diff --git a/MCP_SSL_CHANGES_SUMMARY.md b/MCP_SSL_CHANGES_SUMMARY.md new file mode 100644 index 00000000000..e69de29bb2d diff --git a/docker-compose.yml b/docker-compose.yml index 366fbe51b5a..c268f9ba0ff 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -8,7 +8,7 @@ services: ######################################### ## Uncomment these lines to start proxy with a config.yaml file ## # volumes: - # - ./config.yaml:/app/config.yaml <<- this is missing in the docker-compose file currently + # - ./config.yaml:/app/config.yaml # command: # - "--config=/app/config.yaml" ############################################## diff --git a/docs/my-website/docs/mcp.md b/docs/my-website/docs/mcp.md index 50bd5aefa76..6333f7f0913 100644 --- a/docs/my-website/docs/mcp.md +++ b/docs/my-website/docs/mcp.md @@ -2,7 +2,7 @@ import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; import Image from '@theme/IdealImage'; -# /mcp - Model Context Protocol +# MCP Overview LiteLLM Proxy provides an MCP Gateway that allows you to use a fixed endpoint for all MCP tools and control MCP access by Key, Team. @@ -23,6 +23,43 @@ LiteLLM Proxy provides an MCP Gateway that allows you to use a fixed endpoint fo ## Adding your MCP +### Prerequisites + +To store MCP servers in the database, you need to enable database storage: + +**Environment Variable:** +```bash +export STORE_MODEL_IN_DB=True +``` + +**OR in config.yaml:** +```yaml +general_settings: + store_model_in_db: true +``` + +#### Fine-grained Database Storage Control + +By default, when `store_model_in_db` is `true`, all object types (models, MCPs, guardrails, vector stores, etc.) are stored in the database. If you want to store only specific object types, use the `supported_db_objects` setting. + +**Example: Store only MCP servers in the database** + +```yaml title="config.yaml" showLineNumbers +general_settings: + store_model_in_db: true + supported_db_objects: ["mcp"] # Only store MCP servers in DB + +model_list: + - model_name: gpt-4o + litellm_params: + model: openai/gpt-4o + api_key: sk-xxxxxxx +``` + +**See all available object types:** [Config Settings - supported_db_objects](./proxy/config_settings.md#general_settings---reference) + +If `supported_db_objects` is not set, all object types are loaded from the database (default behavior). + @@ -209,8 +246,203 @@ litellm_settings: -## MCP Tool Filtering +## Converting OpenAPI Specs to MCP Servers +LiteLLM can automatically convert OpenAPI specifications into MCP servers, allowing you to expose any REST API as MCP tools. This is useful when you have existing APIs with OpenAPI/Swagger documentation and want to make them available as MCP tools. + +### Benefits + +- **Rapid Integration**: Convert existing APIs to MCP tools without writing custom MCP server code +- **Automatic Tool Generation**: LiteLLM automatically generates MCP tools from your OpenAPI spec +- **Unified Interface**: Use the same MCP interface for both native MCP servers and OpenAPI-based APIs +- **Easy Testing**: Test and iterate on API integrations quickly + +### Configuration + +Add your OpenAPI-based MCP server to your `config.yaml`: + +```yaml title="config.yaml - OpenAPI to MCP" showLineNumbers +model_list: + - model_name: gpt-4o + litellm_params: + model: openai/gpt-4o + api_key: sk-xxxxxxx + +mcp_servers: + # OpenAPI Spec Example - Petstore API + petstore_mcp: + url: "https://petstore.swagger.io/v2" + spec_path: "/path/to/openapi.json" + auth_type: "none" + + # OpenAPI Spec with API Key Authentication + my_api_mcp: + url: "http://0.0.0.0:8090" + spec_path: "/path/to/openapi.json" + auth_type: "api_key" + auth_value: "your-api-key-here" + + # OpenAPI Spec with Bearer Token + secured_api_mcp: + url: "https://api.example.com" + spec_path: "/path/to/openapi.json" + auth_type: "bearer_token" + auth_value: "your-bearer-token" +``` + +### Configuration Parameters + +| Parameter | Required | Description | +|-----------|----------|-------------| +| `url` | Yes | The base URL of your API endpoint | +| `spec_path` | Yes | Path or URL to your OpenAPI specification file (JSON or YAML) | +| `auth_type` | No | Authentication type: `none`, `api_key`, `bearer_token`, `basic`, `authorization` | +| `auth_value` | No | Authentication value (required if `auth_type` is set) | +| `description` | No | Optional description for the MCP server | +| `allowed_tools` | No | List of specific tools to allow (see [MCP Tool Filtering](#mcp-tool-filtering)) | +| `disallowed_tools` | No | List of specific tools to block (see [MCP Tool Filtering](#mcp-tool-filtering)) | + +### Usage Example + +Once configured, you can use the OpenAPI-based MCP server just like any other MCP server: + + + + +```python title="Using OpenAPI-based MCP Server" showLineNumbers +from fastmcp import Client +import asyncio + +# Standard MCP configuration +config = { + "mcpServers": { + "petstore": { + "url": "http://localhost:4000/petstore_mcp/mcp", + "headers": { + "x-litellm-api-key": "Bearer sk-1234" + } + } + } +} + +# Create a client that connects to the server +client = Client(config) + +async def main(): + async with client: + # List available tools generated from OpenAPI spec + tools = await client.list_tools() + print(f"Available tools: {[tool.name for tool in tools]}") + + # Example: Get a pet by ID (from Petstore API) + response = await client.call_tool( + name="getpetbyid", + arguments={"petId": "1"} + ) + print(f"Response:\n{response}\n") + + # Example: Find pets by status + response = await client.call_tool( + name="findpetsbystatus", + arguments={"status": "available"} + ) + print(f"Response:\n{response}\n") + +if __name__ == "__main__": + asyncio.run(main()) +``` + + + + + +```json title="Cursor MCP Configuration for OpenAPI Server" showLineNumbers +{ + "mcpServers": { + "Petstore": { + "url": "http://localhost:4000/petstore_mcp/mcp", + "headers": { + "x-litellm-api-key": "Bearer $LITELLM_API_KEY" + } + } + } +} +``` + + + + + +```bash title="Using OpenAPI MCP Server with OpenAI" showLineNumbers +curl --location 'https://api.openai.com/v1/responses' \ +--header 'Content-Type: application/json' \ +--header "Authorization: Bearer $OPENAI_API_KEY" \ +--data '{ + "model": "gpt-4o", + "tools": [ + { + "type": "mcp", + "server_label": "petstore", + "server_url": "http://localhost:4000/petstore_mcp/mcp", + "require_approval": "never", + "headers": { + "x-litellm-api-key": "Bearer YOUR_LITELLM_API_KEY" + } + } + ], + "input": "Find all available pets in the petstore", + "tool_choice": "required" +}' +``` + + + + +### How It Works + +1. **Spec Loading**: LiteLLM loads your OpenAPI specification from the provided `spec_path` +2. **Tool Generation**: Each API endpoint in the spec becomes an MCP tool +3. **Parameter Mapping**: OpenAPI parameters are automatically mapped to MCP tool parameters +4. **Request Handling**: When a tool is called, LiteLLM converts the MCP request to the appropriate HTTP request +5. **Response Translation**: API responses are converted back to MCP format + +### OpenAPI Spec Requirements + +Your OpenAPI specification should follow standard OpenAPI/Swagger conventions: +- **Supported versions**: OpenAPI 3.0.x, OpenAPI 3.1.x, Swagger 2.0 +- **Required fields**: `paths`, `info` sections should be properly defined +- **Operation IDs**: Each operation should have a unique `operationId` (this becomes the tool name) +- **Parameters**: Request parameters should be properly documented with types and descriptions + +### Example OpenAPI Spec Structure + +```yaml title="sample-openapi.yaml" showLineNumbers +openapi: 3.0.0 +info: + title: My API + version: 1.0.0 +paths: + /pets/{petId}: + get: + operationId: getPetById + summary: Get a pet by ID + parameters: + - name: petId + in: path + required: true + schema: + type: integer + responses: + '200': + description: Successful response + content: + application/json: + schema: + type: object +``` + +## Allow/Disallow MCP Tools + Control which tools are available from your MCP servers. You can either allow only specific tools or block dangerous ones. @@ -269,210 +501,118 @@ mcp_servers: - If you specify both `allowed_tools` and `disallowed_tools`, the allowed list takes priority - Tool names are case-sensitive -## Using your MCP +--- -### Use on LiteLLM UI +## Allow/Disallow MCP Tool Parameters -Follow this walkthrough to use your MCP on LiteLLM UI +Control which parameters are allowed for specific MCP tools using the `allowed_params` configuration. This provides fine-grained control over tool usage by restricting the parameters that can be passed to each tool. - +### Configuration -### Use with Responses API +`allowed_params` is a dictionary that maps tool names to lists of allowed parameter names. When configured, only the specified parameters will be accepted for that tool - any other parameters will be rejected with a 403 error. -Replace `http://localhost:4000` with your LiteLLM Proxy base URL. - -Demo Video Using Responses API with LiteLLM Proxy: [Demo video here](https://www.loom.com/share/34587e618c5c47c0b0d67b4e4d02718f?sid=2caf3d45-ead4-4490-bcc1-8d6dd6041c02) - - - - - -```bash title="cURL Example" showLineNumbers -curl --location 'http://localhost:4000/v1/responses' \ ---header 'Content-Type: application/json' \ ---header "Authorization: Bearer sk-1234" \ ---data '{ - "model": "gpt-5", - "input": [ - { - "role": "user", - "content": "give me TLDR of what BerriAI/litellm repo is about", - "type": "message" - } - ], - "tools": [ - { - "type": "mcp", - "server_label": "litellm", - "server_url": "litellm_proxy", - "require_approval": "never" - } - ], - "stream": true, - "tool_choice": "required" -}' +```yaml title="config.yaml with allowed_params" showLineNumbers +mcp_servers: + deepwiki_mcp: + url: https://mcp.deepwiki.com/mcp + transport: "http" + auth_type: "none" + allowed_params: + # Tool name: list of allowed parameters + read_wiki_contents: ["status"] + + my_api_mcp: + url: "https://my-api-server.com" + auth_type: "api_key" + auth_value: "my-key" + allowed_params: + # Using unprefixed tool name + getpetbyid: ["status"] + # Using prefixed tool name (both formats work) + my_api_mcp-findpetsbystatus: ["status", "limit"] + # Another tool with multiple allowed params + create_issue: ["title", "body", "labels"] ``` - - +### How It Works -```python title="Python SDK Example" showLineNumbers -""" -Use LiteLLM Proxy MCP Gateway to call MCP tools. +1. **Tool-specific filtering**: Each tool can have its own list of allowed parameters +2. **Flexible naming**: Tool names can be specified with or without the server prefix (e.g., both `"getpetbyid"` and `"my_api_mcp-getpetbyid"` work) +3. **Whitelist approach**: Only parameters in the allowed list are permitted +4. **Unlisted tools**: If `allowed_params` is not set, all parameters are allowed +5. **Error handling**: Requests with disallowed parameters receive a 403 error with details about which parameters are allowed -When using LiteLLM Proxy, you can use the same MCP tools across all your LLM providers. -""" -import openai +### Example Request Behavior -client = openai.OpenAI( - api_key="sk-1234", # paste your litellm proxy api key here - base_url="http://localhost:4000" # paste your litellm proxy base url here -) -print("Making API request to Responses API with MCP tools") +With the configuration above, here's how requests would be handled: -response = client.responses.create( - model="gpt-5", - input=[ - { - "role": "user", - "content": "give me TLDR of what BerriAI/litellm repo is about", - "type": "message" - } - ], - tools=[ - { - "type": "mcp", - "server_label": "litellm", - "server_url": "litellm_proxy", - "require_approval": "never" - } - ], - stream=True, - tool_choice="required" -) - -for chunk in response: - print("response chunk: ", chunk) -``` - - - - -#### Specifying MCP Tools - -You can specify which MCP tools are available by using the `allowed_tools` parameter. This allows you to restrict access to specific tools within an MCP server. - -To get the list of allowed tools when using LiteLLM MCP Gateway, you can naigate to the LiteLLM UI on MCP Servers > MCP Tools > Click the Tool > Copy Tool Name. - - - - -```bash title="cURL Example with allowed_tools" showLineNumbers -curl --location 'http://localhost:4000/v1/responses' \ ---header 'Content-Type: application/json' \ ---header "Authorization: Bearer sk-1234" \ ---data '{ - "model": "gpt-5", - "input": [ - { - "role": "user", - "content": "give me TLDR of what BerriAI/litellm repo is about", - "type": "message" - } - ], - "tools": [ - { - "type": "mcp", - "server_label": "litellm", - "server_url": "litellm_proxy/mcp", - "require_approval": "never", - "allowed_tools": ["GitMCP-fetch_litellm_documentation"] - } - ], - "stream": true, - "tool_choice": "required" -}' -``` - - - - -```python title="Python SDK Example with allowed_tools" showLineNumbers -import openai - -client = openai.OpenAI( - api_key="sk-1234", - base_url="http://localhost:4000" -) - -response = client.responses.create( - model="gpt-5", - input=[ - { - "role": "user", - "content": "give me TLDR of what BerriAI/litellm repo is about", - "type": "message" - } - ], - tools=[ - { - "type": "mcp", - "server_label": "litellm", - "server_url": "litellm_proxy/mcp", - "require_approval": "never", - "allowed_tools": ["GitMCP-fetch_litellm_documentation"] - } - ], - stream=True, - tool_choice="required" -) - -print(response) -``` - - - - -### Use with Cursor IDE - -Use tools directly from Cursor IDE with LiteLLM MCP: - -**Setup Instructions:** - -1. **Open Cursor Settings**: Use `⇧+⌘+J` (Mac) or `Ctrl+Shift+J` (Windows/Linux) -2. **Navigate to MCP Tools**: Go to the "MCP Tools" tab and click "New MCP Server" -3. **Add Configuration**: Copy and paste the JSON configuration below, then save with `Cmd+S` or `Ctrl+S` - -```json title="Basic Cursor MCP Configuration" showLineNumbers +**✅ Allowed Request:** +```json { - "mcpServers": { - "LiteLLM": { - "url": "litellm_proxy", - "headers": { - "x-litellm-api-key": "Bearer $LITELLM_API_KEY" - } - } + "tool": "read_wiki_contents", + "arguments": { + "status": "active" } } ``` -#### How it works when server_url="litellm_proxy" +**❌ Rejected Request:** +```json +{ + "tool": "read_wiki_contents", + "arguments": { + "status": "active", + "limit": 10 // This parameter is not allowed + } +} +``` -When server_url="litellm_proxy", LiteLLM bridges non-MCP providers to your MCP tools. +**Error Response:** +```json +{ + "error": "Parameters ['limit'] are not allowed for tool read_wiki_contents. Allowed parameters: ['status']. Contact proxy admin to allow these parameters." +} +``` -- Tool Discovery: LiteLLM fetches MCP tools and converts them to OpenAI-compatible definitions -- LLM Call: Tools are sent to the LLM with your input; LLM selects which tools to call -- Tool Execution: LiteLLM automatically parses arguments, routes calls to MCP servers, executes tools, and retrieves results -- Response Integration: Tool results are sent back to LLM for final response generation -- Output: Complete response combining LLM reasoning with tool execution results +### Use Cases -This enables MCP tool usage with any LiteLLM-supported provider, regardless of native MCP support. +- **Security**: Prevent users from accessing sensitive parameters or dangerous operations +- **Cost control**: Restrict expensive parameters (e.g., limiting result counts) +- **Compliance**: Enforce parameter usage policies for regulatory requirements +- **Staged rollouts**: Gradually enable parameters as tools are tested +- **Multi-tenant isolation**: Different parameter access for different user groups -#### Auto-execution for require_approval: "never" +### Combining with Tool Filtering -Setting require_approval: "never" triggers automatic tool execution, returning the final response in a single API call without additional user interaction. +`allowed_params` works alongside `allowed_tools` and `disallowed_tools` for complete control: +```yaml title="Combined filtering example" showLineNumbers +mcp_servers: + github_mcp: + url: "https://api.githubcopilot.com/mcp" + auth_type: oauth2 + authorization_url: https://github.com/login/oauth/authorize + token_url: https://github.com/login/oauth/access_token + client_id: os.environ/GITHUB_OAUTH_CLIENT_ID + client_secret: os.environ/GITHUB_OAUTH_CLIENT_SECRET + scopes: ["public_repo", "user:email"] + # Only allow specific tools + allowed_tools: ["create_issue", "list_issues", "search_issues"] + # Block dangerous operations + disallowed_tools: ["delete_repo"] + # Restrict parameters per tool + allowed_params: + create_issue: ["title", "body", "labels"] + list_issues: ["state", "sort", "perPage"] + search_issues: ["query", "sort", "order", "perPage"] +``` +This configuration ensures that: +1. Only the three listed tools are available +2. The `delete_repo` tool is explicitly blocked +3. Each tool can only use its specified parameters + +--- ## MCP Server Access Control @@ -1452,221 +1592,6 @@ curl --location '/v1/responses' \ }' ``` - - -## MCP Cost Tracking - -LiteLLM provides two ways to track costs for MCP tool calls: - -| Method | When to Use | What It Does | -|--------|-------------|--------------| -| **Config-based Cost Tracking** | Simple cost tracking with fixed costs per tool/server | Automatically tracks costs based on configuration | -| **Custom Post-MCP Hook** | Dynamic cost tracking with custom logic | Allows custom cost calculations and response modifications | - -### Config-based Cost Tracking - -Configure fixed costs for MCP servers directly in your config.yaml: - -```yaml title="config.yaml" showLineNumbers -model_list: - - model_name: gpt-4o - litellm_params: - model: openai/gpt-4o - api_key: sk-xxxxxxx - -mcp_servers: - zapier_server: - url: "https://actions.zapier.com/mcp/sk-xxxxx/sse" - mcp_info: - mcp_server_cost_info: - # Default cost for all tools in this server - default_cost_per_query: 0.01 - # Custom cost for specific tools - tool_name_to_cost_per_query: - send_email: 0.05 - create_document: 0.03 - - expensive_api_server: - url: "https://api.expensive-service.com/mcp" - mcp_info: - mcp_server_cost_info: - default_cost_per_query: 1.50 -``` - -### Custom Post-MCP Hook - -Use this when you need dynamic cost calculation or want to modify the MCP response before it's returned to the user. - -#### 1. Create a custom MCP hook file - -```python title="custom_mcp_hook.py" showLineNumbers -from typing import Optional -from litellm.integrations.custom_logger import CustomLogger -from litellm.types.mcp import MCPPostCallResponseObject - - -class CustomMCPCostTracker(CustomLogger): - """ - Custom handler for MCP cost tracking and response modification - """ - - async def async_post_mcp_tool_call_hook( - self, - kwargs, - response_obj: MCPPostCallResponseObject, - start_time, - end_time - ) -> Optional[MCPPostCallResponseObject]: - """ - Called after each MCP tool call. - Modify costs and response before returning to user. - """ - - # Extract tool information from kwargs - tool_name = kwargs.get("name", "") - server_name = kwargs.get("server_name", "") - - # Calculate custom cost based on your logic - custom_cost = 42.00 - - # Set the response cost - response_obj.hidden_params.response_cost = custom_cost - - - - return response_obj - - -# Create instance for LiteLLM to use -custom_mcp_cost_tracker = CustomMCPCostTracker() -``` - -#### 2. Configure in config.yaml - -```yaml title="config.yaml" showLineNumbers -model_list: - - model_name: gpt-4o - litellm_params: - model: openai/gpt-4o - api_key: sk-xxxxxxx - -# Add your custom MCP hook -callbacks: - - custom_mcp_hook.custom_mcp_cost_tracker - -mcp_servers: - zapier_server: - url: "https://actions.zapier.com/mcp/sk-xxxxx/sse" -``` - -#### 3. Start the proxy - -```shell -$ litellm --config /path/to/config.yaml -``` - -When MCP tools are called, your custom hook will: -1. Calculate costs based on your custom logic -2. Modify the response if needed -3. Track costs in LiteLLM's logging system - -## MCP Guardrails - -LiteLLM supports applying guardrails to MCP tool calls to ensure security and compliance. You can configure guardrails to run before or during MCP calls to validate inputs and block or mask sensitive information. - -### Supported MCP Guardrail Modes - -MCP guardrails support the following modes: - -- `pre_mcp_call`: Run **before** MCP call, on **input**. Use this mode when you want to apply validation/masking/blocking for MCP requests -- `during_mcp_call`: Run **during** MCP call execution. Use this mode for real-time monitoring and intervention - -### Configuration Examples - -Configure guardrails to run before MCP tool calls to validate and sanitize inputs: - -```yaml title="config.yaml" showLineNumbers -guardrails: - - guardrail_name: "mcp-input-validation" - litellm_params: - guardrail: presidio # or other supported guardrails - mode: "pre_mcp_call" # or during_mcp_call - pii_entities_config: - CREDIT_CARD: "BLOCK" # Will block requests containing credit card numbers - EMAIL_ADDRESS: "MASK" # Will mask email addresses - PHONE_NUMBER: "MASK" # Will mask phone numbers - default_on: true -``` - - -### Usage Examples - -#### Testing Pre-MCP Call Guardrails - -Test your MCP guardrails with a request that includes sensitive information: - -```bash title="Test MCP Guardrail" showLineNumbers -curl http://localhost:4000/chat/completions \ - -H "Content-Type: application/json" \ - -H "Authorization: Bearer sk-1234" \ - -d '{ - "model": "gpt-3.5-turbo", - "messages": [ - {"role": "user", "content": "My credit card is 4111-1111-1111-1111 and my email is john@example.com"} - ], - "guardrails": ["mcp-input-validation"] - }' -``` - -The request will be processed as follows: -1. Credit card number will be blocked (request rejected) -2. Email address will be masked (e.g., replaced with ``) - -#### Using with MCP Tools - -When using MCP tools, guardrails will be applied to the tool inputs: - -```python title="Python Example with MCP Guardrails" showLineNumbers -import openai - -client = openai.OpenAI( - api_key="your-api-key", - base_url="http://localhost:4000" -) - -# This request will trigger MCP guardrails -response = client.chat.completions.create( - model="gpt-3.5-turbo", - messages=[ - {"role": "user", "content": "Send an email to 555-123-4567 with my SSN 123-45-6789"} - ], - tools=[{"type": "mcp", "server_label": "litellm", "server_url": "litellm_proxy"}], - guardrails=["mcp-input-validation"] -) -``` - -### Supported Guardrail Providers - -MCP guardrails work with all LiteLLM-supported guardrail providers: - -- **Presidio**: PII detection and masking -- **Bedrock**: AWS Bedrock guardrails -- **Lakera**: Content moderation -- **Aporia**: Custom guardrails -- **Custom**: Your own guardrail implementations - -## MCP Permission Management - -LiteLLM supports managing permissions for MCP Servers by Keys, Teams, Organizations (entities) on LiteLLM. When a MCP client attempts to list tools, LiteLLM will only return the tools the entity has permissions to access. - -When Creating a Key, Team, or Organization, you can select the allowed MCP Servers that the entity has access to. - - - - ## LiteLLM Proxy - Walk through MCP Gateway LiteLLM exposes an MCP Gateway for admins to add all their MCP servers to LiteLLM. The key benefits of using LiteLLM Proxy with MCP are: diff --git a/docs/my-website/docs/mcp_control.md b/docs/my-website/docs/mcp_control.md new file mode 100644 index 00000000000..484cb13708c --- /dev/null +++ b/docs/my-website/docs/mcp_control.md @@ -0,0 +1,45 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; +import Image from '@theme/IdealImage'; + +# MCP Permission Management + +Control which MCP servers and tools can be accessed by specific keys, teams, or organizations in LiteLLM. When a client attempts to list or call tools, LiteLLM enforces access controls based on configured permissions. + +## Overview + +LiteLLM provides fine-grained permission management for MCP servers, allowing you to: + +- **Restrict MCP access by entity**: Control which keys, teams, or organizations can access specific MCP servers +- **Tool-level filtering**: Automatically filter available tools based on entity permissions +- **Centralized control**: Manage all MCP permissions from the LiteLLM Admin UI or API + +This ensures that only authorized entities can discover and use MCP tools, providing an additional security layer for your MCP infrastructure. + +:::info Related Documentation +- [MCP Overview](./mcp.md) - Learn about MCP in LiteLLM +- [MCP Cost Tracking](./mcp_cost.md) - Track costs for MCP tool calls +- [MCP Guardrails](./mcp_guardrail.md) - Apply security guardrails to MCP calls +- [Using MCP](./mcp_usage.md) - How to use MCP with LiteLLM +::: + +## How It Works + +LiteLLM supports managing permissions for MCP Servers by Keys, Teams, Organizations (entities) on LiteLLM. When a MCP client attempts to list tools, LiteLLM will only return the tools the entity has permissions to access. + +When Creating a Key, Team, or Organization, you can select the allowed MCP Servers that the entity has access to. + + + + +## Set Allowed Tools for a Key, Team, or Organization + +Control which tools different teams can access from the same MCP server. For example, give your Engineering team access to `list_repositories`, `create_issue`, and `search_code`, while Sales only gets `search_code` and `close_issue`. + + +This video shows how to set allowed tools for a Key, Team, or Organization. + + diff --git a/docs/my-website/docs/mcp_cost.md b/docs/my-website/docs/mcp_cost.md new file mode 100644 index 00000000000..4f5d65fe019 --- /dev/null +++ b/docs/my-website/docs/mcp_cost.md @@ -0,0 +1,121 @@ + +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; +import Image from '@theme/IdealImage'; + +# MCP Cost Tracking + +LiteLLM provides two ways to track costs for MCP tool calls: + +| Method | When to Use | What It Does | +|--------|-------------|--------------| +| **Config-based Cost Tracking** | Simple cost tracking with fixed costs per tool/server | Automatically tracks costs based on configuration | +| **Custom Post-MCP Hook** | Dynamic cost tracking with custom logic | Allows custom cost calculations and response modifications | + +### Config-based Cost Tracking + +Configure fixed costs for MCP servers directly in your config.yaml: + +```yaml title="config.yaml" showLineNumbers +model_list: + - model_name: gpt-4o + litellm_params: + model: openai/gpt-4o + api_key: sk-xxxxxxx + +mcp_servers: + zapier_server: + url: "https://actions.zapier.com/mcp/sk-xxxxx/sse" + mcp_info: + mcp_server_cost_info: + # Default cost for all tools in this server + default_cost_per_query: 0.01 + # Custom cost for specific tools + tool_name_to_cost_per_query: + send_email: 0.05 + create_document: 0.03 + + expensive_api_server: + url: "https://api.expensive-service.com/mcp" + mcp_info: + mcp_server_cost_info: + default_cost_per_query: 1.50 +``` + +### Custom Post-MCP Hook + +Use this when you need dynamic cost calculation or want to modify the MCP response before it's returned to the user. + +#### 1. Create a custom MCP hook file + +```python title="custom_mcp_hook.py" showLineNumbers +from typing import Optional +from litellm.integrations.custom_logger import CustomLogger +from litellm.types.mcp import MCPPostCallResponseObject + + +class CustomMCPCostTracker(CustomLogger): + """ + Custom handler for MCP cost tracking and response modification + """ + + async def async_post_mcp_tool_call_hook( + self, + kwargs, + response_obj: MCPPostCallResponseObject, + start_time, + end_time + ) -> Optional[MCPPostCallResponseObject]: + """ + Called after each MCP tool call. + Modify costs and response before returning to user. + """ + + # Extract tool information from kwargs + tool_name = kwargs.get("name", "") + server_name = kwargs.get("server_name", "") + + # Calculate custom cost based on your logic + custom_cost = 42.00 + + # Set the response cost + response_obj.hidden_params.response_cost = custom_cost + + + + return response_obj + + +# Create instance for LiteLLM to use +custom_mcp_cost_tracker = CustomMCPCostTracker() +``` + +#### 2. Configure in config.yaml + +```yaml title="config.yaml" showLineNumbers +model_list: + - model_name: gpt-4o + litellm_params: + model: openai/gpt-4o + api_key: sk-xxxxxxx + +# Add your custom MCP hook +callbacks: + - custom_mcp_hook.custom_mcp_cost_tracker + +mcp_servers: + zapier_server: + url: "https://actions.zapier.com/mcp/sk-xxxxx/sse" +``` + +#### 3. Start the proxy + +```shell +$ litellm --config /path/to/config.yaml +``` + +When MCP tools are called, your custom hook will: +1. Calculate costs based on your custom logic +2. Modify the response if needed +3. Track costs in LiteLLM's logging system + diff --git a/docs/my-website/docs/mcp_guardrail.md b/docs/my-website/docs/mcp_guardrail.md new file mode 100644 index 00000000000..f71ea2fe5ef --- /dev/null +++ b/docs/my-website/docs/mcp_guardrail.md @@ -0,0 +1,88 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; +import Image from '@theme/IdealImage'; + +# MCP Guardrails + +LiteLLM supports applying guardrails to MCP tool calls to ensure security and compliance. You can configure guardrails to run before or during MCP calls to validate inputs and block or mask sensitive information. + +### Supported MCP Guardrail Modes + +MCP guardrails support the following modes: + +- `pre_mcp_call`: Run **before** MCP call, on **input**. Use this mode when you want to apply validation/masking/blocking for MCP requests +- `during_mcp_call`: Run **during** MCP call execution. Use this mode for real-time monitoring and intervention + +### Configuration Examples + +Configure guardrails to run before MCP tool calls to validate and sanitize inputs: + +```yaml title="config.yaml" showLineNumbers +guardrails: + - guardrail_name: "mcp-input-validation" + litellm_params: + guardrail: presidio # or other supported guardrails + mode: "pre_mcp_call" # or during_mcp_call + pii_entities_config: + CREDIT_CARD: "BLOCK" # Will block requests containing credit card numbers + EMAIL_ADDRESS: "MASK" # Will mask email addresses + PHONE_NUMBER: "MASK" # Will mask phone numbers + default_on: true +``` + + +### Usage Examples + +#### Testing Pre-MCP Call Guardrails + +Test your MCP guardrails with a request that includes sensitive information: + +```bash title="Test MCP Guardrail" showLineNumbers +curl http://localhost:4000/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer sk-1234" \ + -d '{ + "model": "gpt-3.5-turbo", + "messages": [ + {"role": "user", "content": "My credit card is 4111-1111-1111-1111 and my email is john@example.com"} + ], + "guardrails": ["mcp-input-validation"] + }' +``` + +The request will be processed as follows: +1. Credit card number will be blocked (request rejected) +2. Email address will be masked (e.g., replaced with ``) + +#### Using with MCP Tools + +When using MCP tools, guardrails will be applied to the tool inputs: + +```python title="Python Example with MCP Guardrails" showLineNumbers +import openai + +client = openai.OpenAI( + api_key="your-api-key", + base_url="http://localhost:4000" +) + +# This request will trigger MCP guardrails +response = client.chat.completions.create( + model="gpt-3.5-turbo", + messages=[ + {"role": "user", "content": "Send an email to 555-123-4567 with my SSN 123-45-6789"} + ], + tools=[{"type": "mcp", "server_label": "litellm", "server_url": "litellm_proxy"}], + guardrails=["mcp-input-validation"] +) +``` + +### Supported Guardrail Providers + +MCP guardrails work with all LiteLLM-supported guardrail providers: + +- **Presidio**: PII detection and masking +- **Bedrock**: AWS Bedrock guardrails +- **Lakera**: Content moderation +- **Aporia**: Custom guardrails +- **Custom**: Your own guardrail implementations \ No newline at end of file diff --git a/docs/my-website/docs/mcp_usage.md b/docs/my-website/docs/mcp_usage.md new file mode 100644 index 00000000000..ef9d8a5ed1b --- /dev/null +++ b/docs/my-website/docs/mcp_usage.md @@ -0,0 +1,209 @@ + +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; +import Image from '@theme/IdealImage'; + +# Using your MCP + +This document covers how to use LiteLLM as an MCP Gateway. You can see how to use it with Responses API, Cursor IDE, and OpenAI SDK. + +### Use on LiteLLM UI + +Follow this walkthrough to use your MCP on LiteLLM UI + + + +### Use with Responses API + +Replace `http://localhost:4000` with your LiteLLM Proxy base URL. + +Demo Video Using Responses API with LiteLLM Proxy: [Demo video here](https://www.loom.com/share/34587e618c5c47c0b0d67b4e4d02718f?sid=2caf3d45-ead4-4490-bcc1-8d6dd6041c02) + + + + + +```bash title="cURL Example" showLineNumbers +curl --location 'http://localhost:4000/v1/responses' \ +--header 'Content-Type: application/json' \ +--header "Authorization: Bearer sk-1234" \ +--data '{ + "model": "gpt-5", + "input": [ + { + "role": "user", + "content": "give me TLDR of what BerriAI/litellm repo is about", + "type": "message" + } + ], + "tools": [ + { + "type": "mcp", + "server_label": "litellm", + "server_url": "litellm_proxy", + "require_approval": "never" + } + ], + "stream": true, + "tool_choice": "required" +}' +``` + + + + +```python title="Python SDK Example" showLineNumbers +""" +Use LiteLLM Proxy MCP Gateway to call MCP tools. + +When using LiteLLM Proxy, you can use the same MCP tools across all your LLM providers. +""" +import openai + +client = openai.OpenAI( + api_key="sk-1234", # paste your litellm proxy api key here + base_url="http://localhost:4000" # paste your litellm proxy base url here +) +print("Making API request to Responses API with MCP tools") + +response = client.responses.create( + model="gpt-5", + input=[ + { + "role": "user", + "content": "give me TLDR of what BerriAI/litellm repo is about", + "type": "message" + } + ], + tools=[ + { + "type": "mcp", + "server_label": "litellm", + "server_url": "litellm_proxy", + "require_approval": "never" + } + ], + stream=True, + tool_choice="required" +) + +for chunk in response: + print("response chunk: ", chunk) +``` + + + + +#### Specifying MCP Tools + +You can specify which MCP tools are available by using the `allowed_tools` parameter. This allows you to restrict access to specific tools within an MCP server. + +To get the list of allowed tools when using LiteLLM MCP Gateway, you can naigate to the LiteLLM UI on MCP Servers > MCP Tools > Click the Tool > Copy Tool Name. + + + + +```bash title="cURL Example with allowed_tools" showLineNumbers +curl --location 'http://localhost:4000/v1/responses' \ +--header 'Content-Type: application/json' \ +--header "Authorization: Bearer sk-1234" \ +--data '{ + "model": "gpt-5", + "input": [ + { + "role": "user", + "content": "give me TLDR of what BerriAI/litellm repo is about", + "type": "message" + } + ], + "tools": [ + { + "type": "mcp", + "server_label": "litellm", + "server_url": "litellm_proxy/mcp", + "require_approval": "never", + "allowed_tools": ["GitMCP-fetch_litellm_documentation"] + } + ], + "stream": true, + "tool_choice": "required" +}' +``` + + + + +```python title="Python SDK Example with allowed_tools" showLineNumbers +import openai + +client = openai.OpenAI( + api_key="sk-1234", + base_url="http://localhost:4000" +) + +response = client.responses.create( + model="gpt-5", + input=[ + { + "role": "user", + "content": "give me TLDR of what BerriAI/litellm repo is about", + "type": "message" + } + ], + tools=[ + { + "type": "mcp", + "server_label": "litellm", + "server_url": "litellm_proxy/mcp", + "require_approval": "never", + "allowed_tools": ["GitMCP-fetch_litellm_documentation"] + } + ], + stream=True, + tool_choice="required" +) + +print(response) +``` + + + + +### Use with Cursor IDE + +Use tools directly from Cursor IDE with LiteLLM MCP: + +**Setup Instructions:** + +1. **Open Cursor Settings**: Use `⇧+⌘+J` (Mac) or `Ctrl+Shift+J` (Windows/Linux) +2. **Navigate to MCP Tools**: Go to the "MCP Tools" tab and click "New MCP Server" +3. **Add Configuration**: Copy and paste the JSON configuration below, then save with `Cmd+S` or `Ctrl+S` + +```json title="Basic Cursor MCP Configuration" showLineNumbers +{ + "mcpServers": { + "LiteLLM": { + "url": "litellm_proxy", + "headers": { + "x-litellm-api-key": "Bearer $LITELLM_API_KEY" + } + } + } +} +``` + +#### How it works when server_url="litellm_proxy" + +When server_url="litellm_proxy", LiteLLM bridges non-MCP providers to your MCP tools. + +- Tool Discovery: LiteLLM fetches MCP tools and converts them to OpenAI-compatible definitions +- LLM Call: Tools are sent to the LLM with your input; LLM selects which tools to call +- Tool Execution: LiteLLM automatically parses arguments, routes calls to MCP servers, executes tools, and retrieves results +- Response Integration: Tool results are sent back to LLM for final response generation +- Output: Complete response combining LLM reasoning with tool execution results + +This enables MCP tool usage with any LiteLLM-supported provider, regardless of native MCP support. + +#### Auto-execution for require_approval: "never" + +Setting require_approval: "never" triggers automatic tool execution, returning the final response in a single API call without additional user interaction. diff --git a/docs/my-website/docs/observability/posthog_integration.md b/docs/my-website/docs/observability/posthog_integration.md index 7e6a0e1076b..899972b2b48 100644 --- a/docs/my-website/docs/observability/posthog_integration.md +++ b/docs/my-website/docs/observability/posthog_integration.md @@ -55,6 +55,26 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \ }' ``` +### Team-Based Logging + +Configure different PostHog credentials per team using the team callback settings: + +```bash +curl -X POST 'http://localhost:4000/team/{team_id}/callback' \ + -H 'Authorization: Bearer sk-1234' \ + -H 'Content-Type: application/json' \ + -d '{ + "callback_name": "posthog", + "callback_type": "success", + "callback_vars": { + "posthog_api_key": "ph_team_specific_key", + "posthog_api_url": "https://custom.posthog.com" + } + }' +``` + +Now all requests from that team will be logged to their specific PostHog project. + ## Usage with LiteLLM Python SDK ### Quick Start @@ -142,6 +162,31 @@ response = client.chat.completions.create( ) ``` +#### Per-Request Credentials + +You can override PostHog credentials on a per-request basis: + +```python +import litellm + +litellm.success_callback = ["posthog"] + +# Use custom PostHog credentials for this specific request +response = litellm.completion( + model="gpt-3.5-turbo", + messages=[ + {"role": "user", "content": "Hello world"} + ], + posthog_api_key="ph_custom_project_key", + posthog_api_url="https://custom.posthog.com" +) +``` + +This is useful when you need to: +- Log different teams/projects to separate PostHog instances +- Use different PostHog projects for staging vs production +- Route logs based on customer or tenant + #### Disable Logging for Specific Calls Use the `no-log` flag to prevent logging for specific calls: diff --git a/docs/my-website/docs/providers/bedrock_embedding.md b/docs/my-website/docs/providers/bedrock_embedding.md index 69c5f3c86ec..cd492084711 100644 --- a/docs/my-website/docs/providers/bedrock_embedding.md +++ b/docs/my-website/docs/providers/bedrock_embedding.md @@ -265,6 +265,7 @@ print(response) | TwelveLabs Marengo Embed 2.7 | `embedding(model="bedrock/us.twelvelabs.marengo-embed-2-7-v1:0", input=input)` | Supports multimodal input (text, video, audio, image) | | Cohere Embeddings - English | `embedding(model="bedrock/cohere.embed-english-v3", input=input)` | [here](https://github.com/BerriAI/litellm/blob/f5905e100068e7a4d61441d7453d7cf5609c2121/litellm/llms/bedrock/embed/cohere_transformation.py#L18) | Cohere Embeddings - Multilingual | `embedding(model="bedrock/cohere.embed-multilingual-v3", input=input)` | [here](https://github.com/BerriAI/litellm/blob/f5905e100068e7a4d61441d7453d7cf5609c2121/litellm/llms/bedrock/embed/cohere_transformation.py#L18) +| Cohere Embed v4 | `embedding(model="bedrock/cohere.embed-v4:0", input=input)` | Supports text and image input, configurable dimensions (256, 512, 1024, 1536), 128k context length | ### Advanced - [Drop Unsupported Params](https://docs.litellm.ai/docs/completion/drop_params#openai-proxy-usage) diff --git a/docs/my-website/docs/providers/oci.md b/docs/my-website/docs/providers/oci.md index c11d64f4553..1f52fba04f3 100644 --- a/docs/my-website/docs/providers/oci.md +++ b/docs/my-website/docs/providers/oci.md @@ -6,18 +6,27 @@ LiteLLM supports the following models for OCI on-demand GenAI API. Check the [OCI Models List](https://docs.oracle.com/en-us/iaas/Content/generative-ai/pretrained-models.htm) to see if the model is available for your region. +## Supported Models + +### Meta Llama Models - `meta.llama-4-maverick-17b-128e-instruct-fp8` - `meta.llama-4-scout-17b-16e-instruct` - `meta.llama-3.3-70b-instruct` - `meta.llama-3.2-90b-vision-instruct` - `meta.llama-3.1-405b-instruct` +### xAI Grok Models - `xai.grok-4` - `xai.grok-3` - `xai.grok-3-fast` - `xai.grok-3-mini` - `xai.grok-3-mini-fast` +### Cohere Models +- `cohere.command-latest` +- `cohere.command-a-03-2025` +- `cohere.command-plus-latest` + ## Authentication LiteLLM uses OCI signing key authentication. Follow the [official Oracle tutorial](https://docs.oracle.com/en-us/iaas/Content/API/Concepts/apisigningkey.htm) to create a signing key and obtain the following parameters: @@ -83,3 +92,24 @@ response = completion( for chunk in response: print(chunk["choices"][0]["delta"]["content"]) # same as openai format ``` + +## Usage Examples by Model Type + +### Using Cohere Models + +```python +from litellm import completion + +messages = [{"role": "user", "content": "Explain quantum computing"}] +response = completion( + model="oci/cohere.command-latest", + messages=messages, + oci_region="us-chicago-1", + oci_user=, + oci_fingerprint=, + oci_tenancy=, + oci_key=, + oci_compartment_id=, +) +print(response) +``` \ No newline at end of file diff --git a/docs/my-website/docs/providers/openai.md b/docs/my-website/docs/providers/openai.md index d820215948c..dbd0b9927a9 100644 --- a/docs/my-website/docs/providers/openai.md +++ b/docs/my-website/docs/providers/openai.md @@ -171,6 +171,7 @@ os.environ["OPENAI_BASE_URL"] = "https://your_host/v1" # OPTIONAL | gpt-5-2025-08-07 | `response = completion(model="gpt-5-2025-08-07", messages=messages)` | | gpt-5-mini-2025-08-07 | `response = completion(model="gpt-5-mini-2025-08-07", messages=messages)` | | gpt-5-nano-2025-08-07 | `response = completion(model="gpt-5-nano-2025-08-07", messages=messages)` | +| gpt-5-pro | `response = completion(model="gpt-5-pro", messages=messages)` | | gpt-4.1 | `response = completion(model="gpt-4.1", messages=messages)` | | gpt-4.1-mini | `response = completion(model="gpt-4.1-mini", messages=messages)` | | gpt-4.1-nano | `response = completion(model="gpt-4.1-nano", messages=messages)` | @@ -749,4 +750,24 @@ In your logs you should see the forwarded org id ```bash LiteLLM:DEBUG: utils.py:255 - Request to litellm: LiteLLM:DEBUG: utils.py:255 - litellm.acompletion(... organization='my-special-org',) +``` + +## GPT-5 Pro Special Notes + +GPT-5 Pro is OpenAI's most advanced reasoning model with unique characteristics: + +- **Responses API Only**: GPT-5 Pro is only available through the `/v1/responses` endpoint +- **No Streaming**: Does not support streaming responses +- **High Reasoning**: Designed for complex reasoning tasks with highest effort reasoning +- **Context Window**: 400,000 tokens input, 272,000 tokens output +- **Pricing**: $15.00 input / $120.00 output per 1M tokens (Standard), $7.50 input / $60.00 output (Batch) +- **Tools**: Supports Web Search, File Search, Image Generation, MCP (but not Code Interpreter or Computer Use) +- **Modalities**: Text and Image input, Text output only + +```python +# GPT-5 Pro usage example +response = completion( + model="gpt-5-pro", + messages=[{"role": "user", "content": "Solve this complex reasoning problem..."