chore: initial snapshot for gitea/github upload
This commit is contained in:
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from .public_endpoints import router
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__all__ = ["router"]
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@@ -0,0 +1,194 @@
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[
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{
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"agent_type": "a2a",
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"agent_type_display_name": "A2A Standard",
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"description": "Standard A2A protocol",
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"logo_url": "/ui/assets/logos/a2a_agent.png",
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"credential_fields": [],
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"litellm_params_template": {}
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},
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{
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"agent_type": "langgraph",
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"agent_type_display_name": "LangGraph",
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"description": "Connect to LangGraph agents via the LangGraph Platform API",
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"logo_url": "/ui/assets/logos/langgraph.png",
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"model_template": "langgraph/{assistant_id}",
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"credential_fields": [
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{
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"key": "assistant_id",
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"label": "Assistant ID",
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"placeholder": "agent",
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"tooltip": "The assistant/agent ID from your LangGraph deployment",
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"required": true,
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"field_type": "text",
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"default_value": "agent",
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"include_in_litellm_params": false
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},
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{
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"key": "api_base",
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"label": "LangGraph API Base",
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"placeholder": "http://localhost:2024",
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"tooltip": "The base URL for your LangGraph server (e.g., http://localhost:2024 or your deployed LangGraph Cloud URL)",
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"required": true,
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"field_type": "text",
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"default_value": "http://localhost:2024",
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"include_in_litellm_params": true
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},
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{
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"key": "api_key",
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"label": "LangGraph API Key",
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"placeholder": null,
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"tooltip": "API key for authenticating with your LangGraph server (optional for local development)",
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"required": false,
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"field_type": "password",
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"default_value": null,
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"include_in_litellm_params": true
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}
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],
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"litellm_params_template": {
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"custom_llm_provider": "langgraph"
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}
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},
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{
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"agent_type": "bedrock_agentcore",
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"agent_type_display_name": "Bedrock AgentCore",
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"description": "Connect to Amazon Bedrock AgentCore hosted agent runtimes",
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"logo_url": "/ui/assets/logos/bedrock.svg",
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"inherit_credentials_from_provider": "Bedrock",
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"model_template": "bedrock/agentcore/{agent_runtime_arn}",
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"credential_fields": [
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{
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"key": "agent_runtime_arn",
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"label": "Agent Runtime ARN",
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"placeholder": "arn:aws:bedrock-agentcore:us-west-2:123456789012:runtime/my-agent-runtime",
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"tooltip": "The ARN of your Bedrock AgentCore runtime. Find this in your AWS Bedrock console under AgentCore.",
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"required": true,
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"field_type": "text",
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"default_value": null,
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"include_in_litellm_params": false
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}
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],
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"litellm_params_template": {
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"custom_llm_provider": "bedrock"
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}
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},
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{
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"agent_type": "azure_ai_foundry",
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"agent_type_display_name": "Azure AI Foundry",
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"description": "Connect to Microsoft Azure AI Foundry agents",
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"logo_url": "/ui/assets/logos/azure_ai_foundry.png",
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"inherit_credentials_from_provider": "Azure AI",
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"model_template": "azure_ai/agents/{agent_id}",
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"credential_fields": [
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{
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"key": "agent_id",
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"label": "Agent ID",
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"placeholder": "asst_abc123",
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"tooltip": "The agent/assistant ID from your Azure AI Foundry project (e.g., asst_abc123)",
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"required": true,
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"field_type": "text",
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"default_value": null,
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"include_in_litellm_params": false
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},
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{
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"key": "api_base",
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"label": "Azure AI API Base",
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"placeholder": "https://your-resource.services.ai.azure.com/api/projects/your-project",
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"tooltip": "The base URL for your Azure AI Foundry project endpoint (e.g., https://your-resource.services.ai.azure.com/api/projects/your-project)",
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"required": true,
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"field_type": "text",
