Applications using Hugging Face embeddings on Elasticsearch now benefit from native chunking
Elastic (NYSE: ESTC), the Search AI Company, today announced the Elasticsearch Open Inference API now supports Hugging Face models with native chunking through the integration of the semantic_text field. Developers can now quickly ship generative AI (GenAI) applications without the burden of writing custom chunking logic, leveraging the Elasticsearch Open Inference API integration with Hugging Face Inference Endpoints.
“Combining Hugging Face’s embeddings with Elastic’s retrieval relevance tools helps users gain better insights and improve search functionality,” said Jeff Boudier, head of product at Hugging Face. “Hugging Face makes it easy for developers to build their own AI. With this integration, developers get a complete solution to leverage the best open models for semantic search, hosted on Hugging Face multi-cloud GPU infrastructure, to build semantic search experiences in Elasticsearch without worrying about storing or chunking embeddings.”
“Developers are at the heart of our business, and extending more of our GenAI and search primitives to Hugging Face developers deepens our collaboration,” said Matt Riley, global vice president & general manager of search at Elastic. “The integration of our new semantic_text field, simplifies the process of chunking and storing embeddings, so developers can focus on what matters most, building great applications.”
The integration of semantic_text support follows the addition of Hugging Face embeddings models to Elastic’s Open Inference API.
Read the Elastic blog for more information.
About Elastic
Elastic (NYSE: ESTC), the Search AI Company, enables everyone to find the answers they need in real-time using all their data, at scale. Elastic’s solutions for search, observability and security are built on the Elastic Search AI Platform, the development platform used by thousands of companies, including more than 50% of the Fortune 500. Learn more at elastic.co.
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