A Model Context Protocol (MCP) server for Qdrant vector database with semantic search capabilities
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# Qdrant MCP Server [](https://badge.fury.io/py/qdrant-mcp-server) [](https://www.python.org/downloads/) [](https://opensource.org/licenses/MIT) 一个基于 [Qdrant](https://qdrant.tech/) 向量数据库的 [Model Context Protocol (MCP)](https://modelcontextprotocol.io/) 服务器，提供强大的语义搜索和知识管理功能。 ## ✨ 特性 - 🚀 **即插即用**: 使用 `uvx` 一键运行，无需复杂配置 - 🔍 **语义搜索**: 基于向量相似度的智能搜索 - 🧠 **多种嵌入模型**: 支持本地 Sentence-Transformers 和在线 Jina AI - 📝 **完整 CRUD**: 创建、读取、更新、删除知识条目 - 🔧 **灵活配置**: 通过环境变量自定义各项参数 - 🌐 **标准协议**: 完全符合 MCP 规范 ## 📦 安装 ### 使用 uvx (推荐) 最简单的方式是使用 `uvx` 直接运行: ```bash uvx qdrant-mcp-server ``` ### 使用 uv ```bash uv pip install qdrant-mcp-server ``` ### 使用 pip ```bash pip install qdrant-mcp-server ``` ## 🚀 快速开始 ### 1. 配置环境变量 创建 `.env` 文件或设置环境变量: ```bash # Qdrant 连接配置 QDRANT_URL=https://your-qdrant-instance.com:443 QDRANT_API_KEY=your-api-key-here COLLECTION_NAME=my_knowledge_base # 嵌入模型配置 (二选一) # 选项1: 使用本地模型 (默认) EMBEDDING_MODEL=sentence-transformers/all-MiniLM-L6-v2 VECTOR_NAME=fast-all-minilm-l6-v2 # 选项2: 使用 Jina AI 在线模型 # EMBEDDING_MODEL=jina-embeddings-v3 # JINA_TOKEN=your-jina-api-key # VECTOR_NAME=jina-embeddings-v3 ``` ### 2. 在 MCP 客户端中配置 #### Claude Desktop 配置 编辑 `~/Library/Application Support/Claude/claude_desktop_config.json`: ```json { "mcpServers": { "qdrant": { "command": "uvx", "args": ["qdrant-mcp-server"], "env": { "QDRANT_URL": "https://your-instance.com:443", "QDRANT_API_KEY": "your-api-key", "COLLECTION_NAME": "knowledge_base" } } } } ``` #### Roo Code / OpenCode 配置 编辑配置文件 (如 `~/.config/opencode/opencode.json`): ```json { "mcp": { "qdrant": { "type": "local", "command": ["uvx", "qdrant-m…
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