[ramoz]

Interesting. Why did you need to “narrow” the search space using vector space? Did you build custom embeddings and feel confident about retrieval segments?

I did similar in 2019 but typically in reverse, FTS, and a dual tower model to rerank. Vector search was an additional capability but never augmented the FTS.

[ianbutler]

It was in consideration of how slow our FTS at the time was over large amount of documents and the window we wanted to keep response times in and you're correct, we had custom embeddings and we had a reasonably high confidence.

So vector search would reduce the space to like 10k documents and then we'd take the document ids and FTS acted as the final authority on the ranking.