qmd

You can search local notes and documents with semantic understanding by using a hybrid search mechanism that combines traditional keyword matching with vector-based semantic querying and AI-powered reranking. This approach indexes local files to retrieve precise results.

Finding specific information across large collections of meeting transcripts and plain text files requires indexing the documents locally and prompting the AI with a natural language query to retrieve relevant files efficiently without manual searching.

Yes, you can organize and retrieve personal knowledge base files without cloud dependence by utilizing local indexing. This mechanism processes notes, transcripts, and plain text files directly on your machine, ensuring offline access and data privacy.

AI reranking improves search relevance for personal documents by evaluating the contextual meaning of both the query and the indexed text. It adjusts the ranking of initial keyword and semantic matches to surface the most accurate results.

No, you do not need to manually tag your research notes to perform accurate local searches. The system uses vector-based semantic search and AI-enhanced ranking to understand concepts and locate relevant annotations directly from the text.