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"default_value": null,
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"include_in_litellm_params": true
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},
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{
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"key": "api_key",
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"label": "Azure AD Token",
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"placeholder": null,
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"tooltip": "Azure AD Bearer token for authentication. Optional if using Service Principal credentials below. Get via: az account get-access-token --resource https://ai.azure.com",
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"required": false,
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"field_type": "password",
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"default_value": null,
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"include_in_litellm_params": true
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},
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{
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"key": "tenant_id",
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"label": "Azure Tenant ID",
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"placeholder": "xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx",
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"tooltip": "Azure AD Tenant ID for Service Principal authentication. Find in Azure Portal > Azure Active Directory > Overview",
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"required": false,
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"field_type": "text",
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"default_value": null,
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"include_in_litellm_params": true
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},
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{
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"key": "client_id",
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"label": "Azure Client ID",
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"placeholder": "xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx",
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"tooltip": "Application (client) ID of your Service Principal. Find in Azure Portal > App registrations > your app",
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"required": false,
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"field_type": "text",
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"default_value": null,
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"include_in_litellm_params": true
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},
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{
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"key": "client_secret",
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"label": "Azure Client Secret",
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"placeholder": null,
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"tooltip": "Client secret for your Service Principal. Create in Azure Portal > App registrations > your app > Certificates & secrets",
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"required": false,
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"field_type": "password",
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"default_value": null,
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"include_in_litellm_params": true
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}
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],
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"litellm_params_template": {
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"custom_llm_provider": "azure_ai"
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}
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},
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{
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"agent_type": "pydantic_ai_agents",
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"agent_type_display_name": "Pydantic AI",
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"description": "Connect to Pydantic AI agents via A2A protocol (with fake streaming support)",
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"logo_url": "/ui/assets/logos/pydantic.svg",
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"use_a2a_form_fields": true,
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"credential_fields": [
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{
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"key": "api_base",
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"label": "Agent URL",
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"placeholder": "http://localhost:9999",
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"tooltip": "The base URL for your Pydantic AI agent server",
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"required": true,
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"field_type": "text",
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"default_value": "http://localhost:9999",
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"include_in_litellm_params": true
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}
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],
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"litellm_params_template": {
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"custom_llm_provider": "pydantic_ai_agents"
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}
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},
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{
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"agent_type": "vertex_agent_engine",
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"agent_type_display_name": "Vertex AI Agent Engine",
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"description": "Connect to Google Cloud Vertex AI Reasoning Engines",
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"logo_url": "/ui/assets/logos/google.svg",
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"inherit_credentials_from_provider": "Vertex_AI",
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"model_template": "vertex_ai/agent_engine/{reasoning_engine_id}",
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"credential_fields": [
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{
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"key": "reasoning_engine_id",
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"label": "Reasoning Engine Resource ID",
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"placeholder": "projects/123456789/locations/us-central1/reasoningEngines/987654321",
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"tooltip": "The full resource ID of your Vertex AI Reasoning Engine. Find this in Google Cloud Console under Vertex AI > Agent Builder > Your Agent.",
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"required": true,
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"field_type": "text",
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"default_value": null,
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"include_in_litellm_params": false
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}
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],
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"litellm_params_template": {
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"custom_llm_provider": "vertex_ai"
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}
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}
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]
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File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,421 @@
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import json
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import os
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import re
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from importlib.resources import files
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from typing import Any, Dict, List, Optional
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import litellm
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from fastapi import APIRouter, Depends, HTTPException
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from litellm._logging import verbose_logger
