Benchmarking Cohere Rerankers with LanceDB

LanceDB supports reranking, which involves rearranging search results based on metrics independent of initial retrieval scores, across vector, full-text, and hybrid search types. The blog demonstrates using LanceDB with the cohere reranker, CohereReranker, by showcasing its application in different search scenarios and benchmarking its performance with datasets like Uber 10K and LLM Survey Paper Dataset. The Cohere reranker, particularly its v2 and v3 versions, consistently outperforms other rerankers, including the ColBERT model and vector baseline, in terms of retrieval accuracy, achieving notable improvements when integrated with embedding functions like BGE and ColBERT. Although the accuracy difference between Cohere v2 and v3 is minimal on the tested datasets, the latter shows significant improvements in specific settings like semi-structured data, as discussed in Cohere’s blog, and also offers faster API performance. The blog suggests further exploration of reranking performance on more complex datasets in future analyses.