Get Diverse Results and Comprehensive Summaries with Vectara’s MMR Reranker

Result diversity is crucial in search engines, particularly for ambiguous queries, as it helps users explore diverse perspectives and ideas more effectively. Vectara's recent release introduces Maximum Marginal Relevance (MMR) as a reranker to enhance result diversity while maintaining relevance, which is particularly beneficial for ambiguous searches and retrieval augmented generation (RAG) tasks. By using MMR, users can obtain more comprehensive summaries from large language models (LLMs) by presenting them with varied information, thereby reducing inherent data bias. MMR can be implemented in Vectara through an API or UI, allowing users to set a reranker ID and adjust the "diversity factor" to tailor results to their preferences. The implementation of MMR in Vectara has shown that it can provide a broader picture and more complete summaries for ambiguous questions, leading to higher user satisfaction by enabling users to delve deeper or broader into topics of interest.