---
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://docs-v3-preview.elastic.dev/elastic/docs-content/pull/8437/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://docs-v3-preview.elastic.dev/elastic/docs-content/pull/8437/solutions/search/full-text) and [vector search](https://docs-v3-preview.elastic.dev/elastic/docs-content/pull/8437/solutions/search/vector) in one request. For the vector part, you can use managed [semantic search](https://docs-v3-preview.elastic.dev/elastic/docs-content/pull/8437/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, use an [Elasticsearch Vector Database project](https://docs-v3-preview.elastic.dev/elastic/docs-content/pull/8437/solutions/vector-database) when embeddings and similarity search are the primary workload, even if you also run full-text queries. If search is mainly lexical or mixed with time series and analytics, use an [Elasticsearch project](https://docs-v3-preview.elastic.dev/elastic/docs-content/pull/8437/solutions/elasticsearch-solution-project) instead. [Compare Elasticsearch and Vector Database projects](/elastic/docs-content/pull/8437/solutions/vector-database#when-to-use-this-project-type) to choose the right project type for your use case.
The recommended way to use hybrid search in the Elastic Stack is the [`semantic_text` workflow](https://docs-v3-preview.elastic.dev/elastic/docs-content/pull/8437/solutions/search/semantic-search/semantic-search-semantic-text). Check out the [hands-on tutorial](https://docs-v3-preview.elastic.dev/elastic/docs-content/pull/8437/solutions/search/hybrid-semantic-text) for a step-by-step guide.
We recommend implementing hybrid search with the [reciprocal rank fusion (RRF)](https://docs-v3-preview.elastic.dev/elastic/elasticsearch/tree/main/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.