Retrieval augmented generation: Give large language models (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.
Elastic Learned Sparse EncodeR (ELSER): Our sparse vector 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.
Vector database: Get a full vector search experience at scale — create, store, and search vector embeddings. Capture meaning and context of your unstructured data, including text and images with embeddings for dense, sparse, and hybrid retrieval.
Document-level security: Implement role-based access controls, and secure your embeddings at the document level to ensure data is in the right hands.
Bring your own transformer models: Bring public or proprietary transformer models into Elastic, or upload pre-trained models from third-party repositories with support for a variety of supported architectures, such as BERT, BART, ELECTRA, and more.
RRF hybrid ranking: Reciprocal rank fusion (RRF) is a method for combining document rankings from multiple retrieval systems. Hybrid ranking with RRF lets you tune search results from multiple retrievers with less effort.
Data integrations and ingestion libraries: Use familiar tools, such as Elastic Agent or Logstash®, to index your data, as well as an ever-expanding list of integrations — Confluence, Amazon S3, or Google Drive. Leverage native database connectors, such as MySQL and MongoDB, and a web crawler for online sources. For custom app data, use Kibana® APIs or build your own connector with thoughtful frameworks.

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