380 lines
13 KiB
Python
Executable File
380 lines
13 KiB
Python
Executable File
#!/usr/bin/env python3
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"""
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Qdrant MCP 服务器
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此脚本实现了一个 MCP 服务器，与 Qdrant 向量数据库集成
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"""
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import asyncio
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import json
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import os
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import sys
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from typing import Any, Dict, List, Optional
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import logging
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from qdrant_client import QdrantClient
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from qdrant_client.models import Distance, VectorParams, PointStruct, Filter
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# 设置日志
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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class QdrantMCPServer:
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def __init__(self):
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# 从环境变量获取配置
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self.qdrant_url = os.getenv("QDRANT_URL", "http://localhost:6333")
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self.qdrant_api_key = os.getenv("QDRANT_API_KEY", "")
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self.collection_name = os.getenv("COLLECTION_NAME", "mcp")
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self.embedding_model = os.getenv("EMBEDDING_MODEL", "bge-m3")
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# 初始化 Qdrant 客户端
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self.client = QdrantClient(
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url=self.qdrant_url,
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api_key=self.qdrant_api_key if self.qdrant_api_key else None
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)
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# 确保集合存在
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self._ensure_collection_exists()
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logger.info(f"Qdrant MCP 服务器已初始化")
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logger.info(f"Qdrant URL: {self.qdrant_url}")
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logger.info(f"集合名称: {self.collection_name}")
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logger.info(f"嵌入模型: {self.embedding_model}")
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def _ensure_collection_exists(self):
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"""确保集合存在，如果不存在则创建"""
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try:
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collections = self.client.get_collections().collections
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collection_names = [collection.name for collection in collections]
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if self.collection_name not in collection_names:
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# 创建新集合
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self.client.create_collection(
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collection_name=self.collection_name,
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vectors_config=VectorParams(size=1024, distance=Distance.COSINE)
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)
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logger.info(f"已创建新集合: {self.collection_name}")
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else:
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logger.info(f"集合已存在: {self.collection_name}")
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except Exception as e:
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logger.error(f"确保集合存在时出错: {e}")
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raise
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async def handle_request(self, request: Dict[str, Any]) -> Dict[str, Any]:
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"""处理 MCP 请求"""
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method = request.get("method")
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params = request.get("params", {})
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request_id = request.get("id")
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logger.info(f"收到请求: {method}")
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try:
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if method == "initialize":
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result = await self.initialize(params)
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elif method == "tools/list":
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result = await self.list_tools(params)
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elif method == "tools/call":
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result = await self.call_tool(params)
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elif method == "resources/list":
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result = await self.list_resources(params)
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elif method == "resources/read":
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result = await self.read_resource(params)
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else:
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result = {
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"error": {
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"code": -32601,
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"message": f"未知方法: {method}"
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}
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}
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except Exception as e:
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logger.error(f"处理请求时出错: {e}")
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result = {
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"error": {
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"code": -32603,
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"message": f"内部错误: {str(e)}"
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}
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}
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response = {
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"jsonrpc": "2.0",
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"id": request_id,
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**result
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}
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return response
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async def initialize(self, params: Dict[str, Any]) -> Dict[str, Any]:
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"""初始化 MCP 服务器"""
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logger.info("初始化 Qdrant MCP 服务器")
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return {
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"result": {
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"protocolVersion": "2024-11-05",
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"capabilities": {
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"tools": {
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"listChanged": False
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},
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"resources": {
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"subscribe": False,
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"listChanged": False
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}
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},
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"serverInfo": {
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"name": "qdrant-mcp-server",
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"version": "1.0.0"
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}
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}
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}
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async def list_tools(self, params: Dict[str, Any]) -> Dict[str, Any]:
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"""列出可用工具"""
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return {
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"result": {
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"tools": [
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{
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"name": "qdrant_search",
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"description": "在 Qdrant 中搜索相似向量",
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"inputSchema": {
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"type": "object",
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"properties": {
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"query": {
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"type": "string",
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"description": "搜索查询文本"
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},
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"limit": {
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"type": "integer",
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"default": 5,
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"description": "返回结果数量限制"
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}
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},
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"required": ["query"]
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}
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},
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{
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"name": "qdrant_add",
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"description": "向 Qdrant 添加向量",
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"inputSchema": {
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"type": "object",
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"properties": {
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"text": {
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"type": "string",
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"description": "要添加的文本内容"
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},
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"metadata": {
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"type": "object",
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"description": "与文本关联的元数据"
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}
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},
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"required": ["text"]
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}
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},
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{
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"name": "qdrant_delete",
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"description": "从 Qdrant 删除向量",
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"inputSchema": {
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"type": "object",
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"properties": {
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"id": {
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"type": "string",
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"description": "要删除的向量ID"
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}
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},
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"required": ["id"]
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}
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}
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]
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}
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}
