Type to search.

Hybrid search

Serverless

Elastic Cloud Serverless projects are autoscaled environments, fully managed by Elastic and available on Elastic Cloud.

Availability

  • Generally available

    This functionality is generally available and ready for production usage.

Serverless interfaces and procedures might differ from classic Elastic Stack deployments.

Stack

The Elastic Stack includes Elastic's core products such as Elasticsearch, Kibana, Logstash, and Beats.

Availability

  • Generally available

    This functionality is generally available and ready for production usage.

Unless stated otherwise on the page, this functionality is available when your Elastic Stack is deployed on Elastic Cloud Hosted, Elastic Cloud Enterprise, Elastic Cloud on Kubernetes, and self-managed environments.

This documentation corresponds to the latest patch available for each minor version. If you're not using the latest patch, check the release notes for changes.

Hybrid search runs full-text search and vector search in one request. For the vector part, you can use managed semantic search workflows or set up vector fields yourself. Either way, you return one ranked list that combines keyword matching with similarity search.

On Elastic Cloud Serverless, it's recommended to use an Elasticsearch Vector Database project. New users can sign up for a free 14-day trial. For other deployment types, refer to Quick start options. For the Serverless features that differ from Elastic Cloud Hosted, refer to Compare Elastic Cloud Hosted and Serverless.

The recommended way to use hybrid search in the Elastic Stack is the semantic_text workflow. Check out the hands-on tutorial for a step-by-step guide.

For examples of using approximate kNN in hybrid search, refer to Use approximate kNN in hybrid search.

We recommend implementing hybrid search with the reciprocal rank fusion (RRF) algorithm. This approach merges rankings from the full-text and vector queries, giving more weight to documents that score well in either one. The final list balances exact keyword matches with similarity-based matches.