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from litellm.litellm_core_utils.get_blog_posts import (
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BlogPost,
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BlogPostsResponse,
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GetBlogPosts,
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get_blog_posts,
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)
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from litellm.proxy._types import CommonProxyErrors
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from litellm.proxy.auth.user_api_key_auth import user_api_key_auth
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from litellm.types.agents import AgentCard
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from litellm.types.mcp import MCPPublicServer
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from litellm.types.proxy.management_endpoints.model_management_endpoints import (
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ModelGroupInfoProxy,
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)
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from litellm.types.proxy.public_endpoints.public_endpoints import (
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AgentCreateInfo,
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ProviderCreateInfo,
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PublicModelHubInfo,
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SupportedEndpointsResponse,
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)
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from litellm.types.utils import LlmProviders
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router = APIRouter()
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# ---------------------------------------------------------------------------
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# /public/endpoints — helpers
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# ---------------------------------------------------------------------------
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_ENDPOINT_METADATA: Dict[str, Dict[str, str]] = {
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"chat_completions": {"label": "Chat Completions", "endpoint": "/chat/completions"},
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"messages": {"label": "Messages", "endpoint": "/messages"},
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"responses": {"label": "Responses", "endpoint": "/responses"},
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"embeddings": {"label": "Embeddings", "endpoint": "/embeddings"},
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"image_generations": {
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"label": "Image Generations",
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"endpoint": "/images/generations",
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},
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"audio_transcriptions": {
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"label": "Audio Transcriptions",
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"endpoint": "/audio/transcriptions",
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},
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"audio_speech": {"label": "Audio Speech", "endpoint": "/audio/speech"},
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"moderations": {"label": "Moderations", "endpoint": "/moderations"},
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"batches": {"label": "Batches", "endpoint": "/batches"},
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"rerank": {"label": "Rerank", "endpoint": "/rerank"},
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"ocr": {"label": "OCR", "endpoint": "/ocr"},
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"search": {"label": "Search", "endpoint": "/search"},
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"skills": {"label": "Skills", "endpoint": "/skills"},
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"interactions": {"label": "Interactions", "endpoint": "/interactions"},
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"a2a": {"label": "A2A (Agent Gateway)", "endpoint": "/a2a/{agent}/message/send"},
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"container": {"label": "Containers", "endpoint": "/containers"},
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"container_files": {
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"label": "Container Files",
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"endpoint": "/containers/{id}/files",
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},
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"compact": {"label": "Compact", "endpoint": "/responses/compact"},
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"files": {"label": "Files", "endpoint": "/files"},
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"image_edits": {"label": "Image Edits", "endpoint": "/images/edits"},
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"vector_stores_create": {
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"label": "Vector Stores (Create)",
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"endpoint": "/vector_stores",
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},
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"vector_stores_search": {
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"label": "Vector Stores (Search)",
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"endpoint": "/vector_stores/{id}/search",
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},
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"vector_store_files": {
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"label": "Vector Store Files",
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"endpoint": "/vector_stores/{id}/files",
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},
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"video_generations": {
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"label": "Video Generations",
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"endpoint": "/videos/generations",
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},
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"assistants": {"label": "Assistants", "endpoint": "/assistants"},
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"fine_tuning": {"label": "Fine Tuning", "endpoint": "/fine_tuning/jobs"},
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"text_completion": {"label": "Text Completion", "endpoint": "/completions"},
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"realtime": {"label": "Realtime", "endpoint": "/realtime"},
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"count_tokens": {"label": "Count Tokens", "endpoint": "/utils/token_counter"},
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"image_variations": {"label": "Image Variations", "endpoint": "/images/variations"},
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"generateContent": {"label": "Generate Content", "endpoint": "/generateContent"},
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"bedrock_invoke": {"label": "Bedrock Invoke", "endpoint": "/bedrock/invoke"},
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"bedrock_converse": {"label": "Bedrock Converse", "endpoint": "/bedrock/converse"},
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"rag_ingest": {"label": "RAG Ingest", "endpoint": "/rag/ingest"},
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"rag_query": {"label": "RAG Query", "endpoint": "/rag/query"},
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}
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_SLUG_SUFFIX_RE = re.compile(r"\s*\(`[^`]+`\)\s*$")
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# Loaded once on first request; never invalidated (local file, no TTL needed).
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_cached_endpoints: Optional[SupportedEndpointsResponse] = None
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def _clean_display_name(raw: str) -> str:
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return _SLUG_SUFFIX_RE.sub("", raw).strip()
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def _build_endpoints(raw: Dict[str, Any]) -> List[Dict[str, Any]]:
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"""Transform raw provider_endpoints_support_backup.json into the response shape."""
