Elastic Announces Elastic Rerank Model to Power Up Semantic Search
Elastic (NYSE: ESTC) has launched Elastic Rerank, a cross-encoder reranking model designed to enhance semantic search capabilities without requiring reindexing. Built on the DeBERTa v3 architecture, the model demonstrates superior performance compared to larger reranking models, showing a 40% improvement in general retrieval tasks and up to 90% enhancement in question-answering datasets.
The new feature allows developers to semantically boost keyword search with minimal changes to existing data indexing and search processes, offering greater flexibility and cost control. The model is accessible through the Elasticsearch Open Inference API and is available on Elasticsearch Serverless and Elasticsearch 8.17.
Elastic (NYSE: ESTC) ha lanciato Elastic Rerank, un modello di reranking cross-encoder progettato per migliorare le capacità di ricerca semantica senza richiedere il reindicizzazione. Costruito sull'architettura DeBERTa v3, il modello dimostra prestazioni superiori rispetto ai modelli di reranking più grandi, mostrando un 40% di miglioramento nelle attività di recupero generali e fino a un 90% di miglioramento nei dataset di domanda-risposta.
La nuova funzionalità consente agli sviluppatori di aumentare semanticalmente la ricerca basata su parole chiave con minime modifiche ai processi di indicizzazione dei dati esistenti e di ricerca, offrendo maggiore flessibilità e controllo dei costi. Il modello è accessibile tramite l'API di Inference Open di Elasticsearch ed è disponibile su Elasticsearch Serverless e Elasticsearch 8.17.
Elastic (NYSE: ESTC) ha lanzado Elastic Rerank, un modelo de reranking cross-encoder diseñado para mejorar las capacidades de búsqueda semántica sin necesidad de reindexar. Construido sobre la arquitectura DeBERTa v3, el modelo demuestra un rendimiento superior en comparación con modelos de reranking más grandes, mostrando una mejora del 40% en tareas generales de recuperación y hasta una mejora del 90% en conjuntos de datos de preguntas y respuestas.
La nueva función permite a los desarrolladores potenciar la búsqueda por palabras clave semánticamente con cambios mínimos en los procesos de indexación y búsqueda de datos existentes, ofreciendo mayor flexibilidad y control de costos. El modelo está accesible a través de la API de Inference Open de Elasticsearch y está disponible en Elasticsearch Serverless y Elasticsearch 8.17.
Elastic (NYSE: ESTC)는 재색인화 없이 의미 검색 기능을 향상시키기 위해 설계된 교차 인코더 재랭크 모델인 Elastic Rerank를 출시했습니다. DeBERTa v3 아키텍처에 기반한 이 모델은 더 큰 재랭크 모델에 비해 우수한 성능을 보여주며, 일반 검색 작업에서 40% 개선과 질문-답변 데이터 세트에서 최대 90% 향상을 시연합니다.
이 새로운 기능은 개발자들이 기존 데이터 인덱싱 및 검색 프로세스에 최소한의 변경으로 키워드 검색을 의미적으로 강화할 수 있게 해주며, 더 큰 유연성과 비용 관리를 제공합니다. 이 모델은 Elasticsearch Open Inference API를 통해 접근할 수 있으며, Elasticsearch Serverless 및 Elasticsearch 8.17에서 사용할 수 있습니다.
Elastic (NYSE: ESTC) a lancé Elastic Rerank, un modèle de reranking cross-encoder conçu pour améliorer les capacités de recherche sémantique sans nécessiter de réindexation. Basé sur l'architecture DeBERTa v3, le modèle montre des performances supérieures par rapport à des modèles de reranking plus grands, affichant une amélioration de 40% dans les tâches de récupération générales et jusqu'à une amélioration de 90% dans les ensembles de données de questions-réponses.
La nouvelle fonctionnalité permet aux développeurs d'améliorer sémantiquement la recherche par mots-clés avec des modifications minimales des processus d'indexation et de recherche de données existantes, offrant une plus grande flexibilité et un meilleur contrôle des coûts. Le modèle est accessible via l'API d'Inference Open d'Elasticsearch et est disponible sur Elasticsearch Serverless et Elasticsearch 8.17.
Elastic (NYSE: ESTC) hat Elastic Rerank eingeführt, ein Cross-Encoder-Reranking-Modell, das entwickelt wurde, um die semantischen Suchfähigkeiten zu verbessern, ohne dass eine Neuerindexierung erforderlich ist. Basierend auf der DeBERTa v3-Architektur zeigt das Modell eine überlegene Leistung im Vergleich zu größeren Reranking-Modellen und erzielt eine 40% Verbesserung in allgemeinen Abrufaufgaben und bis zu einer 90% Verbesserung in Frage-Antwort-Datensätzen.
Die neue Funktion ermöglicht es Entwicklern, die Schlüsselwortsuche semantisch zu optimieren, mit minimalen Änderungen an den bestehenden Datenindexierungs- und Suchprozessen, was größere Flexibilität und Kostenkontrolle bietet. Das Modell ist über die Elasticsearch Open Inference API zugänglich und ist verfügbar auf Elasticsearch Serverless und Elasticsearch 8.17.
- New Elastic Rerank model shows 40% improvement in retrieval tasks
- Up to 90% enhancement in question-answering datasets
- No reindexing required, reducing implementation costs
- Easy integration with existing search pipelines
- None.
Insights
The introduction of Elastic Rerank represents a significant technical advancement in search technology. The model's ability to deliver a
The integration with Elasticsearch's Open Inference API enables seamless deployment within existing search infrastructures, potentially accelerating adoption among enterprise customers. This could strengthen Elastic's competitive position against rivals like Algolia and Solr, particularly in enterprise search applications where semantic accuracy and performance are important differentiators.
From a market perspective, Elastic Rerank addresses a critical pain point in enterprise search - the balance between search relevance and implementation complexity. By eliminating the need for reindexing while delivering superior performance, this innovation could accelerate enterprise adoption and potentially expand Elastic's market share in the
The seamless integration capabilities and performance improvements position Elastic to capture more value from existing customers through upsells and attract new enterprise clients seeking advanced search capabilities without significant infrastructure changes. This development particularly strengthens Elastic's value proposition in sectors like e-commerce, content management and enterprise knowledge management where search accuracy directly impacts business outcomes.
New cross-encoder reranking model amplifies search experiences through semantic boosting and no required reindexing
“Reranking models provide a semantic boost to any search experience,” said Steve Kearns, general manager, Search at Elastic. “Building a reranking model into the Elasticsearch Open Inference API makes Elastic Rerank effortless to load and use in search pipelines. It allows users to quickly apply the accuracy benefits of semantic ranking to their Elasticsearch data just by adding a few parameters to existing queries.”
Built on the DeBERTa v3 architecture, the Elastic Rerank model outperforms other significantly larger reranking models. Testing indicates a
Support for Elastic Rerank via the Inference API is available on Elasticsearch Serverless and in Elasticsearch 8.17. Read the Elastic blog for more details.
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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