docs(.gitignore/README/QUICKSTART): 更新文档和忽略配置 - 添加IDE配置、日志和数据文件到.gitignore - 重构QUICKSTART.md，提供Docker Compose和本地开发两种部署方式 - 更新README.md，优化项目介绍和架构说明 - 移除旧的agent.py和backend.py文件 ```
This commit is contained in:
8
app/__init__.py
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8
app/__init__.py
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@@ -0,0 +1,8 @@
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"""
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AI Agent 应用模块
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"""
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from .agent import AIAgentService
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from .tools import AVAILABLE_TOOLS, TOOLS_BY_NAME
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__all__ = ["AIAgentService", "AVAILABLE_TOOLS", "TOOLS_BY_NAME"]
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87
app/agent.py
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87
app/agent.py
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"""
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AI Agent 服务类 - 支持多模型动态切换
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接收外部传入的 checkpointer，不负责管理连接生命周期
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"""
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import os
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from dotenv import load_dotenv
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from langchain_community.chat_models import ChatZhipuAI
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from langchain_core.messages import HumanMessage
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from langchain_openai import ChatOpenAI
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from pydantic import SecretStr
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# 本地模块
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from app.graph_builder import GraphBuilder
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from app.tools import AVAILABLE_TOOLS, TOOLS_BY_NAME
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load_dotenv()
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class AIAgentService:
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"""异步 AI Agent 服务，支持多模型动态切换，使用外部传入的 checkpointer"""
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def __init__(self, checkpointer):
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"""
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初始化服务
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Args:
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checkpointer: 已经初始化的 AsyncPostgresSaver 实例
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"""
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self.checkpointer = checkpointer
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self.graphs = {} # 存储不同模型对应的 graph 实例
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def _create_zhipu_llm(self):
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"""创建智谱在线 LLM"""
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api_key = os.getenv("ZHIPUAI_API_KEY")
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if not api_key:
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raise ValueError("ZHIPUAI_API_KEY not set in environment")
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return ChatZhipuAI(
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model="glm-4.7-flash",
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api_key=api_key,
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temperature=0.1,
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max_tokens=4096,
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)
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def _create_local_llm(self):
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"""创建本地 vLLM 服务 LLM"""
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return ChatOpenAI(
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# 原来是 http://localhost:8000/v1
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# 改为 FRP 穿透后的公网地址
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base_url = "http://115.190.121.151:18000/v1",
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api_key=SecretStr(os.getenv("VLLM_LOCAL_KEY", "")),
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model="gemma-4-E2B-it",
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)
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async def initialize(self):
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"""预编译所有模型的 graph（使用传入的 checkpointer）"""
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model_configs = {
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"zhipu": self._create_zhipu_llm,
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"local": self._create_local_llm,
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}
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for model_name, llm_creator in model_configs.items():
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try:
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llm = llm_creator()
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builder = GraphBuilder(llm, AVAILABLE_TOOLS, TOOLS_BY_NAME).build()
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graph = builder.compile(checkpointer=self.checkpointer)
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self.graphs[model_name] = graph
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print(f"✅ 模型 '{model_name}' 初始化成功")
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except Exception as e:
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print(f"⚠️ 模型 '{model_name}' 初始化失败: {e}")
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if not self.graphs:
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raise RuntimeError("没有可用的模型，请检查配置")
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return self
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async def process_message(self, message: str, thread_id: str, model: str = "zhipu") -> str:
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"""处理用户消息，返回最终答案"""
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if model not in self.graphs:
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fallback_model = next(iter(self.graphs.keys()))
