Skip to main content

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.

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.