Elasticsearch Open Inference API Extends Support for Hugging Face Models with Semantic Text
Elastic (NYSE: ESTC) has announced that its Elasticsearch Open Inference API now supports Hugging Face models with native chunking through the integration of the semantic_text field. This advancement allows developers to rapidly deploy generative AI applications without the need to write custom chunking logic. The integration leverages Hugging Face Inference Endpoints, combining Hugging Face's embeddings with Elastic's retrieval relevance tools to enhance insights and search functionality.
Jeff Boudier, head of product at Hugging Face, highlighted that this integration provides developers with a complete solution to utilize open models for semantic search, hosted on Hugging Face's multi-cloud GPU infrastructure. Matt Riley, global VP & GM of search at Elastic, emphasized that this extension of GenAI and search primitives to Hugging Face developers strengthens their collaboration and simplifies the process of chunking and storing embeddings.
Elastic (NYSE: ESTC) ha annunciato che la sua API Open Inference di Elasticsearch ora supporta i modelli di Hugging Face con chunking nativo attraverso l'integrazione del campo semantic_text. Questo progresso consente agli sviluppatori di implementare rapidamente applicazioni di intelligenza artificiale generativa senza la necessità di scrivere logica di chunking personalizzata. L'integrazione sfrutta gli Endpoint di Inferenza di Hugging Face, combinando gli embedding di Hugging Face con gli strumenti di rilevanza di ricerca di Elastic per migliorare le intuizioni e la funzionalità di ricerca.
Jeff Boudier, responsabile del prodotto in Hugging Face, ha sottolineato che questa integrazione offre agli sviluppatori una soluzione completa per utilizzare modelli open per la ricerca semantica, ospitati sull'infrastruttura multi-cloud GPU di Hugging Face. Matt Riley, VP globale e GM della ricerca in Elastic, ha enfatizzato che questa estensione delle primitive GenAI e di ricerca agli sviluppatori di Hugging Face rafforza la loro collaborazione e semplifica il processo di chunking e memorizzazione degli embedding.
Elastic (NYSE: ESTC) ha anunciado que su API Open Inference de Elasticsearch ahora soporta modelos de Hugging Face con chunking nativo a través de la integración del campo semantic_text. Este avance permite a los desarrolladores desplegar rápidamente aplicaciones de IA generativa sin necesidad de escribir lógica de chunking personalizada. La integración aprovecha los Endpoints de Inferencia de Hugging Face, combinando los embeddings de Hugging Face con las herramientas de relevancia de búsqueda de Elastic para mejorar las ideas y la funcionalidad de búsqueda.
Jeff Boudier, jefe de producto en Hugging Face, destacó que esta integración proporciona a los desarrolladores una solución completa para utilizar modelos abiertos para búsqueda semántica, alojados en la infraestructura multi-nube GPU de Hugging Face. Matt Riley, VP global y GM de búsqueda en Elastic, enfatizó que esta extensión de GenAI y las primitivas de búsqueda a los desarrolladores de Hugging Face fortalece su colaboración y simplifica el proceso de chunking y almacenamiento de embeddings.
Elastic (NYSE: ESTC)는 Elasticsearch Open Inference API가 이제 본래의 청킹 방식으로 설정된 Hugging Face 모델을 지원한다고 발표했습니다. 이 발전은 개발자가 사용자 정의 청킹 로직을 작성할 필요 없이 생성적 AI 애플리케이션을 신속하게 배포할 수 있도록 합니다. 통합은 Hugging Face Inference Endpoints를 활용하여 Hugging Face의 임베딩과 Elastic의 검색 관련 도구를 결합해 인사이트와 검색 기능을 강화합니다.
Hugging Face의 제품 책임자인 Jeff Boudier는 이 통합이 개발자에게 Hugging Face의 다중 클라우드 GPU 인프라에 호스팅된 개방형 모델을 사용하여 의미 검색을 위한 완전한 솔루션을 제공한다고 강조했습니다. Elastic의 글로벌 검색 부문 부사장 겸 총괄 매니저인 Matt Riley는 이 GenAI와 검색 프리미티브의 Hugging Face 개발자에 대한 확장이 그들의 협력을 강화하고 임베딩의 청킹 및 저장 프로세스를 단순화한다고 강조했습니다.
