Boomerang

Boomerang is an advanced embedding and retrieval model designed by Vectara to significantly improve the performance of semantic search and Retrieval-Augmented Generation (RAG) systems. Boomerang introduces substantial advancements in multilingual capabilities, retrieval accuracy, and overall performance. For information, check out this blog.

Multilingual capabilities

Boomerang supports text embedding in hundreds of languages, enabling cross-lingual semantic search. This broad language support allows users to query and retrieve information effectively across diverse language datasets, enhancing global accessibility and usability.

Optimized for production use

Boomerang supports real-time applications with low-latency performance, making it suitable for demanding production environments. The model’s embedding size of 768 dimensions strikes an effective balance between high precision and storage efficiency, ensuring optimal operational deployment.

Minimizing hallucinations

In RAG systems, Boomerang helps reduce hallucinations by focusing on the retrieval of contextually relevant information. This leads to more accurate and reliable outputs from generative AI systems, strengthening user confidence in automated responses.