[researchers]

Not OP but I am working on a search engine with vector ranking. Why do you say that vector search would help with narrow queries? In my experience, semantic search helps broaden the query to search for adjacent ideas without exact term marches.

Hybrid approaches that use vector search for broad matches and rerank using BM25 could be what you’re looking for. See https://blog.vespa.ai/efficient-open-domain-question-answeri...

[m-i-l]

> "Hybrid approaches that use vector search for broad matches and rerank using BM25"

Hybrid approaches, e.g. Learning To Rank, normally do it the other way around, given the main benefit of hybrid is to mitigate the cost (time) of vector search, i.e. use a non-vector search (e.g. BM25) to get a broadly relevant set of results first (and quickly), and then the much more computationally expensive vector search to rerank the smaller results set. There are various approaches to try to make vector search more viable across large corpuses, e.g. Locality Sensitive Hashing and Approximate Nearest Neighbour Search, but if you've implemented one of those than I'm not sure there'd be any benefit in retaining a hybrid approach.

[marginalia_nu]

> Why do you say that vector search would help with narrow queries?

I was just asking whether he'd seen better results. I haven't experimented very much with it on my search engine. It's as crude as they get, and in part I want to see how far I can push old fashioned 1970s search algorithms :P