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Here's a bit of a quantification of the point Doug makes. Indeed, for a number of scenarios you get better results if you combine vector search and keyword search into a hybrid retrieval step, and do reranking on top of that.

https://techcommunity.microsoft.com/t5/ai-azure-ai-services-...

(disclaimer: I work in that team)

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This is interesting. I recently built a search tool that needed to locate documents by keyword or by semantics, so I implemented a hybrid search straight away: BM25 + embeddings (from `gte-base`), with a cross-encoder for reranking.

I found that the lexical search was adding nothing; the embeddings alone produced almost identical results for keyword queries. (The re-ranker, however, made a big difference.)

Is this unusual?

It depends on the scenario. For example, for concept-seeking queries, vectors tend to do better (less likely to be an overlap in words between query and content), whereas for keyword searches (a product name, a serial number, project codenames, etc.) BM25 + keywords does much better. If your workload is all concept-seeking queries, it's reasonable that keywords don't add much.

If you look at the table in the section "3. Hybrid Retrieval brings out the best of Keyword and Vector Search" of that article, we shared there the significant variability of metrics as a function of query types.

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