ML SEARCH

Take search to the next level with machine learning

Elastic is loaded with the latest advancements in machine learning (ML) and natural language processing (NLP). Easy to implement, flexible capabilities give you tools to build AI search applications that integrate with generative AI and power semantic and multi-modal search, personalization, and question answering to measurably improve search experiences.

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GENERATIVE AI TOOLS

Build powerful Search AI applications

Use the Elasticsearch Relevance Engine to build the next generation of semantic search applications. Apply Elastic’s vector database and out-of-the-box transformer model for semantic search across domains and hybrid ranking to optimize search using keyword search and semantic retrieval.

  • Tap into the LLM ecosystem

    Streamline code and multi-cloud inference management with Elastic’s open inference API using your own embeddings or Elastic’s proprietary sparse encoder model. Create endpoints and use a diverse range of machine learning models from leading providers.

  • Master long text with automatic chunking

    Handle long text passages easily with semantic_text. Elastic's open inference API with the semantic_text field automatically chunks documents into context-preserving sections, ensuring accurate embeddings and optimal search results. Say goodbye to writing custom chunking logic.

  • Lucene 10 is here!

    Apache Lucene 10 maximizes hardware efficiency for AI search applications. With improved search parallelism for low latency queries, asynchronous I/O operations for better throughput, and optimized CPU and storage efficiency, Lucene 10 paves the way for faster, more efficient search experiences — particularly on large data sets.

  • Elasticsearch with an order of BBQ

    Elastic is the first to offer better binary quantization (BBQ), an optimization for vector databases that amps up performance and efficiency. BBQ offers high ranking quality with the speed and storage efficiency of scalar quantization. With 95% memory reduction and high recall, it scales vector storage affordably for large datasets.

Capabilities

Machine learning at your fingertips

With Elastic, you can build AI search applications with natively run ML and vector search to achieve superior search relevance, performance, and personalization. Categorize data sets, detect anomalies, identify and anticipate trends.

  • Elastic is built with Lucene's vector fields and approximate nearest neighbor (ANN, using HNSW) search, matching search queries with vector-based search concepts that make search applications faster and more accurate — especially at scale. Create, store, and query embeddings with Elastic’s vector database.

  • NLP support & model management

    Support for modern natural language processing lets you use PyTorch, Python, and transformer models with Elasticsearch ingest pipelines for sentiment analysis, text classification, and named entity recognition (NER). Import popular transformer models directly from Hugging Face.

  • Predictive models

    Build and apply predictive models (supervised learning) to classify data into categories or to forecast trends. To apply predictive models in Elastic, convert raw data from Elasticsearch indices to data frames using our Transforms utility.

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Common ML Tasks

Add ML to your search use case

Applications of ML search are limitless, and Elastic's capabilities fuel hyper-relevant search that enhance search experiences and management behind the scenes.

  • Personalization

    Build search that tailors responses to end-user location, customer account or purchase history, or their role inside an organization with named entity recognition and text classification.

  • Natural language search lets users get back faster, more accurate results at scale. Use Elastic's out-of-the-box model or build it using vector search and approximate nearest neighbor algorithms — which capture contextual information and unlock meaning behind search queries.

  • Compare images across large data sets for product discovery and cross-sell, image tracking, and authentication.

  • Question-answering

    Get users relevant answers faster by finding similar questions in your FAQ, help center, or support KB using vector fields and text similarity search. Link your proprietary data for more relevant output when you use LLMs and generative AI to power new experiences.

  • Content enrichment

    Organize content to show five-star customer reviews or similar news stories together on your website, categorize research data, or route customer support issues with named entity recognition, text embedding, zero-shot classification, and sentiment analysis.

  • Trend identification

    Spot and respond to patterns in your search data, such as gaps in your content or product catalog, using machine learning models that classify, cluster, and correlate search analytics and behavior.

Elastic Difference

Make the complex simple

Elastic's tools bring integration, flexibility, and scalability to machine learning, so teams can easily use pretrained models and operate at scale. Tune or build your own models to meet domain-specific needs and innovate for your organization.

  • ML inside

    With ML in core Elasticsearch, integrate your data efficiently without needing to export it to an external endpoint.

  • Flexible applications

    Build modern AI-powered search with maximum configurability and ease of use. Combine generative AI with secure search of your private datastore to ensure privacy and accuracy while leveraging the latest conversational techniques.

  • Scalable for any use case

    test, operationalize, and scale up Elasticsearch‑style. ML is already included with your favorite search platform, which also provides security and observability for your applications.

Try it now on Elastic Cloud

Spin up a fully loaded deployment on the cloud provider you choose. As the company behind Elasticsearch, we bring our features and support to your Elastic clusters in the cloud.

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