notebrain-assistant

To perform semantic search on an Obsidian markdown vault, you need to index the notes using ChromaDB for vector retrieval, enabling meaning-based discovery rather than exact keyword matching. This requires a local Go-based CLI environment to execute the structured JSON data extraction tasks.

You can find hidden relationships and unlinked notes in your knowledge base through graph analysis and backlink traversal. This identifies related concepts across fragmented notes to surface forgotten insights without requiring explicit links.

Yes, a local Go-based CLI environment is required to execute structured JSON-based data extraction and synthesis tasks. This environment facilitates the ChromaDB vector search and graph analysis processes for your knowledge management workflow.

The best way to synthesize information from fragmented markdown notes is using semantic retrieval combined with graph exploration. This approach finds related notes by meaning, traverses backlinks, and retrieves full content for comprehensive knowledge discovery.

Yes, ChromaDB works with Obsidian by indexing markdown vaults to enable vector similarity search. This allows you to query the vault and discover related concepts, even when exact search terms are not present in the text.