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providers: Dict[str, Any] = raw.get("providers", {})
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# Collect endpoint keys in insertion order (union across all providers).
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seen: set = set()
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all_keys: List[str] = []
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for provider_data in providers.values():
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for key in provider_data.get("endpoints", {}):
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if key not in seen:
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seen.add(key)
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all_keys.append(key)
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result: List[Dict[str, Any]] = []
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for key in all_keys:
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meta = _ENDPOINT_METADATA.get(key)
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label = meta["label"] if meta else key.replace("_", " ").title()
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path = meta["endpoint"] if meta else "/" + key.replace("_", "/")
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supporting: List[Dict[str, str]] = [
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{
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"slug": slug,
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"display_name": _clean_display_name(pd.get("display_name", slug)),
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}
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for slug, pd in providers.items()
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if pd.get("endpoints", {}).get(key)
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]
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result.append(
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{"key": key, "label": label, "endpoint": path, "providers": supporting}
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)
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return result
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def _load_endpoints() -> List[Dict[str, Any]]:
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raw = json.loads(
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files("litellm")
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.joinpath("provider_endpoints_support_backup.json")
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.read_text(encoding="utf-8")
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)
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return _build_endpoints(raw)
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# ---------------------------------------------------------------------------
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@router.get(
|
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"/public/model_hub",
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tags=["public", "model management"],
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dependencies=[Depends(user_api_key_auth)],
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response_model=List[ModelGroupInfoProxy],
|
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)
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async def public_model_hub():
|
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import litellm
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from litellm.proxy.proxy_server import (
|
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_get_model_group_info,
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llm_router,
|
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prisma_client,
|
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)
|
||||
from litellm.proxy.health_endpoints._health_endpoints import (
|
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_convert_health_check_to_dict,
|
||||
)
|
||||
|
||||
if llm_router is None:
|
||||
raise HTTPException(
|
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status_code=400, detail=CommonProxyErrors.no_llm_router.value
|
||||
)
|
||||
|
||||
model_groups: List[ModelGroupInfoProxy] = []
|
||||
if litellm.public_model_groups is not None:
|
||||
model_groups = _get_model_group_info(
|
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llm_router=llm_router,
|
||||
all_models_str=litellm.public_model_groups,
|
||||
model_group=None,
|
||||
)
|
||||
|
||||
# Fetch health check information if available
|
||||
health_checks_map = {}
|
||||
if prisma_client is not None:
|
||||
