Scaling vector search using Cohere binary embeddings and Vespa

Cohere's new embedding models, featuring support for binary and int8 vectors, significantly reduce storage requirements and deployment costs by using compact representations that are efficiently processed with Vespa's capabilities. This advancement allows for efficient vector search at scale, with binary vectors compressing data from 1024 floats to just 128 bytes, enabling faster computations through hamming distance. Vespa supports multi-vector indexing, allowing for coarse-to-fine retrieval and ranking pipelines that improve accuracy without additional memory costs. The combination of Cohere's versatile embedding API and Vespa's robust features provides a cost-effective solution for organizations aiming to optimize retrieval-augmented generation (RAG) pipelines and scale large datasets, with applications ranging from compact binary representations to hybrid multilingual searches.