e-05, - "output_cost_per_token": 6e-05, - "litellm_provider": "openai", - "mode": "chat", - "supports_function_calling": true, - "supports_prompt_caching": true, - "supports_system_messages": true, - "supports_tool_choice": true - }, - "gpt-4.1": { - "max_tokens": 32768, - "max_input_tokens": 1047576, - "max_output_tokens": 32768, - "input_cost_per_token": 2e-06, - "output_cost_per_token": 8e-06, - "input_cost_per_token_batches": 1e-06, - "output_cost_per_token_batches": 4e-06, - "cache_read_input_token_cost": 5e-07, - "litellm_provider": "openai", - "mode": "chat", - "supported_endpoints": [ - "/v1/chat/completions", - "/v1/batch", - "/v1/responses" - ], - "supported_modalities": [ - "text", - "image" - ], - "supported_output_modalities": [ - "text" - ], - "supports_pdf_input": true, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_response_schema": true, - "supports_vision": true, - "supports_prompt_caching": true, - "supports_system_messages": true, - "supports_tool_choice": true, - "supports_native_streaming": true - }, - "gpt-4.1-2025-04-14": { - "max_tokens": 32768, - "max_input_tokens": 1047576, - "max_output_tokens": 32768, - "input_cost_per_token": 2e-06, - "output_cost_per_token": 8e-06, - "input_cost_per_token_batches": 1e-06, - "output_cost_per_token_batches": 4e-06, - "cache_read_input_token_cost": 5e-07, - "litellm_provider": "openai", - "mode": "chat", - "supported_endpoints": [ - "/v1/chat/completions", - "/v1/batch", - "/v1/responses" - ], - "supported_modalities": [ - "text", - "image" - ], - "supported_output_modalities": [ - "text" - ], - "supports_pdf_input": true, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_response_schema": true, - "supports_vision": true, - "supports_prompt_caching": true, - "supports_system_messages": true, - "supports_tool_choice": true, - "supports_native_streaming": true - }, - "gpt-4.1-mini": { - "max_tokens": 32768, - "max_input_tokens": 1047576, - "max_output_tokens": 32768, - "input_cost_per_token": 4e-07, - "output_cost_per_token": 1.6e-06, - "input_cost_per_token_batches": 2e-07, - "output_cost_per_token_batches": 8e-07, - "cache_read_input_token_cost": 1e-07, - "litellm_provider": "openai", - "mode": "chat", - "supported_endpoints": [ - "/v1/chat/completions", - "/v1/batch", - "/v1/responses" - ], - "supported_modalities": [ - "text", - "image" - ], - "supported_output_modalities": [ - "text" - ], - "supports_pdf_input": true, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_response_schema": true, - "supports_vision": true, - "supports_prompt_caching": true, - "supports_system_messages": true, - "supports_tool_choice": true, - "supports_native_streaming": true - }, - "gpt-4.1-mini-2025-04-14": { - "max_tokens": 32768, - "max_input_tokens": 1047576, - "max_output_tokens": 32768, - "input_cost_per_token": 4e-07, - "output_cost_per_token": 1.6e-06, - "input_cost_per_token_batches": 2e-07, - "output_cost_per_token_batches": 8e-07, - "cache_read_input_token_cost": 1e-07, - "litellm_provider": "openai", - "mode": "chat", - "supported_endpoints": [ - "/v1/chat/completions", - "/v1/batch", - "/v1/responses" - ], - "supported_modalities": [ - "text", - "image" - ], - "supported_output_modalities": [ - "text" - ], - "supports_pdf_input": true, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_response_schema": true, - "supports_vision": true, - "supports_prompt_caching": true, - "supports_system_messages": true, - "supports_tool_choice": true, - "supports_native_streaming": true - }, - "gpt-4.1-nano": { - "max_tokens": 32768, - "max_input_tokens": 1047576, - "max_output_tokens": 32768, - "input_cost_per_token": 1e-07, - "output_cost_per_token": 4e-07, - "input_cost_per_token_batches": 5e-08, - "output_cost_per_token_batches": 2e-07, - "cache_read_input_token_cost": 2.5e-08, - "litellm_provider": "openai", - "mode": "chat", - "supported_endpoints": [ - "/v1/chat/completions", - "/v1/batch", - "/v1/responses" - ], - "supported_modalities": [ - "text", - "image" - ], - "supported_output_modalities": [ - "text" - ], - "supports_pdf_input": true, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_response_schema": true, - "supports_vision": true, - "supports_prompt_caching": true, - "supports_system_messages": true, - "supports_tool_choice": true, - "supports_native_streaming": true - }, - "gpt-4.1-nano-2025-04-14": { - "max_tokens": 32768, - "max_input_tokens": 1047576, - "max_output_tokens": 32768, - "input_cost_per_token": 1e-07, - "output_cost_per_token": 4e-07, - "input_cost_per_token_batches": 5e-08, - "output_cost_per_token_batches": 2e-07, - "cache_read_input_token_cost": 2.5e-08, - "litellm_provider": "openai", - "mode": "chat", - "supported_endpoints": [ - "/v1/chat/completions", - "/v1/batch", - "/v1/responses" - ], - "supported_modalities": [ - "text", - "image" - ], - "supported_output_modalities": [ - "text" - ], - "supports_pdf_input": true, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_response_schema": true, - "supports_vision": true, - "supports_prompt_caching": true, - "supports_system_messages": true, - "supports_tool_choice": true, - "supports_native_streaming": true - }, - "gpt-4o": { - "max_tokens": 16384, - "max_input_tokens": 128000, - "max_output_tokens": 16384, - "input_cost_per_token": 2.5e-06, - "output_cost_per_token": 1e-05, - "input_cost_per_token_batches": 1.25e-06, - "output_cost_per_token_batches": 5e-06, - "cache_read_input_token_cost": 1.25e-06, - "litellm_provider": "openai", - "mode": "chat", - "supports_pdf_input": true, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_response_schema": true, - "supports_vision": true, - "supports_prompt_caching": true, - "supports_system_messages": true, - "supports_tool_choice": true - }, - "watsonx/ibm/granite-3-8b-instruct": { - "max_tokens": 8192, - "max_input_tokens": 8192, - "max_output_tokens": 1024, - "input_cost_per_token": 0.0002, - "output_cost_per_token": 0.0002, - "litellm_provider": "watsonx", - "mode": "chat", - "supports_function_calling": true, - "supports_tool_choice": true, - "supports_parallel_function_calling": false, - "supports_vision": false, - "supports_audio_input": false, - "supports_audio_output": false, - "supports_prompt_caching": true, - "supports_response_schema": true, - "supports_system_messages": true - }, - "watsonx/mistralai/mistral-large": { - "max_tokens": 131072, - "max_input_tokens": 131072, - "max_output_tokens": 16384, - "input_cost_per_token": 3e-06, - "output_cost_per_token": 1e-05, - "litellm_provider": "watsonx", - "mode": "chat", - "supports_function_calling": true, - "supports_tool_choice": true, - "supports_parallel_function_calling": false, - "supports_vision": false, - "supports_audio_input": false, - "supports_audio_output": false, - "supports_prompt_caching": true, - "supports_response_schema": true, - "supports_system_messages": true - }, - "gpt-4o-search-preview-2025-03-11": { - "max_tokens": 16384, - "max_input_tokens": 128000, - "max_output_tokens": 16384, - "input_cost_per_token": 2.5e-06, - "output_cost_per_token": 1e-05, - "input_cost_per_token_batches": 1.25e-06, - "output_cost_per_token_batches": 5e-06, - "cache_read_input_token_cost": 1.25e-06, - "litellm_provider": "openai", - "mode": "chat", - "supports_pdf_input": true, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_response_schema": true, - "supports_vision": true, - "supports_prompt_caching": true, - "supports_system_messages": true, - "supports_tool_choice": true - }, - "gpt-4o-search-preview": { - "max_tokens": 16384, - "max_input_tokens": 128000, - "max_output_tokens": 16384, - "input_cost_per_token": 2.5e-06, - "output_cost_per_token": 1e-05, - "input_cost_per_token_batches": 1.25e-06, - "output_cost_per_token_batches": 5e-06, - "cache_read_input_token_cost": 1.25e-06, - "litellm_provider": "openai", - "mode": "chat", - "supports_pdf_input": true, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_response_schema": true, - "supports_vision": true, - "supports_prompt_caching": true, - "supports_system_messages": true, - "supports_tool_choice": true, - "supports_web_search": true, - "search_context_cost_per_query": { - "search_context_size_low": 0.03, - "search_context_size_medium": 0.035, - "search_context_size_high": 0.05 - } - }, - "gpt-4.5-preview": { - "max_tokens": 16384, - "max_input_tokens": 128000, - "max_output_tokens": 16384, - "input_cost_per_token": 7.5e-05, - "output_cost_per_token": 0.00015, - "input_cost_per_token_batches": 3.75e-05, - "output_cost_per_token_batches": 7.5e-05, - "cache_read_input_token_cost": 3.75e-05, - "litellm_provider": "openai", - "mode": "chat", - "supports_pdf_input": true, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_response_schema": true, - "supports_vision": true, - "supports_prompt_caching": true, - "supports_system_messages": true, - "supports_tool_choice": true - }, - "gpt-4.5-preview-2025-02-27": { - "max_tokens": 16384, - "max_input_tokens": 128000, - "max_output_tokens": 16384, - "input_cost_per_token": 7.5e-05, - "output_cost_per_token": 0.00015, - "input_cost_per_token_batches": 3.75e-05, - "output_cost_per_token_batches": 7.5e-05, - "cache_read_input_token_cost": 3.75e-05, - "litellm_provider": "openai", - "mode": "chat", - "supports_pdf_input": true, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_response_schema": true, - "supports_vision": true, - "supports_prompt_caching": true, - "supports_system_messages": true, - "supports_tool_choice": true, - "deprecation_date": "2025-07-14" - }, - "gpt-4o-audio-preview": { - "max_tokens": 16384, - "max_input_tokens": 128000, - "max_output_tokens": 16384, - "input_cost_per_token": 2.5e-06, - "input_cost_per_audio_token": 0.0001, - "output_cost_per_token": 1e-05, - "output_cost_per_audio_token": 0.0002, - "litellm_provider": "openai", - "mode": "chat", - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_audio_input": true, - "supports_audio_output": true, - "supports_system_messages": true, - "supports_tool_choice": true - }, - "gpt-4o-audio-preview-2024-12-17": { - "max_tokens": 16384, - "max_input_tokens": 128000, - "max_output_tokens": 16384, - "input_cost_per_token": 2.5e-06, - "input_cost_per_audio_token": 4e-05, - "output_cost_per_token": 1e-05, - "output_cost_per_audio_token": 8e-05, - "litellm_provider": "openai", - "mode": "chat", - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_audio_input": true, - "supports_audio_output": true, - "supports_system_messages": true, - "supports_tool_choice": true - }, - "gpt-4o-audio-preview-2024-10-01": { - "max_tokens": 16384, - "max_input_tokens": 128000, - "max_output_tokens": 16384, - "input_cost_per_token": 2.5e-06, - "input_cost_per_audio_token": 0.0001, - "output_cost_per_token": 1e-05, - "output_cost_per_audio_token": 0.0002, - "litellm_provider": "openai", - "mode": "chat", - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_audio_input": true, - "supports_audio_output": true, - "supports_system_messages": true, - "supports_tool_choice": true - }, - "gpt-4o-audio-preview-2025-06-03": { - "max_tokens": 16384, - "max_input_tokens": 128000, - "max_output_tokens": 16384, - "input_cost_per_token": 2.5e-06, - "input_cost_per_audio_token": 4e-05, - "output_cost_per_token": 1e-05, - "output_cost_per_audio_token": 8e-05, - "litellm_provider": "openai", - 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Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 9.9902e-07, + "output_dbu_cost_per_token": 1.4286e-05, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving", + "supports_tool_choice": true + }, + "databricks/databricks-mpt-7b-instruct": { + "input_cost_per_token": 5.0001e-07, + "input_dbu_cost_per_token": 7.143e-06, + "litellm_provider": "databricks", + "max_input_tokens": 8192, + "max_output_tokens": 8192, + "max_tokens": 8192, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 0.0, + "output_dbu_cost_per_token": 0.0, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving", + "supports_tool_choice": true + }, + "davinci-002": { + "input_cost_per_token": 2e-06, + "litellm_provider": "text-completion-openai", + "max_input_tokens": 16384, + "max_output_tokens": 4096, + "max_tokens": 16384, + "mode": "completion", + "output_cost_per_token": 2e-06 + }, + "deepgram/base": { + "input_cost_per_second": 0.00020833, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0125/60 seconds = $0.00020833 per second", + "original_pricing_per_minute": 0.0125 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/base-conversationalai": { + "input_cost_per_second": 0.00020833, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0125/60 seconds = $0.00020833 per second", + "original_pricing_per_minute": 0.0125 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/base-finance": { + "input_cost_per_second": 0.00020833, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0125/60 seconds = $0.00020833 per second", + "original_pricing_per_minute": 0.0125 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/base-general": { + "input_cost_per_second": 0.00020833, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0125/60 seconds = $0.00020833 per second", + "original_pricing_per_minute": 0.0125 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/base-meeting": { + "input_cost_per_second": 0.00020833, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0125/60 seconds = $0.00020833 per second", + "original_pricing_per_minute": 0.0125 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/base-phonecall": { + "input_cost_per_second": 0.00020833, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0125/60 seconds = $0.00020833 per second", + "original_pricing_per_minute": 0.0125 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/base-video": { + "input_cost_per_second": 0.00020833, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0125/60 seconds = $0.00020833 per second", + "original_pricing_per_minute": 0.0125 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/base-voicemail": { + "input_cost_per_second": 0.00020833, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0125/60 seconds = $0.00020833 per second", + "original_pricing_per_minute": 0.0125 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/enhanced": { + "input_cost_per_second": 0.00024167, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0145/60 seconds = $0.00024167 per second", + "original_pricing_per_minute": 0.0145 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/enhanced-finance": { + "input_cost_per_second": 0.00024167, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0145/60 seconds = $0.00024167 per second", + "original_pricing_per_minute": 0.0145 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/enhanced-general": { + "input_cost_per_second": 0.00024167, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0145/60 seconds = $0.00024167 per second", + "original_pricing_per_minute": 0.0145 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/enhanced-meeting": { + "input_cost_per_second": 0.00024167, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0145/60 seconds = $0.00024167 per second", + "original_pricing_per_minute": 0.0145 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/enhanced-phonecall": { + "input_cost_per_second": 0.00024167, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0145/60 seconds = $0.00024167 per second", + "original_pricing_per_minute": 0.0145 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/nova": { + "input_cost_per_second": 7.167e-05, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0043/60 seconds = $0.00007167 per second", + "original_pricing_per_minute": 0.0043 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/nova-2": { + "input_cost_per_second": 7.167e-05, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0043/60 seconds = $0.00007167 per second", + "original_pricing_per_minute": 0.0043 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/nova-2-atc": { + "input_cost_per_second": 7.167e-05, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0043/60 seconds = $0.00007167 per second", + "original_pricing_per_minute": 0.0043 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/nova-2-automotive": { + "input_cost_per_second": 7.167e-05, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0043/60 seconds = $0.00007167 per second", + "original_pricing_per_minute": 0.0043 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/nova-2-conversationalai": { + "input_cost_per_second": 7.167e-05, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0043/60 seconds = $0.00007167 per second", + "original_pricing_per_minute": 0.0043 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/nova-2-drivethru": { + "input_cost_per_second": 7.167e-05, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0043/60 seconds = $0.00007167 per second", + "original_pricing_per_minute": 0.0043 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/nova-2-finance": { + "input_cost_per_second": 7.167e-05, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0043/60 seconds = $0.00007167 per second", + "original_pricing_per_minute": 0.0043 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/nova-2-general": { + "input_cost_per_second": 7.167e-05, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0043/60 seconds = $0.00007167 per second", + "original_pricing_per_minute": 0.0043 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/nova-2-meeting": { + "input_cost_per_second": 7.167e-05, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0043/60 seconds = $0.00007167 per second", + "original_pricing_per_minute": 0.0043 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/nova-2-phonecall": { + "input_cost_per_second": 7.167e-05, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0043/60 seconds = $0.00007167 per second", + "original_pricing_per_minute": 0.0043 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/nova-2-video": { + "input_cost_per_second": 7.167e-05, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0043/60 seconds = $0.00007167 per second", + "original_pricing_per_minute": 0.0043 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/nova-2-voicemail": { + "input_cost_per_second": 7.167e-05, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0043/60 seconds = $0.00007167 per second", + "original_pricing_per_minute": 0.0043 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/nova-3": { + "input_cost_per_second": 7.167e-05, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0043/60 seconds = $0.00007167 per second", + "original_pricing_per_minute": 0.0043 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/nova-3-general": { + "input_cost_per_second": 7.167e-05, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0043/60 seconds = $0.00007167 per second", + "original_pricing_per_minute": 0.0043 