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print(f"警告: 模型 '{model}' 不可用，已切换到 '{fallback_model}'")
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model = fallback_model
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graph = self.graphs[model]
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config = {"configurable": {"thread_id": thread_id}}
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input_state = {"messages": [HumanMessage(content=message)]}
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result = await graph.ainvoke(input_state, config=config)
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return result["messages"][-1].content
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115
app/backend.py
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115
app/backend.py
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"""
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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 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
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DB_URI = "postgresql://postgres:mysecretpassword@localhost:5432/langgraph_db?sslmode=disable"
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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)
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127
app/graph_builder.py
Normal file
127
app/graph_builder.py
Normal file
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"""
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LangGraph 状态图构建模块 - 完全面向对象风格，无嵌套函数
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"""
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import operator
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import asyncio
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from typing import Literal, Annotated, Any
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from langchain_core.language_models import BaseLLM
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from langchain_core.messages import AnyMessage, AIMessage, ToolMessage, SystemMessage
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from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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from langgraph.graph import StateGraph, START, END
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from typing_extensions import TypedDict
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class MessageState(TypedDict):
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"""对话状态类型定义"""
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messages: Annotated[list[AnyMessage], operator.add]
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llm_calls: int
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class GraphBuilder:
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"""LangGraph 状态图构建器 - 所有节点均为类方法"""
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def __init__(self, llm: BaseLLM, tools: list, tools_by_name: dict[str, Any]):
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"""
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初始化构建器
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Args:
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llm: 大语言模型实例
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tools: 工具列表
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tools_by_name: 名称到工具函数的映射
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"""
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self.llm = llm
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self.tools = tools
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self.tools_by_name = tools_by_name
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self._llm_with_tools = llm.bind_tools(tools)
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self._prompt = self._create_prompt()
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self._chain = self._prompt | self._llm_with_tools
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@staticmethod
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def _create_prompt() -> ChatPromptTemplate:
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"""创建系统提示模板（静态方法，无需访问实例）"""
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return ChatPromptTemplate.from_messages([
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SystemMessage(content=(
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"你是一个个人生活助手和数据分析助手。请说中文。"
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"当用户询问天气或温度时,使用get_current_temperature工具获取信息。"
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"当用户要求读文本文件时,请使用 read_local_file 工具,只能读取 './user_docs' 目录下的文件。"
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"当用户要求读PDF文件时,请使用 read_pdf_summary 工具,只能读取 './user_docs' 目录下的文件。"
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"当用户要求读Excel文件时,请使用 read_excel_as_markdown 工具,只能读取 './user_docs' 目录下的文件。"
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"当用户要求抓取网页时,请使用 fetch_webpage_content 工具。"
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"重要:你的回答必须简洁、直接,不要包含任何关于思考过程的描述。"
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)),
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MessagesPlaceholder(variable_name="message")
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])
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async def call_llm(self, state: MessageState) -> dict:
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"""
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LLM 调用节点（异步方法）
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注意：因为 self._chain.invoke 是同步方法，使用 run_in_executor 避免阻塞事件循环
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"""
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loop = asyncio.get_event_loop()
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response = await loop.run_in_executor(
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None,
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lambda: self._chain.invoke({"message": state["messages"]})
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)
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return {
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"messages": [response],
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"llm_calls": state.get('llm_calls', 0) + 1
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}