}] +) ``` \ No newline at end of file diff --git a/docs/my-website/docs/providers/openai/responses_api.md b/docs/my-website/docs/providers/openai/responses_api.md index e96a2f95225..8d91ca674b7 100644 --- a/docs/my-website/docs/providers/openai/responses_api.md +++ b/docs/my-website/docs/providers/openai/responses_api.md @@ -37,6 +37,29 @@ for event in response: print(event) ``` +#### Image Generation with Streaming +```python showLineNumbers title="OpenAI Streaming Image Generation" +import litellm +import base64 + +# Streaming image generation with partial images +stream = litellm.responses( + model="gpt-4.1", # Use an actual image generation model + input="Generate a gorgeous image of a river made of white owl feathers", + stream=True, + tools=[{"type": "image_generation", "partial_images": 2}], + +) + +for event in stream: + if event.type == "response.image_generation_call.partial_image": + idx = event.partial_image_index + image_base64 = event.partial_image_b64 + image_bytes = base64.b64decode(image_base64) + with open(f"river{idx}.png", "wb") as f: + f.write(image_bytes) +``` + #### GET a Response ```python showLineNumbers title="Get Response by ID" import litellm @@ -150,6 +173,33 @@ for event in response: print(event) ``` +#### Image Generation with Streaming +```python showLineNumbers title="OpenAI Proxy Streaming Image Generation" +from openai import OpenAI +import base64 + +# Initialize client with your proxy URL +client = OpenAI(api_key="sk-1234", base_url="http://localhost:4000") + +stream = client.responses.create( + model="gpt-4.1", + input="Draw a gorgeous image of a river made of white owl feathers, snaking its way through a serene winter landscape", + stream=True, + tools=[{"type": "image_generation", "partial_images": 2}], +) + + +for event in stream: + print(f"event: {event}") + if event.type == "response.image_generation_call.partial_image": + idx = event.partial_image_index + image_base64 = event.partial_image_b64 + image_bytes = base64.b64decode(image_base64) + with open(f"river{idx}.png", "wb") as f: + f.write(image_bytes) + +``` + #### GET a Response ```python showLineNumbers title="Get Response by ID with OpenAI SDK" from openai import OpenAI diff --git a/docs/my-website/docs/providers/vertex.md b/docs/my-website/docs/providers/vertex.md index 2543c15d135..3b6562b51ae 100644 --- a/docs/my-website/docs/providers/vertex.md +++ b/docs/my-website/docs/providers/vertex.md @@ -191,7 +191,7 @@ print(json.loads(completion.choices[0].message.content)) model_list: - model_name: gemini-2.5-pro litellm_params: - model: vertex_ai/gemini-1.5-pro + model: vertex_ai/gemini-2.5-pro vertex_project: "project-id" vertex_location: "us-central1" vertex_credentials: "/path/to/service_account.json" # [OPTIONAL] Do this OR `!gcloud auth application-default login` - run this to add vertex credentials to your env @@ -277,7 +277,7 @@ except JSONSchemaValidationError as e: model_list: - model_name: gemini-2.5-pro litellm_params: - model: vertex_ai/gemini-1.5-pro + model: vertex_ai/gemini-2.5-pro vertex_project: "project-id" vertex_location: "us-central1" vertex_credentials: "/path/to/service_account.json" # [OPTIONAL] Do this OR `!gcloud auth application-default login` - run this to add vertex credentials to your env @@ -981,11 +981,158 @@ curl http://0.0.0.0:4000/v1/chat/completions \ ### **Context Caching** -Use Vertex AI context caching is supported by calling provider api directly. (Unified Endpoint support coming soon.). +#### Unified Endpoint + +Use Vertex AI context caching in the same way as [**Google AI Studio - Context Caching**](../providers/gemini.md#context-caching) + + +##### Example usage + + + + +```python +from litellm import completion + +for _ in range(2): + resp = completion( + model="vertex_ai/gemini-2.5-pro", + messages=[ + # System Message + { + "role": "system", + "content": [ + { + "type": "text", + "text": "Here is the full text of a complex legal agreement" * 4000, + "cache_control": {"type": "ephemeral"}, # 👈 KEY CHANGE + } + ], + }, + # marked for caching with the cache_control parameter, so that this checkpoint can read from the previous cache. + { + "role": "user", + "content": [ + { + "type": "text", + "text": "What are the key terms and conditions in this agreement?", + "cache_control": {"type": "ephemeral"}, + } + ], + }] + ) + + print(resp.usage) # 👈 2nd usage block will be less, since cached tokens used +``` + + + + +```python +from litellm import completion + +# Cache for 2 hours (7200 seconds) +resp = completion( + model="vertex_ai/gemini-2.5-pro", + messages=[ + { + "role": "system", + "content": [ + { + "type": "text", + "text": "Here is the full text of a complex legal agreement" * 4000, + "cache_control": { + "type": "ephemeral", + "ttl": "7200s" # 👈 Cache for 2 hours + }, + } + ], + }, + { + "role": "user", + "content": [ + { + "type": "text", + "text": "What are the key terms and conditions in this agreement?", + "cache_control": { + "type": "ephemeral", + "ttl": "3600s" # 👈 This TTL will be ignored (first one is used) + }, + } + ], + } + ] +) + +print(resp.usage) +``` + + + + +1. Setup config.yaml + +```yaml +model_list: + - model_name: gemini-2.5-pro + litellm_params: + model: vertex_ai/gemini-2.5-pro + vertex_project: "project-id" + vertex_location: "us-central1" + vertex_credentials: "/path/to/service_account.json" +``` + +2. Start proxy + +```bash +litellm --config /path/to/config.yaml +``` + +3. Test it! + +```bash + +curl -X POST 'http://0.0.0.0:4000/chat/completions' \ +-H 'Content-Type: application/json' \ +-H 'Authorization: Bearer sk-1234' \ +-d '{ + "model": "gemini-2.5-flash", + "messages": [ + { + "role": "system", + "content": [ + { + "type": "text", + "text": "Long cache message (must be >= 1024 tokens)", + "cache_control": { + "type": "ephemeral", + "ttl": "7200s" + } + } + ] + }, + { + "role": "user", + "content": [ + { + "type": "text", + "text": "What is the text about?" + } + ] + } + ] +}' + +``` + + + + +#### Calling provider api directly [**Go straight to provider**](../pass_through/vertex_ai.md#context-caching) -#### 1. Create the Cache +##### 1. Create the Cache First, create the cache by sending a `POST` request to the `cachedContents` endpoint via the LiteLLM proxy. @@ -1011,7 +1158,7 @@ curl http://0.0.0.0:4000/vertex_ai/v1/projects/{project_id}/locations/{location} -#### 2. Get the Cache Name from the Response +##### 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. @@ -1030,7 +1177,7 @@ Vertex AI will return a response containing the `name` of the cached content. Th } ``` -#### 3. Use the Cached Content +##### 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`. diff --git a/docs/my-website/docs/providers/vertex_batch.md b/docs/my-website/docs/providers/vertex_batch.md index 4eaa0d69d4b..046c60f2ebd 100644 --- a/docs/my-website/docs/providers/vertex_batch.md +++ b/docs/my-website/docs/providers/vertex_batch.md @@ -1,7 +1,7 @@ import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; -## **Batch APIs** +# Vertex Batch APIs Just add the following Vertex env vars to your environment. diff --git a/docs/my-website/docs/providers/vertex_partner.md b/docs/my-website/docs/providers/vertex_partner.md index 856f054b8e6..48a116eb7a8 100644 --- a/docs/my-website/docs/providers/vertex_partner.md +++ b/docs/my-website/docs/providers/vertex_partner.md @@ -16,7 +16,6 @@ import TabItem from '@theme/TabItem'; | AI21 (Jamba) | `vertex_ai/jamba-*` | [Vertex AI - AI21 Models](https://cloud.google.com/vertex-ai/generative-ai/docs/partner-models/ai21) | | Qwen | `vertex_ai/qwen/*` | [Vertex AI - Qwen Models](https://cloud.google.com/vertex-ai/generative-ai/docs/maas/qwen) | | OpenAI (GPT-OSS) | `vertex_ai/openai/gpt-oss-*` | [Vertex AI - GPT-OSS Models](https://console.cloud.google.com/vertex-ai/publishers/openai/model-garden/) | -| Model Garden | `vertex_ai/openai/{MODEL_ID}` or `vertex_ai/{MODEL_ID}` | [Vertex Model Garden](https://cloud.google.com/model-garden?hl=en) | ## Vertex AI - Anthropic (Claude) @@ -793,112 +792,3 @@ curl http://0.0.0.0:4000/v1/chat/completions \ - -## Model Garden - -:::tip - -All OpenAI compatible models from Vertex Model Garden are supported. - -::: - -#### Using Model Garden - -**Almost all Vertex Model Garden models are OpenAI compatible.** - - - - - -| Property | Details | -|----------|---------| -| Provider Route | `vertex_ai/openai/{MODEL_ID}` | -| Vertex Documentation | [Model Garden LiteLLM Inference](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/open-models/use-cases/model_garden_litellm_inference.ipynb), [Vertex Model Garden](https://cloud.google.com/model-garden?hl=en) | -| Supported Operations | `/chat/completions`, `/embeddings` | - - - - -```python -from litellm import completion -import os - -## set ENV variables -os.environ["VERTEXAI_PROJECT"] = "hardy-device-38811" -os.environ["VERTEXAI_LOCATION"] = "us-central1" - -response = completion( - model="vertex_ai/openai/", - messages=[{ "content": "Hello, how are you?","role": "user"}] -) -``` - - - - - - -**1. Add to config** - -```yaml -model_list: - - model_name: llama3-1-8b-instruct - litellm_params: - model: vertex_ai/openai/5464397967697903616 - vertex_ai_project: "my-test-project" - vertex_ai_location: "us-east-1" -``` - -**2. Start proxy** - -```bash -litellm --config /path/to/config.yaml - -# RUNNING at http://0.0.0.0:4000 -``` - -**3. Test it!** - -```bash -curl --location 'http://0.0.0.0:4000/chat/completions' \ - --header 'Authorization: Bearer sk-1234' \ - --header 'Content-Type: application/json' \ - --data '{ - "model": "llama3-1-8b-instruct", # 👈 the 'model_name' in config - "messages": [ - { - "role": "user", - "content": "what llm are you" - } - ], - }' -``` - - - - - - - - - - - - -```python -from litellm import completion -import os - -## set ENV variables -os.environ["VERTEXAI_PROJECT"] = "hardy-device-38811" -os.environ["VERTEXAI_LOCATION"] = "us-central1" - -response = completion( - model="vertex_ai/", - messages=[{ "content": "Hello, how are you?","role": "user"}] -) -``` - - - - diff --git a/docs/my-website/docs/providers/vertex_self_deployed.md b/docs/my-website/docs/providers/vertex_self_deployed.md new file mode 100644 index 00000000000..b7a71cdbd0e --- /dev/null +++ b/docs/my-website/docs/providers/vertex_self_deployed.md @@ -0,0 +1,229 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Vertex AI - Self Deployed Models + +Deploy and use your own models on Vertex AI through Model Garden or custom endpoints. + +## Model Garden + +:::tip + +All OpenAI compatible models from Vertex Model Garden are supported. + +::: + +### Using Model Garden + +**Almost all Vertex Model Garden models are OpenAI compatible.** + + + + + +| Property | Details | +|----------|---------| +| Provider Route | `vertex_ai/openai/{MODEL_ID}` | +| Vertex Documentation | [Model Garden LiteLLM Inference](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/open-models/use-cases/model_garden_litellm_inference.ipynb), [Vertex Model Garden](https://cloud.google.com/model-garden?hl=en) | +| Supported Operations | `/chat/completions`, `/embeddings` | + + + + +```python +from litellm import completion +import os + +## set ENV variables +os.environ["VERTEXAI_PROJECT"] = "hardy-device-38811" +os.environ["VERTEXAI_LOCATION"] = "us-central1" + +response = completion( + model="vertex_ai/openai/", + messages=[{ "content": "Hello, how are you?","role": "user"}] +) +``` + + + + + + +**1. Add to config** + +```yaml +model_list: + - model_name: llama3-1-8b-instruct + litellm_params: + model: vertex_ai/openai/5464397967697903616 + vertex_ai_project: "my-test-project" + vertex_ai_location: "us-east-1" +``` + +**2. Start proxy** + +```bash +litellm --config /path/to/config.yaml + +# RUNNING at http://0.0.0.0:4000 +``` + +**3. Test it!** + +```bash +curl --location 'http://0.0.0.0:4000/chat/completions' \ + --header 'Authorization: Bearer sk-1234' \ + --header 'Content-Type: application/json' \ + --data '{ + "model": "llama3-1-8b-instruct", # 👈 the 'model_name' in config + "messages": [ + { + "role": "user", + "content": "what llm are you" + } + ], + }' +``` + + + + + + + + + + + + +```python +from litellm import completion +import os + +## set ENV variables +os.environ["VERTEXAI_PROJECT"] = "hardy-device-38811" +os.environ["VERTEXAI_LOCATION"] = "us-central1" + +response = completion( + model="vertex_ai/", + messages=[{ "content": "Hello, how are you?","role": "user"}] +) +``` + + + + + +## Gemma Models (Custom Endpoints) + +Deploy Gemma models on custom Vertex AI prediction endpoints with OpenAI-compatible format. + +| Property | Details | +|----------|---------| +| Provider Route | `vertex_ai/gemma/{MODEL_NAME}` | +| Vertex Documentation | [Vertex AI Prediction](https://cloud.google.com/vertex-ai/docs/predictions/get-predictions) | +| Required Parameter | `api_base` - Full prediction endpoint URL | + +**Proxy Usage:** + +**1. Add to config.yaml** + +```yaml +model_list: + - model_name: gemma-model + litellm_params: + model: vertex_ai/gemma/gemma-3-12b-it-1222199011122 + api_base: https://ENDPOINT.us-central1-PROJECT.prediction.vertexai.goog/v1/projects/PROJECT_ID/locations/us-central1/endpoints/ENDPOINT_ID:predict + vertex_project: "my-project-id" + vertex_location: "us-central1" +``` + +**2. Start proxy** + +```bash +litellm --config /path/to/config.yaml +``` + +**3. Test it** + +```bash +curl http://0.0.0.0:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer sk-1234" \ + -d '{ + "model": "gemma-model", + "messages": [{"role": "user", "content": "What is machine learning?"}], + "max_tokens": 100 + }' +``` + +**SDK Usage:** + +```python +from litellm import completion + +response = completion( + model="vertex_ai/gemma/gemma-3-12b-it-1222199011122", + messages=[{"role": "user", "content": "What is machine learning?"}], + api_base="https://ENDPOINT.us-central1-PROJECT.prediction.vertexai.goog/v1/projects/PROJECT_ID/locations/us-central1/endpoints/ENDPOINT_ID:predict", + vertex_project="my-project-id", + vertex_location="us-central1", +) +``` + +## MedGemma Models (Custom Endpoints) + +Deploy MedGemma models on custom Vertex AI prediction endpoints with OpenAI-compatible format. MedGemma models use the same `vertex_ai/gemma/` route. + +| Property | Details | +|----------|---------| +| Provider Route | `vertex_ai/gemma/{MODEL_NAME}` | +| Vertex Documentation | [Vertex AI Prediction](https://cloud.google.com/vertex-ai/docs/predictions/get-predictions) | +| Required Parameter | `api_base` - Full prediction endpoint URL | + +**Proxy Usage:** + +**1. Add to config.yaml** + +```yaml +model_list: + - model_name: medgemma-model + litellm_params: + model: vertex_ai/gemma/medgemma-2b-v1 + api_base: https://ENDPOINT.us-central1-PROJECT.prediction.vertexai.goog/v1/projects/PROJECT_ID/locations/us-central1/endpoints/ENDPOINT_ID:predict + vertex_project: "my-project-id" + vertex_location: "us-central1" +``` + +**2. Start proxy** + +```bash +litellm --config /path/to/config.yaml +``` + +**3. Test it** + +```bash +curl http://0.0.0.0:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer sk-1234" \ + -d '{ + "model": "medgemma-model", + "messages": [{"role": "user", "content": "What are the symptoms of hypertension?"}], + "max_tokens": 100 + }' +``` + +**SDK Usage:** + +```python +from litellm import completion + +response = completion( + model="vertex_ai/gemma/medgemma-2b-v1", + messages=[{"role": "user", "content": "What are the symptoms of hypertension?"}], + api_base="https://ENDPOINT.us-central1-PROJECT.prediction.vertexai.goog/v1/projects/PROJECT_ID/locations/us-central1/endpoints/ENDPOINT_ID:predict", + vertex_project="my-project-id", + vertex_location="us-central1", +) +``` diff --git a/docs/my-website/docs/providers/wandb_inference.md b/docs/my-website/docs/providers/wandb_inference.md new file mode 100644 index 00000000000..c59f08381c6 --- /dev/null +++ b/docs/my-website/docs/providers/wandb_inference.md @@ -0,0 +1,196 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Weights & Biases Inference +https://weave-docs.wandb.ai/quickstart-inference + +:::tip + +Litellm provides support to all models from W&B Inference service. To use a model, set `model=wandb/` as a prefix for litellm requests. The full list of supported models is provided at https://docs.wandb.ai/guides/inference/models/ + +::: + +## API Key + +You can get an API key for W&B Inference at - https://wandb.ai/authorize + +```python +import os +# env variable +os.environ['WANDB_API_KEY'] +``` + +## Sample Usage: Text Generation +```python +from litellm import completion +import os + +os.environ['WANDB_API_KEY'] = "insert-your-wandb-api-key" +response = completion( + model="wandb/Qwen/Qwen3-235B-A22B-Instruct-2507", + messages=[ + { + "role": "user", + "content": "What character was Wall-e in love with?", + } + ], + max_tokens=10, + response_format={ "type": "json_object" }, + seed=123, + temperature=0.6, # either set temperature or `top_p` + top_p=0.01, # to get as deterministic results as possible +) +print(response) +``` + +## Sample Usage - Streaming +```python +from litellm import completion +import os + +os.environ['WANDB_API_KEY'] = "" +response = completion( + model="wandb/Qwen/Qwen3-235B-A22B-Instruct-2507", + messages=[ + { + "role": "user", + "content": "What character was Wall-e in love with?", + } + ], + stream=True, + max_tokens=10, + response_format={ "type": "json_object" }, + seed=123, + temperature=0.6, # either set temperature or `top_p` + top_p=0.01, # to get as deterministic results as possible +) + +for chunk in response: + print(chunk) +``` + +:::tip + +The above examples may not work if the model has been taken offline. Check the full list of available models at https://docs.wandb.ai/guides/inference/models/. + +::: + +## Usage with LiteLLM Proxy Server + +Here's how to call a W&B Inference model with the LiteLLM Proxy Server + +1. Modify the config.yaml + + ```yaml + model_list: + - model_name: my-model + litellm_params: + model: wandb/ # add wandb/ prefix to use W&B Inference as provider + api_key: api-key # api key to send your model + ``` +2. Start the proxy + ```bash + $ litellm --config /path/to/config.yaml + ``` + +3. Send Request to LiteLLM Proxy Server + + + + + + ```python + import openai + client = openai.OpenAI( + api_key="litellm-proxy-key", # pass litellm proxy key, if you're using virtual keys + base_url="http://0.0.0.0:4000" # litellm-proxy-base url + ) + + response = client.chat.completions.create( + model="my-model", + messages = [ + { + "role": "user", + "content": "What character was Wall-e in love with?" + } + ], + ) + + print(response) + ``` + + + + + ```shell + curl --location 'http://0.0.0.0:4000/chat/completions' \ + --header 'Authorization: litellm-proxy-key' \ + --header 'Content-Type: application/json' \ + --data '{ + "model": "my-model", + "messages": [ + { + "role": "user", + "content": "What character was Wall-e in love with?" + } + ], + }' + ``` + + + + +## Supported Parameters + +The W&B Inference provider supports the following parameters: + +### Chat Completion Parameters + +| Parameter | Type | Description | +| --------- | ---- | ----------- | +| frequency_penalty | number | Penalizes new tokens based on their frequency in the text | +| function_call | string/object | Controls how the model calls functions | +| functions | array | List of functions for which the model may generate JSON inputs | +| logit_bias | map | Modifies the likelihood of specified tokens | +| max_tokens | integer | Maximum number of tokens to generate | +| n | integer | Number of completions to generate | +| presence_penalty | number | Penalizes tokens based on if they appear in the text so far | +| response_format | object | Format of the response, e.g., `{"type": "json"}` | +| seed | integer | Sampling seed for deterministic results | +| stop | string/array | Sequences where the API will stop generating tokens | +| stream | boolean | Whether to stream the response | +| temperature | number | Controls randomness (0-2) | +| top_p | number | Controls nucleus sampling | + + +## Error Handling + +The integration uses the standard LiteLLM error handling. Further, here's a list of commonly encountered errors with the W&B Inference API - + +| Error Code | Message | Cause | Solution | +| ---------- | ------- | ----- | -------- | +| 401 | Authentication failed | Your authentication credentials are incorrect or your W&B project entity and/or name are incorrect. | Ensure you're using the correct API key and that your W&B project name and entity are correct. | +| 403 | Country, region, or territory not supported | Accessing the API from an unsupported location. | Please see [Geographic restrictions](https://docs.wandb.ai/guides/inference/usage-limits/#geographic-restrictions) | +| 429 | Concurrency limit reached for requests | Too many concurrent requests. | Reduce the number of concurrent requests or increase your limits. For more information, see [Usage information and limits](https://docs.wandb.ai/guides/inference/usage-limits/). | +| 429 | You exceeded your current quota, please check your plan and billing details | Out of credits or reached monthly spending cap. | Get more credits or increase your limits. For more information, see [Usage information and limits](https://docs.wandb.ai/guides/inference/usage-limits/). | +| 429 | W&B Inference isn't available for personal accounts. | Switch to a non-personal account. | Follow [the instructions below](#error-429-personal-entities-unsupported) for a work around. | +| 500 | The server had an error while processing your request | Internal server error. | Retry after a brief wait and contact support if it persists. | +| 503 | The engine is currently overloaded, please try again later | Server is experiencing high traffic. | Retry your request after a short delay. | + + +### Error 429: Personal entities unsupported + +The user is on a personal account, which doesn't have access to W&B Inference. If one isn't available, create a Team to create a non-personal account. + +Once done, add the `openai-project` header to your request as shown below: + +```python +response = completion( + model="...", + extra_headers={"openai-project": "team_name/project_name"}, + ... +``` + +For more information, see [Personal entities unsupported](https://docs.wandb.ai/guides/inference/usage-limits/#personal-entities-unsupported). + +You can find more ways of using custom headers with LiteLLM here - https://docs.litellm.ai/docs/proxy/request_headers. diff --git a/docs/my-website/docs/proxy/admin_ui_sso.md b/docs/my-website/docs/proxy/admin_ui_sso.md index 32bf97410cb..bd18dd9c690 100644 --- a/docs/my-website/docs/proxy/admin_ui_sso.md +++ b/docs/my-website/docs/proxy/admin_ui_sso.md @@ -81,6 +81,23 @@ MICROSOFT_TENANT="5a39737 http://localhost:4000/sso/callback ``` +**Using App Roles for User Permissions** + +You can assign user roles directly from Entra ID using App Roles. LiteLLM will automatically read the app roles from the JWT token and assign the corresponding role to the user. + +Supported roles: +- `proxy_admin` - Admin over the platform +- `proxy_admin_viewer` - Can login, view all keys, view all spend (read-only) +- `internal_user` - Normal user. Can login, view spend and depending on team-member permissions - view/create/delete their own keys. + + +To set up app roles: +1. Navigate to your App Registration on https://portal.azure.com/ +2. Go to "App roles" and create a new app role +3. Use one of the supported role names above (e.g., `proxy_admin`) +4. Assign users to these roles in your Enterprise Application +5. When users sign in via SSO, LiteLLM will automatically assign them the corresponding role + diff --git a/docs/my-website/docs/proxy/caching.md b/docs/my-website/docs/proxy/caching.md index 617609cf08a..9cfd796d90f 100644 --- a/docs/my-website/docs/proxy/caching.md +++ b/docs/my-website/docs/proxy/caching.md @@ -278,6 +278,8 @@ Set either `REDIS_URL` or the `REDIS_HOST` in your os environment, to enable cac REDIS_HOST = "" # REDIS_HOST='redis-18841.c274.us-east-1-3.ec2.cloud.redislabs.com' REDIS_PORT = "" # REDIS_PORT='18841' REDIS_PASSWORD = "" # REDIS_PASSWORD='liteLlmIsAmazing' + REDIS_USERNAME = "" # REDIS_USERNAME='my-redis-username' [OPTIONAL] if your redis server requires a username + REDIS_SSL = "True" # REDIS_SSL='True' to enable SSL by default is False ``` **Additional kwargs** diff --git a/docs/my-website/docs/proxy/config_settings.md b/docs/my-website/docs/proxy/config_settings.md index ad3afd59a02..9e66014b146 100644 --- a/docs/my-website/docs/proxy/config_settings.md +++ b/docs/my-website/docs/proxy/config_settings.md @@ -224,6 +224,7 @@ router_settings: | service_account_settings | List[Dict[str, Any]] | Set `service_account_settings` if you want to create settings that only apply to service account keys (Doc on service accounts)[./service_accounts.md] | | image_generation_model | str | The default model to use for image generation - ignores model set in request | | store_model_in_db | boolean | If true, enables storing model + credential information in the DB. | +| supported_db_objects | List[str] | Fine-grained control over which object types to load from the database when `store_model_in_db` is True. Available types: `"models"`, `"mcp"`, `"guardrails"`, `"vector_stores"`, `"pass_through_endpoints"`, `"prompts"`, `"model_cost_map"`. If not set, all object types are loaded (default behavior). Example: `supported_db_objects: ["mcp"]` to only load MCP servers from DB. | | store_prompts_in_spend_logs | boolean | If true, allows prompts and responses to be stored in the spend logs table. | | max_request_size_mb | int | The maximum size for requests in MB. Requests above this size will be rejected. | | max_response_size_mb | int | The maximum size for responses in MB. LLM Responses above this size will not be sent. | @@ -352,7 +353,10 @@ router_settings: | AGENTOPS_SERVICE_NAME | Service Name for AgentOps logging integration | AISPEND_ACCOUNT_ID | Account ID for AI Spend | AISPEND_API_KEY | API Key for AI Spend +| AIOHTTP_CONNECTOR_LIMIT | Connection limit for aiohttp connector. When set to 0, no limit is applied. **Default is 0** +| AIOHTTP_KEEPALIVE_TIMEOUT | Keep-alive timeout for aiohttp connections in seconds. **Default is 120** | AIOHTTP_TRUST_ENV | Flag to enable aiohttp trust environment. When this is set to True, aiohttp will respect HTTP(S)_PROXY env vars. **Default is False** +| AIOHTTP_TTL_DNS_CACHE | DNS cache time-to-live for aiohttp in seconds. **Default is 300** | ALLOWED_EMAIL_DOMAINS | List of email domains allowed for access | ARIZE_API_KEY | API key for Arize platform integration | ARIZE_SPACE_KEY | Space key for Arize platform @@ -505,6 +509,8 @@ router_settings: | EMAIL_SIGNATURE | Custom HTML footer/signature for all emails. Can include HTML tags for formatting and links. | EMAIL_SUBJECT_INVITATION | Custom subject template for invitation emails. | EMAIL_SUBJECT_KEY_CREATED | Custom subject template for key creation emails. +| ENKRYPTAI_API_BASE | Base URL for EnkryptAI Guardrails API. **Default is https://api.enkryptai.com** +| ENKRYPTAI_API_KEY | API key for EnkryptAI Guardrails service | EXPERIMENTAL_MULTI_INSTANCE_RATE_LIMITING | Flag to enable new multi-instance rate limiting. **Default is False** | FIREWORKS_AI_4_B | Size parameter for Fireworks AI 4B model. Default is 4 | FIREWORKS_AI_16_B | Size parameter for Fireworks AI 16B model. Default is 16 @@ -628,6 +634,7 @@ router_settings: | LITELLM_MODE | Operating mode for LiteLLM (e.g., production, development) | LITELLM_RATE_LIMIT_WINDOW_SIZE | Rate limit window size for LiteLLM. Default is 60 | LITELLM_SALT_KEY | Salt key for encryption in LiteLLM +| LITELLM_SSL_CIPHERS | SSL/TLS cipher configuration for faster handshakes. Controls cipher suite preferences for OpenSSL connections. | LITELLM_SECRET_AWS_KMS_LITELLM_LICENSE | AWS KMS encrypted license for LiteLLM | LITELLM_TOKEN | Access token for LiteLLM integration | LITELLM_PRINT_STANDARD_LOGGING_PAYLOAD | If true, prints the standard logging payload to the console - useful for debugging @@ -775,4 +782,6 @@ router_settings: | WEBHOOK_URL | URL for receiving webhooks from external services | SPEND_LOG_RUN_LOOPS | Constant for setting how many runs of 1000 batch deletes should spend_log_cleanup task run | | SPEND_LOG_CLEANUP_BATCH_SIZE | Number of logs deleted per batch during cleanup. Default is 1000 | -| COROUTINE_CHECKER_MAX_SIZE_IN_MEMORY | Maximum size for CoroutineChecker in-memory cache. Default is 1000 | \ No newline at end of file +| COROUTINE_CHECKER_MAX_SIZE_IN_MEMORY | Maximum size for CoroutineChecker in-memory cache. Default is 1000 | +DEFAULT_SHARED_HEALTH_CHECK_TTL | Time-to-live in seconds for cached health check results in shared health check mode. Default is 300 (5 minutes) +DEFAULT_SHARED_HEALTH_CHECK_LOCK_TTL | Time-to-live in seconds for health check lock in shared health check mode. Default is 60 (1 minute) \ No newline at end of file diff --git a/docs/my-website/docs/proxy/dynamic_rate_limit.md b/docs/my-website/docs/proxy/dynamic_rate_limit.md index b3aed6a359e..06d49dfaf0f 100644 --- a/docs/my-website/docs/proxy/dynamic_rate_limit.md +++ b/docs/my-website/docs/proxy/dynamic_rate_limit.md @@ -141,6 +141,7 @@ litellm_settings: "dev": 0.1 # 10% reserved for development (1 RPM) priority_reservation_settings: default_priority: 0 # Weight (0%) assigned to keys without explicit priority metadata + saturation_threshold: 0.50 # A model is saturated if it has hit 50% of its RPM limit general_settings: master_key: sk-1234 # OR set `LITELLM_MASTER_KEY=".."` in your .env @@ -156,6 +157,8 @@ general_settings: `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) +- **saturation_threshold (float)**: Saturation level (0.0 to 1.0) at which strict priority enforcement begins for a model. Saturation is calculated as `max(current_rpm/max_rpm, current_tpm/max_tpm)`. Below this threshold, generous mode allows priority borrowing from unused capacity. Above this threshold, strict mode enforces normalized priority limits. + - Example: When model usage is low, keys can use more than their allocated share. When model usage is high, keys are strictly limited to their allocated share. **Start Proxy** diff --git a/docs/my-website/docs/proxy/guardrails/enkryptai.md b/docs/my-website/docs/proxy/guardrails/enkryptai.md new file mode 100644 index 00000000000..52e66edca40 --- /dev/null +++ b/docs/my-website/docs/proxy/guardrails/enkryptai.md @@ -0,0 +1,276 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# EnkryptAI Guardrails + +LiteLLM supports EnkryptAI guardrails for content moderation and safety checks on LLM inputs and outputs. + +## Quick Start + +### 1. Define Guardrails on your LiteLLM config.yaml + +Define your guardrails under the `guardrails` section: + +```yaml +model_list: + - model_name: gpt-3.5-turbo + litellm_params: + model: openai/gpt-3.5-turbo + api_key: os.environ/OPENAI_API_KEY + +guardrails: + - guardrail_name: "enkryptai-guard" + litellm_params: + guardrail: enkryptai + mode: "pre_call" + api_key: os.environ/ENKRYPTAI_API_KEY + detectors: + toxicity: + enabled: true + nsfw: + enabled: true + pii: + enabled: true + entities: ["email", "phone", "secrets"] + injection_attack: + enabled: true +``` + +#### Supported values for `mode` + +- `pre_call` - Run **before** LLM call, on **input** +- `post_call` - Run **after** LLM call, on **output** +- `during_call` - Run **during** LLM call, on **input**. Same as `pre_call` but runs in parallel as LLM call + +#### Available Detectors + +EnkryptAI supports multiple content detection types: + +- **toxicity** - Detect toxic language +- **nsfw** - Detect NSFW (Not Safe For Work) content +- **pii** - Detect personally identifiable information + - Configure entities: `["pii", "email", "phone", "secrets", "ip_address", "url"]` +- **injection_attack** - Detect prompt injection attempts +- **keyword_detector** - Detect custom keywords/phrases +- **policy_violation** - Detect policy violations +- **bias** - Detect biased content +- **sponge_attack** - Detect sponge attacks + +### 2. Set Environment Variables + +```bash +export ENKRYPTAI_API_KEY="your-api-key" +``` + +### 3. Start LiteLLM Gateway + +```shell +litellm --config config.yaml --detailed_debug +``` + +### 4. Test Request + +**[Langchain, OpenAI SDK Usage Examples](../proxy/user_keys#request-format)** + + + + +```shell +curl -i http://localhost:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer sk-1234" \ + -d '{ + "model": "gpt-3.5-turbo", + "messages": [ + {"role": "user", "content": "Hello, how can you help me today?"