Introducing Vectara’s Chain Rerankers

Vectara has introduced a new feature that allows users to chain different rerankers together. This capability provides more control over how data is retrieved and can be used in combination with various types of rerankers, such as Boomerang for natural language questions/retrieval, Hybrid Search for combining neural and keyword-based systems, Slingshot for cross-attentional neural reranking, MMR reranker for increasing result diversity, and user-defined functions (UDF) reranker for inserting custom business logic. The chain reranker can be used with the Advanced Query API by specifying a chain type for the reranker. Additionally, users can now limit the number of results that come out of each reranker in the chain to improve performance and provide more strict filtering from user-defined functions.