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/nova-3-medical": { + "input_cost_per_second": 8.667e-05, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0052/60 seconds = $0.00008667 per second (multilingual)", + "original_pricing_per_minute": 0.0052 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/nova-general": { + "input_cost_per_second": 7.167e-05, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0043/60 seconds = $0.00007167 per second", + "original_pricing_per_minute": 0.0043 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/nova-phonecall": { + "input_cost_per_second": 7.167e-05, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0043/60 seconds = $0.00007167 per second", + "original_pricing_per_minute": 0.0043 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/whisper": { + "input_cost_per_second": 0.0001, + "litellm_provider": "deepgram", + "metadata": { + "notes": "Deepgram's hosted OpenAI Whisper models - pricing may differ from native Deepgram models" + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/whisper-base": { + "input_cost_per_second": 0.0001, + "litellm_provider": "deepgram", + "metadata": { + "notes": "Deepgram's hosted OpenAI Whisper models - pricing may differ from native Deepgram models" + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/whisper-large": { + "input_cost_per_second": 0.0001, + "litellm_provider": "deepgram", + "metadata": { + "notes": "Deepgram's hosted OpenAI Whisper models - pricing may differ from native Deepgram models" + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/whisper-medium": { + "input_cost_per_second": 0.0001, + "litellm_provider": "deepgram", + "metadata": { + "notes": "Deepgram's hosted OpenAI Whisper models - pricing may differ from native Deepgram models" + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/whisper-small": { + "input_cost_per_second": 0.0001, + "litellm_provider": "deepgram", + "metadata": { + "notes": "Deepgram's hosted OpenAI Whisper models - pricing may differ from native Deepgram models" + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/whisper-tiny": { + "input_cost_per_second": 0.0001, + "litellm_provider": "deepgram", + "metadata": { + "notes": "Deepgram's hosted OpenAI Whisper models - pricing may differ from native Deepgram models" + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepinfra/Gryphe/MythoMax-L2-13b": { + "input_cost_per_token": 7.2e-08, + "litellm_provider": "deepinfra", + "max_input_tokens": 4096, + "max_output_tokens": 4096, + "max_tokens": 4096, + "mode": "chat", + "output_cost_per_token": 7.2e-08, + "supports_tool_choice": true + }, + "deepinfra/NousResearch/Hermes-3-Llama-3.1-405B": { + "input_cost_per_token": 7e-07, + "litellm_provider": "deepinfra", + "max_input_tokens": 131072, + "max_output_tokens": 131072, + "max_tokens": 131072, + "mode": "chat", + "output_cost_per_token": 8e-07, + "supports_tool_choice": true + }, + "deepinfra/NousResearch/Hermes-3-Llama-3.1-70B": { + "input_cost_per_token": 1e-07, + "litellm_provider": "deepinfra", + "max_input_tokens": 131072, + "max_output_tokens": 131072, + "max_tokens": 131072, + "mode": "chat", + "output_cost_per_token": 2.8e-07, + "supports_tool_choice": false + }, + "deepinfra/Qwen/QwQ-32B": { + "input_cost_per_token": 1.5e-07, + "litellm_provider": "deepinfra", + "max_input_tokens": 131072, + "max_output_tokens": 131072, + "max_tokens": 131072, + "mode": "chat", + "output_cost_per_token": 4e-07, + "supports_tool_choice": true + }, + "deepinfra/Qwen/Qwen2.5-72B-Instruct": { + "input_cost_per_token": 1.2e-07, + "litellm_provider": "deepinfra", + "max_input_tokens": 32768, + "max_output_tokens": 32768, + "max_tokens": 32768, + "mode": "chat", + "output_cost_per_token": 3.9e-07, + "supports_tool_choice": true + }, + "deepinfra/Qwen/Qwen2.5-7B-Instruct": { + "input_cost_per_token": 4e-08, + "litellm_provider": "deepinfra", + "max_input_tokens": 32768, + "max_output_tokens": 32768, + "max_tokens": 32768, + "mode": "chat", + "output_cost_per_token": 1e-07, + "supports_tool_choice": false + }, + "deepinfra/Qwen/Qwen2.5-VL-32B-Instruct": { + "input_cost_per_token": 2e-07, + "litellm_provider": "deepinfra", + "max_input_tokens": 128000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 6e-07, + "supports_tool_choice": true + }, + "deepinfra/Qwen/Qwen3-14B": { + "input_cost_per_token": 6e-08, + "litellm_provider": "deepinfra", + "max_input_tokens": 40960, + "max_output_tokens": 40960, + "max_tokens": 40960, + "mode": "chat", + "output_cost_per_token": 2.4e-07, + "supports_tool_choice": true + }, + "deepinfra/Qwen/Qwen3-235B-A22B": { + "input_cost_per_token": 1.3e-07, + "litellm_provider": "deepinfra", + "max_input_tokens": 40960, + "max_output_tokens": 40960, + "max_tokens": 40960, + "mode": "chat", + "output_cost_per_token": 6e-07, + "supports_tool_choice": true + }, + "deepinfra/Qwen/Qwen3-235B-A22B-Instruct-2507": { + "input_cost_per_token": 1.3e-07, + "litellm_provider": "deepinfra", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 6e-07, + "supports_tool_choice": true + }, + "deepinfra/Qwen/Qwen3-235B-A22B-Thinking-2507": { + "input_cost_per_token": 1.3e-07, + "litellm_provider": "deepinfra", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 6e-07, + "supports_tool_choice": true + }, + "deepinfra/Qwen/Qwen3-30B-A3B": { + "input_cost_per_token": 8e-08, + "litellm_provider": "deepinfra", + "max_input_tokens": 40960, + "max_output_tokens": 40960, + "max_tokens": 40960, + "mode": "chat", + "output_cost_per_token": 2.9e-07, + "supports_tool_choice": true + }, + "deepinfra/Qwen/Qwen3-32B": { + "input_cost_per_token": 1e-07, + "litellm_provider": "deepinfra", + "max_input_tokens": 40960, + "max_output_tokens": 40960, + "max_tokens": 40960, + "mode": "chat", + "output_cost_per_token": 3e-07, + "supports_tool_choice": true + }, + "deepinfra/Qwen/Qwen3-Coder-480B-A35B-Instruct": { + "input_cost_per_token": 4e-07, + "litellm_provider": "deepinfra", + "max_input_tokens": 262144, + 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Defaulting to base model pricing", + "supports_function_calling": true, + "supports_system_messages": true, + "supports_tool_choice": true + }, + "ft:gpt-4o-2024-08-06": { + "input_cost_per_token": 3.75e-06, + "input_cost_per_token_batches": 1.875e-06, + "litellm_provider": "openai", + "max_input_tokens": 128000, + "max_output_tokens": 16384, + "max_tokens": 16384, + "mode": "chat", + "output_cost_per_token": 1.5e-05, + "output_cost_per_token_batches": 7.5e-06, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "ft:gpt-4o-2024-11-20": { + "cache_creation_input_token_cost": 1.875e-06, + "input_cost_per_token": 3.75e-06, + "litellm_provider": "openai", + "max_input_tokens": 128000, + "max_output_tokens": 16384, + "max_tokens": 16384, + "mode": "chat", + "output_cost_per_token": 1.5e-05, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "ft:gpt-4o-mini-2024-07-18": { + "cache_read_input_token_cost": 1.5e-07, + "input_cost_per_token": 3e-07, + "input_cost_per_token_batches": 1.5e-07, + "litellm_provider": "openai", + "max_input_tokens": 128000, + "max_output_tokens": 16384, + "max_tokens": 16384, + "mode": "chat", + "output_cost_per_token": 1.2e-06, + "output_cost_per_token_batches": 6e-07, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "gemini-1.0-pro": { + "input_cost_per_character": 1.25e-07, + "input_cost_per_image": 0.0025, + "input_cost_per_token": 5e-07, + "input_cost_per_video_per_second": 0.002, + "litellm_provider": "vertex_ai-language-models", + "max_input_tokens": 32760, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_character": 3.75e-07, + "output_cost_per_token": 1.5e-06, "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing#google_models", + "supports_function_calling": true, + "supports_parallel_function_calling": true, "supports_tool_choice": true }, "gemini-1.0-pro-001": { - "max_tokens": 8192, + "deprecation_date": "2025-04-09", + "input_cost_per_character": 1.25e-07, + "input_cost_per_image": 0.0025, + "input_cost_per_token": 5e-07, + "input_cost_per_video_per_second": 0.002, + "litellm_provider": "vertex_ai-language-models", "max_input_tokens": 32760, "max_output_tokens": 8192, - "input_cost_per_image": 0.0025, - "input_cost_per_video_per_second": 0.002, - "input_cost_per_token": 5e-07, - "input_cost_per_character": 1.25e-07, - "output_cost_per_token": 1.5e-06, - "output_cost_per_character": 3.75e-07, - "litellm_provider": "vertex_ai-language-models", + "max_tokens": 8192, "mode": "chat", - "supports_function_calling": true, + "output_cost_per_character": 3.75e-07, + "output_cost_per_token": 1.5e-06, "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", - "deprecation_date": "2025-04-09", - "supports_tool_choice": true, - "supports_parallel_function_calling": true - }, - "gemini-1.0-ultra": { - "max_tokens": 8192, - "max_input_tokens": 8192, - "max_output_tokens": 2048, - "input_cost_per_image": 0.0025, - "input_cost_per_video_per_second": 0.002, - "input_cost_per_token": 5e-07, - "input_cost_per_character": 1.25e-07, - "output_cost_per_token": 1.5e-06, - "output_cost_per_character": 3.75e-07, - "litellm_provider": "vertex_ai-language-models", - "mode": "chat", "supports_function_calling": true, - "source": "As of Jun, 2024. 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Got max_tokens info here: https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", - "supports_tool_choice": true, - "supports_parallel_function_calling": true + "supports_parallel_function_calling": true, + "supports_tool_choice": true }, "gemini-1.0-pro-002": { - "max_tokens": 8192, + "deprecation_date": "2025-04-09", + "input_cost_per_character": 1.25e-07, + "input_cost_per_image": 0.0025, + "input_cost_per_token": 5e-07, + "input_cost_per_video_per_second": 0.002, + "litellm_provider": "vertex_ai-language-models", "max_input_tokens": 32760, "max_output_tokens": 8192, - "input_cost_per_image": 0.0025, - "input_cost_per_video_per_second": 0.002, - "input_cost_per_token": 5e-07, - "input_cost_per_character": 1.25e-07, - "output_cost_per_token": 1.5e-06, + "max_tokens": 8192, + "mode": "chat", "output_cost_per_character": 3.75e-07, - "litellm_provider": "vertex_ai-language-models", - "mode": "chat", - "supports_function_calling": true, - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", - 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"input_cost_per_image": 0.00032875, - "input_cost_per_audio_per_second": 3.125e-05, - "input_cost_per_video_per_second": 0.00032875, - "input_cost_per_token": 7.8125e-08, - "input_cost_per_character": 3.125e-07, - "input_cost_per_image_above_128k_tokens": 0.0006575, - "input_cost_per_video_per_second_above_128k_tokens": 0.0006575, - "input_cost_per_audio_per_second_above_128k_tokens": 6.25e-05, - "input_cost_per_token_above_128k_tokens": 1.5625e-07, - "input_cost_per_character_above_128k_tokens": 6.25e-07, - "output_cost_per_token": 3.125e-07, - "output_cost_per_character": 1.25e-06, - "output_cost_per_token_above_128k_tokens": 6.25e-07, - "output_cost_per_character_above_128k_tokens": 2.5e-06, - "litellm_provider": "vertex_ai-language-models", - "mode": "chat", - "supports_system_messages": true, - "supports_function_calling": true, - "supports_tool_choice": true, - "supports_response_schema": true, - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", - 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"output_cost_per_token_above_128k_tokens": 6e-07, - "output_cost_per_character_above_128k_tokens": 1.5e-07, - "litellm_provider": "vertex_ai-language-models", - "mode": "chat", - "supports_system_messages": true, - "supports_function_calling": true, - "supports_vision": true, - "supports_response_schema": true, - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", - "supports_tool_choice": true, - "supports_parallel_function_calling": true - }, - "gemini-1.5-flash-exp-0827": { - "max_tokens": 8192, - "max_input_tokens": 1000000, - "max_output_tokens": 8192, - "max_images_per_prompt": 3000, - "max_videos_per_prompt": 10, - "max_video_length": 1, - "max_audio_length_hours": 8.4, - "max_audio_per_prompt": 1, - "max_pdf_size_mb": 30, - "input_cost_per_image": 2e-05, - "input_cost_per_video_per_second": 2e-05, - "input_cost_per_audio_per_second": 2e-06, - "input_cost_per_token": 4.688e-09, - "input_cost_per_character": 1.875e-08, - "input_cost_per_token_above_128k_tokens": 1e-06, - "input_cost_per_character_above_128k_tokens": 2.5e-07, - "input_cost_per_image_above_128k_tokens": 4e-05, - "input_cost_per_video_per_second_above_128k_tokens": 4e-05, - "input_cost_per_audio_per_second_above_128k_tokens": 4e-06, - "output_cost_per_token": 4.6875e-09, - "output_cost_per_character": 1.875e-08, - "output_cost_per_token_above_128k_tokens": 9.375e-09, - "output_cost_per_character_above_128k_tokens": 3.75e-08, - "litellm_provider": "vertex_ai-language-models", - "mode": "chat", - "supports_system_messages": true, - "supports_function_calling": true, - "supports_vision": true, - "supports_response_schema": true, - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", - "supports_tool_choice": true, - "supports_parallel_function_calling": true - }, - "gemini-1.5-flash-002": { - "max_tokens": 8192, - "max_input_tokens": 1048576, - "max_output_tokens": 8192, - "max_images_per_prompt": 3000, - "max_videos_per_prompt": 10, - "max_video_length": 1, - "max_audio_length_hours": 8.4, - "max_audio_per_prompt": 1, - "max_pdf_size_mb": 30, - "input_cost_per_image": 2e-05, - "input_cost_per_video_per_second": 2e-05, - "input_cost_per_audio_per_second": 2e-06, - "input_cost_per_token": 7.5e-08, - "input_cost_per_character": 1.875e-08, - "input_cost_per_token_above_128k_tokens": 1e-06, - "input_cost_per_character_above_128k_tokens": 2.5e-07, - "input_cost_per_image_above_128k_tokens": 4e-05, - "input_cost_per_video_per_second_above_128k_tokens": 4e-05, - "input_cost_per_audio_per_second_above_128k_tokens": 4e-06, - "output_cost_per_token": 3e-07, - "output_cost_per_character": 7.5e-08, - "output_cost_per_token_above_128k_tokens": 6e-07, - "output_cost_per_character_above_128k_tokens": 1.5e-07, - "litellm_provider": "vertex_ai-language-models", - "mode": "chat", - "supports_system_messages": true, - "supports_function_calling": true, - "supports_vision": true, - "supports_response_schema": true, - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#gemini-1.5-flash", - "deprecation_date": "2025-09-24", - "supports_tool_choice": true, - "supports_parallel_function_calling": true - }, - "gemini-1.5-flash-001": { - "max_tokens": 8192, - "max_input_tokens": 1000000, - "max_output_tokens": 8192, - "max_images_per_prompt": 3000, - "max_videos_per_prompt": 10, - "max_video_length": 1, - "max_audio_length_hours": 8.4, - "max_audio_per_prompt": 1, - "max_pdf_size_mb": 30, - "input_cost_per_image": 2e-05, - "input_cost_per_video_per_second": 2e-05, - "input_cost_per_audio_per_second": 2e-06, - "input_cost_per_token": 7.5e-08, - "input_cost_per_character": 1.875e-08, - "input_cost_per_token_above_128k_tokens": 1e-06, - "input_cost_per_character_above_128k_tokens": 2.5e-07, - "input_cost_per_image_above_128k_tokens": 4e-05, - "input_cost_per_video_per_second_above_128k_tokens": 4e-05, - "input_cost_per_audio_per_second_above_128k_tokens": 4e-06, - "output_cost_per_token": 3e-07, - "output_cost_per_character": 7.5e-08, - "output_cost_per_token_above_128k_tokens": 6e-07, - "output_cost_per_character_above_128k_tokens": 1.5e-07, - "litellm_provider": "vertex_ai-language-models", - "mode": "chat", - "supports_system_messages": true, - "supports_function_calling": true, - "supports_vision": true, - "supports_response_schema": true, - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", - "deprecation_date": "2025-05-24", - "supports_tool_choice": true, - "supports_parallel_function_calling": true - }, - "gemini-1.5-flash-preview-0514": { - "max_tokens": 8192, - "max_input_tokens": 1000000, - "max_output_tokens": 8192, - "max_images_per_prompt": 3000, - "max_videos_per_prompt": 10, - "max_video_length": 1, - "max_audio_length_hours": 8.4, - "max_audio_per_prompt": 1, - "max_pdf_size_mb": 30, - "input_cost_per_image": 2e-05, - "input_cost_per_video_per_second": 2e-05, - "input_cost_per_audio_per_second": 2e-06, - "input_cost_per_token": 7.5e-08, - "input_cost_per_character": 1.875e-08, - "input_cost_per_token_above_128k_tokens": 1e-06, - "input_cost_per_character_above_128k_tokens": 2.5e-07, - "input_cost_per_image_above_128k_tokens": 4e-05, - "input_cost_per_video_per_second_above_128k_tokens": 4e-05, - "input_cost_per_audio_per_second_above_128k_tokens": 4e-06, - "output_cost_per_token": 4.6875e-09, - "output_cost_per_character": 1.875e-08, - "output_cost_per_token_above_128k_tokens": 9.375e-09, - "output_cost_per_character_above_128k_tokens": 3.75e-08, - "litellm_provider": "vertex_ai-language-models", - "mode": "chat", - "supports_system_messages": true, - "supports_function_calling": true, - "supports_vision": true, - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", - "supports_tool_choice": true, - "supports_parallel_function_calling": true - }, - "gemini-pro-experimental": { - "max_tokens": 8192, - "max_input_tokens": 1000000, - "max_output_tokens": 8192, - "input_cost_per_token": 0, - "output_cost_per_token": 0, - "input_cost_per_character": 0, - "output_cost_per_character": 0, - "litellm_provider": "vertex_ai-language-models", - "mode": "chat", - "supports_function_calling": false, - "supports_tool_choice": true, - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/gemini-experimental", - "supports_parallel_function_calling": true - }, - "gemini-flash-experimental": { - "max_tokens": 8192, - "max_input_tokens": 1000000, - "max_output_tokens": 8192, - "input_cost_per_token": 0, - "output_cost_per_token": 0, - "input_cost_per_character": 0, - "output_cost_per_character": 0, - "litellm_provider": "vertex_ai-language-models", - "mode": "chat", - "supports_function_calling": false, - "supports_tool_choice": true, - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/gemini-experimental", - "supports_parallel_function_calling": true - }, - "gemini-pro-vision": { - "max_tokens": 2048, - "max_input_tokens": 16384, - "max_output_tokens": 2048, - "max_images_per_prompt": 16, - "max_videos_per_prompt": 1, - "max_video_length": 2, - "input_cost_per_token": 5e-07, "output_cost_per_token": 1.5e-06, - "input_cost_per_image": 0.0025, - "litellm_provider": "vertex_ai-vision-models", - "mode": "chat", - "supports_function_calling": true, - "supports_vision": true, "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", - "supports_tool_choice": true, - "supports_parallel_function_calling": true + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_tool_choice": true }, "gemini-1.0-pro-vision": { - "max_tokens": 2048, + "input_cost_per_image": 0.0025, + "input_cost_per_token": 