diff --git a/.circleci/config.yml b/.circleci/config.yml index 77f3c2b9a7..e5bc82a596 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -715,8 +715,8 @@ jobs: - run: name: Rename the coverage files command: | - mv coverage.xml litellm_router_coverage.xml - mv .coverage litellm_router_coverage + mv coverage.xml litellm_router_unit_coverage.xml + mv .coverage litellm_router_unit_coverage # Store test results - store_test_results: path: test-results @@ -724,8 +724,8 @@ jobs: - persist_to_workspace: root: . paths: - - litellm_router_coverage.xml - - litellm_router_coverage + - litellm_router_unit_coverage.xml + - litellm_router_unit_coverage litellm_security_tests: machine: image: ubuntu-2204:2023.10.1 @@ -3407,6 +3407,110 @@ jobs: - store_test_results: path: test-results + proxy_e2e_anthropic_messages_tests: + machine: + image: ubuntu-2204:2023.10.1 + resource_class: xlarge + working_directory: ~/project + steps: + - checkout + - setup_google_dns + - run: + name: Install Docker CLI (In case it's not already installed) + command: | + curl -fsSL https://get.docker.com | sh + sudo usermod -aG docker $USER + docker version + - run: + name: Install Python 3.10 + 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:$PATH" + conda init bash + source ~/.bashrc + conda create -n myenv python=3.10 -y + conda activate myenv + python --version + - run: + name: Install Dependencies + command: | + export PATH="$HOME/miniconda/bin:$PATH" + source $HOME/miniconda/etc/profile.d/conda.sh + conda activate myenv + pip install "pytest==7.3.1" + pip install "pytest-asyncio==0.21.1" + pip install "boto3==1.36.0" + pip install "httpx==0.27.0" + pip install "claude-agent-sdk" + pip install -r requirements.txt + - run: + name: Install dockerize + command: | + wget https://github.com/jwilder/dockerize/releases/download/v0.6.1/dockerize-linux-amd64-v0.6.1.tar.gz + sudo tar -C /usr/local/bin -xzvf dockerize-linux-amd64-v0.6.1.tar.gz + rm dockerize-linux-amd64-v0.6.1.tar.gz + - run: + name: Start PostgreSQL Database + command: | + docker run -d \ + --name postgres-db \ + -e POSTGRES_USER=postgres \ + -e POSTGRES_PASSWORD=postgres \ + -e POSTGRES_DB=circle_test \ + -p 5432:5432 \ + postgres:14 + - run: + name: Wait for PostgreSQL to be ready + command: dockerize -wait tcp://localhost:5432 -timeout 1m + - attach_workspace: + at: ~/project + - run: + name: Load Docker Database Image + command: | + gunzip -c litellm-docker-database.tar.gz | docker load + docker images | grep litellm-docker-database + - run: + name: Run Docker container with test config + command: | + docker run -d \ + -p 4000:4000 \ + -e DATABASE_URL=postgresql://postgres:postgres@host.docker.internal:5432/circle_test \ + -e LITELLM_MASTER_KEY="sk-1234" \ + -e AWS_ACCESS_KEY_ID=$AWS_ACCESS_KEY_ID \ + -e AWS_SECRET_ACCESS_KEY=$AWS_SECRET_ACCESS_KEY \ + -e AWS_REGION_NAME="us-east-1" \ + --add-host host.docker.internal:host-gateway \ + --name my-app \ + -v $(pwd)/tests/proxy_e2e_anthropic_messages_tests/test_config.yaml:/app/config.yaml \ + litellm-docker-database:ci \ + --config /app/config.yaml \ + --port 4000 \ + --detailed_debug + - run: + name: Start outputting logs + command: docker logs -f my-app + background: true + - run: + name: Wait for app to be ready + command: dockerize -wait http://localhost:4000 -timeout 5m + - run: + name: Run Claude Agent SDK E2E Tests + command: | + export PATH="$HOME/miniconda/bin:$PATH" + source $HOME/miniconda/etc/profile.d/conda.sh + conda activate myenv + export LITELLM_PROXY_URL="http://localhost:4000" + export LITELLM_API_KEY="sk-1234" + pwd + ls + python -m pytest -vv tests/proxy_e2e_anthropic_messages_tests/ -x -s --junitxml=test-results/junit.xml --durations=5 + no_output_timeout: 120m + + # Store test results + - store_test_results: + path: test-results + upload-coverage: docker: - image: cimg/python:3.9 @@ -3428,7 +3532,7 @@ jobs: python -m venv venv . venv/bin/activate pip install coverage - coverage combine llm_translation_coverage llm_responses_api_coverage ocr_coverage search_coverage mcp_coverage logging_coverage audio_coverage litellm_router_coverage local_testing_part1_coverage local_testing_part2_coverage litellm_assistants_api_coverage auth_ui_unit_tests_coverage langfuse_coverage caching_coverage litellm_proxy_unit_tests_part1_coverage litellm_proxy_unit_tests_part2_coverage image_gen_coverage pass_through_unit_tests_coverage batches_coverage litellm_security_tests_coverage guardrails_coverage litellm_mapped_tests_coverage + coverage combine llm_translation_coverage llm_responses_api_coverage ocr_coverage search_coverage mcp_coverage logging_coverage audio_coverage litellm_router_coverage litellm_router_unit_coverage local_testing_part1_coverage local_testing_part2_coverage litellm_assistants_api_coverage auth_ui_unit_tests_coverage langfuse_coverage caching_coverage litellm_proxy_unit_tests_part1_coverage litellm_proxy_unit_tests_part2_coverage image_gen_coverage pass_through_unit_tests_coverage batches_coverage litellm_security_tests_coverage guardrails_coverage litellm_mapped_tests_coverage coverage xml - codecov/upload: file: ./coverage.xml @@ -3478,8 +3582,22 @@ jobs: ls dist/ twine upload --verbose dist/* else - echo "Version ${VERSION} of package is already published on PyPI. Skipping PyPI publish." - circleci step halt + echo "Version ${VERSION} of package is already published on PyPI." + + # Check if corresponding Docker nightly image exists + NIGHTLY_TAG="v${VERSION}-nightly" + echo "Checking for Docker nightly image: litellm/litellm:${NIGHTLY_TAG}" + + # Check Docker Hub for the nightly image + if curl -s "https://hub.docker.com/v2/repositories/litellm/litellm/tags/${NIGHTLY_TAG}" | grep -q "name"; then + echo "Docker nightly image ${NIGHTLY_TAG} exists. This release was already completed successfully." + echo "Skipping PyPI publish and continuing to ensure Docker images are up to date." + circleci step halt + else + echo "ERROR: PyPI package ${VERSION} exists but Docker nightly image ${NIGHTLY_TAG} does not exist!" + echo "This indicates an incomplete release. Please investigate." + exit 1 + fi fi - run: name: Trigger Github Action for new Docker Container + Trigger Load Testing @@ -3488,11 +3606,21 @@ jobs: python3 -m pip install toml VERSION=$(python3 -c "import toml; print(toml.load('pyproject.toml')['tool']['poetry']['version'])") echo "LiteLLM Version ${VERSION}" + + # Determine which branch to use for Docker build + if [[ "$CIRCLE_BRANCH" =~ ^litellm_release_day_.* ]]; then + BUILD_BRANCH="$CIRCLE_BRANCH" + echo "Using release branch: $BUILD_BRANCH" + else + BUILD_BRANCH="main" + echo "Using default branch: $BUILD_BRANCH" + fi + curl -X POST \ -H "Accept: application/vnd.github.v3+json" \ -H "Authorization: Bearer $GITHUB_TOKEN" \ "https://api.github.com/repos/BerriAI/litellm/actions/workflows/ghcr_deploy.yml/dispatches" \ - -d "{\"ref\":\"main\", \"inputs\":{\"tag\":\"v${VERSION}-nightly\", \"commit_hash\":\"$CIRCLE_SHA1\"}}" + -d "{\"ref\":\"${BUILD_BRANCH}\", \"inputs\":{\"tag\":\"v${VERSION}-nightly\", \"commit_hash\":\"$CIRCLE_SHA1\"}}" echo "triggering load testing server for version ${VERSION} and commit ${CIRCLE_SHA1}" curl -X POST "https://proxyloadtester-production.up.railway.app/start/load/test?version=${VERSION}&commit_hash=${CIRCLE_SHA1}&release_type=nightly" @@ -4051,6 +4179,14 @@ workflows: only: - main - /litellm_.*/ + - proxy_e2e_anthropic_messages_tests: + requires: + - build_docker_database_image + filters: + branches: + only: + - main + - /litellm_.*/ - llm_translation_testing: filters: branches: @@ -4235,6 +4371,7 @@ workflows: branches: only: - main + - /litellm_release_day_.*/ - publish_to_pypi: requires: - mypy_linting diff --git a/.circleci/requirements.txt b/.circleci/requirements.txt index 8c44dc1830..a5ec74424f 100644 --- a/.circleci/requirements.txt +++ b/.circleci/requirements.txt @@ -16,4 +16,5 @@ uvloop==0.21.0 mcp==1.25.0 # for MCP server semantic_router==0.1.10 # for auto-routing with litellm fastuuid==0.12.0 -responses==0.25.7 # for proxy client tests \ No newline at end of file +responses==0.25.7 # for proxy client tests +pytest-retry==1.6.3 # for automatic test retries \ No newline at end of file diff --git a/.github/workflows/test-linting.yml b/.github/workflows/test-linting.yml index 35ebffeada..7c5c269f89 100644 --- a/.github/workflows/test-linting.yml +++ b/.github/workflows/test-linting.yml @@ -73,4 +73,4 @@ jobs: - name: Check import safety run: | - poetry run python -c "from litellm import *" || (echo 'šŸšØ import failed, this means you introduced unprotected imports! šŸšØ'; exit 1) \ No newline at end of file + poetry run python -c "from litellm import *" || (echo 'šŸšØ import failed, this means you introduced unprotected imports! šŸšØ'; exit 1) diff --git a/.github/workflows/test-litellm.yml b/.github/workflows/test-litellm.yml index ba32dc1bf5..d9cf2e74a1 100644 --- a/.github/workflows/test-litellm.yml +++ b/.github/workflows/test-litellm.yml @@ -34,7 +34,7 @@ jobs: poetry run pip install "google-genai==1.22.0" poetry run pip install "google-cloud-aiplatform>=1.38" poetry run pip install "fastapi-offline==1.7.3" - poetry run pip install "python-multipart==0.0.18" + poetry run pip install "python-multipart==0.0.22" poetry run pip install "openapi-core" - name: Setup litellm-enterprise as local package run: | diff --git a/.github/workflows/test-model-map.yaml b/.github/workflows/test-model-map.yaml new file mode 100644 index 0000000000..ae5ac402e2 --- /dev/null +++ b/.github/workflows/test-model-map.yaml @@ -0,0 +1,15 @@ +name: Validate model_prices_and_context_window.json + +on: + pull_request: + branches: [ main ] + +jobs: + validate-model-prices-json: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + + - name: Validate model_prices_and_context_window.json + run: | + jq empty model_prices_and_context_window.json diff --git a/.gitignore b/.gitignore index 0248d68c1e..32f1b6f8e1 100644 --- a/.gitignore +++ b/.gitignore @@ -60,10 +60,6 @@ litellm/proxy/_super_secret_config.yaml litellm/proxy/myenv/bin/activate litellm/proxy/myenv/bin/Activate.ps1 myenv/* -litellm/proxy/_experimental/out/_next/ -litellm/proxy/_experimental/out/404/index.html -litellm/proxy/_experimental/out/model_hub/index.html -litellm/proxy/_experimental/out/onboarding/index.html litellm/tests/log.txt litellm/tests/langfuse.log litellm/tests/langfuse.log @@ -76,9 +72,6 @@ tests/local_testing/log.txt litellm/proxy/_new_new_secret_config.yaml litellm/proxy/custom_guardrail.py .mypy_cache/* -litellm/proxy/_experimental/out/404.html -litellm/proxy/_experimental/out/404.html -litellm/proxy/_experimental/out/model_hub.html .mypy_cache/* litellm/proxy/application.log tests/llm_translation/vertex_test_account.json @@ -100,7 +93,6 @@ litellm_config.yaml litellm/proxy/to_delete_loadtest_work/* update_model_cost_map.py tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py -litellm/proxy/_experimental/out/guardrails/index.html scripts/test_vertex_ai_search.py LAZY_LOADING_IMPROVEMENTS.md **/test-results diff --git a/AGENTS.md b/AGENTS.md index 61afbd035f..5a48049ef4 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -51,12 +51,14 @@ LiteLLM is a unified interface for 100+ LLMs that: ### MAKING CODE CHANGES FOR THE UI (IGNORE FOR BACKEND) -1. **Use Common Components as much as possible**: +1. **Tremor is DEPRECATED, do not use Tremor components in new features/changes** + - The only exception is the Tremor Table component and its required Tremor Table sub components. + +2. **Use Common Components as much as possible**: - These are usually defined in the `common_components` directory - Use these components as much as possible and avoid building new components unless needed - - Tremor components are deprecated; prefer using Ant Design (AntD) as much as possible -2. **Testing**: +3. **Testing**: - The codebase uses **Vitest** and **React Testing Library** - **Query Priority Order**: Use query methods in this order: `getByRole`, `getByLabelText`, `getByPlaceholderText`, `getByText`, `getByTestId` - **Always use `screen`** instead of destructuring from `render()` (e.g., use `screen.getByText()` not `getByText`) diff --git a/Dockerfile b/Dockerfile index 0e7a8412bb..2987a44b39 100644 --- a/Dockerfile +++ b/Dockerfile @@ -46,8 +46,8 @@ FROM $LITELLM_RUNTIME_IMAGE AS runtime # Ensure runtime stage runs as root USER root -# Install runtime dependencies -RUN apk add --no-cache bash openssl tzdata nodejs npm python3 py3-pip +# Install runtime dependencies (libsndfile needed for audio processing on ARM64) +RUN apk add --no-cache bash openssl tzdata nodejs npm python3 py3-pip libsndfile WORKDIR /app # Copy the current directory contents into the container at /app @@ -69,8 +69,8 @@ RUN find /usr/lib -type f -path "*/tornado/test/*" -delete && \ # Convert Windows line endings to Unix and make executable RUN sed -i 's/\r$//' docker/install_auto_router.sh && chmod +x docker/install_auto_router.sh && ./docker/install_auto_router.sh -# Generate prisma client -RUN prisma generate +# Generate prisma client using the correct schema +RUN prisma generate --schema=./litellm/proxy/schema.prisma # Convert Windows line endings to Unix for entrypoint scripts RUN sed -i 's/\r$//' docker/entrypoint.sh && chmod +x docker/entrypoint.sh RUN sed -i 's/\r$//' docker/prod_entrypoint.sh && chmod +x docker/prod_entrypoint.sh diff --git a/README.md b/README.md index 914fda384b..77adddf897 100644 --- a/README.md +++ b/README.md @@ -267,6 +267,7 @@ Support for more providers. Missing a provider or LLM Platform, raise a [feature Greptile OpenHands

Netflix

+ OpenAI Agents SDK diff --git a/cookbook/anthropic_agent_sdk/README.md b/cookbook/anthropic_agent_sdk/README.md new file mode 100644 index 0000000000..f113261809 --- /dev/null +++ b/cookbook/anthropic_agent_sdk/README.md @@ -0,0 +1,117 @@ +# Claude Agent SDK with LiteLLM Gateway + +A simple example showing how to use Claude's Agent SDK with LiteLLM as a proxy. This lets you use any LLM provider (OpenAI, Bedrock, Azure, etc.) through the Agent SDK. + +## Quick Start + +### 1. Install dependencies + +```bash +pip install anthropic claude-agent-sdk litellm +``` + +### 2. Start LiteLLM proxy + +```bash +# Simple start with Claude +litellm --model claude-sonnet-4-20250514 + +# Or with a config file +litellm --config config.yaml +``` + +### 3. Run the chat + +```bash +python main.py +``` + +That's it! You can now chat with the agent in your terminal. + +### Chat Commands + +While chatting, you can use these commands: +- `models` - List all available models (fetched from your LiteLLM proxy) +- `model` - Switch to a different model +- `clear` - Start a new conversation +- `quit` or `exit` - End the chat + +The chat automatically fetches available models from your LiteLLM proxy's `/models` endpoint, so you'll always see what's currently configured. + +## Configuration + +Set these environment variables if needed: + +```bash +export LITELLM_PROXY_URL="http://localhost:4000" +export LITELLM_API_KEY="sk-1234" +export LITELLM_MODEL="claude-sonnet-4-20250514" +``` + +Or just use the defaults - it'll connect to `http://localhost:4000` by default. + +## Example Config File + +If you want to use multiple models, create a `config.yaml` (see `config.example.yaml`): + +```yaml +model_list: + - model_name: bedrock-claude-sonnet-4 + litellm_params: + model: "bedrock/us.anthropic.claude-sonnet-4-20250514-v1:0" + aws_region_name: "us-east-1" + + - model_name: bedrock-claude-sonnet-4.5 + litellm_params: + model: "bedrock/us.anthropic.claude-sonnet-4-5-20250929-v1:0" + aws_region_name: "us-east-1" +``` + +Then start LiteLLM with: `litellm --config config.yaml` + +## How It Works + +The key is pointing the Agent SDK to LiteLLM instead of directly to Anthropic: + +```python +# Point to LiteLLM gateway (not Anthropic) +os.environ["ANTHROPIC_BASE_URL"] = "http://localhost:4000" +os.environ["ANTHROPIC_API_KEY"] = "sk-1234" # Your LiteLLM key + +# Use any model configured in LiteLLM +options = ClaudeAgentOptions( + model="bedrock-claude-sonnet-4", # or gpt-4, or anything else + system_prompt="You are a helpful assistant.", + max_turns=50, +) +``` + +Note: Don't add `/anthropic` to the base URL - LiteLLM handles the routing automatically. + +## Why Use This? + +- **Switch providers easily**: Use the same code with OpenAI, Bedrock, Azure, etc. +- **Cost tracking**: LiteLLM tracks spending across all your agent conversations +- **Rate limiting**: Set budgets and limits on your agent usage +- **Load balancing**: Distribute requests across multiple API keys or regions +- **Fallbacks**: Automatically retry with a different model if one fails + +## Troubleshooting + +**Connection errors?** +- Make sure LiteLLM is running: `litellm --model your-model` +- Check the URL is correct (default: `http://localhost:4000`) + +**Authentication errors?** +- Verify your LiteLLM API key is correct +- Make sure the model is configured in your LiteLLM setup + +**Model not found?** +- Check the model name matches what's in your LiteLLM config +- Run `litellm --model your-model` to test it works + +## Learn More + +- [LiteLLM Docs](https://docs.litellm.ai/) +- [Claude Agent SDK](https://github.com/anthropics/anthropic-agent-sdk) +- [LiteLLM Proxy Guide](https://docs.litellm.ai/docs/proxy/quick_start) diff --git a/cookbook/anthropic_agent_sdk/config.example.yaml b/cookbook/anthropic_agent_sdk/config.example.yaml new file mode 100644 index 0000000000..eb1984fc4e --- /dev/null +++ b/cookbook/anthropic_agent_sdk/config.example.yaml @@ -0,0 +1,25 @@ +model_list: + - model_name: bedrock-claude-sonnet-3.5 + litellm_params: + model: "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0" + aws_region_name: "us-east-1" + + - model_name: bedrock-claude-sonnet-4 + litellm_params: + model: "bedrock/us.anthropic.claude-sonnet-4-20250514-v1:0" + aws_region_name: "us-east-1" + + - model_name: bedrock-claude-sonnet-4.5 + litellm_params: + model: "bedrock/us.anthropic.claude-sonnet-4-5-20250929-v1:0" + aws_region_name: "us-east-1" + + - model_name: bedrock-claude-opus-4.5 + litellm_params: + model: "bedrock/us.anthropic.claude-opus-4-5-20251101-v1:0" + aws_region_name: "us-east-1" + + - model_name: bedrock-nova-premier + litellm_params: + model: "bedrock/amazon.nova-premier-v1:0" + aws_region_name: "us-east-1" diff --git a/cookbook/anthropic_agent_sdk/main.py b/cookbook/anthropic_agent_sdk/main.py new file mode 100644 index 0000000000..9bdd2f7364 --- /dev/null +++ b/cookbook/anthropic_agent_sdk/main.py @@ -0,0 +1,196 @@ +""" +Simple Interactive Claude Agent SDK CLI using LiteLLM Gateway + +This example demonstrates an interactive CLI chat with the Anthropic Agent SDK using LiteLLM as a proxy. +LiteLLM acts as a unified interface, allowing you to use any LLM provider (OpenAI, Azure, Bedrock, etc.) +through the Claude Agent SDK by pointing it to the LiteLLM gateway. +""" + +import os +import asyncio +import httpx +from claude_agent_sdk import ClaudeSDKClient, ClaudeAgentOptions + + +class Config: + """Configuration for LiteLLM Gateway connection""" + + # LiteLLM proxy URL (default to local instance) + LITELLM_PROXY_URL = os.getenv("LITELLM_PROXY_URL", "http://localhost:4000") + + # LiteLLM API key (master key or virtual key) + LITELLM_API_KEY = os.getenv("LITELLM_API_KEY", "sk-1234") + + # Model name as configured in LiteLLM (e.g., "bedrock-claude-sonnet-4", "gpt-4", etc.) + LITELLM_MODEL = os.getenv("LITELLM_MODEL", "bedrock-claude-sonnet-4.5") + + +async def fetch_available_models(base_url: str, api_key: str) -> list[str]: + """ + Fetch available models from LiteLLM proxy /models endpoint + """ + try: + async with httpx.AsyncClient() as client: + response = await client.get( + f"{base_url}/models", + headers={"Authorization": f"Bearer {api_key}"}, + timeout=10.0 + ) + response.raise_for_status() + data = response.json() + return [model["id"] for model in data.get("data", [])] + except Exception as e: + print(f"āš ļø Warning: Could not fetch models from proxy: {e}") + print("Using default model list...") + # Fallback to default models + return [ + "bedrock-claude-sonnet-3.5", + "bedrock-claude-sonnet-4", + "bedrock-claude-sonnet-4.5", + "bedrock-claude-opus-4.5", + "bedrock-nova-premier", + ] + + +async def interactive_chat(): + """ + Interactive CLI chat with the agent + """ + config = Config() + + # Configure Anthropic SDK to point to LiteLLM gateway + # Note: We don't add /anthropic to the base URL - LiteLLM handles routing + litellm_base_url = config.LITELLM_PROXY_URL.rstrip('/') + os.environ["ANTHROPIC_BASE_URL"] = litellm_base_url + os.environ["ANTHROPIC_API_KEY"] = config.LITELLM_API_KEY + + # Fetch available models from proxy + available_models = await fetch_available_models(litellm_base_url, config.LITELLM_API_KEY) + + current_model = config.LITELLM_MODEL + + print("=" * 70) + print("šŸ¤– Claude Agent SDK with LiteLLM Gateway - Interactive Chat") + print("=" * 70) + print(f"šŸš€ Connected to: {litellm_base_url}") + print(f"šŸ“¦ Current model: {current_model}") + print("\nType your messages below. Commands:") + print(" - 'quit' or 'exit' to end the conversation") + print(" - 'clear' to start a new conversation") + print(" - 'model' to switch models") + print(" - 'models' to list available models") + print("=" * 70) + print() + + while True: + # Configure agent options for each conversation + options = ClaudeAgentOptions( + system_prompt="You are a helpful AI assistant. Be concise, accurate, and friendly.", + model=current_model, + max_turns=50, + ) + + # Create agent client + async with ClaudeSDKClient(options=options) as client: + conversation_active = True + + while conversation_active: + # Get user input + try: + user_input = input("\nšŸ‘¤ You: ").strip() + except (EOFError, KeyboardInterrupt): + print("\n\nšŸ‘‹ Goodbye!") + return + + # Handle commands + if user_input.lower() in ['quit', 'exit']: + print("\nšŸ‘‹ Goodbye!") + return + + if user_input.lower() == 'clear': + print("\nšŸ”„ Starting new conversation...\n") + conversation_active = False + continue + + if user_input.lower() == 'models': + print("\nšŸ“‹ Available models:") + for i, model in enumerate(available_models, 1): + marker = "āœ“" if model == current_model else " " + print(f" {marker} {i}. {model}") + continue + + if user_input.lower() == 'model': + print("\nšŸ“‹ Select a model:") + for i, model in enumerate(available_models, 1): + marker = "āœ“" if model == current_model else " " + print(f" {marker} {i}. {model}") + + try: + choice = input("\nEnter number (or press Enter to cancel): ").strip() + if choice: + idx = int(choice) - 1 + if 0 <= idx < len(available_models): + current_model = available_models[idx] + print(f"\nāœ… Switched to: {current_model}") + print("šŸ”„ Starting new conversation with new model...\n") + conversation_active = False + else: + print("āŒ Invalid choice") + except (ValueError, IndexError): + print("āŒ Invalid input") + continue + + if not user_input: + continue + + # Send query to agent with loading indicator + print("\nšŸ¤– Assistant: ", end='', flush=True) + + try: + await client.query(user_input) + + # Show loading indicator + print("ā³ thinking...", end='', flush=True) + + # Stream the response + first_chunk = True + async for msg in client.receive_response(): + # Clear loading indicator on first message + if first_chunk: + print("\ršŸ¤– Assistant: ", end='', flush=True) + first_chunk = False + + # Handle different message types + if hasattr(msg, 'type'): + if msg.type == 'content_block_delta': + # Streaming text delta + if hasattr(msg, 'delta') and hasattr(msg.delta, 'text'): + print(msg.delta.text, end='', flush=True) + elif msg.type == 'content_block_start': + # Start of content block + if hasattr(msg, 'content_block') and hasattr(msg.content_block, 'text'): + print(msg.content_block.text, end='', flush=True) + + # Fallback to original content handling + if hasattr(msg, 'content'): + for content_block in msg.content: + if hasattr(content_block, 'text'): + print(content_block.text, end='', flush=True) + + print() # New line after response + + except Exception as e: + print(f"\r\nāŒ Error: {e}") + print("Please check your LiteLLM gateway is running and configured correctly.") + + +def main(): + """Run interactive chat""" + try: + asyncio.run(interactive_chat()) + except KeyboardInterrupt: + print("\n\nšŸ‘‹ Goodbye!") + + +if __name__ == "__main__": + main() diff --git a/cookbook/anthropic_agent_sdk/requirements.txt b/cookbook/anthropic_agent_sdk/requirements.txt new file mode 100644 index 0000000000..1e810bb7d9 --- /dev/null +++ b/cookbook/anthropic_agent_sdk/requirements.txt @@ -0,0 +1,2 @@ +claude-agent-sdk +httpx>=0.27.0 diff --git a/deploy/charts/litellm-helm/templates/deployment.yaml b/deploy/charts/litellm-helm/templates/deployment.yaml index c3e0055e38..4ac5582d06 100644 --- a/deploy/charts/litellm-helm/templates/deployment.yaml +++ b/deploy/charts/litellm-helm/templates/deployment.yaml @@ -38,6 +38,10 @@ spec: serviceAccountName: {{ include "litellm.serviceAccountName" . }} securityContext: {{- toYaml .Values.podSecurityContext | nindent 8 }} + {{- with .Values.extraInitContainers }} + initContainers: + {{- toYaml . | nindent 8 }} + {{- end }} containers: - name: {{ include "litellm.name" . }} securityContext: diff --git a/deploy/charts/litellm-helm/templates/migrations-job.yaml b/deploy/charts/litellm-helm/templates/migrations-job.yaml index f8893a47af..3459fa12d1 100644 --- a/deploy/charts/litellm-helm/templates/migrations-job.yaml +++ b/deploy/charts/litellm-helm/templates/migrations-job.yaml @@ -35,6 +35,10 @@ spec: {{- toYaml . | nindent 8 }} {{- end }} serviceAccountName: {{ include "litellm.serviceAccountName" . }} + {{- with .Values.migrationJob.extraInitContainers }} + initContainers: + {{- toYaml . | nindent 8 }} + {{- end }} containers: - name: prisma-migrations image: "{{ .Values.image.repository }}:{{ .Values.image.tag | default (printf "main-%s" .Chart.AppVersion) }}" diff --git a/deploy/charts/litellm-helm/values.yaml b/deploy/charts/litellm-helm/values.yaml index b75ce64037..cea25974bb 100644 --- a/deploy/charts/litellm-helm/values.yaml +++ b/deploy/charts/litellm-helm/values.yaml @@ -281,6 +281,7 @@ migrationJob: # cpu: 100m # memory: 100Mi extraContainers: [] + extraInitContainers: [] # Hook configuration hooks: diff --git a/docker/Dockerfile.non_root b/docker/Dockerfile.non_root index 8c795f3b17..48109d81a2 100644 --- a/docker/Dockerfile.non_root +++ b/docker/Dockerfile.non_root @@ -170,12 +170,14 @@ RUN sed -i 's/\r$//' docker/entrypoint.sh && \ [ -n "$LITELLM_PROXY_EXTRAS_PATH" ] && chmod -R g+w $LITELLM_PROXY_EXTRAS_PATH || true && \ chmod -R g+rX $PRISMA_PATH && \ chmod -R g+rX /app/.cache && \ - mkdir -p /tmp/.npm /nonexistent /.npm && \ - prisma generate + mkdir -p /tmp/.npm /nonexistent /.npm # Switch to non-root user for runtime USER nobody +# Generate Prisma client as nobody user to ensure correct file ownership +RUN prisma generate + # Prisma runtime knobs for offline containers ENV PRISMA_SKIP_POSTINSTALL_GENERATE=1 \ PRISMA_HIDE_UPDATE_MESSAGE=1 \ diff --git a/docs/my-website/docs/a2a.md b/docs/my-website/docs/a2a.md index d7145e4b83..a7e8b52d99 100644 --- a/docs/my-website/docs/a2a.md +++ b/docs/my-website/docs/a2a.md @@ -68,7 +68,7 @@ Follow [this guide, to add your pydantic ai agent to LiteLLM Agent Gateway](./pr ## Invoking your Agents -Use the [A2A Python SDK](https://pypi.org/project/a2a/) to invoke agents through LiteLLM. +Use the [A2A Python SDK](https://pypi.org/project/a2a-sdk) to invoke agents through LiteLLM. This example shows how to: 1. **List available agents** - Query `/v1/agents` to see which agents your key can access @@ -193,6 +193,120 @@ The logs show: style={{width: '100%', display: 'block', margin: '2rem auto'}} /> + +## Forwarding LiteLLM Context Headers + +When LiteLLM invokes your A2A agent, it sends special headers that enable: +- **Trace Grouping**: All LLM calls from the same agent execution appear under one trace +- **Agent Spend Tracking**: Costs are attributed to the specific agent + +| Header | Purpose | +|--------|---------| +| `X-LiteLLM-Trace-Id` | Links all LLM calls to the same execution flow | +| `X-LiteLLM-Agent-Id` | Attributes spend to the correct agent | + + +To enable these features, your A2A server must **forward these headers** to any LLM calls it makes back to LiteLLM. + +### Implementation Steps + +**Step 1: Extract headers from incoming A2A request** +```python def get_litellm_headers(request) -> dict: + """Extract X-LiteLLM-* headers from incoming A2A request.""" + all_headers = request.call_context.state.get('headers', {}) + return { + k: v for k, v in all_headers.items() + if k.lower().startswith('x-litellm-') + } +``` + +**Step 2: Forward headers to your LLM calls** +Pass the extracted headers when making calls back to LiteLLM: + + + +```python from openai import OpenAI + +headers = get_litellm_headers(request) + +client = OpenAI( + api_key="sk-your-litellm-key", + base_url="http://localhost:4000", + default_headers=headers, # Forward headers +) + +response = client.chat.completions.create( + model="gpt-4o", + messages=[{"role": "user", "content": "Hello"}] +) +``` + + + + +```python +from langchain_openai import ChatOpenAI + +headers = get_litellm_headers(request) + +llm = ChatOpenAI( + model="gpt-4o", + openai_api_key="sk-your-litellm-key", + base_url="http://localhost:4000", + default_headers=headers, # Forward headers +) +``` + + + +```python +import litellm + +headers = get_litellm_headers(request) + +response = litellm.completion( + model="gpt-4o", + messages=[{"role": "user", "content": "Hello"}], + api_base="http://localhost:4000", + extra_headers=headers, # Forward headers +) +``` + + + +```python +import httpx + +headers = get_litellm_headers(request) +headers["Authorization"] = "Bearer sk-your-litellm-key" + +response = httpx.post( + "http://localhost:4000/v1/chat/completions", + headers=headers, + json={"model": "gpt-4o", "messages": [{"role": "user", "content": "Hello"}]} +) +``` + + + +### Result + +With header forwarding enabled, you'll see: + +**Trace Grouping in Langfuse:** + + + +**Agent Spend Attribution:** + + + ## API Reference ### Endpoint diff --git a/docs/my-website/docs/observability/datadog.md b/docs/my-website/docs/observability/datadog.md index 7cf91ced34..6f785be101 100644 --- a/docs/my-website/docs/observability/datadog.md +++ b/docs/my-website/docs/observability/datadog.md @@ -7,6 +7,7 @@ import TabItem from '@theme/TabItem'; LiteLLM Supports logging to the following Datdog Integrations: - `datadog` [Datadog Logs](https://docs.datadoghq.com/logs/) - `datadog_llm_observability` [Datadog LLM Observability](https://www.datadoghq.com/product/llm-observability/) +- `datadog_cost_management` [Datadog Cloud Cost Management](#datadog-cloud-cost-management) - `ddtrace-run` [Datadog Tracing](#datadog-tracing) ## Datadog Logs @@ -73,7 +74,7 @@ Send logs through a local DataDog agent (useful for containerized environments): ```shell LITELLM_DD_AGENT_HOST="localhost" # hostname or IP of DataDog agent LITELLM_DD_AGENT_PORT="10518" # [OPTIONAL] port of DataDog agent (default: 10518) -DD_API_KEY="5f2d0f310***********" # [OPTIONAL] your datadog API Key (agent handles auth) +DD_API_KEY="5f2d0f310***********" # [OPTIONAL] your datadog API Key (Agent handles auth for Logs. REQUIRED for LLM Observability) DD_SOURCE="litellm_dev" # [OPTIONAL] your datadog source ``` @@ -84,6 +85,9 @@ When `LITELLM_DD_AGENT_HOST` is set, logs are sent to the agent instead of direc **Note:** We use `LITELLM_DD_AGENT_HOST` instead of `DD_AGENT_HOST` to avoid conflicts with `ddtrace` which automatically sets `DD_AGENT_HOST` for APM tracing. +> [!IMPORTANT] +> **Datadog LLM Observability**: `DD_API_KEY` is **REQUIRED** even when using the Datadog Agent (`LITELLM_DD_AGENT_HOST`). The agent acts as a proxy but the API key header is mandatory for the LLM Observability endpoint. + **Step 3**: Start the proxy, make a test request Start proxy @@ -161,6 +165,50 @@ On the Datadog LLM Observability page, you should see that both input messages a + + + +## Datadog Cloud Cost Management + +| Feature | Details | +|---------|---------| +| **What is logged** | Aggregated LLM Costs (FOCUS format) | +| **Events** | Periodic Uploads of Aggregated Cost Data | +| **Product Link** | [Datadog Cloud Cost Management](https://docs.datadoghq.com/cost_management/) | + +We will use the `--config` to set `litellm.callbacks = ["datadog_cost_management"]`. This will periodically upload aggregated LLM cost data to Datadog. + +**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: + callbacks: ["datadog_cost_management"] +``` + +**Step 2**: Set Required env variables + +```shell +DD_API_KEY="your-api-key" +DD_APP_KEY="your-app-key" # REQUIRED for Cost Management +DD_SITE="us5.datadoghq.com" +``` + +**Step 3**: Start the proxy + +```shell +litellm --config config.yaml +``` + +**How it works** +* LiteLLM aggregates costs in-memory by Provider, Model, Date, and Tags. +* Requires `DD_APP_KEY` for the Custom Costs API. +* Costs are uploaded periodically (flushed). + + ### Datadog Tracing Use `ddtrace-run` to enable [Datadog Tracing](https://ddtrace.readthedocs.io/en/stable/installation_quickstart.html) on litellm proxy @@ -203,5 +251,5 @@ LiteLLM supports customizing the following Datadog environment variables | `POD_NAME` | Pod name tag (useful for Kubernetes deployments) | "unknown" | āŒ No | \* **Required when using Direct API** (default): `DD_API_KEY` and `DD_SITE` are required -\* **Optional when using DataDog Agent**: Set `LITELLM_DD_AGENT_HOST` to use agent mode; `DD_API_KEY` and `DD_SITE` are not required +\* **Optional when using DataDog Agent**: Set `LITELLM_DD_AGENT_HOST` to use agent mode; `DD_API_KEY` and `DD_SITE` are not required for **Datadog Logs**. (**Note: `DD_API_KEY` IS REQUIRED for Datadog LLM Observability**) diff --git a/docs/my-website/docs/pass_through/openai_passthrough.md b/docs/my-website/docs/pass_through/openai_passthrough.md index d7c98eba7b..49026f8aa2 100644 --- a/docs/my-website/docs/pass_through/openai_passthrough.md +++ b/docs/my-website/docs/pass_through/openai_passthrough.md @@ -1,6 +1,6 @@ # OpenAI Passthrough -Pass-through endpoints for `/openai` +Pass-through endpoints for direct OpenAI API access ## Overview @@ -10,12 +10,27 @@ Pass-through endpoints for `/openai` | Logging | āœ… | Works across all integrations | | Streaming | āœ… | Fully supported | -### When to use this? +## Available Endpoints + +### `/openai_passthrough` - Recommended +Dedicated passthrough endpoint that guarantees direct routing to OpenAI without conflicts. + +**Use this for:** +- OpenAI Responses API (`/v1/responses`) +- Any endpoint where you need guaranteed passthrough +- When `/openai` routes are conflicting with LiteLLM's native implementations + +### `/openai` - Legacy +Standard passthrough endpoint that may conflict with LiteLLM's native implementations. + +**Note:** Some endpoints like `/openai/v1/responses` will be routed to LiteLLM's native implementation instead of OpenAI. + +## When to use this? - For 90% of your use cases, you should use the [native LiteLLM OpenAI Integration](https://docs.litellm.ai/docs/providers/openai) (`/chat/completions`, `/embeddings`, `/completions`, `/images`, `/batches`, etc.) -- Use this passthrough to call less popular or newer OpenAI endpoints that LiteLLM doesn't fully support yet, such as `/assistants`, `/threads`, `/vector_stores` +- Use `/openai_passthrough` to call less popular or newer OpenAI endpoints that LiteLLM doesn't fully support yet, such as `/assistants`, `/threads`, `/vector_stores`, `/responses` -Simply replace `https://api.openai.com` with `LITELLM_PROXY_BASE_URL/openai` +Simply replace `https://api.openai.com` with `LITELLM_PROXY_BASE_URL/openai_passthrough` ## Usage Examples @@ -34,7 +49,7 @@ Make sure you do the following: import openai client = openai.OpenAI( - base_url="http://0.0.0.0:4000/openai", # /openai + base_url="http://0.0.0.0:4000/openai_passthrough", # /openai_passthrough api_key="sk-anything" # ) ``` diff --git a/docs/my-website/docs/providers/gemini.md b/docs/my-website/docs/providers/gemini.md index 23a02f7365..b9ad7820dd 100644 --- a/docs/my-website/docs/providers/gemini.md +++ b/docs/my-website/docs/providers/gemini.md @@ -1840,6 +1840,57 @@ content = response.get('choices', [{}])[0].get('message', {}).get('content') print(content) ``` +## gemini-robotics-er-1.5-preview Usage + +```python +from litellm import api_base +from openai import OpenAI +import os +import base64 + +client = OpenAI(base_url="http://0.0.0.0:4000", api_key="sk-12345") +base64_image = base64.b64encode(open("closeup-object-on-table-many-260nw-1216144471.webp", "rb").read()).decode() + +import json +import re +tools = [{"codeExecution": {}}] +response = client.chat.completions.create( + model="gemini/gemini-robotics-er-1.5-preview", + messages=[ + { + "role": "user", + "content": [ + { + "type": "text", + "text": "Point to no more than 10 items in the image. The label returned should be an identifying name for the object detected. The answer should follow the json format: [{\"point\": [y, x], \"label\": }, ...]. The points are in [y, x] format normalized to 0-1000." + }, + { + "type": "image_url", + "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"} + } + ] + } + ], + tools=tools +) + +# Extract JSON from markdown code block if present +content = response.choices[0].message.content +# Look for triple-backtick JSON block +match = re.search(r'```json\s*(.*?)\s*```', content, re.DOTALL) +if match: + json_str = match.group(1) +else: + json_str = content + +try: + data = json.loads(json_str) + print(json.dumps(data, indent=2)) +except Exception as e: + print("Error parsing response as JSON:", e) + print("Response content:", content) +``` + ## Usage - PDF / Videos / etc. Files ### Inline Data (e.g. audio stream) diff --git a/docs/my-website/docs/providers/sarvam.md b/docs/my-website/docs/providers/sarvam.md new file mode 100644 index 0000000000..d77e9c0c75 --- /dev/null +++ b/docs/my-website/docs/providers/sarvam.md @@ -0,0 +1,89 @@ +# Sarvam.ai + +LiteLLM supports all the text models from [Sarvam ai](https://docs.sarvam.ai/api-reference-docs/chat/chat-completions) + +## Usage + +```python +import os +from litellm import completion + +# Set your Sarvam API key +os.environ["SARVAM_API_KEY"] = "" + +messages = [{"role": "user", "content": "Hello"}] + +response = completion( + model="sarvam/sarvam-m", + messages=messages, +) +print(response) +``` + +## Usage with LiteLLM Proxy Server + +Here's how to call a Sarvam.ai model with the LiteLLM Proxy Server + +1. **Modify the `config.yaml`:** + + ```yaml + model_list: + - model_name: my-model + litellm_params: + model: sarvam/ # add sarvam/ prefix to route as Sarvam provider + api_key: api-key # api key to send your model + ``` + +2. **Start the proxy:** + + ```bash + $ litellm --config /path/to/config.yaml + ``` + +3. **Send a 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 index 91f0a18ea1..3ff007171e 100644 --- a/docs/my-website/docs/providers/vercel_ai_gateway.md +++ b/docs/my-website/docs/providers/vercel_ai_gateway.md @@ -11,7 +11,7 @@ import TabItem from '@theme/TabItem'; | Provider Route on LiteLLM | `vercel_ai_gateway/` | | Link to Provider Doc | [Vercel AI Gateway Documentation ā†—](https://vercel.com/docs/ai-gateway) | | Base URL | `https://ai-gateway.vercel.sh/v1` | -| Supported Operations | `/chat/completions`, `/models` | +| Supported Operations | `/chat/completions`, `/embeddings`, `/models` |

@@ -73,7 +73,7 @@ messages = [{"content": "Hello, how are you?", "role": "user"}] # Vercel AI Gateway call with streaming response = completion( - model="vercel_ai_gateway/openai/gpt-4o", + model="vercel_ai_gateway/openai/gpt-4o", messages=messages, stream=True ) @@ -82,6 +82,33 @@ for chunk in response: print(chunk) ``` +### Embeddings + +```python showLineNumbers title="Vercel AI Gateway Embeddings" +import os +from litellm import embedding + +os.environ["VERCEL_AI_GATEWAY_API_KEY"] = "your-api-key" + +# Vercel AI Gateway embedding call +response = embedding( + model="vercel_ai_gateway/openai/text-embedding-3-small", + input="Hello world" +) + +print(response.data[0]["embedding"][:5]) # Print first 5 dimensions +``` + +You can also specify the `dimensions` parameter: + +```python showLineNumbers title="Vercel AI Gateway Embeddings with Dimensions" +response = embedding( + model="vercel_ai_gateway/openai/text-embedding-3-small", + input=["Hello world", "Goodbye world"], + dimensions=768 +) +``` + ## Usage - LiteLLM Proxy Add the following to your LiteLLM Proxy configuration file: @@ -97,6 +124,11 @@ model_list: litellm_params: model: vercel_ai_gateway/anthropic/claude-4-sonnet api_key: os.environ/VERCEL_AI_GATEWAY_API_KEY + + - model_name: text-embedding-3-small-gateway + litellm_params: + model: vercel_ai_gateway/openai/text-embedding-3-small + api_key: os.environ/VERCEL_AI_GATEWAY_API_KEY ``` Start your LiteLLM Proxy server: diff --git a/docs/my-website/docs/proxy/cli_sso.md b/docs/my-website/docs/proxy/cli_sso.md index cde6bf266d..ad0f033f80 100644 --- a/docs/my-website/docs/proxy/cli_sso.md +++ b/docs/my-website/docs/proxy/cli_sso.md @@ -28,6 +28,37 @@ EXPERIMENTAL_UI_LOGIN="True" litellm --config config.yaml ::: +### Configuration + +#### JWT Token Expiration + +By default, CLI authentication tokens expire after **24 hours**. You can customize this expiration time by setting the `LITELLM_CLI_JWT_EXPIRATION_HOURS` environment variable when starting your LiteLLM Proxy: + +```bash +# Set CLI JWT tokens to expire after 48 hours +export LITELLM_CLI_JWT_EXPIRATION_HOURS=48 +export EXPERIMENTAL_UI_LOGIN="True" +litellm --config config.yaml +``` + +Or in a single command: + +```bash +LITELLM_CLI_JWT_EXPIRATION_HOURS=48 EXPERIMENTAL_UI_LOGIN="True" litellm --config config.yaml +``` + +**Examples:** +- `LITELLM_CLI_JWT_EXPIRATION_HOURS=12` - Tokens expire after 12 hours +- `LITELLM_CLI_JWT_EXPIRATION_HOURS=168` - Tokens expire after 7 days (168 hours) +- `LITELLM_CLI_JWT_EXPIRATION_HOURS=720` - Tokens expire after 30 days (720 hours) + +:::tip +You can check your current token's age and expiration status using: +```bash +litellm-proxy whoami +``` +::: + ### Steps 1. **Install the CLI** diff --git a/docs/my-website/docs/proxy/config_settings.md b/docs/my-website/docs/proxy/config_settings.md index 6d3fa20611..8797e71da7 100644 --- a/docs/my-website/docs/proxy/config_settings.md +++ b/docs/my-website/docs/proxy/config_settings.md @@ -452,6 +452,8 @@ router_settings: | BERRISPEND_ACCOUNT_ID | Account ID for BerriSpend service | BRAINTRUST_API_KEY | API key for Braintrust integration | BRAINTRUST_API_BASE | Base URL for Braintrust API. Default is https://api.braintrustdata.com/v1 +| BRAINTRUST_MOCK | Enable mock mode for Braintrust integration testing. When set to true, intercepts Braintrust API calls and returns mock responses without making actual network calls. Default is false +| BRAINTRUST_MOCK_LATENCY_MS | Mock latency in milliseconds for Braintrust API calls when mock mode is enabled. Simulates network round-trip time. Default is 100ms | CACHED_STREAMING_CHUNK_DELAY | Delay in seconds for cached streaming chunks. Default is 0.02 | CHATGPT_API_BASE | Base URL for ChatGPT API. Default is https://chatgpt.com/backend-api/codex | CHATGPT_AUTH_FILE | Filename for ChatGPT authentication data. Default is "auth.json" @@ -462,6 +464,7 @@ router_settings: | CHATGPT_USER_AGENT_SUFFIX | Suffix to append to the ChatGPT user agent string | CIRCLE_OIDC_TOKEN | OpenID Connect token for CircleCI | CIRCLE_OIDC_TOKEN_V2 | Version 2 of the OpenID Connect token for CircleCI +| CLI_JWT_EXPIRATION_HOURS | Expiration time in hours for CLI-generated JWT tokens. Default is 24 hours. Can also be set via LITELLM_CLI_JWT_EXPIRATION_HOURS | 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 @@ -510,6 +513,8 @@ router_settings: | DD_ENV | Environment identifier for Datadog logs. Only supported for `datadog_llm_observability` callback | DD_SERVICE | Service identifier for Datadog logs. Defaults to "litellm-server" | DD_VERSION | Version identifier for Datadog logs. Defaults to "unknown" +| DATADOG_MOCK | Enable mock mode for Datadog integration testing. When set to true, intercepts Datadog API calls and returns mock responses without making actual network calls. Default is false +| DATADOG_MOCK_LATENCY_MS | Mock latency in milliseconds for Datadog API calls when mock mode is enabled. Simulates network round-trip time. Default is 100ms | DEBUG_OTEL | Enable debug mode for OpenTelemetry | DEFAULT_ALLOWED_FAILS | Maximum failures allowed before cooling down a model. Default is 3 | DEFAULT_A2A_AGENT_TIMEOUT | Default timeout in seconds for A2A (Agent-to-Agent) protocol requests. Default is 6000 @@ -674,6 +679,8 @@ router_settings: | HCP_VAULT_CERT_ROLE | Role for [Hashicorp Vault Secret Manager Auth](../secret.md#hashicorp-vault) | HELICONE_API_KEY | API key for Helicone service | HELICONE_API_BASE | Base URL for Helicone service, defaults to `https://api.helicone.ai` +| HELICONE_MOCK | Enable mock mode for Helicone integration testing. When set to true, intercepts Helicone API calls and returns mock responses without making actual network calls. Default is false +| HELICONE_MOCK_LATENCY_MS | Mock latency in milliseconds for Helicone API calls when mock mode is enabled. Simulates network round-trip time. Default is 100ms | HOSTNAME | Hostname for the server, this will be [emitted to `datadog` logs](https://docs.litellm.ai/docs/proxy/logging#datadog) | HOURS_IN_A_DAY | Hours in a day for calculation purposes. Default is 24 | HIDDENLAYER_API_BASE | Base URL for HiddenLayer API. Defaults to `https://api.hiddenlayer.ai` @@ -712,6 +719,8 @@ router_settings: | LANGSMITH_PROJECT | Project name for Langsmith integration | LANGSMITH_SAMPLING_RATE | Sampling rate for Langsmith logging | LANGSMITH_TENANT_ID | Tenant ID for Langsmith multi-tenant deployments +| LANGSMITH_MOCK | Enable mock mode for Langsmith integration testing. When set to true, intercepts Langsmith API calls and returns mock responses without making actual network calls. Default is false +| LANGSMITH_MOCK_LATENCY_MS | Mock latency in milliseconds for Langsmith API calls when mock mode is enabled. Simulates network round-trip time. Default is 100ms | LANGTRACE_API_KEY | API key for Langtrace service | LASSO_API_BASE | Base URL for Lasso API | LASSO_API_KEY | API key for Lasso service @@ -723,6 +732,7 @@ router_settings: | 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_CLI_JWT_EXPIRATION_HOURS | Expiration time in hours for CLI-generated JWT tokens. Default is 24 hours | LITELLM_DD_AGENT_HOST | Hostname or IP of DataDog agent for LiteLLM-specific logging. When set, logs are sent to agent instead of direct API | LITELLM_DD_AGENT_PORT | Port of DataDog agent for LiteLLM-specific log intake. Default is 10518 | LITELLM_DONT_SHOW_FEEDBACK_BOX | Flag to hide feedback box in LiteLLM UI @@ -843,6 +853,8 @@ router_settings: | 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) +| POSTHOG_MOCK | Enable mock mode for PostHog integration testing. When set to true, intercepts PostHog API calls and returns mock responses without making actual network calls. Default is false +| POSTHOG_MOCK_LATENCY_MS | Mock latency in milliseconds for PostHog API calls when mock mode is enabled. Simulates network round-trip time. Default is 100ms | 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 diff --git a/docs/my-website/docs/proxy/guardrails/onyx_security.md b/docs/my-website/docs/proxy/guardrails/onyx_security.md index 85b0ba9f83..d240902eb5 100644 --- a/docs/my-website/docs/proxy/guardrails/onyx_security.md +++ b/docs/my-website/docs/proxy/guardrails/onyx_security.md @@ -128,6 +128,7 @@ guardrails: mode: ["pre_call", "post_call", "during_call"] # Run at multiple stages api_key: os.environ/ONYX_API_KEY api_base: os.environ/ONYX_API_BASE + timeout: 10.0 # Optional, defaults to 10 seconds ``` ### Required Parameters @@ -137,6 +138,7 @@ guardrails: ### Optional Parameters - **`api_base`**: Onyx API base URL (defaults to `https://ai-guard.onyx.security`) +- **`timeout`**: Request timeout in seconds (defaults to `10.0`) ## Environment Variables @@ -145,4 +147,5 @@ You can set these environment variables instead of hardcoding values in your con ```shell export ONYX_API_KEY="your-api-key-here" export ONYX_API_BASE="https://ai-guard.onyx.security" # Optional +export ONYX_TIMEOUT=10 # Optional, timeout in seconds ``` diff --git a/docs/my-website/docs/proxy/guardrails/quick_start.md b/docs/my-website/docs/proxy/guardrails/quick_start.md index cb6379d49f..ddb215fcb6 100644 --- a/docs/my-website/docs/proxy/guardrails/quick_start.md +++ b/docs/my-website/docs/proxy/guardrails/quick_start.md @@ -405,14 +405,10 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \ ## **Proxy Admin Controls** -### āœØ Monitoring Guardrails +### Monitoring Guardrails Monitor which guardrails were executed and whether they passed or failed. e.g. guardrail going rogue and failing requests we don't intend to fail -:::info - -āœØ This is an Enterprise only feature [Get a free trial](https://www.litellm.ai/enterprise#trial) - ::: #### Setup diff --git a/docs/my-website/docs/proxy/prometheus.md b/docs/my-website/docs/proxy/prometheus.md index cd2b3b68f3..93a0675f09 100644 --- a/docs/my-website/docs/proxy/prometheus.md +++ b/docs/my-website/docs/proxy/prometheus.md @@ -121,8 +121,8 @@ 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", "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"` | +| `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", "user_email", "exception_status", "exception_class", "route", "model_id"` | +| `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", "model_id"` | ### Callback Logging Metrics @@ -130,7 +130,12 @@ Monitor failures while shipping logs to downstream callbacks like `s3_v3` cold s | Metric Name | Description | |----------------------|--------------------------------------| -| `litellm_callback_logging_failures_metric` | Total number of failed attempts to emit logs to a configured callback. Labels: `"callback_name"`. Use this to alert on callback delivery issues such as repeated failures when writing to `s3_v3`. | +| `litellm_callback_logging_failures_metric` | Total number of failed attempts to emit logs to a configured callback. Labels: `"callback_name"`. Use this to alert on callback delivery issues such as repeated failures when writing to `s3_v3`, `langfuse`, or `langfuse_otel` and other otel providers | + +**Supported Callbacks:** +- `S3Logger` - S3 v2 cold storage failures +- `langfuse` - Langfuse logging failures +- `otel` - OpenTelemetry logging failures ## LLM Provider Metrics @@ -191,10 +196,10 @@ Use this for LLM API Error monitoring and tracking remaining rate limits and tok | 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_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", "model_id" | | `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] | +| `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`, `requested_model`, `end_user`, `user`, `model_id` [Note: only emitted for streaming requests] | ## Tracking `end_user` on Prometheus diff --git a/docs/my-website/docs/proxy/ui/page_visibility.md b/docs/my-website/docs/proxy/ui/page_visibility.md new file mode 100644 index 0000000000..06b06f3321 --- /dev/null +++ b/docs/my-website/docs/proxy/ui/page_visibility.md @@ -0,0 +1,121 @@ +import Image from '@theme/IdealImage'; + +# Control Page Visibility for Internal Users + +Configure which navigation tabs and pages are visible to internal users (non-admin developers) in the LiteLLM UI. + +Use this feature to simplify the UI and control which pages your internal users/developers can see when signing in. + +## Overview + +By default, all pages accessible to internal users are visible in the navigation sidebar. The page visibility control allows admins to restrict which pages internal users can see, creating a more focused and streamlined experience. + + +## Configure Page Visibility + +### 1. Navigate to Settings + +Click the **Settings** icon in the sidebar. + +![Navigate to Settings](https://colony-recorder.s3.amazonaws.com/files/2026-01-28/cbb6f272-ab18-4996-b57d-7ed4aad721ea/ascreenshot_ab80f3175b1a41b0bdabdd2cd3980573_text_export.jpeg) + +### 2. Go to Admin Settings + +Click **Admin Settings** from the settings menu. + +![Go to Admin Settings](https://colony-recorder.s3.amazonaws.com/files/2026-01-28/e2b327bf-1cfd-4519-a9ce-8a6ecb2de53a/ascreenshot_23bb1577b3f84d22be78e0faa58dee3d_text_export.jpeg) + +### 3. Select UI Settings + +Click **UI Settings** to access the page visibility controls. + +![Select UI Settings](https://colony-recorder.s3.amazonaws.com/files/2026-01-28/fff0366a-4944-457a-8f6a-e22018dde108/ascreenshot_0e268e8651654e75bb9fb40d2ed366a9_text_export.jpeg) + +### 4. Open Page Visibility Configuration + +Click **Configure Page Visibility** to expand the configuration panel. + +![Open Configuration](https://colony-recorder.s3.amazonaws.com/files/2026-01-28/3a4761d6-145a-4afd-8abf-d92744b9ac9f/ascreenshot_23c16eb79c32481887b879d961f1f00a_text_export.jpeg) + +### 5. Select Pages to Make Visible + +Check the boxes for the pages you want internal users to see. Pages are organized by category for easy navigation. + +![Select Pages](https://colony-recorder.s3.amazonaws.com/files/2026-01-28/b9c96b54-6c20-484f-8b0b-3a86decb5717/ascreenshot_3347ade01ebe4ea390bc7b57e53db43f_text_export.jpeg) + +**Available pages include:** +- Virtual Keys +- Playground +- Models + Endpoints +- Agents +- MCP Servers +- Search Tools +- Vector Stores +- Logs +- Teams +- Organizations +- Usage +- Budgets +- And more... + +### 6. Save Your Configuration + +Click **Save Page Visibility Settings** to apply the changes. + +![Save Settings](https://colony-recorder.s3.amazonaws.com/files/2026-01-28/8a215378-44f5-4bb8-b984-06fa2aa03903/ascreenshot_44e7aeebe25a477ba92f73a3ed3df644_text_export.jpeg) + +### 7. Verify Changes + +Internal users will now only see the selected pages in their navigation sidebar. + +![Verify Changes](https://colony-recorder.s3.amazonaws.com/files/2026-01-28/493a7718-b276-40b9-970f-5814054932d9/ascreenshot_ad23b8691f824095ba60256f91ad24f8_text_export.jpeg) + +## Reset to Default + +To restore all pages to internal users: + +1. Open the Page Visibility configuration +2. Click **Reset to Default (All Pages)** +3. Click **Save Page Visibility Settings** + +This will clear the restriction and show all accessible pages to internal users. + +## API Configuration + +You can also configure page visibility programmatically using the API: + +### Get Current Settings + +```bash +curl -X GET 'http://localhost:4000/ui_settings/get' \ + -H 'Authorization: Bearer ' +``` + +### Update Page Visibility + +```bash +curl -X PATCH 'http://localhost:4000/ui_settings/update' \ + -H 'Authorization: Bearer ' \ + -H 'Content-Type: application/json' \ + -d '{ + "enabled_ui_pages_internal_users": [ + "api-keys", + "agents", + "mcp-servers", + "logs", + "teams" + ] + }' +``` + +### Clear Page Visibility Restrictions + +```bash +curl -X PATCH 'http://localhost:4000/ui_settings/update' \ + -H 'Authorization: Bearer ' \ + -H 'Content-Type: application/json' \ + -d '{ + "enabled_ui_pages_internal_users": null + }' +``` + diff --git a/docs/my-website/docs/rag_ingest.md b/docs/my-website/docs/rag_ingest.md index 1133b85f20..7adc2d70b5 100644 --- a/docs/my-website/docs/rag_ingest.md +++ b/docs/my-website/docs/rag_ingest.md @@ -5,7 +5,7 @@ All-in-one document ingestion pipeline: **Upload ā†’ Chunk ā†’ Embed ā†’ Vector | Feature | Supported | |---------|-----------| | Logging | Yes | -| Supported Providers | `openai`, `bedrock`, `vertex_ai`, `gemini` | +| Supported Providers | `openai`, `bedrock`, `vertex_ai`, `gemini`, `s3_vectors` | :::tip After ingesting documents, use [/rag/query](./rag_query.md) to search and generate responses with your ingested content. @@ -75,6 +75,31 @@ curl -X POST "http://localhost:4000/v1/rag/ingest" \ }" ``` +### AWS S3 Vectors + +```bash showLineNumbers title="Ingest to S3 Vectors" +curl -X POST "http://localhost:4000/v1/rag/ingest" \ + -H "Authorization: Bearer sk-1234" \ + -H "Content-Type: application/json" \ + -d "{ + \"file\": { + \"filename\": \"document.txt\", + \"content\": \"$(base64 -i document.txt)\", + \"content_type\": \"text/plain\" + }, + \"ingest_options\": { + \"embedding\": { + \"model\": \"text-embedding-3-small\" + }, + \"vector_store\": { + \"custom_llm_provider\": \"s3_vectors\", + \"vector_bucket_name\": \"my-embeddings\", + \"aws_region_name\": \"us-west-2\" + } + } + }" +``` + ## Response ```json @@ -265,6 +290,57 @@ When `vector_store_id` is omitted, LiteLLM automatically creates: 4. Install: `pip install 'google-cloud-aiplatform>=1.60.0'` ::: +### vector_store (AWS S3 Vectors) + +| Parameter | Type | Default | Description | +|-----------|------|---------|-------------| +| `custom_llm_provider` | string | - | `"s3_vectors"` | +| `vector_bucket_name` | string | **required** | S3 vector bucket name | +| `index_name` | string | auto-create | Vector index name | +| `dimension` | integer | auto-detect | Vector dimension (auto-detected from embedding model) | +| `distance_metric` | string | `cosine` | Distance metric: `cosine` or `euclidean` | +| `non_filterable_metadata_keys` | array | `["source_text"]` | Metadata keys excluded from filtering | +| `aws_region_name` | string | `us-west-2` | AWS region | +| `aws_access_key_id` | string | env | AWS access key | +| `aws_secret_access_key` | string | env | AWS secret key | + +:::info S3 Vectors Auto-Creation +When `index_name` is omitted, LiteLLM automatically creates: +- S3 vector bucket (if it doesn't exist) +- Vector index with auto-detected dimensions from your embedding model + +**Dimension Auto-Detection**: The vector dimension is automatically detected by making a test embedding request to your specified model. No need to manually specify dimensions! + +**Supported Embedding Models**: Works with any LiteLLM-supported embedding model (OpenAI, Cohere, Bedrock, Azure, etc.) +::: + +**Example with auto-detection:** +```json +{ + "embedding": { + "model": "text-embedding-3-small" // Dimension auto-detected as 1536 + }, + "vector_store": { + "custom_llm_provider": "s3_vectors", + "vector_bucket_name": "my-embeddings" + } +} +``` + +**Example with custom embedding provider:** +```json +{ + "embedding": { + "model": "cohere/embed-english-v3.0" // Dimension auto-detected as 1024 + }, + "vector_store": { + "custom_llm_provider": "s3_vectors", + "vector_bucket_name": "my-embeddings", + "distance_metric": "cosine" + } +} +``` + ## Input Examples ### File (Base64) diff --git a/docs/my-website/docs/routing.md b/docs/my-website/docs/routing.md index 47967775e1..2b3a28edf7 100644 --- a/docs/my-website/docs/routing.md +++ b/docs/my-website/docs/routing.md @@ -830,6 +830,12 @@ asyncio.run(router_acompletion()) +## Traffic Mirroring / Silent Experiments + +Traffic mirroring allows you to "mimic" production traffic to a secondary (silent) model for evaluation purposes. The silent model's response is gathered in the background and does not affect the latency or result of the primary request. + +[**See detailed guide on A/B Testing - Traffic Mirroring here**](./traffic_mirroring.md) + ## Basic Reliability ### Deployment Ordering (Priority) diff --git a/docs/my-website/docs/traffic_mirroring.md b/docs/my-website/docs/traffic_mirroring.md new file mode 100644 index 0000000000..3bdcb0f161 --- /dev/null +++ b/docs/my-website/docs/traffic_mirroring.md @@ -0,0 +1,83 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# A/B Testing - Traffic Mirroring + +Traffic mirroring allows you to "mimic" production traffic to a secondary (silent) model for evaluation purposes. The silent model's response is gathered in the background and does not affect the latency or result of the primary request. + +This is useful for: +- Testing a new model's performance on production prompts before switching. +- Comparing costs and latency between different providers. +- Debugging issues by mirroring traffic to a more verbose model. + +## Quick Start + +To enable traffic mirroring, add `silent_model` to the `litellm_params` of a deployment. + + + + +```python +from litellm import Router + +model_list = [ + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "azure/chatgpt-v-2", + "api_key": "...", + "silent_model": "gpt-4" # šŸ‘ˆ Mirror traffic to gpt-4 + }, + }, + { + "model_name": "gpt-4", + "litellm_params": { + "model": "openai/gpt-4", + "api_key": "..." + }, + } +] + +router = Router(model_list=model_list) + +# The request to "gpt-3.5-turbo" will trigger a background call to "gpt-4" +response = await router.acompletion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "How does traffic mirroring work?"