qmd

You can search markdown notes locally without cloud dependencies by using an on-device hybrid search engine that combines keyword matching, semantic understanding, and LLM reranking for robust document retrieval. Add your collections and execute a search query to retrieve results.

Hybrid search for personal knowledge bases is a mechanism that combines keyword matching, semantic understanding, and LLM reranking. This approach ensures robust local document retrieval by leveraging multiple models for query expansion to find specific information within large markdown collections.

Yes, you can integrate local document retrieval directly with AI agents using Mercury Control Protocol (MCP). This allows AI agents to query your personal knowledge base on-device, utilizing multiple models for query expansion and reranking without cloud dependencies.

Yes, the local search engine supports command-line usage for markdown files. You can add your document collections with context descriptions and use the command-line interface to execute queries and retrieve specific information quickly.

The best way to find specific information in a large collection of markdown files is using a local hybrid search engine. It combines keyword matching, semantic understanding, and LLM reranking to process queries and retrieve relevant documents efficiently on-device.