LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table

Approximate nearest neighbor search (ANNS) is an essential building block for applications like RAG but can sometimes yield results that are overly similar to each other. In certain scenarios, it is desirable for search results to be similar to the query and diverse among themselves. We propose LotusFilter, a post-processing module to diversify ANNS results. We precompute a cut-off table summarizing vectors that are close to each other. During the filtering, LotusFilter greedy looks up the table to delete redundant vectors from the candidates. We demonstrated that the proposed filter operates fast (0.02 [ms/query]) in settings resembling real-world RAG applications, utilizing features such as OpenAI embeddings.