}] +) +``` + + + + +Add `silent_model` to your `config.yaml`: + +```yaml +model_list: + - model_name: primary-model + litellm_params: + model: azure/gpt-35-turbo + api_key: os.environ/AZURE_API_KEY + silent_model: evaluation-model # šŸ‘ˆ Mirror traffic here + - model_name: evaluation-model + litellm_params: + model: openai/gpt-4o + api_key: os.environ/OPENAI_API_KEY +``` + + + + +## How it works +1. **Request Received**: A request is made to a model group (e.g. `primary-model`). +2. **Deployment Picked**: LiteLLM picks a deployment from the group. +3. **Primary Call**: LiteLLM makes the call to the primary deployment. +4. **Mirroring**: If `silent_model` is present, LiteLLM triggers a background call to that model. + - For **Sync** calls: Uses a shared thread pool. + - For **Async** calls: Uses `asyncio.create_task`. +5. **Isolation**: The background call uses a `deepcopy` of the original request parameters and sets `metadata["is_silent_experiment"] = True`. It also strips out logging IDs to prevent collisions in usage tracking. + +## Key Features +- **Latency Isolation**: The primary request returns as soon as it's ready. The background (silent) call does not block. +- **Unified Logging**: Background calls are processed via the Router, meaning they are automatically logged to your configured observability tools (Langfuse, S3, etc.). +- **Evaluation**: Use the `is_silent_experiment: True` flag in your logs to filter and compare results between the primary and mirrored calls. diff --git a/docs/my-website/docs/tutorials/claude_agent_sdk.md b/docs/my-website/docs/tutorials/claude_agent_sdk.md new file mode 100644 index 0000000000..c56784ba2d --- /dev/null +++ b/docs/my-website/docs/tutorials/claude_agent_sdk.md @@ -0,0 +1,115 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Claude Agent SDK with LiteLLM + +Use Anthropic's Claude Agent SDK with any LLM provider through LiteLLM Proxy. + +The Claude Agent SDK provides a high-level interface for building AI agents. By pointing it to LiteLLM, you can use the same agent code with OpenAI, Bedrock, Azure, Vertex AI, or any other provider. + +## Quick Start + +### 1. Install Dependencies + +```bash +pip install claude-agent-sdk +``` + +### 2. Start LiteLLM Proxy + +```yaml title="config.yaml" showLineNumbers +model_list: + - model_name: bedrock-claude-sonnet-3.5 + litellm_params: + model: "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0" + aws_region_name: "us-east-1" + + - model_name: bedrock-claude-sonnet-4 + litellm_params: + model: "bedrock/us.anthropic.claude-sonnet-4-20250514-v1:0" + aws_region_name: "us-east-1" + + - model_name: bedrock-claude-sonnet-4.5 + litellm_params: + model: "bedrock/us.anthropic.claude-sonnet-4-5-20250929-v1:0" + aws_region_name: "us-east-1" + + - model_name: bedrock-claude-opus-4.5 + litellm_params: + model: "bedrock/us.anthropic.claude-opus-4-5-20251101-v1:0" + aws_region_name: "us-east-1" + + - model_name: bedrock-nova-premier + litellm_params: + model: "bedrock/amazon.nova-premier-v1:0" + aws_region_name: "us-east-1" +``` + +```bash +litellm --config config.yaml +``` + +### 3. Point Agent SDK to LiteLLM + +| Environment Variable | Value | Description | +|---------------------|-------|-------------| +| `ANTHROPIC_BASE_URL` | `http://localhost:4000` | LiteLLM proxy URL | +| `ANTHROPIC_API_KEY` | `sk-1234` | Your LiteLLM API key (not Anthropic key) | + +```python title="agent.py" showLineNumbers +import os +from claude_agent_sdk import ClaudeSDKClient, ClaudeAgentOptions + +# Point to LiteLLM proxy (not Anthropic) +os.environ["ANTHROPIC_BASE_URL"] = "http://localhost:4000" +os.environ["ANTHROPIC_API_KEY"] = "sk-1234" # Your LiteLLM key + +# Configure agent with any model from your config +options = ClaudeAgentOptions( + system_prompt="You are a helpful AI assistant.", + model="bedrock-claude-sonnet-4", # Use any model from config.yaml + max_turns=20, +) + +async with ClaudeSDKClient(options=options) as client: + await client.query("What is LiteLLM?") + + async for msg in client.receive_response(): + if hasattr(msg, 'content'): + for content_block in msg.content: + if hasattr(content_block, 'text'): + print(content_block.text, end='', flush=True) +``` + + + +## Why Use LiteLLM with Agent SDK? + +| Feature | Benefit | +|---------|---------| +| **Multi-Provider** | Use the same agent code with OpenAI, Bedrock, Azure, Vertex AI, etc. | +| **Cost Tracking** | Track spending across all agent conversations | +| **Rate Limiting** | Set budgets and limits on agent usage | +| **Load Balancing** | Distribute requests across multiple API keys or regions | +| **Fallbacks** | Automatically retry with different models if one fails | + +## Complete Example + +See our [cookbook example](https://github.com/BerriAI/litellm/tree/main/cookbook/anthropic_agent_sdk) for a complete interactive CLI agent that: +- Streams responses in real-time +- Switches between models dynamically +- Fetches available models from the proxy + +```bash +# Clone and run the example +git clone https://github.com/BerriAI/litellm.git +cd litellm/cookbook/anthropic_agent_sdk +pip install -r requirements.txt +python main.py +``` + +## Related Resources + +- [Claude Agent SDK Documentation](https://github.com/anthropics/anthropic-agent-sdk) +- [LiteLLM Proxy Quick Start](../proxy/quick_start) +- [Complete Cookbook Example](https://github.com/BerriAI/litellm/tree/main/cookbook/anthropic_agent_sdk) diff --git a/docs/my-website/img/a2a_agent_spend.png b/docs/my-website/img/a2a_agent_spend.png new file mode 100644 index 0000000000..15ec769392 Binary files /dev/null and b/docs/my-website/img/a2a_agent_spend.png differ diff --git a/docs/my-website/img/a2a_trace_grouping.png b/docs/my-website/img/a2a_trace_grouping.png new file mode 100644 index 0000000000..05130420aa Binary files /dev/null and b/docs/my-website/img/a2a_trace_grouping.png differ diff --git a/docs/my-website/release_notes/v1.81.3-stable/index.md b/docs/my-website/release_notes/v1.81.3-stable/index.md new file mode 100644 index 0000000000..22b6f43dee --- /dev/null +++ b/docs/my-website/release_notes/v1.81.3-stable/index.md @@ -0,0 +1,423 @@ +--- +title: "v1.81.3-stable - Performance - 25% CPU Usage Reduction" +slug: "v1-81-3" +date: 2026-01-26T10:00:00 +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 \ +docker.litellm.ai/berriai/litellm:v1.81.3.rc.2 +``` + + + + + +``` showLineNumbers title="pip install litellm" +pip install litellm==1.81.3.rc.2 +``` + + + + +--- + +## New Models / Updated Models + +### New Model Support + +| Provider | Model | Context Window | Input ($/1M tokens) | Output ($/1M tokens) | Deprecation Date | +| -------- | ----- | -------------- | ------------------- | -------------------- | ---------------- | +| OpenAI | `gpt-audio`, `gpt-audio-2025-08-28` | 128K | $32/1M audio tokens, $2.5/1M text tokens | $64/1M audio tokens, $10/1M text tokens | - | +| OpenAI | `gpt-audio-mini`, `gpt-audio-mini-2025-08-28` | 128K | $10/1M audio tokens, $0.6/1M text tokens | $20/1M audio tokens, $2.4/1M text tokens | - | +| Deepinfra, Vertex AI, Google AI Studio, OpenRouter, Vercel AI Gateway | `gemini-2.0-flash-001`, `gemini-2.0-flash` | - | - | - | 2026-03-31 | +| Groq | `openai/gpt-oss-120b` | 131K | 0.075/1M cache read | 0.6/1M output tokens | - | +| Groq | `groq/openai/gpt-oss-20b` | 131K | 0.0375/1M cache read, $0.075/1M text tokens | 0.3/1M output tokens | - | +| Vertex AI | `gemini-2.5-computer-use-preview-10-2025` | 128K | $1.25 | $10 | - | +| Azure AI | `claude-haiku-4-5` | $1.25/1M cache read, $2/1M cache read above 1 hr, $0.1/1M text tokens | $5/1M output tokens | - | +| Azure AI | `claude-sonnet-4-5` | $3.75/1M cache read, $6/1M cache read above 1 hr, $3/1M text tokens | $15/1M output tokens | - | +| Azure AI | `claude-opus-4-5` | $6.25/1M cache read, $10/1M cache read above 1 hr, $0.5/1M text tokens | $25/1M output tokens | - | +| Azure AI | `claude-opus-4-1` | $18.75/1M cache read, $30/1M cache read above 1 hr, $1.5/1M text tokens | $75/1M output tokens | - | + +### Features + +- **[OpenAI](../../docs/providers/openai)** + - Add gpt-audio and gpt-audio-mini models to pricing - [PR #19509](https://github.com/BerriAI/litellm/pull/19509) + - correct audio token costs for gpt-4o-audio-preview models - [PR #19500](https://github.com/BerriAI/litellm/pull/19500) + - Limit stop sequence as per openai spec (ensures JetBrains IDE compatibility) - [PR #19562](https://github.com/BerriAI/litellm/pull/19562) + +- **[VertexAI](../../docs/providers/vertex)** + - Docs - Google Workload Identity Federation (WIF) support - [PR #19320](https://github.com/BerriAI/litellm/pull/19320) + +- **[Agentcore](../../docs/providers/bedrock_agentcore)** + - Fixes streaming issues with AWS Bedrock AgentCore where responses would stop after the first chunk, particularly affecting OAuth-enabled agents - [PR #17141](https://github.com/BerriAI/litellm/pull/17141) + +- **[Chatgpt](../../docs/providers/chatgpt)** + - Adds support for calling chatgpt subscription via LiteLLM - [PR #19030](https://github.com/BerriAI/litellm/pull/19030) + - Adds responses API bridge support for chatgpt subscription provider - [PR #19030](https://github.com/BerriAI/litellm/pull/19030) + +- **[Bedrock](../../docs/providers/bedrock)** + - support for output format for bedrock invoke via v1/messages - [PR #19560](https://github.com/BerriAI/litellm/pull/19560) + +- **[Azure](../../docs/providers/azure/azure)** + - Add support for Azure OpenAI v1 API - [PR #19313](https://github.com/BerriAI/litellm/pull/19313) + - preserve content_policy_violation details for images (#19328) - [PR #19372](https://github.com/BerriAI/litellm/pull/19372) + - Support OpenAI-format nested tool definitions for Responses API - [PR #19526](https://github.com/BerriAI/litellm/pull/19526) + +- **Gemini([Vertex AI](../../docs/providers/vertex), [Google AI Studio](../../docs/providers/gemini))** + - use responseJsonSchema for Gemini 2.0+ models - [PR #19314](https://github.com/BerriAI/litellm/pull/19314) + +- **[Volcengine](../../docs/providers/volcano)** + - Support Volcengine responses api - [PR #18508](https://github.com/BerriAI/litellm/pull/18508) + +- **[Anthropic](../../docs/providers/anthropic)** + - Add Support for calling Claude Code Max subscriptions via LiteLLM - [PR #19453](https://github.com/BerriAI/litellm/pull/19453) + - Add Structured output for /v1/messages with Anthropic API, Azure Anthropic API, Bedrock Converse - [PR #19545](https://github.com/BerriAI/litellm/pull/19545) + +- **[Brave Search](../../docs/search/brave)** + - New Search provider - [PR #19433](https://github.com/BerriAI/litellm/pull/19433) + +- **Sarvam ai** + - Add support for new sarvam models - [PR #19479](https://github.com/BerriAI/litellm/pull/19479) + +- **[GMI](../../docs/providers/gmi)** + - add GMI Cloud provider support - [PR #19376](https://github.com/BerriAI/litellm/pull/19376) + + +### Bug Fixes + +- **[Anthropic](../../docs/providers/anthropic)** + - Fix anthropic-beta sent client side being overridden instead of appended to - [PR #19343](https://github.com/BerriAI/litellm/pull/19343) + - Filter out unsupported fields from JSON schema for Anthropic's output_format API - [PR #19482](https://github.com/BerriAI/litellm/pull/19482) + +- **[Bedrock](../../docs/providers/bedrock)** + - Expose stability models via /image_edits endpoint and ensure proper request transformation - [PR #19323](https://github.com/BerriAI/litellm/pull/19323) + - Claude Code x Bedrock Invoke fails with advanced-tool-use-2025-11-20 - [PR #19373](https://github.com/BerriAI/litellm/pull/19373) + - deduplicate tool calls in assistant history - [PR #19324](https://github.com/BerriAI/litellm/pull/19324) + - fix: correct us.anthropic.claude-opus-4-5 In-region pricing - [PR #19310](https://github.com/BerriAI/litellm/pull/19310) + - Fix request validation errors when using Claude 4 via bedrock invoke - [PR #19381](https://github.com/BerriAI/litellm/pull/19381) + - Handle thinking with tool calls for Claude 4 models - [PR #19506](https://github.com/BerriAI/litellm/pull/19506) + - correct streaming choice index for tool calls - [PR #19506](https://github.com/BerriAI/litellm/pull/19506) + +- **[Ollama](../../docs/providers/ollama)** + - Fix tool call errors due with improved message extraction - [PR #19369](https://github.com/BerriAI/litellm/pull/19369) + +- **[VertexAI](../../docs/providers/vertex)** + - Removed optionalĀ vertex_count_tokens_locationĀ param before request is sent to vertex - [PR #19359](https://github.com/BerriAI/litellm/pull/19359) + +- **Gemini([Vertex AI](../../docs/providers/vertex), [Google AI Studio](../../docs/providers/gemini))** + - Supports setting media_resolution and fps parameters on each video file, when using Gemini video understanding - [PR #19273](https://github.com/BerriAI/litellm/pull/19273) + - handle reasoning_effort as dict from OpenAI Agents SDK - [PR #19419](https://github.com/BerriAI/litellm/pull/19419) + - add file content support in tool results - [PR #19416](https://github.com/BerriAI/litellm/pull/19416) + +- **[Azure](../../docs/providers/azure_ai)** + - Fix Azure AI costs for Anthropic models - [PR #19530](https://github.com/BerriAI/litellm/pull/19530) + +- **[Giga Chat](../../docs/providers/gigachat)** + - Add tool choice mapping - [PR #19645](https://github.com/BerriAI/litellm/pull/19645) +--- + +## AI API Endpoints (LLMs, MCP, Agents) + +### Features + +- **[Files API](../../docs/files_endpoints)** + - Add managed files support when load_balancing is True - [PR #19338](https://github.com/BerriAI/litellm/pull/19338) + +- **[Claude Plugin Marketplace](../../docs/tutorials/claude_code_plugin_marketplace)** + - Add self hosted Claude Code Plugin Marketplace - [PR #19378](https://github.com/BerriAI/litellm/pull/19378) + +- **[MCP](../../docs/mcp)** + - Add MCP Protocol versionĀ 2025-11-25Ā support - [PR #19379](https://github.com/BerriAI/litellm/pull/19379) + - Log MCP tool calls and list tools in the LiteLLM Spend Logs table for easier debugging - [PR #19469](https://github.com/BerriAI/litellm/pull/19469) + +- **[Vertex AI](../../docs/providers/vertex)** + - Ensure only anthropic betas are forwarded down to LLM API (by default) - [PR #19542](https://github.com/BerriAI/litellm/pull/19542) + - Allow overriding to support forwarding incoming headers are forwarded down to target - [PR #19524](https://github.com/BerriAI/litellm/pull/19524) + +- **[Chat/Completions](../../docs/completion/input)** + - Add MCP tools response to chat completions - [PR #19552](https://github.com/BerriAI/litellm/pull/19552) + - Add custom vertex ai finish reasons to the output - [PR #19558](https://github.com/BerriAI/litellm/pull/19558) + - Return MCP execution in /chat/completions before model output during streaming - [PR #19623](https://github.com/BerriAI/litellm/pull/19623) + +### Bugs + +- **[Responses API](../../docs/response_api)** + - Fix duplicate messages during MCP streaming tool execution - [PR #19317](https://github.com/BerriAI/litellm/pull/19317) + - Fix pickle error when using OpenAI'sĀ Responses APIĀ withĀ stream=TrueĀ andĀ tool_choiceĀ of typeĀ allowed_toolsĀ (anĀ OpenAI-native parameter) - [PR #17205](https://github.com/BerriAI/litellm/pull/17205) + - stream tool call events for non-openai models - [PR #19368](https://github.com/BerriAI/litellm/pull/19368) + - preserve tool output ordering for gemini in responses bridgeĀ - [PR #19360](https://github.com/BerriAI/litellm/pull/19360) + - Add ID caching to prevent ID mismatch text-start and text-delta - [PR #19390](https://github.com/BerriAI/litellm/pull/19390) + - Include output_item, reasoning_summary_Text_done and reasoning_summary_part_done events for non-openai models - [PR #19472](https://github.com/BerriAI/litellm/pull/19472) + +- **[Chat/Completions](../../docs/completion/input)** + - fix: drop_params not dropping prompt_cache_key for non-OpenAI providers - [PR #19346](https://github.com/BerriAI/litellm/pull/19346) + +- **[Realtime API](../../docs/realtime)** + - disable SSL for ws:// WebSocket connections - [PR #19345](https://github.com/BerriAI/litellm/pull/19345) + +- **[Generate Content](../../docs/generateContent)** + - Log actual user input when google genai/vertex endpoints are called client-side - [PR #19156](https://github.com/BerriAI/litellm/pull/19156) + +- **[/messages/count_tokens Anthropic Token Counting](../../docs/anthropic_count_tokens)** + - ensure it works for Anthropic, Azure AI Anthropic on AI Gateway - [PR #19432](https://github.com/BerriAI/litellm/pull/19432) + +- **[MCP](../../docs/mcp)** + - forward static_headers to MCP servers - [PR #19366](https://github.com/BerriAI/litellm/pull/19366) + +- **[Batch API](../../docs/batches)** + - Fix: generation config empty for batch - [PR #19556](https://github.com/BerriAI/litellm/pull/19556) + +- **[Pass Through Endpoints](../../docs/proxy/pass_through)** + - Always reupdate registry - [PR #19420](https://github.com/BerriAI/litellm/pull/19420) +--- + +## Management Endpoints / UI + +### Features + +- **Cost Estimator** + - Fix model dropdown - [PR #19529](https://github.com/BerriAI/litellm/pull/19529) + +- **Claude Code Plugins** + - Allow Adding Claude Code Plugins via UI - [PR #19387](https://github.com/BerriAI/litellm/pull/19387) + +- **Guardrails** + - New Policy management UI - [PR #19668](https://github.com/BerriAI/litellm/pull/19668) + - Allow adding policies on Keys/Teams + Viewing on Info panels - [PR #19688](https://github.com/BerriAI/litellm/pull/19688) + +- **General** + - respects custom authentication header override - [PR #19276](https://github.com/BerriAI/litellm/pull/19276) + +- **Playground** + - Button to Fill Custom API Base - [PR #19440](https://github.com/BerriAI/litellm/pull/19440) + - display mcp output on the play ground - [PR #19553](https://github.com/BerriAI/litellm/pull/19553) + +- **Models** + - Paginate /v2/models/info - [PR #19521](https://github.com/BerriAI/litellm/pull/19521) + - All Model Tab Pagination - [PR #19525](https://github.com/BerriAI/litellm/pull/19525) + - Adding Optional scope Param to /models - [PR #19539](https://github.com/BerriAI/litellm/pull/19539) + - Model Search - [PR #19622](https://github.com/BerriAI/litellm/pull/19622) + - Filter by Model ID and Team ID - [PR #19713](https://github.com/BerriAI/litellm/pull/19713) + +- **MCP Servers** + - MCP Tools Tab Resetting to Overview - [PR #19468](https://github.com/BerriAI/litellm/pull/19468) + +- **Organizations** + - Prevent org admin from creating a new user with proxy_admin permissions - [PR #19296](https://github.com/BerriAI/litellm/pull/19296) + - Edit Page: Reusable Model Select - [PR #19601](https://github.com/BerriAI/litellm/pull/19601) + +- **Teams** + - Reusable Model Select - [PR #19543](https://github.com/BerriAI/litellm/pull/19543) + - [Fix] Team Update with Organization having All Proxy Models - [PR #19604](https://github.com/BerriAI/litellm/pull/19604) + +- **Logs** + - Include tool arguments in spend logs table - [PR #19640](https://github.com/BerriAI/litellm/pull/19640) + +- **Fallbacks / Loadbalancing** + - New fallbacks modal - [PR #19673](https://github.com/BerriAI/litellm/pull/19673) + - Set fallbacks/loadbalancing by team/key - [PR #19686](https://github.com/BerriAI/litellm/pull/19686) + +### Bugs + +- **Playground** + - increase model selector width in playground Compare view - [PR #19423](https://github.com/BerriAI/litellm/pull/19423) + +- **Virtual Keys** + - Sorting Shows Incorrect Entries - [PR #19534](https://github.com/BerriAI/litellm/pull/19534) + +- **General** + - UI 404 error when SERVER_ROOT_PATH is set - [PR #19467](https://github.com/BerriAI/litellm/pull/19467) + - Redirect to ui/login on expired JWT - [PR #19687](https://github.com/BerriAI/litellm/pull/19687) + +- **SSO** + - Fix SSO user roles not updating for existing users - [PR #19621](https://github.com/BerriAI/litellm/pull/19621) + +- **Guardrails** + - ensure guardrail patterns persist on edit and mode toggle - [PR #19265](https://github.com/BerriAI/litellm/pull/19265) +--- + +## AI Integrations + +### Logging + +- **General Logging** + - prevent printing duplicate StandardLoggingPayload logs - [PR #19325](https://github.com/BerriAI/litellm/pull/19325) + - Fix: log duplication when json_logs is enabled - [PR #19705](https://github.com/BerriAI/litellm/pull/19705) +- **Langfuse OTEL** + - ignore service logs and fix callback shadowing - [PR #19298](https://github.com/BerriAI/litellm/pull/19298) +- **Langfuse** + - Send litellm_trace_id - [PR #19528](https://github.com/BerriAI/litellm/pull/19528) + - Add Langfuse mock mode for testing without API calls - [PR #19676](https://github.com/BerriAI/litellm/pull/19676) +- **GCS Bucket** + - prevent unbounded queue growth due to slow API calls - [PR #19297](https://github.com/BerriAI/litellm/pull/19297) + - Add GCS mock mode for testing without API calls - [PR #19683](https://github.com/BerriAI/litellm/pull/19683) +- **Responses API Logging** + - Fix pydantic serialization error - [PR #19486](https://github.com/BerriAI/litellm/pull/19486) +- **Arize Phoenix** + - add openinference span kinds to arize phoenix - [PR #19267](https://github.com/BerriAI/litellm/pull/19267) +- **Prometheus** + - Added new prometheus metrics for user count and team count - [PR #19520](https://github.com/BerriAI/litellm/pull/19520) + +### Guardrails + +- **Bedrock Guardrails** + - Ensure post_call guardrail checks input+output - [PR #19151](https://github.com/BerriAI/litellm/pull/19151) +- **Prompt Security** + - fixing prompt-security's guardrail implementation - [PR #19374](https://github.com/BerriAI/litellm/pull/19374) +- **Presidio** + - Fixes crash in Presidio Guardrail when running in background threads (logging_hook) - [PR #19714](https://github.com/BerriAI/litellm/pull/19714) +- **Pillar Security** + - Migrate Pillar Security to Generic Guardrail API - [PR #19364](https://github.com/BerriAI/litellm/pull/19364) +- **Policy Engine** + - New LiteLLM Policy engine - create policies to manage guardrails, conditions - permissions per Key, Team - [PR #19612](https://github.com/BerriAI/litellm/pull/19612) +- **General** + - add case-insensitive support for guardrail mode and actions - [PR #19480](https://github.com/BerriAI/litellm/pull/19480) + +### Prompt Management + +- **General** + - fix prompt info lookup and delete using correct IDs - [PR #19358](https://github.com/BerriAI/litellm/pull/19358) + +### Secret Manager + +- **AWS Secret Manager** + - ensure auto-rotation updates existing AWS secret instead of creating new one - [PR #19455](https://github.com/BerriAI/litellm/pull/19455) +- **Hashicorp Vault** + - Ensure key rotations work with Vault - [PR #19634](https://github.com/BerriAI/litellm/pull/19634) + +--- + +## Spend Tracking, Budgets and Rate Limiting + +- **Pricing Updates** + - Add openai/dall-e base pricing entries - [PR #19133](https://github.com/BerriAI/litellm/pull/19133) + - Add `input_cost_per_video_per_second` in ModelInfoBase - [PR #19398](https://github.com/BerriAI/litellm/pull/19398) + +--- + +## Performance / Loadbalancing / Reliability improvements + + +- **General** + - Fix date overflow/division by zero in proxy utils - [PR #19527](https://github.com/BerriAI/litellm/pull/19527) + - Fix in-flight request termination on SIGTERM when health-check runs in a separate process - [PR #19427](https://github.com/BerriAI/litellm/pull/19427) + - Fix Pass through routes to work with server root path - [PR #19383](https://github.com/BerriAI/litellm/pull/19383) + - Fix logging error for stop iteration - [PR #19649](https://github.com/BerriAI/litellm/pull/19649) + - prevent retrying 4xx client errors - [PR #19275](https://github.com/BerriAI/litellm/pull/19275) + - add better error handling for misconfig on health check - [PR #19441](https://github.com/BerriAI/litellm/pull/19441) + +- **Router** + - Fix Azure RPM calculation formula - [PR #19513](https://github.com/BerriAI/litellm/pull/19513) + - Persist scheduler request queue to redis - [PR #19304](https://github.com/BerriAI/litellm/pull/19304) + - pass search_tools to Router during DB-triggered initialization - [PR #19388](https://github.com/BerriAI/litellm/pull/19388) + - Fixed PromptCachingCache to correctly handle messages where cache_control is a sibling key of string content - [PR #19266](https://github.com/BerriAI/litellm/pull/19266) + +- **Memory Leaks/OOM** + - prevent OOM with nested $defs in tool schemas - [PR #19112](https://github.com/BerriAI/litellm/pull/19112) + - fix: HTTP client memory leaks in Presidio, OpenAI, and Gemini - [PR #19190](https://github.com/BerriAI/litellm/pull/19190) + +- **Non root** + - fix logfile and pidfile of supervisor for non root environment - [PR #17267](https://github.com/BerriAI/litellm/pull/17267) + - resolve Read-only file system error in non-root images - [PR #19449](https://github.com/BerriAI/litellm/pull/19449) + +- **Dockerfile** + - Redis Semantic Caching - add missing redisvl dependency to requirements.txt - [PR #19417](https://github.com/BerriAI/litellm/pull/19417) + - Bump OTEL versions to support a2a dependency - resolves modulenotfounderror for Microsoft Agents byĀ @Harshit28jĀ inĀ #18991 + +- **DB** + - Handle PostgreSQL cached plan errors during rolling deployments - [PR #19424](https://github.com/BerriAI/litellm/pull/19424) + +- **Timeouts** + - Fix: total timeout is not respected - [PR #19389](https://github.com/BerriAI/litellm/pull/19389) + +- **SDK** + - Field-Existence Checks to Type Classes to Prevent Attribute Errors - [PR #18321](https://github.com/BerriAI/litellm/pull/18321) + - add google-cloud-aiplatform as optional dependency with clear error message - [PR #19437](https://github.com/BerriAI/litellm/pull/19437) + - MakeĀ grpcĀ dependency optional - [PR #19447](https://github.com/BerriAI/litellm/pull/19447) + - Add support for retry policies - [PR #19645](https://github.com/BerriAI/litellm/pull/19645) + +- **Performance** + - Cut chat_completion latency by ~21% by reducing pre-call processing time - [PR #19535](https://github.com/BerriAI/litellm/pull/19535) + - Optimize strip_trailing_slash with O(1) index check - [PR #19679](https://github.com/BerriAI/litellm/pull/19679) + - Optimize use_custom_pricing_for_model with set intersection - [PR #19677](https://github.com/BerriAI/litellm/pull/19677) + - perf: skip pattern_router.route() for non-wildcard models - [PR #19664](https://github.com/BerriAI/litellm/pull/19664) + - perf: Add LRU caching to get_model_info for faster cost lookups - [PR #19606](https://github.com/BerriAI/litellm/pull/19606) + +--- + +## General Proxy Improvements + +### Doc Improvements + - new tutorial for adding MCPs to Cursor via LiteLLM - [PR #19317](https://github.com/BerriAI/litellm/pull/19317) + - fix vertex_region to vertex_location in Vertex AI pass-through docs - [PR #19380](https://github.com/BerriAI/litellm/pull/19380) + - clarify Gemini and Vertex AI model prefix in json file - [PR #19443](https://github.com/BerriAI/litellm/pull/19443) + - update Claude Code integration guides - [PR #19415](https://github.com/BerriAI/litellm/pull/19415) + - adjust opencode tutorial - [PR #19605](https://github.com/BerriAI/litellm/pull/19605) + - add spend-queue-troubleshooting docs - [PR #19659](https://github.com/BerriAI/litellm/pull/19659) + - docs: add litellm-enterprise requirement for managed files - [PR #19689](https://github.com/BerriAI/litellm/pull/19689) + +### Helm + - Add support for keda in helm chart - [PR #19337](https://github.com/BerriAI/litellm/pull/19337) + - sync Helm chart version with LiteLLM release version - [PR #19438](https://github.com/BerriAI/litellm/pull/19438) + - Enable PreStop hook configuration in values.yaml - [PR #19613](https://github.com/BerriAI/litellm/pull/19613) + +### General + - Add health check scripts and parallel execution support - [PR #19295](https://github.com/BerriAI/litellm/pull/19295) + + +--- + +## New Contributors + + +* @dushyantzz made their first contribution in [PR #19158](https://github.com/BerriAI/litellm/pull/19158) +* @obod-mpw made their first contribution in [PR #19133](https://github.com/BerriAI/litellm/pull/19133) +* @msexxeta made their first contribution in [PR #19030](https://github.com/BerriAI/litellm/pull/19030) +* @rsicart made their first contribution in [PR #19337](https://github.com/BerriAI/litellm/pull/19337) +* @cluebbehusen made their first contribution in [PR #19311](https://github.com/BerriAI/litellm/pull/19311) +* @Lucky-Lodhi2004 made their first contribution in [PR #19315](https://github.com/BerriAI/litellm/pull/19315) +* @binbandit made their first contribution in [PR #19324](https://github.com/BerriAI/litellm/pull/19324) +* @flex-myeonghyeon made their first contribution in [PR #19381](https://github.com/BerriAI/litellm/pull/19381) +* @Lrakotoson made their first contribution in [PR #18321](https://github.com/BerriAI/litellm/pull/18321) +* @bensi94 made their first contribution in [PR #18787](https://github.com/BerriAI/litellm/pull/18787) +* @victorigualada made their first contribution in [PR #19368](https://github.com/BerriAI/litellm/pull/19368) +* @VedantMadane made their first contribution in #19266 +* @stiyyagura0901 made their first contribution in #19276 +* @kamilio made their first contribution in [PR #19447](https://github.com/BerriAI/litellm/pull/19447) +* @jonathansampson made their first contribution in [PR #19433](https://github.com/BerriAI/litellm/pull/19433) +* @rynecarbone made their first contribution in [PR #19416](https://github.com/BerriAI/litellm/pull/19416) +* @jayy-77 made their first contribution in #19366 +* @davida-ps made their first contribution in [PR #19374](https://github.com/BerriAI/litellm/pull/19374) +* @joaodinissf made their first contribution in [PR #19506](https://github.com/BerriAI/litellm/pull/19506) +* @ecao310 made their first contribution in [PR #19520](https://github.com/BerriAI/litellm/pull/19520) +* @mpcusack-altos made their first contribution in [PR #19577](https://github.com/BerriAI/litellm/pull/19577) +* @milan-berri made their first contribution in [PR #19602](https://github.com/BerriAI/litellm/pull/19602) +* @xqe2011 made their first contribution in #19621 + +--- + +## Full Changelog + +**[View complete changelog on GitHub](https://github.com/BerriAI/litellm/releases/tag/v1.81.3.rc)** diff --git a/docs/my-website/sidebars.js b/docs/my-website/sidebars.js index 85573599e4..e45da8583e 100644 --- a/docs/my-website/sidebars.js +++ b/docs/my-website/sidebars.js @@ -139,6 +139,20 @@ const sidebars = { "tutorials/openai_codex" ] }, + { + type: "category", + label: "Agent SDKs", + link: { + type: "generated-index", + title: "Agent SDKs", + description: "Use LiteLLM with agent frameworks and SDKs", + slug: "/agent_sdks" + }, + items: [ + "tutorials/claude_agent_sdk", + "tutorials/google_adk", + ] + }, ], // But you can create a sidebar manually @@ -274,11 +288,19 @@ const sidebars = { "proxy/custom_sso", "proxy/ai_hub", "proxy/model_compare_ui", - "proxy/public_teams", - "proxy/self_serve", - "proxy/ui/bulk_edit_users", "proxy/ui_credentials", "tutorials/scim_litellm", + { + type: "category", + label: "UI User/Team Management", + items: [ + "proxy/access_control", + "proxy/public_teams", + "proxy/self_serve", + "proxy/ui/bulk_edit_users", + "proxy/ui/page_visibility", + ] + }, { type: "category", label: "UI Usage Tracking", @@ -364,6 +386,7 @@ const sidebars = { label: "Load Balancing, Routing, Fallbacks", href: "https://docs.litellm.ai/docs/routing-load-balancing", }, + "traffic_mirroring", { type: "category", label: "Logging, Alerting, Metrics", @@ -775,6 +798,7 @@ const sidebars = { "providers/oci", "providers/ollama", "providers/openrouter", + "providers/sarvam", "providers/ovhcloud", "providers/perplexity", "providers/petals", @@ -921,7 +945,6 @@ const sidebars = { type: "category", label: "LiteLLM Python SDK Tutorials", items: [ - 'tutorials/google_adk', 'tutorials/azure_openai', 'tutorials/instructor', "tutorials/gradio_integration", diff --git a/enterprise/litellm_enterprise/proxy/auth/route_checks.py b/enterprise/litellm_enterprise/proxy/auth/route_checks.py index 6cce781faf..6f7cf9143f 100644 --- a/enterprise/litellm_enterprise/proxy/auth/route_checks.py +++ b/enterprise/litellm_enterprise/proxy/auth/route_checks.py @@ -36,7 +36,7 @@ class EnterpriseRouteChecks: if not premium_user: raise HTTPException( status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, - detail=f"šŸšØšŸšØšŸšØ DISABLING LLM API ENDPOINTS is an Enterprise feature\nšŸšØ {CommonProxyErrors.not_premium_user.value}", + detail=f"šŸšØšŸšØšŸšØ DISABLING ADMIN ENDPOINTS is an Enterprise feature\nšŸšØ {CommonProxyErrors.not_premium_user.value}", ) return get_secret_bool("DISABLE_ADMIN_ENDPOINTS") is True diff --git a/enterprise/litellm_enterprise/proxy/hooks/managed_files.py b/enterprise/litellm_enterprise/proxy/hooks/managed_files.py index 445d2b242b..5ee3372cca 100644 --- a/enterprise/litellm_enterprise/proxy/hooks/managed_files.py +++ b/enterprise/litellm_enterprise/proxy/hooks/managed_files.py @@ -244,6 +244,78 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): return managed_object.created_by == user_id return True # don't raise error if managed object is not found + async def list_user_batches( + self, + user_api_key_dict: UserAPIKeyAuth, + limit: Optional[int] = None, + after: Optional[str] = None, + provider: Optional[str] = None, + target_model_names: Optional[str] = None, + llm_router: Optional[Router] = None, + ) -> Dict[str, Any]: + # Provider filtering is not supported for managed batches + # This is because the encoded object ids stored in the managed objects table do not contain the provider information + # To support provider filtering, we would need to store the provider information in the encoded object ids + if provider: + raise Exception( + "Filtering by 'provider' is not supported when using managed batches." + ) + + # Model name filtering is not supported for managed batches + # This is because the encoded object ids stored in the managed objects table do not contain the model name + # A hash of the model name + litellm_params for the model name is encoded as the model id. This is not sufficient to reliably map the target model names to the model ids. + if target_model_names: + raise Exception( + "Filtering by 'target_model_names' is not supported when using managed batches." + ) + + where_clause: Dict[str, Any] = {"file_purpose": "batch"} + + # Filter by user who created the batch + if user_api_key_dict.user_id: + where_clause["created_by"] = user_api_key_dict.user_id + + if after: + where_clause["id"] = {"gt": after} + + # Fetch more than needed to allow for post-fetch filtering + fetch_limit = limit or 20 + if target_model_names: + # Fetch extra to account for filtering + fetch_limit = max(fetch_limit * 3, 100) + + batches = await self.prisma_client.db.litellm_managedobjecttable.find_many( + where=where_clause, + take=fetch_limit, + order={"created_at": "desc"}, + ) + + batch_objects: List[LiteLLMBatch] = [] + for batch in batches: + try: + # Stop once we have enough after filtering + if len(batch_objects) >= (limit or 20): + break + + batch_data = json.loads(batch.file_object) if isinstance(batch.file_object, str) else batch.file_object + batch_obj = LiteLLMBatch(**batch_data) + batch_obj.id = batch.unified_object_id + batch_objects.append(batch_obj) + + except Exception as e: + verbose_logger.warning( + f"Failed to parse batch object {batch.unified_object_id}: {e}" + ) + continue + + return { + "object": "list", + "data": batch_objects, + "first_id": batch_objects[0].id if batch_objects else None, + "last_id": batch_objects[-1].id if batch_objects else None, + "has_more": len(batch_objects) == (limit or 20), + } + async def get_user_created_file_ids( self, user_api_key_dict: UserAPIKeyAuth, model_object_ids: List[str] ) -> List[OpenAIFileObject]: @@ -297,6 +369,8 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): if ( call_type == CallTypes.afile_content.value or call_type == CallTypes.afile_delete.value + or call_type == CallTypes.afile_retrieve.value + or call_type == CallTypes.afile_content.value ): await self.check_managed_file_id_access(data, user_api_key_dict) @@ -361,12 +435,16 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): data["model_file_id_mapping"] = model_file_id_mapping elif ( call_type == CallTypes.aretrieve_batch.value + or call_type == CallTypes.acancel_batch.value or call_type == CallTypes.acancel_fine_tuning_job.value or call_type == CallTypes.aretrieve_fine_tuning_job.value ): accessor_key: Optional[str] = None retrieve_object_id: Optional[str] = None - if call_type == CallTypes.aretrieve_batch.value: + if ( + call_type == CallTypes.aretrieve_batch.value + or call_type == CallTypes.acancel_batch.value + ): accessor_key = "batch_id" elif ( call_type == CallTypes.acancel_fine_tuning_job.value @@ -382,6 +460,8 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): if retrieve_object_id else False ) + print(f"šŸ”„potential_llm_object_id: {potential_llm_object_id}") + print(f"šŸ”„retrieve_object_id: {retrieve_object_id}") if potential_llm_object_id and retrieve_object_id: ## VALIDATE USER HAS ACCESS TO THE OBJECT ## if not await self.can_user_call_unified_object_id( @@ -673,6 +753,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): bytes=file_objects[0].bytes, filename=file_objects[0].filename, status="uploaded", + expires_at=file_objects[0].expires_at, ) return response @@ -893,8 +974,10 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): delete_response = None specific_model_file_id_mapping = model_file_id_mapping.get(file_id) if specific_model_file_id_mapping: + # Remove conflicting keys from data to avoid duplicate keyword arguments + filtered_data = {k: v for k, v in data.items() if k not in ("model", "file_id")} for model_id, model_file_id in specific_model_file_id_mapping.items(): - delete_response = await llm_router.afile_delete(model=model_id, file_id=model_file_id, **data) # type: ignore + delete_response = await llm_router.afile_delete(model=model_id, file_id=model_file_id, **filtered_data) # type: ignore stored_file_object = await self.delete_unified_file_id( file_id, litellm_parent_otel_span diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20251119131227_add_prompt_versioning/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20251119131227_add_prompt_versioning/migration.sql index a9d9528bd2..43eb240142 100644 --- a/litellm-proxy-extras/litellm_proxy_extras/migrations/20251119131227_add_prompt_versioning/migration.sql +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20251119131227_add_prompt_versioning/migration.sql @@ -1,12 +1,12 @@ -- DropIndex -DROP INDEX "LiteLLM_PromptTable_prompt_id_key"; +DROP INDEX IF EXISTS "LiteLLM_PromptTable_prompt_id_key"; -- AlterTable -ALTER TABLE "LiteLLM_PromptTable" ADD COLUMN "version" INTEGER NOT NULL DEFAULT 1; +ALTER TABLE "LiteLLM_PromptTable" +ADD COLUMN "version" INTEGER NOT NULL DEFAULT 1; -- CreateIndex -CREATE INDEX "LiteLLM_PromptTable_prompt_id_idx" ON "LiteLLM_PromptTable"("prompt_id"); +CREATE INDEX "LiteLLM_PromptTable_prompt_id_idx" ON "LiteLLM_PromptTable" ("prompt_id"); -- CreateIndex -CREATE UNIQUE INDEX "LiteLLM_PromptTable_prompt_id_version_key" ON "LiteLLM_PromptTable"("prompt_id", "version"); - +CREATE UNIQUE INDEX "LiteLLM_PromptTable_prompt_id_version_key" ON "LiteLLM_PromptTable" ("prompt_id", "version"); \ No newline at end of file diff --git a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma index d7aa6e9f0d..ca60b9e1be 100644 --- a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma +++ b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma @@ -760,6 +760,11 @@ model LiteLLM_ManagedVectorStoresTable { updated_at DateTime @updatedAt litellm_credential_name String? litellm_params Json? + team_id String? + user_id String? + + @@index([team_id]) + @@index([user_id]) } // Guardrails table for storing guardrail configurations diff --git a/litellm-proxy-extras/litellm_proxy_extras/utils.py b/litellm-proxy-extras/litellm_proxy_extras/utils.py index 7ffbe95be1..f315572218 100644 --- a/litellm-proxy-extras/litellm_proxy_extras/utils.py +++ b/litellm-proxy-extras/litellm_proxy_extras/utils.py @@ -18,14 +18,15 @@ def str_to_bool(value: Optional[str]) -> bool: return value.lower() in ("true", "1", "t", "y", "yes") - def _get_prisma_env() -> dict: """Get environment variables for Prisma, handling offline mode if configured.""" prisma_env = os.environ.copy() if str_to_bool(os.getenv("PRISMA_OFFLINE_MODE")): # These env vars prevent Prisma from attempting downloads prisma_env["NPM_CONFIG_PREFER_OFFLINE"] = "true" - prisma_env["NPM_CONFIG_CACHE"] = os.getenv("NPM_CONFIG_CACHE", "/app/.cache/npm") + prisma_env["NPM_CONFIG_CACHE"] = os.getenv( + "NPM_CONFIG_CACHE", "/app/.cache/npm" + ) return prisma_env @@ -34,29 +35,28 @@ def _get_prisma_command() -> str: if str_to_bool(os.getenv("PRISMA_OFFLINE_MODE")): # Primary location where Prisma Python package installs the CLI default_cli_path = "/app/.cache/prisma-python/binaries/node_modules/.bin/prisma" - + # Check if custom path is provided (for flexibility) custom_cli_path = os.getenv("PRISMA_CLI_PATH") if custom_cli_path and os.path.exists(custom_cli_path): logger.info(f"Using custom Prisma CLI at {custom_cli_path}") return custom_cli_path - + # Check the default location if os.path.exists(default_cli_path): logger.info(f"Using cached Prisma CLI at {default_cli_path}") return default_cli_path - + # If not found, log warning and fall back logger.warning( f"Prisma CLI not found at {default_cli_path}. " "Falling back to Python wrapper (may attempt downloads)" ) - + # Fall back to the Python wrapper (will work in online mode) return "prisma" - class ProxyExtrasDBManager: @staticmethod def _get_prisma_dir() -> str: @@ -119,7 +119,7 @@ class ProxyExtrasDBManager: stdout=open(migration_file, "w"), check=True, timeout=30, - env=prisma_env + env=prisma_env, ) # 3. Mark the migration as applied since it represents current state @@ -134,7 +134,7 @@ class ProxyExtrasDBManager: ], check=True, timeout=30, - env=prisma_env + env=prisma_env, ) return True @@ -159,14 +159,20 @@ class ProxyExtrasDBManager: @staticmethod def _roll_back_migration(migration_name: str): """Mark a specific migration as rolled back""" - # Set up environment for offline mode if configured + # Set up environment for offline mode if configured prisma_env = _get_prisma_env() subprocess.run( - [_get_prisma_command(), "migrate", "resolve", "--rolled-back", migration_name], + [ + _get_prisma_command(), + "migrate", + "resolve", + "--rolled-back", + migration_name, + ], timeout=60, check=True, capture_output=True, - env=prisma_env + env=prisma_env, ) @staticmethod @@ -178,7 +184,7 @@ class ProxyExtrasDBManager: timeout=60, check=True, capture_output=True, - env=prisma_env + env=prisma_env, ) @staticmethod @@ -228,6 +234,8 @@ class ProxyExtrasDBManager: r"duplicate key value violates", r"relation .* already exists", r"constraint .* already exists", + r"does not exist", + r"Can't drop database.