Vector embeddings are a powerful tool for representing data in a way that machines can understand. But what if we could make them even better?

Binary quantized vector embeddings offer a way to do just that. By compressing vectors by 32x, we can achieve ~25x retrieval speedup while retaining 95+% accuracy. This means faster, more efficient search and analysis, all without sacrificing performance.

Check out this blog post to learn more about this exciting development: https://emschwartz.me/binary-vector-embeddings-are-so-cool/