try:
|
||||
latest_checks = await prisma_client.get_all_latest_health_checks()
|
||||
for check in latest_checks:
|
||||
key = check.model_id if check.model_id else check.model_name
|
||||
if key:
|
||||
health_check_dict = _convert_health_check_to_dict(check)
|
||||
health_checks_map[key] = health_check_dict
|
||||
if check.model_name:
|
||||
health_checks_map[check.model_name] = health_check_dict
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
for model_group in model_groups:
|
||||
health_info = health_checks_map.get(model_group.model_group)
|
||||
if health_info:
|
||||
model_group.health_status = health_info.get("status")
|
||||
model_group.health_response_time = health_info.get("response_time_ms")
|
||||
model_group.health_checked_at = health_info.get("checked_at")
|
||||
|
||||
return model_groups
|
||||
|
||||
|
||||
@router.get(
|
||||
"/public/agent_hub",
|
||||
tags=["[beta] Agents", "public"],
|
||||
dependencies=[Depends(user_api_key_auth)],
|
||||
response_model=List[AgentCard],
|
||||
)
|
||||
async def get_agents():
|
||||
import litellm
|
||||
from litellm.proxy.agent_endpoints.agent_registry import global_agent_registry
|
||||
|
||||
agents = global_agent_registry.get_public_agent_list()
|
||||
|
||||
if litellm.public_agent_groups is None:
|
||||
return []
|
||||
agent_card_list = [
|
||||
agent.agent_card_params
|
||||
for agent in agents
|
||||
if agent.agent_id in litellm.public_agent_groups
|
||||
]
|
||||
return agent_card_list
|
||||
|
||||
|
||||
@router.get(
|
||||
"/public/mcp_hub",
|
||||
tags=["[beta] MCP", "public"],
|
||||
dependencies=[Depends(user_api_key_auth)],
|
||||
response_model=List[MCPPublicServer],
|
||||
)
|
||||
async def get_mcp_servers():
|
||||
from litellm.proxy._experimental.mcp_server.mcp_server_manager import (
|
||||
global_mcp_server_manager,
|
||||
)
|
||||
|
||||
public_mcp_servers = global_mcp_server_manager.get_public_mcp_servers()
|
||||
return [
|
||||
MCPPublicServer(
|
||||
**server.model_dump(),
|
||||
)
|
||||
for server in public_mcp_servers
|
||||
]
|
||||
|
||||
|
||||
@router.get(
|
||||
"/public/model_hub/info",
|
||||
tags=["public", "model management"],
|
||||
response_model=PublicModelHubInfo,
|
||||
)
|
||||
async def public_model_hub_info():
|
||||
import litellm
|
||||
from litellm.proxy.proxy_server import _title, version
|
||||
|
||||
try:
|
||||
from litellm_enterprise.proxy.proxy_server import EnterpriseProxyConfig
|
||||
|
||||
custom_docs_description = EnterpriseProxyConfig.get_custom_docs_description()
|
||||
except Exception:
|
||||
custom_docs_description = None
|
||||
|
||||
return PublicModelHubInfo(
|
||||
docs_title=_title,
|
||||
custom_docs_description=custom_docs_description,
|
||||
litellm_version=version,
|
||||
useful_links=litellm.public_model_groups_links,
|
||||
)
|
||||
|
||||
|
||||
@router.get(
|
||||
"/public/providers",
|
||||
tags=["public", "providers"],
|
||||
response_model=List[str],
|
||||
)
|
||||
async def get_supported_providers() -> List[str]:
|
||||
"""
|
||||
Return a sorted list of all providers supported by LiteLLM.
|
||||
"""
|
||||
|
||||
return sorted(provider.value for provider in LlmProviders)
|
||||
|
||||
|
||||
@router.get(
|
||||
"/public/providers/fields",
|
||||
tags=["public", "providers"],
|
||||
response_model=List[ProviderCreateInfo],
|
||||
)
|
||||
async def get_provider_fields() -> List[ProviderCreateInfo]:
|
||||
"""
|
||||
Return provider metadata required by the dashboard create-model flow.
|
||||
"""
|
||||
|
||||
provider_create_fields_path = os.path.join(
|
||||
os.path.dirname(os.path.dirname(os.path.dirname(__file__))),
|
||||
"proxy",
|
||||
"public_endpoints",
|
||||
"provider_create_fields.json",
|
||||
)
|
||||
|
||||
with open(provider_create_fields_path, "r") as f:
|
||||
provider_create_fields = json.load(f)
|
||||
|
||||
return provider_create_fields
|
||||
|
||||
|
||||
@router.get(
|
||||
"/public/litellm_model_cost_map",
|
||||
tags=["public", "model management"],
|
||||
)
|
||||
async def get_litellm_model_cost_map():
|
||||
"""
|
||||
Public endpoint to get the LiteLLM model cost map.
|
||||
Returns pricing information for all supported models.
|
||||
"""
|
||||
import litellm
|
||||
|
||||
try:
|
||||
_model_cost_map = litellm.model_cost
|
||||
return _model_cost_map
|
||||
except Exception as e:
|
||||
raise HTTPException(
|
||||
status_code=500,
|
||||
detail=f"Internal Server Error ({str(e)})",
|
||||
)
|
||||
|
||||
|
||||
@router.get(
|
||||
"/public/litellm_blog_posts",
|
||||
tags=["public"],
|
||||
response_model=BlogPostsResponse,
|
||||
)
|
||||
async def get_litellm_blog_posts():
|
||||
"""
|
||||
Public endpoint to get the latest LiteLLM blog posts.