5e-07, + "litellm_provider": "vertex_ai-vision-models", + "max_images_per_prompt": 16, "max_input_tokens": 16384, "max_output_tokens": 2048, - "max_images_per_prompt": 16, - "max_videos_per_prompt": 1, + "max_tokens": 2048, "max_video_length": 2, - "input_cost_per_token": 5e-07, - "output_cost_per_token": 1.5e-06, - "input_cost_per_image": 0.0025, - "litellm_provider": "vertex_ai-vision-models", + "max_videos_per_prompt": 1, "mode": "chat", - "supports_function_calling": true, - "supports_vision": true, + "output_cost_per_token": 1.5e-06, "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", + "supports_function_calling": true, + "supports_parallel_function_calling": true, "supports_tool_choice": true, - "supports_parallel_function_calling": true + "supports_vision": true }, "gemini-1.0-pro-vision-001": { - "max_tokens": 2048, + "deprecation_date": "2025-04-09", + "input_cost_per_image": 0.0025, + "input_cost_per_token": 5e-07, + "litellm_provider": "vertex_ai-vision-models", + "max_images_per_prompt": 16, "max_input_tokens": 16384, "max_output_tokens": 2048, - "max_images_per_prompt": 16, - "max_videos_per_prompt": 1, + "max_tokens": 2048, "max_video_length": 2, - "input_cost_per_token": 5e-07, + "max_videos_per_prompt": 1, + "mode": "chat", "output_cost_per_token": 1.5e-06, - "input_cost_per_image": 0.0025, - "litellm_provider": "vertex_ai-vision-models", - "mode": "chat", + "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", "supports_function_calling": true, - "supports_vision": true, - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", - "deprecation_date": "2025-04-09", + "supports_parallel_function_calling": true, "supports_tool_choice": true, - "supports_parallel_function_calling": true + "supports_vision": true }, - "medlm-medium": { - "max_tokens": 8192, - "max_input_tokens": 32768, - "max_output_tokens": 8192, - "input_cost_per_character": 5e-07, - "output_cost_per_character": 1e-06, + "gemini-1.0-ultra": { + "input_cost_per_character": 1.25e-07, + "input_cost_per_image": 0.0025, + "input_cost_per_token": 5e-07, + "input_cost_per_video_per_second": 0.002, "litellm_provider": "vertex_ai-language-models", - "mode": "chat", - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", - "supports_tool_choice": true - }, - "medlm-large": { - "max_tokens": 1024, "max_input_tokens": 8192, - "max_output_tokens": 1024, - "input_cost_per_character": 5e-06, - "output_cost_per_character": 1.5e-05, - "litellm_provider": "vertex_ai-language-models", + "max_output_tokens": 2048, + "max_tokens": 8192, "mode": "chat", - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", + "output_cost_per_character": 3.75e-07, + "output_cost_per_token": 1.5e-06, + "source": "As of Jun, 2024. There is no available doc on vertex ai pricing gemini-1.0-ultra-001. Using gemini-1.0-pro pricing. Got max_tokens info here: https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", + "supports_function_calling": true, + "supports_parallel_function_calling": true, "supports_tool_choice": true }, - "gemini-2.5-pro-exp-03-25": { - "max_tokens": 65535, - "max_input_tokens": 1048576, - "max_output_tokens": 65535, - "max_images_per_prompt": 3000, - "max_videos_per_prompt": 10, - "max_video_length": 1, + "gemini-1.0-ultra-001": { + "input_cost_per_character": 1.25e-07, + "input_cost_per_image": 0.0025, + "input_cost_per_token": 5e-07, + "input_cost_per_video_per_second": 0.002, + "litellm_provider": "vertex_ai-language-models", + "max_input_tokens": 8192, + "max_output_tokens": 2048, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_character": 3.75e-07, + "output_cost_per_token": 1.5e-06, + "source": "As of Jun, 2024. There is no available doc on vertex ai pricing gemini-1.0-ultra-001. Using gemini-1.0-pro pricing. Got max_tokens info here: https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_tool_choice": true + }, + "gemini-1.5-flash": { + "input_cost_per_audio_per_second": 2e-06, + "input_cost_per_audio_per_second_above_128k_tokens": 4e-06, + "input_cost_per_character": 1.875e-08, + "input_cost_per_character_above_128k_tokens": 2.5e-07, + "input_cost_per_image": 2e-05, + "input_cost_per_image_above_128k_tokens": 4e-05, + "input_cost_per_token": 7.5e-08, + "input_cost_per_token_above_128k_tokens": 1e-06, + "input_cost_per_video_per_second": 2e-05, + "input_cost_per_video_per_second_above_128k_tokens": 4e-05, + "litellm_provider": "vertex_ai-language-models", "max_audio_length_hours": 8.4, "max_audio_per_prompt": 1, + "max_images_per_prompt": 3000, + "max_input_tokens": 1000000, + "max_output_tokens": 8192, "max_pdf_size_mb": 30, - "input_cost_per_token": 1.25e-06, - "input_cost_per_token_above_200k_tokens": 2.5e-06, - "output_cost_per_token": 1e-05, - "output_cost_per_token_above_200k_tokens": 1.5e-05, - "litellm_provider": "vertex_ai-language-models", - "mode": "chat", - "supports_system_messages": true, - "supports_function_calling": true, - "supports_vision": true, - "supports_audio_input": true, - "supports_video_input": true, - "supports_pdf_input": true, - "supports_response_schema": true, - "supports_tool_choice": true, - "supported_endpoints": [ - "/v1/chat/completions", - "/v1/completions" - ], - "supported_modalities": [ - "text", - "image", - "audio", - "video" - ], - "supported_output_modalities": [ - "text" - ], - "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing", - "supports_parallel_function_calling": true, - "supports_web_search": true, - "cache_read_input_token_cost": 3.125e-07, - "supports_prompt_caching": true - }, - "gemini-2.0-pro-exp-02-05": { "max_tokens": 8192, + "max_video_length": 1, + 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Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." - }, - "supports_tool_choice": true - }, - "databricks/databricks-mpt-30b-instruct": { - "max_tokens": 8192, - "max_input_tokens": 8192, - "max_output_tokens": 8192, - "input_cost_per_token": 9.9902e-07, - "input_dbu_cost_per_token": 1.4286e-05, - "output_cost_per_token": 9.9902e-07, - "output_dbu_cost_per_token": 1.4286e-05, - "litellm_provider": "databricks", - "mode": "chat", - "source": "https://www.databricks.com/product/pricing/foundation-model-serving", - "metadata": { - "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." - }, - "supports_tool_choice": true - }, - "databricks/databricks-mpt-7b-instruct": { - "max_tokens": 8192, - "max_input_tokens": 8192, - "max_output_tokens": 8192, - "input_cost_per_token": 5.0001e-07, - "input_dbu_cost_per_token": 7.143e-06, - "output_cost_per_token": 0.0, - "output_dbu_cost_per_token": 0.0, - "litellm_provider": "databricks", - "mode": "chat", - "source": "https://www.databricks.com/product/pricing/foundation-model-serving", - "metadata": { - "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. 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Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." - } - }, - "databricks/databricks-gte-large-en": { - "max_tokens": 8192, - "max_input_tokens": 8192, - "output_vector_size": 1024, - "input_cost_per_token": 1.2999e-07, - "input_dbu_cost_per_token": 1.857e-06, - "output_cost_per_token": 0.0, - "output_dbu_cost_per_token": 0.0, - "litellm_provider": "databricks", - "mode": "embedding", - "source": "https://www.databricks.com/product/pricing/foundation-model-serving", - "metadata": { - "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. 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true, - "supports_response_schema": true, - "supports_vision": true, - "source": "https://cloud.sambanova.ai/plans/pricing", - "metadata": { - "notes": "For vision models, images are converted to 6432 input tokens and are billed at that amount" - } - }, - "sambanova/Llama-4-Scout-17B-16E-Instruct": { - "max_tokens": 8192, - "max_input_tokens": 8192, - "max_output_tokens": 8192, - "input_cost_per_token": 4e-07, - "output_cost_per_token": 7e-07, - "litellm_provider": "sambanova", - "mode": "chat", - "supports_function_calling": true, - "supports_tool_choice": true, - "supports_response_schema": true, - "source": "https://cloud.sambanova.ai/plans/pricing", - "metadata": { - "notes": "For vision models, images are converted to 6432 input tokens and are billed at that amount" - } - }, - "sambanova/Meta-Llama-3.3-70B-Instruct": { - "max_tokens": 131072, - "max_input_tokens": 131072, - "max_output_tokens": 131072, - "input_cost_per_token": 6e-07, - "output_cost_per_token": 1.2e-06, - 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"output_cost_per_token": 7e-06, "source": "https://cloud.sambanova.ai/plans/pricing" }, "sambanova/DeepSeek-R1-Distill-Llama-70B": { - "max_tokens": 131072, + "input_cost_per_token": 7e-07, + "litellm_provider": "sambanova", "max_input_tokens": 131072, "max_output_tokens": 131072, - "input_cost_per_token": 7e-07, + "max_tokens": 131072, + "mode": "chat", "output_cost_per_token": 1.4e-06, - "litellm_provider": "sambanova", - "mode": "chat", - "source": "https://cloud.sambanova.ai/plans/pricing" - }, - "sambanova/DeepSeek-R1": { - "max_tokens": 32768, - "max_input_tokens": 32768, - "max_output_tokens": 32768, - "input_cost_per_token": 5e-06, - "output_cost_per_token": 7e-06, - "litellm_provider": "sambanova", - "mode": "chat", "source": "https://cloud.sambanova.ai/plans/pricing" }, "sambanova/DeepSeek-V3-0324": { + "input_cost_per_token": 3e-06, + "litellm_provider": "sambanova", + "max_input_tokens": 32768, + "max_output_tokens": 32768, + "max_tokens": 32768, + "mode": "chat", + "output_cost_per_token": 4.5e-06, + "source": "https://cloud.sambanova.ai/plans/pricing", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, + "sambanova/Llama-4-Maverick-17B-128E-Instruct": { + "input_cost_per_token": 6.3e-07, + "litellm_provider": "sambanova", + "max_input_tokens": 131072, + "max_output_tokens": 131072, + "max_tokens": 131072, + "metadata": { + "notes": "For vision models, images are converted to 6432 input tokens and are billed at that amount" + }, + "mode": "chat", + "output_cost_per_token": 1.8e-06, + "source": "https://cloud.sambanova.ai/plans/pricing", + "supports_function_calling": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "sambanova/Llama-4-Scout-17B-16E-Instruct": { + "input_cost_per_token": 4e-07, + "litellm_provider": "sambanova", + "max_input_tokens": 8192, + "max_output_tokens": 8192, + "max_tokens": 8192, + "metadata": { + "notes": "For vision models, images are converted to 6432 input tokens and are billed at that amount" + }, + "mode": "chat", + "output_cost_per_token": 7e-07, + "source": "https://cloud.sambanova.ai/plans/pricing", + "supports_function_calling": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "sambanova/Meta-Llama-3.1-405B-Instruct": { + "input_cost_per_token": 5e-06, + "litellm_provider": "sambanova", + "max_input_tokens": 16384, + "max_output_tokens": 16384, + "max_tokens": 16384, + "mode": "chat", + "output_cost_per_token": 1e-05, + "source": "https://cloud.sambanova.ai/plans/pricing", + "supports_function_calling": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "sambanova/Meta-Llama-3.1-8B-Instruct": { + "input_cost_per_token": 1e-07, + "litellm_provider": "sambanova", + "max_input_tokens": 16384, + "max_output_tokens": 16384, + "max_tokens": 16384, + "mode": "chat", + "output_cost_per_token": 2e-07, + "source": "https://cloud.sambanova.ai/plans/pricing", + "supports_function_calling": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "sambanova/Meta-Llama-3.2-1B-Instruct": { + "input_cost_per_token": 4e-08, + "litellm_provider": "sambanova", + "max_input_tokens": 16384, + "max_output_tokens": 16384, + "max_tokens": 16384, + "mode": "chat", + "output_cost_per_token": 8e-08, + "source": "https://cloud.sambanova.ai/plans/pricing" + }, + "sambanova/Meta-Llama-3.2-3B-Instruct": { + "input_cost_per_token": 8e-08, + "litellm_provider": "sambanova", + "max_input_tokens": 4096, + "max_output_tokens": 4096, + "max_tokens": 4096, + "mode": "chat", + "output_cost_per_token": 1.6e-07, + "source": "https://cloud.sambanova.ai/plans/pricing" + }, + "sambanova/Meta-Llama-3.3-70B-Instruct": { + "input_cost_per_token": 6e-07, + "litellm_provider": "sambanova", + "max_input_tokens": 131072, + "max_output_tokens": 131072, + "max_tokens": 131072, + "mode": "chat", + "output_cost_per_token": 1.2e-06, + "source": "https://cloud.sambanova.ai/plans/pricing", + "supports_function_calling": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "sambanova/Meta-Llama-Guard-3-8B": { + "input_cost_per_token": 3e-07, + "litellm_provider": "sambanova", + "max_input_tokens": 16384, + "max_output_tokens": 16384, + "max_tokens": 16384, + "mode": "chat", + "output_cost_per_token": 3e-07, + "source": "https://cloud.sambanova.ai/plans/pricing" + }, + "sambanova/QwQ-32B": { + "input_cost_per_token": 5e-07, + "litellm_provider": "sambanova", + "max_input_tokens": 16384, + "max_output_tokens": 16384, + "max_tokens": 16384, + "mode": "chat", + "output_cost_per_token": 1e-06, + "source": "https://cloud.sambanova.ai/plans/pricing" + }, + "sambanova/Qwen2-Audio-7B-Instruct": { + "input_cost_per_token": 5e-07, + "litellm_provider": "sambanova", + "max_input_tokens": 4096, + "max_output_tokens": 4096, + "max_tokens": 4096, + "mode": "chat", + "output_cost_per_token": 0.0001, + "source": "https://cloud.sambanova.ai/plans/pricing", + "supports_audio_input": true + }, + "sambanova/Qwen3-32B": { + "input_cost_per_token": 4e-07, + "litellm_provider": "sambanova", + "max_input_tokens": 8192, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 8e-07, + "source": "https://cloud.sambanova.ai/plans/pricing", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, + "sambanova/DeepSeek-V3.1": { "max_tokens": 32768, "max_input_tokens": 32768, "max_output_tokens": 32768, @@ -18034,1636 +18528,3734 @@ "supports_reasoning": true, "source": "https://cloud.sambanova.ai/plans/pricing" }, - "assemblyai/nano": { - "mode": "audio_transcription", - "input_cost_per_second": 0.00010278, - "output_cost_per_second": 0.0, - "litellm_provider": "assemblyai" - }, - "assemblyai/best": { - "mode": "audio_transcription", - "input_cost_per_second": 3.333e-05, - "output_cost_per_second": 0.0, - "litellm_provider": "assemblyai" - }, - "jina-reranker-v2-base-multilingual": { - "max_tokens": 1024, - "max_input_tokens": 1024, - "max_output_tokens": 1024, - "max_document_chunks_per_query": 2048, - "input_cost_per_token": 1.8e-08, - "output_cost_per_token": 1.8e-08, - "litellm_provider": "jina_ai", - "mode": "rerank" - }, - "snowflake/deepseek-r1": { - "max_tokens": 32768, - "max_input_tokens": 32768, - "max_output_tokens": 8192, - "litellm_provider": "snowflake", + "sambanova/gpt-oss-120b": { + "max_tokens": 131072, + "max_input_tokens": 131072, + "max_output_tokens": 131072, + "input_cost_per_token": 3e-06, + "output_cost_per_token": 4.5e-06, + "litellm_provider": "sambanova", + "mode": "chat", + "supports_function_calling": true, + "supports_tool_choice": true, "supports_reasoning": true, - "mode": "chat" + "source": "https://cloud.sambanova.ai/plans/pricing" }, - "snowflake/snowflake-arctic": { - "max_tokens": 4096, - "max_input_tokens": 4096, - "max_output_tokens": 8192, - "litellm_provider": "snowflake", - "mode": "chat" + "sample_spec": { + "code_interpreter_cost_per_session": 0.0, + "computer_use_input_cost_per_1k_tokens": 0.0, + "computer_use_output_cost_per_1k_tokens": 0.0, + "deprecation_date": "date when the model becomes deprecated in the format YYYY-MM-DD", + "file_search_cost_per_1k_calls": 0.0, + "file_search_cost_per_gb_per_day": 0.0, + "input_cost_per_audio_token": 0.0, + "input_cost_per_token": 0.0, + "litellm_provider": "one of https://docs.litellm.ai/docs/providers", + "max_input_tokens": "max input tokens, if the provider specifies it. if not default to max_tokens", + "max_output_tokens": "max output tokens, if the provider specifies it. if not default to max_tokens", + "max_tokens": "LEGACY parameter. set to max_output_tokens if provider specifies it. IF not set to max_input_tokens, if provider specifies it.", + "mode": "one of: chat, embedding, completion, image_generation, audio_transcription, audio_speech, image_generation, moderation, rerank", + "output_cost_per_reasoning_token": 0.0, + "output_cost_per_token": 0.0, + "search_context_cost_per_query": { + "search_context_size_high": 0.0, + "search_context_size_low": 0.0, + "search_context_size_medium": 0.0 + }, + "supported_regions": [ + "global", + "us-west-2", + "eu-west-1", + "ap-southeast-1", + "ap-northeast-1" + ], + "supports_audio_input": true, + "supports_audio_output": true, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_vision": true, + "supports_web_search": true, + "vector_store_cost_per_gb_per_day": 0.0 }, "snowflake/claude-3-5-sonnet": { - "supports_computer_use": true, - "max_tokens": 18000, + "litellm_provider": "snowflake", "max_input_tokens": 18000, "max_output_tokens": 8192, - "litellm_provider": "snowflake", - "mode": "chat" + "max_tokens": 18000, + "mode": "chat", + "supports_computer_use": true }, - "snowflake/mistral-large": { - "max_tokens": 32000, - "max_input_tokens": 32000, - "max_output_tokens": 8192, + "snowflake/deepseek-r1": { "litellm_provider": "snowflake", - "mode": "chat" + "max_input_tokens": 32768, + "max_output_tokens": 8192, + "max_tokens": 32768, + "mode": "chat", + "supports_reasoning": true }, - "snowflake/mistral-large2": { - "max_tokens": 128000, - "max_input_tokens": 128000, - "max_output_tokens": 8192, + "snowflake/gemma-7b": { "litellm_provider": "snowflake", - "mode": "chat" - }, - "snowflake/reka-flash": { - "max_tokens": 100000, - "max_input_tokens": 100000, + "max_input_tokens": 8000, "max_output_tokens": 8192, - "litellm_provider": "snowflake", - "mode": "chat" - }, - "snowflake/reka-core": { - "max_tokens": 32000, - "max_input_tokens": 32000, - "max_output_tokens": 8192, - "litellm_provider": "snowflake", - "mode": "chat" - }, - "snowflake/jamba-instruct": { - "max_tokens": 256000, - "max_input_tokens": 256000, - "max_output_tokens": 8192, - "litellm_provider": "snowflake", - "mode": "chat" - }, - "snowflake/jamba-1.5-mini": { - "max_tokens": 256000, - "max_input_tokens": 256000, - "max_output_tokens": 8192, - "litellm_provider": "snowflake", + "max_tokens": 8000, "mode": "chat" }, "snowflake/jamba-1.5-large": { - "max_tokens": 256000, + "litellm_provider": "snowflake", "max_input_tokens": 256000, "max_output_tokens": 8192, - "litellm_provider": "snowflake", + "max_tokens": 256000, "mode": "chat" }, - "snowflake/mixtral-8x7b": { - "max_tokens": 32000, - "max_input_tokens": 32000, - "max_output_tokens": 8192, + "snowflake/jamba-1.5-mini": { "litellm_provider": "snowflake", + "max_input_tokens": 256000, + "max_output_tokens": 8192, + "max_tokens": 256000, + "mode": "chat" + }, + "snowflake/jamba-instruct": { + "litellm_provider": "snowflake", + "max_input_tokens": 256000, + "max_output_tokens": 8192, + "max_tokens": 