} + ], + "guardrails": ["enkryptai-guard"] + }' +``` + +**Response: HTTP 200 Success** + +Content passes all detector checks and is allowed through. + + + + + +Expect this to fail if content violates detector policies: + +```shell +curl -i http://localhost:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer sk-1234" \ + -d '{ + "model": "gpt-3.5-turbo", + "messages": [ + {"role": "user", "content": "My email is test@example.com and my SSN is 123-45-6789"} + ], + "guardrails": ["enkryptai-guard"] + }' +``` + +**Expected Response on Failure: HTTP 400 Error** + +```json +{ + "error": { + "message": { + "error": "Content blocked by EnkryptAI guardrail", + "detected": true, + "violations": ["pii"], + "response": { + "summary": { + "pii": 1 + }, + "details": { + "pii": { + "detected": ["email", "ssn"] + } + } + } + }, + "type": "None", + "param": "None", + "code": "400" + } +} +``` + + + + +## Video Walkthrough + + + +## Advanced Configuration + +### Using Custom Policies + +You can specify a custom EnkryptAI policy: + +```yaml +guardrails: + - guardrail_name: "enkryptai-custom" + litellm_params: + guardrail: enkryptai + mode: "pre_call" + api_key: os.environ/ENKRYPTAI_API_KEY + policy_name: "my-custom-policy" # Sent via x-enkrypt-policy header + detectors: + toxicity: + enabled: true +``` + +### Using Deployments + +Specify an EnkryptAI deployment: + +```yaml +guardrails: + - guardrail_name: "enkryptai-deployment" + litellm_params: + guardrail: enkryptai + mode: "pre_call" + api_key: os.environ/ENKRYPTAI_API_KEY + deployment_name: "production" # Sent via X-Enkrypt-Deployment header + detectors: + toxicity: + enabled: true +``` + +### Monitor Mode (Logging Without Blocking) + +Set `block_on_violation: false` to log violations without blocking requests: + +```yaml +guardrails: + - guardrail_name: "enkryptai-monitor" + litellm_params: + guardrail: enkryptai + mode: "pre_call" + api_key: os.environ/ENKRYPTAI_API_KEY + block_on_violation: false # Log violations but don't block + detectors: + toxicity: + enabled: true + nsfw: + enabled: true +``` + +In monitor mode, all violations are logged but requests are never blocked. + +### Input and Output Guardrails + +Configure separate guardrails for input and output: + +```yaml +guardrails: + # Input guardrail + - guardrail_name: "enkryptai-input" + litellm_params: + guardrail: enkryptai + mode: "pre_call" + api_key: os.environ/ENKRYPTAI_API_KEY + detectors: + pii: + enabled: true + entities: ["email", "phone", "ssn"] + injection_attack: + enabled: true + + # Output guardrail + - guardrail_name: "enkryptai-output" + litellm_params: + guardrail: enkryptai + mode: "post_call" + api_key: os.environ/ENKRYPTAI_API_KEY + detectors: + toxicity: + enabled: true + nsfw: + enabled: true +``` + +## Configuration Options + +| Parameter | Type | Description | Default | +|-----------|------|-------------|---------| +| `api_key` | string | EnkryptAI API key | `ENKRYPTAI_API_KEY` env var | +| `api_base` | string | EnkryptAI API base URL | `https://api.enkryptai.com` | +| `policy_name` | string | Custom policy name (sent via `x-enkrypt-policy` header) | None | +| `deployment_name` | string | Deployment name (sent via `X-Enkrypt-Deployment` header) | None | +| `detectors` | object | Detector configuration | `{}` | +| `block_on_violation` | boolean | Block requests on violations | `true` | +| `mode` | string | When to run: `pre_call`, `post_call`, or `during_call` | Required | + +## Observability + +EnkryptAI guardrail logs include: + +- **guardrail_status**: `success`, `guardrail_intervened`, or `guardrail_failed_to_respond` +- **guardrail_provider**: `enkryptai` +- **guardrail_json_response**: Full API response with detection details +- **duration**: Time taken for guardrail check +- **start_time** and **end_time**: Timestamps + +These logs are available through your configured LiteLLM logging callbacks. + +## Error Handling + +The guardrail handles errors gracefully: + +- **API Failures**: Logs error and raises exception +- **Rate Limits (429)**: Logs error and raises exception +- **Invalid Configuration**: Raises `ValueError` on initialization + +Set `block_on_violation: false` to continue processing even when violations are detected (monitor mode). + +## Support + +For more information about EnkryptAI: +- Documentation: [https://docs.enkryptai.com](https://docs.enkryptai.com) +- Website: [https://enkryptai.com](https://enkryptai.com) + diff --git a/docs/my-website/docs/proxy/health.md b/docs/my-website/docs/proxy/health.md index 7e627846b1d..c96753648b8 100644 --- a/docs/my-website/docs/proxy/health.md +++ b/docs/my-website/docs/proxy/health.md @@ -9,13 +9,32 @@ Use this to health check all LLMs defined in your config.yaml | `/health/readiness` | **Load balancer health checks** | Ready to accept traffic - includes DB connection status | | `/health` | **Model health monitoring** | Comprehensive LLM model health - makes actual API calls | | `/health/services` | **Service debugging** | Check specific integrations (datadog, langfuse, etc.) | +| `/health/shared-status` | **Multi-pod coordination** | Monitor shared health check state across pods | ## Summary The proxy exposes: * a /health endpoint which returns the health of the LLM APIs * a /health/readiness endpoint for returning if the proxy is ready to accept requests -* a /health/liveliness endpoint for returning if the proxy is alive +* a /health/liveliness endpoint for returning if the proxy is alive +* a /health/shared-status endpoint for monitoring shared health check coordination across pods + +## Shared Health Check State + +When running multiple LiteLLM proxy pods, you can enable shared health check state to coordinate health checks across pods and avoid duplicate API calls. This is especially beneficial for expensive models like Gemini 2.5-pro. + +**Key Benefits:** +- Reduces duplicate health checks across pods +- Saves costs on expensive model API calls +- Reduces monitoring noise and logging +- Improves resource efficiency + +**Requirements:** +- Redis for shared state coordination +- Background health checks enabled +- Multiple proxy pods + +For detailed configuration and usage, see [Shared Health Check State](./shared_health_check.md). ## `/health` #### Request diff --git a/docs/my-website/docs/proxy/security_encryption_faq.md b/docs/my-website/docs/proxy/security_encryption_faq.md new file mode 100644 index 00000000000..690f67d79a3 --- /dev/null +++ b/docs/my-website/docs/proxy/security_encryption_faq.md @@ -0,0 +1,354 @@ +# LiteLLM Self-Hosted Security & Encryption FAQ + +## Data in Transit Encryption + +### Does the product encrypt data in transit? + +**Yes**, LiteLLM encrypts data in transit using TLS/SSL. + +### Available in both OSS and Enterprise? + +**Yes**, TLS encryption is available in both Open Source and Enterprise versions. + +### In transit between the calling client and the product? + +**Yes**, HTTPS/TLS is supported through SSL certificate configuration. + +**Configuration:** +```bash +# CLI +litellm --ssl_keyfile_path /path/to/key.pem --ssl_certfile_path /path/to/cert.pem + +# Environment Variables +export SSL_KEYFILE_PATH="/path/to/key.pem" +export SSL_CERTFILE_PATH="/path/to/cert.pem" +``` + +**Documentation Reference:** `docs/my-website/docs/guides/security_settings.md` + +### In transit between the product and the LLM providers? + +**Yes**, all connections to LLM providers use TLS encryption by default. + +**Implementation Details:** +- Uses Python's `ssl.create_default_context()` +- Leverages HTTPX and aiohttp libraries with SSL/TLS enabled +- Uses certifi CA bundle by default for SSL verification + +**Code Reference:** `litellm/llms/custom_httpx/http_handler.py` (lines 43-105) + +### Are TCP sessions to the LLM providers shared? + +**Yes**, TCP connections are pooled and reused. + +**Details:** +- Connection pooling is enabled by default +- Default: 1000 max concurrent connections with keepalive +- Sessions are maintained across requests to the same provider +- Reduces overhead of TLS handshakes + +**Code Reference:** `litellm/llms/custom_httpx/http_handler.py` (lines 704-712) + +### Or does the product negotiate a new TLS session with the same LLM provider for every sequential call? + +**No**, TLS sessions are reused through connection pooling. New TLS handshakes are not performed for every request. + +### How is it encrypted? + +**TLS 1.2 and TLS 1.3** + +Uses Python's default SSL context which supports both TLS 1.2 and TLS 1.3. The specific version negotiated depends on: +- Python version +- System SSL library (typically OpenSSL) +- Server capabilities + +**Implementation:** `ssl.create_default_context()` in Python + +### How are these added to the product's configuration? + +#### x.509 Certificate + +**Method 1: CLI Arguments** +```bash +litellm --ssl_certfile_path /path/to/certificate.pem +``` + +**Method 2: Environment Variable** +```bash +export SSL_CERTFILE_PATH="/path/to/certificate.pem" +``` + +#### Private Key + +**Method 1: CLI Arguments** +```bash +litellm --ssl_keyfile_path /path/to/private_key.pem +``` + +**Method 2: Environment Variable** +```bash +export SSL_KEYFILE_PATH="/path/to/private_key.pem" +``` + +#### Certificate Bundle/Chain + +**For client-to-proxy connections:** +Use standard SSL certificate setup with intermediate certificates bundled in the certfile. + +**For proxy-to-LLM provider connections:** + +**Method 1: Config YAML** +```yaml +litellm_settings: + ssl_verify: "/path/to/ca_bundle.pem" +``` + +**Method 2: Environment Variable** +```bash +export SSL_CERT_FILE="/path/to/ca_bundle.pem" +``` + +**Method 3: Client Certificate Authentication** +```yaml +litellm_settings: + ssl_certificate: "/path/to/client_certificate.pem" +``` + +or + +```bash +export SSL_CERTIFICATE="/path/to/client_certificate.pem" +``` + +### Documentation Coverage + +**Primary Documentation:** +- `docs/my-website/docs/guides/security_settings.md` - SSL/TLS configuration guide + +**Additional References:** +- `litellm/proxy/proxy_cli.py` (lines 455-467) - CLI options +- `docs/my-website/docs/completion/http_handler_config.md` - Custom HTTP handler configuration + +--- + +## Data at Rest Encryption + +### Does the product encrypt data at rest? + +**Partially**. Only specific sensitive data is encrypted at rest. + +### What data is stored in encrypted form? + +#### Encrypted Data: +1. **LLM API Keys** - Model credentials in `LiteLLM_ProxyModelTable.litellm_params` +2. **Provider Credentials** - Stored in `LiteLLM_CredentialsTable.credential_values` +3. **Configuration Secrets** - Sensitive config values in `LiteLLM_Config` table +4. **Virtual Keys** - When using secret managers (optional feature) + +#### NOT Encrypted: +1. **Spend Logs** - Request/response data in `LiteLLM_SpendLogs` +2. **Audit Logs** - Change history in `LiteLLM_AuditLog` +3. **User/Team/Organization Data** - Metadata and configuration +4. **Cached Prompts and Completions** - Cache data is stored in plaintext + +### Cached prompts and completions? + +**No**, cached prompts and completions are **NOT encrypted**. + +Cache backends (Redis, S3, local disk) store data as plaintext JSON. + +**Code References:** +- `litellm/caching/redis_cache.py` +- `litellm/caching/s3_cache.py` +- `litellm/caching/caching.py` + +### Configuration data? + +**Partially encrypted**. + +#### What IS Encrypted: +- LLM API keys and credentials in model configurations +- Sensitive values in `LiteLLM_Config` table +- Credential values in `LiteLLM_CredentialsTable` + +#### What is NOT Encrypted: +- Model names and aliases +- Rate limits and budget settings +- User/team/organization metadata +- Non-sensitive configuration parameters + +**Code Reference:** `litellm/proxy/management_endpoints/model_management_endpoints.py` (lines 275-308) + +### Log data? + +**No**, log data is **NOT encrypted**. + +Log data stored in database tables is in plaintext: +- `LiteLLM_SpendLogs` - Contains request/response data, tokens, spend +- `LiteLLM_ErrorLogs` - Error information +- `LiteLLM_AuditLog` - Audit trail of changes + +**Note:** You can disable logging to avoid storing sensitive data: + +```yaml +general_settings: + disable_spend_logs: True # Disable writing spend logs to DB + disable_error_logs: True # Disable writing error logs to DB +``` + +**Documentation:** `docs/my-website/docs/proxy/db_info.md` (lines 52-60) + +### Where is it stored? + +#### In the DB? + +**Yes**, encrypted data is stored in PostgreSQL database. + +**Key Tables with Encrypted Data:** +- `LiteLLM_ProxyModelTable` - Model configurations with encrypted API keys +- `LiteLLM_CredentialsTable` - Credential values +- `LiteLLM_Config` - Configuration secrets + +**Schema Reference:** `schema.prisma` + +#### In the filesystem? + +**No**, encrypted data is not stored in the filesystem by default. + +**Note:** If using disk cache (`disk_cache_dir`), cached data is stored unencrypted. + +#### Somewhere else? + +**Optional:** When using secret managers (AWS Secrets Manager, Azure Key Vault, HashiCorp Vault), encrypted data can be stored externally. + +**Configuration:** +```yaml +general_settings: + key_management_system: "aws_secret_manager" # or "azure_key_vault", "hashicorp_vault" +``` + +**Documentation:** `docs/my-website/docs/secret.md` + +### How is it encrypted? + +**Algorithm:** NaCl SecretBox (XSalsa20-Poly1305 AEAD) + +**NOT AES-256** - LiteLLM uses NaCl (Networking and Cryptography Library) which provides: +- XSalsa20 stream cipher +- Poly1305 MAC for authentication +- Equivalent security to AES-256 + +**Key Derivation:** +1. Takes `LITELLM_SALT_KEY` (or `LITELLM_MASTER_KEY` if salt key not set) +2. Hashes with SHA-256 to derive 256-bit encryption key +3. Uses NaCl SecretBox for authenticated encryption + +**Code Reference:** `litellm/proxy/common_utils/encrypt_decrypt_utils.py` (lines 69-112) + +**Implementation:** +```python +import hashlib +import nacl.secret + +# Derive 256-bit key from salt +hash_object = hashlib.sha256(signing_key.encode()) +hash_bytes = hash_object.digest() + +# Create SecretBox and encrypt +box = nacl.secret.SecretBox(hash_bytes) +encrypted = box.encrypt(value_bytes) +``` + +### Setting the Encryption Key + +**Required Environment Variable:** +```bash +export LITELLM_SALT_KEY="your-strong-random-key-here" +``` + +**Important Notes:** +- ⚠️ **Must be set before adding any models** +- ⚠️ **Never change this key** - encrypted data becomes unrecoverable +- ⚠️ Use a strong random key (recommended: https://1password.com/password-generator/) +- If not set, falls back to `LITELLM_MASTER_KEY` + +**Documentation:** `docs/my-website/docs/proxy/prod.md` (section 8, lines 184-196) + +### Documentation Coverage + +**Primary Documentation:** +- `docs/my-website/docs/proxy/prod.md` (section 8) - LITELLM_SALT_KEY setup +- `docs/my-website/docs/secret.md` - Secret management systems +- `docs/my-website/docs/proxy/db_info.md` - Database information + +**Additional References:** +- `security.md` - General security measures +- `docs/my-website/docs/data_security.md` - Data privacy overview +- `schema.prisma` - Database schema with encrypted fields + +--- + +## Summary of Security Features + +### ✅ Provided Out of the Box + +1. **TLS/SSL encryption** for client-to-proxy connections +2. **TLS encryption** for proxy-to-LLM provider connections (with connection pooling) +3. **Encrypted storage** of LLM API keys and credentials +4. **Support for TLS 1.2 and TLS 1.3** +5. **Connection pooling** to reduce TLS handshake overhead + +### ⚠️ Important Limitations + +1. **Cached data is NOT encrypted** (Redis, S3, disk cache) +2. **Log data is NOT encrypted** (spend logs, audit logs) +3. **Request/response payloads in logs are NOT encrypted** +4. **Uses NaCl SecretBox, NOT AES-256** (equivalent security) +5. **TLS version not explicitly configured** - uses Python/system defaults + +### 🔧 Configuration Requirements + +**For Production Deployments:** + +1. **Set LITELLM_SALT_KEY** before adding any models +2. **Configure SSL certificates** for HTTPS client connections +3. **Consider disabling logs** if they contain sensitive data +4. **Use secret managers** for enhanced security (optional) +5. **Configure CA bundles** if using custom certificates + +--- + +## Quick Start Security Checklist + +```bash +# 1. Generate a strong salt key +export LITELLM_SALT_KEY="$(openssl rand -base64 32)" + +# 2. Set up SSL certificates (for HTTPS) +export SSL_KEYFILE_PATH="/path/to/private_key.pem" +export SSL_CERTFILE_PATH="/path/to/certificate.pem" + +# 3. Configure database +export DATABASE_URL="postgresql://user:password@host:port/dbname" + +# 4. (Optional) Disable logs if they contain sensitive data +# Add to config.yaml: +# general_settings: +# disable_spend_logs: True +# disable_error_logs: True + +# 5. Start LiteLLM Proxy +litellm --config config.yaml +``` + +--- + +## Additional Resources + +- **LiteLLM Documentation:** https://docs.litellm.ai/ +- **Security Settings Guide:** https://docs.litellm.ai/docs/guides/security_settings +- **Production Deployment:** https://docs.litellm.ai/docs/proxy/prod +- **Secret Management:** https://docs.litellm.ai/docs/secret + +For security inquiries: support@berri.ai + diff --git a/docs/my-website/docs/proxy/shared_health_check.md b/docs/my-website/docs/proxy/shared_health_check.md new file mode 100644 index 00000000000..d4b70116309 --- /dev/null +++ b/docs/my-website/docs/proxy/shared_health_check.md @@ -0,0 +1,310 @@ +# Shared Health Check State Across Pods + +This feature enables coordination of health checks across multiple LiteLLM proxy pods to avoid duplicate health checks and reduce costs. + +## Overview + +When running multiple LiteLLM proxy pods (e.g., in Kubernetes), each pod typically runs its own independent health checks on every model. This can result in: + +- **Duplicate health checks** across pods +- **Increased costs** for expensive models (e.g., Gemini 2.5-pro) +- **Redundant monitoring/logging noise** +- **Inefficient resource usage** + +The shared health check state feature solves this by: + +- **Coordinating health checks** across pods using Redis +- **Caching results** with configurable TTL +- **Using distributed locks** to ensure only one pod runs health checks at a time +- **Allowing other pods** to read cached results instead of running redundant checks + +## How It Works + +### 1. Lock Acquisition +When a pod needs to run health checks: +- It attempts to acquire a Redis lock +- If successful, it runs the health checks +- If failed, it waits briefly and checks for cached results + +### 2. Result Caching +After running health checks: +- Results are cached in Redis with a configurable TTL +- Other pods can read these cached results +- Cache includes timestamp and pod ID for tracking + +### 3. Fallback Behavior +If Redis is unavailable or cache is expired: +- Pods fall back to running health checks locally +- System continues to function normally + +## Configuration + +### Enable Shared Health Check + +Add to your `proxy_config.yaml`: + +```yaml +general_settings: + # Enable background health checks (required) + background_health_checks: true + + # Enable shared health check state across pods + use_shared_health_check: true + + # Health check interval (seconds) + health_check_interval: 300 # 5 minutes + +# Redis configuration (required for shared health check) +litellm_settings: + cache: true + cache_params: + type: redis + host: your-redis-host + port: 6379 + password: your-redis-password +``` + +### Environment Variables + +You can also configure using environment variables: + +```bash +# Enable shared health check +export USE_SHARED_HEALTH_CHECK=true + +# Health check TTL (seconds) +export DEFAULT_SHARED_HEALTH_CHECK_TTL=300 + +# Lock TTL (seconds) +export DEFAULT_SHARED_HEALTH_CHECK_LOCK_TTL=60 +``` + +## Requirements + +- **Redis**: Required for shared state coordination +- **Background Health Checks**: Must be enabled (`background_health_checks: true`) +- **Multiple Pods**: Most beneficial with 2+ proxy instances + +## API Endpoints + +### Check Shared Health Check Status + +```bash +GET /health/shared-status +``` + +Returns information about the shared health check coordination: + +```json +{ + "shared_health_check_enabled": true, + "status": { + "pod_id": "pod_1703123456789", + "redis_available": true, + "lock_ttl": 60, + "cache_ttl": 300, + "lock_owner": "pod_1703123456788", + "lock_in_progress": true, + "cache_available": true, + "cache_age_seconds": 45.2, + "last_checked_by": "pod_1703123456788" + } +} +``` + +## Monitoring + +### Health Check Status + +Monitor the shared health check status to ensure proper coordination: + +```bash +curl -H "Authorization: Bearer your-api-key" \ + http://your-proxy-host/health/shared-status +``` + +### Logs + +Look for these log messages: + +``` +INFO: Initialized shared health check manager +INFO: Pod pod_123 acquired health check lock +INFO: Pod pod_123 released health check lock +INFO: Cached health check results for 5 healthy and 0 unhealthy endpoints +DEBUG: Using cached health check results +``` + +## Troubleshooting + +### Common Issues + +#### 1. Shared Health Check Not Working + +**Symptoms**: Each pod still runs independent health checks + +**Solutions**: +- Verify Redis is configured and accessible +- Check that `use_shared_health_check: true` is set +- Ensure `background_health_checks: true` is enabled +- Check Redis connectivity in logs + +#### 2. Redis Connection Issues + +**Symptoms**: Health checks fall back to local execution + +**Solutions**: +- Verify Redis host, port, and credentials +- Check network connectivity between pods and Redis +- Monitor Redis server logs for errors + +#### 3. Lock Not Released + +**Symptoms**: One pod holds the lock indefinitely + +**Solutions**: +- Lock has automatic TTL (default 60 seconds) +- Check pod logs for lock release messages +- Verify Redis TTL settings + +### Debug Mode + +Enable debug logging to see detailed coordination: + +```yaml +general_settings: + set_verbose: true +``` + +## Performance Impact + +### Benefits + +- **Reduced API calls**: Only one pod runs health checks per interval +- **Lower costs**: Especially significant for expensive models +- **Better resource utilization**: Less redundant work across pods +- **Cleaner monitoring**: Reduced noise in logs and metrics + +### Overhead + +- **Redis operations**: Minimal overhead for lock/cache operations +- **Network latency**: Small delay for Redis communication +- **Memory usage**: Negligible additional memory usage + +## Best Practices + +### 1. Redis Configuration + +- Use Redis with persistence enabled +- Configure appropriate memory limits +- Set up Redis monitoring and alerts + +### 2. TTL Settings + +- Set `health_check_interval` to your desired check frequency +- Use default TTL values unless you have specific requirements +- Consider model-specific timeouts for expensive models + +### 3. Monitoring + +- Monitor shared health check status endpoint +- Set up alerts for Redis connectivity issues +- Track health check costs and frequency + +### 4. Scaling + +- Feature works with any number of pods +- More pods = better coordination benefits +- Consider Redis cluster for high availability + +## Example Configuration + +### Complete Example + +```yaml +# proxy_config.yaml +model_list: + - model_name: gpt-4 + litellm_params: + model: gpt-4 + api_key: os.environ/OPENAI_API_KEY + model_info: + health_check_timeout: 30 # 30 second timeout for health checks + +general_settings: + # Enable background health checks + background_health_checks: true + + # Enable shared health check coordination + use_shared_health_check: true + + # Health check interval (5 minutes) + health_check_interval: 300 + + # Health check details + health_check_details: true + +litellm_settings: + # Redis configuration + cache: true + cache_params: + type: redis + host: redis-cluster.example.com + port: 6379 + password: os.environ/REDIS_PASSWORD + ssl: true +``` + +### Kubernetes Example + +```yaml +# deployment.yaml +apiVersion: apps/v1 +kind: Deployment +metadata: + name: litellm-proxy +spec: + replicas: 3 # Multiple pods for coordination + template: + spec: + containers: + - name: litellm-proxy + image: ghcr.io/berriai/litellm:latest + env: + - name: USE_SHARED_HEALTH_CHECK + value: "true" + - name: REDIS_HOST + value: "redis-service" + - name: REDIS_PASSWORD + valueFrom: + secretKeyRef: + name: redis-secret + key: password +``` + +## Migration + +### From Independent Health Checks + +1. **Enable Redis**: Ensure Redis is configured and accessible +2. **Enable Background Health Checks**: Set `background_health_checks: true` +3. **Enable Shared Health Check**: Set `use_shared_health_check: true` +4. **Deploy**: Update your proxy configuration +5. **Monitor**: Check `/health/shared-status` endpoint + +### Rollback + +To disable shared health check: + +```yaml +general_settings: + use_shared_health_check: false + # background_health_checks can remain true for independent checks +``` + +## Related Features + +- [Background Health Checks](./health.md#background-health-checks) +- [Redis Caching](./caching.md) +- [High Availability Setup](./db_deadlocks.md) +- [Health Check Endpoints](./health.md#health-endpoints) diff --git a/docs/my-website/docs/proxy/tag_budgets.md b/docs/my-website/docs/proxy/tag_budgets.md new file mode 100644 index 00000000000..01b82ff8d26 --- /dev/null +++ b/docs/my-website/docs/proxy/tag_budgets.md @@ -0,0 +1,277 @@ +import Image from '@theme/IdealImage'; +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Setting Tag Budgets + +Track spend and set budgets for your API requests using tags. Tags allow you to categorize and monitor costs across different cost centers, projects, and departments. + +## Pre-Requisites + +- You must set up a Postgres database (e.g. Supabase, Neon, etc.) + +## What are Tags? + +Tags are labels you can attach to your LLM requests to track and limit spending by category. + +**Common Use Cases:** +- **Cost Center Tracking**: Allocate LLM costs to specific departments or business units (e.g., "engineering", "marketing", "customer-support") +- **Project-based Budgeting**: Set budgets for different projects or initiatives (e.g., "project-alpha", "chatbot-v2") +- **Customer Attribution**: Track spend per customer or client (e.g., "customer-acme", "customer-techcorp") +- **Feature Monitoring**: Monitor costs for specific features (e.g., "feature-chat", "feature-summarization") + +Tags are added to each request in the `metadata` field to track and enforce budget limits. + +## Setting Tag Budgets + +### 1. Create a tag with budget + +Create a tag to represent a cost center, project, or any budget category. Set `max_budget` ($ value allowed) and `budget_duration` (how frequently the budget resets). + +**Example:** Create a tag for your Engineering department with a monthly $500 budget + +#### API + +Create a new tag and set `max_budget` and `budget_duration` + +```shell +curl -X POST 'http://0.0.0.0:4000/tag/new' \ + -H 'Authorization: Bearer sk-1234' \ + -H 'Content-Type: application/json' \ + -d '{ + "name": "engineering", + "description": "Engineering department cost center", + "max_budget": 500.0, + "budget_duration": "30d" + }' +``` + +**Request Body Parameters:** + +| Parameter | Type | Required | Description | +|-----------|------|----------|-------------| +| `name` | string | Yes | Unique name for the tag (e.g., cost center name) | +| `description` | string | No | Description of what this tag tracks | +| `models` | list[string] | No | Restrict tag to specific models | +| `max_budget` | float | No | Maximum budget in USD | +| `budget_duration` | string | No | How often budget resets (e.g., "30d", "1d") | +| `soft_budget` | float | No | Soft budget limit for warnings | + +**Response:** + +```json +{ + "name": "engineering", + "description": "Engineering department cost center", + "max_budget": 500.0, + "budget_duration": "30d", + "budget_reset_at": "2025-11-10T00:00:00Z", + "created_at": "2025-10-11T00:00:00Z" +} +``` + +#### LiteLLM Admin UI + +Navigate to the **Tag Management** page and click **Create New Tag**. Fill in the tag details and set your budget: + + + +
+ + +**Possible values for `budget_duration`:** + +| `budget_duration` | When Budget will reset | +| --- | --- | +| `budget_duration="1s"` | every 1 second | +| `budget_duration="1m"` | every 1 minute | +| `budget_duration="1h"` | every 1 hour | +| `budget_duration="1d"` | every 1 day | +| `budget_duration="7d"` | every 1 week | +| `budget_duration="30d"` | every 1 month | + +### 2. Use the tag in your requests + +Add tags to your API requests in the `metadata` field: + +:::info Tags Budgets on API Keys + +Currently, tag budget enforcement is only supported per request. If you'd like to set tags on API keys so all requests automatically inherit the tags budgets, please [create a feature request on GitHub](https://github.com/BerriAI/litellm/issues/new?assignees=&labels=enhancement&projects=&template=feature_request.yml&title=%5BFeat%5D%3A). + +::: + + + + + +```python +import openai + +client = openai.OpenAI( + api_key="sk-1234", # Your LiteLLM proxy key + base_url="http://0.0.0.0:4000" +) + +response = client.chat.completions.create( + model="gpt-4", + messages=[{"role": "user", "content": "Hello"}], + extra_body={ + "metadata": { + "tags": ["engineering"] + } + } +) +``` + + + + + +```shell +curl -X POST 'http://0.0.0.0:4000/chat/completions' \ + -H 'Authorization: Bearer sk-1234' \ + -H 'Content-Type: application/json' \ + -d '{ + "model": "gpt-4", + "messages": [{"role": "user", "content": "Hello"}], + "metadata": { + "tags": ["engineering"] + } + }' +``` + + + + + +### 3. Test It + +Make requests until the budget is exceeded: + +```shell +curl -X POST 'http://0.0.0.0:4000/chat/completions' \ + -H 'Authorization: Bearer sk-1234' \ + -H 'Content-Type: application/json' \ + -d '{ + "model": "gpt-4", + "messages": [{"role": "user", "content": "Hello"}], + "metadata": { + "tags": ["engineering"] + } + }' +``` + +**When budget is exceeded, you'll see:** + +```json +{ + "error": { + "message": "Budget has been exceeded! Tag=engineering Current cost: 505.50, Max budget: 500.0", + "type": "budget_exceeded", + "param": null, + "code": "400" + } +} +``` + +## Managing Tags + +### View Tag Information + +Get information about specific tags: + +```shell +curl -X POST 'http://0.0.0.0:4000/tag/info' \ + -H 'Authorization: Bearer sk-1234' \ + -H 'Content-Type: application/json' \ + -d '{ + "names": ["engineering", "marketing"] + }' +``` + +**Response:** + +```json +{ + "engineering": { + "name": "engineering", + "description": "Engineering department cost center", + "spend": 245.50, + "max_budget": 500.0, + "budget_duration": "30d", + "budget_reset_at": "2025-11-10T00:00:00Z", + "created_at": "2025-10-11T00:00:00Z", + "updated_at": "2025-10-11T12:30:00Z" + }, + "marketing": { + "name": "marketing", + "description": "Marketing department cost center", + "spend": 89.20, + "max_budget": 300.0, + "budget_duration": "30d", + "budget_reset_at": "2025-11-10T00:00:00Z", + "created_at": "2025-10-11T00:00:00Z", + "updated_at": "2025-10-11T12:30:00Z" + } +} +``` + +### Update Tag Budget + +Update an existing tag's budget: + +```shell +curl -X POST 'http://0.0.0.0:4000/tag/update' \ + -H 'Authorization: Bearer sk-1234' \ + -H 'Content-Type: application/json' \ + -d '{ + "name": "engineering", + "max_budget": 750.0, + "budget_duration": "30d" + }' +``` + +### Delete Tag + +```shell +curl -X POST 'http://0.0.0.0:4000/tag/delete' \ + -H 'Authorization: Bearer sk-1234' \ + -H 'Content-Type: application/json' \ + -d '{ + "name": "engineering" + }' +``` + +## Multiple Tags per Request + +You can apply multiple tags to a single request to track costs across different dimensions simultaneously. For example, track both the cost center and the specific project: + +```python +response = client.chat.completions.create( + model="gpt-4", + messages=[{"role": "user", "content": "Hello"}], + extra_body={ + "metadata": { + "tags": ["engineering", "project-alpha", "customer-acme"] + } + } +) +``` + +```shell +curl -X POST 'http://0.0.0.0:4000/chat/completions' \ + -H 'Authorization: Bearer sk-1234' \ + -H 'Content-Type: application/json' \ + -d '{ + "model": "gpt-4", + "messages": [{"role": "user", "content": "Hello"}], + "metadata": { + "tags": ["engineering", "project-alpha", "customer-acme"] + } + }' +``` + +**Budget Enforcement:** If any tag exceeds its budget, the request will be rejected. diff --git a/docs/my-website/docs/response_api.md b/docs/my-website/docs/response_api.md index 8bb10bbe36a..80bd2ba6f7b 100644 --- a/docs/my-website/docs/response_api.md +++ b/docs/my-website/docs/response_api.md @@ -14,6 +14,7 @@ Requests to /chat/completions may be bridged here automatically when the provide | Logging | ✅ | Works across all integrations | | End-user Tracking | ✅ | | | Streaming | ✅ | | +| Image Generation Streaming | ✅ | Progressive image generation with partial images (1-3) | | Fallbacks | ✅ | Works between supported models | | Loadbalancing | ✅ | Works between supported models | | Supported operations | Create a response, Get a response, Delete a response | | @@ -56,6 +57,29 @@ for event in response: print(event) ``` +#### Image Generation with Streaming +```python showLineNumbers title="OpenAI Streaming Image Generation" +import litellm +import base64 + +# Streaming image generation with partial images +stream = litellm.responses( + model="gpt-4.1", # Use an actual image generation model + input="Generate a gorgeous image of a river made of white owl feathers", + stream=True, + tools=[{"type": "image_generation", "partial_images": 2}], + +) + +for event in stream: + if event.type == "response.image_generation_call.partial_image": + idx = event.partial_image_index + image_base64 = event.partial_image_b64 + image_bytes = base64.b64decode(image_base64) + with open(f"river{idx}.png", "wb") as f: + f.write(image_bytes) +``` + #### GET a Response ```python showLineNumbers title="Get Response by ID" import litellm @@ -380,6 +404,32 @@ for event in response: print(event) ``` +#### Image Generation with Streaming +```python showLineNumbers title="OpenAI Proxy Streaming Image Generation" +from openai import OpenAI +import base64 + +client = OpenAI(api_key="sk-1234", base_url="http://localhost:4000") + +stream = client.responses.create( + model="gpt-4.1", + input="Draw a gorgeous image of a river made of white owl feathers, snaking its way through a serene winter landscape", + stream=True, + tools=[{"type": "image_generation", "partial_images": 2}], +) + + +for event in stream: + print(f"event: {event}") + if event.type == "response.image_generation_call.partial_image": + idx = event.partial_image_index + image_base64 = event.partial_image_b64 + image_bytes = base64.b64decode(image_base64) + with open(f"river{idx}.png", "wb") as f: + f.write(image_bytes) + +``` + #### GET a Response ```python showLineNumbers title="Get Response by ID with OpenAI SDK" from openai import OpenAI diff --git a/docs/my-website/docs/tutorials/msft_sso.md b/docs/my-website/docs/tutorials/msft_sso.md index f7ad6440f2e..2936f27297f 100644 --- a/docs/my-website/docs/tutorials/msft_sso.md +++ b/docs/my-website/docs/tutorials/msft_sso.md @@ -140,6 +140,54 @@ litellm_settings: +## 4. Using Entra ID App Roles for User Permissions + +You can assign user roles directly from Entra ID using App Roles. LiteLLM will automatically read the app roles from the JWT token during SSO sign-in and assign the corresponding role to the user. + +### 4.1 Supported Roles + +LiteLLM supports the following app roles (case-insensitive): + +- `proxy_admin` - Admin over the entire LiteLLM platform +- `proxy_admin_viewer` - Read-only admin access (can view all keys and spend) +- `org_admin` - Admin over a specific organization (can create teams and users within their org) +- `internal_user` - Standard user (can create/view/delete their own keys and view their own spend) + +### 4.2 Create App Roles in Entra ID + +1. Navigate to your App Registration on https://portal.azure.com/ +2. Go to **App roles** > **Create app role** + +3. Configure the app role: + - **Display name**: Proxy Admin (or your preferred display name) + - **Value**: `proxy_admin` (use one of the supported role values above) + - **Description**: Administrator access to LiteLLM proxy + - **Allowed member types**: Users/Groups + + +4. Click **Apply** to save the role + +### 4.3 Assign Users to App Roles + +1. Navigate to **Enterprise Applications** on https://portal.azure.com/ +2. Select your LiteLLM application +3. Go to **Users and groups** > **Add user/group** +4. Select the user and assign them to one of the app roles you created + + +### 4.4 Test the Role Assignment + +1. Sign in to LiteLLM UI via SSO as a user with an assigned app role +2. LiteLLM will automatically extract the app role from the JWT token +3. The user will be assigned the corresponding LiteLLM role in the database +4. The user's permissions will reflect their assigned role + +**How it works:** +- When a user signs in via Microsoft SSO, LiteLLM extracts the `roles` claim from the JWT `id_token` +- If any of the roles match a valid LiteLLM role (case-insensitive), that role is assigned to the user +- If multiple roles are present, LiteLLM uses the first valid role it finds +- This role assignment persists in the LiteLLM database and determines the user's access level + ## Video Walkthrough This walks through setting up sso auto-add for **Microsoft Entra ID** diff --git a/docs/my-website/img/tag_budget1.png b/docs/my-website/img/tag_budget1.png new file mode 100644 index 00000000000..061e406f490 Binary files /dev/null and b/docs/my-website/img/tag_budget1.png differ diff --git a/docs/my-website/img/tag_budget2.png b/docs/my-website/img/tag_budget2.png new file mode 100644 index 00000000000..f44fd79dd32 Binary files /dev/null and b/docs/my-website/img/tag_budget2.png differ diff --git a/docs/my-website/sidebars.js b/docs/my-website/sidebars.js index 7acd92240c6..6d5d6d68d94 100644 --- a/docs/my-website/sidebars.js +++ b/docs/my-website/sidebars.js @@ -36,6 +36,7 @@ const sidebars = { "proxy/guardrails/aporia_api", "proxy/guardrails/azure_content_guardrail", "proxy/guardrails/bedrock", + "proxy/guardrails/enkryptai", "proxy/guardrails/lasso_security", "proxy/guardrails/guardrails_ai", "proxy/guardrails/lakera_ai", @@ -188,12 +189,13 @@ const sidebars = { type: "category", label: "Budgets + Rate Limits", items: [ + "proxy/users", + "proxy/team_budgets", + "proxy/tag_budgets", "proxy/customers", "proxy/dynamic_rate_limit", "proxy/rate_limit_tiers", - "proxy/team_budgets", "proxy/temporary_budget_increase", - "proxy/users" ], }, "proxy/caching", @@ -333,7 +335,17 @@ const sidebars = { "image_variations", ] }, - "mcp", + { + type: "category", + label: "/mcp - Model Context Protocol", + items: [ + "mcp", + "mcp_usage", + "mcp_control", + "mcp_cost", + "mcp_guardrail", + ] + }, "moderation", { type: "category", @@ -412,6 +424,7 @@ const sidebars = { items: [ "providers/vertex", "providers/vertex_partner", + "providers/vertex_self_deployed", "providers/vertex_image", "providers/vertex_batch", ] @@ -522,22 +535,17 @@ const sidebars = { "providers/oci", "providers/datarobot", "providers/ovhcloud", + "providers/wandb_inference", ], }, { type: "category", label: "Guides", items: [ - { - type: "category", - label: "Tools", - items: [ - "completion/computer_use", - "completion/web_search", - "completion/web_fetch", - "completion/function_call", - ] - }, + "completion/computer_use", + "completion/web_search", + "completion/web_fetch", + "completion/function_call", "completion/audio", "completion/document_understanding", "completion/drop_params", @@ -674,6 +682,7 @@ const sidebars = { items: [ "data_security", "data_retention", + "proxy/security_encryption_faq", "migration_policy", { type: "category", diff --git a/docs/my-website/src/pages/completion/supported.md b/docs/my-website/src/pages/completion/supported.md index 097af2bb4cb..e146e6efc97 100644 --- a/docs/my-website/src/pages/completion/supported.md +++ b/docs/my-website/src/pages/completion/supported.md @@ -8,6 +8,7 @@ | gpt-3.5-turbo-16k | `completion('gpt-3.5-turbo-16k', messages)` | `os.environ['OPENAI_API_KEY']` | | gpt-3.5-turbo-16k-0613 | `completion('gpt-3.5-turbo-16k-0613', messages)` | `os.environ['OPENAI_API_KEY']` | | gpt-4 | `completion('gpt-4', messages)` | `os.environ['OPENAI_API_KEY']` | +| gpt-5-pro | `completion('gpt-5-pro', messages)` | `os.environ['OPENAI_API_KEY']` | ## Azure OpenAI Chat Completion Models For Azure calls add the `azure/` prefix to `model`. If your azure deployment name is `gpt-v-2` set `model` = `azure/gpt-v-2` 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 index 5686876b37c..472e2ea1e0c 100644 --- 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 @@ -5,4 +5,4 @@ */ -- AlterTable -ALTER TABLE "public"."LiteLLM_MCPServerTable" DROP COLUMN "spec_version"; +ALTER TABLE "LiteLLM_MCPServerTable" DROP COLUMN "spec_version"; diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20251006143948_add_mcp_tool_permissions/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20251006143948_add_mcp_tool_permissions/migration.sql new file mode 100644 index 00000000000..51f3be87582 --- /dev/null +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20251006143948_add_mcp_tool_permissions/migration.sql @@ -0,0 +1,3 @@ +-- AlterTable +ALTER TABLE "LiteLLM_ObjectPermissionTable" ADD COLUMN "mcp_tool_permissions" JSONB; + diff --git a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma index ff5d7d6b9f0..a13af1afc5f 100644 --- a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma +++ b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma @@ -25,6 +25,7 @@ model LiteLLM_BudgetTable { organization LiteLLM_OrganizationTable[] // multiple orgs can have the same budget keys LiteLLM_VerificationToken[] // multiple keys can have the same budget end_users LiteLLM_EndUserTable[] // multiple end-users can have the same budget + tags LiteLLM_TagTable[] // multiple tags can have the same budget team_membership LiteLLM_TeamMembership[] // budgets of Users within a Team organization_membership LiteLLM_OrganizationMembership[] // budgets of Users within a Organization } @@ -156,6 +157,7 @@ model LiteLLM_ObjectPermissionTable { object_permission_id String @id @default(uuid()) mcp_servers String[] @default([]) mcp_access_groups String[] @default([]) + mcp_tool_permissions Json? // Tool-level permissions for MCP servers. Format: {"server_id": ["tool_name_1", "tool_name_2"]} vector_stores String[] @default([]) teams LiteLLM_TeamTable[] verification_tokens LiteLLM_VerificationToken[] @@ -244,6 +246,20 @@ model LiteLLM_EndUserTable { blocked Boolean @default(false) } +// Track tags with budgets and spend +model LiteLLM_TagTable { + tag_name String @id + description String? + models String[] + model_info Json? // maps model_id to model_name + spend Float @default(0.0) + budget_id String? + litellm_budget_table LiteLLM_BudgetTable? @relation(fields: [budget_id], references: [budget_id]) + created_at DateTime @default(now()) @map("created_at") + created_by String? + updated_at DateTime @default(now()) @updatedAt @map("updated_at") +} + // store proxy config.yaml model LiteLLM_Config { param_name String @id diff --git a/litellm/__init__.py b/litellm/__init__.py index 60cf327c26f..e461c88efd6 100644 --- a/litellm/__init__.py +++ b/litellm/__init__.py @@ -1190,6 +1190,9 @@ from .llms.azure.responses.transformation import AzureOpenAIResponsesAPIConfig from .llms.azure.responses.o_series_transformation import ( AzureOpenAIOSeriesResponsesAPIConfig, ) +from .llms.litellm_proxy.responses.transformation import ( + LiteLLMProxyResponsesAPIConfig, +) from .llms.openai.chat.o_series_transformation import ( OpenAIOSeriesConfig as OpenAIO1Config, # maintain backwards compatibility OpenAIOSeriesConfig, diff --git a/litellm/_redis.py b/litellm/_redis.py index bcb305985fc..e6ac323ff5a 100644 --- a/litellm/_redis.py +++ b/litellm/_redis.py @@ -177,14 +177,21 @@ def get_redis_url_from_environment(): raise ValueError( "Either 'REDIS_URL' or both 'REDIS_HOST' and 'REDIS_PORT' must be specified for Redis." ) - - if "REDIS_PASSWORD" in os.environ: - redis_password = f":{os.environ['REDIS_PASSWORD']}@" + + if "REDIS_SSL" in os.environ and os.environ["REDIS_SSL"].lower() == "true": + redis_protocol = "rediss" else: - redis_password = "" - + redis_protocol = "redis" + + # Build authentication part of URL + auth_part = "" + if "REDIS_USERNAME" in os.environ and "REDIS_PASSWORD" in os.environ: + auth_part = f"{os.environ['REDIS_USERNAME']}:{os.environ['REDIS_PASSWORD']}@" + elif "REDIS_PASSWORD" in os.environ: + auth_part = f"{os.environ['REDIS_PASSWORD']}@" + return ( - f"redis://{redis_password}{os.environ['REDIS_HOST']}:{os.environ['REDIS_PORT']}" + f"{redis_protocol}://{auth_part}{os.environ['REDIS_HOST']}:{os.environ['REDIS_PORT']}" ) diff --git a/litellm/caching/caching_handler.py b/litellm/caching/caching_handler.py index 17cd50f75aa..6bbc3231224 100644 --- a/litellm/caching/caching_handler.py +++ b/litellm/caching/caching_handler.py @@ -14,10 +14,10 @@ It utilizes the (RedisCache, s3Cache, RedisSemanticCache, QdrantSemanticCache, I In each method it will call the appropriate method from caching.py """ -import time import asyncio import datetime import inspect +import time from typing import ( TYPE_CHECKING, Any, @@ -62,12 +62,10 @@ else: LiteLLMLoggingObj = Any -from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper - - from litellm.litellm_core_utils.core_helpers import ( -_get_parent_otel_span_from_kwargs, + _get_parent_otel_span_from_kwargs, ) +from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper class CachingHandlerResponse(BaseModel): @@ -214,9 +212,7 @@ class LLMCachingHandler: end_time=end_time, cache_hit=cache_hit, ) - cache_key = litellm.cache._get_preset_cache_key_from_kwargs( - **kwargs - ) + cache_key = litellm.cache.get_cache_key(**kwargs) if ( isinstance(cached_result, BaseModel) or isinstance(cached_result, CustomStreamWrapper) @@ -330,9 +326,7 @@ class LLMCachingHandler: end_time=end_time, cache_hit=cache_hit ) - cache_key = litellm.cache._get_preset_cache_key_from_kwargs( - **kwargs - ) + cache_key = litellm.cache.get_cache_key(**kwargs) if ( isinstance(cached_result, BaseModel) or isinstance(cached_result, CustomStreamWrapper) diff --git a/litellm/constants.py b/litellm/constants.py index 3ff9a4b6fb0..54ac3e6d6b8 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -87,6 +87,35 @@ MAX_TOKEN_TRIMMING_ATTEMPTS = int( ########## Networking constants ############################################################## _DEFAULT_TTL_FOR_HTTPX_CLIENTS = 3600 # 1 hour, re-use the same httpx client for 1 hour +# Aiohttp connection pooling constants +AIOHTTP_CONNECTOR_LIMIT = int(os.getenv("AIOHTTP_CONNECTOR_LIMIT", 0)) +AIOHTTP_KEEPALIVE_TIMEOUT = int(os.getenv("AIOHTTP_KEEPALIVE_TIMEOUT", 120)) +AIOHTTP_TTL_DNS_CACHE = int(os.getenv("AIOHTTP_TTL_DNS_CACHE", 300)) + +# SSL/TLS cipher configuration for faster handshakes +# Strategy: Strongly prefer fast modern ciphers, but allow fallback to commonly supported ones +# This balances performance with broad compatibility +DEFAULT_SSL_CIPHERS = os.getenv( + "LITELLM_SSL_CIPHERS", + # Priority 1: TLS 1.3 ciphers (fastest, ~50ms handshake) + "TLS_AES_256_GCM_SHA384:" # Fastest observed in testing + "TLS_AES_128_GCM_SHA256:" # Slightly faster than 256-bit + "TLS_CHACHA20_POLY1305_SHA256:" # Fast on ARM/mobile + # Priority 2: TLS 1.2 ECDHE+GCM (fast, ~100ms handshake, widely supported) + "ECDHE-RSA-AES256-GCM-SHA384:" + "ECDHE-RSA-AES128-GCM-SHA256:" + "ECDHE-ECDSA-AES256-GCM-SHA384:" + "ECDHE-ECDSA-AES128-GCM-SHA256:" + # Priority 3: Additional modern ciphers (good balance) + "ECDHE-RSA-CHACHA20-POLY1305:" + "ECDHE-ECDSA-CHACHA20-POLY1305:" + # Priority 4: Widely compatible fallbacks (slower but universally supported) + "ECDHE-RSA-AES256-SHA384:" # Common fallback + "ECDHE-RSA-AES128-SHA256:" # Very widely supported + "AES256-GCM-SHA384:" # Non-PFS fallback (compatibility) + "AES128-GCM-SHA256", # Last resort (maximum compatibility) +) + ########### v2 Architecture constants for managing writing updates to the database ########### REDIS_UPDATE_BUFFER_KEY = "litellm_spend_update_buffer" REDIS_DAILY_SPEND_UPDATE_BUFFER_KEY = "litellm_daily_spend_update_buffer" @@ -871,6 +900,7 @@ bedrock_embedding_models: set = set( "amazon.titan-embed-text-v1", "cohere.embed-english-v3", "cohere.embed-multilingual-v3", + "cohere.embed-v4:0", "twelvelabs.marengo-embed-2-7-v1:0", ] ) @@ -1027,6 +1057,12 @@ PROXY_BATCH_WRITE_AT = int(os.getenv("PROXY_BATCH_WRITE_AT", 10)) # in seconds DEFAULT_HEALTH_CHECK_INTERVAL = int( os.getenv("DEFAULT_HEALTH_CHECK_INTERVAL", 300) ) # 5 minutes +DEFAULT_SHARED_HEALTH_CHECK_TTL = int( + os.getenv("DEFAULT_SHARED_HEALTH_CHECK_TTL", 300) +) # 5 minutes - TTL for cached health check results +DEFAULT_SHARED_HEALTH_CHECK_LOCK_TTL = int( + os.getenv("DEFAULT_SHARED_HEALTH_CHECK_LOCK_TTL", 60) +) # 1 minute - TTL for health check lock PROMETHEUS_FALLBACK_STATS_SEND_TIME_HOURS = int( os.getenv("PROMETHEUS_FALLBACK_STATS_SEND_TIME_HOURS", 9) ) diff --git a/litellm/experimental_mcp_client/client.py b/litellm/experimental_mcp_client/client.py index 225349b4e8a..b10ddc9e812 100644 --- a/litellm/experimental_mcp_client/client.py +++ b/litellm/experimental_mcp_client/client.py @@ -5,8 +5,9 @@ LiteLLM Proxy uses this MCP Client to connnect to other MCP servers. import asyncio import base64 from datetime import timedelta -from typing import Dict, List, Optional, Union +from typing import Callable, Dict, List, Optional, Union +import httpx from mcp import ClientSession, StdioServerParameters from mcp.client.sse import sse_client from mcp.client.stdio import stdio_client @@ -17,6 +18,8 @@ from mcp.types import TextContent from mcp.types import Tool as MCPTool from litellm._logging import verbose_logger +from litellm.llms.custom_httpx.http_handler import get_ssl_configuration +from litellm.types.llms.custom_http import VerifyTypes from litellm.types.mcp import ( MCPAuth, MCPAuthType, @@ -48,6 +51,7 @@ class MCPClient: timeout: float = 60.0, stdio_config: Optional[MCPStdioConfig] = None, extra_headers: Optional[Dict[str, str]] = None, + ssl_verify: Optional[VerifyTypes] = None, ): self.server_url: str = server_url self.transport_type: MCPTransport = transport_type @@ -62,6 +66,7 @@ class MCPClient: self._task: Optional[asyncio.Task] = None self.stdio_config: Optional[MCPStdioConfig] = stdio_config self.extra_headers: Optional[Dict[str, str]] = extra_headers + self.ssl_verify: Optional[VerifyTypes] = ssl_verify # handle the basic auth value if provided if auth_value: self.update_auth_value(auth_value) @@ -104,10 +109,12 @@ class MCPClient: await self._session.initialize() elif self.transport_type == MCPTransport.sse: headers = self._get_auth_headers() + httpx_client_factory = self._create_httpx_client_factory() self._transport_ctx = sse_client( url=self.server_url, timeout=self.timeout, headers=headers, + httpx_client_factory=httpx_client_factory, ) self._transport = await self._transport_ctx.