faiss VS postgres

There are also FAISS binary indexes[0], so it'd be great to compare binary index vs binary index. Otherwise it seems a little misleading to say it is a FAISS vs not FAISS comparison, since really it would be a binary index vs not binary index comparison. I'm not too familiar with binary indexes, so if there's a significant difference between the types of binary index then it'd be great to explain what that is too.

[0] https://github.com/facebookresearch/faiss/wiki/Binary-indexe...

Or just use FAISS https://github.com/facebookresearch/faiss

You can try txtai (https://github.com/neuml/txtai) with a Faiss backend.

This Faiss wiki article might help (https://github.com/facebookresearch/faiss/wiki/Indexing-1G-v...).

For example, a partial Faiss configuration with 4-bit PQ quantization and only using 5% of the data to train an IVF index is shown below.

faiss={"components": "IVF,PQ384x4fs", "sample": 0.05}

If you want to experiment with vector stores, you can do that locally with something like faiss which has good platform support: https://github.com/facebookresearch/faiss

Doing full retrieval-augmented generation (RAG) and getting LLMs to interpret the results has more steps but you get a lot of flexibility, and there's no standard best-practice. When you use a vector DB you get the most similar texts back (or an index integer in the case of faiss), you then feed those to an LLM like a normal prompt.

The codifer for the RAG workflow is LangChain, but their demo is substantially more complex and harder-to-use than even a homegrown implementation: https://news.ycombinator.com/item?id=36725982

According to this documentation page, faiss-gpu is only supported on Linux, not on Windows.

Indexes for vector databases in high dimensions are nowhere near are effective as the 2-d indexes used in GIS or the 1-d B-tree indexes that are commonly used in databases.

Back around 2005 I was interested in similarity search and read a lot of conference proceedings on the top and was basically depressed at the state of vector database indexes and felt that at least for the systems I was prototyping I was OK with a full scan and later in 2013 I had the assignment of getting a search engine for patents using vector embeddings in front of customers and we got performance we found acceptable with full scan.

My impression today is that the scene is not too different than it was in 2005 but I can't say I haven't missed anything. That is, you have tradeoffs between faster algorithms that miss some results and slower algorithms that are more correct.

I think it's already a competitive business. You have Pinecone which had the good fortune of starting before the gold rush. Many established databases are adding vector extension. I know so many engineering managers who love postgresql and they're just going to load a vector extension and go. My RSS reader YOShInOn uses SBERT embeddings to cluster and classify text and certainly More Like This and semantic search are on the agenda, I'd expect it to take about an hour to get

https://github.com/facebookresearch/faiss

up and working, I could spend more time stuck on some "little" front end problem like getting something to look right in Bootstrap than it would take to get working.

I can totally believe somebody could make a better vector db than what's out there but will it be better enough? A startup going through YC now could spend 2-3 to get a really good product and find customers and that is forever in a world where everybody wants to build AI applications right now.

As the size of the codebase grows, storing and searching through embeddings in memory becomes inefficient. This is where vector databases come into play. Tools like Milvus, Faiss, and others are designed to handle large-scale vector data and provide efficient similarity search capabilities. I've wrtten about how to also use sqlite to store vector embeddings. By integrating a vector database, you can scale your code search tool to handle much larger codebases without compromising on search speed.

But FAISS has their own version ("FastScan") https://github.com/facebookresearch/faiss/wiki/Fast-accumula...

https://github.com/facebookresearch/faiss

- Now, the connection between our API servers and the database was slow (a few hundred ms per query), so we moved to self-hosting Postgres which was pretty painful. We tried to use https://github.com/supabase/postgres, but the documentation was very lacking and we had to make a bunch of modifications to get it to work. After we got it working, it was pretty smooth though - pretty easy to implement backups, etc.

I think Supabase generally does good work, but I don't think they can be given credit for pgvector, if that's what you're indicating (I might have misread).

As I understand, Andrew Kane is the principal author of pgvector, and has worked on it for almost two years before Supabase added support for it.

See also https://github.com/pgvector/pgvector/issues/54 and https://github.com/supabase/postgres/pull/472.

we merged the pgvector PR about 2 weeks ago (https://github.com/supabase/postgres/pull/472). If you're missing anything for your CLI don't hesitate to reach out and we'll see if we can integrate it into the product (my email is in my profile)

as an aside, Pinecone looks great

One of the things they recommend is separating the PostgreSQL DB from the rest of the stack. And they also provide an ansible playbook to set up a postgres DB: https://github.com/supabase/postgres/blob/625899e687047a9da658f3f8cc6dd91ac9769694/ansible/playbook.yml

> entire solution from docker

We PR'd this into our docker-compose today [0]. We're always a bit slammed during Launch Week, so if you spot any problems let use know and we'll patch it up asap.

The extension is also deployed directly into our PG bundle [1] which is available in docker [2]

> The Gui for adding roles and tying them to postgres access is very slick with hasura. Is this done manually via SQL commands with supabase?

I haven't tried Hasura so I don't know if this is a direct comparison. pg_graphql works with Postgres Row Level Security - we provide a GUI for this in our Dashboard, but they are also just native PG Policies, so you can write them in raw SQL

[0] https://github.com/supabase/supabase/pull/6138/files#diff-41...

[1] https://github.com/supabase/postgres

[2] https://hub.docker.com/r/supabase/postgres

and can also PR if it's something useful for everyone: https://github.com/supabase/postgres

Show HN: Postgres Docker image with common extensions\ (23 comments)

Hi! I'm one of the contributors to the repo. Just to clarify, our Docker image [0] only contains the latest version of Postgres (13) and the common extensions listed out here [1]. All the other features such as this [2] and this [3] are only available in the AWS EC2 or DO droplet images. We've since updated our README to make that clearer :-)

You can still connect the DB with a PgBouncer image spun up in another container however. Unfortunately, I can't really recommend you which one since there doesn't seem to be an official Docker image for PgBouncer and I myself have never tried any of the existing ones out there. If you're looking to use PostgREST however, they do have an official Docker image that you can use over here [4].

[0]: https://hub.docker.com/r/supabase/postgres

[1]: https://github.com/supabase/postgres#extensions

[2]: https://github.com/supabase/postgres#enhanced-security

[3]: https://github.com/supabase/postgres#additional-goodies

[4]: https://hub.docker.com/r/postgrest/postgrest/