> ## Documentation Index
> Fetch the complete documentation index at: https://docs.bowerlabs.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Semantic search across your workspace

> Bird and global search now retrieve by meaning, not just keyword — backed by a vector store and an agentic RAG pipeline.

**Shipped 2026-02-08 · Search**

Until today, finding a note meant remembering the exact words you used.
Now you can search by meaning — and so can Bird.

## What's new

* **Vector store + agentic RAG** — every note is embedded into a vector
  store, and Bird retrieves the most relevant notes by semantic
  similarity before answering. The retrieval step is what lets Bird
  answer with grounded citations rather than guesses.
* **Semantic global search** — type a phrase and Bower finds notes that
  *mean* the same thing, even if the wording is different. "The
  experiment with the funny gel" finds the right note without needing
  the title.
* **Hybrid ranking** — semantic and keyword scores are blended so exact
  matches still win when they should ("note titled 2026-02-04") while
  meaning-based queries surface notes you'd never find by string match.

## How this connects to Bird

Bird's inline citations are powered by the same retrieval layer. When
you ask Bird a question, it searches your workspace semantically, picks
the most relevant notes, then writes an answer that cites them
specifically. The retrieval is what stops Bird from making things up.

## Related docs

* [Hybrid semantic search](/ai-and-search/hybrid-search)
* [Bird, your AI assistant](/ai-and-search/bird-ai-assistant)