__aenter__() self._session_ctx = ClientSession( @@ -117,13 +124,15 @@ class MCPClient: await self._session.initialize() else: # http headers = self._get_auth_headers() + httpx_client_factory = self._create_httpx_client_factory() verbose_logger.debug( - "litellm headers for streamablehttp_client: ", headers + "litellm headers for streamablehttp_client: %s", headers ) self._transport_ctx = streamablehttp_client( url=self.server_url, timeout=timedelta(seconds=self.timeout), headers=headers, + httpx_client_factory=httpx_client_factory, ) self._transport = await self._transport_ctx.__aenter__() self._session_ctx = ClientSession( @@ -215,6 +224,41 @@ class MCPClient: return headers + def _create_httpx_client_factory(self) -> Callable[..., httpx.AsyncClient]: + """ + Create a custom httpx client factory that uses LiteLLM's SSL configuration. + + This factory follows the same CA bundle path logic as http_handler.py: + 1. Check ssl_verify parameter (can be SSLContext, bool, or path to CA bundle) + 2. Check SSL_VERIFY environment variable + 3. Check SSL_CERT_FILE environment variable + 4. Fall back to certifi CA bundle + """ + + def factory( + *, + headers: Optional[Dict[str, str]] = None, + timeout: Optional[httpx.Timeout] = None, + auth: Optional[httpx.Auth] = None, + ) -> httpx.AsyncClient: + """Create an httpx.AsyncClient with LiteLLM's SSL configuration.""" + # Get unified SSL configuration using the same logic as http_handler.py + ssl_config = get_ssl_configuration(self.ssl_verify) + + verbose_logger.debug( + f"MCP client using SSL configuration: {type(ssl_config).__name__}" + ) + + return httpx.AsyncClient( + headers=headers, + timeout=timeout, + auth=auth, + verify=ssl_config, + follow_redirects=True, + ) + + return factory + async def list_tools(self) -> List[MCPTool]: """List available tools from the server.""" if not self._session: diff --git a/litellm/files/main.py b/litellm/files/main.py index 18be2c702bf..7bc2c136726 100644 --- a/litellm/files/main.py +++ b/litellm/files/main.py @@ -18,6 +18,7 @@ from litellm import get_secret_str from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.llms.azure.files.handler import AzureOpenAIFilesAPI +from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler from litellm.llms.custom_httpx.llm_http_handler import BaseLLMHTTPHandler from litellm.llms.openai.openai import FileDeleted, FileObject, OpenAIFilesAPI from litellm.llms.vertex_ai.files.handler import VertexAIFilesHandler @@ -268,6 +269,7 @@ def create_file( raise e +@client async def afile_retrieve( file_id: str, custom_llm_provider: Literal["openai", "azure"] = "openai", @@ -308,6 +310,7 @@ async def afile_retrieve( raise e +@client def file_retrieve( file_id: str, custom_llm_provider: Literal["openai", "azure"] = "openai", @@ -422,6 +425,7 @@ def file_retrieve( # Delete file +@client async def afile_delete( file_id: str, custom_llm_provider: Literal["openai", "azure"] = "openai", @@ -462,6 +466,7 @@ async def afile_delete( raise e +@client def file_delete( file_id: str, custom_llm_provider: Literal["openai", "azure"] = "openai", @@ -577,6 +582,7 @@ def file_delete( # List files +@client async def afile_list( custom_llm_provider: Literal["openai", "azure"] = "openai", purpose: Optional[str] = None, @@ -617,6 +623,7 @@ async def afile_list( raise e +@client def file_list( custom_llm_provider: Literal["openai", "azure"] = "openai", purpose: Optional[str] = None, @@ -729,6 +736,7 @@ def file_list( raise e +@client async def afile_content( file_id: str, custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai", @@ -771,6 +779,7 @@ async def afile_content( raise e +@client def file_content( file_id: str, model: Optional[str] = None, diff --git a/litellm/integrations/posthog.py b/litellm/integrations/posthog.py index 5298e538a72..dfa180642c8 100644 --- a/litellm/integrations/posthog.py +++ b/litellm/integrations/posthog.py @@ -26,7 +26,7 @@ from litellm.types.integrations.posthog import ( POSTHOG_MAX_BATCH_SIZE, PostHogEventPayload, ) -from litellm.types.utils import StandardLoggingPayload +from litellm.types.utils import StandardCallbackDynamicParams, StandardLoggingPayload class PostHogLogger(CustomBatchLogger): @@ -72,17 +72,19 @@ class PostHogLogger(CustomBatchLogger): verbose_logger.debug( "PostHog: Sync logging - Enters logging function for model %s", kwargs ) - + + api_key, api_url = self._get_credentials_for_request(kwargs) event_payload = self.create_posthog_event_payload(kwargs) headers = { "Content-Type": "application/json", } - payload = self._create_posthog_payload([event_payload]) + payload = self._create_posthog_payload([event_payload], api_key) + capture_url = f"{api_url.rstrip('/')}/batch/" response = self.sync_client.post( - url=self.capture_url, + url=capture_url, json=payload, headers=headers, ) @@ -92,9 +94,9 @@ class PostHogLogger(CustomBatchLogger): raise Exception( f"Response from PostHog API status_code: {response.status_code}, text: {response.text}" ) - + verbose_logger.debug("PostHog: Sync event successfully sent") - + except Exception as e: verbose_logger.exception(f"PostHog Sync Layer Error - {str(e)}") @@ -122,9 +124,15 @@ class PostHogLogger(CustomBatchLogger): async def _log_async_event(self, kwargs, response_obj=None, start_time=0.0, end_time=0.0): # Note: response_obj, start_time, end_time not used - all data comes from kwargs + api_key, api_url = self._get_credentials_for_request(kwargs) event_payload = self.create_posthog_event_payload(kwargs) - self.log_queue.append(event_payload) + # Store event with its credentials for batch sending + self.log_queue.append({ + "event": event_payload, + "api_key": api_key, + "api_url": api_url + }) verbose_logger.debug( f"PostHog, event added to queue. Will flush in {self.flush_interval} seconds..." ) @@ -257,16 +265,42 @@ class PostHogLogger(CustomBatchLogger): metadata = self._extract_metadata(kwargs) user_id = self._safe_get(metadata, "user_id") if user_id: - return str(user_id) + return str(user_id) end_user = self._safe_get(standard_logging_object, "end_user") if end_user: return str(end_user) trace_id = self._safe_get(standard_logging_object, "trace_id") if trace_id: - return str(trace_id) - + return str(trace_id) + return self._safe_uuid() + def _get_credentials_for_request(self, kwargs: Dict[str, Any]) -> tuple[str, str]: + """ + Get PostHog credentials for this request. + + Checks for per-request credentials in standard_callback_dynamic_params, + falls back to instance defaults from environment variables. + + Args: + kwargs: Request kwargs containing standard_callback_dynamic_params + + Returns: + tuple[str, str]: (api_key, api_url) + """ + standard_callback_dynamic_params: Optional[StandardCallbackDynamicParams] = ( + kwargs.get("standard_callback_dynamic_params", None) + ) + + if standard_callback_dynamic_params is not None: + api_key = standard_callback_dynamic_params.get("posthog_api_key") or self.POSTHOG_API_KEY + api_url = standard_callback_dynamic_params.get("posthog_api_url") or self.posthog_host + else: + api_key = self.POSTHOG_API_KEY + api_url = self.posthog_host + + return api_key, api_url + async def async_send_batch(self): """ Sends the in memory logs queue to PostHog API @@ -282,23 +316,34 @@ class PostHogLogger(CustomBatchLogger): f"PostHog: Sending batch of {len(self.log_queue)} events" ) - headers = { - "Content-Type": "application/json", - } + # Group events by credentials for batch sending + batches_by_credentials: Dict[tuple[str, str], list] = {} + for item in self.log_queue: + key = (item["api_key"], item["api_url"]) + if key not in batches_by_credentials: + batches_by_credentials[key] = [] + batches_by_credentials[key].append(item["event"]) - payload = self._create_posthog_payload(list(self.log_queue)) + # Send each batch to its respective PostHog instance + for (api_key, api_url), events in batches_by_credentials.items(): + headers = { + "Content-Type": "application/json", + } - response = await self.async_client.post( - url=self.capture_url, - json=payload, - headers=headers, - ) - response.raise_for_status() + payload = self._create_posthog_payload(events, api_key) + capture_url = f"{api_url.rstrip('/')}/batch/" - if response.status_code != 200: - raise Exception( - f"Response from PostHog API status_code: {response.status_code}, text: {response.text}" + response = await self.async_client.post( + url=capture_url, + json=payload, + headers=headers, ) + response.raise_for_status() + + if response.status_code != 200: + raise Exception( + f"Response from PostHog API status_code: {response.status_code}, text: {response.text}" + ) verbose_logger.debug( f"PostHog: Batch of {len(self.log_queue)} events successfully sent" @@ -324,8 +369,8 @@ class PostHogLogger(CustomBatchLogger): def _safe_uuid(self) -> str: return str(uuid.uuid4()) - def _create_posthog_payload(self, events: list) -> Dict[str, Any]: - return {"api_key": self.POSTHOG_API_KEY, "batch": events} + def _create_posthog_payload(self, events: list, api_key: str) -> Dict[str, Any]: + return {"api_key": api_key, "batch": events} def _safe_get(self, obj: Any, key: str, default: Any = None) -> Any: if obj is None or not hasattr(obj, 'get'): diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py index 696c67c44d7..eafcab88557 100644 --- a/litellm/litellm_core_utils/litellm_logging.py +++ b/litellm/litellm_core_utils/litellm_logging.py @@ -81,12 +81,12 @@ from litellm.types.llms.openai import ( ) from litellm.types.mcp import MCPPostCallResponseObject from litellm.types.rerank import RerankResponse -from litellm.types.router import CustomPricingLiteLLMParams from litellm.types.utils import ( CachingDetails, CallTypes, CostBreakdown, CostResponseTypes, + CustomPricingLiteLLMParams, DynamicPromptManagementParamLiteral, EmbeddingResponse, GuardrailStatus, diff --git a/litellm/litellm_core_utils/prompt_templates/factory.py b/litellm/litellm_core_utils/prompt_templates/factory.py index c1d8a1cd41b..d2cad0abd93 100644 --- a/litellm/litellm_core_utils/prompt_templates/factory.py +++ b/litellm/litellm_core_utils/prompt_templates/factory.py @@ -2,7 +2,6 @@ import copy import json import mimetypes import re -from litellm._uuid import uuid import xml.etree.ElementTree as ET from enum import Enum from typing import Any, List, Optional, Tuple, cast, overload @@ -13,6 +12,7 @@ import litellm import litellm.types import litellm.types.llms from litellm import verbose_logger +from litellm._uuid import uuid from litellm.llms.custom_httpx.http_handler import HTTPHandler, get_async_httpx_client from litellm.types.files import get_file_extension_from_mime_type from litellm.types.llms.anthropic import * @@ -232,7 +232,6 @@ def ollama_pt( ## MERGE CONSECUTIVE ASSISTANT CONTENT ## while msg_i < len(messages) and messages[msg_i]["role"] == "assistant": assistant_content_str += convert_content_list_to_str(messages[msg_i]) - msg_i += 1 tool_calls = messages[msg_i].get("tool_calls") ollama_tool_calls = [] @@ -258,7 +257,7 @@ def ollama_pt( f"Tool Calls: {json.dumps(ollama_tool_calls, indent=2)}" ) - msg_i += 1 + msg_i += 1 if assistant_content_str: prompt += f"### Assistant:\n{assistant_content_str}\n\n" @@ -365,62 +364,20 @@ def phind_codellama_pt(messages): return prompt -def hf_chat_template( # noqa: PLR0915 - model: str, messages: list, chat_template: Optional[Any] = None -): - # Define Jinja2 environment - env = ImmutableSandboxedEnvironment() - - def raise_exception(message): - raise Exception(f"Error message - {message}") - - # Create a template object from the template text - env.globals["raise_exception"] = raise_exception - - ## get the tokenizer config from huggingface - bos_token = "" - eos_token = "" - if chat_template is None: - - def _get_tokenizer_config(hf_model_name): - try: - url = f"https://huggingface.co/{hf_model_name}/raw/main/tokenizer_config.json" - # Make a GET request to fetch the JSON data - client = HTTPHandler(concurrent_limit=1) - - response = client.get(url) - except Exception as e: - raise e - if response.status_code == 200: - # Parse the JSON data - tokenizer_config = json.loads(response.content) - return {"status": "success", "tokenizer": tokenizer_config} - else: - return {"status": "failure"} - - if model in litellm.known_tokenizer_config: - tokenizer_config = litellm.known_tokenizer_config[model] - else: - tokenizer_config = _get_tokenizer_config(model) - litellm.known_tokenizer_config.update({model: tokenizer_config}) - - if ( - tokenizer_config["status"] == "failure" - or "chat_template" not in tokenizer_config["tokenizer"] - ): - raise Exception("No chat template found") - ## read the bos token, eos token and chat template from the json - tokenizer_config = tokenizer_config["tokenizer"] # type: ignore - - bos_token = tokenizer_config["bos_token"] # type: ignore - if bos_token is not None and not isinstance(bos_token, str): - if isinstance(bos_token, dict): - bos_token = bos_token.get("content", None) - eos_token = tokenizer_config["eos_token"] # type: ignore - if eos_token is not None and not isinstance(eos_token, str): - if isinstance(eos_token, dict): - eos_token = eos_token.get("content", None) - chat_template = tokenizer_config["chat_template"] # type: ignore +def _render_chat_template(env, chat_template: str, bos_token: str, eos_token: str, messages: list) -> str: + """ + Shared template rendering logic for both sync and async hf_chat_template + + Args: + env: Jinja2 environment + chat_template: Chat template string + bos_token: Beginning of sequence token + eos_token: End of sequence token + messages: Messages to render + + Returns: + Rendered template string + """ try: template = env.from_string(chat_template) # type: ignore except Exception as e: @@ -435,7 +392,6 @@ def hf_chat_template( # noqa: PLR0915 bos_token="", ) return True - # This will be raised if Jinja attempts to render the system message and it can't except Exception: return False @@ -469,7 +425,7 @@ def hf_chat_template( # noqa: PLR0915 ) except Exception as e: if "Conversation roles must alternate user/assistant" in str(e): - # reformat messages to ensure user/assistant are alternating, if there's either 2 consecutive 'user' messages or 2 consecutive 'assistant' message, add a blank 'user' or 'assistant' message to ensure compatibility + # reformat messages to ensure user/assistant are alternating new_messages = [] for i in range(len(reformatted_messages) - 1): new_messages.append(reformatted_messages[i]) @@ -495,6 +451,188 @@ def hf_chat_template( # noqa: PLR0915 ) # don't use verbose_logger.exception, if exception is raised +async def _afetch_and_extract_template( + model: str, chat_template: Optional[Any], get_config_fn, get_template_fn +) -> Tuple[str, str, str]: + """ + Async version: Fetch template and tokens from HuggingFace. + + Returns: (chat_template, bos_token, eos_token) + """ + from litellm.litellm_core_utils.prompt_templates.huggingface_template_handler import ( + _extract_token_value, + ) + + bos_token = "" + eos_token = "" + + if chat_template is None: + # Fetch or retrieve cached tokenizer config + if model in litellm.known_tokenizer_config: + tokenizer_config = litellm.known_tokenizer_config[model] + else: + tokenizer_config = await get_config_fn(hf_model_name=model) + litellm.known_tokenizer_config.update({model: tokenizer_config}) + + # Try to get chat template from tokenizer_config.json first + if ( + tokenizer_config.get("status") == "success" + and "tokenizer" in tokenizer_config + and isinstance(tokenizer_config["tokenizer"], dict) + and "chat_template" in tokenizer_config["tokenizer"] + ): + tokenizer_data: dict = tokenizer_config["tokenizer"] # type: ignore + bos_token = _extract_token_value( + token_value=tokenizer_data.get("bos_token") + ) + eos_token = _extract_token_value( + token_value=tokenizer_data.get("eos_token") + ) + chat_template = tokenizer_data["chat_template"] + else: + # Fallback: Try to fetch chat template from separate .jinja file + template_result = await get_template_fn(hf_model_name=model) + if template_result.get("status") == "success": + chat_template = template_result["chat_template"] + # Still try to get tokens from tokenizer_config if available + if ( + tokenizer_config.get("status") == "success" + and "tokenizer" in tokenizer_config + and isinstance(tokenizer_config["tokenizer"], dict) + ): + tokenizer_data: dict = tokenizer_config["tokenizer"] # type: ignore + bos_token = _extract_token_value( + token_value=tokenizer_data.get("bos_token") + ) + eos_token = _extract_token_value( + token_value=tokenizer_data.get("eos_token") + ) + else: + raise Exception("No chat template found") + + return chat_template, bos_token, eos_token # type: ignore + + +def _fetch_and_extract_template( + model: str, chat_template: Optional[Any], get_config_fn, get_template_fn +) -> Tuple[str, str, str]: + """ + Sync version: Fetch template and tokens from HuggingFace. + + Returns: (chat_template, bos_token, eos_token) + """ + from litellm.litellm_core_utils.prompt_templates.huggingface_template_handler import ( + _extract_token_value, + ) + + bos_token = "" + eos_token = "" + + if chat_template is None: + # Fetch or retrieve cached tokenizer config + if model in litellm.known_tokenizer_config: + tokenizer_config = litellm.known_tokenizer_config[model] + else: + tokenizer_config = get_config_fn(hf_model_name=model) + litellm.known_tokenizer_config.update({model: tokenizer_config}) + + # Try to get chat template from tokenizer_config.json first + if ( + tokenizer_config.get("status") == "success" + and "tokenizer" in tokenizer_config + and isinstance(tokenizer_config["tokenizer"], dict) + and "chat_template" in tokenizer_config["tokenizer"] + ): + tokenizer_data: dict = tokenizer_config["tokenizer"] # type: ignore + bos_token = _extract_token_value( + token_value=tokenizer_data.get("bos_token") + ) + eos_token = _extract_token_value( + token_value=tokenizer_data.get("eos_token") + ) + chat_template = tokenizer_data["chat_template"] + else: + # Fallback: Try to fetch chat template from separate .jinja file + template_result = get_template_fn(hf_model_name=model) + if template_result.get("status") == "success": + chat_template = template_result["chat_template"] + # Still try to get tokens from tokenizer_config if available + if ( + tokenizer_config.get("status") == "success" + and "tokenizer" in tokenizer_config + and isinstance(tokenizer_config["tokenizer"], dict) + ): + tokenizer_data: dict = tokenizer_config["tokenizer"] # type: ignore + bos_token = _extract_token_value( + token_value=tokenizer_data.get("bos_token") + ) + eos_token = _extract_token_value( + token_value=tokenizer_data.get("eos_token") + ) + else: + raise Exception("No chat template found") + + return chat_template, bos_token, eos_token # type: ignore + + +async def ahf_chat_template( + model: str, messages: list, chat_template: Optional[Any] = None +): + """HuggingFace chat template (async version)""" + from litellm.litellm_core_utils.prompt_templates.huggingface_template_handler import ( + _aget_chat_template_file, + _aget_tokenizer_config, + strftime_now, + ) + + env = ImmutableSandboxedEnvironment() + env.globals["raise_exception"] = lambda msg: Exception(f"Error message - {msg}") + env.globals["strftime_now"] = strftime_now + + template, bos_token, eos_token = await _afetch_and_extract_template( + model=model, + chat_template=chat_template, + get_config_fn=_aget_tokenizer_config, + get_template_fn=_aget_chat_template_file, + ) + return _render_chat_template( + env=env, + chat_template=template, + bos_token=bos_token, + eos_token=eos_token, + messages=messages, + ) + + +def hf_chat_template( + model: str, messages: list, chat_template: Optional[Any] = None +): + """HuggingFace chat template (sync version)""" + from litellm.litellm_core_utils.prompt_templates.huggingface_template_handler import ( + _get_chat_template_file, + _get_tokenizer_config, + strftime_now, + ) + + env = ImmutableSandboxedEnvironment() + env.globals["raise_exception"] = lambda msg: Exception(f"Error message - {msg}") + env.globals["strftime_now"] = strftime_now + + template, bos_token, eos_token = _fetch_and_extract_template( + model=model, + chat_template=chat_template, + get_config_fn=_get_tokenizer_config, + get_template_fn=_get_chat_template_file, + ) + return _render_chat_template( + env=env, + chat_template=template, + bos_token=bos_token, + eos_token=eos_token, + messages=messages, + ) + + def deepseek_r1_pt(messages): return hf_chat_template( model="deepseek-r1/deepseek-r1-7b-instruct", messages=messages @@ -4032,33 +4170,9 @@ def prompt_factory( elif custom_llm_provider == "azure_text": return azure_text_pt(messages=messages) elif custom_llm_provider == "watsonx": - if "granite" in model and "chat" in model: - # granite-13b-chat-v1 and granite-13b-chat-v2 use a specific prompt template - return ibm_granite_pt(messages=messages) - elif "ibm-mistral" in model and "instruct" in model: - # models like ibm-mistral/mixtral-8x7b-instruct-v01-q use the mistral instruct prompt template - return mistral_instruct_pt(messages=messages) - elif "meta-llama/llama-3" in model and "instruct" in model: - # https://llama.meta.com/docs/model-cards-and-prompt-formats/meta-llama-3/ - return custom_prompt( - role_dict={ - "system": { - "pre_message": "<|start_header_id|>system<|end_header_id|>\n", - "post_message": "<|eot_id|>", - }, - "user": { - "pre_message": "<|start_header_id|>user<|end_header_id|>\n", - "post_message": "<|eot_id|>", - }, - "assistant": { - "pre_message": "<|start_header_id|>assistant<|end_header_id|>\n", - "post_message": "<|eot_id|>", - }, - }, - messages=messages, - initial_prompt_value="<|begin_of_text|>", - final_prompt_value="<|start_header_id|>assistant<|end_header_id|>\n", - ) + from litellm.llms.watsonx.chat.transformation import IBMWatsonXChatConfig + return IBMWatsonXChatConfig.apply_prompt_template(model=model, messages=messages) + try: if "meta-llama/llama-2" in model and "chat" in model: return llama_2_chat_pt(messages=messages) diff --git a/litellm/litellm_core_utils/prompt_templates/huggingface_template_handler.py b/litellm/litellm_core_utils/prompt_templates/huggingface_template_handler.py new file mode 100644 index 00000000000..9305d5bbfc1 --- /dev/null +++ b/litellm/litellm_core_utils/prompt_templates/huggingface_template_handler.py @@ -0,0 +1,139 @@ +import json +from datetime import datetime +from typing import Any, Dict, Union + +from litellm.llms.custom_httpx.http_handler import ( + _get_httpx_client, + get_async_httpx_client, +) +from litellm.types.llms.custom_http import httpxSpecialProvider + + +def strftime_now(fmt: str) -> str: + """ + Custom function for templates that need current date/time formatting (e.g., gpt-oss) + + Args: + fmt: Format string for datetime.now().strftime() + + Returns: + Formatted string + """ + return datetime.now().strftime(fmt) + + +def _get_tokenizer_config(hf_model_name: str) -> Dict[str, Any]: + """ + Fetch tokenizer_config.json from HuggingFace (sync) + + Args: + hf_model_name: HuggingFace model name (e.g., 'openai/gpt-oss-120b') + + Returns: + Dict with 'status' and optionally 'tokenizer' keys + """ + try: + url = f"https://huggingface.co/{hf_model_name}/raw/main/tokenizer_config.json" + client = _get_httpx_client() + response = client.get(url=url) + except Exception as e: + raise e + if response.status_code == 200: + tokenizer_config = json.loads(response.content) + return {"status": "success", "tokenizer": tokenizer_config} + else: + return {"status": "failure"} + + +async def _aget_tokenizer_config(hf_model_name: str) -> Dict[str, Any]: + """ + Fetch tokenizer_config.json from HuggingFace (async) + + Args: + hf_model_name: HuggingFace model name (e.g., 'openai/gpt-oss-120b') + + Returns: + Dict with 'status' and optionally 'tokenizer' keys + """ + try: + url = f"https://huggingface.co/{hf_model_name}/raw/main/tokenizer_config.json" + client = get_async_httpx_client( + llm_provider=httpxSpecialProvider.PromptFactory, + ) + response = await client.get(url=url) + except Exception as e: + raise e + if response.status_code == 200: + tokenizer_config = json.loads(response.content) + return {"status": "success", "tokenizer": tokenizer_config} + else: + return {"status": "failure"} + + +def _get_chat_template_file(hf_model_name: str) -> Dict[str, Any]: + """ + Fetch chat template from separate .jinja file (sync) + + Args: + hf_model_name: HuggingFace model name (e.g., 'openai/gpt-oss-120b') + + Returns: + Dict with 'status' and optionally 'chat_template' keys + """ + template_filenames = ["chat_template.jinja", "chat_template.jinja2"] + client = _get_httpx_client() + + for filename in template_filenames: + try: + url = f"https://huggingface.co/{hf_model_name}/raw/main/{filename}" + response = client.get(url=url) + if response.status_code == 200: + return {"status": "success", "chat_template": response.content.decode("utf-8")} + except Exception: + continue + + return {"status": "failure"} + + +async def _aget_chat_template_file(hf_model_name: str) -> Dict[str, Any]: + """ + Fetch chat template from separate .jinja file (async) + + Args: + hf_model_name: HuggingFace model name (e.g., 'openai/gpt-oss-120b') + + Returns: + Dict with 'status' and optionally 'chat_template' keys + """ + template_filenames = ["chat_template.jinja", "chat_template.jinja2"] + client = get_async_httpx_client( + llm_provider=httpxSpecialProvider.PromptFactory, + ) + + for filename in template_filenames: + try: + url = f"https://huggingface.co/{hf_model_name}/raw/main/{filename}" + response = await client.get(url=url) + if response.status_code == 200: + return {"status": "success", "chat_template": response.content.decode("utf-8")} + except Exception: + continue + + return {"status": "failure"} + + +def _extract_token_value(token_value: Union[None, str, Dict[str, Any]]) -> str: + """ + Extract token string from various formats (string, dict, etc.) + + Args: + token_value: Token value in various formats (None, str, or dict with 'content' key) + + Returns: + Extracted token string + """ + if token_value is None or isinstance(token_value, str): + return token_value or "" + if isinstance(token_value, dict): + return token_value.get("content", "") + return "" \ No newline at end of file diff --git a/litellm/litellm_core_utils/sensitive_data_masker.py b/litellm/litellm_core_utils/sensitive_data_masker.py index 05f1a37ca12..ea0bed30416 100644 --- a/litellm/litellm_core_utils/sensitive_data_masker.py +++ b/litellm/litellm_core_utils/sensitive_data_masker.py @@ -75,7 +75,7 @@ class SensitiveDataMasker: masked_data[k] = self._mask_value(str_value) else: masked_data[k] = ( - v if isinstance(v, (int, float, bool, str)) else str(v) + v if isinstance(v, (int, float, bool, str, list)) else str(v) ) except Exception: masked_data[k] = "" @@ -89,12 +89,14 @@ masker = SensitiveDataMasker() data = { "api_key": "sk-1234567890abcdef", "redis_password": "very_secret_pass", - "port": 6379 + "port": 6379, + "tags": ["East US 2", "production", "test"] } masked = masker.mask_dict(data) # Result: { # "api_key": "sk-1****cdef", # "redis_password": "very****pass", -# "port": 6379 +# "port": 6379, +# "tags": ["East US 2", "production", "test"] # } """ diff --git a/litellm/llms/anthropic/chat/transformation.py b/litellm/llms/anthropic/chat/transformation.py index c7f0a6da61e..691b46af8da 100644 --- a/litellm/llms/anthropic/chat/transformation.py +++ b/litellm/llms/anthropic/chat/transformation.py @@ -878,7 +878,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): prompt_tokens_details = PromptTokensDetailsWrapper( cached_tokens=cache_read_input_tokens, - cache_creation_tokens=cache_read_input_tokens, + cache_creation_tokens=cache_creation_input_tokens, cache_creation_token_details=cache_creation_token_details, ) completion_token_details = ( diff --git a/litellm/llms/anthropic/experimental_pass_through/adapters/streaming_iterator.py b/litellm/llms/anthropic/experimental_pass_through/adapters/streaming_iterator.py index 306bcd9bb2c..47263dc1748 100644 --- a/litellm/llms/anthropic/experimental_pass_through/adapters/streaming_iterator.py +++ b/litellm/llms/anthropic/experimental_pass_through/adapters/streaming_iterator.py @@ -270,7 +270,6 @@ class AnthropicStreamWrapper(AdapterCompletionStreamWrapper): processed_chunk.get("delta", {}).get("stop_reason") is not None ): - self.holding_stop_reason_chunk = processed_chunk else: self.chunk_queue.append(processed_chunk) @@ -380,4 +379,11 @@ class AnthropicStreamWrapper(AdapterCompletionStreamWrapper): self.current_content_block_start = content_block_start return True + # For parallel tool calls, we'll necessarily have a new content block + # if we get a function name since it signals a new tool call + if block_type == "tool_use" and content_block_start.get("name"): + self.current_content_block_type = block_type + self.current_content_block_start = content_block_start + return True + return False diff --git a/litellm/llms/azure/common_utils.py b/litellm/llms/azure/common_utils.py index 04448681b63..54352148c5e 100644 --- a/litellm/llms/azure/common_utils.py +++ b/litellm/llms/azure/common_utils.py @@ -365,6 +365,11 @@ def get_azure_ad_token( azure_ad_token_provider = get_azure_ad_token_provider(azure_scope=scope) except ValueError: verbose_logger.debug("Azure AD Token Provider could not be used.") + except Exception as e: + verbose_logger.error( + f"Error calling Azure AD token provider: {str(e)}. Follow docs - https://docs.litellm.ai/docs/providers/azure/#azure-ad-token-refresh---defaultazurecredential" + ) + raise e ######################################################### # If litellm.enable_azure_ad_token_refresh is True and no other token provider is available, @@ -561,7 +566,9 @@ class BaseAzureLLM(BaseOpenAILLM): "Using Azure AD token provider based on Service Principal with Secret workflow for Azure Auth" ) try: - azure_ad_token_provider = get_azure_ad_token_provider(azure_scope=scope) + azure_ad_token_provider = get_azure_ad_token_provider( + azure_scope=scope, + ) except ValueError: verbose_logger.debug("Azure AD Token Provider could not be used.") if api_version is None: @@ -665,10 +672,6 @@ class BaseAzureLLM(BaseOpenAILLM): ) -> dict: litellm_params = litellm_params or GenericLiteLLMParams() - # If api-key is already in headers, preserve it - if "api-key" in headers: - return headers - api_key = ( litellm_params.api_key or litellm.api_key @@ -693,7 +696,7 @@ class BaseAzureLLM(BaseOpenAILLM): def _get_base_azure_url( api_base: Optional[str], litellm_params: Optional[Union[GenericLiteLLMParams, Dict[str, Any]]], - route: Literal["/openai/responses", "/openai/vector_stores"], + route: Union[Literal["/openai/responses", "/openai/vector_stores"], str], default_api_version: Optional[Union[str, Literal["latest", "preview"]]] = None, ) -> str: """ diff --git a/litellm/llms/azure/passthrough/transformation.py b/litellm/llms/azure/passthrough/transformation.py new file mode 100644 index 00000000000..4e9de4b314f --- /dev/null +++ b/litellm/llms/azure/passthrough/transformation.py @@ -0,0 +1,85 @@ +from typing import TYPE_CHECKING, List, Optional, Tuple + +import httpx + +from litellm.llms.azure.common_utils import BaseAzureLLM +from litellm.llms.base_llm.passthrough.transformation import BasePassthroughConfig +from litellm.secret_managers.main import get_secret_str +from litellm.types.llms.openai import AllMessageValues +from litellm.types.router import GenericLiteLLMParams + +if TYPE_CHECKING: + from httpx import URL + + +class AzurePassthroughConfig(BasePassthroughConfig): + def is_streaming_request(self, endpoint: str, request_data: dict) -> bool: + return "stream" in request_data + + def get_complete_url( + self, + api_base: Optional[str], + api_key: Optional[str], + model: str, + endpoint: str, + request_query_params: Optional[dict], + litellm_params: dict, + ) -> Tuple["URL", str]: + base_target_url = self.get_api_base(api_base) + + if base_target_url is None: + raise Exception("Azure api base not found") + + litellm_metadata = litellm_params.get("litellm_metadata") or {} + model_group = litellm_metadata.get("model_group") + if model_group and model_group in endpoint: + endpoint = endpoint.replace(model_group, model) + + complete_url = BaseAzureLLM._get_base_azure_url( + api_base=base_target_url, + litellm_params=litellm_params, + route=endpoint, + default_api_version=litellm_params.get("api_version"), + ) + return ( + httpx.URL(complete_url), + base_target_url, + ) + + def validate_environment( + self, + headers: dict, + model: str, + messages: List[AllMessageValues], + optional_params: dict, + litellm_params: dict, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + ) -> dict: + return BaseAzureLLM._base_validate_azure_environment( + headers=headers, + litellm_params=GenericLiteLLMParams( + **{**litellm_params, "api_key": api_key} + ), + ) + + @staticmethod + def get_api_base( + api_base: Optional[str] = None, + ) -> Optional[str]: + return api_base or get_secret_str("AZURE_API_BASE") + + @staticmethod + def get_api_key( + api_key: Optional[str] = None, + ) -> Optional[str]: + return api_key or get_secret_str("AZURE_API_KEY") + + @staticmethod + def get_base_model(model: str) -> Optional[str]: + return model + + def get_models( + self, api_key: Optional[str] = None, api_base: Optional[str] = None + ) -> List[str]: + return super().get_models(api_key, api_base) diff --git a/litellm/llms/azure/responses/transformation.py b/litellm/llms/azure/responses/transformation.py index 0050bd163d1..cf9f970995c 100644 --- a/litellm/llms/azure/responses/transformation.py +++ b/litellm/llms/azure/responses/transformation.py @@ -1,6 +1,7 @@ from typing import TYPE_CHECKING, Any, Dict, Literal, Optional, Tuple import httpx +from openai.types.responses import ResponseReasoningItem from litellm._logging import verbose_logger from litellm.llms.azure.common_utils import BaseAzureLLM @@ -38,6 +39,50 @@ class AzureOpenAIResponsesAPIConfig(OpenAIResponsesAPIConfig): model = model.replace("o_series/", "") return model + def _handle_reasoning_item(self, item: Dict[str, Any]) -> Dict[str, Any]: + """ + Handle reasoning items specifically to filter out status=None using OpenAI's model. + Issue: https://github.com/BerriAI/litellm/issues/13484 + OpenAI API does not accept ReasoningItem(status=None), so we need to: + 1. Check if the item is a reasoning type + 2. Create a ResponseReasoningItem object with the item data + 3. Convert it back to dict with exclude_none=True to filter None values + """ + if item.get("type") == "reasoning": + try: + # Ensure required fields are present for ResponseReasoningItem + item_data = dict(item) + if "id" not in item_data: + item_data["id"] = f"rs_{hash(str(item_data))}" + if "summary" not in item_data: + item_data["summary"] = ( + item_data.get("reasoning_content", "")[:100] + "..." + if len(item_data.get("reasoning_content", "")) > 100 + else item_data.get("reasoning_content", "") + ) + + # Create ResponseReasoningItem object from the item data + reasoning_item = ResponseReasoningItem(**item_data) + + # Convert back to dict with exclude_none=True to exclude None fields + dict_reasoning_item = reasoning_item.model_dump(exclude_none=True) + dict_reasoning_item.pop("status", None) + + return dict_reasoning_item + except Exception as e: + verbose_logger.debug( + f"Failed to create ResponseReasoningItem, falling back to manual filtering: {e}" + ) + # Fallback: manually filter out known None fields + filtered_item = { + k: v + for k, v in item.items() + if v is not None + or k not in {"status", "content", "encrypted_content"} + } + return filtered_item + return item + def transform_responses_api_request( self, model: str, @@ -48,12 +93,13 @@ class AzureOpenAIResponsesAPIConfig(OpenAIResponsesAPIConfig): ) -> Dict: """No transform applied since inputs are in OpenAI spec already""" stripped_model_name = self.get_stripped_model_name(model) - return dict( - ResponsesAPIRequestParams( - model=stripped_model_name, - input=input, - **response_api_optional_request_params, - ) + + return super().transform_responses_api_request( + model=stripped_model_name, + input=input, + response_api_optional_request_params=response_api_optional_request_params, + litellm_params=litellm_params, + headers=headers, ) def get_complete_url( @@ -217,15 +263,15 @@ class AzureOpenAIResponsesAPIConfig(OpenAIResponsesAPIConfig): at the correct location (before any query parameters). """ from urllib.parse import urlparse, urlunparse - + # Parse the URL to separate its components parsed_url = urlparse(api_base) - + # Insert the response_id and /cancel at the end of the path component # Remove trailing slash if present to avoid double slashes path = parsed_url.path.rstrip("/") new_path = f"{path}/{response_id}/cancel" - + # Reconstruct the URL with all original components but with the modified path cancel_url = urlunparse( ( diff --git a/litellm/llms/azure_ai/chat/transformation.py b/litellm/llms/azure_ai/chat/transformation.py index 7eb7b767d04..04d2b3a2769 100644 --- a/litellm/llms/azure_ai/chat/transformation.py +++ b/litellm/llms/azure_ai/chat/transformation.py @@ -14,6 +14,7 @@ from litellm.litellm_core_utils.prompt_templates.common_utils import ( from litellm.llms.base_llm.chat.transformation import LiteLLMLoggingObj from litellm.llms.openai.common_utils import drop_params_from_unprocessable_entity_error from