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Search and the Knowledge Base

Find anything in your Glassy library with full-text search, search operators, and the Knowledge Base — a semantic, AI-indexed view of everything you've saved.

Before you start

  • getting-started/03-navigating-the-workspace

What you'll learn

  • Use full-text search across notes, bookmarks, captures, and documents
  • Apply search operators (tag:, type:, collection:) to narrow results
  • Understand the Knowledge Base workspace
  • Recognize when semantic search is working on-device vs. cloud

Your library is only as useful as your ability to retrieve what’s in it. Glassy gives you two complementary search systems: full-text search for keywords and phrases, and the Knowledge Base for semantic, meaning-based discovery.

Press Ctrl +K (or Cmd +K on Mac) anywhere in Glassy to open the global search overlay. Type any word or phrase and press Enter .

Glassy searches across:

  • Note titles and content — full-text match
  • Bookmark titles and URLs — title and URL match
  • Capture content — extracted text from captured pages
  • Documents — document titles and content
  • Tags — tag name match

Results are ranked by relevance, with recent items weighted higher.

Search operators

Narrow your search with operators:

OperatorExampleEffect
tag:tag:meetingsOnly items with the “meetings” tag
type:type:noteOnly notes (note, bookmark, capture)
collection:collection:researchItems in the “research” collection
-tag:-tag:archivedExclude items with the “archived” tag

Operators combine freely:

tag:work type:note -tag:archived

This finds notes tagged “work” that are not tagged “archived”.

The Knowledge Base workspace

The Knowledge Base (in the sidebar under Organize, when enabled) is a dedicated view of everything you’ve saved — notes, bookmarks, captures, documents, and even Obsidian vault files if connected. It’s powered by a hybrid search system that combines:

  1. BM25 full-text search — keyword matching, fast and precise
  2. Vector semantic search — meaning-based matching, finds conceptually related content even without keyword overlap
  3. Reciprocal Rank Fusion (RRF) — merges both result sets, ranked and deduplicated

How content gets indexed

When you save a bookmark, create a note, or sync a vault file, the corpus indexer automatically:

  1. Generates a vector embedding (a mathematical representation of the content’s meaning)
  2. Stores it in the content_embeddings table
  3. Adds the text to an FTS5 full-text index for keyword search

This happens automatically — you don’t need to manually index anything.

Using the Knowledge Base

Open the Knowledge Base workspace to:

  • Search across everything — type a query and get hybrid results
  • Browse by source type — filter to notes, bookmarks, documents, or vault files
  • Discover connections — find content you forgot you had
Knowledge Base search showing hybrid results across bookmarks, notes, and vault files
The Knowledge Base — hybrid search across everything you've saved

When you open a note, the Related Notes panel shows semantically similar notes. If you see an “On Device” badge, the similarity was computed entirely in your browser using local embeddings — no data was sent to any server.

What’s next?

Learn about importing and exporting your data to move content in and out of Glassy — Markdown export, JSON export, and Obsidian import.