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rag-mcp-server-lizhibing
Advanced tools
这是一个基于 MCP (Model Context Protocol) 的 RAG (Retrieval-Augmented Generation) 服务器,它允许 AI 助手访问和管理知识库,进行智能问答和文档检索。
# 在后台启动服务器
python rag_server.py
# 或者直接运行(会阻塞终端)
python rag_server.py
在支持 MCP 的 AI 客户端(如 Claude Desktop)中,添加以下配置:
{
"mcpServers": {
"rag-server": {
"command": "python",
"args": ["/path/to/your/rag-mcp-server/rag_server.py"],
"env": {
"PYTHONPATH": "/path/to/your/rag-mcp-server"
}
}
}
}
启动后,AI 助手就可以使用以下工具:
add_document("文档内容", {"category": "技术", "language": "中文"})
search_documents("人工智能")
get_document("doc_0")
list_documents()
delete_document("doc_0")
get_server_info()
服务器支持以下配置:
max_results
: 最大搜索结果数量 (默认: 5)similarity_threshold
: 相似度阈值 (默认: 0.7)supported_formats
: 支持的文档格式enable_logging
: 是否启用日志记录服务器无法启动
MCP 连接失败
文档添加失败
服务器运行时会输出详细日志,包括:
rag-mcp-server/
├── rag_server.py # 主服务器文件
├── rag_engine.py # RAG 核心引擎
├── mcp_client_config.json # MCP 客户端配置示例
├── requirements.txt # 依赖包列表
└── README.md # 使用说明
你可以通过修改 rag_engine.py
来:
如果遇到问题,请:
注意: 这是一个示例实现,生产环境使用前请进行充分测试和安全评估。
FAQs
RAG MCP Server for document search and retrieval
We found that rag-mcp-server-lizhibing demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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