rag_server/mcp_server/server.py

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 ChatClassifier
from applications.utils.mysql import ContentChunks, Contents, ChatResult
from routes.buleprint import query_search
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 ChatClassifier
from applications.utils.mysql import ContentChunks, Contents, 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()
    content_chunk_mapper = ContentChunks(resource.mysql_client)
    contents_mapper = Contents(resource.mysql_client)
    chat_result_mapper = ChatResult(resource.mysql_client)

    res = []
    for result in query_results["results"]:
        content_chunks = await content_chunk_mapper.select_chunk_content(
            doc_id=result["doc_id"], chunk_id=result["chunk_id"]
        )
        contents = await contents_mapper.select_content_by_doc_id(result["doc_id"])
        if not content_chunks:
            return {"status_code": 500, "detail": "content_chunk not found", "data": {}}
        if not contents:
            return {"status_code": 500, "detail": "contents not found", "data": {}}

        content_chunk = content_chunks[0]
        content = contents[0]
        res.append(
            {
                "contentChunk": content_chunk["text"],
                "contentSummary": content_chunk["summary"],
                "content": content["text"],
                "score": result["score"],
            }
        )

    chat_classifier = ChatClassifier()
    chat_res = await chat_classifier.chat_with_deepseek(query_text, res)

    data = {
        "result": chat_res["summary"],
        "status": chat_res["status"],
        "relevance_score": chat_res["relevance_score"],
    }

    await chat_result_mapper.insert_chat_result(
        query_text,
        dataset_id_strs,
        json.dumps(res, ensure_ascii=False),
        chat_res["summary"],
        chat_res["relevance_score"],
        chat_res["status"],
    )

    return data