diff --git a/.circleci/config.yml b/.circleci/config.yml index caaa91a9dd0..0e53cfc0edb 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -1050,6 +1050,51 @@ jobs: ls python -m pytest -vv tests/test_litellm --cov=litellm --cov-report=xml -x -s -v --junitxml=test-results/junit-litellm.xml --durations=10 -n 8 no_output_timeout: 120m + - run: + name: Rename the coverage files + command: | + mv coverage.xml litellm_mapped_tests_coverage.xml + mv .coverage litellm_mapped_tests_coverage + + # Store test results + - store_test_results: + path: test-results + - persist_to_workspace: + root: . + paths: + - litellm_mapped_tests_coverage.xml + - litellm_mapped_tests_coverage + litellm_mapped_enterprise_tests: + docker: + - image: cimg/python:3.11 + auth: + username: ${DOCKERHUB_USERNAME} + password: ${DOCKERHUB_PASSWORD} + working_directory: ~/project + + steps: + - checkout + - setup_google_dns + - run: + name: Install Dependencies + command: | + python -m pip install --upgrade pip + python -m pip install -r requirements.txt + pip install "pytest-mock==3.12.0" + pip install "pytest==7.3.1" + pip install "pytest-retry==1.6.3" + pip install "pytest-cov==5.0.0" + pip install "pytest-asyncio==0.21.1" + pip install "respx==0.22.0" + pip install "hypercorn==0.17.3" + pip install "pydantic==2.10.2" + pip install "mcp==1.10.1" + pip install "requests-mock>=1.12.1" + pip install "responses==0.25.7" + pip install "pytest-xdist==3.6.1" + pip install "semantic_router==0.1.10" + pip install "fastapi-offline==1.7.3" + - setup_litellm_enterprise_pip - run: name: Run enterprise tests command: | @@ -1476,6 +1521,7 @@ jobs: - run: python ./tests/code_coverage_tests/prevent_key_leaks_in_exceptions.py - run: python ./tests/code_coverage_tests/check_unsafe_enterprise_import.py - run: python ./tests/code_coverage_tests/ban_copy_deepcopy_kwargs.py + - run: python ./tests/code_coverage_tests/check_fastuuid_usage.py - run: helm lint ./deploy/charts/litellm-helm db_migration_disable_update_check: @@ -1779,8 +1825,8 @@ jobs: docker run -d \ -p 4000:4000 \ -e DATABASE_URL=postgresql://postgres:postgres@host.docker.internal:5432/circle_test \ - -e AZURE_API_KEY=$AZURE_BATCHES_API_KEY \ - -e AZURE_API_BASE=$AZURE_BATCHES_API_BASE \ + -e AZURE_API_KEY=$AZURE_API_KEY \ + -e AZURE_API_BASE=$AZURE_API_BASE \ -e AZURE_API_VERSION="2024-05-01-preview" \ -e REDIS_HOST=$REDIS_HOST \ -e REDIS_PASSWORD=$REDIS_PASSWORD \ @@ -3175,6 +3221,12 @@ workflows: only: - main - /litellm_.*/ + - litellm_mapped_enterprise_tests: + filters: + branches: + only: + - main + - /litellm_.*/ - litellm_mapped_tests: filters: branches: @@ -3219,6 +3271,7 @@ workflows: - guardrails_testing - llm_responses_api_testing - litellm_mapped_tests + - litellm_mapped_enterprise_tests - batches_testing - litellm_utils_testing - pass_through_unit_testing @@ -3279,6 +3332,7 @@ workflows: - google_generate_content_endpoint_testing - llm_responses_api_testing - litellm_mapped_tests + - litellm_mapped_enterprise_tests - batches_testing - litellm_utils_testing - pass_through_unit_testing diff --git a/.github/workflows/test-mcp.yml b/.github/workflows/test-mcp.yml new file mode 100644 index 00000000000..2da6980951a --- /dev/null +++ b/.github/workflows/test-mcp.yml @@ -0,0 +1,48 @@ +name: LiteLLM MCP Tests (folder - tests/mcp_tests) + +on: + pull_request: + branches: [ main ] + +jobs: + test: + runs-on: ubuntu-latest + timeout-minutes: 25 + + steps: + - uses: actions/checkout@v4 + + - name: Thank You Message + run: | + echo "### šŸ™ Thank you for contributing to LiteLLM!" >> $GITHUB_STEP_SUMMARY + echo "Your PR is being tested now. We appreciate your help in making LiteLLM better!" >> $GITHUB_STEP_SUMMARY + + - name: Set up Python + uses: actions/setup-python@v4 + with: + python-version: '3.12' + + - name: Install Poetry + uses: snok/install-poetry@v1 + + - name: Install dependencies + run: | + poetry install --with dev,proxy-dev --extras "proxy semantic-router" + poetry run pip install "pytest==7.3.1" + poetry run pip install "pytest-retry==1.6.3" + poetry run pip install "pytest-cov==5.0.0" + poetry run pip install "pytest-asyncio==0.21.1" + poetry run pip install "respx==0.22.0" + poetry run pip install "pydantic==2.10.2" + poetry run pip install "mcp==1.10.1" + poetry run pip install pytest-xdist + + - name: Setup litellm-enterprise as local package + run: | + cd enterprise + python -m pip install -e . + cd .. + + - name: Run MCP tests + run: | + poetry run pytest tests/mcp_tests -x -vv -n 4 --cov=litellm --cov-report=xml --durations=5 diff --git a/Dockerfile b/Dockerfile index addc109e10c..f85582f992d 100644 --- a/Dockerfile +++ b/Dockerfile @@ -41,9 +41,6 @@ RUN pip uninstall jwt -y RUN pip uninstall PyJWT -y RUN pip install PyJWT==2.9.0 --no-cache-dir -# Build Admin UI -RUN chmod +x docker/build_admin_ui.sh && ./docker/build_admin_ui.sh - # Runtime stage FROM $LITELLM_RUNTIME_IMAGE AS runtime diff --git a/cookbook/litellm_proxy_server/cli_token_usage.py b/cookbook/litellm_proxy_server/cli_token_usage.py new file mode 100644 index 00000000000..6ee5555695e --- /dev/null +++ b/cookbook/litellm_proxy_server/cli_token_usage.py @@ -0,0 +1,62 @@ +#!/usr/bin/env python3 +""" +Example: Using CLI token with LiteLLM SDK + +This example shows how to use the CLI authentication token +in your Python scripts after running `litellm-proxy login`. +""" + +from textwrap import indent +import litellm +LITELLM_BASE_URL = "http://localhost:4000/" + + +def main(): + """Using CLI token with LiteLLM SDK""" + print("šŸš€ Using CLI Token with LiteLLM SDK") + print("=" * 40) + #litellm._turn_on_debug() + + # Get the CLI token + api_key = litellm.get_litellm_gateway_api_key() + + if not api_key: + print("āŒ No CLI token found. Please run 'litellm-proxy login' first.") + return + + print("āœ… Found CLI token.") + + available_models = litellm.get_valid_models( + check_provider_endpoint=True, + custom_llm_provider="litellm_proxy", + api_key=api_key, + api_base=LITELLM_BASE_URL + ) + + print("āœ… Available models:") + if available_models: + for i, model in enumerate(available_models, 1): + print(f" {i:2d}. {model}") + else: + print(" No models available") + + # Use with LiteLLM + try: + response = litellm.completion( + model="litellm_proxy/gemini/gemini-2.5-flash", + messages=[{"role": "user", "content": "Hello from CLI token!"}], + api_key=api_key, + base_url=LITELLM_BASE_URL + ) + print(f"āœ… LLM Response: {response.model_dump_json(indent=4)}") + except Exception as e: + print(f"āŒ Error: {e}") + + +if __name__ == "__main__": + main() + + print("\nšŸ’” Tips:") + print("1. Run 'litellm-proxy login' to authenticate first") + print("2. Replace 'https://your-proxy.com' with your actual proxy URL") + print("3. The token is stored locally at ~/.litellm/token.json") diff --git a/cookbook/misc/RELEASE_NOTES_GENERATION_INSTRUCTIONS.md b/cookbook/misc/RELEASE_NOTES_GENERATION_INSTRUCTIONS.md index f24bc8f129e..b0a24ef28e9 100644 --- a/cookbook/misc/RELEASE_NOTES_GENERATION_INSTRUCTIONS.md +++ b/cookbook/misc/RELEASE_NOTES_GENERATION_INSTRUCTIONS.md @@ -4,10 +4,10 @@ This document provides comprehensive instructions for AI agents to generate rele ## Required Inputs -1. **Release Version** (e.g., `v1.76.3-stable`) +1. **Release Version** (e.g., `v1.77.3-stable`) 2. **PR Diff/Changelog** - List of PRs with titles and contributors 3. **Previous Version Commit Hash** - To compare model pricing changes -4. **Reference Release Notes** - Previous release notes to follow style/format +4. **Reference Release Notes** - Use recent stable releases (v1.76.3-stable, v1.77.2-stable) as templates for consistent formatting ## Step-by-Step Process @@ -26,12 +26,12 @@ git diff HEAD -- model_prices_and_context_window.json ### 2. Release Notes Structure -Follow this exact structure based on `docs/my-website/release_notes/v1.76.1-stable/index.md`: +Follow this exact structure based on recent stable releases (v1.76.3-stable, v1.77.2-stable): ```markdown --- -title: "v1.76.X-stable - [Key Theme]" -slug: "v1-76-X" +title: "v1.77.X-stable - [Key Theme]" +slug: "v1-77-X" date: YYYY-MM-DDTHH:mm:ss authors: [standard author block] hide_table_of_contents: false @@ -43,23 +43,42 @@ hide_table_of_contents: false ## Key Highlights [3-5 bullet points of major features] -## Major Changes -[Critical changes users need to know] - -## Performance Improvements -[Performance-related changes] - ## New Models / Updated Models -[Detailed model tables and provider updates] +#### New Model Support +[Model pricing table] + +#### Features +[Provider-specific features organized by provider] + +### Bug Fixes +[Provider-specific bug fixes organized by provider] + +#### New Provider Support +[New provider integrations] ## LLM API Endpoints -[API-related features and fixes] +#### Features +[API-specific features organized by API type] + +#### Bugs +[General bug fixes] ## Management Endpoints / UI -[Admin interface and management changes] +#### Features +[UI and management features] + +#### Bugs +[Management-related bug fixes] ## Logging / Guardrail Integrations -[Observability and security features] +#### Features +[Organized by integration provider with proper doc links] + +#### Guardrails +[Guardrail-specific features and fixes] + +#### New Integration +[Major new integrations] ## Performance / Loadbalancing / Reliability improvements [Infrastructure improvements] @@ -86,21 +105,27 @@ hide_table_of_contents: false **New Models/Updated Models:** - Extract from model_prices_and_context_window.json diff - Create tables with: Provider, Model, Context Window, Input Cost, Output Cost, Features -- Group by provider -- Note pricing corrections -- Highlight deprecated models +- **Structure:** + - `#### New Model Support` - pricing table + - `#### Features` - organized by provider with documentation links + - `### Bug Fixes` - provider-specific bug fixes + - `#### New Provider Support` - major new provider integrations +- Group by provider with proper doc links: `**[Provider Name](../../docs/providers/[provider])**` +- Use bullet points under each provider for multiple features +- Separate features from bug fixes clearly -**Provider Features:** -- Group by provider (Gemini, OpenAI, Anthropic, etc.) -- Link to provider docs: `../../docs/providers/[provider_name]` -- Separate features from bug fixes - -**API Endpoints:** -- Images API -- Video Generation (if applicable) -- Responses API -- Passthrough endpoints -- General chat completions +**LLM API Endpoints:** +- **Structure:** + - `#### Features` - organized by API type (Responses API, Batch API, etc.) + - `#### Bugs` - general bug fixes under **General** category +- **API Categories:** + - Responses API + - Batch API + - CountTokens API + - Images API + - Video Generation (if applicable) + - General (miscellaneous improvements) +- Use proper documentation links for each API type **UI/Management:** - Authentication changes @@ -108,11 +133,19 @@ hide_table_of_contents: false - Team management - Key management -**Integrations:** -- Logging providers (Datadog, Braintrust, etc.) -- Guardrails -- Cost tracking -- Observability +**Logging / Guardrail Integrations:** +- **Structure:** + - `#### Features` - organized by integration provider with proper doc links + - `#### Guardrails` - guardrail-specific features and fixes + - `#### New Integration` - major new integrations +- **Integration Categories:** + - **[DataDog](../../docs/proxy/logging#datadog)** - group all DataDog-related changes + - **[Langfuse](../../docs/proxy/logging#langfuse)** - Langfuse-specific features + - **[Prometheus](../../docs/proxy/logging#prometheus)** - monitoring improvements + - **[PostHog](../../docs/observability/posthog)** - observability integration + - Other logging providers with proper doc links +- Use bullet points under each provider for multiple features +- Separate logging features from guardrails clearly ### 4. Documentation Linking Strategy @@ -211,10 +244,41 @@ This release has a known issue... ::: ``` -**Provider Features:** +**Provider Features (New Models / Updated Models section):** ```markdown +#### Features + - **[Provider Name](../../docs/providers/provider)** - Feature description - [PR #XXXXX](link) + - Another feature description - [PR #YYYYY](link) +``` + +**API Features (LLM API Endpoints section):** +```markdown +#### Features + +- **[API Name](../../docs/api_path)** + - Feature description - [PR #XXXXX](link) + - Another feature - [PR #YYYYY](link) +- **General** + - Miscellaneous improvements - [PR #ZZZZZ](link) +``` + +**Integration Features (Logging / Guardrail Integrations section):** +```markdown +#### Features + +- **[Integration Name](../../docs/proxy/logging#integration)** + - Feature description - [PR #XXXXX](link) + - Bug fix description - [PR #YYYYY](link) +``` + +**Bug Fixes Pattern:** +```markdown +### Bug Fixes + +- **[Provider/Component Name](../../docs/providers/provider)** + - Bug fix description - [PR #XXXXX](link) ``` ### 10. Missing Documentation Check diff --git a/docs/my-website/docs/completion/provider_specific_params.md b/docs/my-website/docs/completion/provider_specific_params.md index a8307fc8a20..250b410c9c4 100644 --- a/docs/my-website/docs/completion/provider_specific_params.md +++ b/docs/my-website/docs/completion/provider_specific_params.md @@ -423,7 +423,7 @@ model_list: curl -X POST 'http://0.0.0.0:4000/chat/completions' \ -H 'Content-Type: application/json' \ -H 'Authorization: Bearer sk-1234' \ --D '{ +-d '{ "model": "llama-3-8b-instruct", "messages": [ { @@ -431,6 +431,56 @@ curl -X POST 'http://0.0.0.0:4000/chat/completions' \ "content": "What'\''s the weather like in Boston today?" } ], - "adapater_id": "my-special-adapter-id" # šŸ‘ˆ PROVIDER-SPECIFIC PARAM - }' -``` \ No newline at end of file + "adapater_id": "my-special-adapter-id" +}' +``` + +## Provider-Specific Metadata Parameters + +| Provider | Parameter | Use Case | +|----------|-----------|----------| +| **AWS Bedrock** | `requestMetadata` | Cost attribution, logging | +| **Gemini/Vertex AI** | `labels` | Resource labeling | +| **Anthropic** | `metadata` | User identification | + + + + +```python +import litellm + +response = litellm.completion( + model="bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0", + messages=[{"role": "user", "content": "Hello!"}], + requestMetadata={"cost_center": "engineering"} +) +``` + + + + +```python +import litellm + +response = litellm.completion( + model="vertex_ai/gemini-pro", + messages=[{"role": "user", "content": "Hello!"}], + labels={"environment": "production"} +) +``` + + + + +```python +import litellm + +response = litellm.completion( + model="anthropic/claude-3-sonnet-20240229", + messages=[{"role": "user", "content": "Hello!"}], + metadata={"user_id": "user123"} +) +``` + + + \ No newline at end of file diff --git a/docs/my-website/docs/completion/shared_session.md b/docs/my-website/docs/completion/shared_session.md new file mode 100644 index 00000000000..ff3da37f34f --- /dev/null +++ b/docs/my-website/docs/completion/shared_session.md @@ -0,0 +1,213 @@ +# Shared Session Support + +## Overview + +LiteLLM now supports sharing `aiohttp.ClientSession` instances across multiple API calls to avoid creating unnecessary new sessions. This improves performance and resource utilization. + +## Usage + +### Basic Usage + +```python +import asyncio +from aiohttp import ClientSession +from litellm import acompletion + +async def main(): + # Create a shared session + async with ClientSession() as shared_session: + # Use the same session for multiple calls + response1 = await acompletion( + model="gpt-4o", + messages=[{"role": "user", "content": "Hello"}], + shared_session=shared_session + ) + + response2 = await acompletion( + model="gpt-4o", + messages=[{"role": "user", "content": "How are you?"}], + shared_session=shared_session + ) + + # Both calls reuse the same session! + +asyncio.run(main()) +``` + +### Without Shared Session (Default) + +```python +import asyncio +from litellm import acompletion + +async def main(): + # Each call creates a new session + response1 = await acompletion( + model="gpt-4o", + messages=[{"role": "user", "content": "Hello"}] + ) + + response2 = await acompletion( + model="gpt-4o", + messages=[{"role": "user", "content": "How are you?"}] + ) + # Two separate sessions created + +asyncio.run(main()) +``` + +## Benefits + +- **Performance**: Reuse HTTP connections across multiple calls +- **Resource Efficiency**: Reduce memory and connection overhead +- **Better Control**: Manage session lifecycle explicitly +- **Debugging**: Easy to trace which calls use which sessions + +## Debug Logging + +Enable debug logging to see session reuse in action: + +```python +import os +import litellm + +# Enable debug logging +os.environ['LITELLM_LOG'] = 'DEBUG' + +# You'll see logs like: +# šŸ”„ SHARED SESSION: acompletion called with shared_session (ID: 12345) +# āœ… SHARED SESSION: Reusing existing ClientSession (ID: 12345) +``` + +## Common Patterns + +### FastAPI Integration + +```python +from fastapi import FastAPI +import aiohttp +import litellm + +app = FastAPI() + +@app.post("/chat") +async def chat(messages: list[dict]): + # Create session per request + async with aiohttp.ClientSession() as session: + return await litellm.acompletion( + model="gpt-4o", + messages=messages, + shared_session=session + ) +``` + +### Batch Processing + +```python +import asyncio +from aiohttp import ClientSession +from litellm import acompletion + +async def process_batch(messages_list): + async with ClientSession() as shared_session: + tasks = [] + for messages in messages_list: + task = acompletion( + model="gpt-4o", + messages=messages, + shared_session=shared_session + ) + tasks.append(task) + + # All tasks use the same session + results = await asyncio.gather(*tasks) + return results +``` + +### Custom Session Configuration + +```python +import aiohttp +import litellm + +# Create optimized session +async with aiohttp.ClientSession( + timeout=aiohttp.ClientTimeout(total=180), + connector=aiohttp.TCPConnector(limit=300, limit_per_host=75) +) as shared_session: + + response = await litellm.acompletion( + model="gpt-4o", + messages=[{"role": "user", "content": "Hello"}], + shared_session=shared_session + ) +``` + +## Implementation Details + +The `shared_session` parameter is threaded through the entire LiteLLM call chain: + +1. **`acompletion()`** - Accepts `shared_session` parameter +2. **`BaseLLMHTTPHandler`** - Passes session to HTTP client creation +3. **`AsyncHTTPHandler`** - Uses existing session if provided +4. **`LiteLLMAiohttpTransport`** - Reuses the session for HTTP requests + +## Backward Compatibility + +- **100% backward compatible** - Existing code works unchanged +- **Optional parameter** - `shared_session=None` by default +- **No breaking changes** - All existing functionality preserved + +## Testing + +Test the shared session functionality: + +```python +import asyncio +from aiohttp import ClientSession +from litellm import acompletion + +async def test_shared_session(): + async with ClientSession() as session: + print(f"āœ… Created session: {id(session)}") + + try: + response = await acompletion( + model="gpt-4o", + messages=[{"role": "user", "content": "Hello"}], + shared_session=session, + api_key="your-api-key" + ) + print(f"Response: {response.choices[0].message.content}") + except Exception as e: + print(f"āœ… Expected error: {type(e).__name__}") + + print("āœ… Session control working!") + +asyncio.run(test_shared_session()) +``` + +## Files Modified + +The shared session functionality was added to these files: + +- `litellm/main.py` - Added `shared_session` parameter to `acompletion()` and `completion()` +- `litellm/llms/custom_httpx/http_handler.py` - Core session reuse logic +- `litellm/llms/custom_httpx/llm_http_handler.py` - HTTP handler integration +- `litellm/llms/openai/openai.py` - OpenAI provider integration +- `litellm/llms/openai/common_utils.py` - OpenAI client creation +- `litellm/llms/azure/chat/o_series_handler.py` - Azure O Series handler + +## Troubleshooting + +### Session Not Being Reused + +1. **Check debug logs**: Enable `LITELLM_LOG=DEBUG` to see session reuse messages +2. **Verify session is not closed**: Ensure the session is still active when making calls +3. **Check parameter passing**: Make sure `shared_session` is passed to all `acompletion()` calls + +### Performance Issues + +1. **Session configuration**: Tune `aiohttp.ClientSession` parameters for your use case +2. **Connection limits**: Adjust `limit` and `limit_per_host` in `TCPConnector` +3. **Timeout settings**: Configure appropriate timeouts for your environment diff --git a/docs/my-website/docs/integrations/index.md b/docs/my-website/docs/integrations/index.md index 9731db6e751..95c922cce89 100644 --- a/docs/my-website/docs/integrations/index.md +++ b/docs/my-website/docs/integrations/index.md @@ -2,4 +2,17 @@ This section covers integrations with various tools and services that can be used with LiteLLM (either Proxy or SDK). +## AI Agent Frameworks +- **[Letta](./letta.md)** - Build stateful LLM agents with persistent memory using LiteLLM Proxy + +## Development Tools +- **[OpenWebUI](../tutorials/openweb_ui.md)** - Self-hosted ChatGPT-style interface + +## Observability & Monitoring +- **[Langfuse](../observability/langfuse_integration.md)** - LLM observability and analytics +- **[Prometheus](../proxy/prometheus.md)** - Metrics collection and monitoring +- **[PagerDuty](../proxy/pagerduty.md)** - Incident response and alerting +- **[Datadog](../observability/datadog.md)** + + Click into each section to learn more about the integrations. \ No newline at end of file diff --git a/docs/my-website/docs/integrations/letta.md b/docs/my-website/docs/integrations/letta.md new file mode 100644 index 00000000000..2afb82542f2 --- /dev/null +++ b/docs/my-website/docs/integrations/letta.md @@ -0,0 +1,928 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Letta Integration + +[Letta](https://github.com/letta-ai/letta) (formerly MemGPT) is a framework for building stateful LLM agents with persistent memory. This guide shows how to integrate both LiteLLM SDK and LiteLLM Proxy with Letta to leverage multiple LLM providers while building memory-enabled agents. + +## What is Letta? + +Letta allows you to build LLM agents that can: +- Maintain long-term memory across conversations +- Use function calling for tool interactions +- Handle large context windows efficiently +- Persist agent state and memory + +## Prerequisites + +```bash +pip install letta litellm +``` + +## Quick Start + + + + +### 1. Start LiteLLM Proxy + +First, create a configuration file for your LiteLLM proxy: + +```yaml +# config.yaml +model_list: + - model_name: gpt-4 + litellm_params: + model: openai/gpt-4 + api_key: os.environ/OPENAI_API_KEY + + - model_name: claude-3-sonnet + litellm_params: + model: anthropic/claude-3-sonnet-20240229 + api_key: os.environ/ANTHROPIC_API_KEY + + - model_name: gpt-3.5-turbo + litellm_params: + model: azure/gpt-35-turbo + api_key: os.environ/AZURE_API_KEY + api_base: os.environ/AZURE_API_BASE + api_version: "2023-07-01-preview" +``` + +Start the proxy: + +```bash +litellm --config config.yaml --port 4000 +``` + +### 2. Configure Letta with LiteLLM Proxy + +Configure Letta to use your LiteLLM proxy endpoint: + +```python +import letta +from letta import create_client + +# Configure Letta to use LiteLLM proxy +client = create_client() + +# Configure the LLM endpoint +client.set_default_llm_config( + model="gpt-4", # This should match a model from your LiteLLM config + model_endpoint_type="openai", + model_endpoint="http://localhost:4000", # Your LiteLLM proxy URL + context_window=8192 +) + +# Configure embedding endpoint (optional) +client.set_default_embedding_config( + embedding_endpoint_type="openai", + embedding_endpoint="http://localhost:4000", + embedding_model="text-embedding-ada-002" +) +``` + + + + +### 1. Configure LiteLLM SDK + +Set up your API keys and configure LiteLLM: + +```python +import os +import litellm + +# Set your API keys +os.environ["OPENAI_API_KEY"] = "your-openai-key" +os.environ["ANTHROPIC_API_KEY"] = "your-anthropic-key" + +# Optional: Configure default settings +litellm.set_verbose = True # For debugging +``` + +### 2. Create Custom LLM Wrapper for Letta + +Create a custom LLM wrapper that uses LiteLLM SDK: + +```python +import letta +from letta import create_client +from letta.llm_api.llm_api_base import LLMConfig +import litellm +from typing import List, Dict, Any + +class LiteLLMWrapper: + def __init__(self, model: str): + self.model = model + + def chat_completions_create(self, messages: List[Dict], **kwargs): + # Use LiteLLM SDK for completion + response = litellm.completion( + model=self.model, + messages=messages, + **kwargs + ) + return response + +# Configure Letta with custom LiteLLM wrapper +client = create_client() + +# Set up LLM configuration using direct SDK integration +llm_config = LLMConfig( + model="gpt-4", # or "claude-3-sonnet", "azure/gpt-35-turbo", etc. + model_endpoint_type="openai", + context_window=8192 +) + +client.set_default_llm_config(llm_config) +``` + + + + +### 3. Create and Use a Letta Agent + + + + +```python +import letta +from letta import create_client + +# Create Letta client +client = create_client() + +# Create a new agent +agent_state = client.create_agent( + name="my-assistant", + system="You are a helpful assistant with persistent memory.", + llm_config=client.get_default_llm_config(), + embedding_config=client.get_default_embedding_config() +) + +# Send a message to the agent +response = client.user_message( + agent_id=agent_state.id, + message="Hi! My name is Alice and I love reading science fiction books." +) + +print(f"Agent response: {response.messages[-1].text}") + +# Send another message - the agent will remember previous context +response = client.user_message( + agent_id=agent_state.id, + message="What did I tell you about my interests?" +) + +print(f"Agent response: {response.messages[-1].text}") +``` + + + + +```python +import letta +from letta import create_client +import litellm +import os + +# Set up environment variables +os.environ["OPENAI_API_KEY"] = "your-openai-key" + +# Create Letta client with LiteLLM integration +client = create_client() + +# Create a new agent +agent_state = client.create_agent( + name="my-assistant", + system="You are a helpful assistant with persistent memory.", + llm_config=client.get_default_llm_config(), + embedding_config=client.get_default_embedding_config() +) + +# Send a message to the agent +response = client.user_message( + agent_id=agent_state.id, + message="Hi! My name is Alice and I love reading science fiction books." +) + +print(f"Agent response: {response.messages[-1].text}") + +# Send another message - the agent will remember previous context +response = client.user_message( + agent_id=agent_state.id, + message="What did I tell you about my interests?" +) + +print(f"Agent response: {response.messages[-1].text}") +``` + + + + +## Advanced Configuration + +### Using Different Models for Different Agents + + + + +```python +from letta import LLMConfig, EmbeddingConfig + +# Create different LLM configurations pointing to your proxy +gpt4_config = LLMConfig( + model="gpt-4", + model_endpoint_type="openai", + model_endpoint="http://localhost:4000", + context_window=8192 +) + +claude_config = LLMConfig( + model="claude-3-sonnet", + model_endpoint_type="openai", # Using OpenAI-compatible endpoint + model_endpoint="http://localhost:4000", + context_window=200000 +) + +# Create agents with different configurations +research_agent = client.create_agent( + name="research-agent", + system="You are a research assistant specialized in analysis.", + llm_config=claude_config # Use Claude for research tasks +) + +creative_agent = client.create_agent( + name="creative-agent", + system="You are a creative writing assistant.", + llm_config=gpt4_config # Use GPT-4 for creative tasks +) +``` + + + + +```python +import os +import litellm +from letta import LLMConfig, EmbeddingConfig + +# Set up API keys for different providers +os.environ["OPENAI_API_KEY"] = "your-openai-key" +os.environ["ANTHROPIC_API_KEY"] = "your-anthropic-key" + +# Create different LLM configurations for direct SDK usage +gpt4_config = LLMConfig( + model="openai/gpt-4", # Using LiteLLM model format + model_endpoint_type="openai", + context_window=8192 +) + +claude_config = LLMConfig( + model="anthropic/claude-3-sonnet-20240229", # Using LiteLLM model format + model_endpoint_type="openai", + context_window=200000 +) + +# Create agents with different configurations +research_agent = client.create_agent( + name="research-agent", + system="You are a research assistant specialized in analysis.", + llm_config=claude_config # Use Claude for research tasks +) + +creative_agent = client.create_agent( + name="creative-agent", + system="You are a creative writing assistant.", + llm_config=gpt4_config # Use GPT-4 for creative tasks +) +``` + + + + +### Function Calling with Tools + + + + +```python +# Define custom tools for your agent +def search_web(query: str) -> str: + """Search the web for information""" + # Your web search implementation + return f"Search results for: {query}" + +def save_note(content: str) -> str: + """Save a note to persistent storage""" + # Your note saving implementation + return f"Note saved: {content}" + +# Create agent with tools (using proxy endpoint) +agent_state = client.create_agent( + name="research-assistant", + system="You are a research assistant that can search the web and save notes.", + llm_config=client.get_default_llm_config(), + embedding_config=client.get_default_embedding_config(), + tools=[search_web, save_note] +) + +# The agent can now use these tools +response = client.user_message( + agent_id=agent_state.id, + message="Search for recent developments in AI and save important findings." +) +``` + + + + +```python +import litellm +import os + +# Set up API keys +os.environ["OPENAI_API_KEY"] = "your-openai-key" + +# Define custom tools for your agent +def search_web(query: str) -> str: + """Search the web for information""" + # Your web search implementation + return f"Search results for: {query}" + +def save_note(content: str) -> str: + """Save a note to persistent storage""" + # Your note saving implementation + return f"Note saved: {content}" + +# Create agent with tools (using LiteLLM SDK directly) +agent_state = client.create_agent( + name="research-assistant", + system="You are a research assistant that can search the web and save notes.", + llm_config=LLMConfig( + model="openai/gpt-4", # Direct model specification + model_endpoint_type="openai", + context_window=8192 + ), + embedding_config=client.get_default_embedding_config(), + tools=[search_web, save_note] +) + +# The agent can now use these tools +response = client.user_message( + agent_id=agent_state.id, + message="Search for recent developments in AI and save important findings." +) +``` + + + + +## Authentication + + + + +If your LiteLLM proxy requires authentication: + +```python +import os +from letta import LLMConfig + +# Set up authenticated configuration +llm_config = LLMConfig( + model="gpt-4", + model_endpoint_type="openai", + model_endpoint="http://localhost:4000", + model_wrapper="openai", + context_window=8192 +) + +# If using API keys with your proxy +os.environ["OPENAI_API_KEY"] = "your-litellm-proxy-api-key" + +client = create_client() +client.set_default_llm_config(llm_config) +``` + +For proxy with authentication enabled: + +```yaml +# config.yaml with auth +general_settings: + master_key: "your-master-key" + +model_list: + - model_name: gpt-4 + litellm_params: + model: openai/gpt-4 + api_key: os.environ/OPENAI_API_KEY +``` + +```python +# Configure Letta with authenticated proxy +llm_config = LLMConfig( + model="gpt-4", + model_endpoint_type="openai", + model_endpoint="http://localhost:4000", + context_window=8192, + api_key="your-master-key" # Proxy master key +) +``` + + + + +With LiteLLM SDK, set up your provider API keys directly: + +```python +import os +import litellm + +# Set up API keys for different providers +os.environ["OPENAI_API_KEY"] = "your-openai-api-key" +os.environ["ANTHROPIC_API_KEY"] = "your-anthropic-api-key" +os.environ["AZURE_API_KEY"] = "your-azure-api-key" +os.environ["AZURE_API_BASE"] = "https://your-resource.openai.azure.com" +os.environ["AZURE_API_VERSION"] = "2023-07-01-preview" + +# Optional: Configure default settings +litellm.api_key = os.environ.get("OPENAI_API_KEY") # Default key +litellm.set_verbose = True # For debugging + +# Use in Letta configuration +from letta import LLMConfig + +llm_config = LLMConfig( + model="openai/gpt-4", # Will use OPENAI_API_KEY automatically + model_endpoint_type="openai", + context_window=8192 +) + +# Or for Azure +azure_config = LLMConfig( + model="azure/gpt-35-turbo", + model_endpoint_type="openai", + context_window=4096 +) +``` + + + + +## Load Balancing and Fallbacks + + + + +LiteLLM proxy's load balancing and fallback features work seamlessly with Letta: + +```yaml +# config.yaml with fallbacks +model_list: + - model_name: gpt-4 + litellm_params: + model: openai/gpt-4 + api_key: os.environ/OPENAI_API_KEY + tpm: 40000 + rpm: 500 + + - model_name: gpt-4 # Same model name for fallback + litellm_params: + model: azure/gpt-4 + api_key: os.environ/AZURE_API_KEY + api_base: os.environ/AZURE_API_BASE + api_version: "2023-07-01-preview" + tpm: 80000 + rpm: 800 + +router_settings: + routing_strategy: "usage-based-routing" + fallbacks: [{"gpt-4": ["azure/gpt-4"]}] +``` + +The proxy handles all routing, load balancing, and fallbacks transparently for Letta. + + + + +With LiteLLM SDK, you can set up routing and fallbacks programmatically: + +```python +import litellm +from litellm import Router + +# Configure router with multiple models +router = Router( + model_list=[ + { + "model_name": "gpt-4", + "litellm_params": { + "model": "openai/gpt-4", + "api_key": os.environ["OPENAI_API_KEY"] + }, + "tpm": 40000, + "rpm": 500 + }, + { + "model_name": "gpt-4", # Same name for fallback + "litellm_params": { + "model": "azure/gpt-4", + "api_key": os.environ["AZURE_API_KEY"], + "api_base": os.environ["AZURE_API_BASE"], + "api_version": "2023-07-01-preview" + }, + "tpm": 80000, + "rpm": 800 + } + ], + fallbacks=[{"gpt-4": ["azure/gpt-4"]}], + routing_strategy="usage-based-routing" +) + +# Create custom completion function for Letta +def custom_completion(messages, model="gpt-4", **kwargs): + return router.completion( + model=model, + messages=messages, + **kwargs + ) + +# Use with Letta by monkey-patching or custom wrapper +litellm.completion = custom_completion +``` + + + + +## Monitoring and Observability + + + + +Enable logging to track your Letta agents' LLM usage through the proxy: + +```yaml +# config.yaml with logging +model_list: + # ... your models + +litellm_settings: + success_callback: ["langfuse"] # or other observability tools + +environment_variables: + LANGFUSE_PUBLIC_KEY: "your-key" + LANGFUSE_SECRET_KEY: "your-secret" +``` + +View metrics in the proxy dashboard: +```bash +# Start proxy with UI +litellm --config config.yaml --port 4000 --detailed_debug +``` + + + + +Set up observability directly in your SDK integration: + +```python +import litellm +import os + +# Configure observability callbacks +os.environ["LANGFUSE_PUBLIC_KEY"] = "your-key" +os.environ["LANGFUSE_SECRET_KEY"] = "your-secret" + +# Set global callbacks +litellm.success_callback = ["langfuse"] +litellm.failure_callback = ["langfuse"] + +# Optional: Set up custom logging +litellm.set_verbose = True + +# Create custom completion wrapper with logging +def logged_completion(messages, model="gpt-4", **kwargs): + try: + response = litellm.completion( + model=model, + messages=messages, + **kwargs + ) + # Custom logging logic here if needed + return response + except Exception as e: + # Custom error handling + print(f"LLM call failed: {e}") + raise + +# Use in Letta configuration +litellm.completion = logged_completion +``` + + + + +## Example: Multi-Agent System + + + + +```python +import letta +from letta import create_client, LLMConfig + +client = create_client() + +# Create specialized agents using proxy endpoints +agents = {} + +# Research agent using Claude for analysis +agents['researcher'] = client.create_agent( + name="researcher", + system="You are a research specialist. Analyze information thoroughly.", + llm_config=LLMConfig( + model="claude-3-sonnet", + model_endpoint="http://localhost:4000", + model_endpoint_type="openai" + ) +) + +# Writer agent using GPT-4 for content creation +agents['writer'] = client.create_agent( + name="writer", + system="You are a content writer. Create engaging, well-structured content.", + llm_config=LLMConfig( + model="gpt-4", + model_endpoint="http://localhost:4000", + model_endpoint_type="openai" + ) +) + +# Coordinator workflow +def research_and_write_workflow(topic: str): + # Research phase + research_response = client.user_message( + agent_id=agents['researcher'].id, + message=f"Research the topic: {topic}. Provide key insights and data." + ) + + research_results = research_response.messages[-1].text + + # Writing phase + write_response = client.user_message( + agent_id=agents['writer'].id, + message=f"Based on this research: {research_results}\n\nWrite an article about {topic}." + ) + + return write_response.messages[-1].text + +# Execute workflow +article = research_and_write_workflow("The future of AI in healthcare") +print(article) +``` + + + + +```python +import letta +from letta import create_client, LLMConfig +import litellm +import os + +# Set up environment +os.environ["OPENAI_API_KEY"] = "your-openai-key" +os.environ["ANTHROPIC_API_KEY"] = "your-anthropic-key" + +client = create_client() + +# Create specialized agents using direct SDK models +agents = {} + +# Research agent using Claude for analysis +agents['researcher'] = client.create_agent( + name="researcher", + system="You are a research specialist. Analyze information thoroughly.", + llm_config=LLMConfig( + model="anthropic/claude-3-sonnet-20240229", + model_endpoint_type="openai" + ) +) + +# Writer agent using GPT-4 for content creation +agents['writer'] = client.create_agent( + name="writer", + system="You are a content writer. Create engaging, well-structured content.", + llm_config=LLMConfig( + model="openai/gpt-4", + model_endpoint_type="openai" + ) +) + +# Cost-conscious agent using GPT-3.5 +agents['reviewer'] = client.create_agent( + name="reviewer", + system="You are an editor. Review and improve content quality.", + llm_config=LLMConfig( + model="openai/gpt-3.5-turbo", + model_endpoint_type="openai" + ) +) + +# Enhanced workflow with multiple agents +def enhanced_workflow(topic: str): + # Research phase + research_response = client.user_message( + agent_id=agents['researcher'].id, + message=f"Research the topic: {topic}. Provide key insights and data." + ) + + research_results = research_response.messages[-1].text + + # Writing phase + write_response = client.user_message( + agent_id=agents['writer'].id, + message=f"Based on this research: {research_results}\n\nWrite an article about {topic}." + ) + + draft_article = write_response.messages[-1].text + + # Review phase + review_response = client.user_message( + agent_id=agents['reviewer'].id, + message=f"Please review and improve this article:\n\n{draft_article}" + ) + + return review_response.messages[-1].text + +# Execute enhanced workflow +article = enhanced_workflow("The future of AI in healthcare") +print(article) +``` + + + + +## Best Practices + + + + +1. **Model Selection**: Use appropriate models for different tasks: + - Claude for analysis and reasoning + - GPT-4 for creative tasks + - GPT-3.5-turbo for simple interactions + +2. **Proxy Configuration**: + - Set appropriate rate limits and timeouts + - Use fallbacks for reliability + - Enable authentication for production + +3. **Memory Management**: Letta handles memory automatically, but monitor usage with large contexts + +4. **Cost Optimization**: + - Use the proxy's budgeting features to control costs + - Set up rate limiting per user/team + - Monitor token usage through proxy dashboard + +5. **Monitoring**: Enable observability to track agent performance and token usage + + + + +1. **Model Selection**: Choose models based on task requirements: + - Use `openai/gpt-4` for complex reasoning + - Use `anthropic/claude-3-sonnet-20240229` for analysis + - Use `openai/gpt-3.5-turbo` for cost-effective simple tasks + +2. **Error Handling**: Implement robust error handling with retries: + ```python + import litellm + from litellm import completion + + # Set up retry logic + litellm.num_retries = 3 + litellm.request_timeout = 60 + + # Custom error handling + def safe_completion(**kwargs): + try: + return completion(**kwargs) + except Exception as e: + print(f"LLM call failed: {e}") + # Implement fallback logic + return completion(model="openai/gpt-3.5-turbo", **kwargs) + ``` + +3. **Cost Management**: + - Use cheaper models for non-critical tasks + - Implement token counting and budgets + - Cache responses when appropriate + +4. **Performance**: + - Use async operations for concurrent requests + - Implement connection pooling + - Monitor response times + +5. **Security**: + - Store API keys securely (environment variables) + - Rotate keys regularly + - Implement rate limiting + + + + +## Troubleshooting + + + + +### Connection Issues +```bash +# Test your LiteLLM proxy +curl -X POST http://localhost:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -d '{ + "model": "gpt-4", + "messages": [{"role": "user", "content": "Hello"}] + }' +``` + +### Configuration Debugging +```python +# Enable verbose logging +import logging +logging.basicConfig(level=logging.DEBUG) + +# Test Letta configuration +client = create_client() +print(client.get_default_llm_config()) +``` + +### Common Proxy Issues +- **Port conflicts**: Make sure port 4000 isn't in use +- **Model not found**: Verify model names match your config.yaml +- **Authentication errors**: Check master key configuration +- **Rate limiting**: Monitor proxy logs for rate limit hits + + + + +### API Key Issues +```python +import os +import litellm + +# Check if API keys are set +print("OpenAI Key:", os.environ.get("OPENAI_API_KEY", "Not set")) +print("Anthropic Key:", os.environ.get("ANTHROPIC_API_KEY", "Not set")) + +# Test direct LiteLLM call +try: + response = litellm.completion( + model="openai/gpt-3.5-turbo", + messages=[{"role": "user", "content": "Hello"}] + ) + print("LiteLLM working:", response.choices[0].message.content) +except Exception as e: + print("LiteLLM error:", e) +``` + +### Configuration Debugging +```python +# Enable verbose logging +litellm.set_verbose = True + +# Test model availability +models = ["openai/gpt-4", "anthropic/claude-3-sonnet-20240229"] +for model in models: + try: + response = litellm.completion( + model=model, + messages=[{"role": "user", "content": "Test"}], + max_tokens=10 + ) + print(f"āœ“ {model} working") + except Exception as e: + print(f"āœ— {model} failed: {e}") +``` + +### Common SDK Issues +- **Import errors**: Ensure `pip install litellm letta` is run +- **Model format**: Use `provider/model` format (e.g., `openai/gpt-4`) +- **API key format**: Different providers have different key formats +- **Rate limits**: Implement exponential backoff for retries + + + + +## Resources + +- [Letta Documentation](https://docs.letta.ai/) +- [LiteLLM Proxy Documentation](../proxy/quick_start.md) +- [LiteLLM SDK Documentation](../completion/input.md) +- [Function Calling Guide](../completion/function_call.md) +- [Observability Setup](../observability/langfuse_integration.md) +- [Router Configuration](../routing.md) \ No newline at end of file diff --git a/docs/my-website/docs/mcp.md b/docs/my-website/docs/mcp.md index 18c99051709..80b4c32d0ab 100644 --- a/docs/my-website/docs/mcp.md +++ b/docs/my-website/docs/mcp.md @@ -114,7 +114,6 @@ mcp_servers: description: "My custom MCP server" auth_type: "api_key" auth_value: "abc123" - spec_version: "2025-03-26" ``` **Configuration Options:** @@ -716,7 +715,6 @@ mcp_servers: url: https://mcp.deepwiki.com/mcp transport: "http" auth_type: "none" - spec_version: "2025-03-26" access_groups: ["dev_group"] ``` diff --git a/docs/my-website/docs/observability/helicone_integration.md b/docs/my-website/docs/observability/helicone_integration.md index 27e972ddebd..22ea051f7cd 100644 --- a/docs/my-website/docs/observability/helicone_integration.md +++ b/docs/my-website/docs/observability/helicone_integration.md @@ -237,7 +237,10 @@ litellm.metadata = { } ``` -### Session Tracking and Tracing + + + +## Session Tracking and Tracing Track multi-step and agentic LLM interactions using session IDs and paths: diff --git a/docs/my-website/docs/providers/azure_ai_img_edit.md b/docs/my-website/docs/providers/azure_ai_img_edit.md new file mode 100644 index 00000000000..0d5408f0af4 --- /dev/null +++ b/docs/my-website/docs/providers/azure_ai_img_edit.md @@ -0,0 +1,260 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Azure AI Image Editing + +Azure AI provides powerful image editing capabilities using FLUX models from Black Forest Labs to modify existing images based on text descriptions. + +## Overview + +| Property | Details | +|----------|---------| +| Description | Azure AI Image Editing uses FLUX models to modify existing images based on text prompts. | +| Provider Route on LiteLLM | `azure_ai/` | +| Provider Doc | [Azure AI FLUX Models ā†—](https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/black-forest-labs-flux-1-kontext-pro-and-flux1-1-pro-now-available-in-azure-ai-f/4434659) | +| Supported Operations | [`/images/edits`](#image-editing) | + +## Setup + +### API Key & Base URL & API Version + +```python showLineNumbers +# Set your Azure AI API credentials +import os +os.environ["AZURE_AI_API_KEY"] = "your-api-key-here" +os.environ["AZURE_AI_API_BASE"] = "your-azure-ai-endpoint" # e.g., https://your-endpoint.eastus2.inference.ai.azure.com/ +os.environ["AZURE_AI_API_VERSION"] = "2025-04-01-preview" # Example API version +``` + +Get your API key and endpoint from [Azure AI Studio](https://ai.azure.com/). + +## Supported Models + +| Model Name | Description | Cost per Image | +|------------|-------------|----------------| +| `azure_ai/FLUX.1-Kontext-pro` | FLUX 1 Kontext Pro model with enhanced context understanding for editing | $0.04 | + +## Image Editing + +### Usage - LiteLLM Python SDK + + + + +```python showLineNumbers title="Basic Image Editing" +import os +import base64 +from pathlib import Path + +import litellm + +# Set your API credentials +os.environ["AZURE_AI_API_KEY"] = "your-api-key-here" +os.environ["AZURE_AI_API_BASE"] = "your-azure-ai-endpoint" +os.environ["AZURE_AI_API_VERSION"] = "2025-04-01-preview" + +# Edit an image with a prompt +response = litellm.image_edit( + model="azure_ai/FLUX.1-Kontext-pro", + image=open("path/to/your/image.png", "rb"), + prompt="Add a winter theme with snow and cold colors", + api_base=os.environ["AZURE_AI_API_BASE"], + api_key=os.environ["AZURE_AI_API_KEY"], + api_version=os.environ["AZURE_AI_API_VERSION"] +) + +img_base64 = response.data[0].get("b64_json") +img_bytes = base64.b64decode(img_base64) +path = Path("edited_image.png") +path.write_bytes(img_bytes) +``` + + + + + +```python showLineNumbers title="Async Image Editing" +import os +import base64 +from pathlib import Path + +import litellm +import asyncio + +# Set your API credentials +os.environ["AZURE_AI_API_KEY"] = "your-api-key-here" +os.environ["AZURE_AI_API_BASE"] = "your-azure-ai-endpoint" +os.environ["AZURE_AI_API_VERSION"] = "2025-04-01-preview" + +async def edit_image(): + # Edit image asynchronously + response = await litellm.aimage_edit( + model="azure_ai/FLUX.1-Kontext-pro", + image=open("path/to/your/image.png", "rb"), + prompt="Make this image look like a watercolor painting", + api_base=os.environ["AZURE_AI_API_BASE"], + api_key=os.environ["AZURE_AI_API_KEY"], + api_version=os.environ["AZURE_AI_API_VERSION"] + ) + img_base64 = response.data[0].get("b64_json") + img_bytes = base64.b64decode(img_base64) + path = Path("async_edited_image.png") + path.write_bytes(img_bytes) + +# Run the async function +asyncio.run(edit_image()) +``` + + + + + +```python showLineNumbers title="Advanced Image Editing with Parameters" +import os +import base64 +from pathlib import Path + +import litellm + +# Set your API credentials +os.environ["AZURE_AI_API_KEY"] = "your-api-key-here" +os.environ["AZURE_AI_API_BASE"] = "your-azure-ai-endpoint" +os.environ["AZURE_AI_API_VERSION"] = "2025-04-01-preview" + +# Edit image with additional parameters +response = litellm.image_edit( + model="azure_ai/FLUX.1-Kontext-pro", + image=open("path/to/your/image.png", "rb"), + prompt="Add magical elements like floating crystals and mystical lighting", + api_base=os.environ["AZURE_AI_API_BASE"], + api_key=os.environ["AZURE_AI_API_KEY"], + api_version=os.environ["AZURE_AI_API_VERSION"], + n=1 +) +img_base64 = response.data[0].get("b64_json") +img_bytes = base64.b64decode(img_base64) +path = Path("advanced_edited_image.png") +path.write_bytes(img_bytes) +``` + + + + +### Usage - LiteLLM Proxy Server + +#### 1. Configure your config.yaml + +```yaml showLineNumbers title="Azure AI Image Editing Configuration" +model_list: + - model_name: azure-flux-kontext-edit + litellm_params: + model: azure_ai/FLUX.1-Kontext-pro + api_key: os.environ/AZURE_AI_API_KEY + api_base: os.environ/AZURE_AI_API_BASE + api_version: os.environ/AZURE_AI_API_VERSION + model_info: + mode: image_edit + +general_settings: + master_key: sk-1234 +``` + +#### 2. Start LiteLLM Proxy Server + +```bash showLineNumbers title="Start LiteLLM Proxy Server" +litellm --config /path/to/config.yaml + +# RUNNING on http://0.0.0.0:4000 +``` + +#### 3. Make image editing requests with OpenAI Python SDK + + + + +```python showLineNumbers title="Azure AI Image Editing via Proxy - OpenAI SDK" +from openai import OpenAI + +# Initialize client with your proxy URL +client = OpenAI( + base_url="http://localhost:4000", # Your proxy URL + api_key="sk-1234" # Your proxy API key +) + +# Edit image with FLUX Kontext Pro +response = client.images.edit( + model="azure-flux-kontext-edit", + image=open("path/to/your/image.png", "rb"), + prompt="Transform this image into a beautiful oil painting style", +) + +img_base64 = response.data[0].b64_json +img_bytes = base64.b64decode(img_base64) +path = Path("proxy_edited_image.png") +path.write_bytes(img_bytes) +``` + + + + + +```python showLineNumbers title="Azure AI Image Editing via Proxy - LiteLLM SDK" +import litellm + +# Edit image through proxy +response = litellm.image_edit( + model="litellm_proxy/azure-flux-kontext-edit", + image=open("path/to/your/image.png", "rb"), + prompt="Add a mystical forest background with magical creatures", + api_base="http://localhost:4000", + api_key="sk-1234" +) + +img_base64 = response.data[0].b64_json +img_bytes = base64.b64decode(img_base64) +path = Path("proxy_edited_image.png") +path.write_bytes(img_bytes) +``` + + + + + +```bash showLineNumbers title="Azure AI Image Editing via Proxy - cURL" +curl --location 'http://localhost:4000/v1/images/edits' \ +--header 'Authorization: Bearer sk-1234' \ +--form 'model="azure-flux-kontext-edit"' \ +--form 'prompt="Convert this image to a vintage sepia tone with old-fashioned effects"' \ +--form 'image=@"path/to/your/image.png"' +``` + + + + +## Supported Parameters + +Azure AI Image Editing supports the following OpenAI-compatible parameters: + +| Parameter | Type | Description | Default | Example | +|-----------|------|-------------|---------|---------| +| `image` | file | The image file to edit | Required | File object or binary data | +| `prompt` | string | Text description of the desired changes | Required | `"Add snow and winter elements"` | +| `model` | string | The FLUX model to use for editing | Required | `"azure_ai/FLUX.1-Kontext-pro"` | +| `n` | integer | Number of edited images to generate (You can specify only 1) | `1` | `1` | +| `api_base` | string | Your Azure AI endpoint URL | Required | `"https://your-endpoint.eastus2.inference.ai.azure.com/"` | +| `api_key` | string | Your Azure AI API key | Required | Environment variable or direct value | +| `api_version` | string | API version for Azure AI | Required | `"2025-04-01-preview"` | + +## Getting Started + +1. Create an account at [Azure AI Studio](https://ai.azure.com/) +2. Deploy a FLUX model in your Azure AI Studio workspace +3. Get your API key and endpoint from the deployment details +4. Set your `AZURE_AI_API_KEY`, `AZURE_AI_API_BASE` and `AZURE_AI_API_VERSION` environment variables +5. Prepare your source image +6. Use `litellm.image_edit()` to modify your images with text instructions + +## Additional Resources + +- [Azure AI Studio Documentation](https://docs.microsoft.com/en-us/azure/ai-services/) +- [FLUX Models Announcement](https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/black-forest-labs-flux-1-kontext-pro-and-flux1-1-pro-now-available-in-azure-ai-f/4434659) \ No newline at end of file diff --git a/docs/my-website/docs/providers/bedrock.md b/docs/my-website/docs/providers/bedrock.md index 157a9e6a797..86e9ac5e3e6 100644 --- a/docs/my-website/docs/providers/bedrock.md +++ b/docs/my-website/docs/providers/bedrock.md @@ -308,6 +308,65 @@ print(response) +## Usage - Request Metadata + +Attach metadata to Bedrock requests for logging and cost attribution. + + + + +```python +import os +from litellm import completion + +os.environ["AWS_ACCESS_KEY_ID"] = "" +os.environ["AWS_SECRET_ACCESS_KEY"] = "" +os.environ["AWS_REGION_NAME"] = "" + +response = completion( + model="bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0", + messages=[{"role": "user", "content": "Hello, how are you?"}], + requestMetadata={ + "cost_center": "engineering", + "user_id": "user123" + } +) +``` + + + +**Set on yaml** + +```yaml +model_list: + - model_name: bedrock-claude-v1 + litellm_params: + model: bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0 + requestMetadata: + cost_center: "engineering" +``` + +**Set on request** + +```python +import openai +client = openai.OpenAI( + api_key="anything", + base_url="http://0.0.0.0:4000" +) + +response = client.chat.completions.create( + model="bedrock-claude-v1", + messages=[{"role": "user", "content": "Hello"}], + extra_body={ + "requestMetadata": {"cost_center": "engineering"} + } +) +``` + + + + ## Usage - Function Calling / Tool calling LiteLLM supports tool calling via Bedrock's Converse and Invoke API's. @@ -1822,6 +1881,59 @@ Here's an example of using a bedrock model with LiteLLM. For a complete list, re | Mixtral 8x7B Instruct | `completion(model='bedrock/mistral.mixtral-8x7b-instruct-v0:1', messages=messages)` | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']` | +## Bedrock Embedding + +### API keys +This can be set as env variables or passed as **params to litellm.embedding()** +```python +import os +os.environ["AWS_ACCESS_KEY_ID"] = "" # Access key +os.environ["AWS_SECRET_ACCESS_KEY"] = "" # Secret access key +os.environ["AWS_REGION_NAME"] = "" # us-east-1, us-east-2, us-west-1, us-west-2 +``` + +### Usage +```python +from litellm import embedding +response = embedding( + model="bedrock/amazon.titan-embed-text-v1", + input=["good morning from litellm"], +) +print(response) +``` + +#### Titan V2 - encoding_format support +```python +from litellm import embedding +# Float format (default) +response = embedding( + model="bedrock/amazon.titan-embed-text-v2:0", + input=["good morning from litellm"], + encoding_format="float" # Returns float array +) + +# Binary format +response = embedding( + model="bedrock/amazon.titan-embed-text-v2:0", + input=["good morning from litellm"], + encoding_format="base64" # Returns base64 encoded binary +) +``` + +## Supported AWS Bedrock Embedding Models + +| Model Name | Usage | Supported Additional OpenAI params | +|----------------------|---------------------------------------------|-----| +| Titan Embeddings V2 | `embedding(model="bedrock/amazon.titan-embed-text-v2:0", input=input)` | `dimensions`, `encoding_format` | +| Titan Embeddings - V1 | `embedding(model="bedrock/amazon.titan-embed-text-v1", input=input)` | [here](https://github.com/BerriAI/litellm/blob/f5905e100068e7a4d61441d7453d7cf5609c2121/litellm/llms/bedrock/embed/amazon_titan_g1_transformation.py#L53) +| Titan Multimodal Embeddings | `embedding(model="bedrock/amazon.titan-embed-image-v1", input=input)` | [here](https://github.com/BerriAI/litellm/blob/f5905e100068e7a4d61441d7453d7cf5609c2121/litellm/llms/bedrock/embed/amazon_titan_multimodal_transformation.py#L28) | +| Cohere Embeddings - English | `embedding(model="bedrock/cohere.embed-english-v3", input=input)` | [here](https://github.com/BerriAI/litellm/blob/f5905e100068e7a4d61441d7453d7cf5609c2121/litellm/llms/bedrock/embed/cohere_transformation.py#L18) +| Cohere Embeddings - Multilingual | `embedding(model="bedrock/cohere.embed-multilingual-v3", input=input)` | [here](https://github.com/BerriAI/litellm/blob/f5905e100068e7a4d61441d7453d7cf5609c2121/litellm/llms/bedrock/embed/cohere_transformation.py#L18) + +### Advanced - [Drop Unsupported Params](https://docs.litellm.ai/docs/completion/drop_params#openai-proxy-usage) + +### Advanced - [Pass model/provider-specific Params](https://docs.litellm.ai/docs/completion/provider_specific_params#proxy-usage) + ## Image Generation Use this for stable diffusion, and amazon nova canvas on bedrock @@ -1901,6 +2013,39 @@ curl -L -X POST 'http://0.0.0.0:4000/v1/images/generations' \ +### Using Inference Profiles with Image Generation + +For AWS Bedrock Application Inference Profiles with image generation, use the `model_id` parameter to specify the inference profile ARN: + + + + +```python +from litellm import image_generation + +response = image_generation( + model="bedrock/amazon.nova-canvas-v1:0", + model_id="arn:aws:bedrock:eu-west-1:000000000000:application-inference-profile/a0a0a0a0a0a0", + prompt="A cute baby sea otter" +) +print(f"response: {response}") +``` + + + + +```yaml +model_list: + - model_name: nova-canvas-inference-profile + litellm_params: + model: bedrock/amazon.nova-canvas-v1:0 + model_id: arn:aws:bedrock:eu-west-1:000000000000:application-inference-profile/a0a0a0a0a0a0 + aws_region_name: "eu-west-1" +``` + + + + ## Supported AWS Bedrock Image Generation Models | Model Name | Function Call | diff --git a/docs/my-website/docs/providers/bedrock_embedding.md b/docs/my-website/docs/providers/bedrock_embedding.md index 430f9a4578c..95ee8d3d228 100644 --- a/docs/my-website/docs/providers/bedrock_embedding.md +++ b/docs/my-website/docs/providers/bedrock_embedding.md @@ -1,4 +1,4 @@ -## Bedrock Embedding +# Bedrock Embedding ## Supported Embedding Models diff --git a/docs/my-website/docs/providers/vertex.md b/docs/my-website/docs/providers/vertex.md index 5a76189de9b..3f4d1068958 100644 --- a/docs/my-website/docs/providers/vertex.md +++ b/docs/my-website/docs/providers/vertex.md @@ -45,7 +45,7 @@ vertex_credentials_json = json.dumps(vertex_credentials) ## COMPLETION CALL response = completion( - model="vertex_ai/gemini-pro", + model="vertex_ai/gemini-2.5-pro", messages=[{ "content": "Hello, how are you?","role": "user"}], vertex_credentials=vertex_credentials_json ) @@ -69,7 +69,7 @@ vertex_credentials_json = json.dumps(vertex_credentials) response = completion( - model="vertex_ai/gemini-pro", + model="vertex_ai/gemini-2.5-pro", messages=[{"content": "You are a good bot.","role": "system"}, {"content": "Hello, how are you?","role": "user"}], vertex_credentials=vertex_credentials_json ) @@ -189,7 +189,7 @@ print(json.loads(completion.choices[0].message.content)) 1. Add model to config.yaml ```yaml model_list: - - model_name: gemini-pro + - model_name: gemini-2.5-pro litellm_params: model: vertex_ai/gemini-1.5-pro vertex_project: "project-id" @@ -210,7 +210,7 @@ curl -X POST 'http://0.0.0.0:4000/chat/completions' \ -H 'Content-Type: application/json' \ -H 'Authorization: Bearer sk-1234' \ -D '{ - "model": "gemini-pro", + "model": "gemini-2.5-pro", "messages": [ {"role": "user", "content": "List 5 popular cookie recipes."} ], @@ -262,7 +262,7 @@ except JSONSchemaValidationError as e: 1. Add model to config.yaml ```yaml model_list: - - model_name: gemini-pro + - model_name: gemini-2.5-pro litellm_params: model: vertex_ai/gemini-1.5-pro vertex_project: "project-id" @@ -283,7 +283,7 @@ curl -X POST 'http://0.0.0.0:4000/chat/completions' \ -H 'Content-Type: application/json' \ -H 'Authorization: Bearer sk-1234' \ -D '{ - "model": "gemini-pro", + "model": "gemini-2.5-pro", "messages": [ {"role": "user", "content": "List 5 popular cookie recipes."} ], @@ -391,7 +391,7 @@ client = OpenAI( ) response = client.chat.completions.create( - model="gemini-pro", + model="gemini-2.5-pro", messages=[{"role": "user", "content": "Who won the world cup?"}], tools=[{"googleSearch": {}}], ) @@ -406,7 +406,7 @@ curl http://localhost:4000/v1/chat/completions \ -H "Content-Type: application/json" \ -H "Authorization: Bearer sk-1234" \ -d '{ - "model": "gemini-pro", + "model": "gemini-2.5-pro", "messages": [ {"role": "user", "content": "Who won the world cup?"} ], @@ -527,7 +527,7 @@ client = OpenAI( ) response = client.chat.completions.create( - model="gemini-pro", + model="gemini-2.5-pro", messages=[{"role": "user", "content": "Who won the world cup?"}], tools=[{"enterpriseWebSearch": {}}], ) @@ -542,7 +542,7 @@ curl http://localhost:4000/v1/chat/completions \ -H "Content-Type: application/json" \ -H "Authorization: Bearer sk-1234" \ -d '{ - "model": "gemini-pro", + "model": "gemini-2.5-pro", "messages": [ {"role": "user", "content": "Who won the world cup?"} ], @@ -815,6 +815,77 @@ Use Vertex AI context caching is supported by calling provider api directly. (Un [**Go straight to provider**](../pass_through/vertex_ai.md#context-caching) +#### 1. Create the Cache + +First, create the cache by sending a `POST` request to the `cachedContents` endpoint via the LiteLLM proxy. + + + + +```bash +curl http://0.0.0.0:4000/vertex_ai/v1/projects/{project_id}/locations/{location}/cachedContents \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer $LITELLM_KEY" \ + -d '{ + "model": "projects/{project_id}/locations/{location}/publishers/google/models/gemini-2.5-flash", + "displayName": "example_cache", + "contents": [{ + "role": "user", + "parts": [{ + "text": ".... a long book to be cached" + }] + }] + }' +``` + + + + +#### 2. Get the Cache Name from the Response + +Vertex AI will return a response containing the `name` of the cached content. This name is the identifier for your cached data. + +```json +{ + "name": "projects/12341234/locations/{location}/cachedContents/123123123123123", + "model": "projects/{project_id}/locations/{location}/publishers/google/models/gemini-2.5-flash", + "createTime": "2025-09-23T19:13:50.674976Z", + "updateTime": "2025-09-23T19:13:50.674976Z", + "expireTime": "2025-09-23T20:13:50.655988Z", + "displayName": "example_cache", + "usageMetadata": { + "totalTokenCount": 1246, + "textCount": 5132 + } +} +``` + +#### 3. Use the Cached Content + +Use the `name` from the response as `cachedContent` or `cached_content` in subsequent API calls to reuse the cached information. This is passed in the body of your request to `/chat/completions`. + + + + +```bash + +curl http://0.0.0.0:4000/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer $LITELLM_KEY" \ + -d '{ + "cachedContent": "projects/545201925769/locations/us-central1/cachedContents/4511135542628319232", + "model": "gemini-2.5-flash", + "messages": [ + { + "role": "user", + "content": "what is the book about?" + } + ] + }' +``` + + + ## Pre-requisites * `pip install google-cloud-aiplatform` (pre-installed on proxy docker image) @@ -835,7 +906,7 @@ import litellm litellm.vertex_project = "hardy-device-38811" # Your Project ID litellm.vertex_location = "us-central1" # proj location -response = litellm.completion(model="gemini-pro", messages=[{"role": "user", "content": "write code for saying hi from LiteLLM"}]) +response = litellm.completion(model="gemini-2.5-pro", messages=[{"role": "user", "content": "write code for saying hi from LiteLLM"}]) ``` ## Usage with LiteLLM Proxy Server @@ -876,9 +947,9 @@ Here's how to use Vertex AI with the LiteLLM Proxy Server vertex_location: "us-central1" # proj location model_list: - -model_name: team1-gemini-pro + -model_name: team1-gemini-2.5-pro litellm_params: - model: gemini-pro + model: gemini-2.5-pro ``` @@ -905,7 +976,7 @@ Here's how to use Vertex AI with the LiteLLM Proxy Server ) response = client.chat.completions.create( - model="team1-gemini-pro", + model="team1-gemini-2.5-pro", messages = [ { "role": "user", @@ -925,7 +996,7 @@ Here's how to use Vertex AI with the LiteLLM Proxy Server --header 'Authorization: Bearer sk-1234' \ --header 'Content-Type: application/json' \ --data '{ - "model": "team1-gemini-pro", + "model": "team1-gemini-2.5-pro", "messages": [ { "role": "user", @@ -975,7 +1046,7 @@ vertex_credentials_json = json.dumps(vertex_credentials) response = completion( - model="vertex_ai/gemini-pro", + model="vertex_ai/gemini-2.5-pro", messages=[{"content": "You are a good bot.","role": "system"}, {"content": "Hello, how are you?","role": "user"}], vertex_credentials=vertex_credentials_json, vertex_project="my-special-project", @@ -1039,7 +1110,7 @@ In certain use-cases you may need to make calls to the models and pass [safety s ```python response = completion( - model="vertex_ai/gemini-pro", + model="vertex_ai/gemini-2.5-pro", messages=[{"role": "user", "content": "write code for saying hi from LiteLLM"}] safety_settings=[ { @@ -1153,7 +1224,7 @@ litellm.vertex_ai_safety_settings = [ }, ] response = completion( - model="vertex_ai/gemini-pro", + model="vertex_ai/gemini-2.5-pro", messages=[{"role": "user", "content": "write code for saying hi from LiteLLM"}] ) ``` @@ -1212,7 +1283,7 @@ litellm.vertex_location = "us-central1 # Your Location ## Gemini Pro | Model Name | Function Call | |------------------|--------------------------------------| -| gemini-pro | `completion('gemini-pro', messages)`, `completion('vertex_ai/gemini-pro', messages)` | +| gemini-2.5-pro | `completion('gemini-2.5-pro', messages)`, `completion('vertex_ai/gemini-2.5-pro', messages)` | ## Fine-tuned Models @@ -1307,7 +1378,7 @@ curl --location 'https://0.0.0.0:4000/v1/chat/completions' \ ## Gemini Pro Vision | Model Name | Function Call | |------------------|--------------------------------------| -| gemini-pro-vision | `completion('gemini-pro-vision', messages)`, `completion('vertex_ai/gemini-pro-vision', messages)`| +| gemini-2.5-pro-vision | `completion('gemini-2.5-pro-vision', messages)`, `completion('vertex_ai/gemini-2.5-pro-vision', messages)`| ## Gemini 1.5 Pro (and Vision) | Model Name | Function Call | @@ -1321,7 +1392,7 @@ curl --location 'https://0.0.0.0:4000/v1/chat/completions' \ #### Using Gemini Pro Vision -Call `gemini-pro-vision` in the same input/output format as OpenAI [`gpt-4-vision`](https://docs.litellm.ai/docs/providers/openai#openai-vision-models) +Call `gemini-2.5-pro-vision` in the same input/output format as OpenAI [`gpt-4-vision`](https://docs.litellm.ai/docs/providers/openai#openai-vision-models) LiteLLM Supports the following image types passed in `url` - Images with Cloud Storage URIs - gs://cloud-samples-data/generative-ai/image/boats.jpeg @@ -1339,7 +1410,7 @@ LiteLLM Supports the following image types passed in `url` import litellm response = litellm.completion( - model = "vertex_ai/gemini-pro-vision", + model = "vertex_ai/gemini-2.5-pro-vision", messages=[ { "role": "user", @@ -1377,7 +1448,7 @@ image_path = "cached_logo.jpg" # Getting the base64 string base64_image = encode_image(image_path) response = litellm.completion( - model="vertex_ai/gemini-pro-vision", + model="vertex_ai/gemini-2.5-pro-vision", messages=[ { "role": "user", @@ -1433,7 +1504,7 @@ tools = [ messages = [{"role": "user", "content": "What's the weather like in Boston today?"}] response = completion( - model="vertex_ai/gemini-pro-vision", + model="vertex_ai/gemini-2.5-pro-vision", messages=messages, tools=tools, ) @@ -2509,150 +2580,6 @@ print("response from proxy", response) -## **Batch APIs** - -Just add the following Vertex env vars to your environment. - -```bash -# GCS Bucket settings, used to store batch prediction files in -export GCS_BUCKET_NAME = "litellm-testing-bucket" # the bucket you want to store batch prediction files in -export GCS_PATH_SERVICE_ACCOUNT="/path/to/service_account.json" # path to your service account json file - -# Vertex /batch endpoint settings, used for LLM API requests -export GOOGLE_APPLICATION_CREDENTIALS="/path/to/service_account.json" # path to your service account json file -export VERTEXAI_LOCATION="us-central1" # can be any vertex location -export VERTEXAI_PROJECT="my-test-project" -``` - -### Usage - - -#### 1. Create a file of batch requests for vertex - -LiteLLM expects the file to follow the **[OpenAI batches files format](https://platform.openai.com/docs/guides/batch)** - -Each `body` in the file should be an **OpenAI API request** - -Create a file called `vertex_batch_completions.jsonl` in the current working directory, the `model` should be the Vertex AI model name -``` -{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "gemini-1.5-flash-001", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} -{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "gemini-1.5-flash-001", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} -``` - - -#### 2. Upload a File of batch requests - -For `vertex_ai` litellm will upload the file to the provided `GCS_BUCKET_NAME` - -```python -import os -oai_client = OpenAI( - api_key="sk-1234", # litellm proxy API key - base_url="http://localhost:4000" # litellm proxy base url -) -file_name = "vertex_batch_completions.jsonl" # -_current_dir = os.path.dirname(os.path.abspath(__file__)) -file_path = os.path.join(_current_dir, file_name) -file_obj = oai_client.files.create( - file=open(file_path, "rb"), - purpose="batch", - extra_body={"custom_llm_provider": "vertex_ai"}, # tell litellm to use vertex_ai for this file upload -) -``` - -**Expected Response** - -```json -{ - "id": "gs://litellm-testing-bucket/litellm-vertex-files/publishers/google/models/gemini-1.5-flash-001/d3f198cd-c0d1-436d-9b1e-28e3f282997a", - "bytes": 416, - "created_at": 1733392026, - "filename": "litellm-vertex-files/publishers/google/models/gemini-1.5-flash-001/d3f198cd-c0d1-436d-9b1e-28e3f282997a", - "object": "file", - "purpose": "batch", - "status": "uploaded", - "status_details": null -} -``` - - - -#### 3. Create a batch - -```python -batch_input_file_id = file_obj.id # use `file_obj` from step 2 -create_batch_response = oai_client.batches.create( - completion_window="24h", - endpoint="/v1/chat/completions", - input_file_id=batch_input_file_id, # example input_file_id = "gs://litellm-testing-bucket/litellm-vertex-files/publishers/google/models/gemini-1.5-flash-001/c2b1b785-252b-448c-b180-033c4c63b3ce" - extra_body={"custom_llm_provider": "vertex_ai"}, # tell litellm to use `vertex_ai` for this batch request -) -``` - -**Expected Response** - -```json -{ - "id": "3814889423749775360", - "completion_window": "24hrs", - "created_at": 1733392026, - "endpoint": "", - "input_file_id": "gs://litellm-testing-bucket/litellm-vertex-files/publishers/google/models/gemini-1.5-flash-001/d3f198cd-c0d1-436d-9b1e-28e3f282997a", - "object": "batch", - "status": "validating", - "cancelled_at": null, - "cancelling_at": null, - "completed_at": null, - "error_file_id": null, - "errors": null, - "expired_at": null, - "expires_at": null, - "failed_at": null, - "finalizing_at": null, - "in_progress_at": null, - "metadata": null, - "output_file_id": "gs://litellm-testing-bucket/litellm-vertex-files/publishers/google/models/gemini-1.5-flash-001", - "request_counts": null -} -``` - -#### 4. Retrieve a batch - -```python -retrieved_batch = oai_client.batches.retrieve( - batch_id=create_batch_response.id, - extra_body={"custom_llm_provider": "vertex_ai"}, # tell litellm to use `vertex_ai` for this batch request -) -``` - -**Expected Response** - -```json -{ - "id": "3814889423749775360", - "completion_window": "24hrs", - "created_at": 1736500100, - "endpoint": "", - "input_file_id": "gs://example-bucket-1-litellm/litellm-vertex-files/publishers/google/models/gemini-1.5-flash-001/7b2e47f5-3dd4-436d-920f-f9155bbdc952", - "object": "batch", - "status": "completed", - "cancelled_at": null, - "cancelling_at": null, - "completed_at": null, - "error_file_id": null, - "errors": null, - "expired_at": null, - "expires_at": null, - "failed_at": null, - "finalizing_at": null, - "in_progress_at": null, - "metadata": null, - "output_file_id": "gs://example-bucket-1-litellm/litellm-vertex-files/publishers/google/models/gemini-1.5-flash-001", - "request_counts": null -} -``` - - ## **Fine Tuning APIs** @@ -2868,7 +2795,3 @@ Once that's done, when you deploy the new container in the Google Cloud Run serv s/o @[Darien Kindlund](https://www.linkedin.com/in/kindlund/) for this tutorial - - - - diff --git a/docs/my-website/docs/providers/vertex_batch.md b/docs/my-website/docs/providers/vertex_batch.md new file mode 100644 index 00000000000..4eaa0d69d4b --- /dev/null +++ b/docs/my-website/docs/providers/vertex_batch.md @@ -0,0 +1,264 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +## **Batch APIs** + +Just add the following Vertex env vars to your environment. + +```bash +# GCS Bucket settings, used to store batch prediction files in +export GCS_BUCKET_NAME="my-batch-bucket" # the bucket you want to store batch prediction files in +export GCS_PATH_SERVICE_ACCOUNT="/path/to/service_account.json" # path to your service account json file + +# Vertex /batch endpoint settings, used for LLM API requests +export GOOGLE_APPLICATION_CREDENTIALS="/path/to/service_account.json" # path to your service account json file +export VERTEXAI_LOCATION="us-central1" # can be any vertex location +export VERTEXAI_PROJECT="my-project" +``` + +### Usage + +Follow this complete workflow: create JSONL file ā†’ upload file ā†’ create batch ā†’ retrieve batch status ā†’ get file content + +#### 1. Create a JSONL file of batch requests + +LiteLLM expects the file to follow the **[OpenAI batches files format](https://platform.openai.com/docs/guides/batch)**. + +Each `body` in the file should be an **OpenAI API request**. + +Create a file called `batch_requests.jsonl` with your requests: +```jsonl +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "gemini-2.5-flash-lite", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "gemini-2.5-flash-lite", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +``` + +#### 2. Upload the file + +Upload your JSONL file. For `vertex_ai`, the file will be stored in your configured GCS bucket provided by `GCS_BUCKET_NAME`. + + + + +```python showLineNumbers title="upload_file.py" +from openai import OpenAI + +oai_client = OpenAI( + api_key="sk-1234", # litellm proxy API key + base_url="http://localhost:4000" # litellm proxy base url +) + +file_obj = oai_client.files.create( + file=open("batch_requests.jsonl", "rb"), + purpose="batch", + extra_body={"custom_llm_provider": "vertex_ai"} +) + +print(f"File uploaded with ID: {file_obj.id}") +``` + + + + +```bash showLineNumbers title="Upload File" +curl --request POST \ + --url http://localhost:4000/v1/files \ + --header 'Content-Type: multipart/form-data' \ + --form purpose=batch \ + --form file=@batch_requests.jsonl \ + --form custom_llm_provider=vertex_ai +``` + + + + +**Expected Response:** + +```json +{ + "id": "gs://my-batch-bucket/litellm-vertex-files/publishers/google/models/gemini-2.5-flash-lite/abc123-def4-5678-9012-34567890abcd", + "bytes": 416, + "created_at": 1758303684, + "filename": "litellm-vertex-files/publishers/google/models/gemini-2.5-flash-lite/abc123-def4-5678-9012-34567890abcd", + "object": "file", + "purpose": "batch", + "status": "uploaded", + "expires_at": null, + "status_details": null +} +``` + +#### 3. Create a batch + +Create a batch job using the uploaded file ID. + + + + +```python showLineNumbers title="create_batch.py" +batch_input_file_id = file_obj.id # from step 2 +create_batch_response = oai_client.batches.create( + completion_window="24h", + endpoint="/v1/chat/completions", + input_file_id=batch_input_file_id, # e.g. "gs://my-batch-bucket/litellm-vertex-files/publishers/google/models/gemini-2.5-flash-lite/abc123-def4-5678-9012-34567890abcd" + extra_body={"custom_llm_provider": "vertex_ai"} +) + +print(f"Batch created with ID: {create_batch_response.id}") +``` + + + + +```bash showLineNumbers title="Create Batch Request" +curl --request POST \ + --url http://localhost:4000/v1/batches \ + --header 'Content-Type: application/json' \ + --data '{ + "input_file_id": "gs://my-batch-bucket/litellm-vertex-files/publishers/google/models/gemini-2.5-flash-lite/abc123-def4-5678-9012-34567890abcd", + "endpoint": "/v1/chat/completions", + "completion_window": "24h", + "custom_llm_provider": "vertex_ai" +}' +``` + + + + +**Expected Response:** + +```json +{ + "id": "7814463557919047680", + "completion_window": "24hrs", + "created_at": 1758328011, + "endpoint": "", + "input_file_id": "gs://my-batch-bucket/litellm-vertex-files/publishers/google/models/gemini-2.5-flash-lite/abc123-def4-5678-9012-34567890abcd", + "object": "batch", + "status": "validating", + "cancelled_at": null, + "cancelling_at": null, + "completed_at": null, + "error_file_id": null, + "errors": null, + "expired_at": null, + "expires_at": null, + "failed_at": null, + "finalizing_at": null, + "in_progress_at": null, + "metadata": null, + "output_file_id": "gs://my-batch-bucket/litellm-vertex-files/publishers/google/models/gemini-2.5-flash-lite", + "request_counts": null, + "usage": null +} +``` + +#### 4. Retrieve batch status + +Check the status of your batch job. The batch will progress through states: `validating` ā†’ `in_progress` ā†’ `completed`. + + + + +```python showLineNumbers title="retrieve_batch.py" +retrieved_batch = oai_client.batches.retrieve( + batch_id=create_batch_response.id, # Created batch id, e.g. 7814463557919047680 + extra_body={"custom_llm_provider": "vertex_ai"} +) + +print(f"Batch status: {retrieved_batch.status}") +if retrieved_batch.status == "completed": + print(f"Output file: {retrieved_batch.output_file_id}") +``` + + + + +```bash showLineNumbers title="Retrieve Batch Status" +curl --request GET \ + --url 'http://localhost:4000/batches/7814463557919047680?provider=vertex_ai' \ + --header 'Authorization: Bearer sk-1234' +``` + + + + +**Expected Response (when completed):** + +```json +{ + "id": "7814463557919047680", + "completion_window": "24hrs", + "created_at": 1758328011, + "endpoint": "", + "input_file_id": "gs://my-batch-bucket/litellm-vertex-files/publishers/google/models/gemini-2.5-flash-lite/abc123-def4-5678-9012-34567890abcd", + "object": "batch", + "status": "completed", + "cancelled_at": null, + "cancelling_at": null, + "completed_at": null, + "error_file_id": null, + "errors": null, + "expired_at": null, + "expires_at": null, + "failed_at": null, + "finalizing_at": null, + "in_progress_at": null, + "metadata": null, + "output_file_id": "gs://my-batch-bucket/litellm-vertex-files/publishers/google/models/gemini-2.5-flash-lite/prediction-model-2025-09-19T21:26:51.569037Z/predictions.jsonl", + "request_counts": null, + "usage": null +} +``` + +#### 5. Get file content + +Once the batch is completed, retrieve the results using the `output_file_id` from the batch response. + +**Important:** The `output_file_id` must be URL encoded when used in the request path. + + + + +```python showLineNumbers title="get_file_content.py" +import urllib.parse +import json + +output_file_id = retrieved_batch.output_file_id +# URL encode the file ID +encoded_file_id = urllib.parse.quote_plus(output_file_id) + +# Get file content +file_content = oai_client.files.content( + file_id=encoded_file_id, + extra_body={"custom_llm_provider": "vertex_ai"} +) + +# Process the results +for line in file_content.text.strip().split('\n'): + result = json.loads(line) + print(f"Request: {result['request']}") + print(f"Response: {result['response']}") + print("---") +``` + + + + +```bash showLineNumbers title="Get File Content" +# Note: The file ID must be URL encoded +curl --request GET \ + --url 'http://localhost:4000/files/gs%253A%252F%252Fmy-batch-bucket%252Flitellm-vertex-files%252Fpublishers%252Fgoogle%252Fmodels%252Fgemini-2.5-flash-lite%252Fprediction-model-2025-09-19T21%253A26%253A51.569037Z%252Fpredictions.jsonl/content?provider=vertex_ai' \ + --header 'Authorization: Bearer sk-1234' +``` + + + + +**Expected Response:** + +The response contains JSONL format with one result per line: + +```jsonl +{"status":"","processed_time":"2025-09-19T21:29:47.352+00:00","request":{"contents":[{"parts":[{"text":"Hello world!"}],"role":"user"}],"generationConfig":{"max_output_tokens":10},"system_instruction":{"parts":[{"text":"You are a helpful assistant."