You can use a hybrid query to combine relevance scores from multiple queries into one score for a given document. A hybrid query contains a list of one or more queries and independently calculates document scores at the shard level for each subquery. The subquery rewriting is performed at the coordinating node level in order to avoid duplicate computations.
Before using a `hybrid` query, you must set up a machine learning (ML) model, ingest documents, and configure a search pipeline with a [`normalization-processor`]({{site.url}}{{site.baseurl}}/search-plugins/search-pipelines/normalization-processor/).
To learn how to set up an ML model, see [Choosing a model]({{site.url}}{{site.baseurl}}/ml-commons-plugin/integrating-ml-models/#choosing-a-model).
Once you set up an ML model, learn how to use the `hybrid` query by following the steps in [Using hybrid search]({{site.url}}{{site.baseurl}}/search-plugins/hybrid-search/#using-hybrid-search).
For a comprehensive example, follow the [Neural search tutorial]({{site.url}}{{site.baseurl}}/ml-commons-plugin/semantic-search#tutorial).
The following table lists all top-level parameters supported by `hybrid` queries.
Parameter | Description
:--- | :---
`queries` | An array of one or more query clauses that are used to match documents. A document must match at least one query clause in order to be returned in the results. The documents' relevance scores from all query clauses are combined into one score by applying a [search pipeline]({{site.url}}{{site.baseurl}}/search-plugins/search-pipelines/index/). The maximum number of query clauses is 5. Required.
## Disabling hybrid queries
By default, hybrid queries are enabled. To disable hybrid queries in your cluster, set the `plugins.neural_search.hybrid_search_disabled` setting to `true` in `opensearch.yml`.