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async def call_tools(self, state: MessageState) -> dict:
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"""
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工具执行节点（异步方法）
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对于每个工具调用，在线程池中执行同步工具函数
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"""
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last_message = state['messages'][-1]
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if not isinstance(last_message, AIMessage) or not last_message.tool_calls:
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return {"messages": []}
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|
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results = []
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loop = asyncio.get_event_loop()
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|
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for tool_call in last_message.tool_calls:
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tool_name = tool_call["name"]
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tool_args = tool_call["args"]
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tool_id = tool_call["id"]
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tool_func = self.tools_by_name.get(tool_name)
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|
||||
if tool_func is None:
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results.append(ToolMessage(content=f"Tool {tool_name} not found", tool_call_id=tool_id))
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||||
continue
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||||
|
||||
try:
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||||
# 同步工具函数在线程池中执行
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||||
observation = await loop.run_in_executor(
|
||||
None,
|
||||
lambda: tool_func.invoke(tool_args)
|
||||
)
|
||||
results.append(ToolMessage(content=str(observation), tool_call_id=tool_id))
|
||||
except Exception as e:
|
||||
results.append(ToolMessage(content=f"Error: {e}", tool_call_id=tool_id))
|
||||
|
||||
return {"messages": results}
|
||||
|
||||
@staticmethod
|
||||
def should_continue(state: MessageState) -> Literal['tool_node', END]:
|
||||
"""
|
||||
条件边判断（静态方法）
|
||||
决定下一步是进入工具节点还是结束
|
||||
"""
|
||||
last_message = state["messages"][-1]
|
||||
if isinstance(last_message, AIMessage) and bool(last_message.tool_calls):
|
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return 'tool_node'
|
||||
return END
|
||||
|
||||
def build(self) -> StateGraph:
|
||||
"""
|
||||
构建未编译的状态图（返回 StateGraph 实例）
|
||||
图中节点直接使用实例方法 call_llm, call_tools
|
||||
"""
|
||||
builder = StateGraph(MessageState)
|
||||
builder.add_node("llm_call", self.call_llm)
|
||||
builder.add_node("tool_node", self.call_tools)
|
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builder.add_edge(START, "llm_call")
|
||||
builder.add_conditional_edges("llm_call", self.should_continue, ["tool_node", END])
|
||||
builder.add_edge("tool_node", "llm_call")
|
||||
return builder
|
||||
103
app/tools.py
Normal file
103
app/tools.py
Normal file
@@ -0,0 +1,103 @@
|
||||
"""
|
||||
工具定义模块 - 纯函数工具，无依赖 AIAgent 类
|
||||
"""
|
||||
|
||||
# 标准库
|
||||
import os
|
||||
from pathlib import Path
|
||||
|
||||
# 第三方库
|
||||
import pandas as pd
|
||||
import pypdf
|
||||
import requests
|
||||
from bs4 import BeautifulSoup
|
||||
from langchain_core.tools import tool
|
||||
|
||||
|
||||
def _file_allow_check(filename: str) -> Path:
|
||||
"""检查用户文件名是否位于允许目录 './user_docs' 下，防止路径遍历攻击。"""
|
||||
allowed_dir = Path("./user_docs").resolve()
|
||||
allowed_dir.mkdir(exist_ok=True)
|
||||
|
||||
file_path = (allowed_dir / filename).resolve()
|
||||
if not str(file_path).startswith(str(allowed_dir)):
|
||||
raise ValueError("错误：非法文件路径。")
|
||||
|
||||
if not file_path.exists():
|
||||
raise FileNotFoundError(f"错误：文件 '{filename}' 不存在。")
|
||||
|
||||
return file_path
|
||||
|
||||
|
||||
@tool
|
||||
def get_current_temperature(location: str) -> str:
|
||||
"""获取指定地点的当前温度。"""
|
||||
return f'当前{location}的温度为25℃'
|
||||
|
||||
|
||||
@tool
|
||||
def read_local_file(filename: str) -> str:
|
||||
"""读取用户指定名称的本地文本文件内容并返回摘要。"""
|
||||
try:
|
||||
file_path = _file_allow_check(filename)
|
||||
with open(file_path, 'r', encoding='utf-8') as f:
|
||||
content = f.read()
|
||||
return f"文件 '{filename}' 的内容开头：\n{content[:1000]}..."
|
||||
except Exception as e:
|
||||
return f"读取文件时出错：{str(e)}"
|
||||
|
||||
|
||||
@tool
|
||||
def read_pdf_summary(filename: str) -> str:
|
||||
"""读取PDF文件并返回内容文本摘要。"""
|
||||
try:
|
||||
file_path = _file_allow_check(filename)
|
||||
text = ""
|
||||
with open(file_path, 'rb') as f:
|
||||
reader = pypdf.PdfReader(f)
|
||||
for page in reader.pages[:3]:
|
||||
text += page.extract_text()
|
||||
return f"PDF文件 '{filename}' 的前几页内容：\n{text[:2000]}..."
|
||||
except Exception as e:
|
||||
return f"读取PDF出错：{e}"
|
||||
|
||||
|
||||
@tool
|
||||
def read_excel_as_markdown(filename: str) -> str:
|
||||
"""读取Excel文件，并将其主要数据转换为Markdown表格格式。"""
|
||||
try:
|
||||
file_path = _file_allow_check(filename)
|
||||
df = pd.read_excel(file_path)
|
||||
markdown_table = df.head(10).to_markdown(index=False)
|
||||
return f"Excel文件 '{filename}' 的数据预览（前10行）：\n{markdown_table}"
|
||||
except Exception as e:
|
||||
return f"读取Excel出错：{e}"
|
||||
|
||||
|
||||
@tool
|
||||
def fetch_webpage_content(url: str) -> str:
|
||||
"""抓取给定URL的网页正文内容，并返回清晰的纯文本。"""
|
||||
try:
|
||||
response = requests.get(url, timeout=10)
|
||||
response.raise_for_status()
|
||||
soup = BeautifulSoup(response.text, 'html.parser')
|
||||
for script in soup(["script", "style"]):
|
||||
script.decompose()
|
||||
text = soup.get_text()
|
||||
lines = (line.strip() for line in text.splitlines())
|
||||
chunks = (phrase.strip() for line in lines for phrase in line.split(" "))
|
||||
text = '\n'.join(chunk for chunk in chunks if chunk)
|
||||
return f"成功抓取网页 {url}，正文内容开头：\n{text[:1500]}..."
|
||||
except Exception as e:
|
||||
return f"抓取网页时出错：{str(e)}"
|
||||
|
||||
|
||||
# 工具列表和映射（全局常量）
|
||||
AVAILABLE_TOOLS = [
|
||||
get_current_temperature,
|
||||
read_local_file,
|
||||
fetch_webpage_content,
|
||||
read_pdf_summary,
|
||||
read_excel_as_markdown
|
||||
]
|
||||
TOOLS_BY_NAME = {tool.name: tool for tool in AVAILABLE_TOOLS}
|
||||
Reference in New Issue
Block a user