I totally agree and hnswlib is actually much faster than FAISS on CPU.

I'm really happy to see `hnswlib` as a Python dependency since I'm the one who implemented PyPI support: https://github.com/nmslib/hnswlib/pull/140

I like Faiss but I tried Spotify's annoy[1] for a recent project and was pretty impressed.

Since lots of people don't seem to understand how useful these embedding libraries are here's an example. I built a thing that indexes bouldering and climbing competition videos, then builds an embedding of the climber's body position per frame. I then can automatically match different climbers on the same problem.

It works pretty well. Since the body positions are 3D it works reasonably well across camera angles.

The biggest problem is getting the embedding right. I simplified it a lot above because I actually need to embed the problem shape itself because otherwise it matches too well: you get frames of people in identical positions but on different problems!

[1] https://github.com/spotify/annoy

I looked a bit and the code, I think it would be kow hanging fruit to add additional sqlite fields except the vector ones. Even if any filtering happens kind of suboptimally in post processing.

I really like your learning series. What you’ve done for understanding conversational memory at https://www.pinecone.io/learn/langchain-conversational-memor... was truly helpful. Thanks!

Awesome. I used faiss during my phd studies on ai based lidar map building and I spent countless hours in the faiss github wiki pages, example code, and issues. Would have loved something like this back then. Bookmarked for the next time I need faiss.

I've actually been looking at Milvus for storing embeddings, why do y'all have options for both Pulsar and Kafka inside Milvus? Seems like unnecessary choices for what we hoped would just be a plug and play vector database

[dead]