74 lines
2.6 KiB
Python
74 lines
2.6 KiB
Python
import asyncio
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import json
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import logging
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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 langchain_openai import ChatOpenAI
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from python_a2a.mcp import MCPClient
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from python_a2a.langchain import to_langchain_tool
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from conf import settings
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# 配置日志
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
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logger = logging.getLogger(__name__)
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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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async def test_mcp_tools():
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# 连接到服务端，端口 8000
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client = MCPClient("http://localhost:8010")
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try:
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# 步骤 1：获取可用工具列表
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tools = await client.get_tools()
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logger.info("可用工具列表：")
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for tool in tools:
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print(tool)
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logger.info(f"- {tool.get('name', '未知')}: {tool.get('description', '无描述')}")
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# 将 MCP tool 转成 LangChain 的工具
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get_weather_tool = to_langchain_tool("http://localhost:8010", "get_weather")
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query_high_frequency_question = to_langchain_tool("http://localhost:8010", "query_high_frequency_question")
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tools=[get_weather_tool, query_high_frequency_question]
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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。工具不需要传参。"),
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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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except Exception as e:
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logger.error(f"MCP 客户端出错：{str(e)}", exc_info=True)
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finally:
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await client.close()
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async def main():
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await test_mcp_tools()
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if __name__ == "__main__":
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asyncio.run(main()) |