litellm.llms.openai.openai import OpenAIConfig +from litellm.llms.xai.chat.transformation import XAIChatConfig from litellm.secret_managers.main import get_secret_str from litellm.types.llms.openai import AllMessageValues from litellm.types.utils import ModelResponse, ProviderField @@ -35,9 +36,24 @@ class AzureAIStudioConfig(OpenAIConfig): for param in supported_params: if param != "tool_choice": filtered_supported_params.append(param) - return filtered_supported_params + supported_params = filtered_supported_params + + # Filter out unsupported parameters for specific models + if not self._supports_stop_reason(model): + supported_params = [param for param in supported_params if param != "stop"] + return supported_params + def _supports_stop_reason(self, model: str) -> bool: + """ + Check if the model supports stop tokens. + """ + if "grok" in model: + # Reuse Xai method for Grok model + xai_config = XAIChatConfig() + return xai_config._supports_stop_reason(model) + return True + def validate_environment( self, headers: dict, @@ -53,9 +69,7 @@ class AzureAIStudioConfig(OpenAIConfig): else: headers["Authorization"] = f"Bearer {api_key}" - headers["Content-Type"] = ( - "application/json" # tell Azure AI Studio to expect JSON - ) + headers["Content-Type"] = "application/json" # tell Azure AI Studio to expect JSON return headers @@ -65,10 +79,7 @@ class AzureAIStudioConfig(OpenAIConfig): """ parsed_url = urlparse(api_base) host = parsed_url.hostname - if host and ( - host.endswith(".services.ai.azure.com") - or host.endswith(".openai.azure.com") - ): + if host and (host.endswith(".services.ai.azure.com") or host.endswith(".openai.azure.com")): return True return False @@ -115,13 +126,9 @@ class AzureAIStudioConfig(OpenAIConfig): # Add the path to the base URL if "services.ai.azure.com" in api_base: - new_url = _add_path_to_api_base( - api_base=api_base, ending_path="/models/chat/completions" - ) + new_url = _add_path_to_api_base(api_base=api_base, ending_path="/models/chat/completions") else: - new_url = _add_path_to_api_base( - api_base=api_base, ending_path="/chat/completions" - ) + new_url = _add_path_to_api_base(api_base=api_base, ending_path="/chat/completions") # Use the new query_params dictionary final_url = httpx.URL(new_url).copy_with(params=query_params) @@ -191,11 +198,7 @@ class AzureAIStudioConfig(OpenAIConfig): dynamic_api_key = api_key or get_secret_str("AZURE_AI_API_KEY") if self._is_azure_openai_model(model=model, api_base=api_base): - verbose_logger.debug( - "Model={} is Azure OpenAI model. Setting custom_llm_provider='azure'.".format( - model - ) - ) + verbose_logger.debug("Model={} is Azure OpenAI model. Setting custom_llm_provider='azure'.".format(model)) custom_llm_provider = "azure" return api_base, dynamic_api_key, custom_llm_provider @@ -211,9 +214,7 @@ class AzureAIStudioConfig(OpenAIConfig): if extra_body and isinstance(extra_body, dict): optional_params.update(extra_body) optional_params.pop("max_retries", None) - return super().transform_request( - model, messages, optional_params, litellm_params, headers - ) + return super().transform_request(model, messages, optional_params, litellm_params, headers) def transform_response( self, @@ -252,47 +253,30 @@ class AzureAIStudioConfig(OpenAIConfig): if should_drop_params and "Extra inputs are not permitted" in error_text: return True - elif ( - "unknown field: parameter index is not a valid field" in error_text - ): # remove index from tool calls + elif "unknown field: parameter index is not a valid field" in error_text: # remove index from tool calls return True elif ( - AzureFoundryErrorStrings.SET_EXTRA_PARAMETERS_TO_PASS_THROUGH.value - in error_text + AzureFoundryErrorStrings.SET_EXTRA_PARAMETERS_TO_PASS_THROUGH.value in error_text ): # remove extra-parameters from tool calls return True - return super().should_retry_llm_api_inside_llm_translation_on_http_error( - e=e, litellm_params=litellm_params - ) + return super().should_retry_llm_api_inside_llm_translation_on_http_error(e=e, litellm_params=litellm_params) @property def max_retry_on_unprocessable_entity_error(self) -> int: return 2 - def transform_request_on_unprocessable_entity_error( - self, e: httpx.HTTPStatusError, request_data: dict - ) -> dict: + def transform_request_on_unprocessable_entity_error(self, e: httpx.HTTPStatusError, request_data: dict) -> dict: _messages = cast(Optional[List[AllMessageValues]], request_data.get("messages")) - if ( - "unknown field: parameter index is not a valid field" in e.response.text - and _messages is not None - ): + if "unknown field: parameter index is not a valid field" in e.response.text and _messages is not None: litellm.remove_index_from_tool_calls( messages=_messages, ) - elif ( - AzureFoundryErrorStrings.SET_EXTRA_PARAMETERS_TO_PASS_THROUGH.value - in e.response.text - ): - request_data = self._drop_extra_params_from_request_data( - request_data, e.response.text - ) + elif AzureFoundryErrorStrings.SET_EXTRA_PARAMETERS_TO_PASS_THROUGH.value in e.response.text: + request_data = self._drop_extra_params_from_request_data(request_data, e.response.text) data = drop_params_from_unprocessable_entity_error(e=e, data=request_data) return data - def _drop_extra_params_from_request_data( - self, request_data: dict, error_text: str - ) -> dict: + def _drop_extra_params_from_request_data(self, request_data: dict, error_text: str) -> dict: params_to_drop = self._extract_params_to_drop_from_error_text(error_text) if params_to_drop: for param in params_to_drop: @@ -300,9 +284,7 @@ class AzureAIStudioConfig(OpenAIConfig): request_data.pop(param, None) return request_data - def _extract_params_to_drop_from_error_text( - self, error_text: str - ) -> Optional[List[str]]: + def _extract_params_to_drop_from_error_text(self, error_text: str) -> Optional[List[str]]: """ Error text looks like this" "Extra parameters ['stream_options', 'extra-parameters'] are not allowed when extra-parameters is not set or set to be 'error'. diff --git a/litellm/llms/bedrock/chat/converse_transformation.py b/litellm/llms/bedrock/chat/converse_transformation.py index a61cfa39e47..d099c9813d6 100644 --- a/litellm/llms/bedrock/chat/converse_transformation.py +++ b/litellm/llms/bedrock/chat/converse_transformation.py @@ -1092,10 +1092,8 @@ class AmazonConverseConfig(BaseConfig): cache_read_input_tokens = usage["cacheReadInputTokens"] input_tokens += cache_read_input_tokens if "cacheWriteInputTokens" in usage: - """ - Do not increment prompt_tokens with cacheWriteInputTokens - """ cache_creation_input_tokens = usage["cacheWriteInputTokens"] + input_tokens += cache_creation_input_tokens prompt_tokens_details = PromptTokensDetailsWrapper( cached_tokens=cache_read_input_tokens diff --git a/litellm/llms/bedrock/common_utils.py b/litellm/llms/bedrock/common_utils.py index 241359d937e..1a599fda59f 100644 --- a/litellm/llms/bedrock/common_utils.py +++ b/litellm/llms/bedrock/common_utils.py @@ -440,7 +440,7 @@ class BedrockModelInfo(BaseLLMModelInfo): """ Abbreviations of regions AWS Bedrock supports for cross region inference """ - return ["us", "eu", "apac", "jp"] + return ["global", "us", "eu", "apac", "jp", "au"] @staticmethod def get_bedrock_route( diff --git a/litellm/llms/custom_httpx/aiohttp_transport.py b/litellm/llms/custom_httpx/aiohttp_transport.py index ab69ea1f8c3..50bbccd6a4b 100644 --- a/litellm/llms/custom_httpx/aiohttp_transport.py +++ b/litellm/llms/custom_httpx/aiohttp_transport.py @@ -152,6 +152,16 @@ class LiteLLMAiohttpTransport(AiohttpTransport): # If we don't have a client or it's not a ClientSession, create one if not isinstance(self.client, ClientSession): + if hasattr(self, "_client_factory") and callable(self._client_factory): + self.client = self._client_factory() + else: + self.client = ClientSession() + # Don't return yet - check if the newly created session is valid + + # Check if the session itself is closed + if self.client.closed: + verbose_logger.debug("Session is closed, creating new session") + # Create a new session if hasattr(self, "_client_factory") and callable(self._client_factory): self.client = self._client_factory() else: @@ -169,14 +179,17 @@ class LiteLLMAiohttpTransport(AiohttpTransport): or session_loop != current_loop or session_loop.is_closed() ): - # Clean up the old session + # Close old session to prevent leaks + old_session = self.client try: - # Note: not awaiting close() here as it might be from a different loop - # The session will be garbage collected - pass + if not old_session.closed: + try: + asyncio.create_task(old_session.close()) + except RuntimeError: + # Different event loop - can't schedule task, rely on GC + verbose_logger.debug("Old session from different loop, relying on GC") except Exception as e: verbose_logger.debug(f"Error closing old session: {e}") - pass # Create a new session in the current event loop if hasattr(self, "_client_factory") and callable(self._client_factory): @@ -193,13 +206,58 @@ class LiteLLMAiohttpTransport(AiohttpTransport): return self.client + async def _make_aiohttp_request( + self, + client_session: ClientSession, + request: httpx.Request, + timeout: dict, + proxy: Optional[str], + sni_hostname: Optional[str], + ) -> ClientResponse: + """ + Helper function to make an aiohttp request with the given parameters. + + Args: + client_session: The aiohttp ClientSession to use + request: The httpx Request to send + timeout: Timeout settings dict with 'connect', 'read', 'pool' keys + proxy: Optional proxy URL + sni_hostname: Optional SNI hostname for SSL + + Returns: + ClientResponse from aiohttp + """ + from aiohttp import ClientTimeout + from yarl import URL as YarlURL + + try: + data = request.content + except httpx.RequestNotRead: + data = request.stream # type: ignore + request.headers.pop("transfer-encoding", None) # handled by aiohttp + + response = await client_session.request( + method=request.method, + url=YarlURL(str(request.url), encoded=True), + headers=request.headers, + data=data, + allow_redirects=False, + auto_decompress=False, + timeout=ClientTimeout( + sock_connect=timeout.get("connect"), + sock_read=timeout.get("read"), + connect=timeout.get("pool"), + ), + proxy=proxy, + server_hostname=sni_hostname, + ).__aenter__() + + return response + async def handle_async_request( self, request: httpx.Request, ) -> httpx.Response: - from aiohttp import ClientTimeout - from yarl import URL as YarlURL - timeout = request.extensions.get("timeout", {}) sni_hostname = request.extensions.get("sni_hostname") @@ -209,28 +267,38 @@ class LiteLLMAiohttpTransport(AiohttpTransport): # Resolve proxy settings from environment variables proxy = await self._get_proxy_settings(request) - with map_aiohttp_exceptions(): - try: - data = request.content - except httpx.RequestNotRead: - data = request.stream # type: ignore - request.headers.pop("transfer-encoding", None) # handled by aiohttp - - response = await client_session.request( - method=request.method, - url=YarlURL(str(request.url), encoded=True), - headers=request.headers, - data=data, - allow_redirects=False, - auto_decompress=False, - timeout=ClientTimeout( - sock_connect=timeout.get("connect"), - sock_read=timeout.get("read"), - connect=timeout.get("pool"), - ), - proxy=proxy, - server_hostname=sni_hostname, - ).__aenter__() + try: + with map_aiohttp_exceptions(): + response = await self._make_aiohttp_request( + client_session=client_session, + request=request, + timeout=timeout, + proxy=proxy, + sni_hostname=sni_hostname, + ) + except RuntimeError as e: + # Handle the case where session was closed between our check and actual use + if "Session is closed" in str(e): + verbose_logger.debug(f"Session closed during request, retrying with new session: {e}") + # Force creation of a new session + if hasattr(self, "_client_factory") and callable(self._client_factory): + self.client = self._client_factory() + else: + self.client = ClientSession() + client_session = self.client + + # Retry the request with the new session + with map_aiohttp_exceptions(): + response = await self._make_aiohttp_request( + client_session=client_session, + request=request, + timeout=timeout, + proxy=proxy, + sni_hostname=sni_hostname, + ) + else: + # Re-raise if it's a different RuntimeError + raise return httpx.Response( status_code=response.status, diff --git a/litellm/llms/custom_httpx/http_handler.py b/litellm/llms/custom_httpx/http_handler.py index 3a67d5127b7..a3ad2c67272 100644 --- a/litellm/llms/custom_httpx/http_handler.py +++ b/litellm/llms/custom_httpx/http_handler.py @@ -12,7 +12,13 @@ from httpx._types import RequestFiles import litellm from litellm._logging import verbose_logger -from litellm.constants import _DEFAULT_TTL_FOR_HTTPX_CLIENTS +from litellm.constants import ( + _DEFAULT_TTL_FOR_HTTPX_CLIENTS, + AIOHTTP_CONNECTOR_LIMIT, + AIOHTTP_KEEPALIVE_TIMEOUT, + AIOHTTP_TTL_DNS_CACHE, + DEFAULT_SSL_CIPHERS +) from litellm.litellm_core_utils.logging_utils import track_llm_api_timing from litellm.types.llms.custom_http import * @@ -94,10 +100,19 @@ def get_ssl_configuration( if ssl_verify is not False: custom_ssl_context = ssl.create_default_context(cafile=cafile) - # If security level is set, apply it to the SSL context + + # Optimize SSL handshake performance + # Set minimum TLS version to 1.2 for better performance + custom_ssl_context.minimum_version = ssl.TLSVersion.TLSv1_2 + + # Configure cipher suites for optimal performance if ssl_security_level and isinstance(ssl_security_level, str): - # Create a custom SSL context with reduced security level + # User provided custom cipher configuration (e.g., via SSL_SECURITY_LEVEL env var) custom_ssl_context.set_ciphers(ssl_security_level) + else: + # Use optimized cipher list that strongly prefers fast ciphers + # but falls back to widely compatible ones + custom_ssl_context.set_ciphers(DEFAULT_SSL_CIPHERS) # Use our custom SSL context instead of the original ssl_verify value return custom_ssl_context @@ -164,7 +179,7 @@ class AsyncHTTPHandler: self, timeout: Optional[Union[float, httpx.Timeout]] = None, event_hooks: Optional[Mapping[str, List[Callable[..., Any]]]] = None, - concurrent_limit=1000, + concurrent_limit=None, # Kept for backward compatibility, but ignored (no limits) client_alias: Optional[str] = None, # name for client in logs ssl_verify: Optional[VerifyTypes] = None, shared_session: Optional["ClientSession"] = None, @@ -173,7 +188,6 @@ class AsyncHTTPHandler: self.event_hooks = event_hooks self.client = self.create_client( timeout=timeout, - concurrent_limit=concurrent_limit, event_hooks=event_hooks, ssl_verify=ssl_verify, shared_session=shared_session, @@ -183,7 +197,6 @@ class AsyncHTTPHandler: def create_client( self, timeout: Optional[Union[float, httpx.Timeout]], - concurrent_limit: int, event_hooks: Optional[Mapping[str, List[Callable[..., Any]]]], ssl_verify: Optional[VerifyTypes] = None, shared_session: Optional["ClientSession"] = None, @@ -209,10 +222,6 @@ class AsyncHTTPHandler: transport=transport, event_hooks=event_hooks, timeout=timeout, - limits=httpx.Limits( - max_connections=concurrent_limit, - max_keepalive_connections=concurrent_limit, - ), verify=ssl_config, cert=cert, headers=headers, @@ -286,7 +295,7 @@ class AsyncHTTPHandler: except (httpx.RemoteProtocolError, httpx.ConnectError): # Retry the request with a new session if there is a connection error new_client = self.create_client( - timeout=timeout, concurrent_limit=1, event_hooks=self.event_hooks + timeout=timeout, event_hooks=self.event_hooks ) try: return await self.single_connection_post_request( @@ -352,7 +361,7 @@ class AsyncHTTPHandler: except (httpx.RemoteProtocolError, httpx.ConnectError): # Retry the request with a new session if there is a connection error new_client = self.create_client( - timeout=timeout, concurrent_limit=1, event_hooks=self.event_hooks + timeout=timeout, event_hooks=self.event_hooks ) try: return await self.single_connection_post_request( @@ -412,7 +421,7 @@ class AsyncHTTPHandler: except (httpx.RemoteProtocolError, httpx.ConnectError): # Retry the request with a new session if there is a connection error new_client = self.create_client( - timeout=timeout, concurrent_limit=1, event_hooks=self.event_hooks + timeout=timeout, event_hooks=self.event_hooks ) try: return await self.single_connection_post_request( @@ -471,7 +480,7 @@ class AsyncHTTPHandler: except (httpx.RemoteProtocolError, httpx.ConnectError): # Retry the request with a new session if there is a connection error new_client = self.create_client( - timeout=timeout, concurrent_limit=1, event_hooks=self.event_hooks + timeout=timeout, event_hooks=self.event_hooks ) try: return await self.single_connection_post_request( @@ -657,7 +666,13 @@ class AsyncHTTPHandler: ) return LiteLLMAiohttpTransport( client=lambda: ClientSession( - connector=TCPConnector(**connector_kwargs), + connector=TCPConnector( + limit=AIOHTTP_CONNECTOR_LIMIT, + keepalive_timeout=AIOHTTP_KEEPALIVE_TIMEOUT, + ttl_dns_cache=AIOHTTP_TTL_DNS_CACHE, + enable_cleanup_closed=True, + **connector_kwargs + ), trust_env=trust_env, ), ) @@ -680,7 +695,7 @@ class HTTPHandler: def __init__( self, timeout: Optional[Union[float, httpx.Timeout]] = None, - concurrent_limit=1000, + concurrent_limit=None, # Kept for backward compatibility, but ignored (no limits) client: Optional[httpx.Client] = None, ssl_verify: Optional[Union[bool, str]] = None, ): @@ -701,10 +716,6 @@ class HTTPHandler: self.client = httpx.Client( transport=transport, timeout=timeout, - limits=httpx.Limits( - max_connections=concurrent_limit, - max_keepalive_connections=concurrent_limit, - ), verify=ssl_config, cert=cert, headers=headers, diff --git a/litellm/llms/custom_httpx/llm_http_handler.py b/litellm/llms/custom_httpx/llm_http_handler.py index ae449bbb15b..7f246e775af 100644 --- a/litellm/llms/custom_httpx/llm_http_handler.py +++ b/litellm/llms/custom_httpx/llm_http_handler.py @@ -13,13 +13,13 @@ from typing import ( cast, ) +from litellm._logging import verbose_logger import httpx # type: ignore import litellm import litellm.litellm_core_utils import litellm.types import litellm.types.utils -from litellm._logging import verbose_logger from litellm.litellm_core_utils.realtime_streaming import RealTimeStreaming from litellm.llms.base_llm.anthropic_messages.transformation import ( BaseAnthropicMessagesConfig, @@ -239,7 +239,7 @@ class BaseLLMHTTPHandler: 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}" @@ -426,6 +426,7 @@ class BaseLLMHTTPHandler: ), json_mode=json_mode, signed_json_body=signed_json_body, + shared_session=shared_session, ) if stream is True: diff --git a/litellm/llms/litellm_proxy/responses/transformation.py b/litellm/llms/litellm_proxy/responses/transformation.py new file mode 100644 index 00000000000..0b81d8be7d8 --- /dev/null +++ b/litellm/llms/litellm_proxy/responses/transformation.py @@ -0,0 +1,48 @@ +""" +Responses API transformation for LiteLLM Proxy provider. + +LiteLLM Proxy supports the OpenAI Responses API natively when the underlying model supports it. +This config enables pass-through behavior to the proxy's /v1/responses endpoint. +""" + +from typing import Optional + +from litellm.llms.openai.responses.transformation import OpenAIResponsesAPIConfig +from litellm.secret_managers.main import get_secret_str +from litellm.types.utils import LlmProviders + + +class LiteLLMProxyResponsesAPIConfig(OpenAIResponsesAPIConfig): + """ + Configuration for LiteLLM Proxy Responses API support. + + Extends OpenAI's config since the proxy follows OpenAI's API spec, + but uses LITELLM_PROXY_API_BASE for the base URL. + """ + + @property + def custom_llm_provider(self) -> LlmProviders: + return LlmProviders.LITELLM_PROXY + + def get_complete_url( + self, + api_base: Optional[str], + litellm_params: dict, + ) -> str: + """ + Get the endpoint for LiteLLM Proxy responses API. + + Uses LITELLM_PROXY_API_BASE environment variable if api_base is not provided. + """ + api_base = api_base or get_secret_str("LITELLM_PROXY_API_BASE") + + if api_base is None: + raise ValueError( + "api_base not set for LiteLLM Proxy responses API. " + "Set via api_base parameter or LITELLM_PROXY_API_BASE environment variable" + ) + + # Remove trailing slashes + api_base = api_base.rstrip("/") + + return f"{api_base}/responses" diff --git a/litellm/llms/oci/chat/transformation.py b/litellm/llms/oci/chat/transformation.py index 18740052480..3ab827797c5 100644 --- a/litellm/llms/oci/chat/transformation.py +++ b/litellm/llms/oci/chat/transformation.py @@ -19,6 +19,13 @@ from litellm.llms.custom_httpx.http_handler import ( ) from litellm.llms.oci.common_utils import OCIError from litellm.types.llms.oci import ( + CohereChatRequest, + CohereMessage, + CohereChatResult, + CohereParameterDefinition, + CohereStreamChunk, + CohereTool, + CohereToolCall, OCIChatRequestPayload, OCICompletionPayload, OCICompletionResponse, @@ -37,13 +44,13 @@ from litellm.types.llms.openai import AllMessageValues from litellm.types.utils import ( Delta, LlmProviders, + ModelResponse, ModelResponseStream, StreamingChoices, ) from litellm.utils import ( ChatCompletionMessageToolCall, CustomStreamWrapper, - ModelResponse, Usage, ) @@ -170,13 +177,16 @@ class OCIChatConfig(BaseConfig): "web_search_options": False, } + # Cohere and Gemini use the same parameter mapping as GENERIC + self.openai_to_oci_cohere_param_map = self.openai_to_oci_generic_param_map.copy() + def get_supported_openai_params(self, model: str) -> List[str]: supported_params = [] vendor = get_vendor_from_model(model) if vendor == OCIVendors.COHERE: - raise ValueError( - "Cohere models are not yet supported in the litellm OCI chat completion endpoint. Use the Cohere API directly." - ) + open_ai_to_oci_param_map = self.openai_to_oci_cohere_param_map + open_ai_to_oci_param_map.pop("tool_choice") + open_ai_to_oci_param_map.pop("max_retries") else: open_ai_to_oci_param_map = self.openai_to_oci_generic_param_map for key, value in open_ai_to_oci_param_map.items(): @@ -195,9 +205,7 @@ class OCIChatConfig(BaseConfig): adapted_params = {} vendor = get_vendor_from_model(model) if vendor == OCIVendors.COHERE: - raise ValueError( - "Cohere models are not yet supported in the litellm OCI chat completion endpoint. Use the Cohere API directly." - ) + open_ai_to_oci_param_map = self.openai_to_oci_cohere_param_map else: open_ai_to_oci_param_map = self.openai_to_oci_generic_param_map @@ -416,21 +424,130 @@ class OCIChatConfig(BaseConfig): def _get_optional_params(self, vendor: OCIVendors, optional_params: dict) -> Dict: selected_params = {} if vendor == OCIVendors.COHERE: - raise ValueError( - "Cohere models are not yet supported in the litellm OCI chat completion endpoint. Use the Cohere API directly." - ) + open_ai_to_oci_param_map = self.openai_to_oci_cohere_param_map + # remove tool_choice from the map + open_ai_to_oci_param_map.pop("tool_choice") + # Add default values for Cohere API + selected_params = { + "maxTokens": 600, + "temperature": 1, + "topK": 0, + "topP": 0.75, + "frequencyPenalty": 0 + } else: open_ai_to_oci_param_map = self.openai_to_oci_generic_param_map - for value in open_ai_to_oci_param_map.values(): - if value in optional_params: - selected_params[value] = optional_params[value] + # Map OpenAI params to OCI params + for openai_key, oci_key in open_ai_to_oci_param_map.items(): + if oci_key and openai_key in optional_params: + selected_params[oci_key] = optional_params[openai_key] # type: ignore[index] + + # Also check for already-mapped OCI params (for backward compatibility) + for oci_value in open_ai_to_oci_param_map.values(): + if oci_value and oci_value in optional_params and oci_value not in selected_params: + selected_params[oci_value] = optional_params[oci_value] # type: ignore[index] + if "tools" in selected_params: - selected_params["tools"] = adapt_tool_definition_to_oci_standard( - selected_params["tools"], vendor - ) + if vendor == OCIVendors.COHERE: + selected_params["tools"] = self.adapt_tool_definitions_to_cohere_standard( # type: ignore[assignment] + selected_params["tools"] # type: ignore[arg-type] + ) + else: + selected_params["tools"] = adapt_tool_definition_to_oci_standard( # type: ignore[assignment] + selected_params["tools"], vendor # type: ignore[arg-type] + ) return selected_params + def adapt_messages_to_cohere_standard(self, messages: List[AllMessageValues]) -> List[CohereMessage]: + """Build chat history for Cohere models.""" + chat_history = [] + for msg in messages[:-1]: # All messages except the last one + role = msg.get("role") + content = msg.get("content") + + if isinstance(content, list): + # Extract text from content array + text_content = "" + for content_item in content: + if isinstance(content_item, dict) and content_item.get("type") == "text": + text_content += content_item.get("text", "") + content = text_content + + # Ensure content is a string + if not isinstance(content, str): + content = str(content) if content is not None else "" + + # Handle tool calls + tool_calls: Optional[List[CohereToolCall]] = None + if role == "assistant" and "tool_calls" in msg and msg.get("tool_calls"): # type: ignore[union-attr,typeddict-item] + tool_calls = [] + for tool_call in msg["tool_calls"]: # type: ignore[union-attr,typeddict-item] + # Parse arguments if they're a JSON string + raw_arguments: Any = tool_call.get("function", {}).get("arguments", {}) + if isinstance(raw_arguments, str): + try: + arguments: Dict[str, Any] = json.loads(raw_arguments) + except json.JSONDecodeError: + arguments = {} + else: + arguments = raw_arguments + + tool_calls.append(CohereToolCall( + name=str(tool_call.get("function", {}).get("name", "")), + parameters=arguments + )) + + if role == "user": + chat_history.append(CohereMessage(role="USER", message=content)) + elif role == "assistant": + chat_history.append(CohereMessage(role="CHATBOT", message=content, toolCalls=tool_calls)) + elif role == "tool": + # Tool messages need special handling + chat_history.append(CohereMessage( + role="TOOL", + message=content, + toolCalls=None # Tool messages don't have tool calls + )) + + return chat_history + + def adapt_tool_definitions_to_cohere_standard(self, tools: List[Dict[str, Any]]) -> List[CohereTool]: + """Adapt tool definitions to Cohere format.""" + cohere_tools = [] + for tool in tools: + function_def = tool.get("function", {}) + parameters = function_def.get("parameters", {}).get("properties", {}) + required = function_def.get("parameters", {}).get("required", []) + + parameter_definitions = {} + for param_name, param_schema in parameters.items(): + parameter_definitions[param_name] = CohereParameterDefinition( + description=param_schema.get("description", ""), + type=param_schema.get("type", "string"), + isRequired=param_name in required + ) + + cohere_tools.append(CohereTool( + name=function_def.get("name", ""), + description=function_def.get("description", ""), + parameterDefinitions=parameter_definitions + )) + + return cohere_tools + + def _extract_text_content(self, content: Any) -> str: + """Extract text content from message content.""" + if isinstance(content, str): + return content + elif isinstance(content, list): + text_content = "" + for content_item in content: + if isinstance(content_item, dict) and content_item.get("type") == "text": + text_content += content_item.get("text", "") + return text_content + return str(content) + def transform_request( self, model: str, @@ -445,28 +562,47 @@ class OCIChatConfig(BaseConfig): vendor = get_vendor_from_model(model) - if vendor == OCIVendors.COHERE: + oci_serving_mode = optional_params.get("oci_serving_mode", "ON_DEMAND") + if oci_serving_mode not in ["ON_DEMAND", "DEDICATED"]: raise Exception( - "Cohere models are not yet supported in the litellm OCI chat completion endpoint. Use the Cohere API directly." + "kwarg `oci_serving_mode` must be either 'ON_DEMAND' or 'DEDICATED'" + ) + + if oci_serving_mode == "DEDICATED": + servingMode = OCIServingMode( + servingType="DEDICATED", + endpointId=model, ) else: - oci_serving_mode = optional_params.get("oci_serving_mode", "ON_DEMAND") - if oci_serving_mode not in ["ON_DEMAND", "DEDICATED"]: - raise Exception( - "kwarg `oci_serving_mode` must be either 'ON_DEMAND' or 'DEDICATED'" - ) + servingMode = OCIServingMode( + servingType="ON_DEMAND", + modelId=model, + ) - if oci_serving_mode == "DEDICATED": - servingMode = OCIServingMode( - servingType="DEDICATED", - endpointId=model, - ) - else: - servingMode = OCIServingMode( - servingType="ON_DEMAND", - modelId=model, - ) + # Build request based on vendor type + if vendor == OCIVendors.COHERE: + # For Cohere, we need to use the specific Cohere format + # Extract the last user message as the main message + user_messages = [msg for msg in messages if msg.get("role") == "user"] + if not user_messages: + raise Exception("No user message found for Cohere model") + + # Create Cohere-specific chat request + chat_request = CohereChatRequest( + apiFormat="COHERE", + message=self._extract_text_content(user_messages[-1]["content"]), + chatHistory=self.adapt_messages_to_cohere_standard(messages), + **self._get_optional_params(OCIVendors.COHERE, optional_params) + ) + + data = OCICompletionPayload( + compartmentId=oci_compartment_id, + servingMode=servingMode, + chatRequest=chat_request + ) + else: + # Use generic format for other vendors data = OCICompletionPayload( compartmentId=oci_compartment_id, servingMode=servingMode, @@ -479,6 +615,111 @@ class OCIChatConfig(BaseConfig): return data.model_dump(exclude_none=True) + def _handle_cohere_response( + self, + json_response: dict, + model: str, + model_response: ModelResponse + ) -> ModelResponse: + """Handle Cohere-specific response format.""" + cohere_response = CohereChatResult(**json_response) + # Cohere response format (uses camelCase) + model_id = model + + # Set basic response info + model_response.model = model_id + model_response.created = int(datetime.datetime.now().timestamp()) + + # Extract the response text + response_text = cohere_response.chatResponse.text + oci_finish_reason = cohere_response.chatResponse.finishReason + + # Map finish reason + if oci_finish_reason == "COMPLETE": + finish_reason = "stop" + elif oci_finish_reason == "MAX_TOKENS": + finish_reason = "length" + else: + finish_reason = "stop" + + # Handle tool calls + tool_calls: Optional[List[Dict[str, Any]]] = None + if cohere_response.chatResponse.toolCalls: + tool_calls = [] + for tool_call in cohere_response.chatResponse.toolCalls: + tool_calls.append({ + "id": f"call_{len(tool_calls)}", # Generate a simple ID + "type": "function", + "function": { + "name": tool_call.name, + "arguments": json.dumps(tool_call.parameters) + } + }) + + # Create choice + from litellm.types.utils import Choices + choice = Choices( + index=0, + message={ + "role": "assistant", + "content": response_text, + "tool_calls": tool_calls + }, + finish_reason=finish_reason + ) + model_response.choices = [choice] + + # Extract usage info + usage_info = cohere_response.chatResponse.usage + from litellm.types.utils import Usage + model_response.usage = Usage( # type: ignore[attr-defined] + prompt_tokens=usage_info.promptTokens, # type: ignore[union-attr] + completion_tokens=usage_info.completionTokens, # type: ignore[union-attr] + total_tokens=usage_info.totalTokens # type: ignore[union-attr] + ) + + return model_response + + def _handle_generic_response( + self, + json: dict, + model: str, + model_response: ModelResponse, + raw_response: httpx.Response + ) -> ModelResponse: + """Handle generic OCI response format.""" + try: + completion_response = OCICompletionResponse(**json) + except TypeError as e: + raise OCIError( + message=f"Response cannot be casted to OCICompletionResponse: {str(e)}", + status_code=raw_response.status_code, + ) + + iso_str = completion_response.chatResponse.timeCreated + dt = datetime.datetime.fromisoformat(iso_str.replace("Z", "+00:00")) + model_response.created = int(dt.timestamp()) + + model_response.model = completion_response.modelId + + message = model_response.choices[0].message # type: ignore + response_message = completion_response.chatResponse.choices[0].message + if response_message.content and response_message.content[0].type == "TEXT": + message.content = response_message.content[0].text + if response_message.toolCalls: + message.tool_calls = adapt_tools_to_openai_standard( + response_message.toolCalls + ) + + usage = Usage( + prompt_tokens=completion_response.chatResponse.usage.promptTokens, + completion_tokens=completion_response.chatResponse.usage.completionTokens, + total_tokens=completion_response.chatResponse.usage.totalTokens, + ) + model_response.usage = usage # type: ignore + + return model_response + def transform_response( self, model: str, @@ -509,46 +750,13 @@ class OCIChatConfig(BaseConfig): status_code=raw_response.status_code, ) - try: - completion_response = OCICompletionResponse(**json) - except TypeError as e: - raise OCIError( - message=f"Response cannot be casted to OCICompletionResponse: {str(e)}", - status_code=raw_response.status_code, - ) - vendor = get_vendor_from_model(model) + + # Handle response based on vendor type if vendor == OCIVendors.COHERE: - raise ValueError( - "Cohere models are not yet supported in the litellm OCI chat completion endpoint. Use the Cohere API directly." - ) + model_response = self._handle_cohere_response(json, model, model_response) else: - iso_str = completion_response.chatResponse.timeCreated - dt = datetime.datetime.fromisoformat(iso_str.replace("Z", "+00:00")) - model_response.created = int(dt.timestamp()) - - model_response.model = completion_response.modelId - - message = model_response.choices[0].message # type: ignore - if vendor == OCIVendors.COHERE: - raise ValueError( - "Cohere models are not yet supported in the litellm OCI chat completion endpoint. Use the Cohere API directly." - ) - else: - response_message = completion_response.chatResponse.choices[0].message - if response_message.content and response_message.content[0].type == "TEXT": - message.content = response_message.content[0].text - if response_message.toolCalls: - message.tool_calls = adapt_tools_to_openai_standard( - response_message.toolCalls - ) - - usage = Usage( - prompt_tokens=completion_response.chatResponse.usage.promptTokens, - completion_tokens=completion_response.chatResponse.usage.completionTokens, - total_tokens=completion_response.chatResponse.usage.totalTokens, - ) - model_response.usage = usage # type: ignore + model_response = self._handle_generic_response(json, model, model_response, raw_response) model_response._hidden_params["additional_headers"] = raw_response.headers @@ -818,26 +1026,21 @@ def adapt_messages_to_generic_oci_standard( def adapt_tool_definition_to_oci_standard(tools: List[Dict], vendor: OCIVendors): new_tools = [] - if vendor == OCIVendors.COHERE: - raise ValueError( - "Cohere models are not yet supported in the litellm OCI chat completion endpoint. Use the Cohere API directly." + for tool in tools: + if tool["type"] != "function": + raise Exception("OCI only supports function tools") + + tool_function = tool.get("function") + if not isinstance(tool_function, dict): + raise Exception("Prop `function` is not a dictionary") + + new_tool = OCIToolDefinition( + type="FUNCTION", + name=tool_function.get("name"), + description=tool_function.get("description", ""), + parameters=tool_function.get("parameters", {}), ) - else: - for tool in tools: - if tool["type"] != "function": - raise Exception("OCI only supports function tools") - - tool_function = tool.get("function") - if not isinstance(tool_function, dict): - raise Exception("Prop `function` is not a dictionary") - - new_tool = OCIToolDefinition( - type="FUNCTION", - name=tool_function.get("name"), - description=tool_function.get("description", ""), - parameters=tool_function.get("parameters", {}), - ) - new_tools.append(new_tool) + new_tools.append(new_tool) return new_tools @@ -877,6 +1080,58 @@ class OCIStreamWrapper(CustomStreamWrapper): if not chunk.startswith("data:"): raise ValueError(f"Chunk does not start with 'data:': {chunk}") dict_chunk = json.loads(chunk[5:]) # Remove 'data: ' prefix and parse JSON + + # Check if this is a Cohere stream chunk + if "apiFormat" in dict_chunk and dict_chunk.get("apiFormat") == "COHERE": + return self._handle_cohere_stream_chunk(dict_chunk) + else: + return self._handle_generic_stream_chunk(dict_chunk) + + def _handle_cohere_stream_chunk(self, dict_chunk: dict): + """Handle Cohere-specific streaming chunks.""" + try: + typed_chunk = CohereStreamChunk(**dict_chunk) + except TypeError as e: + raise ValueError(f"Chunk cannot be casted to CohereStreamChunk: {str(e)}") + + if typed_chunk.index is None: + typed_chunk.index = 0 + + # Extract text content + text = typed_chunk.text or "" + + # Map finish reason to standard format + finish_reason = typed_chunk.finishReason + if finish_reason == "COMPLETE": + finish_reason = "stop" + elif finish_reason == "MAX_TOKENS": + finish_reason = "length" + elif finish_reason is None: + finish_reason = None + else: + finish_reason = "stop" + + # For Cohere, we don't have tool calls in the streaming format + tool_calls = None + + return ModelResponseStream( + choices=[ + StreamingChoices( + index=typed_chunk.index if typed_chunk.index else 0, + delta=Delta( + content=text, + tool_calls=tool_calls, + provider_specific_fields=None, + thinking_blocks=None, + reasoning_content=None, + ), + finish_reason=finish_reason, + ) + ] + ) + + def _handle_generic_stream_chunk(self, dict_chunk: dict): + """Handle generic OCI streaming chunks.""" try: typed_chunk = OCIStreamChunk(**dict_chunk) except TypeError as e: diff --git a/litellm/llms/openai/chat/gpt_5_transformation.py b/litellm/llms/openai/chat/gpt_5_transformation.py index fa357c1bd22..183f60debbd 100644 --- a/litellm/llms/openai/chat/gpt_5_transformation.py +++ b/litellm/llms/openai/chat/gpt_5_transformation.py @@ -41,6 +41,7 @@ class OpenAIGPT5Config(OpenAIGPTConfig): "presence_penalty", "frequency_penalty", "top_logprobs", + "stop", ] return [ diff --git a/litellm/llms/openai/chat/gpt_transformation.py b/litellm/llms/openai/chat/gpt_transformation.py index 204916e3a48..3e18617905c 100644 --- a/litellm/llms/openai/chat/gpt_transformation.py +++ b/litellm/llms/openai/chat/gpt_transformation.py @@ -397,13 +397,13 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig): ) from litellm.types.llms.openai import ChatCompletionToolParam - for message in messages: - message = cast( + for i, message in enumerate(messages): + messages[i] = cast( AllMessageValues, filter_value_from_dict(message, "cache_control") # type: ignore ) if tools is not None: - for tool in tools: - tool = cast( + for i, tool in enumerate(tools): + tools[i] = cast( ChatCompletionToolParam, filter_value_from_dict(tool, "cache_control"), # type: ignore ) diff --git a/litellm/llms/openai/common_utils.py b/litellm/llms/openai/common_utils.py index 00197a67e95..ce470f04aca 100644 --- a/litellm/llms/openai/common_utils.py +++ b/litellm/llms/openai/common_utils.py @@ -207,7 +207,6 @@ class BaseOpenAILLM: ssl_config = get_ssl_configuration() return httpx.AsyncClient( - limits=httpx.Limits(max_connections=1000, max_keepalive_connections=100), verify=ssl_config, transport=AsyncHTTPHandler._create_async_transport( ssl_context=ssl_config @@ -228,7 +227,6 @@ class BaseOpenAILLM: ssl_config = get_ssl_configuration() return httpx.Client( - limits=httpx.Limits(max_connections=1000, max_keepalive_connections=100), verify=ssl_config, follow_redirects=True, ) diff --git a/litellm/llms/openai/openai.py b/litellm/llms/openai/openai.py index 3347e533242..324205237dc 100644 --- a/litellm/llms/openai/openai.py +++ b/litellm/llms/openai/openai.py @@ -1125,6 +1125,7 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): api_base: Optional[str] = None, client: Optional[AsyncOpenAI] = None, max_retries=None, + shared_session: Optional["ClientSession"] = None, ): try: openai_aclient: AsyncOpenAI = self._get_openai_client( # type: ignore @@ -1134,6 +1135,7 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): timeout=timeout, max_retries=max_retries, client=client, + shared_session=shared_session, ) headers, response = await self.make_openai_embedding_request( openai_aclient=openai_aclient, @@ -1197,6 +1199,7 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): client=None, aembedding=None, max_retries: Optional[int] = None, + shared_session: Optional["ClientSession"] = None, ) -> EmbeddingResponse: super().embedding() try: @@ -1223,6 +1226,7 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): timeout=timeout, client=client, max_retries=max_retries, + shared_session=shared_session, ) openai_client: OpenAI = self._get_openai_client( # type: ignore diff --git a/litellm/llms/openai/responses/transformation.py b/litellm/llms/openai/responses/transformation.py index 25078267571..4c82a35c119 100644 --- a/litellm/llms/openai/responses/transformation.py +++ b/litellm/llms/openai/responses/transformation.py @@ -1,12 +1,4 @@ -from typing import ( - TYPE_CHECKING, - Any, - Dict, - Optional, - Union, - cast, - get_type_hints, -) +from typing import TYPE_CHECKING, Any, Dict, Optional, Union, cast, get_type_hints import httpx from openai.types.responses import ResponseReasoningItem @@ -127,13 +119,12 