The elephant in the room is the Curse of Dimensionality. As the number of vector dimensions increase, "nearest neighbor" search behaves in an surprising manner. In a high-dimensional cube randomly sampled vectors are mostly near the edge of the cube, not at all what you expect based on two or three dimensions. There's other hitches, too.
When using vector search, you're making two leaps - that your document can really be summarized in a vector (keeping its important distinguishing semantic features), and that it's possible to retrieve similar documents using high-dimensional vectors. Both of these are lossy and have all sorts of caveats.
What would be great is if these new vector DB companies show examples where the top-ten "search engine results page" based on semantic embedding / vector search is clearly better than a conventionally-tuned, keyword-based system like ElasticSearch. I haven't see that, but I have seen tons of numbers about precision/recall/etc. What's the point of taking 10ns to retrieve good vector results if its unclear if they're solving a real problem?
When using vector search, you're making two leaps - that your document can really be summarized in a vector (keeping its important distinguishing semantic features), and that it's possible to retrieve similar documents using high-dimensional vectors. Both of these are lossy and have all sorts of caveats.
What would be great is if these new vector DB companies show examples where the top-ten "search engine results page" based on semantic embedding / vector search is clearly better than a conventionally-tuned, keyword-based system like ElasticSearch. I haven't see that, but I have seen tons of numbers about precision/recall/etc. What's the point of taking 10ns to retrieve good vector results if its unclear if they're solving a real problem?
https://en.wikipedia.org/wiki/Curse_of_dimensionality