}]}},"response":{"candidates":[{"avgLogprobs":-0.48079710006713866,"content":{"parts":[{"text":"Hello there! It's nice to meet you"}],"role":"model"},"finishReason":"MAX_TOKENS"}],"createTime":"2025-09-19T21:29:47.484619Z","modelVersion":"gemini-2.5-flash-lite","responseId":"S8vNaIvKHdvshMIP_aOtuAg","usageMetadata":{"candidatesTokenCount":10,"candidatesTokensDetails":[{"modality":"TEXT","tokenCount":10}],"promptTokenCount":9,"promptTokensDetails":[{"modality":"TEXT","tokenCount":9}],"totalTokenCount":19,"trafficType":"ON_DEMAND"}}} +{"status":"","processed_time":"2025-09-19T21:29:47.358+00:00","request":{"contents":[{"parts":[{"text":"Hello world!"}],"role":"user"}],"generationConfig":{"max_output_tokens":10},"system_instruction":{"parts":[{"text":"You are an unhelpful assistant."}]}},"response":{"candidates":[{"avgLogprobs":-0.6168075137668185,"content":{"parts":[{"text":"I am unable to assist with this request."}],"role":"model"},"finishReason":"STOP"}],"createTime":"2025-09-19T21:29:47.470889Z","modelVersion":"gemini-2.5-flash-lite","responseId":"S8vNaOneHISShMIP28nA8QQ","usageMetadata":{"candidatesTokenCount":9,"candidatesTokensDetails":[{"modality":"TEXT","tokenCount":9}],"promptTokenCount":9,"promptTokensDetails":[{"modality":"TEXT","tokenCount":9}],"totalTokenCount":18,"trafficType":"ON_DEMAND"}}} +``` diff --git a/docs/my-website/docs/proxy/budget_reset_and_tz.md b/docs/my-website/docs/proxy/budget_reset_and_tz.md index 541ff6a2f0a..340e33afe18 100644 --- a/docs/my-website/docs/proxy/budget_reset_and_tz.md +++ b/docs/my-website/docs/proxy/budget_reset_and_tz.md @@ -29,5 +29,6 @@ Common timezone values: - `US/Pacific` - Pacific Time - `Europe/London` - UK Time - `Asia/Kolkata` - Indian Standard Time (IST) +- `Asia/Bangkok` - Indochina Time (ICT) - `Asia/Tokyo` - Japan Standard Time - `Australia/Sydney` - Australian Eastern Time diff --git a/docs/my-website/docs/proxy/custom_auth.md b/docs/my-website/docs/proxy/custom_auth.md index 3787f9bdd7c..812b80d3e9c 100644 --- a/docs/my-website/docs/proxy/custom_auth.md +++ b/docs/my-website/docs/proxy/custom_auth.md @@ -21,6 +21,169 @@ async def user_api_key_auth(request: Request, api_key: str) -> UserAPIKeyAuth: raise Exception ``` +## UserAPIKeyAuth Fields Reference + +The `UserAPIKeyAuth` object supports the following fields for comprehensive auth configuration: + +### Core Authentication Fields +```python +UserAPIKeyAuth( + # Basic auth fields + api_key: Optional[str] = None, # The API key (will be hashed automatically) + token: Optional[str] = None, # Hashed token for internal use + key_name: Optional[str] = None, # Human-readable key name + key_alias: Optional[str] = None, # Key alias for identification + + # User identification + user_id: Optional[str] = None, # Unique user identifier + user_email: Optional[str] = None, # User email address + user_role: Optional[LitellmUserRoles] = None, # User role (PROXY_ADMIN, INTERNAL_USER, etc.) + + # Team/Organization + team_id: Optional[str] = None, # Team identifier + team_alias: Optional[str] = None, # Team display name + org_id: Optional[str] = None, # Organization identifier +) +``` + +### Budget and Spend Tracking +```python +UserAPIKeyAuth( + # User budgets + max_budget: Optional[float] = None, # Maximum budget for the key + spend: float = 0.0, # Current spend amount + soft_budget: Optional[float] = None, # Soft budget limit (warnings) + model_max_budget: Dict = {}, # Per-model budget limits + model_spend: Dict = {}, # Per-model spend tracking + + # Team budgets + team_max_budget: Optional[float] = None, # Team's maximum budget + team_spend: Optional[float] = None, # Team's current spend + team_member_spend: Optional[float] = None, # This user's spend within the team + + # Budget timing + budget_duration: Optional[str] = None, # Budget reset period + budget_reset_at: Optional[datetime] = None, # When budget resets +) +``` + +### Rate Limiting +```python +UserAPIKeyAuth( + # User limits + tpm_limit: Optional[int] = None, # Tokens per minute limit + rpm_limit: Optional[int] = None, # Requests per minute limit + user_tpm_limit: Optional[int] = None, # User-specific TPM limit + user_rpm_limit: Optional[int] = None, # User-specific RPM limit + + # Team limits + team_tpm_limit: Optional[int] = None, # Team TPM limit + team_rpm_limit: Optional[int] = None, # Team RPM limit + team_member_tpm_limit: Optional[int] = None, # Per-member TPM limit + team_member_rpm_limit: Optional[int] = None, # Per-member RPM limit + + # Per-model limits + rpm_limit_per_model: Optional[Dict[str, int]] = None, # RPM limits by model + tpm_limit_per_model: Optional[Dict[str, int]] = None, # TPM limits by model +) +``` + +### End User Tracking +```python +UserAPIKeyAuth( + # End user identification and limits + end_user_id: Optional[str] = None, # End user identifier + end_user_tpm_limit: Optional[int] = None, # End user TPM limit + end_user_rpm_limit: Optional[int] = None, # End user RPM limit + end_user_max_budget: Optional[float] = None, # End user budget limit +) +``` + +### Model and Route Access +```python +UserAPIKeyAuth( + # Model access control + models: List = [], # Allowed models list + team_models: List = [], # Team's allowed models + aliases: Dict = {}, # Model aliases + + # Route permissions + allowed_routes: Optional[list] = [], # Allowed API routes + allowed_cache_controls: Optional[list] = [], # Cache control permissions + permissions: Dict = {}, # General permissions +) +``` + +### Advanced Configuration +```python +UserAPIKeyAuth( + # Request handling + max_parallel_requests: Optional[int] = None, # Concurrent request limit + allowed_model_region: Optional[AllowedModelRegion] = None, # Geographic restrictions + + # Expiration and status + expires: Optional[Union[str, datetime]] = None, # Key expiration + blocked: Optional[bool] = None, # Whether key is blocked + + # Metadata and configuration + metadata: Dict = {}, # Custom metadata + config: Dict = {}, # Configuration settings + team_metadata: Optional[Dict] = None, # Team metadata + + # Internal tracking + request_route: Optional[str] = None, # Current request route + last_refreshed_at: Optional[float] = None, # Cache refresh timestamp +) +``` + +### Complete Example + +```python +from datetime import datetime, timedelta +from litellm.proxy._types import UserAPIKeyAuth, LitellmUserRoles + +async def user_api_key_auth(request: Request, api_key: str) -> UserAPIKeyAuth: + try: + # Example: Comprehensive auth configuration + if api_key.startswith("sk-admin-"): + return UserAPIKeyAuth( + api_key=api_key, + user_id="admin_user_123", + user_email="admin@company.com", + user_role=LitellmUserRoles.PROXY_ADMIN, + team_id="admin_team", + team_alias="Administrative Team", + max_budget=1000.0, + soft_budget=800.0, + tpm_limit=10000, + rpm_limit=100, + models=["gpt-4", "claude-3-sonnet", "gpt-3.5-turbo"], + allowed_routes=["/chat/completions", "/embeddings"], + expires=datetime.now() + timedelta(days=30), + metadata={"department": "engineering", "cost_center": "ai_ops"} + ) + elif api_key.startswith("sk-team-"): + return UserAPIKeyAuth( + api_key=api_key, + user_id="team_user_456", + user_email="user@company.com", + user_role=LitellmUserRoles.INTERNAL_USER, + team_id="dev_team", + team_alias="Development Team", + max_budget=100.0, + tpm_limit=1000, + rpm_limit=20, + models=["gpt-3.5-turbo", "claude-3-haiku"], + team_member_tpm_limit=500, # Limit within team + end_user_tpm_limit=100, # Per end-user limit + metadata={"project": "chatbot_v2"} + ) + else: + raise Exception("Invalid API key") + except Exception: + raise Exception("Authentication failed") +``` + #### 2. Pass the filepath (relative to the config.yaml) Pass the filepath to the config.yaml diff --git a/docs/my-website/docs/proxy/deploy.md b/docs/my-website/docs/proxy/deploy.md index cdb6f7018fc..6a11d069fb0 100644 --- a/docs/my-website/docs/proxy/deploy.md +++ b/docs/my-website/docs/proxy/deploy.md @@ -13,6 +13,7 @@ To start using Litellm, run the following commands in a shell: ```bash # Get the code curl -O https://raw.githubusercontent.com/BerriAI/litellm/main/docker-compose.yml +curl -O https://raw.githubusercontent.com/BerriAI/litellm/main/prometheus.yml # Add the master key - you can change this after setup echo 'LITELLM_MASTER_KEY="sk-1234"' > .env diff --git a/docs/my-website/docs/proxy/dynamic_rate_limit.md b/docs/my-website/docs/proxy/dynamic_rate_limit.md new file mode 100644 index 00000000000..b3aed6a359e --- /dev/null +++ b/docs/my-website/docs/proxy/dynamic_rate_limit.md @@ -0,0 +1,255 @@ + +# Dynamic TPM/RPM Allocation + +Prevent projects from gobbling too much tpm/rpm. + +Dynamically allocate TPM/RPM quota to api keys, based on active keys in that minute. [**See Code**](https://github.com/BerriAI/litellm/blob/9bffa9a48e610cc6886fc2dce5c1815aeae2ad46/litellm/proxy/hooks/dynamic_rate_limiter.py#L125) + +## Quick Start Usage + +1. Setup config.yaml + +```yaml showLineNumbers title="config.yaml" +model_list: + - model_name: my-fake-model + litellm_params: + model: gpt-3.5-turbo + api_key: my-fake-key + mock_response: hello-world + tpm: 60 + +litellm_settings: + callbacks: ["dynamic_rate_limiter_v3"] + +general_settings: + master_key: sk-1234 # OR set `LITELLM_MASTER_KEY=".."` in your .env + database_url: postgres://.. # OR set `DATABASE_URL=".."` in your .env +``` + +2. Start proxy + +```bash +litellm --config /path/to/config.yaml +``` + +3. Test it! + +```python showLineNumbers title="test.py" +""" +- Run 2 concurrent teams calling same model +- model has 60 TPM +- Mock response returns 30 total tokens / request +- Each team will only be able to make 1 request per minute +""" + +import requests +from openai import OpenAI, RateLimitError + +def create_key(api_key: str, base_url: str): + response = requests.post( + url="{}/key/generate".format(base_url), + json={}, + headers={ + "Authorization": "Bearer {}".format(api_key) + } + ) + + _response = response.json() + + return _response["key"] + +key_1 = create_key(api_key="sk-1234", base_url="http://0.0.0.0:4000") +key_2 = create_key(api_key="sk-1234", base_url="http://0.0.0.0:4000") + +# call proxy with key 1 - works +openai_client_1 = OpenAI(api_key=key_1, base_url="http://0.0.0.0:4000") + +response = openai_client_1.chat.completions.with_raw_response.create( + model="my-fake-model", messages=[{"role": "user", "content": "Hello world!"}], +) + +print("Headers for call 1 - {}".format(response.headers)) +_response = response.parse() +print("Total tokens for call - {}".format(_response.usage.total_tokens)) + + +# call proxy with key 2 - works +openai_client_2 = OpenAI(api_key=key_2, base_url="http://0.0.0.0:4000") + +response = openai_client_2.chat.completions.with_raw_response.create( + model="my-fake-model", messages=[{"role": "user", "content": "Hello world!"}], +) + +print("Headers for call 2 - {}".format(response.headers)) +_response = response.parse() +print("Total tokens for call - {}".format(_response.usage.total_tokens)) +# call proxy with key 2 - fails +try: + openai_client_2.chat.completions.with_raw_response.create(model="my-fake-model", messages=[{"role": "user", "content": "Hey, how's it going?"}]) + raise Exception("This should have failed!") +except RateLimitError as e: + print("This was rate limited b/c - {}".format(str(e))) + +``` + +**Expected Response** + +``` +This was rate limited b/c - Error code: 429 - {'error': {'message': {'error': 'Key= over available TPM=0. Model TPM=0, Active keys=2'}, 'type': 'None', 'param': 'None', 'code': 429}} +``` + + +## [BETA] Set Priority / Reserve Quota + +Reserve TPM/RPM capacity for different environments or use cases. This ensures critical production workloads always have guaranteed capacity, while development or lower-priority tasks use remaining quota. + +**Use Cases:** +- Production vs Development environments +- Real-time applications vs batch processing +- Critical services vs experimental features + +:::tip + +Reserving TPM/RPM on keys based on priority is a premium feature. Please [get an enterprise license](./enterprise.md) for it. +::: + +### How Priority Reservation Works + +Priority reservation allocates a percentage of your model's total TPM/RPM to specific priority levels. Keys with higher priority get guaranteed access to their reserved quota first. + +**Example Scenario:** +- Model has 10 RPM total capacity +- Priority reservation: `{"prod": 0.9, "dev": 0.1}` +- Result: Production keys get 9 RPM guaranteed, Development keys get 1 RPM guaranteed + +### Configuration + +#### 1. Setup config.yaml + +```yaml showLineNumbers title="config.yaml" +model_list: + - model_name: gpt-3.5-turbo + litellm_params: + model: "gpt-3.5-turbo" + api_key: os.environ/OPENAI_API_KEY + rpm: 10 # Total model capacity + +litellm_settings: + callbacks: ["dynamic_rate_limiter_v3"] + priority_reservation: + "prod": 0.9 # 90% reserved for production (9 RPM) + "dev": 0.1 # 10% reserved for development (1 RPM) + priority_reservation_settings: + default_priority: 0 # Weight (0%) assigned to keys without explicit priority metadata + +general_settings: + master_key: sk-1234 # OR set `LITELLM_MASTER_KEY=".."` in your .env + database_url: postgres://.. # OR set `DATABASE_URL=".."` in your.env +``` + +**Configuration Details:** + +`priority_reservation`: Dict[str, float] +- **Key (str)**: Priority level name (can be any string like "prod", "dev", "critical", etc.) +- **Value (float)**: Percentage of total TPM/RPM to reserve (0.0 to 1.0) +- **Note**: Values should sum to 1.0 or less + +`priority_reservation_settings`: Object (Optional) +- **default_priority (float)**: Weight/percentage (0.0 to 1.0) assigned to API keys that have no priority metadata set (defaults to 0.5) + +**Start Proxy** + +```bash +litellm --config /path/to/config.yaml +``` + +#### 2. Create Keys with Priority Levels + +**Production Key:** +```bash +curl -X POST 'http://0.0.0.0:4000/key/generate' \ +-H 'Authorization: Bearer sk-1234' \ +-H 'Content-Type: application/json' \ +-d '{ + "metadata": {"priority": "prod"} +}' +``` + +**Development Key:** +```bash +curl -X POST 'http://0.0.0.0:4000/key/generate' \ +-H 'Authorization: Bearer sk-1234' \ +-H 'Content-Type: application/json' \ +-d '{ + "metadata": {"priority": "dev"} +}' +``` + +**Key Without Priority (uses default_priority weight):** +```bash +curl -X POST 'http://0.0.0.0:4000/key/generate' \ +-H 'Authorization: Bearer sk-1234' \ +-H 'Content-Type: application/json' \ +-d '{}' +``` + +**Expected Response for both:** +```json +{ + "key": "sk-...", + "metadata": {"priority": "prod"}, // or "dev" + ... +} +``` + +#### 3. Test Priority Allocation + +**Test Production Key (should get 9 RPM):** +```bash +curl -X POST 'http://0.0.0.0:4000/chat/completions' \ + -H 'Content-Type: application/json' \ + -H 'Authorization: Bearer sk-prod-key' \ + -d '{ + "model": "gpt-3.5-turbo", + "messages": [{"role": "user", "content": "Hello from prod"}] + }' +``` + +**Test Development Key (should get 1 RPM):** +```bash +curl -X POST 'http://0.0.0.0:4000/chat/completions' \ + -H 'Content-Type: application/json' \ + -H 'Authorization: Bearer sk-dev-key' \ + -d '{ + "model": "gpt-3.5-turbo", + "messages": [{"role": "user", "content": "Hello from dev"}] + }' +``` + +### Expected Behavior + +With the configuration above: + +1. **Production keys** can make up to 9 requests per minute (90% of 10 RPM) +2. **Development keys** can make up to 1 request per minute (10% of 10 RPM) +3. **Keys without explicit priority** get the default_priority weight (0 = 0%), which allocates 0 requests per minute (0% of 10 RPM) +4. Named priorities in `priority_reservation` and keys with `default_priority` operate independently + +**Rate Limit Error Example:** +```json +{ + "error": { + "message": "Key=sk-dev-... over available RPM=0. Model RPM=10, Reserved RPM for priority 'dev'=1, Active keys=1", + "type": "rate_limit_exceeded", + "code": 429 + } +} +``` + +### Demo Video + +This video walks through setting up dynamic rate limiting with priority reservation and locust tests to validate the behavior. + + + diff --git a/docs/my-website/docs/proxy/team_budgets.md b/docs/my-website/docs/proxy/team_budgets.md index 38474066411..03d18797133 100644 --- a/docs/my-website/docs/proxy/team_budgets.md +++ b/docs/my-website/docs/proxy/team_budgets.md @@ -178,188 +178,3 @@ Expect to see this metric on prometheus to track the Remaining Budget for the te ```shell litellm_remaining_team_budget_metric{team_alias="QA Prod Bot",team_id="de35b29e-6ca8-4f47-b804-2b79d07aa99a"} 9.699999999999992e-06 ``` - - -### Dynamic TPM/RPM Allocation - -Prevent projects from gobbling too much tpm/rpm. - -Dynamically allocate TPM/RPM quota to api keys, based on active keys in that minute. [**See Code**](https://github.com/BerriAI/litellm/blob/9bffa9a48e610cc6886fc2dce5c1815aeae2ad46/litellm/proxy/hooks/dynamic_rate_limiter.py#L125) - -1. Setup config.yaml - -```yaml -model_list: - - model_name: my-fake-model - litellm_params: - model: gpt-3.5-turbo - api_key: my-fake-key - mock_response: hello-world - tpm: 60 - -litellm_settings: - callbacks: ["dynamic_rate_limiter"] - -general_settings: - master_key: sk-1234 # OR set `LITELLM_MASTER_KEY=".."` in your .env - database_url: postgres://.. # OR set `DATABASE_URL=".."` in your .env -``` - -2. Start proxy - -```bash -litellm --config /path/to/config.yaml -``` - -3. Test it! - -```python -""" -- Run 2 concurrent teams calling same model -- model has 60 TPM -- Mock response returns 30 total tokens / request -- Each team will only be able to make 1 request per minute -""" - -import requests -from openai import OpenAI, RateLimitError - -def create_key(api_key: str, base_url: str): - response = requests.post( - url="{}/key/generate".format(base_url), - json={}, - headers={ - "Authorization": "Bearer {}".format(api_key) - } - ) - - _response = response.json() - - return _response["key"] - -key_1 = create_key(api_key="sk-1234", base_url="http://0.0.0.0:4000") -key_2 = create_key(api_key="sk-1234", base_url="http://0.0.0.0:4000") - -# call proxy with key 1 - works -openai_client_1 = OpenAI(api_key=key_1, base_url="http://0.0.0.0:4000") - -response = openai_client_1.chat.completions.with_raw_response.create( - model="my-fake-model", messages=[{"role": "user", "content": "Hello world!"}], -) - -print("Headers for call 1 - {}".format(response.headers)) -_response = response.parse() -print("Total tokens for call - {}".format(_response.usage.total_tokens)) - - -# call proxy with key 2 - works -openai_client_2 = OpenAI(api_key=key_2, base_url="http://0.0.0.0:4000") - -response = openai_client_2.chat.completions.with_raw_response.create( - model="my-fake-model", messages=[{"role": "user", "content": "Hello world!"}], -) - -print("Headers for call 2 - {}".format(response.headers)) -_response = response.parse() -print("Total tokens for call - {}".format(_response.usage.total_tokens)) -# call proxy with key 2 - fails -try: - openai_client_2.chat.completions.with_raw_response.create(model="my-fake-model", messages=[{"role": "user", "content": "Hey, how's it going?"}]) - raise Exception("This should have failed!") -except RateLimitError as e: - print("This was rate limited b/c - {}".format(str(e))) - -``` - -**Expected Response** - -``` -This was rate limited b/c - Error code: 429 - {'error': {'message': {'error': 'Key= over available TPM=0. Model TPM=0, Active keys=2'}, 'type': 'None', 'param': 'None', 'code': 429}} -``` - - -#### āœØ [BETA] Set Priority / Reserve Quota - -Reserve tpm/rpm capacity for projects in prod. - -:::tip - -Reserving tpm/rpm on keys based on priority is a premium feature. Please [get an enterprise license](./enterprise.md) for it. -::: - - -1. Setup config.yaml - -```yaml -model_list: - - model_name: gpt-3.5-turbo - litellm_params: - model: "gpt-3.5-turbo" - api_key: os.environ/OPENAI_API_KEY - rpm: 100 - -litellm_settings: - callbacks: ["dynamic_rate_limiter"] - priority_reservation: {"dev": 0, "prod": 1} - -general_settings: - master_key: sk-1234 # OR set `LITELLM_MASTER_KEY=".."` in your .env - database_url: postgres://.. # OR set `DATABASE_URL=".."` in your .env -``` - - -priority_reservation: -- Dict[str, float] - - str: can be any string - - float: from 0 to 1. Specify the % of tpm/rpm to reserve for keys of this priority. - -**Start Proxy** - -``` -litellm --config /path/to/config.yaml -``` - -2. Create a key with that priority - -```bash -curl -X POST 'http://0.0.0.0:4000/key/generate' \ --H 'Authorization: Bearer ' \ --H 'Content-Type: application/json' \ --D '{ - "metadata": {"priority": "dev"} # šŸ‘ˆ KEY CHANGE -}' -``` - -**Expected Response** - -``` -{ - ... - "key": "sk-.." -} -``` - - -3. Test it! - -```bash -curl -X POST 'http://0.0.0.0:4000/chat/completions' \ - -H 'Content-Type: application/json' \ - -H 'Authorization: sk-...' \ # šŸ‘ˆ key from step 2. - -d '{ - "model": "gpt-3.5-turbo", - "messages": [ - { - "role": "user", - "content": "what llm are you" - } - ], -}' -``` - -**Expected Response** - -``` -Key=... over available RPM=0. Model RPM=100, Active keys=None -``` - diff --git a/docs/my-website/docs/proxy/user_onboarding.md b/docs/my-website/docs/proxy/user_onboarding.md new file mode 100644 index 00000000000..baa241d6cdf --- /dev/null +++ b/docs/my-website/docs/proxy/user_onboarding.md @@ -0,0 +1,82 @@ +# User Onboarding Guide + +A step-by-step guide to help admins onboard users to your LiteLLM proxy instance and help users get started with their API key. + +--- + +## For Administrators + +### Step 1: Create a User Account + +You can create a user account via the Admin UI or using the API. + +#### Admin UI +- Go to the (`/ui` endpoint) +- Navigate to the Internal Users section +- Click "Add User" and fill in the required details + +#### API +```bash +curl -X POST http://localhost:4000/user/new \ + -H "Authorization: Bearer " \ + -H "Content-Type: application/json" \ + -d '{"user_email": "user@example.com"}' +``` + +--- + +### Step 2: Grant Access & Permissions + +- Assign the user to a team (optional) +- Set budgets, rate limits, and allowed models as needed +- Generate an API key for the user (via UI or API) + +#### **Generate API Key (API Example)** +```bash +curl -X POST http://localhost:4000/key/generate \ + -H "Authorization: Bearer " \ + -H "Content-Type: application/json" \ + -d '{"user_id": "", "max_budget": 100}' +``` + +--- + +## For End Users + +### Step 3: Validate Your API Key + +Before making LLM calls, validate your key works by calling the `/v1/models` endpoint: + +```bash +curl -X GET http://localhost:4000/v1/models \ + -H "Authorization: Bearer " +``` +- If your key is valid, you'll get a list of available models. +- If invalid, you'll get a 401 error. + +--- + +### Step 4: Hello World - Make Your First LLM Call + +```bash +curl -X POST http://localhost:4000/v1/chat/completions \ + -H "Authorization: Bearer " \ + -H "Content-Type: application/json" \ + -d '{ + "model": "gpt-3.5-turbo", + "messages": [{"role": "user", "content": "Hello!"}] + }' +``` + +--- + +## Troubleshooting +- If you get a 401 error, check with your admin that your key is active and you have access to the requested model. +- Use the `/v1/models` endpoint to quickly check if your key is valid without consuming LLM tokens. + +--- + +## See Also +- [Proxy Quick Start](./quick_start.md) +- [User Management](./users.md) +- [Key Management](./key_management.md) diff --git a/docs/my-website/release_notes/v1.76.0-stable/index.md b/docs/my-website/release_notes/v1.76.0-stable/index.md index 660c8cbcf02..d93568d49dc 100644 --- a/docs/my-website/release_notes/v1.76.0-stable/index.md +++ b/docs/my-website/release_notes/v1.76.0-stable/index.md @@ -1,5 +1,5 @@ --- -title: "[PRE-RELEASE]v1.76.0-stable - RPS Improvements" +title: "v1.76.0-stable - RPS Improvements" slug: "v1-76-0" date: 2025-08-23T10:00:00 authors: diff --git a/docs/my-website/release_notes/v1.77.2-stable/index.md b/docs/my-website/release_notes/v1.77.2-stable/index.md index 6d54db84df4..fdd80693d05 100644 --- a/docs/my-website/release_notes/v1.77.2-stable/index.md +++ b/docs/my-website/release_notes/v1.77.2-stable/index.md @@ -1,5 +1,5 @@ --- -title: "[Pre-Release] v1.77.2-stable - Bedrock Batches API" +title: "v1.77.2-stable - Bedrock Batches API" slug: "v1-77-2" date: 2025-09-13T10:00:00 authors: @@ -21,22 +21,22 @@ import TabItem from '@theme/TabItem'; ## Deploy this version -:::info - -This release is not yet live. - -::: - ``` showLineNumbers title="docker run litellm" +docker run \ +-e STORE_MODEL_IN_DB=True \ +-p 4000:4000 \ +ghcr.io/berriai/litellm:main-v1.77.2-stable ``` + ``` showLineNumbers title="pip install litellm" +pip install litellm==1.77.2.post1 ``` diff --git a/docs/my-website/release_notes/v1.77.3-stable/index.md b/docs/my-website/release_notes/v1.77.3-stable/index.md new file mode 100644 index 00000000000..adea34f9782 --- /dev/null +++ b/docs/my-website/release_notes/v1.77.3-stable/index.md @@ -0,0 +1,258 @@ +--- +title: "[Preview] v1.77.3-stable - Priority Based Rate Limiting" +slug: "v1-77-3" +date: 2025-09-21T10:00:00 +authors: + - name: Krrish Dholakia + title: CEO, LiteLLM + url: https://www.linkedin.com/in/krish-d/ + image_url: https://pbs.twimg.com/profile_images/1298587542745358340/DZv3Oj-h_400x400.jpg + - name: Ishaan Jaff + title: CTO, LiteLLM + url: https://www.linkedin.com/in/reffajnaahsi/ + image_url: https://pbs.twimg.com/profile_images/1613813310264340481/lz54oEiB_400x400.jpg + +hide_table_of_contents: false +--- + +import Image from '@theme/IdealImage'; +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +## Deploy this version + + + + +``` showLineNumbers title="docker run litellm" +docker run \ +-e STORE_MODEL_IN_DB=True \ +-p 4000:4000 \ +ghcr.io/berriai/litellm:main-v1.77.3.rc.1 +``` + + + + + +``` showLineNumbers title="pip install litellm" +pip install litellm==1.77.3 +``` + + + + +--- + +## Key Highlights + +- **+550 RPS Performance Improvements** - Optimizations in request handling and object initialization. +- **Priority Quota Reservation** - Proxy admins can now reserve TPM/RPM capacity for specific keys. + +## Priority Quota Reservation + +This release adds support for priority quota reservation. This allows **Proxy Admins** to reserve TPM/RPM capacity for keys based on metadata priority levels, ensuring critical production workloads get guaranteed access regardless of development traffic volume. + +Get started [here](../../docs/proxy/dynamic_rate_limit#priority-quota-reservation) + + + + +## New Models / Updated Models + +#### New Model Support + +| Provider | Model | Context Window | Input ($/1M tokens) | Output ($/1M tokens) | Features | +| -------- | ----- | -------------- | ------------------- | -------------------- | -------- | +| SambaNova | `sambanova/deepseek-v3.1` | 128K | $0.90 | $0.90 | Chat completions | +| SambaNova | `sambanova/gpt-oss-120b` | 128K | $0.72 | $0.72 | Chat completions | +| OVHCloud | Various models | Varies | Contact provider | Contact provider | Chat completions | +| CompactifAI | Various models | Varies | Contact provider | Contact provider | Chat completions | +| TwelveLabs | `twelvelabs/marengo-embed-2.7` | 32K | $0.12 | $0.00 | Embeddings | + +#### Features + +- **[OVHCloud AI Endpoints](../../docs/providers/ovhcloud)** + - New provider support with comprehensive model catalog - [PR #14494](https://github.com/BerriAI/litellm/pull/14494) +- **[CompactifAI](../../docs/providers/compactifai)** + - New provider integration - [PR #14532](https://github.com/BerriAI/litellm/pull/14532) +- **[SambaNova](../../docs/providers/sambanova)** + - Added DeepSeek v3.1 and GPT-OSS-120B models - [PR #14500](https://github.com/BerriAI/litellm/pull/14500) +- **[Bedrock](../../docs/providers/bedrock)** + - Cross-region inference profile cost calculation - [PR #14566](https://github.com/BerriAI/litellm/pull/14566) + - AWS external ID parameter support for authentication - [PR #14582](https://github.com/BerriAI/litellm/pull/14582) + - CountTokens API implementation - [PR #14557](https://github.com/BerriAI/litellm/pull/14557) + - Titan V2 encoding_format parameter support - [PR #14687](https://github.com/BerriAI/litellm/pull/14687) + - Nova Canvas image generation inference profiles - [PR #14578](https://github.com/BerriAI/litellm/pull/14578) + - Bedrock Batches API - batch processing support with file upload and request transformation - [PR #14618](https://github.com/BerriAI/litellm/pull/14618) + - Bedrock Twelve Labs embedding provider support - [PR #14697](https://github.com/BerriAI/litellm/pull/14697) +- **[Vertex AI](../../docs/providers/vertex)** + - Gemini labels field provider-aware filtering - [PR #14563](https://github.com/BerriAI/litellm/pull/14563) + - Gemini Batch API support - [PR #14733](https://github.com/BerriAI/litellm/pull/14733) +- **[Volcengine](../../docs/providers/volcengine)** + - Fixed thinking parameters when disabled - [PR #14569](https://github.com/BerriAI/litellm/pull/14569) +- **[Cohere](../../docs/providers/cohere)** + - Handle Generate API deprecation, default to chat endpoints - [PR #14676](https://github.com/BerriAI/litellm/pull/14676) +- **[TwelveLabs](../../docs/providers/twelvelabs)** + - Added Marengo Embed 2.7 embedding support - [PR #14674](https://github.com/BerriAI/litellm/pull/14674) + +### Bug Fixes + +- **[Bedrock](../../docs/providers/bedrock)** + - Empty arguments handling in tool call invocation - [PR #14583](https://github.com/BerriAI/litellm/pull/14583) +- **[Vertex AI](../../docs/providers/vertex)** + - Avoid deepcopy crash with non-pickleables in Gemini/Vertex - [PR #14418](https://github.com/BerriAI/litellm/pull/14418) +- **[XAI](../../docs/providers/xai)** + - Fix unsupported stop parameter for grok-code models - [PR #14565](https://github.com/BerriAI/litellm/pull/14565) +- **[Gemini](../../docs/providers/gemini)** + - Updated error message for Gemini API - [PR #14589](https://github.com/BerriAI/litellm/pull/14589) + - Fixed 2.5 Flash Image Preview model routing - [PR #14715](https://github.com/BerriAI/litellm/pull/14715) + - API key passing for token counting endpoints - [PR #14744](https://github.com/BerriAI/litellm/pull/14744) + +#### New Provider Support + +- **[OVHCloud AI Endpoints](../../docs/providers/ovhcloud)** + - Complete provider integration with model catalog and authentication - [PR #14494](https://github.com/BerriAI/litellm/pull/14494) +- **[CompactifAI](../../docs/providers/compactifai)** + - New provider support with documentation - [PR #14532](https://github.com/BerriAI/litellm/pull/14532) + +--- + +## LLM API Endpoints + +#### Features + +- **[/responses](../../docs/response_api)** + - Added cancel endpoint support for non-admin users - [PR #14594](https://github.com/BerriAI/litellm/pull/14594) + - Improved response session handling and cold storage configuration with s3 - [PR #14534](https://github.com/BerriAI/litellm/pull/14534) + - Added OpenAI & Azure /responses/cancel endpoint support - [PR #14561](https://github.com/BerriAI/litellm/pull/14561) +- **General** + - Enhanced rate limit error messages with details - [PR #14736](https://github.com/BerriAI/litellm/pull/14736) + - Middle-truncation for spend log payloads - [PR #14637](https://github.com/BerriAI/litellm/pull/14637) + +#### Bugs + +- **[/chat/completions](../../docs/completion/input)** + - Fixed completion chat ID handling - [PR #14548](https://github.com/BerriAI/litellm/pull/14548) + - Prevent AttributeError for _get_tags_from_request_kwargs - [PR #14735](https://github.com/BerriAI/litellm/pull/14735) +- **[/responses](../../docs/response_api)** + - Fixed cost calculation - [PR #14675](https://github.com/BerriAI/litellm/pull/14675) +- **General** + - Rate limiter AttributeError fix - [PR #14609](https://github.com/BerriAI/litellm/pull/14609) + +--- + +## Spend Tracking, Budgets and Rate Limiting + +- **Responses API Cost Calculation** fix - [PR #14675](https://github.com/BerriAI/litellm/pull/14675) +- **Anthropic Cache Token Pricing** - Separate 1-hour vs 5-minute cache creation costs - [PR #14620](https://github.com/BerriAI/litellm/pull/14620), [PR #14652](https://github.com/BerriAI/litellm/pull/14652) +- **Indochina Time Timezone** support for budget resets - [PR #14666](https://github.com/BerriAI/litellm/pull/14666) +- **Soft Budget Alert Cache Issues** - Resolved soft budget alert cache issues - [PR #14491](https://github.com/BerriAI/litellm/pull/14491) +- **Dynamic Rate Limiter v3** - Priority routing improvements - [PR #14734](https://github.com/BerriAI/litellm/pull/14734) +- **Enhanced Rate Limit Errors** - More detailed error messages - [PR #14736](https://github.com/BerriAI/litellm/pull/14736) + +--- + +## Management Endpoints / UI + +#### Features + +- **Team Member Service Account Keys** - Allow team members to view keys they create - [PR #14619](https://github.com/BerriAI/litellm/pull/14619) +- **Default Budget for JWT Teams** - Auto-assign budgets to generated teams - [PR #14514](https://github.com/BerriAI/litellm/pull/14514) +- **SSO Access Control Groups** - Enhanced token info endpoint integration - [PR #14738](https://github.com/BerriAI/litellm/pull/14738) +- **Health Test Connect Protection** - Restrict access based on model creation permissions - [PR #14650](https://github.com/BerriAI/litellm/pull/14650) +- **Amazon Bedrock Guardrail Info View** - Enhanced logging visualization - [PR #14696](https://github.com/BerriAI/litellm/pull/14696) + +#### Bug Fixes + +- **SCIM v2** - Fix group PUSH and PUT operations for non-existent members - [PR #14581](https://github.com/BerriAI/litellm/pull/14581) +- **Guardrail View/Edit/Delete** behavior fixes - [PR #14622](https://github.com/BerriAI/litellm/pull/14622) +- **In-Memory Guardrail** update failures - [PR #14653](https://github.com/BerriAI/litellm/pull/14653) + +--- + +## Logging / Guardrail Integrations + +#### Features + +- **[DataDog](../../docs/proxy/logging#datadog)** + - Enhanced spend tracking metrics - [PR #14555](https://github.com/BerriAI/litellm/pull/14555) + - Stream support with is_streamed_request parameter - [PR #14673](https://github.com/BerriAI/litellm/pull/14673) + - Fixed tool calls metadata passing - [PR #14531](https://github.com/BerriAI/litellm/pull/14531) +- **[Langfuse](../../docs/proxy/logging#langfuse)** + - Added logging support for Responses API - [PR #14597](https://github.com/BerriAI/litellm/pull/14597) +- **[Langsmith](../../docs/proxy/logging#langsmith)** + - Langsmith Sampling Rate - Key/Team-level tracing configuration - [PR #14740](https://github.com/BerriAI/litellm/pull/14740) +- **[Prometheus](../../docs/proxy/logging#prometheus)** + - Multi-worker support improvements - [PR #14530](https://github.com/BerriAI/litellm/pull/14530) + - User email labels in monitoring - [PR #14520](https://github.com/BerriAI/litellm/pull/14520) +- **[Opik](../../docs/proxy/logging#opik)** + - Fixed timezone issue - [PR #14708](https://github.com/BerriAI/litellm/pull/14708) + +### Bug Fixes + +- **[S3](../../docs/proxy/logging#s3-buckets)** + - Fixed 404 error when using s3_endpoint_url - [PR #14559](https://github.com/BerriAI/litellm/pull/14559) + +#### Guardrails + +- **Tool Permission Guardrail** - Fine-grained tool access control - [PR #14519](https://github.com/BerriAI/litellm/pull/14519) +- **Bedrock Guardrails** - Selective guarding support with runtime endpoint configuration - [PR #14575](https://github.com/BerriAI/litellm/pull/14575), [PR #14650](https://github.com/BerriAI/litellm/pull/14650) +- **Default Last Message** in guardrails - [PR #14640](https://github.com/BerriAI/litellm/pull/14640) +- **AWS exceptions handling despite 200 response** - [PR #14658](https://github.com/BerriAI/litellm/pull/14658) +#### New Integration + +- **[PostHog](../../docs/observability/posthog)** - Complete observability integration for LiteLLM usage tracking and analytics - [PR #14610](https://github.com/BerriAI/litellm/pull/14610) + +--- + + +## MCP Gateway + +- **MCP Server Alias Parsing** - Multi-part URL path support - [PR #14558](https://github.com/BerriAI/litellm/pull/14558) +- **MCP Filter Recomputation** - After server deletion - [PR #14542](https://github.com/BerriAI/litellm/pull/14542) +- **MCP Gateway Tools List** improvements - [PR #14695](https://github.com/BerriAI/litellm/pull/14695) + +--- + +## Performance / Loadbalancing / Reliability improvements + +- **+500 RPS Performance Boost** when sending the `user` field - [PR #14616](https://github.com/BerriAI/litellm/pull/14616) +- **+50 RPS** by removing iscoroutine from hot path - [PR #14649](https://github.com/BerriAI/litellm/pull/14649) +- **7% reduction** in __init__ overhead - [PR #14689](https://github.com/BerriAI/litellm/pull/14689) +- **Generic Object Pool** implementation for better resource management - [PR #14702](https://github.com/BerriAI/litellm/pull/14702) + +--- + +## General Proxy Improvements + +- **Middle-Truncation** for spend log payloads - [PR #14637](https://github.com/BerriAI/litellm/pull/14637) + +#### Security + +- **Security Update** - Bump aiohttp==3.12.14, fix CVE-2025-53643 - [PR #14638](https://github.com/BerriAI/litellm/pull/14638) + +--- + +## New Contributors + +* @luisfucros made their first contribution in [PR #14500](https://github.com/BerriAI/litellm/pull/14500) +* @hanakannzashi made their first contribution in [PR #14548](https://github.com/BerriAI/litellm/pull/14548) +* @eliasto made their first contribution in [PR #14494](https://github.com/BerriAI/litellm/pull/14494) +* @Rasmusafj made their first contribution in [PR #14491](https://github.com/BerriAI/litellm/pull/14491) +* @LingXuanYin made their first contribution in [PR #14569](https://github.com/BerriAI/litellm/pull/14569) +* @ronaldpereira made their first contribution in [PR #14613](https://github.com/BerriAI/litellm/pull/14613) +* @hula-la made their first contribution in [PR #14534](https://github.com/BerriAI/litellm/pull/14534) +* @carlos-marchal-ph made their first contribution in [PR #14610](https://github.com/BerriAI/litellm/pull/14610) +* @akraines made their first contribution in [PR #14637](https://github.com/BerriAI/litellm/pull/14637) +* @mrFranklin made their first contribution in [PR #14708](https://github.com/BerriAI/litellm/pull/14708) +* @tcx4c70 made their first contribution in [PR #14675](https://github.com/BerriAI/litellm/pull/14675) +* @michaeltansg made their first contribution in [PR #14666](https://github.com/BerriAI/litellm/pull/14666) +* @tosi29 made their first contribution in [PR #14725](https://github.com/BerriAI/litellm/pull/14725) +* @gmdfalk made their first contribution in [PR #14735](https://github.com/BerriAI/litellm/pull/14735) +* @FelipeRodriguesGare made their first contribution in [PR #14733](https://github.com/BerriAI/litellm/pull/14733) +* @mritunjaysharma394 made their first contribution in [PR #14678](https://github.com/BerriAI/litellm/pull/14678) + +--- + +## **[Full Changelog](https://github.com/BerriAI/litellm/compare/v1.77.2.rc.1...v1.77.3.rc.1)** diff --git a/docs/my-website/sidebars.js b/docs/my-website/sidebars.js index ae6071b16d8..a131e5c34ec 100644 --- a/docs/my-website/sidebars.js +++ b/docs/my-website/sidebars.js @@ -75,6 +75,7 @@ const sidebars = { type: "category", label: "AI Tools (OpenWebUI, Claude Code, etc.)", items: [ + "integrations/letta", "tutorials/openweb_ui", "tutorials/openai_codex", "tutorials/litellm_gemini_cli", @@ -111,6 +112,7 @@ const sidebars = { label: "Setup & Deployment", items: [ "proxy/quick_start", + "proxy/user_onboarding", "proxy/deploy", "proxy/prod", "proxy/cli", @@ -132,7 +134,6 @@ const sidebars = { label: "All Endpoints (Swagger)", href: "https://litellm-api.up.railway.app/", }, - "proxy/enterprise", "proxy/management_cli", { type: "category", @@ -154,11 +155,9 @@ const sidebars = { "proxy/token_auth", "proxy/service_accounts", "proxy/access_control", - "proxy/cli_sso", - "proxy/custom_auth", "proxy/ip_address", "proxy/email", - "proxy/multiple_admins", + "proxy/custom_auth", ], }, { @@ -169,30 +168,6 @@ const sidebars = { "proxy/team_model_add" ] }, - { - type: "category", - label: "Admin UI", - items: [ - "proxy/ui", - "proxy/admin_ui_sso", - "proxy/custom_root_ui", - "proxy/model_hub", - "proxy/self_serve", - "proxy/public_teams", - "tutorials/scim_litellm", - "proxy/custom_sso", - "proxy/ui_credentials", - "proxy/ui/bulk_edit_users", - { - type: "category", - label: "UI Logs", - items: [ - "proxy/ui_logs", - "proxy/ui_logs_sessions" - ] - } - ], - }, { type: "category", label: "Spend Tracking", @@ -201,7 +176,48 @@ const sidebars = { { type: "category", label: "Budgets + Rate Limits", - items: ["proxy/users", "proxy/temporary_budget_increase", "proxy/rate_limit_tiers", "proxy/team_budgets", "proxy/customers"], + items: ["proxy/users", "proxy/temporary_budget_increase", "proxy/rate_limit_tiers", "proxy/team_budgets", "proxy/dynamic_rate_limit", "proxy/customers"], + }, + { + type: "category", + label: "Enterprise Features", + items: [ + "proxy/enterprise", + { + type: "category", + label: "Admin UI", + items: [ + "proxy/ui", + "proxy/admin_ui_sso", + "proxy/custom_root_ui", + "proxy/model_hub", + "proxy/self_serve", + "proxy/public_teams", + "proxy/ui_credentials", + "proxy/ui/bulk_edit_users", + { + type: "category", + label: "UI Logs", + items: [ + "proxy/ui_logs", + "proxy/ui_logs_sessions" + ] + } + ], + }, + { + type: "category", + label: "SSO & Identity Management", + items: [ + "proxy/cli_sso", + "proxy/admin_ui_sso", + "proxy/custom_sso", + "tutorials/scim_litellm", + "tutorials/msft_sso", + "proxy/multiple_admins", + ], + }, + ], }, { type: "link", @@ -250,6 +266,25 @@ const sidebars = { slug: "/supported_endpoints", }, items: [ + "anthropic_unified", + "apply_guardrail", + "assistants", + { + type: "category", + label: "/audio", + "items": [ + "audio_transcription", + "text_to_speech", + ] + }, + { + type: "category", + label: "/batches", + items: [ + "batches", + "proxy/managed_batches", + ] + }, { type: "category", label: "/chat/completions", @@ -266,11 +301,23 @@ const sidebars = { "completion/http_handler_config", ], }, - "response_api", - "text_completion", "embedding/supported_embedding", - "anthropic_unified", - "mcp", + { + type: "category", + label: "/files", + items: [ + "files_endpoints", + "proxy/litellm_managed_files", + ], + }, + { + type: "category", + label: "/fine_tuning", + items: [ + "fine_tuning", + "proxy/managed_finetuning", + ] + }, "generateContent", { type: "category", @@ -281,21 +328,8 @@ const sidebars = { "image_variations", ] }, - { - type: "category", - label: "/audio", - "items": [ - "audio_transcription", - "text_to_speech", - ] - }, - { - type: "category", - label: "/vector_stores", - items: [ - "vector_stores/search", - ] - }, + "mcp", + "moderation", { type: "category", label: "Pass-through Endpoints (Anthropic SDK, etc.)", @@ -314,36 +348,17 @@ const sidebars = { "proxy/pass_through", ], }, - "rerank", - "assistants", - - { - type: "category", - label: "/files", - items: [ - "files_endpoints", - "proxy/litellm_managed_files", - ], - }, - { - type: "category", - label: "/batches", - items: [ - "batches", - "proxy/managed_batches", - ] - }, "realtime", + "rerank", + "response_api", + "text_completion", { type: "category", - label: "/fine_tuning", + label: "/vector_stores", items: [ - "fine_tuning", - "proxy/managed_finetuning", + "vector_stores/search", ] }, - "moderation", - "apply_guardrail", ], }, { @@ -383,6 +398,7 @@ const sidebars = { items: [ "providers/azure_ai", "providers/azure_ai_img", + "providers/azure_ai_img_edit", ] }, { @@ -392,6 +408,7 @@ const sidebars = { "providers/vertex", "providers/vertex_partner", "providers/vertex_image", + "providers/vertex_batch", ] }, { @@ -523,6 +540,7 @@ const sidebars = { "completion/batching", "completion/mock_requests", "completion/reliable_completions", + "proxy/veo_video_generation", ] }, @@ -544,6 +562,7 @@ const sidebars = { items: [ "set_keys", "completion/token_usage", + "sdk/headers", "sdk_custom_pricing", "embedding/async_embedding", "embedding/moderation", diff --git a/enterprise/litellm_enterprise/integrations/prometheus.py b/enterprise/litellm_enterprise/integrations/prometheus.py index 4a1e8d75435..4451d76bed0 100644 --- a/enterprise/litellm_enterprise/integrations/prometheus.py +++ b/enterprise/litellm_enterprise/integrations/prometheus.py @@ -2328,7 +2328,6 @@ def get_custom_labels_from_tags(tags: List[str]) -> Dict[str, str]: "tag_Service_web_app_v1": "false", } """ - import re from litellm.router_utils.pattern_match_deployments import PatternMatchRouter from litellm.types.integrations.prometheus import _sanitize_prometheus_label_name diff --git a/enterprise/litellm_enterprise/proxy/management_endpoints/internal_user_endpoints.py b/enterprise/litellm_enterprise/proxy/management_endpoints/internal_user_endpoints.py index e60b4d69905..2f53f9e9281 100644 --- a/enterprise/litellm_enterprise/proxy/management_endpoints/internal_user_endpoints.py +++ b/enterprise/litellm_enterprise/proxy/management_endpoints/internal_user_endpoints.py @@ -2,7 +2,6 @@ Enterprise internal user management endpoints """ -import os from fastapi import APIRouter, Depends, HTTPException diff --git a/enterprise/litellm_enterprise/proxy/vector_stores/endpoints.py b/enterprise/litellm_enterprise/proxy/vector_stores/endpoints.py index 43bdfa3844f..bb4b546b8d3 100644 --- a/enterprise/litellm_enterprise/proxy/vector_stores/endpoints.py +++ b/enterprise/litellm_enterprise/proxy/vector_stores/endpoints.py @@ -11,7 +11,7 @@ All /vector_store management endpoints import copy from typing import List, Optional -from fastapi import APIRouter, Depends, HTTPException, Request, Response +from fastapi import APIRouter, Depends, HTTPException import litellm from litellm._logging import verbose_proxy_logger diff --git a/git_model_armor.py b/git_model_armor.