postgres_lsp VS vecs

Thank you for turning me on top Cornucopia, it looks awesome. I've used the very similar aiosql in Python, but I hadn't realized there was a Rust analog.

To tell the truth I've been waiting for postgres_lsp to mature before trying it out, but based on this example [1] I think it does support multiple queries.

Since it uses a parser extracted from Postgres, the nonstandard syntax would probably trip it up, but there's probably a way to fix that.

[1] https://github.com/supabase/postgres_lsp/blob/main/example/f...

Cool. How does this compare to SafeQL, PgTyped, and Postgres language server ?

While code editors have great support for most programming languages, SQL support is underwhelming. We want to make Postgres as simple as Python. Our recently announced Postgres Language Server takes us a step in that direction - eventually it will provide first-class support for Postgres in your favorite code editor including Linting, Syntax Highlighting, Migrations Parsing, SQL Auto-complete, and Intellisense.

Postgres Language Server

supabase/vecs is our Python client for vector projects. Today, we're releasing a new feature for vecs called "adapters".

hey hn, supabase ceo here.

This is an iteration of several things that we’ve developed to support Hugging Face.

1. Python adapters. The first is our python lib, Vecs [0], which now supports “adapters”. Adapters are simply a pipeline of transformations that an input goes through (for example: split the text into chunks, then turn it into an embedding). We’ve included support for Hugging Face sentence transforms to automatically download the models on first run, and then cache them for subsequent runs.

2. Deno support. I want to start by pointing out that most of the hard work to support Hugging Face in Javascript is through Joshua’s continued efforts on Transformers.js (Joshua is an employee at HF). To support this effort we’re rolling out support across all of the Supabase Edge Functions. We’ve started with sentence embeddings for now since that’s the most common use-case for pgvector.

At the moment 98% of Supabase customers use OpenAI to create embeddings in pgvector. With today’s release we’re hoping to add more options, especially for situations where fewer dimensions are viable (for better pgvector performance[2])

A few of the supabase engineers will be in the comments to answer any questions, as well as Joshua from HF (@xenova on HN)

[0] Vecs: https://github.com/supabase/vecs/

[1] Transformers.js: https://huggingface.co/docs/transformers.js/index

[2] pgvector performance with lower dimensions: https://supabase.com/blog/fewer-dimensions-are-better-pgvect...

Vecs is a python client storing and searching vectors backed by Postgres and pgvector. It gives a familiar collection-like interface to upserting and searching.