69 lines
2.5 KiB
Python
69 lines
2.5 KiB
Python
import json
|
||
import asyncio
|
||
from langchain_openai import ChatOpenAI
|
||
from mcp import ClientSession
|
||
from mcp.client.sse import sse_client
|
||
from langchain_mcp_adapters.tools import load_mcp_tools
|
||
from langchain.agents import create_tool_calling_agent, AgentExecutor
|
||
from langchain_core.prompts import ChatPromptTemplate
|
||
from conf import settings
|
||
|
||
# 创建模型
|
||
llm = ChatOpenAI(
|
||
base_url=settings.base_url,
|
||
api_key=settings.api_key,
|
||
model=settings.model_name,
|
||
temperature=0.1
|
||
)
|
||
|
||
# MCP server URL for SSE connection
|
||
server_url = "http://localhost:8001/sse"
|
||
|
||
# 定义mcp客户端
|
||
mcp_client = None
|
||
|
||
# Main async function: connect, load tools, create agent, run chat loop
|
||
async def run_agent():
|
||
global mcp_client
|
||
# 启动 MCP server，通过 SSE 建立异步连接。
|
||
async with sse_client(url=server_url) as streams:
|
||
# 使用读写通道创建 MCP 会话
|
||
async with ClientSession(*streams) as session:
|
||
await session.initialize()
|
||
# 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
|
||
mcp_client = type("MCPClientHolder", (), {"session": session})()
|
||
|
||
# 从 session 自动获取 MCP server 提供的工具列表。
|
||
tools = await load_mcp_tools(session)
|
||
# print(f"tools-->{tools}")
|
||
|
||
# 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。"),
|
||
("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")
|
||
return
|
||
|
||
if __name__ == "__main__":
|
||
asyncio.run(run_agent()) |