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.19-py3-none-any.whl b/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.19-py3-none-any.whl new file mode 100644 index 00000000000..c035bb44215 Binary files /dev/null and b/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.19-py3-none-any.whl differ diff --git a/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.19.tar.gz b/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.19.tar.gz new file mode 100644 index 00000000000..85069c622b0 Binary files /dev/null and b/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.19.tar.gz differ diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20250918083359_drop_spec_version_column_from_mcp_table/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20250918083359_drop_spec_version_column_from_mcp_table/migration.sql new file mode 100644 index 00000000000..5686876b37c --- /dev/null +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20250918083359_drop_spec_version_column_from_mcp_table/migration.sql @@ -0,0 +1,8 @@ +/* + Warnings: + + - You are about to drop the column `spec_version` on the `LiteLLM_MCPServerTable` table. All the data in the column will be lost. + +*/ +-- AlterTable +ALTER TABLE "public"."LiteLLM_MCPServerTable" DROP COLUMN "spec_version"; diff --git a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma index b8f2201d6b5..2b1e20820f9 100644 --- a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma +++ b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma @@ -171,7 +171,6 @@ model LiteLLM_MCPServerTable { description String? url String? transport String @default("sse") - spec_version String @default("2025-03-26") auth_type String? created_at DateTime? @default(now()) @map("created_at") created_by String? diff --git a/litellm-proxy-extras/pyproject.toml b/litellm-proxy-extras/pyproject.toml index 0cb9c35fa62..1c368d58077 100644 --- a/litellm-proxy-extras/pyproject.toml +++ b/litellm-proxy-extras/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "litellm-proxy-extras" -version = "0.2.18" +version = "0.2.19" description = "Additional files for the LiteLLM Proxy. Reduces the size of the main litellm package." authors = ["BerriAI"] readme = "README.md" @@ -22,7 +22,7 @@ requires = ["poetry-core"] build-backend = "poetry.core.masonry.api" [tool.commitizen] -version = "0.2.18" +version = "0.2.19" version_files = [ "pyproject.toml:version", "../requirements.txt:litellm-proxy-extras==", diff --git a/litellm/__init__.py b/litellm/__init__.py index 3c1d6e06966..523ee36b865 100644 --- a/litellm/__init__.py +++ b/litellm/__init__.py @@ -60,6 +60,7 @@ from litellm.constants import ( empower_models, together_ai_models, baseten_models, + WANDB_MODELS, REPEATED_STREAMING_CHUNK_LIMIT, request_timeout, open_ai_embedding_models, @@ -88,6 +89,7 @@ from litellm.types.proxy.management_endpoints.ui_sso import ( LiteLLM_UpperboundKeyGenerateParams, ) from litellm.types.utils import StandardKeyGenerationConfig, LlmProviders +from litellm.types.utils import PriorityReservationSettings from litellm.integrations.custom_logger import CustomLogger from litellm.litellm_core_utils.logging_callback_manager import LoggingCallbackManager import httpx @@ -117,6 +119,7 @@ _custom_logger_compatible_callbacks_literal = Literal[ "logfire", "literalai", "dynamic_rate_limiter", + "dynamic_rate_limiter_v3", "langsmith", "prometheus", "otel", @@ -241,6 +244,7 @@ novita_api_key: Optional[str] = None snowflake_key: Optional[str] = None gradient_ai_api_key: Optional[str] = None nebius_key: Optional[str] = None +wandb_key: Optional[str] = None heroku_key: Optional[str] = None cometapi_key: Optional[str] = None ovhcloud_key: Optional[str] = None @@ -370,6 +374,7 @@ public_model_groups: Optional[List[str]] = None public_model_groups_links: Dict[str, str] = {} #### REQUEST PRIORITIZATION ###### priority_reservation: Optional[Dict[str, float]] = None +priority_reservation_settings: "PriorityReservationSettings" = PriorityReservationSettings() ######## Networking Settings ######## @@ -523,6 +528,7 @@ cometapi_models: Set = set() oci_models: Set = set() vercel_ai_gateway_models: Set = set() volcengine_models: Set = set() +wandb_models: Set = set(WANDB_MODELS) ovhcloud_models: Set = set() ovhcloud_embedding_models: Set = set() @@ -739,6 +745,8 @@ def add_known_models(): oci_models.add(key) elif value.get("litellm_provider") == "volcengine": volcengine_models.add(key) + elif value.get("litellm_provider") == "wandb": + wandb_models.add(key) elif value.get("litellm_provider") == "ovhcloud": ovhcloud_models.add(key) elif value.get("litellm_provider") == "ovhcloud-embedding-models": @@ -837,6 +845,7 @@ model_list = list( | heroku_models | vercel_ai_gateway_models | volcengine_models + | wandb_models | ovhcloud_models ) @@ -919,6 +928,7 @@ models_by_provider: dict = { "cometapi": cometapi_models, "oci": oci_models, "volcengine": volcengine_models, + "wandb": wandb_models, "ovhcloud": ovhcloud_models | ovhcloud_embedding_models, } @@ -1258,6 +1268,7 @@ from .llms.watsonx.chat.transformation import IBMWatsonXChatConfig from .llms.watsonx.embed.transformation import IBMWatsonXEmbeddingConfig from .llms.github_copilot.chat.transformation import GithubCopilotConfig from .llms.nebius.chat.transformation import NebiusConfig +from .llms.wandb.chat.transformation import WandbConfig from .llms.dashscope.chat.transformation import DashScopeChatConfig from .llms.moonshot.chat.transformation import MoonshotChatConfig from .llms.v0.chat.transformation import V0ChatConfig @@ -1334,5 +1345,8 @@ disable_hf_tokenizer_download: Optional[bool] = ( ) global_disable_no_log_param: bool = False +### CLI UTILITIES ### +from litellm.litellm_core_utils.cli_token_utils import get_litellm_gateway_api_key + ### PASSTHROUGH ### from .passthrough import allm_passthrough_route, llm_passthrough_route diff --git a/litellm/_uuid.py b/litellm/_uuid.py new file mode 100644 index 00000000000..05b1adbf75b --- /dev/null +++ b/litellm/_uuid.py @@ -0,0 +1,23 @@ +""" +Internal unified UUID helper. + +Tries to use fastuuid (performance) and falls back to stdlib uuid if unavailable. +""" + +FASTUUID_AVAILABLE = False + +try: + import fastuuid as _uuid # type: ignore + + FASTUUID_AVAILABLE = True +except Exception: # pragma: no cover - fallback path + import uuid as _uuid # type: ignore + + +# Expose a module-like alias so callers can use: uuid.uuid4() +uuid = _uuid + + +def uuid4(): + """Return a UUID4 using the selected backend.""" + return uuid.uuid4() diff --git a/litellm/constants.py b/litellm/constants.py index 9b44613b855..005eb2bb6d0 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -313,6 +313,7 @@ LITELLM_CHAT_PROVIDERS = [ "morph", "lambda_ai", "vercel_ai_gateway", + "wandb", "ovhcloud", ] @@ -448,6 +449,7 @@ openai_compatible_endpoints: List = [ "https://api.lambda.ai/v1", "https://api.hyperbolic.xyz/v1", "https://ai-gateway.vercel.sh/v1", + "https://api.inference.wandb.ai/v1", ] @@ -492,6 +494,7 @@ openai_compatible_providers: List = [ "hyperbolic", "vercel_ai_gateway", "aiml", + "wandb", ] openai_text_completion_compatible_providers: List = ( [ # providers that support `/v1/completions` @@ -507,6 +510,7 @@ openai_text_completion_compatible_providers: List = ( "v0", "lambda_ai", "hyperbolic", + "wandb", ] ) _openai_like_providers: List = [ @@ -757,6 +761,38 @@ nebius_embedding_models: set = set( ] ) +WANDB_MODELS: set = set( + [ + # openai models + "openai/gpt-oss-120b", + "openai/gpt-oss-20b", + + # zai-org models + "zai-org/GLM-4.5", + + # Qwen models + "Qwen/Qwen3-235B-A22B-Instruct-2507", + "Qwen/Qwen3-Coder-480B-A35B-Instruct", + "Qwen/Qwen3-235B-A22B-Thinking-2507", + + # moonshotai + "moonshotai/Kimi-K2-Instruct", + + # meta models + "meta-llama/Llama-3.1-8B-Instruct", + "meta-llama/Llama-3.3-70B-Instruct", + "meta-llama/Llama-4-Scout-17B-16E-Instruct", + + # deepseek-ai + "deepseek-ai/DeepSeek-V3.1", + "deepseek-ai/DeepSeek-R1-0528", + "deepseek-ai/DeepSeek-V3-0324", + + # microsoft + "microsoft/Phi-4-mini-instruct", + ] +) + BEDROCK_INVOKE_PROVIDERS_LITERAL = Literal[ "cohere", "anthropic", @@ -947,6 +983,7 @@ HEALTH_CHECK_TIMEOUT_SECONDS = int( os.getenv("HEALTH_CHECK_TIMEOUT_SECONDS", 60) ) # 60 seconds LITTELM_INTERNAL_HEALTH_SERVICE_ACCOUNT_NAME = "litellm-internal-health-check" +LITTELM_CLI_SERVICE_ACCOUNT_NAME = "litellm-cli" UI_SESSION_TOKEN_TEAM_ID = "litellm-dashboard" LITELLM_PROXY_ADMIN_NAME = "default_user_id" diff --git a/litellm/cost_calculator.py b/litellm/cost_calculator.py index 5d8f5faadf1..36a562b3574 100644 --- a/litellm/cost_calculator.py +++ b/litellm/cost_calculator.py @@ -148,6 +148,8 @@ def cost_per_token( # noqa: PLR0915 ### CALL TYPE ### call_type: CallTypesLiteral = "completion", audio_transcription_file_duration: float = 0.0, # for audio transcription calls - the file time in seconds + ### SERVICE TIER ### + service_tier: Optional[str] = None, # for OpenAI service tier pricing ) -> Tuple[float, float]: # type: ignore """ Calculates the cost per token for a given model, prompt tokens, and completion tokens. @@ -278,6 +280,7 @@ def cost_per_token( # noqa: PLR0915 model=model_without_prefix, usage=usage_block, custom_llm_provider=custom_llm_provider, + service_tier=service_tier, ) return prompt_cost, completion_cost @@ -327,7 +330,7 @@ def cost_per_token( # noqa: PLR0915 elif custom_llm_provider == "bedrock": return bedrock_cost_per_token(model=model, usage=usage_block) elif custom_llm_provider == "openai": - return openai_cost_per_token(model=model, usage=usage_block) + return openai_cost_per_token(model=model, usage=usage_block, service_tier=service_tier) elif custom_llm_provider == "databricks": return databricks_cost_per_token(model=model, usage=usage_block) elif custom_llm_provider == "fireworks_ai": @@ -606,6 +609,8 @@ def completion_cost( # noqa: PLR0915 litellm_model_name: Optional[str] = None, router_model_id: Optional[str] = None, litellm_logging_obj: Optional[LitellmLoggingObject] = None, + ### SERVICE TIER ### + service_tier: Optional[str] = None, # for OpenAI service tier pricing ) -> float: """ Calculate the cost of a given completion call fot GPT-3.5-turbo, llama2, any litellm supported llm. @@ -658,6 +663,10 @@ def completion_cost( # noqa: PLR0915 completion_response=completion_response ) rerank_billed_units: Optional[RerankBilledUnits] = None + + # Extract service_tier from optional_params if not provided directly + if service_tier is None and optional_params is not None: + service_tier = optional_params.get("service_tier") selected_model = _select_model_name_for_cost_calc( model=model, @@ -909,6 +918,7 @@ def completion_cost( # noqa: PLR0915 call_type=cast(CallTypesLiteral, call_type), audio_transcription_file_duration=audio_transcription_file_duration, rerank_billed_units=rerank_billed_units, + service_tier=service_tier, ) _final_cost = ( prompt_tokens_cost_usd_dollar + completion_tokens_cost_usd_dollar @@ -1003,6 +1013,8 @@ def response_cost_calculator( litellm_model_name: Optional[str] = None, router_model_id: Optional[str] = None, litellm_logging_obj: Optional[LitellmLoggingObject] = None, + ### SERVICE TIER ### + service_tier: Optional[str] = None, # for OpenAI service tier pricing ) -> float: """ Returns @@ -1036,6 +1048,7 @@ def response_cost_calculator( litellm_model_name=litellm_model_name, router_model_id=router_model_id, litellm_logging_obj=litellm_logging_obj, + service_tier=service_tier, ) return response_cost except Exception as e: diff --git a/litellm/experimental_mcp_client/client.py b/litellm/experimental_mcp_client/client.py index c97da6624ac..ecb58e18223 100644 --- a/litellm/experimental_mcp_client/client.py +++ b/litellm/experimental_mcp_client/client.py @@ -19,8 +19,6 @@ from litellm._logging import verbose_logger from litellm.types.mcp import ( MCPAuth, MCPAuthType, - MCPSpecVersion, - MCPSpecVersionType, MCPStdioConfig, MCPTransport, MCPTransportType, @@ -48,7 +46,6 @@ class MCPClient: auth_value: Optional[str] = None, timeout: float = 60.0, stdio_config: Optional[MCPStdioConfig] = None, - protocol_version: MCPSpecVersionType = MCPSpecVersion.jun_2025, ): self.server_url: str = server_url self.transport_type: MCPTransport = transport_type @@ -62,7 +59,6 @@ class MCPClient: self._session_ctx = None self._task: Optional[asyncio.Task] = None self.stdio_config: Optional[MCPStdioConfig] = stdio_config - self.protocol_version: MCPSpecVersionType = protocol_version # handle the basic auth value if provided if auth_value: @@ -84,22 +80,24 @@ class MCPClient: """Initialize the transport and session.""" if self._session: return # Already connected - + try: if self.transport_type == MCPTransport.stdio: # For stdio transport, use stdio_client with command-line parameters if not self.stdio_config: raise ValueError("stdio_config is required for stdio transport") - + server_params = StdioServerParameters( command=self.stdio_config.get("command", ""), args=self.stdio_config.get("args", []), - env=self.stdio_config.get("env", {}) + env=self.stdio_config.get("env", {}), ) - + self._transport_ctx = stdio_client(server_params) self._transport = await self._transport_ctx.__aenter__() - self._session_ctx = ClientSession(self._transport[0], self._transport[1]) + self._session_ctx = ClientSession( + self._transport[0], self._transport[1] + ) self._session = await self._session_ctx.__aenter__() await self._session.initialize() elif self.transport_type == MCPTransport.sse: @@ -110,7 +108,9 @@ class MCPClient: headers=headers, ) self._transport = await self._transport_ctx.__aenter__() - self._session_ctx = ClientSession(self._transport[0], self._transport[1]) + self._session_ctx = ClientSession( + self._transport[0], self._transport[1] + ) self._session = await self._session_ctx.__aenter__() await self._session.initialize() else: # http @@ -121,7 +121,9 @@ class MCPClient: headers=headers, ) self._transport = await self._transport_ctx.__aenter__() - self._session_ctx = ClientSession(self._transport[0], self._transport[1]) + self._session_ctx = ClientSession( + self._transport[0], self._transport[1] + ) self._session = await self._session_ctx.__aenter__() await self._session.initialize() except ValueError as e: @@ -184,8 +186,10 @@ class MCPClient: def _get_auth_headers(self) -> dict: """Generate authentication headers based on auth type.""" - headers = {} - + headers = { + "MCP-Protocol-Version": "2025-06-18" + } + if self._mcp_auth_value: if self.auth_type == MCPAuth.bearer_token: headers["Authorization"] = f"Bearer {self._mcp_auth_value}" @@ -196,18 +200,8 @@ class MCPClient: elif self.auth_type == MCPAuth.authorization: headers["Authorization"] = self._mcp_auth_value - # Handle protocol version - it might be a string or enum - if hasattr(self.protocol_version, 'value'): - # It's an enum - protocol_version_str = self.protocol_version.value - else: - # It's a string - protocol_version_str = str(self.protocol_version) - - headers["MCP-Protocol-Version"] = protocol_version_str return headers - async def list_tools(self) -> List[MCPTool]: """List available tools from the server.""" if not self._session: @@ -216,7 +210,7 @@ class MCPClient: except Exception as e: verbose_logger.warning(f"MCP client connection failed: {str(e)}") return [] - + if self._session is None: verbose_logger.warning("MCP client session is not initialized") return [] @@ -245,17 +239,20 @@ class MCPClient: except Exception as e: verbose_logger.warning(f"MCP client connection failed: {str(e)}") return MCPCallToolResult( - content=[TextContent(type="text", text=f"{str(e)}")], - isError=True + content=[TextContent(type="text", text=f"{str(e)}")], isError=True ) if self._session is None: verbose_logger.warning("MCP client session is not initialized") return MCPCallToolResult( - content=[TextContent(type="text", text="MCP client session is not initialized")], + content=[ + TextContent( + type="text", text="MCP client session is not initialized" + ) + ], isError=True, ) - + try: tool_result = await self._session.call_tool( name=call_tool_request_params.name, @@ -270,8 +267,8 @@ class MCPClient: await self.disconnect() # Return a default error result instead of raising return MCPCallToolResult( - content=[TextContent(type="text", text=f"{str(e)}")], # Empty content for error case + content=[ + TextContent(type="text", text=f"{str(e)}") + ], # Empty content for error case isError=True, ) - - diff --git a/litellm/files/main.py b/litellm/files/main.py index 299e52895bf..18be2c702bf 100644 --- a/litellm/files/main.py +++ b/litellm/files/main.py @@ -731,7 +731,7 @@ def file_list( async def afile_content( file_id: str, - custom_llm_provider: Literal["openai", "azure"] = "openai", + custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai", extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, **kwargs, @@ -887,6 +887,32 @@ def file_content( client=client, litellm_params=litellm_params_dict, ) + elif custom_llm_provider == "vertex_ai": + api_base = optional_params.api_base or "" + vertex_ai_project = ( + optional_params.vertex_project + or litellm.vertex_project + or get_secret_str("VERTEXAI_PROJECT") + ) + vertex_ai_location = ( + optional_params.vertex_location + or litellm.vertex_location + or get_secret_str("VERTEXAI_LOCATION") + ) + vertex_credentials = optional_params.vertex_credentials or get_secret_str( + "VERTEXAI_CREDENTIALS" + ) + + response = vertex_ai_files_instance.file_content( + _is_async=_is_async, + file_content_request=_file_content_request, + api_base=api_base, + vertex_credentials=vertex_credentials, + vertex_project=vertex_ai_project, + vertex_location=vertex_ai_location, + timeout=timeout, + max_retries=optional_params.max_retries, + ) else: raise litellm.exceptions.BadRequestError( message="LiteLLM doesn't support {} for 'custom_llm_provider'. Supported providers are 'openai', 'azure', 'vertex_ai'.".format( diff --git a/litellm/integrations/langsmith.py b/litellm/integrations/langsmith.py index 7035aa3a819..7783b704b46 100644 --- a/litellm/integrations/langsmith.py +++ b/litellm/integrations/langsmith.py @@ -39,6 +39,7 @@ class LangsmithLogger(CustomBatchLogger): langsmith_api_key: Optional[str] = None, langsmith_project: Optional[str] = None, langsmith_base_url: Optional[str] = None, + langsmith_sampling_rate: Optional[float] = None, **kwargs, ): self.flush_lock = asyncio.Lock() @@ -49,7 +50,8 @@ class LangsmithLogger(CustomBatchLogger): langsmith_base_url=langsmith_base_url, ) self.sampling_rate: float = ( - float(os.getenv("LANGSMITH_SAMPLING_RATE")) # type: ignore + langsmith_sampling_rate + or float(os.getenv("LANGSMITH_SAMPLING_RATE")) # type: ignore if os.getenv("LANGSMITH_SAMPLING_RATE") is not None and os.getenv("LANGSMITH_SAMPLING_RATE").strip().isdigit() # type: ignore else 1.0 @@ -76,26 +78,14 @@ class LangsmithLogger(CustomBatchLogger): langsmith_base_url: Optional[str] = None, ) -> LangsmithCredentialsObject: _credentials_api_key = langsmith_api_key or os.getenv("LANGSMITH_API_KEY") - if _credentials_api_key is None: - raise Exception( - "Invalid Langsmith API Key given. _credentials_api_key=None." - ) _credentials_project = ( langsmith_project or os.getenv("LANGSMITH_PROJECT") or "litellm-completion" ) - if _credentials_project is None: - raise Exception( - "Invalid Langsmith API Key given. _credentials_project=None." - ) _credentials_base_url = ( langsmith_base_url or os.getenv("LANGSMITH_BASE_URL") or "https://api.smith.langchain.com" ) - if _credentials_base_url is None: - raise Exception( - "Invalid Langsmith API Key given. _credentials_base_url=None." - ) return LangsmithCredentialsObject( LANGSMITH_API_KEY=_credentials_api_key, @@ -200,12 +190,7 @@ class LangsmithLogger(CustomBatchLogger): def log_success_event(self, kwargs, response_obj, start_time, end_time): try: - sampling_rate = ( - float(os.getenv("LANGSMITH_SAMPLING_RATE")) # type: ignore - if os.getenv("LANGSMITH_SAMPLING_RATE") is not None - and os.getenv("LANGSMITH_SAMPLING_RATE").strip().isdigit() # type: ignore - else 1.0 - ) + sampling_rate = self._get_sampling_rate_to_use_for_request(kwargs=kwargs) random_sample = random.random() if random_sample > sampling_rate: verbose_logger.info( @@ -219,6 +204,7 @@ class LangsmithLogger(CustomBatchLogger): kwargs, response_obj, ) + credentials = self._get_credentials_to_use_for_request(kwargs=kwargs) data = self._prepare_log_data( kwargs=kwargs, @@ -245,7 +231,7 @@ class LangsmithLogger(CustomBatchLogger): async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): try: - sampling_rate = self.sampling_rate + sampling_rate = self._get_sampling_rate_to_use_for_request(kwargs=kwargs) random_sample = random.random() if random_sample > sampling_rate: verbose_logger.info( @@ -286,7 +272,7 @@ class LangsmithLogger(CustomBatchLogger): ) async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time): - sampling_rate = self.sampling_rate + sampling_rate = self._get_sampling_rate_to_use_for_request(kwargs=kwargs) random_sample = random.random() if random_sample > sampling_rate: verbose_logger.info( @@ -417,6 +403,17 @@ class LangsmithLogger(CustomBatchLogger): for queue_object in self.log_queue: credentials = queue_object["credentials"] + # if credential missing, skip - log warning + if ( + credentials["LANGSMITH_API_KEY"] is None + or credentials["LANGSMITH_PROJECT"] is None + ): + verbose_logger.warning( + "Langsmith Logging - credentials missing - api_key: %s, project: %s", + credentials["LANGSMITH_API_KEY"], + credentials["LANGSMITH_PROJECT"], + ) + continue key = CredentialsKey( api_key=credentials["LANGSMITH_API_KEY"], project=credentials["LANGSMITH_PROJECT"], @@ -432,6 +429,19 @@ class LangsmithLogger(CustomBatchLogger): return log_queue_by_credentials + def _get_sampling_rate_to_use_for_request(self, kwargs: Dict[str, Any]) -> float: + standard_callback_dynamic_params: Optional[StandardCallbackDynamicParams] = ( + kwargs.get("standard_callback_dynamic_params", None) + ) + sampling_rate: float = self.sampling_rate + if standard_callback_dynamic_params is not None: + _sampling_rate = standard_callback_dynamic_params.get( + "langsmith_sampling_rate" + ) + if _sampling_rate is not None: + sampling_rate = float(_sampling_rate) + return sampling_rate + def _get_credentials_to_use_for_request( self, kwargs: Dict[str, Any] ) -> LangsmithCredentialsObject: @@ -442,9 +452,9 @@ class LangsmithLogger(CustomBatchLogger): Otherwise, use the default credentials. """ - standard_callback_dynamic_params: Optional[ - StandardCallbackDynamicParams - ] = kwargs.get("standard_callback_dynamic_params", None) + standard_callback_dynamic_params: Optional[StandardCallbackDynamicParams] = ( + kwargs.get("standard_callback_dynamic_params", None) + ) if standard_callback_dynamic_params is not None: credentials = self.get_credentials_from_env( langsmith_api_key=standard_callback_dynamic_params.get( diff --git a/litellm/integrations/opik/opik.py b/litellm/integrations/opik/opik.py index 8cbfb9e6535..9f90d2384d8 100644 --- a/litellm/integrations/opik/opik.py +++ b/litellm/integrations/opik/opik.py @@ -3,6 +3,7 @@ Opik Logger that logs LLM events to an Opik server """ import asyncio +from datetime import timezone import json import traceback from typing import Dict, List @@ -291,8 +292,8 @@ class OpikLogger(CustomBatchLogger): "project_name": project_name, "id": trace_id, "name": trace_name, - "start_time": start_time.isoformat() + "Z", - "end_time": end_time.isoformat() + "Z", + "start_time": start_time.astimezone(timezone.utc).isoformat().replace("+00:00", "Z"), + "end_time": end_time.astimezone(timezone.utc).isoformat().replace("+00:00", "Z"), "input": input_data, "output": output_data, "metadata": metadata, @@ -312,8 +313,8 @@ class OpikLogger(CustomBatchLogger): "parent_span_id": parent_span_id, "name": span_name, "type": "llm", - "start_time": start_time.isoformat() + "Z", - "end_time": end_time.isoformat() + "Z", + "start_time": start_time.astimezone(timezone.utc).isoformat().replace("+00:00", "Z"), + "end_time": end_time.astimezone(timezone.utc).isoformat().replace("+00:00", "Z"), "input": input_data, "output": output_data, "metadata": metadata, diff --git a/litellm/litellm_core_utils/cli_token_utils.py b/litellm/litellm_core_utils/cli_token_utils.py new file mode 100644 index 00000000000..2aedb1c19d2 --- /dev/null +++ b/litellm/litellm_core_utils/cli_token_utils.py @@ -0,0 +1,58 @@ +""" +CLI Token Utilities + +SDK-level utilities for reading CLI authentication tokens. +This module has no dependencies on proxy code and can be safely imported at the SDK level. +""" + +import json +import os +from pathlib import Path +from typing import Optional + + +def get_cli_token_file_path() -> str: + """Get the path to the CLI token file""" + home_dir = Path.home() + config_dir = home_dir / ".litellm" + return str(config_dir / "token.json") + + +def load_cli_token() -> Optional[dict]: + """Load CLI token data from file""" + token_file = get_cli_token_file_path() + if not os.path.exists(token_file): + return None + + try: + with open(token_file, 'r') as f: + return json.load(f) + except (json.JSONDecodeError, IOError): + return None + + +def get_litellm_gateway_api_key() -> Optional[str]: + """ + Get the stored CLI API key for use with LiteLLM SDK. + + This function reads the token file created by `litellm-proxy login` + and returns the API key for use in Python scripts. + + Returns: + str: The API key if found, None otherwise + + Example: + >>> import litellm + >>> api_key = litellm.get_litellm_gateway_api_key() + >>> if api_key: + >>> response = litellm.completion( + >>> model="gpt-3.5-turbo", + >>> messages=[{"role": "user", "content": "Hello"}], + >>> api_key=api_key, + >>> base_url="https://your-proxy.com/v1" + >>> ) + """ + token_data = load_cli_token() + if token_data and 'key' in token_data: + return token_data['key'] + return None diff --git a/litellm/litellm_core_utils/custom_logger_registry.py b/litellm/litellm_core_utils/custom_logger_registry.py index 29e58b81010..bb8fa580e3c 100644 --- a/litellm/litellm_core_utils/custom_logger_registry.py +++ b/litellm/litellm_core_utils/custom_logger_registry.py @@ -47,6 +47,7 @@ from litellm.integrations.vector_store_integrations.vector_store_pre_call_hook i VectorStorePreCallHook, ) from litellm.proxy.hooks.dynamic_rate_limiter import _PROXY_DynamicRateLimitHandler +from litellm.proxy.hooks.dynamic_rate_limiter_v3 import _PROXY_DynamicRateLimitHandlerV3 class CustomLoggerRegistry: @@ -86,6 +87,7 @@ class CustomLoggerRegistry: "s3_v2": S3Logger, "aws_sqs": SQSLogger, "dynamic_rate_limiter": _PROXY_DynamicRateLimitHandler, + "dynamic_rate_limiter_v3": _PROXY_DynamicRateLimitHandlerV3, "vector_store_pre_call_hook": VectorStorePreCallHook, "dotprompt": DotpromptManager, "cloudzero": CloudZeroLogger, diff --git a/litellm/litellm_core_utils/duration_parser.py b/litellm/litellm_core_utils/duration_parser.py index 08e5323c30c..9a317cfcf0d 100644 --- a/litellm/litellm_core_utils/duration_parser.py +++ b/litellm/litellm_core_utils/duration_parser.py @@ -158,6 +158,7 @@ def _setup_timezone( "US/Eastern": timezone(timedelta(hours=-4)), # EDT "US/Pacific": timezone(timedelta(hours=-7)), # PDT "Asia/Kolkata": timezone(timedelta(hours=5, minutes=30)), # IST + "Asia/Bangkok": timezone(timedelta(hours=7)), # ICT (Indochina Time) "Europe/London": timezone(timedelta(hours=1)), # BST "UTC": timezone.utc, } diff --git a/litellm/litellm_core_utils/exception_mapping_utils.py b/litellm/litellm_core_utils/exception_mapping_utils.py index 44f08714d80..409f5ebe0bc 100644 --- a/litellm/litellm_core_utils/exception_mapping_utils.py +++ b/litellm/litellm_core_utils/exception_mapping_utils.py @@ -6,6 +6,7 @@ import httpx import litellm from litellm._logging import verbose_logger +from litellm.types.utils import LlmProviders from ..exceptions import ( APIConnectionError, @@ -762,7 +763,7 @@ def exception_type( # type: ignore # noqa: PLR0915 error_str += "XXXXXXX" + '"' raise AuthenticationError( - message=f"{custom_llm_provider}Exception: Authentication Error - {error_str}", + message=f"{custom_llm_provider.capitalize()}Exception: Authentication Error - {error_str}", llm_provider=custom_llm_provider, model=model, response=getattr(original_exception, "response", None), @@ -771,14 +772,14 @@ def exception_type( # type: ignore # noqa: PLR0915 elif "model's maximum context limit" in error_str: exception_mapping_worked = True raise ContextWindowExceededError( - message=f"{custom_llm_provider}Exception: Context Window Error - {error_str}", + message=f"{custom_llm_provider.capitalize()}Exception: Context Window Error - {error_str}", model=model, llm_provider=custom_llm_provider, ) elif "token_quota_reached" in error_str: exception_mapping_worked = True raise RateLimitError( - message=f"{custom_llm_provider}Exception: Rate Limit Errror - {error_str}", + message=f"{custom_llm_provider.capitalize()}Exception: Rate Limit Errror - {error_str}", llm_provider=custom_llm_provider, model=model, response=getattr(original_exception, "response", None), @@ -789,14 +790,14 @@ def exception_type( # type: ignore # noqa: PLR0915 ): exception_mapping_worked = True raise litellm.InternalServerError( - message=f"{custom_llm_provider}Exception - {original_exception.message}", + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", llm_provider=custom_llm_provider, model=model, ) elif "model_no_support_for_function" in error_str: exception_mapping_worked = True raise BadRequestError( - message=f"{custom_llm_provider}Exception - Use 'watsonx_text' route instead. IBM WatsonX does not support `/text/chat` endpoint. - {error_str}", + message=f"{custom_llm_provider.capitalize()}Exception - Use 'watsonx_text' route instead. IBM WatsonX does not support `/text/chat` endpoint. - {error_str}", llm_provider=custom_llm_provider, model=model, ) @@ -804,7 +805,7 @@ def exception_type( # type: ignore # noqa: PLR0915 if original_exception.status_code == 500: exception_mapping_worked = True raise litellm.InternalServerError( - message=f"{custom_llm_provider}Exception - {original_exception.message}", + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", llm_provider=custom_llm_provider, model=model, ) @@ -814,28 +815,28 @@ def exception_type( # type: ignore # noqa: PLR0915 ): exception_mapping_worked = True raise AuthenticationError( - message=f"{custom_llm_provider}Exception - {original_exception.message}", + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", llm_provider=custom_llm_provider, model=model, ) elif original_exception.status_code == 400: exception_mapping_worked = True raise BadRequestError( - message=f"{custom_llm_provider}Exception - {original_exception.message}", + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", llm_provider=custom_llm_provider, model=model, ) elif original_exception.status_code == 404: exception_mapping_worked = True raise NotFoundError( - message=f"{custom_llm_provider}Exception - {original_exception.message}", + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", llm_provider=custom_llm_provider, model=model, ) elif original_exception.status_code == 408: exception_mapping_worked = True raise Timeout( - message=f"{custom_llm_provider}Exception - {original_exception.message}", + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", model=model, llm_provider=custom_llm_provider, litellm_debug_info=extra_information, @@ -846,7 +847,7 @@ def exception_type( # type: ignore # noqa: PLR0915 ): exception_mapping_worked = True raise BadRequestError( - message=f"{custom_llm_provider}Exception - {original_exception.message}", + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", model=model, llm_provider=custom_llm_provider, litellm_debug_info=extra_information, @@ -854,7 +855,7 @@ def exception_type( # type: ignore # noqa: PLR0915 elif original_exception.status_code == 429: exception_mapping_worked = True raise RateLimitError( - message=f"{custom_llm_provider}Exception - {original_exception.message}", + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", model=model, llm_provider=custom_llm_provider, litellm_debug_info=extra_information, @@ -862,7 +863,7 @@ def exception_type( # type: ignore # noqa: PLR0915 elif original_exception.status_code == 503: exception_mapping_worked = True raise ServiceUnavailableError( - message=f"{custom_llm_provider}Exception - {original_exception.message}", + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", model=model, llm_provider=custom_llm_provider, litellm_debug_info=extra_information, @@ -870,7 +871,7 @@ def exception_type( # type: ignore # noqa: PLR0915 elif original_exception.status_code == 504: # gateway timeout error exception_mapping_worked = True raise Timeout( - message=f"{custom_llm_provider}Exception - {original_exception.message}", + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", model=model, llm_provider=custom_llm_provider, litellm_debug_info=extra_information, @@ -1168,9 +1169,9 @@ def exception_type( # type: ignore # noqa: PLR0915 exception_status_code=original_exception.status_code, ) elif ( - custom_llm_provider == "vertex_ai" - or custom_llm_provider == "vertex_ai_beta" - or custom_llm_provider == "gemini" + custom_llm_provider == LlmProviders.VERTEX_AI + or custom_llm_provider == LlmProviders.VERTEX_AI_BETA + or custom_llm_provider == LlmProviders.GEMINI ): if ( "Vertex AI API has not been used in project" in error_str @@ -1178,9 +1179,9 @@ def exception_type( # type: ignore # noqa: PLR0915 ): exception_mapping_worked = True raise BadRequestError( - message=f"litellm.BadRequestError: VertexAIException - {error_str}", + message=f"litellm.BadRequestError: {custom_llm_provider}Exception - {error_str}", model=model, - llm_provider="vertex_ai", + llm_provider=custom_llm_provider, response=httpx.Response( status_code=400, request=httpx.Request( @@ -1193,7 +1194,7 @@ def exception_type( # type: ignore # noqa: PLR0915 if "400 Request payload size exceeds" in error_str: exception_mapping_worked = True raise ContextWindowExceededError( - message=f"VertexException - {error_str}", + message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", model=model, llm_provider=custom_llm_provider, ) @@ -1203,9 +1204,9 @@ def exception_type( # type: ignore # noqa: PLR0915 ): exception_mapping_worked = True raise litellm.InternalServerError( - message=f"litellm.InternalServerError: VertexAIException - {error_str}", + message=f"litellm.InternalServerError: {custom_llm_provider}Exception - {error_str}", model=model, - llm_provider="vertex_ai", + llm_provider=custom_llm_provider, response=httpx.Response( status_code=500, content=str(original_exception), @@ -1216,7 +1217,7 @@ def exception_type( # type: ignore # noqa: PLR0915 elif "API key not valid." in error_str: exception_mapping_worked = True raise AuthenticationError( - message=f"{custom_llm_provider}Exception - {error_str}", + message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", model=model, llm_provider=custom_llm_provider, litellm_debug_info=extra_information, @@ -1224,9 +1225,9 @@ def exception_type( # type: ignore # noqa: PLR0915 elif "403" in error_str: exception_mapping_worked = True raise BadRequestError( - message=f"VertexAIException BadRequestError - {error_str}", + message=f"{custom_llm_provider.capitalize()}Exception BadRequestError - {error_str}", model=model, - llm_provider="vertex_ai", + llm_provider=custom_llm_provider, response=httpx.Response( status_code=403, request=httpx.Request( @@ -1243,9 +1244,9 @@ def exception_type( # type: ignore # noqa: PLR0915 ): exception_mapping_worked = True raise ContentPolicyViolationError( - message=f"VertexAIException ContentPolicyViolationError - {error_str}", + message=f"{custom_llm_provider.capitalize()}Exception ContentPolicyViolationError - {error_str}", model=model, - llm_provider="vertex_ai", + llm_provider=custom_llm_provider, litellm_debug_info=extra_information, response=httpx.Response( status_code=400, @@ -1264,9 +1265,9 @@ def exception_type( # type: ignore # noqa: PLR0915 ): exception_mapping_worked = True raise RateLimitError( - message=f"litellm.RateLimitError: