pgrx VS marqo

Consider also the companies and work behind pgrx [0] and pgzx [1]:

[0] https://github.com/pgcentralfoundation/pgrx

[1] https://github.com/xataio/pgzx

If you like this (I do very much), you might also like pg_idkit[0] which is a little extension with a bunch of other kinds of IDs that you can generate inside PG, thanks to the seriously awesome pgrx[1] and Rust.

[0]: https://github.com/VADOSWARE/pg_idkit

[1]: https://github.com/pgcentralfoundation/pgrx

(disclosure, i work at supabase and have been developing TLEs with the RDS team)

Trusted Language Extensions refer to an extension written in any trusted language. In this case Rust, but it also includes: plpgsql, plv8, etc. See [0]

> PL/Rust is a more performant and more feature-rich alternative to PL/pgSQL

This is only partially true. plpgsql has bindings to low-level Postgres APIs, so in some cases it is just as fast (or faster) than Rust.

> Building a vector index (or any index for that matter) inside Postgres is a more involved process and can not be done via the UDF interface, be it Rust, C or PL/pgSQL

Most PG Rust extensions are written with the excellent pgrx framework [1]. While it doesn't have index bindings right now, I can certainly imagine a future where this is possible[2].

All that said - I think there are a lot of hoops to jump through right now and I doubt it's worth it for the Latern team. I think they are right to focus on developing a separate C extension

[0] TLE: https://supabase.com/blog/pg-tle

[1] pgrx: https://github.com/pgcentralfoundation/pgrx

[2] https://github.com/pgcentralfoundation/pgrx/issues/190#issue...

I’m currently playing with PostgreSQL, foreign data wrappers, and pgrx rust extensions. My development experience has been surprisingly smooth and enjoyable.

My main issue is that joins will be processed locally, so all the foreign data will be fetched before the join happens. But otherwise basic CRUD is easy.

https://wiki.postgresql.org/wiki/Foreign_data_wrappers

https://github.com/pgcentralfoundation/pgrx

https://github.com/supabase/wrappers

I think maybe what you’re really looking for are the files here: https://github.com/pgcentralfoundation/pgrx/tree/c2eac033856...

Those are the internals we currently expose as unsafe “sys” bindings.

As we/contributors identify more that are desired we add them.

pgrx’ focus is on providing safe wrappers and general interfaces to the Postgres internals, which is the bulk of our work and is what will take many years.

As unsafe bindings go, we could just expose everything, and likely eventually will. There’s just some practical management concerns around doing that without a better namespace organization —- something we’ve been working.

The Postgres sources are not small. They are very complex, inconsistent in places, and often follow patterns that are specific to Postgres and not easy to generalize.

If you’ve never built an extension with pgrx, give it a shot one afternoon. It’s very exciting to see your own code running in your database.

pgrx is one of the greatest enabling innovations in the PG ecosystem in a long time.

Awesome to see so many high quality extensions come out of it.

https://github.com/pgcentralfoundation/pgrx

If you're interested in what my Threadripper 3970X does with it, there's some numbers in this PR: https://github.com/tcdi/pgrx/pull/1147

We (Marqo) are doing a lot on 1 and 2. There is a huge amount to be done on the ML side of vector search and we are investing heavily in it. I think it has not quite sunk in that vector search systems are ML systems and everything that comes with that. I would love to chat about 1 and 2 so feel free to email me (email is in my profile). What we have done so far is here -> https://github.com/marqo-ai/marqo

Marqo.ai (https://github.com/marqo-ai/marqo) is doing some interesting stuff and is oss. We handle embedding generation as well as retrieval (full disclosure, I work for Marqo.ai)

Take a look here https://github.com/marqo-ai/local-image-search-demo. It is based on https://github.com/marqo-ai/marqo. We do a lot of image search applications. Feel free to reach out if you have other questions (email in profile).

That sounds much longer than it should. I am not sure on your exact use-case but I would encourage you to check out Marqo (https://github.com/marqo-ai/marqo - disclaimer, I am a co-founder). All inference and orchestration is included (no api calls) and many open-source or fine-tuned models can be used.

Try this https://github.com/marqo-ai/marqo which handles all the chunking for you (and is configurable). Also handles chunking of images in an analogous way. This enables highlighting in longer docs and also for images in a single retrieval step.

As others have correctly pointed out, to make a vector search or recommendation application requires a lot more than similarity alone. We have seen the HNSW become commoditised and the real value lies elsewhere. Just because a database has vector functionality doesn’t mean it will actually service anything beyond “hello world” type semantic search applications. IMHO these have questionable value, much like the simple Q and A RAG applications that have proliferated. The elephant in the room with these systems is that if you are relying on machine learning models to produce the vectors you are going to need to invest heavily in the ML components of the system. Domain specific models are a must if you want to be a serious contender to an existing search system and all the usual considerations still apply regarding frequent retraining and monitoring of the models. Currently this is left as an exercise to the reader - and a very large one at that. We (https://github.com/marqo-ai/marqo, I am a co-founder) are investing heavily into making the ML production worthy and continuous learning from feedback of the models as part of the system. Lots of other things to think about in how you represent documents with multiple vectors, multimodality, late interactions, the interplay between embedding quality and HNSW graph quality (i.e. recall) and much more.

Marqo provides automatic, configurable chunking (for example with overlap) and can allow you to bring your own model or choose from a wide range of opensource models. I think e5-large would be a good one to try. https://github.com/marqo-ai/marqo

Marqo has a similar API to Meilisearch's standard API but uses vector search in the background: https://github.com/marqo-ai/marqo

Have you tried Marqo? check the repo : https://github.com/marqo-ai/marqo