流式传输

api.py

 import uvicorn
 from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
+from sse_starlette import EventSourceResponse
 
+from client.client import user_query, lifespan
 from config.config import config
-from client.client import user_query
-from model.QueryParam import QueryParam
-from model.RestResult import RestResult
 
-app = FastAPI(title="BME MCP服务")
+app = FastAPI(title="BME MCP服务", lifespan=lifespan)
 
 cors = config.get("cors", {})
 app.add_middleware(CORSMiddleware, allow_origins=cors.get("allow_origins", ["*"]),
@@ -16,12 +15,10 @@ app.add_middleware(CORSMiddleware, allow_origins=cors.get("allow_origins", ["*"]
                    allow_headers=cors.get("allow_headers", ["*"]))
 
 
-@app.post(path="/mcp/query", description="调用mcp工具查询")
-async def query(query_param: QueryParam) -> RestResult:
-    if query_param.customer_token not in config["customer"]:
-        return RestResult(code=403, message="无权访问")
-    message = await user_query(query_param)
-    return RestResult(code=200, message=message)
+@app.get(path="/mcp/query", description="调用mcp工具查询")
+async def query(message: str, customer_token: str):
+    return EventSourceResponse(user_query(message, customer_token), media_type="text/event-stream",
+                               headers={"Cache-Control": "no-cache"})
 
 
 if __name__ == "__main__":

client/client.py

+import asyncio
 import json
-from contextlib import AsyncExitStack
+from contextlib import asynccontextmanager
 from datetime import datetime
 from typing import Optional
 
 from loguru import logger
 from mcp import ClientSession, StdioServerParameters
 from mcp.client.stdio import stdio_client
-from openai import OpenAI
+from openai import AsyncOpenAI
 from openai.types.chat import ChatCompletionToolParam, ChatCompletionAssistantMessageParam, \
     ChatCompletionMessageToolCallParam, ChatCompletionToolMessageParam
 from openai.types.chat.chat_completion_message_tool_call_param import Function
@@ -14,7 +15,6 @@ from openai.types.shared_params import FunctionDefinition
 from pydantic import AnyUrl
 
 from config.config import config
-from model.QueryParam import QueryParam
 
 if config["log"]["base_path"]:
     logger.add(config["log"]["base_path"] + "/mcp_client/log_{time:%Y-%m-%d}.log", rotation="1 day", encoding="utf-8",
@@ -27,28 +27,47 @@ class McpClient:
         self.default_system_prompt = None  # mcp_server提供的默认提示词
         self.available_tools: [] = None  # mcp_server提供的tool
         self.session: Optional[ClientSession] = None
-        self.exit_stack = AsyncExitStack()
+        # self.exit_stack = AsyncExitStack()
         active_model = config["model"][config["active"]]
-        self.client = OpenAI(
+        self.client = AsyncOpenAI(
             api_key=active_model["api_key"],
             base_url=active_model["base_url"],
         )
         self.model_name = active_model["model_name"]
-        self.connected = False
-
-    async def connect_to_server(self):
-        """
-        连接mcp_server服务
-        """
-        server_params = StdioServerParameters(
+        self.server_params = StdioServerParameters(
             command=config["server"]["command"],
             args=config["server"]["args"],
         )
-        stdio_transport = await self.exit_stack.enter_async_context(stdio_client(server_params))
-        stdio, write = stdio_transport
-        self.session = await self.exit_stack.enter_async_context(ClientSession(stdio, write))
-        await self.session.initialize()
-        self.connected = True
+
+    async def _session_ping(self):
+        logger.info(f"Start to get stdio client...")
+        async with stdio_client(self.server_params) as client:
+            logger.info(f"Start to get stdio client session...")
+            async with ClientSession(*client) as session:
+                logger.info(f"Start to initialize stdio client session...")
+                await session.initialize()
+                logger.info(f"End to initialize stdio client session...")
+                self.session = session
+                try:
+                    while True:
+                        logger.info(f"Start to ping stdio client session...")
+                        await asyncio.sleep(10)
+                        await session.send_ping()
+                        logger.info(f"End to ping stdio client session...")
+                except Exception as e:
+                    logger.exception(e)
+                    self.session = None
+
+    async def _session_keepalive(self):
+        while True:
+            try:
+                logger.info(f"Start to keep session alive...")
+                await self._session_ping()
+            except Exception as e:
+                logger.exception(e)
+
+    async def start(self):
+        asyncio.create_task(self._session_keepalive())
 
     async def read_mcp(self):
         """
@@ -116,93 +135,84 @@ class McpClient:
             客户名：{customer[0]['customerName']}
             客户全称：{customer[0]['customerFullname']}"""
 
