Vector database built for scalable similarity search

I really don't want another database. I just want to have a solution built in for Postgres, and more specifically, RDS, which we use. I know there will be some extra difficulty that I will have to manage (e.g. reindexing to a new model that is outputting different embeddings), but I really don't want another piece of infrastructure. If anyone from AWS/Google/Azure is listening, please add pgvector [1] into your managed Postgres offerings! 1. https://github.com/pgvector/pgvector

I'd be curious how they implement updating. AFAICT this is the thorniest part of working with existing open source solutions. When working with ANNOY in the past I've had data small enough to be able to recompute the full index in the background every few seconds in a background process and then swap in the built index to the process serving similarity queries. (you can see the VERY "research quality" code on Github, here's a decent starting place https://github.com/hyperstudio/spectacles/blob/master/specta...)…

Seen on HN 7 months ago: https://news.ycombinator.com/item?id=22012300 (152 points/28 comments)