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async def call_tool(self, params: Dict[str, Any]) -> Dict[str, Any]:
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"""调用工具"""
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name = params.get("name")
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arguments = params.get("arguments", {})
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if name == "qdrant_search":
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return await self._search_vectors(arguments)
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elif name == "qdrant_add":
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return await self._add_vector(arguments)
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elif name == "qdrant_delete":
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return await self._delete_vector(arguments)
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else:
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return {
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"error": {
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"code": -32601,
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"message": f"未知工具: {name}"
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}
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}
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async def _search_vectors(self, params: Dict[str, Any]) -> Dict[str, Any]:
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"""搜索相似向量"""
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query = params.get("query", "")
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limit = params.get("limit", 5)
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# 这里应该使用嵌入模型将查询转换为向量
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# 由于我们没有实际的嵌入模型，这里使用一个简单的模拟
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query_vector = [0.1] * 1024 # 模拟向量
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try:
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search_result = self.client.search(
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collection_name=self.collection_name,
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query_vector=query_vector,
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limit=limit
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)
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results = []
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for hit in search_result:
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results.append({
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"id": hit.id,
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"score": hit.score,
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"payload": hit.payload
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})
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return {
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"result": {
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"content": [
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{
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"type": "text",
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"text": f"搜索结果: {json.dumps(results, ensure_ascii=False)}"
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}
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]
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}
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}
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except Exception as e:
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logger.error(f"搜索向量时出错: {e}")
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return {
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"error": {
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"code": -32603,
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"message": f"搜索向量时出错: {str(e)}"
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}
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}
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async def _add_vector(self, params: Dict[str, Any]) -> Dict[str, Any]:
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"""添加向量"""
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text = params.get("text", "")
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metadata = params.get("metadata", {})
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# 生成一个简单的ID
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import hashlib
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vector_id = hashlib.md5(text.encode()).hexdigest()
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# 这里应该使用嵌入模型将文本转换为向量
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# 由于我们没有实际的嵌入模型，这里使用一个简单的模拟
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vector = [0.1] * 1024 # 模拟向量
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try:
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self.client.upsert(
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collection_name=self.collection_name,
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points=[
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PointStruct(
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id=vector_id,
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vector=vector,
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payload={
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"text": text,
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**metadata
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}
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)
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]
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)
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return {
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"result": {
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"content": [
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{
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"type": "text",
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"text": f"已添加向量，ID: {vector_id}"
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}
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]
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}
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}
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except Exception as e:
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logger.error(f"添加向量时出错: {e}")
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return {
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"error": {
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"code": -32603,
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"message": f"添加向量时出错: {str(e)}"
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}
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}
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async def _delete_vector(self, params: Dict[str, Any]) -> Dict[str, Any]:
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"""删除向量"""
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vector_id = params.get("id", "")
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try:
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self.client.delete(
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collection_name=self.collection_name,
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points_selector=[vector_id]
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)
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return {
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"result": {
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"content": [
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{
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"type": "text",
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"text": f"已删除向量，ID: {vector_id}"
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}
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]
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}
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}
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except Exception as e:
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logger.error(f"删除向量时出错: {e}")
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return {
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"error": {
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"code": -32603,
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"message": f"删除向量时出错: {str(e)}"
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}
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}
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async def list_resources(self, params: Dict[str, Any]) -> Dict[str, Any]:
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"""列出资源"""
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return {
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"result": {
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"resources": []
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}
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}
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async def read_resource(self, params: Dict[str, Any]) -> Dict[str, Any]:
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"""读取资源"""
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return {
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"error": {
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"code": -32601,
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"message": "不支持读取资源"
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}
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}
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async def main():
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"""主函数"""
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server = QdrantMCPServer()
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# 从标准输入读取请求
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for line in sys.stdin:
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try:
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request = json.loads(line)
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response = await server.handle_request(request)
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print(json.dumps(response, ensure_ascii=False))
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sys.stdout.flush()
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except json.JSONDecodeError as e:
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logger.error(f"解析 JSON 时出错: {e}")
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error_response = {
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"jsonrpc": "2.0",
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"id": None,
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"error": {
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"code": -32700,
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"message": f"解析 JSON 时出错: {str(e)}"
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}
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}
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print(json.dumps(error_response, ensure_ascii=False))
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sys.stdout.flush()
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except Exception as e:
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logger.error(f"处理请求时出错: {e}")
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error_response = {
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"jsonrpc": "2.0",
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"id": None,
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"error": {
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"code": -32603,
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"message": f"内部错误: {str(e)}"
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}
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}
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print(json.dumps(error_response, ensure_ascii=False))
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sys.stdout.flush()
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if __name__ == "__main__":
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asyncio.run(main()) |