[dd8601fn]

As a real dummy on the subject, maybe you could help me understand where vector search tends to fall over?

I use it to retrieve tool functions by description and it has worked very well for me, but I expect I'm in the "very simple use cases" category that you mentioned.

[jillesvangurp]

That is indeed a simple case. Imagine using that on your Google drive where you might have lots of variations of the same documents with names such as my-doc-draft.doc, my-doc-outdated.doc and my-doc-final-v0.1.3.doc. Which is the best version? Unless you add logic to control this, even the best vector search will not be able to tell apart outdated/incomplete/inaccurate data from the best data. That's why enterprise document search never really got good. Google's own drive is a good example of a search that isn't great.

A good example from an ecommerce vendor that now trains its own models is photo search. They used an off the shelf model to implement that when testing with a photo of some clothes, instead of getting similar products they got random products that featured the same person modeling completely different clothes. The model they used was biased towards faces rather than clothes.