vector-db-benchmark VS instant-distance

Qdrant’s benchmark results are strongly in favor of accuracy and efficiency. We recommend that you consider them before deciding that an LLM is enough. Take a look at our open-source benchmark reports and try out the tests yourself.

Qdrant made their own benchmark. It is quite simple and also takes into consideration more options, so it should be better suited for benchmarking for production purposes.

is noWhen it comes to Supabase, it's using pgvector under the hood, so it would make sense to benchmark it with the other Open Source tools. There is an open PR for that, but it's pretty old: https://github.com/qdrant/vector-db-benchmark/pull/50

P.S.: Perhaps you want to add your database to our benchmarks repo?

Do we have any idea why lucene vector search underperforms? As of lucene 9.1 (and elastic 8.4), it runs the same sort of filtered/categorical HNSW that qdrant runs (https://lucene.apache.org/core/9_1_0/core/org/apache/lucene/...). Qdrant's benchmarking code (https://github.com/qdrant/vector-db-benchmark/blob/9263ba/en...) does use the new filtered ann query with elastic 8.4, so it appears to be a fair benchmark. Why is lucene/elastic so much slower? Is it a rust vs. java thing? Or some memory management issues?

There is also an open-source framework for benchmarking https://github.com/qdrant/vector-db-benchmark

https://github.com/instant-labs/instant-distance is a compact, fairly readable, pretty fast implementation of the paper in Rust.

When I looked at it the Rust-CV HNSW implementation was pretty messy, and it looks like it hasn't seen any commits in 2 years. This is partly why we started instant-distance as an alternative, which I think has come out pretty well (for the particular use cases that it serves).

I believe u/dochtman's implementation of HNSW is about as good as HNSW is going to get. Competing with the scalability and features (like streamed updates) of FAISS is what I hope to accomplish with this project. Based on interest, I'm now leaning towards an MIT license for the implementation.

I wrote an HNSW implementation in pure Rust:

https://github.com/InstantDomain/instant-distance

It works pretty well for us at InstantDomainSearch.

I like to think that this is a fairly idiomatic Rust implementation so it might be easier to follow than Facebook's FAISS. It's kinda similar in design to FAISS, so I think it might achieve similar performance, though we haven't spent enough time benchmarking yet.

We've released the underlying Rust implementation here: https://github.com/InstantDomain/instant-distance with Python bindings at https://pypi.org/project/instant-distance — feedback welcome!