* because it doesn't exist", ] for pattern in idempotent_patterns: @@ -248,7 +256,7 @@ class ProxyExtrasDBManager: if not database_url: logger.error("DATABASE_URL not set") return - + diff_dir = ( Path(migrations_dir) / "migrations" @@ -283,7 +291,7 @@ class ProxyExtrasDBManager: check=True, timeout=60, stdout=f, - env=_get_prisma_env() + env=_get_prisma_env(), ) except subprocess.CalledProcessError as e: logger.warning(f"Failed to generate migration diff: {e.stderr}") @@ -313,7 +321,7 @@ class ProxyExtrasDBManager: check=True, capture_output=True, text=True, - env=_get_prisma_env() + env=_get_prisma_env(), ) logger.info(f"prisma db execute stdout: {result.stdout}") logger.info("āœ… Migration diff applied successfully") @@ -331,12 +339,18 @@ class ProxyExtrasDBManager: try: logger.info(f"Resolving migration: {migration_name}") subprocess.run( - [_get_prisma_command(), "migrate", "resolve", "--applied", migration_name], + [ + _get_prisma_command(), + "migrate", + "resolve", + "--applied", + migration_name, + ], timeout=60, check=True, capture_output=True, text=True, - env=_get_prisma_env() + env=_get_prisma_env(), ) logger.debug(f"Resolved migration: {migration_name}") except subprocess.CalledProcessError as e: @@ -375,7 +389,7 @@ class ProxyExtrasDBManager: check=True, capture_output=True, text=True, - env=_get_prisma_env() + env=_get_prisma_env(), ) logger.info(f"prisma migrate deploy stdout: {result.stdout}") @@ -397,27 +411,42 @@ class ProxyExtrasDBManager: ) if migration_match: failed_migration = migration_match.group(1) - logger.info( - f"Found failed migration: {failed_migration}, marking as rolled back" - ) - # Mark the failed migration as rolled back - subprocess.run( - [ - _get_prisma_command(), - "migrate", - "resolve", - "--rolled-back", - failed_migration, - ], - timeout=60, - check=True, - capture_output=True, - text=True, - env=_get_prisma_env() - ) - logger.info( - f"āœ… Migration {failed_migration} marked as rolled back... retrying" - ) + if ProxyExtrasDBManager._is_idempotent_error(e.stderr): + logger.info( + f"Migration {failed_migration} failed due to idempotent error (e.g., column already exists), resolving as applied" + ) + ProxyExtrasDBManager._roll_back_migration( + failed_migration + ) + ProxyExtrasDBManager._resolve_specific_migration( + failed_migration + ) + logger.info( + f"āœ… Migration {failed_migration} resolved." + ) + return True + else: + logger.info( + f"Found failed migration: {failed_migration}, marking as rolled back" + ) + # Mark the failed migration as rolled back + subprocess.run( + [ + _get_prisma_command(), + "migrate", + "resolve", + "--rolled-back", + failed_migration, + ], + timeout=60, + check=True, + capture_output=True, + text=True, + env=_get_prisma_env(), + ) + logger.info( + f"āœ… Migration {failed_migration} marked as rolled back... retrying" + ) elif ( "P3005" in e.stderr and "database schema is not empty" in e.stderr diff --git a/litellm/__init__.py b/litellm/__init__.py index e5c09702b9..a74a79635f 100644 --- a/litellm/__init__.py +++ b/litellm/__init__.py @@ -80,6 +80,8 @@ import dotenv litellm_mode = os.getenv("LITELLM_MODE", "DEV") # "PRODUCTION", "DEV" if litellm_mode == "DEV": dotenv.load_dotenv() + + #################################################### if set_verbose: _turn_on_debug() @@ -254,6 +256,7 @@ disable_streaming_logging: bool = False disable_token_counter: bool = False disable_add_transform_inline_image_block: bool = False disable_add_user_agent_to_request_tags: bool = False +disable_anthropic_gemini_context_caching_transform: bool = False extra_spend_tag_headers: Optional[List[str]] = None in_memory_llm_clients_cache: "LLMClientCache" safe_memory_mode: bool = False @@ -1467,6 +1470,7 @@ if TYPE_CHECKING: from .llms.azure.chat.gpt_5_transformation import AzureOpenAIGPT5Config as AzureOpenAIGPT5Config from .llms.azure.completion.transformation import AzureOpenAITextConfig as AzureOpenAITextConfig from .llms.hosted_vllm.chat.transformation import HostedVLLMChatConfig as HostedVLLMChatConfig + from .llms.hosted_vllm.embedding.transformation import HostedVLLMEmbeddingConfig as HostedVLLMEmbeddingConfig from .llms.github_copilot.chat.transformation import GithubCopilotConfig as GithubCopilotConfig from .llms.github_copilot.responses.transformation import GithubCopilotResponsesAPIConfig as GithubCopilotResponsesAPIConfig from .llms.github_copilot.embedding.transformation import GithubCopilotEmbeddingConfig as GithubCopilotEmbeddingConfig diff --git a/litellm/_lazy_imports_registry.py b/litellm/_lazy_imports_registry.py index a92c6f95b0..0e52e9a59e 100644 --- a/litellm/_lazy_imports_registry.py +++ b/litellm/_lazy_imports_registry.py @@ -20,25 +20,53 @@ LITELLM_LOGGING_NAMES = ( # Utils names that support lazy loading via _lazy_import_utils UTILS_NAMES = ( - "exception_type", "get_optional_params", "get_response_string", "token_counter", - "create_pretrained_tokenizer", "create_tokenizer", "supports_function_calling", - "supports_web_search", "supports_url_context", "supports_response_schema", - "supports_parallel_function_calling", "supports_vision", "supports_audio_input", - "supports_audio_output", "supports_system_messages", "supports_reasoning", - "get_litellm_params", "acreate", "get_max_tokens", "get_model_info", - "register_prompt_template", "validate_environment", "check_valid_key", - "register_model", "encode", "decode", "_calculate_retry_after", "_should_retry", - "get_supported_openai_params", "get_api_base", "get_first_chars_messages", - "ModelResponse", "ModelResponseStream", "EmbeddingResponse", "ImageResponse", - "TranscriptionResponse", "TextCompletionResponse", "get_provider_fields", - "ModelResponseListIterator", "get_valid_models", "timeout", - "get_llm_provider", "remove_index_from_tool_calls", + "exception_type", + "get_optional_params", + "get_response_string", + "token_counter", + "create_pretrained_tokenizer", + "create_tokenizer", + "supports_function_calling", + "supports_web_search", + "supports_url_context", + "supports_response_schema", + "supports_parallel_function_calling", + "supports_vision", + "supports_audio_input", + "supports_audio_output", + "supports_system_messages", + "supports_reasoning", + "get_litellm_params", + "acreate", + "get_max_tokens", + "get_model_info", + "register_prompt_template", + "validate_environment", + "check_valid_key", + "register_model", + "encode", + "decode", + "_calculate_retry_after", + "_should_retry", + "get_supported_openai_params", + "get_api_base", + "get_first_chars_messages", + "ModelResponse", + "ModelResponseStream", + "EmbeddingResponse", + "ImageResponse", + "TranscriptionResponse", + "TextCompletionResponse", + "get_provider_fields", + "ModelResponseListIterator", + "get_valid_models", + "timeout", + "get_llm_provider", + "remove_index_from_tool_calls", ) # Token counter names that support lazy loading via _lazy_import_token_counter -TOKEN_COUNTER_NAMES = ( - "get_modified_max_tokens", -) +TOKEN_COUNTER_NAMES = ("get_modified_max_tokens",) # LLM client cache names that support lazy loading via _lazy_import_llm_client_cache LLM_CLIENT_CACHE_NAMES = ( @@ -47,9 +75,7 @@ LLM_CLIENT_CACHE_NAMES = ( ) # Bedrock type names that support lazy loading via _lazy_import_bedrock_types -BEDROCK_TYPES_NAMES = ( - "COHERE_EMBEDDING_INPUT_TYPES", -) +BEDROCK_TYPES_NAMES = ("COHERE_EMBEDDING_INPUT_TYPES",) # Common types from litellm.types.utils that support lazy loading via # _lazy_import_types_utils @@ -236,6 +262,7 @@ LLM_CONFIG_NAMES = ( "AzureOpenAIGPT5Config", "AzureOpenAITextConfig", "HostedVLLMChatConfig", + "HostedVLLMEmbeddingConfig", # Alias for backwards compatibility "VolcEngineConfig", # Alias for VolcEngineChatConfig "LlamafileChatConfig", @@ -388,7 +415,10 @@ _UTILS_IMPORT_MAP = { "supports_web_search": (".utils", "supports_web_search"), "supports_url_context": (".utils", "supports_url_context"), "supports_response_schema": (".utils", "supports_response_schema"), - "supports_parallel_function_calling": (".utils", "supports_parallel_function_calling"), + "supports_parallel_function_calling": ( + ".utils", + "supports_parallel_function_calling", + ), "supports_vision": (".utils", "supports_vision"), "supports_audio_input": (".utils", "supports_audio_input"), "supports_audio_output": (".utils", "supports_audio_output"), @@ -419,8 +449,14 @@ _UTILS_IMPORT_MAP = { "ModelResponseListIterator": (".utils", "ModelResponseListIterator"), "get_valid_models": (".utils", "get_valid_models"), "timeout": (".timeout", "timeout"), - "get_llm_provider": ("litellm.litellm_core_utils.get_llm_provider_logic", "get_llm_provider"), - "remove_index_from_tool_calls": ("litellm.litellm_core_utils.core_helpers", "remove_index_from_tool_calls"), + "get_llm_provider": ( + "litellm.litellm_core_utils.get_llm_provider_logic", + "get_llm_provider", + ), + "remove_index_from_tool_calls": ( + "litellm.litellm_core_utils.core_helpers", + "remove_index_from_tool_calls", + ), } _COST_CALCULATOR_IMPORT_MAP = { @@ -442,11 +478,17 @@ _TYPES_UTILS_IMPORT_MAP = { } _TOKEN_COUNTER_IMPORT_MAP = { - "get_modified_max_tokens": ("litellm.litellm_core_utils.token_counter", "get_modified_max_tokens"), + "get_modified_max_tokens": ( + "litellm.litellm_core_utils.token_counter", + "get_modified_max_tokens", + ), } _BEDROCK_TYPES_IMPORT_MAP = { - "COHERE_EMBEDDING_INPUT_TYPES": ("litellm.types.llms.bedrock", "COHERE_EMBEDDING_INPUT_TYPES"), + "COHERE_EMBEDDING_INPUT_TYPES": ( + "litellm.types.llms.bedrock", + "COHERE_EMBEDDING_INPUT_TYPES", + ), } _CACHING_IMPORT_MAP = { @@ -458,294 +500,868 @@ _CACHING_IMPORT_MAP = { _LITELLM_LOGGING_IMPORT_MAP = { "Logging": ("litellm.litellm_core_utils.litellm_logging", "Logging"), - "modify_integration": ("litellm.litellm_core_utils.litellm_logging", "modify_integration"), + "modify_integration": ( + "litellm.litellm_core_utils.litellm_logging", + "modify_integration", + ), } _DOTPROMPT_IMPORT_MAP = { - "global_prompt_manager": ("litellm.integrations.dotprompt", "global_prompt_manager"), - "global_prompt_directory": ("litellm.integrations.dotprompt", "global_prompt_directory"), - "set_global_prompt_directory": ("litellm.integrations.dotprompt", "set_global_prompt_directory"), + "global_prompt_manager": ( + "litellm.integrations.dotprompt", + "global_prompt_manager", + ), + "global_prompt_directory": ( + "litellm.integrations.dotprompt", + "global_prompt_directory", + ), + "set_global_prompt_directory": ( + "litellm.integrations.dotprompt", + "set_global_prompt_directory", + ), } _TYPES_IMPORT_MAP = { "GuardrailItem": ("litellm.types.guardrails", "GuardrailItem"), - "DefaultTeamSSOParams": ("litellm.types.proxy.management_endpoints.ui_sso", "DefaultTeamSSOParams"), - "LiteLLM_UpperboundKeyGenerateParams": ("litellm.types.proxy.management_endpoints.ui_sso", "LiteLLM_UpperboundKeyGenerateParams"), - "KeyManagementSystem": ("litellm.types.secret_managers.main", "KeyManagementSystem"), - "PriorityReservationSettings": ("litellm.types.utils", "PriorityReservationSettings"), + "DefaultTeamSSOParams": ( + "litellm.types.proxy.management_endpoints.ui_sso", + "DefaultTeamSSOParams", + ), + "LiteLLM_UpperboundKeyGenerateParams": ( + "litellm.types.proxy.management_endpoints.ui_sso", + "LiteLLM_UpperboundKeyGenerateParams", + ), + "KeyManagementSystem": ( + "litellm.types.secret_managers.main", + "KeyManagementSystem", + ), + "PriorityReservationSettings": ( + "litellm.types.utils", + "PriorityReservationSettings", + ), "CustomLogger": ("litellm.integrations.custom_logger", "CustomLogger"), - "LoggingCallbackManager": ("litellm.litellm_core_utils.logging_callback_manager", "LoggingCallbackManager"), - "DatadogLLMObsInitParams": ("litellm.types.integrations.datadog_llm_obs", "DatadogLLMObsInitParams"), + "LoggingCallbackManager": ( + "litellm.litellm_core_utils.logging_callback_manager", + "LoggingCallbackManager", + ), + "DatadogLLMObsInitParams": ( + "litellm.types.integrations.datadog_llm_obs", + "DatadogLLMObsInitParams", + ), } _LLM_PROVIDER_LOGIC_IMPORT_MAP = { - "get_llm_provider": ("litellm.litellm_core_utils.get_llm_provider_logic", "get_llm_provider"), - "remove_index_from_tool_calls": ("litellm.litellm_core_utils.core_helpers", "remove_index_from_tool_calls"), + "get_llm_provider": ( + "litellm.litellm_core_utils.get_llm_provider_logic", + "get_llm_provider", + ), + "remove_index_from_tool_calls": ( + "litellm.litellm_core_utils.core_helpers", + "remove_index_from_tool_calls", + ), } _LLM_CONFIGS_IMPORT_MAP = { - "AmazonConverseConfig": (".llms.bedrock.chat.converse_transformation", "AmazonConverseConfig"), + "AmazonConverseConfig": ( + ".llms.bedrock.chat.converse_transformation", + "AmazonConverseConfig", + ), "OpenAILikeChatConfig": (".llms.openai_like.chat.handler", "OpenAILikeChatConfig"), - "GaladrielChatConfig": (".llms.galadriel.chat.transformation", "GaladrielChatConfig"), + "GaladrielChatConfig": ( + ".llms.galadriel.chat.transformation", + "GaladrielChatConfig", + ), "GithubChatConfig": (".llms.github.chat.transformation", "GithubChatConfig"), - "AzureAnthropicConfig": (".llms.azure_ai.anthropic.transformation", "AzureAnthropicConfig"), + "AzureAnthropicConfig": ( + ".llms.azure_ai.anthropic.transformation", + "AzureAnthropicConfig", + ), "BytezChatConfig": (".llms.bytez.chat.transformation", "BytezChatConfig"), - "CompactifAIChatConfig": (".llms.compactifai.chat.transformation", "CompactifAIChatConfig"), + "CompactifAIChatConfig": ( + ".llms.compactifai.chat.transformation", + "CompactifAIChatConfig", + ), "EmpowerChatConfig": (".llms.empower.chat.transformation", "EmpowerChatConfig"), "MinimaxChatConfig": (".llms.minimax.chat.transformation", "MinimaxChatConfig"), - "AiohttpOpenAIChatConfig": (".llms.aiohttp_openai.chat.transformation", "AiohttpOpenAIChatConfig"), - "HuggingFaceChatConfig": (".llms.huggingface.chat.transformation", "HuggingFaceChatConfig"), - "HuggingFaceEmbeddingConfig": (".llms.huggingface.embedding.transformation", "HuggingFaceEmbeddingConfig"), + "AiohttpOpenAIChatConfig": ( + ".llms.aiohttp_openai.chat.transformation", + "AiohttpOpenAIChatConfig", + ), + "HuggingFaceChatConfig": ( + ".llms.huggingface.chat.transformation", + "HuggingFaceChatConfig", + ), + "HuggingFaceEmbeddingConfig": ( + ".llms.huggingface.embedding.transformation", + "HuggingFaceEmbeddingConfig", + ), "OobaboogaConfig": (".llms.oobabooga.chat.transformation", "OobaboogaConfig"), "MaritalkConfig": (".llms.maritalk", "MaritalkConfig"), "OpenrouterConfig": (".llms.openrouter.chat.transformation", "OpenrouterConfig"), "DataRobotConfig": (".llms.datarobot.chat.transformation", "DataRobotConfig"), "AnthropicConfig": (".llms.anthropic.chat.transformation", "AnthropicConfig"), - "AnthropicTextConfig": (".llms.anthropic.completion.transformation", "AnthropicTextConfig"), + "AnthropicTextConfig": ( + ".llms.anthropic.completion.transformation", + "AnthropicTextConfig", + ), "GroqSTTConfig": (".llms.groq.stt.transformation", "GroqSTTConfig"), "TritonConfig": (".llms.triton.completion.transformation", "TritonConfig"), - "TritonGenerateConfig": (".llms.triton.completion.transformation", "TritonGenerateConfig"), - "TritonInferConfig": (".llms.triton.completion.transformation", "TritonInferConfig"), - "TritonEmbeddingConfig": (".llms.triton.embedding.transformation", "TritonEmbeddingConfig"), - "HuggingFaceRerankConfig": (".llms.huggingface.rerank.transformation", "HuggingFaceRerankConfig"), + "TritonGenerateConfig": ( + ".llms.triton.completion.transformation", + "TritonGenerateConfig", + ), + "TritonInferConfig": ( + ".llms.triton.completion.transformation", + "TritonInferConfig", + ), + "TritonEmbeddingConfig": ( + ".llms.triton.embedding.transformation", + "TritonEmbeddingConfig", + ), + "HuggingFaceRerankConfig": ( + ".llms.huggingface.rerank.transformation", + "HuggingFaceRerankConfig", + ), "DatabricksConfig": (".llms.databricks.chat.transformation", "DatabricksConfig"), - "DatabricksEmbeddingConfig": (".llms.databricks.embed.transformation", "DatabricksEmbeddingConfig"), + "DatabricksEmbeddingConfig": ( + ".llms.databricks.embed.transformation", + "DatabricksEmbeddingConfig", + ), "PredibaseConfig": (".llms.predibase.chat.transformation", "PredibaseConfig"), "ReplicateConfig": (".llms.replicate.chat.transformation", "ReplicateConfig"), "SnowflakeConfig": (".llms.snowflake.chat.transformation", "SnowflakeConfig"), "CohereRerankConfig": (".llms.cohere.rerank.transformation", "CohereRerankConfig"), - "CohereRerankV2Config": (".llms.cohere.rerank_v2.transformation", "CohereRerankV2Config"), - "AzureAIRerankConfig": (".llms.azure_ai.rerank.transformation", "AzureAIRerankConfig"), - "InfinityRerankConfig": (".llms.infinity.rerank.transformation", "InfinityRerankConfig"), + "CohereRerankV2Config": ( + ".llms.cohere.rerank_v2.transformation", + "CohereRerankV2Config", + ), + "AzureAIRerankConfig": ( + ".llms.azure_ai.rerank.transformation", + "AzureAIRerankConfig", + ), + "InfinityRerankConfig": ( + ".llms.infinity.rerank.transformation", + "InfinityRerankConfig", + ), "JinaAIRerankConfig": (".llms.jina_ai.rerank.transformation", "JinaAIRerankConfig"), - "DeepinfraRerankConfig": (".llms.deepinfra.rerank.transformation", "DeepinfraRerankConfig"), - "HostedVLLMRerankConfig": (".llms.hosted_vllm.rerank.transformation", "HostedVLLMRerankConfig"), - "NvidiaNimRerankConfig": (".llms.nvidia_nim.rerank.transformation", "NvidiaNimRerankConfig"), - "NvidiaNimRankingConfig": (".llms.nvidia_nim.rerank.ranking_transformation", "NvidiaNimRankingConfig"), - "VertexAIRerankConfig": (".llms.vertex_ai.rerank.transformation", "VertexAIRerankConfig"), - "FireworksAIRerankConfig": (".llms.fireworks_ai.rerank.transformation", "FireworksAIRerankConfig"), + "DeepinfraRerankConfig": ( + ".llms.deepinfra.rerank.transformation", + "DeepinfraRerankConfig", + ), + "HostedVLLMRerankConfig": ( + ".llms.hosted_vllm.rerank.transformation", + "HostedVLLMRerankConfig", + ), + "NvidiaNimRerankConfig": ( + ".llms.nvidia_nim.rerank.transformation", + "NvidiaNimRerankConfig", + ), + "NvidiaNimRankingConfig": ( + ".llms.nvidia_nim.rerank.ranking_transformation", + "NvidiaNimRankingConfig", + ), + "VertexAIRerankConfig": ( + ".llms.vertex_ai.rerank.transformation", + "VertexAIRerankConfig", + ), + "FireworksAIRerankConfig": ( + ".llms.fireworks_ai.rerank.transformation", + "FireworksAIRerankConfig", + ), "VoyageRerankConfig": (".llms.voyage.rerank.transformation", "VoyageRerankConfig"), "ClarifaiConfig": (".llms.clarifai.chat.transformation", "ClarifaiConfig"), "AI21ChatConfig": (".llms.ai21.chat.transformation", "AI21ChatConfig"), "LlamaAPIConfig": (".llms.meta_llama.chat.transformation", "LlamaAPIConfig"), - "TogetherAITextCompletionConfig": (".llms.together_ai.completion.transformation", "TogetherAITextCompletionConfig"), - "CloudflareChatConfig": (".llms.cloudflare.chat.transformation", "CloudflareChatConfig"), + "TogetherAITextCompletionConfig": ( + ".llms.together_ai.completion.transformation", + "TogetherAITextCompletionConfig", + ), + "CloudflareChatConfig": ( + ".llms.cloudflare.chat.transformation", + "CloudflareChatConfig", + ), "NovitaConfig": (".llms.novita.chat.transformation", "NovitaConfig"), "PetalsConfig": (".llms.petals.completion.transformation", "PetalsConfig"), "OllamaChatConfig": (".llms.ollama.chat.transformation", "OllamaChatConfig"), "OllamaConfig": (".llms.ollama.completion.transformation", "OllamaConfig"), "SagemakerConfig": (".llms.sagemaker.completion.transformation", "SagemakerConfig"), - "SagemakerChatConfig": (".llms.sagemaker.chat.transformation", "SagemakerChatConfig"), + "SagemakerChatConfig": ( + ".llms.sagemaker.chat.transformation", + "SagemakerChatConfig", + ), "CohereChatConfig": (".llms.cohere.chat.transformation", "CohereChatConfig"), - "AnthropicMessagesConfig": (".llms.anthropic.experimental_pass_through.messages.transformation", "AnthropicMessagesConfig"), - "AmazonAnthropicClaudeMessagesConfig": (".llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation", "AmazonAnthropicClaudeMessagesConfig"), + "AnthropicMessagesConfig": ( + ".llms.anthropic.experimental_pass_through.messages.transformation", + "AnthropicMessagesConfig", + ), + "AmazonAnthropicClaudeMessagesConfig": ( + ".llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation", + "AmazonAnthropicClaudeMessagesConfig", + ), "TogetherAIConfig": (".llms.together_ai.chat", "TogetherAIConfig"), "NLPCloudConfig": (".llms.nlp_cloud.chat.handler", "NLPCloudConfig"), - "VertexGeminiConfig": (".llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini", "VertexGeminiConfig"), - "GoogleAIStudioGeminiConfig": (".llms.gemini.chat.transformation", "GoogleAIStudioGeminiConfig"), - "VertexAIAnthropicConfig": (".llms.vertex_ai.vertex_ai_partner_models.anthropic.transformation", "VertexAIAnthropicConfig"), - "VertexAILlama3Config": (".llms.vertex_ai.vertex_ai_partner_models.llama3.transformation", "VertexAILlama3Config"), - "VertexAIAi21Config": (".llms.vertex_ai.vertex_ai_partner_models.ai21.transformation", "VertexAIAi21Config"), - "AmazonCohereChatConfig": (".llms.bedrock.chat.invoke_handler", "AmazonCohereChatConfig"), - "AmazonBedrockGlobalConfig": (".llms.bedrock.common_utils", "AmazonBedrockGlobalConfig"), - "AmazonAI21Config": (".llms.bedrock.chat.invoke_transformations.amazon_ai21_transformation", "AmazonAI21Config"), - "AmazonInvokeNovaConfig": (".llms.bedrock.chat.invoke_transformations.amazon_nova_transformation", "AmazonInvokeNovaConfig"), - "AmazonQwen2Config": (".llms.bedrock.chat.invoke_transformations.amazon_qwen2_transformation", "AmazonQwen2Config"), - "AmazonQwen3Config": (".llms.bedrock.chat.invoke_transformations.amazon_qwen3_transformation", "AmazonQwen3Config"), + "VertexGeminiConfig": ( + ".llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini", + "VertexGeminiConfig", + ), + "GoogleAIStudioGeminiConfig": ( + ".llms.gemini.chat.transformation", + "GoogleAIStudioGeminiConfig", + ), + "VertexAIAnthropicConfig": ( + ".llms.vertex_ai.vertex_ai_partner_models.anthropic.transformation", + "VertexAIAnthropicConfig", + ), + "VertexAILlama3Config": ( + ".llms.vertex_ai.vertex_ai_partner_models.llama3.transformation", + "VertexAILlama3Config", + ), + "VertexAIAi21Config": ( + ".llms.vertex_ai.vertex_ai_partner_models.ai21.transformation", + "VertexAIAi21Config", + ), + "AmazonCohereChatConfig": ( + ".llms.bedrock.chat.invoke_handler", + "AmazonCohereChatConfig", + ), + "AmazonBedrockGlobalConfig": ( + ".llms.bedrock.common_utils", + "AmazonBedrockGlobalConfig", + ), + "AmazonAI21Config": ( + ".llms.bedrock.chat.invoke_transformations.amazon_ai21_transformation", + "AmazonAI21Config", + ), + "AmazonInvokeNovaConfig": ( + ".llms.bedrock.chat.invoke_transformations.amazon_nova_transformation", + "AmazonInvokeNovaConfig", + ), + "AmazonQwen2Config": ( + ".llms.bedrock.chat.invoke_transformations.amazon_qwen2_transformation", + "AmazonQwen2Config", + ), + "AmazonQwen3Config": ( + ".llms.bedrock.chat.invoke_transformations.amazon_qwen3_transformation", + "AmazonQwen3Config", + ), # Aliases for backwards compatibility - "VertexAIConfig": (".llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini", "VertexGeminiConfig"), # Alias - "GeminiConfig": (".llms.gemini.chat.transformation", "GoogleAIStudioGeminiConfig"), # Alias - "AmazonAnthropicConfig": (".llms.bedrock.chat.invoke_transformations.anthropic_claude2_transformation", "AmazonAnthropicConfig"), - "AmazonAnthropicClaudeConfig": (".llms.bedrock.chat.invoke_transformations.anthropic_claude3_transformation", "AmazonAnthropicClaudeConfig"), - "AmazonCohereConfig": (".llms.bedrock.chat.invoke_transformations.amazon_cohere_transformation", "AmazonCohereConfig"), - "AmazonLlamaConfig": (".llms.bedrock.chat.invoke_transformations.amazon_llama_transformation", "AmazonLlamaConfig"), - "AmazonDeepSeekR1Config": (".llms.bedrock.chat.invoke_transformations.amazon_deepseek_transformation", "AmazonDeepSeekR1Config"), - "AmazonMistralConfig": (".llms.bedrock.chat.invoke_transformations.amazon_mistral_transformation", "AmazonMistralConfig"), - "AmazonMoonshotConfig": (".llms.bedrock.chat.invoke_transformations.amazon_moonshot_transformation", "AmazonMoonshotConfig"), - "AmazonTitanConfig": (".llms.bedrock.chat.invoke_transformations.amazon_titan_transformation", "AmazonTitanConfig"), - "AmazonTwelveLabsPegasusConfig": (".llms.bedrock.chat.invoke_transformations.amazon_twelvelabs_pegasus_transformation", "AmazonTwelveLabsPegasusConfig"), - "AmazonInvokeConfig": (".llms.bedrock.chat.invoke_transformations.base_invoke_transformation", "AmazonInvokeConfig"), - "AmazonBedrockOpenAIConfig": (".llms.bedrock.chat.invoke_transformations.amazon_openai_transformation", "AmazonBedrockOpenAIConfig"), - "AmazonStabilityConfig": (".llms.bedrock.image_generation.amazon_stability1_transformation", "AmazonStabilityConfig"), - "AmazonStability3Config": (".llms.bedrock.image_generation.amazon_stability3_transformation", "AmazonStability3Config"), - "AmazonNovaCanvasConfig": (".llms.bedrock.image_generation.amazon_nova_canvas_transformation", "AmazonNovaCanvasConfig"), - "AmazonTitanG1Config": (".llms.bedrock.embed.amazon_titan_g1_transformation", "AmazonTitanG1Config"), - "AmazonTitanMultimodalEmbeddingG1Config": (".llms.bedrock.embed.amazon_titan_multimodal_transformation", "AmazonTitanMultimodalEmbeddingG1Config"), + "VertexAIConfig": ( + ".llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini", + "VertexGeminiConfig", + ), # Alias + "GeminiConfig": ( + ".llms.gemini.chat.transformation", + "GoogleAIStudioGeminiConfig", + ), # Alias + "AmazonAnthropicConfig": ( + ".llms.bedrock.chat.invoke_transformations.anthropic_claude2_transformation", + "AmazonAnthropicConfig", + ), + "AmazonAnthropicClaudeConfig": ( + ".llms.bedrock.chat.invoke_transformations.anthropic_claude3_transformation", + "AmazonAnthropicClaudeConfig", + ), + "AmazonCohereConfig": ( + ".llms.bedrock.chat.invoke_transformations.amazon_cohere_transformation", + "AmazonCohereConfig", + ), + "AmazonLlamaConfig": ( + ".llms.bedrock.chat.invoke_transformations.amazon_llama_transformation", + "AmazonLlamaConfig", + ), + "AmazonDeepSeekR1Config": ( + ".llms.bedrock.chat.invoke_transformations.amazon_deepseek_transformation", + "AmazonDeepSeekR1Config", + ), + "AmazonMistralConfig": ( + ".llms.bedrock.chat.invoke_transformations.amazon_mistral_transformation", + "AmazonMistralConfig", + ), + "AmazonMoonshotConfig": ( + ".llms.bedrock.chat.invoke_transformations.amazon_moonshot_transformation", + "AmazonMoonshotConfig", + ), + "AmazonTitanConfig": ( + ".llms.bedrock.chat.invoke_transformations.amazon_titan_transformation", + "AmazonTitanConfig", + ), + "AmazonTwelveLabsPegasusConfig": ( + ".llms.bedrock.chat.invoke_transformations.amazon_twelvelabs_pegasus_transformation", + "AmazonTwelveLabsPegasusConfig", + ), + "AmazonInvokeConfig": ( + ".llms.bedrock.chat.invoke_transformations.base_invoke_transformation", + "AmazonInvokeConfig", + ), + "AmazonBedrockOpenAIConfig": ( + ".llms.bedrock.chat.invoke_transformations.amazon_openai_transformation", + "AmazonBedrockOpenAIConfig", + ), + "AmazonStabilityConfig": ( + ".llms.bedrock.image_generation.amazon_stability1_transformation", + "AmazonStabilityConfig", + ), + "AmazonStability3Config": ( + ".llms.bedrock.image_generation.amazon_stability3_transformation", + "AmazonStability3Config", + ), + "AmazonNovaCanvasConfig": ( + ".llms.bedrock.image_generation.amazon_nova_canvas_transformation", + "AmazonNovaCanvasConfig", + ), + "AmazonTitanG1Config": ( + ".llms.bedrock.embed.amazon_titan_g1_transformation", + "AmazonTitanG1Config", + ), + "AmazonTitanMultimodalEmbeddingG1Config": ( + ".llms.bedrock.embed.amazon_titan_multimodal_transformation", + "AmazonTitanMultimodalEmbeddingG1Config", + ), "CohereV2ChatConfig": (".llms.cohere.chat.v2_transformation", "CohereV2ChatConfig"), - "BedrockCohereEmbeddingConfig": (".llms.bedrock.embed.cohere_transformation", "BedrockCohereEmbeddingConfig"), - "TwelveLabsMarengoEmbeddingConfig": (".llms.bedrock.embed.twelvelabs_marengo_transformation", "TwelveLabsMarengoEmbeddingConfig"), - "AmazonNovaEmbeddingConfig": (".llms.bedrock.embed.amazon_nova_transformation", "AmazonNovaEmbeddingConfig"), + "BedrockCohereEmbeddingConfig": ( + ".llms.bedrock.embed.cohere_transformation", + "BedrockCohereEmbeddingConfig", + ), + "TwelveLabsMarengoEmbeddingConfig": ( + ".llms.bedrock.embed.twelvelabs_marengo_transformation", + "TwelveLabsMarengoEmbeddingConfig", + ), + "AmazonNovaEmbeddingConfig": ( + ".llms.bedrock.embed.amazon_nova_transformation", + "AmazonNovaEmbeddingConfig", + ), "OpenAIConfig": (".llms.openai.openai", "OpenAIConfig"), "MistralEmbeddingConfig": (".llms.openai.openai", "MistralEmbeddingConfig"), - "OpenAIImageVariationConfig": (".llms.openai.image_variations.transformation", "OpenAIImageVariationConfig"), + "OpenAIImageVariationConfig": ( + ".llms.openai.image_variations.transformation", + "OpenAIImageVariationConfig", + ), "DeepInfraConfig": (".llms.deepinfra.chat.transformation", "DeepInfraConfig"), - "DeepgramAudioTranscriptionConfig": (".llms.deepgram.audio_transcription.transformation", "DeepgramAudioTranscriptionConfig"), - "TopazImageVariationConfig": (".llms.topaz.image_variations.transformation", "TopazImageVariationConfig"), - "OpenAITextCompletionConfig": ("litellm.llms.openai.completion.transformation", "OpenAITextCompletionConfig"), + "DeepgramAudioTranscriptionConfig": ( + ".llms.deepgram.audio_transcription.transformation", + "DeepgramAudioTranscriptionConfig", + ), + "TopazImageVariationConfig": ( + ".llms.topaz.image_variations.transformation", + "TopazImageVariationConfig", + ), + "OpenAITextCompletionConfig": ( + "litellm.llms.openai.completion.transformation", + "OpenAITextCompletionConfig", + ), "GroqChatConfig": (".llms.groq.chat.transformation", "GroqChatConfig"), - "GenAIHubOrchestrationConfig": (".llms.sap.chat.transformation", "GenAIHubOrchestrationConfig"), - "VoyageEmbeddingConfig": (".llms.voyage.embedding.transformation", "VoyageEmbeddingConfig"), - "VoyageContextualEmbeddingConfig": (".llms.voyage.embedding.transformation_contextual", "VoyageContextualEmbeddingConfig"), - "InfinityEmbeddingConfig": (".llms.infinity.embedding.transformation", "InfinityEmbeddingConfig"), - "AzureAIStudioConfig": (".llms.azure_ai.chat.transformation", "AzureAIStudioConfig"), + "GenAIHubOrchestrationConfig": ( + ".llms.sap.chat.transformation", + "GenAIHubOrchestrationConfig", + ), + "VoyageEmbeddingConfig": ( + ".llms.voyage.embedding.transformation", + "VoyageEmbeddingConfig", + ), + "VoyageContextualEmbeddingConfig": ( + ".llms.voyage.embedding.transformation_contextual", + "VoyageContextualEmbeddingConfig", + ), + "InfinityEmbeddingConfig": ( + ".llms.infinity.embedding.transformation", + "InfinityEmbeddingConfig", + ), + "AzureAIStudioConfig": ( + ".llms.azure_ai.chat.transformation", + "AzureAIStudioConfig", + ), "MistralConfig": (".llms.mistral.chat.transformation", "MistralConfig"), - "OpenAIResponsesAPIConfig": (".llms.openai.responses.transformation", "OpenAIResponsesAPIConfig"), - "AzureOpenAIResponsesAPIConfig": (".llms.azure.responses.transformation", "AzureOpenAIResponsesAPIConfig"), - "AzureOpenAIOSeriesResponsesAPIConfig": (".llms.azure.responses.o_series_transformation", "AzureOpenAIOSeriesResponsesAPIConfig"), - "XAIResponsesAPIConfig": (".llms.xai.responses.transformation", "XAIResponsesAPIConfig"), - "LiteLLMProxyResponsesAPIConfig": (".llms.litellm_proxy.responses.transformation", "LiteLLMProxyResponsesAPIConfig"), - "VolcEngineResponsesAPIConfig": (".llms.volcengine.responses.transformation", "VolcEngineResponsesAPIConfig"), - "ManusResponsesAPIConfig": (".llms.manus.responses.transformation", "ManusResponsesAPIConfig"), - "GoogleAIStudioInteractionsConfig": (".llms.gemini.interactions.transformation", "GoogleAIStudioInteractionsConfig"), - "OpenAIOSeriesConfig": (".llms.openai.chat.o_series_transformation", "OpenAIOSeriesConfig"), - "AnthropicSkillsConfig": (".llms.anthropic.skills.transformation", "AnthropicSkillsConfig"), - "BaseSkillsAPIConfig": (".llms.base_llm.skills.transformation", "BaseSkillsAPIConfig"), + "OpenAIResponsesAPIConfig": ( + ".llms.openai.responses.transformation", + "OpenAIResponsesAPIConfig", + ), + "AzureOpenAIResponsesAPIConfig": ( + ".llms.azure.responses.transformation", + "AzureOpenAIResponsesAPIConfig", + ), + "AzureOpenAIOSeriesResponsesAPIConfig": ( + ".llms.azure.responses.o_series_transformation", + "AzureOpenAIOSeriesResponsesAPIConfig", + ), + "XAIResponsesAPIConfig": ( + ".llms.xai.responses.transformation", + "XAIResponsesAPIConfig", + ), + "LiteLLMProxyResponsesAPIConfig": ( + ".llms.litellm_proxy.responses.transformation", + "LiteLLMProxyResponsesAPIConfig", + ), + "VolcEngineResponsesAPIConfig": ( + ".llms.volcengine.responses.transformation", + "VolcEngineResponsesAPIConfig", + ), + "ManusResponsesAPIConfig": ( + ".llms.manus.responses.transformation", + "ManusResponsesAPIConfig", + ), + "GoogleAIStudioInteractionsConfig": ( + ".llms.gemini.interactions.transformation", + "GoogleAIStudioInteractionsConfig", + ), + "OpenAIOSeriesConfig": ( + ".llms.openai.chat.o_series_transformation", + "OpenAIOSeriesConfig", + ), + "AnthropicSkillsConfig": ( + ".llms.anthropic.skills.transformation", + "AnthropicSkillsConfig", + ), + "BaseSkillsAPIConfig": ( + ".llms.base_llm.skills.transformation", + "BaseSkillsAPIConfig", + ), "GradientAIConfig": (".llms.gradient_ai.chat.transformation", "GradientAIConfig"), # Alias for backwards compatibility - "OpenAIO1Config": (".llms.openai.chat.o_series_transformation", "OpenAIOSeriesConfig"), # Alias + "OpenAIO1Config": ( + ".llms.openai.chat.o_series_transformation", + "OpenAIOSeriesConfig", + ), # Alias "OpenAIGPTConfig": (".llms.openai.chat.gpt_transformation", "OpenAIGPTConfig"), "OpenAIGPT5Config": (".llms.openai.chat.gpt_5_transformation", "OpenAIGPT5Config"), - "OpenAIWhisperAudioTranscriptionConfig": (".llms.openai.transcriptions.whisper_transformation", "OpenAIWhisperAudioTranscriptionConfig"), - "OpenAIGPTAudioTranscriptionConfig": (".llms.openai.transcriptions.gpt_transformation", "OpenAIGPTAudioTranscriptionConfig"), - "OpenAIGPTAudioConfig": (".llms.openai.chat.gpt_audio_transformation", "OpenAIGPTAudioConfig"), + "OpenAIWhisperAudioTranscriptionConfig": ( + ".llms.openai.transcriptions.whisper_transformation", + "OpenAIWhisperAudioTranscriptionConfig", + ), + "OpenAIGPTAudioTranscriptionConfig": ( + ".llms.openai.transcriptions.gpt_transformation", + "OpenAIGPTAudioTranscriptionConfig", + ), + "OpenAIGPTAudioConfig": ( + ".llms.openai.chat.gpt_audio_transformation", + "OpenAIGPTAudioConfig", + ), "NvidiaNimConfig": (".llms.nvidia_nim.chat.transformation", "NvidiaNimConfig"), "NvidiaNimEmbeddingConfig": (".llms.nvidia_nim.embed", "NvidiaNimEmbeddingConfig"), - "FeatherlessAIConfig": (".llms.featherless_ai.chat.transformation", "FeatherlessAIConfig"), + "FeatherlessAIConfig": ( + ".llms.featherless_ai.chat.transformation", + "FeatherlessAIConfig", + ), "CerebrasConfig": (".llms.cerebras.chat", "CerebrasConfig"), "BasetenConfig": (".llms.baseten.chat", "BasetenConfig"), "SambanovaConfig": (".llms.sambanova.chat", "SambanovaConfig"), - "SambaNovaEmbeddingConfig": (".llms.sambanova.embedding.transformation", "SambaNovaEmbeddingConfig"), - "FireworksAIConfig": (".llms.fireworks_ai.chat.transformation", "FireworksAIConfig"), - "FireworksAITextCompletionConfig": (".llms.fireworks_ai.completion.transformation", "FireworksAITextCompletionConfig"), - "FireworksAIAudioTranscriptionConfig": (".llms.fireworks_ai.audio_transcription.transformation", "FireworksAIAudioTranscriptionConfig"), - "FireworksAIEmbeddingConfig": (".llms.fireworks_ai.embed.fireworks_ai_transformation", "FireworksAIEmbeddingConfig"), - "FriendliaiChatConfig": (".llms.friendliai.chat.transformation", "FriendliaiChatConfig"), - "JinaAIEmbeddingConfig": (".llms.jina_ai.embedding.transformation", "JinaAIEmbeddingConfig"), + "SambaNovaEmbeddingConfig": ( + ".llms.sambanova.embedding.transformation", + "SambaNovaEmbeddingConfig", + ), + "FireworksAIConfig": ( + ".llms.fireworks_ai.chat.transformation", + "FireworksAIConfig", + ), + "FireworksAITextCompletionConfig": ( + ".llms.fireworks_ai.completion.transformation", + "FireworksAITextCompletionConfig", + ), + "FireworksAIAudioTranscriptionConfig": ( + ".llms.fireworks_ai.audio_transcription.transformation", + "FireworksAIAudioTranscriptionConfig", + ), + "FireworksAIEmbeddingConfig": ( + ".llms.fireworks_ai.embed.fireworks_ai_transformation", + "FireworksAIEmbeddingConfig", + ), + "FriendliaiChatConfig": ( + ".llms.friendliai.chat.transformation", + "FriendliaiChatConfig", + ), + "JinaAIEmbeddingConfig": ( + ".llms.jina_ai.embedding.transformation", + "JinaAIEmbeddingConfig", + ), "XAIChatConfig": (".llms.xai.chat.transformation", "XAIChatConfig"), "ZAIChatConfig": (".llms.zai.chat.transformation", "ZAIChatConfig"), "AIMLChatConfig": (".llms.aiml.chat.transformation", "AIMLChatConfig"), - "VolcEngineChatConfig": (".llms.volcengine.chat.transformation", "VolcEngineChatConfig"), - "CodestralTextCompletionConfig": (".llms.codestral.completion.transformation", "CodestralTextCompletionConfig"), - "AzureOpenAIAssistantsAPIConfig": (".llms.azure.azure", "AzureOpenAIAssistantsAPIConfig"), + "VolcEngineChatConfig": ( + ".llms.volcengine.chat.transformation", + "VolcEngineChatConfig", + ), + "CodestralTextCompletionConfig": ( + ".llms.codestral.completion.transformation", + "CodestralTextCompletionConfig", + ), + "AzureOpenAIAssistantsAPIConfig": ( + ".llms.azure.azure", + "AzureOpenAIAssistantsAPIConfig", + ), "HerokuChatConfig": (".llms.heroku.chat.transformation", "HerokuChatConfig"), "CometAPIConfig": (".llms.cometapi.chat.transformation", "CometAPIConfig"), "AzureOpenAIConfig": (".llms.azure.chat.gpt_transformation", "AzureOpenAIConfig"), - "AzureOpenAIGPT5Config": (".llms.azure.chat.gpt_5_transformation", "AzureOpenAIGPT5Config"), - "AzureOpenAITextConfig": (".llms.azure.completion.transformation", "AzureOpenAITextConfig"), - "HostedVLLMChatConfig": (".llms.hosted_vllm.chat.transformation", "HostedVLLMChatConfig"), + "AzureOpenAIGPT5Config": ( + ".llms.azure.chat.gpt_5_transformation", + "AzureOpenAIGPT5Config", + ), + "AzureOpenAITextConfig": ( + ".llms.azure.completion.transformation", + "AzureOpenAITextConfig", + ), + "HostedVLLMChatConfig": ( + ".llms.hosted_vllm.chat.transformation", + "HostedVLLMChatConfig", + ), + "HostedVLLMEmbeddingConfig": ( + ".llms.hosted_vllm.embedding.transformation", + "HostedVLLMEmbeddingConfig", + ), # Alias for backwards compatibility - "VolcEngineConfig": (".llms.volcengine.chat.transformation", "VolcEngineChatConfig"), # Alias - "LlamafileChatConfig": (".llms.llamafile.chat.transformation", "LlamafileChatConfig"), - "LiteLLMProxyChatConfig": (".llms.litellm_proxy.chat.transformation", "LiteLLMProxyChatConfig"), + "VolcEngineConfig": ( + ".llms.volcengine.chat.transformation", + "VolcEngineChatConfig", + ), # Alias + "LlamafileChatConfig": ( + ".llms.llamafile.chat.transformation", + "LlamafileChatConfig", + ), + "LiteLLMProxyChatConfig": ( + ".llms.litellm_proxy.chat.transformation", + "LiteLLMProxyChatConfig", + ), "VLLMConfig": (".llms.vllm.completion.transformation", "VLLMConfig"), "DeepSeekChatConfig": (".llms.deepseek.chat.transformation", "DeepSeekChatConfig"), "LMStudioChatConfig": (".llms.lm_studio.chat.transformation", "LMStudioChatConfig"), - "LmStudioEmbeddingConfig": (".llms.lm_studio.embed.transformation", "LmStudioEmbeddingConfig"), + "LmStudioEmbeddingConfig": ( + ".llms.lm_studio.embed.transformation", + "LmStudioEmbeddingConfig", + ), "NscaleConfig": (".llms.nscale.chat.transformation", "NscaleConfig"), - "PerplexityChatConfig": (".llms.perplexity.chat.transformation", "PerplexityChatConfig"), - "AzureOpenAIO1Config": (".llms.azure.chat.o_series_transformation", "AzureOpenAIO1Config"), - "IBMWatsonXAIConfig": (".llms.watsonx.completion.transformation", "IBMWatsonXAIConfig"), - "IBMWatsonXChatConfig": (".llms.watsonx.chat.transformation", "IBMWatsonXChatConfig"), - "IBMWatsonXEmbeddingConfig": (".llms.watsonx.embed.transformation", "IBMWatsonXEmbeddingConfig"), - "GenAIHubEmbeddingConfig": (".llms.sap.embed.transformation", "GenAIHubEmbeddingConfig"), - "IBMWatsonXAudioTranscriptionConfig": (".llms.watsonx.audio_transcription.transformation", "IBMWatsonXAudioTranscriptionConfig"), - "GithubCopilotConfig": (".llms.github_copilot.chat.transformation", "GithubCopilotConfig"), - "GithubCopilotResponsesAPIConfig": (".llms.github_copilot.responses.transformation", "GithubCopilotResponsesAPIConfig"), - "GithubCopilotEmbeddingConfig": (".llms.github_copilot.embedding.transformation", "GithubCopilotEmbeddingConfig"), + "PerplexityChatConfig": ( + ".llms.perplexity.chat.transformation", + "PerplexityChatConfig", + ), + "AzureOpenAIO1Config": ( + ".llms.azure.chat.o_series_transformation", + "AzureOpenAIO1Config", + ), + "IBMWatsonXAIConfig": ( + ".llms.watsonx.completion.transformation", + "IBMWatsonXAIConfig", + ), + "IBMWatsonXChatConfig": ( + ".llms.watsonx.chat.transformation", + "IBMWatsonXChatConfig", + ), + "IBMWatsonXEmbeddingConfig": ( + ".llms.watsonx.embed.transformation", + "IBMWatsonXEmbeddingConfig", + ), + "GenAIHubEmbeddingConfig": ( + ".llms.sap.embed.transformation", + "GenAIHubEmbeddingConfig", + ), + "IBMWatsonXAudioTranscriptionConfig": ( + ".llms.watsonx.audio_transcription.transformation", + "IBMWatsonXAudioTranscriptionConfig", + ), + "GithubCopilotConfig": ( + ".llms.github_copilot.chat.transformation", + "GithubCopilotConfig", + ), + "GithubCopilotResponsesAPIConfig": ( + ".llms.github_copilot.responses.transformation", + "GithubCopilotResponsesAPIConfig", + ), + "GithubCopilotEmbeddingConfig": ( + ".llms.github_copilot.embedding.transformation", + "GithubCopilotEmbeddingConfig", + ), "ChatGPTConfig": (".llms.chatgpt.chat.transformation", "ChatGPTConfig"), - "ChatGPTResponsesAPIConfig": (".llms.chatgpt.responses.transformation", "ChatGPTResponsesAPIConfig"), + "ChatGPTResponsesAPIConfig": ( + ".llms.chatgpt.responses.transformation", + "ChatGPTResponsesAPIConfig", + ), "NebiusConfig": (".llms.nebius.chat.transformation", "NebiusConfig"), "WandbConfig": (".llms.wandb.chat.transformation", "WandbConfig"), "GigaChatConfig": (".llms.gigachat.chat.transformation", "GigaChatConfig"), - "GigaChatEmbeddingConfig": (".llms.gigachat.embedding.transformation", "GigaChatEmbeddingConfig"), - "DashScopeChatConfig": (".llms.dashscope.chat.transformation", "DashScopeChatConfig"), + "GigaChatEmbeddingConfig": ( + ".llms.gigachat.embedding.transformation", + "GigaChatEmbeddingConfig", + ), + "DashScopeChatConfig": ( + ".llms.dashscope.chat.transformation", + "DashScopeChatConfig", + ), "MoonshotChatConfig": (".llms.moonshot.chat.transformation", "MoonshotChatConfig"), - "DockerModelRunnerChatConfig": (".llms.docker_model_runner.chat.transformation", "DockerModelRunnerChatConfig"), + "DockerModelRunnerChatConfig": ( + ".llms.docker_model_runner.chat.transformation", + "DockerModelRunnerChatConfig", + ), "V0ChatConfig": (".llms.v0.chat.transformation", "V0ChatConfig"), "OCIChatConfig": (".llms.oci.chat.transformation", "OCIChatConfig"), "MorphChatConfig": (".llms.morph.chat.transformation", "MorphChatConfig"), "RAGFlowConfig": (".llms.ragflow.chat.transformation", "RAGFlowConfig"), "LambdaAIChatConfig": (".llms.lambda_ai.chat.transformation", "LambdaAIChatConfig"), - "HyperbolicChatConfig": (".llms.hyperbolic.chat.transformation", "HyperbolicChatConfig"), - "VercelAIGatewayConfig": (".llms.vercel_ai_gateway.chat.transformation", "VercelAIGatewayConfig"), + "HyperbolicChatConfig": ( + ".llms.hyperbolic.chat.transformation", + "HyperbolicChatConfig", + ), + "VercelAIGatewayConfig": ( + ".llms.vercel_ai_gateway.chat.transformation", + "VercelAIGatewayConfig", + ), "OVHCloudChatConfig": (".llms.ovhcloud.chat.transformation", "OVHCloudChatConfig"), - "OVHCloudEmbeddingConfig": (".llms.ovhcloud.embedding.transformation", "OVHCloudEmbeddingConfig"), - "CometAPIEmbeddingConfig": (".llms.cometapi.embed.transformation", "CometAPIEmbeddingConfig"), + "OVHCloudEmbeddingConfig": ( + ".llms.ovhcloud.embedding.transformation", + "OVHCloudEmbeddingConfig", + ), + "CometAPIEmbeddingConfig": ( + ".llms.cometapi.embed.transformation", + "CometAPIEmbeddingConfig", + ), "LemonadeChatConfig": (".llms.lemonade.chat.transformation", "LemonadeChatConfig"), - "SnowflakeEmbeddingConfig": (".llms.snowflake.embedding.transformation", "SnowflakeEmbeddingConfig"), - "AmazonNovaChatConfig": (".llms.amazon_nova.chat.transformation", "AmazonNovaChatConfig"), + "SnowflakeEmbeddingConfig": ( + ".llms.snowflake.embedding.transformation", + "SnowflakeEmbeddingConfig", + ), + "AmazonNovaChatConfig": ( + ".llms.amazon_nova.chat.transformation", + "AmazonNovaChatConfig", + ), } # Import map for utils module lazy imports _UTILS_MODULE_IMPORT_MAP = { "encoding": ("litellm.main", "encoding"), - "BaseVectorStore": ("litellm.integrations.vector_store_integrations.base_vector_store", "BaseVectorStore"), - "CredentialAccessor": ("litellm.litellm_core_utils.credential_accessor", "CredentialAccessor"), - "exception_type": ("litellm.litellm_core_utils.exception_mapping_utils", "exception_type"), - "get_error_message": ("litellm.litellm_core_utils.exception_mapping_utils", "get_error_message"), - "_get_response_headers": ("litellm.litellm_core_utils.exception_mapping_utils", "_get_response_headers"), - "get_llm_provider": ("litellm.litellm_core_utils.get_llm_provider_logic", "get_llm_provider"), - "_is_non_openai_azure_model": ("litellm.litellm_core_utils.get_llm_provider_logic", "_is_non_openai_azure_model"), - "get_supported_openai_params": ("litellm.litellm_core_utils.get_supported_openai_params", "get_supported_openai_params"), - "LiteLLMResponseObjectHandler": ("litellm.litellm_core_utils.llm_response_utils.convert_dict_to_response", "LiteLLMResponseObjectHandler"), - "_handle_invalid_parallel_tool_calls": ("litellm.litellm_core_utils.llm_response_utils.convert_dict_to_response", "_handle_invalid_parallel_tool_calls"), - "convert_to_model_response_object": ("litellm.litellm_core_utils.llm_response_utils.convert_dict_to_response", "convert_to_model_response_object"), - "convert_to_streaming_response": ("litellm.litellm_core_utils.llm_response_utils.convert_dict_to_response", "convert_to_streaming_response"), - "convert_to_streaming_response_async": ("litellm.litellm_core_utils.llm_response_utils.convert_dict_to_response", "convert_to_streaming_response_async"), - "get_api_base": ("litellm.litellm_core_utils.llm_response_utils.get_api_base", "get_api_base"), - "ResponseMetadata": ("litellm.litellm_core_utils.llm_response_utils.response_metadata", "ResponseMetadata"), - "_parse_content_for_reasoning": ("litellm.litellm_core_utils.prompt_templates.common_utils", "_parse_content_for_reasoning"), - "LiteLLMLoggingObject": ("litellm.litellm_core_utils.redact_messages", "LiteLLMLoggingObject"), - "redact_message_input_output_from_logging": ("litellm.litellm_core_utils.redact_messages", "redact_message_input_output_from_logging"), - "CustomStreamWrapper": ("litellm.litellm_core_utils.streaming_handler", "CustomStreamWrapper"), - "BaseGoogleGenAIGenerateContentConfig": ("litellm.llms.base_llm.google_genai.transformation", "BaseGoogleGenAIGenerateContentConfig"), + "BaseVectorStore": ( + "litellm.integrations.vector_store_integrations.base_vector_store", + "BaseVectorStore", + ), + "CredentialAccessor": ( + "litellm.litellm_core_utils.credential_accessor", + "CredentialAccessor", + ), + "exception_type": ( + "litellm.litellm_core_utils.exception_mapping_utils", + "exception_type", + ), + "get_error_message": ( + "litellm.litellm_core_utils.exception_mapping_utils", + "get_error_message", + ), + "_get_response_headers": ( + "litellm.litellm_core_utils.exception_mapping_utils", + "_get_response_headers", + ), + "get_llm_provider": ( + "litellm.litellm_core_utils.get_llm_provider_logic", + "get_llm_provider", + ), + "_is_non_openai_azure_model": ( + "litellm.litellm_core_utils.get_llm_provider_logic", + "_is_non_openai_azure_model", + ), + "get_supported_openai_params": ( + "litellm.litellm_core_utils.get_supported_openai_params", + "get_supported_openai_params", + ), + "LiteLLMResponseObjectHandler": ( + "litellm.litellm_core_utils.llm_response_utils.convert_dict_to_response", + "LiteLLMResponseObjectHandler", + ), + "_handle_invalid_parallel_tool_calls": ( + "litellm.litellm_core_utils.llm_response_utils.convert_dict_to_response", + "_handle_invalid_parallel_tool_calls", + ), + "convert_to_model_response_object": ( + "litellm.litellm_core_utils.llm_response_utils.convert_dict_to_response", + "convert_to_model_response_object", + ), + "convert_to_streaming_response": ( + "litellm.litellm_core_utils.llm_response_utils.convert_dict_to_response", + "convert_to_streaming_response", + ), + "convert_to_streaming_response_async": ( + "litellm.litellm_core_utils.llm_response_utils.convert_dict_to_response", + "convert_to_streaming_response_async", + ), + "get_api_base": ( + "litellm.litellm_core_utils.llm_response_utils.get_api_base", + "get_api_base", + ), + "ResponseMetadata": ( + "litellm.litellm_core_utils.llm_response_utils.response_metadata", + "ResponseMetadata", + ), + "_parse_content_for_reasoning": ( + "litellm.litellm_core_utils.prompt_templates.common_utils", + "_parse_content_for_reasoning", + ), + "LiteLLMLoggingObject": ( + "litellm.litellm_core_utils.redact_messages", + "LiteLLMLoggingObject", + ), + "redact_message_input_output_from_logging": ( + "litellm.litellm_core_utils.redact_messages", + "redact_message_input_output_from_logging", + ), + "CustomStreamWrapper": ( + "litellm.litellm_core_utils.streaming_handler", + "CustomStreamWrapper", + ), + "BaseGoogleGenAIGenerateContentConfig": ( + "litellm.llms.base_llm.google_genai.transformation", + "BaseGoogleGenAIGenerateContentConfig", + ), "BaseOCRConfig": ("litellm.llms.base_llm.ocr.transformation", "BaseOCRConfig"), - "BaseSearchConfig": ("litellm.llms.base_llm.search.transformation", "BaseSearchConfig"), - "BaseTextToSpeechConfig": ("litellm.llms.base_llm.text_to_speech.transformation", "BaseTextToSpeechConfig"), + "BaseSearchConfig": ( + "litellm.llms.base_llm.search.transformation", + "BaseSearchConfig", + ), + "BaseTextToSpeechConfig": ( + "litellm.llms.base_llm.text_to_speech.transformation", + "BaseTextToSpeechConfig", + ), "BedrockModelInfo": ("litellm.llms.bedrock.common_utils", "BedrockModelInfo"), "CohereModelInfo": ("litellm.llms.cohere.common_utils", "CohereModelInfo"), "MistralOCRConfig": ("litellm.llms.mistral.ocr.transformation", "MistralOCRConfig"), "Rules": ("litellm.litellm_core_utils.rules", "Rules"), "AsyncHTTPHandler": ("litellm.llms.custom_httpx.http_handler", "AsyncHTTPHandler"), "HTTPHandler": ("litellm.llms.custom_httpx.http_handler", "HTTPHandler"), - "get_num_retries_from_retry_policy": ("litellm.router_utils.get_retry_from_policy", "get_num_retries_from_retry_policy"), - "reset_retry_policy": ("litellm.router_utils.get_retry_from_policy", "reset_retry_policy"), + "get_num_retries_from_retry_policy": ( + "litellm.router_utils.get_retry_from_policy", + "get_num_retries_from_retry_policy", + ), + "reset_retry_policy": ( + "litellm.router_utils.get_retry_from_policy", + "reset_retry_policy", + ), "get_secret": ("litellm.secret_managers.main", "get_secret"), - "get_coroutine_checker": ("litellm.litellm_core_utils.cached_imports", "get_coroutine_checker"), - "get_litellm_logging_class": ("litellm.litellm_core_utils.cached_imports", "get_litellm_logging_class"), - "get_set_callbacks": ("litellm.litellm_core_utils.cached_imports", "get_set_callbacks"), - "get_litellm_metadata_from_kwargs": ("litellm.litellm_core_utils.core_helpers", "get_litellm_metadata_from_kwargs"), - "map_finish_reason": ("litellm.litellm_core_utils.core_helpers", "map_finish_reason"), - "process_response_headers": ("litellm.litellm_core_utils.core_helpers", "process_response_headers"), - "delete_nested_value": ("litellm.litellm_core_utils.dot_notation_indexing", "delete_nested_value"), - "is_nested_path": ("litellm.litellm_core_utils.dot_notation_indexing", "is_nested_path"), - "_get_base_model_from_litellm_call_metadata": ("litellm.litellm_core_utils.get_litellm_params", "_get_base_model_from_litellm_call_metadata"), - "get_litellm_params": ("litellm.litellm_core_utils.get_litellm_params", "get_litellm_params"), - "_ensure_extra_body_is_safe": ("litellm.litellm_core_utils.llm_request_utils", "_ensure_extra_body_is_safe"), - "get_formatted_prompt": ("litellm.litellm_core_utils.llm_response_utils.get_formatted_prompt", "get_formatted_prompt"), - "get_response_headers": ("litellm.litellm_core_utils.llm_response_utils.get_headers", "get_response_headers"), - "update_response_metadata": ("litellm.litellm_core_utils.llm_response_utils.response_metadata", "update_response_metadata"), + "get_coroutine_checker": ( + "litellm.litellm_core_utils.cached_imports", + "get_coroutine_checker", + ), + "get_litellm_logging_class": ( + "litellm.litellm_core_utils.cached_imports", + "get_litellm_logging_class", + ), + "get_set_callbacks": ( + "litellm.litellm_core_utils.cached_imports", + "get_set_callbacks", + ), + "get_litellm_metadata_from_kwargs": ( + "litellm.litellm_core_utils.core_helpers", + "get_litellm_metadata_from_kwargs", + ), + "map_finish_reason": ( + "litellm.litellm_core_utils.core_helpers", + "map_finish_reason", + ), + "process_response_headers": ( + "litellm.litellm_core_utils.core_helpers", + "process_response_headers", + ), + "delete_nested_value": ( + "litellm.litellm_core_utils.dot_notation_indexing", + "delete_nested_value", + ), + "is_nested_path": ( + "litellm.litellm_core_utils.dot_notation_indexing", + "is_nested_path", + ), + "_get_base_model_from_litellm_call_metadata": ( + "litellm.litellm_core_utils.get_litellm_params", + "_get_base_model_from_litellm_call_metadata", + ), + "get_litellm_params": ( + "litellm.litellm_core_utils.get_litellm_params", + "get_litellm_params", + ), + "_ensure_extra_body_is_safe": ( + "litellm.litellm_core_utils.llm_request_utils", + "_ensure_extra_body_is_safe", + ), + "get_formatted_prompt": ( + "litellm.litellm_core_utils.llm_response_utils.get_formatted_prompt", + "get_formatted_prompt", + ), + "get_response_headers": ( + "litellm.litellm_core_utils.llm_response_utils.get_headers", + "get_response_headers", + ), + "update_response_metadata": ( + "litellm.litellm_core_utils.llm_response_utils.response_metadata", + "update_response_metadata", + ), "executor": ("litellm.litellm_core_utils.thread_pool_executor", "executor"), - "BaseAnthropicMessagesConfig": ("litellm.llms.base_llm.anthropic_messages.transformation", "BaseAnthropicMessagesConfig"), - "BaseAudioTranscriptionConfig": ("litellm.llms.base_llm.audio_transcription.transformation", "BaseAudioTranscriptionConfig"), - "BaseBatchesConfig": ("litellm.llms.base_llm.batches.transformation", "BaseBatchesConfig"), - "BaseContainerConfig": ("litellm.llms.base_llm.containers.transformation", "BaseContainerConfig"), - "BaseEmbeddingConfig": ("litellm.llms.base_llm.embedding.transformation", "BaseEmbeddingConfig"), - "BaseImageEditConfig": ("litellm.llms.base_llm.image_edit.transformation", "BaseImageEditConfig"), - "BaseImageGenerationConfig": ("litellm.llms.base_llm.image_generation.transformation", "BaseImageGenerationConfig"), - "BaseImageVariationConfig": ("litellm.llms.base_llm.image_variations.transformation", "BaseImageVariationConfig"), - "BasePassthroughConfig": ("litellm.llms.base_llm.passthrough.transformation", "BasePassthroughConfig"), - "BaseRealtimeConfig": ("litellm.llms.base_llm.realtime.transformation", "BaseRealtimeConfig"), - "BaseRerankConfig": ("litellm.llms.base_llm.rerank.transformation", "BaseRerankConfig"), - "BaseVectorStoreConfig": ("litellm.llms.base_llm.vector_store.transformation", "BaseVectorStoreConfig"), - "BaseVectorStoreFilesConfig": ("litellm.llms.base_llm.vector_store_files.transformation", "BaseVectorStoreFilesConfig"), - "BaseVideoConfig": ("litellm.llms.base_llm.videos.transformation", "BaseVideoConfig"), - "ANTHROPIC_API_ONLY_HEADERS": ("litellm.types.llms.anthropic", "ANTHROPIC_API_ONLY_HEADERS"), - "AnthropicThinkingParam": ("litellm.types.llms.anthropic", "AnthropicThinkingParam"), + "BaseAnthropicMessagesConfig": ( + "litellm.llms.base_llm.anthropic_messages.transformation", + "BaseAnthropicMessagesConfig", + ), + "BaseAudioTranscriptionConfig": ( + "litellm.llms.base_llm.audio_transcription.transformation", + "BaseAudioTranscriptionConfig", + ), + "BaseBatchesConfig": ( + "litellm.llms.base_llm.batches.transformation", + "BaseBatchesConfig", + ), + "BaseContainerConfig": ( + "litellm.llms.base_llm.containers.transformation", + "BaseContainerConfig", + ), + "BaseEmbeddingConfig": ( + "litellm.llms.base_llm.embedding.transformation", + "BaseEmbeddingConfig", + ), + "BaseImageEditConfig": ( + "litellm.llms.base_llm.image_edit.transformation", + "BaseImageEditConfig", + ), + "BaseImageGenerationConfig": ( + "litellm.llms.base_llm.image_generation.transformation", + "BaseImageGenerationConfig", + ), + "BaseImageVariationConfig": ( + "litellm.llms.base_llm.image_variations.transformation", + "BaseImageVariationConfig", + ), + "BasePassthroughConfig": ( + "litellm.llms.base_llm.passthrough.transformation", + "BasePassthroughConfig", + ), + "BaseRealtimeConfig": ( + "litellm.llms.base_llm.realtime.transformation", + "BaseRealtimeConfig", + ), + "BaseRerankConfig": ( + "litellm.llms.base_llm.rerank.transformation", + "BaseRerankConfig", + ), + "BaseVectorStoreConfig": ( + "litellm.llms.base_llm.vector_store.transformation", + "BaseVectorStoreConfig", + ), + "BaseVectorStoreFilesConfig": ( + "litellm.llms.base_llm.vector_store_files.transformation", + "BaseVectorStoreFilesConfig", + ), + "BaseVideoConfig": ( + "litellm.llms.base_llm.videos.transformation", + "BaseVideoConfig", + ), + "ANTHROPIC_API_ONLY_HEADERS": ( + "litellm.types.llms.anthropic", + "ANTHROPIC_API_ONLY_HEADERS", + ), + "AnthropicThinkingParam": ( + "litellm.types.llms.anthropic", + "AnthropicThinkingParam", + ), "RerankResponse": ("litellm.types.rerank", "RerankResponse"), - "ChatCompletionDeltaToolCallChunk": ("litellm.types.llms.openai", "ChatCompletionDeltaToolCallChunk"), - "ChatCompletionToolCallChunk": ("litellm.types.llms.openai", "ChatCompletionToolCallChunk"), - "ChatCompletionToolCallFunctionChunk": ("litellm.types.llms.openai", "ChatCompletionToolCallFunctionChunk"), + "ChatCompletionDeltaToolCallChunk": ( + "litellm.types.llms.openai", + "ChatCompletionDeltaToolCallChunk", + ), + "ChatCompletionToolCallChunk": ( + "litellm.types.llms.openai", + "ChatCompletionToolCallChunk", + ), + "ChatCompletionToolCallFunctionChunk": ( + "litellm.types.llms.openai", + "ChatCompletionToolCallFunctionChunk", + ), "LiteLLM_Params": ("litellm.types.router", "LiteLLM_Params"), } diff --git a/litellm/a2a_protocol/card_resolver.py b/litellm/a2a_protocol/card_resolver.py new file mode 100644 index 0000000000..7c4c5af149 --- /dev/null +++ b/litellm/a2a_protocol/card_resolver.py @@ -0,0 +1,97 @@ +""" +Custom A2A Card Resolver for LiteLLM. + +Extends the A2A SDK's card resolver to support multiple well-known paths. +""" + +from typing import TYPE_CHECKING, Any, Dict, Optional + +from litellm._logging import verbose_logger + +if TYPE_CHECKING: + from a2a.types import AgentCard + +# Runtime imports with availability check +_A2ACardResolver: Any = None +AGENT_CARD_WELL_KNOWN_PATH: str = "/.well-known/agent-card.json" +PREV_AGENT_CARD_WELL_KNOWN_PATH: str = "/.well-known/agent.json" + +try: + from a2a.client import A2ACardResolver as _A2ACardResolver # type: ignore[no-redef] + from a2a.utils.constants import ( # type: ignore[no-redef] + AGENT_CARD_WELL_KNOWN_PATH, + PREV_AGENT_CARD_WELL_KNOWN_PATH, + ) +except ImportError: + pass + + +class LiteLLMA2ACardResolver(_A2ACardResolver): # type: ignore[misc] + """ + Custom A2A card resolver that supports multiple well-known paths. + + Extends the base A2ACardResolver to try both: + - /.well-known/agent-card.json (standard) + - /.well-known/agent.json (previous/alternative) + """ + + async def get_agent_card( + self, + relative_card_path: Optional[str] = None, + http_kwargs: Optional[Dict[str, Any]] = None, + ) -> "AgentCard": + """ + Fetch the agent card, trying multiple well-known paths. + + First tries the standard path, then falls back to the previous path. + + Args: + relative_card_path: Optional path to the agent card endpoint. + If None, tries both well-known paths. + http_kwargs: Optional dictionary of keyword arguments to pass to httpx.get + + Returns: + AgentCard from the A2A agent + + Raises: + A2AClientHTTPError or A2AClientJSONError if both paths fail + """ + # If a specific path is provided, use the parent implementation + if relative_card_path is not None: + return await super().get_agent_card( + relative_card_path=relative_card_path, + http_kwargs=http_kwargs, + ) + + # Try both well-known paths + paths = [ + AGENT_CARD_WELL_KNOWN_PATH, + PREV_AGENT_CARD_WELL_KNOWN_PATH, + ] + + last_error = None + for path in paths: + try: + verbose_logger.debug( + f"Attempting to fetch agent card from {self.base_url}{path}" + ) + return await super().get_agent_card( + relative_card_path=path, + http_kwargs=http_kwargs, + ) + except Exception as e: + verbose_logger.debug( + f"Failed to fetch agent card from {self.base_url}{path}: {e}" + ) + last_error = e + continue + + # If we get here, all paths failed - re-raise the last error + if last_error is not None: + raise last_error + + # This shouldn't happen, but just in case + raise Exception( + f"Failed to fetch agent card from {self.base_url}. " + f"Tried paths: {', '.join(paths)}" + ) diff --git a/litellm/a2a_protocol/main.py b/litellm/a2a_protocol/main.py index 2d36dbeacd..b326f9e7ed 100644 --- a/litellm/a2a_protocol/main.py +++ b/litellm/a2a_protocol/main.py @@ -6,10 +6,11 @@ Provides standalone functions with @client decorator for LiteLLM logging integra import asyncio import datetime +import uuid from typing import TYPE_CHECKING, Any, AsyncIterator, Coroutine, Dict, Optional, Union import litellm -from litellm._logging import verbose_logger +from litellm._logging import verbose_logger, verbose_proxy_logger from litellm.a2a_protocol.streaming_iterator import A2AStreamingIterator from litellm.a2a_protocol.utils import A2ARequestUtils from litellm.constants import DEFAULT_A2A_AGENT_TIMEOUT @@ -35,13 +36,18 @@ A2ACardResolver: Any = None _A2AClient: Any = None try: - from a2a.client import A2ACardResolver # type: ignore[no-redef] from a2a.client import A2AClient as _A2AClient # type: ignore[no-redef] A2A_SDK_AVAILABLE = True except ImportError: pass +# Import our custom card resolver that supports multiple well-known paths +from litellm.a2a_protocol.card_resolver import LiteLLMA2ACardResolver + +# Use our custom resolver instead of the default A2A SDK resolver +A2ACardResolver = LiteLLMA2ACardResolver + def _set_usage_on_logging_obj( kwargs: Dict[str, Any], @@ -225,7 +231,11 @@ async def asend_message( raise ValueError( "Either a2a_client or api_base is required for standard A2A flow" ) - a2a_client = await create_a2a_client(base_url=api_base) + trace_id = str(uuid.uuid4()) + extra_headers = {"X-LiteLLM-Trace-Id": trace_id} + if agent_id: + extra_headers["X-LiteLLM-Agent-Id"] = agent_id + a2a_client = await create_a2a_client(base_url=api_base, extra_headers=extra_headers) # Type assertion: a2a_client is guaranteed to be non-None here assert a2a_client is not None @@ -490,6 +500,10 @@ async def create_a2a_client( ) httpx_client = http_handler.client + if extra_headers: + httpx_client.headers.update(extra_headers) + verbose_proxy_logger.debug(f"A2A client created with extra_headers={extra_headers}") + # Resolve agent card resolver = A2ACardResolver( httpx_client=httpx_client, diff --git a/litellm/batches/batch_utils.py b/litellm/batches/batch_utils.py index 8a078eeaca..f80eae20f3 100644 --- a/litellm/batches/batch_utils.py +++ b/litellm/batches/batch_utils.py @@ -192,6 +192,9 @@ async def _get_batch_output_file_content_as_dictionary( Get the batch output file content as a list of dictionaries """ from litellm.files.main import afile_content + from litellm.proxy.openai_files_endpoints.common_utils import ( + _is_base64_encoded_unified_file_id, + ) if custom_llm_provider == "vertex_ai": raise ValueError("Vertex AI does not support file content retrieval") @@ -199,8 +202,17 @@ async def _get_batch_output_file_content_as_dictionary( if batch.output_file_id is None: raise ValueError("Output file id is None cannot retrieve file content") + file_id = batch.output_file_id + is_base64_unified_file_id = _is_base64_encoded_unified_file_id(file_id) + if is_base64_unified_file_id: + try: + file_id = is_base64_unified_file_id.split("llm_output_file_id,")[1].split(";")[0] + verbose_logger.debug(f"Extracted LLM output file ID from unified file ID: {file_id}") + except (IndexError, AttributeError) as e: + verbose_logger.error(f"Failed to extract LLM output file ID from unified file ID: {batch.output_file_id}, error: {e}") + _file_content = await afile_content( - file_id=batch.output_file_id, + file_id=file_id, custom_llm_provider=custom_llm_provider, ) return _get_file_content_as_dictionary(_file_content.content) diff --git a/litellm/batches/main.py b/litellm/batches/main.py index 3367f567a7..f7fcaed497 100644 --- a/litellm/batches/main.py +++ b/litellm/batches/main.py @@ -31,7 +31,6 @@ 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 from litellm.types.llms.openai import ( - Batch, CancelBatchRequest, CreateBatchRequest, RetrieveBatchRequest, @@ -868,7 +867,7 @@ async def acancel_batch( extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, **kwargs, -) -> Batch: +) -> LiteLLMBatch: """ Async: Cancels a batch. @@ -912,7 +911,7 @@ def cancel_batch( extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, **kwargs, -) -> Union[Batch, Coroutine[Any, Any, Batch]]: +) -> Union[LiteLLMBatch, Coroutine[Any, Any, LiteLLMBatch]]: """ Cancels a batch. diff --git a/litellm/completion_extras/litellm_responses_transformation/transformation.py b/litellm/completion_extras/litellm_responses_transformation/transformation.py index c98799d254..8e49c90a59 100644 --- a/litellm/completion_extras/litellm_responses_transformation/transformation.py +++ b/litellm/completion_extras/litellm_responses_transformation/transformation.py @@ -17,7 +17,7 @@ from typing import ( Optional, Tuple, Union, - cast, + cast ) from openai.types.responses.tool_param import FunctionToolParam @@ -277,6 +277,8 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): responses_api_request["previous_response_id"] = value elif key == "reasoning_effort": responses_api_request["reasoning"] = self._map_reasoning_effort(value) + elif key == "web_search_options": + self._add_web_search_tool(responses_api_request, value) # Get stream parameter from litellm_params if not in optional_params stream = optional_params.get("stream") or litellm_params.get("stream", False) @@ -727,6 +729,27 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): return Reasoning(effort="minimal", summary="detailed") if auto_summary_enabled else Reasoning(effort="minimal") return None + def _add_web_search_tool( + self, + responses_api_request: ResponsesAPIOptionalRequestParams, + web_search_options: Any, + ) -> None: + """ + Add web search tool to responses API request. + + Args: + responses_api_request: The responses API request dict to modify + web_search_options: Web search configuration (dict or other value) + """ + if "tools" not in responses_api_request or responses_api_request["tools"] is None: + responses_api_request["tools"] = [] + + web_search_tool: Dict[str, Any] = {"type": "web_search"} + if isinstance(web_search_options, dict): + web_search_tool.update(web_search_options) + + responses_api_request["tools"].append(web_search_tool) + def _transform_response_format_to_text_format( self, response_format: Union[Dict[str, Any], Any] ) -> Optional[Dict[str, Any]]: diff --git a/litellm/constants.py b/litellm/constants.py index 49ca3a509b..3c84547d7c 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -980,6 +980,7 @@ BEDROCK_CONVERSE_MODELS = [ "meta.llama3-2-90b-instruct-v1:0", "amazon.nova-lite-v1:0", "amazon.nova-2-lite-v1:0", + "amazon.nova-2-pro-preview-20251202-v1:0", "amazon.nova-pro-v1:0", "writer.palmyra-x4-v1:0", "writer.palmyra-x5-v1:0", @@ -1165,6 +1166,12 @@ LITELLM_CLI_SOURCE_IDENTIFIER = "litellm-cli" LITELLM_CLI_SESSION_TOKEN_PREFIX = "litellm-session-token" CLI_SSO_SESSION_CACHE_KEY_PREFIX = "cli_sso_session" CLI_JWT_TOKEN_NAME = "cli-jwt-token" +# Support both CLI_JWT_EXPIRATION_HOURS and LITELLM_CLI_JWT_EXPIRATION_HOURS for backwards compatibility +CLI_JWT_EXPIRATION_HOURS = int( + os.getenv("CLI_JWT_EXPIRATION_HOURS") + or os.getenv("LITELLM_CLI_JWT_EXPIRATION_HOURS") + or 24 +) ########################### DB CRON JOB NAMES ########################### DB_SPEND_UPDATE_JOB_NAME = "db_spend_update_job" @@ -1326,6 +1333,13 @@ COROUTINE_CHECKER_MAX_SIZE_IN_MEMORY = int( DEFAULT_CHUNK_SIZE = int(os.getenv("DEFAULT_CHUNK_SIZE", 1000)) DEFAULT_CHUNK_OVERLAP = int(os.getenv("DEFAULT_CHUNK_OVERLAP", 200)) +########################### S3 Vectors RAG Constants ########################### +S3_VECTORS_DEFAULT_DIMENSION = int(os.getenv("S3_VECTORS_DEFAULT_DIMENSION", 1024)) +S3_VECTORS_DEFAULT_DISTANCE_METRIC = str( + os.getenv("S3_VECTORS_DEFAULT_DISTANCE_METRIC", "cosine") +) +S3_VECTORS_DEFAULT_NON_FILTERABLE_METADATA_KEYS = ["source_text"] + ########################### Microsoft SSO Constants ########################### MICROSOFT_USER_EMAIL_ATTRIBUTE = str( os.getenv("MICROSOFT_USER_EMAIL_ATTRIBUTE", "userPrincipalName") diff --git a/litellm/cost_calculator.py b/litellm/cost_calculator.py index 1ab7e260a8..490f0288b0 100644 --- a/litellm/cost_calculator.py +++ b/litellm/cost_calculator.py @@ -23,7 +23,11 @@ from litellm.litellm_core_utils.llm_cost_calc.usage_object_transformation import from litellm.litellm_core_utils.llm_cost_calc.utils import ( CostCalculatorUtils, _generic_cost_per_character, + _get_service_tier_cost_key, + _parse_prompt_tokens_details, + calculate_cost_component, generic_cost_per_token, + get_billable_input_tokens, select_cost_metric_for_model, ) from litellm.llms.anthropic.cost_calculation import ( @@ -431,12 +435,18 @@ def cost_per_token( # noqa: PLR0915 model=model, custom_llm_provider=custom_llm_provider ) - if model_info["input_cost_per_token"] > 0: - ## COST PER TOKEN ## - prompt_tokens_cost_usd_dollar = ( - model_info["input_cost_per_token"] * prompt_tokens + if ( + model_info.get("input_cost_per_token", 0) > 0 + or model_info.get("output_cost_per_token", 0) > 0 + ): + return generic_cost_per_token( + model=model, + usage=usage_block, + custom_llm_provider=custom_llm_provider, + service_tier=service_tier, ) - elif ( + + if ( model_info.get("input_cost_per_second", None) is not None and response_time_ms is not None ): @@ -451,11 +461,7 @@ def cost_per_token( # noqa: PLR0915 model_info["input_cost_per_second"] * response_time_ms / 1000 # type: ignore ) - if model_info["output_cost_per_token"] > 0: - completion_tokens_cost_usd_dollar = ( - model_info["output_cost_per_token"] * completion_tokens - ) - elif ( + if ( model_info.get("output_cost_per_second", None) is not None and response_time_ms is not None ): @@ -955,7 +961,10 @@ def completion_cost( # noqa: PLR0915 router_model_id=router_model_id, ) - potential_model_names = [selected_model, _get_response_model(completion_response)] + potential_model_names = [ + selected_model, + _get_response_model(completion_response), + ] if model is not None: potential_model_names.append(model) @@ -1710,10 +1719,16 @@ def default_image_cost_calculator( ) # Priority 1: Use per-image pricing if available (for gpt-image-1 and similar models) - if "input_cost_per_image" in cost_info and cost_info["input_cost_per_image"] is not None: + if ( + "input_cost_per_image" in cost_info + and cost_info["input_cost_per_image"] is not None + ): return cost_info["input_cost_per_image"] * n # Priority 2: Fall back to per-pixel pricing for backward compatibility - elif "input_cost_per_pixel" in cost_info and cost_info["input_cost_per_pixel"] is not None: + elif ( + "input_cost_per_pixel" in cost_info + and cost_info["input_cost_per_pixel"] is not None + ): return cost_info["input_cost_per_pixel"] * height * width * n else: raise Exception( @@ -1833,9 +1848,22 @@ def batch_cost_calculator( if input_cost_per_token_batches: total_prompt_cost = usage.prompt_tokens * input_cost_per_token_batches elif input_cost_per_token: + # Subtract cached tokens from prompt_tokens before calculating cost + # Fixes issue where cached tokens are being charged again total_prompt_cost = ( - usage.prompt_tokens * (input_cost_per_token) / 2 + get_billable_input_tokens(usage) * (input_cost_per_token) / 2 ) # batch cost is usually half of the regular token cost + + # Add cache read cost if applicable + details = _parse_prompt_tokens_details(usage) + cache_read_tokens = details["cache_hit_tokens"] + cache_read_cost_key = _get_service_tier_cost_key( + "cache_read_input_token_cost", None + ) + total_prompt_cost += ( + calculate_cost_component(model_info, cache_read_cost_key, cache_read_tokens) + / 2 + ) if output_cost_per_token_batches: total_completion_cost = usage.completion_tokens * output_cost_per_token_batches elif output_cost_per_token: diff --git a/litellm/experimental_mcp_client/client.py b/litellm/experimental_mcp_client/client.py index 63433c5a46..e2de3cd502 100644 --- a/litellm/experimental_mcp_client/client.py +++ b/litellm/experimental_mcp_client/client.py @@ -4,13 +4,17 @@ LiteLLM Proxy uses this MCP Client to connnect to other MCP servers. import asyncio import base64 -from typing import Any, Awaitable, Callable, Dict, List, Optional, TypeVar, Union +from typing import Any, Awaitable, Callable, Dict, List, Optional, Tuple, TypeVar, Union import httpx from mcp import ClientSession, ReadResourceResult, Resource, StdioServerParameters from mcp.client.sse import sse_client from mcp.client.stdio import stdio_client -from mcp.client.streamable_http import streamable_http_client + +try: + from mcp.client.streamable_http import streamable_http_client # type: ignore +except ImportError: + streamable_http_client = None from mcp.types import CallToolRequestParams as MCPCallToolRequestParams from mcp.types import CallToolResult as MCPCallToolResult from mcp.types import ( @@ -76,104 +80,100 @@ class MCPClient: def _create_transport_context( self, - ) -> tuple[Any, Optional[httpx.AsyncClient]]: - """Create the appropriate transport context based on transport type.""" + ) -> Tuple[Any, Optional[httpx.AsyncClient]]: + """ + Create the appropriate transport context based on transport type. + + Returns: + Tuple of (transport_context, http_client). + http_client is only set for HTTP transport and needs cleanup. + """ http_client: Optional[httpx.AsyncClient] = None if self.transport_type == MCPTransport.stdio: 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", {}), ) - transport_ctx = stdio_client(server_params) - elif self.transport_type == MCPTransport.sse: + return stdio_client(server_params), None + + if self.transport_type == MCPTransport.sse: headers = self._get_auth_headers() httpx_client_factory = self._create_httpx_client_factory() - transport_ctx = sse_client( + return sse_client( url=self.server_url, timeout=self.timeout, headers=headers, httpx_client_factory=httpx_client_factory, - ) - else: - headers = self._get_auth_headers() - httpx_client_factory = self._create_httpx_client_factory() - verbose_logger.debug( - "litellm headers for streamable_http_client: %s", headers - ) - http_client = httpx_client_factory( - headers=headers, - timeout=httpx.Timeout(self.timeout), - ) - transport_ctx = streamable_http_client( - url=self.server_url, - http_client=http_client, - ) - - if transport_ctx is None: - raise RuntimeError("Failed to create transport context") + ), None + # HTTP transport (default) + headers = self._get_auth_headers() + httpx_client_factory = self._create_httpx_client_factory() + verbose_logger.debug( + "litellm headers for streamable_http_client: %s", headers + ) + http_client = httpx_client_factory( + headers=headers, + timeout=httpx.Timeout(self.timeout), + ) + transport_ctx = streamable_http_client( + url=self.server_url, + http_client=http_client, + ) return transport_ctx, http_client + async def _execute_session_operation( + self, + transport_ctx: Any, + operation: Callable[[ClientSession], Awaitable[TSessionResult]], + ) -> TSessionResult: + """ + Execute an operation within a transport and session context. + + Handles entering/exiting contexts and running the operation. + """ + transport = await transport_ctx.__aenter__() + try: + read_stream, write_stream = transport[0], transport[1] + session_ctx = ClientSession(read_stream, write_stream) + session = await session_ctx.__aenter__() + try: + await session.initialize() + return await operation(session) + finally: + try: + await session_ctx.__aexit__(None, None, None) + except BaseException as e: + verbose_logger.debug(f"Error during session context exit: {e}") + finally: + try: + await transport_ctx.__aexit__(None, None, None) + except BaseException as e: + verbose_logger.debug(f"Error during transport context exit: {e}") + async def run_with_session( self, operation: Callable[[ClientSession], Awaitable[TSessionResult]] ) -> TSessionResult: """Open a session, run the provided coroutine, and clean up.""" - transport_ctx = None http_client: Optional[httpx.AsyncClient] = None - session_ctx = None - try: transport_ctx, http_client = self._create_transport_context() - - # Enter transport context - transport = await transport_ctx.__aenter__() - try: - read_stream, write_stream = transport[0], transport[1] - session_ctx = ClientSession(read_stream, write_stream) - - # Enter session context - session = await session_ctx.__aenter__() - try: - await session.initialize() - result = await operation(session) - return result - finally: - # Ensure session context is properly exited - if session_ctx is not None: - try: - await session_ctx.__aexit__(None, None, None) - except Exception as e: - verbose_logger.debug( - f"Error during session context exit: {e}" - ) - finally: - # Ensure transport context is properly exited - if transport_ctx is not None: - try: - await transport_ctx.__aexit__(None, None, None) - except Exception as e: - verbose_logger.debug( - f"Error during transport context exit: {e}" - ) + return await self._execute_session_operation(transport_ctx, operation) except Exception: verbose_logger.warning( "MCP client run_with_session failed for %s", self.server_url or "stdio" ) raise finally: - # Always clean up http_client if it was created if http_client is not None: try: await http_client.aclose() - except Exception as e: - verbose_logger.debug( - f"Error during http_client cleanup: {e}" - ) + except BaseException as e: + verbose_logger.debug(f"Error during http_client cleanup: {e}") def update_auth_value(self, mcp_auth_value: Union[str, Dict[str, str]]): """ @@ -286,7 +286,9 @@ class MCPClient: return [] async def call_tool( - self, call_tool_request_params: MCPCallToolRequestParams + self, + call_tool_request_params: MCPCallToolRequestParams, + host_progress_callback: Optional[Callable] = None ) -> MCPCallToolResult: """ Call an MCP Tool. @@ -295,13 +297,28 @@ class MCPClient: f"MCP client calling tool '{call_tool_request_params.name}' with arguments: {call_tool_request_params.arguments}" ) + async def on_progress(progress: float, total: float | None, message: str | None): + percentage = (progress / total * 100) if total else 0 + verbose_logger.info( + f"MCP Tool '{call_tool_request_params.name}' progress: " + f"{progress}/{total} ({percentage:.0f}%) - {message or ''}" + ) + + # Forward to Host if callback provided + if host_progress_callback: + try: + await host_progress_callback(progress, total) + except Exception as e: + verbose_logger.warning(f"Failed to forward to Host: {e}") + async def _call_tool_operation(session: ClientSession): verbose_logger.debug("MCP client sending tool call to session") return await session.call_tool( name=call_tool_request_params.name, arguments=call_tool_request_params.arguments, - ) + progress_callback=on_progress, + ) try: tool_result = await self.run_with_session(_call_tool_operation) verbose_logger.info( diff --git a/litellm/files/main.py b/litellm/files/main.py index 913ec84626..93a10dac7a 100644 --- a/litellm/files/main.py +++ b/litellm/files/main.py @@ -9,6 +9,7 @@ import asyncio import contextvars import os import time +import uuid from functools import partial from typing import Any, Coroutine, Dict, Literal, Optional, Union, cast @@ -61,7 +62,7 @@ async def acreate_file( file: FileTypes, purpose: Literal["assistants", "batch", "fine-tune"], expires_after: Optional[FileExpiresAfter] = None, - custom_llm_provider: Literal["openai", "azure", "vertex_ai", "bedrock", "hosted_vllm", "manus"] = "openai", + custom_llm_provider: Literal["openai", "azure", "gemini", "vertex_ai", "bedrock", "hosted_vllm", "manus"] = "openai", extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, **kwargs, @@ -106,7 +107,7 @@ def create_file( file: FileTypes, purpose: Literal["assistants", "batch", "fine-tune"], expires_after: Optional[FileExpiresAfter] = None, - custom_llm_provider: Optional[Literal["openai", "azure", "vertex_ai", "bedrock", "hosted_vllm", "manus"]] = None, + custom_llm_provider: Optional[Literal["openai", "azure", "gemini", "vertex_ai", "bedrock", "hosted_vllm", "manus"]] = None, extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, **kwargs, @@ -294,7 +295,7 @@ def create_file( @client async def afile_retrieve( file_id: str, - custom_llm_provider: Literal["openai", "azure", "hosted_vllm", "manus"] = "openai", + custom_llm_provider: Literal["openai", "azure", "gemini", "hosted_vllm", "manus"] = "openai", extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, **kwargs, @@ -493,7 +494,7 @@ def file_retrieve( @client async def afile_delete( file_id: str, - custom_llm_provider: Literal["openai", "azure", "manus"] = "openai", + custom_llm_provider: Literal["openai", "azure", "gemini", "manus"] = "openai", extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, **kwargs, @@ -537,7 +538,7 @@ async def afile_delete( def file_delete( file_id: str, model: Optional[str] = None, - custom_llm_provider: Union[Literal["openai", "azure", "manus"], str] = "openai", + custom_llm_provider: Union[Literal["openai", "azure", "gemini", "manus"], str] = "openai", extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, **kwargs, @@ -680,7 +681,7 @@ def file_delete( ) else: raise litellm.exceptions.BadRequestError( - message="LiteLLM doesn't support {} for 'file_delete'. Only 'openai', 'azure', and 'manus' are supported.".format( + message="LiteLLM doesn't support {} for 'file_delete'. Only 'openai', 'azure', 'gemini', and 'manus' are supported.".format( custom_llm_provider ), model="n/a", diff --git a/litellm/integrations/braintrust_logging.py b/litellm/integrations/braintrust_logging.py index 585de510e8..42e9680a7f 100644 --- a/litellm/integrations/braintrust_logging.py +++ b/litellm/integrations/braintrust_logging.py @@ -9,6 +9,10 @@ import httpx import litellm from litellm import verbose_logger +from litellm.integrations.braintrust_mock_client import ( + should_use_braintrust_mock, + create_mock_braintrust_client, +) from litellm.integrations.custom_logger import CustomLogger from litellm.llms.custom_httpx.http_handler import ( HTTPHandler, @@ -34,6 +38,10 @@ class BraintrustLogger(CustomLogger): self, api_key: Optional[str] = None, api_base: Optional[str] = None ) -> None: super().__init__() + self.is_mock_mode = should_use_braintrust_mock() + if self.is_mock_mode: + create_mock_braintrust_client() + verbose_logger.info("[BRAINTRUST MOCK] Braintrust logger initialized in mock mode") self.validate_environment(api_key=api_key) self.api_base = api_base or os.getenv("BRAINTRUST_API_BASE") or API_BASE self.default_project_id = None @@ -254,6 +262,8 @@ class BraintrustLogger(CustomLogger): json={"events": [request_data]}, headers=self.headers, ) + if self.is_mock_mode: + print_verbose("[BRAINTRUST MOCK] Sync event successfully mocked") except httpx.HTTPStatusError as e: raise Exception(e.response.text) except Exception as e: @@ -399,6 +409,8 @@ class BraintrustLogger(CustomLogger): json={"events": [request_data]}, headers=self.headers, ) + if self.is_mock_mode: + print_verbose("[BRAINTRUST MOCK] Async event successfully mocked") except httpx.HTTPStatusError as e: raise Exception(e.response.text) except Exception as e: diff --git a/litellm/integrations/braintrust_mock_client.py b/litellm/integrations/braintrust_mock_client.py new file mode 100644 index 0000000000..030aa62cd0 --- /dev/null +++ b/litellm/integrations/braintrust_mock_client.py @@ -0,0 +1,131 @@ +""" +Mock HTTP client for Braintrust integration testing. + +This module intercepts Braintrust API calls and returns successful mock responses, +allowing full code execution without making actual network calls. + +Usage: + Set BRAINTRUST_MOCK=true in environment variables or config to enable mock mode. +""" + +import os +import time +from urllib.parse import urlparse + +from litellm._logging import verbose_logger +from litellm.integrations.mock_client_factory import MockClientConfig, MockResponse, create_mock_client_factory + +# Use factory for should_use_mock and MockResponse +# Braintrust uses both HTTPHandler (sync) and AsyncHTTPHandler (async) +# Braintrust needs endpoint-specific responses, so we use custom HTTPHandler.post patching +_config = MockClientConfig( + "BRAINTRUST", + "BRAINTRUST_MOCK", + default_latency_ms=100, + default_status_code=200, + default_json_data={"id": "mock-project-id", "status": "success"}, + url_matchers=[ + ".braintrustdata.com", + "braintrustdata.com", + ".braintrust.dev", + "braintrust.dev", + ], + patch_async_handler=True, # Patch AsyncHTTPHandler.post for async calls + patch_sync_client=False, # HTTPHandler uses self.client.send(), not self.client.post() + patch_http_handler=False, # We use custom patching for endpoint-specific responses +) + +# Get should_use_mock and create_mock_client from factory +# We need to call the factory's create_mock_client to patch AsyncHTTPHandler.post +create_mock_braintrust_factory_client, should_use_braintrust_mock = create_mock_client_factory(_config) + +# Store original HTTPHandler.post method (Braintrust-specific for sync calls with custom logic) +_original_http_handler_post = None +_mocks_initialized = False + +# Default mock latency in seconds +_MOCK_LATENCY_SECONDS = float(os.getenv("BRAINTRUST_MOCK_LATENCY_MS", "100")) / 1000.0 + + +def _is_braintrust_url(url: str) -> bool: + """Check if URL is a Braintrust API URL.""" + if not isinstance(url, str): + return False + + parsed = urlparse(url) + host = (parsed.hostname or "").lower() + + if not host: + return False + + return ( + host == "braintrustdata.com" + or host.endswith(".braintrustdata.com") + or host == "braintrust.dev" + or host.endswith(".braintrust.dev") + ) + + +def _mock_http_handler_post(self, url, data=None, json=None, params=None, headers=None, timeout=None, stream=False, files=None, content=None, logging_obj=None): + """Monkey-patched HTTPHandler.post that intercepts Braintrust calls with endpoint-specific responses.""" + # Only mock Braintrust API calls + if isinstance(url, str) and _is_braintrust_url(url): + verbose_logger.info(f"[BRAINTRUST MOCK] POST to {url}") + time.sleep(_MOCK_LATENCY_SECONDS) + # Return appropriate mock response based on endpoint + if "/project" in url: + # Project creation/retrieval/register endpoint + project_name = json.get("name", "litellm") if json else "litellm" + mock_data = {"id": f"mock-project-id-{project_name}", "name": project_name} + elif "/project_logs" in url: + # Log insertion endpoint + mock_data = {"status": "success"} + else: + mock_data = _config.default_json_data + return MockResponse( + status_code=_config.default_status_code, + json_data=mock_data, + url=url, + elapsed_seconds=_MOCK_LATENCY_SECONDS + ) + if _original_http_handler_post is not None: + return _original_http_handler_post(self, url=url, data=data, json=json, params=params, headers=headers, timeout=timeout, stream=stream, files=files, content=content, logging_obj=logging_obj) + raise RuntimeError("Original HTTPHandler.post not available") + + +def create_mock_braintrust_client(): + """ + Monkey-patch HTTPHandler.post to intercept Braintrust sync calls. + + Braintrust uses HTTPHandler for sync calls and AsyncHTTPHandler for async calls. + HTTPHandler.post uses self.client.send(), not self.client.post(), so we need + custom patching for sync (similar to Helicone). + AsyncHTTPHandler.post is patched by the factory. + + We use custom patching instead of factory's patch_http_handler because we need + endpoint-specific responses (different for /project vs /project_logs). + + This function is idempotent - it only initializes mocks once, even if called multiple times. + """ + global _original_http_handler_post, _mocks_initialized + + if _mocks_initialized: + return + + verbose_logger.debug("[BRAINTRUST MOCK] Initializing Braintrust mock client...") + + from litellm.llms.custom_httpx.http_handler import HTTPHandler + + if _original_http_handler_post is None: + _original_http_handler_post = HTTPHandler.post + HTTPHandler.post = _mock_http_handler_post # type: ignore + verbose_logger.debug("[BRAINTRUST MOCK] Patched HTTPHandler.post") + + # CRITICAL: Call the factory's initialization function to patch AsyncHTTPHandler.post + # This is required for async calls to be mocked + create_mock_braintrust_factory_client() + + verbose_logger.debug(f"[BRAINTRUST MOCK] Mock latency set to {_MOCK_LATENCY_SECONDS*1000:.0f}ms") + verbose_logger.debug("[BRAINTRUST MOCK] Braintrust mock client initialization complete") + + _mocks_initialized = True diff --git a/litellm/integrations/callback_configs.json b/litellm/integrations/callback_configs.json index 6b30b6b736..6a003b8c49 100644 --- a/litellm/integrations/callback_configs.json +++ b/litellm/integrations/callback_configs.json @@ -83,6 +83,33 @@ }, "description": "Datadog Logging Integration" }, + { + "id": "datadog_cost_management", + "displayName": "Datadog Cost Management", + "logo": "datadog.png", + "supports_key_team_logging": false, + "dynamic_params": { + "dd_api_key": { + "type": "password", + "ui_name": "API Key", + "description": "Datadog API key for authentication", + "required": true + }, + "dd_app_key": { + "type": "password", + "ui_name": "App Key", + "description": "Datadog Application Key for Cloud Cost Management", + "required": true + }, + "dd_site": { + "type": "text", + "ui_name": "Site", + "description": "Datadog site URL (e.g., us5.datadoghq.com)", + "required": true + } + }, + "description": "Datadog Cloud Cost Management Integration" + }, { "id": "lago", "displayName": "Lago", @@ -407,4 +434,4 @@ }, "description": "SQS Queue (AWS) Logging Integration" } -] +] \ No newline at end of file diff --git a/litellm/integrations/custom_guardrail.py b/litellm/integrations/custom_guardrail.py index 6a76b57e7f..a5bb530fc5 100644 --- a/litellm/integrations/custom_guardrail.py +++ b/litellm/integrations/custom_guardrail.py @@ -516,7 +516,9 @@ class CustomGuardrail(CustomLogger): from litellm.types.utils import GuardrailMode # Use event_type if provided, otherwise fall back to self.event_hook - guardrail_mode: Union[GuardrailEventHooks, GuardrailMode, List[GuardrailEventHooks]] + guardrail_mode: Union[ + GuardrailEventHooks, GuardrailMode, List[GuardrailEventHooks] + ] if event_type is not None: guardrail_mode = event_type elif isinstance(self.event_hook, Mode): @@ -524,11 +526,21 @@ class CustomGuardrail(CustomLogger): else: guardrail_mode = self.event_hook # type: ignore[assignment] + from litellm.litellm_core_utils.core_helpers import ( + filter_exceptions_from_params, + ) + + # Sanitize the response to ensure it's JSON serializable and free of circular refs + # This prevents RecursionErrors in downstream loggers (Langfuse, Datadog, etc.) + clean_guardrail_response = filter_exceptions_from_params( + guardrail_json_response + ) + slg = StandardLoggingGuardrailInformation( guardrail_name=self.guardrail_name, guardrail_provider=guardrail_provider, guardrail_mode=guardrail_mode, - guardrail_response=guardrail_json_response, + guardrail_response=clean_guardrail_response, guardrail_status=guardrail_status, start_time=start_time, end_time=end_time, diff --git a/litellm/integrations/datadog/datadog.py b/litellm/integrations/datadog/datadog.py index 503e8d8c87..127b0e53fa 100644 --- a/litellm/integrations/datadog/datadog.py +++ b/litellm/integrations/datadog/datadog.py @@ -27,11 +27,16 @@ import litellm from litellm._logging import verbose_logger from litellm._uuid import uuid from litellm.integrations.custom_batch_logger import CustomBatchLogger +from litellm.integrations.datadog.datadog_mock_client import ( + should_use_datadog_mock, + create_mock_datadog_client, +) from litellm.integrations.datadog.datadog_handler import ( get_datadog_hostname, get_datadog_service, get_datadog_source, get_datadog_tags, + get_datadog_base_url_from_env, ) from litellm.litellm_core_utils.dd_tracing import tracer from litellm.llms.custom_httpx.http_handler import ( @@ -80,6 +85,12 @@ class DataDogLogger( """ try: verbose_logger.debug("Datadog: in init datadog logger") + + self.is_mock_mode = should_use_datadog_mock() + + if self.is_mock_mode: + create_mock_datadog_client() + verbose_logger.debug("[DATADOG MOCK] Datadog logger initialized in mock mode") ######################################################### # Handle datadog_params set as litellm.datadog_params @@ -100,7 +111,9 @@ class DataDogLogger( self._configure_dd_direct_api() # Optional override for testing - self._apply_dd_base_url_override() + dd_base_url = get_datadog_base_url_from_env() + if dd_base_url: + self.intake_url = f"{dd_base_url}/api/v2/logs" self.sync_client = _get_httpx_client() asyncio.create_task(self.periodic_flush()) self.flush_lock = asyncio.Lock() @@ -159,18 +172,6 @@ class DataDogLogger( self.DD_API_KEY = os.getenv("DD_API_KEY") self.intake_url = f"https://http-intake.logs.{os.getenv('DD_SITE')}/api/v2/logs" - def _apply_dd_base_url_override(self) -> None: - """ - Apply base URL override for testing purposes - """ - dd_base_url: Optional[str] = ( - os.getenv("_DATADOG_BASE_URL") - or os.getenv("DATADOG_BASE_URL") - or os.getenv("DD_BASE_URL") - ) - if dd_base_url is not None: - self.intake_url = f"{dd_base_url}/api/v2/logs" - async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): """ Async Log success events to Datadog @@ -229,6 +230,9 @@ class DataDogLogger( len(self.log_queue), self.intake_url, ) + + if self.is_mock_mode: + verbose_logger.debug("[DATADOG MOCK] Mock mode enabled - API calls will be intercepted") response = await self.async_send_compressed_data(self.log_queue) if response.status_code == 413: @@ -241,11 +245,16 @@ class DataDogLogger( f"Response from datadog API status_code: {response.status_code}, text: {response.text}" ) - verbose_logger.debug( - "Datadog: Response from datadog API status_code: %s, text: %s", - response.status_code, - response.text, - ) + if self.is_mock_mode: + verbose_logger.debug( + f"[DATADOG MOCK] Batch of {len(self.log_queue)} events successfully mocked" + ) + else: + verbose_logger.debug( + "Datadog: Response from datadog API status_code: %s, text: %s", + response.status_code, + response.text, + ) except Exception as e: verbose_logger.exception( f"Datadog Error sending batch API - {str(e)}\n{traceback.format_exc()}" diff --git a/litellm/integrations/datadog/datadog_cost_management.py b/litellm/integrations/datadog/datadog_cost_management.py new file mode 100644 index 0000000000..2eb94b59dd --- /dev/null +++ b/litellm/integrations/datadog/datadog_cost_management.py @@ -0,0 +1,204 @@ +import asyncio +import os +import time +from datetime import datetime +from typing import Dict, List, Optional, Tuple + +from litellm._logging import verbose_logger +from litellm.integrations.custom_batch_logger import CustomBatchLogger +from litellm.llms.custom_httpx.http_handler import ( + get_async_httpx_client, + httpxSpecialProvider, +) +from litellm.types.integrations.datadog_cost_management import ( + DatadogFOCUSCostEntry, +) +from litellm.types.utils import StandardLoggingPayload + + +class DatadogCostManagementLogger(CustomBatchLogger): + def __init__(self, **kwargs): + self.dd_api_key = os.getenv("DD_API_KEY") + self.dd_app_key = os.getenv("DD_APP_KEY") + self.dd_site = os.getenv("DD_SITE", "datadoghq.com") + + if not self.dd_api_key or not self.dd_app_key: + verbose_logger.warning( + "Datadog Cost Management: DD_API_KEY and DD_APP_KEY are required. Integration will not work." + ) + + self.upload_url = f"https://api.{self.dd_site}/api/v2/cost/custom_costs" + + self.async_client = get_async_httpx_client( + llm_provider=httpxSpecialProvider.LoggingCallback + ) + + # Initialize lock and start periodic flush task + self.flush_lock = asyncio.Lock() + asyncio.create_task(self.periodic_flush()) + + # Check if flush_lock is already in kwargs to avoid double passing (unlikely but safe) + if "flush_lock" not in kwargs: + kwargs["flush_lock"] = self.flush_lock + + super().__init__(**kwargs) + + async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): + try: + standard_logging_object: Optional[StandardLoggingPayload] = kwargs.get( + "standard_logging_object", None + ) + + if standard_logging_object is None: + return + + # Only log if there is a cost associated + if standard_logging_object.get("response_cost", 0) > 0: + self.log_queue.append(standard_logging_object) + + if len(self.log_queue) >= self.batch_size: + await self.async_send_batch() + + except Exception as e: + verbose_logger.exception( + f"Datadog Cost Management: Error in async_log_success_event: {str(e)}" + ) + + async def async_send_batch(self): + if not self.log_queue: + return + + try: + # Aggregate costs from the batch + aggregated_entries = self._aggregate_costs(self.log_queue) + + if not aggregated_entries: + return + + # Send to Datadog + await self._upload_to_datadog(aggregated_entries) + + # Clear queue only on success (or if we decide to drop on failure) + # CustomBatchLogger clears queue in flush_queue, so we just process here + + except Exception as e: + verbose_logger.exception( + f"Datadog Cost Management: Error in async_send_batch: {str(e)}" + ) + + def _aggregate_costs( + self, logs: List[StandardLoggingPayload] + ) -> List[DatadogFOCUSCostEntry]: + """ + Aggregates costs by Provider, Model, and Date. + Returns a list of DatadogFOCUSCostEntry. + """ + aggregator: Dict[Tuple[str, str, str, Tuple[Tuple[str, str], ...]], DatadogFOCUSCostEntry] = {} + + for log in logs: + try: + # Extract keys for aggregation + provider = log.get("custom_llm_provider") or "unknown" + model = log.get("model") or "unknown" + cost = log.get("response_cost", 0) + + if cost == 0: + continue + + # Get date strings (FOCUS format requires specific keys, but for aggregation we group by Day) + # UTC date + # We interpret "ChargePeriod" as the day of the request. + ts = log.get("startTime") or time.time() + dt = datetime.fromtimestamp(ts) + date_str = dt.strftime("%Y-%m-%d") + + # ChargePeriodStart and End + # If we want daily granularity, end date is usually same day or next day? + # Datadog Custom Costs usually expects periods. + # "ChargePeriodStart": "2023-01-01", "ChargePeriodEnd": "2023-12-31" in example. + # If we send daily, we can say Start=Date, End=Date. + + # Grouping Key: Provider + Model + Date + Tags? + # For simplicity, let's aggregate by Provider + Model + Date first. + # If we handle tags, we need to include them in the key. + + tags = self._extract_tags(log) + tags_key = tuple(sorted(tags.items())) if tags else () + + key = (provider, model, date_str, tags_key) + + if key not in aggregator: + aggregator[key] = { + "ProviderName": provider, + "ChargeDescription": f"LLM Usage for {model}", + "ChargePeriodStart": date_str, + "ChargePeriodEnd": date_str, + "BilledCost": 0.0, + "BillingCurrency": "USD", + "Tags": tags if tags else None, + } + + aggregator[key]["BilledCost"] += cost + + except Exception as e: + verbose_logger.warning( + f"Error processing log for cost aggregation: {e}" + ) + continue + + return list(aggregator.values()) + + def _extract_tags(self, log: StandardLoggingPayload) -> Dict[str, str]: + from litellm.integrations.datadog.datadog_handler import ( + get_datadog_env, + get_datadog_hostname, + get_datadog_pod_name, + get_datadog_service, + ) + + tags = { + "env": get_datadog_env(), + "service": get_datadog_service(), + "host": get_datadog_hostname(), + "pod_name": get_datadog_pod_name(), + } + + # Add metadata as tags + metadata = log.get("metadata", {}) + if metadata: + # Add user info + if "user_api_key_alias" in metadata: + tags["user"] = str(metadata["user_api_key_alias"]) + if "user_api_key_team_alias" in metadata: + tags["team"] = str(metadata["user_api_key_team_alias"]) + # model_group is not in StandardLoggingMetadata TypedDict, so we need to access it via dict.get() + model_group = metadata.get("model_group") # type: ignore[misc] + if model_group: + tags["model_group"] = str(model_group) + + return tags + + async def _upload_to_datadog(self, payload: List[Dict]): + if not self.dd_api_key or not self.dd_app_key: + return + + headers = { + "Content-Type": "application/json", + "DD-API-KEY": self.dd_api_key, + "DD-APPLICATION-KEY": self.dd_app_key, + } + + # The API endpoint expects a list of objects directly in the body (file content behavior) + from litellm.litellm_core_utils.safe_json_dumps import safe_dumps + + data_json = safe_dumps(payload) + + response = await self.async_client.put( + self.upload_url, content=data_json, headers=headers + ) + + response.raise_for_status() + + verbose_logger.debug( + f"Datadog Cost Management: Uploaded {len(payload)} cost entries. Status: {response.status_code}" + ) diff --git a/litellm/integrations/datadog/datadog_handler.py b/litellm/integrations/datadog/datadog_handler.py index 26fab77759..e2f30f2f61 100644 --- a/litellm/integrations/datadog/datadog_handler.py +++ b/litellm/integrations/datadog/datadog_handler.py @@ -20,6 +20,14 @@ def get_datadog_hostname() -> str: return os.getenv("HOSTNAME", "") +def get_datadog_base_url_from_env() -> Optional[str]: + """ + Get base URL override from common DD_BASE_URL env var. + This is useful for testing or custom endpoints. + """ + return os.getenv("DD_BASE_URL") + + def get_datadog_env() -> str: return os.getenv("DD_ENV", "unknown") diff --git a/litellm/integrations/datadog/datadog_llm_obs.py b/litellm/integrations/datadog/datadog_llm_obs.py index 6ffdbc0a00..076a147c60 100644 --- a/litellm/integrations/datadog/datadog_llm_obs.py +++ b/litellm/integrations/datadog/datadog_llm_obs.py @@ -18,9 +18,14 @@ import httpx import litellm from litellm._logging import verbose_logger from litellm.integrations.custom_batch_logger import CustomBatchLogger +from litellm.integrations.datadog.datadog_mock_client import ( + should_use_datadog_mock, + create_mock_datadog_client, +) from litellm.integrations.datadog.datadog_handler import ( get_datadog_service, get_datadog_tags, + get_datadog_base_url_from_env, ) from litellm.litellm_core_utils.dd_tracing import tracer from litellm.litellm_core_utils.prompt_templates.common_utils import ( @@ -43,24 +48,35 @@ class DataDogLLMObsLogger(CustomBatchLogger): def __init__(self, **kwargs): try: verbose_logger.debug("DataDogLLMObs: Initializing logger") + + self.is_mock_mode = should_use_datadog_mock() + + if self.is_mock_mode: + create_mock_datadog_client() + verbose_logger.debug("[DATADOG MOCK] DataDogLLMObs logger initialized in mock mode") + if os.getenv("DD_API_KEY", None) is None: 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, set 'DD_SITE=<>', example sit = `us5.datadoghq.com`" ) + # Configure DataDog endpoint (Agent or Direct API) + # Use LITELLM_DD_AGENT_HOST to avoid conflicts with ddtrace's DD_AGENT_HOST + dd_agent_host = os.getenv("LITELLM_DD_AGENT_HOST") self.async_client = get_async_httpx_client( llm_provider=httpxSpecialProvider.LoggingCallback ) self.DD_API_KEY = os.getenv("DD_API_KEY") - self.DD_SITE = os.getenv("DD_SITE") - self.intake_url = ( - f"https://api.{self.DD_SITE}/api/intake/llm-obs/v1/trace/spans" - ) - # testing base url - dd_base_url = os.getenv("DD_BASE_URL") + if dd_agent_host: + self._configure_dd_agent(dd_agent_host=dd_agent_host) + else: + self._configure_dd_direct_api() + + # Optional override for testing + dd_base_url = get_datadog_base_url_from_env() if dd_base_url: self.intake_url = f"{dd_base_url}/api/intake/llm-obs/v1/trace/spans" @@ -78,6 +94,38 @@ class DataDogLLMObsLogger(CustomBatchLogger): verbose_logger.exception(f"DataDogLLMObs: Error initializing - {str(e)}") raise e + def _configure_dd_agent(self, dd_agent_host: str): + """ + Configure the Datadog logger to send traces to the Agent. + """ + # When using the Agent, LLM Observability Intake does NOT require the API Key + # Reference: https://docs.datadoghq.com/llm_observability/setup/sdk/#agent-setup + + # Use specific port for LLM Obs (Trace Agent) to avoid conflict with Logs Agent (10518) + agent_port = os.getenv("LITELLM_DD_LLM_OBS_PORT", "8126") + self.DD_SITE = "localhost" # Not used for URL construction in agent mode + self.intake_url = ( + f"http://{dd_agent_host}:{agent_port}/api/intake/llm-obs/v1/trace/spans" + ) + verbose_logger.debug(f"DataDogLLMObs: Using DD Agent at {self.intake_url}") + + def _configure_dd_direct_api(self): + """ + Configure the Datadog logger to send traces directly to the Datadog API. + """ + if not self.DD_API_KEY: + raise Exception("DD_API_KEY is not set, set 'DD_API_KEY=<>'") + + self.DD_SITE = os.getenv("DD_SITE") + if not self.DD_SITE: + raise Exception( + "DD_SITE is not set, set 'DD_SITE=<>', example site = `us5.datadoghq.com`" + ) + + self.intake_url = ( + f"https://api.