VertexAIException - {error_str}", + message=f"litellm.RateLimitError: {custom_llm_provider}Exception - {error_str}", model=model, - llm_provider="vertex_ai", + llm_provider=custom_llm_provider, litellm_debug_info=extra_information, response=httpx.Response( status_code=429, @@ -1282,18 +1283,18 @@ def exception_type( # type: ignore # noqa: PLR0915 ): exception_mapping_worked = True raise litellm.InternalServerError( - message=f"litellm.InternalServerError: VertexAIException - {error_str}", + message=f"litellm.InternalServerError: {custom_llm_provider}Exception - {error_str}", model=model, - llm_provider="vertex_ai", + llm_provider=custom_llm_provider, litellm_debug_info=extra_information, ) if hasattr(original_exception, "status_code"): if original_exception.status_code == 400: exception_mapping_worked = True raise BadRequestError( - message=f"VertexAIException BadRequestError - {error_str}", + message=f"{custom_llm_provider.capitalize()}Exception BadRequestError - {error_str}", model=model, - llm_provider="vertex_ai", + llm_provider=custom_llm_provider, litellm_debug_info=extra_information, response=httpx.Response( status_code=400, @@ -1306,21 +1307,35 @@ def exception_type( # type: ignore # noqa: PLR0915 if original_exception.status_code == 401: exception_mapping_worked = True raise AuthenticationError( - message=f"VertexAIException - {original_exception.message}", + message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", llm_provider=custom_llm_provider, model=model, ) + if original_exception.status_code == 403: + exception_mapping_worked = True + raise PermissionDeniedError( + message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", + llm_provider=custom_llm_provider, + model=model, + response=httpx.Response( + status_code=403, + request=httpx.Request( + method="POST", + url="https://cloud.google.com/vertex-ai/", + ), + ), + ) if original_exception.status_code == 404: exception_mapping_worked = True raise NotFoundError( - message=f"VertexAIException - {original_exception.message}", + message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", llm_provider=custom_llm_provider, model=model, ) if original_exception.status_code == 408: exception_mapping_worked = True raise Timeout( - message=f"VertexAIException - {original_exception.message}", + message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", llm_provider=custom_llm_provider, model=model, ) @@ -1328,9 +1343,9 @@ def exception_type( # type: ignore # noqa: PLR0915 if original_exception.status_code == 429: exception_mapping_worked = True raise RateLimitError( - message=f"litellm.RateLimitError: VertexAIException - {error_str}", + message=f"litellm.RateLimitError: {custom_llm_provider.capitalize()}Exception - {error_str}", model=model, - llm_provider="vertex_ai", + llm_provider=custom_llm_provider, litellm_debug_info=extra_information, response=httpx.Response( status_code=429, @@ -1343,9 +1358,9 @@ def exception_type( # type: ignore # noqa: PLR0915 if original_exception.status_code == 500: exception_mapping_worked = True raise litellm.InternalServerError( - message=f"VertexAIException InternalServerError - {error_str}", + message=f"{custom_llm_provider.capitalize()}Exception InternalServerError - {error_str}", model=model, - llm_provider="vertex_ai", + llm_provider=custom_llm_provider, litellm_debug_info=extra_information, response=httpx.Response( status_code=500, @@ -1353,71 +1368,20 @@ def exception_type( # type: ignore # noqa: PLR0915 request=httpx.Request(method="completion", url="https://github.com/BerriAI/litellm"), # type: ignore ), ) - if original_exception.status_code == 503: + if original_exception.status_code == 502: exception_mapping_worked = True - raise ServiceUnavailableError( - message=f"VertexAIException - {original_exception.message}", + raise APIConnectionError( + message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", llm_provider=custom_llm_provider, model=model, ) - elif custom_llm_provider == "palm" or custom_llm_provider == "gemini": - if "503 Getting metadata" in error_str: - # auth errors look like this - # 503 Getting metadata from plugin failed with error: Reauthentication is needed. Please run `gcloud auth application-default login` to reauthenticate. - exception_mapping_worked = True - raise BadRequestError( - message="GeminiException - Invalid api key", - model=model, - llm_provider="palm", - response=getattr(original_exception, "response", None), - ) - if ( - "504 Deadline expired before operation could complete." in error_str - or "504 Deadline Exceeded" in error_str - ): - exception_mapping_worked = True - raise Timeout( - message=f"GeminiException - {original_exception.message}", - model=model, - llm_provider="palm", - exception_status_code=original_exception.status_code, - ) - if "400 Request payload size exceeds" in error_str: - exception_mapping_worked = True - raise ContextWindowExceededError( - message=f"GeminiException - {error_str}", - model=model, - llm_provider="palm", - response=getattr(original_exception, "response", None), - ) - if ( - "500 An internal error has occurred." in error_str - or "list index out of range" in error_str - ): - exception_mapping_worked = True - raise APIError( - status_code=getattr(original_exception, "status_code", 500), - message=f"GeminiException - {original_exception.message}", - llm_provider="palm", - model=model, - request=httpx.Response( - status_code=429, - request=httpx.Request( - method="POST", - url=" https://cloud.google.com/vertex-ai/", - ), - ), - ) - if hasattr(original_exception, "status_code"): - if original_exception.status_code == 400: + if original_exception.status_code == 503: exception_mapping_worked = True - raise BadRequestError( - message=f"GeminiException - {error_str}", + raise ServiceUnavailableError( + message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", + llm_provider=custom_llm_provider, model=model, - llm_provider="palm", - response=getattr(original_exception, "response", None), ) - # Dailed: Error occurred: 400 Request payload size exceeds the limit: 20000 bytes elif custom_llm_provider == "cloudflare": if "Authentication error" in error_str: exception_mapping_worked = True diff --git a/litellm/litellm_core_utils/get_llm_provider_logic.py b/litellm/litellm_core_utils/get_llm_provider_logic.py index 414ccb7ab83..69c996d8139 100644 --- a/litellm/litellm_core_utils/get_llm_provider_logic.py +++ b/litellm/litellm_core_utils/get_llm_provider_logic.py @@ -252,6 +252,9 @@ def get_llm_provider( # noqa: PLR0915 elif endpoint == "https://ai-gateway.vercel.sh/v1": custom_llm_provider = "vercel_ai_gateway" dynamic_api_key = get_secret_str("VERCEL_AI_GATEWAY_API_KEY") + elif endpoint == "https://api.inference.wandb.ai/v1": + custom_llm_provider = "wandb" + dynamic_api_key = get_secret_str("WANDB_API_KEY") if api_base is not None and not isinstance(api_base, str): raise Exception( @@ -773,6 +776,13 @@ def _get_openai_compatible_provider_info( # noqa: PLR0915 ) = litellm.AIMLChatConfig()._get_openai_compatible_provider_info( api_base, api_key ) + elif custom_llm_provider == "wandb": + api_base = ( + api_base + or get_secret("WANDB_API_BASE") + or "https://api.inference.wandb.ai/v1" + ) # type: ignore + dynamic_api_key = api_key or get_secret_str("WANDB_API_KEY") if api_base is not None and not isinstance(api_base, str): raise Exception("api base needs to be a string. api_base={}".format(api_base)) diff --git a/litellm/litellm_core_utils/get_supported_openai_params.py b/litellm/litellm_core_utils/get_supported_openai_params.py index d77f53bd798..06e650f938d 100644 --- a/litellm/litellm_core_utils/get_supported_openai_params.py +++ b/litellm/litellm_core_utils/get_supported_openai_params.py @@ -149,6 +149,9 @@ def get_supported_openai_params( # noqa: PLR0915 elif custom_llm_provider == "nebius": if request_type == "chat_completion": return litellm.NebiusConfig().get_supported_openai_params(model=model) + elif custom_llm_provider == "wandb": + if request_type == "chat_completion": + return litellm.WandbConfig().get_supported_openai_params(model=model) elif custom_llm_provider == "replicate": return litellm.ReplicateConfig().get_supported_openai_params(model=model) elif custom_llm_provider == "huggingface": diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py index 0987f2799b3..64986970d00 100644 --- a/litellm/litellm_core_utils/litellm_logging.py +++ b/litellm/litellm_core_utils/litellm_logging.py @@ -26,7 +26,6 @@ from typing import ( cast, ) -import fastuuid as uuid from httpx import Response from pydantic import BaseModel @@ -38,6 +37,7 @@ from litellm import ( turn_off_message_logging, ) from litellm._logging import _is_debugging_on, verbose_logger +from litellm._uuid import uuid from litellm.batches.batch_utils import _handle_completed_batch from litellm.caching.caching import DualCache, InMemoryCache from litellm.caching.caching_handler import LLMCachingHandler @@ -1228,6 +1228,7 @@ class Logging(LiteLLMLoggingBaseClass): "standard_built_in_tools_params": self.standard_built_in_tools_params, "router_model_id": router_model_id, "litellm_logging_obj": self, + "service_tier": self.optional_params.get("service_tier") if self.optional_params else None, } except Exception as e: # error creating kwargs for cost calculation debug_info = StandardLoggingModelCostFailureDebugInformation( @@ -3444,6 +3445,30 @@ def _init_custom_logger_compatible_class( # noqa: PLR0915 dynamic_rate_limiter_obj.update_variables(llm_router=llm_router) _in_memory_loggers.append(dynamic_rate_limiter_obj) return dynamic_rate_limiter_obj # type: ignore + elif logging_integration == "dynamic_rate_limiter_v3": + from litellm.proxy.hooks.dynamic_rate_limiter_v3 import ( + _PROXY_DynamicRateLimitHandlerV3, + ) + + for callback in _in_memory_loggers: + if isinstance(callback, _PROXY_DynamicRateLimitHandlerV3): + return callback # type: ignore + + if internal_usage_cache is None: + raise Exception( + "Internal Error: Cache cannot be empty - internal_usage_cache={}".format( + internal_usage_cache + ) + ) + + dynamic_rate_limiter_obj_v3 = _PROXY_DynamicRateLimitHandlerV3( + internal_usage_cache=internal_usage_cache + ) + + if llm_router is not None and isinstance(llm_router, litellm.Router): + dynamic_rate_limiter_obj_v3.update_variables(llm_router=llm_router) + _in_memory_loggers.append(dynamic_rate_limiter_obj_v3) + return dynamic_rate_limiter_obj_v3 # type: ignore elif logging_integration == "langtrace": if "LANGTRACE_API_KEY" not in os.environ: raise ValueError("LANGTRACE_API_KEY not found in environment variables") @@ -3707,6 +3732,14 @@ def get_custom_logger_compatible_class( # noqa: PLR0915 for callback in _in_memory_loggers: if isinstance(callback, _PROXY_DynamicRateLimitHandler): return callback # type: ignore + elif logging_integration == "dynamic_rate_limiter_v3": + from litellm.proxy.hooks.dynamic_rate_limiter_v3 import ( + _PROXY_DynamicRateLimitHandlerV3, + ) + + for callback in _in_memory_loggers: + if isinstance(callback, _PROXY_DynamicRateLimitHandlerV3): + return callback # type: ignore elif logging_integration == "langtrace": from litellm.integrations.opentelemetry import OpenTelemetry diff --git a/litellm/litellm_core_utils/llm_cost_calc/utils.py b/litellm/litellm_core_utils/llm_cost_calc/utils.py index c5b47763f07..626a3f3625f 100644 --- a/litellm/litellm_core_utils/llm_cost_calc/utils.py +++ b/litellm/litellm_core_utils/llm_cost_calc/utils.py @@ -1,16 +1,18 @@ # What is this? ## Helper utilities for cost_per_token() -from typing import Any, Literal, Optional, Tuple, cast +from typing import Any, Literal, Optional, Tuple, TypedDict, cast import litellm from litellm._logging import verbose_logger from litellm.types.utils import ( + CacheCreationTokenDetails, CallTypes, ImageResponse, ModelInfo, PassthroughCallTypes, Usage, + ServiceTier, ) from litellm.utils import get_model_info @@ -113,9 +115,31 @@ def _generic_cost_per_character( return prompt_cost, completion_cost +def _get_service_tier_cost_key(base_key: str, service_tier: Optional[str]) -> str: + """ + Get the appropriate cost key based on service tier. + + Args: + base_key: The base cost key (e.g., "input_cost_per_token") + service_tier: The service tier ("flex", "priority", or None for standard) + + Returns: + str: The cost key to use (e.g., "input_cost_per_token_flex" or "input_cost_per_token") + """ + if service_tier is None: + return base_key + + # Only use service tier specific keys for "flex" and "priority" + if service_tier.lower() in [ServiceTier.FLEX.value, ServiceTier.PRIORITY.value]: + return f"{base_key}_{service_tier.lower()}" + + # For any other service tier, use standard pricing + return base_key + + def _get_token_base_cost( - model_info: ModelInfo, usage: Usage -) -> Tuple[float, float, float, float]: + model_info: ModelInfo, usage: Usage, service_tier: Optional[str] = None +) -> Tuple[float, float, float, float, float]: """ Return prompt cost, completion cost, and cache costs for a given model and usage. @@ -125,17 +149,27 @@ def _get_token_base_cost( Returns: Tuple[float, float, float, float] - (prompt_cost, completion_cost, cache_creation_cost, cache_read_cost) """ + # Get service tier aware cost keys + input_cost_key = _get_service_tier_cost_key("input_cost_per_token", service_tier) + output_cost_key = _get_service_tier_cost_key("output_cost_per_token", service_tier) + cache_creation_cost_key = _get_service_tier_cost_key("cache_creation_input_token_cost", service_tier) + cache_read_cost_key = _get_service_tier_cost_key("cache_read_input_token_cost", service_tier) + prompt_base_cost = cast( - float, _get_cost_per_unit(model_info, "input_cost_per_token") + float, _get_cost_per_unit(model_info, input_cost_key) ) completion_base_cost = cast( - float, _get_cost_per_unit(model_info, "output_cost_per_token") + float, _get_cost_per_unit(model_info, output_cost_key) ) cache_creation_cost = cast( - float, _get_cost_per_unit(model_info, "cache_creation_input_token_cost") + float, _get_cost_per_unit(model_info, cache_creation_cost_key) + ) + cache_creation_cost_above_1hr = cast( + float, + _get_cost_per_unit(model_info, "cache_creation_input_token_cost_above_1hr"), ) cache_read_cost = cast( - float, _get_cost_per_unit(model_info, "cache_read_input_token_cost") + float, _get_cost_per_unit(model_info, cache_read_cost_key) ) ## CHECK IF ABOVE THRESHOLD @@ -194,7 +228,13 @@ def _get_token_base_cost( except Exception: continue - return prompt_base_cost, completion_base_cost, cache_creation_cost, cache_read_cost + return ( + prompt_base_cost, + completion_base_cost, + cache_creation_cost, + cache_creation_cost_above_1hr, + cache_read_cost, + ) def calculate_cost_component( @@ -238,11 +278,224 @@ def _get_cost_per_unit( verbose_logger.exception( f"litellm.litellm_core_utils.llm_cost_calc.utils.py::calculate_cost_per_component(): Exception occured - {cost_per_unit}\nDefaulting to 0.0" ) + + # If the service tier key doesn't exist or is None, try to fall back to the standard key + if cost_per_unit is None: + # Check if any service tier suffix exists in the cost key using ServiceTier enum + for service_tier in ServiceTier: + suffix = f"_{service_tier.value}" + if suffix in cost_key: + # Extract the base key by removing the matched suffix + base_key = cost_key.replace(suffix, '') + fallback_cost = model_info.get(base_key) + if isinstance(fallback_cost, float): + return fallback_cost + if isinstance(fallback_cost, int): + return float(fallback_cost) + if isinstance(fallback_cost, str): + try: + return float(fallback_cost) + except ValueError: + verbose_logger.exception( + f"litellm.litellm_core_utils.llm_cost_calc.utils.py::_get_cost_per_unit(): Exception occured - {fallback_cost}\nDefaulting to 0.0" + ) + break # Only try the first matching suffix + return default_value +def calculate_cache_writing_cost( + cache_creation_tokens: int, + cache_creation_token_details: Optional[CacheCreationTokenDetails], + cache_creation_cost_above_1hr: float, + cache_creation_cost: float, +) -> float: + """ + Adjust cost of cache creation tokens based on the cache creation token details. + """ + total_cost: float = 0.0 + if cache_creation_token_details is not None: + # get the number of 5m and 1h cache creation tokens + cache_creation_tokens_5m = ( + cache_creation_token_details.ephemeral_5m_input_tokens + ) + cache_creation_tokens_1h = ( + cache_creation_token_details.ephemeral_1h_input_tokens + ) + # add the number of 5m and 1h cache creation tokens to the cache creation tokens + total_cost += ( + cache_creation_tokens_5m * cache_creation_cost + if cache_creation_tokens_5m is not None + else 0.0 + ) + total_cost += ( + cache_creation_tokens_1h * cache_creation_cost_above_1hr + if cache_creation_tokens_1h is not None + else 0.0 + ) + else: + total_cost += cache_creation_tokens * cache_creation_cost + return total_cost + + +class PromptTokensDetailsResult(TypedDict): + cache_hit_tokens: int + cache_creation_tokens: int + cache_creation_token_details: Optional[CacheCreationTokenDetails] + text_tokens: int + audio_tokens: int + character_count: int + image_count: int + video_length_seconds: int + + +def _parse_prompt_tokens_details(usage: Usage) -> PromptTokensDetailsResult: + cache_hit_tokens = ( + cast(Optional[int], getattr(usage.prompt_tokens_details, "cached_tokens", 0)) + or 0 + ) + cache_creation_tokens = ( + cast( + Optional[int], + getattr(usage.prompt_tokens_details, "cache_creation_tokens", 0), + ) + or 0 + ) + cache_creation_token_details = ( + cast( + Optional[CacheCreationTokenDetails], + getattr(usage.prompt_tokens_details, "cache_creation_token_details", None), + ) + or None + ) + text_tokens = ( + cast(Optional[int], getattr(usage.prompt_tokens_details, "text_tokens", None)) + or 0 # default to prompt tokens, if this field is not set + ) + audio_tokens = ( + cast(Optional[int], getattr(usage.prompt_tokens_details, "audio_tokens", 0)) + or 0 + ) + character_count = ( + cast( + Optional[int], + getattr(usage.prompt_tokens_details, "character_count", 0), + ) + or 0 + ) + image_count = ( + cast(Optional[int], getattr(usage.prompt_tokens_details, "image_count", 0)) or 0 + ) + video_length_seconds = ( + cast( + Optional[int], + getattr(usage.prompt_tokens_details, "video_length_seconds", 0), + ) + or 0 + ) + + return PromptTokensDetailsResult( + cache_hit_tokens=cache_hit_tokens, + cache_creation_tokens=cache_creation_tokens, + cache_creation_token_details=cache_creation_token_details, + text_tokens=text_tokens, + audio_tokens=audio_tokens, + character_count=character_count, + image_count=image_count, + video_length_seconds=video_length_seconds, + ) + + +class CompletionTokensDetailsResult(TypedDict): + audio_tokens: int + text_tokens: int + reasoning_tokens: int + + +def _parse_completion_tokens_details(usage: Usage) -> CompletionTokensDetailsResult: + audio_tokens = ( + cast( + Optional[int], + getattr(usage.completion_tokens_details, "audio_tokens", 0), + ) + or 0 + ) + text_tokens = ( + cast( + Optional[int], + getattr(usage.completion_tokens_details, "text_tokens", None), + ) + or 0 # default to completion tokens, if this field is not set + ) + reasoning_tokens = ( + cast( + Optional[int], + getattr(usage.completion_tokens_details, "reasoning_tokens", 0), + ) + or 0 + ) + + return CompletionTokensDetailsResult( + audio_tokens=audio_tokens, + text_tokens=text_tokens, + reasoning_tokens=reasoning_tokens, + ) + + +def _calculate_input_cost( + prompt_tokens_details: PromptTokensDetailsResult, + model_info: ModelInfo, + prompt_base_cost: float, + cache_read_cost: float, + cache_creation_cost: float, + cache_creation_cost_above_1hr: float, +) -> float: + """ + Calculates the input cost for a given model, prompt tokens, and completion tokens. + """ + prompt_cost = float(prompt_tokens_details["text_tokens"]) * prompt_base_cost + + ### CACHE READ COST - Now uses tiered pricing + prompt_cost += float(prompt_tokens_details["cache_hit_tokens"]) * cache_read_cost + + ### AUDIO COST + prompt_cost += calculate_cost_component( + model_info, "input_cost_per_audio_token", prompt_tokens_details["audio_tokens"] + ) + + ### CACHE WRITING COST - Now uses tiered pricing + prompt_cost += calculate_cache_writing_cost( + cache_creation_tokens=prompt_tokens_details["cache_creation_tokens"], + cache_creation_token_details=prompt_tokens_details[ + "cache_creation_token_details" + ], + cache_creation_cost_above_1hr=cache_creation_cost_above_1hr, + cache_creation_cost=cache_creation_cost, + ) + + ### CHARACTER COST + + prompt_cost += calculate_cost_component( + model_info, "input_cost_per_character", prompt_tokens_details["character_count"] + ) + + ### IMAGE COUNT COST + prompt_cost += calculate_cost_component( + model_info, "input_cost_per_image", prompt_tokens_details["image_count"] + ) + + ### VIDEO LENGTH COST + prompt_cost += calculate_cost_component( + model_info, + "input_cost_per_video_per_second", + prompt_tokens_details["video_length_seconds"], + ) + + return prompt_cost + + def generic_cost_per_token( - model: str, usage: Usage, custom_llm_provider: str + model: str, usage: Usage, custom_llm_provider: str, service_tier: Optional[str] = None ) -> Tuple[float, float]: """ Calculates the cost per token for a given model, prompt tokens, and completion tokens. @@ -264,97 +517,45 @@ def generic_cost_per_token( ### Cost of processing (non-cache hit + cache hit) + Cost of cache-writing (cache writing) prompt_cost = 0.0 ### PROCESSING COST - text_tokens = usage.prompt_tokens - cache_hit_tokens = 0 - cache_creation_tokens = 0 - audio_tokens = 0 - character_count = 0 - image_count = 0 - video_length_seconds = 0 + prompt_tokens_details = PromptTokensDetailsResult( + cache_hit_tokens=0, + cache_creation_tokens=0, + cache_creation_token_details=None, + text_tokens=usage.prompt_tokens, + audio_tokens=0, + character_count=0, + image_count=0, + video_length_seconds=0, + ) if usage.prompt_tokens_details: - cache_hit_tokens = ( - cast( - Optional[int], getattr(usage.prompt_tokens_details, "cached_tokens", 0) - ) - or 0 - ) - cache_creation_tokens = ( - cast( - Optional[int], - getattr(usage.prompt_tokens_details, "cache_creation_tokens", 0), - ) - or 0 - ) - text_tokens = ( - cast( - Optional[int], getattr(usage.prompt_tokens_details, "text_tokens", None) - ) - or 0 # default to prompt tokens, if this field is not set - ) - audio_tokens = ( - cast(Optional[int], getattr(usage.prompt_tokens_details, "audio_tokens", 0)) - or 0 - ) - character_count = ( - cast( - Optional[int], - getattr(usage.prompt_tokens_details, "character_count", 0), - ) - or 0 - ) - image_count = ( - cast(Optional[int], getattr(usage.prompt_tokens_details, "image_count", 0)) - or 0 - ) - video_length_seconds = ( - cast( - Optional[int], - getattr(usage.prompt_tokens_details, "video_length_seconds", 0), - ) - or 0 - ) + prompt_tokens_details = _parse_prompt_tokens_details(usage) ## EDGE CASE - text tokens not set inside PromptTokensDetails - if text_tokens == 0: + if prompt_tokens_details["text_tokens"] == 0: text_tokens = ( usage.prompt_tokens - - cache_hit_tokens - - audio_tokens - - cache_creation_tokens + - prompt_tokens_details["cache_hit_tokens"] + - prompt_tokens_details["audio_tokens"] + - prompt_tokens_details["cache_creation_tokens"] ) + prompt_tokens_details["text_tokens"] = text_tokens - prompt_base_cost, completion_base_cost, cache_creation_cost, cache_read_cost = ( - _get_token_base_cost(model_info=model_info, usage=usage) - ) + ( + prompt_base_cost, + completion_base_cost, + cache_creation_cost, + cache_creation_cost_above_1hr, + cache_read_cost, + ) = _get_token_base_cost(model_info=model_info, usage=usage, service_tier=service_tier) - prompt_cost = float(text_tokens) * prompt_base_cost - - ### CACHE READ COST - Now uses tiered pricing - prompt_cost += float(cache_hit_tokens) * cache_read_cost - - ### AUDIO COST - prompt_cost += calculate_cost_component( - model_info, "input_cost_per_audio_token", audio_tokens - ) - - ### CACHE WRITING COST - Now uses tiered pricing - prompt_cost += float(cache_creation_tokens) * cache_creation_cost - - ### CHARACTER COST - - prompt_cost += calculate_cost_component( - model_info, "input_cost_per_character", character_count - ) - - ### IMAGE COUNT COST - prompt_cost += calculate_cost_component( - model_info, "input_cost_per_image", image_count - ) - - ### VIDEO LENGTH COST - prompt_cost += calculate_cost_component( - model_info, "input_cost_per_video_per_second", video_length_seconds + prompt_cost = _calculate_input_cost( + prompt_tokens_details=prompt_tokens_details, + model_info=model_info, + prompt_base_cost=prompt_base_cost, + cache_read_cost=cache_read_cost, + cache_creation_cost=cache_creation_cost, + cache_creation_cost_above_1hr=cache_creation_cost_above_1hr, ) ## CALCULATE OUTPUT COST @@ -363,27 +564,10 @@ def generic_cost_per_token( reasoning_tokens = 0 is_text_tokens_total = False if usage.completion_tokens_details is not None: - audio_tokens = ( - cast( - Optional[int], - getattr(usage.completion_tokens_details, "audio_tokens", 0), - ) - or 0 - ) - text_tokens = ( - cast( - Optional[int], - getattr(usage.completion_tokens_details, "text_tokens", None), - ) - or 0 # default to completion tokens, if this field is not set - ) - reasoning_tokens = ( - cast( - Optional[int], - getattr(usage.completion_tokens_details, "reasoning_tokens", 0), - ) - or 0 - ) + completion_tokens_details = _parse_completion_tokens_details(usage) + audio_tokens = completion_tokens_details["audio_tokens"] + text_tokens = completion_tokens_details["text_tokens"] + reasoning_tokens = completion_tokens_details["reasoning_tokens"] if text_tokens == 0: text_tokens = usage.completion_tokens diff --git a/litellm/litellm_core_utils/object_pooling.py b/litellm/litellm_core_utils/object_pooling.py index 22b371501c1..846e6536f80 100644 --- a/litellm/litellm_core_utils/object_pooling.py +++ b/litellm/litellm_core_utils/object_pooling.py @@ -14,8 +14,9 @@ Memory Management Strategy: - Unlimited pools when maxsize is not specified (eviction controls actual usage) """ -from typing import Type, TypeVar, Optional, Callable -from pond import Pond, PooledObjectFactory, PooledObject +from typing import Any, Callable, Optional, Type, TypeVar + +from pond import Pond, PooledObject, PooledObjectFactory T = TypeVar('T') @@ -50,7 +51,7 @@ class GenericPooledObjectFactory(PooledObjectFactory): pooled_object.keeped_object.__dict__.clear() del pooled_object - def reset(self, pooled_object: PooledObject) -> PooledObject: + def reset(self, pooled_object: PooledObject, **kwargs: Any) -> PooledObject: """Reset the pooled object to a clean state.""" obj = pooled_object.keeped_object # Reset the object by calling its reset method if it exists diff --git a/litellm/litellm_core_utils/streaming_handler.py b/litellm/litellm_core_utils/streaming_handler.py index 322691e28b4..bc0fbdf5c11 100644 --- a/litellm/litellm_core_utils/streaming_handler.py +++ b/litellm/litellm_core_utils/streaming_handler.py @@ -1619,11 +1619,12 @@ class CustomStreamWrapper: completion_start_time=datetime.datetime.now() ) ## LOGGING - executor.submit( - self.run_success_logging_and_cache_storage, - response, - cache_hit, - ) # log response + if not litellm.disable_streaming_logging: + executor.submit( + self.run_success_logging_and_cache_storage, + response, + cache_hit, + ) # log response choice = response.choices[0] if isinstance(choice, StreamingChoices): self.response_uptil_now += choice.delta.get("content", "") or "" diff --git a/litellm/llms/anthropic/chat/transformation.py b/litellm/llms/anthropic/chat/transformation.py index 1a59d677415..6d41655674c 100644 --- a/litellm/llms/anthropic/chat/transformation.py +++ b/litellm/llms/anthropic/chat/transformation.py @@ -45,7 +45,10 @@ from litellm.types.llms.openai import ( OpenAIMcpServerTool, OpenAIWebSearchOptions, ) -from litellm.types.utils import CompletionTokensDetailsWrapper +from litellm.types.utils import ( + CacheCreationTokenDetails, + CompletionTokensDetailsWrapper, +) from litellm.types.utils import Message as LitellmMessage from litellm.types.utils import PromptTokensDetailsWrapper, ServerToolUse from litellm.utils import ( @@ -801,7 +804,15 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): if content.get("citations") is not None: if citations is None: citations = [] - citations.append(content["citations"]) + citations.append( + [ + { + **citation, + "supported_text": content.get("text", ""), + } + for citation in content["citations"] + ] + ) if thinking_blocks is not None: reasoning_content = "" for block in thinking_blocks: @@ -820,6 +831,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): _usage = usage_object cache_creation_input_tokens: int = 0 cache_read_input_tokens: int = 0 + cache_creation_token_details: Optional[CacheCreationTokenDetails] = None web_search_requests: Optional[int] = None if ( "cache_creation_input_tokens" in _usage @@ -842,8 +854,20 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): int, _usage["server_tool_use"]["web_search_requests"] ) + if "cache_creation" in _usage and _usage["cache_creation"] is not None: + cache_creation_token_details = CacheCreationTokenDetails( + ephemeral_5m_input_tokens=_usage["cache_creation"].get( + "ephemeral_5m_input_tokens" + ), + ephemeral_1h_input_tokens=_usage["cache_creation"].get( + "ephemeral_1h_input_tokens" + ), + ) + prompt_tokens_details = PromptTokensDetailsWrapper( cached_tokens=cache_read_input_tokens, + cache_creation_tokens=cache_read_input_tokens, + cache_creation_token_details=cache_creation_token_details, ) completion_token_details = ( CompletionTokensDetailsWrapper( diff --git a/litellm/llms/azure/chat/o_series_handler.py b/litellm/llms/azure/chat/o_series_handler.py index 2f3e9e63996..d0f5153b0eb 100644 --- a/litellm/llms/azure/chat/o_series_handler.py +++ b/litellm/llms/azure/chat/o_series_handler.py @@ -4,7 +4,7 @@ Handler file for calls to Azure OpenAI's o1/o3 family of models Written separately to handle faking streaming for o1 and o3 models. """ -from typing import Any, Callable, Optional, Union +from typing import TYPE_CHECKING, Any, Callable, Optional, Union import httpx @@ -13,6 +13,9 @@ from litellm.types.utils import ModelResponse from ...openai.openai import OpenAIChatCompletion from ..common_utils import BaseAzureLLM +if TYPE_CHECKING: + from aiohttp import ClientSession + class AzureOpenAIO1ChatCompletion(BaseAzureLLM, OpenAIChatCompletion): def completion( @@ -38,6 +41,7 @@ class AzureOpenAIO1ChatCompletion(BaseAzureLLM, OpenAIChatCompletion): organization: Optional[str] = None, custom_llm_provider: Optional[str] = None, drop_params: Optional[bool] = None, + shared_session: Optional["ClientSession"] = None, ): client = self.get_azure_openai_client( litellm_params=litellm_params, @@ -69,4 +73,5 @@ class AzureOpenAIO1ChatCompletion(BaseAzureLLM, OpenAIChatCompletion): organization=organization, custom_llm_provider=custom_llm_provider, drop_params=drop_params, + shared_session=shared_session, ) diff --git a/litellm/llms/azure_ai/image_edit/__init__.py b/litellm/llms/azure_ai/image_edit/__init__.py new file mode 100644 index 00000000000..e0e57bec403 --- /dev/null +++ b/litellm/llms/azure_ai/image_edit/__init__.py @@ -0,0 +1,15 @@ +from litellm.llms.base_llm.image_edit.transformation import BaseImageEditConfig + +from .transformation import AzureFoundryFluxImageEditConfig + +__all__ = ["AzureFoundryFluxImageEditConfig"] + + +def get_azure_ai_image_edit_config(model: str) -> BaseImageEditConfig: + model = model.lower() + model = model.replace("-", "") + model = model.replace("_", "") + if model == "" or "flux" in model: # empty model is flux + return AzureFoundryFluxImageEditConfig() + else: + raise ValueError(f"Model {model} is not supported for Azure AI image editing.") diff --git a/litellm/llms/azure_ai/image_edit/transformation.py b/litellm/llms/azure_ai/image_edit/transformation.py new file mode 100644 index 00000000000..47f612912ce --- /dev/null +++ b/litellm/llms/azure_ai/image_edit/transformation.py @@ -0,0 +1,99 @@ +from typing import Optional + +import httpx + +import litellm +from litellm.llms.azure_ai.common_utils import AzureFoundryModelInfo +from litellm.llms.openai.image_edit.transformation import OpenAIImageEditConfig +from litellm.secret_managers.main import get_secret_str +from litellm.utils import _add_path_to_api_base + + +class AzureFoundryFluxImageEditConfig(OpenAIImageEditConfig): + """ + Azure AI Foundry FLUX image edit config + + Supports FLUX models including FLUX-1-kontext-pro for image editing. + + Azure AI Foundry FLUX models handle image editing through the /images/edits endpoint, + same as standard Azure OpenAI models. The request format uses multipart/form-data + with image files and prompt. + """ + + def validate_environment( + self, + headers: dict, + model: str, + api_key: Optional[str] = None, + ) -> dict: + """ + Validate Azure AI Foundry environment and set up authentication + Uses Api-Key header format + """ + api_key = AzureFoundryModelInfo.get_api_key(api_key) + + if not api_key: + raise ValueError( + f"Azure AI API key is required for model {model}. Set AZURE_AI_API_KEY environment variable or pass api_key parameter." + ) + + headers.update( + { + "Api-Key": api_key, # Azure AI Foundry uses Api-Key header format + } + ) + return headers + + def get_complete_url( + self, + model: str, + api_base: Optional[str], + litellm_params: dict, + ) -> str: + """ + Constructs a complete URL for Azure AI Foundry image edits API request. + + Azure AI Foundry FLUX models handle image editing through the /images/edits + endpoint. + + Args: + - model: Model name (deployment name for Azure AI Foundry) + - api_base: Base URL for Azure AI endpoint + - litellm_params: Additional parameters including api_version + + Returns: + - Complete URL for the image edits endpoint + """ + api_base = AzureFoundryModelInfo.get_api_base(api_base) + + if api_base is None: + raise ValueError( + "Azure AI API base is required. Set AZURE_AI_API_BASE environment variable or pass api_base parameter." + ) + + api_version = (litellm_params.get("api_version") or litellm.api_version + or get_secret_str("AZURE_AI_API_VERSION") + ) + if api_version is None: + # API version is mandatory for Azure AI Foundry + raise ValueError( + "Azure API version is required. Set AZURE_AI_API_VERSION environment variable or pass api_version parameter." + ) + + # Add the path to the base URL using the model as deployment name + # Azure AI Foundry FLUX models use /images/edits for editing + if "/openai/deployments/" in api_base: + new_url = _add_path_to_api_base( + api_base=api_base, + ending_path="/images/edits", + ) + else: + new_url = _add_path_to_api_base( + api_base=api_base, + ending_path=f"/openai/deployments/{model}/images/edits", + ) + + # Use the new query_params dictionary + final_url = httpx.URL(new_url).copy_with(params={"api-version": api_version}) + + return str(final_url) diff --git a/litellm/llms/bedrock/chat/converse_transformation.py b/litellm/llms/bedrock/chat/converse_transformation.py index d99cff6b6bc..a61cfa39e47 100644 --- a/litellm/llms/bedrock/chat/converse_transformation.py +++ b/litellm/llms/bedrock/chat/converse_transformation.py @@ -175,6 +175,77 @@ class AmazonConverseConfig(BaseConfig): and v is not None } + def _validate_request_metadata(self, metadata: dict) -> None: + """ + Validate requestMetadata according to AWS Bedrock Converse API constraints. + + Constraints: + - Maximum of 16 items + - Keys: 1-256 characters, pattern [a-zA-Z0-9\\s:_@$#=/+,-.]{1,256} + - Values: 0-256 characters, pattern [a-zA-Z0-9\\s:_@$#=/+,-.]{0,256} + """ + import re + + if not isinstance(metadata, dict): + raise litellm.exceptions.BadRequestError( + message="requestMetadata must be a dictionary", + model="bedrock", + llm_provider="bedrock", + ) + + if len(metadata) > 16: + raise litellm.exceptions.BadRequestError( + message="requestMetadata can contain a maximum of 16 items", + model="bedrock", + llm_provider="bedrock", + ) + + key_pattern = re.compile(r"^[a-zA-Z0-9\s:_@$#=/+,.-]{1,256}$") + value_pattern = re.compile(r"^[a-zA-Z0-9\s:_@$#=/+,.-]{0,256}$") + + for key, value in metadata.items(): + if not isinstance(key, str): + raise litellm.exceptions.BadRequestError( + message="requestMetadata keys must be strings", + model="bedrock", + llm_provider="bedrock", + ) + + if not isinstance(value, str): + raise litellm.exceptions.BadRequestError( + message="requestMetadata values must be strings", + model="bedrock", + llm_provider="bedrock", + ) + + if len(key) == 0 or len(key) > 256: + raise litellm.exceptions.BadRequestError( + message="requestMetadata key length must be 1-256 characters", + model="bedrock", + llm_provider="bedrock", + ) + + if len(value) > 256: + raise litellm.exceptions.BadRequestError( + message="requestMetadata value length must be 0-256 characters", + model="bedrock", + llm_provider="bedrock", + ) + + if not key_pattern.match(key): + raise litellm.exceptions.BadRequestError( + message=f"requestMetadata key '{key}' contains invalid characters. Allowed: [a-zA-Z0-9\\s:_@$#=/+,.-]", + model="bedrock", + llm_provider="bedrock", + ) + + if not value_pattern.match(value): + raise litellm.exceptions.BadRequestError( + message=f"requestMetadata value '{value}' contains invalid characters. Allowed: [a-zA-Z0-9\\s:_@$#=/+,.-]", + model="bedrock", + llm_provider="bedrock", + ) + def get_supported_openai_params(self, model: str) -> List[str]: from litellm.utils import supports_function_calling @@ -188,6 +259,7 @@ class AmazonConverseConfig(BaseConfig): "top_p", "extra_headers", "response_format", + "requestMetadata", ] if ( @@ -497,6 +569,10 @@ class AmazonConverseConfig(BaseConfig): optional_params["thinking"] = AnthropicConfig._map_reasoning_effort( value ) + if param == "requestMetadata": + if value is not None and isinstance(value, dict): + self._validate_request_metadata(value) # type: ignore + optional_params["requestMetadata"] = value # Only update thinking tokens for non-GPT-OSS models if "gpt-oss" not in model: @@ -686,34 +762,10 @@ class AmazonConverseConfig(BaseConfig): return {} - def _transform_request_helper( - self, - model: str, - system_content_blocks: List[SystemContentBlock], - optional_params: dict, - messages: Optional[List[AllMessageValues]] = None, - headers: Optional[dict] = None, - ) -> CommonRequestObject: - ## VALIDATE REQUEST - """ - Bedrock doesn't support tool calling without `tools=` param specified. - """ - if ( - "tools" not in optional_params - and messages is not None - and has_tool_call_blocks(messages) - ): - if litellm.modify_params: - optional_params["tools"] = add_dummy_tool( - custom_llm_provider="bedrock_converse" - ) - else: - raise litellm.UnsupportedParamsError( - message="Bedrock doesn't support tool calling without `tools=` param specified. Pass `tools=` param OR set `litellm.modify_params = True` // `litellm_settings::modify_params: True` to add dummy tool to the request.", - model="", - llm_provider="bedrock", - ) - + def _prepare_request_params( + self, optional_params: dict, model: str + ) -> Tuple[dict, dict, dict]: + """Prepare and separate request parameters.""" inference_params = copy.deepcopy(optional_params) supported_converse_params = list( AmazonConverseConfig.