Comment by simonw
It still amazes me that the binary trick works.
For anyone who hasn't seen it yet: it turns out many embedding vectors of e.g. 1024 floating point numbers can be reduced to a single bit per value that records if it's higher or lower than 0... and in this reduced form much of the embedding math still works!
This means you can e.g. filter to the top 100 using extremely memory efficient and fast bit vectors, then run a more expensive distance calculation against those top 100 with the full floating point vectors to pick the top 10.
I was taken back when I saw what was basically zero recall loss in the real world task of finding related topics, by doing the same thing you described where we over capture with binary embeddings, and only use the full (or half) precision on the subset.
Making the storage cost of the index 32 times smaller is the difference of being able to offer this at scale without worrying too much about the overhead.