QDrant RagDocs

This RagDocs MCP server provides Retrieval-Augmented Generation capabilities using Qdrant vector database and Ollama/OpenAI embeddings. Built with TypeScript, it offers semantic search and management of documentation through vector similarity. The server implements automatic text chunking, embedding generation, and supports both local and cloud-based Qdrant setups. Key features include adding documents with metadata, semantic search, listing/organizing documents, and deletion. By abstracting vector storage and embedding complexities, it enables easy integration of RAG functionality into AI workflows. This implementation is particularly useful for applications requiring context-aware document retrieval, knowledge management systems, and AI-powered documentation tools.