instant-distance VS hnswlib

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!

How does this compare to hsnwlib - is it faster? https://github.com/nmslib/hnswlib

Last week I released Moodflix (https://moodflix.streamlit.app), a movie recommendation engine based to find movies based on your mood.

Moodflix was created on top of a movie dataset of 10k movies from The Movie Database. I vectorised the films using Hugging Face's T5 model (https://huggingface.co/docs/transformers/model_doc/t5) using the film's plot synopsis, genres and languages. Then I indexed the vectors using hnswlib (https://github.com/nmslib/hnswlib). LLMs can understand a movie's plot pretty well and distill the similarities between a user's query (mood) to the movie's plot and genres.

I have got feedback from close friends around linking movies to other review sites like IMDB or Rotten Tomatoes, linking movies to sites to stream the movie and adding movie posters. I would also love to hear from the community what things you like, what you want to see and what things you consider can be improved.

Actually the "ef" is not epsilon. It is a parameter of the HNSW index: https://github.com/nmslib/hnswlib/blob/master/ALGO_PARAMS.md...

If you want to go larger you could still use some simple setup in conjunction with faiss, annoy or hnsw.

Many vector databases are using Hnswlib and that is a supported vector index alongside Faiss and Annoy.

hnswlib (https://github.com/nmslib/hnswlib) is a strong alternative to faiss that I have enjoyed using for multiple projects. It is simple and has great performance on CPU.

After working through several projects that utilized local hnswlib and different databases for text and vector persistence, I integrated hnswlib with sqlite to create an embedded vector search engine that can easily scale up to millions of embeddings. For self-hosted situations of under 10M embeddings and less than insane throughput I think this combo is hard to beat.

https://github.com/jiggy-ai/hnsqlite

https://github.com/nmslib/hnswlib

Used it to index 40M text snippets in the legal domain. Allows incremental adding.

I love how it just works. You know, doesn’t ANNOY me or makes a FAISS. ;-)

3000 texts doesn't sound like to many, so may be a brute force cos calculation to find the most similar vector would work. If that's taking too much time, may be look at KNN or ANN modules to speed up finding the most similar vector. I use hsnwlib in knn mode for this. SOrt through about 350,000 vectors in about 30-50 msec.

hnswlib is in cpp and has python bindings (you should be able to make your own for other languages).

https://github.com/nmslib/hnswlib

embeddings - The embeddings index file. This is an Approximate Nearest Neighbor (ANN) index with either Faiss (default), Hnswlib or Annoy, depending on the settings.