Elastic (NYSE: ESTC) a annoncé que son API Open Inference d'Elasticsearch prend désormais en charge les modèles Hugging Face avec un chunking natif grâce à l'intégration du champ semantic_text. Cette avancée permet aux développeurs de déployer rapidement des applications d'IA génératives sans avoir besoin d'écrire une logique de chunking personnalisée. L'intégration s'appuie sur les Endpoints d'Inference de Hugging Face, combinant les embeddings de Hugging Face avec les outils de pertinence de recherche d'Elastic pour améliorer les insights et les fonctionnalités de recherche.
Jeff Boudier, responsable produit chez Hugging Face, a souligné que cette intégration offre aux développeurs une solution complète pour utiliser des modèles ouverts pour la recherche sémantique, hébergés sur l'infrastructure GPU multi-cloud de Hugging Face. Matt Riley, VP mondial et GM de la recherche chez Elastic, a souligné que cette extension de GenAI et des primitives de recherche aux développeurs de Hugging Face renforce leur collaboration et simplifie le processus de chunking et de stockage des embeddings.
Elastic (NYSE: ESTC) hat angekündigt, dass seine Elasticsearch Open Inference API nun Hugging Face Modelle mit nativer Chunk-Verarbeitung unterstützt, dank der Integration des semantic_text-Feldes. Dieser Fortschritt ermöglicht es Entwicklern, generative KI-Anwendungen schnell bereitzustellen, ohne benutzerdefinierte Chunking-Logik schreiben zu müssen. Die Integration nutzt die Hugging Face Inference Endpoints und kombiniert die Embeddings von Hugging Face mit den Retrieval-Relevanztools von Elastic, um Einsichten und Suchfunktionen zu verbessern.
Jeff Boudier, Produktleiter bei Hugging Face, betonte, dass diese Integration Entwicklern eine umfassende Lösung bietet, um offene Modelle für die semantische Suche zu nutzen, die auf der Multi-Cloud-GPU-Infrastruktur von Hugging Face gehostet werden. Matt Riley, globaler VP und GM für Suche bei Elastic, hob hervor, dass diese Erweiterung der GenAI- und Suchprimitive die Zusammenarbeit mit Hugging Face-Entwicklern stärkt und den Prozess des Chunkings und der Speicherung von Embeddings vereinfacht.
- Integration of semantic_text field supports Hugging Face models with native chunking
- Simplifies development of generative AI applications
- Improves search functionality and insights through combined technologies
- Strengthens collaboration between Elastic and Hugging Face
- None.
Insights
The integration of Hugging Face models with Elasticsearch's Open Inference API marks a significant advancement in AI-powered search capabilities. By introducing native chunking through the semantic_text field, Elastic has eliminated a major pain point for developers working with large language models. This streamlines the process of implementing semantic search in applications, potentially accelerating time-to-market for GenAI products.
From a technical standpoint, this integration addresses the challenge of efficiently handling and storing embeddings, which are important for understanding context and relevance in search queries. The native chunking feature is particularly valuable as it automates a complex task that previously required custom coding, thus reducing development overhead and potential errors. This could lead to more robust and scalable AI-powered search solutions across various industries.
This collaboration between Elastic and Hugging Face is poised to strengthen Elastic's position in the competitive AI-enhanced search market. By simplifying the integration of advanced language models, Elastic is lowering the barrier to entry for companies looking to implement semantic search capabilities. This could potentially expand Elastic's customer base, particularly among businesses that lack extensive AI expertise but seek to leverage these technologies.
The move aligns with the growing demand for more intelligent, context-aware search solutions across various sectors. As organizations increasingly rely on AI to process and extract insights from vast amounts of data, Elastic's enhanced offering could drive adoption and potentially boost its revenue streams. However, the long-term impact will depend on how effectively Elastic can differentiate its services in a rapidly evolving AI landscape.
Applications using Hugging Face embeddings on Elasticsearch now benefit from native chunking
“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
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