69 lines
2.5 KiB
Python
69 lines
2.5 KiB
Python
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import json
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import asyncio
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from langchain_openai import ChatOpenAI
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from mcp import ClientSession
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from mcp.client.sse import sse_client
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from langchain_mcp_adapters.tools import load_mcp_tools
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from langchain.agents import create_tool_calling_agent, AgentExecutor
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from langchain_core.prompts import ChatPromptTemplate
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from conf import settings
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# 创建模型
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llm = ChatOpenAI(
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base_url=settings.base_url,
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api_key=settings.api_key,
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model=settings.model_name,
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temperature=0.1
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)
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# MCP server URL for SSE connection
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server_url = "http://localhost:8001/sse"
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# 定义mcp客户端
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mcp_client = None
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# Main async function: connect, load tools, create agent, run chat loop
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async def run_agent():
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global mcp_client
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# 启动 MCP server，通过 SSE 建立异步连接。
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async with sse_client(url=server_url) as streams:
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# 使用读写通道创建 MCP 会话
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async with ClientSession(*streams) as session:
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await session.initialize()
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# 动态创建一个临时类 MCPClientHolder，把 session 放进去。这样就可以在函数外部通过 mcp_client.session 调用 MCP 工具
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mcp_client = type("MCPClientHolder", (), {"session": session})()
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# 从 session 自动获取 MCP server 提供的工具列表。
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tools = await load_mcp_tools(session)
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# print(f"tools-->{tools}")
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# 创建prompt模板
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prompt_template = ChatPromptTemplate.from_messages([
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("system", "你是一个乐于助人的助手，能够调用工具回答用户问题。"),
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("human", "{input}"),
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("placeholder", "{agent_scratchpad}"),
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])
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# 构建工具调用代理
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agent = create_tool_calling_agent(llm, tools, prompt_template)
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# 创建代理执行器
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agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
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# 代理调用
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print("MCP客户端启动，输入'quit'退出")
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while True:
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# 接收用户查询
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query = input("\nQuery: ").strip()
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if query.lower() == "quit":
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break
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# 发送用户查询给代理，并打印
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try:
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response = await agent_executor.ainvoke({"input": query})
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print(f"response-->{response}")
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except Exception:
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print("解析有问题")
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return
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if __name__ == "__main__":
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asyncio.run(run_agent())
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