Cosine similarity on a subset of documents for multipass search

How do you calculate cosine on a subset of vectors in a vector index?

Documents

1M -> 10000 -> 100 -> 10

64D -> 128D -> 256D -> 512D (D - vector dimension)

FAISS and ANNOY don't support it.

You can do a filter by ID in Elasticsearch and then run cosine query, but should you?

First search on 1M docs in FAISS with ANN(approximate nearest neighbours) and rest passes on ES.

Is numpy masked array a solution for this?

Let me know your thoughts on Twitter!

Pratik Bhavsar @nlpguy_

Open question How do you calculate cosine on a subset of vectors of a vector index? 1M -> 10000 -> 100 -> 10 64D -> 128D -> 256D -> 512D D - dimension FAISS and ANNOY don't support it. You can do this in Elasticsearch but should you? First search on 1M on FAISS and rest on ES

January 14th 2021