__annotations__.keys() @@ -727,6 +779,11 @@ class AmazonConverseConfig(BaseConfig): ) inference_params.pop("json_mode", None) # used for handling json_schema + # Extract requestMetadata before processing other parameters + request_metadata = inference_params.pop("requestMetadata", None) + if request_metadata is not None: + self._validate_request_metadata(request_metadata) + # keep supported params in 'inference_params', and set all model-specific params in 'additional_request_params' additional_request_params = { k: v for k, v in inference_params.items() if k not in total_supported_params @@ -740,9 +797,16 @@ class AmazonConverseConfig(BaseConfig): self._handle_top_k_value(model, inference_params) ) - original_tools = inference_params.pop("tools", []) + return inference_params, additional_request_params, request_metadata - # Initialize bedrock_tools + def _process_tools_and_beta( + self, + original_tools: list, + model: str, + headers: Optional[dict], + additional_request_params: dict, + ) -> Tuple[List[ToolBlock], list]: + """Process tools and collect anthropic_beta values.""" bedrock_tools: List[ToolBlock] = [] # Collect anthropic_beta values from user headers @@ -784,6 +848,48 @@ class AmazonConverseConfig(BaseConfig): seen.add(beta) additional_request_params["anthropic_beta"] = unique_betas + return bedrock_tools, anthropic_beta_list + + def _transform_request_helper( + self, + model: str, + system_content_blocks: List[SystemContentBlock], + optional_params: dict, + messages: Optional[List[AllMessageValues]] = None, + headers: Optional[dict] = None, + ) -> CommonRequestObject: + ## VALIDATE REQUEST + """ + Bedrock doesn't support tool calling without `tools=` param specified. + """ + if ( + "tools" not in optional_params + and messages is not None + and has_tool_call_blocks(messages) + ): + if litellm.modify_params: + optional_params["tools"] = add_dummy_tool( + custom_llm_provider="bedrock_converse" + ) + else: + raise litellm.UnsupportedParamsError( + message="Bedrock doesn't support tool calling without `tools=` param specified. Pass `tools=` param OR set `litellm.modify_params = True` // `litellm_settings::modify_params: True` to add dummy tool to the request.", + model="", + llm_provider="bedrock", + ) + + # Prepare and separate parameters + inference_params, additional_request_params, request_metadata = ( + self._prepare_request_params(optional_params, model) + ) + + original_tools = inference_params.pop("tools", []) + + # Process tools and collect beta values + bedrock_tools, anthropic_beta_list = self._process_tools_and_beta( + original_tools, model, headers, additional_request_params + ) + bedrock_tool_config: Optional[ToolConfigBlock] = None if len(bedrock_tools) > 0: tool_choice_values: ToolChoiceValuesBlock = inference_params.pop( @@ -813,6 +919,10 @@ class AmazonConverseConfig(BaseConfig): if bedrock_tool_config is not None: data["toolConfig"] = bedrock_tool_config + # Request Metadata (top-level field) + if request_metadata is not None: + data["requestMetadata"] = request_metadata + return data async def _async_transform_request( @@ -1059,9 +1169,7 @@ class AmazonConverseConfig(BaseConfig): return message, returned_finish_reason - def _translate_message_content( - self, content_blocks: List[ContentBlock] - ) -> Tuple[ + def _translate_message_content(self, content_blocks: List[ContentBlock]) -> Tuple[ str, List[ChatCompletionToolCallChunk], Optional[List[BedrockConverseReasoningContentBlock]], @@ -1076,9 +1184,9 @@ class AmazonConverseConfig(BaseConfig): """ content_str = "" tools: List[ChatCompletionToolCallChunk] = [] - reasoningContentBlocks: Optional[ - List[BedrockConverseReasoningContentBlock] - ] = None + reasoningContentBlocks: Optional[List[BedrockConverseReasoningContentBlock]] = ( + None + ) for idx, content in enumerate(content_blocks): """ - Content is either a tool response or text @@ -1199,9 +1307,9 @@ class AmazonConverseConfig(BaseConfig): chat_completion_message: ChatCompletionResponseMessage = {"role": "assistant"} content_str = "" tools: List[ChatCompletionToolCallChunk] = [] - reasoningContentBlocks: Optional[ - List[BedrockConverseReasoningContentBlock] - ] = None + reasoningContentBlocks: Optional[List[BedrockConverseReasoningContentBlock]] = ( + None + ) if message is not None: ( @@ -1214,12 +1322,12 @@ class AmazonConverseConfig(BaseConfig): chat_completion_message["provider_specific_fields"] = { "reasoningContentBlocks": reasoningContentBlocks, } - chat_completion_message[ - "reasoning_content" - ] = self._transform_reasoning_content(reasoningContentBlocks) - chat_completion_message[ - "thinking_blocks" - ] = self._transform_thinking_blocks(reasoningContentBlocks) + chat_completion_message["reasoning_content"] = ( + self._transform_reasoning_content(reasoningContentBlocks) + ) + chat_completion_message["thinking_blocks"] = ( + self._transform_thinking_blocks(reasoningContentBlocks) + ) chat_completion_message["content"] = content_str if ( json_mode is True diff --git a/litellm/llms/bedrock/common_utils.py b/litellm/llms/bedrock/common_utils.py index c7f7acf331d..63c366c9480 100644 --- a/litellm/llms/bedrock/common_utils.py +++ b/litellm/llms/bedrock/common_utils.py @@ -774,7 +774,7 @@ class CommonBatchFilesUtils: Returns: Unique job name (ā‰¤ 63 characters for Bedrock compatibility) """ - import fastuuid as uuid + from litellm._uuid import uuid unique_id = str(uuid.uuid4())[:8] # Format: {prefix}-batch-{model}-{uuid} # Example: litellm-batch-claude-266c398e diff --git a/litellm/llms/bedrock/embed/amazon_titan_v2_transformation.py b/litellm/llms/bedrock/embed/amazon_titan_v2_transformation.py index 8056e9e9b2c..ff748b58e8e 100644 --- a/litellm/llms/bedrock/embed/amazon_titan_v2_transformation.py +++ b/litellm/llms/bedrock/embed/amazon_titan_v2_transformation.py @@ -10,7 +10,7 @@ Docs - https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-tit """ import types -from typing import List, Optional +from typing import List, Optional, Union from litellm.types.llms.bedrock import ( AmazonTitanV2EmbeddingRequest, @@ -30,9 +30,7 @@ class AmazonTitanV2Config: normalize: Optional[bool] = None dimensions: Optional[int] = None - def __init__( - self, normalize: Optional[bool] = None, dimensions: Optional[int] = None - ) -> None: + def __init__(self, normalize: Optional[bool] = None, dimensions: Optional[int] = None) -> None: locals_ = locals().copy() for key, value in locals_.items(): if key != "self" and value is not None: @@ -57,32 +55,56 @@ class AmazonTitanV2Config: } def get_supported_openai_params(self) -> List[str]: - return ["dimensions"] + return ["dimensions", "encoding_format"] - def map_openai_params( - self, non_default_params: dict, optional_params: dict - ) -> dict: + def map_openai_params(self, non_default_params: dict, optional_params: dict) -> dict: for k, v in non_default_params.items(): if k == "dimensions": optional_params["dimensions"] = v + elif k == "encoding_format": + # Map OpenAI encoding_format to AWS embeddingTypes + if v == "float": + optional_params["embeddingTypes"] = ["float"] + elif v == "base64": + # base64 maps to binary format in AWS + optional_params["embeddingTypes"] = ["binary"] + else: + # For any other encoding format, default to float + optional_params["embeddingTypes"] = ["float"] return optional_params - def _transform_request( - self, input: str, inference_params: dict - ) -> AmazonTitanV2EmbeddingRequest: + def _transform_request(self, input: str, inference_params: dict) -> AmazonTitanV2EmbeddingRequest: return AmazonTitanV2EmbeddingRequest(inputText=input, **inference_params) # type: ignore - def _transform_response( - self, response_list: List[dict], model: str - ) -> EmbeddingResponse: + def _transform_response(self, response_list: List[dict], model: str) -> EmbeddingResponse: total_prompt_tokens = 0 transformed_responses: List[Embedding] = [] for index, response in enumerate(response_list): _parsed_response = AmazonTitanV2EmbeddingResponse(**response) # type: ignore + + # According to AWS docs, embeddingsByType is always present + # If binary was requested (encoding_format="base64"), use binary data + # Otherwise, use float data from embeddingsByType or fallback to embedding field + embedding_data: Union[List[float], List[int]] + + if ("embeddingsByType" in _parsed_response and + "binary" in _parsed_response["embeddingsByType"]): + # Use binary data if available (for encoding_format="base64") + embedding_data = _parsed_response["embeddingsByType"]["binary"] + elif ("embeddingsByType" in _parsed_response and + "float" in _parsed_response["embeddingsByType"]): + # Use float data from embeddingsByType + embedding_data = _parsed_response["embeddingsByType"]["float"] + elif "embedding" in _parsed_response: + # Fallback to legacy embedding field + embedding_data = _parsed_response["embedding"] + else: + raise ValueError(f"No embedding data found in response: {response}") + transformed_responses.append( Embedding( - embedding=_parsed_response["embedding"], + embedding=embedding_data, index=index, object="embedding", ) diff --git a/litellm/llms/bedrock/image/amazon_nova_canvas_transformation.py b/litellm/llms/bedrock/image/amazon_nova_canvas_transformation.py index 3ef7a40e9a9..cd33e62af16 100644 --- a/litellm/llms/bedrock/image/amazon_nova_canvas_transformation.py +++ b/litellm/llms/bedrock/image/amazon_nova_canvas_transformation.py @@ -7,12 +7,12 @@ from litellm.types.llms.bedrock import ( AmazonNovaCanvasColorGuidedGenerationParams, AmazonNovaCanvasColorGuidedRequest, AmazonNovaCanvasImageGenerationConfig, + AmazonNovaCanvasInpaintingParams, + AmazonNovaCanvasInpaintingRequest, AmazonNovaCanvasRequestBase, AmazonNovaCanvasTextToImageParams, AmazonNovaCanvasTextToImageRequest, AmazonNovaCanvasTextToImageResponse, - AmazonNovaCanvasInpaintingParams, - AmazonNovaCanvasInpaintingRequest, ) from litellm.types.utils import ImageResponse @@ -67,6 +67,11 @@ class AmazonNovaCanvasConfig: """ task_type = optional_params.pop("taskType", "TEXT_IMAGE") image_generation_config = optional_params.pop("imageGenerationConfig", {}) + + # Extract model_id parameter to prevent "extraneous key" error from Bedrock API + # Following the same pattern as chat completions and embeddings + unencoded_model_id = optional_params.pop("model_id", None) # noqa: F841 + image_generation_config = {**image_generation_config, **optional_params} if task_type == "TEXT_IMAGE": text_to_image_params: Dict[str, Any] = image_generation_config.pop( diff --git a/litellm/llms/bedrock/image/image_handler.py b/litellm/llms/bedrock/image/image_handler.py index 55d94675d14..0103f190d36 100644 --- a/litellm/llms/bedrock/image/image_handler.py +++ b/litellm/llms/bedrock/image/image_handler.py @@ -233,7 +233,17 @@ class BedrockImageGeneration(BaseAWSLLM): Returns: dict: The request body to use for the Bedrock Image Generation API """ - provider = model.split(".")[0] + # Use the existing ARN-aware provider detection method + bedrock_provider = self.get_bedrock_invoke_provider(model) + + if bedrock_provider == "amazon" or bedrock_provider == "nova": + # Handle Amazon Nova Canvas models + provider = "amazon" + elif bedrock_provider == "stability": + provider = "stability" + else: + # Fallback to original logic for backward compatibility + provider = model.split(".")[0] inference_params = copy.deepcopy(optional_params) inference_params.pop( "user", None diff --git a/litellm/llms/custom_httpx/http_handler.py b/litellm/llms/custom_httpx/http_handler.py index 36b543086f5..3a67d5127b7 100644 --- a/litellm/llms/custom_httpx/http_handler.py +++ b/litellm/llms/custom_httpx/http_handler.py @@ -167,6 +167,7 @@ class AsyncHTTPHandler: concurrent_limit=1000, client_alias: Optional[str] = None, # name for client in logs ssl_verify: Optional[VerifyTypes] = None, + shared_session: Optional["ClientSession"] = None, ): self.timeout = timeout self.event_hooks = event_hooks @@ -175,6 +176,7 @@ class AsyncHTTPHandler: concurrent_limit=concurrent_limit, event_hooks=event_hooks, ssl_verify=ssl_verify, + shared_session=shared_session, ) self.client_alias = client_alias @@ -184,6 +186,7 @@ class AsyncHTTPHandler: concurrent_limit: int, event_hooks: Optional[Mapping[str, List[Callable[..., Any]]]], ssl_verify: Optional[VerifyTypes] = None, + shared_session: Optional["ClientSession"] = None, ) -> httpx.AsyncClient: # Get unified SSL configuration ssl_config = get_ssl_configuration(ssl_verify) @@ -199,6 +202,7 @@ class AsyncHTTPHandler: transport = AsyncHTTPHandler._create_async_transport( ssl_context=ssl_config if isinstance(ssl_config, ssl.SSLContext) else None, ssl_verify=ssl_config if isinstance(ssl_config, bool) else None, + shared_session=shared_session, ) return httpx.AsyncClient( @@ -260,7 +264,6 @@ class AsyncHTTPHandler: files: Optional[RequestFiles] = None, content: Any = None, ): - start_time = time.time() try: if timeout is None: @@ -523,7 +526,9 @@ class AsyncHTTPHandler: @staticmethod def _create_async_transport( - ssl_context: Optional[ssl.SSLContext] = None, ssl_verify: Optional[bool] = None + ssl_context: Optional[ssl.SSLContext] = None, + ssl_verify: Optional[bool] = None, + shared_session: Optional["ClientSession"] = None, ) -> Optional[Union[LiteLLMAiohttpTransport, AsyncHTTPTransport]]: """ - Creates a transport for httpx.AsyncClient @@ -544,7 +549,9 @@ class AsyncHTTPHandler: ######################################################### if AsyncHTTPHandler._should_use_aiohttp_transport(): return AsyncHTTPHandler._create_aiohttp_transport( - ssl_context=ssl_context, ssl_verify=ssl_verify + ssl_context=ssl_context, + ssl_verify=ssl_verify, + shared_session=shared_session, ) ######################################################### @@ -612,6 +619,7 @@ class AsyncHTTPHandler: def _create_aiohttp_transport( ssl_verify: Optional[bool] = None, ssl_context: Optional[ssl.SSLContext] = None, + shared_session: Optional["ClientSession"] = None, ) -> LiteLLMAiohttpTransport: """ Creates an AiohttpTransport with RequestNotRead error handling @@ -635,6 +643,18 @@ class AsyncHTTPHandler: trust_env = True verbose_logger.debug("Creating AiohttpTransport...") + + # Use shared session if provided and valid + if shared_session is not None and not shared_session.closed: + verbose_logger.debug( + f"SHARED SESSION: Reusing existing ClientSession (ID: {id(shared_session)})" + ) + return LiteLLMAiohttpTransport(client=shared_session) + + # Create new session only if none provided or existing one is invalid + verbose_logger.debug( + "NEW SESSION: Creating new ClientSession (no shared session provided)" + ) return LiteLLMAiohttpTransport( client=lambda: ClientSession( connector=TCPConnector(**connector_kwargs), @@ -915,12 +935,13 @@ class HTTPHandler: if litellm.force_ipv4: return HTTPTransport(local_address="0.0.0.0") else: - return None + return getattr(litellm, 'sync_transport', None) def get_async_httpx_client( llm_provider: Union[LlmProviders, httpxSpecialProvider], params: Optional[dict] = None, + shared_session: Optional["ClientSession"] = None, ) -> AsyncHTTPHandler: """ Retrieves the async HTTP client from the cache @@ -942,10 +963,12 @@ def get_async_httpx_client( return _cached_client if params is not None: + params["shared_session"] = shared_session _new_client = AsyncHTTPHandler(**params) else: _new_client = AsyncHTTPHandler( - timeout=httpx.Timeout(timeout=600.0, connect=5.0) + timeout=httpx.Timeout(timeout=600.0, connect=5.0), + shared_session=shared_session, ) litellm.in_memory_llm_clients_cache.set_cache( diff --git a/litellm/llms/custom_httpx/llm_http_handler.py b/litellm/llms/custom_httpx/llm_http_handler.py index dc64fea1a33..f8a92c2ac96 100644 --- a/litellm/llms/custom_httpx/llm_http_handler.py +++ b/litellm/llms/custom_httpx/llm_http_handler.py @@ -88,6 +88,7 @@ from litellm.utils import ( ) if TYPE_CHECKING: + from aiohttp import ClientSession from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj from litellm.llms.base_llm.passthrough.transformation import BasePassthroughConfig @@ -236,11 +237,16 @@ class BaseLLMHTTPHandler: client: Optional[AsyncHTTPHandler] = None, json_mode: bool = False, signed_json_body: Optional[bytes] = None, + shared_session: Optional["ClientSession"] = None, ): if client is None: + verbose_logger.debug( + f"Creating HTTP client with shared_session: {id(shared_session) if shared_session else None}" + ) async_httpx_client = get_async_httpx_client( llm_provider=litellm.LlmProviders(custom_llm_provider), params={"ssl_verify": litellm_params.get("ssl_verify", None)}, + shared_session=shared_session, ) else: async_httpx_client = client @@ -290,6 +296,7 @@ class BaseLLMHTTPHandler: headers: Optional[Dict[str, Any]] = None, client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None, provider_config: Optional[BaseConfig] = None, + shared_session: Optional["ClientSession"] = None, ): json_mode: bool = optional_params.pop("json_mode", False) extra_body: Optional[dict] = optional_params.pop("extra_body", None) @@ -469,7 +476,7 @@ class BaseLLMHTTPHandler: if client is None or not isinstance(client, HTTPHandler): sync_httpx_client = _get_httpx_client( - params={"ssl_verify": litellm_params.get("ssl_verify", None)} + params={"ssl_verify": litellm_params.get("ssl_verify", None)}, ) else: sync_httpx_client = client @@ -2283,7 +2290,7 @@ class BaseLLMHTTPHandler: e=e, provider_config=provider_config, ) - + # Store the upload URL in litellm_params for the transformation method litellm_params_with_url = dict(litellm_params) litellm_params_with_url["upload_url"] = api_base @@ -2574,11 +2581,11 @@ class BaseLLMHTTPHandler: "url": transformed_request["url"], "headers": transformed_request["headers"], } - + # Only add data for non-GET requests if method != "get" and transformed_request.get("data") is not None: request_kwargs["data"] = transformed_request["data"] - + batch_response = getattr(sync_httpx_client, method)(**request_kwargs) elif isinstance(transformed_request, dict) and api_base: # For other providers that use JSON requests @@ -2743,12 +2750,14 @@ class BaseLLMHTTPHandler: "url": transformed_request["url"], "headers": transformed_request["headers"], } - + # Only add data for non-GET requests if method != "get" and transformed_request.get("data") is not None: request_kwargs["data"] = transformed_request["data"] - - batch_response = await getattr(async_httpx_client, method)(**request_kwargs) + + batch_response = await getattr(async_httpx_client, method)( + **request_kwargs + ) elif isinstance(transformed_request, dict) and api_base: # For other providers that use JSON requests batch_response = await async_httpx_client.get( diff --git a/litellm/llms/gemini/google_genai/transformation.py b/litellm/llms/gemini/google_genai/transformation.py index 28142f72739..f38c772e355 100644 --- a/litellm/llms/gemini/google_genai/transformation.py +++ b/litellm/llms/gemini/google_genai/transformation.py @@ -121,7 +121,8 @@ class GoogleGenAIConfig(BaseGoogleGenAIGenerateContentConfig, VertexLLM): default_headers = { "Content-Type": "application/json", } - gemini_api_key = self._get_google_ai_studio_api_key(dict(litellm_params or {})) + # Use the passed api_key first, then fall back to litellm_params and environment + gemini_api_key = api_key or self._get_google_ai_studio_api_key(dict(litellm_params or {})) if gemini_api_key is not None: default_headers[self.XGOOGLE_API_KEY] = gemini_api_key if headers is not None: diff --git a/litellm/llms/gemini/image_generation/transformation.py b/litellm/llms/gemini/image_generation/transformation.py index e57364fd288..f136bd0a404 100644 --- a/litellm/llms/gemini/image_generation/transformation.py +++ b/litellm/llms/gemini/image_generation/transformation.py @@ -85,17 +85,25 @@ class GoogleImageGenConfig(BaseImageGenerationConfig): ) -> str: """ Get the complete url for the request - - Google AI API format: https://generativelanguage.googleapis.com/v1beta/models/{model}:predict + + Gemini 2.5 Flash Image Preview: :generateContent + Other Imagen models: :predict """ complete_url: str = ( - api_base - or get_secret_str("GEMINI_API_BASE") + api_base + or get_secret_str("GEMINI_API_BASE") or self.DEFAULT_BASE_URL ) complete_url = complete_url.rstrip("/") - complete_url = f"{complete_url}/models/{model}:predict" + + # Gemini 2.5 Flash Image Preview uses generateContent endpoint + if "2.5-flash-image-preview" in model: + complete_url = f"{complete_url}/models/{model}:generateContent" + else: + # All other Imagen models use predict endpoint + complete_url = f"{complete_url}/models/{model}:predict" + return complete_url def validate_environment( @@ -128,35 +136,52 @@ class GoogleImageGenConfig(BaseImageGenerationConfig): headers: dict, ) -> dict: """ - Transform the image generation request to Google AI Imagen format - - Google AI API format: + Transform the image generation request to Gemini format + + For Gemini 2.5 Flash Image Preview, use the standard Gemini format with response_modalities: { - "instances": [ + "contents": [ { - "prompt": "Robot holding a red skateboard" + "parts": [ + {"text": "Generate an image of..."} + ] } ], - "parameters": { - "sampleCount": 4, - "aspectRatio": "1:1", - "personGeneration": "allow_adult" + "generationConfig": { + "response_modalities": ["IMAGE", "TEXT"] } } """ - from litellm.types.llms.gemini import ( - GeminiImageGenerationInstance, - GeminiImageGenerationParameters, - ) - request_body: GeminiImageGenerationRequest = GeminiImageGenerationRequest( - instances=[ - GeminiImageGenerationInstance( - prompt=prompt - ) - ], - parameters=GeminiImageGenerationParameters(**optional_params) - ) - return request_body.model_dump(exclude_none=True) + # For Gemini 2.5 Flash Image Preview, use standard Gemini format + if "2.5-flash-image-preview" in model: + request_body: dict = { + "contents": [ + { + "parts": [ + {"text": prompt} + ] + } + ], + "generationConfig": { + "response_modalities": ["IMAGE", "TEXT"] + } + } + return request_body + else: + # For other Imagen models, use the original Imagen format + from litellm.types.llms.gemini import ( + GeminiImageGenerationInstance, + GeminiImageGenerationParameters, + ) + request_body_obj: GeminiImageGenerationRequest = GeminiImageGenerationRequest( + instances=[ + GeminiImageGenerationInstance( + prompt=prompt + ) + ], + parameters=GeminiImageGenerationParameters(**optional_params) + ) + return request_body_obj.model_dump(exclude_none=True) def transform_image_generation_response( self, @@ -185,14 +210,30 @@ class GoogleImageGenConfig(BaseImageGenerationConfig): if not model_response.data: model_response.data = [] - - # Google AI returns predictions with generated images - predictions = response_data.get("predictions", []) - for prediction in predictions: - # Google AI returns base64 encoded images in the prediction - model_response.data.append(ImageObject( - b64_json=prediction.get("bytesBase64Encoded", None), - url=None, # Google AI returns base64, not URLs - )) - + + # Handle different response formats based on model + if "2.5-flash-image-preview" in model: + # Gemini 2.5 Flash Image Preview returns in candidates format + candidates = response_data.get("candidates", []) + for candidate in candidates: + content = candidate.get("content", {}) + parts = content.get("parts", []) + for part in parts: + # Look for inlineData with image + if "inlineData" in part: + inline_data = part["inlineData"] + if "data" in inline_data: + model_response.data.append(ImageObject( + b64_json=inline_data["data"], + url=None, + )) + else: + # Original Imagen format - predictions with generated images + predictions = response_data.get("predictions", []) + for prediction in predictions: + # Google AI returns base64 encoded images in the prediction + model_response.data.append(ImageObject( + b64_json=prediction.get("bytesBase64Encoded", None), + url=None, # Google AI returns base64, not URLs + )) return model_response \ No newline at end of file diff --git a/litellm/llms/openai/common_utils.py b/litellm/llms/openai/common_utils.py index aa670df0531..00197a67e95 100644 --- a/litellm/llms/openai/common_utils.py +++ b/litellm/llms/openai/common_utils.py @@ -5,12 +5,15 @@ Common helpers / utils across al OpenAI endpoints import hashlib import json import ssl -from typing import Any, Dict, List, Literal, Optional, Union +from typing import Any, Dict, List, Literal, Optional, TYPE_CHECKING, Union import httpx import openai from openai import AsyncAzureOpenAI, AsyncOpenAI, AzureOpenAI, OpenAI +if TYPE_CHECKING: + from aiohttp import ClientSession + import litellm from litellm.llms.base_llm.chat.transformation import BaseLLMException from litellm.llms.custom_httpx.http_handler import ( @@ -194,7 +197,9 @@ class BaseOpenAILLM: return param_names @staticmethod - def _get_async_http_client() -> Optional[httpx.AsyncClient]: + def _get_async_http_client( + shared_session: Optional["ClientSession"] = None, + ) -> Optional[httpx.AsyncClient]: if litellm.aclient_session is not None: return litellm.aclient_session @@ -205,8 +210,11 @@ class BaseOpenAILLM: limits=httpx.Limits(max_connections=1000, max_keepalive_connections=100), verify=ssl_config, transport=AsyncHTTPHandler._create_async_transport( - ssl_context=ssl_config if isinstance(ssl_config, ssl.SSLContext) else None, + ssl_context=ssl_config + if isinstance(ssl_config, ssl.SSLContext) + else None, ssl_verify=ssl_config if isinstance(ssl_config, bool) else None, + shared_session=shared_session, ), follow_redirects=True, ) @@ -215,10 +223,10 @@ class BaseOpenAILLM: def _get_sync_http_client() -> Optional[httpx.Client]: if litellm.client_session is not None: return litellm.client_session - + # Get unified SSL configuration ssl_config = get_ssl_configuration() - + return httpx.Client( limits=httpx.Limits(max_connections=1000, max_keepalive_connections=100), verify=ssl_config, diff --git a/litellm/llms/openai/cost_calculation.py b/litellm/llms/openai/cost_calculation.py index 304c444e37a..229f75f2657 100644 --- a/litellm/llms/openai/cost_calculation.py +++ b/litellm/llms/openai/cost_calculation.py @@ -18,7 +18,7 @@ def cost_router(call_type: CallTypes) -> Literal["cost_per_token", "cost_per_sec return "cost_per_token" -def cost_per_token(model: str, usage: Usage) -> Tuple[float, float]: +def cost_per_token(model: str, usage: Usage, service_tier: Optional[str] = None) -> Tuple[float, float]: """ Calculates the cost per token for a given model, prompt tokens, and completion tokens. @@ -31,7 +31,7 @@ def cost_per_token(model: str, usage: Usage) -> Tuple[float, float]: """ ## CALCULATE INPUT COST return generic_cost_per_token( - model=model, usage=usage, custom_llm_provider="openai" + model=model, usage=usage, custom_llm_provider="openai", service_tier=service_tier ) # ### Non-cached text tokens # non_cached_text_tokens = usage.prompt_tokens diff --git a/litellm/llms/openai/openai.py b/litellm/llms/openai/openai.py index 1f3cf24457d..3347e533242 100644 --- a/litellm/llms/openai/openai.py +++ b/litellm/llms/openai/openai.py @@ -10,12 +10,16 @@ from typing import ( List, Literal, Optional, + TYPE_CHECKING, Union, cast, ) from urllib.parse import urlparse import httpx + +if TYPE_CHECKING: + from aiohttp import ClientSession import openai from openai import AsyncOpenAI, OpenAI from openai.types.beta.assistant_deleted import AssistantDeleted @@ -355,6 +359,7 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): max_retries: Optional[int] = DEFAULT_MAX_RETRIES, organization: Optional[str] = None, client: Optional[Union[OpenAI, AsyncOpenAI]] = None, + shared_session: Optional["ClientSession"] = None, ) -> Optional[Union[OpenAI, AsyncOpenAI]]: client_initialization_params: Dict = locals() if client is None: @@ -379,7 +384,9 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): _new_client: Union[OpenAI, AsyncOpenAI] = AsyncOpenAI( api_key=api_key, base_url=api_base, - http_client=OpenAIChatCompletion._get_async_http_client(), + http_client=OpenAIChatCompletion._get_async_http_client( + shared_session=shared_session + ), timeout=timeout, max_retries=max_retries, organization=organization, @@ -522,8 +529,9 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): organization: Optional[str] = None, custom_llm_provider: Optional[str] = None, drop_params: Optional[bool] = None, + shared_session: Optional["ClientSession"] = None, ): - super().completion() + super().completion(shared_session=shared_session) try: fake_stream: bool = False inference_params = optional_params.copy() @@ -606,6 +614,7 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): organization=organization, drop_params=drop_params, fake_stream=fake_stream, + shared_session=shared_session, ) data = provider_config.transform_request( @@ -771,6 +780,7 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): drop_params: Optional[bool] = None, stream_options: Optional[dict] = None, fake_stream: bool = False, + shared_session: Optional["ClientSession"] = None, ): response = None data = await provider_config.async_transform_request( @@ -793,6 +803,7 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): max_retries=max_retries, organization=organization, client=client, + shared_session=shared_session, ) ## LOGGING diff --git a/litellm/llms/vertex_ai/batches/transformation.py b/litellm/llms/vertex_ai/batches/transformation.py index a97f312d486..5b6d21b5948 100644 --- a/litellm/llms/vertex_ai/batches/transformation.py +++ b/litellm/llms/vertex_ai/batches/transformation.py @@ -114,7 +114,14 @@ class VertexAIBatchTransformation: """ Gets the output file id from the Vertex AI Batch response """ - output_file_id: str = "" + + output_file_id: str = ( + response.get("outputInfo", OutputInfo()).get("gcsOutputDirectory", "") + + "/predictions.jsonl" + ) + if output_file_id != "/predictions.jsonl": + return output_file_id + output_config = response.get("outputConfig") if output_config is None: return output_file_id diff --git a/litellm/llms/vertex_ai/files/handler.py b/litellm/llms/vertex_ai/files/handler.py index a666a2c37fb..6636bccd6a3 100644 --- a/litellm/llms/vertex_ai/files/handler.py +++ b/litellm/llms/vertex_ai/files/handler.py @@ -1,5 +1,6 @@ import asyncio -from typing import Any, Coroutine, Optional, Union +import urllib.parse +from typing import Any, Coroutine, Optional, Tuple, Union import httpx @@ -9,7 +10,12 @@ from litellm.integrations.gcs_bucket.gcs_bucket_base import ( GCSLoggingConfig, ) from litellm.llms.custom_httpx.http_handler import get_async_httpx_client -from litellm.types.llms.openai import CreateFileRequest, OpenAIFileObject +from litellm.types.llms.openai import ( + CreateFileRequest, + FileContentRequest, + HttpxBinaryResponseContent, + OpenAIFileObject, +) from litellm.types.llms.vertex_ai import VERTEX_CREDENTIALS_TYPES from .transformation import VertexAIJsonlFilesTransformation @@ -105,3 +111,136 @@ class VertexAIFilesHandler(GCSBucketBase): max_retries=max_retries, ) ) + + def _extract_bucket_and_object_from_file_id(self, file_id: str) -> Tuple[str, str]: + """ + Extract bucket name and object path from URL-encoded file_id. + + Expected format: gs%3A%2F%2Fbucket-name%2Fpath%2Fto%2Ffile + Which decodes to: gs://bucket-name/path/to/file + + Returns: + tuple: (bucket_name, url_encoded_object_path) + - bucket_name: "bucket-name" + - url_encoded_object_path: "path%2Fto%2Ffile" + """ + decoded_path = urllib.parse.unquote(file_id) + + if decoded_path.startswith("gs://"): + full_path = decoded_path[5:] # Remove 'gs://' prefix + else: + full_path = decoded_path + + if "/" in full_path: + bucket_name, object_path = full_path.split("/", 1) + else: + bucket_name = full_path + object_path = "" + + encoded_object_path = urllib.parse.quote(object_path, safe="") + + return bucket_name, encoded_object_path + + async def afile_content( + self, + file_content_request: FileContentRequest, + vertex_credentials: Optional[VERTEX_CREDENTIALS_TYPES], + vertex_project: Optional[str], + vertex_location: Optional[str], + timeout: Union[float, httpx.Timeout], + max_retries: Optional[int], + ) -> HttpxBinaryResponseContent: + """ + Download file content from GCS bucket for VertexAI files. + + Args: + file_content_request: Contains file_id (URL-encoded GCS path) + vertex_credentials: VertexAI credentials + vertex_project: VertexAI project ID + vertex_location: VertexAI location + timeout: Request timeout + max_retries: Max retry attempts + + Returns: + HttpxBinaryResponseContent: Binary content wrapped in compatible response format + """ + file_id = file_content_request.get("file_id") + if not file_id: + raise ValueError("file_id is required in file_content_request") + + bucket_name, encoded_object_path = self._extract_bucket_and_object_from_file_id( + file_id + ) + + download_kwargs = { + "standard_callback_dynamic_params": {"gcs_bucket_name": bucket_name} + } + + file_content = await self.download_gcs_object( + object_name=encoded_object_path, **download_kwargs + ) + + if file_content is None: + decoded_path = urllib.parse.unquote(file_id) + raise ValueError(f"Failed to download file from GCS: {decoded_path}") + + decoded_path = urllib.parse.unquote(file_id) + mock_response = httpx.Response( + status_code=200, + content=file_content, + headers={"content-type": "application/octet-stream"}, + request=httpx.Request(method="GET", url=decoded_path), + ) + + return HttpxBinaryResponseContent(response=mock_response) + + def file_content( + self, + _is_async: bool, + file_content_request: FileContentRequest, + api_base: Optional[str], + vertex_credentials: Optional[VERTEX_CREDENTIALS_TYPES], + vertex_project: Optional[str], + vertex_location: Optional[str], + timeout: Union[float, httpx.Timeout], + max_retries: Optional[int], + ) -> Union[ + HttpxBinaryResponseContent, Coroutine[Any, Any, HttpxBinaryResponseContent] + ]: + """ + Download file content from GCS bucket for VertexAI files. + Supports both sync and async operations. + + Args: + _is_async: Whether to run asynchronously + file_content_request: Contains file_id (URL-encoded GCS path) + api_base: API base (unused for GCS operations) + vertex_credentials: VertexAI credentials + vertex_project: VertexAI project ID + vertex_location: VertexAI location + timeout: Request timeout + max_retries: Max retry attempts + + Returns: + HttpxBinaryResponseContent or Coroutine: Binary content wrapped in compatible response format + """ + if _is_async: + return self.afile_content( + file_content_request=file_content_request, + vertex_credentials=vertex_credentials, + vertex_project=vertex_project, + vertex_location=vertex_location, + timeout=timeout, + max_retries=max_retries, + ) + else: + return asyncio.run( + self.afile_content( + file_content_request=file_content_request, + vertex_credentials=vertex_credentials, + vertex_project=vertex_project, + vertex_location=vertex_location, + timeout=timeout, + max_retries=max_retries, + ) + ) diff --git a/litellm/llms/vertex_ai/files/transformation.py b/litellm/llms/vertex_ai/files/transformation.py index 1066a8f0363..f2e5a5b5d25 100644 --- a/litellm/llms/vertex_ai/files/transformation.py +++ b/litellm/llms/vertex_ai/files/transformation.py @@ -261,10 +261,10 @@ class VertexAIFilesConfig(VertexBase, BaseFilesConfig): raise ValueError("file is required") extracted_file_data = extract_file_data(file_data) extracted_file_data_content = extracted_file_data.get("content") - + if extracted_file_data_content is None: raise ValueError("file content is required") - + if FilesAPIUtils.is_batch_jsonl_file( create_file_data=create_file_data, extracted_file_data=extracted_file_data, @@ -283,7 +283,7 @@ class VertexAIFilesConfig(VertexBase, BaseFilesConfig): openai_jsonl_content ) ) - return json.dumps(vertex_jsonl_content) + return "\n".join(json.dumps(item) for item in vertex_jsonl_content) elif isinstance(extracted_file_data_content, bytes): return extracted_file_data_content else: diff --git a/litellm/llms/vertex_ai/gemini/transformation.py b/litellm/llms/vertex_ai/gemini/transformation.py index c59e3bb24e8..ccaf28e5906 100644 --- a/litellm/llms/vertex_ai/gemini/transformation.py +++ b/litellm/llms/vertex_ai/gemini/transformation.py @@ -537,7 +537,11 @@ def sync_transform_request_body( logging_obj=logging_obj, ) else: # [TODO] implement context caching for gemini as well - cached_content = optional_params.pop("cached_content", None) + cached_content = None + if "cached_content" in optional_params: + cached_content = optional_params.pop("cached_content") + elif "cachedContent" in optional_params: + cached_content = optional_params.pop("cachedContent") return _transform_request_body( messages=messages, @@ -584,7 +588,11 @@ async def async_transform_request_body( logging_obj=logging_obj, ) else: # [TODO] implement context caching for gemini as well - cached_content = optional_params.pop("cached_content", None) + cached_content = None + if "cached_content" in optional_params: + cached_content = optional_params.pop("cached_content") + elif "cachedContent" in optional_params: + cached_content = optional_params.pop("cachedContent") return _transform_request_body( messages=messages, @@ -649,5 +657,3 @@ def _transform_system_message( return SystemInstructions(parts=system_content_blocks), messages return None, messages - - diff --git a/litellm/llms/vertex_ai/vertex_llm_base.py b/litellm/llms/vertex_ai/vertex_llm_base.py index 0f0bc776cc9..6d194d41add 100644 --- a/litellm/llms/vertex_ai/vertex_llm_base.py +++ b/litellm/llms/vertex_ai/vertex_llm_base.py @@ -271,17 +271,11 @@ class VertexBase: def is_using_v1beta1_features(self, optional_params: dict) -> bool: """ - VertexAI only supports ContextCaching on v1beta1 - use this helper to decide if request should be sent to v1 or v1beta1 - Returns v1beta1 if context caching is enabled - Returns v1 in all other cases + Returns true if any beta feature is enabled + Returns false in all other cases """ - if "cached_content" in optional_params: - return True - if "CachedContent" in optional_params: - return True return False def _check_custom_proxy( diff --git a/litellm/llms/vllm/common_utils.py b/litellm/llms/vllm/common_utils.py index 8dca3e1de25..e2ed0daafe4 100644 --- a/litellm/llms/vllm/common_utils.py +++ b/litellm/llms/vllm/common_utils.py @@ -11,7 +11,21 @@ from litellm.utils import _add_path_to_api_base class VLLMError(BaseLLMException): - pass + def __init__( + self, + status_code: int, + message: str, + request: Optional[httpx.Request] = None, + response: Optional[httpx.Response] = None, + headers: Optional[Union[httpx.Headers, dict]] = None, + ): + super().