Faiss query, at least for IVFPQ which is the main emphasis of the library, is based on real vectors as input but against quantized database vectors; the so call

Faiss query, at least for IVFPQ which is the main emphasis of the library, is based on real vectors as input but against quantized database vectors; the so called “ADC” (asymmetric distance computation), so you don't have to query based on a database vector. If the data is normalized to lie on a hypersphere, you could find nearest neighbors to a query vector on the opposite point of the hypersphere, for instance. But, as we sometimes find with, say, negative mining, sometimes what you are interested in is not the “hardest” negative or most dissimilar object as those don’t provide much utility in comparison to your current object; you might something that is reasonably close in some ways but quite different otherwise, which is a more useful negative example. This is more difficult.