Introducing Vectara’s Chain Rerankers

Vectara has introduced a new feature that allows users to chain different rerankers, providing enhanced flexibility and control over data retrieval processes. Users can now combine various rerankers, such as Boomerang for fast initial results, Slingshot for accuracy, MMR for diversity, and user-defined functions for custom business logic, in order to create tailored retrieval systems. This feature is particularly beneficial in retrieval augmented generation (RAG) systems, as it helps improve the performance, quality, and relevance of information delivered to large language models (LLMs) by enabling users to specify which results are prioritized or eliminated. The chain reranker functionality also includes the ability to limit the number of results passed between rerankers, further refining the retrieval process. This development offers users the opportunity to optimize their data retrieval strategies to meet specific application needs, enhancing both efficiency and effectiveness.