__init__( + status_code=status_code, + message=message, + request=request, + response=response, + headers=headers, + ) class VLLMModelInfo(BaseLLMModelInfo): @@ -25,7 +39,8 @@ class VLLMModelInfo(BaseLLMModelInfo): api_key: Optional[str] = None, api_base: Optional[str] = None, ) -> dict: - """Google AI Studio sends api key in query params""" + if api_key is not None: + headers["x-api-key"] = api_key return headers @staticmethod @@ -53,7 +68,7 @@ class VLLMModelInfo(BaseLLMModelInfo): endpoint = "/v1/models" if api_base is None or api_key is None: raise ValueError( - "GEMINI_API_BASE or GEMINI_API_KEY is not set. Please set the environment variable, to query Gemini's `/models` endpoint." + "VLLM_API_BASE or VLLM_API_KEY is not set. Please set the environment variable, to query VLLM's `/models` endpoint." ) url = _add_path_to_api_base(api_base, endpoint) diff --git a/litellm/llms/wandb/__init__.py b/litellm/llms/wandb/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/litellm/llms/wandb/chat/__init__.py b/litellm/llms/wandb/chat/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/litellm/llms/wandb/chat/transformation.py b/litellm/llms/wandb/chat/transformation.py new file mode 100644 index 00000000000..1cb2ab492bc --- /dev/null +++ b/litellm/llms/wandb/chat/transformation.py @@ -0,0 +1,27 @@ +""" +Wandb Chat Completions API - Transformation + +This is OpenAI compatible - no translation needed / occurs +""" + +from litellm.llms.openai.chat.gpt_transformation import OpenAIGPTConfig + + +class WandbConfig(OpenAIGPTConfig): + def map_openai_params( + self, + non_default_params: dict, + optional_params: dict, + model: str, + drop_params: bool, + ) -> dict: + """ + map max_completion_tokens param to max_tokens + """ + supported_openai_params = self.get_supported_openai_params(model=model) + for param, value in non_default_params.items(): + if param == "max_completion_tokens": + optional_params["max_tokens"] = value + elif param in supported_openai_params: + optional_params[param] = value + return optional_params diff --git a/litellm/main.py b/litellm/main.py index 44f591d49ae..50de09c3dbf 100644 --- a/litellm/main.py +++ b/litellm/main.py @@ -36,8 +36,12 @@ from typing import ( Union, cast, get_args, + TYPE_CHECKING, ) +if TYPE_CHECKING: + from aiohttp import ClientSession + import dotenv import httpx import openai @@ -374,6 +378,8 @@ async def acompletion( # Optional liteLLM function params thinking: Optional[AnthropicThinkingParam] = None, web_search_options: Optional[OpenAIWebSearchOptions] = None, + # Session management + shared_session: Optional["ClientSession"] = None, **kwargs, ) -> Union[ModelResponse, CustomStreamWrapper]: """ @@ -466,6 +472,16 @@ async def acompletion( ######################################################### ######################################################### + # Log shared session usage + if shared_session is not None: + verbose_logger.debug( + f"šŸ”„ SHARED SESSION: acompletion called with shared_session (ID: {id(shared_session)})" + ) + else: + verbose_logger.debug( + "šŸ”„ NO SHARED SESSION: acompletion called without shared_session" + ) + # Adjusted to use explicit arguments instead of *args and **kwargs completion_kwargs = { "model": model, @@ -506,6 +522,7 @@ async def acompletion( "acompletion": True, # assuming this is a required parameter "thinking": thinking, "web_search_options": web_search_options, + "shared_session": shared_session, } if custom_llm_provider is None: _, custom_llm_provider, _, _ = get_llm_provider( @@ -930,6 +947,8 @@ def completion( # type: ignore # noqa: PLR0915 model_list: Optional[list] = None, # pass in a list of api_base,keys, etc. # Optional liteLLM function params thinking: Optional[AnthropicThinkingParam] = None, + # Session management + shared_session: Optional["ClientSession"] = None, **kwargs, ) -> Union[ModelResponse, CustomStreamWrapper]: """ @@ -1596,6 +1615,7 @@ def completion( # type: ignore # noqa: PLR0915 logging_obj=logging, optional_params=optional_params, litellm_params=litellm_params, + shared_session=shared_session, timeout=timeout, # type: ignore client=client, custom_llm_provider=custom_llm_provider, @@ -1642,6 +1662,7 @@ def completion( # type: ignore # noqa: PLR0915 logging_obj=logging, optional_params=optional_params, litellm_params=litellm_params, + shared_session=shared_session, timeout=timeout, # type: ignore client=client, # pass AsyncOpenAI, OpenAI client custom_llm_provider=custom_llm_provider, @@ -1771,6 +1792,7 @@ def completion( # type: ignore # noqa: PLR0915 logging_obj=logging, optional_params=optional_params, litellm_params=litellm_params, + shared_session=shared_session, timeout=timeout, # type: ignore client=client, custom_llm_provider=custom_llm_provider, @@ -1800,6 +1822,7 @@ def completion( # type: ignore # noqa: PLR0915 logging_obj=logging, optional_params=optional_params, litellm_params=litellm_params, + shared_session=shared_session, timeout=timeout, client=client, custom_llm_provider=custom_llm_provider, @@ -1830,6 +1853,7 @@ def completion( # type: ignore # noqa: PLR0915 logging_obj=logging, optional_params=optional_params, litellm_params=litellm_params, + shared_session=shared_session, timeout=timeout, # type: ignore client=client, custom_llm_provider=custom_llm_provider, @@ -1881,6 +1905,7 @@ def completion( # type: ignore # noqa: PLR0915 model_response=model_response, optional_params=optional_params, litellm_params=litellm_params, + shared_session=shared_session, custom_llm_provider=custom_llm_provider, timeout=timeout, headers=headers, @@ -1954,6 +1979,7 @@ def completion( # type: ignore # noqa: PLR0915 logging_obj=logging, optional_params=optional_params, litellm_params=litellm_params, + shared_session=shared_session, timeout=timeout, client=client, custom_llm_provider=custom_llm_provider, @@ -1980,6 +2006,7 @@ def completion( # type: ignore # noqa: PLR0915 or custom_llm_provider == "openai" or custom_llm_provider == "together_ai" or custom_llm_provider == "nebius" + or custom_llm_provider == "wandb" or custom_llm_provider in litellm.openai_compatible_providers or "ft:gpt-3.5-turbo" in model # finetune gpt-3.5-turbo ): # allow user to make an openai call with a custom base @@ -2044,6 +2071,7 @@ def completion( # type: ignore # noqa: PLR0915 optional_params=optional_params, timeout=timeout, litellm_params=litellm_params, + shared_session=shared_session, acompletion=acompletion, stream=stream, api_key=api_key, @@ -2070,6 +2098,7 @@ def completion( # type: ignore # noqa: PLR0915 client=client, # pass AsyncOpenAI, OpenAI client organization=organization, custom_llm_provider=custom_llm_provider, + shared_session=shared_session, ) except Exception as e: ## LOGGING - log the original exception returned @@ -2110,6 +2139,7 @@ def completion( # type: ignore # noqa: PLR0915 optional_params=optional_params, timeout=timeout, litellm_params=litellm_params, + shared_session=shared_session, acompletion=acompletion, stream=stream, api_key=api_key, @@ -2197,6 +2227,7 @@ def completion( # type: ignore # noqa: PLR0915 model_response=model_response, optional_params=optional_params, litellm_params=litellm_params, + shared_session=shared_session, custom_llm_provider="clarifai", timeout=timeout, headers=headers, @@ -2242,6 +2273,7 @@ def completion( # type: ignore # noqa: PLR0915 model_response=model_response, optional_params=optional_params, litellm_params=litellm_params, + shared_session=shared_session, custom_llm_provider="anthropic_text", timeout=timeout, headers=headers, @@ -2427,6 +2459,7 @@ def completion( # type: ignore # noqa: PLR0915 model_response=model_response, optional_params=optional_params, litellm_params=litellm_params, + shared_session=shared_session, custom_llm_provider="cohere_chat", timeout=timeout, headers=headers, @@ -2694,6 +2727,7 @@ def completion( # type: ignore # noqa: PLR0915 model_response=model_response, optional_params=optional_params, litellm_params=litellm_params, + shared_session=shared_session, custom_llm_provider="openrouter", timeout=timeout, headers=headers, @@ -2756,6 +2790,7 @@ def completion( # type: ignore # noqa: PLR0915 model_response=model_response, optional_params=optional_params, litellm_params=litellm_params, + shared_session=shared_session, custom_llm_provider="vercel_ai_gateway", timeout=timeout, headers=headers, @@ -3242,6 +3277,7 @@ def completion( # type: ignore # noqa: PLR0915 model_response=model_response, optional_params=optional_params, litellm_params=litellm_params, + shared_session=shared_session, custom_llm_provider="watsonx_text", timeout=timeout, headers=headers, @@ -3295,6 +3331,7 @@ def completion( # type: ignore # noqa: PLR0915 model_response=model_response, optional_params=optional_params, litellm_params=litellm_params, + shared_session=shared_session, custom_llm_provider="ollama", timeout=timeout, headers=headers, @@ -3328,6 +3365,7 @@ def completion( # type: ignore # noqa: PLR0915 model_response=model_response, optional_params=optional_params, litellm_params=litellm_params, + shared_session=shared_session, custom_llm_provider="ollama_chat", timeout=timeout, headers=headers, @@ -3348,6 +3386,7 @@ def completion( # type: ignore # noqa: PLR0915 model_response=model_response, optional_params=optional_params, litellm_params=litellm_params, + shared_session=shared_session, custom_llm_provider=custom_llm_provider, timeout=timeout, headers=headers, @@ -3380,6 +3419,7 @@ def completion( # type: ignore # noqa: PLR0915 model_response=model_response, optional_params=optional_params, litellm_params=litellm_params, + shared_session=shared_session, custom_llm_provider="cloudflare", timeout=timeout, headers=headers, @@ -3433,6 +3473,7 @@ def completion( # type: ignore # noqa: PLR0915 logging_obj=logging, optional_params=optional_params, litellm_params=litellm_params, + shared_session=shared_session, timeout=timeout, # type: ignore client=client, custom_llm_provider=custom_llm_provider, @@ -3461,6 +3502,7 @@ def completion( # type: ignore # noqa: PLR0915 model_response=model_response, optional_params=optional_params, litellm_params=litellm_params, + shared_session=shared_session, custom_llm_provider="gradient_ai", timeout=timeout, headers=headers, @@ -3614,7 +3656,7 @@ def completion( # type: ignore # noqa: PLR0915 async_fn=acompletion, stream=stream, custom_llm=custom_handler ) - headers = headers or litellm.headers + headers = headers or litellm.headers or {} ## CALL FUNCTION response = handler_fn( @@ -4412,6 +4454,27 @@ def embedding( # noqa: PLR0915 or "api.studio.nebius.ai/v1" ) + response = openai_chat_completions.embedding( + model=model, + input=input, + api_base=api_base, + api_key=api_key, + logging_obj=logging, + timeout=timeout, + model_response=EmbeddingResponse(), + optional_params=optional_params, + client=client, + aembedding=aembedding, + ) + elif custom_llm_provider == "wandb": + api_key = api_key or litellm.api_key or get_secret_str("WANDB_API_KEY") + api_base = ( + api_base + or litellm.api_base + or get_secret_str("WANDB_API_BASE") + or "https://api.inference.wandb.ai/v1" + ) + response = openai_chat_completions.embedding( model=model, input=input, diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 29100016bb0..832ff36bfb8 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -560,10 +560,14 @@ "cache_creation_input_token_cost": 3.75e-06, "cache_read_input_token_cost": 3e-07, "input_cost_per_token": 3e-06, + "input_cost_per_token_above_200k_tokens": 6e-06, + "output_cost_per_token_above_200k_tokens": 2.25e-05, + "cache_creation_input_token_cost_above_200k_tokens": 7.5e-06, + "cache_read_input_token_cost_above_200k_tokens": 6e-07, "litellm_provider": "bedrock_converse", - "max_input_tokens": 200000, - "max_output_tokens": 64000, - "max_tokens": 64000, + "max_input_tokens": 1000000, + "max_output_tokens": 1000000, + "max_tokens": 1000000, "mode": "chat", "output_cost_per_token": 1.5e-05, "search_context_cost_per_query": { @@ -824,10 +828,14 @@ "cache_creation_input_token_cost": 3.75e-06, "cache_read_input_token_cost": 3e-07, "input_cost_per_token": 3e-06, + "input_cost_per_token_above_200k_tokens": 6e-06, + "output_cost_per_token_above_200k_tokens": 2.25e-05, + "cache_creation_input_token_cost_above_200k_tokens": 7.5e-06, + "cache_read_input_token_cost_above_200k_tokens": 6e-07, "litellm_provider": "bedrock_converse", - "max_input_tokens": 200000, - "max_output_tokens": 64000, - "max_tokens": 64000, + "max_input_tokens": 1000000, + "max_output_tokens": 1000000, + "max_tokens": 1000000, "mode": "chat", "output_cost_per_token": 1.5e-05, "search_context_cost_per_query": { @@ -4787,10 +4795,14 @@ "cache_creation_input_token_cost_above_1hr": 6e-06, "cache_read_input_token_cost": 3e-07, "input_cost_per_token": 3e-06, + "input_cost_per_token_above_200k_tokens": 6e-06, + "output_cost_per_token_above_200k_tokens": 2.25e-05, + "cache_creation_input_token_cost_above_200k_tokens": 7.5e-06, + "cache_read_input_token_cost_above_200k_tokens": 6e-07, "litellm_provider": "anthropic", - "max_input_tokens": 200000, - "max_output_tokens": 64000, - "max_tokens": 64000, + "max_input_tokens": 1000000, + "max_output_tokens": 1000000, + "max_tokens": 1000000, "mode": "chat", "output_cost_per_token": 1.5e-05, "search_context_cost_per_query": { @@ -7547,10 +7559,14 @@ "cache_creation_input_token_cost": 3.75e-06, "cache_read_input_token_cost": 3e-07, "input_cost_per_token": 3e-06, + "input_cost_per_token_above_200k_tokens": 6e-06, + "output_cost_per_token_above_200k_tokens": 2.25e-05, + "cache_creation_input_token_cost_above_200k_tokens": 7.5e-06, + "cache_read_input_token_cost_above_200k_tokens": 6e-07, "litellm_provider": "bedrock_converse", - "max_input_tokens": 200000, - "max_output_tokens": 64000, - "max_tokens": 64000, + "max_input_tokens": 1000000, + "max_output_tokens": 1000000, + "max_tokens": 1000000, "mode": "chat", "output_cost_per_token": 1.5e-05, "search_context_cost_per_query": { @@ -9126,7 +9142,7 @@ "max_tokens": 65535, "max_video_length": 1, "max_videos_per_prompt": 10, - "mode": "chat", + "mode": "image_generation", "output_cost_per_image": 0.039, "output_cost_per_reasoning_token": 3e-05, "output_cost_per_token": 3e-05, @@ -10489,7 +10505,7 @@ "max_tokens": 65535, "max_video_length": 1, "max_videos_per_prompt": 10, - "mode": "chat", + "mode": "image_generation", "output_cost_per_image": 0.039, "output_cost_per_reasoning_token": 3e-05, "output_cost_per_token": 3e-05, @@ -11534,8 +11550,10 @@ }, "gpt-4.1": { "cache_read_input_token_cost": 5e-07, + "cache_read_input_token_cost_priority": 8.75e-07, "input_cost_per_token": 2e-06, "input_cost_per_token_batches": 1e-06, + "input_cost_per_token_priority": 3.5e-06, "litellm_provider": "openai", "max_input_tokens": 1047576, "max_output_tokens": 32768, @@ -11543,6 +11561,7 @@ "mode": "chat", "output_cost_per_token": 8e-06, "output_cost_per_token_batches": 4e-06, + "output_cost_per_token_priority": 1.4e-05, "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -11600,8 +11619,10 @@ }, "gpt-4.1-mini": { "cache_read_input_token_cost": 1e-07, + "cache_read_input_token_cost_priority": 1.75e-07, "input_cost_per_token": 4e-07, "input_cost_per_token_batches": 2e-07, + "input_cost_per_token_priority": 7e-07, "litellm_provider": "openai", "max_input_tokens": 1047576, "max_output_tokens": 32768, @@ -11609,6 +11630,7 @@ "mode": "chat", "output_cost_per_token": 1.6e-06, "output_cost_per_token_batches": 8e-07, + "output_cost_per_token_priority": 2.8e-06, "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -11666,8 +11688,10 @@ }, "gpt-4.1-nano": { "cache_read_input_token_cost": 2.5e-08, + "cache_read_input_token_cost_priority": 5e-08, "input_cost_per_token": 1e-07, "input_cost_per_token_batches": 5e-08, + "input_cost_per_token_priority": 2e-07, "litellm_provider": "openai", "max_input_tokens": 1047576, "max_output_tokens": 32768, @@ -11675,6 +11699,7 @@ "mode": "chat", "output_cost_per_token": 4e-07, "output_cost_per_token_batches": 2e-07, + "output_cost_per_token_priority": 8e-07, "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -11773,8 +11798,10 @@ }, "gpt-4o": { "cache_read_input_token_cost": 1.25e-06, + "cache_read_input_token_cost_priority": 2.125e-06, "input_cost_per_token": 2.5e-06, "input_cost_per_token_batches": 1.25e-06, + "input_cost_per_token_priority": 4.25e-06, "litellm_provider": "openai", "max_input_tokens": 128000, "max_output_tokens": 16384, @@ -11782,6 +11809,7 @@ "mode": "chat", "output_cost_per_token": 1e-05, "output_cost_per_token_batches": 5e-06, + "output_cost_per_token_priority": 1.7e-05, "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_pdf_input": true, @@ -11794,6 +11822,7 @@ "gpt-4o-2024-05-13": { "input_cost_per_token": 5e-06, "input_cost_per_token_batches": 2.5e-06, + "input_cost_per_token_priority": 8.75e-06, "litellm_provider": "openai", "max_input_tokens": 128000, "max_output_tokens": 4096, @@ -11801,6 +11830,7 @@ "mode": "chat", "output_cost_per_token": 1.5e-05, "output_cost_per_token_batches": 7.5e-06, + "output_cost_per_token_priority": 2.625e-05, "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_pdf_input": true, @@ -11919,8 +11949,10 @@ }, "gpt-4o-mini": { "cache_read_input_token_cost": 7.5e-08, + "cache_read_input_token_cost_priority": 1.25e-07, "input_cost_per_token": 1.5e-07, "input_cost_per_token_batches": 7.5e-08, + "input_cost_per_token_priority": 2.5e-07, "litellm_provider": "openai", "max_input_tokens": 128000, "max_output_tokens": 16384, @@ -11928,6 +11960,7 @@ "mode": "chat", "output_cost_per_token": 6e-07, "output_cost_per_token_batches": 3e-07, + "output_cost_per_token_priority": 1e-06, "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_pdf_input": true, @@ -12243,13 +12276,19 @@ }, "gpt-5": { "cache_read_input_token_cost": 1.25e-07, + "cache_read_input_token_cost_flex": 6.25e-08, + "cache_read_input_token_cost_priority": 2.5e-07, "input_cost_per_token": 1.25e-06, + "input_cost_per_token_flex": 6.25e-07, + "input_cost_per_token_priority": 2.5e-06, "litellm_provider": "openai", "max_input_tokens": 400000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", "output_cost_per_token": 1e-05, + "output_cost_per_token_flex": 5e-06, + "output_cost_per_token_priority": 2e-05, "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -12275,13 +12314,19 @@ }, "gpt-5-2025-08-07": { "cache_read_input_token_cost": 1.25e-07, + "cache_read_input_token_cost_flex": 6.25e-08, + "cache_read_input_token_cost_priority": 2.5e-07, "input_cost_per_token": 1.25e-06, + "input_cost_per_token_flex": 6.25e-07, + "input_cost_per_token_priority": 2.5e-06, "litellm_provider": "openai", "max_input_tokens": 400000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", "output_cost_per_token": 1e-05, + "output_cost_per_token_flex": 5e-06, + "output_cost_per_token_priority": 2e-05, "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -12303,6 +12348,7 @@ "supports_response_schema": true, "supports_system_messages": true, "supports_tool_choice": true, + "supports_service_tier": true, "supports_vision": true }, "gpt-5-chat": { @@ -12371,13 +12417,19 @@ }, "gpt-5-mini": { "cache_read_input_token_cost": 2.5e-08, + "cache_read_input_token_cost_flex": 1.25e-08, + "cache_read_input_token_cost_priority": 4.5e-08, "input_cost_per_token": 2.5e-07, + "input_cost_per_token_flex": 1.25e-07, + "input_cost_per_token_priority": 4.5e-07, "litellm_provider": "openai", "max_input_tokens": 400000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", "output_cost_per_token": 2e-06, + "output_cost_per_token_flex": 1e-06, + "output_cost_per_token_priority": 3.6e-06, "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -12403,13 +12455,19 @@ }, "gpt-5-mini-2025-08-07": { "cache_read_input_token_cost": 2.5e-08, + "cache_read_input_token_cost_flex": 1.25e-08, + "cache_read_input_token_cost_priority": 4.5e-08, "input_cost_per_token": 2.5e-07, + "input_cost_per_token_flex": 1.25e-07, + "input_cost_per_token_priority": 4.5e-07, "litellm_provider": "openai", "max_input_tokens": 400000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", "output_cost_per_token": 2e-06, + "output_cost_per_token_flex": 1e-06, + "output_cost_per_token_priority": 3.6e-06, "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -12435,13 +12493,17 @@ }, "gpt-5-nano": { "cache_read_input_token_cost": 5e-09, + "cache_read_input_token_cost_flex": 2.5e-09, "input_cost_per_token": 5e-08, + "input_cost_per_token_flex": 2.5e-08, + "input_cost_per_token_priority": 2.5e-06, "litellm_provider": "openai", "max_input_tokens": 400000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", "output_cost_per_token": 4e-07, + "output_cost_per_token_flex": 2e-07, "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -12467,13 +12529,16 @@ }, "gpt-5-nano-2025-08-07": { "cache_read_input_token_cost": 5e-09, + "cache_read_input_token_cost_flex": 2.5e-09, "input_cost_per_token": 5e-08, + "input_cost_per_token_flex": 2.5e-08, "litellm_provider": "openai", "max_input_tokens": 400000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", "output_cost_per_token": 4e-07, + "output_cost_per_token_flex": 2e-07, "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -15177,13 +15242,19 @@ }, "o3": { "cache_read_input_token_cost": 5e-07, + "cache_read_input_token_cost_flex": 2.5e-07, + "cache_read_input_token_cost_priority": 8.75e-07, "input_cost_per_token": 2e-06, + "input_cost_per_token_flex": 1e-06, + "input_cost_per_token_priority": 3.5e-06, "litellm_provider": "openai", "max_input_tokens": 200000, "max_output_tokens": 100000, "max_tokens": 100000, "mode": "chat", "output_cost_per_token": 8e-06, + "output_cost_per_token_flex": 4e-06, + "output_cost_per_token_priority": 1.4e-05, "supported_endpoints": [ "/v1/responses", "/v1/chat/completions", @@ -15399,13 +15470,19 @@ }, "o4-mini": { "cache_read_input_token_cost": 2.75e-07, + "cache_read_input_token_cost_flex": 1.375e-07, + "cache_read_input_token_cost_priority": 5e-07, "input_cost_per_token": 1.1e-06, + "input_cost_per_token_flex": 5.5e-07, + "input_cost_per_token_priority": 2e-06, "litellm_provider": "openai", "max_input_tokens": 200000, "max_output_tokens": 100000, "max_tokens": 100000, "mode": "chat", "output_cost_per_token": 4.4e-06, + "output_cost_per_token_flex": 2.2e-06, + "output_cost_per_token_priority": 8e-06, "supports_function_calling": true, "supports_parallel_function_calling": false, "supports_pdf_input": true, @@ -16096,10 +16173,12 @@ "openrouter/anthropic/claude-sonnet-4": { "input_cost_per_image": 0.0048, "input_cost_per_token": 3e-06, + "input_cost_per_token_above_200k_tokens": 6e-06, + "output_cost_per_token_above_200k_tokens": 2.25e-05, "litellm_provider": "openrouter", - "max_input_tokens": 200000, - "max_output_tokens": 64000, - "max_tokens": 64000, + "max_input_tokens": 1000000, + "max_output_tokens": 1000000, + "max_tokens": 1000000, "mode": "chat", "output_cost_per_token": 1.5e-05, "supports_assistant_prefill": true, @@ -16900,6 +16979,20 @@ "supports_tool_choice": true, "supports_web_search": true }, + "openrouter/x-ai/grok-4-fast:free": { + "input_cost_per_token": 0, + "litellm_provider": "openrouter", + "max_input_tokens": 2000000, + "max_output_tokens": 30000, + "max_tokens": 2000000, + "mode": "chat", + "output_cost_per_token": 0, + "source": "https://openrouter.ai/x-ai/grok-4-fast:free", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true, + "supports_web_search": false + }, "ovhcloud/DeepSeek-R1-Distill-Llama-70B": { "input_cost_per_token": 6.7e-07, "litellm_provider": "ovhcloud", @@ -18945,10 +19038,14 @@ "cache_creation_input_token_cost": 3.75e-06, "cache_read_input_token_cost": 3e-07, "input_cost_per_token": 3e-06, + "input_cost_per_token_above_200k_tokens": 6e-06, + "output_cost_per_token_above_200k_tokens": 2.25e-05, + "cache_creation_input_token_cost_above_200k_tokens": 7.5e-06, + "cache_read_input_token_cost_above_200k_tokens": 6e-07, "litellm_provider": "bedrock_converse", - "max_input_tokens": 200000, - "max_output_tokens": 64000, - "max_tokens": 64000, + "max_input_tokens": 1000000, + "max_output_tokens": 1000000, + "max_tokens": 1000000, "mode": "chat", "output_cost_per_token": 1.5e-05, "search_context_cost_per_query": { @@ -20281,10 +20378,14 @@ "cache_creation_input_token_cost": 3.75e-06, "cache_read_input_token_cost": 3e-07, "input_cost_per_token": 3e-06, + "input_cost_per_token_above_200k_tokens": 6e-06, + "output_cost_per_token_above_200k_tokens": 2.25e-05, + "cache_creation_input_token_cost_above_200k_tokens": 7.5e-06, + "cache_read_input_token_cost_above_200k_tokens": 6e-07, "litellm_provider": "vertex_ai-anthropic_models", - "max_input_tokens": 200000, - "max_output_tokens": 64000, - "max_tokens": 64000, + "max_input_tokens": 1000000, + "max_output_tokens": 1000000, + "max_tokens": 1000000, "mode": "chat", "output_cost_per_token": 1.5e-05, "search_context_cost_per_query": { @@ -20307,10 +20408,14 @@ "cache_creation_input_token_cost": 3.75e-06, "cache_read_input_token_cost": 3e-07, "input_cost_per_token": 3e-06, + "input_cost_per_token_above_200k_tokens": 6e-06, + "output_cost_per_token_above_200k_tokens": 2.25e-05, + 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}, "watsonx/ibm/granite-3-8b-instruct": { "input_cost_per_token": 0.0002, "litellm_provider": "watsonx", @@ -20988,6 +21219,30 @@ "/v1/audio/transcriptions" ] }, + "vertex_ai/qwen/qwen3-next-80b-a3b-instruct-maas": { + "input_cost_per_token": 1.5e-07, + "litellm_provider": "vertex_ai-qwen_models", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 1.2e-06, + "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing", + "supports_function_calling": true, + "supports_tool_choice": true + }, + "vertex_ai/qwen/qwen3-next-80b-a3b-thinking-maas": { + "input_cost_per_token": 1.5e-07, + "litellm_provider": "vertex_ai-qwen_models", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 1.2e-06, + "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing", + "supports_function_calling": true, + "supports_tool_choice": true + }, "xai/grok-2": { "input_cost_per_token": 2e-06, "litellm_provider": "xai", @@ -21240,6 +21495,35 @@ "supports_tool_choice": true, "supports_web_search": true }, + "xai/grok-4-fast-reasoning": { + "litellm_provider": "xai", + "max_input_tokens": 2e6, + "max_output_tokens": 2e6, + "max_tokens": 2e6, + "mode": "chat", + "input_cost_per_token": 0.2e-06, + "output_cost_per_token": 0.5e-06, + "cache_read_input_token_cost": 0.05e-06, + "source": "https://docs.x.ai/docs/models", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true, + "supports_web_search": true + }, + "xai/grok-4-fast-non-reasoning": { + "litellm_provider": "xai", + "max_input_tokens": 2e6, + "max_output_tokens": 2e6, + "cache_read_input_token_cost": 0.05e-06, + "max_tokens": 2e6, + "mode": "chat", + "input_cost_per_token": 0.2e-06, + "output_cost_per_token": 0.5e-06, + "source": "https://docs.x.ai/docs/models", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_web_search": true + }, "xai/grok-4-0709": { "input_cost_per_token": 3e-06, "litellm_provider": "xai", @@ -21337,4 +21621,4 @@ "supports_vision": true, "supports_web_search": true } -} \ No newline at end of file +} diff --git a/litellm/proxy/_experimental/mcp_server/auth/user_api_key_auth_mcp.py b/litellm/proxy/_experimental/mcp_server/auth/user_api_key_auth_mcp.py index a075de13fb1..6afed97fe93 100644 --- a/litellm/proxy/_experimental/mcp_server/auth/user_api_key_auth_mcp.py +++ b/litellm/proxy/_experimental/mcp_server/auth/user_api_key_auth_mcp.py @@ -28,12 +28,16 @@ class MCPRequestHandler: LITELLM_MCP_SERVERS_HEADER_NAME = SpecialHeaders.mcp_servers.value LITELLM_MCP_ACCESS_GROUPS_HEADER_NAME = SpecialHeaders.mcp_access_groups.value - + # MCP Protocol Version header MCP_PROTOCOL_VERSION_HEADER_NAME = "MCP-Protocol-Version" @staticmethod - async def process_mcp_request(scope: Scope) -> Tuple[UserAPIKeyAuth, Optional[str], Optional[List[str]], Optional[Dict[str, str]], Optional[str]]: + async def process_mcp_request( + scope: Scope, + ) -> Tuple[ + UserAPIKeyAuth, Optional[str], Optional[List[str]], Optional[Dict[str, str]] + ]: """ Process and validate MCP request headers from the ASGI scope. This includes: @@ -49,7 +53,6 @@ class MCPRequestHandler: mcp_auth_header: Optional[str] MCP auth header to be passed to the MCP server (deprecated) mcp_servers: Optional[List[str]] List of MCP servers and access groups to use mcp_server_auth_headers: Optional[Dict[str, str]] Server-specific auth headers in format {server_alias: auth_value} - mcp_protocol_version: Optional[str] MCP protocol version from request header Raises: HTTPException: If headers are invalid or missing required headers @@ -58,39 +61,50 @@ class MCPRequestHandler: litellm_api_key = ( MCPRequestHandler.get_litellm_api_key_from_headers(headers) or "" ) - + # Get the old mcp_auth_header for backward compatibility mcp_auth_header = MCPRequestHandler._get_mcp_auth_header_from_headers(headers) - - # Get the new server-specific auth headers - mcp_server_auth_headers = MCPRequestHandler._get_mcp_server_auth_headers_from_headers(headers) - # Get MCP protocol version from header - mcp_protocol_version = headers.get(MCPRequestHandler.MCP_PROTOCOL_VERSION_HEADER_NAME) + # Get the new server-specific auth headers + mcp_server_auth_headers = ( + MCPRequestHandler._get_mcp_server_auth_headers_from_headers(headers) + ) # Parse MCP servers from header - mcp_servers_header = headers.get(MCPRequestHandler.LITELLM_MCP_SERVERS_HEADER_NAME) + mcp_servers_header = headers.get( + MCPRequestHandler.LITELLM_MCP_SERVERS_HEADER_NAME + ) verbose_logger.debug(f"Raw MCP servers header: {mcp_servers_header}") mcp_servers = None if mcp_servers_header is not None: try: - mcp_servers = [s.strip() for s in mcp_servers_header.split(",") if s.strip()] + mcp_servers = [ + s.strip() for s in mcp_servers_header.split(",") if s.strip() + ] verbose_logger.debug(f"Parsed MCP servers: {mcp_servers}") except Exception as e: verbose_logger.debug(f"Error parsing mcp_servers header: {e}") mcp_servers = None - if mcp_servers_header == "" or (mcp_servers is not None and len(mcp_servers) == 0): + if mcp_servers_header == "" or ( + mcp_servers is not None and len(mcp_servers) == 0 + ): mcp_servers = [] # Create a proper Request object with mock body method to avoid ASGI receive channel issues request = Request(scope=scope) + async def mock_body(): return b"{}" + request.body = mock_body # type: ignore validated_user_api_key_auth = await user_api_key_auth( api_key=litellm_api_key, request=request ) - return validated_user_api_key_auth, mcp_auth_header, mcp_servers, mcp_server_auth_headers, mcp_protocol_version - + return ( + validated_user_api_key_auth, + mcp_auth_header, + mcp_servers, + mcp_server_auth_headers, + ) @staticmethod def _get_mcp_auth_header_from_headers(headers: Headers) -> Optional[str]: @@ -104,10 +118,12 @@ class MCPRequestHandler: Support this auth: https://docs.litellm.ai/docs/mcp#using-your-mcp-with-client-side-credentials If you want to use a different header name, you can set the `LITELLM_MCP_CLIENT_SIDE_AUTH_HEADER_NAME` in the secret manager or `mcp_client_side_auth_header_name` in the general settings. - + DEPRECATED: This method is deprecated in favor of server-specific auth headers using the format x-mcp-{{server_alias}}-{{header_name}} instead. """ - mcp_client_side_auth_header_name: str = MCPRequestHandler._get_mcp_client_side_auth_header_name() + mcp_client_side_auth_header_name: str = ( + MCPRequestHandler._get_mcp_client_side_auth_header_name() + ) auth_header = headers.get(mcp_client_side_auth_header_name) if auth_header: verbose_logger.warning( @@ -115,42 +131,49 @@ class MCPRequestHandler: f"Please use server-specific auth headers in the format 'x-mcp-{{server_alias}}-{{header_name}}' instead." ) return auth_header - + @staticmethod def _get_mcp_server_auth_headers_from_headers(headers: Headers) -> Dict[str, str]: """ Parse server-specific MCP auth headers from the request headers. - + Looks for headers in the format: x-mcp-{server_alias}-{header_name} Examples: - x-mcp-github-authorization: Bearer token123 - x-mcp-zapier-x-api-key: api_key_456 - x-mcp-deepwiki-authorization: Basic base64_encoded_creds - + Returns: Dict[str, str]: Mapping of server alias to auth value """ server_auth_headers = {} prefix = "x-mcp-" - + for header_name, header_value in headers.items(): if header_name.lower().startswith(prefix): # Skip the access groups header as it's not a server auth header - if header_name.lower() == MCPRequestHandler.LITELLM_MCP_ACCESS_GROUPS_HEADER_NAME.lower() or header_name.lower() == MCPRequestHandler.LITELLM_MCP_SERVERS_HEADER_NAME.lower(): + if ( + header_name.lower() + == MCPRequestHandler.LITELLM_MCP_ACCESS_GROUPS_HEADER_NAME.lower() + or header_name.lower() + == MCPRequestHandler.LITELLM_MCP_SERVERS_HEADER_NAME.lower() + ): continue - + # Extract server_alias and header_name from x-mcp-{server_alias}-{header_name} - remaining = header_name[len(prefix):].lower() - if '-' in remaining: + remaining = header_name[len(prefix) :].lower() + if "-" in remaining: # Split on the last dash to separate server_alias from header_name - parts = remaining.rsplit('-', 1) + parts = remaining.rsplit("-", 1) if len(parts) == 2: server_alias, auth_header_name = parts server_auth_headers[server_alias] = header_value - verbose_logger.debug(f"Found server auth header: {server_alias} -> {auth_header_name}: {header_value[:10]}...") - + verbose_logger.debug( + f"Found server auth header: {server_alias} -> {auth_header_name}: {header_value[:10]}..." + ) + return server_auth_headers - + @staticmethod def _get_mcp_client_side_auth_header_name() -> str: """ @@ -162,13 +185,21 @@ class MCPRequestHandler: """ from litellm.proxy.proxy_server import general_settings from litellm.secret_managers.main import get_secret_str - MCP_CLIENT_SIDE_AUTH_HEADER_NAME: str = MCPRequestHandler.LITELLM_MCP_AUTH_HEADER_NAME - if get_secret_str("LITELLM_MCP_CLIENT_SIDE_AUTH_HEADER_NAME") is not None: - MCP_CLIENT_SIDE_AUTH_HEADER_NAME = get_secret_str("LITELLM_MCP_CLIENT_SIDE_AUTH_HEADER_NAME") or MCP_CLIENT_SIDE_AUTH_HEADER_NAME - elif general_settings.get("mcp_client_side_auth_header_name") is not None: - MCP_CLIENT_SIDE_AUTH_HEADER_NAME = general_settings.get("mcp_client_side_auth_header_name") or MCP_CLIENT_SIDE_AUTH_HEADER_NAME - return MCP_CLIENT_SIDE_AUTH_HEADER_NAME + MCP_CLIENT_SIDE_AUTH_HEADER_NAME: str = ( + MCPRequestHandler.LITELLM_MCP_AUTH_HEADER_NAME + ) + if get_secret_str("LITELLM_MCP_CLIENT_SIDE_AUTH_HEADER_NAME") is not None: + MCP_CLIENT_SIDE_AUTH_HEADER_NAME = ( + get_secret_str("LITELLM_MCP_CLIENT_SIDE_AUTH_HEADER_NAME") + or MCP_CLIENT_SIDE_AUTH_HEADER_NAME + ) + elif general_settings.get("mcp_client_side_auth_header_name") is not None: + MCP_CLIENT_SIDE_AUTH_HEADER_NAME = ( + general_settings.get("mcp_client_side_auth_header_name") + or MCP_CLIENT_SIDE_AUTH_HEADER_NAME + ) + return MCP_CLIENT_SIDE_AUTH_HEADER_NAME @staticmethod def get_litellm_api_key_from_headers(headers: Headers) -> Optional[str]: @@ -229,10 +260,14 @@ class MCPRequestHandler: try: allowed_mcp_servers: List[str] = [] allowed_mcp_servers_for_key = ( - await MCPRequestHandler._get_allowed_mcp_servers_for_key(user_api_key_auth) + await MCPRequestHandler._get_allowed_mcp_servers_for_key( + user_api_key_auth + ) ) allowed_mcp_servers_for_team = ( - await MCPRequestHandler._get_allowed_mcp_servers_for_team(user_api_key_auth) + await MCPRequestHandler._get_allowed_mcp_servers_for_team( + user_api_key_auth + ) ) ######################################################### @@ -274,7 +309,9 @@ class MCPRequestHandler: try: key_object_permission = ( await prisma_client.db.litellm_objectpermissiontable.find_unique( - where={"object_permission_id": user_api_key_auth.object_permission_id}, + where={ + "object_permission_id": user_api_key_auth.object_permission_id + }, ) ) if key_object_permission is None: @@ -282,17 +319,21 @@ class MCPRequestHandler: # Get direct MCP servers direct_mcp_servers = key_object_permission.mcp_servers or [] - + # Get MCP servers from access groups - access_group_servers = await MCPRequestHandler._get_mcp_servers_from_access_groups( - key_object_permission.mcp_access_groups or [] + access_group_servers = ( + await MCPRequestHandler._get_mcp_servers_from_access_groups( + key_object_permission.mcp_access_groups or [] + ) ) - + # Combine both lists all_servers = direct_mcp_servers + access_group_servers return list(set(all_servers)) except Exception as e: - verbose_logger.warning(f"Failed to get allowed MCP servers for key: {str(e)}") + verbose_logger.warning( + f"Failed to get allowed MCP servers for key: {str(e)}" + ) return [] @staticmethod @@ -318,10 +359,10 @@ class MCPRequestHandler: return [] try: - team_obj: Optional[LiteLLM_TeamTable] = ( - await prisma_client.db.litellm_teamtable.find_unique( - where={"team_id": user_api_key_auth.team_id}, - ) + team_obj: Optional[ + LiteLLM_TeamTable + ] = await prisma_client.db.litellm_teamtable.find_unique( + where={"team_id": user_api_key_auth.team_id}, ) if team_obj is None: verbose_logger.debug("team_obj is None") @@ -333,21 +374,27 @@ class MCPRequestHandler: # Get direct MCP servers direct_mcp_servers = object_permissions.mcp_servers or [] - + # Get MCP servers from access groups - access_group_servers = await MCPRequestHandler._get_mcp_servers_from_access_groups( - object_permissions.mcp_access_groups or [] + access_group_servers = ( + await MCPRequestHandler._get_mcp_servers_from_access_groups( + object_permissions.mcp_access_groups or [] + ) ) - + # Combine both lists all_servers = direct_mcp_servers + access_group_servers return list(set(all_servers)) except Exception as e: - verbose_logger.warning(f"Failed to get allowed MCP servers for team: {str(e)}") + verbose_logger.warning( + f"Failed to get allowed MCP servers for team: {str(e)}" + ) return [] @staticmethod - def _get_config_server_ids_for_access_groups(config_mcp_servers, access_groups: List[str]) -> Set[str]: + def _get_config_server_ids_for_access_groups( + config_mcp_servers, access_groups: List[str] + ) -> Set[str]: """ Helper to get server_ids from config-loaded servers that match any of the given access groups. """ @@ -359,7 +406,9 @@ class MCPRequestHandler: return server_ids @staticmethod - async def _get_db_server_ids_for_access_groups(prisma_client, access_groups: List[str]) -> Set[str]: + async def _get_db_server_ids_for_access_groups( + prisma_client, access_groups: List[str] + ) -> Set[str]: """ Helper to get server_ids from DB servers that match any of the given access groups. """ @@ -367,21 +416,19 @@ class MCPRequestHandler: if access_groups and prisma_client is not None: try: mcp_servers = await prisma_client.db.litellm_mcpservertable.find_many( - where={ - "mcp_access_groups": { - "hasSome": access_groups - } - } + where={"mcp_access_groups": {"hasSome": access_groups}} ) for server in mcp_servers: server_ids.add(server.server_id) except Exception as e: - verbose_logger.debug(f"Error getting MCP servers from access groups: {e}") + verbose_logger.debug( + f"Error getting MCP servers from access groups: {e}" + ) return server_ids @staticmethod async def _get_mcp_servers_from_access_groups( - access_groups: List[str] + access_groups: List[str], ) -> List[str]: """ Resolve MCP access groups to server IDs by querying BOTH the MCP server table (DB) AND config-loaded servers @@ -390,22 +437,28 @@ class MCPRequestHandler: try: # Import here to avoid circular import - from litellm.proxy._experimental.mcp_server.mcp_server_manager import global_mcp_server_manager - + from litellm.proxy._experimental.mcp_server.mcp_server_manager import ( + global_mcp_server_manager, + ) + # Use the new helper for config-loaded servers server_ids = MCPRequestHandler._get_config_server_ids_for_access_groups( global_mcp_server_manager.config_mcp_servers, access_groups ) # Use the new helper for DB servers - db_server_ids = await MCPRequestHandler._get_db_server_ids_for_access_groups( - prisma_client, access_groups + db_server_ids = ( + await MCPRequestHandler._get_db_server_ids_for_access_groups( + prisma_client, access_groups + ) ) server_ids.update(db_server_ids) return list(server_ids) except Exception as e: - verbose_logger.warning(f"Failed to get MCP servers from access groups: {str(e)}") + verbose_logger.warning( + f"Failed to get MCP servers from access groups: {str(e)}" + ) return [] @staticmethod @@ -418,8 +471,8 @@ class MCPRequestHandler: from typing import List access_groups: List[str] = [] - access_groups_for_key = ( - await MCPRequestHandler._get_mcp_access_groups_for_key(user_api_key_auth) + access_groups_for_key = await MCPRequestHandler._get_mcp_access_groups_for_key( + user_api_key_auth ) access_groups_for_team = ( await MCPRequestHandler._get_mcp_access_groups_for_team(user_api_key_auth) @@ -482,10 +535,10 @@ class MCPRequestHandler: verbose_logger.debug("prisma_client is None") return [] - team_obj: Optional[LiteLLM_TeamTable] = ( - await prisma_client.db.litellm_teamtable.find_unique( - where={"team_id": user_api_key_auth.team_id}, - ) + team_obj: Optional[ + LiteLLM_TeamTable + ] = await prisma_client.db.litellm_teamtable.find_unique( + where={"team_id": user_api_key_auth.team_id}, ) if team_obj is None: verbose_logger.debug("team_obj is None") @@ -502,10 +555,14 @@ class MCPRequestHandler: """ Extract and parse the x-mcp-access-groups header as a list of strings. """ - mcp_access_groups_header = headers.get(MCPRequestHandler.LITELLM_MCP_ACCESS_GROUPS_HEADER_NAME) + mcp_access_groups_header = headers.get( + MCPRequestHandler.LITELLM_MCP_ACCESS_GROUPS_HEADER_NAME + ) if mcp_access_groups_header is not None: try: - return [s.strip() for s in mcp_access_groups_header.split(",") if s.strip()] + return [ + s.strip() for s in mcp_access_groups_header.split(",") if s.strip() + ] except Exception: return None return None @@ -516,4 +573,4 @@ class MCPRequestHandler: Extract and parse the x-mcp-access-groups header from an ASGI scope. """ headers = MCPRequestHandler._safe_get_headers_from_scope(scope) - return MCPRequestHandler.get_mcp_access_groups_from_headers(headers) \ No newline at end of file + return MCPRequestHandler.get_mcp_access_groups_from_headers(headers) diff --git a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py index b35b3ad93d5..ab5d1b10bf3 100644 --- a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py +++ b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py @@ -34,8 +34,6 @@ from litellm.proxy._experimental.mcp_server.utils import ( from litellm.proxy._types import ( LiteLLM_MCPServerTable, MCPAuthType, - MCPSpecVersion, - MCPSpecVersionType, MCPTransport, MCPTransportType, UserAPIKeyAuth, @@ -70,38 +68,6 @@ def _deserialize_env_dict(env_data: Any) -> Optional[Dict[str, str]]: return env_data -def _convert_protocol_version_to_enum( - protocol_version: Optional[str | MCPSpecVersionType], -) -> MCPSpecVersionType: - """ - Convert string protocol version to MCPSpecVersion enum. - - Args: - protocol_version: String protocol version, enum, or None - - Returns: - MCPSpecVersionType: The enum value - """ - if not protocol_version: - return cast(MCPSpecVersionType, MCPSpecVersion.jun_2025) - - # If it's already an MCPSpecVersion enum, return it - if isinstance(protocol_version, MCPSpecVersion): - return cast(MCPSpecVersionType, protocol_version) - - # If it's a string, try to match it to enum values - if isinstance(protocol_version, str): - for version in MCPSpecVersion: - if version.value == protocol_version: - return cast(MCPSpecVersionType, version) - - # If no match found, return default - verbose_logger.warning( - f"Unknown protocol version '{protocol_version}', using default" - ) - return cast(MCPSpecVersionType, MCPSpecVersion.jun_2025) - - class MCPServerManager: def __init__(self): self.registry: Dict[str, MCPServer] = {} @@ -113,8 +79,7 @@ class MCPServerManager: "name": "zapier_mcp_server", "url": "https://actions.zapier.com/mcp/sk-ak-2ew3bofIeQIkNoeKIdXrF1Hhhp/sse" "transport": "sse", - "auth_type": "api_key", - "spec_version": "2025-03-26" + "auth_type": "api_key" }, "uuid-2": { "name": "google_drive_mcp_server", @@ -156,15 +121,13 @@ class MCPServerManager: for server_name, server_config in mcp_servers_config.items(): validate_mcp_server_name(server_name) _mcp_info: Dict[str, Any] = server_config.get("mcp_info", None) or {} - # Convert Dict[str, Any] to MCPInfo properly - mcp_info: MCPInfo = { - "server_name": _mcp_info.get("server_name", server_name), - "description": _mcp_info.get( - "description", server_config.get("description", None) - ), - "logo_url": _mcp_info.get("logo_url", None), - "mcp_server_cost_info": _mcp_info.get("mcp_server_cost_info", None), - } + # Preserve all custom fields from config