The text expansion query uses a natural language processing model to convert the query text into a list of token-weight pairs which are then used in a query against a rank features field.

Example requestedit

GET _search
{
   "query":{
      "text_expansion":{
         "<rank_features_field>":{
            "model_id":"the model to produce the token weights",
            "model_text":"the query string"
         }
      }
   }
}

Top level parameters for text_expansionedit

Top level parameters for <rank_features_field>edit

Exampleedit

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 this tutorial.

GET my-index/_search
{
   "query":{
      "text_expansion":{
         "ml.tokens":{
            "model_id":".elser_model_1",
            "model_text":"How is the weather in Jamaica?"
         }
      }
   }
}

Optimizing the search performance of the text_expansion queryedit

Max WAND is an optimization technique used by Elasticsearch 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 query parameter. Setting track_total_hits to true forces Elasticsearch to consider all documents, resulting in lower query latencies for the text_expansion query. However, other Elasticsearch 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.