sun
feb1a0b280
- 实现了基于LangChain的MCP Agent，支持连接MCP服务器调用工具 - 添加了环境配置文件(.env)，包含LLM模型和API配置信息 - 创建了完整的工具系统，包括BaseTool基类和Bash、Terminate、Add等工具 - 集成了天气查询工具，支持通过中国气象局API获取天气预报信息 - 实现了交互式对话功能，支持多轮工具调用和结果处理 - 添加了详细的CLAUDE.md开发指导文档
97 lines
3.2 KiB
Python
97 lines
3.2 KiB
Python
import os
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import uuid
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from deepagents import create_deep_agent
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from dotenv import load_dotenv, find_dotenv
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from langchain.agents import create_agent
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from langgraph.checkpoint.memory import InMemorySaver
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from app.tools.agent_tools import add, get_weather_by_location
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from langchain_openai import ChatOpenAI
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from app.tools.execute_sql import execute_query
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_ = load_dotenv(find_dotenv())
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checkpointer = InMemorySaver()
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# model = ChatTongyi(
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# model="qwen3-30b-a3b-thinking-2507",
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# dashscope_api_key=os.getenv('LLM_API_KEY'),
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# base_url="https://dashscope.aliyuncs.com/compatible-mode/v1"
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# )
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model = ChatOpenAI(
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model="qwen3-30b-a3b-thinking-2507",
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api_key=os.getenv('LLM_API_KEY'),
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base_url="https://dashscope.aliyuncs.com/compatible-mode/v1"
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)
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# agent = create_agent(
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# model=model,
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# tools=[add, get_weather_by_location],
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# system_prompt="你是一个有帮助的助手。请简洁准确。"
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# )
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agent = create_deep_agent(
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model=model,
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tools=[add, get_weather_by_location, execute_query],
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checkpointer=checkpointer,
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system_prompt="你是一个有帮助的助手。请简洁准确，用中文进行回答。"
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)
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thread_id = str(uuid.uuid4())
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config = {
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"configurable": {
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"thread_id": thread_id
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}
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}
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if __name__ == "__main__":
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# 启用流式输出
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print("=== 开始流式输出 ===")
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stream_result = agent.stream(
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{"messages": [{"role": "user", "content": "查询车上咖啡机数据，不要修改原来的sql，除非运行报错。"
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"sql: select counts(*) from coffee_train"}]},
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config=config
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)
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# 逐块处理流式输出
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full_response = ""
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for chunk in stream_result:
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# 打印每一块内容（调试用途）
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print("接收到数据块:", chunk)
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# 解析消息内容
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if 'messages' in chunk:
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messages = chunk['messages']
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for msg in messages:
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# 打印消息类型和所有属性
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print(f"--- 消息类型: {type(msg).__name__} ---")
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if hasattr(msg, 'content') and msg.content:
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print(f"消息内容: {msg.content}")
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# 处理 AIMessage（带工具调用）
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if hasattr(msg, 'tool_calls') and msg.tool_calls:
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print("正在调用工具...")
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for tool_call in msg.tool_calls:
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print(f" 工具名: {tool_call.get('name', 'N/A')}")
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print(f" 参数: {tool_call.get('args', {})}")
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print(f" 调用ID: {tool_call.get('id', 'N/A')}")
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# 处理 ToolMessage（工具响应）
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elif hasattr(msg, 'name') and msg.name:
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print(f"工具名称: {msg.name}")
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print(f"工具调用ID: {msg.tool_call_id}")
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print(f"工具响应: {msg.content}")
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# 处理普通消息内容
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elif hasattr(msg, 'content'):
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content = msg.content
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full_response += content
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print("\n=== 流式输出结束 ===")
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# 打印最终完整响应
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print("\n=== 最终完整响应 ===")
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print(full_response)
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