feat: demo

demo_mcp/a2a_agent.py

@@ -0,0 +1,74 @@
+import asyncio
+import json
+import logging
+from langchain.agents import create_tool_calling_agent, AgentExecutor
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_openai import ChatOpenAI
+from python_a2a.mcp import MCPClient
+from python_a2a.langchain import to_langchain_tool
+from conf import settings
+
+# 配置日志
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+# 创建模型
+llm = ChatOpenAI(
+    base_url=settings.base_url,
+    api_key=settings.api_key,
+    model=settings.model_name,
+    temperature=0.1
+)
+
+async def test_mcp_tools():
+    # 连接到服务端，端口 8000
+    client = MCPClient("http://localhost:8010")
+    try:
+        # 步骤 1：获取可用工具列表
+        tools = await client.get_tools()
+        logger.info("可用工具列表：")
+        for tool in tools:
+            print(tool)
+            logger.info(f"- {tool.get('name', '未知')}: {tool.get('description', '无描述')}")
+
+        # 将 MCP tool 转成 LangChain 的工具
+        get_weather_tool = to_langchain_tool("http://localhost:8010", "get_weather")
+        query_high_frequency_question = to_langchain_tool("http://localhost:8010", "query_high_frequency_question")
+        tools=[get_weather_tool, query_high_frequency_question]
+
+        # 创建prompt模板
+        prompt_template = ChatPromptTemplate.from_messages([
+            ("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。"),
+            ("human", "{input}"),
+            ("placeholder", "{agent_scratchpad}"),
+        ])
+
+        # 构建工具调用代理
+        agent = create_tool_calling_agent(llm, tools, prompt_template)
+
+        # 创建代理执行器
+        agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
+
+        # 代理调用
+        print("MCP客户端启动，输入'quit'退出")
+        while True:
+            # 接收用户查询
+            query = input("\nQuery: ").strip()
+            if query.lower() == "quit":
+                break
+            # 发送用户查询给代理，并打印
+            try:
+                response = await agent_executor.ainvoke({"input": query})
+                print(f"response-->{response}")
+            except Exception:
+                print("解析有问题")
+    except Exception as e:
+        logger.error(f"MCP 客户端出错：{str(e)}", exc_info=True)
+    finally:
+        await client.close()
+
+async def main():
+    await test_mcp_tools()
+
+if __name__ == "__main__":
+    asyncio.run(main())