[[query-dsl-text-expansion-query]]
== Text expansion query
++++
<titleabbrev>Text expansion</titleabbrev>
++++

The text expansion query uses a {nlp} model to convert the query text into a
list of token-weight pairs which are then used in a query against a
<<sparse-vector,sparse vector>> or <<rank-features,rank features>> field.

[discrete]
[[text-expansion-query-ex-request]]
=== Example request


[source,console]
----
GET _search
{
   "query":{
      "text_expansion":{
         "<sparse_vector_field>":{
            "model_id":"the model to produce the token weights",
            "model_text":"the query string"
         }
      }
   }
}
----
// TEST[skip: TBD]

[discrete]
[[text-expansion-query-params]]
=== Top level parameters for `text_expansion`

`<sparse_vector_field>`:::
(Required, object)
The name of the field that contains the token-weight pairs the NLP model created
based on the input text.

[discrete]
[[text-expansion-rank-feature-field-params]]
=== Top level parameters for `<sparse_vector_field>`

`model_id`::::
(Required, string)
The ID of the model to use to convert the query text into token-weight pairs. It
must be the same model ID that was used to create the tokens from the input
text.

`model_text`::::
(Required, string)
The query text you want to use for search.


[discrete]
[[text-expansion-query-example]]
=== Example

The following is an example of the `text_expansion` query that references the
ELSER model to perform semantic search. For a more detailed description of how
to perform semantic search by using ELSER and the `text_expansion` query, refer
to <<semantic-search-elser,this tutorial>>.

[source,console]
----
GET my-index/_search
{
   "query":{
      "text_expansion":{
         "ml.tokens":{
            "model_id":".elser_model_1",
            "model_text":"How is the weather in Jamaica?"
         }
      }
   }
}
----
// TEST[skip: TBD]

[discrete]
[[optimizing-text-expansion]]
=== Optimizing the search performance of the text_expansion query

https://www.elastic.co/blog/faster-retrieval-of-top-hits-in-elasticsearch-with-block-max-wand[Max WAND]
is an optimization technique used by {es} to skip documents that cannot score
competitively against the current best matching documents. However, the tokens
generated by the ELSER model don't work well with the Max WAND optimization.
Consequently, enabling Max WAND can actually increase query latency for
`text_expansion`. For datasets of a significant size, disabling Max
WAND leads to lower query latencies.

Max WAND is controlled by the
<<track-total-hits, track_total_hits>> query parameter. Setting track_total_hits
to true forces {es} to consider all documents, resulting in lower query
latencies for the `text_expansion` query. However, other {es} queries run slower
when Max WAND is disabled.

If you are combining the `text_expansion` query with standard text queries in a
compound search, it is recommended to measure the query performance before
deciding which setting to use.

NOTE: The `track_total_hits` option applies to all queries in the search request
and may be optimal for some queries but not for others. Take into account the
characteristics of all your queries to determine the most suitable
configuration.