Introducing Knee Reranking: smart result filtering for better results

Vectara has introduced a new feature called Knee Reranking, enhancing its retrieval capabilities by automatically filtering irrelevant or low-quality results from queries to improve the output quality while reducing latency, costs, and hallucinations. This feature is particularly effective for Retrieval Augmented Generation (RAG) systems, which often struggle with determining optimal cutoff points for query results. Unlike traditional methods that rely on fixed score thresholds, Knee Reranking uses a combination of statistical analysis and configurable parameters to identify natural boundaries between relevant and irrelevant results, offering improved precision without sacrificing recall. The system employs a dual-analysis approach using global regression analysis and local pattern detection, with parameters such as sensitivity and early_bias to customize the detection of significant drops in relevance. This advancement is designed to follow the Slingshot reranker in the reranking chain, ensuring optimal filtering across diverse query patterns. It provides a more focused and relevant result delivery by automatically adapting to each query's unique characteristics, making it a significant step forward in result filtering for AI applications.