{self.DD_SITE}/api/intake/llm-obs/v1/trace/spans" + ) + def _get_datadog_llm_obs_params(self) -> Dict: """ Get the datadog_llm_observability_params from litellm.datadog_llm_observability_params @@ -139,6 +187,9 @@ class DataDogLLMObsLogger(CustomBatchLogger): verbose_logger.debug( f"DataDogLLMObs: Flushing {len(self.log_queue)} events" ) + + if self.is_mock_mode: + verbose_logger.debug("[DATADOG MOCK] Mock mode enabled - API calls will be intercepted") # Prepare the payload payload = { @@ -164,13 +215,14 @@ class DataDogLLMObsLogger(CustomBatchLogger): json_payload = safe_dumps(payload) + headers = {"Content-Type": "application/json"} + if self.DD_API_KEY: + headers["DD-API-KEY"] = self.DD_API_KEY + response = await self.async_client.post( url=self.intake_url, content=json_payload, - headers={ - "DD-API-KEY": self.DD_API_KEY, - "Content-Type": "application/json", - }, + headers=headers, ) if response.status_code != 202: @@ -178,9 +230,14 @@ class DataDogLLMObsLogger(CustomBatchLogger): f"DataDogLLMObs: Unexpected response - status_code: {response.status_code}, text: {response.text}" ) - verbose_logger.debug( - f"DataDogLLMObs: Successfully sent batch - status_code: {response.status_code}" - ) + if self.is_mock_mode: + verbose_logger.debug( + f"[DATADOG MOCK] Batch of {len(self.log_queue)} events successfully mocked" + ) + else: + verbose_logger.debug( + f"DataDogLLMObs: Successfully sent batch - status_code: {response.status_code}" + ) self.log_queue.clear() except httpx.HTTPStatusError as e: verbose_logger.exception( diff --git a/litellm/integrations/datadog/datadog_mock_client.py b/litellm/integrations/datadog/datadog_mock_client.py new file mode 100644 index 0000000000..a0a760deb0 --- /dev/null +++ b/litellm/integrations/datadog/datadog_mock_client.py @@ -0,0 +1,28 @@ +""" +Mock client for Datadog integration testing. + +This module intercepts Datadog API calls and returns successful mock responses, +allowing full code execution without making actual network calls. + +Usage: + Set DATADOG_MOCK=true in environment variables or config to enable mock mode. +""" + +from litellm.integrations.mock_client_factory import MockClientConfig, create_mock_client_factory + +# Create mock client using factory +_config = MockClientConfig( + name="DATADOG", + env_var="DATADOG_MOCK", + default_latency_ms=100, + default_status_code=202, + default_json_data={"status": "ok"}, + url_matchers=[ + ".datadoghq.com", + "datadoghq.com", + ], + patch_async_handler=True, + patch_sync_client=True, +) + +create_mock_datadog_client, should_use_datadog_mock = create_mock_client_factory(_config) diff --git a/litellm/integrations/gcs_bucket/gcs_bucket_mock_client.py b/litellm/integrations/gcs_bucket/gcs_bucket_mock_client.py index 6201dc343d..2d14f5eb96 100644 --- a/litellm/integrations/gcs_bucket/gcs_bucket_mock_client.py +++ b/litellm/integrations/gcs_bucket/gcs_bucket_mock_client.py @@ -8,20 +8,28 @@ Usage: Set GCS_MOCK=true in environment variables or config to enable mock mode. """ -import httpx -import json import asyncio -from datetime import timedelta -from typing import Dict, Optional from litellm._logging import verbose_logger +from litellm.integrations.mock_client_factory import MockClientConfig, create_mock_client_factory, MockResponse -# Store original methods for restoration -_original_async_handler_post = None +# Use factory for POST handler +_config = MockClientConfig( + name="GCS", + env_var="GCS_MOCK", + default_latency_ms=150, + default_status_code=200, + default_json_data={"kind": "storage#object", "name": "mock-object"}, + url_matchers=["storage.googleapis.com"], + patch_async_handler=True, + patch_sync_client=False, +) + +_create_mock_gcs_post, should_use_gcs_mock = create_mock_client_factory(_config) + +# Store original methods for GET/DELETE (GCS-specific) _original_async_handler_get = None _original_async_handler_delete = None - -# Track if mocks have been initialized to avoid duplicate initialization _mocks_initialized = False # Default mock latency in seconds (simulates network round-trip) @@ -29,84 +37,59 @@ _mocks_initialized = False _MOCK_LATENCY_SECONDS = float(__import__("os").getenv("GCS_MOCK_LATENCY_MS", "150")) / 1000.0 -class MockGCSResponse: - """Mock httpx.Response that satisfies GCS API requirements.""" - - def __init__(self, status_code: int = 200, json_data: Optional[Dict] = None, url: Optional[str] = None, elapsed_seconds: float = 0.0): - self.status_code = status_code - self._json_data = json_data or {"kind": "storage#object", "name": "mock-object"} - self.headers = httpx.Headers({}) - self.is_success = status_code < 400 - self.is_error = status_code >= 400 - self.is_redirect = 300 <= status_code < 400 - self.url = httpx.URL(url) if url else httpx.URL("") - # Set realistic elapsed time based on mock latency - elapsed_time = elapsed_seconds if elapsed_seconds > 0 else _MOCK_LATENCY_SECONDS - self.elapsed = timedelta(seconds=elapsed_time) - self._text = json.dumps(self._json_data) - self._content = self._text.encode("utf-8") - - @property - def text(self) -> str: - """Return response text.""" - return self._text - - @property - def content(self) -> bytes: - """Return response content.""" - return self._content - - def json(self) -> Dict: - """Return JSON response data.""" - return self._json_data - - def read(self) -> bytes: - """Read response content.""" - return self._content - - def raise_for_status(self): - """Raise exception for error status codes.""" - if self.status_code >= 400: - raise Exception(f"HTTP {self.status_code}") - - -async def _mock_async_handler_post(self, url, data=None, json=None, params=None, headers=None, timeout=None, stream=False, logging_obj=None, files=None, content=None): - """Monkey-patched AsyncHTTPHandler.post that intercepts GCS calls.""" - # Only mock GCS API calls - if isinstance(url, str) and "storage.googleapis.com" in url: - verbose_logger.info(f"[GCS MOCK] POST to {url}") - # Simulate network latency - await asyncio.sleep(_MOCK_LATENCY_SECONDS) - return MockGCSResponse( - status_code=200, - json_data={"kind": "storage#object", "name": "mock-object"}, - url=url, - elapsed_seconds=_MOCK_LATENCY_SECONDS - ) - # For non-GCS calls, use original method - if _original_async_handler_post is not None: - return await _original_async_handler_post(self, url=url, data=data, json=json, params=params, headers=headers, timeout=timeout, stream=stream, logging_obj=logging_obj, files=files, content=content) - # Fallback: if original not set, raise error - raise RuntimeError("Original AsyncHTTPHandler.post not available") - - async def _mock_async_handler_get(self, url, params=None, headers=None, follow_redirects=None): """Monkey-patched AsyncHTTPHandler.get that intercepts GCS calls.""" # Only mock GCS API calls if isinstance(url, str) and "storage.googleapis.com" in url: verbose_logger.info(f"[GCS MOCK] GET to {url}") - # Simulate network latency await asyncio.sleep(_MOCK_LATENCY_SECONDS) - return MockGCSResponse( - status_code=200, - json_data={"data": "mock-log-data"}, + # Return a minimal but valid StandardLoggingPayload JSON string as bytes + # This matches what GCS returns when downloading with ?alt=media + mock_payload = { + "id": "mock-request-id", + "trace_id": "mock-trace-id", + "call_type": "completion", + "stream": False, + "response_cost": 0.0, + "status": "success", + "status_fields": {"llm_api_status": "success"}, + "custom_llm_provider": "mock", + "total_tokens": 0, + "prompt_tokens": 0, + "completion_tokens": 0, + "startTime": 0.0, + "endTime": 0.0, + "completionStartTime": 0.0, + "response_time": 0.0, + "model_map_information": {"model": "mock-model"}, + "model": "mock-model", + "model_id": None, + "model_group": None, + "api_base": "https://api.mock.com", + "metadata": {}, + "cache_hit": None, + "cache_key": None, + "saved_cache_cost": 0.0, + "request_tags": [], + "end_user": None, + "requester_ip_address": None, + "messages": None, + "response": None, + "error_str": None, + "error_information": None, + "model_parameters": {}, + "hidden_params": {}, + "guardrail_information": None, + "standard_built_in_tools_params": None, + } + return MockResponse( + status_code=200, + json_data=mock_payload, url=url, elapsed_seconds=_MOCK_LATENCY_SECONDS ) - # For non-GCS calls, use original method if _original_async_handler_get is not None: return await _original_async_handler_get(self, url=url, params=params, headers=headers, follow_redirects=follow_redirects) - # Fallback: if original not set, raise error raise RuntimeError("Original AsyncHTTPHandler.get not available") @@ -115,18 +98,16 @@ async def _mock_async_handler_delete(self, url, data=None, json=None, params=Non # Only mock GCS API calls if isinstance(url, str) and "storage.googleapis.com" in url: verbose_logger.info(f"[GCS MOCK] DELETE to {url}") - # Simulate network latency await asyncio.sleep(_MOCK_LATENCY_SECONDS) - return MockGCSResponse( - status_code=204, - json_data={}, + # DELETE returns 204 No Content with empty body (not JSON) + return MockResponse( + status_code=204, + json_data=None, # Empty body for DELETE url=url, elapsed_seconds=_MOCK_LATENCY_SECONDS ) - # For non-GCS calls, use original method if _original_async_handler_delete is not None: return await _original_async_handler_delete(self, url=url, data=data, json=json, params=params, headers=headers, timeout=timeout, stream=stream, content=content) - # Fallback: if original not set, raise error raise RuntimeError("Original AsyncHTTPHandler.delete not available") @@ -139,30 +120,26 @@ def create_mock_gcs_client(): This function is idempotent - it only initializes mocks once, even if called multiple times. """ - global _original_async_handler_post, _original_async_handler_get, _original_async_handler_delete - global _mocks_initialized + global _original_async_handler_get, _original_async_handler_delete, _mocks_initialized - # If already initialized, skip + # Use factory for POST handler + _create_mock_gcs_post() + + # If already initialized, skip GET/DELETE patching if _mocks_initialized: return - verbose_logger.debug("[GCS MOCK] Initializing GCS mock client...") + verbose_logger.debug("[GCS MOCK] Initializing GCS GET/DELETE handlers...") - # Patch AsyncHTTPHandler methods (used by LiteLLM's custom httpx handler) - if _original_async_handler_post is None: - from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler - _original_async_handler_post = AsyncHTTPHandler.post - AsyncHTTPHandler.post = _mock_async_handler_post # type: ignore - verbose_logger.debug("[GCS MOCK] Patched AsyncHTTPHandler.post") + # Patch GET and DELETE handlers (GCS-specific) + from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler if _original_async_handler_get is None: - from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler _original_async_handler_get = AsyncHTTPHandler.get AsyncHTTPHandler.get = _mock_async_handler_get # type: ignore verbose_logger.debug("[GCS MOCK] Patched AsyncHTTPHandler.get") if _original_async_handler_delete is None: - from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler _original_async_handler_delete = AsyncHTTPHandler.delete AsyncHTTPHandler.delete = _mock_async_handler_delete # type: ignore verbose_logger.debug("[GCS MOCK] Patched AsyncHTTPHandler.delete") @@ -212,25 +189,4 @@ def mock_vertex_auth_methods(): verbose_logger.debug("[GCS MOCK] Patched Vertex AI auth methods") -def should_use_gcs_mock() -> bool: - """ - Determine if GCS should run in mock mode. - - Checks the GCS_MOCK environment variable. - - Returns: - bool: True if mock mode should be enabled - """ - import os - from litellm.secret_managers.main import str_to_bool - - mock_mode = os.getenv("GCS_MOCK", "false") - result = str_to_bool(mock_mode) - - # Ensure we return a bool, not None - result = bool(result) if result is not None else False - - if result: - verbose_logger.info("GCS Mock Mode: ENABLED - API calls will be mocked") - - return result +# should_use_gcs_mock is already created by the factory diff --git a/litellm/integrations/helicone.py b/litellm/integrations/helicone.py index 198cbaf405..b996813b4e 100644 --- a/litellm/integrations/helicone.py +++ b/litellm/integrations/helicone.py @@ -4,6 +4,11 @@ import os import traceback import litellm +from litellm._logging import verbose_logger +from litellm.integrations.helicone_mock_client import ( + should_use_helicone_mock, + create_mock_helicone_client, +) class HeliconeLogger: @@ -22,6 +27,11 @@ class HeliconeLogger: def __init__(self): # Instance variables + self.is_mock_mode = should_use_helicone_mock() + if self.is_mock_mode: + create_mock_helicone_client() + verbose_logger.info("[HELICONE MOCK] Helicone logger initialized in mock mode") + self.provider_url = "https://api.openai.com/v1" self.key = os.getenv("HELICONE_API_KEY") self.api_base = os.getenv("HELICONE_API_BASE") or "https://api.hconeai.com" @@ -185,7 +195,10 @@ class HeliconeLogger: } response = litellm.module_level_client.post(url, headers=headers, json=data) if response.status_code == 200: - print_verbose("Helicone Logging - Success!") + if self.is_mock_mode: + print_verbose("[HELICONE MOCK] Helicone Logging - Successfully mocked!") + else: + print_verbose("Helicone Logging - Success!") else: print_verbose( f"Helicone Logging - Error Request was not successful. Status Code: {response.status_code}" diff --git a/litellm/integrations/helicone_mock_client.py b/litellm/integrations/helicone_mock_client.py new file mode 100644 index 0000000000..0f4670a1d2 --- /dev/null +++ b/litellm/integrations/helicone_mock_client.py @@ -0,0 +1,32 @@ +""" +Mock HTTP client for Helicone integration testing. + +This module intercepts Helicone API calls and returns successful mock responses, +allowing full code execution without making actual network calls. + +Usage: + Set HELICONE_MOCK=true in environment variables or config to enable mock mode. +""" + +from litellm.integrations.mock_client_factory import MockClientConfig, create_mock_client_factory + +# Create mock client using factory +# Helicone uses HTTPHandler which internally uses httpx.Client.send(), not httpx.Client.post() +_config = MockClientConfig( + name="HELICONE", + env_var="HELICONE_MOCK", + default_latency_ms=100, + default_status_code=200, + default_json_data={"status": "success"}, + url_matchers=[ + ".hconeai.com", + "hconeai.com", + ".helicone.ai", + "helicone.ai", + ], + patch_async_handler=False, + patch_sync_client=False, # HTTPHandler uses self.client.send(), not self.client.post() + patch_http_handler=True, # Patch HTTPHandler.post directly +) + +create_mock_helicone_client, should_use_helicone_mock = create_mock_client_factory(_config) diff --git a/litellm/integrations/langfuse/langfuse.py b/litellm/integrations/langfuse/langfuse.py index 46ada3c393..7bf97665fd 100644 --- a/litellm/integrations/langfuse/langfuse.py +++ b/litellm/integrations/langfuse/langfuse.py @@ -23,6 +23,7 @@ from litellm.constants import MAX_LANGFUSE_INITIALIZED_CLIENTS from litellm.litellm_core_utils.core_helpers import ( safe_deep_copy, reconstruct_model_name, + filter_exceptions_from_params, ) from litellm.litellm_core_utils.redact_messages import redact_user_api_key_info from litellm.integrations.langfuse.langfuse_mock_client import ( @@ -75,9 +76,8 @@ def _extract_cache_read_input_tokens(usage_obj) -> int: # Check prompt_tokens_details.cached_tokens (used by Gemini and other providers) if hasattr(usage_obj, "prompt_tokens_details"): prompt_tokens_details = getattr(usage_obj, "prompt_tokens_details", None) - if ( - prompt_tokens_details is not None - and hasattr(prompt_tokens_details, "cached_tokens") + if prompt_tokens_details is not None and hasattr( + prompt_tokens_details, "cached_tokens" ): cached_tokens = getattr(prompt_tokens_details, "cached_tokens", None) if ( @@ -540,7 +540,6 @@ class LangFuseLogger: verbose_logger.debug("Langfuse Layer Logging - logging to langfuse v2") try: - metadata = metadata or {} standard_logging_object: Optional[StandardLoggingPayload] = cast( Optional[StandardLoggingPayload], kwargs.get("standard_logging_object", None), @@ -706,9 +705,10 @@ class LangFuseLogger: clean_metadata["litellm_response_cost"] = cost if standard_logging_object is not None: - clean_metadata["hidden_params"] = standard_logging_object[ - "hidden_params" - ] + hidden_params = standard_logging_object.get("hidden_params", {}) + clean_metadata["hidden_params"] = filter_exceptions_from_params( + hidden_params + ) if ( litellm.langfuse_default_tags is not None diff --git a/litellm/integrations/langfuse/langfuse_mock_client.py b/litellm/integrations/langfuse/langfuse_mock_client.py index 1dc739ea32..8ed6cff8d4 100644 --- a/litellm/integrations/langfuse/langfuse_mock_client.py +++ b/litellm/integrations/langfuse/langfuse_mock_client.py @@ -9,113 +9,27 @@ Usage: """ import httpx -import json -from datetime import timedelta -from typing import Dict, Optional +from litellm.integrations.mock_client_factory import MockClientConfig, create_mock_client_factory -from litellm._logging import verbose_logger - -_original_httpx_post = None - -# Default mock latency in seconds (simulates network round-trip) -# Typical Langfuse API calls take 50-150ms -_MOCK_LATENCY_SECONDS = float(__import__("os").getenv("LANGFUSE_MOCK_LATENCY_MS", "100")) / 1000.0 - - -class MockLangfuseResponse: - """Mock httpx.Response that satisfies Langfuse SDK requirements.""" - - def __init__(self, status_code: int = 200, json_data: Optional[Dict] = None, url: Optional[str] = None, elapsed_seconds: float = 0.0): - self.status_code = status_code - self._json_data = json_data or {"status": "success"} - self.headers = httpx.Headers({}) - self.is_success = status_code < 400 - self.is_error = status_code >= 400 - self.is_redirect = 300 <= status_code < 400 - self.url = httpx.URL(url) if url else httpx.URL("") - # Set realistic elapsed time based on mock latency - elapsed_time = elapsed_seconds if elapsed_seconds > 0 else _MOCK_LATENCY_SECONDS - self.elapsed = timedelta(seconds=elapsed_time) - self._text = json.dumps(self._json_data) - self._content = self._text.encode("utf-8") - - @property - def text(self) -> str: - return self._text - - @property - def content(self) -> bytes: - return self._content - - def json(self) -> Dict: - return self._json_data - - def read(self) -> bytes: - return self._content - - def raise_for_status(self): - if self.status_code >= 400: - raise Exception(f"HTTP {self.status_code}") - - -def _is_langfuse_url(url) -> bool: - """Check if URL is a Langfuse domain.""" - try: - parsed_url = httpx.URL(url) if isinstance(url, str) else url - hostname = parsed_url.host or "" - - return ( - hostname.endswith(".langfuse.com") or - hostname == "langfuse.com" or - (hostname in ("localhost", "127.0.0.1") and "langfuse" in str(parsed_url).lower()) - ) - except Exception: - return False - - -def _mock_httpx_post(self, url, **kwargs): - """Monkey-patched httpx.Client.post that intercepts Langfuse calls.""" - if _is_langfuse_url(url): - verbose_logger.info(f"[LANGFUSE MOCK] POST to {url}") - return MockLangfuseResponse(status_code=200, json_data={"status": "success"}, url=url, elapsed_seconds=_MOCK_LATENCY_SECONDS) - - if _original_httpx_post is not None: - return _original_httpx_post(self, url, **kwargs) +# Create mock client using factory +_config = MockClientConfig( + name="LANGFUSE", + env_var="LANGFUSE_MOCK", + default_latency_ms=100, + default_status_code=200, + default_json_data={"status": "success"}, + url_matchers=[ + ".langfuse.com", + "langfuse.com", + ], + patch_async_handler=False, + patch_sync_client=True, +) +_create_mock_langfuse_client_internal, should_use_langfuse_mock = create_mock_client_factory(_config) +# Langfuse needs to return an httpx.Client instance def create_mock_langfuse_client(): - """ - Monkey-patch httpx.Client.post to intercept Langfuse calls. - - Returns a real httpx.Client instance - the monkey-patch intercepts all calls. - """ - global _original_httpx_post - - if _original_httpx_post is None: - _original_httpx_post = httpx.Client.post - httpx.Client.post = _mock_httpx_post # type: ignore - verbose_logger.debug("[LANGFUSE MOCK] Patched httpx.Client.post") - + """Create and return an httpx.Client instance - the monkey-patch intercepts all calls.""" + _create_mock_langfuse_client_internal() return httpx.Client() - - -def should_use_langfuse_mock() -> bool: - """ - Determine if Langfuse should run in mock mode. - - Checks the LANGFUSE_MOCK environment variable. - - Returns: - bool: True if mock mode should be enabled - """ - import os - from litellm.secret_managers.main import str_to_bool - - mock_mode = os.getenv("LANGFUSE_MOCK", "false") - result = str_to_bool(mock_mode) - result = bool(result) if result is not None else False - - if result: - verbose_logger.info("Langfuse Mock Mode: ENABLED - API calls will be mocked") - - return result diff --git a/litellm/integrations/langfuse/langfuse_prompt_management.py b/litellm/integrations/langfuse/langfuse_prompt_management.py index 8f73eabad4..3986fc6a6e 100644 --- a/litellm/integrations/langfuse/langfuse_prompt_management.py +++ b/litellm/integrations/langfuse/langfuse_prompt_management.py @@ -300,43 +300,59 @@ class LangfusePromptManagement(LangFuseLogger, PromptManagementBase, CustomLogge ) async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): - standard_callback_dynamic_params = kwargs.get( - "standard_callback_dynamic_params" - ) - langfuse_logger_to_use = LangFuseHandler.get_langfuse_logger_for_request( - globalLangfuseLogger=self, - standard_callback_dynamic_params=standard_callback_dynamic_params, - in_memory_dynamic_logger_cache=in_memory_dynamic_logger_cache, - ) - langfuse_logger_to_use.log_event_on_langfuse( - kwargs=kwargs, - response_obj=response_obj, - start_time=start_time, - end_time=end_time, - user_id=kwargs.get("user", None), - ) + try: + standard_callback_dynamic_params = kwargs.get( + "standard_callback_dynamic_params" + ) + langfuse_logger_to_use = LangFuseHandler.get_langfuse_logger_for_request( + globalLangfuseLogger=self, + standard_callback_dynamic_params=standard_callback_dynamic_params, + in_memory_dynamic_logger_cache=in_memory_dynamic_logger_cache, + ) + langfuse_logger_to_use.log_event_on_langfuse( + kwargs=kwargs, + response_obj=response_obj, + start_time=start_time, + end_time=end_time, + user_id=kwargs.get("user", None), + ) + except Exception as e: + from litellm._logging import verbose_logger + + verbose_logger.exception( + f"Langfuse Layer Error - Exception occurred while logging success event: {str(e)}" + ) + self.handle_callback_failure(callback_name="langfuse") async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time): - standard_callback_dynamic_params = kwargs.get( - "standard_callback_dynamic_params" - ) - langfuse_logger_to_use = LangFuseHandler.get_langfuse_logger_for_request( - globalLangfuseLogger=self, - standard_callback_dynamic_params=standard_callback_dynamic_params, - in_memory_dynamic_logger_cache=in_memory_dynamic_logger_cache, - ) - standard_logging_object = cast( - Optional[StandardLoggingPayload], - kwargs.get("standard_logging_object", None), - ) - if standard_logging_object is None: - return - langfuse_logger_to_use.log_event_on_langfuse( - start_time=start_time, - end_time=end_time, - response_obj=None, - user_id=kwargs.get("user", None), - status_message=standard_logging_object["error_str"], - level="ERROR", - kwargs=kwargs, - ) + try: + standard_callback_dynamic_params = kwargs.get( + "standard_callback_dynamic_params" + ) + langfuse_logger_to_use = LangFuseHandler.get_langfuse_logger_for_request( + globalLangfuseLogger=self, + standard_callback_dynamic_params=standard_callback_dynamic_params, + in_memory_dynamic_logger_cache=in_memory_dynamic_logger_cache, + ) + standard_logging_object = cast( + Optional[StandardLoggingPayload], + kwargs.get("standard_logging_object", None), + ) + if standard_logging_object is None: + return + langfuse_logger_to_use.log_event_on_langfuse( + start_time=start_time, + end_time=end_time, + response_obj=None, + user_id=kwargs.get("user", None), + status_message=standard_logging_object["error_str"], + level="ERROR", + kwargs=kwargs, + ) + except Exception as e: + from litellm._logging import verbose_logger + + verbose_logger.exception( + f"Langfuse Layer Error - Exception occurred while logging failure event: {str(e)}" + ) + self.handle_callback_failure(callback_name="langfuse") diff --git a/litellm/integrations/langsmith.py b/litellm/integrations/langsmith.py index 5893f14105..ebd005f880 100644 --- a/litellm/integrations/langsmith.py +++ b/litellm/integrations/langsmith.py @@ -15,6 +15,10 @@ from pydantic import BaseModel # type: ignore import litellm from litellm._logging import verbose_logger from litellm.integrations.custom_batch_logger import CustomBatchLogger +from litellm.integrations.langsmith_mock_client import ( + should_use_langsmith_mock, + create_mock_langsmith_client, +) from litellm.llms.custom_httpx.http_handler import ( get_async_httpx_client, httpxSpecialProvider, @@ -45,6 +49,12 @@ class LangsmithLogger(CustomBatchLogger): ): self.flush_lock = asyncio.Lock() super().__init__(**kwargs, flush_lock=self.flush_lock) + self.is_mock_mode = should_use_langsmith_mock() + + if self.is_mock_mode: + create_mock_langsmith_client() + verbose_logger.debug("[LANGSMITH MOCK] LangSmith logger initialized in mock mode") + self.default_credentials = self.get_credentials_from_env( langsmith_api_key=langsmith_api_key, langsmith_project=langsmith_project, @@ -388,6 +398,8 @@ class LangsmithLogger(CustomBatchLogger): verbose_logger.debug( "Sending batch of %s runs to Langsmith", len(elements_to_log) ) + if self.is_mock_mode: + verbose_logger.debug("[LANGSMITH MOCK] Mock mode enabled - API calls will be intercepted") response = await self.async_httpx_client.post( url=url, json={"post": elements_to_log}, @@ -400,9 +412,14 @@ class LangsmithLogger(CustomBatchLogger): f"Langsmith Error: {response.status_code} - {response.text}" ) else: - verbose_logger.debug( - f"Batch of {len(self.log_queue)} runs successfully created" - ) + if self.is_mock_mode: + verbose_logger.debug( + f"[LANGSMITH MOCK] Batch of {len(elements_to_log)} runs successfully mocked" + ) + else: + verbose_logger.debug( + f"Batch of {len(self.log_queue)} runs successfully created" + ) except httpx.HTTPStatusError as e: verbose_logger.exception( f"Langsmith HTTP Error: {e.response.status_code} - {e.response.text}" diff --git a/litellm/integrations/langsmith_mock_client.py b/litellm/integrations/langsmith_mock_client.py new file mode 100644 index 0000000000..ef60290823 --- /dev/null +++ b/litellm/integrations/langsmith_mock_client.py @@ -0,0 +1,29 @@ +""" +Mock client for LangSmith integration testing. + +This module intercepts LangSmith API calls and returns successful mock responses, +allowing full code execution without making actual network calls. + +Usage: + Set LANGSMITH_MOCK=true in environment variables or config to enable mock mode. +""" + +from litellm.integrations.mock_client_factory import MockClientConfig, create_mock_client_factory + +# Create mock client using factory +_config = MockClientConfig( + name="LANGSMITH", + env_var="LANGSMITH_MOCK", + default_latency_ms=100, + default_status_code=200, + default_json_data={"status": "success", "ids": ["mock-run-id"]}, + url_matchers=[ + ".smith.langchain.com", + "api.smith.langchain.com", + "smith.langchain.com", + ], + patch_async_handler=True, + patch_sync_client=False, +) + +create_mock_langsmith_client, should_use_langsmith_mock = create_mock_client_factory(_config) diff --git a/litellm/integrations/mock_client_factory.py b/litellm/integrations/mock_client_factory.py new file mode 100644 index 0000000000..2f04fae9f7 --- /dev/null +++ b/litellm/integrations/mock_client_factory.py @@ -0,0 +1,216 @@ +""" +Factory for creating mock HTTP clients for integration testing. + +This module provides a simple factory pattern to create mock clients that intercept +API calls and return successful mock responses, allowing full code execution without +making actual network calls. +""" + +import httpx +import json +import asyncio +from datetime import timedelta +from typing import Dict, Optional, List, cast +from dataclasses import dataclass + +from litellm._logging import verbose_logger + + +@dataclass +class MockClientConfig: + """Configuration for creating a mock client.""" + name: str # e.g., "GCS", "LANGFUSE", "LANGSMITH", "DATADOG" + env_var: str # e.g., "GCS_MOCK", "LANGFUSE_MOCK" + default_latency_ms: int = 100 # Default mock latency in milliseconds + default_status_code: int = 200 # Default HTTP status code + default_json_data: Optional[Dict] = None # Default JSON response data + url_matchers: Optional[List[str]] = None # List of strings to match in URLs (e.g., ["storage.googleapis.com"]) + patch_async_handler: bool = True # Whether to patch AsyncHTTPHandler.post + patch_sync_client: bool = False # Whether to patch httpx.Client.post + patch_http_handler: bool = False # Whether to patch HTTPHandler.post (for sync calls that use HTTPHandler) + + def __post_init__(self): + """Ensure url_matchers is a list.""" + if self.url_matchers is None: + self.url_matchers = [] + + +class MockResponse: + """Generic mock httpx.Response that satisfies API requirements.""" + + def __init__(self, status_code: int = 200, json_data: Optional[Dict] = None, url: Optional[str] = None, elapsed_seconds: float = 0.0): + self.status_code = status_code + self._json_data = json_data or {"status": "success"} + self.headers = httpx.Headers({}) + self.is_success = status_code < 400 + self.is_error = status_code >= 400 + self.is_redirect = 300 <= status_code < 400 + self.url = httpx.URL(url) if url else httpx.URL("") + self.elapsed = timedelta(seconds=elapsed_seconds) + self._text = json.dumps(self._json_data) if json_data else "" + self._content = self._text.encode("utf-8") + + @property + def text(self) -> str: + """Return response text.""" + return self._text + + @property + def content(self) -> bytes: + """Return response content.""" + return self._content + + def json(self) -> Dict: + """Return JSON response data.""" + return self._json_data + + def read(self) -> bytes: + """Read response content.""" + return self._content + + def raise_for_status(self): + """Raise exception for error status codes.""" + if self.status_code >= 400: + raise Exception(f"HTTP {self.status_code}") + + +def _is_url_match(url, matchers: List[str]) -> bool: + """Check if URL matches any of the provided matchers.""" + try: + parsed_url = httpx.URL(url) if isinstance(url, str) else url + url_str = str(parsed_url).lower() + hostname = parsed_url.host or "" + + for matcher in matchers: + if matcher.lower() in url_str or matcher.lower() in hostname.lower(): + return True + + # Also check for localhost with matcher in path + if hostname in ("localhost", "127.0.0.1"): + for matcher in matchers: + if matcher.lower() in url_str: + return True + + return False + except Exception: + return False + + +def create_mock_client_factory(config: MockClientConfig): # noqa: PLR0915 + """ + Factory function that creates mock client functions based on configuration. + + Returns: + tuple: (create_mock_client_func, should_use_mock_func) + """ + # Store original methods for restoration + _original_async_handler_post = None + _original_sync_client_post = None + _original_http_handler_post = None + _mocks_initialized = False + + # Calculate mock latency + import os + latency_env = f"{config.name.upper()}_MOCK_LATENCY_MS" + _MOCK_LATENCY_SECONDS = float(os.getenv(latency_env, str(config.default_latency_ms))) / 1000.0 + + # Create URL matcher function + def _is_mock_url(url) -> bool: + # url_matchers is guaranteed to be a list after __post_init__ + return _is_url_match(url, cast(List[str], config.url_matchers)) + + # Create async handler mock + async def _mock_async_handler_post(self, url, data=None, json=None, params=None, headers=None, timeout=None, stream=False, logging_obj=None, files=None, content=None): + """Monkey-patched AsyncHTTPHandler.post that intercepts API calls.""" + if isinstance(url, str) and _is_mock_url(url): + verbose_logger.info(f"[{config.name} MOCK] POST to {url}") + await asyncio.sleep(_MOCK_LATENCY_SECONDS) + return MockResponse( + status_code=config.default_status_code, + json_data=config.default_json_data, + url=url, + elapsed_seconds=_MOCK_LATENCY_SECONDS + ) + if _original_async_handler_post is not None: + return await _original_async_handler_post(self, url=url, data=data, json=json, params=params, headers=headers, timeout=timeout, stream=stream, logging_obj=logging_obj, files=files, content=content) + raise RuntimeError("Original AsyncHTTPHandler.post not available") + + # Create sync client mock + def _mock_sync_client_post(self, url, **kwargs): + """Monkey-patched httpx.Client.post that intercepts API calls.""" + if _is_mock_url(url): + verbose_logger.info(f"[{config.name} MOCK] POST to {url} (sync)") + return MockResponse( + status_code=config.default_status_code, + json_data=config.default_json_data, + url=url, + elapsed_seconds=_MOCK_LATENCY_SECONDS + ) + if _original_sync_client_post is not None: + return _original_sync_client_post(self, url, **kwargs) + + # Create HTTPHandler mock (for sync calls that use HTTPHandler.post) + def _mock_http_handler_post(self, url, data=None, json=None, params=None, headers=None, timeout=None, stream=False, files=None, content=None, logging_obj=None): + """Monkey-patched HTTPHandler.post that intercepts API calls.""" + if isinstance(url, str) and _is_mock_url(url): + verbose_logger.info(f"[{config.name} MOCK] POST to {url}") + import time + time.sleep(_MOCK_LATENCY_SECONDS) + return MockResponse( + status_code=config.default_status_code, + json_data=config.default_json_data, + url=url, + elapsed_seconds=_MOCK_LATENCY_SECONDS + ) + if _original_http_handler_post is not None: + return _original_http_handler_post(self, url=url, data=data, json=json, params=params, headers=headers, timeout=timeout, stream=stream, files=files, content=content, logging_obj=logging_obj) + raise RuntimeError("Original HTTPHandler.post not available") + + # Create mock client initialization function + def create_mock_client(): + """Initialize the mock client by patching HTTP handlers.""" + nonlocal _original_async_handler_post, _original_sync_client_post, _original_http_handler_post, _mocks_initialized + + if _mocks_initialized: + return + + verbose_logger.debug(f"[{config.name} MOCK] Initializing {config.name} mock client...") + + if config.patch_async_handler and _original_async_handler_post is None: + from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler + _original_async_handler_post = AsyncHTTPHandler.post + AsyncHTTPHandler.post = _mock_async_handler_post # type: ignore + verbose_logger.debug(f"[{config.name} MOCK] Patched AsyncHTTPHandler.post") + + if config.patch_sync_client and _original_sync_client_post is None: + _original_sync_client_post = httpx.Client.post + httpx.Client.post = _mock_sync_client_post # type: ignore + verbose_logger.debug(f"[{config.name} MOCK] Patched httpx.Client.post") + + if config.patch_http_handler and _original_http_handler_post is None: + from litellm.llms.custom_httpx.http_handler import HTTPHandler + _original_http_handler_post = HTTPHandler.post + HTTPHandler.post = _mock_http_handler_post # type: ignore + verbose_logger.debug(f"[{config.name} MOCK] Patched HTTPHandler.post") + + verbose_logger.debug(f"[{config.name} MOCK] Mock latency set to {_MOCK_LATENCY_SECONDS*1000:.0f}ms") + verbose_logger.debug(f"[{config.name} MOCK] {config.name} mock client initialization complete") + + _mocks_initialized = True + + # Create should_use_mock function + def should_use_mock() -> bool: + """Determine if mock mode should be enabled.""" + import os + from litellm.secret_managers.main import str_to_bool + + mock_mode = os.getenv(config.env_var, "false") + result = str_to_bool(mock_mode) + result = bool(result) if result is not None else False + + if result: + verbose_logger.info(f"{config.name} Mock Mode: ENABLED - API calls will be mocked") + + return result + + return create_mock_client, should_use_mock diff --git a/litellm/integrations/opentelemetry.py b/litellm/integrations/opentelemetry.py index 29be248fe8..a357fae335 100644 --- a/litellm/integrations/opentelemetry.py +++ b/litellm/integrations/opentelemetry.py @@ -144,6 +144,7 @@ class OpenTelemetry(CustomLogger): self.OTEL_EXPORTER = self.config.exporter self.OTEL_ENDPOINT = self.config.endpoint self.OTEL_HEADERS = self.config.headers + self._tracer_provider_cache: Dict[str, Any] = {} self._init_tracing(tracer_provider) _debug_otel = str(os.getenv("DEBUG_OTEL", "False")).lower() @@ -615,12 +616,20 @@ class OpenTelemetry(CustomLogger): """Create a temporary tracer with dynamic headers for this request only.""" from opentelemetry.sdk.trace import TracerProvider + # Prevents thread exhaustion by reusing providers for the same credential sets (e.g. per-team keys) + cache_key = str(sorted(dynamic_headers.items())) + if cache_key in self._tracer_provider_cache: + return self._tracer_provider_cache[cache_key].get_tracer(LITELLM_TRACER_NAME) + # Create a temporary tracer provider with dynamic headers temp_provider = TracerProvider(resource=self._get_litellm_resource(self.config)) temp_provider.add_span_processor( self._get_span_processor(dynamic_headers=dynamic_headers) ) + # Store in cache for reuse + self._tracer_provider_cache[cache_key] = temp_provider + return temp_provider.get_tracer(LITELLM_TRACER_NAME) def construct_dynamic_otel_headers( @@ -999,7 +1008,9 @@ class OpenTelemetry(CustomLogger): from opentelemetry._logs import SeverityNumber, get_logger, get_logger_provider try: - from opentelemetry.sdk._logs import LogRecord as SdkLogRecord # type: ignore[attr-defined] # OTEL < 1.39.0 + from opentelemetry.sdk._logs import ( + LogRecord as SdkLogRecord, # type: ignore[attr-defined] # OTEL < 1.39.0 + ) except ImportError: from opentelemetry.sdk._logs._internal import ( LogRecord as SdkLogRecord, @@ -1624,6 +1635,7 @@ class OpenTelemetry(CustomLogger): ) except Exception as e: + self.handle_callback_failure(callback_name= self.callback_name) verbose_logger.exception( "OpenTelemetry logging error in set_attributes %s", str(e) ) diff --git a/litellm/integrations/posthog.py b/litellm/integrations/posthog.py index 468b1a441f..dd7c3627b8 100644 --- a/litellm/integrations/posthog.py +++ b/litellm/integrations/posthog.py @@ -17,6 +17,10 @@ from typing import Any, Dict, Optional, Tuple from litellm._logging import verbose_logger from litellm._uuid import uuid from litellm.integrations.custom_batch_logger import CustomBatchLogger +from litellm.integrations.posthog_mock_client import ( + should_use_posthog_mock, + create_mock_posthog_client, +) from litellm.llms.custom_httpx.http_handler import ( _get_httpx_client, get_async_httpx_client, @@ -40,6 +44,12 @@ class PostHogLogger(CustomBatchLogger): """ try: verbose_logger.debug("PostHog: in init posthog logger") + + self.is_mock_mode = should_use_posthog_mock() + if self.is_mock_mode: + create_mock_posthog_client() + verbose_logger.debug("[POSTHOG MOCK] PostHog logger initialized in mock mode") + if os.getenv("POSTHOG_API_KEY", None) is None: raise Exception("POSTHOG_API_KEY is not set, set 'POSTHOG_API_KEY=<>'") @@ -100,7 +110,10 @@ class PostHogLogger(CustomBatchLogger): f"Response from PostHog API status_code: {response.status_code}, text: {response.text}" ) - verbose_logger.debug("PostHog: Sync event successfully sent") + if self.is_mock_mode: + verbose_logger.debug("[POSTHOG MOCK] Sync event successfully mocked") + else: + verbose_logger.debug("PostHog: Sync event successfully sent") except Exception as e: verbose_logger.exception(f"PostHog Sync Layer Error - {str(e)}") @@ -320,6 +333,9 @@ class PostHogLogger(CustomBatchLogger): verbose_logger.debug( f"PostHog: Sending batch of {len(self.log_queue)} events" ) + + if self.is_mock_mode: + verbose_logger.debug("[POSTHOG MOCK] Mock mode enabled - API calls will be intercepted") # Group events by credentials for batch sending batches_by_credentials: Dict[tuple[str, str], list] = {} @@ -350,9 +366,12 @@ class PostHogLogger(CustomBatchLogger): 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" - ) + if self.is_mock_mode: + verbose_logger.debug(f"[POSTHOG MOCK] Batch of {len(self.log_queue)} events successfully mocked") + else: + 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)}") @@ -429,9 +448,14 @@ class PostHogLogger(CustomBatchLogger): f"PostHog: Failed to flush on exit - status {response.status_code}" ) - verbose_logger.debug( - f"PostHog: Successfully flushed {len(self.log_queue)} events on exit" - ) + if self.is_mock_mode: + verbose_logger.debug( + f"[POSTHOG MOCK] Successfully flushed {len(self.log_queue)} events on exit" + ) + else: + verbose_logger.debug( + f"PostHog: Successfully flushed {len(self.log_queue)} events on exit" + ) self.log_queue.clear() except Exception as e: diff --git a/litellm/integrations/posthog_mock_client.py b/litellm/integrations/posthog_mock_client.py new file mode 100644 index 0000000000..b713587ed6 --- /dev/null +++ b/litellm/integrations/posthog_mock_client.py @@ -0,0 +1,30 @@ +""" +Mock httpx client for PostHog integration testing. + +This module intercepts PostHog API calls and returns successful mock responses, +allowing full code execution without making actual network calls. + +Usage: + Set POSTHOG_MOCK=true in environment variables or config to enable mock mode. +""" + +from litellm.integrations.mock_client_factory import MockClientConfig, create_mock_client_factory + +# Create mock client using factory +_config = MockClientConfig( + name="POSTHOG", + env_var="POSTHOG_MOCK", + default_latency_ms=100, + default_status_code=200, + default_json_data={"status": "success"}, + url_matchers=[ + ".posthog.com", + "posthog.com", + "us.i.posthog.com", + "app.posthog.com", + ], + patch_async_handler=True, + patch_sync_client=True, +) + +create_mock_posthog_client, should_use_posthog_mock = create_mock_client_factory(_config) diff --git a/litellm/integrations/prometheus.py b/litellm/integrations/prometheus.py index bafb0d88c8..2c897cb069 100644 --- a/litellm/integrations/prometheus.py +++ b/litellm/integrations/prometheus.py @@ -229,14 +229,18 @@ class PrometheusLogger(CustomLogger): self.litellm_remaining_api_key_requests_for_model = self._gauge_factory( "litellm_remaining_api_key_requests_for_model", "Remaining Requests API Key can make for model (model based rpm limit on key)", - labelnames=["hashed_api_key", "api_key_alias", "model"], + labelnames=self.get_labels_for_metric( + "litellm_remaining_api_key_requests_for_model" + ), ) # Remaining MODEL TPM limit for API Key self.litellm_remaining_api_key_tokens_for_model = self._gauge_factory( "litellm_remaining_api_key_tokens_for_model", "Remaining Tokens API Key can make for model (model based tpm limit on key)", - labelnames=["hashed_api_key", "api_key_alias", "model"], + labelnames=self.get_labels_for_metric( + "litellm_remaining_api_key_tokens_for_model" + ), ) ######################################## @@ -312,6 +316,18 @@ class PrometheusLogger(CustomLogger): labelnames=self.get_labels_for_metric("litellm_deployment_state"), ) + self.litellm_deployment_tpm_limit = self._gauge_factory( + "litellm_deployment_tpm_limit", + "Deployment TPM limit found in config", + labelnames=self.get_labels_for_metric("litellm_deployment_tpm_limit"), + ) + + self.litellm_deployment_rpm_limit = self._gauge_factory( + "litellm_deployment_rpm_limit", + "Deployment RPM limit found in config", + labelnames=self.get_labels_for_metric("litellm_deployment_rpm_limit"), + ) + 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", @@ -373,15 +389,9 @@ class PrometheusLogger(CustomLogger): self.litellm_llm_api_failed_requests_metric = self._counter_factory( name="litellm_llm_api_failed_requests_metric", documentation="deprecated - use litellm_proxy_failed_requests_metric", - labelnames=[ - "end_user", - "hashed_api_key", - "api_key_alias", - "model", - "team", - "team_alias", - "user", - ], + labelnames=self.get_labels_for_metric( + "litellm_llm_api_failed_requests_metric" + ), ) self.litellm_requests_metric = self._counter_factory( @@ -891,7 +901,7 @@ class PrometheusLogger(CustomLogger): model = kwargs.get("model", "") litellm_params = kwargs.get("litellm_params", {}) or {} - _metadata = litellm_params.get("metadata", {}) + _metadata = litellm_params.get("metadata") or {} get_end_user_id_for_cost_tracking = _get_cached_end_user_id_for_cost_tracking() end_user_id = get_end_user_id_for_cost_tracking( @@ -954,6 +964,8 @@ class PrometheusLogger(CustomLogger): route=standard_logging_payload["metadata"].get( "user_api_key_request_route" ), + client_ip=standard_logging_payload["metadata"].get("requester_ip_address"), + user_agent=standard_logging_payload["metadata"].get("user_agent"), ) if ( @@ -1011,6 +1023,7 @@ class PrometheusLogger(CustomLogger): user_api_key_alias=user_api_key_alias, kwargs=kwargs, metadata=_metadata, + model_id=enum_values.model_id, ) # set latency metrics @@ -1165,26 +1178,15 @@ class PrometheusLogger(CustomLogger): response_cost: float, user_id: Optional[str] = None, ): - _team_spend = litellm_params.get("metadata", {}).get( - "user_api_key_team_spend", None - ) - _team_max_budget = litellm_params.get("metadata", {}).get( - "user_api_key_team_max_budget", None - ) + _metadata = litellm_params.get("metadata") or {} + _team_spend = _metadata.get("user_api_key_team_spend", None) + _team_max_budget = _metadata.get("user_api_key_team_max_budget", None) - _api_key_spend = litellm_params.get("metadata", {}).get( - "user_api_key_spend", None - ) - _api_key_max_budget = litellm_params.get("metadata", {}).get( - "user_api_key_max_budget", None - ) + _api_key_spend = _metadata.get("user_api_key_spend", None) + _api_key_max_budget = _metadata.get("user_api_key_max_budget", None) - _user_spend = litellm_params.get("metadata", {}).get( - "user_api_key_user_spend", None - ) - _user_max_budget = litellm_params.get("metadata", {}).get( - "user_api_key_user_max_budget", None - ) + _user_spend = _metadata.get("user_api_key_user_spend", None) + _user_max_budget = _metadata.get("user_api_key_user_max_budget", None) await self._set_api_key_budget_metrics_after_api_request( user_api_key=user_api_key, @@ -1245,6 +1247,7 @@ class PrometheusLogger(CustomLogger): user_api_key_alias: Optional[str], kwargs: dict, metadata: dict, + model_id: Optional[str] = None, ): from litellm.proxy.common_utils.callback_utils import ( get_model_group_from_litellm_kwargs, @@ -1266,11 +1269,11 @@ class PrometheusLogger(CustomLogger): ) self.litellm_remaining_api_key_requests_for_model.labels( - user_api_key, user_api_key_alias, model_group + user_api_key, user_api_key_alias, model_group, model_id ).set(remaining_requests) self.litellm_remaining_api_key_tokens_for_model.labels( - user_api_key, user_api_key_alias, model_group + user_api_key, user_api_key_alias, model_group, model_id ).set(remaining_tokens) def _set_latency_metrics( @@ -1296,12 +1299,14 @@ class PrometheusLogger(CustomLogger): time_to_first_token_seconds is not None and kwargs.get("stream", False) is True # only emit for streaming requests ): + _ttft_labels = prometheus_label_factory( + supported_enum_labels=self.get_labels_for_metric( + metric_name="litellm_llm_api_time_to_first_token_metric" + ), + enum_values=enum_values, + ) self.litellm_llm_api_time_to_first_token_metric.labels( - model, - user_api_key, - user_api_key_alias, - user_api_team, - user_api_team_alias, + **_ttft_labels ).observe(time_to_first_token_seconds) else: verbose_logger.debug( @@ -1341,7 +1346,7 @@ class PrometheusLogger(CustomLogger): # request queue time (time from arrival to processing start) _litellm_params = kwargs.get("litellm_params", {}) or {} - queue_time_seconds = _litellm_params.get("metadata", {}).get( + queue_time_seconds = (_litellm_params.get("metadata") or {}).get( "queue_time_seconds" ) if queue_time_seconds is not None and queue_time_seconds >= 0: @@ -1365,14 +1370,14 @@ class PrometheusLogger(CustomLogger): standard_logging_payload: StandardLoggingPayload = kwargs.get( "standard_logging_object", {} ) - + if self._should_skip_metrics_for_invalid_key( kwargs=kwargs, standard_logging_payload=standard_logging_payload ): return - + model = kwargs.get("model", "") - + litellm_params = kwargs.get("litellm_params", {}) or {} get_end_user_id_for_cost_tracking = _get_cached_end_user_id_for_cost_tracking() @@ -1396,6 +1401,7 @@ class PrometheusLogger(CustomLogger): user_api_team, user_api_team_alias, user_id, + standard_logging_payload.get("model_id", ""), ).inc() self.set_llm_deployment_failure_metrics(kwargs) except Exception as e: @@ -1413,49 +1419,57 @@ class PrometheusLogger(CustomLogger): ) -> Optional[int]: """ Extract HTTP status code from various input formats for validation. - + This is a centralized helper to extract status code from different callback function signatures. Handles both ProxyException (uses 'code') and standard exceptions (uses 'status_code'). - + Args: kwargs: Dictionary potentially containing 'exception' key enum_values: Object with 'status_code' attribute exception: Exception object to extract status code from directly - + Returns: Status code as integer if found, None otherwise """ status_code = None - + # Try from enum_values first (most common in our callbacks) - if enum_values and hasattr(enum_values, "status_code") and enum_values.status_code: + if ( + enum_values + and hasattr(enum_values, "status_code") + and enum_values.status_code + ): try: status_code = int(enum_values.status_code) except (ValueError, TypeError): pass - + if not status_code and exception: # ProxyException uses 'code' attribute, other exceptions may use 'status_code' - status_code = getattr(exception, "status_code", None) or getattr(exception, "code", None) + status_code = getattr(exception, "status_code", None) or getattr( + exception, "code", None + ) if status_code is not None: try: status_code = int(status_code) except (ValueError, TypeError): status_code = None - + if not status_code and kwargs: exception_in_kwargs = kwargs.get("exception") if exception_in_kwargs: - status_code = getattr(exception_in_kwargs, "status_code", None) or getattr(exception_in_kwargs, "code", None) + status_code = getattr( + exception_in_kwargs, "status_code", None + ) or getattr(exception_in_kwargs, "code", None) if status_code is not None: try: status_code = int(status_code) except (ValueError, TypeError): status_code = None - + return status_code - + def _is_invalid_api_key_request( self, status_code: Optional[int], @@ -1463,23 +1477,23 @@ class PrometheusLogger(CustomLogger): ) -> bool: """ Determine if a request has an invalid API key based on status code and exception. - + This method prevents invalid authentication attempts from being recorded in Prometheus metrics. A 401 status code is the definitive indicator of authentication failure. Additionally, we check exception messages for authentication error patterns to catch cases where the exception hasn't been converted to a ProxyException yet. - + Args: status_code: HTTP status code (401 indicates authentication error) exception: Exception object to check for auth-related error messages - + Returns: True if the request has an invalid API key and metrics should be skipped, False otherwise """ if status_code == 401: return True - + # Handle cases where AssertionError is raised before conversion to ProxyException if exception is not None: exception_str = str(exception).lower() @@ -1492,9 +1506,9 @@ class PrometheusLogger(CustomLogger): ] if any(pattern in exception_str for pattern in auth_error_patterns): return True - + return False - + def _should_skip_metrics_for_invalid_key( self, kwargs: Optional[dict] = None, @@ -1505,18 +1519,18 @@ class PrometheusLogger(CustomLogger): ) -> bool: """ Determine if Prometheus metrics should be skipped for invalid API key requests. - + This is a centralized validation method that extracts status code and exception information from various callback function signatures and determines if the request represents an invalid API key attempt that should be filtered from metrics. - + Args: kwargs: Dictionary potentially containing exception and other data user_api_key_dict: User API key authentication object (currently unused) enum_values: Object with status_code attribute standard_logging_payload: Standard logging payload dictionary exception: Exception object to check directly - + Returns: True if metrics should be skipped (invalid key detected), False otherwise """ @@ -1525,17 +1539,17 @@ class PrometheusLogger(CustomLogger): enum_values=enum_values, exception=exception, ) - + if exception is None and kwargs: exception = kwargs.get("exception") - + if self._is_invalid_api_key_request(status_code, exception=exception): verbose_logger.debug( "Skipping Prometheus metrics for invalid API key request: " f"status_code={status_code}, exception={type(exception).__name__ if exception else None}" ) return True - + return False async def async_post_call_failure_hook( @@ -1576,6 +1590,10 @@ class PrometheusLogger(CustomLogger): litellm_params=request_data, proxy_server_request=request_data.get("proxy_server_request", {}), ) + _metadata = request_data.get("metadata", {}) or {} + model_id = _metadata.get("model_info", {}).get("id") or request_data.get( + "model_info", {} + ).get("id") enum_values = UserAPIKeyLabelValues( end_user=user_api_key_dict.end_user_id, user=user_api_key_dict.user_id, @@ -1590,6 +1608,9 @@ class PrometheusLogger(CustomLogger): exception_class=self._get_exception_class_name(original_exception), tags=_tags, route=user_api_key_dict.request_route, + client_ip=_metadata.get("requester_ip_address"), + user_agent=_metadata.get("user_agent"), + model_id=model_id, ) _labels = prometheus_label_factory( supported_enum_labels=self.get_labels_for_metric( @@ -1629,6 +1650,7 @@ class PrometheusLogger(CustomLogger): ): return + _metadata = data.get("metadata", {}) or {} enum_values = UserAPIKeyLabelValues( end_user=user_api_key_dict.end_user_id, hashed_api_key=user_api_key_dict.api_key, @@ -1644,6 +1666,8 @@ class PrometheusLogger(CustomLogger): litellm_params=data, proxy_server_request=data.get("proxy_server_request", {}), ), + client_ip=_metadata.get("requester_ip_address"), + user_agent=_metadata.get("user_agent"), ) _labels = prometheus_label_factory( supported_enum_labels=self.get_labels_for_metric( @@ -1684,7 +1708,7 @@ class PrometheusLogger(CustomLogger): exception = request_kwargs.get("exception", None) llm_provider = _litellm_params.get("custom_llm_provider", None) - + if self._should_skip_metrics_for_invalid_key( kwargs=request_kwargs, standard_logging_payload=standard_logging_payload, @@ -1716,6 +1740,10 @@ class PrometheusLogger(CustomLogger): "user_api_key_team_alias" ], tags=standard_logging_payload.get("request_tags", []), + client_ip=standard_logging_payload["metadata"].get( + "requester_ip_address" + ), + user_agent=standard_logging_payload["metadata"].get("user_agent"), ) """ @@ -1753,6 +1781,49 @@ class PrometheusLogger(CustomLogger): ) ) + def _set_deployment_tpm_rpm_limit_metrics( + self, + model_info: dict, + litellm_params: dict, + litellm_model_name: Optional[str], + model_id: Optional[str], + api_base: Optional[str], + llm_provider: Optional[str], + ): + """ + Set the deployment TPM and RPM limits metrics + """ + tpm = model_info.get("tpm") or litellm_params.get("tpm") + rpm = model_info.get("rpm") or litellm_params.get("rpm") + + if tpm is not None: + _labels = prometheus_label_factory( + supported_enum_labels=self.get_labels_for_metric( + metric_name="litellm_deployment_tpm_limit" + ), + enum_values=UserAPIKeyLabelValues( + litellm_model_name=litellm_model_name, + model_id=model_id, + api_base=api_base, + api_provider=llm_provider, + ), + ) + self.litellm_deployment_tpm_limit.labels(**_labels).set(tpm) + + if rpm is not None: + _labels = prometheus_label_factory( + supported_enum_labels=self.get_labels_for_metric( + metric_name="litellm_deployment_rpm_limit" + ), + enum_values=UserAPIKeyLabelValues( + litellm_model_name=litellm_model_name, + model_id=model_id, + api_base=api_base, + api_provider=llm_provider, + ), + ) + self.litellm_deployment_rpm_limit.labels(**_labels).set(rpm) + def set_llm_deployment_success_metrics( self, request_kwargs: dict, @@ -1786,6 +1857,16 @@ class PrometheusLogger(CustomLogger): _model_info = _metadata.get("model_info") or {} model_id = _model_info.get("id", None) + if _model_info or _litellm_params: + self._set_deployment_tpm_rpm_limit_metrics( + model_info=_model_info, + litellm_params=_litellm_params, + litellm_model_name=litellm_model_name, + model_id=model_id, + api_base=api_base, + llm_provider=llm_provider, + ) + remaining_requests: Optional[int] = None remaining_tokens: Optional[int] = None if additional_headers := standard_logging_payload["hidden_params"][ @@ -2263,7 +2344,10 @@ class PrometheusLogger(CustomLogger): async def fetch_keys( page_size: int, page: int - ) -> Tuple[List[Union[str, UserAPIKeyAuth, LiteLLM_DeletedVerificationToken]], Optional[int]]: + ) -> Tuple[ + List[Union[str, UserAPIKeyAuth, LiteLLM_DeletedVerificationToken]], + Optional[int], + ]: key_list_response = await _list_key_helper( prisma_client=prisma_client, page=page, @@ -2379,12 +2463,16 @@ class PrometheusLogger(CustomLogger): # Get total user count total_users = await prisma_client.db.litellm_usertable.count() self.litellm_total_users_metric.set(total_users) - verbose_logger.debug(f"Prometheus: set litellm_total_users to {total_users}") + verbose_logger.debug( + f"Prometheus: set litellm_total_users to {total_users}" + ) # Get total team count total_teams = await prisma_client.db.litellm_teamtable.count() self.litellm_teams_count_metric.set(total_teams) - verbose_logger.debug(f"Prometheus: set litellm_teams_count to {total_teams}") + verbose_logger.debug( + f"Prometheus: set litellm_teams_count to {total_teams}" + ) except Exception as e: verbose_logger.exception( f"Error initializing user/team count metrics: {str(e)}" @@ -2412,8 +2500,8 @@ class PrometheusLogger(CustomLogger): self, user_api_team: Optional[str], user_api_team_alias: Optional[str], - team_spend: float, - team_max_budget: float, + team_spend: Optional[float], + team_max_budget: Optional[float], response_cost: float, ): """ @@ -2575,7 +2663,7 @@ class PrometheusLogger(CustomLogger): user_api_key: Optional[str], user_api_key_alias: Optional[str], response_cost: float, - key_max_budget: float, + key_max_budget: Optional[float], key_spend: Optional[float], ): if user_api_key: @@ -2592,7 +2680,7 @@ class PrometheusLogger(CustomLogger): self, user_api_key: str, user_api_key_alias: str, - key_max_budget: float, + key_max_budget: Optional[float], key_spend: Optional[float], response_cost: float, ) -> UserAPIKeyAuth: diff --git a/litellm/litellm_core_utils/core_helpers.py b/litellm/litellm_core_utils/core_helpers.py index 9cb0a00d9f..00695cbfb5 100644 --- a/litellm/litellm_core_utils/core_helpers.py +++ b/litellm/litellm_core_utils/core_helpers.py @@ -351,9 +351,9 @@ def filter_exceptions_from_params(data: Any, max_depth: int = 20) -> Any: # Skip callable objects (functions, methods, lambdas) but not classes (type objects) if callable(data) and not isinstance(data, type): return None - # Skip known non-serializable object types (Logging, etc.) + # Skip known non-serializable object types (Logging, Router, etc.) obj_type_name = type(data).