重构代码，统一config配置

backend/app/backend.py

@@ -0,0 +1,212 @@
+"""
+FastAPI 后端 - 支持动态模型切换，使用 PostgreSQL 持久化记忆
+采用依赖注入模式，优雅管理资源生命周期
+"""
+
+import os
+from .config import DB_URI, BACKEND_PORT
+import uuid
+import json
+from contextlib import asynccontextmanager
+
+from fastapi import FastAPI, HTTPException, WebSocket, WebSocketDisconnect, Depends, Request, Query
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import StreamingResponse
+from pydantic import BaseModel
+from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver
+from .agent.service import AIAgentService
+from .agent.history import ThreadHistoryService
+from .logger import info, error
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    """应用生命周期管理：创建并注入全局服务"""
+    # 1. 创建数据库连接池并初始化表（仅 checkpointer）
+    async with AsyncPostgresSaver.from_conn_string(DB_URI) as checkpointer:
+        await checkpointer.setup()
+        
+        # 2. 构建 AI Agent 服务
+        agent_service = AIAgentService(checkpointer)
+        await agent_service.initialize()
+        
+        # 3. 创建历史查询服务
+        history_service = ThreadHistoryService(checkpointer)
+        
+        # 4. 将服务实例存入 app.state
+        app.state.agent_service = agent_service
+        app.state.history_service = history_service
+        
+        # 应用运行中...
+        yield
+        
+        # 5. 关闭时自动清理数据库连接（async with 负责）
+        info("🛑 应用关闭，数据库连接池已释放")
+
+app = FastAPI(lifespan=lifespan)
+
+# CORS 中间件（允许前端跨域）
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# ========== 健康检查端点 ==========
+@app.get("/health")
+async def health_check():
+    """健康检查端点，用于 Docker 和 CI/CD 监控"""
+    return {"status": "ok", "service": "ai-agent-backend"}
+
+# ========== Pydantic 模型 ==========
+class ChatRequest(BaseModel):
+    message: str
+    thread_id: str | None = None
+    model: str = "zhipu"
+    user_id: str = "default_user"
+
+class ChatResponse(BaseModel):
+    reply: str
+    thread_id: str
+    model_used: str
+    input_tokens: int = 0
+    output_tokens: int = 0
+    total_tokens: int = 0
+    elapsed_time: float = 0.0
+
+# ========== 依赖注入函数 ==========
+def get_agent_service(request: Request) -> AIAgentService:
+    """从 app.state 中获取全局 AIAgentService 实例"""
+    return request.app.state.agent_service
+
+def get_history_service(request: Request) -> ThreadHistoryService:
+    """从 app.state 中获取全局 ThreadHistoryService 实例"""
+    return request.app.state.history_service
+
+# ========== HTTP 端点 ==========
+@app.post("/chat", response_model=ChatResponse)
+async def chat_endpoint(
+    request: ChatRequest,
+    agent_service: AIAgentService = Depends(get_agent_service)
+):
+    """同步对话接口，支持模型选择"""
+    if not request.message:
+        raise HTTPException(status_code=400, detail="message required")
+
+    thread_id = request.thread_id or str(uuid.uuid4())
+    result = await agent_service.process_message(
+        request.message, thread_id, request.model, request.user_id
+    )
+    
+    # 提取 token 统计信息
+    token_usage = result.get("token_usage", {})
+    input_tokens = token_usage.get('prompt_tokens', token_usage.get('input_tokens', 0))
+    output_tokens = token_usage.get('completion_tokens', token_usage.get('output_tokens', 0))
+    elapsed_time = result.get("elapsed_time", 0.0)
+    
+    actual_model = request.model if request.model in agent_service.graphs else next(iter(agent_service.graphs.keys()))
+    
+    return ChatResponse(
+        reply=result["reply"],
+        thread_id=thread_id,
+        model_used=actual_model,
+        input_tokens=input_tokens,
+        output_tokens=output_tokens,
+        total_tokens=input_tokens + output_tokens,
+        elapsed_time=elapsed_time
+    )
+
+# ========== 历史查询接口 ==========
+@app.get("/threads")
+async def list_threads(
+    user_id: str = Query("default_user", description="用户 ID"),
+    limit: int = Query(50, ge=1, le=200, description="返回数量限制"),
+    history_service: ThreadHistoryService = Depends(get_history_service)
+):
+    """获取当前用户的对话历史列表"""
+    threads = await history_service.get_user_threads(user_id, limit)
+    return {"threads": threads}
+
+@app.get("/thread/{thread_id}/messages")
+async def get_thread_messages(
+    thread_id: str,
+    user_id: str = Query("default_user", description="用户 ID"),
+    history_service: ThreadHistoryService = Depends(get_history_service)
+):
+    """获取指定线程的完整消息历史"""
+    messages = await history_service.get_thread_messages(thread_id)
+    return {"messages": messages}
+
+@app.get("/thread/{thread_id}/summary")
+async def get_thread_summary(
+    thread_id: str,
+    user_id: str = Query("default_user", description="用户 ID"),
+    history_service: ThreadHistoryService = Depends(get_history_service)
+):
+    """获取指定线程的摘要信息"""
+    summary = await history_service.get_thread_summary(thread_id)
+    return summary
+
+# ========== 流式对话接口 ==========
+@app.post("/chat/stream")
+async def chat_stream_endpoint(
+    request: ChatRequest,
+    agent_service: AIAgentService = Depends(get_agent_service)
+):
+    """流式对话接口（SSE）"""
+    if not request.message:
+        raise HTTPException(status_code=400, detail="message required")
+
+    thread_id = request.thread_id or str(uuid.uuid4())
+    
+    async def event_generator():
+        try:
+            async for chunk in agent_service.process_message_stream(
+                request.message, thread_id, request.model, request.user_id
+            ):
+                yield f"data: {json.dumps(chunk, ensure_ascii=False)}\n\n"
+            yield "data: [DONE]\n\n"
+        except Exception as e:
+            error(f"流式响应异常: {e}")
+            yield f"data: {json.dumps({'type': 'error', 'message': str(e)}, ensure_ascii=False)}\n\n"
+            yield "data: [DONE]\n\n"
+    
+    return StreamingResponse(
+        event_generator(),
+        media_type="text/event-stream",
+        headers={
+            "Cache-Control": "no-cache",
+            "Connection": "keep-alive",
+            "X-Accel-Buffering": "no",  # 禁用 Nginx 缓冲
+        }
+    )
+
+# ========== WebSocket 端点（可选） ==========
+@app.websocket("/ws")
+async def websocket_endpoint(
+    websocket: WebSocket,
+    agent_service: AIAgentService = Depends(get_agent_service)
+):
+    await websocket.accept()
+    try:
+        while True:
+            data = await websocket.receive_json()
+            message = data.get("message")
+            thread_id = data.get("thread_id", str(uuid.uuid4()))
+            model = data.get("model", "zhipu")
+            user_id = data.get("user_id", "default_user")
+            if not message:
+                await websocket.send_json({"error": "missing message"})
+                continue
+            reply = await agent_service.process_message(message, thread_id, model, user_id)
+            actual_model = model if model in agent_service.graphs else next(iter(agent_service.graphs.keys()))
+            await websocket.send_json({"reply": reply, "thread_id": thread_id, "model_used": actual_model})
+    except WebSocketDisconnect:
+        pass
+
+if __name__ == "__main__":
+    import uvicorn
+    # 使用环境变量或默认端口 8079（避免与 llama.cpp 的 8081 端口冲突）
+    port = int(BACKEND_PORT)
+    uvicorn.run(app, host="0.0.0.0", port=port)