256000, "mode": "chat" }, "snowflake/llama2-70b-chat": { - "max_tokens": 4096, + "litellm_provider": "snowflake", "max_input_tokens": 4096, "max_output_tokens": 8192, - "litellm_provider": "snowflake", - "mode": "chat" - }, - "snowflake/llama3-8b": { - "max_tokens": 8000, - "max_input_tokens": 8000, - "max_output_tokens": 8192, - "litellm_provider": "snowflake", + "max_tokens": 4096, "mode": "chat" }, "snowflake/llama3-70b": { - "max_tokens": 8000, + "litellm_provider": "snowflake", "max_input_tokens": 8000, "max_output_tokens": 8192, - "litellm_provider": "snowflake", - "mode": "chat" - }, - "snowflake/llama3.1-8b": { - "max_tokens": 128000, - "max_input_tokens": 128000, - "max_output_tokens": 8192, - "litellm_provider": "snowflake", - "mode": "chat" - }, - "snowflake/llama3.1-70b": { - "max_tokens": 128000, - "max_input_tokens": 128000, - "max_output_tokens": 8192, - "litellm_provider": "snowflake", - "mode": "chat" - }, - "snowflake/llama3.3-70b": { - "max_tokens": 128000, - "max_input_tokens": 128000, - "max_output_tokens": 8192, - "litellm_provider": "snowflake", - "mode": "chat" - }, - "snowflake/snowflake-llama-3.3-70b": { "max_tokens": 8000, + "mode": "chat" + }, + "snowflake/llama3-8b": { + "litellm_provider": "snowflake", "max_input_tokens": 8000, "max_output_tokens": 8192, - "litellm_provider": "snowflake", + "max_tokens": 8000, "mode": "chat" }, "snowflake/llama3.1-405b": { - "max_tokens": 128000, + "litellm_provider": "snowflake", "max_input_tokens": 128000, "max_output_tokens": 8192, - "litellm_provider": "snowflake", + "max_tokens": 128000, "mode": "chat" }, - "snowflake/snowflake-llama-3.1-405b": { - "max_tokens": 8000, - "max_input_tokens": 8000, - "max_output_tokens": 8192, + "snowflake/llama3.1-70b": { "litellm_provider": "snowflake", + "max_input_tokens": 128000, + "max_output_tokens": 8192, + "max_tokens": 128000, + "mode": "chat" + }, + "snowflake/llama3.1-8b": { + "litellm_provider": "snowflake", + "max_input_tokens": 128000, + "max_output_tokens": 8192, + "max_tokens": 128000, "mode": "chat" }, "snowflake/llama3.2-1b": { - "max_tokens": 128000, + "litellm_provider": "snowflake", "max_input_tokens": 128000, "max_output_tokens": 8192, - "litellm_provider": "snowflake", + "max_tokens": 128000, "mode": "chat" }, "snowflake/llama3.2-3b": { - "max_tokens": 128000, + "litellm_provider": "snowflake", "max_input_tokens": 128000, "max_output_tokens": 8192, + "max_tokens": 128000, + "mode": "chat" + }, + "snowflake/llama3.3-70b": { "litellm_provider": "snowflake", + "max_input_tokens": 128000, + "max_output_tokens": 8192, + "max_tokens": 128000, "mode": "chat" }, "snowflake/mistral-7b": { - "max_tokens": 32000, + "litellm_provider": "snowflake", "max_input_tokens": 32000, "max_output_tokens": 8192, - "litellm_provider": "snowflake", + "max_tokens": 32000, "mode": "chat" }, - "snowflake/gemma-7b": { - "max_tokens": 8000, + "snowflake/mistral-large": { + "litellm_provider": "snowflake", + "max_input_tokens": 32000, + "max_output_tokens": 8192, + "max_tokens": 32000, + "mode": "chat" + }, + "snowflake/mistral-large2": { + "litellm_provider": "snowflake", + "max_input_tokens": 128000, + "max_output_tokens": 8192, + "max_tokens": 128000, + "mode": "chat" + }, + "snowflake/mixtral-8x7b": { + "litellm_provider": "snowflake", + "max_input_tokens": 32000, + "max_output_tokens": 8192, + "max_tokens": 32000, + "mode": "chat" + }, + "snowflake/reka-core": { + "litellm_provider": "snowflake", + "max_input_tokens": 32000, + "max_output_tokens": 8192, + "max_tokens": 32000, + "mode": "chat" + }, + "snowflake/reka-flash": { + "litellm_provider": "snowflake", + "max_input_tokens": 100000, + "max_output_tokens": 8192, + "max_tokens": 100000, + "mode": "chat" + }, + "snowflake/snowflake-arctic": { + "litellm_provider": "snowflake", + "max_input_tokens": 4096, + "max_output_tokens": 8192, + "max_tokens": 4096, + "mode": "chat" + }, + "snowflake/snowflake-llama-3.1-405b": { + "litellm_provider": "snowflake", "max_input_tokens": 8000, "max_output_tokens": 8192, - "litellm_provider": "snowflake", + "max_tokens": 8000, "mode": "chat" }, - "gradient_ai/anthropic-claude-3.7-sonnet": { - "input_cost_per_token": 3e-06, - "output_cost_per_token": 15e-06, - "litellm_provider": "gradient_ai", - "mode": "chat", - "max_tokens": 1024, - "supported_endpoints": ["/v1/chat/completions"], - "supported_modalities": ["text"], - "supports_tool_choice": false + "snowflake/snowflake-llama-3.3-70b": { + "litellm_provider": "snowflake", + "max_input_tokens": 8000, + "max_output_tokens": 8192, + "max_tokens": 8000, + "mode": "chat" }, - "gradient_ai/anthropic-claude-3.5-sonnet": { - "input_cost_per_token": 3e-06, - "output_cost_per_token": 15e-06, - "litellm_provider": "gradient_ai", - "mode": "chat", - "max_tokens": 1024, - "supported_endpoints": ["/v1/chat/completions"], - "supported_modalities": ["text"], - "supports_tool_choice": false + "stability.sd3-5-large-v1:0": { + "litellm_provider": "bedrock", + "max_input_tokens": 77, + "max_tokens": 77, + "mode": "image_generation", + "output_cost_per_image": 0.08 }, - "gradient_ai/anthropic-claude-3.5-haiku": { + "stability.sd3-large-v1:0": { + "litellm_provider": "bedrock", + "max_input_tokens": 77, + "max_tokens": 77, + "mode": "image_generation", + "output_cost_per_image": 0.08 + }, + "stability.stable-image-core-v1:0": { + "litellm_provider": "bedrock", + "max_input_tokens": 77, + "max_tokens": 77, + "mode": "image_generation", + "output_cost_per_image": 0.04 + }, + "stability.stable-image-core-v1:1": { + "litellm_provider": "bedrock", + "max_input_tokens": 77, + "max_tokens": 77, + "mode": "image_generation", + "output_cost_per_image": 0.04 + }, + "stability.stable-image-ultra-v1:0": { + "litellm_provider": "bedrock", + "max_input_tokens": 77, + "max_tokens": 77, + "mode": 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Assumed 50/50 split for input/output." - } - }, - "nscale/black-forest-labs/FLUX.1-schnell": { - "mode": "image_generation", - "input_cost_per_pixel": 1.3e-09, - "output_cost_per_pixel": 0.0, - "litellm_provider": "nscale", - "supported_endpoints": [ - "/v1/images/generations" - ], - "source": "https://docs.nscale.com/docs/inference/serverless-models/current#image-models" - }, - "nscale/stabilityai/stable-diffusion-xl-base-1.0": { - "mode": "image_generation", - "input_cost_per_pixel": 3e-09, - "output_cost_per_pixel": 0.0, - "litellm_provider": "nscale", - "supported_endpoints": [ - "/v1/images/generations" - ], - "source": "https://docs.nscale.com/docs/inference/serverless-models/current#image-models" - }, - "featherless_ai/featherless-ai/Qwerky-72B": { - "max_tokens": 32768, - "max_input_tokens": 32768, - "max_output_tokens": 4096, - "litellm_provider": "featherless_ai", - "mode": "chat" - }, - "featherless_ai/featherless-ai/Qwerky-QwQ-32B": { - "max_tokens": 32768, - "max_input_tokens": 32768, - "max_output_tokens": 4096, - "litellm_provider": "featherless_ai", - "mode": "chat" - }, - "deepgram/nova-3": { - "mode": "audio_transcription", - "input_cost_per_second": 7.167e-05, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0043, - "calculation": "$0.0043/60 seconds = $0.00007167 per second" - } - }, - "deepgram/nova-3-general": { - "mode": "audio_transcription", - "input_cost_per_second": 7.167e-05, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0043, - "calculation": "$0.0043/60 seconds = $0.00007167 per second" - } - }, - "deepgram/nova-3-medical": { - "mode": "audio_transcription", - "input_cost_per_second": 8.667e-05, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0052, - "calculation": "$0.0052/60 seconds = $0.00008667 per second (multilingual)" - } - }, - "deepgram/nova-2": { - "mode": "audio_transcription", - "input_cost_per_second": 7.167e-05, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0043, - "calculation": "$0.0043/60 seconds = $0.00007167 per second" - } - }, - "deepgram/nova-2-general": { - "mode": "audio_transcription", - "input_cost_per_second": 7.167e-05, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0043, - "calculation": "$0.0043/60 seconds = $0.00007167 per second" - } - }, - "deepgram/nova-2-meeting": { - "mode": "audio_transcription", - "input_cost_per_second": 7.167e-05, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0043, - "calculation": "$0.0043/60 seconds = $0.00007167 per second" - } - }, - "deepgram/nova-2-phonecall": { - "mode": "audio_transcription", - "input_cost_per_second": 7.167e-05, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0043, - "calculation": "$0.0043/60 seconds = $0.00007167 per second" - } - }, - "deepgram/nova-2-voicemail": { - "mode": "audio_transcription", - "input_cost_per_second": 7.167e-05, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0043, - "calculation": "$0.0043/60 seconds = $0.00007167 per second" - } - }, - "deepgram/nova-2-finance": { - "mode": "audio_transcription", - "input_cost_per_second": 7.167e-05, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0043, - "calculation": "$0.0043/60 seconds = $0.00007167 per second" - } - }, - "deepgram/nova-2-conversationalai": { - "mode": "audio_transcription", - "input_cost_per_second": 7.167e-05, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0043, - "calculation": "$0.0043/60 seconds = $0.00007167 per second" - } - }, - "deepgram/nova-2-video": { - "mode": "audio_transcription", - "input_cost_per_second": 7.167e-05, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0043, - "calculation": "$0.0043/60 seconds = $0.00007167 per second" - } - }, - "deepgram/nova-2-drivethru": { - "mode": "audio_transcription", - "input_cost_per_second": 7.167e-05, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0043, - "calculation": "$0.0043/60 seconds = $0.00007167 per second" - } - }, - "deepgram/nova-2-automotive": { - "mode": "audio_transcription", - "input_cost_per_second": 7.167e-05, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0043, - "calculation": "$0.0043/60 seconds = $0.00007167 per second" - } - }, - "deepgram/nova-2-atc": { - "mode": "audio_transcription", - "input_cost_per_second": 7.167e-05, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0043, - "calculation": "$0.0043/60 seconds = $0.00007167 per second" - } - }, - "deepgram/nova": { - "mode": "audio_transcription", - "input_cost_per_second": 7.167e-05, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0043, - "calculation": "$0.0043/60 seconds = $0.00007167 per second" - } - }, - "deepgram/nova-general": { - "mode": "audio_transcription", - "input_cost_per_second": 7.167e-05, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0043, - "calculation": "$0.0043/60 seconds = $0.00007167 per second" - } - }, - "deepgram/nova-phonecall": { - "mode": "audio_transcription", - "input_cost_per_second": 7.167e-05, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0043, - "calculation": "$0.0043/60 seconds = $0.00007167 per second" - } - }, - "deepgram/enhanced": { - "mode": "audio_transcription", - "input_cost_per_second": 0.00024167, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0145, - "calculation": "$0.0145/60 seconds = $0.00024167 per second" - } - }, - "deepgram/enhanced-general": { - "mode": "audio_transcription", - "input_cost_per_second": 0.00024167, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0145, - "calculation": "$0.0145/60 seconds = $0.00024167 per second" - } - }, - "deepgram/enhanced-meeting": { - "mode": "audio_transcription", - "input_cost_per_second": 0.00024167, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0145, - "calculation": "$0.0145/60 seconds = $0.00024167 per second" - } - }, - "deepgram/enhanced-phonecall": { - "mode": "audio_transcription", - "input_cost_per_second": 0.00024167, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0145, - "calculation": "$0.0145/60 seconds = $0.00024167 per second" - } - }, - "deepgram/enhanced-finance": { - "mode": "audio_transcription", - "input_cost_per_second": 0.00024167, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0145, - "calculation": "$0.0145/60 seconds = $0.00024167 per second" - } - }, - "deepgram/base": { - "mode": "audio_transcription", - "input_cost_per_second": 0.00020833, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0125, - "calculation": "$0.0125/60 seconds = $0.00020833 per second" - } - }, - "deepgram/base-general": { - "mode": "audio_transcription", - "input_cost_per_second": 0.00020833, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0125, - "calculation": "$0.0125/60 seconds = $0.00020833 per second" - } - }, - "deepgram/base-meeting": { - "mode": "audio_transcription", - "input_cost_per_second": 0.00020833, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0125, - "calculation": "$0.0125/60 seconds = $0.00020833 per second" - } - }, - "deepgram/base-phonecall": { - "mode": "audio_transcription", - "input_cost_per_second": 0.00020833, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0125, - "calculation": "$0.0125/60 seconds = $0.00020833 per second" - } - }, - "deepgram/base-voicemail": { - "mode": "audio_transcription", - "input_cost_per_second": 0.00020833, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0125, - "calculation": "$0.0125/60 seconds = $0.00020833 per second" - } - }, - "deepgram/base-finance": { - "mode": "audio_transcription", - "input_cost_per_second": 0.00020833, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0125, - "calculation": "$0.0125/60 seconds = $0.00020833 per second" - } - }, - "deepgram/base-conversationalai": { - "mode": "audio_transcription", - "input_cost_per_second": 0.00020833, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0125, - "calculation": "$0.0125/60 seconds = $0.00020833 per second" - } - }, - "deepgram/base-video": { - "mode": "audio_transcription", - "input_cost_per_second": 0.00020833, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0125, - "calculation": "$0.0125/60 seconds = $0.00020833 per second" - } - }, - "deepgram/whisper": { - "mode": "audio_transcription", - "input_cost_per_second": 0.0001, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "notes": "Deepgram's hosted OpenAI Whisper models - pricing may differ from native Deepgram models" - } - }, - "deepgram/whisper-tiny": { - "mode": "audio_transcription", - "input_cost_per_second": 0.0001, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "notes": "Deepgram's hosted OpenAI Whisper models - pricing may differ from native Deepgram models" - } - }, - "deepgram/whisper-base": { - "mode": "audio_transcription", - "input_cost_per_second": 0.0001, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "notes": "Deepgram's hosted OpenAI Whisper models - pricing may differ from native Deepgram models" - } - }, - "deepgram/whisper-small": { - "mode": "audio_transcription", - "input_cost_per_second": 0.0001, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "notes": "Deepgram's hosted OpenAI Whisper models - pricing may differ from native Deepgram models" - } - }, - "deepgram/whisper-medium": { - "mode": "audio_transcription", - "input_cost_per_second": 0.0001, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "notes": "Deepgram's hosted OpenAI Whisper models - pricing may differ from native Deepgram models" - } - }, - "deepgram/whisper-large": { - "mode": "audio_transcription", - "input_cost_per_second": 0.0001, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "notes": "Deepgram's hosted OpenAI Whisper models - pricing may differ from native Deepgram models" - } - }, - "elevenlabs/scribe_v1": { - "mode": "audio_transcription", - "input_cost_per_second": 6.11e-05, - "output_cost_per_second": 0.0, - "litellm_provider": "elevenlabs", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://elevenlabs.io/pricing", - "metadata": { - "original_pricing_per_hour": 0.22, - "calculation": "$0.22/hour = $0.00366/minute = $0.0000611 per second (enterprise pricing)", - "notes": "ElevenLabs Scribe v1 - state-of-the-art speech recognition model with 99 language support" - } - }, - "elevenlabs/scribe_v1_experimental": { - "mode": "audio_transcription", - "input_cost_per_second": 6.11e-05, - "output_cost_per_second": 0.0, - "litellm_provider": "elevenlabs", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://elevenlabs.io/pricing", - "metadata": { - "original_pricing_per_hour": 0.22, - "calculation": "$0.22/hour = $0.00366/minute = $0.0000611 per second (enterprise pricing)", - "notes": "ElevenLabs Scribe v1 experimental - enhanced version of the main Scribe model" - } - }, - "bedrock/us-gov-east-1/amazon.titan-embed-text-v1": { - "max_tokens": 8192, - "max_input_tokens": 8192, - "output_vector_size": 1536, - "input_cost_per_token": 1e-07, - "output_cost_per_token": 0.0, - "litellm_provider": "bedrock", - "mode": "embedding" - }, - "bedrock/us-gov-east-1/amazon.titan-embed-text-v2:0": { - "max_tokens": 8192, - "max_input_tokens": 8192, - "output_vector_size": 1024, - "input_cost_per_token": 2e-07, - "output_cost_per_token": 0.0, - "litellm_provider": "bedrock", - "mode": "embedding" - }, - "bedrock/us-gov-east-1/amazon.titan-text-express-v1": { - "max_tokens": 8000, - "max_input_tokens": 42000, - "max_output_tokens": 8000, - "input_cost_per_token": 1.3e-06, - "output_cost_per_token": 1.7e-06, - "litellm_provider": "bedrock", - "mode": "chat" - }, - "bedrock/us-gov-east-1/amazon.titan-text-lite-v1": { - "max_tokens": 4000, - "max_input_tokens": 42000, - "max_output_tokens": 4000, - "input_cost_per_token": 3e-07, - "output_cost_per_token": 4e-07, - "litellm_provider": "bedrock", - "mode": "chat" - }, - "bedrock/us-gov-east-1/amazon.titan-text-premier-v1:0": { - "max_tokens": 32000, - "max_input_tokens": 42000, - "max_output_tokens": 32000, - "input_cost_per_token": 5e-07, - 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"supports_web_search": true } } diff --git a/litellm/mypy.ini b/litellm/mypy.ini index c084de7c563..d4e5e7f6bdf 100644 --- a/litellm/mypy.ini +++ b/litellm/mypy.ini @@ -11,4 +11,8 @@ disable_error_code = ignore_missing_imports = True [mypy-cryptography.hazmat.bindings._rust.x509] +ignore_errors = True + +[mypy-fastuuid.