Cohere has introduced new features in its Embed v3 model to enhance enterprise search capabilities, focusing on compressed embeddings and asynchronous embedding generation through Embed Jobs. These updates aim to improve storage efficiency and reduce query latency by supporting up to 32x compression, which significantly decreases vector database storage requirements without compromising data integrity. Embed Jobs streamlines the process by allowing developers to upload datasets for asynchronous computation on Cohere's servers, reducing errors and improving reliability compared to live API embedding. The improved Embed model integrates with Cohere's Command R and Rerank models, offering best-in-class retrieval-augmented generation (RAG) applications by enhancing the contextual understanding and relevance of retrieved passages. These advancements enable enterprises to leverage large datasets more effectively for various applications, including document classification and information retrieval, while offering substantial cost savings in storage.