semantic-search

Semantic search uses local embedding models to find related notes by conceptual meaning. It retrieves markdown files based on semantic similarity rather than literal keyword matching, enabling meaning-based retrieval across your knowledge base.

Yes, semantic search detects duplicate ideas and partial overlaps by comparing rephrased concepts. It checks whether a new idea already exists in your second brain by finding semantically similar notes, even if the wording differs.

Yes, semantic search requires a local embedding model and a cached vector index to operate. These components process your markdown files and generate the semantic embeddings needed for script-driven search and similarity discovery.

Semantic similarity discovery finds files that are conceptually close to an existing note, surfacing unexpected connections. It ranks related markdown files using semantic embeddings and allows narrowing results by project or category filters.

Semantic search provides meaning-based retrieval to find notes when exact keywords are not enough. It applies a cached vector index to broad recall queries, returning ranked results that match the underlying concept of your search.

Yes, semantic search supports optional project or category filters to narrow note retrieval results. After performing a script-driven search operation, you can refine the ranked semantic matches to focus on specific sections of your knowledge base.