emileriksenkeev gives project search a semantic backbone
This fork can search project documents by meaning, while fixing the quiet scale problems that make early search demos unreliable.
The search uses Voyage, an AI service that turns text into meaning-based representations, and pgvector, a database extension built to compare them. A search for a concept can therefore find relevant material even when the wording differs.
- Semantic document search: users can retrieve project material by the ideas it contains, not only exact keywords.
- Safer embedding runs: temporary service failures are retried, and repeat runs leave documents already processed alone.
- Complete large-workspace coverage: the backfill now works through documents in batches, avoiding a previous 1,000-document ceiling that could silently leave newer files out of search.
Spotted something wrong? Or know the PR text has fresher detail than the writeup above?