qmd

To search markdown notes locally without cloud dependencies, you can use a hybrid search engine that indexes your files entirely on-device. This approach combines BM25 keyword matching and semantic vector search to ensure privacy while querying your personal knowledge base.

Hybrid search for personal knowledge bases combines BM25 keyword matching, semantic vector search, and LLM reranking to improve document retrieval. This mechanism ensures both exact term frequency matching and deep contextual understanding for in-depth administrative and research workflows.

Yes, you need sqlite for database storage and GGUF models for the LLM reranking and semantic understanding. These dependencies allow the local search engine to operate entirely on-device, eliminating cloud requirements while indexing markdown documentation.

The best way to integrate local markdown search into an administrative workflow is using a tool that offers both CLI and MCP integration. This allows seamless command-line querying and direct connection to an AI agent for searching meeting transcripts and documentation.

LLM reranking refines BM25 and vector search results by using a local GGUF model to re-evaluate and sort the most relevant documents. This three-tier hybrid search mechanism ensures high-precision retrieval for complex text file queries without sending data externally.