[laktek]

If you are building using Supabase stack (Postgres as DB with pgVector), we just released a built-in embedding generation API yesterday. This works both locally (in CPUs) and you can deploy it without any modifications.

Check this video on building Semantic Search in Supabase: https://youtu.be/w4Rr_1whU-U

Also, the blog on announcement with links to text versions of the tutorials: https://supabase.com/blog/ai-inference-now-available-in-supa...

[jonplackett]

So handy! I already got some embeddings working with supabase pgvector and OpenAI and it worked great.

What would the cost of running this be like compared to the OpenAI embedding api?

[laktek]

There are no extra costs other than the what we'd normally charge for Edge Function invocations (you get up to 500K in the free plan and 2M in the Pro plan)

[_bramses]

neat! one thing i’d really love tooling for: supporting multi user apps where each has their own siloed data and embeddings. i find myself having to set up databases from scratch for all my clients, which results in a lot of repetitive work. i’d love to have the ability one day to easily add users to the same db and let them get to embedding without having to have any knowledge going in

[kiwicopple]

This is possible in supabase. You can store all the data in a table and restrict access with Row Level Security

You also have various ways to separate the data for indexes/performance

- use metadata filtering first (eg: filter by customer ID prior to running a semantic search). This is fast in postgres since its a relational DB

- pgvector supports partial indexes - create one per customer based on a customer ID column

- use table partitions

- use Foreign Data Wrappers (more involved but scales horizontally)