137 lines
4.6 KiB
Python
137 lines
4.6 KiB
Python
import asyncio
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import logging
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import sys
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from docutils.nodes import description
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from langchain_community.agent_toolkits.load_tools import load_tools
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from langchain_core.tools import tool
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from langchain_openai import ChatOpenAI
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from langchain_mcp_adapters.client import MultiServerMCPClient
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import os
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import dotenv
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from langgraph.checkpoint.memory import InMemorySaver
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from pydantic import BaseModel, Field
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logging.basicConfig(
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level=logging.DEBUG,
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format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
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)
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dotenv.load_dotenv()
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## 设置环境变量
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os.environ['OPENAI_API_KEY'] = os.getenv("OLLAMA_API_KEY")
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os.environ['OPENAI_BASE_URL'] = os.getenv("OLLAMA_BASE_URL")
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# 默认的 'model_name': 'deepseek-ai/DeepSeek-V3.1',
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llm = ChatOpenAI(model="gemma4:e2b")
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def print_optimized_result(agent_response):
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"""
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解析代理响应并输出优化后的结果。
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:param agent_response: 代理返回的完整响应
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"""
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messages = agent_response.get("messages", [])
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steps = [] # 用于记录计算步骤
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final_answer = None # 最终答案
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for message in messages:
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if hasattr(message, "additional_kwargs") and "tool_calls" in message.additional_kwargs:
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# 提取工具调用信息
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tool_calls = message.additional_kwargs["tool_calls"]
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for tool_call in tool_calls:
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tool_name = tool_call["function"]["name"]
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tool_args = tool_call["function"]["arguments"]
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steps.append(f"调用工具: {tool_name}({tool_args})")
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elif message.type == "tool":
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# 提取工具执行结果
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tool_name = message.name
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tool_result = message.content
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steps.append(f"{tool_name} 的结果是: {tool_result}")
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elif message.type == "ai":
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# 提取最终答案
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final_answer = message.content
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# 打印优化后的结果
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print("\n计算过程:")
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for step in steps:
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print(f"- {step}")
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if final_answer:
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print(f"\n最终答案: {final_answer}")
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def greet(name: str) -> str:
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"""用于打招呼的函数"""
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return f"你好呀，{name}"
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class greetInput(BaseModel):
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name: str = Field(description="打招呼的对象")
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async def execute():
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# 1. 创建langchain中的mcp客户端 —— uv add langchain_mcp_adapters
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client = MultiServerMCPClient(
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# mcp.run(transport="streamable-http", host="127.0.0.1", port=9000)
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{
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# 这里是定义服务端信息的，可以有多个服务端
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# 这里是定义服务端信息的，可以有多个服务端
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"weather": {
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"url": "http://localhost:9000/mcp",
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"transport": "streamable_http",
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},
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"math": {
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# "command": "python", # npx uvx
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"command": sys.executable,
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"args": ["./mcp/math_server.py"],
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"transport": "stdio"
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}
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}
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)
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try:
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# 2. 通过客户端获取工具列表
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## 这里是通过服务端获取，所以可能会有异常（比如服务端没有启动，或者网络连接有问题
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tools_mcp = await client.get_tools()
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tools = tools_mcp + tools_internal + [tools_custom]
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# 3. 创建一个智能代理，能够完成 思考--> 行动 --> 观察 --> 思考 --> 行动 --> ... --> 最终答案
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from langchain.agents import create_agent
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## 创建一个带记忆和工具的agent
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agent = create_agent(
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model=llm,
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tools=tools,
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# 配置记忆后，必须增加thread_id, checkpoint_ns, checkpoint_id参数之一，用于区分会话
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checkpointer=InMemorySaver()
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)
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while True:
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user_input = input("请输入你的问题（输入exit则退出） > ")
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if user_input == "exit":
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print("感谢使用，再见👋🏻")
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break
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agent_response = await agent.ainvoke(
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{"messages": [{"role": "user", "content": user_input}]},
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{"configurable": {"thread_id": "1"}},
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)
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## agent会自己去调用 tools ，不需要我们去进行调用
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print_optimized_result(agent_response)
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finally:
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# 资源回收
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if hasattr(client, 'close'):
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client.close()
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if __name__ == '__main__':
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tools_internal = load_tools(["arxiv", "wikipedia"], llm=llm, allow_dangerous_tools=True)
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tools_custom = tool(
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name_or_callable="greet",
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runnable=greet,
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description="用于打招呼的函数",
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args_schema=greetInput
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)
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asyncio.run(execute())
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