Show HN: Rerankers – Models, benchmarks, and papers for RAG (github.com)

A new project titled "Rerankers" has been launched, showcasing a comprehensive repository of models, libraries, and resources tailored for Retrieval-Augmented Generation (RAG) applications. Rerankers play a crucial role in optimizing search results by taking an initial set of retrieved documents and reordering them based on relevance using cross-encoders. While this method is slower than traditional vector searches, as it requires joint encoding of query-document pairs, it significantly enhances accuracy by narrowing candidate results from 50-100 down to the top 3-5. This initiative is particularly noteworthy for the AI/ML community as it offers a structured comparison of top models, open-source platforms, and commercial APIs for reranking. The repository includes evaluation metrics, datasets, and tutorials, making it an essential resource for developers and researchers focusing on enhancing retrieval systems. Key technical discussions highlight the differences between vector search and reranking techniques, as well as various types of reranking methods such as pointwise, pairwise, and listwise scoring. The project's comprehensive leaderboard facilitates real-time comparison of rerankers based on performance metrics like NDCG@10 and latency, empowering users to select the most effective models for their specific RAG tasks.