Elastic Adds Support for Cohere High-Performance Embeddings
Developers can now natively use the Elastic vector database to store and search Cohere’s new int8 text embeddings
With this integration, Elasticsearch developers can experience immediate performance gains, including up to 4x memory savings and up to
“We’re excited to collaborate with Elastic to bring state-of-the-art search solutions to enterprises,” said Jaron Waldman, chief product officer at Cohere. “Elasticsearch delivers strong vector retrieval performance on large datasets, and their native support for Cohere’s Embed v3 models with int8 compression helps unlock gains in performance, efficiency, and search quality for enterprise-grade deployments of semantic search and retrieval-augmented generation (RAG)."
“Developers who want to build more intuitive and accurate semantic search experiences for enterprise use cases need to look at Elasticsearch and Cohere,” said Shay Banon, founder & chief technology officer at Elastic. “Innovation is rarely insular, and our work with the great team at Cohere showcases how we bring developers the best of both worlds. The Cohere and Elastic communities now have great models to generate embeddings with support for inference workloads and seamless integration into the leading search and analytics platform that has invested in creating the best vector database.”
Support for Cohere embeddings is available in preview with Elastic 8.13 and will soon be generally available in an upcoming Elasticsearch release.
About Elastic
Elastic (NYSE: ESTC), the leading search analytics company, securely harnesses search powered AI to enable everyone to find the answers they need in real-time using all their data, at scale. Elastic’s solutions for security, observability and search are built on the Elasticsearch platform, the development platform used by thousands of companies, including more than
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Alexia Russell
Elastic Global PR
PR-Team@elastic.co
Source: Elastic N.V.