What is QMD? A Practical Demo of Local-First AI Search

In this session, we explore QMD hands-on and try to understand what it does well, where it fits, and why it is useful for local-first retrieval workflows. We start with a folder of markdown/plain text documents, index it with QMD, and compare basic keyword search with QMD’s retrieval pipeline. Along the way, we look at how QMD combines BM25 keyword search, vector semantic search, query expansion, hybrid retrieval, reranking, and MCP support for agent workflows. Topics covered: What QMD is How to index local documents CLI-based search and vector search Using QMD with MCP BM25 vs semantic search Hybrid retrieval and reranking Why grounding agent search in real documents matters This is a practical explainer for anyone interested in RAG, retrieval systems, local-first AI tools, or better search over markdown/document repositories.