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): 2. Create a ResponseReasoningItem object with the item data 3. Convert it back to dict with exclude_none=True to filter None values """ - verbose_logger.debug(f"Handling reasoning item: {item}") if item.get("type") == "reasoning": try: # Ensure required fields are present for ResponseReasoningItem item_data = dict(item) if "id" not in item_data: - item_data["id"] = f"reasoning_{hash(str(item_data))}" + item_data["id"] = f"rs_{hash(str(item_data))}" if "summary" not in item_data: item_data["summary"] = ( item_data.get("reasoning_content", "")[:100] + "..." @@ -178,7 +169,7 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): raise OpenAIError( message=raw_response.text, status_code=raw_response.status_code ) - return ResponsesAPIResponse(**raw_response_json) + return ResponsesAPIResponse.model_construct(**raw_response_json) def validate_environment( self, headers: dict, model: str, litellm_params: Optional[GenericLiteLLMParams] @@ -280,6 +271,7 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): ResponsesAPIStreamEvents.MCP_CALL_ARGUMENTS_DONE: MCPCallArgumentsDoneEvent, ResponsesAPIStreamEvents.MCP_CALL_COMPLETED: MCPCallCompletedEvent, ResponsesAPIStreamEvents.MCP_CALL_FAILED: MCPCallFailedEvent, + ResponsesAPIStreamEvents.IMAGE_GENERATION_PARTIAL_IMAGE: ImageGenerationPartialImageEvent, ResponsesAPIStreamEvents.ERROR: ErrorEvent, } diff --git a/litellm/llms/openrouter/chat/transformation.py b/litellm/llms/openrouter/chat/transformation.py index bf57218c91d..d5861c79b2d 100644 --- a/litellm/llms/openrouter/chat/transformation.py +++ b/litellm/llms/openrouter/chat/transformation.py @@ -6,7 +6,8 @@ Calls done in OpenAI/openai.py as OpenRouter is openai-compatible. Docs: https://openrouter.ai/docs/parameters """ -from typing import Any, AsyncIterator, Iterator, List, Optional, Tuple, Union +from enum import Enum +from typing import Any, AsyncIterator, Iterator, List, Optional, Tuple, Union, cast import httpx @@ -20,6 +21,12 @@ from ...openai.chat.gpt_transformation import OpenAIGPTConfig from ..common_utils import OpenRouterException +class CacheControlSupportedModels(str, Enum): + """Models that support cache_control in content blocks.""" + CLAUDE = "claude" + GEMINI = "gemini" + + class OpenrouterConfig(OpenAIGPTConfig): def map_openai_params( self, @@ -48,19 +55,76 @@ class OpenrouterConfig(OpenAIGPTConfig): ) return mapped_openai_params + def _supports_cache_control_in_content(self, model: str) -> bool: + """ + Check if the model supports cache_control in content blocks. + + Returns: + bool: True if model supports cache_control (Claude or Gemini models) + """ + model_lower = model.lower() + return any( + supported_model.value in model_lower + for supported_model in CacheControlSupportedModels + ) + def remove_cache_control_flag_from_messages_and_tools( self, model: str, messages: List[AllMessageValues], tools: Optional[List["ChatCompletionToolParam"]] = None, ) -> Tuple[List[AllMessageValues], Optional[List["ChatCompletionToolParam"]]]: - if "claude" in model.lower(): # don't remove 'cache_control' flag + if self._supports_cache_control_in_content(model): return messages, tools else: return super().remove_cache_control_flag_from_messages_and_tools( model, messages, tools ) + def _move_cache_control_to_content( + self, messages: List[AllMessageValues] + ) -> List[AllMessageValues]: + """ + Move cache_control from message level to content blocks. + OpenRouter requires cache_control to be inside content blocks, not at message level. + + To avoid exceeding Anthropic's limit of 4 cache breakpoints, cache_control is only + added to the LAST content block in each message. + """ + transformed_messages: List[AllMessageValues] = [] + for message in messages: + message_dict = dict(message) + cache_control = message_dict.pop("cache_control", None) + + if cache_control is not None: + content = message_dict.get("content") + + if isinstance(content, list): + # Content is already a list, add cache_control only to the last block + if len(content) > 0: + content_copy = [] + for i, block in enumerate(content): + block_dict = dict(block) + # Only add cache_control to the last content block + if i == len(content) - 1: + block_dict["cache_control"] = cache_control + content_copy.append(block_dict) + message_dict["content"] = content_copy + else: + # Content is a string, convert to structured format + message_dict["content"] = [ + { + "type": "text", + "text": content, + "cache_control": cache_control, + } + ] + + # Cast back to AllMessageValues after modification + transformed_messages.append(cast(AllMessageValues, message_dict)) + + return transformed_messages + def transform_request( self, model: str, @@ -75,6 +139,9 @@ class OpenrouterConfig(OpenAIGPTConfig): Returns: dict: The transformed request. Sent as the body of the API call. """ + if self._supports_cache_control_in_content(model): + messages = self._move_cache_control_to_content(messages) + extra_body = optional_params.pop("extra_body", {}) response = super().transform_request( model, messages, optional_params, litellm_params, headers diff --git a/litellm/llms/snowflake/chat/transformation.py b/litellm/llms/snowflake/chat/transformation.py index 2b92911b055..4c0258d9f4b 100644 --- a/litellm/llms/snowflake/chat/transformation.py +++ b/litellm/llms/snowflake/chat/transformation.py @@ -1,14 +1,15 @@ """ -Support for Snowflake REST API +Support for Snowflake REST API """ -from typing import TYPE_CHECKING, Any, List, Optional, Tuple +import json +from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union import httpx from litellm.secret_managers.main import get_secret_str from litellm.types.llms.openai import AllMessageValues -from litellm.types.utils import ModelResponse +from litellm.types.utils import ChatCompletionMessageToolCall, Function, ModelResponse from ...openai_like.chat.transformation import OpenAIGPTConfig @@ -22,15 +23,25 @@ else: class SnowflakeConfig(OpenAIGPTConfig): """ - source: https://docs.snowflake.com/en/sql-reference/functions/complete-snowflake-cortex + Reference: https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-llm-rest-api + + Snowflake Cortex LLM REST API supports function calling with specific models (e.g., Claude 3.5 Sonnet). + This config handles transformation between OpenAI format and Snowflake's tool_spec format. """ @classmethod def get_config(cls): return super().get_config() - def get_supported_openai_params(self, model: str) -> List: - return ["temperature", "max_tokens", "top_p", "response_format"] + def get_supported_openai_params(self, model: str) -> List[str]: + return [ + "temperature", + "max_tokens", + "top_p", + "response_format", + "tools", + "tool_choice", + ] def map_openai_params( self, @@ -56,6 +67,57 @@ class SnowflakeConfig(OpenAIGPTConfig): optional_params[param] = value return optional_params + def _transform_tool_calls_from_snowflake_to_openai( + self, content_list: List[Dict[str, Any]] + ) -> Tuple[str, Optional[List[ChatCompletionMessageToolCall]]]: + """ + Transform Snowflake tool calls to OpenAI format. + + Args: + content_list: Snowflake's content_list array containing text and tool_use items + + Returns: + Tuple of (text_content, tool_calls) + + Snowflake format in content_list: + { + "type": "tool_use", + "tool_use": { + "tool_use_id": "tooluse_...", + "name": "get_weather", + "input": {"location": "Paris"} + } + } + + OpenAI format (returned tool_calls): + ChatCompletionMessageToolCall( + id="tooluse_...", + type="function", + function=Function(name="get_weather", arguments='{"location": "Paris"}') + ) + """ + text_content = "" + tool_calls: List[ChatCompletionMessageToolCall] = [] + + for idx, content_item in enumerate(content_list): + if content_item.get("type") == "text": + text_content += content_item.get("text", "") + + ## TOOL CALLING + elif content_item.get("type") == "tool_use": + tool_use_data = content_item.get("tool_use", {}) + tool_call = ChatCompletionMessageToolCall( + id=tool_use_data.get("tool_use_id", ""), + type="function", + function=Function( + name=tool_use_data.get("name", ""), + arguments=json.dumps(tool_use_data.get("input", {})), + ), + ) + tool_calls.append(tool_call) + + return text_content, tool_calls if tool_calls else None + def transform_response( self, model: str, @@ -71,6 +133,7 @@ class SnowflakeConfig(OpenAIGPTConfig): json_mode: Optional[bool] = None, ) -> ModelResponse: response_json = raw_response.json() + logging_obj.post_call( input=messages, api_key="", @@ -78,6 +141,26 @@ class SnowflakeConfig(OpenAIGPTConfig): additional_args={"complete_input_dict": request_data}, ) + ## RESPONSE TRANSFORMATION + # Snowflake returns content_list (not content) with tool_use objects + # We need to transform this to OpenAI's format with content + tool_calls + if "choices" in response_json and len(response_json["choices"]) > 0: + choice = response_json["choices"][0] + if "message" in choice and "content_list" in choice["message"]: + content_list = choice["message"]["content_list"] + ( + text_content, + tool_calls, + ) = self._transform_tool_calls_from_snowflake_to_openai(content_list) + + # Update the choice message with OpenAI format + choice["message"]["content"] = text_content + if tool_calls: + choice["message"]["tool_calls"] = tool_calls + + # Remove Snowflake-specific content_list + del choice["message"]["content_list"] + returned_response = ModelResponse(**response_json) returned_response.model = "snowflake/" + (returned_response.model or "") @@ -150,6 +233,95 @@ class SnowflakeConfig(OpenAIGPTConfig): return api_base + def _transform_tools(self, tools: List[Dict[str, Any]]) -> List[Dict[str, Any]]: + """ + Transform OpenAI tool format to Snowflake tool format. + + Args: + tools: List of tools in OpenAI format + + Returns: + List of tools in Snowflake format + + OpenAI format: + { + "type": "function", + "function": { + "name": "get_weather", + "description": "...", + "parameters": {...} + } + } + + Snowflake format: + { + "tool_spec": { + "type": "generic", + "name": "get_weather", + "description": "...", + "input_schema": {...} + } + } + """ + snowflake_tools: List[Dict[str, Any]] = [] + for tool in tools: + if tool.get("type") == "function": + function = tool.get("function", {}) + snowflake_tool: Dict[str, Any] = { + "tool_spec": { + "type": "generic", + "name": function.get("name"), + "input_schema": function.get( + "parameters", + {"type": "object", "properties": {}}, + ), + } + } + # Add description if present + if "description" in function: + snowflake_tool["tool_spec"]["description"] = function[ + "description" + ] + + snowflake_tools.append(snowflake_tool) + + return snowflake_tools + + def _transform_tool_choice( + self, tool_choice: Union[str, Dict[str, Any]] + ) -> Union[str, Dict[str, Any]]: + """ + Transform OpenAI tool_choice format to Snowflake format. + + Args: + tool_choice: Tool choice in OpenAI format (str or dict) + + Returns: + Tool choice in Snowflake format + + OpenAI format: + {"type": "function", "function": {"name": "get_weather"}} + + Snowflake format: + {"type": "tool", "name": ["get_weather"]} + + Note: String values ("auto", "required", "none") pass through unchanged. + """ + if isinstance(tool_choice, str): + # "auto", "required", "none" pass through as-is + return tool_choice + + if isinstance(tool_choice, dict): + if tool_choice.get("type") == "function": + function_name = tool_choice.get("function", {}).get("name") + if function_name: + return { + "type": "tool", + "name": [function_name], # Snowflake expects array + } + + return tool_choice + def transform_request( self, model: str, @@ -160,6 +332,18 @@ class SnowflakeConfig(OpenAIGPTConfig): ) -> dict: stream: bool = optional_params.pop("stream", None) or False extra_body = optional_params.pop("extra_body", {}) + + ## TOOL CALLING + # Transform tools from OpenAI format to Snowflake's tool_spec format + tools = optional_params.pop("tools", None) + if tools: + optional_params["tools"] = self._transform_tools(tools) + + # Transform tool_choice from OpenAI format to Snowflake's tool name array format + tool_choice = optional_params.pop("tool_choice", None) + if tool_choice: + optional_params["tool_choice"] = self._transform_tool_choice(tool_choice) + return { "model": model, "messages": messages, diff --git a/litellm/llms/vertex_ai/common_utils.py b/litellm/llms/vertex_ai/common_utils.py index 8588c3efa27..3e650ecd111 100644 --- a/litellm/llms/vertex_ai/common_utils.py +++ b/litellm/llms/vertex_ai/common_utils.py @@ -1,4 +1,5 @@ import re +from enum import Enum from typing import Any, Dict, List, Literal, Optional, Set, Tuple, Union, get_type_hints import httpx @@ -24,6 +25,68 @@ class VertexAIError(BaseLLMException): super().__init__(message=message, status_code=status_code, headers=headers) +class VertexAIModelRoute(str, Enum): + """Enum for Vertex AI model routing""" + PARTNER_MODELS = "partner_models" + GEMINI = "gemini" + GEMMA = "gemma" + MODEL_GARDEN = "model_garden" + NON_GEMINI = "non_gemini" + + +def get_vertex_ai_model_route(model: str, litellm_params: Optional[dict] = None) -> VertexAIModelRoute: + """ + Determine which handler to use for a Vertex AI model based on the model name. + + Args: + model: The model name (e.g., "llama3-405b", "gemini-pro", "gemma/gemma-3-12b-it", "openai/gpt-oss-120b") + litellm_params: Optional litellm parameters dict that may contain base_model for routing + + Returns: + VertexAIModelRoute: The route enum indicating which handler should be used + + Examples: + >>> get_vertex_ai_model_route("llama3-405b") + VertexAIModelRoute.PARTNER_MODELS + + >>> get_vertex_ai_model_route("gemini-pro") + VertexAIModelRoute.GEMINI + + >>> get_vertex_ai_model_route("gemma/gemma-3-12b-it") + VertexAIModelRoute.GEMMA + + >>> get_vertex_ai_model_route("openai/gpt-oss-120b") + VertexAIModelRoute.MODEL_GARDEN + """ + from litellm.llms.vertex_ai.vertex_ai_partner_models.main import ( + VertexAIPartnerModels, + ) + + # Check base_model in litellm_params for gemini override + if litellm_params and litellm_params.get("base_model") is not None: + if "gemini" in litellm_params["base_model"]: + return VertexAIModelRoute.GEMINI + + # Check for partner models (llama, mistral, claude, etc.) + if VertexAIPartnerModels.is_vertex_partner_model(model=model): + return VertexAIModelRoute.PARTNER_MODELS + + # Check for gemma models + if "gemma/" in model: + return VertexAIModelRoute.GEMMA + + # Check for model garden openai models + if "openai" in model: + return VertexAIModelRoute.MODEL_GARDEN + + # Check for gemini models + if "gemini" in model: + return VertexAIModelRoute.GEMINI + + # Default to non-gemini (legacy vertex models like chat-bison, text-bison, etc.) + return VertexAIModelRoute.NON_GEMINI + + def get_supports_system_message( model: str, custom_llm_provider: Literal["vertex_ai", "vertex_ai_beta", "gemini"] ) -> bool: diff --git a/litellm/llms/vertex_ai/context_caching/transformation.py b/litellm/llms/vertex_ai/context_caching/transformation.py index f3ca699546f..bb40b7665c1 100644 --- a/litellm/llms/vertex_ai/context_caching/transformation.py +++ b/litellm/llms/vertex_ai/context_caching/transformation.py @@ -5,7 +5,7 @@ Why separate file? Make it easy to see how transformation works """ import re -from typing import List, Optional, Tuple +from typing import List, Optional, Tuple, Literal from litellm.types.llms.openai import AllMessageValues from litellm.types.llms.vertex_ai import CachedContentRequestBody @@ -155,13 +155,18 @@ def separate_cached_messages( def transform_openai_messages_to_gemini_context_caching( - model: str, messages: List[AllMessageValues], cache_key: str + model: str, + messages: List[AllMessageValues], + custom_llm_provider: Literal["vertex_ai", "vertex_ai_beta", "gemini"], + cache_key: str, + vertex_project: Optional[str], + vertex_location: Optional[str], ) -> CachedContentRequestBody: # Extract TTL from cached messages BEFORE system message transformation ttl = extract_ttl_from_cached_messages(messages) supports_system_message = get_supports_system_message( - model=model, custom_llm_provider="gemini" + model=model, custom_llm_provider=custom_llm_provider ) transformed_system_messages, new_messages = _transform_system_message( @@ -170,9 +175,14 @@ def transform_openai_messages_to_gemini_context_caching( transformed_messages = _gemini_convert_messages_with_history(messages=new_messages) + model_name = "models/{}".format(model) + + if custom_llm_provider == "vertex_ai" or custom_llm_provider == "vertex_ai_beta": + model_name = f"projects/{vertex_project}/locations/{vertex_location}/publishers/google/{model_name}" + data = CachedContentRequestBody( contents=transformed_messages, - model="models/{}".format(model), + model=model_name, displayName=cache_key, ) diff --git a/litellm/llms/vertex_ai/context_caching/vertex_ai_context_caching.py b/litellm/llms/vertex_ai/context_caching/vertex_ai_context_caching.py index 33a480aa6bb..70b068b5a4d 100644 --- a/litellm/llms/vertex_ai/context_caching/vertex_ai_context_caching.py +++ b/litellm/llms/vertex_ai/context_caching/vertex_ai_context_caching.py @@ -41,8 +41,11 @@ class ContextCachingEndpoints(VertexBase): def _get_token_and_url_context_caching( self, gemini_api_key: Optional[str], - custom_llm_provider: Literal["gemini"], + custom_llm_provider: Literal["vertex_ai", "vertex_ai_beta", "gemini"], api_base: Optional[str], + vertex_project: Optional[str], + vertex_location: Optional[str], + vertex_auth_header: Optional[str], ) -> Tuple[Optional[str], str]: """ Internal function. Returns the token and url for the call. @@ -58,9 +61,15 @@ class ContextCachingEndpoints(VertexBase): url = "https://generativelanguage.googleapis.com/v1beta/{}?key={}".format( endpoint, gemini_api_key ) - + elif custom_llm_provider == "vertex_ai": + auth_header = vertex_auth_header + endpoint = "cachedContents" + url = f"https://{vertex_location}-aiplatform.googleapis.com/v1/projects/{vertex_project}/locations/{vertex_location}/{endpoint}" else: - raise NotImplementedError + auth_header = vertex_auth_header + endpoint = "cachedContents" + url = f"https://{vertex_location}-aiplatform.googleapis.com/v1beta1/projects/{vertex_project}/locations/{vertex_location}/{endpoint}" + return self._check_custom_proxy( api_base=api_base, @@ -80,6 +89,10 @@ class ContextCachingEndpoints(VertexBase): api_key: str, api_base: Optional[str], logging_obj: Logging, + custom_llm_provider: Literal["vertex_ai", "vertex_ai_beta", "gemini"], + vertex_project: Optional[str], + vertex_location: Optional[str], + vertex_auth_header: Optional[str], ) -> Optional[str]: """ Checks if content already cached. @@ -94,8 +107,11 @@ class ContextCachingEndpoints(VertexBase): _, url = self._get_token_and_url_context_caching( gemini_api_key=api_key, - custom_llm_provider="gemini", + custom_llm_provider=custom_llm_provider, api_base=api_base, + vertex_project=vertex_project, + vertex_location=vertex_location, + vertex_auth_header=vertex_auth_header ) try: ## LOGGING @@ -145,6 +161,10 @@ class ContextCachingEndpoints(VertexBase): api_key: str, api_base: Optional[str], logging_obj: Logging, + custom_llm_provider: Literal["vertex_ai", "vertex_ai_beta", "gemini"], + vertex_project: Optional[str], + vertex_location: Optional[str], + vertex_auth_header: Optional[str] ) -> Optional[str]: """ Checks if content already cached. @@ -159,8 +179,11 @@ class ContextCachingEndpoints(VertexBase): _, url = self._get_token_and_url_context_caching( gemini_api_key=api_key, - custom_llm_provider="gemini", + custom_llm_provider=custom_llm_provider, api_base=api_base, + vertex_project=vertex_project, + vertex_location=vertex_location, + vertex_auth_header=vertex_auth_header ) try: ## LOGGING @@ -212,6 +235,10 @@ class ContextCachingEndpoints(VertexBase): client: Optional[HTTPHandler], timeout: Optional[Union[float, httpx.Timeout]], logging_obj: Logging, + custom_llm_provider: Literal["vertex_ai", "vertex_ai_beta", "gemini"], + vertex_project: Optional[str], + vertex_location: Optional[str], + vertex_auth_header: Optional[str], extra_headers: Optional[dict] = None, cached_content: Optional[str] = None, ) -> Tuple[List[AllMessageValues], dict, Optional[str]]: @@ -240,8 +267,11 @@ class ContextCachingEndpoints(VertexBase): ## AUTHORIZATION ## token, url = self._get_token_and_url_context_caching( gemini_api_key=api_key, - custom_llm_provider="gemini", + custom_llm_provider=custom_llm_provider, api_base=api_base, + vertex_project=vertex_project, + vertex_location=vertex_location, + vertex_auth_header=vertex_auth_header ) headers = { @@ -273,6 +303,10 @@ class ContextCachingEndpoints(VertexBase): api_key=api_key, api_base=api_base, logging_obj=logging_obj, + custom_llm_provider=custom_llm_provider, + vertex_project=vertex_project, + vertex_location=vertex_location, + vertex_auth_header=vertex_auth_header ) if google_cache_name: return non_cached_messages, optional_params, google_cache_name @@ -280,7 +314,12 @@ class ContextCachingEndpoints(VertexBase): ## TRANSFORM REQUEST cached_content_request_body = ( transform_openai_messages_to_gemini_context_caching( - model=model, messages=cached_messages, cache_key=generated_cache_key + model=model, + messages=cached_messages, + cache_key=generated_cache_key, + custom_llm_provider=custom_llm_provider, + vertex_project=vertex_project, + vertex_location=vertex_location, ) ) @@ -328,6 +367,10 @@ class ContextCachingEndpoints(VertexBase): client: Optional[AsyncHTTPHandler], timeout: Optional[Union[float, httpx.Timeout]], logging_obj: Logging, + custom_llm_provider: Literal["vertex_ai", "vertex_ai_beta", "gemini"], + vertex_project: Optional[str], + vertex_location: Optional[str], + vertex_auth_header: Optional[str], extra_headers: Optional[dict] = None, cached_content: Optional[str] = None, ) -> Tuple[List[AllMessageValues], dict, Optional[str]]: @@ -356,8 +399,11 @@ class ContextCachingEndpoints(VertexBase): ## AUTHORIZATION ## token, url = self._get_token_and_url_context_caching( gemini_api_key=api_key, - custom_llm_provider="gemini", + custom_llm_provider=custom_llm_provider, api_base=api_base, + vertex_project=vertex_project, + vertex_location=vertex_location, + vertex_auth_header=vertex_auth_header ) headers = { @@ -386,6 +432,10 @@ class ContextCachingEndpoints(VertexBase): api_key=api_key, api_base=api_base, logging_obj=logging_obj, + custom_llm_provider=custom_llm_provider, + vertex_project=vertex_project, + vertex_location=vertex_location, + vertex_auth_header=vertex_auth_header ) if google_cache_name: @@ -394,7 +444,12 @@ class ContextCachingEndpoints(VertexBase): ## TRANSFORM REQUEST cached_content_request_body = ( transform_openai_messages_to_gemini_context_caching( - model=model, messages=cached_messages, cache_key=generated_cache_key + model=model, + messages=cached_messages, + cache_key=generated_cache_key, + custom_llm_provider=custom_llm_provider, + vertex_project=vertex_project, + vertex_location=vertex_location, ) ) diff --git a/litellm/llms/vertex_ai/cost_calculator.py b/litellm/llms/vertex_ai/cost_calculator.py index 119ba2b0366..e98dc75915d 100644 --- a/litellm/llms/vertex_ai/cost_calculator.py +++ b/litellm/llms/vertex_ai/cost_calculator.py @@ -44,6 +44,7 @@ def cost_router( or "mistral" in model or "jamba" in model or "codestral" in model + or "gemma" in model ): return "cost_per_token" elif custom_llm_provider == "vertex_ai" and ( diff --git a/litellm/llms/vertex_ai/gemini/transformation.py b/litellm/llms/vertex_ai/gemini/transformation.py index ccaf28e5906..3d313456d19 100644 --- a/litellm/llms/vertex_ai/gemini/transformation.py +++ b/litellm/llms/vertex_ai/gemini/transformation.py @@ -514,34 +514,35 @@ def sync_transform_request_body( logging_obj: LiteLLMLoggingObj, custom_llm_provider: Literal["vertex_ai", "vertex_ai_beta", "gemini"], litellm_params: dict, + vertex_project: Optional[str], + vertex_location: Optional[str], + vertex_auth_header: Optional[str], ) -> RequestBody: from ..context_caching.vertex_ai_context_caching import ContextCachingEndpoints context_caching_endpoints = ContextCachingEndpoints() - if gemini_api_key is not None: - ( - messages, - optional_params, - cached_content, - ) = context_caching_endpoints.check_and_create_cache( - messages=messages, - optional_params=optional_params, - api_key=gemini_api_key, - api_base=api_base, - model=model, - client=client, - timeout=timeout, - extra_headers=extra_headers, - cached_content=optional_params.pop("cached_content", None), - logging_obj=logging_obj, - ) - else: # [TODO] implement context caching for gemini as well - 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") + ( + messages, + optional_params, + cached_content, + ) = context_caching_endpoints.check_and_create_cache( + messages=messages, + optional_params=optional_params, + api_key=gemini_api_key or "dummy", + api_base=api_base, + model=model, + client=client, + timeout=timeout, + extra_headers=extra_headers, + cached_content=optional_params.pop("cached_content", None), + logging_obj=logging_obj, + custom_llm_provider=custom_llm_provider, + vertex_project=vertex_project, + vertex_location=vertex_location, + vertex_auth_header=vertex_auth_header, + ) + return _transform_request_body( messages=messages, @@ -565,34 +566,34 @@ async def async_transform_request_body( logging_obj: litellm.litellm_core_utils.litellm_logging.Logging, # type: ignore custom_llm_provider: Literal["vertex_ai", "vertex_ai_beta", "gemini"], litellm_params: dict, + vertex_project: Optional[str], + vertex_location: Optional[str], + vertex_auth_header: Optional[str], ) -> RequestBody: from ..context_caching.vertex_ai_context_caching import ContextCachingEndpoints context_caching_endpoints = ContextCachingEndpoints() - if gemini_api_key is not None: - ( - messages, - optional_params, - cached_content, - ) = await context_caching_endpoints.async_check_and_create_cache( - messages=messages, - optional_params=optional_params, - api_key=gemini_api_key, - api_base=api_base, - model=model, - client=client, - timeout=timeout, - extra_headers=extra_headers, - cached_content=optional_params.pop("cached_content", None), - logging_obj=logging_obj, - ) - else: # [TODO] implement context caching for gemini as well - cached_content = 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") + ( + messages, + optional_params, + cached_content, + ) = await context_caching_endpoints.async_check_and_create_cache( + messages=messages, + optional_params=optional_params, + api_key=gemini_api_key or "dummy", + api_base=api_base, + model=model, + client=client, + timeout=timeout, + extra_headers=extra_headers, + cached_content=optional_params.pop("cached_content", None), + logging_obj=logging_obj, + custom_llm_provider=custom_llm_provider, + vertex_project=vertex_project, + vertex_location=vertex_location, + vertex_auth_header=vertex_auth_header, + ) return _transform_request_body( messages=messages, diff --git a/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py b/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py index d3f67967213..cd7ebaca790 100644 --- a/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py +++ b/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py @@ -1792,7 +1792,6 @@ class VertexLLM(VertexBase): gemini_api_key: Optional[str] = None, extra_headers: Optional[dict] = None, ) -> CustomStreamWrapper: - request_body = await async_transform_request_body(**data) # type: ignore should_use_v1beta1_features = self.is_using_v1beta1_features( optional_params=optional_params @@ -1826,6 +1825,13 @@ class VertexLLM(VertexBase): litellm_params=litellm_params, ) + request_body = await async_transform_request_body( + **data, + vertex_project=vertex_project, + vertex_location=vertex_location, + vertex_auth_header=auth_header) # type: ignore + + ## LOGGING logging_obj.pre_call( input=messages, @@ -1913,7 +1919,12 @@ class VertexLLM(VertexBase): litellm_params=litellm_params, ) - request_body = await async_transform_request_body(**data) # type: ignore + request_body = await async_transform_request_body( + **data, + vertex_project=vertex_project, + vertex_location=vertex_location, + vertex_auth_header=auth_header) # type: ignore + _async_client_params = {} if timeout: _async_client_params["timeout"] = timeout @@ -2088,7 +2099,11 @@ class VertexLLM(VertexBase): ) ## TRANSFORMATION ## - data = sync_transform_request_body(**transform_request_params) + data = sync_transform_request_body( + **transform_request_params, + vertex_project=vertex_project, + vertex_location=vertex_location, + vertex_auth_header=auth_header) ## LOGGING logging_obj.pre_call( diff --git a/litellm/llms/vertex_ai/vertex_gemma_models/__init__.py b/litellm/llms/vertex_ai/vertex_gemma_models/__init__.py new file mode 100644 index 00000000000..d06c7a5cd7a --- /dev/null +++ b/litellm/llms/vertex_ai/vertex_gemma_models/__init__.py @@ -0,0 +1,2 @@ +"""Vertex AI Gemma-AI Models Handler""" + diff --git a/litellm/llms/vertex_ai/vertex_gemma_models/main.py b/litellm/llms/vertex_ai/vertex_gemma_models/main.py new file mode 100644 index 00000000000..8203b285ebd --- /dev/null +++ b/litellm/llms/vertex_ai/vertex_gemma_models/main.py @@ -0,0 +1,145 @@ +""" +API Handler for calling Vertex AI Gemma Models + +These models use a custom prediction endpoint format that wraps messages in 'instances' +with @requestFormat: "chatCompletions" and returns responses wrapped in 'predictions'. + +Usage: + +response = litellm.completion( + model="vertex_ai/gemma/gemma-3-12b-it-1222199011122", + messages=[{"role": "user", "content": "What is machine learning?"}], + vertex_project="your-project-id", + vertex_location="us-central1", +) + +Sent to this route when `model` is in the format `vertex_ai/gemma/{MODEL_NAME}` + +The API expects a custom endpoint URL format: +https://{ENDPOINT_NUMBER}.{location}-{REGION_NUMBER}.prediction.vertexai.goog/v1/projects/{PROJECT_ID}/locations/{location}/endpoints/{ENDPOINT_ID}:predict +""" + +from typing import Callable, Optional, Union + +import httpx # type: ignore + +from litellm.utils import ModelResponse + +from ..common_utils import VertexAIError +from ..vertex_llm_base import VertexBase + + +class VertexAIGemmaModels(VertexBase): + def __init__(self) -> None: + pass + + def completion( + self, + model: str, + messages: list, + model_response: ModelResponse, + print_verbose: Callable, + encoding, + logging_obj, + api_base: Optional[str], + optional_params: dict, + custom_prompt_dict: dict, + headers: Optional[dict], + timeout: Union[float, httpx.Timeout], + litellm_params: dict, + vertex_project=None, + vertex_location=None, + vertex_credentials=None, + logger_fn=None, + acompletion: bool = False, + client=None, + ): + """ + Handles calling Vertex AI Gemma Models + + Sent to this route when `model` is in the format `vertex_ai/gemma/{MODEL_NAME}` + """ + try: + import vertexai + + from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( + VertexLLM, + ) + from litellm.llms.vertex_ai.vertex_gemma_models.transformation import ( + VertexGemmaConfig, + ) + except Exception as e: + raise VertexAIError( + status_code=400, + message=f"""vertexai import failed please run `pip install -U "google-cloud-aiplatform>=1.38"`. Got error: {e}""", + ) + + if not ( + hasattr(vertexai, "preview") or hasattr(vertexai.preview, "language_models") + ): + raise VertexAIError( + status_code=400, + message="""Upgrade vertex ai. Run `pip install "google-cloud-aiplatform>=1.38"`""", + ) + try: + model = model.replace("gemma/", "") + vertex_httpx_logic = VertexLLM() + + access_token, project_id = vertex_httpx_logic._ensure_access_token( + credentials=vertex_credentials, + project_id=vertex_project, + custom_llm_provider="vertex_ai", + ) + + gemma_transformation = VertexGemmaConfig() + + ## CONSTRUCT API BASE + stream: bool = optional_params.get("stream", False) or False + optional_params["stream"] = stream + + # If api_base is not provided, it should be set as an environment variable + # or passed explicitly because the endpoint URL is unique per deployment + if api_base is None: + raise VertexAIError( + status_code=400, + message="api_base is required for Vertex AI Gemma models. Please provide the full endpoint URL.", + ) + + # Check if we need to append :predict + if not api_base.endswith(":predict"): + _, api_base = self._check_custom_proxy( + api_base=api_base, + custom_llm_provider="vertex_ai", + gemini_api_key=None, + endpoint="predict", + stream=stream, + auth_header=None, + url=api_base, + ) + # If api_base already ends with :predict, use it as-is + + # Use the custom transformation handler for gemma models + return gemma_transformation.completion( + model=model, + messages=messages, + api_base=api_base, + api_key=access_token, + custom_prompt_dict=custom_prompt_dict, + model_response=model_response, + print_verbose=print_verbose, + logging_obj=logging_obj, + optional_params=optional_params, + acompletion=acompletion, + litellm_params=litellm_params, + logger_fn=logger_fn, + client=client, + timeout=timeout, + encoding=encoding, + custom_llm_provider="vertex_ai", + ) + + except Exception as e: + if hasattr(e, "status_code"): + raise e + raise VertexAIError(status_code=500, message=str(e)) + diff --git a/litellm/llms/vertex_ai/vertex_gemma_models/transformation.py b/litellm/llms/vertex_ai/vertex_gemma_models/transformation.py new file mode 100644 index 00000000000..24b53f0ba4f --- /dev/null +++ b/litellm/llms/vertex_ai/vertex_gemma_models/transformation.py @@ -0,0 +1,354 @@ +""" +Transformation logic for Vertex AI Gemma Models + +Handles the custom request/response format: +- Request: Wraps messages in 'instances' with @requestFormat: "chatCompletions" +- Response: Extracts data from 'predictions' wrapper + +The actual message transformation reuses OpenAIGPTConfig since Gemma uses OpenAI-compatible format. +""" + +from typing import Any, Callable, Dict, List, Optional, Union, cast + +import httpx + +from litellm.llms.base_llm.chat.transformation import BaseLLMException +from litellm.llms.openai.chat.gpt_transformation import OpenAIGPTConfig +from litellm.types.llms.openai import AllMessageValues +from litellm.types.utils import ModelResponse + + +class VertexGemmaConfig(OpenAIGPTConfig): + """ + Configuration and transformation class for Vertex AI Gemma models + + Extends OpenAIGPTConfig to wrap/unwrap the instances/predictions format + used by Vertex AI's Gemma deployment endpoint. + """ + + def __init__(self) -> None: + super().__init__() + + def should_fake_stream( + self, + model: Optional[str], + stream: Optional[bool], + custom_llm_provider: Optional[str] = None, + ) -> bool: + """ + Vertex AI Gemma models do not support streaming. + Return True to enable fake streaming on the client side. + """ + return True + + def _handle_fake_stream_response( + self, + model_response: ModelResponse, + stream: bool, + ) -> Union[ModelResponse, Any]: + """ + Helper method to return fake stream iterator if streaming is requested. + + Args: + model_response: The completed model response + stream: Whether streaming was requested + + Returns: + MockResponseIterator if stream=True, otherwise the model_response + """ + if stream: + from litellm.llms.base_llm.base_model_iterator import MockResponseIterator + return MockResponseIterator(model_response=model_response) + return model_response + + def transform_request( + self, + model: str, + messages: List[AllMessageValues], + optional_params: dict, + litellm_params: dict, + headers: dict, + ) -> dict: + """ + Transform request to Vertex Gemma format. + + Uses parent class to create OpenAI-compatible request, then wraps it + in the Vertex Gemma instances format. + """ + # Get the base OpenAI request from parent class + openai_request = super().transform_request( + model=model, + messages=messages, + optional_params=optional_params, + litellm_params=litellm_params, + headers=headers, + ) + + # Remove params not needed/supported by Vertex Gemma + openai_request.pop("model", None) + openai_request.pop("stream", None) # Streaming not supported, will be faked client-side + openai_request.pop("stream_options", None) # Stream options not supported + + # Wrap in Vertex Gemma format + return { + "instances": [ + { + "@requestFormat": "chatCompletions", + **openai_request, + } + ] + } + + def _unwrap_predictions_response( + self, + response_json: Dict[str, Any], + ) -> Dict[str, Any]: + """ + Unwrap the Vertex Gemma predictions format to OpenAI format. + + Vertex Gemma wraps the OpenAI-compatible response in a 'predictions' field. + This method extracts it so the parent class can process it normally. + """ + if "predictions" not in response_json: + raise BaseLLMException( + status_code=422, + message="Invalid response format: missing 'predictions' field", + ) + + return response_json["predictions"] + + def completion( + self, + model: str, + messages: list, + api_base: str, + api_key: str, + custom_prompt_dict: dict, + model_response: ModelResponse, + print_verbose: Callable, + logging_obj: Any, + optional_params: dict, + acompletion: bool, + litellm_params: dict, + logger_fn: Optional[Callable] = None, + client: Optional[httpx.Client] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + encoding=None, + custom_llm_provider: str = "vertex_ai", + ): + """ + Make completion request to Vertex Gemma endpoint. + Supports both sync and async requests with fake streaming. + """ + if acompletion: + return self._async_completion( + model=model, + messages=messages, + api_base=api_base, + api_key=api_key, + model_response=model_response, + print_verbose=print_verbose, + logging_obj=logging_obj, + optional_params=optional_params, + litellm_params=litellm_params, + timeout=timeout, + encoding=encoding, + ) + else: + return self._sync_completion( + model=model, + messages=messages, + api_base=api_base, + api_key=api_key, + model_response=model_response, + print_verbose=print_verbose, + logging_obj=logging_obj, + optional_params=optional_params, + litellm_params=litellm_params, + timeout=timeout, + encoding=encoding, + ) + + def _sync_completion( + self, + model: str, + messages: list, + api_base: str, + api_key: str, + model_response: ModelResponse, + print_verbose: Callable, + logging_obj: Any, + optional_params: dict, + litellm_params: dict, + timeout: Optional[Union[float, httpx.Timeout]], + encoding: Any, + ): + """Synchronous completion request""" + from litellm.llms.custom_httpx.http_handler import HTTPHandler + from litellm.utils import convert_to_model_response_object + + # Check if streaming is requested (will be faked) + stream = optional_params.get("stream", False) + + # Transform the request using parent class methods + request_data = self.transform_request( + model=model, + messages=messages, + optional_params=optional_params.copy(), + litellm_params=litellm_params, + headers={}, + ) + + # Set up headers + headers = { + "Authorization": f"Bearer {api_key}", + "Content-Type": "application/json", + } + + # Log the request + logging_obj.pre_call( + input=messages, + api_key=api_key, + additional_args={ + "complete_input_dict": request_data, + "api_base": api_base, + }, + ) + + # Make the HTTP request + http_handler = HTTPHandler(concurrent_limit=1) + response = http_handler.post( + url=api_base, + headers=headers, + json=request_data, + timeout=timeout, + ) + + if response.status_code != 200: + raise BaseLLMException( + status_code=response.status_code, + message=f"Request failed: {response.text}", + ) + + response_json = response.json() + + # Unwrap predictions to get OpenAI-compatible response + openai_response = self._unwrap_predictions_response(response_json) + + # Use litellm's standard response converter + model_response = cast( + ModelResponse, + convert_to_model_response_object( + response_object=openai_response, + model_response_object=model_response, + _response_headers={}, + ), + ) + + # Ensure model is set correctly + model_response.model = model + + # Log the response + logging_obj.post_call( + input=messages, + api_key=api_key, + original_response=response_json, + additional_args={"complete_input_dict": request_data}, + ) + + # Return fake stream iterator if streaming was requested + return self._handle_fake_stream_response(model_response=model_response, stream=stream) + + async def _async_completion( + self, + model: str, + messages: list, + api_base: str, + api_key: str, + model_response: ModelResponse, + print_verbose: Callable, + logging_obj: Any, + optional_params: dict, + litellm_params: dict, + timeout: Optional[Union[float, httpx.Timeout]], + encoding: Any, + ): + """Asynchronous completion request""" + from litellm.llms.custom_httpx.http_handler import get_async_httpx_client + from litellm.types.utils import LlmProviders + from litellm.utils import convert_to_model_response_object + + # Check if streaming is requested (will be faked) + stream = optional_params.get("stream", False) + + # Transform the request using parent class async methods + request_data = await self.async_transform_request( + model=model, + messages=messages, + optional_params=optional_params.copy(), + litellm_params=litellm_params, + headers={}, + ) + + # Set up headers + headers = { + "Authorization": f"Bearer {api_key}", + "Content-Type": "application/json", + } + + # Log the request + logging_obj.pre_call( + input=messages, + api_key=api_key, + additional_args={ + "complete_input_dict": request_data, + "api_base": api_base, + }, + ) + + # Make the HTTP request + http_handler = get_async_httpx_client( + llm_provider=LlmProviders.VERTEX_AI, + ) + response = await http_handler.post( + url=api_base, + headers=headers, + json=request_data, + timeout=timeout, + ) + + if response.status_code != 200: + raise BaseLLMException( + status_code=response.status_code, + message=f"Request failed: {response.text}", + ) + + response_json = response.json() + + # Unwrap predictions to get OpenAI-compatible response + openai_response = self._unwrap_predictions_response(response_json) + + # Use litellm's standard response converter + model_response = cast( + ModelResponse, + convert_to_model_response_object( + response_object=openai_response, + model_response_object=model_response, + _response_headers={}, + ), + ) + + # Ensure model is set correctly + model_response.model = model + + # Log the response + logging_obj.post_call( + input=messages, + api_key=api_key, + original_response=response_json, + additional_args={"complete_input_dict": request_data}, + ) + + # Return fake stream iterator if streaming was requested + return self._handle_fake_stream_response(model_response=model_response, stream=stream) + diff --git a/litellm/llms/watsonx/chat/transformation.py b/litellm/llms/watsonx/chat/transformation.py index 6b0dd5a39ae..2c096cafced 100644 --- a/litellm/llms/watsonx/chat/transformation.py +++ b/litellm/llms/watsonx/chat/transformation.py @@ -4,10 +4,14 @@ Translation from OpenAI's `/chat/completions` endpoint to IBM WatsonX's `/text/c Docs: https://cloud.ibm.com/apidocs/watsonx-ai#text-chat """ -from typing import List, Optional, Tuple, Union +from typing import Dict, List, Optional, Tuple, Union from litellm.secret_managers.main import get_secret_str -from litellm.types.llms.watsonx import WatsonXAIEndpoint, WatsonXAPIParams +from litellm.types.llms.watsonx import ( + WatsonXAIEndpoint, + WatsonXAPIParams, + WatsonXModelPattern, +) from ....utils import _remove_additional_properties, _remove_strict_from_schema from ...openai.chat.gpt_transformation import OpenAIGPTConfig @@ -120,3 +124,95 @@ class IBMWatsonXChatConfig(IBMWatsonXMixin, OpenAIGPTConfig): None if model.startswith("deployment/") else api_params["project_id"] ) return payload + + @staticmethod + def _apply_prompt_template_core(model: str, messages: List[Dict[str, str]], hf_template_fn) -> Optional[str]: + """Core logic for applying prompt templates""" + from litellm.litellm_core_utils.prompt_templates.factory import ( + custom_prompt, + ibm_granite_pt, + mistral_instruct_pt, + ) + + if WatsonXModelPattern.GRANITE_CHAT.value in model: + return ibm_granite_pt(messages=messages) + elif WatsonXModelPattern.IBM_MISTRAL.value in model: + return mistral_instruct_pt(messages=messages) + elif WatsonXModelPattern.GPT_OSS.value in model: + hf_model = model.split("watsonx/")[-1] if "watsonx/" in model else model + try: + return hf_template_fn(model=hf_model, messages=messages) + except Exception: + pass + elif WatsonXModelPattern.LLAMA3_INSTRUCT.value in model: + return custom_prompt( + role_dict={ + "system": {"pre_message": "<|start_header_id|>system<|end_header_id|>\n", "post_message": "<|eot_id|>"}, + "user": {"pre_message": "<|start_header_id|>user<|end_header_id|>\n", "post_message": "<|eot_id|>"}, + "assistant": {"pre_message": "<|start_header_id|>assistant<|end_header_id|>\n", "post_message": "<|eot_id|>"}, + }, + messages=messages, + initial_prompt_value="<|begin_of_text|>", + final_prompt_value="<|start_header_id|>assistant<|end_header_id|>\n", + ) + return None + + @staticmethod + async def aapply_prompt_template(model: str, messages: List[Dict[str, str]]) -> Optional[str]: + """Apply prompt template (async version)""" + import litellm + from litellm.litellm_core_utils.prompt_templates.factory import ( + ahf_chat_template, + custom_prompt, + hf_chat_template, + ibm_granite_pt, + mistral_instruct_pt, + ) + + if WatsonXModelPattern.GRANITE_CHAT.value in model: + return ibm_granite_pt(messages=messages) + elif WatsonXModelPattern.IBM_MISTRAL.value in model: + return mistral_instruct_pt(messages=messages) + elif WatsonXModelPattern.GPT_OSS.value in model: + hf_model = model.split("watsonx/")[-1] if "watsonx/" in model else model + try: + # Use sync if cached, async if not + if hf_model in litellm.known_tokenizer_config: + return hf_chat_template(model=hf_model, messages=messages) + else: + return await ahf_chat_template(model=hf_model, messages=messages) + except Exception: + pass + elif WatsonXModelPattern.LLAMA3_INSTRUCT.value in model: + return custom_prompt( + role_dict={ + "system": { + "pre_message": "<|start_header_id|>system<|end_header_id|>\n", + "post_message": "<|eot_id|>", + }, + "user": { + "pre_message": "<|start_header_id|>user<|end_header_id|>\n", + "post_message": "<|eot_id|>", + }, + "assistant": { + "pre_message": "<|start_header_id|>assistant<|end_header_id|>\n", + "post_message": "<|eot_id|>", + }, + }, + messages=messages, + initial_prompt_value="<|begin_of_text|>", + final_prompt_value="<|start_header_id|>assistant<|end_header_id|>\n", + ) + return None + + @staticmethod + def apply_prompt_template(model: str, messages: List[Dict[str, str]]) -> Optional[str]: + """Apply prompt template (sync version)""" + from litellm.litellm_core_utils.prompt_templates.factory import ( + hf_chat_template, + ) + + return IBMWatsonXChatConfig._apply_prompt_template_core( + model=model, messages=messages, hf_template_fn=hf_chat_template + ) + diff --git a/litellm/llms/watsonx/common_utils.py b/litellm/llms/watsonx/common_utils.py index c756be6d458..58b33097cbd 100644 --- a/litellm/llms/watsonx/common_utils.py +++ b/litellm/llms/watsonx/common_utils.py @@ -131,34 +131,102 @@ def _get_api_params( ) -def convert_watsonx_messages_to_prompt( +async def _aconvert_watsonx_messages_core( model: str, messages: List[AllMessageValues], provider: str, custom_prompt_dict: Dict, + apply_template_fn, ) -> str: + """Async core logic for converting watsonx messages to prompt""" + from litellm.types.llms.watsonx import WatsonXModelPattern + # handle anthropic prompts and amazon titan prompts if model in custom_prompt_dict: - # check if the model has a registered custom prompt model_prompt_dict = custom_prompt_dict[model] - prompt = ptf.custom_prompt( + return ptf.custom_prompt( messages=messages, - role_dict=model_prompt_dict.get( - "role_dict", model_prompt_dict.get("roles") - ), + role_dict=model_prompt_dict.get("role_dict", model_prompt_dict.get("roles")), initial_prompt_value=model_prompt_dict.get("initial_prompt_value", ""), final_prompt_value=model_prompt_dict.get("final_prompt_value", ""), bos_token=model_prompt_dict.get("bos_token", ""), eos_token=model_prompt_dict.get("eos_token", ""), ) - return prompt - elif provider == "ibm-mistralai": - prompt = ptf.mistral_instruct_pt(messages=messages) + elif provider == WatsonXModelPattern.IBM_MISTRALAI.value: + return ptf.mistral_instruct_pt(messages=messages) else: - prompt: str = ptf.prompt_factory( # type: ignore + # Try applying specific template first + result = await apply_template_fn(model=model, messages=messages) + if result: + return result + # Fallback to default + return ptf.prompt_factory( model=model, messages=messages, custom_llm_provider="watsonx" + ) # type: ignore + + +def _convert_watsonx_messages_core( + model: str, + messages: List[AllMessageValues], + provider: str, + custom_prompt_dict: Dict, + apply_template_fn, +) -> str: + """Sync core logic for converting watsonx messages to prompt""" + from litellm.types.llms.watsonx import WatsonXModelPattern + + # handle anthropic prompts and amazon titan prompts + if model in custom_prompt_dict: + model_prompt_dict = custom_prompt_dict[model] + return ptf.custom_prompt( + messages=messages, + role_dict=model_prompt_dict.get("role_dict", model_prompt_dict.get("roles")), + initial_prompt_value=model_prompt_dict.get("initial_prompt_value", ""), + final_prompt_value=model_prompt_dict.get("final_prompt_value", ""), + bos_token=model_prompt_dict.get("bos_token", ""), + eos_token=model_prompt_dict.get("eos_token", ""), ) - return prompt + elif provider == WatsonXModelPattern.IBM_MISTRALAI.value: + return ptf.mistral_instruct_pt(messages=messages) + else: + # Try applying specific template first + result = apply_template_fn(model=model, messages=messages) + if result: + return result + # Fallback to default + return ptf.prompt_factory( + model=model, messages=messages, custom_llm_provider="watsonx" + ) # type: ignore + + +async def aconvert_watsonx_messages_to_prompt( + model: str, messages: List[AllMessageValues], provider: str, custom_prompt_dict: Dict +) -> str: + """Async version of convert_watsonx_messages_to_prompt""" + from litellm.llms.watsonx.chat.transformation import IBMWatsonXChatConfig + + return await _aconvert_watsonx_messages_core( + model=model, + messages=messages, + provider=provider, + custom_prompt_dict=custom_prompt_dict, + apply_template_fn=IBMWatsonXChatConfig.aapply_prompt_template, + ) + + +def convert_watsonx_messages_to_prompt( + model: str, messages: List[AllMessageValues], provider: str, custom_prompt_dict: Dict +) -> str: + """Sync version of convert_watsonx_messages_to_prompt""" + from litellm.llms.watsonx.chat.transformation import IBMWatsonXChatConfig + + return _convert_watsonx_messages_core( + model=model, + messages=messages, + provider=provider, + custom_prompt_dict=custom_prompt_dict, + apply_template_fn=IBMWatsonXChatConfig.apply_prompt_template, + ) # Mixin class for shared IBM Watson X functionality diff --git a/litellm/llms/watsonx/completion/transformation.py b/litellm/llms/watsonx/completion/transformation.py index a0b9735a990..3c1229ecd2b 100644 --- a/litellm/llms/watsonx/completion/transformation.py +++ b/litellm/llms/watsonx/completion/transformation.py @@ -228,39 +228,35 @@ class IBMWatsonXAIConfig(IBMWatsonXMixin, BaseConfig): "us-south", ] - def transform_request( - self, - model: str, - messages: List[AllMessageValues], - optional_params: Dict, - litellm_params: Dict, - headers: Dict, - ) -> Dict: - provider = model.split("/")[0] - prompt = convert_watsonx_messages_to_prompt( - model=model, - messages=messages, - provider=provider, - custom_prompt_dict={}, - ) + def _build_request_payload(self, model: str, prompt: str, optional_params: Dict) -> Dict: + """Shared logic to build request payload""" extra_body_params = optional_params.pop("extra_body", {}) optional_params.update(extra_body_params) watsonx_api_params = _get_api_params(params=optional_params) - - watsonx_auth_payload = self._prepare_payload( - model=model, - api_params=watsonx_api_params, - ) - - # init the payload to the text generation call - payload = { + watsonx_auth_payload = self._prepare_payload(model=model, api_params=watsonx_api_params) + + return { "input": prompt, "moderations": optional_params.pop("moderations", {}), "parameters": optional_params, **watsonx_auth_payload, } - return payload + async def atransform_request(self, model: str, messages: List[AllMessageValues], optional_params: Dict, litellm_params: Dict, headers: Dict) -> Dict: + """Async version of transform_request""" + from litellm.llms.watsonx.common_utils import ( + aconvert_watsonx_messages_to_prompt, + ) + + provider = model.split("/")[0] + prompt = await aconvert_watsonx_messages_to_prompt(model=model, messages=messages, provider=provider, custom_prompt_dict={}) + return self._build_request_payload(model=model, prompt=prompt, optional_params=optional_params) + + def transform_request(self, model: str, messages: List[AllMessageValues], optional_params: Dict, litellm_params: Dict, headers: Dict) -> Dict: + """Sync version of transform_request""" + provider = model.split("/")[0] + prompt = convert_watsonx_messages_to_prompt(model=model, messages=messages, provider=provider, custom_prompt_dict={}) + return self._build_request_payload(model=model, prompt=prompt, optional_params=optional_params) def transform_response( self, diff --git a/litellm/main.py b/litellm/main.py index cfb0bef0797..3955a0f32f8 100644 --- a/litellm/main.py +++ b/litellm/main.py @@ -85,6 +85,10 @@ from litellm.litellm_core_utils.prompt_templates.common_utils import ( from litellm.llms.base_llm import BaseConfig, BaseImageGenerationConfig from litellm.llms.bedrock.common_utils import BedrockModelInfo from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler +from litellm.llms.vertex_ai.common_utils import ( + VertexAIModelRoute, + get_vertex_ai_model_route, +) from litellm.realtime_api.main import _realtime_health_check from litellm.secret_managers.main import get_secret_bool, get_secret_str from litellm.types.router import GenericLiteLLMParams @@ -150,7 +154,6 @@ from .llms.bedrock.chat import BedrockConverseLLM, BedrockLLM from .llms.bedrock.embed.embedding import BedrockEmbedding from .llms.bedrock.image.image_handler import BedrockImageGeneration from .llms.bytez.chat.transformation import BytezChatConfig -from .llms.lemonade.chat.transformation import LemonadeChatConfig from .llms.codestral.completion.handler import CodestralTextCompletion from .llms.cohere.embed import handler as cohere_embed from .llms.custom_httpx.aiohttp_handler import BaseLLMAIOHTTPHandler @@ -162,6 +165,7 @@ from .llms.gemini.common_utils import get_api_key_from_env from .llms.groq.chat.handler import GroqChatCompletion from .llms.heroku.chat.transformation import HerokuChatConfig from .llms.huggingface.embedding.handler import HuggingFaceEmbedding +from .llms.lemonade.chat.transformation import LemonadeChatConfig from .llms.nlp_cloud.chat.handler import completion as nlp_cloud_chat_completion from .llms.oci.chat.transformation import OCIChatConfig from .llms.ollama.completion import handler as ollama @@ -192,6 +196,7 @@ from .llms.vertex_ai.multimodal_embeddings.embedding_handler import ( from .llms.vertex_ai.text_to_speech.text_to_speech_handler import VertexTextToSpeechAPI from .llms.vertex_ai.vertex_ai_partner_models.main import VertexAIPartnerModels from .llms.vertex_ai.vertex_embeddings.embedding_handler import VertexEmbedding +from .llms.vertex_ai.vertex_gemma_models.main import VertexAIGemmaModels from .llms.vertex_ai.vertex_model_garden.main import VertexAIModelGardenModels from .llms.vllm.completion import handler as vllm_handler from .llms.watsonx.chat.handler import WatsonXChatHandler @@ -255,6 +260,7 @@ vertex_multimodal_embedding = VertexMultimodalEmbedding() vertex_image_generation = VertexImageGeneration() google_batch_embeddings = GoogleBatchEmbeddings() vertex_partner_models_chat_completion = VertexAIPartnerModels() +vertex_gemma_chat_completion = VertexAIGemmaModels() vertex_model_garden_chat_completion = VertexAIModelGardenModels() vertex_text_to_speech = VertexTextToSpeechAPI() sagemaker_llm = SagemakerLLM() @@ -2872,10 +2878,10 @@ def completion( # type: ignore # noqa: PLR0915 custom_llm_provider=custom_llm_provider, # type: ignore client=client, api_base=api_base, - extra_headers=extra_headers, + extra_headers=headers, ) - elif custom_llm_provider == "vertex_ai": + elif custom_llm_provider == "vertex_ai": vertex_ai_project = ( optional_params.pop("vertex_project", None) or optional_params.pop("vertex_ai_project", None) @@ -2897,7 +2903,9 @@ def completion( # type: ignore # noqa: PLR0915 api_base = api_base or litellm.api_base or get_secret("VERTEXAI_API_BASE") new_params = safe_deep_copy(optional_params or {}) - if vertex_partner_models_chat_completion.is_vertex_partner_model(model): + model_route = get_vertex_ai_model_route(model=model, litellm_params=litellm_params) + + if model_route == VertexAIModelRoute.PARTNER_MODELS: model_response = vertex_partner_models_chat_completion.completion( model=model, messages=messages, @@ -2918,10 +2926,7 @@ def completion( # type: ignore # noqa: PLR0915 timeout=timeout, client=client, ) - elif "gemini" in model or ( - litellm_params.get("base_model") is not None - and "gemini" in litellm_params["base_model"] - ): + elif model_route == VertexAIModelRoute.GEMINI: model_response = vertex_chat_completion.completion( # type: ignore model=model, messages=messages, @@ -2941,9 +2946,31 @@ def completion( # type: ignore # noqa: PLR0915 custom_llm_provider=custom_llm_provider, # type: ignore client=client, api_base=api_base, - extra_headers=extra_headers, + extra_headers=headers, ) - elif "openai" in model: + elif model_route == VertexAIModelRoute.GEMMA: + # Vertex Gemma Models with custom prediction endpoint + model_response = vertex_gemma_chat_completion.completion( + model=model, + messages=messages, + model_response=model_response, + print_verbose=print_verbose, + optional_params=new_params, + litellm_params=litellm_params, # type: ignore + logger_fn=logger_fn, + encoding=encoding, + api_base=api_base, + vertex_location=vertex_ai_location, + vertex_project=vertex_ai_project, + vertex_credentials=vertex_credentials, + logging_obj=logging, + acompletion=acompletion, + headers=headers, + custom_prompt_dict=custom_prompt_dict, + timeout=timeout, + client=client, + ) + elif model_route == VertexAIModelRoute.MODEL_GARDEN: # Vertex Model Garden - OpenAI compatible models model_response = vertex_model_garden_chat_completion.completion( model=model, @@ -2965,7 +2992,7 @@ def completion( # type: ignore # noqa: PLR0915 timeout=timeout, client=client, ) - else: + else: # VertexAIModelRoute.NON_GEMINI model_response = vertex_ai_non_gemini.completion( model=model, messages=messages, @@ -3969,6 +3996,7 @@ def embedding( # noqa: PLR0915 """ azure = kwargs.get("azure", None) client = kwargs.pop("client", None) + shared_session = kwargs.get("shared_session", None) max_retries = kwargs.get("max_retries", None) litellm_logging_obj: LiteLLMLoggingObj = kwargs.get("litellm_logging_obj") # type: ignore mock_response: Optional[List[float]] = kwargs.get("mock_response", None) # type: ignore @@ -4158,6 +4186,7 @@ def embedding( # noqa: PLR0915 client=client, aembedding=aembedding, max_retries=max_retries, + shared_session=shared_session, ) elif custom_llm_provider == "databricks": api_base = api_base or litellm.api_base or get_secret("DATABRICKS_API_BASE") # type: ignore diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 7ed04dc79c9..1dcb01cedba 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -866,6 +866,36 @@ "mode": "audio_transcription", "output_cost_per_second": 0.0 }, + "au.anthropic.claude-sonnet-4-5-20250929-v1:0": { + "cache_creation_input_token_cost": 4.125e-06, + "cache_read_input_token_cost": 3.3e-07, + "input_cost_per_token": 3.3e-06, + "input_cost_per_token_above_200k_tokens": 6.6e-06, + "output_cost_per_token_above_200k_tokens": 2.475e-05, + "cache_creation_input_token_cost_above_200k_tokens": 8.25e-06, + "cache_read_input_token_cost_above_200k_tokens": 6.6e-07, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 200000, + "max_output_tokens": 64000, + "max_tokens": 200000, + "mode": "chat", + "output_cost_per_token": 1.65e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_assistant_prefill": true, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true, + "tool_use_system_prompt_tokens": 346 + }, "azure/ada": { "input_cost_per_token": 1e-07, "litellm_provider": "azure", @@ -3164,6 +3194,42 @@ "supports_function_calling": true, "supports_vision": true }, + "azure_ai/Phi-4-mini-reasoning": { + "input_cost_per_token": 8e-08, + "litellm_provider": "azure_ai", + "max_input_tokens": 131072, + "max_output_tokens": 4096, + "max_tokens": 4096, + "mode": "chat", + "output_cost_per_token": 3.2e-07, + "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/microsoft/", + "supports_function_calling": true + }, + "azure_ai/Phi-4-reasoning": { + "input_cost_per_token": 1.25e-07, + "litellm_provider": "azure_ai", + "max_input_tokens": 32768, + "max_output_tokens": 4096, + "max_tokens": 4096, + "mode": "chat", + "output_cost_per_token": 5e-07, + "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/microsoft/", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_reasoning": true + }, + "azure_ai/MAI-DS-R1": { + "input_cost_per_token": 1.35e-06, + "litellm_provider": "azure_ai", + "max_input_tokens": 128000, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 5.4e-06, + "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/microsoft/", + "supports_reasoning": true, + "supports_tool_choice": true + }, "azure_ai/cohere-rerank-v3-english": { "input_cost_per_query": 0.002, "input_cost_per_token": 0.0, @@ -5294,6 +5360,16 @@ "output_cost_per_token": 0.0, "supports_embedding_image_input": true }, + "cohere.embed-v4:0": { + "input_cost_per_token": 1.2e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 128000, + "max_tokens": 128000, + "mode": "embedding", + "output_cost_per_token": 0.0, + "output_vector_size": 1536, + "supports_embedding_image_input": true + }, "cohere.rerank-v3-5:0": { "input_cost_per_query": 0.002, "input_cost_per_token": 0.0, @@ -7838,19 +7914,19 @@ "tool_use_system_prompt_tokens": 159 }, "eu.anthropic.claude-sonnet-4-5-20250929-v1:0": { - "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, + "cache_creation_input_token_cost": 4.125e-06, + "cache_read_input_token_cost": 3.3e-07, + "input_cost_per_token": 3.3e-06, + "input_cost_per_token_above_200k_tokens": 6.6e-06, + "output_cost_per_token_above_200k_tokens": 2.475e-05, + "cache_creation_input_token_cost_above_200k_tokens": 8.25e-06, + "cache_read_input_token_cost_above_200k_tokens": 6.6e-07, "litellm_provider": "bedrock_converse", "max_input_tokens": 200000, "max_output_tokens": 64000, "max_tokens": 200000, "mode": "chat", - "output_cost_per_token": 1.5e-05, + "output_cost_per_token": 1.65e-05, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, @@ -11820,6 +11896,66 @@ "video" ] }, + "global.anthropic.claude-sonnet-4-5-20250929-v1:0": { + "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": 200000, + "mode": "chat", + "output_cost_per_token": 1.5e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_assistant_prefill": true, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true, + "tool_use_system_prompt_tokens": 346 + }, + "global.anthropic.claude-sonnet-4-20250514-v1:0": { + "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": 1000000, + "max_output_tokens": 64000, + "max_tokens": 64000, + "mode": "chat", + "output_cost_per_token": 1.5e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_assistant_prefill": true, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true, + "tool_use_system_prompt_tokens": 159 + }, "gpt-3.5-turbo": { "input_cost_per_token": 0.5e-06, "litellm_provider": "openai", @@ -12872,6 +13008,72 @@ "supports_tool_choice": true, "supports_vision": true }, + "gpt-5-pro": { + "input_cost_per_token": 1.5e-05, + "input_cost_per_token_batches": 7.5e-06, + "litellm_provider": "openai", + "max_input_tokens": 400000, + "max_output_tokens": 272000, + "max_tokens": 272000, + "mode": "responses", + "output_cost_per_token": 1.2e-04, + "output_cost_per_token_batches": 6e-05, + "supported_endpoints": [ + "/v1/batch", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_native_streaming": false, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_vision": true, + "supports_web_search": true + }, + "gpt-5-pro-2025-10-06": { + "input_cost_per_token": 1.5e-05, + "input_cost_per_token_batches": 7.5e-06, + "litellm_provider": "openai", + "max_input_tokens": 400000, + "max_output_tokens": 272000, + "max_tokens": 272000, + "mode": "responses", + "output_cost_per_token": 1.2e-04, + "output_cost_per_token_batches": 6e-05, + "supported_endpoints": [ + "/v1/batch", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_native_streaming": false, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_vision": true, + "supports_web_search": true + }, "gpt-5-codex": { "cache_read_input_token_cost": 1.25e-07, "input_cost_per_token": 1.25e-06, @@ -13189,6 +13391,19 @@ "/v1/images/generations" ] }, + "gpt-image-1-mini": { + "cache_read_input_image_token_cost": 2.5e-07, + "cache_read_input_token_cost": 2e-07, + "input_cost_per_image_token": 2.5e-06, + "input_cost_per_token": 2e-06, + "litellm_provider": "openai", + "mode": "chat", + "output_cost_per_image_token": 8e-06, + "supported_endpoints": [ + "/v1/images/generations", + "/v1/images/edits" + ] + }, "gpt-realtime": { "cache_creation_input_audio_token_cost": 4e-07, "cache_read_input_token_cost": 4e-07, @@ -13221,6 +13436,37 @@ "supports_system_messages": true, "supports_tool_choice": true }, + "gpt-realtime-mini": { + "cache_creation_input_audio_token_cost": 3e-07, + "cache_read_input_audio_token_cost": 3e-07, + "input_cost_per_audio_token": 1e-05, + "input_cost_per_token": 6e-07, + "litellm_provider": "openai", + "max_input_tokens": 128000, + "max_output_tokens": 4096, + "max_tokens": 4096, + "mode": "chat", + "output_cost_per_audio_token": 2e-05, + "output_cost_per_token": 2.4e-06, + "supported_endpoints": [ + "/v1/realtime" + ], + "supported_modalities": [ + "text", + "image", + "audio" + ], + "supported_output_modalities": [ + "text", + "audio" + ], + "supports_audio_input": true, + "supports_audio_output": true, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_system_messages": true, + "supports_tool_choice": true + }, "gpt-realtime-2025-08-28": { "cache_creation_input_audio_token_cost": 4e-07, "cache_read_input_token_cost": 4e-07, @@ -14219,19 +14465,19 @@ "output_cost_per_token": 1.8e-08 }, "jp.anthropic.claude-sonnet-4-5-20250929-v1:0": { - "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, + "cache_creation_input_token_cost": 4.125e-06, + "cache_read_input_token_cost": 3.3e-07, + "input_cost_per_token": 3.3e-06, + "input_cost_per_token_above_200k_tokens": 6.6e-06, + "output_cost_per_token_above_200k_tokens": 2.475e-05, + "cache_creation_input_token_cost_above_200k_tokens": 8.25e-06, + "cache_read_input_token_cost_above_200k_tokens": 6.6e-07, "litellm_provider": "bedrock_converse", "max_input_tokens": 200000, "max_output_tokens": 64000, "max_tokens": 200000, "mode": "chat", - "output_cost_per_token": 1.5e-05, + "output_cost_per_token": 1.65e-05, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, @@ -14623,6 +14869,54 @@ "/v1/images/generations" ] }, + "low/1024-x-1024/gpt-image-1-mini": { + "input_cost_per_image": 0.005, + "litellm_provider": "openai", + "mode": "image_generation", + "supported_endpoints": [ + "/v1/images/generations" + ] + }, + "low/1024-x-1536/gpt-image-1-mini": { + "input_cost_per_image": 0.006, + "litellm_provider": "openai", + "mode": "image_generation", + "supported_endpoints": [ + "/v1/images/generations" + ] + }, + "low/1536-x-1024/gpt-image-1-mini": { + "input_cost_per_image": 0.006, + "litellm_provider": "openai", + "mode": "image_generation", + "supported_endpoints": [ + "/v1/images/generations" + ] + }, + "medium/1024-x-1024/gpt-image-1-mini": { + "input_cost_per_image": 0.011, + "litellm_provider": "openai", + "mode": "image_generation", + "supported_endpoints": [ + "/v1/images/generations" + ] + }, + "medium/1024-x-1536/gpt-image-1-mini": { + "input_cost_per_image": 0.015, + "litellm_provider": "openai", + "mode": "image_generation", + "supported_endpoints": [ + "/v1/images/generations" + ] + }, + "medium/1536-x-1024/gpt-image-1-mini": { + "input_cost_per_image": 0.015, + "litellm_provider": "openai", + "mode": "image_generation", + "supported_endpoints": [ + "/v1/images/generations" + ] + }, "medlm-large": { "input_cost_per_character": 5e-06, "litellm_provider": "vertex_ai-language-models", @@ -16369,6 +16663,42 @@ "supports_function_calling": true, "supports_response_schema": false }, + "oci/cohere.command-latest": { + "input_cost_per_token": 1.56e-06, + "litellm_provider": "oci", + "max_input_tokens": 128000, + "max_output_tokens": 4000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 1.56e-06, + "source": "https://www.oracle.com/cloud/ai/generative-ai/pricing/", + "supports_function_calling": true, + "supports_response_schema": false + }, + "oci/cohere.command-a-03-2025": { + "input_cost_per_token": 1.56e-06, + "litellm_provider": "oci", + "max_input_tokens": 256000, + "max_output_tokens": 4000, + "max_tokens": 256000, + "mode": "chat", + "output_cost_per_token": 1.56e-06, + "source": "https://www.oracle.com/cloud/ai/generative-ai/pricing/", + "supports_function_calling": true, + "supports_response_schema": false + }, + "oci/cohere.command-plus-latest": { + "input_cost_per_token": 1.56e-06, + "litellm_provider": "oci", + "max_input_tokens": 128000, + "max_output_tokens": 4000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 1.56e-06, + "source": "https://www.oracle.com/cloud/ai/generative-ai/pricing/", + "supports_function_calling": true, + "supports_response_schema": false + }, "ollama/codegeex4": { "input_cost_per_token": 0.0, "litellm_provider": "ollama", @@ -19640,6 +19970,39 @@ "supports_parallel_function_calling": true, "supports_tool_choice": true }, + "together_ai/moonshotai/Kimi-K2-Instruct-0905": { + "input_cost_per_token": 1e-06, + "litellm_provider": "together_ai", + "max_input_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 3e-06, + "source": "https://www.together.ai/models/kimi-k2-0905", + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_tool_choice": true + }, + "together_ai/Qwen/Qwen3-Next-80B-A3B-Instruct": { + "input_cost_per_token": 1.5e-07, + "litellm_provider": "together_ai", + "max_input_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 1.5e-06, + "source": "https://www.together.ai/models/qwen3-next-80b-a3b-instruct", + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_tool_choice": true + }, + "together_ai/Qwen/Qwen3-Next-80B-A3B-Thinking": { + "input_cost_per_token": 1.5e-07, + "litellm_provider": "together_ai", + "max_input_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 1.5e-06, + "source": "https://www.together.ai/models/qwen3-next-80b-a3b-thinking", + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_tool_choice": true + }, "tts-1": { "input_cost_per_character": 1.5e-05, "litellm_provider": "openai", @@ -19848,19 +20211,19 @@ "tool_use_system_prompt_tokens": 159 }, "us.anthropic.claude-sonnet-4-5-20250929-v1:0": { - "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, + "cache_creation_input_token_cost": 4.125e-06, + "cache_read_input_token_cost": 33e-07, + "input_cost_per_token": 33e-06, + "input_cost_per_token_above_200k_tokens": 66e-06, + "output_cost_per_token_above_200k_tokens": 2.475e-05, + "cache_creation_input_token_cost_above_200k_tokens": 8.25e-06, + "cache_read_input_token_cost_above_200k_tokens": 66e-07, "litellm_provider": "bedrock_converse", "max_input_tokens": 200000, "max_output_tokens": 64000, "max_tokens": 200000, "mode": "chat", - "output_cost_per_token": 1.5e-05, + "output_cost_per_token": 1.65e-05, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, @@ -22173,6 +22536,307 @@ "supports_tool_choice": true, "supports_vision": false }, + "watsonx/bigscience/mt0-xxl-13b": { + "max_tokens": 8192, + "max_input_tokens": 8192, + "max_output_tokens": 8192, + "input_cost_per_token": 0.0005, + "output_cost_per_token": 0.002, + "litellm_provider": "watsonx", + "mode": "chat", + "supports_function_calling": false, + "supports_parallel_function_calling": false, + "supports_vision": false + }, + "watsonx/core42/jais-13b-chat": { + "max_tokens": 8192, + "max_input_tokens": 8192, + "max_output_tokens": 8192, + "input_cost_per_token": 0.0005, + "output_cost_per_token": 0.002, + "litellm_provider": "watsonx", + "mode": "chat", + "supports_function_calling": false, + "supports_parallel_function_calling": false, + "supports_vision": false + }, + "watsonx/google/flan-t5-xl-3b": { + "max_tokens": 8192, + "max_input_tokens": 8192, + "max_output_tokens": 8192, + "input_cost_per_token": 0.0001, + "output_cost_per_token": 0.00025, + "litellm_provider": "watsonx", + "mode": "chat", + "supports_function_calling": false, + "supports_parallel_function_calling": false, + "supports_vision": false + }, + "watsonx/ibm/granite-13b-chat-v2": { + "max_tokens": 8192, + "max_input_tokens": 8192, + "max_output_tokens": 8192, + "input_cost_per_token": 0.0005, + "output_cost_per_token": 0.002, + "litellm_provider": "watsonx", + "mode": "chat", + "supports_function_calling": false, + "supports_parallel_function_calling": false, + "supports_vision": false + }, + "watsonx/ibm/granite-13b-instruct-v2": { + "max_tokens": 8192, + "max_input_tokens": 8192, + "max_output_tokens": 8192, + "input_cost_per_token": 0.0005, + "output_cost_per_token": 0.002, + "litellm_provider": "watsonx", + "mode": "chat", + "supports_function_calling": false, + "supports_parallel_function_calling": false, + "supports_vision": false + }, + "watsonx/ibm/granite-3-3-8b-instruct": { + "max_tokens": 8192, + "max_input_tokens": 8192, + "max_output_tokens": 8192, + "input_cost_per_token": 0.00025, + "output_cost_per_token": 0.001, + "litellm_provider": "watsonx", + "mode": "chat", + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_vision": false + }, + "watsonx/ibm/granite-4-h-small": { + "max_tokens": 20480, + "max_input_tokens": 20480, + "max_output_tokens": 20480, + "input_cost_per_token": 0.000625, + "output_cost_per_token": 0.0025, + "litellm_provider": "watsonx", + "mode": "chat", + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_vision": false + }, + "watsonx/ibm/granite-guardian-3-2-2b": { + "max_tokens": 8192, + "max_input_tokens": 8192, + "max_output_tokens": 8192, + "input_cost_per_token": 0.00015, + "output_cost_per_token": 0.0006, + "litellm_provider": "watsonx", + "mode": "chat", + "supports_function_calling": false, + "supports_parallel_function_calling": false, + "supports_vision": false + }, + "watsonx/ibm/granite-guardian-3-3-8b": { + "max_tokens": 8192, + "max_input_tokens": 8192, + "max_output_tokens": 8192, + "input_cost_per_token": 0.00025, + "output_cost_per_token": 0.001, + "litellm_provider": "watsonx", + "mode": "chat", + "supports_function_calling": false, + "supports_parallel_function_calling": false, + "supports_vision": false + }, + "watsonx/ibm/granite-ttm-1024-96-r2": { + "max_tokens": 512, + "max_input_tokens": 512, + "max_output_tokens": 512, + "input_cost_per_token": 0.000625, + "output_cost_per_token": 0.000625, + "litellm_provider": "watsonx", + "mode": "chat", + "supports_function_calling": false, + "supports_parallel_function_calling": false, + "supports_vision": false + }, + "watsonx/ibm/granite-ttm-1536-96-r2": { + "max_tokens": 512, + "max_input_tokens": 512, + "max_output_tokens": 512, + "input_cost_per_token": 0.000625, + "output_cost_per_token": 0.000625, + "litellm_provider": "watsonx", + "mode": "chat", + "supports_function_calling": false, + "supports_parallel_function_calling": false, + "supports_vision": false + }, + "watsonx/ibm/granite-ttm-512-96-r2": { + "max_tokens": 512, + "max_input_tokens": 512, + "max_output_tokens": 512, + "input_cost_per_token": 0.000625, + "output_cost_per_token": 0.000625, + "litellm_provider": "watsonx", + "mode": "chat", + "supports_function_calling": false, + "supports_parallel_function_calling": false, + "supports_vision": false + }, + "watsonx/ibm/granite-vision-3-2-2b": { + "max_tokens": 8192, + "max_input_tokens": 8192, + "max_output_tokens": 8192, + "input_cost_per_token": 0.00015, + "output_cost_per_token": 0.0006, + "litellm_provider": "watsonx", + "mode": "chat", + "supports_function_calling": false, + "supports_parallel_function_calling": false, + "supports_vision": true + }, + "watsonx/meta-llama/llama-3-2-11b-vision-instruct": { + "max_tokens": 128000, + "max_input_tokens": 128000, + "max_output_tokens": 128000, + "input_cost_per_token": 0.00025, + "output_cost_per_token": 0.001, + "litellm_provider": "watsonx", + "mode": "chat", + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_vision": true + }, + "watsonx/meta-llama/llama-3-2-1b-instruct": { + "max_tokens": 128000, + "max_input_tokens": 128000, + "max_output_tokens": 128000, + "input_cost_per_token": 0.0001, + "output_cost_per_token": 0.0002, + "litellm_provider": "watsonx", + "mode": "chat", + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_vision": false + }, + "watsonx/meta-llama/llama-3-2-3b-instruct": { + "max_tokens": 128000, + "max_input_tokens": 128000, + "max_output_tokens": 128000, + "input_cost_per_token": 0.00015, + "output_cost_per_token": 0.0006, + "litellm_provider": "watsonx", + "mode": "chat", + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_vision": false + }, + "watsonx/meta-llama/llama-3-2-90b-vision-instruct": { + "max_tokens": 128000, + "max_input_tokens": 128000, + "max_output_tokens": 128000, + "input_cost_per_token": 0.002, + "output_cost_per_token": 0.008, + "litellm_provider": "watsonx", + "mode": "chat", + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_vision": true + }, + "watsonx/meta-llama/llama-3-3-70b-instruct": { + "max_tokens": 128000, + "max_input_tokens": 128000, + "max_output_tokens": 128000, + "input_cost_per_token": 0.002, + "output_cost_per_token": 0.006, + "litellm_provider": "watsonx", + "mode": "chat", + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_vision": false + }, + "watsonx/meta-llama/llama-4-maverick-17b": { + "max_tokens": 128000, + "max_input_tokens": 128000, + "max_output_tokens": 128000, + "input_cost_per_token": 0.0005, + "output_cost_per_token": 0.002, + "litellm_provider": "watsonx", + "mode": "chat", + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_vision": false + }, + "watsonx/meta-llama/llama-guard-3-11b-vision": { + "max_tokens": 128000, + "max_input_tokens": 128000, + "max_output_tokens": 128000, + "input_cost_per_token": 0.00025, + "output_cost_per_token": 0.001, + "litellm_provider": "watsonx", + "mode": "chat", + "supports_function_calling": false, + "supports_parallel_function_calling": false, + "supports_vision": true + }, + "watsonx/mistralai/mistral-medium-2505": { + "max_tokens": 128000, + "max_input_tokens": 128000, + "max_output_tokens": 128000, + "input_cost_per_token": 0.00225, + "output_cost_per_token": 0.00675, + "litellm_provider": "watsonx", + "mode": "chat", + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_vision": false + }, + "watsonx/mistralai/mistral-small-2503": { + "max_tokens": 32000, + "max_input_tokens": 32000, + "max_output_tokens": 32000, + "input_cost_per_token": 0.0002, + "output_cost_per_token": 0.0006, + "litellm_provider": "watsonx", + "mode": "chat", + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_vision": false + }, + "watsonx/mistralai/pixtral-12b-2409": { + "max_tokens": 128000, + "max_input_tokens": 128000, + "max_output_tokens": 128000, + "input_cost_per_token": 0.00015, + "output_cost_per_token": 0.00015, + "litellm_provider": "watsonx", + "mode": "chat", + "supports_function_calling": false, + "supports_parallel_function_calling": false, + "supports_vision": true + }, + "watsonx/openai/gpt-oss-120b": { + "max_tokens": 8192, + "max_input_tokens": 8192, + "max_output_tokens": 8192, + "input_cost_per_token": 0.004, + "output_cost_per_token": 0.016, + "litellm_provider": "watsonx", + "mode": "chat", + "supports_function_calling": false, + "supports_parallel_function_calling": false, + "supports_vision": false + }, + "watsonx/sdaia/allam-1-13b-instruct": { + "max_tokens": 8192, + "max_input_tokens": 8192, + "max_output_tokens": 8192, + "input_cost_per_token": 0.0005, + "output_cost_per_token": 0.002, + "litellm_provider": "watsonx", + "mode": "chat", + "supports_function_calling": false, + "supports_parallel_function_calling": false, + "supports_vision": false + }, + "whisper-1": { "input_cost_per_second": 0.0001, "litellm_provider": "openai", diff --git a/litellm/passthrough/main.py b/litellm/passthrough/main.py index f4dc1ef6c84..b4a76822022 100644 --- a/litellm/passthrough/main.py +++ b/litellm/passthrough/main.py @@ -242,12 +242,14 @@ def llm_passthrough_route( request_query_params=request_query_params, litellm_params=litellm_params_dict, ) - - # need to encode the id of application-inference-profile for bedrock + + # [TODO: Refactor to bedrockpassthroughconfig] need to encode the id of application-inference-profile for bedrock if custom_llm_provider == "bedrock" and "application-inference-profile" in endpoint: - encoded_url_str = CommonUtils.encode_bedrock_runtime_modelid_arn(str(updated_url)) + encoded_url_str = CommonUtils.encode_bedrock_runtime_modelid_arn( + str(updated_url) + ) updated_url = httpx.URL(encoded_url_str) - + # Add or update query parameters provider_api_key = provider_config.get_api_key(api_key) 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 31032b27b22..e77ad11fae4 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 @@ -109,10 +109,21 @@ class MCPRequestHandler: request.body = mock_body # type: ignore if ".well-known" in str(request.url): # public routes validated_user_api_key_auth = UserAPIKeyAuth() + # elif litellm_api_key == "": + # from fastapi import HTTPException + + # raise HTTPException( + # status_code=401, + # detail="LiteLLM API key is missing. Please add it or use OAuth authentication.", + # headers={ + # "WWW-Authenticate": f'Bearer resource_metadata=f"{request.base_url}/.well-known/oauth-protected-resource"', + # }, + # ) else: 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, @@ -333,6 +344,172 @@ class MCPRequestHandler: verbose_logger.warning(f"Failed to get allowed MCP servers: {str(e)}") return [] + @staticmethod + async def _get_key_object_permission( + user_api_key_auth: Optional[UserAPIKeyAuth] = None, + ): + """Helper to get key object_permission from cache or DB.""" + from litellm.proxy.auth.auth_checks import get_object_permission + from litellm.proxy.proxy_server import ( + prisma_client, + proxy_logging_obj, + user_api_key_cache, + ) + + if not user_api_key_auth: + return None + + # Already loaded + if user_api_key_auth.object_permission: + return user_api_key_auth.object_permission + + # Need to fetch from DB + if user_api_key_auth.object_permission_id and prisma_client: + return await get_object_permission( + object_permission_id=user_api_key_auth.object_permission_id, + prisma_client=prisma_client, + user_api_key_cache=user_api_key_cache, + parent_otel_span=user_api_key_auth.parent_otel_span, + proxy_logging_obj=proxy_logging_obj, + ) + + return None + + @staticmethod + async def _get_team_object_permission( + user_api_key_auth: Optional[UserAPIKeyAuth] = None, + ): + """Helper to get team object_permission from cache or DB.""" + from litellm.proxy.auth.auth_checks import ( + get_object_permission, + get_team_object, + ) + from litellm.proxy.proxy_server import ( + prisma_client, + proxy_logging_obj, + user_api_key_cache, + ) + + if not user_api_key_auth or not user_api_key_auth.team_id or not prisma_client: + return None + + # First get the team object (which may have object_permission already loaded) + team_obj: Optional[LiteLLM_TeamTable] = await get_team_object( + team_id=user_api_key_auth.team_id, + prisma_client=prisma_client, + user_api_key_cache=user_api_key_cache, + parent_otel_span=user_api_key_auth.parent_otel_span, + proxy_logging_obj=proxy_logging_obj, + ) + + if not team_obj: + return None + + # Already loaded + if team_obj.object_permission: + return team_obj.object_permission + + # Need to fetch from DB using object_permission_id + if team_obj.object_permission_id: + return await get_object_permission( + object_permission_id=team_obj.object_permission_id, + prisma_client=prisma_client, + user_api_key_cache=user_api_key_cache, + parent_otel_span=user_api_key_auth.parent_otel_span, + proxy_logging_obj=proxy_logging_obj, + ) + + return None + + @staticmethod + async def get_allowed_tools_for_server( + server_id: str, + user_api_key_auth: Optional[UserAPIKeyAuth] = None, + ) -> Optional[List[str]]: + """ + Get list of allowed tool names for a specific server based on key/team permissions. + Follows same inheritance logic as get_allowed_mcp_servers. + + Args: + server_id: Server ID to check permissions for + user_api_key_auth: User auth + + Returns: + List[str] if restrictions exist, None if no restrictions (allow all) + """ + if not user_api_key_auth: + return None + + try: + # Get key and team object permissions + key_obj_perm = await MCPRequestHandler._get_key_object_permission( + user_api_key_auth + ) + team_obj_perm = await MCPRequestHandler._get_team_object_permission( + user_api_key_auth + ) + + # Extract tool permissions for this server + key_tools = ( + key_obj_perm.mcp_tool_permissions.get(server_id) + if key_obj_perm and key_obj_perm.mcp_tool_permissions + else None + ) + team_tools = ( + team_obj_perm.mcp_tool_permissions.get(server_id) + if team_obj_perm and team_obj_perm.mcp_tool_permissions + else None + ) + + # Apply same inheritance logic as get_allowed_mcp_servers + if team_tools: + if key_tools: + # Both have restrictions → intersection + return list(set(team_tools) & set(key_tools)) + else: + # Only team has restrictions → inherit from team + return team_tools + else: + # No team restrictions → use key restrictions + return key_tools + + except Exception as e: + verbose_logger.warning(f"Failed to get allowed tools for server: {str(e)}") + return None + + @staticmethod + async def is_tool_allowed_for_server( + tool_name: str, + server_id: str, + user_api_key_auth: Optional[UserAPIKeyAuth] = None, + ) -> bool: + """ + Check if a specific tool is allowed for a server based on key/team permissions. + + Args: + tool_name: Name of the tool to check + server_id: Server ID + user_api_key_auth: User auth + + Returns: + True if allowed, False if blocked + """ + allowed_tools = await MCPRequestHandler.get_allowed_tools_for_server( + server_id=server_id, + user_api_key_auth=user_api_key_auth, + ) + + # None means no restrictions (allow all) + if allowed_tools is None: + return True + + # Empty list means no tools allowed + if not allowed_tools: + return False + + # Check if tool is in allowed list + return tool_name in allowed_tools + @staticmethod def is_tool_allowed( allowed_mcp_servers: List[str], @@ -403,41 +580,24 @@ class MCPRequestHandler: user_api_key_auth: Optional[UserAPIKeyAuth] = None, ) -> List[str]: """ - The `object_permission` for a team is not stored on the user_api_key_auth object + Get allowed MCP servers for a team. - first we check if the team has a object_permission_id attached - - if it does then we look up the object_permission for the team + Uses the helper _get_team_object_permission which: + 1. First checks if object_permission is already loaded on the team + 2. If not, fetches from DB using object_permission_id if it exists """ - from litellm.proxy.auth.auth_checks import get_team_object - from litellm.proxy.proxy_server import ( - prisma_client, - proxy_logging_obj, - user_api_key_cache, - ) - if user_api_key_auth is None: return [] if user_api_key_auth.team_id is None: return [] - if prisma_client is None: - verbose_logger.debug("prisma_client is None") - return [] - try: - team_obj: Optional[LiteLLM_TeamTable] = await get_team_object( - team_id=user_api_key_auth.team_id, - prisma_client=prisma_client, - user_api_key_cache=user_api_key_cache, - parent_otel_span=user_api_key_auth.parent_otel_span, - proxy_logging_obj=proxy_logging_obj, + # Use the helper method that properly handles fetching from DB if needed + object_permissions = await MCPRequestHandler._get_team_object_permission( + user_api_key_auth ) - if team_obj is None: - verbose_logger.debug("team_obj is None") - return [] - object_permissions = team_obj.object_permission if object_permissions is None: return [] diff --git a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py index 2c46f9561db..28581c778c6 100644 --- a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py +++ b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py @@ -195,6 +195,7 @@ class MCPServerManager: name=name_for_prefix, alias=alias, server_name=server_name, + spec_path=server_config.get("spec_path", None), url=server_config.get("url", None) or "", command=server_config.get("command", None) or "", args=server_config.get("args", None) or [], @@ -215,15 +216,167 @@ class MCPServerManager: extra_headers=server_config.get("extra_headers", None), allowed_tools=server_config.get("allowed_tools", None), disallowed_tools=server_config.get("disallowed_tools", None), + allowed_params=server_config.get("allowed_params", None), access_groups=server_config.get("access_groups", None), ) self.config_mcp_servers[server_id] = new_server + + # Check if this is an OpenAPI-based server + spec_path = server_config.get("spec_path", None) + if spec_path: + verbose_logger.info( + f"Loading OpenAPI spec from {spec_path} for server {server_name}" + ) + self._register_openapi_tools( + spec_path=spec_path, + server=new_server, + base_url=server_config.get("url", ""), + ) + verbose_logger.debug( f"Loaded MCP Servers: {json.dumps(self.config_mcp_servers, indent=4, default=str)}" ) self.initialize_tool_name_to_mcp_server_name_mapping() + def _register_openapi_tools(self, spec_path: str, server: MCPServer, base_url: str): + """ + Register tools from an OpenAPI specification for a given server. + + This creates "virtual" MCP tools from OpenAPI endpoints that are: + 1. Registered in the global tool registry with server prefix + 2. Mapped to the server for routing + 3. Executed via the local tool handler + + Args: + spec_path: Path to the OpenAPI specification file + server: The MCPServer instance to register tools for + base_url: Base URL for API calls + """ + from litellm.proxy._experimental.mcp_server.openapi_to_mcp_generator import ( + build_input_schema, + create_tool_function, + ) + from litellm.proxy._experimental.mcp_server.openapi_to_mcp_generator import ( + get_base_url as get_openapi_base_url, + ) + from litellm.proxy._experimental.mcp_server.openapi_to_mcp_generator import ( + load_openapi_spec, + ) + from litellm.proxy._experimental.mcp_server.tool_registry import ( + global_mcp_tool_registry, + ) + + try: + # Load OpenAPI spec + spec = load_openapi_spec(spec_path) + + # Use base_url from config if provided, otherwise extract from spec + if not base_url: + base_url = get_openapi_base_url(spec) + + verbose_logger.info( + f"Registering OpenAPI tools for server {server.name} with base URL: {base_url}" + ) + + # Get server prefix for tool naming + server_prefix = get_server_prefix(server) + + # Build headers from server configuration + headers = {} + + # Add authentication headers if configured + if server.authentication_token: + from litellm.types.mcp import MCPAuth + + if server.auth_type == MCPAuth.bearer_token: + headers["Authorization"] = f"Bearer {server.authentication_token}" + elif server.auth_type == MCPAuth.api_key: + headers["Authorization"] = f"ApiKey {server.authentication_token}" + elif server.auth_type == MCPAuth.basic: + headers["Authorization"] = f"Basic {server.authentication_token}" + + # Add any extra headers from server config + # Note: extra_headers is a List[str] of header names to forward, not a dict + # For OpenAPI tools, we'll just use the authentication headers + # If extra_headers were needed, they would be processed separately + + verbose_logger.debug( + f"Using headers for OpenAPI tools (excluding sensitive values): " + f"{list(headers.keys())}" + ) + + # Extract and register tools from OpenAPI paths + paths = spec.get("paths", {}) + registered_count = 0 + + verbose_logger.debug(f"Processing {len(paths)} paths from OpenAPI spec") + + for path, path_item in paths.items(): + for method in ["get", "post", "put", "delete", "patch"]: + if method not in path_item: + continue + + operation = path_item[method] + + # Generate tool name (without prefix initially) + operation_id = operation.get( + "operationId", f"{method}_{path.replace('/', '_')}" + ) + base_tool_name = operation_id.replace(" ", "_").lower() + + # Add server prefix to tool name + prefixed_tool_name = add_server_prefix_to_tool_name( + base_tool_name, server_prefix + ) + + # Get description + description = operation.get( + "summary", + operation.get("description", f"{method.upper()} {path}"), + ) + + # Build input schema using imported function + input_schema = build_input_schema(operation) + + # Create tool function with headers using imported function + tool_func = create_tool_function( + path, method, operation, base_url, headers=headers + ) + tool_func.__name__ = prefixed_tool_name + tool_func.__doc__ = description + + # Register tool with prefixed name in global registry + global_mcp_tool_registry.register_tool( + name=prefixed_tool_name, + description=description, + input_schema=input_schema, + handler=tool_func, + ) + + # Update tool name to server name mapping (for both prefixed and base names) + self.tool_name_to_mcp_server_name_mapping[ + base_tool_name + ] = server_prefix + self.tool_name_to_mcp_server_name_mapping[ + prefixed_tool_name + ] = server_prefix + + registered_count += 1 + verbose_logger.debug( + f"Registered OpenAPI tool: {prefixed_tool_name} for server {server.name}" + ) + + verbose_logger.info( + f"Successfully registered {registered_count} OpenAPI tools for server {server.name}" + ) + + except Exception as e: + verbose_logger.error( + f"Failed to register OpenAPI tools for server {server.name}: {str(e)}" + ) + raise e + def remove_server(self, mcp_server: LiteLLM_MCPServerTable): """ Remove a server from the registry @@ -469,6 +622,10 @@ class MCPServerManager: Returns: List[MCPTool]: List of tools available on the server with prefixed names """ + from litellm.proxy._experimental.mcp_server.tool_registry import ( + global_mcp_tool_registry, + ) + verbose_logger.debug(f"Connecting to url: {server.url}") verbose_logger.info(f"_get_tools_from_server for {server.name}...") @@ -481,7 +638,14 @@ class MCPServerManager: extra_headers=extra_headers, ) - tools = await self._fetch_tools_with_timeout(client, server.name) + ## HANDLE OPENAPI TOOLS + if server.spec_path: + _tools = global_mcp_tool_registry.list_tools(tool_prefix=server.name) + tools = global_mcp_tool_registry.convert_tools_to_mcp_sdk_tool_type( + _tools + ) + else: + tools = await self._fetch_tools_with_timeout(client, server.name) prefixed_or_original_tools = self._create_prefixed_tools( tools, server, add_prefix=add_prefix @@ -597,11 +761,168 @@ class MCPServerManager: Check if the tool is allowed or banned for the given server """ if server.allowed_tools: - return tool_name in server.allowed_tools + return ( + tool_name in server.allowed_tools + or f"{server.name}-{tool_name}" in server.allowed_tools + ) if server.disallowed_tools: - return tool_name not in server.disallowed_tools + return ( + tool_name not in server.disallowed_tools + and f"{server.name}-{tool_name}" not in server.disallowed_tools + ) return True + def validate_allowed_params( + self, tool_name: str, arguments: Dict[str, Any], server: MCPServer + ) -> None: + """ + Filter arguments to only include allowed parameters for the given tool. + + Args: + tool_name: Name of the tool (with or without prefix) + arguments: Dictionary of arguments to filter + server: MCPServer configuration + + Returns: + Filtered dictionary containing only allowed parameters + + Raises: + HTTPException: If allowed_params is configured for this tool but arguments contain disallowed params + """ + from litellm.proxy._experimental.mcp_server.utils import ( + get_server_name_prefix_tool_mcp, + ) + + # If no allowed_params configured, return all arguments + if not server.allowed_params: + return + + # Get the unprefixed tool name to match against config + unprefixed_tool_name, _ = get_server_name_prefix_tool_mcp(tool_name) + + # Check both prefixed and unprefixed tool names + allowed_params_list = server.allowed_params.get( + tool_name + ) or server.allowed_params.get(unprefixed_tool_name) + + # If this tool doesn't have allowed_params specified, allow all params + if allowed_params_list is None: + return None + + # Filter arguments to only include allowed parameters + disallowed_params = [ + param for param in arguments.keys() if param not in allowed_params_list + ] + + if disallowed_params: + raise HTTPException( + status_code=403, + detail={ + "error": f"Parameters {disallowed_params} are not allowed for tool {tool_name}. " + f"Allowed parameters: {allowed_params_list}. " + f"Contact proxy admin to allow these parameters." + }, + ) + + async def check_tool_permission_for_key_team( + self, + tool_name: str, + server: MCPServer, + user_api_key_auth: Optional[UserAPIKeyAuth], + ) -> None: + """ + Check if a tool is allowed based on key/team object_permission.mcp_tool_permissions. + Uses MCPRequestHandler.is_tool_allowed_for_server for consistent inheritance logic. + Raises HTTPException if tool is not allowed. + + Args: + tool_name: Name of the tool to check + server: MCPServer object + user_api_key_auth: User authentication + + Raises: + HTTPException: If tool is not allowed for this key/team + """ + from litellm.proxy._experimental.mcp_server.auth.user_api_key_auth_mcp import ( + MCPRequestHandler, + ) + + if not user_api_key_auth: + return + + # Check if tool is allowed + is_allowed = await MCPRequestHandler.is_tool_allowed_for_server( + tool_name=tool_name, + server_id=server.server_id, + user_api_key_auth=user_api_key_auth, + ) + + if not is_allowed: + raise HTTPException( + status_code=403, + detail={ + "error": f"Tool '{tool_name}' is not allowed for your key/team on server '{server.name}'. Contact proxy admin for access." + }, + ) + + async def _call_openapi_tool_handler( + self, + server: MCPServer, + tool_name: str, + arguments: Dict[str, Any], + ) -> CallToolResult: + """ + Call an OpenAPI tool handler directly. + + For OpenAPI servers, instead of using MCP protocol, we call the tool handler + that was registered during OpenAPI spec parsing. This handler makes direct + HTTP requests to the API. + + Args: + tool_name: The full tool name (with prefix) to call + arguments: Tool arguments to pass to the handler + + Returns: + CallToolResult with the response from the API + """ + from mcp.types import TextContent + + from litellm.proxy._experimental.mcp_server.tool_registry import ( + global_mcp_tool_registry, + ) + + # Get the tool from the registry + tool = global_mcp_tool_registry.get_tool(f"{server.name}-{tool_name}") + if tool is None: + # Tool not found in registry + error_msg = f"OpenAPI tool {tool_name} not found in registry" + verbose_logger.error(error_msg) + return CallToolResult( + content=[TextContent(type="text", text=error_msg)], + isError=True, + ) + + try: + # Call the tool handler with the arguments + # The handler is an async function that makes the HTTP request + handler_result = await tool.handler(**arguments) + + # Convert the handler result (string response) to CallToolResult format + result = CallToolResult( + content=[TextContent(type="text", text=str(handler_result))], + isError=False, + ) + + return result + + except Exception as e: + error_msg = f"Error calling OpenAPI tool {tool_name}: {str(e)}" + verbose_logger.error(error_msg) + return CallToolResult( + content=[TextContent(type="text", text=error_msg)], + isError=True, + ) + async def pre_call_tool_check( self, name: str, @@ -611,7 +932,6 @@ class MCPServerManager: proxy_logging_obj: ProxyLogging, server: MCPServer, ): - ## check if the tool is allowed or banned for the given server if not self.check_allowed_or_banned_tools(name, server): raise HTTPException( @@ -621,6 +941,20 @@ class MCPServerManager: }, ) + ## check tool-level permissions from object_permission + await self.check_tool_permission_for_key_team( + tool_name=name, + server=server, + user_api_key_auth=user_api_key_auth, + ) + + ## filter parameters based on allowed_params configuration + self.validate_allowed_params( + tool_name=name, + arguments=arguments, + server=server, + ) + pre_hook_kwargs = { "name": name, "arguments": arguments, @@ -684,6 +1018,46 @@ class MCPServerManager: verbose_logger.error(f"Guardrail blocked MCP tool call pre call: {str(e)}") raise e + def _create_during_hook_task( + self, + name: str, + arguments: Dict[str, Any], + server_name_from_prefix: Optional[str], + user_api_key_auth: Optional[UserAPIKeyAuth], + proxy_logging_obj: ProxyLogging, + start_time: datetime.datetime, + ): + """Create and return a during hook task for MCP tool calls.""" + from litellm.types.llms.base import HiddenParams + from litellm.types.mcp import MCPDuringCallRequestObject + + request_obj = MCPDuringCallRequestObject( + tool_name=name, + arguments=arguments, + server_name=server_name_from_prefix, + 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 + ) + + return 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 + ) + ) + async def call_tool( self, name: str, @@ -747,95 +1121,92 @@ class MCPServerManager: server=mcp_server, ) - # Get server-specific auth header if available - server_auth_header: Optional[Union[Dict[str, str], str]] = None - if mcp_server_auth_headers and mcp_server.alias: - server_auth_header = mcp_server_auth_headers.get(mcp_server.alias) - elif mcp_server_auth_headers and mcp_server.server_name: - server_auth_header = mcp_server_auth_headers.get(mcp_server.server_name) + # Prepare tasks for during hooks + tasks = [] + if proxy_logging_obj: + during_hook_task = self._create_during_hook_task( + name=name, + arguments=arguments, + server_name_from_prefix=server_name_from_prefix, + user_api_key_auth=user_api_key_auth, + proxy_logging_obj=proxy_logging_obj, + start_time=start_time, + ) + tasks.append(during_hook_task) - # Fall back to deprecated mcp_auth_header if no server-specific header found - if server_auth_header is None: - server_auth_header = mcp_auth_header + # For OpenAPI servers, call the tool handler directly instead of via MCP client + if mcp_server.spec_path: + verbose_logger.debug( + f"Calling OpenAPI tool {name} directly via HTTP handler" + ) + tasks.append( + asyncio.create_task( + self._call_openapi_tool_handler(mcp_server, name, arguments) + ) + ) + else: + # For regular MCP servers, use the MCP client + # Get server-specific auth header if available + server_auth_header: Optional[Union[Dict[str, str], str]] = None + if mcp_server_auth_headers and mcp_server.alias: + server_auth_header = mcp_server_auth_headers.get(mcp_server.alias) + elif mcp_server_auth_headers and mcp_server.server_name: + server_auth_header = mcp_server_auth_headers.get(mcp_server.server_name) - # oauth2 headers - extra_headers: Optional[Dict[str, str]] = None - if mcp_server.auth_type == MCPAuth.oauth2: - extra_headers = oauth2_headers + # Fall back to deprecated mcp_auth_header if no server-specific header found + if server_auth_header is None: + server_auth_header = mcp_auth_header - if mcp_server.extra_headers and raw_headers: - if extra_headers is None: - extra_headers = {} - for header in mcp_server.extra_headers: - if header in raw_headers: - extra_headers[header] = raw_headers[header] + # oauth2 headers + extra_headers: Optional[Dict[str, str]] = None + if mcp_server.auth_type == MCPAuth.oauth2: + extra_headers = oauth2_headers - client = self._create_mcp_client( - server=mcp_server, - mcp_auth_header=server_auth_header, - extra_headers=extra_headers, - ) + if mcp_server.extra_headers and raw_headers: + if extra_headers is None: + extra_headers = {} + for header in mcp_server.extra_headers: + if header in raw_headers: + extra_headers[header] = raw_headers[header] + + client = self._create_mcp_client( + server=mcp_server, + mcp_auth_header=server_auth_header, + extra_headers=extra_headers, + ) - async with client: - # Use the original tool name (without prefix) for the actual call call_tool_params = MCPCallToolRequestParams( name=original_tool_name, arguments=arguments, ) - tasks = [] - if proxy_logging_obj: - # 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, - server_name=server_name_from_prefix, - start_time=start_time.timestamp() if start_time else None, - hidden_params=HiddenParams(), - ) + async def _call_tool_via_client(client, params): + async with client: + return await client.call_tool(params) - during_hook_kwargs = { - "name": name, - "arguments": arguments, - "server_name": server_name_from_prefix, - "user_api_key_auth": user_api_key_auth, - } + tasks.append( + asyncio.create_task(_call_tool_via_client(client, call_tool_params)) + ) - synthetic_llm_data = proxy_logging_obj._convert_mcp_to_llm_format( - request_obj, during_hook_kwargs - ) + try: + mcp_responses = await asyncio.gather(*tasks) - 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 - ) - ) - tasks.append(during_hook_task) + # If proxy_logging_obj is None, the tool call result is at index 0 + # If proxy_logging_obj is not None, the tool call result is at index 1 (after the during hook task) + result_index = 1 if proxy_logging_obj else 0 + result = mcp_responses[result_index] - 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 - # If proxy_logging_obj is not None, the tool call result is at index 1 (after the during hook task) - result_index = 1 if proxy_logging_obj else 0 - result = mcp_responses[result_index] - - return cast(CallToolResult, result) - 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 during result check: {str(e)}" - ) - raise e + return cast(CallToolResult, result) + 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 during result check: {str(e)}" + ) + raise e ######################################################### # End of Methods that call the upstream MCP servers @@ -1009,6 +1380,7 @@ class MCPServerManager: if not server: return { "server_id": server_id, + "server_name": None, "status": "unknown", "error": "Server not found", "last_health_check": datetime.now().isoformat(), @@ -1023,6 +1395,7 @@ class MCPServerManager: return { "server_id": server_id, + "server_name": server.name, "status": "healthy", "tools_count": len(tools), "last_health_check": datetime.now().isoformat(), @@ -1035,6 +1408,7 @@ class MCPServerManager: return { "server_id": server_id, + "server_name": server.name, "status": "unhealthy", "last_health_check": datetime.now().isoformat(), "response_time_ms": round(response_time, 2), diff --git a/litellm/proxy/_experimental/mcp_server/openapi_to_mcp_generator.py b/litellm/proxy/_experimental/mcp_server/openapi_to_mcp_generator.py new file mode 100644 index 00000000000..72288f8e673 --- /dev/null +++ b/litellm/proxy/_experimental/mcp_server/openapi_to_mcp_generator.py @@ -0,0 +1,236 @@ +""" +This module is used to generate MCP tools from OpenAPI specs. +""" + +import json +from typing import Any, Dict, Optional + +import httpx + +from litellm._logging import verbose_logger +from litellm.proxy._experimental.mcp_server.tool_registry import ( + global_mcp_tool_registry, +) + +# Store the base URL and headers globally +BASE_URL = "" +HEADERS: Dict[str, str] = {} + + +def load_openapi_spec(filepath: str) -> Dict[str, Any]: + """Load OpenAPI specification from JSON file.""" + with open(filepath, "r") as f: + return json.load(f) + + +def get_base_url(spec: Dict[str, Any]) -> str: + """Extract base URL from OpenAPI spec.""" + # OpenAPI 3.x + if "servers" in spec and spec["servers"]: + return spec["servers"][0]["url"] + # OpenAPI 2.x (Swagger) + elif "host" in spec: + scheme = spec.get("schemes", ["https"])[0] + base_path = spec.get("basePath", "") + return f"{scheme}://{spec['host']}{base_path}" + return "" + + +def extract_parameters(operation: Dict[str, Any]) -> tuple: + """Extract parameter names from OpenAPI operation.""" + path_params = [] + query_params = [] + body_params = [] + + # OpenAPI 3.x and 2.x parameters + if "parameters" in operation: + for param in operation["parameters"]: + param_name = param["name"] + if param.get("in") == "path": + path_params.append(param_name) + elif param.get("in") == "query": + query_params.append(param_name) + elif param.get("in") == "body": + body_params.append(param_name) + + # OpenAPI 3.x requestBody + if "requestBody" in operation: + body_params.append("body") + + return path_params, query_params, body_params + + +def build_input_schema(operation: Dict[str, Any]) -> Dict[str, Any]: + """Build MCP input schema from OpenAPI operation.""" + properties = {} + required = [] + + # Process parameters + if "parameters" in operation: + for param in operation["parameters"]: + param_name = param["name"] + param_schema = param.get("schema", {}) + param_type = param_schema.get("type", "string") + + properties[param_name] = { + "type": param_type, + "description": param.get("description", ""), + } + + if param.get("required", False): + required.append(param_name) + + # Process requestBody (OpenAPI 3.x) + if "requestBody" in operation: + request_body = operation["requestBody"] + content = request_body.get("content", {}) + + # Try to get JSON schema + if "application/json" in content: + schema = content["application/json"].get("schema", {}) + properties["body"] = { + "type": "object", + "description": request_body.get("description", "Request body"), + "properties": schema.get("properties", {}), + } + if request_body.get("required", False): + required.append("body") + + return { + "type": "object", + "properties": properties, + "required": required if required else [], + } + + +def create_tool_function( + path: str, + method: str, + operation: Dict[str, Any], + base_url: str, + headers: Optional[Dict[str, str]] = None, +): + """Create a tool function for an OpenAPI operation. + + Args: + path: API endpoint path + method: HTTP method (get, post, put, delete, patch) + operation: OpenAPI operation object + base_url: Base URL for the API + headers: Optional headers to include in requests (e.g., authentication) + """ + if headers is None: + headers = {} + + path_params, query_params, body_params = extract_parameters(operation) + all_params = path_params + query_params + body_params + + # Build function signature dynamically + if all_params: + params_str = ", ".join(f"{p}: str = ''" for p in all_params) + else: + params_str = "" + + # Create the function code as a string + func_code = f''' +async def tool_function({params_str}) -> str: + """Dynamically generated tool function.""" + url = base_url + path + + # Replace path parameters + path_param_names = {path_params} + for param_name in path_param_names: + param_value = locals().get(param_name, "") + if param_value: + url = url.replace("{{" + param_name + "}}", str(param_value)) + + # Build query params + query_param_names = {query_params} + params = {{}} + for param_name in query_param_names: + param_value = locals().get(param_name, "") + if param_value: + params[param_name] = param_value + + # Build request body + body_param_names = {body_params} + json_body = None + if body_param_names: + body_value = locals().get("body", {{}}) + if isinstance(body_value, dict): + json_body = body_value + elif body_value: + # If it's a string, try to parse as JSON + import json as json_module + try: + json_body = json_module.loads(body_value) if isinstance(body_value, str) else {{"data": body_value}} + except: + json_body = {{"data": body_value}} + + # Make HTTP request + async with httpx.AsyncClient() as client: + if "{method.lower()}" == "get": + response = await client.get(url, params=params, headers=headers) + elif "{method.lower()}" == "post": + response = await client.post(url, params=params, json=json_body, headers=headers) + elif "{method.lower()}" == "put": + response = await client.put(url, params=params, json=json_body, headers=headers) + elif "{method.lower()}" == "delete": + response = await client.delete(url, params=params, headers=headers) + elif "{method.lower()}" == "patch": + response = await client.patch(url, params=params, json=json_body, headers=headers) + else: + return "Unsupported HTTP method: {method}" + + return response.text +''' + + # Execute the function code to create the actual function + local_vars = { + "httpx": httpx, + "headers": headers, + "base_url": base_url, + "path": path, + "method": method, + } + exec(func_code, local_vars) + + return local_vars["tool_function"] + + +def register_tools_from_openapi(spec: Dict[str, Any], base_url: str): + """Register MCP tools from OpenAPI specification.""" + paths = spec.get("paths", {}) + + for path, path_item in paths.items(): + for method in ["get", "post", "put", "delete", "patch"]: + if method in path_item: + operation = path_item[method] + + # Generate tool name + operation_id = operation.get( + "operationId", f"{method}_{path.replace('/', '_')}" + ) + tool_name = operation_id.replace(" ", "_").lower() + + # Get description + description = operation.get( + "summary", operation.get("description", f"{method.upper()} {path}") + ) + + # Build input schema + input_schema = build_input_schema(operation) + + # Create tool function + tool_func = create_tool_function(path, method, operation, base_url) + tool_func.__name__ = tool_name + tool_func.__doc__ = description + + # Register tool with local registry + global_mcp_tool_registry.register_tool( + name=tool_name, + description=description, + input_schema=input_schema, + handler=tool_func, + ) + verbose_logger.debug(f"Registered tool: {tool_name}") diff --git a/litellm/proxy/_experimental/mcp_server/server.py b/litellm/proxy/_experimental/mcp_server/server.py index 3e7c291810a..77d6abfed62 100644 --- a/litellm/proxy/_experimental/mcp_server/server.py +++ b/litellm/proxy/_experimental/mcp_server/server.py @@ -364,25 +364,25 @@ if MCP_AVAILABLE: def _tool_name_matches(tool_name: str, filter_list: List[str]) -> bool: """ Check if a tool name matches any name in the filter list. - + Checks both the full tool name and unprefixed version (without server prefix). This allows users to configure simple tool names regardless of prefixing. - + Args: tool_name: The tool name to check (may be prefixed like "server-tool_name") filter_list: List of tool names to match against - + Returns: True if the tool name (prefixed or unprefixed) is in the filter list """ from litellm.proxy._experimental.mcp_server.utils import ( get_server_name_prefix_tool_mcp, ) - + # Check if the full name is in the list if tool_name in filter_list: return True - + # Check if the unprefixed name is in the list unprefixed_name, _ = get_server_name_prefix_tool_mcp(tool_name) return unprefixed_name in filter_list @@ -393,34 +393,36 @@ if MCP_AVAILABLE: ) -> List[MCPTool]: """ Filter tools by allowed/disallowed tools configuration. - + If allowed_tools is set, only tools in that list are returned. If disallowed_tools is set, tools in that list are excluded. Tool names are matched with and without server prefixes for flexibility. - + Args: tools: List of tools to filter mcp_server: Server configuration with allowed_tools/disallowed_tools - + Returns: Filtered list of tools """ tools_to_return = tools - + # Filter by allowed_tools (whitelist) if mcp_server.allowed_tools: tools_to_return = [ - tool for tool in tools + tool + for tool in tools if _tool_name_matches(tool.name, mcp_server.allowed_tools) ] - + # Filter by disallowed_tools (blacklist) if mcp_server.disallowed_tools: tools_to_return = [ - tool for tool in tools_to_return + tool + for tool in tools_to_return if not _tool_name_matches(tool.name, mcp_server.disallowed_tools) ] - + return tools_to_return async def _get_tools_from_mcp_servers( @@ -497,10 +499,17 @@ if MCP_AVAILABLE: extra_headers=extra_headers, add_prefix=add_prefix, ) - + filtered_tools = filter_tools_by_allowed_tools(tools, server) + + filtered_tools = await filter_tools_by_key_team_permissions( + tools=filtered_tools, + server_id=server_id, + user_api_key_auth=user_api_key_auth, + ) + all_tools.extend(filtered_tools) - + verbose_logger.debug( f"Successfully fetched {len(tools)} tools from server {server.name}, {len(filtered_tools)} after filtering" ) @@ -513,8 +522,41 @@ if MCP_AVAILABLE: verbose_logger.info( f"Successfully fetched {len(all_tools)} tools total from all MCP servers" ) + return all_tools + async def filter_tools_by_key_team_permissions( + tools: List[MCPTool], + server_id: str, + user_api_key_auth: Optional[UserAPIKeyAuth], + ) -> List[MCPTool]: + """ + Filter tools based on key/team mcp_tool_permissions. + + Note: Tool names in the DB are stored without server prefixes, + but tool names from MCP servers are prefixed. We need to strip + the prefix before comparing. + """ + # Filter by key/team tool-level permissions + allowed_tool_names = await MCPRequestHandler.get_allowed_tools_for_server( + server_id=server_id, + user_api_key_auth=user_api_key_auth, + ) + if allowed_tool_names is not None: + # Strip prefix from tool names before comparing + # Tools are stored in DB without prefix, but come from MCP server with prefix + filtered_tools = [] + for t in tools: + # Get tool name without server prefix + unprefixed_tool_name, _ = get_server_name_prefix_tool_mcp(t.name) + if unprefixed_tool_name in allowed_tool_names: + filtered_tools.append(t) + else: + # No restrictions, return all tools + filtered_tools = tools + + return filtered_tools + async def _list_mcp_tools( user_api_key_auth: Optional[UserAPIKeyAuth] = None, mcp_auth_header: Optional[str] = None, @@ -557,30 +599,7 @@ if MCP_AVAILABLE: ) # Continue with empty managed tools list instead of failing completely - # Get tools from local registry - local_tools = [] - try: - local_tools_raw = global_mcp_tool_registry.list_tools() - - # 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, - ) - local_tools.append(mcp_tool) - except Exception as 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 - all_tools = managed_tools + local_tools - - return all_tools + return managed_tools @client async def call_mcp_tool( @@ -641,33 +660,42 @@ if MCP_AVAILABLE: 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 + # Check if tool exists in local registry first (for OpenAPI-based tools) + # These tools are registered with their prefixed names ######################################################### - 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") - 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, - oauth2_headers=oauth2_headers, - raw_headers=raw_headers, - litellm_logging_obj=litellm_logging_obj, - ) + local_tool = global_mcp_tool_registry.get_tool(name) + if local_tool: + verbose_logger.debug(f"Executing local registry tool: {name}") + response = await _handle_local_mcp_tool(name, arguments) - # Fall back to local tool registry (use original name) - ######################################################### - # Deprecated: Local MCP Server Tool + # Try managed MCP server tool (pass the full prefixed name) + # Primary and recommended way to use external MCP servers ######################################################### else: - response = await _handle_local_mcp_tool(original_tool_name, arguments) + 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") + 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, + oauth2_headers=oauth2_headers, + raw_headers=raw_headers, + litellm_logging_obj=litellm_logging_obj, + ) + + # Fall back to local tool registry with original name (legacy support) + ######################################################### + # Deprecated: Local MCP Server Tool + ######################################################### + else: + response = await _handle_local_mcp_tool(original_tool_name, arguments) ######################################################### # Post MCP Tool Call Hook @@ -739,14 +767,21 @@ if MCP_AVAILABLE: Handle tool execution for local registry tools Note: Local tools don't use prefixes, so we use the original name """ + import inspect + tool = global_mcp_tool_registry.get_tool(name) if not tool: raise HTTPException(status_code=404, detail=f"Tool '{name}' not found") try: - result = tool.handler(**arguments) + # Check if handler is async or sync + if inspect.iscoroutinefunction(tool.handler): + result = await tool.handler(**arguments) + else: + result = tool.handler(**arguments) return [TextContent(text=str(result), type="text")] except Exception as e: + verbose_logger.exception(f"Error executing local tool {name}: {str(e)}") return [TextContent(text=f"Error: {str(e)}", type="text")] def _get_mcp_servers_in_path(path: str) -> Optional[List[str]]: @@ -867,6 +902,7 @@ if MCP_AVAILABLE: await session_manager.handle_request(scope, receive, send) except Exception as e: + raise e verbose_logger.exception(f"Error handling MCP request: {e}") # Instead of re-raising, try to send a graceful error response try: diff --git a/litellm/proxy/_experimental/mcp_server/tool_registry.py b/litellm/proxy/_experimental/mcp_server/tool_registry.py index c08b7979683..bc69095fc43 100644 --- a/litellm/proxy/_experimental/mcp_server/tool_registry.py +++ b/litellm/proxy/_experimental/mcp_server/tool_registry.py @@ -1,6 +1,8 @@ import json from typing import Any, Callable, Dict, List, Optional +from mcp.types import Tool as MCPToolSDKTool + from litellm._logging import verbose_logger from litellm.proxy.types_utils.utils import get_instance_fn from litellm.types.mcp_server.tool_registry import MCPTool @@ -39,12 +41,30 @@ class MCPToolRegistry: """ return self.tools.get(name) - def list_tools(self) -> List[MCPTool]: + def list_tools(self, tool_prefix: Optional[str] = None) -> List[MCPTool]: """ List all registered tools """ + if tool_prefix: + return [ + tool + for tool in self.tools.values() + if tool.name.startswith(tool_prefix) + ] return list(self.tools.values()) + def convert_tools_to_mcp_sdk_tool_type( + self, tools: List[MCPTool] + ) -> List[MCPToolSDKTool]: + return [ + MCPToolSDKTool( + name=tool.name, + description=tool.description, + inputSchema=tool.input_schema, + ) + for tool in tools + ] + def load_tools_from_config( self, mcp_tools_config: Optional[Dict[str, Any]] = None ) -> None: diff --git a/litellm/proxy/_experimental/out/_next/static/WkpkdsewrdPMuTzVGS_5j/_buildManifest.js b/litellm/proxy/_experimental/out/_next/static/WkpkdsewrdPMuTzVGS_5j/_buildManifest.js deleted file mode 100644 index 96ded068de5..00000000000 --- a/litellm/proxy/_experimental/out/_next/static/WkpkdsewrdPMuTzVGS_5j/_buildManifest.js +++ /dev/null @@ -1 +0,0 @@ -self.__BUILD_MANIFEST={__rewrites:{afterFiles:[],beforeFiles:[],fallback:[]},"/_error":["static/chunks/pages/_error-28b803cb2479b966.js"],sortedPages:["/_app","/_error"]},self.__BUILD_MANIFEST_CB&&self.__BUILD_MANIFEST_CB(); \ No newline at end of file diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/1039-1031e149b66e294d.js b/litellm/proxy/_experimental/out/_next/static/chunks/1039-1031e149b66e294d.js new file mode 100644 index 00000000000..74c9c3d33c5 --- /dev/null +++ b/litellm/proxy/_experimental/out/_next/static/chunks/1039-1031e149b66e294d.js @@ -0,0 +1 @@ +"use strict";(self.webpackChunk_N_E=self.webpackChunk_N_E||[]).push([[1039],{41649:function(e,t,n){n.d(t,{Z:function(){return f}});var r=n(5853),a=n(2265),o=n(1526),l=n(7084),i=n(26898),c=n(97324),s=n(1153);let 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