Hello people,

I'm trying to build a facial recognition application, and I have a working API, that takes in an image of a face and spits out a vector that encodes it. I need to run this on a million faces, store them in a db and when the system goes online, it should take a face in, get a vector and compute the distance with all the other vectors to find the closest one. https://1921681001.id/

I'm hearing of locality sensitive hashing, and that mak https://19216811.cam/es sense, but what else can I do at the level of db selection and design that makes these things quicker? TIA