The biggest one is that it's hard to get "zero matches" from an embeddings database. You get back all results ordered by distance from the user's query, but it will really scrape the bottom of the barrel if there aren't any great matches - which can lead to bugs like this one: https://simonwillison.net/2024/Jun/6/accidental-prompt-injec...
The other problem is that embeddings search can miss things that a direct keyword match would have caught. If you have key terms that are specific to your corpus - product names for example - there's a risk that a vector match might not score those as highly as BM25 would have so you may miss the most relevant documents.
Finally, embeddings are much more black box and hard to debug and reason about. We have decades of experience tweaking and debugging and improving BM25-style FTS search - the whole field of "Information Retrieval". Throwing that all away in favour of weird new embedding vectors is suboptimal.
It turns out picking that threshold is extremely difficult - I've tried! The value seems to differ for different searches, so picking eg 0.7 as a fixed value isn't actually as useful as you would expect.
I'll add to what the other commenter noted, but sometimes the difference between results get very granular (i.e. .65789 vs .65788) so deciding on where that threshold should be is little trickier.
The biggest one is that it's hard to get "zero matches" from an embeddings database. You get back all results ordered by distance from the user's query, but it will really scrape the bottom of the barrel if there aren't any great matches - which can lead to bugs like this one: https://simonwillison.net/2024/Jun/6/accidental-prompt-injec...
The other problem is that embeddings search can miss things that a direct keyword match would have caught. If you have key terms that are specific to your corpus - product names for example - there's a risk that a vector match might not score those as highly as BM25 would have so you may miss the most relevant documents.
Finally, embeddings are much more black box and hard to debug and reason about. We have decades of experience tweaking and debugging and improving BM25-style FTS search - the whole field of "Information Retrieval". Throwing that all away in favour of weird new embedding vectors is suboptimal.
>but because embeddings search orders by similarity score it will ALWAYS return results, really scraping the bottom of the barrel if it has to
Why not have a similarity threshold? Say, if the distance is below 0.7, do not accept the search result.
It turns out picking that threshold is extremely difficult - I've tried! The value seems to differ for different searches, so picking eg 0.7 as a fixed value isn't actually as useful as you would expect.
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I'll add to what the other commenter noted, but sometimes the difference between results get very granular (i.e. .65789 vs .65788) so deciding on where that threshold should be is little trickier.