*] +ignore_missing_imports = True ignore_errors = True \ No newline at end of file diff --git a/litellm/proxy/_experimental/mcp_server/auth/user_api_key_auth_mcp.py b/litellm/proxy/_experimental/mcp_server/auth/user_api_key_auth_mcp.py index a075de13fb1..6afed97fe93 100644 --- a/litellm/proxy/_experimental/mcp_server/auth/user_api_key_auth_mcp.py +++ b/litellm/proxy/_experimental/mcp_server/auth/user_api_key_auth_mcp.py @@ -28,12 +28,16 @@ class MCPRequestHandler: LITELLM_MCP_SERVERS_HEADER_NAME = SpecialHeaders.mcp_servers.value LITELLM_MCP_ACCESS_GROUPS_HEADER_NAME = SpecialHeaders.mcp_access_groups.value - + # MCP Protocol Version header MCP_PROTOCOL_VERSION_HEADER_NAME = "MCP-Protocol-Version" @staticmethod - async def process_mcp_request(scope: Scope) -> Tuple[UserAPIKeyAuth, Optional[str], Optional[List[str]], Optional[Dict[str, str]], Optional[str]]: + async def process_mcp_request( + scope: Scope, + ) -> Tuple[ + UserAPIKeyAuth, Optional[str], Optional[List[str]], Optional[Dict[str, str]] + ]: """ Process and validate MCP request headers from the ASGI scope. This includes: @@ -49,7 +53,6 @@ class MCPRequestHandler: mcp_auth_header: Optional[str] MCP auth header to be passed to the MCP server (deprecated) mcp_servers: Optional[List[str]] List of MCP servers and access groups to use mcp_server_auth_headers: Optional[Dict[str, str]] Server-specific auth headers in format {server_alias: auth_value} - mcp_protocol_version: Optional[str] MCP protocol version from request header Raises: HTTPException: If headers are invalid or missing required headers @@ -58,39 +61,50 @@ class MCPRequestHandler: litellm_api_key = ( MCPRequestHandler.get_litellm_api_key_from_headers(headers) or "" ) - + # Get the old mcp_auth_header for backward compatibility mcp_auth_header = MCPRequestHandler._get_mcp_auth_header_from_headers(headers) - - # Get the new server-specific auth headers - mcp_server_auth_headers = MCPRequestHandler._get_mcp_server_auth_headers_from_headers(headers) - # Get MCP protocol version from header - mcp_protocol_version = headers.get(MCPRequestHandler.MCP_PROTOCOL_VERSION_HEADER_NAME) + # Get the new server-specific auth headers + mcp_server_auth_headers = ( + MCPRequestHandler._get_mcp_server_auth_headers_from_headers(headers) + ) # Parse MCP servers from header - mcp_servers_header = headers.get(MCPRequestHandler.LITELLM_MCP_SERVERS_HEADER_NAME) + mcp_servers_header = headers.get( + MCPRequestHandler.LITELLM_MCP_SERVERS_HEADER_NAME + ) verbose_logger.debug(f"Raw MCP servers header: {mcp_servers_header}") mcp_servers = None if mcp_servers_header is not None: try: - mcp_servers = [s.strip() for s in mcp_servers_header.split(",") if s.strip()] + mcp_servers = [ + s.strip() for s in mcp_servers_header.split(",") if s.strip() + ] verbose_logger.debug(f"Parsed MCP servers: {mcp_servers}") except Exception as e: verbose_logger.debug(f"Error parsing mcp_servers header: {e}") mcp_servers = None - if mcp_servers_header == "" or (mcp_servers is not None and len(mcp_servers) == 0): + if mcp_servers_header == "" or ( + mcp_servers is not None and len(mcp_servers) == 0 + ): mcp_servers = [] # Create a proper Request object with mock body method to avoid ASGI receive channel issues request = Request(scope=scope) + async def mock_body(): return b"{}" + request.body = mock_body # type: ignore validated_user_api_key_auth = await user_api_key_auth( api_key=litellm_api_key, request=request ) - return validated_user_api_key_auth, mcp_auth_header, mcp_servers, mcp_server_auth_headers, mcp_protocol_version - + return ( + validated_user_api_key_auth, + mcp_auth_header, + mcp_servers, + mcp_server_auth_headers, + ) @staticmethod def _get_mcp_auth_header_from_headers(headers: Headers) -> Optional[str]: @@ -104,10 +118,12 @@ class MCPRequestHandler: Support this auth: https://docs.litellm.ai/docs/mcp#using-your-mcp-with-client-side-credentials If you want to use a different header name, you can set the `LITELLM_MCP_CLIENT_SIDE_AUTH_HEADER_NAME` in the secret manager or `mcp_client_side_auth_header_name` in the general settings. - + DEPRECATED: This method is deprecated in favor of server-specific auth headers using the format x-mcp-{{server_alias}}-{{header_name}} instead. """ - mcp_client_side_auth_header_name: str = MCPRequestHandler._get_mcp_client_side_auth_header_name() + mcp_client_side_auth_header_name: str = ( + MCPRequestHandler._get_mcp_client_side_auth_header_name() + ) auth_header = headers.get(mcp_client_side_auth_header_name) if auth_header: verbose_logger.warning( @@ -115,42 +131,49 @@ class MCPRequestHandler: f"Please use server-specific auth headers in the format 'x-mcp-{{server_alias}}-{{header_name}}' instead." ) return auth_header - + @staticmethod def _get_mcp_server_auth_headers_from_headers(headers: Headers) -> Dict[str, str]: """ Parse server-specific MCP auth headers from the request headers. - + Looks for headers in the format: x-mcp-{server_alias}-{header_name} Examples: - x-mcp-github-authorization: Bearer token123 - x-mcp-zapier-x-api-key: api_key_456 - x-mcp-deepwiki-authorization: Basic base64_encoded_creds - + Returns: Dict[str, str]: Mapping of server alias to auth value """ server_auth_headers = {} prefix = "x-mcp-" - + for header_name, header_value in headers.items(): if header_name.lower().startswith(prefix): # Skip the access groups header as it's not a server auth header - if header_name.lower() == MCPRequestHandler.LITELLM_MCP_ACCESS_GROUPS_HEADER_NAME.lower() or header_name.lower() == MCPRequestHandler.LITELLM_MCP_SERVERS_HEADER_NAME.lower(): + if ( + header_name.lower() + == MCPRequestHandler.LITELLM_MCP_ACCESS_GROUPS_HEADER_NAME.lower() + or header_name.lower() + == MCPRequestHandler.LITELLM_MCP_SERVERS_HEADER_NAME.lower() + ): continue - + # Extract server_alias and header_name from x-mcp-{server_alias}-{header_name} - remaining = header_name[len(prefix):].lower() - if '-' in remaining: + remaining = header_name[len(prefix) :].lower() + if "-" in remaining: # Split on the last dash to separate server_alias from header_name - parts = remaining.rsplit('-', 1) + parts = remaining.rsplit("-", 1) if len(parts) == 2: server_alias, auth_header_name = parts server_auth_headers[server_alias] = header_value - verbose_logger.debug(f"Found server auth header: {server_alias} -> {auth_header_name}: {header_value[:10]}...") - + verbose_logger.debug( + f"Found server auth header: {server_alias} -> {auth_header_name}: {header_value[:10]}..." + ) + return server_auth_headers - + @staticmethod def _get_mcp_client_side_auth_header_name() -> str: """ @@ -162,13 +185,21 @@ class MCPRequestHandler: """ from litellm.proxy.proxy_server import general_settings from litellm.secret_managers.main import get_secret_str - MCP_CLIENT_SIDE_AUTH_HEADER_NAME: str = MCPRequestHandler.LITELLM_MCP_AUTH_HEADER_NAME - if get_secret_str("LITELLM_MCP_CLIENT_SIDE_AUTH_HEADER_NAME") is not None: - MCP_CLIENT_SIDE_AUTH_HEADER_NAME = get_secret_str("LITELLM_MCP_CLIENT_SIDE_AUTH_HEADER_NAME") or MCP_CLIENT_SIDE_AUTH_HEADER_NAME - elif general_settings.get("mcp_client_side_auth_header_name") is not None: - MCP_CLIENT_SIDE_AUTH_HEADER_NAME = general_settings.get("mcp_client_side_auth_header_name") or MCP_CLIENT_SIDE_AUTH_HEADER_NAME - return MCP_CLIENT_SIDE_AUTH_HEADER_NAME + MCP_CLIENT_SIDE_AUTH_HEADER_NAME: str = ( + MCPRequestHandler.LITELLM_MCP_AUTH_HEADER_NAME + ) + if get_secret_str("LITELLM_MCP_CLIENT_SIDE_AUTH_HEADER_NAME") is not None: + MCP_CLIENT_SIDE_AUTH_HEADER_NAME = ( + get_secret_str("LITELLM_MCP_CLIENT_SIDE_AUTH_HEADER_NAME") + or MCP_CLIENT_SIDE_AUTH_HEADER_NAME + ) + elif general_settings.get("mcp_client_side_auth_header_name") is not None: + MCP_CLIENT_SIDE_AUTH_HEADER_NAME = ( + general_settings.get("mcp_client_side_auth_header_name") + or MCP_CLIENT_SIDE_AUTH_HEADER_NAME + ) + return MCP_CLIENT_SIDE_AUTH_HEADER_NAME @staticmethod def get_litellm_api_key_from_headers(headers: Headers) -> Optional[str]: @@ -229,10 +260,14 @@ class MCPRequestHandler: try: allowed_mcp_servers: List[str] = [] allowed_mcp_servers_for_key = ( - await MCPRequestHandler._get_allowed_mcp_servers_for_key(user_api_key_auth) + await MCPRequestHandler._get_allowed_mcp_servers_for_key( + user_api_key_auth + ) ) allowed_mcp_servers_for_team = ( - await MCPRequestHandler._get_allowed_mcp_servers_for_team(user_api_key_auth) + await MCPRequestHandler._get_allowed_mcp_servers_for_team( + user_api_key_auth + ) ) ######################################################### @@ -274,7 +309,9 @@ class MCPRequestHandler: try: key_object_permission = ( await prisma_client.db.litellm_objectpermissiontable.find_unique( - where={"object_permission_id": user_api_key_auth.object_permission_id}, + where={ + "object_permission_id": user_api_key_auth.object_permission_id + }, ) ) if key_object_permission is None: @@ -282,17 +319,21 @@ class MCPRequestHandler: # Get direct MCP servers direct_mcp_servers = key_object_permission.mcp_servers or [] - + # Get MCP servers from access groups - access_group_servers = await MCPRequestHandler._get_mcp_servers_from_access_groups( - key_object_permission.mcp_access_groups or [] + access_group_servers = ( + await MCPRequestHandler._get_mcp_servers_from_access_groups( + key_object_permission.mcp_access_groups or [] + ) ) - + # Combine both lists all_servers = direct_mcp_servers + access_group_servers return list(set(all_servers)) except Exception as e: - verbose_logger.warning(f"Failed to get allowed MCP servers for key: {str(e)}") + verbose_logger.warning( + f"Failed to get allowed MCP servers for key: {str(e)}" + ) return [] @staticmethod @@ -318,10 +359,10 @@ class MCPRequestHandler: return [] try: - team_obj: Optional[LiteLLM_TeamTable] = ( - await prisma_client.db.litellm_teamtable.find_unique( - where={"team_id": user_api_key_auth.team_id}, - ) + team_obj: Optional[ + LiteLLM_TeamTable + ] = await prisma_client.db.litellm_teamtable.find_unique( + where={"team_id": user_api_key_auth.team_id}, ) if team_obj is None: verbose_logger.debug("team_obj is None") @@ -333,21 +374,27 @@ class MCPRequestHandler: # Get direct MCP servers direct_mcp_servers = object_permissions.mcp_servers or [] - + # Get MCP servers from access groups - access_group_servers = await MCPRequestHandler._get_mcp_servers_from_access_groups( - object_permissions.mcp_access_groups or [] + access_group_servers = ( + await MCPRequestHandler._get_mcp_servers_from_access_groups( + object_permissions.mcp_access_groups or [] + ) ) - + # Combine both lists all_servers = direct_mcp_servers + access_group_servers return list(set(all_servers)) except Exception as e: - verbose_logger.warning(f"Failed to get allowed MCP servers for team: {str(e)}") + verbose_logger.warning( + f"Failed to get allowed MCP servers for team: {str(e)}" + ) return [] @staticmethod - def _get_config_server_ids_for_access_groups(config_mcp_servers, access_groups: List[str]) -> Set[str]: + def _get_config_server_ids_for_access_groups( + config_mcp_servers, access_groups: List[str] + ) -> Set[str]: """ Helper to get server_ids from config-loaded servers that match any of the given access groups. """ @@ -359,7 +406,9 @@ class MCPRequestHandler: return server_ids @staticmethod - async def _get_db_server_ids_for_access_groups(prisma_client, access_groups: List[str]) -> Set[str]: + async def _get_db_server_ids_for_access_groups( + prisma_client, access_groups: List[str] + ) -> Set[str]: """ Helper to get server_ids from DB servers that match any of the given access groups. """ @@ -367,21 +416,19 @@ class MCPRequestHandler: if access_groups and prisma_client is not None: try: mcp_servers = await prisma_client.db.litellm_mcpservertable.find_many( - where={ - "mcp_access_groups": { - "hasSome": access_groups - } - } + where={"mcp_access_groups": {"hasSome": access_groups}} ) for server in mcp_servers: server_ids.add(server.server_id) except Exception as e: - verbose_logger.debug(f"Error getting MCP servers from access groups: {e}") + verbose_logger.debug( + f"Error getting MCP servers from access groups: {e}" + ) return server_ids @staticmethod async def _get_mcp_servers_from_access_groups( - access_groups: List[str] + access_groups: List[str], ) -> List[str]: """ Resolve MCP access groups to server IDs by querying BOTH the MCP server table (DB) AND config-loaded servers @@ -390,22 +437,28 @@ class MCPRequestHandler: try: # Import here to avoid circular import - from litellm.proxy._experimental.mcp_server.mcp_server_manager import global_mcp_server_manager - + from litellm.proxy._experimental.mcp_server.mcp_server_manager import ( + global_mcp_server_manager, + ) + # Use the new helper for config-loaded servers server_ids = MCPRequestHandler._get_config_server_ids_for_access_groups( global_mcp_server_manager.config_mcp_servers, access_groups ) # Use the new helper for DB servers - db_server_ids = await MCPRequestHandler._get_db_server_ids_for_access_groups( - prisma_client, access_groups + db_server_ids = ( + await MCPRequestHandler._get_db_server_ids_for_access_groups( + prisma_client, access_groups + ) ) server_ids.update(db_server_ids) return list(server_ids) except Exception as e: - verbose_logger.warning(f"Failed to get MCP servers from access groups: {str(e)}") + verbose_logger.warning( + f"Failed to get MCP servers from access groups: {str(e)}" + ) return [] @staticmethod @@ -418,8 +471,8 @@ class MCPRequestHandler: from typing import List access_groups: List[str] = [] - access_groups_for_key = ( - await MCPRequestHandler._get_mcp_access_groups_for_key(user_api_key_auth) + access_groups_for_key = await MCPRequestHandler._get_mcp_access_groups_for_key( + user_api_key_auth ) access_groups_for_team = ( await MCPRequestHandler._get_mcp_access_groups_for_team(user_api_key_auth) @@ -482,10 +535,10 @@ class MCPRequestHandler: verbose_logger.debug("prisma_client is None") return [] - team_obj: Optional[LiteLLM_TeamTable] = ( - await prisma_client.db.litellm_teamtable.find_unique( - where={"team_id": user_api_key_auth.team_id}, - ) + team_obj: Optional[ + LiteLLM_TeamTable + ] = await prisma_client.db.litellm_teamtable.find_unique( + where={"team_id": user_api_key_auth.team_id}, ) if team_obj is None: verbose_logger.debug("team_obj is None") @@ -502,10 +555,14 @@ class MCPRequestHandler: """ Extract and parse the x-mcp-access-groups header as a list of strings. """ - mcp_access_groups_header = headers.get(MCPRequestHandler.LITELLM_MCP_ACCESS_GROUPS_HEADER_NAME) + mcp_access_groups_header = headers.get( + MCPRequestHandler.LITELLM_MCP_ACCESS_GROUPS_HEADER_NAME + ) if mcp_access_groups_header is not None: try: - return [s.strip() for s in mcp_access_groups_header.split(",") if s.strip()] + return [ + s.strip() for s in mcp_access_groups_header.split(",") if s.strip() + ] except Exception: return None return None @@ -516,4 +573,4 @@ class MCPRequestHandler: Extract and parse the x-mcp-access-groups header from an ASGI scope. """ headers = MCPRequestHandler._safe_get_headers_from_scope(scope) - return MCPRequestHandler.get_mcp_access_groups_from_headers(headers) \ No newline at end of file + return MCPRequestHandler.get_mcp_access_groups_from_headers(headers) diff --git a/litellm/proxy/_experimental/mcp_server/db.py b/litellm/proxy/_experimental/mcp_server/db.py index d5d9f978908..79e3b0f7623 100644 --- a/litellm/proxy/_experimental/mcp_server/db.py +++ b/litellm/proxy/_experimental/mcp_server/db.py @@ -1,4 +1,4 @@ -import uuid +from litellm._uuid import uuid from typing import Any, Dict, Iterable, List, Optional, Set, Union from litellm._logging import verbose_proxy_logger diff --git a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py index 34a0d604f39..ab5d1b10bf3 100644 --- a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py +++ b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py @@ -34,8 +34,6 @@ from litellm.proxy._experimental.mcp_server.utils import ( from litellm.proxy._types import ( LiteLLM_MCPServerTable, MCPAuthType, - MCPSpecVersion, - MCPSpecVersionType, MCPTransport, MCPTransportType, UserAPIKeyAuth, @@ -70,38 +68,6 @@ def _deserialize_env_dict(env_data: Any) -> Optional[Dict[str, str]]: return env_data -def _convert_protocol_version_to_enum( - protocol_version: Optional[str | MCPSpecVersionType], -) -> MCPSpecVersionType: - """ - Convert string protocol version to MCPSpecVersion enum. - - Args: - protocol_version: String protocol version, enum, or None - - Returns: - MCPSpecVersionType: The enum value - """ - if not protocol_version: - return cast(MCPSpecVersionType, MCPSpecVersion.jun_2025) - - # If it's already an MCPSpecVersion enum, return it - if isinstance(protocol_version, MCPSpecVersion): - return cast(MCPSpecVersionType, protocol_version) - - # If it's a string, try to match it to enum values - if isinstance(protocol_version, str): - for version in MCPSpecVersion: - if version.value == protocol_version: - return cast(MCPSpecVersionType, version) - - # If no match found, return default - verbose_logger.warning( - f"Unknown protocol version '{protocol_version}', using default" - ) - return cast(MCPSpecVersionType, MCPSpecVersion.jun_2025) - - class MCPServerManager: def __init__(self): self.registry: Dict[str, MCPServer] = {} @@ -113,8 +79,7 @@ class MCPServerManager: "name": "zapier_mcp_server", "url": "https://actions.zapier.com/mcp/sk-ak-2ew3bofIeQIkNoeKIdXrF1Hhhp/sse" "transport": "sse", - "auth_type": "api_key", - "spec_version": "2025-03-26" + "auth_type": "api_key" }, "uuid-2": { "name": "google_drive_mcp_server", @@ -156,15 +121,13 @@ class MCPServerManager: for server_name, server_config in mcp_servers_config.items(): validate_mcp_server_name(server_name) _mcp_info: Dict[str, Any] = server_config.get("mcp_info", None) or {} - # Convert Dict[str, Any] to MCPInfo properly - mcp_info: MCPInfo = { - "server_name": _mcp_info.get("server_name", server_name), - "description": _mcp_info.get( - "description", server_config.get("description", None) - ), - "logo_url": _mcp_info.get("logo_url", None), - "mcp_server_cost_info": _mcp_info.get("mcp_server_cost_info", None), - } + # Preserve all custom fields from config while setting defaults for core fields + mcp_info: MCPInfo = _mcp_info.copy() + # Set default values for core fields if not present + if "server_name" not in mcp_info: + mcp_info["server_name"] = server_name + if "description" not in mcp_info and server_config.get("description"): + mcp_info["description"] = server_config.get("description") # Use alias for name if present, else server_name alias = server_config.get("alias", None) @@ -223,7 +186,6 @@ class MCPServerManager: server_name=server_name, url=server_config.get("url", None) or "", transport=server_config.get("transport", MCPTransport.http), - spec_version=server_config.get("spec_version", MCPSpecVersion.jun_2025), auth_type=server_config.get("auth_type", None), alias=alias, ) @@ -239,8 +201,10 @@ class MCPServerManager: env=server_config.get("env", None) or {}, # TODO: utility fn the default values transport=server_config.get("transport", MCPTransport.http), - spec_version=server_config.get("spec_version", MCPSpecVersion.jun_2025), auth_type=server_config.get("auth_type", None), + authentication_token=server_config.get( + "authentication_token", server_config.get("auth_value", None) + ), mcp_info=mcp_info, access_groups=server_config.get("access_groups", None), ) @@ -277,6 +241,14 @@ class MCPServerManager: name_for_prefix = ( mcp_server.alias or mcp_server.server_name or mcp_server.server_id ) + # Preserve all custom fields from database while setting defaults for core fields + mcp_info: MCPInfo = _mcp_info.copy() + # Set default values for core fields if not present + if "server_name" not