Tool

Category: AI Model Optimization Tool

Field: Data Analytics

Type: API

Use Cases:

• Search result refinement in Retrieval-Augmented Generation (RAG)
• Relevance optimization for domain-specific datasets
• Multilingual search refinement
• High-accuracy retrieval for legal, technical, and financial documents

Summary: The Voyage AI Reranker enhances search precision by re-evaluating the relevance of initial search results. Operating as a cross-encoder, it refines candidate documents retrieved through embedding-based or lexical methods, producing more accurate relevancy scores by analyzing complex interactions between queries and documents. This reranker is part of a two-stage retrieval process, improving retrieval tasks within contexts like Retrieval-Augmented Generation (RAG) and domain-specific searches across legal, technical, and multilingual datasets. Voyage offers several model options, including rerank-2 and rerank-2-lite, which allow processing of longer documents or queries with up to 16,000 and 8,000 tokens, respectively. This reranker is particularly effective in situations where users need a robust refinement layer for search, enhancing output accuracy in domains like finance, law, and technical documentation. Compatible with various search platforms and first-stage search models, Voyage AI’s reranking can be seamlessly integrated to optimize retrieval systems, ensuring high recall and relevancy across multiple document types and languages.