Comment by benreesman

Inner product similarity in an embedding space is often a very valuable feature in a ranker, and the effort/wow ratio at the prototype phase is good, but the idea that it’s the only pillar of an IR stack is SaaS marketing copy.

Vector DBs are cool, you want one handy (particularly for recommender tasks). I recommend FAISS as a solid baseline all these years later. If you’re on modern x86_64 then SVS is pretty shit hot.

A search engine that only uses a vector DB is a PoC.

For folks who want to go deeper on the topic, Lars basically invented the modern “news feed”, which looks a lot like a production RAG system would [1].

1. https://youtu.be/BuE3DIJGWOw