in mcp_info: + mcp_info["server_name"] = mcp_server.server_name or mcp_server.server_id + if "description" not in mcp_info and mcp_server.description: + mcp_info["description"] = mcp_server.description + new_server = MCPServer( server_id=mcp_server.server_id, name=name_for_prefix, @@ -284,13 +256,8 @@ class MCPServerManager: server_name=getattr(mcp_server, "server_name", None), url=mcp_server.url, transport=cast(MCPTransportType, mcp_server.transport), - spec_version=_convert_protocol_version_to_enum(mcp_server.spec_version), auth_type=cast(MCPAuthType, mcp_server.auth_type), - mcp_info=MCPInfo( - server_name=mcp_server.server_name or mcp_server.server_id, - description=mcp_server.description, - mcp_server_cost_info=_mcp_info.get("mcp_server_cost_info", None), - ), + mcp_info=mcp_info, # Stdio-specific fields command=getattr(mcp_server, "command", None), args=getattr(mcp_server, "args", None) or [], @@ -347,7 +314,6 @@ class MCPServerManager: user_api_key_auth: Optional[UserAPIKeyAuth] = None, mcp_auth_header: Optional[str] = None, mcp_server_auth_headers: Optional[Dict[str, str]] = None, - mcp_protocol_version: Optional[str] = None, ) -> List[MCPTool]: """ List all tools available across all MCP Servers. @@ -387,7 +353,6 @@ class MCPServerManager: tools = await self._get_tools_from_server( server=server, mcp_auth_header=server_auth_header, - mcp_protocol_version=mcp_protocol_version, ) list_tools_result.extend(tools) verbose_logger.info( @@ -411,7 +376,6 @@ class MCPServerManager: self, server: MCPServer, mcp_auth_header: Optional[str] = None, - protocol_version: Optional[str] = None, ) -> MCPClient: """ Create an MCPClient instance for the given server. @@ -419,18 +383,12 @@ class MCPServerManager: Args: server (MCPServer): The server configuration mcp_auth_header: MCP auth header to be passed to the MCP server. This is optional and will be used if provided. - protocol_version: Optional MCP protocol version to use. If not provided, uses server's default. Returns: MCPClient: Configured MCP client instance """ transport = server.transport or MCPTransport.sse - # Convert protocol version string to enum - protocol_version_enum = _convert_protocol_version_to_enum( - protocol_version or server.spec_version - ) - # Handle stdio transport if transport == MCPTransport.stdio: # For stdio, we need to get the stdio config from the server @@ -447,7 +405,6 @@ class MCPServerManager: auth_value=mcp_auth_header or server.authentication_token, timeout=60.0, stdio_config=stdio_config, - protocol_version=protocol_version_enum, ) else: # For HTTP/SSE transports @@ -458,14 +415,12 @@ class MCPServerManager: auth_type=server.auth_type, auth_value=mcp_auth_header or server.authentication_token, timeout=60.0, - protocol_version=protocol_version_enum, ) async def _get_tools_from_server( self, server: MCPServer, mcp_auth_header: Optional[str] = None, - mcp_protocol_version: Optional[str] = None, ) -> List[MCPTool]: """ Helper method to get tools from a single MCP server with prefixed names. @@ -480,22 +435,18 @@ class MCPServerManager: verbose_logger.debug(f"Connecting to url: {server.url}") verbose_logger.info(f"_get_tools_from_server for {server.name}...") - protocol_version = ( - mcp_protocol_version if mcp_protocol_version else server.spec_version - ) client = None try: client = self._create_mcp_client( server=server, mcp_auth_header=mcp_auth_header, - protocol_version=protocol_version, ) tools = await self._fetch_tools_with_timeout(client, server.name) - + prefixed_tools = self._create_prefixed_tools(tools, server) - + return prefixed_tools except Exception as e: @@ -527,7 +478,7 @@ class MCPServerManager: async def _list_tools_task(): try: await client.connect() - + tools = await client.list_tools() verbose_logger.debug(f"Tools from {server_name}: {tools}") return tools @@ -606,7 +557,6 @@ class MCPServerManager: user_api_key_auth: Optional[UserAPIKeyAuth] = None, mcp_auth_header: Optional[str] = None, mcp_server_auth_headers: Optional[Dict[str, str]] = None, - mcp_protocol_version: Optional[str] = None, proxy_logging_obj: Optional[ProxyLogging] = None, ) -> CallToolResult: """ @@ -657,32 +607,54 @@ class MCPServerManager: "arguments": arguments, "server_name": server_name_from_prefix, "user_api_key_auth": user_api_key_auth, - "user_api_key_user_id": getattr(user_api_key_auth, 'user_id', None) if user_api_key_auth else None, - "user_api_key_team_id": getattr(user_api_key_auth, 'team_id', None) if user_api_key_auth else None, - "user_api_key_end_user_id": getattr(user_api_key_auth, 'end_user_id', None) if user_api_key_auth else None, - "user_api_key_hash": getattr(user_api_key_auth, 'api_key_hash', None) if user_api_key_auth else None, + "user_api_key_user_id": getattr(user_api_key_auth, "user_id", None) + if user_api_key_auth + else None, + "user_api_key_team_id": getattr(user_api_key_auth, "team_id", None) + if user_api_key_auth + else None, + "user_api_key_end_user_id": getattr( + user_api_key_auth, "end_user_id", None + ) + if user_api_key_auth + else None, + "user_api_key_hash": getattr(user_api_key_auth, "api_key_hash", None) + if user_api_key_auth + else None, } - + # Create MCP request object for processing - mcp_request_obj = proxy_logging_obj._create_mcp_request_object_from_kwargs(pre_hook_kwargs) - + mcp_request_obj = proxy_logging_obj._create_mcp_request_object_from_kwargs( + pre_hook_kwargs + ) + # Convert to LLM format for existing guardrail compatibility - synthetic_llm_data = proxy_logging_obj._convert_mcp_to_llm_format(mcp_request_obj, pre_hook_kwargs) - + synthetic_llm_data = proxy_logging_obj._convert_mcp_to_llm_format( + mcp_request_obj, pre_hook_kwargs + ) + try: # Use standard pre_call_hook with call_type="mcp_call" modified_data = await proxy_logging_obj.pre_call_hook( - user_api_key_dict=user_api_key_auth, #type: ignore + user_api_key_dict=user_api_key_auth, # type: ignore data=synthetic_llm_data, - call_type="mcp_call" #type: ignore + call_type="mcp_call", # type: ignore ) if modified_data: # Convert response back to MCP format and apply modifications - modified_kwargs = proxy_logging_obj._convert_mcp_hook_response_to_kwargs(modified_data, pre_hook_kwargs) + modified_kwargs = ( + proxy_logging_obj._convert_mcp_hook_response_to_kwargs( + modified_data, pre_hook_kwargs + ) + ) if modified_kwargs.get("arguments") != arguments: arguments = modified_kwargs["arguments"] - - except (BlockedPiiEntityError, GuardrailRaisedException, HTTPException) as e: + + except ( + BlockedPiiEntityError, + GuardrailRaisedException, + HTTPException, + ) as e: # Re-raise guardrail exceptions to properly fail the MCP call verbose_logger.error( f"Guardrail blocked MCP tool call pre call: {str(e)}" @@ -703,11 +675,9 @@ class MCPServerManager: client = self._create_mcp_client( server=mcp_server, mcp_auth_header=server_auth_header, - protocol_version=mcp_protocol_version, ) async with client: - # Use the original tool name (without prefix) for the actual call call_tool_params = MCPCallToolRequestParams( name=original_tool_name, @@ -716,9 +686,9 @@ class MCPServerManager: tasks = [] if proxy_logging_obj: # Create synthetic LLM data for during hook processing - from litellm.types.mcp import MCPDuringCallRequestObject from litellm.types.llms.base import HiddenParams - + from litellm.types.mcp import MCPDuringCallRequestObject + request_obj = MCPDuringCallRequestObject( tool_name=name, arguments=arguments, @@ -726,28 +696,29 @@ class MCPServerManager: start_time=start_time.timestamp() if start_time else None, hidden_params=HiddenParams(), ) - + during_hook_kwargs = { "name": name, "arguments": arguments, "server_name": server_name_from_prefix, "user_api_key_auth": user_api_key_auth, } - - synthetic_llm_data = proxy_logging_obj._convert_mcp_to_llm_format(request_obj, during_hook_kwargs) - + + synthetic_llm_data = proxy_logging_obj._convert_mcp_to_llm_format( + request_obj, during_hook_kwargs + ) + during_hook_task = asyncio.create_task( proxy_logging_obj.during_call_hook( user_api_key_dict=user_api_key_auth, data=synthetic_llm_data, - call_type="mcp_call" #type: ignore + call_type="mcp_call", # type: ignore ) ) tasks.append(during_hook_task) tasks.append(asyncio.create_task(client.call_tool(call_tool_params))) try: - mcp_responses = await asyncio.gather(*tasks) # If proxy_logging_obj is None, the tool call result is at index 0 @@ -836,19 +807,21 @@ class MCPServerManager: ) verbose_logger.info("Loading MCP servers from database into registry...") - + # perform authz check to filter the mcp servers user has access to prisma_client = get_prisma_client_or_throw( "Database not connected. Connect a database to your proxy" ) db_mcp_servers = await get_all_mcp_servers(prisma_client) verbose_logger.info(f"Found {len(db_mcp_servers)} MCP servers in database") - + # ensure the global_mcp_server_manager is up to date with the db for server in db_mcp_servers: - verbose_logger.debug(f"Adding server to registry: {server.server_id} ({server.server_name})") + verbose_logger.debug( + f"Adding server to registry: {server.server_id} ({server.server_name})" + ) self.add_update_server(server) - + verbose_logger.info(f"Registry now contains {len(self.get_registry())} servers") def get_mcp_server_by_id(self, server_id: str) -> Optional[MCPServer]: @@ -866,7 +839,6 @@ class MCPServerManager: server_name: str, url: str, transport: str, - spec_version: str, auth_type: Optional[str] = None, alias: Optional[str] = None, ) -> str: @@ -882,7 +854,6 @@ class MCPServerManager: server_name: Name of the server url: Server URL transport: Transport type (sse, http, etc.) - spec_version: MCP spec version auth_type: Authentication type (optional) alias: Server alias (optional) @@ -890,7 +861,9 @@ class MCPServerManager: A deterministic server ID string """ # Create a string from all the identifying parameters - params_string = f"{server_name}|{url}|{transport}|{spec_version}|{auth_type or ''}|{alias or ''}" + params_string = ( + f"{server_name}|{url}|{transport}|{auth_type or ''}|{alias or ''}" + ) # Generate SHA-256 hash hash_object = hashlib.sha256(params_string.encode("utf-8")) @@ -1047,11 +1020,12 @@ class MCPServerManager: alias=_server_config.alias, url=_server_config.url, transport=_server_config.transport, - spec_version=_server_config.spec_version, auth_type=_server_config.auth_type, created_at=datetime.datetime.now(), updated_at=datetime.datetime.now(), - description=_server_config.mcp_info.get("description") if _server_config.mcp_info else None, + description=_server_config.mcp_info.get("description") + if _server_config.mcp_info + else None, mcp_info=_server_config.mcp_info, mcp_access_groups=_server_config.access_groups or [], # Stdio-specific fields @@ -1108,7 +1082,6 @@ class MCPServerManager: description=server.description, url=server.url, transport=server.transport, - spec_version=server.spec_version, auth_type=server.auth_type, created_at=server.created_at, created_by=server.created_by, diff --git a/litellm/proxy/_experimental/mcp_server/rest_endpoints.py b/litellm/proxy/_experimental/mcp_server/rest_endpoints.py index 6624eb7e64e..2a9174717d1 100644 --- a/litellm/proxy/_experimental/mcp_server/rest_endpoints.py +++ b/litellm/proxy/_experimental/mcp_server/rest_endpoints.py @@ -1,5 +1,5 @@ import importlib -from typing import Optional, Dict +from typing import Dict, List, Optional from fastapi import APIRouter, Depends, Query, Request @@ -21,9 +21,9 @@ router = APIRouter( ) if MCP_AVAILABLE: + from litellm.experimental_mcp_client.client import MCPTool from litellm.proxy._experimental.mcp_server.mcp_server_manager import ( global_mcp_server_manager, - _convert_protocol_version_to_enum, ) from litellm.proxy._experimental.mcp_server.server import ( ListMCPToolsRestAPIResponseObject, @@ -33,18 +33,24 @@ if MCP_AVAILABLE: ######################################################## ############ MCP Server REST API Routes ################# def _get_server_auth_header( - server, mcp_server_auth_headers: Optional[Dict[str, str]], mcp_auth_header: Optional[str] + server, + mcp_server_auth_headers: Optional[Dict[str, str]], + mcp_auth_header: Optional[str], ) -> Optional[str]: """Helper function to get server-specific auth header with case-insensitive matching.""" if mcp_server_auth_headers and server.alias: normalized_server_alias = server.alias.lower() - normalized_headers = {k.lower(): v for k, v in mcp_server_auth_headers.items()} + normalized_headers = { + k.lower(): v for k, v in mcp_server_auth_headers.items() + } server_auth = normalized_headers.get(normalized_server_alias) if server_auth is not None: return server_auth elif mcp_server_auth_headers and server.server_name: normalized_server_name = server.server_name.lower() - normalized_headers = {k.lower(): v for k, v in mcp_server_auth_headers.items()} + normalized_headers = { + k.lower(): v for k, v in mcp_server_auth_headers.items() + } server_auth = normalized_headers.get(normalized_server_name) if server_auth is not None: return server_auth @@ -62,12 +68,11 @@ if MCP_AVAILABLE: for tool in tools ] - async def _get_tools_for_single_server(server, server_auth_header, mcp_protocol_version): + async def _get_tools_for_single_server(server, server_auth_header): """Helper function to get tools for a single server.""" tools = await global_mcp_server_manager._get_tools_from_server( server=server, mcp_auth_header=server_auth_header, - mcp_protocol_version=mcp_protocol_version, ) return _create_tool_response_objects(tools, server.mcp_info) @@ -100,18 +105,23 @@ if MCP_AVAILABLE: "message": "Successfully retrieved tools" } """ - from litellm.proxy._experimental.mcp_server.auth.user_api_key_auth_mcp import MCPRequestHandler - + from litellm.proxy._experimental.mcp_server.auth.user_api_key_auth_mcp import ( + MCPRequestHandler, + ) + try: # Extract auth headers from request headers = request.headers - mcp_auth_header = MCPRequestHandler._get_mcp_auth_header_from_headers(headers) - mcp_server_auth_headers = MCPRequestHandler._get_mcp_server_auth_headers_from_headers(headers) - mcp_protocol_version = headers.get(MCPRequestHandler.MCP_PROTOCOL_VERSION_HEADER_NAME) - + mcp_auth_header = MCPRequestHandler._get_mcp_auth_header_from_headers( + headers + ) + mcp_server_auth_headers = ( + MCPRequestHandler._get_mcp_server_auth_headers_from_headers(headers) + ) + list_tools_result = [] error_message = None - + # If server_id is specified, only query that specific server if server_id: server = global_mcp_server_manager.get_mcp_server_by_id(server_id) @@ -119,49 +129,67 @@ if MCP_AVAILABLE: return { "tools": [], "error": "server_not_found", - "message": f"Server with id {server_id} not found" + "message": f"Server with id {server_id} not found", } - - server_auth_header = _get_server_auth_header(server, mcp_server_auth_headers, mcp_auth_header) - + + server_auth_header = _get_server_auth_header( + server, mcp_server_auth_headers, mcp_auth_header + ) + try: - list_tools_result = await _get_tools_for_single_server(server, server_auth_header, mcp_protocol_version) + list_tools_result = await _get_tools_for_single_server( + server, server_auth_header + ) except Exception as e: - verbose_logger.exception(f"Error getting tools from {server.name}: {e}") + verbose_logger.exception( + f"Error getting tools from {server.name}: {e}" + ) return { "tools": [], "error": "server_error", - "message": f"Failed to get tools from server {server.name}: {str(e)}" + "message": f"Failed to get tools from server {server.name}: {str(e)}", } else: # Query all servers errors = [] for server in global_mcp_server_manager.get_registry().values(): - server_auth_header = _get_server_auth_header(server, mcp_server_auth_headers, mcp_auth_header) - + server_auth_header = _get_server_auth_header( + server, mcp_server_auth_headers, mcp_auth_header + ) + try: - tools_result = await _get_tools_for_single_server(server, server_auth_header, mcp_protocol_version) + tools_result = await _get_tools_for_single_server( + server, server_auth_header + ) list_tools_result.extend(tools_result) except Exception as e: - verbose_logger.exception(f"Error getting tools from {server.name}: {e}") + verbose_logger.exception( + f"Error getting tools from {server.name}: {e}" + ) errors.append(f"{server.name}: {str(e)}") continue - + if errors and not list_tools_result: - error_message = "Failed to get tools from servers: " + "; ".join(errors) - + error_message = "Failed to get tools from servers: " + "; ".join( + errors + ) + return { "tools": list_tools_result, "error": "partial_failure" if error_message else None, - "message": error_message if error_message else "Successfully retrieved tools" + "message": error_message + if error_message + else "Successfully retrieved tools", } - + except Exception as e: - verbose_logger.exception("Unexpected error in list_tool_rest_api: %s", str(e)) + verbose_logger.exception( + "Unexpected error in list_tool_rest_api: %s", str(e) + ) return { "tools": [], "error": "unexpected_error", - "message": f"An unexpected error occurred: {str(e)}" + "message": f"An unexpected error occurred: {str(e)}", } @router.post("/tools/call", dependencies=[Depends(user_api_key_auth)]) @@ -172,10 +200,11 @@ if MCP_AVAILABLE: """ REST API to call a specific MCP tool with the provided arguments """ - from litellm.proxy.proxy_server import add_litellm_data_to_request, proxy_config - from litellm.exceptions import BlockedPiiEntityError, GuardrailRaisedException from fastapi import HTTPException + from litellm.exceptions import BlockedPiiEntityError, GuardrailRaisedException + from litellm.proxy.proxy_server import add_litellm_data_to_request, proxy_config + try: data = await request.json() data = await add_litellm_data_to_request( @@ -192,9 +221,9 @@ if MCP_AVAILABLE: detail={ "error": "blocked_pii_entity", "message": str(e), - "entity_type": getattr(e, 'entity_type', None), - "guardrail_name": getattr(e, 'guardrail_name', None) - } + "entity_type": getattr(e, "entity_type", None), + "guardrail_name": getattr(e, "guardrail_name", None), + }, ) except GuardrailRaisedException as e: verbose_logger.error(f"GuardrailRaisedException in MCP tool call: {str(e)}") @@ -203,8 +232,8 @@ if MCP_AVAILABLE: detail={ "error": "guardrail_violation", "message": str(e), - "guardrail_name": getattr(e, 'guardrail_name', None) - } + "guardrail_name": getattr(e, "guardrail_name", None), + }, ) except HTTPException as e: # Re-raise HTTPException as-is to preserve status code and detail @@ -216,10 +245,10 @@ if MCP_AVAILABLE: status_code=500, detail={ "error": "internal_server_error", - "message": f"An unexpected error occurred: {str(e)}" - } + "message": f"An unexpected error occurred: {str(e)}", + }, ) - + ######################################################## # MCP Connection testing routes # /health -> Test if we can connect to the MCP server @@ -230,13 +259,19 @@ if MCP_AVAILABLE: from litellm.proxy.management_endpoints.mcp_management_endpoints import ( NewMCPServerRequest, ) - @router.post("/test/connection") - async def test_connection( - request: NewMCPServerRequest, - ): + + async def _execute_with_mcp_client(request: NewMCPServerRequest, operation): """ - Test if we can connect to the provided MCP server before adding it + Common helper to create MCP client, execute operation, and ensure proper cleanup. + + Args: + request: MCP server configuration + operation: Async function that takes a client and returns the operation result + + Returns: + Operation result or error response """ + client = None try: client = global_mcp_server_manager._create_mcp_client( server=MCPServer( @@ -244,20 +279,39 @@ if MCP_AVAILABLE: name=request.alias or request.server_name or "", url=request.url, transport=request.transport, - spec_version=_convert_protocol_version_to_enum(request.spec_version), auth_type=request.auth_type, mcp_info=request.mcp_info, ), mcp_auth_header=None, ) - await client.connect() + return await operation(client) + except Exception as e: - verbose_logger.error(f"Error in test_connection: {e}", exc_info=True) + verbose_logger.error(f"Error in MCP operation: {e}", exc_info=True) return {"status": "error", "message": "An internal error has occurred."