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Comment by imaurer | Hacker News Reader

I am bullish Pgvector because I am “postgres for everything guy”.

Current concerns are the scaling and recall performance.

The author is looking at product quantization along with other ideas: https://github.com/pgvector/pgvector/issues/27

More details on product quantization: https://mccormickml.com/2017/10/13/product-quantizer-tutoria...

A nice repo that tracks the ANN relative performance of different indexes: https://mccormickml.com/2017/10/13/product-quantizer-tutoria...

Also shoutout to Weaviate because they have great docs, are open source and have very informative YouTube channel.

https://weaviate.io/

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Over the past couple of days I tried 11 different vector databases, in order to evaluate and decide which one we'd choose for our use case.

I ended up choosing Weaviate specifically because of the nice docs, but beyond that, time will tell.

Curious if you tried Vespa?

Any thoughts on Milvus?

Have you tried cozodb? Newest kid on the block, looks very promising

I would love to read more about your experience. We need more content with feature, peformance, and architecture comparisons. Currently, there's a lot of developer evangelism hype in the space.

Thanks, that's nice feedback

Yep, we're (https://www.definite.app/) using pgvector and I was initially concerned about scaling, but it doesn't seem it will be a problem for our use case. I definitely wouldn't use it if I was building a feature for Slack, but works for us!

At a glance, your product seems like a fit for my team. However, your landing page doesn’t give specifics. What exactly does it do?

Yes, working on that landing page right now (currently it's pretty week)!

We're building an AI data analyst. You can ask questions of your database and get answers immediately. We also auto generate entire dashboards based on common patterns (e.g. a "Sales Dashboard", "Marketing Dashboard", "Finance / Burn" etc.).

If you want to give it a try (there's a demo database embedded in the app), you can use it here: https://ui.definite.app/

Can you search both by an equality comparison and a vector search in weaviate? I’d like to do something along the lines of `SELECT * FROM table t WHERE cosine_dist(:my_embedding, t.doc_embedding) < :x AND some_column = “XYZ”`

Well Weaviate is graphql and it has filtering and hybrid search which is a great feature that pg can’t fully support because it doesn’t have bm25

https://weaviate.io/developers/weaviate/api/graphql/filters

https://weaviate.io/blog/hybrid-search-explained

I have a ChatGPT session where I have asked it to do a hybrid search using filtering, pg fts and vector search. Looks reasonable just need to test it and write it up somewhere.

Amen. After suffering through many years of people telling me to use document databases when I was much better served with—at most—Postgres with a jsonb field, I feel vindicated enough to feel justified in doing my due diligence before going off the beaten track.

Not that document databases don’t have their place, but…MongoDB is webscale and all that.

Obligatory Youtube video for historical purposes: https://www.youtube.com/watch?v=b2F-DItXtZs

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