qmd is a local semantic search engine and RAG knowledge base indexer that functions as a Model Context Protocol server. It converts local documents, markdown files, and codebases into a searchable database to provide retrieval augmented generation capabilities for AI agents.
The system exposes its search and retrieval tools via stdio or HTTP. It utilizes local model files for embeddings and reranking, supporting query expansion across multiple languages.
The project employs abstract syntax tree based chunking to split source code at function and class boundaries. It implements hybrid vector-keyword indexing and metadata-driven context assignment to improve retrieval accuracy, while operating as a background daemon to maintain model residency in memory.
