---
title: Hybrid search
description: 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...
url: https://www.elastic.co/elastic/docs-builder/docs/4300/solutions/search/hybrid-search
products:
  - Elastic Cloud Enterprise
  - Elastic Cloud Hosted
  - Elastic Cloud Serverless
  - Elastic Cloud on Kubernetes
  - Elastic Documentation
  - Elastic Stack
  - Elasticsearch
applies_to:
  - Elastic Cloud Serverless: Generally available
  - Elastic Stack: Generally available
---

# Hybrid search
Hybrid search runs [full-text search](https://www.elastic.co/elastic/docs-builder/docs/4300/solutions/search/full-text) and [vector search](https://www.elastic.co/elastic/docs-builder/docs/4300/solutions/search/vector) in one request. For the vector part, you can use managed [semantic search](https://www.elastic.co/elastic/docs-builder/docs/4300/solutions/search/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](https://www.elastic.co/elastic/docs-builder/docs/4300/solutions/vector-database). New users can [sign up for a free 14-day trial](https://cloud.elastic.co/serverless-registration?onboarding_token=vector). For other deployment types, refer to [Quick start options](/elastic/docs-builder/docs/4300/get-started/deployment-options#quick-start-options).
The recommended way to use hybrid search in the Elastic Stack is the [`semantic_text` workflow](https://www.elastic.co/elastic/docs-builder/docs/4300/solutions/search/semantic-search/semantic-search-semantic-text). Check out the [hands-on tutorial](https://www.elastic.co/elastic/docs-builder/docs/4300/solutions/search/hybrid-semantic-text) for a step-by-step guide.
For examples of using approximate kNN in hybrid search, refer to [Use approximate kNN in hybrid search](/elastic/docs-builder/docs/4300/solutions/search/vector/knn/approximate-knn-query-examples#combine_approximate_knn_with_other_features).
We recommend implementing hybrid search with the [reciprocal rank fusion (RRF)](https://docs-v3-preview.elastic.dev/elastic/docs-builder/docs/4300/reference/elasticsearch/rest-apis/reciprocal-rank-fusion) 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.