qmd

You can search local markdown notes and meeting transcripts without cloud dependencies by using hybrid BM25, vector search, and LLM reranking. This ensures fast, private semantic retrieval across your local knowledge base.

Yes, you can integrate local knowledge base search into your workflow using either MCP or CLI integration. These options allow you to retrieve decisions, code references, and project context from local archives directly through your existing tools.

The best way to find decisions and code references across large text collections is using hybrid search. It combines keyword matching and vector retrieval with LLM reranking to deliver precise results from documentation and project archives.

Yes, hybrid search works effectively with local text collections and markdown documentation. It leverages BM25 and vector search to perform semantic retrieval, accurately finding ideas, research notes, and project context across these formats.

Your local semantic search needs LLM reranking because it refines initial BM25 and vector search results. Reranking evaluates the contextual relevance of retrieved notes and documents, ensuring the final output prioritizes the most precise answers.