} - return {"status": "ok"} - - + finally: + # Ensure client is properly disconnected before response is sent + if client is not None: + try: + await client.disconnect() + except Exception as e: + verbose_logger.warning(f"Error disconnecting MCP client: {e}") + + @router.post("/test/connection") + async def test_connection( + request: NewMCPServerRequest, + ): + """ + Test if we can connect to the provided MCP server before adding it + """ + + async def _test_connection_operation(client): + await client.connect() + return {"status": "ok"} + + return await _execute_with_mcp_client(request, _test_connection_operation) + @router.post("/test/tools/list") async def test_tools_list( request: NewMCPServerRequest, @@ -266,25 +320,16 @@ if MCP_AVAILABLE: """ Preview tools available from MCP server before adding it """ - try: - client = global_mcp_server_manager._create_mcp_client( - server=MCPServer( - server_id=request.server_id or "", - name=request.alias or request.server_name or "", - url=request.url, - transport=request.transport, - spec_version=_convert_protocol_version_to_enum(request.spec_version), - auth_type=request.auth_type, - mcp_info=request.mcp_info, - ), - mcp_auth_header=None, - ) - list_tools_result = await client.list_tools() - except Exception as e: - verbose_logger.error(f"Error in test_tools_list: {e}", exc_info=True) - return {"status": "error", "message": "An internal error has occurred."} - return { - "tools": list_tools_result, - "error": None, - "message": "Successfully retrieved tools" - } + + async def _list_tools_operation(client): + list_tools_result: List[MCPTool] = await client.list_tools() + model_dumped_tools: List[dict] = [ + tool.model_dump() for tool in list_tools_result + ] + return { + "tools": model_dumped_tools, + "error": None, + "message": "Successfully retrieved tools", + } + + return await _execute_with_mcp_client(request, _list_tools_operation) diff --git a/litellm/proxy/_experimental/mcp_server/server.py b/litellm/proxy/_experimental/mcp_server/server.py index 38619112ccc..2cf91c84dcc 100644 --- a/litellm/proxy/_experimental/mcp_server/server.py +++ b/litellm/proxy/_experimental/mcp_server/server.py @@ -130,7 +130,9 @@ if MCP_AVAILABLE: await _sse_session_manager_cm.__aenter__() _SESSION_MANAGERS_INITIALIZED = True - verbose_logger.info("MCP Server started with StreamableHTTP and SSE session managers!") + verbose_logger.info( + "MCP Server started with StreamableHTTP and SSE session managers!" + ) async def shutdown_session_managers(): """Shutdown the session managers.""" @@ -171,11 +173,18 @@ if MCP_AVAILABLE: """ try: # Get user authentication from context variable - user_api_key_auth, mcp_auth_header, mcp_servers, mcp_server_auth_headers, mcp_protocol_version = ( - get_auth_context() + ( + user_api_key_auth, + mcp_auth_header, + mcp_servers, + mcp_server_auth_headers, + ) = get_auth_context() + verbose_logger.debug( + f"MCP list_tools - User API Key Auth from context: {user_api_key_auth}" + ) + verbose_logger.debug( + f"MCP list_tools - MCP servers from context: {mcp_servers}" ) - verbose_logger.debug(f"MCP list_tools - User API Key Auth from context: {user_api_key_auth}") - verbose_logger.debug(f"MCP list_tools - MCP servers from context: {mcp_servers}") verbose_logger.debug( f"MCP list_tools - MCP server auth headers: {list(mcp_server_auth_headers.keys()) if mcp_server_auth_headers else None}" ) @@ -186,9 +195,10 @@ if MCP_AVAILABLE: mcp_auth_header=mcp_auth_header, mcp_servers=mcp_servers, mcp_server_auth_headers=mcp_server_auth_headers, - mcp_protocol_version=mcp_protocol_version, ) - verbose_logger.info(f"MCP list_tools - Successfully returned {len(tools)} tools") + verbose_logger.info( + f"MCP list_tools - Successfully returned {len(tools)} tools" + ) return tools except Exception as e: verbose_logger.exception(f"Error in list_tools endpoint: {str(e)}") @@ -215,14 +225,21 @@ if MCP_AVAILABLE: """ from fastapi import Request + from litellm.exceptions import BlockedPiiEntityError, GuardrailRaisedException from litellm.proxy.litellm_pre_call_utils import add_litellm_data_to_request from litellm.proxy.proxy_server import proxy_config - from litellm.exceptions import BlockedPiiEntityError, GuardrailRaisedException # Validate arguments - user_api_key_auth, mcp_auth_header, _, mcp_server_auth_headers, mcp_protocol_version = get_auth_context() + ( + user_api_key_auth, + mcp_auth_header, + _, + mcp_server_auth_headers, + ) = get_auth_context() - verbose_logger.debug(f"MCP mcp_server_tool_call - User API Key Auth from context: {user_api_key_auth}") + verbose_logger.debug( + f"MCP mcp_server_tool_call - User API Key Auth from context: {user_api_key_auth}" + ) try: # Create a body date for logging body_data = {"name": name, "arguments": arguments} @@ -249,17 +266,22 @@ if MCP_AVAILABLE: user_api_key_auth=user_api_key_auth, mcp_auth_header=mcp_auth_header, mcp_server_auth_headers=mcp_server_auth_headers, - mcp_protocol_version=mcp_protocol_version, **data, # for logging ) except BlockedPiiEntityError as e: verbose_logger.error(f"BlockedPiiEntityError in MCP tool call: {str(e)}") # Return error as text content for MCP protocol - return [TextContent(text=f"Error: Blocked PII entity detected - {str(e)}", type="text")] + return [ + TextContent( + text=f"Error: Blocked PII entity detected - {str(e)}", type="text" + ) + ] except GuardrailRaisedException as e: verbose_logger.error(f"GuardrailRaisedException in MCP tool call: {str(e)}") # Return error as text content for MCP protocol - return [TextContent(text=f"Error: Guardrail violation - {str(e)}", type="text")] + return [ + TextContent(text=f"Error: Guardrail violation - {str(e)}", type="text") + ] except HTTPException as e: verbose_logger.error(f"HTTPException in MCP tool call: {str(e)}") # Return error as text content for MCP protocol @@ -279,12 +301,62 @@ if MCP_AVAILABLE: ############ Helper Functions ########################## ######################################################## + async def _get_allowed_mcp_servers_from_mcp_server_names( + mcp_servers: Optional[List[str]], + allowed_mcp_servers: List[str], + ) -> List[str]: + """ + Get the filtered MCP servers from the MCP server names + """ + from typing import Set + + filtered_server_ids: Set[str] = set() + # Filter servers based on mcp_servers parameter if provided + if mcp_servers is not None: + for server_or_group in mcp_servers: + server_name_matched = False + + for server_id in allowed_mcp_servers: + server = global_mcp_server_manager.get_mcp_server_by_id(server_id) + + if server: + match_list = [ + s.lower() + for s in [server.alias, server.server_name, server_id] + if s is not None + ] + + if server_or_group.lower() in match_list: + filtered_server_ids.add(server_id) + server_name_matched = True + break + + if not server_name_matched: + try: + access_group_server_ids = ( + await MCPRequestHandler._get_mcp_servers_from_access_groups( + [server_or_group] + ) + ) + # Only include servers that the user has access to + for server_id in access_group_server_ids: + if server_id in allowed_mcp_servers: + filtered_server_ids.add(server_id) + except Exception as e: + verbose_logger.debug( + f"Could not resolve '{server_or_group}' as access group: {e}" + ) + + if filtered_server_ids: + allowed_mcp_servers = list(filtered_server_ids) + + return allowed_mcp_servers + async def _get_tools_from_mcp_servers( user_api_key_auth: Optional[UserAPIKeyAuth], mcp_auth_header: Optional[str], mcp_servers: Optional[List[str]], mcp_server_auth_headers: Optional[Dict[str, str]] = None, - mcp_protocol_version: Optional[str] = None, ) -> List[MCPTool]: """ Helper method to fetch tools from MCP servers based on server filtering criteria. @@ -302,40 +374,15 @@ if MCP_AVAILABLE: return [] # Get allowed MCP servers based on user permissions - allowed_mcp_servers = await global_mcp_server_manager.get_allowed_mcp_servers(user_api_key_auth) + allowed_mcp_servers = await global_mcp_server_manager.get_allowed_mcp_servers( + user_api_key_auth + ) - filtered_server_ids = set() - - # Filter servers based on mcp_servers parameter if provided if mcp_servers is not None: - for server_or_group in mcp_servers: - server_name_matched = False - - for server_id in allowed_mcp_servers: - server = global_mcp_server_manager.get_mcp_server_by_id(server_id) - - if server: - match_list = [s.lower() for s in [server.alias, server.server_name, server_id] if s is not None] - - if server_or_group.lower() in match_list: - filtered_server_ids.add(server_id) - server_name_matched = True - break - - if not server_name_matched: - try: - access_group_server_ids = await MCPRequestHandler._get_mcp_servers_from_access_groups( - [server_or_group] - ) - # Only include servers that the user has access to - for server_id in access_group_server_ids: - if server_id in allowed_mcp_servers: - filtered_server_ids.add(server_id) - except Exception as e: - verbose_logger.debug(f"Could not resolve '{server_or_group}' as access group: {e}") - - if filtered_server_ids: - allowed_mcp_servers = list(filtered_server_ids) + allowed_mcp_servers = await _get_allowed_mcp_servers_from_mcp_server_names( + mcp_servers=mcp_servers, + allowed_mcp_servers=allowed_mcp_servers, + ) # Get tools from each allowed server all_tools = [] @@ -359,15 +406,20 @@ if MCP_AVAILABLE: tools = await global_mcp_server_manager._get_tools_from_server( server=server, mcp_auth_header=server_auth_header, - mcp_protocol_version=mcp_protocol_version, ) all_tools.extend(tools) - verbose_logger.debug(f"Successfully fetched {len(tools)} tools from server {server.name}") + verbose_logger.debug( + f"Successfully fetched {len(tools)} tools from server {server.name}" + ) except Exception as e: - verbose_logger.exception(f"Error getting tools from server {server.name}: {str(e)}") + verbose_logger.exception( + f"Error getting tools from server {server.name}: {str(e)}" + ) # Continue with other servers instead of failing completely - verbose_logger.info(f"Successfully fetched {len(all_tools)} tools total from all MCP servers") + verbose_logger.info( + f"Successfully fetched {len(all_tools)} tools total from all MCP servers" + ) return all_tools async def _list_mcp_tools( @@ -375,7 +427,6 @@ if MCP_AVAILABLE: mcp_auth_header: Optional[str] = None, mcp_servers: Optional[List[str]] = None, mcp_server_auth_headers: Optional[Dict[str, str]] = None, - mcp_protocol_version: Optional[str] = None, ) -> List[MCPTool]: """ List all available MCP tools. @@ -399,11 +450,14 @@ if MCP_AVAILABLE: mcp_auth_header=mcp_auth_header, mcp_servers=mcp_servers, mcp_server_auth_headers=mcp_server_auth_headers, - mcp_protocol_version=mcp_protocol_version, ) - verbose_logger.debug(f"Successfully fetched {len(managed_tools)} tools from managed MCP servers") + verbose_logger.debug( + f"Successfully fetched {len(managed_tools)} tools from managed MCP servers" + ) except Exception as e: - verbose_logger.exception(f"Error getting tools from managed MCP servers: {str(e)}") + verbose_logger.exception( + f"Error getting tools from managed MCP servers: {str(e)}" + ) # Continue with empty managed tools list instead of failing completely # Get tools from local registry @@ -414,10 +468,16 @@ if MCP_AVAILABLE: # Convert local tools to MCPTool format for tool in local_tools_raw: # Convert from litellm.types.mcp_server.tool_registry.MCPTool to mcp.types.Tool - mcp_tool = MCPTool(name=tool.name, description=tool.description, inputSchema=tool.input_schema) + mcp_tool = MCPTool( + name=tool.name, + description=tool.description, + inputSchema=tool.input_schema, + ) local_tools.append(mcp_tool) except Exception as e: - verbose_logger.exception(f"Error getting tools from local registry: {str(e)}") + verbose_logger.exception( + f"Error getting tools from local registry: {str(e)}" + ) # Continue with empty local tools list instead of failing completely # Combine all tools @@ -432,7 +492,6 @@ if MCP_AVAILABLE: user_api_key_auth: Optional[UserAPIKeyAuth] = None, mcp_auth_header: Optional[str] = None, mcp_server_auth_headers: Optional[Dict[str, str]] = None, - mcp_protocol_version: Optional[str] = None, **kwargs: Any, ) -> List[Union[TextContent, ImageContent, EmbeddedResource]]: """ @@ -440,35 +499,46 @@ if MCP_AVAILABLE: """ start_time = datetime.now() if arguments is None: - raise HTTPException(status_code=400, detail="Request arguments are required") + raise HTTPException( + status_code=400, detail="Request arguments are required" + ) # Remove prefix from tool name for logging and processing - original_tool_name, server_name_from_prefix = get_server_name_prefix_tool_mcp(name) - - standard_logging_mcp_tool_call: StandardLoggingMCPToolCall = _get_standard_logging_mcp_tool_call( - name=original_tool_name, # Use original name for logging - arguments=arguments, - server_name=server_name_from_prefix, + original_tool_name, server_name_from_prefix = get_server_name_prefix_tool_mcp( + name + ) + + standard_logging_mcp_tool_call: StandardLoggingMCPToolCall = ( + _get_standard_logging_mcp_tool_call( + name=original_tool_name, # Use original name for logging + arguments=arguments, + server_name=server_name_from_prefix, + ) + ) + litellm_logging_obj: Optional[LiteLLMLoggingObj] = kwargs.get( + "litellm_logging_obj", None ) - litellm_logging_obj: Optional[LiteLLMLoggingObj] = kwargs.get("litellm_logging_obj", None) if litellm_logging_obj: - litellm_logging_obj.model_call_details["mcp_tool_call_metadata"] = standard_logging_mcp_tool_call + litellm_logging_obj.model_call_details[ + "mcp_tool_call_metadata" + ] = standard_logging_mcp_tool_call litellm_logging_obj.model = f"MCP: {name}" # Try managed server tool first (pass the full prefixed name) # Primary and recommended way to use MCP servers ######################################################### - mcp_server: Optional[MCPServer] = global_mcp_server_manager._get_mcp_server_from_tool_name(name) + mcp_server: Optional[ + MCPServer + ] = global_mcp_server_manager._get_mcp_server_from_tool_name(name) if mcp_server: - standard_logging_mcp_tool_call["mcp_server_cost_info"] = (mcp_server.mcp_info or {}).get( - "mcp_server_cost_info" - ) + standard_logging_mcp_tool_call["mcp_server_cost_info"] = ( + mcp_server.mcp_info or {} + ).get("mcp_server_cost_info") response = await _handle_managed_mcp_tool( name=name, # Pass the full name (potentially prefixed) arguments=arguments, user_api_key_auth=user_api_key_auth, mcp_auth_header=mcp_auth_header, mcp_server_auth_headers=mcp_server_auth_headers, - mcp_protocol_version=mcp_protocol_version, litellm_logging_obj=litellm_logging_obj, ) @@ -521,7 +591,6 @@ if MCP_AVAILABLE: user_api_key_auth: Optional[UserAPIKeyAuth] = None, mcp_auth_header: Optional[str] = None, mcp_server_auth_headers: Optional[Dict[str, str]] = None, - mcp_protocol_version: Optional[str] = None, litellm_logging_obj: Optional[Any] = None, ) -> List[Union[TextContent, ImageContent, EmbeddedResource]]: """Handle tool execution for managed server tools""" @@ -556,27 +625,58 @@ if MCP_AVAILABLE: except Exception as e: return [TextContent(text=f"Error: {str(e)}", type="text")] + def _get_mcp_servers_in_path(path: str) -> Optional[List[str]]: + """ + Get the MCP servers from the path + """ + import re + + mcp_servers_from_path: Optional[List[str]] = None + # Match /mcp/ + # Where servers can be comma-separated list of server names + # Server names can contain slashes (e.g., "custom_solutions/user_123") + mcp_path_match = re.match(r"^/mcp/([^?#]+)(?:\?.*)?(?:#.*)?$", path) + if mcp_path_match: + servers_and_path = mcp_path_match.group(1) + + if servers_and_path: + # Check if it contains commas (comma-separated servers) + if ',' in servers_and_path: + # For comma-separated, look for a path at the end + # Common patterns: /tools, /chat/completions, etc. + path_match = re.search(r'/([^/,]+(?:/[^/,]+)*)$', servers_and_path) + if path_match: + # Path found at the end, remove it from servers + path_part = '/' + path_match.group(1) + servers_part = servers_and_path[:-len(path_part)] + mcp_servers_from_path = [s.strip() for s in servers_part.split(',') if s.strip()] + else: + # No path, just comma-separated servers + mcp_servers_from_path = [s.strip() for s in servers_and_path.split(',') if s.strip()] + else: + # Single server case - use regex approach for server/path separation + # This handles cases like "custom_solutions/user_123/chat/completions" + # where we want to extract "custom_solutions/user_123" as the server name + single_server_match = re.match(r"^([^/]+(?:/[^/]+)?)(?:/.*)?$", servers_and_path) + if single_server_match: + server_name = single_server_match.group(1) + mcp_servers_from_path = [server_name] + else: + mcp_servers_from_path = [servers_and_path] + return mcp_servers_from_path + async def extract_mcp_auth_context(scope, path): """ Extracts mcp_servers from the path and processes the MCP request for auth context. Returns: (user_api_key_auth, mcp_auth_header, mcp_servers, mcp_server_auth_headers) """ - import re - - mcp_servers_from_path = None - mcp_path_match = re.match(r"^/mcp/([^/]+)(/.*)?