qmd

You can search Markdown files locally by indexing collections on your machine. This retrieves relevant snippets using BM25 keyword matching and vector embeddings, ensuring all data remains private without cloud uploads.

Yes, you can find specific text snippets inside an Obsidian vault by running local semantic and keyword searches. The tool scans indexed Markdown files and returns only the exact sentences matching your query to reduce token usage.

The best way to retrieve information from large local document sets is building an indexed collection. Indexing enables fast BM25 keyword and vector semantic searches, returning targeted snippets instead of loading entire documents.

Local Markdown search supports both semantic understanding and keyword matching. It uses vector embeddings for semantic context and BM25 algorithms for exact keyword retrieval across indexed knowledge bases.

You index your notes collection by pointing the local search tool at your Markdown files. It builds a searchable index combining BM25 keyword matching and vector embeddings, enabling fast content discovery across your knowledge base.

You should use local search for technical documentation to preserve data privacy. All indexing and querying happen directly on your machine, returning relevant text snippets without exposing your knowledge base to external servers.