237 lines
7.8 KiB
Python
237 lines
7.8 KiB
Python
"""
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FastAPI 后端 - 支持动态模型切换，使用 PostgreSQL 持久化记忆
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采用依赖注入模式，优雅管理资源生命周期
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"""
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import os
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import uuid
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import json
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from contextlib import asynccontextmanager
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from dotenv import load_dotenv
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from fastapi import FastAPI, HTTPException, WebSocket, WebSocketDisconnect, Depends, Request, Query
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from fastapi.middleware.cors import CORSMiddleware
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from fastapi.responses import StreamingResponse
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from pydantic import BaseModel
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from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver
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from app.agent import AIAgentService
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from app.history import ThreadHistoryService
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from app.logger import debug, info, warning, error
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# 加载 .env 文件
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load_dotenv()
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# PostgreSQL 连接字符串配置
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# 优先级：环境变量 DB_URI > Docker 内部服务名 > 本地开发地址
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DB_URI = os.getenv(
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"DB_URI",
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"postgresql://postgres:huang1998@ai-postgres:5432/langgraph_db?sslmode=disable"
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)
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@asynccontextmanager
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async def lifespan(app: FastAPI):
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"""应用生命周期管理：创建并注入全局服务"""
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# 1. 创建数据库连接池并初始化表（仅 checkpointer）
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async with AsyncPostgresSaver.from_conn_string(DB_URI) as checkpointer:
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await checkpointer.setup()
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# 2. 构建 AI Agent 服务
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agent_service = AIAgentService(checkpointer)
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await agent_service.initialize()
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# 3. 创建历史查询服务
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history_service = ThreadHistoryService(checkpointer)
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# 4. 将服务实例存入 app.state
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app.state.agent_service = agent_service
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app.state.history_service = history_service
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# 应用运行中...
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yield
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# 5. 关闭时自动清理数据库连接（async with 负责）
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info("🛑 应用关闭，数据库连接池已释放")
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app = FastAPI(lifespan=lifespan)
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# CORS 中间件（允许前端跨域）
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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# ========== 健康检查端点 ==========
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@app.get("/health")
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async def health_check():
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"""健康检查端点，用于 Docker 和 CI/CD 监控"""
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return {"status": "ok", "service": "ai-agent-backend"}
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# ========== Pydantic 模型 ==========
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class ChatRequest(BaseModel):
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message: str
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thread_id: str | None = None
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model: str = "zhipu"
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user_id: str = "default_user"
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class ChatResponse(BaseModel):
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reply: str
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thread_id: str
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model_used: str
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input_tokens: int = 0
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output_tokens: int = 0
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total_tokens: int = 0
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elapsed_time: float = 0.0
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# ========== 依赖注入函数 ==========
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def get_agent_service(request: Request) -> AIAgentService:
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"""从 app.state 中获取全局 AIAgentService 实例"""
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return request.app.state.agent_service
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def get_history_service(request: Request) -> ThreadHistoryService:
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"""从 app.state 中获取全局 ThreadHistoryService 实例"""
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return request.app.state.history_service
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# ========== HTTP 端点 ==========
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@app.post("/chat", response_model=ChatResponse)
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async def chat_endpoint(
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request: ChatRequest,
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agent_service: AIAgentService = Depends(get_agent_service)
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):
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"""同步对话接口，支持模型选择"""
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if not request.message:
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raise HTTPException(status_code=400, detail="message required")
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thread_id = request.thread_id or str(uuid.uuid4())
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result = await agent_service.process_message(
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request.message, thread_id, request.model, request.user_id
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)
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# 提取 token 统计信息
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token_usage = result.get("token_usage", {})
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input_tokens = token_usage.get('prompt_tokens', token_usage.get('input_tokens', 0))
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output_tokens = token_usage.get('completion_tokens', token_usage.get('output_tokens', 0))
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elapsed_time = result.get("elapsed_time", 0.0)
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actual_model = request.model if request.model in agent_service.graphs else next(iter(agent_service.graphs.keys()))
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return ChatResponse(
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reply=result["reply"],
