refactor(module11): restructure MCP client implementation and migrate to chapter 11

m11_mcp_advanced/final_mcp_main.py

@@ -0,0 +1,107 @@
+import os
+import asyncio
+
+# --- 核心：导入官方库 ---
+from langchain_mcp_adapters.client import MultiServerMCPClient
+
+# LangChain/LangGraph 组件
+from langchain_openai import ChatOpenAI
+from langchain_core.messages import SystemMessage
+from langgraph.graph import StateGraph, MessagesState, START, END
+from langgraph.prebuilt import ToolNode
+
+# 复用你的流式输出模块和配置
+from m11_mcp_advanced.agent_stream import run_agent_with_streaming
+from config import OPENAI_API_KEY, AMAP_MAPS_API_KEY
+
+# === 配置 MCP 服务器 ===
+MCP_SERVERS = {
+    # 方式1.1: 云端代理 —— stdio模式
+    "高德地图": {
+        "transport": "stdio",
+        "command": "npx",
+        "args": ["-y", "@amap/amap-maps-mcp-server"],
+        "env": {**os.environ, "AMAP_MAPS_API_KEY": AMAP_MAPS_API_KEY}
+    },
+
+    # 方式1.2: 云端MCP服务 —— Streamable HTTP模式
+    # "高德地图" :{
+    #     "transport":"streamable_http",
+    #     "url": f"https://mcp.amap.com/mcp?key={AMAP_MAPS_API_KEY}"
+    # },
+
+    # 方式2.1: 本地工具 —— stdio模式
+    # "本地天气":{
+    #     "transport": "stdio",
+    #     "command": "python",
+    #     "args": ["-m", "m10_mcp_basics.stdio_server"],
+    #     "env": None
+    # },
+
+    # 方式2.2:本地MCP服务 —— Streamable HTTP 模式
+    # 注:此方法需要提前运行m10的 streamable_http_server.py
+    # "本地天气":{
+    #     "transport":"streamable_http",
+    #     "url": "http://127.0.0.1:8001/mcp"
+    # }
+}
+
+
+def build_graph(available_tools):
+    """构建图逻辑 (保持不变)"""
+    if not available_tools:
+        print("⚠️ 未加载任何工具")
+
+    llm = ChatOpenAI(
+        model="deepseek-chat",
+        api_key=OPENAI_API_KEY,
+        base_url="https://api.deepseek.com",
+        streaming=True
+    )
+
+    llm_with_tools = llm.bind_tools(available_tools) if available_tools else llm
+
+    sys_prompt = "你是一个地理位置助手，请根据用户需求调用工具查询信息。"
+
+    async def agent_node(state: MessagesState):
+        messages = [SystemMessage(content=sys_prompt)] + state["messages"]
+        return {"messages": [await llm_with_tools.ainvoke(messages)]}
+
+    workflow = StateGraph(MessagesState)
+    workflow.add_node("agent", agent_node)
+
+    if available_tools:
+        workflow.add_node("tools", ToolNode(available_tools))
+
+        def should_continue(state):
+            last_msg = state["messages"][-1]
+            return "tools" if last_msg.tool_calls else END
+
+        workflow.add_edge(START, "agent")
+        workflow.add_conditional_edges("agent", should_continue)
+        workflow.add_edge("tools", "agent")
+    else:
+        workflow.add_edge(START, "agent")
+        workflow.add_edge("agent", END)
+
+    return workflow.compile()
+
+
+async def main():
+    print("🔌 正在初始化 MCP 客户端...")
+
+    client = MultiServerMCPClient(MCP_SERVERS)
+
+    # 显式建立连接并获取工具
+    # 注意：这个 client 对象会保持连接，直到脚本结束
+    tools = await client.get_tools()
+    print(f"✅ 成功加载工具: {[t.name for t in tools]}")
+
+    # 构建并运行
+    app = build_graph(tools)
+    query = "帮我查一下杭州西湖附近的酒店"
+    await run_agent_with_streaming(app, query)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())