Build AI search into your applications

The Elasticsearch Relevance Engine™ (ESRE) is designed to power artificial intelligence-based search applications. Use ESRE to apply semantic search with superior relevance out of the box (without domain adaptation), integrate with external large language models (LLMs), implement hybrid search, and use third-party or your own transformer models.

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See how easy it is to get started with setting up the Elasticsearch Relevance Engine.
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Build advanced RAG-based applications using ESRE.
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Use private, internal data as context with the capabilities of generative AI models to provide up-to-date, reliable responses to user inquiries.
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AI for all developers

Elevate search with AI

Deliver advanced AI relevance capabilities to your application with ESRE no matter your expertise level. ESRE has a suite of features to help you get started or build upon your experience with AI. You have the flexibility and control to deploy machine learning and Generative AI search apps however you see fit.

  • Delivering semantic search to your application shouldn’t require a deep level of expertise. Get the best in class semantic search right out of the box with the Elastic Learned Sparse Encoder model. With a simplified deployment get started quickly delivering semantic search without the heavy lifting of training, and maintaining a machine learning model.

  • Familiar with embeddings & search vectors?

    Convert unstructured data into vector embeddings, efficiently search them using approximate nearest neighbor search. Combine your own domain-specific data in context windows to improve the relevance of LLMs' human-like output.

  • Bring your own transformer models

    Use the Eland Python library to bring your own trained machine learning model, or use a third-party model from a public repository like HuggingFace. Pick from a wide variety of supported architectures that fit your needs.

Elasticsearch Relevance Engine

Elasticsearch - All-in-one vector search power house

Generate embeddings. Store, search and manage vectors. Get semantic search with Elastic’s own Learned Sparse Encoder machine learning model. Ingest all data types. Integrate with rapidly evolving large language models.

  • RRF hybrid ranking

    RRF (Reciprocal Rank Fusion) is a method for combining document rankings from multiple retrieval systems. In the near future, RRF will support blending results from sparse vector models like BM25 and Elastic's retrieval model yielding the best in class zero shot ranking method. Hybrid ranking with RRF lets you tune search results from multiple retrievers with less effort.

  • Elastic Learned Sparse Encoder

    Our new model delivers highly relevant semantic search out of the box, without domain adaptation. It's available with one click while configuring your search application. Elastic Learned Sparse Encoder expands queries with related keywords and relevance scores, so they’re easily interpretable and ready for use right away.

  • Retrieval Augmented Generation

    Give LLMs business-specific information using your private data (not just publicly trained data). Use Elasticsearch for high relevance context windows that draw on your proprietary data to improve LLM output and relevance. Access Generative AI with APIs and plugins integrated with the LLM of your choice.

  • Vector database

    Get a full vector search experience at scale – don’t just store and search embeddings, create them! Capture the meaning and context of your unstructured data, including text and images, with embeddings for dense retrieval. Secure your embeddings at the document level to ensure data is in the right hands.

  • Bring your own transformer models

    Bring your own proprietary transformer model into Elastic. Or upload pretrained models from third-party repositories like the HuggingFace model hub — with support for a variety of supported architectures such as BERT, BART, ELECTRA, and more.

  • Data integrations & ingestion libraries

    Familiar tools such as Elastic Agent or Logstash to index your data. An ever-expanding list of integrations (such as Confluence, S3, or Google Drive). Native database connectors (such as MySQL, MongoDB). A web crawler for online sources. For custom app data, Kibana APIs or build-your-own connector with familiar frameworks.

Chris Brown, Chief Product Officer, Relativity

"I’m thrilled about the benefits we can bring to customers through our investments to harness Elasticsearch within RelativityOne. We're experimenting with ESRE right now and are excited about its potential to deliver powerful, AI-augmented search results to our customers."

Chris BrownChief Product Officer, Relativity

Frequently asked questions

What is Elasticsearch Relevance Engine?

Elasticsearch Relevance Engine is a set of features that help developers build AI search applications and includes:

  • Industry leading advanced relevance ranking features, including traditional keyword search with BM25, a foundation of relevant, hybrid search for all domains.
  • Full vector database capabilities – including the ability to create embeddings, in addition to storage and retrieval of vectors.
  • Elastic Learned Sparse Encoder – our new machine learning model for semantic search across a range of domains Hybrid ranking (RRF) for pairing vector and textual search capabilities for optimal search relevance across a variety of domains.
  • Support to integrate 3rd-party transformer models such as OpenAI GPT-3 and 4 via APIs
  • A full suite of data ingestion tools such as database connectors, 3rd-party data integrations, web crawler, and APIs to create custom connectors
  • Developer tools to build search applications across all types of data: text, images, time-series, geo, multimedia, and more.