while setting defaults for core fields + mcp_info: MCPInfo = _mcp_info.copy() + # Set default values for core fields if not present + if "server_name" not in mcp_info: + mcp_info["server_name"] = server_name + if "description" not in mcp_info and server_config.get("description"): + mcp_info["description"] = server_config.get("description") # Use alias for name if present, else server_name alias = server_config.get("alias", None) @@ -223,7 +186,6 @@ class MCPServerManager: server_name=server_name, url=server_config.get("url", None) or "", transport=server_config.get("transport", MCPTransport.http), - spec_version=server_config.get("spec_version", MCPSpecVersion.jun_2025), auth_type=server_config.get("auth_type", None), alias=alias, ) @@ -239,7 +201,6 @@ class MCPServerManager: env=server_config.get("env", None) or {}, # TODO: utility fn the default values transport=server_config.get("transport", MCPTransport.http), - spec_version=server_config.get("spec_version", MCPSpecVersion.jun_2025), auth_type=server_config.get("auth_type", None), authentication_token=server_config.get( "authentication_token", server_config.get("auth_value", None) @@ -280,6 +241,14 @@ class MCPServerManager: name_for_prefix = ( mcp_server.alias or mcp_server.server_name or mcp_server.server_id ) + # Preserve all custom fields from database while setting defaults for core fields + mcp_info: MCPInfo = _mcp_info.copy() + # Set default values for core fields if not present + if "server_name" not in mcp_info: + mcp_info["server_name"] = mcp_server.server_name or mcp_server.server_id + if "description" not in mcp_info and mcp_server.description: + mcp_info["description"] = mcp_server.description + new_server = MCPServer( server_id=mcp_server.server_id, name=name_for_prefix, @@ -287,13 +256,8 @@ class MCPServerManager: server_name=getattr(mcp_server, "server_name", None), url=mcp_server.url, transport=cast(MCPTransportType, mcp_server.transport), - spec_version=_convert_protocol_version_to_enum(mcp_server.spec_version), auth_type=cast(MCPAuthType, mcp_server.auth_type), - mcp_info=MCPInfo( - server_name=mcp_server.server_name or mcp_server.server_id, - description=mcp_server.description, - mcp_server_cost_info=_mcp_info.get("mcp_server_cost_info", None), - ), + mcp_info=mcp_info, # Stdio-specific fields command=getattr(mcp_server, "command", None), args=getattr(mcp_server, "args", None) or [], @@ -350,7 +314,6 @@ class MCPServerManager: user_api_key_auth: Optional[UserAPIKeyAuth] = None, mcp_auth_header: Optional[str] = None, mcp_server_auth_headers: Optional[Dict[str, str]] = None, - mcp_protocol_version: Optional[str] = None, ) -> List[MCPTool]: """ List all tools available across all MCP Servers. @@ -390,7 +353,6 @@ class MCPServerManager: tools = await self._get_tools_from_server( server=server, mcp_auth_header=server_auth_header, - mcp_protocol_version=mcp_protocol_version, ) list_tools_result.extend(tools) verbose_logger.info( @@ -414,7 +376,6 @@ class MCPServerManager: self, server: MCPServer, mcp_auth_header: Optional[str] = None, - protocol_version: Optional[str] = None, ) -> MCPClient: """ Create an MCPClient instance for the given server. @@ -422,18 +383,12 @@ class MCPServerManager: Args: server (MCPServer): The server configuration mcp_auth_header: MCP auth header to be passed to the MCP server. This is optional and will be used if provided. - protocol_version: Optional MCP protocol version to use. If not provided, uses server's default. Returns: MCPClient: Configured MCP client instance """ transport = server.transport or MCPTransport.sse - # Convert protocol version string to enum - protocol_version_enum = _convert_protocol_version_to_enum( - protocol_version or server.spec_version - ) - # Handle stdio transport if transport == MCPTransport.stdio: # For stdio, we need to get the stdio config from the server @@ -450,7 +405,6 @@ class MCPServerManager: auth_value=mcp_auth_header or server.authentication_token, timeout=60.0, stdio_config=stdio_config, - protocol_version=protocol_version_enum, ) else: # For HTTP/SSE transports @@ -461,14 +415,12 @@ class MCPServerManager: auth_type=server.auth_type, auth_value=mcp_auth_header or server.authentication_token, timeout=60.0, - protocol_version=protocol_version_enum, ) async def _get_tools_from_server( self, server: MCPServer, mcp_auth_header: Optional[str] = None, - mcp_protocol_version: Optional[str] = None, ) -> List[MCPTool]: """ Helper method to get tools from a single MCP server with prefixed names. @@ -483,22 +435,18 @@ class MCPServerManager: verbose_logger.debug(f"Connecting to url: {server.url}") verbose_logger.info(f"_get_tools_from_server for {server.name}...") - protocol_version = ( - mcp_protocol_version if mcp_protocol_version else server.spec_version - ) client = None try: client = self._create_mcp_client( server=server, mcp_auth_header=mcp_auth_header, - protocol_version=protocol_version, ) tools = await self._fetch_tools_with_timeout(client, server.name) - + prefixed_tools = self._create_prefixed_tools(tools, server) - + return prefixed_tools except Exception as e: @@ -530,7 +478,7 @@ class MCPServerManager: async def _list_tools_task(): try: await client.connect() - + tools = await client.list_tools() verbose_logger.debug(f"Tools from {server_name}: {tools}") return tools @@ -609,7 +557,6 @@ class MCPServerManager: user_api_key_auth: Optional[UserAPIKeyAuth] = None, mcp_auth_header: Optional[str] = None, mcp_server_auth_headers: Optional[Dict[str, str]] = None, - mcp_protocol_version: Optional[str] = None, proxy_logging_obj: Optional[ProxyLogging] = None, ) -> CallToolResult: """ @@ -660,32 +607,54 @@ class MCPServerManager: "arguments": arguments, "server_name": server_name_from_prefix, "user_api_key_auth": user_api_key_auth, - "user_api_key_user_id": getattr(user_api_key_auth, 'user_id', None) if user_api_key_auth else None, - "user_api_key_team_id": getattr(user_api_key_auth, 'team_id', None) if user_api_key_auth else None, - "user_api_key_end_user_id": getattr(user_api_key_auth, 'end_user_id', None) if user_api_key_auth else None, - "user_api_key_hash": getattr(user_api_key_auth, 'api_key_hash', None) if user_api_key_auth else None, + "user_api_key_user_id": getattr(user_api_key_auth, "user_id", None) + if user_api_key_auth + else None, + "user_api_key_team_id": getattr(user_api_key_auth, "team_id", None) + if user_api_key_auth + else None, + "user_api_key_end_user_id": getattr( + user_api_key_auth, "end_user_id", None + ) + if user_api_key_auth + else None, + "user_api_key_hash": getattr(user_api_key_auth, "api_key_hash", None) + if user_api_key_auth + else None, } - + # Create MCP request object for processing - mcp_request_obj = proxy_logging_obj._create_mcp_request_object_from_kwargs(pre_hook_kwargs) - + mcp_request_obj = proxy_logging_obj._create_mcp_request_object_from_kwargs( + pre_hook_kwargs + ) + # Convert to LLM format for existing guardrail compatibility - synthetic_llm_data = proxy_logging_obj._convert_mcp_to_llm_format(mcp_request_obj, pre_hook_kwargs) - + synthetic_llm_data = proxy_logging_obj._convert_mcp_to_llm_format( + mcp_request_obj, pre_hook_kwargs + ) + try: # Use standard pre_call_hook with call_type="mcp_call" modified_data = await proxy_logging_obj.pre_call_hook( - user_api_key_dict=user_api_key_auth, #type: ignore + user_api_key_dict=user_api_key_auth, # type: ignore data=synthetic_llm_data, - call_type="mcp_call" #type: ignore + call_type="mcp_call", # type: ignore ) if modified_data: # Convert response back to MCP format and apply modifications - modified_kwargs = proxy_logging_obj._convert_mcp_hook_response_to_kwargs(modified_data, pre_hook_kwargs) + modified_kwargs = ( + proxy_logging_obj._convert_mcp_hook_response_to_kwargs( + modified_data, pre_hook_kwargs + ) + ) if modified_kwargs.get("arguments") != arguments: arguments = modified_kwargs["arguments"] - - except (BlockedPiiEntityError, GuardrailRaisedException, HTTPException) as e: + + except ( + BlockedPiiEntityError, + GuardrailRaisedException, + HTTPException, + ) as e: # Re-raise guardrail exceptions to properly fail the MCP call verbose_logger.error( f"Guardrail blocked MCP tool call pre call: {str(e)}" @@ -706,11 +675,9 @@ class MCPServerManager: client = self._create_mcp_client( server=mcp_server, mcp_auth_header=server_auth_header, - protocol_version=mcp_protocol_version, ) async with client: - # Use the original tool name (without prefix) for the actual call call_tool_params = MCPCallToolRequestParams( name=original_tool_name, @@ -721,7 +688,7 @@ class MCPServerManager: # Create synthetic LLM data for during hook processing from litellm.types.llms.base import HiddenParams from litellm.types.mcp import MCPDuringCallRequestObject - + request_obj = MCPDuringCallRequestObject( tool_name=name, arguments=arguments, @@ -729,28 +696,29 @@ class MCPServerManager: start_time=start_time.timestamp() if start_time else None, hidden_params=HiddenParams(), ) - + during_hook_kwargs = { "name": name, "arguments": arguments, "server_name": server_name_from_prefix, "user_api_key_auth": user_api_key_auth, } - - synthetic_llm_data = proxy_logging_obj._convert_mcp_to_llm_format(request_obj, during_hook_kwargs) - + + synthetic_llm_data = proxy_logging_obj._convert_mcp_to_llm_format( + request_obj, during_hook_kwargs + ) + during_hook_task = asyncio.create_task( proxy_logging_obj.during_call_hook( user_api_key_dict=user_api_key_auth, data=synthetic_llm_data, - call_type="mcp_call" #type: ignore + call_type="mcp_call", # type: ignore ) ) tasks.append(during_hook_task) tasks.append(asyncio.create_task(client.call_tool(call_tool_params))) try: - mcp_responses = await asyncio.gather(*tasks) # If proxy_logging_obj is None, the tool call result is at index 0 @@ -839,19 +807,21 @@ class MCPServerManager: ) verbose_logger.info("Loading MCP servers from database into registry...") - + # perform authz check to filter the mcp servers user has access to prisma_client = get_prisma_client_or_throw( "Database not connected. Connect a database to your proxy" ) db_mcp_servers = await get_all_mcp_servers(prisma_client) verbose_logger.info(f"Found {len(db_mcp_servers)} MCP servers in database") - + # ensure the global_mcp_server_manager is up to date with the db for server in db_mcp_servers: - verbose_logger.debug(f"Adding server to registry: {server.server_id} ({server.server_name})") + verbose_logger.debug( + f"Adding server to registry: {server.server_id} ({server.server_name})" + ) self.add_update_server(server) - + verbose_logger.info(f"Registry now contains {len(self.get_registry())} servers") def get_mcp_server_by_id(self, server_id: str) -> Optional[MCPServer]: @@ -869,7 +839,6 @@ class MCPServerManager: server_name: str, url: str, transport: str, - spec_version: str, auth_type: Optional[str] = None, alias: Optional[str] = None, ) -> str: @@ -885,7 +854,6 @@ class MCPServerManager: server_name: Name of the server url: Server URL transport: Transport type (sse, http, etc.) - spec_version: MCP spec version auth_type: Authentication type (optional) alias: Server alias (optional) @@ -893,7 +861,9 @@ class MCPServerManager: A deterministic server ID string """ # Create a string from all the identifying parameters - params_string = f"{server_name}|{url}|{transport}|{spec_version}|{auth_type or ''}|{alias or ''}" + params_string = ( + f"{server_name}|{url}|{transport}|{auth_type or ''}|{alias or ''}" + ) # Generate SHA-256 hash hash_object = hashlib.sha256(params_string.encode("utf-8")) @@ -1050,11 +1020,12 @@ class MCPServerManager: alias=_server_config.alias, url=_server_config.url, transport=_server_config.transport, - spec_version=_server_config.spec_version, auth_type=_server_config.auth_type, created_at=datetime.datetime.now(), updated_at=datetime.datetime.now(), - description=_server_config.mcp_info.get("description") if _server_config.mcp_info else None, + description=_server_config.mcp_info.get("description") + if _server_config.mcp_info + else None, mcp_info=_server_config.mcp_info, mcp_access_groups=_server_config.access_groups or [], # Stdio-specific fields @@ -1111,7 +1082,6 @@ class MCPServerManager: description=server.description, url=server.url, transport=server.transport, - spec_version=server.spec_version, auth_type=server.auth_type, created_at=server.created_at, created_by=server.created_by, diff --git a/litellm/proxy/_experimental/mcp_server/rest_endpoints.py b/litellm/proxy/_experimental/mcp_server/rest_endpoints.py index 048b25fa35a..2a9174717d1 100644 --- a/litellm/proxy/_experimental/mcp_server/rest_endpoints.py +++ b/litellm/proxy/_experimental/mcp_server/rest_endpoints.py @@ -23,7 +23,6 @@ router = APIRouter( if MCP_AVAILABLE: from litellm.experimental_mcp_client.client import MCPTool from litellm.proxy._experimental.mcp_server.mcp_server_manager import ( - _convert_protocol_version_to_enum, global_mcp_server_manager, ) from litellm.proxy._experimental.mcp_server.server import ( @@ -34,18 +33,24 @@ if MCP_AVAILABLE: ######################################################## ############ MCP Server REST API Routes ################# def _get_server_auth_header( - server, mcp_server_auth_headers: Optional[Dict[str, str]], mcp_auth_header: Optional[str] + server, + mcp_server_auth_headers: Optional[Dict[str, str]], + mcp_auth_header: Optional[str], ) -> Optional[str]: """Helper function to get server-specific auth header with case-insensitive matching.""" if mcp_server_auth_headers and server.alias: normalized_server_alias = server.alias.lower() - normalized_headers = {k.lower(): v for k, v in mcp_server_auth_headers.items()} + normalized_headers = { + k.lower(): v for k, v in mcp_server_auth_headers.items() + } server_auth = normalized_headers.get(normalized_server_alias) if server_auth is not None: return server_auth elif mcp_server_auth_headers and server.server_name: normalized_server_name = server.server_name.lower() - normalized_headers = {k.lower(): v for k, v in mcp_server_auth_headers.items()} + normalized_headers = { + k.lower(): v for k, v in mcp_server_auth_headers.items() + } server_auth = normalized_headers.get(normalized_server_name) if server_auth is not None: return server_auth @@ -63,12 +68,11 @@ if MCP_AVAILABLE: for tool in tools ] - async def _get_tools_for_single_server(server, server_auth_header, mcp_protocol_version): + async def _get_tools_for_single_server(server, server_auth_header): """Helper function to get tools for a single server.""" tools = await global_mcp_server_manager._get_tools_from_server( server=server, mcp_auth_header=server_auth_header, - mcp_protocol_version=mcp_protocol_version, ) return _create_tool_response_objects(tools, server.mcp_info) @@ -104,17 +108,20 @@ if MCP_AVAILABLE: from litellm.proxy._experimental.mcp_server.auth.user_api_key_auth_mcp import ( MCPRequestHandler, ) - + try: # Extract auth headers from request headers = request.headers - mcp_auth_header = MCPRequestHandler._get_mcp_auth_header_from_headers(headers) - mcp_server_auth_headers = MCPRequestHandler._get_mcp_server_auth_headers_from_headers(headers) - mcp_protocol_version = headers.get(MCPRequestHandler.MCP_PROTOCOL_VERSION_HEADER_NAME) - + mcp_auth_header = MCPRequestHandler._get_mcp_auth_header_from_headers( + headers + ) + mcp_server_auth_headers = ( + MCPRequestHandler._get_mcp_server_auth_headers_from_headers(headers) + ) + list_tools_result = [] error_message = None - + # If server_id is specified, only query that specific server if server_id: server = global_mcp_server_manager.get_mcp_server_by_id(server_id) @@ -122,49 +129,67 @@ if MCP_AVAILABLE: return { "tools": [], "error": "server_not_found", - "message": f"Server with id {server_id} not found" + "message": f"Server with id {server_id} not found", } - - server_auth_header = _get_server_auth_header(server, mcp_server_auth_headers, mcp_auth_header) - + + server_auth_header = _get_server_auth_header( + server, mcp_server_auth_headers, mcp_auth_header + ) + try: - list_tools_result = await _get_tools_for_single_server(server, server_auth_header, mcp_protocol_version) + list_tools_result = await _get_tools_for_single_server( + server, server_auth_header + ) except Exception as e: - verbose_logger.exception(f"Error getting tools from {server.name}: {e}") + verbose_logger.exception( + f"Error getting tools from {server.name}: {e}" + ) return { "tools": [], "error": "server_error", - "message": f"Failed to get tools from server {server.name}: {str(e)}" + "message": f"Failed to get tools from server {server.name}: {str(e)}", } else: # Query all servers errors = [] for server in global_mcp_server_manager.get_registry().values(): - server_auth_header = _get_server_auth_header(server, mcp_server_auth_headers, mcp_auth_header) - + server_auth_header = _get_server_auth_header( + server, mcp_server_auth_headers, mcp_auth_header + ) + try: - tools_result = await _get_tools_for_single_server(server, server_auth_header, mcp_protocol_version) + tools_result = await _get_tools_for_single_server( + server, server_auth_header + ) list_tools_result.extend(tools_result) except Exception as e: - verbose_logger.exception(f"Error getting tools from {server.name}: {e}") + verbose_logger.exception( + f"Error getting tools from {server.name}: {e}" + ) errors.append(f"{server.name}: {str(e)}") continue - + if errors and not list_tools_result: - error_message = "Failed to get tools from servers: " + "; ".join(errors) - + error_message = "Failed to get tools from servers: " + "; ".join( + errors + ) + return { "tools": list_tools_result, "error": "partial_failure" if error_message else None, - "message": error_message if error_message else "Successfully retrieved tools" + "message": error_message + if error_message + else "Successfully retrieved tools", } - + except Exception as e: - verbose_logger.exception("Unexpected error in list_tool_rest_api: %s", str(e)) + verbose_logger.exception( + "Unexpected error in list_tool_rest_api: %s", str(e) + ) return { "tools": [], "error": "unexpected_error", - "message": f"An unexpected error occurred: {str(e)}" + "message": f"An unexpected error occurred: {str(e)}", } @router.post("/tools/call", dependencies=[Depends(user_api_key_auth)]) @@ -196,9 +221,9 @@ if MCP_AVAILABLE: detail={ "error": "blocked_pii_entity", "message": str(e), - "entity_type": getattr(e, 'entity_type', None), - "guardrail_name": getattr(e, 'guardrail_name', None) - } + "entity_type": getattr(e, "entity_type", None), + "guardrail_name": getattr(e, "guardrail_name", None), + }, ) except GuardrailRaisedException as e: verbose_logger.error(f"GuardrailRaisedException in MCP tool call: {str(e)}") @@ -207,8 +232,8 @@ if MCP_AVAILABLE: detail={ "error": "guardrail_violation", "message": str(e), - "guardrail_name": getattr(e, 'guardrail_name', None) - } + "guardrail_name": getattr(e, "guardrail_name", None), + }, ) except HTTPException as e: # Re-raise HTTPException as-is to preserve status code and detail @@ -220,10 +245,10 @@ if MCP_AVAILABLE: status_code=500, detail={ "error": "internal_server_error", - "message": f"An unexpected error occurred: {str(e)}" - } + "message": f"An unexpected error occurred: {str(e)}", + }, ) - + ######################################################## # MCP Connection testing routes # /health -> Test if we can connect to the MCP server @@ -234,15 +259,15 @@ if MCP_AVAILABLE: from litellm.proxy.management_endpoints.mcp_management_endpoints import ( NewMCPServerRequest, ) - + async def _execute_with_mcp_client(request: NewMCPServerRequest, operation): """ Common helper to create MCP client, execute operation, and ensure proper cleanup. - + Args: request: MCP server configuration operation: Async function that takes a client and returns the operation result - + Returns: Operation result or error response """ @@ -254,15 +279,14 @@ if MCP_AVAILABLE: name=request.alias or request.server_name or "", url=request.url, transport=request.transport, - spec_version=_convert_protocol_version_to_enum(request.spec_version), auth_type=request.auth_type, mcp_info=request.mcp_info, ), mcp_auth_header=None, ) - + return await operation(client) - + except Exception as e: verbose_logger.error(f"Error in MCP operation: {e}", exc_info=True) return {"status": "error", "message": "An internal error has occurred."} @@ -273,6 +297,7 @@ if MCP_AVAILABLE: await client.disconnect() except Exception as e: verbose_logger.warning(f"Error disconnecting MCP client: {e}") + @router.post("/test/connection") async def test_connection( request: NewMCPServerRequest, @@ -280,13 +305,13 @@ if MCP_AVAILABLE: """ Test if we can connect to the provided MCP server before adding it """ + async def _test_connection_operation(client): await client.connect() return {"status": "ok"} - + return await _execute_with_mcp_client(request, _test_connection_operation) - - + @router.post("/test/tools/list") async def test_tools_list( request: NewMCPServerRequest, @@ -295,13 +320,16 @@ if MCP_AVAILABLE: """ Preview tools available from MCP server before adding it """ + async def _list_tools_operation(client): list_tools_result: List[MCPTool] = await client.list_tools() - model_dumped_tools: List[dict] = [tool.model_dump() for tool in list_tools_result] + model_dumped_tools: List[dict] = [ + tool.model_dump() for tool in list_tools_result + ] return { "tools": model_dumped_tools, "error": None, - "message": "Successfully retrieved tools" + "message": "Successfully retrieved tools", } - + return await _execute_with_mcp_client(request, _list_tools_operation) diff --git a/litellm/proxy/_experimental/mcp_server/server.py b/litellm/proxy/_experimental/mcp_server/server.py index e095f73fcc9..2cf91c84dcc 100644 --- a/litellm/proxy/_experimental/mcp_server/server.py +++ b/litellm/proxy/_experimental/mcp_server/server.py @@ -130,7 +130,9 @@ if MCP_AVAILABLE: await _sse_session_manager_cm.__aenter__() _SESSION_MANAGERS_INITIALIZED = True - verbose_logger.info("MCP Server started with StreamableHTTP and SSE session managers!") + verbose_logger.info( + "MCP Server started with StreamableHTTP and SSE session managers!" + ) async def shutdown_session_managers(): """Shutdown the session managers.""" @@ -171,11 +173,18 @@ if MCP_AVAILABLE: """ try: # Get user authentication from context variable - user_api_key_auth, mcp_auth_header, mcp_servers, mcp_server_auth_headers, mcp_protocol_version = ( - get_auth_context() + ( + user_api_key_auth, + mcp_auth_header, + mcp_servers, + mcp_server_auth_headers, + ) = get_auth_context() + verbose_logger.debug( + f"MCP list_tools - User API Key Auth from context: {user_api_key_auth}" + ) + verbose_logger.debug( + f"MCP list_tools - MCP servers from context: {mcp_servers}" ) - verbose_logger.debug(f"MCP list_tools - User API Key Auth from context: {user_api_key_auth}") - verbose_logger.debug(f"MCP list_tools - MCP servers from context: {mcp_servers}") verbose_logger.debug( f"MCP list_tools - MCP server auth headers: {list(mcp_server_auth_headers.keys()) if mcp_server_auth_headers else None}" ) @@ -186,9 +195,10 @@ if MCP_AVAILABLE: mcp_auth_header=mcp_auth_header, mcp_servers=mcp_servers, mcp_server_auth_headers=mcp_server_auth_headers, - mcp_protocol_version=mcp_protocol_version, ) - verbose_logger.info(f"MCP list_tools - Successfully returned {len(tools)} tools") + verbose_logger.info( + f"MCP list_tools - Successfully returned {len(tools)} tools" + ) return tools except Exception as e: verbose_logger.exception(f"Error in list_tools endpoint: {str(e)}") @@ -220,9 +230,16 @@ if MCP_AVAILABLE: from litellm.proxy.proxy_server import proxy_config # Validate arguments - user_api_key_auth, mcp_auth_header, _, mcp_server_auth_headers, mcp_protocol_version = get_auth_context() + ( + user_api_key_auth, + mcp_auth_header, + _, + mcp_server_auth_headers, + ) = get_auth_context() - verbose_logger.debug(f"MCP mcp_server_tool_call - User API Key Auth from context: {user_api_key_auth}") + verbose_logger.debug( + f"MCP mcp_server_tool_call - User API Key Auth from context: {user_api_key_auth}" + ) try: # Create a body date for logging body_data = {"name": name, "arguments": arguments} @@ -249,17 +266,22 @@ if MCP_AVAILABLE: user_api_key_auth=user_api_key_auth, mcp_auth_header=mcp_auth_header, mcp_server_auth_headers=mcp_server_auth_headers, - mcp_protocol_version=mcp_protocol_version, **data, # for logging ) except BlockedPiiEntityError as e: verbose_logger.error(f"BlockedPiiEntityError in MCP tool call: {str(e)}") # Return error as text content for MCP protocol - return [TextContent(text=f"Error: Blocked PII entity detected - {str(e)}", type="text")] + return [ + TextContent( + text=f"Error: Blocked PII entity detected - {str(e)}", type="text" + ) + ] except GuardrailRaisedException as e: verbose_logger.error(f"GuardrailRaisedException in MCP tool call: {str(e)}") # Return error as text content for MCP protocol - return [TextContent(text=f"Error: Guardrail violation - {str(e)}", type="text")] + return [ + TextContent(text=f"Error: Guardrail violation - {str(e)}", type="text") + ] except HTTPException as e: verbose_logger.error(f"HTTPException in MCP tool call: {str(e)}") # Return error as text content for MCP protocol @@ -287,6 +309,7 @@ if MCP_AVAILABLE: Get the filtered MCP servers from the MCP server names """ from typing import Set + filtered_server_ids: Set[str] = set() # Filter servers based on mcp_servers parameter if provided if mcp_servers is not None: @@ -297,7 +320,11 @@ if MCP_AVAILABLE: server = global_mcp_server_manager.get_mcp_server_by_id(server_id) if server: - match_list = [s.lower() for s in [server.alias, server.server_name, server_id] if s is not None] + match_list = [ + s.lower() + for s in [server.alias, server.server_name, server_id] + if s is not None + ] if server_or_group.lower() in match_list: filtered_server_ids.add(server_id) @@ -306,19 +333,23 @@ if MCP_AVAILABLE: if not server_name_matched: try: - access_group_server_ids = await MCPRequestHandler._get_mcp_servers_from_access_groups( - [server_or_group] + access_group_server_ids = ( + await MCPRequestHandler._get_mcp_servers_from_access_groups( + [server_or_group] + ) ) # Only include servers that the user has access to for server_id in access_group_server_ids: if server_id in allowed_mcp_servers: filtered_server_ids.add(server_id) except Exception as e: - verbose_logger.debug(f"Could not resolve '{server_or_group}' as access group: {e}") + verbose_logger.debug( + f"Could not resolve '{server_or_group}' as access group: {e}" + ) if filtered_server_ids: allowed_mcp_servers = list(filtered_server_ids) - + return allowed_mcp_servers async def _get_tools_from_mcp_servers( @@ -326,7 +357,6 @@ if MCP_AVAILABLE: mcp_auth_header: Optional[str], mcp_servers: Optional[List[str]], mcp_server_auth_headers: Optional[Dict[str, str]] = None, - mcp_protocol_version: Optional[str] = None, ) -> List[MCPTool]: """ Helper method to fetch tools from MCP servers based on server filtering criteria. @@ -344,7 +374,9 @@ if MCP_AVAILABLE: return [] # Get allowed MCP servers based on user permissions - allowed_mcp_servers = await global_mcp_server_manager.get_allowed_mcp_servers(user_api_key_auth) + allowed_mcp_servers = await global_mcp_server_manager.get_allowed_mcp_servers( + user_api_key_auth + ) if mcp_servers is not None: allowed_mcp_servers = await _get_allowed_mcp_servers_from_mcp_server_names( @@ -352,7 +384,6 @@ if MCP_AVAILABLE: allowed_mcp_servers=allowed_mcp_servers, ) - # Get tools from each allowed server all_tools = [] for server_id in allowed_mcp_servers: @@ -375,15 +406,20 @@ if MCP_AVAILABLE: tools = await global_mcp_server_manager._get_tools_from_server( server=server, mcp_auth_header=server_auth_header, - mcp_protocol_version=mcp_protocol_version, ) all_tools.extend(tools) - verbose_logger.debug(f"Successfully fetched {len(tools)} tools from server {server.name}") + verbose_logger.debug( + f"Successfully fetched {len(tools)} tools from server {server.name}" + ) except Exception as e: - verbose_logger.exception(f"Error getting tools from server {server.name}: {str(e)}") + verbose_logger.exception( + f"Error getting tools from server {server.name}: {str(e)}" + ) # Continue with other servers instead of failing completely - verbose_logger.info(f"Successfully fetched {len(all_tools)} tools total from all MCP servers") + verbose_logger.info( + f"Successfully fetched {len(all_tools)} tools total from all MCP servers" + ) return all_tools async def _list_mcp_tools( @@ -391,7 +427,6 @@ if MCP_AVAILABLE: mcp_auth_header: Optional[str] = None, mcp_servers: Optional[List[str]] = None, mcp_server_auth_headers: Optional[Dict[str, str]] = None, - mcp_protocol_version: Optional[str] = None, ) -> List[MCPTool]: """ List all available MCP tools. @@ -415,11 +450,14 @@ if MCP_AVAILABLE: mcp_auth_header=mcp_auth_header, mcp_servers=mcp_servers, mcp_server_auth_headers=mcp_server_auth_headers, - mcp_protocol_version=mcp_protocol_version, ) - verbose_logger.debug(f"Successfully fetched {len(managed_tools)} tools from managed MCP servers") + verbose_logger.debug( + f"Successfully fetched {len(managed_tools)} tools from managed MCP servers" + ) except Exception as e: - verbose_logger.exception(f"Error getting tools from managed MCP servers: {str(e)}") + verbose_logger.exception( + f"Error getting tools from managed MCP servers: {str(e)}" + ) # Continue with empty managed tools list instead of failing completely # Get tools from local registry @@ -430,10 +468,16 @@ if MCP_AVAILABLE: # Convert local tools to MCPTool format for tool in local_tools_raw: # Convert from litellm.types.mcp_server.tool_registry.MCPTool to mcp.types.Tool - mcp_tool = MCPTool(name=tool.name, description=tool.description, inputSchema=tool.input_schema) + mcp_tool = MCPTool( + name=tool.name, + description=tool.description, + inputSchema=tool.input_schema, + ) local_tools.append(mcp_tool) except Exception as e: - verbose_logger.exception(f"Error getting tools from local registry: {str(e)}") + verbose_logger.exception( + f"Error getting tools from local registry: {str(e)}" + ) # Continue with empty local tools list instead of failing completely # Combine all tools @@ -448,7 +492,6 @@ if MCP_AVAILABLE: user_api_key_auth: Optional[UserAPIKeyAuth] = None, mcp_auth_header: Optional[str] = None, mcp_server_auth_headers: Optional[Dict[str, str]] = None, - mcp_protocol_version: Optional[str] = None, **kwargs: Any, ) -> List[Union[TextContent, ImageContent, EmbeddedResource]]: """ @@ -456,35 +499,46 @@ if MCP_AVAILABLE: """ start_time = datetime.now() if arguments is None: - raise HTTPException(status_code=400, detail="Request arguments are required") + raise HTTPException( + status_code=400, detail="Request arguments are required" + ) # Remove prefix from tool name for logging and processing - original_tool_name, server_name_from_prefix = get_server_name_prefix_tool_mcp(name) - - standard_logging_mcp_tool_call: StandardLoggingMCPToolCall = _get_standard_logging_mcp_tool_call( - name=original_tool_name, # Use original name for logging - arguments=arguments, - server_name=server_name_from_prefix, + original_tool_name, server_name_from_prefix = get_server_name_prefix_tool_mcp( + name + ) + + standard_logging_mcp_tool_call: StandardLoggingMCPToolCall = ( + _get_standard_logging_mcp_tool_call( + name=original_tool_name, # Use original name for logging + arguments=arguments, + server_name=server_name_from_prefix, + ) + ) + litellm_logging_obj: Optional[LiteLLMLoggingObj] = kwargs.get( + "litellm_logging_obj", None ) - litellm_logging_obj: Optional[LiteLLMLoggingObj] = kwargs.get("litellm_logging_obj", None) if litellm_logging_obj: - litellm_logging_obj.model_call_details["mcp_tool_call_metadata"] = standard_logging_mcp_tool_call + litellm_logging_obj.model_call_details[ + "mcp_tool_call_metadata" + ] = standard_logging_mcp_tool_call litellm_logging_obj.model = f"MCP: {name}" # Try managed server tool first (pass the full prefixed name) # Primary and recommended way to use MCP servers ######################################################### - mcp_server: Optional[MCPServer] = global_mcp_server_manager._get_mcp_server_from_tool_name(name) + mcp_server: Optional[ + MCPServer + ] = global_mcp_server_manager._get_mcp_server_from_tool_name(name) if mcp_server: - standard_logging_mcp_tool_call["mcp_server_cost_info"] = (mcp_server.mcp_info or {}).get( - "mcp_server_cost_info" - ) + standard_logging_mcp_tool_call["mcp_server_cost_info"] = ( + mcp_server.mcp_info or {} + ).get("mcp_server_cost_info") response = await _handle_managed_mcp_tool( name=name, # Pass the full name (potentially prefixed) arguments=arguments, user_api_key_auth=user_api_key_auth, mcp_auth_header=mcp_auth_header, mcp_server_auth_headers=mcp_server_auth_headers, - mcp_protocol_version=mcp_protocol_version, litellm_logging_obj=litellm_logging_obj, ) @@ -537,7 +591,6 @@ if MCP_AVAILABLE: user_api_key_auth: Optional[UserAPIKeyAuth] = None, mcp_auth_header: Optional[str] = None, mcp_server_auth_headers: Optional[Dict[str, str]] = None, - mcp_protocol_version: Optional[str] = None, litellm_logging_obj: Optional[Any] = None, ) -> List[Union[TextContent, ImageContent, EmbeddedResource]]: """Handle tool execution for managed server tools""" @@ -577,42 +630,39 @@ if MCP_AVAILABLE: Get the MCP servers from the path """ import re + mcp_servers_from_path: Optional[List[str]] = None - # Match /mcp// - # Where can be comma-separated list of server names + # Match /mcp/ + # Where servers can be comma-separated list of server names # Server names can contain slashes (e.g., "custom_solutions/user_123") - mcp_path_match = re.match(r"^/mcp/([^?#]+?)(/[^?#]*)?(?:\?.*)?(?:#.*)?$", path) + mcp_path_match = re.match(r"^/mcp/([^?#]+)(?:\?.*)?(?:#.*)?$", path) if mcp_path_match: - mcp_servers_str = mcp_path_match.group(1) - optional_path = mcp_path_match.group(2) + servers_and_path = mcp_path_match.group(1) - if mcp_servers_str: - # First, try to split by comma for comma-separated lists - if ',' in mcp_servers_str: - # For comma-separated lists, we need to handle the case where the last item - # might include the path (e.g., "zapier,group1/tools" -> ["zapier", "group1/tools"]) - parts = [s.strip() for s in mcp_servers_str.split(",") if s.strip()] - - # If there's an optional path AND the last part contains a slash that matches the optional path, - # remove the path portion from the last server name - if optional_path and len(parts) > 0 and '/' in parts[-1]: - last_part = parts[-1] - # Check if the last part ends with the optional path - if optional_path and last_part.endswith(optional_path.lstrip('/')): - # Remove the path portion from the last server name - parts[-1] = last_part[:-len(optional_path.lstrip('/'))] - - mcp_servers_from_path = parts + if servers_and_path: + # Check if it contains commas (comma-separated servers) + if ',' in servers_and_path: + # For comma-separated, look for a path at the end + # Common patterns: /tools, /chat/completions, etc. + path_match = re.search(r'/([^/,]+(?:/[^/,]+)*)$', servers_and_path) + if path_match: + # Path found at the end, remove it from servers + path_part = '/' + path_match.group(1) + servers_part = servers_and_path[:-len(path_part)] + mcp_servers_from_path = [s.strip() for s in servers_part.split(',') if s.strip()] + else: + # No path, just comma-separated servers + mcp_servers_from_path = [s.strip() for s in servers_and_path.split(',') if s.strip()] else: - # For single server, it might be just a name or contain slashes - # We need to determine where the server name ends and the path begins - # This is tricky - let's use the original logic but handle comma cases differently - single_server_match = re.match(r"^([^/]+(?:/[^/]+)?)(?:/.*)?$", mcp_servers_str) + # Single server case - use regex approach for server/path separation + # This handles cases like "custom_solutions/user_123/chat/completions" + # where we want to extract "custom_solutions/user_123" as the server name + single_server_match = re.match(r"^([^/]+(?:/[^/]+)?)(?:/.*)?$", servers_and_path) if single_server_match: server_name = single_server_match.group(1) mcp_servers_from_path = [server_name] else: - mcp_servers_from_path = [mcp_servers_str] + mcp_servers_from_path = [servers_and_path] return mcp_servers_from_path async def extract_mcp_auth_context(scope, path): @@ -627,7 +677,6 @@ if MCP_AVAILABLE: mcp_auth_header, _, mcp_server_auth_headers, - mcp_protocol_version, ) = await MCPRequestHandler.process_mcp_request(scope) mcp_servers = mcp_servers_from_path else: @@ -636,11 +685,12 @@ if MCP_AVAILABLE: mcp_auth_header, mcp_servers, mcp_server_auth_headers, - mcp_protocol_version, ) = await MCPRequestHandler.process_mcp_request(scope) - return user_api_key_auth, mcp_auth_header, mcp_servers, mcp_server_auth_headers, mcp_protocol_version + return user_api_key_auth, mcp_auth_header, mcp_servers, mcp_server_auth_headers - async def handle_streamable_http_mcp(scope: Scope, receive: Receive, send: Send) -> None: + async def handle_streamable_http_mcp( + scope: Scope, receive: Receive, send: Send + ) -> None: """Handle MCP requests through StreamableHTTP.""" try: path = scope.get("path", "") @@ -649,20 +699,19 @@ if MCP_AVAILABLE: mcp_auth_header, mcp_servers, mcp_server_auth_headers, - mcp_protocol_version, ) = await extract_mcp_auth_context(scope, path) - verbose_logger.debug(f"MCP request mcp_servers (header/path): {mcp_servers}") + verbose_logger.debug( + f"MCP request mcp_servers (header/path): {mcp_servers}" + ) verbose_logger.debug( f"MCP server auth headers: {list(mcp_server_auth_headers.keys()) if mcp_server_auth_headers else None}" ) - verbose_logger.debug(f"MCP protocol version: {mcp_protocol_version}") # Set the auth context variable for easy access in MCP functions set_auth_context( user_api_key_auth=user_api_key_auth, mcp_auth_header=mcp_auth_header, mcp_servers=mcp_servers, mcp_server_auth_headers=mcp_server_auth_headers, - mcp_protocol_version=mcp_protocol_version, ) # Ensure session managers are initialized @@ -686,7 +735,9 @@ if MCP_AVAILABLE: ) await error_response(scope, receive, send) except Exception as response_error: - verbose_logger.exception(f"Failed to send error response: {response_error}") + verbose_logger.exception( + f"Failed to send error response: {response_error}" + ) # If we can't send a proper response, re-raise the original error raise e @@ -699,19 +750,18 @@ if MCP_AVAILABLE: mcp_auth_header, mcp_servers, mcp_server_auth_headers, - mcp_protocol_version, ) = await extract_mcp_auth_context(scope, path) - verbose_logger.debug(f"MCP request mcp_servers (header/path): {mcp_servers}") + verbose_logger.debug( + f"MCP request mcp_servers (header/path): {mcp_servers}" + ) verbose_logger.debug( f"MCP server auth headers: {list(mcp_server_auth_headers.keys()) if mcp_server_auth_headers else None}" ) - verbose_logger.debug(f"MCP protocol version: {mcp_protocol_version}") set_auth_context( user_api_key_auth=user_api_key_auth, mcp_auth_header=mcp_auth_header, mcp_servers=mcp_servers, mcp_server_auth_headers=mcp_server_auth_headers, - mcp_protocol_version=mcp_protocol_version, ) if not _SESSION_MANAGERS_INITIALIZED: @@ -733,7 +783,9 @@ if MCP_AVAILABLE: ) await error_response(scope, receive, send) except Exception as response_error: - verbose_logger.exception(f"Failed to send error response: {response_error}") + verbose_logger.exception( + f"Failed to send error response: {response_error}" + ) # If we can't send a proper response, re-raise the original error raise e @@ -769,7 +821,6 @@ if MCP_AVAILABLE: mcp_auth_header: Optional[str] = None, mcp_servers: Optional[List[str]] = None, mcp_server_auth_headers: Optional[Dict[str, str]] = None, - mcp_protocol_version: Optional[str] = None, ) -> None: """ Set the UserAPIKeyAuth in the auth context variable. @@ -785,13 +836,17 @@ if MCP_AVAILABLE: mcp_auth_header=mcp_auth_header, mcp_servers=mcp_servers, mcp_server_auth_headers=mcp_server_auth_headers, - mcp_protocol_version=mcp_protocol_version, ) auth_context_var.set(auth_user) - def get_auth_context() -> Tuple[ - Optional[UserAPIKeyAuth], Optional[str], Optional[List[str]], Optional[Dict[str, str]], Optional[str] - ]: + def get_auth_context() -> ( + Tuple[ + Optional[UserAPIKeyAuth], + Optional[str], + Optional[List[str]], + Optional[Dict[str, str]], + ] + ): """ Get the UserAPIKeyAuth from the auth context variable. @@ -806,9 +861,8 @@ if MCP_AVAILABLE: auth_user.mcp_auth_header, auth_user.mcp_servers, auth_user.mcp_server_auth_headers, - auth_user.mcp_protocol_version, ) - return None, None, None, None, None + return None, None, None, None ######################################################## ############ End of Auth Context Functions ############# diff --git a/litellm/proxy/_experimental/out/_next/static/fhuPj8WYsuMGymIUE7Xgu/_buildManifest.js 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