Learn about the Elastic Learned Sparse Encoder (ELSER), an AI model for high relevance semantic search across domains.

Searching for meaning, not just words

Introducing Elastic Learned Sparse Encoder, a new text expansion model for semantic search

Superior semantic search out of the box

Why choose Elastic’s Learned Sparse Encoder?

Let’s speak to performance and Elasticsearch as a vector database

The future: The most powerful hybrid search in the market out of the box

Get started with Elastic’s AI-powered search today

The Elastic Learned Sparse Encoder is an AI model for high relevance semantic search across domains. As a sparse vector model, it expands the query with terms that don't exist in the query itself, delivering superior relevance without domain adaptation.

What is the Elastic Learned Sparse Encoder?

ELSER: Introducing Elastic Learned Sparse Encoder

Introducing Elastic Learned Sparse Encoder: Elastic’s AI model for semantic search

Learn about multi-vector documents in Elasticsearch, their use cases, and how to link original context to a multi-vector document.

Introduction

Use cases for multi-vector documents

Multiple vectors per document in Elasticsearch

Usage example

Elasticsearch multi-vector document: Retrieval of originating information

Retrieval of originating information in multi-vector documents

Author

Gilad Gal

Articles

Introducing Elastic Learned Sparse Encoder: Elastic’s AI model for semantic search

Retrieval of originating information in multi-vector documents