You're viewing version 2.13 of the OpenSearch documentation. This version is no longer maintained. For the latest version, see the current documentation. For information about OpenSearch version maintenance, see Release Schedule and Maintenance Policy.
Neural sparse query
Introduced 2.11
Use the neural_sparse query for vector field search in neural sparse search.
Request fields
Include the following request fields in the neural_sparse query:
"neural_sparse": {
"<vector_field>": {
"query_text": "<query_text>",
"model_id": "<model_id>"
}
}
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 and Neural sparse search. |
max_token_score | Float | Optional | (Deprecated) The theoretical upper bound of the score for all tokens in the vocabulary (required for performance optimization). For OpenSearch-provided pretrained sparse embedding 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. This field has been deprecated as of OpenSearch 2.12. |
Example request
GET my-nlp-index/_search
{
"query": {
"neural_sparse": {
"passage_embedding": {
"query_text": "Hi world",
"model_id": "aP2Q8ooBpBj3wT4HVS8a"
}
}
}
}