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 routes.buleprint 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 rag_search(query_text: str):
    dataset_id_strs = "11,12"
    dataset_ids = dataset_id_strs.split(",")
    search_type = "hybrid"

    query_results = await query_search(
        query_text=query_text,
        filters={"dataset_id": dataset_ids},
        search_type=search_type,
    )

    resource = get_resource_manager()
    chat_result_mapper = ChatResult(resource.mysql_client)
    rag_chat_agent = RAGChatAgent()
    chat_result = await rag_chat_agent.chat_with_deepseek(query_text, query_results)
    llm_search_result = await rag_chat_agent.llm_search(query_text)
    decision = await rag_chat_agent.make_decision(chat_result, llm_search_result)
    data = {
        "result": decision["result"],
        "status": decision["status"],
        "relevance_score": decision["relevance_score"],
    }
    await chat_result_mapper.insert_chat_result(
        query_text,
        dataset_id_strs,
        json.dumps(query_results, ensure_ascii=False),
        chat_result["summary"],
        chat_result["relevance_score"],
        chat_result["status"],
        llm_search_result["answer"],
        llm_search_result["source"],
        llm_search_result["status"],
    )

    return data