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The opposite is also true. BM25 prefers lexical matches and brings these candidates back that vector search often doesn’t. I am not disagreeing vectors are use
by softwaredoug 3y ago
The opposite is also true. BM25 prefers lexical matches and brings these candidates back that vector search often doesn’t.
I am not disagreeing vectors are useful, but I think benchmark based evidence is not the same as deploying a solution that must scale, be constantly updated, serve many use cases like filtering, search syntax, etc customers want.
And plus I think there’s a real danger Of herding to vector retrieval (even then one view of it) which cuts off exploration of diverse solutions.
- dmezzetti 3y agoHybrid retrieval can possibly be the best of both worlds. I invested a lot of time with the last txtai release adding a minimal dependency Python-based BM25 component (https://neuml.hashnode.dev/building-an-efficient-sparse-keyword-index-in-python https://neuml.hashnode.dev/building-an-efficient-sparse-keyw...). And keyword-only indexes are supported if one desires. I'm with you 100% on not herding to any one way for any problem. I still remember the pre-2023 world where you weren't pressured to work LLMs into everything.