mirror of
https://gitee.com/wanwujie/deer-flow
synced 2026-04-16 11:24:45 +08:00
411d9d57c3
Add new MCP configuration management endpoint and enhance API documentation with detailed descriptions, examples, and OpenAPI support for better developer experience. Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
115 lines
3.2 KiB
Python
115 lines
3.2 KiB
Python
import logging
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from collections.abc import AsyncGenerator
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from contextlib import asynccontextmanager
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from fastapi import FastAPI
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from src.gateway.config import get_gateway_config
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from src.gateway.routers import artifacts, mcp, models
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# Configure logging
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logging.basicConfig(
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level=logging.INFO,
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format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
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datefmt="%Y-%m-%d %H:%M:%S",
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)
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logger = logging.getLogger(__name__)
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@asynccontextmanager
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async def lifespan(app: FastAPI) -> AsyncGenerator[None, None]:
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"""Application lifespan handler."""
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config = get_gateway_config()
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logger.info(f"Starting API Gateway on {config.host}:{config.port}")
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# Initialize MCP tools at startup
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try:
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from src.mcp import initialize_mcp_tools
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await initialize_mcp_tools()
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except Exception as e:
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logger.warning(f"Failed to initialize MCP tools: {e}")
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yield
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logger.info("Shutting down API Gateway")
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def create_app() -> FastAPI:
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"""Create and configure the FastAPI application.
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Returns:
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Configured FastAPI application instance.
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"""
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app = FastAPI(
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title="DeerFlow API Gateway",
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description="""
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## DeerFlow API Gateway
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API Gateway for DeerFlow - A LangGraph-based AI agent backend with sandbox execution capabilities.
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### Features
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- **Models Management**: Query and retrieve available AI models
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- **MCP Configuration**: Manage Model Context Protocol (MCP) server configurations
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- **Artifacts**: Access thread artifacts and generated files
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- **Health Monitoring**: System health check endpoints
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### Architecture
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LangGraph requests are handled by nginx reverse proxy.
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This gateway provides custom endpoints for models, MCP configuration, and artifacts.
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""",
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version="0.1.0",
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lifespan=lifespan,
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docs_url="/docs",
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redoc_url="/redoc",
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openapi_url="/openapi.json",
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openapi_tags=[
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{
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"name": "models",
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"description": "Operations for querying available AI models and their configurations",
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},
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{
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"name": "mcp",
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"description": "Manage Model Context Protocol (MCP) server configurations",
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},
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{
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"name": "artifacts",
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"description": "Access and download thread artifacts and generated files",
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},
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{
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"name": "health",
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"description": "Health check and system status endpoints",
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},
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],
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)
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# CORS is handled by nginx - no need for FastAPI middleware
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# Include routers
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# Models API is mounted at /api/models
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app.include_router(models.router)
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# MCP API is mounted at /api/mcp
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app.include_router(mcp.router)
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# Artifacts API is mounted at /api/threads/{thread_id}/artifacts
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app.include_router(artifacts.router)
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@app.get("/health", tags=["health"])
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async def health_check() -> dict:
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"""Health check endpoint.
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Returns:
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Service health status information.
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"""
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return {"status": "healthy", "service": "deer-flow-gateway"}
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return app
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# Create app instance for uvicorn
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app = create_app()
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