qmd

Semantic search for local markdown notes and meeting transcripts works by combining BM25 keyword retrieval, vector search, and LLM reranking. This hybrid approach locates relevant information across personal document collections without relying on cloud services.

The best way to search personal knowledge bases without cloud services is using a local search tool that indexes your text collections. It enables semantic knowledge discovery directly on your machine for markdown files, documentation, and transcripts.

To retrieve information from past meeting transcripts stored locally, use a local search tool that supports hybrid retrieval. It indexes your transcripts and allows you to query past decisions or discussion topics directly from your personal archive.

Yes, you can use MCP workflows for document retrieval from personal research archives. Local search tools support MCP and CLI workflows to query text collections, enabling efficient knowledge discovery from your indexed documentation.

Yes, you need local indexing capabilities for semantic knowledge discovery. Local indexing is required to structure your markdown notes and documentation archives so they can be queried using hybrid keyword and vector retrieval methods.

Relying solely on BM25 keyword retrieval for local documents limits matching to exact terms, missing contextual meaning. Combining it with vector semantic search and LLM reranking overcomes this limitation by improving local document discovery accuracy.