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构建并部署 AI Agent 服务 / deploy (push) Failing after 1m17s
docs(quickstart): 更新快速开始文档以支持Nginx反向代理配置 - 修改环境变量配置方式，从直接创建.env文件改为复制模板文件 - 添加必需和可选配置项的详细说明 - 更新Docker Compose启动命令以使用正确的路径 - 增加Nginx反向代理访问方式的说明 - 为本地开发模式添加额外的数据库配置说明 fix(readme): 修正部署说明中的环境变量配置方法 - 将硬编码的环境变量配置改为使用模板文件复制方式 - 更新Docker Compose启动命令路径 - 补充Nginx反向代理访问说明 - 修正数据库配置注意事项 feat(backend): 支持从环境变量读取数据库连接配置 - 添加os模块导入 - 修改DB_URI配置逻辑，优先从环境变量读取 - 适配Docker和本地开发环境的不同数据库连接地址 refactor(docker): 优化Docker Compose配置支持Nginx代理 - 限制后端端口仅本机访问 - 修改前端API URL为相对路径，通过Nginx代理访问 - 限制前端端口仅本机访问 refactor(frontend): 适配Nginx反向代理后端API调用 - 将硬编码的后端API地址改为相对路径 - 支持通过Nginx代理转发请求到后端服务 chore(scripts): 更新启动脚本中的访问地址提示信息 - 修改前端启动成功后的访问地址提示 - 添加Nginx代理访问方式的说明 ```
119 lines
3.7 KiB
Python
119 lines
3.7 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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from contextlib import asynccontextmanager
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from fastapi import FastAPI, HTTPException, WebSocket, WebSocketDisconnect, Depends, Request
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from fastapi.middleware.cors import CORSMiddleware
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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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# PostgreSQL 连接字符串（优先从环境变量读取，适配 Docker 和本地开发）
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DB_URI = os.getenv(
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"DB_URI",
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"postgresql://postgres:mysecretpassword@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
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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. 将服务实例存入 app.state
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app.state.agent_service = agent_service
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# 应用运行中...
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yield
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# 4. 关闭时自动清理数据库连接（async with 负责）
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print("🛑 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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# ========== 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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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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# ========== 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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# ========== 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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reply = await agent_service.process_message(
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request.message, thread_id, request.model
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)
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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(reply=reply, thread_id=thread_id, model_used=actual_model)
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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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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)
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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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uvicorn.run(app, host="0.0.0.0", port=8001) |