__name__ - if obj_type_name in ["Logging", "LiteLLMLoggingObj"]: + if obj_type_name in ["Logging", "LiteLLMLoggingObj", "Router"]: return None if isinstance(data, dict): diff --git a/litellm/litellm_core_utils/get_litellm_params.py b/litellm/litellm_core_utils/get_litellm_params.py index e290101f8b..060e98fd49 100644 --- a/litellm/litellm_core_utils/get_litellm_params.py +++ b/litellm/litellm_core_utils/get_litellm_params.py @@ -93,8 +93,11 @@ def get_litellm_params( "text_completion": text_completion, "azure_ad_token_provider": azure_ad_token_provider, "user_continue_message": user_continue_message, - "base_model": base_model or ( - _get_base_model_from_litellm_call_metadata(metadata=metadata) if metadata else None + "base_model": base_model + or ( + _get_base_model_from_litellm_call_metadata(metadata=metadata) + if metadata + else None ), "litellm_trace_id": litellm_trace_id, "litellm_session_id": litellm_session_id, @@ -139,5 +142,7 @@ def get_litellm_params( "aws_sts_endpoint": kwargs.get("aws_sts_endpoint"), "aws_external_id": kwargs.get("aws_external_id"), "aws_bedrock_runtime_endpoint": kwargs.get("aws_bedrock_runtime_endpoint"), + "tpm": kwargs.get("tpm"), + "rpm": kwargs.get("rpm"), } return litellm_params diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py index f0ea9e3981..f25c84fac2 100644 --- a/litellm/litellm_core_utils/litellm_logging.py +++ b/litellm/litellm_core_utils/litellm_logging.py @@ -335,7 +335,9 @@ class Logging(LiteLLMLoggingBaseClass): self.start_time = start_time # log the call start time self.call_type = call_type self.litellm_call_id = litellm_call_id - self.litellm_trace_id: str = litellm_trace_id if litellm_trace_id else str(uuid.uuid4()) + self.litellm_trace_id: str = ( + litellm_trace_id if litellm_trace_id else str(uuid.uuid4()) + ) self.function_id = function_id self.streaming_chunks: List[Any] = [] # for generating complete stream response self.sync_streaming_chunks: List[ @@ -544,7 +546,10 @@ class Logging(LiteLLMLoggingBaseClass): if "stream_options" in additional_params: self.stream_options = additional_params["stream_options"] ## check if custom pricing set ## - if any(litellm_params.get(key) is not None for key in _CUSTOM_PRICING_KEYS & litellm_params.keys()): + if any( + litellm_params.get(key) is not None + for key in _CUSTOM_PRICING_KEYS & litellm_params.keys() + ): self.custom_pricing = True if "custom_llm_provider" in self.model_call_details: @@ -1633,11 +1638,19 @@ class Logging(LiteLLMLoggingBaseClass): "standard_logging_object" ) ) is not None: - standard_logging_payload["response"] = ( + response_dict = ( result.model_dump() if hasattr(result, "model_dump") else dict(result) ) + # Ensure usage is properly included with transformed chat format + if transformed_usage is not None: + response_dict["usage"] = ( + transformed_usage.model_dump() + if hasattr(transformed_usage, "model_dump") + else dict(transformed_usage) + ) + standard_logging_payload["response"] = response_dict elif isinstance(result, TranscriptionResponse): from litellm.litellm_core_utils.llm_cost_calc.usage_object_transformation import ( TranscriptionUsageObjectTransformation, @@ -2323,18 +2336,28 @@ class Logging(LiteLLMLoggingBaseClass): batch_cost = kwargs.get("batch_cost", None) batch_usage = kwargs.get("batch_usage", None) batch_models = kwargs.get("batch_models", None) - if all([batch_cost, batch_usage, batch_models]) is not None: + has_explicit_batch_data = all( + x is not None for x in (batch_cost, batch_usage, batch_models) + ) + + should_compute_batch_data = ( + not is_base64_unified_file_id + or not has_explicit_batch_data + and result.status == "completed" + ) + if has_explicit_batch_data: result._hidden_params["response_cost"] = batch_cost result._hidden_params["batch_models"] = batch_models result.usage = batch_usage - elif not is_base64_unified_file_id: # only run for non-unified file ids + elif should_compute_batch_data: ( response_cost, batch_usage, batch_models, ) = await _handle_completed_batch( - batch=result, custom_llm_provider=self.custom_llm_provider + batch=result, + custom_llm_provider=self.custom_llm_provider, ) result._hidden_params["response_cost"] = response_cost @@ -3299,6 +3322,7 @@ def _get_masked_values( "token", "key", "secret", + "vertex_credentials", ] return { k: ( @@ -4443,6 +4467,7 @@ class StandardLoggingPayloadSetup: user_api_key_request_route=None, spend_logs_metadata=None, requester_ip_address=None, + user_agent=None, requester_metadata=None, prompt_management_metadata=prompt_management_metadata, applied_guardrails=applied_guardrails, @@ -4523,6 +4548,10 @@ class StandardLoggingPayloadSetup: ) elif isinstance(usage, Usage): return usage + elif isinstance(usage, ResponseAPIUsage): + return ResponseAPILoggingUtils._transform_response_api_usage_to_chat_usage( + usage + ) elif isinstance(usage, dict): if ResponseAPILoggingUtils._is_response_api_usage(usage): return ( @@ -4723,7 +4752,14 @@ class StandardLoggingPayloadSetup: ) -> StandardLoggingPayloadErrorInformation: from litellm.constants import MAXIMUM_TRACEBACK_LINES_TO_LOG - error_status: str = str(getattr(original_exception, "status_code", "")) + # Check for 'code' first (used by ProxyException), then fall back to 'status_code' (used by LiteLLM exceptions) + # Ensure error_code is always a string for Prisma Python JSON field compatibility + error_code_attr = getattr(original_exception, "code", None) + if error_code_attr is not None and str(error_code_attr) not in ("", "None"): + error_status: str = str(error_code_attr) + else: + status_code_attr = getattr(original_exception, "status_code", None) + error_status = str(status_code_attr) if status_code_attr is not None else "" error_class: str = ( str(original_exception.__class__.__name__) if original_exception else "" ) @@ -5128,6 +5164,7 @@ def get_standard_logging_object_payload( model_group=_model_group, model_id=_model_id, requester_ip_address=clean_metadata.get("requester_ip_address", None), + user_agent=clean_metadata.get("user_agent", None), messages=StandardLoggingPayloadSetup.append_system_prompt_messages( kwargs=kwargs, messages=kwargs.get("messages") ), @@ -5193,6 +5230,7 @@ def get_standard_logging_metadata( user_api_key_team_alias=None, spend_logs_metadata=None, requester_ip_address=None, + user_agent=None, requester_metadata=None, user_api_key_end_user_id=None, prompt_management_metadata=None, diff --git a/litellm/litellm_core_utils/llm_cost_calc/utils.py b/litellm/litellm_core_utils/llm_cost_calc/utils.py index 785976ed31..fe06641a38 100644 --- a/litellm/litellm_core_utils/llm_cost_calc/utils.py +++ b/litellm/litellm_core_utils/llm_cost_calc/utils.py @@ -23,6 +23,15 @@ def _is_above_128k(tokens: float) -> bool: return False +def get_billable_input_tokens(usage: Usage) -> int: + """ + Returns the number of billable input tokens. + Subtracts cached tokens from prompt tokens if applicable. + """ + details = _parse_prompt_tokens_details(usage) + return usage.prompt_tokens - details["cache_hit_tokens"] + + def select_cost_metric_for_model( model_info: ModelInfo, ) -> Literal["cost_per_character", "cost_per_token"]: @@ -190,7 +199,6 @@ def _get_token_base_cost( 1000 if "k" in threshold_str else 1 ) if usage.prompt_tokens > threshold: - prompt_base_cost = cast( float, _get_cost_per_unit(model_info, key, prompt_base_cost) ) @@ -566,14 +574,28 @@ def generic_cost_per_token( # noqa: PLR0915 if usage.prompt_tokens_details: prompt_tokens_details = _parse_prompt_tokens_details(usage) - ## EDGE CASE - text tokens not set inside PromptTokensDetails + ## EDGE CASE - text tokens not set or includes cached tokens (double-counting) + ## Some providers (like xAI) report text_tokens = prompt_tokens (including cached) + ## We detect this when: text_tokens + cached_tokens + other > prompt_tokens + ## Ref: https://github.com/BerriAI/litellm/issues/19680, #14874, #14875 - if prompt_tokens_details["text_tokens"] == 0: + cache_hit = prompt_tokens_details["cache_hit_tokens"] + text_tokens = prompt_tokens_details["text_tokens"] + audio_tokens = prompt_tokens_details["audio_tokens"] + cache_creation = prompt_tokens_details["cache_creation_tokens"] + image_tokens = prompt_tokens_details["image_tokens"] + + # Check for double-counting: sum of details > prompt_tokens means overlap + total_details = text_tokens + cache_hit + audio_tokens + cache_creation + image_tokens + has_double_counting = cache_hit > 0 and total_details > usage.prompt_tokens + + if text_tokens == 0 or has_double_counting: text_tokens = ( usage.prompt_tokens - - prompt_tokens_details["cache_hit_tokens"] - - prompt_tokens_details["audio_tokens"] - - prompt_tokens_details["cache_creation_tokens"] + - cache_hit + - audio_tokens + - cache_creation + - image_tokens ) prompt_tokens_details["text_tokens"] = text_tokens @@ -619,7 +641,11 @@ def generic_cost_per_token( # noqa: PLR0915 # Calculate text tokens as remainder when we have a breakdown # This handles cases like OpenAI's reasoning models where text_tokens isn't provided text_tokens = max( - 0, usage.completion_tokens - reasoning_tokens - audio_tokens - image_tokens + 0, + usage.completion_tokens + - reasoning_tokens + - audio_tokens + - image_tokens, ) else: # No breakdown at all, all tokens are text tokens 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 bbe28e3ec2..25ad0a570c 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 @@ -21,11 +21,13 @@ from litellm.types.utils import ( ChatCompletionMessageToolCall, ChatCompletionRedactedThinkingBlock, Choices, + CompletionTokensDetailsWrapper, Delta, EmbeddingResponse, Function, HiddenParams, ImageResponse, + PromptTokensDetailsWrapper, ) from litellm.types.utils import Logprobs as TextCompletionLogprobs from litellm.types.utils import ( @@ -304,6 +306,22 @@ class LiteLLMResponseObjectHandler: "text_tokens": 0, } + # Map Responses API naming to Chat Completions API naming for cost calculator + if usage.get("prompt_tokens") is None: + usage["prompt_tokens"] = usage.get("input_tokens", 0) + if usage.get("completion_tokens") is None: + usage["completion_tokens"] = usage.get("output_tokens", 0) + + # Convert dicts to wrapper objects so getattr() works in cost calculation + if isinstance(usage.get("input_tokens_details"), dict): + usage["prompt_tokens_details"] = PromptTokensDetailsWrapper( + **usage["input_tokens_details"] + ) + if isinstance(usage.get("output_tokens_details"), dict): + usage["completion_tokens_details"] = CompletionTokensDetailsWrapper( + **usage["output_tokens_details"] + ) + if model_response_object is None: model_response_object = ImageResponse(**response_object) return model_response_object diff --git a/litellm/litellm_core_utils/logging_worker.py b/litellm/litellm_core_utils/logging_worker.py index 13a83956ed..d5eca9eeb5 100644 --- a/litellm/litellm_core_utils/logging_worker.py +++ b/litellm/litellm_core_utils/logging_worker.py @@ -415,6 +415,28 @@ class LoggingWorker: """ Safely log a message during shutdown, suppressing errors if logging is closed. """ + # Check if logger has valid handlers before attempting to log + # During shutdown, handlers may be closed, causing ValueError when writing + if not hasattr(verbose_logger, 'handlers') or not verbose_logger.handlers: + return + + # Check if any handler has a valid stream + has_valid_handler = False + for handler in verbose_logger.handlers: + try: + if hasattr(handler, 'stream') and handler.stream and not handler.stream.closed: + has_valid_handler = True + break + elif not hasattr(handler, 'stream'): + # Non-stream handlers (like NullHandler) are always valid + has_valid_handler = True + break + except (AttributeError, ValueError): + continue + + if not has_valid_handler: + return + try: if level == "debug": verbose_logger.debug(message) diff --git a/litellm/litellm_core_utils/prompt_templates/factory.py b/litellm/litellm_core_utils/prompt_templates/factory.py index 30263543fc..98ee5e4fa8 100644 --- a/litellm/litellm_core_utils/prompt_templates/factory.py +++ b/litellm/litellm_core_utils/prompt_templates/factory.py @@ -1632,6 +1632,7 @@ def _sanitize_anthropic_tool_use_id(tool_use_id: str) -> str: def convert_to_anthropic_tool_result( message: Union[ChatCompletionToolMessage, ChatCompletionFunctionMessage], + force_base64: bool = False, ) -> AnthropicMessagesToolResultParam: """ OpenAI message with a tool result looks like: @@ -1677,13 +1678,16 @@ def convert_to_anthropic_tool_result( ] = [] for content in content_list: if content["type"] == "text": - anthropic_content_list.append( - AnthropicMessagesToolResultContent( - type="text", - text=content["text"], - cache_control=content.get("cache_control", None), - ) - ) + # Only include cache_control if explicitly set and not None + # to avoid sending "cache_control": null which breaks some API channels + text_content: AnthropicMessagesToolResultContent = { + "type": "text", + "text": content["text"], + } + cache_control_value = content.get("cache_control") + if cache_control_value is not None: + text_content["cache_control"] = cache_control_value + anthropic_content_list.append(text_content) elif content["type"] == "image_url": format = ( content["image_url"].get("format") @@ -1691,7 +1695,7 @@ def convert_to_anthropic_tool_result( else None ) _anthropic_image_param = create_anthropic_image_param( - content["image_url"], format=format + content["image_url"], format=format, is_bedrock_invoke=force_base64 ) _anthropic_image_param = add_cache_control_to_content( anthropic_content_element=_anthropic_image_param, @@ -2053,6 +2057,12 @@ def anthropic_messages_pt( # noqa: PLR0915 else: messages.append(DEFAULT_USER_CONTINUE_MESSAGE_TYPED) + # Bedrock invoke models have format: invoke/... + # Vertex AI Anthropic also doesn't support URL sources for images + is_bedrock_invoke = model.lower().startswith("invoke/") + is_vertex_ai = llm_provider.startswith("vertex_ai") if llm_provider else False + force_base64 = is_bedrock_invoke or is_vertex_ai + msg_i = 0 while msg_i < len(messages): user_content: List[AnthropicMessagesUserMessageValues] = [] @@ -2162,7 +2172,9 @@ def anthropic_messages_pt( # noqa: PLR0915 ): # OpenAI's tool message content will always be a string user_content.append( - convert_to_anthropic_tool_result(user_message_types_block) + convert_to_anthropic_tool_result( + user_message_types_block, force_base64=force_base64 + ) ) msg_i += 1 @@ -4408,7 +4420,7 @@ def _bedrock_tools_pt(tools: List) -> List[BedrockToolBlock]: ] """ """ - Bedrock toolConfig looks like: + Bedrock toolConfig looks like: "tools": [ { "toolSpec": { @@ -4436,6 +4448,7 @@ def _bedrock_tools_pt(tools: List) -> List[BedrockToolBlock]: tool_block_list: List[BedrockToolBlock] = [] for tool in tools: + # Handle regular function tools parameters = tool.get("function", {}).get( "parameters", {"type": "object", "properties": {}} ) diff --git a/litellm/litellm_core_utils/prompt_templates/image_handling.py b/litellm/litellm_core_utils/prompt_templates/image_handling.py index 5d0bedb776..7137a4e422 100644 --- a/litellm/litellm_core_utils/prompt_templates/image_handling.py +++ b/litellm/litellm_core_utils/prompt_templates/image_handling.py @@ -31,15 +31,19 @@ def _process_image_response(response: Response, url: str) -> str: f"Error: Image size ({size_mb:.2f}MB) exceeds maximum allowed size ({MAX_IMAGE_URL_DOWNLOAD_SIZE_MB}MB). url={url}" ) - image_bytes = response.content + # Stream download with size checking to prevent downloading huge files + max_bytes = int(MAX_IMAGE_URL_DOWNLOAD_SIZE_MB * 1024 * 1024) + image_bytes = bytearray() + bytes_downloaded = 0 - # Check actual size after download if Content-Length was not available - if content_length is None: - size_mb = len(image_bytes) / (1024 * 1024) - if size_mb > MAX_IMAGE_URL_DOWNLOAD_SIZE_MB: + for chunk in response.iter_bytes(chunk_size=8192): + bytes_downloaded += len(chunk) + if bytes_downloaded > max_bytes: + size_mb = bytes_downloaded / (1024 * 1024) raise litellm.ImageFetchError( f"Error: Image size ({size_mb:.2f}MB) exceeds maximum allowed size ({MAX_IMAGE_URL_DOWNLOAD_SIZE_MB}MB). url={url}" ) + image_bytes.extend(chunk) base64_image = base64.b64encode(image_bytes).decode("utf-8") diff --git a/litellm/llms/anthropic/chat/guardrail_translation/handler.py b/litellm/llms/anthropic/chat/guardrail_translation/handler.py index 9d50cc4d92..71d74121a3 100644 --- a/litellm/llms/anthropic/chat/guardrail_translation/handler.py +++ b/litellm/llms/anthropic/chat/guardrail_translation/handler.py @@ -110,6 +110,10 @@ class AnthropicMessagesHandler(BaseTranslation): inputs["tools"] = tools_to_check if structured_messages: inputs["structured_messages"] = structured_messages + # Include model information if available + model = data.get("model") + if model: + inputs["model"] = model guardrailed_inputs = await guardrail_to_apply.apply_guardrail( inputs=inputs, request_data=data, @@ -309,6 +313,14 @@ class AnthropicMessagesHandler(BaseTranslation): inputs["images"] = images_to_check if tool_calls_to_check: inputs["tool_calls"] = tool_calls_to_check + # Include model information from the response if available + response_model = None + if isinstance(response, dict): + response_model = response.get("model") + elif hasattr(response, "model"): + response_model = getattr(response, "model", None) + if response_model: + inputs["model"] = response_model guardrailed_inputs = await guardrail_to_apply.apply_guardrail( inputs=inputs, @@ -552,7 +564,7 @@ class AnthropicMessagesHandler(BaseTranslation): response_content = response.get("content", []) else: response_content = getattr(response, "content", None) or [] - + if not response_content: return False for content_block in response_content: diff --git a/litellm/llms/anthropic/chat/transformation.py b/litellm/llms/anthropic/chat/transformation.py index 82eccee596..1b61b53327 100644 --- a/litellm/llms/anthropic/chat/transformation.py +++ b/litellm/llms/anthropic/chat/transformation.py @@ -290,10 +290,19 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): elif tool_choice == "none": _tool_choice = AnthropicMessagesToolChoice(type="none") elif isinstance(tool_choice, dict): - _tool_name = tool_choice.get("function", {}).get("name") - _tool_choice = AnthropicMessagesToolChoice(type="tool") - if _tool_name is not None: - _tool_choice["name"] = _tool_name + if "type" in tool_choice and "function" not in tool_choice: + tool_type = tool_choice.get("type") + if tool_type == "auto": + _tool_choice = AnthropicMessagesToolChoice(type="auto") + elif tool_type == "required" or tool_type == "any": + _tool_choice = AnthropicMessagesToolChoice(type="any") + elif tool_type == "none": + _tool_choice = AnthropicMessagesToolChoice(type="none") + else: + _tool_name = tool_choice.get("function", {}).get("name") + if _tool_name is not None: + _tool_choice = AnthropicMessagesToolChoice(type="tool") + _tool_choice["name"] = _tool_name if parallel_tool_use is not None: # Anthropic uses 'disable_parallel_tool_use' flag to determine if parallel tool use is allowed @@ -1369,7 +1378,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): else 0 ) completion_token_details = CompletionTokensDetailsWrapper( - reasoning_tokens=reasoning_tokens if reasoning_tokens > 0 else None, + reasoning_tokens=reasoning_tokens if reasoning_tokens > 0 else 0, text_tokens=completion_tokens - reasoning_tokens if reasoning_tokens > 0 else completion_tokens, ) total_tokens = prompt_tokens + completion_tokens diff --git a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py index 1706f045f1..5ba0754b74 100644 --- a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py +++ b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py @@ -168,6 +168,36 @@ class LiteLLMAnthropicMessagesAdapter: return provider_specific_fields.get("signature") return None + def _add_cache_control_if_applicable( + self, + source: Any, + target: Any, + model: Optional[str], + ) -> None: + """ + Extract cache_control from source and add to target if it should be preserved. + + This method accepts Any type to support both regular dicts and TypedDict objects. + TypedDict objects (like ChatCompletionTextObject, ChatCompletionImageObject, etc.) + are dicts at runtime but have specific types at type-check time. Using Any allows + this method to work with both while maintaining runtime correctness. + + Args: + source: Dict or TypedDict containing potential cache_control field + target: Dict or TypedDict to add cache_control to + model: Model name to check if cache_control should be preserved + """ + # TypedDict objects are dicts at runtime, so .get() works + cache_control = source.get("cache_control") if isinstance(source, dict) else getattr(source, "cache_control", None) + if cache_control and model and self.is_anthropic_claude_model(model): + # TypedDict objects support dict operations at runtime + # Use type ignore consistent with codebase pattern (see anthropic/chat/transformation.py:432) + if isinstance(target, dict): + target["cache_control"] = cache_control # type: ignore[typeddict-item] + else: + # Fallback for non-dict objects (shouldn't happen in practice) + cast(Dict[str, Any], target)["cache_control"] = cache_control + def translatable_anthropic_params(self) -> List: """ Which anthropic params, we need to translate to the openai format. @@ -205,12 +235,8 @@ class LiteLLMAnthropicMessagesAdapter: text_obj = ChatCompletionTextObject( type="text", text=content.get("text", "") ) - # Preserve cache_control if present (for prompt caching) - # Only for Anthropic models that support prompt caching - cache_control = content.get("cache_control") - if cache_control and model and self.is_anthropic_claude_model(model): - text_obj["cache_control"] = cache_control # type: ignore - new_user_content_list.append(text_obj) + self._add_cache_control_if_applicable(content, text_obj, model) + new_user_content_list.append(text_obj) # type: ignore elif content.get("type") == "image": # Convert Anthropic image format to OpenAI format source = content.get("source", {}) @@ -225,7 +251,24 @@ class LiteLLMAnthropicMessagesAdapter: image_obj = ChatCompletionImageObject( type="image_url", image_url=image_url_obj ) - new_user_content_list.append(image_obj) + self._add_cache_control_if_applicable(content, image_obj, model) + new_user_content_list.append(image_obj) # type: ignore + elif content.get("type") == "document": + # Convert Anthropic document format (PDF, etc.) to OpenAI format + source = content.get("source", {}) + openai_image_url = ( + self._translate_anthropic_image_to_openai(cast(dict, source)) + ) + + if openai_image_url: + image_url_obj = ChatCompletionImageUrlObject( + url=openai_image_url + ) + doc_obj = ChatCompletionImageObject( + type="image_url", image_url=image_url_obj + ) + self._add_cache_control_if_applicable(content, doc_obj, model) + new_user_content_list.append(doc_obj) # type: ignore elif content.get("type") == "tool_result": if "content" not in content: tool_result = ChatCompletionToolMessage( @@ -233,14 +276,16 @@ class LiteLLMAnthropicMessagesAdapter: tool_call_id=content.get("tool_use_id", ""), content="", ) - tool_message_list.append(tool_result) + self._add_cache_control_if_applicable(content, tool_result, model) + tool_message_list.append(tool_result) # type: ignore[arg-type] elif isinstance(content.get("content"), str): tool_result = ChatCompletionToolMessage( role="tool", tool_call_id=content.get("tool_use_id", ""), content=str(content.get("content", "")), ) - tool_message_list.append(tool_result) + self._add_cache_control_if_applicable(content, tool_result, model) + tool_message_list.append(tool_result) # type: ignore[arg-type] elif isinstance(content.get("content"), list): # Combine all content items into a single tool message # to avoid creating multiple tool_result blocks with the same ID @@ -256,7 +301,8 @@ class LiteLLMAnthropicMessagesAdapter: tool_call_id=content.get("tool_use_id", ""), content=c, ) - tool_message_list.append(tool_result) + self._add_cache_control_if_applicable(content, tool_result, model) + tool_message_list.append(tool_result) # type: ignore[arg-type] elif isinstance(c, dict): if c.get("type") == "text": tool_result = ChatCompletionToolMessage( @@ -266,7 +312,8 @@ class LiteLLMAnthropicMessagesAdapter: ), content=c.get("text", ""), ) - tool_message_list.append(tool_result) + self._add_cache_control_if_applicable(content, tool_result, model) + tool_message_list.append(tool_result) # type: ignore[arg-type] elif c.get("type") == "image": source = c.get("source", {}) openai_image_url = ( @@ -282,7 +329,8 @@ class LiteLLMAnthropicMessagesAdapter: ), content=openai_image_url, ) - tool_message_list.append(tool_result) + self._add_cache_control_if_applicable(content, tool_result, model) + tool_message_list.append(tool_result) # type: ignore[arg-type] else: # For multiple content items, combine into a single tool message # with list content to preserve all items while having one tool_use_id @@ -331,7 +379,8 @@ class LiteLLMAnthropicMessagesAdapter: tool_call_id=content.get("tool_use_id", ""), content=combined_content_parts, # type: ignore ) - tool_message_list.append(tool_result) + self._add_cache_control_if_applicable(content, tool_result, model) + tool_message_list.append(tool_result) # type: ignore[arg-type] if len(tool_message_list) > 0: new_messages.extend(tool_message_list) @@ -344,6 +393,8 @@ class LiteLLMAnthropicMessagesAdapter: ## ASSISTANT MESSAGE ## assistant_message_str: Optional[str] = None + assistant_content_list: List[Dict[str, Any]] = [] # For content blocks with cache_control + has_cache_control_in_text = False tool_calls: List[ChatCompletionAssistantToolCall] = [] thinking_blocks: List[ Union[ChatCompletionThinkingBlock, ChatCompletionRedactedThinkingBlock] @@ -357,10 +408,14 @@ class LiteLLMAnthropicMessagesAdapter: assistant_message_str = str(content) elif isinstance(content, dict): if content.get("type") == "text": - if assistant_message_str is None: - assistant_message_str = content.get("text", "") - else: - assistant_message_str += content.get("text", "") + text_block: Dict[str, Any] = { + "type": "text", + "text": content.get("text", ""), + } + self._add_cache_control_if_applicable(content, text_block, model) + if "cache_control" in text_block: + has_cache_control_in_text = True + assistant_content_list.append(text_block) elif content.get("type") == "tool_use": function_chunk: ChatCompletionToolCallFunctionChunk = { "name": content.get("name", ""), @@ -384,13 +439,13 @@ class LiteLLMAnthropicMessagesAdapter: provider_specific_fields ) - tool_calls.append( - ChatCompletionAssistantToolCall( - id=content.get("id", ""), - type="function", - function=function_chunk, - ) + tool_call = ChatCompletionAssistantToolCall( + id=content.get("id", ""), + type="function", + function=function_chunk, ) + self._add_cache_control_if_applicable(content, tool_call, model) + tool_calls.append(tool_call) elif content.get("type") == "thinking": thinking_block = ChatCompletionThinkingBlock( type="thinking", @@ -411,18 +466,30 @@ class LiteLLMAnthropicMessagesAdapter: if ( assistant_message_str is not None + or len(assistant_content_list) > 0 or len(tool_calls) > 0 or len(thinking_blocks) > 0 ): + # Use list format if any text block has cache_control, otherwise use string + if has_cache_control_in_text and len(assistant_content_list) > 0: + assistant_content: Any = assistant_content_list + elif len(assistant_content_list) > 0 and not has_cache_control_in_text: + # Concatenate text blocks into string when no cache_control + assistant_content = "".join( + block.get("text", "") for block in assistant_content_list + ) + else: + assistant_content = assistant_message_str + assistant_message = ChatCompletionAssistantMessage( role="assistant", - content=assistant_message_str, + content=assistant_content, thinking_blocks=( thinking_blocks if len(thinking_blocks) > 0 else None ), ) if len(tool_calls) > 0: - assistant_message["tool_calls"] = tool_calls + assistant_message["tool_calls"] = tool_calls # type: ignore if len(thinking_blocks) > 0: assistant_message["thinking_blocks"] = thinking_blocks # type: ignore new_messages.append(assistant_message) @@ -532,10 +599,10 @@ class LiteLLMAnthropicMessagesAdapter: ) def translate_anthropic_tools_to_openai( - self, tools: List[AllAnthropicToolsValues] + self, tools: List[AllAnthropicToolsValues], model: Optional[str] = None ) -> List[ChatCompletionToolParam]: new_tools: List[ChatCompletionToolParam] = [] - mapped_tool_params = ["name", "input_schema", "description"] + mapped_tool_params = ["name", "input_schema", "description", "cache_control"] for tool in tools: function_chunk = ChatCompletionToolParamFunctionChunk( name=tool["name"], @@ -548,11 +615,11 @@ class LiteLLMAnthropicMessagesAdapter: for k, v in tool.items(): if k not in mapped_tool_params: # pass additional computer kwargs function_chunk.setdefault("parameters", {}).update({k: v}) - new_tools.append( - ChatCompletionToolParam(type="function", function=function_chunk) - ) + tool_param = ChatCompletionToolParam(type="function", function=function_chunk) + self._add_cache_control_if_applicable(tool, tool_param, model) + new_tools.append(tool_param) # type: ignore[arg-type] - return new_tools + return new_tools # type: ignore[return-value] def translate_anthropic_output_format_to_openai( self, output_format: Any @@ -590,6 +657,41 @@ class LiteLLMAnthropicMessagesAdapter: }, } + def _add_system_message_to_messages( + self, + new_messages: List[AllMessageValues], + anthropic_message_request: AnthropicMessagesRequest, + ) -> None: + """Add system message to messages list if present in request.""" + if "system" not in anthropic_message_request: + return + system_content = anthropic_message_request["system"] + if not system_content: + return + # Handle system as string or array of content blocks + if isinstance(system_content, str): + new_messages.insert( + 0, + ChatCompletionSystemMessage(role="system", content=system_content), + ) + elif isinstance(system_content, list): + # Convert Anthropic system content blocks to OpenAI format + openai_system_content: List[Dict[str, Any]] = [] + model_name = anthropic_message_request.get("model", "") + for block in system_content: + if isinstance(block, dict) and block.get("type") == "text": + text_block: Dict[str, Any] = { + "type": "text", + "text": block.get("text", ""), + } + self._add_cache_control_if_applicable(block, text_block, model_name) + openai_system_content.append(text_block) + if openai_system_content: + new_messages.insert( + 0, + ChatCompletionSystemMessage(role="system", content=openai_system_content), # type: ignore + ) + def translate_anthropic_to_openai( self, anthropic_message_request: AnthropicMessagesRequest ) -> ChatCompletionRequest: @@ -618,13 +720,7 @@ class LiteLLMAnthropicMessagesAdapter: model=anthropic_message_request.get("model"), ) ## ADD SYSTEM MESSAGE TO MESSAGES - if "system" in anthropic_message_request: - system_content = anthropic_message_request["system"] - if system_content: - new_messages.insert( - 0, - ChatCompletionSystemMessage(role="system", content=system_content), - ) + self._add_system_message_to_messages(new_messages, anthropic_message_request) new_kwargs: ChatCompletionRequest = { "model": anthropic_message_request["model"], @@ -655,7 +751,8 @@ class LiteLLMAnthropicMessagesAdapter: tools = anthropic_message_request["tools"] if tools: new_kwargs["tools"] = self.translate_anthropic_tools_to_openai( - tools=cast(List[AllAnthropicToolsValues], tools) + tools=cast(List[AllAnthropicToolsValues], tools), + model=new_kwargs.get("model"), ) ## CONVERT THINKING @@ -843,7 +940,7 @@ class LiteLLMAnthropicMessagesAdapter: role="assistant", model=response.model or "unknown-model", stop_sequence=None, - usage=anthropic_usage, + usage=anthropic_usage, # type: ignore content=anthropic_content, # type: ignore stop_reason=anthropic_finish_reason, ) @@ -992,7 +1089,7 @@ class LiteLLMAnthropicMessagesAdapter: else: usage_delta = UsageDelta(input_tokens=0, output_tokens=0) return MessageBlockDelta( - type="message_delta", delta=delta, usage=usage_delta + type="message_delta", delta=delta, usage=usage_delta # type: ignore ) ( type_of_content, diff --git a/litellm/llms/azure/batches/handler.py b/litellm/llms/azure/batches/handler.py index 3996cb808e..aaefe80168 100644 --- a/litellm/llms/azure/batches/handler.py +++ b/litellm/llms/azure/batches/handler.py @@ -5,12 +5,10 @@ Azure Batches API Handler from typing import Any, Coroutine, Optional, Union, cast import httpx - from openai import AsyncOpenAI, OpenAI from litellm.llms.azure.azure import AsyncAzureOpenAI, AzureOpenAI from litellm.types.llms.openai import ( - Batch, CancelBatchRequest, CreateBatchRequest, RetrieveBatchRequest, @@ -130,9 +128,9 @@ class AzureBatchesAPI(BaseAzureLLM): self, cancel_batch_data: CancelBatchRequest, client: Union[AsyncAzureOpenAI, AsyncOpenAI], - ) -> Batch: + ) -> LiteLLMBatch: response = await client.batches.cancel(**cancel_batch_data) - return response + return LiteLLMBatch(**response.model_dump()) def cancel_batch( self, @@ -160,8 +158,23 @@ class AzureBatchesAPI(BaseAzureLLM): raise ValueError( "OpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment." ) + + if _is_async is True: + if not isinstance(azure_client, (AsyncAzureOpenAI, AsyncOpenAI)): + raise ValueError( + "Azure client is not an instance of AsyncAzureOpenAI or AsyncOpenAI. Make sure you passed an async client." + ) + return self.acancel_batch( # type: ignore + cancel_batch_data=cancel_batch_data, client=azure_client + ) + + # At this point, azure_client is guaranteed to be a sync client + if not isinstance(azure_client, (AzureOpenAI, OpenAI)): + raise ValueError( + "Azure client is not an instance of AzureOpenAI or OpenAI. Make sure you passed a sync client." + ) response = azure_client.batches.cancel(**cancel_batch_data) - return response + return LiteLLMBatch(**response.model_dump()) async def alist_batches( self, diff --git a/litellm/llms/azure/chat/gpt_5_transformation.py b/litellm/llms/azure/chat/gpt_5_transformation.py index 506b7fdfe5..eeb55911ec 100644 --- a/litellm/llms/azure/chat/gpt_5_transformation.py +++ b/litellm/llms/azure/chat/gpt_5_transformation.py @@ -22,7 +22,8 @@ class AzureOpenAIGPT5Config(AzureOpenAIConfig, OpenAIGPT5Config): Accepts both explicit gpt-5 model names and the ``gpt5_series/`` prefix used for manual routing. """ - return "gpt-5" in model or "gpt5_series" in model + # gpt-5-chat* is a chat model and shouldn't go through GPT-5 reasoning restrictions. + return ("gpt-5" in model and "gpt-5-chat" not in model) or "gpt5_series" in model def get_supported_openai_params(self, model: str) -> List[str]: """Get supported parameters for Azure OpenAI GPT-5 models. @@ -37,6 +38,11 @@ class AzureOpenAIGPT5Config(AzureOpenAIConfig, OpenAIGPT5Config): """ params = OpenAIGPT5Config.get_supported_openai_params(self, model=model) + # Azure supports tool_choice for GPT-5 deployments, but the base GPT-5 config + # can drop it when the deployment name isn't in the OpenAI model registry. + if "tool_choice" not in params: + params.append("tool_choice") + # Only gpt-5.2 has been verified to support logprobs on Azure if self.is_model_gpt_5_2_model(model): azure_supported_params = ["logprobs", "top_logprobs"] diff --git a/litellm/llms/azure/cost_calculation.py b/litellm/llms/azure/cost_calculation.py index 96c58d95ff..5b411095ea 100644 --- a/litellm/llms/azure/cost_calculation.py +++ b/litellm/llms/azure/cost_calculation.py @@ -1,11 +1,12 @@ """ Helper util for handling azure openai-specific cost calculation -- e.g.: prompt caching +- e.g.: prompt caching, audio tokens """ from typing import Optional, Tuple from litellm._logging import verbose_logger +from litellm.litellm_core_utils.llm_cost_calc.utils import generic_cost_per_token from litellm.types.utils import Usage from litellm.utils import get_model_info @@ -18,34 +19,15 @@ def cost_per_token( Input: - model: str, the model name without provider prefix - - usage: LiteLLM Usage block, containing anthropic caching information + - usage: LiteLLM Usage block, containing caching and audio token 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="azure") - cached_tokens: Optional[int] = None - ## CALCULATE INPUT COST - non_cached_text_tokens = usage.prompt_tokens - if usage.prompt_tokens_details and usage.prompt_tokens_details.cached_tokens: - cached_tokens = usage.prompt_tokens_details.cached_tokens - non_cached_text_tokens = non_cached_text_tokens - cached_tokens - prompt_cost: float = non_cached_text_tokens * model_info["input_cost_per_token"] - ## CALCULATE OUTPUT COST - completion_cost: float = ( - usage["completion_tokens"] * model_info["output_cost_per_token"] - ) - - ## Prompt Caching cost calculation - if model_info.get("cache_read_input_token_cost") is not None and cached_tokens: - # Note: We read ._cache_read_input_tokens from the Usage - since cost_calculator.py standardizes the cache read tokens on usage._cache_read_input_tokens - prompt_cost += cached_tokens * ( - model_info.get("cache_read_input_token_cost", 0) or 0 - ) - - ## Speech / Audio cost calculation + ## Speech / Audio cost calculation (cost per second for TTS models) if ( "output_cost_per_second" in model_info and model_info["output_cost_per_second"] is not None @@ -55,7 +37,14 @@ def cost_per_token( f"For model={model} - output_cost_per_second: {model_info.get('output_cost_per_second')}; response time: {response_time_ms}" ) ## COST PER SECOND ## - prompt_cost = 0 + prompt_cost = 0.0 completion_cost = model_info["output_cost_per_second"] * response_time_ms / 1000 + return prompt_cost, completion_cost - return prompt_cost, completion_cost + ## Use generic cost calculator for all other cases + ## This properly handles: text tokens, audio tokens, cached tokens, reasoning tokens, etc. + return generic_cost_per_token( + model=model, + usage=usage, + custom_llm_provider="azure", + ) diff --git a/litellm/llms/base_llm/vector_store/transformation.py b/litellm/llms/base_llm/vector_store/transformation.py index 89f2094d5d..935fd53c19 100644 --- a/litellm/llms/base_llm/vector_store/transformation.py +++ b/litellm/llms/base_llm/vector_store/transformation.py @@ -5,8 +5,8 @@ import httpx from litellm.types.router import GenericLiteLLMParams from litellm.types.vector_stores import ( - BaseVectorStoreAuthCredentials, VECTOR_STORE_OPENAI_PARAMS, + BaseVectorStoreAuthCredentials, VectorStoreCreateOptionalRequestParams, VectorStoreCreateResponse, VectorStoreIndexEndpoints, @@ -64,6 +64,30 @@ class BaseVectorStoreConfig: pass + async def atransform_search_vector_store_request( + self, + vector_store_id: str, + query: Union[str, List[str]], + vector_store_search_optional_params: VectorStoreSearchOptionalRequestParams, + api_base: str, + litellm_logging_obj: LiteLLMLoggingObj, + litellm_params: dict, + ) -> Tuple[str, Dict]: + """ + Optional async version of transform_search_vector_store_request. + If not implemented, the handler will fall back to the sync version. + Providers that need to make async calls (e.g., generating embeddings) should override this. + """ + # Default implementation: call the sync version + return self.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=litellm_logging_obj, + litellm_params=litellm_params, + ) + @abstractmethod def transform_search_vector_store_response( self, response: httpx.Response, litellm_logging_obj: LiteLLMLoggingObj diff --git a/litellm/llms/bedrock/base_aws_llm.py b/litellm/llms/bedrock/base_aws_llm.py index 642d15fe3e..1de1c40c43 100644 --- a/litellm/llms/bedrock/base_aws_llm.py +++ b/litellm/llms/bedrock/base_aws_llm.py @@ -1163,7 +1163,7 @@ class BaseAWSLLM: def _sign_request( self, - service_name: Literal["bedrock", "sagemaker", "bedrock-agentcore"], + service_name: Literal["bedrock", "sagemaker", "bedrock-agentcore", "s3vectors"], headers: dict, optional_params: dict, request_data: dict, diff --git a/litellm/llms/bedrock/chat/converse_transformation.py b/litellm/llms/bedrock/chat/converse_transformation.py index cb26a22edf..0d29c1f01a 100644 --- a/litellm/llms/bedrock/chat/converse_transformation.py +++ b/litellm/llms/bedrock/chat/converse_transformation.py @@ -76,6 +76,13 @@ BEDROCK_COMPUTER_USE_TOOLS = [ "text_editor_", ] +# Beta header patterns that are not supported by Bedrock Converse API +# These will be filtered out to prevent errors +UNSUPPORTED_BEDROCK_CONVERSE_BETA_PATTERNS = [ + "advanced-tool-use", # Bedrock Converse doesn't support advanced-tool-use beta headers + "prompt-caching", # Prompt caching not supported in Converse API +] + class AmazonConverseConfig(BaseConfig): """ @@ -298,6 +305,39 @@ class AmazonConverseConfig(BaseConfig): # Check if the model is specifically Nova Lite 2 return "nova-2-lite" in model_without_region + def _map_web_search_options( + self, + web_search_options: dict, + model: str + ) -> Optional[BedrockToolBlock]: + """ + Map web_search_options to Nova grounding systemTool. + + Nova grounding (web search) is only supported on Amazon Nova models. + Returns None for non-Nova models. + + Args: + web_search_options: The web_search_options dict from the request + model: The model identifier string + + Returns: + BedrockToolBlock with systemTool for Nova models, None otherwise + + Reference: https://docs.aws.amazon.com/nova/latest/userguide/grounding.html + """ + # Only Nova models support nova_grounding + # Model strings can be like: "amazon.nova-pro-v1:0", "us.amazon.nova-pro-v1:0", etc. + if "nova" not in model.lower(): + verbose_logger.debug( + f"web_search_options passed but model {model} is not a Nova model. " + "Nova grounding is only supported on Amazon Nova models." + ) + return None + + # Nova doesn't support search_context_size or user_location params + # (unlike Anthropic), so we just enable grounding with no options + return BedrockToolBlock(systemTool={"name": "nova_grounding"}) + def _transform_reasoning_effort_to_reasoning_config( self, reasoning_effort: str ) -> dict: @@ -438,6 +478,10 @@ class AmazonConverseConfig(BaseConfig): ): supported_params.append("tools") + # Nova models support web_search_options (mapped to nova_grounding systemTool) + if base_model.startswith("amazon.nova"): + supported_params.append("web_search_options") + if litellm.utils.supports_tool_choice( model=model, custom_llm_provider=self.custom_llm_provider ) or litellm.utils.supports_tool_choice( @@ -573,6 +617,37 @@ class AmazonConverseConfig(BaseConfig): return transformed_tools + def _filter_unsupported_beta_headers_for_bedrock( + self, model: str, beta_list: list + ) -> list: + """ + Remove beta headers that are not supported on Bedrock Converse API for the given model. + + Extended thinking beta headers are only supported on specific Claude 4+ models. + Some beta headers are universally unsupported on Bedrock Converse API. + + Args: + model: The model name + beta_list: The list of beta headers to filter + + Returns: + Filtered list of beta headers + """ + filtered_betas = [] + + # 1. Filter out beta headers that are universally unsupported on Bedrock Converse + for beta in beta_list: + should_keep = True + for unsupported_pattern in UNSUPPORTED_BEDROCK_CONVERSE_BETA_PATTERNS: + if unsupported_pattern in beta.lower(): + should_keep = False + break + + if should_keep: + filtered_betas.append(beta) + + return filtered_betas + def _separate_computer_use_tools( self, tools: List[OpenAIChatCompletionToolParam], model: str ) -> Tuple[ @@ -730,6 +805,13 @@ class AmazonConverseConfig(BaseConfig): if bedrock_tier in ("default", "flex", "priority"): optional_params["serviceTier"] = {"type": bedrock_tier} + if param == "web_search_options" and value and isinstance(value, dict): + grounding_tool = self._map_web_search_options(value, model) + if grounding_tool is not None: + optional_params = self._add_tools_to_optional_params( + optional_params=optional_params, tools=[grounding_tool] + ) + # Only update thinking tokens for non-GPT-OSS models and non-Nova-Lite-2 models # Nova Lite 2 handles token budgeting differently through reasoningConfig if "gpt-oss" not in model and not self._is_nova_lite_2_model(model): @@ -1044,7 +1126,14 @@ class AmazonConverseConfig(BaseConfig): if beta not in seen: unique_betas.append(beta) seen.add(beta) - additional_request_params["anthropic_beta"] = unique_betas + + # Filter out unsupported beta headers for Bedrock Converse API + filtered_betas = self._filter_unsupported_beta_headers_for_bedrock( + model=model, + beta_list=unique_betas, + ) + + additional_request_params["anthropic_beta"] = filtered_betas return bedrock_tools, anthropic_beta_list @@ -1388,20 +1477,23 @@ class AmazonConverseConfig(BaseConfig): str, List[ChatCompletionToolCallChunk], Optional[List[BedrockConverseReasoningContentBlock]], + Optional[List[CitationsContentBlock]], ]: """ - Translate the message content to a string and a list of tool calls and reasoning content blocks + Translate the message content to a string and a list of tool calls, reasoning content blocks, and citations. Returns: content_str: str tools: List[ChatCompletionToolCallChunk] reasoningContentBlocks: Optional[List[BedrockConverseReasoningContentBlock]] + citationsContentBlocks: Optional[List[CitationsContentBlock]] - Citations from Nova grounding """ content_str = "" tools: List[ChatCompletionToolCallChunk] = [] reasoningContentBlocks: Optional[List[BedrockConverseReasoningContentBlock]] = ( None ) + citationsContentBlocks: Optional[List[CitationsContentBlock]] = None for idx, content in enumerate(content_blocks): """ - Content is either a tool response or text @@ -1446,10 +1538,15 @@ class AmazonConverseConfig(BaseConfig): if reasoningContentBlocks is None: reasoningContentBlocks = [] reasoningContentBlocks.append(content["reasoningContent"]) + # Handle Nova grounding citations content + if "citationsContent" in content: + if citationsContentBlocks is None: + citationsContentBlocks = [] + citationsContentBlocks.append(content["citationsContent"]) - return content_str, tools, reasoningContentBlocks + return content_str, tools, reasoningContentBlocks, citationsContentBlocks - def _transform_response( + def _transform_response( # noqa: PLR0915 self, model: str, response: httpx.Response, @@ -1525,18 +1622,27 @@ class AmazonConverseConfig(BaseConfig): reasoningContentBlocks: Optional[List[BedrockConverseReasoningContentBlock]] = ( None ) + citationsContentBlocks: Optional[List[CitationsContentBlock]] = None if message is not None: ( content_str, tools, reasoningContentBlocks, + citationsContentBlocks, ) = self._translate_message_content(message["content"]) + # Initialize provider_specific_fields if we have any special content blocks + provider_specific_fields: dict = {} + if reasoningContentBlocks is not None: + provider_specific_fields["reasoningContentBlocks"] = reasoningContentBlocks + if citationsContentBlocks is not None: + provider_specific_fields["citationsContent"] = citationsContentBlocks + + if provider_specific_fields: + chat_completion_message["provider_specific_fields"] = provider_specific_fields + if reasoningContentBlocks is not None: - chat_completion_message["provider_specific_fields"] = { - "reasoningContentBlocks": reasoningContentBlocks, - } chat_completion_message["reasoning_content"] = ( self._transform_reasoning_content(reasoningContentBlocks) ) diff --git a/litellm/llms/bedrock/chat/invoke_handler.py b/litellm/llms/bedrock/chat/invoke_handler.py index 17474fa022..1c58a11eeb 100644 --- a/litellm/llms/bedrock/chat/invoke_handler.py +++ b/litellm/llms/bedrock/chat/invoke_handler.py @@ -1476,6 +1476,11 @@ class AWSEventStreamDecoder: reasoning_content = ( "" # set to non-empty string to ensure consistency with Anthropic ) + elif "citationsContent" in delta_obj: + # Handle Nova grounding citations in streaming responses + provider_specific_fields = { + "citationsContent": delta_obj["citationsContent"], + } return ( text, tool_use, diff --git a/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py b/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py index 53e0822979..c936b2cd23 100644 --- a/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py +++ b/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py @@ -53,13 +53,26 @@ class AmazonAnthropicClaudeConfig(AmazonInvokeConfig, AnthropicConfig): model: str, drop_params: bool, ) -> dict: - return AnthropicConfig.map_openai_params( + # Force tool-based structured outputs for Bedrock Invoke + # (similar to VertexAI fix in #19201) + # Bedrock Invoke doesn't support output_format parameter + original_model = model + if "response_format" in non_default_params: + # Use a model name that forces tool-based approach + model = "claude-3-sonnet-20240229" + + optional_params = AnthropicConfig.map_openai_params( self, non_default_params, optional_params, model, drop_params, ) + + # Restore original model name + model = original_model + + return optional_params def transform_request( @@ -90,6 +103,8 @@ class AmazonAnthropicClaudeConfig(AmazonInvokeConfig, AnthropicConfig): _anthropic_request.pop("model", None) _anthropic_request.pop("stream", None) + # Bedrock Invoke doesn't support output_format parameter + _anthropic_request.pop("output_format", None) if "anthropic_version" not in _anthropic_request: _anthropic_request["anthropic_version"] = self.anthropic_version @@ -117,6 +132,26 @@ class AmazonAnthropicClaudeConfig(AmazonInvokeConfig, AnthropicConfig): if "opus-4" in model.lower() or "opus_4" in model.lower(): beta_set.add("tool-search-tool-2025-10-19") + # Filter out beta headers that Bedrock Invoke doesn't support + # AWS Bedrock only supports a specific whitelist of beta flags + # Reference: https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-anthropic-claude-messages-request-response.html + BEDROCK_SUPPORTED_BETAS = { + "computer-use-2024-10-22", # Legacy computer use + "computer-use-2025-01-24", # Current computer use (Claude 3.7 Sonnet) + "token-efficient-tools-2025-02-19", # Tool use (Claude 3.7+ and Claude 4+) + "interleaved-thinking-2025-05-14", # Interleaved thinking (Claude 4+) + "output-128k-2025-02-19", # 128K output tokens (Claude 3.7 Sonnet) + "dev-full-thinking-2025-05-14", # Developer mode for raw thinking (Claude 4+) + "context-1m-2025-08-07", # 1 million tokens (Claude Sonnet 4) + "context-management-2025-06-27", # Context management (Claude Sonnet/Haiku 4.5) + "effort-2025-11-24", # Effort parameter (Claude Opus 4.5) + "tool-search-tool-2025-10-19", # Tool search (Claude Opus 4.5) + "tool-examples-2025-10-29", # Tool use examples (Claude Opus 4.5) + } + + # Only keep beta headers that Bedrock supports + beta_set = {beta for beta in beta_set if beta in BEDROCK_SUPPORTED_BETAS} + if beta_set: _anthropic_request["anthropic_beta"] = list(beta_set) diff --git a/litellm/llms/bedrock/common_utils.py b/litellm/llms/bedrock/common_utils.py index 89b42f5e94..65d237bdbd 100644 --- a/litellm/llms/bedrock/common_utils.py +++ b/litellm/llms/bedrock/common_utils.py @@ -797,7 +797,7 @@ class BedrockEventStreamDecoderBase: def get_anthropic_beta_from_headers(headers: dict) -> List[str]: """ Extract anthropic-beta header values and convert them to a list. - Supports comma-separated values from user headers. + Supports both JSON array format and comma-separated values from user headers. Used by both converse and invoke transformations for consistent handling of anthropic-beta headers that should be passed to AWS Bedrock. @@ -812,8 +812,25 @@ def get_anthropic_beta_from_headers(headers: dict) -> List[str]: if not anthropic_beta_header: return [] - # Split comma-separated values and strip whitespace - return [beta.strip() for beta in anthropic_beta_header.split(",")] + # If it's already a list, return it + if isinstance(anthropic_beta_header, list): + return anthropic_beta_header + + # Try to parse as JSON array first (e.g., '["interleaved-thinking-2025-05-14", "claude-code-20250219"]') + if isinstance(anthropic_beta_header, str): + anthropic_beta_header = anthropic_beta_header.strip() + if anthropic_beta_header.startswith("[") and anthropic_beta_header.endswith("]"): + try: + parsed = json.loads(anthropic_beta_header) + if isinstance(parsed, list): + return [str(beta).strip() for beta in parsed] + except json.JSONDecodeError: + pass # Fall through to comma-separated parsing + + # Fall back to comma-separated values + return [beta.strip() for beta in anthropic_beta_header.split(",")] + + return [] class CommonBatchFilesUtils: diff --git a/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py b/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py index a7065caece..b1c45ea83a 100644 --- a/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py +++ b/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py @@ -54,6 +54,7 @@ class AmazonAnthropicClaudeMessagesConfig( # These will be filtered out to prevent 400 "invalid beta flag" errors UNSUPPORTED_BEDROCK_INVOKE_BETA_PATTERNS = [ "advanced-tool-use", # Bedrock Invoke doesn't support advanced-tool-use beta headers + "prompt-caching-scope" ] def __init__(self, **kwargs): @@ -162,6 +163,49 @@ class AmazonAnthropicClaudeMessagesConfig( return any(pattern in model_lower for pattern in supported_patterns) + def _is_claude_opus_4_5(self, model: str) -> bool: + """ + Check if the model is Claude Opus 4.5. + + Args: + model: The model name + + Returns: + True if the model is Claude Opus 4.5 + """ + model_lower = model.lower() + opus_4_5_patterns = [ + "opus-4.5", "opus_4.5", "opus-4-5", "opus_4_5", + ] + return any(pattern in model_lower for pattern in opus_4_5_patterns) + + def _supports_tool_search_on_bedrock(self, model: str) -> bool: + """ + Check if the model supports tool search on Bedrock. + + On Amazon Bedrock, server-side tool search is supported on Claude Opus 4.5 + and Claude Sonnet 4.5 with the tool-search-tool-2025-10-19 beta header. + + Ref: https://platform.claude.com/docs/en/agents-and-tools/tool-use/tool-search-tool + + Args: + model: The model name + + Returns: + True if the model supports tool search on Bedrock + """ + model_lower = model.lower() + + # Supported models for tool search on Bedrock + supported_patterns = [ + # Opus 4.5 + "opus-4.5", "opus_4.5", "opus-4-5", "opus_4_5", + # Sonnet 4.5 + "sonnet-4.5", "sonnet_4.5", "sonnet-4-5", "sonnet_4_5", + ] + + return any(pattern in model_lower for pattern in supported_patterns) + def _filter_unsupported_beta_headers_for_bedrock( self, model: str, beta_set: set ) -> None: @@ -169,25 +213,33 @@ class AmazonAnthropicClaudeMessagesConfig( Remove beta headers that are not supported on Bedrock for the given model. Extended thinking beta headers are only supported on specific Claude 4+ models. - Advanced tool use headers are not supported on Bedrock Invoke API. + Advanced tool use headers are not supported on Bedrock Invoke API, but need to be + translated to Bedrock-specific headers for models that support tool search + (Claude Opus 4.5, Sonnet 4.5). This prevents 400 "invalid beta flag" errors on Bedrock. Note: Bedrock Invoke API fails with a 400 error when unsupported beta headers are sent, returning: {"message":"invalid beta flag"} + Translation for models supporting tool search (Opus 4.5, Sonnet 4.5): + - advanced-tool-use-2025-11-20 -> tool-search-tool-2025-10-19 + tool-examples-2025-10-29 + Args: model: The model name beta_set: The set of beta headers to filter in-place """ beta_headers_to_remove = set() + has_advanced_tool_use = False - # 1. Filter out beta headers that are universally unsupported on Bedrock Invoke + # 1. Filter out beta headers that are universally unsupported on Bedrock Invoke and track if advanced-tool-use header is present for beta in beta_set: for unsupported_pattern in self.UNSUPPORTED_BEDROCK_INVOKE_BETA_PATTERNS: if unsupported_pattern in beta.lower(): beta_headers_to_remove.add(beta) + has_advanced_tool_use = True break - + + # 2. Filter out extended thinking headers for models that don't support them extended_thinking_patterns = [ "extended-thinking", @@ -204,6 +256,14 @@ class AmazonAnthropicClaudeMessagesConfig( for beta in beta_headers_to_remove: beta_set.discard(beta) + # 3. Translate advanced-tool-use to Bedrock-specific headers for models that support tool search + # Ref: https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-anthropic-claude-messages-request-response.html + # Ref: https://platform.claude.com/docs/en/agents-and-tools/tool-use/tool-search-tool + if has_advanced_tool_use and self._supports_tool_search_on_bedrock(model): + beta_set.add("tool-search-tool-2025-10-19") + beta_set.add("tool-examples-2025-10-29") + + def _get_tool_search_beta_header_for_bedrock( self, model: str, @@ -256,7 +316,7 @@ class AmazonAnthropicClaudeMessagesConfig( Ref: https://aws.amazon.com/blogs/machine-learning/structured-data-response-with-amazon-bedrock-prompt-engineering-and-tool-use/ """ import json - + # Extract schema from output_format schema = output_format.get("schema") if not schema: diff --git a/litellm/llms/cohere/rerank/guardrail_translation/handler.py b/litellm/llms/cohere/rerank/guardrail_translation/handler.py index 6893a5991c..b8133c59f7 100644 --- a/litellm/llms/cohere/rerank/guardrail_translation/handler.py +++ b/litellm/llms/cohere/rerank/guardrail_translation/handler.py @@ -9,6 +9,7 @@ from typing import TYPE_CHECKING, Any, Optional from litellm._logging import verbose_proxy_logger from litellm.llms.base_llm.guardrail_translation.base_translation import BaseTranslation +from litellm.types.utils import GenericGuardrailAPIInputs if TYPE_CHECKING: from litellm.integrations.custom_guardrail import CustomGuardrail @@ -49,8 +50,13 @@ class CohereRerankHandler(BaseTranslation): # Process query only query = data.get("query") if query is not None and isinstance(query, str): + inputs = GenericGuardrailAPIInputs(texts=[query]) + # Include model information if available + model = data.get("model") + if model: + inputs["model"] = model guardrailed_inputs = await guardrail_to_apply.apply_guardrail( - inputs={"texts": [query]}, + inputs=inputs, request_data=data, input_type="request", logging_obj=litellm_logging_obj, diff --git a/litellm/llms/custom_httpx/llm_http_handler.py b/litellm/llms/custom_httpx/llm_http_handler.py index 6a87967c3a..d2ea7e872a 100644 --- a/litellm/llms/custom_httpx/llm_http_handler.py +++ b/litellm/llms/custom_httpx/llm_http_handler.py @@ -7033,17 +7033,31 @@ 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), - ) + # Check if provider has async transform method + if hasattr(vector_store_provider_config, "atransform_search_vector_store_request"): + ( + url, + request_body, + ) = await vector_store_provider_config.atransform_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), + ) + else: + ( + 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 {}) headers, signed_json_body = vector_store_provider_config.sign_request( diff --git a/litellm/llms/gemini/chat/transformation.py b/litellm/llms/gemini/chat/transformation.py index f6d075392b..d5a5ab667a 100644 --- a/litellm/llms/gemini/chat/transformation.py +++ b/litellm/llms/gemini/chat/transformation.py @@ -92,7 +92,7 @@ class GoogleAIStudioGeminiConfig(VertexGeminiConfig): "parallel_tool_calls", "web_search_options", ] - if supports_reasoning(model): + if supports_reasoning(model, custom_llm_provider="gemini"): supported_params.append("reasoning_effort") supported_params.append("thinking") if self.is_model_gemini_audio_model(model): diff --git a/litellm/llms/gemini/files/transformation.py b/litellm/llms/gemini/files/transformation.py index d9ebf69a97..44e09af892 100644 --- a/litellm/llms/gemini/files/transformation.py +++ b/litellm/llms/gemini/files/transformation.py @@ -35,6 +35,26 @@ class GoogleAIStudioFilesHandler(GeminiModelInfo, BaseFilesConfig): def custom_llm_provider(self) -> LlmProviders: return LlmProviders.GEMINI + def validate_environment( + self, + api_key: Optional[str], + headers: dict, + model: str, + messages: list, + optional_params: dict, + litellm_params: dict, + ) -> dict: + """ + Validate environment and add Gemini API key to headers. + Google AI Studio uses x-goog-api-key header for authentication. + """ + api_key = self.get_api_key(api_key) + if not api_key: + raise ValueError("GEMINI_API_KEY is required for Google AI Studio file operations") + + headers["x-goog-api-key"] = api_key + return headers + def get_complete_url( self, api_base: Optional[str], @@ -56,10 +76,12 @@ class GoogleAIStudioFilesHandler(GeminiModelInfo, BaseFilesConfig): if not api_base: raise ValueError("api_base is required") - if not api_key: + # Get API key from multiple sources + final_api_key = api_key or litellm_params.get("api_key") or self.get_api_key() + if not final_api_key: raise ValueError("api_key is required") - url = "{}/{}?key={}".format(api_base, endpoint, api_key) + url = "{}/{}?key={}".format(api_base, endpoint, final_api_key) return url def get_supported_openai_params( @@ -180,7 +202,25 @@ class GoogleAIStudioFilesHandler(GeminiModelInfo, BaseFilesConfig): optional_params: dict, litellm_params: dict, ) -> tuple[str, dict]: - raise NotImplementedError("GoogleAIStudioFilesHandler does not support file retrieval") + """ + Get the URL to retrieve a file from Google AI Studio. + + We expect file_id to be the URI (e.g. https://generativelanguage.googleapis.com/v1beta/files/...) + as returned by the upload response. + """ + api_key = litellm_params.get("api_key") + if not api_key: + raise ValueError("api_key is required") + + if file_id.startswith("http"): + url = "{}?key={}".format(file_id, api_key) + else: + # Fallback for just file name (files/...) + api_base = self.get_api_base(litellm_params.get("api_base")) or "https://generativelanguage.googleapis.com" + api_base = api_base.rstrip("/") + url = "{}/v1beta/{}?key={}".format(api_base, file_id, api_key) + + return url, {"Content-Type": "application/json"} def transform_retrieve_file_response( self, @@ -188,7 +228,40 @@ class GoogleAIStudioFilesHandler(GeminiModelInfo, BaseFilesConfig): logging_obj: LiteLLMLoggingObj, litellm_params: dict, ) -> OpenAIFileObject: - raise NotImplementedError("GoogleAIStudioFilesHandler does not support file retrieval") + """ + Transform Gemini's file retrieval response into OpenAI-style FileObject + """ + try: + response_json = raw_response.json() + + # Map Gemini state to OpenAI status + gemini_state = response_json.get("state", "STATE_UNSPECIFIED") + status = "uploaded" # Default + if gemini_state == "ACTIVE": + status = "processed" + elif gemini_state == "FAILED": + status = "error" + + return OpenAIFileObject( + id=response_json.get("uri", ""), + bytes=int(response_json.get("sizeBytes", 0)), + created_at=int( + time.mktime( + time.strptime( + response_json["createTime"].replace("Z", "+00:00"), + "%Y-%m-%dT%H:%M:%S.