opensearch-docs-cn/_query-dsl/specialized/neural-sparse.md

---
layout: default
title: Neural sparse
parent: Specialized queries
grand_parent: Query DSL
nav_order: 55
---

# Neural sparse query
Introduced 2.11
{: .label .label-purple }

Use the `neural_sparse` query for vector field search in [neural sparse search]({{site.url}}{{site.baseurl}}/search-plugins/neural-sparse-search/). 

## Request fields

Include the following request fields in the `neural_sparse` query:

```json
"neural_sparse": {
  "<vector_field>": {
    "query_text": "<query_text>",
    "model_id": "<model_id>",
    "max_token_score": "<max_token_score>"
  }
}
```

The top-level `vector_field` specifies the vector field against which to run a search query. The following table lists the other `neural_sparse` query fields.

Field | Data type | Required/Optional | Description
:--- | :--- | :--- 
`query_text` | String | Required | The query text from which to generate vector embeddings. 
`model_id` | String | Required | The ID of the sparse encoding model or tokenizer model that will be used to generate vector embeddings from the query text. The model must be deployed in OpenSearch before it can be used in sparse neural search. For more information, see [Using custom models within OpenSearch]({{site.url}}{{site.baseurl}}/ml-commons-plugin/using-ml-models/) and [Neural sparse search]({{site.url}}{{site.baseurl}}/search-plugins/neural-sparse-search/).
`max_token_score` | Float | Optional | The theoretical upper bound of the score for all tokens in the vocabulary (required for performance optimization). For OpenSearch-provided [pretrained sparse embedding models]({{site.url}}{{site.baseurl}}/ml-commons-plugin/pretrained-models/#sparse-encoding-models), we recommend setting `max_token_score` to 2 for `amazon/neural-sparse/opensearch-neural-sparse-encoding-doc-v1` and to 3.5 for `amazon/neural-sparse/opensearch-neural-sparse-encoding-v1`.

#### Example request

```json
GET my-nlp-index/_search
{
  "query": {
    "neural_sparse": {
      "passage_embedding": {
        "query_text": "Hi world",
        "model_id": "aP2Q8ooBpBj3wT4HVS8a",
        "max_token_score": 2
      }
    }
  }
}
```
{% include copy-curl.html %}