A local RAG knowledge base MCP server that exposes semantic document search as tools using zvec for vector storage and Qwen3-Embedding for text embedding.
A local-first document retrieval engine that mounts as an MCP tool for agents to index files, search for relevant passages, and let the agent's own LLM answer.
A local RAG-powered documentation search system that uses vector embeddings and Qdrant to enable semantic search across markdown, HTML, and other file formats. It provides an MCP interface for AI tools like Cursor to intelligently query and retrieve information from local knowledge bases.
Hybrid semantic search (dense vector + BM25) over local knowledge bases and codebases, exposed as MCP tools for AI agents to search and list knowledge bases.