$", path) - if mcp_path_match: - mcp_servers_str = mcp_path_match.group(1) - if mcp_servers_str: - mcp_servers_from_path = [s.strip() for s in mcp_servers_str.split(",") if s.strip()] - + mcp_servers_from_path = _get_mcp_servers_in_path(path) if mcp_servers_from_path is not None: ( user_api_key_auth, mcp_auth_header, _, mcp_server_auth_headers, - mcp_protocol_version, ) = await MCPRequestHandler.process_mcp_request(scope) mcp_servers = mcp_servers_from_path else: @@ -585,11 +685,12 @@ if MCP_AVAILABLE: mcp_auth_header, mcp_servers, mcp_server_auth_headers, - mcp_protocol_version, ) = await MCPRequestHandler.process_mcp_request(scope) - return user_api_key_auth, mcp_auth_header, mcp_servers, mcp_server_auth_headers, mcp_protocol_version + return user_api_key_auth, mcp_auth_header, mcp_servers, mcp_server_auth_headers - async def handle_streamable_http_mcp(scope: Scope, receive: Receive, send: Send) -> None: + async def handle_streamable_http_mcp( + scope: Scope, receive: Receive, send: Send + ) -> None: """Handle MCP requests through StreamableHTTP.""" try: path = scope.get("path", "") @@ -598,20 +699,19 @@ if MCP_AVAILABLE: mcp_auth_header, mcp_servers, mcp_server_auth_headers, - mcp_protocol_version, ) = await extract_mcp_auth_context(scope, path) - verbose_logger.debug(f"MCP request mcp_servers (header/path): {mcp_servers}") + verbose_logger.debug( + f"MCP request mcp_servers (header/path): {mcp_servers}" + ) verbose_logger.debug( f"MCP server auth headers: {list(mcp_server_auth_headers.keys()) if mcp_server_auth_headers else None}" ) - verbose_logger.debug(f"MCP protocol version: {mcp_protocol_version}") # Set the auth context variable for easy access in MCP functions set_auth_context( user_api_key_auth=user_api_key_auth, mcp_auth_header=mcp_auth_header, mcp_servers=mcp_servers, mcp_server_auth_headers=mcp_server_auth_headers, - mcp_protocol_version=mcp_protocol_version, ) # Ensure session managers are initialized @@ -635,7 +735,9 @@ if MCP_AVAILABLE: ) await error_response(scope, receive, send) except Exception as response_error: - verbose_logger.exception(f"Failed to send error response: {response_error}") + verbose_logger.exception( + f"Failed to send error response: {response_error}" + ) # If we can't send a proper response, re-raise the original error raise e @@ -648,19 +750,18 @@ if MCP_AVAILABLE: mcp_auth_header, mcp_servers, mcp_server_auth_headers, - mcp_protocol_version, ) = await extract_mcp_auth_context(scope, path) - verbose_logger.debug(f"MCP request mcp_servers (header/path): {mcp_servers}") + verbose_logger.debug( + f"MCP request mcp_servers (header/path): {mcp_servers}" + ) verbose_logger.debug( f"MCP server auth headers: {list(mcp_server_auth_headers.keys()) if mcp_server_auth_headers else None}" ) - verbose_logger.debug(f"MCP protocol version: {mcp_protocol_version}") set_auth_context( user_api_key_auth=user_api_key_auth, mcp_auth_header=mcp_auth_header, mcp_servers=mcp_servers, mcp_server_auth_headers=mcp_server_auth_headers, - mcp_protocol_version=mcp_protocol_version, ) if not _SESSION_MANAGERS_INITIALIZED: @@ -682,7 +783,9 @@ if MCP_AVAILABLE: ) await error_response(scope, receive, send) except Exception as response_error: - verbose_logger.exception(f"Failed to send error response: {response_error}") + verbose_logger.exception( + f"Failed to send error response: {response_error}" + ) # If we can't send a proper response, re-raise the original error raise e @@ -718,7 +821,6 @@ if MCP_AVAILABLE: mcp_auth_header: Optional[str] = None, mcp_servers: Optional[List[str]] = None, mcp_server_auth_headers: Optional[Dict[str, str]] = None, - mcp_protocol_version: Optional[str] = None, ) -> None: """ Set the UserAPIKeyAuth in the auth context variable. @@ -734,13 +836,17 @@ if MCP_AVAILABLE: mcp_auth_header=mcp_auth_header, mcp_servers=mcp_servers, mcp_server_auth_headers=mcp_server_auth_headers, - mcp_protocol_version=mcp_protocol_version, ) auth_context_var.set(auth_user) - def get_auth_context() -> Tuple[ - Optional[UserAPIKeyAuth], Optional[str], Optional[List[str]], Optional[Dict[str, str]], Optional[str] - ]: + def get_auth_context() -> ( + Tuple[ + Optional[UserAPIKeyAuth], + Optional[str], + Optional[List[str]], + Optional[Dict[str, str]], + ] + ): """ Get the UserAPIKeyAuth from the auth context variable. @@ -755,9 +861,8 @@ if MCP_AVAILABLE: auth_user.mcp_auth_header, auth_user.mcp_servers, auth_user.mcp_server_auth_headers, - auth_user.mcp_protocol_version, ) - return None, None, None, None, None + return None, None, None, None ######################################################## ############ End of Auth Context Functions ############# diff --git a/litellm/proxy/_experimental/out/_next/static/cx_klKzFUqmg6FLvftwd4/_buildManifest.js 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or .pdf\n# response_with_file = client.chat.completions.create(\n# model="').concat(j,'",\n# messages=[\n# {\n# "role": "user",\n# "content": [\n# {\n# "type": "text",\n# "text": "').concat(_,'"\n# },\n# {\n# "type": "image_url",\n# "image_url": {\n# "url": f"data:image/jpeg;base64,{base64_file}" # or data:application/pdf;base64,{base64_file}\n# }\n# }\n# ]\n# }\n# ]').concat(n,"\n# )\n# print(response_with_file)\n");break}case a.KP.RESPONSES:{let e=Object.keys(v).length>0,n="";if(e){let e=JSON.stringify({metadata:v},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();n=",\n extra_body=".concat(e)}let a=b.length>0?b:[{role:"user",content:f}];t='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, "rb") as image_file:\n return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.responses.create(\n model="'.concat(j,'",\n input=').concat(JSON.stringify(a,null,4)).concat(n,'\n)\n\nprint(response.output_text)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg") # or .pdf\n# response_with_file = client.responses.create(\n# model="').concat(j,'",\n# input=[\n# {\n# "role": "user",\n# "content": [\n# {"type": "input_text", "text": "').concat(_,'"},\n# {\n# "type": "input_image",\n# "image_url": f"data:image/jpeg;base64,{base64_file}", # or data:application/pdf;base64,{base64_file}\n# },\n# ],\n# }\n# ]').concat(n,"\n# )\n# print(response_with_file.output_text)\n");break}case a.KP.IMAGE:t="azure"===g?"\n# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.\n# This snippet uses 'client.images.generate' and will create a new image based on your prompt.\n# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.\nimport os\nimport requests\nimport json\nimport time\nfrom PIL import Image\n\nresult = client.images.generate(\n model=\"".concat(j,'",\n prompt="').concat(i,'",\n n=1\n)\n\njson_response = json.loads(result.model_dump_json())\n\n# Set the directory for the stored image\nimage_dir = os.path.join(os.curdir, \'images\')\n\n# If the directory doesn\'t exist, create it\nif not os.path.isdir(image_dir):\n os.mkdir(image_dir)\n\n# Initialize the image path\nimage_filename = f"generated_image_{int(time.time())}.png"\nimage_path = os.path.join(image_dir, image_filename)\n\ntry:\n # Retrieve the generated image\n if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):\n image_url = json_response["data"][0]["url"]\n generated_image = requests.get(image_url).content\n with open(image_path, "wb") as image_file:\n image_file.write(generated_image)\n\n print(f"Image saved to {image_path}")\n # Display the image\n image = Image.open(image_path)\n image.show()\n else:\n print("Could not find image URL in response.")\n print("Full response:", json_response)\nexcept Exception as e:\n print(f"An error occurred: {e}")\n print("Full response:", json_response)\n'):"\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, \"rb\") as image_file:\n return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n # In a real implementation, this would upload the file to OpenAI\n # For this example, we'll just return a placeholder ID\n return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(_,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n model="').concat(j,'",\n input=[\n {\n "role": "user",\n "content": [\n {"type": "input_text", "text": prompt},\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image1}",\n },\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image2}",\n },\n {\n "type": "input_image",\n "file_id": file_id1,\n },\n {\n "type": "input_image",\n "file_id": file_id2,\n }\n ],\n }\n ],\n tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n output\n for output in response.output\n if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n image_base64 = image_data[0]\n image_filename = f"edited_image_{int(time.time())}.png"\n with open(image_filename, "wb") as f:\n f.write(base64.b64decode(image_base64))\n print(f"Image saved to {image_filename}")\nelse:\n # If no image is generated, there might be a text response with an explanation\n text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n if text_response:\n print("No image generated. Model response:")\n print("\\n".join(text_response))\n else:\n print("No image data found in response.")\n print("Full response for debugging:")\n print(response)\n');break;case a.KP.IMAGE_EDITS:t="azure"===g?'\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, "rb") as image_file:\n return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# The prompt entered by the user\nprompt = "'.concat(_,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n model="').concat(j,'",\n input=[\n {\n "role": "user",\n "content": [\n {"type": "input_text", "text": prompt},\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image1}",\n },\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image2}",\n },\n {\n "type": "input_image",\n "file_id": file_id1,\n },\n {\n "type": "input_image",\n "file_id": file_id2,\n }\n ],\n }\n ],\n tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n output\n for output in response.output\n if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n image_base64 = image_data[0]\n image_filename = f"edited_image_{int(time.time())}.png"\n with open(image_filename, "wb") as f:\n f.write(base64.b64decode(image_base64))\n print(f"Image saved to {image_filename}")\nelse:\n # If no image is generated, there might be a text response with an explanation\n text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n if text_response:\n print("No image generated. Model response:")\n print("\\n".join(text_response))\n else:\n print("No image data found in response.")\n print("Full response for debugging:")\n print(response)\n'):"\nimport base64\nimport os\nimport time\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, \"rb\") as image_file:\n return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n # In a real implementation, this would upload the file to OpenAI\n # For this example, we'll just return a placeholder ID\n return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(_,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n model="').concat(j,'",\n input=[\n {\n "role": "user",\n "content": [\n {"type": "input_text", "text": prompt},\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image1}",\n },\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image2}",\n },\n {\n "type": "input_image",\n "file_id": file_id1,\n },\n {\n "type": "input_image",\n "file_id": file_id2,\n }\n ],\n }\n ],\n tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n output\n for output in response.output\n if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n image_base64 = image_data[0]\n image_filename = f"edited_image_{int(time.time())}.png"\n with open(image_filename, "wb") as f:\n f.write(base64.b64decode(image_base64))\n print(f"Image saved to {image_filename}")\nelse:\n # If no image is generated, there might be a text response with an explanation\n text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n if text_response:\n print("No image generated. 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extra_body=".concat(e)}let a=_.length>0?_:[{role:"user",content:h}];t='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, "rb") as image_file:\n return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.chat.completions.create(\n model="'.concat(v,'",\n messages=').concat(JSON.stringify(a,null,4)).concat(n,'\n)\n\nprint(response)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg") # or .pdf\n# response_with_file = client.chat.completions.create(\n# model="').concat(v,'",\n# messages=[\n# {\n# "role": "user",\n# "content": [\n# {\n# "type": "text",\n# "text": "').concat(f,'"\n# },\n# {\n# "type": "image_url",\n# "image_url": {\n# "url": f"data:image/jpeg;base64,{base64_file}" # or data:application/pdf;base64,{base64_file}\n# }\n# }\n# ]\n# }\n# ]').concat(n,"\n# )\n# print(response_with_file)\n");break}case a.KP.RESPONSES:{let e=Object.keys(b).length>0,n="";if(e){let e=JSON.stringify({metadata:b},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();n=",\n extra_body=".concat(e)}let a=_.length>0?_:[{role:"user",content:h}];t='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, "rb") as image_file:\n return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.responses.create(\n model="'.concat(v,'",\n input=').concat(JSON.stringify(a,null,4)).concat(n,'\n)\n\nprint(response.output_text)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg") # or .pdf\n# response_with_file = client.responses.create(\n# model="').concat(v,'",\n# input=[\n# {\n# "role": "user",\n# "content": [\n# {"type": "input_text", "text": "').concat(f,'"},\n# {\n# "type": "input_image",\n# "image_url": f"data:image/jpeg;base64,{base64_file}", # or data:application/pdf;base64,{base64_file}\n# },\n# ],\n# }\n# ]').concat(n,"\n# )\n# print(response_with_file.output_text)\n");break}case a.KP.IMAGE:t="azure"===u?"\n# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.\n# This snippet uses 'client.images.generate' and will create a new image based on your prompt.\n# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.\nimport os\nimport requests\nimport json\nimport time\nfrom PIL import Image\n\nresult = client.images.generate(\n model=\"".concat(v,'",\n prompt="').concat(i,'",\n n=1\n)\n\njson_response = json.loads(result.model_dump_json())\n\n# Set the directory for the stored image\nimage_dir = os.path.join(os.curdir, \'images\')\n\n# If the directory doesn\'t exist, create it\nif not os.path.isdir(image_dir):\n os.mkdir(image_dir)\n\n# Initialize the image path\nimage_filename = f"generated_image_{int(time.time())}.png"\nimage_path = os.path.join(image_dir, image_filename)\n\ntry:\n # Retrieve the generated image\n if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):\n image_url = json_response["data"][0]["url"]\n generated_image = requests.get(image_url).content\n with open(image_path, "wb") as image_file:\n image_file.write(generated_image)\n\n print(f"Image saved to {image_path}")\n # Display the image\n image = Image.open(image_path)\n image.show()\n else:\n print("Could not find image URL in response.")\n print("Full response:", json_response)\nexcept Exception as e:\n print(f"An error occurred: {e}")\n print("Full response:", json_response)\n'):"\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, \"rb\") as image_file:\n return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n # In a real implementation, this would upload the file to OpenAI\n # For this example, we'll just return a placeholder ID\n return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(f,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n model="').concat(v,'",\n input=[\n {\n "role": "user",\n "content": [\n {"type": "input_text", "text": prompt},\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image1}",\n },\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image2}",\n },\n {\n "type": "input_image",\n "file_id": file_id1,\n },\n {\n "type": "input_image",\n "file_id": file_id2,\n }\n ],\n }\n ],\n tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n output\n for output in response.output\n if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n image_base64 = image_data[0]\n image_filename = f"edited_image_{int(time.time())}.png"\n with open(image_filename, "wb") as f:\n f.write(base64.b64decode(image_base64))\n print(f"Image saved to {image_filename}")\nelse:\n # If no image is generated, there might be a text response with an explanation\n text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n if text_response:\n print("No image generated. Model response:")\n print("\\n".join(text_response))\n else:\n print("No image data found in response.")\n print("Full response for debugging:")\n print(response)\n');break;case a.KP.IMAGE_EDITS:t="azure"===u?'\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, "rb") as image_file:\n return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# The prompt entered by the user\nprompt = "'.concat(f,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n model="').concat(v,'",\n input=[\n {\n "role": "user",\n "content": [\n {"type": "input_text", "text": prompt},\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image1}",\n },\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image2}",\n },\n {\n "type": "input_image",\n "file_id": file_id1,\n },\n {\n "type": "input_image",\n "file_id": file_id2,\n }\n ],\n }\n ],\n tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n output\n for output in response.output\n if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n image_base64 = image_data[0]\n image_filename = f"edited_image_{int(time.time())}.png"\n with open(image_filename, "wb") as f:\n f.write(base64.b64decode(image_base64))\n print(f"Image saved to {image_filename}")\nelse:\n # If no image is generated, there might be a text response with an explanation\n text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n if text_response:\n print("No image generated. Model response:")\n print("\\n".join(text_response))\n else:\n print("No image data found in response.")\n print("Full response for debugging:")\n print(response)\n'):"\nimport base64\nimport os\nimport time\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, \"rb\") as image_file:\n return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n # In a real implementation, this would upload the file to OpenAI\n # For this example, we'll just return a placeholder ID\n return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(f,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n model="').concat(v,'",\n input=[\n {\n "role": "user",\n "content": [\n {"type": "input_text", "text": prompt},\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image1}",\n },\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image2}",\n },\n {\n "type": "input_image",\n "file_id": file_id1,\n },\n {\n "type": "input_image",\n "file_id": file_id2,\n }\n ],\n }\n ],\n tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n output\n for output in response.output\n if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n image_base64 = image_data[0]\n image_filename = f"edited_image_{int(time.time())}.png"\n with open(image_filename, "wb") as f:\n f.write(base64.b64decode(image_base64))\n print(f"Image saved to {image_filename}")\nelse:\n # If no image is generated, there might be a text response with an explanation\n text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n if text_response:\n print("No image generated. 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