vault-indexer

Obsidian vault metadata search is accelerated by indexing Markdown files into a SQLite database, replacing slow grep operations with structured queries. This allows fast filtering by tags, dates, paths, and frontmatter attributes across large archives.

Yes, you can filter Markdown notes by tag and date range by querying the indexed SQLite database. The vault indexer extracts frontmatter, tags, and timestamps, enabling structured discovery for specific timeframes without manual file scanning.

Yes, sqlite3, python3, and yq are required dependencies to parse Markdown frontmatter and build the searchable database index. These tools handle metadata extraction and maintain the structured note index for fast vault queries.

The best way to find unorganized documents is using a SQLite vault index to query for notes lacking specific tags or frontmatter. This structured search approach instantly identifies files without metadata, streamlining vault maintenance tasks across thousands of notes.

Vault indexing works for link analysis and knowledge management by extracting note links and titles into the database. This supports structured queries to map note relationships and discover content within Markdown archives.

Obsidian vault search becomes slow when relying on text scanning across thousands of Markdown files. Indexing metadata into a SQLite database resolves this by enabling instant structured queries on tags, dates, and paths instead of manual scanning.