Multi-Vector Retrieval

Multi-vector retrieval indexes each document as multiple embedding vectors rather than a single one, allowing the system to capture different facets of a complex passage. For instance, a document might be indexed with one vector per sentence, one for the summary, and one for key entities. At query time, any of these vectors can match the query embedding, improving recall for documents that are relevant in multiple ways. ColBERT is a prominent multi-vector retrieval model.