from the accompanying blog post: https://www.tigerdata.com/blog/announcing-pg_vectorsearch-sc...

> TLDR: We are launching pg_vectorsearch, a new open-source PostgreSQL extension that makes vector search dramatically faster and more cost-efficient. Its new PRISM index uses partitioning and compression instead of HNSW's graph-based approach, delivering faster queries with a fraction of the memory and significantly faster index builds. At 100 million vectors, PRISM achieved 6 ms query latency on a machine with 4× less memory than HNSW, with index builds over 20× faster.

Does it support filtering though?

Edit: post filtering only

I don't understand how people are efficiently implementing tenant/user/permission scopes with these indexes.

I'm currently running VectorChord and I love it - with a fast backing disk, it's cut my postgres cost in half. Excited to see competition in low-RAM vector index tools. RAMageddon seems to be creating incentives to find more ways to take advantage of how fast modern nvme drives are!

Sadly if cloud providers don't offer it on managed postgres then not many people can use it...

Shouldn't you lead with your benchmarks if you're fast? qps and precision/recall, please.