%f%z", + ) + ) + ), + filename=response_json.get("displayName", ""), + object="file", + purpose="user_data", + status=status, + status_details=str(response_json.get("error", "")) if gemini_state == "FAILED" else None, + ) + except Exception as e: + verbose_logger.exception(f"Error parsing file retrieve response: {str(e)}") + raise ValueError(f"Error parsing file retrieve response: {str(e)}") def transform_delete_file_request( self, @@ -196,7 +269,41 @@ class GoogleAIStudioFilesHandler(GeminiModelInfo, BaseFilesConfig): optional_params: dict, litellm_params: dict, ) -> tuple[str, dict]: - raise NotImplementedError("GoogleAIStudioFilesHandler does not support file deletion") + """ + Transform delete file request for Google AI Studio. + + Args: + file_id: The file URI (e.g., "files/abc123" or full URI) + optional_params: Optional parameters + litellm_params: LiteLLM parameters containing api_key + + Returns: + tuple[str, dict]: (url, params) for the DELETE request + """ + api_base = self.get_api_base(litellm_params.get("api_base")) + if not api_base: + raise ValueError("api_base is required") + + # Get API key from multiple sources (same pattern as get_complete_url) + api_key = litellm_params.get("api_key") or self.get_api_key() + if not api_key: + raise ValueError("api_key is required") + + # Extract file name from URI if full URI is provided + # file_id could be "files/abc123" or "https://generativelanguage.googleapis.com/v1beta/files/abc123" + if file_id.startswith("http"): + # Extract the file path from full URI + file_name = file_id.split("/v1beta/")[-1] + else: + file_name = file_id + + # Construct the delete URL + url = f"{api_base}/v1beta/{file_name}" + + # Add API key as header (Google AI Studio uses x-goog-api-key header) + params = {} + + return url, params def transform_delete_file_response( self, @@ -204,7 +311,34 @@ class GoogleAIStudioFilesHandler(GeminiModelInfo, BaseFilesConfig): logging_obj: LiteLLMLoggingObj, litellm_params: dict, ) -> FileDeleted: - raise NotImplementedError("GoogleAIStudioFilesHandler does not support file deletion") + """ + Transform Gemini's file delete response into OpenAI-style FileDeleted. + + Google AI Studio returns an empty JSON object {} on successful deletion. + """ + try: + # Google AI Studio returns {} on successful deletion + if raw_response.status_code == 200: + # Extract file ID from the request URL if possible + file_id = "deleted" + if hasattr(raw_response, "request") and raw_response.request: + url = str(raw_response.request.url) + if "/files/" in url: + file_id = url.split("/files/")[-1].split("?")[0] + # Add the files/ prefix if not present + if not file_id.startswith("files/"): + file_id = f"files/{file_id}" + + return FileDeleted( + id=file_id, + deleted=True, + object="file" + ) + else: + raise ValueError(f"Failed to delete file: {raw_response.text}") + except Exception as e: + verbose_logger.exception(f"Error parsing file delete response: {str(e)}") + raise ValueError(f"Error parsing file delete response: {str(e)}") def transform_list_files_request( self, diff --git a/litellm/llms/gemini/image_edit/transformation.py b/litellm/llms/gemini/image_edit/transformation.py index 1654113821..c3ea63ad43 100644 --- a/litellm/llms/gemini/image_edit/transformation.py +++ b/litellm/llms/gemini/image_edit/transformation.py @@ -106,7 +106,10 @@ class GeminiImageEditConfig(BaseImageEditConfig): generation_config: Dict[str, Any] = {} if "aspectRatio" in image_edit_optional_request_params: - generation_config["aspectRatio"] = image_edit_optional_request_params[ + # Move aspectRatio into imageConfig inside generationConfig + if "imageConfig" not in generation_config: + generation_config["imageConfig"] = {} + generation_config["imageConfig"]["aspectRatio"] = image_edit_optional_request_params[ "aspectRatio" ] diff --git a/litellm/llms/gigachat/chat/transformation.py b/litellm/llms/gigachat/chat/transformation.py index 90cf67da6b..ba14de1f65 100644 --- a/litellm/llms/gigachat/chat/transformation.py +++ b/litellm/llms/gigachat/chat/transformation.py @@ -31,6 +31,16 @@ else: GIGACHAT_BASE_URL = "https://gigachat.devices.sberbank.ru/api/v1" +def is_valid_json(value: str) -> bool: + """Checks whether the value passed is a valid serialized JSON string""" + try: + json.loads(value) + except json.JSONDecodeError: + return False + else: + return True + + class GigaChatError(BaseLLMException): """GigaChat API error.""" @@ -101,7 +111,11 @@ class GigaChatConfig(BaseConfig): Set up headers with OAuth token. """ # Get access token - credentials = api_key or get_secret_str("GIGACHAT_CREDENTIALS") or get_secret_str("GIGACHAT_API_KEY") + credentials = ( + api_key + or get_secret_str("GIGACHAT_CREDENTIALS") + or get_secret_str("GIGACHAT_API_KEY") + ) access_token = get_access_token(credentials=credentials) # Store credentials for image uploads @@ -193,11 +207,13 @@ class GigaChatConfig(BaseConfig): for tool in tools: if tool.get("type") == "function": func = tool.get("function", {}) - functions.append({ - "name": func.get("name", ""), - "description": func.get("description", ""), - "parameters": func.get("parameters", {}), - }) + functions.append( + { + "name": func.get("name", ""), + "description": func.get("description", ""), + "parameters": func.get("parameters", {}), + } + ) return functions def _map_tool_choice( @@ -281,8 +297,14 @@ class GigaChatConfig(BaseConfig): } # Add optional params - for key in ["temperature", "top_p", "max_tokens", "stream", - "repetition_penalty", "profanity_check"]: + for key in [ + "temperature", + "top_p", + "max_tokens", + "stream", + "repetition_penalty", + "profanity_check", + ]: if key in optional_params: request_data[key] = optional_params[key] @@ -314,7 +336,7 @@ class GigaChatConfig(BaseConfig): elif role == "tool": message["role"] = "function" content = message.get("content", "") - if not isinstance(content, str): + if not isinstance(content, str) or not is_valid_json(content): message["content"] = json.dumps(content, ensure_ascii=False) # Handle None content @@ -441,14 +463,16 @@ class GigaChatConfig(BaseConfig): # Convert to tool_calls format if isinstance(args, dict): args = json.dumps(args, ensure_ascii=False) - message_data["tool_calls"] = [{ - "id": f"call_{uuid.uuid4().hex[:24]}", - "type": "function", - "function": { - "name": func_call.get("name", ""), - "arguments": args, + message_data["tool_calls"] = [ + { + "id": f"call_{uuid.uuid4().hex[:24]}", + "type": "function", + "function": { + "name": func_call.get("name", ""), + "arguments": args, + }, } - }] + ] message_data.pop("function_call", None) finish_reason = "tool_calls" diff --git a/litellm/llms/groq/chat/transformation.py b/litellm/llms/groq/chat/transformation.py index a75ecd8cc7..34ea7b03dd 100644 --- a/litellm/llms/groq/chat/transformation.py +++ b/litellm/llms/groq/chat/transformation.py @@ -323,4 +323,12 @@ class GroqChatCompletionStreamingHandler(OpenAIChatCompletionStreamingHandler): status_code=error.get("code"), message=error.get("message"), body=error ) + # Map Groq's 'reasoning' field to LiteLLM's 'reasoning_content' field + # Groq returns delta.reasoning, but LiteLLM expects delta.reasoning_content + choices = chunk.get("choices", []) + for choice in choices: + delta = choice.get("delta", {}) + if "reasoning" in delta: + delta["reasoning_content"] = delta.pop("reasoning") + return super().chunk_parser(chunk) diff --git a/litellm/llms/hosted_vllm/chat/transformation.py b/litellm/llms/hosted_vllm/chat/transformation.py index 1d21490ea3..e955800b94 100644 --- a/litellm/llms/hosted_vllm/chat/transformation.py +++ b/litellm/llms/hosted_vllm/chat/transformation.py @@ -23,7 +23,7 @@ from ...openai.chat.gpt_transformation import OpenAIGPTConfig class HostedVLLMChatConfig(OpenAIGPTConfig): def get_supported_openai_params(self, model: str) -> List[str]: params = super().get_supported_openai_params(model) - params.append("reasoning_effort") + params.extend(["reasoning_effort", "thinking"]) return params def map_openai_params( @@ -41,6 +41,27 @@ class HostedVLLMChatConfig(OpenAIGPTConfig): _tools = _remove_strict_from_schema(_tools) if _tools is not None: non_default_params["tools"] = _tools + + # Handle thinking parameter - convert Anthropic-style to OpenAI-style reasoning_effort + # vLLM is OpenAI-compatible, so it understands reasoning_effort, not thinking + # Reference: https://github.com/BerriAI/litellm/issues/19761 + thinking = non_default_params.pop("thinking", None) + if thinking is not None and isinstance(thinking, dict): + if thinking.get("type") == "enabled": + # Only convert if reasoning_effort not already set + if "reasoning_effort" not in non_default_params: + budget_tokens = thinking.get("budget_tokens", 0) + # Map budget_tokens to reasoning_effort level + # Same logic as Anthropic adapter (translate_anthropic_thinking_to_reasoning_effort) + if budget_tokens >= 10000: + non_default_params["reasoning_effort"] = "high" + elif budget_tokens >= 5000: + non_default_params["reasoning_effort"] = "medium" + elif budget_tokens >= 2000: + non_default_params["reasoning_effort"] = "low" + else: + non_default_params["reasoning_effort"] = "minimal" + return super().map_openai_params( non_default_params, optional_params, model, drop_params ) diff --git a/litellm/llms/hosted_vllm/embedding/transformation.py b/litellm/llms/hosted_vllm/embedding/transformation.py new file mode 100644 index 0000000000..9c3e8c6c7c --- /dev/null +++ b/litellm/llms/hosted_vllm/embedding/transformation.py @@ -0,0 +1,180 @@ +""" +Hosted VLLM Embedding API Configuration. + +This module provides the configuration for hosted VLLM's Embedding API. +VLLM is OpenAI-compatible and supports embeddings via the /v1/embeddings endpoint. + +Docs: https://docs.vllm.ai/en/latest/serving/openai_compatible_server.html +""" + +from typing import TYPE_CHECKING, Any, List, Optional, Union + +import httpx + +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 +from litellm.utils import convert_to_model_response_object + +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj + + LiteLLMLoggingObj = _LiteLLMLoggingObj +else: + LiteLLMLoggingObj = Any + + +class HostedVLLMEmbeddingError(BaseLLMException): + """Exception class for Hosted VLLM Embedding errors.""" + + pass + + +class HostedVLLMEmbeddingConfig(BaseEmbeddingConfig): + """ + Configuration for Hosted VLLM's Embedding API. + + Reference: https://docs.vllm.ai/en/latest/serving/openai_compatible_server.html + """ + + 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 set up headers for Hosted VLLM API. + """ + if api_key is None: + api_key = get_secret_str("HOSTED_VLLM_API_KEY") or "fake-api-key" + + default_headers = { + "Content-Type": "application/json", + } + + # Only add Authorization header if api_key is not "fake-api-key" + if api_key and api_key != "fake-api-key": + default_headers["Authorization"] = f"Bearer {api_key}" + + # Merge with existing headers (user's headers take priority) + return {**default_headers, **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 Hosted VLLM Embedding API endpoint. + """ + if api_base is None: + api_base = get_secret_str("HOSTED_VLLM_API_BASE") + if api_base is None: + raise ValueError("api_base is required for hosted_vllm embeddings") + + # Remove trailing slashes + api_base = api_base.rstrip("/") + + # Ensure the URL ends with /embeddings + if not api_base.endswith("/embeddings"): + api_base = f"{api_base}/embeddings" + + return api_base + + def transform_embedding_request( + self, + model: str, + input: AllEmbeddingInputValues, + optional_params: dict, + headers: dict, + ) -> dict: + """ + Transform embedding request to Hosted VLLM format (OpenAI-compatible). + """ + # Ensure input is a list + if isinstance(input, str): + input = [input] + + # Strip 'hosted_vllm/' prefix if present + if model.startswith("hosted_vllm/"): + model = model.replace("hosted_vllm/", "", 1) + + return { + "model": model, + "input": input, + **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: + """ + Transform embedding response from Hosted VLLM format (OpenAI-compatible). + """ + logging_obj.post_call(original_response=raw_response.text) + + # VLLM returns standard OpenAI-compatible embedding response + response_json = raw_response.json() + + return convert_to_model_response_object( + response_object=response_json, + model_response_object=model_response, + response_type="embedding", + ) + + def get_supported_openai_params(self, model: str) -> list: + """ + Get list of supported OpenAI parameters for Hosted VLLM embeddings. + """ + return [ + "timeout", + "dimensions", + "encoding_format", + "user", + ] + + def map_openai_params( + self, + non_default_params: dict, + optional_params: dict, + model: str, + drop_params: bool, + ) -> dict: + """ + Map OpenAI parameters to Hosted VLLM format. + """ + for param, value in non_default_params.items(): + if param in self.get_supported_openai_params(model): + optional_params[param] = value + return optional_params + + def get_error_class( + self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers] + ) -> BaseLLMException: + """ + Get the error class for Hosted VLLM errors. + """ + return HostedVLLMEmbeddingError( + message=error_message, + status_code=status_code, + headers=headers, + ) diff --git a/litellm/llms/minimax/chat/transformation.py b/litellm/llms/minimax/chat/transformation.py index ed80ff8aed..3e9dc0209f 100644 --- a/litellm/llms/minimax/chat/transformation.py +++ b/litellm/llms/minimax/chat/transformation.py @@ -1,11 +1,12 @@ """ MiniMax OpenAI transformation config - extends OpenAI chat config for MiniMax's OpenAI-compatible API """ -from typing import Optional +from typing import List, Optional, Tuple import litellm from litellm.llms.openai.chat.gpt_transformation import OpenAIGPTConfig from litellm.secret_managers.main import get_secret_str +from litellm.types.llms.openai import AllMessageValues, ChatCompletionToolParam class MinimaxChatConfig(OpenAIGPTConfig): @@ -73,11 +74,33 @@ class MinimaxChatConfig(OpenAIGPTConfig): else: return f"{base_url}/v1/chat/completions" + def remove_cache_control_flag_from_messages_and_tools( + self, + model: str, + messages: List[AllMessageValues], + tools: Optional[List[ChatCompletionToolParam]] = None, + ) -> Tuple[List[AllMessageValues], Optional[List[ChatCompletionToolParam]]]: + """ + Override to preserve cache_control for MiniMax. + MiniMax supports cache_control - don't strip it. + """ + # MiniMax supports cache_control, so return messages and tools unchanged + return messages, tools + def get_supported_openai_params(self, model: str) -> list: """ Get supported OpenAI parameters for MiniMax. - Adds reasoning_split to the list of supported params. + Adds reasoning_split and thinking to the list of supported params. """ base_params = super().get_supported_openai_params(model=model) - return base_params + ["reasoning_split"] + additional_params = ["reasoning_split"] + + # Add thinking parameter if model supports reasoning + try: + if litellm.supports_reasoning(model=model, custom_llm_provider="minimax"): + additional_params.append("thinking") + except Exception: + pass + + return base_params + additional_params diff --git a/litellm/llms/oci/chat/transformation.py b/litellm/llms/oci/chat/transformation.py index 7af7be2094..84f39ef252 100644 --- a/litellm/llms/oci/chat/transformation.py +++ b/litellm/llms/oci/chat/transformation.py @@ -32,6 +32,7 @@ from litellm.types.llms.oci import ( OCICompletionResponse, OCIContentPartUnion, OCIImageContentPart, + OCIImageUrl, OCIMessage, OCIRoles, OCIServingMode, @@ -1129,7 +1130,7 @@ def adapt_messages_to_generic_oci_standard_content_message( image_url = image_url.get("url") if not isinstance(image_url, str): raise Exception("Prop `image_url` must be a string or an object with a `url` property") - new_content.append(OCIImageContentPart(imageUrl=image_url)) + new_content.append(OCIImageContentPart(imageUrl=OCIImageUrl(url=image_url))) return OCIMessage( role=open_ai_to_generic_oci_role_map[role], diff --git a/litellm/llms/openai/chat/gpt_5_transformation.py b/litellm/llms/openai/chat/gpt_5_transformation.py index 3fffa335fd..05c003c8b7 100644 --- a/litellm/llms/openai/chat/gpt_5_transformation.py +++ b/litellm/llms/openai/chat/gpt_5_transformation.py @@ -19,7 +19,9 @@ class OpenAIGPT5Config(OpenAIGPTConfig): @classmethod def is_model_gpt_5_model(cls, model: str) -> bool: - return "gpt-5" in model + # gpt-5-chat* behaves like a regular chat model (supports temperature, etc.) + # Don't route it through GPT-5 reasoning-specific parameter restrictions. + return "gpt-5" in model and "gpt-5-chat" not in model @classmethod def is_model_gpt_5_codex_model(cls, model: str) -> bool: diff --git a/litellm/llms/openai/chat/guardrail_translation/handler.py b/litellm/llms/openai/chat/guardrail_translation/handler.py index d0ed3f165c..fb00aa28f4 100644 --- a/litellm/llms/openai/chat/guardrail_translation/handler.py +++ b/litellm/llms/openai/chat/guardrail_translation/handler.py @@ -87,6 +87,10 @@ class OpenAIChatCompletionsHandler(BaseTranslation): tools = data.get("tools") if tools: inputs["tools"] = tools + # Include model information if available + model = data.get("model") + if model: + inputs["model"] = model guardrailed_inputs = await guardrail_to_apply.apply_guardrail( inputs=inputs, @@ -297,6 +301,9 @@ class OpenAIChatCompletionsHandler(BaseTranslation): inputs["images"] = images_to_check if tool_calls_to_check: inputs["tool_calls"] = tool_calls_to_check # type: ignore + # Include model information from the response if available + if hasattr(response, "model") and response.model: + inputs["model"] = response.model guardrailed_inputs = await guardrail_to_apply.apply_guardrail( inputs=inputs, @@ -417,6 +424,13 @@ class OpenAIChatCompletionsHandler(BaseTranslation): inputs = GenericGuardrailAPIInputs(texts=texts_to_check) if images_to_check: inputs["images"] = images_to_check + # Include model information from the first response if available + if ( + responses_so_far + and hasattr(responses_so_far[0], "model") + and responses_so_far[0].model + ): + inputs["model"] = responses_so_far[0].model guardrailed_inputs = await guardrail_to_apply.apply_guardrail( inputs=inputs, request_data=request_data, diff --git a/litellm/llms/openai/completion/guardrail_translation/handler.py b/litellm/llms/openai/completion/guardrail_translation/handler.py index 73d08cfead..1f8c6159da 100644 --- a/litellm/llms/openai/completion/guardrail_translation/handler.py +++ b/litellm/llms/openai/completion/guardrail_translation/handler.py @@ -9,6 +9,7 @@ from typing import TYPE_CHECKING, Any, Optional from litellm._logging import verbose_proxy_logger from litellm.llms.base_llm.guardrail_translation.base_translation import BaseTranslation +from litellm.types.utils import GenericGuardrailAPIInputs if TYPE_CHECKING: from litellm.integrations.custom_guardrail import CustomGuardrail @@ -53,8 +54,13 @@ class OpenAITextCompletionHandler(BaseTranslation): if isinstance(prompt, str): # Single string prompt + inputs = GenericGuardrailAPIInputs(texts=[prompt]) + # Include model information if available + model = data.get("model") + if model: + inputs["model"] = model guardrailed_inputs = await guardrail_to_apply.apply_guardrail( - inputs={"texts": [prompt]}, + inputs=inputs, request_data=data, input_type="request", logging_obj=litellm_logging_obj, @@ -80,8 +86,13 @@ class OpenAITextCompletionHandler(BaseTranslation): text_indices.append(idx) if texts_to_check: + inputs = GenericGuardrailAPIInputs(texts=texts_to_check) + # Include model information if available + model = data.get("model") + if model: + inputs["model"] = model guardrailed_inputs = await guardrail_to_apply.apply_guardrail( - inputs={"texts": texts_to_check}, + inputs=inputs, request_data=data, input_type="request", logging_obj=litellm_logging_obj, @@ -154,8 +165,12 @@ class OpenAITextCompletionHandler(BaseTranslation): if user_metadata: request_data["litellm_metadata"] = user_metadata + inputs = GenericGuardrailAPIInputs(texts=texts_to_check) + # Include model information from the response if available + if hasattr(response, "model") and response.model: + inputs["model"] = response.model guardrailed_inputs = await guardrail_to_apply.apply_guardrail( - inputs={"texts": texts_to_check}, + inputs=inputs, request_data=request_data, input_type="response", logging_obj=litellm_logging_obj, diff --git a/litellm/llms/openai/image_generation/cost_calculator.py b/litellm/llms/openai/image_generation/cost_calculator.py index 35caaf6e9b..988d562613 100644 --- a/litellm/llms/openai/image_generation/cost_calculator.py +++ b/litellm/llms/openai/image_generation/cost_calculator.py @@ -8,8 +8,7 @@ from typing import Optional from litellm import verbose_logger from litellm.litellm_core_utils.llm_cost_calc.utils import generic_cost_per_token -from litellm.responses.utils import ResponseAPILoggingUtils -from litellm.types.utils import ImageResponse +from litellm.types.utils import ImageResponse, Usage def cost_calculator( @@ -39,11 +38,18 @@ def cost_calculator( ) return 0.0 - # Transform ImageUsage to Usage using the existing helper - # ImageUsage has the same format as ResponseAPIUsage - chat_usage = ResponseAPILoggingUtils._transform_response_api_usage_to_chat_usage( - usage - ) + # If usage is already a Usage object with completion_tokens_details set, + # use it directly (it was already transformed in convert_to_image_response) + if isinstance(usage, Usage) and usage.completion_tokens_details is not None: + chat_usage = usage + else: + # Transform ImageUsage to Usage using the existing helper + # ImageUsage has the same format as ResponseAPIUsage + from litellm.responses.utils import ResponseAPILoggingUtils + + chat_usage = ResponseAPILoggingUtils._transform_response_api_usage_to_chat_usage( + usage + ) # Use generic_cost_per_token for cost calculation prompt_cost, completion_cost = generic_cost_per_token( diff --git a/litellm/llms/openai/image_generation/guardrail_translation/handler.py b/litellm/llms/openai/image_generation/guardrail_translation/handler.py index 842a64b187..e6340ba470 100644 --- a/litellm/llms/openai/image_generation/guardrail_translation/handler.py +++ b/litellm/llms/openai/image_generation/guardrail_translation/handler.py @@ -9,6 +9,7 @@ from typing import TYPE_CHECKING, Any, Optional from litellm._logging import verbose_proxy_logger from litellm.llms.base_llm.guardrail_translation.base_translation import BaseTranslation +from litellm.types.utils import GenericGuardrailAPIInputs if TYPE_CHECKING: from litellm.integrations.custom_guardrail import CustomGuardrail @@ -52,8 +53,13 @@ class OpenAIImageGenerationHandler(BaseTranslation): # Apply guardrail to the prompt if isinstance(prompt, str): + inputs = GenericGuardrailAPIInputs(texts=[prompt]) + # Include model information if available + model = data.get("model") + if model: + inputs["model"] = model guardrailed_inputs = await guardrail_to_apply.apply_guardrail( - inputs={"texts": [prompt]}, + inputs=inputs, request_data=data, input_type="request", logging_obj=litellm_logging_obj, diff --git a/litellm/llms/openai/openai.py b/litellm/llms/openai/openai.py index 4d62309747..8a8070240d 100644 --- a/litellm/llms/openai/openai.py +++ b/litellm/llms/openai/openai.py @@ -1923,10 +1923,10 @@ class OpenAIBatchesAPI(BaseLLM): self, cancel_batch_data: CancelBatchRequest, openai_client: AsyncOpenAI, - ) -> Batch: + ) -> LiteLLMBatch: verbose_logger.debug("async cancelling batch, args= %s", cancel_batch_data) response = await openai_client.batches.cancel(**cancel_batch_data) - return response + return LiteLLMBatch(**response.model_dump()) def cancel_batch( self, @@ -1962,8 +1962,13 @@ class OpenAIBatchesAPI(BaseLLM): cancel_batch_data=cancel_batch_data, openai_client=openai_client ) + # At this point, openai_client is guaranteed to be a sync OpenAI client + if not isinstance(openai_client, OpenAI): + raise ValueError( + "OpenAI client is not an instance of OpenAI. Make sure you passed a sync OpenAI client." + ) response = openai_client.batches.cancel(**cancel_batch_data) - return response + return LiteLLMBatch(**response.model_dump()) async def alist_batches( self, diff --git a/litellm/llms/openai/responses/guardrail_translation/handler.py b/litellm/llms/openai/responses/guardrail_translation/handler.py index 9b8f15c762..d943662f9e 100644 --- a/litellm/llms/openai/responses/guardrail_translation/handler.py +++ b/litellm/llms/openai/responses/guardrail_translation/handler.py @@ -105,6 +105,10 @@ class OpenAIResponsesHandler(BaseTranslation): inputs["tools"] = tools_to_check if structured_messages: inputs["structured_messages"] = structured_messages # type: ignore + # Include model information if available + model = data.get("model") + if model: + inputs["model"] = model guardrailed_inputs = await guardrail_to_apply.apply_guardrail( inputs=inputs, @@ -150,6 +154,10 @@ class OpenAIResponsesHandler(BaseTranslation): inputs["tools"] = tools_to_check if structured_messages: inputs["structured_messages"] = structured_messages # type: ignore + # Include model information if available + model = data.get("model") + if model: + inputs["model"] = model guardrailed_inputs = await guardrail_to_apply.apply_guardrail( inputs=inputs, request_data=data, @@ -344,6 +352,14 @@ class OpenAIResponsesHandler(BaseTranslation): inputs["images"] = images_to_check if tool_calls_to_check: inputs["tool_calls"] = tool_calls_to_check + # Include model information from the response if available + response_model = None + if isinstance(response, dict): + response_model = response.get("model") + elif hasattr(response, "model"): + response_model = getattr(response, "model", None) + if response_model: + inputs["model"] = response_model guardrailed_inputs = await guardrail_to_apply.apply_guardrail( inputs=inputs, @@ -388,12 +404,15 @@ class OpenAIResponsesHandler(BaseTranslation): tool_calls = model_response_stream.choices[0].delta.tool_calls if tool_calls: + inputs = GenericGuardrailAPIInputs() + inputs["tool_calls"] = cast( + List[ChatCompletionToolCallChunk], tool_calls + ) + # Include model information if available + if hasattr(model_response_stream, "model") and model_response_stream.model: + inputs["model"] = model_response_stream.model _guardrailed_inputs = await guardrail_to_apply.apply_guardrail( - inputs={ - "tool_calls": cast( - List[ChatCompletionToolCallChunk], tool_calls - ) - }, + inputs=inputs, request_data={}, input_type="response", logging_obj=litellm_logging_obj, @@ -417,7 +436,11 @@ class OpenAIResponsesHandler(BaseTranslation): guardrail_inputs["tool_calls"] = cast( List[ChatCompletionToolCallChunk], tool_calls ) - if tool_calls: + # Include model information from the response if available + response_model = final_chunk.get("response", {}).get("model") + if response_model: + guardrail_inputs["model"] = response_model + if tool_calls or text: _guardrailed_inputs = await guardrail_to_apply.apply_guardrail( inputs=guardrail_inputs, request_data={}, @@ -429,8 +452,14 @@ class OpenAIResponsesHandler(BaseTranslation): # tool_calls = model_response_stream.choices[0].tool_calls # convert openai response to model response string_so_far = self.get_streaming_string_so_far(responses_so_far) + inputs = GenericGuardrailAPIInputs(texts=[string_so_far]) + # Try to get model from the final chunk if available + if isinstance(final_chunk, dict): + response_model = final_chunk.get("response", {}).get("model") if isinstance(final_chunk.get("response"), dict) else None + if response_model: + inputs["model"] = response_model _guardrailed_inputs = await guardrail_to_apply.apply_guardrail( - inputs={"texts": [string_so_far]}, + inputs=inputs, request_data={}, input_type="response", logging_obj=litellm_logging_obj, diff --git a/litellm/llms/openai/speech/guardrail_translation/handler.py b/litellm/llms/openai/speech/guardrail_translation/handler.py index 4c2f71477b..e6796fbac2 100644 --- a/litellm/llms/openai/speech/guardrail_translation/handler.py +++ b/litellm/llms/openai/speech/guardrail_translation/handler.py @@ -9,6 +9,7 @@ from typing import TYPE_CHECKING, Any, Optional from litellm._logging import verbose_proxy_logger from litellm.llms.base_llm.guardrail_translation.base_translation import BaseTranslation +from litellm.types.utils import GenericGuardrailAPIInputs if TYPE_CHECKING: from litellm.integrations.custom_guardrail import CustomGuardrail @@ -50,8 +51,13 @@ class OpenAITextToSpeechHandler(BaseTranslation): return data if isinstance(input_text, str): + inputs = GenericGuardrailAPIInputs(texts=[input_text]) + # Include model information if available (voice model) + model = data.get("model") + if model: + inputs["model"] = model guardrailed_inputs = await guardrail_to_apply.apply_guardrail( - inputs={"texts": [input_text]}, + inputs=inputs, request_data=data, input_type="request", logging_obj=litellm_logging_obj, diff --git a/litellm/llms/openai/transcriptions/guardrail_translation/handler.py b/litellm/llms/openai/transcriptions/guardrail_translation/handler.py index ac416f42c8..3d76a21c38 100644 --- a/litellm/llms/openai/transcriptions/guardrail_translation/handler.py +++ b/litellm/llms/openai/transcriptions/guardrail_translation/handler.py @@ -9,6 +9,7 @@ from typing import TYPE_CHECKING, Any, Optional from litellm._logging import verbose_proxy_logger from litellm.llms.base_llm.guardrail_translation.base_translation import BaseTranslation +from litellm.types.utils import GenericGuardrailAPIInputs if TYPE_CHECKING: from litellm.integrations.custom_guardrail import CustomGuardrail @@ -88,8 +89,12 @@ class OpenAIAudioTranscriptionHandler(BaseTranslation): if user_metadata: request_data["litellm_metadata"] = user_metadata + inputs = GenericGuardrailAPIInputs(texts=[original_text]) + # Include model information from the response if available + if hasattr(response, "model") and response.model: + inputs["model"] = response.model guardrailed_inputs = await guardrail_to_apply.apply_guardrail( - inputs={"texts": [original_text]}, + inputs=inputs, request_data=request_data, input_type="response", logging_obj=litellm_logging_obj, diff --git a/litellm/llms/openai_like/embedding/handler.py b/litellm/llms/openai_like/embedding/handler.py index 95a4aa854a..d0d26d5959 100644 --- a/litellm/llms/openai_like/embedding/handler.py +++ b/litellm/llms/openai_like/embedding/handler.py @@ -105,7 +105,8 @@ class OpenAILikeEmbeddingHandler(OpenAILikeBase): custom_endpoint=custom_endpoint, ) model = model - data = {"model": model, "input": input, **optional_params} + filtered_optional_params = {k: v for k, v in optional_params.items() if v not in (None, '')} + data = {"model": model, "input": input, **filtered_optional_params} ## LOGGING logging_obj.pre_call( diff --git a/litellm/llms/openrouter/chat/transformation.py b/litellm/llms/openrouter/chat/transformation.py index b5610852fd..e3770dbbf4 100644 --- a/litellm/llms/openrouter/chat/transformation.py +++ b/litellm/llms/openrouter/chat/transformation.py @@ -26,6 +26,9 @@ class CacheControlSupportedModels(str, Enum): """Models that support cache_control in content blocks.""" CLAUDE = "claude" GEMINI = "gemini" + MINIMAX = "minimax" + GLM = "glm" + ZAI = "z-ai" class OpenrouterConfig(OpenAIGPTConfig): @@ -39,6 +42,7 @@ class OpenrouterConfig(OpenAIGPTConfig): model=model, custom_llm_provider="openrouter" ) or litellm.supports_reasoning(model=model): supported_params.append("reasoning_effort") + supported_params.append("thinking") except Exception: pass return list(dict.fromkeys(supported_params)) diff --git a/litellm/llms/pass_through/guardrail_translation/handler.py b/litellm/llms/pass_through/guardrail_translation/handler.py index c0979e37e6..40433d5341 100644 --- a/litellm/llms/pass_through/guardrail_translation/handler.py +++ b/litellm/llms/pass_through/guardrail_translation/handler.py @@ -11,6 +11,7 @@ from typing import TYPE_CHECKING, Any, List, Optional from litellm._logging import verbose_proxy_logger from litellm.llms.base_llm.guardrail_translation.base_translation import BaseTranslation from litellm.proxy._types import PassThroughGuardrailSettings +from litellm.types.utils import GenericGuardrailAPIInputs if TYPE_CHECKING: from litellm.integrations.custom_guardrail import CustomGuardrail @@ -118,8 +119,13 @@ class PassThroughEndpointHandler(BaseTranslation): return data # Apply guardrail (pass-through doesn't modify the text, just checks it) + inputs = GenericGuardrailAPIInputs(texts=[text_to_check]) + # Include model information if available + model = data.get("model") + if model: + inputs["model"] = model _guardrailed_inputs = await guardrail_to_apply.apply_guardrail( - inputs={"texts": [text_to_check]}, + inputs=inputs, request_data=data, input_type="request", logging_obj=litellm_logging_obj, @@ -178,8 +184,13 @@ class PassThroughEndpointHandler(BaseTranslation): request_data["litellm_metadata"] = user_metadata # Apply guardrail (pass-through doesn't modify the text, just checks it) + inputs = GenericGuardrailAPIInputs(texts=[text_to_check]) + # Include model information from the response if available + response_model = response.get("model") if isinstance(response, dict) else None + if response_model: + inputs["model"] = response_model _guardrailed_inputs = await guardrail_to_apply.apply_guardrail( - inputs={"texts": [text_to_check]}, + inputs=inputs, request_data=request_data, input_type="response", logging_obj=litellm_logging_obj, diff --git a/litellm/llms/s3_vectors/__init__.py b/litellm/llms/s3_vectors/__init__.py new file mode 100644 index 0000000000..e8367949c3 --- /dev/null +++ b/litellm/llms/s3_vectors/__init__.py @@ -0,0 +1 @@ +# S3 Vectors LLM integration diff --git a/litellm/llms/s3_vectors/vector_stores/__init__.py b/litellm/llms/s3_vectors/vector_stores/__init__.py new file mode 100644 index 0000000000..ac24b4a38d --- /dev/null +++ b/litellm/llms/s3_vectors/vector_stores/__init__.py @@ -0,0 +1 @@ +# S3 Vectors vector store integration diff --git a/litellm/llms/s3_vectors/vector_stores/transformation.py b/litellm/llms/s3_vectors/vector_stores/transformation.py new file mode 100644 index 0000000000..df81a78289 --- /dev/null +++ b/litellm/llms/s3_vectors/vector_stores/transformation.py @@ -0,0 +1,254 @@ +from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union + +import httpx + +from litellm.llms.base_llm.vector_store.transformation import BaseVectorStoreConfig +from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM +from litellm.types.router import GenericLiteLLMParams +from litellm.types.vector_stores import ( + VECTOR_STORE_OPENAI_PARAMS, + BaseVectorStoreAuthCredentials, + VectorStoreIndexEndpoints, + VectorStoreResultContent, + VectorStoreSearchOptionalRequestParams, + VectorStoreSearchResponse, + VectorStoreSearchResult, +) + +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj +else: + LiteLLMLoggingObj = Any + + +class S3VectorsVectorStoreConfig(BaseVectorStoreConfig, BaseAWSLLM): + """Vector store configuration for AWS S3 Vectors.""" + + def __init__(self) -> None: + BaseVectorStoreConfig.__init__(self) + BaseAWSLLM.__init__(self) + + def get_auth_credentials( + self, litellm_params: dict + ) -> BaseVectorStoreAuthCredentials: + return {} + + def get_vector_store_endpoints_by_type(self) -> VectorStoreIndexEndpoints: + return { + "read": [("POST", "/QueryVectors")], + "write": [], + } + + def get_supported_openai_params( + self, model: str + ) -> List[VECTOR_STORE_OPENAI_PARAMS]: + return ["max_num_results"] + + def map_openai_params( + self, + non_default_params: dict, + optional_params: dict, + drop_params: bool, + ) -> dict: + for param, value in non_default_params.items(): + if param == "max_num_results": + optional_params["maxResults"] = value + return optional_params + + def validate_environment( + self, headers: dict, litellm_params: Optional[GenericLiteLLMParams] + ) -> dict: + headers = headers or {} + headers.setdefault("Content-Type", "application/json") + return headers + + def get_complete_url(self, api_base: Optional[str], litellm_params: dict) -> str: + aws_region_name = litellm_params.get("aws_region_name") + if not aws_region_name: + raise ValueError("aws_region_name is required for S3 Vectors") + return f"https://s3vectors.{aws_region_name}.api.aws" + + def transform_search_vector_store_request( + self, + vector_store_id: str, + query: Union[str, List[str]], + vector_store_search_optional_params: VectorStoreSearchOptionalRequestParams, + api_base: str, + litellm_logging_obj: LiteLLMLoggingObj, + litellm_params: dict, + ) -> Tuple[str, Dict]: + """Sync version - generates embedding synchronously.""" + # For S3 Vectors, vector_store_id should be in format: bucket_name:index_name + # If not in that format, try to construct it from litellm_params + bucket_name: str + index_name: str + + if ":" in vector_store_id: + bucket_name, index_name = vector_store_id.split(":", 1) + else: + # Try to get bucket_name from litellm_params + bucket_name_from_params = litellm_params.get("vector_bucket_name") + if not bucket_name_from_params or not isinstance(bucket_name_from_params, str): + raise ValueError( + "vector_store_id must be in format 'bucket_name:index_name' for S3 Vectors, " + "or vector_bucket_name must be provided in litellm_params" + ) + bucket_name = bucket_name_from_params + index_name = vector_store_id + + if isinstance(query, list): + query = " ".join(query) + + # Generate embedding for the query + embedding_model = litellm_params.get("embedding_model", "text-embedding-3-small") + + import litellm as litellm_module + embedding_response = litellm_module.embedding(model=embedding_model, input=[query]) + query_embedding = embedding_response.data[0]["embedding"] + + url = f"{api_base}/QueryVectors" + + request_body: Dict[str, Any] = { + "vectorBucketName": bucket_name, + "indexName": index_name, + "queryVector": {"float32": query_embedding}, + "topK": vector_store_search_optional_params.get("max_num_results", 5), # Default to 5 + "returnDistance": True, + "returnMetadata": True, + } + + litellm_logging_obj.model_call_details["query"] = query + return url, request_body + + async def atransform_search_vector_store_request( + self, + vector_store_id: str, + query: Union[str, List[str]], + vector_store_search_optional_params: VectorStoreSearchOptionalRequestParams, + api_base: str, + litellm_logging_obj: LiteLLMLoggingObj, + litellm_params: dict, + ) -> Tuple[str, Dict]: + """Async version - generates embedding asynchronously.""" + # For S3 Vectors, vector_store_id should be in format: bucket_name:index_name + # If not in that format, try to construct it from litellm_params + bucket_name: str + index_name: str + + if ":" in vector_store_id: + bucket_name, index_name = vector_store_id.split(":", 1) + else: + # Try to get bucket_name from litellm_params + bucket_name_from_params = litellm_params.get("vector_bucket_name") + if not bucket_name_from_params or not isinstance(bucket_name_from_params, str): + raise ValueError( + "vector_store_id must be in format 'bucket_name:index_name' for S3 Vectors, " + "or vector_bucket_name must be provided in litellm_params" + ) + bucket_name = bucket_name_from_params + index_name = vector_store_id + + if isinstance(query, list): + query = " ".join(query) + + # Generate embedding for the query asynchronously + embedding_model = litellm_params.get("embedding_model", "text-embedding-3-small") + + import litellm as litellm_module + embedding_response = await litellm_module.aembedding(model=embedding_model, input=[query]) + query_embedding = embedding_response.data[0]["embedding"] + + url = f"{api_base}/QueryVectors" + + request_body: Dict[str, Any] = { + "vectorBucketName": bucket_name, + "indexName": index_name, + "queryVector": {"float32": query_embedding}, + "topK": vector_store_search_optional_params.get("max_num_results", 5), # Default to 5 + "returnDistance": True, + "returnMetadata": True, + } + + litellm_logging_obj.model_call_details["query"] = query + return url, request_body + + def sign_request( + self, + headers: dict, + optional_params: Dict, + request_data: Dict, + api_base: str, + api_key: Optional[str] = None, + ) -> Tuple[dict, Optional[bytes]]: + return self._sign_request( + service_name="s3vectors", + headers=headers, + optional_params=optional_params, + request_data=request_data, + api_base=api_base, + api_key=api_key, + ) + + def transform_search_vector_store_response( + self, response: httpx.Response, litellm_logging_obj: LiteLLMLoggingObj + ) -> VectorStoreSearchResponse: + try: + response_data = response.json() + results: List[VectorStoreSearchResult] = [] + + for item in response_data.get("vectors", []) or []: + metadata = item.get("metadata", {}) or {} + source_text = metadata.get("source_text", "") + + if not source_text: + continue + + # Extract file information from metadata + chunk_index = metadata.get("chunk_index", "0") + file_id = f"s3-vectors-chunk-{chunk_index}" + filename = metadata.get("filename", f"document-{chunk_index}") + + # S3 Vectors returns distance, convert to similarity score (0-1) + # Lower distance = higher similarity + # We'll normalize using 1 / (1 + distance) to get a 0-1 score + distance = item.get("distance") + score = None + if distance is not None: + # Convert distance to similarity score between 0 and 1 + # For cosine distance: similarity = 1 - distance + # For euclidean: use 1 / (1 + distance) + # Assuming cosine distance here + score = max(0.0, min(1.0, 1.0 - float(distance))) + + results.append( + VectorStoreSearchResult( + score=score, + content=[VectorStoreResultContent(text=source_text, type="text")], + file_id=file_id, + filename=filename, + attributes=metadata, + ) + ) + + return VectorStoreSearchResponse( + object="vector_store.search_results.page", + search_query=litellm_logging_obj.model_call_details.get("query", ""), + data=results, + ) + except Exception as e: + raise self.get_error_class( + error_message=str(e), + status_code=response.status_code, + headers=response.headers, + ) + + # Vector store creation is not yet implemented + def transform_create_vector_store_request( + self, + vector_store_create_optional_params, + api_base: str, + ) -> Tuple[str, Dict]: + raise NotImplementedError + + def transform_create_vector_store_response(self, response: httpx.Response): + raise NotImplementedError diff --git a/litellm/llms/vercel_ai_gateway/embedding/__init__.py b/litellm/llms/vercel_ai_gateway/embedding/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/litellm/llms/vercel_ai_gateway/embedding/transformation.py b/litellm/llms/vercel_ai_gateway/embedding/transformation.py new file mode 100644 index 0000000000..7238b05f10 --- /dev/null +++ b/litellm/llms/vercel_ai_gateway/embedding/transformation.py @@ -0,0 +1,176 @@ +""" +Vercel AI Gateway Embedding API Configuration. + +This module provides the configuration for Vercel AI Gateway's Embedding API. +Vercel AI Gateway is OpenAI-compatible and supports embeddings via the /v1/embeddings endpoint. + +Docs: https://vercel.com/docs/ai-gateway/openai-compat/embeddings +""" + +from typing import TYPE_CHECKING, Any, Optional + +import httpx + +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 +from litellm.types.utils import EmbeddingResponse +from litellm.utils import convert_to_model_response_object + +from ..common_utils import VercelAIGatewayException + +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj + + LiteLLMLoggingObj = _LiteLLMLoggingObj +else: + LiteLLMLoggingObj = Any + + +class VercelAIGatewayEmbeddingConfig(BaseEmbeddingConfig): + """ + Configuration for Vercel AI Gateway's Embedding API. + + Reference: https://vercel.com/docs/ai-gateway/openai-compat/embeddings + """ + + def validate_environment( + self, + headers: dict, + model: str, + messages: list, + optional_params: dict, + litellm_params: dict, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + ) -> dict: + """ + Validate environment and set up headers for Vercel AI Gateway API. + + Vercel AI Gateway requires: + - Authorization header with Bearer token (API key or OIDC token) + """ + vercel_headers = { + "Content-Type": "application/json", + } + + # Add Authorization header if api_key is provided + if api_key: + vercel_headers["Authorization"] = f"Bearer {api_key}" + + # Merge with existing headers (user's extra_headers take priority) + merged_headers = {**vercel_headers, **headers} + + return merged_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 Vercel AI Gateway Embedding API endpoint. + """ + if api_base: + api_base = api_base.rstrip("/") + else: + api_base = ( + get_secret_str("VERCEL_AI_GATEWAY_API_BASE") + or "https://ai-gateway.vercel.sh/v1" + ) + + return f"{api_base}/embeddings" + + def transform_embedding_request( + self, + model: str, + input: AllEmbeddingInputValues, + optional_params: dict, + headers: dict, + ) -> dict: + """ + Transform embedding request to Vercel AI Gateway format (OpenAI-compatible). + """ + # Ensure input is a list + if isinstance(input, str): + input = [input] + + # Strip 'vercel_ai_gateway/' prefix if present + if model.startswith("vercel_ai_gateway/"): + model = model.replace("vercel_ai_gateway/", "", 1) + + return { + "model": model, + "input": input, + **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: + """ + Transform embedding response from Vercel AI Gateway format (OpenAI-compatible). + """ + logging_obj.post_call(original_response=raw_response.text) + + # Vercel AI Gateway returns standard OpenAI-compatible embedding response + response_json = raw_response.json() + + return convert_to_model_response_object( + response_object=response_json, + model_response_object=model_response, + response_type="embedding", + ) + + def get_supported_openai_params(self, model: str) -> list: + """ + Get list of supported OpenAI parameters for Vercel AI Gateway embeddings. + + Vercel AI Gateway supports the standard OpenAI embeddings parameters + and auto-maps 'dimensions' to each provider's expected field. + """ + return [ + "timeout", + "dimensions", + "encoding_format", + "user", + ] + + def map_openai_params( + self, + non_default_params: dict, + optional_params: dict, + model: str, + drop_params: bool, + ) -> dict: + """ + Map OpenAI parameters to Vercel AI Gateway format. + """ + for param, value in non_default_params.items(): + if param in self.get_supported_openai_params(model): + optional_params[param] = value + return optional_params + + def get_error_class( + self, error_message: str, status_code: int, headers: Any + ) -> Any: + """ + Get the error class for Vercel AI Gateway errors. + """ + return VercelAIGatewayException( + message=error_message, + status_code=status_code, + headers=headers, + ) diff --git a/litellm/llms/vertex_ai/common_utils.py b/litellm/llms/vertex_ai/common_utils.py index 152b99ca4d..a0e2ddf5e9 100644 --- a/litellm/llms/vertex_ai/common_utils.py +++ b/litellm/llms/vertex_ai/common_utils.py @@ -849,7 +849,7 @@ def get_vertex_model_id_from_url(url: str) -> Optional[str]: `https://${LOCATION}-aiplatform.googleapis.com/v1/projects/${PROJECT_ID}/locations/${LOCATION}/publishers/google/models/${MODEL_ID}:streamGenerateContent` """ - match = re.search(r"/models/([^/:]+)", url) + match = re.search(r"/models/([^:]+)", url) return match.group(1) if match else None 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 b78ac8f9e9..a9ac21bb56 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 @@ -1657,7 +1657,17 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): ## This is necessary because promptTokensDetails includes both cached and non-cached tokens ## See: https://github.com/BerriAI/litellm/issues/18750 if cached_text_tokens is not None and prompt_text_tokens is not None: + # Explicit caching: subtract cached tokens per modality from cacheTokensDetails prompt_text_tokens = prompt_text_tokens - cached_text_tokens + elif ( + cached_tokens is not None + and prompt_text_tokens is not None + and cached_text_tokens is None + ): + # Implicit caching: only cachedContentTokenCount is provided (no cacheTokensDetails) + # Subtract from text tokens since implicit caching is primarily for text content + # See: https://github.com/BerriAI/litellm/issues/16341 + prompt_text_tokens = prompt_text_tokens - cached_tokens if cached_audio_tokens is not None and prompt_audio_tokens is not None: prompt_audio_tokens = prompt_audio_tokens - cached_audio_tokens if cached_image_tokens is not None and prompt_image_tokens is not None: diff --git a/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/experimental_pass_through/transformation.py b/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/experimental_pass_through/transformation.py index fc75376c0c..9b8ff3ecc2 100644 --- a/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/experimental_pass_through/transformation.py +++ b/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/experimental_pass_through/transformation.py @@ -7,6 +7,7 @@ from litellm.llms.anthropic.experimental_pass_through.messages.transformation im from litellm.types.llms.anthropic import ( ANTHROPIC_BETA_HEADER_VALUES, ANTHROPIC_HOSTED_TOOLS, + ANTHROPIC_PROMPT_CACHING_SCOPE_BETA_HEADER, ) from litellm.types.llms.anthropic_tool_search import get_tool_search_beta_header from litellm.types.llms.vertex_ai import VertexPartnerProvider @@ -65,6 +66,10 @@ class VertexAIPartnerModelsAnthropicMessagesConfig(AnthropicMessagesConfig, Vert if existing_beta: beta_values.update(b.strip() for b in existing_beta.split(",")) + # Use the helper to remove unsupported beta headers + self.remove_unsupported_beta(headers) + beta_values.discard(ANTHROPIC_PROMPT_CACHING_SCOPE_BETA_HEADER) + # Check for web search tool for tool in tools: if isinstance(tool, dict) and tool.get("type", "").startswith(ANTHROPIC_HOSTED_TOOLS.WEB_SEARCH.value): @@ -123,3 +128,23 @@ class VertexAIPartnerModelsAnthropicMessagesConfig(AnthropicMessagesConfig, Vert ) # do not pass output_format in request body to vertex ai - vertex ai does not support output_format as yet return anthropic_messages_request + + def remove_unsupported_beta(self, headers: dict) -> None: + """ + Helper method to remove unsupported beta headers from the beta headers. + Modifies headers in place. + """ + unsupported_beta_headers = [ + ANTHROPIC_PROMPT_CACHING_SCOPE_BETA_HEADER + ] + existing_beta = headers.get("anthropic-beta") + if existing_beta: + filtered_beta = [ + b.strip() + for b in existing_beta.split(",") + if b.strip() not in unsupported_beta_headers + ] + if filtered_beta: + headers["anthropic-beta"] = ",".join(filtered_beta) + elif "anthropic-beta" in headers: + del headers["anthropic-beta"] diff --git a/litellm/llms/xai/responses/transformation.py b/litellm/llms/xai/responses/transformation.py index bd422c8d81..82b4771fb4 100644 --- a/litellm/llms/xai/responses/transformation.py +++ b/litellm/llms/xai/responses/transformation.py @@ -1,10 +1,11 @@ -from typing import TYPE_CHECKING, Any, Dict, List, Optional +from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union import litellm from litellm._logging import verbose_logger from litellm.llms.openai.responses.transformation import OpenAIResponsesAPIConfig from litellm.secret_managers.main import get_secret_str from litellm.types.llms.openai import ResponsesAPIOptionalRequestParams +from litellm.types.llms.xai import XAIWebSearchTool, XAIXSearchTool from litellm.types.router import GenericLiteLLMParams from litellm.types.utils import LlmProviders @@ -49,6 +50,85 @@ class XAIResponsesAPIConfig(OpenAIResponsesAPIConfig): return supported_params + def _transform_web_search_tool(self, tool: Dict[str, Any]) -> Union[XAIWebSearchTool, Dict[str, Any]]: + """ + Transform web_search tool to XAI format. + + XAI supports web_search with specific filters: + - allowed_domains (max 5) + - excluded_domains (max 5) + - enable_image_understanding + + XAI does NOT support search_context_size (OpenAI-specific). + """ + xai_tool: Dict[str, Any] = {"type": "web_search"} + + # Remove search_context_size if present (not supported by XAI) + if "search_context_size" in tool: + verbose_logger.info( + "XAI does not support 'search_context_size' parameter. Removing it from web_search tool." + ) + + # Handle filters (XAI-specific structure) + filters = {} + if "allowed_domains" in tool: + allowed_domains = tool["allowed_domains"] + filters["allowed_domains"] = allowed_domains + + if "excluded_domains" in tool: + excluded_domains = tool["excluded_domains"] + filters["excluded_domains"] = excluded_domains + + # Add filters if any were specified + if filters: + xai_tool["filters"] = filters + + # Handle enable_image_understanding (top-level in XAI format) + if "enable_image_understanding" in tool: + xai_tool["enable_image_understanding"] = tool["enable_image_understanding"] + + return xai_tool + + def _transform_x_search_tool(self, tool: Dict[str, Any]) -> Union[XAIXSearchTool, Dict[str, Any]]: + """ + Transform x_search tool to XAI format. + + XAI supports x_search with specific parameters: + - allowed_x_handles (max 10) + - excluded_x_handles (max 10) + - from_date (ISO8601: YYYY-MM-DD) + - to_date (ISO8601: YYYY-MM-DD) + - enable_image_understanding + - enable_video_understanding + """ + xai_tool: Dict[str, Any] = {"type": "x_search"} + + # Handle allowed_x_handles + if "allowed_x_handles" in tool: + allowed_handles = tool["allowed_x_handles"] + xai_tool["allowed_x_handles"] = allowed_handles + + # Handle excluded_x_handles + if "excluded_x_handles" in tool: + excluded_handles = tool["excluded_x_handles"] + xai_tool["excluded_x_handles"] = excluded_handles + + # Handle date range + if "from_date" in tool: + xai_tool["from_date"] = tool["from_date"] + + if "to_date" in tool: + xai_tool["to_date"] = tool["to_date"] + + # Handle media understanding flags + if "enable_image_understanding" in tool: + xai_tool["enable_image_understanding"] = tool["enable_image_understanding"] + + if "enable_video_understanding" in tool: + xai_tool["enable_video_understanding"] = tool["enable_video_understanding"] + + return xai_tool + def map_openai_params( self, response_api_optional_params: ResponsesAPIOptionalRequestParams, @@ -61,7 +141,9 @@ class XAIResponsesAPIConfig(OpenAIResponsesAPIConfig): Handles XAI-specific transformations: 1. Drops 'instructions' parameter (not supported) 2. Transforms code_interpreter tools to remove 'container' field - 3. Sets store=false when images are detected (recommended by XAI) + 3. Transforms web_search tools to XAI format (removes search_context_size, adds filters) + 4. Transforms x_search tools to XAI format + 5. Sets store=false when images are detected (recommended by XAI) """ params = dict(response_api_optional_params) @@ -72,7 +154,13 @@ class XAIResponsesAPIConfig(OpenAIResponsesAPIConfig): ) params.pop("instructions") - # Transform code_interpreter tools - remove container field + if "metadata" in params: + verbose_logger.debug( + "XAI Responses API does not support 'metadata' parameter. Dropping it." + ) + params.pop("metadata") + + # Transform tools if "tools" in params and params["tools"]: tools_list = params["tools"] # Ensure tools is a list for iteration @@ -81,15 +169,36 @@ class XAIResponsesAPIConfig(OpenAIResponsesAPIConfig): transformed_tools: List[Any] = [] for tool in tools_list: - if isinstance(tool, dict) and tool.get("type") == "code_interpreter": - # XAI supports code_interpreter but doesn't use the container field - # Keep only the type field - verbose_logger.debug( - "XAI: Transforming code_interpreter tool, removing container field" - ) - transformed_tools.append({"type": "code_interpreter"}) + if isinstance(tool, dict): + tool_type = tool.get("type") + + if tool_type == "code_interpreter": + # XAI supports code_interpreter but doesn't use the container field + verbose_logger.debug( + "XAI: Transforming code_interpreter tool, removing container field" + ) + transformed_tools.append({"type": "code_interpreter"}) + + elif tool_type == "web_search": + # Transform web_search to XAI format + verbose_logger.debug( + "XAI: Transforming web_search tool to XAI format" + ) + transformed_tools.append(self._transform_web_search_tool(tool)) + + elif tool_type == "x_search": + # Transform x_search to XAI format + verbose_logger.debug( + "XAI: Transforming x_search tool to XAI format" + ) + transformed_tools.append(self._transform_x_search_tool(tool)) + + else: + # Keep other tools as-is + transformed_tools.append(tool) else: transformed_tools.append(tool) + params["tools"] = transformed_tools return params diff --git a/litellm/llms/zai/chat/transformation.py b/litellm/llms/zai/chat/transformation.py index 4380256f0a..fb1d67df35 100644 --- a/litellm/llms/zai/chat/transformation.py +++ b/litellm/llms/zai/chat/transformation.py @@ -1,6 +1,7 @@ -from typing import Optional, Tuple +from typing import List, Optional, Tuple from litellm.secret_managers.main import get_secret_str +from litellm.types.llms.openai import AllMessageValues, ChatCompletionToolParam from ...openai.chat.gpt_transformation import OpenAIGPTConfig @@ -19,6 +20,19 @@ class ZAIChatConfig(OpenAIGPTConfig): dynamic_api_key = api_key or get_secret_str("ZAI_API_KEY") return api_base, dynamic_api_key + def remove_cache_control_flag_from_messages_and_tools( + self, + model: str, + messages: List[AllMessageValues], + tools: Optional[List[ChatCompletionToolParam]] = None, + ) -> Tuple[List[AllMessageValues], Optional[List[ChatCompletionToolParam]]]: + """ + Override to preserve cache_control for GLM/ZAI. + GLM supports cache_control - don't strip it. + """ + # GLM/ZAI supports cache_control, so return messages and tools unchanged + return messages, tools + def get_supported_openai_params(self, model: str) -> list: base_params = [ "max_tokens", diff --git a/litellm/main.py b/litellm/main.py index ce84c8988e..13361c644c 100644 --- a/litellm/main.py +++ b/litellm/main.py @@ -148,7 +148,7 @@ from litellm.utils import ( validate_and_fix_openai_messages, validate_and_fix_openai_tools, validate_chat_completion_tool_choice, - validate_openai_optional_params + validate_openai_optional_params, ) from ._logging import verbose_logger @@ -368,7 +368,7 @@ class AsyncCompletions: @tracer.wrap() @client -async def acompletion( # noqa: PLR0915 +async def acompletion( # noqa: PLR0915 model: str, # Optional OpenAI params: see https://platform.openai.com/docs/api-reference/chat/create messages: List = [], @@ -599,16 +599,8 @@ async def acompletion( # noqa: PLR0915 # Add the context to the function ctx = contextvars.copy_context() func_with_context = partial(ctx.run, func) - - if timeout is not None and isinstance(timeout, (int, float)): - timeout_value = float(timeout) - init_response = await asyncio.wait_for( - loop.run_in_executor(None, func_with_context), - timeout=timeout_value - ) - else: - init_response = await loop.run_in_executor(None, func_with_context) + init_response = await loop.run_in_executor(None, func_with_context) if isinstance(init_response, dict) or isinstance( init_response, ModelResponse ): ## CACHING SCENARIO @@ -616,11 +608,7 @@ async def acompletion( # noqa: PLR0915 response = ModelResponse(**init_response) response = init_response elif asyncio.iscoroutine(init_response): - if timeout is not None and isinstance(timeout, (int, float)): - timeout_value = float(timeout) - response = await asyncio.wait_for(init_response, timeout=timeout_value) - else: - response = await init_response + response = await init_response else: response = init_response # type: ignore @@ -637,14 +625,6 @@ async def acompletion( # noqa: PLR0915 loop=loop ) # sets the logging event loop if the user does sync streaming (e.g. on proxy for sagemaker calls) return response - except asyncio.TimeoutError: - custom_llm_provider = custom_llm_provider or "openai" - from litellm.exceptions import Timeout - raise Timeout( - message=f"Request timed out after {timeout} seconds", - model=model, - llm_provider=custom_llm_provider, - ) except Exception as e: custom_llm_provider = custom_llm_provider or "openai" raise exception_type( @@ -945,6 +925,7 @@ def mock_completion( def responses_api_bridge_check( model: str, custom_llm_provider: str, + web_search_options: Optional[OpenAIWebSearchOptions] = None, ) -> Tuple[dict, str]: model_info: Dict[str, Any] = {} try: @@ -958,6 +939,10 @@ def responses_api_bridge_check( model = model.replace("responses/", "") mode = "responses" model_info["mode"] = mode + + if web_search_options is not None and custom_llm_provider == "xai": + model_info["mode"] = "responses" + model = model.replace("responses/", "") except Exception as e: verbose_logger.debug("Error getting model info: {}".format(e)) @@ -1118,7 +1103,6 @@ def completion( # type: ignore # noqa: PLR0915 # validate optional params stop = validate_openai_optional_params(stop=stop) - ######### unpacking kwargs ##################### args = locals() @@ -1135,7 +1119,9 @@ def completion( # type: ignore # noqa: PLR0915 # Check if MCP tools are present (following responses pattern) # Cast tools to Optional[Iterable[ToolParam]] for type checking tools_for_mcp = cast(Optional[Iterable[ToolParam]], tools) - if LiteLLM_Proxy_MCP_Handler._should_use_litellm_mcp_gateway(tools=tools_for_mcp): + if LiteLLM_Proxy_MCP_Handler._should_use_litellm_mcp_gateway( + tools=tools_for_mcp + ): # Return coroutine - acompletion will await it # completion() can return a coroutine when MCP tools are present, which acompletion() awaits return acompletion_with_mcp( # type: ignore[return-value] @@ -1536,6 +1522,8 @@ def completion( # type: ignore # noqa: PLR0915 max_retries=max_retries, timeout=timeout, litellm_request_debug=kwargs.get("litellm_request_debug", False), + tpm=kwargs.get("tpm"), + rpm=kwargs.get("rpm"), ) cast(LiteLLMLoggingObj, logging).update_environment_variables( model=model, @@ -1563,7 +1551,7 @@ def completion( # type: ignore # noqa: PLR0915 ## RESPONSES API BRIDGE LOGIC ## - check if model has 'mode: responses' in litellm.model_cost map model_info, model = responses_api_bridge_check( - model=model, custom_llm_provider=custom_llm_provider + model=model, custom_llm_provider=custom_llm_provider, web_search_options=web_search_options ) if model_info.get("mode") == "responses": @@ -2361,11 +2349,7 @@ def completion( # type: ignore # noqa: PLR0915 input=messages, api_key=api_key, original_response=response ) elif custom_llm_provider == "minimax": - api_key = ( - api_key - or get_secret_str("MINIMAX_API_KEY") - or litellm.api_key - ) + api_key = api_key or get_secret_str("MINIMAX_API_KEY") or litellm.api_key api_base = ( api_base @@ -2374,6 +2358,33 @@ def completion( # type: ignore # noqa: PLR0915 or "https://api.minimax.io/v1" ) + response = base_llm_http_handler.completion( + model=model, + messages=messages, + api_base=api_base, + custom_llm_provider=custom_llm_provider, + model_response=model_response, + encoding=_get_encoding(), + logging_obj=logging, + optional_params=optional_params, + timeout=timeout, + litellm_params=litellm_params, + shared_session=shared_session, + acompletion=acompletion, + stream=stream, + api_key=api_key, + headers=headers, + client=client, + provider_config=provider_config, + ) + logging.post_call( + input=messages, api_key=api_key, original_response=response + ) + elif custom_llm_provider == "hosted_vllm": + api_base = ( + api_base or litellm.api_base or get_secret_str("HOSTED_VLLM_API_BASE") + ) + response = base_llm_http_handler.completion( model=model, messages=messages, @@ -2413,7 +2424,9 @@ def completion( # type: ignore # noqa: PLR0915 or custom_llm_provider == "wandb" or custom_llm_provider == "clarifai" or custom_llm_provider in litellm.openai_compatible_providers - or JSONProviderRegistry.exists(custom_llm_provider) # JSON-configured providers + or JSONProviderRegistry.exists( + custom_llm_provider + ) # JSON-configured 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 # note: if a user sets a custom base - we should ensure this works @@ -3610,9 +3623,9 @@ def completion( # type: ignore # noqa: PLR0915 "aws_region_name" not in optional_params or optional_params["aws_region_name"] is None ): - optional_params[ - "aws_region_name" - ] = aws_bedrock_client.meta.region_name + optional_params["aws_region_name"] = ( + aws_bedrock_client.meta.region_name + ) bedrock_route = BedrockModelInfo.get_bedrock_route(model) if bedrock_route == "converse": @@ -4724,7 +4737,7 @@ def embedding( # noqa: PLR0915 if headers is not None and headers != {}: optional_params["extra_headers"] = headers - + if encoding_format is not None: optional_params["encoding_format"] = encoding_format else: @@ -4772,9 +4785,32 @@ def embedding( # noqa: PLR0915 client=client, aembedding=aembedding, ) + elif custom_llm_provider == "hosted_vllm": + api_base = ( + api_base or litellm.api_base or get_secret_str("HOSTED_VLLM_API_BASE") + ) + + # set API KEY + if api_key is None: + api_key = litellm.api_key or get_secret_str("HOSTED_VLLM_API_KEY") + + 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=litellm_params_dict, + headers=headers or {}, + ) elif ( custom_llm_provider == "openai_like" - or custom_llm_provider == "hosted_vllm" or custom_llm_provider == "llamafile" or custom_llm_provider == "lm_studio" ): @@ -4866,6 +4902,36 @@ def embedding( # noqa: PLR0915 headers = openrouter_headers + 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=litellm_params_dict, + headers=headers, + ) + 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_str("VERCEL_AI_GATEWAY_API_KEY") + or get_secret_str("VERCEL_OIDC_TOKEN") + ) + response = base_llm_http_handler.embedding( model=model, input=input, @@ -5917,9 +5983,9 @@ def adapter_completion( new_kwargs = translation_obj.translate_completion_input_params(kwargs=kwargs) response: Union[ModelResponse, CustomStreamWrapper] = completion(**new_kwargs) # type: ignore - translated_response: Optional[ - Union[BaseModel, AdapterCompletionStreamWrapper] - ] = None + translated_response: Optional[Union[BaseModel, AdapterCompletionStreamWrapper]] = ( + None + ) if isinstance(response, ModelResponse): translated_response = translation_obj.translate_completion_output_params( response=response @@ -6624,9 +6690,9 @@ def speech( # noqa: PLR0915 ElevenLabsTextToSpeechConfig.ELEVENLABS_QUERY_PARAMS_KEY ] = query_params - litellm_params_dict[ - ElevenLabsTextToSpeechConfig.ELEVENLABS_VOICE_ID_KEY - ] = voice_id + litellm_params_dict[ElevenLabsTextToSpeechConfig.ELEVENLABS_VOICE_ID_KEY] = ( + voice_id + ) if api_base is not None: litellm_params_dict["api_base"] = api_base @@ -6759,9 +6825,7 @@ def speech( # noqa: PLR0915 if text_to_speech_provider_config is None: text_to_speech_provider_config = MinimaxTextToSpeechConfig() - minimax_config = cast( - MinimaxTextToSpeechConfig, text_to_speech_provider_config - ) + minimax_config = cast(MinimaxTextToSpeechConfig, text_to_speech_provider_config) if api_base is not None: litellm_params_dict["api_base"] = api_base @@ -6901,7 +6965,7 @@ async def ahealth_check( custom_llm_provider_from_params = model_params.get("custom_llm_provider", None) api_base_from_params = model_params.get("api_base", None) api_key_from_params = model_params.get("api_key", None) - + model, custom_llm_provider, _, _ = get_llm_provider( model=model, custom_llm_provider=custom_llm_provider_from_params, @@ -7134,9 +7198,9 @@ def stream_chunk_builder( # noqa: PLR0915 ] if len(content_chunks) > 0: - response["choices"][0]["message"][ - "content" - ] = processor.get_combined_content(content_chunks) + response["choices"][0]["message"]["content"] = ( + processor.get_combined_content(content_chunks) + ) thinking_blocks = [ chunk @@ -7147,9 +7211,9 @@ def stream_chunk_builder( # noqa: PLR0915 ] if len(thinking_blocks) > 0: - response["choices"][0]["message"][ - "thinking_blocks" - ] = processor.get_combined_thinking_content(thinking_blocks) + response["choices"][0]["message"]["thinking_blocks"] = ( + processor.get_combined_thinking_content(thinking_blocks) + ) reasoning_chunks = [ chunk @@ -7160,9 +7224,9 @@ def stream_chunk_builder( # noqa: PLR0915 ] if len(reasoning_chunks) > 0: - response["choices"][0]["message"][ - "reasoning_content" - ] = processor.get_combined_reasoning_content(reasoning_chunks) + response["choices"][0]["message"]["reasoning_content"] = ( + processor.get_combined_reasoning_content(reasoning_chunks) + ) annotation_chunks = [ chunk @@ -7188,6 +7252,23 @@ def stream_chunk_builder( # noqa: PLR0915 _choice = cast(Choices, response.choices[0]) _choice.message.audio = processor.get_combined_audio_content(audio_chunks) + # Handle image chunks from models like gemini-2.5-flash-image + # See: https://github.com/BerriAI/litellm/issues/19478 + image_chunks = [ + chunk + for chunk in chunks + if len(chunk["choices"]) > 0 + and "images" in chunk["choices"][0]["delta"] + and chunk["choices"][0]["delta"]["images"] is not None + ] + + if len(image_chunks) > 0: + # Images come complete in a single chunk, collect all images from all chunks + all_images = [] + for chunk in image_chunks: + all_images.extend(chunk["choices"][0]["delta"]["images"]) + response["choices"][0]["message"]["images"] = all_images + # Combine provider_specific_fields from streaming chunks (e.g., web_search_results, citations) # See: https://github.com/BerriAI/litellm/issues/17737 provider_specific_chunks = [ @@ -7271,12 +7352,16 @@ def _get_encoding(): def __getattr__(name: str) -> Any: """Lazy import handler for main module""" if name == "encoding": - # Lazy load encoding to avoid heavy tiktoken import at module load time - _encoding = tiktoken.get_encoding("cl100k_base") + # Use _get_default_encoding which properly sets TIKTOKEN_CACHE_DIR + # before loading tiktoken, ensuring the local cache is used + # instead of downloading from the internet + from litellm._lazy_imports import _get_default_encoding + _encoding = _get_default_encoding() # Cache it in the module's __dict__ for subsequent accesses import sys + sys.modules[__name__].__dict__["encoding"] = _encoding global _encoding_cache _encoding_cache = _encoding return _encoding - raise AttributeError(f"module {__name__!r} has no attribute {name!r}") \ No newline at end of file + raise AttributeError(f"module {__name__!r} has no attribute {name!r}") diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index d958ea4503..6a605f460d 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -354,6 +354,25 @@ "supports_video_input": true, "supports_vision": true }, + "amazon.nova-2-pro-preview-20251202-v1:0": { + "cache_read_input_token_cost": 5.46875e-07, + "input_cost_per_token": 2.1875e-06, + "input_cost_per_image_token": 2.1875e-06, + "input_cost_per_audio_token": 2.1875e-06, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 1000000, + "max_output_tokens": 64000, + "max_tokens": 64000, + "mode": "chat", + "output_cost_per_token": 1.75e-05, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_video_input": true, + "supports_vision": true + }, "apac.amazon.nova-2-lite-v1:0": { "cache_read_input_token_cost": 8.25e-08, "input_cost_per_token": 3.3e-07, @@ -371,6 +390,25 @@ "supports_video_input": true, "supports_vision": true }, + "apac.amazon.nova-2-pro-preview-20251202-v1:0": { + "cache_read_input_token_cost": 5.46875e-07, + "input_cost_per_token": 2.1875e-06, + "input_cost_per_image_token": 2.1875e-06, + "input_cost_per_audio_token": 2.1875e-06, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 1000000, + "max_output_tokens": 64000, + "max_tokens": 64000, + "mode": "chat", + "output_cost_per_token": 1.75e-05, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_video_input": true, + "supports_vision": true + }, "eu.amazon.nova-2-lite-v1:0": { "cache_read_input_token_cost": 8.25e-08, "input_cost_per_token": 3.3e-07, @@ -388,6 +426,25 @@ "supports_video_input": true, "supports_vision": true }, + "eu.amazon.nova-2-pro-preview-20251202-v1:0": { + "cache_read_input_token_cost": 5.46875e-07, + "input_cost_per_token": 2.1875e-06, + "input_cost_per_image_token": 2.1875e-06, + "input_cost_per_audio_token": 2.1875e-06, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 1000000, + "max_output_tokens": 64000, + "max_tokens": 64000, + "mode": "chat", + "output_cost_per_token": 1.75e-05, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_video_input": true, + "supports_vision": true + }, "us.amazon.nova-2-lite-v1:0": { "cache_read_input_token_cost": 8.25e-08, "input_cost_per_token": 3.3e-07, @@ -405,6 +462,25 @@ "supports_video_input": true, "supports_vision": true }, + "us.amazon.nova-2-pro-preview-20251202-v1:0": { + "cache_read_input_token_cost": 5.46875e-07, + "input_cost_per_token": 2.1875e-06, + "input_cost_per_image_token": 2.1875e-06, + "input_cost_per_audio_token": 2.1875e-06, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 1000000, + "max_output_tokens": 64000, + "max_tokens": 64000, + "mode": "chat", + "output_cost_per_token": 1.75e-05, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_video_input": true, + "supports_vision": true + }, "amazon.nova-2-multimodal-embeddings-v1:0": { "litellm_provider": "bedrock", "max_input_tokens": 8172, @@ -3130,7 +3206,7 @@ "supports_reasoning": true, "supports_response_schema": true, "supports_system_messages": true, - "supports_tool_choice": false, + "supports_tool_choice": true, "supports_vision": true }, "azure/gpt-5-chat-latest": { @@ -3162,7 +3238,7 @@ "supports_reasoning": true, "supports_response_schema": true, "supports_system_messages": true, - "supports_tool_choice": false, + "supports_tool_choice": true, "supports_vision": true }, "azure/gpt-5-codex": { @@ -3653,10 +3729,9 @@ "max_input_tokens": 128000, "max_output_tokens": 16384, "max_tokens": 16384, - "mode": "chat", + "mode": "responses", "output_cost_per_token": 1.4e-05, "supported_endpoints": [ - "/v1/chat/completions", "/v1/responses" ], "supported_modalities": [ @@ -10232,6 +10307,48 @@ "mode": "completion", "output_cost_per_token": 5e-07 }, + "deepseek-v3-2-251201": { + "input_cost_per_token": 0.0, + "litellm_provider": "volcengine", + "max_input_tokens": 98304, + "max_output_tokens": 32768, + "max_tokens": 32768, + "mode": "chat", + "output_cost_per_token": 0.0, + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, + "glm-4-7-251222": { + "input_cost_per_token": 0.0, + "litellm_provider": "volcengine", + "max_input_tokens": 204800, + "max_output_tokens": 131072, + "max_tokens": 131072, + "mode": "chat", + "output_cost_per_token": 0.0, + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, + "kimi-k2-thinking-251104": { + "input_cost_per_token": 0.0, + "litellm_provider": "volcengine", + "max_input_tokens": 229376, + "max_output_tokens": 32768, + "max_tokens": 32768, + "mode": "chat", + "output_cost_per_token": 0.0, + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, "doubao-embedding": { "input_cost_per_token": 0.0, "litellm_provider": "volcengine", @@ -13480,6 +13597,79 @@ "supports_vision": true, "supports_web_search": true }, + "gemini-robotics-er-1.5-preview": { + "cache_read_input_token_cost": 0, + "input_cost_per_token": 3e-07, + "input_cost_per_audio_token": 1e-06, + "litellm_provider": "vertex_ai-language-models", + "max_input_tokens": 1048576, + "max_output_tokens": 65535, + "max_tokens": 65535, + "mode": "chat", + "output_cost_per_token": 2.5e-06, + "output_cost_per_reasoning_token": 2.5e-06, + "source": "https://ai.google.dev/gemini-api/docs/models#gemini-robotics-er-1-5-preview", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/completions" + ], + "supported_modalities": [ + "text", + "image", + "video", + "audio" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_output": false, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_prompt_caching": false, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_url_context": true, + "supports_vision": true + }, + "gemini/gemini-robotics-er-1.5-preview": { + "cache_read_input_token_cost": 0, + "input_cost_per_token": 3e-07, + "input_cost_per_audio_token": 1e-06, + "litellm_provider": "gemini", + "max_input_tokens": 1048576, + "max_output_tokens": 65535, + "max_tokens": 65535, + "mode": "chat", + "output_cost_per_token": 2.5e-06, + "output_cost_per_reasoning_token": 2.5e-06, + "source": "https://ai.google.dev/gemini-api/docs/models#gemini-robotics-er-1-5-preview", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/completions" + ], + "supported_modalities": [ + "text", + "image", + "video", + "audio" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_output": false, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_prompt_caching": false, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_url_context": true, + "supports_vision": true, + "supports_web_search": true + }, "gemini-2.5-computer-use-preview-10-2025": { "input_cost_per_token": 1.25e-06, "input_cost_per_token_above_200k_tokens": 2.5e-06, @@ -20549,6 +20739,7 @@ "supports_function_calling": true, "supports_tool_choice": true, "supports_prompt_caching": true, + "supports_reasoning": true, "supports_system_messages": true, "max_input_tokens": 1000000, "max_output_tokens": 8192 @@ -20563,6 +20754,7 @@ "supports_function_calling": true, "supports_tool_choice": true, "supports_prompt_caching": true, + "supports_reasoning": true, "supports_system_messages": true, "max_input_tokens": 1000000, "max_output_tokens": 8192 @@ -20577,6 +20769,7 @@ "supports_function_calling": true, "supports_tool_choice": true, "supports_prompt_caching": true, + "supports_reasoning": true, "supports_system_messages": true, "max_input_tokens": 200000, "max_output_tokens": 8192 @@ -23461,7 +23654,7 @@ "mode": "chat", "output_cost_per_token": 1.02e-06, "supports_function_calling": true, - "supports_prompt_caching": false, + "supports_prompt_caching": true, "supports_reasoning": true, "supports_tool_choice": true }, @@ -23813,8 +24006,11 @@ "max_input_tokens": 400000, "max_output_tokens": 128000, "max_tokens": 128000, - "mode": "chat", + "mode": "responses", "output_cost_per_token": 1.4e-05, + "supported_endpoints": [ + "/v1/responses" + ], "supported_modalities": [ "text", "image" @@ -24153,6 +24349,7 @@ "output_cost_per_token": 1.75e-06, "source": "https://openrouter.ai/z-ai/glm-4.6", "supports_function_calling": true, + "supports_prompt_caching": true, "supports_reasoning": true, "supports_tool_choice": true }, @@ -24166,9 +24363,76 @@ "output_cost_per_token": 1.9e-06, "source": "https://openrouter.ai/z-ai/glm-4.6:exacto", "supports_function_calling": true, + "supports_prompt_caching": true, "supports_reasoning": true, "supports_tool_choice": true }, + "openrouter/xiaomi/mimo-v2-flash": { + "input_cost_per_token": 9e-08, + "output_cost_per_token": 2.9e-07, + "cache_creation_input_token_cost": 0.0, + "cache_read_input_token_cost": 0.0, + "litellm_provider": "openrouter", + "max_input_tokens": 262144, + "max_output_tokens": 16384, + "max_tokens": 16384, + "mode": "chat", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_reasoning": true, + "supports_vision": false, + "supports_prompt_caching": false + }, + "openrouter/z-ai/glm-4.7": { + "input_cost_per_token": 4e-07, + "output_cost_per_token": 1.5e-06, + "cache_creation_input_token_cost": 0.0, + "cache_read_input_token_cost": 0.0, + "litellm_provider": "openrouter", + "max_input_tokens": 202752, + "max_output_tokens": 64000, + "max_tokens": 64000, + "mode": "chat", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_reasoning": true, + "supports_vision": true, + "supports_prompt_caching": false, + "supports_assistant_prefill": true + }, + "openrouter/z-ai/glm-4.7-flash": { + "input_cost_per_token": 7e-08, + "output_cost_per_token": 4e-07, + "cache_creation_input_token_cost": 0.0, + "cache_read_input_token_cost": 0.0, + "litellm_provider": "openrouter", + "max_input_tokens": 200000, + "max_output_tokens": 32000, + "max_tokens": 32000, + "mode": "chat", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_reasoning": true, + "supports_vision": true, + "supports_prompt_caching": false + }, + "openrouter/minimax/minimax-m2.1": { + "input_cost_per_token": 2.7e-07, + "output_cost_per_token": 1.2e-06, + "cache_creation_input_token_cost": 0.0, + "cache_read_input_token_cost": 0.0, + "litellm_provider": "openrouter", + "max_input_tokens": 204000, + "max_output_tokens": 64000, + "max_tokens": 64000, + "mode": "chat", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_reasoning": true, + "supports_vision": true, + "supports_prompt_caching": false, + "supports_computer_use": false + }, "ovhcloud/DeepSeek-R1-Distill-Llama-70B": { "input_cost_per_token": 6.7e-07, "litellm_provider": "ovhcloud", @@ -30382,6 +30646,7 @@ "supports_web_search": true }, "xai/grok-3": { + "cache_read_input_token_cost": 7.5e-07, "input_cost_per_token": 3e-06, "litellm_provider": "xai", "max_input_tokens": 131072, @@ -30396,6 +30661,7 @@ "supports_web_search": true }, "xai/grok-3-beta": { + "cache_read_input_token_cost": 7.5e-07, "input_cost_per_token": 3e-06, "litellm_provider": "xai", "max_input_tokens": 131072, @@ -30410,6 +30676,7 @@ "supports_web_search": true }, "xai/grok-3-fast-beta": { + "cache_read_input_token_cost": 1.25e-06, "input_cost_per_token": 5e-06, "litellm_provider": "xai", "max_input_tokens": 131072, @@ -30424,6 +30691,7 @@ "supports_web_search": true }, "xai/grok-3-fast-latest": { + "cache_read_input_token_cost": 1.25e-06, "input_cost_per_token": 5e-06, "litellm_provider": "xai", "max_input_tokens": 131072, @@ -30438,6 +30706,7 @@ "supports_web_search": true }, "xai/grok-3-latest": { + "cache_read_input_token_cost": 7.5e-07, "input_cost_per_token": 3e-06, "litellm_provider": "xai", "max_input_tokens": 131072, @@ -30452,6 +30721,7 @@ "supports_web_search": true }, "xai/grok-3-mini": { + "cache_read_input_token_cost": 7.5e-08, "input_cost_per_token": 3e-07, "litellm_provider": "xai", "max_input_tokens": 131072, @@ -30467,6 +30737,7 @@ "supports_web_search": true }, "xai/grok-3-mini-beta": { + "cache_read_input_token_cost": 7.5e-08, "input_cost_per_token": 3e-07, "litellm_provider": "xai", "max_input_tokens": 131072, @@ -30482,6 +30753,7 @@ "supports_web_search": true }, "xai/grok-3-mini-fast": { + "cache_read_input_token_cost": 1.5e-07, "input_cost_per_token": 6e-07, "litellm_provider": "xai", "max_input_tokens": 131072, @@ -30497,6 +30769,7 @@ "supports_web_search": true }, "xai/grok-3-mini-fast-beta": { + "cache_read_input_token_cost": 1.5e-07, "input_cost_per_token": 6e-07, "litellm_provider": "xai", "max_input_tokens": 131072, @@ -30512,6 +30785,7 @@ "supports_web_search": true }, "xai/grok-3-mini-fast-latest": { + "cache_read_input_token_cost": 1.5e-07, "input_cost_per_token": 6e-07, "litellm_provider": "xai", "max_input_tokens": 131072, @@ -30527,6 +30801,7 @@ "supports_web_search": true }, "xai/grok-3-mini-latest": { + "cache_read_input_token_cost": 7.5e-08, "input_cost_per_token": 3e-07, "litellm_provider": "xai", "max_input_tokens": 131072, @@ -30793,11 +31068,14 @@ "max_output_tokens": 128000, "mode": "chat", "supports_function_calling": true, + "supports_prompt_caching": true, "supports_reasoning": true, "supports_tool_choice": true, "source": "https://docs.z.ai/guides/overview/pricing" }, "zai/glm-4.6": { + "cache_creation_input_token_cost": 0, + "cache_read_input_token_cost": 1.1e-07, "input_cost_per_token": 6e-07, "output_cost_per_token": 2.2e-06, "litellm_provider": "zai", @@ -30805,6 +31083,8 @@ "max_output_tokens": 128000, "mode": "chat", "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, "supports_tool_choice": true, "source": "https://docs.z.ai/guides/overview/pricing" }, @@ -34452,4 +34732,4 @@ "output_cost_per_token": 0, "supports_reasoning": true } -} +} \ No newline at end of file diff --git a/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py b/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py index ded591a8f5..56feff548a 100644 --- a/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py +++ b/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py @@ -387,25 +387,57 @@ async def callback(code: str, state: str): 1. Try resource_metadata from WWW-Authenticate header (if present) 2. Fall back to path-based well-known URI: /.well-known/oauth-protected-resource/{path} ( - If the resource identifier value contains a path or query component, any terminating slash (/) - following the host component MUST be removed before inserting /.well-known/ and the well-known - URI path suffix between the host component and the path(include root path) and/or query components. + If the resource identifier value contains a path or query component, any terminating slash (/) + following the host component MUST be removed before inserting /.well-known/ and the well-known + URI path suffix between the host component and the path(include root path) and/or query components. https://datatracker.ietf.org/doc/html/rfc9728#section-3.1) 3. Fall back to root-based well-known URI: /.well-known/oauth-protected-resource + + Dual Pattern Support: + - Standard MCP pattern: /mcp/{server_name} (recommended, used by mcp-inspector, VSCode Copilot) + - LiteLLM legacy pattern: /{server_name}/mcp (backward compatibility) + + The resource URL returned matches the pattern used in the discovery request. """ -@router.get(f"/.well-known/oauth-protected-resource{'' if get_server_root_path() == '/' else get_server_root_path()}/{{mcp_server_name}}/mcp") -@router.get("/.well-known/oauth-protected-resource") -async def oauth_protected_resource_mcp( - request: Request, mcp_server_name: Optional[str] = None -): + + +def _build_oauth_protected_resource_response( + request: Request, + mcp_server_name: Optional[str], + use_standard_pattern: bool, +) -> dict: + """ + Build OAuth protected resource response with the appropriate URL pattern. + + Args: + request: FastAPI Request object + mcp_server_name: Name of the MCP server + use_standard_pattern: If True, use /mcp/{server_name} pattern; + if False, use /{server_name}/mcp pattern + + Returns: + OAuth protected resource metadata dict + """ from litellm.proxy._experimental.mcp_server.mcp_server_manager import ( global_mcp_server_manager, ) - # Get the correct base URL considering X-Forwarded-* headers + request_base_url = get_request_base_url(request) mcp_server: Optional[MCPServer] = None if mcp_server_name: mcp_server = global_mcp_server_manager.get_mcp_server_by_name(mcp_server_name) + + # Build resource URL based on the pattern + if mcp_server_name: + if use_standard_pattern: + # Standard MCP pattern: /mcp/{server_name} + resource_url = f"{request_base_url}/mcp/{mcp_server_name}" + else: + # LiteLLM legacy pattern: /{server_name}/mcp + resource_url = f"{request_base_url}/{mcp_server_name}/mcp" + else: + resource_url = f"{request_base_url}/mcp" + return { "authorization_servers": [ ( @@ -414,14 +446,55 @@ async def oauth_protected_resource_mcp( else f"{request_base_url}" ) ], - "resource": ( - f"{request_base_url}/{mcp_server_name}/mcp" - if mcp_server_name - else f"{request_base_url}/mcp" - ), # this is what Claude will call + "resource": resource_url, "scopes_supported": mcp_server.scopes if mcp_server else [], } + +# Standard MCP pattern: /.well-known/oauth-protected-resource/mcp/{server_name} +# This is the pattern expected by standard MCP clients (mcp-inspector, VSCode Copilot) +@router.get(f"/.well-known/oauth-protected-resource{'' if get_server_root_path() == '/' else get_server_root_path()}/mcp/{{mcp_server_name}}") +async def oauth_protected_resource_mcp_standard( + request: Request, mcp_server_name: str +): + """ + OAuth protected resource discovery endpoint using standard MCP URL pattern. + + Standard pattern: /mcp/{server_name} + Discovery path: /.well-known/oauth-protected-resource/mcp/{server_name} + + This endpoint is compliant with MCP specification and works with standard + MCP clients like mcp-inspector and VSCode Copilot. + """ + return _build_oauth_protected_resource_response( + request=request, + mcp_server_name=mcp_server_name, + use_standard_pattern=True, + ) + + +# LiteLLM legacy pattern: /.well-known/oauth-protected-resource/{server_name}/mcp +# Kept for backward compatibility with existing deployments +@router.get(f"/.well-known/oauth-protected-resource{'' if get_server_root_path() == '/' else get_server_root_path()}/{{mcp_server_name}}/mcp") +@router.get("/.well-known/oauth-protected-resource") +async def oauth_protected_resource_mcp( + request: Request, mcp_server_name: Optional[str] = None +): + """ + OAuth protected resource discovery endpoint using LiteLLM legacy URL pattern. + + Legacy pattern: /{server_name}/mcp + Discovery path: /.well-known/oauth-protected-resource/{server_name}/mcp + + This endpoint is kept for backward compatibility. New integrations should + use the standard MCP pattern (/mcp/{server_name}) instead. + """ + return _build_oauth_protected_resource_response( + request=request, + mcp_server_name=mcp_server_name, + use_standard_pattern=False, + ) + """ https://datatracker.ietf.org/doc/html/rfc8414#section-3.1 RFC 8414: Path-aware OAuth discovery @@ -430,15 +503,26 @@ async def oauth_protected_resource_mcp( the well-known URI suffix between the host component and the path(include root path) component. """ -@router.get(f"/.well-known/oauth-authorization-server{'' if get_server_root_path() == '/' else get_server_root_path()}/{{mcp_server_name}}") -@router.get("/.well-known/oauth-authorization-server") -async def oauth_authorization_server_mcp( - request: Request, mcp_server_name: Optional[str] = None -): + + +def _build_oauth_authorization_server_response( + request: Request, + mcp_server_name: Optional[str], +) -> dict: + """ + Build OAuth authorization server metadata response. + + Args: + request: FastAPI Request object + mcp_server_name: Name of the MCP server + + Returns: + OAuth authorization server metadata dict + """ from litellm.proxy._experimental.mcp_server.mcp_server_manager import ( global_mcp_server_manager, ) - # Get the correct base URL considering X-Forwarded-* headers + request_base_url = get_request_base_url(request) authorization_endpoint = ( @@ -470,18 +554,58 @@ async def oauth_authorization_server_mcp( } +# Standard MCP pattern: /.well-known/oauth-authorization-server/mcp/{server_name} +@router.get(f"/.well-known/oauth-authorization-server{'' if get_server_root_path() == '/' else get_server_root_path()}/mcp/{{mcp_server_name}}") +async def oauth_authorization_server_mcp_standard( + request: Request, mcp_server_name: str +): + """ + OAuth authorization server discovery endpoint using standard MCP URL pattern. + + Standard pattern: /mcp/{server_name} + Discovery path: /.well-known/oauth-authorization-server/mcp/{server_name} + """ + return _build_oauth_authorization_server_response( + request=request, + mcp_server_name=mcp_server_name, + ) + + +# LiteLLM legacy pattern and root endpoint +@router.get(f"/.well-known/oauth-authorization-server{'' if get_server_root_path() == '/' else get_server_root_path()}/{{mcp_server_name}}") +@router.get("/.well-known/oauth-authorization-server") +async def oauth_authorization_server_mcp( + request: Request, mcp_server_name: Optional[str] = None +): + """ + OAuth authorization server discovery endpoint. + + Supports both legacy pattern (/{server_name}) and root endpoint. + """ + return _build_oauth_authorization_server_response( + request=request, + mcp_server_name=mcp_server_name, + ) + + # Alias for standard OpenID discovery @router.get("/.well-known/openid-configuration") async def openid_configuration(request: Request): return await oauth_authorization_server_mcp(request) +# Additional legacy pattern support @router.get("/.well-known/oauth-authorization-server/{mcp_server_name}/mcp") -@router.get("/.well-known/oauth-authorization-server") -async def oauth_authorization_server_root( - request: Request, mcp_server_name: Optional[str] = None +async def oauth_authorization_server_legacy( + request: Request, mcp_server_name: str ): - return await oauth_authorization_server_mcp(request, mcp_server_name) + """ + OAuth authorization server discovery for legacy /{server_name}/mcp pattern. + """ + return _build_oauth_authorization_server_response( + request=request, + mcp_server_name=mcp_server_name, + ) @router.post("/{mcp_server_name}/register") diff --git a/litellm/proxy/_experimental/mcp_server/guardrail_translation/handler.py b/litellm/proxy/_experimental/mcp_server/guardrail_translation/handler.py index 8d6d236b88..4d53ae7059 100644 --- a/litellm/proxy/_experimental/mcp_server/guardrail_translation/handler.py +++ b/litellm/proxy/_experimental/mcp_server/guardrail_translation/handler.py @@ -46,8 +46,13 @@ class MCPGuardrailTranslationHandler(BaseTranslation): ) return data + inputs = GenericGuardrailAPIInputs(texts=[content]) + # Include model information if available + model = data.get("model") + if model: + inputs["model"] = model guardrailed_inputs = await guardrail_to_apply.apply_guardrail( - inputs=GenericGuardrailAPIInputs(texts=[content]), + inputs=inputs, request_data=data, input_type="request", logging_obj=litellm_logging_obj, diff --git a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py index e0217cd9e0..92fd54e877 100644 --- a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py +++ b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py @@ -11,7 +11,7 @@ import datetime import hashlib import json import re -from typing import Any, Dict, List, Literal, Optional, Set, Tuple, Union, cast +from typing import Any, Dict, List, Literal, Optional, Set, Tuple, Union, cast, Callable from urllib.parse import urlparse from fastapi import HTTPException @@ -63,7 +63,20 @@ from litellm.types.mcp_server.mcp_server_manager import ( MCPOAuthMetadata, MCPServer, ) -from mcp.shared.tool_name_validation import SEP_986_URL, validate_tool_name + +try: + from mcp.shared.tool_name_validation import SEP_986_URL, validate_tool_name # type: ignore +except ImportError: + SEP_986_URL = "https://github.com/modelcontextprotocol/protocol/blob/main/proposals/0001-tool-name-validation.md" + + def validate_tool_name(name: str): + from pydantic import BaseModel + + class MockResult(BaseModel): + is_valid: bool = True + warnings: list = [] + + return MockResult() # Probe includes characters on both sides of the separator to mimic real prefixed tool names. @@ -90,7 +103,9 @@ def _warn_on_server_name_fields( if result.is_valid: return - warning_text = "; ".join(result.warnings) if result.warnings else "Validation failed" + warning_text = ( + "; ".join(result.warnings) if result.warnings else "Validation failed" + ) verbose_logger.warning( "MCP server '%s' has invalid %s '%s': %s", server_id, @@ -103,7 +118,6 @@ def _warn_on_server_name_fields( _warn("server_name", server_name) - def _deserialize_json_dict(data: Any) -> Optional[Dict[str, str]]: """ Deserialize optional JSON mappings stored in the database. @@ -391,10 +405,13 @@ class MCPServerManager: # Note: `extra_headers` on MCPServer is a List[str] of header names to forward # from the client request (not available in this OpenAPI tool generation step). # `static_headers` is a dict of concrete headers to always send. - headers = merge_mcp_headers( - extra_headers=headers, - static_headers=server.static_headers, - ) or {} + headers = ( + merge_mcp_headers( + extra_headers=headers, + static_headers=server.static_headers, + ) + or {} + ) verbose_logger.debug( f"Using headers for OpenAPI tools (excluding sensitive values): " @@ -1825,6 +1842,7 @@ class MCPServerManager: oauth2_headers: Optional[Dict[str, str]], raw_headers: Optional[Dict[str, str]], proxy_logging_obj: Optional[ProxyLogging], + host_progress_callback: Optional[Callable] = None, ) -> CallToolResult: """ Call a regular MCP tool using the MCP client. @@ -1909,7 +1927,7 @@ class MCPServerManager: ) async def _call_tool_via_client(client, params): - return await client.call_tool(params) + return await client.call_tool(params, host_progress_callback=host_progress_callback) tasks.append( asyncio.create_task(_call_tool_via_client(client, call_tool_params)) @@ -1946,6 +1964,8 @@ class MCPServerManager: proxy_logging_obj: Optional[ProxyLogging] = None, oauth2_headers: Optional[Dict[str, str]] = None, raw_headers: Optional[Dict[str, str]] = None, + host_progress_callback: Optional[Callable] = None, + ) -> CallToolResult: """ Call a tool with the given name and arguments @@ -2021,6 +2041,7 @@ class MCPServerManager: oauth2_headers=oauth2_headers, raw_headers=raw_headers, proxy_logging_obj=proxy_logging_obj, + host_progress_callback=host_progress_callback, ) # For OpenAPI tools, await outside the client context diff --git a/litellm/proxy/_experimental/mcp_server/server.py b/litellm/proxy/_experimental/mcp_server/server.py index 03652ae155..6d54c3871e 100644 --- a/litellm/proxy/_experimental/mcp_server/server.py +++ b/litellm/proxy/_experimental/mcp_server/server.py @@ -8,8 +8,7 @@ import contextlib from datetime import datetime import traceback import uuid -from typing import Any, AsyncIterator, Dict, List, Optional, Tuple, Union, cast - +from typing import Any, AsyncIterator, Dict, List, Optional, Tuple, Union, cast, Callable from fastapi import FastAPI, HTTPException from pydantic import AnyUrl, ConfigDict from starlette.types import Receive, Scope, Send @@ -74,7 +73,11 @@ if MCP_AVAILABLE: AuthContextMiddleware, auth_context_var, ) - from mcp.server.streamable_http_manager import StreamableHTTPSessionManager + + try: + from mcp.server.streamable_http_manager import StreamableHTTPSessionManager + except ImportError: + StreamableHTTPSessionManager = None # type: ignore from mcp.types import ( CallToolResult, EmbeddedResource, @@ -124,8 +127,8 @@ if MCP_AVAILABLE: session_manager = StreamableHTTPSessionManager( app=server, event_store=None, - json_response=True, # Use JSON responses instead of SSE by default - stateless=True, + json_response=False, # enables SSE streaming + stateless=False, # enables session state ) # Create SSE session manager @@ -278,6 +281,30 @@ if MCP_AVAILABLE: verbose_logger.debug( f"MCP mcp_server_tool_call - User API Key Auth from context: {user_api_key_auth}" ) + host_progress_callback = None + try: + host_ctx = server.request_context + if host_ctx and hasattr(host_ctx, 'meta') and host_ctx.meta: + host_token = getattr(host_ctx.meta, 'progressToken', None) + if host_token and hasattr(host_ctx, 'session') and host_ctx.session: + host_session = host_ctx.session + + async def forward_progress(progress: float, total: float | None): + """Forward progress notifications from external MCP to Host""" + try: + await host_session.send_progress_notification( + progress_token=host_token, + progress=progress, + total=total + ) + verbose_logger.debug(f"Forwarded progress {progress}/{total} to Host") + except Exception as e: + verbose_logger.error(f"Failed to forward progress to Host: {e}") + + host_progress_callback = forward_progress + verbose_logger.debug(f"Host progressToken captured: {host_token[:8]}...") + except Exception as e: + verbose_logger.warning(f"Could not capture host progress context: {e}") try: # Create a body date for logging body_data = {"name": name, "arguments": arguments} @@ -307,6 +334,7 @@ if MCP_AVAILABLE: mcp_server_auth_headers=mcp_server_auth_headers, oauth2_headers=oauth2_headers, raw_headers=raw_headers, + host_progress_callback=host_progress_callback, **data, # for logging ) except BlockedPiiEntityError as e: @@ -1341,6 +1369,7 @@ if MCP_AVAILABLE: mcp_server_auth_headers: Optional[Dict[str, Dict[str, str]]] = None, oauth2_headers: Optional[Dict[str, str]] = None, raw_headers: Optional[Dict[str, str]] = None, + host_progress_callback: Optional[Callable] = None, **kwargs: Any, ) -> CallToolResult: """ @@ -1438,6 +1467,7 @@ if MCP_AVAILABLE: oauth2_headers=oauth2_headers, raw_headers=raw_headers, litellm_logging_obj=litellm_logging_obj, + host_progress_callback=host_progress_callback, ) # Fall back to local tool registry with original name (legacy support) @@ -1685,6 +1715,7 @@ if MCP_AVAILABLE: oauth2_headers: Optional[Dict[str, str]] = None, raw_headers: Optional[Dict[str, str]] = None, litellm_logging_obj: Optional[Any] = None, + host_progress_callback: Optional[Callable] = None, ) -> CallToolResult: """Handle tool execution for managed server tools""" # Import here to avoid circular import @@ -1700,6 +1731,7 @@ if MCP_AVAILABLE: oauth2_headers=oauth2_headers, raw_headers=raw_headers, proxy_logging_obj=proxy_logging_obj, + host_progress_callback=host_progress_callback, ) verbose_logger.debug("CALL TOOL RESULT: %s", call_tool_result) return call_tool_result diff --git a/litellm/proxy/_experimental/out/404.html b/litellm/proxy/_experimental/out/404.html index de67ea1428..a8ef84a338 100644 --- a/litellm/proxy/_experimental/out/404.html +++ b/litellm/proxy/_experimental/out/404.html @@ -1 +1 @@ -404: This page could not be found.LiteLLM Dashboard

404

This page could not be found.

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

404

This page could not be found.

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