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thread_id=thread_id,
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model_used=actual_model,
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input_tokens=input_tokens,
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output_tokens=output_tokens,
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total_tokens=input_tokens + output_tokens,
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elapsed_time=elapsed_time
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)
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# ========== 历史查询接口 ==========
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@app.get("/threads")
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async def list_threads(
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user_id: str = Query("default_user", description="用户 ID"),
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limit: int = Query(50, ge=1, le=200, description="返回数量限制"),
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history_service: ThreadHistoryService = Depends(get_history_service)
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):
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"""获取当前用户的对话历史列表"""
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threads = await history_service.get_user_threads(user_id, limit)
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return {"threads": threads}
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@app.get("/thread/{thread_id}/messages")
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async def get_thread_messages(
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thread_id: str,
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user_id: str = Query("default_user", description="用户 ID"),
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history_service: ThreadHistoryService = Depends(get_history_service)
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):
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"""获取指定线程的完整消息历史"""
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messages = await history_service.get_thread_messages(thread_id)
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return {"messages": messages}
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@app.get("/thread/{thread_id}/summary")
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async def get_thread_summary(
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thread_id: str,
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user_id: str = Query("default_user", description="用户 ID"),
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history_service: ThreadHistoryService = Depends(get_history_service)
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):
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"""获取指定线程的摘要信息"""
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summary = await history_service.get_thread_summary(thread_id)
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return summary
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# ========== 流式对话接口 ==========
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@app.post("/chat/stream")
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async def chat_stream_endpoint(
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request: ChatRequest,
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agent_service: AIAgentService = Depends(get_agent_service)
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):
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"""流式对话接口（SSE）"""
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if not request.message:
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raise HTTPException(status_code=400, detail="message required")
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thread_id = request.thread_id or str(uuid.uuid4())
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async def event_generator():
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try:
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async for chunk in agent_service.process_message_stream(
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request.message, thread_id, request.model, request.user_id
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):
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yield f"data: {json.dumps(chunk, ensure_ascii=False)}\n\n"
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yield "data: [DONE]\n\n"
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except Exception as e:
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error(f"流式响应异常: {e}")
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yield f"data: {json.dumps({'type': 'error', 'message': str(e)}, ensure_ascii=False)}\n\n"
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yield "data: [DONE]\n\n"
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return StreamingResponse(
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event_generator(),
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media_type="text/event-stream",
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headers={
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"Cache-Control": "no-cache",
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"Connection": "keep-alive",
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"X-Accel-Buffering": "no", # 禁用 Nginx 缓冲
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}
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)
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# ========== WebSocket 端点（可选） ==========
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@app.websocket("/ws")
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async def websocket_endpoint(
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websocket: WebSocket,
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agent_service: AIAgentService = Depends(get_agent_service)
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):
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await websocket.accept()
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try:
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while True:
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data = await websocket.receive_json()
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message = data.get("message")
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thread_id = data.get("thread_id", str(uuid.uuid4()))
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model = data.get("model", "zhipu")
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user_id = data.get("user_id", "default_user")
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if not message:
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await websocket.send_json({"error": "missing message"})
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continue
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reply = await agent_service.process_message(message, thread_id, model, user_id)
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actual_model = model if model in agent_service.graphs else next(iter(agent_service.graphs.keys()))
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await websocket.send_json({"reply": reply, "thread_id": thread_id, "model_used": actual_model})
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except WebSocketDisconnect:
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pass
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if __name__ == "__main__":
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import uvicorn
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# 使用环境变量或默认端口 8083（避免与 llama.cpp 的 8081 端口冲突）
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port = int(os.getenv("BACKEND_PORT", "8083"))
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uvicorn.run(app, host="0.0.0.0", port=port)
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