[D] Forcing diversification in similarity search

I’m using a vector database for storing image embeddings and using it for similarity search. If I pick top ten most similar vectors I can sometimes end up inside of an echo chamber with almost “duplicates” or too similar images. I would like to diversify the results so that all the results are close to the input vector but different between themselves.

Are there common patterns/algorithms for this type of diversification?

The idea that I have: I want to pick …

database echo embeddings image images inside machinelearning search vector vector database vectors