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* Restructuring TOC Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Resolve merge conflicts Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * More foundational rewrites of ML Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * TOC restructure Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Rename and rewrite search pages and add keyword search Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Small wording change Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Small wording change Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Updated response Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Apply suggestions from code review Co-authored-by: Melissa Vagi <vagimeli@amazon.com> Signed-off-by: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com> * Small rewording Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Move neural search to top of vector search list Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Change terminology Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Reorganize search methods list Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Rename links Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * More link renames Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Apply suggestions from code review Co-authored-by: Nathan Bower <nbower@amazon.com> Signed-off-by: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com> * Implemented editorial comments Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> --------- Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> Signed-off-by: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com> Co-authored-by: Melissa Vagi <vagimeli@amazon.com> Co-authored-by: Nathan Bower <nbower@amazon.com>
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| layout | title | parent | grand_parent | nav_order |
|---|---|---|---|---|
| default | Hybrid | Compound queries | Query DSL | 70 |
Hybrid query
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.
Example
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.
To learn how to set up an ML model, see 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.
For a comprehensive example, follow the Neural search tutorial.
Parameters
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. 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.