rag_server/mcp_server/server.py

import asyncio
import json
from typing import Any, Dict, List

import mcp.types as types
from mcp.server.lowlevel import Server

from applications.resource import get_resource_manager
from applications.utils.chat import RAGChatAgent
from applications.utils.mysql import ChatResult
from applications.utils.search.qwen import QwenClient
from applications.utils.spider.study import study
from routes.blueprint import query_search


def create_mcp_server() -> Server:
    """创建并配置MCP服务器"""
    app = Server("mcp-rag-server")

    @app.call_tool()
    async def call_tool(
            name: str, arguments: Dict[str, Any]
    ) -> List[types.TextContent]:
        """处理工具调用"""
        # ctx = app.request_context
        if name == "rag-search":
            data = await rag_search(arguments["query_text"])
            result = json.dumps(data, ensure_ascii=False, indent=2)
        else:
            raise ValueError(f"Unknown tool: {name}")
        return [types.TextContent(type="text", text=result)]

    @app.list_tools()
    async def list_tools() -> List[types.Tool]:
        return [
            types.Tool(
                name="rag-search",
                title="RAG搜索",
                description="搜索内容并生成总结",
                inputSchema={
                    "type": "object",
                    "properties": {
                        "query_text": {
                            "type": "string",
                            "description": "用户输入的查询文本",
                        }
                    },
                    "required": ["query_text"],  # 只强制 query_text 必填
                    "additionalProperties": False,
                },
            ),
        ]

    return app


async def process_question(question, query_text, rag_chat_agent):
    try:
        dataset_id_strs = "11,12"
        dataset_ids = dataset_id_strs.split(",")
        search_type = "hybrid"

        # 执行查询任务
        query_results = await query_search(
            query_text=question,
            filters={"dataset_id": dataset_ids},
            search_type=search_type,
        )

        resource = get_resource_manager()
        chat_result_mapper = ChatResult(resource.mysql_client)

        # 异步执行 chat 与 deepseek 的对话
        chat_result = await rag_chat_agent.chat_with_deepseek(question, query_results)

        # # 判断是否需要执行 study
        study_task_id = None
        if chat_result["status"] == 0:
            study_task_id = study(question)["task_id"]

        qwen_client = QwenClient()
        llm_search = qwen_client.search_and_chat(user_prompt=question, search_strategy="agent")

        # 执行决策逻辑
        decision = await rag_chat_agent.make_decision(question, chat_result, llm_search)

        # 构建返回的数据
        data = {
            "query": question,
            "result": decision["result"],
            "status": decision["status"],
            "relevance_score": decision["relevance_score"],
        }

        # 插入数据库
        await chat_result_mapper.insert_chat_result(
            question,
            dataset_id_strs,
            json.dumps(query_results, ensure_ascii=False),
            chat_result["summary"],
            chat_result["relevance_score"],
            chat_result["status"],
            llm_search["content"],
            json.dumps(llm_search["search_results"], ensure_ascii=False),
            1,
            decision["result"],
            study_task_id,
        )
        return data
    except Exception as e:
        print(f"Error processing question: {question}. Error: {str(e)}")
        return {"query": question, "error": str(e)}


async def rag_search(query_text: str):
    rag_chat_agent = RAGChatAgent()
    spilt_query = await rag_chat_agent.split_query(query_text)
    split_questions = spilt_query["split_questions"]
    split_questions.append(query_text)

    # 使用asyncio.gather并行处理每个问题
    tasks = [
        process_question(question, query_text, rag_chat_agent)
        for question in split_questions
    ]

    # 等待所有任务完成并收集结果
    data_list = await asyncio.gather(*tasks)
    return data_list