|
||||
|
||||
Fetches from GitHub with a 1-hour in-process cache.
|
||||
Falls back to the bundled local backup on any failure.
|
||||
"""
|
||||
try:
|
||||
posts_data = get_blog_posts(url=litellm.blog_posts_url)
|
||||
except Exception as e:
|
||||
verbose_logger.warning(
|
||||
"LiteLLM: get_litellm_blog_posts endpoint fallback triggered: %s", str(e)
|
||||
)
|
||||
posts_data = GetBlogPosts.load_local_blog_posts()
|
||||
|
||||
posts = [BlogPost(**p) for p in posts_data[:5]]
|
||||
return BlogPostsResponse(posts=posts)
|
||||
|
||||
|
||||
@router.get(
|
||||
"/public/endpoints",
|
||||
tags=["public"],
|
||||
response_model=SupportedEndpointsResponse,
|
||||
)
|
||||
async def get_supported_endpoints() -> SupportedEndpointsResponse:
|
||||
"""
|
||||
Return the list of LiteLLM proxy endpoints and which providers support each one.
|
||||
|
||||
Reads from the bundled local backup file. Result is cached in-process for
|
||||
the lifetime of the server process.
|
||||
"""
|
||||
global _cached_endpoints
|
||||
if _cached_endpoints is None:
|
||||
_cached_endpoints = SupportedEndpointsResponse(endpoints=_load_endpoints()) # type: ignore[arg-type]
|
||||
return _cached_endpoints
|
||||
|
||||
|
||||
@router.get(
|
||||
"/public/agents/fields",
|
||||
tags=["public", "[beta] Agents"],
|
||||
response_model=List[AgentCreateInfo],
|
||||
)
|
||||
async def get_agent_fields() -> List[AgentCreateInfo]:
|
||||
"""
|
||||
Return agent type metadata required by the dashboard create-agent flow.
|
||||
|
||||
If an agent has `inherit_credentials_from_provider`, the provider's credential
|
||||
fields are automatically appended to the agent's credential_fields.
|
||||
"""
|
||||
base_path = os.path.join(
|
||||
os.path.dirname(os.path.dirname(os.path.dirname(__file__))),
|
||||
"proxy",
|
||||
"public_endpoints",
|
||||
)
|
||||
|
||||
agent_create_fields_path = os.path.join(base_path, "agent_create_fields.json")
|
||||
provider_create_fields_path = os.path.join(base_path, "provider_create_fields.json")
|
||||
|
||||
with open(agent_create_fields_path, "r") as f:
|
||||
agent_create_fields = json.load(f)
|
||||
|
||||
with open(provider_create_fields_path, "r") as f:
|
||||
provider_create_fields = json.load(f)
|
||||
|
||||
# Build a lookup map for providers by name
|
||||
provider_map = {p["provider"]: p for p in provider_create_fields}
|
||||
|
||||
# Merge inherited credential fields
|
||||
for agent in agent_create_fields:
|
||||
inherit_from = agent.get("inherit_credentials_from_provider")
|
||||
if inherit_from and inherit_from in provider_map:
|
||||
provider = provider_map[inherit_from]
|
||||
# Copy provider fields and mark them for inclusion in litellm_params
|
||||
inherited_fields = []
|
||||
for field in provider.get("credential_fields", []):
|
||||
field_copy = field.copy()
|
||||
field_copy["include_in_litellm_params"] = True
|
||||
inherited_fields.append(field_copy)
|
||||
# Append provider credential fields after agent's own fields
|
||||
agent["credential_fields"] = (
|
||||
agent.get("credential_fields", []) + inherited_fields
|
||||
)
|
||||
# Remove the inherit field from response (not needed by frontend)
|
||||
agent.pop("inherit_credentials_from_provider", None)
|
||||
|
||||
return agent_create_fields
|
||||
Reference in New Issue
Block a user