-    async def process_query(self, param: QueryParam):
+    async def process_query(self, message: str, customer_token: str):
         """
         处理查询逻辑
-        :param param: 请求参数
+        :param message: 用户原始问题
+        :param customer_token: 客户编码
         :return: 经过mcp加工后的ai回答
         """
-        logger.info(f"--> user origin query: {param}")
+        logger.info(f"--> user origin query, message: {message}, token: {customer_token}")
         messages = [
             {"role": "system", "content": self.default_system_prompt},
-            {"role": "system", "content": self.deal_customer_permission(param.customer_token)},
+            {"role": "system", "content": self.deal_customer_permission(customer_token)},
             {"role": "system", "content": f"如果要使用到当前时间，请使用{datetime.now()}"},
-            {"role": "user", "content": param.message}
+            {"role": "user", "content": message}
         ]
         logger.info(f"--> messages: {messages}")
         logger.info(f"--> model: {self.model_name}")
         # 调用ai
-        ai_response = self.client.chat.completions.create(
+        ai_response = await self.client.chat.completions.create(
             model=self.model_name,
             messages=messages,
             tools=self.available_tools,
             tool_choice="auto"
         )
         logger.info(f"--> ai response: {ai_response}")
-
-        final_text = []
         chat_completion_message = ai_response.choices[0].message
-        final_text.append(chat_completion_message.content) if chat_completion_message.content else None
-        # 防止死循环
-        loop = 0
-        # 只要还存在工具调用，就循环下去
-        while chat_completion_message.tool_calls and loop < config["tool_calls_deep"]:
-            loop = loop + 1
-            logger.info(f"----> Available tools: {chat_completion_message.tool_calls}")
-            # 可能ai一次选取了多个工具，这里循环处理
-            for tool_call in chat_completion_message.tool_calls:
-                tool_name = tool_call.function.name
-                tool_args = json.loads(tool_call.function.arguments)
-                logger.info(f"------> start to call tool...")
-                logger.info(f"------> tool_name: {tool_name}, tool_args: {tool_args}")
-
-                result = await self.session.call_tool(tool_name, tool_args)
-                logger.info(f"------> call result: {result}")
-
-                messages.append(ChatCompletionAssistantMessageParam(
-                    role="assistant",
-                    tool_calls=[ChatCompletionMessageToolCallParam(
-                        id=tool_call.id,
-                        type="function",
-                        function=Function(
-                            name=tool_name,
-                            arguments=json.dumps(tool_args)
-                        )
-                    )]
-                ))
-                messages.append(ChatCompletionToolMessageParam(
-                    role="tool",
-                    tool_call_id=tool_call.id,
-                    content=str(result.content)
-                ))
-
-            ai_response = self.client.chat.completions.create(
-                model=self.model_name,
-                messages=messages,
-                tools=self.available_tools,
-                tool_choice="auto"
-            )
-            logger.info(f"----> ai response: {ai_response}")
-
-            chat_completion_message = ai_response.choices[0].message
-            final_text.append(chat_completion_message.content) if chat_completion_message.content else None
-        return "\n".join(final_text)
-
-    async def cleanup(self):
-        """Clean up resources."""
-        await self.exit_stack.aclose()
-
-
-client = McpClient()
-
-
-async def user_query(param: QueryParam):
+        logger.info(f"----> Available tools: {chat_completion_message.tool_calls}")
+        # 可能ai一次选取了多个工具，这里循环处理
+        for tool_call in chat_completion_message.tool_calls:
+            tool_name = tool_call.function.name
+            tool_args = json.loads(tool_call.function.arguments)
+            logger.info(f"------> start to call tool...")
+            logger.info(f"------> tool_name: {tool_name}, tool_args: {tool_args}")
+            result = await self.session.call_tool(tool_name, tool_args)
+            logger.info(f"------> call result: {result}")
+
+            messages.append(ChatCompletionAssistantMessageParam(
+                role="assistant",
+                tool_calls=[ChatCompletionMessageToolCallParam(
+                    id=tool_call.id,
+                    type="function",
+                    function=Function(
+                        name=tool_name,
+                        arguments=json.dumps(tool_args)
+                    )
+                )]
+            ))
+            messages.append(ChatCompletionToolMessageParam(
+                role="tool",
+                tool_call_id=tool_call.id,
+                content=str(result.content)
+            ))
+        ai_stream_response = await self.client.chat.completions.create(
+            model=self.model_name,
+            messages=messages,
+            tools=self.available_tools,
+            tool_choice="auto",
+            stream=True
+        )
+        async for chunk in ai_stream_response:
+            if chunk.choices[0].finish_reason == "stop":
+                yield "[DONE]"
+            else:
+                yield json.dumps({"content": chunk.choices[0].delta.content})
+
+
+instance = McpClient()
+
+
+@asynccontextmanager
+async def lifespan(app):
+    await instance.start()
+    yield
+
+
+async def user_query(message: str, customer_token: str):
     try:
-        if not client.connected:
-            await client.connect_to_server()
-        await client.read_mcp()
-        result = await client.process_query(param)
-        logger.info(f"Final return: {result}")
-        return result
+        await instance.read_mcp()
+        async for r in instance.process_query(message, customer_token):
+            yield r
     except Exception as e:
         logger.exception(e)

model/QueryParam.py

-from pydantic import BaseModel
-
-
-class QueryParam(BaseModel):
-    """查询参数实体"""
-
-    message: str
-    """用户输入的消息内容"""
-
-    customer_token: str
-    """客户token"""
-

model/RestResult.py

-from pydantic import BaseModel
-
-
-class RestResult(BaseModel):
-    """返回实体"""
-
-    code: int = 200
-    """状态码"""
-
-    message: str
-    """返回信息"""
\ No newline at end of file