Using single vector and multi-vector embeddings to perform a scalable search

Abstract

The present disclosure relates generally to systems and methods for executing a search query using a combination of single-vector searching and multi-vector comparing to more efficiently and accurately identify a set of documents that are responsive to the search query. This involves applying a vector generation model to a search query to generate both single-vector and multi-vector embeddings for the search query. A single-vector search is performed to determine a candidate set of documents that might be relevant to the search query. A more robust multi-vector comparison is then performed to generate a result set including a re-ranked set of documents and/or a further reduced set of documents that is responsive to the search query.